Datathon2_ARcode_final.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPmqmKZgF5/EnLwTiK8373V",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/andysontran/13-CHL5230-F23/blob/main/Datathon2_ARcode_final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "NGHdkQQqFChY",
        "outputId": "ae7d147c-4c47-4d78-d349-7db3d6cc1748"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id  gender   age  hypertension  heart_disease ever_married  \\\n",
              "0      9046    Male  67.0             0              1          Yes   \n",
              "1     51676  Female  61.0             0              0          Yes   \n",
              "2     31112    Male  80.0             0              1          Yes   \n",
              "3     60182  Female  49.0             0              0          Yes   \n",
              "4      1665  Female  79.0             1              0          Yes   \n",
              "...     ...     ...   ...           ...            ...          ...   \n",
              "5105  18234  Female  80.0             1              0          Yes   \n",
              "5106  44873  Female  81.0             0              0          Yes   \n",
              "5107  19723  Female  35.0             0              0          Yes   \n",
              "5108  37544    Male  51.0             0              0          Yes   \n",
              "5109  44679  Female  44.0             0              0          Yes   \n",
              "\n",
              "          work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
              "0           Private          Urban             228.69  36.6  formerly smoked   \n",
              "1     Self-employed          Rural             202.21   NaN     never smoked   \n",
              "2           Private          Rural             105.92  32.5     never smoked   \n",
              "3           Private          Urban             171.23  34.4           smokes   \n",
              "4     Self-employed          Rural             174.12  24.0     never smoked   \n",
              "...             ...            ...                ...   ...              ...   \n",
              "5105        Private          Urban              83.75   NaN     never smoked   \n",
              "5106  Self-employed          Urban             125.20  40.0     never smoked   \n",
              "5107  Self-employed          Rural              82.99  30.6     never smoked   \n",
              "5108        Private          Rural             166.29  25.6  formerly smoked   \n",
              "5109       Govt_job          Urban              85.28  26.2          Unknown   \n",
              "\n",
              "      stroke  \n",
              "0          1  \n",
              "1          1  \n",
              "2          1  \n",
              "3          1  \n",
              "4          1  \n",
              "...      ...  \n",
              "5105       0  \n",
              "5106       0  \n",
              "5107       0  \n",
              "5108       0  \n",
              "5109       0  \n",
              "\n",
              "[5110 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a7ef1d78-5b05-43ea-ae98-a18741a8d777\" 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>id</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>work_type</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>smoking_status</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9046</td>\n",
              "      <td>Male</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>228.69</td>\n",
              "      <td>36.6</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>51676</td>\n",
              "      <td>Female</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>202.21</td>\n",
              "      <td>NaN</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31112</td>\n",
              "      <td>Male</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Rural</td>\n",
              "      <td>105.92</td>\n",
              "      <td>32.5</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>60182</td>\n",
              "      <td>Female</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>171.23</td>\n",
