{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p2r5o8SR4rqH"
      },
      "source": [
        "# **Supervised Learning - Foundations Project: ReCell**"
      ]
    },
    {
      "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: 2/23/2024"
      ],
      "metadata": {
        "id": "OkIwCbuBKeJ1"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3SntBY974rqJ"
      },
      "source": [
        "# Problem Statement"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RA_GXJwuhmVz"
      },
      "source": [
        "## Business Context\n",
        "\n",
        "Buying and selling used phones and tablets used to be something that happened on a handful of online marketplace sites. But the used and refurbished device market has grown considerably over the past decade, and a new IDC (International Data Corporation) forecast predicts that the used phone market would be worth \\$52.7bn by 2023 with a compound annual growth rate (CAGR) of 13.6% from 2018 to 2023. This growth can be attributed to an uptick in demand for used phones and tablets that offer considerable savings compared with new models.\n",
        "\n",
        "Refurbished and used devices continue to provide cost-effective alternatives to both consumers and businesses that are looking to save money when purchasing one. There are plenty of other benefits associated with the used device market. Used and refurbished devices can be sold with warranties and can also be insured with proof of purchase. Third-party vendors/platforms, such as Verizon, Amazon, etc., provide attractive offers to customers for refurbished devices. Maximizing the longevity of devices through second-hand trade also reduces their environmental impact and helps in recycling and reducing waste. The impact of the COVID-19 outbreak may further boost this segment as consumers cut back on discretionary spending and buy phones and tablets only for immediate needs."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Objective\n",
        "\n",
        "The rising potential of this comparatively under-the-radar market fuels the need for an ML-based solution to develop a dynamic pricing strategy for used and refurbished devices. ReCell, a startup aiming to tap the potential in this market, has hired you as a data scientist. They want you to analyze the data provided and build a linear regression model to predict the price of a used phone/tablet and identify factors that significantly influence it."
      ],
      "metadata": {
        "id": "a3_m2zyClMZB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Description\n",
        "\n",
        "The data contains the different attributes of used/refurbished phones and tablets. The data was collected in the year 2021. The detailed data dictionary is given below.\n",
        "\n",
        "\n",
        "- brand_name: Name of manufacturing brand\n",
        "- os: OS on which the device runs\n",
        "- screen_size: Size of the screen in cm\n",
        "- 4g: Whether 4G is available or not\n",
        "- 5g: Whether 5G is available or not\n",
        "- main_camera_mp: Resolution of the rear camera in megapixels\n",
        "- selfie_camera_mp: Resolution of the front camera in megapixels\n",
        "- int_memory: Amount of internal memory (ROM) in GB\n",
        "- ram: Amount of RAM in GB\n",
        "- battery: Energy capacity of the device battery in mAh\n",
        "- weight: Weight of the device in grams\n",
        "- release_year: Year when the device model was released\n",
        "- days_used: Number of days the used/refurbished device has been used\n",
        "- normalized_new_price: Normalized price of a new device of the same model in euros\n",
        "- normalized_used_price: Normalized price of the used/refurbished device in euros"
      ],
      "metadata": {
        "id": "janUsX8IlMxf"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v_-uuGqH-qTt"
      },
      "source": [
        "# Importing necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#!pip install nb_black"
      ],
      "metadata": {
        "id": "QvbxUSO8K5xM"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "zeF8YaNKDPyK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "c6904d36-74f2-4e91-b889-dbc5362bdc6a"
      },
      "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 (good coding practice)\\n%load_ext nb_black\\n\\n# basic libraries for reading, manipulating data and scientific computing\\nimport numpy as np\\nimport pandas as pd\\n\\n# data visualization libraries\\nimport matplotlib.pyplot as plt\\nimport matplotlib.ticker as tick\\nimport seaborn as sns\\n\\n# split arrays or matrices into random train and test subsets\\nfrom sklearn.model_selection import train_test_split\\n\\n# cross-sectional models and methods\\n# OLS - to build linear regression model with ordinary least squares\\nimport statsmodels.api as sm\\nfrom statsmodels.stats.outliers_influence import variance_inflation_factor\\n\\n# statistical functions\\nimport scipy.stats as st\\n\\n# metrics and scoring\\n# quantifying the quality of predictions to check model performance\\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\\n\\nimport warnings\\nwarnings.filterwarnings(\\\"ignore\\\")\";\n",
              "                var nbb_formatted_code = \"# this will help in making the Python code more structured automatically (good coding practice)\\n%load_ext nb_black\\n\\n# basic libraries for reading, manipulating data and scientific computing\\nimport numpy as np\\nimport pandas as pd\\n\\n# data visualization libraries\\nimport matplotlib.pyplot as plt\\nimport matplotlib.ticker as tick\\nimport seaborn as sns\\n\\n# split arrays or matrices into random train and test subsets\\nfrom sklearn.model_selection import train_test_split\\n\\n# cross-sectional models and methods\\n# OLS - to build linear regression model with ordinary least squares\\nimport statsmodels.api as sm\\nfrom statsmodels.stats.outliers_influence import variance_inflation_factor\\n\\n# statistical functions\\nimport scipy.stats as st\\n\\n# metrics and scoring\\n# quantifying the quality of predictions to check model performance\\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\\n\\nimport warnings\\n\\nwarnings.filterwarnings(\\\"ignore\\\")\";\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": [
        "# this will help in making the Python code more structured automatically (good coding practice)\n",
        "%load_ext nb_black\n",
        "\n",
        "# basic libraries for reading, manipulating data and scientific computing\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# data visualization libraries\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as tick\n",
        "import seaborn as sns\n",
        "\n",
        "# split arrays or matrices into random train and test subsets\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# cross-sectional models and methods\n",
        "# OLS - to build linear regression model with ordinary least squares\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "\n",
        "# statistical functions\n",
        "import scipy.stats as st\n",
        "\n",
        "# metrics and scoring\n",
        "# quantifying the quality of predictions to check model performance\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xxhpZv9y-qTw"
      },
      "source": [
        "# Loading the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "ZJwX9wuc4rqL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "ba96aee8-6d35-4a1b-f55e-2ad49c283412"
      },
      "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('used_device_data.csv')\";\n",
              "                var nbb_formatted_code = \"# Loding the dataset\\ndf = pd.read_csv(\\\"used_device_data.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": {}
        }
      ],
      "source": [
        "# Loding the dataset\n",
        "df = pd.read_csv('used_device_data.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# User-defined functions"
      ],
      "metadata": {
        "id": "EK9KOQeD66L-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Functions to carry out the EDA"
      ],
      "metadata": {
        "id": "jMrVbJlo8JrI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Combined boxplot and histogram"
      ],
      "metadata": {
        "id": "3tLcJfcM7Rfn"
      }
    },
    {
      "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 devices', 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": "8AqHM8wq7QJZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "c9109e12-6e29-4674-fd57-fd5ab526f646"
      },
      "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 devices', 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 devices\\\", 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": "iFrriCrA7aiU"
      }
    },
    {
      "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 devices', 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 devices', 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": "SeLOsfYm66VU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "66ffd592-2bfc-4087-f482-89eacd902cb9"
      },
      "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 devices', 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 devices', 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 devices\\\", 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 devices\\\", 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": [
        "## Functions for model check"
      ],
      "metadata": {
        "id": "Fqwbx9tA8W39"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Function to compute different metrics to check performance of a regression model"
      ],
      "metadata": {
        "id": "zUCAy2c88hXL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# function to compute adjusted R-squared\n",
        "def adj_r2_score(predictors, targets, predictions):\n",
        "    r2 = r2_score(targets, predictions)\n",
        "    n = predictors.shape[0]\n",
        "    k = predictors.shape[1]\n",
        "    return 1 - ((1 - r2) * (n - 1) / (n - k - 1))\n",
        "\n",
        "\n",
        "# function to compute MAPE\n",
        "def mape_score(targets, predictions):\n",
        "    return np.mean(np.abs(targets - predictions) / targets) * 100\n",
        "\n",
        "\n",
        "# function to compute different metrics to check performance of a regression model\n",
        "def model_performance_regression(model, predictors, target):\n",
        "    \"\"\"\n",
        "    Function to compute different metrics to check regression model performance\n",
        "\n",
        "    model: regressor\n",
        "    predictors: independent variables\n",
        "    target: dependent variable\n",
        "    \"\"\"\n",
        "\n",
        "    # predicting using the independent variables\n",
        "    pred = model.predict(predictors)\n",
        "\n",
        "    r2 = r2_score(target, pred)  # to compute R-squared\n",
        "    adjr2 = adj_r2_score(predictors, target, pred)  # to compute adjusted R-squared\n",
        "    rmse = np.sqrt(mean_squared_error(target, pred))  # to compute RMSE\n",
        "    mae = mean_absolute_error(target, pred)  # to compute MAE\n",
        "    mape = mape_score(target, pred)  # to compute MAPE\n",
        "\n",
        "    # creating a dataframe of metrics\n",
        "    df_perf = pd.DataFrame(\n",
        "        {\n",
        "            \"RMSE\": rmse,\n",
        "            \"MAE\": mae,\n",
        "            \"R-squared\": r2,\n",
        "            \"Adj. R-squared\": adjr2,\n",
        "            \"MAPE\": mape,\n",
        "        },\n",
        "        index=[0],\n",
        "    )\n",
        "\n",
        "    return df_perf"
      ],
      "metadata": {
        "id": "bdQFVuxZ8ghq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "6417b63f-696d-4e29-ad93-d649c73cbb8d"
      },
      "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 = \"# function to compute adjusted R-squared\\ndef adj_r2_score(predictors, targets, predictions):\\n    r2 = r2_score(targets, predictions)\\n    n = predictors.shape[0]\\n    k = predictors.shape[1]\\n    return 1 - ((1 - r2) * (n - 1) / (n - k - 1))\\n\\n\\n# function to compute MAPE\\ndef mape_score(targets, predictions):\\n    return np.mean(np.abs(targets - predictions) / targets) * 100\\n\\n\\n# function to compute different metrics to check performance of a regression model\\ndef model_performance_regression(model, predictors, target):\\n    \\\"\\\"\\\"\\n    Function to compute different metrics to check regression model performance\\n\\n    model: regressor\\n    predictors: independent variables\\n    target: dependent variable\\n    \\\"\\\"\\\"\\n\\n    # predicting using the independent variables\\n    pred = model.predict(predictors)\\n\\n    r2 = r2_score(target, pred)  # to compute R-squared\\n    adjr2 = adj_r2_score(predictors, target, pred)  # to compute adjusted R-squared\\n    rmse = np.sqrt(mean_squared_error(target, pred))  # to compute RMSE\\n    mae = mean_absolute_error(target, pred)  # to compute MAE\\n    mape = mape_score(target, pred)  # to compute MAPE\\n\\n    # creating a dataframe of metrics\\n    df_perf = pd.DataFrame(\\n        {\\n            \\\"RMSE\\\": rmse,\\n            \\\"MAE\\\": mae,\\n            \\\"R-squared\\\": r2,\\n            \\\"Adj. R-squared\\\": adjr2,\\n            \\\"MAPE\\\": mape,\\n        },\\n        index=[0],\\n    )\\n\\n    return df_perf\";\n",
              "                var nbb_formatted_code = \"# function to compute adjusted R-squared\\ndef adj_r2_score(predictors, targets, predictions):\\n    r2 = r2_score(targets, predictions)\\n    n = predictors.shape[0]\\n    k = predictors.shape[1]\\n    return 1 - ((1 - r2) * (n - 1) / (n - k - 1))\\n\\n\\n# function to compute MAPE\\ndef mape_score(targets, predictions):\\n    return np.mean(np.abs(targets - predictions) / targets) * 100\\n\\n\\n# function to compute different metrics to check performance of a regression model\\ndef model_performance_regression(model, predictors, target):\\n    \\\"\\\"\\\"\\n    Function to compute different metrics to check regression model performance\\n\\n    model: regressor\\n    predictors: independent variables\\n    target: dependent variable\\n    \\\"\\\"\\\"\\n\\n    # predicting using the independent variables\\n    pred = model.predict(predictors)\\n\\n    r2 = r2_score(target, pred)  # to compute R-squared\\n    adjr2 = adj_r2_score(predictors, target, pred)  # to compute adjusted R-squared\\n    rmse = np.sqrt(mean_squared_error(target, pred))  # to compute RMSE\\n    mae = mean_absolute_error(target, pred)  # to compute MAE\\n    mape = mape_score(target, pred)  # to compute MAPE\\n\\n    # creating a dataframe of metrics\\n    df_perf = pd.DataFrame(\\n        {\\n            \\\"RMSE\\\": rmse,\\n            \\\"MAE\\\": mae,\\n            \\\"R-squared\\\": r2,\\n            \\\"Adj. R-squared\\\": adjr2,\\n            \\\"MAPE\\\": mape,\\n        },\\n        index=[0],\\n    )\\n\\n    return df_perf\";\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": "KlUkg1Wy8jdV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "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)\n",
        "        for i in range(len(predictors.columns))\n",
        "    ]\n",
        "    return vif"
      ],
      "metadata": {
        "id": "OTwEk4os8jLb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "19e6c00f-5129-4fdb-8256-0f4ff9bbfa3a"
      },
      "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 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)\\n        for i in range(len(predictors.columns))\\n    ]\\n    return vif\";\n",
              "                var nbb_formatted_code = \"def 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    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 Treating Multicollinearity"
      ],
      "metadata": {
        "id": "4GhhfjDtdbhd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def treating_multicollinearity(predictors, target, high_vif_columns):\n",
        "    \"\"\"\n",
        "    Checking the effect of dropping the columns showing high multicollinearity\n",
        "    on model performance (adj. R-squared and RMSE)\n",
        "\n",
        "    predictors: independent variables\n",
        "    target: dependent variable\n",
        "    high_vif_columns: columns having high VIF\n",
        "    \"\"\"\n",
        "    # empty lists to store adj. R-squared and RMSE values\n",
        "    r2 = []\n",
        "    adj_r2 = []\n",
        "    rmse = []\n",
        "\n",
        "    # build ols models by dropping one of the high VIF columns at a time\n",
        "    # store the adjusted R-squared and RMSE in the lists defined previously\n",
        "    for cols in high_vif_columns:\n",
        "        # defining the new train set\n",
        "        train = predictors.loc[:, ~predictors.columns.str.startswith(cols)]\n",
        "\n",
        "        # create the model\n",
        "        olsmodel = sm.OLS(target, train).fit()\n",
        "\n",
        "        # adding adj. R-squared and RMSE to the lists\n",
        "        r2.append(olsmodel.rsquared)\n",
        "        adj_r2.append(olsmodel.rsquared_adj)\n",
        "        rmse.append(np.sqrt(olsmodel.mse_resid))\n",
        "\n",
        "    # creating a dataframe for the results\n",
        "    temp = pd.DataFrame(\n",
        "        {\n",
        "            \"col\": high_vif_columns,\n",
        "            \"R-squared\": r2,\n",
        "            \"Adj. R-squared after_dropping col\": adj_r2,\n",
        "            \"RMSE after dropping col\": rmse,\n",
        "        }\n",
        "    ).sort_values(by=\"Adj. R-squared after_dropping col\", ascending=False)\n",
        "    temp.reset_index(drop=True, inplace=True)\n",
        "\n",
        "    return temp"
      ],
      "metadata": {
        "id": "A2w-1N4Bdbu8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "ffebfc00-1ff2-4219-e1a2-89f84e28725d"
      },
      "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 = \"def treating_multicollinearity(predictors, target, high_vif_columns):\\n    \\\"\\\"\\\"\\n    Checking the effect of dropping the columns showing high multicollinearity\\n    on model performance (adj. R-squared and RMSE)\\n\\n    predictors: independent variables\\n    target: dependent variable\\n    high_vif_columns: columns having high VIF\\n    \\\"\\\"\\\"\\n    # empty lists to store adj. R-squared and RMSE values\\n    r2 = []\\n    adj_r2 = []\\n    rmse = []\\n\\n    # build ols models by dropping one of the high VIF columns at a time\\n    # store the adjusted R-squared and RMSE in the lists defined previously\\n    for cols in high_vif_columns:\\n        # defining the new train set\\n        train = predictors.loc[:, ~predictors.columns.str.startswith(cols)]\\n\\n        # create the model\\n        olsmodel = sm.OLS(target, train).fit()\\n\\n        # adding adj. R-squared and RMSE to the lists\\n        r2.append(olsmodel.rsquared)\\n        adj_r2.append(olsmodel.rsquared_adj)\\n        rmse.append(np.sqrt(olsmodel.mse_resid))\\n\\n    # creating a dataframe for the results\\n    temp = pd.DataFrame(\\n        {\\n            \\\"col\\\": high_vif_columns,\\n            \\\"R-squared\\\": r2,\\n            \\\"Adj. R-squared after_dropping col\\\": adj_r2,\\n            \\\"RMSE after dropping col\\\": rmse,\\n        }\\n    ).sort_values(by=\\\"Adj. R-squared after_dropping col\\\", ascending=False)\\n    temp.reset_index(drop=True, inplace=True)\\n\\n    return temp\";\n",
              "                var nbb_formatted_code = \"def treating_multicollinearity(predictors, target, high_vif_columns):\\n    \\\"\\\"\\\"\\n    Checking the effect of dropping the columns showing high multicollinearity\\n    on model performance (adj. R-squared and RMSE)\\n\\n    predictors: independent variables\\n    target: dependent variable\\n    high_vif_columns: columns having high VIF\\n    \\\"\\\"\\\"\\n    # empty lists to store adj. R-squared and RMSE values\\n    r2 = []\\n    adj_r2 = []\\n    rmse = []\\n\\n    # build ols models by dropping one of the high VIF columns at a time\\n    # store the adjusted R-squared and RMSE in the lists defined previously\\n    for cols in high_vif_columns:\\n        # defining the new train set\\n        train = predictors.loc[:, ~predictors.columns.str.startswith(cols)]\\n\\n        # create the model\\n        olsmodel = sm.OLS(target, train).fit()\\n\\n        # adding adj. R-squared and RMSE to the lists\\n        r2.append(olsmodel.rsquared)\\n        adj_r2.append(olsmodel.rsquared_adj)\\n        rmse.append(np.sqrt(olsmodel.mse_resid))\\n\\n    # creating a dataframe for the results\\n    temp = pd.DataFrame(\\n        {\\n            \\\"col\\\": high_vif_columns,\\n            \\\"R-squared\\\": r2,\\n            \\\"Adj. R-squared after_dropping col\\\": adj_r2,\\n            \\\"RMSE after dropping col\\\": rmse,\\n        }\\n    ).sort_values(by=\\\"Adj. R-squared after_dropping col\\\", ascending=False)\\n    temp.reset_index(drop=True, inplace=True)\\n\\n    return temp\";\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",
      "metadata": {
        "id": "UvpMDcaaMKtI"
      },
      "source": [
        "# Data Overview"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The initial steps to get an overview of any dataset is to:\n",
        "\n",
        "- [x] Observe the first few rows of the dataset, to check whether the dataset has been loaded properly or not\n",
        "- [x] Get information about the number of rows and columns in the dataset\n",
        "- [x] Find out the data types of the columns \n",
        "- [x] Ensure that data is stored in the preferred format and the value of each property is as expected\n",
        "- [x] Check the statistical summary of the dataset to get an overview of the numerical columns of the data.\n",
        "- [x] Sanity checks.\n",
        "- [x] Observations."
      ],
      "metadata": {
        "id": "WA7OElyD85-g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Displaying few first and last rows of the dataset"
      ],
      "metadata": {
        "id": "YygLXysZOhxx"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "01hJQ7EfMKtK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "ad2eb2e2-13ce-43ae-fbde-d288d3cdf7f4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  brand_name       os  screen_size   4g   5g  main_camera_mp  \\\n",
              "0      Honor  Android        14.50  yes   no            13.0   \n",
              "1      Honor  Android        17.30  yes  yes            13.0   \n",
              "2      Honor  Android        16.69  yes  yes            13.0   \n",
              "3      Honor  Android        25.50  yes  yes            13.0   \n",
              "4      Honor  Android        15.32  yes   no            13.0   \n",
              "\n",
              "   selfie_camera_mp  int_memory  ram  battery  weight  release_year  \\\n",
              "0               5.0        64.0  3.0   3020.0   146.0          2020   \n",
              "1              16.0       128.0  8.0   4300.0   213.0          2020   \n",
              "2               8.0       128.0  8.0   4200.0   213.0          2020   \n",
              "3               8.0        64.0  6.0   7250.0   480.0          2020   \n",
              "4               8.0        64.0  3.0   5000.0   185.0          2020   \n",
              "\n",
              "   days_used  normalized_used_price  normalized_new_price  \n",
              "0        127               4.307572              4.715100  \n",
              "1        325               5.162097              5.519018  \n",
              "2        162               5.111084              5.884631  \n",
              "3        345               5.135387              5.630961  \n",
              "4        293               4.389995              4.947837  "
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              "      <td>4.307572</td>\n",
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              "      <td>480.0</td>\n",
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          "metadata": {},
          "execution_count": 9
        },
        {
          "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 = 9;\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",
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        "# view the last five rows of the dataset\n",
        "df.tail()"
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        "outputId": "de455da8-01db-4372-91f2-cfb989888e45"
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      "execution_count": 10,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "     brand_name       os  screen_size   4g  5g  main_camera_mp  \\\n",
              "3449       Asus  Android        15.34  yes  no             NaN   \n",
              "3450       Asus  Android        15.24  yes  no            13.0   \n",
              "3451    Alcatel  Android        15.80  yes  no            13.0   \n",
              "3452    Alcatel  Android        15.80  yes  no            13.0   \n",
              "3453    Alcatel  Android        12.83  yes  no            13.0   \n",
              "\n",
              "      selfie_camera_mp  int_memory  ram  battery  weight  release_year  \\\n",
              "3449               8.0        64.0  6.0   5000.0   190.0          2019   \n",
              "3450               8.0       128.0  8.0   4000.0   200.0          2018   \n",
              "3451               5.0        32.0  3.0   4000.0   165.0          2020   \n",
              "3452               5.0        32.0  2.0   4000.0   160.0          2020   \n",
              "3453               5.0        16.0  2.0   4000.0   168.0          2020   \n",
              "\n",
              "      days_used  normalized_used_price  normalized_new_price  \n",
              "3449        232               4.492337              6.483872  \n",
              "3450        541               5.037732              6.251538  \n",
              "3451        201               4.357350              4.528829  \n",
              "3452        149               4.349762              4.624188  \n",
              "3453        176               4.132122              4.279994  "
            ],
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              "    <tr>\n",
              "      <th>3451</th>\n",
              "      <td>Alcatel</td>\n",
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              "      <td>32.0</td>\n",
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              "      <td>165.0</td>\n",
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              "      <td>201</td>\n",
              "      <td>4.357350</td>\n",
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              "    <tr>\n",
              "      <th>3452</th>\n",
              "      <td>Alcatel</td>\n",
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              "      <td>15.80</td>\n",
              "      <td>yes</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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            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "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 = \"# view the last five rows of the dataset\\ndf.tail()\";\n",
              "                var nbb_formatted_code = \"# view the last 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": [
        "* The dataset contains information about different attributes of used/refurbished phones and tablets\n",
        "* Many devices seem to have 4g, but not all of them support 5g\n",
        "* Column `main_camera_mp` seems to be missing for some phones"
      ],
      "metadata": {
        "id": "OKgOuFmfPnVc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Get information about the number of rows and columns in the dataset\n"
      ],
      "metadata": {
        "id": "ZQCidP6hPH3N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# view the shape of the dataset\n",
        "df.shape"
      ],
      "metadata": {
        "id": "uoKW9gPnO305",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "30d5aede-465b-440e-cf40-b8cc2d0d6ca8"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3454, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "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 = \"# 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 3454 rows and 15 columns - 15 attributes/factors of 3454 devices"
      ],
      "metadata": {
        "id": "QjmhfyQNPOP3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checking the data types of the columns for the dataset"
      ],
      "metadata": {
        "id": "l05cDLw4RS_h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# view concise summary of the dataset structure\n",
        "df.info()"
      ],
      "metadata": {
        "id": "iE_5UePORZp0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "7f8e7e9a-5135-426a-9eb6-8b3f076a15bf"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3454 entries, 0 to 3453\n",
            "Data columns (total 15 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   brand_name             3454 non-null   object \n",
            " 1   os                     3454 non-null   object \n",
            " 2   screen_size            3454 non-null   float64\n",
            " 3   4g                     3454 non-null   object \n",
            " 4   5g                     3454 non-null   object \n",
            " 5   main_camera_mp         3275 non-null   float64\n",
            " 6   selfie_camera_mp       3452 non-null   float64\n",
            " 7   int_memory             3450 non-null   float64\n",
            " 8   ram                    3450 non-null   float64\n",
            " 9   battery                3448 non-null   float64\n",
            " 10  weight                 3447 non-null   float64\n",
            " 11  release_year           3454 non-null   int64  \n",
            " 12  days_used              3454 non-null   int64  \n",
            " 13  normalized_used_price  3454 non-null   float64\n",
            " 14  normalized_new_price   3454 non-null   float64\n",
            "dtypes: float64(9), int64(2), object(4)\n",
            "memory usage: 404.9+ KB\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 12;\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 11 numeric (*float* and *int* types) and 4 string (*object* type) columns in the dataset\n",
        "* Some columns have missing (`NA`) values: `main_camera_mp`, `selfie_camera_mp`, `int_memory`, `ram`, `battery`, `weight`\n",
        "* The target variable is the `normalized_used_price` which is of *float* type"
      ],
      "metadata": {
        "id": "KKCJgw7H_dH-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Statistical summary of the dataset"
      ],
      "metadata": {
        "id": "rqjFWNwOBJkO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe(include=np.number).T"
      ],
      "metadata": {
        "id": "Lr0qv_alBJsG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "ab8bce19-e580-4a37-9ddd-b64db0df7118"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        count         mean          std          min  \\\n",
              "screen_size            3454.0    13.713115     3.805280     5.080000   \n",
              "main_camera_mp         3275.0     9.460208     4.815461     0.080000   \n",
              "selfie_camera_mp       3452.0     6.554229     6.970372     0.000000   \n",
              "int_memory             3450.0    54.573099    84.972371     0.010000   \n",
              "ram                    3450.0     4.036122     1.365105     0.020000   \n",
              "battery                3448.0  3133.402697  1299.682844   500.000000   \n",
              "weight                 3447.0   182.751871    88.413228    69.000000   \n",
              "release_year           3454.0  2015.965258     2.298455  2013.000000   \n",
              "days_used              3454.0   674.869716   248.580166    91.000000   \n",
              "normalized_used_price  3454.0     4.364712     0.588914     1.536867   \n",
              "normalized_new_price   3454.0     5.233107     0.683637     2.901422   \n",
              "\n",
              "                               25%          50%          75%          max  \n",
              "screen_size              12.700000    12.830000    15.340000    30.710000  \n",
              "main_camera_mp            5.000000     8.000000    13.000000    48.000000  \n",
              "selfie_camera_mp          2.000000     5.000000     8.000000    32.000000  \n",
              "int_memory               16.000000    32.000000    64.000000  1024.000000  \n",
              "ram                       4.000000     4.000000     4.000000    12.000000  \n",
              "battery                2100.000000  3000.000000  4000.000000  9720.000000  \n",
              "weight                  142.000000   160.000000   185.000000   855.000000  \n",
              "release_year           2014.000000  2015.500000  2018.000000  2020.000000  \n",
              "days_used               533.500000   690.500000   868.750000  1094.000000  \n",
              "normalized_used_price     4.033931     4.405133     4.755700     6.619433  \n",
              "normalized_new_price      4.790342     5.245892     5.673718     7.847841  "
            ],
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              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <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",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>screen_size</th>\n",
              "      <td>3454.0</td>\n",
              "      <td>13.713115</td>\n",
              "      <td>3.805280</td>\n",
              "      <td>5.080000</td>\n",
              "      <td>12.700000</td>\n",
              "      <td>12.830000</td>\n",
              "      <td>15.340000</td>\n",
              "      <td>30.710000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>main_camera_mp</th>\n",
              "      <td>3275.0</td>\n",
              "      <td>9.460208</td>\n",
              "      <td>4.815461</td>\n",
              "      <td>0.080000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>48.000000</td>\n",
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              "    <tr>\n",
              "      <th>selfie_camera_mp</th>\n",
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              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>8.000000</td>\n",
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              "    <tr>\n",
              "      <th>int_memory</th>\n",
              "      <td>3450.0</td>\n",
              "      <td>54.573099</td>\n",
              "      <td>84.972371</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>1024.000000</td>\n",
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              "    <tr>\n",
              "      <th>ram</th>\n",
              "      <td>3450.0</td>\n",
              "      <td>4.036122</td>\n",
              "      <td>1.365105</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>12.000000</td>\n",
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              "    <tr>\n",
              "      <th>battery</th>\n",
              "      <td>3448.0</td>\n",
              "      <td>3133.402697</td>\n",
              "      <td>1299.682844</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>2100.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>4000.000000</td>\n",
              "      <td>9720.000000</td>\n",
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              "    <tr>\n",
              "      <th>weight</th>\n",
              "      <td>3447.0</td>\n",
              "      <td>182.751871</td>\n",
              "      <td>88.413228</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>142.000000</td>\n",
              "      <td>160.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>855.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>release_year</th>\n",
              "      <td>3454.0</td>\n",
              "      <td>2015.965258</td>\n",
              "      <td>2.298455</td>\n",
              "      <td>2013.000000</td>\n",
              "      <td>2014.000000</td>\n",
              "      <td>2015.500000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>2020.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>days_used</th>\n",
              "      <td>3454.0</td>\n",
              "      <td>674.869716</td>\n",
              "      <td>248.580166</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>533.500000</td>\n",
              "      <td>690.500000</td>\n",
              "      <td>868.750000</td>\n",
              "      <td>1094.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>normalized_used_price</th>\n",
              "      <td>3454.0</td>\n",
              "      <td>4.364712</td>\n",
              "      <td>0.588914</td>\n",
              "      <td>1.536867</td>\n",
              "      <td>4.033931</td>\n",
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              "      <td>4.755700</td>\n",
              "      <td>6.619433</td>\n",
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              "    <tr>\n",
              "      <th>normalized_new_price</th>\n",
              "      <td>3454.0</td>\n",
              "      <td>5.233107</td>\n",
              "      <td>0.683637</td>\n",
              "      <td>2.901422</td>\n",
              "      <td>4.790342</td>\n",
              "      <td>5.245892</td>\n",
              "      <td>5.673718</td>\n",
              "      <td>7.847841</td>\n",
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          "data": {
            "text/plain": [
              "            count  unique      top  freq\n",
              "brand_name   3454      34   Others   502\n",
              "os           3454       4  Android  3214\n",
              "4g           3454       2      yes  2335\n",
              "5g           3454       2       no  3302"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>unique</th>\n",
              "      <th>top</th>\n",
              "      <th>freq</th>\n",
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              "      <th>brand_name</th>\n",
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              "\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "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 = \"df.describe(include='object').T.fillna('')\";\n",
              "                var nbb_formatted_code = \"df.describe(include=\\\"object\\\").T.fillna(\\\"\\\")\";\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": [
        "# Mode\n",
        "df.mode().loc[0,:]"
      ],
      "metadata": {
        "id": "r0jRTXxIVBU0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "ef1a08bf-55ef-4b19-ca41-6a169a082deb"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "brand_name                 Others\n",
              "os                        Android\n",
              "screen_size                  12.7\n",
              "4g                            yes\n",
              "5g                             no\n",
              "main_camera_mp               13.0\n",
              "selfie_camera_mp              5.0\n",
              "int_memory                   16.0\n",
              "ram                           4.0\n",
              "battery                    4000.0\n",
              "weight                      150.0\n",
              "release_year               2014.0\n",
              "days_used                   564.0\n",
              "normalized_used_price    3.535145\n",
              "normalized_new_price      5.13574\n",
              "Name: 0, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "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 = \"# Mode\\ndf.mode().loc[0,:]\";\n",
              "                var nbb_formatted_code = \"# Mode\\ndf.mode().loc[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": [
        "# `selfie_camera_mp` mean value (exclusding value 0)\n",
        "df.selfie_camera_mp.apply(lambda x: x if x>0 else None).mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "I0Qrroa58ka6",
        "outputId": "cc3cdc34-f678-4e0b-d785-28c355086ebe"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.629123937884558"
            ]
          },
          "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 = \"# `selfie_camera_mp` mean value (exclusding value 0)\\ndf.selfie_camera_mp.apply(lambda x: x if x>0 else None).mean()\";\n",
              "                var nbb_formatted_code = \"# `selfie_camera_mp` mean value (exclusding value 0)\\ndf.selfie_camera_mp.apply(lambda x: x if x > 0 else None).mean()\";\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": [
        "## Sanity checks"
      ],
      "metadata": {
        "id": "CpGAkI_lxdsv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Sanity model is a model where all target values ​​are constant and equal to the average of the target column. \n",
        "# Let's estimate the value of the MAE and RMSE metrics of the sanity model.\n",
        "# We will consider a model valuable if its MAE and RMSE are less than values of the sanity check model.\n",
        "\n",
