{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "AT5OogJVFbwu",
      "metadata": {
        "id": "AT5OogJVFbwu"
      },
      "source": [
        "# **Supervised Learning - Classification Project: INN Hotels**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Student: Alexey Tyurin\n",
        "- Group: Oct'22 C Sun - MLS Grp B\n",
        "- Batch: PGP-DSBA-UTA-OCT22-C\n",
        "- Date: 3/10/2023 - 3/17/2023"
      ],
      "metadata": {
        "id": "xYALyHkXGn2Z"
      },
      "id": "xYALyHkXGn2Z"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# INN Hotels - Problem Statement"
      ],
      "metadata": {
        "id": "HO1OZbjV78JI"
      },
      "id": "HO1OZbjV78JI"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Context\n",
        "\n",
        "A significant number of hotel bookings are called-off due to cancellations or no-shows. The typical reasons for cancellations include change of plans, scheduling conflicts, etc. This is often made easier by the option to do so free of charge or preferably at a low cost which is beneficial to hotel guests but it is a less desirable and possibly revenue-diminishing factor for hotels to deal with. Such losses are particularly high on last-minute cancellations. \n",
        "\n",
        "The new technologies involving online booking channels have dramatically changed customers’ booking possibilities and behavior. This adds a further dimension to the challenge of how hotels handle cancellations, which are no longer limited to traditional booking and guest characteristics. \n",
        "\n",
        "The cancellation of bookings impact a hotel on various fronts:\n",
        "* Loss of resources (revenue) when the hotel cannot resell the room.\n",
        "* Additional costs of distribution channels by increasing commissions or paying for publicity to help sell these rooms.\n",
        "* Lowering prices last minute, so the hotel can resell a room, resulting in reducing the profit margin.\n",
        "* Human resources to make arrangements for the guests."
      ],
      "metadata": {
        "id": "l_PXG7z8GsjZ"
      },
      "id": "l_PXG7z8GsjZ"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Objective\n",
        "The increasing number of cancellations calls for a Machine Learning based solution that can help in predicting which booking is likely to be canceled. INN Hotels Group has a chain of hotels in Portugal, they are facing problems with the high number of booking cancellations and have reached out to your firm for data-driven solutions. You as a data scientist have to analyze the data provided to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds."
      ],
      "metadata": {
        "id": "DTiRD6byHH8Q"
      },
      "id": "DTiRD6byHH8Q"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Description\n",
        "The data contains the different attributes of customers' booking details. The detailed data dictionary is given below."
      ],
      "metadata": {
        "id": "9w0hOaFWHH-u"
      },
      "id": "9w0hOaFWHH-u"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Dictionary**\n",
        "\n",
        "* Booking_ID: unique identifier of each booking\n",
        "* no_of_adults: Number of adults\n",
        "* no_of_children: Number of Children\n",
        "* no_of_weekend_nights: Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel\n",
        "* no_of_week_nights: Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel\n",
        "* type_of_meal_plan: Type of meal plan booked by the customer:\n",
        "    * Not Selected – No meal plan selected\n",
        "    * Meal Plan 1 – Breakfast\n",
        "    * Meal Plan 2 – Half board (breakfast and one other meal)\n",
        "    * Meal Plan 3 – Full board (breakfast, lunch, and dinner)\n",
        "* required_car_parking_space: Does the customer require a car parking space? (0 - No, 1- Yes)\n",
        "* room_type_reserved: Type of room reserved by the customer. The values are ciphered (encoded) by INN Hotels.\n",
        "* lead_time: Number of days between the date of booking and the arrival date\n",
        "* arrival_year: Year of arrival date\n",
        "* arrival_month: Month of arrival date\n",
        "* arrival_date: Date of the month\n",
        "* market_segment_type: Market segment designation.\n",
        "* repeated_guest: Is the customer a repeated guest? (0 - No, 1- Yes)\n",
        "* no_of_previous_cancellations: Number of previous bookings that were canceled by the customer prior to the current booking\n",
        "* no_of_previous_bookings_not_canceled: Number of previous bookings not canceled by the customer prior to the current booking\n",
        "* avg_price_per_room: Average price per day of the reservation; prices of the rooms are dynamic. (in euros)\n",
        "* no_of_special_requests: Total number of special requests made by the customer (e.g. high floor, view from the room, etc)\n",
        "* booking_status: Flag indicating if the booking was canceled or not."
      ],
      "metadata": {
        "id": "iJQmLCqWHIBd"
      },
      "id": "iJQmLCqWHIBd"
    },
    {
      "cell_type": "markdown",
      "id": "dirty-island",
      "metadata": {
        "id": "dirty-island"
      },
      "source": [
        "# Importing necessary libraries and data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "statewide-still",
      "metadata": {
        "id": "statewide-still"
      },
      "outputs": [],
      "source": [
        "#!pip install nb_black"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# this will help in making the Python code more structured automatically (help adhere to good coding practices)\n",
        "%load_ext nb_black\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# Libraries to help with reading and manipulating data\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# libaries to help with data visualization\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Removes the limit for the number of displayed columns\n",
        "pd.set_option(\"display.max_columns\", None)\n",
        "# Sets the limit for the number of displayed rows\n",
        "pd.set_option(\"display.max_rows\", 200)\n",
        "# setting the precision of floating numbers to 5 decimal points\n",
        "pd.set_option(\"display.float_format\", lambda x: \"%.5f\" % x)\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "from statsmodels.tools.sm_exceptions import ConvergenceWarning\n",
        "\n",
        "warnings.simplefilter(\"ignore\", ConvergenceWarning)\n",
        "\n",
        "# Library to split data\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# To build model for prediction\n",
        "import statsmodels.stats.api as sms\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.tools.tools import add_constant\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# To build model for prediction\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn import tree\n",
        "\n",
        "# To tune different models\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# To get diferent metric scores\n",
        "from sklearn.metrics import (\n",
        "    f1_score,\n",
        "    accuracy_score,\n",
        "    recall_score,\n",
        "    precision_score,\n",
        "    confusion_matrix,\n",
        "    make_scorer,\n",
        "    roc_auc_score,\n",
        "    precision_recall_curve,\n",
        "    roc_curve,\n",
        "    )\n"
      ],
      "metadata": {
        "id": "jwr21_2uHY3O",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "29c84d1f-5c52-4ab2-a15e-6d19c1522896"
      },
      "id": "jwr21_2uHY3O",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 2;\n",
              "                var nbb_unformatted_code = \"# this will help in making the Python code more structured automatically (help adhere to good coding practices)\\n%load_ext nb_black\\n\\nimport warnings\\nwarnings.filterwarnings(\\\"ignore\\\")\\n\\n# Libraries to help with reading and manipulating data\\nimport pandas as pd\\nimport numpy as np\\n\\n# libaries to help with data visualization\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\\n\\n# Removes the limit for the number of displayed columns\\npd.set_option(\\\"display.max_columns\\\", None)\\n# Sets the limit for the number of displayed rows\\npd.set_option(\\\"display.max_rows\\\", 200)\\n# setting the precision of floating numbers to 5 decimal points\\npd.set_option(\\\"display.float_format\\\", lambda x: \\\"%.5f\\\" % x)\\n\\nwarnings.filterwarnings(\\\"ignore\\\")\\nfrom statsmodels.tools.sm_exceptions import ConvergenceWarning\\n\\nwarnings.simplefilter(\\\"ignore\\\", ConvergenceWarning)\\n\\n# Library to split data\\nfrom sklearn.model_selection import train_test_split\\n\\n# To build model for prediction\\nimport statsmodels.stats.api as sms\\nfrom statsmodels.stats.outliers_influence import variance_inflation_factor\\nimport statsmodels.api as sm\\nfrom statsmodels.tools.tools import add_constant\\nfrom sklearn.linear_model import LogisticRegression\\n\\n# To build model for prediction\\nfrom sklearn.tree import DecisionTreeClassifier\\nfrom sklearn import tree\\n\\n# To tune different models\\nfrom sklearn.model_selection import GridSearchCV\\n\\n# To get diferent metric scores\\nfrom sklearn.metrics import (\\n    f1_score,\\n    accuracy_score,\\n    recall_score,\\n    precision_score,\\n    confusion_matrix,\\n    make_scorer,\\n    roc_auc_score,\\n    precision_recall_curve,\\n    roc_curve,\\n    )\";\n",
              "                var nbb_formatted_code = \"# this will help in making the Python code more structured automatically (help adhere to good coding practices)\\n%load_ext nb_black\\n\\nimport warnings\\n\\nwarnings.filterwarnings(\\\"ignore\\\")\\n\\n# Libraries to help with reading and manipulating data\\nimport pandas as pd\\nimport numpy as np\\n\\n# libaries to help with data visualization\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\\n\\n# Removes the limit for the number of displayed columns\\npd.set_option(\\\"display.max_columns\\\", None)\\n# Sets the limit for the number of displayed rows\\npd.set_option(\\\"display.max_rows\\\", 200)\\n# setting the precision of floating numbers to 5 decimal points\\npd.set_option(\\\"display.float_format\\\", lambda x: \\\"%.5f\\\" % x)\\n\\nwarnings.filterwarnings(\\\"ignore\\\")\\nfrom statsmodels.tools.sm_exceptions import ConvergenceWarning\\n\\nwarnings.simplefilter(\\\"ignore\\\", ConvergenceWarning)\\n\\n# Library to split data\\nfrom sklearn.model_selection import train_test_split\\n\\n# To build model for prediction\\nimport statsmodels.stats.api as sms\\nfrom statsmodels.stats.outliers_influence import variance_inflation_factor\\nimport statsmodels.api as sm\\nfrom statsmodels.tools.tools import add_constant\\nfrom sklearn.linear_model import LogisticRegression\\n\\n# To build model for prediction\\nfrom sklearn.tree import DecisionTreeClassifier\\nfrom sklearn import tree\\n\\n# To tune different models\\nfrom sklearn.model_selection import GridSearchCV\\n\\n# To get diferent metric scores\\nfrom sklearn.metrics import (\\n    f1_score,\\n    accuracy_score,\\n    recall_score,\\n    precision_score,\\n    confusion_matrix,\\n    make_scorer,\\n    roc_auc_score,\\n    precision_recall_curve,\\n    roc_curve,\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Loding the dataset\n",
        "df = pd.read_csv('/content/INNHotelsGroup.csv')"
      ],
      "metadata": {
        "id": "pDSw6b7jHlHl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "0bfa40e5-16e9-4f5b-9748-811675454f8c"
      },
      "id": "pDSw6b7jHlHl",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 3;\n",
              "                var nbb_unformatted_code = \"# Loding the dataset\\ndf = pd.read_csv('/content/INNHotelsGroup.csv')\";\n",
              "                var nbb_formatted_code = \"# Loding the dataset\\ndf = pd.read_csv(\\\"/content/INNHotelsGroup.csv\\\")\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# User-defined functions"
      ],
      "metadata": {
        "id": "5DQD3hCUHsTd"
      },
      "id": "5DQD3hCUHsTd"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Functions to carry out the EDA"
      ],
      "metadata": {
        "id": "StIkp_ijHtpC"
      },
      "id": "StIkp_ijHtpC"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Combined boxplot and histogram"
      ],
      "metadata": {
        "id": "z-4pYb-MYDlM"
      },
      "id": "z-4pYb-MYDlM"
    },
    {
      "cell_type": "code",
      "source": [
        "# functiom to create combined boxplot and histogram\n",
        "def histogram_boxplot(data, feature, figsize=(10, 5), fmt='{:.1f}',\n",
        "                      title=None, kde=True, bins=None, step=None, xlabel=None):\n",
        "    x = data[feature]\n",
        "    colors = sns.color_palette(\"Spectral\")\n",
        "    title = title if title else f'Distribution for `{feature}` column'\n",
        "\n",
        "    x_min, x_max = x.min(), x.max()\n",
        "\n",
        "    if (bins == None) & (step == None):\n",
        "        bins = int(3 * np.log(x.nunique())) + 2\n",
        "    elif bins == None:\n",
        "        x_min = step * round(x_min / step, 0)\n",
        "        x_max = step * round(x_max / step, 0) + step\n",
        "        bins = int((x_max - x_min) / step)\n",
        "\n",
        "    bw = (x_max - x_min) / bins\n",
        "\n",
        "    fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=figsize,\n",
        "                           gridspec_kw={\"height_ratios\": (0.3, 0.7)})\n",
        "\n",
        "    plt.suptitle(title, fontsize=16)\n",
        "    box = sns.boxplot(data=data, x=feature, ax=ax[0], showmeans=True, color=colors[1])\n",
        "    hist = sns.histplot(data=data, x=feature, kde=kde, ax=ax[1], color=colors[4], bins=bins)\n",
        "\n",
        "    ax[0].set_xlabel('')\n",
        "    ax[1].axvline(x.median(), color=\"grey\", linestyle=\"-\")\n",
        "    ax[1].axvline(x.mean(), color=\"darkgreen\", linestyle=\"--\")\n",
        "    ax[1].set_ylabel('Number of bookings', fontsize=12)\n",
        "    if xlabel: ax[1].set_xlabel(xlabel, fontsize=12)\n",
        "\n",
        "    labels = [fmt.format(x) for x in np.arange(x_min, x_max, bw)]\n",
        "    ticks =  [x+bw/2 for x in np.arange(x_min, x_max, bw)]\n",
        "\n",
        "    ax[1].set_xticks(ticks)\n",
        "    ax[1].set_xticklabels(labels)\n",
        "\n",
        "    plt.show();"
      ],
      "metadata": {
        "id": "gEoG4Q-TYDuN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "31e71850-d424-4c29-864e-153b1e6bf85b"
      },
      "id": "gEoG4Q-TYDuN",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 4;\n",
              "                var nbb_unformatted_code = \"# functiom to create combined boxplot and histogram\\ndef histogram_boxplot(data, feature, figsize=(10, 5), fmt='{:.1f}',\\n                      title=None, kde=True, bins=None, step=None, xlabel=None):\\n    x = data[feature]\\n    colors = sns.color_palette(\\\"Spectral\\\")\\n    title = title if title else f'Distribution for `{feature}` column'\\n\\n    x_min, x_max = x.min(), x.max()\\n\\n    if (bins == None) & (step == None):\\n        bins = int(3 * np.log(x.nunique())) + 2\\n    elif bins == None:\\n        x_min = step * round(x_min / step, 0)\\n        x_max = step * round(x_max / step, 0) + step\\n        bins = int((x_max - x_min) / step)\\n\\n    bw = (x_max - x_min) / bins\\n\\n    fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=figsize,\\n                           gridspec_kw={\\\"height_ratios\\\": (0.3, 0.7)})\\n\\n    plt.suptitle(title, fontsize=16)\\n    box = sns.boxplot(data=data, x=feature, ax=ax[0], showmeans=True, color=colors[1])\\n    hist = sns.histplot(data=data, x=feature, kde=kde, ax=ax[1], color=colors[4], bins=bins)\\n\\n    ax[0].set_xlabel('')\\n    ax[1].axvline(x.median(), color=\\\"grey\\\", linestyle=\\\"-\\\")\\n    ax[1].axvline(x.mean(), color=\\\"darkgreen\\\", linestyle=\\\"--\\\")\\n    ax[1].set_ylabel('Number of bookings', fontsize=12)\\n    if xlabel: ax[1].set_xlabel(xlabel, fontsize=12)\\n\\n    labels = [fmt.format(x) for x in np.arange(x_min, x_max, bw)]\\n    ticks =  [x+bw/2 for x in np.arange(x_min, x_max, bw)]\\n\\n    ax[1].set_xticks(ticks)\\n    ax[1].set_xticklabels(labels)\\n\\n    plt.show();\";\n",
              "                var nbb_formatted_code = \"# functiom to create combined boxplot and histogram\\ndef histogram_boxplot(\\n    data,\\n    feature,\\n    figsize=(10, 5),\\n    fmt=\\\"{:.1f}\\\",\\n    title=None,\\n    kde=True,\\n    bins=None,\\n    step=None,\\n    xlabel=None,\\n):\\n    x = data[feature]\\n    colors = sns.color_palette(\\\"Spectral\\\")\\n    title = title if title else f\\\"Distribution for `{feature}` column\\\"\\n\\n    x_min, x_max = x.min(), x.max()\\n\\n    if (bins == None) & (step == None):\\n        bins = int(3 * np.log(x.nunique())) + 2\\n    elif bins == None:\\n        x_min = step * round(x_min / step, 0)\\n        x_max = step * round(x_max / step, 0) + step\\n        bins = int((x_max - x_min) / step)\\n\\n    bw = (x_max - x_min) / bins\\n\\n    fig, ax = plt.subplots(\\n        nrows=2,\\n        ncols=1,\\n        sharex=True,\\n        figsize=figsize,\\n        gridspec_kw={\\\"height_ratios\\\": (0.3, 0.7)},\\n    )\\n\\n    plt.suptitle(title, fontsize=16)\\n    box = sns.boxplot(data=data, x=feature, ax=ax[0], showmeans=True, color=colors[1])\\n    hist = sns.histplot(\\n        data=data, x=feature, kde=kde, ax=ax[1], color=colors[4], bins=bins\\n    )\\n\\n    ax[0].set_xlabel(\\\"\\\")\\n    ax[1].axvline(x.median(), color=\\\"grey\\\", linestyle=\\\"-\\\")\\n    ax[1].axvline(x.mean(), color=\\\"darkgreen\\\", linestyle=\\\"--\\\")\\n    ax[1].set_ylabel(\\\"Number of bookings\\\", fontsize=12)\\n    if xlabel:\\n        ax[1].set_xlabel(xlabel, fontsize=12)\\n\\n    labels = [fmt.format(x) for x in np.arange(x_min, x_max, bw)]\\n    ticks = [x + bw / 2 for x in np.arange(x_min, x_max, bw)]\\n\\n    ax[1].set_xticks(ticks)\\n    ax[1].set_xticklabels(labels)\\n\\n    plt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Labeled barplots"
      ],
      "metadata": {
        "id": "pCaIBs_FYNV4"
      },
      "id": "pCaIBs_FYNV4"
    },
    {
      "cell_type": "code",
      "source": [
        "# function to create labeled barplots\n",
        "def labeled_barplot(data, feature, perc=False, n=None, percfmt=\"{:.1f}%\", rnd=0,\n",
        "                    figsize=(10, 5), xlabel=None, xlo=0, title=None, sort=True, \n",
        "                    orient='h'):\n",
        "    \"\"\"\n",
        "    Barplot with percentage at the top\n",
        "\n",
        "    data: dataframe\n",
        "    perc: whether to display percentages instead of count (default is False)\n",
        "    n: displays the top n category levels (default is None, i.e., display all levels)\n",
        "    figsize: size of figure (default (10, 5))\n",
        "    xlabel: label for x axis\n",
        "    \"\"\"\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=figsize)\n",
        "    plt.xticks(rotation=xlo, fontsize=14)\n",
        "    if orient == 'h':\n",
        "      sns.countplot(data=data, x=feature, palette=\"Paired\",\n",
        "                    order=data[feature].value_counts().index[:n] if sort else None)\n",
        "    else:\n",
        "      sns.countplot(data=data, y=feature, palette=\"Paired\",\n",
        "                    order=data[feature].value_counts().index[:n] if sort else None)\n",
        "\n",
        "    # draw lables\n",
        "    for p in ax.patches:\n",
        "      if orient == 'h':\n",
        "        if perc == True: label = percfmt.format(100 * p.get_height() / data.shape[0])\n",
        "        else: label = p.get_height()\n",
        "        ax.annotate(label, (p.get_x() + p.get_width() / 2, p.get_height()),\n",
        "                    ha=\"center\", va=\"center\", size=11, xytext=(0, 5),\n",
        "                    textcoords=\"offset points\")\n",
        "        ax.set_ylabel('Number of bookings', fontsize=14)\n",
        "        if xlabel: ax.set_xlabel(xlabel, fontsize=14)\n",
        "      else:\n",
        "        if perc == True:\n",
        "          label = percfmt.format(100 * p.get_width() / data.shape[0])\n",
        "        else:\n",
        "          label = p.get_height()\n",
        "        ax.annotate(label, (p.get_width(), p.get_y() + p.get_height() / 2),\n",
        "                    ha=\"center\", va=\"center\", size=11, xytext=(15, 0),\n",
        "                    textcoords=\"offset points\")\n",
        "        ax.set_xlabel('Number of bookings', fontsize=14)\n",
        "        if xlabel: ax.set_ylabel(xlabel, fontsize=14)\n",
        "\n",
        "    if title: plt.title(title, fontsize=16)\n",
        "    else: plt.title(f'Distribution for `{feature}` column', fontsize=16)\n",
        "\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "3HOqRP6WYNfg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "0a5849f3-b155-4762-8c60-1248e0e26ded"
      },
      "id": "3HOqRP6WYNfg",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 5;\n",
              "                var nbb_unformatted_code = \"# function to create labeled barplots\\ndef labeled_barplot(data, feature, perc=False, n=None, percfmt=\\\"{:.1f}%\\\", rnd=0,\\n                    figsize=(10, 5), xlabel=None, xlo=0, title=None, sort=True, \\n                    orient='h'):\\n    \\\"\\\"\\\"\\n    Barplot with percentage at the top\\n\\n    data: dataframe\\n    perc: whether to display percentages instead of count (default is False)\\n    n: displays the top n category levels (default is None, i.e., display all levels)\\n    figsize: size of figure (default (10, 5))\\n    xlabel: label for x axis\\n    \\\"\\\"\\\"\\n\\n    fig, ax = plt.subplots(figsize=figsize)\\n    plt.xticks(rotation=xlo, fontsize=14)\\n    if orient == 'h':\\n      sns.countplot(data=data, x=feature, palette=\\\"Paired\\\",\\n                    order=data[feature].value_counts().index[:n] if sort else None)\\n    else:\\n      sns.countplot(data=data, y=feature, palette=\\\"Paired\\\",\\n                    order=data[feature].value_counts().index[:n] if sort else None)\\n\\n    # draw lables\\n    for p in ax.patches:\\n      if orient == 'h':\\n        if perc == True: label = percfmt.format(100 * p.get_height() / data.shape[0])\\n        else: label = p.get_height()\\n        ax.annotate(label, (p.get_x() + p.get_width() / 2, p.get_height()),\\n                    ha=\\\"center\\\", va=\\\"center\\\", size=11, xytext=(0, 5),\\n                    textcoords=\\\"offset points\\\")\\n        ax.set_ylabel('Number of bookings', fontsize=14)\\n        if xlabel: ax.set_xlabel(xlabel, fontsize=14)\\n      else:\\n        if perc == True:\\n          label = percfmt.format(100 * p.get_width() / data.shape[0])\\n        else:\\n          label = p.get_height()\\n        ax.annotate(label, (p.get_width(), p.get_y() + p.get_height() / 2),\\n                    ha=\\\"center\\\", va=\\\"center\\\", size=11, xytext=(15, 0),\\n                    textcoords=\\\"offset points\\\")\\n        ax.set_xlabel('Number of bookings', fontsize=14)\\n        if xlabel: ax.set_ylabel(xlabel, fontsize=14)\\n\\n    if title: plt.title(title, fontsize=16)\\n    else: plt.title(f'Distribution for `{feature}` column', fontsize=16)\\n\\n    plt.show()\";\n",
              "                var nbb_formatted_code = \"# function to create labeled barplots\\ndef labeled_barplot(\\n    data,\\n    feature,\\n    perc=False,\\n    n=None,\\n    percfmt=\\\"{:.1f}%\\\",\\n    rnd=0,\\n    figsize=(10, 5),\\n    xlabel=None,\\n    xlo=0,\\n    title=None,\\n    sort=True,\\n    orient=\\\"h\\\",\\n):\\n    \\\"\\\"\\\"\\n    Barplot with percentage at the top\\n\\n    data: dataframe\\n    perc: whether to display percentages instead of count (default is False)\\n    n: displays the top n category levels (default is None, i.e., display all levels)\\n    figsize: size of figure (default (10, 5))\\n    xlabel: label for x axis\\n    \\\"\\\"\\\"\\n\\n    fig, ax = plt.subplots(figsize=figsize)\\n    plt.xticks(rotation=xlo, fontsize=14)\\n    if orient == \\\"h\\\":\\n        sns.countplot(\\n            data=data,\\n            x=feature,\\n            palette=\\\"Paired\\\",\\n            order=data[feature].value_counts().index[:n] if sort else None,\\n        )\\n    else:\\n        sns.countplot(\\n            data=data,\\n            y=feature,\\n            palette=\\\"Paired\\\",\\n            order=data[feature].value_counts().index[:n] if sort else None,\\n        )\\n\\n    # draw lables\\n    for p in ax.patches:\\n        if orient == \\\"h\\\":\\n            if perc == True:\\n                label = percfmt.format(100 * p.get_height() / data.shape[0])\\n            else:\\n                label = p.get_height()\\n            ax.annotate(\\n                label,\\n                (p.get_x() + p.get_width() / 2, p.get_height()),\\n                ha=\\\"center\\\",\\n                va=\\\"center\\\",\\n                size=11,\\n                xytext=(0, 5),\\n                textcoords=\\\"offset points\\\",\\n            )\\n            ax.set_ylabel(\\\"Number of bookings\\\", fontsize=14)\\n            if xlabel:\\n                ax.set_xlabel(xlabel, fontsize=14)\\n        else:\\n            if perc == True:\\n                label = percfmt.format(100 * p.get_width() / data.shape[0])\\n            else:\\n                label = p.get_height()\\n            ax.annotate(\\n                label,\\n                (p.get_width(), p.get_y() + p.get_height() / 2),\\n                ha=\\\"center\\\",\\n                va=\\\"center\\\",\\n                size=11,\\n                xytext=(15, 0),\\n                textcoords=\\\"offset points\\\",\\n            )\\n            ax.set_xlabel(\\\"Number of bookings\\\", fontsize=14)\\n            if xlabel:\\n                ax.set_ylabel(xlabel, fontsize=14)\\n\\n    if title:\\n        plt.title(title, fontsize=16)\\n    else:\\n        plt.title(f\\\"Distribution for `{feature}` column\\\", fontsize=16)\\n\\n    plt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Stacked barplot"
      ],
      "metadata": {
        "id": "l2qA884UYX4G"
      },
      "id": "l2qA884UYX4G"
    },
    {
      "cell_type": "code",
      "source": [
        "def stacked_barplot(data, predictor, target):\n",
        "    \"\"\"\n",
        "    Print the category counts and plot a stacked bar chart\n",
        "\n",
        "    data: dataframe\n",
        "    predictor: independent variable\n",
        "    target: target variable\n",
        "    \"\"\"\n",
        "    count = data[predictor].nunique()\n",
        "    sorter = data[target].value_counts().index[-1]\n",
        "    tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(\n",
        "        by=sorter, ascending=False\n",
        "    )\n",
        "    print(tab1)\n",
        "    print(\"-\" * 120)\n",
        "    tab = pd.crosstab(data[predictor], data[target], normalize=\"index\").sort_values(\n",
        "        by=sorter, ascending=False\n",
        "    )\n",
        "    tab.plot(kind=\"bar\", stacked=True, figsize=(count + 5, 5))\n",
        "    plt.legend(\n",
        "        loc=\"lower left\", frameon=False,\n",
        "    )\n",
        "    plt.legend(loc=\"upper left\", bbox_to_anchor=(1, 1))\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "GXk4FPPTYX_u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "48c58dd3-d2bd-4823-ccba-570c51df8c4f"
      },
      "id": "GXk4FPPTYX_u",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 6;\n",
              "                var nbb_unformatted_code = \"def stacked_barplot(data, predictor, target):\\n    \\\"\\\"\\\"\\n    Print the category counts and plot a stacked bar chart\\n\\n    data: dataframe\\n    predictor: independent variable\\n    target: target variable\\n    \\\"\\\"\\\"\\n    count = data[predictor].nunique()\\n    sorter = data[target].value_counts().index[-1]\\n    tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(\\n        by=sorter, ascending=False\\n    )\\n    print(tab1)\\n    print(\\\"-\\\" * 120)\\n    tab = pd.crosstab(data[predictor], data[target], normalize=\\\"index\\\").sort_values(\\n        by=sorter, ascending=False\\n    )\\n    tab.plot(kind=\\\"bar\\\", stacked=True, figsize=(count + 5, 5))\\n    plt.legend(\\n        loc=\\\"lower left\\\", frameon=False,\\n    )\\n    plt.legend(loc=\\\"upper left\\\", bbox_to_anchor=(1, 1))\\n    plt.show()\";\n",
              "                var nbb_formatted_code = \"def stacked_barplot(data, predictor, target):\\n    \\\"\\\"\\\"\\n    Print the category counts and plot a stacked bar chart\\n\\n    data: dataframe\\n    predictor: independent variable\\n    target: target variable\\n    \\\"\\\"\\\"\\n    count = data[predictor].nunique()\\n    sorter = data[target].value_counts().index[-1]\\n    tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(\\n        by=sorter, ascending=False\\n    )\\n    print(tab1)\\n    print(\\\"-\\\" * 120)\\n    tab = pd.crosstab(data[predictor], data[target], normalize=\\\"index\\\").sort_values(\\n        by=sorter, ascending=False\\n    )\\n    tab.plot(kind=\\\"bar\\\", stacked=True, figsize=(count + 5, 5))\\n    plt.legend(\\n        loc=\\\"lower left\\\",\\n        frameon=False,\\n    )\\n    plt.legend(loc=\\\"upper left\\\", bbox_to_anchor=(1, 1))\\n    plt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Distributions wrt target"
      ],
      "metadata": {
        "id": "MmZ1yxPgYjUM"
      },
      "id": "MmZ1yxPgYjUM"
    },
    {
      "cell_type": "code",
      "source": [
        "def distribution_plot_wrt_target(data, predictor, target):\n",
        "\n",
        "    fig, axs = plt.subplots(2, 2, figsize=(12, 10))\n",
        "\n",
        "    target_uniq = data[target].unique()\n",
        "\n",
        "    axs[0, 0].set_title(\"Distribution of target for target=\" + str(target_uniq[0]))\n",
        "    sns.histplot(\n",
        "        data=data[data[target] == target_uniq[0]],\n",
        "        x=predictor,\n",
        "        kde=True,\n",
        "        ax=axs[0, 0],\n",
        "        color=\"teal\",\n",
        "        stat=\"density\",\n",
        "    )\n",
        "\n",
        "    axs[0, 1].set_title(\"Distribution of target for target=\" + str(target_uniq[1]))\n",
        "    sns.histplot(\n",
        "        data=data[data[target] == target_uniq[1]],\n",
        "        x=predictor,\n",
        "        kde=True,\n",
        "        ax=axs[0, 1],\n",
        "        color=\"orange\",\n",
        "        stat=\"density\",\n",
        "    )\n",
        "\n",
        "    axs[1, 0].set_title(\"Boxplot w.r.t target\")\n",
        "    sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette=\"gist_rainbow\")\n",
        "\n",
        "    axs[1, 1].set_title(\"Boxplot (without outliers) w.r.t target\")\n",
        "    sns.boxplot(\n",
        "        data=data,\n",
        "        x=target,\n",
        "        y=predictor,\n",
        "        ax=axs[1, 1],\n",
        "        showfliers=False,\n",
        "        palette=\"gist_rainbow\",\n",
        "    )\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "gss5an1AYjbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "2fb390c5-4894-4b21-d198-69607569667f"
      },
      "id": "gss5an1AYjbc",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 7;\n",
              "                var nbb_unformatted_code = \"def distribution_plot_wrt_target(data, predictor, target):\\n\\n    fig, axs = plt.subplots(2, 2, figsize=(12, 10))\\n\\n    target_uniq = data[target].unique()\\n\\n    axs[0, 0].set_title(\\\"Distribution of target for target=\\\" + str(target_uniq[0]))\\n    sns.histplot(\\n        data=data[data[target] == target_uniq[0]],\\n        x=predictor,\\n        kde=True,\\n        ax=axs[0, 0],\\n        color=\\\"teal\\\",\\n        stat=\\\"density\\\",\\n    )\\n\\n    axs[0, 1].set_title(\\\"Distribution of target for target=\\\" + str(target_uniq[1]))\\n    sns.histplot(\\n        data=data[data[target] == target_uniq[1]],\\n        x=predictor,\\n        kde=True,\\n        ax=axs[0, 1],\\n        color=\\\"orange\\\",\\n        stat=\\\"density\\\",\\n    )\\n\\n    axs[1, 0].set_title(\\\"Boxplot w.r.t target\\\")\\n    sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette=\\\"gist_rainbow\\\")\\n\\n    axs[1, 1].set_title(\\\"Boxplot (without outliers) w.r.t target\\\")\\n    sns.boxplot(\\n        data=data,\\n        x=target,\\n        y=predictor,\\n        ax=axs[1, 1],\\n        showfliers=False,\\n        palette=\\\"gist_rainbow\\\",\\n    )\\n\\n    plt.tight_layout()\\n    plt.show()\";\n",
              "                var nbb_formatted_code = \"def distribution_plot_wrt_target(data, predictor, target):\\n    fig, axs = plt.subplots(2, 2, figsize=(12, 10))\\n\\n    target_uniq = data[target].unique()\\n\\n    axs[0, 0].set_title(\\\"Distribution of target for target=\\\" + str(target_uniq[0]))\\n    sns.histplot(\\n        data=data[data[target] == target_uniq[0]],\\n        x=predictor,\\n        kde=True,\\n        ax=axs[0, 0],\\n        color=\\\"teal\\\",\\n        stat=\\\"density\\\",\\n    )\\n\\n    axs[0, 1].set_title(\\\"Distribution of target for target=\\\" + str(target_uniq[1]))\\n    sns.histplot(\\n        data=data[data[target] == target_uniq[1]],\\n        x=predictor,\\n        kde=True,\\n        ax=axs[0, 1],\\n        color=\\\"orange\\\",\\n        stat=\\\"density\\\",\\n    )\\n\\n    axs[1, 0].set_title(\\\"Boxplot w.r.t target\\\")\\n    sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette=\\\"gist_rainbow\\\")\\n\\n    axs[1, 1].set_title(\\\"Boxplot (without outliers) w.r.t target\\\")\\n    sns.boxplot(\\n        data=data,\\n        x=target,\\n        y=predictor,\\n        ax=axs[1, 1],\\n        showfliers=False,\\n        palette=\\\"gist_rainbow\\\",\\n    )\\n\\n    plt.tight_layout()\\n    plt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Functions to check and improve a classification model"
      ],
      "metadata": {
        "id": "8HgO1K5rdmxH"
      },
      "id": "8HgO1K5rdmxH"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Function to plot the confusion_matrix and to compute metrics"
      ],
      "metadata": {
        "id": "7egP1-loV3DK"
      },
      "id": "7egP1-loV3DK"
    },
    {
      "cell_type": "code",
      "source": [
        "# Defining a function to plot the confusion_matrix and to compute\n",
        "# different metrics to check performance of a classification model\n",
        "\n",
        "def model_performance(model, predictors, target, threshold=0.5, calc_only=False):\n",
        "  \"\"\"\n",
        "  Function to plot the confusion_matrix with percentages and to compute\n",
        "  different metrics to check classification model performance\n",
        "\n",
        "  model: classifier\n",
        "  predictors: independent variables\n",
        "  target: dependent variable\n",
        "  threshold: threshold for classifying the observation as class 1\n",
        "  \"\"\"\n",
        "\n",
        "  pred = round(pd.Series(model.predict(predictors) > threshold))\n",
        "\n",
        "  cm = confusion_matrix(target, pred)\n",
        "  labels = np.asarray([f'{item:.0f}\\n{item / cm.flatten().sum():.2%}' for item in cm.flatten()]).reshape(2, 2)\n",
        "\n",
        "  # creating a dataframe of metrics\n",
        "  metrics = pd.DataFrame(\n",
        "      {\"Accuracy\": accuracy_score(target, pred), \n",
        "       \"Recall\": recall_score(target, pred), \n",
        "       \"Precision\": precision_score(target, pred), \n",
        "       \"F1\": f1_score(target, pred)\n",
        "       },\n",
        "       index=[0],\n",
        "       )\n",
        "\n",
        "  if calc_only != True:\n",
        "    plt.figure(figsize=(6, 4))\n",
        "    sns.heatmap(cm, annot=labels, fmt=\"\")\n",
        "    plt.ylabel(\"True label\")\n",
        "    plt.xlabel(\"Predicted label\")\n",
        "    plt.show();\n",
        "\n",
        "    display(metrics);\n",
        "\n",
        "  return metrics"
      ],
      "metadata": {
        "id": "p2FjHbZOdm3S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "6ec9abd9-611b-48f7-ed05-4ae3592b5e8d"
      },
      "id": "p2FjHbZOdm3S",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 8;\n",
              "                var nbb_unformatted_code = \"# Defining a function to plot the confusion_matrix and to compute\\n# different metrics to check performance of a classification model\\n\\ndef model_performance(model, predictors, target, threshold=0.5, calc_only=False):\\n  \\\"\\\"\\\"\\n  Function to plot the confusion_matrix with percentages and to compute\\n  different metrics to check classification model performance\\n\\n  model: classifier\\n  predictors: independent variables\\n  target: dependent variable\\n  threshold: threshold for classifying the observation as class 1\\n  \\\"\\\"\\\"\\n\\n  pred = round(pd.Series(model.predict(predictors) > threshold))\\n\\n  cm = confusion_matrix(target, pred)\\n  labels = np.asarray([f'{item:.0f}\\\\n{item / cm.flatten().sum():.2%}' for item in cm.flatten()]).reshape(2, 2)\\n\\n  # creating a dataframe of metrics\\n  metrics = pd.DataFrame(\\n      {\\\"Accuracy\\\": accuracy_score(target, pred), \\n       \\\"Recall\\\": recall_score(target, pred), \\n       \\\"Precision\\\": precision_score(target, pred), \\n       \\\"F1\\\": f1_score(target, pred)\\n       },\\n       index=[0],\\n       )\\n\\n  if calc_only != True:\\n    plt.figure(figsize=(6, 4))\\n    sns.heatmap(cm, annot=labels, fmt=\\\"\\\")\\n    plt.ylabel(\\\"True label\\\")\\n    plt.xlabel(\\\"Predicted label\\\")\\n    plt.show();\\n\\n    display(metrics);\\n\\n  return metrics\";\n",
              "                var nbb_formatted_code = \"# Defining a function to plot the confusion_matrix and to compute\\n# different metrics to check performance of a classification model\\n\\n\\ndef model_performance(model, predictors, target, threshold=0.5, calc_only=False):\\n    \\\"\\\"\\\"\\n    Function to plot the confusion_matrix with percentages and to compute\\n    different metrics to check classification model performance\\n\\n    model: classifier\\n    predictors: independent variables\\n    target: dependent variable\\n    threshold: threshold for classifying the observation as class 1\\n    \\\"\\\"\\\"\\n\\n    pred = round(pd.Series(model.predict(predictors) > threshold))\\n\\n    cm = confusion_matrix(target, pred)\\n    labels = np.asarray(\\n        [f\\\"{item:.0f}\\\\n{item / cm.flatten().sum():.2%}\\\" for item in cm.flatten()]\\n    ).reshape(2, 2)\\n\\n    # creating a dataframe of metrics\\n    metrics = pd.DataFrame(\\n        {\\n            \\\"Accuracy\\\": accuracy_score(target, pred),\\n            \\\"Recall\\\": recall_score(target, pred),\\n            \\\"Precision\\\": precision_score(target, pred),\\n            \\\"F1\\\": f1_score(target, pred),\\n        },\\n        index=[0],\\n    )\\n\\n    if calc_only != True:\\n        plt.figure(figsize=(6, 4))\\n        sns.heatmap(cm, annot=labels, fmt=\\\"\\\")\\n        plt.ylabel(\\\"True label\\\")\\n        plt.xlabel(\\\"Predicted label\\\")\\n        plt.show()\\n\\n        display(metrics)\\n\\n    return metrics\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Function for Variance Inflation Factor"
      ],
      "metadata": {
        "id": "PeRrDB_kYpRv"
      },
      "id": "PeRrDB_kYpRv"
    },
    {
      "cell_type": "code",
      "source": [
        "# Defining a function for Variance Inflation Factor\n",
        "\n",
        "def checking_vif(predictors):\n",
        "  vif = pd.DataFrame()\n",
        "  vif[\"feature\"] = predictors.columns\n",
        "\n",
        "  # calculating VIF for each feature\n",
        "  vif[\"VIF\"] = [variance_inflation_factor(predictors.values, i) for i in range(len(predictors.columns))]\n",
        "\n",
        "  return vif"
      ],
      "metadata": {
        "id": "-LyVgUwkYvDs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "51591752-8cc0-46c7-e06b-1f3e0de6eff5"
      },
      "id": "-LyVgUwkYvDs",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 9;\n",
              "                var nbb_unformatted_code = \"# Defining a function for Variance Inflation Factor\\n\\ndef checking_vif(predictors):\\n  vif = pd.DataFrame()\\n  vif[\\\"feature\\\"] = predictors.columns\\n\\n  # calculating VIF for each feature\\n  vif[\\\"VIF\\\"] = [variance_inflation_factor(predictors.values, i) for i in range(len(predictors.columns))]\\n\\n  return vif\";\n",
              "                var nbb_formatted_code = \"# Defining a function for Variance Inflation Factor\\n\\n\\ndef checking_vif(predictors):\\n    vif = pd.DataFrame()\\n    vif[\\\"feature\\\"] = predictors.columns\\n\\n    # calculating VIF for each feature\\n    vif[\\\"VIF\\\"] = [\\n        variance_inflation_factor(predictors.values, i)\\n        for i in range(len(predictors.columns))\\n    ]\\n\\n    return vif\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Function for Multicollinearity problem solving"
      ],
      "metadata": {
        "id": "244oPG27df1-"
      },
      "id": "244oPG27df1-"
    },
    {
      "cell_type": "code",
      "source": [
        "# Defining a function for multicollinearity problem solving\n",
        "\n",
        "def mc_solving(model_perf, X, y):\n",
        "# Outputting the VIF value for top-10 of all the variables\n",
        "# Removing variables with high VIF\n",
        "\n",
        "  predictors = X.copy()\n",
        "  target = y.copy()\n",
        "  res = model_perf.copy()\n",
        "\n",
        "  res['feature to remove'] = '-= Base model with all vars =-'\n",
        "  res['VIF'] = 0.0\n",
        "  n = 0\n",
        "\n",
        "  vif = checking_vif(predictors).sort_values(by='VIF', ascending=False)\n",
        "  vif = vif[vif['VIF']>=5]\n",
        "  var_to_remove = vif.iloc[0].feature\n",
        "\n",
        "  while (len(vif) > 1) & (var_to_remove != ''):\n",
        "    n += 1\n",
        "    r = res.copy()\n",
        "    print(f'\\033[91m\\nIteration # {n}\\033[0m')\n",
        "    for feature in vif.feature[1:]:\n",
        "      item  = list(model_performance(\n",
        "          sm.Logit(target, predictors.drop(feature, axis=1)).fit(method='bfgs', disp=False),\n",
        "          predictors.drop(feature, axis=1), target, calc_only=True).values.flatten())\n",
        "      \n",
        "      item += [feature]\n",
        "      item += [vif[vif['feature'] == feature]['VIF'].values[0]]\n",
        "\n",
        "      r.loc[len(r.index)] = item\n",
        "    \n",
        "    display(r.sort_values('Recall', ascending=False))\n",
        "    \n",
        "    var_to_remove = r[1:].sort_values('Recall', ascending=False)['feature to remove'].iloc[0]\n",
        "    print(f'\\033[1m\\nColumn to remove [{var_to_remove}]:\\033[0m', sep='')\n",
        "    var_to_remove = input()\n",
        "\n",
        "    if var_to_remove:\n",
        "      predictors.drop(var_to_remove, axis=1, inplace=True)\n",
        "      vif = checking_vif(predictors).sort_values(by='VIF', ascending=False)\n",
        "      vif = vif[vif['VIF']>=5]\n",
        "    else:\n",
        "      break\n",
        "\n",
        "  print(f'\\n\\033[32m\\033[1mMulticollinearity in the dataset has been resolved.\\033[0m')\n",
        "\n",
        "  return predictors.columns"
      ],
      "metadata": {
        "id": "-bsu5m6PdgA9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "0e79960b-d889-4eb3-8bfd-9fbafe52b63d"
      },
      "id": "-bsu5m6PdgA9",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 10;\n",
              "                var nbb_unformatted_code = \"# Defining a function for multicollinearity problem solving\\n\\ndef mc_solving(model_perf, X, y):\\n# Outputting the VIF value for top-10 of all the variables\\n# Removing variables with high VIF\\n\\n  predictors = X.copy()\\n  target = y.copy()\\n  res = model_perf.copy()\\n\\n  res['feature to remove'] = '-= Base model with all vars =-'\\n  res['VIF'] = 0.0\\n  n = 0\\n\\n  vif = checking_vif(predictors).sort_values(by='VIF', ascending=False)\\n  vif = vif[vif['VIF']>=5]\\n  var_to_remove = vif.iloc[0].feature\\n\\n  while (len(vif) > 1) & (var_to_remove != ''):\\n    n += 1\\n    r = res.copy()\\n    print(f'\\\\033[91m\\\\nIteration # {n}\\\\033[0m')\\n    for feature in vif.feature[1:]:\\n      item  = list(model_performance(\\n          sm.Logit(target, predictors.drop(feature, axis=1)).fit(method='bfgs', disp=False),\\n          predictors.drop(feature, axis=1), target, calc_only=True).values.flatten())\\n      \\n      item += [feature]\\n      item += [vif[vif['feature'] == feature]['VIF'].values[0]]\\n\\n      r.loc[len(r.index)] = item\\n    \\n    display(r.sort_values('Recall', ascending=False))\\n    \\n    var_to_remove = r[1:].sort_values('Recall', ascending=False)['feature to remove'].iloc[0]\\n    print(f'\\\\033[1m\\\\nColumn to remove [{var_to_remove}]:\\\\033[0m', sep='')\\n    var_to_remove = input()\\n\\n    if var_to_remove:\\n      predictors.drop(var_to_remove, axis=1, inplace=True)\\n      vif = checking_vif(predictors).sort_values(by='VIF', ascending=False)\\n      vif = vif[vif['VIF']>=5]\\n    else:\\n      break\\n\\n  print(f'\\\\n\\\\033[32m\\\\033[1mMulticollinearity in the dataset has been resolved.\\\\033[0m')\\n\\n  return predictors.columns\";\n",
              "                var nbb_formatted_code = \"# Defining a function for multicollinearity problem solving\\n\\n\\ndef mc_solving(model_perf, X, y):\\n    # Outputting the VIF value for top-10 of all the variables\\n    # Removing variables with high VIF\\n\\n    predictors = X.copy()\\n    target = y.copy()\\n    res = model_perf.copy()\\n\\n    res[\\\"feature to remove\\\"] = \\\"-= Base model with all vars =-\\\"\\n    res[\\\"VIF\\\"] = 0.0\\n    n = 0\\n\\n    vif = checking_vif(predictors).sort_values(by=\\\"VIF\\\", ascending=False)\\n    vif = vif[vif[\\\"VIF\\\"] >= 5]\\n    var_to_remove = vif.iloc[0].feature\\n\\n    while (len(vif) > 1) & (var_to_remove != \\\"\\\"):\\n        n += 1\\n        r = res.copy()\\n        print(f\\\"\\\\033[91m\\\\nIteration # {n}\\\\033[0m\\\")\\n        for feature in vif.feature[1:]:\\n            item = list(\\n                model_performance(\\n                    sm.Logit(target, predictors.drop(feature, axis=1)).fit(\\n                        method=\\\"bfgs\\\", disp=False\\n                    ),\\n                    predictors.drop(feature, axis=1),\\n                    target,\\n                    calc_only=True,\\n                ).values.flatten()\\n            )\\n\\n            item += [feature]\\n            item += [vif[vif[\\\"feature\\\"] == feature][\\\"VIF\\\"].values[0]]\\n\\n            r.loc[len(r.index)] = item\\n\\n        display(r.sort_values(\\\"Recall\\\", ascending=False))\\n\\n        var_to_remove = (\\n            r[1:].sort_values(\\\"Recall\\\", ascending=False)[\\\"feature to remove\\\"].iloc[0]\\n        )\\n        print(f\\\"\\\\033[1m\\\\nColumn to remove [{var_to_remove}]:\\\\033[0m\\\", sep=\\\"\\\")\\n        var_to_remove = input()\\n\\n        if var_to_remove:\\n            predictors.drop(var_to_remove, axis=1, inplace=True)\\n            vif = checking_vif(predictors).sort_values(by=\\\"VIF\\\", ascending=False)\\n            vif = vif[vif[\\\"VIF\\\"] >= 5]\\n        else:\\n            break\\n\\n    print(\\n        f\\\"\\\\n\\\\033[32m\\\\033[1mMulticollinearity in the dataset has been resolved.\\\\033[0m\\\"\\n    )\\n\\n    return predictors.columns\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Function for high p-value variables treatment"
      ],
      "metadata": {
        "id": "bl-XW0SOeC1H"
      },
      "id": "bl-XW0SOeC1H"
    },
    {
      "cell_type": "code",
      "source": [
        "# Defining a function to remove variables with high p-value from a model\n",
        "def pv_solving(predictors):\n",
        "  selected_features = predictors.columns.tolist()\n",
        "  max_p_value = 1\n",
        "\n",
        "  while len(selected_features) > 0:\n",
        "      model = sm.Logit(y_train, predictors[selected_features]).fit(disp=False, method='bfgs')\n",
        "\n",
        "      if max(model.pvalues) > 0.05:\n",
        "          selected_features.remove(model.pvalues.idxmax())\n",
        "          print(f'Column `{model.pvalues.idxmax()}` has been removed')\n",
        "      else:\n",
        "          break\n",
        "\n",
        "  return selected_features"
      ],
      "metadata": {
        "id": "NmrFbqi2gQCG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "8c152218-3351-4c33-b0d3-d020d2c4e99b"
      },
      "id": "NmrFbqi2gQCG",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 11;\n",
              "                var nbb_unformatted_code = \"# Defining a function to remove variables with high p-value from a model\\ndef pv_solving(predictors):\\n  selected_features = predictors.columns.tolist()\\n  max_p_value = 1\\n\\n  while len(selected_features) > 0:\\n      model = sm.Logit(y_train, predictors[selected_features]).fit(disp=False, method='bfgs')\\n\\n      if max(model.pvalues) > 0.05:\\n          selected_features.remove(model.pvalues.idxmax())\\n          print(f'Column `{model.pvalues.idxmax()}` has been removed')\\n      else:\\n          break\\n\\n  return selected_features\";\n",
              "                var nbb_formatted_code = \"# Defining a function to remove variables with high p-value from a model\\ndef pv_solving(predictors):\\n    selected_features = predictors.columns.tolist()\\n    max_p_value = 1\\n\\n    while len(selected_features) > 0:\\n        model = sm.Logit(y_train, predictors[selected_features]).fit(\\n            disp=False, method=\\\"bfgs\\\"\\n        )\\n\\n        if max(model.pvalues) > 0.05:\\n            selected_features.remove(model.pvalues.idxmax())\\n            print(f\\\"Column `{model.pvalues.idxmax()}` has been removed\\\")\\n        else:\\n            break\\n\\n    return selected_features\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "desperate-infection",
      "metadata": {
        "id": "desperate-infection"
      },
      "source": [
        "# Data Overview\n",
        "\n",
        "- [x] Observations\n",
        "- [x] Sanity checks"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Displaying few first and last rows of the dataset"
      ],
      "metadata": {
        "id": "61Wnldw0IMgj"
      },
      "id": "61Wnldw0IMgj"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "persistent-juice",
      "metadata": {
        "id": "persistent-juice",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "outputId": "b29e9eba-bf23-4b7c-a19c-27dfd2aaa76d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Booking_ID  no_of_adults  no_of_children  no_of_weekend_nights  \\\n",
              "0   INN00001             2               0                     1   \n",
              "1   INN00002             2               0                     2   \n",
              "2   INN00003             1               0                     2   \n",
              "3   INN00004             2               0                     0   \n",
              "4   INN00005             2               0                     1   \n",
              "\n",
              "   no_of_week_nights type_of_meal_plan  required_car_parking_space  \\\n",
              "0                  2       Meal Plan 1                           0   \n",
              "1                  3      Not Selected                           0   \n",
              "2                  1       Meal Plan 1                           0   \n",
              "3                  2       Meal Plan 1                           0   \n",
              "4                  1      Not Selected                           0   \n",
              "\n",
              "  room_type_reserved  lead_time  arrival_year  arrival_month  arrival_date  \\\n",
              "0        Room_Type 1        224          2017             10             2   \n",
              "1        Room_Type 1          5          2018             11             6   \n",
              "2        Room_Type 1          1          2018              2            28   \n",
              "3        Room_Type 1        211          2018              5            20   \n",
              "4        Room_Type 1         48          2018              4            11   \n",
              "\n",
              "  market_segment_type  repeated_guest  no_of_previous_cancellations  \\\n",
              "0             Offline               0                             0   \n",
              "1              Online               0                             0   \n",
              "2              Online               0                             0   \n",
              "3              Online               0                             0   \n",
              "4              Online               0                             0   \n",
              "\n",
              "   no_of_previous_bookings_not_canceled  avg_price_per_room  \\\n",
              "0                                     0            65.00000   \n",
              "1                                     0           106.68000   \n",
              "2                                     0            60.00000   \n",
              "3                                     0           100.00000   \n",
              "4                                     0            94.50000   \n",
              "\n",
              "   no_of_special_requests booking_status  \n",
              "0                       0   Not_Canceled  \n",
              "1                       1   Not_Canceled  \n",
              "2                       0       Canceled  \n",
              "3                       0       Canceled  \n",
              "4                       0       Canceled  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c2d2f6a3-7417-487f-9c0f-9a83592c9a96\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Booking_ID</th>\n",
              "      <th>no_of_adults</th>\n",
              "      <th>no_of_children</th>\n",
              "      <th>no_of_weekend_nights</th>\n",
              "      <th>no_of_week_nights</th>\n",
              "      <th>type_of_meal_plan</th>\n",
              "      <th>required_car_parking_space</th>\n",
              "      <th>room_type_reserved</th>\n",
              "      <th>lead_time</th>\n",
              "      <th>arrival_year</th>\n",
              "      <th>arrival_month</th>\n",
              "      <th>arrival_date</th>\n",
              "      <th>market_segment_type</th>\n",
              "      <th>repeated_guest</th>\n",
              "      <th>no_of_previous_cancellations</th>\n",
              "      <th>no_of_previous_bookings_not_canceled</th>\n",
              "      <th>avg_price_per_room</th>\n",
              "      <th>no_of_special_requests</th>\n",
              "      <th>booking_status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>INN00001</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Meal Plan 1</td>\n",
              "      <td>0</td>\n",
              "      <td>Room_Type 1</td>\n",
              "      <td>224</td>\n",
              "      <td>2017</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>Offline</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>65.00000</td>\n",
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              "      <td>Online</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>106.68000</td>\n",
              "      <td>1</td>\n",
              "      <td>Not_Canceled</td>\n",
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              "    <tr>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>2018</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>Online</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>60.00000</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>2</td>\n",
              "      <td>Meal Plan 1</td>\n",
              "      <td>0</td>\n",
              "      <td>Room_Type 1</td>\n",
              "      <td>211</td>\n",
              "      <td>2018</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "      <td>Online</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>Canceled</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>INN00005</td>\n",
              "      <td>2</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>48</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>Online</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>94.50000</td>\n",
              "      <td>0</td>\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 12;\n",
              "                var nbb_unformatted_code = \"# view the first five rows of the dataset\\ndf.head()\";\n",
              "                var nbb_formatted_code = \"# view the first five rows of the dataset\\ndf.head()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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      "source": [
        "# view the first five rows of the dataset\n",
        "df.head()"
      ]
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    {
      "cell_type": "code",
      "source": [
        "# view the first five rows of the dataset\n",
        "df.tail()"
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          "height": 270
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        "id": "ar3V_adUIOyj",
        "outputId": "8ef48b03-f370-41a1-cddf-78610a0d0c4c"
      },
      "id": "ar3V_adUIOyj",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Booking_ID  no_of_adults  no_of_children  no_of_weekend_nights  \\\n",
              "36270   INN36271             3               0                     2   \n",
              "36271   INN36272             2               0                     1   \n",
              "36272   INN36273             2               0                     2   \n",
              "36273   INN36274             2               0                     0   \n",
              "36274   INN36275             2               0                     1   \n",
              "\n",
              "       no_of_week_nights type_of_meal_plan  required_car_parking_space  \\\n",
              "36270                  6       Meal Plan 1                           0   \n",
              "36271                  3       Meal Plan 1                           0   \n",
              "36272                  6       Meal Plan 1                           0   \n",
              "36273                  3      Not Selected                           0   \n",
              "36274                  2       Meal Plan 1                           0   \n",
              "\n",
              "      room_type_reserved  lead_time  arrival_year  arrival_month  \\\n",
              "36270        Room_Type 4         85          2018              8   \n",
              "36271        Room_Type 1        228          2018             10   \n",
              "36272        Room_Type 1        148          2018              7   \n",
              "36273        Room_Type 1         63          2018              4   \n",
              "36274        Room_Type 1        207          2018             12   \n",
              "\n",
              "       arrival_date market_segment_type  repeated_guest  \\\n",
              "36270             3              Online               0   \n",
              "36271            17              Online               0   \n",
              "36272             1              Online               0   \n",
              "36273            21              Online               0   \n",
              "36274            30             Offline               0   \n",
              "\n",
              "       no_of_previous_cancellations  no_of_previous_bookings_not_canceled  \\\n",
              "36270                             0                                     0   \n",
              "36271                             0                                     0   \n",
              "36272                             0                                     0   \n",
              "36273                             0                                     0   \n",
              "36274                             0                                     0   \n",
              "\n",
              "       avg_price_per_room  no_of_special_requests booking_status  \n",
              "36270           167.80000                       1   Not_Canceled  \n",
              "36271            90.95000                       2       Canceled  \n",
              "36272            98.39000                       2   Not_Canceled  \n",
              "36273            94.50000                       0       Canceled  \n",
              "36274           161.67000                       0   Not_Canceled  "
            ],
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              "      <td>INN36274</td>\n",
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              "      <td>2018</td>\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 13;\n",
              "                var nbb_unformatted_code = \"# view the first five rows of the dataset\\ndf.tail()\";\n",
              "                var nbb_formatted_code = \"# view the first five rows of the dataset\\ndf.tail()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Distribution of variables in the data"
      ],
      "metadata": {
        "id": "WBQZJqAXddYa"
      },
      "id": "WBQZJqAXddYa"
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_style(\"darkgrid\")\n",
        "df.hist(figsize=(15, 12))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "jHryH-6XdTLp",
        "outputId": "299524c8-b294-46a8-e588-8aca659503ae"
      },
      "id": "jHryH-6XdTLp",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x864 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 14;\n",
              "                var nbb_unformatted_code = \"sns.set_style(\\\"darkgrid\\\")\\ndf.hist(figsize=(15, 12))\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"sns.set_style(\\\"darkgrid\\\")\\ndf.hist(figsize=(15, 12))\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in df.select_dtypes(include='object').columns[1:]:\n",
        "  print(df.value_counts(col))\n",
        "  print('-'*60)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "id": "eKV_dmSSIxNk",
        "outputId": "9b6cb59a-aba2-46ed-a5b5-97352ef1d1ac"
      },
      "id": "eKV_dmSSIxNk",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "type_of_meal_plan\n",
            "Meal Plan 1     27835\n",
            "Not Selected     5130\n",
            "Meal Plan 2      3305\n",
            "Meal Plan 3         5\n",
            "dtype: int64\n",
            "------------------------------------------------------------\n",
            "room_type_reserved\n",
            "Room_Type 1    28130\n",
            "Room_Type 4     6057\n",
            "Room_Type 6      966\n",
            "Room_Type 2      692\n",
            "Room_Type 5      265\n",
            "Room_Type 7      158\n",
            "Room_Type 3        7\n",
            "dtype: int64\n",
            "------------------------------------------------------------\n",
            "market_segment_type\n",
            "Online           23214\n",
            "Offline          10528\n",
            "Corporate         2017\n",
            "Complementary      391\n",
            "Aviation           125\n",
            "dtype: int64\n",
            "------------------------------------------------------------\n",
            "booking_status\n",
            "Not_Canceled    24390\n",
            "Canceled        11885\n",
            "dtype: int64\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 15;\n",
              "                var nbb_unformatted_code = \"for col in df.select_dtypes(include='object').columns[1:]:\\n  print(df.value_counts(col))\\n  print('-'*60)\";\n",
              "                var nbb_formatted_code = \"for col in df.select_dtypes(include=\\\"object\\\").columns[1:]:\\n    print(df.value_counts(col))\\n    print(\\\"-\\\" * 60)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Booking_ID'].nunique()-df.shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "apZdCeJTJK1s",
        "outputId": "b17da21a-9a58-4596-9d21-9b9e6b2de8ea"
      },
      "id": "apZdCeJTJK1s",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 16;\n",
              "                var nbb_unformatted_code = \"df['Booking_ID'].nunique()-df.shape[0]\";\n",
              "                var nbb_formatted_code = \"df[\\\"Booking_ID\\\"].nunique() - df.shape[0]\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The data contains the different attributes of customers' booking details\n",
        "* There are three types of meal plans those are 1, 2, and 3, and `Not Selected`\n",
        "* There are seven types of rooms those are from 1 to 7\n",
        "* There are five types of market segments those are `Online`, `Offline`, `Corporate`, `Complementary`, and `Aviation`\n",
        "* There are two values (classes) of the dependent variable `booking_status`: `Not_Canceled` and `Canceled'\n",
        "* The `Booking_ID` column is containing unique values"
      ],
      "metadata": {
        "id": "4s_yLNgVIfVb"
      },
      "id": "4s_yLNgVIfVb"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Get information about the number of rows and columns in the dataset"
      ],
      "metadata": {
        "id": "1I7JaeuxK-Fb"
      },
      "id": "1I7JaeuxK-Fb"
    },
    {
      "cell_type": "code",
      "source": [
        "# view the shape of the dataset\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "9aQHTJsjK9N7",
        "outputId": "588d6aab-dc1b-4af8-e273-90fc76a2389f"
      },
      "id": "9aQHTJsjK9N7",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(36275, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 17;\n",
              "                var nbb_unformatted_code = \"# view the shape of the dataset\\ndf.shape\";\n",
              "                var nbb_formatted_code = \"# view the shape of the dataset\\ndf.shape\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset contains 36275 rows and 19 columns - 19 attributes/factors of 36275 bookings"
      ],
      "metadata": {
        "id": "R5ZYNnIVLFcY"
      },
      "id": "R5ZYNnIVLFcY"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checking the data types of the columns for the dataset"
      ],
      "metadata": {
        "id": "Ed31SnjCLTDS"
      },
      "id": "Ed31SnjCLTDS"
    },
    {
      "cell_type": "code",
      "source": [
        "# view concise summary of the dataset structure\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "KsE1SrSsK9RB",
        "outputId": "d57984e5-25b6-40b9-afbb-b3b09275a693"
      },
      "id": "KsE1SrSsK9RB",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 36275 entries, 0 to 36274\n",
            "Data columns (total 19 columns):\n",
            " #   Column                                Non-Null Count  Dtype  \n",
            "---  ------                                --------------  -----  \n",
            " 0   Booking_ID                            36275 non-null  object \n",
            " 1   no_of_adults                          36275 non-null  int64  \n",
            " 2   no_of_children                        36275 non-null  int64  \n",
            " 3   no_of_weekend_nights                  36275 non-null  int64  \n",
            " 4   no_of_week_nights                     36275 non-null  int64  \n",
            " 5   type_of_meal_plan                     36275 non-null  object \n",
            " 6   required_car_parking_space            36275 non-null  int64  \n",
            " 7   room_type_reserved                    36275 non-null  object \n",
            " 8   lead_time                             36275 non-null  int64  \n",
            " 9   arrival_year                          36275 non-null  int64  \n",
            " 10  arrival_month                         36275 non-null  int64  \n",
            " 11  arrival_date                          36275 non-null  int64  \n",
            " 12  market_segment_type                   36275 non-null  object \n",
            " 13  repeated_guest                        36275 non-null  int64  \n",
            " 14  no_of_previous_cancellations          36275 non-null  int64  \n",
            " 15  no_of_previous_bookings_not_canceled  36275 non-null  int64  \n",
            " 16  avg_price_per_room                    36275 non-null  float64\n",
            " 17  no_of_special_requests                36275 non-null  int64  \n",
            " 18  booking_status                        36275 non-null  object \n",
            "dtypes: float64(1), int64(13), object(5)\n",
            "memory usage: 5.3+ MB\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 18;\n",
              "                var nbb_unformatted_code = \"# view concise summary of the dataset structure\\ndf.info()\";\n",
              "                var nbb_formatted_code = \"# view concise summary of the dataset structure\\ndf.info()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* There are 14 numeric (1 *float* and 13 *int* types) and 5 string (*object* type) columns in the dataset\n",
        "* The target variable is the `booking_status ` which is of *object* type"
      ],
      "metadata": {
        "id": "JOSE9hbeLWgl"
      },
      "id": "JOSE9hbeLWgl"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checking for missing values"
      ],
      "metadata": {
        "id": "99YzvGv_L6n3"
      },
      "id": "99YzvGv_L6n3"
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "B1LbS4SvK9UB",
        "outputId": "3355f2a1-4ad9-4d31-a307-1cba5845333c"
      },
      "id": "B1LbS4SvK9UB",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 19;\n",
              "                var nbb_unformatted_code = \"df.isna().sum().sum()\";\n",
              "                var nbb_formatted_code = \"df.isna().sum().sum()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* There are no missing (Null, NA) values in the dataset."
      ],
      "metadata": {
        "id": "RwS9qbWYMBFh"
      },
      "id": "RwS9qbWYMBFh"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dropping the duplicate values"
      ],
      "metadata": {
        "id": "VZOxPw4aMEK7"
      },
      "id": "VZOxPw4aMEK7"
    },
    {
      "cell_type": "code",
      "source": [
        "# checking for duplicate values\n",
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "n5jO-_eGMETy",
        "outputId": "9577e96f-f9b4-46a3-dfc4-3135856f66ed"
      },
      "id": "n5jO-_eGMETy",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 20;\n",
              "                var nbb_unformatted_code = \"# checking for duplicate values\\ndf.duplicated().sum()\";\n",
              "                var nbb_formatted_code = \"# checking for duplicate values\\ndf.duplicated().sum()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are no duplicate values in the dataset"
      ],
      "metadata": {
        "id": "Xz5Q4uY1MEcL"
      },
      "id": "Xz5Q4uY1MEcL"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Statistical summary of the dataset\n",
        "\n",
        "Let's check the statistical summary of the data."
      ],
      "metadata": {
        "id": "ovq8DTRVMo9O"
      },
      "id": "ovq8DTRVMo9O"
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        },
        "id": "nHpsXe1tMoHc",
        "outputId": "2c8c6290-7ffd-4965-bfdf-495da408e2f4"
      },
      "id": "nHpsXe1tMoHc",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           count       mean      std  \\\n",
              "no_of_adults                         36275.00000    1.84496  0.51871   \n",
              "no_of_children                       36275.00000    0.10528  0.40265   \n",
              "no_of_weekend_nights                 36275.00000    0.81072  0.87064   \n",
              "no_of_week_nights                    36275.00000    2.20430  1.41090   \n",
              "required_car_parking_space           36275.00000    0.03099  0.17328   \n",
              "lead_time                            36275.00000   85.23256 85.93082   \n",
              "arrival_year                         36275.00000 2017.82043  0.38384   \n",
              "arrival_month                        36275.00000    7.42365  3.06989   \n",
              "arrival_date                         36275.00000   15.59700  8.74045   \n",
              "repeated_guest                       36275.00000    0.02564  0.15805   \n",
              "no_of_previous_cancellations         36275.00000    0.02335  0.36833   \n",
              "no_of_previous_bookings_not_canceled 36275.00000    0.15341  1.75417   \n",
              "avg_price_per_room                   36275.00000  103.42354 35.08942   \n",
              "no_of_special_requests               36275.00000    0.61966  0.78624   \n",
              "\n",
              "                                            min        25%        50%  \\\n",
              "no_of_adults                            0.00000    2.00000    2.00000   \n",
              "no_of_children                          0.00000    0.00000    0.00000   \n",
              "no_of_weekend_nights                    0.00000    0.00000    1.00000   \n",
              "no_of_week_nights                       0.00000    1.00000    2.00000   \n",
              "required_car_parking_space              0.00000    0.00000    0.00000   \n",
              "lead_time                               0.00000   17.00000   57.00000   \n",
              "arrival_year                         2017.00000 2018.00000 2018.00000   \n",
              "arrival_month                           1.00000    5.00000    8.00000   \n",
              "arrival_date                            1.00000    8.00000   16.00000   \n",
              "repeated_guest                          0.00000    0.00000    0.00000   \n",
              "no_of_previous_cancellations            0.00000    0.00000    0.00000   \n",
              "no_of_previous_bookings_not_canceled    0.00000    0.00000    0.00000   \n",
              "avg_price_per_room                      0.00000   80.30000   99.45000   \n",
              "no_of_special_requests                  0.00000    0.00000    0.00000   \n",
              "\n",
              "                                            75%        max  \n",
              "no_of_adults                            2.00000    4.00000  \n",
              "no_of_children                          0.00000   10.00000  \n",
              "no_of_weekend_nights                    2.00000    7.00000  \n",
              "no_of_week_nights                       3.00000   17.00000  \n",
              "required_car_parking_space              0.00000    1.00000  \n",
              "lead_time                             126.00000  443.00000  \n",
              "arrival_year                         2018.00000 2018.00000  \n",
              "arrival_month                          10.00000   12.00000  \n",
              "arrival_date                           23.00000   31.00000  \n",
              "repeated_guest                          0.00000    1.00000  \n",
              "no_of_previous_cancellations            0.00000   13.00000  \n",
              "no_of_previous_bookings_not_canceled    0.00000   58.00000  \n",
              "avg_price_per_room                    120.00000  540.00000  \n",
              "no_of_special_requests                  1.00000    5.00000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aa348a33-0a27-4883-9e18-7581a86476cb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>no_of_adults</th>\n",
              "      <td>36275.00000</td>\n",
              "      <td>1.84496</td>\n",
              "      <td>0.51871</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>4.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no_of_children</th>\n",
              "      <td>36275.00000</td>\n",
              "      <td>0.10528</td>\n",
              "      <td>0.40265</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>10.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no_of_weekend_nights</th>\n",
              "      <td>36275.00000</td>\n",
              "      <td>0.81072</td>\n",
              "      <td>0.87064</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>7.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no_of_week_nights</th>\n",
              "      <td>36275.00000</td>\n",
              "      <td>2.20430</td>\n",
              "      <td>1.41090</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>3.00000</td>\n",
              "      <td>17.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>required_car_parking_space</th>\n",
              "      <td>36275.00000</td>\n",
              "      <td>0.03099</td>\n",
              "      <td>0.17328</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lead_time</th>\n",
              "      <td>36275.00000</td>\n",
              "      <td>85.23256</td>\n",
              "      <td>85.93082</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>17.00000</td>\n",
              "      <td>57.00000</td>\n",
              "      <td>126.00000</td>\n",
              "      <td>443.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arrival_year</th>\n",
              "      <td>36275.00000</td>\n",
              "      <td>2017.82043</td>\n",
              "      <td>0.38384</td>\n",
              "      <td>2017.00000</td>\n",
              "      <td>2018.00000</td>\n",
              "      <td>2018.00000</td>\n",
              "      <td>2018.00000</td>\n",
              "      <td>2018.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arrival_month</th>\n",
              "      <td>36275.00000</td>\n",
              "      <td>7.42365</td>\n",
              "      <td>3.06989</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>5.00000</td>\n",
              "      <td>8.00000</td>\n",
              "      <td>10.00000</td>\n",
              "      <td>12.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arrival_date</th>\n",
              "      <td>36275.00000</td>\n",
              "      <td>15.59700</td>\n",
              "      <td>8.74045</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>8.00000</td>\n",
              "      <td>16.00000</td>\n",
              "      <td>23.00000</td>\n",
              "      <td>31.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>repeated_guest</th>\n",
              "      <td>36275.00000</td>\n",
              "      <td>0.02564</td>\n",
              "      <td>0.15805</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no_of_previous_cancellations</th>\n",
              "      <td>36275.00000</td>\n",
              "      <td>0.02335</td>\n",
              "      <td>0.36833</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>13.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no_of_previous_bookings_not_canceled</th>\n",
              "      <td>36275.00000</td>\n",
              "      <td>0.15341</td>\n",
              "      <td>1.75417</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>58.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_price_per_room</th>\n",
              "      <td>36275.00000</td>\n",
              "      <td>103.42354</td>\n",
              "      <td>35.08942</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>80.30000</td>\n",
              "      <td>99.45000</td>\n",
              "      <td>120.00000</td>\n",
              "      <td>540.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no_of_special_requests</th>\n",
              "      <td>36275.00000</td>\n",
              "      <td>0.61966</td>\n",
              "      <td>0.78624</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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      "cell_type": "code",
      "source": [
        "df.describe(include='object').T"
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "lyY1yf9CVFaD",
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      "id": "lyY1yf9CVFaD",
      "execution_count": 22,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     count unique           top   freq\n",
              "Booking_ID           36275  36275      INN00001      1\n",
              "type_of_meal_plan    36275      4   Meal Plan 1  27835\n",
              "room_type_reserved   36275      7   Room_Type 1  28130\n",
              "market_segment_type  36275      5        Online  23214\n",
              "booking_status       36275      2  Not_Canceled  24390"
            ],
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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      "cell_type": "markdown",
      "source": [
        "* The `no_of_adults` ranges from 0 to 4. Usually, bookings are made for 2 adult persons\n",
        "* The `no_of_children` ranges from 0 to 10. Usually, bookings are made with no children\n",
        "* The `no_of_weekend_nights` ranges from 0 to 7. Usually, bookings are made for 1 weekend night\n",
        "* The `no_of_week_nights` ranges from 0 to 17. Usually, bookings are made for 1-2 weeknights\n",
        "* The `required_car_parking_space`: 3% of the bookings require a car parking space\n",
        "* The `lead_time` ranges from 0 to 443 days. The average is 85, the median is 57, and the distribution is right-skewed or one-sided. \n",
        "* The `arrival_year`, `arrival_month`, `arrival_date`: bookings were made from year 2017-07-01 to 2018-12-31. There are some bookings made on 2018-02-29 that date does not exist as 2018 was not leap. Data need to be treated\n",
        "* The `repeated guest`: 2.6% of bookings were made by repeated guests. Most of the bookings made by new customers\n",
        "* The `no_of_previous_cancellations` ranges from 0 to 13. Usually, bookings are made with no previous cancellations\n",
        "* The `no_of_previous_bookings_not_canceled` ranges from 0 to 58. Usually, bookings are made for the first time. But it seems there are some repeated guests with up to 58 of not canceled bookings in their past\n",
        "* The `avg_price_per_room` ranges from 0 to 540 euros, the average is 103 euros, and the distribution is slightly right-skewed. \n",
        "* The `no_of_special_requests` ranges from 0 to 5. Usually, bookings are made with no special requests.\n",
        "* There is a big difference between the 75th percentile and the maximum in many columns - outliers need to be checked\n",
        "---\n",
        "* There are four unique meal plan types. Plan 1 is the most popular\n",
        "* There are seven unique room types. Room Type 1 is the most popular\n",
        "* There are five unique types of the market segment. `Online` is the most popular\n",
        "* There are two unique values of booking_status. `Not_Canceled` is the most frequent"
      ],
      "metadata": {
        "id": "TZM87-sjM4cW"
      },
      "id": "TZM87-sjM4cW"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Sanity checks"
      ],
      "metadata": {
        "id": "ht11IZN_Yt3p"
      },
      "id": "ht11IZN_Yt3p"
    },
    {
      "cell_type": "code",
      "source": [
        "# Sanity model is a model where all target values ​​are constant and equal to the 'Cancelled' value of the target column\n",
        "# Let's estimate the values of main Classification metric of the sanity model.\n",
        "# We will consider a model valuable if its scores (except Recall) are higher than values of the sanity check model.\n",
        "\n",
        "y_sanity_test = (df['booking_status'].sample(frac = 0.3, random_state = 42) == 'Canceled').astype(int)\n",
        "\n",
        "print('Sanity scores:')\n",
        "print(f'Accuracy: {accuracy_score(y_sanity_test, [1] * len(y_sanity_test)):.4f}')\n",
        "print(f'Recall: {recall_score(y_sanity_test, [1] * len(y_sanity_test)):.4f}')\n",
        "print(f'Precision: {precision_score(y_sanity_test, [1] * len(y_sanity_test)):.4f}')\n",
        "print(f'F1: {f1_score(y_sanity_test, [1] * len(y_sanity_test)):.4f}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
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        "outputId": "55350939-9b6b-4e28-c680-a43850b8ace1"
      },
      "id": "PQ9ZTMGpYuDK",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sanity scores:\n",
            "Accuracy: 0.3314\n",
            "Recall: 1.0000\n",
            "Precision: 0.3314\n",
            "F1: 0.4978\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 23;\n",
              "                var nbb_unformatted_code = \"# Sanity model is a model where all target values \\u200b\\u200bare constant and equal to the 'Cancelled' value of the target column\\n# Let's estimate the values of main Classification metric of the sanity model.\\n# We will consider a model valuable if its scores (except Recall) are higher than values of the sanity check model.\\n\\ny_sanity_test = (df['booking_status'].sample(frac = 0.3, random_state = 42) == 'Canceled').astype(int)\\n\\nprint('Sanity scores:')\\nprint(f'Accuracy: {accuracy_score(y_sanity_test, [1] * len(y_sanity_test)):.4f}')\\nprint(f'Recall: {recall_score(y_sanity_test, [1] * len(y_sanity_test)):.4f}')\\nprint(f'Precision: {precision_score(y_sanity_test, [1] * len(y_sanity_test)):.4f}')\\nprint(f'F1: {f1_score(y_sanity_test, [1] * len(y_sanity_test)):.4f}')\";\n",
              "                var nbb_formatted_code = \"# Sanity model is a model where all target values \\u200b\\u200bare constant and equal to the 'Cancelled' value of the target column\\n# Let's estimate the values of main Classification metric of the sanity model.\\n# We will consider a model valuable if its scores (except Recall) are higher than values of the sanity check model.\\n\\ny_sanity_test = (\\n    df[\\\"booking_status\\\"].sample(frac=0.3, random_state=42) == \\\"Canceled\\\"\\n).astype(int)\\n\\nprint(\\\"Sanity scores:\\\")\\nprint(f\\\"Accuracy: {accuracy_score(y_sanity_test, [1] * len(y_sanity_test)):.4f}\\\")\\nprint(f\\\"Recall: {recall_score(y_sanity_test, [1] * len(y_sanity_test)):.4f}\\\")\\nprint(f\\\"Precision: {precision_score(y_sanity_test, [1] * len(y_sanity_test)):.4f}\\\")\\nprint(f\\\"F1: {f1_score(y_sanity_test, [1] * len(y_sanity_test)):.4f}\\\")\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
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              "            "
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    {
      "cell_type": "markdown",
      "id": "seasonal-calibration",
      "metadata": {
        "id": "seasonal-calibration"
      },
      "source": [
        "# Exploratory Data Analysis (EDA)\n",
        "\n",
        "- EDA is an important part of any project involving data.\n",
        "- It is important to investigate and understand the data better before building a model with it.\n",
        "- A few questions below will help us approach the analysis in the right manner and generate insights from the data\n",
        "- A thorough analysis of the data, in addition to the questions mentioned below, should be done."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "classified-traveler",
      "metadata": {
        "id": "classified-traveler"
      },
      "source": [
        "**Leading Questions**:\n",
        "1. What are the busiest months in the hotel?\n",
        "2. Which market segment do most of the guests come from?\n",
        "3. Hotel rates are dynamic and change according to demand and customer demographics. What are the differences in room prices in different market segments?\n",
        "4. What percentage of bookings are canceled? \n",
        "5. Repeating guests are the guests who stay in the hotel often and are important to brand equity. What percentage of repeating guests cancel?\n",
        "6. Many guests have special requirements when booking a hotel room. Do these requirements affect booking cancellation?"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Univariate Analysis"
      ],
      "metadata": {
        "id": "L4yPk7M-a4tE"
      },
      "id": "L4yPk7M-a4tE"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`no_of_adults`**"
      ],
      "metadata": {
        "id": "de4W7dQ6bgas"
      },
      "id": "de4W7dQ6bgas"
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_barplot(data=df, feature='no_of_adults',\n",
        "                perc=True, n=None, percfmt=\"{:.0f}%\", sort=False,\n",
        "                figsize=(10, 5), xlabel='No of adults', xlo=0,\n",
        "                title='No of adult guests per booking')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "YgnTaCEDa4Mv",
        "outputId": "0eb43761-69f9-4251-a6b8-b5e37c38c12c"
      },
      "id": "YgnTaCEDa4Mv",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 24;\n",
              "                var nbb_unformatted_code = \"labeled_barplot(data=df, feature='no_of_adults',\\n                perc=True, n=None, percfmt=\\\"{:.0f}%\\\", sort=False,\\n                figsize=(10, 5), xlabel='No of adults', xlo=0,\\n                title='No of adult guests per booking')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(\\n    data=df,\\n    feature=\\\"no_of_adults\\\",\\n    perc=True,\\n    n=None,\\n    percfmt=\\\"{:.0f}%\\\",\\n    sort=False,\\n    figsize=(10, 5),\\n    xlabel=\\\"No of adults\\\",\\n    xlo=0,\\n    title=\\\"No of adult guests per booking\\\",\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The average number of adults per booking is 1.84, mode and median are 2\n",
        "* Zero means that the booking is for children only. Their adults perhaps made sepateted bookings for themselves.\n",
        "* 72% of bookings were made for 2 adults, 21% - for 1 adult, 6% - for 3 adults, no adults, and 4 adults - less than 1% of the bookings"
      ],
      "metadata": {
        "id": "Tsz97VOhZwZc"
      },
      "id": "Tsz97VOhZwZc"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`no_of_children`**"
      ],
      "metadata": {
        "id": "3QkgVyPfcHsB"
      },
      "id": "3QkgVyPfcHsB"
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_barplot(data=df, feature='no_of_children',\n",
        "                perc=True, n=None, percfmt=\"{:.0f}%\", sort=False,\n",
        "                figsize=(10, 5), xlabel='No of children', xlo=0,\n",
        "                title='No of children per booking')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "ea5f68d9-a13d-4560-946f-fefc74d8e229",
        "id": "i9U_S521cHsB"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 25;\n",
              "                var nbb_unformatted_code = \"labeled_barplot(data=df, feature='no_of_children',\\n                perc=True, n=None, percfmt=\\\"{:.0f}%\\\", sort=False,\\n                figsize=(10, 5), xlabel='No of children', xlo=0,\\n                title='No of children per booking')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(\\n    data=df,\\n    feature=\\\"no_of_children\\\",\\n    perc=True,\\n    n=None,\\n    percfmt=\\\"{:.0f}%\\\",\\n    sort=False,\\n    figsize=(10, 5),\\n    xlabel=\\\"No of children\\\",\\n    xlo=0,\\n    title=\\\"No of children per booking\\\",\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "i9U_S521cHsB"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Most of the bookings were made for adults only (no children) - 93%\n",
        "* The average number of children per booking is 0.11, and the mode and median are 0\n",
        "* There are some bookings in the dataset where the number of children is 9 and 10. Probably false values, data need to be treated accordingly\n",
        "* 4% of bookings are for 2 children, 3% - for 1 child, more than 2 children - less than 1% of the bookings"
      ],
      "metadata": {
        "id": "K_eID1bkbF1j"
      },
      "id": "K_eID1bkbF1j"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`no_of_weekend_nights`**"
      ],
      "metadata": {
        "id": "TBMs6VcGdF3j"
      },
      "id": "TBMs6VcGdF3j"
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_barplot(data=df, feature='no_of_weekend_nights',\n",
        "                perc=True, n=None, percfmt=\"{:.0f}%\", sort=False,\n",
        "                figsize=(10, 5), xlabel='No of weekend nights', xlo=0,\n",
        "                title='No of weekend nights per booking')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "811a8baf-a3b3-4bc1-a3d2-b73074ed59b2",
        "id": "ZnYQHO_0dF3n"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 26;\n",
              "                var nbb_unformatted_code = \"labeled_barplot(data=df, feature='no_of_weekend_nights',\\n                perc=True, n=None, percfmt=\\\"{:.0f}%\\\", sort=False,\\n                figsize=(10, 5), xlabel='No of weekend nights', xlo=0,\\n                title='No of weekend nights per booking')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(\\n    data=df,\\n    feature=\\\"no_of_weekend_nights\\\",\\n    perc=True,\\n    n=None,\\n    percfmt=\\\"{:.0f}%\\\",\\n    sort=False,\\n    figsize=(10, 5),\\n    xlabel=\\\"No of weekend nights\\\",\\n    xlo=0,\\n    title=\\\"No of weekend nights per booking\\\",\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "ZnYQHO_0dF3n"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Most of the bookings do not consist of weekend nights - 47%\n",
        "* The average number of weekend nights per booking is 0.81, and the median is 1\n",
        "* 28% of bookings are for 1 weekend night, 25% - for 2 weekend nights, more than 2 weekend nights - less than 1% of the bookings"
      ],
      "metadata": {
        "id": "plzmFxD5bB0Z"
      },
      "id": "plzmFxD5bB0Z"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`no_of_week_nights`**"
      ],
      "metadata": {
        "id": "9CAQjszddXPe"
      },
      "id": "9CAQjszddXPe"
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_barplot(data=df, feature='no_of_week_nights',\n",
        "                perc=True, n=None, percfmt=\"{:.0f}%\", sort=False,\n",
        "                figsize=(10, 5), xlabel='No of week nights', xlo=0,\n",
        "                title='No of week nights per booking')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "9535e488-4d01-4867-a158-bb75b8133714",
        "id": "cJ4OJps5dXPe"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 27;\n",
              "                var nbb_unformatted_code = \"labeled_barplot(data=df, feature='no_of_week_nights',\\n                perc=True, n=None, percfmt=\\\"{:.0f}%\\\", sort=False,\\n                figsize=(10, 5), xlabel='No of week nights', xlo=0,\\n                title='No of week nights per booking')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(\\n    data=df,\\n    feature=\\\"no_of_week_nights\\\",\\n    perc=True,\\n    n=None,\\n    percfmt=\\\"{:.0f}%\\\",\\n    sort=False,\\n    figsize=(10, 5),\\n    xlabel=\\\"No of week nights\\\",\\n    xlo=0,\\n    title=\\\"No of week nights per booking\\\",\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "cJ4OJps5dXPe"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Most of the bookings are for 2 weekday nights - 32%\n",
        "* The average number of weekday nights per booking is 2.2, and the median is 2\n",
        "* 7% of bookings are for no weekday nights (weekend only), 26% - for 1 weekend night, 22% - for 3 weekend nights, 8% - 4, 4% - 5, 1% - 6, more than 7 weekday nights - less than 1% of the bookings"
      ],
      "metadata": {
        "id": "skEyaA8rdoz-"
      },
      "id": "skEyaA8rdoz-"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`type_of_meal_plan`**"
      ],
      "metadata": {
        "id": "R30yu_lcdl43"
      },
      "id": "R30yu_lcdl43"
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_barplot(data=df, feature='type_of_meal_plan',\n",
        "                perc=True, n=None, percfmt=\"{:.0f}%\", sort=False,\n",
        "                figsize=(10, 5), xlabel='Type of meal plan', xlo=0,\n",
        "                title='Types of meal plan distribution')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "cafdeb08-1a68-4687-aa84-5128a0c2e5dd",
        "id": "GbbpSOkSdl43"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 28;\n",
              "                var nbb_unformatted_code = \"labeled_barplot(data=df, feature='type_of_meal_plan',\\n                perc=True, n=None, percfmt=\\\"{:.0f}%\\\", sort=False,\\n                figsize=(10, 5), xlabel='Type of meal plan', xlo=0,\\n                title='Types of meal plan distribution')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(\\n    data=df,\\n    feature=\\\"type_of_meal_plan\\\",\\n    perc=True,\\n    n=None,\\n    percfmt=\\\"{:.0f}%\\\",\\n    sort=False,\\n    figsize=(10, 5),\\n    xlabel=\\\"Type of meal plan\\\",\\n    xlo=0,\\n    title=\\\"Types of meal plan distribution\\\",\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "GbbpSOkSdl43"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Most of the bookings are with `Meal Plan 1` selected - 7%\n",
        "* 14% of bookings - no meal plan selected\n",
        "* 9% - `Meal Plan 2`\n",
        "* `Meal Plan 3` - less than 1% of the bookings"
      ],
      "metadata": {
        "id": "4J-VxqgUe6Yg"
      },
      "id": "4J-VxqgUe6Yg"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`required_car_parking_space`**"
      ],
      "metadata": {
        "id": "di93fuhvd3r3"
      },
      "id": "di93fuhvd3r3"
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_barplot(data=df, feature='required_car_parking_space',\n",
        "                perc=True, n=None, percfmt=\"{:.0f}%\", sort=False,\n",
        "                figsize=(10, 5), xlabel='Required car parking space', xlo=0,\n",
        "                title='Required car parking space distribution')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "ee7635ad-3ac8-49f4-e115-baba4422e77b",
        "id": "JHyUflJ2d3r3"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 29;\n",
              "                var nbb_unformatted_code = \"labeled_barplot(data=df, feature='required_car_parking_space',\\n                perc=True, n=None, percfmt=\\\"{:.0f}%\\\", sort=False,\\n                figsize=(10, 5), xlabel='Required car parking space', xlo=0,\\n                title='Required car parking space distribution')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(\\n    data=df,\\n    feature=\\\"required_car_parking_space\\\",\\n    perc=True,\\n    n=None,\\n    percfmt=\\\"{:.0f}%\\\",\\n    sort=False,\\n    figsize=(10, 5),\\n    xlabel=\\\"Required car parking space\\\",\\n    xlo=0,\\n    title=\\\"Required car parking space distribution\\\",\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "JHyUflJ2d3r3"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 97% of the bookings are with no request for car parking space"
      ],
      "metadata": {
        "id": "DJSAQfVMfmDu"
      },
      "id": "DJSAQfVMfmDu"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`room_type_reserved`**"
      ],
      "metadata": {
        "id": "vv61PE7neMno"
      },
      "id": "vv61PE7neMno"
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_barplot(data=df, feature='room_type_reserved',\n",
        "                perc=True, n=None, percfmt=\"{:.0f}%\", sort=True,\n",
        "                figsize=(10, 5), xlabel='Room type reserved', xlo=90,\n",
        "                title='Room type reserved distribution')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "outputId": "a8a853d9-69b6-40b5-bd42-7fb948e6e89e",
        "id": "JKe9SlVeeMnp"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 30;\n",
              "                var nbb_unformatted_code = \"labeled_barplot(data=df, feature='room_type_reserved',\\n                perc=True, n=None, percfmt=\\\"{:.0f}%\\\", sort=True,\\n                figsize=(10, 5), xlabel='Room type reserved', xlo=90,\\n                title='Room type reserved distribution')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(\\n    data=df,\\n    feature=\\\"room_type_reserved\\\",\\n    perc=True,\\n    n=None,\\n    percfmt=\\\"{:.0f}%\\\",\\n    sort=True,\\n    figsize=(10, 5),\\n    xlabel=\\\"Room type reserved\\\",\\n    xlo=90,\\n    title=\\\"Room type reserved distribution\\\",\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "JKe9SlVeeMnp"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Most of the bookings are with `Room Type 1` selected - 78%\n",
        "* 17% of bookings - `Room Type 4`\n",
        "* 3% of bookings - `Room Type 6`\n",
        "* 2% of bookings - `Room Type 2`\n",
        "* 1% of bookings - `Room Type 5`\n",
        "* `Room Type 7` and `Room Type 3` - less than 1% of the bookings"
      ],
      "metadata": {
        "id": "BkUikTSggXNr"
      },
      "id": "BkUikTSggXNr"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`lead_time`**"
      ],
      "metadata": {
        "id": "esG61JnkfZvg"
      },
      "id": "esG61JnkfZvg"
    },
    {
      "cell_type": "code",
      "source": [
        "histogram_boxplot(data=df, feature='lead_time', step = 30,\n",
        "                  xlabel='Lead time, days', fmt='{:.0f}',)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "outputId": "0632c7af-7359-4c4c-d71a-9acb19b22772",
        "id": "PDt8m9GpfZvm"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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Xc/311zNp0iRKS0t55plniImJYfLkyXXuu0uXLpx00kk89thjeDwe7HY7r732Wo2eP5FQoaJPQt60adOAylNBSUlJnHHGGcyfP5/hw4cf8ZRh27ZtiYiI4B//+Af79+8nOjqaLl268MILL3hHx5533nlMmjSJ1157jb///e+YpsmPP/54TDl79+5N3759mT9/PpmZmZx66qk899xz1U6t9evXjwceeIDnn3+eNWvWcMYZZ/D444/XGD1Yn1wXX3wxERER/M///A833ngj0dHRDBo0iJkzZxIVFXVMr8VXDz/8MCeeeCJvv/02ixYtIiUlhWuvvZabb77Zr+0e7pJLLqFVq1YsWbKEd999F7fbTWpqKmeddRann346UDkh8sKFC3n00UeZOnUqKSkpXHHFFRQUFNS4z+/ChQt58MEHefLJJ7FaraSnp3Pvvfdy00031SvXddddx2+//cY999xDaWkpffr04eWXX260110lNjaWf/3rX/z973/nueeeIzs7m9jYWE455ZQaA1LqKy4ujtdff50FCxbw3HPPkZ+fT1JSEv369fP2SA8aNIhnn32Wv//970yfPp2wsDD69OnDzJkzjzhdi81m4x//+Adz5sxh1qxZxMfHc+WVV9KtWzfde1lCkmEebYiiiIiIiBz3dE2fiIiISAjQ6V0RkSBjmma1QQu1OdbJkEUkdOlTQ0QkyCxfvvyod5k41mtDRSR06Zo+EZEgk5eXx+7du4+4zrGOAheR0KWiT0RERCQEaCCHiIiISAhQ0SciIiISAlT0iYiIiIQAFX0iIiIiIUBFn4iIiEgIUNEnIiIiEgJU9ImIiIiEABV9IiIiIiFARZ+IiIhICFDRJyIiIhICVPSJiIiIhAAVfSIiIiIhQEWfiIiISAhQ0SciIiISAlT0iYiIiIQAFX0iIiIiIUBFn4iIiEgIsAU6QCB5PB7cbjPQMY7KajWCJmewZFGOmoIli3LUFCxZlKOmYMmiHDUFS5ZgyQEQFmZt0PYhXfS53Sb5+aWBjnFUCQlRQZMzWLIoR03BkkU5agqWLMpRU7BkUY6agiVLsOQASE6ObdD2Or0rIiIiEgJU9ImIiIiEABV9IiIiIiFARZ+IiIhICAjpgRzHm/feW8m+fXsDmsFms+JyuX1ev7i4CICYmIZdfNrQHP7i7xytWrXmggvG+G3/IiISOlT0HUf27dtL5q4dpMUEroPWVc/1i4o9AMQ4cwKaw1/8mSPz4LETERFpDCr6jjNpMRYm94gIdAyfvbCtHOC4yhwsqo6diIhIY9A1fSIiIiIhQEWfiIiISAhQ0SciIiISAlT0iYiIiIQAFX0iIiIiIUBFn4iIiEgIUNEnIiIiEgJU9ImIiIiEABV9IiIiIiFARZ+IiIhICFDRJyIiIhICVPSJiIiIhAAVfX60bdvnbNv2eaBjiIgENX1WijQNW6ADNGdffrkVgB49egU4iYhI8NJnpUjTUE+fiIiISAhQ0SciIiISAlT0iYiIiIQAFX0iIiIiIUBFn4iIiEgIUNEnIiIiEgJU9ImIiIiEABV9IiIiIiFARZ+IiIhICFDRJyIiIhICdBs2EREJuJ07d3DvvTOrLbNarbjd7lrXv+qq63jxxefq3N/o0eP53/9dVm2ZzRbGeeedz4cfvo/NFobL5axXxtTUNPLycomJiSM39wBWq43x4y/mrbde9Wa1Wm1cf/0tLFq0ANM0q20fERHJPffMoaiokDfeeIWLL/4zsbFxFBUVVnvtd9xxL489Ntf7uFWrEzBNE5vNxqRJVwJU2/7ppx9n//5soqNjKSkpYvToP9GnTz+fXtPhWaocmmfu3MfZt28P//M/i7jmmhtp1ap1nesFg8bI9OijcyguLiI2Np558+Ydcd1582ZTVlZGZGQ0d999/zG111TU0yfNXo5Zwa2OreSaFYGOIiL1UFfBB/Cvf71yxG0PL/gAXC4nH374vvf7+srKysThcJCbe+BgPhfLlv2rWla328XSpa/VKPgAysvLAPj444/444+drF//kffxoQ5/vG/fHjIz97J79x+sX/9Rje33788GoKSk6OBrf9vn13T4vury1luvU1FRwdKlr/m87+NZcXHlsSwqKjjqumVlZQf/LPFrpsagok+avVdcO/jGzOcV145ARxGRWuzcWf+/m1UFVKDVVphmZ2fVuf6DD97Ltm1bMU2TL7/8nH379rB16+Zq6xz++FBffLGl2vZPPfVIrett2fLpUbMXFRVW21dRUSFAjR7Xe++dyf79Wd7Xtm/f3jrXC7TGyPToo3MO28c9da47b97swx7fX+/2mpJO7/pRcXERRUVF/M//LGrQfmw2Ky6Xm3379hJrqfmvR6lbjlnBB569mMBqz17+bLYj0QgPdCyfFDtMivbt9fn9U/U+CTTlqClYsihH4FVUlGO1WgEwTQ9Ll75er+3dbjeGYXi3z83NqXW9//3ft496ivfjjz/y9kiapof16z/i8sv/fNQMS5e+xi233F6v3MeTql6+Kvn5+XWuW9XL99/Hwd3bp54+adZece3AQ+WHmgdTvX0iEnD/PRXsPmKvYF2qCrUjnf72xfbtX1bL8vXXX/q03bFkluCgnj4/iomJJSYmlmuuuaFB+0lIiCI/v7SyxydvZ+OECwFVvXyug0WfC/O46u2LsRvEtGjt8/un6n0SaMpRU7BkCdYcwXBasCn9d9CHlaSklvUuogzDwDTNIw508UXXrj358sst3izduvX0abuUlNRjblMCSz190mwd2stXRb19IhJI4eER3tOzhmFhwoRL67W91Wr1nh42DAuJiUm1rjd69J+Ouq/zzju/WpZzzz3fpwwTJkzyMe3xKSYmttrjhISEOteNjIw87HG0PyI1GhV90mx9ZxZ4e/mquDD51jz6aCwRaTonn9yu3ttEREQefaUmUFWAHepIPWF//etcevTojWEY9OzZi1atTqB37/7V1jn88aHOOqtPte1vvfWuWtfzZcqW2Ni4avuqmrLl8GlO5s59nOTkVO9rq5qypbb1Aq0xMt15Z/XBGXPnPlTnunffPeewx/fXu72mpKJPmq1n7f34KDyjxtezdt/mrxKRwKqtoKpyySVHHnAwevT4GststjAyMkZ4v6+v1NQ07HY7iYktD+azMX78JdWyWq02JkyY5O1BO1RVoXreeedz0kkne3vWzjuveg/b4Y9btTqBtLTWnHjiSZx77vk1tk9OTgEgOjr24Gs/ei/foW0duq+6TJx4KeHh4c2+l69KVW9fbGz8Udet6u0L9l4+AMOsbTKhEOF0uv16fUvVqMvGvqZvco+IxojXJF7YVg5wXGUOFi9sK4cWJ+uavuM8BwRPlmDN0ViflY2RJVCUo6ZgyRIsOQCSk2OPvtIRqKdPREREJASo6BMREREJASr6REREREKAij4RERGREKCiT0RERCQEqOgTERERCQEq+kRERERCgIo+ERERkRCgok9EREQkBNgCHaA569mzd6AjiIgEPX1WijQNFX1+1KNHr0BHEBEJevqsFGkaOr0rIiIiEgJU9ImIiIiEABV9IiIiIiFARZ+IiIhICFDRJyIiIhICVPSJiIiIhAAVfSIiIiIhQEWfiIiISAhQ0SciIiISAlT0iYiIiIQAFX0iIiIiIUBFn4iIiEgIsAU6gNRPZrGHF7aVBzqGz/YVewCOq8zBIrPYQ1qLQKcQEZHmQkXfcaRVq9aBjoDNZsXlcvu8fmxYUeU3MbEBzeEv/syR1iI4fuYiItI8qOg7jlxwwZhARyAhIYr8/NJAx1AOERGRetI1fSIiIiIhQEWfiIiISAhQ0SciIiISAlT0iYiIiIQAFX0iIiIiIcAwTdMMdAgRERER8S/19ImIiIiEABV9IiIiIiFARZ+IiIhICFDRJyIiIhICVPSJiIiIhAAVfSIiIiIhQEWfiIiISAhQ0SciIiISAlT0iYiIiIQAFX0iIiIiIUBFn4iIiEgIUNEnIiIiEgJU9ImIiIiEABV9IiIiIiFARZ+IiIhICFDRJyIiIhICVPSJiIiIhAAVfSIiIiIhQEWfiIiISAhQ0SciIiISAlT0iYiIiIQAFX0iIiIiIUBFn4iIiEgIaJKib9asWfTv359Ro0Z5lz366KMMHz6c0aNHc9NNN1FYWOh97tlnnyUjI4Nhw4axadMm7/KNGzcybNgwMjIyWLx4sXf5rl27mDhxIhkZGUyfPh2Hw9EUL0tERETkuGGYpmn6u5GtW7cSFRXFnXfeybvvvgvAJ598Qr9+/bDZbDz++OMAzJw5k19++YUZM2awdOlSsrKymDx5Mh988AEAw4YN44UXXiA1NZUJEyYwf/58Tj31VKZNm8bQoUMZOXIks2fPplOnTkyaNOmouTweD263319+g1mtRtDkrG+W/PxcABISEgOWI7c0B4DEqKRGzVDfHP4WLFmUo6ZgyaIcNQVLFuWoKViyBEsOgLAwa4O2tzVSjiPq3bs3u3fvrrZs4MCB3u+7d+/O6tWrAVi7di0jR47EbrfTpk0b2rZty/bt2wFo27Ytbdq0AWDkyJGsXbuW9u3b8+mnn/Lkk08CMG7cOBYuXOhT0ed2m+TnlzbKa/SnhISooMlZ3ywrVrwDwNixFwUsx/jn/lSZ5br3GjVDfXP4W7BkUY6agiWLctQULFmUo6ZgyRIsOQCSk2MbtH1QXNP39ttvM2jQIACysrJIS0vzPpeamkpWVlady/Py8oiLi8Nmq6xf09LSyMrKatoXICIiIhLkmqSn70gWLVqE1WrlwgsvbPK2rVaDhISoJm+3vqxWS9DkrG8Wm63y3xWNnb8+OfyVob45/C1YsihHTcGSRTlqCpYsylFTsGQJlhyNIaBF37Jly1i/fj0vvvgihmEAlT14mZmZ3nWysrJITU0FqHV5ixYtKCwsxOVyYbPZyMzM9K5/NDq9W3/1zeJyeQAaPX99cvgrQ31z+FuwZFGOmoIli3LUFCxZlKOmYMkSLDngOD69u3HjRpYsWcKiRYuIjIz0Lk9PT2fVqlU4HA527drFzp076dq1K126dGHnzp3s2rULh8PBqlWrSE9PxzAM+vbt6x3ssXz5ctLT0wP1skRERESCUpP09M2YMYMtW7aQl5fHoEGDuOWWW1i8eDEOh4PJkycD0K1bN+bMmcNpp53GiBEjuOCCC7BarcyePRurtXK0yuzZs7n22mtxu9386U9/4rTTTgMqR/3eeuutLFiwgNNPP52JEyc2xcuS48RrVy4NdAQREZGAa5IpW4KV0+kOmi7bIwmmruX6j959Ewjs6F1/CpYcEDxZlKOmYMmiHDUFSxblqClYsgRLDmj46d2AD+Ro7lokRmGzNmxeHTj2H7TL7SYvNzjerIHy/KfPAXB1v+sCnERERCRwVPT5mc1q5d9/bG3QPiIiwigvdx7Ttmef1LtBbTcH7/xnOaCiT0REQltQzNMnIiIiIv6lok9EREQkBKjoExEREQkBKvpEREREQoAGckizt+K69wIdQUREJODU0yciIiISAlT0SbP3901P8/dNTwc6hoiISECp6JNm78MfVvPhD6sDHUNERCSgVPSJiIiIhAAVfSIiIiIhQEWfiIiISAjQlC3S7EWERQQ6goiISMCp6JNm719XLQt0BBERkYDT6V0RERGREKCiT5q9J9c9ypPrHg10DBERkYBS0SfN3qZfN7Dp1w2BjiEiIhJQKvpEREREQoBPRd8LL7zA999/D8BXX33FueeeS3p6Otu2bfNrOBERERFpHD4VfS+++CInnngiAE8++SRXXXUVN9xwA/PmzfOpkVmzZtG/f39GjRrlXZafn8/kyZMZOnQokydPpqCgAADTNHnwwQfJyMhg9OjRfPvtt95tli9fztChQxk6dCjLly/3Lv/mm28YPXo0GRkZPPjgg5im6VMuERERkVDhU9FXVFREbGwsxcXF/Pjjj1x++eVMnDiR3377zadGxo8fz5IlS6otW7x4Mf3792fNmjX079+fxYsXA7Bx40Z27tzJmjVrmDt3Lvfffz9QWSQuXLiQN998k7feeouFCxd6C8X777+fuXPnsmbNGnbu3MnGjRt9ff0SAlpEJdIiKjHQMURERALKp6KvVatWfPnll7z33nv06tULq9VKcXExVqvVp0Z69+5NfHx8tWVr165l7NixAIwdO5aPPvqo2nLDMOjevTuFhYVkZ2fzySefMGDAABISEoiPj2fAgAFs2rSJ7OxsiouL6d69O4ZhMHbsWNauXVuPQyDN3QuXvcILl70S6BgiIiIB5dPkzHfccQdTp07Fbrfz9NNPA/Dxxx/TpUuXY244JyeHlJQUAJKTk8nJyQEgKyuLtLQ073ppaWlkZWXVWJ6amlrr8qr1fWG1GiQkRB3za/BVRERYg7a3GEaD9tGYr9FqtdRrfzabpdEzHEsOfwmWHBA8WZSjpmDJohw1BUsW5agpWLIES47G4FPRN3jwYD755JNqy4YPH87w4cMbJYRhGBiG0Sj7qg+32yQ/v9SvbSQnx1Je7mzQPiIiwhq0j8Z8jQkJUfXan8vlafQM9c3x4Af3A/DXYfc3aob65vC3YMmiHDUFSxblqClYsihHTcGSJVhyQGVN0RA+FX27du2qdbndbic5ORmLpf4zvyQlJZGdnU1KSgrZ2dkkJlZec5WamkpmZqZ3vczMTFJTU0lNTWXLli3e5VlZWfTp06fO9Y8nZZ4yyj2lRFtisVvsgY7T7Hz+x5ajryQiItLM+VT0ZWRkYBiGd1Tsob1yFouF9PR07rvvPlq2bOlzw+np6axYsYIpU6awYsUKhgwZ4l3+yiuvMHLkSL7++mtiY2NJSUlh4MCBzJ8/3zt445NPPmHGjBkkJCQQExPDV199Rbdu3VixYgWXX365zzkCyTQ95LpyyHfnAZBLDhGWSFrakgm3RAQ4nYiIiDQnPhV9c+fOZcuWLdxyyy2kpaWxb98+Fi1aRPfu3enduzdPPPEEc+bM8V7vd7gZM2awZcsW8vLyGDRoELfccgtTpkxh+vTpLF26lNatW7NgwQKg8lTyhg0byMjIIDIy0jstTEJCAjfeeCMTJkwA4KabbiIhIQGA++67j1mzZlFeXs6gQYMYNGhQAw+L/3lMD3sdu6gwK4izxpNgbUGxp5gCVx77HHs4MbwtNsOnH4+IiIjIUflUVTzzzDN8+OGHhIeHA9C2bVvuu+8+hg0bxsaNG3nkkUcYOnRondvPnz+/1uUvvfRSjWWGYXDffffVuv6ECRO8Rd+hunTpwrvvvuvLSwkahe4CKswKUsLSiLXGAdDCkki0JZrdjj/Icuyltb1NQK51FBERkebHp4vxPB4Pu3fvrrZs7969eDyVF+lHRkbidrsbP10z5TE95LtyibBEegu+KnZLOClhaZSb5Rxw7Q9QwualVXxrWsW3DnQMERGRgPKpp+/KK6/kyiuv5E9/+hNpaWlkZmaybNkyrrjiCqByQuXu3bv7M2ezUuDOx42bVFvt10DGWGMp95RR4M4nxhpDBPG1rie+WXTRkqOvJCIi0sz5VPRdd911dOzYkdWrV/Ptt9+SnJzMQw895L127vzzz+f888/3a9DmoqqXL9ISRaQlss71Em0tKXYXkeM8QIIZV+d6IiIiIr7weaTA8TJAItgVuPPx4CGxjl6+KhbDQqKtJftdWRQ5i7BTd4EoR/bXd+8E4MFRjwY4iYiISOD4VPQ5HA6WL1/O999/T2lp9QkKH3vsMb8Ea66K3YVEGJFE+DAlS6w1jnx3HlnlWZwY1laDOo7RN/v+E+gIIiIiAedT0XfXXXfxww8/cN5559VrLj6pzulx4DAdJNmSfVrfMAwSbUlkOfdR5C4kzqZr+0REROTY+FT0bdq0ibVr1xIXp2vLGqLEUwJAtCXa522iLTFEWiPJc+cSa41Tb5+IiIgcE5+mbGnVqhUOh8PfWZq9EncxdsNOWD1utWYYBi3DW+IynZR4iv2YTkRERJozn3r6xo4dy4033sgVV1xBUlJStef69+/vl2DNjdt0UW6W0cKaWO9tY8NiCTPCyHflEm2JUW9fPbVveWqgI4iIiAScT0XfK6+8AtS8s4ZhGKxdu7bxUzVDJe6Dp3atMfXe1jAMEqwt2O/KptxTRqQ1qrHjNWtPjqv99oAiIiKhxKeib926df7O0eyVeIqxYcNuhB/T9jHWOHJdOeS5c1X0iYiISL35dE2fNIzH9FDmKSXaeuynZi2GhXhbAmWeUio8FY2csHm7bflUbls+NdAxREREAqrOnr4RI0bw/vvvAzB48OA6i5X169f7JVhzUu4px8Qksh6jdmsTZ00gz5VLviuXVHurRkrX/P164JdARxAREQm4Oou+uXPner9//PHHmyRMc1VulgH4NCHzkVgNK3HWeArc+SSZLbEZYY0RT0REREJAnUVfr169vN+3bduW1NTUGut8//33/knVzJR7yrAbdqyGtcH7ire1oMCdT74rn5Zhvk3yLCIiIuLTNX3XXHMN+fn51ZZt376d6667zh+ZmhWP6aHcU06EpXHunRtmhBFjiaXQnY/bdDfKPkVERKT586nou+iii7j66qspKamcduTLL7/kxhtv5KGHHvJruOYg31GAiafRij6ABFsiJiaF7oJG22dzdmarLpzZqkugY4iIiASUT1O2XHHFFRQWFjJlyhSmTJnCrFmzeOKJJzj77LP9ne+4t79sPwARRuMVfeGWcCItURS48oi3JmAxNAj7SB4c9WigI4iIiAScT0UfwM0330xxcTG33norzz77LL179/ZnrmYju2w/VqzYDJ8PtU8SrIns8+ym2F1EnC2+UfctIiIizU+dlUht07R4PB5M02TmzJneZQ2dsuXFF1/krbfewjAMOnTowMMPP0x2djYzZswgPz+fzp0789hjj2G323E4HNxxxx18++23JCQk8NRTT3HiiScC8Oyzz7J06VIsFgt//etfOeeccxqUq7HsL9tPhCWy0W+dFmmJxG6Ek+/OJdYap1uzHcENb14LwKKLlgQ4iYiISODUWfQ1xTQtWVlZ/POf/+S9994jIiKCadOmsWrVKjZs2MBVV13FyJEjmT17NkuXLmXSpEm89dZbxMXF8eGHH7Jq1SqeeOIJFixYwC+//MKqVatYtWoVWVlZTJ48mQ8++ACrteGjZRui3FNOiauEJFvjj7I1DIMWtkSynPso9ZQc0+3dQsW+gr2BjiAiIhJwdRZ9ffr0aZIAbreb8vJybDYb5eXlJCcn8+mnn/Lkk08CMG7cOBYuXMikSZNYt24dN998MwDDhg1jzpw5mKbJ2rVrGTlyJHa7nTZt2tC2bVu2b99Ojx49muQ11CXflQvQqIM4DhVticFm2Mh35aroExERkSPyaQSA0+nk6aefZsiQIXTp0oUhQ4bw9NNP43A4GtR4amoqV199Needdx4DBw4kJiaGzp07ExcXh81WWY+mpaWRlZUFVPYMtmpVeScKm81GbGwseXl5ZGVlkZaWVm2/VdsEUp4rF6thJfwY77d7NIZhkGBtQblZTpmn1C9tiIiISPPg0+iCxx9/nO3bt/PAAw/QunVr9u7dyz/+8Q+Ki4u5++67j7nxgoIC1q5dy9q1a4mNjWXatGls2rTpmPdXX1arQUJClN/2n2xJJNoTjmE2bHStxTCIiKj97ht2syX5hXnku3NJiKz92r7GfI1Wq6Ve+7PZLI2eob45/JWhvjn8LViyKEdNwZJFOWoKlizKUVOwZAmWHI3Bp6Jv9erVrFy5khYtWgDQrl07zjjjDMaMGdOgou/f//43J554IomJiQAMHTqUL7/8ksLCQlwuFzabjczMTO/dQFJTU9m3bx9paWm4XC6Kiopo0aIFqampZGZmeveblZVV6x1EDud2m+Tn+6+HrCWtSU6O5d9/bG3QfiIiwigvd9b5fII1kQOubPJKC4iy1ry/b2O+xoSEqHrtz+XyNHqG+ubo3rqXXzLUN4e/BUsW5agpWLIoR03BkkU5agqWLMGSAyA5ObZB2/vUBWWaZr2W+6p169Z8/fXXlJWVYZommzdv5tRTT6Vv37588MEHACxfvpz09HQA0tPTWb58OQAffPAB/fr1wzAM0tPTWbVqFQ6Hg127drFz5066du3aoGzHkzhrPDbDRq7rQIN/Js3RX4fdz1+H3R/oGCIiIgHlU0/f8OHDueGGG7jpppto3bo1e/bsYdGiRYwYMaJBjXfr1o1hw4Yxbtw4bDYbp59+OhdffDHnnnsut956KwsWLOD0009n4sSJAEyYMIGZM2eSkZFBfHw8Tz31FACnnXYaI0aM4IILLsBqtTJ79uyAj9xtSoZh0MKaxH5XFiWeYmKsDfuXgIiIiDQ/hulD15DD4WDRokW8++67ZGdnk5KSwsiRI7nxxhux2+1NkdMvnE6337tsm+L0LlT2uu5y/A6YtLGf7L227+yTerN/f1GD2j9Ufbu5V6x4E4CxYy9qtAz1zTH51T8D8MJlrzRqhvrm8LdgyaIcNQVLFuWoKViyKEdNwZIlWHJAw0/v+tTTZ7fbmTZtGtOmTWtQY+I/hmGQZGtJpnMv+e48WtgSAx0paOSV5gY6goiISMD5fG+wzz77jBUrVnh7+saMGUO/fv38mU3qKdoaQ5Q7mjxXDrHWWGxG7SN+RUREJPT4NJDjrbfeYvr06SQnJ5ORkUFKSgq33XYbb775pr/zST21tKUAcMC5P8BJREREJJj41NO3ZMkSXnjhBTp16uRdNmLECKZOncpFFzXutVrSMGGWMBJsieS5cih1lwQ6joiIiAQJn4q+/Px82rdvX21Zu3btKCgo8EsoaZgEawuK3UVkOzOpcFcEOk7AndN+cKAjiIiIBJxPp3d79uzJI488QllZGQClpaU89thjAb+3rdTOYlhIDUvDjZvPsraE/Nx9t6XfyW3pdwY6hoiISED5VPQ98MAD/PDDD/Tq1Yuzzz6b3r1788MPP/DAAw/4O58co3BLBIm2lvxe/Ad7HbsDHUdEREQCzKfTuykpKbz66qtkZmZ6R++mpaX5O5s0UIK1BbYwg+9K/0O8LSFkJ22+5MXxAPzrqmUBTiIiIhI4PvX0ARQWFrJlyxbvV2FhoT9zSSMwDIOBaQOxGha+LN6C03PkCZ6bq3JnOeXO8kDHEBERCSifir7NmzeTnp7Oyy+/zH/+8x9eeeUV0tPT2bx5s7/zSQNFh0XRI6Y3ZZ5Svi75IuSv7xMREQlVPp3enTt3LnPmzOGCCy7wLnv//fd54IEHWL16td/CSeNoYUvi9KgufFe6nR/KvuX0qDMDHUlERESamE89fdnZ2QwbNqzasoyMDA4cOOCXUNL4Tgo/mbbhp/B7xQ5+K/8l0HFERESkiflU9I0ZM4ZXX3212rLXX3+dsWPH+iOT+EmnyDNJC2vNj2XfsadiV6DjNJmMTsPJ6DQ80DFEREQCqs7Tu5MmTcIwDAA8Hg//+te/WLJkCampqWRlZZGTk0O3bt2aLKg0nGEYdI3ugaPYwTelX2EzbKTaWwU6lt/ddM7UQEcQEREJuDqLvokTJ1Z7rNutNQ8Ww0rPmD5sLdrMVyWf04PepNg1/Y6IiEhzV2fRN27cuKbMIU3IZtjoFdOPrcWb2VbyOT2NPiSHpQQ6lt+Mfa5yANKK694LcBIREZHA8XmePmlewixh9IrpR4w1hm3FWzjg3B/oSCIiIuJHKvpCmN1ip3dMf6Ks0XxZvIUcp0Zji4iINFcq+kKc3RJO75izibRE8mXxZ+Sq8BMREWmW6iz6Dh24sXDhQr8FKCwsZOrUqQwfPpwRI0awbds28vPzmTx5MkOHDmXy5MkUFBQAYJomDz74IBkZGYwePZpvv/3Wu5/ly5czdOhQhg4dyvLly/2WtzkKt4TTJ/ZsIiyRfF78GTk61SsiItLs1Fn07dy5k4qKCgCef/55vwV46KGHOOecc1i9ejUrV66kffv2LF68mP79+7NmzRr69+/P4sWLAdi4cSM7d+5kzZo1zJ07l/vvvx+A/Px8Fi5cyJtvvslbb73FwoULvYWi+CbcEkGf2AFEWaL4ovgz9juzAx2p0VzYZRwXdtHAJBERCW11jt4dMmQIw4YN44QTTqCiooLLLrus1vUOn7S5PoqKiti6dSuPPPIIAHa7Hbvdztq1a3n55ZcBGDt2LJdffjkzZ85k7dq1jB07FsMw6N69O4WFhWRnZ7NlyxYGDBhAQkICAAMGDGDTpk2MGjXqmLOFoqoev63Fm/myeAs9ons1i+lcru53XaAjiIiIBFydRd/DDz/M559/zp49e/jPf/7DhAkTGr3x3bt3k5iYyKxZs/jhhx/o3Lkz99xzDzk5OaSkVE4hkpycTE5ODgBZWVmkpf23CElLSyMrK6vG8qoJpAU8pofk5Nh6bBFLy5ZD+Wj3Or4q+Zxz4gdyUkybamv4uj+X212Pdv2n1FEKQJQ9KsBJREREAqfOog+gV69e9OrVC6fT6Zd5+1wuF9999x333nsv3bp148EHH/Seyq1iGIb3ziCNzWo1SEjwfyEQERHWoO0thnHM+7AYFr7M/qre26WEp1DmKmPD3o2cGHUi8fZ4bxaPafq0j54p3bHZKq8gaOzjbLVafN7nhKcqe3w/unVdo2aobw5/C5YsylFTsGRRjpqCJYty1BQsWYIlR2M4YtFXZcKECXz22WesWLGC7OxsUlJSGDNmDP369WtQ42lpaaSlpXlv5zZ8+HAWL15MUlKSt53s7GwSExOByh68zMxM7/aZmZmkpqaSmprKli1bvMuzsrLo06fPUdt3u03y80sb9BqOJjk5lvJyZ4P2ERER1qB9HOu2abbW7PPsYXfpbhxOF7HWuHpncbk8AI1+nBMSonzep78y1DeHvwVLFuWoKViyKEdNwZJFOWoKlizBkgN8P9NWF5+mbHnrrbeYPn06ycnJZGRkkJKSwm233cabb77ZoMaTk5NJS0tjx44dAGzevJn27duTnp7OihUrAFixYgVDhgwB8C43TZOvvvqK2NhYUlJSGDhwIJ988gkFBQUUFBTwySefMHDgwAZlk8pbtrWyn0iEEUm2M5Mid2GgI4mIiMgx8qmnb8mSJbzwwgt06tTJu2zEiBFMnTq1wffkvffee7n99ttxOp20adOGhx9+GI/Hw/Tp01m6dCmtW7dmwYIFAAwePJgNGzaQkZFBZGQk8+bNAyAhIYEbb7zRe93hTTfd5B3UIQ1jMSy0sp9ApnMv2c5MjHKTGOIDHUtERETqyaeiLz8/n/bt21db1q5du0aZFuX0009n2bJlNZa/9NJLNZYZhsF9991X634mTJjgl8EmcrDwC2tNljOTrPIsKqwOEm0t/XatpYiIiDQ+n07v9uzZk0ceeYSysjIASktLeeyxx+jRo4dfw0nwMAwLqWGtaGFvQb47jyznPjymJ9CxfHJJz8u4pGftUw6JiIiECp96+h544AFuvfVWevXqRXx8PAUFBfTo0YMnn3zS3/kkiBiGQauIVhgeK7muA+x17CLNfgI2w6e3UcBccpYKPhEREZ9+W6ekpPDqq6+SmZnpHVV76Lx4EjoMw6CFLRG7YSfLuY/dFb+Tam9NpCUy0NHqlFNSOc9jUnRSgJOIiIgETr26aKqmWBGJtsZwgnESWc697HXsIsnWknhri6C8zu+a1y4HYMV17wU4iYiISOD4dE2fSG3CLeGcYD+JaEsMOa4D7HPuwWU2bE5CERER8Q8VfdIgVsNKalgrWtpSKPeUsavidwpdBZg+3rVDREREmsZRiz6Px8PmzZtxOBxNkUeOQ4ZhEG9LoI29LXYjnP2uLPY6dpNf0fApfURERKRxHLXos1gs3Hjjjdjt9qbII8exMIud1vYTSbal4DArePf3VZS6S46bqV1ERESaM58GcvTu3ZuvvvqK7t27+zmOHO8MwyDOlkC0NRZbhMEecxcV7nJ+LfuJthHtAjK9y1V9r2nyNkVERIKNT7+BW7duzXXXXceQIUNIS0urNkJz2rRpfgsnxy+rYaV/am++tn5NqaeEn8t/4I+K32gf2ZET7SdhMZructKxXf/UZG2JiIgEK5+KvoqKCs4//3wAsrKy/BpImherYSXWGkff2IH8VPYd35VuZ2f5r5wa0ZFW9hOaZIqXPfm7ATgh4US/tyUiIhKsfCr6Hn74YX/nkGauhS2RPjED2O/M4ufyH9he+iU7yn/m1MiOpIa18mvxd9NbUwDN0yciIqHN5wusfv31V1avXk1OTg6zZ89mx44dOBwOOnXq5M980owYhkGKPY3ksFQynXv5pexHvir5nDhrPKdFdqKlLSUoJ3cWERFpDny6sOr999/nsssuIysrixUrVgBQUlLCI4884s9s0kwZhkEr+wkMiDuXLlHdcZpOvij+jM+KPiHHeSDQ8URERJoln3r6nn76aV588UU6derE+++/D0CnTp344Ycf/BpOmjeLYeGE8JNoZT+R3Y4/+LXsJ7YW/5tkWwodozoTY40NdEQREZFmw6eiLzc3l44dOwJ4T78ZhqFTcXJEHtNDWJgVgOTkIxdwqXShm+d0fsz/if/kfsP/Fa7ntPhT6ZbUlQhbRK3bHG2fLrebvNzSYwsvIiLSzPhU9HXu3JmVK1cyduxY77JVq1bRtWtXf+WSZsBiWCisKALg339s9Xm7E8JOIteVw08FP/NLwa8k2BJJsCZgHDLNS0REGOXlR77P79kn9QbghoG3HEN6ERGR5sWnou+ee+7hmmuuYenSpZSWlnLNNdfw22+/8fzzz/s7n4Qgq2ElOSyFeGs8Oa4D5LoOUOQuIMmWTLQ1pt77G3b6CD+kFBEROb74VPS1b9+e999/n48//phzzz2XVq1ace655xIdHe3vfBLC7JZwWtlPoNRdwgHXfjKde4lyR5NkSyaCMJ/388v+nwE4Nfk0f0UVEREJej7fFiEyMpKzzjqLPn360KtXr0Yt+NxuN2PHjuX6668HYNeuXUycOJGMjAymT5+Ow+EAwOFwMH36dDIyMpg4cSK7d+/27uPZZ58lIyODYcOGsWnTpkbLJoEXZY2mjb0tSbZkyjxl7HLsJKssy+d7+t6+Yhq3r9CdY0REJLT5VPTt3buXSZMmkZ6ezvXXX096ejqTJk1iz549jRLin//8J+3bt/c+fuKJJ7jqqqv48MMPiYuLY+nSpQC89dZbxMXF8eGHH3LVVVfxxBNPAPDLL7+watUqVq1axZIlS3jggQdwu92Nkk2Cg2EYJNhacFL4ycRa4zhQcYA/Kn6jyF2IaZqBjiciIhL0fCr67rzzTjp37szWrVvZvHkzW7Zs4cwzz+Suu+5qcIDMzEzWr1/PhAkTADBNk08//ZRhw4YBMG7cONauXQvAunXrGDduHADDhg1j8+bNmKbJ2rVrGTlyJHa7nTZt2tC2bVu2b9/e4GwSfGyGjZSwNE6JOQWbEUa2M5O9jl1UeMoDHU1ERCSo+VT0ffvtt9xxxx1ERUUBEB0dze23384333zT4ADz5s1j5syZWCyVUfLy8oiLi8Nmq7zcMC0tzXu/36ysLFq1agWAzWYjNjaWvLw8srKySEtL8+4zNTVV9whu5qJsUZxgb0OyLRWn6WS34w+ynVm4TVego4mIiAQlnwZydO/ene3bt3PWWWd5l33zzTf06NGjQY1//PHHJCYmcuaZZ/LZZ581aF/Hwmo1SEiI8ns7ERG+DzqojcUwGrSPhrbfkCwWi9HoGapyREbaiaQlSWYL9pfvJ6cihxJPESkRKSTaEwFISIjCZrN4v29sVqulSd5DvgiWLMpRU7BkUY6agiWLctQULFmCJUdjqLPo+9vf/ub9vk2bNkyZMoVzzz2XtLQ0MjMz2bBhA6NGjWpQ419++SXr1q1j48aNVFRUUFxczEMPPURhYSEulwubzUZmZiapqalAZQ/evn37SEtLw+VyUVRURIsWLUhNTSUzM9O736ysLO82R+J2m+Tn+3fy3uTk2KPOJ3c0vsxJdyQNbb8hWTwes9Ez1JYjwUgiyh5TOcq3LJOc8lxaRiURWZbALefcBuCXn3VCQpTf30O+CpYsylFTsGRRjpqCJYty1BQsWYIlBxz9pgRHU+fp3czMTO+Xw+Fg6NCh2O12cnNzsdvtZGRkUFFR0aDGb7vtNjZu3Mi6deuYP38+/fr148knn6Rv37588MEHACxfvpz09HQA0tPTWb58OQAffPAB/fr1wzAM0tPTWbVqFQ6Hg127drFz505NHB2C7JZwWoWdQFpYa8Bk475N/F/hejqc1IFB7c8NdDwREZGAqrOn7+GHH27KHNXMnDmTW2+9lQULFnD66aczceJEACZMmMDMmTPJyMggPj6ep556CoDTTjuNESNGcMEFF2C1Wpk9ezZWqzVg+SVwDMMg2hpDlCWa1i1S2Jb9Ne/sWEakJZLz2w4jzX4CFsPnmYpERESaDZ+u6QMoKyvj999/p7S0ehdnz549GyVI37596du3L1B5OrlqmpZDhYeH8/TTT9e6/Q033MANN9zQKFnk+GcYBqfEnUx0eSIP/+shyjxlpFySwo9l33FieFtODG9LpCUy0DFFRESajE9F34oVK5gzZw5hYWFERER4lxuGwfr16/2VTaTBDMPAbgnHbgmnZ0xfdpX/xq/lP/Fr+U+0sCWSFnYCafZWhFsijr4zERGR45hPRd/jjz/OM888w4ABA/ydR8RvUsJSSQlLpdRdwj7HHvY59vB92X/4vuw/JNlakmpvTXJYqnoARUSkWfKp6AsLC6NPnz7+ziLSJKKs0bSP7ED7yA4UuQvJdOxln2MP35VWTugda42jZVgKKWGpxFtb6BpAERFpFnwq+qZNm8YjjzzCTTfdRGJior8ziTSZWGscsZFxnBrRkWJPEQec2WQ7s9hZ/iu/lf9CmBFGS1sKyWEptAxLwW4JD3RkERGRY+JT0XfyySfz9NNP89prr3mXmaaJYRh8//33fgsn0hjuHnrfUdcxDKOyALTGcUrEqTg9TnJc+8l2ZnHAmcU+Z+V9puOtLUgJSyU5LJVYa5y/o4uIiDQan4q+O+64gzFjxnDBBRdUG8ghEsw8pofk5FhGJp9/TNu3JhHoiGma5FTksqd4D3tK9vJz+Q/8XP4DkdZITuIkTo1vT2J4ixrbu9xu8nKDY0JPERERn4q+/Px8pk2bhmEY/s4j0mgshoV//7GV/+yqvFavS5uGT9idYCQREx5PqbuEUk8JP+X/xI/5PxJuhBNrjSfGGovVqJwj8uyTeje4PRERkcbi0xXq48ePZ+XKlf7OIuIXz378D579+B+Ntj+bYSPOFk+avTUd4jqQZEvGxOSAK5vfK3aQ7czC4XE0WnsiIiKNwaeevu3bt/Pqq6+yaNEiWrZsWe25V1991S/BRI4HNouNBFsL4q0JVJgVFLoLKHYXUuQuYONeDycYpxBniw90TBEREd+KvosuuoiLLrrI31lEjluGYRBhRBBhiSDRlkSBK4+9pXv53fMHLW0ptI88jRa2pEDHFBGREOZT0Tdu3Dh/5xBpNmyGjaSwZIadcD5f7vkPv1fs4LOi/yM5LJUOkadr1K+IiASET0VfbffBrTJhwoRGCyPSnNitdtpHduDkiHbsLP+N3yp+5v8K13OCvQ2nRnYk0hIV6IgiIhJCfCr6Dh/EceDAAXbt2kWPHj1U9EnQmzp0RkDbtxo22keeRpvwtuwo/5nfK35jn2MPJ4WfQruI07Bb7AHNJyIiocGnou/ll1+usWzp0qX8+uuvjR5IpLF1SOsQ6AgA2C12OkV1pm3EKfxS9iM7K35lt+N3Tgk/jZMjTsFq+PTXUURE5Jgc801Fx48fz9tvv92YWUT8YuuOLWzdsSXQMbwiLVF0ie7BgLhzaWFL4ufy79lYsJY/Kn7DY3oCHU9ERJopn7oWPJ7qv4jKysp45513iI2N9Usokcb00ifPA9C7XZ8AJ6ku1hrHWTF9yXPl8FPZ93xX+h9+K/+V0yI60sp+oiZDFxGRRuVT0XfGGWfU+AWUmprK3Llz/RJKJJS0sCXRJ2YAB1zZ/FT2PdtLt7Gj/BdOjexIalgrFX8iItIofCr61q5dW+1xZGQkiYmJfgkk0lxU3fvXVynEcbrZnt+L/+DrA1/zVcnnxNnjOLPFGZwcezJWi/Wo+zi0Pd37V0REDuVT0XfCCSf4O4dIs1N1799j0dKSRmRYDHnOXP6d9SmfZm0lzhpPnC2eMCOs1m0iIsIoL3d6H+vevyIicqgjFn2XX375EU8tGYbBSy+91OihREKdYRjEWGOJtsRQ5imlwJ1PvjuXfHcukZZIYixxRFtjsBpH7/0TERGBoxR9F154Ya3Ls7KyePnllykvL29Q4/v27eOOO+4gJycHwzC46KKLuPLKK8nPz+fWW29lz549nHDCCSxYsID4+HhM0+Shhx5iw4YNRERE8Mgjj9C5c2cAli9fzqJFiwC44YYbdBcR8Zp5waxARzhmhmEQZY0myhqN0+OkyFNIkbuQ/a4s9ruyiDAiibJGE2mJxG6qABQRkbodseibOHFitcd5eXksXryYN998kwsuuICbbrqpQY1brVbuuusuOnfuTHFxMX/6058YMGAAy5Yto3///kyZMoXFixezePFiZs6cycaNG9m5cydr1qzh66+/5v777+ett94iPz+fhQsX8vbbb2MYBuPHjyc9PZ34eN3oXqBty7aBjtAowixhJFqSaGFNpMKsoNRdTImnmFzXAQD2OgzsRjjhlnDCDDu7infhcBlEWCIJM8I0IEREJMT5dE1fcXExS5Ys4dVXX+Xcc89l+fLlnHTSSQ1uPCUlhZSUFABiYmJo164dWVlZrF271jsh9NixY7n88suZOXMma9euZezYsRiGQffu3SksLCQ7O5stW7YwYMAAEhISABgwYACbNm1i1KhRDc4ox79PftoEwMAO5wQ4SeMwDIMII4IISwSJtMRluij3lOG0VFDqLKPYXYQHD+v37vduYzNsRNkisVvthFns2C1h2K127BY7YYd8H24NJ/yQ78Msx1YstkiM0iASEZEgc8Sir7y8nJdeeonnn3+evn378tprr3Haaaf5Jcju3bv5/vvv6datGzk5Od5iMDk5mZycHKDytHJaWpp3m7S0NLKysmosT01NJSsryy855fjzr09fBZpP0Xc4m2EjxhpLREQi5YYT0zTx4OaM1A58se9rXKYLl+nE5XZR4i7DY5bgwY3H9ODBg4l5xP1bsGI1LNiMMGyGjTAj7OD3YdgNe43rCiMiwuiZ0t2Pr1hERI7FEYu+9PR0PB4P1157LWeeeSYHDhzgwIED1dbp379/g0OUlJQwdepU7r77bmJiYqo9ZxiG305LWa0GCQn+v+l9RETtoy19ZTGMBu2joe03JIvFYjR6hvrkiIgI81sGX3L4o01fsyRFJNEy+uhTK3lMD27T7f2z6svlcVV77PQ4KfWU4Dbd1ba3GTbCreFEWCMIt4SDOxKXx9Ukf7eOxGq1BDxDlWDJohw1BUsW5agpWLIES47GcMSiLyIiAoDXX3+91ucNw6gxh199OZ1Opk6dyujRoxk6dCgASUlJZGdnk5KSQnZ2tndOwNTUVDIzM73bZmZmkpqaSmpqKlu2/Pc2W1lZWfTpc/S7L7jdJvn5/j0FlZwcW20ajWNx+FQc9dXQ9huSxeMxGz1DfXKUlzv9lsGXHP5osz5Z6te+5eB/YXhLR+PgF8DBDj2P6cFlunCaDpymgwqPA4e7glJXrrfX8NeiX4myRBNjjfV+xVrjiLZEY2miEccJCVF+//vtq2DJohw1BUsW5agpWLIESw6gXnO/1uaIRd+6desatPOjMU2Te+65h3bt2jF58mTv8vT0dFasWMGUKVNYsWIFQ4YM8S5/5ZVXGDlyJF9//TWxsbGkpKQwcOBA5s+fT0FBAQCffPIJM2bM8Gt2kVBlMSzYDTt27NWWm6aJ03SCzU1iZAsyC/dT7C5ivzOr2inkCCOSSGsUkZZIIi1RRFqiiLBEEGGJJNwSgQ2bBp2IiPiBTwM5/OWLL75g5cqVdOjQgTFjxgAwY8YMpkyZwvTp01m6dCmtW7dmwYIFAAwePJgNGzaQkZFBZGQk8+bNAyAhIYEbb7yRCRMmAHDTTTd5B3WISNMwDAO7YSfCHkbXpC608hQB4DHdlHhKKHIXUuIupsxTSpmnlFznAcrNmtM+WbESbqkcqBJuiSDCiCDcEkmEJYJoawzRlhgshqWpX56IyHEvoEVfr169+PHHH2t9rrZJnw3D4L777qt1/QkTJniLPpFD3TvmgUBHCDk1b0GXANS8s4/bdFPqLKXUVUqpq4wyV9nB7ysfF7ryyXKX4TE93m0sWIi3x9EysiXJES1Ji0ojOiy62n5dbjeHbCIiIgS46BNpCqnxqYGOEHIacgs6ABsRxBFBnK0FptXEgweX6cRhOnB4HJS7yvm1YAc/F/wCgN2wE2WpnMQ6wohkQNs+OD3uo7QiIhJaVPRJs/fRtx8CcH7njAAnkWNhGAZWrFgNK+FEeAeUmKaJw3RQ5imh1FNCvjuPfHceFix49jk4IfJEIs14bIY+5kREQEWfhIAVX7wNqOhrbgzDIPzgHUgSSMRjuin1lFLqKWFfaSY7i37HgpXksGRSw1qRHJZGmKXpptAREQk2KvpEpFmwGFbv9DD92pzF3qJMfs3bSZZjH1nOTAwMkmzJpNpbkRqWht0SHujIIiJNSkWfiDQ7FsNCamQq4RWxnB55JgXuPDId+8hy7uPb0q/5lq9JtCWRHJZGkq0lsdY4TRMjIs2eij4RadYMwyDBlkiCLZGO5hkUuQvJcu4jy7GPH8u+BSDMCKOFLYlEW0sSbUnEWGM1LYyINDsq+kQkZBiGQZwtnjhbPKdFdqLcU0au8wC5rhxyXAfIdlbe8ceClThrPAm2BOJtLYi3tiDSEqneQBE5rqnok2bvwQmPBDqCNDGP6SEszOrDLYtiaUOK91GJs4Tssv0cKM8hp/wAuyp+Z2fFDgDCreG0jEiiZURLWkYkkRSRRLi19usCXW43ebnBcdsmEZEqKvqk2UuISgh0BGliFsPCl9lfNejex3EkEmtvgcOsoNxTToVZTnbpAfaU7PWuE2aEEW789+4h4UY4hmHh7JN6N8bLaDKmaeLBffCeyi7cphOX6T54+7yqm+hV/t9m2Agz7NiMMOxGWJPdS1lEGk5FnzR7q75+F4CR3UYFOIkcbyqnhaks6Kq4TTcVngoqzHLKPWWUeUopPnjLOaicRmZLNoQ7oom3tSDKEl37zv2kRWIUNmtlIeYxPZQ4SyhxlVDmKmN/cTnllFPmLqfcVXbwz3LK3eXV7o9cH1bDSkxYDLFhscTZY4m2RUOZnRhrLOEaIS0SVFT0SbP3voo+aURWw0qUNYooooDKXjI3rsreQE855Z7Ku4W4TBcANiOMlhVJRJtxxFsrrxFsrGLIY3q8hWflVxmebAf7irNwmU5cuGrZysB2cLJrq2HDZoQRb43AYlixYMFiWKj6D8Pg8KsYPaYHN248pgcPbtymG6fLSZYzmz0le6oVj3bDTow1jlhrbLU/RSQwVPSJiDSAYRjYCCPGGkaMtfIawn5tzmJn1j4KXHnku/IpdheQ6cj0bhNhiSTGEkOkJYoISxR2ix27Ycd6sPACAzdu3KYLt1n5p9N0UWGWUX6wsKzwlFFhVtTIE+2KPthGFGEHi7rKLxvRERE4Ktx+G5BimibdWp/Brv1ZFLuLKXYXUuQuYnfFH7j5723xooujiTJiiLXGEWWJOnhqvLJH1W7YNWBGxE9U9ImINDKLYSHWGkesNY4Tw9uSkBDFgbxCCl0FFLjzKHDlU+oppcC5F6fp+3WHNiOMCCOCCEskcWFxRFgiibREEmGJOlhARpCaEl/nfY+tFiuG4Wmsl1mDYRjEhMXQMsykZdh/B8iYpll5GtxdRJG7kAprKblleRxwZtc4rWxgEHbwWkGrt0fSigVrtV5I7/eGBQvWymsMDxbPlX+GE2GJxKprDkW8VPSJiDQyj+mpMXK4VUoLWtECOLnacpfHRYW7gnJ3xcGePQ+maWKz2Aiz2LAZtoPfh2GzBP9Hdm2vvVIckFZtidt0U+Yqo8xVTpmrlFJXGWXuMhxuBy7TjdvjwmW6cHncB3s8HThMDx7TjdvjwW1Wnmau+rM2EUYEUdZooizRRFmjibHEEmeLJ9yIUI+ihJzg/wQRETnOWAxLtd62iIiwBo0kro9Ajxw+/LXX5ujHw8DAdvC/aoupcZHhQVXXGp6Z2pGs3HwcngrKDt6LudRdQrYzE4fD4V0/zLATZ42jpTuJcHc08dYEoizRKgSlWVPRJ83eE5cuCHQEEfGzqtO9SRFJeMLsta7jNJ0UuwspdBVS5C6g0F3Az0U/e3sJKwe1xB+ckDuBeFsCEZbIpnwZIn6lok+avYiwiKOvJCLNQt2nl6sk1lg/31FATnkOOeU5HCjP4bfyX7zXGkZaI0mKSCIpIvGok3KDJuaW4KaiT5q9ZZ8vBWB8rwkBTiIi/ubL6eVDVT/VbCHRSCYhPOm/k3J7yskqzWJ3yW7vNjYjjHDDTphhJ+zg4JGwg6OvA316XeRIVPRJs7fuu48AFX0i4huLYSHCiKx2avfQSbkrPOU4TAclnhIOmYkGCxZyd2Zj89gJt0QSYYnAbgknzAjzftmMsMpi0QjT9YPS5JpV0bdx40YeeughPB4PEydOZMqUKYGOJCIizcDhk3JD5VQ0TtOJ03TgMB24TCfRYdEUlhdT4MzHYTqOsEcqB6pYKovByLIILG4bdsvBHkTv1DPVv7cazerXtjSxZvPucbvdzJkzhxdeeIHU1FQmTJhAeno6p556aqCjiYhIM2QYRuW8gNiputlev9ZnYTEsALg9lb2DDrcDh8dBxcE/HW6Hd1nV4wp3BaUUk+esoMJTc9LtKlbDWlkAWu2EW+3YLeHYrfaDxaINR7mJBQPDsFT+SWUWEw8eTDymp/L7g6OdK6fCceOmciJwo8ykwun0Pq51KhyDg3Mo2rAa1oN3eLEd7MUMq5xe6JDezf/2cFb+WXV8pOk1m6Jv+/bttG3bljZt2gAwcuRI1q5dq6JPRESaTH2vKTQII5ww4iMSKq8ttFb2IHrwHCzG3JXzEh783m268eDG5XJT4SrGYxZWPl9Z0tUrq4GBzTsXpBWbYcOKjYhwOzYjEpvFVuvk1iYmbo8bp3ceRRcVnjIcbicVbsdRc1ixHrwe8r/FoAUrhlGZqer/YU4r5RVOTO9t/w7+Z1b/0zQ9lQNvjMpsnoOPq/6seq0Wo7IMNgyjzseHflkNCwYWrFmWGs95JwY/+BVtiyHJ1apexz8Qmk3Rl5WVRVrafyf+TE1NZfv27QFMJCIiUn+GYXjvRlIf/dqcxf/9sQWoLH5MTDAry56qwqbaf7VcU1htYIu7xtOV+QAbYf8tIAzACmef0pvs7EI8uHGaLpwHT3k7Pc6Dp8GdlY8P+77UU+LtUfxvbrC4DTCNGndgqbpjiwULhlH5vIGFqEg7WcX7D7ln9H/vHW16917Ln6aJaVY+cuHGxPXfNUwTwwJuj+eQdCameeg+TGLDYjk7JviLPsM0TfPoqwW/1atXs2nTJh566CEAVqxYwfbt25k9e3aAk4mIiIgEXrM5sZ6amkpm5n9vaJ6VlUVqamoAE4mIiIgEj2ZT9HXp0oWdO3eya9cuHA4Hq1atIj09PdCxRERERIJCs7mmz2azMXv2bK699lrcbjd/+tOfOO200wIdS0RERCQoNJtr+kRERESkbs3m9K6IiIiI1E1Fn4iIiEgIUNEX5DZu3MiwYcPIyMhg8eLFTdZuRUUFEyZM4MILL2TkyJE8/fTTAOzatYuJEyeSkZHB9OnTcTiOfJuhxlBYWMjUqVMZPnw4I0aMYNu2beTn5zN58mSGDh3K5MmTKSgo8HsOgJdeeolRo0YxcuRIXnzxRYAmyTJr1iz69+/PqFGjvMseffRRhg8fzujRo7npppsoLCz0Pvfss8+SkZHBsGHD2LRpk9+zPPPMM5xzzjmMGTOGMWPGsGHDBr9nqS3H999/z0UXXcSYMWMYP368d65O0zR58MEHycjIYPTo0Xz77beNlmPfvn1cfvnlXHDBBYwcOZKXXnoJgPfff5+RI0fSqVMn/vOf/1Tbxh/HpK4cVZ5//nk6duxIbm4uEJhjMn36dO97JD09nTFjxni38ccxqe/nmMPhYPr06WRkZDBx4kR2797t1xx33303F154IaNHj2bq1KmUlJT4NceRspimyVNPPcWwYcMYMWIE//znP73L/fE+qSvH5s2bGTduHKNGjeLOO+/E5XL5NUcVt9vN2LFjuf766wG47bbbGDZsGKNGjWLWrFk4nc4myVFblrvuusv792XMmDF8//33x57FlKDlcrnMIUOGmH/88YdZUVFhjh492vz555+bpG2Px2MWFxebpmmaDofDnDBhgrlt2zZz6tSp5rvvvmuapmnee++95quvvur3LHfccYf55ptvmqZpmhUVFWZBQYH56KOPms8++6xpmqb57LPPmo899pjfc/z444/myJEjzdLSUtPpdJpXXnmluXPnzibJsmXLFvObb74xR44c6V22adMm0+l0mqZpmo899pi33Z9//tkcPXq0WVFRYf7xxx/mkCFDTJfL5dcsTz/9tLlkyZIa6/ozS205Jk+ebK5fv940TdNcv369+ec//9n7/TXXXGN6PB5z27Zt5oQJExolg2maZlZWlvnNN9+YpmmaRUVF5tChQ82ff/7Z/OWXX8xff/3V/POf/2xu377du76/jkldOUzTNPfu3WteffXV5rnnnmvm5OSYphmYY3Kohx9+2HzmmWdM0/TfManv59grr7xi3nvvvaZpmua7775rTps2rcEZjpSjqKjIu868efO8nyP+ynGkLEuXLjVnzpxput1u0zRN88CBA6Zp+u99UluOL774whw0aJC5Y8cO0zRNc8GCBd7Pfn++X03TNJ9//nlzxowZ5pQpU7zteTwe0+PxmLfeeqv3PeLvHLVlufPOO83333+/xnrHkkU9fUHs0FvL2e12763lmoJhGERHV95N0uVy4XK5MAyDTz/9lGHDhgEwbtw4v+cpKipi69atTJgwAQC73U5cXBxr165l7NixAIwdO5aPPvrIrzkAfv31V7p27UpkZCQ2m43evXuzZs2aJsnSu3dv4uPjqy0bOHAgNlvlAPzu3bt756lcu3YtI0eOxG6306ZNG9q2bduod6epLUtd/JmlthyGYXh7S4qKikhJSfHmGDt2LIZh0L17dwoLC8nOzm6UHCkpKXTu3BmAmJgY2rVrR1ZWFu3bt6ddu3Y11vfXMakrB8DDDz/MzJkzq92BIRDHpIppmrz//vveXlp/HZP6fo6tW7eOcePGATBs2DA2b96M2QhjHevKERMTA1Qej/Lycu/6/spxpCyvv/46N910ExZLZVmQlJQE+O99UlsOq9VKWFgYp5xyCgADBgxgzZo1fs0BkJmZyfr1672/ZwAGDx5ceRcTw6Br167e968/c9SVpS7HkkVFXxCr7dZyh35w+pvb7WbMmDGcffbZnH322bRp04a4uDhvoZGWlub3PLt37yYxMZFZs2YxduxY7rnnHkpLS8nJyfH+Qk9OTiYnJ8evOQA6dOjAF198QV5eHmVlZWzcuJHMzMyAZDnc22+/zaBBg4DAvW9effVVRo8ezaxZs7ynuJs6y913381jjz3G4MGDefTRR5kxY0atOfz13t29ezfff/893bp1q3Odpjgmh+b46KOPSElJoVOnTkfM0ZTH5PPPPycpKYmTTz651iyNeUzq8zmWlZVFq1aVt9Ky2WzExsaSl5fnlxxVx2PWrFkMGDCAHTt2cPnll/s9R11Zdu3axXvvvcf48eO59tpr2blzpzeLv94nh+fo2rUrbrfbeznE6tWrvf+Y9WeOefPmMXPmTG/Beyin08nKlSs555xz/J7jSFmeeuopRo8ezbx587yXIxxLFhV9Uier1crKlSvZsGED27dvZ8eOHU2eweVy8d1333HppZeyYsUKIiMja1zbWPWvMX9r37491157Lddccw3XXnstnTp1qvEXs6myHGrRokVYrVYuvPDCJm33UJdeeikffvghK1euJCUlhUceeSQgOV5//XVmzZrFhg0bmDVrFvfcc0+TtV1SUsLUqVO5++67vT04gXBoDqvVyrPPPsu0adMCnuXQY/Luu+9WuxbTn4Lhc6y2HD/99BNQ2Qu7adMm2rdvz3vvvRewLA6Hg/DwcJYtW8ZFF13E3Xff3eQ5fv75Z+bPn8/DDz/MhAkTiI6OrrUQa0wff/wxiYmJnHnmmbU+/8ADD9CrVy969erl1xxHyjJjxgxWr17N22+/TUFBQYOu71fRF8SC5dZycXFx9O3bl6+++orCwkLvhbWZmZl+z5OWlkZaWpr3X8XDhw/nu+++IykpyduNnZ2dTWJiol9zVJk4cSLLli3j1VdfJT4+npNPPjlgWQCWLVvG+vXreeKJJ7zFZiDeNy1btsRqtWKxWJg4caL3X+pNnWX58uUMHToUgBEjRnhPER6eo7Hfu06nk6lTpzJ69Ghv+3Xx5zE5PMcff/zB7t27vQMnMjMzGT9+PPv37w/YMXG5XHz44YdccMEF3mVN8T7x5XMsNTWVffv2eXMWFRXRokULv+Q4dLCK1Wpl5MiR3lOZTZHj8CypqalkZGQAkJGRwY8//ujN4s/3yeE5evTowWuvvcbSpUvp3bu3tzfYXzm+/PJL1q1bR3p6OjNmzODTTz/l9ttvB2DhwoXk5uYya9Ys7/r+PB51ZUlJScEwDOx2O+PHj6/z89WXLCr6glggby2Xm5vrHQ1aXl7Ov//9b9q3b0/fvn354IMPgMpfsP7Ok5ycTFpamvdf55s3b6Z9+/akp6ezYsUKAFasWMGQIUP8mqNK1anbvXv3smbNGkaPHh2wLBs3bmTJkiUsWrSIyMhI7/L09HRWrVqFw+Fg165d7Ny5k65du/o1y6HXkXz00Ufeu+E0dZaUlBS2bNkCwKeffur9hVH1MzJNk6+++orY2FjvKfmGMk2Te+65h3bt2jF58uSjru+vY1Jbjo4dO7J582bWrVvHunXrSEtLY9myZSQnJwfsmPz73/+mXbt21U5L+euY1PdzLD09neXLlwPwwQcf0K9fv0bpua8txymnnMLvv/8OVB6vdevWea8B9VeOurK0a9eO888/n88++wyALVu2+P3vTl05qj5jHQ4Hzz33HJdccolfc9x2221s3LiRdevWMX/+fPr168cTTzzBW2+9xSeffML8+fOr9Tb68+9NXVmqPl9N06zx+VrfLM3mNmzNUSBvLZednc1dd92F2+3GNE2GDx/Oeeedx6mnnsqtt97KggULOP3005k4caLfs9x7773cfvvtOJ1O2rRpw8MPP4zH42H69OksXbqU1q1bs2DBAr/nALjlllvIz8/HZrNx3333ERcXx5QpU/yeZcaMGWzZsoW8vDwGDRrELbfcwuLFi3E4HN5fqt26dWPOnDmcdtppjBgxggsuuACr1crs2bOxWq1+zbJlyxZ++OEHAE444QTmzJkD4NcsteWYO3cu8+bNw+VyER4e7s0xePBgNmzYQEZGBpGRkcybN69RMgB88cUXrFy5kg4dOninIJkxYwYOh4O5c+eSm5vL9ddfz+mnn87//M//+O2Y1JVj8ODBta4fiGMyePBg3nvvPUaOHFltfX8dk/p+jk2YMIGZM2eSkZFBfHw8Tz31VIMz1JXj3HPPZdKkSZSUlGCaJh07duSBBx7wa466spx33nmcddZZ3H777bz00ktERUXx0EMPAf57n9SV49FHH2X9+vV4PB4uvfRS+vfv79ccdbnvvvto3bo1F198MVDZ+3nzzTc3eQ6A22+/nby8PEzTpFOnTt73ybFk0W3YREREREKATu+KiIiIhAAVfSIiIiIhQEWfiIiISAhQ0SciIiISAlT0iYiIiIQAFX0iErKWLVvGpZde6vP677zzDldffbUfE/muY8eO3nneRER8oaJPRIJSeno6//73vwPW/u7du+nYsaP3zg0AF154Ic8//3zAMomINISKPhEREZEQoKJPRI4rHo+HxYsXc/7559O3b1+mTZtGfn6+9/mpU6cyYMAAzjrrLC677DJ+/vln73N5eXn85S9/oWfPnkyYMIE//vijznb+/Oc/A9C7d2969OjBtm3bapwO7tixI6+++ipDhw6lR48eLFiwgD/++INLLrmEnj17Mm3aNBwOh3f9jz/+mDFjxtCrVy8uueQS711MfLFkyRIGDhzIwIEDWbp0abXn1q9fz9ixY+nZsyeDBw/mmWee8T43ZcoUXn755Wrrjx49mg8//BDTNJk3bx79+/enZ8+ejB49mp9++snnTCJyfFHRJyLHlZdffpmPPvqIV155hU2bNhEfH++93RrAoEGD+OCDD9i8eTNnnHGG9+bpAHPmzCE8PJxPPvmEefPm8fbbb9fZziuvvALA1q1b2bZtGz169Kh1vU8++YRly5bx5ptvsmTJEu69914ef/xxNmzYwM8//8yqVasA+O6777j77ruZM2cOn332GRdffDE33nhjtaKwLhs3buT555/n+eefZ82aNWzevLna85GRkTz66KN8/vnnPPvss7z++ut89NFHAIwdO5Z33nnHu+4PP/xAdnY2gwcP5pNPPuHzzz/ngw8+4IsvvmDBggUkJCQcNY+IHJ9U9InIceVf//oXt956K2lpadjtdm6++WY++OAD77V3EyZMICYmBrvdzi233MIPP/xAUVERbrebNWvWMHXqVKKioujQoQPjxo1rcJ5rr72WmJgYTjvtNDp06MCAAQNo06YNsbGxDBo0iO+++w6AN954g4svvphu3bphtVoZN24cYWFhfPXVV0dt4/3332f8+PF06NCBqKgobr755mrP9+3bl44dO2KxWOjUqRMjR45ky5YtAAwZMoSdO3eyc+dOAFauXMmIESOw2+3YbDZKSkrYsWMHpmnSvn37Rrt5vIgEH1ugA4iI1MfevXu56aabsFj++29Wi8VCTk4OLVu25KmnnmL16tXk5uZ618nLy6O8vByXy0WrVq2827Vu3brBeVq2bOn9Pjw8vMbjAwcOeHOvWLHC24MI4HQ6yc7OPmob2dnZnHnmmd7HJ5xwQrXnv/76a5544gl+/vlnnE4nDoeD4cOHezOMGDGCd955h5tvvpl3332Xp59+GoD+/ftz2WWXMWfOHPbs2cPQoUO58847iYmJOYYjISLBTkWfiBxX0tLSmDdvHmeddVaN51asWMHatWt54YUXOPHEEykqKqJ3796YpkliYiI2m419+/bRvn17APbt21dnO4ZhNGruVq1a8Ze//IUbbrih3tumpKRUy7p3795qz9922238+c9/ZsmSJYSHh/PQQw+Rl5fnfX7cuHHccccdnHXWWURGRlY7VX3FFVdwxRVXkJOTw/Tp01myZAnTp0+v/wsUkaCn07siErScTicVFRXeL5fLxaWXXsqCBQvYs2cPALm5ud7r10pKSrDb7bRo0YKysjLmz5/v3ZfVaiUjI4OFCxdSVlbGL7/8wvLly+tsOzExEYvFwq5duxrltUycOJF//etffP3115imSWlpKevXr6e4uBiAu+66i7vuuqvWbYcPH87y5cv55ZdfKCsrY+HChdWeLykpIT4+nvDwcLZv3867775b7fkePXpgsVh45JFHuPDCC73Lt2/fztdff43T6SQyMhK73V6tB1VEmhf97RaRoDVlyhS6du3q/XrmmWe44oorSE9P5+qrr6ZHjx5cdNFFbN++HagctNC6dWvOOeccRo4cSffu3avtb/bs2ZSWljJgwADuuusuxo8fX2fbkZGR/OUvf+HSSy+lV69ePl17dyRdunRh7ty5zJkzh969ezN06FCWLVvmfX7fvn307Nmz1m0HDx7MlVdeyZVXXklGRgb9+vWr9vx9993H008/TY8ePfj73//OiBEjauxjzJgx/PTTT4wZM8a7rKSkhL/+9a/06dOH8847j4SEBK655poGvU4RCV6GaZpmoEOIiIQyh8PBmDFjeOeddwgLC/NLGytWrOCNN97g9ddf98v+RST46Zo+EZEAs9vtvP/++37bf1lZGa+99hqTJk3yWxsiEvx0eldEpBnbtGkT/fv3JykpiVGjRgU6jogEkE7vioiIiIQA9fSJiIiIhAAVfSIiIiIhQEWfiIiISAhQ0SciIiISAlT0iYiIiIQAFX0iIiIiIeD/Azd6X9TFyCMmAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 31;\n",
              "                var nbb_unformatted_code = \"histogram_boxplot(data=df, feature='lead_time', step = 30,\\n                  xlabel='Lead time, days', fmt='{:.0f}',)\";\n",
              "                var nbb_formatted_code = \"histogram_boxplot(\\n    data=df,\\n    feature=\\\"lead_time\\\",\\n    step=30,\\n    xlabel=\\\"Lead time, days\\\",\\n    fmt=\\\"{:.0f}\\\",\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "PDt8m9GpfZvm"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The distribution of lead time looks an exponential by nature\n",
        "* There are many outliers (lead time is more than nine months).\n",
        "* Most of the booking were made with zero lead time - customers made their booking on the day of arrival\n",
        "* The average lead time is 85 days, and the median is 57 days\n",
        "* The maximum lead time is the dataset is 443 days"
      ],
      "metadata": {
        "id": "1oAiqsIBit9Z"
      },
      "id": "1oAiqsIBit9Z"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`arrival_year`, `arrival_month`, `arrival_date`**"
      ],
      "metadata": {
        "id": "zl8yiMhMgd3t"
      },
      "id": "zl8yiMhMgd3t"
    },
    {
      "cell_type": "code",
      "source": [
        "ddf = df[['arrival_year', 'arrival_month', 'arrival_date', 'booking_status']]\n",
        "ddf['arrival'] = df.apply(lambda x: f\"{x['arrival_year']}-{x['arrival_month']:02d}\", axis=1)"
      ],
      "metadata": {
        "id": "FrtIFNsCdkTn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "eb80f340-fc1a-41e4-f39e-8b5c83963219"
      },
      "id": "FrtIFNsCdkTn",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 32;\n",
              "                var nbb_unformatted_code = \"ddf = df[['arrival_year', 'arrival_month', 'arrival_date', 'booking_status']]\\nddf['arrival'] = df.apply(lambda x: f\\\"{x['arrival_year']}-{x['arrival_month']:02d}\\\", axis=1)\";\n",
              "                var nbb_formatted_code = \"ddf = df[[\\\"arrival_year\\\", \\\"arrival_month\\\", \\\"arrival_date\\\", \\\"booking_status\\\"]]\\nddf[\\\"arrival\\\"] = df.apply(\\n    lambda x: f\\\"{x['arrival_year']}-{x['arrival_month']:02d}\\\", axis=1\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_barplot(data=ddf.sort_values(by='arrival'), feature='arrival',\n",
        "                perc=True, n=None, percfmt=\"{:.0f}%\", sort=False,\n",
        "                figsize=(10, 5), xlabel='Arrival year-month', xlo=90,\n",
        "                title='Month of arrival distribution')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "MEjrKE6RhmZl",
        "outputId": "2719b5ad-5e73-4f1f-c981-d883e8182156"
      },
      "id": "MEjrKE6RhmZl",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 33;\n",
              "                var nbb_unformatted_code = \"labeled_barplot(data=ddf.sort_values(by='arrival'), feature='arrival',\\n                perc=True, n=None, percfmt=\\\"{:.0f}%\\\", sort=False,\\n                figsize=(10, 5), xlabel='Arrival year-month', xlo=90,\\n                title='Month of arrival distribution')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(\\n    data=ddf.sort_values(by=\\\"arrival\\\"),\\n    feature=\\\"arrival\\\",\\n    perc=True,\\n    n=None,\\n    percfmt=\\\"{:.0f}%\\\",\\n    sort=False,\\n    figsize=(10, 5),\\n    xlabel=\\\"Arrival year-month\\\",\\n    xlo=90,\\n    title=\\\"Month of arrival distribution\\\",\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Most of the bookings were made in October-2018 - 9%\n",
        "* Less of the bookings were made in July-2017\n",
        "* Number of bookings by months in 2017 seems to be significantly less than in 2018"
      ],
      "metadata": {
        "id": "MBeZ9kPBo5GZ"
      },
      "id": "MBeZ9kPBo5GZ"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`market_segment_type`**"
      ],
      "metadata": {
        "id": "TeZkUsY4lQMY"
      },
      "id": "TeZkUsY4lQMY"
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_barplot(data=df, feature='market_segment_type',\n",
        "                perc=True, n=None, percfmt=\"{:.0f}%\", sort=True,\n",
        "                figsize=(10, 5), xlabel='Market segment type', xlo=0,\n",
        "                title='Market segment type distribution')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "4fdd6e6d-f5c5-42d1-f83d-36652626382d",
        "id": "aZsQsl3tlQMZ"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 34;\n",
              "                var nbb_unformatted_code = \"labeled_barplot(data=df, feature='market_segment_type',\\n                perc=True, n=None, percfmt=\\\"{:.0f}%\\\", sort=True,\\n                figsize=(10, 5), xlabel='Market segment type', xlo=0,\\n                title='Market segment type distribution')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(\\n    data=df,\\n    feature=\\\"market_segment_type\\\",\\n    perc=True,\\n    n=None,\\n    percfmt=\\\"{:.0f}%\\\",\\n    sort=True,\\n    figsize=(10, 5),\\n    xlabel=\\\"Market segment type\\\",\\n    xlo=0,\\n    title=\\\"Market segment type distribution\\\",\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "aZsQsl3tlQMZ"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Most of the bookings came from the `Online` segment - 64% \n",
        "* 29% of bookings - `Offline` \n",
        "* 6% of bookings - `Corporate` \n",
        "* 1% of bookings - `Complimentary` \n",
        "* less than 1% of bookings - `Aviation`"
      ],
      "metadata": {
        "id": "Jjkv8bDfsgau"
      },
      "id": "Jjkv8bDfsgau"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`repeated_guest`**"
      ],
      "metadata": {
        "id": "79wvbYM6ll8_"
      },
      "id": "79wvbYM6ll8_"
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_barplot(data=df, feature='repeated_guest',\n",
        "                perc=True, n=None, percfmt=\"{:.0f}%\", sort=True,\n",
        "                figsize=(10, 5), xlabel='Repeated guest', xlo=0,\n",
        "                title='Repeated guest distribution')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "58cc34bf-47e3-418d-9373-4aa64b6f5629",
        "id": "E1BrwLRull8_"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 35;\n",
              "                var nbb_unformatted_code = \"labeled_barplot(data=df, feature='repeated_guest',\\n                perc=True, n=None, percfmt=\\\"{:.0f}%\\\", sort=True,\\n                figsize=(10, 5), xlabel='Repeated guest', xlo=0,\\n                title='Repeated guest distribution')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(\\n    data=df,\\n    feature=\\\"repeated_guest\\\",\\n    perc=True,\\n    n=None,\\n    percfmt=\\\"{:.0f}%\\\",\\n    sort=True,\\n    figsize=(10, 5),\\n    xlabel=\\\"Repeated guest\\\",\\n    xlo=0,\\n    title=\\\"Repeated guest distribution\\\",\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "E1BrwLRull8_"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 97% of the booking made for the repeated guests"
      ],
      "metadata": {
        "id": "ztboheN5tIqh"
      },
      "id": "ztboheN5tIqh"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`no_of_previous_cancellations`**"
      ],
      "metadata": {
        "id": "DQimL3yJl0mR"
      },
      "id": "DQimL3yJl0mR"
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_barplot(data=df, feature='no_of_previous_cancellations',\n",
        "                perc=True, n=None, percfmt=\"{:.0f}%\", sort=False,\n",
        "                figsize=(10, 5), xlabel='No of previous cancellations', xlo=0,\n",
        "                title='No of previous cancellations distribution')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "6cfe687f-aeff-4ab7-ba9c-24b82f996b93",
        "id": "V7n8UaWwl0mS"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 36;\n",
              "                var nbb_unformatted_code = \"labeled_barplot(data=df, feature='no_of_previous_cancellations',\\n                perc=True, n=None, percfmt=\\\"{:.0f}%\\\", sort=False,\\n                figsize=(10, 5), xlabel='No of previous cancellations', xlo=0,\\n                title='No of previous cancellations distribution')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(\\n    data=df,\\n    feature=\\\"no_of_previous_cancellations\\\",\\n    perc=True,\\n    n=None,\\n    percfmt=\\\"{:.0f}%\\\",\\n    sort=False,\\n    figsize=(10, 5),\\n    xlabel=\\\"No of previous cancellations\\\",\\n    xlo=0,\\n    title=\\\"No of previous cancellations distribution\\\",\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "V7n8UaWwl0mS"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 99% of the booking were made for the customers have no cancellations prior to the current booking.\n",
        "* Very few customers have more than one cancellation.\n",
        "* Some customers canceled their prvious booking 13 times."
      ],
      "metadata": {
        "id": "NdVxeeUUtYYQ"
      },
      "id": "NdVxeeUUtYYQ"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`no_of_previous_bookings_not_canceled`**"
      ],
      "metadata": {
        "id": "wj7EcEFOmCpc"
      },
      "id": "wj7EcEFOmCpc"
    },
    {
      "cell_type": "code",
      "source": [
        "histogram_boxplot(data=df, feature='no_of_previous_bookings_not_canceled', step = 10,\n",
        "                  xlabel='No of previous bookings not canceled', fmt='{:.0f}',)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "outputId": "6ea417ce-4f05-4047-94e3-440319152ec1",
        "id": "10lNHW1QmCpc"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 37;\n",
              "                var nbb_unformatted_code = \"histogram_boxplot(data=df, feature='no_of_previous_bookings_not_canceled', step = 10,\\n                  xlabel='No of previous bookings not canceled', fmt='{:.0f}',)\";\n",
              "                var nbb_formatted_code = \"histogram_boxplot(\\n    data=df,\\n    feature=\\\"no_of_previous_bookings_not_canceled\\\",\\n    step=10,\\n    xlabel=\\\"No of previous bookings not canceled\\\",\\n    fmt=\\\"{:.0f}\\\",\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "10lNHW1QmCpc"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 98% of the bookings were made for customers who have no non-canceled booking prior the current booking. Most of the customers are first-time clients.\n",
        "* Very few customers have more than 1 booking not canceled previously.\n",
        "* Some customers have not canceled their bookings around 60 times."
      ],
      "metadata": {
        "id": "ZL3MErK8uJKn"
      },
      "id": "ZL3MErK8uJKn"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`avg_price_per_room`**"
      ],
      "metadata": {
        "id": "7qn6OCbgmsjA"
      },
      "id": "7qn6OCbgmsjA"
    },
    {
      "cell_type": "code",
      "source": [
        "histogram_boxplot(data=df, feature='avg_price_per_room', step = 25,\n",
        "                  xlabel='Avgerage price per room, euros', fmt='{:.0f}',)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "outputId": "b2dd76c5-6691-43f9-9d90-558c99928311",
        "id": "HNK-qZh8msjG"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 38;\n",
              "                var nbb_unformatted_code = \"histogram_boxplot(data=df, feature='avg_price_per_room', step = 25,\\n                  xlabel='Avgerage price per room, euros', fmt='{:.0f}',)\";\n",
              "                var nbb_formatted_code = \"histogram_boxplot(\\n    data=df,\\n    feature=\\\"avg_price_per_room\\\",\\n    step=25,\\n    xlabel=\\\"Avgerage price per room, euros\\\",\\n    fmt=\\\"{:.0f}\\\",\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "HNK-qZh8msjG"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The distribution of average price per room is bell-shaped, right skewed.\n",
        "* There are outliers on both sides.\n",
        "* The mean value of the average price per room per booking is \\$103.42. The median is \\$99.45\n",
        "* The minimum is \\$0 - some rooms are free of charge (complimentary).\n",
        "* The maximum is \\$540."