              "      <td>34.4</td>\n",
              "      <td>smokes</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1665</td>\n",
              "      <td>Female</td>\n",
              "      <td>79.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>174.12</td>\n",
              "      <td>24.0</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5105</th>\n",
              "      <td>18234</td>\n",
              "      <td>Female</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>83.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5106</th>\n",
              "      <td>44873</td>\n",
              "      <td>Female</td>\n",
              "      <td>81.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Urban</td>\n",
              "      <td>125.20</td>\n",
              "      <td>40.0</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5107</th>\n",
              "      <td>19723</td>\n",
              "      <td>Female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>82.99</td>\n",
              "      <td>30.6</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5108</th>\n",
              "      <td>37544</td>\n",
              "      <td>Male</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Rural</td>\n",
              "      <td>166.29</td>\n",
              "      <td>25.6</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5109</th>\n",
              "      <td>44679</td>\n",
              "      <td>Female</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Govt_job</td>\n",
              "      <td>Urban</td>\n",
              "      <td>85.28</td>\n",
              "      <td>26.2</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5110 rows × 12 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a7ef1d78-5b05-43ea-ae98-a18741a8d777')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-a7ef1d78-5b05-43ea-ae98-a18741a8d777 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-a7ef1d78-5b05-43ea-ae98-a18741a8d777');\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",
              "\n",
              "\n",
              "<div id=\"df-c79e8f80-94e0-4efb-90ae-8ebe55450064\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c79e8f80-94e0-4efb-90ae-8ebe55450064')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c79e8f80-94e0-4efb-90ae-8ebe55450064 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# Import necessary libraries\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix, classification_report, roc_auc_score, roc_curve\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Load the Cardiovascular Event Dataset\n",
        "data = pd.read_csv('Cardiovascular Event Dataset.csv')\n",
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DATA PRE-PROCESSING**"
      ],
      "metadata": {
        "id": "ZbSAE_-qWfya"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove the id row\n",
        "data.drop('id', axis=1, inplace=True)\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "NW-QhF8qI-iO",
        "outputId": "cf38270c-41a8-4a88-dc20-5188c1034072"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
              "0       Male  67.0             0              1          Yes        Private   \n",
              "1     Female  61.0             0              0          Yes  Self-employed   \n",
              "2       Male  80.0             0              1          Yes        Private   \n",
              "3     Female  49.0             0              0          Yes        Private   \n",
              "4     Female  79.0             1              0          Yes  Self-employed   \n",
              "...      ...   ...           ...            ...          ...            ...   \n",
              "5105  Female  80.0             1              0          Yes        Private   \n",
              "5106  Female  81.0             0              0          Yes  Self-employed   \n",
              "5107  Female  35.0             0              0          Yes  Self-employed   \n",
              "5108    Male  51.0             0              0          Yes        Private   \n",
              "5109  Female  44.0             0              0          Yes       Govt_job   \n",
              "\n",