        "y_train, y_test = train_test_split(df['normalized_used_price'], test_size=0.3, random_state=42)\n",
        "\n",
        "train_price_mean = y_train.mean()\n",
        "MAE_sanity = mean_absolute_error(y_test, [train_price_mean] * len(y_test))\n",
        "RMSE_sanity = mean_squared_error(y_test, [train_price_mean] * len(y_test))**0.5\n",
        "print(f'Sanity MAE for the mean value is {MAE_sanity:.2f}')\n",
        "print(f'Sanity RMSE for the mean value is {RMSE_sanity:.2f}')"
      ],
      "metadata": {
        "id": "z55sh8kmxlzM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "d44f8ba3-c4cf-4c4f-e129-fa2fe024c910"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sanity MAE for the mean value is 0.44\n",
            "Sanity RMSE for the mean value is 0.58\n"
          ]
        },
        {
          "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 = \"# Sanity model is a model where all target values \\u200b\\u200bare constant and equal to the average of the target column. \\n# Let's estimate the value of the MAE and RMSE metrics of the sanity model.\\n# We will consider a model valuable if its MAE and RMSE are less than values of the sanity check model.\\n\\ny_train, y_test = train_test_split(df['normalized_used_price'], test_size=0.3, random_state=42)\\n\\ntrain_price_mean = y_train.mean()\\nMAE_sanity = mean_absolute_error(y_test, [train_price_mean] * len(y_test))\\nRMSE_sanity = mean_squared_error(y_test, [train_price_mean] * len(y_test))**0.5\\nprint(f'Sanity MAE for the mean value is {MAE_sanity:.2f}')\\nprint(f'Sanity RMSE for the mean value is {RMSE_sanity:.2f}')\";\n",
              "                var nbb_formatted_code = \"# Sanity model is a model where all target values \\u200b\\u200bare constant and equal to the average of the target column.\\n# Let's estimate the value of the MAE and RMSE metrics of the sanity model.\\n# We will consider a model valuable if its MAE and RMSE are less than values of the sanity check model.\\n\\ny_train, y_test = train_test_split(\\n    df[\\\"normalized_used_price\\\"], test_size=0.3, random_state=42\\n)\\n\\ntrain_price_mean = y_train.mean()\\nMAE_sanity = mean_absolute_error(y_test, [train_price_mean] * len(y_test))\\nRMSE_sanity = mean_squared_error(y_test, [train_price_mean] * len(y_test)) ** 0.5\\nprint(f\\\"Sanity MAE for the mean value is {MAE_sanity:.2f}\\\")\\nprint(f\\\"Sanity RMSE for the mean value is {RMSE_sanity:.2f}\\\")\";\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 for duplicate values"
      ],
      "metadata": {
        "id": "3BH6EMhe0Ibq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "id": "1QaD81Co0Hwk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ef8b1562-2189-4c04-ac03-91aeea594b9e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "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 = \"df.duplicated().sum()\";\n",
              "                var nbb_formatted_code = \"df.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": "uNWwXGuh0PJT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checking for missing values"
      ],
      "metadata": {
        "id": "n-XFMm3O0Wol"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "pU7izY3I0Wol",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "31d85598-cbe6-497a-edb5-60e6a678c3f4"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "brand_name                 0\n",
              "os                         0\n",
              "screen_size                0\n",
              "4g                         0\n",
              "5g                         0\n",
              "main_camera_mp           179\n",
              "selfie_camera_mp           2\n",
              "int_memory                 4\n",
              "ram                        4\n",
              "battery                    6\n",
              "weight                     7\n",
              "release_year               0\n",
              "days_used                  0\n",
              "normalized_used_price      0\n",
              "normalized_new_price       0\n",
              "dtype: int64"
            ]
          },
          "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.isnull().sum()\";\n",
              "                var nbb_formatted_code = \"df.isnull().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 missing values in many columns."
      ],
      "metadata": {
        "id": "Be8jItpa0Wol"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Observations:**"
      ],
      "metadata": {
        "id": "dBdb1o0x0XE_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* We can see that the used device normzlized prices vary between 1.54 and 6.62\n",
        "  * The mean price is 4.36 normalized euros\n",
        "* There are 34 brands in the dataset, most of the devices fell into the category of \"Others\"\n",
        "* All devices are divided into 4 groups by operating systems. 93% of the devices operate on Android\n",
        "* Screen size vary between 5.08cm\tand 30.71cm\n",
        "  * The mean size is 13.71cm\n",
        "  * The most frequent size is 12.7cm\n",
        "* 4G/5G availability\n",
        "  * 68% of the devices support 4G\n",
        "  * 4% support 5G\n",
        "* Resolution of the rear camera vary from 0.3MP to 48MP (one device has 80 kilo-pixels main camera)\n",
        "  * The average is 9.46MP (13MP appears most frequently in the dataset)\n",
        "  * There are 179 missing values in this column\n",
        "* Resolution of the front camera vary from 0.3MP to 32MP\n",
        "  * The average is 6.55MP (5MP appears most frequently in the dataset)\n",
        "  * The mean exclusing 0 is 6.6MP\n",
        "  * There are 2 missing values in this column, but many rows contain 0 value in this column\n",
        "* Amount of ROM (`int_memory`) is between 8Mb and 1Tb\n",
        "  * The mean of ROM is 54.6GB, median is 32GB (skewed right), mode is 16GB\n",
        "  * 4 missing values in this column\n",
        "* Amount of RAM is between 16Mb and 12Gb\n",
        "  * The mean, median and mode of RAM are 4GB\n",
        "  * 4 missing values in this column\n",
        "* Energy capacity of the device battery varies between 500mAh and 9720mAh\n",
        "  * The mean is 3133mAh (4000mAh appears most frequently)\n",
        "  * 6 missing values in this column\n",
        "* Device weight varies from 69g to 855g, average weight is 183g, median is 160g, mode is 150g\n",
        "  * 7 missing values in this column\n",
        "* The device model year is represented by 8 years in the dataset, from 2013 to 2020\n",
        "  * Most of the devices in the dataset were released in 2014\n",
        "* The terms of used/refurbished devices usage vary from 3 months to 3 years\n",
        "  * The mean is 1 year and 10 months approximately\n",
        "  * The median is 1 year and 11 months approximately\n",
        "  * The term of 564 days (1 year 7 months) appears most frequently\n",
        "* The normalized price of a new device of the same model vary between 2.90 and 7.85\n",
        "  * The mean of normalized price of a new device of the same model is 5.23 normalized euros. \n",
        "* The price of a used device is on average 83.4% of a new device price of the same model\n",
        "  * In other words, a new device is 20% more expensive than the same model of used device.\n",
        "* Sanity checks\n",
        "  * To be better than simple average, the MAE of the model need to be less than 0.44, RMSE - less than 0.58\n",
        "* There are no duplicate values in the dataset.\n",
        "* There are missing values in many columns."
      ],
      "metadata": {
        "id": "q_CsmDgfCxSY"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "__7ciGcIDPyk"
      },
      "source": [
        "# Exploratory Data Analysis (EDA)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- 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 have been mentioned below to help us approach the analysis in the right manner and generate insights from the data.\n",
        "- A thorough analysis of the data has been done, in addition to the questions mentioned below."
      ],
      "metadata": {
        "id": "3bGVKmh75ri8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Univariate analysis"
      ],
      "metadata": {
        "id": "-tHomyo8UYEA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Continuous variables"
      ],
      "metadata": {
        "id": "jM7dAwc7M5cl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`normalized_used_price`**"
      ],
      "metadata": {
        "id": "CxeTJiVDUQz0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "histogram_boxplot(data=df, feature='normalized_used_price', fmt='{:.1f}', bins=20,\n",
        "                  xlabel='Normalized price of the used/refurbished device')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "J6J3_qG4UO3E",
        "outputId": "581faaf4-12c4-4f0d-99f2-76f0ae10b9d6"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"histogram_boxplot(data=df, feature='normalized_used_price', fmt='{:.1f}', bins=20,\\n                  xlabel='Normalized price of the used/refurbished device')\";\n",
              "                var nbb_formatted_code = \"histogram_boxplot(\\n    data=df,\\n    feature=\\\"normalized_used_price\\\",\\n    fmt=\\\"{:.1f}\\\",\\n    bins=20,\\n    xlabel=\\\"Normalized price of the used/refurbished device\\\",\\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 normalized used device prices look symmetric (bell shaped), unimodal, close to normally distributed\n",
        "* Normalized used device price is approximately 4.4 on average \n",
        "* The range is 1.5-6.6\n",
        "* There are many outliers below and above the overall pattern in a distribution"
      ],
      "metadata": {
        "id": "4lef0CsLYG9Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`normalized_new_price`**"
      ],
      "metadata": {
        "id": "7jMdhpwIUu4J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "histogram_boxplot(data=df, feature='normalized_new_price', fmt='{:.1f}', bins=20,\n",
        "                  xlabel='Normalized price of the new device')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "outputId": "c69ea984-44a9-4098-86b1-a98afabb9193",
        "id": "UfKhiia5Uu4J"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 21;\n",
              "                var nbb_unformatted_code = \"histogram_boxplot(data=df, feature='normalized_new_price', fmt='{:.1f}', bins=20,\\n                  xlabel='Normalized price of the new device')\";\n",
              "                var nbb_formatted_code = \"histogram_boxplot(\\n    data=df,\\n    feature=\\\"normalized_new_price\\\",\\n    fmt=\\\"{:.1f}\\\",\\n    bins=20,\\n    xlabel=\\\"Normalized price of the new device\\\",\\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 normalized new device prices look symmetric (bell shaped), unimodal, close to normally distributed\n",
        "* Normalized used device price is approximately 5.2 on average \n",
        "* The range is 2.9-7.8\n",
        "* There are many outliers below and above the overall pattern in a distribution"
      ],
      "metadata": {
        "id": "e9kP-NrWYmx4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`battery`**"
      ],
      "metadata": {
        "id": "8bp01bWINExf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "histogram_boxplot(data=df.dropna(), feature='battery', step=500, fmt='{:,.0f}', figsize=(12.5, 6),\n",
        "                  xlabel='Energy capacity of the device battery in mAh')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "outputId": "ad8777db-4259-40d2-8939-faa570024064",
        "id": "ULPZShZhNExg"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 22;\n",
              "                var nbb_unformatted_code = \"histogram_boxplot(data=df.dropna(), feature='battery', step=500, fmt='{:,.0f}', figsize=(12.5, 6),\\n                  xlabel='Energy capacity of the device battery in mAh')\";\n",
              "                var nbb_formatted_code = \"histogram_boxplot(\\n    data=df.dropna(),\\n    feature=\\\"battery\\\",\\n    step=500,\\n    fmt=\\\"{:,.0f}\\\",\\n    figsize=(12.5, 6),\\n    xlabel=\\\"Energy capacity of the device battery in mAh\\\",\\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": [
        "# Log transformation of the energy capacity\n",
        "bat = df[['battery']].dropna()\n",
        "bat['battery'] = bat['battery'].map(np.log)\n",
        "histogram_boxplot(data=bat, feature='battery', bins=9, \n",
        "                  title='Log transformation of the energy capacity',\n",
        "                  xlabel='Log energy capacity of the device battery')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "outputId": "40433217-4234-4637-d213-04206b117146",
        "id": "8ZG70C1cNExg"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 23;\n",
              "                var nbb_unformatted_code = \"# Log transformation of the energy capacity\\nbat = df[['battery']].dropna()\\nbat['battery'] = bat['battery'].map(np.log)\\nhistogram_boxplot(data=bat, feature='battery', bins=9, \\n                  title='Log transformation of the energy capacity',\\n                  xlabel='Log energy capacity of the device battery')\";\n",
              "                var nbb_formatted_code = \"# Log transformation of the energy capacity\\nbat = df[[\\\"battery\\\"]].dropna()\\nbat[\\\"battery\\\"] = bat[\\\"battery\\\"].map(np.log)\\nhistogram_boxplot(\\n    data=bat,\\n    feature=\\\"battery\\\",\\n    bins=9,\\n    title=\\\"Log transformation of the energy capacity\\\",\\n    xlabel=\\\"Log energy capacity of the device battery\\\",\\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 battery distribution has many outliers on the right\n",
        "* There are several more frequent values: 2000mAh, 3000mAh, 4000mAh\n",
        "* The normalized battery capacity looks multimodal, with three outliers on the left\n",
        "* The battery amount is approximately 3100 mAh on average\n",
        "* The range is from 500mAh to 9720mAh\n",
        "* It seems that log-transformation of this variable might be usefult for the modeling"
      ],
      "metadata": {
        "id": "zkMpDw_7NExg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`weight`**"
      ],
      "metadata": {
        "id": "wE2sP_1CSoO-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "histogram_boxplot(data=df.dropna(), feature='weight', step=50, fmt='{:.0f}',\n",
        "                  xlabel='Weight of the device in grams')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "outputId": "befd29ae-67a8-48ca-dd90-2d2c2a5c82ee",
        "id": "_hXjlqs-SoO_"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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v5ZxzzgHg29/+NqtXr+akk07ixhtv5IILLmDfffcF4OGHH+bqq6+mpaWFmpoaNm7cyOc//3mampoyn7eOjg4uu+wyLrroIjo6Orj88sszy8iu85ZbbgHgox/9KIsXL85sz9NPP52f//znmTiz/elPf+Kmm27i6KOP5v7778/EPnv2bO68806OP/54PvCBD7BixQq+/vWv09/fD0AkEmH27Nl86EMf4oc//CEzZszgmGOO4eqrr+bss8/m9ttvB+Doo4/mmmuuYdKkSbS3t/Oxj32MadOmcfnll3PIIYdw//33Z7bZrFmzuOqqqzjuuOO44oor6O/vx9056aSTOOaYYzLfQccffzxXXHEFLS0tJJNJ2traBrVpWvZ3FpD3eXbb5rZN7ndeIdL7xwUXXJBZn9z508s95ZRTuO666wA477zzaG5u3qHO7HVO72tNTU185zvfYfXq1TvUA3DFFVfkXeZHPvIRfvnLX+7QBunY2tvb+d73vkdbW9sO+2D2NMO16SmnnMLixYtH1GaFGMm2aG9v36ENhltGvjbO3RfGu6G2a6V+083dK1JxOS1cuNCXLFlSsuVfd9113HPPPRxxxBGcdtppfOUrX2HNmjWYGe7OkUceyWmnnVay+kfrsssuo3/dy5z5lrqdTvvjx3oACpo2n+8/1cF985dxw+k30FLfMqpllMpll11GW1sbHR0dQJB0rV69OvO+mVFfX09XV1fe+VtbW2lraxuUIB155JE8/PDDQ86TSCRobm5mzZo1RVmHRCIxZF2jma7Qecwss99/9rOfzUyXnqe1tZUvf/nLmc9EIpGgu7ubI444Anfn7rvv3qGub33rWwB85jOf2SHpbG1tZc8998x83l588UXWrFlDa2srW7ZsoaurK7OM7DrTcc2aNYu1a9dmtmc0GiWZTGbizHbeeeftEFt3dzfZ35lXXHFFpp7h2i1dT/pvdtnO2jqRSLBw4ULuueeevPvhFVdckfkOyvd+dpumZX9nuXve59ltm9s2ud95hUjvH9nrkzt/erkzZ87MtGn6+zO3ztx1Tu8b6X0qt57s/S13mTNnzmTt2rU7tEE6tuuuuy4zb+4+mD3NcG2aXUcxfw9Gsi2y1yP7d2moZeRr49x9YbwbaruW+jfdzB5x94W55To8uova29t54IEHcHfuv/9+nn322cyXTfrL/f7776e9vb2SYVbcUy2v0RZv44onrqh0KDsYGBjIJGzAoIQNgu04XKKzevXqHX5877rrrmHn6erqKlrCll5eMacrdJ70fp+boKafr169mocffjizrl1dXbg79913H/fdd1/eup5//nkefvjhHdo0vbz77rsPd+fee+/NLHf16tWZOru6urj77rsH1Zm2Zs2aQdszXcfq1atZuXJlZro//elPeWPL/U/uz372syG3Y3a96Xqy1ynf+uVr666uLu69994h98Obb7458x001PzPP/985nXud9b999+/w/Pcts1um9z5C/lue/bZZwdtn/Q2zJ4/e7nZbXr//fezcuXKQXWuWLFih3VevXo199xzT952u++++zI9lwD33XffoGWk94v0dNmxtbe3D5p39erVeadJLyt3Genn6TqK+Xswkm2Rux733XffDrEPtT2y2zh7XxjvhtuulfpNV0/bLrruuuu47777Mv+Lrq2tzfvF2dzczG677VaSGEZr5cqVxJLdXHRoYqfT7kpP20bv5bSee0lFUkQ9yt9t+jsSvvM6y+WVV17J+wMqhYlGo0D+JCT9/kjaN5FI0NvbW/Ztkt2LkNvLNtbtrI2ze9uyv7PSRwOAQc9zZbdN7nfe4YcfvtNeh+xe2Ny40/NnLzebmTFz5kzWrVuXqXO33XbLvB6tWbNm5V1Guh3SseXrEc6NP/2jnl5Wehn52rTQNivESLZFuscoO54jjzxyUOyFbI+J1NuW2wa5+0Ype9smXE+bmS0ysyVmtmT9+vUlq+ehhx4a9L/ooXoltm7dWrIYxrqfD7yCE3xROM4TiScqHNFgSth2TTKZHLYNR9q+XV1dFdkmuT2s1WRn7ZX9vZT9nZX9Az7cf+Cz2yb3O+9vf/vbTuMb6nsxe/7s5WZL91Jl15n9erSGWka6HdKxPfTQQ0MuI3ua7GWll5GvTQtts0KMZFs89NBDO8STG3sh26OaPycjNdR2LeY2HKlxOxDB3a8EroSgp61U9Rx00EE77WkrR1Y+Gulz2kppo/fy59RqPBJsgpSlWNa4jCs+ecWYObftC1/4wqDDozIy46mnrVoV0tOWlv2dNZKetnzzR6NR3v72t+80vqHOi8yeP3u52SrZ0/b2t799pz1t6WlG0tNWSJsVYiTb4qCDDtqhpy039kK2RzV/TkYqtw1y941KGLc9beVywgknEIkEzRiJRDIjTLJFIhGOP/74coc2Jvx84BVSDP7SSnlqTJ3bNm3atEqHUNUikQhnnHHGkO/ney8Wi2WSvVyLFi0adnnp+YaaH4JRoiN11llnZZ5/8IMfLGieww47bMT1jEb6Oyaf4447btj3IWjTtOzvrFgsRiwW2+F5bttmt03ud14h322534vp5WfPn73cbLFYjLPPPntQnWeddVbeac1s0Ot865l+nW8Z2ftlOrYTTjhh0Lzp6XKnya4rvYzcenPXeVeNZFuccMIJg7ZrLBbbIfZCtkf2vjDeDbVdK/mbrqRtFzU3N3PIIYdgZhx66KHsv//+zJo1C9j+BXLooYdO2Et+POvtDOQkbf2pfh5f93hlAsojFosxadKkzOvc/0ma2aCeilytra07/MgdeeSRw86TSCQy+0kxDFfXaKYrdJ70fn/ggQcOmi79vLW1lQMPPDCzrolEAjPjsMMOy5vwJBIJ9t13Xw488MC8SVlrayuHHXYYZsbhhx+eWW5ra2umzkQiwRFHHDGozrRZs2YN2p7pOlpbWwddyuDYY4/NG1tuUvDxj398yO2YXW++RDPf+uVr60QiweGHHz7kfnjiiSdmvoOGmj/7kh+531mHHnroDs9z2za7bXLnL+S7bf/99x+0fdLbMHv+7OVmt+mhhx7KnDlzBtU5d+7cHda5tbWVd7zjHXnb7bDDDuPQQw/NvHfYYYcNWkZ6v0hPlx1bc3PzoHlbW1vzTpNeVu4y0s/TdRTz92Ak2yJ3PQ477LAdYh9qe2S38US65Mdw27VSv+lK2orghBNOYM8998xk3meddRZ1dXXMmjWLurq6CdvLBvDDmoP5v9r38LFn38EZG87gqU8+xVOffIpff+DXlQ5tkGnTpjF37lxqa2s566yzMj0txx9/PHvuuSfnnnsudXV1vO1tbwOCbvPa2lpqamo466yzBvUMtba2cvzxxw/qXZgyZQqtra3E43Eg6PnI/p9+Y2Nj5kd8xowZg2Krra0Fgv/d7bXXXoPKpk2bRm1tLeeeey719fWZeYbqyVq0aFFm3dLLgCCBaG1tZe7cuSxYsIA3velNmfU855xzMslKPB7nne9856C2Se/f6elmz56daa/0/8rTn4lzzz03M88JJ5yQSZBPOukkzGxQj1C6TVtaWmhtbc1sm+zPW3q5Z511Vqb+9DKy61ywYAELFizgrLPOGrQ9zzzzzEFxZku309FHHz0o9qOOOiqz/ul60ts1vZ3mzp3LOeecQ21tLfPmzePMM8/EzDjzzDMzsZxxxhmYWebL/2Mf+1hmHbJ/XBctWpRZ53POOYeamprM9jjppJOA7d9B5557LrW1tcyePZuZM2fu0KZp2W041PPsth1u/kJlb5+h5k+Xn3XWWZl2yu75yZ4ne53TcZ5wwgnMnj07bz0nnHDCkMs8++yz87ZBdlzz5s3Luw8W2qbpfa/Yvwcj2Rb52mC4ZeRr44lmqO1aKRo9WkKXXXYZAJ/73OfKXnchynmdth8/1kN8tz3GZFuM9e0kIiITy4QbPSoiIiIynihpExEREakCStpEREREqoCSNhEREZEqoKRNREREpAooaRMRERGpAkraRERERKqAkjYRERGRKqCkTURERKQKxHY+iYzW3LlzKx2CFEDbSUREqoGSthI65ZRTKh2CFEDbSUREqoEOj4qIiIhUASVtIiIiIlVASZuIiIhIFVDSJiIiIlIFlLSJiIiIVAElbSIiIiJVQEmbiIiISBVQ0iYiIiJSBZS0iYiIiFQBJW0iIiIiVUBJm4iIiEgVUNImIiIiUgV0w/gJbm1nih8/1rPT6dZ0pgAKmnaoeubuNqpZRUREBCVtE9rcuXMLnrbe1gMQnz59dHXtNrL6REREZDAlbRPYKaecUukQREREpEA6p01ERESkCihpExEREakCStpEREREqoCSNhEREZEqoKRNREREpAqYu1c6hpIzs/XA8jJX2wJsKHOdqn9sxaD6tQ9M9PrHQgyqX/tANda/u7vvcI2tCZG0VYKZLXH3haq/ciodg+rXPjDR6x8LMah+7QPjqX4dHhURERGpAkraRERERKqAkrbSuVL1V1ylY1D9lVfpGCZ6/VD5GFR/5VU6hnFTv85pExEREakC6mkTERERqQJK2kRERESqgJI2ERERkSqgpE1ERESkCihpExEREakCStpEREREqoCSNhEREZEqoKRNREREpAooaRMRERGpAkraRERERKqAkjYRERGRKqCkTURERKQKKGkTERERqQJK2kRERESqgJI2ERERkSqgpE1ERESkCihpExEREakCStpEREREqoCSNhEREZEqEKt0AOXQ0tLi8+fPr3QYVW3jxo0ATJs2bXTzd4bzN45ufhERkYnikUce2eDu03PLy5K0mdk1wPuAde7+hrDsl8A+4SSTgS3ufoCZzQeeA14I33vQ3c8L53kbcC1QD9wKXODuvrP658+fz5IlS4q2PhPRtddeC8AZZ5wxqvmP+sZRANz5+TuLEo+IiMh4ZWbL85WXq6ftWuC7wE/TBe7+kfRzM7sMaM+a/mV3PyDPcn4AnAv8jSBpOxb4Y/HDFRERERlbynJOm7vfDWzK956ZGXAKcP1wyzCzWcAkd38w7F37KfDBIocqIiIiMiaNhYEI7wDa3P2lrLIFZvaYmd1lZu8Iy2YDK7OmWRmW5WVmi8xsiZktWb9+ffGjFhERESmjsZC0ncbgXrY1wDx3fwvwWeA6M5s00oW6+5XuvtDdF06fvsO5fCIiIiJVpaKjR80sBpwEvC1d5u69QG/4/BEzexnYG1gFzMmafU5YJlXg1n+8tdIhiIiIVLVKX/Lj3cDz7p457Glm04FN7p40s9cBewGvuPsmM+sws4MJBiJ8Ari8IlFPAFv7O3l649NELUp9rJ6B1ACxyOh3l0RtoojRiYiITDzluuTH9cBRQIuZrQT+3d2vBk5lxwEIRwBfNbN+IAWc5+7pQQyfZvslP/6IRo6WRPdAN7evvJ2ugW6iFqUv1cem3k1MrZ066mV+/47vA/Dpd366WGGKiIhMKGVJ2tz9tCHKz8hT9hvgN0NMvwR4Q1GDk0H6kn3cvuoOugd6eM+cdzO9voVt/V18n++xpW8LvcleaqO1I17u4iWLASVtIiIiozUWBiLIGHLPmntp723nyNYjmF7fAkBDPMHk2skkPcW9a+4j5akKRykiIjLxKGmTjA09G1ndtYYDWt5Ma8OsQe/FI3EmxZtY3bWGVzperVCEIiIiE5eSNsl4dvNzxCNx9mreK+/79bF6ptRM5rnNz1PA3cNERESkiJS0CQCd/Z28tvU19mrek5pofMjp9p2yL1v6trC2u62M0YmIiEilL/khY8Rzm18AYN/J+w473YKm+Ty64TGe3/w8sxIzC16+bhQvIiKya9TTJvQl+1javpT5TbvTEB/+emrRSJS9m/di5bZVdPR1lClCERERUdImvLr1VQZ8gP2n7FfQ9HtP3psIEZ7f8mLBdXzzz9/km3/+5mhDFBERmfCUtAmvda5kUnwSU+sKu3huIlbPvKZ5vNLxSsGX//j9k7/n90/+flfCFBERmdCUtE1wfck+2rramNs4Z+cTZ5nftDv9qX7WdmlAgoiISDkoaZvgVm1bjePMGWHSNisxk6hFWdG5okSRiYiISDYlbVVu9/m7Y2ajfnzz6m/SvqGdmY0zhp3urrvu4q677sq83uN1ezC7oZUVnSt1zTYREZEy0CU/qtxry1/j8fVPjGrelKd4dstzNNc081jb48NOe8+N9wBw4Tf+CYADpr+ZuY1zea1zBRt6NmZueTWU+pr6UcUoIiIiASVtE9i2gW2kPEVzfNKo5p/d0IphrOhcsdOk7Y8X/HFUdYiIiEhAh0cnsI6+DgyjMd44qvlro7XMSMxgRefKIkcmIiIiuZS0TVDuTkf/VpriTURs9LvBvMa5dPR30N7bPux0X/v91/ja77826npEREQmurIkbWZ2jZmtM7Ons8ouMbNVZvZ4+Dg+672LzWypmb1gZu/NKj82LFtqZv9SjtjHq75UH/2pfppG2cuWNrchGHW6ctuqYae7/bnbuf2523epLhERkYmsXD1t1wLH5in/H3c/IHzcCmBm+wOnAq8P5/m+mUXNLAp8DzgO2B84LZxWRmHbwDYAGuINu7ScRDxBc00za7vWFiMsERERGUJZkjZ3vxvYVODkJwI3uHuvu78KLAUOCh9L3f0Vd+8DbginlVHY1r+NqEWpjdTu8rJmJmbQ1r2OpCeLEJmIiIjkU+lz2s43syfDw6dTwrLZQPYVW1eGZUOV52Vmi8xsiZktWb9+fbHjrnqdA9toiDVgZru8rJn1M0l6ko09G4sQmYiIiORTyaTtB8AewAHAGuCyYi7c3a9094XuvnD69OnFXHTV60sG57M17uKh0bQZid0Ahr2l1bTGaUxrnFaU+kRERCaiil2nzd0zv/Bm9iMgfTfxVcDcrEnnhGUMUy4jkDmfLVacpK02WsvU2qms6VrLm6a9Me80v/n73xSlLhERkYmqoJ42M9vfzGaEzxvN7Ctm9u9mlhhtxWY2K+vlh4D0yNJbgFPNrNbMFgB7AQ8BDwN7mdkCM6shGKxwy2jrn8g6B4Lz2eqidUVb5szEDDZ0b2AgNVC0ZYqIiMh2hR4evR6YHD7/JnAEcDDww0JmNrPrgQeAfcxspZmdDXzdzJ4ysyeBdwL/BODuzwCLgWeBPwGfcfekuw8A5wN/Bp4DFofTyght699GQyxRlPPZ0mYmZpIixbru/OcPXvzbi7n4txcXrT4REZGJptDDo/Pd/QULfuVPIrjkRjfwaiEzu/tpeYqvHmb6S4FL85TfCtxaUMSSV3+qn75UH9NqpxZ1ubvVT8cw1natpbVh1g7vP/DyA0WtT0REZKIpNGnrMbMmgmTtNXffYGYxoHjH16QstvUX5/psueKRONPrW4YdjCAiIiKjV+jh0euAvwI/IbhQLsBbKbCnTcaObQNdRIhQH60v+rJn1M9gU+8m+lP9RV+2iIjIRFdQT5u7/5OZHQP0u/sdYXGK8Dw0qR5dA13Ux+qLej5b2m7103GcDd0bmJXnEKmIiIiMXsGX/HD3v5jZXDM72N0fdPclpQxMii/lKXqSPbTUleZ6aS11LQCs79kxaZszZU5J6hQREZkoCkrazGwewQjSAwAHGs3sZOBYdz+ndOFJMfUke3CcRGzUV2oZVk20hik1k/OOIP35OT8vSZ0iIiITRaHntP0Q+APQBKRPWLoNeE8pgpLS6BroAqC+REkbwPT66WzoWU/KUyWrQ0REZCIqNGk7CPgvd08R9LTh7u1Ac6kCk+LrGugmZjFqIvGS1TG9fjr9qQG29LUPKr/whgu58IYLS1aviIjIeFfoOW1twJ7Ai+kCM9sfeK0UQUlpdA10lezQaNr0+uA+r+u71zO1dkqm/PEVj5e0XhERkfGu0J62bwK/N7MzgZiZnQb8EvjvkkUmRTWQGqAv1UciVvxLfWRrjDVQH61j/RB3RhAREZHRKfSSH9eY2UbgU8AK4BPAl9z9phLGJkXUPdANUPKeNjNjev30IW9nJSIiIqMzkkt+3AzcXMJYpIS6kulBCKXtaYPgEOlrnSvKcjhWRERkoijo8KiZfcfMDs0pO9TM/rckUUnRdQ10UxutJWrRktc1vS59XtuGTNneM/Zm7xl7l7xuERGR8arQnrbTgItyyh4BbgIuLGI8UgLuTtdAF5Pik8pS39S6KUQtyvru9ezeNA+AKz9xZVnqFhERGa8KHYjgeaaNjmB+qaD+VD9JT5Z8EEJa1KJMrZ3Chp4NO59YREREClJo0nUP8B9mFgEI/14SlssY150MBiGU43y2tJa6Fjb1bs5cZHfRTxex6KeLyla/iIjIeFNo0nYB8G5gjZk9BKwmuBvCPxQys5ldY2brzOzprLJvmNnzZvakmd1oZpPD8vlm1m1mj4ePK7LmeZuZPWVmS8Pz7Ip/1/NxKD1ytC5aV7Y6W+qnkfQkm3s3A/Bi24u82PbiTuYSERGRoRSUtLn7SuCtwAeBb4R/3xaWF+Ja4NicstuAN7j7mwgu2ntx1nsvu/sB4eO8rPIfAOcCe4WP3GVKHt3JbuqidUSsfEez0zeP39CzsWx1ioiIjGcF/4q7e8rdH3D3X7n7g+EtrQqd925gU07ZX9x9IHz5IDBnuGWY2SxgUli3Az8lSB5lGO5O90AP9WXsZQNoiDVQF63TeW0iIiJFMuToUTN7zt33C5+vILznaC53n1eEOM4iuMNC2gIzewzoAP7N3e8BZgPZPXsrw7K8zGwRsAhg3rxihFidBnyAAR8o6/lsEFxkt6WuhQ3d6mkTEREphuEu+XFu1vPTSxWAmX0RGAB+ERatAea5+0Yzextwk5m9fqTLdfcrgSsBFi5cmDfhnAjS57PVR8ubtAG01E1j5baV9CZ7OWDuAWWvX0REZDwZMmlz93uzXj7j7kU/zmVmZwDvA94VHvLE3XuB3vD5I2b2MrA3sIrBh1DnhGUyjPTI0bpYeQ+PArTUB+e1bezZxP+e+r9lr19ERGQ8KfScttfM7FYzO93MGopRsZkdC3wB+IC7d2WVTzcLLttvZq8jGHDwiruvATrM7OBw1Ogn0G21dqp7oIfaSHnuhJBrWu00AJ3XJiIiUgSFJm3zgN8D5wFrzex6M3u/mRV0RwUzux54ANjHzFaa2dnAd4Em4LacS3scATxpZo8DvwbOc/f0IIZPA1cBS4GXgT8WGP+E1Z3spr4CvWwANdE4zTXNbOjZwOlXnc7pV5XsKLuIiMi4V1DSFR4a/T7wfTPbneC2VpcC1wDTC5j/tDzFVw8x7W+A3wzx3hLgDYXELDCQGqA/1V+R89nSWuqmsbJzFSs3F3p1GBEREclnNBfu2g2YAbQAW4oajRRVZhBCmUeOZmupa6E31UvSkxWLQUREZDwoKGkzs/3N7GtmtpTgJvEAH3T3vUoWmeyyzO2rKtzTBsH9T0VERGT0Cjo8CtxHcMjyU8AdI7mwrlRO90A3NZEaopHyD0JIm1w7mahF6U/1l/U2WiIiIuNNoUnbDHfvK2kkUnTdyfLfCSFXxCJMq5vKHrP2ZI/m11U0FhERkWpWaNLWb2bnEgxAaHH3N5nZEcBMd19cuvBktAZSSfpSfUytnVLpUGipa+HEwz/IqXucUulQREREqlahAxG+CpxNcIeB9D2hVgL/XIqgZNf1jIHz2dJa6lpIeYrNvZsrHYqIiEjVKjRpOwN4n7vfwPZ7kL4K6HjXGDUWRo6mtdRN4zs3fZuPXvnRSociIiJStQo9PBoFOsPn6aStMatMxpjuZDfxSJxYpNBNXDqJWIKuni4G+jWCVEREZLQK7Wm7FfiWmdUChLeR+hrwu1IFJrume6DygxDSzIx4NE5/aqDSoYiIiFStQpO2zwKzgHagmaCHbXd0TtuYlPQkvaneMXFoNC0eiZH0AXqTvZUORUREpCoVehurDuBDZjaDYCDCCndfW9LIZNR6BnqAsTEIIS0eiQPBzeNnN8yucDQiIiLVZ8ikzczy9cKtDx+Z93Wh3bEncyeEMdTTdsz+x/D0xmfY0L1RSZuIiMgoDNfTNsD2QQfDqdzl9iWv7oFuYhYjZpUfhJB2yfsv4XfL/sCGng2VDkVERKQqDferviDr+QnAycB/AsvZfj7bb0oXmoxWd7KH+lgdwXiRsaOlbhqvda7A3cdcbCIiImPdkEmbuy9PPzezzwIL3X1LWPSimS0BlgA/KGmEMiIpT9GT7GFSfHqlQxnkuG8fx7b+Ls79wCK29m9lUs2kSockIiJSVQodPdoMJHLKEmF5QczsGjNbZ2ZPZ5VNNbPbzOyl8O+UsNzM7DtmttTMnjSzt2bN88lw+pfM7JOF1j9R9CTDQQhj6Hw2gO6+blLJJAAbejZWOBoREZHqU2jS9hPg/8xskZkdZ2aLgD+H5YW6Fjg2p+xfgNvdfS/g9vA1wHHAXuFjEWFvnplNBf4deDtwEPDv6URPApk7IYyhkaNpsUhwnp3OaxMRERm5Qs9U/wKwFPgI0AqsAb4L/KjQitz9bjObn1N8InBU+PwnwJ0E58qdCPzU3R140Mwmm9mscNrb3H0TgJndRpAIXl9oHONdd7KbqEUzl9golUg0MrLz0k4I/hwy8zBeTLzI208+aMR1ztt9HsuXLd/5hCIiIuNQoddpSwFXhI9imuHua8Lna4EZ4fPZwIqs6VaGZUOVSyh9J4RSn+ifSqZ4fP0TBU9/9pVnA3DkkUeyoWcDj657jEjeq8oM7YDpbx7R9CIiIuPJmLkmhLu7mRVyiZGChIdwFwHMmzevWIsd09KDEFrqplU6lB0cse8RACRi9ThO90A3DfGGCkclIiJSPSqdtLWZ2Sx3XxMe/lwXlq8C5mZNNycsW8X2w6np8jvzLdjdrwSuBFi4cGHRksGxrDfZi+Nj8ny2Tx4RjBnpTwU3je9KKmkTEREZiZEdnyq+W4D0CNBPAjdnlX8iHEV6MNAeHkb9M3CMmU0JByAcE5YJY/NOCLnikTjxSJyuga5KhyIiIlJVhkzazOzBrOf/vqsVmdn1wAPAPma20szOBv4LeI+ZvQS8O3wNcCvwCsHghx8BnwYIByB8DXg4fHw1PShBgpGjESLURGoqHcoOzr7y7Mx5bYloPd1K2kREREZkuMOje5tZnbv3AJ8DvrIrFbn7aUO89a480zrwmSGWcw1wza7EMl51D3RTH6sf83cbqI8laO/vYCA1QCxS6SP0IiIi1WG4X8ybCe58sAyoN7O7803k7keUIjAZGXenO9nDtNqplQ5lpxpiwXWauwa6dGcEERGRAg13G6szzexwYD5wIHB1uYKSkcsMQhjD57OlpWNU0iYiIlK4YY9Nufu9wL1mVuPuI7n7gZRZZhDCGBw5mitiEeqidXSFd28QERGRnSv04rrXmNlRwCcILma7CviZu99RutBkJLoHujGM2mhtpUPJ65g3HjPodSKWYEvfFtx9zJ+DJyIiMhYUdMkPMzsHWExw14LfEtzG6nozO7eEsckIdCW7y3InhNH6yCEf4SOHfCTzOhGrJ+UpelO9FYxKRESkeozk3qPvcffMfYvM7JfAbxjB/UelNNyDOwxMHcODELr7wsO3NcHh20RmMEI3ddG6isUlIiJSLQq9uO404NmcsheAsZslTCA94SCExBgehHD+tedz/rXnZ17XRmqJWETXaxMRESlQoUnbvcC3zCwBYGYNwDeA+0sVmBSuOxkkPtUwcjTNzEhE63VnBBERkQIVmrSdB7wZaDezNmBL+PpTJYpLRiB9J4TayNgchDCURCxBd7KHlKcqHYqIiMiYV+jo0TXAEWY2B2gFVrv7ypJGJgXrqpI7IeSqD89r6x7QzeNFRER2ZkT3EAoTNSVrY0gsHqMn2UNL3bRKhzJiiayL7CppExERGZ5u/Fjl5u47L7wTQqLSoQzrA2/7wA5l8UiceCROV1IX2RUREdkZJW1VbsEbFgCQGON3QjjxbSfmLW+IJejs36aL7IqIiOzETgcimFnEzI42s5pyBCQj87o3vo6oRYlH4pUOZVibt21m87bNO5Q3xBoY8AH6U/0ViEpERKR67DRpc/cUcLO795UhHhmhBW94HYkqGIRw0S8u4qJfXLRDeSIWnMu2bWBbuUMSERGpKoVe8uNuMzu42JWb2T5m9njWo8PMLjSzS8xsVVb58VnzXGxmS83sBTN7b7Fjqib9qQFm7zmb+ujYPp9tOHXRWqIWUdImIiKyE4We07Yc+KOZ3QysADz9hrt/ebSVu/sLwAEAZhYluBH9jcCZwP+4+zezpzez/YFTgdcTXHrk/8xsb3dPjjaGarapZxORaGRM3wlhZ8yMRKyBbf26yK6IiMhwCk3a6oGbwudzShMK7wJedvflwxzqOxG4wd17gVfNbClwEPBAiWIa0zb0rAe238ezWjXEEmzt38pAaoBYRGNjRERE8in04rpnljoQgh6067Nen29mnwCWAJ9z983AbODBrGlWhmUT0rruDaxdtpY3vfWNlQ5llzRkzmvrorlmUoWjERERGZsKPacNM9vXzL5kZt8NX+9jZm8qRhDhyNQPAL8Ki34A7EFw6HQNcNkolrnIzJaY2ZL169cXI8wxxd3Z0LOBpY+/VOlQCnLKwR/mlIM/nPe9+lg9hum8NhERkWEUlLSZ2YeBewh6tT4RFjcB3ypSHMcBj7p7G4C7t7l7Mhy5+iOCQ6AQnPM2N2u+OWHZDtz9Sndf6O4Lp0+fXqQwx47O/k56kj289Fh1JG3vfdOxvPdNx+Z9L2IR6mP1dPUraRMRERlKoT1tXwXe7e7nAemT/p8guGl8MZxG1qFRM5uV9d6HgKfD57cAp5pZrZktAPYCHipSDFVlfc8GAJY+trTCkRRm7Za1rN2ydsj3G2INdCW7dfN4ERGRIRR61vduwJPhc8/66/knL5yZNQDvAT6VVfx1MzsgXP6y9Hvu/oyZLQaeBQaAz0zUkaMbujcQsxgrX1pR6VAK8sXFXwTg6kVX532/IZZgPcF5bU3xxjJGJiIiUh0KTdoeAT4O/DSr7FSK0Mvl7tuAaTllHx9m+kuBS3e13mq3vmcDLXXT8NQu581jQvqG8dv6O5W0iYiI5FFo0vaPwF/M7Gygwcz+DOwNHFOyyGRI/akBNvdu5g1TX1/pUIomalES0Xo6NRhBREQkr0Iv+fG8me0LvA/4PcEFdn/v7p2lDE7y29izEceZXtdS6VCKqiHeyPqe9SQ9SdSilQ5HRERkTCn4kh/u3gXcB9wJ3KOErXLWhxfVbakfX0lbY3iItEt3RxAREdlBQT1tZjYP+AVwMLAZmGJmDwKnu/vyEsYneWzo3sCk+CRqo7WVDqVgn3jHkKcpZjTEGjCMzoFOmmqayhCViIhI9Si0p+0nBIMRJrv7bsAUgjsV/KRUgUl+7s667vXsVl9d1547cr+jOHK/o4adJmIRErEEnbpem4iIyA4KHYjwNuAYd+8HcPdOM/tnYGPJIpO8NvdtoS/Vx4zEjEqHMiLL1i8DYP70+cNO1xhvoK17HQOpJLGIzmsTERFJK7Sn7UG235UgbSET9EbtldTW1QbAjPrdKhzJyHztxq/xtRu/ttPpGmLB5T50SysREZHBhuxpM7OvZr18GbjVzP5AMHJ0LnA8cF1pw5NcbV1tNMYbM9c1G28S4X1IO/s7dfN4ERGRLMMdHp2b8/q34d/dgF7gRqCuFEFJfu5OW/c65jbOqXQoJROxCA2xBjoHNDhZREQk25BJm7ufWc5AZOe2VOn5bCPVFG9kTfda+pJ91ERrKh2OiIjImFDoQATMLAHsCQy6x5C731/soCS/tVV6PttINcWbWNO9lq39nUyLTq10OCIiImNCoddp+wTwXaAP6M56y4F5JYhL8ljXvY6GWAONVXhvznOPPrfgaWujtcQjcbb2b2VanZI2ERERKLyn7evA37n7baUMRobm7rR1rWN24+xKhzIqB+95cMHTmhlN8Sa29G4h5SkiVvCNO0RERMatQn8N+whuXyUVsqVvC72pXmZW6aHR51c/z/Orny94+qZ4IylSdA3ollYiIiJQeNL2JeBbZja+bnZZRTLns1XpIIRv/P4bfOP33yh4+vQh4K39GkUqIiIChSdtLwIfANrMLBk+UmaWLEYQZrbMzJ4ys8fNbElYNtXMbjOzl8K/U8JyM7PvmNlSM3vSzN5ajBjGutXbVjOpZlJVns82GlGL0hBrYGv/1kqHIiIiMiYUmrT9DPgp8GZg7/CxV/i3WN7p7ge4+8Lw9b8At7v7XsDt4WuA48K69wIWAT8oYgxj0kBqgLXdbbQmWisdSlk1xZvoSfbQn+qvdCgiIiIVV+hAhGnAl93dSxlMjhOBo8LnPyE4p+6fw/KfhrE8aGaTzWyWu68pY2xl1dbdRspTzG6YVelQyqop3sjabujo0yhSERGRQnvafgx8vIRxOPAXM3vEzBaFZTOyErG1QPpkrtkEt9JKWxmWjVurt60halF2q9JBCKNVF62jJhKno7+90qGIiIhUXKE9bQcB55vZF4G27Dfc/YgixHG4u68ys92A28xs0DBDd3czG1EvX5j8LQKYN6+6LyW3ettqZtTPIBYp+FrIY84/vPcfRjyPmTGpppmNPRtJpopy+qSIiEjVKjQL+FH4KAl3XxX+XWdmNxIkiW3pw55mNgtYF06+isH3RZ0TluUu80rgSoCFCxeW87BuUW3t20pH/1b2nlzM0wfL74DdDxjVfM3xSWzo2UCHBiSIiMgEV1DS5u4/KVUAZtYARNx9a/j8GOCrwC3AJ4H/Cv/eHM5yC0Gv3w3A24H28Xw+2+quYNVmN1T3IITHlz8OjDx5S8QSxCxGR58OkYqIyMRW6G2szhrqPXe/ZhdjmAHcaGbpeK5z9z+Z2cPAYjM7G1gOnBJOfytwPLAU6ALG9Y3tV29bQ2O8kaZ4U6VD2SWX//lyAK5edPWI5jMzmmsmsal3M/HaeClCExERqQqFHh7NHYQwE9gDuA/YpaTN3V8huJRIbvlG4F15yh34zK7UWS0GUgOs6VrDHpNeR5jUTkiTaprZ2LuJNx7+xkqHIiIiUjGFHh59Z25Z2Pu2X9EjkozV21aT9CTzmqp7IMWuaow1ELUoC99zYKVDERERqZhduRP3tcDZRYpD8lje+Rq10VpmTLBLfeQyMybFm3jLu96qUaQiIjJhFZS0mVkk59FIcDmNLSWNbgJLppKs2raKuQ1ziNiu5Nbjw+TayTRMamDlth0GCouIiEwIhZ7TNkBwAdxsq4BzixuOpK3uWkN/amDcHBr9/Ps+v0vzN8Ya2dS2iVcaXmX3cdImIiIiI1Fo0rYg5/U2d99Q7GBku9c6V1ATqWFmYsbOJ64C+7buu0vzmxkP/O5+pp0zjZ6BHupidUWKTEREpDoUdNzN3ZfnPJSwlVDSk6zsXMmcxtlELVrpcIriwaUP8uDSB3dpGffedC+Os2zr8iJFJSIiUj2G7WkzszvY8bBoNnf3HS7LIbumrauNvlQf8xrHz2HAH/01uKHGwXsePOplrHppJVNqp/BKx6vsO2WfYoUmIiJSFXZ2ePTnQ5TPBv4RSBQ3HAF4peNVaiI1tCZmVTqUMed1kxbwyPpHae9tp7m2udLhiIiIlM2wh0fd/ersB3ATwbXZPgf8FqjuG2KOQX3JPl7rXMH8pt2JRsbHodFiWtA0H8NY2vFypUMREREpq0Iv+THJzL5GcOuoGcBb3X2Ru68saXQT0LKty0l6kj2b96h0KGNSfayeeY1zWdr+MgOpgUqHIyIiUjbDJm1mVm9mFwOvEPSwHe7uH3d3dXOUyNKOl5lcM5mptVMrHcqYtc/kfehL9fFqx7JKhyIiIlI2OzunbRlBYvd1YAkww8wGXYPC3f9amtAmns29W9jYs5GF09827u41+qUPfaloy9qtfjpTaqfw/JYX2LN5j3HXViIiIvnsLGnrJhg9+vdDvO/A64oa0QT2csfLGMaCpvmVDqXo5k+fX7RlmRn7TN6bB9v+Rlv3unFzLTsREZHhDJu0ufv8MsUx4SVTSV7peJW5jXPG5YVj73ruTgCO3O+ooixvQdN8Hl3/GC9seUFJm4iITAiF3hFBSuyVra/Sm+xl78njc0DuT+/5GVC8pC0WibFX8548u/k5Ovo6mFQzqSjLFRERGasqeidyM5trZneY2bNm9oyZXRCWX2Jmq8zs8fBxfNY8F5vZUjN7wczeW7noi8fdeW7zc0ytncLMevUaFWq/KfsSsQhPbXy60qGIiIiUXEWTNoIb0X/O3fcHDgY+Y2b7h+/9j7sfED5uBQjfOxV4PXAs8H2z6r/P06ptq2nv62D/KfvppPoRqI/Vs/fkvXh16zI6+joqHY6IiEhJVTRpc/c17v5o+Hwr8BzB3RaGciJwg7v3uvurBNeNO6j0kZbWs5ufIxFLsHvT7pUOpeq8fsr+RCzCkxufqnQoIiIiJVXpnrYMM5sPvAX4W1h0vpk9aWbXmNmUsGw2sCJrtpUMn+SNeRt7NtLW3cZ+k4NDfTIy9bF69pm8N8u2LqddvW0iIjKOjYkswcwagd8AF7p7B/ADYA/gAGANcNkolrnIzJaY2ZL169cXM9yienLjU8QjcfZs3rPSoZTUpadcyqWnXFqSZe8/ZX+iFuWxDY+XZPkiIiJjQcWTNjOLEyRsv3D33wK4e5u7J909BfyI7YdAVwFzs2afE5btwN2vdPeF7r5w+vTppVuBXdDWtY6V21bx+qn7UxONVzqckpo5eSYzJ88sybLrY3W8YdrrWdG5glXbVpekDhERkUqr9OhRA64GnnP3b2WVz8qa7ENAenjgLcCpZlZrZguAvYCHyhVvMbk7j254jESsnv0m71vpcEruz0/+iT8/+aeSLX//yfsxKd7Ew+seJplKlqweERGRSql0T9thwMeBo3Mu7/F1M3vKzJ4E3gn8E4C7PwMsBp4F/gR8xt2r8hd6RecKNvRs4E3T3kQsMv4vl7f4wV+x+MFflWz50UiUA3c7kK39nTyz+dmS1SMiIlIpFc0W3P1eIN81Lm4dZp5LgdKcHFUmSU/y2IbHaa6ZxB6TdBewYmltmMXujfN4etMz7N44j+ba5kqHJCIiUjSV7mmbkJ7e9Awd/Vt5W8tbNWK0yBbutpBYJMbda+5hIDVQ6XBERESKRhlDkew+f3fMbKePOXvP5dE1j/HA7+5nTtOcguYZ7iGDJWL1HDbzULb0tbNk/SOVDkdERKRoxv/JVGXy2vLXeHz9E8NO4+4s7XiZvlQfZ3/8bD71yU/tcr0HTH/zLi9jvJnd0Mrrp+zPM5ufZWZiBvOb5lc6JBERkV2mpK2M1vdsoDvZzbyGuRNi8EG2b37sm2Wt74CWN7Ouex33rX2A+mg9MxK6p6uIiFQ3HR4tk87+TtZ2r6U5Ponmmol3gvyUhilMaZiy8wmLJGIR3jn7KJrijdyx+k429WwqW90iIiKloKStDPpS/bzWuYLaSC1zGudMyHPRbn7kZm5+5Oay1lkbreVds48mHqnh9lV3KHETEZGqpqStxFKe4rWty0l5it2b5hG1aKVDqohbHrmFWx65pez1NsQbePecdxGxCH9ecRsrO/PeQENERGTMU9JWQilPsbzzNbqS3cxtnENdtK7SIU1IzTWTOG7ee5lUM4k7V9/FM5ueIeWpSoclIiIyIhPrbPgycndWdK5ga/9WZidmT8jz2IotEo3s0qHlmvpaFv3Xp/BjncW3/Yqr/vVHrH5l5/cqnbf7PJYvWz7qekcr5Sm6B7rpTfXRn+zL/O0Prz9nZhiW+VsTqaE2WkNNtIbaSC010ZoJN+BFRGQ80zd6CaQ8xYptK2nv72BWYhbT6qZWOqRxIZVM7fSyKjvj7mzp20L0rVH++4/fYGrtVKbXtVATrRlynlJfViWZStLRv5X2vnbae9uDv33tdPRv3eUewZpIDQ3xBhpiDTTEEzTEGmiKN9FcO4mmeJMu7iwiUkWUtBVZf6qf5VuX05XsZlb9TKbXtVQ6JMliZkypnUJjvJG13W1s7N3Ipt5NTK5pZnLtZBpjjSUbKNKfGqCjL52UdbClt52Ovna29nfieGa6xngjzTWT+N3Pf8dLT75I55ZOtm3toqt9G9u2dtGzrRuASCQS9j5GiMajJBrraZjcSGNzAw3NjTRObmTqzKlMmzWNaa3TmDarhYbmhkw9A30DtL3WxuqXV/Ha86/x6tOv8OrTy5jSNLkiPYsiIjI8JW1F1DXQxfLO1xhIDQT3vtQh0YzvnvHdSocwSDwSZ27DHGbU7cb6nvVs7tvC5r4txCxGY9gzVR9LUDtMD1w+A6kBOvu30dm/la39nXT2d9LRF/SibRvYlpnOMCbVNDG5djK7N+1Oc01wKZhJNZMyhzTf9aWjd7lnMVcylaQ31Utvspeeul6m7jeVBXsv4MD39mWm2bBqA3etvpupdVOZVjuNaXVTqY3WFjUOEREZOSVtRZD0JCdf+GGWdrxMPBJnj0mvIxFLVDqsMaW+pr7SIeRVE61hdsNsZiVmsbV/K1v62uns38aWvvbMNN+597t87cZL2dbeSdfWLlIpJ5VMUVMXp74pQUNTgvqmBInGehqnNA1afs+2nqA3a+kqVr28itUvr2b10lW0vdZGciBZ7tUlGomSiCR22D+TnqR7oIfugS4eePR+FsxfwGudKzLvN8WbmFY3jZa6abTUtTC1dgrRyMQcCS0iUilK2nZRylP8+bXb+MDfn8iUmim0JmbpxyyPXz7wSwA+cshHKhxJfhGL0FzTTHNNM+5Of6qf7mQ3vcle7rrrTt532vtJepKkJ8HBcSIWIWoRIhYlalGiFiEWiW8fEBCpITolis01OGzkMZXzFmVRi9IYb6Ax3sAPLvo+3//c9+hN9rKpdzMbezayoWcjbV1tLNu6DIAIEabUTg4SuLqpTK6dTHNNM/EJMPDB3RnwAXqTvfQl++hL9YV/+zP7SNKTpFIpkp7cPmAka9AIBhGMiEWJRWLEIzFiFiceiRGPxMOyODXRmgl7mSAR2dH4/4YtsYhFeN2kBfzbyV/kx7/8caXDGbP+8tRfgLGbtGUzM2rCUZgAV3/xKv5h0T9UOKryq43WMisxk1mJmZmyrv4uNvRsZEPPBjb0bOTljld4of3FzPuN8UYmh4d5G+MNJGINNMQSNMQbqInUjOkLS6c8RW+yj55kNz0DPXQnu+ka6KY7/ch6nfTCekmNYH2zz1kcqdpILXWxOupj9dRH64Ln0XrqY3UkYolMG+s/iyLjX1UmbWZ2LPBtIApc5e7/Vcl49p2yD4/99dFKhiBSNCO5tIpFjN3m7sacveYyZ+85zN5zNnP2msOM+TOJ18QHTdvX00dneydd7dvo2trFtvBvX08f/b391NfV8+nzPh30WkaC3suIRQb1UEXMMLaXRdLvpafDACeFk/IU7ilSYc/YQKqf/tQA/al++lP9wXl9yR4efeox6ifVM2nqJCLRHUfTdnd2s2X9Fjav20z7ui1sWb+F9g3tmXXpbN/GtvZtdHduX5eBvgEG+gdwH5ysWcSIRCLU1NWQHEgSq4lRl6ijrqGOuoZ66hvqqE3UUddYR31DPU1Tmmhuad7+mD6Z5pZmaut3PMewY1MHm9ZsYlPbJjat2Zh5vnHNRjav3cSmtZtobW3VIBORKlZ1SZuZRYHvAe8BVgIPm9kt7v5sZSMTGR+KdWmVAd+eIPWl+umv6yfZnCSZSpLyJAMe/E254zhdXV08seFJIpHSXoYklUrRs62Hbe3b6NjYTvvGDt593LuIWYxYJEYsEidm4SHLSIzo1CjMK24MB0x/86jb2N1JkaI/FSSifWEbT501ldkzZmfaPF9vYPuGdv6w/I8kYgkaYgkS8QSJWD3xSHA4vyYaHN6PR2qIR2Jl7RlNr1fKd3x4mISnPJWTkKfCeRwPp4Pth6DTaXzwentajxkRIplTHCxzqkMkU579SP8nQaTSqi5pAw4Clrr7KwBmdgNwIqCkTWSMMDPiFiceie984tABe7+Zx9Y9jqf/uWeuU5c+vBiU55R41nuw/Ucasnrosn6EiWAt23+AD5j+Zs5Y/8ldXudyMTOiRIlGozDMqN6kJ7N6FvvoT/Vzx+1/Zb8z96Ozfytt3W30p/qHrgcLz6+LEgl7PaOZhCaaSXjStm+F7dLb8LHHHyXpKWLxWPiIEs08Dx81Y/vnKDuhi1puYhcNz1Ec3DaR8FzX3L/pXuRI1vPsv+lEslCOg3vWZye9JZyUO9tfBdOlghNzB33WBn/Gtr/ensRauG5Bb3d2smtDJLuZnnAiOb3lFs5jgy4SXgy5vdvZ65W9zulp83+35LRPMHFm355UM3jAWTmN7U9JfrOBFVmvVwJvr1AsIlJEmRP1AdSxsUuiFiR2dVmJ3U++ci0//vI1mdd1DXU0tzSTmNRAorGexKQEiaYG6pvqaZjUQKIpQU19zaDEavvzOLF4cB5d5nfS0z/46ecw0NfPQP8A73j3EXkHZeQri1g69c5+n7zTBj1omakzP8EAH33PaZCeL2JYJEIk/BuLRYnGo8TisSCBjA1OJKOxaGZ9o7Ho9vJ4lGgs3Qbp59nvx6ipjYfzx4nVxIjn+Ss7sgI+9LtyfmgxRC3KR/c6tWL1W25WOtaZ2cnAse5+Tvj648Db3f38nOkWAYvCl/sAL5Q1UGgBNpS5TtU/tmJQ/doHJnr9YyEG1a99oBrr393dp+cWVmNP2ypgbtbrOWHZIO5+JXBluYLKZWZL3H2h6q+cSseg+rUPTPT6x0IMql/7wHiqvxpvPPgwsJeZLTCzGuBU4JYKxyQiIiJSUlXX0+buA2Z2PvBngkt+XOPuz1Q4LBEREZGSqrqkDcDdbwVurXQcO1GxQ7OqP6PSMaj+yqt0DBO9fqh8DKq/8iodw7ipv+oGIoiIiIhMRNV4TpuIiIjIhKOkrQjMbJmZPWVmj5vZkrBsqpndZmYvhX+nFLnOa8xsnZk9nVWWt04LfMfMlprZk2b21hLVf4mZrQrb4XEzOz7rvYvD+l8ws/cWof65ZnaHmT1rZs+Y2QVheVnaYJj6y9kGdWb2kJk9EcbwlbB8gZn9Lazrl+GAHcysNny9NHx/fonqv9bMXs1qgwPC8qLvh+Fyo2b2mJn9PnxdlvUfpv5yr3/B3z8l+i7IV385PweTzezXZva8mT1nZoeUef3z1V/O9d8nq57HzazDzC4sVxsMU3852+CfLPgOetrMrrfgu2mBlel7YIj6S/M94OmLIeox6gewDGjJKfs68C/h838B/rvIdR4BvBV4emd1AscDfyS4XOnBwN9KVP8lwEV5pt0feAKoBRYALwPRXax/FvDW8HkT8GJYT1naYJj6y9kGBjSGz+PA38J1WwycGpZfAfx9+PzTwBXh81OBX5ao/muBk/NMX/T9MFzuZ4HrgN+Hr8uy/sPUX+71X0aB3z+liGGI+sv5OfgJcE74vAaYXOb1z1d/2dY/Z/lRYC2weznbYIj6y9IGBBfcfxWoD18vBs4o1/fAMPVfSwm+B9TTVjonEnyYCf9+sJgLd/e7gU0F1nki8FMPPAhMNrNZJah/KCcCN7h7r7u/CiwluB3ZrtS/xt0fDZ9vBZ4j+PCUpQ2GqX8opWgDd/fO8GU8fDhwNPDrsDy3DdJt82vgXWajv3fMMPUPpej7oZnNAU4ArgpfG2Va/3z170TR138ndZXlu2AUcRXtc2BmzQT/gbwawN373H0LZVr/YeofStG/B3K8C3jZ3ZdTmX0gu/6hlKINYkC9mcWABLCGMn4P5Kl/9TDT7lL7K2krDgf+YmaPWHAnBoAZ7r4mfL4WmFGGOIaqM9+tv4ZLMHbF+WGX7zW2/ZBwSesPu7ffQtDTU/Y2yKkfytgGFhyaexxYB9xG8L/WLe4+kKeeTAzh++3AtGLW7+7pNrg0bIP/MbP0fZRK0Qb/C3wBSIWvp1HG9c9Tf1q51h9G9v1Tihjy1Q/l+RwsANYDP7bgEPVVZtZA+dZ/qPqhAt+FBD1H14fPK/F7kF0/lKEN3H0V8E3gNYJkrR14hDJ9D+Sr393/Er5d9O8BJW3Fcbi7vxU4DviMmR2R/aYHfaJlHaZbiTqBHwB7AAcQ7LyXlbpCM2sEfgNc6O4d2e+Vow3y1F/WNnD3pLsfQHBnkIOAfUtZ387qN7M3ABeHcRwITAX+uRR1m9n7gHXu/kgplr8L9Zdl/bNU+vsnX/3l+hzECE7T+IG7vwXYRnAoMKPE6z9U/ZX4LqwBPgD8Kve9Mn0X5tZfljYIk8ETCRLoVqABOLYUdRVav5mdTom+B5S0FUGYaePu64AbCX4829JdnuHfdWUIZag6C7r1165y97bwRzwF/IjtXd4lqd/M4gQJ0y/c/bdhcdnaIF/95W6DtPCQzB3AIQTd7elrMGbXk4khfL8Z2Fjk+o8NDx27u/cCP6Z0bXAY8AEzWwbcQHA45NuUb/13qN/Mfl7G9QdG/P1T9Bjy1V/Gz8FKYGVWD++vCZKocq1/3vor9D1wHPCou7eFr8v9ezCo/jK2wbuBV919vbv3A78l+GyW63sgX/2Hlup7QEnbLjKzBjNrSj8HjgGeJri11ifDyT4J3FyGcIaq8xbgE+GolYMJum/X5FvArsg5Lv8hgnZI13+qBaN2FgB7AQ/tYl1GcB7Jc+7+ray3ytIGQ9Vf5jaYbmaTw+f1wHsIzq27Azg5nCy3DdJtczLw1/B/4MWs//msHwojOI8kuw2Ktg3c/WJ3n+Pu8wkOy/zV3T9GmdZ/iPpPL9f6h3WM9Pun2J+DvPWX63Pg7muBFWa2T1j0LuBZyrT+Q9Vfzu+BLKcx+NBkuX8PBtVfxjZ4DTjYzBLhZy69D5Tle2CI+p8r2feAF2nUyER9AK8jGAnzBPAM8MWwfBpwO/AS8H/A1CLXez1Bl3M/wf/2zh6qToJRKt8jON/pKWBhier/Wbj8J8Mdc1bW9F8M638BOK4I9R9O0N3/JPB4+Di+XG0wTP3lbIM3AY+FdT0NfDlrn3yI4ATfXwG1YXld+Hpp+P7rSlT/X8M2eBr4OdtHmBZ9P8yK5Si2j94sy/oPU3/Z1p8Rfv+U4HMwVP3l/BwcACwJ67oJmFKu9R+m/rKtf7jMBoLeouassnK2Qb76y7kPfAV4PvzM/YxgZGrZvgeGqL8k3wO6I4KIiIhIFdDhUREREZEqoKRNREREpAooaRMRERGpAkraRERERKqAkjYRERGRKqCkTUSGZGYfM7O/7HxKMLMzzOzeEsayj5k9bmZbzewfC5zHzWzPEsWzzMzeXYTldJrZ64oRU9YynzGzo4q5TBGpPCVtIuOMmV1sZn/MKXtpiLJTh1uWu//C3Y8pUlx3mtk5u7CILwB3uHuTu3+nBMuvCHdvdPdXirzM17v7ncVcpohUnpI2kfHnbuBQM4tC5srkceAtOWV7htNWi90JLuAqY0TWbYJEpAyUtImMPw8TJGkHhK/fQXBLlxdyyl5299Vm1mxmV5vZGjNbZWb/kZXcDTrkaWbHmNkLZtZuZt83s7tye7fM7JtmttnMXjWz48KyS8M6vxseDvxuvsDN7APhob0tYc/ZfmH5X4F3Zs2/d858wy3/3WGv4hYz+154W5n0fGeZ2XNhvH82s92HalQz+7iZLTezjWb2xZz3Imb2L2b2cvj+YjObGr73RzM7P2f6J8zspPB55hCumdWb2WVhPe1mdq8FtwjDzA42s/vD9XhiuMOf2YduzeySMJ6fhoeWnzGzhcPMO+Q2DveH+8zsf8xsI3CJme1hZn8N13uDmf3CwtubZcXyeTN70sy2hfvajLBdtprZ/1lw023MrM7Mfh4ua4uZPWxmM4aKVWSiUdImMs64ex/wN+CIsOgI4B7g3pyydC/btcAAQc/bWwjuH7nDYUYzayG4IfbFBLfIeQE4NGeyt4flLcDXgavNzNz9i2EM54eHA8/PmY8wEbseuBCYDtwK/M7Matz96Jz5X8xZ5+GW/z7gQILbbp0CvDes70TgX4GTwvruYfC9G7Nj2x/4AfBxoDVc/zlZk/wDwf0Fjwzf30xwqxrCZZ6Ws6zdgT/kqeqbwNsI2nUqwSHhlJnNDqf/j7D8IuA3ZjY9X7x5fIDgpvaTCW4pNFTSXOg2fgWYAVxKcFue/wzXez+Cm2FfkjPP3xHcm3Zv4P3AHwnafjrB71D6HMVPEtzAe25Y/3lAd4HrKDLuKWkTGZ/uYnuC9g6ChOSenLK7wl6M44EL3X2bu68D/ofgBui5jgeecfffuvsA8B1gbc40y939R+6eBH4CzCL4cS/ER4A/uPtt7t5PkMDUs2PSMFL/5e5b3P01gh7HA8Ly84D/dPfnwvX5f8ABQ/S2nUxwb9G73b0X+BKQynr/PIL7bq4M378EONmCw4c35iz3Y8Bvw+kyzCwCnAVc4O6r3D3p7veH050O3Orut7p7yt1vI7jf5fEFtsG94bxJgnsjvnmI6QrZxqvd/XJ3H3D3bndfGm6zXndfD3yLIHnNdrm7t7n7KoL98G/u/pi794Tt85Zwun6CZG3PcP0fcfeOAtdRZNxT0iYyPt0NHB4eopvu7i8B9xOc6zYVeEM4ze4Eh1LXhIejtgA/BHbLs8xWYEX6hQc3Ll6ZM83arPe7wqeNBcbcCizPmj8V1je7wPmHkp10dGXFszvw7az13kTQa5Svvtx130Zwg+y03YEbs5b1HJAEZrj7VoJesnQifBrwizx1tBDczPrlPO/tDnw4vfywjsMJkuJC5LZBneU/H62Qbbwi+0V4qPMGCw6tdxDcHLslZ562rOfdeV6nt8nPgD8DN5jZajP7upnFh181kYlDSZvI+PQAwWGmc4H7AMIei9Vh2Wp3f5XgB7gXaHH3yeFjkru/Ps8y15B1SDA8N2xOnumG4jt5fzVBcpK9/LnAqiItP9cK4FNZ6z3Z3evd/f48064JY0nHliDoEcpe1nE5y6oLe5YgPERqZocQJGZ35KljA9AD7DFErD/LWX6Du//XCNd5ZwrZxrnt/P/Csje6+ySCXkFjFNy9392/4u77E/Swvg/4xGiWJTIeKWkTGYfcvZvg8NlnCQ5Hpd0blt0dTrcG+AtwmZlNCk+o38PMcg9vQdBb9EYz+2DYS/MZYOYIwmoDhrse2WLgBDN7V9i78jmChDJfEjWa5ee6ArjYzF4PYMGAjA8PMe2vgfeZ2eFmVgN8lcHfn1cAl6YPgZrZ9PCcubRbCRLSrwK/DHsRBwnLrgG+ZWatZhY1s0PMrJag9+r9ZvbesLzOzI4ys5EkzYUYzTZuAjqB9vDcu8+PtnIze6eZvdGCgTAdBIdLd2grkYlKSZvI+HUXwWHO7Ave3hOWZV/q4xNADfAswQn0vybPYTd33wB8mGCAwUZgf4LEsDd32iF8m+A8r81mtsN11tz9BYJemssJep3eD7w/HFixy8vPU9+NwH8THIrrAJ4Gjhti2mcIEpjrCHqjNjP4sOG3CU7w/4uZbQUeJDhhPz1/L/Bb4N3hMoZyEfAUwQjgTWF8EXdfAaQHTqwn6Hn7PEX+Dh/lNv4K8FagnSDp++0uhDCTYP/rIDjEfBfBIVMRASw4ZUFEZGTCE+dXAh9z93yH+6TKaRuLjC3qaRORgoWH5yaHh+z+leDcpQcrHJYUkbaxyNilpE1ERuIQgtGN6cOXHwzPn5PxQ9tYZIzS4VERERGRKqCeNhEREZEqoKRNREREpAooaRMRERGpAkraRERERKqAkjYRERGRKqCkTURERKQK/H+2oUQW8J6sNAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"histogram_boxplot(data=df.dropna(), feature='weight', step=50, fmt='{:.0f}',\\n                  xlabel='Weight of the device in grams')\";\n",
              "                var nbb_formatted_code = \"histogram_boxplot(\\n    data=df.dropna(),\\n    feature=\\\"weight\\\",\\n    step=50,\\n    fmt=\\\"{:.0f}\\\",\\n    xlabel=\\\"Weight of the device in grams\\\",\\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": [
        "# Tablets\n",
        "wt = df[['weight']].dropna()\n",
        "wt['type'] = wt['weight'].apply(lambda x: 'tablet' if x > 250 else 'phone')\n",
        "histogram_boxplot(data=wt[wt['type']=='tablet'], feature='weight', step=50,\n",
        "                  title='Weight of tablets in grams', fmt='{:.0f}',\n",
        "                  xlabel='Tablet weight, g')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "outputId": "c555c05c-242c-4592-97c7-8667eccdbf94",
        "id": "z2I23OppSoO_"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"# Tablets\\nwt = df[['weight']].dropna()\\nwt['type'] = wt['weight'].apply(lambda x: 'tablet' if x > 250 else 'phone')\\nhistogram_boxplot(data=wt[wt['type']=='tablet'], feature='weight', step=50,\\n                  title='Weight of tablets in grams', fmt='{:.0f}',\\n                  xlabel='Tablet weight, g')\";\n",