      ],
      "metadata": {
        "id": "eFdMoLZLvCcU"
      },
      "id": "eFdMoLZLvCcU"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`no_of_special_requests`**"
      ],
      "metadata": {
        "id": "fB6ZRntbnJq2"
      },
      "id": "fB6ZRntbnJq2"
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_barplot(data=df, feature='no_of_special_requests',\n",
        "                perc=True, n=None, percfmt=\"{:.0f}%\", sort=False,\n",
        "                figsize=(10, 5), xlabel='No of special requests', xlo=0,\n",
        "                title='No of special requests distribution')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "de32ec4f-8a47-40e0-8845-e089b2233070",
        "id": "RdRKz1WMnJq3"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 39;\n",
              "                var nbb_unformatted_code = \"labeled_barplot(data=df, feature='no_of_special_requests',\\n                perc=True, n=None, percfmt=\\\"{:.0f}%\\\", sort=False,\\n                figsize=(10, 5), xlabel='No of special requests', xlo=0,\\n                title='No of special requests distribution')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(\\n    data=df,\\n    feature=\\\"no_of_special_requests\\\",\\n    perc=True,\\n    n=None,\\n    percfmt=\\\"{:.0f}%\\\",\\n    sort=False,\\n    figsize=(10, 5),\\n    xlabel=\\\"No of special requests\\\",\\n    xlo=0,\\n    title=\\\"No of special requests distribution\\\",\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "RdRKz1WMnJq3"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The distribution looks like an exponential by nature\n",
        "* Most of the bookings (55%) were made with no special requests\n",
        "* 31% - 1 special request\n",
        "* 12% - 2 special requests\n",
        "* 2% -3 special requests\n",
        "* 4 or 5 special requests - less than 1%"
      ],
      "metadata": {
        "id": "2uhhprC7wOTd"
      },
      "id": "2uhhprC7wOTd"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`booking_status`**"
      ],
      "metadata": {
        "id": "DYeG96zRnaJl"
      },
      "id": "DYeG96zRnaJl"
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_barplot(data=df, feature='booking_status',\n",
        "                perc=True, n=None, percfmt=\"{:.0f}%\", sort=False,\n",
        "                figsize=(10, 5), xlabel='Booking status', xlo=0,\n",
        "                title='Booking status distribution')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "91ca5fcd-b19a-4c25-b45b-fbd8ebb7842e",
        "id": "RuFvHIbnnaJl"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 40;\n",
              "                var nbb_unformatted_code = \"labeled_barplot(data=df, feature='booking_status',\\n                perc=True, n=None, percfmt=\\\"{:.0f}%\\\", sort=False,\\n                figsize=(10, 5), xlabel='Booking status', xlo=0,\\n                title='Booking status distribution')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(\\n    data=df,\\n    feature=\\\"booking_status\\\",\\n    perc=True,\\n    n=None,\\n    percfmt=\\\"{:.0f}%\\\",\\n    sort=False,\\n    figsize=(10, 5),\\n    xlabel=\\\"Booking status\\\",\\n    xlo=0,\\n    title=\\\"Booking status distribution\\\",\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "RuFvHIbnnaJl"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 33% of the bookings were canceled\n",
        "* 67% of the bookings were not canceled"
      ],
      "metadata": {
        "id": "L9xkE_l0wpmz"
      },
      "id": "L9xkE_l0wpmz"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Bivariate Analysis"
      ],
      "metadata": {
        "id": "GvPotEBMbDC6"
      },
      "id": "GvPotEBMbDC6"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Pairplot of continuous numerical variables**"
      ],
      "metadata": {
        "id": "-W7CB-VtQ7ci"
      },
      "id": "-W7CB-VtQ7ci"
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(df[['no_of_weekend_nights', \n",
        "                 'no_of_week_nights', \n",
        "                 'lead_time', \n",
        "                 'avg_price_per_room', \n",
        "                 'no_of_special_requests', \n",
        "                 'no_of_adults',\n",
        "                 'no_of_children']]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        },
        "id": "dW8v6Gj5Q6Kx",
        "outputId": "7bc1725a-c4f6-409d-f524-9b4a08f80ef4"
      },
      "id": "dW8v6Gj5Q6Kx",
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1260x1260 with 56 Axes>"
            ],
            "image/png": 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dzb6j7Xe/+13eMrfbjcWLF6OpqalWYRAREREREREREVVFzQbafvazn+Gvf/0r3v72twMA/vznP6OjowPHjh3Dpz/9aVx11VW1CoWIiIiIiIiIiKjiajbQpqoqfvWrX6G5uRkAMDAwgNtvvx0//elP8dGPfrTgQNuSJUvgdDphNpshCAIeffTRWoVMRERERERERERUspoNtJ08eTI9yAYATU1NOHnyJLxeLyyW4mE89NBDaGxsrHaIRDRViUDPkIzeE360eqxYMEMClIz1FqDHJ6N3JIo2jxXzvRIQK1DHSFS7DgAwAf3hGAaCMpqdVrTYBSAxwTLjxao3EXjhmA8DgShmuq1Q1DiGwzG0eqzwSGb0+WV4HBL8keQxznRbYbOY4St2TnKNnqMT/iicVgHNThGBaBxvjETQ5rZhjlvU3OZUVMWpsIKgrOI0jw0ttgL7mkjZYszAgZMjOO4Lo9klodFugVcsox7SlwAcGh5r//MaJCABHPUr6PNH0eSSEJRjsFoEyDEVLqsFCcQRj5vR6x/rD3L7h35/kXZuTtbf68/I6XhOXKX0J1Sf9OjnS7mHVUPq/nrCX/j+Wg1sP1OPEZ+PjBhT6p520o829+g9TdU5pmqYzLmfTP/AvoVKVLOBtgsuuAD//M//jCuvvBIA8Nvf/hYXXHABQqEQ3G53rcIgoulGBPZ2+7Bh535ElDhsohmbV3Ti8nZv8oZsAfYe1Fh/hnfsj4Hx6gAAE/DMsRGs274vXWbr6i5cOMczdgMer0wp+9GTRnxrl7bj4acPYygk4+6rOtFgF3DoeASbdh3QLJN3TnLlnKN5TXbceOkZWfVtWdmJ5Z22rG2efyOA1weC2Lanu/D5n2jZYszAEz0+rN8xdi42Lu9Am0dCZ7OTD131QgD2vpad0//6gS7IsTju/MXYsjVL2vHIs0dwzXmn49DACM6b34wNOw+kc/Tmy9rTdeT+npdfGrmzZWUnLlvgHRtsK9ZXUH3To5/X695Syv21Gkq5H1N9MeLzkRFj0rinbV7RicsXeafWYNtkzv1k+gf2LTQB5lrtaOPGjXj/+9+Pl156CS+99BKuuuoqbNy4EQ6HAz/84Q+Lbnv99ddj1apVeOSRR2oULRFNFT1DcvpGDAARJY4NO/ejZ0hOrvcVWO+TS64DSP7rVurGmyqzbvs+9IdjJZcpZT960opv255urDp3DiJKcmDCZZXSg2JaZXLPSa7cc7Ssa3Zefet37MfLfYGsbQ6cHEkPnKXKae1rImWLOepX0gMlqTo27TqAYDQ+oXpIX4eG83P6YF8gPciWWnbf491Y1jUb9z3ejavOPT09yAYkczSzjtzfc/NLK3fW79iPo/6xvw5K6U+oPunRz+t1bynl/loNbD9TjxGfj4wYk9Y9bcPO/Tg0bIznyEqZzLmfTP/AvoUmomZvtJlMJlx55ZXpN9pK9ZOf/AStra0YHBzEddddh4ULF+L888/XLCsIJni9joJ1CYK56HqjqJc4gfqJ1UhxFstTI8VZinqIt/eEP31DTIkocfSORPHWOd5x15dSBwB0Hx7SLOOLqmhv85RUppT91EKhHC0Un8k09nN/IDpumcxzkiv3HJlM0KzvjZEIOmbNTG8TT2iXy93XRMoW81xvn2YdQTk2oXoqwSjtsNZxVKIv7T2Zn9OF8iOViwM5OZ6bo4VyNpUXhXKnLxDF2bMbABTvK4xyvathqh2bVo7q0c/rdW/Ra7+l3I+rqd7yuB6eS43yfGT4mDTuaREljl5/FOfoFFOmcvMpN0cnc+4n0z+Uuq1R2k0uI8Y1lWOq+kDbP/3TP+EnP/kJ3vrWt8KU+ksLQCKRgMlkwvPPP190+9bWVgDJ73S74oorsG/fvoIDbaqagM8XKliX1+sout4o6iVOoH5i1SPOlhbtj0QXy9N6OZ8p9RBvq8cKm2jOujHaRDNaPVb4fCG0jbO+lDoAwGu1aJbxWoWSy5Syn0qaaI4Wii+RGPu5xTV+mcxzkqvQOcr9vc1jyzqvgkm7XO6+JlK2mNYCx+mULBOqpxKM0g6rFUc1+9I2d/51LJQfiUTxHB/v93Q7L7D9TFdp/Ymqxg1xvavBKLk8URPJ0Vr383rtE0BJ99dqKOV+XE1GzONCOQrUx3OpXjlcbzFp3dNsohmtbv1iyjRePpXal07m3E+mfyh1W6O0m1xGjKveYirWl+aq+kdHf/KTnwAAXnjhBTz//PPp/1K/FxMKhRAIBNI///GPf0R7e3u1QyaiKWTBDAmbV3TCJia7u9T3OCyYIQEA5nsLrPdKJdcBAC12AVtXd2WV2bq6Cy12S8llStmPnrTiW7u0HY8+fww20Yy7r+pEICpj4/KOgmVyz0mu3HO068XjefVtWdmJs2a6srZ58ywP1i5tL3r+J1q2mDluEVtWZp+Ljcs74LSaJ1QP6WteQ35OL5rpwt1XZS9bs6Qdu/cdx5ol7fj580eweUVHVo5m1pH7e25+aeXOlpWdmJsxyUcp/QnVJz36eb3uLaXcX6uB7WfqMeLzkRFj0rqnbV7RifkNxniOrJTJnPvJ9A/sW2giTIlEomZf3aeqKgYGBqCqY9/GeNpppxUsf/ToUdx8883pbZctW4abbrqpYHlFUflGW43VS6xGeqOtWJ7Wy/lMqZt4x5ttLWPW0VaPNflHwKRnHZWSN96is45qlKnhzHDl5GgqvoFAFC0uK2Lx5KyjM91WNFizZx0dDMpoyZp1tMA5yTV6jk76o3BIo7OOysmPi7a6bZjrFuH15ORexkyiIVnFLLe18L4mUrYYM3A8GMMJXxhNOs46apR2WOs32irWl6ZmaPNH0eq2Jv8gSc06GoiiySkhJMcgjc466rRaYEIcasmzjmrkfcaso6mcLj7r6FgdRrne1VCvxzbhHNVjBlC9Zx0tdn+thlLux1VixDwu9hZG3TyX6pXD9RaT1j3NIBMhlPtGm2aOTubcT6Z/KGFbQ7WbDEaMq95imsgbbTUbaPvhD3+Ib33rW2hubobZPPYi3a5duyq2Dw601V69xMqBtupgvNVTi1jLGmiDMc6jEWIwShxGiKGacUyHvnQieGzGU899aa3wWPU1JQbaRjGm0tRjTFPlfm/EmABjxlVvMU1koK1m7zk+/PDD+M1vfoMZM2bUapdEREREREREREQ1U/XvaEtpa2uD2136CCAREREREREREVE9qdkbbXPnzsXHPvYxXHbZZZCksS8qvO6662oVAhERERERERERUdXUbKDttNNOw2mnnQZFUaAoen9LJBERERERERERUWXVbKDtlltuKbp+y5YtWL9+fY2iISIiIiIiIiIiqqyafUfbeJ5//nm9QyAiIiIiIiIiIiqbYQbaiIiIiIiIiIiI6hkH2oiIiIiIiIiIiCrAMANtiURC7xCIiIiIiIiIiIjKZpiBtmuvvVbvEIiIiIiIiIiIiMpW9VlHb7zxxqLr77//fgDAqlWrqh0KERERERERERFR1VR9oO2Tn/wkAOB3v/sdBgYGsGLFCgDAL3/5SzQ1NVV790RERERERERERDVR9YG2Cy64AABwzz334NFHH00vX7JkSclvsamqitWrV6O1tRUPPPBAVeIkIiIiIiIiIiKajKoPtKWEw2EcPXoUc+fOBQAcPXoU4XC4pG0ffvhhLFq0CIFAoJohElGpRKBnSEbvCT9aPVYsmCEBSoEyI1HtMuOtr0aZQvGWUocZOOpX0OuPoM1twxy3CMQneN5KYQL6wzF0Hx6C12pBi10AjDJXjAAcDyh4ZfgUwlEVLrsFoWgMDXYRJlMCatyEYFSB0yoiLMfgtokIRGOIJ+JwSiL6A1E0u6wIyQpEswCP3YJEPI6ICvhCClpcEiKKiqCsYoZDgi8so8VphdViwkBQQUCOweuQEIwqcAdlBCMKXFYRiUQcZrOA4YiMGQ4JESWOU0EZLW4rorEY3FYRTtGMk8PJ6xtS4jgVktFgF+ELKXBaLelygAn9gSicVgskwQSHKKDNbcGh0fyY1WCDSzLjVFCGVbTglaFBiBYBgagCt03EqYCMtgYrEgngmC+CWQ1WmAGcCilodkpQVBWCWUAgoqDZaS3v+ubkotsdT+fNQFAuv96pYPTcPNfbh1aXtfR2mnv+3AKODSuIxFSYTCbE1ARCsoqZbglqIgE5lsBgUEazS4JdFCCYTRgJKxiJxOCxW2ATBJwKyWh0ihiOKBBMZrisFsQTKkwmAQOBKOyiAIckQBRMUFVAVmMwmwX0jkTR5rFi/gwJx4YVBGUVSlyFzWJBIBLDfDUBr8UEIBnzcESBVbSM5ZRDQH+IuWBopdxDq7XPYve5au63lscKABagxyePtSevBMRqsF89TJf+X69cYkzGNJnjnEx/qFdfSnWnZgNtd9xxBz72sY9h7ty5SCQSOHHiBDZt2jTudm+88QaeeOIJ3HjjjfjBD35Q/UCJqDgR2Nvtw4ad+xFR4rCJZmxe0YnL271jN5rxylSijlrGYgae6PFh/Y6xMltWduKyBd7KDraZgGeOjWDd9n3p/Wxd3YUL53j0f2gWgKePjuCN4QjufezVdHxrlrTjkWeP4ObL2vHvT3Tj8GAYNtGMu5Z34PBgCN/7Yw+uOe903Pd4d9421140Hy0uCV9/7FV88uIFeK0/gG17xsp9/t1nYjAQxWBQyVp+67sW47/+fBjXnHc6Hn/5DXzgbafj2384iJsvXYTeERmbdh1Il71reQd+9lw3rj7vdPzmbydxwcIm/PdfjuTFdMeVZyGWCGHrb19JL1u7tB1NThEv95qzrv1tVyyGVTDjP59KHtsjz+bXt3ZpOx5++jCGQnLWzxuXd+D+PxxMn6cJX98CudjsEnHTj18wXt7UUrntNKfdffBts/DO9lb4QjIAICir2LanGzMcEm591xlQE6asHPvqqrOhxhO48xf7Na9/Kt+vf8cCOKyWvHJtDTY4JDNOBWNZ9W5e0QlRSOAbew7m5dfW1V2QLGZ85dcv5a3bsrIT39rbXX6OUXWVcs+ZCvvUc78WYO9Bjf2e4Z16g21Gfm6oJL1yiTEZ02SOU69tadqp2ayjl1xyCX73u9/hS1/6Eu6880785je/wT/8wz+Mu91XvvIVrFu3DmazYSZIJZrWeobk9A0GACJKHBt27kfPkFxymUrUUctYjvqV9B/vqTLrd+zHUX9l76r94Vj6YTm1n3Xb96E/rP9fBoeGZRzsC6QH2YBkfPc93o1lXbOxYed+LOuanV5+164DGAjKWNY1Oz0IkLvN1t++goP9QSzrmo3BkJweTEuV+9rvXoFDEvOW/9vvX03Xe+3FC3HXrgNY1jUbDquYHqjIjOPaixdi484D+MQ7F2Dbnm7NmAZDcnqQLbVs255uOCQx79rf+9irGAyNHZtWfdv2dGPVuXPyft40Gmuq3ESvb6FcDEbjhsybWiq3nea2u6vOPR0H+wMYCMoYCI7l5apz58Ah5edYz0AwPXiWWpZ5zVM5MhCUNcv1DAThsUl59W7YuR+iYNHMr3Xb9+HAyRHNdet37J9UjlF1lXLPmQr71HW/vgL79VV3v3ow8nNDJemVS4zJmCZznHptS9NPzd5oA4D9+/fj+PHjUFUVL7/8MgDgqquuKlh+7969aGxsRGdnJ/70pz+NW78gmOD1OoqsNxddbxT1EidQP7EaKc5ieWqkOAvpPeFP32BSIkocvSNRvHWOt6QylaijlrE819unWaYvEMXZsxu0T1QZug8Pae7HF1XR3uap2H7Go5WjvSf9iCegGZ/JNPb/zOXxBNLrCm2TKlOo7qAcK7p9eHS9yQQEo9plU2WGgkrWtpkmuv/MYyt2jMV+Tv0+ketbKBeDcixvWe3zprb9V26elttOc9vdgD+KeMabIKl1hXKsWLvI/LlQuXgC6PdHC17XQvk1XvvK/L3WuVCuergHToRmX1rCPafS9NjndNxvSi3zuBLPDfXwXKr3NWVMlVFuPuXm6GSOsxbbGqXd5DJiXFM5ppoNtK1btw5Hjx7FWWedBUEQAAAmk6noQNvzzz+Pxx9/HE8++SSi0SgCgQA+//nP42tf+5pmeVVNwOcLFazP63UUXW8U9RInUD+x6hFnS4tbc3mxPK2H89nqscImmrNuNDbRjFaPNR37eGUqUUdNY3Fpl5npslb0enmtFs39eK1CVfJiIjna5raiu9evGV8iMfb/zOXm0cGFYtukyggm7XJOSfucpLZ3jK4HAKdNu6x9tMwMp5gum1tuovvPPDat+jLPR6GfU79P5PoWykWnlH07r2beFFKt/qvUPC23nea2uxa3Fa/1j30nbOY6rRwrlDu519xi1i5nNiX3Wey6jpeDhfad+r3WuVCuergHaplIX1rKPafS9Ninnvtt02m/KbXM41KfGwrlKFAfz6V65RJjqqzx8qnk+/0kjrMW2xql3eQyYlz1FlOxvjRXzT6PuX//fvzkJz/BXXfdhfXr12P9+vW48847i27zuc99Dk8++SQef/xx3HvvvbjwwgsLDrIRUW0smCFh84rOrAGGzSs6k18GWmKZStRRy1jmuEVsWZldZsvKTsx1i5M+n5la7AK2ru7K2s/W1V1osdf05WNN8xokLJrpwm1XLM6Kb82SduzedxybV3Ri977j6eV3Le9As1PCrhePY82Sds1t1r3nTJzR4sSuF4+j0SFh7dLscp9/95kIyUre8lvftRi79yXrfeip13HX8g7sevE4QhEFG5d3ZJW9a3kHHn7qdWxa0YEf/F8P1i5t14yp0SFh3XvOzFq2dmk7QrKSd+1vu2Ixmhxjx6ZV39ql7Xj0+WN5P29c3pF1niZ6fQvlotNqNmTe1FK57TS33f38+SNY1OJCk1NCk3MsL7c/dwyhaH6OzW924u6rOgte/1S+NzklzXILmp0Yich59W5e0QlFjWnm19bVXeiY5dFct2Vl56RyjKqrlHvOVNinnvud7y2wX29196sHIz83VJJeucSYjGkyx6nXtjT9mBKJRE2+KnPNmjW48847MXPmzLK2/9Of/oT//M//xAMPPFCwjKKofKOtxuolViO90VYsT+vlfFZkNlA9Zh2dTCwZMz22um3JP96LfcF6uUZnD/NFVXitQvJhuUq99IRzdHTW0YCsJmcdtVkQkmNosIkwmTNmHZVEhGMxuCURATmGeCIBp2TBQCCKJqcVISUGi9mMBpsFicTorKPh5KycUUVFSFbR4JAwHE7OoGYbnXU0KKtosIsIycnZRlMznCIRh8kswB+R4dWYddQliXBJo7OOuq0IxUZnHbWJ8IVTs46qcFstyJ111C4KmJWaddQfRZvHBnfGrKMhJQZRyJh1NCijzTM262ibxwrBBAyFFDQ6JcSyZh2Vyru+Obn45jY3/IFIxqxzZdY7SbV+o00zT0fPTV8gipkua+ntNGvWPgktbovmrKMtLglxJGcdPRWU0eiU4JCyZx112yywW5Kzjs5wivBHYjCbTHBJFsSRnHV0MCDDJpphlwRIo7OOKmoMJrOAPn8UM93JfujYsIKgoiKmxmG1CMlZR5scRWYdldDisGTMOqpPLpSrbu6BOSbcl+oxa53es47Wer8Zs462eqzJQbYafW1ZzfM4t//SaPPF3sKom+dSI872yJgmpNw32jRzVK+ZQ0vY1lDtJoMR46q3mCbyRlvNBto+9rGP4eWXX0ZXVxdEcexfl++///6K7YMDbbVXL7FyoK06GG/11CLWcnIUMMZ5NEIMRonDCDFUM47p0JdOBI/NeOq5L60VHqu+psRA2yjGVJp6jGmq3O+NGBNgzLjqLaaJDLTV7L3iz3zmM7XaFRERERERERERUc3VbKDtggsuwPHjx3H48GFcfPHFCIfDUFW1VrsnIiIiIiIiIiKqqppNhvDTn/4Ua9aswYYNGwAAvb29uPnmm2u1eyIiIiIiIiIioqqq2UDbj3/8Y/zkJz+By+UCAMyfPx+nTp2q1e6JiIiIiIiIiIiqqmYDbZIkQZLGpr6NxWo09Q8REREREREREVEN1Ow72s4//3zcf//9iEQi+OMf/4j/+q//wpIlS2q1eyIiIiIiIiIioqqq2Rttn//859HY2IjFixfjkUcewaWXXopbb721VrsnIiIiIiIiIiKqqpq90fbNb34Ta9euxQc/+EEAgKqq+NznPoevf/3rtQqBiIiIiIiIiIioamr2Rtsbb7yBBx54AAAgyzI+85nPYP78+bXaPRERERERERERUVXVbKDtK1/5Cl599VU88MADuPHGG3HBBRfgM5/5TK12n2Y2m2q+TyIiIiIiIiIimvqqPtB24MABHDhwAH//+99x7bXX4le/+hXmzZuH888/HwcOHKj27tPMZhM2/fLv+Nre1zjYRkREREREREREFVf172i75557sn73eDw4ePAg7rnnHphMJjz88MPVDiHtVFCGGovXbH9ERERERERERDR9VH2g7Yc//GG1d0FERERERERERKS7ms06OjAwgHvvvRd9fX347ne/i4MHD+KFF17A1VdfXXCbaDSKj3zkI5BlGaqq4j3veQ/WrFlTq5CJiIiIiIiIiIhKVrOBti984QtYtWoV7r//fgDA/PnzceuttxYdaJMkCQ899BCcTicURcGHP/xhXHLJJTjnnHNqFDURaRKBniEZvSf8aPVYsWCGBCgFyoxEtcuMt74aZYrFS+MTgSM+Bc/19sFltcAimJBIJOCyWiCrCfSNROGyWuC2CQjJCiSLBWoigeGQgga7CJPJhJGwgkaHhKiqIhBR4bZbAMRhNgkYCSvw2EWcCsqY1WBDSFExGJDR6rYiKMfglCywmIHBoIKWiIpgVIbFLGAoLMMhWdDkEBGUVQwEZHjsFjhEAf6IAo9NRFSNYyikoMklQjILGInEEE/E4bGJCMgqAtEYWlwSFEVFg12CYAJ6/VE0O61ocQjoD8dwbDgKm2iGy2pBLK5CMAl4rrcPDklAg00ETMDRU2G0uK2wWczwhWU4rRacCsqwiwIa7CKcogl9AQUhWUWjQ0JEiaHBJqHFJeCQL5nHbR4r5jVIQAI46lfQ64+gxWVFIhGHUxTRYheARIFrZAF6MuqZ75WAWIGyJuBUVMWpsIKgrOI0jw0ttiJ1T1Xm5Hnu80cx22tDWIkjICtosEsYHM2lsBKDaBbgdYiQY3H0+6OwSwKcVguCUQUOyQK7KODESAQNNhEDgShaPbZ0HjlEC0SLCWYz4LAICClx9I5EMdNthdMqIKqoOBVUMLvBhha7gP5QDMMRBVbRgkBESeZh7nU3Af3hGE74o3BaBTTaRDRaNa7faLmBoKxdD9WeHvekUu6V1dxvre+/eh3vVDHab3QfHoLXajFGv2HEZznGpJ/JHOdk+gf2LVSimg20DQ0N4R//8R/x4IMPJndsscBsLj4Xg8lkgtPpBADEYjHEYjGYTJzIgEhXIrC324cNO/cjosRhE83YvKITl7d7x24045WpRB2VLEPjE4EnDvqwfsfYeVy7tB1umwVumwW3b/9b1vJZDTb86JluLH1TG379t5N479mzsG1PN2Y4JFx70Txs29ONiBLHvCY7Pn3ZGfiPJw7imvNOx32Pd2PxTBf+6e3zsGnXgXSddy3vwM+e68bqt52On/zpMIYjCm669AzcNVpmXpMdN192BjbsPJAVx8NPH8ZQSMbape349d9O4gNvm4OgrOK//3IEn7x4AUKKmo7FJppx67sW47/+fBgfOv90PPz0YUgWEz6zpB13/mLsuL/43rNglyx558IpCfj2H17HUEjGF997FtQE8P9+83JWmdO8Ntz72Ks4PBiGTTRjzZJ2HBoYwXnzW7JzdGUnGuwWrP3vv6aXbVzWge3Pv4KPX7QAF87x5P/RYwH2HtTI9TO8+YNtJuD5NwJ4fSCYdfxbV3dp1z1VmYEnepJ5ncq7+/9wEB+7cD6+9rsX0udlzZJ2PP7yG/jg+fOwIee6t3lsuO9P3bjm/HkAEvjMf72Ql+epsgub7TgUVrPydNOKDthFE77661cxFJJx91Wd+O8/H8aSs9pw3+Ma1wYATMAzx0awbvu+nPqdOLfNNXb9NMpNu2tsNHrck/S6D063/U4VRuw3jHhNGZN+JnOcem1L007VZx1NcTgcGBoaSg+U/fWvf4Xb7R53O1VVsXLlSlx88cW4+OKL8Za3vKXaoRJRET1DcvoGAwARJY4NO/ejZ0guuUwl6qhkGRpfz5CcHlgCkudx255u9PmjeK0/mLf89YEgrr14Ibbt6cYNlyxKDzisOndO+mcAWNY1Gxt3HsCyrtnpQYUbLlmUHmRL1XnXrgO49uKF2LTrAG64ZBGWdc1OD7Kl6kkNXmTGsercOemfb7hkEQaCMrbt6cayrtkYDMlZsUSUOP7t969iWdfs9LbLumanB9lSZQaC2udiICin9zcQlNODbJllXusPYlnX7PSy+x7vxlXnnp6fozv2Q4klspZt2p08B+u270N/OP81tR5fgVz35ed6fziGAydH8o6/UN1T1VG/kr6Wqbxb1jUbX/vdK1nn5b7Hu3HtxQvTg2yp5dv2dKNnMJnr63fsh120aOZ5qqzHbs3L0407D8DrsKZz585f7Me1Fy9Mt4dUucxr0x+Opf8Izqz/wMmRrOunVW66XWOj0eOepNd9cLrtd6owYr9hxGvKmPQzmePUa1uafmr60dGbbroJR44cwYc+9CEMDQ1h27Zt424nCAJ27NiBkZER3HzzzXj11VexePHiAmVN8HodBesyARAsZng89nIPoyYEwVz0OIykXmI1UpzF8tRIcRbSe8KfvsGkREY/BvXWOd6SylSijkqWMSI9c0ErRwudx7jGv26nloflGCJKHOFoLL2tyYSselK/Zy7PLJ9ZZ7o+OVawntxtUi9Bp7aLJ8aWp37W2iZ320yFtosnkN6mlDKpZQP+qGbZoBzLW5Y6B76oiva25NtNqVyZSK53Hx4qGGNm3RNR65ytRF/6XG9fXt4VyqXUuc9dnpnrqWtWqI7+Atd6KKTk5WqhayMIZviiasFYMq9f9+Ghil7jaquHe+BETKQvreY9Sa/74HTbb0q95XFunhqx39D7mjKmyii3beTm6GSOsxbbGrUPMGJcUzmmmg20dXR04Ec/+hF6enqQSCSwYMECiKJY8vYejwdvf/vb8b//+78FB9pUNQGfL6S5zmw2IQFAjcUxMhJGXOuvQ4Pweh0Fj8No6iVWPeJsadF+Y7NYntbD+Wz1WGETzVk3GptoRqvHmo59vDKVqKOSZYyoFrkwkRwtdB7NGp/mTy23SxbYRDMcVkvWtlr1ZC7PLZ9al6rPLlkK1pP7eyIx9rNDskAwje0v9bPWNqn/mzTKFNrObALUeOllUsta3Nrn1ill36Izz4HXKqSvUSpX2iaQ616rpWCMmXVPRLVytpp9aavLmpd3gPZ5cUjaeZmZ65nXTKtsoWs9wyHm5Wqha6OqcXgLtBGzCdm5UaBcude42urhHqilEn1pNe9Jet0Hp9t+U4yYx4VyFMjPUyP2G3pfU8ZUGeO1jVL70skcZy22NWIfABgzrnqLqVhfmqtmHx0Nh8N48MEH8dBDD2Hx4sU4fvw49u7dW3SbU6dOYWRkBAAQiUTw1FNPYeHChbUIl4gKWDBDwuYVnVl/kG5e0Zn8MtASy1SijkqWofEtmCFhy8rs87h2aTtmuq1Y1OLMW76w2YmHn3oda5e24ztPvoa1S9thE83Y/tyx9M8AsOvF49i0ogO7XjyONUuSy7/z5GvYuLwjq867lnfg4adex8blHfjuk69h14vHcVdGmV0vHsfmFR15cTz6/LH0z9958jU0OSWsXdqOXS8eR6NDyorFJia/o233vuPpbXe9eBx3X5V93E1O7XPR7JTS+2tySrj9yrPyyixqcWL3vuPpZWuWtOPnzx/Jz9GVnRAtpqxlG5clz8HW1V1osef/O9l8b4Fc9+bneotdwJtnefKOv1DdU9Uct5i+lqm82/XicXz+3WdmnZc1S9rx0FOvY7PGdV/QlMz1LSs7EVZimnmeKjsSjubl6aYVHfCFouncufuqTjz01Ovp9pAql3ltWuwCtq7uyqu/Y5Yn6/pplZtu19ho9Lgn6XUfnG77nSqM2G8Y8ZoyJv1M5jj12pamH1MikajJq12f/exn0dHRgR07dmD37t0Ih8P40Ic+hB07dhTc5uWXX8YXvvAFqKqKRCKBK6+8ErfcckvB8oqiFn2jbf1vXoEai+Mry97EN9oqpF5iNdIbbcXytF7OZ0VmA9Vj1tE6miFIzzfaCubo6Kyj/YHk7KKiYEI8kYBTskCJJ9Dvj8JptcAlCQgrCiTBAhUJDIdiaLBbCs46akIcJpOAkdEZQk8FZczy2BCKqTgVkDFzdNZRh2SBaAZOBRU0u6wIyQoEswBfWIFdEtDoEBGSVQwGZbhtyVkgAxEFbpsIWY3DF1LQ6BQhCQJGojHE42OzjgajMTQ7JSgxFQ02EYLZNDrrqIQWhwX94RiOD0dhFc1wSRbEEslZRwcCMuySGR6bCJMJODoURosrOevo8OhsqKdCCuxisoxLGpt1dIZDQlSJocEmosVlSc466o+i1W3F/JxZR5tdViARh1O0JP/YybiFZeVKxqyjrR5rcpCthFlHQ7KKWW5rXt0TUes32irWl6ZmHQ1EcVqDbfQjoAo8NgmngjJcNguiSvIjmzNGZx0d8MuwSck32EKKApvFAock4ORIBB6biMFAFDPdNghmoM8vwy4JEAUTTCbAKSZnHe0biaLFbYVTEhCNpWYdTV6D/FlHpfS1SR/b6KyAJ/1ROCQBjfZSZh2VJnWNq61u7oE5yulLa35P0nvW0emyXxgzj4u9haGZp6P9hi+qwmsVjNFvGPFZjjFNSLlvtGnmqF4zh5awrRH7AMCYcdVbTBN5o61mA22rVq3Co48+iquuugq/+MUvAAArVqzAzp07K7YPDrTVXr3EyoG26mC81WPIgbZRRjiPRojBKHEYIYZqxjEd+tKJ4LEZTz33pbXCY9XXhAfaRhnxWBhTaeoxpqlyvzdiTIAx46q3mAz50VFJkhCJRNKzjh45cgSSxNcsiYiIiIiIiIhoaqjZh+1vueUW3HDDDTh58iQ+97nP4YUXXsBXv/rVWu2eiIiIiIiIiIioqmo20PbOd74THR0dePHFF5FIJPClL30JjY2Ntdo9ERERERERERFRVdVsoO3zn/88LrjgArztbW/DokWLarVbIiIiIiIiIiKimqjZQNsHPvABPPvss7j77rtx5MgRvPnNb8Z5552Hj3/847UKgYiIiIiIiIiIqGpqNtB24YUX4vzzz8ff/vY3/OlPf8J///d/o7u7mwNtREREREREREQ0JdRsoO3jH/84wuEwzjnnHJx33nn42c9+hqamplrtnoiIiIiIiIiIqKrMtdrRmWeeCVEU0d3djVdeeQWvvvoqIpFIrXZPRERERERERERUVTV7o+2LX/wiACAQCODnP/85vvjFL6K/vx/79++vVQhERERERERERERVU7OBth/96Ed49tlnceDAAcyePRurV6/G2972tlrtnoiIiIiIiIiIqKpqNtAWjUZx3XXXoaOjAxZL/m6Hh4fR0NBQq3CIiIiIiIiIiIgqqmbf0Xb99dfjLW95i+YgGwB84hOfqFUoREREREREREREFVezgbbxJBIJvUMgIiIiIiIiIiIqW80+Ojoek8mUt+zkyZP4l3/5FwwODsJkMuGDH/wgPv7xj+sQHVGViUDPkIzeE360eqxYMEMClAJlRqLVLTOROiYTb62Op9R4aXwicHRYQffIKahxICKraHZLSMSBwZCMJqeEiKJiJBKDy2qBRTDBLgoIRmMYDscw022FaDGhdzgKl80CNaHCJYkIyir8kRjcVgvskgBfWIYkCLCLZsQTwFBIhtchYTiswG2zwGox46WhAdhEC0bCChrsIkymOOIJM/pGomh2S3CIAgQBCEXjGAjIcNssEAUTGuwWjETUdL4kEIcJZgQiClw2EcNhBV67iKgag2ASEIjE4LQJMMMEkwnw2CzwR8e2b1BVnAoqMJkSMJsE9PujaHFbEZZjkCwCwkoMXruEBFTE42bIagwOUYQ/GkNIVtHismK214zDp1SMRBQ0OyWElOT5aHKJQMKEN0b35XUI8Efi8IVkSIIZHrsIRY3jZV8EgUjy/MYScQyHFLR5rBDMZpjNwEhYRZ8/WQcQR1RJHocci8EpiRBMwKmQjHktEo6dUtHrn2R701NGW2/zWGGXBBwbCsPrEBFWYrBaBDglIZm/Sgw20QJ/NAYlFkfjaP4GZRVNTgl20Qx/RIVvNMdGIsnzGlbi6PdH0eaxQVZV+CMqml0S5FgcvrCCJqeEoKxAMCdzOBiNwSZZEJYVuKwi4okEhkIK7KIAl9UCJa5CMJkhx+LwR2Pw2C0QzWacCspw2SxwShb4RttAWFYx0hfEDIeIwaCcbjP9ARktLgmxeBy+kAKHlMx3UTCjPxBFs1OCmgD6/VE0uSQ4JQF9/iga7RLmNIg4OqxgKCzDYxPhC8mY4ZQQT8SRSJgxEEhe63keCVD1vsBThB73JL3arl73Xz2OV69jNQH94RgGgjKanVa02AVgsu8vGPG5iTHVb0wWoMc3dm+e75WA2CTrnMxxTqZ/mMy2ZuCoX0GvP4I2tw1z3CIQL3FbKo2BzrFhBtq0CIKAL3zhC+jo6EAgEMDq1avxjne8A2eccYbeoRFVjgjs7fZhw879iChx2EQzNq/oxOXt3rGOu1Zlptp+Si1D4xOBpw+NwBeSEZRVbNvTnT6f695zJtySgJ6BIO597NX08tuuWIwWtxV3PPq39LK1S9vx8NOHMRSSsWlFBxQ1jLt/+VLWepfVgh1/PYbVbzsdm3YdSK9bs6Qdjzx7BJ++7AxYLWZ84dHnEVHiOG9eAz543unYsHOs7KYVHXBIAtb9bF962b+u7sThwURWuc0rOvDvTxzE4cFw1j5uvOQM3P/k2PLbrlgMr90Ck8mM9Tv2Z20/r8mKQ4NRbNyZH+s1552OR549glsub8djfz+B9549G28M+7PO391XdeLlE0OY1+xJn8MZDgnXXjQvq1xurJtWdCCqqPjKr1/OOuff/+MhDIVkfPn9nbCYTbh9+9+yzsuel07irac3YVaDDT965hW8u2MWrnhTE/6veyTn3NRZW9KIb+PyDvzkT4fxal8Aa5a04/GX38AH3nY6fvbcEXzk7fPxxsgItu3pzjvf735zM971ptPw709045rzTsd9j3dj8UwX/unt87Bp1wHN65OZ26nr/6HzT4dDFPCfT/XghncuhBoP457fvJy1zYJmB94YjuYtT9V12xWLMavBhlMhBd/4/av48AXzsOb3r+a1md7hcFYurF3aDqckYO/LfXh356ystrRxeQcEJPDIXw7nHWdEiWNekx2fvuyMrJzevLITly/0crBtsvRoR3q13em0X72O1QQ8c2wE67aP3eu2ru7ChXM85Q+2GbGvZ0z1G5MF2HtQI6YzvOUPtk3mOPXa1gw80ePLeobcsrITly3wcrCtUgx2jqv+0dGjR4+WVE7ro6MzZ85ER0cHAMDlcmHhwoXo7e2taHxEeusZktMdNgBElDg27NyPniG55mWm2n5KLUPj6xmScbA/gIGgnB5cAJLnc+tvX4HDKqYH2VLL733sVfQMBLOWbdvTjVXnzkFEiWPjzgPo80fz1vcHorj24oXpgYHUuvse78ayrtnYuPMAXs+o99qLF6YHiFJlN+48gO6+QNYyr8OaV27DzgNY1jU7bx+bdmcvv/exV+GQxPTNO3N7EyzpAYncelL/X79jPz5y4QL0DATzzt+dv9iPd3XMRn8gmj6Hq86dk1cuN9aNOw9gICjnnfPU+f3Sz/fjtf5g3nn5yIULsG1PN14fCOLaixfi3sdeRb9f1Tg39dWWtOLbtOsAbrhkUfqaXHvxQty16wCuvXghegbHrkXu+f7IhQuwYef+9DWMKHHccMmidE5qXZ/M3E5d9217ujEYkrGsK3l9U4NpmdvEVGguT9V172OvorsvgJ6BIJZ1zca//f7VvLL9gWheLmzb042BoIxPvHNBXlvatOsAHFZR8zgBpNtZ1rXesR+Hho1xreuZHu1Ir7Y7nfar17H2h2PpQbbUftdt34f+cPmvCxmxr2dMdRyTr0BMvvJjmsxx6rXtUb+S9wy5fsd+HPUb4V8qpwajneOqv9G2du1aPProo/j4xz+Ohx56qGC5H/zgB0XrOXbsGF566SW85S1vKVhGEEzweh0F15sACBYzPB77eGHrShDMRY/DSOolViPFmZunvSf86Q4hJaLE0TsSxVvneGtaZqrtp9QyRqRnzmr1pb0n/IiP/nuI1vkMyjHN5fGcf0OJKHGkvimg0Pp4AghHteszmfK3K1Q2t+5TQaVgnVr7yF0eLLCfXn+kaKyp//uCCuIJ7fPX749krUttM16s5ZxfX0gZO8+j163QMUykLdU6Z0vtS8NyLOvniBJHOBorer6HRnMlc3lmno13fXJz1WRCwWtfKK+0rmOh/eZe58zlQwXyPijHEIsn8o6z2H56/VGcU8V+00j36koo1JfW+p6k131wOu1Xr2PtPjykuV9fVEV7m6ekOsp5Lq01xjS9Y6pkjtZiW6172XO9fZrb9gWiOHt2Q9H9VooR77GVjKlS57hSMVV9oC0ej+P+++/HoUOH8P3vfz9v/XXXXQcA8Hq9BesIBoNYs2YNvvjFL8LlchUsp6oJ+HwhzXVmswkJAGosjpGRMOJaT6UG4fU6Ch6H0dRLrHrE2dLi1lyem6etHitsojmrY7CJZrR6rOlytSoz1fZTahkjqkXOlpqjQPI8HuzzA4Dm+XRKFs3l5pyv37SJZqReYC603mwCHFbt+hKJ/O0Klc2tu9EpFqxTax+5y5027f20emxFY0393+sUIQxon78Wtw0H+wJZ60qJtZzz63WI6XX20etW6Bgm0paqlbOT7UvtkiX9s2P0eB1WCwQTCp7vVK5kLs/Ns2LXJzdX1ThgMRdoOwXySus6xhPadeRe58zlhfLeKVngsVvyjjOzTN61dle336yXZ4pcE+1La31P0us+OJ32q9exegvc/7xWIWu/hXIUKO+5tNYYU/3G1DaBmCr5t1MhtdhW617W6tLedqardtfGiPfYSsZUqXNcLKZifWmuqn909N5774XZbIaqqggGg3n/jUdRFKxZswbLly/Hu9/97mqHS1RzC2ZI2LyiM+uPnc0rOpNfrlnjMlNtP6WWofEtmCFhUYsLTU4Ja5e2Z53Pde85E6GogtuuWJy1/LYrFmNBszNr2dql7Xj0+WOwicnvC5vptuatb3FZ8dBTr2Pj8o6sdWuWtGP3vuPYtKIDCzPqfeip17F5RXbZTSs60D7TlbXMF4rmldu8ogO79x3P28fGZdnLb7tiMUJRBVtW5uZSBxKJGDat0I419f8tKzvx42d6ML/ZmXf+7r6qE78/cBzNLmv6HG5/7lheudxYN63oQLNTyjvnqfP75fd3YlGLM++8/PiZHqxd2o6FzU48/NTrye/Scwka56a+2pJWfBuXd+C7T76WviYPPfU67lregYeeeh3zm8auRe75/tEzPdi8ohO7XkxeQ5toxneefC2dk1rXJzO3U9d97dJ2NDkk7N6XvL5fuPKsvG0sZmguT9V12xWL0T7ThQXNTux68ThufdfivLItLmteLqxd2o5mp4Tv/19PXlvauLwDoaiieZwAsOvF43k5vXllJ+Y3GONa1zM92pFebXc67VevY22xC9i6uitrv1tXd6HFXv67FEbs6xlT/cY031sgJm/5MU3mOPXado5bzHuG3LKyE3Pd4rjbUmmMdo5NCa0vR6uCP/zhD7j00ksntE0ikcDtt9+OhoYGfOlLXxq3vKKoRd9oW/+bV6DG4vjKsjdV5I02c8Y/IVfyDTkjjjYXUi+xGumNNs081WMGTr1nAzXSMRuQnm+0FexLR2cdjcRUxONAWFbR5JKARPaso/5IDE6NWUdb3FZIGbOOxhNxOEULgqPbuKwWOArNOmqXMBxR4Br913t/VIFNsGAkkjPrqD+KJmdyVsW8WUctJjTYkrOO9o1EMdNtRcKUMeuoVcRwJDnrqKyqMJvMyVlHrQLMpuSso26bBYHUrKNuKxocAk4FFZhNCZhSs466rAgrY7OONtgkwJScdVRRVdhHZ7rMnXXUH1XQ5BibdbTRKcIEE3pHY/U6BfjDcfjCMkTBjAa7iJgaR1iJwx9Jnl81EcdwKIZWtwSLUGDW0RjgtlqgxGJwihYIZlNFZh2t9Rtt4/WlmbOONthFRGMqJMEMpzV71tFANAa52KyjEQUNNhH+iIJWtxXhWIFZR9XkjK+NTglBOQbBbIZNNCMUjcEuWhBSkjkeTyTgC8VgE81wSRYoiWSuKbE4AlEVbpsAi2DGUFCB0yrAJVngC4/OOjo6q+8Mu4hTQRnO0TYzEJDR7JKgxuMYCsXgkIT0rKMDgWSbyJp1VBTQF4hihl3C3IxZR902EcMhGTMcEhJItqmBQDLX5zVUf9bRenmmyFVOX6rbjJh6zTo6Hfar17FmzToqJQfZcv4sKfYWRtnPpbXGmOo3ptSso6mYCsw6WvG/nQqp8rYF72UZM2K2um3JAaB4frFqMeI9tuIxVeAcV+qNtpoNtPn9fnzrW9/CX/7yFwDABRdcgJtvvhlud+Fgn332WXzkIx/B4sWLYTYnRyZvu+22ggN2tRxoM5tN+Nre13AqKKPRKeHzly+q2GCbERtBIfUSq+EH2kbVy/lMYbzVY8iBtlFGOI9GiMEocRghhmrGMR360ongsRlPPfeltcJj1deEB9pGGfFYGFNp6jGmqXK/N2JMgDHjqreYJjLQVvXvaEv54he/iPb2dmzbtg0AsGPHDtxxxx341re+VXCb8847D6+88kqtQpywU0EZA4Go3mEQEREREREREZEB1Gyg7ciRI/jmN7+Z/v2WW27BypUra7V7IiIiIiIiIiKiqqr6ZAgpNpsNzz77bPr35557DjabrVa7rxtmranDiIiIiIiIiIjI8Gr2RtumTZvwL//yLwgEAgAAj8eDe+65p1a7rwup730TLGbc+g8LKjrBAhERERERERERVVfNBtrOOuss7Ny5Mz3Q5nK5stb//Oc/x/vf//5ahWNYp4IyBEvNXjQkIiIiIiIiIqIKqfmIjsvlyhtkA4CHH3641qEQERERERERERFVjGFenUok+DFJIiIiIiIiIiKqX4YZaDOZOAkAERERERERERHVL8MMtPGNNiIiIiIiIiIiqmeGGWg799xz9Q6BiIiIiIiIiIiobDWbddTv9+Ob3/wmnn32WQDABRdcgJtvvhlutxsAsGHDhlqFQkREREREREREVHE1e6Pti1/8IlwuF7Zt24Zt27bB5XLhjjvuqNXuiYiIiIiIiIiIqqpmb7QdOXIE3/zmN9O/33LLLVi5cmWtdk9ERERERERERFRVNXujzWazpT82CgDPPfccbDZb0W3uuOMOXHTRRVi2bFm1wyMiIiIiIiIiIpqUmr3Rdtddd+H2229HIBAAAHg8Htxzzz1Ft1m1ahU++tGP4vbbb69FiET6EYGeIRm9J/xo9VixYIYEKAXKjESrW2YidRSLl6YeETg6rODvQ4OIKCpmuq2IxxMIKXGE5Bg8NgusFgGDQRkNdhFhOQavQ0JYUTEQkNHmsQIwoc8fQbPLCqtgxpGhMFo9VqhxFYJZgNViRiCqQDQLGI7ImOGQoKhxDIdjaHZJEM1mxBIqegJjOeoQBRwaDGGmxwqL2YRTQRlNLgkhWUUkpsJjFTEcUdDoSC7zR2Jo9VgBAH3+KBqdEnwhBV6HCJdVQFCOQY0DajyBYDQGuyjAYxcRUxPoD0ThkAS4rBYMRxS0yCpCURW9/mQsVgsQUYCBQBSzGmyQ1TiGw0ry3AgCAooCm8UCX0iG1yHBF1bgHT1XJpMJjaPnq8+f3D4eT+Dk6HHOcAgYCqkYDMhwWS3w2Cxo9ZjwtxPDCMoqQnIMXocIwWTCGyNRtHmsEM1j57jRIeC4LwqbaMFAIIqZbivmz5Cy2vvpMwQIqgDEc669GTjqV9Drj6DNbcMct5hfRiNfxu1LKk0EDg3J6H8jAI/NArdNQK9fRqNDhEMSMDASRbPLCrsIxE3AcDiOoKwiLMfgslnQ5BRhMQFD4Tj8URkuSURQTubASDgGl92CmKrCbrFgIJi8DjbRjJGIArskwGYR0O+PwiYKcNssUOMJnBptD2aTCX2jeWIxmXB8OJL82WzCSCSWbFMeKxQ1joiSzL02jxWxeAL9/mROWwUzXujrR4PdAsCEoZCMRqcEh1XAgD8Cq0WAYDZjOKygxSUhkQCO+SKY1WDFPK+EEyMxhJRkfofkGGY4RVgFAf5oDP5IDA12CxrtIholATABxwIK3hiJJs+NQwQSCQwEZDQ7rWixC0ApE8abgP5wDAPBCW43lelxD9WjPWbut9bPCzr1P7ocqwAcGk4ea5vHinkNEqBOsk4jPucxpvqNyQL0+JIxtXmsmO+VgNgk65zMcU6mf5jMtqnzMNpWK3IeyLBqNtC2aNEi3HDDDThy5Aj8fj/cbjd+//vf46yzziq4zfnnn49jx47VKkQifYjA3m4fNuzcj4gSh000Y/OKTlze7h3ruGtVplL7oalHBJ4+NILjvjC27elGRIljXpMdN156BjbtOpDOhduuWAyrYMYDfziIT75jIU4M+7Fp1wHMcEi49qJ56W1tohkbl3fgJ386jFf7Ati0ogN7XjqJ93XNhhJL4M4dY/m1dmk7Hn76MIZCMu6+qhOSYMa/bN+XXr9pRQd+87eTeLrnFNYubcev/3YS7z17Fv77L0dwzXmn45Fnj+CTFy9Ad28ga/+3XbEY3//jIQyFZKxZ0o7HX34D1140H76QgkgsjnsfezUr1vv/cBCHB8PpmBa3uvD3EyPYsPNARlvowL8/cRByLJF3vF+48iw02EXc8fPn08vWLGnHI88m4/zr0UG8602zsGGn9vnavKIT//5Ed1YMC5udOOYLY+tvX9HcJvPcbVnZCUVVsXn3S+nrd/Nl7TltuQOXtHsgIGOwzQw80ePD+oxrsmVlJy5b4C082KZHPyECTxzMjnPt0nY4RAFf/uXfceOlZ6Tz7dsffSv8YTUrn22iGV99/9mwCCZ87XevpHPnwxfMw7/9/tX0Obvp0jNw167n8/bxn0/14CNvn5fOqVTOaOVC6rpIFlNeG9q8sgP/vrfwdqn8zly+aUUHbBYThsNhfOXXL2fl3ANPvo6hkIx/Xd2FsBzDqZCCbXu6McMh4aZLFyIoq3nton2mE4NBBXf+Yn/Btr51dRcunOMpPmhmAp45NoJ1Ge21pO2mMp3ahi737em0X72OVQD2vqax30Xe8gfbjPicx5jqNyYLsPegRkxneMsfZJrMceq1bTXOAxlazT46etNNN2Hv3r2wWq1obW2Fw+GAw+Go1e6JDKtnSE53ugAQUeLYsHM/eobkmpep1H5o6ukZknGwf2ygCgCWdc1O/+ENJHPh3sdexWBIxrUXL0Q8gfT6VefOydo2osSxadcB3HDJIkSUODbuPICPXLgAr/UH04NsqXLb9nRj1blzEFHiuPMX+3GwP5C1fuPOA/jEOxeky95wySJs29ONZV2zcd/jyf8PhuS8/d/72Kvpeu97vBvXXrwQB/uDGAjK6UG2zFiXdc3OikkUzOlBttTyDTuT5bSO957fvIyewWDWslR89z3ejY9cuCBdn9b2G3buz4tBURPY+ttXCm6Tee7W79iPN0aiWdcvvy0fwJFTKo76x54Yj/qV9OBVqtz6HfuzymjlS637iZ4hOS/ObXu6MRiS07mayjfBZM7L54gSxx0//xu6+wJZuZMaZAOS5+yunJzP3EdmTqVypth10WpDG3YU3y6V37ltQDALGAjKeTmXiudgfwDHhyPpbVedOwcDQe12EYzG04NsqePOjXPd9n3oDxf/66A/HEsPsk1ku6lMr7ahx317Ou1Xr2M9NKy930PD5e/XiM95jKmOY/IViMmnT47qtm0VzgMZW83eaOvt7cX3vve9qu5DEEzwegsP3pkACBYzPB57ZfZnMcNiESpepwmoWH3VJgjmoufcKIwUZ26e9p7wpzvdlIgSR+9IFG+d461pmUrtx6iMlAfj0TNWrb6094Qf8QSyrr3JBM1ciCeAcDSW/r1Y2bA8Vs4XUvL2kVpnMmXXn7veF1Ky6kxtk/p/KfWG5Vi67mJlU7+fCirj1ql1brTKR5Q4hjLqK3S+cmMIRmMlb5O7/0Lle/0RiIIZZ89uAAA819unWa4vEE2XAbJzthb9RKl9aTwxdqypfDsVLJxrmeVzz1GxnM/cJrW8WC6Uuy6V37nLgxn5q1VXbm4XaxfBnH0UOm5fVEV7mweFdB8emtB29dRHl6JQX1rre6he9+3ptF/djvVkgf36ozinxP2W81xaa4xpesdUyRytxbZa9zIjXBsj3mOnckw1G2h761vfildeeQVnnnlm1fahqgn4fCHNdWazCQkAaiyOkZEw4rlPoxNkNpugxuKIxdSK1ylYzBWprxa8XkfBc24kesTZ0uLWXJ6bp60eK2yiOavztYlmtHqs6XK1KlOp/RhVveQrUJtYS81RIHndD/b5Na997u9mE+CwWmAyZa/XKmuXLOmfk98vpl0ukciuP1Nq29TPDin5vVmp3wGUVK9DskAwFT6uREaXbBPNaHSKBcvlHnux2BMJ7fpKicFps5S8TaH957Vltw0Ws2mszbu02/xMV3abz8zZSvYTk+1LzSZAjWfnW6NThDBQ+BrFE2O5U2rOp/aReb6L5UK561L5nbvcKVkK5hcAzdwu1C6cBfaR+7vXKhS9nl6rdj2FtqunPjrTRPvSWt9D9bpvT6f96nWsbe4C+3Vn77dQjgLlPZfWGmOq35jaJhBTJf92KqQW22rdyyZyHqrFiPfYeoupWF+aq2YfHX3uueewevVqvOc978Hy5cvT/xFNdwtmSNi8ojPrj7rNKzqTX65Z4zKV2g9NPQtmSFjU4sLape3pa7/rxePYuLwjKxduu2IxmhwSHnrqdZiB9Prtzx3L2tYmJr/37LtPvgabmPyOqR8/04OFLU7cvTI7v9Yubcejzx+DTTTj7qs6cUaLK2v9phUd+MH/9aTLfufJ17B2aTt2vXgca5Yk/9/okPL2f9sVi9P1rlnSjoeeeh2LWpxockq47YrFebHu3nc8KyZFjWPzio6ctpAsp3W8X7jyLCxocmYtW7OkHbv3JeP80TM96fq0tt+8ojMvBtFswrr3nFlwm8xzt2VlZ/pBL3X98ttyB05vFDDXLaav/Ry3iC0512TLys6sMlr5Uut+YsEMKS/OtUvb0eSQsHvf8ax8UxPxvHy2icnvaGuf6crKnVvftTjrnN2Vk/OZ+8jMqVTOFLsuWm1o88ri26XyO7cNqHEVzU4pL+dS8SxqceG0Blt62+3PHUOTU7tdOK3JtlasrW9d3YUWe/F/r22xC9i6umvC201lerUNPe7b02m/eh3rvAbt/c5vKH+/RnzOY0z1G9N8b4GYvPrkqF7bVuM8kLGZEolETV6bOn78uOby2bNnF9zmtttuw5///GcMDQ2hqakJn/nMZ3D11VcXLK8oatE32tb/5hWosTi+suxNFXn77Iu7X8JAIDmDWSXrFCxmbLnyTL7RVkFGeqNNM09rNaNoKWUqtR8Dqpd8BfR9o61gXzo666g/GkNUjqPZLSGRSM46GpZVuKwCbKKAUyEZHlv2rKODARkzPVaYUrOOOq2wWsw4OhTGTLcVKlQIEGAVx2YdHYkoaLCLiMWzZx1VEypicRP6RqKYmZp19FQIM11WWITRWUedEkKKimhMhTs166g9ucwfjWGmywqzCej1R9HokOCLKGiwiXDbcmcdVWEXzfDYxOTsjxmzjo5EFDS7rEVnHVXUOIZDMbhsyRkpg4oCq2CBL5ycdXQ4rKRnaM2bddRjQzyRwBujx5madfRUUIbTaoHbakGbx4QTw6MzZ0ZVNDgtEEymZNt0WyEK2rOODgaT964FmbOOuq04vXH8WUdb3bbkIFtOmbycrVA/MdG+9NCQjIGADPforKN9fhleuwiHVcDgSBTNTgl2yZQz62gyf5tcY7OOBqIKnJIle9ZRmwWxePaso1bRDH9EgV0cnXU0IMMmmjNmHVXQYLeMzTrqTs40enw4kvxZSM06GsdMtwQlHkdUSSAQjaHVbYU6OtNnozM562ifPwrP6KyjvlByZl6HVcBAIAqrxZycdTSkoNktAQnguC85u+n8GbmzjqqY4bSkZx0NRJIzBzfaRTRax2Yd7fVH4bRa0OgQYUrPOiolB8smPOto8e3qqY/OVE5fqtuMmHrNOjod9qvXsaZmHR3tX+ZrzDpa7C2Msp9La40x1W9MGbNttnqsycElja/qrPjfToVUeduC97ISz0O1GPEeW28xTeSNtpoNtNUCB9pqz4iNQ4vhB9pG1cv5TGG81WPIgbZRRjiPRojBKHEYIYZqxjEd+tKJ4LEZTz33pbXCY9XXhAfaRhnxWBhTaeoxpqlyvzdiTIAx46q3mAz50VEiIiIiIiIiIqKpjANtREREREREREREFcCBNiIiIiIiIiIiogrgQBsREREREREREVEFcKCNiIiIiIiIiIioAjjQRkREREREREREVAEcaCMiIiIiIiIiIqoADrQRERERERERERFVAAfaiIiIiIiIiIiIKoADbURERERERERERBXAgTYiIiIiIiIiIqIK4EAbERERERERERFRBXCgjYiIiIiIiIiIqAIMP9D25JNP4j3veQ+uuOIKPPjgg3qHQ0REREREREREpMmidwDFqKqKzZs34/vf/z5aW1vxgQ98AEuWLMEZZ5yhd2hElSUCPUMyek/40eqxYsEMCVAKlBmJVrfMROooFi9NPaPXvf+kHy1uK/wRBS0uKyKxOPr9UbS4rQjJMTglC2yigMODIZzmtcFsSiCsJDAYlNHskuCyWoAEMBKNQY6pcEoW9PqjaHZJsIsCev1ReGwWeO0W+KMqwrKKkKKiySFBspjhj8hwR2KIKHGcCspw2yzwOkSEZBX+iAKHZMFIREGTU4IcU2E2mWETzZDVOIaCCjx2CyxmM4ZCMhocEiJyDE0uK0JRFb3+ZN6LQgKAGRElDn80hhl2ESMRBS6biOGwAo/NAodkxqHjPgwGZbS5rYjHEwgqcQQiMTS7JFhFEwSTGSORGAYDMlobrLBazAhEFVjMAvpGomh2S2jzWNA3MrZvizmBWNyEoZAMp2SBYAYsghkem4BgNI7+QBQNdhEhOQarRcAMRUUgqqJvJIqZHis8NgHHhiJw20QkEgmMRGJocUuY5THjyKmx/Zw+Q8CRITXd1k+fIUCAkNX+mxwCnFZklZtUe8/oX+bMsCEeB04MRyrXj+T0TafNEPB6nwyX1QIT4lDiJpw+Q8DRIRV9/ijmeO2IqnEMBpL5azGbAACxeAKBaAzNLisiioqRiAKnZMFwWIFdssBjs8AfiSEQjWGm2wpRMGEopCCiqGh0SjgVVOC2WdDotGA4nDx3Mz1W2EUzBgJRuK0iZDUGpyQirMQxGJQx022FMrosEFURiMbQ4rIiEE3us8UloD+gYmQgCI9NRL8/irYGG8wmoN8fRYNdQliJwS4m87/RISGWiEEwmZFImPDG6PWb4RAwElYRVuIIy8l4g7ICh5g8PpsowGE1Ix43od8fRavbijluEUgA/eEYBoIymp1WtNgFADnLHAL6QzllEqPXxqSxfaLAdTSSasStxz20lHt7Nfdb6+cFPY5Xr2MVgEPDyWNt81gxr0EC1EnWacTnPMbEmCpV52T6B722peobvd93Hx6C12qZ9P3e0ANt+/btw7x58zB37lwAwPve9z7s2bOHA200tYjA3m4fNuzcj4gSh000Y/OKTlze7h3rfGtVplL7oalH47p//t1nYiQSwx2P/i29bOOyDmx/vhsfPO90PPPaAC5c1AQ1YcKmXQfSZf519dmIKHH89NkjWP2207Fp1/Nj2y/vgIAEvvzLv+NzVyxGf0DG1t++MpZrKztgE8044Ytiw85knefNa8A158/Dt/Z245rzTsd9j3eny29a0QG7aMKpYAz3/OZlzHBIuPaiedi2Z6zMhmVvQp9fxvodY8e2ZWUnJIsJ9z72al6da5a045Fnj+DGS8+AYErgR88cwQfPnwt/JJZV779+oAtyLI47f5HRVlZ2osEmYO0jzyGixPHBt83CefOb08eSbE8d+OmzR/Ds4WHYRDPWLm1Hk1OE2WzOqisVx82XnYF/f+IgDg+G0+3xmdf68asDvVi7tB0PP30YQyEZd1/ViW8+3o3Dg2HMa7Lj5sva89rx2+a5ce1//jm9bMfNF+HJbn9l2ntGDmldh0n3I5p9UwfOmGnFn3pOYbbXhp6+ERxqdGPDjv1YPNOFf3r7vOzcHL1m33y8G59792IMBGR88/FufPiCefi337+KiBLHvCY7brz0jKztNi7vwP1/GLsGt75rMR77+0l88LzTs67tphUdsFtM+I+9B/HJdyzEkVPRrHq++v6zcSoUzMr5z7/7TPzwmUO4+bJ2/PTZw1hyVhvue7xb8xxuXNaB+588kI5jy8pOmEwJ3PmLA1m5HY2puPuXL+XEP7Zd7vHcfVUn3FYL1j7y1/Q22645B3IsjnXb96XPyy2Xt2e1o62ru3DhHA8A4JljI+myWeuMPNhmqkLcetxD9bpvT6f96nWsArD3NY39LvKWP9hmxOc8xsSYKlVnPW5L1VeF+72hPzra29uLtra29O+tra3o7e3VMSKiyusZktOdLgBElDg27NyPniG55mUqtR+aerSu+9d+9wp6BoJZyzbtPoBrL16IDTsP4APnnw6HJKYHElJlDvYHsWFnslzuuk27DsBhFbGsazYO9o8NOKTWb9hxAF67lB68AIBrL16I9Tv2Y1nX7PSAWKr8xp0H4HVYcc9vXkZEiWPVuXPSAxOpMm+MRNODA6ll63fsx2v9Qc0673u8G8u6ZidjlUTccMki9PmjefUe7AukB8bG4t8PRUV62VXnnp51LMn2lDw3qd+37emGQxLz6krFsWHnASzrmp2x/X584PzT09uuOncOIkpywC9VLrldfjseDKhZy4ZDasXae2YOaV2HyfYj2n3TAcRUC7bt6cbB/iAuPKMVG0av9Q2XLMrPzdFrtqxrNrx2Kf1zapAtde608jbzGvzb719Nt4O8fHRace3FCxFPIK+ensH8nP/a715JX69rL16Yzketc7hpd3Yc63fsh81iycvtPn+0aPy5v9/5i/04cHIka5sDJ8ceSFPnJbcdrdu+D/3hGPrDsayymeuMrBpx63EP1eu+PZ32q9exHhrW3u+h4Ur3pfo+5zEmxlSpOutxW6q+atzvDf1G20QJggler6PgehMAwWKGx2OvzP4sZlgsQsXrNAEVq6/aBMFc9JwbhZHizM3T3hP+dKNOiShx9I5E8dY53pqWqdR+jMpIeTAePWPV6ksLXfd4zr/yRJQ4wnLyY52DgShCUTVvu3hitFw0pllnUI7BZBorl7s++RG9seWpekymAuWDY+W1yhTaTzyhXT5zX0E5VrCOQvWmtgGAgYwBj8wy4YwyESWOYIFzlYrDZMpePhiIZpXJ/bnQcfX6I1nLev2Rktv7eDmbmUMF9z+BfqTkvnT0GOIJoC/jeLTyL3XNTCbg1Gje5MZaLCcyf0+1g9xyqY+YYgI5k9pnZsylxpGZb6llWu02d7vc33O3yY21UDy+qJr+WWtde5snvcxofXT34aGS4i5kIn1pNe+het23p9N+dTvWk4X6vSjOqXRfquNzHmOa3jFVMkdrsa3WvcwI18Zo91jAODFN9n6vxdADba2trXjjjTfSv/f29qK1tbVgeVVNwOcLaa4zm01IAFBjcYyMhBHPfWKcILPZBDUWRyymVrxOwWKuSH214PU6Cp5zI9EjzpYWt+by3Dxt9VhhE81ZjdsmmtHqsabL1apMpfZjVPWSr0BtYi01R4HC191syt7WJpphlyywiWY0uawwmeS87QRTspzDatGs0ylZkEgAFjM0189wiFnLU/Wk1ueVd2aXLxSP1rHFE9rrEomMWAvUUahepzR2621xa59Xe0YZm2iG06Z9rlJxJDJuF6lzn1km9+dC56rVbUOmVo+t5PY+Xs7m5lCp9U66L3Xb0tdzZsbxaOVf6poBQONo3hSKVetaZP7ukLSv2QyHiOGwCaYJ5EzqOufGXEocmfmWWqbVbnO3y/09d5tCseb+7rUKAEwF12VeQ6P10d4CfVRu3JXoS6t5D9Xrvj2d9qvXsbYVuIe0uq0l5ShQ3nNprTGm6RFTJf92qkQ85W6rdS8zwrUx2j0WME5Mk73fazH0R0fPPvtsHDp0CEePHoUsy/jlL3+JJUuW6B0WUUUtmCFh84rOrD/oNq/oTH5BZo3LVGo/NPVoXffPv/tMLGh2Zi3buKwDDz/1Ojav6MDP/nIEoaiCjcs7ssosanFi84oOPPTU63nrNi7vQCiqYPe+41jU4sS695yZnWsrO+ALy9i8Ymy7h556HVtWdmLXi8exZkl7VvlNKzrgC0XxhSvPgk00Y/tzx7B2aXaZVo8VW1ZmH9uWlZ1Y1OLUrHPNknbs3nc8Gaus4DtPvoYWtzWv3kUzXbj7qpy2srITojA2mPPz549kHUuyPSXPYer3tUvbEYoqeXWl4ti8ogO79x3P2L4TP/vLkfS2jz5/DDYx+T1bqXK7Xjyu2Y6bXELWsga7ULH2nplDWtdhsv2Idt/UAYsQw9ql7TijxYlnunuxefRaf+fJ1/Jzc/Sa7XrxOHxhOf3zre9anC6368XjmnmbeQ1ufddiPDTaDvLyMRjFQ0+9DjOQV8/8pvyc//y7zxy9zp146KnX0/modQ43LsuOY8vKTkRisbzcnjn6x3mh+HN/v/uqTnTM8mRt8+ZZHmxd3ZV1XnLb0dbVXWixJ79QOLNs5jojq0bcetxD9bpvT6f96nWs8xq09zu/odJ9qb7PeYyJMVWqznrclqqvGvd7UyKRMPRrU3/4wx/wla98BaqqYvXq1bjpppsKllUUtegbbet/8wrUWBxfWfamirx99sXdL2EgEEWzy1rROgWLGVuuPJNvtFWQkd5o08zTWs0oWkqZSu3HgOolXwF932gr2JemZh31R9HissIfVdDssiKamnXUZUVISc46ahcFHDoVwmkeG8xmIKwkZwhtdEpw25KzjvpHZx11jM462uSU4JDGZh1tsFsQGJ11NCzH0egUIQlm+KMy3FYJkVgcQ6HkjJJeu4iQoiZnaBQt8I/Ouiir2bOO+oIxuO3C2KyjdgkRZfxZR712Ef6IApdVxHBEgdtqgVMSEFWTx9XqsiKeSCCkxOGPxNDkkmDLnHU0KKPVbYVVzJh1dPSYZzVozzrqC8lwZMw66rYKCMnx5KyVNhFhJTnrqNcuIignZ9Gc6c6ZdRQJjISTs6Ce1lBk1lG3Fac3Tm7W0ZJyNnPWUa8N8cT4s45Opi89bYaA1/pkuK0WmExxKGrhWUebXcnZQ4HkrKPBaDIvsmcdjcEuCVmzjra4rZDSs47G0egQcSqczJHUrKN9I8lZTe2SGYOBKFxWEbKanHE31TZaXFYo8RicooiArKZnPQ3mzjoaUZKzjgaiaHOPzjoakNFgFxFRYrCNzjo6wyEhnkjmfyKB5MynbitmOIvMOhpRYLMIcEhmqAkTBgJRzHRZMTdv1lEp/fCZtcxhyZh1dLSM5qyjOesmkkO1VkLc5falusyIqdeso9Nhv3oda2rW0dFZgudrzDpa7C2Msp9La40xTfmYKv630yTjKXfbgvcyna+NEe+xhopp9H7vi6rwWoUJ3e81qzP6QNtEcKCt9gzVOIow/EDbqHo5nymMt3oMOdA2ygjn0QgxGCUOI8RQzTimQ186ETw246nnvrRWeKz6mvBA2ygjHgtjKk09xjRV7vdGjAkwZlz1FtNEBtqM/c4+TZo540tV6mHgjoiIiIiIiIioXk2rgbZGpwQ1Fs8afCqX2WxCo1NK11vJOgWLuWL1ffeZI/CFFXjtIm648PSqDLZVItZaqHScHLgkIiIiIiIiokxT6qOjREREREREREREejH0rKNERERERERERET1ggNtREREREREREREFcCBNiIiIiIiIiIiogqo6mQId9xxB5544gk0NTVh9+7dAIDPfvaz6OnpAQD4/X643W7s2LEjb9slS5bA6XTCbDZDEAQ8+uij1QyViIiIiIiIiIhoUqo60LZq1Sp89KMfxe23355e9o1vfCP98z333AOXy1Vw+4ceegiNjY0l70+WYxgeDhdc73JZEQhES65PL/USJ1A/seoRZ0uLW3N5sTytl/OZwnirpxaxlpOjgDHOoxFiMEocRoihmnFMh750InhsxlPPfWmt8Fj1VShHgfrrSxlTaeoxpqlyvzdiTIAx46q3mIr1pbmq+tHR888/Hw0NDZrrEokEfv3rX2PZsmUV25/JZCq63mIRKravaqqXOIH6idVIcRbLUyPFWQrGWz16xloPfakRYgCMEYcRYgBqH8dU6ksngsdWP+qhL60VHqtx1VtfyphKM5ViYo5WhhHjmsox6fYdbc8++yyampowf/78gmWuv/56rFq1Co888kjtAiMiIiIiIiIiIiqDKZFIJKq5g2PHjuHGG29Mf0dbysaNGzFv3jx88pOf1Nyut7cXra2tGBwcxHXXXYf169fj/PPPL7qveDwOVS18OIJghqrGJ34QNVYvcQL1E6secYqi9mh4sTytl/OZwnirpxaxlpOjgDHOoxFiMEocRoihmnFMh750InhsxlPPfWmt8Fj1VShHgfrrSxlTaeoxpqlyvzdiTIAx46q3mIr1pbmq+h1thcRiMTz22GNFJzhobW0FADQ1NeGKK67Avn37xh1oU9UEfL5QwfVer6PoeqOolziB+olVjzgLfYa7WJ7Wy/lMYbzVU4tYy8lRwBjn0QgxGCUOI8RQzTimQ186ETw246nnvrRWeKz6Kva9QvXWlzKm0tRjTFPlfm/EmABjxlVvMRnmO9oKeeqpp7Bw4UK0tbVprg+FQggEAumf//jHP6K9vb2WIdJUYQaOBhXseaUPx4JKZTLeBPRHYnhpMIT+iAoU/woWoqljNPf/cngI/VEVp2R1+rQDtvvpp9A1r1YuMMemj8y+tFbXevR56Nk3/JV7HiKqpWo800+WHm2ZkgTgUEDGb1/qxeGADBjvq75omqvqG2233XYb/vznP2NoaAiXXHIJPvOZz+Dqq6/Gr371K7zvfe/LKtvb24s777wT3/nOdzA4OIibb74ZAKCqKpYtW4ZLLrmkmqHSVGQGnujxYf2O/YgocdhEM7as7MRlC7xAuW+omoBnjo1g3fZ96Tq3ru7ChXM8QFU/hE2kM43cX7u0HQ8/fRhDIXlqt4Ni7Z6mpkLXfK4Hzxytwj2A95bpQ49rXY3nIaJaMmIOs9/WjwDsfc2HDTvH8mHzik5cvsgLqHoHR5RU9e9oqyVFUafEK/r1Eidg7FiPBhV8+Ht/QkQZuwPbRDP+6/q3Y65TLKvO/kgMqx58Jq/ORz91IVps2ePWhV4tLZanRj6fWhhv9ej50VGtHC2U+9e/cyH+fe/Bgu2gWmp5LYu1+/Y2j+45ZZS8rvVHR6vZlxa65v91/ds17yuTzf2J3FuMcr2roV6PrRJ9aTX7z2o8D01UvV7bchjxWIt93KkenkuNkMO59GjLE2GUa5ep3I+O5ubooYCMj/3nn/PO/Q8/eQHmu6TKBVwmI557wJhx1VtMhv/oKFEt9PojWR0wAESUOHr9kbLrHAjKmnUOBOWy6ySqB4VyPzXj+lRuB2z300+ha17ovjLZXGCOTR96XOtqPA8R1ZIRc5j9tn56R6IF8iGqU0RE+TjQRlNWm9sGm5id4jbRjFa3rew6m51WzTqbnfr/6wlRNRXK/dQ70VO5HbDdTz+Frnmh+8pkc4E5Nn3oca2r8TxEVEtGzGH22/pp82if+1a3VaeIiPJxoI2mrDluEVtWdqY74tT3Ocx1l/+KeYtdwNbVXVl1bl3dhRa7/q+IE1WTVu6vXdqOR58/NuXbAdv99FPoms9xi1XJBebY9KHHta7G8xBRLRkxh9lv62deg4TNK7LzYfOKTsxv4CAnGQe/o82A6iVOoA5iNQNH/Qr6AlHMdFmTN+TJfmmqCegPxzAQlNHslJI3VI1WxO9oM556itdo39EGIJ37vqgKr1WAYDah1x8t2g6qpebXskC7N0JOGSGGasahW19aqK8v8R5Qsf3lMMr1roZ6PbbJ9qU16T9Hn4d6/RG0um2VeR6agHq9tuUw4rHW+3e0AajOM/1k6dGWS2SoazeqUt/RBiA56+iwjF5/FK1ua3KQzSATIRjx3APGjKveYprId7RxyJ2mtjgw1yni7NkNyQZTiRtyAmixWca+6NQgN1SiqhvN/cwJABqbHOl1Uxrb/fRT6JpXKxeYY9NHbl9ai2s9+jyU/uJ4vQcoiCaqGs/0k6VHW6YkFZjvknDOHG/y3BtkkI0ohR8dJSIiIiIiIiIiqgAOtNHUZk5OCb7nlT4cCyrMeKJKMiWnt39pMIT+iAqYxllOVC9KyWHmOZVrNHf+cniodrnDfKV6p0e7GY8RY5ou+DceGRw/OkpTlxl4oseH9Tv2I6LE01+cetkCrzFeNyeqZybgmWMjWLd9X7p9bV3dhQvnevDMUY3lczz8SAXVh0K5nZnDpZQh0qJH7jBfqd4ZMYeNGNN0wb/xqA5w7JemrKN+Jd0BA0BEiWP9jv046ld0joyo/vWHY+mHSyDZvtZt34ejfkVzeX84pme4RCUrlNuZOVxKGSIteuQO85XqnRFz2IgxTRf8G4/qAQfaaMrq9UfSHXBKRImj1x/RKSKiqWMgKBdsX1rLB4JyLcMjKluh3M7M4VLKEGnRI3eYr1TvjJjDRoxpuuDfeFQPONBGU1ab2wabmJ3iNtGMVrdNp4iIpo5mp7Vg+9Ja3uyUahkeUdkK5XZmDpdShkiLHrnDfKV6Z8QcNmJM0wX/xqN6wIE2mrLmuEVsWdmZ7ohTn9+f6xZ1joyo/rXYBWxd3ZXVvrau7sJct6i5vMXOrwSl+lAotzNzuJQyRFr0yB3mK9U7I+awEWOaLvg3HtUDUyKRmDJf16goKny+UMH1Xq+j6HqjqJc4gTqI1Zz8HH9fIIqZLmuyA67Rl2S2tLg1lxfLU8OfzxyMt3pqEWs5OQpkxGZKfkfJQFBGs1NKPlwmUHh5BRnlWhohDiPEUM04dOlLS8nhGuS5FqNc72qo12ObcI6O5o4vqsJrFWqTOzrla0q9XttyGPFYC+UoUEfPpXq0m3qMaZShrt2o8WKaUF+q49944zHiuQeMGVe9xVSsL83FIXea2uLAXKeIs2c3JBuMQTpgoikhAbTYLGixWdK/F11OVC9KyWHmOZVrNHfa2zzJZ5Na5A7zleqdHu1mPEaMabrg33hkcFUdaLvjjjvwxBNPoKmpCbt37wYAfPOb38RPf/pTNDY2AgBuu+02XHrppXnbPvnkk/jyl7+MeDyOq6++Gp/61KeqGSpN1Oi/4HQfHoLXakGLXZj8zUUEeoZk9I5E0eqxYsEMCZjs5DEWoMcno/eEH20eK+Z7JYCTARFNSjyeQH8k+WbEDKeEcCyOgdF/UVRiKhpsUmX6hBQzcCygoNcfhdtqQbNThCeeU3nW2xrW4vufSFma+gTg0HDy3tPmsWJegwTEkzkyHFFgFS0YCslwWAU02kQ02gT0hzLyx5HzeyqfTMCpqIpTYQVBWcVpHhtabMw1wtjzzgl/5Z53xjP69kevP4I2tw1zDPT2B1FJ9Gg340ndP0760eYevX+oOsc0XRgxH4gyVHWgbdWqVfjoRz+K22+/PWv5Jz7xCVx//fUFt1NVFZs3b8b3v/99tLa24gMf+ACWLFmCM844o5rhUqlMwDPHRtJTWqe+k+DCOZ7y/4AQgb3dPmzYuT9d5+YVnbi83Vt+p2kB9h7UqPMMLwfbiMplAn7/Sj8+9z9/xQyHhGsvmodte7rTbWzNknY88uwRfP6KMyfXJ6SYgT8c8uHOX4y147VL23HGzAjOmelMD2iU3CdVo/+i+iUAe1/Lvk9sWdkJj82Cr/z6JVxz3um47/Gx/P7ie8+Cw2pJ5+O8Jjtuubwd63fsz86nuR48fzKA1weCWe2DuUZVed4Zjxl4oseXladbVnbisgVeDrZRfdCj3YxH4/6xeUUnLl/k5WBbtRkxH4hyVHUyhPPPPx8NDQ0T3m7fvn2YN28e5s6dC0mS8L73vQ979uypQoRUjv5wLP1HKpCcTnnd9n3oD5c/etUzJKc7y1SdG3buR89Q+VNk9/gK1OnjtNtE5eoPx/C5//krIkocq86dkx5EAJJt7L7Hu7Gsa/ak+4SUo34lPaiR2se2Pd342/HhdP0T6ZOq0X9R/To0nH+fWL9jPw6cHMGyrtnpQbbUuoGgnJWPy7pmpwcvUmXWbd+Ho34FB06O5LUP5hpV43lnPEf9Sl6ert+xH0f9/IuU6oMe7WY8WvePDTv349Aw/86oNiPmA1EuXb6j7cc//jF+8YtfoLOzE1/4whfyBuN6e3vR1taW/r21tRX79u0bt15BMMHrdRRZby663iiMHmf34aF0x5YSUeLwRVW0t3nKqrP3hF+zzt6RKN46x2uYOiuhWJ4a/drnYrzVo2esxXI0s/2bTNBsY6nlk+kTUp7r7dPcRzyBdP0T6ZMq3X8ZIaeMEIMecVSiL+09qX2fiCe08zueyF5WqA30BaJ5ZVPrJtsujHK9q2GqHZtWjurxbFKoH+0LRHH27In/g3g5ptq1LabejrUenkuN+Exf6P7R64/iHB3/zkgxyrXLVG5MuTlqxHzIZMRzDxgzrqkcU80H2v7pn/4Jn/70p2EymbBt2zbcc889+OpXv1qRulU1wVlHa8BrtcAmmrM6OJtohtcqlB13q8eqWWerx1p2nW1VqHMiCs1KUixPjX7tczHe6tFz1tGiOZrT/rXaWCIx+T4hpdWl3Y7NJqTrn0ifVOn+ywg5ZYQYqhlHNfvSNnfh/Ion8vNbMGnnfO7vM13WgmUn2y6Mcr2roV6PbSI5Wo3nnfEU6kdnumrzPATU77UthxGPtdhMefXwXKpHuxlPoftHq1u/mDIZ5dplKnfW0dwcNWI+ZDLiuQeMGVe9xTSRWUer+tFRLc3NzRAEAWazGVdffTX+9re/5ZVpbW3FG2+8kf69t7cXra2ttQyTimixC9i6ugs2MZk+qe+dabGXP267YIaEzSs6s+rcvKIz+cWWZZrvLVCnt/w6iaa7FruAr199DmyiGdufO4a1S9uz2tiaJe3Yve/4pPuElDluEXdfld2O1y5tx9mzG9L1T6RPqkb/RfVrXkP+fWLLyk50zPJg14vHsWZJdn43OaWsfNz14nFsWdmZl09z3SLePMuT1z6Ya1SN553xzHGLeXm6ZWUn5rrFqu2TqJL0aDfj0bp/bF7RifkN/Duj2oyYD0S5TIlEoqpfyXvs2DHceOON6VlH+/r6MHPmTADAD37wA7z44ov4t3/7t6xtYrEY3vOe9+AHP/hBejKEr3/962hvby+6L0VR+UZbrYzO2ueLqvBaheQfDkaedTRVZw1nHS004l0sT+vi2mdgvNWj5xtt4/WlHo8dr/X5MRCU4XVKiMTiGAxE0eyyIhZT0WATK9MnpOTOOuoQMb/ZhZGR8FiZrJlEpeL7n0jZcRghp4wQQzXjqHpfmpo1zh9Fq9ua/CNJa9ZRSUCjPXfWUQktDkv276l8yph1NCSrmOW2VqRdGOV6V0O9HtuEc7QazzvjyZh1tNVtSw6y1XAihHq9tuUw4rEWewujbp5L9Wg349G6fxhkIgRDXbtR5b7RppmjRsyHUUY894Ax46q3mCbyRltV/1n1tttuw5///GcMDQ3hkksuwWc+8xn8+c9/xssvvwwAmD17NjZv3gwg+dbanXfeie985zuwWCzYsGEDbrjhBqiqitWrV487yEY1lgBabBa0t3mSiViJP6gVYIFLwgKXlP590mLJOt86x5uMk99BTTRpZrMJLTYLWmxjt5B0u02p5D/hxIE5DhFzHGNvX5jNprz9ZcVUbP8TKUtTnwrMd0mYn8rh0T+SMnNkrjPjzZ94Tv7k/p7KpwTQKAlolISxbZlrBKSfd9LPJrX44zCezON0LnO2Uao3erSb8YzeP85JxWSQQbZpwYj5QJShqgNt9957b96yq6++WrNsa2srvvOd76R/v/TSS3HppZdWLTYiIiIiIiIiIqJK4heFkHFkfZzLiha7ULmPo57wG+61YqK6ZAYOnBzBcV8YM91WJJDAcCgGj90Cr9WS87G6CrVjIr2MfvxzMKxgJBKD22qBx2ZBIKKgwS5BMCH9kSE1nmDeU2n0eDbJ+Ohom9uGOTX+6CjRpBnxmX60XT3X24dWl5XtqpaMmA9EGTjQRsZgAp45NoJ12/chosTTXxp94RxP+X+siMDebh827NyfrnPzik5c3u5lR0xUDjPwRI8P63eMtam1S9vx8NOHMRSSse49Z6LFbcW//KyC7ZhILybg+TcCOHwqhHsfezUr511WC777f/vxofNPx6//dhLvPXsWtu3pZt7T+PR4NtHou7es7MRlC7wcFKD6YMRnerYr/RgxH4hy1HzWUSIt/eFYepANACJKHOu270N/uPwvVesZktMdcKrODTv3o2dIrkjMRNPNUb+SfqAEkm1q255urDp3DiJKHFt/+woO9gUq2o6J9NIfjuHAyZH0IBswlvP9gSiWdc3Gtj3duOGSRelBtlQZ5j0VoseziVbfvX7Hfhz18y9Sqg9GfKZnu9KPEfOBKBcH2sgQBoJyurNMiShxDATL7zB7R6KadfaORMuuk2g66/VHNNuUyTT2czznDZ7JtmMivQwEZcQT0Mz5eAIwmZI/h6Oxit+/aOrS49mkUN/d649UbZ9ElWTEZ3q2K/0YMR+IcnGgjQyh2WmFTcxOR5toRrNTKrDF+Fo92nW2eqxl10k0nbW5bZptKpEY+zl3MtDJtmMivTQ7rRBM0Mx5swlIJJI/O6yWit+/aOrS49mkUN/d6rZVbZ9ElWTEZ3q2K/0YMR+IcnGgjQyhxS5g6+qudKeZ+o6bFnv5XyO4YIaEzSs6s+rcvKIz+WWZRDRhc9witqzMblNrl7bj0eePwSaase49Z+KMma6KtmMivbTYBbx5lge3XbE4L+dbXFbs3ncca5e24ztPvoa1S9uZ91QSPZ5NtPruLSs7MdctVm2fRJVkxGd6tiv9GDEfiHKZEonElPmqXkVR4fOFCq73eh1F1xtFvcQJVDjWrFlHpeQfKZWadXQkWvMZaVpa3JrLi+VpPV17gPFWUy1iLSdHYQaOB2M44QujxW0FkIAvFIPHZoHXljvraIXasQajXEsjxGGEGKoZh6596eiso6dGZx11WS1w2ywIRhQ02EQIZpPGrKPVy3vAONe7Gur12Caco3o8m2TMOtrqtiUHA2r4he31em3LYcRjLZSjQB09l+r4TF/QaLvqC0Qx02WtebsqxlDXbtR4MU2oLzViPowy4rkHjBlXvcVUrC/NxX9uJeNIAC02C1pslvTvk6YAC1wS3jrHm2wwBumAiepWHOiY5cHszLd1XNas9RVvx0R6SQCNkoBGSQAaMpbbxvK/scmR/pl5TyXR49kkDsx1ipjrFNO/E9UVIz7Tj7ars2c3JGNiu6odI+YDUYaSPzr661//GoFAAADwH//xH7jllltw4MCBqgVGRERERERERERUT0oeaPuP//gPuFwuPPvss3j66afxgQ98AHfddVcVQ6NpxwT0R2J4aTCE/ogKmMbfZFwWoCcg4zd/78WhgMx3OIkmywQcPhXCS4MhnJLVybfZctp9RgwFt6lGf0LTT6E8Gl1+0BfG0aCClwZDOBZUcNAXzs835iLlMgNHgwr2vNKHY0GlNt+YPLrPZ9/w126fQDr//3J4iPlPk6NHuxmPABwKyPjtS704HJABQe+AppHJ/I3H+zLVQMkpKQjJnuMPf/gDPvjBD+Kyyy7DN77xjWrFRdONCXjm2AjWbd+HiBJPf5n0hXM85X8ExwLsPejDhp3703VuXtGJy8/wArFKBk80TWS00xkOCddeNA/b9nSX32bLafelbFON/oSmn0J5NNeDZ46O4GuPvYJrzjsd9z0+1gbWLGnHI88eweevODOZb2AuUg4z8ESPD+t3jD2bbFnZicsWeKv3sTM99gmwL6bK0SuHixGAva9p/J2xyAuoOsU0XUzmbzz2S1QjJf9bQGtrKzZs2IBf/epXuPTSSyHLMuJxfhCdKqM/HEt3eAAQUeJYt30f+sPlj4j1+OR0B5yqc8PO/ejxyRWJmWi6yWynq86dkx5kA8prs+W0+1K2qUZ/QtNPoTw66lewbvs+LOuanR5kS62/7/FuLOuanc435iLlOupX0oMFQDIn1u/Yj6P+6n3BkB77BNgXU+XolcPFHBrW/jvj0DD/zqi2yfyNx36JaqXkgbZvfOMbeOc734nvfe978Hg88Pl8+Jd/+ZdqxkbTyEBQTnd4KREljoFg+Ter3pGoZp29I9Gy6ySazjLbqcmESbfZctp9KdtUoz+h6adQHvX6I4go8YJtILV8ICgzFylPKn8ypfJqKu0TYF9MlaNXDhdT8O8MP//OqLbJ/I3HfolqpeSBtg0bNuDd73435s+fDwCYOXMmduzYUXSbO+64AxdddBGWLVuWXvb//t//w5VXXonly5fj5ptvxsjIiOa2S5YswfLly7Fy5UqsWrWq1DCpTjU7rbCJ2eloE81odkpl19nm0a6z1WMtsAURFZPbTifbZstp96VsU43+hKafQnnU6rall2utTyTG8o25SLnaMvInJZVXU2mfAPtiqhy9criYgn9nuPl3RrVN5m889ktUKyUPtB08eDDrd1VVx511dNWqVfjud7+btewd73gHdu/ejV27dmH+/Pl44IEHCm7/0EMPYceOHXj00UdLDZPqVItdwNbVXVl/vGxd3YUWe/mzF8z3Sti8ojOrzs0rOrHAy46UqByZ7XT7c8ewdmn7pNpsOe2+lG2q0Z/Q9FMoj+a6RWxd3YVdLx7HmiXZbWDNknbs3nc8nW/MRco1xy1iy8rsZ5MtKzsx1y1OqX0C7IupcvTK4WLmNWj/nTG/gX9nVNtk/sZjv0S1YkokEkW/9u+BBx7A/fffj2g0Cpst+a8GiUQCkiThgx/8ID73uc8V3cGxY8dw4403Yvfu3XnrHnvsMfzmN7/B17/+9bx1S5Yswc9+9jM0NjaWfDCKosLnCxVc7/U6iq43inqJE6hwrKbk5+YHgjKanVKyw5vsl1Jakp/j7x2JotVjTXbANfoIfkuLW3N5sTytp2sPMN5qqkWs5eQoTMCwCpwYCqHVbYUaT0yuzZbT7jNiKLhNNfoTDUbIKSPEUM04dO1LC+XR6PLhiAKraEEgosBlExFVYmiwidn5VuFcNMr1roZ6PbYJ56g5+Z1TfYEoZrqsycGCan/t8eg+e/0RtLpttdknkM5/X1SF1ypUrS82EiPmcaEcBerouVSPdjMeIfldbb3+KFrd1uQgm0EmQjDUtRs1XkwT6ksn8zdelZ8RjXjuAWPGVW8xFetLc407dPvP//zP+Od//md8/etfH3dQbaK2b9+O9773vQXXX3/99TCZTLjmmmtwzTXXVHTfZEAJoMVmQYvNkv590mLAApeEt87xJhsMv+eSaHISwLxGBxoy3oeeVJstp93nxqC1TTX6E5p+CuVR7nJn6q0KMbtcsTpo+ooDc50izp7dkHw2qcVgweg+56ZytVYDFKP5397mSR4r85/KpUe7GY8KzHdJOCf1d4ZBBtmmhcn8jcf7MtXAuG+0Zert7cXx48ehqmO9yPnnn190m0JvtH3729/G/v378a1vfQsmk0lzX62trRgcHMR1112H9evXj7uveDwOVS18OIJghqoaoVcurl7iBOonVj3iFEVBc3mxPK2X85nCeKunFrGWk6OAMc6jEWIwShxGiKGacUyHvnQieGzGU899aa3wWPVVKEeB+utLGVNp6jGmqXK/N2JMgDHjqreYivWluUr+MPLXvvY1/OpXv8KiRYsgCGM7GG/wS8ujjz6KJ554Aj/4wQ80B9kAoLW1FQDQ1NSEK664Avv27Rt3X6qa4EdHayXj9e1WlxVzKvH6tgj0DGW8AjxDAiY7a3c14ixRoVdLi+VpXVz7DIy3evT86GjBHB191f6NgAy7ZIZTtCAYVdBgk9BiFyr3L4Kj+znhj8JpFdBoE9FoFbI+ijf20VHr2L6zPgpgrWxMBXg8drzW58/eJ2obh1HyutYfHa16X2oCTkVVRNQ4Qkocg0EZp3lscNvMGAgoCMoqTvPY0GIrI/8mkatGud7VUK/HNuEczfjIU5vHivm1+FqLajxjTUC9XttyGPFYi33cqW6eS3V8pi9I53alKevj2paaPAuVqtyPjmrmqBHP/ShDtZsMRoyr3mKq6EdHU1LfpyZJk/uCxyeffBLf/e538aMf/Qh2u12zTCgUQjweh8vlQigUwh//+Ed8+tOfntR+qYLMwBM9PqzfsR8RJZ7+QtLLFnjLv+GJwN5uHzbsHKtz84pOXN7uLb/TrEacRNOVCXjm2AjWbd+Xbk9rl7bDIQr4z6f24/NXnIkL53gm/zBXYD8Lm504t80FIH/91tVduHCuB88c1VheiZiKxPr7V/rxuf/5a3qf2645B3IsXts4qPJMwPNvBOCPKlBUYNOuA+nruXF5B+7/w0EcHgyXl38aOc4cmUYswN6DGs87Z3irN9hWjWcsoloy4jO9EdvVdLm/GPHcE+UoedbRuXPnQlEmlrm33XYbPvShD6GnpweXXHIJ/ud//gdbtmxBMBjEddddh5UrV2LDhg0Akh8V/f/+v/8PADA4OIgPf/jDWLFiBa6++mpceumluOSSSya0b6qeo34lfaMDgIgSx/od+3HUX37P1jMkpzvLVJ0bdu5Hz5BsqDiJpqv+cCz94AYk29O2Pd0YDMlY1jUb67bvQ3948n8lFtrPgZMj6A/HNNev274PR/2K5vJKxFQs1tQgW2qfB06O1DwOqrz+cAwHTo7AZrGkB9mA5PXctOsAlnXNTv8+0fwrlMPMkemhx1fgecdX/vPOuPuswjMWUS0Z8ZneiO1qutxfjHjuiXKN+0bbli1bYDKZYLfbcdVVV+Giiy7KeqvtzjvvLLjtvffem7fs6quv1izb2tqK73znOwCSg3o7d+4cN3jSR68/ku7YUiJKHL3+yNiX7E60zpGodp0jUSxwlfcWZTXiJJquBoKyZnuKJwCTKfnzQFAe+2LZKuxnICinf89dX6i9VyKmicQaT2jHV804qPIGgjLiCSAYjWlez8xvvZho/hXKcebI9FCN5x0j7pOokoz4TG/EdjVd7i9GPPdEucZtcZ2dnQCAjo4OLFmypOoBkfG1uW2wieasDs4mmtHqtpVdZ6vHql2nx2qoOImmq2andhs1mwA1nvy52Tn5h5ti+0nWbyrYrrWWVyKmicQqmFDzOKjymp1WCCbAabNoXs/MaaQmmn+Fcpw5Mj20VeF5ZzzVeMYiqiUjPtMbsV1Nl/uLEc89Ua5xPzr6/ve/v+h/NP3McYvYsrITNjGZPqnvSZjrLv9flBbMkLB5RXadm1d0Jr/Y0kBxEk1XLXYBW1d3ZbWntUvb0eSQsHvfcWxd3YUW++T/tbTQfjpmedBit2iu37q6C3PdoubySsRULNavX31O1j7fPMtT8zio8lrsAt48y4OIEsPG5R1Z13Pj8g7s3nc8/ftE869QDjNHpof53gLPO97q/SFcjWcsoloy4jO9EdvVdLm/GPHcE+UyJRKJkr4acfny5XnL3G43Ojs7cdNNN2HGjBkVD26iFEXlrKO1kjHzz0yXNXmjM/isoxWLs0SFZiUplqd1ce0zMN7q0XPW0YI5mjnrqGiGQ7IgFFXQYBOTD3EVnnX0pD8KhySg0V5s1lFpbN9ZMzlKlY2pgOxZR6X0w2wt4zBKXtd61tGq96Uas47O8tjgGZ11NCSrmOW2lpd/k8hVo1zvaqjXY5twjmbMOtrqsSYH2Tjr6JRhxGMtNlNe3TyX6vhMX5ARZ77MmnVUqMmzUKnKnXVUM0eNeO5HGardZDBiXPUWU1VmHf2Hf/gHCIKAZcuWAQB+9atfIRwOo7m5GXfccQfuv//+kndKU0AcmOsUcfbshmQiVuJGpwALXNLYZ+sr0VlWI06i6SoBtNgsaG/zjN2AUv9KWsmHuNH9ZH2fSCJ7/bxGBxrMOetyt6vBg6XZbNLcZ63joCpIAI2SAEAA7MAZGR9J8TYIWeVS/y/5uuuQq2QgseTzzlvneJN9aS2+p7waz1hEtWTEZ3olpy0boV3lPqtN1fuLEc89UYaSB9qefvpp/PznP0//fuaZZ+L9738/fv7zn2u+7UZERERERERERDSdlDzQpqoq9u3bh66uLgDAvn37oKoqAEAQhGKb0lSUel33hL9yr+tmfJSizWPF/Fp8lIKIJsYMHDg5gsFgFA02EcNhBQ6rgEabiEa7gKMjCobCMhpsIkYiCpxWC+yigD5/FM5UucyPgaZkfZTOihZ79kdFs9Y5BBw+FRr96GhO2VLqy1h/Yry4KmW8eMhYMj6+7HWIiMbiGInE4LFb0GQX0WhL5nqvP4I2tw1z3CKQAE7JKvqDCvyRGGa6rTi9QUR/IOe6o8RcYM5MXdV4hip1n7X+mNXox/2e6+1Dq8uabCu1eBOJ7Wfq0aPd1GNMerW5WjPiuSfKUPJA2913340vfelLCAaDAACn04kvf/nLCIVC+NSnPlW1AMmARGBvtw8bdu5HRImnv4Dy8nZv+R2cBdh7UKPOM7wcbCMyCjPwRI8P39rbjWvOOx33Pd6dbq+3XbEYbR4bvv7YK3nr1i5tx8NPH8ZQSMbape1Y2OzEuW2urIG0Z46NYN32felttq7uwoVzPADy121Z2Ylv7e3G4cFwdtlS6ktor9eMq1LGi4eMJeN6zXBIuPaiedi2JzvXZzXY8LXfvZLOwX/9QBesghmvDwSzym5Z2YlH/nIYzx4ehk00Y9s150COxcfPhXHaBNWxajxDGXGfQPqesX7H/qw2cdkCb3X/8GefO/XolcP1FpNeba7WjHjuiXKMO+toSldXF3bt2oUdO3Zgx44d2LVrF7q6uuBwOPCP//iP1YyRDKZnSE53bAAQUeLYsHM/eobk8uv0FajTV36dRFRZR/0K1u/Yj2Vds9MDaUCyvd772Ks42B/QXLdtTzdWnTsn/fOBkyPoD4+NoPeHY+k/iFLbrNu+D/3hmOa6VAy5ZUupr9B6rbgqZbx4yFgyr9eqc+ekB86AsVzv7gtk5eDBvgAOnBzJK7t+x35ce/HC9O8HTo6UlAvMmamrGs9QRtwnMHbPyG0TR/3V/UuY7Wfq0SuH6y0mvdpcrRnx3BPlGveNth07dmDlypX4/ve/r7n+uuuuq3hQZGy9I9F0x5YSUeLoHYmOfcmuAeokosrq9UcQUeIwmaDZXuMJFFxnMmWXGwjK6S+CHwjKmtsMBOX0z4XqyyxbSn0tNkvB9blxVcp48ZCxZF6v8XI9JZ4YW5dbNizHssqVkgvFcqa97CMjI9DjeUevZ6zUPSNvv/4I5jrFqu2Xfe7UY8S/EwwZk05trtaMeO6Jco37Rls4HAYABINBzf9o+mn1WGETs1PHJprRmjEj20S1VaFOIqqsNrct3U612qvZhILrEonscs3OsQehZqd2+292SgXXJRL5ZUupr9j63LgqZbx4yFhyr1ehXMnMQcGU/E+rrF2yZJUrJReYM1NXNZ6hjLhPIPuekbVft62q+2X7mXr0yuFijBiTXm2u1ox47olyjTvQ9qEPfQgAcMstt2j+R9PPghkSNq/ozPqDe/OKzuSXUJZpvrdAnV4+FBEZxRy3iC0rO7HrxeNYs6Q9q73edsVinNHi0ly3dmk7Hn3+WPrnjlketNjHBh9a7AK2ru7K2mbr6i602C2a67as7MTufcfzypZSX6H1WnFVynjxkLFkXq/tzx3D2qX5ud4+05WVg4tmuvDmWZ68sltWduLhp15P//7mWZ6ScoE5M3VV4xnKiPsExu4ZuW1irru6b9aw/Uw9euVwvcWkV5urNSOee6JcpkQiUdLXgp46dQo//elPcfz4ccRiYx+D+OpXv1q14CZKUVT4fKGC671eR9H1RlEXcVZj9qqMWUdbPdbkIFuFvk5Dj3Pa0uLWXF4sT+vi2mdgvNVTi1jLyVGYgePBWPaso5KARnv2rKMemwh/RIFTssAuCej3R8fKjTvrqJT8g0hz1lEJLQ4LhmMYnXU0p2wp9WWsPzleXOMo+TqNF88kGCWvqxWHLn1poVlHbRY0ObJnHW1125J/xOTMOtritmJe1qyjUvoP/ZJyoUDOGOV6V0O9HtuEc1SPGUB1nnW0LxDFTJc12VZqPutoZfvc8RgxjwvlKFBHz6V65XC9xaRXmyvBePk0ob7UiOd+lKHaTQYjxlVvMRXrS3OV/E87n/70p/G2t70NF110EQRBKHkHNEUpwAKXhLfO8SYTsRIdWyxZZ/qz9fzOWiLjiQMdszxjN6DM78JQgblOcex7QDLWzcr8XhytP3YSQIvNMvb9OYki6+LAvEYHGszIL1tKfVrrC9VTKePFQ8ailR8NGetzc330j5hGUUCjN+MZKaZ93UvKBebM1FWNZ6gS95l+xqrVH6TxZFs5e3ZD8lhr9Qc/28/Uo0e7GY8RY9KrzdWaEc89UYaSB9rC4TDWrVs3ocrvuOMOPPHEE2hqasLu3bsBAD6fD7feeiuOHz+O2bNn4xvf+AYaGhrytv35z3+Ob3/72wCAm266Ce9///sntG8iIiIiIiIiIqJaKnmg7bLLLsMf/vAHXHrppSVXvmrVKnz0ox/F7bffnl724IMP4qKLLsKnPvUpPPjgg3jwwQfzBvB8Ph++9a1vYfv27TCZTFi1ahWWLFmiOSA3paRegT3hr+wrsNV4tbYasRr4FWAiAmACggkVh4750OuPoskpjX4xvAmBqAJJEDAcVmCXBFgtZpwKKnDbBDQ7RShKAgNBGQ6rBcMRBY12CXNGP26X+njPrAYr/NHk7FhtblveerdNRESJodEhwTcYwklfCM1OK1rsyTeITkVVnAorCMoqTvPY0GLT+Cjo6EcqhsIyGmwiQtFYVh394RhO+KNwWgU02nI+Tmoa24esxtHktOK1o0MY8MuY7bUhAaB3JII2jw0uyYyTw1G0uq1Q48ljb3ZZIZiAXn90bJ+VfMti9Niyzt9U/ZfsQkY/LtZ9eAheq6X0c5z1MTMrWtwCjg0rGInGICtxzHCKGIkosIsCbBYBQVlFSFbhtlsQVVR47SKCsgp/JIY2jxVRNQ4llkBIjsFts8AimOARLWi0CegPJXPMLgqQRBOsZgHBqIJGhzSWKzk5ORxRYBUtCEQUnBYHGgQU+Gi1FS2O5D6y6uHbPMZSree9UvZZ62csPY4V0Kc/LLf/MSIjHoteuVRvMY3m/nO9fWh1Wafus4Be555/r1KJSh5oe/jhh/HAAw9AkiRYLBYkEgmYTCY8//zzBbc5//zzcezYsaxle/bswQ9/+EMAwFVXXYWPfexjeQNt//d//4d3vOMd8Hq9AIB3vOMd+N///V8sW7as1HDrjwjs7fZhw879iCjx9Jc6Xt7unVzjrUa99VInEVWOCTgSiOC1gQg27BhrpxuXd6DFLcEXVHBnxvK1S9vx8NOHIVlMuPHSM7Bp14H0ujVL2vHIs0dwy+XtaHaJuOnHL2DxTBf+6e3zssptWdmZXp9aduu7FsMumvGVX7+cXrbtmnMAAK8PBLFtT3d6+dbVXbhwjmfsjwMz8ESPD9/a241rzjsd9z3enVWHHItj3fZ9WcewsNmJc9tcAIDn3wjg9YEg/vsvR3DzpYtwYjiKTbsOYIZDwrUXzcva98blHfjd/pO4YGFT1vLUeRkKyfnxTcbosa3PuAZbVnbisgXeqfmArcUEPHNsJOsalnSOc7Z795ubsaxrDo77wlnX7tZ3LcYMhwWDQSVveSonZzgk3HTpQgRlNavMbVcshtdugdlsxp2/yG4nTkmAzWLG3076ce9jr2bFLlnM+MqvX8rL1/RxIf+Yt6zsxLf2duPwYLj0c0C1o8fzjl7PWHrtV4/+sNz+x4iMeCxG/DvBiDFNl2eB6danUV0ad9bRlBdeeAEvv/wy9u3bh+effx4vvPBC1iBbd3d3SfUMDg5i5syZAICWlhYMDg7mlent7UVbW1v699bWVvT29pYaal3qGZLTjRYAIkocG3buR8+QbLh666VOIqqc/nAMsbg5PcgGJNvppl0HIAnm9CBbavm2Pd1Yde4cLOuanR48S6277/FuLOuajfU79iMYjSOixHHDJYvyymWuTy37t9+/ioGgnLXswMkRHDg5kh7YSC1ft30f+sNjX/Z41K9g/Y79WNY1Oz1okVlH6o+KzGM4cHIE/eEY+sOx9D7+f/bePD6Oo877//Tdc2p0jCTHp2zLCZEiIJjEZNmQWDGBxZaTOCzwwGY58mPhlzz2YjALWWJHcjh+mA04wD4cyxFzhABO4oOFkDjXw+aAJGDHIsSyLV+yrcPWMWdfM78/ero1R89odIy6Z1Tv1ysvZ6rr+FTVt6qrS139Xds2H26BM/XecuWCnLI793Xjw29vygk32sVK33Qw6pbdfqdDc2flNxhTc/qwmDbOTvfBVU04OhjO6buvP3EEbp6zDDds8pYrF2AoIufEue9xPa2xyWaE7zzQk3rTkzM32dK1d58bs7RXo15WdTZsfDJtQJg97Fjv2LXGsqtcO+bDqc4/TsSJdXHic4ITNc2VtcBcm9MI5cmM+bn+7Gc/i0ceeWRSaSiKAkVRMyUBDEMhEHAXuE4XvG4n/WdD5qA1iCsJ9I9JePOCgKPyLZc803FS3xeyUyfpLAait3TYqdXKRntODuNCRLIcp8NRxTLcmN7zXYsrCURkfeEek1TLeMb19LBE1l/Ujd9W6UckDc2N+ps/L/cPZJSdnYdV+kQSGJG0jDgUBUTS9FrlF1cSGIlM3C7p+qYKw9AYCFv3zUBYwhXzZ+ezC7Nts9l22nNyeEIbsCI73XBEyWsPkTx2atggReW3pUJpC13La18puyxkY8W2gVMopzm6GKzm0lKvd6ywo0w7yzXm+uxySzkfTnX+cQIzNZeWErtsqdw02WH7k2Gqc3y2jTp9TnPqvcyJuipZ04xttCWTxb1LXFtbi4GBAdTX12NgYAA1NTU5cRoaGvDHP/7R/N3f34+rrrpqwrw1LVnQPawT3ccaNPgFiBydMXhFjkaDX5iW5lLkWy55pmNH3+dz/1vITp1so1YQvaVjNrROxkYDAguGpizHabWbswxPpjYJ8l0TORoeXr8NuQXWMp5xPT2Mzvr7DEONX8tOHxAYsy4NXn2esYrL5NFJU0BAYABQZhwA8IiZei3L9uRvFyt9UyUQcJt1yy6r3jszc2ixOkpRVrF2GshjQxO1cXa6Gg8HZsi6T7P73QhPt8l8tlQobaFriWR+2wasx2T6kmym7Gw2KKc5Op3JzKWlXu9YYUeZtpZrw3w41flntshno8DMzaWlxC5bKjtNDlgLFGKiOb7YudTpc5pT72VO1FVumgrNpdkUfXR0Iop9M2316tV49NFHAQCPPvoo2tvbc+K8/e1vxx/+8AeMjo5idHQUf/jDH/D2t799pqQ6kqZqHl0drRkPgV0drfoHFh2Wb7nkSSAQZo6giwFLJ9C1PnOcblvXAllL4N6s8E3tzXj4lTPYd7AP29a1ZFzbuLoZ+w/1Yfv6VngEGiJH4/vPHsuJl37dCPvUDStQl3LCYIRdPs+Py+f5sam9OSN8x4Y2BF3jG3ULfBy2r2/FvoN92Li6OSePHRvacurQMs+PoEv/ELRRxr6DfYjGFVPv7pfP5JS9bV0LfvyH3pxwo12s9E0Ho27Z7bfQx81I/uVA0MXk9GExbZyd7qcv9GJZ0JvTd5+6YQWikmIZbtjk7pfPoNbD58TZvEZPe+9NueOkzsMjGlewec2KHO0t8/yW9mrUy6rO29e3Yv+hvkm1AWH2sGO9Y9cay65y7ZgPpzr/OBEn1sWJzwlO1DRX1gJzbU4jlCdUsthX0Sbg5ptvzjk6unnzZvzxj3/E8PAwamtr8b//9//GDTfcgH/913/FuXPncMkll+Ab3/gGAoEAXn31VfziF7/AF7/4RQDAr3/9a3z3u98FAHziE5/Ahg0bJtSgKFrZvtEGoHReTErpddTpeaZw0htthezU8TaaBdFbOux8oy2vjaa8jg6GNQyEJNQYXkdBISwr4BgGYymvozxLYySiwCukvI6qujdFl8BiLK6g2sXrC788XkcbfGLOda/IQUp5HaUZJuV1lDcX/4ZH0KisYZ5P0MMLeB31ixxikpqRx2BMxbmQBDfPoMaV3+uooiVQ4xEQllUMhWXMrxr3OtrgE+ETLLyOengwNJXyOspb65sCpq2kedkz228WP34822+0WdppylPeiKQhIDDFt3GG504eQR+b43U0FFchcrTpdTQma/CKLGRVQ1Wa19EGnwA5YXgd1eATGN3rKD/udfRcSILIMeBZCjzDIJrjdTTTJjO8jla7C3gd5RF0s2leR2fOzmaDcpqj05n0XGqH1zq7vY7Odrl2zIdTnX9mgUJvYczoXFpKnOjt0YmaUrY/EJZQ7xVmfS1QiKm+0WZpow6e05x6L3OirnLTNJk32mZso+0f//Ef8ctf/nImspoyZb/RlqJcdALlo5VstJUGord0OHKjLYUT2tEJGpyiwwkaSqljLsylk4HUzXmU81w6W5C62sukN9pSOLEuRFNxlKOmSrnfO1ET4Exd5aapJEdHd+7cmfFb0zR8+tOfNn/bvclGIBAIBAKBQCAQCAQCgUAg2EnRh+3Pnz+P7373u/iXf/kXyLKMTZs24fLLLy+ltrmH8Srq2VD5HB2daa0EAsG5pI4j/OlcP+p8AgSGxrnROBr8Ary8flSyziMg6GNwNqRC0hKIyxokJYGgl0dMVlHl4sFQwOnRODwCgxox7XgmA5wY1eeqRr+AxVU8oFlL0bQETkf0Y0GNPhEL8h2NSGk+NxZHnZdHjYtFgJugvIyjeAKCLmZSxw+NY36SoprH/SzzSTvaVLAOhNkj3SaqBFSJDM6PSRA4FkNhCfU+AWFJgcAy4BgaoZgCl8BC0TQILIuRqAIXxyDgYhFTNEiqBpFjcSEio8ajjwEPrx+fdvFZ9l9IywTjgVBm2LGGsuuYFQv0juh1bfQLWBLgAXXiZNNmqvM4QSfVfj0nhxEQWGe0nxOfPZyoKbW2eLl/AA1eoXLXFtNp++msv5x4XJjgSIreaPvSl76Ez3zmM/jud7+LF198Eddeey0+/OEPl1DaHIMDnuoZwda9hxFXEubHFa9vDkxv8JYi31JpJRAIzoUGnu4dwd17xsf9pvZm7Hr+JIajMrata8GDL57EZY0etL9hHqKKhvOjcew80GPG/9QNK/DzP57E+9+6yEy3qb0ZS+s8uHK+F08dtZhXlgVyNxdoYN/h8xlatq9vxXVNgcyFkoXmbeta0Ojn0drgsS5veQAvnBzDlt2HzPAdG9qwaoG/8EMGBbxwZgxfe/x1vG/lIjz00im8b+Ui3P9kj3U+Ftos60CYPRjgqWOZNvHlW67AhbCMr/3+dTPs8++6DJKWwH2PH8mwq+88042TF2Lm790vn8LqyxozbGDb2hZ859nxeKb9N3oz7ctCizkeCOWNHWsou9ZtLPLOsyXdbEvNx5Oexwk6Tmw/Jz57OFHTXFlbTKftp9NGTuxzgmOZ8Ohod3c3uru78de//hW33XYb/vu//xuLFy/GW9/6VnR3d8+GxjlB77BsDloAiCsJbN17GL3DsuPyLZVWAoHgXE6HFHNRAujjfueBHtxy5QLElQQ693Xj9muX4aYrF0HRkugdipibbEb8rz9xBGvb5mek23mgB93nxtA7Yj2vnBjNnVestNy95zBOh5QJ43Xu60ZESuQtr3dENh8ujPAtuw9hMFb4qXAwpmLL7kNY2zYf9z/ZY/6bL59i60CYPU6M5tpE71DE3GQzwi5EZXOTzQjr3NeNtW3zM37fds3SHBvo3J8Zz7D/bPuy0pJvPBDKCzvWUHat2wrNs6XEmI8nO48TdJzYfk589nCiprmytphO20+njZzY5wTnMuEbbV/5ylcyfvv9fhw9ehRf+cpXQFEUdu3aVTJxc4n+MckctAZxJYH+MQlN3qm7DC5FvqXSSiAQnEt/KG457ilq/P9jsoqhUBIxWUMiibzxs9MlkgXmlZCEJVnzSj4t/aE4Fnq4CeNFZBX9Y9b68ukYisgIivlvmUMROad+hfIptg6E2cOq763suJBtp/+OyWpR8RJJ5NhXofFAKG/sWEPZtW6zq1xjPs4ud6J5nKDjxPZz4rOHIzXNkbXFdNp+Om3kxD4nOJcJZ8uf/OQns6FjztPgFyBydMbgFTkaDX7BcfmWSiuBQHAujT7RctwbfqtFjoaLZxH08rgQkcFQyBs/Ox1NAY355hVf7ryST0uDTywqnodn885j+XTUeQovoOo8ejojvvFvvnyKrQNh9rDqeys7LmTb6b/dPFtUPJpCjn1NZjwQygs71lB2rdvy2nGJyzXm48nO4wQdJ7afE589nKhprqwtptP202kjJ/Y5wbkU7XV0aGgId911F26//XYAwNGjR/GrX/2qZMLmGk3VPLo6WjMe0Lo6WvUPLDos31JpJRAIzmWBj8P29ZnjflN7Mx5+5Yz5Tar/evYYHnnlFDiawpI6Dza1N2fE/9QNK7D/UF9Guk3tzWiZ58eSgPW8sqQqd16x0rJ9fSsW+rgJ421b1wKPQOcvL8Bjx4a2jPAdG9oQdBX+u1TQxWDHhjbsO9iHjaubzX/z5VNsHQizx+KqXJtYUufBZ955aUZYjZvH5jUrcuxq/6G+jN8PPHc8xwa2rc2MZ9h/tn1Zack3HgjlhR1rKLvWbfnm2aZAacs15uPJzuMEHSe2nxOfPZyoaa6sLabT9tNpIyf2OcG5UMlksqjPWt5+++245ZZb8J3vfAd79+6Fqqq4+eabsW/fvlJrLBpF0TAyEs17PRBwF7xuO6XyYlJKr6Nl4nHFjr4PBn2W4YXs1PE2mgXRWzpmQ+ukbTTlpel8KI46rwCeoXF+LI56rwCfYHgd5RH0sTleR+u8POKyiiqRA0NTODMah5tnUOOy8DoaktDgE/RNhTxeFn0+EX89H0J/KI4Gn6gvkAp4HT0/FkdtPq+j2eVleKvj9YeLPHfKjH4q6HXUIp80r1cF6zABTrHrUumY1bk0zSYafQKqXAz6xyTwLIuhiISgV0BEViGwNDiaRiiuwMWzUBMaeMPrKM+gSmQRT3kdFTgWFyMyatw8YqoKN8fq6bgs+y+gJd0+ndLfpaBc6zZpG7VjDWW311GjXFu8jhaex2caJ9pxPhsF8thpqv1GJA0BgZnV9suLE589nKgptbYYCEuo9wpTXluUgonGxqTm0um0/XTWX0WU68Q5AHCmrnLTVGguzaboP00MDw/jH/7hH/C9731PT8iyoOmiX4gjFIMCNHl5vHlBQO/cmZqoU/maZ8dnIt9SaSUQCM4lASz0cLhifpV5A5rv8pqXA7Vu/X8U4BLjOy6etPTu8b8W1gTTLhiLdw1Y4uXHv8mWZ5MNABiGxkIPN/49jXwLpJTmjO9uTFReEgiK7Pi3aIp9uMhOh1SZRtnZ+WRrc8hCeE6TbRMAAtW6XTf5jLC0v1xnf5Ml/Xfat4zGv93C58bLZ1+TGA+EMsOONVQp1oLFoGbVdba+pz/VeZygk2q/5ka/3m9OaD8nPns4UVP2Wq1S1xbTafvprL/smksJZUfRO2VutxvDw8OgUl/x/ctf/gKfr/gdPQKBQCAQCAQCgUAgEAgEAqGSKfqNts997nP45Cc/iVOnTuH9738/hoeHsXPnzlJqm3sYr6KeDZXP0dGZ1kogEJwNA7x6dhQDIQkenoFPZBFXExiOKKj3CZBVFTRNQ0skILIsRmIyPAILmkoCoDEQkjDPL4KhKYzGFcQVDQE3j7CkQGD0/OZ5WZweVXBuLI667OOeVqQd2XQLHKKyioisYXGNiFA8kZsPM36UqdEvIOBicG5EQp1HQNDNYDCq4mxIgkdgUCNyqBEZjMgaLsZUDIVlXFIlYoGPw4ik4djpYQyFZTT6BSz25z/qOikyjjwJCLoK1J0Gus+N4dxoDEGvgGQyAQ/HFU4zlXLKhVSdek4OIyCwU2sHN4PBmIq+UQlekUUymQDLMBgKS6j3CdASScQUFW6OM4+TJpEAkjQGwxKCPgFxRYXAMmAoCgNhCbUeHjxDp2xRgKSoqBJ5sDQwFC6y/a3qhgrsw7mAHWsou4642bVerMT5ba7jxGcPosk+plNPNnMduGQyR9rTjp02+vT1YMW+NUiYFkVvtLW0tOCnP/0pent7kUwm0dTUBI6rrA8r2goHPNUzgq17DyOuJMyPK17fHJje5FiKfEullUAgOBsGeKZ3BF94dHzsb2pvhodn8H+eOY7hqIzOjhb86qVT2HDlInzn2aM4eSGGlYur8N6Vi7Btb7eZbtu6FnznGf264Sjh5388idvetgRBr4DP7j6UEbfRz6O1zpP7oEQBL5wZw9cefx0fvaYJUUXDzgM9WFHvxQeuXozOfZllLqwWcG5Uxt17DmeEP/jiSYzGFdx5fXPGtc1rVqC5wYPzo3JGXl3rW+EVGGz+5cGMsOuXBqa32Zaqz5a0+u/Y0IZVC/y5daeBp3tHMuuytgW7X3kd//y2Jus0UymnXJhKnbLSLK514X+vbjZtfHGtC5+4djk693dn2ITA0Pjy7/5sxrnjuuXYmmbf96xrgZpI4N7fvJYxVnY9fxLDURlfvvkKHBmIYMdjrxenNU/deJbGpof+Ujl9OBewYw1l17rNrnIrcX6b6zjx2YNoso/p1JMFnjpqkXZ5YOLNNot11/b1rbiuKUA22wg5FH10NBaL4Xvf+x4eeOABrFixAn19fXjqqadKqW1O0TssmwMeAOJKAlv3HkbvsOy4fEullUAgOJsTo7K5AQHoY3/ngR4MRWTccuUCxJUEtu3txm3XLEXn/m6sbZsPALjtmqXmJpuRrnPf+PW4ksDXnziCtW3zseOx13F0MJwTNyIlMBjLXQENxlRs2X0Ia9vm40JUxs4DPYgrCdx+7TJzYyw9n0SSMhdI6eG3X7sMa9vm51y77/EjYCg6J6+tew4jkUBO2InR6c2DRn3S892y+5Bl3U+HlNy67NfbP1+aqZRTLkylTtlp1rbNz7DxtW3zzU02I8/7Hj+CC1E5I87WLPu+Z183BkJSzlgxxknvhfFNtmK05qtb97mxiurDuYAdayi71m12lVuJ89tcx4nPHkSTfUynnr0jedKOTJzWat11957DOB2qpF1MwkxR9Ebb5z//eXAch7/85S8AgIaGBnzjG9+YUqHHjx/H+vXrzf+uvPJK/PjHP86I8+KLL+Itb3mLGedb3/rWlMoqF/rHxhfkBnElgf4xyXH5lkorgUBwNvnGfiIJpD7fibiSQExWEVcSZlhMUi3TGdfTfxv5ZceNyPoRoGyGIrKZNpEc3/jKV+ZwVLEMj8mqWX72tYsR6zQRWc0J6w9Nbx406pOdr1Xd+0PxvHXJl2Yq5ZQLU6lTdppsG8hnE+k2WkwcI8yw+XRbLUZrvrpZlVHOfTgXsGMNZde6za5yK3F+m+s48dmDaLKP6dRzWmnzrLv6Q/EiVBPmGkUfHT116hS+8Y1v4De/+Q0AwOVyIZmc2vvXS5cuxZ49ewAAmqbh2muvxZo1a3LirVy5Et/97nenVEa50eAXIHJ0xuAVORoNfsFx+ZZKK4FAcDaNecY+TQFaYvy3i2chcjSMW4RbYC3Tpd9CjN9GfumIHA0Pz6LOk+XhEUCdR9cEAAwFs5x8ZVa7OctwF8+a/599rcZjncbDZ95CRY5Gg29686BRn+yyrOre6BPz1iVfmqmUUy5MpU750kz028pGi4lj2Hy6rRajNZ9OqzLKuQ/nAnasoexat9lVbiXOb3MdJz57EE32MZ165lvLFpU2z7qrwSdOsgaEuUDRb7TxPI94PG56HT116hR4fvo3rOeffx4LFy7E/Pnzp51XOdNUzaOro9V8YDTOizdVT6+NS5FvqbQSCARns7iKx703ZY79Te3NqPPwePiVMxA5Gp0dLdj13HFsW9uC/Yf6AAAPPHccnR0tGem2rRu/bnyjbf+hPmy58VIsD3pz4noEGkFX7t+Ggi4GOza0Yd/BPtS4eWxqb4bI0fj+s8ewbV1umTSVxPb1rTnh//XsMew72JdzbfOaFdCSiZy8uta3gqaRE7akanrzoFGf9Hx3bGizrPsCH5dbl7V6++dLM5VyyoWp1Ck7zb6DfRk2vu9gH7atbcmxiVo3nxGnK8u+71nXgnqfkBG2qb3ZHCdLaj3YcuOlRWvNV7eWef6K6sO5gB1rKLvWbXaVW4nz21zHic8eRJN9TKeeSwJ50gYmTmu17tq+vhULfeS79YRcqGSRr6X94Q9/wHe+8x0cPXoUf/d3f4c///nP+PKXv4yrr756WgI+//nPo6WlBR/60Icywl988UVs3LgRDQ0NqK+vx7/927+hubm5YF6KomFkJJr3eiDgLnjddkrlEaqUXkdn23vVFLGj74NBn2V4ITt1vI1mQfSWjtnQOhUbBaN/o2IgJMHNM/ALLOKafiQz6BWgpLyOJhIJCFleR5OgMRiS0OgXwZpeRxMIuDlEJBUcQ8MnsLjEp3sdPT8WR20er6MZ7WPhdTQqa1hULSIkJXLzyet1lEfQzWIwquJcqn41rkyvoxfCMhr9Ihb6da+jF1KeSBt9AhZXlcLrKK8/HBbwOtoXUXFuNIY6rwAkE/BwbOE0UymnCEpls5Oy01SdRiQNAYGZWju42bxeR4NeAQkkEZM1uDkWQxFJb3fD62hEQtAjQNJU8Eyu11HdFgVIqgq/wIMzvY4W0f5WdcPM9qHdlNMcnc6k51I71lB2ex2d7XJneH6bDE6043w2CpTRutSJzx5E06SYyJ4mNZdOp55pXkcb/IK+yTYFr6MNPlHfZMtyhOCocZOGE3WVm6ZCc2k2RW+0AcDw8DAOHjyIZDKJN77xjaipqSm6ICtkWcbf//3f4ze/+Q3q6uoyroXDYVAUBY/Hg2eeeQZf/OIX8fvf/75gfolEApqWvzoMQ0PTEnmvO4Vy0QmUj1Y7dHIcYxleyE7LpT0NiN7SMRtap2KjgDPa0QkanKLDCRpKqWMuzKWTgdTNeZTzXDpbkLraSz4bBcpvLiWaiqMcNVXK/d6JmgBn6io3TYXm0myKfof6M5/5DK666iq85S1vwbJly4ouoBDPPvssWlpacjbZAMDr9Zr//453vAOdnZ24ePFiwc09TUuW9xttKcpFJ1A+Wp30RlshOy2X9jQgekuHnW+0lcNc6gQNTtHhBA2l1DEX5tLJQOrmPMp5Lp0tSF3tpdBbGOU2lxJNxVGOmirlfu9ETYAzdZWbpsm80Vb0Rtutt96Kl156Cffeey9OnTqFyy+/HCtXrsQ///M/F11YNr/5zW/wnve8x/La4OAg6urqQFEUDh06hEQigerq6imXVRYYr8CeDZXP0dGZ1kogEJwLA/SFFfx5YBB+FwOeYdA/JqHGw4FjaERlDWFJRZ2Xx3BUQZWLQ0xW4eJZxGUVHEujzs1BUZMYjStw8SxCkgZJ1eBPxfXwLEZiCqpcLBiKRt9oHA1+AXFVhZtjkUwmMRpT0ZQAJFVF34h+vE9NaHBxLEIxBR6Rw2hMQZ2HxwIvB1DAyVEZ50MS6n0C3ByT8j6qIejloWgaKIrGUFhGg0/AAh8HJDOPo8YVDWNxFW6egcDR4GgaY5KKSH8YVS5WP2LqZsy5ttEvYEmABxL6EYPhmIwqkcNYXIGLZ1Aj6kdSB6P60ab5AQEXY5o5T7NUEgLDIuhiJnfcKeO4lDBx+snGdzqpIx0v9w+gwZvqy4n+UMoAJ0b1fpsfEJFMAmFZgYtjMRSW4RdZiDyDCxEZ9V4eFIAxSUM4rqLWy4NlAJamkdCSOB+S4BFYsDTg4Vl4OApxLYmYksSFiAS/yOnHRkUeIzEZAsMg4OIQVzXEUp5s/WLKnnj9r6Y5/WMw1b6rtD4vR+xYQ9l9dHS214ts5icClkzmaBjBmTjx2YNoso/p1HM686GDj+YSnEXRG22rVq3CW9/6Vrz66qt48cUX8Ytf/AI9PT1T3miLRqN47rnn0NXVZYY9+OCDAIAPfOADeOyxx/Dggw+CYRiIooj77rvPdMRQkXDAUz0j2Lr3MOJKwvww4/XNgekN3lLkWyqtBALBuTDAsydG8e+PvGqO+03tzfjtq+dw61sWICJr2Hmgx7z2qRtW4Od/PIlPXLscu1/pwerLGvHQS6fwiXcsx+6XT2Fd23xElfE0i2td+MS1y9G5/5WM/Hc9fxLDURnb1rZg9ys9aH9DI3776jmsfeMluO/xI3nTblzdjHtfOoVPv/NSyGoCX3j08HjcdyxH575uy9/Gh23rvBzu2fdXfPSaJkhawixL5Gh8+ZYrMBiSMsK23Hgpgl4Bn919yAy796ZW8CyN//j963jfykW4/8nx9tm8ZgUaq0R89teH8LamGrzrinnYtndcQ2dHCw68dg7r2hZg1QJ/0RsoL5wZw5Y0DTs2tOVPP9n4TocGnu4dwd17Dmf05XVNgfybbQzw1LHs+9nlSILCtr2Ztljr5nAhFMdITM2w9c6OFjA0oKgJ3P/kMQxHZWxqb4aHZ9DgFxGWtYxxs3F1Mx566RT+adUS/O7wWXxw1RKcH41n5Ll5zQo013sQjms5/fNOn2vqfVdpfV6O2LGGsmvdZle5LPDUUYtylwfIZlu54sRnD6LJPqZTT7vSEuYcRX+j7Z//+Z8Ri8Xwpje9CStXrsRb3vIW1NbWllrfpChnZwi9YRm3/fCPOe6Cd330KjR5p+4pphT5lkprKXHS0dGy+ehsERC9pcNpzhBOhGX8k8W4/+qtb8TRgRC+9+zxnGsfe/tS/OAPx/HVW9+Iz/76YMbv7DR3XL8cP/iDdR7ffuqoWdZnf33Q/LeYtAyNguXkS/u1W9+I186HctIDwMb25Zb1/fi1S3H/gaM5YVoClmUY8R/46FvxLz95Oef6d//pLfiXn7yMhz++CkEx/9/FDFsZjKu45Xsv5OSTL/1k40+E3c4QTkcU/K8fvJhTn59/7Gos9Fh7BLOy62994M34TJp9Gfl87dY34kgeWzeuaQmY9vrxa5diRb3PMq9CYyE9T6u0++98O2R5an03030+05TTHJ3OZOZSO9ZQdq3b5lq5Bk6043J3hmB3nxJNM8NMOUOYTj1nI61Txk02TtRVbpomc3SULjbipZdeCo7j0NPTg9dffx1HjhxBPB4vuiBCYfrHpIxBCwBxJYH+Mclx+ZZKK4FAcC75xn1MUpFIwvIaRaXiyGrmb4s0xjWrPMyyUvnEJLXotBOVky9tRFYt0wPWYXElgUTWn62MsHxlGPGHI4rl9ZGoHj4UkVEMQxHZMp986Scb3+n0h+LW96ZQ/rWKlV1HsuzLyCci57d141q6vSaS+fNKHxuF8rQKHwhJU+67SuvzcsSONZRd67a5Vi6hdDixT4km+5hOPe1KS5h7FL3Rdtddd+FnP/sZvvnNbyIQCOCuu+7CypUrS6ltTtHgFyBymd0hcjQa/ILj8i2VVgKB4Fwa84x7t8CCoWB5LZnU/3XxbMbvQmms8jD+38jHLbBFpy22nOzfHl5/u8cqfb486ayvG6SHFYpf4+Esrwfcenidp7i/Qtd5rPsoX/rJxnc6jT7R+t7kE/OnsbBrj2htXx4+v916eBY0hQx7pan8eZljYYI8rcLrfcKU+67S+rwcsWMNZde6za5y892vyDq1fHHiswfRZB/TqaddaQlzj6I32n7605/iX//1X3HzzTfjwIED2LBhA77//e+XUtucoqmaR1dHqzl4jTPfTdXTW/yWIt9SaSUQCM5lcRWPL958Rca439TejO8/ewy1Hh6b2pszrn3qhhXYf6gP29a2YNdzx7FxdbP+e10LHnjuOGrcmWn2HdTjZuf/8CtnIHK0mY9R5uY1KwqmNcpbVu/FvTe1ZsZd15L3t8jp3/XyCDT2HexDjZvPKEvkaCyp8+SEbbnxUiwPejPC7r2pFcvrvdh3sA8bV2e2z+Y1K7C8Xo//oz/0orMjU0NnRwt+9kIvdmxoQ9BV3JG+oIvBjg1tGfkUSj/Z+E5ngY/D9vWtOX250Gd9bBTQ7Tr7fhaVlJz+2NTejKikoNEv5Nh6Z0cL4qqKeX7BtNdN7c2o8/DgaCpn3Bi2+Zl3XooHnjuOJXWenDw3r1kBj0Bb9s/CgGvKfVdpfV6O2LGGsmvdZle5SwJ5yg2QdWq54sRnD6LJPqZTT7vSEuYeRX+j7Qc/+AFWrlyJlpYWsGzugmx0dBRVVVUzLnAylPM32gCUzotJKb2OlonHFfKNttJA9JYOp32jDYDpdXQgJMPvYsEzNAZCEqrdmV5Haz08RuMK/CKHmKLCxbGIKyo4mkadZ9zrqMizCMsaJEUz47p5FqNpXkfPjsZR7xcgqSpElgWQxFhMxZJaT4bXUS2hQTS8jgq6d88aN69vsqR7HfUKcPMMhmMK4rKGOi8PVdMAik55lRT0NHm8jrp4BiKnex0NSSrCku4lstad6XW0wS/oD3VpXkf9IodQXIGLY3SvklZeR1OeUTkqCYFh9A2QCe7SGbaS4VGSnzj9ZOMXq2MGmZSdpryODoSl8b4s1utoSMIlVSKQBMKKAhfLYigiwyewcHEMLkRl1Ht4UBSFsVTf17p1r6MMTSOZSKI/JMGd8jrq5lh4+XGvoxcjMnwiC0nV4BM4jMZ1r6NVLg5SyutoVNbgE1L2IWR7HdX7J1CVauep9t0M9vlMU05zdDqTnkvtWEPZ7XV0tstN8zpqzsez5AjBiXZc7t9oA+DMZw+iaVLM1DfaANjnObSItI4aN2k4UVe5aZrMN9qK3mibiJtvvhmPPPLITGQ1Zcp+oy1FuegEykcr2WgrDURv6XDkRlsKJ7SjEzQ4RYcTNJRSx1yYSycDqZvzKOe5dLYgdbWXithoS0E0FUc5aqqU+70TNQHO1FVumiaz0TZjZwVmaL9ubmPskJ8Nlc8bbTOtlUAgOBcOOBtScapvFJKqISSpcHEMXDwDL88gLGm4GJXNb0txDI2RmIKYoqHeKyAkKQiIPCQtgcGQhFovDxfLYDgmwSfwiCoaorIKr8jCxTIISQpcHIvRqAKBo+ETWCSSSVyIyGiIK5DUJPrHJMyrEsDSNM6NxlDrFRCKKxBYJvWdLAoDIQnzqkTIagIjMQUBF4eRqAKvyEJWNbjMt+g4aAkNLM1gLK5A5PS3jeKKhriSQFhSUe3hQIMy3zyr0jQMhRWE4ip8qW/HeTlGfxvJ6raYentqICQh6BMQV1T4eA4LinnzCgAo4KKkYTCqICypaPQJ8PnSEhpvZ41JaPQLWFzFA1rqWuptr/5QHI0+sfgy5woUENY0DEU1jERl1HoFRGXdJmu9AsZiMhiahldgUe2mMRxJICSriCua/hYik0RMBi5GZATcnGl7QZ+g27XAgqYo/a3GlM0ZbznWeDhoCWAkpsDNMeA5Cn6OzbSjVN8fOz2CcFzBJX4RDAX0hyTUeQQEXXlsjuA87FhD2f1G22yvFwvNhaUi9bZoz8lhBAR29sZkxluqFTwXOPHZg2iyj+nU06634QhzihnbaKMoauJIhPxwwFM9I9i69zDiSsI88319c2B6g7cU+ZZKK4FAcC4c8MqZMOKqhv4xCfc9fsQc/3e9+zK4eBZ37xmfEzavWYGgT8DnH37VDNv5/jfhtZEQ7tnXbYZtW9cCD0fhtXNh7DzQY4Z/4T1vAEvTuGffK2bYpvZmNFaJ+L+vD+CtS+vQmZbPpvZm7Hr+JIajMjaubsZDL53C+9+6CAEXiwf/eBrvvmIefvGnU3jfykW4/8nxcoy471u5CA+9dAqfuHY5dr9yCqsva8RDL53C5jUrMBSWseOx11Ht5nHb2xZn6OzqaMG3nz6Kkxdipg6fyCLoFXBlozfzYYcBnjqWOXduW9uC3a/04H1vXYzrmgKFN74o4JXzYRwfimRouPemVrxjSQCgcvPv6mjF9csCQBJ4uncko4+2r2+duMy5AgUcH4vh5EUJW/cctuzrT92wAj//40nccd0yiByLvpFYTj9888keyGoyJ+22dS1gqDi+/fQx09YMW7Qqa1N7Mzw8gwa/qNsRrPt+85oV+NH/nMBwVMaODW1YtcBfmQ/YlYQdayi71m12lWsx15pzYak22yjghTNj2LL7kFnmrIxJu8qdbZz47EE02cd06mlXWsKco2hnCITS0jssm4MW0F0Fb917GL3DsuPyLZVWAoHgXHqHZShaEscGI+YmG6CP/6GIbG7gGGH3PX4EvUORjDBFTZqbbEZY575uBDyCuXlghA+EpJy4Ow/0oHcoglvfusjcZEu/dsuVCxBXErj/yR6sbZuPnQd6cG5Mwu3XLsPOA3qYsclmpDPiGv927u/GbdcsNX8fG4xgx2OvI64kcMuVC3J0bt3bjbVt8zN0DIQkdJ8bw2As84NAJ0Zz506jvLv3HMbpUOFV2mBMRfe5sRwNX3hUT2uV/9a9h3FiVMbpkJLTR8WUOVcYjKlIJGlsTbWRVV9//YkjWNs2H26ew9HBsGU/rG2bb5m2c1833DyXYWuGLVrF33mgB0MR2bSjfH1/3+NHTLvfsvtQjs0RnIcdayi71m12lVtoLiwVgzHV3OwyypyNMWlXubONE589iCb7mE497UpLmHtMuNF2+vTpojIiR0enR/+YZA5ag7iSQP+Y5Lh8S6WVQCA4l/4xCRFJRSKJnPFvFRZXEkhk3RYikmoZbziiTCrPobD1HGS8WG38vxE/lirXCLNKl/5vTB6Pn66jUPpsjYkkMBTJXHjlmzuN8vpDcRRiKCLnbZf+UDz/3ByS0B+K501H0Nt2MCQV1df5xoFxPV/aiKzm2FqhstLtqFDfp9t9ts0RnIcdayi71m2OKzdUunKHIrJlmaUek3aVO9s48dmDaLKP6dTTrrSEuceEG22bNm0CAPzzP/9zwXg//vGPZ0TQXKXBL5iugg1EjkaDX3BcvqXSSiAQnEuDX4BHZMFQyBn/VmH699Ey8/CIrGW8ag83qTyDXus5yPh7j/H/Rny3MF5uvnTp/7pS35hLJnN1FCo3XSNNAXWeTHfvjXnmTqO8Bp+IQtR5hLzt0uAT8+bf4BPQ6BPzpiPobRv0CUX1db5xkG2D2dc8PJtxbaKy0u2oUN+nl5ltcwTnYccayq51m13lFpoLS0Wdx7rMUo9Ju8qdbZz47EE02cd06mlXWsLcY8KNtkQige985zs4ceIEfvSjH+X8ZxAIBEqps+JpqubR1dGasQDv6mjVP7DosHxLpZVAIDiXpmoeHE1hadCDzWtWZIz/Wg+P7esz54TNa1agqc6TEcYxFO5Z15IRtm1dC0YiEja1N2eEB31CTtxN7c1oqvPgV386hW0W1x5+5Yz53bX9h/qwqb0Z8/wCvv/sMWxqb8a+g33YuDqzHCOu8e+2tS3Y9dxx8/fSoAdbbrwUIkdj98tncnR2dbRg/6G+DB31PgEt8/wIujI/g7q4KnfuNMrbvr4VC31cwT4IuhhcPs+fo+Hem/S0Vvl3dbRiSRWPBT4up4+KKXOuEHQxoKkEulJtZNXXn7phBfYf6kNUUrAs6LXsh/2H+izTblvXgqismDaYbotW8Te1N6POw5t2lK/vN69ZYdr9jg1tOTZHcB52rKHsWrfZVW6hubBUBF0MdmxoyyhzNsakXeXONk589iCa7GM69bQrLWHuQSUnOPN5/PhxPPHEE9i1axfe//7351y/8847SyZuspS9G/VSeTEppdfRMvG4YkffV4qL6kIQvaVjNrRO2kZTXkdVLQlJ1RCWtNQbWQw8PIOIpGE4qsDNM6mNNRqjMQUxOYGgj0dYUlBl6XVUhk/gEFU0xCQNHpHRvY7KFl5HkcSFsIIGPz/uddQvgGVonBuLodaT5XUUFAbDEhr9ImRtAq+jIgctmd/raETSEPCwoDHuTbLKxZheR70CCxc3ea+jXp7TN7wm4XV0KKogJKlo8Al4Q4MPIeMIqOFpLyShwSfoD5YWXkcbfGLxZRZJqWx21ubSvF5HNdR6eITiCiiago9nUe0Z9zoqyQnU+XjwLBCTkxiOKKhys+NeR70CYooKT5bX0bG4Ap+Q6XV0NKbAxTHgWQp+3trr6LCkIRJXMM8vgKGolNdRXn+wLvOveJTTHJ3OVObSWV9D2e11dLbLLTQXloqU988RSUNAYGZvTGZ4HbWeC/LZKFBG61InPnsQTZNiInua1Fxql+fQItI6atyk4URd5aap0FyazYQbbQbPPPMM3vGOdxSdsR2U/UZbinLRCZSPVrLRVhqI3tLhyI22FE5oRydocIoOJ2gopY65MJdOBlI351HOc+lsQepqLxWx0ZaCaCqOctRUKfd7J2oCnKmr3DRNZqOt6PeKr7zySnz5y1/Gn/70JwDAVVddhTvuuAM+X/GFpbN69Wp4PB7QNA2GYfDwww9nXE8mk/jiF7+IZ555BqIo4itf+QpaWlqmVBaBQCAQCAQCgUAgEAgEAoFQaoreaLvrrrvQ3NyMnTt3AgD27NmDz3/+8/jWt7415cIfeOAB1NTUWF579tlnceLECfz+97/HwYMHcc899+BXv/rV1AoyXvE8G3L20clS6Cw3rQQCwbmkxv3oYARVLg5DYQm1XgEensHFiIyorGFBtQtxRcP5MQm1Hh4+kUVc0XAxIqPGox8fFVgWNJ0ABQaDqSOUHK0fgavx8BiNKaj18GAoCqNxBWoyAb/AYzAkwSuy8AsMTkdHcW40jnlVImKKhoikosbDQ00kEYqrqPXwiMgaYrKGard+JJQGAyWhgWMYDIQk1PsERGQVHp5FEglQoHExIqPeJyCuaFC0BFw8a2rkGQqjMQVunsVASEKdT0BA0xCREhiJyXBxLMKSiioXB4YCYkoCMUVDVeqoqt/FQmRpCCyNsKShPzTeRqMx/fjpvCoRaiKJi2EJNR4BIk/hzHAcLo6BV2BR46HRH1IRlzX4RQ5hWUP4fBi1Xh7VLgYBNs+R1YyjRQKCrlS8rPvDomoGF0MYv15upOr56tEheAQGIstgNJpVZwMa6IsoOD8mwc0z8AkslKQGN8chKmvwCBQUlUJIUhGOq6j18hBYGiNRGS6exYWU/bs5BsMxxbRBhk5A02j9yJpfAEMnIatAOK6iys0hmUziYkQ/LipyDLwihQthDbKmQWAYhCQFHp7DhYhuo0no8atcHBiaQvRi1LTLBr+ARFI/Tl3n5eHhGZwfi8MvclBUDVUij6CHwekx/chwo0/Eghk+MkyYAnasoew+Ojrb60U7j+dWwtrYiXUhmspXUymYTj3tOnaa9gkPcj8uEfnWuzZQ9EbbqVOn8M1vftP8feedd2L9+vUlEQUABw4cwE033QSKovCmN70JY2NjGBgYQH19/eQy4oCnekawde9hxJWE+dHC65sDU590yiXPctNKmBG2/e51dL7rUrtlECqN1Lj/9tM9eN/KRbj/yR5z/Hd2tOA/nz6KKpHD/7p6Me7Z1424ksDiWhf+3+uWY9vebjPup25YgZdPDqH9DfMywje1N2PX8ycxHJXxqRtWYDgi4UJEwS/+dAr/66rF+PoTf86I6+EZ/PrlM3j3FfOw80BPxrXfvnouJ7yzowUvnxjCW5bUZZS7bW0Ldr/Sg39cuQhPvHYOr/aFcdvbFuMXfzqVU8/t61uhaBq69r+WNve14InXzuFNC2vNuItrXfjEO5ajc994ORtXN+Ohl07hjuuWQ+QYfHb3oZw2qnbzuO1ti3PqY7TLpvZmzA+IaKzi0T8ax1/PhXLqWO/j0VrnyVxUUMALZ8awJVWm8bHsVUv8FnN5C65q8uOFM2NYtcBfXpttFvVMb78dG9rG60QDz5wYwRcePZwR94r5PpwcCmE0EkV9wIu+4VhGG/9/G66ApCSwde8refu6q6MV3366BycvxLC41oU7rluOrXlsvbOjBQEXh7+dHcYlNT5866nc8bVtXQsefPEkjgyEcde7L0NMSeDrTxyxzG/7+lYIDNC5/69438pF+MvpC7jhDZdk9PH29a24rilAFvd2Yccayq5121wqt5LWxk6sC9FUvppKwXTqaVdaGni6dwR37yH345KRb71r03p2Qq+jBqIo4qWXXjJ/v/zyyxBFcVqFf+xjH8Mtt9yChx56KOdaf38/Ghsbzd+NjY3o7++fdBm9w7I5GAAgriSwde9h9A7LU9ZdLnmWm1bCzCCppf7aL2EuYoz7tW3zzU0AQB//2/Z2Y23bfNx+7TJzkw0A1rbNNze1jLhff+IIPriqKSd854Ee3HLlAjOOm+ew80AP1rbNNzcV0uMORWTcfu0ycxMk/ZpV+La93bjpykU55Xbu78Zt1yzF1r3d+OCqJtxy5QKz3Ox63r3nMM6PSVlzn54uPe7atvnmxosR7/4n9Ty37u3G0cGwZRsZZedrl50HenB0MAIkGbgFzrKOESmBwZia0XeDMdVcdBhxt+w+lGcu78bAmIYtuw/l5ON0rOqZ3n7pdTodUsxNtvS4DM3g7j2H0bawDkcHwjltfGwwYm6aAdZ9bYwT4/rWAra+bW83FC2JVcsbcPce6/HVua8bt1+7DHElgaGIbDkejPzu3nMYAY9g5vPBVU05fXz3nsM4HaqkJ67ywo41lF3rtrlUbiWtjZ1YF6KpfDWVgunU0660p0OKuclmpCX345kl33rXrvVs0W+0dXZ24rOf/SzC4TAAwO/34ytf+cqUC37wwQfR0NCACxcu4CMf+QiWLl2Kt771rVPODwAYhkIg4M4I6z8bMhvbIK4k0D8m4c0LAlMqp1zyLFW+pdJaShiGzrENu7Cy0/Fr09fJMMys1dVJ7VoM5aTXTq2F5lKKguX4pyggJqkZ1/LFHYkoefMw/j+SyitfHokkEJNVy2vZOozwoZBkHT+Vz0hUMcsrVG522HBWfQq1UXYe6XELpUsvvz8UR1TSLONGZN3jXXOj3wzvOTmcd862DA/F9fbIymciZttms+00Xz3T28+o08v9A5ZxB1M20h+KI5HM7Y/ssIn6rJg+jcgqkqFkQbuLyapl+Vb5DafZcb6xNhCWcMX8KjiNcpqji2G21qUTYde6bS6VW45rY4NsO3ViXYim8tWUzlTn+Jm00dlIa1XPfOuO2bwfO/EeO5Oa8q0D7VrPFr3Rdtlll2Hv3r3mRpvX6824/sgjj+Dmm28uuuCGhgYAQG1tLdasWYNDhw5lbLQ1NDTg/Pnz5u/z58+bafKhackcDxENfgEiR2c0usjRaPALU/ZwUS55lpvWUuIkr6NWdmowEzo1rbCXs5nEiZ5iClFOeu30OlpoLgVgOf6TScAtsJbXsn8HPFzePIz/94hswfJoCnDz1uXl0xH0Wc9drlQ+ATdnhhUqNx2Ro1Gdpz5W9cuXhxF3onahKaDBJ2KAkizjengWAYHJ6L9AnvbIO5f7RL09svKZiNn2Opptp/nqmd5+Rp0avNZ1N2ykwS/i6EA4Jw5DWfdRvjInum70Wf0E48vFswXLT8+v2s2ZtpZvrNV7nXm/Lqc5Op2pzKWzuYaya902l8p1+tq4kKe8bDt1Yl2IpvLVlM5UvY7OpI3ORlqreuZbd8zm/diJ99iZ1JRvHTiT69nJeB0t+uiogdfrzdlkA4Bdu3YVnUc0GjU37KLRKP7nf/4Hzc3NGXFWr16NRx99FMlkEn/5y1/g8/km/302AE3VPLo6WjMWsF0drfqHC6dIueRZbloJBIJzMcb9voN92Li6OWP8d3a0YP+hPnz/2WO4Z12LeW3fwT50drRkxP3UDSvwsxd6c8I3tTfj4VfOmHGikoJN7c3Yd7APn7phRU7cOg+P7z97DJvam3OuWYV3drTgkVdO5ZS7bW0Ldj13HF0dLfjZC73Y/fIZs9zsem5f34rGrA0RI1163H0H+7BtXWY5G1c3Y/+hPnR1tGB50GvZRkbZ+dplU3szlgc9AKUhGlcs6+gRaARdmX9DC7oY7NjQlhF3x4a2PHN5C+r9evzsfJyOVT3T2y+9Tgt8HO69qTUnrpbQsH19Kw6dGsKyem9OGy8NetDV0VKwr7s6WrH/UJ95vauArXd2tIBjKDzf04/t663H17Z1LfivZ49B5GjUenjL8WDkt319K0YiEvYf0vP52Qu9OX28fX0rFvq4kvcHwRo71lB2rdvmUrmVtDZ2Yl2IpvLVVAqmU0+70i7wcdi+ntyPS0m+9a5d61kqmUzOyKfhbrrpJjz66KNFxT19+jTuuOMOAPrbN2vXrsUnP/lJPPjggwCAD3zgA0gmk+jq6sL//b//Fy6XC1/60pdwxRVXFMxXUfK8yVNKr5tOz7PctJYIJ73RltdOMTM6P7f/r/jK2sunlUexOPEvI4UoJ712vtE20Vw6GtM9IF5Iecb0CGleRwMuxFUN/WO6B1GfwCKupryOunmEZRUCy4Cmk6BA6x49vQJYhsJAutdRNw+G1r2OaokkfKLu5dQjsPAJDBJJZHkd1VDj4aClvI7WuHlEFd3raMDNIZHUQKV5HTXKjSoq3DwL/Uu0BbyOegXwrO511Air8woIuJlMr6Mpz5Km11FZQ5Wbw0hMgU9g4eJo8CyNSJbX0bGYglBcQ6NfgJrUvY5WewS4eAp9wxIEjoaPZ1HjtfA6Kqmodae8jnLFeB3l9UVHXq+jyfHrk2C232iztNNUPc+HZXh4BiJneB3lc+uU5XXUK7BQ83kdTbWxwKW8jnIsLkZ1T7qG19GopHu4ZegktIRuz/V+ASydhJTldXQ4qqDazUFkU15HI9ZeR4NeAaCSGA6r8LtZ3euopOo2GJZQ79W9khpeR908g/5QHD6Bg6pqqBI5BD2s6XW0wSfqi/rMkxWOoZzm6HSmOpfa4hHTLq+jc6FcB6+NC72FYWmnTqwL0VS+mlJM9Y22GbfREqfNW880r6N23I+deI+dcU351rszpGkyb7TN2EbbzTffjEceeWQmspoyhTYwAGcalxXlohMoH61ko600lEv/G5STXkdutKVwQjs6QYNTdDhBQyl12DWXOhVSN+dRznPpbEHqai+T3mhL4cS6EE3FUY6aKuV+70RNgDN1lZumkh4dzccM7dcRCAQCgUAgEAgEAoFAIBAIZcmMHVi98sorZyqrmcd4xfNsyNlHJ0uhk0AgEGYKDjiRmqNqfTxElgINGhxLIRTXMBDS50MtoYGhGVS5GIzFNAyFZfhEFm6ewWhchsAyqHVzGI1piCn6MbiBMf2YXUxR4RP071UMhmXUe3lEUscjA24WPM1gKCLDPxYHQ9MYDOtHOLWEBoDGaEw/QsfRFC7EZFSLevpEMgGRSx359AmgqQSoJI3BlDaeo8BSNEJxBR6BhZZIQtGSCEsq/C4WIssgFJfh4jmzfv1jEhrCMgQWiClJJBIJsAyDgZCEWi8PgaUgsgzCkqYfP/UJiMkqLgkIGI5q5v3DJzK4GFEQkTT4RNY8VjsSVRBw60d0a70Cqt0Meodi8PAs5ldzGBjTj582hGUsqmZwdiSBcFxBnUdA0M1AYzScGdYwGJbhFVj4XSy0RAL9YzKCPh6LAzxOj+hHGBp9IhY4+Ehh0aTdR+dVCXBxDIajCsKSika/AA9H49yoZLZRWNUwHE8gpujHNUOSBlnVUO3mEZU1JJGAwLAYCuv9xzAUxmIK3KnjwzUeHi6OhounMRLVx8C8KgEMReHsaBxBn4C4osLDc/oRZ1lFwMWBZ3TbdfMsOIYCx9BIQoOi6fl7eBZekYXIURn58iyNvx4fgotncTEiw+9i4WIZXIjI8IkcRuMKql0ckEzAL3DQkkmcHZPgERjUiBxqRAaDUeNIhYCgiwGo8aMsFWMHTseO9Z7dRzhne21r59HRSljHO7EuRFP5aioF06mnXcdOaXK/nUsUvdEWCoXwzW9+Ey+99BIA4KqrrsIdd9wBn09/fW7r1q2lUThdOOCpnhFs3XsYcSVhfrTw+ubA1CedcsmTQCAQZgqLOWrbuhbUuBmMxRO4e894eGdHC3rOj+DSedUZ4Zvam+EVWOz5yxn848rF+OVLJ7HhykXo3P/KeJ5rW7D7lR50vHE+KIrC8cEwdh7oMa9vXrMC+w+exbuvmJcR3tnRgv98+ihOXohB5Gjce1MrOBp4/VwYB147n1NOV0cLfvnSKbx0ctTUFnCxYCgKcTWBiKzllCswNPYdOor3rlyEbXu7M/J64rVzuP7SeejcPx7+5VuugKIlsHXPeNi961swEJIz2qWrowXfTmlfubgK7125CP/59FG8b+Ui3P9kT0a8186OYH5AQN+IC1szNLSi2g38Pz85BJGj8cgnr8bhs5Gc9r8kIOIH/7cXRwbC2L6+FYoi4a49r5sf5b2uKVC+i74sG11c68In3rEcnfu6M+zkZy+cxGhcwRdvbkH/mIIHXzyB2962BIdGQvjFn07ho9c04Ui/td3c949vxFhMxT37MsPCkoatWW296/mTGI7K+Py7LoOkJXDf40cy7OlH/3MCw1HZtD2PwOKzu18142y58VIEvXxG2F3vvgwxJYGvP3HEsqyNq5vx0Eun8Ml3LAdNRbF1718z0roFFl94dFznzve9CSFJzQgreztwOnas9+xaY86lcitpHe/EuhBN5aupFEynnnalpYGne0cy1mXkflvZFH109K677oLX68XOnTuxc+dOeL1efP7zny+lthmhd1g2BwMAxJUEtu49jN5hueLzJFQm2373ut0SCHMQqzmqc183XDxnLhqM8G17u3FDy/yc8J0HejAYlnDbNUuxde9h3HbNUnNjysxzfzduu2YphiIyBsOSudllXL/v8SO4/dplOeHb9nZjbdt88/cXHj0Mhmaw80CPZTlb9+rlpGs7NybBLXAYisiW5V6IyrjtmqXmJlt6Xh9c1ZRTRu9QxNxkM8JODcdy2mVrmnYj/7Vt881NtvR4N14xH20L68xNtvFrh1Hlcpu/Q/GkZfsfG4zg9muXIa7om6PLG6rN63fvOYzTofJdiWfb6Nq2+eYmGzBuJ7dfuwxr2+aDoRj8+yOv4rZrluLoYAQ7D/Rgbdt8XIjKee0mkQDu2ZcbttWirW+5cgHiSgIXorK5yWZcv+/xI+Z1w/aODkYy4ux47PWcsKGIbG6yWZV1/5N6He7Z1w03z+WkNTbUjLDuc2M5YeVuB07HjvWeXWvMuVRuJa3jnVgXoql8NZWC6dTTrrSnQ0rOuozcbyubojfaTp06hY0bN2LhwoVYuHAh7rzzTpw+fbqU2maE/jHJNGiDuJJA/5hU8XkSKhNJ1eyWQJiD5JujhqOKZfhgKG4ZnkgCMVlFXEkgJqmWcWKyikQSSCSR97pVOEVl/o4UUU62tkiq7Im057RDJLcdrPLJl7eh3dBKUdbxBkNx9Odp2/5Q3PydL45RB6s02b/LjWwbzdeGMVkFRQGDIcm0D6NfKGq8j6zsJlJkWHqfTtTnRr8ksj51a4SnU0xeRr0jafadL22+/MrZDpyOHes9u9aYc6ncSlrHO7EuRFNxOFFTKZhOPW1LW8TajVBZFL3RJoqieWwUAF5++WWIolgSUTNJg1+AyGVWU+RoNPiFis+TQCAQZop8c1S1m7MMD/pEy3CaAtw8C5Gj4RZYyzgungVDAQwFy+tG+uzwdJ88IkfDU0Q52do8qbIn0p59rcaT2w5W+eTL29CerjVfuzb4rdu2wTd+T84Xh6Zg1js7TfbvciOfjWb/Nuof9AmmfaT3i/H/VnbjEYsLS+/Tifrc6BeaQk6c7LBi8komx+1/orT58itnO3A6dqz37FpjzqVyK2kd78S6EE3F4URNpWA69bQrbWOedTG531YuRW+03XPPPejq6sLq1auxevVqbN++HZ2dnaXUNiM0VfPo6mjNeHDp6mjVP1xY4XkSCATCTGE1R21b14KYrGD7+szwzo4WPNHdlxO+qb0ZQa+AB547jq6OVjzw3HFsW9uSmefaFux67jhqPTzqvAI2tTdnXN+8ZgW+/+yxnPDOjhbsP9Rn/r73plZoCQ2b2psty+nq0MtJ1zbPLyAqKaj18Jbl1rp5PPDccXR25Ob10xd6c8pYUudB1/rMsIXVrpx26UrTbuS/72AfNq5uzon32Kt9OHRqCF05GloxGouav30CZdn+y4Ie/Nezx8xvgxztHzavb1/fioU+bmYMxgaybXTfwT5sW9eSYyf/9ewx7DvYBy2p4Ys3X4EHnjuOZUEPNrU3Y9/BPtS4+bx2Q1PAPetyw7os2vrhV87om7BuHpvXrMixJ+O6YXvLg56MOFtuvDQnrNbD41M3rMhb1sbVzdh/qA/3rGtBVFZy0t57U6bOy+f5c8LK3Q6cjh3rPbvWmHOp3EpaxzuxLkRT+WoqBdOpp11pF/i4nHUZud9WNlQymUxOHA2QZRm/+93vcOrUKYRCIdMJwp133llSgZNBUTSMjERzL5TSQ6jT8ywxgYDbus0dhh06g0GfZXheO0VxOj+3/6/4ytrLp3x9JimX/jcoJ72zoXXSNmp4HQ1JqPXwEDkKFGjwKa+jgyEJ9T4BCWigwaDKnet1dCyugGNo1Ho4jKV7HU2lNTw0UhQwFJYRTPc6mvLWeCEiw+diwFA0hsISaj16mckkjbGYghoPD56hcDGqIODiMr2OhnWPkzSdAJJ6Xl6BBc+mvI5KCtwCi0TK62hEUuETWYhcmtfRpAYGjKlZ4DK9jhreKAWWgsgV73U0KmnwpnsdjSkIiBwuRPU66l5H43DzDBakex31C1leR3kE3Wym11GRhV9goSV1r6N1Xh5Lqse9jjb4RH2xN40P8pbKZidlp2n30Ua/ADc/7nW0wSfAyxteR/U2Gvc6qsLDswhLGmRNQ8CV5XU0IiHo1b2OhmIKXGleR0WOhjvldXQwpJfL0NZeR6OyhioXm/I6KsPNM2leRxNQNGAspsLNMym7G/c62ugXIHA0hiOy6XXUJ7Jwc+NeR8fius2nex09NybBzTOocWV7HeURdLEZXkdnwg6mQznN0elMZS61zSOmXV5H50K5Dl7H57NRYOK51DF1IZrKV1OKieb4qd7vneZ1NG89aXvvt068x5abpkJzaTZFb7R97GMfg9/vx+WXXw6GYczwj370o0UXVmoKbWAAzuxIK8pFJ1A+WslGW2kol/43KCe9jtxoS+GEdnSCBqfocIKGUuoo1VxarpC6OY9ynktnC1JXe5n0RlsKJ9aFaCqOctRUKfd7J2oCnKmr3DRNZqONnTiKTn9/P37wgx8UnTGBQCAQCAQCgUAgEAgEAoEwlyh6o+3Nb34zXn/9dVx66aWl1FMajFc8z4acfcyzFDoJBAu2/e51dL6rDMcywV444OSIgpfOD8ArsPAKDLSkhkSSNo9RKpoKnmGhJZIYjipo9AugKApjcQUxRYNfZOEVWEhqAlFZA00nITD60dEaDw83z2A0JoNjaVS7OIzG9GNzDX4BsqaBYxj4RQZ950YRkTVEJA1+F4uYosInsEgmKQyEJNR6ebA0hQSSoEBhJKqgxs1BUhNQtAQ8PIvBsAy/yMIjMJA0FVSSwUBYrweQAEsxCMsq4oqGWo/+/Q1ZS2AsrsLFMfDwDM7FVfSHJCyoFhGKaWZ6SVEh8izUVHw3x8AjsAjLCsTUUcR6nwAXrx811fWMh4XjGrSkBpZm0D+mhwfcDEaiKkZiCtxc6mghT6N/IIRQXMWFsIwGv4AqF4PeC3E0+HioCeDcaNw8XnpqePzI6qJqBqE4oKhJnBmV0ODnoSWAvpG4eQ8aDBnHDAUEXQxQ1DvwhW2opEdasu6jtR4GJ4fj8AschsL6MeGgl4OLA/pDGoZTx3PDkgK/iwNHU+gP6UcyazwMolISYVlDVFLhFVm4OAZxVYWLZRGSVERlDXVeHrKqgaJohOIqAm4WHEMjImkAlQBH633b6BfAMxTOjkrwCiw8Ag1Ng3m0V+QoMBQFj8BgNDbeTzSVgKwBIstgNC7DG5FAgcZgSIJXZMEzFPwii5iSQEjS7bXarR+/ZikaF6Iyatw8YoqGsZiKBr8ANaEhmaQgKRq8Apc6ciwg6B4/WjovIGA0puF86hju4ioeyHZ6TQGDsTQbcacfTZ0hm6lE7Fjv2X2Ec7bXtnYeHa2EujrxmYRoKl9NLNA7omtq9AtYEuABdeJkBZlOPe06dsoAJ0bHP29heV8lVAxFb7S9/PLLeOSRRzB//nzw/PgH//bt21cSYTMGBzzVM4Ktew8jriTMjxZe3xyY+qRTLnkSCHmQVDKrEyYJBzx9dAR370mfoy4HQGfMW9vWtmD3Kz1of0MjfvvqOdz6lgWIyBp2Hugx43z11jacH43j993nsOEti9C575Xx9OtaUCWyePr1PqxcUoete7vNa50dLTjw2jnc8IZLQNNJfOHR8Wtd6y5HP2Tcs687Iy+GSiKuJPDrl8/g3VfMwy/+dAr/66rF+PoTR8x4X3jPGyCwTEbdtq9vhaRquPc3r2WU/59PH8XJCzHzI/SNVSK8Ao1Xz4Qy2uHz77oMajKJHY+9boZtam9Go1/ENw50m3l89dY2SEoCn9jzSlq7tuClE0N4y5I6bEvVf3GtC3dctzyjPTavWYHmBg/Oj8roTKt3V0crLqni8McTw7jv8SMZ+X47Tf9XN7QBAD67+1CGxl3Pn8RwVEZXRytUVcJde16HyNHYsaENqxb4p75xUur7nEX+X91wBWJKAv/v3j+bYXe9+zIEPDz+4/ev430rF+H+J3ty6n/FfC/Wti3A+bF4VhteDpFj0DcylmHT96xrwf95ZrxtDVu9/tJ56Nyfad8PvngSo3EFd17fnGFzm9qbEfTxSCaprHGm28PyhipUuzkcH4xm2NXmNSuwqMaF3qFohqbPvesysAwFnqZwfDCSYwu/fOkUVl/WmFH/7etb8a2nelAlcvjA1Ytz7Or6ZYHxhwIKeOHMGLak7GdxrSunTtO2mUrEjvWeXWvMuVRuJdXVic8kRFP5amKBp45aaFoemPpm23TqaVdaBnjqmEXa9PsqoaIo2uvo97//fTz22GP44Q9/iO985zvmf06nd1g2DRoA4koCW/ceRu+wXPF5EggEwkzROyybD9CAPke5eS5n3urc343brlmKnQd6cPu1yzAUkc2HfyPO0YEw7nv8CG67Zqn5IG+m39eNRBK46cpF5qaScW3b3m58cFUTtu49DJFlM7UInLnJlp6Xm+cwFJFx+7XLsPNAD9a2zTc32Yx4AyEpp2537zmMgZCUU/7atvnm750HetA7FIFX4HPa4UJUNjdDMuJfiGTkcXQgnFP21r3duOnKReYmGwCsbZuf0x73PX4EDEXntOHWvYfBs5y5sZKeb0bZg2Fzky1d4y1XLjDzWd5QbV7bsvsQBmNT/xN0qe9zVvkfHYxktGNcSeBLv/0bjg6EsbZtvrnJZFwz6v/BVU04OhjOaUM3z+HoYCTHpu/Zl9m2hq127s+1yduvXYa1bfNz+n3ngR6ILJvXHu57/AhcHJtjV/c9fgSqhhxNX/nd3zAQkuAWrG3htmuW5tT/7j2HsbZtPm6/dpmlXZ0YHe+rwZhqbrIBsKzTdG2mErFjvWfXGnMulVtJdXXiMwnRVMaaRvJoGrHHRu1Ke2LUOm36fZVQWRS90TZ//nzL/5xO/9j4g5JBXEmgf0yq+DwJBAJhprCaoyKSajlvxWTV/DeRRE4cIyyWJ31EVnEhbD0njkQVM04xWiIpDYYmisqvJzttIustHCN9dpxBC62F8kzPI1+8oaw8rXTHlQQuRhTre0cobhleTNlGHCOfDF2RqS8IS32fs8p/on7IV//hiGKZNiJZ27SVbYzk6ZuYrOYtO58dG+Mhr53nCU8kC4zTPOEUhbzX+kPjfTUUkYuy0enYTCVix3rPrjXmXCq3kurqxGcSoqk45oqm6eTpuLQh8qxfqRS90TaTnDt3Dv/0T/+Ef/iHf8B73vMePPDAAzlxXnzxRbzlLW/B+vXrsX79enzrW9+aUlkNfgEil1lNkaPR4BemlF855UkgEAgzhdUc5RFZy3nLxevhbp4FQyEnjhHmFqzTe3gWtV7rOTHg5sw4xWjx8CxoCnDz49fz6clOS1PICUv3023ECVpoLZRneh754lnlafW7xsNZ3zt8omV4MWUbcYx80q/VeXhMlVLf56zyn6hv89W/xsNZpvWI1jZtZRuBPH3jStmupb3msWNjPOS18zzhNJV/bOQbf8kk8l5r8I33VZ2nOBudjs1UInas9+xaY86lciuprk58JiGaisOJmhodZqN2pc3bDj7yrF+p2LLRxjAMPve5z+G///u/8dBDD+HnP/85jh49mhNv5cqV2LNnD/bs2YM777xzSmU1VfPo6mjNeMDq6mjVP1w4RcolTwKBQJgpmqp5bF+fOUdFJSVn3tq2tgW7njuOTe3N+P6zx1Dr4bGpvTkjzrJ6LzavWYEHnjuObetaMtOvawFNAY++cgpdHZnXOjta8LMXetHV0Yq4qmZqiSu4xyKvqKygzsPj+88ew6b2Zuw72IdP3bAiI17QJ+TUbfv6VtT7hJzy9x/qM39vam9GU50HYUnOaYcaN48tN16aEbapvRlNtZ6MPJbVe3PK7upowSOvnEJnWv33HezLaY/Na1ZASyZy2rCroxWyqmDzmhU5+WaUHfTiqxvacjQ+/MoZM5+j/cPmtR0b2hB0Ff1pV0sbKuV9zir/ZUFPRjuKnP6NtuX1Xuw72IeNq5st6//TF3qxLOjNacOopGBZ0JNj0/esy2xbw1a3rc21yf969hj2HezL6fdN7c2IK6qlPTz6yilsXrMCMVnNsavNa1aApZGj6XPvugz1PgHRuLUtPPDc8Zz6b1/fiv2H+vD9Z49Z2tWSqvG+CroY7EizH6s6TddmKhE71nt2rTHnUrmVVFcnPpMQTeWraUkgj6aAPTZqV9rFVdZp0++rhMqCSiaTtn+i9pOf/CQ+9KEP4e/+7u/MsBdffBE//OEP8d3vfrfofBRFw8hINPdCKb3xOD3PEhMIuK3b3GHYoTMY9FmG57VTFKfzc/v/iq+svXzK14uNUwzl0v8G5aR3NrRO2kZTXkcHw7rXRC/PIJHUoCV1D4hBnwBF08AzDLRkEiNRBQ2+ca+jcSUBr8DAK7KQU15HGRrgGSbX6yijv702FtMwGJJQ79fzZmnd66ikJhCRNUQlDT4Xi7iswSMyQMrraI2HB8dQSCSToCgKIxEFNZ5Mr6NDEd0LpYdnICc0UEl63OsolQALw+toAjUeDhR0r6OhmAaRp+HmGfCMnmZ+lYhQXMNgWG8HWVUhcLrX0VBcg4vX366LyAqElNfRoFeAW9C9jrp5FkNZXkcTSQ0MzWBgTM+zysVgNKZiNKpC5Gn4BBYugYaqQfc6GpF176SG11EvDzWZ8jrqE7CoxsLraAxQtCT6RiXUF/Q6yusbJgVWDEXZ7BTuc5Oy06z8az0MTl3U7fVCJNfr6EhMQZXhdVTkwDEUBkK6F9AaD4uolDC923pFJsfraEzWPdLKmu51NBxXUZXmdZSikmBpxuxbgaVwblSCJ83r6FBEhkdgIbAUWDrL66hPAEMnIKmAyDEYiytw8wxoisZQWM+HZyj4RP2bhWFJRVxOIOBhLb2OhuIqgl4BiaSGRI7XUR5BNzvudbRKwGhcw/mQrmPJhF5HM9MXYzPZlNMcnc5U5lLbPGLa5XV0LpTr4Lrms1GguLnUEc8kRFP5ajK8jhqa8ngdnc793kleR/Peywyvo4XuqyXEiffYctNUaC7NxvaNtjNnzuBDH/oQ9u/fD6/Xa4a/+OKL2LhxIxoaGlBfX49/+7d/Q3Nzc8G8EokENC1/dRiGhqYl8l53CuWiEygfrXbo5DjGMryQnRaj844H/4xvf+DNU75ebJxiKJf+NygnvbOhdSo2CjijHZ2gwSk6nKChlDpKNZeWK6RuzqOc59LZgtTVXvLZKFB+cynRVBzlqKlS7vdO1AQ4U1e5aSo0l2Zj6/v8kUgEGzduxF133ZWxyQYALS0tePLJJ+HxePDMM8/gjjvuwO9///uC+WlasuCOqBN3TK0oF51A+Wh10htthey0GJ2alv+NuGKuFxunGMql/w3KSa+db7SVw1zqBA1O0eEEDaXUUaq5tFwhdXMe5TyXzhakrvZS6C2McptLiabiKEdNlXK/d6ImwJm6yk3TZN5os22jTVEUbNy4EevWrcM73/nOnOvpG2/veMc70NnZiYsXL6KmpmbyhRmveJ4NOfuYZyl0EggEwkyRmqPk0Rh4hkH/mH6k081RACjE5AQGwxJqvTwElsZwVEa1Sz+yNhZX4eEZuAUWLo6GqiWhJZMpz5ky6nwCBIaCi6cRkRIISSriioaglwdA4exoHLWpo6VaQkNvSH/1vtEvwCsyuBBWEJZU1Hg5IElhJCoj4OahJjRwNIP+kIQ6Lw8Xx+DMcAxBnwBV0+DmOYQkFYqmH6EbCkuo8wqQNRUczYCmKAzHFFS7OIzGFHhEFlFJRZWLA00lcT6uIixruBCR0egXwDEUorJep6BPQFxV4eZYRGQVVSIHWUuAoSnIanL8qKebwXBEw0BIv5+omgaKosDS48f+GvwMTgxJ8AgcLkYkVLl4jMYVVLk4eGQNozEldWyRA0Ppx2eDPgFaQgPPsHALNKpdQFgCLqTKaqpzISYncD7tKGn/WBJnR2No9IlY4OOABHLud3UeBj6G0a8VYS+zfXTrxLCMwfNhBNz6seAzw3HU+wT4RQbHh6JYWONGNNVm1S4OF6MKvAIDkWMQkhQ0+AXE5ARGojJcPAtJ0eDiWIRlBT6Rg6QkEJVV+FwsWJpGRFLh4hgMR/W0SOrHb+t8PASWApIUtKQGmmIQllSoiQR8AofhqIxaDw+WpjEaVxBTNPhEFiLL4EJERpVLd8hgHHGu83IYjWkYHoigxs1hOKrAxdHwCixiigqWYZBMJjEcVeB3sRBZOpW3iqisotrNgaNpPS7N4GJUhi+Vr6QmcXY0rh+lFmgwFA1J1SCrSURkFR6BRUDkQCGJobCMOo+AoIsp7lho2vFSn8ghrqioEvni01cidqz37D7WONtrWzuPjs52XWngdEhBfyieOXdPByc+kxBN5avJODJ5LoRGn4DFM3Fkcjr1dPCxU4KNpNYrPSeHERDYaa9TbNloSyaT+Pd//3csXboUH/nIRyzjDA4Ooq6uDhRF4dChQ0gkEqiurp58YRzwVM8Itu49jLiSMD88eH1zYOqGXS55EggEwkyRmqOeeO0s2t8wD9v2dqfNVS2ocnPY9Iu/mGGdHbpTg6MDEdz3+BEzfFN7M+ZXu1DlYnF2RELnvu6MNPU+Hn87H8bOAz0ZaXY9fxLDURn/34YrIKtJ3L3ncEa6X710Cn0jEm572+KMtJ0dLfjPp4/i5IWY+TH6B188idG4gk+/81L0nRnDL/50Cu9buQj3Pzme7ss3X4GL0Qh2PX8i59rG1c146KVT+Mw7L0VU1jLawqq83S/34B9XLsJgyoW7osGs9zsvr8MNb5iHrWl53LOuBWoigXt/85oZ9tVb2yCrCfzLT1/J0XHHdcvx7aePQlaTlvU/8No5rLn8EswL8Dg/KuMLjx5GtZvPidvV0YK3LvHjEz97DcNRGdvXt+K65QGLe1MLFteKWFrlyv8wZ8c9jQOePjqSYRvpttPZ0YK/nR3BSCyA7zxzFP/rqsX4+hOZtvmWxVXoPhvG/QeO4H0rF+Ghl06Z/370miZElUzb3HLjpWApCl/+3d8sy9y2rgVujoLIMTg+NJpja4trXfjEO5ZnjIPNa1ZAYGjc+5u/4hPvWI4HXzyJIwNhdHW04pcvncSay+dhU5buxioRP3vhBNrf0GiWvXnNCjRWifiP379u2mPnustBUVSGvW1qb4aHZ/B/njmO4aiML99yBSKSilBcNetqpXPHhjasWuAvvAilgBfOjGHL7kO542fNpROnr0RsGhu2rDHnUrl21ZUGnu7NnPe2r2/FdU2BqW+2OfGZhGgqX00M8NQxC03LAlPfbJtOPcsxLaH0WKxXilrnFMAWr6Mvv/wy9uzZgxdeeAHr16/H+vXr8cwzz+DBBx/Egw8+CAB47LHHsHbtWnR0dODee+/FfffdB4qiJl1W77BsGjQAxJUEtu49jN5hecr6yyVPAoFAmCmMOeqDq5rMjSXAmKu6oajJjLBte7vh4lhzk80I33mgB0cHwuAZxnxoT0/D0LT5cJ+e5pYrFyCuJHBsMGI+UKSnu+2apbjlygU5abft7cbatvnm78593bj92mVY2zYfRwf0TZO1bfPNjQ8jXu+FCHY89rrltfuf1NP0DIRz2sKqvNuuWYqte7shsixEls2o9wdXNZmbHkaae/Z1YyAkZYQdHQjjC48ettSxNVVmvvp/cFUT7t5zGAzFmHlYxd26txuDIc1s67v3HM5zb+pGIknjdCj/ytCOe1rvsJxjG+m2s21vN268Yj469+ntZWyypcdFksa/P/Kq2e/p/16IyjlttuOx13EhKucts3NfNxiagaLB0tbWts3PGQf3PX4EF6Kyee32a5eZ7XfbNUstdfcORXDbNUszyr7v8SM4OhDOsMe+0XiOve080IOhiGym6x2KYCAkZdTVSueW3YcwGLP4mnUagzHVXLQa6Yz2LCZ9JWLX2LBjjTmXyrWrrqdDSs68d/eewwXn54lw4jMJ0VS+mk6MWms6MWrPc3M5piWUHqv1ynTXKba80bZy5Uq8/vrrBeN86EMfwoc+9KFpl9U/Nv6wYhBXEugfk9DknZo73XLJk0AgEGYKY44ajiiWc1VEVnPDJNUybiIJDIat57yLefI3/s6SSMLyekxWQVHW19L/RpMe18jLKl2ha0Z4Pi1W5ZltlJUmX3smsv56Vqis9DKt4oxE9TKG0to8X736Q/GMvPLdm4bCEliawkIPByvsuKflKzO9PoOheMF+7c+6nv5vvj7I7qvsMo2xYVVuPh2J5Pi1WFr6WIExZdhZetlGPgaF6pA+xozwiXQORWQExfxLyaGInLdPiklfiThpbJR6jTmXyrWtrqk5K6fcUDzv/Dxhng58JiGaKlBTSMISG56byzEtofTkW69MZ51iyxtts0mDX4DIZVZT5Gj9OyoVnieBQCDMFMYcVePhLOcqD8/mhomsZVyaAoJe6zkvX/6Gf2yGguV1V6r8Qmmz46bnlZ2u0DUjz3xarMoz2ii7TfLVl856gbtQWellWsUJuPUy6rLa3PKe4xMz8sp3b6rzCmjwiciHHfe0fGWm