              "     Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
              "0             Urban             228.69  36.6  formerly smoked       1  \n",
              "1             Rural             202.21   NaN     never smoked       1  \n",
              "2             Rural             105.92  32.5     never smoked       1  \n",
              "3             Urban             171.23  34.4           smokes       1  \n",
              "4             Rural             174.12  24.0     never smoked       1  \n",
              "...             ...                ...   ...              ...     ...  \n",
              "5105          Urban              83.75   NaN     never smoked       0  \n",
              "5106          Urban             125.20  40.0     never smoked       0  \n",
              "5107          Rural              82.99  30.6     never smoked       0  \n",
              "5108          Rural             166.29  25.6  formerly smoked       0  \n",
              "5109          Urban              85.28  26.2          Unknown       0  \n",
              "\n",
              "[5110 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7cee4332-4669-41f0-bf3b-b70aa57c3522\" 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>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>work_type</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>smoking_status</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>228.69</td>\n",
              "      <td>36.6</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Female</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>202.21</td>\n",
              "      <td>NaN</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Rural</td>\n",
              "      <td>105.92</td>\n",
              "      <td>32.5</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Female</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>171.23</td>\n",
              "      <td>34.4</td>\n",
              "      <td>smokes</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Female</td>\n",
              "      <td>79.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>174.12</td>\n",
              "      <td>24.0</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5105</th>\n",
              "      <td>Female</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>83.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5106</th>\n",
              "      <td>Female</td>\n",
              "      <td>81.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Urban</td>\n",
              "      <td>125.20</td>\n",
              "      <td>40.0</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5107</th>\n",
              "      <td>Female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>82.99</td>\n",
              "      <td>30.6</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5108</th>\n",
              "      <td>Male</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Rural</td>\n",
              "      <td>166.29</td>\n",
              "      <td>25.6</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5109</th>\n",
              "      <td>Female</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Govt_job</td>\n",
              "      <td>Urban</td>\n",
              "      <td>85.28</td>\n",
              "      <td>26.2</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5110 rows × 11 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7cee4332-4669-41f0-bf3b-b70aa57c3522')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-7cee4332-4669-41f0-bf3b-b70aa57c3522 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-7cee4332-4669-41f0-bf3b-b70aa57c3522');\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",
              "\n",
              "\n",
              "<div id=\"df-7a4ab1c7-3801-4f13-9010-6eb34862ee67\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7a4ab1c7-3801-4f13-9010-6eb34862ee67')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7a4ab1c7-3801-4f13-9010-6eb34862ee67 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Exploration of the dataset, dropping the 'count' row for better visualization\n",
        "description = data.describe()\n",