              "                var nbb_formatted_code = \"# Tablets\\nwt = df[[\\\"weight\\\"]].dropna()\\nwt[\\\"type\\\"] = wt[\\\"weight\\\"].apply(lambda x: \\\"tablet\\\" if x > 250 else \\\"phone\\\")\\nhistogram_boxplot(\\n    data=wt[wt[\\\"type\\\"] == \\\"tablet\\\"],\\n    feature=\\\"weight\\\",\\n    step=50,\\n    title=\\\"Weight of tablets in grams\\\",\\n    fmt=\\\"{:.0f}\\\",\\n    xlabel=\\\"Tablet weight, g\\\",\\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": [
        "# Phones\n",
        "histogram_boxplot(data=wt[wt['type']=='phone'], feature='weight', step=10,\n",
        "                  title='Weight of phones in grams', fmt='{:.0f}',\n",
        "                  xlabel='Phone weight, g')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "j6QumWTrVT3-",
        "outputId": "a7fdb1fe-b80c-45ad-dd0d-731ef1f979cc"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"# Phones\\nhistogram_boxplot(data=wt[wt['type']=='phone'], feature='weight', step=10,\\n                  title='Weight of phones in grams', fmt='{:.0f}',\\n                  xlabel='Phone weight, g')\";\n",
              "                var nbb_formatted_code = \"# Phones\\nhistogram_boxplot(\\n    data=wt[wt[\\\"type\\\"] == \\\"phone\\\"],\\n    feature=\\\"weight\\\",\\n    step=10,\\n    title=\\\"Weight of phones in grams\\\",\\n    fmt=\\\"{:.0f}\\\",\\n    xlabel=\\\"Phone weight, g\\\",\\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 weight distribution has many outliers on both sides\n",
        "* Heavy devices (>250g) might be considered tablets (to be verified together with the screen size)\n",
        "* Heavier devices (tablets):\n",
        "  * The distribution is multimodal, right-skewed\n",
        "  * The weight is approximately 400g on average (median is ~350g)\n",
        "  * The range is from 250g to 850g (250g is the assumed threshold)\n",
        "* Lighter devices (phones):\n",
        "  * The distribution is bell-shaped, close to normal, with many outliers on both sides\n",
        "  * The weight is approximately 160g on average\n",
        "  * The range is from 69g to 250g (as the assumed threshold)"
      ],
      "metadata": {
        "id": "ukfVpEv1SoPA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`screen_size`**"
      ],
      "metadata": {
        "id": "lnAh2FL1Uv9Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "histogram_boxplot(data=df, feature='screen_size',\n",
        "                  xlabel='Screen size, cm', fmt='{:.0f}',)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "outputId": "4b8cfe01-13d9-46ca-9217-2bd86a5acd1e",
        "id": "6ZiHd-KaUv9Y"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"histogram_boxplot(data=df, feature='screen_size',\\n                  xlabel='Screen size, cm', fmt='{:.0f}',)\";\n",
              "                var nbb_formatted_code = \"histogram_boxplot(\\n    data=df,\\n    feature=\\\"screen_size\\\",\\n    xlabel=\\\"Screen size, cm\\\",\\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": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "si = df[['screen_size']]/2.54\n",
        "si['screen_size'] = si['screen_size'].map(int)\n",
        "labeled_barplot(data=si, feature='screen_size',\n",
        "                perc=True, n=None, percfmt=\"{:.1f}%\", sort=False,\n",
        "                figsize=(10, 5), xlabel='Screen size, in', xlo=0,\n",
        "                title='Screen size in inches')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "w-FUzXiyee8O",
        "outputId": "39ac301c-7edb-4264-9282-cfd526c4c918"
      },
      "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": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"si = df[['screen_size']]/2.54\\nsi['screen_size'] = si['screen_size'].map(int)\\nlabeled_barplot(data=si, feature='screen_size',\\n                perc=True, n=None, percfmt=\\\"{:.1f}%\\\", sort=False,\\n                figsize=(10, 5), xlabel='Screen size, in', xlo=0,\\n                title='Screen size in inches')\";\n",
              "                var nbb_formatted_code = \"si = df[[\\\"screen_size\\\"]] / 2.54\\nsi[\\\"screen_size\\\"] = si[\\\"screen_size\\\"].map(int)\\nlabeled_barplot(\\n    data=si,\\n    feature=\\\"screen_size\\\",\\n    perc=True,\\n    n=None,\\n    percfmt=\\\"{:.1f}%\\\",\\n    sort=False,\\n    figsize=(10, 5),\\n    xlabel=\\\"Screen size, in\\\",\\n    xlo=0,\\n    title=\\\"Screen size in inches\\\",\\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 screen size distribution is right-skewed, multi-modal\n",
        "* Screen size is approximately 13.7 cm on average\n",
        "* The range is 5.1cm - 30.7cm (2\" - 12\")\n",
        "* The most frequent (~41%) screen size is 12.7cm (5\")\n",
        "* There are outliers below and above the overall pattern in a distribution\n",
        "* Gaps on the histogram are caused by the conversion of inches to cm (screen sizes are usually given in inches)\n",
        "* The distribution of screen size in inches is right-skewed, close to normal\n",
        "* Devices with a screen size of more than 6.7\" (17 cm) are highly likely tablets"
      ],
      "metadata": {
        "id": "-5156mGNZcLN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`days_used`**"
      ],
      "metadata": {
        "id": "a3Vv0-cU_Tkf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "histogram_boxplot(data=df, feature='days_used', xlabel='Days used', fmt='{:.0f}', figsize=(12, 5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "outputId": "7063cbb1-f74d-4e64-bff9-b2594907a4c0",
        "id": "cnSUYMb6_Tkg"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"histogram_boxplot(data=df, feature='days_used', xlabel='Days used', fmt='{:.0f}', figsize=(12, 5))\";\n",
              "                var nbb_formatted_code = \"histogram_boxplot(\\n    data=df, feature=\\\"days_used\\\", xlabel=\\\"Days used\\\", fmt=\\\"{:.0f}\\\", figsize=(12, 5)\\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 `days_used` distribution seems to be close to uniform distribution\n",
        "* The range is between 91 and 1094 days (approx. 3 years)\n",
        "* There are two subsets: devices used less than 1.5 years and more\n",
        "* Most of the devices were in use for about 1.7 years\n",
        "* Least of the devices were in use for less than six months\n",
        "* The `days_used` column seems to be negatively correlated with the `release_year`"
      ],
      "metadata": {
        "id": "g6EYdfCA_Tkg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`main_camera_mp`**"
      ],
      "metadata": {
        "id": "oU7QS3ZlUwWN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "histogram_boxplot(data=df.fillna(0), feature='main_camera_mp', fmt='{:.0f}',\n",
        "                  xlabel='Resolution of the rear camera in megapixels')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "outputId": "7aaa9cff-9ef8-48c1-8ffa-8c2046caf729",
        "id": "QUvWvVqAUwWN"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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2qWwL35OIiMhIKWmTAR3WGOOdz6od8+v+7F89AJFcu1Sy70lERGSk1KdNREREpAIoaRMRERGpAEraRERERCqAkjYRERGRCqCkTURERKQCKGkTERERqQBK2kREREQqgJI2ERERkQqgpE1ERESkAihpExEREakAStpEREREKoCSNhEREZEKoKStCFavXs3q1aujDkNExpD+7kVkrFVFHcB4sHHjxqhDEJExpr97ERlrqmkTERERqQBK2kREREQqgJI2ERERkQqgpE1ERESkAihpExEREakAStpEREREKoCSNhEREZEKoKRNREREpAIoaRMRERGpAEraRERERCqAkjYREZEJbO/evVxwwQXs3bs36lDK2l133cX73vc+7rnnnshiUNImFW239/Lx5F3s8d6oQxERqUhXX301a9as4Zprrok6lLJ26aWXArBq1arIYlDSJhXtV6knedDb+FXqyahDERGpOHv37uW2227D3bn11ltV2zaAu+66i3Q6DUA6nY6stq0qkquOMzt37qS3t5cLLrgg6lCKZtOmTVSlM1GHMajd3st1mS048KfMFt7qhzPFaqIOq1+7uzOkNm0aV5+RiW7Tpk3U1JTn502kUFdffTWZTPBvfSaT4ZprruGss86KOKryk61ly1q1ahXHH3/8mMcxbmvazOxsM7vbzO7euXNn1OFICfwq9SQZHIAMrto2EZFhuvPOOw+qQbrjjjsijqg8Ze/RQK/HyritaXP3i4GLAZYvX+6lvNb06dMB+MQnPlHKy4ypCy64gL4da6MOY0DZWrZUmLSl8LKubZtaFyMxY+64+oxMdKo1lfHgxBNP5B//+AfpdJp4PM5JJ50UdUhlKR6PH5SoxePxSOIYtzVtMr7l1rJlqbZNRGR4XvGKVxCLBalALBbj9NNPjzii8rRy5cqDXr/rXe+KJA4lbVKRHva9+2vZslI4D7k60YqIFKq5uZlnP/vZmBnPec5zaG5ujjqksnTCCSfsr12Lx+OR9GeDcdw8KuPbj6tPjjoEEZFx4RWveAVbt25VLdsQVq5cySWXXBJZLRsoaRMREZnQmpub1d+2ACeccAInnHBCpDGoeVRERESkAihpExEREakAStpEREREKoCSNhEREZEKoKRNREREpAIoaRMRERGpAEraRERERCqAkjYRERGRCqCkTURERKQCaEWEIpg3b17UIYjIGNPfvYiMNSVtRXDGGWdEHYKIjDH93YvIWFPzqIiIiEgFUNImIiIiUgGUtImIiIhUACVtIiIiIhVASZuIiIhIBVDSJiIiIlIBlLSJiIiIVAAlbSIiIiIVQEmbiIiISAVQ0iYiIiJSAZS0iYiIiFQAJW0iIiIiFUALxsuAtnVk+Nm/esb8uls7MgCRXLtUtnVkmDcj6ihERKSSKWmTfs2bNy+ya9fZTgAS06dHFkOxzZsR7T0VEZHKp6RN+nXGGWdEHYKIiIjkUJ82ERERkQqgpE1ERESkAihpExEREakAStpEREREKoCSNhEREZEKYO4edQwlZ2Y7gfVRxzEM04BdUQcxAMU2cuUcn2IbGcU2MoptZBTbyJRzbANZ4O6HzHs1IZK2SmNmd7v78qjj6I9iG7lyjk+xjYxiGxnFNjKKbWTKObbhUvOoiIiISAVQ0iYiIiJSAZS0laeLow5gEIpt5Mo5PsU2MoptZBTbyCi2kSnn2IZFfdpEREREKoBq2kREREQqgJI2ERERkQqgpE1ERESkAihpExEREakAStpEREREKoCSNhEREZEKoKRNREREpAIoaRMRERGpAEraRERERCqAkjYRERGRCqCkTURERKQCKGkTERERqQBK2kREREQqgJI2ERERkQqgpE1ERESkAihpExEREakAStpEREREKoCSNhEREZEKoKRNREREpAJURR3AWJg2bZovXLgw6jCkQLt37wZg6tSpQ+/bEe7bOPS+IiIileCee+7Z5e7T88snRNK2cOFC7r777qjDkAJdeumlAKxcuXLIfVecvwKAGz91Y8niERERGUtmtr6/cjWPioiIiFSAMUnazGyVme0wswdzyi43s3vDxzozuzcsX2hm3TnbfpRzzPFm9oCZrTGz75qZjUX8IiIiIlEbq+bRS4HvAb/IFrj7m7PPzewCYG/O/mvdfVk/5/kh8F7gDuAa4DTg2uKHKyIiIlJexqSmzd1vBvb0ty2sLTsDuGywc5jZLKDJ3W93dydIAF9b5FBFREREylI5DER4PrDd3Z/IKVtkZv8C2oHPu/stwBxgU84+m8IymcCu+cg1UYcgIiIyJsohaTuLg2vZtgLz3X23mR0P/NHMjh3uSc3sbOBsgPnz5xclUCk/9TX1UYcwqPX71rOjeyez6g9jZv1hJGLl8CcnIiKVKNJvEDOrAl4PHJ8tc/deoDd8fo+ZrQWWAJuBuTmHzw3L+uXuFwMXAyxfvtyLHryUhR/c8AMAPvDCD0QcyaEynuGuHffQne7m0bbHiFucl817CVNrNaeciIgMX9RTfrwYeNTd9zd7mtl0M4uHzw8HjgSedPetQLuZnRz2g3s7cEUUQUv5WH33albfvTrqMPq1vXsH3elunnvYs3nRnFMBWNv+ZMRRiYhIpRqrKT8uA24DlprZJjN7d7jpTA4dgHAKcH84Bcj/Au9z9+wghg8APwXWAGvRyFEpY+va11FlVcxvnM/shlnMqj+MzZ1bCMbRiIiIDM+YNI+6+1kDlK/sp+x3wO8G2P9u4GlFDU6kBNKZNBs6NjK/cR5VYT+2OQ1z2NS5mfZkO801zRFHKCIilSbq5lGRcWlL1xaSmSQLmxbsL5vTMBuATZ0DdsUUEREZkJI2kRJ4at96auI1zKqftb+sIdFAS3ULmzu3RBiZiIhUKs0/IBWtHBeK78v0saljE4ubDidmB/+/aE7DbB5ufYRkOkl1vDqiCEVEpBKppk2kyLZ3bSftaRZMOnR+wLmNc3CcrV1bI4hMREQqmZI2qWjfuu5bfOu6b0UdxkFae9sA+p2PbVrtNKpj1WoiFRGRYVPSJhXtqvuv4qr7r4o6jIO09rbSmGgkEUscsi1mMWY1zFJNm4iIDJuSNpEia+1tY3LN5AG3T62ZTFeqm2Q6OYZRiYhIpVPSJlJEqUyKfX37mFzdMuA+zdXBHG17k3vHKCoRERkPlLSJFFFbci+OM7mmZcB9sklbm5I2EREZBk35IRWtrrou6hAO0trbCjBo82hjopG4xdnbq6RNREQKp6RNKtq1Hy2v5Wdbe9uosioaE40D7mNmNFc3q3lURESGRc2jIkXU1tvK5JoWzGzQ/Zqrm9Q8KiIiw6KkTSraeVedx3lXnRd1GAC4O629bbQM0p8tq7mmma5UF8l0X+kDExGRcUFJm1S06x+5nusfuT7qMACCJCyTHLQ/W1aLRpCKiMgwKWkTKZLsSgiDjRzNag6nBFHSJiIihRqTpM3MVpnZDjN7MKfsXDPbbGb3ho/Tc7adY2ZrzOwxM3tZTvlpYdkaM/uPsYhdpFDZkaMt1UPXtDUmGohZTEmbiIgUbKxq2i4FTuun/Nvuvix8XANgZscAZwLHhsf8wMziZhYHvg+8HDgGOCvcV6QstCbbaEw0Uh0/dPmqfDGL0VzdTJum/RARkQKNyZQf7n6zmS0scPfXAL9x917gKTNbA5wYblvj7k8CmNlvwn0fLna8UjmmNh66KHtU2nrb9vdVK0RzdRM7u3eVMCIRERlPop6n7UNm9nbgbuAT7t4KzAFuz9lnU1gGsDGv/KSBTmxmZwNnA8yfP7+YMUsZ+d37fxd1CEAwcrSjr4PZDbMKPqaluoV1+9bTl+nrd3F5ERGRXAU1j5rZMWY2M3zeaGb/aWZfMrP6UVz7h8BiYBmwFbhgFOc6hLtf7O7L3X359OnTi3lqkUN0p7tJe5pJiUkFH5Ndzqo92V6qsEREZBwptE/bZUBL+PxbwCnAycCPR3phd9/u7ml3zwA/4UAT6GZgXs6uc8OygcplAjvn9+dwzu/PiToM9iU7AJg0yEoI+ZprwjVI1a9NREQKUGjz6EJ3f8yCad5fTzAQoBt4aqQXNrNZ7r41fPk6IDuy9Ergf8zsQmA2cCRwJ2DAkWa2iCBZOxP4fyO9vowPt629LeoQAOjo2wdA4zBq2iYlGjGMfeGxIiIigyk0aesxs0kEydoGd99lZlVAbSEHm9llwApgmpltAr4ErDCzZYAD64B/A3D3h8xsNcEAgxTwQXdPh+f5EHAdEAdWuftDBcYvUlL7+jowjMZEQ8HHxCxGfVU9HX0dJYxMRETGi0KTtv8B/gZMAr4Xlh1HgTVt7n5WP8WXDLL/V4Gv9lN+DXBNIdcUGUv7+vbREM69NhyNiUYlbSIiUpCCkjZ3/7iZvRToc/cbwuIM8PGSRSZSQfb1ddA4jP5sWY2JBrZ0bh16RxERmfAKnvLD3f9sZvPM7GR3v93d7y5lYCKFmDt5btQhANDR18H8xnlD75inMdEYjDzNpInH4iWITERExouCkjYzm08wgnQZQR+0RjN7I3Cau7+ndOGJDO5X7/lV1CGQTPfRm+4d1sjRrGwfuI5UJ83VTcUOTURExpFCO+D8GLiaoE9bX1j2F+AlpQhKpJKMZORoVrZJVf3aRERkKIU2j54IvMLdM2bmAO6+18wKX7NHpAQ+9puPAXDRmRdFFsO+vuHP0ZbVWKWkTUREClNo0rYdOAJ4PFsQLta+oRRBiRTq3o33Rh3C/nnWRlLTVldVR8xidPZ1FjssEREZZwptHv0WcJWZvROoMrOzgMuBb5QsMpEKsa+vg5p4DdXx4a8famY0VDWopk1ERIZU6JQfq8xsN8EEuBuBtwNfcPc/ljA2kYrQkdw3oqbRLM3VJiIihRjOlB9XAFeUMBaRirSvr4PpddNHfHxjooENPXuKGJGIiIxHBTWPmtl3zew5eWXPMbOLShKVSIGWzFzCkplLIrt+2tN0pbpGXdPWm+mlL9M39M4iIjJhFVrTdhbwybyye4A/Ah8rYjwiw3Lx2y+O9PqdfZ04zqQRDELIyp32Y3LN5GKFJiIi40yhAxG8n33jwzheZFzaP91H9Shq2qrCCXY1glRERAZRaNJ1C/AVs2A17PDnuWG5SGTO/sXZnP2LsyO7fnYAQXa+tZHQBLsiIlKIQptHPwpcBWw1s/XAfGAr8KpSBSZSiMe3Pz70TiXU2ddJzGLUVdWN+Bw18RqqrEpJm4iIDKqgmjZ33wQcB7wWOD/8eXxYPiQzW2VmO8zswZyy883sUTO738z+YGYtYflCM+s2s3vDx49yjjnezB4wszXh4Agr9I2KlEJHqpOGqnpG81E0MxoTDWoeFRGRQRXcJ83dM+5+m7v/1t1vd/fMMK5zKXBaXtlfgKe5+zMIVlo4J2fbWndfFj7el1P+Q+C9wJHhI/+cImOqs6+ThrBP2mhorjYRERnKgEmbmT2S83yjmW3o71HIRdz9ZmBPXtmf3T0VvrwdmDvYOcxsFtAUJowO/IKgxk+kJBYsXICZDfp4YsMTXP7Ly4fcb6jHqh+uYmvr1oL3X7BwQdS3R0RExthgfdrem/P8rSWO410Ey2JlLTKzfwHtwOfd/RZgDpDbHLspLJMJbNm8ZSU794b1G7h3530Dbs94hgdbH+KMt53Bh8/+8KiutbN7J1u7t3H39nuoig3d1XTZ9GeO6noiIlJ5Bvx2cPe/57x8yN13lSIAM/sckAJ+HRZtBea7+24zOx74o5kdO4Lzng2cDTB//vxihStl5qIzL4rs2tnJcBOx6lGfqzpevf+chSRtIiIy8RTap22DmV1jZm81s9F34AmZ2UrglcBbwiZP3L3X3XeHz+8B1gJLgM0c3IQ6Nyzrl7tf7O7L3X359OkjX2JIZCDJMGmrjg1/ofh8ifAcyUxy1OcSEZHxqdCkbT7BlB/vA7aZ2WVm9iozG3GVgJmdBnwaeLW7d+WUTzezePj8cIIBB0+6+1ag3cxODkeNvh2thTrhvfWnb+WtPy11633/kukgwaouRk1b7EBNm4iISH8KnfJjl7v/wN2fBzwNuA/4KkFT5pDM7DLgNmCpmW0ys3cD3wMmAX/Jm9rjFOB+M7sX+F/gfe6eHcTwAeCnwBqCGrhrC7m+jF+bWjexqbWgmWeKri+sFUsUoaYtbnEM2197JyIikm8kNWUzgJnANKCtkAPc/ax+ii8ZYN/fAb8bYNvdBEmjSOSSmT4SscSo5mjLMjOqY9X0pdU8KiIi/Suops3MjjGz88xsDcEi8QCvdfcjSxaZSJnry/QVpT9bViKeUE2biIgMqNCatn8Q1H79G3DDMCfWFRmXkplkUSbWzaqOJdib6i7a+UREZHwpNGmb6e5qt5Gy8+zFz47kuu5e/Jq2WDVpT5PxDDEreLESERGZIApN2vrM7L3AWcA0d3+GmZ0CHObuq0sXnsjg/uv1/xXJdYs5R1tWdc60H7Xx2qKdV0RExodC/zv/ZeDdwMUE039AsCLBZ0oRlEi52z9HW7x4NW37p/1Iq1+biIgcqtCkbSXwSnf/DeBh2VPA4aUISia2VCZV8Hxlb/jhG3jDD99Q4ogOlZ3uoxhztGUdmGBXSZuIiByq0ObRONARPs8mbY05ZSJFsTfZzp7eVhznqfanWNS0aND9d3fsHqPIDpYs4hxtWdlz9WlVBBER6UehNW3XABeaWQ1AuCLBecD/lSowmXh60j3csPkGjCCB+fu2W3m87Ymow+pXMtNHlVUVdcCAmZGIadoPERHpX6HfOP8OzAL2As0ENWwLUJ82KZK0p7lpy810prpoqWlhck0Ls+tnc8eOO9nSWdDCG2OqL50s6sjRrOpYtWraRESkX4UuY9Xu7q8jSNROBha7++vcfV9Jo5MJY8O+jezo3smzZ54UrDKAsWL2KdTEa1jbvjbq8A6RzPSRiBevP1uWatpERGQgA/ZpM+u33Wdn+Ni/XRPtSjGs27eeungdiyYt4mZuBiAeizOvcR7r2teRyqSoih36cX3R0S8a61D3z9HWHGsq+rmrYwnaMn24e1GWxxIRkfFjsIEIKQ4MOhhMvEixyASVTPexpWsLS5qPPCRRWdA4nzV717ClayvzG+cdcuwXXvmFsQpzv5SncLyoc7RlZc/Zl+mjugQ1eSIiUrkGax5dRDClx+HAh4GbgNOAo8OfNwAfKnWAMv5t6txIxjMsnLTgkG2H1c+kJlbD+n0bIoisf8l0drqPEvRpi2vaDxER6d+ANW3uvj773Mz+HVju7m1h0eNmdjdwN/DDkkYo4966fetpqKpnWu20Q7bFLMa8xrms71hPOpMmHju4Yvfl33k5ANd+9NoxiRUOrIZQzDnasg7UtCWB4q1rKiIila/Q0aPNQH1eWX1YLjJiveletnZuY8GkBQP24VowaQF9mRRburYcsq072U13cmwXWc/WgiWKuBpCVrUm2BURkQEUmrT9HPirmZ1tZi83s7OB68LygpjZKjPbYWYP5pRNMbO/mNkT4c/JYbmZ2XfNbI2Z3W9mx+Uc845w/yfM7B2FXl/K08aOTWTov2k0q9yaSPsySeIWJ27F784ZsxhVVqVpP0RE5BCFJm2fBr4LvBm4EDgT+F5YXqhLCfrC5foP4Hp3PxK4PnwN8HLgyPBxNmETrJlNAb4EnAScCHwpm+hJZVq/bz2NiUam1EwZcJ+YxZjbOJdNnZtxL2RsTGklM8miroSQLxFLkNT6oyIikqfQedoy7v4jd3+Rux/t7qeGr9OFXsjdbwb25BW/hgO1dT8HXptT/gsP3A60mNks4GXAX9x9j7u3An/h0ERQKkTa02zv3sHchjlDTm8xs246fZk+2pPtYxTdwJKZvpL0Z8uqjiUKXntVREQmjkLXHi2Vme6ene5+GzAzfD4H2Jiz36awbKByqUC7e/aQ9jQz62YOuW92kMKunl001xzoSvnKZ7yyZPH1x93pS/cxqaqxZNdIxKtp79unudpEROQgUSdt+7m7m1nR2r7CfndnA8yfP79Yp5Ui2t61HYCZ9TOG3LepuolELMHOnl0sbl68v/yTL/tkyeLrT9rTZMiUZDWErOpYAsdJe5oqK5s/URERiVjxVrseme1hsyfhzx1h+WYgdybVuWHZQOWHcPeL3X25uy+fPn160QOX0dvevZ2W6hZq4jVD7mtmTKudxq6e3WMQ2cCS+6f7KGWfturwWhqMICIiBwyYtJnZ7TnPv1Si618JZEeAvgO4Iqf87eEo0pOBvWEz6nXAS81scjgA4aVhmVSYjGfY0b2TmfVDN41mTaudSltvG32Z1P6yFeevYMX5K0oQYf+yozpLsRpClqb9EBGR/gxW07bEzGrD558Y7YXM7DLgNmCpmW0ys3cDXwdeYmZPAC8OXwNcAzwJrAF+AnwAwN33AOcBd4WPL4dlUmF29+wO+7MN3TSaNb12Go6zJ8LatrGsaetLq6ZNREQOGKzDzBUEKx+sA+rM7Ob+dnL3Uwq5kLufNcCmQ1b89mBehw8OcJ5VwKpCrinla1t32J9tGEnb1LqpQDAYYTg1dMWUTCeJESvJHG1ZcYsRI6aaNhEROchgy1i908yeBywETgAuGaugZPzb0bWD5upmaqtqh945VBuvZVJiEjsjrGnry/SRiCdKOqrTzKiOa9oPERE52KBD09z978Dfzaza3Qte/UBkMNn+bIubDh/2sdNqp7Kta3tk02EkM8mSztGWlYhVayCCiIgcpKD5BNx9lZmtAN5OMC/aZuCX7n5D6UKT8Wp3zx5SnhpRE+e02mk8tW8dXakuGhINnLH8jBJEOLC+TB8NVfnL8BZfdSxBV6qr5NcREZHKUVDSZmbvAb4G/BS4A5gPXGZmX3D3n5QwPhmHdoT92WbUDX8qlul12Ul2d9OQaOADL/xAUWMbTNrTpD1d0pGjWYlY9f7rlbL/nIiIVI5CZ+78NPASd78vW2BmlwO/IxjdKVKw7d07aUpMoq6qbtjHttS0ECPG7p7dLJg0n67eoDaqvqb0tV/Z9UBLOXI0K3uNvnQf8SolbSIiUvjkulOBh/PKHgMGXuVbpB/uzs7uncwYxqjRXHGL01zdRFuyDYDTv3s6p3/39CJGOLD9c7SVcDWErOw11K9NRESyCk3a/g5caGb1AGbWAJwP3FqqwGR8akvuJZlJjjhpg6C2rbW3rXhBFWgs5mjL0gS7IiKSr9Dm0fcBlwN7zWwPQQ3brcBAc69JhdrRvYPdPXuIW5zaqlrmNswhZsVb7WxHd7BS2Uj6s2VNrpnMU/vW0ZvuLVZYBUlmkhg2JuuBVlkVhmnaDxER2a/Q0aNbgVPMbC4wG9ji7ptKGpmMua1d27h+099wfH/ZsZOP4bjpzyraNXZ076AuXkdjonHE55hc0wJA2xjXtiXTSarj1WMy1YiZkYgl1DwqIiL7DavKIEzUlKyNQ3t793LTlptpqm7iRXNOxQzu3/0AD7U+TEtNC4c3LRr1NdydHd07mVE3fVSJT0vNZIAxbyIdqznasqpjmmBXREQOKH07j5S9nnQPf9tyI3GLc+qcFTQkgpGYJ844gfZkO7dvv4Om6iam1U4d1XU6U510pbpG1Z8NoC5eS028htbeVlY+Z+WozlUodyeZTtJY1TAm14Ng2o99ffvG7HoiIlLeitdZSSrWA7sfpLOvkxfOfsFBzZYxi3HK7OdTG6/lH1tvJeOZUV1nR/dOYHT92SBoOpxc3UJbso2Vz13JyueuHNX5CpHyFBkyVI/ByNGs6ngiuO4o77uIiIwPQyZtZhYzs1PNbOy+rWTM9KR7eGLvGhY1LWRaOHFtrtp4LcdNfxbtfe1s7Ng4qmvt6N5BIpagJeyTNhrZEaQ723eya9+uUZ9vKMl00LdsbJtHg2upiVRERKCApM3dM8AV7q4e0ePQY62Pk/Y0x04+dsB95jfOoynRxAN7HsLdB9xvKDu6dzC9dnpRRqNOrplM2tO8/kev540/euOozzeU7ICA6nhNya+VlU3axnqUrIiIlKdCvz1vNrOTSxqJjLm+TB+Ptj3GvIa5tNQ0D7hfzGI8bcoxtPa2sqVzy4iu1dnXyd5kO4flrTe6YOECzOygx0033cRNN910SHnu46XPewkA9z5w75D7jvSRa3/SNgZztGXVhAmiRpCKiAgUPhBhPXCtmV0BbIQDc0K4+xdHenEzW0ow/1vW4cAXgRbgvcDOsPyz7n5NeMw5wLuBNPARd79upNef6J5oW0Myk+RpUwauZcta1LSI+3bfzwN7HmR2w+xhj/7c0rkVgDkNsw8q37B+A/fuvO+gslv+cAsAHzv/4wOeL+MZHmx9iEVLD6c6Vs0ll14yrHgKsWz6M/c/700nScQSRZ2zbihxixOzmGraREQEKDxpqwP+GD6fW6yLu/tjwDIAM4sDm4E/AO8Evu3u38rd38yOAc4EjiWYL+6vZrbE3dPFimmiyHiGR9oeYWbdzH77suWLWYxjpxzDnTvuZkf3Dmbm1ZgNZUvXFuqr6mmuHrhGbzhiFqMmVjNmnfTHeroPCAZc1MRq6E2rpk1ERAqfXPedpQ4EeBGw1t3XD1KL8xrgN+7eCzxlZmuAE4HbxiC+cWVr11a6Ut2cMP2Ego9Z3LSYe3fdz6Ntjw0ract4hq1d21gwaUFRJ6atraodu6QtnWRSYtKYXCtXdbya7lTXmF9XRETKT8FtPWZ2lJl9wcy+F75eambPKGIsZwKX5bz+kJndb2arzGxyWDaHoHk2a1NYJsO0du+T1MRqmNM4e+idQ1WxKo5oXszGjk109hWeSOzs3klfpo859bNGEuqAauO1vHDZqbzxpDcU9bz5Mp4h5SlqxnC6j6yaWA3JTJ+m/RARkcKSNjN7E3ALQYL09rB4EnBhMYIIpxN5NfDbsOiHwGKCptOtwAUjOOfZZna3md29c+fOoQ+YQHrTvWzs3MSipoXELT6sY5e0LMFxntj7RMHHbOnaimEcVn/YcEMdVG28lpOPOplTjl1R1PPmi2K6j6xsoqiF40VEpNCati8DL3b39xEMAAC4D3jmwIcMy8uBf7r7dgB33+7u6XC6kZ8QNIFC0OdtXs5xc8OyQ7j7xe6+3N2XT58+uslcx5t1+9aT8QyLmw4f9rGTEo3MbZjDE3vXkM4U1pVwS+cWptdNL/rEtHXxWna372bD7g1FPW++3v3TfYx90padYiSpwQgiIhNeoUnbDOD+8Lnn/Bz5pF0HO4ucplEzy21Hex3wYPj8SuBMM6sxs0XAkcCdRYphwli790km10xmSu2UER2/tGUpPeke1ncMnSx1p7rZ09vK7IbiNo0CJGIJfnzNj/ja779S9HPnyiZMkdS0Zedq07QfIiITXqFJ2z3A2/LKzqQICZOZNQAvAX6fU/xNM3vAzO4HXgh8HMDdHwJWAw8DfwI+qJGjw9PW28bu3t0jqmXLmlV/GE2JSTza+tiQk+1u6gwqQufUF953rlBmRsxiZChtf69kJknMYsNuSi6GuMWJW0w1bSIiUvCUHx8B/mxm7wYazOw6YAnw0tEG4O6dwNS8svwEMXfbV4Gvjva6E9Xa9icxjIWTFo74HGbG0ZOP5o4dd7KlawtzGvofC+LuPN72OE3VTUyumdzvPqMVI0bKU7h7UUem5kpmktTEqkt2/sGYGdWa9kNERCiwps3dHwWOAr4PfB74GfB0dy+8N7pELuMZnmx/ijkNc6irqh3VuRY3H05DVQP37bp/wNq27d3b2dPbyjEtR5cs4YlbDPCSrs/Zmx77Odpy1cSr6c2opk1EZKIreMoPd+8C/gHcCNzi7h2lCkpKY0vnVnrSPSxuHnnTaFbc4jxj6tPZ3btnfxNovodbH6EmXsPhTYtGfb2BZFco6En3lOT87kFCOJZrjuarjtXQp2k/REQmvEKn/JhvZrcA64CrgXVmdouZLShlcFJca9ufpCZew9wBmjOH6/CmRUxKTOK+3YfWtu1N7mVz5xaWtiwhHitdX7C3P+/tvHz56SVL2pKZJI5HMkdb1oFpP9REKiIykRVa0/ZzgsEILe4+A5gM3B2WSwXoTfeyqXMTiyYtKtr6mTGL8YypT6e1t5VH2h49aNsjrY8StzhLm5cU5VoDeeGxp3LikpPoLlHS1hMOAKiNj645eTT2Lxyvfm0iIhNaoQMRjgde6u59AO7eYWafAXaXLDIpqqfa1wVzsxWhaTTXwkkLWL9vA/fs/Cc9qR6eNuVY7t19H0/sXcORzUdQO8q+c0NZt3Mdezp3EZs8oyTn7w2TwZpYlM2j4bQfGkEqIjKhFZq03U4wwe0/csqWozU/K8ba9nButiKP4oxZjBfMfj537riLh1of5tG2x0h7mqUtS3jWtGVFvVZ/zvvDeSQzST51xqfJeKZotYhZveleElZV0ibeoVTFqohbXHO1iYhMcAMmbWb25ZyXa4FrzOxqgrU/5wGnA/9T2vCkGHb37GZP7x6WTz++JOePWYyTZpzIpEQjmzu3cty0ZUyrm1aSa/V7/bCVvzfdS11VXVHP3ZPupSbCptGsmnhNyfrtiYhIZRispm1e3uvs5LczgF7gD0D032YypEfbHqfKqkY1oe5QzIxjpxzLsVOOLdk1BpKtXetO9xQ9aetN95ZsjrnhqIvX0trbVtL56EREpLwNmLS5+zvHMhApjZ5UD+v2reOIpsWRrJ05FmJmGEZPqhuKmGBNnTWVDBlqI5zuI6s2XkeGPcFEv2UQj4iIjL1C+7RhZvXAEUBjbrm731rsoKR4nmhfQ8YzLG1ZGnUoJWTUxmuLPoJ09uJg6a1yaB7NTobck+5R0iYiMkEVlLSZ2duB7wFJoDtnkwPzSxCXFEHGMzze9gSH1R9GS01z1OGUxHtPfS8A9VV1tCaL23w4e3Ewn1151LQFSVt3qofm6vH5uxQRkcEVOtTum8Ab3H2au8/LeShhCy1YuAAzK6vHSaedTFeqiws+fX7Ut6dkTj7iZE4+4uSg+dAzRZ2Advbi2cQtTlWs4ArpkolZjJpYDT3p7qF3FhGRcanQb6MkwfJVMoAN6zdw7877og5jP3ff3zT61//9K3bZ+Oy8/uiWYFLf+TOCxTm6U91Faz6cc8ScSCfVzVdbVUtXqivqMEREJCKFJm1fAC40s/90912lDEiKozXZSk+6h/kN80j3pcsqocy1bPozR3X8+VcFtYg/ee9PMIzudA8tRYjL3Zm9eE5Z9R+ri9exN7mXVCYddSgiIhKBQptHHwdeDWw3s3T4yJiZvj3KUMYzbOvaTn28bsL0f4pZjNp4Dd2p4jQfpjxFY0tjWfRny6qLZwcjqIlURGQiKrSm7ZfAL4DLOXggQlGY2TpgH5AGUu6+3MymhNdbSLBQ/Rnu3mpBL/PvEEzu2wWsdPd/FjumSrazZycpT7Ggfv6EmtOrrqqOvcn2ogxGyC4ZVU41bbU5I0hFRGTiKbSmbSrwRXd/0N3X5j6KGMsL3X2Zuy8PX/8HcL27HwlcH74GeDlwZPg4G/hhEWOoeL3pXnZ276I50URDoiHqcMZUXbyOtKfpy/SN+lzlsFB8vkQsQZVV0Z1S0iYiMhEVmrT9DHhbKQPpx2uAn4fPfw68Nqf8Fx64HWgxs1ljHFtZSmXSPLVvHWbGrPqJd0uyqyF0F6H5sCfdQ2d7J1UW/cjRXMF8dGoeFRGZiAr9RjoR+JCZfQ7YnrvB3U8pQhwO/NnMHPixu18MzHT3reH2bcDM8PkcgvVPszaFZVtzyjCzswlq4pg/f/zPTOLubOhYT1+mj8MnLRq3qx/k+/DLPrz/+YG5zLpH3ZevO9XNugef4tkLTx7VeYqtrqqWXT27iVdFt4C9iIhEo9Ck7Sfho1Se5+6bzWwG8BczezR3o7t7mNAVLEz8LgZYvnz5sI6tNKlMik2dm+lIdTK3Ye6EahZdtmDZ/ufBYITRr4yQ8Qzd6W7W3v8kvHKUARZZbbwOx5l75NyoQxERkTFWUNLm7j8feq+Rc/fN4c8dZvYHgpq97WY2y923hs2fO8LdN3PwYvZzw7IJx91p72tnc+cWUp5iVt1hTCmDxc3H0r3r7wUOJG918Vr29XWMajBCdgTqk/evKUaIRZVNyI8+6ZiIIxERkbFW6DJW7xpom7uvGk0AZtYAxNx9X/j8pcCXgSuBdwBfD39eER5yJUFT7W+Ak4C9Oc2o45K74zhpT5PM9NGXTtKZ6qQ92U6fp6iN17KoYeH+Pl0TyX9f998AXHL2JUDQr6012UZfpm/ETcRdYZ+xtfc/WZwgi6g6lqAmVsOxz31a1KGIiMgYK7R5NH8QwmHAYuAfwKiSNoK+an8Ia0WqgP9x9z+Z2V3AajN7N7AeOCPc/xqC6T7WEEz58c5RXr/oUpkUu3t309nXGSRZ4WjGmBlGLPhpMWIc+jODk/EMaU+T8TTp8Hk+w5iUmMTM6iZaqpuJWaFjSsa3+qqgJqoz1TXipK071UUilmDvzrYiRlY8jYlGjlq+lHQmTTymvm0iIhNFoc2jL8wvC2vfjh5tAO7+JHDItPjuvht4UT/lDnxwtNcthVQmxbbubbT2tuE4dfE66qrqaI41AZBxx8kEPz1DhpyfmT4yYZNe3OJUxxLErJa4xYhZnLjFiFucRCxBIlZNTbxaiVo/6uK1xCxGZ6qDyTUtIzpHV6qb+nj51lo2Jhqpqa9lV88uZtbPHPoAEREZF0Yzn8GlwC7gU8UJpbJNmzONte1PkswkmVwzmWm108pqNv2JwsxoqGqgo69zRMenMimSmSRTaqYUObLiaaxqIJ1Ks6Vrq5I2EZEJpNA+bflVOvXAW4G2YgdUiXb37OGLvzmXlPexaNIiGifQ6M1y1FDVwL6+ffRl+kjEEsM6tischFBfxv0D47E4T96/lpnPmRF1KCIiMoYKrWlLEcyllmsz8N7ihlN5Mp7hlq23kOpLsbhpcVnNoD8RfOqVh1b0NiYaoBs6+zppGWYTaVeqC6DsB3U8dNtDLDluKcl0csLMySciMtEV2ilqEXB4zmOmu8939+tKFlmFiFmMU2Y9ny+fea4StggcNfsojpp91EFldfE6YsToTA2/ibQ73UVtvIa4lXcH/4dufRDH2da1feidRURkXCh0IML6UgdSyabUTqFtR1vUYUxIt6+5HYCTjziwcoGZ0ZCop2OYSZu705XqpinRVNQYS2HNfWuosiq2dm1l/qR5Qx8gIiIVb9Ckzcxu4NBm0Vzu7oeM8BQZKz/5W7BQR27SBmG/tu7tpDIpqmKF9QLoTfeS9jT1VfVFj7PY0n1pZjXMYn3HBpZPP15Tf4iITABDfZv9aoDyOcBHCAYkiJSdhv3ztXUWvA5pe187AE3Vk0oWVzEtbVnCxo6NrNu3nsXNh0cdjoiIlNigSZu7X5L72symAucQDEC4nGDlApGyU19Vj2F09HUUnLTtTbZTH68b9ojTqBxWN5Pm6mYebXuUw5sWjXjZLhERqQwFDUQwsyYzO49gFYKZwHHufra7byppdCIjZBasGLE32U4wH/Pgkukk3elumqrLvz9blplxVMtS9vS2srNnZ9ThiIhIiQ2atJlZnZmdAzxJsPrB89z9be6+dkyiExmFyTUtpDxFR1/HkPseaBqtnKQNYFHTIqpj1Tza+ljUoYiISIkN1adtHUFi903gbmCmmR00Bbu7/600oYkM7Quv+8KA2yYlJhG3GK3JNiYN0U+tPdlOTaym4qZtScSqOKJ5MY+0Psq+vg4mJRqjDklEREpkqKStm2D06PsH2O4E87aJRGLh9IUDbotZjObqFlp7W0n77AHnXktlUnSkOpleO71EUZbWUS1LebztCf6+9R+8dN6Ly36OORERGZlBm0fdfaG7LxrkoYRNInXTIzdy0yM3Drh9cnULjrM32T7gPvv69gHQXGFNo1kNiQaec9jJ7OrZxT07/xV1OCIiUiKjWTBeJHK/uOWXALzg6BX9bq+vqqc6Vk1bbytTaiYfst3d2d27h4RVURcv76WrBrNg0gKO7t7FI22PMq12Koc3LYo6JBERKbJCl7EqCTObZ2Y3mNnDZvaQmX00LD/XzDab2b3h4/ScY84xszVm9piZvSy66KUSmBkt1S10pDrpSfcesr0t2UZXqosZdTMrfsqM46Y/ixl10/nHtlv5585/kc6kow5JRESKKNKkjWAh+k+4+zHAycAHzeyYcNu33X1Z+LgGINx2JnAscBrwAzN14JHBTa2dQtzibOjYQMYz+8vTmTRbu7ZRF6/rtxau0sQsxqlzTuXI5iN4qPVhrt14HTu6NRWIiMh4EWnS5u5b3f2f4fN9wCMEqy0M5DXAb9y9192fIpg37sTSRyqVLBFLML9hHj3pHjZ3btk/b9v27u2kPMWchtkVX8uWlYhVcfLMk1gx+wV0p7q5buOfuX7T39jVszvq0EREZJSirmnbz8wWAs8C7giLPmRm95vZKjPLVoPMATbmHLaJwZM8EQAmVU9iRu0MWpOtrOtYzxN7n2BX726m1EypiLVGh2te41xeu+g1HDdtGbt79nDthj9x67bb6E51Rx2aiIiMUFkMRDCzRuB3wMfcvd3MfgicRzClyHnABcC7hnnOs4GzAebPn1/cgKVsfPWMrxa878y6GfSke+hKdVEbr2V67XRm1FXmNB+FSMSqOHbKsSxpWcIDux/kkdZH2dCxkRNnLOfwJg38FhGpNJEnbWaWIEjYfu3uvwdw9+05238CXBW+3AzMyzl8blh2CHe/GLgYYPny5UOvYyQV6bCWwwre18xYOGlBCaMpT4lYguOmP4sjmhdz+/Y7+Me229jT08px059FzMqmsl1ERIYQ9ehRAy4BHnH3C3PKZ+Xs9jrgwfD5lcCZZlZjZouAI4E7xypeKT/X3f8nrrv/T1GHURGaqpt48dwXcVTLUh5pe5S/bb6Bvkwq6rBERKRAUde0PRd4G/CAmd0bln0WOMvMlhE0j64D/g3A3R8ys9XAwwQjTz/o7prXYAJbfftvAXjZM06LOJLKELMYJ8xYTktNC3dsv5Mbt9zEqbNXEI9pELaISLmLNGlz978D/Q3bu2aQY74KFN6RSUQOcWTzEcQwbt1+OzdtvYUXzH6+lr8SESlz6tAiMkEtbl7MiTNOYHPnZm7ddvv+qVBERKQ8Rd08KiIRWtqyhGQ6yb2776OpehLPnPqMqEMSEZEBKGkTmeCeNuVY2vv2cf/uB5iUmKR1S0VEypSSNqlo33rLt6IOoeKZGSfPPJHOvg5u2347jYkGZtTNiDosERHJoz5tUtEmN0xmckPlrxs6XLF4DDMr2qMqVsUbnvF6tjy5md/e9ztmzp854nMtWDjx5sITERkLqmmTinbFPVcA8JrjXxNxJGMrk85w7877in7e3nQva9rX8t9/+z6LmxZTNYKpQJZNf2bR4xIREdW0SYW78p4rufKeK6MOY9yoidewoHE+yUySDR3rNaJURKSMKGkTkYM0JhqZ0zCHjlQnm7o2K3ETESkTah4VkUNMqZlMMt3Ljp6d1MRqmFE3PeqQREQmPCVtItKvmXUz6c0k2da9jbjFmFo7NeqQREQmNCVtItIvM2New1wynmFz1xbSnmZ67XTM+lt5rnRSmRRdqW7SniLjGcCojldTE6smEUuMeTwiIlFR0iYV7Xsrvxd1CONazGIsbFzAxs6NbOveTl8mxaz6w4hZabrD7uvrYEfXdnb37mFPTyvtfe30pnsH3D8RSzApMYmm6iYm17QwuWYyk2smUxevVTInIuOOkjapaHXVdVGHMO4FNW7zqLIqdvXupjPVwbyGedRVjf7e96R72Na1na1d29jWtY2Ovg4AqqyKKbWTmd84j4aqBuoT9VRZFTGL4TjJdJLedC+dfZ3s69vHzu4drNu3bv95a+I1TK6ZzJSayfuTuabqJuI2/ClMRETKhZI2qWiX33Y5AG9+9psjjmR8MzNmN8ymMdHIps7NrGlfy+SaFqbVTqM2XnvQvtmJf/tT21DH0uVLOebkYzjm2cey4OhgIt7ujm4eueNhHrrtIR6+/WG2rBn+qNX6pnrmLZ3P/KMOPOYcOZfqmmoAUskUuzbtZPnTl1NfVR88EvUHnlfVkYglRnB3RETGhpI2qWh/fuDPgJK2sdJU3cSSqnq2dW+ntbeVPb2tNFQ10JhooL6qIWiunDyJ6x/6GylPkcqk6E0n6Un30J3qoivdDYBhNFTV05BopDHRSP3kOk6afyK8qbjxuju9mV66Uz301PbwwLr76Ty6ix3dO0lmkofsn4glqK+qoy4nkWuoaqC5uomm6iZq1ewqIhGqyKTNzE4DvgPEgZ+6+9cjDklkwqiKVTG3YQ6H1c1kV89u2vva2d69Y//2//7H93m47ZGDjokRo7aqlhm104Mkraq+ZP3icpkZtfHa/bWB337/hVzoFwDZAQ5ddKa6goQy1U1Xqjt83sW2rm10p7pxDtT4VceqwwSumZaaZlqqW2ipaVEfOhEZExWXtJlZHPg+8BJgE3CXmV3p7g9HG5nIxFIVq+Kw+pkcxkzSmTRd6S5SmTTnferLfPabnyVuVVTF4tTEaspmlOdgTbf9sZgxZeYUZh0+i1mHz2b24tnMOnw2c4+YQ9PU5v377Wvdx6bHN7HpiY1semIT257ays5NO9mzbQ+ZdKaga81fMJ/169YP+z2JyMRRcUkbcCKwxt2fBDCz3wCvAZS0iUQkHoszKTYJgL9ddj0XfvfCiCPqXzHXbE1lUvSke4JHzRRmTp/JsScfG05LckB1LEEiVk11LEE8VkWVxfcntHGLYxgxi/H6576Ojr4O4hbf/8gmmIaVRdIr5Su/D2huDfFBr73/fTxv44HT5Z4n/DzagdeW/8xySw88L8XnN/uenZyf+99mUOqeIe0ZMp4+5Gcm3Jbe/zz4mfEMjgc/3clw4KdhPGPq04v+XgpViUnbHGBjzutNwEkRxSIiE1RVrIrGWNAnL8vd6cv00ZtJkkwn6cskSYbPO1KdpDNpMvRf8/aNa8/nD09dUdC1938RBt+QB31JRik/USjSSaO9/jBEff1KcUhSZweXd3d34+5BomdBwmcYFrP9ZbFYNKtwJnuSkSZtVmnrCprZG4HT3P094eu3ASe5+4fy9jsbODt8uRR4bEwDHZ1pwK6ogxiAYhu5co5PsY2MYhsZxTYyim1kyjm2gSxw90PWD6zEmrbNwLyc13PDsoO4+8XAxWMVVDGZ2d3uvjzqOPqj2EaunONTbCOj2EZGsY2MYhuZco5tuKKpXxydu4AjzWyRmVUDZwJXRhyTiIiISElVXE2bu6fM7EPAdQRTfqxy94ciDktERESkpCouaQNw92uAa6KOo4TKuVlXsY1cOcen2EZGsY2MYhsZxTYy5RzbsFTcQAQRERGRiagS+7SJiIiITDhK2sqMmZ1mZo+Z2Roz+4+o48lnZnEz+5eZXRV1LFlmttTM7s15tJvZxyKMZ5WZ7TCzB3PK3mRmD5lZxswiG8U0QGznmtnmnPt3ehnFtszMbg/jutvMTowotnlmdoOZPRz+Hj8alp9vZo+a2f1m9gczaymX2MJtHw7je8jMvhlBbLVmdqeZ3RfG8J9h+SIzuyP8d+7ycFBZucR2SVh2v5n9r5k1DnWuMYztlpy/0y1m9sexji0nxoO+C8zsQ+Hv081sWlRx9RdbTvl3zawjqriKwt31KJMHwcCKtcDhQDVwH3BM1HHlxfjvwP8AV0UdyyD3cBvBHDdRxXAKcBzwYE7Z0QTzBd4ILC+z2M4FPlkGv7v+Yvsz8PLw+enAjRHFNgs4Lnw+CXgcOAZ4KVAVln8D+EYZxfZC4K9ATbhtRgSxGdAYPk8AdwAnA6uBM8PyHwHvL6PYmnL2uRD4j3KJLW+f3wFvH+vYcq5/0HcB8CxgIbAOmBZVXP3FFpYtB34JdEQZ22gfqmkrL/uX6HL3JJBdoqssmNlc4BXAT6OOZRAvAta6e2SLOLr7zcCevLJH3D3yCZ77i61cDBCbA03h82Zgy5gGlQ3Cfau7/zN8vg94BJjj7n9291S42+0E80aWRWzA+4Gvu3tvuG1HBLG5u2drNhLhw4FTgf8Ny38OvLZcYnP3dgAL1l2qY1hrMpQ2tux2M2siuId/HOvYwusf8l3g7v9y93VRxJOrv9gsWLP8fODTUcVVLEraykt/S3TNiSiW/lxE8KEvbAXsaJwJXBZ1EBXoQ2Fz0Cozmxx1MDk+BpxvZhuBbwHnRBsOmNlCglqFO/I2vQu4dswDypEX2xLg+WEz5E1mdkJEMcXN7F5gB/AXgtaEtpxkN7J/5/Jjc/c7wvKfEdTYHwX8dznFFnotcH02wYzARZTvd8FFHBrbh4Ar3X1rJBEVkZI2KYiZvRLY4e73RB3LQMJ+Ma8Gfht1LBXmh8BiYBmwFbgg0mgO9n7g4+4+D/g4cEmUwYT9m34HfCz3C9PMPgekgF+XUWxVwBSCJr9PAavD2qMx5e5pd19GUAt5IkEiVBbyYzOzp4Xl7wRmE9RavrmcYgudRUT/OS3n74L+YjOz2cCbiCj5LjYlbeWloCW6IvJc4NVmto6g2fZUM/tVtCEd4uXAP919e9SBVBJ33x5+QWSAnxB8sZaLdwC/D5//lghjM7MEQVL0a3f/fU75SuCVwFs87DxTJrFtAn4fNrXdSVDzEFkHcXdvA24Ang20mFl2ntDI/53Lie20nLI0wb91b4gorGwcbeTEFnbyPxG4OqKQyvm74JDYgIeAI4A1YXm9ma2JLMJRUtJWXsp2iS53P8fd57r7QoK4/ubub404rHyR/e+zkpnZrJyXrwMeHGjfCGwBXhA+PxV4IoogwhqqS4BH3P3CnPLTCJpiXu3uXeUUG0F/pxeG+ywhGNw0potmm9n07IhaM6sDXkJQe3UD8MZwt3cAV4xlXIPE9piZHRGWGUHN/aNlEls2jjcSdLDvGeu4oLy/CwaIbbK7H+buC8PyLnc/ItJAR6EiV0QYr1xLdI2YmTUQ/MP2b2UQy2XACmCamW0CvkTQwf6/genA1WZ2r7u/rExiW2Fmywg6Oq8jons4QGzvBb4T1sr0AGdHERvB/+DfBjwQ9jMC+CzwXaAG+EvY8ni7u7+vTGJbBayyYAqVJPCOCGoCZwE/DzuCx4DV7n6VmT0M/MbMvgL8i2iavQ+JjaD26pawo78RjOB/fznE5u7Z6SvOBL4eQUyDMrOPEPwH5jDgfjO7xt3fE3FY445WRBARERGpAGoeFREREakAStpEREREKoCSNhEREZEKoKRNREREpAIoaRMRERGpAEraRMqUmS00M8+ZhHS4x7/FzP5c7LgKuO5zzewJM+sws9cWsP+o3qeMnpk938wiXxu3lMzss2Y26nWTw89qxc7zJZVNU36IFCicTXsmkAY6gD8BH8pZ2LnY11sIPAUkctZpHPW+pWZm1xOs8/edAbavA97j7n8NXy+kTGIXGYqZOXCku1fsrPpSuVTTJjI8r3L3RoJ1Op9FGSxgXoYWECwdE7li196Fk52WvUqJU0SGR0mbyAi4+zaClSuWZcvM7GQzu9XM2szsPjNbkbNtpZk9aWb7zOwpM3tLWB4zs8+b2Xoz22FmvzCz5v6uaWbrzOzFOa/PzVnz7+bwZ1vYLPns8Jp/z9n/OWZ2l5ntDX8+J2fbjWZ2npn9I4zxz+Eah/0ys/ea2Roz22NmV1qwKDNmthY4HPi/MI6avON+CczP2f7pnM1vMbMNZrbLggXYs8fEzOw/zGytme02s9VmNmWAuFaY2SYz+4yZbQN+NtTxZvZbM9sW3pebzezYnG2XmtkPzewaM+skXBYq75pTzOxnZrbFzFrN7I9h+WQzu8rMdoblV5nZ3Lx7/pXwM9NhZv9nZlPN7Ndm1h7+jhbm7H+Umf0lvOePmdkZg8VpZq8ws3+F59poZucO8vtcYcEqFNnX68zsk2Z2f3hfLjez2gGOXRl+br4dfvafDD9rK8Pr7jCzd+TsX2Nm3wp/19vN7EcWLNWU3f5pM9sa3s/3WE5z5GDvyQ40s58dHrvVzD6Zs33/34uZvdmCv8Om8PXLw8/A9PD1u8zskfD3dp2ZLRjgvZ9uZg9b8DezOfd6IiXh7nrooUcBD4Ilnl4cPp8LPAB8J3w9B9gNnE7wn6GXhK+nAw1AO7A03HcWcGz4/F3AGoJEp5FgcfRfhtsWEiwtVZV//fD1ucCv+ts3LFsJ/D18PgVoJVjuqIpgndZWYGq4/UZgLbAEqAtff32A+3AqwRqWxxEs4fTfwM393aeh7mNe7D8Jr/1MoBc4Otz+UeD28J7XAD8GLhvg3CuAFPCNcN+6oY4PfweTwm0XAffmbLsU2EuwVFQMqO3nmlcDlwOTgQTwgrB8KsFi4/Xh+X8L/DHnuBvD3/1ioBl4GHgceHH4O/oF8LNw3wZgI/DOcNuzwt/BMQPFGd6Lp4evnwFsB147yH3blPc7uhOYTfDZeQR43wDHrgzv+TsJlt/7CrAB+H54T18K7AMaw/2/TbCm8pTwvvwf8F/httOAbcCx4X37VfjZOCInzn7fEwc+R5eF9+vpwE4O/M2eS/j3Er7+dXjfphKscfvKsPw14e/l6PBefx64Nee43Hi2As8Pn08Gjov63yk9xvcj8gD00KNSHuEXWUf4BeTA9UBLuO0zhMlWzv7XESyG3QC0EXyB1+Xtcz3wgZzXS4G+8Msi+yVUjKTtbcCdede+DVgZPr8R+HzOtg8AfxrgPlwCfDPndWMY88L+4hzgPvaXtM3NKbsTODN8/gjwopxts7L3qJ9zryBYZ7M2p2w4x7eEsTSHry8FfjHIe5kFZIDJBXx+lgGtOa9vBD6X8/oC4Nqc168iTCCBNwO35J3vx8CXCokz3Oci4NsDbFvBoUnbW3NefxP40QDHrgSeyHn99PAezswp2x2+fwM6gcU5254NPBU+X0WYwIWvjyAnSRrsPeV8jo7Ki/uS/L+XnN/1BoL/fP04p/xa4N05r2NAF7AgfJ2btG0gWKu3aajfvx56FOOh5lGR4Xmtu08i+JI7Csg2IS4A3hQ2D7WZWRvwPGCWu3cSfOm+D9hqZleb2VHhcbOB9TnnX0+QsM0sctz518lea07O6205z7sIkrEhz+XBQIzdeecaiYGuvwD4Q859fYRgMMhA92inu/fkvB7weDOLm9nXw6bTdoJkBQ78XiGo4RrIPGCPu7fmbzCzejP7sQVN3+0ETdgtdnB/s+05z7v7eZ17D07K+3y9hWBx7n7jNLOTzOyGsHl2L8Hnb8Am734U+nno733g7v29l+kENWj35LyPP4XlEHy2ct/HSN5T7jHrw3Mewt3bCGo/n0aQMGctAL6TE98egmSzv8/3Gwhq19eb2U1m9uz+riVSLEraREbA3W8iqN34Vli0kaCmrSXn0eDuXw/3v87dX0JQM/MoQVMgBM0yuf1l5hM0NeV+4WV1EnzhZeV+YfsQIedfJ3utzUMcN+S5zKyBoImp0HMNFWu+jcDL8+5trbsPdL388w92/P8jaA57MUET5cLwGCsw3o3AFDNr6WfbJwhqTk9y9ybglH7OXaiNwE1576HR3d8/SJz/Q9AMOc/dm4EfjfDaxbSLIIE7Nud9NHswuAeC5sa5OfvPyzu+kPeUe8x8gs/rIcxsGUHT+GXAd3M2bQT+Le9e17n7rfnncPe73P01wAzgj8DqAd63SFEoaRMZuYuAl5jZMwn63rzKzF4W1t7Uhp2755rZTDN7TZjc9BI0sWbCc1wGfNzMFplZI/A14HLvf+qLe4EzzSxhZsuBN+Zs2xme8/ABYr0GWGJm/8/MqszszcAxwFUjeN+XAe80s2UWDDT4GnCHu68r8Pjtg8TZnx8BX812Bjez6Wb2miIdP4ngd7KbICH+2jDOi7tvJWhO+4EFAw8SZpZNziYRJChtFgx8+NJwzp3nKoLf39vCayTM7AQzO3qQYyYR1AL2mNmJBAlqpNw9Q/Aflm+b2QwAM5tjZi8Ld1lN8Nk62szqgS/knaKQ9/SFsJbzWIJ+dpfn72DBoIpfAZ8N95ljZh8IN/8IOCc8HjNrNrM39XOOagvmQmx29z6CfquZ/P1EiklJm8gIuftOgs7iX3T3jQQ1Np8lSKA2Ap8i+BuLAf9O8D/+PcALgGwNySrglwRNZ08BPcCHB7jkFwg6rbcC/0lQ65CNpQv4KvCPsFnn5LxYdwOvJKj92Q18mqDj9a4RvO+/hrH8jqBmZDFw5jBO8V/A58M4Cxlt9x2C2pU/m9k+gkEFJw3jeoMd/wuCJrTNBAMBbh/GebPeRtBH7lFgB/CxsPwigoEQu8Lz/mkE5wbA3fcRdOg/k+BztI0Dgy0G8gHgy+F7/iLlUwv0GYKO/reHzcZ/JaiRxN2vJaj1uiG7T3hMb/izkPd0U3js9cC33L2/Cab/C9jo7j90917grcBXzOxId/8Dwb39TRjfg8DLB3gvbwPWhfu9j6DJWqRkNLmuiIiUpbAm8UGgZoDa59x9F6JJmmWcU02biIiUDTN7nQVzuU0mqPH6PyVhIgElbSIiUk7+jaCZeS3BKN/3D767yMSh5lERERGRCqCaNhEREZEKoKRNREREpAIoaRMRERGpAEraRERERCqAkjYRERGRCqCkTURERKQC/H9/jPqI/8MCQgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"histogram_boxplot(data=df.fillna(0), feature='main_camera_mp', fmt='{:.0f}',\\n                  xlabel='Resolution of the rear camera in megapixels')\";\n",
              "                var nbb_formatted_code = \"histogram_boxplot(\\n    data=df.fillna(0),\\n    feature=\\\"main_camera_mp\\\",\\n    fmt=\\\"{:.0f}\\\",\\n    xlabel=\\\"Resolution of the rear camera in megapixels\\\",\\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": [
        "mc = pd.DataFrame(df['main_camera_mp'].fillna(0).map(int))\n",
        "labeled_barplot(data=mc, feature='main_camera_mp', \n",
        "                perc=True, n=None, percfmt=\"{:.0f}%\", sort=False,\n",
        "                figsize=(10, 5), xlabel='Main camera, MP', xlo=0,\n",
        "                title='Resolution of the rear camera in megapixels')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "wluX25dYkTHA",
        "outputId": "64245ea6-a38c-47d5-8d59-7a9335e99c67"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"mc = pd.DataFrame(df['main_camera_mp'].fillna(0).map(int))\\nlabeled_barplot(data=mc, feature='main_camera_mp', \\n                perc=True, n=None, percfmt=\\\"{:.0f}%\\\", sort=False,\\n                figsize=(10, 5), xlabel='Main camera, MP', xlo=0,\\n                title='Resolution of the rear camera in megapixels')\";\n",
              "                var nbb_formatted_code = \"mc = pd.DataFrame(df[\\\"main_camera_mp\\\"].fillna(0).map(int))\\nlabeled_barplot(\\n    data=mc,\\n    feature=\\\"main_camera_mp\\\",\\n    perc=True,\\n    n=None,\\n    percfmt=\\\"{:.0f}%\\\",\\n    sort=False,\\n    figsize=(10, 5),\\n    xlabel=\\\"Main camera, MP\\\",\\n    xlo=0,\\n    title=\\\"Resolution of the rear camera in megapixels\\\",\\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": [],
      "metadata": {
        "id": "G88-2SqYYiFj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The main camera resolution is approximately 9.5MP on average\n",
        "* The range is 265KP - 48MP\n",
        "* The most frequent (~30%) main camera resolution is 13MP\n",
        "* There are five outliers: one 41MP and four 48MP, one device with 0.08MP (probably a mistake)\n",
        "* 7% of devices have a main camera resolution less than 1MP"
      ],
      "metadata": {
        "id": "3NHzRXRdrn_c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`selfie_camera_mp`**"
      ],
      "metadata": {
        "id": "e_6SkPNL3DZ1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "histogram_boxplot(data=df.fillna(0), feature='selfie_camera_mp', fmt='{:.0f}', step=1,\n",
        "                  xlabel='Resolution of the front camera in megapixels')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "kFqzdSku3Dpu",
        "outputId": "f5689ea6-ea8d-4f0a-aebf-fe9559443288"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"histogram_boxplot(data=df.fillna(0), feature='selfie_camera_mp', fmt='{:.0f}', step=1,\\n                  xlabel='Resolution of the front camera in megapixels')\";\n",
              "                var nbb_formatted_code = \"histogram_boxplot(\\n    data=df.fillna(0),\\n    feature=\\\"selfie_camera_mp\\\",\\n    fmt=\\\"{:.0f}\\\",\\n    step=1,\\n    xlabel=\\\"Resolution of the front camera in megapixels\\\",\\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": [
        "sc = pd.DataFrame(df['selfie_camera_mp'].fillna(0).map(int))\n",
        "labeled_barplot(data=sc, feature='selfie_camera_mp', \n",
        "                perc=True, n=None, percfmt=\"{:.0f}%\", sort=False,\n",
        "                xlabel='Selfie camera, MP', xlo=0,\n",
        "                title='Resolution of the front camera in megapixels')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "J_RqsfTw3S7d",
        "outputId": "1b517c9e-7144-463e-8b69-cb0ac8db2dc1"
      },
      "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": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"sc = pd.DataFrame(df['selfie_camera_mp'].fillna(0).map(int))\\nlabeled_barplot(data=sc, feature='selfie_camera_mp', \\n                perc=True, n=None, percfmt=\\\"{:.0f}%\\\", sort=False,\\n                xlabel='Selfie camera, MP', xlo=0,\\n                title='Resolution of the front camera in megapixels')\";\n",
              "                var nbb_formatted_code = \"sc = pd.DataFrame(df[\\\"selfie_camera_mp\\\"].fillna(0).map(int))\\nlabeled_barplot(\\n    data=sc,\\n    feature=\\\"selfie_camera_mp\\\",\\n    perc=True,\\n    n=None,\\n    percfmt=\\\"{:.0f}%\\\",\\n    sort=False,\\n    xlabel=\\\"Selfie camera, MP\\\",\\n    xlo=0,\\n    title=\\\"Resolution of the front camera in megapixels\\\",\\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 selfie camera resolution is approximately 6.6MP on average\n",
        "* The range is 256KP- 32MP\n",
        "* The most frequent (~23%) main camera resolution is 5MP\n",
        "* There are five outliers - more than 16MP selfie camera\n",
        "* About 15% devices have a main camera resolution less than 1MP\n",
        "* 39 devices do not have a selfie camera"
      ],
      "metadata": {
        "id": "AFX_7YQ63D3m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`int_memory`**"
      ],
      "metadata": {
        "id": "90nLj8eOAyvf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "histogram_boxplot(data=df, feature='int_memory', fmt='{:.0f}', step=64,\n",
        "                  xlabel='Amount of internal memory (ROM) in GB')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "5LFkBmhTAy5n",