16feJxbs1wZ/7nXj33x9kN1X2WV6eBYeni1YrlWeRt+m27ZbyD+mDDtLL9vIx6BQHdLHWL542b/rPIUfDuo8+fukmPSViJPGRqnXmHOpXLvq2pg2p2WUW2B+nggnPpMQTcXhRE2N+TT57HluLse0hNKTb70ynXVKxW+0NVXz6OpozVjgdnW06h8frPA8CQQCYaYw5qifvtCLzo6WrLmqBRxLZYR1drQgJqvYvGZFRvim9mYsr/dC1jRsW9eSk0ZLJLCpvTknzcOvnIHI0Vga9GD7+tacdLueO47dL5/JSdvZ0YL9h/rM39vWteC/nj2GfQf7sKzei03tzdh3sA8bV2emW1LrwZYbL7W8tnF1M/Yf6sPyem9OW1iVt+u54+jqaEFcVRFX1Ix6//SFXnRl5XHPuhbU+4SMsGX1Xtx7U6uljq5Umfnq/7MXerF9fSu0pGbmYRW3q6MFQR9jtvX29a157k0toKkEFvryvy1hxz2tqZrPsY102+nsaMHvXu3DtnUt2HewD5+6Idc2gQS+ePMVZr+n/1vj5nPabMuNl6LWzectc9u6FmgJDRwDS1vbd7AvZxxsXrMCtW4e+w/1mfZqtN8Dzx231N1U58Gu545nlL15zQosr/dm2OMlVWKOvW1qb0adhzfTLanzIOgTMupqpXPHhjYEXYX/yht0Mdixoc3SbotJX4nYNTbsWGPOpXLtqusCH5cz721f31pwfp4IJz6TEE3lq2lxlbWmJVX2PDeXY1pC6bFar0x3nUIlk8mK+QytomjWrlhL6SHU6XmWGCe65LXCDp353P/mtVMUp/Nz+/+Kr6y9fMrXi41TDOXS/wblpHc2tE7aRg2vo5oGnmYwEJYQ9Apw8xSSoBDP8jo6EpURSHkdDcU1uHj97R4XR0NNZHkd9QrgWQruNK+jkpxArY8DBQrnRuOoSfM6qmoUBsISGnxZXkc9HAAKI1EFATdneh0dCOm6XCyDMyOZXkfDkgo55XX0QlhCrUeAktDA0jRoisJITEHA8DoqsIjKKvwiB4ZJgqMYhGUNF1MeRHk2zeuoV4CkqRBZ3SukfwKvo4NhCfUpXaAocDSNi1EZ1YbX0QsSPDyHiymPlGNxBX6Rg0dgMBpTEIlr8LtZMBSFwbCEOo+ABDSwNAsPT6PaneV1tNaFmJLyOuoTsKhm3Otog0/UH9SyvY76BNR5rb2O5tjsDN3TJmWnKa+jQ2EZVS4WHkH3Ohr0CagyvI5WuxFVdK+jAReHkagCt8DAxTEISwrqU15HR6MKRJ4xvY5GZAVe0+uoBp/IgGVoRCUVAsdgJN3raMpLrsDpXkcTll5HFdR4dE+go3H9eK9XYCCyDC7GZN3GKOBCRIFHYMa9jkYV1Lh13QKnex2Nq7on0WQyiZGoonsv5ca9jsYkDQEPm+F1dDgqwyOwCKa8jp4bjcMj6LbC0OleRzV4BAZVIgfa9DrK64vPSXod1dtP98Brlb6c5uh0pjqX2uIR0y6vo3OhXLvqmuZ1NGPuTiOfjQL551LHPZMQTeWryfA6GtLXEUvyeB2d7P3eqZ5D897LbO4bJ95jHaUptV4ZkTQEBMZynVJoLs3Jbk5stKVwVEcWoFx0AuWjlWy0TT5OMZRL/xuUk15HbrSlcEI7OkGDU3Q4QUMpdZRqLi1XSN2cRznPpbMFqau9THqjLYUT60I0FUc5aqqU+70TNQHO1FVumiaz0VbxR0cJBAKBQCAQCAQCgUAgEAiE2WBufBzDeE3zbMjZxzxLoZNAIBBmitQcNXA2hDofj4CLRRLAWEzFcOqoZlxR4RZYUEkKAyEJdT4BLo5CXEkiJqvwCCwuRhSIHA0Xz8DFMhiLKwhJKup9PFQN6A9JqPXwqPGw5jHSuKKh3idAVhOIKio8IRkjMQVekQHH0FBUDSzDmHOyi6PBMhTCcQ39YxLq/QIYOgFNozEQ0n+LLI0zIzHU+wSwNIXhmAwXx0JSNIgcg5iiws3rR/REjoZHYOHmaITiGoYiEuq8AqIXo2AoGjxLIeBmMRbTj4DWeQWMxRVUiRyisgqeZeDhGWjJJKKyXtfhiIb+kIRGvwCaBi6EZVR7ODAUjcGQBJ+LhcgyODsaR71PQBIJcDQDmgJOD8fR4BewqJrBn8+MmPVeVM2ASTC4GNMwGNWP0zb6BMz3cRiM6Mf36jwCgi4GADAYV3FmVIKLo1ElcpjnZ2fvWMMs3EfnBRj0DsrwCSzUhAYPyyHoY3ByRIGsqvAIHKKyhgsRGXVeHn6RhZsHLoQTCEkyXByHCxEJl1SJSAAYCsnwiSxkVYObZzESU1Dl4hCVFXgFDkoigdGoCq/AwsXToEEhImuIKRqq3RxGYgp8AguWpjES048FR2UNYUlFvU+Ai6cgK0BU0TXN84tgaOB86riNmkhi4FwIQZ+AsKzAw+vHQS+EZfhdHGKKChfHYjQmo8bDQ01qYCgaFFI2lTpSmgTAMzSqBAoXowkMx2TUunlEZA0hSUWjXwBDUegbjcMnsKjzcKgRGAxGs2yoYs5EFCDt6OuM1duO9Z7dRzhne21r59HRSljHO7EuRBPRNFN52nXslAV6R/S0jX4BSwI8oBaZllAcpbhnT5HK32jjgKd6RrB172HElYT54cHrmwNTH+DlkieBQCDMFBZzVGdHC9w8gy2/PmSGfeqGFXBxNL7027+lzWUteOK1c3jTwlrc/2SPGb6pvRmXBET85PkTkNUkPnD1YnTu6zavf3XDFTg/JuG+x4+YYXe9+zJQFIUv/vef0/K/HFqSykjb1dGKKjeLTb/4C+JKAotrXbjjuuXYunc8zrZ1LXjwxZM4MhDGPetaQCOJbz/zV7xv5SI89NIpvG/lohy98wMi/uPxIzh5IWZ+2P2hl07hjuuW4eRFGlv3jLePce0T71iO3S+fwoa3LMLiWhEUgD+fGsvR8vvD5/C25XUZ9d3U3oxdz5/EcFTGVzdcgZGoktW2rfj20z2mnq6OFlzb7Mczxy5k5LN9fSse+tNJvHRyFCJHY+f73gRZD+m+FQABAABJREFUTWDL7vG+23LjpQh6BXw2Laxk96FZu4+2YEkdjxd6L6KxSsRvXz2OGy6/BL/800l89O+W4uTFeIbdfO29b0Rc0fDNJ3vM/q9287j975uw47HXc/rWsJWPXtMENRnNiNPZ0QJJ0TL66zPvvBQ8Q+FLv/0bqt08bnvbYuw8MG5j997UCp6h8Nndr1raQPr/f/5dl0FNJjPK3La2Bd95ttu0h+3rWyGwwGd3H87Ir8bNwefi0CPrdf3oNU040h/O0LJ5zQr86H9OYDgqY/OaFWisEvHZtLG+Y0MbVi3wV/ZmGwW8cGYsY5xMu952rPfsWmPOpXIraR3vxLoQTUTTTOVpV1oWeOqoRdrlAbLZNlOU4p49DSr+6GjvsGwaNADElQS27j2M3mG54vMkEAiEmcJqjtq2txs9A+GMsK8/cQRDETlrLuvGB1c1mZtWRvjOAz04NhjBbdcsxe3XLjM3PIzrRwcj5maREfal3/4Ng2EpI8zNczlpt+49DEVNmmFr2+abG1tGnM593bj92mWIKwncs68bboHD2rb5uP/JHvPfbL1HByNY2zbfDDPiunnO3GTLvta5rxu3XbMUnfu6wVAMKIq21PLhtzfl1HfngR7ccuUCsz2MTZv0eqbr2bq3G6cuajn53L3nMG67Zqn5u/vc+ELECNvx2Os4OhjOyb8U96HZu492A0kOOw/0oHcogg+uasLWVFskksixmyP9IXzh0cMZ/X/LlQvMzSwjXrqNrG2bjwtROSfOtr3dOWPha79/3Qy75coF5saWcf0Ljx7G0cFIXhtI/3+rMjv3d2fYw917DiPgFnLy6xuNI5GAWdcLUTlHy32PHzHLuu/xIziaNda37D6EwVhlPx0MxtSccTLdetux3rNrjTmXyq2kdbwT60I0EU0zladtaUfypB0pvznCqZTinj0dKn6jrX9s/IHMIK4k0D8mVXyeBAKBMFPkm6MSWX8hyhc2ElXypo/JKmKSmnM9kURRZUYs0saVBCLy+I2VoqzziqXiGPGNePniJ5J6XulhFJVfg5FPTNavD4TiGAxZt+VwxLqNjPLytUe2nv5QvGBdC+Vl1XeluA/N6n001R6JJMw2jkmqZZ8Z7ZLe//lsId1WJtOeRlghG7MqK/v/i7WH4aiSEyeRBCIpmyykP71cK11Dkcp+QEjfKDWYbr3tWO/ZtcacS+VW0jreiXUhmopjrmiaTp7lmJZQHKW4Z0+Hit9oa/ALELnMaoocjQa/UPF5EggEwkyRb46iKRQVFnBzedO7eBZugc25zlAoqkyPmJtW5Gh4eDYnLPu3KxUnPb4RL1/Z6b66RY5GMplfQzI5Xo7I0aj3iQj6rNuy2mPdRkZ5+dojW0+DTyxY10J5WfVdKe5Ds3ofTbUHTcFsY7fAWvZZerukXyvUt8nk5NozPazYNEYfT8Ueqt2cZRkefrz+E+WVT1edh0clU+extqnp1NuO9Z5da8y5VG4lreOdWBeiqTjmiqbp5GlX2kYH9k2lUYp79nSo+I22pmoeXR2tGQvnro5W/cOFFZ4ngTAdtv3udbslEByE1RzV2dGC5npvRtinbliBOg+fNZe14Gcv9GLj6uaM8E3tzVgW9GDXc8fx/WePYdu6lozry4IebF6zIiPsrndfhqBXyAiLSkpO2q6OVnAsZYbtO9iHro7MONvWteC/nj0GkaNxz7oWRCUF+w72YePqZvPfbL3Lgx7sP9Rnhm1c3Yz9h/oQlRR0rc9sH+PatnUt2PXccWxb1wItqSGZTFhq+fEfenPqu6m9GQ+/csZsj7vefVlOPdP1dHW0YFENk5PP9vWt2PXccfP35fP82LGhLSPOlhsvxfKgNyf/UtyHZu8+2gJQCja1N6OpzoOfvdCLrvWteOC546CBHLtpbvDh3ptaM/p/98tnsOXGSy371vi3xs3nxOnsaMkZC59556Vm2O6Xz2BTe6aN3XtTK5YHPXltIP3/rcrctrYlwx62r2/FSFTKyW9+lQiahlnXGjefo2XzmhVmWZvXrMDyrLG+Y0Mbgq7K/tRv0MXkjJPp1tuO9Z5da8y5VG4lreOdWBeiiWiaqTztSrskkCdtoPzmCKdSinv2dKCSyaQNn4YrDYqiYWQkmnuhlJ7NnJ5niQkE3NZt7jDs0BkM+izD89opitP5uf1/xVfWXj7l6zMZp1z636Cc9M6G1knbqOF1NOUVNOBmgSQwFlcxElXgd3GQVA1ungGFlNdRr+5J0fQ6yrMYjioQuEyvo2FJQ52Xg5YABkISarK8jkpyAnU+HkoigZg07vHRIzDg2XGvowNjUsp7Iw2WphCWdM+e9T4BDJ2EplEYCEuoT23WnRmJIegVwDEURmIKRI6BpGgQOAZxRS9nNKXXw7Nw87rX0QsRCbVeAVFZtfY66hEwJinwi7onSJ4Z9zoak1UEU15HDQ+oDA1cCCsIeFiwFI2hkAyPqLfPubE4glZeR30CFtUwODWse1Y1fhteR4eiujfXBp+ABRleR3lz0TEYV9E3qm/E+EUOl0zD6+ikbbbIe96k7DQrz1yvoyyCPlb3Oqqp8PC619GLERm1Xh5+gYVbMLyOKnBxLC5EJMzziwCFVL+wUFJeR0fjeh9HZQUegYOqJTEWU+DJ9joq615HR+MKvAILhqYxmuV1NOgV4BbGvY5ejMho8Itgad0Tb9ArQEsmMZgaVxFZgTvL62hcUSEaXkfdPDRooECDpiy8jtI0qsQ8Xkd9Ahg6zeuom0ONmO51NGVDM7yCdOQcneHBzLreU51LbfGIaZfX0blQroPX8flsFChuLnVEXYimitc0nfu9k7yO5r2XpXkdbfAL+ibbLH4+zIn32BnXVMQ9ezqaCs2lOVLmxEZbCicalxXlohMoH61ko600ccql/w3KSa8jN9pSOKEdnaDBKTqcoKGUOko1l5YrpG7Oo5zn0tmC1NVeJr3RlsKJdSGaiqMcNVXK/d6JmgBn6io3TZPZaLPt6Oizzz6LG2+8EWvWrMH3vve9nOuyLONf//VfsWbNGrz3ve/FmTNnbFBJIMwun/71QbslEAgEAoFAIBAIBAKBQJgithxY1TQNXV1d+NGPfoSGhgbceuutWL16NZYvX27G+dWvfgW/34/HH38cv/nNb/C1r30N3/jGN6ZWoPGK59mQs495lkInoazI9pRCIDgKDjg9quCvwxcQVzTU+wQkE/qxNp/IotrDISYnzCNqAkfBxbEISyqGwjIWVrsgKQkMhiUEfQLUhAaWZjAY0n97eAaypkFW9eOjDX4BPpHBmYtx1Hp5JJJJDIVluHkG7oiMJJLgaBpqAogpCgSGxWBYP4rJ0TQisoqwpB9XrfVwCEmaOWcrmopkkoZPZDESleESGFBJChcjMrwCC4GjUedlcDGcQEhWEVc0XFIl4GJUgZtjcSEi68cDIxJoisa50TjqvAK0hIZaD49QXMNwTEGVi8PFsKwf3/RzGJE0XIzp7dHgF8DTAMNQ+nFalkV/6lium2fQ4KPGj4X6BSyqZnJ+XwwB/mx3kMWS8Xq9gKCLmfj1eiceSUkn7T46r0qAW6BxbCCGBr8Av8CgVmCABAAa6I/pxyQvRnTbBJKIqSqoJIOQrMAncBiNKaj16Mc7Q3EVdV4e9X4Gw5EkQpKKcFzVP2RMJRGRElATGrw8Z9pHtYtDTNFwISqjysWljpxyuBiRIHIMvAJr2nWdlwcFIJbygOsTWTApt59+FwtFTSIsa7h4LoRGvwCOpnDeOA7KMvDxNAJ8+vFOAUF32m+vAIYCLkZlCByLcFwpvt8JM4sd6z27j3DO9trWjvrSwOmQgpf7B9Dg1Y/so1yXdcYRt7P6fLNklo+4WeLE5ySiqThKMTamU0+7jp0ywIlRPW2jX8DiKh7QikxLKDts2Wg7dOgQFi9ejIULFwIA3vOe9+DAgQMZG21PPvkk7rzzTgDAjTfeiK6uLiSTSVAUZZlnXjjgqZ4RbN17GHElYX548PrmwNQnnXLJk0AgEGYKDnj+xBj6RmLYeaDHnKc2tTdj1/MnwbMU/t/rlmPb3u6Ma5cERNz3+BFUiRw+cPVidO4bv97Z0YJfvXQKL50chcjR+OqGKyBrSXzh0cMZcV48NoTLLqnCfY8fych7XpUInqXxwz8cx4YrF6Fz/yuodvP45DuWIiJrps6Vi6vw3pWLMrRtW9uC3a+cQvsbGlHr4RBXEvjSb/9mXt/5vjacHUFOfe+9qRXffLIbJy/ETB0ensH/eeY4hqMytq1rwZnhGL799DG8b+Ui3P+knnZxrQt3vfsNuBhVMtpg+/pWPP7Xs1h92Tzcs+8VM/y+f3wjXu/XsHVP+j2hBd9++qhZdldHK968yIMnXh/EVZd4J7dZQgEvnBnDlt2HzPx3bGjDqgX+/Pk4/T5loc+wz+GojM6OFtT7ebTWe/DK2TAuRvS+qHbzuP3vm/Di8UG0v2Ee/vPpo3jfykV46KVT+Og1TTg6EM6wga/e2obzo/EMe9y2rgW7Xz6F1Zc1mn1uhH/nGb3PFte68Ml3LM/o503tzWisEvGjP/TiyEA4I77hhEBkaVR7eKiJJD7/8KsZbf/bV8/i+d6L2LxmRerbhAw2/eIvps3deX0z7k6zobvefRliSgJff+JI8f1OmFnsGEd2jd25VC4NPN07kjHetq9vxXVNgfLbbGOBp45atN/ygH2bbU68/xBNxVGKsTGdetqVlgGeOmaRdlmAbLZVKLYcHe3v70djY6P5u6GhAf39/Tlx5s2bBwBgWRY+nw/Dw8OTLqt3WDYNGtDfGNq69zB6h+Up6y+XPAkEAmGm6B2WcXRwfMMB0OepnQd6cMuVC7C2bb65kZV+7dhgBGvb5uP2a5eZG0zG9W17u3HbNUvN30cHI+YmW3qcW9+6yNzUSM/7+FAEiQRw2zVL0blfz/uWKxdgKCJn6LztmqU52jr362XvPNCDMyNxDEXkjOt+l2BZ3y88ehhr2+Zn6BiKyLjlygV6vvu64eY5rG2bb264AMDatvlQtGROG9y95zA+uKoJ92SFJxIwN9mMsK17uzPK3rr3MIYjSXz6V3/BYGxyTz+DMdXcZDPy27L7UMF8nH6fstJn2KdhSwxFo3dEhqKO98UtVy7AjsdexwdXNWFbqo3vf7IHa9vm40JUzrGBowPhHHvs3KfbU3qfG+FGn61tm5/TzzsP9KB3KILbr12WEz+uJHDf40cwFJHRMxBG71Akp+0//PYmM97xoQgUNZlhc3dn2dBQRDY32YywifqdMLPYMY7sGrtzqdzTISVnvN295zBOh5zwV4jJ0TuSp/1G7JvrnXj/IZqKoxRjYzr1tCvtiVHrtCdGnbGGIsw8FeWXnWEoBALujLD+syHToA3iSgL9YxLevCAwpXLKJc/ZgGHonDZ3Ik7SaWWnBhQwoU6GYQrGmej6TMW548E/gwLwrQ+8uWA+TsJJdjARdmrNN5cmkrnHm+NKAsaLxlbXEkmAooCYpFpej8njD/j58r8QlvLmHZFVIC0dReXmU6hsI59sBkPShPXNrqPxOyKroKjMtBQFRPLoGIkoOeH54maX3R+K63lIGpob/bkVyUPPyWFrLQXymeg+Nds2m22n+fSl981wVAFNqYimta/RV8OpfjB+W9kSkN9ODXvKV362TRjXE0mY4yCffVkRVxIYiSoZ8SJp48mqvHzaJ2s/paKc5uhimK116UTYtcacS+W+3D9gWeZAWMIV86tKUuZMUexcauczCdFUvppmYmzMpI3ORlqre1n/uTxpQxLeNEt948R7bCVrsmWjraGhAefPnzd/9/f3o6GhISfOuXPn0NjYCFVVEQqFUF1dXTBfTUvmeIho8AsQOTrDsEWORoNfmLKHi3LJczZwoqcQK5zkddTKTg2SwIQ6Na2wF7OJrs9UnEhcBsMwZdH/BuVir4C9XkfzzaVHB0KW81QytdFkdY2mAC0BuAXW8rqLH78NMXnyqPVaz480BXh4FklkpsvOp1DZRj45beMTcGwwnLe+VnU0fntSdcpO6xGtdQQ8XNFxs8tu8Il6HsLkxmIgT5sUymei+9Rsex3NttN8+ow2Ezka1W4OLo5Bfyizf0SORk2qH4zfgLVN5rNTN19cn1nZsSvNZqzsywqRoxFwcxnxPDybE6cY7ZO1n1JRTnN0OpOdS2d7vWfXGnMulduQ5z5V73XGOr6Qp7xsO2104DOJE5+TiKYiNU1ibEz3fl9MPWcjrdW9rNGXJ61v9vrGiffYctPkeK+jV1xxBU6cOIHTp09DlmX85je/werVqzPirF69Go888ggA4LHHHsOqVasm/302AE3VPLo6WjMWz10drfqHC6dIueRJIBAIM0VTNY9lQS82tTdnzFOb2pvx8CtnsO9gHzo7WnKuLQt6sP9QH77/7DFsW5d5vbOjBbueO27+Xhb04N6bWnPi/PpPp/RvVWXlvbTOA5oGHnjuOLat1fPe/fIZ1Hr4DJ0PPHc8R9u2tXrZm9qbsSAgos7DZ1wfi0mW9b33plbsP9SXoaPOw+PhV87o+a5rQVRWsO9gHzauHk+772AfOJrKaYPt61vxsxd6cU9WOE0BXeuz7wktGWV3dbSi2kPhP977JgRdk/u7WdDFYMeGtoz8d2xoK5iP0+9TVvoM+zRsSUsmsCTAg2PG+2L3y2ew5cZL8dMXetHZ0WL23b6Dfahx8zk2sKzem2OP29a14IHnjmf0uRFu9Nm+g305/bypvRlNdR7817PHcuKLnP6NtjoPj+Z6L5rqPDlt/+M/9JrxltZ5wLFUhs1tz7KhWg+PT92wYlL9TphZ7BhHdo3duVTuAh+XM962r2/FQh9XsjJLxZJAnvYL2DfXO/H+QzQVRynGxnTqaVfaxVXWaZdUOWMNRZh5qGQyacvnb5955hl86UtfgqZp2LBhAz75yU9i586daG1tRXt7OyRJwpYtW/Daa6+hqqoKX//6103nCflQlDxv4JTSQ6jT8ywxTtyFtsJJb7TltVMA//7b1/HFd19aMN/P7f8rvrL28ilfn6k4//rIq2AYBv/RUTgfJ1Eu9grY+0Zbobn09KiCcOrYXZ2XB5JAf1iCT2ARcOtOBQyvozxLwc3n8TrqFaAmda+jQ2EJdd5Mr6ODId17aF6vozyDJJJgaRpaAogrCngLr6MRSYObZ0yvowNj+nVF05BMUqbXUZFnQIPCcESBW2AgcpleRyUlgcYqHsMpr6MXIzLcAgsXR4OmqMJeRyMy6r0CFqZ5Hb0QllHvE8Az+pGIkagKnmUwEJJQ7eHhyfY66hOwqMbK62gSy+p9GBuLTd4AMryO8vpmyzS8js72G22Wdpqmr9EvwCPSOD4QQ71Pt6W6PF5HF1S7QCGJmKqBStIIywq8htdRN4+Yoset9eR6Ha33CaDpJMJpXkcN+wi4OMQVDRejMvwiBzWhwcXp10WOhpdnkYRu17VpXkejsgaPwIClM72ORmRNtyefAJ6h0B+S4REYuFgGPiHb6yiPoJvN+M3QVJbX0SL7fZYopzk6nanMpbO+3rPb6+hcKDflWXEgLOlzvoO8jhZ6C8PSTg2vo0b7OcnrqJOek4im4ihybEz1fu80r6N572WG19GQvq5bMsteR514jy03TZN5o822jbZSUGgDA3BmR1pRLjqB8tFKNtpmPg7ZaCstjtxoS+GEdnSCBqfocIKGUuqYip06pU1KAamb8yjnuXS2IHW1l0lvtKVwYl2IpuIoR02Vcr93oibAmbrKTZPjj44SCAQCgUAgEAgEAoFAIBAIlQbZaCMQHIRxbr8QAstM6/pMxRE5BiJXOM6XD/RMWA6BQCAQCAQCgUAgEAiVQkUdHSUQCAQCgUAgEAgEAoFAIBDsgrzRRiAQCAQCgUAgEAgEAoFAIMwAZKONQCAQCAQCgUAgEAgEAoFAmAHIRhuBQCAQCAQCgUAgEAgEAoEwA5CNNgKBQCAQCAQCgUAgEAgEAmEGYO0WMJPIsorR0Vje616vgHBYmkVFU6NcdALlo9UOncGgzzK8kJ2WS3saEL2lYza0TsVGAWe0oxM0OEWHEzSUUsdcmEsnA6mb8yjnuXS2IHW1l3w2CpTfXEo0FUc5aqqU+70TNQHO1FVumgrNpdlU1BttFEUVvM6yzCwpmR7lohMoH61O0lnITp2ksxiI3tJhp9ZymEudoAFwhg4naABmX0clzaWTgdStfCiHuXS2IHV1LuU2lxJNxVFJmoiNzgxO1FXJmipqo41AIBAIBAKBQCAQCAQCgUCwC7LRRiAQCAQCgUAgEAgEAoFAIMwAZKONQCAQCAQCgUAgEAgEAoFAmAEqyhlCXjigd1hG/9kQGvwCmqp5QLFbFIEwSQw7HpOIHRMIBEKpoIEoNPSPaugPkfmWUALIupRAmDw0cDqk4OX+ATR4BSzwcUDCZk1kLNsHaXuCw6n8jTYOeKpnBFv3HkZcSUDkaHR1tOL65gAZjITygdgxgUAglB4a6I8r+OvZMLbu7SbzLWHmIfdzAmHy0MDTvSO4e8/4uNm+vhXXNQXs22wjY9k+SNsTyoCKPzraOyybgxAA4koCW/ceRu+wbLMyAqF4iB0TCARC6TkdUhCVkuYmG0DmW8LMQu7nBMLkOR1SzE02QB83d+85jNMh+3ZVyFi2D9L2hHKg4jfa+sckcxAaxJUE+sckmxQRCJNnRu2YA3rDMl44G0JvWAa4GRJJIBAIZU5/KI7+UNxyvr0QJusGwvQh61ICYfLkm5f7Q3GbFJGxbCek7QnlQMUfHZ0fECFydMZgFDkal1SJNqoiECZHg1+wtOMGvzC5jMir1gQCgZCXRp8IJZm0nG8FjtH/MEHmSsI0IOtSAmHyNPqsx02Dz75xQ8ayfZC2J5QDFf9GGwVgU3szRE6vqsjR2NTeDJqyVxeBMBmaqnl0dbRm2HFXR6v+4c9JQF61JhAIhPws8HFw8xS+ePMVGfPtxtXN6NzXTeZKwrQh61ICYfIs8HHYvj5zHbx9fSsW+uw7lkHGsn2QtieUAxX/RtuZkTh2PX8SH3v7UlAUkEwCu54/iQXVbixwkzNzhDJBAa5vDmDXR6+altfRQq9aN3knt2lHIBAIFUcCaBA5nBXYjHXDT144iXOjcTJXEqYNWZcSCFMgAVzXFMDPP3Y1BsIS6r2Cvslmo9dRMpbtg7Q9oRyo+I22Br+A4aiMbz911Ayb0pE7AsFuFKDJy48/5E3h+NKMHUElEAiESiUBBNwcfvCH42SuJMw4ZF1KIEyRBLDQw+GK+VUYGYnauskGkLFsJ6TtCeVAxW+0GUfusr9JNZW3gTLg9GN403m7iECYcSawy5KNBwKBQKggrObK7etbIbA0NE7DkfMS6jwCgi4GSIKsCQhF01TNY+f73wRFTSIiqfCILDiGIjZDIEwEBQzGVPScHEZAYMfnX5sgY9k+SNsTyoGK32ibqSN3GZAPyhOcSDF2WYrxQCAQCJVGaq78yUevwtnROI4NhnHf40cwHJXR1dGCSxtE3PK957FjQxtWLfGTNQFhUoxG1Rx7IRAIBaCAF86MYcvuQ+a42bGhDasW+G3dbCNj2T5I2xOcTsU7QwBgHrl71+UN+rG7aS58yQflCU6kaLtMjYdVl/hmZDwQCARCRaLoz2//tvsQ7j9wFOdG46l5tRuSyiCuJLBl9yGyJiBMCmIvBMLkGYyp5iYbAHP+HYyptmkiY9k+SNsTyoG5sdE2wxT6oDyBYBfELgkEAmFmyTuvhuLj/0/mXsIkIPZCIEyeoYhsOW6GIvZtrJCxbB+k7QnlANlomwLGB+XTIR9gJNgNsUsCgUCYWfLOqz5x/P/J3EuYBMReCITJU+exHjd1Hvu8QJOxbB+k7QnlANlomwLGR5KNAZ7xQXkCwSaIXRIIBMLMYj2vtkBgNfMbQWTuJUwGYi8EwuQJuhjs2NCWMW52bGhD0GXf58bJWLYP0vaEcmDWZydN07BhwwY0NDTgu9/9Lk6fPo3NmzdjZGQELS0t+OpXvwqe5yHLMj772c+iu7sbgUAAX//617FgwYLZlmsN+aA8wYkQuyQQCISZxWJeXVTN4Mh5CQ9/fJX+kEfmXsJkIPZCIEyeJLBqgR8Pf3wVRiQNAYHR518bHSGQsWwjpO0JZcCsv9G2a9cuLFu2zPz9ta99DR/+8Ifx+OOPw+/349e//jUA4Fe/+hX8fj8ef/xxfPjDH8bXvva12ZZaGPJBeYITIXZJIBAIM0vWvMooDN5Q60ZQTHvII3MvYTLMsJMuAmFOkASCIou3Lq7OnH/thIxl+yBtT3A4s7rRdv78eTz99NO49dZbAQDJZBIvvPACbrzxRgDAzTffjAMHDgAAnnzySdx8880AgBtvvBHPP/88kkknzKgEAoFAIBAIBAKBQCAQCARCLrN6dPRLX/oStmzZgkgkAgAYHh6G3+8Hy+oyGhsb0d/fDwDo7+/HvHnzdJEsC5/Ph+HhYdTU1OTNn2EoBALuAtfpgtedQrnoBMpHq5N0FrJTJ+ksBqK3dNiptRzmUidocIoOJ2iwQ0clzaWTgdStfCiHuXS2IHV1LuU2lxJNxVFJmoiNzgxO1FXJmmZto+2pp55CTU0NWltb8eKLL5akDE1LYmQkmvd6IOAueN0plItOoHy02qEzGPRZhhey03JpTwOit3TMhtap2CjgjHZ0ggan6HCChlLqmAtz6WQgdXMe5TyXzhakrvaSz0aB8ptLiabiKEdNlXK/d6ImwJm6yk1Tobk0m1nbaHvllVfw5JNP4tlnn4UkSQiHw/jiF7+IsbExqKoKlmVx/vx5NDQ0AAAaGhpw7tw5NDY2QlVVhEIhVFdXz5Zce+CA3mEZ/WdD5KOOcw3S9wQCgTAzUMDJi1GcHY6iziMg6GJm9ls+xnyd/gFmFRiMqajxUTg1rJGPMxMKY8c938puiW0SygknrpWdqGmuQNqe4HBmbaPt05/+ND796U8DAF588UX88Ic/xH/8x39g48aNeOyxx/Ce97wHjzzyCFavXg0AWL16NR555BG8+c1vxmOPPYZVq1aBoqjZkjv7cMBTPSPYuvcw4krCdFN8fXOATBqVDul7AoFAmBko4IUzY9iy+5A5n+7Y0IZVC/wzs9mWZ75+40IvanwUnu0Zw9a93WQuJ+THjns+WWcQyh0n2rATNc0VSNsTyoBZ9zqazZYtW/CjH/0Ia9aswcjICN773vcCAG699VaMjIxgzZo1+NGPfoTPfOYzNivNggZORxS8dD6EMxFl2i3ZOyybkwUAxJUEtu49jN5heQbEEpzMjPb9DNslgUAglBODMdXcZAP0+XTL7kMYjKmTz4wCBuMqXrsQxaCk4aKsofei9Xw9Gk3g1EXN3GRLv0bu44R07FjvkTUmYdKk5r8/nRzGYFwDbH7XwYk27ERNcwVb2z59beCAsUFwLrPqDMHg6quvxtVXXw0AWLhwIX7961/nxBEEAffff//MFDjTr5bSwNO9I7h7z/gu+vb1rbiuKQAkppZl/5hkThYGcSWB/jFJd1lMqFhmrO9p4PhoDIkkDUVLIq4lcHw0hqVVrinbJYFAIJQTQxHZcj4disgIipNY8li8GbepvRkLa9zW83Uobv5/zjVyHyekYcd6j6wxCZOi1G8GTwEn2rATNc0VbGt7B44NgnOp/PddUq+W3vbDP+J/P/hn3PbDP+KpnhGAm3qWp0OKuckG6AP77j2HcTo09d27Br8AkcvsDpGj0eAXpi6UUBbMVN+HNA0nL8TxkR//CRt/8Rd85Md/wskLcYQ0bSblEggEgmOp81jPp3WeyS28rd6M23mgB/U+6/wb/SLmVYl5rpH7OGGcRhvWe/Oq8tktsU1CLjP6ZvAMkW/c2GnDZFzZh11t78SxQXAuFb/RVopXS/tD8YJ/0Z4KTdU8ujpazUnDOGveVE3+IlLpzFTfD4Wtji11YyictdFGXnkmEAgVStDFYMeGtoz5dMeGNgRdk3uBP9+bcTFZxbZ1LRn5b1vXApGlwTP6W2/p1za1N0NgK36pRZgEbp5BZ0emDXV2tMDDMyUrk8tjmzyxTYIFhd4MtguRpfPOvXZB5nz7EFjrts/efJtpnDg2CM7FlqOjs0kpXi1t9Ol/tU7PV+RoNPjEqQtVgOubA9j10auIR6i5xgz1fX8oj62HJDT5UrZOXnkmEAiVTBJYtcCP/Xe+PeV1lNc32SY5vxlvxmXf50FRePDFk/jqrW9ETFbh4ln817PH8P9cuxQAhV3Pn8TH3r4UFAUkk8Cu509iQbUb8y4p3h08obI5PhTF7149h+/+01swElUQcHP48R964eZZ1JfITk5djOW1zUuIbRKyyDf/TfbN4JnkxMVYnrl3GRptsuGTBcYVmfNLy4kL+du+lPbgxLFBcC4Vv9FmHMvL2RSbxqulC3wctq9vzflG20IfN71vYSlAk5fHmxcEMDISJZtsc4kZ6PtibD3fK88Pf3zV5L5fRCAQCE4lCSyucaOKHv89WYw347K/0eYTWBwZCGPjg38244ocjTqvAIamMByV8e2njmZcI5+AIKTT4BfwfO9FPHVkyAwTORqfvH55ScsktkkoFqv5z3wz2KY/yjb4Bcu5104bJuPKPuxqeyeODYJzqfh3WxdVM+jKekW/q6MFi6qn8Yp+AriuKYCff+xq/J8Pvhk//9jV03KEQCDMBMUcQSWvPBMIBEIRpN6Me/jjq7Drwyvx8MdXYXVzHWrctMU824Jf/elUnvUG+QQEIROOTloeHeXo0j2lkc+TECZF2vz389uvxsMfX2X7yQcn2nBJnjEJRWFb21usDeweGwTnUvGvsFwMAdc2+7HrI1ehPxRHg1/EomoGF0NJBKdx0hMJYKGHw0IPZ/6eNjPtHZVQWRj2ke94aRFHUOs8Ar60/lIsb6g2x8PR88PklWcCgTD3oPS3fIciMuo8AoIuJnOxzABhNYFRSYPfk0RCTeLIUBzN9R78/ParcX5Mgk9kEZNVvHx6BL1Dir7eIJ+AIBRAoFlwlJaxLj1zYQwCXcIHxLn4eZKJxjehMEkgKLJobvTrJy3sbjsH2nDJnjEJE2Jr26fGhnkSyO6xQXAsFb/RFvQxeKpnxHSIYPwF5PrmgLMWGCnvqI7XSbCHYuyDBp4+OpJzpDn9bcugjwHLCrjtR3/MyCfoY4mdEQiEucNE36tkgaeOZs65m9qb8dtXz+HdV8zDzgM9GeGffucKfPm3f8ORgXBmPmReJWQR9DE4fJ6d3fvwXFtjku/RVh4OtOGyecasQEjbE8qBij86WgqvowBm3HNjyXQSKoJi7ON0SDE32Yw4d+85jNMhZVL5FEXK/v90cph4LiUQCGVHvu9VDsZUAEDvSO5cufNAD26/dpm5yZYefnQggr9fUZ+TD4GQjR3rvbm2xpxofBPKDyfasBM1zRVI2xPKgYp/o60UXkdL8ZeykuhM00teny9virGP/lA8j9fRuHnEeUbsbKbsn9glgUCwiULfqwyKbN65MplMmF7OAGD3y2dwbjSORBJmWHo+BEI2JV3vOahMOxmKyKh287jlygUZY5WMy/LFiTbcPyZZ2lmljisnQdqeUA5U/N2mwS9gca0La9vmmwNx38G+aXklGYypeOMiT8a58MYqBoMRdco38FJ4RwVAXp+vEIqx40afaB3HJ04qn4kYjKm4YkGm/df7GAzGJmH/xC4JBIKN1Hms77l1Hh6grO/Ji2tdkNUkfvCH4+a89cWbr8CLxwZAU0DQI5j5zAsI6A3LGAhJqPcJ0DQNVQJP/qBAKN16z2FlmqT+qNZzchgBgZ2VMdDgE3Db2xbnHPFu8BFvkOWKrTach/kBEXdcvwz3/uY1086+8J43YH6AfKCt1MwPiPjI3y3BfY8fMdt+85oVpO0JjoJKJpMVs+RTFE3/YGc6HDAUUxCKJc1NAZ9Ioc7FTfkMt8ZpeLZnDFv3dqedC2/Btc1+MMrUPmYbZzQ8fyw3z7ct80PUpv6B3MG4ilu+90LOjenhj6+akb/qBQLu3DZ3IHboDAZ9luGWdppaiI5ImvVCtJhvUzBANKmhf1Qzbb3Bz8BNMYCmR4lQGv7Ym2tnVzf54U6m7IzWj6H2h+Jo9IlY4OMynH3MhP0XbZcz/NZbudgrMDtaJ2WjaTihHZ2gwSk6nKChlDomO5delDQMSxrCcQWX+EUERYt5I99m/0I/Xjg9hq89/jret3IR7n9y/EH96//4Jnzql3/Jmbf+84NXgkICY3ENVy/x4tSwhv4xfQ4+NTSGL+z9GzaubsZDL53CZ9ZcihVBD/pD0pTnNKf0dyko17pNxkY1TsMfT0RwdDCMRBJgKGBZ0IurlnimvIackBKshYvCpj+qlXrtOxFOtON8NgoUvuc7pi4c8PzJMRwdSBs39V68bbHftm9yDcgKNnznxRw72/2Jq1HPc/aISsMxfZfGRJqKnUttbfsink2c2PaAM3WVm6ZCc2k2Ff9GWzyh4dUzYesNLExtQXPqombmBxjnwrux6yNXock3tTyHxhJYUM2NvyXkE6EmZAyOJbDQM/WF10THYwgOoIiF6Ik83yL4yUevwpLUK9IareHFfBtgqc3a/jHV0nYf+MhbsdTH6A4Vegs7VDg1nMf+P3oVmrzF2WpRdkneeiMQCJOBAl45H8bxoUjGmyyW80YSWLXAj4c/viq1WOYRdLEYjI5/2+knL5zEx96+FAwNvKHRj4isWs5bfzk9grcvr4NHUPBsTyjrDyItuLfjMnxh79/wsbcvxZbdh/Dxa5fi/gNHyZw2Rzk7msD5sTi+9+z4m5Gb16zA2VEXFrpL53n01TMR6z/WlZDBuPW30h7+l1UICqVbg5K1b+VxLqQiJmsZ46azowXnQirm2dSnZ0es7ezsiIz6evs32ioZ29qePJsQJkHFO0M4N2K9KXBuRJtynv2hPN8JCElTzjOuqOjpj+MPx4bwen8Y/3NsCD39cUjK9D7c6hM5iFxmN4scDa9IbgBOoZiP9ub9NkWazeXbAD51cdzWw3HrB8VwXC+rGIcKhb6TUSzGsa10zGNbKcjHjAkEwmQYjKnoPjeW46wg77yRBIIiizfUuvWH72TmA/q50Ti+/dRR3H/gKLrPjeHEhajlvKUlgAthGUhyFn8Q6caiWj/iSgIUpYclUotxMqfNTUKSah53AnQ7uO/xIwhJpbMDuz4cfmbUer3QNzr19XIxFLPGIJQXEVnDtqw17ra93YjIU3+emy4unrG0Mxdfug1zgo5dbU+eTQiToeI32kqxKWZ8JyCd6X4noMbNwyMyWFHvw8KAC80NPnhEBjXu6S0K4oqKjaubTb0iR2Pj6uZpb+ARZo5Cf3k1aMxnc2nfGynG1uf5Rct85qVstz8Uxxfe3YxffnwVvvmBN+GX/7IKX3h3M/pDcTN+0fZPA6cjCl46H8KZiJIx2wRdDHZsaMuwyx0b2hB0jf9Vsph2IRAIBIOhiIxEEsXPG2new89EFBwdiWX8cWpelYg7rl+Oje3LsXJxAG9aUIUv3nxFzv10/6E+eEW2oEMakaORTML8d0JthIolrmiodvO44/rluHO1/l+1m4ckJyZOPEVm4g9kU8HFWT8MiyV+GC5mjUEoLy6ErdeEF2ycPxka2NSe+Yy1qb0ZdMU/XduPXW1Pnk0Ik6Hi7zil+HhmUzWPro7WnFfwm6r5KX8ngKYBLQF85tcHM16Jnu6EUSXyeOilU6aXtGQSeOilU7hu+ZunlzFhxij4Ue4Ui6usbW5JFW9+f21elXU+jWm2zjHAVze04bNprzx/dUMbOEb3jHD5JW68eFzFbT/6Y8bRp8svcZtHR4uy/4mOoOY5tpX+2nUx7UIgEAgGdR4BfoHBxvbl5ltju18+g+GonDtvWBz/+Py7LgNFAdvXt+JbT/WY32hbUe/FgoALW/d2Y0W9F1//xzfhtfNj0BL6/fT9b10EL89AYEXr9YZPNL/Rtqm9GbueP5lxncxpc4sFeT7ifUl15TlD8AkMNq9ZkVNXb6nf+ClijUEoL+ZVWTv8avTb9/F7F8fAwzP4+LVLkUgCNAV4eAYulrzRVmrsanvybEKYDP8/e28eJkV5dg+f6uqq3pfZZ5gNhhkQZlhkNxoXUKMJm4DGaCAC/sibqBiNxsQoCBrfuMREo0aJikDivsv3xmgQJEaIAi6ACzMsA4PD7L0vtXR/f3R3ddd01XRP9xQ9DHWuyyuhp7vq6epnuZ/7Ofc5Qz7RVpVHSiYFqvLIjJNih3sYPLatUZS8emxbI4YXTsjYUrgnwOPxbU3CNQHg8W1NeGDRBNjT1L2SQpGBxC0XjU6qJVcDjsGD2Mlrn78RD1xQa8fGZdPQ5gqi1KrDcDsNJBAT9drIaU5vly1DwmmyxRD538SFSXidBdpcaeivscAFdfG2lFh1SUlmuRLU55ZPR6UpWrYcLdsS9FJ69ce0nosKFSpURFFkJJFv1uGhN/aJ5sCaQlPSvNG7/CPPSMPH8nh4SyPyjDTuXzge/2/TLgTYEG6YWYcbXvgUATaEL467sHbzl7h8SgWGFxgxb2I57AYtNASBqjyNZLxRaidxZsiK82vPxDFnAD2+yMm35Jw2wAYwKgYfglxYsnR0SvV0QKG9WrGFxNq59UkarsUWZfsXy/OosBvw4KIJ8DIcTLQWGgLgeB7AyZQwIU7ivVQoAZtBg5+fXyuUj8YICXZD7uhjpEa6X2nJHPe3HDj9nmxQMs+Y1qb57DNca9W9iYr+YMgn2o5289JJsYIJGRsXtLuDaO7y47GtTaLXO9zBjBNt7gCLX148CnYDjW4vi3wThVElJniCLJDhNQGop3qnAhJ+o4jrKJn8GxHAzua+xTcPd/mxcUezqK9v3NGMijwjSoZFHFKO9vACmy0GPaUREmnt7iBGFZtx7bkj4Q9yMOq0+Ov2g+K+TQA7j/Tdlr5KqIREWz+ei9p3VahQkQodPg53vCFO8D+8pRGvrZiRNG/0Lv9YMKlCpO3W6Y2X2nGhsOi9rc4AHtnShL9cPQl8CHjk/YO4Z349/nPQA5ZlsGHpNLRHTY1G5EcOIWptkVOOIoNZfk5TRZZPC7TLyDy0u4OoNCqTfPqq1Q+H1y8y3PrsaCe+aqUwpdSsyD0BgNZq0dztTDoALLcXKHZPAOpYGoLo8HCSGm3rr5kKmzU3DLIjXX785YNDWDCpAgQRqUz6yweHcPv3x6B0WPrOhAOK06TvH+sJSD771XPGorgkxZyWzTNS9yYq+oEhn2hrk0mKtbmDGGHJLIFVbJGmjRaZM6fgl9n02HvchV+9slt0UlNbNACU6BTMIRWDANHfqK7UGrET7vUbyYlvJlrVF1t16PExor6upzQoTtRx60OnZYSZxvB8I340vRq/SihhXj2nHlX5xn61pdQiX0KVyXNR+64KFSpSoT9Og73LP2JGBUAk6Xak0ws9pcGoYjOMOlJyPjPQJB7bGnEPNdJaPPJ+NFH3/mHhPYnzIoA+5zSpuXXDjsMovWQMOj1BlFr0qLCoRkanOoy0dH8yKlhOadZr8dCWQwiw4vjgr0umKHZPIGL80Nuc5OEtjRhXbgMU1EtLJ05RcWqhW2Z+7/YygMIl0HIokYm7E/WTTzZOl75vpEnJZ5/OPDqwz0hly6qQx9AZcTIoseoka/qz0aVgOA6rZo/BCVcQoTBAEpH7MDyHTHn/7oC0m86GpdNQRKm1/qc70tlA5htJ3DO/QWB06CkN7pnfgHxTvP+kGg8+hscTH4hLmJ/4oAkPLJwA6Mi021JhoXD3vIYkjbZKCyVovalQoULFQCJmZNA7gSHlst27/GPhmSU4t7Ywwvax6vFZcyfunlePijwjur0MHrv6TBw44YGX4UESQIGJBsdH5rY7Z4+VdXSWSvLJoffcOr7cioWTqrD4mY9F8+ichtxpEqnIHmadFmvnjoWRpuANcjDptfAFWZh1yoXkVp1WUlrCquA9AcAtMy6UdFgF+pd0V3FqoEiO5JDDpJaOJPDnH52JIBsSxrJOq4Eu3fJFBXC69H2TzDxqolN/x6ye0WnCGFQxMBg6I04GVXkkbphZl5R8yEajrcSqQ6uTwbrth0TBbzbJu75KCWoyZN6pGDooNOkwpdqGJd+pEUo6N3x0SCS+adEBpTYa66+Zig53EEUWHfgwD4sOQl+vyiNx3fm1STotsfHgCrKCAHjs7ytn1sEVZIFoP0xLCDQEnD/CjueWTxfKVNQkmwoVKpREzGW79/wVcdnulWxLKP/IsxD4d6NbpK12/6LxCLIhLH32E+QZaSw5q1qUpLj3snEIhYGNy6bBSBP48luPwICLld6b9FqU2dOPC3rPrdeeO1JgF0e+X0Trsq7YjHLVPfGUBakJAYRGZH61dl4DSI1yuzQmxOG7dXmYXDVNSCYb6ciYUXIrUGShpZMjCguHq4LlQw9+hsNDl09AKAwhsaIB4GczJzlkC6tBg2/aWaxKOFReO69hYKqRMsTp0vcpTVhyHqW0qefRbJ7R6cIYVDEwGPI94ltnCH9+v1HE0Pnz+42oKZyISmNmTLEeH49Ht4qv+ejWxqyMC3LlCKXi1ECRicQPp4pLOu+e14Aik1ZwHe3xASwfRogIIRQGWD4EPhxGjw8ojO4xj3bLmB0snYYRFhJWPRUvf4r+/ZH3G7Fx2bR4W9IVAg0BlSYqrsmmJtlUqFChIGyGfrpsR8s4exscAcAJhx8P/SsyF/bWbwuwIdz++l4sP6cGT394CGvnNmB8pRV/uHw8XAEeXxztxIX15ehwB9Dj52Hv62CPihgsxYxltv3yLJz/hx0IsCH4GWk20AlXAOUG5XS1VCgLhiPw0ifNuH/RBNHB2W2XjFHsnkVmCh8fdiUdsk0boSwLIxwK4a459bjr7fh975pTj3BY2YBAFSwfeigw6dDqdCX1pcoEaZOTjU4PLzmWRxSMgTkLI7tsUGQkJStKiozaIRWHBznIz6Mpts7ZzA+nC2NQxcBgyPcIV0CGoRNggQxFZ/u8ZsbGBSGsmVuf5KYDYgBmRS1w2MHIulWqGPxokXHxfP7a6aiI9uMwgHYXk9SHEnXR2mSYkzHNwg6Zv4vMENIxbwAAEjjijPe7ahstJAUBqO56KlSoGFCQBHDl1Kqk8jg5Z7gYpNb0O2ePRZ6RRqszINJviyHAhoTXV721DxuXTkORRY9Pm49jVKkd16z/OCGh0YAL6uzJyTYK2NroSHIp3fbLs3DgRBBGnVbyAK7UqpaOnsrwsRwWTqoSa6HOrodPQWZOu5yjePSQTSloNSRe2X00shlmOBhoLTZ+dAi3XzpWsXsCUAXLhyB8LC8k2YBIH77r7f2Rg2BdbpJacmM5lyy7Dh8nIoOEwxEySH3JmUMqEZTVPJrF/HC6MAZVDAxy54l8kqDTkpIMHZ0280lZjvVj1WUuUszywO4jnXjmmql45EcTsf6aqdh9pBNstgkxLbC1yYElz3yMG57/FIuf+RhbmxynQYp1aKGvBFkMcjp/7kA8uzXMFjEpSISe0qDMFtm4xTQwev89yegjygSZWp0XWbglkmxbDzqw+JmPcf3zn+LHz3yMrQcdQGzYRTUOFqzbiSXP7sKCdTuws8WlnKaoBjjmZbHlm3a0eNnTYOZToeL0Q5s7KDgvXz+zFjfOqgMfCuNQtw8dAV52fpFa09dtP4jffn8Mrp9Zi9ElFlQXGESf0VMahKPzXmwubncHcWF9uURCYx8O9zBJ9z3cwwhJtsT3Hu3hMabAiGorjbvnNQhzcoyZcEaxymY7lWHQarFms7iPrNm8HwatcoFZOjGEEgiwHJbMqIbdoIVOS8Ju0GLJjOpoObfCiMYpYwqM0nGKilMKfZl55QoGSnos6xUcy6nQ6WUEE8BH32/CY1ub0NzlR6c3eQ06lZH1PEoAAT4ML8sjyIfT3n/E2HCJ67LAhlOhoheGfK9w+lnJidnlZzN2qen0SE/2nd7MnUzzDBQmDy/Esmc/EbGR7IbsHMYOO6QD+Y3LpsUZSpkgykZqbO6BXadV2UgKwyLDbLAkCBnL9ktPXOePJICbLxqFh947IPSzmy8ahZhua1UeibVzG5IYFlX5/dM0POKU7neblk3DcDOdvsbBQLDeNMC2w44kGv35I+xDikavQsXpjkJT3AGuzKbH4hnVeHRrU0rB4t5M3jKbHj+cUiXSflk9px5PfBDZsMRY7Jt2NgOIzMU2gxYEQaDDHZDdDPZec1O5QMtpXZKkelJwKqNDZq3uyCKGTIUymzQLo1RheZJiC43jriBWbNot0kkuVrWHVfQTg1FiR7YKxKPcWE6F04VxldU8qgF2tLjQ1O5BKAx8fcKNkcVmnFVhTb0vUNmyKvqBIZ9oK7bocMXkMsyfVIXOqED863uOorA3Q6cfKDTLTGJZXJPlQ5JspBf/33TEaUD9R8pAPhOojisnHYUmSjJBVphQ/pyOI1Obh8HYUhM2Lo0LIjMsizYPg3IjBZInMa484e8WPSx6AiTfvz4o2+/cQQw30+lpHAxQPzsmU3b73PLpcf04FSpUnPJI1F1ZMKkiiaV266tfYP1PpsCmo0RJ+7Io0zf2XqnPrnl7P/54xUT4WR51xUb4mDAq8w0otugBgscTWw/DHWTxq0vGpL0ZTGvjqGpdDjkUWaTdv5OY4wMIo04j6Tpq0iubtHUFQ4IZGRAZS3e8EZG9sGaok5w2NJH1v80dQKlFjwrVkOmUBqUJS0rsUAqaiKSC7FjOoRPq6aJPmM08+q2Xw/Eev8jU8MZZdfg2z4hh6TDTomxZYb8yhJ6rioHFSUu0BYNBXH311WAYBjzP43vf+x5WrlyJY8eO4eabb4bD4UB9fT3uv/9+0DQNhmHwq1/9Cvv374fdbscf//hHVFRU9Pu+Fr0GU4YXiZhia+c2wGrIPLgIh3msnl0vUFZjdeHhMJ/6wzJocweRZ6SxYFKFMGG8ursFbW4Gw7JgtZUqcAKkOq6cfOTrScwdX4CJFfEEWVUeCTJECrpnHC/dLzk+3i9HFOjx2TE3Vr31qWg8TKy0AIhoOyxY99+k/iLHNJNjNMr2u2jwkc6JW4efw4Ydh5OETkfOru9XP2uTY5i4A2qiTYWKoYSEk+ajTulx/59D3Xjq34dESXsCwO2XnoFOL4NQGKjKM8gy4asK9Piq1ZvE+r1hVg2OO1hoNWE8/ZOpaOnxwUhH5qwrplRjRB6dxAouMJKSG8cCpRMQKnIKigxLun9TpHK7tWPdAbQ7fXh26TR0uAMotujxzt7jaOk2o6j45JestrsZQV9WEahM9iEHNkQIEjudniCKzBHixIjC6py1SauRHsvaHCb/EAZmVFrx3PLpaPcEUWLWDckkczbP3hVgkwyOHt7SiIZh1vQSbSpUpImTVn9A0zQ2bNiAt956C2+88Qb+/e9/47PPPsODDz6Ia665Bu+99x6sViteeeUVAMDLL78Mq9WK9957D9dccw0efPDBjO7b4+MlS9h6fJknxUgNiSe2Nwk6MMvPqcET25ugITIPjm1GCkvPHo6nPzyER99vwlP/PoSlZw+HzZjdgB9up7F2rljjZe3cBoywZ04h7ouNpEIZ8Boe2xtdWLL+Y9zw/GdY8szH2N7oAq+J92ONRiPdLzXxYe7wS48Hhz9ynbR+WwLY1+nFoW4/jnX7cKjHh32dXpG+QbVNut8Nt0X6XToaB94EodPbXtuLW1/5HAsnVcHbT22XUou0Ll2iSYQKFSqGCKInzbVFZslxHw7HD4c6/JG5pNPLwM+GsG57ZP097vBLfva40w+Gh+QcyvEaOH1BNLb7sHzDJ7jt1cicdcXUamkjBADHe4J4Z28rnlw8GQ9fORFPLp6Md/a24nhP7jSHVCgPlickjQlYXimRUqDcrseYYRGTjhue/ww/Wf8xxgyzY5hdWeaNkSYlx5KeVnb7IcdkP+buhwaGikEFWhvG1BER4sTK5z/D0mc/wdQRRdBpc5fU4kLSY5kLKTeWU4IAdh5z4aqn/4v/+dse/Ojp/2LnMQU1kHOEbJ59gA1J7nV6v6ZCRbY4aWlbgiBgMpkAABzHgeM4EASBnTt34g9/+AMA4LLLLsOjjz6Kq666Cu+//z6uv/56AMD3vvc9rF27FuFwGATRv5miXaaErd2deelkt5cVhCYT0eNjMTzDa5KERigLjLXxofcO4G/Lp2d0PQEccEGtHRuXTUObK4gSqy6SZMtCh/Z0qf8fTDjandoxzOXnJPuly88BUSZZqvFg0VP46+LxsBmMAnPO6fPBrI+fPDtYHu3uZHdTh00Pe8xkhAcuGGnHpmXT0OYOosSiiyTZYnnBdDQOCI2k0OmmZdP69ewqLJSk1XnlEDzhU6FCRQSVdkNS+UyirlpiqbrNQOGP/4qvvy/tapEss9u4oxk1hWZZhqzdqMOzHx1KYuGOKDBKxgYWPYUdh7ux9UCn8Jqe0uCGmXXJXyhBr3JYCLCRUMtVTlEoIumRAj4mhMe2NQlOhADw2LYmPLBogqLmiFa9VnIsWXXKbj9aXdKM1hMulcmeNgaZFjPLEZLJ0/7GhAMJubHcruBYToXTpeoom2df3ksuAoisveW2NA8eyIgWdZsriFKrDtWJ+xsVKhJwUkccz/NYsGABjh49iquuugqVlZWwWq3QRh1CSktL0dbWBgBoa2tDWVlZpJFaLSwWC3p6epCfn9+vexZbpWu4i7Oon9dTGskBqqMyP6GTFdR0B1GdbVDAASPMdHziydLsqchA4i9XnwlvMARvkINJr4WJ1gy5+v+TihQBTV+OYTHRTwOlkezrhoST4wILLdl3C6JJ0jK7Bo0dwA0vfCwqixpXrhEYGT1+aXfTjUunwW5JYHWGAZIgQBKAliCS+0YKjYO+zB36ldBOEBRv9wRRbNapSTYVKoY4NBpCSOa3uoP4/LgTm3Y2o9UZACA+HOq9/rY6A9i4oxnrFk+G08/BRJP47Rv70OoMIN9EyWhh6uHwBvHjGcPR1O5GKBwxn/nxjOFwB1nAQifNcVwoudx/zdx6EL0nJ1UXdUihRCYuLVFQ16nHx+KHU6oE7cFY4tnhYwEFEwIaDQETTWLFuTUIhQENAZhoEiSpLL1GTrM2Gy3l0wqDcM7p8MhUXHiYjEkO2UJuj5lLjba0NJCHALJ59kV6Eg//cCL2t7qEtXpsmTU9d2IS2HrQkSQfccFIu5psU5GEkzriSJLEm2++CZfLheuuuw6HDh0a4OsTsNuNotdMDC9Zw22iyaT3pguzj5UUprfotBlf0+IISLtK6jO/plLg+RA6jyRrX1iG6wetGxpJagbNc+zdT0OhMP71TQd++fJnwvP8w+UTceHoImg0kdWjxMNIB+ZWnXCtPIbHz8+vTWKa5Rlp4T1WlsfqOfVY83aCjtuceqGffdrikHWpPbMiouPWdcIjuYh3+RicWWkHEOkjm/efEESQY05js+tL0+4jwwK85Jgosxky+i3t1kg/4PlTI8OWyz4rNZeK/5778TQY2jBY2jEY2pCLdkj101AojGMOP5qO9qDIrMPIYgtGFlsQ4MLo8UVK4GNz7MhiCzQaAkUeJmmu6fExMNIktBoCXV4WV0ypwEu7WrD+w8NJumpr59bjn3uP4wcTyvHJkZ4kgeUymx1OHqjONwrta3cHQWtJvLrnqMAyCoeBx7c1Yc3cBowtMwjzf3O3T5KhsPn6c1Cdn/vffSAxWPryQEGqj9p4HtedX5e0UbMZM49LUyHPwyQZfDzyfiM2Lpum6PPed7ATf/ngkKA/zIeAv3xwCPde1oAxJVbF7htyBSQ1a0Ph0EnpX6daP+7dTwfjnGMbhPskGyc9lq0G5cZyKgwLQfI5DcszDoo+menY6N1Hs3n2oVAY3Ldu0Vr9h8snwGqJr7ty+Py4E49ta+zFDm5ETdFETCi3Zf09lcZgbNdQblNOUttWqxXTp0/HZ599BpfLBY7joNVqceLECZSUlAAASkpK0NraitLSUnAcB7fbjby8vD6vy/NhOBw+0WsOPytJl7/3snFJ700Xw2waVOcb8eCiCfAyHEy0FhRJYJhNk/E19ZRGJMZMEkCBiYaByvyaSuGYV1r7YmShadBS8u1240l/jkVFFsnXe/fTjgCHUSU6kRNonpHAwXa3cPpUlUdKLihVeaRwLW+Qx+O9+vrj25pw/8IJwnu0GqDURmPd4sno8bLIM1HgwyFoScDh8PVZ0hK7hkWvlVzEzTqt8J4WHyvpNDay0JS2APIwIylZ8llu0mb8W+aiH2SKk9HWdPtobwyG5zgY2jBY2jEY2qBkO9Lup32wMKYNMyeVqrtcfgDAiLyIpuRj2xoxe3w5SA0wuSoP7a4gbn7586Ty0SJLJzYsnYb2qDPzgRM9WL/jGC4YU4oXPjkqmoNf+OQo6odZ8W2PDzYyuX2xctYY0w4A9hztQbGREub/b3t8kvPytz0+2Abn2VbGGCx9ub/oz1zq9PGSG7UHFk6Ag1Tmu3fJMMS7vEFFn3e+iUKPjxFJWugpDfKNlKL3teoovLrnm0gZN8PBQGux8aNDWDu7/qT0r8HYj+X6KJDcTwfjnGOgScl9kpEmc/asu7ys5Fi+97JxcGhz0yYbCUm2li0a5+caqcZGunNpNs++I8Dh/n9+Lfrs/f/8GiPyDClZf11eRpId3O1lRO0bjHMAMDjbdaq1qa+5tDdOWqKtu7sbWq0WVqsVgUAAH330Ef7f//t/mD59Ov75z3/iBz/4AV5//XXMnDkTADBz5ky8/vrrOPPMM/HPf/4TM2bM6Lc+GwAEOV5yQAS5zPmdJ1xhHOr0JmlOlFj1KDdkdk2K1EBHkaLs+pq59dAOQoaY6uI4sMi3ENje6ElKop1bZxHKNSMabRJMswSNNmdAujTEFYiWLQHwBUNobPMmsTHtehowkihJ4RYKAGYdKam5YqbjZaN9lbqm7TSWUPLZFt3QqiWfKlSokEMqbRrZUnUWuKDODi05Cre/vlc0r+UZabRGHUwf3tKIP10xEe4gh5+s/1i0Vo8vt4LhQ5JzMMOHMMysk2zfI+9HNgqxRISe0oAPQVTmo+qiDi14WOm12seyUEowTbaU0qRsiVs4TEhWgIQVVmYv0pP4yVkjcOsr8US5YLikllunxGCcc5wBFrRWvE+6a049nAEWlUo62PYBRmaPyXJqoKo0snn2HT7pZFmnL3V5bZ6RkmYHL82dVqCKwYuTlsVpb2/HkiVLMGfOHCxatAjf+c53cMEFF+DWW2/F+vXrcdFFF8HhcODyyy8HACxatAgOhwMXXXQR1q9fj1tuuSWj+xopreSAMFCZ5xjdAU7SFtgdzFz8zOlnJXWvnP4BcEgiItn7r7p86AjwWTvPqC6OA4ujPdJJtKM98WRwX4mrGGx66cnfmmBk4A5ykqYbsb5bYiWxdm59L7fQehRb40k0p59FgZHCg4sm4L6F4/DgogkoMFKRhF4UFp1Wso9Y+iuAHAIqTRSmlFoiSVw1dlGhQoUMOr0M8ow0rrugFtfPjPyXZ6TTcsTucHNCkg2Ir+sLJlUI7wmwITj9yYzu1W/txy+/NxrWPuZgUkPIaufEztNiwf7mL46LNrRSLs2/v2w8irJ0JVeRG5go6X5ioJRLFoQRxo2z6kR96MZZdQChbNap2xeEXa8VxQt2vVYo41YMCYZLG6+ZgtdWzFA1DfuBdJzhTzZMtBZ3vS3eJ9319n6Y6Ny1SS+zx9RRZIpPKofuII8Wh19w0X5y+yG0OPzoDg4tAbFsnr2eIjP+rCfASq7j3qDqaKwiGVnNTrt27UJzczMWLlyI7u5ueL1eVFZWSr73jDPOwBtvvJH0emVlJV555ZWk13U6HR555JFsmgcAcMkMCHeABayZneR5gpzMIMs80cby0lbDbLZ6UgoImqoujgOLdBzIKvOlHXIq8+LJTadfehPn9McZbX6Wl3xPgIm8dqybhcvnj5exWvT44lgnWnoMGG2PLEB6msTwIgMQJtHmDqPYqgOgRTghb19ooiRZb4U5OnVUoULF0EeJRYclZ1Uns83TEEeWS4IlEun1lAYmnVbyfZ1uBgZaI/m3bi+DJc98hqeWTJGcx6cOz8fKWbXgQ8CLu47iyqlVaO7xw2nQwqanUWQgMaPSiid/PBm7j/aADwF/3PINyItGq8mDUxByRj9d3iBqLMowhjrdLDbuaBbpAW7c0YzhhSYMV5DVZtVT+OaEB6ve/lI0JmuKzYrdUxrKMugEaIBjbha729pRYtah4lSNixMSlY4gD7uOzDkb0OXvYz+XIzMEp0ybXP7M95hZtynI4YF/fiNKIj3wz28wbulU5NO5SwAONLJ59t6+9vEp4oUY2aSvyh8VKmLIONH26KOPYt++fTh8+DAWLlwIlmVx66234oUXXhjI9mUNJZyHCsy0pDB9fhaU6jyj9DXthuwWjw4/hw07Dkd0KoIcjDotNnx0CCNn12fuPqO6OA4oZB3IEhaKUAi4/dIzcO8/vhaC1dsvPQOhhKCnQIbqn9gv7QZpxzxblBnhYzgcdwTREM2XhwEcdwRRVRg/CSuxaPFFiwdNHV6EwkBTuwcji0wYX2EWgrB8mkRNoUnkNFZTaEK+Lrf28CpUqBi64ENhSbb5eSMLUn5WrlQqposcSxDYjZTsfB0Kh2VdnfOMNA51eJIOIB5YOB55hsgBBEEAs8eXY+OOZvT4GCw/pwZPf3gIDywcj5EFRvz0b7tF104si1Vx6qBYJi4tUtARc5hdJ6mVVqZwMiDAhvDp0S48GdWFzTdR+NvOwxiXIBquCHLhmqkBth1ONgo7f4T91IyPo87wdaXWiFZRjmM3u1EmftXn7gC30EzLzvm5gkMmAeXwp04inUrI5tnnm6Q/m2dM/VmSgCSRgMxA3krF0EfG0dl7772HN954A5dddhmAiHmB1+sdsIYNFAxRt8Pe7odGOvOq2TyTtDB9njHzkwKzXiPpjmoxZFfd62U5LJxUhV8l6FSsnl0PL8tlF5xHS/rGldsiC/CpGEQMEkSMDpJ/+6o8UtBoY0M87EZKlLiyGymwIR5AJMjwBFnJvu5lWCCqnWczaLF2bn3SvWzRcoARhQY4fHZck6A/tHZuPUYUxplz3Z4QjjsCSc56FXYjTKboGAgDk0rNqLTpReLjuQ7UVKhQMXQRKx2NORwCwKu7W0R6Z3KIlUolbsxvunAUCITx8JUTYTdowYbCKLFSSev/3fMacKzbiye2H8LKmXUi3Ze1cxtAkWEsOasa9/7ja+QZaYFVpCGAUUUmtLmDeGRLU1KbCCKuM/foj86UZtKl8d1UDC6U2EisnduQFEOW2EhAoeouPgRJrbSQwmsypQ1j1pgy/HTTbpGmYRbqLWkhlV6jEjjmljYKe275dFW/eACg02qwdl4DViUkMtfOa4COyp2WdZFFeiwXWcmc7YsMFCmZRDLk8DkpgWKr9LMvTmMeDXIhyWQZk0YV2TFnQJIdPKrYjPwi0wB9OxVDBRmvNhRFgSAIwaDA5xtcbhExuIM8QqGQyCHUx7BwB3kUaDNLjPV4ZYTpl02D3ZzZNR1eXkh+xK+5HxuXTkOeJQuqL6ER7M1j112zeT82LVNFGwcLIkYH0r99zOiAgAa/enVv0sK5MeF3HF6ogzPAiZJxtFaD4QU6YcF3+nmRC284HHHhvX/hBBRYSPT4ZNqS0LfdjLRG4ZOLJwsJPQDCaaik+Hi6ICIBcyRZp0ORQWXEqVChQhp2Iy1ZOmpL45Qa4UjSa93iydjVHCnPfPajI2h1BqCnNHju2ungQmG0Opmk9f/ON/dhxbk1aO7yY9POSABOaoAzSiy4759f4+55DajIMyLAhtDqDIhYRXXFZowsMElujMLRuS7AhmDSSW+eVEOEUw8nnGLX0cg63IjhhRMwIsMYMhU6PAx0pEYUH+hIDTo8jKJC8kSYlNQfVlo4/FsZXdtWd1CxRJtqFKYsglwIj23tNW62NuIPl08AckTUanOd/LGcCja9tHSLNYfMPyWQzTza7WXxj72tgiuxkdbir9sPYniBCTUpWH8mHSnJDjYOobJcFQOHjFebSy+9FKtWrYLL5cJLL72EV199FVdcccVAtm1AwPEhdHlZrHpLrA9RmZ/5UYOcplZ7gqZWv68pExS0u4MYkYVmh5wWSKcniOE50jRQIUZfRgex377dHZRkanS4432uzcXjV698IZmMiy067e4gmrv8ogUCADrcEW2Yrj60Y2L3CbC8ZFuCbD/HVKokWi5KP1SoUHHKws/ykocAkyrtQBoi3m3uIFpdYnZZmU2PJWdV42CHF3e+uQ/XfrdGco6MMYMSE2l//tGZYLgwPEEOoXAYN86qxUu7WtDqDACIzM+N7R6MKjQlselWzqzDpp3Nwvvy9VTSe1QXxVMTcutwuzvzGDIV8owU/rq9CUu+UyOSEfnV98Yocr8Y2mRil7YsY9tUkEtMK7kZltduUo3CBgKdHkZy3HR6GFTn6MAhF2M5FcrMWkyqtmLD0mlodwdQbNEDBI9hZq1ijNlcIJtnP8ymx6XjykTVXjfOqkOZLfVYzZdJZOYbhlYiU8XAIONE2/Lly/Gf//wHJpMJhw8fxsqVK3H22WcPZNsGBFwIkoH3U0umZHzNMpu0vkZpFloXctcsyVI/Q134Bz9KrKl/+2HRzV7vib3MmlDS6ZMW8+7xMcKiI3ev4ui9CszSfy8wxttSmSfdlvK8fvTVNJJouSj9UKFCxakLt4zAsSfIIR3KQ6FJBxfDC3NgmU2PxTOq4Wd5UUlYX1puia8RAJacVQ0/w+GXL+8V5sqYBlssmTal0i4Ij3d6GYQJAqve2iew6R5YOB75OnLQiZOryAxy2sFKarSFw7ykjEhY4Z13hV0mXrArG4PmYjOsGoUpCyN98pOnqZCLsZwKXo5Hc2cgSSKmxETDRAwd1lU2z57SSOcGzqtNreear1M1qFWkj6wKts8++2xcd911+OlPf4r6+no4HI4BatbAwcdIB94+JnOHUA0ISZt0TRZCiJHT7uRrhrMctRUWCvcvGo+Vs2px/cxa3DirFvcvGh9Z+FUMClTlkVg7r0H026+d1xDRaItCjqnh5+JBcmFU3DMRekqD/ISyKS7EY83cetG91sytBx+KXMfhY7FyprgfrpxZB0cgblvtZ6QFx/1M+n010aTjvgXj8MCiCdiw4zA6/PFxKecC2Oll0r6PChUqTh8Ms+ol58B0Bd+LTCQoksDqOZE5csGkCjzyfiNCYQhz0au7W5LmyLvm1Ivm39j6fazHh4e3NKI8zwggPlf+bn4DHlg0ARoNUG7Xwayn0NTjR4APR9tBY91Vk7Dxmil4bcWM+AFEtBx/anVe5LBBDepPSbA8h9Wzxevw6tn14ELKJb0IgpSUESEU3niHIL2hVbrr5utITKqyYv01U/HIlROx/pqpmFRljWyGlUKCUdgTP56E55ZPP3WNEAYhLDqt5D7JosvdwWsuxnIqtHukJWDaPUOIzobsnn2nR2Z/4UljfxEGJpWZcX5dEerLLLigrgiTyszqeqxCEhnPTi+88AL+/Oc/Q6fTgSAIhMNhEASBLVu2DGT7skaeUdpZxJ6OZosMWl3SQohV+caMdRi6vdLW6yMKTRiR5clI7/Sf6osyuNDmDsOsI0U6ghpN5PVh0UNfj5wVdYAHouZdDC8t7smG4p8jNSS2fNWKJxdPhsPHwm6k8Pedh7H8nFoAEYOFF3cdFfXDF3cdxQOLJgjX6PZKl5f2eIOoTrP/e1kOV08fjqZ2N0LhiIvP1dOHi0w65FwAVU0iFSpUSCFbN7BvPRyKzDp0uAJ4cvFkOH1x97bYXNTqDGDTzmasOLcG5TYDjvb4wUQPPBJPuI0UiSeihjEd7qBwjwAbwmctDjyypQnVBQZcd34dbnr5M/xwSpXIREHE8FUD+KGFsAav7jkq6AMZaC02fnQIv7hwtGK3VELyJL37SuuWtbsCqFJQGw4awOHnwXIc/AyPLi8DiiQAO5Qtn1ONwhSDK8CizKYXzbNlNj1cARZQsi/1hRyM5VRIR45mSCCLZ18ow4YrTOf5EMDOY6qsjYr0kHGi7ZlnnsHbb7+N/Pz8gWzPgMPHcLj3snrQpBbeIAeTXguG4+BnOACZTThFFmmb9KIsbJOtBq3kNa1Zlsh96+XQ0uNPcoj8Ns+IYWlo1qhQHl6Gx80vfZ404T9zzVQg+vsXp7EouAMcPj7UhScXT0aPl0W+icL6Dw9jZKEZsEbeY6ZJ/GB8OXY39wgJrh+ML4cpesrL8Tz+57xarHk7TjlfPUd8QiRXXprfj4SwliRxwpXsXDq8wCi8R8oFUNUkUqFChRza3EHJObC+1IL8hLlFDq4Ai1KbFjpKi59u2o1rv1sDPaURWGyxRFiPj0GJVY8ePxPRnSIAl59DXbEFOkoDng+jsd2DhZMr8Pbnx1GQMDfqKQ1oMnICP3t8OVa9tQ/Lz6kRrg2oZfJDHXpKg4WTKkQHTQsnVSjqnlhq1WFKtS1Jo604S3mSVCiUk6NQuLTuhJdDt5dNimVOeDmUqmMqPUR1dBube2DXaXNuRmXSafFZcxfOri2JaI9Z9djZ2IaKUSU5a5Oe0uDKaVWR8rBo8u/KaVU5dUJNR45mKEBPaXD1dPGzv3p6es+eDYWxek590vyQjtS0Kmujoj/IuEdUVlbCYDAMZFsUQZlNh3ZXELe/HtelWDu3HiW2zCccrSYsaTGt1WS+Aum0pKT1ui5DZ9QYXAFWkrbfMMyqJtoGCbplSiR7vAwQXRgZPiTZPxLZasMLDLi4oQw/3bRbtHBUFcS1UFxBBiwfFiW47pnfAE+QQQltgJ7SgiTCSS69elLcV6RYI/1hSnqC0s6lDYsnx08mwxDpFhWaaOkkmwY45mbR5g6g1KJHhaqHokLFaYkymw4XN5Rh1Zv7MHt8OUgNsOQ7w1GZpn4kqQEa2wIYWWTEinNrYKRJ3Dl7LO7e/KXAYqstMsNioHDXW/vQ3OVHdYEBPz+/Fo9u/Rp5Eq6na+c24MuWLgCRjcGds8eixKLDxmVTcaTThwAbAkFAcg3o9DLiwH2QbXxVZAaLnoSWJEXr8N3zGmDVK1fWaDWQuGKKWKNt7dx62AzKlo46/KwoSS3IUfhZQEEmnYfh8cQHcYd1AHjigyb8fsF44QBTRR8ggD0nPNjf6hKSwWPLrJhUmrsSuXwjiWH5Fvxk/ceicZNvzJ3umNVAIhQicItoXCk7llMhFI5IxKxO0GhbM7ce4fDQKh21G0kc6iDwm9fFzz4vjTnN4WdF80M4HJkf1s5rwLAU80Onl5E0eElar1WoQBaJtl/+8pe48sorMWHCBNB0fLG84447BqRhAwWnX7pWfeOyaSjI2HpZA4RDScmIbCTvHD4GBooUUaINFAlHgEFVFrbgATYkGcD3fk1F7iDHVktkSLr8HNb/54hoUVj/nyNYO7ceiJ4Mu/y8cDoDRDVY3t6PTYl9PazBHW/sE73njjf2Yf01UwEAPpYXHHoT2/LUT6YAiPTDTk9Qssx5ZJEJlYn0/T4SYGn3y6gmkbB4SSTZth12JIkPq7ooKlScfnAHQ3jig6akMszfzR+Hc4fb+p4TNECnl0VTuwcgIDiPltn0wlxXV2yBVkPgZ3/bLcxVs8eXCxuaBZMqkg4QVr21DxuXTsPKWcCU6nzc+eZeNHf5o4cc41BdEDmwlCuT7whEnJlLLDoc6PCq5SpDAD0+TmSuEWBDuPPNfXh26VTkW5TZoDt80rHwhqXTFLsnANgMWkk5ivsXjlfsnkDEGKX3PLByZl3axiinO7qDPA51epOrDvIMyM+R+UCXl5ccN5uWTYMl4/1cdnB4eax6a5/knK/kuOoLGoLE7iOdeOaaqejyBFFg1uGNPUcxoqA6J+1RCt0e+WdvS/HsAywv6VgaZFJvHPLMyQdqN86qg12VtVEhgYwTbatWrcKMGTMwatQoaDS5o8imgpwuRVsWuhTuIIfHth3E7PHlQjb77c+P4+55DRnrBNgMFHY0tePC+nJ0RO2Y39t/HGeUWjK6XgzlNmnX0fIsGH0qBhY6bURM+64ECvNdc+qhI+PjymagUG7XYXSpRSj7KLfrYEvobydk+voJVxDDo33dE5Bx5QtwgFUHb5CTPKnxJWjB5ZtoyTLn/MRFJkUCrMgirZ1Y1M8xeczNSgZezy2fnrFeogoVKk5NtLkDmD2+PKkM87dv7E05J3zr5XC8x4+PmjpwVk2BSJPttT0tuHxKhfDexHkrxkYrs+lxRqkF1363BkBk3mx1BgRtnEe2NGHlrFo0d/mFa9zxxl785ceTcddb+5JYP79fMB7HnAHc+OJnCLAhrJxVK2x6Y59Xy1VOTXR6ZBgRHgYjs5Ag6QvtMrpN7e4gahTUbdJrSUk5Cj2lbBLCbqCS5oFH3m/ExqXTFL3vUEGHj8WWr05E9K8SSo3Hl9tylmjr6GPcDFeQHdkXBqMeGsvzmDqiCMue/UQUf7M5NGhQAm3uoGR/SOfZl8gQHIqtqX8zPxOSrMiZXJUHKGumrOIURMbRGcdx+M1vfjOQbVEEStSqh8NhLPvOCHT5GIFSvew7IxAOZ36sXGonccawPFyTQIm+Z34DyvJIgE39eTmQBHDr90bjgX9+I1z31u+NTlscWhZqCcuAoccXxD/3tQq6QnkmCs9+eBilNjqhvJfH5b3KPtbMrUeiqq9cXy9OCNzzTJSMOUhkA1okc1JTmOBSS2s1krR0WhtPDB5zs3h0a6OobOPRrY0YWTgRlSYK/iCHWy4ejQffjffLWy4eHdFO7Eeyus0tLbTc5g6oiTYVKk4zlNn0+OqEO6M5wRWIbCwXTqrCr1/7Qkh8jSo242fn1+KrEy580+aGVUcmzaHVBQb8cEoVbk2Yn1fOrMOmnc3o8TEojc7NfEKzymx6LJhUAU+AxS0XnwGKBO5fNAFHOr0IciGU2XT4yfpPhPskOp8mfi+1XOXUQ5lNL7nOllmV26WVysQHpQrrNh3r9uP5/zaLBMuf2n4QK84biWFl2R0k94VEI5MYAmwITj8LDCVBeIUQRggLJ4ljztWz6xHKYalAgYmSHDf5OYz1cjWu+oKJ1uLON3eLEkF3vrkPz/+/6TlrkxIot0vPo+X21PNouZnCvZeNw+2v7xU+e+9l41BuplKapXTJGMJ1eYeY2YSKAUHG0dm5556LF198ERdccIGodNRutw9EuwYMVj2JtXPrBcp8TJcim/p5q56Cj+WTKNUWfeaT/QlHSLKk72/Lp6PalHlb29xBvPnpcSHIMdJa/HX7QdQVmtISh5YEAexsUR1XBgr5Jh2m1RSItNVunFWHPGPiQk0KiS0g0j9WR0ugYzDRpGQCzJRgZ8+HQ5L6grHgiQ9B8qRmU8J9vEEej28Taxs8vi2qfRK1We/xM7hqWjX++K+4ptxNF45Cj59BpYmCTU9j005xKeymnUfwlyvP7NezK7VIMzZLLL0WWjUxrELFkIeGIDChwp7enNALATaEJd+pETaWm3Y248ZZdSi26nHTS5+J5rHbLz0D9/7jawTYEN7+/DhWz67Hz5/bk8SeWXFuDcpsBtBaDe5bOB6tDj/KbJF2LJ5RLWKwrZ1bj24fg+c/PooeH4OqfENSMJ+VC3N0DozoXerUOTDHkFpn/36tchthnVYjKf6t0ypbkVJk1eFAuwcrn/9UeC3CXlc2EVFkoVFdYEiqPOkva/50hV6rxZrN4jltzeb9oljwZIMkCMlx87cctskss8c051Cjrd0tw/xzM6gwDJ0DaIKQnkfTSiiGI6SBRLkmWqtJa02UM4RT2uBFxamJjBNtmzdvBgA8+eSTwmsEQWDLli3Zt2oA0djuw86D4lr1Vz45CgOtRcGwzE7T5ITc1yUKufcTnR7pDHmnJ4jqLE5rSqw6XDquTHQqdeOsOpRkUZ6gOq4MLIKsNA15SnWe8B65Euj2hBLoI10+vLM3woxz+FjYjRFmnEWvRUm0BFmrIfFYAtMsHAYe29qIh66YCCC98tNuLyOpbdCdYN5g01NCki12jT/+64AQEBUZSfzy4tFoavcgFAa0GuCXF49GkVHbL221CguFu+c1JJWoViYaIqSTGFY3oSpUnPI47vDjpU+O4Z75DcLBVewwodLa90l1uU2Pva0uYc5qdQbgCfJ4OHriDcTnsesvqMXjV03CnmMOhMNAU7tHct6sK7bgr9sP4v+dOxK//8fX6PExuHFWHYDkDcKqt/bjwUUT8LPzaqDRELAaxOzjV3e3JJnQpO3CrB6ODSq097GeV2UYQ6bC0R55ZlmZgswyu4HE2rkNgpZSTLDcprCAvYnS4Ofn1yYfPNKDV+pmMKHTI12W1+kJ5qxM80QfZZrVOWpTu5vBS7uOisbVxo8OocQ2CoV5uTEMzDdR+Nl5Nej0xquufnZeDexDrMqj1SmzX3EGUyYUWzws3vrsGK6eMUJwKP/bzsMot9WhIsUc7PKzuOnCUUlEAldAWYMXFacmMs6KvP/++wPZDsVQYtXh//a34bXPWoXXInbMmYtC+llecnD3fq0/MNLJ5Sh6SgNjlloIPpkkzowR+RnrLHTKuGSqJSyZQU47pcMdD2jSKQstMNPYcbgbWw90it6z4ryRwr+7PTJJMg+DKiOVVql1URrmDV0yfaTby6DaTKM7wOOEMyBihd580Sh0F5r61y9DwPkj7Hhu+XS0uQMosejFSTakkRhWN6EqVAwJlFr02HG4G11eRtj4mGgtHL4gdh519Tmmi4wkKvMNorlNzg2U4UPwMDwefT8yj14/s1ZyTvz6hBsH2j0oseiweEY1Nu1sxsNbGvH4VZMkr3ug3Q2zTosJlTY88M7XIt22Hh+DmkITXlsxA44gD7uOTC/JBvVwbLDBrNdK9hezgr9FoZmWZJYVKCzg7Q7w0GjETuYBjoMnwKMgi2qNVOj2cZJVABuumQq7NXdso1MFuShvToV0jMNONgIsj13NTuxq/lT0ejqi+kohFA7DyyRXXWUjbzQYYZWZRy1pzKNMiMOsMWWiSqI1c+vBhDjEjN/kUGSm8a3D38u8sB/schWnFfq9qu/YsQNnnXUW3n33Xcm/X3zxxVk3akBBhPDwDyeA5QFvkINJrwWlAaDJfBLMM8rpXGUeJJl12qTT6htn1cGsyy7wOuEKSJ5KnXAFUmbt5WDRU5KUfHMWpbOnM+REORODhzyjND09LyFQpbWETGlIXI/PrNfK/HaRflZiJSUZYqVWUkhemWgS9y8cj6YOj3BaNrLIDFNCgsysk14ATdH+3O1n8dB7YsbbQ+8dwLhhU/qfAA4BlSYqrr/Ua2insuJWN6EqVAwNJDJcVz7/aZJWWl9j+piLxbP/OSQqvycJ6XLNs2sLcKDVg+tn1gIAdh7swJ2zx+LuzV8K8+bKmXV4cddRrJ3bgDaXBy/uOorffH8MDrS5QZEaTKm2YVezU7hudYEBk6vzsLfFCYYNY874cry6pwXLz6kBqQG+W1uI4RYaCAN1pVY4HL60DwLUw7HBBateOt6zZhnv9QWtBpLxgcKeBPAxPP78fpO0eZhJufs6A9LGTq6o8ZNiGCIyFRwfljykn5rT0tGQZHxKErlLahVbdJIxdWEOtbqCrPRv99clU3LWJiVgNWhx80WjhL1E7MDemsaaRoRl5HjSMEvhQsDT/zks/OahcOTfD10+MduvpGIIot+r+ieffIKzzjoLW7dulfz7YEu0GSkKRwKBJE2qcnuG+mQAQqEw1swZi+POgJBoGGbTI5TFYupnWZTa9KIMealNDz+bOrveFyrsBkkKcbk9c0ozF+IlXaT4EJ9VW09XhMMhrJ5djzWbE57n7HogHA8eWB6oLTZi49JpAnNLS4bB8gCilRChEKDXQnRyzPIc+IR+yYZ4/GLWKBzu8gr94RezRoGLuhG1uTgQRFjUDwkijBMuDiOi9ukszyPQS6Nw7dx6sDwPIPKeQhMluZEojCZ35VihfjYDV6QUZZ8lFp3kyWysfFrdhKpQMUQQAs6vsWP9NVNxtMsHoy6iSdrqDABAn2O6zR3Au1924oSTwcM/nIggH4KZJpMEk39zyRlobPOgwxsEEJlDr54xHGYdiT9feSY8QQ4lNj3cAQ53z2sATQI3vbQXi2dUCxIOT0XnTOAodjU7UV1gwP+cW5uk03ndzDrsPe7Ey7taMKXSnnFZSqFJ+jBHPYHPDZwBBuV5BtE6W55ngCvIJBggDSx0WhI2g1YUH2g0AE0qm2lTwjwsHeSZtJLrfjYH4ikxhNjx7TJyNh2e3JVphqEBx/OiccPxPMLIXTmwntaIZFBIIiKDYshhiXJAruqKGVquo+Ewj+p8Y2ROE4g0BMJp6M9k4xbrDDD44ZQqkcbqypl1cAYYYIiV56rIHv1ecVauXAkA+PnPf47KykrR344dOzYwrRpA+BheSLIBUS2UN/dFROR1mQUYBloLgiCSEg0GbeYBi15L4R97jwj14nkmCn/feRgrzq3L+JpAZG2XohBnAy1JCkk2ICqS+vZ+PK+gkO9Qhomm8Oqeb5I0HtbOqRfew3JhHGjz4a6E5OZdc+oxptQi5DZ1Wg10lBahECI/PAHoKC30CWLHFprCfpc7qT9UR40xglwYd7yxP2lD9lTCSZifDeOxBDMEAHgsZoYQzd/m0yRqCk2igKim0IR8XSQJZtVLs0L7bSiSRmDLh6RP984bWQBA3YSqUDFkQAA7j4rng5Uz69DhYdDjY/oc0zFjlS+Ou/DFcRc6XF4snFwFmiLx+FWT0OkJot0dRCgchi+QvKbqtRqs2fwVAAivFZh08DEsrp4eD8qBuCbbhqXT8J+DnagttghJuNjfH97SiAcWTcBT/z6Uta5qkYHEAwvHJ82T6ZaeqhhYmCgKf/s8Eu85vCzs0Xjvp1nGe32B4cJ45sNDWPKdGiAc+dmf+fAQfnPpWMXuCUQqIAbaPCwdUBqN5Lr/goLOi0OJHV8sI/hemEvB9zCwZvNXSW3KpRlCkAnjeI8/qX9X5RmzEGfKDnkm6fg6b4glgfiQBoc6vUnJ9HRKiWVlctL4rFlHJa3nj7zfmBYbTsXph4yngZUrV+L1118XvXbjjTfitddey7pRAwlZ0Vl3XES+v/AGOaGEL3a9VW/txzM/mQJkehpJ8Lho7DDRifbd8xpAENmxxJx+VjLYaBhmRVmGC3+XTClet5fJuBz1dAYbCmPWmNIkw4rEbutjebyyOyq4GuRg1Gmx4aND+OXFZwh9rtvHwBvkk2j1PT4GpdGSFDkjj78uiRh5eAKc5HhxBzjh3+4AJ3ma4w5yAKKLVBiYVGpGpU0fZZrRok2dM8CK9Idi1+ivmGg6gW0qxlram1DVMEGFikENqfkg5v5ZV2SWTywRgDE6X9755j5UF+gxdbgdLY4AfpvAZls5M+JC+suXk5NiDy6aACASrN8zvwHdniD++K8D6PExuGd+g0wcEsAjW5pw/cxayTXVF4yUv/lZHsecAfAWfWTekWh/n3NTGJhRYcVrK2ZIzscqTi4IIiypD0QQyv0gjgCDmWeI44yTwcJQwjwsHbS7pdf9djeDcoWcF4cSO57leMkYjeNyx4qSNevKoRmCXP8eV27LfD+YJVg+JFlSyfK5K7FVAtnMLToKkiYt+jS6UbeXwahiM649d6SwH/vr9oPo8TEp2XAqTj/0exY4ePAgmpqa4Ha7RTptHo8HwWBwQBs3ECiw0NI2vFmwVdwyyQhPgAdsGV40TOLRBDdIAHh0a2PWNd/yJXqZT7j5JkqSkp+nJtkyQqszgI07mkVOoBt3NGNEoQnDDGYAAMPxWDi5ShQkr55TD4aPBz1GSosXP2lMSsbddskY4T0+uf4QFW4tkhkvRQmLR55R5jSn96liGJFEVizATNhD5BtovLjrqOg7v7jrKP7Yz/6eTmBbKHcyG5sD0tmEDqGSEBUqhiq+lSkHGTfMhoklJtkkW2xs5xlp3HxhHUYXW9DpZfHb1/ciz0hjyVnVqMgzws9w0BCE5D1IDYE//+hMlFh1uP+dr7Cr2Ykymx7Lz6mBnpI2Oyqz6vHoj84EpdVgmE2PtQkabzfOqoOB1mDxjGrRZveBheNxscUgtL3Dz+Fgtw9N7R68tKsFPT5Gem7qYz5WcXLB8GFs+SriEJ7oeFddMDL1hzOEkdJKrtvrr5mq2D2BPszDFBaLN8gYjBmyNBjrC0OJHW/T03j/6xNCpYWRjsST59cW5KxNhRYaF48tTHKKVNrQoy94Gen9oC+HZZp+loeO1IgqSnSkJjNplkGMbIwJGRZ4aVdzUv++7ZIxAl9ADoVmGj+aXp20HytQHUdVSKDfibbDhw9j27ZtcLvdIp02k8mEu+++e0AbNxCw6bW4Z34D7ngjnrW+Z35DWmKJsteUMUOwZqH9oFTNd75JOnGSn0VSLBSC5CnCphzSt09l5Jtp9PgYkROontIgPyF4MNBaPPHBflEi9okPmvBAlEUBAH6Ok0zGBTgOQORaVoOcS08k+NRpNbj1e6PxwD+/Ea5x6/dGQ5dQfurwsZKLm9OXPhutwkLh+gvqkth3vR1DU8EiU4KaaMxBEpDUiyM1cZOIVJvQoVQSokLFUIVJJ725Nuvk2aeJY7vVGYAzwMPHhvB5iwN5Rhr/c24NfCyPW6Pz6q8vGS15jyNdXtz3zjfC2m3RUbh8ahW+PuFCc5cXt196Bu79x9ei+e5bVwC/euULLD+nBk9/eEi45qhiMyrzjCA1BIIcjzwjjVZnQJh3xlx/DmxkcvI/Zvygzk2DGwQROumMNqeflTYH8LOKmgPIOwMqqw1niK7zvdd9PaWcdtZQKtEuMpJY8p0RaGp3C9pjS74zAkVGbb9itIFEqUWLC3uNm7Vz61Fmzd08J2/8lTtnW6uewv++82lyie3yobVHy5fZi+elsRfv8bOYM75c1L/njC+Hw596HxMOR/ZfvfdjDybsx1SoiKHfs9OFF16ICy+8EJ9++inOPPNM2fc9+eST+OlPf5pV4wYCAZYHRRKizD5FRoJXZHiyRZPSCzhNZr6AGxQ6bfQGOdx2yRm47514gH/bJWfAx8STL/1Fqxx92xXEcDWj3284/dJllM6ECd8VYCUTsYmllnpSizVv7xH1oTVvi110tBqNJKVcG+27nd4giiw01kVP2vNMFBx+Bl3eePlp2ifFfZUzEUChmcK6xZPRHT2Z5EMhgEC/EGA5yWcXTDARaXMHJRmD9aUW5BekZ4oylEpCVKgYqsjXS5uw5PdRKtZ7bBME0OWJCLdfPqUCXT5G0N8BgA07miXn0PX/OQIgvnY/fvUk/Pzve0RlKeuvmQKGD8Ht51FkpfGTZz5BgA2BICBc/4JRhfjxWcPxeYsDoXDEwW7xjGps2tksJNva3UEwOlKyTHb5OTV4bGuTOjcNYmTjeJcpSiw6XPvdEUmHaMVZaP+lA4ueEjn5xpKKFoXKN2PwMRyMFCmK/Y0UCT/DAUoxoBLY8Y4gD7uOPCWTbADQHeBxwhkQaY/dfNEodBea+u8MP0Bod/OSsj0bl07DCEtu2mSgNZL9W8mEbir0eKUPw3u8LKpPQXalHEhCej9DEqmffaGJxsF2T5K2XoEx9fNxBVhJg5f+St+oOD2QcRTWV5INAN55551BkWjjQsBtr+5NSgoklbn1A3KlflX5RlRmyBTzBqWtyL2JulcZIM9Eg+zyiYINkgDsaUwmcii1SttZlyhpmT6EYTdQkmWUiWw1axrimx19uETFdAPcfhabP/9WRJf+6/aDqC0yASYa+QYaJ5wMfvVK/MRw9Zx6VOXF+7Uh+lpv11lDYmBBAHtOeLC/1SUsRGPLrJhUagbCQIubxdcnPMmOYAa6Xzp/NpkS1PNr4/NToUmHcrsOo0stQkltuV3Xr3KOoVQSokLFUEW+jsS4cosoga8hwoIJiwjRgwANqUka24UWGm+/exy/vHg0CBC49rs1ACJrcqszgPX/OYIHF03A121unFFiwe/+7yvB2RSIzLufHXP02hDuw4pza1BhN2LnwQ5MH1kocRJP4/vjh+G65+IJupUz6/DirqNYMKkCj21tipTym3X4us0l+nyZTY8FkypQlWfAjbNqszJPUKEs5Bzv2tNwvMsUIQAbdxwRsTA27jiCiZUTFblfDCwfwuMJ5knhMPD4tib86YcTEXMpVwIGSotnPjosxKl8CHjmo8PKs06i7Pi6UiscDt8pmWQDAEeAExIYQKR/PvTeAUwon5qzRFubOyi5T0rHKVIpEGFCun9fMTEn7QEgK1Wgp3LHslMCx50Byf3MMJsBlSkqwbx96VWn0EXPM9I40JacpMvLYl+tYuhCseNOpa2700WXR5qJ0u1lMjZDKLToQGvj1BuCAGgtkZUbT76JltQ9y89y4AbZkFCuEoOe0mRV5jk8j8Z159cliUiOyKMBNovGnqZi88PzaPzyotFo6ojYg2s1wC8vGi16np1e6cC80xsPMArMtGQCNLEE1W6kcem4siTjhVji1c+FJB1ln10aZ1byITFtOkajfmBhPIDtDvLo8Ig1Gzs8QXQHDcinSbhlFrn6fgokkwRw5dSqPstCi0wkrpgi1lNYO7cBRSYtkKZkxVAqCVGhYsiCAFqdTFJJ+uh8o6zmYp6RFrHgdh7swFk1ebjl4jPgCXKiA4VYaWaPj8GRLi/CUffGHh8jaoae0qC37nSADSEUBla9tQ/PXDMVXR5G2Aztb3Fg3eIpcPpZNLW7RaWiMZYaQUCYd0gN0NTuET5fZtMnabnVFplVDclBihKZw8piBQ8rnX4ZeRI/o6gpQaebQXOXXySNEXu9QkFWm5fh8ItZo3C4yyvEVb+YNQreLKo5Tic4ZbSoXQFO0VLjvlBu10vuk8rt+py0B4gk/xguPskSRMTht92dO3M4Iy19GG6kc8eyUwLD7HosmlwhKv9cNLkCZbbU/cHLSOu7+YKpNwW5MnhRcWpCsUQbQRCp33QSIF8/n/lX12uB686vFSjMMZ2AbNzKfQwvOXCfWjIF2biOnnBJnwBlU+bZ4mSFJFusrave2ofnr52e+cJCAPs6vfAGQ/AGObgYHm1eDRoKZQSshxBanGyEcpwAV4BFi5MVnmeRjKB/UUJy10hr8PPza5Mo7ImLq1emn/11yRTASKFTJjHd6WEwMsqQcPpZycDZ6WeBaNLPw/JwB7ikEx8PyyOfJhFIV8SUBI44GbS5ghEmpY0WJcfSKQs95pLur88tn57y1CsRtFYsLktrh1bQokLFqY5jblZIsgGRsX7nm8ljvbcu28YdzVhxbg1GFpqg0RD4osWF8RU2QZctdq2Yg6lVT4HSEHh069dJiTo9pcGds8di3faDorbpKQ30Wg2Wn1ODLk8QnZ4g7l80Hhs/OoyL68uwYtOupIReLNlGaoCpw/Px/THFKDJo0egI4qVdLULZ/IJJFUmMZ1WnbfBCS4YlY0gtqVywo5Q8SSpYDNI6SkqXjtr0FNoSWKZAxFTKpk9PLiJjRA+MG5t7YNdpT9kDY4u+D73LHCEUDkvGr7nUHsszSpvD2bPQ7M4WfBjY+nWy2crIotqctUkJkERkT9N7n5GOilOpTXpPlU5llk8uSZdDAwwVgxeK7RR7M9paW1uxePFifP/738cPfvADbNiwAQDgcDiwdOlSXHzxxVi6dCmcTqfw+XvuuQcXXXQR5syZg/3792fUDrOOFAmgxgaiOQvqc5CDpE5AkMv4koo511TlG/Cz82qEiYckgJ+dV4OqPEPG15Sz2G5zZ+4662B5nHAxuOWVz3Hba3txy8uf44SLgWOIueRIwc/Fk1KPvt+EJ7cfijjbJtio24wk1s5rEPXjtfMaYDPE+3GQDUvqviSetsn2s+gpTqGZTtKW6O3Sa4kaKvR+jzlhQ+ePBkC9A6KY222F3YDqAgOuu6AW18+M/FddYECFPWGRI4GtBx1Y/MzHuP75T/HjZz7G1oMOUbVJoUknGEk8+n4THtvahB4fIyrp7JQpqe30pN9fO/wcbnzxMzyyJXKfR7Y04cYXP0OHP4tBr0KFigFFmzsgszaJN9y9ddlanQE8sqUJdiOFVmcAD29pRLeMzs2oEgtCoRD+N6p7mpio+/OPzsQDiybg9T3H8MMpVaL5+p75DbAZKDz94SHc8PxnuO+db8DyYdx2yRis2bw/KQGyYFKF8NmJFXY4fIGIhmUYKLZE5r1NOyOHDFV5BhnGs5hpp2JwgOMJyRiS45U7oPbIMJQ8AWXXME8wokGbOBZWzqyDJ5hN+UNq8OEwun2sKK7q9rHglay2iTJlF6zbiaue+i8WrNuBnS2ufmvPDgaYKK3k3slI5y6B1CNjxOXwKduX+oKGICRjXU0OySYcz+GC0RHTiF+8+BlWbNqNC0aXgeOH1n7KE5QmDnjSYKWFQpDs36E0pocY8SERekqTVVWbiqELxRJtl1xyiejfJEni17/+Nf7v//4PL774Ip577jk0NTVh3bp1OOuss/Duu+/irLPOwrp16wAA27dvx5EjR/Duu+/i7rvvxl133ZVROzQEAYteixXn1uD6mbVYcW4NLHptVpNgm1yiyZV5oimmAZWI3s6TmSAcjmf8Y8GGl+GzOmCLsQR7tzUblmC3n5MsWew+DRIZvIyLK5fQxXq8HBAO4cFFE3DfwnERnZFwCD2++PPpS/clhmK5BSLKRDPTpEjINcaKSzzF9AY5ycDZz8bb4gnKBPXRbDSlAX5+fi2e/jDSL5/69yH8/PxaUAkln0ecjCQT7YgzvnmMlXQmtkUo6YzCbpJOHtr7Mbb6MkNQoULF4ECpRS851kss4lISqfW2usAAHUXCE+Rw7XdrYKI1ktcqMtMothokE3U+hsOD736NK6dWC9qRK2fV4qErJiLAcPjd/30lms9++/peWRZxrFT0d5eNwzv7jkNDkOjxcwABVNoNeGDheEHCgiCAG2fVikpmVA3JwYt2mbW6I4vDylQwRxlKiYgckCnLUErUoL1+Zi2Wn1ODF3cdhS2bEpA0EIujesdVSaz5AYScO/mpeCAX5DhU5xsjMeeCcXjw8gmozjeC4XL3XWxRdmQi9JQGVoXZkX0hnbj7ZENLapMOb9Zs3g8tObQ02txyhwdpsF6OO+Ja67F5aeOOZnzrCKT8bIBjo1VsCcSHufUI5nBsqBi86Hdm5O677+6zLPSOO+4AAPzP//yP6PXi4mIUFxcDAMxmM2pqatDW1oYtW7Zg06ZNAID58+dj8eLFuPXWW7FlyxbMnz8fBEFg4sSJcLlcaG9vF66RLlrdAbz0yTFce+5IQQj9r9sPomimDsMM5n5dK4YSqwzlNAvxYXeQxT3zG3DHG3FtmXvmN0RO/fpR3tYbrgArrYU1zIpyQ2aJMVOUJdibKm3KgiUot9no8jCoGeKizgGWx6hic1IfDSSwGRkujMe2HUzSdLln3jjhPTE2Wu9+mZisdQVYrJ1Xj1VvJpSszKuHJ8ACRgphRFxFE0ske7uJWvQUPjvWhScXT4bDy8JuovD3nYcxZXhewnu0kho0lijrrdvHSbLvNiydCnvUPUo2oe1OKHtOcPmKaPvRSbppDp+0q2s6Nt7Cs1XNEFSoGPSosFCS62ilhYqowUdRZCDx+8vG4dev70WekcbS71TDbtThmvURF9DqAgNqi0xJ69w98xvw+TEHaorMkvOBVU/h15eMgUYD3HLxGWh3BVBk1aOp3YMRhSZBey2GABuCUcbFeVKlHY9fNQm/fWMfWp0BFNtMeOrfh/DAwvG4eIwBMyqtuGnWKPz69b2idXjjjoiGnKohOXhRkMZaPdAwUKTk2DAoLJDO8iFcPb06yRmQCymX8AIAd4rDPiUwlNzJaa0WLQ5X0u9Wbs+8GiZbDEQsN9AotsjIuuRw39Ihk/zr9AQz1iYfjJCdR9PQNo+xwhMlcNL93cJhDV7adVQwYTDQWmz86BB+ceHo9Bp+muqRn67o98zf0NAAANizZw+amprw/e9/H0DEZXTkyJFpXaOlpQVfffUVJkyYgK6uLiF5VlRUhK6uLgBAW1sbSktLhc+Ulpaira2t34m2QhONH8+oEqh7BAH8eEZVVgGNkSaxdm59kr6GMQvtgnwjDR/D48FFE+BlOJhoLTQaZO1i4k9XC6sfIDVxlmAsGWPRa0FmwRIcZtNLTpil1tyJnJ4slFr1+NF0sVj/6jn1KE1gJ7BhXlLIODFYNUXZaL012hLLpK16Cj3eYKSfBTmY9FqwHAdL9HTZz4bwq1e+SPodNi6bJpjf5hlJXDgmQktP7P95xvh9Cs2UpAZNoTlyH0dAugTA6eeA6EInl9Au7r0QRl2+hEC214JlN6Z2dU0F1QxBhYpTAGHAouu1Num0AAkcdsW1HofbaZRYItpqVgOFNlcAD/0rzp6dPb4cv34tkoS7/oJaDLMbQJMatDh88DM8enwMHv7hBLA8hHmUJjUAEcbK5z8TknX/c25tkvHMxh3NQrJNT2nwzQlX0ubxnvkNeHxbI84fXYJWZwB6SoNwOM6QGXP9OWAYTkiyAfFDtPU/mQKbTqvOTYMYFj2Ju+c1JJl2WBRkl7F8GC983BzZHEYP9DZ8dAi3XTJGsXsCQJDjYddrRTGHL8AiyCmbaCsySW/CCxRMZlr00np0ZoXZe0rAx3B4d39rUn8ZN8wKZHhIny3sRgrdHh+eXToNHe4Aii16vLf/OOwJh7wnG2ad9FjOpZZdkUzyb6iVNlr00ocHFkPqZ283klg7tyHJ1M9uTP1ZM01i1pjSpLU9LbJJghFT4l5CNS4auuj3bHnZZZcBAJ5//nk899xz0Gojl7jyyitx9dVXp/y81+vFypUrcfvtt8NsFjPKCILIykSBJAnY7WKh09YABz5M4JZeSQwdqUl6b7rYc6ADeSYt1l8zFR2eIIrMOniCDL51BDB6VFFG1zx63IHmLl8SS6zEosu4nQBQ4GUkJ9x8E5Xxdfc2deKxrQcFgwU+BDy29SB+N78BY8usGV3TYglJLlZjSy0g01G2TAEyi997oNG7nx481iPr9GkvsQAArF5GUsh407JpwrUOt3TD2IuNZqRJ+HkOdnvkd2n2OnDCxeDhLftF/awyPwy73YiuEx7JBFi3l8GZFXYAwKFjDkl9mQ1Lp2F4pSXaFun3bFw2DTUVFhh6/JL9Uk/FfyeNl8FDl09AKBzfzGoAaAn067fkXX78z7m1ApVeT2mwenY9QqFQv65zscWAMdefg3Z3EMUWHSrtBmg0yulw5LLPSs2l4r/nfjwNhjYMlnYMhjbkoh29++mRLh9ufPEz0bxSXWDADTNH4Y439ooC6hk1duw40oOHtzTi2u/WiD6Tb6SExPzIYjM6XAHBvTvy+bFwBXjRerV6Tj3yjFr84sI6eII89FpNUvnOw1saceOsOtz3zjfCpuAP7x4AANFBgNvP4uL6Mqz/zxHBXOHR95uE63R4gkLiLREBNoQwCNSVZrYODwYMlr48UJCaS4+3OuFnONFa7Wc4cHxYse/u7PTikoZhos3hLRePhivAwh5d25WA1cfiq1YPVr39pSjmqC2xKPo77+vy4qYLR+GP/4ozsm66cBQ8QU6x73vY48BvLjkDXT5GcEHMN9Lgwsr9rgOF3v005PRh4aQq8QHw7HqEkLvvUqLhMarUjmvWfyw6wC2xkrBbc9Omlm+dCHK8aCwHOR5BNgR7kSUnbXJ1eiWZf0qP9XSR6Rzfu4+eaHOB1BCiZ09qiMg8Wtj39Y9968SuIx1RB/AgCsw6vLHnKGqLTagZ1vfvdtTrQKlNL7pvqU2PUK9xLvU9m7t9kuXlm68/B9X5J6cPD8Y1dii3KeNjCafTCY/HA7vdDgDw+XyCkYEcWJbFypUrMWfOHFx88cUAgIKCAqEktL29Hfn5+QCAkpISnDhxQvjsiRMnUFJS0uf1eT4Mh8Mnes3L8HjigyYhiAWAJz5owu8XjE96b7oos+qwv9WNG54XJ+/qyywZX1PODXLd4skZXxMA+DAvyb7jw6GMr2ugNYI2DBBhCdJaAgZKk1Vbzx9hx3PLp6PNHUCJRY9KCwW3O3W9fDqw241ZtU2ENGm/RTKLbO9+2umWd/qMvU9OmNvhY4X3aAgSD777jVCuGQoDD777Df5w+UThPXK6JU8tmQKHwwezXt6lN3aNvswFYu/pS4PG4fDBqtfi5otGJZUlWBLuY9WRaAxwuCvBovyuOfWoK+rfbxkKEXh1TzLN+6YLR/e7T9g0gM0WORV0ufz9+mx/kXWfTaOfpttHB7xtA4DB0IbB0o7B0AYl25FuP23u9ibNO7PHlwtJNiCu9bhx2TSMKrEIr8fmvTKbHlYDhQfe/UaUGIiVfUbKPSnh8A5A9G9+mHUWlNuNePDdr3H9BXWSc2C53YD7FzaguTuAcrsePb7I/B8rYYkxiI90erBwckUk8RZgRSw4A0WCJgjJudquIwdFX8gUg6Uv9xf9mUvdQR5P/+ewaK1++j+Hcc/8cYp9d7uBxoPvfioaBw+++w3+tny6os/bE+QUiW1TgSZJPPex2JH8uY+b8TsFn7FNRyLIh0QuiDdfNAq2QTIm5fookNxPtRotnti+X7x32t6E+xdOyNl3afPw0ge4S6fBGMpNm3wsj7/++5BoLP/134fwv5cp189SwazTSlZxZLPvHUikmuPTnUu9DI8//euA6Nn/6V8HcN/C1N/T4WdwzqhicHwIfAjg+BDOGVUMp59N+Vk/G8KOpnbMn1SFTncQRRYdXt9zFEUTK0Wflfqe3/b4JOOCb3t8sCmmmi/GYFxjT7U29TWX9kbGibYVK1bgsssuw/Tp0xEOh/HJJ5/ghhtukH1/OBzGb3/7W9TU1GDp0qXC6zNnzsQbb7yBFStW4I033sCsWbOE1//2t7/hBz/4AT7//HNYLJZ+l40CEZ0GqZI7d5CDUAvXT3gZXpKBtP6aqUCGOgxK2QUTYVJUS26kI/TvX30v83KBQhOF/zmvVngGsURjrCwwY4SAShOFypgmnbKVBZlBAdpviVUnqWeWqPlnoDSS79HT8ZnZFWAk+7orwAg6f3Lioe6obolVr8XqOfVJv601oV/L0dITtQ1KU5R9hsJhFFl0ohOhIosO4YSH6A6GhCRbrJ13vb0fzy2fDrs2fVo+TRGSNO/EZPGQg0pPV3EawqRL1jsjNdLMr3Z3ENaosc+ru1sEBsCCSRUCUy323oe3NGL5OTVCMsyboP9UZtNj8YzqpHm3xxuUnAObOjw4r64Iq976EuePLkw6cFg5sw63vvI5rrugDsUWGm99dhzXfCci2BxjyDB8COUWWi1nP0XB8tJSEKyCroByB2RdniCqs9ABToWAAvIl6cBi0GL52SPQ6Y2wy7QaYPnZIwSdWCXgZkLCWAYi3/Oh9w5gUtV05CushTfQcAVYmXiSBSy50fnqS7d3RI7aFOSkx3KAy53Dp05L4Ofn1ybJyOipoRXzugLS+3tXIC5BI4disx6eoBe7mnsi7NPOCHu9KI3yWlITxuThhVj27Cei56slUy+8qt7z6YeMV5yFCxfi3HPPxeeffw4AuOWWW1BUJF82uXv3brz55psYNWoU5s2bBwC4+eabsWLFCvziF7/AK6+8gmHDhuFPf/oTAOC8887DBx98gIsuuggGgwH33ntvRu206SnJkrsNS6dldD0A6JIT7vcyqLVmlrwrtUkPvtIMrxeDM8Bi5hniJEO2i6UzEJJMNP5t+XTYTadWMNFfyLlKvbZiRsZit7SWwA0z65J0BhITQYUWGr+8aBSaOrxCScIvLxqFwgRhUwOllezr66+ZKrwn30RJJuzyjfFE3Ku7jybpclReNFpIIvsZDqtmj8EJV1BoS4lVBz/DAYi0hw+HJQ0zQtEdoI8J4Tev7U3q708tmQJEmbqtroDkODvhCsSTsWnASmlh6lVSa6JJWHNoU680lOinKlQMduTrKdx+6RnC5pokgEnVebJJ/1tf+VxIsH1yuAsbl02TZeMmqlqYEsxezii14NYEdlts3v3r4im4b+F4HOrw4KVdLejxMVg5sw6bdjajvsyGNXPr8fj7TfjxWdV4cNEEHGh3gw8Bm3ZGNNxWvbkPDy6agMunVOGBd79Gc5dfYMgUmui0jGAEUMDhnohGXYlVhxF5NMAq8QuoSAdGWnqtfnbp1BSfzOaepOTa39vsaKAhKxavsCi7O8BCR5EidtmaufURgzGF1sATTvmYRclkphKwGcTatkD/tW0HGrKH0lnuk7JBOnH3yQeBlyXE+n/7g7E5bNPAI5v9PcuH4PAyotccXgas3QCg7zmR1JCSZm4bl6W+r6r3fPohq9WGpmkUFxcjGAziyJEjOHLkCKZOlZ5cpkyZgm+++Ubybxs2bEh6jSAIrF69OpvmAYgstlILnyeYeaKpzKaXnOyzEe4nQEgK2WejWQcAeQbpiWhTGhOCHIZSMNFfKOEqxYXCQpItdr073tiHv18b/418wRCOOwKioPHGWXUotxuBKLvLI+Oy5U1gb5p0WkmTApMu0nY+FMKc8eVoancLG9U548vB8/Hr2gwU2t2MqC13z2uALcFi3eFjBevsGHV9445mjCw0A2ad7Cl3kI2fAsq5qBb0M0DP15EoserRmbCollj1yNcNXaefoeR+pkJFusg3kDDQWqxL0FN7YNH4JP3PtXMbQIRDaO7yY9POZtx+6Rkw0FoseeZjXPvdGsl5JybHqKc0CPG8wBjore8GRMbaJ83deGRLk6CxxvMhuAIcLp9SAatBiyDH4oqpVejysjja7RM02BKv4WUi7swxNl2MIXPBqOLIXiBmBGPQosPP4atOX3KZOAVsbXQkiT5fUGdXk205gkeGWe5Jg4mRKfKMFH5x4Sgc7owf1v3iwlHIMygbs8nFtposY9tU0BAayc2wkgkQOSZ/SQ4dKDOFK8Bi2XdGiPTmln1nBNyB3Dl8ajVhyfg1HSaRUvCmEXefbHS4g2C46DMJAwQAhgujw82gQuHxfjLhCWa+vw9yIXgZPmlPlY5JS7sMs7LdnYaraxiYUWkVZJJKLXpU9HJFVzG0kPGO6+WXX8bGjRtx4sQJnHHGGfj8888xceJEbNy4cSDblzWsBmkXIEsWLkBaDSFZOkllIYzuZTgE2V6CmiwPL8Nldfrmk1kE/EEu48VyKAUT/YUStN8uj/Ri0e1hURV1nfUw8jonsbJQm0xftyYsrJ4gJ6lx8ezSqSihSVj1FHxs8uJjTRgvDBdOKq2688192JhwimSitZLW2TFn3kIZR7BEN+B8g3QZa35vx6tUWmRhYFKpGZU2PRxBHnYdOeRPj1R6uorTEcdcbNLcdOsrX+DlFTOwcdm0OKPLTqPDw0NPadDqDMBu0gms78Qy0sSDBKuBwspZteBDwNGeAB6NJr4ASI612NlEgA1h3faDWHHuSOEz67Yfwuo59Xjig68wZ0I5SEL6GgZam8SmC7AhHOz0oMpmiMx16LtM/HAPIyTZYp+PadSl3BSoUARycalVwU0wy4dxwpl8WKe0ALeXlY5tfSynqHul0y/nbM4CCjGgqm20pJPhcBsN5K6SMCPkG2k0tnmS+kueMXdzBhciZDXacoVcjOVUKDDT+NH0arGRxZx6FGQr7zPIILfnsaWxv2f5sOSe6q9LpqT8bEk2e2AC2HlMlXU5nZCx9N7GjRuFcs9Nmzbh9ddfh9U6+JyuHH4WK2fWQU9FvmqsdNLhz/wo1xlgJUsnnYHMr8mHgHv/8TUe2dKER99vwiNbmnDvP75GKMssd6FZJ3z3GLLdcFfnRYKJxGe6dm4DhucN/aA9RvtN/O4C7TdDWKIGBInQUxqYEhKssjonTPw1ub7uTOjrTp908OnyRTTaPDKmHN4ErcAOGa2XDk9Q1P6bLxolasvNF42CLvpvZ4DFTReK/37ThaMip6VR2CkSpVYaDy6agPsWjsODiyag1ErDnqh1EtUiW7BuJ5Y8uwsL1u3AzhZX5AgvEVHmx9TqvAija4gvaEr0UxUqBjvkyj6POwMYYaYxY5glklzixGPEn3Ag1eoMYNPOCBv3savOxP2LJoAmCbB8CC/vasFre1pQZNYJ748l5nrPu6/taRHaMHt8Oe7e/GVSzHD55Eq8ursFhWYdbpwlvsZdc+rx1PaD0FMahBPmKz2lwd7jLmGua/GykmXiHf7InC6ra+QKIiU0wDEvi10n3GjxsplHjATQEeDwVZcPHQE+eX4+zaBEXJoKcqYEnqg+q1KQi23TII5khUIzjeoCA667oBbXz4z8V11gQIGSh008cMFIOzYtm4ZHrzoTm5ZNwwUj7adckg2I6EZL9ZdsdaOzgSyTyJPGXKYQHL6TP5ZTgQ9Bco/KKzzmTjYYPpS0bt44qw5MGhtnP8sjz0iL5oc8Iw1/Gv07jLDkfcNpbCzkZF1i67WKoYeMd100TUOni2RvGYbByJEjcfjw4QFr2EDBqpd3X8kUSoi7BmWvmd2iRhKQ1Mois2DfHXOweGlXc5LBQm2RqV/aWack+qOLkyb8HCdpxZ1YRpkvwwDLS3je+UZKsq8/mKCpYaCTxcL1lEYwVfAx0gxIHxNfBGTNEBJERHVaDSryDHhw0QR4GQ4mWguNBtBrI/ehtRpJR7DfXzZO+iGFIbk5S1uLLMp6a2zugV2nlXWKHTJQoJ+qUDHYIVtuLrW5ThgjLpYXfa7VGcB/D3VgZJEJt7++V5iXb75oFHSkBt86/cL7Y4m5FefWoNxmgEmnxQPvfi24hALyhgwxuYnHtx3Eiu+OwLrFk+EJctCAwOPbmnCg3RNlvsUdSWM6b7G5bs3c+j7LxGVP31OxejTAtsMOUcnt3fMacP4Ie//KXFRjliQUmGj8TmKt/sPlExW7p1KGW6kgf0io7H1NNInrzq9LYpeZdQrrCPPAcDONiRX2iGPdKZhkA1IbZ+UCcnNZcQ6raWwG6T3m/Qsz32Nmi6xKG08huAKcpETNXYX1QAreT6GZxpKzqpP2xoVpPJ9ur7Q0Tk2hCSNSmCmosi6nHzL+VUtLS+FyuXDhhRdi6dKlsFqtGDZs2EC2bUBgokiRLoVWE9GlMGUhAJsvU5aVn8VJmUWBElcAaHMHJSeE+lIL8gsyKxlw+BlJHS9HgBn6iTYgrosTmxSz3CzY9bTkQv1QQtBNgpBOmCbUFOkpUtJpKFHs2KzT4o4fjEG7O25kUGTRwRzVaMszSm9U7QnlAgFWOjEY4OJmCK4AA0+AS9LScAVYlOq0yNdTuHJqVdL3yU+g23cHeXx9wpP0nmEWPfKj3ymtRYsA9pzwYH+rS/jOY8usmFRqHtobvQHupypUDHawHI/fXHKGSFco30iDk3OAi44RD8fhnvkNglZmdYEBPzu/Dtc9twd5RhoLJlWAICJ6PKBJvLSrBXfOHiuw1Hp8DAqMNLp9DChthI1219v7BQODMaVWyXn1aI8PCyZV4LGtTbjvn9/g8asmgdRocLzHh6unV+Nojx/P/7cZ8yaWo8JugJ7W4n//7ysAwHUX1IIg4pqxzV1+0bVjrPURedLlbKkMEY65k8tw73xzH55bPr1f67xqzJIMLUFIOmJmIz+SCkVmGTmBNFz2soHNIG3AZDMqGyt2e1k8tq1RJOb/2LZG3Dt/HArtQ9u0ayBQInOgWqxwf+kLWhL43wXjRDqDwwtNyKWhq4EiJaWEDDlsVPEgTEgqgWKzTmQaRxARc7miNPTXA6w0Y/OZn0wB0PfcVGCiJe+bTg5AlXU5/ZBxlPPYY48BAG644QZMnz4dbrcb3/3udwesYQOFEBEGHwqLdAbumd8AEJnvOp1+BjddOAp//NcB4Zo3XTgKTj8j6GX1/5qs5AbBmaWVdqFJeiLKZlDnGWl8I6XdYFAnikxg0mkkXUfN+nidTps7IJkwHV5gFDY93V4GW75qxZOLJ8PhZWE3Ufj7zsMos+lRQhsAAKEwD52WTDIyCIV5ABSc0ZKW3kk0pz8ugGukpU/wpo2IM+e0Gi0e27a/V5DbhPsXRt6TryNRU2gS6bbUFJpEBgXdflZyIZxYYRMSbeksWt1BHoc6vUn9dXieQbiOChUqTn2YdBSCfEg01m++aBSMur7XZT4EvLv/Wzy7dCocPhY2A4Udh7qQZ6SxeEa1aD68c/ZYFJlpaBHG0z+Zgm4viyIzjeZuH/73nbgJw+8uGweaJGCgtXhiW6PkvLppZzMWTq6AntJg1eyx+O0b+9DqDKDMpscdPxgDggC+P74MWk3kOiaaxE/PHQEChOheq2fX44ntTUJiT+RixgIX1NnFGnVpuI4OlOuzeoKfjE5vEH5W3E9vunAUOr1BDFOovN8dZJNMQe6e1wBvkM04bk0HAZbDL2aNwuGuBBOGWaMQTDiYUwJcKIQfTqlKGnN8tnospwkqrZRkf6myUjlj6bn8HBw+VjRubrvkDLiMFJCj/UeHJ4h8A4n110xFhzuIIosOLl8Qnd4gynI0vwU4Dqtn12PN5v2iNULpMXey4WVYSXKBl2GBFIl8OfM4TzB15/azHH52Xi3uSkiu3jWnXkQ2kIPqOnr6IatZYNeuXWhubsbChQvR3d2NtrY2VFZWDlTbBgRBVtrRceOyaRkbwui0pGTZ2+/my5S9pYFCE40jvZIBN180Kussd5GRxC0Xj0Zju0cIcm65eDSKjNqMXU68MtoN45ZMSTm5qUiG08/jz+83ivrTn99vxAOLJiDfHE0omXWS5gKJp9EaTRgXjC7DTzftFi2uZIKuTiik6dPIwG5IXX4aBiTZaIlrhDvISga57pgbUBiYNMyMfBONNlcQpVYdqnsJBnvTKHVJZ9FKJ2GnQoWKUx9+lsdD7x0QjfWH3juADddMTRZeTzBRsRoonFNbjGvWf4InF09Gpydy4HX5lIok1+512w9i5aw6tLuCWL1hFwJsCCtn1Qprd+x9v319L5afU4PNXxzHrRefAUqrwYOLJuBQpxdBLoRNO5vR42MwpToP4XANLNFNWZlNjyVnVeOXL3+OPGOkvOXBd+OHenfOHgurXosbZtbibzuPotUZwJrN+3H/ogloanfj7JEFqLHqxEE7C4ww0/GyoTTkgwbK9Vk9wU+GkdYKB7VApL/88V8HFHXEtBkotPT4xaYEHC8yOlICZh2F/d+6k00YMqyoSBcmnTZp7D7yfiP+tix3wvmnFHjg/JqI3lybO4gSiy7npg4mWov7ogcMQOQ3ve+dr3P6m+abaHx9woMbX/pExGg7w2bIWZuMlBav7mkU5H0MtBYbPzqEX186JmdtUgJ6rRar39oj6g+r39qPTWn0B3kTi9RpERNN4a63xfe96+39+Pvy6akbrcq6nHbIONH26KOPYt++fTh8+DAWLlwIlmVx66234oUXXhjI9mWNLo/0aWqXh8m4Vt1Ek5KJhmzKURk+hL//t1nEAPr7f5sxvsKW8TWBCJunpcefFOR0F5mQnyG1WU67QWlR3aGKdncQzV1+URINiFh0x/qol5FmmnmZ+AkKTWqxZrN48l+zeX8kqZxwL0ntBk8QIyw0CAKSNPjEipYOmXLk6gIjhkc3TzY9hfe/PhFZ6IMcjLqIjt+U4dGFXgNsO9S3/s8wq15yISxLpL+nsWilk7BToULFqQ+37Ck1B9HJmoRu2D3zG7B27liEQiGUWHXY8NEhXPvdkaI1+dXdLZg9vhzNXT5RYi0UltZgIzXAD6dU4Xf/9xV+NK0KBWYar+45htnjy3H5lApMqLDDrNNg8xfHQWrKsWBSBUgNhNhiwaQK0SFBnpFGmysALWEAw4Xw8/NH4vFtB9HqDIDleIwps0ZcmQcgaE/b9TkF1BP8ZHjkYqgAp5gjJsuHcc//91XSerpR4SSFnAnDusWTFT2Y7ZExfnL4WVQPIZ0qRRECTFoN8o00TFoy48P5gUJHH+zYXP2mATYkaTyw/pqpQI5ybTSpwcLJVUmuozSZsf/hoES3T7o/9PhYDE/RH3yMtASOn+kVK0igyyu9j+ryMqhKhx2syrqcVsg40fbee+/hjTfewGWXXQYAKCkpgdfrHbCGDRTMUUfH3sGFOQtKLxcOwUSTopNBE02CC2e+CnmDnCQDyJvGoO8LHV5pNs+4chvyM9Sp6JfgtIqUkDMXELHVCI0k0+zueQ3Cezpl3EC7PPGEnax2Q/ReXV4G7+6LlJ/2eFnkmSg8++FhFEyvQlX0902HXcfwHBZO6rXQz64Hy0eSW+no/xQZSMnShSQ2pgbwciG4gjyMulDEGS8hh5ZWwi6B3VJo0g19swQVKoYg5MZ6vonGV10+YWxL6Ybd8cY+rL9mKlg+hGPdXtx88WiccAbx9IeHRGuyVgO4gsnJe6n7Tqyw43CnF0u/U40Smx6FZh1uunA0Dnd68NKuFqzbfghr5zbggcvH4cbnv8Dt3x8DLhQ3ViKIeAKvzKZPKmO9cVYdVnx3BO775zegtCRuevEz3DO/AecNt2e9KU50fY4Z2ph0mojrc3/mRvUEPwl5Jmk2RZ6CJZxy8UGnR1mBdH8ajulKwKpXRvf4tMEg1LY109L7OROduxL0HtlkD6NY0jwVXAEOW7+Ox/H5Jgp/23kYlXmGnJWzKgEjJW3uZqBSJxSteunqnT9cPiHlZ/Uy99WncV8Vpx8y7hUURYEgCBDRo16fzzdgjRpIWHSkpA2vJQvnoR4vi1d2t6C22IJKuwF1xRa8srsFPd7MGV1WPSVJc7em0JZJBbk6dG8W7DOdVoNbvzda9Exv/d5o0NosJxkC6Ahw+KrLh44AL+kyORShIUJYO7de9DzXzq0HqYn/bmZdpFRoTGmkz40ps+CGmbUwJwQYBVGx40RENpnxxd5Ik1jT615r5tbDGB0PRWYdZo0twe7mHjR1eLCnuQezxpaIkmg+hsP9CxqwcdlU/OmHE7Fx2VTcv6BB5Eyq1WjxxPYmLD+nBtfPrMW1363BE9uboNVE7tMXsy6GDh+HR7c2CtdYfk4NHt3aiA5fQt8lga0HHVj8zMe4/vlP8eNnPsbWgw4gYXgX6SOMisTvLDAqACGgfOfrdnx81IF/ft2GPSc8p03/U6FiqEBqrN8zvwG/eOkzLHl2Fxas24GdLS5Z3bCj3T60u4PgeIDnITiOxv7+yPuNaCi3gyQgmmtf3d0iGWf89o19eODdb1Bq08PPhLB8wy784sXP8OT2Q1g8oxp5Rhqr3tqHcEiDJWdVgwBQGk0WxhD7/wsmJZexPrylETVFZqycWYdvHT4hYXjUzWY/f4WBhiIThtn0sOq1KLfp0VBkymyTHT3BH1NgjJzin8ZJNgDgw2HJ/sKHlXswRTLxQZHC4vZy9+1vCXJ/EWAjOlWJz3j17HoEWIUrL6Jx7CfNPad0HNsd5NHcHWHuPvp+E57cfgjN3T50p6FhpRQs+oHfz2WLQpn+XWjKofEAEcLMMyIyMr948TOs2LQbM88oA5GFNvlghEmnlewP6SRe+XAYV06twtMfRvr30x8ewpVTq9Kag00yeYVcJnxVDF5k3CsuvfRSrFq1Ci6XCy+99BJeffVVXHHFFQPZtgEBQUQcSBJPZQMcByKL1c+q1+LScWUits6Ns+pg0Wc+2Tv8rMjdDIgE744EEfpMUGCSZp9l45Da7WOgJQgRo09LEHD4GAzL9LREopTngYXjMaPCOuSD8nBYg5d2HU0qs/zVJXE9BVoLhMIEbknoc2vnNYBOyMP6ZcpL/Wy8vNTpZyUNEyryDCihSXChENwBLqnUmEsQELYbKLS5gvhVghbcmrn1qEpwDHUHWSz7zgiRucey74yAJ6rRlg6Lr9PLwKanMLrUIjyXfx+gRCLaR5yM4KYHRDafq97ah03Lpomo47RWI+qviUnhQWeWEGXXNTb3wK7Tquw6FSrSRRiYUWnFc8uno90TRKGJxu/f+Upw5AywEcfL55dPl5x/agpNCHIh+Fhe9pCqzRVAXYkZf7xiIo51e1FmN6K5y4u6YjM2LJuKHi+LL1td2LijGa3OAACgqcObpOH2SFSX87GtTWh3BwXmeXWBQSjZjCXwHt7SKGK3JbbHFeDw/tcnML2mCECkvNTpZ7HT6cfIAhOK9BnOHwSw89jpuSYrDYePlZRfGFlkBhRKfIXCYdx+6RmC0ylJROLDkILJPQBwB1jcv3AcmjriZggji0zwMMqaMBhoLZ7Yvl/0jJ/Y3oQHFqZmrGSMIRTHOgKcpN7lhPKpOdO2LbVqUJ5nEMVy5XkGlNo0aelOKgEfw2LtnLEw6ih4gxxMei18ARY+NnfGAzSpldQQU7pM/GTDFWBRbtNh3eLJ6I4y9xzeYEQLOsXc4vJz+MfeVkHHzkhr8dftB1FbnHoONutIlNv14n5o14sM7FSoiCHjRNvy5cvxn//8ByaTCYcPH8bKlStx9tlnD2TbBgQOH4MuD4s//iuuM3LThaNQZGYydnciNRrJcsx0BBjlYNVrseSs6iTdN2uWNF9XQDr54g5mnsDTaUnB8SwGPaXJSshXqpTn1le/wGsrZgx5ZzJXgMXlkyvR1O4WAtHLJ1fCHYj/Rj4mjFW9Si1XvRk19Yj+jFoNKUmFTjTpYHkeEysLRIYJK2fWgeUj12W4sGTffmrJFOEaDB/GyykSg/lGGo19OtOGhQ1k4t+JhGi0wKzDj6ZXJ+lM5Ccsgm0uaWZcmzsoJNo6/BxufPGzpP4a61uDyixhCAXqKlScdBDAnlZxudOc8eU47ggKSa8AG0KA5ZJ0w265eDQ+PdqDXc1dmDexEsUyhwFWgxZHunzYuOMIfjilCrcmHn7MrUeeicYjW8R6m3IabkSUGVdi1WFUsRlfHHehucuPT5s7sWHpNHR4gii26PD41WfCQGnx9ufHhaRhrD0EgGXnjMTdm78UjBR+/tye1PNHinL503lNVhomWispv2BUcL3xs5yk06mfVZahZNFTokRzrAKiIk9ZMwSXn5XUvnUFooZMCmAojRmXjI6gS0EdwVT41hmCw8uIXnN4GXzrNKDSmJvkn0VHoRWM6BB89Zx6WHS5+73b3UFJ4kai7vNQgElHotUZFh36r55Tj7I0GI6FJlqSMJOOSU+3l8OHje2YP6kKnZ4gisw6vL7nKIoseuRb0+iHqlTNaYWsZoKzzz57UCbXEmGQcXfKJrPf4ZGexDo9wZQCjHIgNYR08m55dicQOm3q5Et/4e2zHDWzBfhbmVLCVnfwlAtQ+osis04yKXVGmVV4T6cniFHFZlx77kghufXX7QdF+ioWvVbSpMOS8Px0WmknrliSNB2jCy8jrb/mSzBmkBNA/uviyYCJQpeXkT3Rj2nBuRlOUmR2w9KpQHQhLZXRnCuxiJlxkto0UWbcYDJLGEqBugoVJxvdQR5troDoNYIAlpxVjfve+QZAjDlLo8PHik6kC0w03EEWdSV2fNPmFoL2RDOAG2fVIRwGHvjnN1h+Tk3SXLrqrf14dulUrJxVi1A0cH51d4tQatp7ntIQwN3zGrD/uBO/vHg0jjt84HjAqNPiJ+s/Fh2GvLjrKH5+fi0e39aE5i6/kCihtBq4/CwWTq6AVUfioX+JzRNanX58Y6IRYHkMs+ojDDekTuinmjdVZA6TLsICX/1WvG+tmVsPk045RoSRoqRj4aXKslwCLI8H/vmN6L4P/PMbPPOTKQCUY7TJ6TObFEyADKUxY6SldaiUTAangjvI4d5/JB/yP6mwsUZf4MOQjFNzyR4rseokiRvFOUqQKoYwIfns0yG9sCFpUsHkND4b5HhMqi7EsmfjTrN3zalHkEtjz6Aepp926PfMf+aZZwq6bIkIh8MgCAJ79uwZkIYNFBwyQpUOX+aMrlKrHkvPHi7QqvWUBjdfNAolVn3G7eySWaB7vKzg5JgJlHBItRulRWbthswXOpNu8C3qJwvuAIctXyU7dI4rtwHR4KwyzyDJ7qqwx22NGF7apIPh4880ldtZvonGlGoblnynRtSWPGO8D5ooGXfThIDdJ5O88kaTV8Y0TvQ7ZRyDOzwMaqKJtGobjfsXjkdThyehLMUssqE36qQDbkM04E7LLOEkYSgF6ipUnGw4ghy8DJ90aDGyyAwgrs/oDobwq1e+kNywdXoipXWdbgb5JlqYT2OHAZdPqRDYaFJj9YQzKLr/qtljYdZFtOLueENs7FJi1eHON/cJibMbZ9WBD4Vx7z++Es2vsTLT1W/tx5OLJ+OTIz3QayOizze+8KnomjfOqoOX4WGgNCgy0ejysbh24y5RUD+ywJgyoV9okinvV02PsgbHA49vaxIdND2+rQkPLlKurNHhl3fhVIrhBQAeCeOQyMGssgdZsq6CbHYGY31hKI0ZLUlIVh1oydyJzgVkjDWCChtr9IUOGZJALtljGkKauPH3a6fnpD0CBlgWRVbr2Z2a9JLN72aipUtz00nwqYfppx/6/at++umnSrRDMRhlXGqySeCEw5DULvj78swnMYNCLibuAAuzTitKvph12khZYoYnQC6/dDmqK4ty1Hw9Jbmo52eRvDtVoCHCkgwxTYJwqZ+RthDfuHQaEM3vtrmCeGV3SxLr7afmkUKy1i7jdmaP9gUTrcEVU8RtWTu3HiY63g875NzLvEGMiAbscmVXRdHklc2gTWKLrJ5TD1vCQiPHVitNTICFIuYcidBpNSLHPadM+bQrWpobE1DvfcKUC2e8oRSoq1BxssHw0qfUTy6ejOtn1uLsmnzU2vXY1eqWDtBdQYwts+KrVhcKzDQcflayDDRRBLn3WD3U6RHdf+3mL/H41ZPgDTJ4IKoHk2ek0eby4843GzF7fLnAjH/hk6P4+Xm1km2LJfbanAE8+n4TVs6qFbE7AmzEuXnFuTV49P0m6CkN7pw9Fi98cjQpqH/0R2emTOgXGQbPvDjU0O4OSpY1prNBzBSxeHKgY8xUsBmlY3CrwrGdRcZVUMlk5lAaM2EABSYqom8d0x4Lsjn9GmVyFQy23MVHhWnoDZ9syCWROt1BVOWI+acEkytmRJHJs8/PQr+8S4bA0+1jUs7f6mH66Ych/6saKFIygWOgMk+0tcskGjo8QVRlKO5q1Wsl25mtRpvVQOGpD/cLwTwfAp768BD+dMXEzK+pQACTryNRU2gSJQRrCk3I1w392nUtSWLN5l5JtF4Msb5cOmPJrXKbHrMnDBMlyW6+aBTKbHGmpTconXTyRss+fWyk/CmxLaveitLgo2uXbBItYXEjEMbNF41KYn1qoj+ml+HxxAfiE/0nPmjC/QvHCyw+ObZatZ0GopWsUqViba4AuhOMDPINtGR//ePlEyMfCAMzKqx4bcWMqGYCnbPAeCgF6ipUnGwEZJi07a4gnvr3IXx/TDEQBkot0ixWu4GCSUdiVIkFBWZKsvzs7c+P4+55DXh0a2PSXLp2bj3++K/GpPt/dswBAHhkS5NQ8mmgNfjhlKqkudgmwxgPRxN8pTY97l84DmadVvK7xkpWA2wId2/+UjBcSHyPHINclNAfRPPiUEOBeeBNqlLBRJOSa7LSVQO+oAyzjFFWLN4bZCXHl1fJ+yaMGUeQh11HnrJjhtQAYYgNuNbMrQeZQ713PgTcNWcs7nr7S6FNd80Zi1Ao9WeVAstH3G1jMXzsoJzjc+fOajVIJ7ctOUzkKMHkYkOZP3uaJPDQFRMQCkFIJGuIyOupYDdIr9FWfer9v3qYfvphyCfaumW0oGqLzBknxSwypWjmLLQf9JQG5fZebjp2Q9anjQwfkiwd5bJYmViOlwxguHTq0+UQBiaVmlFp0592Qb2coH+7O55EK7Gl1iILQYZpmUAXp2U0+/73sohmn+xJWIIWnJfhJNloiQHsCReD9f85IrrP+v8cQVX+WFQYaTh80kLFDh8LRL9Tt49HhyeYJKLc7eOFJJqLkS4VczGc8J4KC4XrL6jDnW+Ky7YqLVSc+RYGivTa+IKfq343hAJ1FSpONspt0gm0Tk9QlLCusFC4e15D0pwAAth33IlJ1XY0tfvQ4w0mJQl+OKUKfpbD5ZMrMbLYjHWLJ6PdHcQJZwA+hkOPTyzWrac04BOYtwE2oo315OLJgilN7PVH3m/Ew1eeKZmYeHHXUayZW4/1Hx7G1gOd+POPzpRNyMUQYENJG2M9pUG+nkovoT9Y5sUhBiNFSq6h2Uh6pIJOS6LEqhPFmCVWHfRaZRNtpEYjGXPcPa9B0ftqiNzcNzZm6kqtcDh8p+yY0UAjaAgCkblk9Vv78bcsKneyRaeXAcuHRX2Y5cPo9DKoyBFTiwhr8Oqeo4J7pYHWYuNHh/CLi0bnpD0AoCOlCSY6hcd6X1CCyUUg82dv1pM42MkLJnN6SoO18xowsij1Mwqw0ocH6Wi0qYfppx+GfKLNpCcltaBMabiSyEEJ7QJ3kEeA5TCq2AIvw8FEa+FjWLiDPAqymBy7vWzcwjihnLCm0IThGVKbbXoanx3rwpOLJ8PhZWE3Ufj7zsM4v7Yg43YCUCaoH2BNACUgK+ifIFyqISAZmGsSNlGyCTtXnC6uI6U1+2ILcJEcDd4Ub4uOIrH169bI7+9jYTdGfv+lZ48U3lNsoSXHXWHMuCGNZLUjwEmKKDdcE7eXD8q4pD6d4JKKEHD+CDueWz4d7Z4gis06cZINGDgXoIG4zhAJ1FWoONmQKgO/Z34D6orMGGbSihLrheZIWZRRp4GB0qLLw8Ci14IkNdje2IV12w/h2u/W4O3Pjydt1q+cWgUNQYj00VbOrMPmL77FvZeNw+2v701Kkq2d14DrZ9YCiBgkyM3X35xw49U9x/DoVZPg9rPIN9M41O7BLy8+A39492vMHl+OHYe70dLjSwr2b5xVh407moXr6SkNxpRahbk2FtTn60iVrZZDdHqDIBGOlOXF4r0gi05vEGUKsU66fQweeu+AUN0Qikqg3D2vASU6Q+oLZAizTtqkKZuD6XRg0Wlx3fkjYaQpgbFSnT8yp26QpxK8AWlNP1+QBTIkKWQLu4HCPf/fl6I+/NSHhxQtB04Fo47ExfVlSZUkpiyqprJFhycoSTCpKcycYJItlGBy6SnpZ29IY8/s9MWTbEC0eufNfdi4dBryLX1/3kBpJZP49y8cn7rRKlP8tMOQX3G0JCFJlyezSIppNJAUnZfwiEgbfobHqre+TJqE1i2eDJgyv26ekZK0MLZncfpTZCJx0dhhwml8jA1QZNIKAvSDAqeIu4uRJiUdyBJLOnxMSLLU8nfzxwHGyHusMi5biXTxdndAMvE6vNCIKhMVYatJULG9bJytZtNrMWtMmej3XzO3HjZD/D6kRiOdjI5mBgtN0pp8hQn90pmGvbycOG6A7dURQ0ClicK4clskedUryTYg/eQU6W8qVAxZJASxh7r9ONDuxh/ePYAeHyMaix1+Dj/7+6c4a0Q+Lh0/DKvejCTMVs6qFRJsATaEV3e3YPGMalEy66YLR6G+3IJrNySz0e5fNAEBhsNDV0zE1ydc4EORAPx/zq3Fql6mB3LzdcMwK4JcOe58Yx9anfGy+Otn1qK5yw9SA6ycGUmo0VoC6xZPxq7mHtCkBkaKFBh1sfmnttCA9T+ZAh/Do8yiEwX1KlstNzBQWqx6+9Ok3z7m/q0E+HBIshIhFFb2h3cHWZRa9aJ4udSqhzuYuU5wOiBJAIRGVPq4dl5D5HUlQQJHnAzaWt0otehQnWDMpCgG6rAwCotMeZxZlzvdZC/Dnfxy4BRg+BD0Wo2of+u1GjA5rGe16KTNxrIhmGQLRZhc4bDks0/ngm0y1TttCZVEcvCyHK6aVi24OMfiAh+T5kBXmeKnFYZ+oo3QoNRKY93iyej2ssg3UXD4gqCIzEsyaZnP6jSZX9MnkzDwpztwZUCTGknGz/NZuM+0uFm8+ElzkktmbZEpZ/RtKZwq7i49PhYv70qmP5dfOBrF0WSbn+EkSy0TJ3aLXovbLz0DnV5G0DQrMNGiE9xiix6LJlegqd0tvGfR5AoUmyM6bmZai1f3NCa15VeXjBHdU7qkYBoQPcU74UpI6DEcjHScSVluMCOfJjGu3IJ1iyejx8siz0RBQ4RFmnwWvbSOkDkhWBgIx9CB6ienSn9ToWKoI8CHcSA6xwHJY7HTyyDPSGPFeSOx9NlPhDEbCsedRPWUBq3OADbtjDADSA1wZqUNHA90e1hc+90avLq7RUiGBdiQMK++/XmTwLqIMdGau/zC+x7e0ognfzwJN104ShSsr5lbj6Z2N57+8JBkSaie0mDaiHwc6vDgiikVGF5ogsMbhIEi8fCWxsh3OrcGdcVmjMw3CpsYuy1hg6UG9TmHJxAxxFgwqUI4oH11d4vg/q0EzDQlJCiAeHJ4YxpOednASGnx5/82RpzME2KK2xJiCiXgZ8J4bGujcDgJAI9tbcT9CycoZToKkMDWgw6seiuhHG1uAy4YaVc22abAIZ9LxkTKnUNGm05LSvZhJRPUqeD0sZImZNfPrANyZIhgNUprficehp90KCCL0uWVfvZltjpUpWDKyZqtpTH/mmktnvtYzBh87uNmPLAoDUabitMOQ373x3Ac/GwYv+rFvmF4DkBmi0WrO4i/fHBICJL4EPCXDw5h9ZyxKM8w0VSchXtKX3AHWMmALhvXUQ/DYtFksTPlXXPq4WGyPKEc4BO5U8Xdxcdw2NXsxK7mT3u9Ho/M5Eo6ixIcbhx+BnqKFOmVrZ1bD0eAwbDoAsuHI6+PKraInKT4cOS6OorA5b1cR9fMrYeBitM1u2Sea7eXRbUp7jr6o2mVooTej6ZVxg0TCKDVySRpJI3ONwq/eb4htRNt2o6hfZQQp91PUvTPU6W/qVAxZEEA3/T4EAoTqCk0I99EYVSREf/7zgG0OgPCWCyx6PCz82okHZT1lAav7m4RkmCtzgCe/vBQ5BDDw4rmrJsuHIVQOAwvw4MkgCnD84AwMKLQiN++Hnnf7ZeOFjmLxpJznzQ78OruFiGJd1ZNATSaMGjSjMevnoTPjjmiSbvjWHHuSLy+5xhWzqzDb1/fi9njy/H0h4ciZTIUiQIThXWLJ8Md4DDMqkdFrDRebv0c4LVWRf9QZKGx5KzqZEa3Qo6jQMRQSSoW7HDH9VeVgIYArp4xXBQLXD1jODRZVICkA3eQlWSduIMskIKxkimOOBk8tq1Xcm9bI0YUTlDMTRZQ5pCP1GgEiZieKEnhbzsPY3yFbSCb3i84/dLlrC4/q1iCOhXsJkrShMxuzF3Mx3Jhyaorls/xJD/Asih2k1by2dvSePZ6rQb/u2AcDnd6hXlpeKFJ0FLtCxqCwM/PG4njzgBCYUCrAX5+3khosilrUzFkMeR3f1oNKc2+yeIUz2aQpuVm4+iiJYEHFo1HY3vcYbG22Awqy1/IrKckAzpzGu4octBptbjr7T2iZ3rX2/uzOxlVwvr5FHF3kXK2603z1mo0ePjKCWC5uEMORQLaBKVrI6XFqrfEv4vgGBqFhiDAh8VOUqvn1AsLxLeOII53e7Bh6TS0uwMoserx4YE22A0USksifcZES7c3sdQ1FA7D4eeSTApC0R/zmJvFo71Omx/d2oiRhRNRGTstDUuXaIuQjt5Bir6VVj9Jo38OWH87BXQFVagYjHBxPLq9LJo6osFzZ8Sp+L5FDbjzjf3CWORDEQHtEUVm0Zjd/k071sytx+PbmjCq1CxoaFn0WlAaDW544VPR/PrHfx3AinNr8Oj7TdE5cBSe+7gZN84ahceuOhN8OMJeeuhfyZpt4TDQ6gzgtT0tWDCpAi09ftgNFFqdHtz7j6+F9985eyxe33MMl02qxIsfH0Vzlx8EETe7+dX3RqPMbsKu5h6EwsD9//wat1w0Wn7tVEvccw6WD+OFT46K1r8XPjmKKdV5it2z1KbHdReMRLs7KMSY110wEqVWfeoPZ4EAx4PlQkkHgIFszLPSgE1PCUk2ID5eNynI4Ov2STud9vhYRRNtShzyWXSkpESIJYflh4VmGldMLsP8SVXodAdRZNHh9T1HFXXrTQWS0EibkOXQNOK4IyBJBrn9+2NQOYiqjrIFSWjw9/82i+bRv/+3GZOrJqb8bLePkZyXenxMSp1MhuMRBiH67F1z6sEoPKepODWRQ6Pmk4Munwz7ppczWH9g0EYcXfRU5PHFkgjGbMQvwwR8UffER99vwpPbD0UYTeHsMuR+lpcsHfX31rDqB+ScKTvcwcyvKXMi1+HnMr5mTBMg8XcS2E6DCCZKK9mfTHRiO8Nw+njc8srnuO21vbjl5c/h9PFI3Bn1yPR1h48V/s3yYcFQIfb3NW/vF066yqw6VBZY8JP1H+OG5z/Dkmc+RmWBRUSnNtKp+7+fDQkbietn1uLa79bghU+OCvft8jH44ZQqPP1hpL8/9e9D+OGUKtG4POaMBAt89CvFgoWWBN2i6KNBkV6LMQXGSFDZa7OYqm+l00/S6Z8D0t+im+AF63biqqf+iwXrdmBniwtQD8pUqEgJZzCE446AaB097vAjwIRx/QV1KIqedMfK63u8Adwzf5wwZmePL0Oplcbv5o9DhysYmW9f3YubX/ocThlx8MTy1D/+KyI2f/vre8FwYfB8WDBGiL3nkfcbcefsery2pwVlNj0Wz6jG0x8ewq2vfIEbXvgUXoZHnpEW3n/35i8xvaYId2/+EtfNrMOjPzoTNYUmXD+zFnlGGnkmGtc/twePbInPow++943s2qnEWquif/AyrOT6F9GaUgY0ScAd4ERjwx3gQGuVXVwoDYlVvQ67V721H5RG2YSNHPO+y5t57J8KeUbp8ly7QdnkRuyQLxHZHioHubAkSSHI5S4bX2QhMWV4EZY9+wlWvvAZlj77CaYML0KxNXfJvy4JVrTS/SwVSqw6gQzy6PtNeGxrE3p8jMhgbSjAE5CeRz1BNuVnDbRWcl4ypMFu0ZIk/hLVzI7tb/7yQRO06QpAaoBjXha7TrjR4mVPg0zM6Y3BlXFQAFYZQU9LFoyuLi8jKShfV2zOWKPMK6N7tf6aqUAWTDmXX1pQ3h3ggAwZ4PlmWvKZZnOq9K1M8q7VHcy87E4BTQAlEAyFpGneCWKqfiYk6H4AsUUh4pAT0xuxG6V/l0Tji27Zsk8GsOrA8GGhNCr2tzvf3Cc6BRb1/wT9tbpis8BGYzhe8mQ3Zn9t08voxSyN38ekk3YMNvZmtaVAyr6VBisurRPjAXATUnXeVKjIHJ4gJ3mw9PhVk3Dnm/uEcVRo0qHUQoPhgT+/f0Ao3xxXYcO3ziCau5zCaXXsOse6fZLza6KWfIANoSrfgDwjja9OuDC2zCo5bwRYHpdPqUBdsQW3RtnFie1dfk6NMO8F2BAIAsgz0nD6GGFzEGO7mXRajCo247ujikEQQJDjcc1Zw+EMSpeGqiXuuYeRSr3+DTScfumxMa7chlIFnTjlRMfbPalFx7OBScbZ3KTgd5WLr3p8jKLfVQmheamy+gAbQofCv1tfaHPx0nHwsmkYYc5Nss1ASfczQw5dRw2UdFmkgRpaGR09pZWcRzekMY+2y7h+pzMvOfzSzFWHnwVSMVc1wLbDjiTZnPNH2MUmbSqGDIbWqJOAL8hh5Uwx+2blzDr42cxPDvNNtODkedtre3HrK5/j0nFlWSWaujzKnL4VWWjJk66CbCyVtZFyw8RnmvjvTGDSkZLt7G9SJQlRttPU6jxJttNgQIcrKMncanfFGYKywaqIRRiWZJolfulii/TJZ3HUPEBefy3eD616bbz/vxrv/4ml01aZRFoswe0NSrNDvAknUXJMPyPdv0A5rb6VghWX9olxiuukQl+bYBUqVPQNHyNtKuSNvh4bR0VGEuV5Rtzxxj7BZOaRLU3wMyH89vW9IlOEGF7a1YI7Z49Nmo9e29MivEdPaXDc4ceSs6qRZ6BAkxrJeSPPSGHd9kP4ps0t2V6dVoPrLqjF9TNrceOsWphoEpdPqUg6gb9785fYd9yJH02rxuYvjgtMJZOewq9f34slz+4Ss2IJQEtJz4cGBRMQKsTo9MqxYDKvCkgFt4yLtzugLJNxmE0v2d+ULlm16qTjB4uC/bzEokN1gUEYu9fPrEV1gQFF/TBnyggJh3wbr5mC11bMyLoUPFWsmAu0ySRH2lzKjZtU0NGEZD/TUbkrQ+jyBuHwsSL2qsPH5pRlByAiixLg8ElzDzoCfNaVGtnMo6U26f5dkkb/thsyZ64ec7OSZIZj7tQsPBWnJoZ8ZGXRU3hx11GRO8iLu47igUUTMr4my4clTwazOY0slnFAyXZRM1Ik1s6tF52Cr51bn1UCq8fHgERY0K8x0RFBfYePQUWGFPl8fWrh+6GKIotOkrlVlGCEUSLXPxKo4N1eFht3iJ1wNu5oxohCE0YIJgRh3DWnHne9He8Pd82pB4hIRGY3UqguMIjEu9/+/DisCb8DqSEk+//flsf7f6dHWni5yxsRXpazjrck3McbZGGkxEw/I0XCF2SBxHJMTWTxanMHUGpJEAKPYiD6liLW5BI4VXQFVagYjCiSMRWiSQIrZ9VGEv0E0OHj8OkxR1KQ7kgQ2+59HVpLoNCsw5OLJ2N3cw9oUgMjFWHdxt6/cmYdNu1sRo+PwZ+umIg1m/cnOffdM38c/vjeN7L30VMa1BWbcdNLnwmfufmiURheYJLcVITCwJrN+wUWXIANYdWb+0T/jrFiAWBfi0PSTdAVSOM0XsWAoFCmnxaYlEtiFMpUIhQo/JtrCEiuv6TCeQguHMIwu14UPwyz6wXjJyWgpzT4+fm1QnVKTNfs5DOJBuDhEiGsnduQ5KAKIne0G7k4OJ3kiFIgQUhWpJBE7ngsRorCfe+I9UTve+drRRmzKaGANqiciWBRGiaCNElg9Zx6QUonRhjRpVFK3yWT4Ov2pjaWaXMHpJPF7kBcn1rFkMJJTbT95je/wbZt21BQUIDNmzcDABwOB2666SYcP34c5eXl+NOf/gSbzYZwOIzf/e53+OCDD6DX6/H73/8e9fX1/b6nkSJx/QV1STTNbPTUOtxBnDUiH9ecM0Jw41n/4eGsqPAaIiSZENNkuah1+Vg8tq1JlHx5bFsT7pk/DsUZJttsehpr3v5SSMaEw8DmL47jD5dPzLid+ToSNYUm0WJVU2hCvi5LIfhoEmZ3WztKzLqkJMxgQJDjsHp2PdZsTpjwZ8eccSP9ieN5yUWB4+Nae2adFuV2HUaXWoSS5nK7DuaEE1xPkINGIknqDfCACQiHQ5KBIpHQD9NxHR1mM0iacJRZDQAAPyv9nQNs/Dvb9DSe+Wif0M/4EPDMR4fxlyvPjN84DRp2Wn0rlQtfGJhRacVzy6fLJvTSuk4KnKyEngoVQxFGWoN75jfgjjfi88Fdc+px7z++QnOXH+u2H8IDC8ej0EQhFE5OchnoCNvr1d0tWDV7LNZu/hIBNoTqAgNuu+QMsFwITj+LqcPz8fC/vsFxRxAPLpqAr9vcCIeBTTub0RrVkGRDITBcGKFwGPfMb0CxRYceL4MCsw7HHZET91d3tyQlvVbNHovfv/OVaJP00HsH8NAVEyQ3FaNLLMgz0kg0PIuVmyb+O8bmcwV5vP358aTDxz9msX6r6B8CDIfbLjkD970TN7247ZIzEODi699Ag9YSSWPjnvkNaW0ss0GLIyB5AFiZZ8xYaiUd+JkQNu04giXfqRHioQ0fHcIvLhwNGJW5p8PPyUrAFCpZSqhAEkNLkABC4liRYaOv5wY2AymZ/LMZc9embi+Dz472YNHUuEHDy58cRZnNgKocJU56/DJ6zX7lHHdTQQlZFJbnsWZufdJ+hQul1iB3Bng88YF4b/zEB0343fxxKEshLZUnK9OT+tmWWvSSZIYSi7IMXxW5w0lNtC1YsAA//vGPcdtttwmvrVu3DmeddRZWrFiBdevWYd26dbj11luxfft2HDlyBO+++y4+//xz3HXXXXj55Zf7fU+Hn8WLnzQLelIGWouN0cW2JEP3nOGFRlwyLtmNZ3h+5qt3OKzBS7uOJrXztkvGZHxNAPAxnFAakwg/k7kZQijE42fn1SaxosIhHkCGC0sYmFRqRqVNn7G+VRJOkVp4I0Xh1T2NSb/9ry8dK7yH0pIgiXBS0ENp4324wEzh8ilVIqvrNXPrUWCmEu6lxaq3P01aIJ5dOhUAQMq49CaehJlltU/ibWH5kCTrbWrUVU2rIfHE9l6L3PYm/H7BeOEaRQYSt1w0us+kkxwN+7nl09M/HUonSCWAncfSeE+2we4poiuoQsVgRIcriI8aO/Ds0mnocAdQZNHjtlc/R3OXH0A8uP/bsmnYebBDFKRXFxhAkYSQ+DLRJJafU4OqPAPK7JHkWOJBx5q59Six6uAJ8njq34eS5kNaSyYdNqycWYeH/nUAv7x4NHiew5+2HMSmnc1YcW4NRpVYcMLph0lHCu2NIcCGwIXCSYmS1bPrseGjQ1hyVrXo/b214+KsWAJvf348SV/m3svGodJKAapp2klBvkmHHj8rOvzJN1HINyrHzGH5MGgtIbonrSUEIySlkCjMHsPJKEHkQjzmjC9HU7tb0KmaM74cXEi54K9P6Q0FheiVSGK4gxxWvfVl0ry2bvFkIEfOla3OILSkOA5meA4nnEHkZ7H/ygZldgOmjyzEsmc/Ea0NZfbcJU7MtFYymZO1FE8WUEIbNM9Iwx3gRP2BIgG7IXXCyxOQ3hunU0pv1mlx97yGpL2lOY2y9AorhesuqMOqhM+undegrr9DGCc10TZ16lS0tLSIXtuyZQs2bdoEAJg/fz4WL16MW2+9FVu2bMH8+fNBEAQmTpwIl8uF9vZ2FBcX9+ue7gCHXc1O7Gr+VPS6J5i5LoVPxrhg47JpQIbJO6eflWynMx1xxT4gm3nPoiSTIDR4ZfdRkRnEhgFICsb0rRLF5bPBgCRhTgI0GmDZOTUIhQBEA+BlUXHuGBguJBn0PP2TKcK/PQHpfrlp2TQURoVinXLmGH4OsOjQKeugFGdrGihSshQkUfy1VUZLo9UVRLWZll3kPAEuHpCmYS6QDg27w8/hxhc/S3p2sSA0nSB1oN6TFqLjoK7UCofDpybZVKhIEyUWHepKrbhm/ccIsCGsnFUrmbTq9DL42fm1ONDmwZo59bAZKfhZHntbnHjjswjby6Kn8PSHh3Dtd2tQatcnuTWvfms/nvnJVHR7fLj5olF46L0DIqaQhkDSYcMj70eMDu54Yy/+eMVE/Pz8kXjqw0Ooyjfh/S9PoK7UikMdXsk1+5sTHmz+4jgeumIimto9CHIhPLG9CbPHl+PhLY1RPc64ZuoTHzQJ/757XkPEcTUM3HLRaGzYcRgPXTERX59wgQ8BD285AOqi0VnrOqlID16Gx22v7k36jTcszTyGTIVwGPjVK8n3TDQ6UgJGmpBkIRl1yjLpbHoaPtYjmJrE4hRbFkZoqVBiUUYCJhWUSGLI6V36mdydUuu0JG5/fX/S811/zdSctckblI67NyydBuQosWUzUJKVKUq73/YFJWRR3AEeN774edI1Ny6dlpJBGtMvTy47Td0eb5CDn+FEhxZ+hoM3yKX8zTu8nJBkA6KGHglGTSqGHnL+q3Z1dQnJs6KiInR1dQEA2traUFpaKryvtLQUbW1t/U60FcjoUuSlQfGUg5wgZ7srdX22HOTcUa1ZBgUcz0tqcvFpUGvl4AmymHlGqYg5tXJmXUTIfhBpvJwqtfBcKASGC+FQgkPQiEKT6OTVG5QR+Q7Gf8cTckKx7iCGR3+XmDFA735mjAb3chpHhQmaB63OgKTrblW+EZXRk85SOS2NaBKt3K6X/Hu5vVdAmiL5WmqRvk4iDTtVEJpOkDpQ70kL0fLTxuYe2HXafpefqlBxuoIPi/VTpcpDY5o+jR3epMOCY11e3HLxGWhsd4MNRcxlAiwPh0/avKXbx+CV3S34ydnDRUE3QUQOyaQ+QxCR//3qhAsAcN/C8Xjmw4O45uyRWPbsJ8gz0knlpKtn1+P5j5vR3OXHzS99JnIljV2vwETjvgXjcLTHj+f/24zVs+ux55gD4TDw6NZG1JeciSK9FjMqrCi/dCyuevq/ovap7sYnD+0y5kYd7iBqFCrtkuuP2R7mpoKPCeOxbY295EsaIzrJCoaLHoaXZNU3LJmiGCOLC2VeypYNlEhi2AzSlQsWfe5YUXJ92OVnFWUM9oVcjOVUcDMyJcxLp2ZcyZUtlJBFkTOJa3OnlnEiAMmxSqSR/2f4MO79x9dJY+OvS6b08akIvpVpc6s7qK69QxSD6lclCAJEOr1cBiRJwG4X04d1AU5S+0yv1SS9N12UeBjpUyurLuNrBnp8+P2CcUnJlgDPwW63Z3RNADD5Ofzlg32iIOcvHzThkR+emXFbTR5GZDABRDReHlw0IeNrAgDPh/B1uwcnXAGU2QwYXWQCSWYuKDoswEv+TmU2Q1btzBa9+ynhZdDqDCSdvFbkxdtpl+lzdgMlvEe2X5rj/dLkZ3HP/HrotVp4gxxMei0CLAcTTcJuN4J3BSTZav8/e2ce3kZ57f+vZkajffFux06cxc6CnQUSSGhpIAlb2xC2QHvpL5Tt5tJLCmUtLZCQsJQLoVAutIVbytqWfUvubdkSSCmkJSwBQiC7s+B4t7VrFun3h6yxxhpZsqSxbOd8noenjSyN3hmddzvvOd8jRyPKNaoCIpbOrlGlZCydXYMql1l5Dx8SUwqNut1WeDqTo0CuOWUyTBw3qN/G4YhohnAfVelQbKdK1t5sV7mtcLutGBOBZpj9mCKr0pYxEe1rDPY96YhEoti0ux2fH+pRnm1jtQvzJ5WCYYauipXWWKr+e/bjZ74YDm0YLu0YDm0oRDv62+nnu9pV/U9LA+3e82bBaTYmnSr/5u2d+ONFcxQttyIrj5+cOBGsAah0qh36VS4zzptTAyNjwHWnTcEtr36hipyrLbFgzZmNmuNBNBoveODAoe4A3t/dgVljSxASpL55ureCtF+QMaXCgbVvfIXFM6rx2SGP4qzrf72mziAMBuChjbtgNjLwCzIe3NAXMdwdllFf6QQA7Ozu0lzsJ76nUAwXW84XWmOpHmvIdLh8guY850pYQ+hB6zdeCFLfTtpgAAQpilZvGEfXuHX7Xt9hHyaX23HZ/EmqA0FfWNLtfo1+Ec9rSMDc+N2jdH3GzkgU9543C9c+/6lqnJtU7sh43dDfTssiUc01WrmDL1j/LEnRb4pthWtTIfpyOroOezWLkXX5BbjHFhWkTQBwqsOCaStOQJsvjDK7CWPdlkGta/vbaKpnX5HBs/+k1Yd/7m7HHy86VqWtV2TlMaPaPeBn/S2+FIEP6rFFay6ze7XbbDPrOw4nMhzn2NHcpoI72kpKSpSU0NbWVhQXFwMAKioqcPjwYeV9hw8fRkVFxYDXkuVoLNUqAW9IQFiKqE6bw1IE3rCQ9N5MYZko7j53Bna1+ZTN8KQyOzgm+fszxWE2orknrHK2rF7SAIfJmPU1AaDNF4bLbFQJ5P99hxHtvnDW1/WGRVxwXC3ue6tvAr765MnwhcXs26qDntoYK6vphKm2cTk900wpK3Novt7fTgMpTl4fWTZbeZ9fEHHT96ahzRdWbK7UbkJAkJT3cExU8345tu/7TByDSMSA6xKiEdcsaYSJY9DdHYAnJGsKF9eVNSjXYAyx1Jf+jkHGAOU9LZ4wPtzTO4n1TqrPf7gfJTYeY8wB7O3wY/3Wb5QFqZWPLYInldrgHKRv9aQJbqVIQYXDjLEOI7zekPL3iCzjl9+dina/oDy7EhuPiCyjuzsAFwfNgikuru9+XCw0T+Nc7ODek45OQcau1uR0l3EuM4p1SEPI1Eb743Zbh6QPDcRwaMNwacdwaIOe7cjUTm0mNsmZsOGrw3jy4uMQEiQl/Xx7e0BzoSxIUZVAckCUMb7YFitY03twUGTlNbXXntrcBAD40dxxqHCasf0bD35x+lT8KkHw/sqF9Xh2y35cffJkrH3jKyyfPwlWIwu/IMMblsExwHNbDqIrIODKhfVYt/UQojOq0dQR1HSuxa8X/9/FM6qVMeNgV99zMRsZuE2s8qzcKXQ2E99TKIaLLQ+WwYylAxW/0uveGSaCa0+dgl2tfevWa0+dAiaHdWsmVLhMmoWRKpwmXb93bJEF/za3VpV5seqMBtS4Lbp9b09ASCEBk/1+I1OOG2NPktnweNRp86lsFEi205AYQW2xJaZ/1Xsoa2SAkKivvQwEazDg5u9PQ6u3bx1c5jCBNRgK1iYjG9Xsy0a2cM+p0mnGT06cqFrz/uTEiahwmAs+troYoHZcUWy/0c8+42Q6lpqM0NyLm4zp19wVTjOOnajW1lt1RkNG41KqCs6lNl71Wa25rMikLb1TNIRz73CcY0damwYaS/tTcEfbwoUL8corr2D58uV45ZVXsGjRIuX1p59+Gt///vexdetWOByOQaeNAgDPcfj7zhb8aF5fhdCnN+/FlIq6rNvMGVgIckS1Gb79rEawOVTjCYkR/DahOigA/PadXnF4S9aXRYndqLnYKM4hddLGGxUnW7zt9721I6ZJkCW66KlF+pwwrb4wyu0mjB2GVUdTaWEEEgpWWI1cks1df9oUWFRh4AwkWVY5lWNVSfs8V56QjLe2f4OHl81W9YfakjqUGlm4rUbNyqWuBG0HbzhFSkb1HKD3fSU2Ht+ZUo7NezoQiQK7W334zpRypTCDy8rhu9OrVHZ51aJ6OC1ZDEkRYKzN2Gcn/X7fzoCAoKh+dlefPBmdAQHFvAVtAUnT9lRpVBnoxWX0njS0BUTNZzuj2qWLo40gRhNOM6epTeMws32pJNHYIUV8oVzlMuOcY2pgMTLgOQMu+dYElXNs1RkNKLbb8OJHO/Hw/zsGPMfigz0duOw7E/HiRwfR3BPCAxt24jc/PBrtvjBu661UGh9nrlpUDzPHYEqVE83dIdx2ZiMCYQlnzKzGyx8fwDnHjMVv3v4KRVYe582pwTWnTEarN4Rnt+zHz0+fhtvWf6lyrt12ZiM8QRFrl84Eyxhw8/eOwu3/9yUuP7EOPUEB15xcjylVTnx+sAcrFtZh3dZDuO6UKaqxiKobFxZRNmDLPvVB1Msf78fY4nG6fScDNpZil4AnKGKsO4cFZkbfa9Cc0/506Vxdvzcgykm6iqvX5aalnA6zUVuaw8wNwdydZ43joCThYHcI97z+tWrNWWzndXt+6egOCfCH5aS1XE9IKJgcjCgb8Nb25qQ19fiS7PeYuZKq4IccLfDmJ8+yKBHZgKCg1lsPChIicvoouYCgPT48dtGxQJoUTpdFO4gjk+q3xSYWE0ttqn3axFIbik0kETNaGVJH2zXXXIN//etf6Orqwvz58/HTn/4Uy5cvx89+9jO88MILGDNmDO6//34AwIknnoh3330Xp5xyCiwWC+68886svlOSZZxy1BhVhdDbzmzMSTNBiESUyl9ArIPe/MoXeOrS7B1NvrCUVAnsyoX1vUUbstcekGTgxTwXLuhOUTq6J4fS0brpqfU6YaZXu2Ke6WHmZAMGOB1J0E1hGUZZ8ACxZ3PP61/j6YTFakCQsXr99qTr/CFBk0SMyFg0Lblibrw/RCFrVi6NJjw4X0i7oIIvoVoPYzDgsEY6rFKZN2rAMx+q04+f+XA/5tTmP6zdZOQ0HcN/7n122WmrpZjIc1zs+sIpilXkULyFII4UPCFJ88Dq7nNn9IkjG4AD3UHcekYDfvfurqR596pF9Siy8mjuCSmL73uWzsC/nzABLZ6wKnIhHsnW3BOCkTEoTjagb5xZsaAOQTGiOjmPR6v9/PRpuOa5T1Fk5bFsXm3S/N/cHURXQMBtZzbCxBnwPxfOwR//vgcbd7QDiI3vDy+bjdvPnA4YIii182j3Carx/a6zp2PeWKd67svDoQCRPYIkY3ypM8kmBEk/LS8hEoE3JCXNyaKOVTiB1OvF7qCAcTo6RwbSzspWSzkdvNGgGa3Cc0Mn+5AvRCmqueb844/T61DphcOkfcivd0GPgQhLEhZMUa+pVy1ugCBL0FWEcAAiUe2sk0ghx3cDsPmgJ+lwJ5cCPHI0gs6AmHSfmTgUU1UI7vALqEuj99cVkPHgRrXu5IMbY7qTbnsaZ1sUOKbSjrEuM829RwjZC2Blwa9//Wu899572LZtGzZt2oTzzjsPRUVFeOKJJ/DGG2/g8ccfV/TIDAYDVq1ahbfeegvr1q3D9OnTs/pOnuM0o1V4LnsfY4cvRQf1CVlf02U2Kovs+PUe2LAz52IIAUHC0tkxx8nPX/oc17+wFUtnj0NAyH7jbudjaSeJmI0MbDlE3FT1auD0v2als3AlsocK1mDANadMVu4/roXBJugVdqWYFLr8fTbnF7SdNImRcTzLaoqkGtne3y6q/ffEoaKit9BBInFNijjesKR5ih13GPWEYunHj763Bw9u2IU//H0PLjiuFj0h9Yk7WGCfT8A/v/GiyScAWZiYL6QtoOvr/a64kHD/+1EJCfcuEs55ZDMufHwLznnkA2w+6Enpb8uWSod2Wyp0rlpGEKMBbyh2YJU4rvxgzjiVozpehXh8qRXXnzo1ad79zds7cc4xNcr7Q2IEPMfCaTUpTrb46w9siL3XbGTQGdAeo8vsJs25ffGManx1OKa5ds4xNZrvmVblxB9+PAdd/jB2tPpx8yuf49gJJahymZX3He4J4eZXP8fnB73Y1epLOqW/8eXP0RbQmO97DwWmlcYOP7a3B9AWkvM+phHJWHlO8/e28vqdfQtSVHNODkv67vCcZqPmnJbr2jYd8QPM/t9bnEOBgHQ4jRxKbEasXToT/3XOdKw9byZKbEY4dfxd9cKT4kDVGyrcoV9XiqI03QExxSf0x8RxWL2+X2TU+m057TFzRZK1+7okF86b0xaUFCdbvE3Xv/gZ2oLZ21NQ1L7PkJj+PityWGu3esNo6gjioY278OCGXXho4y40dQTR6g1n1vD43FtijR3Kk5NtVDPyRv9B0pbqVMsXRm2Wp2lWPlkHZt3WQ7Dm4GjyhlM4A8LZR4nF2srh1nUfqwaiW9dty+kEqNRm1Dy1K83hdNLCMZrCq1ZuSH3BBaHFF8ZX3/QkiXLWllhR0xuJZjdra+rYEyKubDyX1i7TVcxt92n/vd2nrqCk9Vsl/lJhMaJ5nXDva26L9snk04l2yQIbd3dj5Wt94dlrljRiwSQ3MIiD/3QVuTJJo2oLSlj75teqSJm1b36N3/3w6LxWCqq2G3H7WY1KxGw8Lb3GPvxSngliuOG0aB9YPXbRsTjoF1HjMCoRrO1eAXva/ZrjVGJNptoSC2w8qzl2Fll5TKt04LYzG1HhNKO2xKIqimA2MrCZuJTfEa+KGq8c2v89nX5BpacZ12L70dxxCIoRsEysMMMVJ07CQ+/uxn+eWDe46FwdogyI9KSKpuj0C0qF8HyTSSS6HgRFCb86ezr2dvQV+hpfYkNQ1Dfix8wxmtpZVqN+a8piMwuGYVR99vazGlFsZkfc/G03cZhT68KF35qoyoaxmQq3bbSbtVNzrQVKZQVia2qtwgPxNXUhSJUZ4StgZkR2mSMDk2pMy8QZzKXQ1uPY9BNfiY3HqUeVJklSlejoxCdGLqPe0WZPIfprz+GEyWUx4vIT65IqKibqWA0WR++pX/92OnI89UtV/rjVG856QReSosqpXVwkNRAWEZKiQJbN7QwIMLGMKm/dxDKKjtZoprbIgrmT1KKcq5c0YGxR330HBAlXnzw5qQBFbLEaO4ExGxlNu0w8tSl3aDudynpPccrS/B2ILSwe+8c+Vdj0Y//Yh7FFfY5Bl1Xbnl29f08VFdrpF1Hba5f7egTFyRb/+8rXvsBTlxyntl029t4WTxiVThNqXbzKEVdmYfG7Hx0Nfzii2KuNZ/ocaRmkUfWERM3U7p6QmN+S3BHgxPHDX1eQIIYjPSkiHrYe7MH9b+3AbWc2Yla1IzYWWTgcVeXQHKeKrUY88G9HQ5BklNhM+PKbHkytcqreW+Uy48Lja1Wb6lWLG/D7TbHTbbORwS2Lj0KJXXssjEaB9Z8dwi2Lj0KrJ6T5nh2t3iSn4aUnTESl04ybE/RhVp3RgEu/PQHWFOudqMEQi1Trt4dIFWWg0qck8o6N1/6drDo6MdLN/Xph4Tm0+dQFfq45ZTJqivVd10WjAMMgtk4VJNh4DiFJ0jV9ri0gacrKjMT+5DCzmjIiDnPhnFoWjsWNp0/FXQkamjeePhUWY+HaVO02axb7GOMqXDZOsU1bjkbPaM50lNpMmoEApTm0KZcxTZQNeE6jQvANGcgqFds4nNxPgmfNkgYU20ZWHyeGhlEfLuS2cli9pM/ZEJ8s3NmIrvciyVFNEcVcwnKj0ahm+mA0mtuqIGX4fA4nLe3+MLoCEq7rTUe97vmt6ApIaPdnGDargcnI4Vd/+woPvB0LxX3g7V341d++gsk4+gcub1jWTNf0hfu8RSU2HhZjzBG5YmEdls+fCIuRQbGl73dkGIOmXTIJ4Rk2E4s1/frDmiUNsPWeCAYFSeWci2/iEgVHnWYOXQFBFTbdFRDgSFhIekIirjt1iuo61506BZ7edE1Lb1ToFQvqsGJh7L/aEgvMfJ+tpoq+a0kMz+6Nelv2x39hxV8+wf/747+wcXe3OsXUALT7RJW9tvtEdYpUmlBuk1E71UcX++zVFVw0pTymT0hONoLICLuZ0xxXJpfbcdl3JuLBjTvhDUdwx1nTAUTxTXcwVqwgYZz6+elTUWLnccMLW3Hd85/hJ3/6CDazEeu3HlStJc6bU5OUtrJ6/TZce+pUXHPKZNy9dCb8IRGekIzrT1OPhVefPBnrPzuEC46rxbP/2g+zkcUti49KGnef33JQdX+h3ii2/V2BpHF+fKkNB7sCSde5cmE9Vr72hTpFxwC0hSQ0dQdTRhkQ+uG2cprzcC7r0nRwrCHJ1q9aVA8jq2+ucFiMKNHvQMy+fv3mDiW6XS8CooybX9mGFX/5BD9/8XOs+MsnuPmVbQiI+ungDRS1M9IQpKjmulTQOdV4IMKyhNpSCx5ZNhu/+cEsPLJsNmpLLRDlwkVqMQZopi8yBdxdW3r3uf33vRYdoznTUWZlsWJBvUrWYcWCepRZsx/zTJz2mGbOQBOx1RPGlqYeXNk7Plz5l0+wpakno/TPnqCcJCOx8rVt6AlmOLb0zr/bO0iu4Uhg1HsxIhEgLKorMYZFOadTrYHC/rMVdw2KMtxmTnX6FgiLCOW4KLBwLFad0ZAU5WTNoQqSy5x/QdIBdbQKVE1oqEiXzgkAohzFnX/9KunkJvGZp0qTbvcJGNsbSeYLCyhz8nhk2Wx09oY8y9EI/GEB4C2wmTiwhpDaDgVRddJu4VntNN+EFNUymwn+sKTqd2UOHmW22EmTO4Oo0Eqn9mlVooZCJlFvGVW07a2GFItoMyVVQzqS7ZMgRgqRaESz6uj9b+3AjlYfrlxYj66ggIYqO7p6+7uVZ7FiQR0mVzjwxTc9GOO24KpnPlGNF7et/xK/Pn8WXvlkPx5eNhvdAREmjtEcE/Z3+MExBtz7xldYPKMa277pwbG1RbjhtCnoDIhgDMAYtxmXfnsCfvfuHjT3hPDZIQ9qSyy4e+lM7Gr1YnK5A2IkkiSibjYymDXWjfvf3IErFtSpUpV6ghKe+XA/fn76VFW0cbxYg5Kik5Auetl3Jg6YVk/ogxyJwN5vvccwgKxjuFWLJ4QnP2hS2caTHzRhUpkNY8x23b43JGpXVc91bZsOfzhFNfewft+bWM04zkjtT6mKSeSSDZMrNp5Hc48Pe9t705DbgfGlNpSW6We/6Tic6kDYE0Z1DllOudDcE8KfNjeporX+sGk3ViysR2V5Ybb9bQFJcx2eS7Rnc09Yc0yrK7OjIs19ljp4zQi7TNI/UwYBZJIuTHINRxyj3tHWExLx3q7WpFzq8aU2jMny9LAoRVqc25pD6qjJiP1SAAd7vIqORYmNh92U20B9qCeIv/wzecC9/KRJGGNxZHXNQIr8/1jUU3YTcDodrazJczlpPahI4VBKLC6QSQqwJUVpeVPCKRbPcmj1+OG2JhQuCIpwmWy9/zLgoXd2J00+9543S3l/UJRQZjepnWh2U28aa8xeBTmCn7/4eUrHYECQNKPvHr9ojlJau7aIx93nzsCuNp/SJyaV2TG+iAd6tW8HinqLP5e0FW0zmPgyts80DjuCIPTDbOQ0ozBWLKjDZ4d24IENO/GnS+fik4MelZN/5eJpEOUILEYW3SmKGuxq9aG+wo2PmrpQV+7A4Z4grlxUpxzavfjRQXQFBEyvceG/396Bn58+DV8d9sDKs/jiGw8qXWYERRlPb96ProCA/1k2B9VuE845pgYsA0yrdOLR93Zj4dRKPLJpN06aWo6fnz4VXx/24rktsWvfftZ0WHkDvju9KilVqbrXebe/I4BH39uTcqxKTBd98aODuHJhvSolvr8+JZF/xAjAGqIospsQ8UZRYufhCYYh6uhoK7LySiR6nNi6VV+nidOivV7WuxiC05Je1zbfsAZg1eJpaPaElTVLpdMElhl5ISvDMf0wJMkQJRmTyx39ZGtkoEA6bS6LUdNh4yyQkw0AbCYudrD0l0+U12J6oYVLsdVDo81lNaLabcKUSoeiI1jtNikSNQPhNHNYsaBecf6ZjQxuO7MRzgzaUunUToOtSFOtFCC5hiORUf+r8hywqF8u9eolDchFCoMxALee0YBbExbqt57RgFzmUkGWNcsxC7KMrIXPAFQ4zJoDbrkje/0AR4qFUy5OwUwE6QfNCDk5EGQZt5/ZqNLcuf3Mxt7fPkaq6K7yhOguh4nD9adNUUqym40Mrj9tChwJxm4wRBEUo7ihX38wMLEH0hMUcMFxtUlacD0hQYncYgwMfvFyshPtsYuOVf7dnkKDrb1X7NmX4rQ5MV32G4+ENl9Y1SeuP20KvvFIipM8k6i3yt6KtsnvifWBTCa+jOxzhNgbQYxW2lMcSJTZTcr/94aTnfyHPWGsWb8dRVYe/3Xu9KTxorbEgpljXTjcE8IYlwUvf3wAU8e4kuZrq5HFgQ4/zj5mLK557lMUWfkk/Z4rF9bjqc1N2HqwG+fNGaeKvrtl8VF4a9thnN5YpXJ+rVnSCH9YxH9v2IFVi4/C29sPxw7PEkTKAcBt4fHPPa1JxYruOnt6bKwC0BOWcNl3JgKIOQef2hyLCGgc40Ct20JOtiHAxBnQE5Rx1bMfJvzGDahy6+eQcZpZ3HZmY9LG0qWz5lZXQExy5sYiS0VMyKHQVzqCoraubVjSL6ItIErgWFY1Ltx2ZiMCooTiHIqlFQKeNWhmw/A6pxoPTBRy1KDWxTyjAYUcsIptHK44qT6paFch9bp4jtEsWMcXsLicHsEUZqMB5/fTEYyl5Ke30e6gdqbLHy+ag/I0fdVsZDQj5zNJzdXD4UgMb0b9r8oYWM0T7idzSHMUIxF83NQeqxLpC6PMbsLLH+/H+FJr1teUI9p5/n+4cE7W1wRiTkGtATeXudIT0l44ecM5pNFlIEg/WEbKyYHTZEQ7K6gixDjWAGeC45IxQHPRwyaM6yYjoxlplqjRJ0jQ7A9PXBzrD84M0oJ7QqJmlSVPSAR6T3RSVkntdfoVWbVPSxNP171hCU9+sE9V6fPJD/ZhSoUD6N001rp4rFnSmLTIGZ9QEMHOM5rPzmGKPZeMJr4M7HOk2BtBjFYsfKqqdJzy/7UisiPRWH9t7gnhsff2qsaUObUu/PhbE/FRUxciUeCb7iCWHF2Dy5/+KGm+vv/8WXDbeFz02L8QEiM455hkHbcHNuzE8vkTManMjquf+1T1t9vWf4m7l85UNg7x11e+9gUeXjYbLrMREQDnHqPeXKxa3AAxImP1+m14eNlsbD3Qg0eWzcZXzR5Uuq2KDk7/g4C40+/R9/b0jVMDzbkUsZsXQkJUU+PnyYuPi9c2yjt+IQJJVsuoSLIMvxBBcQ5SIukoshqx4avDSlaFlY85hueMTy86ngtWI4ciK5ckg6GncL4UNWhu3nORVSkUHMOANUSTnh9XQPExSYZmJkTiIe9Q4w3JmvIlT196HIpthXGudvtF/PXzZtVhzP9s2o26MjtQoIjEMqu2o7/MymWtQxxMMY4+kcE46k1ZhVkGnAN/1i9Imvuoxy8+FumieHTL3iKGLaN+95eJ/tVgMRiimD0+uUqkIYcVp1/Q7vQBIbfTt296tHU5xhX3VYgcLDzH4tkt+1XXfHbLftx1zoyc2hoXpE90buTCSDk5CEsR3PhS6jRLIBYhppUCvGJhPcb2Oqd8YRk3JDh64td58pLjUNa7uEylu9HmDWOig0+pP9jRG4kGAFUO7SpLFQlRkjae1XTw2nrbIciy5t/FhAhOQZY1o+tEOaF9MrCgzo0nLzlOqTo63s0DCdq4zT1hzWdX7ZwMd4k184kvjX2OFHsjiNGK3cRpjisHuwLK/9/b4U/q76wBymvzJ5cBiOCRZbOxv8OP8WV2fH6wRxWlcvtZjZp93RMSEUVU+ZvBAM33jSu2QoxE8LOT6+ELy3jxo4No7omluAdTSDN4gxJ+8b2psc3m+n6bzfXb8Miy2QiJEXzU1IUH3t6lOOCefH8P1ixu0DwIiDv96svs6Q+2KGI3b6SSgmjxhnWL8gpLMlav3540zz120RzkkjWRDreFxYXHj8eu1j5ZlAuPHw+3RV8nBM8y6PCLWPnal6qxoL5cP0dRJlIWI4WQFMs6ONjtVRyzpTY+lqZZoK1j5wD62MggbU8PBtLrqi2Q86TIZsR3p1epDmOuWlSPogLqCbcFJDy4cadq3/jgxp1oqDg66/Vxuv3MQFh5TjP905JB5Gl3QHuO7glIQJqKp3o4HInhzajf/WWifzVYDNEUUXIXZ39qlbLce47h5qUOk6YuR6k9+/sPSxJ+MGdcUkSbniH52TBSTg5Sis76+hZnDjPXJ4wdjRWp4TkD7AmaCwNNOnGncrr+YDcNHIkGACEpohl9ecy4IuU9npCAapdJVXSh2x+Gpzfq0ROStIWZSxuU0yS7STu6ThWNagA+PuTDtmYPIlFgV6sXnVVOHFNpV+mraaVPx+0gX2nLI8XeCGK0IkQkjHGbVVE7NUVW2M2xis1PftAEAEkR2SU2Xkkzqy2x4T///DF+dnI9xhXb0O0Xk8a7A50B7YhcCw8zz6j+pvW+wz0hPLhxFy49YSIefW+PElnWFRAwtsSq+Zl9HX5c98Iu3H3uDO1FflBEbYkF8XOIuAPu9/9vNsosHLa3BzQ/N7PahRnltrRjHUXs5o9U83BFmk1aLqSK4PBmEMGRCz0hGd0BUfVad0BET0hGkY4RP96wpLlOaVw2G8hBT3kgyhzav2tZDuvtQtEdFPG7d/comQtyBPjdu3uw+oyGtM4EvSh1aGdC5LKfyblNqQpgFLBN0ah2hlQhIyvb/QKaOoKqvWj89Wznj5T7mQzs025iNAuy2fj0jnhbqsj5DD6rh8ORGN6M+l9VlGWsXtKQlEstydk7hVq8YUwut+Oy+ZNUYbmtOZxGWnkWN39/Glq9fSKqZQ5Tzo42I2PQPOHPpaS7nddOBThufFH6Dw8huui+6UCZw4Q5tS5c+K2JKs2dxMWZw2TEsn6nwsuOH68SFC7JQLw2Cu3+EO09SmGZFKnGCfNHuy9VddO+U6QiqwnNPYJKC27VGQ2otcT+7jRzmg5gR8IpdypR8u6ACPQ6DjvDMva0+5O0ksYXWRRNlDILi9/96Gj4wxFFQNfGM312kKe05ZFibwQxWmHA4n8/O4QfzZuA7oAIt9WIP23ei3Nnj8MDb/eNNXFdsnHFFkwstWF7swdvftmMP150LHoCsQrD1W4rvjrsQbXLkjQOPbflYNKp9DWnTAbHxqo73nf+LGzvLYTQv0LzLYuPwoMbdiEkRpSIt3hkmcXIgme152wg9l6GMWiO8y6LET8/fRpuW/+l8npIjECUItjS7EWZPTbPbGnqUX2uymHKaHyiiN38wTIRzagGltFvorAYtQ/RMongyAVJjgzq9XwRFGVNiYugqN/3mlJoY5kKqI2VLXZee41mLaCgvollNPWxTVz2+5lcEWQJa5Y0KCmMcZ0wQc6+OFyupKyEWsDISj0OouWIrDmORpB+f58qDTkTZ6QtReS8jU8/D+rhcCSGN6P+V2UMDN7e3oyHl81WLbwv/vakrK85rtiCf5tbq9ZIOaMBY4ssWV9TjEgwcckiqmIkt2IIHT4hZfnjsVme6ll5FufOHpd0/7k6BfNOggOlOyzDbWKHpdNDisg4r5+g5+olDZAifZNFUJLxTXcoyaE0xm1Rqi3Ff4f+JzSJGm1hEZr94f/NmwAAMHEsbDyrigix8SxMbN9vm/LUNuEUKSjK2lVFLz4WAAcLx2oWbjAnaMWYjaxmaHfi/XQGk6NNfvP2TsyqcfWJDxuAdp+YNBmjDH22kI+0ZR10BoczDGNARMcqeQQxWDwhESdPrYQ3KCEkyPAyBpw8tTIpYry5J4RH39uD5fMnwsqz2NvmxQ+PG49fvPQZ7lk6E2Yjg73tfkSiQFdASBrveM4AloFqnDSxDEQ5gnafgFte7RuD7zi7Ef/9w6Ox9VAPolHAGxLR3BOC2cgg2tt9QmIEk8sdONgdQIdf1Jyzz51dAwB45N3dSYclq85owL4OPzxBCc09IaWdZiODsBTBir98ouhXAk3Y0tSjzCEHekIos9jTjlMUsZs/5KgBkUhEZT+RSASyjsOpkcv/oWsmMAaDZqEvxqDv9xbZjPjJiRPR7heUw8mfnDgRbqt+255Wr7ZUS12ZHdUWnbdbedZP5FI4/PW2l4HoDAhg0E83LiyiKyCgukBVPp1mHu2cqOrLRo6B01y4cbHKpT1WVxYoEhHI4MA7C6xGDjyn1rfmOQMsXPq+lkkmUSra/QKsRvU+yWpk0REQMDZNei7No0ceo97R5jBzmlVHHTmUHQ33Og36OxFySR2NRBhNEdVcRT7tZlbzVMqeQ6WpzoCI37+7SyVS//t3d+GOs6ajbBg628rMHOornejuDgxLpweXqmBHgj0FBVnTofTIstmALfYe1mBAmYPHI8tmo8svoshmhCBHwCYsaK08i6PHlaj6w1WL6hUnqZVn4LYa0e4XlM+4rUZYTX3OrXRRcUDMwas1iXX4BExymBAUJHAGg7oAhMGAkCApaR0ui1EztNuVEMXnF7SrlyZqGx7wiqpQbSAWqj2pdFbfpJivRWqedQaHIwxjwC2vbYMkyfjFyfXkbCOGDWV2E9q8QlJVOpuJSUoXvfrkyagussBp5nBq4xhc/PiHuPSEiegKCFh1RgMO9wSxbushXPKtCUlRaavOaMB//unjpMXyExcfh9+8rS5kcNPLX+CepTPx4IaYbtqlJ0xU5Bae2tykfHb7YS8efW8P7jh7Oi7+Vi08CdptiU65Ha0+VDhNscMSvwgja8Arnx7AkpljUWw14cbTpyCK2GHHtEon2nwhVLnMOOeYGhzsDuDaU6di68FueEMynvwglq6aSfpnxhG7VDAhPREDVq37Msl+cllDpoNlgBKbMUncnmX0dZyIclSXQl/pYKHt4GN1dPBZUkSBmfVeF+ugnxiJRjUPXSPRwnVmu8mI/1z3yZD2m3QIUgS/SKOxPNSwBkPSnHXNKZN17+sDksmB9yAR5Ch+/mJ2z748RcBAeQYpv26LEXf875dKEIAcAf74/l7cs3Rm2s9S5suRx6h3tMmRKNwWdeUhIwvIOUwWA3nCs00d9YUkzTB3X0jKSeTTxGqL0idGKA0WQZY1NdrEHNJxj2TaUqRitiXYU1DUdiglvmZAzCH3WWtP7AS3HagrtyNxamUZA+or7MnOuN4JuM0n4skP9iWlsV518hSUuGM2E4n0RYnGr9E/SjRd1JvLzGPdZzti35OQfrxmcYPyfkmOajq0/3TpXOU9Y5xmzai3qoSTu66goGmvXcHe0ycS+R403QEBkkTKrcTwQpC0D8Eev/hY/O2LZiVd1MZzuOeNr/CL705DUIzpm8VTOa08hzv/bzt+eOw4XHfqVLR5Q6gtseHRH8+BLySh3ReGJEc0x+MWb0jb8R+WlLRRQZTxyLLZ2PaNB+fOrgFrAMaVWNHmDaPIyuOmlz9XtNuuPnky/vyvJvzw2HF48oMmJS0pIMi45rmYQ6+2xILLT6xTORevWlSP57ccRFdAwMrFR+Hy+RPxq799hZAYwSObYtd96eODSvRbRmkrmUTs0liaEZ0pZBG6AoJuxRCMDAMzz2FHgvzEpHI7OJ03376w9trWH5YG/mCOBEVtLVk9HXxGBvjld6eqouhKbDyMOmeO6qGfaObYpOg1I2tQZR0MNT1B7Yr3PSER0KnfpCPlfrCAaZptfgGVvYfuiRrJbX4h6yJ4uXLAK2oGk/z50rlpo8BSkcuzj0SjmnvjSAYTVYmNxRUn1SWlC5dkojl5hGW+EEeAoy0oytjTHkjqTC4rD2Q5AY1xmzWdCFVO8wCfGphyl0mzkmMuRRuA2IJOK5S9vtye9eBmNxkVpwXQpzHzZK4nOEfoSXiJXVtbrSRhoihLKbja956wHMHBrmDSCW6Vq88ug4KEdq+QpHFhNTKAxQg5EsEpR6mrFV198mREIn3fG5YkzK4tVUXFXX3yZFUxjJRaGr1ib2U2FufPUadfr1nSiDIbh7i8QrwKXyIhMYJmTwjjem23zMLi2lOnYFerT1nYXnvqFFUFH5dZ217jp15tQQlr3/xaFfG29s2v8bsfkjgpQYwkUlVzbPcJOL2xCs9u2Y/FM6rx0scHcfP3p6LYbkSnT0Rp78EAAHiCIpo6gnjygyZce+pk3J2Q3n7VonrYTRx4jtEcjw3Q1k+rLrLgnqUzYWQNaPWGsKfNr5rrrzt1Cv7yr/1YNq8WT21uUrTb7ntrR8zBF5bxs0X1aPOF8dA7u/DDY8dhxYI6lDtMqC6y4NIntiQ5FC49YSIe2rgLa9Z/ieXzJ6r+ft9bO7BiQR3WvrFjcGkraSJ2qWBCZqSKpijVMbVLikRwSGN9MMaV/bo1E4psRs21rVvnCogBQbv4Q2K0e76x8RxMRrUEzOolDbBmoN2UC3roJ9pMDMaWWDC+1I42bxhlDhPkqAybqXB6c6U2XjMduJBpd4UobJK2TXYTvvKrNZJXL2lAbUnh2pTqEKrFG8p6L1qZ6tlnsG/uCWpLNEwqswNpotrafTIeemeX6rMPvbMLd587E05HZs620Z75MqLp9UXsbOqC28Tlnoafv5YNTyQdwtZZg7Z2QS4ng0KK07c5tbkVGHBajJqh7M4c9AwyEakfNEfwSXiRldMUUy1SaXpon74kPpxQChtKtHWOZRXnV/w9t67bpjhJ7TyHP/+rSeV0+vO/mnBvQki0ndeuBvp0gqO1OyTA1S+SlGWAnpCAMRYOBzwiHnpHnc750Ds7UVfWl85ZmYHORGdIxuEetXbdNadMRmepTdFoC4S1F9zBsATYefSERM2It56QSJtDghhBpCwIY+Vx3fNb8dAFx+DmV75Ac08IVp7F7hYfuoMSplU5sObMRjy0cSfm1DbCbGRwzjE1uPmVL5LG0+XzY1G4/VNRr1xYj//ZtDvp9dvPmo4vv+mBJyxj3dZD+MV3p+Fnz36quu7aN77GigV1SlGExMqhXQERd/zvdpX2ml+Q8eCG2Jx+5aI6zfGtuDdyISRGUO2yYMXCOgBQ0lHjhzd3nDUdZVYObYHcD7lGbcGEPB8CMinWkHqmNYbEKJ75cL9qzn3mw/2YXu3S7TsBgIGhIBUQXVbtscClo5ZXQJA1ZUCeuOjYrA/2M0EP3SdBBpq7haRUP7fFBBQoqE2ORjXTgXPJUMoVBtp9WW8NwoEIS5G0cjRDTaVDO0ClwpG9o9+QYhzN5NkX23jNvXFxBk6/Vm9Ys6BBW0JBOGKEooMvYgSvfDIjVUlzXw5h680ebcHTCaU2jLHYs7qmXuHH0RThsdEcJianxag5YDpzEHvVLaooz55pPegKSHhuy36liquF5/Dk+3swrmQqintPRzr8Iv76eXPsPQmVbuvL7RhrjdmHL4VDKdHWW1JUI2r1hjHBzsMT1nY6ecJ9TtSBNlO1ve+xGTn8x1PJGkZxZ1x3inTO7lCfmKjdxGpqwSXqC3aHJEWHIt6OX7+5AzOrj1UcbSnLr/cuQk1GDs9uUW9Ant2yH/MmzMro9ysIR2j0J0EMhMPM4e5zp2NXm78vPa7MBruZRUiMwBMUlbQjk5FFd1BS5sa//PuxuGVxA8ycAavOaEBHipR+K8+iym3Foe4AHl42G56giK8O+/DU5iY094TQ5hNw6QkTMbXSDgvP4bb129DUEVTGuF2tPs3rTiyz4acL61Bf4cBXzV6sWFiHdVsPwWpkseqMo/Dsv/bjqGo3WAaoL3dgRrUT35lcjqOqnJrj24RSG+5e2oiHNu7GoZ6gohF35cJ6PLtlP8ocJiyfPxEzxtix+UB+FpajUuhZh4V3c0+obz7vlU74n027Mb7Ehuos15DpCEvakh9hnSU/PCFR0949oRwOZjNAlmXNqHo5ot/9elLsNzw5SsCkQw/dp4Aga2rb3rN0JlAgLeZMDpOHmlT7wdoSa9aRWrnSkWKN3qFjano6apxGrFnSiJWv9Tlu1yxpxFinERkUCdWkuUf72Y8rtqYt9hcFcPP3p6HVG1bWCmUOU0bdJVWxiUJGMRL5QY+o/FHvaEuVllecw8LPymsXGMil6maq8OPyHDtuT1DUXNBNKrMDWV47JGqf5ofE7Bcw7QFtx0t7IIeT8BESJdfuE3CoO4yvD3uVBc2h7rBSOAAAyuw8Fs8co0q1vOaUyarUUaeF03aAJjy/lKHWvd9jMXKaaZaJRTmKbdqO1sSToI4UUY+dgZgzzm42YsNXh1WOwyfe34M546cp72/zCnhewwE5xjUZJb0VfntSLOJ7Eha2Rhaa1ViNvSXhfSmci76wCOixUMrVSTZC7JoghpqgKEGQYxU845XNQqKEkCjDbIwVenn0vVg0xDFj56gifAwGBntauzF7fBEqnLxmel9tiQUOsxHXJ4zDd5w9Hes/O6REnMUrmj68bLaSXg/0jaXxqqb9x085Avz3hl24/axGfNLUielj3bjmlCmQI1H84e+78cPjavHfG3YqTrvVSxrw/Jb9KLfzuGXxUbht/Zeq8Wv1+m247cxG/OdJdfhTb9GFeBvuO38WdrV6UV9mhyhHc1tYJoxnFQ7TqBN61mPhXWQz4rvT1RINVy2qR5GOG3OH2ah5oHTPuekFvHPBlepg1qyvE4JjWbzw0X7V2veJ9/fghtOmpf9wlhRZte+1SG9drCgwr9aJJy85Di2eMCqdJox380AOMnjeFJH+vrC+DtKBCGRQ/GqoqXCaNPeDZRmI6uuF06y9F8ilCGCutAUkPLelKWk931hlR1mW7Ur17DPZN3cFRPSPN4lGEcvMSuMfsPCMZhaStYBp1UR+0CMqf9Q72swcoxkVY+ay7xBOE6cZJZbLIGYA8KtzpmNve99J/PhSG3LVqXVZtRd0rhxKnHMMq1qwRaOxBdsdZ03P+pqmFA6eXEKdR4peTKXTpKk7kagzIEWimpFbT1/a93ysRk6z0lCiPgjPGjSdTnyvdlqq02dvSFQcV2ZOO9IsUSQ3VdSjo3dxHRQknHvMOJVdrlrcgKAoAeCV793S1IMtTZ8ktSeOxchqfo/F2Ne/2/zqKrnRaKxK7l1nT4fbxcJkZDVt74mLc6v4q0kenGQjxa4JYqhhYKNixzMAAQAASURBVIAoAze/slU1NoVEGQ9dcAwe2tjXz32CpNpI/uHve3DVonr0BCWExSgefS/m9Iqnj5qNDG48fRqufk6d9nnTy5/jtz86RqlCGh/Ltn/j1RxLw6KI315wDD492I1INFa85fIT69Dlj0XQ3fzKF3jogmOwJiESbtXiBjzzryYsnlGNhzbuQjwV6O6lM3HvG1/hl987CvcsnYmAIKHNG1ai67oCIla9tg2XnjARnx3yKG2QIlGcWFeKYjOLzQc9mu1s9obTHwZojGe/+cGsUSX0rMvCm2E0I3OeTij0k2+CgoRLvjUBHYG+dcYl35qgmnP1QI5qVymPIJL+wzngC4tYOLVStca4cmE9fIIIve7XyDKaewMjq/MGnAU27upOihZaMMmddbSQlU9/6DrUFMyROQAp924F9LlYee0ieLYCRSICwMGesOZ6/lBPGGXl2Y2jueybS6w8drf6ktKQi63pxwZfKJJSo628cKZI5AE9ovJH/a7sUE8I/9zdjj9edCzaewU9n/9wP1wWI6rM2YXo94REVLnMqrLXVS5zLL0uywFfjETR5g0naU1V5ShUy8KgqctxbA7ab2Yjg0u/PUFxDHEMcOm3J8BkzH5m6fCnELH2Z1/J9ZsU6bjN3vCwckgYWUZTdyJxcdbm1V7st3kF1PYOAJ2BMErtRpUumiBL6AqEUd2b1nuoJ4S//FN9qvSHTbtx+UmTMMbiSFl0ocTW5/QLCDLCoqyy/7Aox04Ve59rICzhpu9NQ5uvLyy71G7qXdSbYOE5/H7TNpVd/n7TLlV57NJURSISBjxbCqe3LcG56A1JEKS+nZ7BAAhSFN6QDLgAf4qUW39YyjrqMxUZOcnSpDuPWh0kgsgRQaNScdwhdcMLW3Hlwnoc6g6juScEE6t2sBf1LrClSBSRaBQLp1ai0xfGigV1qHSZUeYwoScQO4iocplVle9YA/Doj+coVZjve/NrzJtUphkRZ2BY/Oef+5xytyw+Ci9+tB8nTalQ2rz1YLfKqbZ6fewe9rX7ccWCOuV7DYjiB3PG4apnPlE5EwAoEXwhMYJEyRqzkcGkEguKeRZtAQm7Wn2a46wgR3Hzq9vQFRBw19kz8O1aJ/r7RrTGs6ue/RQvLZ+HaSXW2JtGsJMN0Gfh3ZpCGLzVG0KtTlFtNhOHgJi8zrDpHeUSZfHbfpvS3/ZuSvVEt6JdA3A4RRrhpFI7xuQgrZKOfT2C4mQDYve68rUv8NQlx2UtPeNNeeiqbxrsQHQGRM11ZVdALFiFTynF3k3vIiMD0RkQYDWyqjW61ciiM1C4qqM2k/ahuNWUvfMvl31zUJQHSEMe+Bl1+LU12jr8pNE20tEjDX/U78rK7DzmTSrFJY9/qArxLM1hULabjWjxhGLpKb0OjYAgws5bs76mJGtHLOWqPeALi/iP+RNx2NM3Mf3H/Ik5hX8HRQk8p66sdOsZDQjlcDJaPICIdbakHNgLeKqjhSdBJwjoG/Abq10Y0+s4cdu0Q8HdCZNmsZVHm1fAL19WR3O4E55hic2EnpCoSlPtCYmKI80A4MbTp+Kuv32VEMUxVXVCJMpR3PnXr5La8j8JtlpqN6HdJyRV3yqxxr6nO6CdltAd7LPLkCDhF6dPVZ3AF1t5hEUJ8YmwJySi0ql2elc6zegJi4o+RrE9VdUzTnlu2qek+Z8w0zrJMoh4G5U6SASRB7pSpKwLkqxstOPVOINin4N9RrUT/za3FqvXbcM9S2fCAOCBDTtx2XcmYvPuNlxywiR81NSF+nIHakssSWNXleso2E0sfv3mDghSFOfNqUF9uT0pIu6WxQ1Y8eePVWP9beu/xKUnTERIir0WTyMdVxwrYBAvXhASJNSV23FNb0Sd2cjg0R/PwfUvqB1dz27Zj5u+Nw1SJIoDHQHUlliU8Tu+ETnQHUJZpR3tfgHPbTmIq0+erBS4iY/Fd/1tOy761ng8/v4+3PjyZ/ifC+dgapE5N6f/CNSW1GPhnVo3VD8HRljS1rd69Mf66lu1+bQ3pe06C4e3pdBYbPfF9Gj1wJJCVsbM6xvelEp7tyUHjWd7ivRDWw6OkVwps/No6vAnOVZy2c/liqjT3i0XTByLP76/F4tnVMNgAOQI8Mf39+aUdZQrNqN2lJ3VmL095bJvzqUqcandhNoSi/J8gVhkemkB04WJPBEF5tU48dLyeegOy3Cb2Jyj8ke9ow0wqEI8gViI573nzcr6imFRxkPv7E7qZL86ezrSecJT4U/V6cO5aQ8U2UzYfjg5PLZhTPYDgjFF5crHc0i1c5o4zZRGRw4ROsVmI3753amqlMwSG49iHatOZUOqIgb+hCIGZlZ7kkpMgQ6lqDSU+LsIsoSfLZqMvR19odY/WzQZohxzknrDIsodJlVUHGMAvEJftGZQ1NbJCCZMUCmrHvWeJhdZtU+bEyuR2UxG2MwsSuxqvSWrqe/3s/McHtqwExd+a6JK9+Hnp/fpsESj2lXP4m0RIxHNZytF8p/aks5JlklRED02fgQxGkjlwCjrjUwNiRGwDHDOrCpFO6rIyuMnJ9UpKaF72/3g2dg1Nn3digu/NV5xbtWWWLBycQOu0HCWLZ8/UYlCe37LQZw3pwZ15Xb85odHIxKJIoooulI4plgmthkyGxlcffJk/Lk3TfTR9/YoxQuqi6y4782vlLHBbmLhDUq47DsTAQCbvm7Fd6dXwWkx4rqEdLk1ZzaivtyGo6qcaPOE8dt3d6MrIOCl5fNQaotp3ESiUaxdOhM7Wr2QI1BST+97a4fimOzwhdFtN8KdIBEwKKf/SNWWTFh45ysdljFo64bqmW6Wep2hr75VpVN7U5qr/nA6UkXn6+nMTJWyZ9H5cFcPjWerkdWUIrHl4BjJFTmFhIreFWwHIheHjV7YTZxm1pG9gBptnqB2QbeGKkfWmWC5PPsSG685LmVSdTQoSPjJiXVJhVaCQobBJiPwwOmIIgqUmTnUVzrR3R3I+bcZ9Y42T0jABcfVqk5rrz55MjwhIWuh85Aka2pdhKTsB1ZHqtMjc26TmjekHS01vdqVdblxX6pKrqHsU+16AgKcZgaPLJutpN94QwI8AQHlvCWraxZbWbhtPNr9gvKa28aj2MrmJBKbbzJJkTzsDWtOUhNKbUooeLsvRXRBQlEFG2/E3lBA9R5vSERtb5qPjTdi2zfepIXiceOLlfeXpIg+TGxvW4q03bZ4ddMMKpFFEYUcgWrTuHpJAxJHvfFFPM6fU6vSYVmzpBHji3hAzKwtPUFJM91jcrkdyPMJVZmVxd1LZ2BXq6+vKmK5HWVWDojEIvS0Iv16QmJfhEgUmDfWiT9fOhct3hAqHWbUOIxJaV0EcaTRExQ1C/XsavEBiI1T8+tL4alx49Z127By8TQUW0348nCfThnLADXFFpiNDL4zuVyJSKtymbF4RjU6/QIu+85EJdIsnkZa7bLAbuYwo9qJ0xur8MCGnSiy8qpo2qsW1WmOnTNq3BAkGdedOhlPbW7CD+aMw1Obm5QDiN/9aDZEWcJ/zK/D7f/3JQQpiguPr8XPEhyAl8+vQ7MnmDTfr3z1C/zhwjlo7gkiJEZQZudxy/enYU9nEOVOE+47fxZ2t/nwVYsXD25QRx2FxFjaqdnIYE+7Hy4LD3epRRmCB+P0H9Hakr0L78QxOBc6/QI+3JMsaVJi4xUpiHxjNxUmQslhZnHFSXVJwuFOi77fG5YkrFrcgNXrE5yZixsQlvXTpONZBpX9ZGUqXWaY9NZoi0Y079UQzX5REBIl1JXbVGtyKRJBSOrLKBhqOgMCiqy8Km3/xY8OFjR11G7mNB02hYz86wkKMBnZpIySnqCgyMgMNW4rr60Xbsn+d3OkePaODPbNDGPA5SfWJR12sBkIvFl5DreuUx+23bpuW2YOXwPwRbsf/nAsmMIjyGjxM2gstZGzbZQyzFc3uWPjOcXJBsQ6xH1v7chJ6LzIwmOXnBy+7M5hwDBz2qdHlhxPj7wpnGLeUPaeJncKQVJ3DpFipXYeITl2vfhYY+W5nELCD3pEHOoKJkXzHXRbCqZToAXLGHD9aVNwz+tfK+28/rQpqgG/OEVRi0QRWK0qeWYjg/IEZ5EgyZp6cIIsAzDCL2g7Zh9ZNltxTPeEBFx36hSsfaOvvdedOgU9Cc7rVCes8eqmtpSL/r4hKSxHB4yKA4A2r4Qt+9piGxZfGGV2E17+eD8aK+3KpmiM26z5XXEdhzFOs2a6R1U2OiRpTqo6Q3KSpsT1p01BZ0hGMc/CyGkXZlBN4AZg84ERGBlCEDpTbOXx6YEOPLxsNrr9Itw2I/60eS9m1BTDbGTwy+9OBQMDPj3YDZfZCLvZCJ5jUO2y4KpFddj4VSucZiOi0WisknZvymmVy4xl82qTHHh/+6JZcarFX79l8VF4ZNNuhMQIzjmmRjWePrflYFLEy+1nNcLEAjtbAii28Vg8o1qJKANiY8C2b3qw9o0dMBsZrFx8FIqsvKoow+IZ1Vi9fhsu+85Ezfn+4/3d+O07u3DHWdPx7/Mn4prn++aR285sxIwaFz5q6tIek3kWvz5/Fna1+uAXJHSGY2MVgEFFe5G2ZB/lDhOOnaiWNFl1RgPKdUylZA0GzTVmJhvLXOj0S4qTDYjrh23DYxcdi2Knfs4IM8fhxY93JlU5TIx2zzfesACHicOc2iJ0+kUU24wQpAi8YRFVOtq4zWjEix9/nXSvaxY3ZH9NE4/DHh/2xIXm24EJpTaU2bPTts4Hbou2DIirQM4jADCy2g4bntO3Xw2Eheew6rWPk9fOORSXy5VUmmjHjHUDWf5+ViOn+eytxvTX84akJD3X1eu2xfY6afayqSQqMnH4dosyDnuEpDbXuMyqaHEiR4ZR1OCoX930BLUjZ3qC2UdfBUVZM3z5jz9OL6KYis6AALeZi6Xs9abJBUIiugICxubgFNIjpFyKyJpROXIOp2cygFavkFSZKpdiEN6wttOoYdnsrEOV9cAbEsEZDKpTUM5ggDehuIbBMHD6IwA4TCzuPncGdrUl/C5ldtXpjhjBAAKgmZVP5xgGf/viG2VRZ+U5PPH+Hly5aLLyHotRuzJpPIUiJEqakSchse97Onzap5cdfkHRWOkJiZhU7lJtWK4+ebIqAszIxDaTt7zap5V025mNSqVV1gDNFGPW0G+hlG7gziA1qjskKQ7V+LO95/Wv0XjRsSjm2ZRRiR1+QZnAM44M0XmiMSBmlwwDRCLk4SMKj5VnsHhmDT5q6lI2h4tn1mBckQXjS2zgjQzESAQmjsGKhfXY3eZTbdpWndGA37+7C+fPGYvnthzAdadOhdnI4JxjapSxKh7BFpJkXHvaFKx8VS1CHtdce2jjLhgM6irJzT0xsfS1S2dCjETgMnNYvf5L3Hj6NPzm7Zgm3PrPDqnGvHVbDyGYcP0167/E6jMaVNdN/B6t+b6uzIaQGMFNr3yO5fMnqtp7y6tf4JqTY9XW+jsB7zx7Oqw8i+3NHjy35SC6AgLuOGs65te6+saSDKO9SFuyD1GjaMfqTCMisoTnGLgt/daYYRG8zuURO1I4WDv9gq6i+lIkgvPnjAUDAL3rqvPnjNVFEiIOz3HY3erpW0u0x9YSU8c4dftOIBZZ+uPjJ+D6hIPYXOUkwpIMT1BUveYJighLMlCgaK1M1sFDTtSgqmoPxKrar00o7DXUpHIEdQcFoEBi/alS133hWIG0bPCGJc1nf8dZ09M670JiRLM9YTH9+GAxaut/W4zpx9LOoLaD74mLj4XbQY62vGAAPj7sw7Zmj7KnO6rKiWMq7QVxto16R5uN146cyUUQ35tqwOitYpgNpTYee9r8WLnuS9Vp4+QKR9btBGLRUqsWT0NzQjGESqcJHJv9aYuNNyIaFVSvRaNRWPnszaknKGtGLz1x8XEoyXLwCYkRTUdN/9+u0Fh4Dr/6W3JxgcTFQ2eKxWpXgtNJkKLoDqgLEPzyu1MhSFbF/xtMo2mQiWPWZeFw7uxxqui6VWc0wJXg5PGEJDy/ZX+SM27cqVNRzrMwGzk8u2W/Kl3z2S37cdc5M5RrFNmM+MmJE1UOsJ+cOFEVxWdOEbH6l8vmKu8JSDIMhqhqgxESJQR6F4ydAQGCHFU9t+tOnYLOgIDieNpyBgN3Jg6wnpCkaZOe3kpeZSmiEhNFVjOKDBkCPSSnxYg739wBAPjFyfXkbCMKTlCMFT6YXB7TdXRZjbCZGLR6w9jZ6kWZw4R9bRIcJg4mI5O0aVu9LlYJ+fUvDuOX35sGjjHg9jMbsb8rMGBkW/8ItMRMsf79uSsgYPthLx59bw8e/n+zsXLxUeBYBvf/YBZcVg6TK+y44YW+frtqcQP+8q8m5fMhMQJriojgFz86qHmAwXEM1iw5CkW9+lTXnzYZT2/eHyuyIEbgCct48f0mXHh8Le5ZOhMhUUapg8etr21DU0dQdZ83vfI5HvvxHLhMxkE570lbso+WFHIGrTmI16eD5xgwDKOSYrj9rEbwnL6OtlSR9mU6a7RJ0QgAg1qvcEkDZB3nqUgkqpkxENV7btRBR1CUI5r3IsqFWz+ndCAFhKyLu+VKQJA05T4Cmep16cBQFvgqZJvCkqz57GMZOgOTKjPLlUEQhsPMaWoxOjLQwGv3CTh+QjEuOmECunqjXh97by86fAIm6jwmHil0hmXsafcnjV3jiyx90fhDyLB3tG3atAl33HEHIpEIzjvvPCxfvnxQn7ebtMVJ7TmcyFhTeLNzqSyUqnJNriecnpAAC88BCCuvWXgOnpCgVLQcLCEpgm+6Q0lGPMZtyfZgAq3esKYDotWbfWWqcgevGWauZ3pGNrSluPe4hhiQWti3JMEBExAlVTXQkBjBnX/9Co9dNEc53UmZ9muN/Z1jDPivc6djd1tfsYSJZTYYExyzcgSaJzJ/TnBuecMSFk6tVDnjrlxYD2/v6VVQ1F6chBN0Ds0so7nQMyfsYlM5IDv9fWXMo5EoAmEZ+zuDqoi1+OLXzHNKGmz882vf+FrlrOsMy2jxhFTf0+IJoTNh4M7EAVZi00596BNgjWqOV4aEFXMmkSGZFFXIB90BMf2bCGKIiCKKSFSt63jHWdNjGzHENsKTyu1Ys/5L/PJ70zT7K8sA/2/eOPQERKx8bRuKrDxWnnFUUmRb/P2JlUyBWF+cO6EYtSUWvPjRwaR0vatPnozH398XO1GXZHT4BKxZ33fAtnLxUfjZyfV44v2Y8271+pjz77NDHuX6Xf4wfn3+LHx1OOb437y7TRHXf2pzE5bPn4i6MjvsJg6r12/Dv58wAVYTp4p4ibejKyCAMcSi7f7rb18DAK5cVKdEAGvd5z/2dOIPf98zOOe9Ds6AkUoqXdZiHaP7goKk6A0Csd/05le+wFOXHKtrhFIUEc2I8qjOoqIWzoiV/dLnVr62DU9fOjfNJ7NHlKMDZgwMHbmnLUoR4JkP96vWEM98uB+N1YWrXJnJOnio4VPIfTx2UfbyRLkSFCXcekZDsli/VDjnX6oslrCYveafw6xdVC2TCMeAIOGm701Dm68vCKXUbsqooEFIisDGs6osJBvPKvJHA1FXbsX3ZozBfzz1kWo8nFhmzeieifR0BkXNcXhWjYscbf2RZRlr1qzBY489hoqKCixduhQLFy5EXV1dxteIwoASmzEpXD6XiajIatSsGFWUQzpiJuLw2WA1GvFFjzfJWTG2KPtOHRS0c+1jOl7ZXbPabdZ0QFS7s08dlSLai55jxxcwzFyDVPc+JiFt1mllNNNCXdY+p1NqPT4Z6M1cMHHaKZ2mXm2AkCSj3SckaYiNcZuB3tOa1gGKC8TTnN2WgSdAnmGx4avDSRFvM2r6QkI9KVJ/GxNSf1MJ0dr7OZO0HHZxBioiEXfWeQRJ8xoeQVIGbodZ24lpN/eNC9Goduru073PpSsgpizMMK53E5ZJZEhGRRUIYpQRiQArX1UfAtz0yudYsaAOD2/a03vIxuGC42rx5Tce5UQ9fsjBGoAZNS5YeQ4X96ajA0C7N4yrFtWnrLgc9/3H+9kvX/4cy+dPQqmdhz8sqRbkZo5BmZ3HeXNqYGQYxckWv9aa3gqmy+bVKpFyidf/xelTERQjSZVQI9EIHr/4WLR4QmAMDJ54fw/OnzMO155cDyPHqTTd4pG/y+dPRG2xDUa2L/LObGQwodSmeZ/xwgjRaJbFDPJcVGCk4jBz+NU507G3ve9Aa3ypDU4dtaa6Atrrg+6AlPeiP4kwBgZBQd0HgoIExqBvJF27T3ud0u4LozbLQmjpCKaQ3gjqXYFShwh2ORLRXEPIOqbepsMAaB5E6lxqYkACgnaWQiyirTAOQAvPQZACqj4nSDIsGWiX6UWqLJZ5E2ZlfU1PCmkoTzD9vtllMaKFCycVjHBmoDXeHRDxwkcHcdn8SaridFcsqEsrSeUNykmHWLe8+gWevuQ4lNgpdTQf+DOQQBpKhvWO67PPPkNtbS3Gjh0LAPj+97+Pt99+e1COtg5/GF0BCStf6zsxvvrkyegMhLOuvhKSoqq88Gg0lhd+73mzsh5XU23SHebcFgSZiNsPllCKzUb/1waDFImk0F7I/lRoIAeKXpW9siHVKeichFMZXzCCw55QUgGOsUUWuK2xwdmcKtIyQTfAE5Tw23fUtvvbd3bh9rOmY4yZgyBFNTXEHv1x34msK0VUXOIE1RNIoY0YiE2AdrN2+qkjYcOWiV6cwRDFf55Ul+Q4ZAx9q0tB0n6+/9N7yhx/RgM9t3CKazyacFKdyYndYY/24v+wN4xaO5+6MEPi5J1BZIjJyGk6Ov+sw2l+Oq02pldsm1JLCb1JpQdVZjep+v19b+1AkZXHnWc3osUTVm3crjt1CsYWW5XrnHNMDe74v+0osvL45femaY4Vs2uLcOWiOsgRKM6x29Z/ibVLZ+KXL3+R9P77zp+Fq5/7NGXxgkgUSgTZo+/tweRyB9aeNwMlNh5fHe6rCl3lMuMHc8bhij9/rBpznt2yHz+YMw4P9RZA+Ne+Ts3vqS93oNzB4/fv7MbdS2dCkGS4rTx2tXg175MxQEkhjV/jSCxmkCut3hA6+h1oXXfqFLRZwygv1ieywcqn0BXS+YQ/LEZUkfbx79U7yivV/eYiG5MOp0U7pVtPByqgT0Vfi1E7UiuXQnK5cqhX47L/QeS4YmvBCpz1rywdd/4VMk2zUH1uIDxh7cNfX1jMei9q4rT7uCmDIoJhKaIpV/R4BvZd7jBpFqfLJB1+oMyX2gKlP482xjhTFMArUGpuIQ8C0tLS0oLKykrl3xUVFWhpaRnUNawpNJxy8ey3esNo6gjioY278OCGXXho4y40dQTR5g2n/3AKugMirlxYr2zu44NQdzC31KxUJ/C5OMVKelPXEsk17aHDp+2Y6fBlf//xhVYiei+0smEgvZY43rCkmVocS8WMYeFjadKJNnTVonqYE+7XF5Y0bdfXe51MqtSGex1K/W1VlfbZz1HV/zV/WFsQ1C/0fU88vab/NUoS7MwARnOyTBzaAqL2PcVPmV1mo+ZzcyY4uVM7l/vu2WXmlRO7FQvrcOkJE/Hslv1wJVyn0qndd+LVWMvMsWi1xLYo0WqJ9EaGTCuxxhbS/XxYvhQRsr5Q/lM941ptd721EyzLgGEMyn8sy+BXb+3Er97aqTjcCEIvUo75vdG4ITGiiDI394RQ5jAnOdDXvvE1inoPE4C+QgPNPSHc+X/bk8a+28+aju3NHjzwdmw8TdRq86fQc91+2KO8rtXeeMQYy0CpCn3vGzuwr92PYiuvcgJqbYYXz6hW/rcrICIS1f4eAPjn3i5s3NGOK//yCa57/jPc8soXmF3rxh1nTVfd55olDbDzrEqPrn/KOpEZZk5broDXsepc3Jb6z3OszrsAfxpdWL2wmzjN+7VnoKOULX5Be22UuK7Rg4E279nSleKwtJByESU2XjmIjK9fuwICigo4BvlTZPj4CxQ9E2tTYfrcQPAco7k+NuYwABlZg2YfN2aw1hwoECMdgRS/eSbP152wtohjNjJwZRBJR2RGxvuoIWJUHUOyrAFut/o00NvqT7HhlOAeV5TV9zi6QylSxLik788UpzecUhw+22sCgMsbTnnClu11P2lrw3WnTlEWivHT2K6AgJnV2VWDKPOLmu0sc5iybmdRWNYMMy+yGnN6prnS305LPNq/UbGNV94XaPGlnDjj7+FDEipdZlW4eKXLDBPLKO8p8wvaz7n3u+ypbNvUZy9cd0jTVu84a7ryHntA1Hz2tt7r+Fq178cXkuEeF7uGH8CaJQ1YmRCttmZJA0rtfc+l/bD2dTr8AmbVuAEA5UFt24pfx+GIYF+XOsy+usiCKeV2sL2LgFpZu5rfuGKb0hZnJIobTpuGa5//VGnvvefNwqRyh+JksttNWLOkEStf+yLhnhrRWOkA17vJOtVhwbQVJ6DNF0aZ3YSxbsugnVRjItrtHVNkzcj2tcbSRAwAOI5R/usJinBZjLh74244zBy8IQndAQFji63w9S4+nE7LoO4hfRuZgvbj4dSO4dCGQrSjv51a/ILmuHOwKwCgd1xNiMht8YQ0x482bwhrzmzEyle/UD4Xd7bFNdCqXRbs7wrCYeawr0PW7G82s3aES1zKJVXxgqc2NymRcl83e9DiCaMrIKDKbcH2Zo9yzf5VTePtj7/OMkCxzYh1Ww8lfc+qMxrQ3B1IqsLYFRBQZDVhxhg3ZlS78HWLFztavXh6cxO+O70KXb16d1rjWz4YLracL4ZqXZoO3i9q6grxLKvr83Z4tNcUNpO+3/tFZxusRvX9Wo0sPCEB7jFZVi1LA5/B2kgPcp3vgWQ7LfZprxWLEtalQ401JGnK9tiM+trSQPhTrc3DcsHalKrP2XXucwNREQV+eOy4pLm5wmnO2kZNAe0xzcSln0NKU+yNE/cXqfCn2L8k7sdi7U1uR6jTr535Ig2dvQzHOTbfbYrvo1q9YZQ7sttH5atNw9rRVlFRgcOHDyv/bmlpQUVFRcr3y3IU3d0B1WtlDm3R2RI7n/TeTHFZtCuOOM1cTte8/MS6pAnEZcn+mgDgNLO4/rQpSjqg2RjT3HKY2KyvW2bjccf/fqlaTDy1eR/uO39W1tesdRk1BXNrXcasr1lpYVFdZElyoFTm+EwzpaxMu2Jsfzt1mFjNxYM94TdKWbnLblLeU2wz4HAPG6u4J0iw8RyMrAFFNoPyHqeF1XzODkvsuwaqphO/ht3E4kdza1UC39ecMhk2vq+9NS4jqt39nr3bgrG9v2dpKlFbW1+/tBli/Xft0pnK/dhMDGwGKO+pTPFcyhOeS43TiNvPalSEoGNRKI1KWwDg+BonalwWtHhDqHCYMdZhhNfbV/zAzUFTF83NQfVbHjfGnpTS6fEEVb//gkluPHXJcWjxhlHhMGG8i4fPp46GdTFA7bgidHcHkj6fCS5Wu70uVt3eTG00EYaJlWaQpEjSfz1BEaJoRE9QRFdAgM3IQpIiMADwekOIRgdOH42nl2aSbup2W4ekH6djOLRjOLRBz3Zkaqcmlkk6bLDxLH737h5l7jMb+wokpUo9KXOYsWtnCx676FgEBEmla9kVEGDmWNz/9k50BQTcd/4srNt6KGlcvWpRPQJhMen1VYuPwu837QYAxXEX0yO1YFebD09tbkJXQMCVC+ux8tUvsHz+JDyyaTduP6sRZiMDt6VPIzbe3v7tj/ZGsM2scSMSieA/5k/Cw5t249ITJoJlYhWTPcGY406KRJVrJI4THk8QbhaYW+3AxGIL5ox1o8JhwomTSgYc33JluNjyYBnMWJpqXVqaw7o0HWZjrKJeYpRTXAZCz+dt47mkgiDXnDIZdl7fdZjDZMQf3/9S0W+VI8Af39+Le8+bqdv3Oi2cpjPBkcPeIBNyne+BZDu18tp6vlZj9nuHXOE5A4qsnGpNyLGx1wvVpgpnqqq6+vXldBRZtfuc2zY0ex8tHAwwsdSmmpsnltrgYJA8PmY4llpNDNw2XjWmuW08rKb0Y5rTwmnuuzLpq5UpfvOKfr+51lzmTsh8SXTG35/D/nmwDMc5Vo82uRjA5YplC2WzThmoTQONpf0xRNPtfAqIJEk47bTT8PjjjyvFEO69917U19drvl8U5eSHYgTe3d2dtMk+cZIbyDYC2gj8syl2yhsfMCaXOzC31pHTNT/9xo+wGFEmEJORwawxtuyvCQAMsOUbL7Yf7mvrtEoH5oxxIOuiTwzwzt7uJGfNSRPc2V8TADhgb7eAFk8YFU4TJrh5INeIewY44BXR6guj3G7CWIcxtzYOglQdMclOjcDHh3wQpWifg4wz4Jhqe99vn4kdG4H2oARvMKI4jBwWBqUWTvWer9sDYA0s2n1hlNpNkKMyppRaY+8xApubPNjZ6lPspb7cjnm1TtU1PtjnUQozMAagrsyO48c71bZqBPZ2JfyeRXzf31ng3b0a9zPBDSRGXxti+iMpq9RlaosDtSVT0rUlz+Q88WTQ3oxtNAGGMeCm//sKNiOrRLJp/W9XQMD4EpvymsEAOM0cPCEp6X+7AyKKrEb84pTJAIA739wBAPjFyfWIRKJJjjeGMcDptMDjCRZc+204LFqGQxv0bMegxtKDPohybCx1mY1wWjgERRk7WnyoKbLg7ztacOKUSnzd4kWpnYeJY1WbyVVnNGBHczemjXHjuS378e/z6xCWZIRFCTzHYXebD89vOYiugIDbzmxEqcMIi5GDHIlAlGOC6HYTC441gGMN4BkWAVFCh08EyxhgQBQ+QVaNfavOaIAoyyi2miBHIzAyDA52BTCp3IHm7gBKHWawjAEhUcb1L3ym6ALVV9jRE5Rw08ufq07Hn92yHysW1KPCycPOc/CGJViMHDwhEWYjC4YBfCEZe9p9YABMG+NCVI4Mi0qgw8WWB8ugxlI91qXpMAJfHPaDZRh09Y63ciSCxsoc15gZfG/aNYVO37txVzdWJqwN1pzZiAV1bv2+lwU2H9C417FO9bpGD3KY7wENO2WBj5s11qVVdv3vJRVG4IsWP1hDgg1HI2is0NmG07RpyPtyBm3acsCH7b1VqWP7PifmjLUXrk2AYqPdYRluE5tyrhnMfD/gfmYgjMAn3/ghJOy5eSODozPZcxuB9/b04BcJ8+6vzp6OEya6VJ/VnMsY4N19GvYy3j1ke9PhOMeOtDaNGkcbALz77ru48847Icsyzj33XPzkJz9J+d6Um8N8bLJH6jUBxdmUGK2Tc4cuoAMrGwrRiQe78E772w/Ve/L1PelggX09giqyK6sF3CDsezgO5qkYirYOpaMtk/fGnXH7O4MwAPivMxsAxBxvBkDliPMJMhw8i1+cMlkVKTfUjrfhYFPDoQ16tiOXsXRcEYtPDgZRZuMRRQQ2jkOZlUNbILYpLXaY4AtLaPcJqHCYwHMMmntCqC2xwBuS0eIJKxWgBVkCy7Bo84ZRbOPBswZEAESiETBg0O4T4DBzsBgZyNEoLEY25vQLS2AYgGdZtPsEFNuMMBgM8ARFuC1GyNEoegIS7GYWRo5BTyDWNzxhETzDwm5iYeEZhMSoUqG8yGpEV1BEqZUHywItnth3hyUZJpZBscWIYhOr7TQb4kODwTBcbHmwDHos1Wu9NxCF+M4j7Xvzta7RgUE52oDheS+FsiVqU95IN8bnfe+UCp0/m/I+9diTD4LhOMeOtDaNKkfbYBhocwgMzx9Si5HSTmDktHXYO9p6GSnPMw61Vz+OREdb/78lOt76/9sryKhxmVWRcnFnXD6mtf5prKn+Phxsaji0Qc92HAlj6WCgext+ZGOjwMi932ygey0sg3a09TIc74XalBkjsU2jZb4fjm0Chme7RlqbBuNoG9YabQRBEMSRS3dARKLPrP+/E1+LO+fufHOHZmrquGKLZtpqJmms/d/T36GXzhmnhVY6bDrSXa/QabQEQRAEQRAEQZCjjSAIghgkbisPK2dQosm0/heIFXkZ6D3ZvDf+vxzHaH7GE8pV2BFwmDn89h/7YDdpT5GJfw9KEXT5whjjtsAXlmA3cfCFY234z2+P14yuMxgM+O0/9invAaBcL/Ea8f/1BEU4LUbN6xkMBqx9cwdkOZLy+4aSbCpQkoOQIAiCIAiCGE2MqtRRgiAIgiAIgiAIgiAIgigUTKEbQBAEQRAEQRAEQRAEQRCjAXK0EQRBEARBEARBEARBEEQeIEcbQRAEQRAEQRAEQRAEQeQBcrQRBEEQBEEQBEEQBEEQRB4YVVVHBUFCT08w5d/tdhN8vvAQtig7Rko7gZHT1kK0s6zMofn6QHY6Up5nHGqvfgxFW7OxUWB4PMfh0Ibh0o7h0AY923EkjKWDge5t+DGSx9Khgu61sKSyUWDkjaXUpswYiW0aLfP9cGwTMDzbNdLaNNBY2p9RFdFmMBgG/DvHsUPUktwYKe0ERk5bh1M7B7LT4dTOTKD26kch2zoSxtLh0AZgeLRjOLQBGPp2jKaxdDDQvY0cRsJYOlTQvQ5fRtpYSm3KjNHUJrLR/DAc2zWa2zSqHG0EQRAEQRAEQRAEQRAEUSjI0UYQBEEQBEEQBEEQBEEQeYAcbQRBEARBEARBEARBEASRB44MR5sBaAtJ+LCpC20hGRhYMoMghie9dry9I0B2TAx/yF4JgiC0oXUpQYwOqC8XDnr2xDBnVFUd1cQAbD7owfUvfoaQGIHZyOCec2dgXo0TiBa6cQSRIWTHxEiC7JUgCEIbGh8JYnRAfblw0LMnRgCjPqKtLSgpnRAAQmIE17/4GdqCUoFbRhCZQ3ZMjCTIXgmCILSh8ZEgRgfUlwsHPXtiJDDqHW3tfkHphHFCYgTtfqFALSKIwUN2TIwkyF4JgiC0ofGRIEYH1JcLBz17YiQw6h1tpTYTzEb1bZqNDEptfIFaRBCDh+yYGEmQvRIEQWhD4yNBjA6oLxcOevbESGDUO9rKLCzuOXeG0hnjOdxlltEvT0eMHsiOiZEE2StBEIQ2ND4SxOiA+nLhoGdPjARGvzVGgXk1Try0fB66wzLcJjbWCUkokRhJJNhxu19AqY0nOyaGL2SvBEEQ2tC6lCBGB9SXCwc9e2IEMPodbQAQBcrMHOornejuDlAnJEYmvXZcZuaUfxPEsIXslSAIQhtalxLE6ID6cuGgZ08Mc0Z96ihBEARBEARBEARBEARBDAXkaCMIgiAIgiAIgiAIgiCIPDCkqaMLFy6EzWYDwzBgWRYvvfQSuru7cfXVV+PQoUOorq7G/fffD5fLhWg0ijvuuAPvvvsuzGYz7rrrLjQ0NAxlcwmCIAiCIAiCIAiCIAgiY4Y8ou2JJ57Aq6++ipdeegkA8Mgjj+D444/HG2+8geOPPx6PPPIIAGDTpk3Yt28f3njjDdx222249dZbh7qpBEEQBEEQBEEQBEEQBJExBU8dffvtt3HWWWcBAM466yy89dZbqtcNBgNmzZoFj8eD1tbWAraUIAiCIAiCIAiCIAiCIFIz5I62Sy+9FOeccw6effZZAEBHRwfKy8sBAGVlZejo6AAAtLS0oLKyUvlcZWUlWlpahrq5BEEQBEEQBEEQBEEQBJERQ6rR9pe//AUVFRXo6OjAxRdfjIkTJ6r+bjAYYDAYsr4+yxrgdlsH+Dsz4N+HCyOlncDIaetwaudAdjqc2pkJ1F79KGRbR8JYOhzaMFzaMRzaUIh2jKaxdDDQvY0cRsJYOlTQvQ5fRtpYSm3KjNHUJrLR/DAc2zWa2zSkjraKigoAQElJCU455RR89tlnKCkpQWtrK8rLy9Ha2ori4mLlvYcPH1Y+e/jwYeXzqZDlKLq7Ayn/7nZbB/z7cGGktBMYOW0tRDvLyhyarw9kpyPlecah9urHULQ1GxsFhsdzHA5tGC7tGA5t0LMdR8JYOhjo3oYfI3ksHSroXgtLKhsFRt5YSm3KjJHYptEy3w/HNgHDs10jrU0DjaX9GbLU0UAgAJ/Pp/z/f/zjH6ivr8fChQvxyiuvAABeeeUVLFq0CACU16PRKD799FM4HA4lxZQgCIIgCIIgCIIgCIIghhtDFtHW0dGBK664AgAgyzIWL16M+fPnY/r06fjZz36GF154AWPGjMH9998PADjxxBPx7rvv4pRTToHFYsGdd945VE0lCIIgCIIgCIIgCIIgiEEzZI62sWPH4rXXXkt6vaioCE888UTS6waDAatWrRqKphEEQRAEQRAEQRAEQRBEzgx51VGCIAiCIAiCIAiCIAiCGI2Qo40gCIIgCIIgCIIgCIIg8gA52giCIAiCIAiCIAiCIAgiD5CjjSAIgiAIgiAIgiAIgiDyADnaCIIgCIIgCIIgCIIgCCIPkKONIAiCIAiCIAiCIAiCIPIAOdoIgiAIgiAIgiAIgiAIIg+Qo40gCIIgCIIgCIIgCIIg8gA52giCIAiCIAiCIAiCIAgiD5CjjSAIgiAIgiAIgiAIgiDyADnaCIIgCIIgCIIgCIIgCCIPkKONIAiCIAiCIAiCIAiCIPIAl82HZFnGO++8g0OHDkGWZeX1iy++OG8NIwiCIAiCIAiCIAiCIIiRRFaOtssvvxwmkwmTJ08Gw1BQHEEQBEEQBEEQBEEQBEFk5Wg7fPgw1q1bl++2EARBEARBEARBEARBEMSIJatwtPnz5+O9997Ld1sIgiAIgiAIgiAIgiAIYsSSVUTbrFmzsGLFCkQiEXAch2g0CoPBgI8//jjf7SMIgiAIgiAIgiAIgiCIEUFWjrZf/epXeOaZZzBlyhQYDIZ8t4kgCIIgCIIgCIIgCIIgRhxZpY5WVVVh8uTJ5GQjCIIgCIIgCIIgCIIgiF6yimgbO3Ysli1bhvnz54PneeX1iy++OO1nZVnGueeei4qKCjz88MM4cOAArrnmGnR3d6OhoQF33303eJ6HIAi44YYbsG3bNrjdbtx3332oqanJprkEQRAEQRAEQRAEQRAEoTtZRbTV1NTg+OOPhyiK8Pv9yn+Z8OSTT2LSpEnKv9euXYuLLroIb775JpxOJ1544QUAwPPPPw+n04k333wTF110EdauXZtNUwmCIAiCIAiCIAiCIAhiSMgqom3FihUAoDjXbDZbRp87fPgw3nnnHVx++eV4/PHHEY1GsXnzZtx7770AgLPPPhsPPvggLrjgAmzYsEH5ntNOOw1r1qxRii4QBEEQBEEQBEEQBEEQxHAjK0fbjh07cMMNN6CnpwcAUFRUhP/6r/9CfX39gJ+78847cf311ysOuq6uLjidTnBcrBmVlZVoaWkBALS0tKCqqirWSI6Dw+FAV1cXiouLs2kyQRAEQRAEQRAEQRAEQehKVo62lStX4sYbb8S8efMAAP/85z9xyy234Jlnnkn5mY0bN6K4uBiNjY345z//mV1r08CyBrjd1gH+zgz49+HCSGknMHLaOpzaOZCdDqd2ZgK1Vz8K2daRMJYOhzYMl3YMhzYUoh2jaSwdDHRvI4eRMJYOFXSvw5eRNpZSmzJjNLWJbDQ/DMd2jeY2ZeVoCwQCipMNAObOnYtAIDDgZz7++GNs2LABmzZtQjgchs/nwx133AGPxwNJksBxHA4fPoyKigoAQEVFBZqbm1FZWQlJkuD1elFUVDTgd8hyFN3dqdvhdlsH/PtwYaS0Exg5bS1EO8vKHJqvD2SnI+V5xqH26sdQtDUbGwWGx3McDm0YLu0YDm3Qsx1Hwlg6GOjehh8jeSwdKuheC0sqGwVG3lhKbcqMkdim0TLfD8c2AcOzXSOtTQONpf3JqhjC2LFj8dBDD+HgwYM4ePAgfvvb32Ls2LEDfubaa6/Fpk2bsGHDBvz617/GvHnzcO+992Lu3Ll4/fXXAQAvv/wyFi5cCABYuHAhXn75ZQDA66+/jnnz5pE+G0EQBEEQBEEQBEEQBDFsycrRduedd6Krqws//elP8dOf/hSdnZ248847s2rA9ddfj8ceewynnHIKuru7cd555wEAli5diu7ubpxyyil47LHHcN1112V1fYIgCIIgCIIgCIIgCIIYCrJKHXW5XLj55pvh8/lgMBgyrjoaZ+7cuZg7dy6AWHTcCy+8kPQek8mEBx54IJvmJWMA2oISdjZ1wW3iUGZhgWh+Lk0Qw4peW2/3Cyi1mcjWiaGDbI8gCCIzaF1KEIReHCnjy5Fyn8SIJStH29dff42f//znqqqjd911FyZPnpzXxuUFA7D5oAfXv/gZQmIEZiODe86dgXk1TuqMxOiCbJ0oFGR7BEEQmUHjJUEQenGkjC9Hyn0SI5qsUkdXrVqFG2+8ERs3bsTGjRvx85//HCtXrsx32/JCW1BSOiEAhMQIrn/xM7QFpQK3jCDyC9k6USjI9giCIDKDxkuCIPTiSBlfjpT7JEY2Q1Z1tFC0+wWlE8YJiRG0+wWUmbO6/RiUJkUMM9r9AoqsPM45pgbxuiEvfnQwd1sniDToNs4WChrfCYLQiYKNlzSuEUT+GWbpi6NuPZaCnO+TxkNiCMiqx8Wrjp555pkAgNdeey1t1dFCUWozwWxkVJ3RbGRQauOzvyiFqxLDkAqHCRceX4vfvL1TscurFtWjwmEqdNOIUY4u42yhoPGdIAgdKch4SeMaQeSfYdivRtV6bAByus9h+LsRo5Ocq45eeeWV6OrqyrrqqN6UWVjcc+4MmI2xW413pjJL9l59ClclhiNyJKo42YCYXf7m7Z2QIzRrEPqixzhbKGh8JwhCTwoxXtK4RhD5Zzj2q9G0HhuIXO5zOP5uxOhk0L1OlmWsWLECTz31lB7tyT9RYF6NEy8tn4fusAy3iY11whx8D0dKWC4xsiC7JApGwjgbC8Pncx5nCwX1I4IgdEWHdWk6aFwjiPwzLPtVAcaXgpDDfQ7L340YlQzamliWBcMw8Hq9cDgcerRJZww5X+FICcslRhYZ2yXpEhB6EAXKzFzfIiXRpkaQzdH4ThCE7vSOl/WVTnR3B3QfD0ttJtSWWLB4RrWi4bpu6yEa1wgiB4Ztvxri8aVgZHmfOa/zRtCaligsWbltrVYrzjjjDHzrW9+C1WpVXr/55pvz1rC8oUMedjxctf81R+WJATFiyMguSZeAGGpGmM3R+E4QxGijzMpixYJ63PLqF8q4dtuZjSizckAk/ecJgkiG+tXIJKd13ghb0xKFJStH26mnnopTTz01323RhVR52C8tn5d9eOgoSpMiRhEZ2KUu/YEgBmDE2RyN7wRBjDLaApLiDABi4/Atr34xfMdhghgBUL8aoeSwzhtxa1qioGRlEWefffaAf//pT3+K//7v/86qQflGtzzsgdKkCKJQpLFL0iUghpoRaXM0vhMEMYoYkeMwQQxzqF+NYLJc59FvTgyGrKqOpuPAgQN6XDYr4nnYiZDeDnGkQv2BGGrI5giCIAoLjcMEkX+oXx150G9ODAZdHG0GQ+4FB/LFkVLmmCAygfoDoTsGoC0kYXtHAG0hGWVWsjmCIIhCQnM/QeQf6ldHHvSbE4PhiLAKnmOwfP5ERKIAY4j9myCOVKg/ELqRSiR2LGmeEQRBFBKa+wki/1C/OvKg35zIFF0cbdHo8NlBtQUlXPXsp0klfEm0kDgSof5A6Ek6kVjSPCMIghh6aO4niPxD/erIg35zYjAM2gUryzKuvfbaAd9z3XXXZd2gfDOQaCFBHGlQfyD0hOyLIAhi+EFjM0HkH+pXRx70mxODYdCONpZl8c0330AQUhvUCSeckFOj8gmJFhJEH9QfCD0h+yIIghh+0NhMEPmH+tWRB/3mxGDIKsZx7Nix+Ld/+zcsXLgQVqtVef3iiy/OW8PyRVy0sL9mUM4aQYZY+GhMc8iEMgtL6VCEvmRic2neo1t/II4oIpEo2kLJdjYo+6IxlCCIIxUGOOAV8VFLKyrsJtQ4jEAk/ceyheZ+gsg/ZRYWv/nBLGxr9iASBVgDcFSVk/rVUNG7jtzZ1AW3iRuSdSSNpcRgyMrRNm7cOIwbNw7RaBR+vz/fbco7eRctTCX4XeOkTkboQyY2l8l7osC8GhKmJ3LAALz1dRuuff5TTTvLyL5oDCUI4kiFAd7Z241bXv1CGf9uO7MRJ01w6+dso7mfIPKPAfCGJTyyaY/Sl28/qxEwgPqW3hRwHUnFEIhMycrRtmLFCgBAMBiExWLJa4PyjR6ihekEvwki32RicxnbZRQkTE9kTVtQwrXPf4oiK49zjqmBwQDsavNhcpkNxTybkX3RGEoQxJHKAa+oONmA2Ph3y6tf4M+XzsVYm1G/Lz7S5n6KmiZ05oBXxM2vqPvyza8MQV8mCraObAtKuPOv27F4RjUMBiASBe7863b87odH0/qVSCIri/jkk09w0003IRAI4J133sFXX32FZ555BrfeemvKz4TDYfzoRz+CIAiQZRmnnXYarrzyShw4cADXXHMNuru70dDQgLvvvhs8z0MQBNxwww3Ytm0b3G437rvvPtTU1Ay6rQOJFmbbIfS4JkEMRCY2R3ZJ6I4B2N0ZQJGVx7J5tXhgw07lJHFcsQ3za10ZbWTIVgmCOFJp9YY1x79WX5g25/mCoqaJIYD6cuEo1DqyJyTiB3PGqda/Vy6sR09IpPUrkURWsY533nknHn30UbjdbgDA1KlTsWXLlgE/w/M8nnjiCbz22mt45ZVX8Pe//x2ffvop1q5di4suughvvvkmnE4nXnjhBQDA888/D6fTiTfffBMXXXQR1q5dm01TdREttJo4zWtaTDl2MA7Y6xPwty9bsM8nZOkGJUYjpTYTakssuGJBHVYsjP1XW2JR2XHGdskC+3wC/vmNF00+AWCH4g6I0UBbUMKuVh/Om1OjLDKA2OLmplc+xx5PGFvb/GgLy7HUiRQUXEzWALSFJGzvCKAt1K+tDHAwIOKjFh92dIfQKQ58LwRBEIOh1M5rjn8leo9/A417o4xU0S5tQUnfL+59xh82dY36Z0wAJXZec21eTML4yeS5b2SyL9IDI8cmrX8f2LATHEebKSKZrF05VVVVqn8zzMA+O4PBAJvNBgCQJAmSJMFgMGDz5s249957AQBnn302HnzwQVxwwQXYsGGDkqJ62mmnYc2aNYhGozAYBtcz9RCqDIoSVi1uwOr12xRv9qrFDQhJEoAsOzgHbNzVjZWv9Wl2rFnSiAV1bkDndQEx/CmzsbjipPok+yizcYAce09AlLDqjAasXpdgl2c0ICgm2CULbNytYWeT3Mp1CCIV7X4Bz205iF98byouPWEi4sPxix8dRHNPCP/Y3YEH3t6VNnqgoGKyA0U6GIB393UrqSBmI4OrFtVjYqkNx1TaKRKCIIicYQ1RrF7SgFWv9c3Vq5c0gGN0HGAKGeFVAMHygkS7UBTdEQfLRHDFSXVYmdCX1yxpAMfoWNlkJKJD3yhzpNgXOThAzG/zE+n0CyrpFCC2Bu7yCxhvJwcroSariLaqqip8/PHHMBgMEEURjz76KCZNmpT2c7Is48wzz8S3vvUtfOtb38LYsWPhdDrBcbFJr7KyEi0tLQCAlpYWxZnHcRwcDge6uroG39gEocoHN+zCw5v2wBuWcvKkm40cfr9pFy49YSJWLKzDpSdMxO837YKJy37y3tstKIMFEFsQrHztC+ztFrJvKDFqOOARNe3jgKdvNrHxRvz+3X52+e4uWPm+8PV9Pdp2tq+H7IxIT6nNBJ4zwB+S8Oh7sTH1D3/fg2XzalFbYgHPMrhiQR0u+85E7GrzoTOcwnubIMz95EVz8NLyeUO2GRko0kFLb+U3b+/EtmaP/pEQBEEcEYhRA377jnqu/u07uyBG9At/KmSE1+aDHpzzyGZc8Id/4pxHPsDmgx7dI70KETVdsGdMFAwpwihONiC+pt4GKULi+Ino0Tf2dqXYN3fpu58pd5hw4fG1qjXwhcfXosxh0vV7iZFJVp6hW2+9FXfccQdaWlrwne98ByeccAJWrlyZ9nMsy+LVV1+Fx+PBFVdcgT179mTz9QNc3wC326p6bVuzR1Oo8pl/n4eGKmdW39PR4kNTRxAPbdyler3bL8Bd487qmq3feLXz/L1hHJ3lNYcClmWSnvlwZDi1U8tO+/6m3c4Pm1s07eOwN4Tp1S4AwGftbZp26QmKcPe+p6VZ285avGHMysLOhtNzzYSR1N5CtjWVjTojUdx25nQsf2pLUtj8Az88Gt6wiJte7jtdHFtswxmNlWAY7V2V2wXUZ93G7J7PzqYuzT7QHZbhCYmaf4tEge6wjPrK5DljONjUcGhDIdqRzVg6GqB7Gzlo2WjrN17NuVrP9d7nu9q11xA+QXNcyxf7OgKaG+x1V5yA8SX6/c7OSBT3njdLVR373vNmYVK5I+V8lCsDzS16PuN8MNLG0uHSppYUe7cWT2H3bpFIFAe6g9i1vwtldhPGui262X0m5KNv9LfRQj37Pd4u/OZtderob97eidkXH6tq33Cx0f4Mx3aN5jZl5WgrLi5W0j2zwel0Yu7cufj000/h8XggSRI4jsPhw4dRUVEBAKioqEBzczMqKyshSRK8Xi+KiooGvK4sR9HdHVC91uoJaTuwPCFUW7KLQKuwx07K+lcyLbWbkr4/U8qd2tcsd2R/zaHA7bYO6/bFKUQ7y8ocmq9r2WmcVO2M60DEq9wAwLqth1Bi45X3mzlW04ZMHKO8pzKFnVVmaWcj5fePM5LaOxRtzcZGEYlojqlyJIoH3t6pSin97w07cFS5TZdUnWyfj7tXy7B/H3CbWFg4RvNvjAFwm1jN7xsONjUc2qBnO/I5lo4G6N6GH4Ox0QqnSXM+13O9Z+G1xzaLkdH1eTd1+jXni/2dfrh1ljQ6bowdLy2f11t1lEeZhYPHE9Tt+waaW4aDTaeyUWDkjaXDpU3Dcu9mAD4+7EuSTCqk/MVg+kamY2lFimdf4czw2TOxqrEt3hAqHWbUOIxABhm/3UHtA9nuoKT63uFio/0Zju0aaW0aaCztT1axrQcOHMDll1+OefPm4fjjj8dPfvITHDhwYMDPdHZ2wuPxAABCoRDef/99TJo0CXPnzsXrr78OAHj55ZexcOFCAMDChQvx8ssvAwBef/11zJs3b9D6bADgtmkLVbpzCB+vcRhx+1mNSli62cjg9rMaMdaRfYUZY69mR+I1Vy9pgFFPzQ5ixFBu47BiQb0qVHnFgnqU2/ocGBxrwFWL6lU2dNWienBsX79xmVisOkNtZ6vOaIDLTCKeRGakSskpsnJYPn+SykZ/MGccekI6imVkQVwfLrEPxPXhtMb2qxbVoyGu60kQBJEj44t4XHGSej6/4qR6TCjSL62x2GzENadMVo1t15wyGcUWfSsjmo2s5nxhMg7BmiMKlJk5TCuxxg57dF5ODzS3EHlimBWbMLMGzb2bmStcwzrDMva0+1WSSXva/amlPIYAPfpGiZXVfPYl1gzGFgZ4Z283Lnj0n/jJnz7Bvz36T7yztzsjr4id5zT9Clae9lFEMllZ+LXXXosLLrgADz74IADgf//3f3HNNdfg+eefT/mZ1tZW3HjjjZBlGdFoFKeffjoWLFiAuro6XH311bj//vsxbdo0nHfeeQCApUuX4vrrr8cpp5wCl8uF++67L5umIiRKuPzEuiSB+JCYQ+GCKFBiM2Lt0pnwhyXYzBxsPJPTJC4naHYYDEA0Cvz2nV1Yu3Rm9hclRg3doQhueVWdAn3Lq1/gL5fNhb13UuEYAxxmDsvnT0QkCjAGwGHmwCWEizf3hPGXfzbh7qUzERQkWHgOf9i0G9XOyXDH0ziyPOUhjgzKLCzuPHs6fvny58qYesfZjQiIEdy2/kuVjT67ZT+OHT8T2zsCKLWZhkQEOy0J+nCJkQ6Ixv524ng3/nLZXLR4w3CYOJRajSg2DYN2EwQxKjjYI+Khd9TRvw+9sxP15bNQY9XH8VVsZlHlMqvWB1UuM4rNrK7zO8cA1582Bfe8/rUyX1x/2hRwo1HCKmFu6Q7LcJvYoSnwc6QwDItNSJEoSmwcHrvoWLT5wiizm+APC5AihfvR2wKiZmrjjGoXigvlDNKhbxzqCuNvnzfj4WWz0R0Q4bYa8fh7e1HlMMGZJi39gFfU3FP9+dK5GGsbeAy2m1hcffJk7Gn3KxGDV588GQ4TOdqIZLJytAWDQZx11lnKv88880w8+uijA35m6tSpeOWVV5JeHzt2LF544YWk100mEx544IFsmqfCxhuxet3Hqs60et02PH3p3Kyv2RmWsbPVj1+/uUMZ7K85ZTLGOMxZD2JdAUFb9y0gAFTF5IinxRtOqeEXX5ibOBY2Ewd4w8p7bCYOpoSS06U2E3pCIr4+7FUW+D0hsU8guPeUJz4BmY0MbjuzESdNcJOzjVCqx40rtmL5/IkYV2yFjeewr8OPpo6AykarXGb8YM44LPvjvwa/KO79npgjTAcHXW+kg5LSmnjtCFBjNao3vLRRIggiT3QFBVxwXC3ue6tvDXn1yZPRFRR0c7S1BSRc/8JnSWlWLy2fp18VTgBuqxGldl7l4Cu183DpHElXMHrnlvpKZyztiOaOvJFKUF9vGx4Iu5nFnnYZK/7yoWrNPLa4cE4XX1jSrIrpDRe4KEee+0ap3YQP9nZi44525TWzkcH1p0xO+9kWb0jzGbV4Q2kdbTAY0NwTwiOb9qiq008oteVyO8QoJauRaf78+XjkkUfwve99DwaDAf/3f/+HE088Ed3d3QAAt9udxybmRldAu8R3T0AA0nWmFHQERcXJFr/er9/cgelj5mTtaHNZec1cc5eVnGwEYE+hb2Az9XXhnqCIX7z0edJ7Hr1wDip6T1rKrCxWLKhPcqSVWTkgktspDzHKSThNLrLyuPD4Wpg5Flc/9yku+85EAFDZ6DnH1OCBDTsHvygehqfWBEEQ+cLOGxUnGxAbG+97aweevPg43b6zOcVhXbM3rKuTwheS8fMXk9clT19yHEqHIn2UGDV8UyAbHojugKy5Zn7i4uNQ7CiMfVe7zLjw+Folqi3uCBrjMhekPXrBGoCrFtUn3SebQdGHapclxTOypP2sJyRpRgw2VrswpkB2SAxfsrKIv/71rwCAZ555RvX6//7v/8JgMODtt9/OvWV5wmJkNUVnzTlM8J6QpDnYe0IS4Mrumt6QiCsX1isbU7ORwZUL6+ENiRTRRsDYq7/Wf1IwJuivecMSJpfbcdn8SQiGJVhNHP5n027VKVZbQNJcFMSdHy1e7eIhGZ3yEKOaxNPk5p4QnvygCSsXH6XYy7qth1RjGMtA05ba/cKAi+K0p9a90W47m7rgNnGxaDdDhunOekfKEQRBpKEnhZh2T1AEHPqs9+xmTnMtbNd5Y3jYE9aMHDnsDaOW1rb54wiY22wm7YJfhdTGah0g22SiTn05HawBmo6gE+tKCtIevWjxhvHkB00qyaUnP2hCQ6UDxWlSRzkm+2fkS+ED8IUyjBjkgL3dAlo8YVQ6TRjv5oECBxsS+pHVDLthw4YB//6Pf/wD3/72t7NqUL5xWTj850l1WPVan0bb6iUNcOUgwOg0a0cX5bJgKbbweHbLftWA8eyW/bjvvFlZX5MYQWg5DxIWSQwD2HhWlX5h41kkHtyMK7bg3+bW4oYXtqr0CMcW9Z3QtPu1Izzjzo9Kh1m7io9Dh5OwI2BhOJrobzvNPSFwjAFmI4MXPzqIZfNqlTGMZYBja4u1qzMPVIjGkPrUut0voMzCJUW7/eYHs+AXZPzy5c9RZOVx3pwaTCqzo77UhjJzgk1RpBxBEMMAu1nbYWDXUeNHjkRw+fw6rF6foFe8uAGRiL6aEDVu7eia6lEWXVNQjpC5rdhs1Dxw1rugx0BUu7XXzGPchbPvdl+Kdb5PQJlp9ERcldpN4BOKThgMAM8ZBl5j9pLLMypzaGeglWZycMABG3d1Y+VrfVlFa5Y0YkGdm5xtoxRd5EjXrl2rx2WzIihGFCcbEOtIq17bhqCY/eLCxnOa1R3tfPYDWI3LqKpC9eh7sSpUY10URTTq6V0knfPIZlzwh3/inEc+wOaDHlU1JSfPqaLXgFiUmzPB5vxhWSn6AfTpEQaEvkpDDrNRswKY3RyzMz0q6mqScM8XPr5F856J4UWpXV1ttMplRiQq47c/OgbnzakBwwA/PHYcJlfYYTGyuOf1r5LGyTvPng4xCmzvCCRXDOu1ic8P9WjaaKmN14x229bsUZxsy+bV4pFNe3DVM5/inIfVNpUqUq4tSKsbgiCGDqtRew1pzWENmQ4zxylONqB3fbB+G0ycvhtvnjPgmQ9jBzArFtbhsu9MxDMf7oepgFUZRxtHytxWbGIxsdSG5fNjtrR8/kRMLLXFihUVCKY3fbF/X2YLaN5Oi/Y63zHKdBGNLPCzkyeD7b1V1hD7tzGDsaXUZkq5zkxHmZ3FbWeq90m3ndmI8gxShfd2C4qTDYj11ZWvfYG93ULazxIjE11m2Gh0+ByhpBKRb/GGMT7LsPWQLGtGF4VlGdk+0gM9It7a/k2seopfhNtmxJ8270VdmY1S9kY5mQi88ixQ6TKjusiKLr+IIpsRciSCxHV5iye9rXvD2inKvrAY0yyMAg5Tv8qlpvxXzRqOorbEwLAG4O6lM7Cr1QeOYTB3QhEOdAVx87MfJ1QfnY6Z1TaMdVswvsSKMS4LTqwrwe6OAA52BeENivjB/2xWRaONdZnR7hfgMBux9s2vIUjRJBuNl4Hf3h5IsvFINGY/6TTh0kVzEgRBDAVBUXsNGZSyX0OmwxfSTlf1hcSs9Yozod0n4AdzxiWtOdp9AsqKdR53j5CoeZrbCkdzj3b64vgSG6oL5NgSIxHNSr+SztGraUmTuTNYPGEZoiRjcrkD/rAEm5lDICzCE5bh5gZ2epVZWNxz7oykKNBMKqG2+SJ4cONO1W/+4MaduPe8Wai1Dfy9KfdpnjAmUCr9qESXEdhgGD4nVaX2HEI8U9ATEPG7d/comhNyBPjdu3uwekkDYDdldc2uoICTp1bCG5QQEmV4QwacPLUSXUGBHG2jnEwWSZ5wFN1BCXvj5aTbgfGlNnisUdh7M0NLMwhnNhlZbPjqMO5eOhNBQYKV5/DE+3swu9YNIDYJXvXsp0nXyLcDjBaGI4/OgICwGMH7u9pw4bcmotUr4EBnAEVWHs09MW2/m17+HI9fdCwmOU3KuNUWilW7u/SEiSpNjCIrjz3tfsXe4huwpzY34anNfQvXb08sRp3bDET7TiETbYc1xGzUYBhYE07rs5meYOaNI2TjRxBEarqDIl746GBMT7V3Hv6fTbvxnwvqAEd2a8h0xKNc+o9/eke5mIycShYFiMmizD53pq7fe6SkUwLa8+KQz21DwFCtTweD3cxppi/qrX04EKIcQZXTjLVLZ8IvSLDxHBgDIMkFdLTp1B+NHIsdrd7YvsgQ2xdlCs8xqsMOnsssya/NF4Yg9TXaYAAEKYp2Xxi1afbrlU7tvlrh1GfcJwrPqN/RWnlWicKId8RJ5facxDOtPIeugICHNu5SXstVkLPcbkKrV8B1/fS1yrN03Kmgzd2wJp7Omaz51zdghyMRCKKs+pwgyggnnFDZeBarzmhQ0kfjNmRLsEuDIYrz54xT6bitWdIAxhAziIwdYExMfP6jllZU2E2pxedTcKQsDEcTRo7Fm19+gx8ep9YBvPrkyXj8/X2Ks63dL8DJcyizsmgLSNjfHcJ9P5gFO89iXFED2v1hPL15P845piZJjPaBDbFTwoc27sJDG3fBbGTwvWnlynildQpZYuNx9cmTERCkAW0qqxNMI7C3KyZaW+E0YUIRD4hZPsAjaONHEERqKhwmfHd6lWocvWpRPSp0crIBgBCJYPUZR+FQT0hZC49xmXWPcvGFRc2INp8gAtBvvj+SouZzic4ZSbT7BRw/oRgXnTABXX4RxTYjHntvb0EPaO0mRlMH3MbrosyUERYjh62dniQtuypXccHapEd/jADwBkU8smmPcp+//O5URIrSVw7NxWlbZDXi4m+Px6/f3KF87zWnTIbbmv7QYnwRjzVLGpM02nJaWxLDGl1Gpurqaj0umxUmzoCwGFF1xNvObMxJH4JlUpUUzr6dAVFbX+uJi48FcplAaHM37JEiMlYtbkgSKZYjMoDYwM0A6AyoJ5SrFtVjUsJ1uvwCbCYGjyybraSXdgcFdPkFVPaKe7IGFiv7aRaufG0b/nTpXAAZOsAY4J293Ur10nifOmmCO2Nn25GyMBxNeEIifnjsePznnz9W2c99b+3AigV1WPvGDpiNDBgAP3nmY6xYUK+ykasW1ePJD5rQFRBw9cmTEUVU06kbH0c1bSIKzKtx4qXl89AdluE2sTjQE8Kdf92OHx47DrcsPgq3rf9S26aiwLyxTvz50rlo8YZQ4TDHtAdT2awR2LhTQ7S23p3VguhI2vgRBJEaKRLVrHg355LjdPtOIwMwDJO0Fk6TYZUzDrMxKaX/gQ078aSO9wocYVHzCfNi7ECdH5VrqWq3CadPr8J/PPWRyqlV7S5kNJBBUwf8L/8+t2At8oUlzfGlcdlsIANnkB7o0h+jUdz5169U93nnX7/CU5ccq2t7eJZRnGzxz/36zR3482Xpf/M2r4TntjQpWUUWnsOT7+9BY6V99I1LBIAsHW3BYBB//OMf0dzcjNtvvx379u3D3r17sWDBAgDAgw8+mNdG5kJPQFY2e0CsQ9zy6hd48uLjUJyBcKEWNp5DsdWoCjktthphNWbfSQaqgDIxh1NO2twNf1iGxe837VLl+/9+0y5Vxdn4RNl/4vzDhXOU99jNRuxpD+CGFz5XFiE3nj4VNe6+MtepSpG3+cIYZzOizMri9rMacfMrfc6F289qRJmVUxwSB7yiZp/686VzM09zPkIWhqMJl4XHuzvbNO2ntsSGKxfV4ZhxRXjiH3uxeEZ1ko385u2+aLX73tqBxy46FnNqXdjS1KNcy2xkcFJ9GeaMdae2iShQZuZQX+lEd3cAZRY7fvfDo9HuF1DhMKW2KQb4R1MPbny5r38MdOiwt0tbtPbJS47LSkvjiNr4EQSRks6A9ljQFRCz1g5OhxQxaM7bT+ns8OpMsbbt9Au6ahIdcVHzvfOiMpeMwrVUZ1DWdGo9dclxcNoLUxAh5ZraK6CmQBptITGi2ab+rw0levTHzoC27mRXQML4NNlgubSn3af9m7f7BIxN48hs9wvY0tSDLU2fJL1O68DRSVYxWL/4xS/A8zw+/fRTAEBFRQXuv//+PDYrf6QqhtDqDWd9zSoHB0u/6lAWnsMYZ/adpNSRogJKjqmjA23uiOGBLySiqSOIhzbuwoMbYilzTR3BmEhxL96QpPk7ekN9VaWkSAR3/U19unPX375SpYY4LZx2NaLeAb4zJCMQllRVnQJhCZ2hvrTVFm9Isy0t3tDgbrx3YTitxBqbYEbhwnA00RUQEIlC034MAB54excuf/ojnNxQiWKrUdNG4ho9ITGCD/Z04Pw5tZhT61Kuc8+5MzDWbhycTSTYUTHPatuUAfiyPag42eJtGKgy20CitdmQS5UrgiBGDxYjqzkW9H8tnwxULElPylOsbcvyIYsyAPGo+cTKgEqEMzEiKZQND0Qh+nI6qt1mzTYVMvJPj/6Yy7PPpT3mHL6X1oFHHlmNBPv378e///u/g+stC26xWIZVpdFEKl3aRp2L8OC+LgE3vPgZHng75hh54O1duOHFz7CvK3vnlSRLWLW4QdXpVy1ugBSR03xyYHTr1IaYyPmHTV1oC8nA8Kl/MeLI5DeKF/VIek/CiXCXX/t0p9vf50iwGllcc8pklZ1dc8pkWIyx08DOoIg7//qVyrbv/OtX6Az2Of0qHdqTeIXDnM3tDw1krzljM7FYt/UQrlyoLmV/+1mN6PTHFrohMYLb1n+Jhmqnpo3EpwmzkYEcAVa+9gV+fvo0PHnRHLy0fF4sugyx32p7R0D7t+r/WzJp3o9YZO9H+7sGdehQ4czv3EEbP4IgAMBu4jTnYbtJv7GgrEAOLyAmtZJ4r1ctqh+SKTgudh4/NMxU7JwYnqTcz+mobZgOm4nTtG8bX7h5XZSjmm0Sc9tO5kZCFsufL5vbt97LwXVgT/HsMxpHE6REfvejo/HnS+di3tjM2pPLb07rwCOPrH5ZnucRCoWU6qL79+8Hzw9Pb6wBUdx+VgPMHKeU/w2JEnIpjHrYE0aRlVeqjgLAix8dRIsnnHXYv5FlsfHrZjy8bDa6/SLcNiP+tHkvLvvOpPQfHgBdtLBI9y2vZPIbGVkGd5/bCLfVhM5eEdjuQBjGBGFAm4lDbYkFi2dUK3a5bushWEx9IfUdAQFuM6eqRhQIi+gMxEKe/YKs6YwICH0zdI3TqCnmOdZpBAo5kaeC7DUvFJuNuPzEOvz+3ViaM8sAUyudeOWT/bj42xPx0AVHwxeS0O4Pw2CI4rc/OgafHuhGJAps3t2GS0+YhJ2tPly1qA7jSqxY+/qO3nD7MOZUOdAWlLCrK4gWr4gbX/6sNyXVgjVLGmGIRmOFXKwsNh/o+y1rSyxJWnBav227vy8ar3+qgN1sjDnn+tnChHyL1lK6NEEQAEKihGlVdjx20bFo84ZR5jBBisgISRLiuqz5xsQasGbJUbDyRmUtHAiLMLH6uryaPSE8+UGTShrjyQ+aUFtizVxqIgvaghLu/Ot2ZT0UiQJ3/nU7fvfDoylFa4RSamU15+QyW2HSRoFYtMrUKjueuPg4tHpDKHeYERBFMAU8zP2mJ6jZ58aXWDHG4ihcw/rJfuS69glJEsa4zSoZpzFuM8KZjKMG4ONmH7Y1exCJAl8d9uKoKieOqbSnbZcB2r95Rn4FWgcecWQ12/z0pz/FZZddhubmZlx77bX45JNP8Ktf/SrfbcsLcjSCaNSgquZ525mNiESzz1WvKTLjP+ZPVNL04lpY1e7sI3pMLIvvTq/GR01dsYpQ7cB3p1fDxOY4gejQqUn3Lf+kKzMdkkSEJWB5ggjsbWc2IiyJiHdjp5nDtadOUVXYvfbUKXAl/CYlVh6He0I4mFAOu8xhQok15iAe4zRrOiOqEqJ42vwS3tr+DR5OKLrwp817h62YJ9lrfig2sxhbZMKvzpkOQYoiKMjwhkScPLUSnx7oURWHWbOkEQ+9sxNNHcHYv89sxF1/2678+/azGvGTkyai2GoCyzD4siOI/96wAxd+a6LiZKtymfGDOePwH0/32fxdZ0/HfW/3idBqacFp/bYVDhNsPIvbzmzEwa4AnttyEF0BAbcsPgpXP/8prjtlSrLjVQQW1Lvx5CXH5afqKHBE6OgQBDEwLguPbd94lMJEsTGzAQ1jnLp9Z0CUEIV6Lbx6SQMCon7OPSC2vvjh7CrMqytFqzeECqcZvEFGqU3fKKSekIgLjqvFfW/1VQa8+uTJ6AmJNO+PUFr9Mh56Z6fKgfTQOzsxfulMTCiQRhvDRNHhE/GTVz5R6RoXF6joAABUucz4Tl0Rvl1XgvZeR36bx49KZ4GzTgyx9fjOpi64TRzKLGxOayDOwGJfqwffrqtQxpYPdragylmR9rOdYRl72v1JBebGF1lQzA9sS2yuvzmtA48ospptvv3tb+Ooo47C1q1bEY1GcdNNN6G4uHBlgweCNXC45dWPkgRgn740t4owWlpYuVSZCYgSDveEkjp9pdME5BpSmudOTaLe+SWTMtOcgdUUMn46QchYikSSbOiaUyajJsEBLEUikOSo6j03nj4VUq/jmTVA89SbTTiq6QmJmDW2RFX56cqF9cN2AUv2mh+6BRl+IYJIJILth2OngOu2HsLqJY1Yue7LpKIBN313GsaVWtHVG4F5xUmTcMOLMRu++ZUv8OvzZ+Ga5z5VbGjV4gYc7g4o1znnmJqkanU3vvy5UlABAAwGpP9tDcCONr/KEXjL4qNQ4TThgbdizsCUjlcRmGDn+0S7UznZeheQscMMU84LSIIgRi9+QcZb25uVw6pimxFPb96L8aU2IM0mL1t4ltUUkn9a52IITguDqdVFaPOGEQjLaPWGMbW6CE6rvmmcvJFTnGxAX4XsXNf+ROHo8AmKnnEiehfWGAhRNijFwwAo65tC2pndxOCE+gps3tOBSBTY3ebDCfUVcJgLmDptAD4+3BdBxhqQcQRZKsw8MHtiCSLRKKJRQI5EMXtiCcwZmEJnUMQzH+5XnLYA8MyH+zGrxpXW0TYcf3Ni+JLVLvPNN9/EvHnzcNJJJwEAPB4P3nrrLZx88sn5bFte6EpZ3UlAbZZh6+3eFBv3HKrMyBGkrSo5XCi1mTRTFEnMMTsycQSlqlLWGRBQ27vACIkRzZLTf/xxnw1Foe0kfrK3HLYnLCLSLwL01jMa4AmLyuRj5Fg8u0U9QT27ZT+OHT8zvw8mT5C95gdPOFZV6bb1X6oiBDpT2K/TalQ5Y9csacCCyaXYuKMdITGCrw57VHa4ev02PLxsthJRmcqJ5rJw+PnpU1BTZIWNZzV/WyPHoC0ko8zCakY03rb+S6xYUIfPDnmU15Icrxk6zyKRKKUmEwSRMZIs4/sz1BkM359RDUnWT3uhLUX1zzZ/3xpCDyTJgE6/iNXr+qL3Vp3RgGqXBdAxCKnDry2c3+XPfu1PFBanWVsexaGjtmE6Uq1/OgtoZ55gBIe6g0mBGzVuC1wFSrPNJYIsFSaWxTdd/qTI4LKa9NGyohzBsnnjsfaNr5XPXnfqFIhy+my3VGNLxr85HcweUWTl3n7wwQfhcPTleTudTjz44IN5a1Q+cZqNmuKZTnP2A6DLqn1NVw6hwgFBu6pkojZW1hjSi4UPhjIrixUL6vHoe3vw4IZd+MPf92DFgnqUWSk6KBsyKYbgsmgXQ3BZ+t7jyaAyadqCCQYGt65Tn3rfum4bYOj7bk9IxLUn12PuhCLUldlx3PgiXHtyPbzhXHLqUpAH2yV7zQ9+QVKcbEBfhADPJVdbMhsZ7G7zKe8tsvI42B3EeceOw4qFdagtsaD/eiYkRvD5wZ6kYgv9rzulMjb3XP/CVtz/1g5cfmKd6re9YkE9vm7x4ifPfIzNBz3Y0xXUtPmQpI4gNZu4vo1fr67fOY9sxoWPb8E5j3yAzQc9mvZ3oDuomZp8wCfmbcwlCGL0YDZy+KY7Fn3+4IZdeHjTHnzTHYLZqN+cZDdpVxzXswADAHjDkuJkA3oPVdZtgzesXe05Xzgt2ut0O0Wxj1jKHcak+f7yE+tQ7iic49Rh1u5XjgLamVeQNAM3vIK+fW4gOoOiZpsSC60Nlp6grDjZ4tdc+do29ATT75vtZqPiZIt/du0bX8c0e9OQyq+Q0W8+iLUlMTrIaiSIRJI9vrKOJ3G54AmJ+MXpU9EREJRw1WIrD09IBBzZneLJkQh++d2pisA2awBKbDxkjeeSKXHnXf/0QVeuaaM6hOu2BSTNNEbSvMqOMguL3/3oaPjDESVd08YzKi29TOzYnqIYgi1hIW3hWU07M/OxSaPFq13oo9XbV+ij0mnG5z4BNyREK61e0oBJ/auOpju1yeDv+YgUInvNDwFB1rSNg10BXLmwXknzjOsHdvjDqHLFbGLZvFrV39csacRzW5qUa1e5zDhvTg1qS2zgGGD14mmwmo24ZfFRqgi6VWc04Otmj7Jg+87k8qRN3MpXv8Dy+RPxgznjsPbNr3H9aVMVm69ymXHOMTVgGWByhQNVLjO6AgKuXFiP61/YiisW1OPoagc6Axno+vXa7/6eEC77zkS8+NFBNPeElPfvavNhf1cQ67Ye0taAIwjiiMQXlvD29sO4e+lMBMMSrCYOT7y/B41jnIBO2k42E4urFtWrUuivWlQPm1HfCJeOFBE/HX4BdVlWcM4Ef1jC1SdPTtJoi2nSFa5KJZE9vnBEKcYUX4P8/t1duP/8WXBbCxOpFRQlzbV5qIAlPgUpotnnBDH7PWquZFJobbC0+bQjy9r8YUxIs79v92nvddp9scJwAxGStH/zsJT+Xkgz+sgjq1+1sbERv/rVr/CjH/0IAPCnP/0JDQ0NeW1Yviix8djbL1z1mlMmK+Lv2RCWZHD9KjVxrAFhKftBzMgwms67xKqS2aBHuC5pXuUZBvCGZVURg0nl9li8ae+4XZzCjosT7NhqYjSLIVj5PhuKl8Puv9iOn2pXOE248PjapL+XJyyIvWFZU+vliYuPQ3ncptI5yTJwouVrQiJ7zQ8VDm3bYAA8sbmvwhVjiC1ifvP2Tly5sB6RaDRJa23lazGNti+++RRFVj7puquXNOCJ9/dCkKKxzWhvhVybmUVPUFC+a6zbovnbRqLAAxtioslxR+CzW/bjB3PGqRx+tyw+Cp6giCc/aEJzTwgrX/0C950/C76wdnSoYjMa9nvlwno8tTl2HbORQVNnEI++twdXLqzH2je/pmp3BEEAiAmon3vMONyQINGwanEDGB0llKLRKKqL1BX6qovMiOrs/S93mDQP98od+jq7Smw8DhmDqvu1GBkUW0gyYqTSGRBwxYmTYDX1aQiPc09CZ0BATYGKD7gtRuySk9fmrgLO9W6LduCGu4AFGjIptDZYylKMLWX29Nd02Yya61lXBpkuDpMRYY3fPJPoYNqPHHlkNa3fcsstMBqN+NnPfoaf/exn4HkeK1euzHfb8oIgy5q6VUIO0WdFFhOCYkQV9h8UIyjKYQLv8Asw9KsNbDAY0OEXsr4moE+4riNF2GwmIbdEMoe8olLEIG5Ph3tCOOTt+40EKYUdJ0SSsgZG8zpswurdyDKYUGrF2qUz8V/nTsfapTMxodSqOHQZg0ERCF2xsA6XfWcinvlwP5gE24yfBF2xoA4rFsb+K7LyaPeFlfekcpK1BaWM/g4MPCENhkxSc4n0GAzaOpKTK53oCgh4aGMslaPKacFfP29GSIzggQ07UWY3af6OjAFYe95M/P7/HYP6cjvuOmcGnrzkWKw+Yxp6AgIuOWESfvG9afAEwjjQFcQ9b3yFDp+ACqdZSR35pieo+dtGo7HvYJlYJN5Tm5tw7alTkxx+t63/EnIkinOOqVHs3QDgYFdgQJvRst8HNuzEOcfUKE63lz4+qLy+eEb1oO2WIIjRiZHhsHp9v3TK9dvAMfpF5fAcg2KbEcdPLMHkcjuOn1iCYpsRRk5H7x4A1hDB6iUNKjmA1UsawBr0ja4JijJe23oIdeUOjHVbUF/uwGtbDyGUQdRJTvTKXXzY1EWyAXmmzG6CvZ8Ott1izMixoheRqEFzbR4p4A/fHRSTJDiuXFiP7hz2fbkiRqK4apG6TVctqkcuQXZBQcKqxeqxZdXiBgTF9CmyTNSguZ5lMvndotD8zTM5s6D9yJFHVu5Tq9WK6667blCfaW5uxg033ICOjg4YDAacf/75+PGPf4zu7m5cffXVOHToEKqrq3H//ffD5XIhGo3ijjvuwLvvvguz2Yy77rorq6i57oB2ZEJPQASyPFELSjLueV2d233P61/j8YuPRZaPFMVWHrtafUmRZ8U5RN4B+oTrhkQpKVXsyoX1COtcJn604he1nWiP/ngO4s/TG9L+Hb0hGXDG/u0LS/jTP5tUYfV/+mcTGsY4gd6TEm8ojJ6gpESkxRe9TrOASpMF3pCQFPVz5cJ6eEOiktJS5TJrngTF0wSBmJNMMyy799Qmk1Od+ITU/7RqsBNSmYXFPefOSIqeS0zNJdLT6tUO0//iUI9ic9Eo8PtNu7B4RjU+OxQrdpDq1PHLZg8+P9CN06dXqexx1RkNcFuNuG39l+gKCFi1uAGbd7fhB3PG4b837MSZs6oV22IZBg/+29HY0+aDJywrIfy/37QHZiODaZVOBEQJXQEBO1q8mu0fX2rDPa9/haaOIGpLLJhdWwSLkcN958/CXX/bjqaOmDPvjrOmKzaTyn4nV9hx6QkTlci2+OssA1pIEQQBIHZYpTn/+cKYmKWkSToMBgOauwVFRiGe4u+u0tdJEYkyeH7LfiUy2cJzePL9Pfj56dN0/d5oVDtqMBrVcdLPk9wFoY2c4rdL9fpQ0JmrML4O2E2cqmBZNBorWHbXOTMK0h4A+KYniCc/aFK16ckPmjC+xIoxFkf6C2hg4Tm8+PHOpBT8G05LP7akWs8myuSkoj2D4nSpoP3IkcegvEJ33HEHbrrpJlx++eWaf//973+f8rMsy+LGG29EQ0MDfD4fzj33XHz729/GSy+9hOOPPx7Lly/HI488gkceeQTXX389Nm3ahH379uGNN97A1q1bceutt+L5558f3N0hdgKitckryeEExBNMIToflLJ33onyAFVHsx+sU4br5hC27zLzmoP4SXVHZ33NIxlfWMLxE4px0QkT0OUXUWwz4rH39sIfkgFX7D3uFBp+iaHgohzRdJJJCarzHMth1WsfJ6V9PnnxcQAAE8clRf08sGEnHrvoWOUaYUnbVh+7qM9WKxwmXPzt8YoDMR5aXdFrd5k40cosLH7zg1lJ+oKDnpCiwLwaJ15aPg/dYRluE0uTWhZYeU7zNwOAhzbuUr037lw1GxnsavVi1RkNqqpzVy2qx18/b8Z1p03F8qe2qCM71m3D2qUzcc4xNXho4y6lGulHTV247tSpsJkYjC+xYl9HAIIcweeHelDtNkOQoxDkCKrcFlxx0kQ4LDyMrAH/2NaGX58/C5FoVLP9PMtgzZmN8ARFlNpNeOwfu/HGl+3KRpTnDGANDKaUWxWbSWW/jMGAR9/bk/T67HFFqW0ukwpUVKWKIEYNFU7t8aNCx3TKgCBrapU+eclxQJYyIpnQ4RewpakHW5o+Ub3e6RfSbmhzwciymlGDT15ynG7fSfpL+tMTlJIq2Fa5CtceR4o0zUIWQ3BbOVxzymTsbvMjEsX/Z+/L46Oo7/efndmZvY/cCYQEQsKVcAgBrV9FBbUeXCJaW+uJP3pIsVpt1VYQUGvVarVaW49aQa33Sav1wKNWUcGLQyARCFfuZO9jjt3fH7s7u5P9TLLsZtiA87xefVU2uzOfmXnP53g+7/fzQE8B15w2Bs5cNb9zQLnNCFafzBbT6QBWr0NZX23nQ0CIFzBrXLmMTF82qw4hQQDQf99SZGWJmtaFGWyIFppZ8nosk6q2KHDcCDueWnws2r0hlNuMqLQxQP7k8zSojEPKGZ8/fz4A4PLLLyf+rz+UlpZKGWlWqxU1NTVob2/HO++8gwULFgAAFixYgLfffhsApM91Oh2mTJkCj8eDjo6OQ70+CJEIMV01J+MCE9llxp5DJxZUyDwL5ug6WmKMseep1y+x59ke00TjxjPHIyEfp6eAG88cn9Mxv8sotxlw5sQK/GTtJvzymS+xZO0mnDmxAmX2ZKfNiwJWzO2TIj23HnxK6aiFJZNkZjb5XJR2cTrjZZ/eENmV1BtKppx7Fd1Nk20Ro1Fill5i5zGxqzNQXHKCvESby0EHMQmtliMbmFma2JeyffQqE6WbRiZGYFUXW/HmllbcuWgyls6qxV9+NBVrPm7BiWNK0eomO4L6OUGa/IT4CDa19OK+d5px3fNfgaEotHvCUly8/OUBiFEd7n+3Gfe904xlT38BHUXh9te3Y+k/v8D0UcV4b3srSu0GrOpTwnTDGeOwp9uPn6zdhF/880tc9o/PMHt8BU4ZUywtRHe2+3Ht819he7tfCh1S/N5+ziQ8/tGutJKN358zEROKTYok24AOVErfoZKOvC09AS2sNWg4QmAz0lg1r0HWT6ya1wCbSV3CSynzRk1YFNxOzSq7nSoJpXenSFwMNgZL7kIDGYmNuL4bc33v+eFEICwQyzQzKV9UC2Eugi4fJ5s7d/k4hPN4nyrtDK48pS7NIX6EPftEEhNDXvOYMnBvthlpXHlyn/acXAd7Bn2wW6E01x3KoDRXB2zY58GPHv0EP3vyC/zw0U+wYZ/mOno045BGuoaGBoiiiGeeeQZ//OMfsz7p/v378c0332Dy5Mno7u5GaWkpAKCkpATd3d0AgPb2dpSXl0u/KS8vR3t7u/TdTOGKC133TVcdXWLNOvssChAF5XNJMCjIIGMp28Ymsnli2RDsoGTzJAiQ1NRXDdmBE6NEi+q1KTuvDK3HX9/fKi/Re78Zdy6aLH2nN0AmyVwBHojvHBdbyTsxRfG/O5V2alJKmO0Z7OAd9JAnua2eMEpK9BnF5aDtDmvlHIOCbh9H7Et/ddoYKR4SWWC9/jDuWjQZ+3oDWP7KFlw1uw77ewO4f30z/vzDY9Ab4KDTKWfJWVg9EtUgRoZCIikzxEfgDsqt6+dMGp42+V69bhsWn1AjZcQ9cvE0fNjUBTNL49FLGvHJ7h6IEcAbFnD/u81pGZ4PX9SId3d2IcRHoNMRYo+UJWnWg6ZG4a63dmDxCTWgKWBaVUGMZFOY32YS46Tv3PXWDlw9ewyuf2mzFtMaNBxhcAVEPPBek6wvfeC9Jtxx7mQU2tQh22wGcl+biYB3LrCyZLdTq4pZdABQrEI1S7bn1GQDBgc9/ZHFKjrY9geHiSFW+NyVMjc/3PCEBaK80UMXTcPwPBkidPoFLO+TUbv8lS05ZXsqrXl6A/yA2bKBcMyUS77u2hJbdw1wi5Se+Z0ZPHMt6/W7h0N+qjRN4+DBg+A4Dix76IOH3+/HsmXLcOONN8Jqtcr+ptPp0gwBDq1tOjidZtln5t6gJNSdgJGhYuRBn+9mCk+HH69vbpXVhT/8wbcYXWKFc0RBVsf8osNPtCLvDfKYUunM6pipcDqAupyPEkNLT4DYUaxbegKqC7O7p2qDprN/3oONvnHa3krWjurwhqVn39nqBSckV9A6HcAJUXT5wjgm/h1jb5BcGpcS66aQgJXz6tM02kx6Gk6nGcGeADEOg5wApzN2nrDCdzhBlM5j9XJkAsXIyK69v7hsauklE4dhEXXl9oFvdBxHYrwC+Y1ZYl/qChH7UotBjzsXTUaAE9DpDaPbH8btr+/A0lm1AJKlxXefF8to6/KF8KcfTIYgAq5AOC0eV8ytR4Dj8d+dHVg2uxYjCszo8IZQ4TCi1R2CGInIdAhZmiLGycgiM5bOqoXVQGNfb0jaGLjj3Im4753YNSydVUueqAU5VDiM6A1wEuEX4iNwhQSwrB4d3jBKbQaMLrWBYWiIcSbw9PEmjK+wS38f4TSBopTHtExinPSdOZOGSyRb4jdKMS2KEWzv8KHNE0KFw4SxJRbQObpZK+FwxywpTvPVlsMJ7dqOHJBitOOgFy3dwbSS+05vGFNHONVphydEJLxoCqre76+7OmFmaJn7p5mh0Rvk0DBMvZo/T5efqCXsDfFwDsKcmgRvb4B4j40G/ZCP6SOhLy3xkeeVJTZD3tr3VZcfP5pRnTYfdqsYZwMh0O5TqJCK5O0+bW7uIrapzcdlPJ/vG6NKa55M1vftrV6ijnR7yrpLCZ4uP1Gmx9PnmZPem8G4D7liqLzPqTia25QVfTpixAj88Ic/xKxZs2A2Jxtx2WWX9fs7nuexbNkyzJ07F6effjoAoKioCB0dHSgtLUVHRwcKCwsBAGVlZWhra5N+29bWhrKysn6PL4pRuFwB2WcWQ6xWva9WlNWgT/tupii3x0r9UuvCr5pdhzK7Ietj2k16PPWpPFvkqU9bcPvCSVkfUy0c7A0QO4qDrgAcuazfVNQhcjrNh/0+lpSQBT77xmmRhZxFVmhhpe+V2Q1EA4ISWzLmrAqxbkuJ9U5fGJwQkU16OSGCTn8YLlcATjMLE0PJ/m6KZ7QljmEx6InfsaScp8BA3sUuMNDJax/geTsVduCdqccAABrY4+bQ7gmj3G5AtYMFUiquFeO1NyVeh6AG1uGI2UxjFABYhsLyOROwat02GSmWahhw1ew6qWyU0kGWiba93Yv71zejusiEK0+ulbI4G6sdeOTiRnT7ORRbWfT6wyhzmPDj40ZKO46JicwbW1oR4COSDpqRoXDP+VOIcdLlC+OFTfvx27PGY2eHF1ecWIMPdnTAaWbxh3Mnwszqweop4m9NehrnNVbCqKfxxpZWXHlKbaxUntJh8ZrPJOOEVfMaoKd0KDAyUsw4KMDhNKAzKOCT3d396q6ZFM6fGuOk94CmMHBMAwAFvLfblSaAfvIopyqaIGrF7KHEqdptGQrQrm3o4VBitFxJoy2HOeRAMLN6DHMaZeP2MKcRZjb7uXAmMOhpvPb1AVx8fA2CnAAzGxMs/+WpY1U9r8PIYP32tjSh9Okjx6t23v09AWLWd325DbYhUBqmFKPAkdGXilERq+bVS3OHWMl1PSJRMW/tsxkZvLWtVTL7SMT3lBHqxdlAcJjJc2e7ic5bmwwKcx2DnkprU6Z9qcPEpOn/rphbD4eJGfA6ldZUmfTBttS+JeWZT6uWP3PSe2NiFeacTPp9UAtD5X1OxZHWpv760r7IimirqqpCVVUVotEo/H5/Rr+JRqP47W9/i5qaGhkhN2vWLLz88stYsmQJXn75ZcyePVv6/IknnsDZZ5+Nr776Cjab7ZDLRgGgwEyh3C6fXJTbjSgwZ88IBRSMC46pckrujocKC0vjl6eOwe6upHjlL08dMzjp9YNMIpgUCBATm0Pa63e4vI+ldfj9wonSs6d1wMhii0z7itKRraifuuJY6TuFFnKsF1oo6R7ajQz++mVzbNKb6tITdwDjxShue3172rN9YnHyPJwQIX7n75c0JttioFFTbJG1pabYgkJDPPYyeN4ZufPQwLvfumSEzKp5DThltFMi22xGcqmr1RjPD/8Ox96hwB/mUWFn8egljTjQG8Qwpwm/fXkzWrqDAJIx+evvj8VVs+tQZGbQ4eOwdFYtaB1gZGJ92XnTRshKpTe2uHHFmo147LLpaGrzosPHgdXrsd8VwBUn1gCI7TTet74J95w/BVc/+6XsPbj9jW9wy4IG/O7lLbLJd5GNxU9n1uDa+IZIdZEJPz2pFsue/kJGRP/h3In4zQubZZMtPyegsdqJvd0BnD1pmLRb/dAHuyTC74yGCvzkiU3S725Z0ICJFTYUGmhs2DdAPMVj7q63dqRlXfSNcdJ70FhVkFGJ0j4vTxRAf2rxsRiRJ0c0DRq+67AYaKye35BGgFsM6pZTugN8+r+LVD0lHCY9LjxuJJo7vNL85sLjRsKucplUBBGc1yh3HV05rx5RFVXHiy0GYta3Vjo6OGAoPR54r7lPyXUz/njelLy1yWmicf70almcrZrfAKdZ3Xe5PziMDLF/sRvzN+abDRSRFDOzuWRnRPHX95vTJHXuu2DKgL/UQYenP9srq454+rO9mD6ycMDf5vLMC40MMQmh0KTNx45WZDXSLV26FADg8/kAIK0ElIRNmzbhlVdewZgxYyRThWuuuQZLlizBL3/5Szz//PMYNmwY/vSnPwEATjrpJLz//vs47bTTYDKZcNttt2XTVHT6RPzxrR2Ss0gkCvzxrR24c9FkWK3ZdYTuILku3JOD6ygnRKDvU2Kkp3TgxBwnBSqQCL0BnpiS3xscuC5eCd/lunWGptBHTx60LvZ5Ah2+MDHNudMXxoi45kKnV8SvU+4hEJvkrb18BizxWOdFARce22fSe+xI8GLMpadTySzBG5asyv1hEWNKrbhi5mhZ6bQ/nJJGFgWmllsxwmEkarBl9Lwz0HHb4+YUdRYSsRjiBWK8hnkBAPOdjr1DgdPE4tvOAH77cozoWja7ViLZEgjxEYwsNsNqYGI26UYG+3sD+Oene/GjGdWocBhRYjWkxfIHOzogCFHYTSxK7Ub4OUGmAblsVh3WbmgBpYNsYvTCpv1o6Q7CHeCx9JRalNuN2NsbwD1vN6E3wOHGM8dh6Sm1CAkRjC2z4brnv5Kd2x8W4DRZJUI4kYHQG+Dw+GXT4TQbcMu/v5LFxn3rm/C3i6bhJ2tjJFuFw4iFUyuxtyeA4QUGuEM0mjp9uOLEGrywaT9a3SFc98LXMXLLxqAzIMAdTsbc2g0tkqbbyXUlGGFl0vrmMSUWPHZJIwKciAqbASVmfUYW8e3eEPF9bveGNKJNg4Y8wR3kMaKAxZrLZqDdG0KZ3YhIRIA7yKOEUWeB7g0JxA2yRy5uzHqDOBOEBBHeoJzg8wZ5hEURWS5BMkOUkiQJgKT+5uOXqec6WmKi8eCFx8AfjsAfFmAx6mFhKc3lfJDQ4+eIJde9fk6anx5ueEMiHni3STYveeDdJvzxvMkotOSHbPNzIiysDg9dNA09fh6FFga+EIcAL6rqMNwfKOjIpNgPpmR9zHZPmCip0+7hMGwAUtEV5HDFCTXo9IWltdAVJ9TAHeSAAWIpl2c+YBKChqMOWY1ymzdvxo033ihls1mtVtx2221oaGhQ/E1jYyN27NhB/Nvjjz+e9plOp8OKFSuyaZ4MvjCPi44bibve3CEtSK49fSz84aRA/KFCqdSvIEeRyVZ3SLa4vOa0MRjuyN76GFCHwLIZ9UQhyNsXZm+I0J9b09FOdgQFMWZYkAJXgEfIkZyIVtgU0pxTiN0uH0ckwLr9nEQ6mRgG7mBAFmfXnzEO1UVxbTUlweSUZ1BiY/HDY+W7OSvm1qPE1ud9igIlRr2MOJPamunz7ucYQGygJRMJYemaHUaWGK8n1x5zaG35jkOMAPet34nFJ9SgzM6irjSWOh2JxoiyE8eUwsRQoHQUrnv+K1k56eXHj8LfP9qNhVMr4bSwslhOZJr9v7Ub5WXGZhat7pBEbl1zah08IUFWNnrDGeMQ4EVYDXqU2o24683tEvlX4TDCz4mS2cGy2bUoMLO46LhqGem6en4D3tvega8PeGTX2+oOoc1DJqo8QQFXnFgDq4GGDjrc8/ZOnFVfhmClE1/u70IkGrOKv+i4aqzd0IJWdwjvNXViRIEZ97/bhLmTh0vHbXWHpIVD4winfJKnsFFSYrLLSOhhBWY4aKS9H+U2I7lEzZbbuKJBg4bsUWBh8PU+L5a/+rksE3vSiMzLUQ4VAY7sFh7I0dl+IIiRWFZbocUgkU+iKCKisgFil4LraJcvjJq+c5XBgg7whkU0d/ikxfvoUmvMUVBbROcMM0srVNPkL3vMHxYU1pgCkKdMRloH+MIRXPPcppT+pR7leSxfbvdyRFKsw8thWJbZXAUWBpf938g0uZwCy8Dz9mKLAbs6/Wlr7iLLwMkyvjBP1OXzZfLMB0hC0HD0IauczRtvvBErVqzA+vXrsX79eixfvhw33HDDYLdtUOAwslIHCMQd297cAbsx+w6Q1uliAqdM0tr3qtl1aRlph4KQEJE6i0Q7735rJ0JCbrORgwoZSq3e7C3ObQY9fnnqGCQSrqQy1xzcq4otBul+JjAoKfc6oDMk4LOWXnSGxKFpoRyNPf9UK+6QEJFE2AGAj0SJpaNCJPml0hQC7DcvbsZ1z3+FHx5bjZIUQpkTI7j9je2y49z+xnYpc9JuonHjmeOwbHYtls6qxVWza3HjmeNgT3m2YYFssR7uG6vxe/9NdyDt3g/W805o3fQ9TioBWWKiceOZ42XxeuOZ42OD2yC2Zcihn/ufDdwhDj+aUY1PdnWCoWlc9o/PcN87MVv0H86oxrqvD+CuN3fip09swg8aq1DhMEpxGuBF/Or0cRhVbIaZofH0Z3ul+CG5ht77ThMWTq2Uzh3iIxhTbsdNr2xBgZnFlafU4pen1oHRU7j3nSYp3n80oxq/OWMsls6qxY1njZedJxIFzmusTLODv+mVLVgyc7TsWo0MhV1dfgx3mmOacqfE3oels2pRXWRCU0dMby7Aibjn7Z0oMLP4vzGl+PlTn0v35AeNVXhm414snFoJIxNzTr3plS2YM2m4dI6+5+wbc0obJZ1BQSKhxxeZYwYIhIlapS1WQpI6Vq2e34ARdi2bTYOGfMETFImZ2J6geqRXwlE8FUZmEJztB4BJTyHAR3FtfF5y7XNfIcBHYdSrY8iSQImNPK6XZFl1kgkO+gUc6A3K5nIHeoM46BdUO+d3CSY2tqmbOp6tmFsPU07lh7nBYmCIa0yLIX9jrBiBTJ4j1r9sRa4FUrnAbqRx8feq8eiHsXfjkf/uwsXfq4bNmD1JSoEirpupDKiNACcQfxvgBn5XzSwjkWyJ397z9k6Ys5JPGoqLUg2DiayYEZqm0diY1GNqbGyEXj80Mz86+9nVqsoy1bjNGyYKno4qtqAyy0mLPywQSwN94dwmXhYDeQfInMMOUJmVxo4OyHYCbj1nIsqtNMAP/HsSMtLjOlQcIdpbQiRK7PAfTdE8a1PI3GrzhFEdX5yLEchSs4HYv+88N2k57Vaww/YEYmXPtI6CgaFlz3blvHrQKSRyt0IGWLefQ01iEqsDtnT5pRIKDyei3U+hodgSIwjMNO5YNClt57fErD8kkfZqB4tV8xrSNNpG9jFE4OJEZmocJKBK7OUbKsS+hY0Ztqya34BNLb2y0siV67Zi8Qk1eCCePXbf+ibZv0usBrS5Arjt9R2yUtBWdwg6HVnYf2SRGZOG26VMOYOewnWnj8Uwpwm3v/EN5kwajj+93ZQ22Vkyswb3r29OO88HOzqw+MQa4rkiiEr9ZOrvLjl+JH46sxYr123FmFIrlswcjTGnjYWRoTFpuB1mlsbiE2owocKGXz2XXmKaKAldNa8eBj2FOxdNBqun8MD6prRy5nvOi72n33QHJC3NRLZlojw18V67Q3xm2ZYR4OQaJ/5+yXTs7w3AyOqx5qNdMOqpIdcPatDwXUF/Eg2jsqy0GAi+MI/V8+thYvRSZlmQE2KZNyqdEwACPHlj7rFLpwMm1U4LXhRwx7kT0dyZ1L4dXWIBL6pHZnpCPFH3qWGYHcNMQ3ONdCQhGiWXH+ZTo62/zMl8lbP2BjjierI3kL28T65gaJqYLJCq/3yo6E9SZ6D1vTeHNbcrQF4DuQIZVModIetSDYOHrHr+6dOnY/ny5Tj77LOh0+nw73//GzNmzMDWrVsBAPX19YPayFygVApnySH7ym7QEwVPc8noKuxTTiWVT+XYUashvHjAzeO3L22WdZi/fWkznr7iWAzPdnc0Az2uQ8WRor3lC5NLOlI1z5TKlYtSMmDcIZ5sOR3mgXiphEkh9d4Y3xF0BXmirskjFzeiLC7UXGQhO6alply7eBFtHi5N+LTSYYRTT6MnJKKNUCrdU2xB4aGQwCJwymgn1l4+A+3eMMpshjSSbcA4UCH28g01Yj/Ii/hBY5WkTUYizBII8RHp30aGwt7eAGrjpaZ9ibjEd/rGU68/jB8eWy2LoWWz6nD7G9/gB41VoBScNxNJnqnnefHz/TijoQKtLrIdfKnNINNpW7shptM2ocKGJWs34XujCnH25GGSsUIinm1GGtc+9zWuUCDwaAporC7Ani6/pBt31ew6nDu1Ei98vh9Xza5DZYEZrJ6CPyxg4UMbZJOvMSUWVBeZ0t7r+vLJ6DQmDW7sEeVg7fQLuPzxz2Tt23Jw6PWDGjR8V5DIturbD5VY1cu2shn1aPdwaX2YNYeMkkygtDHX4+cAu3rXa2UZHBQ42Rxj9fwGWHMx7RoAkWiUOAeLRI/gycQQQpcvTNRo684jqaUot5LDejBXFCmsJ4vyqMvqD5E3+QMhfkBNNCUUmhnyujmDdajTRP6tIwNC3MgorKOYgTPpjpR1qYbBQ1ZPdfv27QCA+++/X/b5tm3boNPpsGbNmtxbNkgwszSu+/5Y3PmfZP38dd8fm1NGl4GhcOeiiXCYWElo0h3kYMjgJVNCWIgQ2f7UrKZsUGigMbXKjscunY5ObxglNgMoXSQn4cU2hd3Ydm84e6INGFCP61BxpGhvWVg9qotMkmEHENN3So1RM0PjD+dOxLcpu7M1JRaYU4STHSaynX3jyPHSd6wGPfF9SEwKMtFx8XNkM4wAxwOIEXo9QYG4i/34ZdPhtNHoDPDELL76isZDI9oAQARGWtnkTl2fDamM4mCQYy/fUCP2DXpaeuaJDKuQIOLGs8bjrje3S6XOFQ4jLju+GrWlNtxx7kQUmFn8eX0TzKweV55SK8W4PU7cvvbVAaycVy8RvIl44sRoWgzdt74JS0+pxX3rm3DnosmyyU6Fw4jzGisx3GHC0lm1UrYdTQELp8ZKRgvMbFrsrpxXjxc37cXkqiKsXrdNNuna0xVAgZnFhd8biaVPfS6dK6YfF4TVYMP1Z4zF6FIbqNm1iEQhndfIUJhc6cRd/9mBnR0+iVi8950mPHxxI248azzaPCFc9/xXWHxCjaQ9l7jWxORr1bwGyd00ce49PQFc/VxywfyHhZNQamXgMLIosdHY3cuh3RNGmd2AqgL6iOgHNWj4riDACbh94UTsSnEaH1VsQYCPmRKpgWhURxyT11yunjkAAJTaDMT5jZqkIgBwYhT39xEsv//dJty1aHL/P8wBFoM+TZrgvvVNeGKxuvf4uwKHiUFjtQMXH18jm+Pa8+jYaGZjcitdfk56l4ssbE5rzFwhREBcT65R0QhkIBQrbNDnItFC6cjOodOqCzL6PekePX7Z9AF/Z2JpYgJLJlqB/ck5afOxoxNZPdW1a9f2+/eXXnoJ55xzTlYNGmyEeB6FZlbm8FFoZhHieQDZdc5GRocgF8V1z6cKTTbAxGRfa+3PIKspK1DA3p5wms1zjdOURkhkCotCVlQ+BxYS1OjY1YCJofDTk2rTsr9MKcQtTUcgRNLLdWk6yQhxooBzp8rt7FfMqZccRQHAxOpQWWDCXYsmw88JsLB6MHodTGwsdu0mhnjP7Cm7PFYDQzQXSC1R7W8Xu8ZmUCT0/H31EXSxHaBE5k6J6dAJ4iMlDgYTalxzIvOywmFMMxRYNa8Bz25sQYXDiJ+dVIOQEMHPn0oKfa+cV48CM4utB914duN+9AY43HrORPztomnghAiiEPGXH03F5/tciEaBN7a04sJjq4kxUm43osDMYneXXyLNCszpO7jLZtXhmY17MabUJutfdTrgzkWTEeAEDHea8Ke3d2DWuHKIkQiWzKzBmFIbdnZ4sebjFtx+7kSc11iJr/e7ZIRe4voT5/1/azamnXfpKXV44uM9kslCYiIY4iP4bE9PbCdcT6HAzMKgp4jX2uXnoIM8c2/h1Erc+06TrOxhV5cP7iCLySNovNvkTSul/uDX/4eZd/xv0GJBgwYN2aPYakCXT55tdcuCBhSrSD71KIzJvX5OtXJVAIhCxDWnjZFtEl5z2hhEdeqaMChm+IeyN0IbCD1+nmhI1evnJYmPIwrx+VdTSy+cBn1W86/BBEVFcV6jfI67cl49KCp/jfKHeQR5uTTJ1aeOiW08q6x/qIRehdLG3gCHUWoZgQwANSRagrxAfMdDGWxYeEPkNYg3FJPR6Q8MpUO5wyjjFcodRjAZ6LSrIeekYWhDFQXJoZTRpqf1uOGlzbjvnWbcv74Z973TjBte2gw9nT1zHOCiRCHbAJd9Z1+oIMhemOPg3OLhJZIt0dabXtmCFk+WYmqImSGQzCByTpUeZPH2RMee2k6pYx9CiADEnebUajBO0BHLdVN5KZbWY+W6PsdZtxVMSqyHeWBXpz8mTPzCZlz7/FfY1elHOB4OYkTEHedOlJkh3HHuRIgpFmE6XRQ/P7lWEjV99MNd+PnJtdClTHZKrAoGA/GFRKGFLMxcaE6J97iWwcKHNuDif2zEwoc+xob9nkOOixIzjVsWyAXhb1nQENODO0qhRuw74iRsIjusb/936fE1uPGs8UTNwRWvbsXOdi/+9sEuXHRcNQrMLH770mZ8vd+Nu97cDivLoifAgdYB/93ZgXOnVsJi1BNjZG9vAOc1ViIsRPDMxr245/wp+N3Z49N2J+9b34QVc+phiEsFXHv6GCyfMwFPf7YXv/jnF1jx6la0eUI44ArjvvVNKDQbYGJoRBDFfe80o9UdgsvPYUSBGZFo0rwg9foTpFff866e34C739qJd3d2Se1OZPwZGQosHRPx7fJzWDi1EqOKLcRrtRqZNLMOnQ6Se2riHXz5ywMwMDTcCiLrbS5xyPeDGjR8VxDgRPzuZfl7+ruXt6jqAKo05haoTAAxlB7tnrDMIKDdEwZDqdv/2I0MMbvMpqJIfZGFIRpSFeaxZC9rpMy/fvTIJ1nPvwYTFCiitEkm4vdqwcDoicL4bB51y4uU5tf5JHtTJFqeuuJYvLjkuJx1yYwMOYPUyAx87xNJBfLjURllR9KUDkY9hTGlNowoMGFMqQ1GPQVaN/DLkZBz6rt+zkXOScPQhio9QXQI6RF0KKRpduQgOtuuIEzf4cn+mLwo4vozxkmOkEaGwvVnjIsLt2b/Anb5wjirvgyLplehK146+txne3PSNHCHeBSamT5ZgkxspzDbHRw1BCJTOnZXWITTQA9J7a1epV3QFOHSLp9CKaCPw4j4Pc8kLr0hgZgu3TDcAZj0cBgN6OnjkKXT6eBIcemldBRKbQwev2wGOrwhlNqMCHCcbLITRZSYWh2N33x/WFAoP03uRGWsZTBA1ltPSEQgLMjiNRAW0BMSk2WqmWTODUJ23WGDCrpzrgCPWxZMxN4ePzHOtrd7YTXQqCo0S9mOJoaCzaBHgcUAWgf8YlYtntm4Fxd/rxq+sIiaYguuP2M8Vq7bipbuoESCBsIC7vzPdtw0Z4KsnDOhCXfDmeNg0OtwQu0kPPT+tzhr0jBimxIGAMtTMnpTdeV+9/IWLD2lFne9uVNy3tXrdLhqdi2e3bgfRlYPNhrFa18dkOI11bxBycihN8CjN8ABiE2kbpozQTJouOa0MagusuCKE2tQV2pDJBJBJBIhvg9hXsAIq0m2E0zr0t1T50wajhWvbsXtCycR29PuDR9VGoQaNBzJUByrVTRDcAV4XH3qGIkUSGTeuILqZXgBMVLxyU9aZOVdT37SggkVdlUzfhQNJ3xh1bJ6xCh501Tt8lw1MBS1pNr7Wc/lS+S/PzMENTNF+4MnRJZ38YbVfdcHRFyipa7cDpcrkPMcRGl9n4mpTK9Cf9gb4DN6brwowmk2QIhEYTfp4QqEMyKhC000hjtNsvXIcKcJhSY66yozDUMbqvSWugxY3cOFYuvAIvKHigoHuSyrLAdhV50uigKLnLwqsDDIIBO1X1QVmnDc6GJc/o/PUkqJ6lFZmL3dk9PMICREYDEwknsVQ+tysolXbVAf5I5dDRSm7IKmlo6m7oLaTWTB1VThzkziUsl4wReOkWu8GMH+uD19KkFW6TQBSJghUDjQG8EX+7oQiQLNHT6MLrFiZFGSaPMEBaIzb12pFbAaYGJpYvlpY4obaEY6Yzrg8zYftrZ6pLKUCRV2TC23Ss+6J8jj1a8OpOl61JVaY0RbJiTvkegUNMi6c6U2FiFewJgyGzHOLCyNSZUO7OkKSHpj1UUm/PSkWlyXEtvXnj4WBRYG976kTH4tmVmDlu4gvCGeaFJwwBVENAp8uW8fzp48DPt6AsQ21ZbZpL4PSDdiCPExd9P7f3gMogBGFJrx3MZ9mDjCiRvPGgdXgMNf3mvGDxqrpHgdVy6/ftJ5GUoni+1iqwHnTquEUU/BQFO46ukvpGu/ac4EeEM88X04ufaYNNLUZtLDFRRwxYk1AGKacAnCr9BCLv0usxvIsXAkkccaNBwlGOYwEt/TCrtRtXMWWVi0uYOyOaaJoVBkVnfhzUdEYnmXIEYG/nEOKLOT50OlA5SF5YKOPBCoamEoahwrmYLlM1OrQuFdLlfxXR4IBj15fn3rgol5a5MaqLCT731ZBve+xMqindAflmTwnnb6wmh1c/j1C8k57NWnjoHVGEb5AJVd+zw8/vjWDkmzUowAf3xrB+45bwpGHImZrxoGhCr5tkMpo82oj5EWqWmaffWvDhWUTkdM/aRzYMUo0Lj7rZ1IzD0iUeDut3ZCl+Mj8oVELO+Tar381a3wh7KnzqORlPLDFzfj2udi5YfRHOZN/QlEHu2IRCBZli+dVYsrTqzBX99vRkq1JnQAMeb6gvSdVN671EYu6UwIE/s4kZjx5kspaenxR2KES6kNI5wm1JXZEOIF9AaSDbabGMmZ9/71zXjg3Wb0BjgpLZuhKCz+v1Gg403RU8Di/xsFhk62rVix/DQ5EPaERezq8svKUnZ1+dGTom0YRVTSrkuUc5w7tQoJRkGJ5O0MJjP7MvnO0Q5ejOLXL2zGrf/6BstmyePs1nMmotxhRCAsos0TwhUnxmL5gulVaTv8d725Ay3dgTTya+HUSunfibLpxz9qgVFP45H/7pJiaNmsOqz5uAX3rW/CxcfX4Hcvb8GzG/entemq2XUybbUEQrzcEXWYI7bpEIiTzRccW4XtrS5EocMBVxBzJw/HG1taMWfScNBUbExZOS82pnywowM3zZmQNr789f1vpdh/9MNdCAsR3L++GUE+gt/Hs5YTbVm9bhsEMYofzaiWlWNfML0KLb1BNLuC6AyKKDHpMb7YjJbuEH725Oe4f30zHvlvrBTXxMQcrx77cLfUtkR7Vs1rwKgCwuRxkEqzNWjQcGhQGs/VfPWiiOLR/+2WzTEf/d9uKctcLZj05PIuA6OuJpHNSGNVWl9YD7tJvfOWO8hzljIVyT210FeyAMi/tqfdqCc/0zyKyEcjMc3B1DZdc9qYvG5YGRkKF0yvSptP5GLYNxRB6cj3ns6gIxUiEdz2+naZrNRtr2+HEBl4IWtSKBc2ZVCy2u4NSc65ibVRS3cQ7d7QwI3WcERCld5p6tSpahw2K/QGOegpyMTfg7wAV4DH8Cxrog+6Q8RsnapCs1TGd6hwBcnCra4QD+SQ5q6Uat3uzT593hMmkzENFzVmXQrwXRaIVBLtdac8e3eQJ8bc6BKrJNzZ4+cUv5MQ4qUoYNX8eix/JWm8sGp+vUR4KZkUBFOINiEagRjV4do+GXhCCtPqHSB1vcvPQYzKzR1+c8Y4dPs5VMZjiI9EiQ6pqc3rCfLEWJxS6ZDKQhmaJmrXrY2Xc2SyczsUd3cPNxJ9Sas7hLUbknE2Y2QhOFHEwd4geDEqe6Y3zZkQd+hMTiISRFrCuTRBelnjLqRGhpJl8poYCg9eOBVhIQITS6PTE8LN8+rh8nOgdTp8b1Qh3t3ZJWvTuDIbbv33N7EsMkK/Eo1rrq2aOwHuEI+dHV5EojE3vKWn1GHBlBG4JsXVMzXj7vozx8FA6/CPS6djvyuIP8cz5GgKOKaqAIhGsLPDJ51r1bx6rPloFwCApsilpqNLrBAjUTx26XQ0dfjQ7gljzcex7L2rZtchyIuoK7ViuMOE61+SE773rW/CNafWYdX8Bix/ZQsA4KGLpsEd5FFmN8ZINoIk51AsDdKg4buAAwpzyBEFZtWyGvxhsnC4PywAKpInnhBP7PO8IQHIoQpkIPQGRDy7cW/MhZ0TYGZjmexVReNRaFNnXslQVJqD9sp59WDpI4/gUEO8PldwQgRAVLaeC3B87PM8rRU6/RwMNCXLjDLQFDr9XN4ylHxhHmaGlrXJzNCqv+sDYpDNNTp85Hvf4UuuI5TgCpDXOu6AAAxgSuNTMFLwZdCnldsUsvBs+cuA1KAusppNP/bYY2mfWa1WNDQ0YPz48Vi+fHnODRssFJpZ7OsOYl9vUHoRiy0sCnLoAIc5jGD1yZWgTgewel1OqcJOM1m4NVdtB6X0+VzKXBUdUvs6Rh4CEgKRfTW9vgsCkQ7TwM++xMZKGWIJ9M3uKrSQv5Nagtrt5xDmI7KBKcxHYgSXiUGRhUV1kUlKawZi5ENByqAlimQdkscunZ5yXnLq+m3nxFLXC8wMfvHPL2TH+MMb2/HE4uQ1e0Ic0THYE+YwLF4yG+RFMjHIJ4nB/jQ0RlrZjBw6v4vOpX1RklKG3+oO4YF3myVS7L53mrFsdi0eej09W2vJzBrc9448Ji0sneZcetOcCVKpKY0oqotM+NGMavwhhWhNOHpeML1KIqJWzqsHALy7s0tq0+ITatDqDuGFTfvTCN/V8xsQ5gU8dNE0dHg5/OzJz2XHv//dJsyfMlzm6hkWRMnVtK7Uiiuf+hyLT6iRSmQT75yRofDYpY149JJG9Ph5OE0MdrT24NffH492bwhldiM+au7Exha37H580+bBfe80SwuzbQdcaHWHUOEwwm5ipH5x2exaYizXltowsdSc1GEzMRhfaIpNYhV8bzTyWIOG/GCYw0gcq8sd6i22TCw5s0xt/bBMnMzVgDvIY9a4cpkkx7JZdfAEc9u87g+d/jA4QT6/4oQIOv1hac5yJIHVy0kMVp9fwtAbFrD81W1psfTQRdPy5vBZYGYkKYjUNuVTl89qYPDa181pcinXnzk+b21SQ34ll3uvKCuVQemoU0GiIxP5pEobg9XzGySTwsScdISNiTnjaTjqkFXPv2XLFmzZsgWnnHIKAODdd9/F2LFj8fTTT+OMM87A//t//29QG5kLeCFK1GeaUGHP+ph6SoefnlQrkQ2JjB4mk3xVBXT7yWRAT47W6/Z4+vzylB22WKp19rs/ifLDNNIhB2v6QgONmmKLbFCvKbag0JCjZtAQsycnwR0k7/h6UkSKo1EdkYhMhTdMziLzpexi2Y0MVn+4TSLSIlHgkQ934c5FkwHESOOfn1ybtiNLpcyvevycjIQAYjpRPX5O2s0xMzQumF6V1l5zvFxE+Ri8lH1nYPS44aXP+x1E7UbygGczJge8gXaQMtm5zXh39yjWvDKzeqyYWy/r966aXYfXN7fiylNqMdxhIsZxTbFVuv+J/qfcYcSStZvSSLl7LzgGuzp9KLYacPvCSbj+xa/TCK8bz5qAhz9oxg1njcfOdi8OuoJYclIN3t3ZheoiE26e14CtB9y4/syxEMQoDHoKj17SiC/2uhDkI7j/3Sb8oLEKzR0+3BEn8RJtSOi3mRWIwEcvmQpKR+OOcyehwMzixc/3p2XrbWpx4a43d8LIULhlQT3sZjMufuzTlOtvANCCjS1u6R6u+bhF+v2KV7fi75dOx/Z2H85rrJTMIABI7qd9Y7mq0AxEDk2TTyOPNWjIDygd8Luzx6PDG5a0RUtshoxKnrKFJ4N5hhpgaYp4rWpneRWaGVxFIBbXqkiAOIwMlv57aJEu2aIzKOCqZ75Mu5Z8ZjxzQoRoHBbLdMsP8vVe9QeGpnBeY5WMZF45r14mzXK4oUYGvVfh3ntDA9/7aDRWdnr3W0kzhGtOG4NMlK/ECNnsTczkxxHg5FFOPLX42Njmq82okWxHObKK7ra2Nrz44ouwWCwAgF/84hf4yU9+gieffBILFy4cUkSblxNwfp8OZ9W8evhS3A0PFb1BnpjR89BF01CRZYdRaCYvegpyFKrd2xNCiZ2VO0TyPPb2hlBUYsnqmEJUIDLyYlRA1g6pUWBquRUjHMbBc8Y7QgTsTQy5bNaYomFywBXCp7u68beLpqHXz6PQwuCxD3ejsiBZrkzrKGIW2ar5DdJxPCEeFx03Ene9mcwSuvb0sdLAxIsRon36Py5LZqsNcxil7J7UQWZYym58t0IZa12pFZVmBjajnngMWwoBrJSJ1p3i5tQb5HHDGePQHeCkSXyhmYU7ZZJTaWewal4Dlr+6JaUPaMAIOyO5/Ay4c5uJi+dgxRsF7PPy2NTegTKrAZVDZBA+4ArizS2tUgyW2lg8/MEunNFQgfvWN+GKE2vImQtGvZThRVPA3z/chQtmVEvfS5SQGvQU9JQOT3+2V3IgveGMcQgJEZkz1Iq59bj8/2qwvd0rnafTx+GvPz4GQT6CL/b2Qk9RqC6y4PbXv5GOtWxWnUSM3be+CX88bzIxvmgKqHSapdLoxOer123Dgz+ehptf/VrSa1s+dwIefLcZXx/wSNc7sdKBZ35yHFq6/Cizm/D/1myUHWf5q1vw8EWNcAV5sHoKN7+6VUbWFZhZ9Pg4/Or0MSizGfBcSuktKUPvznMnYYTTBI8neEjPM0Ee3xUX5qUpoLGqACVm/ZCINw0ajla4QjxKrCycJjZpKEXFPh+o5ClbKGaWGdXNBEpkzqZKClx7+lh0+7mspVYyQW+AnLGb6uY+2OhW2EDszXHDPB/o8nO4ZnYNplQVS9nYX7Z05TXjucRmwOITR6E5LvVA64DFJ45CSR418IwK83e1NQj7gyckEOfxD1/ciGF5enZqZNBbFfo0q2HgfqVLoeS3O8ChaoCKN1eALOUzqtgiSfn0iwgwwsIkS4u1+dZRjayiu7u7GyybHDQYhkFXVxeMRqPs86EAu4HBz179vM9CZyueXHxs1sfkhAixwwjz2b8tLK0jsutsjlucxVYWWw640eV3Sw6RRRYWDcMdWR+TofQw6MN4KL7gLrAwcAXCYKjB7MBz39rtDCnsoPzkOJQM4AxzOGFgdGmZQivm1sPAJO9BVYEJpzdU4CfxLKDEd6oKku6xVoOemEVmY5PXWmBisfRNecnmXW/ukN4Hd5BcFuwJCtIAEhLIGn1TLm1Egmi1m/TE0phEuYieoojHSN35LbYaiGWsRSmZk0UWFrvjZgip701hCkHd6Rfw9jcH8beLpsHl5+G0MHhyw240lFtRYtRnvnM7gIvnoOzYUcB7u11pJPbJo5x5H4wdJj1m1BTJYvCe86fg6mdj945EAl17+lhEEcUnu3skDbQfNFahyMpi2examFkaNiMjZW311UPrDnDSswWSmxr3nD8lzRn3mConejrlsZB6rL5uo1GQs8PqKxzgRXIfv/WAO03n6KY5E9C5PmbU8Luzx2Pzfjc4MYLJlU60uwPE4xx0B3HQFUR1kVkmQ1ARJ7FT9Q8TGW+t7hBa3SE8s3Evnlp8LHwhXiJ8qWyMeKLAcSPsuOGM8djWFnPt3bS3F0FelLn2atCgYXBRbGGx5aA3bcxvqMjeDX4g+BUy3gM5bDpngkIzi2X/TJ9zPJHDHDwTWFiyU7uaur8FZoa4gZhJSVnOGORs+qoiA1xhKz78NsVdvtSKEUX5I7UEMYqDrlDa2F/pNOetTVYDTVy72fKoLx3MQGv5cEONDPogJxD7tCA/cJ9WYGZw27/l1T1//2i3VN3TH0ptBuL6piSHqi4NRy+yYhvmzp2L888/H7NnzwYArF+/HnPmzEEgEMDo0aMHtYG5olMhK6bTFx6QtVaCQ4FFd+SgJ+YOcTD1Ea80MTTcOe5w8mIEfk5MG5j4HKzVw4IIV0BAc2cgtqvUFSM8Smwiss5oUyH7bL+b/OwPuMMoKR06RFs0qpNcRxO7I399v1nW4XNihJhFmVoGQekiqCmxxIRiE7vktA4UlbwHXQolyt3+2C5OJpNTJTLOHRSAeEW2nqKwat4EmFlGaksgzEMfr0FV2m12BTgpE00QRWIZqyAmJwucGJEmOIlj3P3WTlkGnp8XMGucnKS8eW49/LyAEqN+0HbaBuM4+7y8RLIlfn/TK1vw1OJj8279TSJHv2nzSP9ONUmoLjShwxtGgYXBlU99IZsEPbNxL5bPqcdDH+zC4hNq8Ke308t77jl/Cra2eqCnKOI9TT1vgqT9y4+mYlVKmWXiWAlyrcDMYkKFDUtn1YLWAYEwj5vmTJCRfKvnN6DAqocYIZNwo0usErGYOMfqddtw56LJcAfCMDI07n/3G+l4t54zEdVFJrR0B2XH2dsTxKMf7sJVs+tw+8KJ+HhXjIgcV2ZLy6S7950mSefOyMTcxDp94SQZRuofGWB3L4d2TxhldoOiIUIPJ2J3tz9tfBhZZEKh/ug3otGgIR/whUVlnVOVMk5YmqybemtcN1Ut9CiMiz1+DtUqjmkWA000JrAY1OvXdNCRNxAvU7l0VIX5sysQwYHeYNrYUOU0wWrOz9gQ4MibvA9fnNzkPdxwBXliZpQrxGN4nnTjzHkgmQdCiYnGvT+IzesS2YgTKuw5VS7RFLmK55YFA/dp3jDZhM4XzqTsNErsW7TdSQ0kZDWiX3nllZg5cyY+//xzAMDKlSsxcWIssP/4xz8OXusGARZ28N0sXUHyzqA7B4dQM8vglS/TxSt/fUZu4pWcEO1nYMoOOp2OSN7pdNlnoalRv29iKIWSzKHlAOUKcOCEZAet08WemyuQ7PA7FNxjO7xhqQyC0tEI8yE0d/qlgWx0iQWULlnSaVZIczfF74mZJe/Opb4vRRYFEdGUnSl3iCc6k3pCPGBmUKhwjNRSaT1NE9PfUyet/epjxDPwdBSNm/ssam5+bau0o15sMaCx2pH27h3qTttg7Ni1e0PE62n3hvJOtJE2LfpqhrW6Q3j0w124+7zJKLYa8POn5NnEz2zci1+dPg6+kIA7F01GWCCbWfjDAl776gCuP3M88Z723ScI8bENBdKxdLpkptivnpNLCLz0+V7JMbS+wgGXP4Q9nQEY9BSuPnWMrGT1qtl1CPAC0UV1d5cftaVWXNOHhPvtS5tx9/lTpM9Ts+wSffFffzxN6kuXza6VlR5ZDTQEMYrqIgue/clxKDLT6PKL6PCGsM8fY84OuEIo83FJMo0B3m1ypZVKn1LnTCPbOnxk196Jwx0odGpEmwYNaqA3oFBiGOBUc+I0s/3rpqoFq5E851CT8AJiOkphXuxj/CQiElFvMexWcFh1q2jAAKikfxUmk1oNFzXmzXggpGB+FcpjppZBT+O1r9N1wH91+ri8tYmmQNQQy7f5LSfIS8jvPHdSTsdjKBD7tEw8O2wsIyPpgBhJd+e5A2e0BbgI/vKePDniL+8145b5E4FMFJmOYi1nDenIqge+5ZZbcNZZZ+GSSy4Z7PYMOuwmhliWl0v2mdPEEFn0TF5QJfCiiHOnyrXkVsypBy/mNoCoMTAJIpm8eyQH8k6N+n2HgpOp2pokh4oym4FYblCaMjFTMqBI1aYQxCjaPOG0MsrUtHqLQU+8J5Z4Ka03RN6d88YJMgCgqAjRYIOikiOFhdVj5WtykmXla0mSrNhMY9X8BixPKZFcNb8BJZbk5FtxJzzAYVT83hSYBybslLJau3xhVFsYlFhpnN9Y3UfHsQElVj1wCEa6GRsm9IOhbP1dZE2Pwde+OoBbFjTgdy9vkfVbf33/W5w6oVz23QqHET/oo5f5px9MIV5vpy+MOZOG4/bXv8HNc+slojTRf//1/WZZ24xMTN+NdKxoFDivsRL3vtMkW9wecAUxe3w5/vDGDum7D144FRtbXLAZaFA6SO9BQoOjN8ARXVRHFluwPSXLLoEQH8GuTh/uWDQZzR1e1Jba8Pt/f4NWdwh/vqABZXYr2r1hPLvkOISFCNo8Ydx7wRREEUW7OwybUY+bX9uKlu4gqotMuPLkOhmBdtOcCfCGeNz15nZceXIdxldYEQ1B+k6iDctf3YI1l89I0wka5mCw5rIZkgbP3i4Prn1hG7yhPoGfkiFXXWRCmI+1tb9sOQ0aNJBRYSfrnFbk4Fw/ELr8HF7f3Io7Fk1GkBNgZmNC8rWlVlW10kwMTZyDq51dE+AjuC3FBRuI9dW5zFMHglOh2kXt0lE15s++MLlyIaZvnR84zfm5v/0hGo1g0TT5vObmufWIRvOn9WHQ07Cw8gopC0vDQOdv80wNMtjC6mPGWinXWe4wwsIOfLwgLxAz2jIpOw3yAlq6g7LS0djnGayrjxDtcA2Dh6yiu76+Hg8++CB2796N0047DWeddZaU0TbU4OcEvLBpb2xykbLbMPL0sVmn6Bv1FH556hjs7oplDukp4JenjskpU0pP0fjrB80ydv2vHzTjjhzIO0CdgSmokDmSS/2/GvX7FRZ9spSSE2Bh9WD0OgyzDC2xb06BuGysTmZusbQOfzh3Ir5NyVarKbHAkKLhFxREYhnlo5ck0+q7/RyKzIysvDQQ4iVhYlZP4+8f7U7TLbh9YXLnSRQpPNBnN+eB95plRHOnQgZepy+MUTYWAS6KZz9rkU36H/9oFxrK62GJc4dKWW+FKTGhpDvjT0n/thrIafQJcnGPiyMSE2svnyEXTR5oFyqueZWLm9BQtv52mui0tv1iVh2e/lQuCvvXD5oxZ9JwjCq2yO77wqmV0nMCYvf5969/k1a+mXDT1emAlu4gyhwGKV7NBj1e3LQPFx5bLcu6vPrUMXjwvea0WEgQUQUmFgVmlugkWuEwotUdQoGZhZ6mYDPoMbrEAj4Swc+f/CLtPowukbuoXn3qGBx0BRQdQWtKrFi9bhta3SGsmDMeC6dW4vQJRej2i/jw2y44jAyESAQ3vLhZdg9e39yKOZMqsGJuPbp9HCwGGtc8m27QsGRmDX7QWIUH3mvCdaePQ4ATMabUKhk0JL7b7gmhwESj1RWOxa+NxqYmb5/Mt3rcvWgCilPjPiVDrsDMphEEStlyGjRoICMkiHjnm7a0eemUEQ6oVQJXaGGxaFqlTEh+0bRKmZ6pGghyIlEa4/fnTARy2PAeCL4QmShK20QYRPQEyPORHhUNGAB15s8lNvL8qySPrtTuEI/V8+thYvTS/DXICbFKibw5fJIrJtR0tx0IFkNsjdfl56TPnGYGFmP+UtrUIIODQgSvbz6AC48bJZnEPbFhN644sXbA37J6Om0+et/6plj5/gAoJmw6GxkKRRnEoBqEo4ahjaye6jnnnINzzjkHLpcLb775Ju666y60trbizTffHOz25YywIGLWuHLZbsOyWXUI52AH3ekLo9vHpbko2Y36rF1H3SFyvbgnh3JUIObKSBr4XTlYTzvNeqJIvdOcfSdRYqOJzpAlNn32C7i4ftHOlInlhIqht2vQn45gYnIWFiPo6hNz131/LCpSnD69ChNLX0gE4t4XhWYWe3sCWP7aNtlxRscn25wgEuOQE5Ikars3TNzN6fCGpUyzYoUMvOK4WGinn8PcScNlk/65k4bHBt04ARbgBPz+nInY3Z0kF0cWWWTizVYjg/Xb0xcsM0aOTzmvjpjFZ4ybTSiV5abe/4x2oXTAhn0Z7FT1R9hFktbfHb4wSq2GIUGyAYA3JCLICbLdw1hmWDgtFqoKTQCisr6HppB2n1u6g/CG+DT3pvMaKxGNAtVFJnR5ORm5t2xWHdZ9dRB/+dFUhAURgA63vxFzF3WHeNz/o6mIRKIwsTR+9/JmtHQHsXRWLc5rTCf6Vq/bhsUn1ODFz/fj4u9VSw6hRobCLQsaiPpqbDzjc1SxBW3uEEwMBW+81DWhQZdw8TxmhBNrPtqDVncI1UUm2Ewsvml1obnTgt++JCfWEiWpCaL9zxccg+ZOH37+ZCwzdNnsWmKcRqKQtOj8XMxt7K5Fk7H0n1/I2l1qM2JHewBL//kFjAyFNZfNIBDMsazTkSlZart7k0T0wqmVaZsCStlyADLWitOg4bsEIRIhVjAIEfU6ekFBr1fNcwKAN0zO/vCF1S33UySKVCRkCswK1S4ZCKzngsHIpu8Li4FKy1a/ZUFDXsmaEkvMkfva5+USELkQirmiPQNZl8ONHr+ANR/viZWzpmxk/+r0cSi05yerTQ0yOMAJOGVsH5O4OfUIZmDworxeEgYs3xejIu46bzJ2tifXL3VlNkSiA+uUq0E4ahjayOmp7t27F7t27cLBgwczMkG44YYb8N5776GoqAjr1q0DALhcLlx99dU4cOAAhg8fjj/96U9wOByIRqO49dZb8f7778NoNOL2229HfX39IbfRqNdnzVorwcLqcdebO2THvOvNHTkd02liiO1ck+OuSKHCwH9XDgM/S9P46Um1aaUAbA5pybt7OTzwXlOfLKkmjCyenLUtek9YhDvIYUypTdr9cgc59IRFFOZRFLQvihQGoCJLsrPnhCju/I885u78zw5ZGYSFpYkEqIlNTowi0QjxOIndNxNLfl9S47DMTnYDLU0pY2V0ZHKLibsjWgx6BPj0Sb8pxQ3WbmSws92XVgpbXZQsheWEgUuuaR1FTKOndbH7olSWW5ziIJTJLlRnUMBdb+2QZaXe9dYOPHjBMckBNBPCLgoYaR3sRgZGmhoyxHCQE4mlOKRSyr09Qegp4OUvD+Da08eg2GqAniZrJgY4Mc29aVy5HX944xtcf8b4NPOB+9bHzAG+3O9Cmd0IhgJ+dfo46BAFQ1PY1+1Hb5DHtKoC/Or0sfj181/jhU378avTxxAnODpdsrQ09Ty/e3kLHrxwKn725OcoMLM4r7ESNcVWHHQF8dzGmJ7S4hNqcO87TbjvgmOwbPYY/POTPVgyc3Sai+qubj+Wz5mAne0+nDO1Cpf947O0DNaEaUPiM3dIrp+mlDEXjUIiMk2sHiE+giiisqy7VfPq8cbmAxg3zCkdX1kPMIwQJ0gkcLsnuZDQ6dLJ0li2XDi9nz4ErTgNGr5LYGk9Vq7rI62wbiseV1E0n1dB8iMTmBR0YXOpAMkENAWiYLmaHi9mhsKvTh+L5g6fVO3yq9PHwqzytSIKHFdpx4tLjotv4LE5kWwAwAlAICzfWAuEBXACclw5Zo+wGJUkS4CUzaE8Zo8VWVjifLgwj+SfNywQE0x8YQFAfpwx1SCDzQy5H83EfMRiUNBvz0A70soy2MPJ3W9Xz2+AlR3Y/bbYQl4/5ZMs1qAusuou77jjDrz99tsYMWIEzj77bPz85z+H3W4f8HcLFy7Ej3/8Y/zmN7+RPnvooYfwve99D0uWLMFDDz2Ehx56CNdddx0++OAD7NmzB2+++Sa++uor3HzzzXjuuecOua2KOgMZsNZK8IQEjCm14oqZo6Usmoc/+DanYyqJ46a6MGYDMRrF4v8bhS4/Jw38i/9vFCI5jMDuEE90zHroomkYZspuBG73kLOkiAu4DBEQRKIgf0AYWkRbkCdbVIdStAKU4tgfTpZB2E0MkQBN1SPsCZDFenvjpQ1d/eiZjZJMFyJpWlGr5jWATtFok+nBpLwjtSVWjLAw0iS/v0m/nyOXwj58cVKMV0/RWLlua78DbbsnhAff3yW9W2IEePD9XVg+dzxGmBkIgki8/2JKFl8mu1BKWanuEC8j4/ol7DLVb8iDmGpAQe+xvsKOZbNrpZ29QjOLv36wCwDws5NqUGo34pfPfIkCM5t2n685bQxKUohOIxNz6gyEOaya34B2TxhXnFiDFzbtlwwIQnwEVYVm/PHNnWD1OiybPUY2obxqdh2e27gfD32wC7edMxH3XXAMvj7gRoUjpn+X2s/SOmDCMAdcCi64TR0+PH75dOztDqZl1a3d0CIRT/6wAE+Aw7LZY7AkvruaOMZ965tw3w+OQbc/RpytnFuvSPglYGQoFJpZ2fde2LSfGKdrN7TAyFA4ZoQT97y1M5a1YTPg8ctmoMMbQonNiP9sPoB/bjyAOxYVS8crsyvpARpw/kMbpNgrc8i/p/SbvkjNhEtcY7/Zbxo0fEfQrej+HUaNSqL5AU4gzjEDKgvJO0wMbjxznDQHpXUxcsKhslZuu4fDkxuS8hQmVo9HPvgWPz+lFsNN6tzjsBiTUEldgK+cV49wX/ce1ZG9MVkCnpBA3Fh7+OJGDMtT5k3qpk8CIT6CjhzWCbnCYqBxw5njIIiQNvQbho1T3eyjPxSolLiRE1LIYFdYhNNA50wGd/nDxD6ty5+srlGCmaGJyQCZmMN4QqI0HwRi9/emV7bg8ctmoHiA35eYaSw9pS5NHqbEPLQkjTQMHrLqLauqqvD0009j37594DgOO3bExKSnT+8/o2v69OnYv3+/7LN33nkHa9euBQAsWLAAF110Ea677jq88847WLBgAXQ6HaZMmQKPx4OOjg6UlpYeUlvtRrI+kz1LQggAiqwMfnisXDx9xdx6FFqznziUWMmC+KlZNdkgECdiUgf+G88ch2BYBKzZHTOoZLDAZ99LlNnJWUVlOThwhcUokRB8/LLsMw/VgJnVD1hu4DArxXEy5ngxQrzeJxYnB1fLALbfmbiBRiIUeQGdQm4VWhicObEijQApjDtnesPkSb8vhTgMcGRyMVUL0KPg8pVacl1sNaA3wKVlTRXHMwYdRpZ4/0+uPUb6fiZp7waGnA34VNzdFBiYsMtIvyFPYqo2Ql9aXWRCb4CX9S/L50zAJcdX4/GPWuAJCejyx0wCWt0hrN2Q1HObPrIAvBjBkx+34O7zJiPAiyg0s/jLe02YNa4cv3tFnhW2dkMLWt0hGBkKFlaPVncIV55SK5VgAjFzjCAv4pez67DPFcS97+zEbedMxCP/3YWt+124Y9EkHOgNyvrZWxY0YHSJhfh8KwvM+F9zt3R9iWdy3/omPHTRNHT5QmisdqDcYQQnRuAOkmM2gqhk/OFQ0M2MJ3tK1ytEo7LvtbpDeGbjXjx6SSO6fBy+7fRh7YaYQcMtCybitS8PYGeHD6vm1cPMUtje5peZjayYW49HPvhWOufHTe3Ecv0RhTSWzoppnNz11g488/+mSuYnL2zanzY5XTWvHlWFdFqWmtKiKJfNEw0ajgYomhvlON/rD04LQ5xjOnKQ/MgUBoZOI58GgQvqF1aDHu4Qjx1tXmmO4Q7xsBrUu15OiBKd0nOpdskIOmBLlx/+cHzThxPR7qfQUGzJek7g6y+hIE/IxBTscIPS6dAbENI2uCudKgd4P3AF+3G/zefYGwVKjHrUldvhcgVynq8Oc2RvKhPgeVT0MVKocBgR4AUMVP7Z4SUTfB3egTdKOgMCkaTTNNoGGUPI2TWrp0pRFC655BK0tbVh3Lhx+OqrrzBlyhSsWbPmkI/V3d0tkWclJSXo7u4GALS3t6O8vFz6Xnl5Odrb2w+ZaPNzAm44Yxy6A5ws48LPZZ9CK0YgE3cFYv/ORYchxJOttP9+aVLIPhvYjQwe/d82WVsf/d9u/PG87NtKWnAbmdzs2kcVsEQR+Fw0fbp9CqSGj0NNHgfmvvCFeVxxQg06fWEp6/CKE2pi2WrxQdGoJzt3pZZfdHrJ19vp41AdJ4QsLE3cXU4QbQylS3N5vHluPZgU04Ueheyf3gCf3EWKghjPT8R31CrsBiybNRqtnrDUjmWzRssyY0oUBEdThdptRoaYhm1L2S2PIErcuUpkdZaYafxiVl2aFknqDlMmae8+BdLPF+KBOMGoJKKaIOwyyZzLl5gqpdOluc3+9qwJWPb0F7K2rFq3DXctmoyfzqxBWBAREtKzoChdLNZ2tPrww+OqIYpR7O0JoMxuxOzx5Wmxk9Age/TDXbj61DFo6fEDgEz3rcJhTDM7uPrUMQjxIm5fOAl6WocOT4hYIvrnC44hvl9///BbnDetivhM2twhGFkaFx8/SioFvWp2LZGMdJgYXHFiTUzPLcSnxeM1p42BGIli6axaiehdNb8h7XsXHluNbl8YX7b04PSGYah0xo7NMhSOHV2MWePL4QqE8cHObry3ox2PXjIdvX4OpXYDOr0h7OzwAYg9j6piO06oceCpxcfCywkIcSLKHAZc8PDnaOkOSvdvX6+AmXV2rLl8Rsx1tNCExuoCtHlihh9VhTRoIb3vV2PzRIOGowFCJIrfnT0eHd7k+FdiM0CMqrgSiOqIY7LaWS7uIE8knx66aJqqmVE2o56Y4W9TkWhTmnN2+znUqtjvuQQRbR6OQPYY4cxS0mVEgYmYUFBZYBrk1mcOp5kmOt47zfnLHvOGBOJ68PaFk1CWp6w2g54s02HQ509fTw1wQhRPf7ZXdu+f/mwvplUVDPhbVq+HJxiQfeYJ8hieQXxXFZiIBF9VBr/VNNoOA4aYs2tWT3Xt2rV4/vnncf7552Pt2rX49ttvcc899+TcGJ1OB50u+10AmtbB6ZTXSJdwApo7/Gk6TyVWQ9p3M4Wrw4/Ljx8lI+8uP34UXEEezkpnVsf0dfgUFukinFXZtRMAtvSQ2+oLC3AOz66tDk7EredMlIl533rORDhMTNb3dPNBN8KCKNtdCAsi9rl5TBzmyOqYRd4wcbAptLBZtxMAIpEo9rmC6PCGUWozYITTBIrKPG77xqnNHwYnRtKMDixGWvreDlcP0bnr1gUT4ayI3R+7K0S8XptBLx3nm94ucGI0zcjDF+bhHOZAOydieIEBj106HV2+MIqtBggREUZ9si0WhfOYDcnvfKEQzz0BDpMrndgXcIHRUzL9vBAvIIqodIw9fheRIBOjye+EegLEyXSIF+B0OgEA7g4fsYx1dIkVzsoC7OsNELVIvCIwoiD5nE61GPDE4mPR5gmhwm7EhDIr9CmCL46QSM46NCfjzdsbwDWnjZE5Zl5z2hgY489oWIRcmjeswCwdo6mll3hvXWERdeXJEv5c4pTUlzqE2PWl3qeQQnbrzg4vTAyNY6qc+M0LX0smAamltQ8xFG44Yxz2dPll9+OmORMkY4DUY44rt+HBC6eiKS5Aa2QoTK50SveL5Gp6z9s7JQ25BHlPau/ubj9MjFzHz8xSmDtpOA66g8Rnst8VhImhQafolj27UZ7x1VjtwM9OrsPHu7rjf485plpYGncumowAJ8AfFlBkZfGbFzbLYrily49Ckx4PXTQN7qCAMrsBlC4KTohiXIUTvBjBExta8PUBDy773gh8f+JwdHpDGFNuB01FsGlPD8RIUiSgssCAtYtnwB3gYxqJnIB2v4g9PQHcmNKX33jmOPg5EX5ORIAT8G2HD//8pAfnTa8CEE0pcdUBOsBoMMISJ5JpmpLiZoyVIWbMjSm3wKIfnLIxUpwm/0bl1M8PZWjXduSAFKOiOwA9RcnG4Zvn1kOMRFS7dm+Xn9j3eUPZz1szAddJPi8nqHetANDU0kMkQH5/zkQ4KwaWuskGpT6OOFaU2rJfb2SC5r29xGqGxy6djpFVtoyO0TdOd+93EY+55vIZcJZldszBxrf7evH2N63420XT4ArwcJoZPLlhN6qKzKgZkZ82+br8RMkQPyfA6RyY8FED1kD6Zt5Vs+tgTVkL5BPZ9vF9Y9StcO89YR7OEc5+jxVyhfDo/3ZLm/SRaCwJ5ZYFEwdsW4vPRdy0mF5dIPst6TozmeOrjaE4xg5mm1p6AsRkhHVLT0B1YebnGKw2ZUW0sSwLgyG2O8NxHEaPHo3du3dn1YCioiKpJLSjowOFhYUAgLKyMrS1tUnfa2trQ1lZWb/HEsVoLB01BWEuQtR5euLyGWnfzRTFVhbfdvrShNyLLWzWx+yvpC/bYwKA1cBApwvKPtPpYmL0uRxXT0G2KNVTsUqAbI/pCwu45V/fpF3/QxdNy/qYDK0jZqmwel32134ITHlJCXng7xunFCiiQcGTi4+VvufnBHBC8gQ6XWw3J8CJ0neMLE3cJTcxyRgyM2Qjj8T7IIgR7O8NE3aBGekYBSaGeF8LTMnvOM3kElSnKfmOiBHI9PNWzouZnST+3u4JY83HLWmOlFWFZrgsse9YDQxWvvZ52mTwiZR7V2w1EMtYi62xtrR7QkQtkscuaYQtQSpQwHu7XWkZlyePckpZb4EQR9TQCob55DW5gzDQcrLKQFNodwdh0wEOPYiZnQ598r44DeS+wmlI6SsyjNNMYxQAfCER676OWakHwiKcZgZtnhCuml2LZzcmNdSMDAUxEstofPDCabh5bj0eeLcJvzp9nPQMEs+qO8CllWWuXreNaLCgA3DjS1vQG+CwYm497r3gGBxwBaQJZUIvrcJhlKX0J7I1Q3wE+3sDxHs3usSKVeu2pjmMLplZg+c2Kmuj9QY4WSZzqzuENR+34K5Fk6GndQhwIq586nPZ7578pAXzpwzH8le3xbMCx0On08liguNFvPD5fpzRUIEV65KOWqvm1eOB95qljLNbFkxEkYVGp1fApY99KiO0fnrKKFzy902yzzyBACwmE5b+8wsUmFncPLce976zMy3j+ZLvjcQf3tghxd/3G8px2T8+w5hSK354bLXs3U81OHA6zbK4OaXOKWXCJVxHeR8P1yGmKR9KnCbQty1HE7RrG3o4lBhlaD1u7jNu3RwnMdS69v6yv9W8306FUnmnSd3zcmKEuMnMixHVzsvSUeLmgoFW7qcGA/1l0qWeVylGgfQ47VRyY/eG8/Z+dvs4vLmtC29u65J9Pn/KiLy1yWlU1kPLV5toHTCq2Iy7Fk2GnxNgYfXQ07HPh0LfOlAfn2lfmsu95yMikaQTMugfWhVkMdo8YVSnto9wnQ4axOoYB334ns1QHGMHs00HewPE53OwNwBHJkmd8bLTmJagnlh22l9f2hdZEW3l5eXweDw49dRTcdlll8Fut2PYsGHZHAqzZs3Cyy+/jCVLluDll1/G7Nmzpc+feOIJnH322fjqq69gs9kOuWwU6Md62RdGdbYi+xy5zPORFJH2Q4XDxBAdkpym3Hb+I5Eo0dI9Esk+f9IXFqXsiwSMDIXHL5uB0ixNBtTQfaOhA62LygabAMeDykEcRI2yvY5+7MGr4pkiRRYWPzupRlby+bOTalBkYVJ+I8Cgp9OccEJCUnNAKW2528+h2soiyJN13h67dDoQz4oO8CIxu+7ORZOBeKp8b4AnkhO9wVh5qSCCWE6SqvNWaiNrq6Xq2LiD5OvxBDmpXFPJbS1xLj8nDigSvc/LE3UVnlp8LEbEz5OJ1puB0eP3b6STegkdt86AgPvflbvv3v9uE+rLks6lmZSxqhGnvChi9vgKLH9lS9oE5arZdVjzcYx4uvrUMfjHR3sQ4iPYtLcXj/x3F5bPmQBEo2nPKhIlu1iOLrFKi7NE7Nz55nZcdFw11m5owcrXtuJP50+BJyigusiMhy6aBk9IQHWRKa1tN82ZgAqHEa3uEJ7duD+NyFw2qw6r1m3FnEnDZbEW4iOIRJGmLTe2zIbb/v2NRCzGrOST6A1w+KbNC5oCUdtt8QkxQi3xWacvjFv/nb7JcMeiyWnE5PJXt0rupCE+gt+9vBl/v3Q6lr/6ZZ/vxYR5+3625rIZuDhOyC2cWomWbh9xsjk8nsmZiPO7Fk1GiI/gipmjCW1SMDjQARv2DJ30fQ0ahgr6IzHU0i8M8QJ+OrNWMg8yMjGH7lTTJTUgiGTpBkHNMlkANgODvgUyOl1sc04tcKIOz26UGzCs+WgXfn3GeNXOCfRTpp+DREpJHnQEB0KpjezYmE+Ntm6FOXWvn8ubFml3gEOrO4w/xOeaRobCb84YB1ZPYViOa8qhhFzuvZVlsH57m6zS5fGPdqFx5MBZiErvW2kmcaiCQ7AGOTLR1FaECmWnWa24HnjgAQDAL37xCxx77LHwer048cQTB/zdNddcg08//RS9vb2YOXMmfvGLX2DJkiX45S9/ieeffx7Dhg3Dn/70JwDASSedhPfffx+nnXYaTCYTbrvttmyaqjhY5GIyoOhkGs5eJNQbFvCX9+TkxV/ey73On1MgGR7OwdJdiRjqzEAIUglKphVWY/bX7gnyeGLDXlwxczQQjb0jT2zYi2tPG5M1IapGfb1V8dqTx6N1OiJhSqXMJBmKJpJBqeSVmSVbWpviBGmPwvX1+DnJUbfbR3aI7U5xJi0wM/0aPCgNkN0BTtJ5YygdcYKeqhdnMZB3yy0pk2klJ9VOX8yZaISTLKha6UgKqrZ7Q0Qyrt0bkoi2EhONa08bO6COG9EEIq7j1uXniPdWFl8ZDNRqxKmeorHi1a1Yekpt2i7ive804c5Fk9HU4YUxrgNiZChEo0ndtjsXTU57VrSOnEY/3GnC45fNwP++7YIYgWSEkCCqHni3GYhnbAI67O3244H3duGmORNwzbNy0mn1um3Sb3oDHOwmRhaXiWPTfXa6jEzSoKDVHcID78bKTxefUCPL3kt15UzNdjt3WiXxGdAUwKacjEQ2FphZ6EAmIVMXjyE+ohjfHd5QWnafK8Tj/h8eAz1NIciJsBpo/D+CS+qjlzTKjuWPk4lBhbGPZHCQLy1BDRqGOhRJDBUJA4YmO3SrLdTfG+CJmekjiy2AitfLiSJxzsSJInLRPe4Pnd4wNra4sbHli7TP1SRdRjpZYibdSCcLZLksCfECrj51DO55OynrcPWpY+Ibt/khkMSoSHRsjETVdc7tD1aFCgOLilqAA8HM6iWSDYi96394Yzv+McSM4HJFLvdeEEWcO7VKrkE4px6COHAsCaJI1AoUItnEYf5MM45WZJKMoAQ15q059wQzZmQupHr33XcTP3/88cfTPtPpdFixYkXW7UogGiXvpkVz2E1TcoB05OBk2q2wwM5VRFXRuTGc/cCUE5uvADNDFjm1ZGC1rNhOmwHnTx+B5g6vlAV2/vQROe3y5cSUK0CpFDM1m9ETFoiEaf1F0yTSsFNhwZ1qdW1mFSyt40RbkZVc8lmYcn2ZkNdmliZqpyXOo0gupgyQnb6worZaZfya3SGeOBl0p7iODrQzK4pkQdWTRhdJ3x/uIIufDnOkiJ9mQICV2Qy48pTRsvLeK09JmkBkHF9x96ZU8i0VasRpe9xpqcxuJMbZjnYv7l/fLJVcGvU01m5okf5+wBVIy3KsLjKnZZitmteA21/fhkuOl5ePJo6ji5Nz21o9kvbaynn1GO40oFlBGzDxm2Wz6tDc4cWjH+5Kuzfjy+0ywuya08bI3gcjE9NRevD9Zuk315w2Bi5/GA9f3IjP9vRAjACf7e7GDWeNh1WB1J5QYYdBr8Off3gMLCwNE0vhkf8mv1cRd9Jq7vASf586dMWyFozE75Xa0s0hbjtnInwhXiqVvvb0McT75QrwsmNZ2FicmRUmtiSDgy4/h2f+3zSEBRrt3hDK7EYYaFET/dXwnYdOFyWO+TqdemkNXgWzHm+IlzbR1ECRlSVmpheZ1SVrhAjZkCmXTeaBoEZmWUYQgFNq+5Tp50CyAbHMPxMjl7kwMRSsbP4yonSgiJvJj+eRQGLogTeEDze8Cu7n3qCgKrl9uMHoFe69fuB7r6fIGw+piQlKsLAMrEYhVi0V15imdLHPB8QQE+o/KhEFjhthx1OLj0W7N4RymxGVNkaS+ekPqiQpZPWrIwjuoEDcTRtdYs26wzHQNFHQ3KDPnhQqV2mAVnJuLMphd81mIJNiuWSf6XTAsxv3pqXxLj97QtbHhC7mCNR3RzOXDYRcmHIlRKIRYinmny+YAiB2T5VKa4Nc8jNF6/M+BNhwp9zSerjTKBFg+gxcR3kxguu+P1bSlTMyMfMGIZI8b6cvBBp9ynbDPLp8YZQVmuEw6YkDpD2lI3OYGKK2WiqhXWpl0eYKpk0GS1MIpShEYll2VBdrrzvE40czqtPJujAvdax6mjxpP6kuScalgxBoA8RkiZnGLQsa+nVAzQRqxGmZ3YDzGisVdc4SBFCIj6DSacI9bzfJMr+GOc1ocwXw6CWNCHAiaEqHla9tBSdEsWRmDaoKzSixGfCvr/bj4uNr4DCRSR1KB6lUNXG+mItdIza19BB/01hdgONqpmHzfg+sBn0aubdyXmyRmxpHZXYDwoKIOxdNRku3H1VFFrj8YcyfMhyRaOw9fex/e9Ab4HDt6WNQajOAEyKYf0wlvu3w4t3tHWkxvnzOBHgCHJa/tk02dtxx7iT8Ov6szmusxL3vNKHAzKYRk6vmNeCB95qk61o9vwEvbGpJi+9V8xpgZnVpmYc3vrQZS2bWJD8TIsT7Ret0WDqrFq99dQBXzR4DhtbByFB4+INv0wiCVfPI7tBjyg34oMmTNk7MrLNjdy8n023L1llag4YjEb1+Hv/8RF5i+MgH32LprDqMtKizEC5S2HwpVOl8CZhZmqg7albZkTGotMnMqZf9VGCiiXONgsPhiikAo6xsMnMuB5INiBkdkbRr/35JI9TKCBwI7iCZLHbnkUBiaQo2o142d7AZ9bKs9cONQgt5w7wgh43WIYkoiPc+kzlul5+cmNCdkpigBD4SQUt3IG39kkmyiZbpfxigAzbsy47MVCNJ4ah/qsVKu2k53LQ2TwiP/W+PjBh57H97MKLAhBFZliQaGZq4w2nKUvMsAT2tI6aUZ8L4KyHAR4jOP6NLrUCWGWg9AQ5zJw2XZZ/NnTQcPUEeFVlqCnT6eSI5MnG4A4XOLO+rCvX1bR5yKWabh0OFMV6WqJBpVmxN3hudDgq75Mlj8mIUIV6MuX2m6NbxYhRgYkSb06xPE1HVp7hVRqIRFFlY2eBWZGERSUm1Mer1eOD9bWlaGr9fOAlAbBLnNMkHSKdJj7AgItEtUTod8fmtvTy54xSNgjgZ/OcVxybbG6HwTiJe/Tyclli8XvZ/owEAej0tkWyJ89zz9k7ZeQ64yIPyAVcYJaXxbjSDnaouhZicFI/JnpBIdEDtCYkoPJS+oM+OTpnNiBEZ7ugogRNF1BRb8Yc3tiuaAwBxp1sjg94AJ/37lgUT0eYKwBMWceNLm3HjmRPwi38mxcATmWlLZtZgzuQR2NjSg2Irm07qzG/AiAIjrnn26zRX0nZPCMfWFGK40yQjd25Z0IBXv9iPsRUOPP1ZzPn00Q93Y/EJNaApYNJwB6xGPTbu6UVdqQ0HXAF4QyLufmsn5kwajkc/3IYVc+rxyAff4uRxpWlZdkCM5OYjwC3/2iy7J69vbsWSmTUYX27Ht50+uIPpz//ut3biqtl1+Pul03HQFYSZpRHiI2nacMeOKkSpTY9bF0yEK8DDHxZgM+rx3+ZedHp5WX/M6oFvO8mCsHoquQB4YVO60cNVs+uwct029Aa4mAOshcGoQgOeWnwsvJwARKNYc/kMdHrDKLEaMKqQTJTt7RWl55A49wPvNYOixuB3L2+WjUcJMwUNGr4LsJn02Nnhw7J/JksMY/2meoSM1UgRCS+rUV1CIMAJiEYjaVq5AU4Acpzf9gdlYlE9sqHVw+O5xIZxikZbpXMs7NnOOTNFXMA7Ni81EAW8DwWekEJWVEgEHDm2NUuY+zGNyxeMDAW7kUGHNyx9ZjfGJE3yBVZPEZMhDPr8tUkNMLQuTW88EomCoQZe3+ay8aCk0/7QRdMAS/+/VSNjSoMcuZCZaiQpHPVPNSyIxOyzXHQaSqxkkfaiHHTfDrqCxB3Ooll1KCsd4M3tB56gkBRnTckU++WpY1GRpYaAkvPPedOqUZUl0WgxMAjw6U6uuegcKGnp+XPQ0gMwYNneocJmIuuM2VI6BANNznRKzaLs8nHEGLrylFpUxyeX3pCA5a9uSzvXIxc3AkY9eDGKXz7zVdrfU0knA63H9S9+nvadJ1K+ExZEYpYYJ8R2k31hAbROFyP84qnXgRAf0zmMv0dKpbCdvjBGxndt+3P/SZSXhgUBU0YU4Sdrkw6My2bVxUm9GMlLOkZvgJPOY1QoAzSmTPAy6dy9A+g79gR5RQdUGdE20KQ6hx0dJTA0DX84gN4AJxFABj2FulIrbn8jZg6QuLcPvteMJTNrMKbMBiNDY3Xc0TPx96YOL/E+RKJAmzsYs1r/cDeuPGm0bIEGXRTuoCCReKn3aJjTiBte3JzMkCsww2rQw8fxOHVCOa557issPqFGIpUeeLcZFQ4jTAwt25lcNqsOL34ec1FNOJmuXLdVIsyIWaM2Iy77x2eyZ5/Qk7vvnWbcce5EUDodxEi6IUSIj8DPiTjoCoLV09jRliwZTdWGO6muGL1+AZsPuNN2Utd83ILrX9iMhVMrQVNAXalNscyzpjg5nrS6Q3hm4178/dLpcAd4fNPmwZqPWyQSc/W6bVhz+QyEOeCTPT1p560tMisSZO2Ed3POpOESyQbEtOj2uwLYtF+PQgurZbdp+G4gCmJWuJro8vFEo53fnzMRRQ71iIqwEFWec6iILj+H35wxLk0QvifAodqiTkaWLywQNdq8uc45B4IK5Wg2FXSTc4Wi9EkOEjO5otPHYd3X+3HhcaNkiQc/Pm4UigtMAx9ABRx0B/HEBlLGbC3KS615aZMaEKNIK9FlaB0yyVkN8jxuOGOczJW40MwimIE5TIBTqjAa+MxqZExpkCMnMjMlmSbmOkrnnExz1BNt7qBAzD5bMXcCYM/yoCroa9iMjMIOZ26PiI+ImDWuXFZ+t2xWnazM71AxkKB+NgjHdwT67hDkMhkrUkifVnNHMxtwvEjMEOKFZKftCvL483r5JPnP65tw64KJkllGoYUlxlBBihbKQEYeikYXKeRWp4KVfKePk5x8rQaGmCX2RNxd02FksKPNJyuhu2p2XSwrMo5ihbLnVC04JRON1PfGwpItwBPkoUOR6ExOxlkFHQ42ZZDPpHNXKodMlMwqDeCpDqiZTKrVSE+3G2jYTIx0HxIE0B/OnYib5zVgU0uvzFzg6wMe/PmHx2DpU5+n3XuSMUKiLLTMYUSbJ4zrzxiPRz/8FsfWlICmgGNGOMEJETz832+xYk69zD1v1fwGPP7RLrR0BwEkM+TuveAYdHo5BPkgQnxEIs4SWDi1Mq3fSRBkj364S1YOO7rYglZ3ELcvnITdXT48u3E/egMcrppdB1eAXNKS0IYzsnrct34rHrpomuJ1O80svt7vwnMb07PMbpozAdc+/xVunltP7CdjuqOQ/WbF3HrccMY4yeU20a9EEZXaYGQoLD2lDrf/+xtitl6Ij8AV4GDVG8ml06OVS6dJekU0lbz/FY50DTktu03DdwGsngZD6WSZywylA0urRxh4QwIxc94TElTNUFLDPCwTFFtYtLvlshK0Dqpqw5UruGKqrdGmxnhvZoYeqdXt52BmaNkzNTM0ugOcZEp1uBHkRWLiwaJp1XlpDxDbwCKtBZwq6yIebvT6edy3/lvJ8EmMAPet/xar5tUDA6zznCYWO0W/LLnjmtPGyLSxlaAkyZSJyaIaGVMa5MiZzIwn09SV2+FyBXJ+Lkc90aZUOlqcg0aZyy8QM4d+MasOyFLvIsgLRLIlxq5nP0gb9XoiyZCL05RewQ1Sn4P4pxqTsYiCEUZEZVv5Q4WJ1RMdOhvjDp1ArPyCNElOJWCCXH8xFIt3pV3KBDGlaHSQEtcDHQMAehT0D3r8sd3kYAbEqiCKaYRKX1egEhtD1EQpsSUHy4Ey4wL93rfYdespHSysfIJnYWlZSW0mnXuhiSFm2BbGB/dCs4K2RsoEKZNJtRrp6W2eEMpsBrS5grhz0WQEwgI6fWHc/dZO/OHcSXjkv+kGA1QfYivRjlZXIG2z4qrZdagsMGP5K1uk7Leb5kyAP8RjmNOMG1/aAlavw4o59WD1wGOXTcf+niAMDI1giEub6Ib4CAQxFldXnFgjlXKk3t++xFvidzQFWTlsdZEJnBjFHSkZKIm2GfQ0Wrr9igRaouw0QZjeds5E3PjS5rTrXr1uK+ZOHi7LGNTpYrHmCfJo6Q6iqd0rM+14YVMs825kkQVX93FbXfnaVlw1u47o/Pu3H09DgBPB0DocdAXx9QEPZo4tJV6D08xmFU+jCtKd8KZWFUjnWDi1Mm1sWv7qFqy5fIaqDn0aNOQbAY6HzRhzmU7AZmQQ5HmopX+l5NBnVdkdsVhhwzMX+ZZMoNORZSWeSpGVGGwMtzO48uS6NKmWSjuTs2Zaf1BjvO8OKJNalVlWruQKp4nBLf9KSpKIEeDvH+3GXSlz5cMNpc1eq8oahP1BcS3A5c8xVg3YjDRxfW/J4N5HosCTn7TI5lNPftKCxuqCAX/rC/PE0lxfmAcGInxVkB/SIMdQIzOPeqLNzJJL7kw57MrYzeTsM3uWWmIAYDMwRLIl1wHEq6Cz4AsJ2TtNxUmGvqRDLlDDDMIbIhthjCnN3ghDDXhDPH7QWJU2KHpDPBBfcNoVsq7sKcYABoYmxtAfzp0kfcfK6olET8JVMMgJRHIrlaxzmMlGBqkmBQNpaSjFZWqJhU5H4a8f9DGJ+KAZty9MXo83KOIv78m/85f3mvHHRZNRaE1m+vVHXhWaWOJ9+9P5U5L3jaFB9dF9oCgdrCn9SCadeyFLo7rQLHt3qgvNKDTESj9DCoR7mBeQWIBlMqlWIz2dpih0+znc9vqOtL+5/HwacXbNaWOwT8E4oarIAk88G6zQzMJi0MNpYfCnt3ZIWWkhPoLV67bh/h9NxU0vb5HKGX/+1Oe474JjYGYj2NcbQCQKjC2zEc/ji8dZQovsmY17ZfeX1oH4u+kjC/G7lzdL5bDXnzE+jchavW4b7lg0Gb//9zcAQJx4OcwsHljfhK8PeJJZvxERTyyegW4fDyMbeycC4RiR/sKm/bhpzgSsXrdNyhhcNitWGlrhMMJiZHD32/LYeGbjXtiMemJMjCg0487/bJeIy1vPmYjnN7bg2U2t0u8TZCJJr23VvAaMcrLo9JGzmPuNJx44pa6PE14K+aZEcrZ7whrRpuGohpll8OQnTbj4+BqZltcNZ+Zg/jQArAZyhpJVZX2rsCgQDZY4QT1TAgBwKUhCuAJc1lrKA2Gfm5dItsT5lr+6BU8tPlbVjCs1xnubgcHfP0onte44N3+kFkPr8POTa9M2VvPp8GnU08QycGMeM/9MrB7rt7elSQY1jhyYRDqSYGTI9z6T9b0ryBHXXa7gwP2DidXj2Y1NaVqMv/7++MwaPsjyQxr6YIiRmUc90dbpDYPV6/DQRdPQG+BRYGbgCnLo8odRypqzOqY/zBMXw36OR7a7BayewpUn16Yt1HJ1rrErlKrZTNk/eqVOzJSD6+pwC0MkRCut2Qu4D7MbibsdFUOIZANiZSQkoueWBROl7yhmXXHJrKsgLxAHjpBMNyCKMrshzV1RF++BzAY9Xvg8fQD5zRnJAcQX4lHVhyyqKjTHdnPiu+OKWhrxSX2RgrlDallHgnxIz+JLXrOikYQ3LJWxFpn1xFLvYkusrcOtDH4xq67f2Cs00CixGmSCtyVWg0SQxW/twJ17FJhabsUIh5H4HYeRJU6QTq5NluhlMqlWY0cnlhFBJlyiANxBTooJo56CGImC1iFNgPvWBROh0+nwwPvfYs6k4QjyQYwrt2NPpw8bW9yycyYWRn2ND74+4MaGbzvxg+nVuOmVLSgws2nxtmxWHXoDHIwMJRkLLJxaCT0FPHxRI9q9IdiNDG49ZyJ+m5Jhdsv8Bhzs8ePa08chLIioKjSjU6Gkeme7VyLjQryIu8+fAl+IR7efQ5ATsfzVL6Tjrp7fAFoHQEfjx49+KjverQvqpXZ6QzyWzKzBuHIbtrd5pVLcK0+pxep122SLuPvWN+EvF07FtoNu4nPZ2e7FT0+qhTvISRlss8eXo9RhwbgyG+58c7t0bxN6bWsvn4G2BDHmZAEhh3ji+zjhpZBvIV4c9M0VDRqOBIiRCM6cOEwm6XHd98dCjGYv6TEQXEEeVoPcgMhq0MMd4jFcxQwlK8vg+U3pc4rf5uIonwGUtW/Vu9Z2b4i8eeANqUq0qTHeh5TmkxloWKkFX1gkmk388tSxQA6JDrkgyAsoNMvNwQrNbPw+5adNAU7Aj48bKTOX+/FxI+Nz56NnE8sb5lHYx5it0MLCx/HAQGQZk321V4gXiJJMIeHour9HNIYQmXnUE21mVo/lr26VdmWiUWDd1wdyyhRzGMkZMH88b0rWx+zyhYnilVedWodhOZBiRj1FZPwNORB4w6x6tFhYWdmD08JimE2ffXq8LkY2pnaYrJ4CctioKjEOrfRRJdgNNC6YXpVGStlS0p8dCllXqTFXoJSZ9YPkdw66w3j0v7txxczRsjj72cm1GGZiYKQpnDutSjaArJhbD2OKW5GeovHKl7vTxF+vOLFW+k6XL0zMJqwtsWCEmQFLg1jyyaa44VbYjcSJckWKDkImmZBOhkahhZGJ6jN6HZwMHSPSIsBJI539O3QOQJClfm/Azr2f75RYaZzfWC27/6vmNaDEmny3MppUq7CjYzNS8IQE/OXCqfhynwuRaEx/5sqTY5sM//x0b9rE/KrZdVj78Z6YOUGhGW3uEO56cwcuOb4a86cMh56iMLLYgr09fkyoIBsNGPsQ+EaGQjQKbGxx49QJYdy5aDJ2tHsBAEtPqUUkCowstgDRCIyMHlefOgb3vL0Tre4QHv1wF249ZyKMLIVIJApBjCAaEfHXH09DgBPA0hRW/2ublAG2Ym49rnv+K8ydPJxMMEYhZe8NKzChpSuAUcVm6HQ6SXicpoDJlU7QFNAbCCMSpdIWY/e/+6200fD4Ry24+HvV0NMUTAwtGT+k6pslEOIj2HbQgyc/2Usk4tduaEFvgMPiE2qkDLnHL5uBR/77FW44Y1yaYcm1p43FSBsr6TFK/flgxlOcfAtQIjELsMxB5+SOq0HDUIc7IOCR/+6WjY+P/Hd3TFsoB1Ot/mA3Mnjkw62yDKVHPtyFu3OYt2YCHXQ4vb5CNqZdc9oYULlM7jIArYsSXVZpSr3JX7mNPGcpsxlVOycAVcb7EosBz2zcmjaffPCHxwxeuw8RQU4kmk1kIkKvFkysHk9+sjMtO/XGs9QlkvuD08jgm1ZvmrlcdWF2ySVDFWZGj/s2pGcGX3/mwJll/csV9d8HF5vJ78bJdfl7NzQMXRz1RJuBIWeKGZjsiSa7icK1p49FU4cPkSigp4BrTx8Lhyn7YyqJVxbkKF5pM1AosckzmEpsBtgN2be10ydg3VfpLju1hWOy1oPY4+bw6+e/TpugrL18RnLRd6hQwT1EDUQADC8wyZ7R8AKTrJlFFoqo/VFspaTrMbIUls0eI8vOufWciTCxyWddYmPhDvHY0eaVdAncIR4l8Xu83xUi6g/+7JRalBtjRgUmlsZpE4bJXDxXz2+QieQWmMnaiAkxVgNDw27Uy8gvmoLMfrzERC77LjHrpYV4tSNdB2rVvAaMdLBIWA91BgTc9u9v0sj2By84JhmvEWCEhUnuOpMW+odhh2SPiyOWnsjeg0wn1YPc3k5vGAdcnGzhcts5E6GnozjQy+Gi46pRZjfirkWTsavLD4bWYUyZFTrdMIwsNIOLRHD/u80I8RH889O9+OnMWrmhwdwJaZmHv184EWaWxrLZtRKx94PGKqzd0AIjQyHAidjR7iXqw/3lwqkI8yKe+rRFIryOqSqAgQaufe4rzJk0HDQFTKl0wmmi4QsJ4KIR3L5wEnr8HAotLK5/8WuppLMvkbV6fgM8QR6LT6jBY//bg95ALKOvzRNGhYPFH86dhC4fhyILCzNDISQKuPSxr/HghcekLcZ6AxyGOY14+KJG+DkBZTYWgA5tKSX6YxTKY4N8RMrYS0z8xpbZcNu/v5Gy1RLveoiPoMMbwuITavDXD3aB1evw90umQxDEgRdngxxPZtCYMMyKNZfNkAhus0EHM/JXcqNBw+FAiZ08PpbY1MuGsBkp/Or0sWhOmbf+6vSxsOcwb80EXf4wDLR8E9VAU+jyh3PaRB4QUQrPfNaSlv10o4rluZU2hkjupW3cqYFB7p9LjDSuPW1s+oaeMX9z6AonuRKi3Jm/LCK7kcIlx49CU0r22CXHj8ppPZgrvJxI1ECeeHHjgJleRxJKbDQuPn6ULHPv4uNHocQ28Gad1UiuzsjEVTfnd0MX01qOzd8NKDHRQ25dqmHwcNQTbZwQxQN9NJweeK85lgmU5cZhWIhCEKOy3YLfnzMRYSGaddaoJ0QWV/SEBk6B7f+4Eaz5aHca4//bsybAbs5uQdPl54guOz+eMTJroq3dQy7NaveGsyfagEF3D1EDriCHUFjAmFKbRDoFwjzcQR6V8XT4Dq+IB95r6hPHTRhZPBmj4lpknd4wRhQYZAtX6CLo9IZR5IztZNmMNJGws5lix7Aa9UTCN1UwmRMFVDhYPHbpdHT5wii2GiBGRHARAYgvkovNNFbNb8DylAnnqvkNKI7HXCAcwdXPfpU2yD25+FggTth1BgSi0+qEVIJMBE4Z7cTay2eg3RtGmc0gI9mAWLySyktzEQpWCxm/B3lIi9bTetz0itxB9MaXNsfJUhG0DnCYRLR7wuDECCgdBTECVBWa4LQwMOop/PXH0+APC7AbGQR5AY9dOh2uAIcCiwFbD7jB0Do8fFEjWt1BOMws9vUEcMOLSeL4pjkT8Myne9Eb4LBibj2sBj3uenN7Ggl22zkTMbzAgN++uEUi1CZXOuENhPHPzQdxx7mT0eYJwWlmsK/bDx1lQpndgHZPGDSlg8NEwx03IAAgI7LGV9hgNzL4zQtfy0pagRgZ9o//7cK8ycPhCfkQEiISsfvHuNPqg+99m5bNuWJOPe5+cweOry1BbakNP3zkU9x93mSU2Y1S5vDB3kAa8XzbORNx7zs7pTYmstYWn1AjtS2ReZf47zKbEb/455cwMhSuPnUMbnjpaxnp3Bk6TBPACFBmZLCP56GndNBTOpQZD8OCVIOGPEMQRfz+nIm4IWVT7PfnTIQQEaFWuVkgHEGbO5TmsldVYILTpB65bWb1kvNxAkaGysmQKxP4Qjwx+8kXykCwPFtEgJNHxbLjO3xhlFoNh4dkUwnEKpM8ws9FiBrDAS6St4o9X1iAGJGvB29fOBG+kAi7iu9Vv21S0uZW2en3cKPHJ6LTG5bd++u+PxY9PhPMA6xvjbSCtl6Gzs9Zvxs6YMN+TxpJd1ylfUiuTzXkjqG1ylQBXb4QeZHtC6E6y8HWExSkCRIQ68BueGkz/n5JI8rYLK20WT0eeG9rGiF4Z45mCG2eEHGy0eYJZe0