        "description\n",
        "description = description.drop('count')\n",
        "\n",
        "# Plot some graphs to visually show data distribution\n",
        "\n",
        "# Box and whisker plots (only looking at continuous variables)\n",
        "description.plot(kind='box', subplots=True, layout=(2, 7), figsize=(10, 5), color='#7569c9')\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Bar graphs (for categorical variables)\n",
        "data['gender'].value_counts().plot(kind='bar')\n",
        "plt.xlabel('Gender')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()\n",
        "data['hypertension'].value_counts().plot(kind='bar')\n",
        "plt.xlabel('Hypertension')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()\n",
        "data['heart_disease'].value_counts().plot(kind='bar')\n",
        "plt.xlabel('Heart Disease')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()\n",
        "data['ever_married'].value_counts().plot(kind='bar')\n",
        "plt.xlabel('Ever Married')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()\n",
        "data['work_type'].value_counts().plot(kind='bar')\n",
        "plt.xlabel('Work Type')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()\n",
        "data['Residence_type'].value_counts().plot(kind='bar')\n",
        "plt.xlabel('Residence Type')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()\n",
        "data['smoking_status'].value_counts().plot(kind='bar')\n",
        "plt.xlabel('Smoking Status')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ImfAwLv8GIft",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "51fd2037-97e6-4410-a0b9-1661f485c916"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 14 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkQAAAHNCAYAAAD/t2TXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAxhklEQVR4nO3deVxUdd//8fcAgmCAksLILSKuaYq5lHKnlmniWiZXd5p7tOgFVy6Z5nXlltclZaltpnaXopV7VqZlouaSopWKphnuoQnq7QJBsQjz+yOdXxNmOgIz+n09H4/zeHjO9zPnfM48mnx7zvfMWGw2m00AAAAG83B1AwAAAK5GIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGM/L1Q3cCIqKinTixAn5+/vLYrG4uh0AAHAVbDabfv75Z4WGhsrD48rXgAhEV+HEiRMKCwtzdRsAAMAJx44dU7Vq1a5YQyC6Cv7+/pJ+e0MDAgJc3A0AALgaWVlZCgsLs/89fiUEoqtw6TZZQEAAgQgAgBvM1Ux3YVI1AAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHherm4A7q3Gcytd3QLK0NEXu7i6BQBwCa4QAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACM59JAlJCQoDvvvFP+/v4KDg5W9+7dlZqa6lBz7733ymKxOCyDBg1yqElLS1OXLl3k5+en4OBgPfvss7pw4YJDzfr169W0aVP5+Piodu3aSkxMLO3TAwAANwiXBqINGzYoLi5OW7duVVJSkgoKCtShQwfl5OQ41D3xxBNKT0+3L5MnT7aPFRYWqkuXLsrPz9eWLVs0d+5cJSYmauzYsfaaI0eOqEuXLmrbtq1SUlI0dOhQPf744/riiy/K7FwBAID7cuk3Va9atcphPTExUcHBwdq+fbvatGlj3+7n5yer1XrZfaxevVrff/+91qxZo5CQEN1xxx2aOHGiRo0apfHjx8vb21szZ85URESEpkyZIkmqX7++vvrqK02bNk3R0dGld4IAAOCG4FY/3ZGZmSlJCgoKctj+wQcf6P3335fValW3bt00ZswY+fn5SZKSk5PVqFEjhYSE2Oujo6M1ePBg7d27V02aNFFycrLat2/vsM/o6GgNHTq0dE8IANwYP81jFn6a58rcJhAVFRVp6NChuvvuu9WwYUP79kcffVTh4eEKDQ3V7t27NWrUKKWmpmrZsmWSpIyMDIcwJMm+npGRccWarKws/frrr/L19XUYy8vLU15enn09Kyur5E4UAAC4HbcJRHFxcdqzZ4+++uorh+1PPvmk/c+NGjVS1apV1a5dOx06dEi1atUqlV4SEhI0YcKEUtk3AABwP27x2H18fLxWrFihL7/8UtWqVbtibYsWLSRJBw8elCRZrVadPHnSoebS+qV5R39WExAQUOzqkCSNHj1amZmZ9uXYsWPOnRgAALghuDQQ2Ww2xcfH66OPPtK6desUERHxl69JSUmRJFWtWlWSFBUVpe+++06nTp2y1yQlJSkgIEANGjSw16xdu9ZhP0lJSYqKirrsMXx8fBQQEOCwAACAm5dLA1FcXJzef/99zZ8/X/7+/srIyFBGRoZ+/fVXSdKhQ4c0ceJEbd++XUePHtXy5cvVr18/tWnTRpGRkZKkDh06qEGDBurbt6927dqlL774Qs8//7zi4uLk4+MjSRo0aJAOHz6skSNH6ocfftBbb72lxYsXa9iwYS47dwAA4D5cGohmzJihzMxM3Xvvvapatap9WbRokSTJ29tba9asUYcOHXTbbbfpmWeeUUxMjD799FP7Pjw9PbVixQp5enoqKipKffr0Ub9+/fTCCy/YayIiIrRy5UolJSWpcePGmjJlit555x0euQcAAJJcPKnaZrNdcTwsLEwbNmz4y/2Eh4frs88+u2LNvffeq507d15TfwAAwAxuMakaAADAlQhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxXBqIEhISdOedd8rf31/BwcHq3r27UlNTHWpyc3MVFxenW2+9VbfccotiYmJ08uRJh5q0tDR16dJFfn5+Cg4O1rPPPqsLFy441Kxfv15NmzaVj4+PateurcTExNI+PQAAcINwaSDasGGD4uLitHXrViUlJamgoEAdOnRQTk6OvWbYsGH69NNPtWTJEm3YsEEnTpxQjx497OOFhYXq0qWL8vPztWXLFs2dO1eJiYkaO3asvebIkSPq0qWL2rZtq5SUFA0dOlSPP/64vvjiizI9XwAA4J4sNpvN5uomLjl9+rSCg4O1YcMGtWnTRpmZmapSpYrmz5+vv/3tb5KkH374QfXr11dycrJatmypzz//XF27dtWJEycUEhIiSZo5c6ZGjRql06dPy9vbW6NGjdLKlSu1Z88e+7F69uyp8+fPa9WqVX/ZV1ZWlgIDA5WZmamAgIDSOXk3VeO5la5uAWXo6ItdXN0CyhCfb7OY+Pm+lr+/3WoOUWZmpiQpKChIkrR9+3YVFBSoffv29prbbrtN1atXV3JysiQpOTlZjRo1sochSYqOjlZWVpb27t1rr/n9Pi7VXNrHH+Xl5SkrK8thAQAANy+3CURFRUUaOnSo7r77bjVs2FCSlJGRIW9vb1WsWNGhNiQkRBkZGfaa34ehS+OXxq5Uk5WVpV9//bVYLwkJCQoMDLQvYWFhJXKOAADAPblNIIqLi9OePXu0cOFCV7ei0aNHKzMz074cO3bM1S0BAIBS5OXqBiQpPj5eK1as0MaNG1WtWjX7dqvVqvz8fJ0/f97hKtHJkydltVrtNV9//bXD/i49hfb7mj8+mXby5EkFBATI19e3WD8+Pj7y8fEpkXMDAADuz6VXiGw2m+Lj4/XRRx9p3bp1ioiIcBhv1qyZypUrp7Vr19q3paamKi0tTVFRUZKkqKgofffddzp16pS9JikpSQEBAWrQoIG95vf7uFRzaR8AAMBsLr1CFBcXp/nz5+uTTz6Rv7+/fc5PYGCgfH19FRgYqNjYWA0fPlxBQUEKCAjQP/7xD0VFRally5aSpA4dOqhBgwbq27evJk+erIyMDD3//POKi4uzX+UZNGiQ3nzzTY0cOVKPPfaY1q1bp8WLF2vlSp6wAAAALr5CNGPGDGVmZuree+9V1apV7cuiRYvsNdOmTVPXrl0VExOjNm3ayGq1atmyZfZxT09PrVixQp6enoqKilKfPn3Ur18/vfDCC/aaiIgIrVy5UklJSWrcuLGmTJmid955R9HR0WV6vgAAwD251fcQuSu+hwimMPF7SkzG59ssJn6+b9jvIQIAAHAFAhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxnApEhw8fLuk+AAAAXMapQFS7dm21bdtW77//vnJzc0u6JwAAgDLlVCDasWOHIiMjNXz4cFmtVj311FP6+uuvS7o3AACAMuFUILrjjjv02muv6cSJE5o9e7bS09PVqlUrNWzYUFOnTtXp06dLuk8AAIBSc12Tqr28vNSjRw8tWbJEL730kg4ePKgRI0YoLCxM/fr1U3p6ekn1CQAAUGquKxB9++23+vvf/66qVatq6tSpGjFihA4dOqSkpCSdOHFCDz74YEn1CQAAUGq8nHnR1KlTNWfOHKWmpqpz586aN2+eOnfuLA+P3/JVRESEEhMTVaNGjZLsFQAAoFQ4FYhmzJihxx57TAMGDFDVqlUvWxMcHKx33333upoDAAAoC04FogMHDvxljbe3t/r37+/M7gEAAMqUU3OI5syZoyVLlhTbvmTJEs2dO/e6mwIAAChLTgWihIQEVa5cudj24OBgTZo06ar3s3HjRnXr1k2hoaGyWCz6+OOPHcYHDBggi8XisHTs2NGh5uzZs+rdu7cCAgJUsWJFxcbGKjs726Fm9+7dat26tcqXL6+wsDBNnjz56k8WAADc9JwKRGlpaYqIiCi2PTw8XGlpaVe9n5ycHDVu3FjTp0//05qOHTsqPT3dvixYsMBhvHfv3tq7d6+SkpK0YsUKbdy4UU8++aR9PCsrSx06dFB4eLi2b9+ul19+WePHj9fbb7991X0CAICbm1NziIKDg7V79+5iT5Ht2rVLt95661Xvp1OnTurUqdMVa3x8fGS1Wi87tm/fPq1atUrffPONmjdvLkl644031LlzZ73yyisKDQ3VBx98oPz8fM2ePVve3t66/fbblZKSoqlTpzoEJwAAYC6nrhD16tVLTz/9tL788ksVFhaqsLBQ69at05AhQ9SzZ88SbXD9+vUKDg5WvXr1NHjwYJ05c8Y+lpycrIoVK9rDkCS1b99eHh4e2rZtm72mTZs28vb2ttdER0crNTVV586du+wx8/LylJWV5bAAAICbl1NXiCZOnKijR4+qXbt28vL6bRdFRUXq16/fNc0h+isdO3ZUjx49FBERoUOHDumf//ynOnXqpOTkZHl6eiojI0PBwcEOr/Hy8lJQUJAyMjIkSRkZGcVu74WEhNjHKlWqVOy4CQkJmjBhQomdBwAAcG9OBSJvb28tWrRIEydO1K5du+Tr66tGjRopPDy8RJv7/dWmRo0aKTIyUrVq1dL69evVrl27Ej3W740ePVrDhw+3r2dlZSksLKzUjgcAAFzLqUB0Sd26dVW3bt2S6uUv1axZU5UrV9bBgwfVrl07Wa1WnTp1yqHmwoULOnv2rH3ekdVq1cmTJx1qLq3/2dwkHx8f+fj4lMIZAAAAd+RUICosLFRiYqLWrl2rU6dOqaioyGF83bp1JdLcHx0/flxnzpyxfzt2VFSUzp8/r+3bt6tZs2b2YxcVFalFixb2mn/9618qKChQuXLlJElJSUmqV6/eZW+XAQAA8zgViIYMGaLExER16dJFDRs2lMVicerg2dnZOnjwoH39yJEjSklJUVBQkIKCgjRhwgTFxMTIarXq0KFDGjlypGrXrq3o6GhJUv369dWxY0c98cQTmjlzpgoKChQfH6+ePXsqNDRUkvToo49qwoQJio2N1ahRo7Rnzx699tprmjZtmlM9AwCAm49TgWjhwoVavHixOnfufF0H//bbb9W2bVv7+qV5O/3799eMGTO0e/duzZ07V+fPn1doaKg6dOigiRMnOtzO+uCDDxQfH6927drJw8NDMTExev311+3jgYGBWr16teLi4tSsWTNVrlxZY8eO5ZF7AABg5/Sk6tq1a1/3we+9917ZbLY/Hf/iiy/+ch9BQUGaP3/+FWsiIyO1adOma+4PAACYwanvIXrmmWf02muvXTHMAAAA3CicukL01Vdf6csvv9Tnn3+u22+/3T5Z+ZJly5aVSHMAAABlwalAVLFiRT300EMl3QsAAIBLOBWI5syZU9J9AAAAuIxTc4ik374Acc2aNZo1a5Z+/vlnSdKJEyeUnZ1dYs0BAACUBaeuEP3444/q2LGj0tLSlJeXp/vvv1/+/v566aWXlJeXp5kzZ5Z0nwAAAKXGqStEQ4YMUfPmzXXu3Dn5+vratz/00ENau3ZtiTUHAABQFpy6QrRp0yZt2bJF3t7eDttr1Kihn376qUQaAwAAKCtOXSEqKipSYWFhse3Hjx+Xv7//dTcFAABQlpwKRB06dNCrr75qX7dYLMrOzta4ceOu++c8AAAAyppTt8ymTJmi6OhoNWjQQLm5uXr00Ud14MABVa5cWQsWLCjpHgEAAEqVU4GoWrVq2rVrlxYuXKjdu3crOztbsbGx6t27t8MkawAAgBuBU4FIkry8vNSnT5+S7AUAAMAlnApE8+bNu+J4v379nGoGAADAFZwKREOGDHFYLygo0C+//CJvb2/5+fkRiAAAwA3FqafMzp0757BkZ2crNTVVrVq1YlI1AAC44Tj9W2Z/VKdOHb344ovFrh4BAAC4uxILRNJvE61PnDhRkrsEAAAodU7NIVq+fLnDus1mU3p6ut58803dfffdJdIYAABAWXEqEHXv3t1h3WKxqEqVKrrvvvs0ZcqUkugLAACgzDgViIqKikq6DwAAAJcp0TlEAAAANyKnrhANHz78qmunTp3qzCEAAADKjFOBaOfOndq5c6cKCgpUr149SdL+/fvl6emppk2b2ussFkvJdAkAAFCKnApE3bp1k7+/v+bOnatKlSpJ+u3LGgcOHKjWrVvrmWeeKdEmAQAASpNTc4imTJmihIQEexiSpEqVKunf//43T5kBAIAbjlOBKCsrS6dPny62/fTp0/r555+vuykAAICy5FQgeuihhzRw4EAtW7ZMx48f1/Hjx/Xhhx8qNjZWPXr0KOkeAQAASpVTc4hmzpypESNG6NFHH1VBQcFvO/LyUmxsrF5++eUSbRAAAKC0ORWI/Pz89NZbb+nll1/WoUOHJEm1atVShQoVSrQ5AACAsnBdX8yYnp6u9PR01alTRxUqVJDNZiupvgAAAMqMU4HozJkzateunerWravOnTsrPT1dkhQbG8sj9wAA4IbjVCAaNmyYypUrp7S0NPn5+dm3P/LII1q1alWJNQcAAFAWnJpDtHr1an3xxReqVq2aw/Y6deroxx9/LJHGAAAAyopTV4hycnIcrgxdcvbsWfn4+Fx3UwAAAGXJqUDUunVrzZs3z75usVhUVFSkyZMnq23btiXWHAAAQFlw6pbZ5MmT1a5dO3377bfKz8/XyJEjtXfvXp09e1abN28u6R4BAABKlVNXiBo2bKj9+/erVatWevDBB5WTk6MePXpo586dqlWrVkn3CAAAUKqu+QpRQUGBOnbsqJkzZ+pf//pXafQEAABQpq75ClG5cuW0e/fu0ugFAADAJZy6ZdanTx+9++67Jd0LAACASzg1qfrChQuaPXu21qxZo2bNmhX7DbOpU6eWSHMAAABl4ZoC0eHDh1WjRg3t2bNHTZs2lSTt37/focZisZRcdwAAAGXgmgJRnTp1lJ6eri+//FLSbz/V8frrryskJKRUmgMAACgL1zSH6I+/Zv/5558rJyenRBsCAAAoa05Nqr7kjwEJAADgRnRNgchisRSbI8ScIQAAcKO7pjlENptNAwYMsP+Aa25urgYNGlTsKbNly5aVXIcAAACl7JoCUf/+/R3W+/TpU6LNAAAAuMI1BaI5c+aUVh8AAAAuc12TqgEAAG4GBCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOO5NBBt3LhR3bp1U2hoqCwWiz7++GOHcZvNprFjx6pq1ary9fVV+/btdeDAAYeas2fPqnfv3goICFDFihUVGxur7Oxsh5rdu3erdevWKl++vMLCwjR58uTSPjUAAHADcWkgysnJUePGjTV9+vTLjk+ePFmvv/66Zs6cqW3btqlChQqKjo5Wbm6uvaZ3797au3evkpKStGLFCm3cuFFPPvmkfTwrK0sdOnRQeHi4tm/frpdfflnjx4/X22+/XernBwAAbgzX9E3VJa1Tp07q1KnTZcdsNpteffVVPf/883rwwQclSfPmzVNISIg+/vhj9ezZU/v27dOqVav0zTffqHnz5pKkN954Q507d9Yrr7yi0NBQffDBB8rPz9fs2bPl7e2t22+/XSkpKZo6dapDcAIAAOZy2zlER44cUUZGhtq3b2/fFhgYqBYtWig5OVmSlJycrIoVK9rDkCS1b99eHh4e2rZtm72mTZs28vb2ttdER0crNTVV586du+yx8/LylJWV5bAAAICbl9sGooyMDElSSEiIw/aQkBD7WEZGhoKDgx3Gvby8FBQU5FBzuX38/hh/lJCQoMDAQPsSFhZ2/ScEAADcltsGIlcaPXq0MjMz7cuxY8dc3RIAAChFbhuIrFarJOnkyZMO20+ePGkfs1qtOnXqlMP4hQsXdPbsWYeay+3j98f4Ix8fHwUEBDgsAADg5uW2gSgiIkJWq1Vr1661b8vKytK2bdsUFRUlSYqKitL58+e1fft2e826detUVFSkFi1a2Gs2btyogoICe01SUpLq1aunSpUqldHZAAAAd+bSQJSdna2UlBSlpKRI+m0idUpKitLS0mSxWDR06FD9+9//1vLly/Xdd9+pX79+Cg0NVffu3SVJ9evXV8eOHfXEE0/o66+/1ubNmxUfH6+ePXsqNDRUkvToo4/K29tbsbGx2rt3rxYtWqTXXntNw4cPd9FZAwAAd+PSx+6//fZbtW3b1r5+KaT0799fiYmJGjlypHJycvTkk0/q/PnzatWqlVatWqXy5cvbX/PBBx8oPj5e7dq1k4eHh2JiYvT666/bxwMDA7V69WrFxcWpWbNmqly5ssaOHcsj9wAAwM5is9lsrm7C3WVlZSkwMFCZmZnGzSeq8dxKV7eAMnT0xS6ubgFliM+3WUz8fF/L399uO4cIAACgrBCIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGc+tANH78eFksFofltttus4/n5uYqLi5Ot956q2655RbFxMTo5MmTDvtIS0tTly5d5Ofnp+DgYD377LO6cOFCWZ8KAABwY16ubuCv3H777VqzZo193cvr/7c8bNgwrVy5UkuWLFFgYKDi4+PVo0cPbd68WZJUWFioLl26yGq1asuWLUpPT1e/fv1Urlw5TZo0qczPBQAAuCe3D0ReXl6yWq3FtmdmZurdd9/V/Pnzdd9990mS5syZo/r162vr1q1q2bKlVq9ere+//15r1qxRSEiI7rjjDk2cOFGjRo3S+PHj5e3tXdanAwAA3JBb3zKTpAMHDig0NFQ1a9ZU7969lZaWJknavn27CgoK1L59e3vtbbfdpurVqys5OVmSlJycrEaNGikkJMReEx0draysLO3du7dsTwQAALgtt75C1KJFCyUmJqpevXpKT0/XhAkT1Lp1a+3Zs0cZGRny9vZWxYoVHV4TEhKijIwMSVJGRoZDGLo0fmnsz+Tl5SkvL8++npWVVUJnBAAA3JFbB6JOnTrZ/xwZGakWLVooPDxcixcvlq+vb6kdNyEhQRMmTCi1/QMAAPfi9rfMfq9ixYqqW7euDh48KKvVqvz8fJ0/f96h5uTJk/Y5R1artdhTZ5fWLzcv6ZLRo0crMzPTvhw7dqxkTwQAALiVGyoQZWdn69ChQ6pataqaNWumcuXKae3atfbx1NRUpaWlKSoqSpIUFRWl7777TqdOnbLXJCUlKSAgQA0aNPjT4/j4+CggIMBhAQAANy+3vmU2YsQIdevWTeHh4Tpx4oTGjRsnT09P9erVS4GBgYqNjdXw4cMVFBSkgIAA/eMf/1BUVJRatmwpSerQoYMaNGigvn37avLkycrIyNDzzz+vuLg4+fj4uPjsAACAu3DrQHT8+HH16tVLZ86cUZUqVdSqVStt3bpVVapUkSRNmzZNHh4eiomJUV5enqKjo/XWW2/ZX+/p6akVK1Zo8ODBioqKUoUKFdS/f3+98MILrjolAADghtw6EC1cuPCK4+XLl9f06dM1ffr0P60JDw/XZ599VtKtAQCAm8gNNYcIAACgNBCIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjGRWIpk+frho1aqh8+fJq0aKFvv76a1e3BAAA3IAxgWjRokUaPny4xo0bpx07dqhx48aKjo7WqVOnXN0aAABwMWMC0dSpU/XEE09o4MCBatCggWbOnCk/Pz/Nnj3b1a0BAAAXMyIQ5efna/v27Wrfvr19m4eHh9q3b6/k5GQXdgYAANyBl6sbKAv/93//p8LCQoWEhDhsDwkJ0Q8//FCsPi8vT3l5efb1zMxMSVJWVlbpNuqGivJ+cXULKEMm/jduMj7fZjHx833pnG0221/WGhGIrlVCQoImTJhQbHtYWJgLugHKTuCrru4AQGkx+fP9888/KzAw8Io1RgSiypUry9PTUydPnnTYfvLkSVmt1mL1o0eP1vDhw+3rRUVFOnv2rG699VZZLJZS7xeulZWVpbCwMB07dkwBAQGubgdACeLzbRabzaaff/5ZoaGhf1lrRCDy9vZWs2bNtHbtWnXv3l3SbyFn7dq1io+PL1bv4+MjHx8fh20VK1Ysg07hTgICAvgfJnCT4vNtjr+6MnSJEYFIkoYPH67+/furefPmuuuuu/Tqq68qJydHAwcOdHVrAADAxYwJRI888ohOnz6tsWPHKiMjQ3fccYdWrVpVbKI1AAAwjzGBSJLi4+Mve4sM+D0fHx+NGzeu2G1TADc+Pt/4Mxbb1TyLBgAAcBMz4osZAQAAroRABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeEZ9DxFwJSdPntSIESO0du1anTp1qtivIxcWFrqoMwDO6NGjx1XXLlu2rBQ7wY2AQARcNGDAAKWlpWnMmDGqWrUqP+QL3OCu9jesAIkvZgTs/P39tWnTJt1xxx2ubgUAUMaYQwRcFBYWVuw2GQDADFwhAi5avXq1pkyZolmzZqlGjRqubgdACVu6dKkWL16stLQ05efnO4zt2LHDRV3BXXCFCLjokUce0fr161WrVi35+/srKCjIYQFw43r99dc1cOBAhYSEaOfOnbrrrrt066236vDhw+rUqZOr24Mb4AoRcNHcuXOvON6/f/8y6gRASbvttts0btw49erVS/7+/tq1a5dq1qypsWPH6uzZs3rzzTdd3SJcjEAEALjp+fn5ad++fQoPD1dwcLCSkpLUuHFjHThwQC1bttSZM2dc3SJcjFtmwGXk5uYqKyvLYQFw47JarTp79qwkqXr16tq6dask6ciRIzxMAUkEIsAuJydH8fHxCg4OVoUKFVSpUiWHBcCN67777tPy5cslSQMHDtSwYcN0//3365FHHtFDDz3k4u7gDrhlBlwUFxenL7/8UhMnTlTfvn01ffp0/fTTT5