        "outputId": "608d1f9a-d6bf-4ed5-90d5-ddae548da571"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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Bc/dLgEsA5syZ46XOb+LEiQB8/vOfz7v8oosuonP1iwPKI1P7looBXArfWgs3vi5B9aTp3eZfSuWo9RMRERnuhmQN3FCUXfuWoVo4ERGR0ps3b95274844ojyFCSLArgK8Yxv3Fr7lpHCedo3lqlEkjt8hIaTGB70uYsMPyeffPJ278t9/xsM0SbUoeiXNXO7XXY5bYNYEhERkeFn3rx5W8eB2xkogBMZANW+DE/63EWGn5NPPnmHmrhyUhOqiIiISIVRACciIiJSYRTAiYiIiFQYBXAiIiIiFUYBnIiIiEiFUQAnIiIiUmEUwImIiIhUGAVwIiIiIhVGAZyIiIhIhdFMDEUwY8aMchehbIbzsYuIiJSLArgiOOmkk8pdhLIZzscuIiJSLmpCFREREakwCuBEREREKowCOBEREZEKowBOREREpMIogBMRERGpMArgRERERCqMAjgRERGRCqMATkRERKTCKIATERERqTAK4EREREQqjAI4ERERkQqjAE5ERESkwmgy+0Gyckuay//VlnfZii1pgG6XF7LvGZP6XTQRERGpMArgBsGMGTN6XF5nTQBUT5zYv/1P6j0PERERGToUwA2Ck046qdxFEBERkSFE98CJiIiIVBgFcCIiIiIVRgGciIiISIVRACciIiJSYRTAiYiIiFQYc/dyl6GkzKwJWFzuchRgArBmGOY93PMfzsc+3PMfzsde7vyH87EP9/wr4dhnuXuv44oN+QCuUpjZfHefM9zyHu75D+djH+75D+djL3f+w/nYh3v+Q+nY1YQqIiIiUmEUwImIiIhUGAVwO49Lhmnewz3/4Xzswz3/4Xzs5c5/OB/7cM9/yBy77oETERERqTCqgRMRERGpMArgRERERCqMAjgRERGRCqMATkRERKTCKIATERERqTAK4EREREQqjAI4ERERkQqjAE5ERESkwiiAExEREakwCuBEREREKowCOBEREZEKowBOREREpMIogBMRERGpMArgRERERCqMAjgRERGRCqMATkRERKTCKIATERERqTAK4EREREQqjAI4ERERkQpTVe4ClNqECRN89uzZ5S5Gj9auXQvA+PHje193S1y3sfd1RUREpLI8+uija9x9Ym/rDfkAbvbs2cyfP7/cxejRFVdcAcAZZ5zR67rzfjAPgPu+cF/JyiMiIiLlYWaLC1lPTagiIiIiFUYBnIiIiEiFUQAnIiIiUmEUwImIiIhUmCHfiWGoueNTd5S7CCIiIlJmCuAGUVe6izVta2hqW8Omjs28dsIB1FXV9Wkf9bX1JSqdiIiIVAoFcIPoryv+ztLmpVvfj6kZzX7j9u3TPn52788A+PhbP17UsomIiEjl0D1wgyTtaVa2rGTWyFmctPv7GFUzihUtK/q8nxvm38AN828oQQlFRESkUgxKAGdmI8zsYTN7wsyeNrNvxvRdzewhM1toZtebWU1Mr43vF8bls7P2dV5Mf97MjhqM8hfDhvYNpDzF9IZp1CZrmVI/hVWtq+lKd5W7aCIiIlJhBqsGrh043N0PAA4EjjazucD3gB+6+x7AeuCsuP5ZwPqY/sO4Hma2H3Ay8CrgaOBnZpYcpGMYkKa2NQBMrAuzY0yp34Uu76KpramcxRIREZEKNCgBnAdb4tvq+HDgcOD3Mf1K4N3x9fHxPXH5EWZmMf06d29395eBhcBBpT+CgWtqbaIuWUdjVQMAu9RPxjBWtKwsc8lERESk0gzaPXBmljSzx4HVwN3Ai8AGd0/FVZYC0+LracASgLh8IzA+Oz3PNju1prY1TKybQIhDoTpRzYQRE1jR3Pf74ERERGR4G7ReqO7eBRxoZmOAm4F9SpWXmZ0NnA0wc+bMUmVTsNZUK1s6t7D3mL22S5/SsAsL1j5J2tMkrLBYWpPYi4iIyKD3QnX3DcC9wBuBMWaWCSKnA8vi62XADIC4fDSwNjs9zzbZeVzi7nPcfc7EiRNLcRh90tQa738bsX1ZptRPAaAj3TnoZRIREZHKNVi9UCfGmjfMrA54O/AsIZB7X1ztdODW+Pq2+J64/C/u7jH95NhLdVdgT+DhwTiGgWhqayJhCcbVjt0ufcKI8VQnqunoai94XxfeeSEX3nlhsYsoIiIiFWSwauCmAPea2QLgEeBud78d+BLwOTNbSLjH7dK4/qXA+Jj+OeDLAO7+NHAD8AzwJ+ATsWl2p9bU2sT42vEkE9t3mE1Ygsl1k/tUA3f7gtu5fcHtxS6iiIiIVJBBuQfO3RcAr82T/hJ5epG6extwYjf7+jbw7WKXsVS60l2sbV/HPmP2zrt8bO0YurwLxwe5ZCIiIlKpNBNDia1rX0fa01vHf8s1qmYU4BrQV0RERAqmAK7ENnVsAmBMzZi8y0MAB107f0uwiIiI7CQ0mX2JbelsBqChuj7v8lHVIwFIbR0Or2d1NXXFKZiIiIhULAVwJbYl1Ux9VT3Jbmb8qknWkCBRcBPqHz/9x2IWT0RERCqQmlBLbEvnFhqrG3pcJ5moIqUmVBERESmQArgSa+7cQmNVY4/rVFmSrnRhTagX3H4BF9x+QTGKJiIiIhVKAVwJpT1NS6qVhl5q4KoSVaRJ017AgL73PHsP9zx7T7GKKCIiIhVIAVwJNadacJzG6p5r4DL3x23q2DwYxRIREZEKpwCuhLZ0bgHo9R64qkToS5IZckRERESkJwrgSqh56xAihdTAGZs6FcCJiIhI7zSMSAlt6dyCYTRU5R8DLlvSkgXVwI1vHF+MoomIiEgFUwBXQs2dYQy4hPVe0VllSTYWEMDd+LEbi1E0ERERqWAFNaGa2X5mNjm+bjSzb5rZN8ys96qlYWxLqvcx4DKSiSo2d24m7ekSl0pEREQqXaH3wF0LjImvLwQOBeYCvyxBmYaMLZ3Nvd7/llFlSdKepjnV0uN65910HufddF4xiiciIiIVqtAm1Nnu/ryZGfAeYD+gFXi5ZCWrcF3eRUuqhcaqwmvgIPREHdlD0PfAiw8UpXwiIiJSuQoN4NrMbCQhcHvF3deYWRUwonRFq2wtnaEmrbcx4DKq4lhwmzs2Q2Exn4iIiAxThQZwvwX+AowEfhLTXodq4LpV6BhwGQlLkLTk1u1EREREulPQPXDu/lngq8DH3D0TwKWBzxayvZnNMLN7zewZM3vazD4d0883s2Vm9nh8HJu1zXlmttDMnjezo7LSj45pC83sy4Ue6GDbksqMAVd4dVpjdaMCOBEREelVwcOIuPtdMRCb6+4Puvv8PuSTAj7v7o/FpthHzezuuOyH7n5h9spmth9wMvAqYCrwZzPbKy7+KfB2YCnwiJnd5u7P9KEsgyIzBlx9AWPAZTRWN2wN/Lozfez0gRZNREREKlxBAZyZzST0RD0QcKDRzN4HHO3uH+5te3dfAayIrzeb2bPAtB42OR64zt3bgZfNbCFwUFy20N1fiuW6Lq670wVwzZ3NNFQ3FDQGXEZjVSOrW5t6XOfqD1890KKJiIhIhSs0uvgl8AfCPXCdMe1uQk1Yn5jZbOC1wEMx6VwzW2Bml5nZ2Jg2DViStdnSmNZd+k5nS2czDQX2QM1orG6kM91JR1dHiUolIiIiQ0GhAdxBwHfdPU2ogcPdNwKj+5KZmTUCNwKfcfdNwM+B3Qk1eyuAi/qyvx7yOdvM5pvZ/Kamnmu0SqU51UxDdd/GOc50eOjpPrjPXPcZPnPdZwZSNBEREalwhQZwq4A9shPifWqvFJqRmVUTgrdr3P0mAHdf5e5dMTD8FduaSZcBM7I2nx7Tukvfjrtf4u5z3H3OxIkTCy1i0bg7bak26pJ1fdouM+hvTwHc40se5/Eljw+keCIiIlLhCg3gLgRuN7MzgSozOwW4HvheIRvHAYAvBZ5194uz0qdkrXYC8FR8fRtwspnVmtmuwJ7Aw8AjwJ5mtquZ1RA6OtxW4DEMmvZ0O2nSferAAFk1cL10ZBAREZHhraBODO5+mZmtBT5KuAftNOBr7n5Lgfm8Gfgg8KSZPR7TvgKcYmYHEpplF8X94+5Pm9kNhM4JKeAT7t4FYGbnAncCSeAyd3+6wDIMmtZUKwB1VX2rgatJ1FCdqNZQIiIiItIjc/dyl6Gk5syZ4/Pn92XEk/6ZNXsWrywOLcqvPuTVfOHSL3HBKd/khcde6HXbM844A4ArrriCC275NutXruPic7q5HfAd8fkP4WnmrJksXrR4gKUXERGRnYGZPeruc3pdr5AAzsz+hzCsxz+z0t4EnOTunxlIQUttsAI4M+PxpicAWNe+jqXNy9hn9N7UJGt63fZvN/8NgLec8BYWbV5Me7qdvUfvlXfdb930LQC+/p6vA3DgxAMY6kG4iIjIcFFoAFfoQL6nAP+Zk/YocAvwmT6VbBhIpVMAVCUKHid5q5pENZs7N+PuhFsHt5cJ3ERERGT4KrQTg+dZN9mH7YeVznSKpCX6NIhvRk2yBsfpCrf8iYiIiOyg0Ajjb8B/mYWIJD6fH9MlR2e6k6pEdb+2rUmEJteOdP7BfL9107e2NqOKiIjI8FRoG9+ngduBFWa2GJhJGHj3XaUqWCVLeYpq63vzKUB1JoDr6sg7DMniNeqwICIiMtwVOozIUjN7HXAwYfDcJcDDcQBeydGZ7uzzNFoZNclQc9ddDZyIiIhIwdVEMVh7oIRlGRLcnVQ6RXU/OjAAJC1J0pJ0pDt7X1lERESGpW6jDDN71t33ja+XEOdAzeXuM0tUtorU5V043u974CDcB6cJ7UVERKQ7PVUTfSTr9amlLshQkRlCpL/3wEHoiZqZzSHX3lP27vd+RUREZGjoNspw979nvX3a3dcMQnkqXqeHps/qAdXAVbMpvSnvWHBffNcXB1Q+ERERqXyFDiPyipndYWanmln/7s4fJgYyiG9GTSKMBZfyVLGKJSIiIkNIoQHcTMIwIucAK83sWjN7l9kA2gmHqM50MWrgtg0lkusr15/HV64/r9/7FhERkcpXUADn7mvc/WfufgiwP/AE8G3CWHCSpTOdItHPWRgyqnsYSmTVxtWs2ri63/sWERGRytefKGMSMBmYAGwoammGgDCIb/9r36D32RhERERkeCsogDOz/czsAjNbSJjAHuDd7r5nyUpWocI0WgNrWU5YgiqroqNLY8GJiIjIjgqNNP4B3Ah8FLhXMzB0L5VOUV9VN+D91CRrVAMnIiIieRUawE12d0UTvXD3WAM3asD7qklU05xq2SH9gJmvGfC+RUREpLIVGsB1mtlHgFOACe7+GjM7FNjF3W8oXfEqS9rTOD6gQXwzahI1bEhv3GEsuE8d/ekB71tEREQqW6GdGL4FnAVcQhhSBGAp8KVCNjazGWZ2r5k9Y2ZPm9mnY/o4M7vbzF6Iz2NjupnZ/5jZQjNbYGavy9rX6XH9F8zs9EIPdDBkBvEdyDRaGdWxI0On5kQVERGRHIUGcGcA73T369g2J+rLwG4Fbp8CPu/u+wFzgU+Y2X7Al4F7YmeIe+J7gGOAPePjbODnEAI+4BvAwcBBwDcyQd/OoDMzjdYAOzFAuAcOduyJ+vmrP8fnr/7cgPcvIiIilavQAC4JbImvMwFcY1Zaj9x9hbs/Fl9vBp4FpgHHA1fG1a4E3h1fHw/8xoMHgTFmNgU4Crjb3de5+3rgbuDoAo+h5FLp4tXA1STyjwW3oWUjG1o2Dnj/IiIiUrkKDeDuAC42s1oITZzABcD/9TVDM5sNvBZ4iNA5IjMY8ErC+HIQgrslWZstjWndpe8UUsWsgds6G4OaUEVERGR7hQZwnwOmABuB0YSat1kUeA9chpk1EoYj+Yy7b8pe5u7Ottq9ATGzs81svpnNb2pqKsYuC9LpKQwj0a/xkbdnZlQnqjWUiIiIiOyg0Km0Nrn7CYSgbS6wu7ufEJtDC2Jm1YTg7Rp3vykmr4pNo8TnzBxRy4AZWZtPj2ndpeeW9xJ3n+PucyZOnFhoEQcslU5RnajertfoQNQkNBaciIiI7KjbAM7MErkPoAl4FFidldar2OR6KfCsu1+cteg2INOT9HTg1qz002Jv1LnAxtjUeidwpJmNjZ0XjoxpO4VUEWZhyFaTqKYzZ0L7g3c/iIN3P6hoeYiIiEjl6SnaSFFYk2aygHXeDHwQeNLMHo9pXwG+C9xgZmcBi4GT4rI7gGOBhUALcCaAu68zswuAR+J633L3dQXkPyg6PUVtorZo+6tO1NDpKdKeJhFj5bOP+GjR9i8iIiKVqacAbtes1+8A3gd8hxBoZe5/u7GQTNz970B37YpH5FnfgU90s6/LgMsKyXewpdIpGqsaira/zFAinelOapPFCwxFRESksnUbwLn74sxrM/scMMfdN8Skf5vZfGA+cYy24a6quoou7ypyE+q2seAyAdwnLv84AD8982dFy0dEREQqS6HdJUcD9Tlp9TFdgFHjw/ynVTbwMeAyto4Fl3UfXFtnO22d7UXLQ0RERCpPodVFVwJ/NrMfEcZhmwF8im2D8A57oyeOAYozBlxGdaIaw9QTVURERLZTaLTxRUKHgvcDU4EVwE+AX5WoXBVn9IRQGVnMJlQzoyZRQ3uXAjgRERHZpqBow93TwC/iQ/IYE2vgijGNVrbaZC3tXWoyFRERkW2KV100zI2eGGvgrJBRVQpXm6xhc+dm3B0z49B9Di3q/kVERKTyKIArkjETxpC05Nbx2oqlNlmL41t7op5+6Om9byQiIiJDWnGjjWFs9ITRRb3/LSMzMLCaUUVERCSjp6m0Hsx6/Y3BKU7lGj1hNNVFHEIkIzP+W3vsiXrWJWdx1iVnFT0fERERqRw91cDtZWYj4uvPD0ZhKtnoiWNKUgOXtCRJS6oGTkRERLbqKeK4lTDjwiKgzsz+mm8ldx/2d9W7e8maUM2M2kQNHQrgREREJOppKq0zzewQYDbwBuDSwSpUpelMd1JbV0u1laZPSG2yli2dW0qybxEREak8PUYccRL6v5tZjbtr1oVutHa1AcUdxDdbbbKW9R0b6PKukuxfREREKkuhA/leZmbzgNOAacAy4Cp3v7d0RascbalWoPiD+GbUxI4MHV0dHPnqI0uSh4iIiFSOggI4M/sw8N/Ar4GHgJnAtWb2NXcf9tNptXaFAK5kTahZQ4m8/43vL0keIiIiUjn6Mhfq2939iUyCmV0P3IjmQ6U1Veom1BoA2tPttHaEYLGupq4keYmIiMjOr9CIYzzwTE7a88C44hanMrWmWkl1pEgWeRqtjIQlqE5U097VzrlXnQvApWerT4mIiMhwVehMDH8HLjazegAzawB+APyzkI3N7DIzW21mT2WlnW9my8zs8fg4NmvZeWa20MyeN7OjstKPjmkLzezLBZa95Nq62ti4ZgNmVrI8ahO1tHd1lGz/IiIiUjkKDeDOAQ4ANprZKmBDfP/RAre/Ajg6T/oP3f3A+LgDwMz2A04GXhW3+ZmZJc0sCfwUOAbYDzglrlt2ralWNqzZWNI8apO1cTBfL2k+IiIisvMrtBfqCuBQM5sOTAWWu/vSQjNx97+a2ewCVz8euM7d24GXzWwhcFBcttDdXwIws+viurlNu4OutauNjYMQwKVJ4+4lrekTERGRnV+fJrN396Xu/nBfgrdenGtmC2IT69iYNg1YkrXO0pjWXXrZtaZa2di0oaR51CXDrGZp0iXNR0RERHZ+fQrgiuznwO7AgcAK4KJi7djMzjaz+WY2v6mpqVi7zcvdmdE4neceea6k+YyoCgHcEa95G8e9/riS5iUiIiI7t9KMe1EAd1+VeW1mvwJuj2+XATOyVp0e0+ghPXfflwCXAMyZM6ekN42ZGXMnH8wD/1dQf45+S1qS2kQtb9n/UGaPnFXSvERERGTn1msNnJklzOxwM6spZsZmNiXr7QlApofqbcDJZlZrZrsCewIPA48Ae5rZrrEsJ8d1h40RVSNYtXkV65vXl7soIiIiUka91sC5e9rMbnX3kf3NxMyuBeYBE8xsKfANYJ6ZHUjoVrmI2KPV3Z82sxsInRNSwCfcwySgZnYucCeQBC5z96f7W6ZKVJes44e3XER9VQOXnX1ZuYsjIiIiZVJoE+pfzWyuuz/Yn0zc/ZQ8yd2OROvu3wa+nSf9DuCO/pRhKKirCrMvpF0dGURERIazQgO4xcAfzexWQk/QrfeVufvXS1Ew2dHWnqihQlJERESGqUIDuDrglvh6emmKIr2pSlRhJFQDJyIiMswVOpDvmaUuiBQmYQrgREREhruChxExs32AE4HJ7n6ume0N1Lr7gpKVTnZw3Jzj2Ni5ibSnSVg5h/ETERGRcikoAjCzE4G/EWY+OC0mjwQuLlG5pBtHH3AMc/eZS2tXW7mLIiIiImVSaBXOt4C3ufs5QOYO+icIE9rLINq0eQNrN62lLdVa7qKIiIhImRQawE0CMk2lnvVc0lkOZEfn33g+l9zxC1oUwImIiAxbhQZwjwIfzEk7mTBDggwqI2FJmlPN5S6IiIiIlEmhnRg+BdxlZmcBDWZ2J7AXcGTJSibdSlqCjnQHnenOchdFREREyqDQYUSei71Q30mYdH4JcLu7byll4SS/pCUBaE61lLkkIiIiUg4FDyPi7i1m9g/gZWC5grfySViCBAmaO/URiIiIDEcFBXBmNhO4BpgLrAfGmtmDwKnuvriE5ZMcp70l3IpYX1WvGjgREZFhqtBODFcSOjKMcfdJwFhgfkyXQXTYvvM4bN95NFQ30NbVRsPohnIXSURERAZZoU2orweOdPdOAHffYmZfAtaWrGSS16KmRQBMHDORVcBer9+7rOURERGRwVdoDdyDwEE5aXOAB4pbHOnNBTdfwAU3X0BdVR2GsfccBXAiIiLDTbc1cGb2ray3LwJ3mNkfCD1QZwDHAr8tbfGkOwlLUF9Vz95v2KfcRREREZFB1lMT6oyc9zfF50lAO3AzMKIUhZLCNFTVM3u/2XSmO6lOVJe7OCIiIjJIug3g3P3MwSyI9F1DdSPJqiQrW1Yxo3F6uYsjIiIig6TQe+Aws3oze42ZvSn7UeC2l5nZajN7KittnJndbWYvxOexMd3M7H/MbKGZLTCz12Vtc3pc/wUzO70vBzoUNVTV09bcxvLm5eUuioiIiAyiQseBOw34CdABZM+i7sDMAnZxRdz+N1lpXwbucffvmtmX4/svAccAe8bHwcDPgYPNbBzwDULnCQceNbPb3H19IccwVHzk8I9sfZ2wBM889AzjjxyPu2NmZSyZiIiIDJZCa+C+D7zX3Se4+4ysRyHBG+7+V2BdTvLxbBtH7krg3Vnpv/HgQWCMmU0BjgLudvd1MWi7Gzi6wPIPGXP3mMvcPeZuff/k3xbQnGpmU+fmMpZKREREBlOhAVwHcF+R857s7ivi65XA5Ph6GqGna8bSmNZd+rDy3PLneG75c1vfP/HXJwDUjCoiIjKMFBrAfQ242MwmlKIQ7u6EZtGiMLOzzWy+mc1vamoq1m53Cj+4/Qf84PYfbH2/ZmkTo6pHKYATEREZRgoN4P4NHAesMrOu+EibWdcA8l4Vm0aJz6tj+jK2H8JkekzrLn0H7n6Ju89x9zkTJ04cQBErw9SGKaxqXU0qnSp3UURERGQQFBrAXUXogHAAsFd87Bmf++s2INOT9HTg1qz002Jv1LnAxtjUeidwpJmNjT1Wj4xpw960hql0eRerWleVuygiIiIyCAqdC3U88PXY1NlnZnYtMA+YYGZLCb1JvwvcYGZnAYuBk+LqdxBmeVgItABnArj7OjO7AHgkrvctd8/tGDEsTaqbRNKSLG9ewbSGYXdboIiIyLBTaAB3OfBBth8GpGDufko3i47Is64Dn+hmP5cBl/WnDENZVaKKyXWTWda8nDeUuzAiIiJScoUGcAcB55rZV4Ht2unc/dCil0q69cmjPpk3fWrDFOY3LWdzx2ZG1owc5FKJiIjIYCo0gPtVfEiZHTjrwLzp0xqmMr/pUZa3LGfvmr0Ht1AiIiIyqAoK4Nz9yt7XksHw+OLHgR0DuVE1oxhZ3ciy5hXsPUYBnIiIyFBW6FRaH+puWbwvTQbJ/975vwBcevalOyybWj+VhZtepCvdRTKRHOyiiYiIyCAptAn1gznvdwF2B/6BOhXsNKY2TOX5jf9mVetqpjZMKXdxREREpEQKbUJ9a25arJXbt+glkn6bXD+ZhCVY3rJcAZyIiMgQVuhAvvlcAZxVpHJIEVQnqphcN0nTaomIiAxxBQVwZpbIeTQCZwMbSlo66bOpDVPZ2LGJLZ1byl0UERERKZFC74FLseNk88uAjxS3ONKbL7zzCz0un9YwlUebHmN583L2GjOQmc5ERERkZ1VoALdrzvtmd19T7MJI7/aZuk+Py0dVj6KxupFlCuBERESGrEI7MSwudUGkMA8ufBCAuXvMzbvczJim4URERESGtB4DODO7lx2bTrO5u+8wn6mUzq/+EibE6C6AA5jWmBlOZBVTG6YOVtFERERkkPRWA3d1N+nTgE8B9cUtjhTD5LrJJC3JsublCuBERESGoB4DOHffbrh/MxsPnEfovHA98K3SFU36qypRxS71k1nWvIw5/nrMrNxFEhERkSIqdBiRUWZ2AbAQmAy8zt3PdvelJS2d9Nu0hmls7tzC5s7N5S6KiIiIFFmPAZyZ1ZnZecBLhFkXDnH3D7r7i4NSOum3TNPp0uZlZS6JiIiIFFtv98AtIgR53wfmA5PNbHL2Cu7+l9IUTfL52glfK2i9kdWNjK4ZxbLm5ew3VjOeiYiIDCW9BXCthF6oH+tmuQO7DaQAZrYI2Ax0ASl3n2Nm4wj32M0mBJEnuft6Czdz/Rg4FmgBznD3xwaSf6WZPXF2wetOb5jOM+ufpb2rndpkbekKJSIiIoOqxyZUd5/t7rv28BhQ8Jblre5+oLvPie+/DNzj7nsC98T3AMcAe8bH2cDPi5R/xbj/2fu4/9n7Clp3RuMMHGdZCedGbUm1sHjzYjq6OkqWh4iIiGyv0JkYBtvxwLz4+krgPuBLMf037u7Ag2Y2xsymuPuKspSyDH7zt6sAOGzfeb2uO2HEeOqSI1iyZSm7jcqdTGNgVrWs4ql1T7OiZSWOU19Vx8GTDmZ647Si5iMiIiI7KqgXaok5cJeZPWpmZ8e0yVlB2UpCz1cI488tydp2aUyTPMyM6Y3TWd68nK50V9H2u659Pfcsu5cNHRvYf9yreOvUw6hO1HDv8vt4ePUjRctHRERE8tsZArhD3P11hObRT5jZodkLY21bT7NB7MDMzjaz+WY2v6mpqYhF3fkkkgnMrNvH2SeeTcpTvP7wOT2uV+ijYXQDVz1wFTWJGo6deQwHTjiA6Y3TecfMY9h7zF48v+HfLN78SrlPi4iIyJBW9iZUd18Wn1eb2c3AQcCqTNOomU0BVsfVlwEzsjafHtNy93kJcAnAnDlz+hT8VZp0V5rHm57ofrmneWbDs/zXld9mRsP0AeXl7izaspj1zes5dOpbqKuq27osmUgyZ+LraWpdw8OrH2Fy/SRGJEcMKD8RERHJr6w1cGbWYGYjM6+BI4GngNuA0+NqpwO3xte3AadZMBfYOJzuf+uPhCUYWT2STR2bCJWZ/beufR2bOzdzzXeuZlLdxLx5vWmXuXR0dfDI6vkDyktERES6V+4auMnAzXGqpyrgt+7+JzN7BLjBzM4CFgMnxfXvIAwhspAwjMiZg1/k8vr2Sd/u8zajq0exsWMjLakWGqob+pVvl3exqnU1DVX13PPbP8M1+dcbWzuWV4/fnyfWLmC3kbsyTZ0aREREiq6sAZy7vwQckCd9LXBEnnQHPjEIRdtp7TJmlz5vM7JmJNZsrO9Y3+8Arqm1iZSnmF0/q9d19x/3Kl7c9BKPr13A1IapmotVRESkyHaGTgzSB3cu+BN3LvhTn7ZJWpIxNWPY0L6RLu97b9SOdCdNbWsYUzOa+qr6XtdPWIJXj9ufde3rSjoGnYiIyHClAK7C3PDg77jhwd/1ebtxtWNJk2Zjx8Y+b7uqZSUAu9QVXvu326hdaahqYMHaJwd8752IiIhsTwHcMFFfVU9topZ17ev7tF1rqpX1HRuYMGI8NckaoPehS8yMZCLJ/375x6xtX8sBhx1YlCFMZs3uvflWRERkOCh3JwYZJGbG2NqxrGxdSVtXW0FDfLg7y1tWkLQkk0ZM2pre29AlW9fzNM9v/DffuPwb7D5q9wHfC3fgxB1ulxQRERmWVAM3jIytHQNQcC3c5s7NNKeamVw3iWQi2ef8EpZg0oiJtHS1siW1pc/bi4iISH4K4IaR6kQ1o6pHsb59Pal0qsd13Z0VLSupSdQwvnZ8v/McWzuW6kQ1q1pX6144ERGRIlETaoW58AMXDmj7yXWTeGHTQla2rmJ6Q/djtDW1raE93c6sxlkDavpMWIKJIyayvGU5W1LNjKxu7Pe+REREJFANXIUZ2zCWsQ1j+719XVUdE2rHs659HS2plrzrNHc2s7J1JaOqRzGqemS/88oYVzuWKqtidevq3lcWERGRXimAqzC3Pnortz56a+8r9mBy3WSqrIplzct3aNZMpVO80ryEmkQNMxqmF2UQ3oQlmFQ3keZUM1s6dS+ciIjIQCmAqzC3PXobtz1624D2kUwkmVK/C61drSxpXrr1fri2rjYWb3mFVDrFzMaZ/eq40J1xteOosipWtKzUvXAiIiIDpHvghqkxNWNo62qnqa2JTZ2baKiqZ3PnFgxjWsM06qvqippfwhLsUr8LS5uXsqFjA2Nr+98MLCIiMtwpgBumzIwp9bswtnYsK1pW0JpqZdKIiUwYMYGqRGkui7E1Y1jbtoaVrasYXTOahKkCWEREpD8UwA1zI5K17Dpy9qDkFYLGqby0+SWa2tYwuW5S7xuJiIjIDlQFIoOqsbqBUdWjWN26mrau9nIXR0REpCKpBq7C/OSMn5S7CAM2tWEqL2x8gVe2vMIeo3avuKbUjq4OVrauojXVSke6A4AxNaMZUzuWxqqGovTcFRER6YkCuApTV1PczgXlUJOoZmbDDF7esojlLcuZ3jC93EXqVSqd4sVNL/HypkWsaVuDk78n7cjqRnYbtRu7jdqVRg1aLCIiJaIArsJc/8D1ALz/je8vc0kGZmTNSCaNmMjqtibqknWMH9H/6bpKqTXVynMbnuffG16gI93B2Nqx7D/uVUxtmMLI6pFUJ6pxnA3tG1nXvo7Fm1/hibULeGLtAmY2zmT/cfvttMcmIiKVSwFchbnrybuAyg/gIAwo3NrVyrKW5aQ8xaQRk3aa5scN7Rt4Zv1zvLz5ZVJdKR675zH+dPkd/PvRf/e67fip43nr+4/giP84gle2vMJT/3iS2y+5nWcefLpPZZg5ayaLFy3u7yFIkbk7bV3tNKea6ci6f7M2OYLG6gZqEjU7zfUrIkNfRQZwZnY08GMgCfza3b9b5iJJP5gZsxpnsax5GataV9PR1cHU+qlFHUC4L9ydlS0reWb9cyxvWU7SkuwxaneOP/g47px/Fx96/5l92l+Xd7G2bR1Vh1Sx/5tfvbWmcUyBQ6gcOPGA/h6KFEF7VztNrU00ta2hqXUNa9vWkvJUt+tXJ6qZOGICE+omMDEOyVOTrB7EEovIcFJxAZyZJYGfAm8HlgKPmNlt7v5MeUsm/ZGwBNMbplOTrGFV62o2dm5i4ogJjK+dQNUgBHKZIGvJliUs2ryYllQLI5IjOGD8a9hrzJ6MSI5g1eJV/dp30pJMqpvIhBHjWd++gTVta1javJTlLcsZXT2aUTUjaaxuJGnlCVglmDV7FsuWL2PmPrPY7dW7sfsBu7P7AXswZdcpAHSlunjlucUsfHwhK19ewdoVa9m0bjO4Ywlj9PjRjJ86nim7TWOP1+7B9D2nk0gkSKfTLH1hKS898SIvLXiRFxe8yLKFy0h3pVW7KiIDVnEBHHAQsNDdXwIws+uA4wEFcBXKzJhcN5lR1aNY1bp666M+WUdjdSO1yVqqEzXssusUmjubSSaSJK2KKktu12SVmaIr08HAcVLpFB3pTjq7OsJzuoMtnc1s7tzMhvaNrGlbQ5d3YRhTG6bw2gkHMqvI04glLMH4EeMYVzuWllQLa9vXsbFzI+s71mMYtcla6pJ1jEjWUp2opjpRTdKSTJw+kdZUK1WJKpKWrKjeurnTpXXX6WOH5XlWy922p3ch7x2XdaY76Uh30NHVQXt8bkm1sLlzM2f+4EPsccCeW/OpsirqqupoqKqnPj5eO+lAOLTHQ9iqK91FS1cLLakWRu0/il332ZV5J70VAMOoq6rjj9fdwZNrn6K+qp6aZA01iRpqkjXUJmqoSlRhliCBYWZYfBbpTSHTFPb2t9jnbQvcXaH55l+rgOPKu0r+7bo8Tdq7SHkXXd5FVzo+577Ok1aTrOHV4/Yv6FhKrRIDuGnAkqz3S4GDy1QWKaK6qjpmj5xFa6qVjR2b2NK5mdVtTVuXf/9PP+Cml2/ZbhvD+vWFVJ2oYlT1aPYcvQeT6iaxS/1kapO1Az2EHpkZDdUNNFQ34O40p5rZ3LmF1q5WNnduZn3H+u3Wv+ieH/L7l27afh/Yju+th2X0/MXZ2xdyn7etMPVV9bS3dTB+xPgQrCXrqE5UDyhgSiaSjEyMZGT1SCD8qHakO2hJtdKaaqGlq5UDDj2Qx9c+0af9JkjslIFc7nW3s+nvdVrwdgX801GSfGVQpdNpOts6WP1KE68+bucI4KzSJhY3s/cBR7v7h+P7DwIHu/u5WeucDZwd3+4NPD/oBe27CcCaYZj3cM9/OB/7cM9/OB97ufMfzsc+3POvhGOf5e4Te9tRJdbALQNmZL2fHtO2cvdLgEsGs1ADZWbz3X3OcMt7uOc/nI99uOc/nI+93PkP52Mf7vkPpWOvnJtqtnkE2NPMdjWzGuBk4LYyl0lERERk0FRcDZy7p8zsXOBOwjAil7l73wbYEhEREalgFRfAAbj7HcAd5S5HkZWzybfczc3DOf/hfOzDPf/hfOzlzn84H/twz3/IHHvFdWIQERERGe4q8R44ERERkWFNAVyZmdnRZva8mS00sy8PUp5jzOz3ZvacmT1rZm/MWvZ5M3Mzm1DE/C4zs9Vm9lRW2g9i/gvM7GYzGxPTq83sSjN7MpbtvBLkfYCZPRDz+D8zGxXT325mj8b0R83s8IHkHfc5w8zuNbNnzOxpM/t0TD/fzJaZ2ePxcWzWNq+J5Xs6lmVEP/MeYWYPm9kTcV/fjOnXxGvuqXh+qmO6mdn/xGtxgZm9boDH3l3+R5jZY/G4/25me+Rs9954DRalp5aZJc3sX2Z2e076/5jZlqz3M+Nn9a94/MfuuLc+570ofoaPm9n8mHZiPB/p7GMs9vXXTd7XZ11zi8zs8az1i3LdZe2v4O+ZElx7e2cd5+NmtsnMPmNm48zsbjN7IT6PzdnuDWaWsjBcVVHzjss+Gc/H02b2/ZhW1O+8uM/PxjyeMrNr49/ipfFvcUH8XBrjup+z8P20wMzuMbNZJcr/8Ph3/1Q83qqs9efFc/W0md0/wLw/HfN4OnPeY/oO5z6mnxevu+fN7Kh+5JfvNybvdWZmH4jn+Ukz+6eZHZCzr7zfVd1ydz3K9CB0wngR2A2oAZ4A9huEfK8EPhxf1wBj4usZhM4hi4EJRczvUOB1wFNZaUcCVfH194Dvxdf/AVwXX9cDi4DZRc77EeCw+PpDwAXx9WuBqfH1/sCyIhz7FOB18fVI4N/AfsD5wH/mWb8KWAAcEN+PB5L9zNuAxvi6GngImAscG5cZcC3wsbjOscAfY/pc4KEBHnt3+f8b2Demfxy4ImubkcBfgQeBOUW6/j4H/Ba4PSttDnAVsCUr7ZKsc7EfsKgIeS/K/VsC9iWMT3lf9jEW+/rLl3fO8ouArxf7usvaf8HfM8W+9nLKkQRWArOA7wNfjulfJn7vZK33F8L91e8rQd5vBf4M1MZlk+Jzsb/zpgEvA3Xx/Q3AGcCorHUuzjoPbwXq4+uPAdcP8Jjz5f8hwgD8e8W0bwFnxddjCDMpzcw+L/3Me3/gqXgeq+L53qOHc78f4Xe3FtiV8Hvcp+ue/L8xea8z4E3A2Pj6mNzrnDzfVT09VANXXlunBXP3DiAzLVjJmNlowgV3KYC7d7j7hrj4h8AXKXhylMK4+1+BdTlpd7lvnRn8QcJ4fsS8G+J/Z3VAB7CpmHkDexGCBIC7gffGdf/l7stj+tNAnZkNaHoGd1/h7o/F15uBZwlfcN05Eljg7k/Ebda6e1c/83Z3z9QwVceHu/sdcZkDD7Pt3B8P/CYuehAYY2ZT+pN3T/nHx6iYPhpYnrXZBYSAvq2/+WYzs+nAO4BfZ6UlgR8QrvXtitxDuYrG3Z919x0GFy/F9dcdMzPgJEIAD0W87uL++/o9U9RrL8cRwIvuvjjmc2VMvxJ4d9Z6nwRuBFYXKd/cvD8GfNfd2wHcPZNPUb/zoirC9VNFCGaWu/sm2PrZ18V8cfd73b0lbpf9XVzM/JuBDnf/d1y+9XuXEMDe5O6vxPIM5PzvSwiKWuLvy/3Ae+j+3B9PCJ7b3f1lYCHhd7lg3fzG5L3O3P2f7p6Zcme7c53vu6o3CuDKK9+0YD39uBfDrkATcHmsqv21mTWY2fGE//j7Nr9PcXyI8N83wO8Jf+wrgFeAC909949joJ5mW6B8ItsPDJ3xXuCxzB98MZjZbEIty0Mx6dxYnX5ZVlPOXoCb2Z2xuSE3yOhrnkkLzWSrgbvd/aGsZdXAB4E/xaSiX4/d5P9h4A4zWxrz/25c93XADHf/w0DyzPEjQrCQzko7F7jN3VfkrHs+cGos1x2EH/SBcuAuC02iZ/e69jbFuP56yvstwCp3fyG+L+p1R9+/Z0r5XXgy2wLVyVmf+0pgMoCZTQNOAH5epDzz5b0X8BYze8jM7jezN8T0on7nufsy4MK4rxXARne/C8DMLicc9z7A/+bZ/Cy2fRcXLX9CLVyVbbtl4H1s+97dCxhrZvfFa/W0AWT/FOEcjzezekLN7gy6P/eluu7yXmc5cs/1j9jxu6pHCuCGnypCde/P3f21hC+O84GvAF8f7MKY2VeBFHBNTDoI6AKmEn4EPm9muxU52w8BHzezRwlNdh05ZXoVoRboo8XKMN5vciPwmfif8M+B3YEDCV9yF8VVq4BDgA/E5xPM7Ij+5uvuXe5+IOE/vYPMLHsSv58Bf3X3v/V3//3M/7PAse4+HbgcuNjMEoRmnc8XK28zeyew2t0fzUqbSgja8/14nUJozp1O+OK/KpZrIA5x99cRmks+YWaHFlDuYl1/PeV9CtsCCyjydcdO8j1jYbD344Df5S6LNdCZWsAfAV9y94J/PPuRdxUwjtBE/AXghlgbVtTvvPjP4PFxX1MJtXunArj7mTHtWeD9OdudSri14Af9zbu7/AnX1cnAD83sYWAz4ZghnJfXE2qfjgK+ZmZ79Sdvd3+W8LdzF+Ef08djPt2d+5LLuc4AMLO3EgK4L8X3O3xXFUIBXHn1Oi1YCSwFlmbVxPye8EW7K/CEmS2K5XjMzHYpZUHM7AzgncAH4kUOoTr9T+7eGau5/0H4Uikad3/O3Y9099cTfsRezCrTdOBm4DR3f7G7ffRFrOm6EbjG3W+KZVgVg5s08Cu2VdsvJQRVa2Kzxh2Ez2dAYvPVvcDRsUzfACYS7rnIKNn1mJX/MYT7rDLX3/WE+0JGEu5fuS9eg3OB22xgHRneDBwX93cdcDih9nUPYGFMrzezhXH9swg1Bbj7A8AIwryF/RZrIzJNNjfTS/NMMa+/7vKOzVrvIZz7jGJfd339ninVtXcMoSZzVXy/KtM0G58zTWlzgOtiud4H/MzM3l3kvJcSmgrd3R8m1LRMoPjfeW8DXnb3JnfvBG4i/I0B4Z8qwt9DpgkTM3sb8FXguCK0OuTN390fcPe3uPtBhFtYMs2pS4E73b3Z3dfEZQfk3XMB3P1Sd3+9ux8KrI/5dHfuS3XddXedYWavITSTHu/ua2PyDt9VZnZ1b5kogCuvQZ8WzN1XAkvMbO+YdAThS2aSu89299mEi/11cd2SMLOjCdXFx2XdfwGh2v3wuE4D4Yf8uSLnPSk+J4D/B/wivh8D/IFw8+k/ipSXEe4DetbdL85Kz76/5wRC1T+Em7tfbWb18Yf2MMINvv3Je6Jt691bB7wdeM7MPkz4T/eUnBqH24DTLJhLaHrJbWYcaP7PAqOz/sN+O+HcbHT3CVnX4IOEa2N+f/N39/PcfXrc38nAX9x9rLvvkpVPi7tnesG+Qvh7wMz2JQRwTf3NPzYZjsy8Jtxn9lQP64+hSNdfL3m/DXjO3ZdmbVK06w769T1T1GsvS25N423A6fH16cCtsby7ZpXr98DH3f2WIud9C+FmeuL1X0OY1LzY33mvAHPjZ2mEc/+sxd7eMe24TB5m9lrgl4S/t2Lc/9dd/pnv3VpCzdMv4vq3AoeYWVVs9jyY8D3RL1n5zCT8o/Jbuj/3twEnm1mtme0K7Em4L3ig8l5nsUw3AR/0bfcDdvdddWqvuXiRevro0e9eM8cS/kN4EfjqIOV5IDCf0OvsFmKvmKzliyhuL9RrCc2EnYQv7bMIN4suIVRxPw78Iq7bSGhyeJrwA/KFEuT96XjO/024/yozoPX/IzT1PJ716HePqLjPQwjV5wuy9nksoQfkkzH9NmBK1janxuN/Cvj+APJ+DfCvmMdTbOtxmIrXW6Y8mXQDfhqXPckAe4H2kP8Jcf9PEHpi7pZn2/sGmn/O/uaRp2cX2/dC3Y9Q+/FEPC9HDjDP3eK+noif51ezjn8p0A6sItQ+FPX66y7vuOwK4Jw82xTlusva34EU+D1T7Gsv7rMBWAuMzkobD9wDvEDolTguz3ZXMMBeqN3kXQNcHc/vY8DhMb2o33lxn98kBGhPEb5rauO1/WRMu4bYKzWeh1VZ19xtJcr/B4TA7HnCrSTZ638hHvtTucv6kfff4r6eAI7o6dzHZV+N193zwDH9yC/fb0ze64xQ87Y+61zPz7O/eRTYC1UzMYiIiIhUGDWhioiIiFQYBXAiIiIiFUYBnIiIiEiFUQAnIiIiUmEUwImIiIhUGAVwItJvcdyuy81sfRxhPXf5B8zsrnKUra/M7Awz+3u5y1EOZnZtEQau7W/enzSz7/WyzpaBzE4gMhQpgBMpIQvz+623Ek1KPlBFCFoOIQzGO93DCOvbcfdr3P3IQSqL9EMcGf4Atg02eoaZdcWgaZOZPRGn+sneptbMvmNmr5hZq5m9YGZfiAO3Zta5z8zczA7I2fbmmD4vJv0K+EBmANZ83L3R3V/q5/HVmNnXzex5M2s2s2Vm9kczOzJrnUXxOLbEv9c/mFm+OZJFdhoK4ERKxMLk9W8hDOR7XHlLUzKzgEXu3lzugsQZBIa1fp6DjxKmecseFPQBd28ExhDmzL0uM6tG9DvCCPvHEqZB+yBwNvDjnH3/G9g6ObmZjQfeSNYMF+7eRpjUeyCTmPfk94S5OU8DxhKm8/oxYe7NbO+KxzyFMLBtvvlyRXYaCuBESuc0wpRQV7BtWhUAzOwKM/tZrAnYYmb/MLNdzOxHsQbguTjFTWb9fWONxgYze9rMjstadp+F6bEy77eryYq1HefEWpINZvbT2PS5L2E6mzfGMmzIdxBmNtXMbjOzdWa20Mw+EtPPIowsntn+m3m2HVBZYk3PhbGmZ5WZ/cLCtFyY2TwzW2pmXzKzlcDlZna+md1gZr8xs83xXM3Jyv/LZvZiXPaMmZ3Q66cYtpsdy36mmS2Jn9E5ZvYGM1sQj+UnOdt8yMyejeveaWazcs7Dx+N52GxmF5jZ7mb2Twu1XjdYmF4vs/5H4rlfFz+LqTn7+oSZvQC8EM/pRTlluc3MPtvN4R0D3J9vgYep1q4izCywZ9zXEYSpud7r7k+5e8rdHyTM5PAJi1M2RdcA7zezZHx/CmFe1o6crO5jx4Aqu/xu26aCuiIe4x/iuXvIzHbvZru3EWqIj3f3h9y9Iz7+5O6f7uaY2whB337dlUdkZ6AATqR0TiP8gF0DHGVmk3OWn0SYPmkCYVqlBwjTvEwg/IBcDGBm1cD/AXcBk4BPAtfYtnkmC/FO4A2E6a1OAo5y92eBc4i1Le4+ppttryNMETOVMNH3f5vZ4e5+ac723yhBWb4L7EWYlmkPYBrw9ax97QKMI9QEnh3TjotlHkOYpiw7sHqRUCs6mjDdz9W2/by0vTmYEMi8H/gRYRqetwGvAk4ys8MAzOx44CuEuRgnEqb3uTZnX0cBryfMfflF4BJCEDQD2J8Q7GBmhwPfIZyrKcDieHzZ3h3Lth9wJXCKhbl+MbMJsYy/zT0YC3Nv7kqYRmgHMfA6kzBN0OKY/HbgIXdfkr2uh4nrlxLnk42WE6Y1yjRXngb8Jk9Wz9K3CcxPJnx+YwnT8n27m/XeFsu6tJvlO7AwH+f7Cf98iey0FMCJlICZHUIIKm5w90cJgcN/5Kx2s7s/Gv/jvxloc/ffuHsXcD2QqYGbS5gv8bux9uAvwO3EH/gCfdfdN7j7K8C9hICokOOYAbwZ+JK7t7n744Rat4E0dxVUFjMzQlD2WXdf5+6bgf8m/HhnpIFvuHu7u7fGtL+7+x3xPF5FVmDg7r9z9+Xunnb36wlzFe5w714PLojn4S7CvKXXuvtqd19GCNIyn9k5wHfc/Vl3T8VyH5hdC0eYb3STu2fmH73L3V9y942EJsXMvj4AXObuj7l7O3AeoaZydta+vhPPUau7PwxsZFsgdTJwn7uvynM8Y+Lz5pz0ubEWtA24EDjVt010PoEw92M+K+LybL8hTFS/DzDG3R/Is91mQlBdqJvd/eF4bq+h++t5ArAy88bMxsXa0o1m1paz7i3xmDcSgtQf9KE8IoNOAZxIaZxO+EFeE9//lpxmVMJ9Nhmted43xtdTgSWxOStjMaE2qlArs163ZO27N1OBTPDU37z7W5aJQD3waPzR3QD8KaZnNMUAuKf9j7B4b5iZnWZmj2ftb392DDh6UuhnNgv4cVY+6wgTtmeft758/pnaL9x9C2Gi9Ox9bVcbRqiFOzW+PpUQyOazIT6PzEl/MNaCjiXUYr4la9kaQk1gPlPi8mw3AYcD5/ZQjpGEwKlQhV5Da8kqawxyxxBqPnM7Fr07LhsRy3q/me3ShzKJDCoFcCJFFu/ROgk4zMxWxvuzPgscYDk98gq0HJiRaRKLZgLL4utmQqCT0ZcfHe9l+XJgnJll/8Bn511MuWVZQwhkXuXuY+JjdLzRvLttuhVrv35F+HEeH3+snyIEVsW2BPhoVrnHuHudu/+zH/taTggIga3NnuPZ/jPIPQ9XA8fH621f4JZ8O46dT14kNFPnW74F+BjwQdt2T+afgYMtp5emmR1MaP79S84+Wgg1ih+j+wBuX+CJbpYNxD3AG8xseqEbuHuXu98EdBF6WYvslBTAiRTfuwlf/vsRmnYOJPxA/Y3+NT0+RKhl+KKZVVsYfuFdbLsP6nHgPWZWH2/0PqsP+14FTM++YT5bvM/pn8B3zGyEhSEnziIECMW2XVlijeOvgB9aHGLCzKaZ2VH93H8DIdBpivs6k1ADVwq/AM4zs1fFvEab2Yn93Ne1wJlmdqCF4Wj+m3Bf16LuNoj3fD1CCJhuzGpezucO4LAe9rWO0Gz+9fj+z4TA6EYze5WZJc1sLuGa+Lm7v5BnN18BDuuhzIcRgryiik3d9xKaRw+2MKRINeG2hLwsOJ5Q+/hsscskUiwK4ESK73Tgcnd/xd1XZh6Em+k/YH0c6sHdOwgB2zGEWqmfAae5+3NxlR8SevWtIjSdXdOH3f8FeBpYaWa5TV8ZpwCzCTVBNxPuOftzX45hAGX5EuEm9QfNbBOh9qcvnTe2cvdngIsInUVWAa8G/jHQQneT183A9wjDb2wi1PQd0899/Rn4GnAj4R6z3dn+PsDuXEk4xu5qvTIuIVyXPdVE/gg4NgbwAO8lBEZ/ArYQgrdLCR1s8h3DcnfPO8afmY0gDEdyZS/l7K8TCPeMXk1oMn6ZcF9h7j8C/2dmW4BNhE4Rp8f7E0V2Srb90D8iIjIUmNmhhKBllvfyRW9mvyV0uLllMMqWk/cngRnu/sXBzlukkimAExEZYmIz4XXAE+7+rXKXR0SKT02oIiJDiIVBkTcQel/+qKyFEZGSUQ2ciIiISIVRDZyIiIhIhVEAJyIiIlJhFMCJiIiIVBgFcCIiIiIVRgGciIiISIVRACciIiJSYf4/ERiYFE/KdjIAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"histogram_boxplot(data=df, feature='int_memory', fmt='{:.0f}', step=64,\\n                  xlabel='Amount of internal memory (ROM) in GB')\";\n",
              "                var nbb_formatted_code = \"histogram_boxplot(\\n    data=df,\\n    feature=\\\"int_memory\\\",\\n    fmt=\\\"{:.0f}\\\",\\n    step=64,\\n    xlabel=\\\"Amount of internal memory (ROM) in GB\\\",\\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": [
        "rom = df[['int_memory']].dropna()\n",
        "rom['int_memory'] = rom['int_memory'].apply(lambda x: '<16' if x<16 else x)\n",
        "labeled_barplot(data=rom, feature='int_memory', rnd=0,\n",
        "                perc=True, n=None, percfmt=\"{:.0f}%\", sort=True,\n",
        "                figsize=(10, 5), xlabel='ROM, GB', xlo=0,\n",
        "                title='Amount of internal memory (ROM) in GB')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "gNz4ucw_CJYK",
        "outputId": "6bb60702-83fd-4787-d471-66b0945d3007"
      },
      "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": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"rom = df[['int_memory']].dropna()\\nrom['int_memory'] = rom['int_memory'].apply(lambda x: '<16' if x<16 else x)\\nlabeled_barplot(data=rom, feature='int_memory', rnd=0,\\n                perc=True, n=None, percfmt=\\\"{:.0f}%\\\", sort=True,\\n                figsize=(10, 5), xlabel='ROM, GB', xlo=0,\\n                title='Amount of internal memory (ROM) in GB')\";\n",
              "                var nbb_formatted_code = \"rom = df[[\\\"int_memory\\\"]].dropna()\\nrom[\\\"int_memory\\\"] = rom[\\\"int_memory\\\"].apply(lambda x: \\\"<16\\\" if x < 16 else x)\\nlabeled_barplot(\\n    data=rom,\\n    feature=\\\"int_memory\\\",\\n    rnd=0,\\n    perc=True,\\n    n=None,\\n    percfmt=\\\"{:.0f}%\\\",\\n    sort=True,\\n    figsize=(10, 5),\\n    xlabel=\\\"ROM, GB\\\",\\n    xlo=0,\\n    title=\\\"Amount of internal memory (ROM) in GB\\\",\\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 int_memory amount is approximately 55GB on average\n",
        "* The range is wide - from 8MB to 1TB\n",
        "* The most frequent (~37%) ROM amount is 16GB\n",
        "* There are three outliers - 256GB, 512GB, 1024GB"
      ],
      "metadata": {
        "id": "r1UIzVXBJwMn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`ram`**"
      ],
      "metadata": {
        "id": "VBcwGWD8LXpd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "histogram_boxplot(data=df, feature='ram', step=1, fmt='{:.0f}',\n",
        "                  xlabel=' Amount of RAM in GB')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "outputId": "55d97b73-11fd-4793-90d2-5a6ea56e9165",
        "id": "QtPGVTKlLXpe"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"histogram_boxplot(data=df, feature='ram', step=1, fmt='{:.0f}',\\n                  xlabel=' Amount of RAM in GB')\";\n",
              "                var nbb_formatted_code = \"histogram_boxplot(\\n    data=df, feature=\\\"ram\\\", step=1, fmt=\\\"{:.0f}\\\", xlabel=\\\" Amount of RAM in GB\\\"\\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=df, feature='ram', rnd=0,\n",
        "                perc=True, n=None, percfmt=\"{:.0f}%\", sort=False,\n",
        "                figsize=(10, 5), xlabel='RAM, GB', xlo=0,\n",
        "                title='Amount of RAM in GB')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "896d891d-9f4d-4c97-a310-54312c26df58",
        "id": "Pied_OZ3LXpe"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"labeled_barplot(data=df, feature='ram', rnd=0,\\n                perc=True, n=None, percfmt=\\\"{:.0f}%\\\", sort=False,\\n                figsize=(10, 5), xlabel='RAM, GB', xlo=0,\\n                title='Amount of RAM in GB')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(\\n    data=df,\\n    feature=\\\"ram\\\",\\n    rnd=0,\\n    perc=True,\\n    n=None,\\n    percfmt=\\\"{:.0f}%\\\",\\n    sort=False,\\n    figsize=(10, 5),\\n    xlabel=\\\"RAM, GB\\\",\\n    xlo=0,\\n    title=\\\"Amount of RAM in GB\\\",\\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 RAM amount is approximately 4GB on average\n",
        "* The range is 16MB and 12GB\n",
        "* The most frequent (~81%) RAM amount is 4GB\n",
        "* All values except 4GB may be considered outliers"
      ],
      "metadata": {
        "id": "bgGw3rS1LXpe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Discrete variable"
      ],
      "metadata": {
        "id": "684yGc4VNKFk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`year_release`**"
      ],
      "metadata": {
        "id": "JVrCillcY-1r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "histogram_boxplot(data=df, feature='release_year', xlabel='Release year', fmt='{:.0f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "outputId": "629c216c-04b5-4a3c-921a-e26111935cb8",
        "id": "ETgtQeEQY-1r"
      },
      "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": {
            "needs_background": "light"
          }
        },
        {
          "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='release_year', xlabel='Release year', fmt='{:.0f}')\";\n",
              "                var nbb_formatted_code = \"histogram_boxplot(data=df, feature=\\\"release_year\\\", xlabel=\\\"Release year\\\", fmt=\\\"{:.0f}\\\")\";\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 year of release distribution seems to be close to uniform distribution\n",
        "* The range is eight years - from 2013 to 2022\n",
        "* Most of the devices were released in 2014\n",
        "* The least of the devices were released in 2020"
      ],
      "metadata": {
        "id": "rB5RXoz1-gDA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Categorical variables"
      ],
      "metadata": {
        "id": "gKJV69lGNIhQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`4g`**\n",
        "\n"
      ],
      "metadata": {
        "id": "bFLKjiFMUwGe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_barplot(data=df, feature='4g', xlabel='4g', perc=True, \n",
        "                title='Whether 4G is available or not')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "6ffe65e3-93ad-4654-e8bb-843e76c70df7",
        "id": "80SccpKcUwGf"
      },
      "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": {
            "needs_background": "light"
          }
        },
        {
          "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='4g', xlabel='4g', perc=True, \\n                title='Whether 4G is available or not')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(\\n    data=df,\\n    feature=\\\"4g\\\",\\n    xlabel=\\\"4g\\\",\\n    perc=True,\\n    title=\\\"Whether 4G is available or not\\\",\\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 4g technology is available in ~68% of the devices"
      ],
      "metadata": {
        "id": "COU--JnCiQmw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`5g`**"
      ],
      "metadata": {
        "id": "wRFR_T1rUwPC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_barplot(data=df, feature='5g', xlabel='5g', perc=True, title='Whether 5G is available or not')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "77183b62-3a74-40c0-b3c5-52ab05c1f49f",
        "id": "iPQPIf1WUwPC"
      },
      "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": {
            "needs_background": "light"
          }
        },
        {
          "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='5g', xlabel='5g', perc=True, title='Whether 5G is available or not')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(\\n    data=df,\\n    feature=\\\"5g\\\",\\n    xlabel=\\\"5g\\\",\\n    perc=True,\\n    title=\\\"Whether 5G is available or not\\\",\\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 5g technology is available in ~4% of the devices"
      ],
      "metadata": {
        "id": "Ixfy--PEiiMV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`os`**"
      ],
      "metadata": {
        "id": "GWgFITO9O-Er"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_barplot(data=df, feature='os', xlabel='os', perc=True, title='Operating system')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "7db8ac8e-1078-461e-bc80-5a04a3e45770",
        "id": "dfE9AiWZO-Es"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"labeled_barplot(data=df, feature='os', xlabel='os', perc=True, title='Operating system')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(data=df, feature=\\\"os\\\", xlabel=\\\"os\\\", perc=True, title=\\\"Operating system\\\")\";\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 four OS in the dataset\n",
        "* 93% devices run on Android"
      ],
      "metadata": {
        "id": "sSjZANurO-Es"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`brand_name`**"
      ],
      "metadata": {
        "id": "GqxovOC4PjBV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "labeled_barplot(data=df, feature='brand_name', xlabel='Brand name', \n",
        "                figsize=(8, 10), perc=True, title='Brands', orient='v')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "outputId": "fb7b6a06-900f-464f-a8b0-cc84e2ab9fec",
        "id": "VeGRw4MLPjBV"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"labeled_barplot(data=df, feature='brand_name', xlabel='Brand name', \\n                figsize=(8, 10), perc=True, title='Brands', orient='v')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(\\n    data=df,\\n    feature=\\\"brand_name\\\",\\n    xlabel=\\\"Brand name\\\",\\n    figsize=(8, 10),\\n    perc=True,\\n    title=\\\"Brands\\\",\\n    orient=\\\"v\\\",\\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": [
        "* There are 34 manufacturing brands in the dataset\n",
        "* 14.5% of devices are from other brands\n",
        "* Highest number of devices of the known brand is from Samsung (9.9%)\n",
        "* Lowest number of devices of the known brand is from Infinix (0.3%)"
      ],
      "metadata": {
        "id": "P1-B8h9gPjBW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Bivariate analysis"
      ],
      "metadata": {
        "id": "d52CPcpHYeCA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pairplot of continuous numerical variables"
      ],
      "metadata": {
        "id": "z8CiUyhGYg-x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_cols = ['normalized_used_price', 'normalized_new_price', 'days_used', 'weight', 'battery']\n",
        "sns.pairplot(df[num_cols]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 903
        },
        "id": "KBjVaoOEA3S-",
        "outputId": "fbd3d6a0-85b1-4f43-9f03-3699696c3c55"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 30 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"num_cols = ['normalized_used_price', 'normalized_new_price', 'days_used', 'weight', 'battery']\\nsns.pairplot(df[num_cols]);\";\n",
              "                var nbb_formatted_code = \"num_cols = [\\n    \\\"normalized_used_price\\\",\\n    \\\"normalized_new_price\\\",\\n    \\\"days_used\\\",\\n    \\\"weight\\\",\\n    \\\"battery\\\",\\n]\\nsns.pairplot(df[num_cols])\";\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": [
        "* Visuzlly, the normalized new and used prices are sntrongly positevely correlated\n",
        "* The weight and battery seem to be  positevely correlated\n",
        "* Weight and price are correlated and it seems there are two subsets of data"
      ],
      "metadata": {
        "id": "WCUSTF89Bq-3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`Correlation heatmap`**"
      ],
      "metadata": {
        "id": "ShouwSljMkO6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 7))\n",
        "sns.heatmap(df.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();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        },
        "id": "7QUf1QNfMc1G",
        "outputId": "cd0a81a0-b0ce-41e8-d0df-46bbc054ea06"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"plt.figure(figsize=(12, 7))\\nsns.heatmap(df.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, 7))\\nsns.heatmap(df.corr(), annot=True, vmin=-1, vmax=1, fmt=\\\".2f\\\", cmap=\\\"Spectral\\\")\\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": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* `screen_size`, `battery`, and `weight` are mutually strongly correlated (but not always one caused another), as all three tie each other thru device size - the size of the device is strongly influenced by all three factors\n",
        "* `normalized_new_price` and `normalized_used_price` have a strong positive correlation\n",
        "* `release_year` and `days_used` have a strong negative correlation\n",
        "* moderate positive correlation between `selfie_camera_mp` and `release_year` might be explained by the technical development and improvements of the camera capacity within the last eight years"
      ],
      "metadata": {
        "id": "bieVkVPssq80"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`release_year`** and **`days_used`**"
      ],
      "metadata": {
        "id": "5TLmykR4CyAs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(12, 6))\n",
        "sns.boxplot(data=df, x='release_year', y='days_used', ax=ax)\n",
        "plt.xlabel('Release year', fontsize=12)\n",
        "plt.ylabel('Days used', fontsize=12)\n",
        "plt.title('Days used vs Release year', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "HtMGbw_JBqXr",
        "outputId": "c9630943-926d-4bbc-cb12-d48dbce20e75"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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njjUzGju2JEnqPYZuddU+sx/hwwvWNF1G4z6xaHbTJUiSpB4y7UN3L0yJ6JXpEOCUCEmSpDpM+9DdarW49pc3sXHWbo3VEOsTgGt+fVdjNQBst9Yv+OwlvfCGEHrnTaFvCCVJk9m0D90AG2ftxoMHHNF0GY3b8abvNV2C2rRaLa698VqY03AhG6sf1955bXM1rGzu0JIkdYOhW+plc2DjIRubrqJx213u1U0lSZOb/5NJkiRJNTN0S5IkSTUzdEuSJEk1c063pGmhF64G0ytXggGvBiNJ25qhW9K00Gq1uOW669irwRpGPlpced11DVYBzV6cVJKmJ0O3pGljL+AEoukyGreQbLoESZp2nNMtSZIk1czQLUmSJNXM0C1JkiTVzNAtSZIk1czQLUmSJNXM0C1JkiTVzEsGSpK2uW58WdHw8DAAfX19E9qPXxQkaVswdEuSJqV169Y1XYIkdczQLUna5roxsjyyj8HBwQnvS5Lq5pxuSZIkqWaGbkmSJKlmhm5JkiSpZoZuSZIkqWaGbkmSJKlmhm5JkiSpZoZuSZIkqWaGbkmSJKlmhm5JkiSpZoZuSZIkqWaGbkmSJKlmhm5JkiSpZoZuSZIkqWaGbkmSJKlmhm5JkiSpZoZuSZIkqWaGbkmSJKlmhm5JkiSpZoZuSZIkqWaGbkmSJKlmM5suoGnDw8Nst3YVO970vaZLadx2a+9leHhD02VIkiRNOY50S5IkSTWb9iPdfX193P3QTB484IimS2ncjjd9j76+vZouQ5IkacqZ9qFb0vQwPDzMamAh2XQpjVsKrBkebroMSZpWGp9eEhHviYgbI+KGiDg3InaMiP0i4sqIaEXE1yNi+7LuDuVxqyyf33D5kiRJ0hY1OtIdEfOAAeCAzFwXEecDxwCHA5/LzPMi4kvACcAXy88VmdkfEccAnwb+qqHyNcrw8DAPrJ7BJxbNbrqUxt2+egaPn+BI4vDwMKyC7S5v/L1x81bCcE6sP/v6+lh5zz2cQHSnpklsIcmcvr6my5CkaaUX/jefCewUETOBWVSffL4Y+GZZfiZwdLl/VHlMWX5oRPg/qCRJknpaoyPdmXlnRHwGuANYB/wIuAZYmZkj164bBuaV+/OAJWXbDRGxCtgduKd9vxFxInAiwD777FP301DR19fHgxuW8uEFa5oupXGfWDSbHSc4ktjX18fyWM7GQzZ2qarJa7vLt6NvniOzvWJwcJBWq9V0GQwNDQEwMDDQaB39/f2N1yCp9zU9vWRXqtHr/YCVwDeAwya638w8FTgVYMGCBZ41JUld1Gq1uPGXNzNn1hMbrWPj+uqDzjt/fW9jNaxcu6yxY0uaXJq+eslLgN9m5nKAiPgW8DxgTkTMLKPdfcCdZf07gb2B4TIdZReguX9tJWmamjPrifzF049puozGXXbLeU2XIGmSaHpO9x3AwRExq8zNPhS4CbgMeE1Z5zjgu+X+heUxZfmlmelItiRJknpao6E7M6+kOiFyMfDLUs+pwEnAeyOiRTVne2HZZCGwe2l/L/DBbV60JEmSNE5NTy8hMz8KfHRU82+Ag8ZY90HgtduiLkmSJKlbmp5eIkmSJE15hm5JkiSpZoZuSZIkqWaGbkmSJKlmhm5JkiSpZoZuSZIkqWaGbkmSJKlmhm5JkiSpZoZuSZIkqWaGbkmSJKlmhm5JkiSpZoZuSZIkqWaGbkmSJKlmhm5JkiSpZoZuSZIkqWYzmy5AkiRNzODgIK1Wa0L7GB4eBqCvr2+r99Hf38/AwMCE6pCmKkO3JEli3bp1TZcgTWmGbkmSJrlujC6P7GNwcHDC+5L0h5zTLUmSJNXMkW5J0rgMDw+zau1qLrvlvKZLadzKtcvIYadlSNoyR7olSZKkmjnSLUkal76+PuKhe/mLpx/TdCmNu+yW85jXt3vTZUiaBBzpliRJkmpm6JYkSZJqZuiWJEmSamboliRJkmq2yRMpI6KjQJ6ZG7tXjiRJkjT1bO7qJRuA7GAfM7pUiyRJkjQlbS5079d2/5XAa4B/Am4H9gVOAi6or7RtZ7u197HjTd9r7Pjx4P0A5I5PaKwGqPoB9mq0Bo2yEra7vOFZYGvKz9kN1rASmNfg8SVJmqBNhu7MvH3kfkS8F1iQmStL068iYhGwCPhirRXWrL+/v+kSGBpaDcD+T2k68O7VE/2hSq/8LoaGhgDYf97+zRUxr3f6Q5KkrdHpl+PsAsyiGm8aMau0T2oDAwNNl/D7GgYHBxuuRL2kF16bMLVen3cBCzuaNVePe8vPpr9K5S5gTsM1SNJ002noPhP4cUR8HlgC7A0MlHZJ6nm9MFK+vHxqMGf/Bj81oArcvdAfkjSddBq6PwC0gL8CngwsBb4A/J+a6tIkdceaGXxiUZOTf+HutdUc6D1nNXdhnTvWzOCpjR1dY+mFTw6m0qcGkqTx6Sh0l8sCfqncpDH1ysjZ+jKauOP85kYTn0rv9IckSWpeR6E7IgJ4K3AMMDcz/zQiXgjslZnn11mgJo9eGEkERxMlSVLv6fRaZH8PnEA1nWSf0jZMddlASZIkSZvRaeg+HjgiM8/j0S/M+S3wR3UUJUmSJE0lnYbuGTz6FRkjoXt2W5skSZKkTeg0dF8EfDYidoDfz/H+B+D/r6swSZIkaaroNHS/F3gSsIrqC3HW8OhXwUuSJEnajE4vGXg/8D8i4olUYXtJZt5Va2WSJE0Dg4ODtFqtpstgqFxutekrUfX39zdeg1SHTi8ZOBdYl5nLIuJe4E0R8Qjw1XINb0mStBVarRY3XH89O2/f6ffV1WPDhkcAuP3mGxurYfX6DY0dW6pbp3/h3wPeDlwL/CPwKuBh4NnAe+opTZKk6WHn7Wdy0J67Nl1G4666e0XTJUi16TR0PxW4rtw/FvhzqnndN2LoliRJkjar09D9CLB9RDwVWJWZd0TEdlSXDZQkSZK0GZ2G7u8D5wO7A+eVtgOAO+soSpIkSZpKOg3dbwWOo5rHfXZp2wP4WA01SZIkSVNKp5cMfAg4dVTb5XUUJEnqfSvXLuOyW87b8oo1WvNgddLd7B2bOwFx5dplzGP3xo4vafLo9JKBZ/Po178/Rma+qasVSZJ6Wn9/f9MlADA0dB8A857SXOidx+490x+Selun00tGX7V/L+A1wDndLUeS1Ot65YtLRuoYHBxsuBJJ2rJOp5d8fHRbRCwEPjrRAiJiDnAa8Ayq0fS3ALcCXwfmA7cBr8vMFRERwCnA4cBa4PjMXDzRGiRJkqQ6bTeBba8DXtSFGk4BfpCZTweeCdwMfBC4JDP3By4pjwFeAexfbicCX+zC8SVJkqRadTqn+8WjmmYBxwA3TeTgEbEL8ELgeIDMXA+sj4ijgEPKamcClwMnAUcBZ2VmAldExJyIeFJmLp1IHZIkSVKdOp3TvXDU4weoRrpfP8Hj7wcsB06PiGcC1wDvAvZsC9J3AXuW+/OAJW3bD5c2Q7ckSZJ6Vqdzuver8fgHAu/MzCsj4hQenUoycuyMiDGvnLIpEXEi1fQT9tlnn27VKkmSJG2Viczp7oZhYDgzryyPv0kVwu+OiCcBlJ/LyvI7gb3btu9jjG/FzMxTM3NBZi6YO3dubcVLkiRJnWg0dGfmXcCSiHhaaTqUap74hVTfgEn5+d1y/0LgTVE5GFjlfG5JkiT1uk7ndNfpncA5EbE98BvgzVRvBs6PiBOA24HXlXUvorpcYIvqkoFv3vblSpIkSePTeOjOzOuABWMsOnSMdRN4R901SZIkSd3U6SUDDwDuzcy7I2I28H5gI/DPmbm2zgIlSZKkya7TOd3nAnPK/c9QXVv7YODLNdQkSZIkTSmdTi+Zn5m3lq9h/0vgAGAd8NvaKpMkSZKmiE5D94MRsTNV2L4jM++JiJnAjvWVJkmSJE0NnYburwGXAjsDXyhtB+JItyRJkrRFnX4j5Xsi4mXAw5l5WWneCLyntsokSZKkKaLTq5ccBfx7Zm4YacvMRbVVJUmSJE0hnU4v+XvgtIj4OnB229e2S5KkCRgeHmb1+g1cdfeKpktp3Or1GxgeHm66DKkWHV0yMDOfCbyE6oolF0TErRHx4YiYX2dxkiRJ0lTQ8TdSZub1wPUR8QGqb4s8Gfh4RPwn1fW6z83MjfWUKUnS1NTX18cjq1dx0J67Nl1K4666ewV9fX1NlyHVYlxfAx8RTwGOLbeNwEeAO4D/Dbya6hrekiRJktp0eiLlO4A3AvsDXwfemJlXtC2/AFhWS4U9bnBwkFarNaF9DA0NATAwMDCh/fT39094H02zP7urV/pzKvQlTLw/fW0+qldemzA1+lNS7+t0pPsVVNNJLszMh0YvzMy1EeEo91baaaedmi5hSrE/u8v+7B77srvsT9Vlom8KR04GnehUGd8QTi2dXqf7iA7W+dHEy5l8/GPoLvuzu+zP7rI/u8e+1FS2bt26pktQD+p4TndEHAm8CNgDiJH2zHxTDXVJkiQ1YqJvCke2Hxwc7EY5miI6umRgRHyU6gol2wGvBe4FXg6srK0ySZIkaYroKHQDbwFempnvAdaXn68C5tdVmCRJkjRVdBq652TmDeX++oh4XGZeRTXdRJIkSdJmdDqn+9cR8d8y80bgBuD/iYgVgN9ZK0mSJG1Bp6H7w8Du5f6HgHOA2cD/qqMoSZIkaSrp9JKBF7XdvxLor60iSZIkaYrp9BspDwBeAOwG3Af8NDNvqrMwSZIkaarYbOiOiAAWAscBw8DvgHnAkyPibOAtmZm1VylJkiRNYlu6esmJwCHAwZm5b2Y+NzP3AZ5LNfL91zXXJ0mSJE16WwrdbwQGMvPq9sby+N1luSRJkqTN2FLoPgD4ySaW/aQslyRJkrQZWwrdMzJz9VgLSnunX64jSZIkTVtbunrJ4yLiL4DYyu0lSZKkaW9LoXkZ8JUtLJckSZK0GZsN3Zk5fxvVIUmSJE1ZzsmWJEmSamboliRJkmpm6JYkSZJqZuiWJEmSamboliRJkmpm6JYkSZJqZuiWJEmSauY3SkqS1LDV6zdw1d0rGq1h7YZHAJg1c0ZjNaxev6GxY0t1M3RLktSg/v7+pksAYGhoCIB999+/0Tp6pT+kbovMbLqGWi1YsCAXLVrUdBmSJPW0gYEBAAYHBxuuZGIGBwdptVqN1jDyBmb/ht/AQPUmZuR3q/pFxDWZuWCsZY50S5KkKaPVanHrDTez9857NVbD4zZUp8ytvb3ZKUNLVt/V6PH1WIZuSZI0pey9816876A3N11G406+6vSmS1Abr14iSZIk1czQLUmSJNXM0C1JkiTVzNAtSZIk1czQLUmSJNXM0C1JkiTVzNAtSZIk1czQLUmSJNWsJ0J3RMyIiGsj4nvl8X4RcWVEtCLi6xGxfWnfoTxuleXzGy1ckiRJ6kBPhG7gXcDNbY8/DXwuM/uBFcAJpf0EYEVp/1xZT5IkSeppjYfuiOgDXgmcVh4H8GLgm2WVM4Gjy/2jymPK8kPL+pIkSVLPajx0A58HPgBsLI93B1Zm5obyeBiYV+7PA5YAlOWryvqSJElSz2o0dEfEEcCyzLymy/s9MSIWRcSi5cuXd3PXkiRJ0rg1PdL9PODIiLgNOI9qWskpwJyImFnW6QPuLPfvBPYGKMt3Ae4dvdPMPDUzF2Tmgrlz59b7DCRJkqQtaDR0Z+aHMrMvM+cDxwCXZuYbgMuA15TVjgO+W+5fWB5Tll+ambkNS5YkSZLGremR7k05CXhvRLSo5mwvLO0Lgd1L+3uBDzZUnyRJktSxmVteZdvIzMuBy8v93wAHjbHOg8Brt2lhkiRJ0gT16ki3JEmSNGUYuiVJkqSaGbolSZKkmhm6JUmSpJr1zImUkiRp6wwODtJqtSa0j6GhIQAGBga2eh/9/f0T2l5TTzdem8PDwwD09fVNaD9Nvz4N3ZIkiZ122qnpEqQxrVu3rukSusLQLUnSJOfosnpVN16bI/sYHByc8L6aZOiWJElTxvDwMA+sXs3JV53edCmNW7L6Lh4//EDTZajwREpJkiSpZo50S5KkKaOvr4+1j6zgfQe9uelSGnfyVaczq2/XpstQ4Ui3JEmSVDNDtyRJklQzQ7ckSZJUM0O3JEmSVDNDtyRJklQzQ7ckSZJUM0O3JEmSVDNDtyRJklQzQ7ckSZJUM0O3JEmSVDNDtyRJklQzQ7ckSZJUM0O3JEmSVDNDtyRJklQzQ7ckSZJUs5lNFyBJkqTeNDg4SKvVarSGoaEhAAYGBhqtA6C/v3+r6zB0S5IkaUytVosbbriB2bNnN1bDww8/DMBtt93WWA0Aa9asmdD2hm5JkiRt0uzZsznwwAObLqNxixcvntD2zumWJEmSamboliRJkmpm6JYkSZJqZuiWJEmSauaJlJIkaUpZsvouTr7q9MaOv2ztfQA8cdZujdUAVT88jV0brUGPMnRLkqQpo7+/v+kSeHjoHgBm7dts4H0au/ZEf6hi6JYkSVNGL3yBykgNg4ODDVeiXuKcbkmSJKlmhm5JkiSpZoZuSZIkqWaGbkmSJKlmhm5JkiSpZoZuSZIkqWaGbkmSJKlmhm5JkiSpZoZuSZIkqWaGbkmSJKlmfg28JEmSxjQ8PMzq1atZvHhx06U0bvXq1QwPD2/19o50S5IkSTVzpFuSJElj6uvrY8OGDRx44IFNl9K4xYsX09fXt9XbO9ItSZIk1czQLUmSJNWs0dAdEXtHxGURcVNE3BgR7yrtu0XExRExVH7uWtojIgYjohURv4gIP+uQJElSz2t6pHsD8L7MPAA4GHhHRBwAfBC4JDP3By4pjwFeAexfbicCX9z2JUuSJEnj02jozsylmbm43F8N3AzMA44CziyrnQkcXe4fBZyVlSuAORHxpG1btSRJkjQ+TY90/15EzAeeDVwJ7JmZS8uiu4A9y/15wJK2zYZLmyRJktSzeiJ0R8Rs4ALg3Zl5f/uyzEwgx7m/EyNiUUQsWr58eRcrlSRJksav8dAdEY+jCtznZOa3SvPdI9NGys9lpf1OYO+2zftK22Nk5qmZuSAzF8ydO7e+4iVJkqQONH31kgAWAjdn5mfbFl0IHFfuHwd8t639TeUqJgcDq9qmoUiSJEk9qelvpHwe8EbglxFxXWn7W+BTwPkRcQJwO/C6suwi4HCgBawF3rxNq5UkSZK2QqOhOzN/BsQmFh86xvoJvKPWoiRJ0rQ2ODhIq9Xa6u2HhoYAGBgYmFAd/f39E96HekfTI92SJElTyk477dR0CepBhm5JkqQ2ji6rDo1fvUSSJEma6hzpliRJ0iatWbOGxYsXN3b8tWvXAjBr1qzGaoCqHybC0C1JkqQx9ff3N13C709MnT9/frOFMLH+MHRLkiRpTL0wv32khsHBwYYrmRjndEuSJEk1M3RLkiRJNTN0S5IkSTUzdEuSJEk1M3RLkiRJNTN0S5IkSTUzdEuSJEk1M3RLkiRJNTN0S5IkSTUzdEuSJEk1M3RLkiRJNTN0S5IkSTUzdEuSJEk1M3RLkiRJNTN0S5IkSTUzdEuSJEk1M3RLkiRJNTN0S5IkSTUzdEuSJEk1M3RLkiRJNZvZdAGSJEmamgYHB2m1WhPax9DQEAADAwMT2k9/f/+E9zERhm5JkiT1rJ122qnpErrC0C1JkqRaNDmy3Guc0y1JkiTVzNAtSZIk1czQLUmSJNXM0C1JkiTVzNAtSZIk1czQLUmSJNXM0C1JkiTVzNAtSZIk1czQLUmSJNXM0C1JkiTVzNAtSZIk1czQLUmSJNXM0C1JkiTVLDKz6RpqFRHLgdubrqMDewD3NF3EFGJ/dpf92T32ZXfZn91lf3aPfdldk6U/983MuWMtmPKhe7KIiEWZuaDpOqYK+7O77M/usS+7y/7sLvuze+zL7poK/en0EkmSJKlmhm5JkiSpZobu3nFq0wVMMfZnd9mf3WNfdpf92V32Z/fYl9016fvTOd2SJElSzRzpliRJkmpm6K5JROwdEZdFxE0RcWNEvKu07xYRF0fEUPm5a2l/ekT8V0Q8FBF/07afHSPiqoi4vuzn4009pyZ1qz/b9jcjIq6NiO9t6+fSC7rZnxFxW0T8MiKui4hFTTyfJnW5L+dExDcj4paIuDkintvEc2pSF//tfFp5TY7c7o+Idzf0tBrT5dfne8o+boiIcyNixyaeU1O63JfvKv1443R8XcJW9ecbIuIX5f+bn0fEM9v2dVhE3BoRrYj4YFPPaUucXlKTiHgS8KTMXBwROwPXAEcDxwP3Zeanygtj18w8KSKeCOxb1lmRmZ8p+wng8Zm5JiIeB/wMeFdmXrHNn1SDutWfbft7L7AAeEJmHrHtnklv6GZ/RsRtwILMnAzXT+26LvflmcBPM/O0iNgemJWZK7fpE2pYt//Wyz5nAHcCz8nMyfC9DV3Txf+L5lH9/3NAZq6LiPOBizLzjG39nJrSxb58BnAecBCwHvgB8PbMbG3jp9SorejPPwduzswVEfEK4GOZ+Zzy9/0r4KXAMHA18PrMvKmBp7VZjnTXJDOXZubicn81cDMwDzgKOLOsdibVC4zMXJaZVwMPj9pPZuaa8vBx5Tbt3il1qz8BIqIPeCVwWv2V96Zu9ud0162+jIhdgBcCC8t666db4IbaXpuHAr+eboEbut6fM4GdImImMAv4Xb3V95Yu9uUfA1dm5trM3AD8BPjL+p9Bb9mK/vx5Zq4o7VcAfeX+QUArM3+Tmeup3tActU2exDgZureBiJgPPBu4EtgzM5eWRXcBe3aw/YyIuA5YBlycmVfWVOqkMNH+BD4PfADYWEd9k00X+jOBH0XENRFxYj1VTg4T7Mv9gOXA6VFNfTotIh5fW7GTQBdemyOOAc7tbnWTz0T6MzPvBD4D3AEsBVZl5o/qq7a3TfC1eQPwgojYPSJmAYcDe9dV62SwFf15AvD9cn8esKRt2XBp6zmG7ppFxGzgAuDdmXl/+7Ks5vZscdQ6Mx/JzGdRvas7qHw0NS1NtD8j4ghgWWZeU1+Vk0c3Xp/A8zPzQOAVwDsi4oXdr7T3daEvZwIHAl/MzGcDDwA9Ozexbl16bVKm6RwJfKPrRU4iXfi3c1eq0cP9gCcDj4+IY2sqt6dNtC8z82bg08CPqKaWXAc8Ukuxk8B4+zMi/oIqdJ+0zYrsEkN3jcoc7AuAczLzW6X57jKPaWQ+07JO91c+ar4MOKzLpU4KXerP5wFHlnnI5wEvjoiv1lRyT+vW67OMgJGZy4BvU33UN610qS+HgeG2T7K+SRXCp50u/9v5CmBxZt7d/Uonhy7150uA32bm8sx8GPgW8Od11dyruvjv5sLM/O+Z+UJgBdWc5GlnvP0ZEX9KNTX0qMy8tzTfyWM/KegrbT3H0F2TcgLkQqpJ/59tW3QhcFy5fxzw3S3sZ25EzCn3d6I6UeCWrhfc47rVn5n5oczsy8z5VB85X5qZ0260pouvz8eXE2AoUyFeRvXR6bTRxdfmXcCSiHhaaToU6LkTgerWrf5s83qm8dSSLvbnHcDBETGr7PNQqjm400Y3X5vlJEsiYh+q+dxf6261vW+8/Vn66lvAGzOz/U3K1cD+EbFf+WTrmLKP3pOZ3mq4Ac+n+kjkF1QfHV1HNW9rd+ASYAj4MbBbWX8vqpGu+4GV5f4TgD8Fri37uQH4SNPPbTL356h9HgJ8r+nnNpn7E/gj4PpyuxH4f5t+bpO1L8uyZwGLyr6+Q3XWfuPPcRL35+OBe4Fdmn5eU6Q/P0416HMDcDawQ9PPbxL35U+p3lRfDxza9HObJP15GtWnAiPrLmrb1+FUnxb8mh7+f8hLBkqSJEk1c3qJJEmSVDNDtyRJklQzQ7ckSZJUM0O3JEmSVDNDtyRJklQzQ7ckTVIRcXlEvLXpOiRJW2bolqQGRcRtEbEuItZExF0RcUb5WmRJ0hRi6Jak5r0qM2dTfTnOs4EPNVtO74qImU3XIElbw9AtST0iq6+C/yFV+AYgIg6OiJ9HxMqIuD4iDtnU9hHxloi4OSJWRMQPI2LftmWnRMSSiLg/Iq6JiBe0LTsoIhaVZXdHxGfblnV0/Ih4f0RcMKptMCJOKfd3iYiFEbE0Iu6MiE9ExIyy7CkRcWlE3BsR90TEORExp20/t0XESRHxC+ABg7ekycjQLUk9IiL6gFcArfJ4HvDvwCeA3YC/AS6IiLljbHsU8LfAXwJzqb5m+ty2Va6mCvO7AV8DvhERO5ZlpwCnZOYTgKcA54/3+MBXgcNGwnIJxscAZ5XlZwAbgH6q0fyXASPz0QP4J+DJwB8DewMfG7X/1wOvBOZk5oYxji9JPc3QLUnN+05ErAaWAMuAj5b2Y4GLMvOizNyYmRcDi4DDx9jH24F/ysybSyj9JPCskdHuzPxqZt6bmRsy82RgB+BpZduHgf6I2CMz12TmFeM9fmYuBf4DeG1pOgy4JzOviYg9yzbvzswHMnMZ8DmqUE5mtjLz4sx8KDOXA58FXjTqEIOZuSQz13XWpZLUWwzdktS8ozNzZ+AQ4OnAHqV9X+C1ZWrHyohYCTwfeNIY+9gXOKVtvfuoRpDnAUTE35SpJ6vK8l3ajnMC8FTgloi4OiKO2IrjA5xJFdQpP89u28/jgKVt+/ky8MRS254RcV6ZdnI/1aj5HjzWkk0cU5ImBefFSVKPyMyfRMQZwGeAo6mC5tmZ+bYONl8C/GNmnjN6QZm//QHgUODGzNwYESuoQjmZOQS8PiK2o5qe8s2I2H2cxwf4DvDFiHgGcEQ55khtDwF7bGJqyCeBBP4kM++LiKOBL4xaJzusQZJ6kiPdktRbPg+8NCKeSTXi+6qIeHlEzIiIHSPikDL3e7QvAR+KiP8Gvz9xcWSqx85U86mXAzMj4iPAE0Y2jIhjI2JuZm4EVpbmjeM8Ppn5IPBNqjnjV2XmHaV9KfAj4OSIeEJEbFdOnhyZQrIzsAZYVeaRv3/cvSZJPc7QLUk9pMxpPgv4SGYuAUZOkFxONWL8fsb4tzszvw18GjivTNG4geqkTKiuiPID4FfA7cCDPHa6xmHAjRGxhuqkymMyc914jt/mTOBPeHRqyYg3AdsDNwErqML5yDSVjwMHAquoTtz81mb2L0mTUmT6iZ0kqTsiYh/gFmCvzLy/6XokqVc40i1J6ooyJ/y9wHkGbkl6LE+klCRNWEQ8HribavrKYQ2XI0k9x+klkiRJUs2cXiJJkiTVzNAtSZIk1czQLUmSJNXM0C1JkiTVzNAtSZIk1czQLUmSJNXs/wLN1C9GNirniAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"fig, ax = plt.subplots(figsize=(12, 6))\\nsns.boxplot(data=df, x='release_year', y='days_used', ax=ax)\\nplt.xlabel('Release year', fontsize=12)\\nplt.ylabel('Days used', fontsize=12)\\nplt.title('Days used vs Release year', fontsize=16)\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"fig, ax = plt.subplots(figsize=(12, 6))\\nsns.boxplot(data=df, x=\\\"release_year\\\", y=\\\"days_used\\\", ax=ax)\\nplt.xlabel(\\\"Release year\\\", fontsize=12)\\nplt.ylabel(\\\"Days used\\\", fontsize=12)\\nplt.title(\\\"Days used vs Release year\\\", 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": [