cpIbwqhpmCEcKCs0sbn51W1rG1X0pJZ/tClpkHd6kcLiVZbBhd09aCepxowql73uComLGVKGVhl1BMNmW6kwWpbD5gCvtO8emnMdK06guNMjIOEoXgZWOLdxb3WQtk1ZPCFWWpOh/RgSZCIy0ppS79akiUCNe1cJQfg8Udco6vLh/fbNE3Dz5yV5Jt+ye86egyxtGS3cAT3+2F786bSyaO33S5KSu1IpWVxC7uvz48/pYtluFw4iLjqvGfldQmkAlzrV63TY8fHEjxEgUrF6H5z/bh/lThmNMuVXKpBMiERRZWcy//2OE+Iik+2ZkKNz7g2Nw6oRhuOSxT2Ulli+vb8Z13x+P7e1eFFkZ0DoKTrN8xzNRevrYpY3YsKtHKulMIJaxyeDKU+pw40tb0kg4XhClbIc3NrdKhKIxvvkxe3w5hheYUGFjYg6y8WwPWbmyRS8t4oqtBtiM6ZmuK+bU468fNEttump2zExBKkO20Vg6qxaULiYtwAnR2Dtl0h/+CWAmmaQaNBxl0FM0OEGULdQ4QYSeUm9h7g2LEkEBxPrTu9/aGXP6VlHfyhfiyYv+XAy5MkDexv14nzZxuCO2uXuE9mmdQQFXPfNl2v1LdTc/3HAHeDz2vz2yddJj/9uDlSqWXA8ETtDh+hfl68HrX9yMtZfPyEt7AKDMRo79kjzdI7Xg5yISUQbE7v2d/9kR69MGWN9W2hnss/ap9rIaMnIIzuXd6AwK0hwr0ebrXvg6r++VBnVx1D/VQoXBttCSfYejXNudvU6AJ5490ZdU8ASTzpPZQA3NCDWEVzMpATxawQkRXPy9kWk7K5yYfGZlCgRMaQoB488gXdwdJE96E3Hm43gMdxrlZaxOY8zoI77w94YF4nn+dtG05OAWBWrsJnQGBfAGPRwsjRKTQYqPTAilwZooDzWr5/4wlN+DEoXJWyJMQ3wE97y9E4tPqMGjH+7C6vkNePC9Zuzs8OHhixtxy7CJKLUzKLcb0e4NochqwFtbDqLcacG729skt82EKP9vvj+OGKtf7XPhrjd3SiRSucOI1eu2SdlnALBsdi3xt3wkImVZJj57ZuNeLJk5WiLfHvlvLD6KrekGLavm1aOpw4dnN6aXkt40ZwKue+5rnN9YSSThHEYGtYUmSQvQbtSjyGLDAVcYV80eA7uRwTBLsiw6EZuyzEWxDzGFpLlApzeMEpsBQV7AnYsmo8sXRqnNAD1FobLAjDK7AWY2glP++D9Zu5bMrEGxhdUmgBo0HCaIEWDFa9vS+tInFx+r2jkVHehVznKxm8nlfnaVxetLzDSxjDNVekKDMjJxNz/cKLSQS64LVTSaGAhdPvJ96vZzuVXj5AAxIhI1kCMqZszmA/3rrA0AAfhetR3lDmPSFT1u/jQQcnk3huJ7pUFdHPVP1cSAWJKZyxhvUnDeM+Wg+8bqKeIxc03VVkUzQg3tswxKAI9W9AZ4ojDyynn1GBnfgeJE8sDJi8kbFODIg04gRSjWPoATl1HPYM3HJHHRCSnHFInnCff5rL/yxkwIpUEjyI4QrT4AQ/o9oHSRtL40IbifQIiPoLrQhAd+NBX3vr0TXx/wAAA+2d2D+9c346GLpsJhZhAWIrj8H58hxEfQWO3ABTOqpTJhmgLGl9tx0BVU1CRLnOved5pwz/lTZCQbgJjzKWlxZ9Snxe6cScPT3DwTBFPp8FiJ9N6egKRXOHNsKXoDnEwTjdLFNkVa3SE8u3F/WlaoFLd9M7gAlJSmDMPZ9Mk8cEylM5Y9ASBRQzMqZZI/3GTDN90BnP+3jbKfhvgI6kqtKDHp8U1XQJsAatBwGNCh4E7Z4UtmdA82lJy+C3PUAR4IVgNNnLtYDOqW1XUGBMmQJjGvuv/dJtSXHaP1ZxlgKFYC+MICMZZ8YQHIU7tsRj1Z68uQvxizMAye27gjTZ9w1Zz6vLVJDdhN5Htvz/T9Fvq4ome452AzktdR1gwcjYfie6VBXRz1o02AA54l2EFf9/3xWWu0WQx6YnmdJYeO1apwzJw76xTNCEVHxWyghvbZACWARyssBj1xly51IsrSNN75phV/u2ia3OnzhKTTZ6GFPJEuSEmhdgV5ojuhO8QDNhaFJgoXzJC7Xt6yoAFFKcKuI5wm4nkqnYfwQmVCKA2mk9YRoNUnIf4eTEmQJ0PkPYhGKXCCiD+dPwVBQQRDUbjzze2yEskEabuz3SuRbAmNMCNDYWe7D/e/24x7LzgGS2bWwGFkUD/cgUvj2WSJdyDR//XtE0nEnifEp8Xja18dwC0LJuJ3Lyc1kFbNa8Cebn/ad5XcPLv8HMYXmRFmIU3sAaDTx0ntSmiiLZsVK88EYiWmaz5uwSMXNyIiRgbFAW4woDTBG11oBqLaBFCDhsOFIiv5XSvKodJiIBhoikhSGFTW3WIpwGlOd/1WmWc7orRZhyKGYiWAidHjL30kdv7yXjPuODc3iZ1cYFWQW7Gw+TP1KTHRuOR7o3Bdyjw+389ODZgZmqjZZ2IyvPdZmhKEeIG4jgrzAgbKGByK75UGdXHUjzZdvjBRo6zLF0ZNlg5PxTYKw519XCKdJhTbqKwXxSFeQLldXrJXbjcilMGLOyA0HZwhDRurJw4W1hS9P0EUccrYCpnT54o59XHx5Bh6AzyuPnUM7nk7eZyrTx0DV0r5caGZwTMb96bZUt8V1wK06mmU9zU6iIqw6pMuPiUGhYHCcIgDRSbEah5E/zWQ0e3n0Obl8LtXtqHCYcTF36vGkpmjpWywxAQzLIh4+rO9ACBNQJ7ZuBdXnzoGT33aglvPmQggirpSK5o6fGjp8hOJriAvoq7Uil9/fyx6AjzGldmIxF6Pn0tbQP5iVh1YOoqHL2qEK8ih2GrA3m4fHv1wd9oEaWpVQb8EU9+JUW+AQ02xBQ9f3IjP9vSgrtSGu/q0qzfAofAwxm0kEh3QxGCgCZ42AdSg4fAgyAtYMade5rq8Yk49grwAtVTdO7xhPLkh3VH8F7PqVHX/bHWF8cD6ZlwxczSA2DTigfXNuPa0MXAWmVU7r7ZxkCOGYCWAN0SW2PHGN4rzgZ4AB6tBL1u7WQ169Aa5rHWwc8YQfHZqoMvPwcTQsntvYmj0BDK49zmYEjiMLHEddXLtMQM3ejATCDQcETjqibZMtK0OFWEOcJj0aKwuQG+AR4GZQTQaRTgsyVgdMkysHk9+sjOtZO/Gs9SzItcwNFBh0aPc0YdkdRhlek2snsYLn6dnZv7mjPHScZwmBk992iLr/J/6tEVmqFFsofGr08eiuSMmSK+ngF+dPhYllqTT529f2ioZM2xv82Hd1wdkroTflUFcgxxlVgO+0cX6z1Z3CH94Ywcq4nE7ocIOM0tjT5cff/9wN+ZPGY7RJVZYDTQOuEJYPb8BQT6CWxZMRJAXcOu/vsGPZlTjoQ924YoTa4h99PGji8AJImidDqV2I4qtLJaeUicrg79lQQPK7Qb8Ka4NR1PAMSOcONAbwB4vB0oXQLGFxW3/3g5Wr8PSU+qkciKaAqZVFWBCial/gikl3v28gKiOQrsnjEI9hXMmlaEzIOLq2WNwfYqD4GElqHTA2zs68avnvux/wjjQBE+bAGrQcFhgZfV44fOmtPH8+jPHD/zjLFFoYYmO4gUqE0/FVgPxvGoTXtrGwWBDl+8GoEBB769A5fLn/mA3Mlj5YdLMTIwAj3y4S9q8HhrI/7NTAw4jg9VZ3vtcNGlLTDSuPW1s9n2LlkDwncJRT7RVOmmsmt8gCWAbGQqr5jdgRAEN8Nkdk+ej+MkTn6d19i8uOS7rO0rpgEXTqmQlezfPrQedXzdtDYcDEeB7VfaYSLwnjHK7ASP7iHJWO1mc3ygv6Vw1rwEjC1gpjk0MjZ+eVIuVr6Xsks+thzkljTosAD0+TmaHff0Z4xAWLbDoYztEDiODseU2BMMCzAY9/ruTSS+3OJJKMTUMCirtDEaXWmVlEr3x3dxefxh/eXcfTh5XisUn1sAT4HH76/Isr6WzanH/+mZUOIz47VnjcW08ll/YlG4ssGJuPaysHl4xAk6ISCRWdZEJf/vxNOiiUVQWmhEKx7K4fnvmBIR4AQ4jgxKzHmUWViKLaEqHkec0xIgjsx71ZcckiSSzHp1+AcUWBk8tPha+EE8mmKJAiVWPLc2+NF3BOQ2lGGVj80ZQdQYFiWQDBpgwDjTB0yaAGjSoDiND47xG+Xxv5bz6zEueskC5g0ozd7llQQMqHFTG2kTZgKGB3y+ciN1dfkSiAK0DRhZbwOhVXvxrGwe5QQd83ubD1laP9NwmVNgxtdyat3voNJFj2GnK30LJYqDx85Nr00qyrcb8lY7mkq11JMFppnHlybVpGuxO88D3/qA3jAIzi4VTK6GLd0UvbNqPVm84o9Jyq5GWlcNbDNpiXQMZRz3RtrdXRGuPF49fNgMdcY2yj5vbsbfXjFHW7DrCLj+HMaVWXDFztERGPPzBtzlpP/jDIp7fFM9Yih/z8Y924ZrTxgIZCCxqOIJBAVs6/KB1FMRIFEFexJYOPxqKLVJGW6dXwNvfHIxptPl5OC0xjbaGcqsUc/t6A9jZ6sI/LpuBTm8IJTYj/rP5AJwmPcrKbQCADh+P29/YLluU3/7GdjxycSMKnTQqHAZceFx12gKgwtEnAzSubdDU0gunQZ+xtoGGIxedfgECz2H6yEL86QdTYGZpWA16dPrC4MUoVs6PCe1GIsDHPQGcO60yNnFxh2BkKBj1FCocRiycWgl/iltUqzuEtRtasPSUWtSUWGBi9bAb9Wj3hlBoYvF/I03piyUAnx70pWVx1RaYgEgfsghAYaJEKfVvOmDDvvTJ6PhiMzGWd7s4iWQDYu/O8le3YFTxDIy0snkjqFJdrBL3V6cD3JygLSw1aBiC6PZz2LSnC3+/dDq64m7BL32+FxUOI0pZkyrndPujeHNr+hxiQtkYlKgnDQdXSIA3yMs29248cxxcIQHOLOfgGUPbOMgaPWER7Z6Q7LN2Twg9BSYU5kl/7EBvGB81dcbem7i0yfOf7UWphYVdxTLk/tDj54nv8ogCE4od+blP3xUH8TY3j51tyTVPqc2It7YeQIXTBKez/3tfYGZwxYmjcOd/dkj90nXfHwtnBuW+PWER29t8abp8w2zGzN6NLLXhNByZOHreOAUwVBSjSu3437ddiESB5g4fRpfawdDZR3Wx3YAfHisnI1bMrUeRPYdyVFHErHHlsmMum1UHTtQE1Y52dIVFdHi4tB2xLpsRxfEdbr8g4MyJw7GppTe2u9gFnDlxOPyCgJL4a1xTYoYn5JSE5RPHqSlOTkD8A9hh+7iITPg9xMf+/c8rjoVTHx9AviO7ZRrk0OmBiE6PHz/6iWxyYWZo/P2j3fjV6WPBCRHZbvOyWXVYv70Ni08YDSCKWxY0YNW6rZg7eTjBlECHa56V93/PbNyLpafU4eRRTtliqTNEzuJKZqUZUGKm0RkgTGbikxx3+NAmo+2eMPHdafOEkzqD/d5AdSZXCS2iAjOLi46rljIDH/mv9l5q0DAUwdDAMdXFkvNyYg6pYkIbPGGeqPPqCfMxfVWVEAVw2+vyzb3bXt+ONZdPV+2cGnKHh4vNCfsSpB5OyBvRVmYz4LjRRdiwqxuRKPBthw/HjS5CWQ5SQLlChyiOqerzLs+pRz4H3dTNtwSORgdxPRXF6DL5mmfF3HroqYHvvU6nk0g2IHZ/7vzPDjx1xbED/rYnyGN3pyeNXO2pdAz8bmjrp+8chnyu4wcffIDvf//7OO200/DQQw8d8u+joHDAFcJDH+zC/eub8bcPduGAK4RoNPtL94dF/PX9Ziw+oQZLZ9XiihNr8Nf3mxEIZ28PaNTrpQUSEHvp71vfBIM+j+nHGg4LvCGRSG55Q8l40lM02tzyOG5zh6CnkvHhDpKP4045TrE1pnGRipjbWYwoaPeSyYR2b1j6t9JuWWdQxfoTDXmHP5Qk0YDYc7/3nSZ0BzjMmTQczR2+tL/ft74JPz25Dlc/+yV+9uQXuPKpz/GDxip8sKMDy2bVSbF4XmOltDuY+ts5k4bjple2YJ9XXuevNJF8r6kTF/9jIxY+9DHe2+3Cz57+Qvr3hv0egIpNchY+tAH/29WjOBkloTyu95kKI0OhPJMNFl3yvLL26ABQwD4/j41tXuz384c8KpeYaPzxvCk4r7EybQwZ8L3UxUjLb7oD6AyJmUm5ZPMbDRo0SKApWpJ4AGLv6srXtoKm1JvvURQtmS9I51y3FZRO3TmmK8AT+1lXQJsvDGXodDoiQarT5bHDV3AIz+dKllZ4r9R8lwdCYvMtFUejEQhNZ9+PdiisdTpT1jpKMLE6NI6MkavLnv4Sl/3jMzSOLIaZHfjd0NZP3z0MaWpbFEWsWrUKjz32GMrKyrBo0SLMmjULtbW1GR/DGxZw7Cgb1lw2A+3eEMrsRugpAd6wAGTpCNMb5HH58aPQHeAk7YLLjx8lc3c8VLiDPLEc1R3kgRwy5VQDBezz8tjU3oEyqwGVNkZzM80S7QpaAR3eMEbFnZS8YQHTqq2yOAZ4WRx3Khyn0xvGqHhcekJ8mkPjynn1sYw2KwubQU8Um7Wm7Hh3+TnieQ55tyweQ+3eEMptRnIMDVYWkFbqmjM6fTFyK+E4WllgRiAsoMDCwqDXgaKA97ZbceKYUhSaGdSWWdHpDcNqoPHYpdNg0usREiPo8nK48ezxMOh1mFjZiGBYhIgoCsys7Lhd/jBGl1hw7elj0O4Nw8cJsBr0MDMUjIQ4rS4yob7Cjj//8BhYWBpCNIrV8+vxmxe2oNUdwnUvfI3nf/I96HS6uE6nCeU2Fm1eDi9s2g8AuPh71QiJUTR7Qig2M4hEYruXvrCIEiuL1fMb+pgxTMSEcgsCAT49lqOxEoOeIA9vWERzpw8FZhat7hAKzCyaOn0otDI44ArLNERvXTARdSUmGGgaYjSKg54wbEY9jHoa7kDMQZXWxfqNxDtx6tgSGPQ64sRxrzuEkGiAL8SjzGaAGInG3ierAftcQVz1zAAmCqnQdmM1aMgZnd4wWX7ElxyrBxvtnjBe+tl0eEM6aQ5hM0TxbVdynqEGjAyN0ycU48LjRqHXz6PQwuCJDbvTiABVwAC7ezm0e8IosxswKkXTVvVzHvQevnOqgG6FzaweP4/qPBE2vUEBEyut4AVKimGGjsAVFFBoy1OZpk9h3u0LoyZPTqhD1ghkkOfhnd4wvjeqEJeekOxbHvtwd0b9qJmlUV1kkowUAOC1rw7AlEG2JifqoNeJsvXY/m4PwuLARNt3JdtQQxJD+ql+/fXXqK6uxogRIwAAZ599Nt55551DItpGl7L4qMmD5a9+LhNLPL7OnvXgV2Zj8W2HT5ZSfdXsOpTm0KmOKDQRy1ErC9TR68gJFPDebpdswbl6fgNOHuXUyLYsMLrUhIu/V51W719Tmnz2o0pYfNzswfJXv5DF8fdq7ZKQcWWBkXicygKjdJzqQiN8IaGPHTaFqsIYmeu0MFgxtz7NUKEghZQusxuI5zmk9P1MYmiwFvUaOTAoGOY0oLrIhMuPH4UAL+K6lL7q1nMmwmqg8dOTR+MPb2zHDxqrZCVKMaFvHX79whZZXNGI4oH3v8WqefX42Uk18HPy466e34C1G1rQ0h1M6mA4jVj78R6ZgUJ1kQk/O6lWRhpdNbsOw50m3HDGGPz+jZ0osbLY1uqVmRmsmFOPDd924qczayBEozK9jpXz6hHmRWlX38hQuHPRJPztomnY1NILMQL8ef1OOEx6uIO8rGR29fwGVDhYbD7glb0ny2bV4Y0trTijoQL3rW9CbelkiWQDYhOu3768GQ9eOBUHXF7c8q9vZNez5uMW9AY42X/fee4knD7ehGE2I5Ekj0SBHz36iURk9n1vE+RfJjou3xXtFw0a1MSoYjNxvletos7U9GozPmjypAmHz8xhLpwJakpYnDpeXrK6al49akpYVU0YwADvNrnSzGtOqXOqd735OKdKsBnJm64WQ/4ytcrteny6Oz2GZ4zK31yuqpA8f6/K59otzqelzvOj8c/zFocqzMNrS804Y2JF2lxzdMnA/WiFgyWaWJQ7Bl7HO0w09kRpXJxSsrpqXj0cpoHfjWKrgfheFau0waIh/xjSpaPt7e0oLy+X/l1WVob29vZDOsbBXlHqlIGEgPVWHOzNvszTGxLTypzufadJVup3qAiERWIKbJDL/phqYZ+XlwgSINZWUnmXhszgD0WI8eQPJTviVhc5jltdyfjwhclx6U8pae72i/j1C5tx3zvNuH99M+57pxm/fmEzuv2x7wT6lEUvPiFeFp0ShwGe3N6AkDnLmkkMDVaKtZaqPTgQI8D1Z4xHd4BLe/6/fWkzeCGKSASYM2l4Wgnjile3wmk2pPVvZgODOZOGI8jFdvT6HvemV7ZgzqTh0r/vfacJ33b6cWxNCdZuaMHiE2qwbHYt/nDuZNzcp/+8950mNHf64LQYsHBqJZbMHJ1mZrBy3VZcfHwNugNcml7Hile3ynYfQ3wE1z3/NTa19OK+d5rxwLvNaOkOYvMBd1rJ7E2vbEEkqiOWw14xc7R0f4IKmolf7HPJShsS17NwamXaf1/3wtfY5wpKu9iJTBEjQ+GmORPwhzdiZN3CqenluYnjpJ67tZ/Sif52Y79ruH7dtnw3QcMRCp/CfM+fg/zIQNirMBfem8NcOBMcVJi7HHSpe97dvWTzmt296vVV+TinWjDqaVw1u042nlw1u05VZ9yB0OEhx1KHJ3/rJE5hPswdwnx4sLG7l8OvX/i6zzz/67zGoRrzcEW5nODA8dAbIP/WFRj4t+4gOQ4zOS8fiRLfKz5/4aJBZRxVW9A0rYPTKWey2w96iQuDdk8Yx1Q6szqPt81HPKY3JKSdP1O0KbSzzRPGlCzbqRY2tXcQ29rhC2PicEeeWtU/aJrK+tkMNvrGaSbPPpM49ijEpTsowFltzug4bQe9aOkO4oF3m2XfSW2L0vNv94bRMCyz559JDDW19BK/4wqLqCu3Z3SewTzO4UY+Y5bUl2442I7dXX5EomSdFH9cPFmnI/+9N8ATf6PTxUrnlY6bKgkT4iOIRGPnaHWHpDitLbUSfxuJAr0BHjQFRBElfifICYrnjvTZaSV9pvTbXj9ZmyjIJck1s0KpthgB+krhpN6Lvv/d6QujuqoAp9tMGL/0BDR3+rD5gAfeEI+W7iAA5eeSeh4jQ8FiZBTjblgExPYOKzDD6TQf9pglxWnyb+q2JQLlc6uNoTSeDTaOtms7XPPSgZCPc37Xzpuvax0M9I3TT1vbsebj2GaWTgdEo8Caj1tQVWjGxAzneYMNxfvrDeOYEc68tGmDQptaPWFMztMzH4pxOBjz8L4xmst1Ho7fksayTe0dxPdqVLEFEypK+z3vYGEojrFHc5uGNNFWVlZI6J1nAAEAAElEQVSGtrY26d/t7e0oKytT/L4oRuFyBeTHsJPTNMvshrTvZooSG6uY+pntMdVop1ooU0h9LbUOvbYm4HSaD3vbSkpsxM/7xmkmzz6T72QSlwMdJ6O2DMLzz+QYTgUSwmmgD+lZDtZxDjcOR8xmGqNALHaaO7ygdWSyxcLqZf/u+/eCPpqYqb+xGPWKx41G5b+hdLHsutTPyhTKJildzMb95LoSRBAlfsfEKp+b6kN2kT5T+m2BhSF+PrLILH3+8Aff4pYFDWlOrc9s3Iv5U4annTtxL/r+d0nKe+OggCqHCb985ktccWKNrA39XaNUStrPe+GgQdR+cdCAyxVQLWYPJU4TUPv9EQRROv7167bh9jkTVDtXX+RjPDtcOFKv7VD70sM938vXHPO7dN6hPo9XilEgPU5LbQb0BjjZpquRoVBiy9+1DMX7q7UpMxzKPHww105KOBy/JY1lZVbye3U4189DcYw90trUX1/aF0O6dHTixInYs2cP9u3bB47j8K9//QuzZs06pGOMKmCxal6DLE1z1byGmEBplqh2xkSxU4+5en4DRuZwTDXaqRYqbQzx+kfYsjOX+K4jk2efyXcyicuBjpPJeQbj+WdyDFIpnCToeggYrON81zGqgMXoEiuKLGxa6vvq+Q1g4oYIr311QOYomtC+cAXCss9WzK1HIMzjta8OIBDiMbLYknbclfPqse7rA9K/r5pdh9EllrTPnvm0BbcskMfTVbPrUFtiAaWLYoSVQbU9PbZXzKnHmo92YUKFPS1GVs6rR7GF7fMupH/WMNyRdu7V8xtA6dJLBO48dxJqHAbpXF8f8ODNrQfx4IXTsGx2rFT7mY178bOTalFqM6Rdz4uf70/77zvPnYQRTrkeTCLmU5/FC5v2p7XnlgUNsLI0ls6qxZKZNagptqCwPw2eKHBcpR0vLjkOay5txItLjjvqtQ4TJaKppaK/e/0b2XdSy4RSv6eVl2ogIR/zvXzNMb9L5z2S5vEDYaRT4Vqc+buWoXh/tTZlBjXm4blcZ75+q62fv3vQRaPRIT1Ffv/993HbbbdBFEWce+65+NnPfqb4XZ4XyeyjGs5DR8ox1ULcMbLDF0ap1RDrJIZwjflQymgjxmkmz/5wfSeTYwzG809xHS2zGcnHkLmOstm7JsWP4wqLcBro/LsvZYB8ZrT115fuc/MICSLECBDkRBRaGBj0FHTQQUcBPX4ekUgEJlaPTm8YJVYDRIgw0npwYgRdPg6lNgNYPYUuXxg2AwMfx6PIYgAnRhDkRIT4COwmPUx6GkFBhCcgwMhSsLJ6mFgKnqAAPyei3MaC1unQ7g2jxG5AgIuVUZpZGlaDHg4DBbs+xdmKBva4ObR7wyi1GaCLRmBh9NJkrzMooNUb+32xRe46ajfqUWjWS58FOBEVNgNGl9rg8QXTYznFdTTxXSnudPJzlVoZuEMRtHtDKLYagGgEdgMDMRpFqycMq0EPIxN3HbWwoCld3HU09k44HYRYiZ/DHeJhYPTprqMWFiVmPToDg/B+xXG4M9oU43QQ23LNy1tw94IG/Oa1rWD0FG45czx+++9tEMQoTCwNIRIFL0TA6CnoKR08QQF3L2iQ/XawMRR3fwcLR+q1ZdOX5s0R83DPMb9L5x3C8/j+sjCIcaoHdrtSrsWpsoFFJhiK91drU2bIcB4+6GsnJaj8W8WxLJO1j4oYimPskdamQ8loG/JE26Ggv0k3MDQfJAlHSjuBI6etQ55oi+NIuZ8JaO1VD0OSaItjKNzHodCGodKOodAGNduRz770t//eBoamJGOiP8ytl4g2Pa1L+/8gF8HdCxrwu9e/QSAsSv99y5njAQxOmelQed5q4Ei9tiO5Lz1c0K41vzhkoi2OoXgtWpsyw5HYpqNl7TQU2wQMzXYdaW06akpHNWjQoEGDBg0aDhVX/vOLQTsWHxcF1NO6tLLRvmBoHW7+z3bwQgQmlsLvXv8GfLy09Hevf6NYZqpBgwYNGjRo0KDh6IFGtGnQoEGDBg0ajirwYu61GCRSjRcGPm7quXkhIhF00WhUIuIS/wY0XTcNGjRo0KBBg4ajDZoauAYNGjRo0KDhqILNoMfK/+yQfSZEolh95jgAwK1v7YQQiUJP6RT/HwAYOrYfqdPFPsv2/4VIFAxNQadLqnWYWBq3vrUTelontcfIUFj5nx39tiuq00EXjWbU/v6+k3o/bnp9u/TfqVD6XIMGDRo0aNCgQYMyjiqNNg0aNGjQoEGDBg0aNGjQoEGDBg0a8gWtdFSDBg0aNGjQoEGDBg0aNGjQoEGDhkGARrRp0KBBgwYNGjRo0KBBgwYNGjRo0DAI0Ig2DRo0aNCgQYMGDRo0aNCgQYMGDRoGARrRpkGDBg0aNGjQoEGDBg0aNGjQoEHDIOCoch3lOAFud1Dx71arAT5f+DC2KDscKe0Ejpy25qOdJSU24uf9xemRcj8T0NqrHg5HW7OJUWBo3Meh0Iah0o6h0AY12/Fd6EsPBdq1DT0cyX3p4YJ2rfmFUowCR15fqrUpMxyJbTpaxvuh2CZgaLbrSGtTf31pXxxVGW06na7fv+v19GFqSW44UtoJHDltHUrt7C9Oh1I7M4HWXvWQz7YeCX3pUGgDMDTaMRTaABz+dhxNfemhQLu2IwdHQl96uKBd69DFkdaXam3KDEdTm7QYHRwMxXYdzW06qog2DRo0aNCgQYMGDRo0aNCgQYMGDRryhSFfOjpr1ixYLBZQFAWapvHiiy/mu0kaNGjQoEGDBg0aNGjQoEGDBg0aNKRhyBNtAPD444+jsLAw+wPQwB43h/ZWL8ptBlQ7WEDMsVGJY3rCKLcP0jF1QGdQQFNLL5wGPUpMNBDN8Zhq4Uhq63cJFLDPy6PdG0K5zYhKGwNE+nyHAXb3xmK3zG7AqAIW4FVoSzxGuvwcii0GLUaOFuiAlp4ADvYG5M815XlXOA1wB0W0pfaPkcGNh0gkis6QwvG02NMwGBgojvTAHlesLy22sSgy6+HUp78PQy4Gh3LbNGjQoOFIQmJOfdCr7pz6UPBdWaMNxXuvQUMKjgiiLSfQwLvfurD81S0I8REYGQqr5jXglNHO7IkxNY6pw/9n797j5Krr+/G/zpzb3Hc2m91NSCDXDam7REAURItAANHmAgnSVlqU6ldrwaQiiFBJSPAnfL9YbLwVtFaJtdZWxFyqrZBwsUXQBCUmcsllcyXZS7KXuZ7bzO+P2Zmd2Tmzmd2dOXsmeT0fDx7snPmcz+d9Pud9Pmfmk7P7wUtHB3H3k7vydT6ychEumxl23+BYT7GeTTzAc539uH/TcF4+uLwDV86JDE+2ycCze21yty1S3ZsTc+TMVO68nhvGS0ey2xe0BPHnl87Cui17inLsnIiCj298pTr5IADPvNGDz/7H70rrA3OPquB0Y5gEPLuveCxdu7Qd08IKOpoD+evBdTnIsZmIqDqc+kw9FmfLGO/GvicaoS7+RtvHPvYxrFixAj/60Y/GvO/BAT1/EQJAykhjzebdODigjzueWtTZkzTzg2Kuzruf3IWepDnuOmulnmI9mxyJGvlJNiB7Xu7ftBtHosN3nM4++9zt7Bt/7tphjpyZyp3XI1Ejv/3jV8zLT7LlyqzZvBvpjFC1fOhJmvlJtpH1MfeoGk6XR539pWPpui17ENfSRdeD3b6TidcHEVF1OPWZeizOljHejX1PNJLrn2j74Q9/iNbWVpw8eRK33XYb5s6di3e+8522ZUVRQCTiL9rWdTyavwhzUkYaXVENF86MjCumWtS591CfbZ39moW2aeFx1Vkr9RRrjih6SnJjstjl6fB7449zZ1e37Xnpjmm4YEYDAKDrrTK5O6jhonHkbrl43ZojbsqD05nMWMvlaLnz2h3T8tuTmmlbpi9hlGwbbz6Mll+5n6vV1um4IafcEMNkxFGrsRQ4/RhWbiyN62bR9WC370RN5NjcOjbnuCWXq2W0HM2+f2Yd72h4rO5Vy7G0FtwSU7U/U1fDmTrGj8xRN/Z9Ibfk6EhujOtMjsn1E22tra0AgKamJlx77bXYtWtX2Yk2y8qgvz9RtG1aSIVX9hRdjF7Zg9aQWlK2UrWoM6JKtnVGVHHcddZKPcWaE4n4HY+tuTlku90uT3MmEmdr0D4vW4LDedkaLpO74fHlbrl43Zojk5EH4+VErGPN0XLntTD3/GXKNPrloromkg+j5RcgOJp7bsgpN8RQyzicHkuB049h08qMpQFFQkuZsbhaOTiRY3Pr2Jzjllweq/HkKFC/xzsePNbJVS5HgdqOpbXglpiq/Zm6Gup9jK90LHVj3xdyS46O5Ma46i2m0cbSkVz9q6OJRAKxWCz/8//+7/+ira1tTHXMalCwflkHvHL2UHO/wz27QRl3XLWos9kn4pGVi4rqfGTlIjT73DcXWk+xnk1mhmQ8uLw4Lx9c3oFzQ8MTHHMa7XN3TuP4c9cOc+TMVO68zgzJ+e3ffmE/1i5tL8kxj5CpWj40+0T8/YcutK2PuUfVcLo8mh0pHUvXLm1HQPXg3ILrwW7fycTrg4ioOpz6TD0WZ8sY78a+JxpJyGQyrv3TiEeOHMHtt98OALAsC0uWLMGnPvWpsuUNw7KffcytEBrV0BpSsxNi1Vp1tJp1Dq0S069ZiKhidlB069mpp1jhrifayuYpqhBnwaqjrSFvdpKthquOjhpv0cp2iityxI3/alLOZD7RNlqOQgAGLAytOlpwXgtXHW1QMZCycKJwfExXNx/CYR/2d0ft63Mw99yQU26IoZZxTMpYCpw+j3KrjkY1NAUUTA2UW3W0ujk44WNz4dic45ZcHqtxjaWo3+MdDx7r5BrtKYyaj6VV5qqYqviZumpc/B1tvE+02eaoG/t+iKtytIAb46q3mMbyRJurp7fPPfdcbN68eeIVWcDsoIILZ0aynTbRCbGCOmcHlfzrCcsAzV4JbdPC2ThdMijaqqdYzyZp4NyAjHMDcv51CQOYE1QwJ5e7tbopDeVIs1fKv6YzQAaYNcWPBs/w69z/C893JChi1ojxsZr54PEI5etj7lE1nC6PzBGfAwrLuDkH3RwbEVE9GfpMfVHuO6YbJnrOlu9obux7ogKu/tVRIiIiIiIiIiKiesGJNiIiIiIiIiIioirgRBsREREREREREVEVcKKNiIiIiIiIiIioCjjRRkREREREREREVAWcaCMiIiIiIiIiIqoCTrQRERERERERERFVASfaiIiIiIiIiIiIqoATbURERERERERERFXAiTYiIiIiIiIiIqIq4EQbERERERERERFRFXCijYiIiIiIiIiIqAo40UZERERERERERFQFnGgjIiIiIiIiIiKqAk60ERERERERERERVYFjE20///nPEYvFAADf/OY3cccdd2DPnj1ONU9ERERERERERFRTjk20ffOb30QwGMSOHTvwq1/9CjfddBMeeOABp5onIiIiIiIiIiKqKccm2kRRBAA8//zzuPnmm3HllVfCMAynmiciIiIiIiIiIqopxybaWltbsWbNGvzsZz/D+973Pui6jnQ67VTzRERERERERERENeXYRNs//MM/4L3vfS++853vIBwOo7+/H5/73Oecap6IiIiIiIiIiKimHJtoW7NmDa677jrMnj0bANDS0oJNmzY51TwREREREREREVFNOTbRtm/fvqLXlmVVvOqoZVm44YYb8MlPfrIWoREREREREREREU2YVOsGHn/8cTz22GPQNA0XX3wxACCTyUBRFNx8880V1bFx40bMmzcPsVhsfEHIQGefjq63omgNq5jTqAATXYchV+egVv06qxkn1QcPcCRqYGdXN1qDKmaGZGDknzCsJOeqUWYsdYyWqwLQkzTRG9cxNaCi2ScCmfF20AQNxbL3UB8iqjS5sdQzGXgramL/kT4YVgZJ3UTIK8EjCPB4BGimCVWUoJsmfIqMmGYiZVhoDakw0kBvVMPUkIIMMkgZ2f1bQio0M42emI6pQQUBWUTKMCF6RMRSRjZ3/CJOpSwM6iY0MwPNsBCJG9AtE5JHRFdUQ1NAQaNfQkJPozuqYUpAQV/CQFNAxqywAlgjjmWs+SkCBwey18W0sIpZEQU9MROdR/shCcJwrCPrmeh1MDQ2dEVTmBby2o8NNKwa99GRfd4g4+igga6ohuaggnQG6BrUML1BRdArIpZKI6FbiGnZfE7DQibjQTRlIKBKiKVMhH0y0pkM+uIGWsMqBAADKQN+WUJPLDvWyqIHx/qTaA6qSOgmPIKABq+M6QEJyAzn0cwMkNIcGFvdNIYTkbMq+VzqNDd+T2JMlanF5/CJHOdEvsfXYg6Azkg1n2j75Cc/iU9+8pP4+7//e3z2s58d8/4nTpzAc889h7/+67/G9773vbEHIAPP7u3Hms27kTLS8MoerF/WgavaIuO/KOqlTqoPHuC5zn7cv2n43D+4vANXzokMf6ipJD+qUaZa7QjAS0cHcfeTu/JlHlm5CJfNDDv/Rc1NsdQzGXjp0CD64jriuoUN2/bm+/POaxfAK3kQ8sr4+e6D+EDHDJwYjGLDtr1o9Cu49d2zisqvXdqOx57fB93MlLy3blk7VMmDz//k9/ltX7yhA+l0GifjRr7sJbMa8KFLzsPazXuK9v3mc/tw6GQSXtmDVVe34Us7DuP2K9tw1bzI8GTbWHNCBJ7dX5rz/77jEK5eOA1f3b7Xvp6J5l4lYwMNq8Z91KbP1y/rwDee24sGr4w/v3QW1m3J5tysJh8+e935ONaXLMrhcrmxenEbNv7qEPoSOr7wJ38EyePBA1teKboufvjyIbzZHcOqq9vwox2H8WfvPA8zGn0IqSI+9YPf2l5PNRnPOG4Snb3ceO9x4/ckxlSZWtxPJnKck7UvnXUc+9XRK6+8EolEAgCwadMmPPTQQzh27Nhp9/vSl76Eu+++Gx7P+ELt7NPzFwMApIw01mzejc4+fVz11VOdVB+ORI38hxkge+7v37QbR6LDI3Yl+VGNMtVqpydp5m+ouTJ3P7kLPUmzSr1WOTfFUs86+3Ts7Y6hN67nv+QD2f589Ok30RvX0Xkyjlsum4POk/F8mRUXzywpv27LHixZNMP2vbWb9+BAb7xo2xd+uht+RS4qe+vlc/OTbIX7Llk0I//6q9v3YsmiGVizeTcODow/Pw8O2Of8rZfPzU+k2NUz0dyrZGygYdW4j9r1+ZrNu7Fk0Qx8/Ip5+Uk2AFiyaAb2dcdKcrhcbmzYthcrLp6JlJF96vKBLXtKrouPXzGvKHc3bNuLfd0xxLV02eupFuMZx02is5cb7z1u/J7EmCpTi/vJRI5zsvals0/Nn2jLeeCBB7B582a8/vrr+O53v4sPfehDuOeee/Av//IvZfd59tlnMWXKFHR0dODll18+bRuiKCAS8Rdt63ormr8YclJGGl2DGi6aGRnXsdRLnU4QRU9Jn7uRm+Icmac7u7ptz313TMMFMxoAVJYf1ShTrXb2HuqzLdOvWWibFh6ld6rPTbGMxWTmbLmxND30L492/Zl7ry9uIJ0ZLiMI9uUF4fR1FW6L62ZR2aRmjlpv4euUkUZXVMOF48zPruP2OZ/U7WPI1TPR3KtkbADcM745HcfIPK3GfbRcnwtCac4JAopyvbB8udzI5edo+xWWzV0P8aHt5a6nao9nkzVuuiWXq8VuLC1+/8w63tHwWN1rPJ9LnebG70mMqTLVuJ9U837vxL5uHQPcGNeZHJNjE22SJEEQBDzzzDO45ZZb8KEPfQg//vGPR93nlVdewfbt2/HCCy9A0zTEYjHcdddd+PKXv2xb3rIy6O9PFG1rDavwyp6ii8Ire9AaVkvKVqpe6nRCJOJ3dXw5kxFnc3PIdvvIPG0N2p/7luDwua8kP6pRplrtRFTJtkxEFR0/D26KZSycyNlKcxTInvd93VEAsO1Pz9AEwpSADLG3uIxd+UwmO2kwWl2F2wJK8Xn0lzmvmQxKXntlD1pD48/PaSH7nPcro9cz0dyrZGwA3DMO1yqOisfSKtxHy/V5JmOfc2KZHC6XG7n8LLefT5GKyuauh4AiFZWr9Xg2WeOmW3J5rMYylhaq1+MdDx7r5CqXo8D4Ppc6zY3fkxhTZcZyP3Hifu/Evm4cAwB3xlVvMY02lo7k2K+OBgIBPP7449i8eTOuvPJKpNNpmOboj4x+9rOfxQsvvIDt27fj0UcfxWWXXVZ2kq2cOY0K1i/rgFfOHmrud6nnNCrjPpZ6qZPqw8yQjAeXF5/7B5d34NyQnC9TSX5Uo0y12mn2iXhk5aKiMo+sXIRmn2Nz+66MpZ7NaVQwvyWIpoCC1YvbivrzzmsXYGpAwZymAP7lpU7Mbgrkyzy582hJ+bVL27F11zHb99Yta8fcqYGibV+8oQMJ3Sgq+8SLB7BuWXvJvlt3Hcu/XnV1G7buOob1yzowu2H8+TmrwT7nn3jxAFZd3Va2nonmXiVjAw2rxn3Urs/XL+vA1l3H8O0X9mPt0uGc2/LqMcxrCZbkcLncWL24DT955Si8sgfNIRUPLG0vuS7+6YX9Rbm7enEb5rcEEVA9Za+nWoxnHDeJzl5uvPe48XsSY6pMLe4nEznOydqXzj5CJpNx5M/a9vT0YOvWrbjgggtwySWX4K233sKvf/1r3HDDDRXt//LLL+Of//mf8fjjj5ctYxiW/exjLVcIdXudNebGWWg7bnqizTZPh1Z36o5paAmq2Q8zI//grBtXHR2tTNGKdUr2hjrJq472axYiqji5sVRoMp9oG20sfStqImlYQ6uOWgiqIkSPAMEjwDAtKKI4YtXRNFpDSnbV0ZiGqQEFEDJIGhmkdAvNQQWalUZvTEdTUIFfFqEVrTqqoNkv5Vcd1c0MUqaFBp8CY2jV0dwqoxGfhKSRRk9UQ6NfQX/SQKNfzk6yjbrqaAX5mVt1NKqhNaRi9tCqozEzXbDqqE09E70OClbAbA15bccGt4zDTj/RZpun1biPjuxzu1VHo9nVZ0NeEfFUGnHdQlw3MTWoArCQzngQ1QwElBGrjiYMtARVeITiVUdbQioUqXTV0bBXxjkjVx2d4i9YdbSGY+skjOFuyeWxGvNYOqRej3c8eKyTa7SnMMb9udRpbvyexJgqU+HncMfu9zXe141jAODOuOotprE80ebYRBsAHDt2DIcOHcLll1+OZDIJy7IQDAarVv+Z8oGmXuIE6idW10+0DamX/sxhvLXjyom2IW7oRzfE4JY43BBDLeM4G8bSseCxuU89j6VO4bFOrjFPtA1x47EwpsrUY0xnyv3ejTEB7oyr3mJy5a+O/vu//ztWrVqFNWvWAAC6urpw++23O9U8ERERERERERFRTTk20faDH/wAP/zhD/NPsM2ePRunTp1yqnkiIiIiIiIiIqKacmyiTVEUKMrwHwo83UIIRERERERERERE9cSx5aPe+c534rHHHkMqlcL//u//4l//9V9x9dVXO9U8ERERERERERFRTTn2RNtdd92FKVOmYMGCBfjRj36E973vffjbv/1bp5onIiIiIiIiIiKqKceeaPN4PLj55ptx8803O9UkERERERERERGRYxybaLv66qshCELJ9m3btjkVAhERERERERERUc04NtH25JNP5n/WdR0///nPMTAw4FTzRERERERERERENeXY32hrbGzM/9fa2oqPfvSjeP75551qnoiIiIiIiIiIqKYce6Jtz549+Z/T6TR2794N0zSdap6IiIiIiIiIiKimHJtoe/jhh4cblSTMmDED//AP/+BU80RERERERERERDXl2ETb97//faeaIiIiIiIiIiIicpxjE23f/e53R33/tttucygSIiIiIiIiIiKi6nNsom337t34/e9/j6uvvhoA8Oyzz+KCCy7A7NmznQqBiIiIiIiIiIioZhybaDtx4gR+8pOfIBgMAgDuuOMOfPKTn8SXv/xlp0IgIiIiIiIiIiKqGY9TDfX29kJRlPxrRVHQ29vrVPNEREREREREREQ15dgTbTfccANuuukmXHvttQCAZ555BjfeeKNTzRMREREREREREdWUYxNtn/rUp3DFFVdgx44dAICHHnoIb3vb25xqnoiIiIiIiIiIqKYc+9VRAEgmkwgGg/jIRz6CadOm4ciRI042T0REREREREREVDOOTbR9/etfxz/90z/hW9/6FgDAMAzcfffdTjVPRERERERERERUU45NtD399NP4x3/8R/h8PgBAa2sr4vG4U80TERERERERERHVlGMTbbIsQxAECIIAAEgkEk41TUREREREREREVHOOLYbwgQ98AGvWrMHg4CD+/d//HU8++SRuvvnmUffRNA233HILdF2HZVl4//vfj1WrVo29cRno7NPR9VYUrWEVcxoVwBjngYysc1Crfp3VjLNWsUpAZ3821mlhFbMjCmBOsE4B6Ema6I3rmBpQ0ewTgcwE66wXlZz7Ss5jNcqMpY7R4hWBgwPZeqaFVcxqUACr4P1cDg29b5tDleREJWU8wJGogZ1d3WgNqpgZkoH0GOsgQAaODZrYH+2DYWaQMEw0+CQoooiBlIGAIsG0LIgeESfjGqYGVSR1E4oswkpbUEQJ/QkdAUWCV/bASGcQTZlo8MmIaQb8igQrnd1/MGki6BXhl0UIABJGGifjOlpCKnyyiB2vdyPolRBURAykdEgeET7FA1HwIKqZiGsmmgIKBlIGQl4JAVmEYWUQ1UykM2k0eBXEdAuxlInWsIpMBjiZ0NHglRFNGWj0y4hpJlRZhFf04ERUQ4NPhmaaaAl7kdAs9CUMRGI6TsZ1BNXsMYmCkO+LpGHCp0hIGWkkdQszI14YVgZH+5OYGlQwxS8BGWAgZeFUQodXFuFTRKiiB9NDEmACpzQLp5IG4rqFc8JeNHsLcnMob/cfPgWP4EFPTMM5DV7MDI7I74KyJTleuD2oQhSArqhWv9dBpffRkf3hF9GTyL4O+WSkkUY6LSCaMhDyykiZJiSPiLg2nK+SR4QsCfBKHuiWBWSy56A5pCKkihhMWeiOamgJq/DJAiwLSBkWBlMmfIqIoCpB9KShmQJ6otn9BlMGwl4ZPtmDk3ENYa+CmGYiZVho9CuwMhkkTsYRUmX0xjQ0BVUYVjY2RfTgVEKHX5EQTRloCqjQDRMNXiV7LjGOcY5jI9HZqVbfSRjT2RlTLUzkOCfy3bgW36vpjOTIRFsmk8EHP/hBHDhwAIFAAJ2dnVi1ahXe8573jLqfoih44oknEAgEYBgGPvzhD+OKK67AhRdeWHnjMvDs3n6s2bwbKSMNr+zB+mUduKotMv6Lol7qrFW9EvDsPps650fGP9kmAC8dHcTdT+7K1/nIykW4bGb4zP9QX8k5cqpMtdoRgWf325SZF8lOtlWSQ5XkRCVlPMBznf24f9NwWw8u78CVcyLZyYizOffGQgZ+dWgQ/XEdcd3Chm178/119/vPxxS/gh++fBBXnT8d67buyb+36uo2bH/9BG56x3l4YMsr+e2rF7choIj4x+cPoC+h58utvPi8ov2/eEM70mlgzebhbWuXtuOHLx/Cm90xrF7cBr8sYsuuY/jLd8/GW/2potg+c80C/OuvD+H2q9qATBrfeG4//uryOUgYsaJyX/iTP0Jcs/CVZ94siv1HOw7jz955Hjb+6hD6Ejruuu589CdMfOWZN/Hhd80qKr96cRum+GUokoi1m/cMtVPcV6sXt+XrWru0HVMCMlb/2++K3p/W4MXxARHTwjJePNBftH8+N5HN2yd+1VnSZ0X5DZTP8XPDeOlI8fbC+OruOqj0fjeiP2Y1+XDHVW35MeKSWQ3403fOyr+e1eTDX18xvySvc7kRUEQ0+GU8+vSbOHQyieveNhXX/NH0opz9fysvQH/CwJd+/np+230fWAifIhWNTbl6/+bK+Yh4Rbx8/FTR+b/z2gVQRQ8e+q/fDl8PS9rx5CuHseztM5A00kU5mcv/+z7wR9DN9NjGOY6NRGenWn0nYUxnZ0y1MJHjnKx96azjyK+OCoKAT3ziE3jPe96De+65B/fcc89pJ9ly+wUCAQCAaZowTTP/q6eV6uzT8xcDAKSMNNZs3o3OPn3sB1JnddYs1v4ydfaPv86epJn/MJ+r8+4nd6EnOdHH5NyvknPkVJlqtXNwwL7MwYGhdirIoUpyopIyR6JG/otsrsz9m3bjSNSouA7Knvd93TH0xvX8F38g21+P/Pcb6DwZxy2XzclPRuTe++r2vbj18rl4YEvx9g3b9qI3rmPFxTOLyo3c3ytJ+QmL3LZ1W/bg41fMy9dzMqHj1svnYn9PvCS2rzzzJpYsmoE1m3bDr8hYsmgGTiZKj6E7quUnKApjX7JoBjZs25uP88u/eAMHeuNYsmhGSfkN2/bi2EAKnSfjZdsprGvdlj0wzEzJ+529cRhWBpoplOyfy81c3tr1WWF+A+Vz/EjUKNleGF+9XQeV3u9G9seSRTOKxohbL59b9HrJohm2eZ3Ljd64jv092XMOALdcNqckZ/f1xPOTbLltvXG9ZGzK1bt28x6E/WrJ+X/06TdxMqEXXw9b9+DWy+eiN66X5GQu//ccHxzzOMexkejsVKvvJIzp7IypFiZynJO1L519HPvV0be97W3YtWsXFi1aNKb9LMvCihUrcPjwYXz4wx/G29/+9rJlRVFAJOIv2tb1VjR/MeSkjDS6BjVcNDMypljqrc5a1VuLOvce6rOts1+z0DYtPK46C4mipyQ3JsvIPK2kP50qU7V2jpcpE9VwYYXtVJITlZTZ2dVtW6Y7puGCGQ01z73xmsycLTeWpoeeYrHrr3QG6Isbtu8lNbPsPrl/OylXLl5m36RuFtWT1E2kM/axCUL2/3HdhCDAttzp9i2MMxd3uWMCULadkXXFddO2jrhuoiuaKZubo/VZYX4D5a+T7ph22vjGch04nbPjGUuB0v4YeS5H9mm5c53bXnjOAfvrYDw511Pm/KRHPE2Wux5Gq6/ce6Od38kYG910r64Gu7G0+P0z63hHw2N1r/GOpU5iTPUbU6HxXhvVzFEn9nXrGODGuM7kmBybaHv11VexZcsWnHPOOfmVRwFgy5Yto+4niiI2bdqEwcFB3H777XjzzTexYMEC27KWlUF/f/EiC61hFV7ZU3RReGUPWsNqSdlK1Uudtap3Wg3qjAz9faORdUZUcULHn68/4q9KPWPR3Byy3T4yTys5R06VqVY700JlyoSyZSrJoUpyopIyrUH7tlqC2bZqnXvj5UTOVpqjQPa87+uOAoBtf3kEYEpAtn3PX6aPPQJgpUcvF/Da7+tTpKJ6/IoEUbCPLZPJ/j8wtI9dudPtm8kUx53OlO8HIPv+aHXmfs7FNLKOgCKhpcx1FFFFAMKofZvLb6D8dVLu2iiMbyzXQa1ytppjKVC+P3Kvy/VpudzInfNcLttdB+PJueYy58cz4sH+3PUwWn2Sx/690c7vZIyNk3GvroaxjKWF6vV4x4PHOrnK5Sgw/rHUSYypfmMqdLpro9r3eztO7OvGMQBwZ1z1FtNoY+lIjq06+p3vfAdPP/00nnjiCTz22GP5/3IGBgZG3T8cDuPSSy/FL3/5yzG1O6dRwfplHfDK2UPN/S71nEZl7AdRZ3XWqt7ZkTJ1RsZfZ7NPxCMrFxXV+cjKRWj2OTYXPGkqOUdOlalWO7Ma7MvMbsiWqSSHKsmJSsrMDMl4cHlxWw8u78C5IbniOih73ue1BNEUULB6cVtRf939/vMxpymAf3mpE2uXtBe9t+rqNjzx4gE8sLR4++rFbZgaUPCTV44WlRu5f8owsX5Z8ba1S9vxTy/sz9fT5FfwxIsHMLc5UBLbZ65ZgK27jmH98g4kdANbXj2GKf7SY2gOqfjMNQtKYt+66xhWL27Lx3nXdedj7tQAtrx6rKT86sVtmNHgxZymQNl2Cutau7Q9+8f0R7w/Z2oAsihAlTIl++dyM5e3dn1WmN9A+RyfGZJLthfGV2/XQaX3u5H9seXVY0VjxBMvHih6veXVY7Z5ncuNqQEF85oD2LrrGADgX17qLMnZec0B3PeBhUXbmgJKydiUq3fdsnYMJrSS83/ntQvQ5FeKr4cl7dj44gE0BZSSnMzl/9umh8c8znFsJDo71eo7CWM6O2OqhYkc52TtS2cfIZPJuOJP2t5444146qmniradOnUKkiQhHA4jlUrhr/7qr/B//s//wVVXXWVbh2FY9rOPtVwh1O111qreghUjW8NqdoKkqquOKtkP81XKTjc90Wabp06tKFpJmWq1k1t1NKqhNaRmJ9nKrDpaNocqyYlKygytOtod09ASVLOTEOkx1uGwyXyibbSx9NigiZRpwbAySOoWwj4JiuixXXW0KaAiZZhQJBHpTBqyKKI/YcCviPDJxauOxjUDXllCOpOGx+NBNGkiqA6tOipkVx09FdfRPLTq6NH+JIJqbtVRA5LHU37VUVVGQPEUrDqaQYNXzq46qploCaoAgFMJHeGCVUfjWnbFVK/oQVdUQ9gnQzMttIRUJPShVUd98hhWHVVhWMCx/iSaggqmDE1YZFcdNeCVPWVXHU3oFqaH1OLcHMrbQd2ER/CgN6ZhWthbmt8FZUtyfMR20SMMrTo69uvA6Sfaxj2WAqX94Zfyq44GvTIgpGGlBcQ0A0G1dNXRuGZCFD3ZCVHRAyNdsOpoUEXIm111NLeaqE8ZWnXUtBBNWvAqHgQVCaKYgWYiu19AxaBWuOpoNh/jmomkkUajX0Y6k0FcNxFUZZyKaZgSUGGkLUgeT3bV0aQOvywhljLRGFBgGCYavHJ+cmzM45zDY6Mb/2W7EmMeS4fU6/GOB491co32FMaExlInMab6jWnIeJ9oq3qO1nhfN44BgDvjqreYxvJEm2sm2m644Qb89Kc/Ldr2+uuv4/Of/zwsy0Imk8H111+PO+64o2wdZ8oHmnqJE6ifWF0/0TakXvozh/HWjisn2oa4oR/dEINb4nBDDLWM42wYS8eCx+Y+9TyWOoXHOrnGPNE2xI3HwpgqU48xnSn3ezfGBLgzrnqLaSwTba55/t9uNdGFCxeWTL4REREREdUzQRDw4M9ew6r3zIZL/s2biIiIqsSxv9FGRERERERZsdRE/+YGERERuZFrJtr4r3lERERERERERFTPav6ro/39/aO+H4lEAADf+973ah0KERERERERERFRzdR8om3FihUQBMH2iTVBELBt2zYAwxNuRERERERERERE9ajmE23bt2+vdRNERERERERERESTztFVRwcGBnDo0CFompbf9s53vtPJEIiIiIiIiIiIiGrCsYm2//iP/8DGjRtx4sQJLFy4EK+++iouvPBCbNy40akQiIiIiIiIiIiIasaxVUc3btyIH//4xzjnnHPw/e9/H0899RTC4bBTzRMREREREREREdWUYxNtiqJAVVUAgK7rmDdvHjo7O51qnoiIiIiIiIiIqKYc+9XRadOmYXBwENdccw1uu+02hMNhnHPOOU41T0REREREREREVFOOTbR94xvfAAB8+tOfxqWXXopoNIo//uM/dqp5IiIiIiIiIiKimqr5RFssFkMwGER/f39+24IFCwAAiUQCiqLUOgQiIiIiIiIiIqKaq/lE22c/+1k8/vjjWLFiBQRBQCaTKfr/tm3bah0CERERERERERFRzdV8ou3xxx8HAGzfvr3WTREREREREREREU0ax1YdffrppxGNRvOvBwcH8cwzzzjVPBERERERERERUU05NtH29a9/HaFQKP86HA7j61//ulPNExERERERERER1ZRjE23pdLpkm2VZTjVPRERERERERERUU45NtHV0dOChhx7C4cOHcfjwYTz00ENob293qnkiIiIiIiIiIqKacmyi7f7774csy/jbv/1bfOYzn4GqqlizZo1TzRMREREREREREdVUzVcdzfH7/bjrrruQSCTg9/udapaIiIiIiIiIiMgRjj3R9sorr+CDH/wgPvjBDwIAXn/9dTzwwANONU9ERERERERERFRTjk20PfTQQ/jOd76DSCQCAFi4cCF27Ngx6j7Hjx/HX/7lX+KDH/wg/uRP/gRPPPGEA5ESERERERERERGNnWO/OgoA06dPL3rt8Yw+zyeKIj7/+c+jvb0dsVgMK1euxHve8x7Mnz9/bA3LQGefjq63omgNq5jTqADGWKMvU+egVv06qxlnPcUqAZ392TinhVXMjiiAOcE660Ul/VnJeaxGmbHUMZF4q3U8leTN6eL1AEeiBrqiKUwLeTEzJAOlCyWfngD0JE30xnVMDaho9olAZhz1uJUMnIia2He4DyfjOpqDChp8EvoSJk7GdUwdeh1LWeiKamgOqQDS8AgeDCYMNPhkWMigL24gpEqQRAHpTAaqJCJlmAh7FehmGlHdRMqw0OiXoYge9CUNBBQJ0ZSBRr8CK5PJn0ufIiChZdAV1XBuoxdmGjg+kEJrWEVmqG3NSMMjZOCVJWimBTMNxDUTU4MKNNOE6BEhi4CVFvK55pdF9CcNJHQTQa8EDwQAgF8VoRlpdEc1TA2pMAYSEOBBUM3WrZkZxFImWkIqvIoHAwkD0ZQJvyIioGaPQRAEBFUJVsaCABG9MQ1TgwrSaaA/ZSDik3EypqE17AUAHOvPHY8F0xSgyh4Mpgw0+VUAGew+2YupQQWmlcGgZiKpW5gWVhFSPOiO6lBlCVbagiB40BPTEfZKCPskpAwLCd2CT87GNT3sRbO3znN2xLV+TkTEgR4dKcNCxC9DEICIKsEwM+iN65jdrOLEoIXemIbWkBdGOo2+uIFzIioMC9BME7Io5vPCJ4uI6zo8ENGXNNDokzE4lJenEjqmhVWkjDS6BjVMDSkIeyVEUyZ6YjpaQyoMy4IsiTDTaUSTJsI+GSnDREDJ1uNXRaiiiOMDKUwJKPArIpDJwEinIXtERDUTWm8cU/wKYrqFWMpEa1hF0OvBqbiJ3lj2OgwqEiQpg8FkGj1RDdMbvMhkgJ6ohilBBdGUgaAqQzdNhLwyNDONvoSB5qAKzTSHrsk0koaFc86EvCCisanVdxLGxJjc8F10It+NJ2tfqjuOTbRNnz4dr7zyCgRBgGEY2LhxI+bNmzfqPi0tLWhpaQEABINBzJ07F11dXWObaJOBZ/f2Y83m3UgZaXhlD9Yv68BVbZHxJ3a91FlPsUrAs/ts6pwfOfMn2yrpT6fK1Fs7leTN6erxAM919uP+TcPvP7i8A1fOiYxtsk0AXjo6iLuf3JWv55GVi3DZzPCZ8QVVBl45FsOpuIF1W/YgZaQxq8mH26+cjzWb9xT0bTu+8dw+HDqZxKwmH/7myvlYW/D+6sVt2PirQ+hL6Fi9uA0BRQQANPhlRDULB3ri2LBtb778fR9YiJBPxqf+5ZWSOhRJwO1XtmHN5t1o9Cu49d2zivZ9cHkHkrqJza8ewy2XzkY0FUdct4rKPLC0Hfu6+rFgWqToONYta8d/7DiMHYcG4JU9uPPaBYj4JGQgFB3P2iXtePaN4/jABTNwYiCVr7vcsftlEf/8Yic+9p458KsSvvDT3VjQEsSfXzoLjz2/D396yXn46va9tv21blk7tr12HBed14Qmv4x93XE8+vSbaPQr+NT75pYc2xdv6IDiATa+9CZuesd5eGBLcSxNARl9CRNfeebNMyNnba/1djzz2nH84g+9Rfl0709+jzsXz8WJqK/ovK9e3Iaf//44PnDBdPz28Eks/qPpRefw6x++CCdjOr75XOm5uu8DCxHTTNz7k9+XzYF1y9qRTqexbutr+W2rrm7Dj3YcxoffNQv/+utD+LN3npc/52uXtmOKPzuJa2UE/OClg1i6aAbe7Irlz/UlsxrwoUvOK87Lpe2YEpDx+rE+/NvO4yXXRq7NP73kPPxox+GiNh9ecQG6oxoeffoMyQsiGptafSdhTIzJDd9F63FfqkuO/eroAw88gB/84Afo6urCH//xH+O1114b06qjR48exWuvvYa3v/3tY2q3s0/PJzQApIw01mzejc4+fUz11GOd9RRrZ3+ZOvsndvz1oJL+dKpM3bVTQd6crp4jUSM/yZZ7//5Nu3EkOra7Xk/SzE+y5eq5+8ld6EmeGTPFnX06DDOTn2QDgCWLZuQnKYBc3+7BkkUz8u+vHfH+hm17seLimfmfe+M6euM69vfEYZiZ/GRArvyXfv46OnvjtnVk28+euxUXzyzZ9/5Nu9Eb13Hr5XPReTKO3rheUuaBLXtwTXvpcazdvAe3Xj43//rRp9+EX5FLjmfd1j245bI56OyNF9Vd7thPJnQsWTQDvXEdX/hpNvaPXzEP67Zk+y03cWPXX2s3Z9vasG0v/KqcnwhZcfFM22P7wk93IxJQcevlc/OTbIX1+hU5P8mW217POWt/rWf7LPe6MJ8uPG9qyXnfsG0vPn7FPGzYthe3XDan5BwqogdrN9ufq5G5apcDazfvwfFBrWjbV7fvxZJFM/CVZ97EkkUzis75ui17YFhAxK+iszeOWy+fi5OJ4nN96+VzS/Nyyx4YZgaXzW+1vTZybeb+X9jmgd54Prdy5es5L4hobGr1nYQxMaZqxTSROutxX6pPjj3RNmXKFPz93//9uPaNx+NYtWoV7rvvPgSDwbLlRFFAJFK8omnXW9F8QuekjOyvdVw0MzKueOqlzlrVWy91FhJFT0luTJaReVrJsTtV5kxrp5IyO7u6bd/vjmm4YEYDKrX3UJ9tPf2ahbZp4YrryZnMnC03liY0s+gYBQG2xywIlb2fMtJIFzwhEx9Rv10ZuzpGayudAZKama/DrkxPNGW7PambRa/LxdcfN5DOoKK+SWey7xWWTw7VW0l/9SeMklhG1le4b1/CQEq3bN8rezx1krOVjqX9CaPodS4Xusqd96F+6YsbJe+fGto22vnNqaRMbluubOH/c+/FdRNWJlOUy4X1Jsucx7huIhPNjJpXdm2Wy6Xx5sVYuOleXQ12Y2khAUBDg8+5gCbRmXZuR1Nvxzqez6VOY0xnd0zVzFEn9rUbA9xwbtw4Np3JMTk20XbkyBH8f//f/4ff/e53EAQBF154Ie677z6ce+65o+5nGAZWrVqFpUuX4rrrrhu1rGVl0N+fKNrWGlbhlT1Fie2VPWgNqyVlK1UvddZTrNNqdPw5kYi/KvWMRXNzyHb7yDytpD+dKlNv7VSSN6eNJWj/fktwbLkXUSXbeiKqOK7ccyJnK81RINuP3VHYHuPI15mCiYTR3vfKHniE4bIBr30fFpYprEMQiusvt69flSAK5eNpCXltt/sUqeh1ufgiARlib2V94xEAKw1InuH3/UO5U0l/RfyybSyiYL9vo1/GgCDYvlf2eFyWsxMdSyN+ueh1Lp9aw/bnPXc+pgTkkvdz23JlK8nVSvN55P9z7wWU7N/UK8zlwnr9ZcaegCKhOaTija5Y2byya7NcLo03L8ZiMu7V1TCWsTRHEARkAAwMJJHJnPm/k1uv53Y83His5XIUGN/nUqcxprMjpmp+d6pGPOPd124McMO5cePYVG8xjTaWjuTYr45+9rOfxfXXX4//+Z//wS9/+Utcf/31uPPOO0fdJ5PJ4O/+7u8wd+5c3HbbbeNqd06jgvXLOoo+GK9f1pH944PjVC911lOssyNl6oxM7PjrQSX96VSZemunkrw5XT0zQzIeXF78/oPLO3BuaPjLeSWafSIeWbmoqJ5HVi5Cs8/RNWdqZk6jAlkUsHZpe/4Yt7x6DOuXtY/o23Zs3XUs//66Ee+vXtyGn7xyNP/z1ICCqQEF85oDkEUBqxe3FZW/7wMLMWdqwLaObPvZc/fkzqMl+z64vANTAwqeePEAZjcF0BRQSso8sLQdT+8pPY51y9qx8cUD+dd3XrsACc0oOZ61S9rxg5c6MXtqoKjucsfe5FewddcxNAUUfPGGbOzffmE/1i5tx5ZXj2HV1W1l+2vdsmxbqxe3IZEycOe1C/LHbndsX7yhA/1xDU+8eAAPLC2NJaEZ+Mw1C86YnLW/1rN9lntdmE+/O9Rbct5XL27Dt1/Yj9WL2/AvL3WWnEPdSmPdMvtzNTJX7XJg3bJ2TB/6sJ3bturqNmzddQyfuWYBtu46VnTO1y5thywC/QkNc6YG8MSLBzDFX3yun3jxQGleLm2HLAl4aV+X7bWRazP3/8I250wN5HMrV76e84KIxqZW30kYE2OqVkwTqbMe96X6JGQc+me0pUuXYsuWLUXbli1bhs2bN5fdZ8eOHbjllluwYMGC/Aqld955J973vvfZljcMy372sZarbrq9znqKtWD1yNawmp0sqdKfhHHTE222eerUiqKVlKm3dirJm9PVU7DqaGvIm51kS2PsilYdVbJfTMc5wk7mE22jjaUnoiZiunXaVUe7oxqmBlVASMOD7CqZYa+MNDLojxsIqBLk3KqjooiUObzqaEw3kdLTiAQkyB4P+lPZVUdjKQMNPgVpZNA1qKElpMI/tOpod1TDjIgXVgY4MZhCS1BFRrBfddRKZ39NdUpAgW5Z8AgeqBJg5lYdDanwK7lVRy0EVBHi0GNIflmEZqbzx2daFjKCgJAiQbOyq47GNRPNweyqo4NJA9GkBa+SXZnUbtXRkzEdTUEZ6TQwkMquznoyrqEl5IWAoVVHQ9njMczsh7NoykSjX4GADE4lDDQFFVhDq46m9DRaQgrCqs2qo3EdIVVC2Jvti6JVR0OqK3N2ImPp8KqjaUT8EgQAEa/9qqMtIS+sdBqn4gamN6gw04BumpBEEd1D+eZThlcd7U9mV4gdTBmI+BX0J3W0BFVoZhpdUQ1NAQUNXglRLbsaaEvBqqNWOoPBpInQ0HkIKBKimgGfLEKVClYdlUUAGRjpDGQxe951y0Kjb3jV0ZaQipBveNXRpoCSXdVXymAwkUZvXMO0kBcZAD0xDVMCCqKagaCSXXU0qMrQreyv2E4NqtBNE8rQqqMpw5pwXoyFG/9luxJjHkuRfaLtS9v24b7F8/lE2xnGjcc62lMY4/5c6jTGdMbHVPXvThOMZ7z7lh0DJvncuHFsqreYxvJEm2MTbY888ggaGhrwwQ9+EIIg4Gc/+xkGBwfxsY99DAAQiUQm3MZoH2iybbjvRNqplziB+onV9RNtQ+qlP3MYb+24cqJtiBv60Q0xuCUON8RQyzjOhrF0LHhs7sOJttOr13M7Hm481jFPtA1x47EwpsrUY0xnyv3ejTEB7oyr3mIay0SbY78H8POf/xwA8G//9m9F2//zP/8TgiBg27ZtToVCRERERERERERUdY5NtG3fvt2ppoiIiIiIiIiIiBzn2GIIP//5zxGLxQAA3/zmN3HHHXfgD3/4g1PNExERERERERER1ZRjE23f/OY3EQwGsWPHDvzqV7/CTTfdhLVr1zrVPBERERERERERUU05NtEmiiIA4Pnnn8fNN9+MK6+8EoYx2UugEBERERERERERVYdjE22tra1Ys2YNfvazn+F973sfdF1HOp12qnkiIiIiIiIiIqKacmyi7R/+4R/w3ve+F9/5zncQDofR39+Pz33uc/n3BwYGnAqFiIiIiIiIiIio6hybaPP5fLjuuuswe/ZsAEBLSwve+9735t//6Ec/6lQoREREREREREREVefYRNvpZDKZyQ6BiIiIiOiMJAgCBEGY7DCIiIjOeK6ZaOONn4iIiIio+gRBwPpfvIH1v3iDn7mJiIhqTJrsAIiIiIiIqLaiSWOyQyAiIjor1PyJtiNHjlRUjr86SkRERERERERE9azmE22rV68GAHzkIx8Ztdz3vve9WodCRERERERERERUMzX/1dF0Oo3HHnsMBw8exHe/+92S92+77TYAQCQSqXUoRERERERERERENVPzJ9oeffRReDweWJaFeDxe8h8REREREREREdGZoOZPtM2dOxef+MQncP755+N973tfrZsjIiIiIiIiIiKaFI6tOnrxxRfjoYcewm9+8xsAwLve9S7cfvvtCIVCToVARERERERERERUMzX/1dGc++67D4FAABs2bMCGDRsQDAZx7733OtU8ERERERERERFRTTn2RNvhw4fxta99Lf/6jjvuwPLly51qnoiIiIiIiIiIqKYce6LN6/Vix44d+dc7d+6E1+t1qnkiIiIiIiIiIqKacuyJtnXr1uFzn/scYrEYACAcDuPhhx92qnkiIiIiIiIiIqKacmyibeHChdi8eXN+oi0YDBa9/9RTT+HGG28s2nbvvffiueeeQ1NTE7Zu3epUqERERERERERERGPm2ERbzsgJtpyNGzeWTLStWLECf/EXf4F77rlnYo3KQGefjq63omgNq5jTqADGxKrM1zmoVb/OasZZT7HWS5wC0JM00RvXMTWgotknAhkH4qykf6pRZix1TCRep46nknir1Y4HOBI10BVNYVrIi5khGUiPsczQ+zu7utEaVO3rqCQHK4llrIb6IH4qgYAioTeqoTmkQhEFQABMC0gYFhK6iaAqwSd7IEBAT0xDg0/BQMpAxCcjqIqI6yZSRga6aSHklXEypqEpqEKVgHRaQMq0YFhp+GQJJ+M6AqoEVRageDywMhYOxnQMJA2EfTI8AiCLAjQzg5MxHSGvhAa/BMPMoGtQQ9grwaeISJkmJI+I/oSBKX4ZpxI6vLKIoCohaRjwCCKiKQONfgVWxoLkEdEb0+FXsmVkj4C+pIGEbqE5qKIvqSMS1wEAhmVB9ojoiekIeiWEvRLS6Qy6ohoifhlx3UTEq0ASgcGUBSANVZLQPajhvCk+aGYaJwY1TAurUCUPDp1KojWsIpOxIAgi+hIGmoMKPAJwfEDD1JAMZAT0xjQ0xXT0JQw0+mX4FQ+O9adw7hQv4qlsna1hFS0hESkD0MwMBlMmYikTfzTdh+6oVZTTtjlekPuzmnzQjOF6q3afqpYR1/p5jSJ+cyiB1rCKdNpCWFXQEABODKZxKqZjeoMXcd1CdzR7PD4ZUCQRsVQaKdOET5YQ00wkdAsRvwyfnM3dWMqEX5Hgk0WcSuho9MvQTBOqJOFU3IBX9iCgSPAIGHpfQUK3oFsWwl4Zp+I6AoqEoFeCYWVwKq4j4s9ubwpkz/NAykBQldGf0OFTJDT4JJhWBntO9cKvZHOnJazCJ3tgWhl4BMBKA3HdQkwz0RxSoJsWJMkDZAT0RDW0hFRkkIYqSpA9QG9MR8grI2maED0exFImpoe9aPaJOJWycCppIK5bOCfsRbPXZpwZORb5RfQkRoxNqME9k4hqq1bfSRgTY3LDd7yJfOecyL4S0Nmf3XdaWMXsiAKYFe5LlZnI9/Shffce6kNElSb8ecXxibZyMpnSo3jnO9+Jo0ePTqxiGXh2bz/WbN6NlJGGV/Zg/bIOXNUWGf8FXi911lOs9VKnALx0dBB3P7krX+cjKxfhspnh8V+IlcTpVJkzrR0nY/EAz3X24/5Nw2UeXN6BK+dEhie4TlemkjoqycFK6hmroT749x2HsPLi87Bu6ytFdYd9Eg6dTODRp9/Mb1+9uA0BRcQ/Pn8AfQkdq65uw492HMbtV7bBKwN///Re/Okl5+Gr2/fm9/l/KxdhMGXg2788UPLe6sVtCHklhH0y/v4Xb+DQyWR2n5sugG5m8IWf7i4p+41n96MvoWP14jacE/Hi+786iGvfNh2rnhmO885rF6A5qOLep7LHNKvJh9uvnI81m/cU1TetwYt/eObNfLt3XXc+vv/SQdy/5G3oixtF/b16cRsiPglf3b4/f+zbXz+Ble84D0/uPJzvwwUtQfz5pbOwbstwW2uXtuOHLx/Cm90xrF3SjidfOYyli2bg0Mk4Hn36TTT6Fdz67lnYsG24b4b7dj5ee6sfBxr8Re+vX9aOt50TwIv7+7Bh217cuXguoimj6BjXL+uAJJi488d/KMrxXO7btVuV+1S12F6n7bhghh83/uOvsXZJO559oxPXtZ+DL/x0N949Zwquv2A61hb0wYPLO+CVPXjixU7cculsnBgczB9vubzY+KtD6EvoeGBpO/7x+T35/Mjl/493HsUHLpiOf/vNYducbgrI6EuY+EpBTq5d2g7JA/zND35bVHZGREVcz2DdlleKjrE5rCCWsnC0L1l0ftYta4eADNZs/kPRMZqWhXVbXxtub0k7HnthXz72R25ahOMDqaLruWScGTEWzWry4Y6r2oqug0dWLoIiebD6R7+r3j2TiGqrVt9JGBNjcsN3vMnaVwKe3Wez7/wIJ9uqZSLf02vwHd+xxRBORxCEmtTb2afnExoAUkYaazbvRmeffsbXWU+x1kudPUkzfwHm6rz7yV3oSY5/hKwkTqfKnGntOBnLkejwREuuzP2bduNI1Ki4TCV1VJKDldQzVrk+uPXyuVi3dU9J3YaZyX8pz23fsG0veuM6Vlw8Eykjja9u34sli2ZgzebdiPhVLFk0Iz/pkNtnX08MX/zP12zf27BtL7qjGvZ1x7Bk0Yzhfbrj+Um2kWVzbW/Ythf7e+K49fK5+QmNXNlHn34TnSfj+W3ZGPeU1NfZGy9q98u/eANLFs2AInpK+nvDtr04PqgVHfutl8/Fui17ivrw41fMy0+y5fZdt2UPPn7FvOzPW7PlTyb0fP+uuHhmfjIlt89w3+7B+y+YUfL+ms17kNCQ337heVNLjnHN5t2YOSVc9Low9+3arcZ9qlrsr9M9iKaEfF/ectmcfK589L1z8pNsufL3b9qNvd0x3Hr5XHSejBcdb7m8yJ3jB7bsKcqPXP5//Ip52LBtb9mc9itySU6u27IHPlkqKRvxqyX5smbzHvhlGXu7YyXnZ+3mPfArcskxHh/UitvbWhz73u5YyfU8cpwZORYtWTSj5Dq4+8ld2HN8sKr3TCKqrVp9J2FMjKlaMU2kzknbt7/Mvv3u+Ax1JpjI9/RafMd39RNtYyWKAiIRf9G2rrei+Q7LSRlpdA1quGhmZFzt1Eudtar3bK5z76E+2zr7NQtt08IV1TEyTyuJ06kyZ1o7Tsays6vbtkx3TMMFMxoqKlNJHZXkYCX1jGa0sTSpmbZ1Z38VtHR7OgPk/h0lZaQhCNn/9yWM/M+F0pnicnb1AcN1Fu5zurbTGZSNP11wCxqt7cJ2c3GeihsVtZ8c6qPCGMrFk9TNop8Lj7FcfLntPdGUfb4WbO8apUzR64IJmXLt5q4DUfSU5E0tVTyWDh1Tykijr+Bc9Y1y3pKaWZJXo/X7yJ9H1jVaTsdHuaZGbiuXa93RVNnrwK6e9IiPXCNjL1dX4Tgzciw63TVbrp5CTudQrdmNpYUEAA0NPufikbK/yutkm/m2z7BzO5p6O9bxfC51GmM6u2OqZo46sa/dGOCGc+PGsamaMU3ke3o1vuOP5JqJtosvvnjCdVhWBv39iaJtrWEVXtlT1HFe2YPWsFpStlL1Umc9xVovdUZUybbOiCqW1NncHLKtY2SeVhKnU2XOtHYcjSVoX6YlWHmZSuqoJAcrqQeoPEcL+8Bfpv2AYr8993ejcq8zmez/G/1yflvhPqKQ3Wb3Xq4+YLjOwn1O17ZHQNn4PSMeqj5dfYXHMyUgV9S+f6iPCmMoF49PkYp+HnmMdvvk+rY55C2Tr8PbC38uKhPyjthHPW27uesgEvFP6H5VzoTH0qFj8soeNBacq9HOm18t7fPc+3b9PvLnkXWNltMBb/lrqpBX9pSNuSXkxb7uWMX12OV7Yezljr1wnCk3FlVybdndMwHULIdqbSxjaY4gCMgAGBhIVuUfm09HEARYpgU42Gahej234+HGYy2Xo8D4Ppc6jTGdHTFV87tTNeIZ7752Y8A0F5wbN45N1YxpLN/Tx7vvaGPpSI796mg0GsWXvvQlrFixAitWrMDDDz+MaDSaf3/NmjU1aXdOo4L1yzqKPuSuX9aR/cOFZ3id9RRrvdTZ7BPxyMpFRXU+snIRmn3jn7OuJE6nypxp7TgZy8yQnP/7TrkyDy7vwLkhueIyldRRSQ5WUs9Y5frgiRcPYO2S9pK6ZUnAndcuKNq+enEbpgYU/OSVo/DK2b8jtnXXMaxf1oH+hIYtrx7DqqvbivaZ1xzEF/7kj2zfW724DS0hFfNbgti669jwPi0BfPGGDtuyubZXL27DvOYAnnjxAD5zTXGcd167AHOaAvltW149hvXL2kvqmzM1UNTuXdedj627jkG30iX9vXpxG6aH1aJjf+LFA1i7tL2oD7/9wn6sXVrc1tql7finF/Znf17Sjo0vHsAUv5Lv3yd3HsXqxcV9M9y37fjv3x8reX/9snb4FeS3/+5Qb8kxrl/WgaOnBoteF+a+XbvVuE9Vi/112o6QN5Pvyx+81JnPle/+TyfWLSvN5baWIJ548QBmNwWKjrdcXuTO8QNL24vyI5f/335hP1Yvbiub0wnNKMnJtUvbkTTMkrL9Ca0kX9Yva0fCMDC/JVhyftYta0dCN0qOcfrQB/58e0uKY5/fEiy5nkeOMyPHoi2vHiu5Dh5ZuQjt08NVvWcSUW3V6jsJY2JM1YppInVO1r6zI2X2jbjjM9SZYCLf02vxHV/IOPRPWp/+9KfR1taWX1l006ZNeP311/H1r3+97D533nknfv3rX6Ovrw9NTU349Kc/jQ996ENlyxuGZT9bWcvVLN1eZz3FWi91Fq1momQvQJurqNyMt22eTsYKnJO9GuiZeMwFK322hrzZia30GMsMvd8d09ASVO3rqCQHK4hlTDla0Adx3URAltAb19AcLF11NKlZCHjFglVHdTT45Oyqo14ZAa+IhG4hZaRhWBaCavGqo1ZagDa06qhXlrIrNKoSVEmA7PEgjTTSaQEDSQMNPhmC3aqjvuxqjl2DGkJeCf7cqqOCiP6UgUafjL5EdgIiu+qoCY/gQTRlotEvI52xIJZZdTSpW2gKquhP6mjwZts30hZkoXTV0e6ohrBPRtIwEfYqkEUgmrKyKz9KErqjGs5rLF119HBfMrtCZMaCABF9yYJVRwc1TA0OrToa19AUUNGfMBAZWnX0rX4NM6eow6uOhlS0hKuz6uh5U3zQTftVR51+oq2SsfS8RhE7DifQElSRzlgIqzIaAkLJqqM90dwKnsOrjmqmCe/QqqNJPY0Gv1Sw6qgFvyLCK4voy686akGVxFFXHc3le1/CgF8REcqtOprQEfHJ6IvrmFKy6qgBnyIi7JVgpTOIpgz4lGzutARV+BQPDCsDMbfqqDG06mhAgW5ZkEQPgOJVRxVRgjK06mjQm10x1ZNbdTSkotkv5VcdTehWdpvdODNyLPJLBauOKvkPqJXcMwF3/mt7JcY8liL7hNmXtu3DfYvnO/ZE292bdgMAHlnewSfaasiNxzraUxjj/lzqNMZ0xsdU9e9OE4xnvPuWHQMKVh1tDavZSTYH/2SpG8emqsdU4ff00fbt1yxEVNF237E80ebYRNvy5cuxadOm026biNE+0ADuTC479RInUD+xTkac4/ngXS/9mcN4a8eJWMeTo4A7+tENMbglDjfEUMs4zoaxdCx4bO7DibbTq9dzOx5uPNYxT7QNceOxMKbK1GNMZ8r93o0xAe6Mq95icuWvjnq9XuzYsSP/eufOnfB6vaPsQUREREREREREVD8c+yMZDzzwAO655x7EYjEAQDgcxsMPP+xU80RERERERERERDXl2ETbvHnz8PGPfxyHDx9GNBpFKBTCM888g4ULFzoVAhERERERERERUc04NtH2qU99CuFwGG9729vg9/udapaIiIiIiIiIiMgRjk20dXV14Tvf+Y5TzRERERERERERETnKscUQLrroIrzxxhtONUdEREREREREROQox55o27lzJ5566inMmDEDiqLkt2/ZssWpEIiIiIiIiIiIiGrGsYm2b3/72041RURERERERERE5DjHJtpmzJjhVFNERERERERERESOc+xvtBEREREREREREZ3JONFGRERERERERERUBZxoIyIiIiIiIiIiqgJOtBERERERUU0IgoAHf/YaBEGY7FCIiIgcwYk2IiIiIiKqmVjKnOwQiIiIHMOJNiIiIiIiIiIioirgRBsREREREREREVEVcKKNiIiIiIiIiIioCjjRRkREREREREREVAWcaCMiIiIiIiIiIqoCTrQRERERERERERFVASfaiIiIiIiIiIiIqsD1E20vvPAC3v/+9+Paa6/Ft771rckOh4iIiIiIiIiIyJY02QGMxrIsrF+/Ht/97nfR2tqKm266CVdffTXmz58/topkoLNPR9dbUbSGVcxpVABjgsHl6hzUql9nNeOsp1jrJc5aqCTOSvqnGmXGUsdE4nXqeCqJt5I6JKCzP1tmWljF7IgCmCPKeIAjUQNd0RSmhbyYGZKB9Dj7ZbS+FYCepIneuI6pARXNPhHIjKPMWA3F1nM8iqlBFSnDRMSvIGlYOBnT0RJSoUge9Cd0+BQJ3YMazol4gQxwbCCFGREvBABdUQ1TAgqADDJIQ4CI7kENTSEFQUVEb1yHX5EQ8YmIpiykjDRimomQKsGriMhkLHTGsn3YElYRkEWcimvwKhJ6YxqmBlUkdAMBRYYsCuiOavDKIgKKCL8ioj9p4lQ8G68kAjEtDd2yEFJlxFIGwj4ZppVBwjARUmX0pww0eGVEUwZCQ/9XJBFhr4Su7kFEUxZODtWnShlohoCuaPb8ZmAhk/FAlQRYFpAwTDR4FQxqJmIpE80hBWGfiFNxM9+HquzBYNIEhDRkj4SemIbWkIrWBhFdAxa6o0PHaGSPscGyMJCw0BXN5qYienC0P4nmkAq14OepAREDyTSimomEbmFaWIUgCDg+kIJfERHxyYCQwbE+DUFVQtgrIagKGEyZMNICugY1zJ3qR1y3Khun7XIdw9tmNnqRTgNvDaRqMuZPC6vwqyIO9ibQElJhpi0YFtAUyOZsXDfhkyWcjGmYHvHCAwEp00IsZSLsk2FlLIiCCDNtQfFIiBsWkroJvyIhpIrQzAx6YhpaQirSSCOdFjCQNDA1qCCumVAkEcGhcl2DKUwJKPDJHkgeASkjjRND/aJIAgaTFpKGhalBBQnDhCKK6IlqQ9cUMJC00BRQkNAtnHgripawClUU4FM8GEha6B7UMDWkQPIAHsGDlGFCErM5P5DU4ZUlRDUTKcPC1IACAQKOD6ZwToMXfsWDU3EDMc1Cg19GTDMwxa8gpHrQEzfRG9MxNaigOSBhMJXGicFRxrfR1GJMckK9xk00EW78/MyY6jcmETg4oKPreBTTQipmNSiANcE6J3KcE/nOOVn7UmVcdM929UTbrl27MGvWLJx77rkAgD/5kz/Btm3bxjbRJgPP7u3Hms27kTLS8MoerF/WgavaIuNP7Hqps55irZc6a6GSOJ0qc6a1U61YJODZfTZl5keGJ9s8wHOd/bh/03CZB5d34Mo5keEvo9WIRQBeOjqIu5/clS/zyMpFuGxmePhGUkmZsbKJ7a7rzkdT0MC9P/l9ftsDS9sR9kr45L+8kt+2enEbfv774/jABdOxYdve/Pa1S/4IkigW9dnape2I+CR8Y/te3Pru2ehPGPjSz1/Pv//56xdiSkDG554cbvP/rrwAp+IG/u9/DZdbdXUbfrTjMP7snedh468OoS+hY/2ytwHw5I9hVpMPf3PlfHzzuX3400vOw1e3D8d257ULoIoe/POLfyh5L1f3x94zBz5FKop//bJ2fOO5fTh0Mgmv7MG6Ze3Y9tpxLP6j6dj22nEsefsM7HkrWtQP65d14BvP7c3vs35ZO5557TiuOn861m19JR/r7Ve2FfV/Lo7br2wr2n/14rb8Ma9d2o4fvnwIb3bHsH55B5BJY83mPxQd53f/92C+7GPP7yuqZ0ajD4oo4G9/9CrePWcKrr9gOtZu3nP6MbVMLjf4Jaz+t9+h0a/g1nfPKumHao/5hce/dmk7fnuoF++YPRX/seMwVl58XlH//s2V8/PHNqvJh09f1YZf/OEt/MmiGegajOLRp99EykjjklkN+NAl5xX1w7pl7fhmwXl/YGk7frzzMD50yXlF29cubUejX8JDP38dh04mS9q1q2v9sg4c74tiWmOwqNwjNy1CykiXXD+ikIEqi/jPXcdwzR9Nx/SIih2H+vLxF16TN71jJuK6VXQePnPNAvzrr/+QvzbKxVkyvo2mFmOSE+o1bqKJcOPnZ8ZUvzGJwLP7bWKaFxn/ZNtEjrMe96XKuOye7epfHe3q6sK0adPyr1tbW9HV1TWmOjr79HxCA0DKSGPN5t3o7NPHHVe91FlPsdZLnbVQSZxOlTnT2qlaLP1lyvQPlzkSNfJfeHNl7t+0G0eiw3fPasTSkzTzN5Bcmbuf3IWepDmmMmNlF9uXf/EGOnvjRdse2LIH6QyKtm3Ythcfv2Je/st8bvvxQa2kz9Zt2QMrDdx6+Vzs64nnJ9ly7z/8X69jX09xm/t74vlJtty2r27fiyWLZmDDtr1YcfFMpIw0/IpcdAxLFs3A2s17sGTRjPxEWm7/R59+EycTuu17ubp743pJ/GuG6su9Xrt5D265bE7+//u64yX9sGbz7qJ91gyVXbd1T1GsI/s/F8fI/QuPed2WPfj4FfOy9W7aDb8ilxxnYdmR9ezrjsG0sq8/+t45+YmWwtjtxtRyuWyYGaSMNFZcPNO2H6o95hce/7ote3DDxdkJslsvn1vSv4XHtmTRDHxh027cctkc7O+J5yepgGxujuyHtSPO+wNb9uTLFW5ft2UPTAv5bSPbtatrzebduGx+a0m5vd0x2+vHr8jY3xPHLZfNwZrNeyB5xKL4C6/J3rhech6+8sybRddGuThHjm+jqcWY5IR6jZtoItz4+Zkx1W9MBwfsYzo4MDnf8epxX6qM2+7Zrn6ibaxEUUAk4i/a1vVWNN/ZOSkjja5BDRfNjIyrnXqps1b1ns11VsPIPK0kTqfKnGntOBnLzq5u2zLdMQ0XzGioWix7D/XZlunXLLRNC1dcZjRjGUvTI/6FKGWkEdfNkm1JzSzZv3BCzm7/cu+PbLNcOUEY/j8AxEfEUPh+uXbKvScIo7db+Lo/YSBlpNEXNyrepy9u2MZ6umMcWV/KSCM51J/lzk1h2ZH1pDPI7zMyplyZXH6KoiefN+XyJVdXueMZy/hc6VhaePwnY5ptPo6MJ/fa7pzZ5bJd3yV103Z7XDfz20Y7r4Wvu6OpMV0/6QzyedczdMwl/aKZFV875eIsHN9GU+mYVJhDblCLsbSQAKChwTfRMCsmSiLgcJs5Th/rZHJbHp/OeD6XOo0x1XFMx8vEFNVwYZXv95UcpxP72o0Bbjg3bhybqhnTRO/Z1Y7J1RNtra2tOHHiRP51V1cXWltby5a3rAz6+xPFdYRVeGVPUad7ZQ9aw2pJ2YrjqpM66ynWeqlzLJqbQ7bbR+ZpJXE6VeZMa6dasUyrpJ2gfZmWYHVjiaiSbZmIKo6pDFB5jo4Wu0co3tcrexBQpJJtfpuYRAG2dQYUCelR3h/ZZrlymczw/wEg4LXvl9z/7dpJZ8rXLXnKv1f4OuKX4ZU9mBKQIfZWts+UgGxb7nTHOLI+r+yBb+h8lDs3hWVH1uMRkN+nXEy5/IxE/KfN9cL2Kx2fJzqWFh5/09B1apePdq/tzlm5fUf2nU+RbLcHFKlk2+nqag15x3T9eATk8665zNjkV6WKr51ycRaOb6OpdEwqzCE3qMVYmiMIAjIABgaSyGQytmWqSRAEWGb297ScarOwbSePdbK5LY+B8jkKjO9zqdMYU/3GNC1UJqZQ9e/3lRynE/vajQFuODduHJuqGVOl9+yJxDTaWDqSq3919IILLsDBgwdx5MgR6LqO//zP/8TVV189pjrmNCpYv6yj6MvU+mUd+T/KPB71Umc9xVovddZCJXE6VeZMa6dascyOlCkTGS4zMyTjweXFZR5c3oFzQ3JVY2n2iXhk5aKiMo+sXIRmnzSmMmNlF9td152POVMDRdseWNoOj1A8gbV6cRu+/cJ+rF7cVrR9Wlgt6bO1S9sheoCNLx7AvOYA7vvAwqL3P3/9QsxvLm5zbnMA91xfXG7V1W3YuusYVi9uw09eOQqv7EFCM4qOYcurx7BuWTu2vHoMq64uju3Oaxegya/YvperuymglMS/flk7tu46ln+9blk7fvBSJ9Yta8e/vNSJeS2Bkn5Yv6yjaJ/1Q2XXLmkvinVk/+fiGLl/4TGvXdqOf3phf7be5R1I6EbJcRaWHVnP/JYgJDH7+rv/kz2OSsbUcrksSwK8sgdP7jxq2w/VHvMLj3/t0nb89JXDWLesHU+8eKCkfwuPbcurx/DF5R34l5c6Mbc5gDuvXZB/74kXD5T0w7oR5/2Bpe3YOFSucPvape2QROS3jWzXrq71yzrwq31dJeXmtwRtr5+EbmBecwA/eKkT65e1w0xbRfEXXpNNAaXkPHzmmgXYuutYURx2cY4c30ZTizHJCfUaN9FEuPHzM2Oq35hmNdjHNLthcr7j1eO+VBm33bOFjMv/aen555/Hl770JViWhZUrV+JTn/pU2bKGYdnPPtZyNUu311lPsdZLnRUqN+Ntm6eTsQLnZK8GWm/HXLDqaGtYzU6yjbLqaGvIm/0Smh5RphqxFK2oo2RvIKOuOmpfZkw5WhBbb0xDU0BFyjQR8ZVZdVSW0B3TML3BC2HEqqPdUQ2NfgUQMshkMhAED7qjGpoCCgKKiFND+0f8w6uOxjULAVWETxaRgYV0xoPuQQ3NIXVoHw1eScLJeDa2hGEioEhDq47q8MqesquOxodWHQ3mVh31yjDTw6uODgxtK1l1VJUgikA0ZeFUXM+u8ikBmpE9xpaQioyQRiYjQBEFpNNAwjTRoA6vOjo1qKDBP7zqaPPQv/wOJk0IQgaSR8yvbDltaNXRnqH+TxoG/LKMBr+YX3W0NZxdafTYQBJTg8WrjjYFRAwWrDraGlbhsVt1tF9DUJEQ8koIjWHV0ZJ/ATzdqqMRL9KZ0686OpGxNKCKOHQygalBFVa5VUfjGqaHvfAI2VVH45qJkFeGlUlDFDz5VUcThoWEbsGviGVXHR1MGpgSUJDQ7Vcd9coeyLlVR4dyRJUEDKYsJPXsyqJJ04QydN5zOdWfzK4Wmhjq++ah/byKB4NJK3/9yKLNqqMpHV5JQmzoV16nBGR4yq066pMR0w1M8SkIeT3oHVp1tCmgoDk4vOpo2fFtNBWMSW781/aajKXIPuX1pW37cN/i+Y490Xb3pt0AgEeWdzj+RJuTx5prE8CkPEHnxjwe7SmMcX8udRpjqt+YcquORrOrqM8us+po1b87lVPjfcuOAZN8btw4NlU9pkq+I00gprE80eb6ibaxGO0DDeDO5LJTL3EC9RPrZMQ5ng/e9dKfOYy3dpyIdTw5CrijH90Qg1vicEMMtYzjbBhLx4LH5j6caDt9204f6/pfvAEAWHPd+Y5Ptrkxj8c80TbEjcfCmCpTjzGdKfd7N8YEuDOueotpLBNtfPadiIiIiIjOGNHkZD/CQ0REZzNX/402IiIiIiIiIiKiesEn2oiIiIiIHBb0Ssj+KTHhdEUnTBCAkE/O/+xEm4Vtn03H+pVte/Gxd86E03+c5wz6a0BERHXvjPobbURERERERERERJOFvzpKRERERERERERUBZxoIyIiIiIiIiIiqgJOtBEREREREREREVUBJ9qIiIiIiIiIiIiq4IxadVTXTQwMJMu+HwyqiMU0ByMan3qJE6ifWCcjzubmkO320fK0Xvozh/HWjhOxjidHAXf0oxticEscboihlnGcDWPpWPDY3Keex1Kn8FgnV7kcBepvLGVMlanHmM6U+70bYwLcGVe9xTTaWDrSGfVEmyCMvny3JIkORTIx9RInUD+xuinO0fLUTXFWgvHWzmTGWg9jqRtiANwRhxtiAJyP40waS8eCx1Y/6mEsdQqP1b3qbSxlTJU5k2JijlaHG+M6k2M6oybaiIiIiIiIiIiIJktdTLRZloUbbrgBn/zkJyc7FCIiIiIiIiIiIlt18TfaNm7ciHnz5iEWi42vAhno7NPR9VYUrWEVcxoVwJhgUBLQ2a+ja1DDtLCK2REFMKtU51vR6tVJ9cEDHIka2NnVjdagipkhGUiPKJPL40GtfB6LwMGB4byc1aAA1ogytchdOjsIwCnNwv4j/dAMEyFVRixloDWkwspkcHxQQ9ArIWVYCKoy4roBvywhoZmY3qAiqqXRFU1hWsibzfEM0JM00RvXMTWgotmXfVS7aJtfRE9iRJnMcDwl+2fKx15xWapfQ+d576E+RFRpcs6zXa6hwvwr2DfglWFYFqIpCy0hBelMBnE9Db03gWmh7LVxZNBAVzSFGQ0+SB7grUENAVXEFK+MKeqINni9uEfuXn08immhMvfqaqvkM0Qt263mZ3C3csP4Q3S2OJvGFqpLrp9oO3HiBJ577jn89V//Nb73ve+NvQIZeHZvP9Zs3o2UkYZX9mD9sg5c1RYZ/8UoAc/us6lzfmT8Exa1qJPqgwd4rrMf928aPvcPLu/AlXMiw5NtleSxCDy736bMvMjwB3jmGY2XALxyIoYDvXH8228O408vOQ9f3b4XjX4Ft757FjZs25vPqVVXt2H76yew8h3nYd2WV7CgJYg/v3QW1m3ZU5TjU4MyPvWD3+a3bfjTC6Gbadz95K6icl9/di8OnUzCK3vwyMpFuGxmGOl0Bi8dHSwqm3uv5IuNgMrLUv1yw3kuE4MiebD6R78bPS6bfVcvbsPGXx2CIgn46/fNL7qG1i/vwDee3QvdzJRcg6sXt2Hu1AAunhbMtjGWvnFDP57JKrlXV1stPgu7ud3JwOuGyDln09hCdcv1vzr6pS99CXfffTc8nvGF2tmn5y9CAEgZaazZvBudffq4Y+rsL1Nnv7vqpPpwJGrkJ9mA7Lm/f9NuHIkO3ykqyeODA/ZlDg4Ml2Ge0Xj1JE3sOT6IDdv2YsmiGfjq9uyX+hUXz8x/wQeyOfXV7Xtx6+Vz85MCH79iXv7nXJn7N+1GXEsXbdtzfPhLSmG5JYtm5F/f/eQu9CRNHOlPlpTNvWcXe6VlqX654TyXi2HP8cHTxmW374Zte7Hi4plYsmhGyTW0ZujasLsGN2zbiz3HB/NtjKVv3NCPZ7JK7tXVVovPwm5udzLwuiFyztk0tlD9cvUTbc8++yymTJmCjo4OvPzyy6ctL4oCIhF/0baut6L5izAnZaTRNajhopmRccVVL3U6QRQ9JX3uRm6Kc2Se7uzqtj333TENF8xoAFBZfnQdL1MmquHCXJkq55mb+rUS9RTvZMZqN5buPdSHdCabL4KAfB4V/pyTMtJI6mZ+e1IzbcvE9eIvILn6R5YrXGwqZaTRr1kQdMu2bL9moW1auCT2SsuOlRtyyg0xTEYcI/O0lue5UuViSI94osUurnL75vJ/rO+lM8i3MZa+mcx+dEsuV4vt59IK7tXVNlmfMev1s+14uGH8GS+7PB1+z33XJGOqzJkU08gcdfvY4sa+B9wZ15kck6sn2l555RVs374dL7zwAjRNQywWw1133YUvf/nLtuUtK4P+/kTRttawCq/sKboYvbIHrWG1pGylptVJnU6IRPyuji9nMuJsbg7Zbh+Zp61B+3PfEhw+95Xk8bRQmTKhgjJVzrN6Of859RSvE7FWmqMAEFEliEI2XwAU5ZFdTvkVKb/dr0q2ZQJK8S0oV//IcpmCSQqv7EFEFaEo9nVGVNE29krLjpUbcsoNMdQyjkrztJbnuVLlYvAIxeXs4iq3byaTndAe63seAfk2xtI3k9mPbsnlsRrLWFrJvbraavFZ2M3tTgY3jD+jKZejgH2e5rjxmmRMlanHmCr+7uTyscWNfQ+4M656i2m0sXQkV//q6Gc/+1m88MIL2L59Ox599FFcdtllZSfZypnTqGD9so6iL4frl3Vk/2DiOM2OlKkz4q46qT7MDMl4cHnxuX9weQfODcn5MpXk8awG+zKzG4bLMM9ovJp9It42PYzVi9uw5dVjWHV1G7yyB0/uPIrVi9uKcmrV1W144sUDWLu0HV7Zg2+/sD//c67Mg8s7EFA9RdveNj2MR1YuKim3ddex/OtHVi5Cs0/CuRFfSdnce3axV1qW6pcbznO5GNqnh08bl92+qxe34SevHMWWV4+VXEPrh64Nu2tw9eI2tE8P59sYS9+4oR/PZJXcq6utFp+F3dzuZOB1Q+Scs2lsofolZDKZuvgTnS+//DL++Z//GY8//njZMoZh2c8+1mKlpYKVG1vDanaiolqrjlazzhpz4yy0HTc90Wabp0OrjnbHNLQE1ewk20RWHY1qaA2p2Q/uo6w6OtE8q5fzn1NP8U7mE21lx9KhVUf7NAu6YSJYZtVRzbDgV2UkdAM+WUJyxKqjrSFvNseLVh1V8l9Iirb5pYJVR4fKZIb6ZyBRun9FqyiepuwYuCGn3BBDLeMYU54Oned+zUJEFat2nsfELtdQYf4V7Ov3yjBHrDqa0NPQzDSmBRU0B6T8qqPnNPgge4Djgxr8iogpvtOtOjo518vpuCWXx2rMY2kl9+pqm+xVR51udzK4YfwpY7SnMMrmKdx5TTKmytRjTGMaS108trix7wF3xlVvMY3liba6mWirxGg3CsCdJ9JOvcQJ1E+srp9oG1Iv/ZnDeGvHlRNtQ9zQj26IwS1xuCGGWsZxNoylY8Fjc596HkudwmOdXJxoqy3GVJmqTrRVWOdkcGNMgDvjqreYzphfHSUiIiIiIiIiIqoXnGgjIiIiIiIiIiKqAk60ERERERERERERVQEn2oiIiIiIiIiIiKqAE21ERERERERERERVwIk2IiIiIiIiIiKiKuBEGxERERERERERURVwoo2IiIiIiIiIiKgKONFGRERERERERERUBZxoIyIiIiIiIiIiqgJOtBEREREREREREVUBJ9qIiIiIiIiIiIiqgBNtREREREREREREVcCJNiIiIiIiIiIioirgRBsREREREREREVEVcKKNiIiIiIiIiIioCjjRRkREREREREREVAWcaCMiIiIiIiIiIqoCTrQRERERERERERFVASfaiIiIiIiIiIiIqoATbURERERERERERFXAiTYiIiIiIiIiIqIq4EQbERERERERERFRFXCijYiIiIiIiIiIqAo40UZERERERERERFQFnGgjIiIiIiIiIiKqAk60ERERERERERERVQEn2oiIiIiIiIiIiKqAE21ERERERERERERVIE12AKPRNA233HILdF2HZVl4//vfj1WrVk12WERERERERERERCVcPdGmKAqeeOIJBAIBGIaBD3/4w7jiiitw4YUXjq0iGejs09H1VhStYRVzGhXAmGBwuToHterXWc046ylWETg4kI1zWljFrAYFsCZYZ72opD8rOY/VKDOWOiYSb7WOxwMciRroiqYwLeTFzJAMpO3L7OzqRmtQLS1TSR0C0JM00RvXMTWgotknAhmcXWTgUJ+OHSdiCHklKLIAVRQxkDSQMixMCSgAgHQmA93MQDMthLwykroJRcqWmxpUENNMNHhlWOkMjg9qmN6gQhQEdMU0tARVxHULCd3EtAYvUrqFgaQBnyxBlgRIHgECBPzmRBemBBQEFQn9SQNx3USjX0FCNzA1oCJhpNE1qOG8KT4YVvbn5pAKRRQQ0yzENBMtIRVWJoOuQQ0tIRWaZUIUPAgqEtKZDJJGGifjOpqDChTJg96ojqagAiAN0xIwkDLQENPRE83WbVgmZFGCKAAJPY2UYaExIGMwZcAni5BFD/riBryyCFURIAkenBhMoSWkIq6ZiPhkKJIHgykL3dFszrcERQREEW/FTAymDCQNCxG/jAZFwhRVBAAcOpXAW32J4rwcma9+ET2JsyB/7cYmc5Rrd6if4oYJj0dEd1RDyCshqIroTxrwKxISuoGAIiOmGwgqMgaSBiJ+GVYmg764gZkRbz7fZkS8EAAc7U+hNayiwefBQDKN/qSBiE9Gbyyba6IADKZMhLwyYpoJM51GUJXRn9DR6FcgewQcHcjmhiBk8y3klRDTTAx2xxD2ydAMC2GvDD2dBjKAYWWQ0E34FQl+RUSDz4PBZBonBlOY0eCDJAJH+zX4ZA+CqoSoZiDiVSB5gN6YjqlBFWYmg7cGUgipEqYGZExRqpgnHEOH1erzXiVtVvOz4FjadfJYC9t18ngn61h5bdHZaCLX20TGB15vVCFXT7QJgoBAIAAAME0TpmlCEISxVSIDz+7tx5rNu5Ey0vDKHqxf1oGr2iLjv/nVS531FKsIPLvfps55kTN/sq2S/nSqTL214wGe6+zH/ZuGyzy4vANXzokMT5SdrkwldQjAS0cHcfeTu/JlHlm5CJfNDJ89N1cZeG5fcT/d94GFUGURazfvKThH7UgZFr7zv53400vOw492HMafXnIevrp9b1GZo31JfPE/X8tvW724DT///XF84ILp2LBtLxr9Cj7+x3PwyH+/UVQmoGQnl/7x+QPoS+hYt6wd33xuHw6dTMIre3DP9QvRlzBx709+jwUtQfz5pbOwbks2vllNPvzNlfOL4l29uA0bf3UIfQkdDyxtx/bXj+PGi85Ff9LM7+eVPVi7tB0/fPkQFEnAzZfMwjee21tyXGuXtOPZNw7iqoXTi/a99/qF0Kw0Hn36zZJjKTwOryzg9a7UiP7swKypKl45NIgN24bbuvv952NmxAcAWP2j3xXn5blhvHRkOF9nNflwx1VtRefujMxfmzHj/61cBAGwv3aRva6f+FUnPnTJeSV5Mb3Bi69v34urF07Dj3YcxoffNQtfeab4HHYPJDB4TgRrbHJqRkTFzZech288t68kV+68dgEiXgl/OB7Fv/2m9BopzMv1y9rx5ol+tE2LFMX4mWsWoNEvIWWkEdetovy489oFmBb24n/2duGX+/pw67tnFb2/enEbZk3xY293X8k1lmt39eI2zJ0awMXTghPPE46hw2r1ec9tbZ5t7U7WsfLamhSST4GVGe7gqG5C8MpFZURBgJnUnQ7t7DCR620i+/J6ozEQMpmMq9PCsiysWLEChw8fxoc//GHcfffdZcsahoX+/kTRts6Yjlv/+ddIGcOPp3hlDzb+1bswJ6iMK6Z6qbOeYj0Y0/GXNnV+/6/ehdkTOP6cSMRfkhu11twcst0+Mk8r6U+nytRbO0fiBj78nZdLyvzrxy7FuQG5ojKV1NGTMrHiWy+VlPnJJy5Ds7c2/17hRM5WmqOA/flYtXg+vvXCgZJ++cQVc2Glge/8zwF87L1z8Z3/sS/z1W37irb9v5vejs/9+FWkjDRuv2p+2f0AwEoD33h2H7yyBx9771x849l9JXV/9c8vytcHoGyduf29sgeP/+U7EE2auKtgv8L4AOBzP3617HE9/pfvwCe/v3NM/ZRr+4nb3oWPfLc058ttz/XFyH78149dWpTT5Y672vlbq5ydyFharu9/8onLAAArvvVSUd4VlvnEFXMxvyU06vn+3m3vwkdtzs3H3jsX508bfd8v3/R23DXK+4V5Wa6dL9/0drzZHS2bX5fNbcL/7js5avujtfuJK+bi+oUtE86TSsfQybhXV8NEx9JqfN4bzWS0eba1O1nHWum1VS5HAfs8zXHjNemGmASvjE9u3JF/LUkiTLP4yYDHb70EmZQTjzTac0M/jXS6mKr53amciexb7/cyN8ZVbzGNNpaO5Oon2gBAFEVs2rQJg4ODuP322/Hmm29iwYIFZcoKiET8Rdu63ooWXQwAkBr6FY+LZkbGFVO91FmremtS5/EydUY1XDiB488RRU9JbkyWkXlaSX86Vabe2tnZ1W1bpjum4YIZDRWVqaSOvYf6bMv0axbapoVRC5OZs5WOpekMbPslnQEEIftz7v92ZUZuS+pmvuzp9ss93Jxrw65MUjOL6ihXZ2Fd/QkDKd2yLZfUTSAz+nH1J4wx91PudXc0ZZ+LZbaP7MN8+ZhW0XFXO3+dztlKxtJyfd+vWfmfR+ZJbns6g3xOluvDnjLnRhCG86/cvvHT1F2YG+XaievmqPl1MqaVb7/McRe2m86gKnlS6Rjqpnt1NTj1ufR0JqPNs63dyTrWanw+scvT4ffcd026IaaobkKSxPxrASh6DQAej4DQJMbphn4aabwxjee7UzkT2bfe72VujOtMjsn1E2054XAYl156KX75y1+WnWizrEzJ7GNrWIVX9pTMPLeG1XHPntZLnfUU67RQmTpDEzv+HDc90TYyTyvpT6fK1F07QfsyLcHKy1RSR0SVbMtEVLFmeTWZT7RVOpaKAmz7xSNkJzi8sie/za5MIa/sgV8p7ufR9rPSw9sKn8suLOMvc95Gvs7t75U9iPhlRD2CbTmfIkHA6McV8ctj6qfC42gJee1zscz2kX2YL18mp2udv04/0VbJWFqu7yOqCCB7nsvliUdAPidz2yo9N5nMcP6V2zdwmroL87K5TDsBRRo1v5qCKtAVs9/Xa3/che16BFQlTyodQ934L9uVmOhYWo3Pe6OZjDbPtnYn61grvbZGewrDLk/z9bvwmnRDTIJXLnqCze6JtnS6fL86wQ39NNJ4n2gbz3enciayb73fy9wYV73FNJYn2jzVCqgWTp06hcHBQQBAKpXCiy++iLlz546pjjmNCtYv6yj6MLt+WUf2jx6OU73UWU+xzmqwr3N2Q+0et3eLSvrTqTL11s7MkIwHlxeXeXB5B84NyRWXqaSOZp+IR1YuKirzyMpFaPbVzb9VTNicRqWkn5oCytDfFis8R+2YGlCw5dVjWHV1W/7/I8u0DE2u57atXtyGb7+wH6sXZ8s+ufMo7n7/+SVlpgYUTA0o+MkrR+GVPVi3rB1bdx3Ll7nn+oWYMzUAr+zBt1/Yj7VLh+Pb8uqxknhXL27L1/XA0nb84KVOiAKK9vPK2b/R9k8v7McTLx7A+mUdtse1dkl2/5H7TvEruPPaBbbHUngcgGXTnx2AYOX7Jbf97vefj/bpYbRPD5fk5bkhuShft7x6rOTcnYn5azdmzGsOlr12c9f1Ey8esM2LuVMDeOLFA/k8/sw1pefwv35/DOvL5FQ2V9ptc+XOaxcgoRlYvdj+GinMy/XL2vHMntLc/cw1C5DQDTQFlJL8uPPaBZjfHMRPXzmMJ3ceLXl/9eI2eADbayzX7urFbWifHq5KnnAMHVarz3tua/Nsa3eyjpXXFp2NJnK9TWRfXm80Fq7+G22vv/46Pv/5z8OyLGQyGVx//fW44447ypYv+zcGarnqptvrrKdYc6uORjW0htTsJFuVFkJw0xNttnnq1IqilZSpt3YKVgxtDXmzE2Rp+zLdQ6talpSppI6iVYaU7E21hqOn2/5GG4D8qqO9cR1BVYIiCVAlEYNJA0kjjSkBGQIAa2jVUd2yEFSHVx0dTBqYElAQ14dXHT0xdG4lj4DumIbmgIq4YSGpWWhtUKEZFvqTJnyKCFkcXnW0K5pCY0BBaGjV0YRuIeKTkTBMNAUUJA2bVUeDKhQpu+poXDPRPLTqaPfQiqSGaQGCgJA6vOroqbiOqQWrjk4JKtlVIE0BA5qBBm92JcmpQRWGZUEWxdOvOqqIUCUBkie76mhzUEVCL7PqaEBEQCpcdTSNiE9Cgzq86uiAhaFVRwvycmS++qWCVUdrk7+T/TfaANiPGeYo1+6IVUd7YhqCqoSAImIgZcAvS0gaBvyKjPjQ6qOjrTp6ToMXHmFo1dGQigZ/6aqjzUEVkqfcqqMGGv0yZI+AYwMpNIdUeIQ0jMJVR5Mmwj6pzKqjFnyKiIAsIuzzIJrKrjp6ToMPsggc69fglT0IqBJimoEGrwI5v+qoAjOD4VVH/XI2x2qy6qh9DrrxX7YrMZ6xdNJWxJysVUfPhnYn61gruLb4N9qqi3+jbXyq9TfaAEzseqvaqqP1dS9zY1z1FtNYnmhz9UTbWI12owDceSLt1EucQP3E6vqJtiH10p85jLd2XDnRNsQN/eiGGNwShxtiqGUcZ8NYOhY8Nvep57HUKTzWycWJturiRNv4VHWircI6J4MbYwLcGVe9xXTG/OooERERERERERFRveBEGxERERERERERURVwoo2IiIiIiIiIiKgKHJtoSyQSSKezf128s7MT27Ztg2FM3u+tExERERERERERVZNjE21/8Rd/AU3T0NXVhY997GPYtGkTPv/5zzvVPBERERERERERUU05NtGWyWTg8/nwi1/8An/+53+Or371q9i3b59TzRMREREREREREdWUoxNtv/3tb7FlyxZceeWVAJD/VVIiIiIiIiIiIqJ659hE29/93d/h8ccfxzXXXIO2tjYcOXIEl156qVPNExERERERERER1ZTkVEO9vb147LHH8q/PPfdcvOMd73CqeSIiIiIiIiIioppy7Im2b33rWxVtIyIiIiIiIiIiqkc1f6Lt+eefxwsvvICuri588YtfzG+PxWIQRbHWzRMRERERERERETmi5hNtra2taG9vx/bt29He3p7fHggEcO+999a6eSIiIiIiIiIiIkfUfKJt4cKFWLhwIZYtWwZJcuxPwhERERERERERETmq5jNfS5cuHfX9LVu21DoEIiIiIiIiIiKimqv5RFvhSqNERERERERERERnqppPtM2YMaPWTRAREREREREREU06x/5o2kUXXQRBEAAAhmHANE34fD688sorToVARERERERERERUM45NtP32t7/N/5zJZLBt2zb87ne/c6p5IiIiIiIiIiKimvJMRqOCIOCaa67B//zP/0xG80RERERERERERFXn2BNtv/jFL/I/p9Np7N69G6qqOtU8ERERERERERFRTTk20fbss8/mfxZFETNmzMA3v/lNp5onIiIiIiIiIiKqKccm2h566CGnmiIiIiIiIiIiInJczSfaHnzwwfxqo3a+8IUv1DoEIiIiIiIiIiKimqv5YggdHR1ob2+HpmnYs2cPZs2ahVmzZuG1116Druu1bp6IiIiIiIiIiMgRNX+i7cYbbwQA/PCHP8S//uu/QpKyTf7Zn/0Zbrnlllo3T0RERERERERE5IiaP9GWMzAwgFgsln+dSCQwMDDgVPNEREREREREREQ15dhiCJ/4xCdw44034tJLL0Umk8FvfvMbfPrTn3aqeSIiIiIiIiIioppybKJt5cqVuOKKK/Dqq68CAO666y40Nzc71TwREREREREREVFNOfarowCgKApaWlrQ0NCAgwcP4je/+Y2TzRMREREREREREdWMY0+0/cd//Ac2btyIEydOYOHChXj11Vdx4YUXYuPGjWX3OX78OD73uc/h5MmTEAQBN998Mz7ykY84FTIREREREREREVHFHJto27hxI3784x/j5ptvxve//33s378fX/nKV0bdRxRFfP7zn0d7eztisRhWrlyJ97znPZg/f/7YGpeBzj4dXW9F0RpWMadRAYwJHExhnYNa9eusZpz1FGu9xCkAPUkTvXEdUwMqmn0ikHEgzkr6pxplxlLHROJ16ngqibda7YjAwYFsmWlhFbMaFMAaUcYDHIka6IqmMC3kxcyQDKRt6jgexbRQmToqycEa5mnP8SiaQypShgm/IsHKZDCYMNHgl5HJZDCQNBBQJEiiAJ8sIqqZGEyamBZWYaUz6IpqaAmpUEQBvQkdQUVGf0JHUJUQ9klIp4GYbkIzLIS9Mox0GoMpEyGvBJ8kAkIanbFsP7eEVQQUEW/1J9EUVGCmM9CNDJKGialBFWY6jVNxA1OCMmTBA91Kw0xnEE2ZCKoSFFmAJHjQn8rG0RPV0BxSIYtATDOhiBIGUwYafDIGkwaCXgkJ3YQiivArIk5ZUQwkLXRHs+fcTGfQnzDQGlaRzmSQMtKIaSYCioTQUHtxzcKpuI6QV0ZCN3FOxItoysrnjSp7MJg0ASEN2SOhJ6ahNaSitUFE94CF3riOBr+MpGHCK4loNC0MFsSgiB4c7U+iOaTCK3lwpC+JlpCK6Y0iugdMmJaArqiG2VN9yKSBmJ6NZ1pYRdgr4o2uRD7He6ImpoQEHO7LxjeryQfNSONEJeO03TWD4W0zG71Ip4G3BlI1GfOnN2T74nBfti8sywIEAY1+GXHNwmDKwBS/iqhmIpYy0RxSoEhASBXRl0gjZZjwyRKShoWoZqLBK8ErS9BMCxAA0yrOo3QaUEUBGQg41p89prBXxP6eBJpDKpK6CUXyQJVE9MQ0+GQRQVWCX/XgVMzI53hQlRBNmYhpJpqDKmKaAVUW0eiX0J+wEO2No8ErozemozEgQ/J40BNNoSmoQjdNiB4R0ZSJpoACScxAszKQBA9imomkYaE1pCIDIJoykdAtTG/wQjfT6IpqmBpU4Fc8QEZABhmkjAy6o9nrbIpfRCyVxonBgrErM2Kc8YvoSYwYd1CDschJk3XPr7ZafMYaS7tOHmthu04e79l0rESTbSLX20SumcnalyozkXv20L57D/UhokoTvt87NtGmKApUVQUA6LqOefPmobOzc9R9Wlpa0NLSAgAIBoOYO3cuurq6xjbRJgPP7u3Hms27kTLS8MoerF/WgavaIuNP7Hqps55irZc6BeClo4O4+8ld+TofWbkIl80Mj/9CrCROp8qcae04GYsIPLvfpsy8yPBEmQd4rrMf928aLvPg8g5cOSeSnWyrpI5KctChPF11dRt+tOMw/uyd52Hjrw6hL6Fj9eK2/M93XrsAzSEV9/7k92j0K7j13bOwYdve/P4PrbgA/QkD//e/fpvfdue1C9ASUvH5n/w+v62wzrvffz6agwo+9+Tw++uWtePl/b248LxGxHWrqI07r12Ara++hZveMRMej4CEbuHRp98sqjvklWBZGTxUEMeDyzugSAIefXoP/vSS8/DV7XtLjvv//PFcqJJYdD5zsSqSgL+5cj7Wbt5T9N6MRh/+/hdv4NDJ5FA77TgZ07GmoNy6Ze3YebAXF503Feu2voKUkcasJh9uv7LNtv9vv7IN33hub77Owv5au7QdP3z5EN7sjmH98g5M8cu444e/RaNfwWeumQ8rI2DdluG21y/vwGvH+vDDHcewflkHrmgL4YW9g1izeY/tOSw7ppa5Zhr8Elb/2+/GVtcEcrSoL5a047eHe3HJ7Kn4xnP78Mkr5uKNE7GiGB5ecQFEj4AnXuzELZfOxqlEDI/89xto9Cu4/ap5SKczo+ZROp29wP7x+QPoS+hYv6wdL+3vxc/2dOHe6xciA+Dh/3o9v999H1gInyIV5dDape147Pl9+fN513Xn4/svHcTtV7Xh339zCNe+bTq+8sybtn24dkk7HntheN8v3tCBpqCM148XH+e6Ze345nP7oJuZkjoeXN6BsE9Ef8Isyt/1y9rxjeeK6w6pElb/6Hf5HL3jqraiY3lk5SIokidfpipjkZMm655fbZPR5tnW7tl0rESTbSJ5X4/7UmUmcs+uwf3esb/RNm3aNAwODuKaa67Bbbfdhk996lM455xzKt7/6NGjeO211/D2t799TO129un5hAaAlJHGms270dmnj6meeqyznmKtlzp7kmb+AszVefeTu9CTNGsap1NlzrR2nIzl4IB9mYMDw2WORI38l9Bcmfs37caRqFFxHZXkoFN5+tXte7Fk0Qxs2LYXKy6eiZSRLvr50affRGdvHCkjjRUXz8x/mc/t39kbx/8dmnTIbXv06TdxYGif3LbCOh/57zewr6f4/bWb9+Cmd56H3rhe0sajT7+Jj18xD71xHd1RLT85Ulh3d1TDyYRecl7298SxZNGM/CTbyOPujmol5zMX65JFM/KTFIXv7euOYcmiGfltPlnKT7IVHs8NF5+HdVuHty9ZNKNs/6/ZvLuozsL+WrdlDz5+xbxsLg3FmjsffkXOT7Ll823Tbrz/ghn53Dt8ysrHZ3cOy42p5a4Zw8yMua5K2bVZ1Bdbs/26ZvMeLFk0AycGtZIYPv+T32Nvdwy3Xj4XnSfjeOS/38jH2x3VTptHvXEdvXE93+aaodxMGWmcTOj5Sbbcfr1xvSSH1m3ZU3Q+v/yLN7LnedNu3Hr5XHzlmTfL9uG6rcX7fuGnu+GX5ZJya4f6wK6O+zftRtirlORvrt8K695zfLAoR0cey91P7ioqU42xyEmTdc+vtslo82xr92w6VqLJNpG8r8d9qTITuWfX4n7v2BNt3/jGNwAAn/70p3HppZciGo3ij//4j/PvDwwMoKGhwXbfeDyOVatW4b777kMwGCzbhigKiET8Rdu63ormOywnZaTRNajhopmRcR1LvdRZq3rP5jr3HuqzrbNfs9A2LVxRHSPztJI4nSpzprXjaCzHy5SJarhwqMzOrm7bMt0xDRfMaKiojkpycKJ5OpaxVBCG/1+4Lffz0AM++XKF0pnSbYX7jGxntPdPxrSy9SV1M79PJe0VbreLOxdPufYKYy1XZ05cM23L9ca0ou2jxVHYpl0MSd3M/xwf+lkQyrfdE03lf+6KpvJlysWQuw5E0ZPPm3L5Utj+RMfnSsfSwr44OdSvo52/dAZIambR+7nyheXs9ssZ2SZg397pcqjwdcpII1lwzkbLicLX3QXnsFy/jHyvJ6pVVPfIY670ei43FhXmkBs4OZZO9PPeaCajzbOt3Xo+Vrs8HX7PXdck4I6YoroJSRLzrwWg6DUAeDwCQpMYpxv6aaTxxjSe707lOLGv3XFO1hhR6EzKCTsTuWdX4zv+SI5NtBV617veVbLtox/9KJ566qmS7YZhYNWqVVi6dCmuu+66Ueu1rAz6+xNF21rDKryyp6jjvLIHrWG1pGyl6qXOeoq1XuqMqJJtnRFVLKmzuTlkW8fIPK0kTqfKnGntOBnLtFCZMqGCeoL2ZVqC2TKV1FFJDlaap5Xm6Gh9kMkM/79wW+5nT8EX85H7i0LptpH7nK7O3LamoAqxO2Zbn1+RIAr2MdjVV7g9nbHfJ5MBJE/594RRjs0quI8HvPbnqrlMrpyu/+36y6dI+Z8DyvBtv2zbIW/+59awt6jMaNdBJOI/7TVT2H6l4/NEx9LCvmga6ldg9Pzzq1LJ++IouTwyj3LnONdmufbKxWB3Pr2yB/4R13Yl+7aEvGXLlcvT5jJj0ci67a7FSq5nu3smgKIccgMnx9KJft4bzWS0eba16/ZjLZejgH2e5rjtmgTcEZPglWGaw39AV5LEotcAkE6X71cnuKGfRjpdTNX87lSOE/vaHedkjRGF6jEnxlTXGL6nj3ff0cbSkRz71dHTyWRKHynIZDL4u7/7O8ydOxe33XbbuOqd06hg/bKO/Adrr5z9fejcH2U+k+usp1jrpc5mn4hHVi4qqvORlYvQ7Bv/nHUlcTpV5kxrx8lYZjXYl5ndMFxmZkjGg8uLyzy4vAPnhuSK66gkB53K01VXt2HrrmNYvbgNP3nlKLyyp+jnO69dgDlTA/DKHjy58yhWL24r2n/21ADuuX5h0bY7r12AuUP75LYV1nn3+8/H/Obi99cta8ePf3MYTQGlpI07r12Ab7+wH00BBc0hFXdeu6Ck7paQiia/UnJe5jUHsOXVY1h1dZvtcTeH1JLzmYt1y6vHsG5Ze8l781uC2LrrWH5bUjexfkS5dcva8dQrh7F2yfD2La8eK9v/65d1FNVZ2F9rl7bjn17Yn82loVhz5yOhGVi7tLjt9cs78N+/P5bPvfMaxXx8duew3Jha7pqRJWHMdVXKrs2ivljSjp++chjrl7Vjy6vH0BpWS2J4eMUFaGsJ4okXD2B2UwB3v//8fLzNIfW0eTQ1oGBqQMm3uX4oN72yB1P8Cj4/It+bAkpJDq1d2l50Pu+67vzseV7egSdePIDPXLOgbB+uXVK87xdv6EDCMErKrVuWLWdXx4PLOzCY0kvyd/2y0rrbp4eLcnTksTyyclFRmWqMRU6arHt+tU1Gm2dbu2fTsRJNtonkfT3uS5WZyD27Fvd7IWM3wzUJbrzxxpIn2nbs2IFbbrkFCxYsgMeTPeg777wT73vf+2zrMAzLfraylqtZur3Oeoq1XuosWs1EyV6ANldRuRlv2zydjBU4J3s10DPxmHMrhkazq0TOPs2qo60hb3aSLT3GOirJwQrKjClHC/qgJ6qhOagiZWZXZcwgg4GiVUdN+BUR8tCqozHNxEDKxLRQdtXR7lh2f0UUcDKhI6DIGEgY8Ksiwj5paCXM7KqjIa8MM51GNGkh6BXhlUUIQhqGJaB7MLtCaEAVcbw/hSkBBVYmDc3IIGlYaAoqsNJp9MUNNAZkyJ7hVUdjKRMBVYIiCZA8HgykdARkGT2x4VVH45oFWRSLVh0NqBJShgVZ9MCviAioHgwkLfQMraSazmTQnzDRElKQRvbvkcU1C34lu8Kkml911EDIKyFhmDgn7EVUy64a2hoaXnVUEDKQPNkVKltCKlrDInoGLZyM6wj7ZGiGBVnyYEpARnRo1dGWsApV9ODoQBLNgey/nh7tT6I5aLPqaJMPmUx21dG+uJ5fJXPUVUen+KCZ9quOlvyr5OlWHY14kc6cftXR8Y6l08MqFGlo1dGgCittARDQGLBZdVQzMTWgQJWLVx31ytnzHdMshLwifOVWHZUEpDNlVh3tTaA5WLzqaG9Mhyp7EFIk+L3ZVUejqWyOF646OjWoIp5bddQnoT9pIaoZaPDKOBnXEfFnVx3tjaUwJaDCMC14PB5EUyamBBTIYgYpMwPZk111NGWk0RzKnodoykRStzAt7IVupdEd1dAUVOCXPQBGrDoaUjElMLzqaH7syowYZ/xSwaqjSv4DaiX3TNsccoMajqWTsiLmZK06eja06+JjHe0pjLJ5Cndek26ISfDK+OTGHfnXdk+0PX7rJcikJu+v3Luhn0Ya7xNt4/7uVE6N9y17nJO86mg95sSYVfg9fbR9+zULEVUc0/3etjo3T7SN1Wg3CsCdyWWnXuIE6ifWyYhzzB+8UT/9mcN4a8eJWMeTo4A7+tENMbglDjfEUMs4zoaxdCx4bO5Tz2OpU3isk4sTbdXFibbxqepEW4V1TgY3xgS4M656i8lVvzp65MiRisq5ZL6PiIiIiIiIiIhoXGo+0bZ69WoAwEc+8pFRy33ve9+rdShEREREREREREQ1U/O/RptOp/HYY4/h4MGD+O53v1vyfm6Rg0gkUutQiIiIiIiIiIiIaqbmT7Q9+uij8Hg8sCwL8Xi85D8iIiIiIiIiIqIzQc2faJs7dy4+8YlP4Pzzzy+7WigREREREREREVG9q/lEW87FF1+Mhx56CL/5zW8AAO9617tw++23IxSqfOUGIiIiIiIiIiIit6r5r47m3HfffQgEAtiwYQM2bNiAYDCIe++916nmiYiIiIiIiIiIasqxJ9oOHz6Mr33ta/nXd9xxB5YvX+5U80RERERERERERDXl2BNtXq8XO3bsyL/euXMnvF6vU80TERERERERERHVlGNPtK1btw6f+9znEIvFAADhcBgPP/ywU80TERERERERERHVlGMTbQsXLsTmzZvzE23BYLDo/aeeego33nijU+EQERERERERERFVlWO/OpoTDAZLJtkAYOPGjU6HQkREREREREREVDWOT7SVk8lkJjsEIiIiIiIiIiKicXPNRJsgCJMdAhERERERERER0bi5ZqKNT7QREREREREREVE9c81E28UXXzzZIRAREREREREREY2bY6uORqNRfO1rX8OOHTsAAO9617tw++23IxQKAQDWrFnjVChERERERERERERV59gTbffddx+CwSA2bNiADRs2IBgM4t5773WqeSIiIiIiIiIioppy7Im2w4cP42tf+1r+9R133IHly5c71TwREREREREREVFNOfZEm9frzf/aKADs3LkTXq/XqeaJiIiIiIiIiIhqyrEn2h544AHcc889iMViAIBwOIyHH37YqeaJiIiIiIiIiIhqyrGJtnnz5uHjH/84Dh8+jGg0ilAohGeeeQYLFy50KgQiIiIiIiIiIqKacWyi7VOf+hTC4TDe9ra3we/3O9UsERERERERERGRIxybaOvq6sJ3vvMdp5ojIiIiIiIiIiJylGOLIVx00UV44403nGqOiIiIiIiIiIjIUY490bZz50489dRTmDFjBhRFyW/fsmWLUyEQERERERERERHVjGMTbd/+9redaoqIiIiIiIiIiMhxjk20zZgxw6mmiIiIiIiIiIiIHOfY32gjIiIiIiIiIiI6kzn2RNt43HvvvXjuuefQ1NSErVu3jr8iGejs09H1VhStYRVzGhXAmGBwuToHterXWc046ynWeomzFiqJs5L+qUaZsdQxkXidOp5K4q2kDg9wJGqgK5rCtJAXM0MykB5HmUr7ZbS+FYCepIneuI6pARXNPhHIjCOWsZKBIwMGOuN9sCzgVFzHjIgXhpVBX0JHxK9gIGmg0S9DEgQcH9TQElJgpYGemIaWkAqv7MGJQQ0NPhkxzcAUvwLNTKMnpmNqUIFPFnFiUEOjX0bIK6I/qQMZD+KaiQafjKhmIOJTcDDWj5NxHQ0+GbppYVqDioGEha5otl9bwyJ6omn0xjT4FREhVULcMOARPJgeltETs/LnoDUsoi+eRjRlIa6bmBpUkNRNNAZUJLRsndMbVDT4RJyKW+guaKPzaD+6BzXMiHiRBtCf0BHyyuhLZPuhJSji+KCJkzEdrWEVspiBX5HQl8jWM71BhU8RcSpmIKaZaAmrUEQBp+I6wl4ZUc1EQrcwNahAlTzQzDT6EwbCPhlx3USDV8Yb/acQTZloDimQxAwMU4DkycBIC/ljTJkmpvgVJPU0ThTk3sGhXGwKKmjwSdDMNE4MaAiqEkJeETPCMoDhnJ3d5EPKKK6j7Jh6ujytUY4WXj8tIRFdgyYiPglIA11RDTMaVfQn0+hL6Gj0K9BMCzHNwpSADGSA/qSBiF+GZpqQPRIShomAKkEz0ojrJsJeCWGvjMGUiVNxHS0hFT7Zg5huobcgj7sGNTQGZHgEAT1RDQFFQlAVkTBMZDICfLIHPkXEQDKbH9MaVPhkESfjOhK6heaQAtnjQX/SQNKw0BJSEdMMeOM6dDONmGYioEjwKyJ8igdH+1JoDioIKB50RQ1EUyaaggpaAiJ64tl8n9agotEr4li/NnxOAJzSLJxKGojrFs4Je9HstRlTqHom47NJLT5jjaVdpz+HTcbx1stnTqIzwUSut4mMD5M1llJlKvmO5BBXT7StWLECf/EXf4F77rln/JXIwLN7+7Fm826kjDS8sgfrl3XgqrbI+C+KeqmznmKtlzproZI4nSpzprVTrVg8wHOd/bh/03CZB5d34Mo5keGJgUrKVCMWAXjp6CDufnJXvswjKxfhspnh4RtJJbGMlQz86uAgUoYJKyNg3ZY9aPQruPXds7Bh2958O6uubsOPdhzGn73zPPz6wElc1zEd67bsyb+/dmk7fvjyIbzZHcP6pW9D96COB0a8LyKDL/3sD/jstQtgWMDf/fT3+fc/c80CBNQUvvifr+W3fenGdnQN6iX9tuNgD/5953F4ZQ9WL27D9AYvEqkUjp5S82VnNfnw2WvPx7H+ZNFx3HP9QkTiBu79ye8L6mzHN57bh0Mnk0Vt/HJfX9l+uP3K+XjmteP4xR96s+fqpkVIGcmic7N26f/f3p3HOVXdfwP/ZLlZJ5mF2RAQWQbUQVTqrhUBWZRVcWmfVlurP1wLSqV1BcG1Ra2AWrW2tbZ91P6KsvlYq2y2KrIpCFUBRWAQZmOWLJPc5OY+f2QSsk9m5ia5mfm8Xy9fztx7cs73nvu9594cMjnVeGFjZL3V6FNgwKb9x8J1Duxjxu1jqjA/4nUPTa3GoUY3Hnvny6jzvGV/Pb53UikWrNodVdbpkTD37zui+ui5DXvD7S6cVo3nI45vzrgq9Cs2w6jTYs4bn2FYeQF+eO7AqPOZdEztKE8zlKPx1081qirM+GR/E0yCFpu+bsQFVWV4YMWuqPyNzeWBfcy4dfRQ/G7jbvzsgkFw+6So8/vw9BF4dn2w784aWIhrzhoY1e6CqdWwChp82ujC0+/tCW8P5eFfN32LH5xzIgIBYP6q3VHx/n3rQWw90BKOIfL6+PXM01Db6oyqc+74Yai0G2HUaTDnjc9wy+ih4Xwa2MeM2y+piopt4bRq/PPzI/h4/zEsufYMAMA3Da6o44sbU0g5uXg2ydXzUG9qN1+eOYl6gu5cb7l6LWVeOu+RskjVfzp69tlno7CwsFt17G86/uYLADy+AOav2oX9TWKPrzOfYs2XOjMhnTizVaantaNULIccvvCEQKjMgyt34ZDD16kySsRS3+YP30BCZeYt34n6Nn+nYums/U0i9tU7YTEI4YmWK0f1D785D7WzdN1eTBnZD0vW7sVPLxoULhvav3D1btx08RB4fAFYjEJ4EiFyv8UoYMrIfthX7wpPsoX2//b9PahzeKO2GXT6hP02Y9SJ4d+XrN2LbxpcGFpRHFU22I4z7jh+/c8vsb/BFVPnbkwZ2S+ujVT9MH/VbvzovEHh7XvrnHHnZuHq2Hp3Q9Bpo+qcMrJfeJItVO6h1bvR4BLjzvOMUSeGJ9kiywYCiOujyHYXxBzfkrV7sa/OCZ8kw+ML4KaLh8Sdz2Rjakd5mqkcjc+D3fD5tWhwiahp9uCqs0/EAyt2xeVv7DmcMrIfHmo/L41uMe78PrjyeN9df8HguHYXrt6NIqsxPCEW2affNLhw/QWDYdLrw5NskfFef8HgqBgi939d74qr8+n39mBfvQt2ixFTRvaLyqdgDkbHtmDVbvz0okHw+ALYfaQVu4+0xh1f7JhCysnFs0munod6U7v58sxJ1BN053rL1Wsp89J5j5RNqv5EW2fpdBoUFVmittV+5wh3dojHF0Btqxdn9i/qUjv5Umem6u3NdSohNk/TiTNbZXpaO0rFsq22LmGZOqcXp/UrTLuMErHsPdCUsEyzV0JVpT3tWFJJNpYGZMDl9Yfr1miQsJ3Q9maXL+H+NjF4w4usK3K/S/RDowECcuL6AzH/KpWsnkanN+51tQ5PVNnOtBM6ttg2OuwH9/HJo2RtxdbbFNN3ydpIFGOD05u0XztqN/b3gIzw69qS9HMoP3U6bThvOsrT7uYo0Imx1OEJ91NDxCRtZJ/G9m/o91T5EeqrZP3S5E6c/wEZwWsgSb2h6yPROU+Vqw0RuRiKLVnehHIy1C8djSnZEplDPUG2nks7kqvnod7UrlqfOdORKE+P71PfNamGmByiH3q9Lvy7Boj6HQC0Wg1sOYxTDf0Uq6sxdeW9UzLZeK0a+x5QZ1xKxpTOe6RsxtSjJtokSUZzsztqW4U9+J1AkZ1uErSosBvjyqYrX+rMp1jzpc7OKCuzJdwem6fpxJmtMj2tHcViKUhcprygk2UUiKXIqE9Ypsio61QsQPo5Gop9X50DVlN0+4nakeX2mKxCwv1mQ/DWE1tXaL/VoIcsA3pt4vq1EZNBqerpU2CMe12F3RRXVqdJr53QscW1UetM3Q8WocO2YustTtJ36cRYluT8Ww36uLKx7cb+rtUg/DpLktwL5WdRkSXtPE03RwEFxlKbCXvrnMG6bNFlOsploONzlqxfii2Jz6FWA5gNemiT1GuOOE+dydXSAiO+POpMeB7jzkN7Tuo0KcpEjCnZEplD+aSzY2m2n01y9TzUm9rN9TNnR5LlKJA4T0PUeE2qISaNSYDfL4V/1+t1Ub8DQCCQvF+zQQ39FKujmJR875RMNl6rxr4H1BmXkjGl8x6puzGlGktjqfpPR5UwqNiARdNGhB+YTULwb6kHFRt6fJ35FGu+1JkJ6cSZrTI9rR2lYulvE/Dw9OgyD08fgQE2oVNllIilzKzD4pkjo8osnjkSZebjb87TiaWzBhUbMKSsAG6vDwumVsMkaLF8Ww3mjKuKamf22Cqs2XkYc8ZV4ZX/7A+XDe1fMLUaL3/wNUyCFm6PDw8l2O/2+rBm52EMKbPi0RmnRe2/69Jh4UUVQttEvz9hv63YfjD8+5xxVRhcasW+o01RZVfvOIwhZQVxx/GrSSdjUKk1ps5qrNl5OK6NVP2waFo1/rZpf3j70PKCuHOzYGpsvdXwSYGoOlfvOIxFMa97aGo1Sq2GuPP81vaDWDitOq6sVou4Popsd2HM8c0ZV4Wh5QUQdBqYBC1+/8HXcecz2ZjaUZ5mKkfj86Aagj6AUqsB/YtM+N8tB/HIjBFx+Rt7DlfvOIyHplZj9Y7DKLEY4s7vw9OP992fP/omrt0FU6vR7PJi7vhhUdtDefjqR9/A4/Nj0bTY/qzGqx99ExVD5P7BZda4OueOH4ahZVa0ur1Ys/NwVD6t3nE4LraF06rxyn/2wyRocWpfO07ta487vtgxhZSTi2eTXD0P9aZ28+WZk6gn6M71lqvXUual8x4pmzSyLOfgq+HSV1NTg1tuuSWtVUd9Pinx7GMmV7NUe535FGu+1JmmZDPeCfM0Fytw5no10Hw75ogVEitspuCEQKALZZSIJWpFHUPwBpJi1dFksXQqR9tjO9Tig18OhFcdPaHQBH9ARpM7uFJjaNVRnUaDow4vyguSrDpqEuASfe2rPspocAZXvjTrdTjq8KLYLMBm1qGlTYQcseqo0+tDockASZaDK3N2YtVRt88HDbToWxix6qjNiHK7Ds3uiFVHrQZ4/H4UmY1wi8E6K+1GFFniVx2tbZFQ5/SiX2HEqqNGAU1tPhSbBZTb2lcdbV+d0qgDzAbd8VVH7UaYje2rjop+lBcYYdCnWHXUF0CLxweb6fiqo25RgsMTXC3VoJfh9WsgtK86WufworzACK/kR7HZgDZfglVHHV70sRpQaNLDKwX3Fxj0sBl16FcYs+poiRkef+JVR+P+BbCjPE3neulsnkZeP+3nNt1VR4utAjQAmt2+4Gq2kgRBq4taddQtSigw6mA3C3AkWHW00SkG81jQodYRXD1XC00wD416WA06tLWvOmoStLCEVh11iaiwGWExRKw6WmCAoAuuOuoRAyi1GeASfTAJeoj+AFweCWajDlZBB7NRi8NNHvSxGlBg1KLO4YPD60eJNWLVUUewT0rMoVVHDeGHz9Cqo25RQl+bMfGYkgVq/Nf2dHRlLM3Zipi5WnW0N7Sr4tUIU30KI2meQp3XpBpi0pgE3Pzq1vDviT7R9uL1Z0H25C4B1NBPsbr6ibYuv3dKJsOvVWPfA+qMS/GY0nmP1I2YOvOJNlVPtM2dOxebN29GU1MT+vTpg5///Oe4+uqrk5ZPdaMA1JlcieRLnED+xJqLODv94I386c8Qxps52Yi1KzkKqKMf1RCDWuJQQwyZjKM3jKWdwWNTn3weS7OFx5pbnGhTFifaukbRibY068wFNcYEqDOufIupMxNtqv67gKeffjrXIRAREREREREREaWlx39HGxERERERERERUTZwoo2IiIiIiIiIiEgBnGgjIiIiIiIiIiJSACfaiIiIiIiIiIiIFKDqxRCIiIiIiIiod9ObDZBkGQ7RD41JSFhGp9HA3yZmOTIionicaCMiIiIiIiLVkmQZN7+6FXq9Dn6/lLDMi9efleWoiIgS45+OEhERERERERERKYATbURERERERERERArgRBsREREREREREZECONFGRERERERERESkAE60ERERERERERERKYATbURERERERERERArgRBsREREREREREZECONFGRERERERERESkAE60ERERERERERERKYATbURERERERERERArgRBsREREREREREZECONFGRERERERERESkAE60ERERERERERERKYATbURERERERERERArgRBsREREREREREZECONFGRERERERERESkAE60ERERERERERERKYATbURERERERERERArgRBsREREREREREZECONFGRERERERERESkAE60ERERERERERERKYATbURERERERERERApQ/UTbBx98gIkTJ2L8+PF46aWXch0OERERERERERFRQvpcB5CKJElYtGgR/vSnP6GiogJXXXUVxo4di6FDh3auIgHY3ySi9jsHKuxGDCo2AL5uBheqs9WrfJ1KxgkAGqC+zY8Gl4hSqxFlZh0gK1AvKUcP7G8OnvtKuxEnFRkAf0yZdHJOiTKdqUPpXO2KdPK7vczeA00oMup5DXSVANS0+LC39RicHj/MBj0MOg3k9r40Czq0+XwwCwJcoh9Orx92swC36IfVoIdJ0MFq0MDhCcDjl6DRaOCXZLhFCX2sAgKQoYUGoiTjmEtEhd0IQaeBwyO116WHoNPAotej5ZgTdQ4RrR4/rAYdCox6+GUJkLWoc3hRZjPCqNOgzR+AX5Lh8UkoLTDA6w/A65dgEvRobfOhyCyg1eOD2aBDiUlAibE9N7RAjdOHo61e2E16mA06NLpEFFsESAEZLW0+FFkMaKl3odgiQA4EUGQW4PAGcKTVg9ICA0rMehQZdKh3+9Hi8UHQ69DgFFFaYIBBB4gSUOvwoqTAAINWgwKDDiWGNHMzIu9PCACFOmQ2p/PlPtI+ltYfcaBvoQltvmDu9LEaIEOGXdAfP8dA/HFZguerwSWi0BrMl5Y2H/pYDXCLwboKzXoY9Tq0ekRYDALqHV6U24zQ64DaVhEFRj2KrQI8YgB1Di8KjHrYTDoUmrU41Og93n/Ikz4l5eXiHpqJ59bOtJvt54VcHK8WOOTwYVttHSoKjOhvE4BAhtukXk9vNkCS428eDtEPjUkAAOg0GvjbxGyHpl7deabpztiSq3GYckLVE207d+7EwIEDMWDAAADA5MmTsXbt2s5NtAnA+r3NmL9qFzy+AEyCFoumjcCYqqKuJ3a+1AkAGmBTTSvmLd8ZrnfxzJE4r7+dD/RqoQfW70tw7ocWHZ9sSyc/lCijVDvZkk5+8xpQhgB8/G0r6p1eLH73q3BfzhlXBatBFy5WYTdi9xFnVJnZY6vwxtaDuO2SoSiyCHB7fXB4JLhECUvW7kWxxYBbRw+GSdBCkjVYuHp3+LULp1Xjf7cexNYDLTAJWsybOBz9i0w4cKwNT7+3JyIHTwWgjcrLh6ePgNcv4ZG3vwhvu2fSySg0C3hm7W5ce9aJWLpub9SxDC61YtQJBdi4vxkPrNgVtc8i6PDI2//FD84+Ea9+fABNbhF3XToM/3fzAdx+yVAY9F7c8+bn4dcsmFqNEquA33/wNcaeXBnV1qJp1Xhuwz4caGwLl9VpZBSaDRhVWZA6N7Od0/lyDUWMpcUWA64/fyCWrD3e5/dddjIAoMJuCvYxoo9rYB8z7hhThQdXHn/961sO4mcXDMK+OmdcXW2+AH77/p6o8/3aJwfQ4vHhtkuGYsGq3VH506/YjKNNTjy99hssufYMiP6A+vuUlJeLe2iu7tu9qV0tsGF/Mx5cGX0PumRQESfbKKMkWcbNr26N267X6+D3SwCAF68/K9thqVd3nmm6M7ao6f0TZYWq/3S0trYWlZWV4d8rKipQW1vbqTr2N4nhhAYAjy+A+at2YX9T12f186VOIDhbHxpIQvXOW74T9W2xH5eiXNnfnOTcNx8/9+nkhxJllGonW9LJb14DytjfJGJf/fEJNCDYl0vW7kWDSwz/55MQV2bpur2YMrIfFqzaDZ9fhqDTo8ElhicurhzVHw2u4KeDQpNsodcuWLUb118wOPz74ne/gk9CeJIttN1iEOLy8sGVu1Dn8EZte+KfX2J/owtTRvYLT3xFHsvuI634tkUMT7JF7mt0i5gysh+WrN2LK0f1h6d9omXKyH6Yv2o3vmlwRb1m4erg8V5/weC4tuav2o0pI/tFlbUYBOw+0tphbmY7p/PlGoocS68c1T+cX0Aw5sfe+RINLjHcx7HHNWVkv/Cb5NDrp4zsh0a3GFdXg0sMT7KFti1cvRs3XTwknOux+bOvzokzTiyFxxfA7iOtedGnpLxc3ENzdd/uTe0ecvjC40eozQdX7sIhB99BE6lJd55pujO2qOn9E2WHqj/R1lk6nQZFRZaobbXfOcIJHeLxBVDb6sWZ/Yu61E6+1AkAew80Jay32SuhqtLe5XpDdDptXJ+rkZrijM3TdM59tsoo1U62pJPfmb4GMiWXOZtsLA3ISNiXgYh/AXR5/QnLaDTB/7tEPyAjqi6NJvh7ste2if6o311ifLlkrw3E/OtkaFsonkT7aiMm55K9TqOJP7ZEbYWON1mfxJYNyOgwN7Od0+m2l+2cTTWWpjq/QLCPQ9tCIl8TeZ4T5X2ya6FN9HeQW56Ur0/nHKrpfqa0nnZs2Xou7Uiu7tu9qd1ttXUJ26xzenFav8KMtKmURHl6fJ96rkmH6Ider4MGwU9rJaLVamDLQryhWEISxZSrWBLFlK1YOtLVfFIyR7vzDJXu2JIoJjW8f1LT9RzSk2NS9URbRUUFjh49Gv69trYWFRUVSctLkozmZnd0HXYjTII2KrFNghYVdmNc2bTjypM6AaDIqE9Yb5FR1616w/UXWRSpJ9NyEWdZmS3h9tg8rUzj3KeTH0qUUaqdbEknvzN9DWRKNnI23RwFgud9X50jYV9qIyaMrKbE/S3Lwf9bDXpAA+g0iCqn0yR/rdmgj/o9+H1v0eWSvTYytshtARlJy1faEue4VgNIgePHE3tsidqyGvSQkbityK9UCZXVatBhbmY7p9NtL1M529WxNFU+FBl1ADQJy0S+HojP1WTbInM1WdsVNlPK16dzDvPlvtsV+XpsnR1Ls30PzdV9uze1W1GQuM3yguw/GyWSLEeBxHkaoqZrUmMS4PdLUX8SGSsQSH4smYglJFFMuYolUUzZiqUjHeVTZ8bSdOuMK9+NZ6h0x5ZEManh/ZOarueQfIsp1VgaS9V/Onraaafh22+/xaFDhyCKIt5++22MHTu2U3UMKjZg0bQR4Qfm0N9DDyo2dDmufKkTAMrMOiyeOTKq3sUzR6LMrOo51l7lpKIk577o+LlPJz+UKKNUO9mSTn7zGlDGoGIDhpQVYN7E4VF9OWdcFUqthvB/ghZxZWaPrcKanYexcFo1BL0GPn/wy+nnjKuCSdBi+baa4JfNe31YMLU66rULp1Xj1Y++Cf8+b+JwCDpg7vhhUeXcXl9cXj48fQTK2yfNQtvumXQyBvWxYvWOw5g9tiruWKr72jGw0IBHZoyI29fHYsCanYcxZ1wV3txeA5OgxV2XDsOanYexaFo1Bpdao16zYGrweP/80TdxbS2aVo01Ow9HlXWLPlT3tXeYm9nO6Xy5hiLH0uXbasL5BQRjvu+yk1FqNYT7OPa4Vu84jIenR79+9Y7DKLEY4urqYzXgrkuHxZ3vlz/4Gqt3BHM9Nn+Glhfgs4MNMAlanNrXnhd9SsrLxT00V/ft3tRuf5sQHj9CbT48fQQG2ISMtUlEndedZ5rujC1qev9E2aGR5QTLlKjIxo0b8dhjj0GSJMycORO33npr0rI+n5R49jGTK4SqvU4gZmUVQ3AgUeisq3EWOhE1faItYZ6GVh0Nnft8WXVUDavmpJPf7WWavRKKjDpFr4FMyeUn2lKNpTUtPrT5JTg9EswGXXDV0fbdJr0OHr8PZn1o1VEJNpMebb7Eq45qNRr4IlYdlRH8U4fQqqPlNiMM+uCqoy6vHzaTHoJeA7NeD5OgQZ1DhMMjwWzQwha76miBEUa9Bh5/AD5JhtcnoY/VAK8UgChJMOr1aPX4UGgS4PD4YBZ0KDHHrzpa275qpKV91dEis4CALKOlzY8ii4CWNh+KzQJk+fiqo0dbPeiTYNVRvT5YRx+rAcbIVUetBhh0GhQIuugVMVOJXHW02JLlVUcTX2fZ/kRbqrG03uENrzrq8koosQqQZRl2Q6pVRw0os+iDq466RRSag/kSueqoyyvBZtLBpNeh1SvCIgiodwZXuRV0GtS2BvOl2CKE/2yswKiHzaBDoSW06qgh/EDflXtzvtx3uyJfj60rY2nW76G5XnW0N7TbvupondOL8gJjcJJNJQshpPoURtI8hbquSY1JwM2vbk35ibYXrz8LsifzCRaKJSRRTLmKJVFM2YqlI139RJviOdqd98ZpjC1JY8rx+yc1Xc8h+RZTZz7RpvqJts5IdREC6jyRieRLnED+xKr6ibZ2+dKfIYw3c1Q50dZODf2ohhjUEocaYshkHL1hLO0MHpv65PNYmi081tziRFtmYgnhRFt6VDPRlmFqjAlQZ1z5FlOP+dNRIiIiIiIiIiKifMGJNiIiIiIiIiIiIgXwW3eJiIiIiIgoiscfgMaUekEHnUYDf5uYpYiIiPJDj/qONiIiIiIiIiIiolzhn44SEREREREREREpgBNtRERERERERERECuBEGxERERERERERkQI40UZERERERERERKSAHrXqqCj60dLSlnR/QYERTqc3ixF1Tb7ECeRPrLmIs6zMlnB7qjzNl/4MYbyZk41Yu5KjgDr6UQ0xqCUONcSQyTh6w1jaGTw29cnnsTRbeKy5lSxHgfwbSxlTevIxpp5yv1djTIA648q3mFKNpbF61CfaNBpNyv16vS5LkXRPvsQJ5E+saoozVZ6qKc50MN7MyWWs+TCWqiEGQB1xqCEGIPtx9KSxtDN4bPkjH8bSbOGxqle+jaWMKT09KSbmqDLUGFdPjqlHTbQRERERERERERHlCifaiIiIiIiIiIiIFKCKibZ7770X559/PqZMmRLe1tzcjBtuuAETJkzADTfcgJaWlq43oAHqPX5sOdCEeo8EpP4kP1H+as/1LxrdzHXKPeYj9STMZ8pnzF8i5fE9JhEloYrFEK688kr8+Mc/xq9+9avwtpdeegnnn38+Zs2ahZdeegkvvfQS5s2b1/nKNcCmmlbMW74THl8AJkGLxTNH4rz+dkBW8CCIco25TmrCfKSehPlM+Yz5S6Q8XldElIIqPtF29tlno7CwMGrb2rVrMWPGDADAjBkz8P7773ep7vo2f3gABACPL4B5y3eivs3frZiJ1Ia5TmrCfKSehPlM+Yz5S6Q8XldElIoqPtGWSGNjI8rLywEAZWVlaGxs7PA1Op0GRUWWqG17DzSFB8AQjy+AZq+Eqkq7cgErSKfTxh2HWuVLrGqKM1GeHt/X9Thzketq6td05FO8uYw1VY4G93ccW6bzUS3nUg1xqCGGXMSRqbE0ETU9S6jlfGdCTzs2JcZSJaghf3vauU0l3441m2OpEtQSkxquq1TU0k+RuhoTc1QZaoyrJ8ek2om2SBqNpsMl0gFAkmQ0N7ujthUZ9TAJ2qiB0CRoUWTUxZVVi6Iii2pji5UvseYizrIyW8LtifI0pDtx5iLX8+X8h+RTvNmItSs5CqQXW6bzUS3nUg1xqCGGTMaR7bE0YX0qepZQy/nOhHw9tkyOpUpQQ/7m67ntCjUea7IcBbI7lipBLTGp4bpKRS39FKmjmLoylpoKTPD6pZTt6jQa+NvE9APtJjX2PaDOuPItplRjaSxV/OloIn369EFdXR0AoK6uDiUlJV2qp8ysw+KZI2ESgoca+vv5MnNezDESpY25TmrCfKSehPlM+Yz5S6Q8Xlfq4AsEcPOrW1P+J8n80jzKPtWOBGPHjsWKFSswa9YsrFixAuPGjetaRTJwXn873px1Hpq9EoqMuuAAyOuNepqIXG9wiSi1GpjrlDvMR+pJmM+Uz5i/RMrje0wiSkEVE21z587F5s2b0dTUhIsvvhg///nPMWvWLNx55534xz/+gRNOOAHPPPNM1xuQgTKTHlWV9uDHADkAUk/VnutlJn34d6KcYT5ST8J8pnzG/CVSHt9jElESqphoe/rppxNu//Of/5zlSIiIiIiIiIiIiLpGtd/RRkRERERERERElE840UZERERERERERKQATrQREREREREREREpgBNtRERERERERERECuBEGxERERERERERkQI40UZERERERERERKQATrSpiRY45PJh7Vd1qHH5eHYomgao9/jxRaMb9R4J0OQ+li0HmnIfC2VdICCj3uPHjnoX9rV6cExkDlAPlmjsVdN4TPknF/dQ5izlOzU+e6oxJiJSBX2uA6B2WmDD/mY8uHIXPL4ATIIWD08fgUsGFQGBXAdHOacBNtW0Yt7yneH8WDxzJM7rbwfkXhwLZZ8GeP+rOvzif3eEz/+ccVUYXGrFqMoC5gD1LEnGO4NeizlvfMYxkDovF/dQ3rcp36kxh9UYExGpBj8zpRKHHL7wJBsAeHwBPLhyFw45fDmOjNSgvs0fvpEDwfyYt3wn6tv8vToWyr76Nn94kg0Inv8la/di95FW5gD1OMnGu91HWjkGUpfk4h7K+zblOzXmsBpjIiL14ESbStQ6POGBOsTjC6DW4clRRKQmDS4xYX40uMReHQtlX7LzH5DBHKAeJ1W+x25j/lM6cnEP5X2b8p0ac1iNMRGRenCiTSUqbSaYhOjTYRK0qLCZchQRqUmp1ZgwP0qthl4dC2VfsvOv1YA5QD1OqnyP3cb8p3Tk4h7K+zblOzXmsBpjIiL14ESbSvS3CXh4+ojwgB36jrYBNiHHkZEalJl1WDxzZFR+LJ45EmXm7H/NoppioewrM+vw1NWnR53/OeOqUN3XzhygHifZeFfd184xkLokF/dQ3rcp36kxh9UYExGpB0cCtQgAlwwqwv+98VzUOb0oLzAGJ9m4EAIBgAyc19+ON2edhwaXiFKrIXgjz8WXrUbE0uyVUGTU5S4Wyj4ZuHR4Od6cdR6OOLywGHQoMQsoMeqYA9TzJBt7AXWMx5R/cnEPVdMzBFFXqPHZU40xEZFqcKJNTQLAAKuA0/oVornZzUk2iiYDZSY9ykz68O+5jqWq0h7MVT5U9CparSY6FwHmAPVcScZe1YzHlH9ycQ9V0zMEUVeo8dlTjTERkSrwT0eJiIiIiIiIiIgUwIk2IiIiIiIiIiIiBXCijYiIiIiIiIiISAGcaCMiIiIiIiIiIlIAJ9qIiIiIiIiIiIgUwIk2IiIiIiIiIiIiBXCijYiIiIiIiIiISAH6XAfQkVdeeQX/+7//C41Gg2HDhuHxxx+H0WjMdVhERERERERERERRVP2JttraWrz66qtYvnw51qxZA0mS8Pbbb+c6LCIiIiIiIiIiojiqnmgDAEmS4PF44Pf74fF4UF5enuuQiIiIiIiIiIiI4qj6T0crKirws5/9DGPGjIHRaMSFF16Iiy66KNdhERERERERERERxdHIsiznOohkWlpa8POf/xzPPPMMbDYb5syZg4kTJ2L69OkJywcCAUhS8sPR6bSQpECmwlVMvsQJ5E+suYhTEHQJt6fK03zpzxDGmznZiLUrOQqoox/VEINa4lBDDJmMozeMpZ3BY1OffB5Ls4XHmlvJchTIv7GUMaUnH2PqyljqFCXc8tdtKdv93Y9HwWbI3ueL1Nj3gDrjyreYUo2lsVT9ibaPPvoI/fv3R0lJCQBgwoQJ+PTTT5NOtEmSjOZmd9L6ioosKferRb7ECeRPrLmIs6zMlnB7qjzNl/4MYbyZk41Yu5KjgDr6UQ0xqCUONcSQyTh6w1jaGTw29cnnsTRbeKy5lSxHgfwbSxlTevIxpq6MpTqLAX6/lLLdQCD1WKw0NfY9oM648i2mVGNpLFV/R9sJJ5yAHTt2oK2tDbIs4+OPP8aQIUNyHRYREREREREREVEcVX+i7fTTT8fEiRNxxRVXQK/X45RTTsG1116b67CIiIiIiIiIiIjiqHqiDQBmz56N2bNn5zoMIiIiIiIiIiKilFT9p6NERERERERERET5ghNtRERERERERERECuBEGxERERERERERkQI40UZERERERERERKQATrQREREREREREREpgBNtRERERERERERECtArXaEoinj33Xdx+PBh+P3+8PY77rhD6aaIiIiIiIiIiIhUQ/GJtltvvRU2mw3V1dUwGAxKV09ERERERERERKRKik+01dbW4g9/+IPS1RIREREREREREama4t/RduaZZ+Krr75SuloiIiIiIiIiIiJVU/wTbdu2bcNbb72Ffv36Rf3p6OrVq5VuioiIiIiIiIiISDUUn2j7/e9/r3SVREREREREREREqqf4n47269cPR44cwaZNm9CvXz+YzWYEAgGlmyEiIiIiIiIiIlIVxSfann32Wbz88st46aWXAAA+nw/z5s1TuhkiIiIiIiIiIiJVUXyi7b333sPvfvc7mM1mAEBFRQVcLpfSzRAREREREREREamK4hNtgiBAo9FAo9EAANxut9JNEBERERERERERqY7iiyFcdtllmD9/PlpbW/H3v/8dy5cvxzXXXKN0M0RERERERERERKqi6ESbLMu4/PLL8c0338BqtWL//v2YPXs2LrzwQiWbISIiIiIiIiIiUh1FJ9o0Gg1mzZqF1atXc3KNiIiIiIiIiIh6FcW/o+3UU0/Fzp07la6WiIiIiIiIiIhI1RT/jrYdO3Zg9erVOOGEE8IrjwLA6tWrlW6KiIiIiIiIiIhINRSfaPvDH/6gaH2tra144IEHsGfPHmg0Gjz22GM488wzFW2DiIiIiIiIiIiouxSbaGtubgYAWK1WpaoEADz66KP4/ve/j6VLl0IURXg8HkXrJyIiIiIiIiIiUoJiE21XXnklNBoNZFnGkSNHYLfbAQQ/kda3b1+sW7eu03U6HA5s2bIFTzzxBADAYDDAYDAoFTIREREREREREZFiFJtoC02kPfDAAxg/fjxGjx4NANi4cSPWrl3bpTprampQUlKCe++9F19++SWqq6tx//33w2KxKBU2ERERERERERGRIjSyLMtKVjh16tS4hQ8SbUvH559/jmuvvRavvfYaTj/9dDzyyCMoKCjAnXfembB8IBCAJCU/HJ1OC0kKdDqObMuXOIH8iTUXcQqCLuH2VHmaL/0ZwngzJxuxdiVHAXX0oxpiUEscaoghk3H0hrG0M3hs6pPPY2m28FhzK1mOAvk3ljKm9ORjTF0ZS52ihFv+ui1lu7/78SjYDIp/NX1Saux7QJ1x5VtMqcbSWIpnXHl5OZ5//nlMmzYNQHC10fLy8i7VVVlZicrKSpx++ukAgEmTJuGll15KWl6SZDQ3u5PuLyqypNyvFvkSJ5A/seYizrIyW8LtqfI0X/ozhPFmTjZi7UqOAuroRzXEoJY41BBDJuPoDWNpZ/DY1Cefx9Js4bHmVrIcBfJvLGVM6cnHmLoyluosBvj9Usp2A4HUY7HS1Nj3gDrjyreYUo2lsbRKBRTy1FNP4dixY7jjjjtwxx13oLGxEU899VSX6iorK0NlZSW++eYbAMDHH3+MIUOGKBkuERERERERERGRIhT/RFtRUREeeOABxep78MEHcffdd8Pn82HAgAF4/PHHFaubiIiIiIiIiIhIKYpNtN1yyy0p97/wwgtdqveUU07Bm2++2aXXEhERERERERERZYtiE20/+9nPlKqKiIiIiIiIiIgo7yg20XbOOecoVRUREREREREREVHeUWyibc6cOViyZAmmTp2acP/q1auVaoqIiIiIiIiIiEh1FJtou//++wF0/bvYiIiIiIiIiIiI8pliE23l5eUAgH79+ilVJRERERERERERUd5QbKIt5F//+heefPJJNDY2QpZlyLIMjUaD7du3K90UERERERERERGRaig+0bZ48WK88MILGDJkiNJVExERERERERERqZZW6Qr79OnDSTYiIiIiIiIiIup1FPtE27/+9S8AwIgRI3DnnXfi0ksvhcFgCO+fMGGCUk0RERERERERERGpjmITbevXrw//bDab8eGHH0bt50QbERERERERERH1ZIpNtD3++ONKVUVERERERERERJR3FP+Otl/96ldobW0N/97S0oJ7771X6WaIiIiIiIiIiIhURfGJtq+++gp2uz38e2FhIb744gulmyEiIiIiIiIiIlIVxSfaAoEAWlpawr83NzdDkiSlmyEiIiIiIiIiIlIVxb6jLeRnP/sZrr32WkyaNAkA8M9//hO33HKL0s0QERERERERERGpiuITbTNmzMCIESOwadMmAMCzzz6LoUOHhve3tLSgsLBQ6WaJiIiIiIiIiIhySvGJNgAYOnRo1ORapJ/+9Kd46623MtEsERERERERERFRzij+HW0dkWU5200SERERERERERFlXNYn2jQaTbabJCIiIiIiIiIiyrisT7QRERERERERERH1RIpNtB06dCitcvzTUSIiIiIiIiIi6okUm2ibM2cOAOAnP/lJynKvvPJKp+uWJAkzZszAzTff3JXQiIiIiIiIiIiIMk6xVUcDgQBeeOEFfPvtt/jTn/4Ut/+GG24AABQVFXW67ldffRVDhgyB0+nsbphEREREREREREQZodgn2p5++mlotVpIkgSXyxX3X1cdPXoUGzZswFVXXaVUqERERERERERERIpT7BNtgwcPxqxZszB8+HCMHj1aqWrx2GOPYd68ed2arCMiIiIiIiIiIso0xSbaQkaNGoXHH38cW7ZsAQCcc845uP3222Gz2Tpd1/r161FSUoIRI0bgk08+6bC8TqdBUZElxX5tyv1qkS9xAvkTq5riTJWnaoozHYw3c3IZaz6MpWqIQS1xqCGGXMTRk8bSzuCx5Y98GEuzhceqXvk2ljKm9PSkmFLlqFOUoNfrUr5eq9XAltXnE/X1PaDOuHpyTIpPtN13332oqqrCkiVLAAArV67Evffei2effbbTdW3fvh3r1q3DBx98AK/XC6fTibvvvhtPPvlkwvKSJKO52Z20vqIiS8r9apEvcQL5E2su4iwrSzy5nCpP86U/Qxhv5mQj1q7kKKCOflRDDGqJQw0xZDKO3jCWdgaPTX3yeSzNFh5rbiXLUSD/xlLGlJ58jKkrY6nOYoDfL6VsNxBIPRYrTY19D6gzrnyLKdVYGkvxibaDBw9i2bJl4d/vuOMOTJ8+vUt1/eIXv8AvfvELAMAnn3yCP/7xj0kn2YiIiIiIiIiIiHJJscUQQkwmE7Zu3Rr+fdu2bTCZTEo3Q0REREREREREpCqKf6Jt4cKF+OUvfwmn0wkAsNvteOKJJ7pd77nnnotzzz232/UQERERERERERFlguITbSeffDJWrVoVnmgrKCiI2v/WW2/hiiuuULpZIiIiIiIiIiKinFL8T0dDCgoK4ibZAODVV1/NVJNEREREREREREQ5k7GJtmRkWc52k0RERERERERERBmX9Yk2jUaT7SaJiIiIiIiIiIgyjp9oIyIiIiIiIiIiUkDWJ9pGjRqV7SaJiIiIiIiIiIgyTvFVRx0OB5YtW4atW7cCAM455xzcfvvtsNlsAID58+cr3SQREREREREREVHOKT7Rdt9996GqqgpLliwBAKxcuRL33nsvnn32WaWbSp8A7G8SUfudAxV2IwYVGwBfN+vUA/ubRdS2elFpN+KkIgPg72adWuCQw4dttXWoKDCiv00AAt2sk/KDDvi2RUTtEQcqbUYMLDQAUkyZdHIulOut3uS5HmqrvZ64ttrzsNbhQaXNlDgPmau9kxY44vbjy2+Pwenxo8QqwGLQodXjQyAAFJoE9LXq08sFDVDf5keDS0Sp1Ygyiw717ojfzTog0TcNaIBjXglfH2qG0+PDCXYTyiw6HGrtIGcTtZmsjY7KRuw7IQAU6pC8HsquiLHpBLsJei3Q4ExxvtPJiY7KRO4vMEKnAWod3tQ5FjvO2gUca5NwrM0HlygF89oUnXPHvMH9bQ1uVNqMx/d38hgq7EaIkoxmjx9Ojx99C43oZ+UYnlWZeC7tSCaeW9ORi2Ol7uN5Sw/7iYiSUHyi7eDBg1i2bFn49zvuuAPTp09Xupn0CcD6vc2Yv2oXPL4ATIIWi6aNwJiqoq4PhHpg/b4EdQ4t6vpDixbYsL8ZD648XufD00fgkkFFfPjt6XTA+q8T5NOQouMTYOnkXDq53lFb6eQhc7V30gKbalpR09SGJWv3hs/9wmnVsBn1+OOH32DcKZXoV2zG+f3tqXNBE6xr3vKd8PgCGNjHjDvGVEXl1OKZI3Fef3vchMb2o0580+CKimHR9BF4bv1eHGhsS56PMW0mbaOjsuhEPZRdEWNTscWA688fGJUncecpnZzoqEyC/XPGVeHVjw+gyS0mzo2YMXRgHzN+MX44Dje3JY4XifN+8cyROG+AHZsOpX8MxRYDbh09GC5RiqrrkRkjMPqkIo7h2ZCJ59KOZOK5NR25OFbqPp639LCfiCgFxb+jzWQyhf9sFAC2bdsGk8mkdDNp298khgdAAPD4Api/ahf2N4ldr7M5SZ3NXa/zkMMXfugO1fngyl045OBI3dN925I4n75tOZ5P6eRcOrneUVvp5CFztXc65PBhb50z/OYcCJ77Bat2IyAD118wGEvW7sW+OmeHuVDf5g9PDADAlJH94nJq3vKdqG/zx71u95HWuBjmr9yFKSP7hX9PlI+xbSZro6OynamHsitybLpyVP+4PIk9T+mcy47KJNq/ZO1eXDmqf9LciB1Dp4zsh3318ddWZM4lyvt5y3fikMPXqWO4clR/NLjEuLoeWMExPFsy8VzaYZsZeG5Nq90cHCt1H89bethPRJSK4hNtDz30EBYtWoSxY8di7NixePjhh7Fw4UKlm0lbbas3PACGeHwB1LZ61VWnw5O4Toeny3VSfkiaTw5vx2VaFSrT3lY6echc7Z1qHR4EZCQ89y7RjzbRD48vgICMDnOhwSVG1aPRJK63wSXGvS5ZDBpN9O+xMcS2mayNjsp2ph7KrsixKZ2cSudcdlQm2f5QPibKjdgxVKNB0rwO5Vyy/cnG42THkKotjuHZkYlnSDW2mct2qXt43tLDfiKiVBSfaBsyZAhuuukmXHnllRg/fjwuvfRSvP/++0o3k7YKuxEmIfowTYIWFXZjl+uszESdNlPiOm25+zQgZUfSfLIZOy4TkXPp5HpHbaWTh8zV3qnSZoJOg4Tn3mrQw2zQwyRoodWgw1wotSbOw9jfS62GuNcli0GWo3+PjSFZm7FtdFS2M/VQdsWOTR2dp3TOZUdlku0P5WOi3Eg0hibL61DOJdtfkWQ8TnUMqeqizMvEc2lHMvHcmo5cHCt1H89bethPRJSK4hNtt956K9avXw+j0YiKigpYLBZYLBalm0nboGIDFk0bER4IQ38/P6i462+KTipKUmdR1+vsbxPw8PToOh+ePgIDbEKX66T8MLAwcT6dVHg8n9LJuXRyvaO20slD5mrv1N8mYGh5AeaMq4o69wunVUOrAV796BvMGVeFoeUFHeZCmVmHxTNHhutZveNwXE4tnjkSZWZ93OtO7WuPi2HR9BFYs/Nw+PdE+RjbZrI2OirbmXoouyLHpuXbauLyJPY8pXMuOyqTaP+ccVV4c3tN0tyIHUNX7ziMIWXx11ZkziXK+8UzR2KATejUMSzfVoM+VkNcXY/M4BieLZl4Lu1IJp5b05GLY6Xu43lLD/uJiFLRyLKs6Nc3T5kyBWvWrFGyyrT5fBKam93xO9JZibGzIlZvqrAbgw8rCq06Wuf0orzAGHzoVfkXExcVWRL3ucrkIs6yMlvC7QnzNLQSqMOLCpsxOPGVYtXRpDnXmVVHk7UVsRpehc2UOA/zMFeB/MlXIDuxdipHgfCqoy0eP1xeP4otAsyCDk6vD1IAsJsEnNClVUcNKLPoI1YdNQQnClKsOtrkleDy+NDXZkSZVR9edTRpziZqM1kbHZWNXHW02JLzVUfVkteZiqNTeRoxNvW1myCEVx1Ncr7TyYmOysTs12k17auOpsix2HE2YtVRtygF8zom58KrjvoCqCyIqLuTx1BhM0IMBFcddXn9qLAZ0b9AHWO4WnK5szo9lmbiubQjmXhuTUcujjXH1JjHyXIUSJKnKj5vqupf9lOndBRTp8dSADqLAf/zypaU7b54/VmQPdk7MWrse0CdceVbTKnG0liK/xP8mWeeia+++grDhw9Xuuqu8wGDCgw4s39RsNOUuM78wToHFRjCv3dbABhgFXBav8JgnCp46KUskYCTCgw4I5SjsZNsQHo554spkyjX29s6KVQmtq32PBxgFcK/x2Gu9k4BoK9Jj1Mq7dE3IJM+qkxaZKDMpEdZ6LWBmN+TTVzJQIlBh8HltuMxSGnkbKI2U02OpSobsU+NDwi9WuzYBKDMmOJ8p5MTHZWJ3Q+gpI8leX0RcYZzVgrmdYlBF1Vv5M+h/eGck4/v6/QxAKjsynVLysjEc2lHMvHcmo5cHCt1H89bethPRJSE4hNt27Ztw1tvvYV+/frBYDj+0dnVq1cr3RQREREREREREZFqKD7R9vvf/17pKomIiIiIiIiIiFRP8Ym2fv36KV0lERERERERERGR6im+6igREREREREREVFvxIk2IiIiIiIiIiIiBXCijYiIiIiIiIiISAGKf0ebko4cOYJf/vKXaGxshEajwTXXXIOf/OQnuQ6LiIiIiIiIiIgojqon2nQ6He655x5UV1fD6XRi5syZuPDCCzF06NDOVSQA+5tE1H7nQIXdiEHFBsDXzeBCdbZ6la9TyTjzKVYNUN/mR4NLRKnViDKzDpC7WWe+SKc/0zmPSpTpTB3diTeddrTAIYcPtQ4PKm0m9LcJQKAbx6z0tdXbCMC3TSIaa50osgho8/lhEfQw6LRwin60iRJKLAY0tflQajWgzSfB4fWjxCoAsgZ1Di9KbUaUFujQ6JRQ2+rFgGITNNCgxeODW5RQYTcCMlDT7EGl3Qi7WYdjLj8aXSLKbUYUW3RocUvYWutEgVEPg04Dq1EPp9ePJreIPlYjmtt8KDILAALQanRocHrRp0CATqOF0+uHxyfBbtZDlgGjXgdR8sOk18PhleD0+FFmM8Bi0KHJ7YPT40eBUQ+7WQ+XKKHBKaLCZoRT9MNq0EPj8kCv0SEgywjIQKNLhNWgg9WoR5NbhN0kQKMBGpwiSqwGOL0+2IwCbGYdnB4JTW4f7CYBrW0+FFsF+HwSCk2G4+OfBjjmlXCszQeXKGFAkQlSQMZRhxcFJgFNbh/6uHwYaBcAKcE5SzWudmbMzZfxOeZaLzTr0NImwaCT0eaTUWISUGLUAQgeT4vHB6Ogh9PjCx6XRYd6d/A4iwoMkCQZLlFCm0+C3SSgxeOD3aRHgUEPl+iHKMlwevyosBuhQTBvy+1G2I06eHwB1Dq8sBh0KDDq4Q9IkGUtWj0+FJoFNLtFFFkMaG7zobzAAL1WC38gAF9ARpsoweOTUGQxwC36YRJ0KDbr0dwmwdHggt0koN7hRWWhCRoAR1s9KC0wwif5odXo4PD40MdqhF4nwyvJ0Efkfh+rAT5JRqvHhwq7EUa9Fq1tEhpdIgpMetiMOgg6LZrbfHB5g9eKW/SjxGKAWadFrcN7PAcQkxcR/afqPMm1XNyTMvEs2Jl2s33/zcXx9qRnDTUeC2NKT/v9eu+BJhQZ9RyHiXJE1RNt5eXlKC8vBwAUFBRg8ODBqK2t7dxEmwCs39uM+at2weMLwCRosWjaCIypKur6QJgvdeZTrBpgU00r5i3fGa5z8cyROK+/veffHNLpz2yVUVM7WmDD/mY8uPJ4mYenj8Alg4qOT7YpFS91TAA27Is+H3ddOgzFFj0aXT4sWbs3vP3uCcNxpLkNj73zJYotBlx//sDw/oF9zLj9kqGYv2o3ii0G3Dp6MFyiFPX6ueOH4U8ffosmt4hF06rx/hdH8K//NmBgHzPuGFMVFcN9l50Ms0EftW322Cq8sfUgbr9kKJ7bsA+FJgE3XDQIR1s8Ue3MmzgcNqMOZoMeNU2tUfsWTavGcxv24UBjGwb2MeO2S4Ziward4f0LplZj/ZffYsrIfvD4JAAazI/YP2dcFSyCDn/86L+4ZfRQvPbJAeypc0bEVoW/bz2AsSdXYum6vXGx3z1+OM4bYMf2I0580+DCkrV74/oyVP6x9vrGDCmKnmxLNa6iE2NuvozPCa/1aug1EvyyDlu/bcCgMjtOriyA0yPhyfe+wrVnnRju/8j8CuWmxx/A0+/tiTs/c8cPQ4NTxOJ3v4o6569+fKA9b0fguQ17caCxrT2OUyFDg+c37ItqM3QdeUQfBJ0OHn8gLk/vunQY/u/mA7h9TBX+vuV4ziTKhwVTqvHCB/vC7T4yYwT6FAj48ogz4TVm0Gvirqk546pQWWjCM+/vCdcTOu4fnH1i+BiXXHsGRH8gnBeJrk9V5kmu5eKelKv7YG9qtyc9a6jxWBhTevLlfk3UC+TNd7TV1NTgiy++wOmnn96p1+1vEsMDIAB4fAHMX7UL+5vELseSL3XmU6z1bf7wTSFU57zlO1Hf5u9ynfkinf7MVhk1tXPI4Qu/YQuVeXDlLhxy+NJuJ90y1LH9TWLc+fjt+3tgMQjhN/Ch7U/+6ys0uER4fAFcOap/1P4pI/uFJ6SuHNUfDS4x7vVPv7cHV47q336uduNH5w0KvzY2hgZXfFxL1+0NtzNlZD/cdPEQ7G+frIost/jdr2AxCNhb54zbF3ptqN3QJFto/8LVwbj21btgMQjhYwrtX7J2LxrdIqaM7IeFq3fjpouHxMS2C9dfMDg84RIb+7zlO3HI4cPuI8cnAGP7Mra+b1uiczrVuNqZMTdfxufE1/pu9C+xY/6q3Zgx6kQsWbsXLm8w/ikj+0X1f2R+hXIzNMkWqi/U31/Xu8KTbKF9S9bujcjbXeH88fgCsBgELGjPqdhz/tv396DIYoQUQMI8/e37e4LneGV0ziTKh4Vrdke1+8CKXbAI8ddo6BpLdE0tWbsX+xtcUfWEjjvyGHcfaY3Ki0R1qTFPci0X96Rc3Qd7U7s96VlDjcfCmNKTL/drot5A1Z9oC3G5XJg9ezbuu+8+FBQUJC2n02lQVGSJ2lb7nSM82IR4fAHUtnpxZv+iLsWTL3Vmqt5M1Ln3QFPCOpu9Eqoq7V2qM5JOp43LjVyJzdN0+jNbZdTUzrbauoRl6pxenNavUNF+UaNc5mxnxlKX6E+4PdD+L6caDaL2R/6u0QABGQlfr9Ec/7nZ7UtYF5D69aH/t3n9Scu5xOT7QjEkajcUV0AGXN7kfRB6bZvoj4utLcnrQvvrnN6o2JLFEdpe6/DijIicTjWuhn5OtC92zE13fM52zqY9ljo88PgCaHR6g+e8vd+7k5vp5G3oZwBJ2wyVbXL54PFJHeZzZM6kyofI3+vajz9VrLH7QrmbKIbQ9thYk14nnbiPq+lerYRsPZd2JFf3wd7Ubr4+awBdey7NNsaUnky/n+quro7xicbSEKcoQa/XpXy9VquBLavPJ+q8l6kxrp4ck+on2nw+H2bPno2pU6diwoQJKctKkozmZnfUtgq7ESZBGzXomAQtKuzGuLLpypc68ynWIqM+YZ1FRl23jj9cf5FFkXo6o6zMlnB7bJ6m05/ZKqOqdgoSlykvUL5f1CgbOZtujgLJ+9FqSHztaiPepCfaH/pdp0m8X5aP/1xkEZLWler1of9bjPqk5ayG5PvkiD+zSDg+WQToNIDVlLwPpEDwZ7NBHxebJcm4F9pfXmCMiy1V+QpbdE6nGlcBTdpjbrrjc6Zytttjqc0Ek6BFn/YxJXS+Qvu7kpt6bcd5G5k/HbVZbBXQ6tF0mM+xOZNO3pa3H3+icpok7YVyN1EMofqTxdqd+3gu7tVKUGIszeQ9KVf3wd7UrtqfNZLlKNC159JsY0zpyfT7qe7qaIzvzFgaorMY4Pcn+pLa4wKB5K/PBLXey9QYV77FlGosjaXqPx2VZRn3338/Bg8ejBtuuKFLdQwqNmDRtBFRD7iLpo0IflllF+VLnfkUa5lZh8UzR0bVuXjmSJSZVT8X3G3p9Ge2yqipnf42AQ9Pjy7z8PQRGGAT0m4n3TLUsUHFhrjzcdelw+AWfZgzripq+90ThqPUaoBJ0GL5tpqo/at3HMaiadXhfX2shrjXzx0/DG9ur2k/V9X426b94dfGxtDHGh/X7LFVWLMz2M6anYfx+w++xkml1rh25k0cDrfow9Dygrh9odeG2l3YHnNo/4KpwbiGlFnh9vrCxxTaP2dcFfpYDFiz8zAWTK3Gyx98HRPbCPz5o28we2xVwtgXzxyJATYBp/a1h2OL7cvY+k4qjM7pVONqZ8bcfBmfE1/r1ag51opF06qxYvtBzBlXBashGP/qHYej+j8yv0K5OXf8sIT9PbjMinkTh8ed8+N5OyKcPyZBC7fXh4XTquPaDF1HzW4vdBokzNO7Lh0WPMfTo3MmUT4smFId1e4jM0bA7Yu/RkPXWKJras64KgwqtUbVEzruyGM8ta89Ki8S1aXGPMm1XNyTcnUf7E3t9qRnDTUeC2NKT77cr4l6A40sy6r9asStW7fiRz/6EYYNGwatNjhgzJ07F6NHj05Y3ueTEs8+ZnLVTbXXmU+xRq1qZwjeFBTKTjV9oi1hnmZrRdF0yqipnYhVRytspuAkWyCmjFLxqkwuP9GWaiz9tklEo0tEoVmA1y/BLOjaVx2V0CZKKLYIaI5YddTplVBk1UMja1DnDK5WWFqgQ6MruOpo/yITtJrjq46W24KrNx5uX72xMMmqow0uEdaYVUeb3SJKEqw62uj0oiRq1dFA+6qjMow6HXwBP4w6PRyiBKfXjzKrARZjcNVRl0eCxahDYfuqo41OEWU2I9xicMVVjTYQt+poaJXJyFVHG50iii0GOMX2VUdNOji9MauOWgT4/BIKTcLx8S9i1VG3KKF/oQmSLKPW4YXVGOzrEqshzVVHY8bVzoy5aZTN9ifa0hlLQ6uOGnUy3KKMEnOqVUcNKLPog6tmukUUWgwIBGS4fMHctpsEtHp8sEWsOuqT5GDOFBih1bSvOmozwm7Stf/ZpgizQZt81VGzAc2eZKuOBlBkEeAW/TAKOpSEVh31+o6vOmo3QaOJXHVUglajhcPjR4nVAEEnw+OXIWiP536JVYBfktHq8aPcFpwQb22T0OgWUWDUw2bQQdAHVx11eyXYQquOmg0w60OrjhrCb9yi8iLUf128j6vxX7bT0ZWxNGcrYuZq1dHe0K6KnzVSfQqjy8+l2caY0tN+v272Sigy6hR9P9VdXf1EW9KxFMFPtP3PK1tStvvi9WdB9mTvxKj1XqbGuPItps58