o1a5ZefPFF9e7d29UtAnBSUVGRioqK5OX12/cRL1y4UFu2bFGdOnX01FNPydvb28UdwtUIRMBF1atX17x583TvvfcqICBAO3bsUO3atfXee+9pwYIF+uyzz1zdIgAnXLhwQZMmTdJjjz2matWquboduClumQEXnT17VjVr1pQkBQQE2OcbtGrVShs3bnRlawCug5eXlyZPnqwLFy64uhW4MQIRcFHNmjV15MgRSb89ort48WJJ0qeffqqKFSu6sDMA16tdu3basGGDq9uAG+PHXYGLBg4cqF27dumee+7Rc889p27duunNN99UQUGBpk6d6ur2AFyHTp066bnnntN3332nZs2aqUKFCg7jDzzwgIs6g7tgDhHwJ44ePWqfRxQZGenqdgBcBw+PP78hYrFYVFhYWIbdwB0RiAAAgPGYQwT8ztq1a9W1a1fVqlVLtWrVUteuXbVmzRpXtwUAKGVcIQIueuuttzRkyBD97W9/U1RUlCRp69atWrp0qaZNm6a4uDgXdwjAWS+88MIVx8eOHVtGncBdEYiAi6pVq6bnnntO8fHxDtunT5+uSZMm6aeffnJRZwCuV5MmTRzWCwoKdOTIEXl5ealWrVrasWOHizqDuyAQARfdcsstSklJUe3atR22HzhwQE2aNFF2draLOgNQGrKysjRgwAA99NBD6tu3r6vbgYsxhwi46IEHHtBHH31UbPsnn3yirl27uqAjAKUpICBAEyZM0JgxY1zdCtwA30MEo73++uv2Pzdo0ED/+c9/tH79eoc5RJs3b9YzzzzjqhYBlKLMzExlZma6ug24AW6ZwWgRERFXVWexWHT48OFS7gZAafn9P34kyWazKT09Xe+9957atGmjBQsWuKgzuAsCEQDgpvfHf/x4eHioSpUquu+++zR69Gj5+/u7qDO4C+YQAfrtiZNatWpp3759rm4FQCk4cuSIw3Lo0CGtX79et956q+rUqePq9uAGCESApHLlyik3N9fVbQAoYXl5eRo9erSaN2+uu+++Wx9//LEkac6cOapVq5Zee+01DRs2zLVNwi1wywy4aNKkSdq/f7/eeecdeXnxvAFwMxg1apRmzZql9u3ba8uWLTp9+rQGDhyorVu36p///KcefvhheXp6urpNuAH+rw9c9M0332jt2rVavXq1GjVqVOzXsJctW+aizgA4a8mSJZo3b54eeOAB7dmzR5GRkbpw4YJ27doli8Xi6vbgRghEwEUVK1ZUTEyMq9sAUIKOHz+uZs2aSZIaNmwoHx8fDRs2jDCEYghEwEVz5sxxdQsASlhhYaG8vb3t615eXrrllltc2BHcFYEIxqtUqdJl/7UYGBiounXrasSIEbr//vtd0BmA62Wz2TRgwAD5+PhIknJzczVo0CBuiaMYAhGM9+qrr152+/nz57V9+3Z17dpVS5cuVbdu3cq2MQDXrX///g7rffr0cVEncHc8ZQb8halTp2rp0qXasmWLq1sBAJQSAhHwF/bv36+WLVvq7Nmzrm4FAFBK+GJG4C/k5eU5TMoEANx8CETAX3j33Xd1xx13uLoNAEApYlI1jDd8+PDLbs/MzNSOHTu0f/9+bdy4sYy7AgCUJQIRjLdz587Lbg8ICND999+vZcuWFfulbADAzYVJ1QAAwHjMIQIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgBu5+jRo7JYLEpJSfnTmvXr18tisej8+fNl1heAmxeBCMA1GzBggCwWiywWi8qVK6eIiAiNHDlSubm5JbL/sLAwpaenq2HDhiWyP1epUaOG/X263DJgwABXtwjgIr6HCIBTOnbsqDlz5qigoEDbt29X//79ZbFY9NJLL133vj09PWW1WkugS9f65ptvVFhYKEnasmWLYmJilJqaqoCAAEmSr6+vK9sD8DtcIQLgFB8fH1mtVoWFhal79+5q3769kpKS7ONFRUVKSEhQRESEfH191bhxYy1dutQ+fu7cOfXu3VtVqlSRr6+v6tSpozlz5ki6/C2zzz77THXr1pWvr6/atm2ro0ePFuvpq6++UuvWreXr66uwsDA9/fTTysnJsY/XqFFDkyZN0mOPPSZ/f39Vr15db7/9tsM+jh8/rl69eikoKEgVKlRQ8+bNtW3bNvv4J598oqZNm6p8+fKqWbOmJkyYoAsXLlz2PapSpYqsVqusVquCgoIkScHBwQoJCVGrVq30v//7vw71KSkpslgsOnjwoCTJYrFoxowZ6tSpk3x9fVWzZk2H91CSjh07pv/5n/9RxYoVFRQUpAcffPCy7w2AKyMQAbhue/bs0ZYtWxx+BDchIUHz5s3TzJkztXfvXg0bNkx9+vTRhg0bJEljxozR999/r88//1z79u3TjBkzVLly5cvu/9ixY+rRo4e6deumlJQUPf7443ruueccag4dOqSOHTsqJiZGu3fv1qJFi/TVV18pPj7eoW7KlClq3ry5du7cqb///e8aPHiwUlNTJUnZ2dm655579NNPP2n58uXatWuXRo4cqaKiIknSpk2b1K9fPw0ZMkTff/+9Zs2apcTERP3nP/+5pvfLYrHoscceswfAS+bMmaM2bdqodu3a9m1jxoxRTEyMdu3apd69e6tnz57at2+fJKmgoEDR0dHy9/fXpk2btHnzZt1yyy3q2LGj8vPzr6knwHg2ALhG/fv3t3l6etoqVKhg8/HxsUmyeXh42JYuXWqz2Wy23Nxcm5+fn23Lli0Or4uNjbX16tXLZrPZbN26dbMNHDjwsvs/cuSITZJt586dNpvNZhs9erStQYMGDjWjRo2ySbKdO3fOvu8nn3zSoWbTpk02Dw8P26+//mqz2Wy28PBwW58+fezjRUVFtuDgYNuMGTNsNpvNNmvWLJu/v7/tzJkzl+2rXbt2tkmTJjlse++992xVq1a9bP3vffnllw79/vTTTzZPT0/btm3bbDabzZafn2+rXLmyLTEx0f4aSbZBgwY57KdFixa2wYMH249dr149W1FRkX08Ly/P5uvra/viiy/+sicA/x9ziAA4pW3btpoxY4ZycnI0bdo0eXl5KSYmRpJ08OBB/fLLL7r//vsdXpOfn68mTZpIkgYPHqyYmBjt2LFDHTp0UPfu3fXf//3flz3Wvn371KJFC4dtUVFRDuu7du3S7t279cEHH9i32Ww2FRUV6ciRI6pfv74kKTIy0j5usVhktVp16tQpSb/dsmrSpIn99tYf7dq1S5s3b3a4IlRYWKjc3Fz98ssv8vPz+/M37A9CQ0PVpUsXzZ49W3fddZc+/fRT5eXl6eGHH77ieUZFRdlvJe7atUsHDx6Uv7+/Q01ubq4OHTp01b0AYFI1ACdVqFDBfmtn9uzZaty4sd59913FxsYqOztbkrRy5Ur913/9l8PrfHx8JEmdOnXSjz/+qM8++0xJSUlq166d4uLi9MorrzjVT3Z2tp566ik9/fTTxcaqV69u/3O5cuUcxiwWi/2W2F9Ncs7OztaECRPUo0ePYmPly5e/5p4ff/xx9e3bV9OmTdOcOXP0yCOPXFOoys7OVrNmzRxC4CVVqlS55n4AkxGIAFw3Dw8P/fOf/9Tw4cP16KOPqkGDBvLx8VFaWpruueeeP31dlSpV1L9/f/Xv31+tW7fWs88+e9lAVL9+fS1fvtxh29atWx3WmzZtqu+//95h/s21ioyM1DvvvKOzZ89e9ipR06ZNlZqael3H+L3OnTurQoUKmjFjhlatWqWNGzcWq9m6dav69evnsH7pKlvTpk21aNEiBQcH259cA+AcJlUDKBEPP/ywPD09NX36dPn7+2vEiBEaNmyY5s6dq0OHDmnHjh164403NHfuXEnS2LFj9cknn+jgwYPau3evVqxYYb+t9UeDBg3SgQMH9Oyzzyo1NVXz589XYmKiQ82oUaO0ZcsWxcfHKyUlRQcOHNAnn3xSbFL1lfTq1UtWq1Xdu3fX5s2bdfjwYX344YdKTk629zxv3jxNmDBBe/fu1b59+7Rw4UI9//zzTr1nnp6eGjBggEaPHq06deoUuz0mSUuWLNHs2bO1f/9+jRs3Tl9//bX9nHr37q3KlSvrwQcf1KZNm3TkyBGtX79eTz/9tI4fP+5UT4CpCEQASoSXl5fi4+M1efJk5eTkaOLEiRozZowSEhJUv359dezYUStXrlRERIQkydvbW6NHj1ZkZKTatGkjT09PLVy48LL7rl69uj788EN9/PHHaty4sWbOnKlJkyY51ERGRmrDhg3av3+/WrdurSZNmmjs2LEKDQ296nPw9vbW6tWrFRwcrM6dO6tRo0Z68cUX5enpKUmKjo7WihUrtHr1at15551q2bKlpk2bpvDwcCffNSk2Nlb5+fkaOHDgZccnTJighQsXKjIyUvPmzdOCBQvUoEEDSZKfn582btyo6tWrq0ePHqpfv75iY2OVm5vLFSPgGllsNpvN1U0AgKk2bdqkdu3a6dixYwoJCXEYs1gs+uijj9S9e3fXNAcYhDlEAOACeXl5On36tMaPH6+HH364WBgCULa4ZQYALrBgwQKFh4fr/Pnzmjx5sqvbAYzHLTMAAGA8rhABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOP9P7LpRbBLiS4GAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Since