        "* Distributions of the number of days being in used for devices issued in 2013-2016 look the same\n",
        "* The average time of usage for devices released more than 4 years ago (in 2013-2016) is ~815 days (2 years and 3 months)\n",
        "* Since 2017 the average usage time decreased by 5 months on average every year\n",
        "* Devices issued in 2020 were in use on average for 229 days (7 months)"
      ],
      "metadata": {
        "id": "cjDjonSuDaqJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`release_year`** and **`normalized_used_price`**"
      ],
      "metadata": {
        "id": "YsokYXXmFuCF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(12, 6))\n",
        "sns.boxplot(data=df, x='release_year', y='normalized_used_price', ax=ax)\n",
        "plt.xlabel('Release year', fontsize=12)\n",
        "plt.ylabel('Normalized Used Price', fontsize=12)\n",
        "plt.title('Normalized Used Price vs Release year', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "outputId": "e0dacbf4-6e84-42f0-d833-6a3376982f28",
        "id": "W3TVFms5FuCG"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"fig, ax = plt.subplots(figsize=(12, 6))\\nsns.boxplot(data=df, x='release_year', y='normalized_used_price', ax=ax)\\nplt.xlabel('Release year', fontsize=12)\\nplt.ylabel('Normalized Used Price', fontsize=12)\\nplt.title('Normalized Used Price vs Release year', fontsize=16)\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"fig, ax = plt.subplots(figsize=(12, 6))\\nsns.boxplot(data=df, x=\\\"release_year\\\", y=\\\"normalized_used_price\\\", ax=ax)\\nplt.xlabel(\\\"Release year\\\", fontsize=12)\\nplt.ylabel(\\\"Normalized Used Price\\\", fontsize=12)\\nplt.title(\\\"Normalized Used Price vs Release year\\\", 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 average normalized used price was growing with the release year\n"
      ],
      "metadata": {
        "id": "h7FQHp1hFuCG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`screen_size`** and **`normalized_used_price`**"
      ],
      "metadata": {
        "id": "LllTK-lKHFVg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "round(df['screen_size']/2.54,0).value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "8Tx1qnjFHO30",
        "outputId": "e6020835-67ab-4ce4-8c4c-13ecab0a3b35"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.0     1360\n",
              "6.0      762\n",
              "4.0      598\n",
              "7.0      284\n",
              "8.0      112\n",
              "10.0     106\n",
              "3.0      106\n",
              "2.0       92\n",
              "9.0       20\n",
              "12.0      11\n",
              "11.0       3\n",
              "Name: screen_size, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "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 = \"round(df['screen_size']/2.54,0).value_counts()\";\n",
              "                var nbb_formatted_code = \"round(df[\\\"screen_size\\\"] / 2.54, 0).value_counts()\";\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": [
        "ssnp = df[['screen_size', 'normalized_used_price']]\n",
        "ssnp['screen_size_in'] = round(ssnp['screen_size']/2.54,0)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12, 6))\n",
        "sns.boxplot(data=ssnp, x='screen_size_in', y='normalized_used_price', ax=ax)\n",
        "plt.xlabel('Screen size, in', fontsize=12)\n",
        "plt.ylabel('Normalized Used Price', fontsize=12)\n",
        "plt.title('Normalized Used Price vs Screen size in inches', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "outputId": "b55f437b-5981-4064-c757-f2153d57b553",
        "id": "pR456f0nHFVg"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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YZ1tm3h4RJ1IcdLyRItn8JcUQdmMxfz8ingr8HcXwaYdSdDn5fhl7Mz5BMbrFjcCmcc99gyJZe2lZ9hBFX/i/m6S8Oyg+L6dQvP9LKd6nzRQt0R+qq+N3yzq+FXgPxSgMWym+a5PKzG0RcQLFAdj/ByyheM+upC5BnaXXaCpfp+gi8UaKz8sW4C8z818mWP9xFH13zypv9T5GMVpFU5/buW5f3uMGZTXzXv6c4uD2byiuO9hF0W/8lPJgX6rU2LA1kiRJkuo46oUkSZLUgImyJEmS1ICJsiRJktSAibIkSZLUgImyJEmS1MCcHR7u8MMPz+OOO67dYUiSJKmDfe9737s1M5c0em7OJsrHHXccV1zRzMyxkiRJ0sxExNaJnrPrhSRJktSAibIkSZLUgImyJEmS1ICJsiRJktSAibIkSZLUgImyJEmS1ICJsiRJktSAibIkSZLUgImyJGleGh4eZtWqVezYsaPdoUjqUCbKkqR5aWBggM2bNzMwMNDuUCR1KBNlSdK8Mzw8zIYNG8hM1q9fb6uypEqYKEuS5p2BgQFGR0cBGB0dtVVZUiVMlCVJ886mTZsYGRkBYGRkhI0bN7Y5IkmdyERZkjTvLFu2jJ6eHgB6enpYvnx5myOS1IlMlCVJ806tVmPBguJf2IIFC6jVam2OSFInMlGWJM07vb29nHTSSQA8+9nPZvHixW2OSFInMlGWJEmSGjBRliTNO8PDw1x66aUAfP3rX3d4OEmVMFGWJM07Dg8nqRVMlCVJ847Dw0lqBRNlSdK84/BwklrBRFmSNO84PJykVjBRliTNO729vZxyyilEBCtWrHB4OEmV6Gl3AJIkzUStVmPr1q22JkuqjImyJGle6u3tZfXq1e0OQ1IHs+uFJEmS1ICJsiRJktSAibIkSZLUgImyJEmS1ICJsiRJktSAibIkSZLUgImyJEmS1ICJsiRJktSAibIkSZLUgImyJEmS1ICJsiRJktSAibKkjjQ8PMyqVavYsWNHu0ORJM1TJsqSOtLAwACbN29mYGCg3aFIkuapnnYHIEmzbXh4mPXr15OZrF+/nlqtxuLFi9sdlqQutWbNGgYHB5tef2hoCIC+vr6mt+nv72flypXTjk2TM1GW1HEGBgYYGRkBYM+ePQwMDHD22We3OarpacU/VvCfqzQX7d69u90hqGSiLKnjbNy4kcwEIDO55JJL5l2iPF3+Y5XmrukejJ577rkAXHjhhVWEo2kwUZbUcZYsWcK2bdseeLx06dI2RjMz/mOVpPbzYj5JHWf79u17Pb7lllvaFIkkaT4zUZbUcZYvX05EABARnHzyyW2OSJI0H5koS+o4tVqNnp6iZ1lPTw+1Wq3NEUmS5qOWJcoR8YiI+GxEXB0RV0XEM1q1b0ndpbe3lxUrVhARnHrqqQ4NJ0makVZezPd/ga9k5u9GxP7AAS3ct6QuU6vV2Lp1q63JkqQZa0mLckQcCjwb+AhAZt6Xmbe3Yt+SulNvby+rV6+2NbmDOU25pKq1quvFo4HtwL9FxA8i4sMRcWCL9i1J6kBOUy6paq1KlHuApwDvz8wnA3cBbxy/UkS8OiKuiIgrxg/vJEnSmOHhYTZs2PDANOW2KkuqQqsS5RuAGzLz8vLxZykS571k5gcz84TMPGHJkiUtCk2SNN8MDAwwOjoKwOjoqK3KkirRkkQ5M28Gro+Ix5eLTgZ+2op9S5I6z6ZNmxgZGQFgZGSEjRs3tjkiSZ2olaNevAb4VDnixRbgFS3ctySpgyxbtoy1a9eSmUQEy5cvb3dI6iJr1qxhcHCwsvK3bNkCPDg1fRX6+/tZuXJlZeV3ipYlypn5Q+CEVu1PktS5Tj/9dL785S8DkJmcccYZbY5I3WRwcJCfXzXIUYceW0n5PffvD8DdQ3sqKf/GndsqKbcTtbJFWZKkWXHxxRcTEQ+0KK9du5azzz673WGpixx16LG85sTz2h3GjLznGxe0O4R5wymsJUnzzqZNm8hMoGhRto+ypCqYKEuS5p1ly5YREQD2UZZUGbteSF1geHiYCy64gPPPP9+Z6tQR7KMszW3TveBxaGgIgL6+vqa3acUFibYoS13AGczUacb6KAMP9FGWNH/t3r2b3bt3tzuMh7BFWepw42cwq9Vqtipr3mvUR9mL+aS5Y7otvWND4V144YVVhDNjJspSh2s0g9l8SyhmMmbpXD2Np9mxbNky1q1bx8jICD09PfZRllQJu15IHa5bZzCbq6fxNDtqtRoLFhT/whYsWECtVmtzRJI6kS3KUofrhJa3mbTyztXTeJodvb29nHLKKaxdu5YVK1bYnUhSJWxRljpcrVbbq+uFLW/qFLVajSc84Ql+piVVxhZlqQvUX/QkdYre3l5Wr17d7jAkdTATZanDDQwM7JUoz8eL+SRJ7TOTC6qna8uWLcCD3eaqMJMLtk2UpQ43/uK9Sy65xERZkvbB0NAQd+28m/d844J2hzIjN+7cyoEc0PT6g4ODDF71c449+JGVxbT/SNEbeM8Nuyopf9uum2e0nYmy1OGWLFnCtm3bHni8dOnSNkYjSZqPjj34kZz3m69sdxgzdsHlH53RdibKUofbvn37Xo9vueWWNkUiSZ2hr6+Pu9nDa048r92hzMh7vnEBB/QtbHcY84KjXkgdbvny5XtN9XvyySe3OSJJkuYHE2Wpw9Vqtb0SZYfSkiSpOSbKUhdweDhJkqbPRFnqcI2Gh5MkSVMzUZY6XKPh4SRJ0tRMlNV1hoeHWbVqFTt27Gh3KC2xZMmSvR47PJwkSc1xeDh1nYGBATZv3tw1M9SNHw7ul7/8ZZsikaTOcePObZVNOHLrXcXv9OEHHlFJ+Tfu3MZj+/orKbvTmCirqwwPD7NhwwYyk/Xr11Or1Vi8eHG7w6rU0qVL95pw5IgjqvnhlaRu0d9fbZI5suU+gMrGOn5sX3/ldegUJsrqKgMDA4yOjgIwOjraFa3KTjgiSbNr5cqVlZZ/7rnnAnDhhRdWuh9NzT7K6iqbNm1iZGQEgJGRkYdc6NaJli9fvtdjJxyRJKk5JsrqKsuWLaOnpziR0tPT85AkshOdfvrpez0+44wz2hSJJEnzi10v1FVqtRobNmwAYMGCBV0xS93FF19MRJCZRARr167t+O4mkuauNWvWMDg42PT6Q0NDAPT19TW9TX9/f+XdI9QdbFFWV+nt7eWUU04hIlixYkXHX8gHRXeT+glHuqG7iaTOsXv3bnbv3t3uMNSlbFFW16nVamzdurUrWpOh6G6ybt06RkZGuqa7iaS5a7otvV7YpnayRVldp7e3l9WrV3dFazIUBwb1I310ywGCJEn7yhZlqQvUd72QNHfYX1fzwdDQEHfvupMLLv9ou0OZsa27buKAoV3T3s5EWepwAwMDeyXK3TB2tNSp7Ks7P033gGjLli3Ag91OmuEBUTVMlKUON/7ivUsuucREWZoj7K+rRhYtWtTuEPbS19fHntFdnPebr2x3KDN2weUfZWHfwdPezkRZ6nBLlizZawrrpUuXtjEaSeo+tvTOXybK6jrDw8NccMEFnH/++V1xQZ9TWEuS9tW2XTdX2kf5l3cPA3DEAb2VlL9t1830Y4uyNKWBgQE2b97cNX11ly9fztq1ax+YcMQprDVXeWGbNDf19/dXvo/7thSNOguPnn4y24x+Dp5RPUyU1VWGh4dZv349mcm6deuo1Wod36pcq9VYv349e/bsYeHChQ4Pp47hhW1Sa7Ti4HKu9r83UVZXGRgYYGRkBICRkZF52ao83VY3KKbrBjjooIO44IILmtrGlje1mhe2SZprnHBEXWXjxo17DZV2ySWXtDmi1liwYAELFizwQj5JkqbBFmV1lU4YAWImrby2vEmSNH22KKurOAKEJElqVtOJckQsjIiTIuIPyscHRsSB1YUmzb5nPvOZez0+8cQT2xSJJEma65pKlCPifwE/Az4EfKRc/Bxg/k76LUmSJE2i2Rbl9wN/m5m/Auwpl30NeFYlUUkV+da3vrXX429+85ttikSSJM11zSbKTwA+Wd5PgMy8C3h4FUFJVXnGM56x1+PxXTEkSZLGNJsoXwf87/oFEfE04NrZDkiq0r333jvpY0mSpDHNDg/3N8CXI2INsH9EnAesBF5VWWRSBex6IUmSmtVUi3JmXgScBiyh6Jv8KOBFmbm+wtikWTc22chEjyVJksY0PeFIZv4A+PMKY5Eq9/CHP5y77rprr8eSJEmNNJUoR8TngHdl5qV1y04CXpeZv1tVcNJsq0+SGz2WqrJmzRoGBwcrK3/Lli3Ag7MwVqW/v39Gs0Oqc3XCZ9vPtSbSbIvyc4DfG7fsW8AXmt1RRFwH7ALuB0Yy84Rmt5Vmy1FHHcWNN974wOOjjz66jdF0r6r/scLc++c6ODjIT665GnoPrSaYvB+An9x6UzXlAwzvrK5szVuDg4Ncc80gi3uPraT8zP0B2H7rninWnJkdw9sqKVedodlEeTdwIHBH3bKDeHBM5WYty8xbp7mNNGse/ehH75UoH3/88W2MpnsVSeOVcPj+Fe5lBICfDP+smuJvvW/62/Qeyn5nnjT7sbTI/RddOvVK6kqLe4/ljDPf3O4wZmTtRW9rdwiaw5pNlNcBH4iIP83MOyLiEOC9wFeqC02afd///vf3enzFFVe0KRJx+P7EC49sdxQzll+ssOVWkjQnNDuO8huAQ4AdEXELsAM4FDhnGvtKYH1EfC8iXt1ohYh4dURcERFXbN++fRpFS81xwhFJktSsplqUM/M24PkRcSRwNHB9Zt48zX09KzNvjIilwIaIuDozvz5uPx8EPghwwgknOG6XJGle8cI2qbNMmChHRGQ5yGxEjLU8/7K8PbAsM0eb2VFm3lj+vSUiPg88Dfj65FtJs2v8BCPf+MY3WLVqVZuikdRpiv73P2e/3r5Kyh/N/QD46a3VjNhz//BQJeWq+0z3oHEmB4GtOKibrEV5J0V3Cyiuihnfwhvlsv2m2klEHAgsyMxd5f0VwFunH65m2/DwMBdccAHnn38+ixcvbnc4lVuyZAnbtj14hfPSpUvbGI2kTrRfbx8HnDk/px24+6J/nfY2Q0ND3LHr7nl7Udzw8Fb23HdAu8PoeosWLWp3CA1Nlig/oe7+o/dxP0cAn4+IsX0OZKYXAs4BAwMDbN68mYGBAc4+++x2h1O58X3fb7nlljZFIklS5+qU7jsTJsqZeT1AROwHfAw4NTPvnclOMnML8BszirCFuq11dXh4mA0bNpCZrF+/nlqt1vH1Xr58OWvXriUziQhOPvnkdockSfNaX18fC2/dM6+Hh1ty+MJ2h6E5aspRLzLzfooW5WZHyJi36ltXu8HAwACjo0UX89HR0a6od61Wo6enOD5cuHAhtVqtzRFJkqS5qtnk9++B90fEoyJiv4hYMHarMrhWGt+6umPHjnaHVLlNmzYxMlJMyjAyMsLGjRvbHFH1ent7WbFiBRHBihUrOr4FXZIkzVyzie6HgZcBW4D7KGbkG2H6M/PNWd3Yurps2bIHWld7enpYvnx5myNqjVqtxhOe8ARbkyVJ0qSaTZQfXd6Or7uNPe4I3di6WqvVWLCg+AgsWLCgaxLH3t5eVq9ebWuyJEma1KQTjkQxTMWrgCcC38/Mf29FUO2wbNky1q1bx8jISNe0rvb29nLKKaewdu3aed0NYbpjNQ4NFeOE9vU1P86pA/BLktR9ppqZbzVQAy4FLoiIR2fm31UfVuvVajU2bNgAdFfraq1WY+vWrV1TX4Ddu3e3OwRJHWpoaIj7d901o/GI54L7h4cYuu/AdochzRlTJcq/DzwnM38WEb8GfBHoyES5U1pXp2usG8J8Nt2W3rFZfy688MIqwpFU6oTpnMEzSlI3mypRPjQzfwaQmT+NiI7OHruxdVVqh6GhIdh1H/nFm9odyszdeh9D9zrd72SK6ZyvIRYfXkn5Wc4Xe+X24UrKB8gdt05r/b6+Pm6/9a55PTNf3+G2KEtjpkqUIyIeTTFdNcB+4x6PTSbSETqhdVWS5pJYfDgLz3xhu8OYsT0XfbHdIUhqo6kS5QOBa6lLjIH682gJ7DfbQUnqbH19fQwP30m88Mh2hzJj+cWb6Ott/oJQqZPtGN7G2oveVknZd+z8JQCHHHpEJeXvGN7GksP7Kylb89+kiXJmdsyEIpIkafb191ebZO664z6AyqaZXnJ4f+V10Pw1VYuyJGkWFP2yd3L/RZe2O5SZG76dofuy3VFojqn6QkcvwFY72WIsSZIkNWCLsiS1QF9fH8O3BvudeVK7Q5mx+y+6lL7D52+/8la5f3iosnGUR3cWo3AsOLSakUTuHx6Cwx9bSdnSfGSiLEnSLKm6r+uWO4oL246vagi3wx9rf12pjomyJEmzxP66UmeZMFGOiOsphn+bVGYeO6sRSV2m6tnLoDUzmDl7mSSp00zWovySuvtPBV4O/AuwFXgUcDbw8epCk7rD4OAgV139Yw6ucN7LkfKQ94ZbflxJ+bt2VFKsJEltNWGinJlfG7sfEe8DTs3MG+uWXQx8BfinSiOUusDBi+Fpp7Y7ipn7zrp2R6C5aGhoiLxj17ye3S6Hb2Voz73tDkNSmzQ7PFwfcOe4ZXcCR81uOGq14eFhVq1axY4dNglKkiTVa/Zivi8BX4qItwE3AMcA55XLNY8NDAywefNmBgYGOPvss9sdjqQO0tfXx46Fwyw884XtDmXG9lz0RfqW9LY7DElt0myL8krgW8Aa4PvA+4HLy+Wap4aHh9mwYQOZyfr1621VliRJqtNUopyZuzPzjZnZn5kPL/++MTPvqTpAVWdgYIDR0VEARkdHGRgYaHNEkiRJc0fT4yhHxCnAi4GlmflbEXECcEhmbqwsOlVq06ZNjIyMADAyMsLGjRvtfqHWufU+8os3VVf+zuKzzaEVDRd/633gGXlJ6mhN/QeJiNcArwM+DPxuufgeiuHinllNaJqJ6YzJu2jRIu6+++69Hjczzq7j5WpftWLmry07i7Gjj+89vpod9LamHpKk9mm2qeUc4OTMvC4i/rpcdjXw+EqiUkssXbr0gX7JEcHSpUvbHJG6RSsOtJzBTJK0r5pNlA8Gri/vj83WtxC4b9Yj0j6ZbgJSq9XYsWMHz3/+8+12IUmSVKfZRPnrwBuBt9ctey2wadYjmkXTnRp4aGgIKIY0atZ874awdOlSdu/eTa1Wa3cokiRJc0qzifJrgP+OiFcBB0fENcAu4MzKImuD3bt3tzuEllu4cCH9/f0sXlzh/MmSJEnzUFOJcmbeFBFPBZ4KPIqiG8Z3MnO0yuD21XRbeu3TqHYYGhpi1x3zexroXTtgaGSo3WFoDsodt1Y2hXXesROAOOTQSsqHIn6ccETqWk2Pm5SZCXwH+E5ELANOAr5WVWCSpPmt6lFBtuwqEuXjq0xkl/Q6uonUxZodHu5rwPmZ+Y1y1IvXAyMR8b7M/P8qjVDqcH19fYz23MrTTm13JDP3nXXQt7T5vv3qDlVfv+FZQElVa3YK6ycC3y7vvwpYBjwdp7CWJElSh2q268UCICOiH4jM/ClARBxWWWSSJElSGzWbKF8GvBc4Evg8QJk031pRXJLUeYZ3cv9Fl1ZT9s47i7+HHlRN+QDDO+HwI6srX5LmmGYT5bOANwDbgbHOYL8C/N8KYpKkjlP5hW13lFN2V5nIHn6kF7ZJ6irNDg83DJw/btmXK4lIkjqQF7ZJhelOBrZlS3EQOPYZb8Z8nwxMc8ekiXJEfIIHp6weswfYCvxnZl5VVWCSJEmLFi1qdwjqYlO1KF/bYNlC4PHAtyOiZsuyJElqli29mk8mTZQz8+8nei4ingf8I2CiLEnSDNgNQZrbmp6Zr4FLAK/qkCSpReyGILXWviTKfcDtsxSHJEldx5ZeaW6b6mK+4xssXggcB7wZ+EwFMUmSJElt18zFfAlE3bL7gW3Ap4G3VhSX1FV27YDvrKuu/Lt3FX8POLia8nftAJZWU7YkSe0y1cV8C1oViNStWjGBw5Y7iwuAjl7a6CTRLFjamnpIktRK+9JHWZp1070CfCZmctX4dE3nKvNW9FF0MgpJkqbPRFlzyuDgINdc/RMOf0SFOxkt/gzf/JNKir/19kqKlSRJLWairDnn8EfAi5bFlOvNVZ/bNH4yS0mSNB/ZB1mSJElqYMIW5YhoKonOzNFmdxYR+wFXADdm5pnNbidJkiS12mRdL0Yohoabyn7T2N/rgKuAQ6axjSRJktRyk7UaPxo4vry9BvgacBrwq+XfTcDZze4oIo4Gng98eKbBSpIkSa0yYYtyZm4dux8RrwdOyMzby0U/i4grKLpRvL/Jfb0b+CtgwikPIuLVwKsBjj322CaLlSRJkmZfsxfzHQocMG7ZAeXyKUXEmcAtmfm9ydbLzA9m5gmZecKSJUuaDE2SJEmafc0OD/cx4H8i4t3A9cAxwGvL5c04EXhBRJwBLAIOiYhPZuZLphmvJEmS1BLNJsp/BVwL/AHQB9wEvBf4UDMbZ+Z5wHkAEfFcYJVJsiRJkuayphLlcgi4NeVNkiRJ6nhN9VGOwqsi4pKI+HG57NkR8fvT3WFmftUxlCVJkjTXNXsx31uBP6boajE2HMUNwF9XEZQkSZLUbs0mymcBZ2bmf/DgJCS/oBhjWZIkSeo4zSbK+wF3lvfHEuWD6pZJkiRJHaXZUS/WAv8cEX8JRZ9l4B+A/64qMHWnoaEhdt0Bn9vUzOzpc9Ott8O9o0PtDkOad9asWcPg4GDT62/ZsgWAc889t+lt+vv7Wbly5bRjk9Sdmm1Rfj1wJLCTYpKRO4FHAW+sKC5Jkia1aNEiFi1a1O4wJHWwZoeHuwP4nYhYSpEgX5+ZN1cambpSX18fwwuGedGyaHcoM/a5TUnvI/vaHYY079jSK2muaXZ4uI9GxIrMvCUzvzuWJEfEv1YbniRJktQezfZRfgmwIiLenZmrxy3/89kPS5IeNN2+q2D/VUnSvmu2j/Ju4OnAiyPiExGxf7l8/p4fl9TR7L8qSdpXzbYok5k3RMRJwEeAyyLid3hwqDhJqoytvJKkdmg2UQ6AzLwHqEXEG4HvAA+rKjBJ6matGCoN7G4iSZNpNlF+a/2DzPzHiPgR8PuzH5IkabrsZiJJs6/Z4eEubLDsYuDiWY9IkmQrryTNARMmyhHxlcw8rbx/KRP0R87MZ1cUmyRJktQ2k7Uof7zu/oerDkSSJEmaSyZMlDNzoO7+x1oTjiRJkjQ3TNb14pXNFJCZH529cDTeTCZamI6ZXik/HV5VL0mS5qPJul68tIntEzBRrtDg4CDXXvUTjj6kmrldFo4UXc9333hlJeXfcMf0h9q+9Xb43KbqhujeeWfx99CDqin/1tuh95HVlC1Jklpnsq4Xy1oZiCZ29CHBOc/Yf+oV56B3f+u+aa3f399fUSQP2lm2ovc+8vhKyu99ZGvqIUmSqtX0zHxjIiKom7o6M0dnNSJ1tVZ00RjrZnLhhQ8Z9VCSJOkBC5pZKSKOiojPR8QwMALsqbtJkiRJHaepRBlYA9wHnAzcCTwF+BLgFVqSJEnqSM12vXgmcGxm3hURmZk/iog/Br4JfKi68CRJkqT2aLZF+X6KLhcAt0fEEuAu4KhKopIkSZLarNkW5cuBM4DPA+uATwP3AFdUFJekCcxkbO2ZjJc938e/Hh4e5oILLuD8889n8eLF7Q5HkjQPNdui/FLga+X9c4BNwJVArYKYJM2yRYsWsWjRonaH0VIDAwNs3ryZgYGBqVeWJKmBplqUM/P2uvv3AP9QVUCTcZY6qTVD6M13w8PDbNiwgcxk/fr11Go1W5UlSdPWVKIcET3AHwJPBvaazywzX11BXA0NDg4yeNXVHHvoYZWUv//9xZDQe4Z+WUn523beVkm5kvY2MDDA6GjxfR4dHWVgYICzzz67zVFJkuabZvsofxL4X8DFQDVZZJOOPfQw3vSsFe0MYcbeftn6doegLtVt/XU3bdrEyEhx/fHIyAgbN240UZYkTVuzifJpwDGZuavKYCRVo76/bjckjMuWLWPdunWMjIzQ09PD8uXL2x2SJGkeavZivs1A5zdDSR1ofH/dHTt2tDukytVqNRYsKH7eFixYQK3mdceSpOmbzqgXH46IcyPiZfW3KoOTtO8a9dftdL29vZxyyilEBCtWrOiK7iaSpNnXbNeLs4CTgMMoxk8ek8DHZzkm1RkaGuLuO5J3f+u+docyIzfckRwQQ+0Oo6t1a3/dWq3G1q1bbU2WJM1Ys4ny64AnZ+ZVVQYjafZ1a3/d3t5eVq9e3e4wJEnzWLOJ8i+BbVUGosb6+vrYnTs45xn7tzuUGXn3t+5jUV9fu8PoarVajQ0bNgD215UkaTqa7aP8LuCTEfH0iDi+/lZlcJL2nf11JUmamWZblN9X/n3huOUJ7Dd74Uiqgv11JUmavilblCMigMcC+2fmgnE3k2RJkiR1pCkT5cxM4MfAaPXhSKpC/YQj3WJ4eJhVq1Z1xbjRkqRqNNtH+QfA46oMRFI1unHCEejOgwNJ0uxqNlH+KvCViHhLRPxxRLxy7FZhbJJmQTdOONKtBweSpNnVbKJ8IvAL4DnASyhm6ntpeV/SHNZowpFO140HB5Kk2ddUopyZyya4dcfMBdI8tmzZMnp6igFuumXCkW48OJAkzb5mW5SJiMMi4mURcV7597AqA5M0O2q1GgsWFF/1bplwpBsPDiRJs6+pRDkingEMAiuBXwf+FBgsl0uaw7pxwpFuPDiQJM2+ZluU3w38eWY+MzP/MDNPBP4M+JfKIpM0a2q1Gk94whO6JmHsxoMDSdLsazZRfhzwmXHLPgs8ZnbDkVSF2267jS1btnD77be3O5SW6baDA0nS7Gs2Uf458OJxy36PojuGpDnune98J3fffTfveMc72h1Ky/T29rJ69WpbkyVJM9bT5HrnABdFxGuBrcBxFNNan1lNWJJmy7XXXsu2bdsA2Lp1K1u2bOH4449vc1SSJM19TSXKmfnNiOgHng/0Af8NrM3Mpkbxj4hFwNeBh5X7/Gxm/t10gx0aGuLunXfw9svWT3fTOWHrzts4gPvbHYa6zDvf+c69Hr/jHe/gAx/4QJuikSRp/mi2RZnMvA345Az3cy+wPDPvjIiFwGURcXFmfnuG5XWVG+5I3v2t+yope/tdCcCSA6OS8m+4I3nMUZUUrSaNtSaP2bp1a5sikSRpfpk0UY6ITUBOskpm5slT7SQzE7izfLiwvE1WbkN9fX3sYT/e9KwV0910Tnj7ZetZ2HfEtLbp7++vKJrCni1bAFh0VDWn4h9zVPV10OQOOugg7rzzzr0eS5KkqU3VojxRC/JRwGuBA5rdUUTsB3yPYqSM92Xm5c1u281WrlxZafnnnnsuABdeeGGl+1H7jM1QN9FjSZLU2KSjXmTmR+pvwBeAXwXeAHyOYti4pmTm/Zn5JOBo4GkR8cTx60TEqyPiioi4Yvv27dOohqSJnHzy3id9nve857UpEkmS5pdmZ+Y7JCL+AbgWOAJ4Sma+OjNvmO4OM/N2YBNwWoPnPpiZJ2TmCUuWLJlu0ZIaqNVqe03n7LjCkiQ1Z9JEOSIeHhHnAVsoWpKflZkvzcxpjZ8cEUsi4hFjZQKnAFfPLGRJ09Hb28upp55KRHDaaad1zbjCw8PDrFq1ih07mhqcZ97rtvpKUitM1aJ8HfB64J3AvwJHRMTy+luT+zkS2BQRPwa+C2zIzItmGrSk6enGWeoGBgbYvHkzAwMD7Q6lJbqtvpLUClNdzHcPxegUfzbB8wlMOVxCZv4YePL0QpM0W8ZmqesWw8PDbNiwgcxk/fr11Gq1jm5J77b6SlKrTHUx33GZ+ehJbk7vJc0D3XZafmBggNHRUQBGR0c7vpW12+orSa3S1MV8kua3bjstv2nTpgeGwRsZGWHjxo1tjqha3VZfSWqVpmfmk+aqNWvWMDjY/PWl1157Lffeey/nnHMOCxcubGqb/v7+yse0rko3npZftmwZ69atY2RkhJ6eHpYvb/Zyivlp2bJlrF27lswkIjq+vpLUKrYoq+uMjo4yOjrKLbfc0u5QWqIbT8vXajUWLCh+3hYsWNDxFzGefvrpFBOgQmZyxhlntDkiSeoMtihr3ptOS+/w8DCveMUrALjzzjs577zzOr51tdFp+bPPPrvNUVWrt7eXU045hbVr17JixYqOf48vvvhiIuKBFuW1a9d2/HssSa1gi7K6Sje2ri5btmyvCUe65bR8Nw2Jt2nTpr1alO2jLEmzw0RZXaUbL3rqtm4IY8aGxOv01mTo3oMhSaqaibK6SjcmFGPdECKiK7ohdKNuPRiSpKqZKKurdGtC0U3dELqRB0OSVA0TZXWVbk0ouqkbQrfyYEiSZp+JsrqOCUV36LbZCD0YkqTZZ6KsrmNC0R26bTZCSdLsM1GW1HHGz0bYLa3KkqTZZaIsqeN043jZkqTZZ6IsqeN043jZkqTZZ6IsqeN043jZkqTZZ6IsqeN063jZkqTZZaIsqeN063jZkqTZ1dPuAKZr287bePtl6ysp+5d37QLgiAMPrqT8bTtvo7/viErKlrS3Wq3G1q1bbU2WJM3YvEqU+/v7Ky3/vi13AbCwomS2v++IyusgqTA2XrYkSTM1rxLllStXVlr+ueeeC8CFF15Y6X4kSZI099lHWZIkSWrARFmSJElqwERZkiRJasBEWZIkSWrARFmSJElqwERZkiRJasBEWZIkSWrARFmSJElqYF5NOKKprVmzhsHBwabX37JlC/DgZCvN6O/vr3zyF0mSpHYzUe5yixYtancIkiRJc5KJcoexpVeSJGl22EdZkiRJasBEWZIkSWrARFmSJElqwERZkiRJasBEWZIkSWrARFmSJElqwERZkiRJasBEWZIkSWrARFmSJElqwERZkiRJasBEWZIkSWrARFmSJElqwERZkiRJasBEWZIkSWrARFmSJElqwERZkiRJasBEWZIkSWrARFmSJElqoCWJckQcExGbIuKnEbE5Il7Xiv1KkiRJM9XTov2MAG/IzO9HxMHA9yJiQ2b+tEX7lyRJkqalJS3KmXlTZn6/vL8LuAo4qhX7liRJkmai5X2UI+I44MnA5Q2ee3VEXBERV2zfvr3VoUmSJEkPaGmiHBEHAf8FnJOZd4x/PjM/mJknZOYJS5YsaWVokiRJ0l5alihHxEKKJPlTmfm5Vu1XkiRJmolWjXoRwEeAqzLzn1uxT0mSJGlftKpF+UTgpcDyiPhheTujRfuWJEmSpq0lw8Nl5mVAtGJfkiRJ0mxwZj5JkiSpARNlSZIkqQETZUmSJKkBE2VJkiSpARNlSZIkqQETZUmSJKkBE2VJkiSpARNlSZIkqQETZUmSJKkBE2VJkiSpARNlSZIkqQETZUmSJKkBE2VJkiSpARNlSZIkqQETZUmSJKkBE+UuNzw8zKpVq9ixY0e7Q5EkSZpTTJS73MDAAJs3b2ZgYKDdoUiSJM0pJspdbHh4mA0bNpCZrF+/3lZlSZKkOibKXWxgYIDR0VEARkdHbVWWJEmqY6LcxTZt2sTIyAgAIyMjbNy4sc0RSZIkzR097Q6gSmvWrGFwcLDp9bds2QLAueee2/Q2/f39rFy5ctqxzQXLli1j3bp1jIyM0NPTw/Lly9sdkiRJ0pxhi3KdRYsWsWjRonaH0TK1Wo0FC4qPwIIFC6jVam2OSJIkae7o6Bbl+drS2yq9vb2ccsoprF27lhUrVrB48eJ2hyRJkjRndHSirKnVajW2bt1qa7IkSdI4Jspdrre3l9WrV7c7DEmSpDnHPsqSJElSAybKkiRJUgMmypIkSVIDJsqSJElSAybKkiRJUgMmypIkSVIDJsqSJElSAybKkiRJUgMmypIkSVIDJsqSJElSAybKkiRJUgORme2OoaGI2A5sbcOuDwdubcN+28k6dwfr3Pm6rb5gnbuFde4O7arzozJzSaMn5myi3C4RcUVmntDuOFrJOncH69z5uq2+YJ27hXXuDnOxzna9kCRJkhowUZYkSZIaMFF+qA+2O4A2sM7dwTp3vm6rL1jnbmGdu8Ocq7N9lCVJkqQGbFGWJEmSGujKRDkijomITRHx04jYHBGva7BORMS/RMS1EfHjiHhKO2KdLRGxKCK+ExE/Kuv89w3WeVhEfLqs8+URcVwbQp1VEbFfRPwgIi5q8FzH1RcgIq6LiJ9ExA8j4ooGz3faZ/sREfHZiLg6Iq6KiGeMe77T6vv48r0du90REeeMW6ej6gwQEX9Z/nZdGRH/LyIWjXu+477PEfG6sr6bx7/H5fPz/n2OiI9GxC0RcWXdssURsSEifl7+PWyCbV9ervPziHh566LeNxPU+ffK93k0IiYc9SEiTouIa8r3/I2tiXjfTVDnC8vf7R9HxOcj4hETbNveOmdm192AI4GnlPcPBn4G/Nq4dc4ALgYCeDpwebvj3sc6B3BQeX8hcDnw9HHr/Dmwprz/YuDT7Y57Fur9emAAuKjBcx1X37Iu1wGHT/J8p322Pwb8SXl/f+ARnVzfcXXbD7iZYgzQjq0zcBTwC+Dh5ePPAGeNW6ejvs/AE4ErgQOAHuB/gMd02vsMPBt4CnBl3bJ3Am8s778ReEeD7RYDW8q/h5X3D2t3ffahzr8KPB74KnDCBNvtBwwCx5e/dT8an7vM1dsEdV4B9JT33zHB+9z2Ondli3Jm3pSZ3y/v7wKuovghrvdC4ONZ+DbwiIg4ssWhzpqyHneWDxeWt/Ed1F9IkXQAfBY4OSKiRSHOuog4Gng+8OEJVumo+k5Dx3y2I+JQih/gjwBk5n2Zefu41Tqmvg2cDAxm5vjJmTqxzj3AwyOihyJ5HBr3fKd9n3+VIvG9OzNHgK8BLxq3zrx/nzPz68COcYvr38uPAb/dYNNTgQ2ZuSMzbwM2AKdVFedsalTnzLwqM6+ZYtOnAddm5pbMvA/4D4rXas6boM7ry882wLeBoxts2vY6d2WiXK88PfdkihbWekcB19c9voGHJtPzStkN4YfALRQ/MBPWufzw7gR6Wxrk7Ho38FfA6ATPd1p9xySwPiK+FxGvbvB8J322Hw1sB/6t7GLz4Yg4cNw6nVTf8V4M/L8Gyzuqzpl5I7Aa2AbcBOzMzPXjVuu07/OVwEkR0RsRB1C0Hh8zbp2Oep/rHJGZN5X3bwaOaLBOp9Z9Mp1c51dSnB0Zr+117upEOSIOAv4LOCcz72h3PFXLzPsz80kUR21Pi4gntjmkykTEmcAtmfm9dsfSBs/KzKcApwN/ERHPbndAFeqhOJ33/sx8MnAXxanajhcR+wMvAP6z3bFUreyj+kKKA6M+4MCIeEl7o6pWZl5FcTp6PfAV4IfA/e2MqR2yOP/u8FwdLCLeBIwAn2p3LI10baIcEQspkuRPZebnGqxyI3sfvR9dLpv3ylPTm3joaaoH6lye3jwUGG5pcLPnROAFEXEdxama5RHxyXHrdFJ9H1C2vpGZtwCfpzh1Va+TPts3ADfUnR35LEXiXK+T6lvvdOD7mfnLBs91Wp2fB/wiM7dn5h7gc8Azx63Tcd/nzPxIZv7vzHw2cBvF9TT1Ou19HvPLsS4k5d9bGqzTqXWfTMfVOSLOAs4E/qg8KBqv7XXuykS57Lf2EeCqzPznCVb7EvCy8qrip1Oc6rtpgnXnvIhYMnZFaUQ8HDgFuHrcal8Cxq4c/l1g4wQf3DkvM8/LzKMz8ziK09MbM3N8C1TH1HdMRBwYEQeP3ae4WOLKcat1zGc7M28Gro+Ix5eLTgZ+Om61jqnvOH9I424X0Hl13gY8PSIOKH+/T6a4tqReJ36fl5Z/j6XonzwwbpVOe5/H1L+XLwe+2GCddcCKiDisPOOwolzWyb4LPDYiHl2eUXoxxWs1L0XEaRTdI1+QmXdPsFr769zKKwfnyg14FsWpnB9TnM76IUX/r5XAynKdAN5HcbXlT5jgKtT5cgN+HfhBWecrgb8tl7+V4kMKsIjiNO61wHeA49sd9yzV/bmUo150en0prgz+UXnbDLypXN7Jn+0nAVeUn+0vUFwB37H1Let0IEVr6aF1yzq9zn9PcXB/JfAJ4GFd8H2+lOLA70fAyZ34PlMc7N0E7KE4Q/THFH3LLwF+TjHax+Jy3ROAD9dt+8ry/b4WeEW767KPdf6d8v69wC+BdeW6fcDaum3PoDizMDj2+z4fbhPU+VqK/sc/LG9jo9bMqTo7M58kSZLUQFd2vZAkSZKmYqIsSZIkNWCiLEmSJDVgoixJkiQ1YKIsSZIkNWCiLEldJCLOj4gPV7yPzRHx3Cr3IUmt4PBwkjSFiHgW8E7gCRTTCF8FnJOZ321rYJKkSvW0OwBJmssi4hDgIuDPgM8A+wMnUUwMMJ1yejJzZPYjlCRVxa4XkjS5xwFk5v/LzPsz857MXJ+ZPx5bISJeFRFXRcSuiPhpRDylXH5dRPx1RPwYuCsieiLi6RHxzYi4PSJ+VN9FISIOjYiPRMRNEXFjRLwtIvYrnzsrIi6LiNURcVtE/CIiTp8o6HK/N5YxXRMRJ5fL3xIRnyzvvzci7qy7jUTEW8rn+iLivyJie7mv1zb7gpX1fl7d/j4TER8vY9kcESc0W5YktZOJsiRN7mfA/RHxsYg4PSIOq38yIn4PeAvwMuAQ4AUU00uP+UPg+cAjgCOALwNvAxYDq4D/iogl5br/DowAjwGeDKwA/qSurN8ErgEOp+gK8pGIiPEBR8TjgbOBp2bmwcCpwHXj18vMszPzoMw8CHgWcBvwxYhYAPw3xdTJRwEnA+dExKmTv1QTegHwH+Vr8CXgvTMsR5JaykRZkiaRmXdQJJEJfAjYHhFfiogjylX+BHhnZn43C9dm5ta6Iv4lM6/PzHuAlwBrM3NtZo5m5gbgCuCMsrwzKPo+35WZtwDvAl5cV9bWzPxQZt4PfAw4kiL5Hu9+4GHAr0XEwsy8LjMHJ6pjmah/AXhNZv4AeCqwJDPfmpn3ZeaWsu4vnqiMKVxW1vl+4BPAb8ywHElqKRNlSZpCZl6VmWdl5tHAE4E+4N3l08cAEyahwPV19x8F/F7Z7eL2iLidIgk/snxuIXBT3XMfAJbWbX9zXUx3l3cPahDvtcA5FC3dt0TEf0REX6PgImIh8FlgIDP/oy7OvnFxnk/jpLwZN9fdvxtYFBFeIyNpzvOHSpKmITOvjoh/B/60XHQ90D/ZJnX3rwc+kZmvGr9SRBxJcYHg4bNx0V9mDgAD5cWIHwDeAby0warvAe4A3jwuzl9k5mP3NQ5Jms9sUZakSUTEr0TEGyLi6PLxMRT9jr9drvJhYFVE/O8oPCYiHjVBcZ8EfisiTo2I/SJiUUQ8NyKOzsybgPXAP0XEIRGxICL6I+I5M4j58RGxPCIeBuwG7gFGG6z3p8BzgD/KzPrnvwPsKi8IfHgZ6xMj4qnlds+NCMcWldTxTJQlaXK7KC6iuzwi7qJIkK8E3gCQmf8JvB0YKNf9AsWFeg+RmdcDL6ToxrCdouX2XB78LX4ZxfBzP6W4sO6zFN0yputhwD8Ct1J0e1gKnNdgvT8EjgeG6ka+OL/sS3wm8CTgF2U5HwYOLbc7BvjmDOKSpHnFCUckSdNSzuz3n5m5rt2xSFKVTJQlSZKkBux6IUmSJDVgoixJkiQ1YKIsSZIkNWCiLEmSJDVgoixJkiQ1YKIsSZIkNWCiLEmSJDVgoixJkiQ18P8Dt/Q657zpJkcAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"ssnp = df[['screen_size', 'normalized_used_price']]\\nssnp['screen_size_in'] = round(ssnp['screen_size']/2.54,0)\\n\\nfig, ax = plt.subplots(figsize=(12, 6))\\nsns.boxplot(data=ssnp, x='screen_size_in', y='normalized_used_price', ax=ax)\\nplt.xlabel('Screen size, in', fontsize=12)\\nplt.ylabel('Normalized Used Price', fontsize=12)\\nplt.title('Normalized Used Price vs Screen size in inches', fontsize=16)\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"ssnp = df[[\\\"screen_size\\\", \\\"normalized_used_price\\\"]]\\nssnp[\\\"screen_size_in\\\"] = round(ssnp[\\\"screen_size\\\"] / 2.54, 0)\\n\\nfig, ax = plt.subplots(figsize=(12, 6))\\nsns.boxplot(data=ssnp, x=\\\"screen_size_in\\\", y=\\\"normalized_used_price\\\", ax=ax)\\nplt.xlabel(\\\"Screen size, in\\\", fontsize=12)\\nplt.ylabel(\\\"Normalized Used Price\\\", fontsize=12)\\nplt.title(\\\"Normalized Used Price vs Screen size in inches\\\", 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": [
        "* Average normalized used price is growing with screen size increase until 6\"\n",
        "* The average prices are stable for devices with screen sizes from 6\" to 10\"\n",
        "* Devices with screen sizes 8\" and mor are tablets\n",
        "* Prices for tablets grow significantly for devices with a screen size of 11\" and more"
      ],
      "metadata": {
        "id": "fp9X4RSPHFVh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`os`** and **`normalized_used_price`**"
      ],
      "metadata": {
        "id": "D4rcEdz2Kk8j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(12, 6))\n",
        "sns.boxplot(data=df, x='os', y='normalized_used_price', ax=ax)\n",
        "plt.xlabel('Operating System', fontsize=12)\n",
        "plt.ylabel('Normalized Used Price', fontsize=12)\n",
        "plt.title('Normalized Used Price vs OS', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "outputId": "b0b97966-919c-4d11-f1a0-8d2e80506f50",
        "id": "lBjie4waKk8j"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"fig, ax = plt.subplots(figsize=(12, 6))\\nsns.boxplot(data=df, x='os', y='normalized_used_price', ax=ax)\\nplt.xlabel('Operating System', fontsize=12)\\nplt.ylabel('Normalized Used Price', fontsize=12)\\nplt.title('Normalized Used Price vs OS', fontsize=16)\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"fig, ax = plt.subplots(figsize=(12, 6))\\nsns.boxplot(data=df, x=\\\"os\\\", y=\\\"normalized_used_price\\\", ax=ax)\\nplt.xlabel(\\\"Operating System\\\", fontsize=12)\\nplt.ylabel(\\\"Normalized Used Price\\\", fontsize=12)\\nplt.title(\\\"Normalized Used Price vs OS\\\", 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": [
        "* Average normalized used price is higher for iOS devices\n",
        "* Average normalized used price is lower for Other devices\n",
        "* Average normalized used price of Windows devices is lower than Android and iOS devices"
      ],
      "metadata": {
        "id": "QSY0A6RZKk8j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`weight`, `normalized_used_price`, `4g`/`5g`**"
      ],
      "metadata": {
        "id": "YqS9AY0TdKxR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(12, 5))\n",
        "sns.scatterplot(data=df, x='weight', y='normalized_used_price', hue='4g', palette='Paired', ax=ax1);\n",
        "sns.scatterplot(data=df, x='weight', y='normalized_used_price', hue='5g', palette='Spectral_r', ax=ax2);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "W3HffJtcdK8o",
        "outputId": "9927c7bc-8764-40b8-c4d0-6061b9df9927"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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, (ax1, ax2) = plt.subplots(1,2, figsize=(12, 5))\\nsns.scatterplot(data=df, x='weight', y='normalized_used_price', hue='4g', palette='Paired', ax=ax1);\\nsns.scatterplot(data=df, x='weight', y='normalized_used_price', hue='5g', palette='Spectral_r', ax=ax2);\";\n",
              "                var nbb_formatted_code = \"fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\\nsns.scatterplot(\\n    data=df, x=\\\"weight\\\", y=\\\"normalized_used_price\\\", hue=\\\"4g\\\", palette=\\\"Paired\\\", ax=ax1\\n)\\nsns.scatterplot(\\n    data=df,\\n    x=\\\"weight\\\",\\n    y=\\\"normalized_used_price\\\",\\n    hue=\\\"5g\\\",\\n    palette=\\\"Spectral_r\\\",\\n    ax=ax2,\\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": [
        "* Assuming that devices heavier than 250g are tablets, we can conclude that\n",
        "  * Devices with no 4g in general cost less in their segment\n",
        "    * This is true for both phones and tablets, but the distinguish is more visible for the phones segment\n",
        "  * Phones with 5g are in the top price segment\n",
        "  * There are just a few tablets with 5g"
      ],
      "metadata": {
        "id": "cNs9TrMjdLF3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`5g`, `normalized_used_price`, `brand_name`, `release_year`**"
      ],
      "metadata": {
        "id": "TxohUBsBuWsB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "comp = df[df['5g']=='yes'].groupby(['brand_name','release_year']).agg({'normalized_used_price': 'mean', '5g': 'count', 'weight': 'mean'}).reset_index()\n",
        "comp = comp.rename(columns = {'5g': '# of devices'})\n",
        "sizes = [50*n for n in range(1, 11)]\n",
        "sns.scatterplot(data=comp, x='brand_name', y='normalized_used_price', hue='release_year', size='# of devices', palette='Paired_r', \n",
        "                sizes=sizes, alpha=0.7);\n",
        "plt.title('Number of devices with 5g by year, price, and brand')\n",
        "plt.xlabel('Brand name')\n",
        "plt.ylabel('Normalized used price')\n",
        "plt.xticks(rotation=90);\n",
        "plt.legend(bbox_to_anchor=(1.0, 1.02), handleheight=2.2);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "dXqk8Deui9R_",
        "outputId": "b3433353-4517-403d-ca04-272dbefb7ec1"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"plt.figure(figsize=(10, 5))\\ncomp = df[df['5g']=='yes'].groupby(['brand_name','release_year']).agg({'normalized_used_price': 'mean', '5g': 'count', 'weight': 'mean'}).reset_index()\\ncomp = comp.rename(columns = {'5g': '# of devices'})\\nsizes = [50*n for n in range(1, 11)]\\nsns.scatterplot(data=comp, x='brand_name', y='normalized_used_price', hue='release_year', size='# of devices', palette='Paired_r', \\n                sizes=sizes, alpha=0.7);\\nplt.title('Number of devices with 5g by year, price, and brand')\\nplt.xlabel('Brand name')\\nplt.ylabel('Normalized used price')\\nplt.xticks(rotation=90);\\nplt.legend(bbox_to_anchor=(1.0, 1.02), handleheight=2.2);\";\n",
              "                var nbb_formatted_code = \"plt.figure(figsize=(10, 5))\\ncomp = (\\n    df[df[\\\"5g\\\"] == \\\"yes\\\"]\\n    .groupby([\\\"brand_name\\\", \\\"release_year\\\"])\\n    .agg({\\\"normalized_used_price\\\": \\\"mean\\\", \\\"5g\\\": \\\"count\\\", \\\"weight\\\": \\\"mean\\\"})\\n    .reset_index()\\n)\\ncomp = comp.rename(columns={\\\"5g\\\": \\\"# of devices\\\"})\\nsizes = [50 * n for n in range(1, 11)]\\nsns.scatterplot(\\n    data=comp,\\n    x=\\\"brand_name\\\",\\n    y=\\\"normalized_used_price\\\",\\n    hue=\\\"release_year\\\",\\n    size=\\\"# of devices\\\",\\n    palette=\\\"Paired_r\\\",\\n    sizes=sizes,\\n    alpha=0.7,\\n)\\nplt.title(\\\"Number of devices with 5g by year, price, and brand\\\")\\nplt.xlabel(\\\"Brand name\\\")\\nplt.ylabel(\\\"Normalized used price\\\")\\nplt.xticks(rotation=90)\\nplt.legend(bbox_to_anchor=(1.0, 1.02), handleheight=2.2)\";\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": [
        "* Prices of devices with 5g released in 2020 dropped for most of the brands in comparison to 2019\n",
        "* The average price of devices from Samsung, Oppo, and Vivo released in 2020 is slightly higher than the average price of their devices released in 2019\n",
        "* There were 9 brands with devices released in 2019 with 5g. 7 more brands joined them in 2020\n",
        "* THere were a few devices with 5g by Lenovo released in 2019, but no devices with 5g released in 2020 from this brand in our dataset"
      ],
      "metadata": {
        "id": "d6BWojpBrkJg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Additional analysis"
      ],
      "metadata": {
        "id": "j5wDK0h2arzY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* We can not create a new variable related to the dependent variable and add it to the model. However, it is essential to investigate the derivative variable `discount` and analyze its behavior for different groups or devices\n",
        "* Let's create a temporary column `discount` as $1-{{normalized\\_used\\_price} \\over {normalized\\_new\\_price}}$\n"
      ],
      "metadata": {
        "id": "I577xjyAayjw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df.copy()\n",
        "df1['discount'] = 1 - df1['normalized_used_price'] / df1['normalized_new_price']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "rEibcUbFar-F",
        "outputId": "d534b31a-003c-45e5-fe69-f6d07fdfee8c"
      },
      "execution_count": 52,
      "outputs": [
        {
          "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 = \"df1 = df.copy()\\ndf1['discount'] = 1 - df1['normalized_used_price'] / df1['normalized_new_price']\";\n",
              "                var nbb_formatted_code = \"df1 = df.copy()\\ndf1[\\\"discount\\\"] = 1 - df1[\\\"normalized_used_price\\\"] / df1[\\\"normalized_new_price\\\"]\";\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(f'The aveage discount for all devices is {df1.discount.mean():.2%}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "vuKwYsfjdod4",
        "outputId": "54e350a4-e431-4993-971b-0fd834b2b2ca"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The aveage discount for all devices is 16.43%\n"
          ]
        },
        {
          "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 = \"print(f'The aveage discount for all devices is {df1.discount.mean():.2%}')\";\n",
              "                var nbb_formatted_code = \"print(f\\\"The aveage discount for all devices is {df1.discount.mean():.2%}\\\")\";\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(f'The aveage discount for devices with 5G is {df1[df1[\"5g\"]==\"yes\"].mean()[\"discount\"]:.2%}')\n",
        "print(f'The aveage discount for devices w/o 5G is {df1[df1[\"5g\"]==\"no\"].mean()[\"discount\"]:.2%}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "EgG9ABWdqGzs",
        "outputId": "b6e3bdc4-28a0-4333-abb9-08a93423e114"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The aveage discount for devices with 5G is 16.14%\n",
            "The aveage discount for devices w/o 5G is 16.44%\n"
          ]
        },
        {
          "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 = \"print(f'The aveage discount for devices with 5G is {df1[df1[\\\"5g\\\"]==\\\"yes\\\"].mean()[\\\"discount\\\"]:.2%}')\\nprint(f'The aveage discount for devices w/o 5G is {df1[df1[\\\"5g\\\"]==\\\"no\\\"].mean()[\\\"discount\\\"]:.2%}')\";\n",
              "                var nbb_formatted_code = \"print(\\n    f'The aveage discount for devices with 5G is {df1[df1[\\\"5g\\\"]==\\\"yes\\\"].mean()[\\\"discount\\\"]:.2%}'\\n)\\nprint(\\n    f'The aveage discount for devices w/o 5G is {df1[df1[\\\"5g\\\"]==\\\"no\\\"].mean()[\\\"discount\\\"]:.2%}'\\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": [
        "sns.barplot(data=df1.groupby('os').mean()[['discount']].reset_index(), x='os', y='discount');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "iGWHimxXqx0H",
        "outputId": "030a32a8-a3ab-4d59-f040-7885e5f0ce90"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"sns.barplot(data=df1.groupby('os').mean()[['discount']].reset_index(), x='os', y='discount');\";\n",
              "                var nbb_formatted_code = \"sns.barplot(\\n    data=df1.groupby(\\\"os\\\").mean()[[\\\"discount\\\"]].reset_index(), x=\\\"os\\\", y=\\\"discount\\\"\\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": [
        "del df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "XS0zLUbSidfN",
        "outputId": "f6353f41-23f3-4489-8f02-c7392df59208"
      },
      "execution_count": 56,
      "outputs": [
        {
          "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 = \"del df1\";\n",
              "                var nbb_formatted_code = \"del df1\";\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 discount for used devices is ~16% off to new device price of the same model (in normalized prices)\n",
        "* The highest discounts are for iOS devices (more than 20%).\n",
        "* `Other` and `Windows` devices have higher discount that devices work on Android\n",
        "* The aveage discount for devices w/o 5G is 0.3% higher than for devices with 5G\n",
        "* **That might be important to explain the regression coefficents of the model**"
      ],
      "metadata": {
        "id": "PRyR5JcEdovb"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oEyqzdJBb0jU"
      },
      "source": [
        "## Questions:\n",
        "\n",
        "1. What does the distribution of normalized used device prices look like?\n",
        "2. What percentage of the used device market is dominated by Android devices?\n",
        "3. The amount of RAM is important for the smooth functioning of a device. How does the amount of RAM vary with the brand?\n",
        "4. A large battery often increases a device's weight, making it feel uncomfortable in the hands. How does the weight vary for phones and tablets offering large batteries (more than 4500 mAh)?\n",
        "5. Bigger screens are desirable for entertainment purposes as they offer a better viewing experience. How many phones and tablets are available across different brands with a screen size larger than 6 inches?\n",
        "6. A lot of devices nowadays offer great selfie cameras, allowing us to capture our favorite moments with loved ones. What is the distribution of devices offering greater than 8MP selfie cameras across brands?\n",
        "7. Which attributes are highly correlated with the normalized price of a used device?"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####**1. What does the distribution of normalized used device prices look like?**"
      ],
      "metadata": {
        "id": "mRHmipbLSylm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "histogram_boxplot(data=df, feature='normalized_used_price',\n",
        "                  xlabel='Normalized price of the used/refurbished device in euros')"
      ],
      "metadata": {
        "id": "TwttUDWcTRm7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "outputId": "46a2bae5-fa11-402b-e236-9414853c3cc1"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"histogram_boxplot(data=df, feature='normalized_used_price',\\n                  xlabel='Normalized price of the used/refurbished device in euros')\";\n",
              "                var nbb_formatted_code = \"histogram_boxplot(\\n    data=df,\\n    feature=\\\"normalized_used_price\\\",\\n    xlabel=\\\"Normalized price of the used/refurbished device in euros\\\",\\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": [
        "# outliers\n",
        "\n",
        "iqr15 = 1.5*st.iqr(df['normalized_used_price'])\n",
        "outs = ((df['normalized_used_price']<(df['normalized_used_price'].quantile(0.25) - iqr15)).sum(),\n",
        "        (df['normalized_used_price']>(df['normalized_used_price'].quantile(0.75) + iqr15)).sum())\n",
        "\n",
        "print(f'There are {outs[0]} and {outs[1]} outliers below and above respectively the overall pattern in a distribution.')\n"
      ],
      "metadata": {
        "id": "yLgUb0RKe9tG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b0761a6d-7afe-4d27-ec04-1ea57b99d7b3"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 74 and 11 outliers below and above respectively the overall pattern in a distribution.\n"
          ]
        },
        {
          "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 = \"# outliers\\n\\niqr15 = 1.5*st.iqr(df['normalized_used_price'])\\nouts = ((df['normalized_used_price']<(df['normalized_used_price'].quantile(0.25) - iqr15)).sum(),\\n        (df['normalized_used_price']>(df['normalized_used_price'].quantile(0.75) + iqr15)).sum())\\n\\nprint(f'There are {outs[0]} and {outs[1]} outliers below and above respectively the overall pattern in a distribution.')\";\n",
              "                var nbb_formatted_code = \"# outliers\\n\\niqr15 = 1.5 * st.iqr(df[\\\"normalized_used_price\\\"])\\nouts = (\\n    (\\n        df[\\\"normalized_used_price\\\"]\\n        < (df[\\\"normalized_used_price\\\"].quantile(0.25) - iqr15)\\n    ).sum(),\\n    (\\n        df[\\\"normalized_used_price\\\"]\\n        > (df[\\\"normalized_used_price\\\"].quantile(0.75) + iqr15)\\n    ).sum(),\\n)\\n\\nprint(\\n    f\\\"There are {outs[0]} and {outs[1]} outliers below and above respectively the overall pattern in a 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": [
        "* The normalized used device price variable look symmetric (bell shaped), unimodal, close to normally distributed\n",
        "* Normalized used device price is approximately 4.4 on average (the range is 1.54-6.62)\n",
        "* There are 74 and 11 outliers below and above the overall pattern in a distribution respectively\n",
        "* It seems that the original prices in euros were converted to the normalized form with the log transformation"
      ],
      "metadata": {
        "id": "7fKuYjH2V3Qk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####**2. What percentage of the used device market is dominated by Android devices?**"
      ],
      "metadata": {
        "id": "5aE_YolUsCWX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "display(pd.concat({'Frequency': df.value_counts('os', normalize=False), \n",
        "                   'Percentage': df.value_counts('os', normalize=True)}, axis=1) \\\n",
        "        .style.format({'Frequency': '{:,.0f}'.format, \n",
        "                       'Percentage': '{:.1%}'.format}))\n",
        "\n",
        "print(f'\\n\\033[1mTotal:\\033[0m {df.shape[0]:,.0f} devices.')"