ok3VE22dleoiBNR5IhPJlziB/IlV9RNt7fKlP0MYb+aocqKtnRr6UQ0xqCUONcSQyTh6w1jaGTw29cnnsTRbeKy51emJtnZqPBbGlJ58jIkTbZmlxrjyLaYe86ejRERERERERERE+YITbURERERERERERArgRBsREREREREREZECONFGRERERERERESkAE60ERERERERERERKYATbURERERERERERArgRBsREREREREREZEC9LkOICsEYH+TiNrvHKiwGzGo2AD4FKqz1at8nUrGmU+xaoD6Nj8aXCJKrUaUmXWA3M0680U6/ZnOeVSiTGfq6E686bSjB/Y3B8tU2o04qcgA+GPKaIFDDh9qHR5U2kzobxOAQMJezryensMC8G37eS+1GWDSa9HS5oPZoEdLmw82kx6iX4JJ0MPh8cFuEuDw+qDXamE36eEPyDjmElFoFqDVaFDnCJ57vVaLeqcXhWYBx1wiygqMCEBGbasX5TYj9FoZUkCDWocXpQUGmAUdPt1Thz5WA2QZqHeKsJn0sJt08PrbX2c3IpgIWtQ7vCixGmARdPBKfhj1erhFCY0uEX3tJui0QE2zB/2LTJACQItXhFUQcMzlRWmBES7RB51GB0GngVHQQq/VQPTLOOYWUen1wy1KqHeKqLQbodVoUO/0wiLoYDXq0eASUWwWIOhk+CQNGpwiCkx6mAQNtNCgQNChxKRDvcePQ80emAUdCs0CBC3Q4BBRWmCEGJBxpMWDEqsAg06LJrcIq1EPj09CmdUIe0AO5p7Hj5oWL8yCFoUmAX2t+mAX9PS8jBQxNlXajSi06HC0RYRblFBhNyIQkNHk9uEEuwllpoh+0AGHHT60ev0Q/QEUWwyQ5ACOuXzt/a5Dc5sXZkFAoyuYTwVGPUR/AHUOLwqMeui0gMWghywDzva8KLQIcHl9ELRaFBj1aPaIMOl10GuD147drG/PfxHFFgGtbT7YzQI0GqCu1Ys+BQZYDTrUO70othgQCACbvqtFhd0Iu1mHJpcfx1wiyu1GBGQZLW4fSguMcHp8EPRalJgElBjbjzNRHiBiW4ERvoCM71ralB1Le1P+pStTz3vptKnks2Bn2s3msUa2m83jzdWxZkL7dbv3QBOKjHp1XLdq7F/GRERJ9PyJNgFYv7cZ81ftgscXgEnQYtG0ERhTVdT1QSdf6synWDXApppWzFu+M1zn4pkjcV5/e+5v7JmWTn9mq4ya2tED6/clKDO06PhkmxbYsL8ZD648Xubh6SNwyaCi7E+29fQcTnDOFk6rhugP4NH/90V424Ip1Xjhg9040NgGk6DF7LFVeGPrQdwyeihe2LgPol/G9ecPxJK1e8OvmTt+GIw6LX7+2qfhbXPGVeHVjw+gX5ER15x1Iuav2n28janV2PJNA84dUooF7dsH9jHjltFDsXD17oh8qcZzG/aFY1kwtRr9i434ut4RVW7OuCq88/kRXHZaX7y+5SCuPetELF13PL7QMfzg7BNhNehQZDVAAxlvbjuMCSP6YuHq3Si2GOKOa864KlgEHV7auA/XnDUwqu/mjKuCzaSHSdBCr9Xinjc/T3ockfFF1h+Mazd+OfFk6LUazHnjs6jX9Cs24/wBdmw62IPzMlKSHH0+IgfmTRyOl/+9H01u8Xg/aIGPD7XicFNb3Pl79eMDaHKL4Rx9/J+fRuWhTiPjt+/vQ5NbxH2XnYxCi4Dvmj0JzlMwf/pYBDR7/Hj6vT3h/fdddjLafAHMeX9PwrYXTK2GXgssXP1f/ODsE8PbF00bgec27A0fW+Rr7p4wHH/Z9C1+cPaJGFxqxai+Bdh0KDoPllx7BkR/IGpbZB2KjKU9fVzsikw976mtzd7Wbq6ONRPUeN2qsX8ZExGl0OP/dHR/kxgebADA4wtg/qpd2N8k9vg68ynW+jZ/+IYeqnPe8p2ob4v9+FLPk05/ZquMqtppTlKm+XiZQw5feJItVObBlbtwyJH9p4mensOJztmCVbtR7/RGbVu4ZjemjOwX/n3pur2YMrIfFq4Obr9yVP/wJESozNPv7UGjW4zatmTtXlw5qj+uv2BweJIt3Mbq3bjq7BPDk2wAwm1E50t0LAtX74Zeq4srt2TtXtx08RAsWRuMNTTJFnsMS9buRYNLxL46JwrNBvz0okHhuhId15K1e9HoFtuPYVfcvjqHFya9PjzJluw4IuNLFNcv/ncHdh9pjXvNvjonvm0Re3ReRkqWo5E5sPjdr3DlqP5R/fBtS/CcJjp/obKJcnTh6t2wGIRwmQaXiK/rXUnP05K1e2ExCuFJttD+BpeI376/J2nbC1fvhlnQh+sIbZ+/alfUsUXue/JfX4XL7z7SikMOX1we7D7SGrctsg4lxtKePi52Raae99TWZm9rN1fHmglqvG7V2L+MiYhS6fETbbWtx98Ehnh8AdS2ent8nZmqNxN1NrjEhHU2uHr+jSGd/sxWmbxrx+FJXMbhQbb19BxOdj4CMf+67fEFoNHE/x76f+jndOtp8/oTlm90RseTrN7YWBqciY8j1E6qekJxBmSgye1Dk8sXLpvquJIdQ0AGXDH7ktWTrI7IuBLVX+tIfLw9JS8jJcvRRPkY+rnBFfzTsoCcOn+S9bFL9IfLhHIj1XmKPd+h13XUdqid2O2pji0yNxKNlem0292xtKePi12Rqec9tbXZ29rN1bFmghqvWzX2L2MiolR6/ERbhd0IkxB9mCZBiwq7scfXmal6M1FnqTVxnaVWQ5frzBfp9Ge2yqipncp0ythMicvYTMi2np7Dyc6ZVoO4bbIc/3vk9s7UYzHqE5bvU5A4no5iKU3yush2ktUTilOrAYotAkqsQlTZZMeV7Bi0GsBqSrwvVXzJ4kpUf4WtZ+dlpGQ5migfQz+XWg2otBuh06TOn2R9bG3/TjYA0GmQsh6ToE14vtNpO9RO7PZUxxaZG4nGynTa7e5Y2tPHxa7I1POe2trsbe3m6lgzQY3XrRr7lzERUSo9fqJtULEBi6aNiHoDtWjaiOAXQ/bwOvMp1jKzDotnjoyqc/HMkSgz9/yvEUynP7NVRk3tnFSUpEzR8TL9bQIenh5d5uHpIzDAJnTqHCihp+dwonO2cFo1yiImrkxC8Dva1uw8HP599tgqrNl5GAumBrcv31aDOeOqol4zd/ww9LEYorbNGVeFN7fX4M8ffYNF06qj25hajX9sOYiFEdtX7wi2EZ0v0bEsmFoNf0CKKzdnXBV+/8HXmDOuCqt3HMbssdHxhY5hzrgqlFoNGFpegJY2EX/6z/5wXYmOa864KvSxGNqPYUTcvnKbER6fH09ceVrK44iML1FcT119Oqr72uNeM7S8ACcVGnp0XkZKlqOROTBv4nC8ub0mqh8GFhowpLwg4fkLlU2UowumVsMt+sJl+lgNGFxmTXqe5oyrgtvjw9zxw6L297EacNelw5K2vWBqNdp8/nAdoe2Lpo2IOrbIfXdPGB4uX93Xjv42IS4PTu1rj9sWWYcSY2lPHxe7IlPPe2prs7e1m6tjzQQ1Xrdq7F/GRESpaGRZljsulh98PgnNze74HZlcdVPtdeZTrFErkxmCN3SFsrOoyJI4NzKorMyWcHvCPM3WiqLplFFTOxGrjlbYjcFJthSrjlbYTME3hkm+vDvjeaBgDmcjZzuVo8DxVUfbV/8Mrzoq6NHi8aHAqIdPSrzqqM2khxSQcczlQ6FFDy0SrDpqEnDMHVz9UE6y6mgfqwEWgw61rV70sQqJVx11eFFeYAQ0Ha86Wmk3Qa8FDjd70K991dFWrwiLIKDJ7UUfqxEu0Q+dVgtBq4FRr4VeF1x1tMktosJuDK86WmE3QqcJrixqNmhhFfRodIsoilx11CWiwKiHUa+BThO96mhNsxdGQRssH1511AAxgISrjnp9EkotBgwpt6HV0YZ6jx+HW7wwCVrYTQJOSLjqqLJja6RM5Wx3xtKi9lVH20QJZTYjZFlGs9uHvjZjdD/ErDpaZDEgELfqqAizoEejK7gCaHCV3ZhVRwU9ZARXHW0TJRSaBbhEP/RaDQqMwevEqNcmXXXU4fHBZopeddQi6NDg8qLIHFxl97sWT/yqo7bgSr0tbj9KCwzBVUd1WpSYk606agi/aT6+6qgBvgDwXUtbh2Npp3Qi/3Jxr1ZCV8bSnK2ImatVR3tDu7k61jQky1EgSZ62X7fNXglFRl3G7hudosb+ZUyd0tEY3+mxFIDOYsD/vLIlZbsvXn8WZE/2OkGt9zI1xpVvMaUaS2P1jom2dmo8kYnkS5xA/sSq+om2dvnSnyGMN3NUOdHWTg39qIYY1BKHGmLIZBy9YSztDB6b+uTzWJotPNbc6vREWzs1HgtjSk8+xsSJtsxSY1z5FlNnJtp6/J+OEhERERERERERZQMn2oiIiIiIiIiIiBTAiTYiIiIiIiIiIiIFcKKNiIiIiIiIiIhIAZxoIyIiIiIiIiIiUgAn2oiIiIiIiIiIiBTAiTYiIiIiIiIiIiIF6HMdQEc++OADPProowgEArj66qsxa9aszlciAPubRNR+50CF3YhBxQbA183AQnW2epWvU8k48ynWfIlTA9S3+dHgElFqNaLMrAPkLMSZTv8oUaYzdXQn3mwdTzrxKtWOHtjfHCxTaTfipCID4O9iv6TqWy1wyOFDrcODSpsJ/W0CEIgpowO+bTkey8BCAyChewTgQJOIL5vdMOr0qHN4cUKRCZCBwy0e9C00QgbQ5BJhNeih0wJWox4Ojx+tHj/6FAjQyBrUOrwotxlh0Gng9vmh0+iC2+xGWA061Du9sBr0EP1+GPV6NLlFmAQdLAYdjHotAghgf83xPrQZdWh2i9DrdKhzelFqNcLj88NmEhCQA2hw+lBsEWDQaSEGAvBLMhweP2xGPaxGHTz+APwBCQadHnWtwTjMggZtvgCaXD7YzXoIWi1aPD4UGAW0eHywm/SwCDo0NTrQ6PKjyS2itMCINtEPs0GPJrcPZQUGSIEAjrl9sAg6WI166LQByLIOTq8fTq8fdrMeZQUCmlwSah3B47EadWhy+QBNAIJWj3qnFxU2IyoKdahrkdDgElFoEdDm88Ok16HYL6G1TUKdI3iuDTotaprbUGYzwtj+c7nNiBKrDk1uCW4x+N+JJSb4JcApSjjmEtHXboTJoMOBxjbYTXoUWvSw6DWwGoGDTRJqW704qY8ZHl8AR9MZpxPlOo5v619sQiAAfNfiydiYX2jWYV+dG+U2I/ySBH9Ag76FAprcElo8PlTYTHB6/Wh0iqiwG2ExalBg1KLeIcEXkGDU6SFKAbT5/LAIerT5JBQY9QjIMnzteVRg1MMkaCEDaHSKKC0wwOH1w2LQo8isQ0ubhCZ3MAfbRD+sRj2OuXwwCVrYTXrodVo0uX1wi8G6CgzBnDzm8qHcZoRGI6PeGTw//gCwva4eJRYBTe7gdWYx6BCQZbh9ATg9fpQWGGA36+D1S/D4AH9Agkmvh8Prh8cnocQiABoNNACMOi0qrXrUu4L3tAqbEVJARoNLRLndiBbP8bw6qdiAekc37n2ZuHdmQ67u+UrLxDNWZ9rN5rFGtpvN4+1Nx5oLuepfxtQzYyLKMFVPtEmShEWLFuFPf/oTKioqcNVVV2Hs2LEYOnRo+pUIwPq9zZi/ahc8vgBMghaLpo3AmKqirl/g+VJnPsWaL3VqgE01rZi3fGe4zsUzR+K8/vauP3inE2e2yvS0drIZix5Yvy9BmaFFxyfblIhFC2zY34wHVx4v8/D0EbhkUNHxyTYdsP7rBPUMKer6ZJsAbNjXjDe2HMDMUSdi4Zrt4brnjKvCqx8fQJNbjPp57vhhKLMZce+bn6PYYsD15w/EkrV7w6/79czTIPrlqGNZNK0aNpMeD63ajWvPOhFL1+2NaqdvoQlmQYc5b3wW3r7k2tPR3ObHglW7w9tmj63CG1sP4kfnDsSaHd/hqu/1h1argVuU8PR7e6LqLLMZ4JeAh1Zvj+qv5zbsheiX4+IO1X3npcMgBWQ8sGJX3L6fXTAINU1uLH73q6i2BpdaceBYS1QMobYONLaF+2Drtw0488TScD8P7GPG7ZdURZ3TUFu3X1IV9frIc7BgajVe++QA9tQ5sWj6CEAOYP6q/6LYYsBdlw6FJGuwcPXxfntoajX+b3v5OeOqMOHUUnyw14H5q3YlPIdJx9QkuVxo0WPO6591rq5O5GhsmwunVeOfnx/Bx/uPYcHUanxd24xhlUV4bsM+zBk3FPUOMer4H5kxAga9Fmt21GDyyH5ocrnwp4/24/+cMxD/d/MB/OyCQdDpEueR1aDD7zZ+gya3iIemVmPdl9/i0lP64rkN+3DtWSfija0H43L6vstOhsWoj8qh2Lrmjh+GP334LZrcIu677GSIkow5rx/Pq8jrLHJM6FtkwLZvGnFSuR3fNbdG9fV9l50Mm1mAzy9hv0GPp/71VVSunz+oBJNO6xt1TcXmaafufZm4d2ZDru75SstFm72t3d50rLmgxuNkTPkbE1EWqPpPR3fu3ImBAwdiwIABMBgMmDx5MtauXdupOvY3ieELGwA8vgDmr9qF/U1il+PKlzrzKdZ8qbO+zR9+4A7VOW/5TtS3xX5sSdk4s1Wmp7WT1Viak5RpVjaWQw5feGIqVObBlbtwyHH8aeXblsT1fNvSvevpwZW7cP0Fg7Fwze6oupes3YsrR/WP+/np9/Zgf4MLHl8AV47qH36jH3rd1/WuuGOZv2o3pAAwZWS/8IREZDvfNLjgk+So7T4J4QmB0Lal6/Ziysh+ePq9Pbjp4iFocImoc3jDkyORdZr0ejy0endcf00Z2S9h3KG69ze4whMksfsa3WJ4ki2yLZ8kx8UQaiuyD2aMOjGqn6eM7Bd3TkNtxb4+8hwsXL0bN108JFjvyl2wGITw+bAYhPAkU+i1D0WUX7J2LxqdUrjdRH2RbExNlss+v9zputKVqM0Fq3bjpxcNCvfFpdX9MH/VbkwZ2Q8GnT7u+B9YsQv76pz40XmD8HW9C0/880tMGdkPv31/T/i8JsujBpcY7veHVu/Gj84bFG4rdK5ic7rBJcblUGxdT7+3J/xzg0vEk/+KzqvI6yy07cGVu6DT6HDe0Ap8Xe+K6+vH3vkS+xtcsBgE7KtzxuX6Ty8aFHdNxeZZZ+59mbh3ZkOu7vlKy0Wbva3d3nSsuaDG42RM+RsTUTao+hNttbW1qKysDP9eUVGBnTt3Ji2v02lQVGSJruM7R/jCDvH4Aqht9eLM/kVdiytP6sxUvb25zr0HmhLW2eyVUFVpT6uO2DxNJ85slelp7fTEWLbV1iUsU+f04rR+hcF2jiSpx+HFGWnkfqqxtM3rT1i3RpP450D7pz40GsS9LiDHb/P4AnCJ/oTlQ/W5xOg3ua4UMXl8AbSJ/nAcydrr6JgS7UsWf6p9HbUV+r3B6Y0ql6w/QttjXx8Ze1t7f4XaD9WXrN8iy9c6POEyyWII5adOpw3nTbJcjmy/u+NzumNps9sX/rm+/XhSHX9ABppcvvA5jOzjVHkUkBHV781uX9zr070GYusK/ZyqfOy2Bqe3w9e42q+N2FxvcvnSytN0733p3jsjc0gNunvPz9ZzaUdy0WZvazefjzVRnh7fp45rMlf9y5jUEVOqHHWKEvR6XcrX63VaBCyGlGUErRYmvTKfQVLLdRNLjXH15JhUPdHWWZIko7nZHbWtwm6ESdBGXeAmQYsKuzGubLrypc58ijVf6ixq/y6e2DqLjLq4OsvKbAnriM3TdOLMVpme1k42Y6nMViwFicuUF0TEYktSjy0699PN0cjYLUmuAVlO/LM24o157Ot0mvhtJkELq0GfsHyovtD+EKspeUwmQQuLQQ+dJnEMofaSvV6TJEZZBvTazu9L1Vbk72VJznOyY4x9feQ5MEf0Z2TfJeu3yPIVdlNUmVT5WVRk6TDXI9tPd3zu7lhaZBHCP5fbgseT6vi1GqDEKkDXgHDZ0P9T5ZFWA0iB6HZjX5/uNRBbV+h8piofySRoUVpghFajwb46Z8prKdRWZK6XWIW08jTRvS+RdO+dkTmkBunG3ZWxVOnnvVRy0WZva1ftx5osR4HEeRqilmsyV/3LmLIbU2fG0hCdxQC/P/X3okgBGbP+vCVlmRevPwsepydlmXSp5bqJpca48i2mVGNpLFX/6WhFRQWOHj0a/r22thYVFRWdqmNQsQGLpo2IeshdNG1E+EuZuyJf6synWPOlzjKzDotnjoyqc/HMkSgzd33OOp04s1Wmp7WTzVhOKkpSpkjZWPrbBDw8PbrMw9NHYIBNCJcZWJi4npMKu3c9PTx9BP780TdYMKU6qu4546rw5vaauJ/njh+GQaVWmAQtlm+rwZxxVVGvG1xmjTuWRdOqodMCq3ccxuyxVXHtDC61QtBporYLWmDhtOiYZo+twpqdhzF3/DD8/oOv0cdqQJnNiLnjh8XV6fH78dDU6rj+WrPzcMK4Q3WfVGrFIzNGJNxXYjFg3sThcW0JOk1cDKG2Ivvgre0Ho/p59Y7Dcec01Fbs6yPPwYKp1Xj5g6+D9U4fAbfoC58Pt9eHBTHH/VBE+TnjqtDHqgu3m6gvko2pyXJZ0Gs6XVe6ErW5cFo1XvnP/nBfvLf7MBZNq8bqHYch+v1xx//IjBEYWl6Av27aj8FlVtwz6WSs3nEYd106DKt3BM9rsjwqtRrC/f7Q1Gr8bdP+cFuzx1YlzOk+VkNcDsXWNXf8sPDPfawG3D0hOq8ir7PQtoenj4AkS9i0rxaDy6xxfX3fZSdjUKkVbtGHoeUFcbn+p//sj7umYvOsM/e+TNw7syFX93yl5aLN3tZubzrWXFDjcTKm/I2JKBs0siyr9mto/X4/Jk6ciFdeeSW8GMJTTz2FqqqqhOV9Pinx7GMmV7NUe535FGu+1Bm1Apkh+MCd4CpKNuOdME9zsQJnrlcD7YnHHLHqaIXdGJxk6+qqo6naiVh1tMJmCk6yBWLKhFYddQRXrDwpwaqjncrR9tgONInw+P3BFTqdXpxQGFx1NLRyJDTBPzuzGHTQawGLQQ+nN3rV0TqnF2UFwVVH2/x+aKFDncOLMltwxc0GpxcWQQ9RCq06Gpwcshh0MOq0CGgC8PoQ7CObETaTDs1tIvTa4IqlfdpXHS0wCZBlGY1OH4os+vCqo5Iko7V9tcgCow7e9lVHhfaVVMtt7auOigE0u/2wmXVRq462enwoMOphNehgFDQ4FrvqqKBHc5sPpVYDJDmAJrcfJkGLAqMeOq0MWdaGVx21mfQotyVedVSjkcPHVG4zorJ91dFGlwi7WYDXJ0HQa1FiFeBoX3W03B5cafRwSxtKC5KvOtomSuhfYoLUvupokyu46qbZoMPBxjbYTHrYzXpYhfRXHY37F8COVh0tMiEgd7zqaHfG0kKzDl/Xu1FaYIQUCK46WmkX0OyW0OrxoTy06qhLRLnNiAKjBtb2VUdDK9GKUgAenwSzoEObT4LVqIecZNXRY04RJVYDXO15UGQJrjra7PahyBxcKdZq1KPJ5YNB0MJu1EPQB1cdbRMlWI06WA3BnGxy+9o/lSajweFDRaEBUgBocIootghocgevM6tBhwBktIkBOEKrjpp08EoSPCIgyRKM+uB16PEFUGzRh1cdNei06Jtk1dEyuxEOTzAv41cdTX7vSyqNe6ca/2U7nbi7MpbmbEXMXK062hvaVfGxpvoURtI8hcquSTWursqYFI2p02Mpgp9o+59XUn9a7aWfnJ3WJ9pkT+qO0psNkFJMneg0GvjbRHVdNxHUGFe+xdSZT7SpeqINADZu3IjHHnsMkiRh5syZuPXWW5OWTXURAuo8kYnkS5xA/sSaizi7crPIl/4MYbyZk41Yu5KjgDr6UQ0xqCUONcSQyTh6w1jaGTw29cnnsTRbeKy51SMm2toxpvTkY0xqn2jTmATc/OrWDutQY98D+ZkTuaDURJu6P7MPYPTo0Rg9enSuwyAiIiIiIiIiIkpJ1d/RRkRERERERERElC840UZERERERERERKQA1X9HGxERERERERERUT7gJ9qIiIiIiIiIiIgUwIk2IiIiIiIiIiIiBXCijYiIiIiIiIiISAGcaCMiIiIiIiIiIlIAJ9qIiIiIiIiIiIgUwIk2IiIiIiIiIiIiBfSKibYPPvgAEydOxPjx4/HSSy/lOpykjhw5guuuuw6XX345Jk+ejD//+c+5DiklSZIwY8YM3HzzzbkOJaXW1lbMnj0bkyZNwmWXXYZPP/001yEllC95GjJ27FhMnToV06dPx5VXXpnrcKLce++9OP/88zFlypTwtubmZtxwww2YMGECbrjhBrS0tOQwwmiJ4l22bBm+//3vY/r06Zg+fTo2btyYwwiDspmjnTmHsizjkUcewfjx4zF16lTs3r1bkRiSjcnZjsPr9eKqq67CtGnTMHnyZCxduhQAcOjQIVx99dUYP3487rzzToiiCAAQRRF33nknxo8fj6uvvho1NTWKxAHEj/u5iKEj+TaWRlJLzmVSPuRQpuVzjnZGvj3XKiVfno9TUWOOJnouyDU15niyZwY16M610VFOZvuekc65/+STT/C9730v/Cz/7LPPZjQmoOP3Z7l4bvjmm2/CfTB9+nSMGjUKr7zySlSZbPRVd94fvvXWW5gwYQImTJiAt956K70G5R7O7/fL48aNkw8ePCh7vV556tSp8t69e3MdVkK1tbXyrl27ZFmWZYfDIU+YMEG1scqyLP/xj3+U586dK8+aNSvXoaT0y1/+Uv773/8uy7Ise71euaWlJccRxcunPA0ZM2aM3NjYmOswEtq8ebO8a9cuefLkyeFtv/71r+UXX3xRlmVZfvHFF+Xf/OY3uQovTqJ4ly5dKr/88ss5jCpatnO0M+dww4YN8o033igHAgH5008/la+66ipFYkg2Jmc7jkAgIDudTlmWZVkURfmqq66SP/30U3n27NnymjVrZFmW5QcffFD+29/+JsuyLP/1r3+VH3zwQVmWZXnNmjXynDlzFIlDluPH/VzEkEo+jqWR1JJzmaT2HMq0fM/Rzsi351ql5MvzcTJqzdFEzwW5psYcT/bMoAZdvTbSycls3zPSOfebNm3K+jjQ0fuzXD83+P1++YILLpBramqitmejr7r6/rCpqUkeO3as3NTUJDc3N8tjx46Vm5ubO2yvx3+ibefOnRg4cCAGDBgAg8GAyZMnY+3atbkOK6Hy8nJUV1cDAAoKCjB48GDU1tbmOKrEjh49ig0bNuCqq67KdSgpORwObNmyJRynwWCA3W7PcVTx8ilP88HZZ5+NwsLCqG1r167FjBkzAAAzZszA+++/n4PIEksUr9pkO0c7cw5D2zUaDc444wy0trairq6u2zEkG5OzHYdGo4HVagUA+P1++P1+aDQabNq0CRMnTgQAXHHFFeHzsW7dOlxxxRUAgIkTJ+Ljjz+GLMvdjiN23JdlOesxdCTfx1K15Fym5EMOZVq+52hn5NNzrVLy5fk4FbXmqBqfldSY48meGXKtO9dGOjmZ7XuGGs99OnL93PDxxx9jwIAB6NevX9baDOnq+8P//Oc/uPDCC1FUVITCwkJceOGF+Pe//91hez1+oq22thaVlZXh3ysqKvLiIqipqcEXX3yB008/PdehJPTYY49h3rx50GrVnUI1NTUoKSnBvffeixkzZuD++++H2+3OdVhx8jVPb7zxRlx55ZV44403ch1KhxobG1FeXg4AKCsrQ2NjY44j6tjf/vY3TJ06Fffee2/O/9RVDTma7BzGxlZZWal4bJFjci7ikCQJ06dPxwUXXIALLrgAAwYMgN1uh16vj2urtrYWffv2BQDo9XrYbDY0NTV1O4bYcb+pqSnrMXREDXmqlFznXCbkQw5lWk/K0c5Q+3OtUvLl+TiV3pqj3aWmHI99ZlBDTN25NtLJyVzeM1Kd+88++wzTpk3DTTfdhL1792YlnlTvz3L93PD2228n/fPvXPRVOu8Puzom5u9doAdzuVyYPXs27rvvPhQUFOQ6nDjr169HSUkJRowYketQOuT3+/Hf//4XP/zhD7FixQqYzWbVfNdEvnvttdfw1ltv4fe//z3+9re/YcuWLbkOKW0ajUYV/7qXyg9/+EO89957WLlyJcrLy/HEE0/kOiRVyeY5TDUmZysOnU6HlStXYuPGjdi5cye++eabjLcZKZ/G/Z5ADTmnNOZQ76X251qlMMd7L7XleOwzw549e3IaT0++NlKd++rqaqxbtw6rVq3Cddddh9tvvz3j8aj5/Zkoili3bh0mTZoUty8XfRVL6eerHj/RVlFRgaNHj4Z/r62tRUVFRQ4jSs3n82H27NmYOnUqJkyYkOtwEtq+fTvWrVuHsWPHYu7cudi0aRPuvvvuXIeVUGVlJSorK8P/wjBp0iT897//zXFU8fItTwGE4+vTpw/Gjx+PnTt35jii1Pr06RP+aHRdXR1KSkpyHFFqpaWl0Ol00Gq1uPrqq/H555/nNB415Giycxgb29GjRxWLLdGYnIs4Qux2O84991x89tlnaG1thd/vj2uroqICR44cARD8xwaHw4Hi4uJutZto3H/00UezGkM61JCn3aW2nFNKvuRQpvWEHO2MfHiuVVJ1WgAAGKdJREFUVUo+PR+n0ttytLvUnOOhZ4Z0/swtk7p7baSTk7m4Z3R07gsKCsJ/xjt69Gj4/X4cO3YsozF19P4sl88NH3zwAaqrq1FaWhq3Lxd9BaT3/rCrY2KPn2g77bTT8O233+LQoUMQRRFvv/02xo4dm+uwEpJlGffffz8GDx6MG264IdfhJPWLX/wCH3zwAdatW4enn34a5513Hp588slch5VQWVkZKisrw5/++PjjjzFkyJAcRxUvn/IUANxuN5xOZ/jnDz/8EFVVVTmOKrWxY8dixYoVAIAVK1Zg3LhxuQ2oA5Hfl/D+++/nvH/VkKPJzmFouyzL+Oyzz2Cz2cIfA++OZGNytuM4duwYWltbAQAejwcfffQRhgwZgnPPPRfvvvsugOBqSKHzMXbs2PCKSO+++y7OO++8bv8LXaJx/6mnnspqDOlQQ552h1pyLhPyJYcyLd9ztDPy5blWKfn0fJxKb8rR7lJjjid6Zhg8eHBOY+rutZFOTmb7npHOua+vrw9/T9zOnTsRCAQyOvmXzvuzXD43vP3225g8eXLCfdnuq5B03h9edNFF+M9//oOWlha0tLTgP//5Dy666KIO69YrHaza6PV6zJ8/HzfddBMkScLMmTNz/oY1mW3btmHlypUYNmwYpk+fDgCYO3cuRo8enePI8tuDDz6Iu+++Gz6fDwMGDMDjjz+e65Di5FOeAsG/Zw99pFeSJEyZMgUXX3xxjqM6bu7cudi8eTOamppw8cUX4+c//zlmzZqFO++8E//4xz9wwgkn4Jlnnsl1mGGJ4t28eTO+/PJLAEC/fv2waNGinMaY7RztzDkcPXo0Nm7ciPHjx8NsNuOxxx5TJIZkY3K246irq8M999wDSZIgyzImTZqEMWPGYOjQobjrrrvwzDPP4JRTTsHVV18NALjqqqswb948jB8/HoWFhfjtb3+rSByJzJs3L+cxRMq3sTSWWnIum9SWQ5mW7znaGXyuzU9qzdFEzwWh8SJX1JjjyZ4Z8lmynFyyZAlGjBiBcePGZf2ekezcf/fddwCCXwHz7rvv4rXXXoNOp4PJZMLTTz+d0cm/ZO/PXnvttXBMuXpucLvd+Oijj6Lez0TGlY2+6sx7i88//xyvv/46Hn30URQVFeG2224LL+Rx++23o6ioqMP2NHJPWMKJiIiIiIiIiIgox3r8n44SERERERERERFlAyfaiIiIiIiIiIiIFMCJNiIiIiIiIiIiIgVwoo2IiIiIiIiIiEgBnGgjIiIiIiIiIiJSACfaiIiIiIiIiIiIFMCJNhX6+uuvMX36dMyYMQMHDx7MSpvXXXcdPv/8c0Xr/OSTT3DzzTd36bU/+MEPOiwzduxYHDt2LGG727dv71K7pJx8zePXXnsNK1asSFnmzTffxKJFixLue+GFF7rVPmXPmWeeqUg9qfIBAN5//33s27cv/PuSJUvw0UcfKdI29W6ff/45HnnkkVyHQT1ULu7jkf7nf/4Hra2tKcskexZUGz4b5D+lroeOnhkAoKamBlOmTAEAfPHFF9i4cWOX26Oeqav5eM899+Cf//xn3Pba2lrMnj0bQOr38Pky5qoBJ9pUaO3atZg4cSJWrFiBE088Mdfh5MTrr7/e5ddu3rwZn376qYLRUFfkax7/8Ic/xIwZM7r8+hdffFG5YKhHiJ1omzNnDi644IIcRkQ9gd/vx2mnnYYHHngg16EACMajRBlSj1zfx3//+9/DbrcrUpcsywgEAorU1RV8Nsh/uboeONFGiSidjxUVFVi6dGmXXpvr8VWt9LkOIF/V1NTgf/7nf/C9730Pn376KSoqKvD8889j//79WLBgAdra2nDiiSfiscceQ2FhYcI6vvjii7iyn332Gf785z9Dq9Xi448/xl/+8pe417388sswGAy4/vrr8dhjj+HLL7/Eq6++io8//hj/+Mc/8NRTT+E///kPli1bBlEUMWDAADz++OOwWq3YtWsXnnjiCbjdbhQXF+Pxxx9HeXl5uO5AIID77rsPFRUVmD17Np588kls3rwZoijiRz/6EX7wgx/gk08+wbPPPovi4mLs2bMH1dXVePLJJ6HRaPDBBx/gscceg9lsxve+972Ufbhs2TJ89913qKmpwXfffYef/OQnuP766wEEP2ny6aefIhAIYNGiRdi0aRP69u0LvV6PmTNnYtKkSQCAv/71r1i/fj38fj+eeeYZGI1GvP7669BqtVi1ahUefPBB1NfX47nnnoNWq4XNZsPf/va3rp72Hqc35PFdd92VMO4zzzwT119/PdavXw+TyYTnn38epaWlWLZsGSwWC2688Ubs3LkT999/P7RaLS644AL8+9//xpo1awAAdXV1uPHGG3Ho0CFceuml+OUvf4knn3wSHo8H06dPx9ChQ/Hwww/jzjvvxNGjRxEIBHDbbbfh8ssvV+DMkdJefvllvPPOOxBFEePHjw//q95tt92Go0ePwuv14vrrr8e1114LAFi+fDleeukl2Gw2nHzyyTAYDAnr3b59O9atW4fNmzfjd7/7HZYtW4bnn38el1xyCSZNmoSxY8di8uTJ+OCDD6DT6fDwww/j6aefxoEDB3DjjTfihz/8Ycr4KLdi8yMQCODgwYP41a9+BSD4yYVdu3Zh/vz5eO6557Bq1SqUlJSgb9++qK6uxo033piw3uuuuw7Dhw/Hli1bIEkSHnvsMYwcORLLli3DwYMHcejQIZxwwgm49tpr8cc//hEvvvgiXC4XHnnkEezatQsAcMcdd2DixIlJx9FExo4di0mTJuHf//43jEYjnnrqKQwcOBDHjh3DggUL8N133wEA7rvvPnzve9+Li+fpp5+Oq/PNN9/Ev/71L7jdbgQCATz77LO47777cOjQIZjNZixatAgnn3wympubE25ftmwZampqcOjQIRw5cgT33nsvPvvsM/z73/9GeXk5XnjhBQiCoMTpzEu5vI+73e6E97jO5lGy3B07diz+8Y9/oKSkJOlY3FHf3HjjjTj99NOxe/duvPTSS3jnnXcSjqW/+93vsGLFirjr87rrrsMvf/lLnHbaaTh27BiuuuoqrFu3DpIkJXxGrqurw1133QWn0wlJkvDQQw9hw4YNfDbIklxeD0Dnnxnuueee8PMAcPz9T4goili6dCk8Hg+2bduGm2++GaWlpXj00UcBABqNBn/9619RUFCgWB+ScnKdjytWrMAf/vAHaDQaDB8+HIsXLwYAbN26Fa+88grq6+sxb948TJo0CTU1NbjlllvC73NCmpqa8Itf/AK1tbU444wzIMty+NjSGV+T9YHJZFKwp1VKpi45dOiQfMopp8j//e9/ZVmW5dmzZ8srVqyQp0yZIn/yySeyLMvyM888Iz/yyCNJ60hWdunSpfLLL7+c9HWffvqp/POf/1yWZVn+4Q9/KM+cOVMWRVFetmyZ/Nprr8mNjY3y//k//0d2uVyyLMvyiy++KC9btkwWRVG+9tpr5cbGRlmWZfntt9+W77nnHlmWZfnHP/6x/Omnn8p33XWX/Pzzz8uyLMuvv/66/Nxzz8myLMter1e+4oor5IMHD8qbNm2SR40aJR85ckSWJEm+5ppr5C1btsgej0e++OKL5f3798uBQECePXu2PGvWrKTHsXTpUvnaa6+VvV6v3NjYKJ9zzjmyKIqyLMvyGWecIcuyLL/zzjvyTTfdJEuSJNfV1clnnXWW/M4778iyLMtjxoyRX331VVmWZfmvf/2rfN999yXsvylTpshHjx6VZVmWW1paksbTG/WGPE5m2LBh8tq1a2VZluVf//rX4VyPjHvy5Mny9u3bZVmW5cWLF8uTJ0+WZVmWly9fLo8dO1ZubW2VPR6PfMkll8jfffedLMvHc1eWZfmf//ynfP/994d/b21tTRkTZVfoXP373/+WH3jgATkQCMiSJMmzZs2SN2/eLMuyLDc1NcmyLMttbW3y5MmT5WPHjsm1tbXy6NGj5cbGRtnr9crXXnutvHDhwqTt/OpXvwqPW7G/jxkzRv7b3/4my7IsP/roo/KUKVNkh8MhNzY2yueff36H8VFuxeZHfX29fOmll4b333jjjfKWLVvkHTt2yNOmTZM9Ho/scDjk8ePHpxwff/zjH4fHjs2bN4fHnqVLl8pXXHGF3NbWJsuyLG/atCl8n/3Nb34TNVY3NzcnHUeTGTNmTHjsfOutt8J1z507V96yZYssy7J8+PBhedKkSQnjSWT58uXy97///XBfLVq0KBzDRx99JE+bNi3l9qVLl8o/+MEPZFEU5S+++EIeOXKkvGHDBlmWZfm2226T33vvvaRt9wa5vI8nu8d1No8S5W6ontC9PtFYHFsmUd8MHz5c/vTTT2VZTj6Wfv755/KUKVNkt9stOxwO+dJLLw0f949//GN5586dsizLcmNjozxmzBhZlpM/I//hD38IH7vf75cdDocsy3w2yJZcXg+y3Plnhtjng1CeHDp0KOqZM/IZ4+abb5a3bt0qy7IsO51O2efzpdc5lHW5zMc9e/bIEyZMiBtDf/WrX8k///nPZUmS5L1794afWSJzLvLZ4uGHHw7fm9evXy8PGzZMbmxsTHt8TdYHvQE/0dYN/fv3xymnnAIAqK6uxqFDh+BwOHDOOecAAK644grMmTMn4WsdDkfaZWNVV1dj9+7dcDqdMBgMOPXUU7Fr1y5s3boVDzzwAHbs2IF9+/aFPwnh8/lwxhlnYP/+/dizZw9uuOEGAMFP/ZSVlYXrnT9/Pi677DLceuutAIAPP/wQX331Fd59991wzAcOHIAgCBg5ciQqKysBACeffDIOHz4Mq9WK/v3746STTgIATJs2DX//+99THsvo0aNhMBhQUlKCkpISNDY2husFgG3btmHSpEnQarUoKyvDueeeG/X6CRMmAABGjBiB9957L2EbZ555Ju655x5cdtllGD9+fFp93Jv09DxORhAEjBkzBkAwfz788MOo/a2trXC5XOHv8ZoyZQo2bNgQ3n/++efDZrMBAIYMGYLDhw+jb9++UXUMGzYMv/71r7F48WKMGTMGZ511Vlp9Q9n14Ycf4sMPPwz/ybDb7ca3336Ls88+G3/5y1/CY8uRI0dw4MABNDQ04JxzzkFJSQkA4PLLL8e3337b5fbHjRsHIJgvbrc7/C/TBoMBra2tKeOj3IrNj5qaGgwYMACfffYZBg4ciG+++Qbf+9738Oc//xnjxo2D0WiE0WgMjz2pTJ48GQBw9tlnw+l0hr+rauzYsQn/Jfjjjz+O+kRZYWEh1q9fn3AcTSX0vUCTJ0/G448/DgD46KOPov782el0wuVypYwn0oUXXoiioiIAwfv6smXLAATH0ebmZjidzqTbAeDiiy+GIAgYNmwYJEnCxRdfDCB4zdTU1KRsuzfI1X081T2uM3mUKHdjJRqLi4uLO4zxhBNOCOd8srHU5XLh0ksvhdlsBhDM6Y4ke0Y+7bTTcN9998Hv9+PSSy8Nn5dIfDbIrFxdD0DiPFX6mWHUqFF44oknMHXqVEyYMCHpJ5RJHXKVj5s2bcKkSZPCeRe6BwPApZdeCq1Wi6FDh6KhoSFlPVu2bMGzzz4LALjkkkuixud0xte+ffvG9cHhw4fTOoZ8x4m2boj8UyGdTtfhF7YqRRAE9O/fH2+++SbOPPNMDB8+HJ988gkOHjyIIUOG4ODBg7jwwgvj/oTjq6++QlVVFd54442E9Z555pn45JNP8LOf/QxGoxGyLOOBBx7A97///ahyn3zySdyxS5LUpWOJraez398S+nMRrVabNIZFixZhx44d2LBhA2bOnInly5en9XDWW/T0PE7VvkajAZA6f5JJ5xoYNGgQ3nzzTWzcuBHPPPMMzjvvPNxxxx2daocyT5ZlzJo1K24Rlk8++QQfffQR3njjDZjNZlx33XXwer2Ktx85jkXmlVarhd/vTxof5Vay/Lj88svxzjvvYPDgwRg/fnx4nOms2NeFfg9NBqRDluWE42hnBQIB/P3vf084pqYTT2diTiR0XWi12m6P3T1Rru7jnb3HpcqjVLozFlsslvDPycbSV155JenrdTpd+E+lRFGMqivRMzIQ/FqTjRs34p577sENN9wQ972vfDbIrFxdD13JU51OF/5uq0AgAJ/P12E7s2bNwujRo7Fx40b88Ic/xMsvv4whQ4YocgykvFzlYyrJvu6ks9IZX2tqauL6IBPP0mrExRAUZLPZYLfbsXXrVgDAypUrk37ioDNlEznrrLPwxz/+EWeffTbOOussvP766zjllFOg0WhwxhlnYPv27Thw4ACA4Izy/v37MWjQIBw7diz8t/8+nw979+4N13nVVVdh9OjRmDNnDvx+Py666CK89tpr4UF///79cLvdSWMaPHgwDh8+HF755O233077eJIZNWoU/vWvfyEQCKChoQGbN2/u8DVWqzX8L+0AcPDgQZx++umYM2cOiouLcfTo0W7H1ZP1tDzuKrvdDqvVih07dgAA/t//+39pvU6v14evmdraWpjNZkyfPh033ngj/vvf/3Y5Hsqciy66CMuXLw+PG7W1tWhsbITD4UBhYSHMZjO+/vprfPbZZwCAkSNHYsuWLWhqaoLP50u4elOk2DFJqfgot5Llx/jx47F27VqsWbMm/Km0UaNGYf369fB6vXC5XFGfjk0mNOZs3boVNpst/AnaZC644IKo7yBtaWlJOo6m8s4774TbD32i96KLLor6Dpgvvviiw/iTOeuss7Bq1SoAwTemxcXFKCgoSLqdOi9b9/FU97jO5FGi3I2U7FrrrGRj6dlnn433338fHo8HTqcT69evD7+mX79+4e+Oixzrkz0jHz58GKWlpbjmmmtw9dVXY/fu3QD4bJBL2boeuvLM0K9fv3COrFu3LuFEW6L3NcOHD8esWbNw2mmndTimk7pkKx/PO+88/POf/0RTUxMAoLm5uUvxnn322Vi9ejUAYOPGjXHjcwifVePxE20K+/Wvfx3+wsLQlw4rUTbWWWedhRdeeAFnnHEGLBYLjEZj+KPnJSUlePzxxzF37tzwv77deeedGDRoEJYuXYpHHnkEDocDkiThJz/5CaqqqsL13nDDDXA4HOEvdj98+DCuvPJKyLKM4uJiPP/880ljMhqNWLRoEWbNmhVeDKE7by4BYOLEifj4449x+eWXo2/fvjj11FM7fLMxZswYzJ49G2vXrsWDDz6IV155BQcOHIAsyzjvvPNw8skndyum3qCn5bFW27V/U3j00UfxwAMPQKvV4uyzz07rTd8111yDadOm4dRTT8WMGTPwm9/8BlqtFnq9Hg899FCX4qDMuuiii/D111+H/xXOYrFg8eLFuPjii/H666/jsssuw6BBg8Ifjy8vL8cdd9yBH/zgB7DZbAn/NCjS5ZdfjgcffBB/+ctfurSiU7L4+vTp0+m6SDnJ8qOwsBBDhgzBvn37MHLkSADBN1pjx47FtGnT0KdPHwwbNqzDe5nRaMSMGTPg9/vx2GOPdRjPrbfeikWLFmHKlCnQarW44447MGHChKTjaDItLS2YOnUqDAZD+JNw999/PxYtWoSpU6dCkiScddZZWLRoUTrdFOeOO+7Afffdh6lTp8JsNuOJJ55IuZ26Jhv38T179iS9x3Umj5Llbkiya62zko2l1dXVuPzyyzF9+nSUlJTgtNNOC7/mZz/7Ge688078/e9/x+jRo8Pbr7766oTPyJs3b8Yf/vAH6PV6WCwW/PrXvwbAZ4Ncy8b10JVnhmuuuQa33XYbpk2bhu9///tRnxAKOffcc/HSSy9h+vTpuPnmm7Ft2zZ88skn0Gg0qKqqCv8pPeWPbORjVVUVbrnlFlx33XXQarU49dRTu3Rfvf322/GLX/wCkydPxplnnokTTjghYblk42tX34P1BBo59HloIpVyuVywWq1oamrC1Vdfjddeey3qO7mIMiWUewDw0ksvoa6uDg888ECOoyKifBQaT9ra2vCjH/0IDz/8MKqrqxOWjVzpMJsiV3kk6qp8z6PI1ceJiIi6gp9oI9W75ZZb0NraCp/Ph9tuu42TbJQ1GzduxIsvvghJknDCCSfwExZE1GXz58/Hvn374PV6ccUVVySdZCMiIiKi/MZPtGXBwoULsX379qht119/PWbOnJnydU1NTfjpT38at/2VV17Jqy/zX758OV599dWobaNGjcKCBQtyFBF1Rb7m8dVXXx31BcYA8Jvf/AbDhw/PeNvUu/zud7+L+762SZMmdbgCLvVeXR1Xu+v222+PW63z7rvvTvjF7un697//jSeffDJqW//+/fHcc891uU5SVr7exzui9vhInXrq9UD5ifnY83CijYiIiIiIiIiISAG999vpiIiIiIiIiIiIFMSJNiIiIiIiIiIiIgVwoo2IiIiIiIiIiEgBnGgjIiIiIiIiIiJSACfaiIiIiIiIiIiIFPD/AWmPNhQI54ztAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 41;\n",
              "                var nbb_unformatted_code = \"sns.pairplot(df[['no_of_weekend_nights', \\n                 'no_of_week_nights', \\n                 'lead_time', \\n                 'avg_price_per_room', \\n                 'no_of_special_requests', \\n                 'no_of_adults',\\n                 'no_of_children']]);\";\n",
              "                var nbb_formatted_code = \"sns.pairplot(\\n    df[\\n        [\\n            \\\"no_of_weekend_nights\\\",\\n            \\\"no_of_week_nights\\\",\\n            \\\"lead_time\\\",\\n            \\\"avg_price_per_room\\\",\\n            \\\"no_of_special_requests\\\",\\n            \\\"no_of_adults\\\",\\n            \\\"no_of_children\\\",\\n        ]\\n    ]\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Correlation heatmap**"
      ],
      "metadata": {
        "id": "5gVB1e5sQ-hb"
      },
      "id": "5gVB1e5sQ-hb"
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "mechanical-interference",
      "metadata": {
        "id": "mechanical-interference",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 604
        },
        "outputId": "26c3bfdb-aea5-4598-dd1c-20837532d325"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 42;\n",
              "                var nbb_unformatted_code = \"plt.figure(figsize=(12, 10))\\nsns.heatmap(pd.concat([df, pd.Series((df['booking_status'] == 'Canceled').astype(int))], axis=1).corr(), annot=True, vmin=-1, vmax=1, fmt=\\\".2f\\\", cmap=\\\"Spectral\\\")\\nplt.xlabel('')\\nplt.ylabel('')\\nplt.title('Correlation heatmap representing the correlation\\\\nbetween numeric variables of dataset', fontsize=16)\\nplt.show();\";\n",
              "                var nbb_formatted_code = \"plt.figure(figsize=(12, 10))\\nsns.heatmap(\\n    pd.concat(\\n        [df, pd.Series((df[\\\"booking_status\\\"] == \\\"Canceled\\\").astype(int))], axis=1\\n    ).corr(),\\n    annot=True,\\n    vmin=-1,\\n    vmax=1,\\n    fmt=\\\".2f\\\",\\n    cmap=\\\"Spectral\\\",\\n)\\nplt.xlabel(\\\"\\\")\\nplt.ylabel(\\\"\\\")\\nplt.title(\\n    \\\"Correlation heatmap representing the correlation\\\\nbetween numeric variables of dataset\\\",\\n    fontsize=16,\\n)\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(12, 10))\n",
        "sns.heatmap(pd.concat([df, pd.Series((df['booking_status'] == 'Canceled').astype(int))], axis=1).corr(), annot=True, vmin=-1, vmax=1, fmt=\".2f\", cmap=\"Spectral\")\n",
        "plt.xlabel('')\n",
        "plt.ylabel('')\n",
        "plt.title('Correlation heatmap representing the correlation\\nbetween numeric variables of dataset', fontsize=16)\n",
        "plt.show();"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Correlations between independent variables\n",
        "  * There is a moderate positive correlation between `no_of_adults` / `no_of_children` and `avg_price_per_room`.\n",
        "  * There is a weak positive correlation between `arrival_year` and `avg_price_per_room`. Prices grow.\n",
        "  * There is a weak positive correlation between `no_of_special_requests` and `avg_price_per_room`. The more requests the more price.\n",
        "  * There is a weak negative correlation between `repeated_guest` and `avg_price_per_room`. The repeated guest knows how to save.\n",
        "  * There is a weak positive correlation between `no_of_special_requests` and `no_of_adults`. The more people the more requests.\n",
        "  * There is a weak negative correlation between `repeated_guest` and `no_of_adults`. The repeated guests travel alone.\n",
        "  * There is a moderate positive correlation between `repeated_guest` / `no_of_previous_bookings_not_canceled`, and `no_of_previous_cancellations`. Data need to be treated to avoid multicollinearity\n",
        "  * There is a weak positive correlation between `no_of_week_nights` and `no_of_weekend_nights`.\n",
        "  * There is a weak positive correlation between `lead_time` and `no_of_week_nights`. Long stays are planned in advance.\n",
        "  * There is a moderate negative correlation between `arrival_year` and  `arrival_month`due to not equal number of months in 2017/18\n",
        "\n",
        "* Correlations between the dependent variable and predictors\n",
        "  * There is a close to a moderate negative correlation between the `no_of_special_requests` and the `booking_status`. The higher number of special requests the fewer odds that the booking is cancellated.\n",
        "\n",
        "  * There is a moderate positive correlation between `booking_status` and `lead_time`. The higher the lead time the higher odds of cancellation."
      ],
      "metadata": {
        "id": "yDSAGWK3ohmA"
      },
      "id": "yDSAGWK3ohmA"
    },
    {
      "cell_type": "code",
      "source": [
        "temp = df[['room_type_reserved', 'no_of_children', 'no_of_adults']]\n",
        "temp['guests'] = temp['no_of_children'] + temp['no_of_adults']\n",
        "stacked_barplot(temp, \"room_type_reserved\", \"guests\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "id": "U6MeMOs5kvac",
        "outputId": "b2aff6e4-a25c-47ec-d10f-8443f402b6b5"
      },
      "id": "U6MeMOs5kvac",
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "guests                 1      2     3    4   5  10  11  12    All\n",
            "room_type_reserved                                               \n",
            "Room_Type 2           18    598    27   46   2   0   1   0    692\n",
            "All                 7552  23942  3851  912  15   1   1   1  36275\n",
            "Room_Type 1         6847  19534  1729   19   0   1   0   0  28130\n",
            "Room_Type 3            3      4     0    0   0   0   0   0      7\n",
            "Room_Type 4          556   3572  1913   15   0   0   0   1   6057\n",
            "Room_Type 5           98    117    36   14   0   0   0   0    265\n",
            "Room_Type 6           16     73   117  755   5   0   0   0    966\n",
            "Room_Type 7           14     44    29   63   8   0   0   0    158\n",
            "------------------------------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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ptLRU2dmD9eCDD+uyyy7TggW/UmnpST3xxGPq2rWrFi58tVHrUvABAAAQ8XzVvjNPvgniev4899y8c26/5ZaMizo2BR8AAAAR79tKr9/HWoYLrsEHAAAAbISCDwAAANgIBR8AAACwEQo+AAAAYCMBFfxt27Zp0KBByszM1NKlS8+5z4YNGzR06FBlZWVp6tSpQR0SAAAAQGD8PkXH5/MpNzdXy5cvl9vt1ujRo5WRkaHOnTvX71NYWKilS5fq97//vRITE3XixImQDg0AAACEu9mzc7Rjx3a1adNWb7yxWpL08ssv6f33t8vlcun730/R00/nKCEhoVHr+i34BQUFSk1NVUpKiiQpKytLmzdvblDw//SnP+mnP/2pEhMTJUnt2rVr1BAAAACASfHxUYqOjg7aetXV1aqsrLngPllZ2Ro9+k7l5s6s33bDDTdp4sRH5XK5tGjRQq1Y8VtNnjylUcf2W/A9Ho/at29f/9rtdqugoKDBPoWFhZKku+66S3V1dZo8ebIGDBhwwXWdTodat27ZqGEjDfmYQ/Zmkb9Z5G8O2ZtF/maZzj86Olo5OTlBWy8nJ8dvwe/du4+OHTvWYNuNN/ar/3OPHmnasmVzo48dlC+68vl8+uc//6mVK1equLhY9957r95++21ddtllF/gZS6Wlp4Jx+HNKSmrcrzIuRaHMJ9TCPX+yN4v8zSJ/c8jeLPI3i154trfffku33npbo3/O7022brdbxcXF9a89Ho/cbvdZ+2RkZCgqKkopKSnq2LFj/Vl9AAAAAI2zfPkyuVwuDR48tNE/67fgp6WlqbCwUEVFRaqurlZ+fr4yMjIa7HPrrbdq9+7dkqSSkhIVFhbWX7MPAAAAIHDr1/9FO3Zs17PPzpbD4Wj0z/u9RMflcmnmzJkaP368fD6fRo0apS5dumjhwoXq0aOHBg4cqJtvvlk7duzQ0KFD5XQ6NW3aNLVp06ZJ/0EAAABApNq5c4dWrVqhxYuXKTY2rklrBHQNfnp6utLT0xtsmzLlf+/mdTgcmjFjhmbMmNGkIQAAAIBI88wzM7R//z6VlpYqO3uwHnzwYeXl/VbV1TV67LGJks7caDt9+lONWjcoN9kCAAAA4ay6ujqoT9Gprq72u89zz807a9vw4SMu+tgUfAAAAES8ysoav4+1DBd+b7IFAAAAED4o+AAAAICNUPABAAAQkSzLMj1Ck/ibm4IPAACAiBMXF6vy8rKwK/mWZam8vExxcbHn3YebbAEAABBxUlJSVFRUpK+/Pmp6lEaLi4u94JfKUvABAADCnM/rVfdDB02P0WQ+r7fZjxkVFaVOnTo1+3GbAwUfAAAgzDljY5W2Is30GE124P4DUrk9HlF5KeAafAAAAMBGKPgAAACAjVDwAQAAABuh4AMAAAA2QsEHAAAAbISCDwAAANgIBR8AAACwEQo+AAAAYCMUfAAAAMBGKPgAAACAjVDwAQAAABuh4AMAAAA2QsEHAAAAbISCDwAAANgIBR8AAACwEQo+AAAAYCMUfAAAAMBGKPgAAACAjVDwAQAAABuh4AMAAAA2QsEHAAAAbISCDwAAANgIBR8AAACwEQo+AAAAYCMUfAAAAMBGKPgAAACAjVDwAQAAABuh4AMAAAA2QsEHAAAAbISCDwAAANiIy/QAAILLqvHKkVNmeowms2q8pkcAACCsUfABm3FExarjk/mmx2iywvlZkmpMjwEAQNjiEh0AAADARgIq+Nu2bdOgQYOUmZmppUuXnne/TZs26eqrr9aBAweCNiAAAACAwPkt+D6fT7m5uVq2bJny8/O1fv16HT58+Kz9KioqlJeXp2uvvTYkgwIAAADwz2/BLygoUGpqqlJSUhQdHa2srCxt3rz5rP0WLlyoBx98UDExMSEZFAAAAIB/fgu+x+NR+/bt61+73W55PJ4G+3z66acqLi7WLbfcEvQBAQAAAATuop+iU1dXp/nz52vevHmN+jmn06HWrVte7OFtjXzMIXuzyN8s8jeH7M0if7PIP3j8Fny3263i4uL61x6PR263u/51ZWWlPv/8c40dO1aSdPz4cU2cOFGLFy9WWlraedf1+SyVlp66mNkvKCkpIWRrN5dQ5hNq4Z4/2ZtF/maRvzlkbxb5m0UvDB6/BT8tLU2FhYUqKiqS2+1Wfn6+FixYUP9+QkKCdu3aVf/6vvvu07Rp0y5Y7gEAAACEht+C73K5NHPmTI0fP14+n0+jRo1Sly5dtHDhQvXo0UMDBw5sjjkBAAAABCCga/DT09OVnp7eYNuUKVPOue/KlSsvfioAAAAATcI32QIAAAA2QsEHAAAAbISCDwAAANjIRT8HHwCAS4VV45Ujp8z0GE1i1XhNjwDAJij4AADbcETFquOT+abHaJLC+VmSakyPgTBVVevVgfsPmB6jyapq+QduMFHwAQAAwlyMK1bKSTQ9RpPF5JSJf+AGD9fgAwAAADZCwQcAAABshIIPAAAA2AgFHwAAALARCj4AAABgIxR8AAAAwEYo+AAAAICNUPABAAAAG6HgAwAAADZCwQcAAABshIIPAAAA2AgFHwAAALARCj4AAABgIxR8AAAAwEYo+AAAAICNUPABAAAAG6HgAwAAADZCwQcAAABshIIPAAAA2AgFHwAAALARCj4AAABgIxR8AAAAwEYo+AAAAICNUPABAAAAG6HgAwAAADZCwQcAAABshIIPAAAA2AgFHwAAALARCj4AAABgIxR8AAAAwEZcpgeAPVk1XjlyykyP0SRWjdf0CAAAAE1GwUdIOKJi1fHJfNNjNEnh/CxJNabHAAAAaBIu0QEAAABshIIPAAAA2AgFHwAAALARCj4AAABgIxR8AAAAwEYCeorOtm3bNGfOHNXV1WnMmDGaMGFCg/eXL1+u1atXy+l0qm3btpo7d66uuOKKkAwMAAAuPT6vV90PHTQ9RpP5vDwiGfbht+D7fD7l5uZq+fLlcrvdGj16tDIyMtS5c+f6fbp37661a9cqLi5Ob7zxhp5//nn9+te/DuXcAADgEuKMjVXaijTTYzTZgfsPSOU8Ihn24PcSnYKCAqWmpiolJUXR0dHKysrS5s2bG+xz0003KS4uTpLUq1cvFRcXh2ZaAAAAABfkt+B7PB61b9++/rXb7ZbH4znv/mvWrNGAAQOCMx0AAACARgnqN9m+9dZb+uSTT7Rq1Sq/+zqdDrVu3TKYh7cd8jGH7M0if7PI3xyyN4v8zSL/4PFb8N1ud4NLbjwej9xu91n7ffDBB1qyZIlWrVql6Ohovwf2+SyVlp5q5LiBS0pKCNnazSWU+YRauOdP9maRv1nkbw7Zm0X+ZtELg8fvJTppaWkqLCxUUVGRqqurlZ+fr4yMjAb7fPbZZ5o5c6YWL16sdu3ahWxYAAAAABfm9wy+y+XSzJkzNX78ePl8Po0aNUpdunTRwoUL1aNHDw0cOFC/+tWvdOrUKU2ZMkWS1KFDBy1ZsiTkwwMAAABoKKBr8NPT05Went5g27/LvCS9/vrrQR0KAAAAQNPwTbYAAACAjVDwAQAAABuh4AMAAAA2QsEHAAAAbISCDwAAANgIBR8AAACwEQo+AAAAYCMUfAAAAMBGKPgAAACAjVDwAQAAABuh4AMAAAA2QsEHAAAAbISCDwAAANgIBR8AAACwEQo+AAAAYCMUfAAAAMBGKPgAAACAjVDwAQAAABuh4AMAAAA2QsEHAAAAbISCDwAAANgIBR8AAACwEQo+AAAAYCMu0wMAgJ1YNV45cspMj9FkVo3X9AgIU1W1Xh24/4DpMZqsqpbPPuyDgg8AQeSIilXHJ/NNj9FkhfOzJNWYHgNhKMYVK+Ukmh6jyWJyyhTOn31OLuC7KPgAAABhjpML+C6uwQcAAABshIIPAAAA2AgFHwAAALARCj4AAABgIxR8AAAAwEYo+AAAAICNUPABAAAAG6HgAwAAADZCwQcAAABshIIPAAAA2AgFHwAAALARCj4AAABgIxR8AAAAwEYo+AAAAICNuEwPECpWjVeOnDLTYzSZVeM1PQIAAADCkG0LviMqVh2fzDc9RpMVzs+SVGN6DAAAAIQZ2xZ8AADQfPjNOXDpCKjgb9u2TXPmzFFdXZ3GjBmjCRMmNHi/urpa06ZN06effqrWrVvrpZde0ve///2QDAwAAC49/OYcuHT4vcnW5/MpNzdXy5YtU35+vtavX6/Dhw832Gf16tW67LLL9O6772rcuHF64YUXQjYwAAAAgPPzW/ALCgqUmpqqlJQURUdHKysrS5s3b26wz5YtWzRy5EhJ0qBBg7Rz505ZlhWaiQEAAACcl9+C7/F41L59+/rXbrdbHo/nrH06dOggSXK5XEpISNDJkyeDPCoAAAAAf4zdZBsV5VRSUkJIj3HmerrwFep8Qi2c8yd7s8jfLPI3h+zNIn+zwj3/S4nfM/hut1vFxcX1rz0ej9xu91n7fP3115Kk2tpalZeXq02bNkEeFQAAAIA/fgt+WlqaCgsLVVRUpOrqauXn5ysjI6PBPhkZGVq3bp0kadOmTbrpppvkcDhCMzEAAACA83JYAdwNu3XrVs2dO1c+n0+jRo3SxIkTtXDhQvXo0UMDBw5UVVWVfvnLX+rgwYNKTEzUSy+9pJSUlOaYHwAAAMB3BFTwAQAAAIQHv5foAAAAAAgfFHwAAADARij4AAAAgI1Q8ANQUVGho0ePnrX90KFDBqbBv+3YscP0CBHh+PHjOn78uCSppKRE77zzjv7nf/7H8FSR6cUXXzQ9QsQqKirSO++8oyNHjpgeJSIcO3ZMVVVVkiTLsrR27Vo999xzeuONN1RbW2t4OnvbvHlzffYIX9xk68eGDRs0d+5ctWvXTrW1tZo3b5569uwpSRo5cmT940HR/G655Rb97W9/Mz2Grf3hD3/Q0qVLJUnjx4/XunXr1KVLF+3bt0/jx4/XmDFjDE9oX7Nnz27w2rIsvfXWWxoxYoQk6emnnzYwVeSYNGmSXn31VUnSe++9p7lz5+rGG2/U/v379dBDD+mOO+4wPKG9DRs2TKtXr1ZcXJyef/55FRUVaeDAgfrwww8lSfPmzTM8oX317NlTcXFxGjBggIYNG6b+/fvL6XSaHguNZOybbMPFa6+9pjfffFPJyckqKCjQtGnTNHXqVGVmZop/G4Xeww8/fN73SktLm2+QCPW73/1O+fn58nq9ysjI0DvvvKOkpCSVlZVp7NixFPwQevfdd3X99derf//+9X/X5Ofn65prrjE8WWQ4duxY/Z+XLVumFStWKCUlRSUlJRo3bhwFP8Tq6uoUFxcnSdq5c6fWrFmjFi1a6Pbbb9fw4cMNT2dvnTp10ooVK7Rp0yb99re/1YwZM3Trrbdq2LBhuuGGG0yPhwBR8P2oq6tTcnKypDP/qs3Ly9PDDz+sr7/+mi/zagb79u3T888/r5YtWzbYblmWCgoKDE0VOVwul+Li4hQXF6eUlBQlJSVJkhITE/n8h1h+fr4WLlyo7du3a9q0aXK73Vq0aJFGjhxperSI8N3Pd21tbf13u7Rt21YtWnB1a6h16NBBO3fuVL9+/XTFFVfo66+/1hVXXKGTJ0+aHs32HA6HEhMT9ZOf/EQ/+clPdPz4cW3cuFELFixQcXGxtm7danpEBICC70d8fLyOHj2qK6+8UpKUnJysvLw8PfLII1yH3AyuvfZaxcbGnvOswVVXXWVgosjicDhUU1OjqKio+kt1JKmqqkp1dXUGJ7O/Vq1a6amnntInn3yiX/ziF7rlllv4rWEzOnTokK677jpZlqWamhp98803Sk5OVnV1tXw+n+nxbG/27NmaNm2aFi1apISEBI0YMULdunVTeXm5ZsyYYXo8W/v//55JSkrS2LFjNXbsWH311VeGpkJjcQ2+H4cOHVJcXJxSU1MbbK+pqdHGjRv5VSFs7dixY0pOTpbL1fBcgMfj0ZEjR/TDH/7Q0GSRxbIsvfHGG/roo4/0wgsvmB4non377bc6cuSIevfubXqUiHDkyBH94x//kM/nU/v27ZWWlsZvUEJs165duvHGG02PgYtEwQcAAABshH8GAwAAADZCwQcAAABshILfCF6vV1988YXpMSIW+ZtF/uaQvVnkbxb5m0P24YuCH6AtW7bo9ttv1/jx4yVJBw8evOAz2hFc5G8W+ZtD9maRv1nkbw7ZhzcKfoAWLVqkNWvW6LLLLpMkde/encdFNSPyN4v8zSF7s8jfLPI3h+zDGwU/QC6XSwkJCabHiFjkbxb5m0P2ZpG/WeRvDtmHN77oKkCdO3fW22+/LZ/Pp8LCQq1cuZLnIDcj8jeL/M0he7PI3yzyN4fswxvPwQ/Q6dOntWTJEr3//vuyLEs333yzJk2apJiYGNOjRQTyN4v8zSF7s8jfLPI3h+zDGwW/kSoqKiSd+Rp5ND/yN4v8zSF7s8jfLPI3h+zDE5foBKigoEBPPfWUKisrJZ35oM+dO1c9evQwPFlkIH+zyN8csjeL/M0if3PIPsxZCMiwYcOsPXv21L/es2ePNWzYMIMTRRbyN4v8zSF7s8jfLPI3h+zDG0/RCZDT6VTfvn3rX/ft21cuF78AaS7kbxb5m0P2ZpG/WeRvDtmHN67BD9CcOXNUVVWlrKwsORwObdiwQTExMRo+fLgk6ZprrjE8ob2Rv1nkbw7Zm0X+ZpG/OWQf3ij4AbrvvvvO+57D4VBeXl4zThN5yN8s8jeH7M0if7PI3xyyD28U/AD5fD45nU7TY0Qs8jeL/M0he7PI3yzyN4fswxvX4Afotttu03/913/pyJEjpkeJSORvFvmbQ/Zmkb9Z5G8O2Yc3zuAHqKKiQhs2bNCbb76puro6jRo1SllZWTwXtpmQv1nkbw7Zm0X+ZpG/OWQf3ij4TbB7925NnTpV5eXlGjRokCZNmqTU1FTTY0UM8jeL/M0he7PI3yzyN4fsww8F34/a2lq5XC75fD797W9/05tvvqmvvvpKt99+u7Kzs7V371699NJL2rRpk+lRbYn8zSJ/c8jeLPI3i/zNIXt74IGmfowZM0br1q3TbbfdphtvvFEPPPCArrvuuvr3Bw8erL179xqc0N7I3yzyN4fszSJ/s8jfHLK3B87g+zFixAj9+c9/VmVlpeLj402PE3HI3yzyN4fszSJ/s8jfHLK3B87g+1FSUqLly5ef9/2f/exnzThN5CF/s8jfHLI3i/zNIn9zyN4eKPh+1NXVqbKy0vQYEYv8zSJ/c8jeLPI3i/zNIXt7oOD7kZSUpMmTJ5seI2KRv1nkbw7Zm0X+ZpG/OWRvD3zRlR+B3qJQVlYW4kkiE/mbRf7mkL1Z5G8W+ZtD9vZAwffj9ddfD2i/cePGhXSOSEX+ZpG/OWRvFvmbRf7mkL09UPD9aN26dUD78TCi0CB/s8jfHLI3i/zNIn9zyN4eKPhB4nA4TI8Q0cjfLPI3h+zNIn+zyN8csr+0UfABAAAAG6HgBwm/qjKL/M0if3PI3izyN4v8zSH7SxvfZNsIZWVl+vrrr+Xz+eq3XXPNNZKk0tLSgK9bQ9OQv1nkbw7Zm0X+ZpG/OWQfvij4Afr1r3+tdevW6corr6zf5nA4lJeXZ3CqyEH+ZpG/OWRvFvmbRf7mkH1444uuArRx40a9++67io6ONj1KRCJ/s8jfHLI3i/zNIn9zyD68cQ1+gLp27ary8nLTY0Qs8jeL/M0he7PI3yzyN4fswxuX6ATowIEDmjRpkrp27aqoqKj67UuWLDE4VeQgf7PI3xyyN4v8zSJ/c8g+vFHwA5SVlaU777xTXbt2VYsW//uLjxtuuMHgVJGD/M0if3PI3izyN4v8zSH78MY1+AGKjY3V2LFjTY8RscjfLPI3h+zNIn+zyN8csg9vnMEP0Lx58xQdHa2MjIwGN5z8+3FRCC3yN4v8zSF7s8jfLPI3h+zDG2fwA/TZZ59Jkv7+97/Xb+NxUc2H/M0if3PI3izyN4v8zSH78MYZfAAAAMBGOIMfoPLyci1atEh79uyRdOYmk0ceeUQJCQmGJ4sM5G8W+ZtD9maRv1nkbw7ZhzfO4Afo0UcfVZcuXTRy5EhJ0ltvvaVDhw5p0aJFhieLDORvFvmbQ/Zmkb9Z5G8O2Yc5CwEZPnx4QNsQGuRvFvmbQ/Zmkb9Z5G8O2Yc3vsk2QLGxsdq7d2/963379ik2NtbgRJGF/M0if3PI3izyN4v8zSH78MYlOgE6ePCgpk+froqKClmWpcTERM2fP1/dunUzPVpEIH+zyN8csjeL/M0if3PIPrxR8BupoqJCktSqVSvDk0Qm8jeL/M0he7PI3yzyN4fswxNP0QkQd5ObRf5mkb85ZG8W+ZtF/uaQfXjjDH6AuJvcLPI3i/zNIXuzyN8s8jeH7MOckVt7wxB3k5tF/maRvzlkbxb5m0X+5pB9eOMpOgHibnKzyN8s8jeH7M0if7PI3xyyD29cohMg7iY3i/zNIn9zyN4s8jeL/M0h+/BGwW+kf99NHhcXp/z8fA0fPtzwRJGF/M0if3PI3izyN4v8zSH78MQlOn5UVFTotddeU25urnbs2KH4+Hj9+c9/VmZmpjZu3Gh6PNsjf7PI3xyyN4v8zSJ/c8jeHjiD78fEiROVmJioXr16aefOnSopKZFlWXrqqafUvXt30+PZHvmbRf7mkL1Z5G8W+ZtD9jbRvPf0hp9hw4bV/7m2tta66aabLK/Xa3CiyEL+ZpG/OWRvFvmbRf7mkL09cImOHy7X/34XmNPpVPv27RUTE2NwoshC/maRvzlkbxb5m0X+5pC9PXCJjh/du3dXXFycJMmyLFVVVSk2NlaWZcnhcGj//v2GJ7Q38jeL/M0he7PI3yzyN4fs7YGCDwAAANgIl+gAAAAANkLBBwAAAGyEgg8AAADYCAUfAAAAsBEKPgCE0HvvvafDhw+bHuOS8eabbyo3N9f0GABgaxR8ABHHsizV1dU1y7FMFfza2tqgr9mcuQEAms7lfxcACH9ffvmlHnjgAV177bX69NNP1bNnTx04cEAOh0MTJ07U0KFDZVmWfvWrX2n79u0Ntu/atUsvv/yyEhIS9Pnnn2vIkCHq2rWr8vLyVFVVpVdeeUVXXnnlWcfcv3+/tmzZot27d2vx4sV6+eWXNWXKFK1bt06SVFhYqP/4j//QunXrlJGRocGDB2v79u2KiYnRggULlJqaqpKSEs2aNUvHjh2TJP3nf/6n+vTpc87/xpdffllHjx5VUVGRvve97+npp58+58/u3r1bc+bMkSQ5HA6tWrVKrVq10rJly7Rx40ZVV1crMzNTjz322Fm5DRkyRJWVlZo+fbqkM2fkP/nkE82cOVNvvfWWVq5cqZqaGl177bWaNWuWnE6n1q5dq6VLlyohIUHdunVTdHR00P/3BQB8R7N+by4AGFJUVGRdffXV1kcffWT99a9/tcaNG2fV1tZax48ft9LT0y2Px3Pe7R9++KHVp08fy+PxWFVVVVb//v2thQsXWpZlWa+//ro1e/bs8x53+vTp1saNG+tf33vvvdZnn31mWZZlLViwwMrLy7Msy7J+/OMfW6+++qplWZa1bt06a8KECZZlWdYTTzxh7dmzx7Isy/rqq6+swYMHn/dYv/nNb6yRI0dap0+fvuDPPvTQQ9bevXsty7KsiooKq6amxtq+fbv19NNPW3V1dZbP57MmTJhg7d69u0FulmVZJ06csG699db6Yz7wwAPWnj17rMOHD1sPPfSQVV1dbVmWZc2aNctat26d5fF4rPT0dOvEiRNWVVWVdeedd1rPPvus3/+9AABNxxl8ABHje9/7nnr16qW5c+cqKytLTqdTl19+ua6//nodOHBA+/btO+f2Vq1aKS0tTcnJyZKkK6+8Uj/60Y8kSV27dtWuXbsCnmHMmDFau3atZsyYoQ0bNmj16tX17w0bNkySlJWVpXnz5kmSPvjggwaX+FRUVKiyslLx8fHnXD8jI0OxsbEX/NnrrrtO8+fPV3Z2tm677TbFx8drx44d2rFjh0aMGCFJOnXqlAoLC9WhQ4f63CSpbdu2SklJ0d///nelpqbqiy++UJ8+ffS73/1On3zyiUaPHi1J8nq9ateunQoKCnTDDTeobdu2kqShQ4eqsLAw4LwAAI1HwQcQMVq2bNnkn/3uZSUtWrSof92iRQv5fL6A1xk0aJBeeeUV3XTTTbrmmmvUpk2bC+5fV1enP/3pT4qJiQlo/X9/xfyFfnbChAlKT0/X1q1bdffdd2vZsmWyLEsTJkzQXXfd1WDfL7/88qzchg4dqo0bN6pTp07KzMyUw+GQZVkaOXKkpk6d2mDf9957L6C5AQDBw022ACJO3759tXHjRvl8PpWUlGjv3r3q2bPnebdfjPj4eFVWVta/jomJUf/+/ZWTk6M77rijwb4bN26UJG3YsEG9e/eWJPXv318rV66s3+fgwYMBH/t8P3v06FFdffXVmjBhgtLS0vSPf/xD/fv319q1a+tn9Xg8OnHixDnXzczM1ObNm7V+/XplZWVJkvr166dNmzbV/0xpaam++uor9ezZU3v27NHJkydVU1Ojv/71rwHPDwBoGs7gA4g4mZmZ+uijj3T77bfL4XDol7/8pZKSks67/YsvvmjysYYOHapnnnlGK1eu1G9+8xtdeeWVys7O1rvvvqv+/fs32LesrEzZ2dmKjo7Wiy++KEl66qmnlJubq+zsbPl8PvXt2zfgx0ye72dXrFihXbt2yeFwqEuXLhowYICio6N15MiR+jP4LVu21PPPP68WLc4+D5SYmKgf/OAHOnz4cP0/gDp37qzHH39cP//5z1VXV6eoqCjNnDlTvXr10uTJk3XXXXcpISFB3bt3b3KWAIDAOCzLskwPAQCR5L//+79VXl6uxx9/vH5bRkaG1qxZU3+tOgAATcUZfABoRo888oiOHj2qFStWmB4FAGBTnMEHgCBYvHjxWdeXDx48WBMnTgz6sdauXau8vLwG26677jrNmjUr6McCAIQfCj4AAABgIzxFBwAAALARCj4AAABgIxR8AAAAwEYo+AAAAICNUPABAAAAG/l/0bAbWXyfSI0AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 43;\n",
              "                var nbb_unformatted_code = \"temp = df[['room_type_reserved', 'no_of_children', 'no_of_adults']]\\ntemp['guests'] = temp['no_of_children'] + temp['no_of_adults']\\nstacked_barplot(temp, \\\"room_type_reserved\\\", \\\"guests\\\")\";\n",
              "                var nbb_formatted_code = \"temp = df[[\\\"room_type_reserved\\\", \\\"no_of_children\\\", \\\"no_of_adults\\\"]]\\ntemp[\\\"guests\\\"] = temp[\\\"no_of_children\\\"] + temp[\\\"no_of_adults\\\"]\\nstacked_barplot(temp, \\\"room_type_reserved\\\", \\\"guests\\\")\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stacked_barplot(df[df['no_of_children']<4], \"no_of_children\", \"room_type_reserved\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "id": "An3cHB6Al54c",
        "outputId": "27b22306-6538-4f28-a7e0-3478bb08280f"
      },
      "id": "An3cHB6Al54c",
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "room_type_reserved  Room_Type 1  Room_Type 2  Room_Type 3  Room_Type 4  \\\n",
            "no_of_children                                                           \n",
            "0                         26785          482            7         5850   \n",
            "All                       28129          691            7         6056   \n",
            "1                          1309           25            0          191   \n",
            "2                            34          179            0           15   \n",
            "3                             1            5            0            0   \n",
            "\n",
            "room_type_reserved  Room_Type 5  Room_Type 6  Room_Type 7    All  \n",
            "no_of_children                                                    \n",
            "0                           241          121           91  33577  \n",
            "All                         265          966          158  36272  \n",
            "1                            13           64           16   1618  \n",
            "2                            11          776           43   1058  \n",
            "3                             0            5            8     19  \n",
            "------------------------------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 44;\n",
              "                var nbb_unformatted_code = \"stacked_barplot(df[df['no_of_children']<4], \\\"no_of_children\\\", \\\"room_type_reserved\\\")\";\n",
              "                var nbb_formatted_code = \"stacked_barplot(df[df[\\\"no_of_children\\\"] < 4], \\\"no_of_children\\\", \\\"room_type_reserved\\\")\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* `Room Type 1` \"Double\" - is most popular, capacity is up to 2 adults + child\n",
        "* `Room Type 4` \"Double+\" followed by the `Room Type 1`. Capacity is up to 3 adults\n",
        "* `Room Type 2` \"Family\" capacity is up to 3 persons (Adult+Child(ren))\n",
        "* `Room Type 6` \"Family+\" - capacity is up to 4 persons. Usually for 2 adults + 2 children"
      ],
      "metadata": {
        "id": "Uc3tgVdch4df"
      },
      "id": "Uc3tgVdch4df"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Let's see how the `no_of_weekend_nights` varies across the `no_of_week_nights`**"
      ],
      "metadata": {
        "id": "tgebDbb3OHce"
      },
      "id": "tgebDbb3OHce"
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14,6));\n",
        "ax = sns.scatterplot(data=df, x='no_of_week_nights', y='no_of_weekend_nights')\n",
        "\n",
        "ax.set_xticks(np.arange(0, 18, step=1))\n",
        "plt.show()\n",
        "\n",
        "display(df.pivot_table(index='no_of_weekend_nights', columns='no_of_week_nights', aggfunc='count', values='Booking_ID').fillna(0).astype(int).replace({0: ''}));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "id": "Jf8nKJ_zOHQ3",
        "outputId": "276da1f5-883c-4577-e21f-ff6773e73592"
      },
      "id": "Jf8nKJ_zOHQ3",
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "no_of_week_nights       0     1     2     3     4    5    6   7   8   9   10  \\\n",
              "no_of_weekend_nights                                                           \n",
              "0                       78  5082  5745  4180  1529  258                        \n",
              "1                     1522  1940  3406  2071   743  313                        \n",
              "2                      787  2466  2293  1588   718  973  126  63  38  14   5   \n",
              "3                                                    53   31  39  14   9   7   \n",
              "4                                                    17   32  11  10  11  32   \n",
              "5                                                                         18   \n",
              "6                                                                              \n",
              "7                                                                              \n",
              "\n",
              "no_of_week_nights     11 12 13 14 15 16 17  \n",
              "no_of_weekend_nights                        \n",
              "0                                           \n",
              "1                                           \n",
              "2                                           \n",
              "3                                           \n",
              "4                     13  2        1        \n",
              "5                      4  5  1  1  5        \n",
              "6                         2  4  6  4  2  2  \n",
              "7                                        1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5bbb464b-13ee-4ad3-9362-9548ca572ee4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "        vertical-align: middle;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>no_of_week_nights</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no_of_weekend_nights</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>78</td>\n",
              "      <td>5082</td>\n",
              "      <td>5745</td>\n",
              "      <td>4180</td>\n",
              "      <td>1529</td>\n",
              "      <td>258</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1522</td>\n",
              "      <td>1940</td>\n",
              "      <td>3406</td>\n",
              "      <td>2071</td>\n",
              "      <td>743</td>\n",
              "      <td>313</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>787</td>\n",
              "      <td>2466</td>\n",
              "      <td>2293</td>\n",
              "      <td>1588</td>\n",
              "      <td>718</td>\n",
              "      <td>973</td>\n",
              "      <td>126</td>\n",
              "      <td>63</td>\n",
              "      <td>38</td>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>53</td>\n",
              "      <td>31</td>\n",
              "      <td>39</td>\n",
              "      <td>14</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>17</td>\n",
              "      <td>32</td>\n",
              "      <td>11</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>32</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>1</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>18</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5bbb464b-13ee-4ad3-9362-9548ca572ee4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5bbb464b-13ee-4ad3-9362-9548ca572ee4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5bbb464b-13ee-4ad3-9362-9548ca572ee4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 45;\n",
              "                var nbb_unformatted_code = \"plt.figure(figsize=(14,6));\\nax = sns.scatterplot(data=df, x='no_of_week_nights', y='no_of_weekend_nights')\\n\\nax.set_xticks(np.arange(0, 18, step=1))\\nplt.show()\\n\\ndisplay(df.pivot_table(index='no_of_weekend_nights', columns='no_of_week_nights', aggfunc='count', values='Booking_ID').fillna(0).astype(int).replace({0: ''}));\";\n",
              "                var nbb_formatted_code = \"plt.figure(figsize=(14, 6))\\nax = sns.scatterplot(data=df, x=\\\"no_of_week_nights\\\", y=\\\"no_of_weekend_nights\\\")\\n\\nax.set_xticks(np.arange(0, 18, step=1))\\nplt.show()\\n\\ndisplay(\\n    df.pivot_table(\\n        index=\\\"no_of_weekend_nights\\\",\\n        columns=\\\"no_of_week_nights\\\",\\n        aggfunc=\\\"count\\\",\\n        values=\\\"Booking_ID\\\",\\n    )\\n    .fillna(0)\\n    .astype(int)\\n    .replace({0: \\\"\\\"})\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The distribution of numbers of weekend nights looks reasonable in comparison with the number of weekday nights per booking"
      ],
      "metadata": {
        "id": "SMy6t3fqQJkS"
      },
      "id": "SMy6t3fqQJkS"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Let's see how the target variable varies across the number of special requests**"
      ],
      "metadata": {
        "id": "hU74KSczqjlw"
      },
      "id": "hU74KSczqjlw"
    },
    {
      "cell_type": "code",
      "source": [
        "stacked_barplot(df, \"no_of_special_requests\", \"booking_status\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "1-bLbt3Wo7bg",
        "outputId": "c467529f-5d61-4a8d-a121-6b13bf9b2afe"
      },
      "id": "1-bLbt3Wo7bg",
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "booking_status          Canceled  Not_Canceled    All\n",
            "no_of_special_requests                               \n",
            "All                        11885         24390  36275\n",
            "0                           8545         11232  19777\n",
            "1                           2703          8670  11373\n",
            "2                            637          3727   4364\n",
            "3                              0           675    675\n",
            "4                              0            78     78\n",
            "5                              0             8      8\n",
            "------------------------------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 792x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 46;\n",
              "                var nbb_unformatted_code = \"stacked_barplot(df, \\\"no_of_special_requests\\\", \\\"booking_status\\\")\";\n",
              "                var nbb_formatted_code = \"stacked_barplot(df, \\\"no_of_special_requests\\\", \\\"booking_status\\\")\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Bookings made with more than two special  requests have a high chance to be not canceled."
      ],
      "metadata": {
        "id": "1A0Sk4Vwr3bK"
      },
      "id": "1A0Sk4Vwr3bK"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Let's see how the average price per room varies by the number of special requests**"
      ],
      "metadata": {
        "id": "QxkUVzPzC2eP"
      },
      "id": "QxkUVzPzC2eP"
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(12, 6))\n",
        "sns.boxplot(data=df, x='no_of_special_requests', y='avg_price_per_room', ax=ax, showfliers=False)\n",
        "plt.xlabel('Number of special requests', fontsize=12)\n",
        "plt.ylabel('Average price per room, euro', fontsize=12)\n",
        "plt.title('Price vs. No of requests', fontsize=16)\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "outputId": "939a53a2-e5d2-4a0e-8701-c8c3a1a1a4d5",
        "id": "etPK-X94C2eP"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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HHyo1NVUVK1ZUUFCQFi5cqJCQEKu+hwDKFpO5pP+iAwBgZ7GxsdqzZ48SExPtHQUAJDHnGgAAALAayjUAAABgJUwLAQAAAKyEM9cAAACAlVCuAQAAACspN5fiKywsVEEBM1wAAABgWxUq3Ph6/+WmXBcUmJWdnXPrJwIAAAB3oFq1Kjdcx7QQAAAAwEoo1wAAAICVUK4BAAAAK6FcAwAAAFZCuQYAAACshHINAAAAWAnlGgAAALASyjUAAABgJZRrAAAAwEoo1wAAAICVUK4BAAAAK3GxdwAAuFNJSVuVmLjZ6uNmZ2dJkjw9vaw+tiRFREQqPLyLTcYGANgH5RoAbiAry7blGgBQ/pjMZrPZ3iGsIS+vQNnZOfaOAaAcmTw5VpI0bdpsOycBAJQl1apVueE65lwDAAAAVkK5BgAAAKyEcg0AAABYCeUaAAAAsBKuFgIAsCtHvJQil1EEcCOUawBAucSlFAHYA+UaAGBX4eFdbHIWmEspArAHyjUAACgRW03pkZjWA8dFuQYAAGUO03rgqCjXAACgRGw1pUdiWg8cF5fiAwAAAKyEcg0AAABYCeUaAAAAsBLKNQAAAGAllGsAAADASkqlXJ8+fVpPPfWUunfvrh49euijjz6SJGVnZysqKkoPPvigoqKidO7cOUmS2WzWa6+9psjISPXq1Uv//Oc/SyMmAAAAcEdKpVw7OzsrNjZWGzdu1Oeff65PP/1UR48e1eLFi9W2bVtt2rRJbdu21eLFiyVJO3bsUEpKijZt2qTp06drypQppRETAAAAuCOlUq59fX3VuHFjSZK7u7sCAwOVlpamrVu3qm/fvpKkvn37asuWLZJkWW4ymdS8eXP99ttvSk9PL42oAAAAQImV+k1kTpw4oR9++EHNmjVTRkaGfH19JUnVqlVTRkaGJCktLU3+/v6Wbfz9/ZWWlmZ57vU4O5vk6elm2/AA/lBcXIrOP/Bvi2Pi+Dk2jh8cVamW64sXLyo6OloTJ06Uu7t7sXUmk0kmk6nEYxcUmJWdnXOnEQHAIj+/UJL4t8VBcfwcG8cPZVm1alVuuK7UrhaSl5en6Oho9erVSw8++KAkycfHxzLdIz09Xd7e3pIkPz8/paamWrZNTU2Vn59faUUFAAAASqRUyrXZbNakSZMUGBioqKgoy/KIiAjFxcVJkuLi4tSlS5diy81msw4ePKgqVarcdEoIAAAAUBaUyrSQ7777TvHx8QoKClKfPn0kSTExMRo+fLjGjh2r1atXq0aNGpo3b54kqVOnTtq+fbsiIyN11113aebMmaUREwAAALgjpVKuW7ZsqR9//PG6665e8/r3TCaTXn31VVvHAgAAAKyKOzQCAAAAVkK5BgAAAKyEcg0AAABYCeUaAAAAsBLKNQAAAGAllGsAAADASijXAAAAgJVQrgEAAAAroVwDAAAAVkK5BgAAAKyEcg0AAABYCeUaAAAAsBLKNQAAAGAllGsAAADASijXAAAAgJVQrgEAAAAroVwDAAAAVkK5BgAAAKyEcg0AAABYiYu9AwBlQVLSViUmbrbJ2NnZWZIkT08vq48dERGp8PAuVh8XAACUDOUasLGsLNuVawAAULZQrgFJ4eFdbHYGePLkWEnStGmzbTI+AAAoO5hzDQAAAFgJ5RoAAACwEso1AAAAYCWUawAAAMBKKNcAAACAlVCuAQAAACuhXAMAAABWUirXuZ4wYYKSkpLk4+OjhIQESdLYsWOVnJwsSTp//ryqVKmi+Ph4nThxQt27d1e9evUkSc2aNdO0adNKIyYAAABwR0qlXPfr109PPvmkXn75ZcuyefPmWb6ePXu23N3dLY9r166t+Pj40ogGAAAAWE2pTAsJCwuTh4fHddeZzWZ99dVX6tmzZ2lEAQAAAGzG7rc/37dvn3x8fFS3bl3LshMnTqhv375yd3fX2LFj1bJly1uO4+xskqenmw2TAiXj4lL0Myzfn46HY+fYOH6OjeMHR2X3cp2QkFDsrLWvr6+2bdsmLy8vHT58WKNHj9aGDRuKTRu5noICs7Kzc2wdF7ht+fmFksT3pwPi2Dk2jp9j4/ihLKtWrcoN19n1aiH5+fnavHmzunfvblnm6uoqLy8vSVJISIhq165t+eAjAAAAUJbZtVx/8803CgwMlL+/v2VZZmamCgoKJEnHjx9XSkqKatWqZa+IAAAAgGGlMi0kJiZGe/bsUVZWljp27KgxY8Zo4MCB2rhxo3r06FHsuXv37tXbb78tFxcXOTk5aerUqfL09CyNmHcsKWmrEhM3W33c7OwsSZKnp5fVx46IiFR4eBerjwsAAPBHZKhc5+Xl6f3331dcXJzOnDkjX19f9enTRyNGjJCrq+stt587d+51l8+ePfuaZV27dlXXrl2NxPrDyMqyXbkGAACA9Rgq13PmzNGhQ4c0bdo01ahRQ6dOndJ7772nCxcuaOLEibbO6DDCw7vY5Czw5MmxkqRp0679YQQAAABlh6Fy/fXXXys+Pt7yQcPAwEDde++96tOnD+UaAAAA+A9DH2g0m823tRwAAAD4IzJUrh966CGNHDlSO3fu1LFjx7Rjxw6NHj1a3bp1s3U+AAAAwGEYmhYyfvx4vf/++5o2bZrS09Pl5+en7t27a9SoUbbOBwAAADiMW5brgoIC/fnPf9b06dP1/PPPl0YmAAAAwCHdclqIs7Ozdu3aJZPJVBp5AAAAAIdlaM71oEGDtGDBAuXm5to6DwAAAOCwDM25/vjjj3X27FktW7ZM3t7exc5iJyUl2SobAAAA4FAM30QGAAAAwM0ZKtetWrWydQ4AAADA4Rkq1/Pnz7/hOq4gAgAAABQxVK5TU1OLPT5z5oz27t2rBx54wCahAAAAAEdkqFzPmjXrmmU7duzQhg0brB4IAAAAcFSGLsV3Pe3bt9eWLVusmQUAAABwaIbOXB8/frzY40uXLikhIUHVq1e3SSgAAADAERkq15GRkTKZTDKbzZKku+66S40aNdLs2bNtGg4AAABwJIbK9ZEjR2ydAwAAAHB4hudc5+Xlad++fdq4caMkKScnRzk5OTYLBgAAADgaQ2euf/zxR40cOVKurq5KS0tT9+7dtXfvXq1du1bz5s2zcUQAAADAMRg6cz1lyhRFR0fr66+/lotLUR8PCwvTd999Z9NwAAAAgCMxVK6PHj2qPn36SJJMJpMkyc3NTVeuXLFdMgAAAMDBGCrXAQEBOnz4cLFlhw4dUu3atW0SCgAAAHBEhuZcP//883r22Wf12GOPKS8vT4sWLdJnn32m6dOn2zofAAAA4DAMnbnu3LmzlixZoszMTIWFhenkyZNasGCB2rdvb+t8AAAAgMMwdOZaku69915NmTLFhlEAAAAAx2b4OtcAAAAAbo5yDQAAAFgJ5RoAAACwklIp1xMmTFDbtm3Vs2dPy7IFCxaoQ4cO6tOnj/r06aPt27db1i1atEiRkZHq2rWrdu7cWRoRAQAAgDtW4nI9YcIErV69WgUFBbd8br9+/bRkyZJrlg8ePFjx8fGKj49Xp06dJBXdsGbDhg3asGGDlixZoqlTpxraBwAAAGBvd3TmOiEhQb17977l88LCwuTh4WFozK1bt6pHjx5ydXVVrVq1VKdOHR06dOhOYgIAAAClwvCl+P7XrFmzJEl5eXkl3vknn3yiuLg4hYSEKDY2Vh4eHkpLS1OzZs0sz/Hz81NaWlqJ9wEAAACUlhKX66sqVKhQou0ef/xxjRo1SiaTSfPnz9fs2bMthb0knJ1N8vR0K/H2ZZmLS9EvGMrr6yvvOH6Oi2Pn2Dh+jo3jB0dlqFwfOXJEM2fO1JEjR5STkyNJMpvNMplMOnz4cIl2XLVqVcvXAwcO1IgRIyQVnalOTU21rEtLS5Ofn98txysoMCs7O6dEWcq6/PxCSSq3r6+84/g5Lo6dY+P4OTaOH8qyatWq3HCdoXIdExOjBx98UK+88ooqVapklVDp6eny9fWVJG3ZskUNGjSQJEVEROiFF15QVFSU0tLSlJKSoqZNm1plnwAA/BEtXbpYKSk/2zvGbUlOLso7eXKsnZMYV7duoIYMGW7vGLAzQ+X67Nmzev7552UymUq0k5iYGO3Zs0dZWVnq2LGjxowZoz179ujIkSOSpICAAE2bNk2S1KBBA3Xr1k3du3eXs7OzJk+eLGdn5xLtFwAASCkpP+vfR/4tTzdfe0cxzLmwoiQp/dds+wYxKDsn3d4RUEYYKtd9+/bV+vXrDV0Z5Hrmzp17zbKBAwfe8PkjR47UyJEjS7QvAABwLU83X3Vu+Ji9Y5Rb2458Zu8IKCMMlevhw4fr0Ucf1aJFi+Tj41Ns3fLly20SDAAAAHA0hsp1dHS0atasqcjISFWsWNHWmQAAAACHZKhc//DDD9q9e7dcXV1tnQcAAABwWIbu0NiyZUsdO3bM1lkAAAAAh2bozHXNmjU1ZMgQRUZGXjPn+vnnn7dJMAAAAMDRGCrXly9fVnh4uPLy8ord4AUAAADAfxkq13dyW3IAAADgj8JQuZaklJQUJSQkWO6s2LNnT9WtW9eG0QAAAADHYugDjYmJierXr5+Sk5Pl4eGh5ORk9e/fX1u3brV1PgAAAMBhGDpz/dZbb+m9995TmzZtLMt2796t6dOnq0uXLjYLB6B8Wbp0sVJSfrZ3DMOSk4uyTp4ca+ckt6du3UANGTLcqmM62rGTOH4A7MNQuU5NTVXLli2LLWvRogUfbgRwW1JSftbhfx+SPO2dxCDnoj8Opx+yb47bkW2bYVNSftaxf34vf9sMbxNu//nz4j+/t2uO28H/VQHHZ6hcN2zYUEuXLtXw4f/9SXrZsmVq1KiRzYIBKKc8pcLwQnunKLeckgzN9isRf0lDZbLZ+JA+kNneEQDcIUPlesqUKRo5cqSWL1+u6tWr6/Tp07rrrru0cOFCW+cDAAAAHIahcl2/fn1t3LhRBw8etFwtpFmzZqpQoYKt8wEAAAAO47Z+f2g2m1VYWCiz2SyTiV8NAgAAAL9n6Mz1sWPHNHLkSF25ckX+/v46ffq0KlasqIULF6p+/fq2zggAAAA4BEPleurUqXrkkUc0dOhQyxnrDz74QFOmTNGKFStsGhAAAABwFIamhRw5ckRRUVHFpoIMGjRIR44csVkwAAAAwNEYKte+vr7as2dPsWX79u2Tr6+vTUIBAAAAjsjQtJBx48Zp1KhRCg8PV40aNXTq1CklJSVpzpw5ts4HAAAAOAxDZ647d+6stWvXqkGDBrp48aIaNGigNWvW6IEHHrB1PgAAAMBh3PLMdUFBgUJDQ7Vv3z6NGjWqNDIBAAAADumWZ66dnZ1Vt25dZWVllUYeAAAAwGEZmnPdq1cvjRgxQk8//bT8/f2LrWvbtq1NggEAAACOxlC5XrlypSRpwYIFxZabTCZt3brV+qkAAAAAB2SoXCcmJto6BwAAAODwDF0tBAAAAMCtUa4BAAAAK6FcAwAAAFZyyznXZrNZJ06cUI0aNeTs7FyinUyYMEFJSUny8fFRQkKCJOn111/Xtm3bVKFCBdWuXVuzZs3S3XffrRMnTqh79+6qV6+eJKlZs2aaNm1aifYLAAAAlKZbnrk2mUzq1auXTCZTiXfSr18/LVmypNiydu3aKSEhQevXr1fdunW1aNEiy7ratWsrPj5e8fHxFGsAAAA4DEPTQho1aqTk5OQS7yQsLEweHh7FlrVv314uLkUnzps3b67U1NQSjw8AAACUBYYuxdeqVSs988wzevjhh+Xv71/sLPaAAQPuOMSXX36pbt26WR6fOHFCffv2lbu7u8aOHauWLVve8T4AAAAAWzNUrvfv36+AgADt2bOn2HKTyXTH5fr999+Xs7OzevfuLUny9fXVtm3b5OXlpcOHD2v06NHasGGD3N3dbzqOs7NJnp5ud5SlrHJxKfoFQ3l9feUdx++/rr4XsC0XFyerf79x7EoPx89x2eLYwfEYKtcrVqywyc7XrFmjpKQkffjhh5az4a6urnJ1dZUkhYSEqHbt2kpOTlaTJk1uOlZBgVnZ2Tk2yWlv+fmFklRuX9/tWLp0sVJSfrZ3jNuSnFyUNzo62s5JjKtbN1BDhgy3+rhXv5dhW/n5hVb/94JjV3o4fo7LFscOZVO1alVuuM5QuZakrKwsbd++XWfPntWwYcOUlpYms9ksf3//EoXasWOHlixZoo8//lh33XWXZXlmZqY8PDzk7Oys48ePKyUlRbVq1SrRPlD+pKT8rOQf/6Ha7gX2jmKYh4p+cCw4ud/OSYz59ULJrgoEAAAMlus9e/ZozJgxCgkJ0f79+zVs2DD98ssvWrp0qRYuXHjL7WNiYrRnzx5lZWWpY8eOGjNmjBYvXqzc3FxFRUVJ+u8l9/bu3au3335bLi4ucnJy0tSpU+Xp6XlHLxLlS233Ar3S8oK9Y5Rbr+27+RQsAABwY4bK9cyZMzVv3jy1bdtWYWFhkorK8KFDhwztZO7cudcsGzhw4HWf27VrV3Xt2tXQuAAAAEBZYugTDidPnlTbtm0lyTI3ukKFCioocJxfzQMAAAC2ZujMdf369bVz50516NDBsuybb75RUFCQzYIBAADryM7OUnbOGW078pm9o5Rb2Tnpcs022zsGygBD5To2NlbPPvuswsPDdfnyZU2ePFmJiYl67733bJ0PAAAAcBiGynXz5s21bt06rVu3Tv3791f16tW1evXqEl8pBAAAlB5PTy/l/mZS54aP2TtKubXtyGdcgAGSbuNSfH5+fho2bJiysrLk5eVV7C6NAAAAAAx+oPG3337T+PHj1bRpU7Vr105NmzbV+PHjlZ2dbeN4AAAAgOMwVK4nTJigK1euKC4uTvv371dcXJxyc3M1ceJEW+cDAAAAHIahaSF///vftWvXLlWqVElS0dVDZs+eXezqIQCA8is7O0tnJX0groZgS6clVc3OsncMAHfA0JnrwMBAnTx5stiyU6dOqV69ejYJBQAAADgiQ2eu27ZtqyFDhqhPnz7y9/dXamqq1q1bpz59+mj16tWW5w0YMMBmQa1l6dLFSkn52d4xbktyclHeyZNj7Zzk9tStG6ghQ4bbOwYAK/D09FKFkyc0VHyY3ZY+kFmVPb3sHQPAHTBUrg8cOKDatWvrwIEDlmW1atXS/v37tX//fklFd250hHKdkvKzDh/5UYVu3vaOYpipsOgwHfr1jJ2TGOeUk2nvCCiDsrOzpGzJKcnQL81QEtlStivTCgDAXgyV6xUrVtg6R6kqdPPW5Xt72jtGuVbpXwn2jgAAAFDqDF/nGgDulKenl07kHldheKG9o5RbTklO8mRaAQDYDb+bBQAAAKyEcg0AAABYCeUaAAAAsBLDc66PHTumr7/+WmfPntWrr76qY8eOKS8vTw0bNrRlPgAAAMBhGDpz/dVXX+nJJ59UWlqa4uPjJUk5OTmaPXu2TcMBAAAAjsTQmeu3335by5YtU8OGDfXVV19Jkho2bKgjR47YNBwAAADgSAyduc7MzFRwcLCkopvFXP3z6tcAAAAADJbrxo0bW6aDXLVhwwY1bdrUJqEAAAAAR2RoWsikSZM0dOhQrV69Wjk5ORo6dKiSk5O1dOlSW+cDAAAAHIahcl2/fn199dVX2rZtm8LDw1W9enWFh4ercuXKts4HAAAAOAxD5TotLU2VKlVS9+7dLcvOnTuntLQ0+fn52SwcAADAH9nSpYuVkvKzTcbOzs5SVlaWTca2FS8vL3l6etlk7Lp1AzVkyPA7HsdQuR41apRmzpwpDw8Py7LU1FS98sor+uKLL+44BAAAAK6VkvKzfvzXYVVxNXxrEsOuFBQqt6DQ6uPaUlbuZeWcTbP6uOdz8602lqEjlZKSYrlayFXBwcH6+Wfb/CQFAACAIlVcXdTKzzZna1FkT5r1zuAbulqIt7e3fvnll2LLfvnlF3l6elotCAAAAODoDJ257t+/v8aMGaNx48apVq1a+vXXXzV//nwNHDjQ1vmAYrKzs5R53lmv7XO3d5Ry65fzzvLOdqw5eAAAlBWGyvXw4cPl4uKi119/XampqfL399fAgQMVFRVl63wAAACAwzBUrp2cnDRs2DANGzasxDuaMGGCkpKS5OPjo4SEBElSdna2xo0bp5MnTyogIEDz5s2Th4eHzGazZsyYoe3bt6tSpUqaPXu2GjduXOJ9o/zw9PRSlYvJeqXlBXtHKbde2+cuZxt9EhsAgPLuhnOu9+7da/n622+/veF/RvXr109Lliwptmzx4sVq27atNm3apLZt22rx4sWSpB07diglJUWbNm3S9OnTNWXKlNt8WQAAAEDpu+GZ66lTp1rOME+aNOm6zzGZTNq6dauhHYWFhenEiRPFlm3dulUrVqyQJPXt21dPPfWUxo8fr61bt6pv374ymUxq3ry5fvvtN6Wnp8vX19fQvgAAAAB7uGG5vlqsJWnz5s1ydna2+s4zMjIshblatWrKyMiQVHTTGn9/f8vz/P39lZaWRrkGAABAmXbLOdcFBQUKDQ3Vvn375OrqarMgJpNJJpOpxNs7O5vk6el2y+e5uBi6+iCswMXFydAxud0xC6w6Iq7HFsfu6riwPVv93UPp4Pg5Lo6dY7PW8btluXZ2dlbdunWVlZVl9Vud+/j4WKZ7pKeny9vbW5Lk5+en1NRUy/NSU1Nvue+CArOys3Nuuc/8fMe6E5Ejy88vNHRMbndM2J4tjt3VcWF7/N1zbBw/x8Wxc2y3c/yqVatyw3WGfhzq1auXRowYobVr15b4A43XExERobi4OElSXFycunTpUmy52WzWwYMHVaVKFaaEAAAAoMwzdCm+lStXSpIWLFhQbPntfKAxJiZGe/bsUVZWljp27KgxY8Zo+PDhGjt2rFavXq0aNWpo3rx5kqROnTpp+/btioyM1F133aWZM2fexksCAAAA7MNQuU5MTLzjHc2dO/e6yz/66KNrlplMJr366qt3vE8AAABHlp2dpawredp6/IzVxy40m+Vok06cJDndwWf0biTfbJable5ObKhcS0UfbDx48KDS09Pl5+enZs2a2eQKIgAAAChStWo1ZWVZp/T9r/z8POXn59tkbFtxcXGRi0sFq4/rqqL32hoMlesjR45o9OjRunLlivz9/ZWamqqKFSvqnXfeUaNGjawSpLRkZ2fJKSdDlf6VcOsno8SccjKUnW34ZzcAAHAdkye/Zu8IuE2G2s/EiRP1pz/9SVFRUTKZTDKbzfrwww81adIkrVmzxtYZAQAAAIdgqFynpKRo0KBBlutQm0wmPf3009d8wNEReHp66dff8nX53p72jlKuVfpXgjw9vewdA4AVpUr6QGZ7xzDswn/+dLdrituTKqm+vUMAuCOGynWnTp2UmJioyMhIy7Jt27YpPDzcVrkAAGVI3bqB9o5w29KTf5Yk+dVznOz15ZjvNYD/MlSuCwoKNG7cOIWEhFjmXB8+fFhdunTRSy+9ZHneG2+8YbOgAAD7GTJkuL0j3LbJk2MlSdOmzbZzEgB/JIbKdVBQkIKCgiyP77nnHrVv395moQAAAABHZKhcP/fcc7bOAQAAADg8rpUGoHRlS05JTvZOYczl//xZya4pbk+2JF97h0BZlJ2Trm1HPrN3DMMu512UJFWqUNnOSYzJzkmXrzztHQNlAOUaQKlxtA9qJf/nA3H1fB0ot6/jvc+wPUf8nkhOzpQk+dYOsHMSY3zl6ZDvM6yPcg2g1Djah+L4QBzKC0f7uyfx9w+Oy0F+NwsAAACUfYbKtdls1qpVq/T000+rV69ekqS9e/dq48aNNg0HAAAAOBJD5Xr+/PlavXq1Hn30UZ0+fVqS5O/vryVLltg0HAAAAOBIDJXrtWvXauHCherRo4flFug1a9bU8ePHbRoOAAAAcCSGynVBQYEqVy66FM7Vcn3x4kW5ubnZLhkAAADgYAyV606dOmnWrFnKzc2VVDQHe/78+ercubNNwwEAAACOxFC5njBhgs6cOaMWLVro/PnzCg0N1alTp/Tiiy/aOh8AAADgMAxd59rd3V3vvvuuzp49q1OnTql69eqqVq2arbMB1/XrBWe9ts/d3jEMO5dbNJXKw9Vs5yTG/HrBWfXsHQIAAAdlqFwXFhZKkry9veXt7W1Z5uTEZbJRuhzx7lfn/nOXP+8Ax8heT475PgMAUBYYKtf33nuv5YOMv+fs7CxfX189+OCDGjNmjOVDj4CtcJcxAABQlhkq13/+85+1ZcsWDR8+XP7+/jp9+rSWLFmiTp06qV69enr33Xc1c+ZMzZgxw9Z5AQAAgDLLULletmyZ1q5dqypVqkiS6tWrp5CQEPXr109btmxRcHCw+vXrZ9OgAAAAQFlnaNL0hQsXdOnSpWLLLl26pPPnz0uSqlatqsuXL1s/HQAAAOBADJ257tu3r4YMGaKnn35a/v7+SktL0/Lly/Xwww9Lkv72t7+pXj2uLwAAAIA/NkPl+qWXXlKdOnW0YcMGpaenq1q1anriiSf0yCOPSJLatGmj1q1b2zQoAAAAUNYZKtdOTk56/PHH9fjjj193fcWKFa0aCgAAAHBEhsq1JJ09e1aHDh1SVlaWzOb/3gxjwIABNgkGAAAAOBpD5XrLli0aP3686tSpo6NHj+qee+7RTz/9pPvuu49yDQAAAPyHoXI9b948zZw5U926dVNYWJji4uL05Zdf6ujRo7bOZxNOOZmq9K8Ee8cwzJRXdKUWc4W77JzEOKecTEnV7B0DAACgVBkq16dOnVK3bt2KLXv44YfVrl07vfzyyyXe+c8//6xx48ZZHh8/flzR0dE6f/68Vq1aZbnVekxMjDp16lTi/fyeI97WOfk/t8+uV9uRymo1h3yvAQAA7oShcu3j46OzZ8+qatWqCggI0IEDB+Tl5aXCwsI72nlgYKDi4+MlSQUFBerYsaMiIyO1Zs0aDR48WEOHDr2j8a+H22cDAADAVgzdRGbgwIH67rvvJEmDBw/W008/rT59+tzw6iEl8e2336pWrVoKCAiw2pgAAABAaTJ05nrYsGFycirq4X379lWrVq106dIl1a9f32pBNmzYoJ49e1oef/LJJ4qLi1NISIhiY2Pl4eFx0+2dnU3y9HSzWp6yxMWl6L0vr6+vvOP4OS6OnWPj+Dk2jh8c1S3LdUFBgUJDQ7Vv3z65urpKkmrUqGHVELm5uUpMTNQLL7wgSXr88cc1atQomUwmzZ8/X7Nnz9asWbNukdOs7Owcq+YqK/Lzi6bflNfXV95x/BwXx86xcfwcG8cPZVm1alVuuO6W00KcnZ1Vt25dZWVlWTXU7+3YsUONGzdW1apVJUlVq1aVs7OznJycNHDgQH3//fc22zcAAABgLYamhfTq1UsjRozQ008/LX9//2Lr2rZte8chNmzYoB49elgep6eny9fXV1LRNbYbNGhwx/sAAAAAbM1QuV65cqUkacGCBcWWm0wmbd269Y4C5OTk6JtvvtG0adMsy+bMmaMjR45IkgICAoqtAwAAAMoqQ+U6MTHRZgHc3Ny0e/fuYsvmzJljs/0BAAAAtmLoUnySlJeXp3379mnjxo2Sis445+TwIQMAAADgKkNnrn/88UeNHDlSrq6uSktLU/fu3bV3716tXbtW8+bNs3FEAAAAwDEYOnM9ZcoURUdH6+uvv5aLS1EfDwsLs9xYBgAAAIDBcn306FH16dNHUtGHGKWiudJXrlyxXTIAAADAwRgq1wEBATp8+HCxZYcOHVLt2rVtEgoAAABwRIbmXD///PN69tln9dhjjykvL0+LFi3SZ599punTp9s6HwAAAOAwDJ257ty5s5YsWaLMzEyFhYXp5MmTWrBggdq3b2/rfAAAAIDDMHTmOjMzU/fee6+mTJli4zgAAACA4zJ85vqZZ57RunXruLY1AAAAcAOGyvW2bdsUHh6ulStXql27doqJiVFiYqLy8/NtnQ8AAABwGIbKtbe3t/70pz9p5cqVSkhIUMOGDfXWW28x5xoAAAD4HcO3P78qIyNDZ8+eVVZWlu6++25bZAIAAAAckqEPNB49elQJCQnasGGDLl++rG7duum9995T06ZNbZ0PAAAAcBiGyvXjjz+uBx98UNOmTVPr1q3l5FR0wruwsNDyNQAAAPBHZ6hc79q1S66urpbHP/74o+Li4rR+/Xr97W9/s1k4AAAAwJEYKteurq7KzMzU+vXrFRcXpyNHjqhly5aaNGmSrfMBAIAyKilpqxITN9tk7OTknyVJkyfHWn3siIhIhYd3sfq4gHSLcp2Xl6fExEStXbtWf/vb31S7dm316NFDp06d0rx58+Tj41NaOQEAwB+Il5eXvSMAJXLTct2uXTuZTCb169dPY8aMUePGjSVJK1euLJVwAACg7AoP78IZYOB/3PTTiMHBwTp//rz+8Y9/6Pvvv9e5c+dKKxcAAADgcG5arlesWKHNmzerXbt2Wrp0qdq1a6cRI0YoJyeHuzMCAAAA/+OW19ELCAjQ6NGjtWnTJn344YeqVq2anJyc1Lt3b73xxhulkREAAABwCIauFnJVy5Yt1bJlS73yyivavHmz4uLibBQLAAAAcDy3Va6vqlixonr27KmePXtaOw8AAADgsLi9IgAAAGAllGsAAADASijXAAAAgJVQrgEAAAAroVwDAAAAVkK5BgAAAKykRJfis7aIiAhVrlxZTk5OcnZ21po1a5Sdna1x48bp5MmTCggI0Lx58+Th4WHvqAAAAMANlZkz1x999JHi4+O1Zs0aSdLixYvVtm1bbdq0SW3bttXixYvtnBAAAAC4uTJTrv/X1q1b1bdvX0lS3759tWXLFvsGAgAAAG6hTEwLkaShQ4fKZDLp0Ucf1aOPPqqMjAz5+vpKkqpVq6aMjIybbu/sbJKnp1tpRC11Li5FPwOV19dX3nH8HBfHzrFx/ADYQ5ko1ytXrpSfn58yMjIUFRWlwMDAYutNJpNMJtNNxygoMCs7O8eWMe0mP79Qksrt6yvvOH6Oi2Pn2Dh+AGylWrUqN1xXJqaF+Pn5SZJ8fHwUGRmpQ4cOycfHR+np6ZKk9PR0eXt72zMiAAAAcEt2L9c5OTm6cOGC5etdu3apQYMGioiIUFxcnCQpLi5OXbp0sWNKAAAA4NbsPi0kIyNDo0ePliQVFBSoZ8+e6tixo5o0aaKxY8dq9erVqlGjhubNm2ffoAAAAMAt2L1c16pVS+vWrbtmuZeXlz766CM7JAIAAABKxu7TQgAAAIDygnINAAAAWAnlGgAAALASyjUAAABgJZRrAAAAwEoo1wAAAICVUK4BAAAAK6FcAwAAAFZCuQYAAACshHINAAAAWAnlGgAAALASyjUAAABgJZRrAAAAwEoo1wAAAICVUK4BAAAAK6FcAwAAAFbiYu8AAIA/tqSkrUpM3Gz1cZOTf5YkTZ4ca/WxIyIiFR7exerjAnB8lGsAQLnk5eVl7wgA/oAo1wAAuwoP78JZYADlBnOuAQAAACuhXAMAAABWQrkGAAAArIRyDQAAAFgJ5RoAAACwEso1AAAAYCVcig+Q7W5iIXEjCwAA/kgo14CNcSMLAAD+OCjXgLiJBQAAsA7mXAMAAABWYtcz16dPn9ZLL72kjIwMmUwmPfLIIxo0aJAWLFigVatWydvbW5IUExOjTp062TMqAAAAcEt2LdfOzs6KjY1V48aNdeHCBfXv31/t2rWTJA0ePFhDhw61ZzwAAADgtti1XPv6+srX11eS5O7ursDAQKWlpdkzEgAAAFBiZeYDjSdOnNAPP/ygZs2aaf/+/frkk08UFxenkJAQxcbGysPD46bbOzub5OnpVkppS5eLS9HU+PL6+oCyir97AIDbVSbK9cWLFxUdHa2JEyfK3d1djz/+uEaNGiWTyaT58+dr9uzZmjVr1k3HKCgwKzs7p5QSl678/EJJKrevDyir+LsHALieatWq3HCd3a8WkpeXp+joaPXq1UsPPvigJKlq1apydnaWk5OTBg4cqO+//97OKQEAAIBbs2u5NpvNmjRpkgIDAxUVFWVZnp6ebvl6y5YtatCggT3iAQAAALfFrtNCvvvuO8XHxysoKEh9+vSRVHTZvYSEBB05ckSSFBAQoGnTptkzJgAAAGCIXct1y5Yt9eOPP16znGtaAwAAwBHZfc41AAAAUF6UiauFlBdJSVuVmLjZ6uMmJ/8sSZo8OdbqY0dERCo8vIvVxwUAAPgjolw7AC8vL3tHAAAAgAEms9lstncIa8jLK+BatMAflK1/a1SvXqDVx5b4zREAOKqbXeeaM9cAcAP81ggAcLs4cw0AAADchjJ9h0YAAACgvKBcAwAAAFZCuQYAAACshHINAAAAWAnlGgAAALASyjUAAABgJZRrAAAAwEoo1wAAAICVUK4BAAAAK6FcAwAAAFZCuQYAAACshHINAAAAWAnlGgAAALASk9lsNts7BAAAAFAecOYaAAAAsBLKNQAAAGAllGsAAADASijXAAAAgJVQrgEAAAAroVwDAAAAVuJi7wC4tR07dmjGjBkqLCzUwIEDNXz4cHtHgkETJkxQUlKSfHx8lJCQYO84uA2nT5/WSy+9pIyMDJlMJj3yyCMaNGiQvWPBoCtXruhPf/qTcnNzVVBQoK5duyo6OtresXAbCgoK1L9/f/n5+WnRokX2joPbEBERocqVK8vJyUnOzs5as2aNvSOVKsp1GVdQUKBp06Zp2bJl8vPz04ABAxQREaF77rnH3tFgQL9+/fTkk0/q5ZdftncU3CZnZ2fFxsaqcePGunDhgvr376927drxd89BuLq66qOPPlLlypWVl5enJ554Qh07dlTz5s3tHQ0GLV++XPXr19eFCxfsHQUl8NFHH8nb29veMeyCaSFl3KFDh1SnTh3VqlVLrq6u6tGjh7Zu3WrvWDAoLCxMHh4e9o6BEvD19VXjxo0lSe7u7goMDFRaWpqdU8Eok8mkypUrS5Ly8/OVn58vk8lk51QwKjU1VUlJSRowYIC9owC3jXJdxqWlpcnf39/y2M/Pj//BA6XsxIkT+uGHH9SsWTN7R8FtKCgoUJ8+fXT//ffr/vvv5/g5kJkzZ2r8+PFycqKmOKqhQ4eqX79++vzzz+0dpdTxXQsAN3Hx4kVFR0dr4sSJcnd3t3cc3AZnZ2fFx8dr+/btOnTokP7973/bOxIM2LZtm7y9vRUSEmLvKCihlStXau3atfrrX/+qTz75RHv37rV3pFJFuS7j/Pz8lJqaanmclpYmPz8/OyYC/jjy8vIUHR2tXr166cEHH7R3HJTQ3XffrdatW2vnzp32jgID9u/fr8TEREVERCgmJkZ///vf9eKLL9o7Fm7D1Z7i4+OjyMhIHTp0yM6JShfluoxr0qSJUlJSdPz4ceXm5mrDhg2KiIiwdyyg3DObzZo0aZICAwMVFRVl7zi4TZmZmfrtt98kSZcvX9Y333yjwMBAO6eCES+88IJ27NihxMREzZ07V23atNGbb75p71gwKCcnx/Ih1JycHO3atUsNGjSwc6rSxdVCyjgXFxdNnjxZw4YNs1yW6I/2TerIYmJitGfPHmVlZaljx44aM2aMBg4caO9YMOC7775TfHy8goKC1KdPH0lFx7NTp052TgYj0tPTFRsbq4KCApnNZj300EPq3LmzvWMB5V5GRoZGjx4tqehzDz179lTHjh3tnKp0mcxms9neIQAAAIDygGkhAAAAgJVQrgEAAAAroVwDAAAAVkK5BgAAAKyEcg0AAABYCeUaAAyIjY3VW2+9ZZd9m81mTZgwQWFhYRowYIBdMvzewoULNWnSJEPPtef7BgD2QLkG4JAiIiLUtm1b5eTkWJZ98cUXeuqpp+yYyja+++477dq1S9u3b9fq1avtHUcjRozQjBkz7B2jVOzevfsPd41eAHeGcg3AYRUWFmr58uX2jnHbCgoKbuv5J0+eVEBAgNzc3GyUqGzIz8+3dwQAuGOUawAOa+jQoVq6dKnlNte/d+LECQUHBxcrbE899ZS++OILSdKaNWv02GOPaebMmWrZsqW6dOmi/fv3a82aNerUqZPatm2rtWvXFhszKytLUVFRCg0N1ZNPPqmTJ09a1h07dkxRUVFq1aqVunbtqo0bN1rWxcbG6tVXX9Uzzzyj5s2ba/fu3dfkTUtL04gRI9SqVStFRkZq1apVkorOxr/yyis6ePCgQkND9fbbb1+z7S+//KInn3xSLVq0UOvWrTV27FjLuuDgYC1fvlxdunRR69at9frrr6uwsNCyfvXq1erWrZvCwsI0dOjQYq/pp59+srym+++/XwsXLpQkLViwQC+++KLledHR0WrXrp1atGihP/3pT/rpp5+uyXg9vz8GrVu31oIFC5Sbm6vXX39d4eHhuv/++zV58mRdvnzZss2SJUvUvn17tW/fXqtXr1ZwcLB++eUXScWP79XxH3/8ccvjmx2j7du3q3v37goNDVWHDh30wQcfKCcnR88884zS09MVGhqq0NBQpaWl6dChQ+rXr5/uu+8+3X///Zo1a5ah1wvgj4FyDcBhhYSEqFWrVvrggw9KtP2hQ4cUHBys3bt3q2fPnoqJidH333+vzZs3a86cOZo2bZouXrxoef769es1atQo7d69Ww0bNrQUzJycHA0ZMkQ9e/bUN998o7feektTp07V0aNHLdsmJCRoxIgR2r9/v1q0aHFNlpiYGPn7+2vnzp16++23NXfuXH377bcaOHCgpk6dqubNm+vAgQOKjo6+Ztv58+erXbt22rt3r3bs2KEnn3yy2PrNmzfryy+/1Nq1a5WYmKgvv/xSkrRlyxYtWrRI77zzjr799lu1aNFCL7zwgiTpwoULioqKUocOHbRz505t2rRJbdu2ve772LFjR/3f//2fvv32W917773FireRY1CrVi3t2rVLI0eO1Jtvvqnk5GTFxcVp06ZNSk9P17vvvitJ2rFjh5YuXaqlS5dq06ZN+vbbbw3v51bHaNKkSZo2bZoOHDighIQEtWnTRm5ubvrrX/8qX19fHThwQAcOHJCfn59mzJihp59+Wvv379fmzZvVrVs3wzkAlH+UawAOLTo6Wh9//LEyMzNve9uaNWuqf//+cnZ2Vvfu3XX69GmNHj1arq6uat++vVxdXfXrr79anh8eHq6wsDC5urpq3LhxOnjwoE6fPq2kpCQFBASof//+cnFx0b333quuXbvq66+/tmzbpUsXtWjRQk5OTqpYsWKxHKdPn9b+/fv14osvqmLFimrUqJEGDhyo+Ph4Q6/DxcVFp06dUnp6uipWrKiWLVsWW//MM8/I09NTNWrU0NNPP62EhARJ0meffabhw4erfv36cnFx0YgRI/TDDz/o5MmTSkpKUtWqVTVkyBBVrFhR7u7uatas2XX3P2DAALm7u8vV1VVjxozRkSNHdP78eUPZfX199dRTT8nFxUUVK1bUqlWrNHHiRHl6esrd3V3PPvusNmzYIEn66quv1K9fPwUFBcnNzU3PPfecoX1IuuUxcnFx0dGjR3XhwgV5eHiocePGNxzLxcVFv/76qzIzM1W5cmU1b97ccA4A5Z+LvQMAwJ0ICgpSeHi4Fi9erPr169/Wtj4+PpavK1WqJEmqWrWqZVnFihWLnbn29/e3fF25cmV5eHgoPT1dJ0+e1KFDh4qV2oKCAvXu3dvyuHr16jfMkZ6eLg8PD7m7u1uW1ahRQ4cPHzb0OsaPH6/58+drwIAB8vDwUFRUVLGrivx+3wEBAUpPT5cknTp1SjNnztTrr79uWW82m5WWlqbTp0+rdu3at9x3QUGB3nrrLX399dfKzMyUk1PROZusrCxVqVLlltv//j3NzMzUpUuX1K9fv2J5rk5jSU9PV0hISLHXYtStjtHbb7+t999/X3/5y18UHBysF154QaGhodcda8aMGXr77bfVrVs31axZU88995w6d+5sOAuA8o1yDcDhRUdH6+GHH9aQIUMsy65++O/y5cuW0nrmzJk72k9qaqrl64sXL+rcuXPy9fVV9erVFRYWpmXLlpVoXF9fX507d04XLlywZD19+rT8/PwMbV+tWjW99tprkqR9+/YpKipKYWFhqlOnjmWsBg0aSCoq1L6+vpKKSveIESOK/RBw1alTp4rNSb6R9evXa+vWrVq2bJlq1qyp8+fPKywsTGaz2VB2k8lk+drLy0uVKlXShg0brvvafX19dfr06WIZf++uu+7SpUuXLI/Pnj1r+fpWx6hp06Z6//33lZeXp08++URjx47V9u3bi+W7qm7dupo7d64KCwu1adMmRUdHa/fu3eX+A6cAjGFaCACHV6dOHXXv3l0rVqywLPP29pafn5/i4+NVUFCg1atX6/jx43e0n+3bt2vfvn3Kzc3V/Pnz1axZM1WvXl3h4eFKSUlRXFyc8vLylJeXp0OHDunYsWOGxq1evbpCQ0M1d+5cXblyRUeOHNHq1auvW3qv56uvvrIUfw8PD5lMJssZZEn64IMPdO7cOZ0+fVrLly9X9+7dJUmPPfaYFi9ebPkA4vnz5/XVV19JKpoCc+bMGX344YfKzc3VhQsX9I9//OOafV+8eFGurq7y8vLSpUuXNHfuXEOZr8fJyUkDBw7UzJkzlZGRIanog547d+6UJD300ENau3atjh49qkuXLumdd94ptn2jRo20efNmXbp0Sb/88kuxyxbe7Bjl5uZq3bp1On/+vCpUqKDKlStb3j8fHx9lZ2cXm+YSHx9vOUt/9913W7IDgES5BlBOjB49utg1ryVp+vTp+uCDD9S6dWsdPXr0hr/mN6pnz55699131bp1a/3zn//UnDlzJEnu7u764IMPtHHjRnXo0EHt27fXm2++qdzcXMNjz507VydPnlSHDh303HPPacyYMbr//vsNbfv9999r4MCBCg0N1ciRIzVp0iTVqlXLsr5Lly7q16+f+vbtq/DwcMuUkcjISA0bNkwxMTG677771LNnT+3YscPympYuXapt27apXbt26tq163WvctK3b1/VqFFDHTp0UI8ePe54/vH48eNVp04dPfLII7rvvvs0ePBgJScnS5I6deqkQYMGadCgQYqMjFSbNm2KbTto0CBVqFBB999/v15++WX16tXLsu5Wxyg+Pl4RERG677779Nlnn1mObf369dWjRw898MADatmypaXs9+jRQ6GhoZoxY4beeusty7QiADCZjf7uDgDgcIKDg7Vp0ybLFJHypry/PgCOhzPXAAAAgJVQrgEAAAArYVoIAAAAYCWcuQYAAACshHINAAAAWAnlGgAAALASyjUAAABgJZRrAAAAwEoo1wAAAICV/D8WKrDdmcsBTAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 47;\n",
              "                var nbb_unformatted_code = \"fig, ax = plt.subplots(figsize=(12, 6))\\nsns.boxplot(data=df, x='no_of_special_requests', y='avg_price_per_room', ax=ax, showfliers=False)\\nplt.xlabel('Number of special requests', fontsize=12)\\nplt.ylabel('Average price per room, euro', fontsize=12)\\nplt.title('Price vs. No of requests', fontsize=16)\\nplt.show();\";\n",
              "                var nbb_formatted_code = \"fig, ax = plt.subplots(figsize=(12, 6))\\nsns.boxplot(\\n    data=df, x=\\\"no_of_special_requests\\\", y=\\\"avg_price_per_room\\\", ax=ax, showfliers=False\\n)\\nplt.xlabel(\\\"Number of special requests\\\", fontsize=12)\\nplt.ylabel(\\\"Average price per room, euro\\\", fontsize=12)\\nplt.title(\\\"Price vs. No of requests\\\", fontsize=16)\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "etPK-X94C2eP"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The median prices are growing with the growing number of special requests"
      ],
      "metadata": {
        "id": "duEXK8K4C2eQ"
      },
      "id": "duEXK8K4C2eQ"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Let's see how the target variable varies across the number of adult guests**"
      ],
      "metadata": {
        "id": "Omj8FcIRCISK"
      },
      "id": "Omj8FcIRCISK"
    },
    {
      "cell_type": "code",
      "source": [
        "stacked_barplot(df, \"no_of_adults\", \"booking_status\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "outputId": "bebb757f-a01f-48f0-ce6c-00197b8aa4b4",
        "id": "NRzZpakrCISK"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "booking_status  Canceled  Not_Canceled    All\n",
            "no_of_adults                                 \n",
            "All                11885         24390  36275\n",
            "2                   9119         16989  26108\n",
            "1                   1856          5839   7695\n",
            "3                    863          1454   2317\n",
            "0                     44            95    139\n",
            "4                      3            13     16\n",
            "------------------------------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 48;\n",
              "                var nbb_unformatted_code = \"stacked_barplot(df, \\\"no_of_adults\\\", \\\"booking_status\\\")\";\n",
              "                var nbb_formatted_code = \"stacked_barplot(df, \\\"no_of_adults\\\", \\\"booking_status\\\")\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "NRzZpakrCISK"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The highest percent of cancelations occurred in the bookings for 3 of adults guests - 37%\n",
        "* The lowest percent of cancelations occurred in the bookings for 4 of adults guests - 19%"
      ],
      "metadata": {
        "id": "g1DTUYEmCISL"
      },
      "id": "g1DTUYEmCISL"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Let's see how the target variable varies across the market segment type**"
      ],
      "metadata": {
        "id": "DX4Pv57zAQ9I"
      },
      "id": "DX4Pv57zAQ9I"
    },
    {
      "cell_type": "code",
      "source": [
        "stacked_barplot(df, \"market_segment_type\", \"booking_status\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "outputId": "1b9eb535-5dab-41a6-d09b-bbdf0322355e",
        "id": "j_Uo3RP6AQ9I"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "booking_status       Canceled  Not_Canceled    All\n",
            "market_segment_type                               \n",
            "All                     11885         24390  36275\n",
            "Online                   8475         14739  23214\n",
            "Offline                  3153          7375  10528\n",
            "Corporate                 220          1797   2017\n",
            "Aviation                   37            88    125\n",
            "Complementary               0           391    391\n",
            "------------------------------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 49;\n",
              "                var nbb_unformatted_code = \"stacked_barplot(df, \\\"market_segment_type\\\", \\\"booking_status\\\")\";\n",
              "                var nbb_formatted_code = \"stacked_barplot(df, \\\"market_segment_type\\\", \\\"booking_status\\\")\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "j_Uo3RP6AQ9I"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The proportion of canceled bookings made Online is slightly higher than those from Offline or Aviation \n",
        "* Corporate segment shows low cancellations proportion"
      ],
      "metadata": {
        "id": "Qf7C8M4zAQ9I"
      },
      "id": "Qf7C8M4zAQ9I"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Let's analyze the relation between Lead time and Segment**"
      ],
      "metadata": {
        "id": "MT3IKbMRnY9D"
      },
      "id": "MT3IKbMRnY9D"
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(12, 6))\n",
        "sns.boxplot(data=df, x='market_segment_type', y='lead_time', ax=ax, showfliers=False)\n",
        "plt.xlabel('Market Segment Type', fontsize=12)\n",
        "plt.ylabel('Lead time, days used', fontsize=12)\n",
        "plt.title('Lead time vs. Segment', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "XJh1SRy7nZIW",
        "outputId": "84544e1b-49ed-4b06-b03b-d8c12d3c48c7"
      },
      "id": "XJh1SRy7nZIW",
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 50;\n",
              "                var nbb_unformatted_code = \"fig, ax = plt.subplots(figsize=(12, 6))\\nsns.boxplot(data=df, x='market_segment_type', y='lead_time', ax=ax, showfliers=False)\\nplt.xlabel('Market Segment Type', fontsize=12)\\nplt.ylabel('Lead time, days used', fontsize=12)\\nplt.title('Lead time vs. Segment', fontsize=16)\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"fig, ax = plt.subplots(figsize=(12, 6))\\nsns.boxplot(data=df, x=\\\"market_segment_type\\\", y=\\\"lead_time\\\", ax=ax, showfliers=False)\\nplt.xlabel(\\\"Market Segment Type\\\", fontsize=12)\\nplt.ylabel(\\\"Lead time, days used\\\", fontsize=12)\\nplt.title(\\\"Lead time vs. Segment\\\", fontsize=16)\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The bookings that came from the Complementary, Aviation, and Corporate segments have the lead time less than a month on average\n",
        "* The bookings made Offline have a higher median and higher range of lead time compared with the Online bookings\n"
      ],
      "metadata": {
        "id": "-bEPeHkp--RC"
      },
      "id": "-bEPeHkp--RC"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Let's analyze the relation between Price and Segment**"
      ],
      "metadata": {
        "id": "bggwucOR8ZRb"
      },
      "id": "bggwucOR8ZRb"
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(12, 6))\n",
        "sns.boxplot(data=df, x='market_segment_type', y='avg_price_per_room', ax=ax, showfliers=False)\n",
        "plt.xlabel('Market Segment Type', fontsize=12)\n",
        "plt.ylabel('Average price per room, euro', fontsize=12)\n",
        "plt.title('Price vs. Segment', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "outputId": "fcdb0ecf-cf5d-48c4-f9f3-671345d95c11",
        "id": "Komyt_m38ZRb"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 51;\n",
              "                var nbb_unformatted_code = \"fig, ax = plt.subplots(figsize=(12, 6))\\nsns.boxplot(data=df, x='market_segment_type', y='avg_price_per_room', ax=ax, showfliers=False)\\nplt.xlabel('Market Segment Type', fontsize=12)\\nplt.ylabel('Average price per room, euro', fontsize=12)\\nplt.title('Price vs. Segment', fontsize=16)\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"fig, ax = plt.subplots(figsize=(12, 6))\\nsns.boxplot(\\n    data=df, x=\\\"market_segment_type\\\", y=\\\"avg_price_per_room\\\", ax=ax, showfliers=False\\n)\\nplt.xlabel(\\\"Market Segment Type\\\", fontsize=12)\\nplt.ylabel(\\\"Average price per room, euro\\\", fontsize=12)\\nplt.title(\\\"Price vs. Segment\\\", fontsize=16)\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "Komyt_m38ZRb"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The bookings made Online have higher median and higher range of prices.\n",
        "* The bookings came from Offline and Corporate segments are almost similar. The Corporate one has slightly lower median price\n",
        "* The bookings came from Aviation segment have narroew range of prices.\n",
        "* The Complementary segment has low or zero prices."
      ],
      "metadata": {
        "id": "San94fCC8vYP"
      },
      "id": "San94fCC8vYP"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Let's analyze the relation between Average price per room and Status**"
      ],
      "metadata": {
        "id": "aiXiK1O5BvQf"
      },
      "id": "aiXiK1O5BvQf"
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(12, 6))\n",
        "sns.boxplot(data=df, x='avg_price_per_room', y='booking_status', ax=ax, showfliers=False)\n",
        "plt.ylabel('Booking status', fontsize=12)\n",
        "plt.xlabel('Average price per room, euros', fontsize=12)\n",
        "plt.title('Average price per room vs. Booking status', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "KuU4Z8_qBvYu",
        "outputId": "309cc695-cd27-409d-f064-9f96503b2ff8"
      },
      "id": "KuU4Z8_qBvYu",
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 52;\n",
              "                var nbb_unformatted_code = \"fig, ax = plt.subplots(figsize=(12, 6))\\nsns.boxplot(data=df, x='avg_price_per_room', y='booking_status', ax=ax, showfliers=False)\\nplt.ylabel('Booking status', fontsize=12)\\nplt.xlabel('Average price per room, euros', fontsize=12)\\nplt.title('Average price per room vs. Booking status', fontsize=16)\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"fig, ax = plt.subplots(figsize=(12, 6))\\nsns.boxplot(\\n    data=df, x=\\\"avg_price_per_room\\\", y=\\\"booking_status\\\", ax=ax, showfliers=False\\n)\\nplt.ylabel(\\\"Booking status\\\", fontsize=12)\\nplt.xlabel(\\\"Average price per room, euros\\\", fontsize=12)\\nplt.title(\\\"Average price per room vs. Booking status\\\", fontsize=16)\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The median price of canceled bookings is slightly higher than the median price of not canceled bookings"
      ],
      "metadata": {
        "id": "10HHnWJGD3el"
      },
      "id": "10HHnWJGD3el"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Let's analyze the relation between Year-Month of arrival and Status**"
      ],
      "metadata": {
        "id": "l_gg2D3drJX1"
      },
      "id": "l_gg2D3drJX1"
    },
    {
      "cell_type": "code",
      "source": [
        "tab = pd.crosstab(ddf['arrival'], ddf['booking_status'], normalize=\"index\")\n",
        "tab.plot(kind=\"bar\", stacked=True, figsize=(12, 6))\n",
        "plt.legend(loc=\"upper left\", bbox_to_anchor=(1, 1))\n",
        "plt.xlabel('Year-Month of arrival', fontsize=12)\n",
        "plt.ylabel('Booking Status', fontsize=12)\n",
        "plt.title('Year-Month of arrival and Booking status', fontsize=16)\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "KxSSXUH3rJhB",
        "outputId": "6999e7e4-6fec-45f4-ba61-9438e2e05ed5"
      },
      "id": "KxSSXUH3rJhB",
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 53;\n",
              "                var nbb_unformatted_code = \"tab = pd.crosstab(ddf['arrival'], ddf['booking_status'], normalize=\\\"index\\\")\\ntab.plot(kind=\\\"bar\\\", stacked=True, figsize=(12, 6))\\nplt.legend(loc=\\\"upper left\\\", bbox_to_anchor=(1, 1))\\nplt.xlabel('Year-Month of arrival', fontsize=12)\\nplt.ylabel('Booking Status', fontsize=12)\\nplt.title('Year-Month of arrival and Booking status', fontsize=16)\\nplt.show();\";\n",
              "                var nbb_formatted_code = \"tab = pd.crosstab(ddf[\\\"arrival\\\"], ddf[\\\"booking_status\\\"], normalize=\\\"index\\\")\\ntab.plot(kind=\\\"bar\\\", stacked=True, figsize=(12, 6))\\nplt.legend(loc=\\\"upper left\\\", bbox_to_anchor=(1, 1))\\nplt.xlabel(\\\"Year-Month of arrival\\\", fontsize=12)\\nplt.ylabel(\\\"Booking Status\\\", fontsize=12)\\nplt.title(\\\"Year-Month of arrival and Booking status\\\", fontsize=16)\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Most cancellations (~65%) were made in July-2017 \n",
        "* Less cancellations (>5%) were made in December-2017 and January-2018"
      ],
      "metadata": {
        "id": "I3DEufTtrvxD"
      },
      "id": "I3DEufTtrvxD"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Let's analyze the relation between Average Price per Room and Booking status**"
      ],
      "metadata": {
        "id": "zJApFlTQrDs4"
      },
      "id": "zJApFlTQrDs4"
    },
    {
      "cell_type": "code",
      "source": [
        "distribution_plot_wrt_target(df, \"avg_price_per_room\", \"booking_status\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "V3iLbEesrD33",
        "outputId": "0ec579c6-28b7-449d-a2b6-cb66c2f4135c"
      },
      "id": "V3iLbEesrD33",
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 54;\n",
              "                var nbb_unformatted_code = \"distribution_plot_wrt_target(df, \\\"avg_price_per_room\\\", \\\"booking_status\\\")\";\n",
              "                var nbb_formatted_code = \"distribution_plot_wrt_target(df, \\\"avg_price_per_room\\\", \\\"booking_status\\\")\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The distributions are quite similar.\n",
        "* Most of the cancellations occur at booking with slightly higher average price per room. However, for more accurate conclusion t-test need to be done, as the means seems very close to each others."
      ],
      "metadata": {
        "id": "kmQ63gM1t9fm"
      },
      "id": "kmQ63gM1t9fm"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Let's analyze the relation between Lead Time and Booking status**"
      ],
      "metadata": {
        "id": "O03747_friag"
      },
      "id": "O03747_friag"
    },
    {
      "cell_type": "code",
      "source": [
        "distribution_plot_wrt_target(df, \"lead_time\", \"booking_status\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "pDGFiDQkrucE",
        "outputId": "d27989e6-5546-4381-c044-60bce1538201"
      },
      "id": "pDGFiDQkrucE",
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 55;\n",
              "                var nbb_unformatted_code = \"distribution_plot_wrt_target(df, \\\"lead_time\\\", \\\"booking_status\\\")\";\n",
              "                var nbb_formatted_code = \"distribution_plot_wrt_target(df, \\\"lead_time\\\", \\\"booking_status\\\")\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The median lead time for bookings that were canceled is different from the bookings that were not canceled. Most of the cancellations occur at booking with a higher lead time. \n",
        "* The higher the lead time, the higher odds of a booking cancelation."
      ],
      "metadata": {
        "id": "ZWa61lRmvAw4"
      },
      "id": "ZWa61lRmvAw4"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Leading Questions"
      ],
      "metadata": {
        "id": "ZGffT1KmbJFx"
      },
      "id": "ZGffT1KmbJFx"
    },
    {
      "cell_type": "markdown",
      "source": [
        ">**1. What are the busiest months in the hotel?**"
      ],
      "metadata": {
        "id": "pusLkjh7vUBh"
      },
      "id": "pusLkjh7vUBh"
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_barplot(data=df.sort_values(by='arrival_month'), feature='arrival_month',\n",
        "                perc=True, n=None, percfmt=\"{:.0f}%\", sort=False,\n",
        "                figsize=(10, 5), xlabel='Arrival month', xlo=90,\n",
        "                title='Month of arrival distribution')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "ilhJJCZ_HN4B",
        "outputId": "129d524f-65a0-4936-8b38-b3cc4ec2d743"
      },
      "id": "ilhJJCZ_HN4B",
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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p/P0HM3LkUH777Tw1atRi8eIFjBoVVFCxJZ+pcBMRESmEGjf2zNX2trZ2JCYmkpaWRnJyEnZ29uzcuYOGDT144onK+RNSCpyucRMREbEyb701hQEDXmL+/Dncvn0bgPbtO3L58iX8/V/mqae8cXJyYseObbz88gALp5W89Fg8ZD45OVXLgYiIiFXq2bMzc+cuNE2NRkVF4uZWnqSkJBYvfo+EhASmTn07w36zZ8/g+ef9SElJYevWTdjbOzB06AjKl69Q0KcguaTlQERERB4Rbm7lAXBwcKB791788MPJDNucOHEMAC+vfxESMo/Jk6fTpUt3Vq1aXqBZJe+pcBMREbESd+/eJT4+HgCj0ch//7uLWrXqpNsmOTmZlSvfZ/jwkQAkJiZiMNhgMBi4e1ezT9ZOU6UiIiKFUEjIPCIi/o8bN2JwcnLG0dGJd99dQHDw+P//KMc0qlWrzqhRQZQrV8603+rVq3BzK0+HDp0A+PzzMDZs+AQ7O3smTZpC3br1LXVKYqbspkpVuImIiIgUItkVbloORERExAIcSxWlSFF7S8fI4F5iMrduJ1o6hmRBhZuIiIgFFClqT2DHhZaOkcHSL0aDCrdCSzcniIiIiFgJFW4iIiIiVkKFm4iIiIiVUOEmIiIiYiVUuImIiIhYCRVuIiIiIlZChZuIiIiIlVDhJiIiImIlVLiJiIiIWAkVbiIiIiJWQoWbiIiIiJUo0GeV+vr6UqJECWxsbLC1tWXLli3ExsYyevRoLl++TKVKlQgJCcHJyQmj0cg777xDREQERYsWZc6cOTRo0ACAsLAw3n//fQCGDRtG9+7dC/I0RERERCyiwEfc1qxZw7Zt29iyZQsAK1asoFmzZnz11Vc0a9aMFStWALBv3z4uXrzIV199xdtvv8306dMBiI2NJTQ0lP/85z9s3LiR0NBQ4uLiCvo0RERERAqcxadKd+/eTbdu3QDo1q0b//3vf9O1GwwGPD09uXXrFtHR0ezfvx8fHx+cnZ1xcnLCx8eHb775xoJnICIiIlIwCnSqFODVV1/FYDDw4osv8uKLLxITE4OrqysALi4uxMTEABAVFUX58uVN+5UvX56oqKgM7W5ubkRFRWV7TFtbA87OxfPhbERERB49+plZeBVo4fbpp5/i5uZGTEwM/v7+1KhRI12/wWDAYDDk+XFTU43Exibk+fuKiIg8LBeXUpaOkCX9zLSs7L4bBTpV6ubmBkDZsmVp27Ytp06domzZskRHRwMQHR1NmTJlTNtGRkaa9o2MjMTNzS1De1RUlOl9RURERB5lBVa4JSQkEB8fb/r7gQMHqF27Nr6+vmzduhWArVu30rp1awBTu9Fo5MSJE5QqVQpXV1eaN2/O/v37iYuLIy4ujv3799O8efOCOg0RERERiymwqdKYmBgCAwMBSE1NpVOnTrRs2ZJGjRoxatQoNm3aRMWKFQkJCQGgVatWRERE0LZtW4oVK8asWbMAcHZ2Zvjw4fTs2ROAwMBAnJ2dC+o0RERERCzGYDQajZYOkd+Sk1M1Xy8iIoWKi0spAjsutHSMDJZ+MZpr125bOsZjrdBc4yYiIiIiD0+Fm4iIiIiVUOEmIiIiYiVUuImIiIhYiQJ/coKIiEhBCg0NISJiD1evXmHt2s+oUaMWAJMmjeXKlSvY2BgoVqw4o0cHUbu2OykpKUyZMoErV65QqdITzJgxGzs7O2JjY5k8OYiQkGXY29tb+KzkcaURNxEReaS1aPEsoaErKF++Qrr2yZPfYs2aT/noo/X06dOP2bNnAHDo0EFKlXJkzZpPKVmyJIcOHQRg2bJFBAQMV9EmFqURNxEReaQ1buyZaXvJkiVNf4+Pj8dguD+WYWdnx717iQDcu5eIvb09x48fxcbGhsaNvfI9r0h2VLiJiMhja86ctzl8+DsA5s9fDECTJt7s3bubAQP60KBBQxo39mLMmBHMnj3fklFFABVuIiLyGJs4cQoAX34ZzrJli5g/fzE2NjZMmBBs2uajj1bSqVNXIiOvMnfu/af4DBjwKrVr17FIZnm86Ro3ERF57D3/vB/Hjh0lLi42XfulS39w+vQPdOjQiZCQ+QQGvsHw4SNZtEijb2IZKtxEROSxk5CQQFRUpOn1/v37cHR0xNHRKd12S5YsYOTIsQAkJt7FYDBgY2NDQoIeoyiWoalSERF5pIWEzCMi4v+4cSOGUaMCcXR0YvHi95kyZSKJiXexsbHF0dGRd99diMFgMO23a9cX1K1bnypVqgLw6qtDGTfuDQACA0da5FxE9JB5ERERC9BD5iUr2T1kXiNuIiJi1Uo7F8fO3tbSMdJJSU7lpgYMJB+ocBMREatmZ2/LoZ0/WDpGOt4dGlk6gjyidHOCiIiIiJVQ4SYiIiJiJVS4iYiIiFgJFW4iIiIiVkKFm4iIiIiVUOEmIiIiYiVUuImIiIhYCRVuIiIiIlZChZuIiIiIlVDhJiIiImIlVLiJiIiIWAkVbiIiIiJWQoWbiIiIiJVQ4SYiIiJiJVS4iYiIiFgJFW4iIiIiVkKFm4iIiIiVeKjCLTExkW+//ZbLly/ndR4RERERyYJZhdvEiRP55JNPAEhKSqJXr14MGjSI559/noiIiHwNKCIiIiL3mVW47d+/H09PTwD27NnDnTt3OHDgAK+//jqhoaH5mU9ERERE/j+zCre4uDjKli0LwDfffEO7du0oW7YsHTt25Pz58/kaUERERETuM6twc3Fx4ZdffiE1NZX9+/fTrFkzABISErC3t8/XgCIiIiJyn505G/Xo0YPRo0fj6uqKra2tqXA7efIkNWrUyNeAIiIiInKfWYXbiBEjqF27NlevXuX555/HwcHh/s52drz22mv5GlBERERE7jOrcANo3759hrbu3bvn+oCpqam88MILuLm58cEHH3Dp0iXGjBlDbGwsDRo0YO7cuTg4OJCUlMT48eM5ffo0zs7OLFy4kCeeeAKADz74gE2bNmFjY0NwcDAtWrTIdQ4RERERa2NW4bZ169Ys+4oUKULVqlWpX7++WQdcu3YtNWvWJD4+HoD58+czcOBA/Pz8mDp1Kps2baJv375s3LgRR0dHvv76a8LDw5k/fz4hISGcP3+e8PBwwsPDiYqKwt/fn127dmFra2vW8UVERESslVmF21tvvUVycjIpKSnY2Ny/nyEtLQ07u/u7p6SkUL9+fVatWkWZMmWyfJ/IyEj27t3L0KFDWb16NUajke+++4733nsPuD+CFxoaSt++fdmzZw8jRowA7o/2zZgxA6PRyO7du/Hz88PBwYHKlStTtWpVTp06hZeX1z/6IEREREQKO7MKt5CQEJYuXcqkSZNo1KgRAD/88APvvvsuw4YNw83NjUmTJjF79mzmzZuX5fvMmjWLoKAg7ty5A8DNmzdxdHQ0FYDly5cnKioKgKioKCpUqHA/pJ0dpUqV4ubNm0RFRdG4cWPTe7q5uZn2yYqtrQFn5+LmnKqIiGRi3ry5/Pe/X3P58mXCwrZRu3btbNuTk5MZO3Y0f/55mcqVK/Peewuws7Pj5s2bjBo1klWr/o29vYMlTynfWfPPHWvO/qgzq3CbM2cOc+bMSVcweXl5MXHiRCZNmsTOnTuZMGEC48ePz/I9/u///o8yZcrQsGFDDh069M+T50JqqpHY2IQCPaaIyKOkadPmdOnSk8DAwdy+fdf0f2pW7QcOfEPRoiX48MNPmDXrLXbt2o2PTwtmz57DoEFDuXMnBUjJk2wuLqXy5H3yWk4/dwprbsg5u+Sv7L4bZhVuly9fpmjRohnaixYtanpe6RNPPMGtW7eyfI9jx46xZ88e9u3bx71794iPj+edd97h1q1bpKSkYGdnR2RkJG5ubsD9kbSrV69Svnx5UlJSuH37NqVLl8bNzY3IyEjT+0ZFRZn2ERGR/NG4sWeu2u3s7Lh3LxGAe/cSsbe35/jxo9jY2NC4sS5tEXlYZi3A6+HhwZw5c7h27Zqp7dq1a8ydO9c0Cvf7779nW0CNHTuWffv2sWfPHhYsWMDTTz/Ne++9h7e3N7t27QIgLCwMX19fAHx9fQkLCwNg165dPP300xgMBnx9fQkPDycpKYlLly5x8eJFPDw8Hu7sRUQkXzRp4k3x4iUYMKAPJUqUpHFjL1atWs7w4SMtHU3Eqpk14jZz5kwCAwN57rnncHV1BSA6Oppq1aqxdOlSAO7evcuwYcNyHSAoKIjRo0cTEhJCvXr16NWrFwA9e/YkKCiItm3b4uTkxMKFCwGoXbs2HTp0oGPHjtja2jJ16lTdUSoiUsjY2NgwYUKw6fVHH62kU6euREZeZe7cWQAMGPAqtWvXsVREEatkVuFWrVo1duzYwf79+7lw4QIANWrUwMfHB4PBAECbNm3MPqi3tzfe3t4AVK5cmU2bNmXYpkiRIixevDjT/YcNG/ZQRaKIiBS8S5f+4PTpH/D3H8zw4a8xZcr9VQJmzXqL0NAVlo4nYlXMXoDXYDDQokULLXYrIiK5smTJAkaOHAtAYuJdDAYDBoOBhARdAC+SW2YXbidPnuTgwYPExMRgNBrT9QUHB2exl4iIPApCQuYREfF/3LgRw6hRgTg6OvHxx//Jsv2BXbu+oG7d+lSpUhWAV18dyrhxbwAQGKjr3URyy2D8exWWiX//+9/MmzePqlWrmq5xM72BwcDatWvzLWBeSE5O1a3NIiKPKBeXUhza+YOlY6Tj3aER167dznYbF5dSBHZcWECJzLf0i9E5Zpf89Y+XA1m7di3BwcH069cvz0KJiEjhUcbJDluHYpaOkUFq0l1uxOXNem8ijwKzCrf4+HhatWqV31lERMRCbB2KcXF6dUvHyKDa9AuARn9EHjBrHTc/Pz/27duX31lEREREJBtmjbhVqFCBJUuWcOzYMdzd3bG3t0/X7+/vny/hREREROR/zCrcNm7cSPHixTl+/DjHjx9P12cwGFS4iYiIiBQAswq3PXv25HcOEREREcmBWde4iYiIiIjlZTniNnPmTMaMGUPx4sWZOXNmtm+iBXhFRERE8l+WhdvPP/9MSkqK6e8iIiIiYllZFm7r1q3L9O8iIiIiYhlmXeN26tSpLPu2bduWZ2FEREREJGtmFW5Dhgzh119/zdC+detWpk2bluehRERERCQjswo3f39/Xn31VSIjI01tW7duZfr06SxcWPgekCsiIiLyKDJrHbeAgABu3rzJwIEDWb9+PREREUyfPp1Fixbx7LPP5nNEEREREQEzCzeACRMmEBsbS+/evbl+/TqLFy/Wg+dFREREClCWhdtXX32Voa1ly5YcPHgQPz8/7t27Z9qmXbt2+ZdQRERERIBsCreRI0dmudPmzZvZvHkzcP9ZpWfPns37ZCIiIiKSTpaF208//VSQOUREREQkB3pWqYiIiIiVMPvmhL1797Jy5UrOnz+PwWCgVq1aDB48WDcoiIiIiBQQs0bcNm7cyIgRI6hSpQrjxo1j7NixPPHEEwQGBrJp06b8zigiIiIimDnitnLlSiZOnEi/fv1Mbb169aJBgwasXLmSnj175ltAEZGcfPvtflatep+UlBQcHZ14881puLq6MWXKBK5cuUKlSk8wY8Zs7OzsiI2NZfLkIEJClmFvb2/p6CIiuWLWiNuVK1do0aJFhvaWLVty+fLlPA8lImKuW7du8c4705g+fRZr126gc+duzJ8/h0OHDlKqlCNr1nxKyZIlOXToIADLli0iIGC4ijYRsUpmFW4VK1bkwIEDGdr3799PpUqV8jyUiIi5Ll++ROnSZalSpSoAzZr5cPjwQRIT73LvXiIA9+4lYm9vz/HjR7GxsaFxYy9LRhYReWhmTZUOGjSImTNncubMGby87v+Hd+zYMbZt28aUKVPyNaCISHYqV67KjRsxnD17mnr1GvDVVzsBeOKJKhQvXoIBA/rQoEFDGjf2YsyYEcyePd/CiUVEHp5ZhdtLL71E2bJl+fDDD/n6668BqFGjBiEhIbRp0yZfA4qIZKdkyZK89dYsFi9eQFJSEk8//QwlS5bC1taWCROCTdt99NFKOnXqSmTkVebOnQXAgAGvUrt2HUtFFxHJNbOXA2nbti1t27bNzywiIg+lSRNvmjTxBuDGjRg+/XQdlSo9Yeq/dOkPTp/+AX//wQwf/hpTpszAaDQya9ZbhIausFRsEZFcM7twAzh48CC//vqraR03b2/v/MolImK2mJjrlC1bjrS0ND74YCldu75AsWLFTP1Llixg5MixACQm3sVgMGAwGEhISLBUZBGRh2JW4RYVFUVgYCCnT5/G1dUVgOjoaBo2bEhoaChubm75GlJEJDsrV77PDz+cJDk5maZNn2bo0BGmvl27vqBu3fqmmxdefXUo48a9AUBgYNbPZBYRKYzMKtxmzpyJra0tX331FZUrVwbg0qVLBAUF8c4777B48eJ8DSkikp2JE7O+Sap9+47pXvv4tMDHJ+PyRiIi1sCswu3AgQOsW7fOVLQBVK5cmcmTJzNw4MD8yiYij6nSJe2xK1bU0jEySLmbyM34ZEvHEJECcuDAN6xa9T5GIxiNRgYNGoyPT0uLLu5t9jVuBoPBrDYRkX/KrlhRjjzV1NIxMmjy/WFQ4SbyWDAajbz99lSWLVtJjRq1OH/+HMOGvcq0aW///8W932PWrLc4dOggPj4tCmxxb7MW4G3WrBlvv/02V69eNbVduXKFWbNm0axZs3wLJyIiItbvwIFv8Pfvy8CBfRkwoA8REXtISUlh0qSxDBjQhzffDCIlJQWA2NhYAgMHk5xs+V+SbGxsiI+PByA+/jZly5bD3t7Boot7mzXiFhwczLBhw2jTpk26mxPq1KlDcHBwDnuLiIjI46qwjlzlxGAwMGPGbCZNGkvRosVISEhg3rxF1K/fgL17d1tscW+zCrcKFSoQFhbGt99+y2+//QZAzZo1eeaZZ/I1nIiIiFi/wjhylZOUlBTWrVvN7Nnv4eHhyalTJ5g6dSIff7zRoot75+oaNx8fH3x8fPIliIjI46gwXvwskpcK68hVTs6f/4WYmGt4eHgC4OHhSbFixfj99wvUq9cAsMzi3mYXbv/973/56KOPOH/+PHB/xM3f319PUxAReUjWOoUkkhuFdeQqJy4urkRHR/PHHxepUqUaFy9e4MaNG+meymKJxb3NKtw+/PBDFixYQLdu3ejevTsAJ06cYOzYsbzxxhu8+uqrOb7HvXv3ePnll0lKSiI1NZX27dszcuRILl26xJgxY4iNjaVBgwbMnTsXBwcHkpKSGD9+PKdPn8bZ2ZmFCxfyxBP3P6wPPviATZs2YWNjQ3BwMC1aaE0mEbFO1jiFJJIbhXXkKidly5Zj3LiJBAdPwGC4fy/npElTcXR0Aiy3uLfZhdvUqVPp3bu3qa1nz554eHiwePFiswo3BwcH1qxZQ4kSJUhOTqZv3760bNmSjz76iIEDB+Ln58fUqVPZtGkTffv2ZePGjTg6OvL1118THh7O/PnzCQkJ4fz584SHhxMeHk5UVBT+/v7s2rULW1vbh/8UREQswFqnkERyo7COXJmjXbsOtGvXIdM+Sy3ubVbhdufOnUyfS+rt7c2dO3fMOpDBYKBEiRLA/WHTlJQUDAYD3333He+99x4A3bt3JzQ0lL59+7Jnzx5GjLj/2Jr27dszY8b96nv37t34+fnh4OBA5cqVqVq1KqdOncLLS7+Fioh1sdYpJJHcKEwjV86l7LEvWvgW905OTCT2tnnLn5hVuLVp04Zdu3YREBCQrn3Xrl34+vqaHSw1NZUePXrwxx9/0LdvXypXroyjoyN2dvdjlC9fnqioKOD+81ErVKhwP6SdHaVKleLmzZtERUXRuHFj03u6ubmZ9smKra0BZ+fiZucUEclKXv5f8uOPP3Lz5nVatrx/h37Lls9QokQJYmKuUrFiIwB+//0iv/xyhtGj3+CVV/oxe/a7GI1GgoPfZPXqtXmWpTCz1v+/rTU35H323r1foHfvFzLte/HFnule+/m1x8+vfZ4e/wF7e1vWDCh8K2IMWPMtzrbmXbuaZeH20Ucfmf5etWpVVqxYwaFDh/D09ATuX+N28uTJXD3yytbWlm3btnHr1i0CAwNNS4vkt9RUI7Gxlh1uFRHzubiUsnSELOXl/yXFijkSGRnFqVNnTFNI169fx8mpnOk477wzixEjRhMbm0B8/B1u375/7dutW/F5msWaP/PCmt1ac0PO2cuUKYqtmYVGQUlNTebGjcRst7GWzzy7nFkWbuvWrUv32tHRkQsXLnDhwoV0bWFhYaYpTXM5Ojri7e3NiRMnuHXrFikpKdjZ2REZGYmbmxtwfyTt6tWrlC9fnpSUFG7fvk3p0qVxc3MjMjLS9F5RUVGmfURErElhmkISyQ1bW3tu3/7U0jHSKVWqD5B94fYoyLJw27NnT54e6MaNG9jZ2eHo6EhiYiLffvstgwcPxtvbm127duHn50dYWJhp6tXX15ewsDC8vLzYtWsXTz/9NAaDAV9fX8aOHYu/vz9RUVFcvHgRDw+PPM0qIlJQCuPFzyJSeJm9jts/FR0dzcSJE0lNTcVoNPL888/z3HPPUatWLUaPHk1ISAj16tWjV69ewP27VoOCgmjbti1OTk4sXLgQgNq1a9OhQwc6duyIra0tU6dO1R2lUqhoQdXHWxnnItjaO1g6RjqpyUnciL1n6RgikgcKrHCrW7cuW7duzdBeuXJlNm3alKG9SJEiLF68ONP3GjZsGMOGDcvriCL/mBZUFVt7B67vmWHpGOmU850KqHATeRTYWDqAyKNGC6qKiEh+KbARN5HHgRZUFRGR/JRl4TZp0iQmT55MyZIlOXLkCF5eXqb11kQkc1pQVURE8lOWU6Xbt2/n7t27ALzyyivExcUVWCgRa5XdM/keePBMvg4dOhESMp/AwDcYPnwkixZp9E1ERLKX5RBapUqV+Pjjj/Hx8cFoNHL8+HGcnJwy3bZJkyb5FlDEmljzM/lERKTwy7JwCwoKIjg4mA8++ACDwZDlIrsGg4GzZ8/mW0ARa2LNC6pevXqFSZPGmV7Hx9/mzp07bN/+lZYyEREpJLIs3Nq0aUObNm24desWTZs2JTw8nDJlyhRkNhGrZK0LqlaoUJHVq9ebXi9a9B6pqSkcOnRQS5mIiBQSOd5t4OjoyNq1a6latapuTpDHUikne4o6FLV0jAwSkxK5HZecL++dnJzM11/v5L33QomNvamlTERECgmzKrGmTZuSlJTEpk2b+PXXXwGoVasWnTt3xsGhcK0QLpLXijoU5bklPpaOkcH/vX6A2+RP4bZ/fwTlyrni7l6XtLQ0LWUiIlJImFW4nT9/nsGDB3P79m3q1Lm/XMHGjRsJDQ1l1apV1KxZM19DikjBCg//HD+/LsD9BYW1lImISOFg1pMT3nnnHerWrcvevXtZv34969evZ+/evbi7uzNr1qz8zigiBejatWhOnDhGu3bPZ+jTUiYiIpZlVuF27NgxxowZQ8mSJU1tJUuWZPTo0Rw9ejTfwolIwdu5cwfNmjXHyck5Q19mS5nY2NhoKRMRkQJi1lRpkSJFuHXrVob227dvU6RIkTwPJSKW88UXOxg1alyG9sK8lImIyOPCrMLtueeeY8qUKbz99tt4enoCcPz4caZNm4avr29+5hORAvbZZ1sybS/MS5mIiDwuzCrcJk+ezIQJE3j55ZextbUFIC0tDV9fX9588818DSgiD6+Mc1FsC9k6a6nJydyITbR0DBERq2RW4ebo6Mj777/P77//bloOpGbNmlStWjVfw4nIP2Nrb0/sp+tz3rAAOffpC6hwExF5GLlaUbdq1aoq1qRA6PFLIiIiGelRCFIo6fFLIiIiGZm1HIiIJT14/JKfX1fs7Oz0+CUREXlsqXCTQu+vj19q0sSb4sVLMGBAH0qUKEnjxl6sWrWc4cO1HIWIiDz6cpwqTUlJYcOGDbRp0wY3N7eCyCSSjh6/JCIicl+OI252dnbMmzePlJSUgsgjko4evyQiIvI/Zk2VNm7cmDNnzuR3FpEM9PglERGR/zHrrtLevXszZ84cLl++TMOGDSlWrFi6/gYNGuRLOBE9fklEROR/zCrcxo69P6oxZ86cDH0Gg4GzZ8/mbSqR/0+PXxIREfkfswq33bt353cOeQw4ly6KvV3hWmctOSWZ2JtaxV9ERKyDWYVbpUqV8juHPAbs7ezZcXqZpWOk06nBcPT4JRERsRZmr+MWERHBkCFD6NixI1evXgVg48aNHDx4MN/CiYiIiMj/mFW4ff7554waNYqqVavy559/mpYGSU1NZdWqVfkaUERERETuM6twW7VqFTNnzuTNN9/E1tbW1O7p6akbE0REREQKiFmF2++//46np2eG9uLFixMfH5/XmUREREQkE2YVbq6urly8eDFD+5EjR6hSpUpeZxIRERGRTJhVuPXu3ZuZM2dy9OhRAK5evUpYWBjz5s2jT58++RpQRERERO4zazmQwYMHEx8fz6BBg7h37x6vvPIKDg4ODBo0iJdffjm/M4qIiIgIZhZuAKNHj2bo0KGcP38eo9FIzZo1KVGiRH5mExEREZG/MLtwg/uPtypSpAhAurtLRURERCT/mVW4JSUlMW/ePDZs2EBycjJGoxEHBwd69+5NUFCQqZgTERERkfxjVuE2bdo0Dhw4wMyZM/Hy8gLg+PHjLFiwgDt37jB79ux8DSkiIiIiZhZuX375JaGhofj4+JjaKleuTNmyZXn99ddVuImIiIgUALOWAylevDhubm4Z2t3c3ChatGiehxIRERGRjMwq3Pr160doaCiJiYmmtsTERJYtW0a/fv3MOtDVq1fp378/HTt2xM/PjzVr1gAQGxuLv78/7dq1w9/fn7i4OACMRiMzZ86kbdu2dO7cmdOnT5veKywsjHbt2tGuXTvCwsLMPlkRERERa5blVOnQoUPTvT58+DAtW7bE3d0dgF9++YWUlBQSEhLMOpCtrS0TJ06kQYMGxMfH88ILL+Dj48OWLVto1qwZAQEBrFixghUrVhAUFMS+ffu4ePEiX331FSdPnmT69Ols3LiR2NhYQkND2bx5MwaDgR49euDr64uTk9M/+BhERERECr8sC7fSpUune92+fft0r5944olcHcjV1RVXV1cASpYsSY0aNYiKimL37t2sW7cOgG7dutG/f3+CgoLYvXs33bp1w2Aw4Onpya1bt4iOjubw4cP4+Pjg7OwMgI+PD9988w2dOnXKVR4RERERa5Nl4ZafNxz8+eefnD17lsaNGxMTE2Mq6FxcXIiJiQEgKiqK8uXLm/YpX748UVFRGdrd3NyIiorK9ni2tgacnYvnw5nIo8CavxvWmt1ac4P1ZrfW3GC92a01N1hvdmvNDeZnz9UCvHnhzp07jBw5kjfffJOSJUum6zMYDBgMhjw/ZmqqkdhY86Z0Jf+4uJSydIRM5fTdKKy5wXqzW2tusN7s1pobrDe7teYG681urbkhffbscppVuMXFxbFkyRIOHTrEjRs3SEtLS9d/8OBBs0IlJyczcuRIOnfuTLt27QAoW7Ys0dHRuLq6Eh0dTZkyZYD7I2mRkZGmfSMjI3Fzc8PNzY3Dhw+b2qOiomjatKlZxxcRERGxZmYVbhMmTODcuXN0796dsmXLPtSomNFoZPLkydSoUQN/f39Tu6+vL1u3biUgIICtW7fSunVrU/vHH3+Mn58fJ0+epFSpUri6utK8eXMWLFhguvt0//79jBkzJtd5RERERKyNWYXboUOH+Pjjj2nQoMFDH+jo0aNs27aNOnXq0LVrVwDGjBlDQEAAo0aNYtOmTVSsWJGQkBAAWrVqRUREBG3btqVYsWLMmjULAGdnZ4YPH07Pnj0BCAwMNN2oICIiIvIoM6twq1KlSobp0dx66qmn+PnnnzPte7Cm218ZDAamTZuW6fY9e/Y0FW6SvZ49O+Pg4ICDw/3nyQ4b9jr16jVg8uQg4uJi8fDwYty4iQD88cdFQkNDmDs3xIKJRUREJCtmLcA7efJkFixYwE8//URqamp+Z5I8NnPmu6xevZ7Vq9fj7d2Mr7/eyZNPPsXatRv444+L/PbbeQAWL17AyJFjLZxWREREsmLWiFvVqlVJTEyke/fumfafPXs2T0NJ/rK1tSMxMZG0tDSSk5Ows7Nn584dNGzowRNPVLZ0PBEREcmCWYXbmDFjiI+PJzg4+KFvThDLeeutKYCRRo08GTIkkPbtO/LOO9Pw93+Zli2fxcnJiR07thESsszSUUVERCQbZhVuP/74Ixs3bqROnTr5nUfy2NKlK3FzK09SUhKLF7/HwoVzmTr1bWbOnGvaZvbsGbz22lBOnDjG1q2bsLd3YOjQEZQvX8GCyUVEROTvzLrGrWbNmsTHx+d3FskHbm73nzLh4OBA9+69+OGHk+n6T5w4BoCX178ICZnH5MnT6dKlO6tWLS/wrCIiIpI9swq3UaNGMWfOHL799luuX79ObGxsuj9SON29e9dUcBuNRv77313UqvW/UdPk5GRWrnyf4cNHApCYmIjBYIPBYODuXT1pQkREpLAxa6o0ICAAgEGDBqW7vs1oNGIwGHRzQiF140YMwcHjSUtLIzU1jWrVqjN27ERT/yefrKFTp644OTkDMGDAq7z2Wn/s7OyZNGmKhVKLiIhIVswq3NauXZvfOSQfVKr0BB99tD7L/oEDX0v3ukuX7nTpkvmdwyIiImJ5ZhVuehZo4VHSsSjFithbOkYGd+8lE38r0dIxREREHmlmFW6nT5/Otv+fPApLcqdYEXv+FVT4RkCPznuFeFS4iYiI5CezCrcXXngBg8GA0Wg0tf31Wjdd4yYiIiKS/8wq3Hbv3p3udUpKCmfOnGH58uWMGTMmX4KJiIiISHpmFW6VKlXK0Fa1alVKlSpFaGgorVq1yvNgIiIiIpKeWeu4ZeWJJ57gp59+yqssIiIiIpINs0bc/r7IrtFo5Nq1a4SGhlK9evX8yCUiIiIif2NW4fb0009neLC80WikQoUKLFy4MF+CiYiIiEh6D7UAr42NDaVLl6Zq1arY2Zn1FiIiIiLyD2kBXhERERErkW3hZu4D5J2dnfMgioiIiIhkJ9vCLbNr2/7OYDBw5syZPA0lIiIiIhllW7hl93D5b775hrVr12Jra5vnoUREREQko2wLt8yubTtz5gxz587l+++/56WXXmL48OH5Fk5ERERE/sfsW0IvXbpESEgIX375JW3btuWLL76gSpUq+ZlNRERERP4ix8Lt5s2bLF26lM8++4wnn3ySTz/9FA8Pj4LIJiIiIiJ/kW3h9v777/Pvf/+bSpUqsWzZMlq2bFlQuURERETkb7It3BYtWkTRokUpX74869evZ/369Zlut3z58nwJJyIiIiL/k23h1q1btxyXAxERERGRgpFt4TZnzpyCyiEiIiIiObCxdAARERERMY8KNxERERErocJNRERExEqocBMRERGxEircRERERKyE2Y+8etxNmjSWK1euYGNjoFix4oweHUT16jWZMmUCV65coVKlJ5gxYzZ2dnbExsYyeXIQISHLsLe3t3R0EREReURoxM1Mkye/xZo1n/LRR+vp06cfs2fP4NChg5Qq5ciaNZ9SsmRJDh06CMCyZYsICBiuok1ERETylAo3M5UsWdL09/j4eAwGG+zs7Lh3LxGAe/cSsbe35/jxo9jY2NC4sZelooqIiMgjSlOluTBnztscPvwdAPPnL6Zaters3bubAQP60KBBQxo39mLMmBHMnj3fwklFRETkUaTCLRcmTpwCwJdfhrNs2SLmz1/MhAnBpv6PPlpJp05diYy8yty5swAYMOBVateuY5G8IiIi8mjRVOlDeP55P44dO0pcXKyp7dKlPzh9+gc6dOhESMh8AgPfYPjwkSxapNE3ERERyRsFVrhNmjSJZs2a0alTJ1NbbGws/v7+tGvXDn9/f+Li4gAwGo3MnDmTtm3b0rlzZ06fPm3aJywsjHbt2tGuXTvCwsIKJHtCQgJRUZGm1/v378PR0RFHRydT25IlCxg5ciwAiYl3MRgM2NjYkJCQUCAZRURE5NFXYFOlPXr0oF+/fkyYMMHUtmLFCpo1a0ZAQAArVqxgxYoVBAUFsW/fPi5evMhXX33FyZMnmT59Ohs3biQ2NpbQ0FA2b96MwWCgR48e+Pr64uTklM2R/7nExLtMmTKRxMS72NjY4ujoyLvvLsRgMACwa9cX1K1bnypVqgLw6qtDGTfuDQACA0fmazYRERF5fBRY4dakSRP+/PPPdG27d+9m3bp1AHTr1o3+/fsTFBTE7t276datGwaDAU9PT27dukV0dDSHDx/Gx8cHZ2dnAHx8fPjmm2/SjeLlhzJlyrJixeos+9u375jutY9PC3x8WuRrJhEREXn8WPTmhJiYGFxdXQFwcXEhJiYGgKioKMqXL2/arnz58kRFRWVod3NzIyoqKsfj2NoacHYunq7NYGPAzrZwXeKXkpqGMc1o6RgP7e+fsbWw1txgvdmtNTdYb3ZrzQ3Wm91ac4P1ZrfW3GB+9kJzV6nBYDBNPea11FQjsbHprzVzcSlF2NHz+XK8h9X9X7W4duN2ttu4uJQqoDS59/fP+O8Ka3ZrzQ3Wm91ac4P1ZrfW3GC92a01N1hvdmvNDemzZ5fTokNOZcuWJTo6GoDo6GjKlCkD3B9Ji4z8380AkZGRuLm5ZWiPiorCzc2tYEOLiIiIWIhFCzdfX1+2bt0KwNatW2ndunW6dqPRyIkTJyhVqhSurq40b96c/fv3ExcXR1xcHPv376d58+YWPAMRERGRglNgU6Vjxozh8OHD3Lx5k5YtW/L6668TEBDAqFGj2LRpExUrViQkJASAVq1aERERQdu2bSlWrBizZt1fzNbZ2Znhw4fTs2dPAAIDA003KoiIiIg86gqscFuwYEGm7WvWrMnQZjAYmDZtWqbb9+zZ01S4iYiIiDxOCtdtlSIiIiKSJRVuIiIiIlZChZuIiIiIlVDhJiIiImIlVLiJiIiIWAkVbiIiIiJWQoWbiIiIiJVQ4SYiIiJiJVS4iYiIiFgJFW4iIiIiVkKFm4iIiIiVUOEmIiIiYiVUuImIiIhYCRVuIiIiIlZChZuIiIiIlVDhJiIiImIlVLiJiIiIWAkVbiIiIiJWQoWbiIiIiJVQ4SYiIiJiJVS4iYiIiFgJFW4iIiIiVkKFm4iIiIiVUOEmIiIiYiVUuImIiIhYCRVuIiIiIlZChZuIiIiIlVDhJiIiImIlVLiJiIiIWAkVbiIiIiJWQoWbiIiIiJVQ4SYiIiJiJVS4iYiIiFgJFW4iIiIiVkKFm4iIiIiVUOEmIiIiYiVUuImIiIhYCRVuIiIiIlZChZuIiIiIlVDhJiIiImIlrLZw27dvH+3bt6dt27asWLHC0nFERERE8p1VFm6pqanMmDGDVatWER4ezo4dOzh//rylY4mIiIjkK6ss3E6dOkXVqlWpXLkyDg4O+Pn5sXv3bkvHEhEREclXBqPRaLR0iNz68ssv+eabb3jnnXcA2Lp1K6dOnWLq1KkWTiYiIiKSf6xyxE1ERETkcWSVhZubmxuRkZGm11FRUbi5uVkwkYiIiEj+s8rCrVGjRly8eJFLly6RlJREeHg4vr6+lo4lIiIikq/sLB3gYdjZ2TF16lRee+01UlNTeeGFF6hdu7alY4mIiIjkK6u8OUFERETkcWSVU6UiIiIijyMVbiIiIiJWQoWbiIiIiJVQ4SZSgJKTky0dQURErJhuThApQA0bNmTbtm3UrFnT0lFEHmvR0dF8+umnHD16lGvXrmFjY0PlypVp3bo1PXr0wNbW1tIRRTKlwi0PXb16lcWLFzN79mxLR8ng1q1bHDt2DEdHR7y8vDAYDKa+hIQEPvzwQ0aMGGHBhFn75ZdfOHHiBF5eXtSuXZvz58+zevVqkpKS6NKlC82bN7d0xAxmzpyZafsnn3yCn58fzs7OAAQHBxdgqocTFxdHWFgYv//+Oy4uLnTv3p0KFSpYOlYG33//PWXKlKFGjRrA/c96/fr1XLlyhUqVKvHyyy/Tp08fC6fM3NChQ+nQoQMdOnTAwcHB0nHMlpaWxgcffMDJkyd59tlneemll9i8eTMrVqwgLS2Ntm3bMmrUqEJ3Tj/88AP+/v5UqVKFokWLcuLECTp16kRycjL79++nZs2arFq1ipIlS1o6qkgGKtzy0E8//UT37t05e/aspaOkc+7cOfz9/blx4wZpaWnUr1+fJUuWUKlSJQCuX79OixYtCl1ugIiICAIDAylRogQJCQmEhoYyYcIE6tWrR1paGkeOHGHVqlU888wzlo6aTt26dalbty6lSpVK137kyBEaNmxIsWLFMBgMrF271kIJs9a8eXO2b99O6dKluXTpEn369CEtLY1atWrx22+/kZiYyIYNGwrdqGGnTp0IDg7m6aef5pNPPmH+/Pm88sor1KhRgwsXLvDxxx8zevRoXn75ZUtHzaBu3boYDAZKlSpFly5d6N27N3Xq1LF0rByFhISwfv16fH19+e677+jevTvr169n4MCB2NjYsHr1al588UVGjRpl6ajp9OnTBx8fH9Mvq9u2beOTTz7hP//5D3FxcQwYMICnnnqqUP9ilZCQwI4dOzh+/DjXrl0DwMXFhSeffBI/Pz+KFy9u4YS5d/36dT777LNCO4iQlpbGr7/+iqOjY4anNd27d4+dO3fSrVu3/A9iFLOFhYVl+2fp0qXGunXrWjpmBkOGDDEGBgYa79y5Y4yKijKOHDnS2KJFC+OFCxeMRqPReO3atUKZ22g0Gl988UXjggULjEaj0bhjxw5jkyZNTK+NRqNx/vz5Rn9/f0vFy9Ly5cuNvr6+xkOHDqVrr1+/vvHcuXMWSmUed3d34/Xr141Go9E4evRoY79+/Yx37twxGo1GY2JiojEgIMD4+uuvWzJipho1amT8888/jUaj0di1a1fjli1b0vV/8cUXxvbt21siWo7c3d2NZ86cMS5btszYunVrY926dY29evUybty40ZiQkGDpeFny9fU17tmzx2g0Go3nzp0z1q1b17ht2zZT/1dffWVs06aNpeJlycPDw/jHH3+YXqemphobNGhgvHbtmtFoNBr3799vbN68uaXi5ejcuXPG5s2bG728vIwBAQHGSZMmGSdNmmQMCAgwPvnkk8YWLVoU+v9nMnP27NlC+7PoypUrRj8/P6O7u7uxbt26xuHDhxtjY2NN/QX5c9Qqn5xgKRMnTqRYsWJZ9hsL6eDlyZMnWbt2LcWLF6d48eIsWrSI2bNn079/f9auXZthVKgwOXfuHO+++y4AHTp0YPz48bRv397U37lzZ7Zs2WKpeFkaMmQI3t7eBAUF0aFDB0aNGoWNjfXdC3Ty5Elmzpxp+u29SJEiDB8+nJEjR1o4WUYlSpTg5s2bVKpUiejoaOrVq5euv0GDBly9etVC6XLm6urKsGHDGDZsGPv37+c///kP06dPZ/bs2fj5+dG7d28aNmxo6ZjpXLt2DXd3dwBq1aqFra1tus+9fv36ptGgwqRs2bJERUVRuXJl4P55pKSkmKZGq1atSlxcnCUjZuutt97iX//6F++++y5FihRJ13fv3j0mTpzIW2+9xbp16yyUMHNHjhzJtv/ixYsFE+QhzJ8/HycnJ8LDw7l9+zZz5syhX79+rFmzhjJlyhRoFhVuueDq6kpwcDDt2rXLtP/s2bP06NGjgFPlLCkpKd01bQCTJk3CaDTSv39/3nvvPQslM8+DgsfGxgYHB4d0hWaJEiW4ffu2paJly9PTky1btjBlyhR69epV6D/nv3rwfUlKSqJs2bLp+sqWLcuNGzcsEStbrVq14pNPPmH27Nl4e3uzc+dO6tata+r/4osvqFatmuUC5kLz5s1p3rw5N27cYPPmzWzatImNGzcWussZXFxcOHfuHBUrVuS3334jNTWV8+fPmx5BeP78+QL/oWaO1q1bM23aNMaNG4eDgwPvv/8+TZo0oWjRogD89ttvGabCCpNTp06xefPmDEUb3P/latiwYfTq1csCybLXv39/DAZDtoMcf/9ZVVgcOnSI999/33SJyLp16xg3bhz9+vUr8AJZhVsuNGjQgNOnT2dZuOX0hbSU6tWr8+OPP1KrVq107W+++SZpaWkMHz7cQslyVqlSJS5evGj6zXjDhg3pLoy/evUq5cqVs1S8HJUqVYqQkBD+85//mK4Vswb9+vXD1taW+Ph4fvvtt3TXW129epXSpUtbMF3mxo4dS58+fejbty+NGzdm9erVHDlyhJo1a3LhwgVOnDjB0qVLLR0zV8qUKcPgwYMZPHgwhw4dsnScDDp37sz48ePx9fXl0KFDBAQEMHfuXGJiYjAYDKxcuTLdCHlhMWrUKK5du8aIESNITU3F09OTefPmmfptbGwYM2aMBRNmz9HRkYsXL2b4P/2B33//HUdHxwJOlbPSpUszadIkWrRokWn/L7/8wsCBAws2lJnu3LmTbtDA3t6eBQsWMHr0aPr168eCBQsKLIsKt1x49dVXuXPnTpb9VapUKZQXm7dt25YdO3ZketFkcHAwqampfPrppwUfzAwvvvhiurXP/n7BdkREBN7e3gUdK9d69+5NkyZNOHHiBOXLl7d0nGz9/cLgEiVKpHu9Z88ennrqqYKMZBYXFxfCwsJYuXIlu3fvxmg0curUKSIjI/Hy8uLTTz+lUaNGlo6ZqYoVK+Y4lV4Yv+cjR4403ZX50ksvERAQQJ06dZg3bx53797F19eXN954w9IxMyhRogQhISHcu3ePlJSUDN/xwnin+l/17t2bCRMmMGTIEJ555hnTL6/Xr1/n22+/ZeXKlQwYMMDCKTOqX78+ly5dyvIXPycnp0I5+AFQuXJlfvrpp3Sj9ra2tixcuJBRo0YxdOjQAsuiu0pFRESszIoVK1i7di3Xr183TS8ajUbKlSvHgAEDGDx4sIUTZvT111+TkJBA165dM+2Pi4tjz549dO/evYCT5WzevHn89NNP/Pvf/87Ql5KSwsiRI9mzZw8//fRTvmdR4SYiImKlLl26xPXr1wEoV66c6bISyVspKSkkJiZmubZfamoqkZGRpmW28pP13eYmIiIiwP0pPC8vL7y8vExF29WrV5k0aZKFk+VeYc5tZ2eX7YLM0dHRhIaGFkgWFW4iIiKPkLi4OLZu3WrpGLlmrbmhYLPr5gQRERErklOBcOXKlYIJkkvWmhsKV3Zd4yYiImJF6tatm+Ni8Pfu3St06/5Za24oXNk14iYiImJFrHUxeGvNDYUru65xExERsSIPFoPPSmFdDN5ac0Phyq4RNxEREStirYvBW2tuKFzZdY2biIiIiJXQVKmIiIiIlVDhJiIiImIlVLiJyCOpf//+zJgxI0/f09fXN9NnFRZm7u7ufPnll5aOISJ5RIWbiBQap0+fpl69erz00kv/+L2WLFnCmDFj8iCVdViyZAmdOnWydAwRyWcq3ESk0Ni4cSN9+/bl3Llz/Prrrzlun5ycnKEtKSkJAGdn52yfLSgiYo1UuIlIoZCYmMiOHTvo3bs37du3Z9OmTen6//zzT9zd3dmxYwevvPIKHh4ebNiwgYkTJzJkyBBWrFhBy5YtadWqFZB+qnTBggWZLo750ksvMXPmTABOnTrFoEGD8Pb25sknn6RPnz4cP348V+fwYNQrLCwMX19fPD09mTRpEklJSXzyySe0atUKb29vZs+eTVpammm/uLg4JkyYQJMmTfDw8GDgwIGcO3fO1L9lyxa8vLw4ePAgnTp1wtPTk/79+3Pp0iVTf2hoKOfOncPd3R13d3e2bNmS7v1HjhyJp6cnrVu3Ztu2bbk6LxEpPFS4iUih8OWXX1KxYkXc3d3p2rUrW7duzXREbcGCBfTt25fw8HDatGkDwOHDh/n5559ZtWoVq1evzrBPly5dOH36dLpRvEuXLnH8+HG6dOkCwJ07d+jSpQvr169n48aN1KtXj4CAAG7evJmr87h8+TK7d+9m+fLlLFmyhC+//JJhw4bx448/8uGHHzJz5kw+/vhjvv76a9M+EydO5OTJkyxbtoyNGzdStGhRXnvtNRITE03bJCUl8cEHHzBr1iw+++wzbt++zfTp0wHo2LEjgwYNonr16uzfv5/9+/fTsWNH075Lly41FWwdO3Zk8uTJhfq5kCKSNRVuIlIobN68ma5duwLQtGlTihUrxu7duzNs169fP55//nkqV65M+fLlAShSpAizZ8+mTp06uLu7Z9inVq1a1K9fn+3bt5vatm/fTrVq1fDw8ACgWbNmdOvWjZo1a1KzZk2mTJlCkSJF2LdvX67OIzU11ZSlRYsWtGjRgtOnT/PWW29Rs2ZN2rZty5NPPsmhQ4cAuHjxInv27OHtt9+mSZMmuLu7M2/ePOLj49PlTUlJYerUqXh4eFC3bl0GDRrEoUOHMBqNFC1alOLFi2NnZ4eLiwsuLi4ULVrUtG/Xrl3p2rUrVatW5Y033sDW1pYjR47k6rxEpHBQ4SYiFvf7779z9OhR08X1BoOBzp07Z5guBWjYsGGGttq1a+Pg4JDtMbp06cKOHTtMr7dv307nzp1Nr2NiYpg6dSrt27fnX//6F08++SQxMTFcvXo1V+dSoUIFSpUqZXpdtmxZqlWrli5f2bJliYmJAeDXX3/FxsYGT09PU3+pUqWoU6cO58+fN7U5ODhQo0YN02tXV1eSk5OJi4vLMdNfi1k7OzvKlCnDjRs3cnVeIlI46JFXImJxGzduJDU1leeee87U9uChLlevXqVChQqm9mLFimXYv3jx4jkew8/Pj3nz5nH8+HEcHBz47bffTCN8ABMmTCAmJoZJkyZRqVIlHBwcGDhwYKbTtdmxt7dP99pgMGTa9tdr3LJiMBhMf7ezs8u0z5z3yWxfc/YTkcJHhZuIWFRKSgpbt25l7NixPPvss+n6xo8fz+bNmxkxYsQ/Po6rqytPP/0027dvx8HBAS8vLypXrmzqP3r0KMHBwaYM169f59q1a//4uDmpWbMmaWlpnDhxgiZNmgAQHx/PL7/8kukNFVmxt7cnNTU1v2KKSCGhqVIRsai9e/dy8+ZNevXqRZ06ddL96dixI1u2bCGvHqncpUsXvvjiC8LDw9NNkwJUr16dzz//nPPnz3Pq1ClGjx6dYaQsP1SrVo3WrVszdepUvv/+e37++WfGjRtHyZIlM2TMTqVKlbhy5QqnT5/mxo0bpmVRROTRosJNRCxq06ZNeHt7U7p06Qx9HTp04PLlyxw4cCBPjtW2bVsSExO5efNmursuAWbNmkVCQgI9evRgzJgxvPDCC1SqVClPjpuT2bNn4+HhwbBhw+jVqxeJiYmsWrUq3Q0GOWnfvj2tWrVi4MCBNGvWLN31fCLy6DAY8+pXWRERERHJVxpxExEREbESKtxERERErIQKNxERERErocJNRERExEqocBMRERGxEircRERERKyECjcRERERK6HCTURERMRK/D+OpAdxY1oXRgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 56;\n",
              "                var nbb_unformatted_code = \"labeled_barplot(data=df.sort_values(by='arrival_month'), feature='arrival_month',\\n                perc=True, n=None, percfmt=\\\"{:.0f}%\\\", sort=False,\\n                figsize=(10, 5), xlabel='Arrival month', xlo=90,\\n                title='Month of arrival distribution')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(\\n    data=df.sort_values(by=\\\"arrival_month\\\"),\\n    feature=\\\"arrival_month\\\",\\n    perc=True,\\n    n=None,\\n    percfmt=\\\"{:.0f}%\\\",\\n    sort=False,\\n    figsize=(10, 5),\\n    xlabel=\\\"Arrival month\\\",\\n    xlo=90,\\n    title=\\\"Month of arrival distribution\\\",\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mos = pd.pivot_table(data=df, index='arrival_month', columns='arrival_year', values='Booking_ID', aggfunc='count', margins=True).fillna(0)\n",
        "mos['2017_%'] = 100 * mos[2017] / mos[2017]['All']\n",
        "mos['2018_%'] = 100 * mos[2018] / mos[2018]['All']\n",
        "mos.apply(lambda x: x.astype(int))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "xyMO3hjcvUJY",
        "outputId": "7ee9c386-09a7-4100-da2d-530ba503b0e3"
      },
      "id": "xyMO3hjcvUJY",
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "arrival_year   2017   2018    All  2017_%  2018_%\n",
              "arrival_month                                    \n",
              "1                 0   1014   1014       0       3\n",
              "2                 0   1704   1704       0       5\n",
              "3                 0   2358   2358       0       7\n",
              "4                 0   2736   2736       0       9\n",
              "5                 0   2598   2598       0       8\n",
              "6                 0   3203   3203       0      10\n",
              "7               363   2557   2920       5       8\n",
              "8              1014   2799   3813      15       9\n",
              "9              1649   2962   4611      25       9\n",
              "10             1913   3404   5317      29      11\n",
              "11              647   2333   2980       9       7\n",
              "12              928   2093   3021      14       7\n",
              "All            6514  29761  36275     100     100"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2b3db463-4e19-4375-aaba-fe7fa0e89ab3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>arrival_year</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>All</th>\n",
              "      <th>2017_%</th>\n",
              "      <th>2018_%</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arrival_month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1014</td>\n",
              "      <td>1014</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1704</td>\n",
              "      <td>1704</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>2358</td>\n",
              "      <td>2358</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>2736</td>\n",
              "      <td>2736</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>2598</td>\n",
              "      <td>2598</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>3203</td>\n",
              "      <td>3203</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>363</td>\n",
              "      <td>2557</td>\n",
              "      <td>2920</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1014</td>\n",
              "      <td>2799</td>\n",
              "      <td>3813</td>\n",
              "      <td>15</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1649</td>\n",
              "      <td>2962</td>\n",
              "      <td>4611</td>\n",
              "      <td>25</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1913</td>\n",
              "      <td>3404</td>\n",
              "      <td>5317</td>\n",
              "      <td>29</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>647</td>\n",
              "      <td>2333</td>\n",
              "      <td>2980</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>928</td>\n",
              "      <td>2093</td>\n",
              "      <td>3021</td>\n",
              "      <td>14</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>6514</td>\n",
              "      <td>29761</td>\n",
              "      <td>36275</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2b3db463-4e19-4375-aaba-fe7fa0e89ab3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </style>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 57;\n",
              "                var nbb_unformatted_code = \"mos = pd.pivot_table(data=df, index='arrival_month', columns='arrival_year', values='Booking_ID', aggfunc='count', margins=True).fillna(0)\\nmos['2017_%'] = 100 * mos[2017] / mos[2017]['All']\\nmos['2018_%'] = 100 * mos[2018] / mos[2018]['All']\\nmos.apply(lambda x: x.astype(int))\";\n",
              "                var nbb_formatted_code = \"mos = pd.pivot_table(\\n    data=df,\\n    index=\\\"arrival_month\\\",\\n    columns=\\\"arrival_year\\\",\\n    values=\\\"Booking_ID\\\",\\n    aggfunc=\\\"count\\\",\\n    margins=True,\\n).fillna(0)\\nmos[\\\"2017_%\\\"] = 100 * mos[2017] / mos[2017][\\\"All\\\"]\\nmos[\\\"2018_%\\\"] = 100 * mos[2018] / mos[2018][\\\"All\\\"]\\nmos.apply(lambda x: x.astype(int))\";\n",
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              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ">* The simple answer is the busiest months in the hotel is **October** followed by **September**\n",
        "* However, there are no data for the first half of 2017 in the dataset. Therefore, we can not say what was the busiest month on average for two years\n",
        "* Most of the bookings of 2018 (11%) were in October\n",
        "* More than 10% of all bookings of 2018 were in June (no data for June 2017)\n",
        "* Based on incomplete data of 2017, we can say that October was the busiest month this year (29% of bookings made in Jul-Dec were in October)\n",
        "* The next busiest month of 2017 based on the half-year data was September - 25% of bookings made in Jul-Dec of 2017 were in Sepember"
      ],
      "metadata": {
        "id": "snC3zh7F0BYF"
      },
      "id": "snC3zh7F0BYF"
    },
    {
      "cell_type": "markdown",
      "source": [
        ">**2. Which market segment do most of the guests come from?**"
      ],
      "metadata": {
        "id": "L0QA-HGNvUPw"
      },
      "id": "L0QA-HGNvUPw"
    },
    {
      "cell_type": "code",
      "source": [
        "q2 = df.groupby('market_segment_type').agg({'market_segment_type': 'count', 'no_of_adults': 'sum', 'no_of_children': 'sum'}).rename(columns={'market_segment_type': 'bookings'})\n",
        "q2['no_of_guests'] = q2['no_of_adults'] + q2['no_of_children']\n",
        "\n",
        "print('\\nActual numbers:')\n",
        "display(q2)\n",
        "print('\\n\\nPercentages:')\n",
        "display(round(100 * q2 / q2.sum(axis=0),1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "R-_DepLSvUWK",
        "outputId": "dfa7af48-661c-4cb0-a979-26cafd18cf99"
      },
      "id": "R-_DepLSvUWK",
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Actual numbers:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                     bookings  no_of_adults  no_of_children  no_of_guests\n",
              "market_segment_type                                                      \n",
              "Aviation                  125           127               0           127\n",
              "Complementary             391           580              49           629\n",
              "Corporate                2017          2481              20          2501\n",
              "Offline                 10528         18715             222         18937\n",
              "Online                  23214         45023            3528         48551"
            ],
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              "      <th></th>\n",
              "      <th>bookings</th>\n",
              "      <th>no_of_adults</th>\n",
              "      <th>no_of_children</th>\n",
              "      <th>no_of_guests</th>\n",
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              "      <td>127</td>\n",
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              "      <th>Complementary</th>\n",
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              "      <td>629</td>\n",
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              "    <tr>\n",
              "      <th>Corporate</th>\n",
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              "      <td>2501</td>\n",
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              "      <td>18937</td>\n",
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              "      <th>Online</th>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "          element.innerHTML = '';\n",
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          "metadata": {}
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        {
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          "text": [
            "\n",
            "\n",
            "Percentages:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                     bookings  no_of_adults  no_of_children  no_of_guests\n",
              "market_segment_type                                                      \n",
              "Aviation              0.30000       0.20000         0.00000       0.20000\n",
              "Complementary         1.10000       0.90000         1.30000       0.90000\n",
              "Corporate             5.60000       3.70000         0.50000       3.50000\n",
              "Offline              29.00000      28.00000         5.80000      26.80000\n",
              "Online               64.00000      67.30000        92.40000      68.60000"
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              "      <td>0.30000</td>\n",
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              "      <th>Complementary</th>\n",
              "      <td>1.10000</td>\n",
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              "      <td>0.90000</td>\n",
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              "      <td>0.50000</td>\n",
              "      <td>3.50000</td>\n",
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              "      <th>Offline</th>\n",
              "      <td>29.00000</td>\n",
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              "      <td>26.80000</td>\n",
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              "      <th>Online</th>\n",
              "      <td>64.00000</td>\n",
              "      <td>67.30000</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 58;\n",
              "                var nbb_unformatted_code = \"q2 = df.groupby('market_segment_type').agg({'market_segment_type': 'count', 'no_of_adults': 'sum', 'no_of_children': 'sum'}).rename(columns={'market_segment_type': 'bookings'})\\nq2['no_of_guests'] = q2['no_of_adults'] + q2['no_of_children']\\n\\nprint('\\\\nActual numbers:')\\ndisplay(q2)\\nprint('\\\\n\\\\nPercentages:')\\ndisplay(round(100 * q2 / q2.sum(axis=0),1))\";\n",
              "                var nbb_formatted_code = \"q2 = (\\n    df.groupby(\\\"market_segment_type\\\")\\n    .agg(\\n        {\\\"market_segment_type\\\": \\\"count\\\", \\\"no_of_adults\\\": \\\"sum\\\", \\\"no_of_children\\\": \\\"sum\\\"}\\n    )\\n    .rename(columns={\\\"market_segment_type\\\": \\\"bookings\\\"})\\n)\\nq2[\\\"no_of_guests\\\"] = q2[\\\"no_of_adults\\\"] + q2[\\\"no_of_children\\\"]\\n\\nprint(\\\"\\\\nActual numbers:\\\")\\ndisplay(q2)\\nprint(\\\"\\\\n\\\\nPercentages:\\\")\\ndisplay(round(100 * q2 / q2.sum(axis=0), 1))\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ">- Most of the guests 48551 (68.6%) come from `Online` market segment\n",
        "- Most of the bookings 23214 (64%) come from `Online` market segment"
      ],
      "metadata": {
        "id": "pJc8Kdg28TFm"
      },
      "id": "pJc8Kdg28TFm"
    },
    {
      "cell_type": "markdown",
      "source": [
        ">**3. Hotel rates are dynamic and change according to demand and customer demographics. <br>What are the differences in room prices in different market segments?**"
      ],
      "metadata": {
        "id": "avIOz1pRvUcQ"
      },
      "id": "avIOz1pRvUcQ"
    },
    {
      "cell_type": "code",
      "source": [
        "q3 = df.groupby('market_segment_type').avg_price_per_room\n",
        "display(q3.describe())\n",
        "print('\\n')\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12, 6))\n",
        "sns.boxplot(data=df, x='market_segment_type', y='avg_price_per_room', ax=ax, showfliers=False)\n",
        "plt.xlabel('Market Segment Type', fontsize=12)\n",
        "plt.ylabel('Average Price per Room', fontsize=12)\n",
        "plt.title('Average Price per Room vs Market Segment Type', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "3cqCF6zPvUw7",
        "outputId": "81c09836-a9b7-405f-fdd0-f0ba48a680a6"
      },
      "id": "3cqCF6zPvUw7",
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                          count      mean      std      min      25%  \\\n",
              "market_segment_type                                                    \n",
              "Aviation              125.00000 100.70400  8.53836 79.00000 95.00000   \n",
              "Complementary         391.00000   3.14176 15.51297  0.00000  0.00000   \n",
              "Corporate            2017.00000  82.91174 23.69000 31.00000 65.00000   \n",
              "Offline             10528.00000  91.63268 24.99560 12.00000 75.00000   \n",
              "Online              23214.00000 112.25685 35.22032  0.00000 89.00000   \n",
              "\n",
              "                          50%       75%       max  \n",
              "market_segment_type                                \n",
              "Aviation             95.00000 110.00000 110.00000  \n",
              "Complementary         0.00000   0.00000 170.00000  \n",
              "Corporate            79.00000  95.00000 220.00000  \n",
              "Offline              90.00000 109.00000 540.00000  \n",
              "Online              107.10000 131.75000 375.50000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a0188c9f-081b-44e2-adc1-9a90dff81a9c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
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              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
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              "      <th>market_segment_type</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Aviation</th>\n",
              "      <td>125.00000</td>\n",
              "      <td>100.70400</td>\n",
              "      <td>8.53836</td>\n",
              "      <td>79.00000</td>\n",
              "      <td>95.00000</td>\n",
              "      <td>95.00000</td>\n",
              "      <td>110.00000</td>\n",
              "      <td>110.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Complementary</th>\n",
              "      <td>391.00000</td>\n",
              "      <td>3.14176</td>\n",
              "      <td>15.51297</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
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              "      <td>0.00000</td>\n",
              "      <td>170.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Corporate</th>\n",
              "      <td>2017.00000</td>\n",
              "      <td>82.91174</td>\n",
              "      <td>23.69000</td>\n",
              "      <td>31.00000</td>\n",
              "      <td>65.00000</td>\n",
              "      <td>79.00000</td>\n",
              "      <td>95.00000</td>\n",
              "      <td>220.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Offline</th>\n",
              "      <td>10528.00000</td>\n",
              "      <td>91.63268</td>\n",
              "      <td>24.99560</td>\n",
              "      <td>12.00000</td>\n",
              "      <td>75.00000</td>\n",
              "      <td>90.00000</td>\n",
              "      <td>109.00000</td>\n",
              "      <td>540.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Online</th>\n",
              "      <td>23214.00000</td>\n",
              "      <td>112.25685</td>\n",
              "      <td>35.22032</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>89.00000</td>\n",
              "      <td>107.10000</td>\n",
              "      <td>131.75000</td>\n",
              "      <td>375.50000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a0188c9f-081b-44e2-adc1-9a90dff81a9c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      background-color: #E8F0FE;\n",
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              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a0188c9f-081b-44e2-adc1-9a90dff81a9c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a0188c9f-081b-44e2-adc1-9a90dff81a9c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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          "text": [
            "\n",
            "\n"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 59;\n",
              "                var nbb_unformatted_code = \"q3 = df.groupby('market_segment_type').avg_price_per_room\\ndisplay(q3.describe())\\nprint('\\\\n')\\n\\nfig, ax = plt.subplots(figsize=(12, 6))\\nsns.boxplot(data=df, x='market_segment_type', y='avg_price_per_room', ax=ax, showfliers=False)\\nplt.xlabel('Market Segment Type', fontsize=12)\\nplt.ylabel('Average Price per Room', fontsize=12)\\nplt.title('Average Price per Room vs Market Segment Type', fontsize=16)\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"q3 = df.groupby(\\\"market_segment_type\\\").avg_price_per_room\\ndisplay(q3.describe())\\nprint(\\\"\\\\n\\\")\\n\\nfig, ax = plt.subplots(figsize=(12, 6))\\nsns.boxplot(\\n    data=df, x=\\\"market_segment_type\\\", y=\\\"avg_price_per_room\\\", ax=ax, showfliers=False\\n)\\nplt.xlabel(\\\"Market Segment Type\\\", fontsize=12)\\nplt.ylabel(\\\"Average Price per Room\\\", fontsize=12)\\nplt.title(\\\"Average Price per Room vs Market Segment Type\\\", fontsize=16)\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ">* Room prices of `Online` market segment (~112EUR) are higher than from other segments and have wider range (IQR: 89EUR - 132EUR)\n",
        "* `Aviation`: ~101EUR (IQR: 95EUR - 110EUR)\n",
        "* `Offline`: ~92EUR (IQR: 75EUR - 109EUR)\n",
        "* `Corporate`: ~83EUR (IQR: 65EUR - 95EUR)\n",
        "* `Complimentary` is free in most cases, but may be up to 170EUR in some cases"
      ],
      "metadata": {
        "id": "T_NNebq4-Ynd"
      },
      "id": "T_NNebq4-Ynd"
    },
    {
      "cell_type": "markdown",
      "source": [
        ">**4. What percentage of bookings are canceled?**"
      ],
      "metadata": {
        "id": "Tl04T31DwRAf"
      },
      "id": "Tl04T31DwRAf"
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_barplot(data=df, feature='booking_status',\n",
        "                perc=True, n=None, percfmt=\"{:.0f}%\", sort=False,\n",
        "                figsize=(10, 5), xlabel='Booking status', xlo=0,\n",
        "                title='Booking status distribution')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "ck-LQ9xYwRLe",
        "outputId": "8c8fb0b4-b94d-444b-fe07-b21ad6b66eb8"
      },
      "id": "ck-LQ9xYwRLe",
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 60;\n",
              "                var nbb_unformatted_code = \"labeled_barplot(data=df, feature='booking_status',\\n                perc=True, n=None, percfmt=\\\"{:.0f}%\\\", sort=False,\\n                figsize=(10, 5), xlabel='Booking status', xlo=0,\\n                title='Booking status distribution')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(\\n    data=df,\\n    feature=\\\"booking_status\\\",\\n    perc=True,\\n    n=None,\\n    percfmt=\\\"{:.0f}%\\\",\\n    sort=False,\\n    figsize=(10, 5),\\n    xlabel=\\\"Booking status\\\",\\n    xlo=0,\\n    title=\\\"Booking status distribution\\\",\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ">* 33% of the bookings in the datatset were cancelled"
      ],
      "metadata": {
        "id": "2m93iop5Cvp-"
      },
      "id": "2m93iop5Cvp-"
    },
    {
      "cell_type": "markdown",
      "source": [
        ">**5. Repeating guests are the guests who stay in the hotel often and are important to brand equity.<br>What percentage of repeating guests cancel?**"
      ],
      "metadata": {
        "id": "6s5SFHCHvU3Z"
      },
      "id": "6s5SFHCHvU3Z"
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_barplot(data=df[df['repeated_guest']==1], feature='booking_status',\n",
        "                perc=True, n=None, percfmt=\"{:.0f}%\", sort=False,\n",
        "                figsize=(10, 5), xlabel='Booking status', xlo=0,\n",
        "                title='Booking status distribution')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "52UIgs1KvU-a",
        "outputId": "b1f93cd6-3fc9-45c2-da04-1cbe6acf251d"
      },
      "id": "52UIgs1KvU-a",
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 61;\n",
              "                var nbb_unformatted_code = \"labeled_barplot(data=df[df['repeated_guest']==1], feature='booking_status',\\n                perc=True, n=None, percfmt=\\\"{:.0f}%\\\", sort=False,\\n                figsize=(10, 5), xlabel='Booking status', xlo=0,\\n                title='Booking status distribution')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(\\n    data=df[df[\\\"repeated_guest\\\"] == 1],\\n    feature=\\\"booking_status\\\",\\n    perc=True,\\n    n=None,\\n    percfmt=\\\"{:.0f}%\\\",\\n    sort=False,\\n    figsize=(10, 5),\\n    xlabel=\\\"Booking status\\\",\\n    xlo=0,\\n    title=\\\"Booking status distribution\\\",\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ">* 2% of the bookings came from repeating guests were cancelled"
      ],
      "metadata": {
        "id": "KoefpJM2DAn4"
      },
      "id": "KoefpJM2DAn4"
    },
    {
      "cell_type": "markdown",
      "source": [
        ">**6. Many guests have special requirements when booking a hotel room.<br>Do these requirements affect booking cancellation?**"
      ],
      "metadata": {
        "id": "XwHzxIArvVE7"
      },
      "id": "XwHzxIArvVE7"
    },
    {
      "cell_type": "code",
      "source": [
        "q6 = pd.pivot_table(data=df, index='no_of_special_requests', columns='booking_status', values='Booking_ID', aggfunc='count')/df.shape[0]\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "sns.heatmap(q6, annot=True, fmt=\".0%\", cmap=\"Spectral\")\n",
        "plt.xlabel('Booking Status')\n",
        "plt.ylabel('No of special requests')\n",
        "plt.title('Heatmap representing the %% of bookings for\\nguests with special requests by their booking status', fontsize=16)\n",
        "plt.show();\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "sklTitUobJS6",
        "outputId": "28346323-84b2-4e02-bffd-7ccf8acf23b8"
      },
      "id": "sklTitUobJS6",
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 62;\n",
              "                var nbb_unformatted_code = \"q6 = pd.pivot_table(data=df, index='no_of_special_requests', columns='booking_status', values='Booking_ID', aggfunc='count')/df.shape[0]\\n\\nplt.figure(figsize=(8, 8))\\nsns.heatmap(q6, annot=True, fmt=\\\".0%\\\", cmap=\\\"Spectral\\\")\\nplt.xlabel('Booking Status')\\nplt.ylabel('No of special requests')\\nplt.title('Heatmap representing the %% of bookings for\\\\nguests with special requests by their booking status', fontsize=16)\\nplt.show();\";\n",
              "                var nbb_formatted_code = \"q6 = (\\n    pd.pivot_table(\\n        data=df,\\n        index=\\\"no_of_special_requests\\\",\\n        columns=\\\"booking_status\\\",\\n        values=\\\"Booking_ID\\\",\\n        aggfunc=\\\"count\\\",\\n    )\\n    / df.shape[0]\\n)\\n\\nplt.figure(figsize=(8, 8))\\nsns.heatmap(q6, annot=True, fmt=\\\".0%\\\", cmap=\\\"Spectral\\\")\\nplt.xlabel(\\\"Booking Status\\\")\\nplt.ylabel(\\\"No of special requests\\\")\\nplt.title(\\n    \\\"Heatmap representing the %% of bookings for\\\\nguests with special requests by their booking status\\\",\\n    fontsize=16,\\n)\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stacked_barplot(df, \"no_of_special_requests\", \"booking_status\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "mFL-RH-PHTaD",
        "outputId": "e87c2477-9a39-4e00-dcdb-3a9695303e88"
      },
      "id": "mFL-RH-PHTaD",
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "booking_status          Canceled  Not_Canceled    All\n",
            "no_of_special_requests                               \n",
            "All                        11885         24390  36275\n",
            "0                           8545         11232  19777\n",
            "1                           2703          8670  11373\n",
            "2                            637          3727   4364\n",
            "3                              0           675    675\n",
            "4                              0            78     78\n",
            "5                              0             8      8\n",
            "------------------------------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 792x360 with 1 Axes>"
            ],
            "image/png": 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gEAI+AAAAYBACPgAAAGAQAj4AAABgEAI+AAAAYBACPgAAAGAQAj4AAABgEAI+AAAAYBACPgAAAGAQAj4AAABgEAI+AAAAYBACPgAAAGAQAj4AAABgEAI+AAAAYBACPgAAAGAQAj4AAABgEAI+AAAAYBACPgAAAGAQAj4AAABgEAI+AAAAYBACPgAAAGAQAj4AAABgEAI+AAAAYBACPgAAAGAQAj4AAABgEAI+AAAAYBACPgAAAGAQAj4AAABgEAI+AAAAYBACPgAAAGAQAj4AAABgEAI+AAAAYBACPgAAAGAQAj4AAABgEAI+AAAAYBACPgAAAGAQAj4AAABgEAI+AAAAYBACPgAAAGAQAj4AAABgEAI+AAAAYBACPgAAAGCQegX8jIwMxcXFKTY2VikpKTWO2bBhg/r376/4+Hg98cQTjVokAAAAgPqxuhvgcDiUlJSkJUuWyGazadiwYYqJidEVV1zhGpOdna2UlBS9+eabCgkJ0fHjx5u0aAAAAAA1c3sHPzMzUxEREQoPD5e/v7/i4+OVnp5eZczq1at17733KiQkRJJ0wQUXNE21AAAAAOrk9g6+3W5X+/btXa9tNpsyMzOrjMnOzpYkjRw5UpWVlZo0aZJ69+5d53l9fS1q0ybwDEo+v3HNvBN98U70xXvRG+9EX7wTfUFDuQ349eFwOHTgwAGtWLFCeXl5uu+++7Ru3Tq1bt26jmOcOnWquDGmr1NYWHCTz9GcmuOaNQf64p3oi/eiN96Jvngn+tJwpl2z853bJTo2m015eXmu13a7XTabrdqYmJgY+fn5KTw8XJdeeqnrrj4AAACA5uM24EdGRio7O1s5OTkqLy9XWlqaYmJiqozp27evdu/eLUk6ceKEsrOzFR4e3jQVAwAAAKiV2yU6VqtV06dP1/jx4+VwOJSQkKDOnTtrwYIF6tatm/r06aNbbrlF27dvV//+/eXr66spU6aobdu2zVE/AAAAgF+o1xr86OhoRUdHV9k2efJk188Wi0VPPfWUnnrqqcatDgAAAECD8E22AAAAgEEI+AAAAIBBCPgAAACAQQj4AAAAgEEI+AAAAIBBCPgAAACAQQj4AAAAgEEI+AAAAIBBCPgAAACAQQj4AAAAgEEI+AAAAIBBCPgAAACAQQj4AAAAgEEI+AAAAIBBCPgAAACAQQj4AAAAgEEI+AAAAIBBCPgAAACAQQj4AAAAgEEI+AAAAIBBCPgAAACAQQj4AAAAgEEI+AAAAIBBCPgAAACAQQj4AAAAgEEI+AAAAIBBCPgAAACAQQj4AAAAgEEI+AAAAIBBCPgAAACAQQj4AAAAgEEI+AAAAIBBCPgAAACAQQj4AAAAgEEI+AAAAIBBCPgAAACAQQj4AAAAgEEI+AAAAIBBrJ4uoKk5T5fKkpjv6TIahfN0qadLAAAAgJczPuBb/Frq0qlpni6jUWTPipd02tNlAAAAwIuxRAcAAAAwCAEfAAAAMEi9An5GRobi4uIUGxurlJSUWsdt2rRJV111lb766qtGKxAAAABA/bkN+A6HQ0lJSVq8eLHS0tK0fv167d+/v9q4wsJCLV++XNddd12TFAoAAADAPbcBPzMzUxEREQoPD5e/v7/i4+OVnp5ebdyCBQs0YcIEtWjRokkKBQAAAOCe24Bvt9vVvn1712ubzSa73V5lzN69e5WXl6dbb7210QsEAAAAUH9n/ZjMyspKzZo1SzNnzmzQcb6+FrVpE3i20593uGbeib54J/riveiNd6Iv3om+oKHcBnybzaa8vDzXa7vdLpvN5npdVFSk7777TqNHj5YkHTt2TBMnTtTChQsVGRlZ63kdDqdOnSo+m9rrJSwsuMnnaE7Ncc2aA33xTvTFe9Eb70RfvBN9aTjTrtn5zm3Aj4yMVHZ2tnJycmSz2ZSWlqZ58+a59gcHB2vXrl2u16NGjdKUKVPqDPcAAAAAmobbgG+1WjV9+nSNHz9eDodDCQkJ6ty5sxYsWKBu3bqpT58+zVEnAAAAgHqo1xr86OhoRUdHV9k2efLkGseuWLHi7KsCAAAAcEb4JlsAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIFZPF4Dzk/N0qSyJ+Z4uo1E4T5d6ugQAAAAXAj48wuLXUpdOTfN0GY0ie1a8pNOeLgMAAEASS3QAAAAAoxDwAQAAAIMQ8AEAAACDEPABAAAAg9Qr4GdkZCguLk6xsbFKSUmptn/JkiXq37+/Bg4cqDFjxujw4cONXigAAAAA99wGfIfDoaSkJC1evFhpaWlav3699u/fX2VMly5dtHbtWq1bt05xcXGaO3dukxUMAAAAoHZuA35mZqYiIiIUHh4uf39/xcfHKz09vcqYXr16KSAgQJLUvXt35eXlNU21AAAAAOrkNuDb7Xa1b9/e9dpms8lut9c6fs2aNerdu3fjVAcAAACgQRr1i67ee+89ff3111q5cqXbsb6+FrVpE9iY058XuGbeib54J/riveiNd6Iv3om+oKHcBnybzVZlyY3dbpfNZqs2bseOHVq0aJFWrlwpf39/txM7HE6dOlXcwHIbLiwsuMnnaE7Ncc2aA33xTvTFe9Eb70RfvBN9aTjTrtn5zu0SncjISGVnZysnJ0fl5eVKS0tTTExMlTHffPONpk+froULF+qCCy5osmIBAAAA1M3tHXyr1arp06dr/PjxcjgcSkhIUOfOnbVgwQJ169ZNffr00Zw5c1RcXKzJkydLkjp06KBFixY1efEAAAAAqqrXGvzo6GhFR0dX2fZzmJekpUuXNmpRAAAAAM4M32QLAAAAGISADwAAABiEgA8AAAAYhIAPAAAAGISADwAAABiEgA8AAAAYhIAPAAAAGISADwAAABiEgA8AAAAYpF7fZAvg/OA8XSpLYr6ny2gUztOlni4BAACPIOADcLH4tdSlU9M8XUajyJ4VL+m0p8sAAKDZsUQHAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADBIvQJ+RkaG4uLiFBsbq5SUlGr7y8vL9eijjyo2NlbDhw/XoUOHGr1QAAAAAO65DfgOh0NJSUlavHix0tLStH79eu3fv7/KmHfeeUetW7fW5s2bNXbsWL344otNVjAAAACA2rkN+JmZmYqIiFB4eLj8/f0VHx+v9PT0KmM+/PBDDRkyRJIUFxennTt3yul0Nk3FAAAAAGrlNuDb7Xa1b9/e9dpms8lut1cb06FDB0mS1WpVcHCwTp482cilAgAAAHDH6qmJ/fx8FRYW3CxzZc+Kb5Z5mkNzXbPmQF+8E33xYon5nq6g0RjVG/rinegLzmNu7+DbbDbl5eW5Xtvtdtlstmpjjhw5IkmqqKhQQUGB2rZt28ilAgAAAHDHbcCPjIxUdna2cnJyVF5errS0NMXExFQZExMTo7///e+SpE2bNqlXr16yWCxNUzEAAACAWlmc9fg07NatW5WcnCyHw6GEhARNnDhRCxYsULdu3dSnTx+VlZXpySef1L59+xQSEqI///nPCg8Pb476AQAAAPxCvQI+AAAAgHMD32QLAAAAGISADwAAABiEgA8AAAAYxGPPwT+XZWVlKT09XUePHpUktWvXTn369NHll1/u4coA75SVlaWjR4/q2muvVVBQkGt7RkaGevfu7cHKzm+ZmZmSpGuvvVb79+/Xxx9/rE6dOik6OtrDleGXpkyZojlz5ni6DPzKp59+qq+++kqdO3dWVFSUp8sBquBDtg2UkpKitLQ0xcfHu74PwG63u7Y9+OCDHq4QNVm7dq0SEhI8XcZ5afny5Vq1apUuv/xyffvtt5o2bZr69u0rSRoyZIjrEbtoXi+//LIyMjJUUVGhm2++WV9++aVuuukm7dixQ1FRUZo4caKnSzwv/f73v6+2bdeuXbrpppskSYsWLWrukvBfw4YN05o1ayRJq1ev1qpVqxQbG6tt27YpJiaGf//hVbiD30Br167V+vXr5efnV2X72LFjNWDAAP4D91IvvfQSAd9D3nnnHb377rsKCgrSoUOH9Mgjj+jw4cMaM2aMuL/gOZs2bVJqaqrKy8t18803KyMjQ61atdK4ceM0fPhwAr6H2O12XX755Ro+fLgsFoucTqe+/vprPfDAA54u7bxXUVHh+vntt9/WkiVLFBoaqgceeEAjRozg3394FQJ+A1ksFh09elQXX3xxle3Hjh3jy708bODAgbXu++GHH5qxEvxSZWWla1nOJZdcohUrVuiRRx5Rbm4uAd+DfH195evrq4CAAHXs2FGtWrWSJLVs2VI+Pnw8y1PWrl2r5cuXa9GiRZoyZYq6dOmiFi1aqGfPnp4u7bxXWVmp/Px8VVZWyul0KjQ0VJIUGBgoX19fD1cHVEXAb6Bp06Zp7NixioiIUIcOHSRJubm5OnjwoJ599lkPV3d+O378uF5//XW1bt26ynan06mRI0d6qCpccMEF2rdvn7p06SJJCgoK0quvvqpp06bpu+++83B15y8/Pz+VlJQoICBA7777rmt7QUEBAd+DfHx8NHbsWN1xxx1KTk7WhRdeKIfD4emyIKmwsFBDhw6V0+l03exr166dioqKuFkBr8Ma/DNQWVmpzMxM2e12SZLNZlNkZCR/wXvYtGnTNHToUPXo0aPavieeeELz5s3zQFXIy8uTr6+vwsLCqu377LPPdMMNN3igKpSXl8vf37/a9hMnTujYsWO66qqrPFAVfu2jjz7S559/rscff9zTpaAWJSUl+uGHHxQeHu7pUgAXAj4AAABgEN6HBQAAAAxCwAcAAAAMQsAHAAAADELAB3BOysrK0qBBgzR48GAdPHiw2eefMGGCfvzxxzrHxMTE6MSJE81U0Znjy5MAwCwEfADnpPT0dMXFxSk1NVUdO3Zs9vlfe+21ao9kPVNOp1OVlZWNcq4z8eqrr3psbgBA4+M5+ACazKFDhzRhwgTdcMMN2rNnj2w2m1555RV9//33eu6551RSUqKOHTsqOTlZISEhNZ5j37591cZ+8cUXWrZsmXx8fLRz506tWLGi2nHFxcV69NFHlZeXp8rKSj300EPq37+/YmJidMcdd+jjjz9WixYtNG/ePEVEROjEiRN67rnnlJubK+mnx67ecMMNKioq0owZM/T1119LkiZNmqS4uDjFxMRozZo1Cg0N1UMPPaS8vDyVlZVp9OjRGjFiRL2uzbhx43Tddddp7969SklJ0caNG7Vx40aVl5crNjZWjzzyiCRp4cKFSk1NVWhoqDp06KCuXbtq3LhxGjVqlKZMmaLIyEidOHFCw4YN04cffiiHw6EXX3xRu3fvVnl5ue69916NHDlSR48e1WOPPabCwkI5HA4lJibqo48+UmlpqQYNGqQrrrhCL7zwQo3XDQBw7iDgA2hSBw4c0Pz58zVjxgxNnjxZmzZt0uLFi/Xss8+qZ8+eWrBggV5++WU9/fTTNR4/ZcqUGseOHDlSgYGBGjduXI3Hffzxx2rXrp1SUlIk/fQFTj8LDg7WunXrlJqaquTkZL366qv605/+pDFjxqhHjx7Kzc3VuHHjtHHjRr3yyitq1aqV1q1bJ0nKz8+vNldycrLatGmj0tJSDRs2TLfffrvatm1br2sze/Zsde/eXdu2bdOBAwe0Zs0aOZ1OTZw4UZ988okCAgK0YcMGpaamyuFwaMiQIeratWud512zZo2Cg4O1du1alZeXa+TIkbr55pu1efNmRUVFaeLEiXI4HCopKVGPHj20atUqvffee5KkTZs21XrdAADnBgI+gCZ1ySWXuL7FtmvXrsrJyVFBQYF69uwpSRoyZIgmT55c47EFBQX1HvtrV155pWbPnq25c+fqtttuq/IFaAMGDJAkxcfHa+bMmZKkHTt2aP/+/a4xhYWFKioq0s6dOzV//nzX9preaVixYoU2b94sSTpy5IgOHDhQr4B/0UUXqXv37pKk7du3a/v27Ro8eLCkn96ByM7OVlFRkfr27auAgABJP63rd2f79u3697//rU2bNkn66ToeOHBAkZGRmjZtmioqKtS3b19XX36prusGADg3EPABNKlffluqr6+v2w+mNpbLLrtM7777rrZu3aq//OUv6tWrlyZNmlTr+MrKSq1evVotWrRo0Dy7du3Sjh079PbbbysgIECjRo1SWVlZvY4NDAx0/ex0OvXggw9q5MiRVcYsXbq01uN9fX3183cVlpeXVznXM888o1tuuaXaMStXrtTWrVs1depU3X///a4/KH7W0OsGAPA+fMgWQLMKDg5W69at9emnn0qS3nvvPd14441nPfbX7Ha7AgICNGjQII0bN07ffPONa9/GjRslSRs2bND1118vSYqKiqqyln/fvn2SpP/5n//RqlWrXNt/vUSnoKBAISEhCggIUFZWlr744ot61fdrUVFRWrt2rYqKilz1Hz9+XDfeeKM++OADlZaWqrCwUFu2bHEdc/HFF7s+G/D+++9XOdebb76p06dPS5K+//57FRcX6/Dhw7rwwgt11113afjw4dq7d68kyWq1usbWdd0AAOcG7uADaHazZ892fXA2PDzctUzmbMf+0nfffac5c+bIx8dHVqtViYmJrn35+fkaOHCg/P39Xctvnn76aSUlJWngwIFyOBzq0aOHkpKSNHHiRCUlJWnAgAHy8fHRpEmTdPvtt7vO1bt3b7311lvq16+fLrvsMteSm4aKiopSVlaW6w5+YGCg5s6dq65du6p///4aNGiQQkNDFRkZ6TrmgQce0KOPPqrVq1crOjratX348OE6fPiwhg4dKqfTqbZt2+qVV17R7t279frrr8tqtSowMFCzZ8+WJN1111268847dc0112jw4MG1XjcAwLnB4vz5/V0AOA/88uk356KXXnqpzg8XAwDAEh0AAADAINzBB+AVnn/+eX3++edVto0ePVoJCQl1Hnfy5EmNHTu22valS5fW60k2Tc3b6wMAmIeADwAAABiEJToAAACAQQj4AAAAgEEI+AAAAIBBCPgAAACAQQj4AAAAgEH+HxNFJIjOrjS4AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 63;\n",
              "                var nbb_unformatted_code = \"stacked_barplot(df, \\\"no_of_special_requests\\\", \\\"booking_status\\\")\";\n",
              "                var nbb_formatted_code = \"stacked_barplot(df, \\\"no_of_special_requests\\\", \\\"booking_status\\\")\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stacked_barplot(df, \"booking_status\", \"no_of_special_requests\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "ZQK9FAa-IAn3",
        "outputId": "ab89fdc5-e1fe-48d5-b8f5-3b412cdbf287"
      },
      "id": "ZQK9FAa-IAn3",
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "no_of_special_requests      0      1     2    3   4  5    All\n",
            "booking_status                                               \n",
            "Not_Canceled            11232   8670  3727  675  78  8  24390\n",
            "All                     19777  11373  4364  675  78  8  36275\n",
            "Canceled                 8545   2703   637    0   0  0  11885\n",
            "------------------------------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 64;\n",
              "                var nbb_unformatted_code = \"stacked_barplot(df, \\\"booking_status\\\", \\\"no_of_special_requests\\\")\";\n",
              "                var nbb_formatted_code = \"stacked_barplot(df, \\\"booking_status\\\", \\\"no_of_special_requests\\\")\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ">* There is no information about cancellations for the booking with more than two special requests in the dataset\n",
        "* 43% of cancellations happened when a guest had no special requests during their booking process\n",
        "* Most of the not canceled bookings had no special requests\n",
        "* Biggest group (31%) of those with no special request who did not cancel the booking\n",
        "* Among those who canceled the booking, most did not have any special requests, some had one request and few had two requests\n",
        "* Bookings with 1-2 special requests tend to be not canceled in comparison with those with no special requests"
      ],
      "metadata": {
        "id": "r4spUDXsECeP"
      },
      "id": "r4spUDXsECeP"
    },
    {
      "cell_type": "markdown",
      "id": "alleged-spirituality",
      "metadata": {
        "id": "alleged-spirituality"
      },
      "source": [
        "# Data Preprocessing\n",
        "\n",
        "- Missing value treatment (if needed)\n",
        "- Feature engineering (if needed)\n",
        "- Outlier detection and treatment (if needed)\n",
        "- Preparing data for modeling \n",
        "- Any other preprocessing steps (if needed)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Missing value treatment (if needed)"
      ],
      "metadata": {
        "id": "eqYToZrEGyyg"
      },
      "id": "eqYToZrEGyyg"
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Total number of missing values is {df.isnull().sum().sum()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "5V5eoK8yGy68",
        "outputId": "63ad62ef-99eb-495a-eef9-c61151b39eb6"
      },
      "id": "5V5eoK8yGy68",
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of missing values is 0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 65;\n",
              "                var nbb_unformatted_code = \"print(f'Total number of missing values is {df.isnull().sum().sum()}')\";\n",
              "                var nbb_formatted_code = \"print(f\\\"Total number of missing values is {df.isnull().sum().sum()}\\\")\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* There is no missing values (NaN, None, etc.) in the dataset\n",
        "* Statistical summary of the dataset shows that there is no unexpected zeroes in the dataset as well"
      ],
      "metadata": {
        "id": "tjX16HupILt-"
      },
      "id": "tjX16HupILt-"
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Feature engineering (if needed)"
      ],
      "metadata": {
        "id": "7RG5jsbsGzDW"
      },
      "id": "7RG5jsbsGzDW"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Dropping the columns with all unique values**"
      ],
      "metadata": {
        "id": "289fCQtKMTti"
      },
      "id": "289fCQtKMTti"
    },
    {
      "cell_type": "code",
      "source": [
        "df['Booking_ID'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "ENs1tzUYMT4K",
        "outputId": "4b0a508d-0aca-43dc-f310-a9d59e34fc3e"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "36275"
            ]
          },
          "metadata": {},
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 66;\n",
              "                var nbb_unformatted_code = \"df['Booking_ID'].nunique()\";\n",
              "                var nbb_formatted_code = \"df[\\\"Booking_ID\\\"].nunique()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "ENs1tzUYMT4K"
    },
    {
      "cell_type": "markdown",
      "source": [
        "The `Booking_ID` column contains only unique values, so we can drop it"
      ],
      "metadata": {
        "id": "KJB0Xe-lMdqo"
      },
      "id": "KJB0Xe-lMdqo"
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop('Booking_ID', axis=1)"
      ],
      "metadata": {
        "id": "Vy57s5KTMhVy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "9de6a63c-0e70-4ace-adb5-730b38a72bcb"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 67;\n",
              "                var nbb_unformatted_code = \"df = df.drop('Booking_ID', axis=1)\";\n",
              "                var nbb_formatted_code = \"df = df.drop(\\\"Booking_ID\\\", axis=1)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "Vy57s5KTMhVy"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Adding column with day of week of booking (canceled)**"
      ],
      "metadata": {
        "id": "U8AVID-u_9b3"
      },
      "id": "U8AVID-u_9b3"
    },
    {
      "cell_type": "code",
      "source": [
        "# Adding column with day of week of booking\n",
        "\n",
        "#dowt = {0: 'Mon', 1: 'Tue', 2: 'Wed', 3: 'Thu', 4: 'Fri', 5: 'Sat', 6: 'Sun'}\n",
        "\n",
        "# Converting arrival columns to date format at first\n",
        "#df['arrival'] = df.apply(lambda x: f\"{x['arrival_month']}/{x['arrival_date']}/{x['arrival_year']}\", axis=1)\n",
        "\n",
        "# There are some false values `2/29/2018` in the data which requires pre-processing. \n",
        "# It is important to thoroughly clean and preprocess the data before modeling to ensure that the dataset is accurate and reliable.\n",
        "\n",
        "# Removing the false values\n",
        "#df = df[df['arrival'] != '2/29/2018']\n",
        "\n",
        "# Updating the false values\n",
        "#df.loc[df['arrival'] == '2/29/2018', 'arrival'] = '2/28/2018'\n",
        "\n",
        "# Adding DOW to the dataset as text\n",
        "#df['arrival_dow'] = pd.to_datetime(df['arrival']).dt.weekday.replace(dowt)\n",
        "\n",
        "#df.value_counts('arrival_dow')\n",
        "\n",
        "# Dropping temporary column to avoid multicollinearity and 'arrival_date'\n",
        "#df = df.drop(['arrival', 'arrival_date'], axis=1)\n"
      ],
      "metadata": {
        "id": "4Xz3aRvw2a-W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "38be6d53-d7e2-4533-e79b-818e96fa147f"
      },
      "id": "4Xz3aRvw2a-W",
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 68;\n",
              "                var nbb_unformatted_code = \"# Adding column with day of week of booking\\n\\n#dowt = {0: 'Mon', 1: 'Tue', 2: 'Wed', 3: 'Thu', 4: 'Fri', 5: 'Sat', 6: 'Sun'}\\n\\n# Converting arrival columns to date format at first\\n#df['arrival'] = df.apply(lambda x: f\\\"{x['arrival_month']}/{x['arrival_date']}/{x['arrival_year']}\\\", axis=1)\\n\\n# There are some false values `2/29/2018` in the data which requires pre-processing. \\n# It is important to thoroughly clean and preprocess the data before modeling to ensure that the dataset is accurate and reliable.\\n\\n# Removing the false values\\n#df = df[df['arrival'] != '2/29/2018']\\n\\n# Updating the false values\\n#df.loc[df['arrival'] == '2/29/2018', 'arrival'] = '2/28/2018'\\n\\n# Adding DOW to the dataset as text\\n#df['arrival_dow'] = pd.to_datetime(df['arrival']).dt.weekday.replace(dowt)\\n\\n#df.value_counts('arrival_dow')\\n\\n# Dropping temporary column to avoid multicollinearity and 'arrival_date'\\n#df = df.drop(['arrival', 'arrival_date'], axis=1)\";\n",
              "                var nbb_formatted_code = \"# Adding column with day of week of booking\\n\\n# dowt = {0: 'Mon', 1: 'Tue', 2: 'Wed', 3: 'Thu', 4: 'Fri', 5: 'Sat', 6: 'Sun'}\\n\\n# Converting arrival columns to date format at first\\n# df['arrival'] = df.apply(lambda x: f\\\"{x['arrival_month']}/{x['arrival_date']}/{x['arrival_year']}\\\", axis=1)\\n\\n# There are some false values `2/29/2018` in the data which requires pre-processing.\\n# It is important to thoroughly clean and preprocess the data before modeling to ensure that the dataset is accurate and reliable.\\n\\n# Removing the false values\\n# df = df[df['arrival'] != '2/29/2018']\\n\\n# Updating the false values\\n# df.loc[df['arrival'] == '2/29/2018', 'arrival'] = '2/28/2018'\\n\\n# Adding DOW to the dataset as text\\n# df['arrival_dow'] = pd.to_datetime(df['arrival']).dt.weekday.replace(dowt)\\n\\n# df.value_counts('arrival_dow')\\n\\n# Dropping temporary column to avoid multicollinearity and 'arrival_date'\\n# df = df.drop(['arrival', 'arrival_date'], axis=1)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Converting some vars to binary\n",
        "df['repeated_guest'] = df['repeated_guest'].astype('uint8')\n",
        "df['required_car_parking_space'] = df['required_car_parking_space'].astype('uint8')\n",
        "df['previous_cancellations'] = (df['no_of_previous_cancellations'] != 0).astype('uint8')\n",
        "df['children'] = (df['no_of_children'] > 0).astype('uint8')\n",
        "\n",
        "# Converting type_of_meal_plan to discrete numeric var as it represents number of meal times\n",
        "mp_repl = {'Not Selected': 0, 'Meal Plan 1': 1, 'Meal Plan 2': 2, 'Meal Plan 3': 3}\n",
        "df['type_of_meal_plan'] = df['type_of_meal_plan'].replace(mp_repl).astype('int')\n",
        "\n",
        "# Dropping insignificant vars\n",
        "df.drop(['no_of_previous_cancellations', \n",
        "         'no_of_previous_bookings_not_canceled', \n",
        "         'no_of_adults', 'no_of_children',\n",
        "         'arrival_date',\n",
        "         ], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "hZ6_6gSJ-rIE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "f95298de-9c8c-4f58-f349-0404ef9909db"
      },
      "id": "hZ6_6gSJ-rIE",
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 69;\n",
              "                var nbb_unformatted_code = \"# Converting some vars to binary\\ndf['repeated_guest'] = df['repeated_guest'].astype('uint8')\\ndf['required_car_parking_space'] = df['required_car_parking_space'].astype('uint8')\\ndf['previous_cancellations'] = (df['no_of_previous_cancellations'] != 0).astype('uint8')\\ndf['children'] = (df['no_of_children'] > 0).astype('uint8')\\n\\n# Converting type_of_meal_plan to discrete numeric var as it represents number of meal times\\nmp_repl = {'Not Selected': 0, 'Meal Plan 1': 1, 'Meal Plan 2': 2, 'Meal Plan 3': 3}\\ndf['type_of_meal_plan'] = df['type_of_meal_plan'].replace(mp_repl).astype('int')\\n\\n# Dropping insignificant vars\\ndf.drop(['no_of_previous_cancellations', \\n         'no_of_previous_bookings_not_canceled', \\n         'no_of_adults', 'no_of_children',\\n         'arrival_date',\\n         ], axis=1, inplace=True)\";\n",
              "                var nbb_formatted_code = \"# Converting some vars to binary\\ndf[\\\"repeated_guest\\\"] = df[\\\"repeated_guest\\\"].astype(\\\"uint8\\\")\\ndf[\\\"required_car_parking_space\\\"] = df[\\\"required_car_parking_space\\\"].astype(\\\"uint8\\\")\\ndf[\\\"previous_cancellations\\\"] = (df[\\\"no_of_previous_cancellations\\\"] != 0).astype(\\\"uint8\\\")\\ndf[\\\"children\\\"] = (df[\\\"no_of_children\\\"] > 0).astype(\\\"uint8\\\")\\n\\n# Converting type_of_meal_plan to discrete numeric var as it represents number of meal times\\nmp_repl = {\\\"Not Selected\\\": 0, \\\"Meal Plan 1\\\": 1, \\\"Meal Plan 2\\\": 2, \\\"Meal Plan 3\\\": 3}\\ndf[\\\"type_of_meal_plan\\\"] = df[\\\"type_of_meal_plan\\\"].replace(mp_repl).astype(\\\"int\\\")\\n\\n# Dropping insignificant vars\\ndf.drop(\\n    [\\n        \\\"no_of_previous_cancellations\\\",\\n        \\\"no_of_previous_bookings_not_canceled\\\",\\n        \\\"no_of_adults\\\",\\n        \\\"no_of_children\\\",\\n        \\\"arrival_date\\\",\\n    ],\\n    axis=1,\\n    inplace=True,\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Outlier detection and treatment (if needed)"
      ],
      "metadata": {
        "id": "t9tzGTPsGzT3"
      },
      "id": "t9tzGTPsGzT3"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Let's check for outliers in the data.**"
      ],
      "metadata": {
        "id": "LM4T3wtBM9xc"
      },
      "id": "LM4T3wtBM9xc"
    },
    {
      "cell_type": "code",
      "source": [
        "# outlier detection using boxplot\n",
        "numeric_columns = df.select_dtypes(include=[int, float]).columns.tolist()\n",
        "\n",
        "plt.figure(figsize=(12, 10))\n",
        "\n",
        "for i, variable in enumerate(numeric_columns):\n",
        "    plt.subplot(4, 4, i + 1)\n",
        "    plt.boxplot(df[variable], whis=1.5)\n",
        "    plt.tight_layout()\n",
        "    plt.title(variable)\n",
        "\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "pF5YSO3dGzdg",
        "outputId": "5f94f956-10d6-4bed-aae2-b259b466c275"
      },
      "id": "pF5YSO3dGzdg",
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 70;\n",
              "                var nbb_unformatted_code = \"# outlier detection using boxplot\\nnumeric_columns = df.select_dtypes(include=[int, float]).columns.tolist()\\n\\nplt.figure(figsize=(12, 10))\\n\\nfor i, variable in enumerate(numeric_columns):\\n    plt.subplot(4, 4, i + 1)\\n    plt.boxplot(df[variable], whis=1.5)\\n    plt.tight_layout()\\n    plt.title(variable)\\n\\nplt.show();\";\n",
              "                var nbb_formatted_code = \"# outlier detection using boxplot\\nnumeric_columns = df.select_dtypes(include=[int, float]).columns.tolist()\\n\\nplt.figure(figsize=(12, 10))\\n\\nfor i, variable in enumerate(numeric_columns):\\n    plt.subplot(4, 4, i + 1)\\n    plt.boxplot(df[variable], whis=1.5)\\n    plt.tight_layout()\\n    plt.title(variable)\\n\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Instead of drop rows with outliers we updade them with new values\n",
        "\n",
        "cols = ['avg_price_per_room', 'lead_time'] # 'no_of_weekend_nights', 'no_of_week_nights'\n",
        "\n",
        "for col in cols:\n",
        "  q1, q3 = df[col].quantile([0.25,0.75])\n",
        "  iqr = q3 - q1\n",
        "  df.loc[df[col] > q3 + 1.5 * iqr, col] = q3 + 1.5 * iqr"
      ],
      "metadata": {
        "id": "KzMn10-ZKJ_y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "3d8c2615-1dbb-49c6-cc24-4e686e7e6938"
      },
      "id": "KzMn10-ZKJ_y",
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 71;\n",
              "                var nbb_unformatted_code = \"# Instead of drop rows with outliers we updade them with new values\\n\\ncols = ['avg_price_per_room', 'lead_time'] # 'no_of_weekend_nights', 'no_of_week_nights'\\n\\nfor col in cols:\\n  q1, q3 = df[col].quantile([0.25,0.75])\\n  iqr = q3 - q1\\n  df.loc[df[col] > q3 + 1.5 * iqr, col] = q3 + 1.5 * iqr\";\n",
              "                var nbb_formatted_code = \"# Instead of drop rows with outliers we updade them with new values\\n\\ncols = [\\n    \\\"avg_price_per_room\\\",\\n    \\\"lead_time\\\",\\n]  # 'no_of_weekend_nights', 'no_of_week_nights'\\n\\nfor col in cols:\\n    q1, q3 = df[col].quantile([0.25, 0.75])\\n    iqr = q3 - q1\\n    df.loc[df[col] > q3 + 1.5 * iqr, col] = q3 + 1.5 * iqr\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat_features = ['arrival_year', 'arrival_month', 'room_type_reserved', 'market_segment_type'] \n",
        "\n",
        "for col in cat_features:\n",
        "  df[col] = df[col].astype('category')"
      ],
      "metadata": {
        "id": "V4N6YhPctV_x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "9236fa24-f978-43e2-98b0-f2458116f6d8"
      },
      "id": "V4N6YhPctV_x",
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 72;\n",
              "                var nbb_unformatted_code = \"cat_features = ['arrival_year', 'arrival_month', 'room_type_reserved', 'market_segment_type'] \\n\\nfor col in cat_features:\\n  df[col] = df[col].astype('category')\";\n",
              "                var nbb_formatted_code = \"cat_features = [\\n    \\\"arrival_year\\\",\\n    \\\"arrival_month\\\",\\n    \\\"room_type_reserved\\\",\\n    \\\"market_segment_type\\\",\\n]\\n\\nfor col in cat_features:\\n    df[col] = df[col].astype(\\\"category\\\")\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**\n",
        "\n",
        "* There are quite a few outliers in the data.\n",
        "* They are proper values.\n",
        "* However, we replaced upper outliers' in `avg_price_per_room` and `lead_time` columns with the Q3 + 1.5 * IQR values, as it is important to interpret regression coefficients"
      ],
      "metadata": {
        "id": "gqhqY7sVNMgJ"
      },
      "id": "gqhqY7sVNMgJ"
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Preparing data for modeling"
      ],
      "metadata": {
        "id": "_25n5Vq2Gzle"
      },
      "id": "_25n5Vq2Gzle"
    },
    {
      "cell_type": "code",
      "source": [
        "X = sm.add_constant(pd.get_dummies(df.drop(['booking_status'], axis=1), drop_first=True))\n",
        "y = (df[['booking_status']] == 'Canceled').astype(int).rename(columns = {'booking_status': 'cancelled'})\n",
        "\n",
        "# Splitting data in train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=1)"
      ],
      "metadata": {
        "id": "bvOLUACbGztf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "dec9ddb6-dac7-498e-cf1a-7d16ec93fec0"
      },
      "id": "bvOLUACbGztf",
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 73;\n",
              "                var nbb_unformatted_code = \"X = sm.add_constant(pd.get_dummies(df.drop(['booking_status'], axis=1), drop_first=True))\\ny = (df[['booking_status']] == 'Canceled').astype(int).rename(columns = {'booking_status': 'cancelled'})\\n\\n# Splitting data in train and test sets\\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=1)\";\n",
              "                var nbb_formatted_code = \"X = sm.add_constant(\\n    pd.get_dummies(df.drop([\\\"booking_status\\\"], axis=1), drop_first=True)\\n)\\ny = (\\n    (df[[\\\"booking_status\\\"]] == \\\"Canceled\\\")\\n    .astype(int)\\n    .rename(columns={\\\"booking_status\\\": \\\"cancelled\\\"})\\n)\\n\\n# Splitting data in train and test sets\\nX_train, X_test, y_train, y_test = train_test_split(\\n    X, y, test_size=0.30, random_state=1\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Shape of Training set : \", X_train.shape)\n",
        "print(\"Shape of Testing set : \", X_test.shape)\n",
        "print(\"Percentage of classes in training set:\")\n",
        "print(y_train.value_counts(normalize=True))\n",
        "print(\"Percentage of classes in test set:\")\n",
        "print(y_test.value_counts(normalize=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "Vhcls8MtP2pi",
        "outputId": "a84a88f3-7ede-4983-aba8-aa1318380719"
      },
      "id": "Vhcls8MtP2pi",
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of Training set :  (25392, 33)\n",
            "Shape of Testing set :  (10883, 33)\n",
            "Percentage of classes in training set:\n",
            "cancelled\n",
            "0           0.67064\n",
            "1           0.32936\n",
            "dtype: float64\n",
            "Percentage of classes in test set:\n",
            "cancelled\n",
            "0           0.67638\n",
            "1           0.32362\n",
            "dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 74;\n",
              "                var nbb_unformatted_code = \"print(\\\"Shape of Training set : \\\", X_train.shape)\\nprint(\\\"Shape of Testing set : \\\", X_test.shape)\\nprint(\\\"Percentage of classes in training set:\\\")\\nprint(y_train.value_counts(normalize=True))\\nprint(\\\"Percentage of classes in test set:\\\")\\nprint(y_test.value_counts(normalize=True))\";\n",
              "                var nbb_formatted_code = \"print(\\\"Shape of Training set : \\\", X_train.shape)\\nprint(\\\"Shape of Testing set : \\\", X_test.shape)\\nprint(\\\"Percentage of classes in training set:\\\")\\nprint(y_train.value_counts(normalize=True))\\nprint(\\\"Percentage of classes in test set:\\\")\\nprint(y_test.value_counts(normalize=True))\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Any other preprocessing steps (if needed)"
      ],
      "metadata": {
        "id": "AW3IjP0_Gz0l"
      },
      "id": "AW3IjP0_Gz0l"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* No any other preprocessing steps are needed"
      ],
      "metadata": {
        "id": "JdhHu1nQPhFw"
      },
      "id": "JdhHu1nQPhFw"
    },
    {
      "cell_type": "markdown",
      "id": "difficult-union",
      "metadata": {
        "id": "difficult-union"
      },
      "source": [
        "## EDA\n",
        "\n",
        "- It is a good idea to explore the data once again after manipulating it."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Univariate analysis**"
      ],
      "metadata": {
        "id": "1hGpKt8CtAtF"
      },
      "id": "1hGpKt8CtAtF"
    },
    {
      "cell_type": "code",
      "source": [
        "# Numerical variables\n",
        "cols = X.select_dtypes(include=[int, float]).columns.tolist()[1:]\n",
        "plt.figure(figsize=(12, 12))\n",
        "\n",
        "for i, var in enumerate(cols):\n",
        "  plt.subplot(4, 3, i + 1)\n",
        "  sns.boxplot(data=X, x=var)\n",
        "  plt.tight_layout(pad=2)\n",
        "\n",
        "plt.tight_layout(pad=2);\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "id": "wLcI6y0hmejp",
        "outputId": "59958b3b-bfa9-43e8-f193-071bf46ba8c8"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 75;\n",
              "                var nbb_unformatted_code = \"# Numerical variables\\ncols = X.select_dtypes(include=[int, float]).columns.tolist()[1:]\\nplt.figure(figsize=(12, 12))\\n\\nfor i, var in enumerate(cols):\\n  plt.subplot(4, 3, i + 1)\\n  sns.boxplot(data=X, x=var)\\n  plt.tight_layout(pad=2)\\n\\nplt.tight_layout(pad=2);\\nplt.show();\";\n",
              "                var nbb_formatted_code = \"# Numerical variables\\ncols = X.select_dtypes(include=[int, float]).columns.tolist()[1:]\\nplt.figure(figsize=(12, 12))\\n\\nfor i, var in enumerate(cols):\\n    plt.subplot(4, 3, i + 1)\\n    sns.boxplot(data=X, x=var)\\n    plt.tight_layout(pad=2)\\n\\nplt.tight_layout(pad=2)\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "wLcI6y0hmejp"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Bivariate analysis**"
      ],
      "metadata": {
        "id": "1tWlGBOSs4wp"
      },
      "id": "1tWlGBOSs4wp"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Correlation heatmap**"
      ],
      "metadata": {
        "id": "iOrpi_4-I9VI"
      },
      "id": "iOrpi_4-I9VI"
    },
    {
      "cell_type": "code",
      "source": [
        "numeric = list(X.select_dtypes(include=[int, float]).columns[1:])\n",
        "\n",
        "plt.figure(figsize=(12, 7))\n",
        "sns.heatmap(pd.concat([X[numeric], y], axis=1).corr(), annot=True, vmin=-1, vmax=1, fmt=\".2f\", cmap=\"Spectral\")\n",
        "plt.xlabel('')\n",
        "plt.ylabel('')\n",
        "plt.title('Correlation heatmap representing the correlation\\nbetween different variables of dataset', fontsize=16)\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "1hjrpIvBtZoJ",
        "outputId": "dafb2b62-0da4-443b-dc18-7d72b024fd10"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 76;\n",
              "                var nbb_unformatted_code = \"numeric = list(X.select_dtypes(include=[int, float]).columns[1:])\\n\\nplt.figure(figsize=(12, 7))\\nsns.heatmap(pd.concat([X[numeric], y], axis=1).corr(), annot=True, vmin=-1, vmax=1, fmt=\\\".2f\\\", cmap=\\\"Spectral\\\")\\nplt.xlabel('')\\nplt.ylabel('')\\nplt.title('Correlation heatmap representing the correlation\\\\nbetween different variables of dataset', fontsize=16)\\nplt.show();\";\n",
              "                var nbb_formatted_code = \"numeric = list(X.select_dtypes(include=[int, float]).columns[1:])\\n\\nplt.figure(figsize=(12, 7))\\nsns.heatmap(\\n    pd.concat([X[numeric], y], axis=1).corr(),\\n    annot=True,\\n    vmin=-1,\\n    vmax=1,\\n    fmt=\\\".2f\\\",\\n    cmap=\\\"Spectral\\\",\\n)\\nplt.xlabel(\\\"\\\")\\nplt.ylabel(\\\"\\\")\\nplt.title(\\n    \\\"Correlation heatmap representing the correlation\\\\nbetween different variables of dataset\\\",\\n    fontsize=16,\\n)\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "1hjrpIvBtZoJ"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Moderate positive correlation between the `lead_time` and the dependant variable\n",
        "* Moderate negative correlation between the `no_of_special_requests` and the dependant variable\n",
        "* Weak correlations between some independent variables - multicollinearity exists"
      ],
      "metadata": {
        "id": "utTY34wR-1iI"
      },
      "id": "utTY34wR-1iI"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Pairplot**"
      ],
      "metadata": {
        "id": "hia1ckKGtZ8R"
      },
      "id": "hia1ckKGtZ8R"
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(pd.concat([X[['no_of_week_nights', 'lead_time', 'avg_price_per_room']], y], axis=1), hue='cancelled');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "JMW9nGvBsbaj",
        "outputId": "6b95f480-f116-4cfb-f708-cdd1da3d696d"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 595.875x540 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 77;\n",
              "                var nbb_unformatted_code = \"sns.pairplot(pd.concat([X[['no_of_week_nights', 'lead_time', 'avg_price_per_room']], y], axis=1), hue='cancelled');\";\n",
              "                var nbb_formatted_code = \"sns.pairplot(\\n    pd.concat([X[[\\\"no_of_week_nights\\\", \\\"lead_time\\\", \\\"avg_price_per_room\\\"]], y], axis=1),\\n    hue=\\\"cancelled\\\",\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "JMW9nGvBsbaj"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* More expensive bookings and bookings with higher lead time cancel more often\n",
        "* **After manipulating, the data look consistent**"
      ],
      "metadata": {
        "id": "FioOyGrG8vrL"
      },
      "id": "FioOyGrG8vrL"
    },
    {
      "cell_type": "markdown",
      "id": "third-projection",
      "metadata": {
        "id": "third-projection"
      },
      "source": [
        "## Checking Multicollinearity\n",
        "\n",
        "- In order to make statistical inferences from a logistic regression model, it is important to ensure that there is no multicollinearity present in the data."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Outputting the VIF value for top-10 of all the variables\n",
        "\n",
        "checking_vif(X).sort_values(by='VIF', ascending=False).head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "KDWsj5HBg59g",
        "outputId": "224e281a-2610-468b-de1c-dc8698c7e5ae"
      },
      "id": "KDWsj5HBg59g",
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              feature       VIF\n",
              "0                               const 360.23873\n",
              "32         market_segment_type_Online  68.92564\n",
              "31        market_segment_type_Offline  62.07633\n",
              "30      market_segment_type_Corporate  16.53643\n",
              "26                   arrival_month_10   6.05903\n",
              "25                    arrival_month_9   5.63316\n",
              "24                    arrival_month_8   4.77618\n",
              "29  market_segment_type_Complementary   4.38834\n",
              "22                    arrival_month_6   4.11209\n",
              "23                    arrival_month_7   3.93208"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5afd54aa-47b7-49ae-a46c-492b1ca45e81\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>feature</th>\n",
              "      <th>VIF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>const</td>\n",
              "      <td>360.23873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>market_segment_type_Online</td>\n",
              "      <td>68.92564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>market_segment_type_Offline</td>\n",
              "      <td>62.07633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>market_segment_type_Corporate</td>\n",
              "      <td>16.53643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>arrival_month_10</td>\n",
              "      <td>6.05903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>arrival_month_9</td>\n",
              "      <td>5.63316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>arrival_month_8</td>\n",
              "      <td>4.77618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>market_segment_type_Complementary</td>\n",
              "      <td>4.38834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>arrival_month_6</td>\n",
              "      <td>4.11209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>arrival_month_7</td>\n",
              "      <td>3.93208</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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          },
          "metadata": {},
          "execution_count": 78
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      "source": [
        "Multicollinearity exists in the dataset\n",
        "\n",
        "* To make statistical inferences from a logistic regression model, it is essential to treat the dataset, remove columns cause multicollinearity, and ensure that there is no multicollinearity present in the final model data\n",
        "* Some of the categorical levels of a variable have VIF>5 which can simply be ignored, but we will treat them also to simplify the model"
      ],
      "metadata": {
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    {
      "cell_type": "markdown",
      "id": "domestic-iceland",
      "metadata": {
        "id": "domestic-iceland"
      },
      "source": [
        "# Building a Logistic Regression model"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* We will now perform logistic regression using statsmodels, a Python module that provides functions for the estimation of many statistical models, as well as for conducting statistical tests, and statistical data exploration.\n",
        "\n",
        "* Using statsmodels, we will be able to check the statistical validity of our model - identify the significant predictors from p-values that we get for each predictor variable."
      ],
      "metadata": {
        "id": "97cWd0Us097N"
      },
      "id": "97cWd0Us097N"
    },
    {
      "cell_type": "code",
      "source": [
        "# fitting logistic regression model\n",
        "lg = sm.Logit(y_train, X_train).fit(method='bfgs', disp=False)\n",
        "print(lg.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 836
        },
        "id": "-OP0_USh1Dah",
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      },
      "id": "-OP0_USh1Dah",
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                           Logit Regression Results                           \n",
            "==============================================================================\n",
            "Dep. Variable:              cancelled   No. Observations:                25392\n",
            "Model:                          Logit   Df Residuals:                    25359\n",
            "Method:                           MLE   Df Model:                           32\n",
            "Date:                Sat, 18 Mar 2023   Pseudo R-squ.:                  0.3394\n",
            "Time:                        05:44:39   Log-Likelihood:                -10630.\n",
            "converged:                      False   LL-Null:                       -16091.\n",
            "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
            "=====================================================================================================\n",
            "                                        coef    std err          z      P>|z|      [0.025      0.975]\n",
            "-----------------------------------------------------------------------------------------------------\n",
            "const                                -3.1708      0.286    -11.069      0.000      -3.732      -2.609\n",
            "no_of_weekend_nights                  0.1019      0.020      5.117      0.000       0.063       0.141\n",
            "no_of_week_nights                     0.0268      0.012      2.158      0.031       0.002       0.051\n",
            "type_of_meal_plan                    -0.1519      0.043     -3.562      0.000      -0.235      -0.068\n",
            "required_car_parking_space           -1.0735      0.123     -8.723      0.000      -1.315      -0.832\n",
            "lead_time                             0.0172      0.000     57.991      0.000       0.017       0.018\n",
            "repeated_guest                       -0.6252      0.299     -2.088      0.037      -1.212      -0.038\n",
            "avg_price_per_room                    0.0202      0.001     23.034      0.000       0.018       0.022\n",
            "no_of_special_requests               -1.4522      0.030    -48.593      0.000      -1.511      -1.394\n",
            "previous_cancellations               -0.2100      0.464     -0.453      0.651      -1.119       0.699\n",
            "children                              0.1295      0.080      1.626      0.104      -0.027       0.285\n",
            "room_type_reserved_Room_Type 2       -0.2589      0.129     -2.014      0.044      -0.511      -0.007\n",
            "room_type_reserved_Room_Type 3       -0.0037      1.296     -0.003      0.998      -2.544       2.537\n",
            "room_type_reserved_Room_Type 4       -0.2698      0.051     -5.251      0.000      -0.371      -0.169\n",
            "room_type_reserved_Room_Type 5       -0.1408      0.201     -0.700      0.484      -0.535       0.253\n",
            "room_type_reserved_Room_Type 6       -0.1049      0.128     -0.819      0.413      -0.356       0.146\n",
            "room_type_reserved_Room_Type 7       -0.0082      0.267     -0.031      0.975      -0.532       0.516\n",
            "arrival_year_2018                     0.4220      0.063      6.647      0.000       0.298       0.546\n",
            "arrival_month_2                       0.4971      0.133      3.742      0.000       0.237       0.758\n",
            "arrival_month_3                       0.4433      0.126      3.519      0.000       0.196       0.690\n",
            "arrival_month_4                       0.1195      0.124      0.961      0.337      -0.124       0.363\n",
            "arrival_month_5                      -0.3568      0.130     -2.751      0.006      -0.611      -0.103\n",
            "arrival_month_6                      -0.0978      0.127     -0.772      0.440      -0.346       0.151\n",
            "arrival_month_7                      -0.2575      0.130     -1.987      0.047      -0.511      -0.004\n",
            "arrival_month_8                      -0.2814      0.129     -2.187      0.029      -0.533      -0.029\n",
            "arrival_month_9                      -0.2988      0.130     -2.302      0.021      -0.553      -0.044\n",
            "arrival_month_10                     -0.0513      0.127     -0.405      0.685      -0.299       0.197\n",
            "arrival_month_11                      0.3937      0.129      3.053      0.002       0.141       0.646\n",
            "arrival_month_12                     -1.7475      0.146    -11.987      0.000      -2.033      -1.462\n",
            "market_segment_type_Complementary    -0.2704      0.498     -0.543      0.587      -1.247       0.706\n",
            "market_segment_type_Corporate        -0.9638      0.262     -3.675      0.000      -1.478      -0.450\n",
            "market_segment_type_Offline          -1.9073      0.251     -7.585      0.000      -2.400      -1.414\n",
            "market_segment_type_Online           -0.0954      0.248     -0.385      0.700      -0.581       0.390\n",
            "=====================================================================================================\n"
          ]
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              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 79;\n",
              "                var nbb_unformatted_code = \"# fitting logistic regression model\\nlg = sm.Logit(y_train, X_train).fit(method='bfgs', disp=False)\\nprint(lg.summary())\";\n",
              "                var nbb_formatted_code = \"# fitting logistic regression model\\nlg = sm.Logit(y_train, X_train).fit(method=\\\"bfgs\\\", disp=False)\\nprint(lg.summary())\";\n",
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    {
      "cell_type": "markdown",
      "source": [
        "**Observations**\n",
        "\n",
        "* Negative values of the coefficient show that the probability of a booking being cancelled decreases with the increase of the corresponding attribute value.\n",
        "\n",
        "* Positive values of the coefficient show that the probability of a booking being cancelled increases with the increase of the corresponding attribute value.\n",
        "\n",
        "* p-value of a variable indicates if the variable is significant or not. If we consider the significance level to be 0.05 (5%), then any variable with a p-value less than 0.05 would be considered significant."
      ],
      "metadata": {
        "id": "NL6eIAuBqzNT"
      },
      "id": "NL6eIAuBqzNT"
    },
    {
      "cell_type": "markdown",
      "id": "historic-season",
      "metadata": {
        "id": "historic-season"
      },
      "source": [
        "## Model performance evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model can make wrong predictions as**:\n",
        "\n",
        "1. Predicting that a booking was cancelled, but in reality, the booking was not cancelled.\n",
        "\n",
        "2. Predicting that a booking was not cancelled, but in reality, the booking was cancelled.\n",
        "\n",
        "**Which case is more important?** \n",
        "\n",
        "* Both the cases are important as:\n",
        "\n",
        "  * If we predict a booking was cancelled, but  actually, the booking was not cancelled then overbooking may happened and the hotel might lose resources.\n",
        "\n",
        "  * If we predict a a booking was not cancelled, but  actually, the booking was cancelled, then the hotel will incur costs due to room downtime.\n",
        "\n",
        "\n",
        "**How to reduce this loss?**\n",
        "\n",
        "* We need to reduce both False Negatives and False Positives\n",
        "\n",
        "* $F_1$ should be maximized as the greater the $F_1$, the higher the chances of reducing both False Negatives and False Positives and identifying both the classes correctly\n",
        "  * $F_1$ is computed as\n",
        "  $$F_1 = \\frac{2 \\times Precision \\times Recall}{Precision + Recall}$$"
      ],
      "metadata": {
        "id": "JV-SCqEXrdLu"
      },
      "id": "JV-SCqEXrdLu"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Functions to calculate different metrics and confusion matrix has been created so that we don't have to use the same code repeatedly for each model.**\n",
        "\n",
        "* The `model_performance_classification_statsmodels` function will be used to check the model performance of models.\n",
        "* The `confusion_matrix_statsmodels` function will be used to plot confusion matrix."
      ],
      "metadata": {
        "id": "Y2LLyVcwwI2c"
      },
      "id": "Y2LLyVcwwI2c"
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Training performance:\")\n",
        "lg_train = model_performance(lg, X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "udy9h4VwwHB5",
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      },
      "id": "udy9h4VwwHB5",
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training performance:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Accuracy  Recall  Precision      F1\n",
              "0   0.80754 0.63889    0.74105 0.68619"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Precision</th>\n",
              "      <th>F1</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.80754</td>\n",
              "      <td>0.63889</td>\n",
              "      <td>0.74105</td>\n",
              "      <td>0.68619</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-47affaf6-c1ed-4f87-bdbe-a3a05050e9de')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-47affaf6-c1ed-4f87-bdbe-a3a05050e9de button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-47affaf6-c1ed-4f87-bdbe-a3a05050e9de');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 80;\n",
              "                var nbb_unformatted_code = \"print(\\\"Training performance:\\\")\\nlg_train = model_performance(lg, X_train, y_train)\";\n",
              "                var nbb_formatted_code = \"print(\\\"Training performance:\\\")\\nlg_train = model_performance(lg, X_train, y_train)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Testing performance:\")\n",
        "lg_test = model_performance(lg, X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "eiowQEdZ6pzm",
        "outputId": "29ddfe22-0cfd-4e4b-dc8e-577d22a1400f"
      },
      "id": "eiowQEdZ6pzm",
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing performance:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
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          "data": {
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              "   Accuracy  Recall  Precision      F1\n",
              "0   0.81016 0.64282    0.73698 0.68668"
            ],
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              "      <th>Accuracy</th>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-592fb85f-e567-4add-89eb-c94c965402bb');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {}
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        {
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            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 81;\n",
              "                var nbb_unformatted_code = \"print(\\\"Testing performance:\\\")\\nlg_test = model_performance(lg, X_test, y_test)\";\n",
              "                var nbb_formatted_code = \"print(\\\"Testing performance:\\\")\\nlg_test = model_performance(lg, X_test, y_test)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
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              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**\n",
        "\n",
        "* The $F_1$ of the model is ~0.69 and we will try to maximize it further\n",
        "\n",
        "* The variables used to build the model might contain multicollinearity, which will affect the p-values\n",
        "\n",
        "* We will have to remove multicollinearity from the data to get reliable coefficients and p-values"
      ],
      "metadata": {
        "id": "O3xu8kJoxCaI"
      },
      "id": "O3xu8kJoxCaI"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Detecting and Dealing with Multicollinearity"
      ],
      "metadata": {
        "id": "K9m56nJ1xc6b"
      },
      "id": "K9m56nJ1xc6b"
    },
    {
      "cell_type": "code",
      "source": [
        "# Outputting the VIF value for top-10 of all the variables\n",
        "# Removing variables with high VIF\n",
        "\n",
        "selected_features = mc_solving(lg_train, X_train, y_train)\n",
        "\n",
        "print('\\nSelected features are: ')\n",
        "print(pd.Series(selected_features))"
      ],
      "metadata": {
        "colab": {
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      },
      "id": "pveZRK6SxBpx",
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[91m\n",
            "Iteration # 1\u001b[0m\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Accuracy  Recall  Precision      F1               feature to remove  \\\n",
              "1   0.80738 0.64451    0.73755 0.68789      market_segment_type_Online   \n",
              "2   0.80632 0.64187    0.73625 0.68583     market_segment_type_Offline   \n",
              "4   0.80734 0.63936    0.74027 0.68613                arrival_month_10   \n",
              "0   0.80754 0.63889    0.74105 0.68619  -= Base model with all vars =-   \n",
              "5   0.80707 0.63853    0.74002 0.68554                 arrival_month_9   \n",
              "3   0.80612 0.63685    0.73849 0.68392   market_segment_type_Corporate   \n",
              "\n",
              "       VIF  \n",
              "1 70.70870  \n",
              "2 63.64456  \n",
              "4  5.97573  \n",
              "0  0.00000  \n",
              "5  5.59455  \n",
              "3 16.91017  "
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              "      <td>0.73755</td>\n",
              "      <td>0.68789</td>\n",
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              "      <td>0.73625</td>\n",
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              "      <td>arrival_month_10</td>\n",
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              "      <th>0</th>\n",
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              "      <td>0.63889</td>\n",
              "      <td>0.74105</td>\n",
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              "      <td>-= Base model with all vars =-</td>\n",
              "      <td>0.00000</td>\n",
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              "      <th>5</th>\n",
              "      <td>0.80707</td>\n",
              "      <td>0.63853</td>\n",
              "      <td>0.74002</td>\n",
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              "      <td>5.59455</td>\n",
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              "      <th>3</th>\n",
              "      <td>0.80612</td>\n",
              "      <td>0.63685</td>\n",
              "      <td>0.73849</td>\n",
              "      <td>0.68392</td>\n",
              "      <td>market_segment_type_Corporate</td>\n",
              "      <td>16.91017</td>\n",
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            "\u001b[1m\n",
            "Column to remove [market_segment_type_Online]:\u001b[0m\n",
            "market_segment_type_Offline\n",
            "\u001b[91m\n",
            "Iteration # 2\u001b[0m\n"
          ]
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              "   Accuracy  Recall  Precision      F1               feature to remove     VIF\n",
              "1   0.80687 0.64152    0.73786 0.68632                arrival_month_10 5.96690\n",
              "2   0.80659 0.64080    0.73754 0.68578                 arrival_month_9 5.58753\n",
              "0   0.80754 0.63889    0.74105 0.68619  -= Base model with all vars =- 0.00000"
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              "      <th>1</th>\n",
              "      <td>0.80687</td>\n",
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              "      <td>0.73786</td>\n",
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              "      <td>0.80659</td>\n",
              "      <td>0.64080</td>\n",
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              "      <td>0.63889</td>\n",
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              "      <td>0.68619</td>\n",
              "      <td>-= Base model with all vars =-</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          "text": [
            "\u001b[1m\n",
            "Column to remove [arrival_month_10]:\u001b[0m\n",
            "arrival_month_10\n",
            "\n",
            "\u001b[32m\u001b[1mMulticollinearity in the dataset has been resolved.\u001b[0m\n",
            "\n",
            "Selected features are: \n",
            "0                                 const\n",
            "1                  no_of_weekend_nights\n",
            "2                     no_of_week_nights\n",
            "3                     type_of_meal_plan\n",
            "4            required_car_parking_space\n",
            "5                             lead_time\n",
            "6                        repeated_guest\n",
            "7                    avg_price_per_room\n",
            "8                no_of_special_requests\n",
            "9                previous_cancellations\n",
            "10                             children\n",
            "11       room_type_reserved_Room_Type 2\n",
            "12       room_type_reserved_Room_Type 3\n",
            "13       room_type_reserved_Room_Type 4\n",
            "14       room_type_reserved_Room_Type 5\n",
            "15       room_type_reserved_Room_Type 6\n",
            "16       room_type_reserved_Room_Type 7\n",
            "17                    arrival_year_2018\n",
            "18                      arrival_month_2\n",
            "19                      arrival_month_3\n",
            "20                      arrival_month_4\n",
            "21                      arrival_month_5\n",
            "22                      arrival_month_6\n",
            "23                      arrival_month_7\n",
            "24                      arrival_month_8\n",
            "25                      arrival_month_9\n",
            "26                     arrival_month_11\n",
            "27                     arrival_month_12\n",
            "28    market_segment_type_Complementary\n",
            "29        market_segment_type_Corporate\n",
            "30           market_segment_type_Online\n",
            "dtype: object\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 82;\n",
              "                var nbb_unformatted_code = \"# Outputting the VIF value for top-10 of all the variables\\n# Removing variables with high VIF\\n\\nselected_features = mc_solving(lg_train, X_train, y_train)\\n\\nprint('\\\\nSelected features are: ')\\nprint(pd.Series(selected_features))\";\n",
              "                var nbb_formatted_code = \"# Outputting the VIF value for top-10 of all the variables\\n# Removing variables with high VIF\\n\\nselected_features = mc_solving(lg_train, X_train, y_train)\\n\\nprint(\\\"\\\\nSelected features are: \\\")\\nprint(pd.Series(selected_features))\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train1 = X_train.copy()[selected_features]\n",
        "X_test1 = X_test.copy()[selected_features]"
      ],
      "metadata": {
        "id": "8fC7YAE0xVuA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "25aaa1ce-5c82-4b61-fecb-cf53c9b7ec3d"
      },
      "id": "8fC7YAE0xVuA",
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 83;\n",
              "                var nbb_unformatted_code = \"X_train1 = X_train.copy()[selected_features]\\nX_test1 = X_test.copy()[selected_features]\";\n",
              "                var nbb_formatted_code = \"X_train1 = X_train.copy()[selected_features]\\nX_test1 = X_test.copy()[selected_features]\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fitting logistic regression model\n",
        "lg1 = sm.Logit(y_train, X_train1).fit(disp=False, method='bfgs')\n",
        "print(lg1.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 801
        },
        "id": "Ty-wDYMmwHEv",
        "outputId": "34817511-4e37-4d5b-ac1b-58744b130afd"
      },
      "id": "Ty-wDYMmwHEv",
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                           Logit Regression Results                           \n",
            "==============================================================================\n",
            "Dep. Variable:              cancelled   No. Observations:                25392\n",
            "Model:                          Logit   Df Residuals:                    25361\n",
            "Method:                           MLE   Df Model:                           30\n",
            "Date:                Sat, 18 Mar 2023   Pseudo R-squ.:                  0.3353\n",
            "Time:                        05:44:59   Log-Likelihood:                -10697.\n",
            "converged:                      False   LL-Null:                       -16091.\n",
            "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
            "=====================================================================================================\n",
            "                                        coef    std err          z      P>|z|      [0.025      0.975]\n",
            "-----------------------------------------------------------------------------------------------------\n",
            "const                                -4.6478      0.110    -42.236      0.000      -4.864      -4.432\n",
            "no_of_weekend_nights                  0.0871      0.020      4.394      0.000       0.048       0.126\n",
            "no_of_week_nights                     0.0216      0.012      1.740      0.082      -0.003       0.046\n",
            "type_of_meal_plan                    -0.1316      0.042     -3.150      0.002      -0.213      -0.050\n",
            "required_car_parking_space           -1.1218      0.126     -8.927      0.000      -1.368      -0.875\n",
            "lead_time                             0.0163      0.000     58.141      0.000       0.016       0.017\n",
            "repeated_guest                       -0.5889      0.382     -1.542      0.123      -1.338       0.160\n",
            "avg_price_per_room                    0.0176      0.001     21.011      0.000       0.016       0.019\n",
            "no_of_special_requests               -1.4588      0.030    -49.018      0.000      -1.517      -1.401\n",
            "previous_cancellations               -0.2115      0.551     -0.384      0.701      -1.292       0.869\n",
            "children                              0.2398      0.079      3.038      0.002       0.085       0.394\n",
            "room_type_reserved_Room_Type 2       -0.2464      0.127     -1.940      0.052      -0.495       0.003\n",
            "room_type_reserved_Room_Type 3       -0.0052      1.286     -0.004      0.997      -2.525       2.515\n",
            "room_type_reserved_Room_Type 4       -0.2377      0.051     -4.647      0.000      -0.338      -0.137\n",
            "room_type_reserved_Room_Type 5       -0.0933      0.202     -0.462      0.644      -0.489       0.303\n",
            "room_type_reserved_Room_Type 6        0.0041      0.127      0.032      0.975      -0.245       0.253\n",
            "room_type_reserved_Room_Type 7        0.0199      0.271      0.074      0.941      -0.511       0.551\n",
            "arrival_year_2018                     0.4978      0.061      8.153      0.000       0.378       0.617\n",
            "arrival_month_2                       0.4850      0.092      5.274      0.000       0.305       0.665\n",
            "arrival_month_3                       0.4141      0.078      5.277      0.000       0.260       0.568\n",
            "arrival_month_4                       0.0860      0.072      1.199      0.231      -0.055       0.227\n",
            "arrival_month_5                      -0.2661      0.075     -3.557      0.000      -0.413      -0.119\n",
            "arrival_month_6                      -0.0842      0.070     -1.210      0.226      -0.221       0.052\n",
            "arrival_month_7                      -0.2347      0.071     -3.300      0.001      -0.374      -0.095\n",
            "arrival_month_8                      -0.2773      0.068     -4.072      0.000      -0.411      -0.144\n",
            "arrival_month_9                      -0.2288      0.066     -3.463      0.001      -0.358      -0.099\n",
            "arrival_month_11                      0.3884      0.077      5.016      0.000       0.237       0.540\n",
            "arrival_month_12                     -1.9107      0.102    -18.795      0.000      -2.110      -1.711\n",
            "market_segment_type_Complementary    -0.2582      0.746     -0.346      0.729      -1.720       1.204\n",
            "market_segment_type_Corporate         0.0061      0.123      0.049      0.961      -0.235       0.248\n",
            "market_segment_type_Online            1.6558      0.051     32.325      0.000       1.555       1.756\n",
            "=====================================================================================================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 84;\n",
              "                var nbb_unformatted_code = \"# fitting logistic regression model\\nlg1 = sm.Logit(y_train, X_train1).fit(disp=False, method='bfgs')\\nprint(lg1.summary())\";\n",
              "                var nbb_formatted_code = \"# fitting logistic regression model\\nlg1 = sm.Logit(y_train, X_train1).fit(disp=False, method=\\\"bfgs\\\")\\nprint(lg1.summary())\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Training performance:\")\n",
        "lg_train1 = model_performance(lg1, X_train1, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "yjWd1Q5d5xln",
        "outputId": "0ac857ad-993a-40cd-d9d0-7efa4d8ced88"
      },
      "id": "yjWd1Q5d5xln",
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training performance:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
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              "   Accuracy  Recall  Precision      F1\n",
              "0   0.80687 0.64152    0.73786 0.68632"
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              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 85;\n",
              "                var nbb_unformatted_code = \"print(\\\"Training performance:\\\")\\nlg_train1 = model_performance(lg1, X_train1, y_train)\";\n",
              "                var nbb_formatted_code = \"print(\\\"Training performance:\\\")\\nlg_train1 = model_performance(lg1, X_train1, y_train)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Testing performance:')\n",
        "lg_test1 = model_performance(lg1, X_test1, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "oCxwlsPR6nxa",
        "outputId": "53aeddd7-b524-4bb0-f65c-186435b400a0"
      },
      "id": "oCxwlsPR6nxa",
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing performance:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
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          "metadata": {}
        },
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          "data": {
            "text/plain": [
              "   Accuracy  Recall  Precision      F1\n",
              "0   0.81071 0.65219    0.73340 0.69041"
            ],
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              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Recall</th>\n",
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              "      <th>F1</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.81071</td>\n",
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              "      <td>0.69041</td>\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6fac8d28-eb7a-4151-9ce0-088978538aaf button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6fac8d28-eb7a-4151-9ce0-088978538aaf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 86;\n",
              "                var nbb_unformatted_code = \"print('Testing performance:')\\nlg_test1 = model_performance(lg1, X_test1, y_test)\";\n",
              "                var nbb_formatted_code = \"print(\\\"Testing performance:\\\")\\nlg_test1 = model_performance(lg1, X_test1, y_test)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
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              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
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              "                    }\n",
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              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Removing high p-value variables"
      ],
      "metadata": {
        "id": "7qfQLvUGzVAc"
      },
      "id": "7qfQLvUGzVAc"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* For other attributes present in the data, the p-values are high only for few dummy variables and since only one (or some) of the categorical levels have a high p-value we will drop them iteratively as sometimes p-values change after dropping a variable. So, we'll not drop all variables at once.\n",
        "\n",
        "* Instead, we will do the following repeatedly using a loop:\n",
        "  - Build a model, check the p-values of the variables, and drop the column with the highest p-value.\n",
        "  - Create a new model without the dropped feature, check the p-values of the variables, and drop the column with the highest p-value.\n",
        "  - Repeat the above two steps till there are no columns with p-value > 0.05.\n",
        "\n",
        "Note: *The above process can also be done manually by picking one variable at a time that has a high p-value, dropping it, and building a model again. But that might be a little tedious and using a loop will be more efficient.*"
      ],
      "metadata": {
        "id": "By0rbk_M6AmT"
      },
      "id": "By0rbk_M6AmT"
    },
    {
      "cell_type": "code",
      "source": [
        "selected_features = pv_solving(X_train1)\n",
        "\n",
        "print('\\nSelected features are: ')\n",
        "print(pd.Series(selected_features))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "DFKCfiKfzEPO",
        "outputId": "df38c6d2-dd62-4dde-a696-60c29b6c1c68"
      },
      "id": "DFKCfiKfzEPO",
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column `room_type_reserved_Room_Type 3` has been removed\n",
            "Column `room_type_reserved_Room_Type 6` has been removed\n",
            "Column `room_type_reserved_Room_Type 7` has been removed\n",
            "Column `market_segment_type_Corporate` has been removed\n",
            "Column `market_segment_type_Complementary` has been removed\n",
            "Column `previous_cancellations` has been removed\n",
            "Column `room_type_reserved_Room_Type 5` has been removed\n",
            "Column `arrival_month_6` has been removed\n",
            "Column `arrival_month_4` has been removed\n",
            "Column `no_of_week_nights` has been removed\n",
            "\n",
            "Selected features are: \n",
            "0                              const\n",
            "1               no_of_weekend_nights\n",
            "2                  type_of_meal_plan\n",
            "3         required_car_parking_space\n",
            "4                          lead_time\n",
            "5                     repeated_guest\n",
            "6                 avg_price_per_room\n",
            "7             no_of_special_requests\n",
            "8                           children\n",
            "9     room_type_reserved_Room_Type 2\n",
            "10    room_type_reserved_Room_Type 4\n",
            "11                 arrival_year_2018\n",
            "12                   arrival_month_2\n",
            "13                   arrival_month_3\n",
            "14                   arrival_month_5\n",
            "15                   arrival_month_7\n",
            "16                   arrival_month_8\n",
            "17                   arrival_month_9\n",
            "18                  arrival_month_11\n",
            "19                  arrival_month_12\n",
            "20        market_segment_type_Online\n",
            "dtype: object\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 87;\n",
              "                var nbb_unformatted_code = \"selected_features = pv_solving(X_train1)\\n\\nprint('\\\\nSelected features are: ')\\nprint(pd.Series(selected_features))\";\n",
              "                var nbb_formatted_code = \"selected_features = pv_solving(X_train1)\\n\\nprint(\\\"\\\\nSelected features are: \\\")\\nprint(pd.Series(selected_features))\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train2 = X_train1.copy()[selected_features]\n",
        "X_test2 = X_test1.copy()[selected_features]"
      ],
      "metadata": {
        "id": "tcfcDfqYwHHX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "3de5e883-d451-4344-c156-21a80ccfda4e"
      },
      "id": "tcfcDfqYwHHX",
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 88;\n",
              "                var nbb_unformatted_code = \"X_train2 = X_train1.copy()[selected_features]\\nX_test2 = X_test1.copy()[selected_features]\";\n",
              "                var nbb_formatted_code = \"X_train2 = X_train1.copy()[selected_features]\\nX_test2 = X_test1.copy()[selected_features]\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lg2 = sm.Logit(y_train, X_train2).fit(disp=False, method='bfgs')\n",
        "print(lg2.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "id": "zPt05YE67LZS",
        "outputId": "754ff723-d2ad-4c60-f056-b30ecb139d1d"
      },
      "id": "zPt05YE67LZS",
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                           Logit Regression Results                           \n",
            "==============================================================================\n",
            "Dep. Variable:              cancelled   No. Observations:                25392\n",
            "Model:                          Logit   Df Residuals:                    25371\n",
            "Method:                           MLE   Df Model:                           20\n",
            "Date:                Sat, 18 Mar 2023   Pseudo R-squ.:                  0.3349\n",
            "Time:                        05:45:02   Log-Likelihood:                -10703.\n",
            "converged:                      False   LL-Null:                       -16091.\n",
            "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
            "==================================================================================================\n",
            "                                     coef    std err          z      P>|z|      [0.025      0.975]\n",
            "--------------------------------------------------------------------------------------------------\n",
            "const                             -4.8021      0.104    -46.206      0.000      -5.006      -4.598\n",
            "no_of_weekend_nights               0.1238      0.020      6.264      0.000       0.085       0.163\n",
            "type_of_meal_plan                 -0.1168      0.042     -2.766      0.006      -0.200      -0.034\n",
            "required_car_parking_space        -1.1245      0.127     -8.888      0.000      -1.372      -0.877\n",
            "lead_time                          0.0171      0.000     61.171      0.000       0.017       0.018\n",
            "repeated_guest                    -0.6609      0.287     -2.305      0.021      -1.223      -0.099\n",
            "avg_price_per_room                 0.0185      0.001     23.647      0.000       0.017       0.020\n",
            "no_of_special_requests            -1.5451      0.030    -50.764      0.000      -1.605      -1.485\n",
            "children                           0.3104      0.072      4.312      0.000       0.169       0.452\n",
            "room_type_reserved_Room_Type 2    -0.2557      0.128     -1.992      0.046      -0.507      -0.004\n",
            "room_type_reserved_Room_Type 4    -0.1716      0.050     -3.411      0.001      -0.270      -0.073\n",
            "arrival_year_2018                  0.4201      0.059      7.118      0.000       0.304       0.536\n",
            "arrival_month_2                    0.4694      0.086      5.458      0.000       0.301       0.638\n",
            "arrival_month_3                    0.4088      0.071      5.769      0.000       0.270       0.548\n",
            "arrival_month_5                   -0.2964      0.067     -4.410      0.000      -0.428      -0.165\n",
            "arrival_month_7                   -0.2335      0.064     -3.630      0.000      -0.360      -0.107\n",
            "arrival_month_8                   -0.2637      0.061     -4.318      0.000      -0.383      -0.144\n",
            "arrival_month_9                   -0.2371      0.060     -3.973      0.000      -0.354      -0.120\n",
            "arrival_month_11                   0.3701      0.072      5.167      0.000       0.230       0.511\n",
            "arrival_month_12                  -1.9272      0.097    -19.823      0.000      -2.118      -1.737\n",
            "market_segment_type_Online         1.7602      0.050     34.983      0.000       1.662       1.859\n",
            "==================================================================================================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 89;\n",
              "                var nbb_unformatted_code = \"lg2 = sm.Logit(y_train, X_train2).fit(disp=False, method='bfgs')\\nprint(lg2.summary())\";\n",
              "                var nbb_formatted_code = \"lg2 = sm.Logit(y_train, X_train2).fit(disp=False, method=\\\"bfgs\\\")\\nprint(lg2.summary())\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Training performance:')\n",
        "lg_train2 = model_performance(lg2, X_train2, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "v2gptcEQ7RJZ",
        "outputId": "bb19d1d4-5fe9-4657-abfd-353fe5cff3a5"
      },
      "id": "v2gptcEQ7RJZ",
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training performance:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
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        {
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          "data": {
            "text/plain": [
              "   Accuracy  Recall  Precision      F1\n",
              "0   0.80600 0.64738    0.73251 0.68732"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Recall</th>\n",
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              "      <th>F1</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.80600</td>\n",
              "      <td>0.64738</td>\n",
              "      <td>0.73251</td>\n",
              "      <td>0.68732</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f488db86-981e-4c22-a0da-0fa0616bc861')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f488db86-981e-4c22-a0da-0fa0616bc861 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f488db86-981e-4c22-a0da-0fa0616bc861');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 90;\n",
              "                var nbb_unformatted_code = \"print('Training performance:')\\nlg_train2 = model_performance(lg2, X_train2, y_train)\";\n",
              "                var nbb_formatted_code = \"print(\\\"Training performance:\\\")\\nlg_train2 = model_performance(lg2, X_train2, y_train)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('\\nTesting performance:')\n",
        "lg_test2 = model_performance(lg2, X_test2, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "6ArnENGx7KSi",
        "outputId": "a788c39f-97c1-4f23-beb6-dce884530ebd"
      },
      "id": "6ArnENGx7KSi",
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Testing performance:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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fY0d388svZziR+Av6//QmDQYD7Tv4YG9vx5bNa2jV6gmSkn5lytQ5xvIx0Z8ydtxbvP3PCTz55P+xd+8B1qz9osraVm1U4KZbWm3hv6adnJzw9vbm5MmTODk5kZKSgouLCykpKcbxda1WS3JysrFscnIyWq0WrVZLQkKC8bxOp6Njx44l1isDq5VM61wfAKd6DvTp0ZWfT/1Kk8aN+Hh5KF+uXYHvP3rS6JEGxZY/ePgYLZu7lysRK4rCqk83Mib4eT5cu4EpIa8wdFA/Nmze+tDtEZWjX7/enDjxMykp9/9rLTPzLq+NnEz7Dj6MCJ6Ac30nzp+/VOSa9PQMvtv/PX19ehU57+fnw/HjJ6lTpzZNmjTmuefHMCRwgHEFqviTCtqc6969e9y9e9f48/fff0+zZs3w8vIiJiYGgJiYGPr06QNgPK8oComJidStWxcXFxc8PT2Jj48nPT2d9PR04uPj8fT0LLFuWSlaie5l56AYDNSuXYt72TkcSjjO2ODnSb2ThlM9BwwGA6vWbeKZAN9i77Fjz3f4evcqV71f79xLjy4dsLerS3ZuLhqNBRYaC3L+s+e6MD/PBgU8cLgFwN7ejnv3ssnPz+fVV57nYPwRMjPvUr++I/n5BaSnZ1CzZk3+0acH7y79wFjOysqKN8aPxM9/OM2aNTFuqWtpaYmNjQ3Z2TlV0bRqQymomFkuqamphISEAIUTGgYOHEiPHj3w8PBg4sSJREVF4ebmxvLlywHo2bMn+/fvx9vbG1tbW0JDQwFwcHBg3LhxDB06FICQkBDjA9LiyErRSnTl2g3emL4AAH2BHl+fXox++TnWfxnDpq++AeAfPbsycUxw4cygm6nMWbycD5cVlrmXnYN34Evs2vwJdev8Ma1x7/7veSfsQ26npVO3Th1aNGtCRNgiALJzchg3dQ4RyxdhbWXFj4m/sHDZSqytrfjXnLd4vHHDKv4tVCw1rhStVcuWC+eO0uyJLmRkZAIwamThrpsRH6+nc6d2rF27HEVROHXqV0aOmkpaWjoeHi1Zu2Y5lpYWWFhYEBW1jYWLlhvvO2H8a6SlZfDZ+i8B+Hz9Slq1eoJdu/bx9vTQKm9nZaqIlaJ3pw0u87V13i19TrgpSEIX1YoaE7p4eBWS0Kf6l/naOkvNc/hShlyEEAJUsfRfEroQQgCKJHQhhFCJCnooakqS0IUQAmTIRQghVEMSuhBCqIOZTvgrF0noQggB0kMXQgjVkIQuhBDqoBRU3OZcpiIJXQghAKp/PpeELoQQIAuLhBBCPSShCyGESsiQixBCqIMMuQghhEooBZLQhRBCHWTIRQgh1KEC3xFtMpLQhRACpIcuhBBqIT10IYRQCaXA1BE8PEnoQgiB9NCFEEI1VJ3QFyxYgEajKbbgzJkzKyUgIYQwCaX4fFddFJvQW7duXZVxCCGESam6hz548OAin7Ozs7G1ta30gIQQwhQUQ/XvoVuUdsGJEyfw9fWlf//+AJw5c4a5c+dWdlxCCFGlDHpNmQ9zVWpCDw0NZc2aNTg4OADQokULjh07VtlxCSFElVIMZT/MVZlmuTRo0KDIZwuLUv8OCCFEtaKGIZdSE3qDBg04fvw4Go2G/Px8PvvsM9zd3asiNiGEqDJK9d9ssfQhl7lz57JhwwZ0Oh3du3fn9OnTzJ49uypiE0KIKqMYNGU+ykKv1xMQEMDo0aMBuHLlCsOGDcPb25uJEyeSl5cHQF5eHhMnTsTb25thw4Zx9epV4z1WrVqFt7c3ffv25eDBg6XWWWpCd3R0ZNmyZRw6dIjDhw+zdOlS6tWrV6YGCSFEdVHRD0X/dzRj6dKljBgxgj179mBnZ0dUVBQAmzdvxs7Ojj179jBixAiWLl0KwNmzZ9m+fTvbt29n9erVzJs3D71eX2KdpSb0K1euMGbMGDp37kyXLl0YO3YsV65cKVODhBCiuqjIHnpycjLfffcdQ4cOLby3onD48GH69u0LFE4Lj4uLA2Dfvn3GaeJ9+/blhx9+QFEU4uLiGDBgADY2NjRq1IjGjRtz8uTJEustNaFPmTKFfv36ER8fz8GDB+nXrx+TJ08utUFCCFGdKIqmzEdkZCSBgYHGIzIyssi9QkNDmTZtmnECyZ07d7Czs8PKqvCxpaurKzqdDgCdTmeceGJlZUXdunW5c+cOOp0OV1dX4z21Wq2xTHFKfSianZ1NQECA8bO/vz9r1qwpw69HCCGqj/JMRwwKCiIoKOiB33377bc4OjrSunVrjhw5UkHRlU2xCT0tLQ2AHj16EBERga+vLxqNhh07dtCzZ8+qik8IIaqEoYL2cjl+/Dj79u3jwIED5ObmcvfuXRYtWkRGRgYFBQVYWVmRnJyMVqsFCnveN27cwNXVlYKCAjIzM6lXrx5arZbk5GTjfXU6nbFMcYpN6IGBgWg0GpT/zOXZtGmT8TuNRsOUKVMeqtFCCGFOlApK6FOmTDHmxyNHjrB27VqWLVvGhAkTiI2NZcCAAURHR+Pl5QWAl5cX0dHRtG3bltjYWDp37oxGo8HLy4spU6YQHByMTqfj4sWLPPnkkyXWXWxC37dvX4U0TgghqoPKXtI/bdo0Jk2axPLly2nZsiXDhg0DYOjQoUybNg1vb2/s7e0JCwsDoFmzZvTv3x9fX18sLS2ZPXs2lpaWJdahUZTSp9P/9ttvnD171jhvEigyrl4Z8m+dr9T7i+rJ1q27qUMQZqgg79pD3+OU+4AyX/t/57Y/dH2VodSHouHh4Rw5coRz587Rs2dPDhw4QLt27So9oQshRFWqqDF0Uyp12mJsbCzr1q2jfv36vPPOO2zdupXMzMyqiE0IIapMeaYtmqtSe+g1atTAwsICKysr7t69i5OTEzdu3KiK2IQQosqoYS+XUhN669atycjIYNiwYQQGBlKrVi3atm1bFbEJIUSVUcOQS5keiv7X1atXuXv3Li1atKjMmAB5KCoeTB6KigepiIeixxv5l/nap69sfej6KkOxPfSkpKRiCyUlJdGqVatKCUgIIUxBDT30YhP64sWLiy2k0Wj47LPPKiWg/2rUtOxTiMTfxwi3LqYOQaiUOT/sLKtiE/r69eurMg4hhDApVffQhRDi70QFk1wkoQshBIDeUP3flSwJXQghgHLsnmu2Sv2TpCgKW7duJTw8HIDr16+X+tYMIYSobhQ0ZT7MVZleEp2YmMj27YWb0dSuXZt58+ZVemBCCFGVDErZD3NVakI/efIkc+bMoUaNGgDY29uTn59f6YEJIURVMqAp82GuSh1Dt7KyQq/Xo9EUNuL27dvG9+QJIYRamPNQSlmVmtCHDx9OSEgIqamphIWFsWvXLiZOnFgFoQkhRNXR/x0S+qBBg2jVqhWHDx9GURQ++OAD3N3dqyI2IYSoMmqY5VJqQr9+/Tq2trb07t27yDk3N7dKDUwIIarS3yKhjx492vhzbm4uV69e5fHHHzfOehFCCDX4W4yhb9u2rcjnpKQkvvjii0oLSAghTMFQ/fN5+VeKtmrVShYWCSFUx5ynI5ZVqQn9k08+Mf5sMBg4deoULi4ulRqUEEJUNb2pA6gApSb0rKws48+Wlpb07NmTvn37VmpQQghR1QwalffQ9Xo9WVlZvPXWW1UVjxBCmIQZr+gvs2ITekFBAVZWVhw/frwq4xFCCJNQ9bTFYcOGER0dTYsWLRgzZgz9+vWjVq1axu99fHyqJEAhhKgKf4tZLnl5edSrV48jR44UOS8JXQihJqpe+p+amsonn3xCs2bN0Gg0KMofI0waFTw8EEKIP1N1D91gMBSZ4SKEEGqm6jF0Z2dnXn/99aqMRQghTKaiZrnk5ubywgsvkJeXh16vp2/fvkyYMIErV64wefJk0tLSaNWqFUuWLMHGxoa8vDzefPNNkpKScHBwICwsjIYNGwKwatUqoqKisLCwYObMmXTv3r3Euovd2PzPQyxCCKF2Bk3Zj5LY2Niwbt06vv76a2JiYjh48CCJiYksXbqUESNGsGfPHuzs7IiKigJg8+bN2NnZsWfPHkaMGMHSpUsBOHv2LNu3b2f79u2sXr2aefPmodeXvPyp2IT+6aeflu+3IYQQ1ZihHEdJNBoNtWvXBgqnfxcUFKDRaDh8+LBxUebgwYOJi4sDYN++fQwePBiAvn378sMPP6AoCnFxcQwYMAAbGxsaNWpE48aNS912pdghFwcHh1LCFkII9dCX46FoZGQkkZGRxs9BQUEEBQX9cS+9nsDAQC5fvszzzz9Po0aNsLOzw8qqMOW6urqi0+kA0Ol0NGjQACh8Q1zdunW5c+cOOp2Op556ynhPrVZrLFOccm/OJYQQalSeh6L/m8D/l6WlJVu3biUjI4OQkBDOnz//8AGWgbwcVAghqLghlz+zs7OjU6dOJCYmkpGRQUFBAQDJyclotVqgsOd948YNoHCIJjMzk3r16qHVaklOTjbeS6fTGcsURxK6EEJQOMulrEdJbt++TUZGBgA5OTkcOnQId3d3OnXqRGxsLADR0dF4eXkB4OXlRXR0NACxsbF07twZjUaDl5cX27dvJy8vjytXrnDx4kWefPLJEuuWIRchhKDiFhalpKTwz3/+E71ej6Io9OvXj969e9O0aVMmTZrE8uXLadmyJcOGDQNg6NChTJs2DW9vb+zt7QkLCwOgWbNm9O/fH19fXywtLZk9ezaWlpYl1q1RzHR+oqtDS1OHIMzQQIdWpg5BmKHVF6Me+h5hj75Y5msnXf78oeurDNJDF0II/iYvuBBCiL8DVe/lIoQQfyeq3stFCCH+TszyYWI5SUIXQgjAoIKULgldCCGQh6JCCKEaMoYuhBAqIbNchBBCJWQMXQghVKL6p3NJ6EIIAcgYuhBCqIZeBX10SegmEha+EO++vbh18za9ug4CYPb8qXj3601+fj4XL1xhYsh0MtIzqVfPgdWfLadN29ZEfhHD9DcXGu/z1TfrcNE6k5OTA8Czg1/j1q3bJmmTeDj1Gjjx6nvjsatvj6LAgY17iPtkB/6Tn6WtdwcMioHMWxmsnRpOesodAJ6b8woevduSl53H2qnhXE66wBNdWhE0a4Txvg3cH2HV+DASdx81UcuqBzX00GW3RRPp3LU9WVn3WPHhYmNC79m7K/EHjqDX65k5dwoAC+cuo1YtW1o/2ZIWLZvRomWz+xL6vJlL+CkxySTtqGpq3m3R3tkBe5d6XE66QI3aNZm1bQkrRy3hTnIqOXezAegzwpcGzRry+YwIPHq1xWuEL/8esYgmbZvx7JxXCA14u8g9a9vXIXT/CqZ1Hk1eTp4pmlUlKmK3xcmPPVvma9+7uOmh66sM8oILEzl86Bhpd9KKnNv/7SHjW71/PPYTDdwK305y7142CYePk5ubW9VhiiqUfjONy0kXAMjNyuHGuWvUc3U0JnMAm1o14D99sDY+Hfjhq+8AOH/id2rVrYW9s0ORe7bz7czP3yWqOplXlIp6wYUpyZCLmXruxUC2frWzTNcuXxmK3qBn+9d7CHv3w0qOTFQFp4bOPPp/j3E+8XcABk99ji6BPcnOvMe7z80FwEHrxO3rqcYyd5Jv4+DqRPrNNOO5Dn7d2LP6m6oMvdpSw5CL9NDN0BtTRlNQoGfLl9tKvXbcyGn07uaPf/8X6dSlHcOe9a+CCEVlqlGrJuM+nErk/E+NvfPopRt5s+sYDm89iNfL/cp0H3tnBxo+8ShJBxIrMVr10KOU+TBXktDNTNDzAXj37UXIyGlluj75RgoAWXfvER31DW2f9qjM8EQls7SyZOxHUzkcc5DjsUfu+/5IzEHa9esMQJouFUc3J+N39VwdSUv+o8fefmBXjscmoC9Qwy4llc+AUubDXElCNyO9+3gSMuFVXn5uHNnZOaVeb2lpiaOjAwBWVlZ49+3FmdO/V3KUojK9/K9x3Dh7lT1r/hgmcXnM1fhzG+8O3Dh3DYDEPcfoEtgLgCZtm5Gdea/IcEvHQZ4kbIuvkrjVQMbQxV/24eqldPXsiKOTA8eTvuXdxeFMmDQSGxsbImPWAPDj0Z94a/I8AI6e3EudurWxsbam34A+PBv4GlevXGfjV6uxtrbC0sKSA/sP8fm6zaZslngITdu3oOuQnlw9fYnZO94FIHrJF3gG9cG1iRuKQSH12k3Wz4gA4Odvj+PR+2lC94eTl53LJ9M+MN7LqaEzjg2c+O3wKZO0pToy5553Wcm0RVGtqHnaovjrKmLa4sjHhpX52o8vmmfHSXroQggBKCrooVf5GPqWLVuqukohhCiVzHL5C1asWFHVVQohRKkM5TjMVaUMufj5+RX73a1btyqjSrP0oP1a/Pz7MvWfr9PsiSb093qm2CX7Dyr7X6+OeoERrz2PQW9g7+79LJizlA6d2vKv9+aQn5fPmFencuH8Jezs6xLxSRjPDRmJmT4q+Vsqbs+Wdr5dGDTxGRo0fYRF/m9z6edzZS4Lhcv8R4dPwqmhC6lXU/go5D3uZWTxdL9O+E9+lqy0u6wc9S+y0u7i/KiWwDefZ9XrYVXdfLNlUMF/I5WS0FNTU1mzZg12dnZFziuKwrPPln2/hOou8osY1n78BSs+XGw8d+b077wyfDzvLp9X7rIA3bp3pK9vH/p4BpCXl0/9+o4AjHk9mBeGjabRo4/w8itBzJ25hElTx/D+e6skmZsZQ4GeLxeuK7Jny6mDJ7n+62U+GPMuL4WOLnfZG2ev0n9sAKcP/czOD2PoPzaA/uMGs2Xx5/QZ4cuiQW/xdL9OdPLvzr51OwmY+hzRSzdWYavNnxr+K6mUIZdevXqRlZXFI488UuRo2LAhnTp1qowqzdKD9mv5/bfznDt78S+VBXj5lWdZEfYxeXn5AMadFQvyC7C1tcXWtib5+QU0fqwRbo804FC87LBnborbs+XGuWvozl//S2WhcI76oajvADgU9R1tvTsAYDAYsLKxxsa2BvoCPc06tCTjZhopF5MrqYXVkxoWFlVKDz00NLTY75YtW1YZVf5tNGn6GJ27tuPtWW+Qm5vHvJlLSDzxC++HRbDio8Xk5OTw+ui3mLPgTRYv/LepwxWl+N89Wx6mrJ2zg3FhUfrNNOz+s1HXzg+imbJhNmm6O6ye9D5jVk4hYrwMtfwvNcxykWmL1YyVpRUO9ezx/ceztH3ag4hPw+j4lDdJP59hgHfhcFbnru3R6W6i0WhYtfY98vPzmTtzCbduppZyd1GVHrRnS0WW/e9Q26n4k5yKPwlAl8Ce/PzdcbRNGuAzchD30rPYNG+t7MYIFKggocvS/2rm+vVkdmzbA8CJ4z9jMBhwcqpX5JqJU8cQtuRDprw1jgVzlrLhsyheG/2iKcIVxShtz5a/UjbjZppx+1x7Zwcyb6UXKWdT04auQ3vx7We7GDQpiLVTwjl77DSdAno8dHvUQCnH/8yVJPRqZtf2OLp1L3wO0cT9MaytrUlNvWP8/pnn/Inbc4C0tHRsa9liMBgwGAzY1rI1VcjiAR60Z8vDlk3ce4yuQ3sB0HVoLxL3FH1+0ne0P3Gf7kBfoMemhg0oCgaDgo2tzV9uh5pU1LTFGzduMHz4cHx9fRkwYADr1q0DIC0tjeDgYHx8fAgODiY9vfAPrqIoLFy4EG9vb/z8/EhK+mPmW3R0ND4+Pvj4+BAdHV1qG2TpfyX6834tN1NSeXdxOGl30ln0rxk41XckIz2DX34+w3NDRqJ1dea99xfywjOjiy27cf0WrK2tCQtfSGuPluTl5zNv1hK+P1DYS7O1rcnnX35E0ODXKCgooFOXdixeOpu8/HzGvTa1TA9jzZ0alv43bd+Cf0Yt5OrpSxiUwvQQveQLrGpY89zcV6nraEd2RhaXT19k+UsLsXepx4h/jeXfwaHFlv35uxPUdqjDmJVTcHSrT+q1m6wKeY+s9LsA2LvU4+XFY3j/lXcAjFMkszOyCB+1hLu3M0zzy6ggFbH0f/CjxU+3/l/Rl4vf2jolJYWbN2/SqlUr7t69y5AhQ1i5ciVfffUVDg4OjBo1ioiICNLT05k2bRr79+9n/fr1fPzxx/z0008sWrSIzZs3k5aWxpAhQ9iyZQsajYbAwEC++uor7O3ti61bErqoVtSQ0EXFq4iE7v/owDJfu/Vy2f9lNXbsWF588UXmz5/P+vXrcXFxISUlheHDhxMbG8vs2bPp2LEjAwcW1t+3b1/Wr19PQkICCQkJzJ8/H+C+6x5EHooKIQSUa0l/ZGQkkZGRxs9BQUEEBQXdd93Vq1c5ffo0Tz31FKmpqbi4uADg7OxMamrhJAWdToer6x9bJLu6uqLT6e47r9Vq0el0JcYlCV0IISjf9rnFJfA/y8rKYsKECUyfPp06deoU+U6j0aDRaP5SnCWRh6JCCEHhw8myHqXJz89nwoQJ+Pn54ePjA4CTkxMpKYVvGEtJScHRsXBBmFarJTn5j0VeycnJaLXa+87rdDq0Wm2J9UpCF0IIKm6Wi6IozJgxgyZNmhAcHGw87+XlRUxMDAAxMTH06dOnyHlFUUhMTKRu3bq4uLjg6elJfHw86enppKenEx8fj6enZ4l1y5CLEEJQcStFf/zxR7Zu3Urz5s3x9y98afvkyZMZNWoUEydOJCoqCjc3N5YvXw5Az5492b9/P97e3tja2hpX2js4ODBu3DiGDh0KQEhICA4ODiXWLbNcRLUis1zEg1TELJd/NOpb5mv3Xol96Poqg/TQhRAC0CvmvNN52UhCF0IIZHMuIYRQDXnBhRBCqET1T+eS0IUQAijfwiJzJQldCCGQhC6EEKohs1yEEEIlZJaLEEKohJmusSwXSehCCIGMoQshhGpID10IIVRCX+o+iuZPEroQQiArRYUQQjVklosQQqiE9NCFEEIlpIcuhBAqIT10IYRQCVn6L4QQKiFDLkIIoRKK9NCFEEIdZOm/EEKohCz9F0IIlZAeuhBCqITeIGPoQgihCjLLRQghVELG0IUQQiVkDF0IIVRCeuhCCKESangoamHqAIQQwhwYUMp8lObtt9+mS5cuDBw40HguLS2N4OBgfHx8CA4OJj09HSj8l8HChQvx9vbGz8+PpKQkY5no6Gh8fHzw8fEhOjq61HoloQshBIWJtaxHaQIDA1m9enWRcxEREXTp0oXdu3fTpUsXIiIiADhw4AAXL15k9+7dLFiwgLlz5wKFfwDCw8P58ssv2bx5M+Hh4cY/AsWRhC6EEBRun1vWozQdOnTA3t6+yLm4uDgCAgIACAgIYO/evUXOazQa2rRpQ0ZGBikpKcTHx9OtWzccHBywt7enW7duHDx4sMR6ZQxdCCGo/HnoqampuLi4AODs7ExqaioAOp0OV1dX43Wurq7odLr7zmu1WnQ6XYl1SEIXQgjK94KLyMhIIiMjjZ+DgoIICgoqc3mNRoNGoylXfGUhCV0IIQBDObbPLW8CB3ByciIlJQUXFxdSUlJwdHQECnveycnJxuuSk5PRarVotVoSEhKM53U6HR07diyxDhlDF0IIKvah6IN4eXkRExMDQExMDH369ClyXlEUEhMTqVu3Li4uLnh6ehIfH096ejrp6enEx8fj6elZYh3SQxdCCCp2YdHkyZNJSEjgzp079OjRg/HjxzNq1CgmTpxIVFQUbm5uLF++HICePXuyf/9+vL29sbW1JTQ0FAAHBwfGjRvH0KFDAQgJCcHBwaHEejWKmS6PcnVoaeoQhBka6NDK1CEIM7T6YtRD38PK5pEyX1uQd+2h66sMZpvQhRBClI+MoQshhEpIQhdCCJWQhC6EECohCV0IIVRCEroQQqiEJHQhhFAJSehCCKESktDN3IEDB+jbty/e3t7G/ZPF39uDXp4gBEhCN2t6vZ758+ezevVqtm/fzjfffMPZs2dNHZYwsQe9PEEIkIRu1k6ePEnjxo1p1KgRNjY2DBgwgLi4OFOHJUzsQS9PEAIkoZu1v7LBvRDi70sSuhBCqIQkdDP2vxvf63Q6tFqtCSMSQpgzSehmzMPDg4sXL3LlyhXy8vLYvn07Xl5epg5LCGGmZPtcM7d//35CQ0PR6/UMGTKEsWPHmjokYWJ/fnmCk5MT48ePZ9iwYaYOS5gBSehCCKESMuQihBAqIQldCCFUQhK6EEKohCR0IYRQCUnoQgihEpLQRYlatmyJv78/AwcOZMKECWRnZ//le/3zn/9k165dAMyYMaPEjcaOHDnC8ePHy12Hl5cXt2/fLvP5P2vbtm256lqxYgVr1qwpVxkhKpMkdFGimjVrsnXrVr755husra3ZtGlTke8LCgr+0n0XLVpE06ZNi/0+ISGBEydO/KV7C/F3ZWXqAET10b59e3799VeOHDnCv//9b+zs7Lhw4QI7duxg6dKlJCQkkJeXxwsvvMCzzz6LoigsWLCA77//ngYNGmBtbW281/Dhw3nzzTfx8PDgwIEDhIWFodfrqVevHosWLWLTpk1YWFjw9ddfM2vWLJo0acKcOXO4fv06ANOnT6ddu3bcuXOHKVOmoNPpaNOmDWVZVjFu3DiSk5PJzc3lpZdeIigoyPhdaGgo33//PfXr1ycsLAxHR0cuX77MvHnzuHPnDjVr1mTBggW4u7tX/C9YiIelCFGCNm3aKIqiKPn5+cqYMWOUDRs2KIcPH1aeeuop5fLly4qiKMqmTZuUlStXKoqiKLm5ucrgwYOVy5cvK7GxscqIESOUgoICJTk5WWnXrp2yc+dORVEU5cUXX1ROnjyppKamKj169DDe686dO4qiKMr777+vrF692hjH5MmTlaNHjyqKoijXrl1T+vXrpyiKoixYsEBZsWKFoiiK8u233yrNmzdXUlNT72tH7969jef/W0d2drYyYMAA5fbt24qiKErz5s2VrVu3KoqiKCtWrFDmzZunKIqivPTSS8qFCxcURVGUxMREZfjw4Q+MUQhTkx66KFFOTg7+/v5AYQ996NChnDhxAg8PDxo1agTA999/z6+//kpsbCwAmZmZXLp0iaNHjzJgwAAsLS3RarV07tz5vvsnJibSvn17470cHBweGMehQ4eKjLnfvXuXrKwsjh49Snh4OAC9evUq0z7h69evZ8+ePQDcuHGDS5cuUa9ePSwsLPD19QXA39+f119/naysLE6cOMEbb7xhLJ+Xl1dqHUKYgiR0UaL/jqH/r1q1ahl/VhSFmTNn0r179yLX7N+/v8LiMBgMfPnll9SoUeOh7nPkyBEOHTpEZGQktra2DB8+nNzc3Adeq9FoUBQFOzu7B/4OhDA38lBUPDRPT082btxIfn4+ABcuXODevXt06NCBnTt3otfrSUlJ4ciRI/eVbdOmDceOHePKlSsApKWlAVC7dm2ysrKK1LF+/Xrj59OnTwOFb+/Ztm0bUPgHJD09vcRYMzMzsbe3x9bWlnPnzpGYmGj8zmAwGP+VsW3bNtq1a0edOnVo2LAhO3fuBAr/eJ05c6Y8vx4hqowkdPHQhg0bRtOmTQkMDGTgwIHMnj0bvV6Pt7c3jRs3xtfXl7feeos2bdrcV9bR0ZH58+czfvx4Bg0axKRJkwDo3bs3e/bswd/fn2PHjjFjxgx++eUX/Pz88PX1ZePGjQCEhIRw7NgxBgwYwJ49e3Bzcysx1h49elBQUED//v1ZtmxZkZhq1arFyZMnGThwIIcPHyYkJASAd999l6ioKAYNGsSAAQPYu3dvxfzihKhgstuiEEKohPTQhRBCJSShCyGESkhCF0IIlZCELoQQKiEJXQghVEISuhBCqIQkdCGEUIn/B1fpC4IyijRrAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Accuracy  Recall  Precision      F1\n",
              "0   0.80906 0.65503    0.72776 0.68948"
            ],
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Precision</th>\n",
              "      <th>F1</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.80906</td>\n",
              "      <td>0.65503</td>\n",
              "      <td>0.72776</td>\n",
              "      <td>0.68948</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-79051123-a475-458f-aeae-bb698dbf4ea2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-79051123-a475-458f-aeae-bb698dbf4ea2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-79051123-a475-458f-aeae-bb698dbf4ea2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 91;\n",
              "                var nbb_unformatted_code = \"print('\\\\nTesting performance:')\\nlg_test2 = model_performance(lg2, X_test2, y_test)\";\n",
              "                var nbb_formatted_code = \"print(\\\"\\\\nTesting performance:\\\")\\nlg_test2 = model_performance(lg2, X_test2, y_test)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Now no categorical feature has p-value greater than 0.05, so we'll consider the features in *X_train2* as the final ones and *lg2* as final model.**"
      ],
      "metadata": {
        "id": "bjRZ1i2m7vKt"
      },
      "id": "bjRZ1i2m7vKt"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checking Assumptions of Logistic Regression"
      ],
      "metadata": {
        "id": "ouY4rvNEhZnq"
      },
      "id": "ouY4rvNEhZnq"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1. The Response Variable is Binary**\n",
        "\n",
        "Logistic regression assumes that the response variable only takes on two possible outcomes."
      ],
      "metadata": {
        "id": "oZoh_GTGhbzi"
      },
      "id": "oZoh_GTGhbzi"
    },
    {
      "cell_type": "code",
      "source": [
        "y.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "id": "fIgGD2EHh-BN",
        "outputId": "455ff47d-de2d-4bfc-e386-4a1bbfe186d1"
      },
      "id": "fIgGD2EHh-BN",
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cancelled\n",
              "0            24390\n",
              "1            11885\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 92
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 92;\n",
              "                var nbb_unformatted_code = \"y.value_counts()\";\n",
              "                var nbb_formatted_code = \"y.value_counts()\";\n",
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              "            }, 500);\n",
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      "cell_type": "code",
      "source": [
        "y.value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "id": "1yHR3WKUU8oc",
        "outputId": "21d602f5-defe-4cfc-a94c-0245b60c0f0e"
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      "id": "1yHR3WKUU8oc",
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cancelled\n",
              "0           0.67236\n",
              "1           0.32764\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 93;\n",
              "                var nbb_unformatted_code = \"y.value_counts(normalize=True)\";\n",
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              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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              "                }\n",
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              "            "
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ">**Yes**, the response variable is binary:\n",
        "  * Booking status `cancelled` is **0** (booking is not cancelled)\n",
        "  * Booking status `cancelled` is **1** (booking is cancelled)\n",
        "\n"
      ],
      "metadata": {
        "id": "LLgMPQghh-RF"
      },
      "id": "LLgMPQghh-RF"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2. There is No Multicollinearity Among Explanatory Variables**\n",
        "\n",
        "Logistic regression assumes that there is no severe multicollinearity among the explanatory variables.\n",
        "\n",
        "Multicollinearity occurs when two or more explanatory variables are highly correlated to each other, such that they do not provide unique or independent information in the regression model. If the degree of correlation is high enough between variables, it can cause problems when fitting and interpreting the model."
      ],
      "metadata": {
        "id": "lxZlcKKFh7Yv"
      },
      "id": "lxZlcKKFh7Yv"
    },
    {
      "cell_type": "markdown",
      "source": [
        "> **Yes**, there is no multicollinearity among explanatory bariables\n",
        "The multicollinearity was detect and treated by using the variance inflation factor (VIF), which measures the correlation and strength of correlation between the predictor variables in a regression model.\n",
        "See section `Detecting and Dealing with Multicollinearity` above."
      ],
      "metadata": {
        "id": "u8VhHsVZlflo"
      },
      "id": "u8VhHsVZlflo"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3. There are No Extreme Outliers**\n",
        "Logistic regression assumes that there are no extreme outliers or influential observations in the dataset.\n",
        "\n",
        "We decided to keep the outliers in the model as they are proper values"
      ],
      "metadata": {
        "id": "Vh8Sb_frh7cw"
      },
      "id": "Vh8Sb_frh7cw"
    },
    {
      "cell_type": "markdown",
      "source": [
        "> **Yes**, there are no extreme outliers. See section `Outlier detection and treatment` above"
      ],
      "metadata": {
        "id": "n2l9iQAumSm-"
      },
      "id": "n2l9iQAumSm-"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**5. There is a Linear Relationship Between Explanatory Variables and the Logit of the Response Variable**\n",
        "\n",
        "Logistic regression assumes that there exists a linear relationship between each explanatory variable and the logit of the response variable. Recall that the logit is defined as:\n",
        "\n",
        "$$logit(p)  = \\log(\\frac{p}{1-p}),$$ where $p$ is the probability of a positive outcome.\n",
        "\n",
        "How to check this assumption: The easiest way to see if this assumption is met is to use a Box-Tidwell test."
      ],
      "metadata": {
        "id": "eY6qFqHLh7gU"
      },
      "id": "eY6qFqHLh7gU"
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.genmod.generalized_linear_model import GLM\n",
        "from statsmodels.genmod import families\n",
        "\n",
        "dflog = df.copy()\n",
        "\n",
        "dflog['lead_time_log'] = dflog['lead_time'] * np.log1p(dflog['lead_time'])\n",
        "dflog['no_of_special_requests_log'] = dflog['no_of_special_requests'] * np.log1p(dflog['no_of_special_requests'])\n",
        "dflog['avg_price_per_room_log'] = dflog['avg_price_per_room'] * np.log1p(dflog['avg_price_per_room'])\n",
        "dflog['type_of_meal_plan_log'] = dflog['type_of_meal_plan'] * np.log1p(dflog['type_of_meal_plan'])\n",
        "\n",
        "cols_to_keep = ['lead_time', \n",
        "                'no_of_special_requests', \n",
        "                'type_of_meal_plan',\n",
        "                'avg_price_per_room', \n",
        "                'lead_time_log', \n",
        "                'no_of_special_requests_log', \n",
        "                'avg_price_per_room_log',\n",
        "                'type_of_meal_plan_log'\n",
        "                ]\n",
        "\n",
        "X_ = sm.add_constant(dflog[cols_to_keep])\n",
        "y_ = y\n",
        " \n",
        "# Build model and fit the data (using statsmodel's Logit)\n",
        "logit_results = GLM(y_, X_, family=families.Binomial()).fit()\n",
        "\n",
        "# Display summary results\n",
        "print(logit_results.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "vN0iaCpr7gj7",
        "outputId": "ce401d59-5929-42b9-ec05-408c7ff5f8b1"
      },
      "id": "vN0iaCpr7gj7",
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 Generalized Linear Model Regression Results                  \n",
            "==============================================================================\n",
            "Dep. Variable:              cancelled   No. Observations:                36275\n",
            "Model:                            GLM   Df Residuals:                    36266\n",
            "Model Family:                Binomial   Df Model:                            8\n",
            "Link Function:                  Logit   Scale:                          1.0000\n",
            "Method:                          IRLS   Log-Likelihood:                -16696.\n",
            "Date:                Sat, 18 Mar 2023   Deviance:                       33393.\n",
            "Time:                        05:45:03   Pearson chi2:                 3.70e+04\n",
            "No. Iterations:                     7   Pseudo R-squ. (CS):             0.2914\n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================================\n",
            "                                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "----------------------------------------------------------------------------------------------\n",
            "const                         -6.4168      0.300    -21.366      0.000      -7.005      -5.828\n",
            "lead_time                      0.0386      0.002     16.832      0.000       0.034       0.043\n",
            "no_of_special_requests        -1.2210      0.075    -16.310      0.000      -1.368      -1.074\n",
            "type_of_meal_plan             -1.1551      0.086    -13.383      0.000      -1.324      -0.986\n",
            "avg_price_per_room             0.1779      0.016     11.360      0.000       0.147       0.209\n",
            "lead_time_log                 -0.0043      0.000    -10.574      0.000      -0.005      -0.003\n",
            "no_of_special_requests_log     0.0895      0.073      1.234      0.217      -0.053       0.232\n",
            "avg_price_per_room_log        -0.0268      0.003     -9.820      0.000      -0.032      -0.021\n",
            "type_of_meal_plan_log          0.4299      0.078      5.503      0.000       0.277       0.583\n",
            "==============================================================================================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 94;\n",
              "                var nbb_unformatted_code = \"from statsmodels.genmod.generalized_linear_model import GLM\\nfrom statsmodels.genmod import families\\n\\ndflog = df.copy()\\n\\ndflog['lead_time_log'] = dflog['lead_time'] * np.log1p(dflog['lead_time'])\\ndflog['no_of_special_requests_log'] = dflog['no_of_special_requests'] * np.log1p(dflog['no_of_special_requests'])\\ndflog['avg_price_per_room_log'] = dflog['avg_price_per_room'] * np.log1p(dflog['avg_price_per_room'])\\ndflog['type_of_meal_plan_log'] = dflog['type_of_meal_plan'] * np.log1p(dflog['type_of_meal_plan'])\\n\\ncols_to_keep = ['lead_time', \\n                'no_of_special_requests', \\n                'type_of_meal_plan',\\n                'avg_price_per_room', \\n                'lead_time_log', \\n                'no_of_special_requests_log', \\n                'avg_price_per_room_log',\\n                'type_of_meal_plan_log'\\n                ]\\n\\nX_ = sm.add_constant(dflog[cols_to_keep])\\ny_ = y\\n \\n# Build model and fit the data (using statsmodel's Logit)\\nlogit_results = GLM(y_, X_, family=families.Binomial()).fit()\\n\\n# Display summary results\\nprint(logit_results.summary())\";\n",
              "                var nbb_formatted_code = \"from statsmodels.genmod.generalized_linear_model import GLM\\nfrom statsmodels.genmod import families\\n\\ndflog = df.copy()\\n\\ndflog[\\\"lead_time_log\\\"] = dflog[\\\"lead_time\\\"] * np.log1p(dflog[\\\"lead_time\\\"])\\ndflog[\\\"no_of_special_requests_log\\\"] = dflog[\\\"no_of_special_requests\\\"] * np.log1p(\\n    dflog[\\\"no_of_special_requests\\\"]\\n)\\ndflog[\\\"avg_price_per_room_log\\\"] = dflog[\\\"avg_price_per_room\\\"] * np.log1p(\\n    dflog[\\\"avg_price_per_room\\\"]\\n)\\ndflog[\\\"type_of_meal_plan_log\\\"] = dflog[\\\"type_of_meal_plan\\\"] * np.log1p(\\n    dflog[\\\"type_of_meal_plan\\\"]\\n)\\n\\ncols_to_keep = [\\n    \\\"lead_time\\\",\\n    \\\"no_of_special_requests\\\",\\n    \\\"type_of_meal_plan\\\",\\n    \\\"avg_price_per_room\\\",\\n    \\\"lead_time_log\\\",\\n    \\\"no_of_special_requests_log\\\",\\n    \\\"avg_price_per_room_log\\\",\\n    \\\"type_of_meal_plan_log\\\",\\n]\\n\\nX_ = sm.add_constant(dflog[cols_to_keep])\\ny_ = y\\n\\n# Build model and fit the data (using statsmodel's Logit)\\nlogit_results = GLM(y_, X_, family=families.Binomial()).fit()\\n\\n# Display summary results\\nprint(logit_results.summary())\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
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              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hh = pd.DataFrame()\n",
        "\n",
        "hh['logit'] = np.log(lg2.predict(X_train2)/(1-lg2.predict(X_train2)))\n",
        "hh['lead_time'] = X_train2['lead_time']\n",
        "hh['avg_price_per_room'] = X_train2['avg_price_per_room']\n",
        "hh['no_of_special_requests'] = X_train2['no_of_special_requests']\n",
        "hh['type_of_meal_plan'] = X_train2['type_of_meal_plan']\n",
        "\n",
        "fig, ax = plt.subplots(2, 2, figsize=(18, 12))\n",
        "sns.regplot(data=hh, x='lead_time', y='logit', marker='o', scatter_kws={'s':0.1, 'color': 'blue'}, line_kws={'color': 'red'}, ax=ax[0,0])\n",
        "sns.regplot(data=hh, x='avg_price_per_room', y='logit', marker='o', scatter_kws={'s':0.1, 'color': 'blue'}, line_kws={'color': 'red'}, ax=ax[0,1])\n",
        "sns.regplot(data=hh, x='no_of_special_requests', y='logit', marker='o', scatter_kws={'s':0.1, 'color': 'blue'}, line_kws={'color': 'red'}, ax=ax[1,0])\n",
        "sns.regplot(data=hh, x='type_of_meal_plan', y='logit', marker='o', scatter_kws={'s':0.1, 'color': 'blue'}, line_kws={'color': 'red'}, ax=ax[1,1])\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "5-afH3BobxhI",
        "outputId": "208fa87c-496f-46ca-cd5c-e39bce82c2a3"
      },
      "id": "5-afH3BobxhI",
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x864 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 95;\n",
              "                var nbb_unformatted_code = \"hh = pd.DataFrame()\\n\\nhh['logit'] = np.log(lg2.predict(X_train2)/(1-lg2.predict(X_train2)))\\nhh['lead_time'] = X_train2['lead_time']\\nhh['avg_price_per_room'] = X_train2['avg_price_per_room']\\nhh['no_of_special_requests'] = X_train2['no_of_special_requests']\\nhh['type_of_meal_plan'] = X_train2['type_of_meal_plan']\\n\\nfig, ax = plt.subplots(2, 2, figsize=(18, 12))\\nsns.regplot(data=hh, x='lead_time', y='logit', marker='o', scatter_kws={'s':0.1, 'color': 'blue'}, line_kws={'color': 'red'}, ax=ax[0,0])\\nsns.regplot(data=hh, x='avg_price_per_room', y='logit', marker='o', scatter_kws={'s':0.1, 'color': 'blue'}, line_kws={'color': 'red'}, ax=ax[0,1])\\nsns.regplot(data=hh, x='no_of_special_requests', y='logit', marker='o', scatter_kws={'s':0.1, 'color': 'blue'}, line_kws={'color': 'red'}, ax=ax[1,0])\\nsns.regplot(data=hh, x='type_of_meal_plan', y='logit', marker='o', scatter_kws={'s':0.1, 'color': 'blue'}, line_kws={'color': 'red'}, ax=ax[1,1])\\nplt.show();\";\n",
              "                var nbb_formatted_code = \"hh = pd.DataFrame()\\n\\nhh[\\\"logit\\\"] = np.log(lg2.predict(X_train2) / (1 - lg2.predict(X_train2)))\\nhh[\\\"lead_time\\\"] = X_train2[\\\"lead_time\\\"]\\nhh[\\\"avg_price_per_room\\\"] = X_train2[\\\"avg_price_per_room\\\"]\\nhh[\\\"no_of_special_requests\\\"] = X_train2[\\\"no_of_special_requests\\\"]\\nhh[\\\"type_of_meal_plan\\\"] = X_train2[\\\"type_of_meal_plan\\\"]\\n\\nfig, ax = plt.subplots(2, 2, figsize=(18, 12))\\nsns.regplot(\\n    data=hh,\\n    x=\\\"lead_time\\\",\\n    y=\\\"logit\\\",\\n    marker=\\\"o\\\",\\n    scatter_kws={\\\"s\\\": 0.1, \\\"color\\\": \\\"blue\\\"},\\n    line_kws={\\\"color\\\": \\\"red\\\"},\\n    ax=ax[0, 0],\\n)\\nsns.regplot(\\n    data=hh,\\n    x=\\\"avg_price_per_room\\\",\\n    y=\\\"logit\\\",\\n    marker=\\\"o\\\",\\n    scatter_kws={\\\"s\\\": 0.1, \\\"color\\\": \\\"blue\\\"},\\n    line_kws={\\\"color\\\": \\\"red\\\"},\\n    ax=ax[0, 1],\\n)\\nsns.regplot(\\n    data=hh,\\n    x=\\\"no_of_special_requests\\\",\\n    y=\\\"logit\\\",\\n    marker=\\\"o\\\",\\n    scatter_kws={\\\"s\\\": 0.1, \\\"color\\\": \\\"blue\\\"},\\n    line_kws={\\\"color\\\": \\\"red\\\"},\\n    ax=ax[1, 0],\\n)\\nsns.regplot(\\n    data=hh,\\n    x=\\\"type_of_meal_plan\\\",\\n    y=\\\"logit\\\",\\n    marker=\\\"o\\\",\\n    scatter_kws={\\\"s\\\": 0.1, \\\"color\\\": \\\"blue\\\"},\\n    line_kws={\\\"color\\\": \\\"red\\\"},\\n    ax=ax[1, 1],\\n)\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> **Yes**. According to the Box-Tidwell test, the relationships between the explanatory variables and the Logit of the response variables are not linear for all the explanatory variables. However, visually, the relationships are close to linear. We will take this into account when we make conclusions about the coefficients' interpretations."