      ],
      "metadata": {
        "id": "0KzTkzqDsCt5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "outputId": "4b6cae60-74bf-43e6-e1d2-ba46ced51e53"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f9b20a4bdf0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_ed685_\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >Frequency</th>\n",
              "      <th class=\"col_heading level0 col1\" >Percentage</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >os</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_ed685_level0_row0\" class=\"row_heading level0 row0\" >Android</th>\n",
              "      <td id=\"T_ed685_row0_col0\" class=\"data row0 col0\" >3,214</td>\n",
              "      <td id=\"T_ed685_row0_col1\" class=\"data row0 col1\" >93.1%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ed685_level0_row1\" class=\"row_heading level0 row1\" >Others</th>\n",
              "      <td id=\"T_ed685_row1_col0\" class=\"data row1 col0\" >137</td>\n",
              "      <td id=\"T_ed685_row1_col1\" class=\"data row1 col1\" >4.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ed685_level0_row2\" class=\"row_heading level0 row2\" >Windows</th>\n",
              "      <td id=\"T_ed685_row2_col0\" class=\"data row2 col0\" >67</td>\n",
              "      <td id=\"T_ed685_row2_col1\" class=\"data row2 col1\" >1.9%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ed685_level0_row3\" class=\"row_heading level0 row3\" >iOS</th>\n",
              "      <td id=\"T_ed685_row3_col0\" class=\"data row3 col0\" >36</td>\n",
              "      <td id=\"T_ed685_row3_col1\" class=\"data row3 col1\" >1.0%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\u001b[1mTotal:\u001b[0m 3,454 devices.\n"
          ]
        },
        {
          "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 = \"display(pd.concat({'Frequency': df.value_counts('os', normalize=False), \\n                   'Percentage': df.value_counts('os', normalize=True)}, axis=1) \\\\\\n        .style.format({'Frequency': '{:,.0f}'.format, \\n                       'Percentage': '{:.1%}'.format}))\\n\\nprint(f'\\\\n\\\\033[1mTotal:\\\\033[0m {df.shape[0]:,.0f} devices.')\";\n",
              "                var nbb_formatted_code = \"display(\\n    pd.concat(\\n        {\\n            \\\"Frequency\\\": df.value_counts(\\\"os\\\", normalize=False),\\n            \\\"Percentage\\\": df.value_counts(\\\"os\\\", normalize=True),\\n        },\\n        axis=1,\\n    ).style.format({\\\"Frequency\\\": \\\"{:,.0f}\\\".format, \\\"Percentage\\\": \\\"{:.1%}\\\".format})\\n)\\n\\nprint(f\\\"\\\\n\\\\033[1mTotal:\\\\033[0m {df.shape[0]:,.0f} devices.\\\")\";\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": [
        "* 93.1% of the used device market is dominated by Android devices"
      ],
      "metadata": {
        "id": "EoSiiw-l8S8L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####**3. The amount of RAM is important for the smooth functioning of a device. How does the amount of RAM vary with the brand?**"
      ],
      "metadata": {
        "id": "AAeKsgZA9j5A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# top 5 RAM amount devices\n",
        "display(pd.concat({'Frequency': df.value_counts('ram', normalize=False), \n",
        "                   'Percentage': df.value_counts('ram', normalize=True)}, \n",
        "                  axis=1).head(5).style.format({'Frequency': '{:,.0f}'.format, \n",
        "                                               'Percentage': '{:.1%}'.format}))\n"
      ],
      "metadata": {
        "id": "8F1yV9EMUJCU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "87dac8fa-56a6-4093-bdda-a8d25539f802"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f9b20a4b160>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_a78b8_\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >Frequency</th>\n",
              "      <th class=\"col_heading level0 col1\" >Percentage</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >ram</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_a78b8_level0_row0\" class=\"row_heading level0 row0\" >4.0</th>\n",
              "      <td id=\"T_a78b8_row0_col0\" class=\"data row0 col0\" >2,815</td>\n",
              "      <td id=\"T_a78b8_row0_col1\" class=\"data row0 col1\" >81.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a78b8_level0_row1\" class=\"row_heading level0 row1\" >6.0</th>\n",
              "      <td id=\"T_a78b8_row1_col0\" class=\"data row1 col0\" >154</td>\n",
              "      <td id=\"T_a78b8_row1_col1\" class=\"data row1 col1\" >4.5%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a78b8_level0_row2\" class=\"row_heading level0 row2\" >8.0</th>\n",
              "      <td id=\"T_a78b8_row2_col0\" class=\"data row2 col0\" >130</td>\n",
              "      <td id=\"T_a78b8_row2_col1\" class=\"data row2 col1\" >3.8%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a78b8_level0_row3\" class=\"row_heading level0 row3\" >2.0</th>\n",
              "      <td id=\"T_a78b8_row3_col0\" class=\"data row3 col0\" >90</td>\n",
              "      <td id=\"T_a78b8_row3_col1\" class=\"data row3 col1\" >2.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a78b8_level0_row4\" class=\"row_heading level0 row4\" >0.25</th>\n",
              "      <td id=\"T_a78b8_row4_col0\" class=\"data row4 col0\" >83</td>\n",
              "      <td id=\"T_a78b8_row4_col1\" class=\"data row4 col1\" >2.4%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\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 = \"# top 5 RAM amount devices\\ndisplay(pd.concat({'Frequency': df.value_counts('ram', normalize=False), \\n                   'Percentage': df.value_counts('ram', normalize=True)}, \\n                  axis=1).head(5).style.format({'Frequency': '{:,.0f}'.format, \\n                                               'Percentage': '{:.1%}'.format}))\";\n",
              "                var nbb_formatted_code = \"# top 5 RAM amount devices\\ndisplay(\\n    pd.concat(\\n        {\\n            \\\"Frequency\\\": df.value_counts(\\\"ram\\\", normalize=False),\\n            \\\"Percentage\\\": df.value_counts(\\\"ram\\\", normalize=True),\\n        },\\n        axis=1,\\n    )\\n    .head(5)\\n    .style.format({\\\"Frequency\\\": \\\"{:,.0f}\\\".format, \\\"Percentage\\\": \\\"{:.1%}\\\".format})\\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": [
        "fig, ax = plt.subplots(figsize=(12, 6))\n",
        "pd.crosstab(index=df['brand_name'], columns=df['ram']).plot(\n",
        "    kind='bar', stacked=True, color=sns.color_palette('Paired'), ax=ax)\n",
        "plt.xlabel('Brand name', fontsize=12)\n",
        "plt.ylabel('Number of devices', fontsize=12)\n",
        "plt.title('The amount of RAM by the brand', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "QCebwYmS9uev",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "outputId": "8586498d-9c9d-4fba-8026-057fada4b653"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"fig, ax = plt.subplots(figsize=(12, 6))\\npd.crosstab(index=df['brand_name'], columns=df['ram']).plot(\\n    kind='bar', stacked=True, color=sns.color_palette('Paired'), ax=ax)\\nplt.xlabel('Brand name', fontsize=12)\\nplt.ylabel('Number of devices', fontsize=12)\\nplt.title('The amount of RAM by the brand', fontsize=16)\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"fig, ax = plt.subplots(figsize=(12, 6))\\npd.crosstab(index=df[\\\"brand_name\\\"], columns=df[\\\"ram\\\"]).plot(\\n    kind=\\\"bar\\\", stacked=True, color=sns.color_palette(\\\"Paired\\\"), ax=ax\\n)\\nplt.xlabel(\\\"Brand name\\\", fontsize=12)\\nplt.ylabel(\\\"Number of devices\\\", fontsize=12)\\nplt.title(\\\"The amount of RAM by the brand\\\", 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": "code",
      "source": [
        "plt.figure(figsize=(15, 5))\n",
        "sns.barplot(data=df, x=\"brand_name\", y=\"ram\", palette='Spectral',\n",
        "            order=df.groupby('brand_name').mean()['ram'].sort_values()[::-1].index)\n",
        "plt.xticks(rotation=90)\n",
        "plt.xlabel('Brand name', fontsize=12)\n",
        "plt.ylabel('Average RAM, GB', fontsize=12)\n",
        "plt.title('Average amount of RAM by the brand', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "eXyE8ibSjkhJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "outputId": "f29c7de7-a196-444e-f704-8939df01a9b8"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"plt.figure(figsize=(15, 5))\\nsns.barplot(data=df, x=\\\"brand_name\\\", y=\\\"ram\\\", palette='Spectral',\\n            order=df.groupby('brand_name').mean()['ram'].sort_values()[::-1].index)\\nplt.xticks(rotation=90)\\nplt.xlabel('Brand name', fontsize=12)\\nplt.ylabel('Average RAM, GB', fontsize=12)\\nplt.title('Average amount of RAM by the brand', fontsize=16)\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"plt.figure(figsize=(15, 5))\\nsns.barplot(\\n    data=df,\\n    x=\\\"brand_name\\\",\\n    y=\\\"ram\\\",\\n    palette=\\\"Spectral\\\",\\n    order=df.groupby(\\\"brand_name\\\").mean()[\\\"ram\\\"].sort_values()[::-1].index,\\n)\\nplt.xticks(rotation=90)\\nplt.xlabel(\\\"Brand name\\\", fontsize=12)\\nplt.ylabel(\\\"Average RAM, GB\\\", fontsize=12)\\nplt.title(\\\"Average amount of RAM by the brand\\\", 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": "code",
      "source": [
        "# Variety of ram among a brand's devices\n",
        "df[['brand_name', 'ram']].drop_duplicates().value_counts('brand_name').head()"
      ],
      "metadata": {
        "id": "YpZTcTI_apdp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "outputId": "2adc0435-0a49-4829-9b51-2b7e5215c7ef"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "brand_name\n",
              "Samsung    9\n",
              "Others     8\n",
              "Nokia      8\n",
              "Huawei     8\n",
              "LG         8\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "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 = \"# Variety of ram among a brand's devices\\ndf[['brand_name', 'ram']].drop_duplicates().value_counts('brand_name').head()\";\n",
              "                var nbb_formatted_code = \"# Variety of ram among a brand's devices\\ndf[[\\\"brand_name\\\", \\\"ram\\\"]].drop_duplicates().value_counts(\\\"brand_name\\\").head()\";\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(f\"{df[df['ram']==df['ram'].min()].value_counts('brand_name').index.values} has models with minimum RAM\")\n",
        "print(f\"{df[df['ram']==df['ram'].max()].value_counts('brand_name').index.values} has models with maximum RAM\")"
      ],
      "metadata": {
        "id": "IPUTbL5Ac1Pa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "10bc24b5-1308-455d-d008-b4f055059015"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Nokia'] has models with minimum RAM\n",
            "['Samsung' 'Oppo' 'Huawei' 'Motorola' 'OnePlus' 'Xiaomi'] has models with maximum RAM\n"
          ]
        },
        {
          "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 = \"print(f\\\"{df[df['ram']==df['ram'].min()].value_counts('brand_name').index.values} has models with minimum RAM\\\")\\nprint(f\\\"{df[df['ram']==df['ram'].max()].value_counts('brand_name').index.values} has models with maximum RAM\\\")\";\n",
              "                var nbb_formatted_code = \"print(\\n    f\\\"{df[df['ram']==df['ram'].min()].value_counts('brand_name').index.values} has models with minimum RAM\\\"\\n)\\nprint(\\n    f\\\"{df[df['ram']==df['ram'].max()].value_counts('brand_name').index.values} has models with maximum RAM\\\"\\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": [
        "* Overwhelming majority of the devices have 4GB RAM (4GB of RAM is in 81.6% of devices)\n",
        "* All other amounts except 4GB might be considered as outliers\n",
        "* Samsung is the brand with largest variety of models with different amounts of RAM\n",
        "* Nokia is the brand that has models with minimal RAM of 16MB\n",
        "* Several brands have models with a maximum RAM of 12 GB\n",
        "* The maximum average amount of RAM is observed in the OnePlus brand\n",
        "* The minimum average amount of RAM is observed in the Celcon brand"
      ],
      "metadata": {
        "id": "9a1kjwfQUGLy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####**4. A large battery often increases a device's weight, making it feel uncomfortable in the hands. How does the weight vary for phones and tablets offering large batteries (more than 4500 mAh)?**"
      ],
      "metadata": {
        "id": "I5UGcs4cm4pH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bw = data=df[df['battery']>4500]\n",
        "bw[['weight','battery']].corr()"
      ],
      "metadata": {
        "id": "1nxTtWNfzMTt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "b90893f8-ce9d-4957-96d1-bf608a30aaa3"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           weight   battery\n",
              "weight   1.000000  0.757622\n",
              "battery  0.757622  1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-afabdd4a-268f-415e-8cb0-1b55fa400a8e\">\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>weight</th>\n",
              "      <th>battery</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>weight</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.757622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>battery</th>\n",
              "      <td>0.757622</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-afabdd4a-268f-415e-8cb0-1b55fa400a8e')\"\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-afabdd4a-268f-415e-8cb0-1b55fa400a8e 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-afabdd4a-268f-415e-8cb0-1b55fa400a8e');\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": 65
        },
        {
          "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 = \"bw = data=df[df['battery']>4500]\\nbw[['weight','battery']].corr()\";\n",
              "                var nbb_formatted_code = \"bw = data = df[df[\\\"battery\\\"] > 4500]\\nbw[[\\\"weight\\\", \\\"battery\\\"]].corr()\";\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": [
        "sns.lmplot(x='weight', y='battery', data=bw, height=6, aspect=1.5)\n",
        "plt.xlabel('Weight, g', fontsize=12)\n",
        "plt.ylabel('Energy capacity, mAh', fontsize=12)\n",
        "plt.title('Weight vs Energy capacity for large batteries (> 4500 mAh)', fontsize=14)\n",
        "plt.show();\n"
      ],
      "metadata": {
        "id": "wbj6BSWNklWf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "6070c183-7eb3-49bd-ddec-3cd90c407b57"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"sns.lmplot(x='weight', y='battery', data=bw, height=6, aspect=1.5)\\nplt.xlabel('Weight, g', fontsize=12)\\nplt.ylabel('Energy capacity, mAh', fontsize=12)\\nplt.title('Weight vs Energy capacity for large batteries (> 4500 mAh)', fontsize=14)\\nplt.show();\";\n",
              "                var nbb_formatted_code = \"sns.lmplot(x=\\\"weight\\\", y=\\\"battery\\\", data=bw, height=6, aspect=1.5)\\nplt.xlabel(\\\"Weight, g\\\", fontsize=12)\\nplt.ylabel(\\\"Energy capacity, mAh\\\", fontsize=12)\\nplt.title(\\\"Weight vs Energy capacity for large batteries (> 4500 mAh)\\\", fontsize=14)\\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": [
        "plt.figure(figsize=(12, 5))\n",
        "sns.barplot(data=bw, x=\"brand_name\", y=\"weight\", palette='Spectral',\n",
        "            order=bw.groupby('brand_name').mean()['weight'].sort_values()[::-1].index)\n",
        "plt.xticks(rotation=90)\n",
        "plt.xlabel('Brand name', fontsize=12)\n",
        "plt.ylabel('Average weight, g', fontsize=12)\n",
        "plt.title('Average weight of devices with large batteries', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "pwGFBda_xK3B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "outputId": "beaa06eb-af4c-4c0d-c290-0beb2952180e"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"plt.figure(figsize=(12, 5))\\nsns.barplot(data=bw, x=\\\"brand_name\\\", y=\\\"weight\\\", palette='Spectral',\\n            order=bw.groupby('brand_name').mean()['weight'].sort_values()[::-1].index)\\nplt.xticks(rotation=90)\\nplt.xlabel('Brand name', fontsize=12)\\nplt.ylabel('Average weight, g', fontsize=12)\\nplt.title('Average weight of devices with large batteries', fontsize=16)\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"plt.figure(figsize=(12, 5))\\nsns.barplot(\\n    data=bw,\\n    x=\\\"brand_name\\\",\\n    y=\\\"weight\\\",\\n    palette=\\\"Spectral\\\",\\n    order=bw.groupby(\\\"brand_name\\\").mean()[\\\"weight\\\"].sort_values()[::-1].index,\\n)\\nplt.xticks(rotation=90)\\nplt.xlabel(\\\"Brand name\\\", fontsize=12)\\nplt.ylabel(\\\"Average weight, g\\\", fontsize=12)\\nplt.title(\\\"Average weight of devices with large batteries\\\", 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 relationship between weight and energy capacity for devices with large batteries is considered strong as coefficient correlation is larger than 0.7\n",
        "* The heaviest device with a large battery is manufactured by Google (517g)\n",
        "* Lightest device with a large battery is manufactured by Micromax (118g)\n",
        "* Acer is the brand with largest variety of models with different weights of devices with a large battery\n",
        "* Devices with weight more than 250g are mostly tablets. Considering also that weight and batary are strongly correlated, we can conclude that most of the devices with large batteries are tablets"
      ],
      "metadata": {
        "id": "sDu7RMMfm0ma"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####**5. Bigger screens are desirable for entertainment purposes as they offer a better viewing experience. How many phones and tablets are available across different brands with a screen size larger than 6 inches?**"
      ],
      "metadata": {
        "id": "szpQduGw1ftg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bs = df[df['screen_size']>6*2.54]\n",
        "bs.value_counts('brand_name', normalize=True).head()"
      ],
      "metadata": {
        "id": "It3j-73taVMe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "outputId": "739383d6-2829-4845-c240-8cfe8122f987"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "brand_name\n",
              "Huawei     0.135578\n",
              "Samsung    0.108280\n",
              "Others     0.090082\n",
              "Vivo       0.072793\n",
              "Honor      0.065514\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "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 = \"bs = df[df['screen_size']>6*2.54]\\nbs.value_counts('brand_name', normalize=True).head()\";\n",
              "                var nbb_formatted_code = \"bs = df[df[\\\"screen_size\\\"] > 6 * 2.54]\\nbs.value_counts(\\\"brand_name\\\", normalize=True).head()\";\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=bs, feature='brand_name', perc=False, \n",
        "                xlabel='Brand name', figsize=(13,5), xlo=90,\n",
        "                title='Devices available across different brands with a screen size larger than 6\"')"
      ],
      "metadata": {
        "id": "V-oH_b5EWRnL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "0308249e-4b9f-42b2-caf9-923e1651d57a"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 936x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"labeled_barplot(data=bs, feature='brand_name', perc=False, \\n                xlabel='Brand name', figsize=(13,5), xlo=90,\\n                title='Devices available across different brands with a screen size larger than 6\\\"')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(\\n    data=bs,\\n    feature=\\\"brand_name\\\",\\n    perc=False,\\n    xlabel=\\\"Brand name\\\",\\n    figsize=(13, 5),\\n    xlo=90,\\n    title='Devices available across different brands with a screen size larger than 6\\\"',\\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": [
        "plt.figure(figsize=(12, 5))\n",
        "sns.barplot(data=bs, x=\"brand_name\", y=\"screen_size\", palette='Spectral',\n",
        "            order=bs.groupby('brand_name').mean()['screen_size'].sort_values()[::-1].index)\n",
        "plt.xticks(rotation=90)\n",
        "plt.xlabel('Brand name', fontsize=12)\n",
        "plt.ylabel('Average screen size, cm', fontsize=12)\n",
        "plt.title('Average screen size of devices with large screen', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "u-qmsHhpbxUq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "outputId": "215b7f4e-28b5-4a3f-ce7d-59de75f145fb"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"plt.figure(figsize=(12, 5))\\nsns.barplot(data=bs, x=\\\"brand_name\\\", y=\\\"screen_size\\\", palette='Spectral',\\n            order=bs.groupby('brand_name').mean()['screen_size'].sort_values()[::-1].index)\\nplt.xticks(rotation=90)\\nplt.xlabel('Brand name', fontsize=12)\\nplt.ylabel('Average screen size, cm', fontsize=12)\\nplt.title('Average screen size of devices with large screen', fontsize=16)\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"plt.figure(figsize=(12, 5))\\nsns.barplot(\\n    data=bs,\\n    x=\\\"brand_name\\\",\\n    y=\\\"screen_size\\\",\\n    palette=\\\"Spectral\\\",\\n    order=bs.groupby(\\\"brand_name\\\").mean()[\\\"screen_size\\\"].sort_values()[::-1].index,\\n)\\nplt.xticks(rotation=90)\\nplt.xlabel(\\\"Brand name\\\", fontsize=12)\\nplt.ylabel(\\\"Average screen size, cm\\\", fontsize=12)\\nplt.title(\\\"Average screen size of devices with large screen\\\", 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": [
        "* Huawei is the brend with maximum number of devices (149, ~13.6%) with screen size larger than 6\"\n",
        "* Micosoft is the brand with minimum number of devices (1) with screen size larger than 6\"\n",
        "* The largest average screen size belongs to Microsoft device (25.6cm, 10\")\n",
        "* The smallest average screen size belongs to Meizu devices (15.3cm, ~6\")\n",
        "* Google is the brand with largest variety of models with different screen sizes of devices with a screen size large than 6\""
      ],
      "metadata": {
        "id": "DgDYVW_tcNCE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####**6. A lot of devices nowadays offer great selfie cameras, allowing us to capture our favorite moments with loved ones. What is the distribution of devices offering greater than 8MP selfie cameras across brands?**"
      ],
      "metadata": {
        "id": "iZrS9AFyWFCa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bc = df[df['selfie_camera_mp']>8]\n",
        "print(bc.value_counts('brand_name', normalize=False).head())\n",
        "print(bc.value_counts('brand_name', normalize=False).tail())"
      ],
      "metadata": {
        "id": "3KFobr9tWEjy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "outputId": "e327fd4a-4916-4d4c-93d6-6ee52ff6ca3c"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "brand_name\n",
            "Huawei     87\n",
            "Vivo       78\n",
            "Oppo       75\n",
            "Xiaomi     63\n",
            "Samsung    57\n",
            "dtype: int64\n",
            "brand_name\n",
            "Coolpad       3\n",
            "Micromax      2\n",
            "Panasonic     2\n",
            "BlackBerry    2\n",
            "Acer          1\n",
            "dtype: int64\n"
          ]
        },
        {
          "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 = \"bc = df[df['selfie_camera_mp']>8]\\nprint(bc.value_counts('brand_name', normalize=False).head())\\nprint(bc.value_counts('brand_name', normalize=False).tail())\";\n",
              "                var nbb_formatted_code = \"bc = df[df[\\\"selfie_camera_mp\\\"] > 8]\\nprint(bc.value_counts(\\\"brand_name\\\", normalize=False).head())\\nprint(bc.value_counts(\\\"brand_name\\\", normalize=False).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": "code",
      "source": [
        "labeled_barplot(data=bc, feature='brand_name', perc=True, percfmt='{:.0f}%',\n",
        "                xlabel='Brand name', figsize=(15,5), xlo=90,\n",
        "                title='Distribution of devices offering greater than 8MP selfie cameras across brands')"
      ],
      "metadata": {
        "id": "zQzFWLCbW7la",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "outputId": "5ef76e74-211c-43a5-96e3-49465dd64ca5"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"labeled_barplot(data=bc, feature='brand_name', perc=True, percfmt='{:.0f}%',\\n                xlabel='Brand name', figsize=(15,5), xlo=90,\\n                title='Distribution of devices offering greater than 8MP selfie cameras across brands')\";\n",
              "                var nbb_formatted_code = \"labeled_barplot(\\n    data=bc,\\n    feature=\\\"brand_name\\\",\\n    perc=True,\\n    percfmt=\\\"{:.0f}%\\\",\\n    xlabel=\\\"Brand name\\\",\\n    figsize=(15, 5),\\n    xlo=90,\\n    title=\\\"Distribution of devices offering greater than 8MP selfie cameras across brands\\\",\\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": [
        "plt.figure(figsize=(12, 5))\n",
        "sns.barplot(data=bc, x=\"brand_name\", y='selfie_camera_mp', palette='Spectral',\n",
        "            order=bc.groupby('brand_name').mean()['selfie_camera_mp'].sort_values()[::-1].index)\n",
        "plt.xticks(rotation=90)\n",
        "plt.xlabel('Brand name', fontsize=12)\n",
        "plt.ylabel('Average screen size, cm', fontsize=12)\n",
        "plt.title('Average resolution of selfie camera of devices with greater than 8MP', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "bIfZDaf-W7th",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "outputId": "9e343c0a-6b97-49fa-e0fc-5d4f0e6bf585"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"plt.figure(figsize=(12, 5))\\nsns.barplot(data=bc, x=\\\"brand_name\\\", y='selfie_camera_mp', palette='Spectral',\\n            order=bc.groupby('brand_name').mean()['selfie_camera_mp'].sort_values()[::-1].index)\\nplt.xticks(rotation=90)\\nplt.xlabel('Brand name', fontsize=12)\\nplt.ylabel('Average screen size, cm', fontsize=12)\\nplt.title('Average resolution of selfie camera of devices with greater than 8MP', fontsize=16)\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"plt.figure(figsize=(12, 5))\\nsns.barplot(\\n    data=bc,\\n    x=\\\"brand_name\\\",\\n    y=\\\"selfie_camera_mp\\\",\\n    palette=\\\"Spectral\\\",\\n    order=bc.groupby(\\\"brand_name\\\").mean()[\\\"selfie_camera_mp\\\"].sort_values()[::-1].index,\\n)\\nplt.xticks(rotation=90)\\nplt.xlabel(\\\"Brand name\\\", fontsize=12)\\nplt.ylabel(\\\"Average screen size, cm\\\", fontsize=12)\\nplt.title(\\n    \\\"Average resolution of selfie camera of devices with greater than 8MP\\\", 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": [
        "fig, ax = plt.subplots(figsize=(12, 8))\n",
        "sns.boxplot(data=bc, y=\"brand_name\", x=\"selfie_camera_mp\", ax=ax)\n",
        "plt.xlabel('Selfie camera resolution, MP', fontsize=12)\n",
        "plt.ylabel('Brand name', fontsize=12)\n",
        "plt.title('Distribution of selfie cameras resolution (>8MP) across brands', fontsize=16)\n",
        "ax.xaxis.set_major_formatter(tick.FormatStrFormatter('%0.0f'))\n",
        "plt.xticks(np.arange(min(bc['selfie_camera_mp']), max(bc['selfie_camera_mp'])+1, 1))\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "JpRhCgVcbe3k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "outputId": "ba08c060-7632-4b02-f258-de7f8611531e"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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VnjqeDXxe0uC8NrYi6SVaqzYi4uWIuIgkcdg+Lb6NJNnbOiJmFXn01ntYimuA7YCnu2mjp4OJGcAHilxgmW84SYKV7ytrGXOhrt65kd1sQ2Giv9ZxRuI+kgvP4c33rC/6/P5GxOKIuJrkAHELkoR8bT7/Xb3Jb8SfDg36cEG9vuwjbgL2Ud7F4unzz/QSS190Desq9gNeH5Q0lSQxvxhYn+TC3M0j4tC0t77wtrfzSYbRrVNQ/kGSg6GiB/LpkJaTSXqrC4dvrBFJY0g+z739f/f6mU2/F24FvqRe7uLTjT69lz18x+TXeTQifkhyoFDK/80+6dDJrvbHk5y5uL3bJd7UldC/8Top+Y2Cj5SwbDF3khw4faGg/MAidYHSXhOrDPfoW62ZIGlTkiEK44BPk5zOv46kp6AoSZ8mucvHFSS3g1ufpOfpNd7cUXbdW/0oSf8EVpbY61TMC8AMJbd4XAYck7aZ/4M6F6YxnaXktnxvI0mY2grWVVJcEbFS0nEkPePnkwwTeQtwEslYybMKl1lDPya53uBqJbdJG0HSK9VG0hPfJ2ky0vU+vEhy14gvk44tj4hXJR0N/F7J7ST/mbb1FpLxsTMj4i/F1t0HvyQ5rX6LpF+SJBfrkyT/H42IfXtZ9ovAv5T8euUDJBes7gsckR4kXAMcIukB4HGSIQaFSeRaiYiZki4g6fH+BUkvbCfJBXb7AMdExGO9rOYa4BhJP0yX34PkYsk3pENlfk1yxuVxkosfDyUZ93vDGsRd0vsr6SckPYY3kpydeCvJ//B9kfyeBWvx+e9q84+Sjie5GPZ7JBdP5nss3c7DJC0i+d9+tJsDwZ+S7J+ul3QqSU/rMSRJ109KfX16EhFPS5pHMlyl8K4op5DckeinwF8iolhPfKHfkRwU/C39324n6UQ5CPhlwdmOwljKsk15Ppj+vbnHWiV8ZlPNJAn77UruYPUsyQHShIjo8c5alPBe9vYdI2kkyfCi6SQHaCtI9hEb8+Z1ND1pJ/lOOY3k83ki8Crd3w0p379IPrfnptu+Rbr806xBh29EPCrpL8BP0mFHd5Ocid0nv16J37tWaVEFVwT74UdvD968u03Xo52kF+5ykkS/8G4441n16v93kCQmT5L0TC0gueDog3nLDCa5XeGLJAlSFKzraz3ENT6v7CmSL92vATmSZOBeYI8iy3+dJAlpJ+nZ3InV797RW1yHFqzzS8D9absLSW7ptkVBnaeA84vEE8AJJbwfe5PsqNtJEqQrgXcU1Cn1rjuHkPSKvZjG/CTJl9eGBfX2IUnyXgWW8mby9q6C7cp/7Uq6605atnHa7pMk499fJLlg9tslbMNmwFSSU9rLSS4kPYf0riYkif+FJL13L5N82b+/MDaSO7A828378rOCsq7P3tZ5ZYNIhh/dT/I5b0uf/5ykpz//c1Ps8zyMZAjBApIv46tJDkDf+Fyk23oOSdK7lKSX9yZgr15eo27bLeX9JTkjdm36Gi9LX+M/k94JZA0+/9MKynYhSViWptv2JQruupP3P/sESeIUwG49rPODJEnWYpLx8NcDHyio0917PpMid+UqUu9U4Iki5WN6W7ab9X0ybbvrM3AfyW1p8+/41ON72d3nlh7uWlRk2T8Cs0qo1+tnNq/u+0hum/sKyb7rEZID4PzX/NZu2unxvaSX7xiS5PwPJMPTFpN8zu+ml7uc5b2OJwE/JDlAeZ1k3zShyGemu/i/kG7v62kMBxZ+vrt7X/Pet93yyoanr/uidHuuIjlD0KfvXT8q/1D6ZpiZmVmNkdRAcgZqt0juMFTzlNz//nmgOSL+3Ft9M+uex+ibmZnVqIjIAWeT/DhYvfg6yRm1c7IOxKzWOdE3MzOrbT8G7l6Lu6hUm2Ukwz86eq1pZj3y0B0zMzMzszrkHn0zMzMzszrk22tWwKabbhrjx4/POgwzMzMzq3OzZ89+KSJGF5vnRL8Cxo8fz6xZa3r7dTPrTlNTE21tbYwdOzbrUMzMzN7Q0NBAU1NTJm2nv6dRlBN9M6sZ8+fPp33pUkYzuPfKZmZm/WBeW9Efja4KTvTNrKasN3gIP/roXlmHYWZmBsDPbrk26xC65UTfrJ+1tLQAZHaKz8zMzMpn/uLXGNy6MuswiqqLRF/S4ogYkTd9KNAYEf+bXVRmxeVyuaxDMDMzszJZ1rECtbdnHUZRvr3mGpBUFwdIZmZmZla/6j7RlzRe0g2S5ki6XtK4tHyapN9Iuk3SE5L2T8sl6TRJcyU9IOmAtHw3SbdIugp4KMNNMjMzMzPrVb30TA+TdF/e9CjgqvT5b4FzIuIcSYcBvwH2S+dtAewCbJfWvwT4HDAB2AHYlORnxW9O6+8IbB8RT1ZsS6zutba20t7eTnNzc9ah1Jz29nYG+ce8zczMSlIvPfrtETGh6wEclzdvZ+Av6fPzSBL7LldERGdEPARsnpbtAlwQESsj4gXgJuD96by7ukvyJR0uaZakWQsWLCjTZpmZmZmZrZl66dFfU8vynquE+ku6mxERU4GpAI2Nje5ztG51/djTlClTMo6k9kyaNIlYtjzrMMzMzGpCvfTo9+Q24MD0+WTgll7q3wIcIGmwpNHArsBdFYzPzMzMzKzsBkKP/pHA2ZKOBhYAX+ml/uUkw33uBwL4XkTMl7RdZcO0gaKhoSHrEMzMzKxM1h0ylMHDhmUdRlF1kejn30M/nZ4GTEufzwP2KLLMocXWEREBHJ0+8ufPBGaWK2YbuPxDWWZmZvVjzIgNGDp2894rZmAgDN0xMzMzMxtw6qJH38wGjtdXdvCzW67NOgwzMzMA5rUtYusq7dF3om9mNWPMmDG0tbVV7SlSMzMbeLYeu3nVXn/nRN/MakZLS0vWIZiZmdUMJ/pm/aylpYVcLleWdbW2tgJv3pt/IGhoaPAFzWZmZiVwom/Wz3K5HI8//BDjRm6w1uta8sprACxn5VqvqxY83fZa1iGYmZnVDCf6ZhkYN3IDjv3oB9Z6PSfdkvyWWznWVQu6ttfMzMx659trmpmZmZnVoQHXoy9pcf4PbEk6FGgEngc+nxa/B3ggfX4WcA3wK2Ab4DXgceDIiHihf6KufV0XUXpsta2NFxYvZXB6XYKZmZn1bMAl+t2JiJOAk+CNg4EJ6fP1SJL+70bE39Ky3YDRgBP9EpXr4lMb2F7vWMmg9vaswzAzM6sJTvR790Xg9q4kHyAiZmYXjpmZmZlZ7wZioj9M0n1506OAq3qovz0wu6IRDQCtra20t7fT3NycdSiZy+VyrLOyI+swzMzMrM4NxES/vWtYDqwyRn+tSDocOBxg3Lhxa7s6MzMzM7O1MhAT/b56EPhYb5UiYiowFaCxsTEqHVSt6fpBpylTpmQcSfaam5tZ3vpM1mGYmZlZnfPtNXv3F+DDkj7VVSBpV0nbZxiTmZmZmVmP3KPfi4hol/Rp4FeSfgWsAOYA38o0sBrT0NCQdQhWB9YbMpjBw4ZlHYaZmVlNGHCJfv499NPpacC0Xuo8Auxd6djqme+fb+Ww+YjhrJMOAzMzM7OeeeiOmZmZmVkdGnA9+mbV4Om21zjplrvWej3zXnkNoCzrqgVPt73G1u7QNzMzK4kTfbN+Vs7rFdanFWDADGfZeqyv9zAzMyuVE32zfubrFczMzKw/ONE3s5rR1NREW1vbG7/LYGZrp6GhwZ0PZnXMib6Z1Yz58+fTvmQJm0Rb1qGY1byn2zqyDsHMKsyJvpnVlHWHiO99eKOswzCreT+/7ZWsQzCzCvPtNc3MzMzM6pB79M2sZixbtgxWRtZhmJmZvaGlpQWozptt1EyPvqTFBdOHSvpdBnH8SdK7+rtdM4POzk46neebmVkVyeVy5HK5rMMoyj36fRQRX8s6BjMzMzOz3tRFoi9pGnB1RFySTi+OiBGSRgBXAhsDQ4EfRcSVko4GlkXEbyT9EtghIvaQtAfw1YiYLGkicCKwLpADvhIRiyXNBJojYla/b6iZmVmZvLhkJctzOZqbm7MOxaym5XI5hg0blnUYRdXM0B1gmKT7uh7AT0pY5nVgUkTsCOwOnC5JwC3AR9M6jcAISUPTspslbQr8CPh4uuws4Ls9NSTpcEmzJM1asGDBmmyfmZmZmVnZ1FKPfntETOiakHQoSZLeEwH/J2lXoBN4C7A5MBvYSdKGwDLgnnRdHwW+CXwIeBfw7+S4gHWA23tqKCKmAlMBGhsbPYrYzMyq2mbrD2bdtzQwZcqUrEMxq2nVfFaslhL9nnSQnp2QNIgkMQeYDIwGdoqIFZKeAtZLnz8JHArcBswh6fHfGngYaACui4iD+nMjzMzMzMzKpZaG7vTkKWCn9PlnScbjA4wEXkwT+92BrfKWuQVoBm5Onx8B3BsRAdwBfETS1gCS1pe0bcW3wszMzMysTOol0f8j8DFJ9wM7A0vS8ulAo6QHgIOBR/KWuQXYArg9Il4gGc9/C0BELCDp7b9A0hySYTvb9cN2mFkPBg0axCBlHYWZmdmbGhoaaGhoyDqMompm6E5EjCiYngZMS5+/QDKuvssxaflLJIl/sfVdz5s9/0TEtgXzbwDeX2S53dYgfDMrg3XXXZfOZSuzDsPMzOwN1fhDWV1qJtE3MwNY1hH8/LZXsg7DrOY93dbBNm/JOgozqyQn+mZWM8aMGUNbWxvrjh2bdShmNW+bt1C1ww3MrDyc6JtZzWhpack6BDMzs5pRLxfjmpmZmZlZHif6ZlYzWlpa3KtvZmZWIif6ZlYzZsyYwYwZM7IOw8zMrCY40TczMzMzq0O+GNfMasbSpUuzDsHMzKxm1HyPvqTFJdT5qKQHJd0n6S2SLilhmX9I2qgsQZpZWUQEEZF1GGZmZjVhoPToTwZOjojz0+n9e1sgIvapbEhmZmZmZpVT8z36XSTtJmmmpEskPSJpuhJfA74A/DQtGy9pbrrMoZIuk3SNpP9I+nne+p6StKmk90uaI2k9SeunZwa2z2o7zczMzMxKUW89+u8D3g20Av8GPhIRf5K0C3B1RFwiaXzBMhPS5ZYBj0r6bUQ80zUzIu6WdBXwM2AYcH5EzK38ppiZmZmZrbm66dFP3RURz0ZEJ3AfML6EZa6PiLaIeB14CNiqSJ2fAJ8AGoGfF5mPpMMlzZI0a8GCBWsUvJmZmZlZudRbor8s7/lKSjtjUcoymwAjgA2A9YqtJCKmRkRjRDSOHj26xHDNzMzMzCqj3hL9SvkD8GNgOnBqxrGYmZmZmfWq3sbol52kg4EVEfEXSYOB2yTtERE3ZB2b2UAjKesQzMzMaoZ8T+rya2xsjFmzZmUdhlndmTRpEgCXX355xpGYmZlVB0mzI6Kx2DwP3TEzMzMzq0NO9M3MzMzM6pDH6JtZzZg4cWLWIZiZmdUMJ/pmVjOampqyDsHMzKxmONE3s5rR1NREW1sbY8eOzToUM7N+09DQ4I4OWyNO9M2sZsyfP5+lS5cwTC9lHYqZWb948ZWsI7Ba5kTfzGrKOkPgoN296zKzgeGCGzuyDsFqmO+6Y2ZmZmZWh2ou0Ze0OOsYzNZGS0sLLS0tWYdhZmZmZVDN3+s+/23Wz3K5XNYhmJmZWZlU8/d6zfXoFyOpQdI1kmZLukXSdmn5NEm/kXSbpCck7Z+WS9JpkuZKekDSAWn5hZI+lbfeaZL2l7SepLPTuvdK2j2bLTUzMzMzK0299OhPBY6IiP9I+iBwBrBHOm8LYBdgO+Aq4BLgc8AEYAdgU+BuSTcDFwFfAP4uaR1gT6AJ+AYQEfGe9CBihqRtI+L1/tpAqx+tra20t7fT3NycdSg1p729PesQzMz61cuLYVEu5++MKpbL5Rg2bFjWYRRV84m+pBHAh4GLJXUVr5tX5YqI6AQekrR5WrYLcEFErARekHQT8H7gn8CvJa0L7A3cHBHtknYBfgsQEY9ImgdsC8zJi+Nw4HCAcePGVWZjzczMzMxKVPOJPsnwo1ciYkI385flPVc3dQCIiNclzQT2Ag4ALiw1iIiYSnJmgcbGxih1ORt4un7sacqUKRlHUnsmTZpEx/IlWYdhZtZvNh4BG2/R4O+MKlbNZ1tqfox+RLwKPCnp8/DG+PsdelnsFuAASYMljQZ2Be5K510EfAX4KHBNXv3J6fq3BcYBj5Z1Q8zMzMzMyqgWE/3hkp7Ne3yXJAn/qqT7gQeBfXtZx+Ukw27uB24AvhcR89N5M4CPAf+KiOVp2RnAIEkPkBwIHBoRyzAzMzMzq1I1N3QnIro7ONm7SN1DC6ZHpH8DODp9FC6zAhhVUPY6SS+/2VpraGjIOgQzMzMrk2r+Xq+5RN+s1jU1NWUdgpmZmZVJNX+vO9E3s5qyvAMuuLEj6zDMzPrFi6/AxltkHYXVKif6ZlYzxowZQ1tbGxtvMTbrUMzM+sXGW1T30BCrbk70zaxmtLS0ZB2CmZlZzajFu+6YmZmZmVkv3KNv1s+amppoa2t744ezrG8aGhqq+sInMzOzauFE36yfzZ8/n6VLlzB00EtZh1JzFr6cdQRmZma1w4m+WQaGDoHP7OmRc331t+s7sw7BzMysZjjRtz7ruiDSwyesv7W9Bis6W7MOw8zMrCa4SzElaXE35V+SNEfSg5Lul/QnSRv1c3hVJZfLkcvlsg7DBqCODmhvb886DDMzszfceOONTJw4kZtvvjnrUFbjRL8HkvYGvgN8MiLeDewI3AZsnmlgZmZmZlYVTjvtNABOOeWUjCNZnRP9nh0LNEfEcwARsTIizoqIRzOOy8zMzMwyduONN9LRkfxae0dHR9X16nuMfs/eDdyTdRDVprW1lfb2dpqbm7MOpSZ56ImZmVl96OrN73LKKaew6667ZhTN6tyjXyJJ75F0n6ScpAOKzD9c0ixJsxYsWJBFiGZmZmbWj7p687ubzpp79Hv2IMm4/Bsj4gFggqTfAcMKK0bEVGAqQGNjY/RrlP2s64eepkyZknEktWnSpEmsWL4k6zDMzMxsLQ0ZMmSV5H7IkOpKrd2j37OTgSmS3ppXtlqSb2ZmZmYDz9FHH73K9Pe///2MIinOif6bhkt6Nu/x3Yj4B/Ab4J+SHpJ0G7ASuDbbULPV0NBAQ0ND1mHYADRkCAwb5mNtMzOrDrvvvvsbvfhDhgypqvH54KE7b4iIogc9EXEOcE4/h1PV/ENZlpWRG8DoMWOzDsPMzOwNRx99NCeffHLV9eaDE30zMzMzszW2++67s/vuu2cdRlFO9M0ysKID/nZ9Z9Zh1JyFL8PoMVlHYWZmVhuc6Jv1szFjxtDW1uYhKGtg9Bh8fYiZmVmJnOib9bOWlpasQzAzM7MBwIm+mdWMlpYWcrlc1mFYL1pbWxk5cqQPas3MMuZE38xqRi6X4+GH57DRxllHYj1ZuBDa29uzDsPMbMBzom9mNWWjjWGPico6DOvBZRfV9Y+Dm5nVDP9glpmZmZlZHaqbRF9SSDo/b3qIpAWSru5luUZJv6l8hGaJlpYWj122urZyJSxbtizrMMzMBrx6GrqzBNhe0rCIaAc+ATzX20IRMQuYVengzLr4YlKrdxHQ2enfiTAzy1rd9Oin/gF8Kn1+EHBB1wxJ60s6S9Jdku6VtG9avltXr7+kf0i6L320STpE0qGSfpe3nqsl7dZvW2RmZmZmtgbqqUcf4ELguDRxfy9wFvDRdN6xwA0RcZikjYC7JP0rf+GI2AdA0k7A2cAVwKT+Cd0GitbWVtrb22lubs46lJqTy+VwR7GZmVlp6irRj4g5ksaT9Ob/o2D2ROCzkrqyq/WAcYXrkLQpcB7whYhok0q7u4ekw4HDAcaNW221ZmZmZmb9qq4S/dRVwBRgN2CTvHIB/xURj+ZXlrR53vPBJGcFfhIRc9PiDlYd4rResUYjYiowFaCxsdH3lrNujR07FoApU6ZkHEntaW5u5vn5c7IOw8zMrCbU2xh9SIbrnBgRDxSUXwscqbSLXtL7iix7CjAnIi7MK3sKmCBpkKQtgQ9UIGYzMzMzs7Kqux79iHgWKHa7zJ8CvwLmSBoEPAl8uqBOM/CgpPvS6eOAv6V1HwIeBu4pf9Q2kDQ0NGQdgllFSTBoUD32I5mZ1Za6SfQjYkSRspnAzPR5O/D1Xup0NyB/cnmiNIOmpqasQzCrqMGDYZ111s06DDOzAc9dLmZmZmZmdahuevTNbGB45WW4YYavd69mHR2wzjpZR2FmZk70zaxm+PqG2hCdrYwcOTLrMMzMBjwn+mZWM3x9g5mZWemc6JtZzWhqaqKtre2N3yIws9rQ0NDgA3WzDDjRN7OaMX/+fJYsXcLKIS9lHYqZlei1RVlHYDZwOdE3s5oyZAi8/5Pd3QnXzKrN3f/0xfNmWfHtNc3MzMzM6pB79M2AlpYWwBd7mpmV29JXobWjNeswzAakquvRlxSSzs+bHiJpgaSre1luN0kfLmMc0yTtX671WXXL5XLkcrmswzAzqzsrV0B7e3vWYZgNSFWX6ANLgO0lDUunPwE8V8JyuwF9SvQl+YyGmZmZmdWlak10/wF8CrgEOAi4APgogKRRwFnA24GlwOHAq8ARwEpJXwKOBJ5J620KLAC+EhFPS5oGvA68D/i3pHOBM4HhQA44LCJezg9G0nHAZ4BhwG3A1yPCVxfVkdbWVtrb22lubs46FOtBe3s7/sczMzMrTTX26ANcCBwoaT3gvcCdefNOBO6NiPcCPwTOjYinSJL1X0bEhIi4BfgtcE5abzrwm7x1vBX4cER8FzgXOCat9wBwfJF4fhcR74+I7UmS/U8XVpB0uKRZkmYtWLBgrTbezMzMzGxtVWWPfkTMkTSepDf/HwWzdwH+K613g6RNJG1YZDU7A59Ln58H/Dxv3sURsVLSSGCjiLgpLT8HuLjIunaX9D2SXv9RwIPA3wpingpMBWhsbHSnY43p+gGmKVOmZByJ9WTSpEksW74k6zDMzMxqQlUm+qmrgCkkY+83KfO6S84U0rMKZwCNEfGMpBOA9cocj5mZmZlZWVXr0B1IxtefGBEPFJTfAkyG5E47wEsR8SrwGrBBXr3bgAPT55PT5VYREW3Ay5I+mhZ9GbipoFpXUv+SpBGA78RjZmZmZlWvanv0I+JZVh1X3+UE4CxJc0guxj0kLf8bcImkfUkuxj0SOFvS0aQX43bT1CHAmZKGA08U1ouIVyT9EZgLzAfuXpvtsurU0NCQdQhmZnVp8FAYNmxY7xXNrOyqLtGPiBFFymYCM9Pni4D9itR5jOTC3Xx7FKl3aMH0fcCHeqoXET8CftRb7Fa7/ENZZmaVMXxDGLvZ2KzDMBuQqi7RNzPrSUcH3P1PX+9uViteWwRslnUUZgOTE30zqxljxoyhra3NvYNmtWQzD480y4oTfTOrGS0tLVmHYGZmVjOq+a47ZmZmZma2htyjb2Y1o6WlhVwul3UY1ovW1lZGjhzpMzBmZhlzom9mNSOXyzH3kTmsV+6f0LOyWroQ2tvbsw7DzGzAc6JvZjVlvU1g/Gc96rCaPXJ2Z9YhmJkZHqNv1u9aWlo8pMHMMud9kVn9q6tEX1JIOj1vulnSCb0sM03S/kXK/yTpXRUI0wa4XC7nceZmljnvi8zqX10l+sAy4HOSNl3bFUXE1yLioTLEZGZmZmbW7+ot0e8ApgLfKZwhabykGyTNkXS9pHFF6vw07eEfLGmmpMa0vEXSLEkPSjqx8pthZmZmZrZ26vFi3N8DcyT9vKD8t8A5EXGOpMOA3wD7dc2UdBqwAfCViAhJ+cseGxGLJA0Grpf03oiYU9GtsLrV2tpKe3s7zc3NWYdSc3K5HMsj6yisN50d0N7hz3i1y+VyDBs2LOswzKyC6q1Hn4h4FTgX+GbBrJ2Bv6TPzwN2yZv3Y2BkRBwREcXSiC9Iuge4F3g3sNrYfUmHp73+sxYsWLC2m2FmZmZmtlbqsUcf4FfAPcDZJda/G9hJ0qiIWJQ/Q9LbgGbg/RHxsqRpwHqFK4iIqSTDhmhsbHSfo3Vr7NixAEyZMiXjSGpPc3Mzjy/wybRqN2gIDBs6zJ/xKuczLmb1r+569AHSZP2vwFfzim8DDkyfTwZuyZt3DXAK8HdJGxSsbkNgCdAmaXPgkxUJ2szMzMysjOq1Rx/gdOB/86aPBM6WdDSwAPhKfuWIuDhN8q+StE9e+f2S7gUeAZ4B/l3xyK2uNTQ0ZB2CmZn3RWYDQF0l+hExIu/5C8DwvOl5wB5Fljk07/lZwFnp5G7F6pitraampqxDMDPzvshsAKjLoTtmZmZmZgNdXfXom1n9e30hPHVVZ9ZhWA86VwBDs47CzMyc6JtZzfCY4trQuqKVkSNHZh2GmdmA50TfzGqGxxSbmZmVzmP0zaxmtLS00NLSknUYZmZmNcGJvpnVjBkzZjBjxoyswzAzM6sJTvTNzMzMzOqQE30zMzMzszpU1Ym+pLdKulLSfyTlJP1a0jpruK6Zkh6VdL+kf0t6R155Y3kjN7NKWLp0KUuXLs06DDMzs5pQtYm+JAGXAVdExDbAtsAI4KS1WO3kiNgBOAc4be2jNLP+FBFERNZhmJmZ1YSqTfSBPYDXI+JsgIhYCXwHOEzS/0i6TNI1aW//z7sWkjRR0u2S7pF0saQRRdZ9M7B1YaGkxXnP95c0LX3+eUlz07MBN5d3M83MzMzMyq+aE/13A7PzCyLiVeBpkvv/TwAOAN4DHCBpS0mbAj8CPh4ROwKzgO8WWfdngAf6EMtxwF7p2YDP9nE7zMzMzMz6XS3/YNb1EdEGIOkhYCtgI+BdwL+TkT+sA9yet8x0Se3AU8CRfWjr38A0SX8lGU60GkmHA4cDjBs3ri/bYWZmZmZWdtWc6D8E7J9fIGlDYBzQASzLm7WSZFsEXBcRB3WzzskRMauHNvMH/673RmHEEZI+CHwKmC1pp4hYuMqCEVOBqQCNjY0eRGxmZmZmmarmoTvXA8MlHQwgaTBwOjAN6O62G3cAH5G0dbrM+pK27UObL0h6p6RBwKSuQkkNEXFnRBwHLAC27PPWmJmZmZn1o6pN9CO5tcYk4POS/gM8BrwO/LCHZRYAhwIXSJpDMmxnuz40+33gauA24Pm88tMkPSBpbjrv/j6s08zMzMys38m3qiu/xsbGmDWrpxFCZrYm9tprLwCuvfbajCMxMzOrDpJmR0TR34Sq5jH6ZmarGD58eNYhmJmZ1YyqHbpjZmZmZmZrzj36ZlYzJk6cmHUIZmZmNcOJvpnVjKampqxDMDMzqxkeumNmZmZmVofco29mNaOlpYVcLpd1GNaL1tZWAMaOHZtxJGZm/aOhoaEqzzo70TezmpHL5ZjzyIOw6YisQ7GevLoYgJfWWZFxIGZm/eClxVlH0C0n+mZWWzYdwZB9d8g6CutBx5XJbwr6fTKzgaBrn1eNnOib9bOWlhbAF5aamZnVhbZ2Wpe3Zh1FUXWb6Et6K/B74F0kFx1fDRwdEcszDcwGPI8xNzMzqyMrVtJOe9ZRFFWXd92RJOAy4IqI2AbYFhgBnJRpYGZmZmZm/aQuE31gD+D1iDgbICJWAt8BDpP0P5KulDRT0n8kHQ8gabykRyRNl/SwpEskDU/n7SnpXkkPSDpL0rqZbZmZmZmZWQlKHrojaRNgH2CLiPi5pLHAoIh4tmLRrbl3A7PzCyLiVUlPk2zzB4DtgaXA3ZL+DrwEvAP4akT8W9JZwP9I+h0wDdgzIh6TdC7QBPyqvzbG6ktrayvt7e00NzdnHUrNSYY9+U4uZmZmpSipR1/Sx4BHgcnAj9PibYCWCsVVaddFxMKIaCcZ4rNLWv5MRPw7fX5+Wv4O4MmIeCwtPwfYtXCFkg6XNEvSrAULFlQ4fDMzMzOznpXao/8r4ICIuF7Sy2nZnSQ949XoIWD//AJJGwLjgA4gCupHwd/C8l5FxFRgKkBjY2PJy9nA0/UjQlOmTMk4ktrT3NzMnJfmZR2GmZlZTSh1jP74iLg+fd6VxC6neu/acz0wXNLBAJIGA6eTDMFZCnxC0ihJw4D9gK5e/HGSdk6ffxG4leRMxnhJW6flXwZu6o+NMDMzMzNbU6Um+g9J2qug7OPAA2WOpywiIoBJwOcl/Qd4DHgd+GFa5S7gUmAOcGlEzErLHwW+IelhYGOgJSJeB74CXCzpAaATOLPfNsbqTkNDAw0NDVmHYWZmZuUwdDDDhg3LOoqiSu2RPwq4Or1odZikPwCfAfatWGRrKSKeIYlxFcmdN3k2IvYrslhHRHypyLquB95X7hhtYPIPZZmZmdWRkcMYu+nYrKMoqqQe/Yi4A9gBeBA4C3gS+EBE3F3B2MzMzMzMbA2VPMY+Ip4Dfl7BWPpFREwjGatfWP4UyS03zayavbSYjivvzzoK68lLiwH8PpnZwPDSYtg06yCKKynRlzQS+CbJ8JUR+fMiYmIF4jIzW42vbagNrctbAar2VLaZWVltWr3fT6X26F8MDAYuB9orF46ZWfd8fYOZmVnpSk30PwRsGhHLKxmMmVlPWlqS3+hzwm9mZta7Um+veSuwXSUDMTPrzYwZM5gxY0bWYZiZmdWEUnv0DwX+IelO4IX8GRHxk3IHZWZmZmZma6fURP8kYEvgKWDDvPIoWtvMrAKWLl2adQhmZmY1o9ShOwcCEyJi/4j4ct7j4EoG1xNJb5V0paT/SMpJ+rWkdSRNkLRPXr0TJDVnFadZobPOOouJEydyzjnnZB1KzYkIkh++Lm7hwoUcddRRLFq0qB+jskKzZ89m77335t577806FOuB3yez+ldqov8EsKKSgfSFkp+3vQy4IiK2AbYlue3nScAEYJ/ul+5zW4PLtS4zgAsvvBCA6dOnZxxJ/Zk+fTpz5871a5uxk046ic7OTn76059mHYr1wO+TWf0rNdE/D7hK0kGS9sh/VDK4HuwBvB4RZwNExErgO8DXSH7U6wBJ90k6IK3/LkkzJT0h6ZtdK5H0JUl3pXX/0JXUS1os6XRJ9wM7SzpF0kOS5kia0q9banXlrLPOWmXavfrls3DhQmbMmEFEcO2117pXPyOzZ89m8eLkB7MWL17s3uIq5ffJbGAoNdH/BrAF8H/An/Mef6pQXL15NzA7vyAiXiW5huBnwEURMSEiLkpnbwfsBXwAOF7SUEnvBA4APhIRE4CVwOS0/vrAnRGxA/AwMAl4d0S8N12/2Rrp6s3v4p7n8pk+fTqdnZ0AdHZ2+rXNyEknnbTKtHuLq5PfJ7OBoaREPyLe1s3j7ZUOsEz+HhHLIuIl4EVgc2BPYCfgbkn3pdNd27MSuDR93ga8DvxZ0ueAolcDSjpc0ixJsxYsWFC5LTGzom644QY6OjoA6Ojo4Prrr884ooGpq5e4u2mrDn6fzAaGUnv0q81DJEn6GyRtCIwDOorUX5b3fCXJ3YYEnJP2/E+IiHdExAlpndfT4UBERAfJmYBLgE8D1xQLKCKmRkRjRDSOHj16zbfMzNbIHnvswZAhyY3EhgwZwp577plxRAPTiBEjepy26uD3yWxgKCnRl7ShpF9Imi1pnqSnux6VDrAb1wPDJR2cxjcYOB2YRnKf/w1KXMf+kjZL1zFK0laFlSSNAEZGxD9IrgPYoSxbYAPSgQceuMr05MmTu6lpfTV58mQGDUp2aYMGDfJrm5Fjjz12lekf//jHGUViPfH7ZDYwlNqjfwawI/ATYBRwJPA08MsKxdWjSO6vNwn4vKT/AI+RDK/5IXAjycW3+RfjFlvHQ8CPgBmS5gDXkVyHUGgD4Oq0zq3Ad8u6MTagHHbYYatMH3LIIRlFUpskkdx0a3WbbLIJEydORBJ77bUXo0aN6ufoDGCnnXZ6o3d4xIgRvO9978s4IivG75PZwFDqD2ZNBN4ZEQslrYyIKyXNAv5Gdsn+M8BnisxaBry/h+W2z3t+EXBRkToj8p4/TzJ0x6wsDjzwQC688EL3OK+B4cOH9zh/8uTJzJs3z69txo499liOPfZY9xJXOb9PZvVPPf34zBuVpJeAMRHRIelZkrvevAa8EhEb9rz0wNPY2BizZs3KOgyzujNp0iQALr/88owjMTMzqw6SZkdEY7F5pfbo3w98jGRc+y0kQ3kWkwyZMTMzMzOzKlNqov/fJHepAfgWyf30NwIOrkBMZmZFTZw4MesQzMzMakZJQ3esbzx0x8zMzMz6QzmG7iBpIjABWOVmuxFx3FpFZ2ZWopaWFgCampoyjsTMzKz6lXof/d8B55P8SNWWBQ8zs34xY8YMZsyYkXUYZmZmNaHUHv0vAjukt7Q0MzMzM7MqV+oPZr0EvFLBOMzMzMzMrIxKTfRPB6ZL2lnS2/MflQzOrB49/vjj7LfffjzxxBNZh1Jzli5dytKlS7MOw8z6YPbs2ey9997ce++9WYdiNuCUmui3AJ8G/g08nvf4T4XiWo2klZLukzRX0t8kbbSG6zk0vebALBOnnnoqS5cu5eSTT846lJoTEfhOYWa15aSTTqKzs5Of/vSnWYdiNuCUlOhHxKBuHoMrHWCe9oiYEBHbA4uAb/Rj22Zl8fjjjzNv3jwA5s2b5159M6trs2fPZvHixQAsXrzYvfpm/azUHv1qczvwFgBJDZKukTRb0i2StkvLPyPpTkn3SvqXpM0LVyJpmqQWSXdIekLSbpLOkvSwpGl59SZKul3SPZIuljSicF1mpTj11FNXmXavvpnVs5NOOmmVaffqm/Wvmkv0JQ0G9gSuSoumAkdGxE5AM3BGWn4r8KGIeB9wIfC9bla5MbAz8J10nb8E3g28R9IESZsCPwI+HhE7ArOA7xaJ63BJsyTNWrBgQRm21OpRV29+d9NmZvWkqze/u2kzq6ySfzCrCgyTdB9JT/7DwHVpz/qHgYslddVbN/37VuAiSVsA6wBPdrPev0VESHoAeCEiHgCQ9CAwPl3Pu4B/p22sQ3JGYRURMZXkoIPGxkYPIraittpqq1WS+6222irDaMzMKmvEiBGrJPcjRviEuFl/qqUe/faImABsBYhkjP4g4JV07H7X451p/d8Cv4uI9wBfB9brZr3L0r+dec+7poekbV2Xt/53RcRXy7plNmAcc8wxq0z/4Ac/yCgSM7PKO/bYY1eZ/vGPf5xRJGYDUy0l+gBExFLgm8BRwFLgSUmfB1Bih7TqSOC59Pkha9HkHcBHJG2dtrG+pG3XYn02gG299dZv9OJvtdVWvP3tvkOtmdWvnXba6Y1e/BEjRvC+970v44jMBpZuE31Jz0h6urdHfwbbJSLuBeYABwGTga9Kuh94ENg3rXYCyZCe2SQ/+LWmbS0ADgUukDSHZNjOdmscvA14xxxzDMOHD3dv/hqQRN4wPTOrAcceeyyDBg1yb75ZBtTdPaklfSxv8v0kveK/AeaRDJ/5X+DciDi90kHWmsbGxpg1a1bWYZjVnUmTJgFw+eWXZxyJmZlZdZA0OyIai83r9mLciLgpbwW/B/aKiOfyyv4JXEPyq7lmZmZmZlZFSr3rzlig8J5Yi0nvZW9m1h8mTpyYdQhmZmY1o9RE/yrgKkk/A54FtgR+wJv3sjczq7impqasQzAzM6sZpd515wiSi1DPBO5J/96ZlpuZmZmZWZUpqUc/Il4Hvp8+zMwy0dTURFtbG2PHjs06FOtFQ0ODz8CYmWWs5F/GlfQOYAdglZ+1i4izyh2UmVkx8+fPZ8nSpSwcWks/6j3wxMJFWYdgZmaUmOhL+iFwHHA/yY9UdQnAib6Z9Z+hQxj6mU9nHYX1YMXfrs46BDMzo/Qe/W8DH4iIORWMxSwzLS0tgC/2NDMrN+9fzbJT6sW47cAjlQykkiTdKGmvgrJvS3pSkq87MHK5HLlcLuswzMzqjvevVu+OP/54Jk6cyE9+8pOsQ1lNqYn+j4HfStpC0qD8RyWDK6MLgAMLyg4EDomIUzKIx8zMzMzqwO233w7ArbfemnEkqys1UZ8G/DfJPfRXpI+O9G8tuAT4lKR1ACSNJ/kRsAZJv5M0UtK8rgMXSetLekbSUEkTJN0haY6kyyVtnN1mmJmZmVm1OP7441eZrrZe/VLH6L+tolFUWEQsknQX8EngSpLe/L+SXExMRLRJug/4GHAj8Gng2ohYIelc4MiIuEnST4DjSa5ZsDrS2tpKe3s7zc3NWYdiPWhvb886BCtBtL1K7tXX/P9kQDJ0Z9iwYVmHYVYRXb35XaqtV7+kHv2ImNfdo9IBllH+8J0D0+l8FwEH5M2/SNJIYKOIuCktPwfYtdjKJR0uaZakWQsWLChv5GZmZmZmfdSX++h/lqTHe1NAXeURcXAF4qqEK4FfStoRGB4RsyW9J2/+VcD/SRoF7ATcQMFvBvQkIqYCUwEaGxujfGFbf+j6AaYpU6ZkHIn1ZNKkSSxZsTzrMKwXGrkhDaM38/+TAfjMjlmGSurRl3Q88Ie0/ueBhcBewCsVi6zMImIxybCcs1i9N79r/t3Ar4GrI2JlRLQBL0v6aFrty8BNhcuamZmZ2cCz8847rzK9yy67ZBRJcaVejHsY8ImI+A6wPP37GWB8pQKrkAtIft13tUQ/dRHwpfRvl0OA0yTNASYA1XWVhZVFQ0MDDQ0NWYdhZlZ3vH+1enbiiSeuMn3cccdlFElxpQ7d2Sgi5qbPl0saGhF3SfpYpQKrhIi4glWHHU0juaNQ1/Ql+fPTsvuAD/VHfJYd/5CLmVlleP9q9W7nnXfm9ttvr7refCg90c9JendEPAjMBZokvQy8XLnQzMzMzMyqW2GvfjUpNdH/EbBJ+vz7wF9ILlT9n0oEZWbWrRUdrPjb1VlHYT2IhYtg9GZZh2FmNuD1muinPyL1OnAHQETcBWxd4bjMzFYzZswY2traGOsksrqN3sxjss3MqkCviX5EdEq6MiI26I+AzMy609LSknUIZmZmNaPUoTs3S/pQRNxR0WjMzHrQ0tJCLpfLOgzrRWtrKyNHjvSBmZlZxkpN9OcB/5R0JfAM8MYPQkVEdd1HyMzqVi6XY84jjzBok9FZh2I96Fy4kPb29qzDMDMb8EpN9IcBV6TP35pX7l+ANbN+NWiT0az7mc9nHYb1oH3aGVmHYGZmlJjoR8RXKh2ImZmZmZmVT6m/jPsGSZtJ+pyk7SoRUAntbynpSUmj0umN0+kPS7qkwm0fIengSrZh9a+lpcVjl80sc94XmZXH73//eyZOnMiZZ56ZdSir6THRl/QWSZdJekTS2ZLeDTwMnAnMkXRgv0SZJyKeAVqAU9KiU4CpEXFbROxf4bbPjIhzK9mG1b9cLucLSs0sc94XmZXHlVdeCcBll12WcSSr661H/0ySX7/9DiDgWuBrEbEZ8Hngh5UNr1u/BD4k6dvALsAUSeMlzQVIn98i6Z708eG0XJJOkzRX0gOSDkjLd5N0k6QrJT0h6RRJkyXdldZrSOudIKk5m002MzMzs2ry+9//fpXpauvV722M/oeBLSJiuaSbgFdIL8qNiCslZdK7HRErJB0NXANMTKfzq7wIfCIiXpe0DXAB0Ah8DpgA7ABsCtwt6eZ0mR2AdwKLgCeAP0XEByR9CzgS+HbFN8wGhNbWVtrb22lu9jFjX+VyOTp9C4Dq19FBe0eHP+NVLpfLMWzYsKzDMKtpXb35XS677DKOOOKIjKJZXW89+kMjYjlARCwFFkdE/tesii/WLz4JPA9sX2TeUOCPkh4ALgbelZbvAlwQESsj4gXgJuD96by7I+L5iFgG5IAZafkDwPjegpF0uKRZkmYtWLBgTbfJzMzMzKwseuvRHyJpd95M6AunB1cssh5ImgB8AvgQcKukCwuqfAd4gaSXfhDwegmrXZb3vDNvupPSfkF4KjAVoLGx0X2O1q2xY8cCMGXKlIwjqT3Nzc3MXbAw6zCsN0OGMGzoUH/Gq5zPuJjVv9569F8EzgL+nD4WFky/WNHoilAyRqcF+HZEPA2cBhR+m4wEno+ITuDLvHlAcgtwgKTBkkYDuwJ39U/kZmZmZlZP9t1331WmP/e5z2UUSXE9JvoRMT4i3tbTo78CzfPfwNMRcV06fQbJ2Pqt8uqcARwi6X5gO2BJWn45MAe4H7gB+F5EzO+XqM3MzMysrnzjG99YZbqaxudD6b+MWzXyh8ik0yuBHdPJ7dOy/wDvzVvsmLQ8gKPTR/46ZwIz86Z3KzYvIk4oy0bYgNbQ0JB1CGZm3heZlcm+++7LlVdeWXW9+VCDib5ZrWtqaso6BDMz74vMyuQb3/jGaj371cKJvpnVlM6FC1j2t4uzDsN6smIFDB2adRRmZgOeE30zqxkealAbWlcsY+TIkVmHYWY24DnRN7Oa4aEGZmZmpevt9ppmZmZmZlaD3KNv1s9aWlrI5XJZh1GTWltbGTlyJC0tLVmHYmZmVvWc6Jv1s1wux5xHHmXQJmOyDqXmdC5cSHt7e9ZhmJmZ1QQn+mYZGLTJGIZ99pCsw6g5S84+NesQzMzMaobH6JuRDKfxcBCz8vD/k5lZdRhwPfqSJgHHFxTvADwGtAPjgLb08RLwNeBh4NG8+r+IiHMrH631F4+ZNysf/z+ZmVWHAZfoR8TlwOVd05IOByYDu0dEp6RpwNURcUk6fzyQi4gJ/R+tmZmZmdmaGXCJfj5J2wLHAR+OiM6s4zEzMzMzK5cBm+hLGgr8BTgqIp7upXqDpPvypo+MiFsqFpz1u9bWVtrb22lubq54W7lcjs7w5TFrpKOD9o4V/fI+2ZrL5XIMGzYs6zDMzAa8AZvoAz8FHoyIi0qo2+vQnXQI0OEA48aNW/vozMzMzMzWwoBM9CXtBvwXsGO51hkRU4GpAI2NjVGu9Vr/GDt2LABTpkypeFvNzc3MXdBW8Xbq0pAhDBs6pF/eJ1tzPuNiZlYdBlyiL2lj4GzgixHxWtbxmJmZmZlVwoBL9IEjgM2AFkn55Sf3MIyncIz+WRHxmwrFZxloaGjIOgSzuuH/JzOz6jDgEv2IOBk4uYf5hxZMPwX4qrI619TUlHUIZnXD/09mZtXBt/4wMzMzM6tDA65H36wadC6cT/tV52QdRu1ZsRyGerdlZmZWCn9jmvUzj19ec60rNmHkyJFZh2FmZlYTnOib9TOPXzYzM7P+4ETfrJ+1tLSQy+WyDqMmtba2Am/+7oGZ1YaGhgZ3cphlwIm+WT/L5XLMeeQxBm/ylqxDqTkrX10MwMtDl2QciZmVauXC57IOwWzAcqJvloHBm7yFDT57ZNZh1JzXrvotgF87sxrS9X9rZv3Pt9esIy0tLbS0tGQdhpmZmdmAUc35V9X06Es6FvgisBLoBL4eEXdmG1Vt8bhvMzMzs/5VzflXVST6knYGPg3sGBHLJG0KrJNxWGZmZmZmNatahu5sAbwUEcsAIuKliGiVdJykuyXNlTRVkgAkzZT0S0mzJD0s6f2SLpP0H0k/S+usL+nvku5Plz8gLX8qPZBAUqOkmenzEySdla77CUnf7ApO0o8lPSrpVkkXSGru35fHzMzMzKxvqqJHH5gBHCfpMeBfwEURcRPwu4j4CYCk80h6/f+WLrM8IholfQu4EtgJWATkJP0S2A1ojYhPpcuX8is72wG7AxsAj0pqASYA/wXsAAwF7gFmr/UWV0Brayvt7e00N/s4pJrlcjk6o1r+9czMKquz7SVyr873d5PVrVwux7Bhw7IOo6iq6NGPiMUkifrhwALgIkmHArtLulPSA8AewLvzFrsq/fsA8GBEPJ+eEXgC2DIt/4SkUyV9NCLaSgjl7xGxLCJeAl4ENgc+AlwZEa9HxGu8eaCxCkmHp2cYZi1YsKCPr4CZmZmZWXlVTbdiRKwEZgIz08T+68B7gcaIeEbSCcB6eYssS/925j3vmh4SEY9J2hHYB/iZpOvTswMdvHmAk78+Ctazkj68PhExFZgK0NjYGKUuV05dPyI0ZcqULJq3EjU3N/PgAt8H3swGhkEjN6Vh9Pr+brK6Vc1nq6qiR1/SOyRtk1c0AXg0ff6SpBHA/n1c51hgaUScD5wG7JjOeork7AEkQ3J682/gM5LWS+P4dF/iMDMzMzPLQrX06I8AfitpI5Ie98dJhvG8AswF5gN393Gd7wFOk9QJrAC6fnv7RODPkn5KcgahRxFxt6SrgDnACyRDgkoZBtTvGhoasg7BzMzMbECp5vyrKhL9iJgNfLjIrB+lj8L6u+U9n0lewp4/D7i2yLK3ANsWKT+hYHr7vMkpEXGCpOHAzVTpxbhNTU29VzIzMzOzsqnm/KsqEv0aMFXSu0jG9J8TEfdkHZCZmZmZWU+c6JcgIr6YdQxWX1YufI7Xrvpt1mHUnJULnwPwa2dWQ1YufA5Gr3Yi3cz6gRN9s35WzWP5ql3rihEAjB29fsaRmFnJRm/r/Z5ZRpzom/Wzah7LZ2ZmZvXDib5ZP2tqaqKtre2N3z2oNg0NDT4YMTMzqwNO9M362fz581mytJ3l62ySdSirWfLS01mHYGZmZmXiRN8sA4OHrsv2+/4g6zBWM/fKk7MOwczMzMqkKn4Z18zMzMzMyss9+mb9bNmyZXR2Zh1FbWppaQF8QbOZmVkpqrpHX9IYSRdKykmaLekfkvp0M15J4yXNLVM8h0r6XTnWZQNXZ2cnEc7010QulyOXy2UdhpmZWU2o2h59SQIuJ/kl2gPTsh2AzYHHsozNzMzMzKzaVW2iD+wOrIiIM7sKIuJ+JU4DPgkE8LOIuCg9MPh5YXn+CiUdCkwCRgJvAc6PiBPTeVcAWwLrAb+OiKlp+VeAHwCvAPcDyyq1wWZZe73tRXJty2lubs46lKJyuRzDhg3LOgwzM7OaUM2J/vbA7CLlnwMmADsAmwJ3S7oZ+HA35YU+kK57aVrn7xExCzgsIhZJGpaWXwqsA5wI7AS0ATcC9xYLVtLhwOEA48aNW5PtNTMzMzMrm2pO9LuzC3BBRKwEXpB0E/D+HsrnFCx/XUQsBJB0WbrcLOCbkialdbYEtgHGADMjYkFa/yKg6DUC6RmAqQCNjY1Rro0160/rjdyM8Zuuy5QpU7IOpahqPdNgZmZWjar5YtwHSXrSy60wCQ9JuwEfB3aOiB1Ieu3Xq0DbZmZmZmb9opoT/RuAddMhMQBIei/JWPkDJA2WNBrYFbgLuKWb8kKfkDQqHaKzH/BvkjH7L0fEUknbAR9K694JfEzSJpKGAp+vxIaamZmZmZVb1Q7diYhIh9L8StIxwOvAU8C3gREkF8YG8L2ImC/pcmDnIuXjC1Z9F3Ap8FaSi3FnSXoAOELSw8CjwB1pDM9LOgG4neQA475Kba8NHIMGDcJ311wzDQ0NWYdgZmZWM6o20QeIiFbgC0VmHZ0+8utGN+VPkVx82+XZiNivoM4ykrv1FIvhbODsPoZu1q11112X11c4018T/qEsMzOz0lV1om9Wr1auWMbcK0/OOozVLHnpadh0m6zDMDMzszIYUIl+REwDpmUchg1wY8aMoa2tjbGbrpt1KKvbdBsPjzEzM6sTAyrRN6sGLS0tWYdgZmZmA0A133XHzMzMzMzWkBN9s37W0tLiXn0zMzOrOCf6Zv1sxowZzJgxI+swzMzMrM450TczMzMzq0O+GNesny1dujTrEMzMzGwAGDA9+pL2kxSStss6FhvYIoLk993MzMzMKmfAJPrAQcCt6d+1IslnQszMzMysqg2IRF/SCGAX4KvAgWnZYElTJM2VNEfSkWn5TpJukjRb0rWStkjLZ0r6laRZwLey2hYzMzMzs1IMlJ7pfYFrIuIxSQsl7QR8ABgPTIiIDkmjJA0FfgvsGxELJB0AnAQclq5nnYhozGIDzMzMzMz6YqAk+gcBv06fX5hOvw04MyI6ACJikaTtge2B6yQBDAaez1vPRd01IOlw4HCAcePGlTt+MzMzM7M+qftEX9IoYA/gPZKCJHkP4O5i1YEHI2Lnbla3pLt2ImIqMBWgsbHRV1qamZmZWaYGwhj9/YHzImKriBgfEVsCTwL3A1/vurA2PSB4FBgtaee0bKikd2cVuJmZmZnZmhoIif5BwOUFZZcCWwBPA3Mk3Q98MSKWkxwYnJqW3Qd8uB9jtQFAEunQMDMzM7OKqfuhOxGxe5Gy3+RNfrdg3n3ArkWW2a3csdnANHz48KxDMDMzswFgIPTom5mZmZkNOE70zczMzMzqUN0P3TGrNhMnTsw6BDMzMxsAnOib9bOmpqasQzAzM7MBwIm+WT9ramqira2NsWPHZh1Kv2hoaPDBjZmZWQac6Jv1s/nz57N0aTvDBo/OOpSKe3HRvKxDMDMzG7Cc6JtlYJ0h63HQJ3+UdRgVd8E/f5Z1CGZmZgOW77pjZmZmZlaHBkSiL2k/SSFpu6xjMVu2bBkrVi7POoya1NLSQktLS9ZhmNWFhQsXctRRR7Fo0aKsQzGzChkQiT5wEHBr+tcsU52dnUR0Zh1GTcrlcuRyuazDMKsL06dPZ+7cuUyfPj3rUMysQuo+0Zc0AtgF+CpwYFq2haSbJd0naa6kj6bli/OW21/StPT559N690u6uf+3wszMrHwWLlzIjBkziAiuvfZa9+qb1amBcDHuvsA1EfGYpIWSdgJ2A66NiJMkDQaG97KO44C9IuI5SRtVNlyz+vHya/NZtHg5zc3NZVlfLpdj2LBhZVmX2UA2ffp0OjuTM4udnZ1Mnz6dI488MuOozKzc6r5Hn2S4zoXp8wvT6buBr0g6AXhPRLzWyzr+DUyT9N/A4GIVJB0uaZakWQsWLChP5GZmZhVwww030NHRAUBHRwfXX399xhGZWSXUdY++pFHAHsB7JAVJkh7A0cCuwKdIEvhfRMS56bwu63U9iYgjJH0wrT9b0k4RsTC/rYiYCkwFaGxszF+P2YC18QZj2HjzoUyZMqUs6yvXmQGzgW6PPfbgmmuuoaOjgyFDhrDnnntmHZKZVUC99+jvD5wXEVtFxPiI2BJ4kiTJfyEi/gj8Cdgxrf+CpHdKGgRM6lqJpIaIuDMijgMWAFv272aYmZmVz+TJkxk0KEkBBg0axOTJkzOOyMwqod4T/YOAywvKLgWmAfdLuhc4APh1Ou/7wNXAbcDzecucJukBSXPTefdXMmgzM7NK2mSTTZg4cSKS2GuvvRg1alTWIZlZBdT10J2I2L1I2W+A33RT/xLgkiLlnyt/dDZQDRo0iE7fXXONNDQ0ZB2CWd2YPHky8+bNc2++WR2r60TfrBqtu+66dCz3ZRxroqmpKesQzOrGJptswumnn551GGZWQU70zTKwvON1Lvjnz7IOo+JeXDSPjTffOuswzMzMBiQn+mb9bMyYMbS1tbHx5kOzDqXiNt58aw+3MTMzy4gTfbN+1tLSknUIZmZmNgDU+113zMzMzMwGJCf6ZlYzWlpafEbEzMysRE70zaxmzJgxgxkzZmQdhpmZWU1wom9mZmZmVod8Ma6Z1YylS5dmHYKZmVnN6JcefUkrJd0n6X5J90j6cFo+XtLcNVznTEmNRcqfkvRA2t4DkvZd2/jNrDpEBBH+sTEzM7NS9FePfntETACQtBdwMvCxCra3e0S8JOkdwAzgylIXlDQ4IlZ2N21mZmZmVguyGKO/IfByYWHau39L2uP/Rq9/Ou+YtHf+fkmnFCw3SNI0ScV+ZnSVtiR9SdJdaW//HyQNTssXSzpd0v3AzgXTx0q6Im8dn5B0+dq+CGZmZmZmldRfPfrDJN0HrAdsAexRpM6LwCci4nVJ2wAXAI2SPgnsC3wwIpZKGpW3zBBgOjA3Ik7KK79RkoC3A18AkPRO4ADgIxGxQtIZwGTgXGB94M6IOCqt+8Z0up6HJY2OiAXAV4CzyvGimJmZmZlVShZDd3YGzpW0fUGdocDvJE0AVgLbpuUfB86OiKUAEbEob5k/AH8tSPLhzaE7DcD1kmYCewI7AXcnuTvDSA4uSNu7NG/5N6YjIiSdB3xJ0tnAzsDBhRso6XDgcIBx48b19nqYmZmZmVVUv991JyJul7QpMLpg1neAF4AdSIYUvV7C6m4Ddpd0ekSsVj8icpJeAN4FCDgnIn5QZD2vF4zDL5w+G/hbGtPFEdFRpK2pwFSAxsZGXy1oZmZmZpnq9zH6krYDBgMLC2aNBJ6PiE7gy2kdgOuAr0gani6fP3Tnz8A/gL9KWu2gRdJmwNuAecD1wP5pGZJGSdqqlJgjohVoBX5EkvSbmZmZmVW1/h6jD0nP+iERsTIdQtPlDOBSSQcD1wBLACLimnQ4zyxJy0kS+x92LRQRv5A0EjhP0uS0+EZJK0mGA30/Il4AXpD0I2CGpEHACuAbJAcBpZgOjI6Ih/u47WZWJgX7DDMzM+tBvyT6ETG4m/KngO3T5/8B3ps3+5i8eqcApxQsu1ve8+PzZo3vIY6LgIuKlI/oaTq1C/DH7tZtZpU3fPjwrEMwMzOrGf5l3BJImk1yhuGorGMxMzMzMyuFE/0SRMROWcdgZjBx4sSsQzAzM6sZTvTNrGY0NTVlHYKZmVnNyOKXcc3MzMzMrMLco29mFdPS0kIulyvb+lpbWwEYO3Zs2dbZnYaGBp9BMDOzmuZE38wqJpfL8Z+HHmfcBuX5teglry0BYNnK5WVZX3eefu3piq7fzMysPzjRN7OKGrfBOL73gR/2XrEEP7/r/wDKtr7e2jEzM6tlTvTN+llLSwvgC0ur2YtLX2Bwa9Gf/zAzM6sZNXkxrqTNJf1F0hOSZku6XdIkSY2SfpN1fGY9yeVyZR23buX3+srXaW9vzzqMmrVw4UKOOuooFi1alHUoZmYDWs0l+pIEXAHcHBFvT+9xfyDw1oiYFRHfzDRAM7MBbvr06cydO5fp06dnHYqZ2YBWc4k+sAewPCLO7CqIiHkR8VtJu0m6GkDSKElXSJoj6Q5J703LT5B0lqSZ6RmBNw4MJH1J0l2S7pP0B0mD0/KJ6VmDeyRdLGlEP2+zmVlNWLhwITNmzCAiuPbaa92rb2aWoVoco/9u4J4S6p0I3BsR+0naAzgXmJDO2w7YHdgAeFRSC7A1cADwkYhYIekMYLKkfwA/Aj4eEUskHQN8F/hJOTfKBo7W1lba29tpbm7OOpSKy+VyrNOxTtZhWD+aPn06nZ2dAHR2djJ9+nSOPPLIjKMyMxuYarFHfxWSfi/pfkl3F8zaBTgPICJuADaRtGE67+8RsSwiXgJeBDYH9gR2Au6WdF86/XbgQ8C7gH+n5YcAWxWJ43BJsyTNWrBgQbk308ysJtxwww10dHQA0NHRwfXXX59xRGZmA1ct9ug/CPxX10REfEPSpsCsPqxjWd7zlSSvg4BzIuIH+RUlfQa4LiIO6mmFETEVmArQ2NgYfYjFBpiuH3uaMmVKxpFUXnNzM8ueqew976267LHHHlxzzTV0dHQwZMgQ9txzz6xDMjMbsGqxR/8GYD1J+fcmHF6k3i3AZABJuwEvRcSrPaz3emB/SZuly4yStBVwB/ARSVun5etL2natt8LMrA5NnjyZQYOSr5ZBgwYxefLkjCMyMxu4ai7Rj4gA9gM+JulJSXcB5wDHFFQ9AdhJ0hzgFJIhNz2t9yGSsfgz0mWuA7aIiAXAocAFafntJGP8zdZIQ0MDDQ0NWYdhPVhv8HoMGzYs6zBq0iabbMLEiRORxF577cWoUaOyDsnMbMCqxaE7RMTzJLfULGZmWmcRyQFB4bInFExvn/f8IuCiIsvcALx/TeM1y+cfyqp+mw3fnHXH+iLiNTV58mTmzZvn3nwzs4zVZKJvZmbVa5NNNuH000/POgwzswHPib6ZVdTTrz3Nz+/6vzKtax5A2dbXfTtPsw1bV7QNMzOzSnOib2YVU+5rEdZvXR+g4sNqtmFrX0dhZmY1z4m+mVWMr0cwMzPLTs3ddces1rW0tNDS0pJ1GGZmZlbnnOib9bMZM2YwY8aMrMMwMzOzOudE38zMzMysDjnRNzMzMzOrQ74Y16yfLV26NOsQzMzMbADItEdfUkg6P296iKQFkq5Opz8r6fvZRWhWfhFBRGQdhpmZmdW5rHv0lwDbSxoWEe3AJ4DnumZGxFXAVaWsSJIARURnRSI1MzMzM6sh1TBG/x/Ap9LnBwEXdM2QdKik36XPN5d0uaT708eHJY2X9Kikc4G5wJaSTpM0V9IDkg5Il91N0k2SrpT0hKRTJE2WdFdaryGt9xlJd0q6V9K/JG2elv9a0nHp870k3SypGl47MzMzM7OiqiFZvRA4UNJ6wHuBO7up9xvgpojYAdgReDAt3wY4IyLeDTQCE4AdgI8Dp0naIq23A3AE8E7gy8C2EfEB4E/AkWmdW4EPRcT70ri+l5b/ADhA0u5pHF8pPHMg6XBJsyTNWrBgwZq9EmZmZmZmZZJ5oh8Rc4DxJL35/+ih6h5AS7rMyohoS8vnRcQd6fNdgAvS+S8ANwHvT+fdHRHPR8QyIAd03cj8gbR9gLcC10p6ADgaeHfa3lLgv4HrgN9FRK7IdkyNiMaIaBw9enRfXgIzMzMzs7LLPNFPXQVMIW/YTh8sKbHesrznnXnTnbx5rcJvSRL59wBfB9bLW+Y9wEJg7BrEaGZmZmbWr6ol0T8LODEiHuihzvVAE4CkwZJGFqlzC8kQm8GSRgO7Anf1IY6RvHkx8CFdhZK2Ao4C3gd8UtIH+7BOMzMzM7N+VxWJfkQ8GxG/6aXat4Dd02E1s4F3FalzOTAHuB+4AfheRMzvQygnABdLmg28BG/czefPQHNEtAJfBf6UXlNg1meSSD5WZmZmZpUj38+7/BobG2PWrFlZh2FVatKkSQBcfvnlGUdiZmZmtU7S7IhoLDavKnr0zczMzMysvLL+wSyzAWfixIlZh2BmZmYDgBN9s37W1NSUdQhmZmY2AHjojpmZmZlZHXKPvlkNa2lpIZdb7ffb1lhraysAY8eW7+ciGhoafBbDzMwsA070zWpYLpfj8YceZdyGY8qyviWvvgbA8s62XmqW5ulX+3J3WzMzMysnJ/pmNW7chmM4dudDy7Kuk26fBlD29ZmZmVn/8xh9s37W0tJCS0tL1mHUJL92ZmZmpcukR1/SSuCBtP2HgUMiYmkWsXRHUiNwcER8M+tYrL6Uc0z9QOPXzszMrHRZ9ei3R8SEiNgeWA4ckVEc3YqIWU7yzczMzKxWVcPQnVuArSV9RtKdku6V9C9JmwNIOkHSWZJmSnpC0hvJt6QrJM2W9KCkw9OywZKmSZor6QFJ30nLJ0i6Q9IcSZdL2jgtnynpVEl3SXpM0kfT8t0kXZ0+HyHp7HR9cyT9Vz+/RmZmZmZmfZLpxbiShgCfBK4BbgU+FBEh6WvA94Cj0qrbAbsDGwCPSmqJiBXAYRGxSNIw4G5JlwLjgbekZwuQtFG6jnOBIyPiJkk/AY4Hvp3OGxIRH5C0T1r+8YJQfwy0RcR70nVuXM7XwQaW1tZW2tvbaW5uXut15XI51umohuP14l5YsojluZfKsq2QbO+wYcPKsi4zM7N6l1WiP0zSfenzW4A/A+8ALpK0BbAO8GRe/b9HxDJgmaQXgc2BZ4FvSpqU1tkS2AZ4FHi7pN8CfwdmSBoJbBQRN6V1zwEuzlv/Zenf2SQHCoU+DhzYNRERLxdWSM8oHA4wbty43rbfzMzMzKyiskr02yNiQn5Bmpj/IiKukrQbcELe7GV5z1cCQ9I6Hwd2joilkmYC60XEy5J2APYiGfv/BeA7vcTTtf6VrOFrEhFTgakAjY2NsSbrsIGh68eopkyZstbram5uZvmz5bnnfSVsvv4o1nnryLJsK1C2MwNmZmYDQTWd8x8JPJc+P6TE+i+nSf52wIcAJG0KDIqIS4EfATtGRBvwctf4e+DLwE3FVtqN64BvdE146I6ZmZmZVbtq+sGsE4CLJb0M3AC8rZf61wBHSHqYZLjOHWn5W4CzJXUdxPwg/XsIcKak4cATwFf6ENvPgN9LmkvS638ibw73MeuThoaGrEOoWX7tzMzMSpdJoh8RI4qUXQlcWaT8hILp7fMmP9lNEzsWWc99pL3+BeW75T1/iXSMfkTMBGamzxdT2lkGs141NTVlHULN8mtnZmZWumoaumNmZmZmZmVSTUN3zGwNPP3qfE66fVpZ1jXv1fkAZVvf06/OZ2tGlmVdZmZm1jdO9M1qWLnHrK/fugSAdcaWJznfmpEeV29mZpYRJ/pmNcxj1s3MzKw7HqNv1s9aWlpoaWnJOgwzMzOrc070zfrZjBkzmDFjRtZhmJmZWZ1zom9mZmZmVoec6JuZmZmZ1aG6TvQlHSvpQUlzJN0n6YNZx2S2dOlSli5dmnUYNenxxx9nv/3244knnsg6FDMzs6pXt4m+pJ2BTwM7RsR7gY8Dz2QblRlEBBGRdRg16dRTT2Xp0qWcfPLJWYdiZmZW9eo20Qe2AF6KiGUAEfFSRLRK2lPSvZIekHSWpHUBJD0l6URJ96TztpM0SNJ/JI1O6wyS9HjXtJn1n8cff5x58+YBMG/ePPfqm5mZ9aKeE/0ZwJaSHpN0hqSPSVoPmAYcEBHvIfkdgfwbkb8UETsCLUBzRHQC5wOT0/kfB+6PiAX9thVmBiS9+fncq29mZtazuk30I2IxsBNwOLAAuAj4OvBkRDyWVjsH2DVvscvSv7OB8enzs4CD0+eHAWcXa0/S4ZJmSZq1YIGPA8zKras3v7tpMzMzW1XdJvoAEbEyImZGxPHA/wL79bLIsvTvStJfDY6IZ4AXJO0BfAD4ZzdtTY2IxohoHD3aI3vMym2rrbbqcdrMzMxWVbeJvqR3SNomr2gCkAPGS9o6LfsycFMJq/sTyRCeiyNiZVkDNbOSHHPMMatM/+AHP8goEjMzs9pQt4k+MAI4R9JDkuYA7wK+D3wFuFjSA0AncGYJ67oqXV/RYTtmVnlbb731G734W221FW9/+9szjsjMzKy6Dck6gEqJiNnAh4vMuh54X5H64/OezwJ2y5u9A8lFuI+UN0obiCRlHULNOuaYY2hubnZvvpmZWQnqNtEvF0nfJ7kzz+Te6pqVYvjw4VmHULO23nprrrjiiqzDMDMzqwn1PHSnLCLilIjYKiJuzToWMzMzM7NSuUffrJ9NnDgx6xDMzMxsAHCib9bPmpqaeq9kZmZmtpYUEVnHUHckLQCy+jWfTYGX6qid/mzL21QbbdVbO/3ZlrepNtryNtVGW96m2mir3topZquIKPojTk7064ykWRHRWC/t9Gdb3qbaaKve2unPtrxNtdGWt6k22vI21UZb9dZOX/liXDMzMzOzOuRE38zMzMysDjnRrz9T66yd/mzL21QbbdVbO/3ZlrepNtryNtVGW96m2mir3trpE4/RNzMzMzOrQ+7RNzMzMzOrQ07064Skb0maK+lBSd8u87rPkvSipLl5ZaMkXSfpP+nfjSvUzufTbeqUVLar2btp6zRJj0iaI+lySRtVqJ2fpm3cJ2mGpLFr2053beXNO0pSSNq0Eu1IOkHSc+k23Sdpn7Vtp7u20vIj0/fqQUk/r0Q7ki7K256nJN23tu300NYESXekbc2S9IEKtbODpNslPSDpb5I2LEM7W0q6UdJD6fvxrbS8EvuI7toq636ih3YqsY/orq2y7ie6aydvfjn3Ed1tU1n3Ez1tUwX2Ed1tU1n3Ez20U9Z9RA/tVGIfsZ6kuyTdn7Z1Ylr+Nkl3Sno8fR3XqVA7/5u2UZbPdy9tTZf0qJJ87CxJQ8vR3lqJCD9q/AFsD8wFhpP8CNq/gK3LuP5dgR2BuXllPwe+nz7/PnBqhdp5J/AOYCbQWOFtmggMSZ+fWsFt2jDv+TeBMyu1TWn5lsC1JL/tsGmFtukEoLlc708vbe2efsbXTac3q9Rrlzf/dOC4Cm7TDOCT6fN9gJkVaudu4GPp88OAn5ahnS2AHdPnGwCPAe+q0D6iu7bKup/ooZ1K7CO6a6us+4nu2kmny72P6G6byrqf6KGdSuwjun398uqs9X6ih20q6z6ih3YqsY8QMCJ9PhS4E/gQ8FfgwLT8TKCpQu28DxgPPFWOz3cvbe2TzhNwwdpuUzke7tGvD+8E7oyIpRHRAdwEfK5cK4+Im4FFBcX7Auekz88B9qtEOxHxcEQ8urbrLrGtGenrB3AH8NYKtfNq3uT6QFkulOnmfQL4JfC9fmin7Lppqwk4JSKWpXVerFA7AEgS8AWSnfZa66atALp6zkYCrRVqZ1vg5vT5dcB/laGd5yPinvT5a8DDwFuozD6iaFvl3k/00E4l9hHdtVXW/UQP7xOUfx/RU1tl00M7ldhH9LhN5dpP9NBOWfcRPbRTiX1ERMTidHJo+ghgD+CStHyt9xHdtRMR90bEU2uz7j609Y90XgB3UYZ9xNpyol8f5gIflbSJpOEkR5RbVrjNzSPi+fT5fGDzCrfX3w4D/lmplUs6SdIzwGTguAq2sy/wXETcX6k28vxvOtTgLJVhmEYPtiX5vN8p6SZJ769gWwAfBV6IiP9UsI1vA6eln4kpwA8q1M6DJAk4wOcp835C0niS3rM7qfA+oqCtiumhnbLvIwrbqtR+Ir+dSu8jirx+FdlPFLRT0X1EN5+Jsu8nCtr5NhXaRxS0U5F9hKTB6bCmF0kOIHLAK3kHzs9ShoPBwnYiomL7h57aSofsfBm4plLtl8qJfh2IiIdJTiPPIPlQ3Qes7Mf2gzL1BFUDSccCHcD0SrUREcdGxJZpG/9biTbSg74fUsEDiTwtQAMwAXie5BR2pQwBRpGcJj0a+Gvam1YpB1Gm3vweNAHfST8T3wH+XKF2DgP+R9JsktP1y8u1YkkjgEuBbxf0Rpd9H9FTW+XUXTuV2EcUa6sS+4n8dki2oWL7iCLbVJH9RJF2KraP6OGzV9b9RJF2KrKPKNJORfYREbEyIiaQ9HB/ANiuHOvtrR1J21einRLaOgO4OSJuqVT7pXKiXyci4s8RsVNE7Aq8TDLerpJekLQFQPp3rU+NVgNJhwKfBianyUmlTacMp0a70QC8Dbhf0lMkO6N7JI0pd0MR8UK60+sE/kiyI6+UZ4HL0rOjdwGdQFkusCokaQjJMLiLKrH+PIcAl6XPL6ZCr19EPBIREyNiJ5KkJFeO9aa9V5cC0yOiazsqso/opq2y666dSuwjStimsuwnirRTsX1EsW2qxH6im9euIvuIHj4TZd1PdNNO2fcR3bxHFdlHdImIV4AbgZ2BjdLXDpLP3nMVaGfvcq2z1LYkHQ+MBr5b6bZL4US/TkjaLP07jmSH85cKN3kVyY6H9O+VFW6v4iTtTTJO9bMRsbSC7WyTN7kv8Egl2omIByJis4gYHxHjSb78doyI+eVuqyuhS00iGU5WKVeQXGyHpG2BdYCXKtTWx4FHIuLZCq2/SyvwsfT5HkBFhgnl7ScGAT8iuQBubdcpkt7FhyPiF3mzyr6P6KGtsuqunUrsI3poq6z7iWLtVGof0cM2lXU/0cPn4QrKvI/o5bNXtv1ED+2UdR/Rw3tUiX3EaKV3qJI0DPgEyTUBNwL7p9XWeh/RTTsV+X7tri1JXwP2Ag5KD2izFxlfDexHeR7ALcBDwP3AnmVe9wUkp1lXkHwRfBXYBLieZGfzL2BUhdqZlD5fBrwAXFvBbXoceIZk6NN9lOFuON20cynJF9wc4G8kF95VZJsK5j9Fee6oUWybzgMeSLfpKmCLCr5P6wDnp6/hPcAelXrtgGnAEeXYll62aRdgdvr/eyewU4Xa+RbJ2b7HgFMg+dHEtWxnF5JhOXPy/nf2qdA+oru2yrqf6KGdSuwjumurrPuJ7topqFOufUR321TW/UQP7VRiH9Ht60cZ9xM9bFNZ9xE9tFOJfcR7gXvTtuaS3pkIeDvJBauPk5ylWLdC7XyTZP/QQXLA9KcKblMHyVmQrte0LHdrW5uHfxnXzMzMzKwOeeiOmZmZmVkdcqJvZmZmZlaHnOibmZmZmdUhJ/pmZmZmZnXIib6ZmZmZWR1yom9m1s8khaSt0+fDJP1NUpukiyVNljQj6xjrnaQTJJ2/Fsv/U9Ihvdc0M8uOE30zszUgaRdJt6UJ+iJJ/5b0/jVY1f7A5sAmEfH5iJgeERPLHK6thWIHBRHxyYg4px/afkrSckmbFpTfmx4wjk+np6X1Fqefx+skbVfp+MysujnRNzPrI0kbAlcDvwVGAW8BTiT5waa+2gp4LCI6yhdh9ZA0eC2XH1KuWGrYk8BBXROS3gMML1Lv5xExAngr8CLJDzmZ2QDmRN/MrO+2BYiICyJiZUS0R8SMiJjTVUHSYZIelvSypGslbVW4EkknAscBB6Q9sV+VdKikW/PqbJf2zi6S9KikL3QXlKRRks6W1Jq2e0VavrGkqyUtSMuvlvTWvOVmSvpZeoZicTqUaBNJ0yW9Kunurp7j3mJKe5ZbJP1D0hJgd0mfSnugX5X0jKQTetiG3SQ9K+kYSfOBsyUNkvR9STlJCyX9VdKotP56ks5Py19JY908nTdW0lVpnI9L+u+e2iwoe0rSxyXtDfww7z26P+81+1r6fJCkH0maJ+lFSedKGpnOG5/2vB8i6WlJL0k6trvt78Z5wMF504cA53ZXOSKWAn8Btu9jO2ZWZ5zom5n13WPASknnSPqkpI3zZ0ralyQ5/BwwGrgFuKBwJRFxPPB/wEURMSIi/lywnvWB60iSts2AA4EzJL2rm7jOI+npfXda/5dp+SDgbJKzB+OAduB3BcseCHyZ5OxEA3B7uswo4GHg+D7E9EXgJGAD4FZgCUmiuhHwKaBJ0n7dbAPAmLTdrYDDgSOB/YCPAWOBl4Hfp3UPAUYCWwKbAEek2wdwIfBsusz+wP9J2qOHdlcTEdew6nu0Q5Fqh6aP3YG3AyNY/fXdBXgHsCdwnKR39iGMO4ANJb0zPUNyINDt9QWSRgCTgXv70IaZ1SEn+mZmfRQRr5IkbgH8EViQ9hxvnlY5Ajg5Ih5Oh+T8HzChWK9+Lz4NPBURZ0dER0TcC1wKfL6woqQtgE8CR0TEyxGxIiJuSuNdGBGXRsTSiHiNJAn/WMEqzo6IXES0Af8EchHxrzT+i4H39SGmKyPi3xHRGRGvR8TMiHggnZ5DctBT2H6+TuD4iFgWEe0kr+exEfFsRCwDTgD2T4f1rCBJ8LdOz67MjohXJW0JfAQ4Jo3hPuBPrNozXi6TgV9ExBMRsRj4AXBgwbCjE9MzP/cD9wPFDhh60tWr/wmSA6/nitRplvQK8DjJwcahfWzDzOqMxz6ama2BiHiYNJFKL3o8H/gVyVjqrYBfSzo9bxGR9JbP60MzWwEfTJO3LkNIkr5CWwKLIuLlwhmShpP07u8NdJ192EDS4IhYmU6/kLdIe5HpEX2I6ZmC9j8InEIylGQdYF2Sg4fuLIiI1/OmtwIul9SZV7aS5CLm80i2/UJJG5G8D8eS9OIvSg9suswDGntod02NZdX3dR7Ja7J5Xtn8vOdLefP1LNV5wM3A2+h+2M6UiPhRH9drZnXMPfpmZmspIh4hufCxa0z0M8DXI2KjvMewiLitj6t+BripYD0jIqKpm7qj0mS30FEkw0Y+GBEbArum5epjPKXGFAXL/AW4CtgyIkYCZ/bSduHyzwCfLGhzvYh4Lj1zcWJEvAv4MMkZh4OBVpLXY4O89YyjeE/4EvIubk2Hx4zuIZ5CrSQHI/ntdLDqwdJaiYh5JBfl7gNcVq71mll9c6JvZtZH6cWoR3Vd0JoOEzmIZCw1JInsDyS9O50/UtJqw21KcDWwraQvSxqaPt5fbHx3RDxPMuTmjPTi26GSuhL6DUh65V9JL2I9fg1i6XNMeTYg6V1/XdIHSMbw98WZwEldQ58kjU6vg0DS7pLekybnr5IM5emMiGeA24CT0wt23wt8leJj2x8D1ksvGh4K/IjkrEOXF4Dxkrr7zrwA+I6kt6Xj47vG9Pd6J6X0QuDeDiS6fBXYIyKWlFjfzAY4J/pmZn33GvBB4M70zjJ3AHNJes6JiMuBU0mGk7yazvtkXxtJh51MJLn4spVk+MeprJqE5vsySaL7CMntFb+dlv8KGAa8lMZ6TV9jWYuYAP4H+Imk10juMvTXPjb7a5IzAjPSddxB8vpDcuHuJSRJ/sPATbw5jOggYHwa5+Uk4/7/VWSb2tIY/0TS47+E5CLeLl3DjBZKuqdIfGfx5tCaJ4HXSS4gLsWWJAckvUqvoZhV4nrNzFBEqR0JZmZmVk6S/gRcHBHXZh2LmdUfJ/pmZmZmZnXIQ3fMzMzMzOqQE30zMzMzszrkRN/MzMzMrA450TczMzMzq0NO9M3MzMzM6pATfTMzMzOzOuRE38zMzMysDjnRNzMzMzOrQ/8P0fiWqunmk5EAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"fig, ax = plt.subplots(figsize=(12, 8))\\nsns.boxplot(data=bc, y=\\\"brand_name\\\", x=\\\"selfie_camera_mp\\\", ax=ax)\\nplt.xlabel('Selfie camera resolution, MP', fontsize=12)\\nplt.ylabel('Brand name', fontsize=12)\\nplt.title('Distribution of selfie cameras resolution (>8MP) across brands', fontsize=16)\\nax.xaxis.set_major_formatter(tick.FormatStrFormatter('%0.0f'))\\nplt.xticks(np.arange(min(bc['selfie_camera_mp']), max(bc['selfie_camera_mp'])+1, 1))\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"fig, ax = plt.subplots(figsize=(12, 8))\\nsns.boxplot(data=bc, y=\\\"brand_name\\\", x=\\\"selfie_camera_mp\\\", ax=ax)\\nplt.xlabel(\\\"Selfie camera resolution, MP\\\", fontsize=12)\\nplt.ylabel(\\\"Brand name\\\", fontsize=12)\\nplt.title(\\\"Distribution of selfie cameras resolution (>8MP) across brands\\\", fontsize=16)\\nax.xaxis.set_major_formatter(tick.FormatStrFormatter(\\\"%0.0f\\\"))\\nplt.xticks(np.arange(min(bc[\\\"selfie_camera_mp\\\"]), max(bc[\\\"selfie_camera_mp\\\"]) + 1, 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": "markdown",
      "source": [
        "* Huawei is the brand offering the maximum number of devices (87, ~13%) with greater than 8MP selfie cameras\n",
        "* Acer is the brand offering one device with greater than an 8MP selfie camera\n",
        "* Devices with the highest average selfie camera resolution belong to Oppo (22.12MP)\n",
        "* Devices with the smallest average selfie camera resolution belong to Acer (13MP) among that higher 8MP\n",
        "* Lenovo is the brand with the largest variety of models with different selfie camera resolutions of devices with a selfie camera resolution greater than 8MP"
      ],
      "metadata": {
        "id": "bl29o1pBY7Aq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####**7. Which attributes are highly correlated with the normalized price of a used device?**"
      ],
      "metadata": {
        "id": "R-RhX25KhqzD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 7))\n",
        "sns.heatmap(df.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": {
        "id": "Bq4sRx8EhvJ4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        },
        "outputId": "2b4a394f-3149-41ea-f158-d615463fd1c3"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"plt.figure(figsize=(12, 7))\\nsns.heatmap(df.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 = \"plt.figure(figsize=(12, 7))\\nsns.heatmap(df.corr(), annot=True, vmin=-1, vmax=1, fmt=\\\".2f\\\", cmap=\\\"Spectral\\\")\\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": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The correlation coefficient of 0.83 indicates a strong positive correlation between the *normalized price of a used device* and *normalized price of a new device* of the same model\n",
        "* The correlation coefficient of -0.36 indicates a weak negative correlation between the *normalized price of a used device* and number of days the used/refurbished device has been used\n",
        "* The attributes positively correlated with the `normalized_used_price` are:\n",
        "  * Moderate: `screen_size`, `main_camera_mp`, `selfie_camera_mp`, `ram`, `battery`, `release year`\n",
        "  * Week: `weight`\n",
        "  * No correlation: `int_memory` ($<$0.3)\n",
        "\n",
        "**NB:** *In interpreting correlation it is important to remember that correlation is not causation. There may or may not be a causative connection between the two correlated variables.Moreover, if there is a connection it may be indirect.*"
      ],
      "metadata": {
        "id": "9OxN0ludi47s"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pVn5toJ7MKte"
      },
      "source": [
        "# Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Duplicate value check\n",
        "- Missing value treatment\n",
        "- Feature engineering (if needed)\n",
        "- Outlier detection and treatment (if needed)\n",
        "- Preparing data for modeling\n",
        "- Any other preprocessing steps (if needed)"
      ],
      "metadata": {
        "id": "YcceZiPd5vGV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's create a copy of the dataset for preprocessing - `dfpp`"
      ],
      "metadata": {
        "id": "AUT3MNg4N8WU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfpp = df.copy()"
      ],
      "metadata": {
        "id": "C8RRkpm3N7Sr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "076ae8e2-3f9f-40c2-8c2b-f7db65f5ba39"
      },
      "execution_count": 76,
      "outputs": [
        {
          "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 = \"dfpp = df.copy()\";\n",
              "                var nbb_formatted_code = \"dfpp = df.copy()\";\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": [
        "##**Duplicate Value Check**"
      ],
      "metadata": {
        "id": "VYeZeefw0pF6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfpp.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "002b40bb-0bee-4ac4-f461-57a81fab07b8",
        "id": "Sz3ZKevQ0pF6"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "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 = \"dfpp.duplicated().sum()\";\n",
              "                var nbb_formatted_code = \"dfpp.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": "jkiBicu10pF6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Missing Value Treatment**"
      ],
      "metadata": {
        "id": "x5g3TfmzozY0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "missed = dfpp.isnull().sum()\n",
        "missed = missed[missed>0]\n",
        "\n",
        "print('Columns with missing values:')\n",
        "print(missed)\n",
        "print(f'Missing values occur in about {missed.sum()/dfpp.shape[0]:.1%} of rows.')"
      ],
      "metadata": {
        "id": "TF8rvwWkov2j",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "outputId": "7e5be7d4-d3ea-48a5-c8b5-22c691774dd2"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columns with missing values:\n",
            "main_camera_mp      179\n",
            "selfie_camera_mp      2\n",
            "int_memory            4\n",
            "ram                   4\n",
            "battery               6\n",
            "weight                7\n",
            "dtype: int64\n",
            "Missing values occur in about 5.8% of rows.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 78;\n",
              "                var nbb_unformatted_code = \"missed = dfpp.isnull().sum()\\nmissed = missed[missed>0]\\n\\nprint('Columns with missing values:')\\nprint(missed)\\nprint(f'Missing values occur in about {missed.sum()/dfpp.shape[0]:.1%} of rows.')\";\n",
              "                var nbb_formatted_code = \"missed = dfpp.isnull().sum()\\nmissed = missed[missed > 0]\\n\\nprint(\\\"Columns with missing values:\\\")\\nprint(missed)\\nprint(f\\\"Missing values occur in about {missed.sum()/dfpp.shape[0]:.1%} of rows.\\\")\";\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": [
        "# distibution review using histplot\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "for i, variable in enumerate(missed.index):\n",
        "    plt.subplot(2, 3, i + 1)\n",
        "    sns.histplot(data=dfpp, x=variable)\n",
        "    plt.tight_layout(pad=2)\n",
        "\n",
        "plt.show();"
      ],
      "metadata": {
        "id": "DwxGWU7Ckcv9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "74ffff29-efca-412e-b5c4-b20b4950379f"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 79;\n",
              "                var nbb_unformatted_code = \"# distibution review using histplot\\n\\nplt.figure(figsize=(12, 6))\\nfor i, variable in enumerate(missed.index):\\n    plt.subplot(2, 3, i + 1)\\n    sns.histplot(data=dfpp, x=variable)\\n    plt.tight_layout(pad=2)\\n\\nplt.show();\";\n",
              "                var nbb_formatted_code = \"# distibution review using histplot\\n\\nplt.figure(figsize=(12, 6))\\nfor i, variable in enumerate(missed.index):\\n    plt.subplot(2, 3, i + 1)\\n    sns.histplot(data=dfpp, x=variable)\\n    plt.tight_layout(pad=2)\\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": [
        "Let's fix the missing values in the data.\n",
        "* For the variables contained missing values, we will impute the missing values in each column with the median grouped by `brand_name` as all of the columns have skewed distributions\n"
      ],
      "metadata": {
        "id": "nzEsykgHo95j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filling NA with median by grouping by brand_name\n",
        "\n",
        "for col in missed.index:\n",
        "  dfpp[col] = dfpp[col].fillna(value=dfpp.groupby(['brand_name'])[col].transform('median'))\n",
        "\n",
        "missed = dfpp.isnull().sum()\n",
        "missed = missed[missed>0]\n",
        "\n",
        "print('Columns with missing values:')\n",
        "print(missed)"
      ],
      "metadata": {
        "id": "XzuqnnxrrVMH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "b6f59f6d-8bc5-4b84-acbc-a5d1c1f7bbd4"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columns with missing values:\n",
            "main_camera_mp    10\n",
            "dtype: int64\n"
          ]
        },
        {
          "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 = \"# Filling NA with median by grouping by brand_name\\n\\nfor col in missed.index:\\n  dfpp[col] = dfpp[col].fillna(value=dfpp.groupby(['brand_name'])[col].transform('median'))\\n\\nmissed = dfpp.isnull().sum()\\nmissed = missed[missed>0]\\n\\nprint('Columns with missing values:')\\nprint(missed)\";\n",
              "                var nbb_formatted_code = \"# Filling NA with median by grouping by brand_name\\n\\nfor col in missed.index:\\n    dfpp[col] = dfpp[col].fillna(\\n        value=dfpp.groupby([\\\"brand_name\\\"])[col].transform(\\\"median\\\")\\n    )\\n\\nmissed = dfpp.isnull().sum()\\nmissed = missed[missed > 0]\\n\\nprint(\\\"Columns with missing values:\\\")\\nprint(missed)\";\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": [
        "* After the first step of treating the missing values, there are still some missing values in the `main_camera_mp` column.\n",
        "* We will input the remaining missing values in the `main_camera_mp` column with the median value of this column."
      ],
      "metadata": {
        "id": "9xxRSpXXjJD_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfpp['main_camera_mp'] = dfpp['main_camera_mp'].fillna(value=dfpp['main_camera_mp'].median())\n",
        "\n",
        "print(f'Total number of missed values in the dataset after transformation is {dfpp.isnull().sum().sum()}.')\n"
      ],
      "metadata": {
        "id": "-iI8CZxYI7iI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c288e0bf-19e8-439f-b700-ced28386b894"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of missed values in the dataset after transformation 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 = 81;\n",
              "                var nbb_unformatted_code = \"dfpp['main_camera_mp'] = dfpp['main_camera_mp'].fillna(value=dfpp['main_camera_mp'].median())\\n\\nprint(f'Total number of missed values in the dataset after transformation is {dfpp.isnull().sum().sum()}.')\";\n",
              "                var nbb_formatted_code = \"dfpp[\\\"main_camera_mp\\\"] = dfpp[\\\"main_camera_mp\\\"].fillna(\\n    value=dfpp[\\\"main_camera_mp\\\"].median()\\n)\\n\\nprint(\\n    f\\\"Total number of missed values in the dataset after transformation is {dfpp.isnull().sum().sum()}.\\\"\\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": [
        "* Missing values in the columns imputed with median by grouping the data by `brand name` \n",
        "* Ten missing values of `main_camera_mp` column imputed with median of the column\n",
        "* All the missing values in the dataset have been treated"
      ],
      "metadata": {
        "id": "M7Ty7MTuJXuG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Feature engineering (if needed)**"
      ],
      "metadata": {
        "id": "ts8pTzZ38n-V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "> Let's create a feature `type` to tag tablets and phones\n",
        "based on  the `screen_size` column. \n",
        "* Assume that devices with screen size > 17cm (6.7\") are tablets"
      ],
      "metadata": {
        "id": "gUPycWc9DAz9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfpp['type'] = dfpp['screen_size'].apply(lambda x: 'tablet' if x > 6.7*2.54 else 'phone')\n",
        "dfpp['type'].describe()"
      ],
      "metadata": {
        "id": "hR0-Tw38DA9E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "6b5c1057-385c-4eef-a0ea-8207ca9f3794"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      3454\n",
              "unique        2\n",
              "top       phone\n",
              "freq       3052\n",
              "Name: type, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 82
        },
        {
          "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 = \"dfpp['type'] = dfpp['screen_size'].apply(lambda x: 'tablet' if x > 6.7*2.54 else 'phone')\\ndfpp['type'].describe()\";\n",
              "                var nbb_formatted_code = \"dfpp[\\\"type\\\"] = dfpp[\\\"screen_size\\\"].apply(\\n    lambda x: \\\"tablet\\\" if x > 6.7 * 2.54 else \\\"phone\\\"\\n)\\ndfpp[\\\"type\\\"].describe()\";\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": [
        "> Let's create a new column `years_since_release` from the release_year column\n",
        "* We will consider the year of data collection, 2021, as the baseline\n",
        "* We will drop the release_year column"
      ],
      "metadata": {
        "id": "eIavwLttfCY-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfpp['years_since_release'] = 2021 - dfpp['release_year']\n",
        "dfpp = dfpp.drop('release_year', axis=1)\n",
        "dfpp['years_since_release'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "id": "H8Z5cOm4d_g9",
        "outputId": "e59033d3-5955-4c6e-b0f1-f874977d18fa"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    3454.000000\n",
              "mean        5.034742\n",
              "std         2.298455\n",
              "min         1.000000\n",
              "25%         3.000000\n",
              "50%         5.500000\n",
              "75%         7.000000\n",
              "max         8.000000\n",
              "Name: years_since_release, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "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 = \"dfpp['years_since_release'] = 2021 - dfpp['release_year']\\ndfpp = dfpp.drop('release_year', axis=1)\\ndfpp['years_since_release'].describe()\";\n",
              "                var nbb_formatted_code = \"dfpp[\\\"years_since_release\\\"] = 2021 - dfpp[\\\"release_year\\\"]\\ndfpp = dfpp.drop(\\\"release_year\\\", axis=1)\\ndfpp[\\\"years_since_release\\\"].describe()\";\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": "7Bokx2VK8xRl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# outlier detection using boxplot\n",
        "num_cols = dfpp.select_dtypes(include=np.number).columns.tolist()\n",
        "\n",
        "plt.figure(figsize=(12, 10))\n",
        "plt.suptitle('Outlier detection using boxplot', fontsize=16, y=1.01)\n",
        "\n",
        "for i, variable in enumerate(num_cols):\n",
        "    plt.subplot(4, 4, i + 1)\n",
        "    sns.boxplot(data=dfpp, x=variable)\n",
        "    plt.tight_layout(pad=2)\n",
        "\n",
        "plt.show();"
      ],
      "metadata": {
        "id": "qS1C5W5K8xdu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "outputId": "9fd502aa-ead3-44f9-aa48-a5319632e6a6"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 11 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"# outlier detection using boxplot\\nnum_cols = dfpp.select_dtypes(include=np.number).columns.tolist()\\n\\nplt.figure(figsize=(12, 10))\\nplt.suptitle('Outlier detection using boxplot', fontsize=16, y=1.01)\\n\\nfor i, variable in enumerate(num_cols):\\n    plt.subplot(4, 4, i + 1)\\n    sns.boxplot(data=dfpp, x=variable)\\n    plt.tight_layout(pad=2)\\n\\nplt.show();\";\n",
              "                var nbb_formatted_code = \"# outlier detection using boxplot\\nnum_cols = dfpp.select_dtypes(include=np.number).columns.tolist()\\n\\nplt.figure(figsize=(12, 10))\\nplt.suptitle(\\\"Outlier detection using boxplot\\\", fontsize=16, y=1.01)\\n\\nfor i, variable in enumerate(num_cols):\\n    plt.subplot(4, 4, i + 1)\\n    sns.boxplot(data=dfpp, x=variable)\\n    plt.tight_layout(pad=2)\\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": [
        "# Use this code to remove outliers\n",
        "\n",
        "#num_cols = df.select_dtypes(include=np.number).columns.tolist()\n",
        "\n",
        "#for col in num_cols:\n",
        "#  q1, q3 = df[col].quantile([0.25,0.75])\n",
        "#  iqr = q3 - q1\n",
        "#  df = df[(df[col] <= q3 + 1.5 * iqr) & (df[col] >= q1 - 1.5 * iqr)]\n"
      ],
      "metadata": {
        "id": "fx-t4UcKy60e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "8c305bae-9140-4150-a94d-2f13fcbc8464"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 85;\n",
              "                var nbb_unformatted_code = \"# Use this code to remove outliers\\n\\n#num_cols = df.select_dtypes(include=np.number).columns.tolist()\\n\\n#for col in num_cols:\\n#  q1, q3 = df[col].quantile([0.25,0.75])\\n#  iqr = q3 - q1\\n#  df = df[(df[col] <= q3 + 1.5 * iqr) & (df[col] >= q1 - 1.5 * iqr)]\";\n",
              "                var nbb_formatted_code = \"# Use this code to remove outliers\\n\\n# num_cols = df.select_dtypes(include=np.number).columns.tolist()\\n\\n# for col in num_cols:\\n#  q1, q3 = df[col].quantile([0.25,0.75])\\n#  iqr = q3 - q1\\n#  df = df[(df[col] <= q3 + 1.5 * iqr) & (df[col] >= q1 - 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": "markdown",
      "source": [
        "* There are quite a few outliers in the data\n",
        "* However, we will not treat them as they are **proper** values"
      ],
      "metadata": {
        "id": "QfNCknUHN63h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Preparing data for modeling**"
      ],
      "metadata": {
        "id": "XU0Vf1TR8xuO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* We want to predict the normalized price of used device\n",
        "* Before we proceed to build a model, we'll have to encode categorical features\n",
        "* We'll split the data into train and test to be able to evaluate the model that we build on the train data\n",
        "* We will build a Linear Regression model using the train data and check it's performance"
      ],
      "metadata": {
        "id": "tdgvK1xh3Lp9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# defining X and y variables\n",
        "X = dfpp.drop(['normalized_used_price'], axis=1)\n",
        "y = dfpp[\"normalized_used_price\"]"
      ],
      "metadata": {
        "id": "mEtflLjT8x5U",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "71738356-522b-455f-fd0b-3a62d554cb33"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 86;\n",
              "                var nbb_unformatted_code = \"# defining X and y variables\\nX = dfpp.drop(['normalized_used_price'], axis=1)\\ny = dfpp[\\\"normalized_used_price\\\"]\";\n",
              "                var nbb_formatted_code = \"# defining X and y variables\\nX = dfpp.drop([\\\"normalized_used_price\\\"], axis=1)\\ny = dfpp[\\\"normalized_used_price\\\"]\";\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": [
        "# let's add the intercept to data\n",
        "X = sm.add_constant(X)"
      ],
      "metadata": {
        "id": "6O527Oy840Qm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "507c28ba-4185-4b18-fd47-6e22bcaae9bf"
      },
      "execution_count": 87,
      "outputs": [
        {
          "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 = \"# let's add the intercept to data\\nX = sm.add_constant(X)\";\n",
              "                var nbb_formatted_code = \"# let's add the intercept to data\\nX = sm.add_constant(X)\";\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 dummy variables\n",
        "dummies = ['brand_name', 'os', '4g', '5g', 'type'] \n",
        "\n",
        "X = pd.get_dummies(data=X, columns=dummies, drop_first=True)"
      ],
      "metadata": {
        "id": "M-dpGpHasWGd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "1bd0958d-c574-4a4f-933e-fac1bac33acf"
      },
      "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 = \"# creating dummy variables\\ndummies = ['brand_name', 'os', '4g', '5g', 'type'] \\n\\nX = pd.get_dummies(data=X, columns=dummies, drop_first=True)\";\n",
              "                var nbb_formatted_code = \"# creating dummy variables\\ndummies = [\\\"brand_name\\\", \\\"os\\\", \\\"4g\\\", \\\"5g\\\", \\\"type\\\"]\\n\\nX = pd.get_dummies(data=X, columns=dummies, drop_first=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": [
        "# splitting the data in 70:30 ratio for train to test data\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)\n",
        "\n",
        "print(\"Number of rows in train data =\", X_train.shape[0])\n",
        "print(\"Number of rows in test data =\", X_test.shape[0])"
      ],
      "metadata": {
        "id": "qH3PLaZF6D3H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "d457f799-e036-45b9-8dc0-3770a33e9994"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of rows in train data = 2417\n",
            "Number of rows in test data = 1037\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 = \"# splitting the data in 70:30 ratio for train to test data\\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)\\n\\nprint(\\\"Number of rows in train data =\\\", X_train.shape[0])\\nprint(\\\"Number of rows in test data =\\\", X_test.shape[0])\";\n",
              "                var nbb_formatted_code = \"# splitting the data in 70:30 ratio for train to test data\\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)\\n\\nprint(\\\"Number of rows in train data =\\\", X_train.shape[0])\\nprint(\\\"Number of rows in test data =\\\", X_test.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": [
        "##**Any other preprocessing steps (if needed)**"
      ],
      "metadata": {
        "id": "r5-9YVgR8yC9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* No other preprocessing steps are needed\n",
        "* Data is ready to build a model"
      ],
      "metadata": {
        "id": "TUc561Dm6Jcc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "4tPKFOe8kBJM"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KNzFis7eEaXj"
      },
      "source": [
        "# EDA"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* It is a good idea to explore the data once again after manipulating it."
      ],
      "metadata": {
        "id": "dsAA8UpkXN7u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Univariate analysis**"
      ],
      "metadata": {
        "id": "1hGpKt8CtAtF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Numerical variables\n",
        "cols = X.select_dtypes(include='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.subplot(4, 3, 9);\n",
        "sns.boxplot(x=y);\n",
        "plt.tight_layout(pad=2);\n",
        "\n",
        "plt.subplot(4, 3, 10)\n",
        "sns.boxplot(data=X, x='days_used');\n",
        "plt.tight_layout(pad=2)\n",
        "\n",
        "plt.subplot(4, 3, 11)\n",
        "sns.boxplot(data=X, x='years_since_release');\n",
        "plt.tight_layout(pad=2)\n",
        "\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 855
        },
        "id": "wLcI6y0hmejp",
        "outputId": "a8fa2b04-b6f0-4b01-e44f-58d070fed274"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 11 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"# Numerical variables\\ncols = X.select_dtypes(include='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.subplot(4, 3, 9);\\nsns.boxplot(x=y);\\nplt.tight_layout(pad=2);\\n\\nplt.subplot(4, 3, 10)\\nsns.boxplot(data=X, x='days_used');\\nplt.tight_layout(pad=2)\\n\\nplt.subplot(4, 3, 11)\\nsns.boxplot(data=X, x='years_since_release');\\nplt.tight_layout(pad=2)\\n\\nplt.show();\";\n",
              "                var nbb_formatted_code = \"# Numerical variables\\ncols = X.select_dtypes(include=\\\"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.subplot(4, 3, 9)\\nsns.boxplot(x=y)\\nplt.tight_layout(pad=2)\\n\\nplt.subplot(4, 3, 10)\\nsns.boxplot(data=X, x=\\\"days_used\\\")\\nplt.tight_layout(pad=2)\\n\\nplt.subplot(4, 3, 11)\\nsns.boxplot(data=X, x=\\\"years_since_release\\\")\\nplt.tight_layout(pad=2)\\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": [
        "# Categorical variables\n",
        "plt.figure(figsize=(12, 4))\n",
        "sns.countplot(data=dfpp, x='brand_name');\n",
        "plt.xticks(rotation=90, fontsize=12)\n",
        "plt.xlabel('brand_name', fontsize=12)\n",
        "plt.show();\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "for i, var in enumerate(dfpp.select_dtypes(include='object').columns[1:]):\n",
        "  plt.subplot(2, 2, i + 1)\n",
        "  sns.countplot(data=dfpp, x=var);\n",
        "  plt.xticks(fontsize=12)\n",
        "  plt.xlabel(var, fontsize=12)  \n",
        "  plt.tight_layout(pad=1)\n",
        "\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 907
        },
        "id": "Uf17G_qHokv5",
        "outputId": "49bdffb0-f212-4d52-b808-7b7d51c1cbce"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"# Categorical variables\\nplt.figure(figsize=(12, 4))\\nsns.countplot(data=dfpp, x='brand_name');\\nplt.xticks(rotation=90, fontsize=12)\\nplt.xlabel('brand_name', fontsize=12)\\nplt.show();\\n\\nplt.figure(figsize=(10, 8))\\nfor i, var in enumerate(dfpp.select_dtypes(include='object').columns[1:]):\\n  plt.subplot(2, 2, i + 1)\\n  sns.countplot(data=dfpp, x=var);\\n  plt.xticks(fontsize=12)\\n  plt.xlabel(var, fontsize=12)  \\n  plt.tight_layout(pad=1)\\n\\nplt.show();\";\n",
              "                var nbb_formatted_code = \"# Categorical variables\\nplt.figure(figsize=(12, 4))\\nsns.countplot(data=dfpp, x=\\\"brand_name\\\")\\nplt.xticks(rotation=90, fontsize=12)\\nplt.xlabel(\\\"brand_name\\\", fontsize=12)\\nplt.show()\\n\\nplt.figure(figsize=(10, 8))\\nfor i, var in enumerate(dfpp.select_dtypes(include=\\\"object\\\").columns[1:]):\\n    plt.subplot(2, 2, i + 1)\\n    sns.countplot(data=dfpp, x=var)\\n    plt.xticks(fontsize=12)\\n    plt.xlabel(var, fontsize=12)\\n    plt.tight_layout(pad=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": [
        "**Bivariate analysis**"
      ],
      "metadata": {
        "id": "1tWlGBOSs4wp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Bivariate analysis\n",
        "plt.figure(figsize=(6, 10))\n",
        "sns.boxplot(data=dfpp, y='brand_name', x='normalized_used_price');\n",
        "plt.xticks(fontsize=12)\n",
        "plt.xlabel('normalized_used_price', fontsize=12)\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612
        },
        "id": "WOTwY_Y8qflh",
        "outputId": "ce5257d9-385c-4641-89ca-80134f6faa5e"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"# Bivariate analysis\\nplt.figure(figsize=(6, 10))\\nsns.boxplot(data=dfpp, y='brand_name', x='normalized_used_price');\\nplt.xticks(fontsize=12)\\nplt.xlabel('normalized_used_price', fontsize=12)\\nplt.show();\";\n",
              "                var nbb_formatted_code = \"# Bivariate analysis\\nplt.figure(figsize=(6, 10))\\nsns.boxplot(data=dfpp, y=\\\"brand_name\\\", x=\\\"normalized_used_price\\\")\\nplt.xticks(fontsize=12)\\nplt.xlabel(\\\"normalized_used_price\\\", fontsize=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": [
        "plt.figure(figsize=(10, 8))\n",
        "for i, var in enumerate(dfpp.select_dtypes(include='object').columns[1:]):\n",
        "  plt.subplot(2, 2, i + 1)\n",
        "  sns.boxplot(data=dfpp, x=var, y='normalized_used_price');\n",
        "  plt.xlabel(var, fontsize=12)\n",
        "  plt.tight_layout(pad=1)\n",
        "\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 587
        },
        "id": "uGt_jA7ntQBi",
        "outputId": "d3061220-e96f-459f-afa2-0d292c7f6177"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"plt.figure(figsize=(10, 8))\\nfor i, var in enumerate(dfpp.select_dtypes(include='object').columns[1:]):\\n  plt.subplot(2, 2, i + 1)\\n  sns.boxplot(data=dfpp, x=var, y='normalized_used_price');\\n  plt.xlabel(var, fontsize=12)\\n  plt.tight_layout(pad=1)\\n\\nplt.show();\";\n",
              "                var nbb_formatted_code = \"plt.figure(figsize=(10, 8))\\nfor i, var in enumerate(dfpp.select_dtypes(include=\\\"object\\\").columns[1:]):\\n    plt.subplot(2, 2, i + 1)\\n    sns.boxplot(data=dfpp, x=var, y=\\\"normalized_used_price\\\")\\n    plt.xlabel(var, fontsize=12)\\n    plt.tight_layout(pad=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": [
        "**Correlation heatmap**"
      ],
      "metadata": {
        "id": "iOrpi_4-I9VI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 7))\n",
        "sns.heatmap(dfpp.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": 568
        },
        "id": "1hjrpIvBtZoJ",
        "outputId": "c3758870-c987-481f-af5c-351aa3b278b1"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"plt.figure(figsize=(12, 7))\\nsns.heatmap(dfpp.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 = \"plt.figure(figsize=(12, 7))\\nsns.heatmap(dfpp.corr(), annot=True, vmin=-1, vmax=1, fmt=\\\".2f\\\", cmap=\\\"Spectral\\\")\\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": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Pairplot**"
      ],
      "metadata": {
        "id": "hia1ckKGtZ8R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "numeric = ['normalized_used_price', 'normalized_new_price', 'days_used', 'weight', 'battery', 'type']\n",
        "sns.pairplot(dfpp[numeric], hue='type');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 903
        },
        "id": "JMW9nGvBsbaj",
        "outputId": "52227e4e-2b5e-4160-a357-1ba7b17ca364"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 967.125x900 with 30 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"numeric = ['normalized_used_price', 'normalized_new_price', 'days_used', 'weight', 'battery', 'type']\\nsns.pairplot(dfpp[numeric], hue='type');\";\n",
              "                var nbb_formatted_code = \"numeric = [\\n    \\\"normalized_used_price\\\",\\n    \\\"normalized_new_price\\\",\\n    \\\"days_used\\\",\\n    \\\"weight\\\",\\n    \\\"battery\\\",\\n    \\\"type\\\",\\n]\\nsns.pairplot(dfpp[numeric], hue=\\\"type\\\")\";\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": [
        "* On the pair plot, we can see different behavior of some variables by device type (phone/tablet). That might be essential for modeling\n",
        "* **After manipulating, the data look consistent**"
      ],
      "metadata": {
        "id": "FioOyGrG8vrL"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HeUzI1OB4rqM"
      },
      "source": [
        "# Model Building - Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "id": "ZNRiMg0wMKth",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "fc7aac25-7794-47ab-b5a3-5bd40f87f7ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                              OLS Regression Results                             \n",
            "=================================================================================\n",
            "Dep. Variable:     normalized_used_price   R-squared:                       0.845\n",
            "Model:                               OLS   Adj. R-squared:                  0.842\n",
            "Method:                    Least Squares   F-statistic:                     263.2\n",
            "Date:                   Fri, 24 Feb 2023   Prob (F-statistic):               0.00\n",
            "Time:                           06:08:10   Log-Likelihood:                 124.19\n",
            "No. Observations:                   2417   AIC:                            -148.4\n",
            "Df Residuals:                       2367   BIC:                             141.1\n",
            "Df Model:                             49                                         \n",
            "Covariance Type:               nonrobust                                         \n",
            "=========================================================================================\n",
            "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-----------------------------------------------------------------------------------------\n",
            "const                     1.3211      0.074     17.947      0.000       1.177       1.465\n",
            "screen_size               0.0238      0.004      6.160      0.000       0.016       0.031\n",
            "main_camera_mp            0.0209      0.002     13.493      0.000       0.018       0.024\n",
            "selfie_camera_mp          0.0135      0.001     11.941      0.000       0.011       0.016\n",
            "int_memory                0.0001   6.98e-05      1.662      0.097   -2.09e-05       0.000\n",
            "ram                       0.0233      0.005      4.523      0.000       0.013       0.033\n",
            "battery               -1.689e-05   7.28e-06     -2.321      0.020   -3.12e-05   -2.62e-06\n",
            "weight                    0.0010      0.000      7.270      0.000       0.001       0.001\n",
            "days_used              4.152e-05   3.09e-05      1.343      0.179   -1.91e-05       0.000\n",
            "normalized_new_price      0.4312      0.012     35.038      0.000       0.407       0.455\n",
            "years_since_release      -0.0238      0.005     -5.174      0.000      -0.033      -0.015\n",
            "brand_name_Alcatel        0.0157      0.048      0.329      0.742      -0.078       0.109\n",
            "brand_name_Apple      -2.777e-05      0.147     -0.000      1.000      -0.289       0.289\n",
            "brand_name_Asus           0.0153      0.048      0.319      0.749      -0.079       0.109\n",
            "brand_name_BlackBerry    -0.0287      0.070     -0.408      0.683      -0.167       0.109\n",
            "brand_name_Celkon        -0.0450      0.066     -0.677      0.498      -0.175       0.085\n",
            "brand_name_Coolpad        0.0217      0.073      0.298      0.766      -0.121       0.165\n",
            "brand_name_Gionee         0.0457      0.058      0.790      0.429      -0.068       0.159\n",
            "brand_name_Google        -0.0321      0.085     -0.379      0.705      -0.198       0.134\n",
            "brand_name_HTC           -0.0125      0.048     -0.260      0.795      -0.107       0.082\n",
            "brand_name_Honor          0.0321      0.049      0.652      0.514      -0.064       0.129\n",
            "brand_name_Huawei        -0.0019      0.044     -0.042      0.966      -0.089       0.085\n",
            "brand_name_Infinix        0.1633      0.093      1.752      0.080      -0.019       0.346\n",
            "brand_name_Karbonn        0.0950      0.067      1.415      0.157      -0.037       0.227\n",
            "brand_name_LG            -0.0125      0.045     -0.276      0.783      -0.101       0.076\n",
            "brand_name_Lava           0.0344      0.062      0.551      0.582      -0.088       0.157\n",
            "brand_name_Lenovo         0.0457      0.045      1.011      0.312      -0.043       0.134\n",
            "brand_name_Meizu         -0.0125      0.056     -0.223      0.824      -0.122       0.097\n",
            "brand_name_Micromax      -0.0329      0.048     -0.687      0.492      -0.127       0.061\n",
            "brand_name_Microsoft      0.0955      0.088      1.081      0.280      -0.078       0.269\n",
            "brand_name_Motorola      -0.0107      0.050     -0.215      0.830      -0.108       0.087\n",
            "brand_name_Nokia          0.0707      0.052      1.365      0.172      -0.031       0.172\n",
            "brand_name_OnePlus        0.0718      0.078      0.926      0.355      -0.080       0.224\n",
            "brand_name_Oppo           0.0132      0.048      0.275      0.783      -0.081       0.107\n",
            "brand_name_Others        -0.0074      0.042     -0.176      0.861      -0.090       0.075\n",
            "brand_name_Panasonic      0.0571      0.056      1.021      0.307      -0.053       0.167\n",
            "brand_name_Realme         0.0333      0.062      0.539      0.590      -0.088       0.155\n",
            "brand_name_Samsung       -0.0310      0.043     -0.718      0.473      -0.116       0.054\n",
            "brand_name_Sony          -0.0613      0.050     -1.215      0.225      -0.160       0.038\n",
            "brand_name_Spice         -0.0137      0.063     -0.216      0.829      -0.138       0.111\n",
            "brand_name_Vivo          -0.0145      0.049     -0.299      0.765      -0.110       0.081\n",
            "brand_name_XOLO           0.0159      0.055      0.290      0.772      -0.092       0.124\n",
            "brand_name_Xiaomi         0.0875      0.048      1.816      0.069      -0.007       0.182\n",
            "brand_name_ZTE           -0.0051      0.047     -0.108      0.914      -0.098       0.088\n",
            "os_Others                -0.0535      0.033     -1.611      0.107      -0.119       0.012\n",
            "os_Windows               -0.0198      0.045     -0.438      0.661      -0.108       0.069\n",
            "os_iOS                   -0.0701      0.147     -0.477      0.633      -0.358       0.218\n",
            "4g_yes                    0.0530      0.016      3.342      0.001       0.022       0.084\n",
            "5g_yes                   -0.0728      0.032     -2.307      0.021      -0.135      -0.011\n",
            "type_tablet               0.0091      0.031      0.290      0.772      -0.052       0.071\n",
            "==============================================================================\n",
            "Omnibus:                      223.759   Durbin-Watson:                   1.911\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              424.043\n",
            "Skew:                          -0.619   Prob(JB):                     8.32e-93\n",
            "Kurtosis:                       4.637   Cond. No.                     1.79e+05\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 1.79e+05. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        },
        {
          "output_type": "display_data",
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              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 96;\n",
              "                var nbb_unformatted_code = \"oslmodel = sm.OLS(y_train, X_train).fit()\\nprint(oslmodel.summary())\";\n",
              "                var nbb_formatted_code = \"oslmodel = sm.OLS(y_train, X_train).fit()\\nprint(oslmodel.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",
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              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "oslmodel = sm.OLS(y_train, X_train).fit()\n",
        "print(oslmodel.summary())"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Interpreting the Regression Results:\n",
        "\n",
        "1. **Adjusted R-squared**: It reflectsthe fit of the model.\n",
        "  * Adjusted R-squared values generally range from 0 to 1, where a higher value generally indicates a better fit, assuming certain conditions are met.\n",
        "  * In our case, the value for adj. R-squared is **0.842**, which is good.\n",
        "\n",
        "2. ***`const`* coefficient**: It is the Y-intercept.\n",
        "  * It means that if all the predictor variable coefficients are zero, then the expected output (i.e., Y) would be equal to the const coefficient.\n",
        "  * In our case, the value for const coefficient is **1.3211**\n",
        "\n",
        "3. **Coefficient of a predictor variable**: It represents the change in the output Y due to a change in the predictor variable (everything else held constant).\n",
        "  * In our case, the coefficient of `screen_size` is **0.0238**.\n",
        "  * We cannot interpret the coefficients of predictors until check linear regression assumptions, as for example, multicollinearity can cause problems when we interpret the model's results.\n",
        "\n",
        "4. **$P>|t|$**\n",
        "  * Some of the variables in the model have p-value > 0.05. So, they are not significant and we'll drop them on the next steps."
      ],
      "metadata": {
        "id": "HMEjK0kuJ5_r"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jvoU3F6oMKti"
      },
      "source": [
        "# Model Performance Check"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's check the performance of the model using different metrics.\n",
        "\n",
        "* We will be using metric functions defined in sklearn for RMSE, MAE, and $R^2$\n",
        "* We will define a function to calculate MAPE and $R^2_{adj}$\n",
        "  * The mean absolute percentage error (MAPE) measures the accuracy of predictions as a percentage, and can be calculated as the average absolute percent error for each predicted value minus actual values divided by actual values. It works best if there are no extreme values in the data and none of the actual values are 0.\n",
        "  * We have created a function which print out all the above metrics in one go"
      ],
      "metadata": {
        "id": "EXMYVbw0LwIF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# checking model performance on train set (seen 70% data)\n",
        "print(\"Training Performance\\n\")\n",
        "model_train_perf = model_performance_regression(oslmodel, X_train, y_train)\n",
        "model_train_perf"
      ],
      "metadata": {
        "id": "N1HPiNTJQHwg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
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      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Performance\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       RMSE       MAE  R-squared  Adj. R-squared      MAPE\n",
              "0  0.229852  0.180275   0.844929        0.841652  4.325608"
            ],
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              "\n",
              "  <div id=\"df-7b62300c-e989-4c14-8946-eb3716a001b9\">\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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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\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>RMSE</th>\n",
              "      <th>MAE</th>\n",
              "      <th>R-squared</th>\n",
              "      <th>Adj. R-squared</th>\n",
              "      <th>MAPE</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.229852</td>\n",
              "      <td>0.180275</td>\n",
              "      <td>0.844929</td>\n",
              "      <td>0.841652</td>\n",
              "      <td>4.325608</td>\n",
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              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7b62300c-e989-4c14-8946-eb3716a001b9')\"\n",
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              "  </svg>\n",
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              "      \n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-7b62300c-e989-4c14-8946-eb3716a001b9 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-7b62300c-e989-4c14-8946-eb3716a001b9');\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
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        {
          "output_type": "display_data",
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            "application/javascript": [
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              "            setTimeout(function() {\n",
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              "                var nbb_unformatted_code = \"# checking model performance on train set (seen 70% data)\\nprint(\\\"Training Performance\\\\n\\\")\\nmodel_train_perf = model_performance_regression(oslmodel, X_train, y_train)\\nmodel_train_perf\";\n",
              "                var nbb_formatted_code = \"# checking model performance on train set (seen 70% data)\\nprint(\\\"Training Performance\\\\n\\\")\\nmodel_train_perf = model_performance_regression(oslmodel, X_train, y_train)\\nmodel_train_perf\";\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",
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              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking model performance on test set\n",
        "print(\"Testing Performance\\n\")\n",
        "model_test_perf = model_performance_regression(oslmodel, X_test, y_test)\n",
        "model_test_perf"
      ],
      "metadata": {
        "id": "yYPHOz-_QOKN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
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      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing Performance\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       RMSE       MAE  R-squared  Adj. R-squared      MAPE\n",
              "0  0.238438  0.184799   0.842374         0.83438  4.504304"
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RMSE</th>\n",
              "      <th>MAE</th>\n",
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              "      <th>Adj. R-squared</th>\n",
              "      <th>MAPE</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.238438</td>\n",
              "      <td>0.184799</td>\n",
              "      <td>0.842374</td>\n",
              "      <td>0.83438</td>\n",
              "      <td>4.504304</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\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",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      height: 32px;\n",
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              "\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",
              "    }\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-a8da1bdf-8ec8-4a37-86d2-cf3cd3579854 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-a8da1bdf-8ec8-4a37-86d2-cf3cd3579854');\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": 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 = \"# checking model performance on test set\\nprint(\\\"Testing Performance\\\\n\\\")\\nmodel_test_perf = model_performance_regression(oslmodel, X_test, y_test)\\nmodel_test_perf\";\n",
              "                var nbb_formatted_code = \"# checking model performance on test set\\nprint(\\\"Testing Performance\\\\n\\\")\\nmodel_test_perf = model_performance_regression(oslmodel, X_test, y_test)\\nmodel_test_perf\";\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",
        "* The training $R^2$ is **0.845**, so the model is not underfitting\n",
        "* The train and test RMSE and MAE are comparable, so the model is not overfitting either\n",
        "* MAE suggests that the model can predict normalized used device price within a mean error of **0.18** on the test data\n",
        "* MAPE of 4.5 on the test data means that we are able to predict within **4.5\\%** of the normalized used device price"
      ],
      "metadata": {
        "id": "h_NZ_X1GM_BX"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a9GxSQf-qH8e"
      },
      "source": [
        "# Checking Linear Regression Assumptions"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* In order to make statistical inferences from a linear regression model, it is important to ensure that the assumptions of linear regression are satisfied.\n",
        "* We will be checking the following Linear Regression assumptions:\n",
        "  1. **No Multicollinearity**\n",
        "  2. **Linearity of variables**\n",
        "  3. **Independence of error terms**\n",
        "  4. **Normality of error terms**\n",
        "  5. **No Heteroscedasticity**\n"
      ],
      "metadata": {
        "id": "UdGv3pQF50xP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**TEST FOR MULTICOLLINEARITY**"
      ],
      "metadata": {
        "id": "cebN7tgBOer4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Multicollinearity occurs when predictor variables in a regression model are correlated. This correlation is a problem because predictor variables should be independent. If the correlation between variables is high, it can cause problems when we fit the model and interpret the results. When we have multicollinearity in the linear model, the coefficients that the model suggests are unreliable.\n",
        "* There are different ways of detecting (or testing) multicollinearity. One such way is by using the Variance Inflation Factor, or VIF.\n",
        "* **Variance Inflation Factor (VIF)**: Variance inflation factors measure the inflation in the variances of the regression parameter estimates due to collinearities that exist among the predictors. It is a measure of how much the variance of the estimated regression coefficient $\\beta_k$  is \"inflated\" by the existence of correlation among the predictor variables in the model.\n",
        "  * If VIF is 1, then there is no correlation among the $k$th predictor and the remaining predictor variables, and hence, the variance of $\\beta_k$ is not inflated at all.\n",
        "\n",
        "* **General Rule of thumb**:\n",
        "  * If VIF is between 1 and 5, then there is low multicollinearity.\n",
        "  * If VIF is between 5 and 10, we say there is moderate multicollinearity.\n",
        "  * If VIF is exceeding 10, it shows signs of high multicollinearity.\n",
        "\n",
        "The function to check VIF was defined above"
      ],
      "metadata": {
        "id": "apTRNJopOh4i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Variance Inflation Factor**"
      ],
      "metadata": {
        "id": "udTosfuoka-7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Outputting the VIF value for top-10 of all the variables\n",
        "checking_vif(X_train).sort_values(by='VIF', ascending=False).head(10)"
      ],
      "metadata": {
        "id": "UQFrGNrNQdqp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "f6735d00-4c84-459c-c3ae-cff15cc647bb"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                feature         VIF\n",
              "0                 const  242.768994\n",
              "12     brand_name_Apple   13.125023\n",
              "46               os_iOS   11.846791\n",
              "1           screen_size    9.910225\n",
              "34    brand_name_Others    9.735730\n",
              "37   brand_name_Samsung    7.545436\n",
              "7                weight    6.675499\n",
              "21    brand_name_Huawei    5.986725\n",
              "10  years_since_release    5.006897\n",
              "24        brand_name_LG    4.863838"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>feature</th>\n",
              "      <th>VIF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <th>12</th>\n",
              "      <td>brand_name_Apple</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>os_iOS</td>\n",
              "      <td>11.846791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>screen_size</td>\n",
              "      <td>9.910225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>brand_name_Others</td>\n",
              "      <td>9.735730</td>\n",
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              "    <tr>\n",
              "      <th>37</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
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              "      <td>6.675499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>brand_name_Huawei</td>\n",
              "      <td>5.986725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>years_since_release</td>\n",
              "      <td>5.006897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>brand_name_LG</td>\n",
              "      <td>4.863838</td>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-46c33896-59b3-4678-ac34-02c84324c57f');\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": 99
        },
        {
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              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 99;\n",
              "                var nbb_unformatted_code = \"# Outputting the VIF value for top-10 of all the variables\\nchecking_vif(X_train).sort_values(by='VIF', ascending=False).head(10)\";\n",
              "                var nbb_formatted_code = \"# Outputting the VIF value for top-10 of all the variables\\nchecking_vif(X_train).sort_values(by=\\\"VIF\\\", ascending=False).head(10)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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              "            "
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    {
      "cell_type": "markdown",
      "source": [
        "* There are multiple columns with very high VIF values, indicating presence  of strong multicollinearity\n",
        "* We will systematically drop numerical columns with VIF > 5\n",
        "* We will ignore the VIF values for dummy variables and the constant (intercept)"
      ],
      "metadata": {
        "id": "IoVFxxXsQzhc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Outputting the columns with VIF value is 5 and higher, except the dummy vars and the constant\n",
        "vif = checking_vif(X_train)\n",
        "vif = vif[~vif.feature.str.startswith(tuple(dummies+['const']))]\n",
        "vif = vif[vif['VIF']>5]\n",
        "print(\"VIF:\")\n",
        "vif.sort_values(by='VIF', ascending=False)"
      ],
      "metadata": {
        "id": "lS1AhETcQmqm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "outputId": "497031a0-0b89-4de2-ef21-5a561d181b93"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "VIF:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                feature       VIF\n",
              "1           screen_size  9.910225\n",
              "7                weight  6.675499\n",
              "10  years_since_release  5.006897"
            ],
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              "      <th></th>\n",
              "      <th>feature</th>\n",
              "      <th>VIF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>screen_size</td>\n",
              "      <td>9.910225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>weight</td>\n",
              "      <td>6.675499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>years_since_release</td>\n",
              "      <td>5.006897</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cbf7a14d-4f1f-4af2-923a-05ba10b0a594 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cbf7a14d-4f1f-4af2-923a-05ba10b0a594');\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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              "  "
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          },
          "metadata": {},
          "execution_count": 100
        },
        {
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          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 100;\n",
              "                var nbb_unformatted_code = \"# Outputting the columns with VIF value is 5 and higher, except the dummy vars and the constant\\nvif = checking_vif(X_train)\\nvif = vif[~vif.feature.str.startswith(tuple(dummies+['const']))]\\nvif = vif[vif['VIF']>5]\\nprint(\\\"VIF:\\\")\\nvif.sort_values(by='VIF', ascending=False)\";\n",
              "                var nbb_formatted_code = \"# Outputting the columns with VIF value is 5 and higher, except the dummy vars and the constant\\nvif = checking_vif(X_train)\\nvif = vif[~vif.feature.str.startswith(tuple(dummies + [\\\"const\\\"]))]\\nvif = vif[vif[\\\"VIF\\\"] > 5]\\nprint(\\\"VIF:\\\")\\nvif.sort_values(by=\\\"VIF\\\", ascending=False)\";\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 three variables with high VIF. We need to choose one of them to drop from the dataset"
      ],
      "metadata": {
        "id": "DMs2aDiCYa-B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Removing Multicollinearity**"
      ],
      "metadata": {
        "id": "u-qJTZhGRcyp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To remove multicollinearity\n",
        "  1. Drop every column one by one that has a VIF score greater than 5.\n",
        "  2. Look at the adjusted R-squared and RMSE of all these models.\n",
        "  3. Drop the variable that makes the least change in adjusted R-squared.\n",
        "  4. Check the VIF scores again.\n",
        "  5. Continue till you get all VIF scores under 5.\n",
        "\n",
        "We've defined a function that will help us do this."
      ],
      "metadata": {
        "id": "yHGWLZYBkiKt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Columns with hight VIF\n",
        "treat = treating_multicollinearity(X_train, y_train, vif.feature)\n",
        "treat"
      ],
      "metadata": {
        "id": "wpOcBmNNQmxc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "673585fc-4255-471f-c05d-464e7e063bf4"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   col  R-squared  Adj. R-squared after_dropping col  \\\n",
              "0  years_since_release   0.843175                           0.839996   \n",
              "1          screen_size   0.842443                           0.839249   \n",
              "2               weight   0.841467                           0.838253   \n",
              "\n",
              "   RMSE after dropping col  \n",
              "0                 0.233527  \n",
              "1                 0.234072  \n",
              "2                 0.234796  "
            ],
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              "      <th>col</th>\n",
              "      <th>R-squared</th>\n",
              "      <th>Adj. R-squared after_dropping col</th>\n",
              "      <th>RMSE after dropping col</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>years_since_release</td>\n",
              "      <td>0.843175</td>\n",
              "      <td>0.839996</td>\n",
              "      <td>0.233527</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>screen_size</td>\n",
              "      <td>0.842443</td>\n",
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              "      <td>0.234072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>weight</td>\n",
              "      <td>0.841467</td>\n",
              "      <td>0.838253</td>\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 101;\n",
              "                var nbb_unformatted_code = \"# Columns with hight VIF\\ntreat = treating_multicollinearity(X_train, y_train, vif.feature)\\ntreat\";\n",
              "                var nbb_formatted_code = \"# Columns with hight VIF\\ntreat = treating_multicollinearity(X_train, y_train, vif.feature)\\ntreat\";\n",
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    {
      "cell_type": "markdown",
      "source": [
        "* The `screen_size` and `years_since_release` make the least change in adjusted R-squared\n",
        "* However, the `screen_size` has higher VIF\n",
        "* We will drop the `screen_size` and check the VIF scores again."
      ],
      "metadata": {
        "id": "3suI_1SnSwOc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Iteration #2**"
      ],
      "metadata": {
        "id": "KQUid_WpXtAf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop `screen_size` from train and test\n",
        "X_train2 = X_train.drop('screen_size', axis=1)\n",
        "X_test2 = X_test.drop('screen_size', axis=1)\n",
        "\n",
        "# Check VIF now\n",
        "vif = checking_vif(X_train2)\n",
        "vif = vif[~vif.feature.str.startswith(tuple(dummies+['const']))]\n",
        "vif = vif[vif['VIF']>5]\n",
        "print(\"VIF after dropping \", treat['col'][0])\n",
        "vif.sort_values(by='VIF', ascending=False)\n",
        "\n"
      ],
      "metadata": {
        "id": "GlCz2HIzQm0w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "5e5914e8-5df4-4fa7-d132-754825e107b5"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "VIF after dropping  years_since_release\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [feature, VIF]\n",
              "Index: []"
            ],
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              "      <th>feature</th>\n",
              "      <th>VIF</th>\n",
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              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-9241d2ae-30bd-4614-babc-f9ff926d43b4 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-9241d2ae-30bd-4614-babc-f9ff926d43b4');\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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              "  "
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          "metadata": {},
          "execution_count": 102
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 102;\n",
              "                var nbb_unformatted_code = \"# Drop `screen_size` from train and test\\nX_train2 = X_train.drop('screen_size', axis=1)\\nX_test2 = X_test.drop('screen_size', axis=1)\\n\\n# Check VIF now\\nvif = checking_vif(X_train2)\\nvif = vif[~vif.feature.str.startswith(tuple(dummies+['const']))]\\nvif = vif[vif['VIF']>5]\\nprint(\\\"VIF after dropping \\\", treat['col'][0])\\nvif.sort_values(by='VIF', ascending=False)\";\n",
              "                var nbb_formatted_code = \"# Drop `screen_size` from train and test\\nX_train2 = X_train.drop(\\\"screen_size\\\", axis=1)\\nX_test2 = X_test.drop(\\\"screen_size\\\", axis=1)\\n\\n# Check VIF now\\nvif = checking_vif(X_train2)\\nvif = vif[~vif.feature.str.startswith(tuple(dummies + [\\\"const\\\"]))]\\nvif = vif[vif[\\\"VIF\\\"] > 5]\\nprint(\\\"VIF after dropping \\\", treat[\\\"col\\\"][0])\\nvif.sort_values(by=\\\"VIF\\\", ascending=False)\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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              "            "
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          "metadata": {}
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    {
      "cell_type": "markdown",
      "source": [
        "* We have dealt with multicollinearityin the data\n",
        "* Let's rebuild the model using yje updated set of predictors variables"
      ],
      "metadata": {
        "id": "43rsJqLKX3UL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "olsmod2 = sm.OLS(y_train, X_train2).fit()\n",
        "print(olsmod2.summary())"
      ],
      "metadata": {
        "id": "AMksJckZQm4I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3c6b0240-061a-4bd7-c0c3-eb52513c116a"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                              OLS Regression Results                             \n",
            "=================================================================================\n",
            "Dep. Variable:     normalized_used_price   R-squared:                       0.842\n",
            "Model:                               OLS   Adj. R-squared:                  0.839\n",
            "Method:                    Least Squares   F-statistic:                     263.8\n",
            "Date:                   Fri, 24 Feb 2023   Prob (F-statistic):               0.00\n",
            "Time:                           06:08:14   Log-Likelihood:                 104.97\n",
            "No. Observations:                   2417   AIC:                            -111.9\n",
            "Df Residuals:                       2368   BIC:                             171.8\n",
            "Df Model:                             48                                         \n",
            "Covariance Type:               nonrobust                                         \n",
            "=========================================================================================\n",
            "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-----------------------------------------------------------------------------------------\n",
            "const                     1.5030      0.068     22.117      0.000       1.370       1.636\n",
            "main_camera_mp            0.0224      0.002     14.457      0.000       0.019       0.025\n",
            "selfie_camera_mp          0.0141      0.001     12.433      0.000       0.012       0.016\n",
            "int_memory             9.989e-05   7.02e-05      1.422      0.155   -3.79e-05       0.000\n",
            "ram                       0.0244      0.005      4.707      0.000       0.014       0.035\n",
            "battery               -7.634e-06   7.17e-06     -1.064      0.287   -2.17e-05    6.43e-06\n",
            "weight                    0.0014      0.000     12.352      0.000       0.001       0.002\n",
            "days_used              2.608e-05    3.1e-05      0.840      0.401   -3.48e-05    8.69e-05\n",
            "normalized_new_price      0.4417      0.012     35.968      0.000       0.418       0.466\n",
            "years_since_release      -0.0304      0.005     -6.732      0.000      -0.039      -0.022\n",
            "brand_name_Alcatel        0.0200      0.048      0.417      0.676      -0.074       0.114\n",
            "brand_name_Apple          0.0843      0.148      0.569      0.569      -0.206       0.374\n",
            "brand_name_Asus           0.0079      0.048      0.165      0.869      -0.087       0.103\n",
            "brand_name_BlackBerry    -0.0292      0.071     -0.413      0.680      -0.168       0.110\n",
            "brand_name_Celkon        -0.0351      0.067     -0.524      0.600      -0.166       0.096\n",
            "brand_name_Coolpad        0.0247      0.074      0.336      0.737      -0.119       0.169\n",
            "brand_name_Gionee         0.0334      0.058      0.574      0.566      -0.081       0.148\n",
            "brand_name_Google        -0.0458      0.085     -0.538      0.591      -0.213       0.121\n",
            "brand_name_HTC           -0.0213      0.049     -0.439      0.661      -0.117       0.074\n",
            "brand_name_Honor          0.0400      0.050      0.806      0.420      -0.057       0.137\n",
            "brand_name_Huawei        -0.0043      0.045     -0.095      0.924      -0.092       0.084\n",
            "brand_name_Infinix        0.1533      0.094      1.633      0.103      -0.031       0.337\n",
            "brand_name_Karbonn        0.1060      0.068      1.567      0.117      -0.027       0.239\n",
            "brand_name_LG            -0.0200      0.046     -0.437      0.662      -0.110       0.070\n",
            "brand_name_Lava           0.0429      0.063      0.681      0.496      -0.081       0.166\n",
            "brand_name_Lenovo         0.0415      0.046      0.911      0.362      -0.048       0.131\n",
            "brand_name_Meizu         -0.0192      0.057     -0.341      0.733      -0.130       0.092\n",
            "brand_name_Micromax      -0.0349      0.048     -0.723      0.470      -0.130       0.060\n",
            "brand_name_Microsoft      0.0907      0.089      1.018      0.309      -0.084       0.265\n",
            "brand_name_Motorola      -0.0206      0.050     -0.412      0.681      -0.119       0.078\n",
            "brand_name_Nokia          0.0540      0.052      1.036      0.300      -0.048       0.156\n",
            "brand_name_OnePlus        0.0806      0.078      1.032      0.302      -0.073       0.234\n",
            "brand_name_Oppo           0.0115      0.048      0.238      0.812      -0.083       0.106\n",
            "brand_name_Others        -0.0189      0.042     -0.445      0.656      -0.102       0.064\n",
            "brand_name_Panasonic      0.0602      0.056      1.069      0.285      -0.050       0.171\n",
            "brand_name_Realme         0.0351      0.062      0.563      0.573      -0.087       0.157\n",
            "brand_name_Samsung       -0.0385      0.044     -0.884      0.377      -0.124       0.047\n",
            "brand_name_Sony          -0.0706      0.051     -1.388      0.165      -0.170       0.029\n",
            "brand_name_Spice         -0.0223      0.064     -0.350      0.727      -0.147       0.103\n",
            "brand_name_Vivo          -0.0088      0.049     -0.180      0.857      -0.105       0.087\n",
            "brand_name_XOLO           0.0209      0.055      0.377      0.706      -0.088       0.129\n",
            "brand_name_Xiaomi         0.0845      0.049      1.740      0.082      -0.011       0.180\n",
            "brand_name_ZTE           -0.0105      0.048     -0.219      0.827      -0.104       0.083\n",
            "os_Others                -0.1319      0.031     -4.269      0.000      -0.192      -0.071\n",
            "os_Windows               -0.0138      0.046     -0.303      0.762      -0.103       0.075\n",
            "os_iOS                   -0.1773      0.147     -1.206      0.228      -0.465       0.111\n",
            "4g_yes                    0.0519      0.016      3.243      0.001       0.021       0.083\n",
            "5g_yes                   -0.0878      0.032     -2.768      0.006      -0.150      -0.026\n",
            "type_tablet               0.1008      0.028      3.623      0.000       0.046       0.155\n",
            "==============================================================================\n",
            "Omnibus:                      237.036   Durbin-Watson:                   1.911\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              451.703\n",
            "Skew:                          -0.647   Prob(JB):                     8.20e-99\n",
            "Kurtosis:                       4.676   Cond. No.                     1.79e+05\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 1.79e+05. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 103;\n",
              "                var nbb_unformatted_code = \"olsmod2 = sm.OLS(y_train, X_train2).fit()\\nprint(olsmod2.summary())\";\n",
              "                var nbb_formatted_code = \"olsmod2 = sm.OLS(y_train, X_train2).fit()\\nprint(olsmod2.summary())\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
              "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
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              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Interpreting the Regression Results:**\n",
        "  1. **`std err`**: It reflects the level of accuracy of the coefficients.\n",
        "    * The lower it is, the higher is the level of accuracy.\n",
        "\n",
        "  2. $P>|t|$: It is p-value.\n",
        "    * For each independent feature, there is a null hypothesis and an alternate hypothesis. Here $\\beta_i$ is the coefficient of the $i$th independent variable.\n",
        "      * $H_0$: Independent feature is not significant ($\\beta_i=0$)\n",
        "      * $H_a$: Independent feature is that it is significant ($\\beta_i\\neq0$)\n",
        "    * $P>|t|$ gives the p-value for each independent feature to check that null hypothesis. We are considering 5% as significance level.\n",
        "      * A p-value of less than 0.05 is considered to be statistically significant.\n",
        "    \n",
        "  3. **Confidence Interval**: It represents the range in which our coefficients are likely to fall (with a likelihood of 95%)."
      ],
      "metadata": {
        "id": "QSOv3-HzYppv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**\n",
        "\n",
        "* We can see that $R^2_{adj}$ has dropped from **0.842** to **0.839**\n",
        "  * That shows that the dropped column `screen_size` did not have much effect on the model\n",
        "* As there is no multicollinearity, we can look at the p-values of predictor variables to check their significance"
      ],
      "metadata": {
        "id": "-xv0LWsnbzP4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Dealing with high p-value variables**\n",
        "\n",
        "* Some of the dummy variables in the data have p-value > 0.05. So, they are not significant and we'll drop them\n",
        "* But sometimes p-values change after dropping a variable. So, we'll not drop all variables at once\n",
        "* Instead, we will do the following:\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": "J8D-BCptbzYG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# initial list of columns\n",
        "predictors = X_train2.copy()\n",
        "selected_features = predictors.columns.tolist()\n",
        "max_p_value = 1\n",
        "\n",
        "while len(selected_features) > 0:\n",
        "    model = sm.OLS(y_train, predictors[selected_features]).fit()\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",
        "print('\\nSelected features are: ')\n",
        "print(pd.Series(selected_features))"
      ],
      "metadata": {
        "id": "LVLoKpbTSMrN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 967
        },
        "outputId": "5716af38-6b1f-4d92-ef52-f5b2cd315592"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column `brand_name_Huawei` has been removed\n",
            "Column `brand_name_Vivo` has been removed\n",
            "Column `brand_name_ZTE` has been removed\n",
            "Column `os_Windows` has been removed\n",
            "Column `brand_name_Spice` has been removed\n",
            "Column `brand_name_Meizu` has been removed\n",
            "Column `brand_name_BlackBerry` has been removed\n",
            "Column `brand_name_Motorola` has been removed\n",
            "Column `brand_name_HTC` has been removed\n",
            "Column `brand_name_LG` has been removed\n",
            "Column `brand_name_Others` has been removed\n",
            "Column `brand_name_Celkon` has been removed\n",
            "Column `brand_name_Google` has been removed\n",
            "Column `brand_name_Micromax` has been removed\n",
            "Column `brand_name_Coolpad` has been removed\n",
            "Column `brand_name_Apple` has been removed\n",
            "Column `brand_name_Asus` has been removed\n",
            "Column `days_used` has been removed\n",
            "Column `brand_name_XOLO` has been removed\n",
            "Column `brand_name_Oppo` has been removed\n",
            "Column `brand_name_Realme` has been removed\n",
            "Column `battery` has been removed\n",
            "Column `brand_name_Gionee` has been removed\n",
            "Column `brand_name_Lava` has been removed\n",
            "Column `brand_name_Alcatel` has been removed\n",
            "Column `int_memory` has been removed\n",
            "Column `brand_name_OnePlus` has been removed\n",
            "Column `brand_name_Microsoft` has been removed\n",
            "Column `brand_name_Honor` has been removed\n",
            "Column `brand_name_Infinix` has been removed\n",
            "Column `brand_name_Panasonic` has been removed\n",
            "Column `brand_name_Nokia` has been removed\n",
            "Column `brand_name_Lenovo` has been removed\n",
            "\n",
            "Selected features are: \n",
            "0                    const\n",
            "1           main_camera_mp\n",
            "2         selfie_camera_mp\n",
            "3                      ram\n",
            "4                   weight\n",
            "5     normalized_new_price\n",
            "6      years_since_release\n",
            "7       brand_name_Karbonn\n",
            "8       brand_name_Samsung\n",
            "9          brand_name_Sony\n",
            "10       brand_name_Xiaomi\n",
            "11               os_Others\n",
            "12                  os_iOS\n",
            "13                  4g_yes\n",
            "14                  5g_yes\n",
            "15             type_tablet\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 = 104;\n",
              "                var nbb_unformatted_code = \"# initial list of columns\\npredictors = X_train2.copy()\\nselected_features = predictors.columns.tolist()\\nmax_p_value = 1\\n\\nwhile len(selected_features) > 0:\\n    model = sm.OLS(y_train, predictors[selected_features]).fit()\\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\\nprint('\\\\nSelected features are: ')\\nprint(pd.Series(selected_features))\";\n",
              "                var nbb_formatted_code = \"# initial list of columns\\npredictors = X_train2.copy()\\nselected_features = predictors.columns.tolist()\\nmax_p_value = 1\\n\\nwhile len(selected_features) > 0:\\n    model = sm.OLS(y_train, predictors[selected_features]).fit()\\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\\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",
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              "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
              "                        }\n",
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              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Iteration #3**"
      ],
      "metadata": {
        "id": "nXLTnWbEelTU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train3 = X_train2[selected_features]\n",
        "X_test3 = X_test2[selected_features]"
      ],
      "metadata": {
        "id": "6XaHN0qrSUso",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "da4cb795-de2a-468d-e36a-3bccfef25449"
      },
      "execution_count": 105,
      "outputs": [
        {
          "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 = \"X_train3 = X_train2[selected_features]\\nX_test3 = X_test2[selected_features]\";\n",
              "                var nbb_formatted_code = \"X_train3 = X_train2[selected_features]\\nX_test3 = X_test2[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": [
        "olsmod3 = sm.OLS(y_train, X_train3).fit()\n",
        "print(olsmod3.summary())"
      ],
      "metadata": {
        "id": "33T5mVPHSYUi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 766
        },
        "outputId": "c0d95037-831d-45eb-de48-c73185b8db3f"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                              OLS Regression Results                             \n",
            "=================================================================================\n",
            "Dep. Variable:     normalized_used_price   R-squared:                       0.840\n",
            "Model:                               OLS   Adj. R-squared:                  0.839\n",
            "Method:                    Least Squares   F-statistic:                     842.4\n",
            "Date:                   Fri, 24 Feb 2023   Prob (F-statistic):               0.00\n",
            "Time:                           06:08:15   Log-Likelihood:                 88.791\n",
            "No. Observations:                   2417   AIC:                            -145.6\n",
            "Df Residuals:                       2401   BIC:                            -52.94\n",
            "Df Model:                             15                                         \n",
            "Covariance Type:               nonrobust                                         \n",
            "========================================================================================\n",
            "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
            "----------------------------------------------------------------------------------------\n",
            "const                    1.5265      0.049     31.288      0.000       1.431       1.622\n",
            "main_camera_mp           0.0221      0.001     15.251      0.000       0.019       0.025\n",
            "selfie_camera_mp         0.0142      0.001     13.191      0.000       0.012       0.016\n",
            "ram                      0.0223      0.005      4.484      0.000       0.013       0.032\n",
            "weight                   0.0014   9.89e-05     13.672      0.000       0.001       0.002\n",
            "normalized_new_price     0.4413      0.011     39.442      0.000       0.419       0.463\n",
            "years_since_release     -0.0294      0.003     -8.673      0.000      -0.036      -0.023\n",
            "brand_name_Karbonn       0.1118      0.055      2.048      0.041       0.005       0.219\n",
            "brand_name_Samsung      -0.0404      0.016     -2.458      0.014      -0.073      -0.008\n",
            "brand_name_Sony         -0.0697      0.030     -2.294      0.022      -0.129      -0.010\n",
            "brand_name_Xiaomi        0.0801      0.026      3.125      0.002       0.030       0.130\n",
            "os_Others               -0.1238      0.027     -4.553      0.000      -0.177      -0.070\n",
            "os_iOS                  -0.0915      0.045     -2.034      0.042      -0.180      -0.003\n",
            "4g_yes                   0.0497      0.015      3.300      0.001       0.020       0.079\n",
            "5g_yes                  -0.0726      0.031     -2.375      0.018      -0.133      -0.013\n",
            "type_tablet              0.1060      0.027      3.929      0.000       0.053       0.159\n",
            "==============================================================================\n",
            "Omnibus:                      247.012   Durbin-Watson:                   1.906\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              491.301\n",
            "Skew:                          -0.656   Prob(JB):                    2.07e-107\n",
            "Kurtosis:                       4.777   Cond. No.                     2.40e+03\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 2.4e+03. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        },
        {
          "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 = \"olsmod3 = sm.OLS(y_train, X_train3).fit()\\nprint(olsmod3.summary())\";\n",
              "                var nbb_formatted_code = \"olsmod3 = sm.OLS(y_train, X_train3).fit()\\nprint(olsmod3.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",
              "            "
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking model performance on train set (seen 70% data)\n",
        "print(\"Training Performance\\n\")\n",
        "mod3_train_perf = model_performance_regression(olsmod3, X_train3, y_train)\n",
        "mod3_train_perf"
      ],
      "metadata": {
        "id": "dgyw5XBySYdD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "outputId": "57d9be4d-0ecf-426c-d2ac-bc3899a5fe6b"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Performance\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       RMSE       MAE  R-squared  Adj. R-squared      MAPE\n",
              "0  0.233243  0.182023    0.84032        0.839255  4.375982"
            ],
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              "      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-a9ff954b-a2b3-450a-98c4-12cd352841dc 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-a9ff954b-a2b3-450a-98c4-12cd352841dc');\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": 107
        },
        {
          "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 = \"# checking model performance on train set (seen 70% data)\\nprint(\\\"Training Performance\\\\n\\\")\\nmod3_train_perf = model_performance_regression(olsmod3, X_train3, y_train)\\nmod3_train_perf\";\n",
              "                var nbb_formatted_code = \"# checking model performance on train set (seen 70% data)\\nprint(\\\"Training Performance\\\\n\\\")\\nmod3_train_perf = model_performance_regression(olsmod3, X_train3, y_train)\\nmod3_train_perf\";\n",
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              "                        }\n",
              "                        break;\n",
              "                    }\n",
              "                }\n",
              "            }, 500);\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking model performance on test set (seen 30% data)\n",
        "print(\"Test Performance\\n\")\n",
        "mod3_test_perf = model_performance_regression(olsmod3, X_test3, y_test)\n",
        "mod3_test_perf"
      ],
      "metadata": {
        "id": "iwpT7MFxSYgd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "outputId": "2a1203fc-f0c7-4e31-9b17-32dac7c428f2"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Performance\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       RMSE       MAE  R-squared  Adj. R-squared      MAPE\n",
              "0  0.239841  0.184879   0.840512         0.83801  4.516599"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-64c3533f-79fc-46ee-af5a-45fa162516fe\">\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>RMSE</th>\n",
              "      <th>MAE</th>\n",
              "      <th>R-squared</th>\n",
              "      <th>Adj. R-squared</th>\n",
              "      <th>MAPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.239841</td>\n",
              "      <td>0.184879</td>\n",
              "      <td>0.840512</td>\n",
              "      <td>0.83801</td>\n",
              "      <td>4.516599</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-64c3533f-79fc-46ee-af5a-45fa162516fe')\"\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",
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              "      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-64c3533f-79fc-46ee-af5a-45fa162516fe 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-64c3533f-79fc-46ee-af5a-45fa162516fe');\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": 108
        },
        {
          "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 = \"# checking model performance on test set (seen 30% data)\\nprint(\\\"Test Performance\\\\n\\\")\\nmod3_test_perf = model_performance_regression(olsmod3, X_test3, y_test)\\nmod3_test_perf\";\n",
              "                var nbb_formatted_code = \"# checking model performance on test set (seen 30% data)\\nprint(\\\"Test Performance\\\\n\\\")\\nmod3_test_perf = model_performance_regression(olsmod3, X_test3, y_test)\\nmod3_test_perf\";\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": [
        "###**Observations**\n"
      ],
      "metadata": {
        "id": "hZ6hE3Lnf9Vv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Now no feature has p-value greater than 0.05, so we'll consider the features in `x_train3` as the final set of predictor variables and `olsmod3` as the final model to move forward with\n",
        "* Now adjusted R-squared is **0.840**, i.e., our model is able to explain ~**84%** of the variance\n",
        "* The adjusted R-squared in `olsmod2` (where we considered the variables without multicollinearity) was **0.842**\n",
        "* This shows that the variable we dropped was not affecting the model\n",
        "RMSE and MAE values are comparable for train and test sets, indicating that the model is not overfitting\n",
        "* **There is no multicollinearity in the dataset**\n",
        "\n",
        "**Now we'll check the rest of the assumptions on olsmod3**\n",
        "1. Linearity of variables\n",
        "2. Independence of error terms\n",
        "3. Normality of error terms\n",
        "4. No Heteroscedasticity\n"
      ],
      "metadata": {
        "id": "6lH_3GTtkttk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**TEST FOR LINEARITY AND INDEPENDENCE**"
      ],
      "metadata": {
        "id": "lUlkWriDf_5B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "**Why the test?**\n",
        "* Linearity describes a straight-line relationship between two variables, predictor variables must have a linear relation with the dependent variable.\n",
        "* The independence of the error terms (or residuals) is important. If the residuals are not independent, then the confidence intervals of the coefficient estimates will be narrower and make us incorrectly conclude a parameter to be statistically significant.\n",
        "\n",
        "**How to check linearity and independence?**\n",
        "* Make a plot of fitted values vs residuals.\n",
        "* If they don't follow any pattern, then we say the model is linear and residuals are independent.\n",
        "* Otherwise, the model is showing signs of non-linearity and residuals are not independent.\n",
        "\n",
        "**How to fix if this assumption is not followed?**\n",
        "* We can try to transform the variables and make the relationships linear."
      ],
      "metadata": {
        "id": "J3N6dmKUolzq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Residuals**"
      ],
      "metadata": {
        "id": "il8KP5fulTX_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# let us create a dataframe with actual, fitted and residual values\n",
        "df_pred = pd.DataFrame()\n",
        "\n",
        "df_pred[\"Actual Values\"] = y_train  # actual values\n",
        "df_pred[\"Fitted Values\"] = olsmod3.fittedvalues  # predicted values\n",
        "df_pred[\"Residuals\"] = olsmod3.resid  # residuals\n",
        "\n",
        "df_pred.head()"
      ],
      "metadata": {
        "id": "zxub3qHcTdat",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "2a0e2424-0aaf-4a1c-b321-4b25cbac6d1a"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Actual Values  Fitted Values  Residuals\n",
              "3026       4.087488       3.870447   0.217040\n",
              "1525       4.448399       4.579927  -0.131527\n",
              "1128       4.315353       4.284748   0.030605\n",
              "3003       4.282068       4.197748   0.084320\n",
              "2907       4.456438       4.492847  -0.036408"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-17d4d52f-a838-4706-8a43-f47aa80854d0\">\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>Actual Values</th>\n",
              "      <th>Fitted Values</th>\n",
              "      <th>Residuals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3026</th>\n",
              "      <td>4.087488</td>\n",
              "      <td>3.870447</td>\n",
              "      <td>0.217040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1525</th>\n",
              "      <td>4.448399</td>\n",
              "      <td>4.579927</td>\n",
              "      <td>-0.131527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1128</th>\n",
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              "      <td>0.084320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2907</th>\n",
              "      <td>4.456438</td>\n",
              "      <td>4.492847</td>\n",
              "      <td>-0.036408</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <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",
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              "      \n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-17d4d52f-a838-4706-8a43-f47aa80854d0 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-17d4d52f-a838-4706-8a43-f47aa80854d0');\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
        },
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              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 109;\n",
              "                var nbb_unformatted_code = \"# let us create a dataframe with actual, fitted and residual values\\ndf_pred = pd.DataFrame()\\n\\ndf_pred[\\\"Actual Values\\\"] = y_train  # actual values\\ndf_pred[\\\"Fitted Values\\\"] = olsmod3.fittedvalues  # predicted values\\ndf_pred[\\\"Residuals\\\"] = olsmod3.resid  # residuals\\n\\ndf_pred.head()\";\n",
              "                var nbb_formatted_code = \"# let us create a dataframe with actual, fitted and residual values\\ndf_pred = pd.DataFrame()\\n\\ndf_pred[\\\"Actual Values\\\"] = y_train  # actual values\\ndf_pred[\\\"Fitted Values\\\"] = olsmod3.fittedvalues  # predicted values\\ndf_pred[\\\"Residuals\\\"] = olsmod3.resid  # residuals\\n\\ndf_pred.head()\";\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": [
        "# let's draw a scatterplot to compare Actual values vs. Fitted Values\n",
        "sns.scatterplot(x=df_pred['Actual Values'], y=df_pred['Fitted Values'])\n",
        "sns.lineplot([1,7], [1,7], color='r', linestyle='dashdot');"
      ],
      "metadata": {
        "id": "Q0qXGDNthrCc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "d3b19e5b-ca72-41c4-a489-6ebf2799f01b"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"# let's draw a scatterplot to compare Actual values vs. Fitted Values\\nsns.scatterplot(x=df_pred['Actual Values'], y=df_pred['Fitted Values'])\\nsns.lineplot([1,7], [1,7], color='r', linestyle='dashdot');\";\n",
              "                var nbb_formatted_code = \"# let's draw a scatterplot to compare Actual values vs. Fitted Values\\nsns.scatterplot(x=df_pred[\\\"Actual Values\\\"], y=df_pred[\\\"Fitted Values\\\"])\\nsns.lineplot([1, 7], [1, 7], color=\\\"r\\\", linestyle=\\\"dashdot\\\")\";\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": [
        "# let's plot the fitted values vs residuals\n",
        "\n",
        "sns.residplot(data=df_pred, x=\"Fitted Values\", y=\"Residuals\", color=\"purple\", lowess=True)\n",
        "plt.xlabel(\"Fitted Values\")\n",
        "plt.ylabel(\"Residuals\")\n",
        "plt.title(\"Fitted vs Residual plot\")\n",
        "plt.show();"
      ],
      "metadata": {
        "id": "uGgYIKcbTdds",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "6339fc29-8963-48b5-a917-77cf6a0d3cf9"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"# let's plot the fitted values vs residuals\\n\\nsns.residplot(data=df_pred, x=\\\"Fitted Values\\\", y=\\\"Residuals\\\", color=\\\"purple\\\", lowess=True)\\nplt.xlabel(\\\"Fitted Values\\\")\\nplt.ylabel(\\\"Residuals\\\")\\nplt.title(\\\"Fitted vs Residual plot\\\")\\nplt.show();\";\n",
              "                var nbb_formatted_code = \"# let's plot the fitted values vs residuals\\n\\nsns.residplot(\\n    data=df_pred, x=\\\"Fitted Values\\\", y=\\\"Residuals\\\", color=\\\"purple\\\", lowess=True\\n)\\nplt.xlabel(\\\"Fitted Values\\\")\\nplt.ylabel(\\\"Residuals\\\")\\nplt.title(\\\"Fitted vs Residual plot\\\")\\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": [
        "###**Observations**"
      ],
      "metadata": {
        "id": "2l81-iBhlGlq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The scatter plot shows the distribution of residuals (errors) vs fitted values (predicted values).\n",
        "* If there exist any pattern in this plot, we consider it as signs of non-linearity in the data and a pattern means that the model doesn't capture non-linear effects.\n",
        "* **We see no pattern in the plot above. Hence, the assumptions of linearity and independence are satisfied.**"
      ],
      "metadata": {
        "id": "4yLdYjDVjD-9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **TEST FOR NORMALITY**"
      ],
      "metadata": {
        "id": "czAai3TcjQRb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Why the test?**\n",
        "* Error terms, or residuals, should be normally distributed. If the error terms are not normally distributed, confidence intervals of the coefficient estimates may become too wide or narrow. Once confidence interval becomes unstable, it leads to difficulty in estimating coefficients based on minimization of least squares. Non-normality suggests that there are a few unusual data points that must be studied closely to make a better model.\n",
        "\n",
        "**How to check normality?**\n",
        "* The shape of the histogram of residuals can give an initial idea about the normality.\n",
        "* It can also be checked via a Q-Q plot of residuals. If the residuals follow a normal distribution, they will make a straight line plot, otherwise not.\n",
        "* Other tests to check for normality includes the Shapiro-Wilk test.\n",
        "  * Null hypothesis: Residuals are normally distributed\n",
        "  * Alternate hypothesis: Residuals are not normally distributed\n",
        "\n",
        "**How to fix if this assumption is not followed?**\n",
        "* We can apply transformations like log, exponential, arcsinh, etc. as per our data."
      ],
      "metadata": {
        "id": "8WR1bC04ouKE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Normality**"
      ],
      "metadata": {
        "id": "-3bYV_fUmz0_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=df_pred, x=\"Residuals\", kde=True)\n",
        "plt.title(\"Normality of residuals\")\n",
        "plt.show();"
      ],
      "metadata": {
        "id": "U93k3sOaTdgc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "3797bfa5-29db-4984-d0a0-5f79fab4e10b"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"sns.histplot(data=df_pred, x=\\\"Residuals\\\", kde=True)\\nplt.title(\\\"Normality of residuals\\\")\\nplt.show();\";\n",
              "                var nbb_formatted_code = \"sns.histplot(data=df_pred, x=\\\"Residuals\\\", kde=True)\\nplt.title(\\\"Normality of residuals\\\")\\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 histogram of residuals does have a bell shape, left skewed.\n",
        "* Let's check the Q-Q plot."
      ],
      "metadata": {
        "id": "aJzKwqorl3Ym"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pylab\n",
        "import scipy.stats as stats\n",
        "\n",
        "stats.probplot(df_pred[\"Residuals\"], dist=\"norm\", plot=pylab)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "-mpzaNdPTdjI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "9e0165d2-495a-4a1c-b86a-7a31eafc7f20"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"import pylab\\nimport scipy.stats as stats\\n\\nstats.probplot(df_pred[\\\"Residuals\\\"], dist=\\\"norm\\\", plot=pylab)\\nplt.show()\";\n",
              "                var nbb_formatted_code = \"import pylab\\nimport scipy.stats as stats\\n\\nstats.probplot(df_pred[\\\"Residuals\\\"], dist=\\\"norm\\\", plot=pylab)\\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 residuals more or less follow a straight line except for the tails\n",
        "* Let's check the results of the Shapiro-Wilk test."
      ],
      "metadata": {
        "id": "sTSC3MocmKLf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stats.shapiro(df_pred[\"Residuals\"])"
      ],
      "metadata": {
        "id": "reBZEE3OTdmu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a3bbe84a-235b-4ad3-c1c9-b5f8fb8eda6c"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ShapiroResult(statistic=0.9675482511520386, pvalue=6.226413491682132e-23)"
            ]
          },
          "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 = \"stats.shapiro(df_pred[\\\"Residuals\\\"])\";\n",
              "                var nbb_formatted_code = \"stats.shapiro(df_pred[\\\"Residuals\\\"])\";\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**"
      ],
      "metadata": {
        "id": "pfkWfJGkmuE_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Since p-value < 0.05, the residuals are not normal as per the Shapiro-Wilk test.\n",
        "* Strictly speaking, the residuals are not normal.\n",
        "* However, as an approximation, we can accept this distribution as close to being normal.\n",
        "* **So, the assumption is satisfied.**"
      ],
      "metadata": {
        "id": "lmE_c7iwmwv5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**TEST FOR HOMOSCEDASTICITY**"
      ],
      "metadata": {
        "id": "r9Cv1knQnBN4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* **Homoscedascity**: If the variance of the residuals is symmetrically distributed across the regression line, then the data is said to be homoscedastic.\n",
        "* **Heteroscedascity**: If the variance is unequal for the residuals across the regression line, then the data is said to be heteroscedastic.\n",
        "\n",
        "**Why the test?**\n",
        "* The presence of non-constant variance in the error terms results in heteroscedasticity. Generally, non-constant variance arises in presence of outliers.\n",
        "\n",
        "**How to check for homoscedasticity?**\n",
        "* The residual vs fitted values plot can be looked at to check for homoscedasticity. In the case of heteroscedasticity, the residuals can form an arrow shape or any other non-symmetrical shape.\n",
        "* The goldfeldquandt test can also be used. If we get a p-value > 0.05 we can say that the residuals are homoscedastic. Otherwise, they are heteroscedastic.\n",
        "  * Null hypothesis: Residuals are homoscedastic\n",
        "  * Alternate hypothesis: Residuals have heteroscedasticity\n",
        "\n",
        "**How to fix if this assumption is not followed?**\n",
        "* Heteroscedasticity can be fixed by adding other important features or making transformations."
      ],
      "metadata": {
        "id": "fH3ltMm3o1NB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Homoscedascity**"
      ],
      "metadata": {
        "id": "yT_W2FuDno48"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.stats.api as sms\n",
        "from statsmodels.compat import lzip\n",
        "\n",
        "name = [\"F statistic\", \"p-value\"]\n",
        "test = sms.het_goldfeldquandt(df_pred[\"Residuals\"], X_train3)\n",
        "lzip(name, test)"
      ],
      "metadata": {
        "id": "EDXxL2MmT6D4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "04bd5cb6-c1c5-4486-aa89-b6b51cf882bd"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('F statistic', 1.0190491334733005), ('p-value', 0.3723058892893009)]"
            ]
          },
          "metadata": {},
          "execution_count": 115
        },
        {
          "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 = \"import statsmodels.stats.api as sms\\nfrom statsmodels.compat import lzip\\n\\nname = [\\\"F statistic\\\", \\\"p-value\\\"]\\ntest = sms.het_goldfeldquandt(df_pred[\\\"Residuals\\\"], X_train3)\\nlzip(name, test)\";\n",
              "                var nbb_formatted_code = \"import statsmodels.stats.api as sms\\nfrom statsmodels.compat import lzip\\n\\nname = [\\\"F statistic\\\", \\\"p-value\\\"]\\ntest = sms.het_goldfeldquandt(df_pred[\\\"Residuals\\\"], X_train3)\\nlzip(name, 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": [
        "###**Observations**"
      ],
      "metadata": {
        "id": "fJll4CDuneN0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Since p-value > 0.05, we can say that the residuals are homoscedastic.\n",
        "* **So, this assumption is satisfied.**"
      ],
      "metadata": {
        "id": "ct_mR3Qsne3E"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Predictions on test data**"
      ],
      "metadata": {
        "id": "-KWQI8ClpPYP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we have checked all the assumptions of linear regression and they are satisfied, let's go ahead with prediction."
      ],
      "metadata": {
        "id": "deRARavrpd0l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# predictions on the test set\n",
        "pred = olsmod3.predict(X_test3)\n",
        "\n",
        "df_pred_test = pd.DataFrame({\"Actual\": y_test, \"Predicted\": pred})\n",
        "df_pred_test['Diff'] = df_pred_test['Predicted']-df_pred_test['Actual']\n",
        "df_pred_test['Diff, %'] = round(100*df_pred_test['Diff']/df_pred_test['Actual'],2)\n",
        "df_pred_test.sample(10, random_state=42)"
      ],
      "metadata": {
        "id": "xsorzUSyT6Gb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "6f962fdd-df78-4066-a744-406f7ae236cb"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Actual  Predicted      Diff  Diff, %\n",
              "2360  4.597541   4.728633  0.131092     2.85\n",
              "1413  3.463546   3.774185  0.310639     8.97\n",
              "1884  3.875359   4.049946  0.174587     4.51\n",
              "1922  3.747620   3.732205 -0.015415    -0.41\n",
              "396   4.170843   3.857928 -0.312914    -7.50\n",
              "340   5.342526   5.322020 -0.020506    -0.38\n",
              "1556  3.780547   3.577012 -0.203534    -5.38\n",
              "2597  4.225227   4.103573 -0.121654    -2.88\n",
              "2341  3.696103   3.998392  0.302289     8.18\n",
              "1768  3.479700   3.714218  0.234518     6.74"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-000acfa1-b8cc-4985-a24c-3501d2060857\">\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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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "      <th>Diff</th>\n",
              "      <th>Diff, %</th>\n",
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              "      <th>2360</th>\n",
              "      <td>4.597541</td>\n",
              "      <td>4.728633</td>\n",
              "      <td>0.131092</td>\n",
              "      <td>2.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1413</th>\n",
              "      <td>3.463546</td>\n",
              "      <td>3.774185</td>\n",
              "      <td>0.310639</td>\n",
              "      <td>8.97</td>\n",
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              "    <tr>\n",
              "      <th>1884</th>\n",
              "      <td>3.875359</td>\n",
              "      <td>4.049946</td>\n",
              "      <td>0.174587</td>\n",
              "      <td>4.51</td>\n",
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              "    <tr>\n",
              "      <th>1922</th>\n",
              "      <td>3.747620</td>\n",
              "      <td>3.732205</td>\n",
              "      <td>-0.015415</td>\n",
              "      <td>-0.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>4.170843</td>\n",
              "      <td>3.857928</td>\n",
              "      <td>-0.312914</td>\n",
              "      <td>-7.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>340</th>\n",
              "      <td>5.342526</td>\n",
              "      <td>5.322020</td>\n",
              "      <td>-0.020506</td>\n",
              "      <td>-0.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1556</th>\n",
              "      <td>3.780547</td>\n",
              "      <td>3.577012</td>\n",
              "      <td>-0.203534</td>\n",
              "      <td>-5.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2597</th>\n",
              "      <td>4.225227</td>\n",
              "      <td>4.103573</td>\n",
              "      <td>-0.121654</td>\n",
              "      <td>-2.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2341</th>\n",
              "      <td>3.696103</td>\n",
              "      <td>3.998392</td>\n",
              "      <td>0.302289</td>\n",
              "      <td>8.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1768</th>\n",
              "      <td>3.479700</td>\n",
              "      <td>3.714218</td>\n",
              "      <td>0.234518</td>\n",
              "      <td>6.74</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-000acfa1-b8cc-4985-a24c-3501d2060857')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-000acfa1-b8cc-4985-a24c-3501d2060857 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-000acfa1-b8cc-4985-a24c-3501d2060857');\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 116
        },
        {
          "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 = \"# predictions on the test set\\npred = olsmod3.predict(X_test3)\\n\\ndf_pred_test = pd.DataFrame({\\\"Actual\\\": y_test, \\\"Predicted\\\": pred})\\ndf_pred_test['Diff'] = df_pred_test['Predicted']-df_pred_test['Actual']\\ndf_pred_test['Diff, %'] = round(100*df_pred_test['Diff']/df_pred_test['Actual'],2)\\ndf_pred_test.sample(10, random_state=42)\";\n",
              "                var nbb_formatted_code = \"# predictions on the test set\\npred = olsmod3.predict(X_test3)\\n\\ndf_pred_test = pd.DataFrame({\\\"Actual\\\": y_test, \\\"Predicted\\\": pred})\\ndf_pred_test[\\\"Diff\\\"] = df_pred_test[\\\"Predicted\\\"] - df_pred_test[\\\"Actual\\\"]\\ndf_pred_test[\\\"Diff, %\\\"] = round(100 * df_pred_test[\\\"Diff\\\"] / df_pred_test[\\\"Actual\\\"], 2)\\ndf_pred_test.sample(10, random_state=42)\";\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": [
        "# let's draw a scatterplot to compare Actual values vs. Predicted Values\n",
        "sns.scatterplot(x=df_pred_test['Actual'], y=df_pred_test['Predicted'])\n",
        "sns.lineplot([1,7], [1,7], color='r', linestyle='dashdot');"
      ],
      "metadata": {
        "id": "0a90HxZDd_vM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "1deff3a1-e929-49ee-8c3c-ee65368a798c"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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 = \"# let's draw a scatterplot to compare Actual values vs. Predicted Values\\nsns.scatterplot(x=df_pred_test['Actual'], y=df_pred_test['Predicted'])\\nsns.lineplot([1,7], [1,7], color='r', linestyle='dashdot');\";\n",
              "                var nbb_formatted_code = \"# let's draw a scatterplot to compare Actual values vs. Predicted Values\\nsns.scatterplot(x=df_pred_test[\\\"Actual\\\"], y=df_pred_test[\\\"Predicted\\\"])\\nsns.lineplot([1, 7], [1, 7], color=\\\"r\\\", linestyle=\\\"dashdot\\\")\";\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 observe here that our model has returned pretty good prediction results, and the actual and predicted values are comparable"
      ],
      "metadata": {
        "id": "FB3eT8FZpNeN"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jRYSDgFZMKtm"
      },
      "source": [
        "# Final Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "id": "x_Sqvs4TMKtn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "e57ba4cf-73b2-497e-b203-5cee00e55a6c"
      },
      "outputs": [
        {
          "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 = \"X_train_final = X_train3.copy()\\nX_test_final = X_test3.copy()\";\n",
              "                var nbb_formatted_code = \"X_train_final = X_train3.copy()\\nX_test_final = X_test3.copy()\";\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_train_final = X_train3.copy()\n",
        "X_test_final = X_test3.copy()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_final = sm.OLS(y_train, X_train_final).fit()\n",
        "print(model_final.summary())"
      ],
      "metadata": {
        "id": "HvAjtBiNUJHU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 766
        },
        "outputId": "d72a6037-ef86-4bc2-b909-e2ed61681693"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                              OLS Regression Results                             \n",
            "=================================================================================\n",
            "Dep. Variable:     normalized_used_price   R-squared:                       0.840\n",
            "Model:                               OLS   Adj. R-squared:                  0.839\n",
            "Method:                    Least Squares   F-statistic:                     842.4\n",
            "Date:                   Fri, 24 Feb 2023   Prob (F-statistic):               0.00\n",
            "Time:                           06:08:18   Log-Likelihood:                 88.791\n",
            "No. Observations:                   2417   AIC:                            -145.6\n",
            "Df Residuals:                       2401   BIC:                            -52.94\n",
            "Df Model:                             15                                         \n",
            "Covariance Type:               nonrobust                                         \n",
            "========================================================================================\n",
            "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
            "----------------------------------------------------------------------------------------\n",
            "const                    1.5265      0.049     31.288      0.000       1.431       1.622\n",
            "main_camera_mp           0.0221      0.001     15.251      0.000       0.019       0.025\n",
            "selfie_camera_mp         0.0142      0.001     13.191      0.000       0.012       0.016\n",
            "ram                      0.0223      0.005      4.484      0.000       0.013       0.032\n",
            "weight                   0.0014   9.89e-05     13.672      0.000       0.001       0.002\n",
            "normalized_new_price     0.4413      0.011     39.442      0.000       0.419       0.463\n",
            "years_since_release     -0.0294      0.003     -8.673      0.000      -0.036      -0.023\n",
            "brand_name_Karbonn       0.1118      0.055      2.048      0.041       0.005       0.219\n",
            "brand_name_Samsung      -0.0404      0.016     -2.458      0.014      -0.073      -0.008\n",
            "brand_name_Sony         -0.0697      0.030     -2.294      0.022      -0.129      -0.010\n",
            "brand_name_Xiaomi        0.0801      0.026      3.125      0.002       0.030       0.130\n",
            "os_Others               -0.1238      0.027     -4.553      0.000      -0.177      -0.070\n",
            "os_iOS                  -0.0915      0.045     -2.034      0.042      -0.180      -0.003\n",
            "4g_yes                   0.0497      0.015      3.300      0.001       0.020       0.079\n",
            "5g_yes                  -0.0726      0.031     -2.375      0.018      -0.133      -0.013\n",
            "type_tablet              0.1060      0.027      3.929      0.000       0.053       0.159\n",
            "==============================================================================\n",
            "Omnibus:                      247.012   Durbin-Watson:                   1.906\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              491.301\n",
            "Skew:                          -0.656   Prob(JB):                    2.07e-107\n",
            "Kurtosis:                       4.777   Cond. No.                     2.40e+03\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 2.4e+03. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 119;\n",
              "                var nbb_unformatted_code = \"model_final = sm.OLS(y_train, X_train_final).fit()\\nprint(model_final.summary())\";\n",
              "                var nbb_formatted_code = \"model_final = sm.OLS(y_train, X_train_final).fit()\\nprint(model_final.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",
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              "            "
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking model performance on train set (seen 70% data)\n",
        "print(\"Training Performance\\n\")\n",
        "model_final_train_perf = model_performance_regression(model_final, X_train_final, y_train)\n",
        "model_final_train_perf"
      ],
      "metadata": {
        "id": "dMna9usPUJJ6",
        "colab": {
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          "height": 117
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      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Performance\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       RMSE       MAE  R-squared  Adj. R-squared      MAPE\n",
              "0  0.233243  0.182023    0.84032        0.839255  4.375982"
            ],
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              "\n",
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              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RMSE</th>\n",
              "      <th>MAE</th>\n",
              "      <th>R-squared</th>\n",
              "      <th>Adj. R-squared</th>\n",
              "      <th>MAPE</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0.233243</td>\n",
              "      <td>0.182023</td>\n",
              "      <td>0.84032</td>\n",
              "      <td>0.839255</td>\n",
              "      <td>4.375982</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-60fdd8eb-07d0-455a-9e86-e92d839f8a4e')\"\n",
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              "    .colab-df-convert {\n",
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              "          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",
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              "    </div>\n",
              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 120
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
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              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 120;\n",
              "                var nbb_unformatted_code = \"# checking model performance on train set (seen 70% data)\\nprint(\\\"Training Performance\\\\n\\\")\\nmodel_final_train_perf = model_performance_regression(model_final, X_train_final, y_train)\\nmodel_final_train_perf\";\n",
              "                var nbb_formatted_code = \"# checking model performance on train set (seen 70% data)\\nprint(\\\"Training Performance\\\\n\\\")\\nmodel_final_train_perf = model_performance_regression(\\n    model_final, X_train_final, y_train\\n)\\nmodel_final_train_perf\";\n",
              "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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              "                        }\n",
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              "            }, 500);\n",
              "            "
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking model performance on test set (seen 30% data)\n",
        "print(\"Test Performance\\n\")\n",
        "model_final_test_perf = model_performance_regression(model_final, X_test_final, y_test)\n",
        "model_final_test_perf"
      ],
      "metadata": {
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          "height": 117
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      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Performance\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       RMSE       MAE  R-squared  Adj. R-squared      MAPE\n",
              "0  0.239841  0.184879   0.840512         0.83801  4.516599"
            ],
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              "      <th></th>\n",
              "      <th>RMSE</th>\n",
              "      <th>MAE</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.239841</td>\n",
              "      <td>0.184879</td>\n",
              "      <td>0.840512</td>\n",
              "      <td>0.83801</td>\n",
              "      <td>4.516599</td>\n",
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              "        \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",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "    }\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-39280485-5de0-4bc2-9db3-3a52dd8c432b 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-39280485-5de0-4bc2-9db3-3a52dd8c432b');\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",
              "  "
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          },
          "metadata": {},
          "execution_count": 121
        },
        {
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          "data": {
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            ],
            "application/javascript": [
              "\n",
              "            setTimeout(function() {\n",
              "                var nbb_cell_id = 121;\n",
              "                var nbb_unformatted_code = \"# checking model performance on test set (seen 30% data)\\nprint(\\\"Test Performance\\\\n\\\")\\nmodel_final_test_perf = model_performance_regression(model_final, X_test_final, y_test)\\nmodel_final_test_perf\";\n",
              "                var nbb_formatted_code = \"# checking model performance on test set (seen 30% data)\\nprint(\\\"Test Performance\\\\n\\\")\\nmodel_final_test_perf = model_performance_regression(model_final, X_test_final, y_test)\\nmodel_final_test_perf\";\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",
        "* The model is able to explain ~**84%** of the variation in the data\n",
        "* The train and test RMSE and MAE are low and comparable. So, our model is not suffering from overfitting\n",
        "* The MAPE on the test set suggests we can predict within **4.5%** of the normalized price of used devices\n",
        "* **Hence, we can conclude the model `olsmodel_final` is good for prediction as well as inference purposes**"
      ],
      "metadata": {
        "id": "0vrFNoUCrKjC"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2BkZh6eHluZK"
      },
      "source": [
        "# Actionable Insights and Recommendations "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. The model is able to explain ~84% of the variation in the data and within 4.5% of the normalized price of used devices on the test data, which is good. This indicates that the model is good for prediction as well as inference purposes.\n",
        "2. The most significant predictors of the used device normalized price are the normalized price of a new device of the same model, the weight of the device, 4g and 5g availability, os, brand, the resolution of the main and selfie cameras, release year , the amount of RAM, and the type of device (tablet or phone).\n",
        "3. If resolution of the `main_camera_mp` of an used device increases by one unit (MP), then its normalized price increases by 0.0221 units, all other variables held constant.\n",
        "4. If resolution of the `selfie_camera_mp` of an used device increases by one unit (MP), then its normalized price increases by 0.0142 units, all other variables held constant.\n",
        "5. If the `ram` of an used device increases by one unit (GB), then its normalized price increases by 0.0223 units, all other variables held constant.\n",
        "6. If the `weight` of an used device increases by one unit (g), then its normalized price increases by 0.0014 units, all other variables held constant.\n",
        "7. If the `normalized_new_price` of an used device increases by one unit, then its normalized price increases by 0.4413 units, all other variables held constant.\n",
        "8. If the `years_since_release` of an used device increases by one unit (year), then its normalized price decreases by 0.0294 units, all other variables held constant.\n",
        "10. The normalized price of used device with 4g support will be 0.0497 units higher than device without 4g, all other variables held constant.\n",
        "11. The normalized price of used device with 5g support will be 0.0726 units less than device without 5g, all other variables held constant$^1$.\n",
        "12. The normalized price of used tablet will be 0.1060 units higher than used phone, all other variables held constant.\n",
        "13. The normalized price of used device runs on iOS will be 0.0915 units less than device runs on Android, all other vars held constant$^1$.\n",
        "14. The normalized price of used device runs on 'Others' OS will be 0.1238 units less than device runs on Android, all other vars held const$^1$.\n",
        "15. Brands. The normalized price of used device of the following brands will be (all other variables held constant):\n",
        "  * Karbonn: 0.1118 units higher than device manufactured by Acer, \n",
        "  * Samsung: 0.0404 units less than device manufactured by Acer, \n",
        "  * Sony: 0.0697 units less than device manufactured by Acer, \n",
        "  * Xiaomi: 0.0801 units higher than device manufactured by Acer.\n",
        "\n",
        "$^1$ *iOS devices, as well as devices with 5G, cost more than others on average. That might cause a question about the negative coefficient of the regression for these variables.*\n",
        "\n",
        "*Indeed, for these devices (`iOS`, `Others` os; `5G`) prices are higher than for others, but the discounts for these devices are also higher - see sub-section 'Additional analysis' in the EDA section above. So, if a device works on `iOS`/`Others` or supports 5G, its `normalized_used_price` will be less than the price of the device that works on `Android` (or without `5G`) if all other variables are held constant.*"
      ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "**Business recommendations**\n",
        "\n",
        "* Since, ReCell wants us to predict the price of a used phone/tablet and identify factors that significantly influence it, they need to focus on phones and tablets released after 2019 and have main and selfie cameras' resolutions above average.\n",
        "\n",
        "* Weight, 4g, 5g, brand, and amount of RAM also influence the price of used devices.\n",
        "\n",
        "* It might be helpful to perform a customer segmentation analysis to gain better insights into customers' preferences and develop sales and marketing programs based on these insights.\n",
        "\n",
        "* ReCell can suggest phone/tablet repair service, sales of accessories and related products, trade-in options to attract more customers, and increases in the top and bottom lines.\n",
        "\n",
        "* ReCell can work on developing different options like rent or lease devices."
      ],
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        "___"
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