      ],
      "metadata": {
        "id": "fCfCkmXQ7gts"
      },
      "id": "fCfCkmXQ7gts"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**6. The Sample Size is Sufficiently Large**\n",
        "\n",
        "Logistic regression assumes that the sample size of the dataset if large enough to draw valid conclusions from the fitted logistic regression model.\n"
      ],
      "metadata": {
        "id": "6TFSDdyHh7kH"
      },
      "id": "6TFSDdyHh7kH"
    },
    {
      "cell_type": "markdown",
      "source": [
        "> **Yes**, the sample size is sufficiently large.\n",
        "As a rule of thumb, we should have a minimum of 10 cases with the least frequent outcome for each explanatory variable. \n",
        "We have 33 explanatory variables, and the expected probability of the least frequent outcome is 0.33. Then we should have a sample size of at least (10*33) / 0.33 = 1000. 1000 is less than the number of rows in our dataset"
      ],
      "metadata": {
        "id": "U6vQz1VDTn-T"
      },
      "id": "U6vQz1VDTn-T"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Interpretations based on coefficients obtained from the logistic regression model"
      ],
      "metadata": {
        "id": "GyEsWSeZ7V43"
      },
      "id": "GyEsWSeZ7V43"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Converting coefficients to odds**\n",
        "\n",
        "* The coefficients ($\\beta$s) of the logistic regression model are in terms of $\\log(odds)$ and to find the odds,<br>we have to take the exponential of the coefficients\n",
        "* Therefore, **$odds = e^\\beta$**\n",
        "* The percentage change in odds is given as $(e^\\beta - 1) * 100$"
      ],
      "metadata": {
        "id": "pE23HItk78YH"
      },
      "id": "pE23HItk78YH"
    },
    {
      "cell_type": "code",
      "source": [
        "# converting coefficients to odds\n",
        "odds = np.exp(lg2.params)\n",
        "\n",
        "# finding the percentage change\n",
        "perc_change_odds = (np.exp(lg2.params) - 1) * 100\n",
        "\n",
        "# adding the odds to a dataframe\n",
        "pd.DataFrame({\"Odds\": odds, \"Change_odd%\": perc_change_odds}, index=X_train2.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "J0rbTu7x8AEv",
        "outputId": "befc555c-9ffa-4077-d81a-4c94b1eaf71b"
      },
      "id": "J0rbTu7x8AEv",
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  Odds  Change_odd%\n",
              "const                          0.00821    -99.17876\n",
              "no_of_weekend_nights           1.13180     13.17971\n",
              "type_of_meal_plan              0.88977    -11.02331\n",
              "required_car_parking_space     0.32482    -67.51789\n",
              "lead_time                      1.01729      1.72904\n",
              "repeated_guest                 0.51637    -48.36335\n",
              "avg_price_per_room             1.01870      1.87032\n",
              "no_of_special_requests         0.21329    -78.67147\n",
              "children                       1.36401     36.40118\n",
              "room_type_reserved_Room_Type 2 0.77434    -22.56621\n",
              "room_type_reserved_Room_Type 4 0.84229    -15.77127\n",
              "arrival_year_2018              1.52214     52.21364\n",
              "arrival_month_2                1.59900     59.89972\n",
              "arrival_month_3                1.50504     50.50376\n",
              "arrival_month_5                0.74351    -25.64883\n",
              "arrival_month_7                0.79173    -20.82733\n",
              "arrival_month_8                0.76823    -23.17672\n",
              "arrival_month_9                0.78894    -21.10600\n",
              "arrival_month_11               1.44793     44.79255\n",
              "arrival_month_12               0.14556    -85.44395\n",
              "market_segment_type_Online     5.81345    481.34480"
            ],
            "text/html": [
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Odds</th>\n",
              "      <th>Change_odd%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>const</th>\n",
              "      <td>0.00821</td>\n",
              "      <td>-99.17876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no_of_weekend_nights</th>\n",
              "      <td>1.13180</td>\n",
              "      <td>13.17971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>type_of_meal_plan</th>\n",
              "      <td>0.88977</td>\n",
              "      <td>-11.02331</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>required_car_parking_space</th>\n",
              "      <td>0.32482</td>\n",
              "      <td>-67.51789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lead_time</th>\n",
              "      <td>1.01729</td>\n",
              "      <td>1.72904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>repeated_guest</th>\n",
              "      <td>0.51637</td>\n",
              "      <td>-48.36335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_price_per_room</th>\n",
              "      <td>1.01870</td>\n",
              "      <td>1.87032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no_of_special_requests</th>\n",
              "      <td>0.21329</td>\n",
              "      <td>-78.67147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>children</th>\n",
              "      <td>1.36401</td>\n",
              "      <td>36.40118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>room_type_reserved_Room_Type 2</th>\n",
              "      <td>0.77434</td>\n",
              "      <td>-22.56621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>room_type_reserved_Room_Type 4</th>\n",
              "      <td>0.84229</td>\n",
              "      <td>-15.77127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arrival_year_2018</th>\n",
              "      <td>1.52214</td>\n",
              "      <td>52.21364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arrival_month_2</th>\n",
              "      <td>1.59900</td>\n",
              "      <td>59.89972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arrival_month_3</th>\n",
              "      <td>1.50504</td>\n",
              "      <td>50.50376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arrival_month_5</th>\n",
              "      <td>0.74351</td>\n",
              "      <td>-25.64883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arrival_month_7</th>\n",
              "      <td>0.79173</td>\n",
              "      <td>-20.82733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arrival_month_8</th>\n",
              "      <td>0.76823</td>\n",
              "      <td>-23.17672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arrival_month_9</th>\n",
              "      <td>0.78894</td>\n",
              "      <td>-21.10600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arrival_month_11</th>\n",
              "      <td>1.44793</td>\n",
              "      <td>44.79255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arrival_month_12</th>\n",
              "      <td>0.14556</td>\n",
              "      <td>-85.44395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>market_segment_type_Online</th>\n",
              "      <td>5.81345</td>\n",
              "      <td>481.34480</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c3e19f90-cfaa-4552-b91e-11a83670bb8e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 96;\n",
              "                var nbb_unformatted_code = \"# converting coefficients to odds\\nodds = np.exp(lg2.params)\\n\\n# finding the percentage change\\nperc_change_odds = (np.exp(lg2.params) - 1) * 100\\n\\n# adding the odds to a dataframe\\npd.DataFrame({\\\"Odds\\\": odds, \\\"Change_odd%\\\": perc_change_odds}, index=X_train2.columns)\";\n",
              "                var nbb_formatted_code = \"# converting coefficients to odds\\nodds = np.exp(lg2.params)\\n\\n# finding the percentage change\\nperc_change_odds = (np.exp(lg2.params) - 1) * 100\\n\\n# adding the odds to a dataframe\\npd.DataFrame({\\\"Odds\\\": odds, \\\"Change_odd%\\\": perc_change_odds}, index=X_train2.columns)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Coefficient interpretations**\n",
        "\n",
        "* `children`: The odds of cancelation of a booking with child(ren) are 1.36 times more than a booking for adults only (**36% increase** in the odds of the booking being canceled), holding all other features constant.\n",
        "\n",
        "* `required_car_parking_space`: The odds of cancelation of a booking with a requirement of a car parking space are 3 times (1/0.32) less than for a booking without such requirement (**68% fewer** odds of the booking being canceled), holding all other features constant.\n",
        "\n",
        "* `repeated_guest`: The odds of cancelation of a booking for a repeated guest are 2.00 times (1/0.52) less than for a booking for a new customer (**48% fewer** odds of the booking being canceled), holding all other features constant.\n",
        "\n",
        "*  `arrival_year`\n",
        "  * `arrival_year_2018`: The odds of cancelation of a booking with an arrival date in 2018 are 1.52 times more than for a booking with an arrival date in 2017 (**52% increase** in the odds of the booking being canceled), holding all other features constant.<br>*The dropped category `arrival_year_2017` is taken as a reference level.*\n",
        "\n",
        "* `market_segment_type`: \n",
        "  * `market_segment_type_Online`: The odds of cancelation of a booking comes from the `Online` market segment type are 5.81 times more than for a booking comes from another segment (**481% increase** in the odds of the booking being canceled), holding all other features constant.<br>*The dropped categories `Aviation`, `Complementary`, and `Offline` are taken as a reference level.*\n",
        "\n",
        "* `no_of_weekend_nights`: Holding all other features constant 1 unit increase (1 night) in `no_of_weekend_nights` will increase the odds of a booking being canceled by 1.13 times (**13% increase**).\n",
        "\n",
        "* `no_of_special_requests`: Holding all other features constant 1 unit increase (1 request) in `no_of_special_requests` will decrease the odds of a booking being canceled by 4.69 (1/0.21) times (**79% decrease**).\n",
        "\n",
        "* `avg_price_per_room`: Holding all other features constant, 1 unit increase (1 euro) in `avg_price_per_room` will increase the odds of a booking being canceled by ~1.02 times (**2% increase**).\n",
        "<br>**NB:** *Since a linear relationship between the explanatory variable `avg_price_per_room` and the Logit of the response variable `booking_status` has not been confirmed using the Box-Tidwell test, the interpretation may be incorrect.*\n",
        "\n",
        "* `lead_time`: Holding all other features constant, 1 unit increase (1 day) in `lead_time` will increase the odds of a booking being canceled by ~1.02 times (**2% increase**).\n",
        "<br>**NB:** *Since a linear relationship between the explanatory variable `lead_time` and the Logit of the response variable `booking_status` has not been confirmed using the Box-Tidwell test, the interpretation may be incorrect.*\n",
        "\n",
        "* `type_of_meal_plan`: Holding all other features constant, 1 unit increase (1 meal time) in `type_of_meal_plan` will decrease the odds of a booking being canceled by ~1.12 (1/0.89) times (**11% decrease**).\n",
        "<br>**NB:** *Since a linear relationship between the explanatory variable `type_of_meal_plan` and the Logit of the response variable `booking_status` has not been confirmed using the Box-Tidwell test, the interpretation may be incorrect.*\n",
        "\n",
        "* `room_type_reserved`: \n",
        "  * `room_type_reserved_Room_Type 2`: The odds of a booking with `Room_Type 2` being canceled is 1.29 (1/0.77) times less than the booking with selected `Room_Type 1, 3, 5, 6, or 7` (**23% decrease**), holding all other features constant.\n",
        "  * `room_type_reserved_Room_Type 4`: The odds of a booking with `Room_Type 4` being canceled is 1.19 (1/0.84) times less than the booking with selected `Room_Type 1, 3, 5, 6, or 7` (**16% decrease**), holding all other features constant.<br>*The dropped categories `Room_Type 1`, `Room_Type 3`, `Room_Type 5`, `Room_Type 6` and `Room_Type 7` are taken as a reference level.*\n",
        "\n",
        "* `arrival_month`:\n",
        "  * The odds of a booking with an arrival date in Feb, Mar, and Nov being canceled is **more** than the booking with an arrival in Jan, Apr, Jun, or Oct, holding all other features constant.\n",
        "    * `arrival_month_2`: 1.60 times or 60% more;\n",
        "    * `arrival_month_3`: 1.51 times or 51% more;\n",
        "    * `arrival_month_11`: 1.45 times or 45% more;\n",
        "  * The odds of a booking with an arrival date in May-Sep and Dec being canceled is **less** than the booking with an arrival in Jan, Apr, Jun, or Oct, holding all other features constant.\n",
        "    * `arrival_month_5`: 1.34 (1/0.74) times or 26% less;\n",
        "    * `arrival_month_7`: 1.26 (1/0.79) times or 21% less;\n",
        "    * `arrival_month_8`: 1.30 (1/0.77) times or 23% less;\n",
        "    * `arrival_month_9`: 1.27 (1/0.79) times or 21% less;\n",
        "    * `arrival_month_12`: 6.87 (1/0.15) times or 85% less.\n",
        "<br>*The dropped categories `January`, `April`, 'June` and `October` are taken as a reference level.*\n"
      ],
      "metadata": {
        "id": "Td6137WN9LPl"
      },
      "id": "Td6137WN9LPl"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checking performance of the new model"
      ],
      "metadata": {
        "id": "nkI46Vw5azkX"
      },
      "id": "nkI46Vw5azkX"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Training set performance**"
      ],
      "metadata": {
        "id": "7RgqgGiOa-gr"
      },
      "id": "7RgqgGiOa-gr"
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Training performance:\")\n",
        "lg_train2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "id": "xKPV3pSIbLQ4",
        "outputId": "b89a9f10-840c-4c74-b6df-86e0109b1f50"
      },
      "id": "xKPV3pSIbLQ4",
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training performance:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Accuracy  Recall  Precision      F1\n",
              "0   0.80600 0.64738    0.73251 0.68732"
            ],
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              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
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              "      <th>F1</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.80600</td>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a17dd33d-83fb-4b5e-b967-34adee70415a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a17dd33d-83fb-4b5e-b967-34adee70415a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 97
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 97;\n",
              "                var nbb_unformatted_code = \"print(\\\"Training performance:\\\")\\nlg_train2\";\n",
              "                var nbb_formatted_code = \"print(\\\"Training performance:\\\")\\nlg_train2\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
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              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Testing set performance**"
      ],
      "metadata": {
        "id": "HGBJsirwbO49"
      },
      "id": "HGBJsirwbO49"
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Test performance:\")\n",
        "lg_test2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "outputId": "5e775708-a1da-4b56-e99c-03a63515009b",
        "id": "muZyje_JbO4-"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test performance:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Accuracy  Recall  Precision      F1\n",
              "0   0.80906 0.65503    0.72776 0.68948"
            ],
            "text/html": [
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Precision</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.80906</td>\n",
              "      <td>0.65503</td>\n",
              "      <td>0.72776</td>\n",
              "      <td>0.68948</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-df6a3247-14fd-4471-9166-f1a46ea39b8d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-df6a3247-14fd-4471-9166-f1a46ea39b8d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-df6a3247-14fd-4471-9166-f1a46ea39b8d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 98
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 98;\n",
              "                var nbb_unformatted_code = \"print(\\\"Test performance:\\\")\\nlg_test2\";\n",
              "                var nbb_formatted_code = \"print(\\\"Test performance:\\\")\\nlg_test2\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "muZyje_JbO4-"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The model is giving a good $F_1$ score of ~0.68732 and ~0.68948 on the train and test sets respectively\n",
        "* As the train and test performances are comparable, the model is not overfitting\n",
        "* Moving forward we will try to improve the performance of the model"
      ],
      "metadata": {
        "id": "PYx2SnBFbhUc"
      },
      "id": "PYx2SnBFbhUc"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Performance Improvement"
      ],
      "metadata": {
        "id": "b-VNZSx1bv0z"
      },
      "id": "b-VNZSx1bv0z"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Let's see if the $F_1$ score can be improved further by changing the model threshold\n",
        "* First, we will check the ROC curve, compute the area under the ROC curve (ROC-AUC), and then use it to find the optimal threshold\n",
        "* Next, we will check the Precision-Recall curve to find the right balance between precision and recall as our metric of choice is $F_1$ score"
      ],
      "metadata": {
        "id": "pOYJFFrPb1st"
      },
      "id": "pOYJFFrPb1st"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ROC Curve and ROC-AUC"
      ],
      "metadata": {
        "id": "fKHtJsFXb_HQ"
      },
      "id": "fKHtJsFXb_HQ"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ROC-AUC on training set**"
      ],
      "metadata": {
        "id": "eaThHV5CcBaA"
      },
      "id": "eaThHV5CcBaA"
    },
    {
      "cell_type": "code",
      "source": [
        "logit_roc_auc_train = roc_auc_score(y_train, lg2.predict(X_train2))\n",
        "fpr, tpr, thresholds = roc_curve(y_train, lg2.predict(X_train2))\n",
        "plt.figure(figsize=(7, 5))\n",
        "plt.plot(fpr, tpr, label=\"Logistic Regression (area = %0.2f)\" % logit_roc_auc_train)\n",
        "plt.plot([0, 1], [0, 1], \"r--\")\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.title(\"Receiver operating characteristic\")\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "QwfGHPgHbLTX",
        "outputId": "b935dbdd-62b7-4fe4-b451-8232d68112e5"
      },
      "id": "QwfGHPgHbLTX",
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ],
            "image/png": 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LY/v27bz55pvMmDEjp6IIIYQQ95VjRbBevXr4+fml+/qOHTvo3r07Go2GWrVqER8fz82bN3MqjhBCCHEPh02bFhUVRUhIiP1xSEgIUVFRFClSxFGRhBDCqVltihSLFYtVYbEpzFYbUQlGTFYbtxJNaDUabEphtanU/yuw3f23xaYwWWxY775utaW+brEqwmOTCfR0sa9nsSlsCmxKcSU6mcLerqBAofjn5nzq7n+pD1Tq43+99s9zd/+Z5vXULf33tf+fT/Wf57j7vKtey+YxzbPUZk43d6hGkzq3nng4Op1W2i0LpN2yRtrt4RmMFm4bTEREJnAnIYVbiSZMFhu3Eo2Yrak3Dbgek4xOq8FsVRy+EoO/hwspFithd5LwcNGRbLZmWx6tBnRaDVqNBsvdohji645OCzqtFp0m9f8arYZztwyUCPBAowENGjSa1G1oAI1GAxoNmrvbvPsSmrvP/f+y///iP89r7j6h+eeluwtrABeLiUYHfmRv0y64uWS9lDmsCAYHBxMZGWl/HBkZSXBwcIbryQTaWSOT8maNtFvWFOR2u5VoJCw6iZsJJlIsVkxWRUKKmbhkC656LQaThZgkM0aLjWuxyei1Wi5HZ66t3PRajBYbRX3d0AJWm40Khb0oX8gLnRaK+rpjtiqCfd3QazXotRpMFhvlg7xw12vR67R4uujQakGnSS1wWq0GnQa0Gg2uei0uutQCp/mnOuVB+qN/4jN6JPq/z9Kze1PMjZpkfVvZmOuhhIaGsmbNGjp16sTx48fx8fGRoVAhRK5Rd4f9zHeHAZPNViw2Ze/1pA4HKuKSzQCYrIrbiUZsCsJjk1EKTFYb4THJ3Ew0EhadnKn9+rjp8XXX4+/hQmEvV2KSzfR+vCg6rYZyhb3w9nKjkJuWIt5uuOq0uOq0eLjqcNPLFW2kpOC1YC4eHy3GFhxC3Lr1j1QAIQeL4Lhx4zh06BAxMTE0b96cl19+GYvFAsDTTz9NixYt2L17N23btsXDw4M5c+bkVBQhRAFgsSmuxSRzPT6FiLgUog0mzDaFxZpazCxWGwaTlYi4FOJSzNxKNGEwZX34UK/V4Ouux8tVR/kgb6qF+GAwWSni7Ua1oj6UCvDAz8MFX3c9LneLmU6bce+qIPeiM+I3oC+ue34lecCzGGbMRvmmf/JlZmnUv482OgGbTXHnTqKjYzgd+cXKGmm3rMmtdrtw28CUzWcwW23ciEvhv/eiddFp0Gk06O/+31WvJdDTFbPVRnF/D6qGeOOqSx0mtNkULjotfu56dFqN/T+bTeHlpsPTVY+rToO7XkeQtyvuWbyJa0bkM/cfycmg04GrKy67fwWbDXOr1vcsltX7CTrdiTFCiIJHKcUdg4lEo5UUi5XoJDMXbxvYc/EOl+8kUczXjQH1SvCYnzs+bnpKF/KkdIAHep0MIToz/aGD+IwZibFnH5Jem4y5Ravs30e2b1EIIR5CstnKqRsJGEwWjBYbEXEpxCSbCY9JRqvREBGfQlh0Eub/dvPualDKnw9718zl1CJHJSXhNfdNPJZ/jK14Ccz1G+bYrqQICiGyncFo4ebd69Piks3cNpiISUo9Dnc9PgU3nZYj12IzPC7nptdSvagPnasFE+TtRrCPG75uerzcdBTxdqOIjxvucsJIvqI/chifkUPRX75E8vNDMUybifLO2lBnpvaXY1sWQuRriUYLpyMTuG0wERadREKKhSPX4ohJMhNz94zKBykZ4EH1oj7UeswPX3cXKhXxwtfdBRfdPyec6DN1IonIZ3Q60GiI3bAZc9OsXQD/MKQICiEeyGixceG2gZ3nbnPuViKGu8Xv36OTOq0GHzc9fu56/Dz09H6iOJ46DYGeLigF/h4uBHi6UMzPHT93fZ6+Bk3kPpd9e3HZu4ekCVOwPF6bmN8PpxbDXCBFUAhhl2i0cP6WgSPXYjkblcilO0lcjUl7/VsxXze61gjB00VPxSJePObnTtUQH1z+dRKKnOEoMiUxEe/Zb+Dx2SdYypQleeQolI9vrhVAkCIoRIGilOJydBJHwuMwWmzcuTuUGZNs5uSNhHuW93TRUbOYL60rFuaJEv5UDPKSXpzIFi6/7cbnlVFow6+S9MKLGCZPB8/cn2pPiqAQ+dDtRCOXo5MIj03hemwKJquNsDtJHLgSk2Y5nQa83fQkGi2UDvSgVIAnTcoGUvsxP0oGeqCVgidygCYuFt/n+mMLCiL2h5+wNMi5sz8zIkVQCCditSkSjalzT95JMhERl8LfNxM5f8uA2aq4HpdMstlKstl2z7ol/N15skoRAj1daFcpiGJ+7vh7uEjPTuQa/dE/sdSqg/LzJ+6rb7FUq+GQ3l+aTA7duxAiDaUU8SkWUiw2IuNTiIw38tulO0TGGzkTlYApnWvl/lGvpD86rYa6JfwpU8iTYB83ini7EuDpmkvvQIh7aeLj8Jo5DY/Vq4hf8inGXk9hqdfA0bEAKYJCOIzRYiMhxUxEvJGTN+L5/kRkuncTcNNreaKEP1abokYxX7xcdZQO9MTDRUdxf3dCfN1zOb0QmeOy82d8xo1GG3mDpFFjMXbs4uhIaUgRFCIXJJmsnLwRz6GrsZitNjafiiLRaMH2r45dgIcL9Uv6UzrQk/JBXvi46Snh70GIrxt+Hi6OCy9EFnm9NRPPxQuxVKpM7IovsDxRz9GR7iFFUIhsZLUpTkcmcDg8lthkM9diU0hIMXP0enya5fzc9dQvGUDz8oVw02mpU8KP4v4eDkotRDZTCjQazA0akjRmPIbxE8E9b45WSBEUIossdwvelegkfr8cTVh06jV1/57jUq/VYFOKIt6udK4WTPPyhalcxFtmQhH5kiY2Bu9pk7GWLEXSa5MxtWmPqU17R8d6ICmCQjwEm1L88vcttp25yd5L0WleK+zlSrOyhWhUOoDqRX0pFeiR5gJyIfIz15+24f3qGLS3b5E0boKj42SaFEEhMhCbbLb39r4+EkGSOXXC55blC1G7uB/lCntRJdgbX3c5bicKHk1MNN5TJ+H+zVdYqlYn9sv/YalZy9GxMk2KoBD/ERmfwtbTNzl6PY4DYWkvLq9b0p/GpQPoV+cx6eUJAeiuheO2eSOGVyeRNPZVcHWuy3GkCApB6uUKnx+6yv+ORhCXYrE/XyLAg7ol/KhXMoDKRbwpESAnrwihuXMHt22bSRnwLJYaj3Pnz1OowoUdHStLpAiKAstqUxy5Fsumk1FsO3PT/nzvx4vSqkJh6pX0JyDASyaCFuJfXDdtxGfiODRxsZiat8RWspTTFkCQIigKGKUUP5yM5Mi1OI5diyMi3ghAmUBPBtQtTrvKQbi75N4M9kI4C83t23hPfhX3jRsw16xFwjcbsZUs5ehYj0yKoCgwbErxyncn2Xc59ThfvZL+9K3zGN1qhODlKr8KQqTLYiGgQyjaGxEYpkwn6aUx4JI/TgST33xRYLy365K9AO58qTE+7vLxF+JBNNF3UAGBoNeTOHMO1rLlsFau4uhY2UpObxP53tFrcSzYcYF1R64D8Mf45lIAhXgQpXDb8A2BjergvnY1AKaOnfNdAQTpCYp86ui1OFYevMrBKzH2+Tm9XHV0rhbs2GBC5HHaqEi8X3sFtx+3YH6iLuY8creHnCJFUOQbkfEprDl8jR/P3LRf5lCzmC+FvVwZ2aQ0pQs59r5lQuR1rpt/wGfcKDQpKSS+MZvkES+BLn+fKCZFUDg1pRTHr8dzOTqJOT+fB8BVp6HX40XpUi2YakV9HZxQCCfi4Y61QiUSFn+MtXwFR6fJFVIEhVM7HB7Li9/8ZX88unkZBtYr4cBEQjgRpXD7ei3aO3dIfmk0ptbtMIW2BU3BmeBdiqBwWharzV4Alz5Vk9KBnhTycq4pm4RwFG3EdbzHj8Ztx8+Ymjb//6HPAlQAQYqgcELHr8dx5FocX90929PLVUed4n5oCtgvrxBZohTua1fjNX0KGouZxLfmkzzkBdAWzIsFpAgKp2Cx2pj7y3l+OnsLo8UGgLebjpeblWFgveJSAIXIJN2lC3i/NhZz/YYkLPoQW5myjo7kUFIERZ73x9UYpm45S3SSGYCBdYvToWoRyhbykpvTCpEZSuGy/3fMjZtiLVeB2M3bsdSqU2B7f/8mRVDkWV8duc7GvyK5cNuAVgPtKgUxpV0FmeJMiIegDb+Kzysv47rnV2K2/IylXgMsdeo6OlaeId8mIs/54a9IPv8jnKsxyQAMa1SS/nWLS/ET4mHYbLh/sRKvmdMASHh7EZYn6jk4VN4j3yoiT1BK8dulaFb/Ec6x6/EAtK8cxKutyuPvmT8m6hUiN/kOHojb1k2YmrciYdEH2EqUdHSkPEmKoHCoyPgUfr1wh3d/vWh/rmHpABZ2q4arXo5XCPFQbLbUSxw0GowdOmFq3ZaUAc8WuMseHoYUQeEQSimGfXWc4xHx9ufaVQpiWONSlA6U6c2EeFjay5fweWUUxt59SRnwLMa+zzg6klOQIihylVKKVYfCWX8sgpuJJgDmdalC3RL++HnIsKcQD81mw+PTpXjNmYXS6UnpP8jRiZyKFEGRK87fSuS7E5FsP/v/k1s3L1eI6e0rSvETIot0ly7gM+YlXA7ux9imHYnvLMZW7DFHx3IqUgRFjrl428AfV2P58cxNzkQlAFDU153axf2Y3akKbnLMT4hHort8Cd3ZM8S/vyR1+FOO/T00KYIiR2w9HcUb2/62P65d3I+5navI3J5CPCLdhfPoDx/C2K8/ptbtiP7zL5Svn6NjOS0pgiLb/HNbozk/n+dydBIAk9uUp13lIni7yUdNiEditeKx5EO85s9G+fph7NwNvL2lAD4i+WYS2eLYtTje3XWRM1GJ6LUaqoX40KNmCN1qFHV0NCGcnu7c3/iMGYnLn4cxduhMwtuLwNvb0bHyBSmC4pGYrTaW77vC54fC8XbTS89PiGymiYnGv30rcHMlftlnGLv3kmN/2ShHv6n27NnDW2+9hc1mo0+fPgwfPjzN6xEREUycOJGEhASsViuvvvoqLVq0yMlIIpvciE/h+xM3+OxgOAB+7nrWDKxDiK+7g5MJkT9oI29gCymKCggkYfFHmBs2QRUp4uhY+U6OFUGr1cqsWbNYuXIlwcHB9O7dm9DQUMqXL29fZsmSJXTo0IFnnnmGCxcuMHz4cHbu3JlTkUQ2uBqTzKA1RzCYrAC46jQMb1yaQXI7IyGyh9mM56IFeC6cT9wX6zCHtsXUtYejU+VbOVYET5w4QalSpShRogQAnTp1YseOHWmKoEajITExEYCEhASKyF85ed7rm89gMFkJ9HTho941KVPIU25nJEQ20Z06iX7cKFyOHiGlW08sNWs7OlK+l2NFMCoqipCQEPvj4OBgTpw4kWaZUaNGMWTIENasWUNycjIrV67MqTjiEZksNo5ei+PszURKBnjw7WCZjV6I7OTx8Qd4vTUD/P2JW/EFpi7dHR2pQHDo2QtbtmyhR48eDB48mKNHjzJhwgQ2b96M9gE3etRowN9f5pZ8WDqdNkvtduJaHF8cuMLG4xH25wY0LFVgfgZZbbeCTtrt4WlCCqN69IQPPsAzIBBpvdyRY0UwODiYyMhI++OoqCiCg4PTLLN+/Xo+/fRTAGrXro3RaCQmJoZChQqlu12lIDY2KWdC52P+/p4P1W5XY5KZvOk0524ZgNTbGlUN8SG0QmFCfN0LzM/gYdtNpJJ2ywSTCc9FC7CVKEnKMwOhRz/o+bS0XRYFBflkab0cm7eqRo0ahIWFER4ejslkYsuWLYSGhqZZpmjRouzfvx+AixcvYjQaCQwMzKlIIpMu3DLQ67M/OHfLQKsKhdk4tD6zO1XhmSeKy9mfQmQD/YljBLRridfC+eiPHUl9Uk4sc4gc6wnq9XqmT5/O0KFDsVqt9OrViwoVKrB48WKqV69O69atmTRpElOnTmXVqlVoNBrmzZsnZxg6iFKKbWdusupQOJfvpP4V+lz9ErzUrIyDkwmRjxiNeC56G8/F72IrHETc6q8xte/g6FQFmkYppRwd4mHYbIo7dxIdHcPpPGiIJcVs5ekv/uRabAo6rYb2lYPo9XgxahbzzeWUeY8MTWWNtNv9uezdg3/PzqT060/irDko/4B7lpG2y5qsDofKtB4F3LXYZHqs+AMAnQZ2v9xE7u4gRHZKScHlwD7MLUMxN21O9K/7sFar7uhU4i75tivAzFabvQC+1LQ0B8Y1lwIoRDbS//kHAW2a4fdMb7TXUmdXkgKYt8g3XgEVdieJxu/tBaB7jRCea1DSwYmEyEdSUvCaNR3/Tm3RJCYSt+Z/2IqXcHQqcR8yHFrA/H0zkRe/OUH83bu7t6sUxMQ2FRycSoh8xGwmoH0r9GdOkTzweQwz3kT5yPH1vEqKYAFittqYse1v4lMsVAzyYmC9EjxZRaaqEyJbmM3g4gIuLiQPeg5ruQqYW4ZmvJ5wKBkOLQCUUnx75BqN39vLhdsGqgR78+WgJ6QACpFN9Af2E9CsPq47tgOQMuQFKYBOQnqC+dzpyARe/OYEBpMVL1cdHasGM6yRHP8TIlsYDHjNnYXHJ0uxlSiJ8pDJzpyNFMF8LCrByLNfHgVgSofKdKxYWM7+FCKbuBzYh8/okejCLpM8eBiJU2fK3d6dkBTBfOrvqEQGrEmdjmlWx0o83ai0XIArRDbSXbwAShH73RbMTZo5Oo7IIimC+Ux4TDJfH73O10dT7/owunkZOlQJzmAtIURmuPz+G9pbNzF270XKMwNJ6dEbPGUI1JlJEcxHjlyL5YWvU+/ZWK6wJx2qBDOwnlybJMQjS0zE+83peKz8FHPNWhi79gCtVgpgPiBFMJ/Yee4WEzedAWB6+4p0qR6SwRpCiMxw2bMLn3Evow2/StILL2GYPC21AIp8QYqgk7sSncTL3/7FjXgjAB/0qk7D0nI7KiGyg+7c3/j37oqlXHliN23HUr+BoyOJbCZF0IlN3nSaX87dBqBNxcJMaF2eAE9XB6cSwvlpr4RhK1Uaa8VKxH+yCmO7DuDh4ehYIgdIn94J3YhPYfhXx+wF8M2OlZnTuYoUQCEekSY+Du9XRhHYqA66v1KPrxu79ZQCmI9JT9CJKKX48LfLfPHHNQCK+rqx7tkn8HKVH6MQj8p1x3a8x49BG3mD5JfGYK1Q0dGRRC6Qb08ncfRaHDN+/JuIuBQA3utZnSZl5NifEI9MKbxfHYvH6pVYKlUm9rPVWOrUdXQqkUukCOZxBpOFC7cMjP/+FAlGC8888RijmpXBRScj2UJkC40G22OPYXjlVZLGTQQ3N0cnErlIimAetu9yNNO2nrXf9ujd7tVoVq6Qg1MJ4fw0MdF4T52EsXtPTG2fJGncBEdHEg4iRTCPemfnBb4+GkGQtyuzO1ambGFPKgTJvIRCPCrXbVvwfm0s2ug7WB6v5eg4wsGkCOYxZquNsRtOcuhqLAAf9KpBucJejg0lRD6gib6D95QJuG/4Bku1GsSuW4+lxuOOjiUcTIpgHvLNsQgW7bqI2aoA2DC4HiUC5NRsIbKD6/YfcfvhOwwTppA0ehy4yiVF4iGKYHJyMh5yrUyOORWZwNs7LgCp0541KRtIoFz3J8Qj0dy+jf7UX5hbtMLY9xks9RtgLVve0bFEHpLhKYZHjhyhY8eOdOjQAYCzZ88yY8aMnM5VoFyPS2bU+tQLcz/t9zhdqodIARTiEbn+8B2Bzevj+8LzYDCARiMFUNwjwyI4d+5cVqxYgb+/PwCVK1fm8OHDOZ2rwPjjagw9V/xBotHKO92q8vhjfo6OJIRT09y6he+QQfgNfRbrYyWI3bAFvOS4uri/TA2HFi1aNM1jrcygni2OX4/jxW/+AuDjPjWoVzLAwYmEcG6aO3cIbF4fTUICia+/QfJLY0Avpz6I9GX46ShatChHjhxBo9FgNpv54osvKFeuXG5ky7eUUiz89aL9xref9ntceoBCPIqUFHB3RxUqRNKLYzC1exJrpcqOTiWcQIZduhkzZvDll18SFRVF8+bNOXPmDG+88UZuZMu3Pj8Ubr8GcHHP6lIAhcgqpXD79n8UeqI6+hPHAEh+eawUQJFpGfYEL1++zMKFC9M89+eff/LEE0/kWKj8bH9YNB/tDaOEvzvfDq6HRqNxdCQhnJI2KhLv18bi9uNWzE/UQ8lxP5EFGfYEZ8+enannRMaik0yM/vYkWg0sfepxKYBCZJHbt/8joGl9XHftJHHmHGI3b8daroKjYwknlG5P8OjRoxw9epTo6GhWrlxpfz4xMRGr1Zor4fKbF79JvQxieONSFPGRSXqFyCrdpYtYK1UmYfFHUvzEI0m3CJrNZpKSkrBarRgMBvvz3t7evP/++7kSLr9QSjHvlwtcvJ1EyQAPhjQs5ehIQjgXpXD76ktswcGYQ9uS9MprqZNe63SOTiacXLpFsH79+tSvX58ePXrw2GOP5WamfGf1H9fYcOIGvu561g2SY6lCPAzt9Wv4jB+N685fSOneE3NoW7nsQWSbDD9JHh4ezJ8/nwsXLmA0Gu3Pf/HFFzkaLL+IjE/hg98u4+/hwubhDXDVyzWWQmSKUrh/+QVeb7yOxmohYe4CUp4f5uhUIp/J8Bv51VdfpWzZsly7do1Ro0bx2GOPUaNGjdzIli9sOHEDSL0TvJsUQCEyzfWXn/AZ9zKWmo8TvWs/KUNeAJmoQ2SzDD9RsbGx9OnTB71eT/369Zk7dy4HDhzIjWxO71psMqsOhlMtxIdqIT6OjiNE3qcUunN/A2Bq0564z9cR9+0mbKXLODiYyK8yLIL6u2PvRYoUYdeuXZw+fZq4uLgcD5YfvPztXyhgcMOSjo4iRJ6nvRKGX++u+LdvhTbyBmg0mDp0kt6fyFEZHhMcOXIkCQkJTJw4kTfffBODwcCUKVNyI5tT+/nvW1yLTaF+SX+alyvk6DhC5F02G+6rVuA9azpKo8Ew8y1swSGOTiUKiAyLYKtWrQDw8fFh9erVQOqMMSJ9f99MZMrmMwC88WQlB6cRIg8zmfDr1xPXvXswtWhFwrsfYCshIyci96RbBK1WK9u2bSMqKopmzZpRsWJFfv31V5YtW0ZKSgrff/99LsZ0HkopJm86DcDo5mXkonghHsTVFUv1mhh7PUXKMwNBZlESuSzdIvj6669z48YNatasyezZsylSpAgnT57k1VdfpU2bNrmZ0al891ck4bEpPFWrGAPrlXB0HCHyHO2li/i8NhbD9FlYHq+NYdYcR0cSBVi6RfDkyZP88MMPaLVajEYjTZo04eeffyYgQO55lx6z1cbcn8/jotMwtmVZR8cRIm+x2fD4dCleb81EubiijYiAx2s7OpUo4NItgi4uLvab57q5uVGiRAkpgA9gU8p+HHBYo1K46OSMNiH+obt4Hp8xL+Fy6ADGtu1JfGcxtqLFHB1LiPSL4KVLl+jSpYv98dWrV9M83rRpU4Yb37NnD2+99RY2m40+ffowfPjwe5bZunUrH374IRqNhsqVK99z2yZnYLEpnvn8Ty5HJwEwSIZBhUjD7fsN6P4+S/yHyzD26SfH/kSekW4R3Lp16yNt2Gq1MmvWLFauXElwcDC9e/cmNDSU8uXL25cJCwtj+fLlrFu3Dj8/P+7cufNI+3QEpRQztp3lcnQSPm56trzQAJ1WfsGF0J0/h/b2LcyNmpA0ehwpA56VSx9EnpNuEXzUSbNPnDhBqVKlKFEitVfUqVMnduzYkaYI/u9//6N///74+aXeWb1QIee7nm7tn9f56ewtAH4c0VDmBhXCYkH7zgICZs7AWrY8Mbv2gYuLFECRJ+XYVOxRUVGEhPz/hz44OJgTJ06kWSYsLAyAfv36YbPZGDVqFM2bN3/gdjUa8Pf3zPa8WRGXbOa93ZcA+HNKa3w9XBycKH06nTbPtJszkXZ7SKdPoxs2BO0ff2Dr1h31wYf4B8gd3x+GfOZyl0PvR2K1Wrly5QqrV68mMjKSAQMGsGnTJnx9fdNdRymIjU3KxZTpW74vDICp7SpgM5qJNZodG+gB/P0980y7ORNpt8zTnT5FQLsWKG9vLGvWEtO2U+pfrdJ+D0U+c1kTFJS1+ZkzNXaXkpLCpUuXHmrDwcHBREZG2h9HRUURHBx8zzKhoaG4uLhQokQJSpcube8d5nWJRguf7L8KQMeqwRksLUT+pUlMAMBapSqGCVOI/u0P1FNPyckvwilkWAR37txJt27dGDp0KABnzpxhxIgRGW64Ro0ahIWFER4ejslkYsuWLYSGhqZZpk2bNhw6dAiA6OhowsLC7McQ8zKL1Ub3T1Nzv/FkRbkcQhRMZjOe775N4BPV0V69AhoNyaPHoYKCHJ1MiEzLcDj0ww8/ZP369QwcOBCAKlWqcP369Yw3rNczffp0hg4ditVqpVevXlSoUIHFixdTvXp1WrduTbNmzfj999/p2LEjOp2OCRMmOMW1iPN2XCAuxYJOq5FeoCiQdCf/wmfMi7j8dZyUHr1QXt6OjiRElmRYBPV6PT4+WRtrbdGiBS1atEjz3JgxY+z/1mg0TJ48mcmTJ2dp+46QkGJh41+RFPZyZcsLDdDKkI8oSJTC8515eC5agAoIJG7ll5g6dcl4PSHyqAyLYPny5dm0aRNWq5WwsDBWr15N7doFd6qjD3+7DMD0JytKARQFj0aDNvIGxm49SXxrPirQ+S5rEuLfMjyYNW3aNC5cuICrqyvjx4/H29ub119/PTey5Tknb8Sz4cQNABqUyvvDtkJkC5MJz3mz0R8/CkDi/HdJWPKpFECRL2TYE7x06RKvvPIKr7zySm7kybMi4lJ4fu0xAD7vX1t6gaJA0B8/is/oF9GfOQU6HZbHa4PeoVdWCZGtMvw0z5s3j9u3b9O+fXs6duxIxYoVcyNXnnPhtgGA5+qXoGpI1o6RCuE0jEY8F87H84NF2IKKELfma0ztOjg6lRDZLsMiuHr1am7dusW2bduYPn06BoOBDh068OKLL+ZGvjzj6yPX0SCTY4uCwX3NKrzee4eUfv1JnDUH5S/D/yJ/ytQFbkFBQQwaNIiZM2dSuXJlPv7445zOlafYlOLQ1VhKF/LEx12GgkQ+lZKC7szp1H8OGkzshs0kvL9ECqDI1zL8Rr948SJbt25l+/bt+Pv706FDByZNmpQb2fKMmwlGABqXDnRwEiFyhv7wIXzGvIgmIYHog8fAwwNz0wfP4ytEfpBhEZwyZQodOnTg008/vWfas4Ji5cFwAFpXLOzgJEJks+RkvOa/hcfSD7EVLUbCex+Bh4ejUwmRazIsgl9//XVu5MizjBYbG07coE5xP2oUS39ibyGcjebWLfy7tkd/8QLJgwZjeGMWykc+46JgSbcIjhkzhsWLF6e5m/y/ZebO8vnB4auxAIRWkF6gyCeUAo0GVbgw5sZNSZy3EHOLVo5OJYRDpFsE/7kgfunSpbkWJi96e8d5ANpWlkmBhfNzObAPr9cnEr9yDbaSpUhc+L6jIwnhUOmeHVqkSBEA1q5dy2OPPZbmv7Vr1+ZaQEc6E5VARLyRBqX8CfR0dXQcIbLOYMDr9Qn4deuANi4ObfQdRycSIk/I8BKJffv23fPcnj17ciRMXvPFoWsAjG9V3sFJhMg6l317CWzZCM9PlpI8ZDjRu/djqVXH0bGEyBPSHQ5du3Yt69atIzw8PM1xQYPBQJ06BeMX6HRkPABlCnk6OIkQWef27Teg0RC7cRvmRk0cHUeIPCXdItilSxeaN2/Ou+++y/jx4+3Pe3l54e/vnxvZHGrtn9eIiDfSo2aIo6MI8dBc9uxC+ftjqVmLxJlvgVYLnvLHnBD/le5wqEajoXjx4kyfPh0vLy/7fwCxsbG5lc8hEo0WFu26BMArLcs5OI0QmadJiMf71bH49+6K58K3U5/09pYCKEQ60u0Jjh8/nmXLltGzZ080Gg1KKftrGo2GHTt25EpAR9hyKgqAgXWL4+Gic3AaITLHZddOfMa9jPb6NZJGvoxh0lRHRxIiz0u3CC5btgyAnTt35lqYvGJ/WAw6DYxoUtrRUYTIFNft2/Ab0BdL+QrEbt6OpV4DR0cSwilkeHbon3/+SVJSEgAbN25k7ty5RERE5HgwRzGYLBy8EkOdEv646jM1v7gQDqOJiQbA1KoNibPmELNjrxRAIR5Cht/yM2bMwMPDg7Nnz7Jy5UpKlizJhAkTciNbrjt+PY6XvvkLi00xqF5xR8cRIl2auFi8x75EQItGaGJjwMWF5BGjZN5PIR5ShkVQr9ej0Wj45Zdf6N+/P/3798dgMORGtlx1/HocQ786zqnIBGoW86Wh3DFC5FGuv/xEQPOGuH+9FmPfZ1DuUviEyKoMJ9D28vJi2bJl/PDDD3z55ZfYbDYsFktuZMtV7+1OPRv0/V7VqVfC37FhhLgfoxGfV8fg/vVaLFWqEvv5WrnoXYhHlGFPcNGiRbi6ujJnzhyCgoKIjIxkyJAhuZEt18Qlmzl5I4HHi/nSqHQgep0cCxR5kKsrmrhYDONeI2b7bimAQmSDDL/tg4KC6NKlCwkJCfz666+4ubnRvXv3XIiWez7aexmA5xuUdHASIdLSxETj/cootFfCQKMhftVakiZNAzc3R0cTIl/IsAhu3bqVPn368OOPP7Jt2zb7v/OLExHxfHciklIBHjQpK8cBRd7hunUzgU3r4/71WlwOHUh9UiujFEJkpwyPCS5dupT169dTqFAhAKKjo3nuued48sknczxcbjgVmQDApDYVHJxEiFSaO3fwfv013Desx1y9JglfbcBao6ajYwmRL2VYBJVS9gII4O/vn2b2GGe35o9wdFqN3DVe5BmeixfitmkjhomvkzR6HLi4ODqSEPlWhkWwadOmDBkyhE6dOgGpw6PNmzfP8WC5Iclk5WaiiaZlA3GTC+OFA2lu3UIbE421YiWSXptESr/+WKtWc3QsIfK9DIvgxIkT2b59O3/++ScAffv2pW3btjkeLDdExKcA0Kai3DVeOIhSuG3cgPfkV7E+VoLYn3ejfHylAAqRS9ItgmFhYcyfP5/w8HAqVqzIxIkTCQ4Ozs1sOc5gTL3e0UWncXASURBpbt7EZ+I43Lb8gLl2HRIWLwGNfBaFyE3pjgFOmTKFVq1a8f7771OtWjXefPPN3MyVK05HJQJQ1NfdwUlEQaM7c5rAZvVw/eUnEqfNInbLL1grV3F0LCEKnHR7ggaDgaeeegqAsmXL0qNHj1wLlVsS7/YEKwR5OTiJKDBsNtBqsZavgLFTV5JHjMJasZKjUwlRYKVbBI1GI6dPn7afCZqSkpLmcbVqzn3MwmpTLN93BTe9Vk6KETlPKdzWf43nB4uI3bgNFRBI4rsfODqVEAVeukUwKCiIuXPn2h8XLlzY/lij0fDFF1/kfLocNHv7OQDKFvJEI8dhRA7SRt7A+9UxuG3/EXPd+mgSE1EBMjGDEHlBukVw9erVuZkjV6WYrfx09ia+7npWPF3L0XFEfqUUbl+vxXvaZDTGFBJnzSF52EjQ6RydTAhxV4aXSORHf16Lw2xVvNKyNC4yWbbIQW4/fIe1chUSFn+EtWx5R8cRQvxHgSyCJyPiAagrt0wS2U0p3NetwdSkGbZSpUlYugLl7SNzfgqRRxXI38zVh68BUMTH1cFJRH6ivRaOX7+e+Ix9CY9VKwBQvn5SAIXIwzL87VRKsXHjRj788EMAIiIiOHHiRI4Hyyl/XI3BaLHRp1YxvFwLZEdYZDelcF+9ioDmDXE5eICEeQsxTJvp6FRCiEzIsAjOmDGDY8eOsWXLFiD1TvMzZzrvL/hfEal3jehZs6iDk4j8wuOTJfiMH42lVm2id+8nZfAw6f0J4SQy7AqdOHGC7777zn4jXT8/P8xmc07nyjG3DSYAHvOXWWLEI7DZ0Ny+jSpShJSnB2Dz8cXY9xkpfkI4mQx/Y/V6PVar1X4tXXR0NFon/kW/HpcMgLtcIC+ySHslDL/eXfHv1RmMRpSPL8anB0gBFMIJZfhbO3DgQF566SXu3LnDokWLePrpp3nhhRcytfE9e/bQvn172rZty/Lly9Nd7qeffqJSpUr89ddfmU+eRQajFT93vVwgLx6ezYb7iuUEtmiE/thRkl94CVzl5CohnFmGw6Fdu3alWrVqHDhwAKUUH3/8MeXKlctww1arlVmzZrFy5UqCg4Pp3bs3oaGhlC+f9lqpxMREvvjiCx5//PGsv4tM+jM8luMR8TzzxGM5vi+Rv2hu38Z36CBc9+3F1Ko1CQvfx1a8hKNjCSEeUYY9wYiICDw8PGjVqhWhoaF4eHgQERGR4YZPnDhBqVKlKFGiBK6urnTq1IkdO3bcs9zixYsZNmwYbm5uWXsHD2Huz+fRaTU8V1++vMTDUf7+oNOR8N5HxH21QQqgEPlEhj3Bfw99Go1Grl27RpkyZexni6YnKiqKkJAQ++Pg4OB7Lq04deoUkZGRtGzZkhUrVjxs9oeilOJabDJFfNwI8JQhLJEx3aUL6Oa9iWb+IlRAIHHrf5D7/QmRz2RYBDdt2pTm8alTp1i7du0j79hmszFv3rw0k3RnhkYD/v6eD72/PedvYVXQr17JLK3v7HQ6bYF831litaL94H2006eBuzv+1y6hyhR3dCqnIp+3rJO2y10PfbV4tWrVMnWxfHBwMJGRkfbHUVFRae5MbzAYOHfuHIMGDQLg1q1bjBw5kiVLllCjRo10t6sUxMYmPWxsfjmZmqVFGf8sre/s/P09C+T7fli6C+fxGT0S3eFDGNt3QLtsGbGe/iBt91Dk85Z10nZZExTkk6X1MiyCK1eutP/bZrNx+vRpihQpkuGGa9SoQVhYGOHh4QQHB7NlyxYWLlxof93Hx4eDBw/aHw8cOJAJEyY8sAA+ikt3DIDcRV48mNecWegunCP+408w9noK/wAvKYBC5GMZFkGDwWD/t06no0WLFrRv3z7jDev1TJ8+naFDh2K1WunVqxcVKlRg8eLFVK9endatWz9a8odgMFk4eCWWZmUD0coxHfEfunN/o9zdsZUsRcLcdwBQ/xq1EELkXw8sglarFYPBwMSJE7O08RYtWtCiRYs0z40ZM+a+y+bk/Qs//i0MgE7V5ItN/IvFgsfHH+C1YA6mNu2JX7lGip8QBUy6RdBisaDX6zly5Ehu5skRey9H46bXElqhsKOjiDxCd/YMPmNG4nL0CMZOXUmY/66jIwkhHCDdItinTx++++47KleuzIgRI3jyySfx9Pz/M5batWuXKwEf1bFrcUTEpdCzZlGZJUYA4LLzZ/wGPY3y8SH+k1UYu/aQSx+EKKAyPCZoMpkICAhIcxILOEcRtFhtvPbDaQDaVQ5ycBrhcBYL6PVY6jckZcCzGF6djCosowNCFGTpFsE7d+6wcuVKKlSogEajQSllf81ZelQ7z98mNtnM7I6VeULuIl9wmc14Ll6I69bNxG79BeXtQ+K8hRmvJ4TI99ItgjabLc2Zoc7oRrwRgOblCzk4iXAU3V8n8BnzIi4nT5DSszcakxHlLpfJCCFSpVsEg4KCGDVqVG5myXZ/RcQD4Ca3TSp4zGY8Fy3A8713Uqc8W7UWU8fOjk4lhMhj0q0O/x7+dEY2pdh98Q7BPm5ybWBBpNHg+vNPGLv3InrvISmAQoj7SrcnuGrVqlyMkf1uJqQOhTYrG+jgJCLXGI14fvw+yc8NQQUEEvv9VvDycnQqIUQelm4R9Pf3z8UY2S/RZAWg1mN+Dk4icoP+2BF8Ro9Ef/YMtqAipAx4VgqgECJD+fZg2T89wQBPFwcnETkqJQWv2TPw79AaTVwccevWpxZAIYTIhIe+i4SziE4yARDoJfcOzM+8Z07FY8VykvsPwjDzLZSv9PyFEJmXb4vg+Vupl3cU95PT4fOd5GQ0CQmoIkVIGjMeY9snMYe2cXQqIYQTyrfDoRdvGygZ4IG7i87RUUQ20v9xkIDWTfEdMRiUwhZSVAqgECLL8mURVEpx8EospQPl7sz5RlISXtOn4N+5HZqUFJJGj5P5PoUQjyxfDofuD4sBoKlcHpEv6M6fw3dgX/SXLpL87BAMb8xCeWftLtJCCPFv+a4IWm2KmT/+jZ+7ng5Vijg6jsgGtqJFsRV7jNgF72Fu1iLjFYQQIpPy3XDouVuJRCeZaVG+kBwPdGIu+3/Hd2BfSElBefsQt2GzFEAhRLbLd0XwdmLqpREtysstcpxSYiLek1/Fv1sH9GfPoIu45uhEQoh8LN8VwfDYZABCfNwcnEQ8LJe9ewhs2Rj3zz4hadgIonftx1q2vKNjCSHysXx3THDTySgAQnylCDoVpfB6czpKpyVu4zbMDRs7OpEQogDId0UwLDqJIG9XfN1lujRn4PLbbizVqqMCCxG/YjW2wELgKZe2CCFyR74aDj1+PQ6LTdG5WrCjo4gMaBLi8R4/Bv9eXfB8fxEAtuIlpAAKIXJVvuoJbjqVOhTavrJcGpGXufy6A59xL6O9EUHSS2MwTJji6EhCiAIqXxXBP67E4Ouup1xhuYVOXuW+8lN8Jo7DUrESsVt+xvJEPUdHEkIUYPmmCF6JTiIi3ig30c2rUlLA3R1Th04YoiJJGvsquMvk5kIIx8o3xwRXHLgKwItNyzg4ifg3TWwMPqNH4vd0L7DZsIUUJWnSVCmAQog8Id8UweMR8Xi56igfJEOheYXrT9sIaNYAt2++wly/AVitjo4khBBp5IvhUKUUdwwmutcIcXQUAWji4/Ce/Bru33yFpUo1Ytd8jeXx2o6OJYQQ98gXPUGDyYrRYiPIWy6QzwuUVof+zz8wjJ9IzM+7pQAKIfKsfNETvHV3vtAgb1cHJym4NNF38PzgPQwTXwdvb2J2HwA3+aNECJG35Yue4B9XYwHwdc8XNd3puG7+gcBmDfBY9hEuhw6kPikFUAjhBJy+CCakWFiw8wIAlYp4OzhNwaK5fRuf4c/hN3gA1pCixGzfjbl5S0fHEkKITHP6rtP4jacAGFC3uBwTzGU+L7+A655dGCZNJenlV8BF5msVQjgXpy6CFpvi6LU4/Nz1jGlR1tFxCgTNrVug16ECAjHMnIPBasVapaqjYwkhRJY49XDomcgEALrJpRE5Tyncvv+WwGb18J6eOtentWIlKYBCCKfm1EXw/K1EAEIrBjk4Sf6miYrC9/kB+A5/HmvpMiSNGuvoSEIIkS2cejj0jsEMQHE/mYIrp7j8/hu+gwegSUoicfqbJI94CfRO/bERQgg7p/42u5Nkwk2vxc9DTsjIKdbyFTDXqYth1lysFSo6Oo4QQmQrpx4OvXTbQKCnFMBspRRuX6/F99lnUie8Dg4hft23UgCFEPmSUxfBo9fjKVNI7kSeXbQ3IvAd8BS+L49Ae+c2mrhYR0cSQogc5dRFECDRKHcmeGRK4bZuDQHNGuC6dw+Js+cRu3EbKkDuzSiEyN+c9phgotECwBMl/BycJB9ITsZr4Xws1aqTsOhDbGXLOTqREELkCqctgu/tugRA6UAZDs0SpXDb8A3GTl3B05PY77diK/YYaJ1+cEAIITItR7/x9uzZQ/v27Wnbti3Lly+/5/WVK1fSsWNHunTpwrPPPsv169czve34uz3BJ6sUyba8BYU2/Cp+T3XHd+RQ3L/6EgBb8RJSAIUQBU6OfetZrVZmzZrFp59+ypYtW9i8eTMXLlxIs0yVKlX49ttv2bRpE+3bt2fBggWZ3v4fV2OoFuKDVqPJ7uj5l1K4f/4ZAc0b4vLHIRLmv0vKoOcdnUoIIRwmx4rgiRMnKFWqFCVKlMDV1ZVOnTqxY8eONMs0bNgQDw8PAGrVqkVkZGSmt59otCL17+F4TZ+Mz2tjsdSpS/SeA6Q8P1R6f0KIAi3HjglGRUUREvL/c3oGBwdz4sSJdJdfv349zZs3z9S2U8ypZ4TWKOr7aCELApsNUlLA35OU/s9irVCJlIHPIX9BCCFEHjkxZuPGjZw8eZI1a9ZkuKxGA3N2XASg0mN++PvLiTHpunQJ3QvDoGgx+PJLfBo+AQ2fQCaZyzydTiufsSyQdss6abvclWNFMDg4OM3wZlRUFMHBwfcst2/fPpYuXcqaNWtwdXXNcLs2m2LbqdTtti9fiNjYpOwLnV/YbLh/thzv2TNQOj2GWX1wt1iJjUt2dDKn4+/vKZ+xLJB2yzppu6wJCvLJ0no5dkCoRo0ahIWFER4ejslkYsuWLYSGhqZZ5vTp00yfPp0lS5ZQqFChTG3XYEodCu1aPRi9Vob0/ksbfhW/Hp3wmTIBU6MmxOw5QEr/QTL8KYQQ95FjPUG9Xs/06dMZOnQoVquVXr16UaFCBRYvXkz16tVp3bo1b7/9NklJSYwZMwaAokWLsnTp0gdu13D30oinaj+WU9Gdm6sruuvXiX9/Cca+z0jxE0KIB9AopZSjQzyM8OgkWi74lf2vNEMjX/AA6C6cx/3zFRhmzkk929Niued2RzLEkjXSblkj7ZZ10nZZk+eGQ3OKTSlKBHhIAQSwWvH4+AMCQpvg/vVadJdSTxiS+/0JIUTmOF0RNJptuOqcLna2050/h3/ndnjPeB1Ty1BifjuEtXwFR8cSQgin4nRdBq1WQ6KpgN85wmbD9/n+aG/dJH7Jpxh79pFjf0IIkQVOVwSTTRbKFNBJs3Xn/sZaoiR4eJCw5FOsRUJQ97nsRAghROY43biiAlx0BazXY7HgsXghAaFN8Fy8MPWpGo9LARRCiEfkdD1BAG83p4ydJbrTp/AZ8yIux4+S0rUHyUNecHQkIYTIN5yympQv7OXoCLnC7asv8Rk/GuXnR9yKLzB16e7oSEIIka843XAopJ4ck6/dvXTTUvsJjN16Ev3bH1IAhRAiBzhlEdTl1zMhTSY8F8zFZ/RIAKyVKpPw8SeoTE4pJ4QQ4uE4ZRH093DKUdwH0v91nIB2LfFaMDd1xhez2dGRhBAi33PKapKvJs42GvFc9Daei9/FVqgwcZ+vw9Shk6NTCSFEgeCUPUFdPiqCmthYPD77BGOvp4j57aAUQCGEyEVO2RN0+iJoNOK+bg0pg55HBQcT/dsfcs2fEEI4gFMWQRetU3ZgAdAf/ROf0SPR/30Wa+kymFuGSgEUQggHccpqEuST8R3o85yUFLzefAP/Dq3RxMcTt2495pahGa8nhBAixzhlT9Db1fli+w4egNsv20ke8CyGGbNRvn6OjiSEEAWe81UTQO8sc4cmJ6fe3cHdnaTR40keNhJzq9aOTiWEEOIupxwOdYaL5fUHDxAQ2gSv+W8BYGnYSAqgEELkMU5ZBF31eTh2UhJe0ybh37U9GrMZkxQ+IYTIs5xyODSv3llef/RPfEYMQX/5EsmDh5E4dSZ4ezs6lhBCiHQ4ZRHMq9cJKk8vcHEh9rstmJs0c3QcIYQQGcibXaoH0OSx44Euv/+G18xpQOqE1zF7DkoBFEIIJ+F0RTDPSEzEe+I4/Ht0wm3LD2hiolOfd+IL+YUQoqBxvm/su/facySXPbsIbNkI91UrSHrhRaJ37UcFBDo6lhBCiIfkdMcEHV4CExPxHf4cNv8AYn/4CUuDho5OJPIRq9VCTMwtLBaTo6M8kqgoDSoP/MHqjKTtHkyvdyUgIAidLnvKl9MVQb2Dhhv1fxzE8kQ98PYm7uvvsFSsDB4eDski8q+YmFu4u3vi5RWS545/PwydTovVanN0DKckbZc+pRQGQzwxMbcoXLhotmzT+YZDc5kmPg7v8aMJ6NQW96++BMDyeG0pgCJHWCwmvLx8nboACpFTNBoNXl6+2TpS4nQ9wdzksvNnfMaNRht5g6RRY0np0dvRkUQBIAVQiPRl9++H0/UEc+v7wXPebPz79UL5+BC79RcM02dJ708UCG3bPvolPmfOnOa99xak+/qNGxFs3/6j/fHZsw9e/r9GjRrO00/35Nlnn2bo0EGcP//3I+XNTnv37mb16lXZsi2jMYVRo4ZjtVqzZXs5YfXqlfTt252nn+7JwYP777vM4cOHGDy4P8899wwjRw7h2rVw+2s7dvzMgAF9GDDgKWbMeB2AmJgYxo17OVfyS0/wv2w20GoxN2tBktWKYfxEcHd3dCohnEqVKlWpWLFyuq/fuBHBL7/8SLt2TwJQuXJVKleu+lD7eOON2VSuXJUtW37go48W8957Hz9SZgCr1YpOp3ukbTRt2oKmTVs8chaAzZt/oHnzVpnOpJRCKYU2l86duHz5Er/8sp3Vq//H7du3GDv2Rdat23BP3nfemce8eQspXboMGzZ8w+efr+D112cQHn6VNWtW8vHHK/D19SXm7qVmAQEBFC5cmBMnjlGzZq0cfQ9SBO/SxMbgPW0ytkKFMcyYjblJM7noXYi7zp//mwUL5mI0plCsWHEmT56Or68vZ86cYt68N9FotNSr14ADB35n9er/ceTIYb788gvefvs9jh79k8WLFwKpIzkfffQJS5d+yJUrl3nuuWfo0KETFSpU4quv1vD22++RlJTEe+8t4OzZ02g0Gp5/fhgtW6Y/B2/16jVZt241AMnJySxa9DaXL1/EYrEwePBwmjVrSUpKCm+9NYPLly9SokQpbt++xfjxE6lcuSpt2zaja9eeHD58iHHjJhIZGcH69V9hNluoWrUa48dPAmDevDftmTp16krfvv355puv2LjxW3Q6HaVLl2HmzLls3bqJs2dPM27cRG7ciGDu3FnExcXi7x/A5MlvEBISwltvzcDLy4uzZ89w584dXnzxZVq1anPPe/v55x95443ZACQlJTF58ngSEuKxWCwMGzaSZs1acuNGBOPGjaJq1er8/fdZ3nlnMTt3/szOnb9gNpto3rwVQ4a8AMDkyeOJiorCZDLRp08/unXr+Uifi717d9OmTTtcXV0pVuwxihcvwZkzp6hevWaa5TQaMBgMABgMiRQuHATApk3f0bPnU/j6+gIQ8K9LzZo1a8H27T9KEcwNrj9tw/vVMWhv3yLpldccHUcIALaciuKHk5HZus2u1UPoVC34odebPfsNxo59jdq1n+DTT5eycuUnjBkznjlzZjJx4lSqV6/JkiUf3HfddevWMG7cBGrWrEVSUhKurq6MGDHKXvQAjhw5bF9+1apP8fLy5osvvgYgPj7+gdkOHtxHs2YtAfjii8944ol6TJnyBgkJCQwb9ix16zbgu+/W4+Pjw5o133Dp0gWef76/ff3k5GSqVq3Oyy+/QljYZb788nOWLPkMvV7PO+/MY/v2bZQpU45bt26yevX/AEhISABgzZpVfPPND7i6utqf+7dFixbQoUNnOnTozObNG1m8eAFz56b+QXD79m0+/vhTrlwJY9KkcfcUQbPZTETEdYoWLQaAq6src+YswMvLm9jYWF544Tl7j/PatXBef30m1avX4NChA4SHh/PJJ5+jlGLSpHEcO3aEWrXq3P3jxQ+jMYWhQwfRsmUofn7+afb7/vsLOXLkz3veS+vW7Rg48Lk0z926dZNq1WrYHwcFFeHWrZv3rDtp0jRee20Mbm5ueHl5sWzZSgDCw68CMHLkYKxWG4MHD6dhw8ZA6ujAJ58suWdb2a1AF0FNTDTer0/Eff3XWKpWJ/bL/2HJ4b86hHA2iYmJJCQkULv2EwB06NCZadMmkpCQQFJSkv2v/rZtn2Tfvt/uWb9Gjcf54INFtGvXgRYtWlGkyIOL8OHDh5g5c4798T+9hP+aOXMqFouF5OQkVq5cC8ChQwfYu3c369atAcBkMhIVFclffx2jT5+nAShbtjzlypW3b0en09GyZSgAf/55iL//PsPQoYOA1GNyAQEBNGnSnIiI6yxa9DaNGjWlfv3U64PLlavArFlTadaspb0Q/9upUyeYMyf1WOeTT3ZiyZL37a81b94SrVZLmTJliY6OvmfduLhYvP8zAf+yZR9x/PhRNBott27dIjr6DgAhIUWpXr2GvQ3++OOAvdAnJydx7dpVatWqwzfffMWePbsAuHkzivDw8HuK4OjR4+/b3o/i66/XsmDBYqpVq87atV/wwQeLmDRpGlarlfDwcD74YDk3b0YxatRwPv/8K3x8fAgICOT27dvZnuW/CnQR1N66heu2LRhenUTS2FfB1dXRkYSw61QtOEu9trxm4MDnaNy4Kfv372XkyCG8++6H2bLdN96YTaVKVfjoo8UsWrSAOXMWoJTirbfepmTJ0pnejqurq/0YllKKDh06M2LEqHuWW7VqHYcO7Wfjxm/ZufNnpkx5gwUL3uP48aP8/vsevvjiMz7//KtM79fFxeVfj+69ON7V1Q2T6f8vBdi+fRuxsbGsWLEGvV5P795d7K+7/+u8BaUUAwY8R/fuvdJs78iRwxw+fIhly1bi7u7OqFHDMZmM9+z3YXqCQUFFuHkzyv741q2bBAUVSbNMTEwMFy6co1q16gCEhrbj1Vdftq9ftWp19Ho9xYo9RokSJbl27SpVqlTDZDLi5uZ2T47s5nxnhz7q+nfu4L5iGQDWipWIPnKSpAlTpAAKkQ5vb298fHw5fvwoAD/+uIVaterg4+ODp6cnp06dBGDHju33Xf/69WuUK1eeAQOeo0qVqly5EoanpxdJSUn3Xb5evQZs2PCN/fGDhkM1Gg3Dho3g1Km/uHIljAYNGrF+/df2GVfOnTsLpPZGd+78GUg9mePixQv33d4TT9Rn164d9hM04uPjiIy8QWxsLErZaNmyNcOGjeTcub+x2WzcvBlFnTp1GTlyNImJiSQnJ6fZXvXqNfnll5+A1CJWs2btdN/Lf/n6+mKz2TAaUwtVYmIiAQEB6PV6jhw5TGTkjfuu16BBI7Zs+cHevrdu3SQmJhqDIREfH1/c3d25ciWM06dP3nf90aPHs2rV2nv++28BBGjSpDm//LIdk8lERMR1wsPDqVKlWpplfHx8MBgSuXr1CgCHDx+gVKnSADRr1pKjR1MLbmxsLOHhVylW7DEArl69Spky5TLdXllVoHqCrpu+x2fiODRxcZhbhGItX0Hm/BTiP1JSUujRo6P9cd++zzB16ox/nRjzGJMnvwHApEnTefvt2Wg0WmrVqnPP8B3A//63liNHDqPVailduiwNGzZGq9Wi1Wp59tmn6dixMxUqVLIv/+yzQ3j33fkMHPgUWq2OwYOH0aJFaLp53dzc6ddvAGvXfsG4cRNYvHghzz7bD5tNUaxYMd5++z169OjDW2+9wYABfShZsjRlypTDy+verGXKlGXYsJG88soolLKh0+kZN24ibm5uzJ07E5sttbi+8MJL2Gw2Zs2ahsGQiFKK3r374ePjk2Z7r7wygTlzZrJu3Wr7iTEPo169Bpw4cYx69RrQrl0HJk58hUGD+lK5clV7Ifmv+vUbEhZ2mREjngfAw8OT6dPfpEGDxnz//Qb69+9NyZKlqFq1+kNluZ+yZcsRGtqGAQP6oNPpGDdugr1X/eqro5k0aRqFCwcxYcJUpk6dgEajxcfHh8mTpwOpBfuPPw4wYEAftFotL7442j48e+TIYRo3bvLIGTOiUU42Sd3ZG/EU0j9cf1Bz6xbek1/F/YfvMNesRcL7S7BWrZbxivmIv78nsbH3/8tbpC+32y0y8gohIaVybX+PKikpCU9PTwBWr17FnTu3GTv21Tw39ZfVasViseDm5sb169cYO/ZF1q799j9DknnDv9vu77/P8r//fcm0aW86OFXue+mlYcydu/C+x4Tv93sSFORzz3KZkf97glYr/t2eRHf1CoYp00l6aQzkwQ++EM5o//69rF69CqvVQkhIUaZMmeHoSPdlNKbw8ssjsFgsgGLcuIl5sgD+V6VKlaldu262XL/oTGJiYujbt3+6J0VlJ+frCUbGU0iXcU9Qc/s2KjAQtFpcdv6MrVhxrJWr5ELCvEl6glkjPcGsyWs9QWcibZex7OwJOt2JMRlSCrcN3xDYtC4ed0+AMYe2LdAFUAghxP05XRF8UB9QGxWJ77PP4DtiCNYyZTE94GC6EHmVkw3OCJGrsvv3wwmPCd6/DLpu24LPmJFoUlJIfGM2ySNeggI0hi7yB73eFYMhXm6nJMR9/HM/Qb0++y5pc8IieH/Kzw9rxcokvPcR1vIVHB1HiCwJCAgiJuYWiYmxjo7ySDQauTt6VknbPdg/d5bPLjl6YsyePXt46623sNls9OnTh+HDh6d53WQyMWHCBE6dOoW/vz+LFi2iePHiD9zm35EJBOpIPfb39Vp018JJejV1gluUyr17LTkZOTEma6TdskbaLeuk7bImz50YY7VamTVrFp9++ilbtmxh8+bNXLiQdpaGb775Bl9fX37++Weee+453nnnnUxtWxtxHd9neuM7eiQue/eAxZL6ghRAIYQQDyHHiuCJEycoVaoUJUqUwNXVlU6dOrFjx440y+zcuZMePXoA0L59e/bv35/hMICfIY6AZg1w3f87iW/NJ27DZtDnm1FdIYQQuSjHimBUVBQhISH2x8HBwURFRd2zTNGiRQHQ6/X4+PgQExPzwO0WiY3CUqMm0b/uI3nYSMilm0cKIYTIf5yuC6V94glcf/+NQo4O4oSyOmZe0Em7ZY20W9ZJ2+WeHOtGBQcHExn5/zcEjYqKIjg4+J5lbtxInQndYrGQkJBAQEBATkUSQggh0sixIlijRg3CwsIIDw/HZDKxZcsWQkPTXrweGhrKd999B8BPP/1Ew4YN5dooIYQQuSZHL5HYvXs3c+bMwWq10qtXL0aOHMnixYupXr06rVu3xmg08tprr3HmzBn8/PxYtGgRJUqUyKk4QgghRBpON4G2EEIIkV3k1EohhBAFlhRBIYQQBVaeLYJ79uyhffv2tG3bluXLl9/zuslkYuzYsbRt25Y+ffpw7do1B6TMezJqt5UrV9KxY0e6dOnCs88+y/Xr1x2QMu/JqN3+8dNPP1GpUiX++uuvXEyXd2Wm3bZu3UrHjh3p1KkT48ePz+WEeVNG7RYREcHAgQPp3r07Xbp0Yffu3Q5ImfdMnjyZRo0a0blz5/u+rpRi9uzZtG3bli5dunDq1KmMN6ryIIvFolq3bq2uXr2qjEaj6tKlizp//nyaZdasWaOmTZumlFJq8+bNasyYMQ5Imrdkpt3279+vkpKSlFJKffnll9JuKnPtppRSCQkJ6plnnlF9+vRRJ06ccEDSvCUz7Xb58mXVrVs3FRsbq5RS6vbt246Imqdkpt2mTp2qvvzyS6WUUufPn1etWrVyRNQ859ChQ+rkyZOqU6dO9319165dasiQIcpms6mjR4+q3r17Z7jNPNkTzKkp1/K7zLRbw4YN8fDwAKBWrVppruUsqDLTbgCLFy9m2LBhuLm5OSBl3pOZdvvf//5H//798fPzA6BQIZnmIjPtptFoSExMBCAhIYEiRYo4ImqeU69ePftn6X527NhB9+7d0Wg01KpVi/j4eG7evPnAbebJIphTU67ld5lpt39bv349zZs3z41oeVpm2u3UqVNERkbSsmXLXE6Xd2Wm3cLCwrh8+TL9+vXjqaeeYs+ePbkdM8/JTLuNGjWKTZs20bx5c4YPH87UqVNzO6ZT+m/bhoSEPPA7EPJoERQ5b+PGjZw8eZKhQ4c6OkqeZ7PZmDdvHhMnTnR0FKdjtVq5cuUKq1evZuHChUybNo34+HhHx8rztmzZQo8ePdizZw/Lly9nwoQJ2Gw2R8fKl/JkEZQp17ImM+0GsG/fPpYuXcqSJUtwdc2+OzQ7q4zazWAwcO7cOQYNGkRoaCjHjh1j5MiRBf7kmMz+noaGhuLi4kKJEiUoXbo0YWFhuZw0b8lMu61fv54OHToAULt2bYxGY4Ef6cqM/7ZtZGTkfb8D/y1PFkGZci1rMtNup0+fZvr06SxZskSOz9yVUbv5+Phw8OBBdu7cyc6dO6lVqxZLliyhRo0aDkzteJn5vLVp04ZDhw4BEB0dTVhYWIGfFSoz7Va0aFH2798PwMWLFzEajQQGBjoirlMJDQ3l+++/RynFsWPH8PHxyfB4ap68i4Rer2f69OkMHTrUPuVahQoV0ky51rt3b1577TXatm1rn3KtoMtMu7399tskJSUxZswYIPWXbenSpQ5O7liZaTdxr8y0W7Nmzfj999/p2LEjOp2OCRMmFPgRm8y026RJk5g6dSqrVq1Co9Ewb968Av9HPsC4ceM4dOgQMTExNG/enJdffhnL3ZuqP/3007Ro0YLdu3fTtm1bPDw8mDNnTobblGnThBBCFFh5cjhUCCGEyA1SBIUQQhRYUgSFEEIUWFIEhRBCFFhSBIUQQhRYUgSFAKpUqUK3bt3s/z3oriS1a9d+5P1NmjSJ0NBQunXrRo8ePTh69OhDb+P111/nwoULAPdc5tKvX79Hzgj/3y6dO3dmxIgRGc72cubMGbnjgXAqcomEEKQWtswWoodZNj2TJk2iZcuWPPnkk+zdu5f58+ezadOmLG8vOzJltN2JEydSunRpRo4cme7yGzZs4OTJk0yfPj3bswiRE6QnKMR9GAwGnn32WXr06EGXLl345Zdf7lnm5s2b9O/f395TOnz4MAB79+6lb9++9OjRg9GjR2MwGB64r3r16nH16lUg9X6PnTt3pnPnzqxatQqApKQkhg8fTteuXencuTNbt24FYODAgfz111+88847pKSk0K1bN/v9+v7prb7yyivs2rXLvq9Jkybx448/YrVamT9/Pr169aJLly589dVXGbZJrVq17JMRnzhxgr59+9K9e3f69evHpUuXMJlMvP/++2zdupVu3bqxdetWkpKSmDx5Mr1796Z79+73bUchHCr77vQkhPOqXLmy6tq1q+ratat68cUXldlsVgkJCUoppe7cuaPatGmjbDabUkqpWrVqKaWUWrFihfr444+VUqn3iEtISFB37txRzzzzjDIYDEoppZYtW6Y++OCDe/Y3ceJEtW3bNqWUUlu3blW9e/dWf/31l+rcubMyGAwqMTFRdezYUZ06dUr9+OOP6vXXX7evGx8fr5RSasCAAfb7Gv6T6R//PN6+fbuaMGGCUkopo9GomjdvrpKTk9VXX32lPvroI/vzPXr0UFevXr0n5z/bsVgs6uWXX1a7d+9WSqXeW9FsNiullPr999/VqFGjlFJKffvtt2rmzJn29RcuXKi+//57pZRScXFxql27dva2ESIvyJPTpgmR29zd3dm4caP9sdls5t133+WPP/5Aq9USFRXF7du3CQoKsi9To0YNpkyZgsVioU2bNlSpUoVff/2VCxcu8PTTT9u3U6tWrfvu8+2332bJkiUEBgby1ltvsX//ftq0aYOnpycAbdu25fDhwzRr1oz58+ezYMECWrVqRd26dTP9vpo3b85bb72FyWRiz5491K1bF3d3d37//Xf+/vtvfvrpJyD1nnVXrly5Z17Pf3qYUVFRlCtXjiZNmtiXnzhxIleuXEGj0WA2m++7/71797Jz504+++wzAIxGIzdu3KBcuXKZfg9C5CQpgkLcx6ZNm4iOjmbDhg24uLgQGhqK0WhMs0y9evVYs2YNu3fvZtKkSTz//PP4+vrSpEkT3n333Qz3MWHCBJ588kn7438mTP6vMmXKsGHDBnbv3s17771Hw4YNGTVqVKbeh5ubG/Xr1+e3335j27ZtdOzYEQClFFOnTqVZs2YPXP+fPw6Sk5MZMmQIX375JYMGDWLx4sU0aNCAjz76iGvXrjFo0KB0t/H+++9TtmzZTOUVIrfJMUEh7iMhIYFChQrh4uLCgQMHuH79+j3LXL9+ncKFC/PUU0/Rp08fTp06Ra1atThy5AhXrlwBUo/nXb58OVP7rFu3Lr/88gvJyckkJSXxyy+/ULduXaKiovDw8KBbt24MGTKE06dP37OuXq9PtzfWsWNHNmzYYO9VAjRt2pR169bZ17l8+TJJSUnpZvPw8GDq1KmsXLnSfuuyf25R88/dXAC8vLzSHANt2rQpa9asQd09/+5+2YVwJOkJCnEfXbp0YeTIkXTp0oXq1avftydz6NAhVqxYgV6vx9PTk/nz5xMYGMjcuXMZN24cJpMJgLFjx1KmTJkM91mtWjV69uxJnz59AOjduzdVq1blt99+4+2330ar1aLX65kxY8Y96z711FN07dqVqlWrsnDhwjSvNWnShAkTJtC6dWv7/SP79OnD9evX6dmzJ0opAgIC+Pjjjx+Yr2rVqlSqVInNmzczdOhQJk2axJIlS2jRooV9mQYNGrB8+XK6devGCy+8wIsvvsicOXPo2rUrNpuN4sWLs2zZsgzbQojcIpdICCGEKLBkOFQIIUSBJUVQCCFEgSVFUAghRIElRVAIIUSBJUVQCCFEgSVFUAghRIElRVAIIUSBJUVQCCFEgfV/bNynumIUnVQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 99;\n",
              "                var nbb_unformatted_code = \"logit_roc_auc_train = roc_auc_score(y_train, lg2.predict(X_train2))\\nfpr, tpr, thresholds = roc_curve(y_train, lg2.predict(X_train2))\\nplt.figure(figsize=(7, 5))\\nplt.plot(fpr, tpr, label=\\\"Logistic Regression (area = %0.2f)\\\" % logit_roc_auc_train)\\nplt.plot([0, 1], [0, 1], \\\"r--\\\")\\nplt.xlim([0.0, 1.0])\\nplt.ylim([0.0, 1.05])\\nplt.xlabel(\\\"False Positive Rate\\\")\\nplt.ylabel(\\\"True Positive Rate\\\")\\nplt.title(\\\"Receiver operating characteristic\\\")\\nplt.legend(loc=\\\"lower right\\\")\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"logit_roc_auc_train = roc_auc_score(y_train, lg2.predict(X_train2))\\nfpr, tpr, thresholds = roc_curve(y_train, lg2.predict(X_train2))\\nplt.figure(figsize=(7, 5))\\nplt.plot(fpr, tpr, label=\\\"Logistic Regression (area = %0.2f)\\\" % logit_roc_auc_train)\\nplt.plot([0, 1], [0, 1], \\\"r--\\\")\\nplt.xlim([0.0, 1.0])\\nplt.ylim([0.0, 1.05])\\nplt.xlabel(\\\"False Positive Rate\\\")\\nplt.ylabel(\\\"True Positive Rate\\\")\\nplt.title(\\\"Receiver operating characteristic\\\")\\nplt.legend(loc=\\\"lower right\\\")\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Logistic Regression model is giving a good performance on training set.\n",
        "* ROC-AUC score of 0.86 on training is good."
      ],
      "metadata": {
        "id": "NA0yNgkpchB8"
      },
      "id": "NA0yNgkpchB8"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Optimal threshold using AUC-ROC curve"
      ],
      "metadata": {
        "id": "Bs5oYg5ZckBF"
      },
      "id": "Bs5oYg5ZckBF"
    },
    {
      "cell_type": "code",
      "source": [
        "# Optimal threshold as per AUC-ROC curve\n",
        "# The optimal cut off would be where tpr is high and fpr is low\n",
        "\n",
        "fpr, tpr, thresholds = roc_curve(y_train, lg2.predict(X_train2))\n",
        "\n",
        "optimal_idx = np.argmax(tpr - fpr)\n",
        "optimal_threshold_auc_roc = thresholds[optimal_idx]\n",
        "print(f'Optimal threshold AUC-ROC: {optimal_threshold_auc_roc:.5f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "KP2V2cEgbLWA",
        "outputId": "fd869cd3-8616-4c2d-daf0-84decfe8537b"
      },
      "id": "KP2V2cEgbLWA",
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal threshold AUC-ROC: 0.32799\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 100;\n",
              "                var nbb_unformatted_code = \"# Optimal threshold as per AUC-ROC curve\\n# The optimal cut off would be where tpr is high and fpr is low\\n\\nfpr, tpr, thresholds = roc_curve(y_train, lg2.predict(X_train2))\\n\\noptimal_idx = np.argmax(tpr - fpr)\\noptimal_threshold_auc_roc = thresholds[optimal_idx]\\nprint(f'Optimal threshold AUC-ROC: {optimal_threshold_auc_roc:.5f}')\";\n",
              "                var nbb_formatted_code = \"# Optimal threshold as per AUC-ROC curve\\n# The optimal cut off would be where tpr is high and fpr is low\\n\\nfpr, tpr, thresholds = roc_curve(y_train, lg2.predict(X_train2))\\n\\noptimal_idx = np.argmax(tpr - fpr)\\noptimal_threshold_auc_roc = thresholds[optimal_idx]\\nprint(f\\\"Optimal threshold AUC-ROC: {optimal_threshold_auc_roc:.5f}\\\")\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Checking model performance on training set**"
      ],
      "metadata": {
        "id": "BNKh9aPrc3Lo"
      },
      "id": "BNKh9aPrc3Lo"
    },
    {
      "cell_type": "code",
      "source": [
        "# checking model performance for this model\n",
        "print(\"Training performance:\")\n",
        "lg_train_auc_roc = model_performance(lg2, X_train2, y_train, threshold=optimal_threshold_auc_roc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "J8FrkA2jbLYd",
        "outputId": "bc35d3cd-df6f-4ebe-d547-dc3c93b2dcf7"
      },
      "id": "J8FrkA2jbLYd",
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training performance:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
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          "data": {
            "text/plain": [
              "   Accuracy  Recall  Precision      F1\n",
              "0   0.77784 0.78178    0.63145 0.69862"
            ],
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              "      <th>Accuracy</th>\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-32607648-e7c2-40fd-b4df-4f995acc2c8a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-32607648-e7c2-40fd-b4df-4f995acc2c8a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 101;\n",
              "                var nbb_unformatted_code = \"# checking model performance for this model\\nprint(\\\"Training performance:\\\")\\nlg_train_auc_roc = model_performance(lg2, X_train2, y_train, threshold=optimal_threshold_auc_roc)\";\n",
              "                var nbb_formatted_code = \"# checking model performance for this model\\nprint(\\\"Training performance:\\\")\\nlg_train_auc_roc = model_performance(\\n    lg2, X_train2, y_train, threshold=optimal_threshold_auc_roc\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Accuracy of model has reduced but the other metrics have increased.\n",
        "* The model is still giving a good performance."
      ],
      "metadata": {
        "id": "I_0AxvzsdkEe"
      },
      "id": "I_0AxvzsdkEe"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Checking model performance on test set**"
      ],
      "metadata": {
        "id": "SX3XVHlUeDCs"
      },
      "id": "SX3XVHlUeDCs"
    },
    {
      "cell_type": "code",
      "source": [
        "logit_roc_auc_train = roc_auc_score(y_test, lg2.predict(X_test2))\n",
        "fpr, tpr, thresholds = roc_curve(y_test, lg2.predict(X_test2))\n",
        "plt.figure(figsize=(7, 5))\n",
        "plt.plot(fpr, tpr, label=\"Logistic Regression (area = %0.2f)\" % logit_roc_auc_train)\n",
        "plt.plot([0, 1], [0, 1], \"r--\")\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.title(\"Receiver operating characteristic\")\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "VXWeRsxIeDPz",
        "outputId": "cd20442a-ee98-4841-9147-20ee45b2cd20"
      },
      "id": "VXWeRsxIeDPz",
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 102;\n",
              "                var nbb_unformatted_code = \"logit_roc_auc_train = roc_auc_score(y_test, lg2.predict(X_test2))\\nfpr, tpr, thresholds = roc_curve(y_test, lg2.predict(X_test2))\\nplt.figure(figsize=(7, 5))\\nplt.plot(fpr, tpr, label=\\\"Logistic Regression (area = %0.2f)\\\" % logit_roc_auc_train)\\nplt.plot([0, 1], [0, 1], \\\"r--\\\")\\nplt.xlim([0.0, 1.0])\\nplt.ylim([0.0, 1.05])\\nplt.xlabel(\\\"False Positive Rate\\\")\\nplt.ylabel(\\\"True Positive Rate\\\")\\nplt.title(\\\"Receiver operating characteristic\\\")\\nplt.legend(loc=\\\"lower right\\\")\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"logit_roc_auc_train = roc_auc_score(y_test, lg2.predict(X_test2))\\nfpr, tpr, thresholds = roc_curve(y_test, lg2.predict(X_test2))\\nplt.figure(figsize=(7, 5))\\nplt.plot(fpr, tpr, label=\\\"Logistic Regression (area = %0.2f)\\\" % logit_roc_auc_train)\\nplt.plot([0, 1], [0, 1], \\\"r--\\\")\\nplt.xlim([0.0, 1.0])\\nplt.ylim([0.0, 1.05])\\nplt.xlabel(\\\"False Positive Rate\\\")\\nplt.ylabel(\\\"True Positive Rate\\\")\\nplt.title(\\\"Receiver operating characteristic\\\")\\nplt.legend(loc=\\\"lower right\\\")\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Logistic Regression model is giving a good performance on testing set.\n",
        "* ROC-AUC score of 0.87 on testing is slightly higher than on training."
      ],
      "metadata": {
        "id": "HsdFdJ44brrk"
      },
      "id": "HsdFdJ44brrk"
    },
    {
      "cell_type": "code",
      "source": [
        "# checking model performance for this model\n",
        "print(\"Test performance:\")\n",
        "lg_test_auc_roc = model_performance(lg2, X_test2, y_test, threshold=optimal_threshold_auc_roc)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "hJkNmwtveDUS",
        "outputId": "a71104e6-0f0b-4bf1-a226-3bc9f1b7c0e1"
      },
      "id": "hJkNmwtveDUS",
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test performance:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Accuracy  Recall  Precision      F1\n",
              "0   0.78186 0.78705    0.63057 0.70018"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d8bed578-5ee4-4c5c-9379-6d21d8eaf527\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Precision</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.78186</td>\n",
              "      <td>0.78705</td>\n",
              "      <td>0.63057</td>\n",
              "      <td>0.70018</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d8bed578-5ee4-4c5c-9379-6d21d8eaf527')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d8bed578-5ee4-4c5c-9379-6d21d8eaf527 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d8bed578-5ee4-4c5c-9379-6d21d8eaf527');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 103;\n",
              "                var nbb_unformatted_code = \"# checking model performance for this model\\nprint(\\\"Test performance:\\\")\\nlg_test_auc_roc = model_performance(lg2, X_test2, y_test, threshold=optimal_threshold_auc_roc)\";\n",
              "                var nbb_formatted_code = \"# checking model performance for this model\\nprint(\\\"Test performance:\\\")\\nlg_test_auc_roc = model_performance(\\n    lg2, X_test2, y_test, threshold=optimal_threshold_auc_roc\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Accuracy of model has reduced but the other metrics have increased.\n",
        "* The model is still giving a good performance on testing."
      ],
      "metadata": {
        "id": "OLwOinc4boD5"
      },
      "id": "OLwOinc4boD5"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Let's use Precision-Recall curve and see if we can find a better threshold**\n"
      ],
      "metadata": {
        "id": "D3mjvy6UcEXx"
      },
      "id": "D3mjvy6UcEXx"
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Precision-Recall Curve"
      ],
      "metadata": {
        "id": "3Me3NwXNeWH9"
      },
      "id": "3Me3NwXNeWH9"
    },
    {
      "cell_type": "code",
      "source": [
        "y_scores = lg2.predict(X_train2)\n",
        "precisions, recalls, thresholds = precision_recall_curve(y_train, y_scores)\n",
        "\n",
        "plt.figure(figsize=(10, 7))\n",
        "plt.plot(thresholds, precisions[:-1], \"b--\", label=\"precision\")\n",
        "plt.plot(thresholds, recalls[:-1], \"g--\", label=\"recall\")\n",
        "plt.xlabel(\"Threshold\")\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.ylim([0, 1])\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "4dMyjwNgeDXl",
        "outputId": "698359cf-920b-4ab4-f23a-5350ab156bbb"
      },
      "id": "4dMyjwNgeDXl",
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 104;\n",
              "                var nbb_unformatted_code = \"y_scores = lg2.predict(X_train2)\\nprecisions, recalls, thresholds = precision_recall_curve(y_train, y_scores)\\n\\nplt.figure(figsize=(10, 7))\\nplt.plot(thresholds, precisions[:-1], \\\"b--\\\", label=\\\"precision\\\")\\nplt.plot(thresholds, recalls[:-1], \\\"g--\\\", label=\\\"recall\\\")\\nplt.xlabel(\\\"Threshold\\\")\\nplt.legend(loc=\\\"upper left\\\")\\nplt.ylim([0, 1])\\nplt.show();\";\n",
              "                var nbb_formatted_code = \"y_scores = lg2.predict(X_train2)\\nprecisions, recalls, thresholds = precision_recall_curve(y_train, y_scores)\\n\\nplt.figure(figsize=(10, 7))\\nplt.plot(thresholds, precisions[:-1], \\\"b--\\\", label=\\\"precision\\\")\\nplt.plot(thresholds, recalls[:-1], \\\"g--\\\", label=\\\"recall\\\")\\nplt.xlabel(\\\"Threshold\\\")\\nplt.legend(loc=\\\"upper left\\\")\\nplt.ylim([0, 1])\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tmp = pd.DataFrame(data={'Threshold': thresholds, 'Precision': precisions[:-1], 'Recall': recalls[:-1]})\n",
        "tmp['diff'] = abs(tmp['Precision'] - tmp['Recall'])\n",
        "tmp[tmp['diff']==0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "MFxV7yvgeyJr",
        "outputId": "41251261-560f-4dae-c13e-c25840d0c189"
      },
      "id": "MFxV7yvgeyJr",
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Threshold  Precision  Recall    diff\n",
              "12760    0.43542    0.69772 0.69772 0.00000"
            ],
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Threshold</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>diff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12760</th>\n",
              "      <td>0.43542</td>\n",
              "      <td>0.69772</td>\n",
              "      <td>0.69772</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2eeca48d-7831-4aa2-a7cd-4cf6671b3dc5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2eeca48d-7831-4aa2-a7cd-4cf6671b3dc5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2eeca48d-7831-4aa2-a7cd-4cf6671b3dc5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 105
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 105;\n",
              "                var nbb_unformatted_code = \"tmp = pd.DataFrame(data={'Threshold': thresholds, 'Precision': precisions[:-1], 'Recall': recalls[:-1]})\\ntmp['diff'] = abs(tmp['Precision'] - tmp['Recall'])\\ntmp[tmp['diff']==0]\";\n",
              "                var nbb_formatted_code = \"tmp = pd.DataFrame(\\n    data={\\\"Threshold\\\": thresholds, \\\"Precision\\\": precisions[:-1], \\\"Recall\\\": recalls[:-1]}\\n)\\ntmp[\\\"diff\\\"] = abs(tmp[\\\"Precision\\\"] - tmp[\\\"Recall\\\"])\\ntmp[tmp[\\\"diff\\\"] == 0]\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "optimal_threshold_curve = tmp[tmp['diff']==0]['Threshold'].values[0]\n",
        "optimal_threshold_curve"
      ],
      "metadata": {
        "id": "KsVHC-CQei-v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "74471b77-0078-4bec-9cb4-498a070023af"
      },
      "id": "KsVHC-CQei-v",
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4354191129890816"
            ]
          },
          "metadata": {},
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 106;\n",
              "                var nbb_unformatted_code = \"optimal_threshold_curve = tmp[tmp['diff']==0]['Threshold'].values[0]\\noptimal_threshold_curve\";\n",
              "                var nbb_formatted_code = \"optimal_threshold_curve = tmp[tmp[\\\"diff\\\"] == 0][\\\"Threshold\\\"].values[0]\\noptimal_threshold_curve\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* At the threshold of 0.43542, we get balanced recall and precision."
      ],
      "metadata": {
        "id": "ftNEzZOZejx0"
      },
      "id": "ftNEzZOZejx0"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Checking model performance on training set**"
      ],
      "metadata": {
        "id": "DV2Odk0UgJTo"
      },
      "id": "DV2Odk0UgJTo"
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Training performance:\")\n",
        "lg_train_pr_curve = model_performance(lg2, X_train2, y_train, threshold=optimal_threshold_curve)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "Okhs9RNDgMaB",
        "outputId": "257f02fa-96dc-4ea4-e1aa-503483d571ad"
      },
      "id": "Okhs9RNDgMaB",
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training performance:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Accuracy  Recall  Precision      F1\n",
              "0   0.80092 0.69772    0.69780 0.69776"
            ],
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Precision</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.80092</td>\n",
              "      <td>0.69772</td>\n",
              "      <td>0.69780</td>\n",
              "      <td>0.69776</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a2f0ea88-bf0c-41d5-b242-5152a22bd01a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a2f0ea88-bf0c-41d5-b242-5152a22bd01a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a2f0ea88-bf0c-41d5-b242-5152a22bd01a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 107;\n",
              "                var nbb_unformatted_code = \"print(\\\"Training performance:\\\")\\nlg_train_pr_curve = model_performance(lg2, X_train2, y_train, threshold=optimal_threshold_curve)\";\n",
              "                var nbb_formatted_code = \"print(\\\"Training performance:\\\")\\nlg_train_pr_curve = model_performance(\\n    lg2, X_train2, y_train, threshold=optimal_threshold_curve\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Model is performing well on training set.\n",
        "* There is some improvements in the model performance as the default threshold is 0.50 and here we get 0.44 as the optimal threshold.\n",
        "* Model has given a balanced performance in terms of precision and recall."
      ],
      "metadata": {
        "id": "FFBUyCsKgViG"
      },
      "id": "FFBUyCsKgViG"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Checking model performance on test set**"
      ],
      "metadata": {
        "id": "uOZ6ZEkSgeOx"
      },
      "id": "uOZ6ZEkSgeOx"
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Test performance:\")\n",
        "lg_test_pr_curve = model_performance(lg2, X_test2, y_test, threshold=optimal_threshold_curve)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "AKJaFIpvgMf4",
        "outputId": "09e528fc-49bd-4118-fd68-b4a3ab9dbdf5"
      },
      "id": "AKJaFIpvgMf4",
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test performance:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Accuracy  Recall  Precision      F1\n",
              "0   0.80198 0.70670    0.68928 0.69788"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Precision</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.80198</td>\n",
              "      <td>0.70670</td>\n",
              "      <td>0.68928</td>\n",
              "      <td>0.69788</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d9181007-a351-4025-887b-f61ee78d3da0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d9181007-a351-4025-887b-f61ee78d3da0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d9181007-a351-4025-887b-f61ee78d3da0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 108;\n",
              "                var nbb_unformatted_code = \"print(\\\"Test performance:\\\")\\nlg_test_pr_curve = model_performance(lg2, X_test2, y_test, threshold=optimal_threshold_curve)\";\n",
              "                var nbb_formatted_code = \"print(\\\"Test performance:\\\")\\nlg_test_pr_curve = model_performance(\\n    lg2, X_test2, y_test, threshold=optimal_threshold_curve\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Model is performing well on test set.\n",
        "* Model performance has improved as compared to our initial model.\n",
        "* Model has given a balanced performance in terms of precision and recall."
      ],
      "metadata": {
        "id": "NlA88Q6tdYQW"
      },
      "id": "NlA88Q6tdYQW"
    },
    {
      "cell_type": "markdown",
      "id": "stylish-collaboration",
      "metadata": {
        "id": "stylish-collaboration"
      },
      "source": [
        "## Final Model Summary"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Performance Comparison and Final Model Selection"
      ],
      "metadata": {
        "id": "esgT7VRZgrUR"
      },
      "id": "esgT7VRZgrUR"
    },
    {
      "cell_type": "code",
      "source": [
        "# training performance comparison\n",
        "\n",
        "models_train_comp_df = pd.concat(\n",
        "    [lg_train.T, lg_train1.T, lg_train2.T, lg_train_auc_roc.T, lg_train_pr_curve.T,], axis=1)\n",
        "\n",
        "models_train_comp_df.columns = \\\n",
        "  [\"LR\", \"LR-mc\", \"LR-pv\",\n",
        "   f\"LR-{optimal_threshold_auc_roc:.2f}\", \n",
        "   f\"LR-{optimal_threshold_curve:.2f}\"]\n",
        "\n",
        "print(\"Training performance comparison:\")\n",
        "models_train_comp_df.T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "1Xi46qEigqi7",
        "outputId": "1fd1d601-ea50-42fb-c984-f8b5438a6394"
      },
      "id": "1Xi46qEigqi7",
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training performance comparison:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Accuracy  Recall  Precision      F1\n",
              "LR        0.80754 0.63889    0.74105 0.68619\n",
              "LR-mc     0.80687 0.64152    0.73786 0.68632\n",
              "LR-pv     0.80600 0.64738    0.73251 0.68732\n",
              "LR-0.33   0.77784 0.78178    0.63145 0.69862\n",
              "LR-0.44   0.80092 0.69772    0.69780 0.69776"
            ],
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              "\n",
              "  <div id=\"df-552b6f50-e1e7-4cba-b4c8-dad9d56660b9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Precision</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>LR</th>\n",
              "      <td>0.80754</td>\n",
              "      <td>0.63889</td>\n",
              "      <td>0.74105</td>\n",
              "      <td>0.68619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LR-mc</th>\n",
              "      <td>0.80687</td>\n",
              "      <td>0.64152</td>\n",
              "      <td>0.73786</td>\n",
              "      <td>0.68632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LR-pv</th>\n",
              "      <td>0.80600</td>\n",
              "      <td>0.64738</td>\n",
              "      <td>0.73251</td>\n",
              "      <td>0.68732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LR-0.33</th>\n",
              "      <td>0.77784</td>\n",
              "      <td>0.78178</td>\n",
              "      <td>0.63145</td>\n",
              "      <td>0.69862</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LR-0.44</th>\n",
              "      <td>0.80092</td>\n",
              "      <td>0.69772</td>\n",
              "      <td>0.69780</td>\n",
              "      <td>0.69776</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-552b6f50-e1e7-4cba-b4c8-dad9d56660b9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-552b6f50-e1e7-4cba-b4c8-dad9d56660b9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-552b6f50-e1e7-4cba-b4c8-dad9d56660b9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 109
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 109;\n",
              "                var nbb_unformatted_code = \"# training performance comparison\\n\\nmodels_train_comp_df = pd.concat(\\n    [lg_train.T, lg_train1.T, lg_train2.T, lg_train_auc_roc.T, lg_train_pr_curve.T,], axis=1)\\n\\nmodels_train_comp_df.columns = \\\\\\n  [\\\"LR\\\", \\\"LR-mc\\\", \\\"LR-pv\\\",\\n   f\\\"LR-{optimal_threshold_auc_roc:.2f}\\\", \\n   f\\\"LR-{optimal_threshold_curve:.2f}\\\"]\\n\\nprint(\\\"Training performance comparison:\\\")\\nmodels_train_comp_df.T\";\n",
              "                var nbb_formatted_code = \"# training performance comparison\\n\\nmodels_train_comp_df = pd.concat(\\n    [\\n        lg_train.T,\\n        lg_train1.T,\\n        lg_train2.T,\\n        lg_train_auc_roc.T,\\n        lg_train_pr_curve.T,\\n    ],\\n    axis=1,\\n)\\n\\nmodels_train_comp_df.columns = [\\n    \\\"LR\\\",\\n    \\\"LR-mc\\\",\\n    \\\"LR-pv\\\",\\n    f\\\"LR-{optimal_threshold_auc_roc:.2f}\\\",\\n    f\\\"LR-{optimal_threshold_curve:.2f}\\\",\\n]\\n\\nprint(\\\"Training performance comparison:\\\")\\nmodels_train_comp_df.T\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# testing performance comparison\n",
        "\n",
        "models_test_comp_df = pd.concat(\n",
        "    [lg_test.T, lg_test1.T, lg_test2.T, lg_test_auc_roc.T, lg_test_pr_curve.T,], axis=1)\n",
        "\n",
        "models_test_comp_df.columns = \\\n",
        "  [\"LR\", \"LR-mc\", \"LR-pv\",\n",
        "   f\"LR-{optimal_threshold_auc_roc:.2f}\", \n",
        "   f\"LR-{optimal_threshold_curve:.2f}\"]\n",
        "\n",
        "print(\"Testing performance comparison:\")\n",
        "models_test_comp_df.T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "ImxeZGkTgMiZ",
        "outputId": "9fd1407e-cc24-4149-9f23-b8f8b41f4e90"
      },
      "id": "ImxeZGkTgMiZ",
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing performance comparison:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Accuracy  Recall  Precision      F1\n",
              "LR        0.81016 0.64282    0.73698 0.68668\n",
              "LR-mc     0.81071 0.65219    0.73340 0.69041\n",
              "LR-pv     0.80906 0.65503    0.72776 0.68948\n",
              "LR-0.33   0.78186 0.78705    0.63057 0.70018\n",
              "LR-0.44   0.80198 0.70670    0.68928 0.69788"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4d5fbcf5-c9c1-4582-8c25-341ca71e59b0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Precision</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>LR</th>\n",
              "      <td>0.81016</td>\n",
              "      <td>0.64282</td>\n",
              "      <td>0.73698</td>\n",
              "      <td>0.68668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LR-mc</th>\n",
              "      <td>0.81071</td>\n",
              "      <td>0.65219</td>\n",
              "      <td>0.73340</td>\n",
              "      <td>0.69041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LR-pv</th>\n",
              "      <td>0.80906</td>\n",
              "      <td>0.65503</td>\n",
              "      <td>0.72776</td>\n",
              "      <td>0.68948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LR-0.33</th>\n",
              "      <td>0.78186</td>\n",
              "      <td>0.78705</td>\n",
              "      <td>0.63057</td>\n",
              "      <td>0.70018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LR-0.44</th>\n",
              "      <td>0.80198</td>\n",
              "      <td>0.70670</td>\n",
              "      <td>0.68928</td>\n",
              "      <td>0.69788</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d5fbcf5-c9c1-4582-8c25-341ca71e59b0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4d5fbcf5-c9c1-4582-8c25-341ca71e59b0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4d5fbcf5-c9c1-4582-8c25-341ca71e59b0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 110
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 110;\n",
              "                var nbb_unformatted_code = \"# testing performance comparison\\n\\nmodels_test_comp_df = pd.concat(\\n    [lg_test.T, lg_test1.T, lg_test2.T, lg_test_auc_roc.T, lg_test_pr_curve.T,], axis=1)\\n\\nmodels_test_comp_df.columns = \\\\\\n  [\\\"LR\\\", \\\"LR-mc\\\", \\\"LR-pv\\\",\\n   f\\\"LR-{optimal_threshold_auc_roc:.2f}\\\", \\n   f\\\"LR-{optimal_threshold_curve:.2f}\\\"]\\n\\nprint(\\\"Testing performance comparison:\\\")\\nmodels_test_comp_df.T\";\n",
              "                var nbb_formatted_code = \"# testing performance comparison\\n\\nmodels_test_comp_df = pd.concat(\\n    [\\n        lg_test.T,\\n        lg_test1.T,\\n        lg_test2.T,\\n        lg_test_auc_roc.T,\\n        lg_test_pr_curve.T,\\n    ],\\n    axis=1,\\n)\\n\\nmodels_test_comp_df.columns = [\\n    \\\"LR\\\",\\n    \\\"LR-mc\\\",\\n    \\\"LR-pv\\\",\\n    f\\\"LR-{optimal_threshold_auc_roc:.2f}\\\",\\n    f\\\"LR-{optimal_threshold_curve:.2f}\\\",\\n]\\n\\nprint(\\\"Testing performance comparison:\\\")\\nmodels_test_comp_df.T\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Almost all the three models are performing well on both training and test data without the problem of overfitting\n",
        "* The model with threshold of **0.33** is giving the best $F_1$ score **0.70** on testing data. Therefore it can be selected as the final model"
      ],
      "metadata": {
        "id": "iw-nAl_XiQ4G"
      },
      "id": "iw-nAl_XiQ4G"
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Final Model"
      ],
      "metadata": {
        "id": "eOjIGTKyBfxk"
      },
      "id": "eOjIGTKyBfxk"
    },
    {
      "cell_type": "code",
      "source": [
        "final_model = sm.Logit(y_train, X_train2).fit(method='bfgs', disp=False)\n",
        "\n",
        "print(\"Final model performance on test data:\")\n",
        "final_model_performance = model_performance(final_model, X_test2, y_test, threshold=optimal_threshold_auc_roc)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "hWVjh_jFBf7C",
        "outputId": "2d071e43-effd-4fc7-e3be-d9c710affb2b"
      },
      "id": "hWVjh_jFBf7C",
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final model performance on test data:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Accuracy  Recall  Precision      F1\n",
              "0   0.78186 0.78705    0.63057 0.70018"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-210b9d58-8bc7-4f39-8ee1-9864aaa71b5b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Precision</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.78186</td>\n",
              "      <td>0.78705</td>\n",
              "      <td>0.63057</td>\n",
              "      <td>0.70018</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-210b9d58-8bc7-4f39-8ee1-9864aaa71b5b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-210b9d58-8bc7-4f39-8ee1-9864aaa71b5b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-210b9d58-8bc7-4f39-8ee1-9864aaa71b5b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 111;\n",
              "                var nbb_unformatted_code = \"final_model = sm.Logit(y_train, X_train2).fit(method='bfgs', disp=False)\\n\\nprint(\\\"Final model performance on test data:\\\")\\nfinal_model_performance = model_performance(final_model, X_test2, y_test, threshold=optimal_threshold_auc_roc)\";\n",
              "                var nbb_formatted_code = \"final_model = sm.Logit(y_train, X_train2).fit(method=\\\"bfgs\\\", disp=False)\\n\\nprint(\\\"Final model performance on test data:\\\")\\nfinal_model_performance = model_performance(\\n    final_model, X_test2, y_test, threshold=optimal_threshold_auc_roc\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test_preidct = round(final_model.predict(X_test2) > optimal_threshold_auc_roc)\n",
        "print(f'Final model Recall on test data is {recall_score(y_test, y_test_preidct):.5f}')\n",
        "print(f'Final model Precisison on test data is {precision_score(y_test, y_test_preidct):.5f}')\n",
        "print(f'Final model F1 score on test data is {f1_score(y_test, y_test_preidct):.5f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "id": "7wAJ0o-rEsZz",
        "outputId": "dc14c380-10ee-4b29-cc03-b4dc08c179a7"
      },
      "id": "7wAJ0o-rEsZz",
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final model Recall on test data is 0.78705\n",
            "Final model Precisison on test data is 0.63057\n",
            "Final model F1 score on test data is 0.70018\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 112;\n",
              "                var nbb_unformatted_code = \"y_test_preidct = round(final_model.predict(X_test2) > optimal_threshold_auc_roc)\\nprint(f'Final model Recall on test data is {recall_score(y_test, y_test_preidct):.5f}')\\nprint(f'Final model Precisison on test data is {precision_score(y_test, y_test_preidct):.5f}')\\nprint(f'Final model F1 score on test data is {f1_score(y_test, y_test_preidct):.5f}')\";\n",
              "                var nbb_formatted_code = \"y_test_preidct = round(final_model.predict(X_test2) > optimal_threshold_auc_roc)\\nprint(f\\\"Final model Recall on test data is {recall_score(y_test, y_test_preidct):.5f}\\\")\\nprint(\\n    f\\\"Final model Precisison on test data is {precision_score(y_test, y_test_preidct):.5f}\\\"\\n)\\nprint(f\\\"Final model F1 score on test data is {f1_score(y_test, y_test_preidct):.5f}\\\")\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations from Logistic Regression model**\n",
        "\n",
        "* We have been able to build a predictive model that can predict which booking is going to be canceled in advance with an $F_1$ score of **0.69862** on the training data set. The model can help the hotel staff in formulating profitable policies for cancellations and refunds.\n",
        "\n",
        "* All the logistic regression models have given a generalized performance on the training and test set.\n",
        "\n",
        "* Coefficients of continuous variables such as the number of weekend nights, lead time, and the average price per room, are positive and increase in these will lead to an increase in the chances of a booking being canceled.\n",
        "\n",
        "* Coefficients of categorical binary variables such as the presence of children among the guests, and arrival in February, March, or November are also positive and 1s in these will lead to an increase in chances of a booking being canceled.\n",
        "\n",
        "* Coefficients of continuous variables such as the number of meals (type_of_meal_plan), number of special requests are negative and increase in these will lead to decrease in the chances of a booking being canceled.\n",
        "\n",
        "* Coefficients of categorical binary variables such as repeated guest, car parking space requirement, reservation of Room_Type 2 and Room_Type 4, and arrival from May to September or December are also negatibe and 1s in these will lead to decrease in chances of a booking being canceled.\n",
        "\n",
        "* Using the **default** threshold the model gives a low recall but good precision score. This model maximizes True Positives and minimizes False Positives. We can permit some False Positives as our goal is to predict all Positives.\n",
        "\n",
        "* Using a **0.33** threshold the model gives a high recall but lower precision score. This model maximizes True Positive and minimizes False Negative. It catches maximum Positive cases. And this is what we are focusiog on in this project."
      ],
      "metadata": {
        "id": "P00CnDuredk3"
      },
      "id": "P00CnDuredk3"
    },
    {
      "cell_type": "markdown",
      "id": "amazing-fluid",
      "metadata": {
        "id": "amazing-fluid"
      },
      "source": [
        "# Building a Decision Tree model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "id": "neither-hydrogen",
      "metadata": {
        "id": "neither-hydrogen",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "198cbac9-589f-4c15-fd6c-ce154f4cd731"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 113;\n",
              "                var nbb_unformatted_code = \"X = df.drop(['booking_status'], axis=1)\\ny = round(df['booking_status'] == 'Canceled')\\n\\nX = pd.get_dummies(X, drop_first=True)\\n\\n# Splitting data in train and test sets\\nX_train, X_test, y_train, y_test = train_test_split(\\n    X, y, test_size=0.30, random_state=42\\n)\";\n",
              "                var nbb_formatted_code = \"X = df.drop([\\\"booking_status\\\"], axis=1)\\ny = round(df[\\\"booking_status\\\"] == \\\"Canceled\\\")\\n\\nX = pd.get_dummies(X, drop_first=True)\\n\\n# Splitting data in train and test sets\\nX_train, X_test, y_train, y_test = train_test_split(\\n    X, y, test_size=0.30, random_state=42\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "X = df.drop(['booking_status'], axis=1)\n",
        "y = round(df['booking_status'] == 'Canceled')\n",
        "\n",
        "X = pd.get_dummies(X, drop_first=True)\n",
        "\n",
        "# Splitting data in train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.30, random_state=42\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = DecisionTreeClassifier(random_state=42)\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "fXwg6EuOkrll",
        "outputId": "55c4524c-7694-4622-85eb-4c90747552a7"
      },
      "id": "fXwg6EuOkrll",
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 114
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 114;\n",
              "                var nbb_unformatted_code = \"model = DecisionTreeClassifier(random_state=42)\\nmodel.fit(X_train, y_train)\";\n",
              "                var nbb_formatted_code = \"model = DecisionTreeClassifier(random_state=42)\\nmodel.fit(X_train, y_train)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Model Evaluation"
      ],
      "metadata": {
        "id": "o8X8O3mUkyXO"
      },
      "id": "o8X8O3mUkyXO"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model evaluation criterion**\n",
        "\n",
        "**Model can make wrong predictions as:**\n",
        "\n",
        "\n",
        "* Predicting that a booking will not be canceled but in reality, the booking will be canceled (FN)\n",
        "* Predicting that a booking will be canceled but in reality, the booking will not be canceled (FP)\n",
        "\n",
        "**Which case is more important?**\n",
        "\n",
        "* If we predict that a booking will not be canceled but in reality, the booking will be canceled, then the hotel will receive less revenue/profit\n",
        "\n",
        "* If we predict that a booking will be canceled but in reality, the booking will not be canceled, then the hotel may face problems of overbooking and bear additional expenses to accommodate guests in another hotel for example\n",
        "\n",
        "**How to reduce the losses?**\n",
        "\n",
        "* We want the recall to be maximized, the greater the recall score higher are the chances of minimizing the False Negatives."
      ],
      "metadata": {
        "id": "mWFM1IDGk3HY"
      },
      "id": "mWFM1IDGk3HY"
    },
    {
      "cell_type": "code",
      "source": [
        "dt_train = model_performance(model, X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "T2gNkciTkrrU",
        "outputId": "0e2bebcc-133d-4b5c-8bc2-868e28d4a598"
      },
      "id": "T2gNkciTkrrU",
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
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          "metadata": {}
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          "data": {
            "text/plain": [
              "   Accuracy  Recall  Precision      F1\n",
              "0   0.99330 0.98345    0.99596 0.98967"
            ],
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              "      <td>0.99330</td>\n",
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              "          const element = document.querySelector('#df-a268d632-13ac-4ddf-ae47-f1994fbc2667');\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 115;\n",
              "                var nbb_unformatted_code = \"dt_train = model_performance(model, X_train, y_train)\";\n",
              "                var nbb_formatted_code = \"dt_train = model_performance(model, X_train, y_train)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Model is able to perfectly classify all the data points on the training set.\n",
        "* Almost no errors on the training set, each sample has been classified correctly\n",
        "* As we know a decision tree will continue to grow and classify each data point correctly if no restrictions are applied as the trees will learn all the patterns in the training set.\n",
        "* This generally leads to overfitting of the model as Decision Tree will perform well on the training set but will fail to replicate the performance on the test set.\n",
        "* To verify if the model is overfitting, we need to check the performance on test set"
      ],
      "metadata": {
        "id": "V63B954eTEZq"
      },
      "id": "V63B954eTEZq"
    },
    {
      "cell_type": "code",
      "source": [
        "dt_test = model_performance(model, X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "d6S44tbbSUuJ",
        "outputId": "e260088b-ceca-42a7-bdc2-2b5cd6aac2d0"
      },
      "id": "d6S44tbbSUuJ",
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Accuracy  Recall  Precision      F1\n",
              "0   0.86529 0.80177    0.79385 0.79779"
            ],
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              "  <thead>\n",
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              "      <th>Accuracy</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.86529</td>\n",
              "      <td>0.80177</td>\n",
              "      <td>0.79385</td>\n",
              "      <td>0.79779</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2a84b335-1ed8-48a2-b7d9-19e4422c3a7b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2a84b335-1ed8-48a2-b7d9-19e4422c3a7b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2a84b335-1ed8-48a2-b7d9-19e4422c3a7b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 116;\n",
              "                var nbb_unformatted_code = \"dt_test = model_performance(model, X_test, y_test)\";\n",
              "                var nbb_formatted_code = \"dt_test = model_performance(model, X_test, y_test)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The model is overfitting the data as expected and not able to generalize well on the test set\n",
        "* However all the performance metrics are better than in Logistic Regression model"
      ],
      "metadata": {
        "id": "GwBiPaKHTuep"
      },
      "id": "GwBiPaKHTuep"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3KQY8iHUYp6J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "802376a7-4b35-4c39-9c29-fadc9a0c061a"
      },
      "id": "3KQY8iHUYp6J",
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 116;\n",
              "                var nbb_unformatted_code = \"dt_test = model_performance(model, X_test, y_test)\";\n",
              "                var nbb_formatted_code = \"dt_test = model_performance(model, X_test, y_test)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualizing the Decision Tree**"
      ],
      "metadata": {
        "id": "SpTv8tdbYt8R"
      },
      "id": "SpTv8tdbYt8R"
    },
    {
      "cell_type": "code",
      "source": [
        "feature_names = list(X_train.columns)\n",
        "importances = model.feature_importances_\n",
        "indices = np.argsort(importances)"
      ],
      "metadata": {
        "id": "3kAp2VFmYt8R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "73a6b6df-5b94-42d7-9e71-7aa7313e7fd4"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 117;\n",
              "                var nbb_unformatted_code = \"feature_names = list(X_train.columns)\\nimportances = model.feature_importances_\\nindices = np.argsort(importances)\";\n",
              "                var nbb_formatted_code = \"feature_names = list(X_train.columns)\\nimportances = model.feature_importances_\\nindices = np.argsort(importances)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "3kAp2VFmYt8R"
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 10))\n",
        "out = tree.plot_tree(\n",
        "    model,\n",
        "    feature_names=feature_names,\n",
        "    filled=True,\n",
        "    fontsize=9,\n",
        "    node_ids=False,\n",
        "    class_names=None,\n",
        ")\n",
        "# below code will add arrows to the decision tree split if they are missing\n",
        "for o in out:\n",
        "    arrow = o.arrow_patch\n",
        "    if arrow is not None:\n",
        "        arrow.set_edgecolor(\"black\")\n",
        "        arrow.set_linewidth(1)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "outputId": "45f98e3b-4982-428a-ca57-3aee1a9f98c8",
        "id": "VbnpPukDYt8R"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 118;\n",
              "                var nbb_unformatted_code = \"plt.figure(figsize=(20, 10))\\nout = tree.plot_tree(\\n    model,\\n    feature_names=feature_names,\\n    filled=True,\\n    fontsize=9,\\n    node_ids=False,\\n    class_names=None,\\n)\\n# below code will add arrows to the decision tree split if they are missing\\nfor o in out:\\n    arrow = o.arrow_patch\\n    if arrow is not None:\\n        arrow.set_edgecolor(\\\"black\\\")\\n        arrow.set_linewidth(1)\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"plt.figure(figsize=(20, 10))\\nout = tree.plot_tree(\\n    model,\\n    feature_names=feature_names,\\n    filled=True,\\n    fontsize=9,\\n    node_ids=False,\\n    class_names=None,\\n)\\n# below code will add arrows to the decision tree split if they are missing\\nfor o in out:\\n    arrow = o.arrow_patch\\n    if arrow is not None:\\n        arrow.set_edgecolor(\\\"black\\\")\\n        arrow.set_linewidth(1)\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "id": "VbnpPukDYt8R"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Do we need to prune the tree?**"
      ],
      "metadata": {
        "id": "RK_MKzRenLAh"
      },
      "id": "RK_MKzRenLAh"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Yes**, let's use pruning techniques to try and reduce overfitting."
      ],
      "metadata": {
        "id": "uT3sAS4Sm5hL"
      },
      "id": "uT3sAS4Sm5hL"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Decision Tree (Pre-pruning)"
      ],
      "metadata": {
        "id": "KVeNITCQm8xD"
      },
      "id": "KVeNITCQm8xD"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Before pruning the tree let's check the important features.**"
      ],
      "metadata": {
        "id": "ywWldYGUU79M"
      },
      "id": "ywWldYGUU79M"
    },
    {
      "cell_type": "code",
      "source": [
        "feature_names = list(X_train.columns)\n",
        "importances = model.feature_importances_\n",
        "indices = np.argsort(importances)\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.title(\"Feature Importances\")\n",
        "plt.barh(range(len(indices)), importances[indices], color=\"violet\", align=\"center\")\n",
        "plt.yticks(range(len(indices)), [feature_names[i] for i in indices])\n",
        "plt.xlabel(\"Relative Importance\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "K0PULoIEU8Ks",
        "outputId": "5c04205f-246e-41e8-c64b-f3ace447c368"
      },
      "id": "K0PULoIEU8Ks",
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 119;\n",
              "                var nbb_unformatted_code = \"feature_names = list(X_train.columns)\\nimportances = model.feature_importances_\\nindices = np.argsort(importances)\\n\\nplt.figure(figsize=(8, 8))\\nplt.title(\\\"Feature Importances\\\")\\nplt.barh(range(len(indices)), importances[indices], color=\\\"violet\\\", align=\\\"center\\\")\\nplt.yticks(range(len(indices)), [feature_names[i] for i in indices])\\nplt.xlabel(\\\"Relative Importance\\\")\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"feature_names = list(X_train.columns)\\nimportances = model.feature_importances_\\nindices = np.argsort(importances)\\n\\nplt.figure(figsize=(8, 8))\\nplt.title(\\\"Feature Importances\\\")\\nplt.barh(range(len(indices)), importances[indices], color=\\\"violet\\\", align=\\\"center\\\")\\nplt.yticks(range(len(indices)), [feature_names[i] for i in indices])\\nplt.xlabel(\\\"Relative Importance\\\")\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Lead time is the most important feature followed by average price per room"
      ],
      "metadata": {
        "id": "MYHk5wm-VMQG"
      },
      "id": "MYHk5wm-VMQG"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Using GridSearch for Hyperparameter tuning of our tree model**\n",
        "\n",
        "* Hyperparameter tuning is tricky in the sense that there is no direct way to calculate how a change in the hyperparameter value will reduce the loss of our model, so we usually resort to experimentation. i.e we'll use Grid search\n",
        "* Grid search is a tuning technique that attempts to compute the optimum values of hyperparameters. \n",
        "* It is an exhaustive search that is performed on a the specific parameter values of a model.\n",
        "* The parameters of the estimator/model used to apply these methods are optimized by cross-validated grid-search over a parameter grid."
      ],
      "metadata": {
        "id": "_9bOlDGDnBP0"
      },
      "id": "_9bOlDGDnBP0"
    },
    {
      "cell_type": "code",
      "source": [
        "# Choose the type of classifier.\n",
        "estimator = DecisionTreeClassifier(random_state=42)\n",
        "\n",
        "# Grid of parameters to choose from\n",
        "parameters = {\n",
        "    \"class_weight\": [None, \"balanced\"],\n",
        "    \"max_depth\": np.arange(4, 8, 2),\n",
        "    \"max_leaf_nodes\": [25, 50, 75],\n",
        "    \"min_samples_split\": [5, 10, 20],\n",
        "    \"criterion\": [\"entropy\", \"gini\"],\n",
        "    \"splitter\": [\"best\", \"random\"],\n",
        "    \"min_impurity_decrease\": [1e-5, 1e-4],\n",
        "}\n",
        "\n",
        "# Type of scoring used to compare parameter combinations\n",
        "acc_scorer = make_scorer(f1_score)\n",
        "\n",
        "# Run the grid search\n",
        "grid_obj = GridSearchCV(estimator, parameters, scoring=acc_scorer, cv=5)\n",
        "grid_obj = grid_obj.fit(X_train, y_train)\n",
        "\n",
        "# Set the clf to the best combination of parameters\n",
        "estimator = grid_obj.best_estimator_\n",
        "\n",
        "# Fit the best algorithm to the data.\n",
        "estimator.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "id": "ZhOrk8Ogmy-d",
        "outputId": "d20aa835-4258-477b-a283-e37986c89c86"
      },
      "id": "ZhOrk8Ogmy-d",
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=6, max_leaf_nodes=50,\n",
              "                       min_impurity_decrease=1e-05, min_samples_split=20,\n",
              "                       random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=6, max_leaf_nodes=50,\n",
              "                       min_impurity_decrease=1e-05, min_samples_split=20,\n",
              "                       random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=6, max_leaf_nodes=50,\n",
              "                       min_impurity_decrease=1e-05, min_samples_split=20,\n",
              "                       random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 120
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 120;\n",
              "                var nbb_unformatted_code = \"# Choose the type of classifier.\\nestimator = DecisionTreeClassifier(random_state=42)\\n\\n# Grid of parameters to choose from\\nparameters = {\\n    \\\"class_weight\\\": [None, \\\"balanced\\\"],\\n    \\\"max_depth\\\": np.arange(4, 8, 2),\\n    \\\"max_leaf_nodes\\\": [25, 50, 75],\\n    \\\"min_samples_split\\\": [5, 10, 20],\\n    \\\"criterion\\\": [\\\"entropy\\\", \\\"gini\\\"],\\n    \\\"splitter\\\": [\\\"best\\\", \\\"random\\\"],\\n    \\\"min_impurity_decrease\\\": [1e-5, 1e-4],\\n}\\n\\n# Type of scoring used to compare parameter combinations\\nacc_scorer = make_scorer(f1_score)\\n\\n# Run the grid search\\ngrid_obj = GridSearchCV(estimator, parameters, scoring=acc_scorer, cv=5)\\ngrid_obj = grid_obj.fit(X_train, y_train)\\n\\n# Set the clf to the best combination of parameters\\nestimator = grid_obj.best_estimator_\\n\\n# Fit the best algorithm to the data.\\nestimator.fit(X_train, y_train)\";\n",
              "                var nbb_formatted_code = \"# Choose the type of classifier.\\nestimator = DecisionTreeClassifier(random_state=42)\\n\\n# Grid of parameters to choose from\\nparameters = {\\n    \\\"class_weight\\\": [None, \\\"balanced\\\"],\\n    \\\"max_depth\\\": np.arange(4, 8, 2),\\n    \\\"max_leaf_nodes\\\": [25, 50, 75],\\n    \\\"min_samples_split\\\": [5, 10, 20],\\n    \\\"criterion\\\": [\\\"entropy\\\", \\\"gini\\\"],\\n    \\\"splitter\\\": [\\\"best\\\", \\\"random\\\"],\\n    \\\"min_impurity_decrease\\\": [1e-5, 1e-4],\\n}\\n\\n# Type of scoring used to compare parameter combinations\\nacc_scorer = make_scorer(f1_score)\\n\\n# Run the grid search\\ngrid_obj = GridSearchCV(estimator, parameters, scoring=acc_scorer, cv=5)\\ngrid_obj = grid_obj.fit(X_train, y_train)\\n\\n# Set the clf to the best combination of parameters\\nestimator = grid_obj.best_estimator_\\n\\n# Fit the best algorithm to the data.\\nestimator.fit(X_train, y_train)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt_train_pre = model_performance(estimator, X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "UPjFSsR3mzD0",
        "outputId": "baa13edc-847d-4882-b9a6-cf3c52f064fc"
      },
      "id": "UPjFSsR3mzD0",
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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              "   Accuracy  Recall  Precision      F1\n",
              "0   0.84216 0.72083    0.77858 0.74859"
            ],
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 121;\n",
              "                var nbb_unformatted_code = \"dt_train_pre = model_performance(estimator, X_train, y_train)\";\n",
              "                var nbb_formatted_code = \"dt_train_pre = model_performance(estimator, X_train, y_train)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt_test_pre = model_performance(estimator, X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "EBHHnZoAmzJb",
        "outputId": "2bbd5ece-1f92-48a7-e531-2829c21ebbe4"
      },
      "id": "EBHHnZoAmzJb",
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
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          "data": {
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              "   Accuracy  Recall  Precision      F1\n",
              "0   0.84232 0.71417    0.78994 0.75015"
            ],
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              "      <th>Accuracy</th>\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0f85a619-666e-4ade-922d-0fd83afec8f1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0f85a619-666e-4ade-922d-0fd83afec8f1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 122;\n",
              "                var nbb_unformatted_code = \"dt_test_pre = model_performance(estimator, X_test, y_test)\";\n",
              "                var nbb_formatted_code = \"dt_test_pre = model_performance(estimator, X_test, y_test)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
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              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The model is giving a generalized result now.\n",
        "* The $F_1$ scores on both the train and test data are coming to be around 0.75 which shows that the model is able to generalize well on unseen data."
      ],
      "metadata": {
        "id": "tmEX8aainQ4x"
      },
      "id": "tmEX8aainQ4x"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualizing the Decision Tree**"
      ],
      "metadata": {
        "id": "AZIFzTzHXYIs"
      },
      "id": "AZIFzTzHXYIs"
    },
    {
      "cell_type": "code",
      "source": [
        "feature_names = list(X_train.columns)\n",
        "importances = estimator.feature_importances_\n",
        "indices = np.argsort(importances)"
      ],
      "metadata": {
        "id": "9SGAP_vNmzMS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "ffc1c018-ab5a-4fb9-e308-268f0959ef6b"
      },
      "id": "9SGAP_vNmzMS",
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 123;\n",
              "                var nbb_unformatted_code = \"feature_names = list(X_train.columns)\\nimportances = estimator.feature_importances_\\nindices = np.argsort(importances)\";\n",
              "                var nbb_formatted_code = \"feature_names = list(X_train.columns)\\nimportances = estimator.feature_importances_\\nindices = np.argsort(importances)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 10))\n",
        "out = tree.plot_tree(\n",
        "    estimator,\n",
        "    feature_names=feature_names,\n",
        "    filled=True,\n",
        "    fontsize=9,\n",
        "    node_ids=False,\n",
        "    class_names=None,\n",
        ")\n",
        "# below code will add arrows to the decision tree split if they are missing\n",
        "for o in out:\n",
        "    arrow = o.arrow_patch\n",
        "    if arrow is not None:\n",
        "        arrow.set_edgecolor(\"black\")\n",
        "        arrow.set_linewidth(1)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "sbSAgbdgnXch",
        "outputId": "0005975c-92b1-42c5-a830-938b94d94af3"
      },
      "id": "sbSAgbdgnXch",
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 124;\n",
              "                var nbb_unformatted_code = \"plt.figure(figsize=(20, 10))\\nout = tree.plot_tree(\\n    estimator,\\n    feature_names=feature_names,\\n    filled=True,\\n    fontsize=9,\\n    node_ids=False,\\n    class_names=None,\\n)\\n# below code will add arrows to the decision tree split if they are missing\\nfor o in out:\\n    arrow = o.arrow_patch\\n    if arrow is not None:\\n        arrow.set_edgecolor(\\\"black\\\")\\n        arrow.set_linewidth(1)\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"plt.figure(figsize=(20, 10))\\nout = tree.plot_tree(\\n    estimator,\\n    feature_names=feature_names,\\n    filled=True,\\n    fontsize=9,\\n    node_ids=False,\\n    class_names=None,\\n)\\n# below code will add arrows to the decision tree split if they are missing\\nfor o in out:\\n    arrow = o.arrow_patch\\n    if arrow is not None:\\n        arrow.set_edgecolor(\\\"black\\\")\\n        arrow.set_linewidth(1)\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Text report showing the rules of a decision tree -\n",
        "print(tree.export_text(estimator, feature_names=feature_names, show_weights=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "077fmwwbnXfZ",
        "outputId": "db443396-92a8-419d-bba0-4c0350f327a6"
      },
      "id": "077fmwwbnXfZ",
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "|--- lead_time <= 151.50\n",
            "|   |--- no_of_special_requests <= 0.50\n",
            "|   |   |--- market_segment_type_Online <= 0.50\n",
            "|   |   |   |--- lead_time <= 92.50\n",
            "|   |   |   |   |--- no_of_weekend_nights <= 0.50\n",
            "|   |   |   |   |   |--- avg_price_per_room <= 178.44\n",
            "|   |   |   |   |   |   |--- weights: [2421.00, 84.00] class: 0.0\n",
            "|   |   |   |   |   |--- avg_price_per_room >  178.44\n",
            "|   |   |   |   |   |   |--- weights: [4.00, 18.00] class: 1.0\n",
            "|   |   |   |   |--- no_of_weekend_nights >  0.50\n",
            "|   |   |   |   |   |--- lead_time <= 65.50\n",
            "|   |   |   |   |   |   |--- weights: [1250.00, 152.00] class: 0.0\n",
            "|   |   |   |   |   |--- lead_time >  65.50\n",
            "|   |   |   |   |   |   |--- weights: [213.00, 121.00] class: 0.0\n",
            "|   |   |   |--- lead_time >  92.50\n",
            "|   |   |   |   |--- lead_time <= 116.50\n",
            "|   |   |   |   |   |--- arrival_month_4 <= 0.50\n",
            "|   |   |   |   |   |   |--- weights: [316.00, 239.00] class: 0.0\n",
            "|   |   |   |   |   |--- arrival_month_4 >  0.50\n",
            "|   |   |   |   |   |   |--- weights: [2.00, 67.00] class: 1.0\n",
            "|   |   |   |   |--- lead_time >  116.50\n",
            "|   |   |   |   |   |--- no_of_week_nights <= 0.50\n",
            "|   |   |   |   |   |   |--- weights: [8.00, 18.00] class: 1.0\n",
            "|   |   |   |   |   |--- no_of_week_nights >  0.50\n",
            "|   |   |   |   |   |   |--- weights: [418.00, 79.00] class: 0.0\n",
            "|   |   |--- market_segment_type_Online >  0.50\n",
            "|   |   |   |--- lead_time <= 13.50\n",
            "|   |   |   |   |--- avg_price_per_room <= 99.24\n",
            "|   |   |   |   |   |--- arrival_month_2 <= 0.50\n",
            "|   |   |   |   |   |   |--- weights: [487.00, 44.00] class: 0.0\n",
            "|   |   |   |   |   |--- arrival_month_2 >  0.50\n",
            "|   |   |   |   |   |   |--- weights: [83.00, 39.00] class: 0.0\n",
            "|   |   |   |   |--- avg_price_per_room >  99.24\n",
            "|   |   |   |   |   |--- lead_time <= 3.50\n",
            "|   |   |   |   |   |   |--- weights: [304.00, 55.00] class: 0.0\n",
            "|   |   |   |   |   |--- lead_time >  3.50\n",
            "|   |   |   |   |   |   |--- weights: [179.00, 191.00] class: 1.0\n",
            "|   |   |   |--- lead_time >  13.50\n",
            "|   |   |   |   |--- avg_price_per_room <= 111.76\n",
            "|   |   |   |   |   |--- avg_price_per_room <= 72.22\n",
            "|   |   |   |   |   |   |--- weights: [217.00, 115.00] class: 0.0\n",
            "|   |   |   |   |   |--- avg_price_per_room >  72.22\n",
            "|   |   |   |   |   |   |--- weights: [746.00, 1026.00] class: 1.0\n",
            "|   |   |   |   |--- avg_price_per_room >  111.76\n",
            "|   |   |   |   |   |--- required_car_parking_space <= 0.50\n",
            "|   |   |   |   |   |   |--- weights: [443.00, 1268.00] class: 1.0\n",
            "|   |   |   |   |   |--- required_car_parking_space >  0.50\n",
            "|   |   |   |   |   |   |--- weights: [32.00, 1.00] class: 0.0\n",
            "|   |--- no_of_special_requests >  0.50\n",
            "|   |   |--- no_of_special_requests <= 1.50\n",
            "|   |   |   |--- market_segment_type_Online <= 0.50\n",
            "|   |   |   |   |--- type_of_meal_plan <= 0.50\n",
            "|   |   |   |   |   |--- lead_time <= 63.00\n",
            "|   |   |   |   |   |   |--- weights: [17.00, 0.00] class: 0.0\n",
            "|   |   |   |   |   |--- lead_time >  63.00\n",
            "|   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1.0\n",
            "|   |   |   |   |--- type_of_meal_plan >  0.50\n",
            "|   |   |   |   |   |--- lead_time <= 91.50\n",
            "|   |   |   |   |   |   |--- weights: [906.00, 4.00] class: 0.0\n",
            "|   |   |   |   |   |--- lead_time >  91.50\n",
            "|   |   |   |   |   |   |--- weights: [153.00, 16.00] class: 0.0\n",
            "|   |   |   |--- market_segment_type_Online >  0.50\n",
            "|   |   |   |   |--- lead_time <= 9.50\n",
            "|   |   |   |   |   |--- lead_time <= 4.50\n",
            "|   |   |   |   |   |   |--- weights: [677.00, 25.00] class: 0.0\n",
            "|   |   |   |   |   |--- lead_time >  4.50\n",
            "|   |   |   |   |   |   |--- weights: [392.00, 47.00] class: 0.0\n",
            "|   |   |   |   |--- lead_time >  9.50\n",
            "|   |   |   |   |   |--- required_car_parking_space <= 0.50\n",
            "|   |   |   |   |   |   |--- weights: [3302.00, 927.00] class: 0.0\n",
            "|   |   |   |   |   |--- required_car_parking_space >  0.50\n",
            "|   |   |   |   |   |   |--- weights: [190.00, 2.00] class: 0.0\n",
            "|   |   |--- no_of_special_requests >  1.50\n",
            "|   |   |   |--- lead_time <= 89.50\n",
            "|   |   |   |   |--- no_of_week_nights <= 3.50\n",
            "|   |   |   |   |   |--- weights: [2140.00, 0.00] class: 0.0\n",
            "|   |   |   |   |--- no_of_week_nights >  3.50\n",
            "|   |   |   |   |   |--- no_of_week_nights <= 10.00\n",
            "|   |   |   |   |   |   |--- weights: [301.00, 43.00] class: 0.0\n",
            "|   |   |   |   |   |--- no_of_week_nights >  10.00\n",
            "|   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0\n",
            "|   |   |   |--- lead_time >  89.50\n",
            "|   |   |   |   |--- no_of_special_requests <= 2.50\n",
            "|   |   |   |   |   |--- arrival_month_11 <= 0.50\n",
            "|   |   |   |   |   |   |--- weights: [386.00, 82.00] class: 0.0\n",
            "|   |   |   |   |   |--- arrival_month_11 >  0.50\n",
            "|   |   |   |   |   |   |--- weights: [18.00, 17.00] class: 0.0\n",
            "|   |   |   |   |--- no_of_special_requests >  2.50\n",
            "|   |   |   |   |   |--- weights: [88.00, 0.00] class: 0.0\n",
            "|--- lead_time >  151.50\n",
            "|   |--- avg_price_per_room <= 100.04\n",
            "|   |   |--- no_of_special_requests <= 0.50\n",
            "|   |   |   |--- market_segment_type_Online <= 0.50\n",
            "|   |   |   |   |--- arrival_year_2018 <= 0.50\n",
            "|   |   |   |   |   |--- lead_time <= 214.50\n",
            "|   |   |   |   |   |   |--- weights: [114.00, 48.00] class: 0.0\n",
            "|   |   |   |   |   |--- lead_time >  214.50\n",
            "|   |   |   |   |   |   |--- weights: [127.00, 0.00] class: 0.0\n",
            "|   |   |   |   |--- arrival_year_2018 >  0.50\n",
            "|   |   |   |   |   |--- lead_time <= 197.50\n",
            "|   |   |   |   |   |   |--- weights: [178.00, 69.00] class: 0.0\n",
            "|   |   |   |   |   |--- lead_time >  197.50\n",
            "|   |   |   |   |   |   |--- weights: [276.00, 517.00] class: 1.0\n",
            "|   |   |   |--- market_segment_type_Online >  0.50\n",
            "|   |   |   |   |--- avg_price_per_room <= 2.50\n",
            "|   |   |   |   |   |--- weights: [11.00, 3.00] class: 0.0\n",
            "|   |   |   |   |--- avg_price_per_room >  2.50\n",
            "|   |   |   |   |   |--- arrival_month_12 <= 0.50\n",
            "|   |   |   |   |   |   |--- weights: [0.00, 524.00] class: 1.0\n",
            "|   |   |   |   |   |--- arrival_month_12 >  0.50\n",
            "|   |   |   |   |   |   |--- weights: [6.00, 75.00] class: 1.0\n",
            "|   |   |--- no_of_special_requests >  0.50\n",
            "|   |   |   |--- no_of_weekend_nights <= 0.50\n",
            "|   |   |   |   |--- lead_time <= 180.50\n",
            "|   |   |   |   |   |--- lead_time <= 159.50\n",
            "|   |   |   |   |   |   |--- weights: [12.00, 6.00] class: 0.0\n",
            "|   |   |   |   |   |--- lead_time >  159.50\n",
            "|   |   |   |   |   |   |--- weights: [51.00, 3.00] class: 0.0\n",
            "|   |   |   |   |--- lead_time >  180.50\n",
            "|   |   |   |   |   |--- market_segment_type_Online <= 0.50\n",
            "|   |   |   |   |   |   |--- weights: [24.00, 4.00] class: 0.0\n",
            "|   |   |   |   |   |--- market_segment_type_Online >  0.50\n",
            "|   |   |   |   |   |   |--- weights: [27.00, 123.00] class: 1.0\n",
            "|   |   |   |--- no_of_weekend_nights >  0.50\n",
            "|   |   |   |   |--- market_segment_type_Online <= 0.50\n",
            "|   |   |   |   |   |--- no_of_week_nights <= 5.50\n",
            "|   |   |   |   |   |   |--- weights: [152.00, 2.00] class: 0.0\n",
            "|   |   |   |   |   |--- no_of_week_nights >  5.50\n",
            "|   |   |   |   |   |   |--- weights: [2.00, 1.00] class: 0.0\n",
            "|   |   |   |   |--- market_segment_type_Online >  0.50\n",
            "|   |   |   |   |   |--- no_of_week_nights <= 8.50\n",
            "|   |   |   |   |   |   |--- weights: [336.00, 82.00] class: 0.0\n",
            "|   |   |   |   |   |--- no_of_week_nights >  8.50\n",
            "|   |   |   |   |   |   |--- weights: [2.00, 6.00] class: 1.0\n",
            "|   |--- avg_price_per_room >  100.04\n",
            "|   |   |--- arrival_month_12 <= 0.50\n",
            "|   |   |   |--- no_of_special_requests <= 2.50\n",
            "|   |   |   |   |--- weights: [0.00, 2113.00] class: 1.0\n",
            "|   |   |   |--- no_of_special_requests >  2.50\n",
            "|   |   |   |   |--- weights: [37.00, 0.00] class: 0.0\n",
            "|   |   |--- arrival_month_12 >  0.50\n",
            "|   |   |   |--- no_of_special_requests <= 0.50\n",
            "|   |   |   |   |--- weights: [58.00, 0.00] class: 0.0\n",
            "|   |   |   |--- no_of_special_requests >  0.50\n",
            "|   |   |   |   |--- avg_price_per_room <= 107.74\n",
            "|   |   |   |   |   |--- weights: [4.00, 1.00] class: 0.0\n",
            "|   |   |   |   |--- avg_price_per_room >  107.74\n",
            "|   |   |   |   |   |--- weights: [4.00, 13.00] class: 1.0\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 125;\n",
              "                var nbb_unformatted_code = \"# Text report showing the rules of a decision tree -\\nprint(tree.export_text(estimator, feature_names=feature_names, show_weights=True))\";\n",
              "                var nbb_formatted_code = \"# Text report showing the rules of a decision tree -\\nprint(tree.export_text(estimator, feature_names=feature_names, show_weights=True))\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importance of features in the tree building\n",
        "\n",
        "importances = estimator.feature_importances_\n",
        "indices = np.argsort(importances)[-15:]\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.title(\"Feature Importances\")\n",
        "plt.barh(range(len(indices)), importances[indices], color=\"violet\", align=\"center\")\n",
        "plt.yticks(range(len(indices)), [feature_names[i] for i in indices])\n",
        "plt.xlabel(\"Relative Importance\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "77E0zgkYnXkc",
        "outputId": "a971e672-d9f2-4841-ea34-8e713e324328"
      },
      "id": "77E0zgkYnXkc",
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 126;\n",
              "                var nbb_unformatted_code = \"# importance of features in the tree building\\n\\nimportances = estimator.feature_importances_\\nindices = np.argsort(importances)[-15:]\\n\\nplt.figure(figsize=(8, 8))\\nplt.title(\\\"Feature Importances\\\")\\nplt.barh(range(len(indices)), importances[indices], color=\\\"violet\\\", align=\\\"center\\\")\\nplt.yticks(range(len(indices)), [feature_names[i] for i in indices])\\nplt.xlabel(\\\"Relative Importance\\\")\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"# importance of features in the tree building\\n\\nimportances = estimator.feature_importances_\\nindices = np.argsort(importances)[-15:]\\n\\nplt.figure(figsize=(8, 8))\\nplt.title(\\\"Feature Importances\\\")\\nplt.barh(range(len(indices)), importances[indices], color=\\\"violet\\\", align=\\\"center\\\")\\nplt.yticks(range(len(indices)), [feature_names[i] for i in indices])\\nplt.xlabel(\\\"Relative Importance\\\")\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* In the pre tuned decision tree also, lead time and Online segment are the most important features."
      ],
      "metadata": {
        "id": "LaOxTbJvn5nP"
      },
      "id": "LaOxTbJvn5nP"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations from the pre-pruned decision tree**\n",
        "* The tree become more simple, number of nodes and branches decreased significantly\n",
        "* The performance metrics of the model has been generalized.\n",
        "* The most important features are:\n",
        "  * Lead time\n",
        "  * Market Segment - Online\n",
        "  * Number of special requests\n",
        "  * Average price per room\n",
        "  * Arrivak month - December\n",
        "* The rules obtained from the decision tree can be interpreted as:\n",
        "  * The lead time is the most important factor of the booking cancelation located in the root\n",
        "  * if lead time < 152 days and no special requests and not Online:\n",
        "    * if lead time < 93 and weekdays only \n",
        "      * if price is more 178.44 then the booking will be canceled\n",
        "      * if price is less or equal to 178.44 then the booking will not be canceled\n",
        "\n",
        "<code>\n",
        "    --- lead_time <= 151.50<br>\n",
        "    |   |--- no_of_special_requests <= 0.50<br>\n",
        "    |   |   |--- market_segment_type_Online <= 0.50<br>\n",
        "    |   |   |   |--- lead_time <= 92.50<br>\n",
        "    |   |   |   |   |--- no_of_weekend_nights <= 0.50<br>\n",
        "    |   |   |   |   |   |--- avg_price_per_room <= 178.44<br>\n",
        "    |   |   |   |   |   |   |--- weights: [2421.00, 84.00] class: 0.0<br>\n",
        "    |   |   |   |   |   |--- avg_price_per_room >  178.44<br>\n",
        "    |   |   |   |   |   |   |--- weights: [4.00, 18.00] class: 1.0<br>\n",
        "</code>\n",
        "\n",
        "and so on we can go through each of the branches."
      ],
      "metadata": {
        "id": "10y1Zic_nyK1"
      },
      "id": "10y1Zic_nyK1"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Decision Tree (Post pruning)"
      ],
      "metadata": {
        "id": "0rMRA4kYn-Ea"
      },
      "id": "0rMRA4kYn-Ea"
    },
    {
      "cell_type": "markdown",
      "source": [
        "The `DecisionTreeClassifier` provides parameters such as\n",
        "``min_samples_leaf`` and ``max_depth`` to prevent a tree from overfiting. Cost\n",
        "complexity pruning provides another option to control the size of a tree. In\n",
        "`DecisionTreeClassifier`, this pruning technique is parameterized by the\n",
        "cost complexity parameter, ``ccp_alpha``. Greater values of ``ccp_alpha``\n",
        "increase the number of nodes pruned. Here we only show the effect of\n",
        "``ccp_alpha`` on regularizing the trees and how to choose a ``ccp_alpha``\n",
        "based on validation scores."
      ],
      "metadata": {
        "id": "CVDf0Zp8oAWs"
      },
      "id": "CVDf0Zp8oAWs"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Total impurity of leaves vs effective alphas of pruned tree**\n",
        "\n",
        "Minimal cost complexity pruning recursively finds the node with the \"weakest\n",
        "link\". The weakest link is characterized by an effective alpha, where the\n",
        "nodes with the smallest effective alpha are pruned first. To get an idea of\n",
        "what values of ``ccp_alpha`` could be appropriate, scikit-learn provides\n",
        "`DecisionTreeClassifier.cost_complexity_pruning_path` that returns the\n",
        "effective alphas and the corresponding total leaf impurities at each step of\n",
        "the pruning process. As alpha increases, more of the tree is pruned, which\n",
        "increases the total impurity of its leaves."
      ],
      "metadata": {
        "id": "j9cvGs2_oAah"
      },
      "id": "j9cvGs2_oAah"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Cost Ccomplexity Pruning**"
      ],
      "metadata": {
        "id": "_dQAor-Yd4Zd"
      },
      "id": "_dQAor-Yd4Zd"
    },
    {
      "cell_type": "code",
      "source": [
        "clf = DecisionTreeClassifier(random_state=42, class_weight=\"balanced\")\n",
        "path = clf.cost_complexity_pruning_path(X_train, y_train)\n",
        "ccp_alphas, impurities = abs(path.ccp_alphas), path.impurities"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
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      "id": "3eDMaKxknXnC",
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              "                var nbb_formatted_code = \"clf = DecisionTreeClassifier(random_state=42, class_weight=\\\"balanced\\\")\\npath = clf.cost_complexity_pruning_path(X_train, y_train)\\nccp_alphas, impurities = abs(path.ccp_alphas), path.impurities\";\n",
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        "pd.DataFrame(path)"
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              "      ccp_alphas  impurities\n",
              "0        0.00000     0.00975\n",
              "1        0.00000     0.00975\n",
              "2        0.00000     0.00975\n",
              "3        0.00000     0.00975\n",
              "4        0.00000     0.00975\n",
              "...          ...         ...\n",
              "1632     0.00904     0.32867\n",
              "1633     0.01017     0.33884\n",
              "1634     0.01199     0.35084\n",
              "1635     0.03448     0.41979\n",
              "1636     0.08021     0.50000\n",
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        "fig, ax = plt.subplots(figsize=(10, 5))\n",
        "ax.plot(ccp_alphas[:-1], impurities[:-1], marker=\"o\", drawstyle=\"steps-post\")\n",
        "ax.set_xlabel(\"effective alpha\")\n",
        "ax.set_ylabel(\"total impurity of leaves\")\n",
        "ax.set_title(\"Total Impurity vs effective alpha for training set\")\n",
        "plt.show()"
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          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 129;\n",
              "                var nbb_unformatted_code = \"fig, ax = plt.subplots(figsize=(10, 5))\\nax.plot(ccp_alphas[:-1], impurities[:-1], marker=\\\"o\\\", drawstyle=\\\"steps-post\\\")\\nax.set_xlabel(\\\"effective alpha\\\")\\nax.set_ylabel(\\\"total impurity of leaves\\\")\\nax.set_title(\\\"Total Impurity vs effective alpha for training set\\\")\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"fig, ax = plt.subplots(figsize=(10, 5))\\nax.plot(ccp_alphas[:-1], impurities[:-1], marker=\\\"o\\\", drawstyle=\\\"steps-post\\\")\\nax.set_xlabel(\\\"effective alpha\\\")\\nax.set_ylabel(\\\"total impurity of leaves\\\")\\nax.set_title(\\\"Total Impurity vs effective alpha for training set\\\")\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we train a decision tree using the effective alphas. The last value in `ccp_alphas` is the alpha value that prunes the whole tree, leaving the tree, `clfs[-1]`, with one node."
      ],
      "metadata": {
        "id": "3b4tVA_OoNMq"
      },
      "id": "3b4tVA_OoNMq"
    },
    {
      "cell_type": "code",
      "source": [
        "clfs = []\n",
        "for ccp_alpha in ccp_alphas:\n",
        "    clf = DecisionTreeClassifier(random_state=42, ccp_alpha=ccp_alpha, class_weight=\"balanced\")\n",
        "    clf.fit(X_train, y_train)\n",
        "    clfs.append(clf)\n",
        "\n",
        "print(f'Number of nodes in the last tree is: {clfs[-1].tree_.node_count} with ccp_alpha: {ccp_alphas[-1]:.5f}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "QzwcTVQqoHV5",
        "outputId": "27696023-1aa1-4a3b-a1e4-4632ead9170b"
      },
      "id": "QzwcTVQqoHV5",
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of nodes in the last tree is: 1 with ccp_alpha: 0.08021\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 130;\n",
              "                var nbb_unformatted_code = \"clfs = []\\nfor ccp_alpha in ccp_alphas:\\n    clf = DecisionTreeClassifier(random_state=42, ccp_alpha=ccp_alpha, class_weight=\\\"balanced\\\")\\n    clf.fit(X_train, y_train)\\n    clfs.append(clf)\\n\\nprint(f'Number of nodes in the last tree is: {clfs[-1].tree_.node_count} with ccp_alpha: {ccp_alphas[-1]:.5f}')\";\n",
              "                var nbb_formatted_code = \"clfs = []\\nfor ccp_alpha in ccp_alphas:\\n    clf = DecisionTreeClassifier(\\n        random_state=42, ccp_alpha=ccp_alpha, class_weight=\\\"balanced\\\"\\n    )\\n    clf.fit(X_train, y_train)\\n    clfs.append(clf)\\n\\nprint(\\n    f\\\"Number of nodes in the last tree is: {clfs[-1].tree_.node_count} with ccp_alpha: {ccp_alphas[-1]:.5f}\\\"\\n)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For the remainder, we remove the last element in\n",
        "``clfs`` and ``ccp_alphas``, because it is the trivial tree with only one\n",
        "node. Here we show that the number of nodes and tree depth decreases as alpha\n",
        "increases."
      ],
      "metadata": {
        "id": "WVqKQOQloSW_"
      },
      "id": "WVqKQOQloSW_"
    },
    {
      "cell_type": "code",
      "source": [
        "clfs = clfs[:-1]\n",
        "ccp_alphas = ccp_alphas[:-1]\n",
        "\n",
        "node_counts = [clf.tree_.node_count for clf in clfs]\n",
        "depth = [clf.tree_.max_depth for clf in clfs]\n",
        "fig, ax = plt.subplots(2, 1, figsize=(10, 7))\n",
        "ax[0].plot(ccp_alphas, node_counts, marker=\"o\", drawstyle=\"steps-post\")\n",
        "ax[0].set_xlabel(\"alpha\")\n",
        "ax[0].set_ylabel(\"number of nodes\")\n",
        "ax[0].set_title(\"Number of nodes vs alpha\")\n",
        "ax[1].plot(ccp_alphas, depth, marker=\"o\", drawstyle=\"steps-post\")\n",
        "ax[1].set_xlabel(\"alpha\")\n",
        "ax[1].set_ylabel(\"depth of tree\")\n",
        "ax[1].set_title(\"Depth vs alpha\")\n",
        "fig.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "SRuRf8QfoHZJ",
        "outputId": "ed7db169-fd09-4118-f91b-794cb356df5f"
      },
      "id": "SRuRf8QfoHZJ",
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 131;\n",
              "                var nbb_unformatted_code = \"clfs = clfs[:-1]\\nccp_alphas = ccp_alphas[:-1]\\n\\nnode_counts = [clf.tree_.node_count for clf in clfs]\\ndepth = [clf.tree_.max_depth for clf in clfs]\\nfig, ax = plt.subplots(2, 1, figsize=(10, 7))\\nax[0].plot(ccp_alphas, node_counts, marker=\\\"o\\\", drawstyle=\\\"steps-post\\\")\\nax[0].set_xlabel(\\\"alpha\\\")\\nax[0].set_ylabel(\\\"number of nodes\\\")\\nax[0].set_title(\\\"Number of nodes vs alpha\\\")\\nax[1].plot(ccp_alphas, depth, marker=\\\"o\\\", drawstyle=\\\"steps-post\\\")\\nax[1].set_xlabel(\\\"alpha\\\")\\nax[1].set_ylabel(\\\"depth of tree\\\")\\nax[1].set_title(\\\"Depth vs alpha\\\")\\nfig.tight_layout()\";\n",
              "                var nbb_formatted_code = \"clfs = clfs[:-1]\\nccp_alphas = ccp_alphas[:-1]\\n\\nnode_counts = [clf.tree_.node_count for clf in clfs]\\ndepth = [clf.tree_.max_depth for clf in clfs]\\nfig, ax = plt.subplots(2, 1, figsize=(10, 7))\\nax[0].plot(ccp_alphas, node_counts, marker=\\\"o\\\", drawstyle=\\\"steps-post\\\")\\nax[0].set_xlabel(\\\"alpha\\\")\\nax[0].set_ylabel(\\\"number of nodes\\\")\\nax[0].set_title(\\\"Number of nodes vs alpha\\\")\\nax[1].plot(ccp_alphas, depth, marker=\\\"o\\\", drawstyle=\\\"steps-post\\\")\\nax[1].set_xlabel(\\\"alpha\\\")\\nax[1].set_ylabel(\\\"depth of tree\\\")\\nax[1].set_title(\\\"Depth vs alpha\\\")\\nfig.tight_layout()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f1_train, f1_test = [], []\n",
        "train_scores, test_scores = [], []\n",
        "\n",
        "for clf in clfs:\n",
        "  pred_train, pred_test = clf.predict(X_train), clf.predict(X_test)\n",
        "  values_train, values_test = f1_score(y_train, pred_train), f1_score(y_test, pred_test)\n",
        "  f1_train.append(values_train)\n",
        "  f1_test.append(values_test)\n",
        "  train_scores.append(clf.score(X_train, y_train))\n",
        "  test_scores.append(clf.score(X_test, y_test))"
      ],
      "metadata": {
        "id": "OYVPQ3ZVoHbt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "e300e393-d4e6-4fa6-a592-35260e02147a"
      },
      "id": "OYVPQ3ZVoHbt",
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 132;\n",
              "                var nbb_unformatted_code = \"f1_train, f1_test = [], []\\ntrain_scores, test_scores = [], []\\n\\nfor clf in clfs:\\n  pred_train, pred_test = clf.predict(X_train), clf.predict(X_test)\\n  values_train, values_test = f1_score(y_train, pred_train), f1_score(y_test, pred_test)\\n  f1_train.append(values_train)\\n  f1_test.append(values_test)\\n  train_scores.append(clf.score(X_train, y_train))\\n  test_scores.append(clf.score(X_test, y_test))\";\n",
              "                var nbb_formatted_code = \"f1_train, f1_test = [], []\\ntrain_scores, test_scores = [], []\\n\\nfor clf in clfs:\\n    pred_train, pred_test = clf.predict(X_train), clf.predict(X_test)\\n    values_train, values_test = f1_score(y_train, pred_train), f1_score(\\n        y_test, pred_test\\n    )\\n    f1_train.append(values_train)\\n    f1_test.append(values_test)\\n    train_scores.append(clf.score(X_train, y_train))\\n    test_scores.append(clf.score(X_test, y_test))\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(15, 5))\n",
        "ax.set_xlabel(\"alpha\")\n",
        "ax.set_ylabel(\"F1 score\")\n",
        "ax.set_title(\"F1 score vs alpha for training and testing sets\")\n",
        "ax.plot(ccp_alphas, f1_train, marker=\"o\", label=\"train\", drawstyle=\"steps-post\")\n",
        "ax.plot(ccp_alphas, f1_test, marker=\"o\", label=\"test\", drawstyle=\"steps-post\")\n",
        "ax.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "z7AwWwthoHkh",
        "outputId": "297f7624-495a-46cd-b6d7-45a25cbe1881"
      },
      "id": "z7AwWwthoHkh",
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 133;\n",
              "                var nbb_unformatted_code = \"fig, ax = plt.subplots(figsize=(15, 5))\\nax.set_xlabel(\\\"alpha\\\")\\nax.set_ylabel(\\\"F1 score\\\")\\nax.set_title(\\\"F1 score vs alpha for training and testing sets\\\")\\nax.plot(ccp_alphas, f1_train, marker=\\\"o\\\", label=\\\"train\\\", drawstyle=\\\"steps-post\\\")\\nax.plot(ccp_alphas, f1_test, marker=\\\"o\\\", label=\\\"test\\\", drawstyle=\\\"steps-post\\\")\\nax.legend()\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"fig, ax = plt.subplots(figsize=(15, 5))\\nax.set_xlabel(\\\"alpha\\\")\\nax.set_ylabel(\\\"F1 score\\\")\\nax.set_title(\\\"F1 score vs alpha for training and testing sets\\\")\\nax.plot(ccp_alphas, f1_train, marker=\\\"o\\\", label=\\\"train\\\", drawstyle=\\\"steps-post\\\")\\nax.plot(ccp_alphas, f1_test, marker=\\\"o\\\", label=\\\"test\\\", drawstyle=\\\"steps-post\\\")\\nax.legend()\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# creating the model where we get highest train and test recall\n",
        "index_best_model = np.argmax(f1_test)\n",
        "best_model = clfs[index_best_model]\n",
        "print(best_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "kvAr6ESooHnP",
        "outputId": "5202968b-db94-43cb-ce9f-f9233b0f3706"
      },
      "id": "kvAr6ESooHnP",
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DecisionTreeClassifier(ccp_alpha=6.192049136120569e-05, class_weight='balanced',\n",
            "                       random_state=42)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 134;\n",
              "                var nbb_unformatted_code = \"# creating the model where we get highest train and test recall\\nindex_best_model = np.argmax(f1_test)\\nbest_model = clfs[index_best_model]\\nprint(best_model)\";\n",
              "                var nbb_formatted_code = \"# creating the model where we get highest train and test recall\\nindex_best_model = np.argmax(f1_test)\\nbest_model = clfs[index_best_model]\\nprint(best_model)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Checking performance on training set**"
      ],
      "metadata": {
        "id": "WA6fQ3wJgTYe"
      },
      "id": "WA6fQ3wJgTYe"
    },
    {
      "cell_type": "code",
      "source": [
        "dt_train_post = model_performance(best_model, X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "-ogZT4hcobJo",
        "outputId": "faeafea4-8407-49c9-dd63-f00932db1118"
      },
      "id": "-ogZT4hcobJo",
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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              "   Accuracy  Recall  Precision      F1\n",
              "0   0.93447 0.95736    0.85806 0.90499"
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              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Checking performance on testing set**"
      ],
      "metadata": {
        "id": "Ls7JwTgogVTa"
      },
      "id": "Ls7JwTgogVTa"
    },
    {
      "cell_type": "code",
      "source": [
        "dt_test_post = model_performance(best_model, X_test, y_test)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "mslKG8htobPU",
        "outputId": "d072818b-be56-40cb-8b48-1edcc8e5a01b"
      },
      "id": "mslKG8htobPU",
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
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              "   Accuracy  Recall  Precision      F1\n",
              "0   0.86833 0.85694    0.77121 0.81182"
            ],
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-2ae28561-01f7-4aeb-9f30-e5123c5a98fe');\n",
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              "                                                     [key], {});\n",
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              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 136;\n",
              "                var nbb_unformatted_code = \"dt_test_post = model_performance(best_model, X_test, y_test)\";\n",
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    {
      "cell_type": "markdown",
      "source": [
        "* In the post-pruned tree also, the model is giving a generalized result since the recall scores on both the train and test data are coming to be around **0.90** which shows that the model is able to generalize well on unseen data."
      ],
      "metadata": {
        "id": "Q1X8yV9OokVw"
      },
      "id": "Q1X8yV9OokVw"
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 10))\n",
        "\n",
        "out = tree.plot_tree(\n",
        "    best_model,\n",
        "    feature_names=feature_names,\n",
        "    filled=True,\n",
        "    fontsize=9,\n",
        "    node_ids=False,\n",
        "    class_names=None,\n",
        ")\n",
        "for o in out:\n",
        "    arrow = o.arrow_patch\n",
        "    if arrow is not None:\n",
        "        arrow.set_edgecolor(\"black\")\n",
        "        arrow.set_linewidth(1)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "svLUt3m8oiP9",
        "outputId": "db6e2ea3-a0d1-4cb8-e36d-e0a43c83f7e9"
      },
      "id": "svLUt3m8oiP9",
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 137;\n",
              "                var nbb_unformatted_code = \"plt.figure(figsize=(20, 10))\\n\\nout = tree.plot_tree(\\n    best_model,\\n    feature_names=feature_names,\\n    filled=True,\\n    fontsize=9,\\n    node_ids=False,\\n    class_names=None,\\n)\\nfor o in out:\\n    arrow = o.arrow_patch\\n    if arrow is not None:\\n        arrow.set_edgecolor(\\\"black\\\")\\n        arrow.set_linewidth(1)\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"plt.figure(figsize=(20, 10))\\n\\nout = tree.plot_tree(\\n    best_model,\\n    feature_names=feature_names,\\n    filled=True,\\n    fontsize=9,\\n    node_ids=False,\\n    class_names=None,\\n)\\nfor o in out:\\n    arrow = o.arrow_patch\\n    if arrow is not None:\\n        arrow.set_edgecolor(\\\"black\\\")\\n        arrow.set_linewidth(1)\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Text report showing the rules of a decision tree -\n",
        "print(tree.export_text(best_model, feature_names=feature_names, show_weights=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NrgrY8npoiSn",
        "outputId": "6d54da0e-739c-4063-e7cc-e889d5660ccd"
      },
      "id": "NrgrY8npoiSn",
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "|--- lead_time <= 151.50\n",
            "|   |--- no_of_special_requests <= 0.50\n",
            "|   |   |--- market_segment_type_Online <= 0.50\n",
            "|   |   |   |--- lead_time <= 90.50\n",
            "|   |   |   |   |--- no_of_weekend_nights <= 0.50\n",
            "|   |   |   |   |   |--- avg_price_per_room <= 178.44\n",
            "|   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50\n",
            "|   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 107.00\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [78.64, 1.53] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room >  107.00\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [5.19, 6.13] class: 1.0\n",
            "|   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50\n",
            "|   |   |   |   |   |   |   |   |--- weights: [1238.89, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50\n",
            "|   |   |   |   |   |   |   |--- repeated_guest <= 0.50\n",
            "|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time <= 16.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_8 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_8 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time >  16.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_12 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_12 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [19.29, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time <= 14.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [11.13, 23.01] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time >  14.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [8.90, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |--- repeated_guest >  0.50\n",
            "|   |   |   |   |   |   |   |   |--- weights: [114.24, 1.53] class: 0.0\n",
            "|   |   |   |   |   |--- avg_price_per_room >  178.44\n",
            "|   |   |   |   |   |   |--- arrival_month_9 <= 0.50\n",
            "|   |   |   |   |   |   |   |--- weights: [2.23, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |--- arrival_month_9 >  0.50\n",
            "|   |   |   |   |   |   |   |--- weights: [0.74, 27.61] class: 1.0\n",
            "|   |   |   |   |--- no_of_weekend_nights >  0.50\n",
            "|   |   |   |   |   |--- lead_time <= 65.50\n",
            "|   |   |   |   |   |   |--- arrival_month_2 <= 0.50\n",
            "|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 3.50\n",
            "|   |   |   |   |   |   |   |   |--- arrival_month_8 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_9 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_3 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_3 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_9 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [63.80, 3.07] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- arrival_month_8 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time <= 1.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [8.90, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time >  1.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
            "|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
            "|   |   |   |   |   |   |   |--- no_of_weekend_nights >  3.50\n",
            "|   |   |   |   |   |   |   |   |--- weights: [0.74, 18.40] class: 1.0\n",
            "|   |   |   |   |   |   |--- arrival_month_2 >  0.50\n",
            "|   |   |   |   |   |   |   |--- lead_time <= 1.50\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 62.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [0.74, 52.15] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room >  62.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [4.45, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |--- lead_time >  1.50\n",
            "|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [41.54, 4.60] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [2.23, 4.60] class: 1.0\n",
            "|   |   |   |   |   |--- lead_time >  65.50\n",
            "|   |   |   |   |   |   |--- avg_price_per_room <= 99.98\n",
            "|   |   |   |   |   |   |   |--- lead_time <= 81.50\n",
            "|   |   |   |   |   |   |   |   |--- arrival_month_3 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time <= 66.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_10 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.97, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_10 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.97, 12.27] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time >  66.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
            "|   |   |   |   |   |   |   |   |--- arrival_month_3 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 69.75\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 74.00\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.19, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time >  74.00\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  69.75\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.38\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 12.27] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.38\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |--- lead_time >  81.50\n",
            "|   |   |   |   |   |   |   |   |--- weights: [43.03, 3.07] class: 0.0\n",
            "|   |   |   |   |   |   |--- avg_price_per_room >  99.98\n",
            "|   |   |   |   |   |   |   |--- lead_time <= 81.00\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 123.25\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time <= 68.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [2.23, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time >  68.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_5 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.97, 116.56] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_5 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.23, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room >  123.25\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [6.68, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |--- lead_time >  81.00\n",
            "|   |   |   |   |   |   |   |   |--- arrival_month_9 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [13.35, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- arrival_month_9 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [0.74, 3.07] class: 1.0\n",
            "|   |   |   |--- lead_time >  90.50\n",
            "|   |   |   |   |--- lead_time <= 116.50\n",
            "|   |   |   |   |   |--- arrival_month_4 <= 0.50\n",
            "|   |   |   |   |   |   |--- avg_price_per_room <= 93.58\n",
            "|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50\n",
            "|   |   |   |   |   |   |   |   |--- type_of_meal_plan <= 1.50\n",
            "|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.07] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [97.18, 9.20] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- type_of_meal_plan >  1.50\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 83.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 10.74] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  83.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 103.00\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.68, 1.53] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time >  103.00\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1.0\n",
            "|   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 75.07\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 58.75\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [11.13, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  58.75\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 97.00\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time >  97.00\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room >  75.07\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_6 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_11 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_11 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 4.60] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_6 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.13] class: 1.0\n",
            "|   |   |   |   |   |   |--- avg_price_per_room >  93.58\n",
            "|   |   |   |   |   |   |   |--- avg_price_per_room <= 101.51\n",
            "|   |   |   |   |   |   |   |   |--- arrival_month_6 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 98.60\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 97.00\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.16, 7.67] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time >  97.00\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.68, 36.81] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  98.60\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 101.22] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |--- arrival_month_6 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 96.27\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.13] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  96.27\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [11.13, 1.53] class: 0.0\n",
            "|   |   |   |   |   |   |   |--- avg_price_per_room >  101.51\n",
            "|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [3.71, 52.15] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 109.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [37.09, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  109.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 21.47] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
            "|   |   |   |   |   |--- arrival_month_4 >  0.50\n",
            "|   |   |   |   |   |   |--- weights: [1.48, 111.96] class: 1.0\n",
            "|   |   |   |   |--- lead_time >  116.50\n",
            "|   |   |   |   |   |--- avg_price_per_room <= 89.88\n",
            "|   |   |   |   |   |   |--- lead_time <= 139.50\n",
            "|   |   |   |   |   |   |   |--- lead_time <= 138.50\n",
            "|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_8 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_8 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [1.48, 7.67] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50\n",
            "|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 8.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [38.58, 1.53] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  8.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1.0\n",
            "|   |   |   |   |   |   |   |--- lead_time >  138.50\n",
            "|   |   |   |   |   |   |   |   |--- arrival_month_11 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- arrival_month_11 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.60] class: 1.0\n",
            "|   |   |   |   |   |   |--- lead_time >  139.50\n",
            "|   |   |   |   |   |   |   |--- weights: [96.44, 1.53] class: 0.0\n",
            "|   |   |   |   |   |--- avg_price_per_room >  89.88\n",
            "|   |   |   |   |   |   |--- avg_price_per_room <= 96.28\n",
            "|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 94.75\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time <= 125.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.68, 46.01] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time >  125.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 92.25\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [16.32, 7.67] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  92.25\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room >  94.75\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 19.94] class: 1.0\n",
            "|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50\n",
            "|   |   |   |   |   |   |   |   |--- weights: [5.93, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |--- avg_price_per_room >  96.28\n",
            "|   |   |   |   |   |   |   |--- avg_price_per_room <= 115.81\n",
            "|   |   |   |   |   |   |   |   |--- children <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [83.09, 1.53] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- children >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1.0\n",
            "|   |   |   |   |   |   |   |--- avg_price_per_room >  115.81\n",
            "|   |   |   |   |   |   |   |   |--- lead_time <= 120.50\n",
            "|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [3.71, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- lead_time >  120.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [2.23, 13.80] class: 1.0\n",
            "|   |   |--- market_segment_type_Online >  0.50\n",
            "|   |   |   |--- lead_time <= 9.50\n",
            "|   |   |   |   |--- lead_time <= 2.50\n",
            "|   |   |   |   |   |--- arrival_month_2 <= 0.50\n",
            "|   |   |   |   |   |   |--- avg_price_per_room <= 178.78\n",
            "|   |   |   |   |   |   |   |--- avg_price_per_room <= 97.66\n",
            "|   |   |   |   |   |   |   |   |--- arrival_month_8 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_4 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [120.18, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_4 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.23, 3.07] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.16, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- arrival_month_8 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.35\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 4.60] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.97, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.35\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [21.51, 3.07] class: 0.0\n",
            "|   |   |   |   |   |   |   |--- avg_price_per_room >  97.66\n",
            "|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_10 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
            "|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_10 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [32.64, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [0.74, 3.07] class: 1.0\n",
            "|   |   |   |   |   |   |--- avg_price_per_room >  178.78\n",
            "|   |   |   |   |   |   |   |--- arrival_month_12 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |--- arrival_month_5 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- children <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [7.42, 9.20] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- children >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 10.74] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |--- arrival_month_5 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [2.23, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |--- arrival_month_12 >  0.50\n",
            "|   |   |   |   |   |   |   |   |--- weights: [2.23, 0.00] class: 0.0\n",
            "|   |   |   |   |   |--- arrival_month_2 >  0.50\n",
            "|   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50\n",
            "|   |   |   |   |   |   |   |--- weights: [25.96, 6.13] class: 0.0\n",
            "|   |   |   |   |   |   |--- no_of_weekend_nights >  0.50\n",
            "|   |   |   |   |   |   |   |--- avg_price_per_room <= 80.00\n",
            "|   |   |   |   |   |   |   |   |--- weights: [1.48, 26.07] class: 1.0\n",
            "|   |   |   |   |   |   |   |--- avg_price_per_room >  80.00\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 111.50\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 100.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [5.19, 1.53] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  100.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.07] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room >  111.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [2.23, 0.00] class: 0.0\n",
            "|   |   |   |   |--- lead_time >  2.50\n",
            "|   |   |   |   |   |--- avg_price_per_room <= 88.96\n",
            "|   |   |   |   |   |   |--- avg_price_per_room <= 78.50\n",
            "|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 2.50\n",
            "|   |   |   |   |   |   |   |   |--- weights: [76.41, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |--- no_of_weekend_nights >  2.50\n",
            "|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1.0\n",
            "|   |   |   |   |   |   |--- avg_price_per_room >  78.50\n",
            "|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50\n",
            "|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_6 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [28.93, 1.53] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_6 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_2 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_7 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.16, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_7 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_2 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 4.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.23, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time >  4.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.48, 6.13] class: 1.0\n",
            "|   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 85.62\n",
            "|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 3.00\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [2.23, 12.27] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  3.00\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room >  85.62\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [4.45, 0.00] class: 0.0\n",
            "|   |   |   |   |   |--- avg_price_per_room >  88.96\n",
            "|   |   |   |   |   |   |--- arrival_year_2018 <= 0.50\n",
            "|   |   |   |   |   |   |   |--- no_of_week_nights <= 7.50\n",
            "|   |   |   |   |   |   |   |   |--- arrival_month_8 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [31.90, 1.53] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- arrival_month_8 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 159.00\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.68, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  159.00\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1.0\n",
            "|   |   |   |   |   |   |   |--- no_of_week_nights >  7.50\n",
            "|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1.0\n",
            "|   |   |   |   |   |   |--- arrival_year_2018 >  0.50\n",
            "|   |   |   |   |   |   |   |--- arrival_month_12 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 120.80\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 118.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  118.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [11.13, 1.53] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  120.80\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_11 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_11 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.23, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50\n",
            "|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
            "|   |   |   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_9 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.45, 47.54] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_9 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |--- arrival_month_12 >  0.50\n",
            "|   |   |   |   |   |   |   |   |--- weights: [14.10, 0.00] class: 0.0\n",
            "|   |   |   |--- lead_time >  9.50\n",
            "|   |   |   |   |--- required_car_parking_space <= 0.50\n",
            "|   |   |   |   |   |--- avg_price_per_room <= 99.82\n",
            "|   |   |   |   |   |   |--- lead_time <= 19.50\n",
            "|   |   |   |   |   |   |   |--- arrival_month_12 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 78.90\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 64.14\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.19, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.48, 6.13] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  64.14\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [26.71, 1.53] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room >  78.90\n",
            "|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_9 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_9 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 98.05\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  98.05\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.60] class: 1.0\n",
            "|   |   |   |   |   |   |   |--- arrival_month_12 >  0.50\n",
            "|   |   |   |   |   |   |   |   |--- weights: [48.22, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |--- lead_time >  19.50\n",
            "|   |   |   |   |   |   |   |--- avg_price_per_room <= 60.99\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 20.60\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [23.00, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room >  20.60\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_12 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 84.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time >  84.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_12 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [8.16, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |--- avg_price_per_room >  60.99\n",
            "|   |   |   |   |   |   |   |   |--- type_of_meal_plan <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_7 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.19, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_7 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 27.00\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [11.13, 18.40] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time >  27.00\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11\n",
            "|   |   |   |   |   |   |   |   |--- type_of_meal_plan >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.82\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 68.72\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  68.72\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.82\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 21\n",
            "|   |   |   |   |   |--- avg_price_per_room >  99.82\n",
            "|   |   |   |   |   |   |--- arrival_year_2018 <= 0.50\n",
            "|   |   |   |   |   |   |   |--- type_of_meal_plan <= 1.50\n",
            "|   |   |   |   |   |   |   |   |--- lead_time <= 74.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [43.77, 9.20] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- lead_time >  74.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.07] class: 1.0\n",
            "|   |   |   |   |   |   |   |--- type_of_meal_plan >  1.50\n",
            "|   |   |   |   |   |   |   |   |--- weights: [0.00, 33.74] class: 1.0\n",
            "|   |   |   |   |   |   |--- arrival_year_2018 >  0.50\n",
            "|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 130.65\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time <= 49.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_12 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_12 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time >  49.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11\n",
            "|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room >  130.65\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_11 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_12 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_12 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_11 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 143.24\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  143.24\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
            "|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 175.71\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time <= 31.00\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [5.93, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time >  31.00\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 32.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.07] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time >  32.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room >  175.71\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.13] class: 1.0\n",
            "|   |   |   |   |--- required_car_parking_space >  0.50\n",
            "|   |   |   |   |   |--- no_of_week_nights <= 8.00\n",
            "|   |   |   |   |   |   |--- weights: [51.93, 0.00] class: 0.0\n",
            "|   |   |   |   |   |--- no_of_week_nights >  8.00\n",
            "|   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1.0\n",
            "|   |--- no_of_special_requests >  0.50\n",
            "|   |   |--- no_of_special_requests <= 1.50\n",
            "|   |   |   |--- market_segment_type_Online <= 0.50\n",
            "|   |   |   |   |--- lead_time <= 71.50\n",
            "|   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50\n",
            "|   |   |   |   |   |   |--- weights: [611.28, 1.53] class: 0.0\n",
            "|   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50\n",
            "|   |   |   |   |   |   |--- no_of_week_nights <= 2.50\n",
            "|   |   |   |   |   |   |   |--- weights: [8.16, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |--- no_of_week_nights >  2.50\n",
            "|   |   |   |   |   |   |   |--- weights: [0.74, 4.60] class: 1.0\n",
            "|   |   |   |   |--- lead_time >  71.50\n",
            "|   |   |   |   |   |--- type_of_meal_plan <= 0.50\n",
            "|   |   |   |   |   |   |--- weights: [0.00, 9.20] class: 1.0\n",
            "|   |   |   |   |   |--- type_of_meal_plan >  0.50\n",
            "|   |   |   |   |   |   |--- lead_time <= 91.50\n",
            "|   |   |   |   |   |   |   |--- weights: [64.54, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |--- lead_time >  91.50\n",
            "|   |   |   |   |   |   |   |--- arrival_month_10 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |--- arrival_month_5 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 169.69\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [108.31, 13.80] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  169.69\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |--- arrival_month_5 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time <= 101.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 4.60] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time >  101.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [3.71, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |--- arrival_month_10 >  0.50\n",
            "|   |   |   |   |   |   |   |   |--- weights: [0.74, 4.60] class: 1.0\n",
            "|   |   |   |--- market_segment_type_Online >  0.50\n",
            "|   |   |   |   |--- lead_time <= 9.50\n",
            "|   |   |   |   |   |--- lead_time <= 4.50\n",
            "|   |   |   |   |   |   |--- no_of_weekend_nights <= 3.50\n",
            "|   |   |   |   |   |   |   |--- arrival_month_11 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |--- weights: [464.40, 27.61] class: 0.0\n",
            "|   |   |   |   |   |   |   |--- arrival_month_11 >  0.50\n",
            "|   |   |   |   |   |   |   |   |--- type_of_meal_plan <= 1.50\n",
            "|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 1.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [11.13, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time >  1.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.16, 6.13] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [17.06, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- type_of_meal_plan >  1.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1.0\n",
            "|   |   |   |   |   |   |--- no_of_weekend_nights >  3.50\n",
            "|   |   |   |   |   |   |   |--- weights: [1.48, 3.07] class: 1.0\n",
            "|   |   |   |   |   |--- lead_time >  4.50\n",
            "|   |   |   |   |   |   |--- arrival_month_9 <= 0.50\n",
            "|   |   |   |   |   |   |   |--- avg_price_per_room <= 139.50\n",
            "|   |   |   |   |   |   |   |   |--- arrival_month_2 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 5.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [39.32, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time >  5.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
            "|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [1.48, 1.53] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |--- arrival_month_2 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 89.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [15.58, 4.60] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  89.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 91.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 4.60] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  91.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |   |--- avg_price_per_room >  139.50\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 144.25\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 143.25\n",
            "|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.48, 4.60] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  143.25\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.07] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room >  144.25\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time <= 7.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [27.45, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time >  7.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.93, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |--- arrival_month_9 >  0.50\n",
            "|   |   |   |   |   |   |   |--- avg_price_per_room <= 115.81\n",
            "|   |   |   |   |   |   |   |   |--- weights: [9.64, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |--- avg_price_per_room >  115.81\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 167.75\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 125.00\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 7.67] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  125.00\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.19, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.45, 10.74] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room >  167.75\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [5.19, 0.00] class: 0.0\n",
            "|   |   |   |   |--- lead_time >  9.50\n",
            "|   |   |   |   |   |--- required_car_parking_space <= 0.50\n",
            "|   |   |   |   |   |   |--- avg_price_per_room <= 118.64\n",
            "|   |   |   |   |   |   |   |--- lead_time <= 61.50\n",
            "|   |   |   |   |   |   |   |   |--- arrival_month_12 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 7.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 67.36\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [63.06, 7.67] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  67.36\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 21\n",
            "|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  7.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [1.48, 7.67] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |--- arrival_month_12 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 8.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [117.95, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  8.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.07] class: 1.0\n",
            "|   |   |   |   |   |   |   |--- lead_time >  61.50\n",
            "|   |   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_7 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 74.38\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  74.38\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_7 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- children <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.97, 61.35] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- children >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.23, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_11 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_10 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 16\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_10 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_11 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 70.69\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.93, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  70.69\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
            "|   |   |   |   |   |   |--- avg_price_per_room >  118.64\n",
            "|   |   |   |   |   |   |   |--- arrival_month_5 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |--- lead_time <= 141.50\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_12 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_6 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 25\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_6 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_12 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 101.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [54.90, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time >  101.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 13.80] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |--- lead_time >  141.50\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 142.74\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  142.74\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 15.34] class: 1.0\n",
            "|   |   |   |   |   |   |   |--- arrival_month_5 >  0.50\n",
            "|   |   |   |   |   |   |   |   |--- lead_time <= 71.50\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 119.42\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 1.53] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  119.42\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [13.35, 4.60] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- lead_time >  71.50\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 138.20\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  138.20\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [6.68, 16.87] class: 1.0\n",
            "|   |   |   |   |   |--- required_car_parking_space >  0.50\n",
            "|   |   |   |   |   |   |--- no_of_week_nights <= 7.50\n",
            "|   |   |   |   |   |   |   |--- lead_time <= 150.00\n",
            "|   |   |   |   |   |   |   |   |--- weights: [140.95, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |--- lead_time >  150.00\n",
            "|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1.0\n",
            "|   |   |   |   |   |   |--- no_of_week_nights >  7.50\n",
            "|   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1.0\n",
            "|   |   |--- no_of_special_requests >  1.50\n",
            "|   |   |   |--- lead_time <= 89.50\n",
            "|   |   |   |   |--- no_of_week_nights <= 3.50\n",
            "|   |   |   |   |   |--- weights: [1587.56, 0.00] class: 0.0\n",
            "|   |   |   |   |--- no_of_week_nights >  3.50\n",
            "|   |   |   |   |   |--- no_of_special_requests <= 2.50\n",
            "|   |   |   |   |   |   |--- arrival_month_12 <= 0.50\n",
            "|   |   |   |   |   |   |   |--- lead_time <= 8.50\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 133.58\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [20.77, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room >  133.58\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 141.60\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 3.07] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  141.60\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [6.68, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |--- lead_time >  8.50\n",
            "|   |   |   |   |   |   |   |   |--- lead_time <= 19.50\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time <= 10.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [8.90, 4.60] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time >  10.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.71, 13.80] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |--- lead_time >  19.50\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time <= 25.00\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [16.32, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time >  25.00\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 82.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time >  82.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [9.64, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |--- arrival_month_12 >  0.50\n",
            "|   |   |   |   |   |   |   |--- weights: [28.93, 0.00] class: 0.0\n",
            "|   |   |   |   |   |--- no_of_special_requests >  2.50\n",
            "|   |   |   |   |   |   |--- weights: [44.51, 0.00] class: 0.0\n",
            "|   |   |   |--- lead_time >  89.50\n",
            "|   |   |   |   |--- no_of_special_requests <= 2.50\n",
            "|   |   |   |   |   |--- arrival_month_11 <= 0.50\n",
            "|   |   |   |   |   |   |--- lead_time <= 142.50\n",
            "|   |   |   |   |   |   |   |--- lead_time <= 115.50\n",
            "|   |   |   |   |   |   |   |   |--- type_of_meal_plan <= 1.50\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_5 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_3 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_3 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.07] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_5 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [14.10, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- type_of_meal_plan >  1.50\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time <= 99.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [4.45, 1.53] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time >  99.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 9.20] class: 1.0\n",
            "|   |   |   |   |   |   |   |--- lead_time >  115.50\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.42\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_8 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 69.88\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.71, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  69.88\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_8 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [6.68, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.42\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 132.24\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [75.67, 6.13] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  132.24\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 139.72\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  139.72\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |--- lead_time >  142.50\n",
            "|   |   |   |   |   |   |   |--- avg_price_per_room <= 125.10\n",
            "|   |   |   |   |   |   |   |   |--- lead_time <= 148.50\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 91.10\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [2.23, 7.67] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  91.10\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.93, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |   |   |--- lead_time >  148.50\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_9 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 1.53] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [11.13, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_9 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1.0\n",
            "|   |   |   |   |   |   |   |--- avg_price_per_room >  125.10\n",
            "|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time <= 148.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [5.19, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- lead_time >  148.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [1.48, 15.34] class: 1.0\n",
            "|   |   |   |   |   |--- arrival_month_11 >  0.50\n",
            "|   |   |   |   |   |   |--- lead_time <= 107.50\n",
            "|   |   |   |   |   |   |   |--- avg_price_per_room <= 130.88\n",
            "|   |   |   |   |   |   |   |   |--- weights: [2.23, 23.01] class: 1.0\n",
            "|   |   |   |   |   |   |   |--- avg_price_per_room >  130.88\n",
            "|   |   |   |   |   |   |   |   |--- weights: [3.71, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |--- lead_time >  107.50\n",
            "|   |   |   |   |   |   |   |--- avg_price_per_room <= 106.67\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 79.05\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room >  79.05\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [7.42, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |--- avg_price_per_room >  106.67\n",
            "|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1.0\n",
            "|   |   |   |   |--- no_of_special_requests >  2.50\n",
            "|   |   |   |   |   |--- weights: [65.28, 0.00] class: 0.0\n",
            "|--- lead_time >  151.50\n",
            "|   |--- avg_price_per_room <= 100.04\n",
            "|   |   |--- no_of_special_requests <= 0.50\n",
            "|   |   |   |--- market_segment_type_Online <= 0.50\n",
            "|   |   |   |   |--- arrival_year_2018 <= 0.50\n",
            "|   |   |   |   |   |--- lead_time <= 214.50\n",
            "|   |   |   |   |   |   |--- lead_time <= 169.00\n",
            "|   |   |   |   |   |   |   |--- weights: [67.51, 13.80] class: 0.0\n",
            "|   |   |   |   |   |   |--- lead_time >  169.00\n",
            "|   |   |   |   |   |   |   |--- lead_time <= 198.50\n",
            "|   |   |   |   |   |   |   |   |--- weights: [0.00, 50.61] class: 1.0\n",
            "|   |   |   |   |   |   |   |--- lead_time >  198.50\n",
            "|   |   |   |   |   |   |   |   |--- type_of_meal_plan <= 1.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [17.06, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- type_of_meal_plan >  1.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.20] class: 1.0\n",
            "|   |   |   |   |   |--- lead_time >  214.50\n",
            "|   |   |   |   |   |   |--- weights: [94.21, 0.00] class: 0.0\n",
            "|   |   |   |   |--- arrival_year_2018 >  0.50\n",
            "|   |   |   |   |   |--- arrival_month_12 <= 0.50\n",
            "|   |   |   |   |   |   |--- type_of_meal_plan <= 1.50\n",
            "|   |   |   |   |   |   |   |--- lead_time <= 197.00\n",
            "|   |   |   |   |   |   |   |   |--- lead_time <= 178.50\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 81.76\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [34.13, 1.53] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.93, 7.67] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  81.76\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_9 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_9 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.93, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- lead_time >  178.50\n",
            "|   |   |   |   |   |   |   |   |   |--- repeated_guest <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [71.22, 6.13] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |--- repeated_guest >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.13] class: 1.0\n",
            "|   |   |   |   |   |   |   |--- lead_time >  197.00\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.06\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 75.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 227.00\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.93, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time >  227.00\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  75.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.06\n",
            "|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.71, 7.67] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [2.23, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |--- type_of_meal_plan >  1.50\n",
            "|   |   |   |   |   |   |   |--- arrival_month_10 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_9 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.30\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [52.67, 1.53] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.30\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |   |   |   |--- arrival_month_9 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [9.64, 6.13] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1.0\n",
            "|   |   |   |   |   |   |   |--- arrival_month_10 >  0.50\n",
            "|   |   |   |   |   |   |   |   |--- weights: [8.16, 7.67] class: 0.0\n",
            "|   |   |   |   |   |--- arrival_month_12 >  0.50\n",
            "|   |   |   |   |   |   |--- weights: [51.19, 0.00] class: 0.0\n",
            "|   |   |   |--- market_segment_type_Online >  0.50\n",
            "|   |   |   |   |--- avg_price_per_room <= 2.50\n",
            "|   |   |   |   |   |--- no_of_weekend_nights <= 0.50\n",
            "|   |   |   |   |   |   |--- weights: [1.48, 4.60] class: 1.0\n",
            "|   |   |   |   |   |--- no_of_weekend_nights >  0.50\n",
            "|   |   |   |   |   |   |--- weights: [6.68, 0.00] class: 0.0\n",
            "|   |   |   |   |--- avg_price_per_room >  2.50\n",
            "|   |   |   |   |   |--- weights: [4.45, 918.69] class: 1.0\n",
            "|   |   |--- no_of_special_requests >  0.50\n",
            "|   |   |   |--- no_of_weekend_nights <= 0.50\n",
            "|   |   |   |   |--- lead_time <= 180.50\n",
            "|   |   |   |   |   |--- lead_time <= 159.50\n",
            "|   |   |   |   |   |   |--- no_of_week_nights <= 2.50\n",
            "|   |   |   |   |   |   |   |--- avg_price_per_room <= 94.38\n",
            "|   |   |   |   |   |   |   |   |--- weights: [0.74, 7.67] class: 1.0\n",
            "|   |   |   |   |   |   |   |--- avg_price_per_room >  94.38\n",
            "|   |   |   |   |   |   |   |   |--- weights: [2.23, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |--- no_of_week_nights >  2.50\n",
            "|   |   |   |   |   |   |   |--- weights: [5.93, 1.53] class: 0.0\n",
            "|   |   |   |   |   |--- lead_time >  159.50\n",
            "|   |   |   |   |   |   |--- lead_time <= 177.50\n",
            "|   |   |   |   |   |   |   |--- weights: [27.45, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |--- lead_time >  177.50\n",
            "|   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50\n",
            "|   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 96.92\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.60] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  96.92\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |--- no_of_week_nights >  3.50\n",
            "|   |   |   |   |   |   |   |   |--- weights: [7.42, 0.00] class: 0.0\n",
            "|   |   |   |   |--- lead_time >  180.50\n",
            "|   |   |   |   |   |--- market_segment_type_Online <= 0.50\n",
            "|   |   |   |   |   |   |--- avg_price_per_room <= 96.37\n",
            "|   |   |   |   |   |   |   |--- lead_time <= 279.75\n",
            "|   |   |   |   |   |   |   |   |--- weights: [14.84, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |--- lead_time >  279.75\n",
            "|   |   |   |   |   |   |   |   |--- weights: [2.97, 3.07] class: 1.0\n",
            "|   |   |   |   |   |   |--- avg_price_per_room >  96.37\n",
            "|   |   |   |   |   |   |   |--- weights: [0.00, 3.07] class: 1.0\n",
            "|   |   |   |   |   |--- market_segment_type_Online >  0.50\n",
            "|   |   |   |   |   |   |--- no_of_special_requests <= 2.50\n",
            "|   |   |   |   |   |   |   |--- arrival_month_12 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 171.77] class: 1.0\n",
            "|   |   |   |   |   |   |   |--- arrival_month_12 >  0.50\n",
            "|   |   |   |   |   |   |   |   |--- lead_time <= 276.50\n",
            "|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.07] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [5.93, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- lead_time >  276.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [2.23, 13.80] class: 1.0\n",
            "|   |   |   |   |   |   |--- no_of_special_requests >  2.50\n",
            "|   |   |   |   |   |   |   |--- weights: [10.39, 0.00] class: 0.0\n",
            "|   |   |   |--- no_of_weekend_nights >  0.50\n",
            "|   |   |   |   |--- market_segment_type_Offline <= 0.50\n",
            "|   |   |   |   |   |--- no_of_week_nights <= 8.50\n",
            "|   |   |   |   |   |   |--- avg_price_per_room <= 74.73\n",
            "|   |   |   |   |   |   |   |--- arrival_month_12 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |--- weights: [44.51, 1.53] class: 0.0\n",
            "|   |   |   |   |   |   |   |--- arrival_month_12 >  0.50\n",
            "|   |   |   |   |   |   |   |   |--- lead_time <= 217.50\n",
            "|   |   |   |   |   |   |   |   |   |--- weights: [4.45, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |--- lead_time >  217.50\n",
            "|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 55.92\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.48, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  55.92\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.97, 10.74] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [2.97, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |--- avg_price_per_room >  74.73\n",
            "|   |   |   |   |   |   |   |--- no_of_special_requests <= 2.50\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.72\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 79.60\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 162.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
            "|   |   |   |   |   |   |   |   |   |   |--- lead_time >  162.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  79.60\n",
            "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 12.27] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.72\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 93.01\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.23, 6.13] class: 1.0\n",
            "|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
            "|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  93.01\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_6 <= 0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
            "|   |   |   |   |   |   |   |   |   |   |--- arrival_month_6 >  0.50\n",
            "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [7.42, 0.00] class: 0.0\n",
            "|   |   |   |   |   |   |   |--- no_of_special_requests >  2.50\n",
            "|   |   |   |   |   |   |   |   |--- weights: [11.87, 0.00] class: 0.0\n",
            "|   |   |   |   |   |--- no_of_week_nights >  8.50\n",
            "|   |   |   |   |   |   |--- weights: [1.48, 9.20] class: 1.0\n",
            "|   |   |   |   |--- market_segment_type_Offline >  0.50\n",
            "|   |   |   |   |   |--- weights: [114.24, 4.60] class: 0.0\n",
            "|   |--- avg_price_per_room >  100.04\n",
            "|   |   |--- arrival_month_12 <= 0.50\n",
            "|   |   |   |--- no_of_special_requests <= 2.50\n",
            "|   |   |   |   |--- weights: [0.00, 3240.72] class: 1.0\n",
            "|   |   |   |--- no_of_special_requests >  2.50\n",
            "|   |   |   |   |--- weights: [27.45, 0.00] class: 0.0\n",
            "|   |   |--- arrival_month_12 >  0.50\n",
            "|   |   |   |--- no_of_special_requests <= 0.50\n",
            "|   |   |   |   |--- weights: [43.03, 0.00] class: 0.0\n",
            "|   |   |   |--- no_of_special_requests >  0.50\n",
            "|   |   |   |   |--- avg_price_per_room <= 107.74\n",
            "|   |   |   |   |   |--- lead_time <= 194.00\n",
            "|   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1.0\n",
            "|   |   |   |   |   |--- lead_time >  194.00\n",
            "|   |   |   |   |   |   |--- weights: [2.97, 0.00] class: 0.0\n",
            "|   |   |   |   |--- avg_price_per_room >  107.74\n",
            "|   |   |   |   |   |--- weights: [2.97, 19.94] class: 1.0\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 138;\n",
              "                var nbb_unformatted_code = \"# Text report showing the rules of a decision tree -\\nprint(tree.export_text(best_model, feature_names=feature_names, show_weights=True))\";\n",
              "                var nbb_formatted_code = \"# Text report showing the rules of a decision tree -\\nprint(tree.export_text(best_model, feature_names=feature_names, show_weights=True))\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- We can see that the observation we got from the pre-pruned tree is also matching with the decision tree rules of the post pruned tree."
      ],
      "metadata": {
        "id": "KqScuXvaoqkS"
      },
      "id": "KqScuXvaoqkS"
    },
    {
      "cell_type": "code",
      "source": [
        "importances = best_model.feature_importances_\n",
        "indices = np.argsort(importances)[-15:]"
      ],
      "metadata": {
        "id": "8SGQi9ldoiVk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "7250724a-027c-491a-ca6e-3e4c193370a7"
      },
      "id": "8SGQi9ldoiVk",
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 139;\n",
              "                var nbb_unformatted_code = \"importances = best_model.feature_importances_\\nindices = np.argsort(importances)[-15:]\";\n",
              "                var nbb_formatted_code = \"importances = best_model.feature_importances_\\nindices = np.argsort(importances)[-15:]\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importance of features in the tree building\n",
        "\n",
        "importances = estimator.feature_importances_\n",
        "indices = np.argsort(importances)[-15:]\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.title(\"Feature Importances\")\n",
        "plt.barh(range(len(indices)), importances[indices], color=\"violet\", align=\"center\")\n",
        "plt.yticks(range(len(indices)), [feature_names[i] for i in indices])\n",
        "plt.xlabel(\"Relative Importance\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "yzf8PParoiYK",
        "outputId": "6e89016a-1078-49d7-af91-767fad837cca"
      },
      "id": "yzf8PParoiYK",
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 140;\n",
              "                var nbb_unformatted_code = \"# importance of features in the tree building\\n\\nimportances = estimator.feature_importances_\\nindices = np.argsort(importances)[-15:]\\n\\nplt.figure(figsize=(8, 8))\\nplt.title(\\\"Feature Importances\\\")\\nplt.barh(range(len(indices)), importances[indices], color=\\\"violet\\\", align=\\\"center\\\")\\nplt.yticks(range(len(indices)), [feature_names[i] for i in indices])\\nplt.xlabel(\\\"Relative Importance\\\")\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"# importance of features in the tree building\\n\\nimportances = estimator.feature_importances_\\nindices = np.argsort(importances)[-15:]\\n\\nplt.figure(figsize=(8, 8))\\nplt.title(\\\"Feature Importances\\\")\\nplt.barh(range(len(indices)), importances[indices], color=\\\"violet\\\", align=\\\"center\\\")\\nplt.yticks(range(len(indices)), [feature_names[i] for i in indices])\\nplt.xlabel(\\\"Relative Importance\\\")\\nplt.show()\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
              "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
              "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The feature importance is similar to what we got in pre-pruned tree.\n",
        "* The lead time, average room price, market segment type `Online`, and no of special requests are the most important features for the post-pruned tree\n"
      ],
      "metadata": {
        "id": "NzMFeppkoxB9"
      },
      "id": "NzMFeppkoxB9"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Performance Comparison and Conclusions"
      ],
      "metadata": {
        "id": "3EkKgRnZnk9V"
      },
      "id": "3EkKgRnZnk9V"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Comparison of Models and Final Model Selection"
      ],
      "metadata": {
        "id": "vu6kP8Gdo4jy"
      },
      "id": "vu6kP8Gdo4jy"
    },
    {
      "cell_type": "code",
      "source": [
        "# training performance comparison\n",
        "\n",
        "models_train_comp_df = pd.concat([dt_train.T, dt_train_pre.T, dt_train_post.T], axis=1)\n",
        "models_train_comp_df.columns = [\"Decision Tree\", \"Decision Tree (Pre-Pruning)\", \"Decision Tree (Post-Pruning)\"]\n",
        "\n",
        "print(\"Training performance comparison:\")\n",
        "models_train_comp_df.T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "vBtk0F4to334",
        "outputId": "81abbde9-cad2-460c-f731-026474a0d347"
      },
      "id": "vBtk0F4to334",
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training performance comparison:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              Accuracy  Recall  Precision      F1\n",
              "Decision Tree                  0.99330 0.98345    0.99596 0.98967\n",
              "Decision Tree (Pre-Pruning)    0.84216 0.72083    0.77858 0.74859\n",
              "Decision Tree (Post-Pruning)   0.93447 0.95736    0.85806 0.90499"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Precision</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Decision Tree</th>\n",
              "      <td>0.99330</td>\n",
              "      <td>0.98345</td>\n",
              "      <td>0.99596</td>\n",
              "      <td>0.98967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Decision Tree (Pre-Pruning)</th>\n",
              "      <td>0.84216</td>\n",
              "      <td>0.72083</td>\n",
              "      <td>0.77858</td>\n",
              "      <td>0.74859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Decision Tree (Post-Pruning)</th>\n",
              "      <td>0.93447</td>\n",
              "      <td>0.95736</td>\n",
              "      <td>0.85806</td>\n",
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              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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              "                var nbb_cell_id = 141;\n",
              "                var nbb_unformatted_code = \"# training performance comparison\\n\\nmodels_train_comp_df = pd.concat([dt_train.T, dt_train_pre.T, dt_train_post.T], axis=1)\\nmodels_train_comp_df.columns = [\\\"Decision Tree\\\", \\\"Decision Tree (Pre-Pruning)\\\", \\\"Decision Tree (Post-Pruning)\\\"]\\n\\nprint(\\\"Training performance comparison:\\\")\\nmodels_train_comp_df.T\";\n",
              "                var nbb_formatted_code = \"# training performance comparison\\n\\nmodels_train_comp_df = pd.concat([dt_train.T, dt_train_pre.T, dt_train_post.T], axis=1)\\nmodels_train_comp_df.columns = [\\n    \\\"Decision Tree\\\",\\n    \\\"Decision Tree (Pre-Pruning)\\\",\\n    \\\"Decision Tree (Post-Pruning)\\\",\\n]\\n\\nprint(\\\"Training performance comparison:\\\")\\nmodels_train_comp_df.T\";\n",
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    {
      "cell_type": "code",
      "source": [
        "# testing performance comparison\n",
        "\n",
        "models_test_comp_df = pd.concat([dt_test.T, dt_test_pre.T, dt_test_post.T], axis=1)\n",
        "models_test_comp_df.columns = [\"Decision Tree\", \"Decision Tree (Pre-Pruning)\", \"Decision Tree (Post-Pruning)\"]\n",
        "\n",
        "print(\"Testing performance comparison:\")\n",
        "models_test_comp_df.T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
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      },
      "id": "PuVThmWZoibL",
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing performance comparison:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "                              Accuracy  Recall  Precision      F1\n",
              "Decision Tree                  0.86529 0.80177    0.79385 0.79779\n",
              "Decision Tree (Pre-Pruning)    0.84232 0.71417    0.78994 0.75015\n",
              "Decision Tree (Post-Pruning)   0.86833 0.85694    0.77121 0.81182"
            ],
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              "      <th>Recall</th>\n",
              "      <th>Precision</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>Decision Tree</th>\n",
              "      <td>0.86529</td>\n",
              "      <td>0.80177</td>\n",
              "      <td>0.79385</td>\n",
              "      <td>0.79779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Decision Tree (Pre-Pruning)</th>\n",
              "      <td>0.84232</td>\n",
              "      <td>0.71417</td>\n",
              "      <td>0.78994</td>\n",
              "      <td>0.75015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Decision Tree (Post-Pruning)</th>\n",
              "      <td>0.86833</td>\n",
              "      <td>0.85694</td>\n",
              "      <td>0.77121</td>\n",
              "      <td>0.81182</td>\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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        {
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              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 142;\n",
              "                var nbb_unformatted_code = \"# testing performance comparison\\n\\nmodels_test_comp_df = pd.concat([dt_test.T, dt_test_pre.T, dt_test_post.T], axis=1)\\nmodels_test_comp_df.columns = [\\\"Decision Tree\\\", \\\"Decision Tree (Pre-Pruning)\\\", \\\"Decision Tree (Post-Pruning)\\\"]\\n\\nprint(\\\"Testing performance comparison:\\\")\\nmodels_test_comp_df.T\";\n",
              "                var nbb_formatted_code = \"# testing performance comparison\\n\\nmodels_test_comp_df = pd.concat([dt_test.T, dt_test_pre.T, dt_test_post.T], axis=1)\\nmodels_test_comp_df.columns = [\\n    \\\"Decision Tree\\\",\\n    \\\"Decision Tree (Pre-Pruning)\\\",\\n    \\\"Decision Tree (Post-Pruning)\\\",\\n]\\n\\nprint(\\\"Testing performance comparison:\\\")\\nmodels_test_comp_df.T\";\n",
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              "            "
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    {
      "cell_type": "markdown",
      "source": [
        "* The post-pruned Decision tree model demonstrates good improvements in Recall and F1 score with a slight decline in Precision metric\n",
        "* We choose the post-pruned decision tree as the best model since it gives higher scores on the train and test sets than the pre-pruned decision tree."
      ],
      "metadata": {
        "id": "1PVQH9Ejo-ys"
      },
      "id": "1PVQH9Ejo-ys"
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Conclusions"
      ],
      "metadata": {
        "id": "LYIo-JZvlhR-"
      },
      "id": "LYIo-JZvlhR-"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Decision tree model with default parameters is overfitting the training data and is not able to generalize well.\n",
        "* Pre-pruned tree has given a generalized performance with balanced values of precision and recall.\n",
        "* Post-pruned tree gives high Recall and F1 scores compared to other models, but the difference between precision and recall is quite high.\n",
        "* We recommend the hotel use the pre-pruned decision tree model to predict bookings cancelations."
      ],
      "metadata": {
        "id": "GMoyWxKJtKCf"
      },
      "id": "GMoyWxKJtKCf"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusions and Recommendations\n"
      ],
      "metadata": {
        "id": "9QMijGvapE6o"
      },
      "id": "9QMijGvapE6o"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The Decision Tree model performs better than the Logistic regression on the dataset.\n",
        "* Both models pointed the sames variables as importat for the cancelation prediction. The following variables are the most important in predicting whether a booking will be canceled or not:\n",
        "  * Lead Time\n",
        "  * Number of special requests\n",
        "  * Average price per room are important\n",
        "  * Arrival in December\n",
        "  * Online segment\n",
        "\n",
        "* The **number of weekend nights**, **lead time**, and the **average price per room**, are positive and increase in these will lead to an *increase* in the chances of a booking being canceled.\n",
        "  * The presence of **children** among the guests, and arrival in **February, March, or November** are also lead to an *increase* in chances of a booking being canceled.\n",
        "\n",
        "* The number of meals (type_of_meal_plan), number of special requests are negative and increase in these will lead to *decrease* in the chances of a booking being canceled.\n",
        "  * The **repeated guest**, **car parking space requirement**, reservation of **Room_Type 2** and **Room_Type 4**, and arrival from **May to September or December** are also lead to *decrease* in chances of a booking being canceled.\n",
        "\n",
        "* The model built can be used to predict if a booking is going to canceled or not and can correctly identify **85%** of the booking cancelations"
      ],
      "metadata": {
        "id": "433E3koIpHWi"
      },
      "id": "433E3koIpHWi"
    },
    {
      "cell_type": "markdown",
      "id": "nasty-retailer",
      "metadata": {
        "id": "nasty-retailer"
      },
      "source": [
        "## Actionable Insights and Recommendations\n",
        "\n",
        "- What profitable policies for cancellations and refunds can the hotel adopt?\n",
        "- What other recommedations would you suggest to the hotel?"
      ]
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        "1. The lead time, price, bookings coming online, and special requests presence are the most important features impacted booking cancelations. \n",
        "  - Bookings with a special requests and the bookings with a lead time of fewer than 151 days are less likely to be canceled.\n",
        "  - **Actions**:\n",
        "    1. Discount, complimentary upgrade class of room, complimentary breakfast for those with a lead time of more than 5 months to avoid or lower chance of cancelation, as such benefits will make the booking made in advance more attractive closer to the arrival.\n",
        "    2. Motivate early-bird customers to confirm a booking by suggesting some perk closer to the arrival date\n",
        "    3. Improve the online booking system to motivate customers to make special requests during their booking process\n",
        "\n",
        "2. Review and implement cancellation policies \n",
        "  * Cancelation policy for online booking needs to be developed\n",
        "  * Cancelation policy needs to be articulated to the customer during the submission\n",
        "  * The higher cost bookings or bookings in the most popular and limited room types, or high season should imply a partial refund (or not refundable advance payment)    \n",
        "\n",
        "3. August-October are the months with the highest percentage of cancelations. Motivational programs during this timeframe may reduce the number of cancelations.\n",
        "\n",
        "4. To reduce the probability of a canceled booking, the 'special requests' option needs to be available for online bookings\n",
        "\n",
        "5. Prices for advanced booking might be less than prices for short lead time bookings to avoid last-minute cancelations\n",
        "\n",
        "6. Loyalty programs need to be developed as repeated guests tend to keep their booking with no cancellations\n",
        "\n",
        "7. We can use an overbooking approach for bookings with a high probability of cancelation. \n",
        "  * The detailed process needs to be developed and contracts with near hotels need to be negotiated to mitigate risks of overbooking, in case this approach is selected."
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