smoking_status has more than 1500 Unknown values, which is about 30% of the sample, we decided to exclude this variable from our analysis\n",
        "data.drop ('smoking_status', axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "oxbxi-cCVywu"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Since gender=Other only has 1 observation in the dataset, we decided to drop this row from our analysis\n",
        "data.drop(3116,axis=0,inplace=True)"
      ],
      "metadata": {
        "id": "UK5eTgIXjn8a"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove outliers in BMI, specifically the observations with BMI=97.6, 92, and 78\n",
        "data.drop(2128,axis=0,inplace=True)\n",
        "data.drop(4209,axis=0,inplace=True)\n",
        "data.drop(928,axis=0,inplace=True)"
      ],
      "metadata": {
        "id": "hMgr4RchS7TQ"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Since children in the dataset skew age and BMI (and as BMI is not as accurate for youth), we decided to restrict our models to adults only, so remove work_type=children\n",
        "data = data[data.work_type != 'children']"
      ],
      "metadata": {
        "id": "_7RFtFsBUkI7"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Code the binary variables gender, ever_married, Residence_type into numerical values;\n",
        "data['gender'] = data['gender'].map({'Male': 0, 'Female': 1})\n",
        "data['ever_married'] = data['ever_married'].map({'No': 0, 'Yes': 1})\n",
        "data['Residence_type'] = data['Residence_type'].map({'Rural': 0, 'Urban': 1})"
      ],
      "metadata": {
        "id": "x2pyllaNWeUd"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create one-hot coding for work_type\n",
        "data['work_type_Private'] = np.where(data['work_type']=='Private', 1, 0)\n",
        "data['work_type_Selfemployed'] = np.where(data['work_type']=='Self-employed', 1, 0)\n",
        "data['work_type_Govtjob'] = np.where(data['work_type']=='Govt_job', 1, 0)\n",
        "data['work_type_Never'] = np.where(data['work_type']=='Never_worked', 1, 0)"
      ],
      "metadata": {
        "id": "GPS9w42pZgLA"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values in each column\n",
        "print(data.isnull().sum())"
      ],
      "metadata": {
        "id": "HmbOblbtJ0in",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c14c7631-b9f1-46ba-a850-13c3d5fc653f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gender                      0\n",
            "age                         0\n",
            "hypertension                0\n",
            "heart_disease               0\n",
            "ever_married                0\n",
            "work_type                   0\n",
            "Residence_type              0\n",
            "avg_glucose_level           0\n",
            "bmi                       185\n",
            "stroke                      0\n",
            "work_type_Private           0\n",
            "work_type_Selfemployed      0\n",
            "work_type_Govtjob           0\n",
            "work_type_Never             0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# For BMI, replace zeros with nan to handle missing values\n",
        "data[['bmi']] = data[['bmi']].replace(0, np.nan)\n",
        "\n",
        "# Fill missing values with median\n",
        "data.fillna(data.median(), inplace=True)\n",
        "\n",
        "# Check to see if there are no more missing values\n",
        "print(data.isnull().sum())"
      ],
      "metadata": {
        "id": "49JamvpQKOBR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04916cde-2edd-447a-9ca1-c201b570b3a1"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gender                    0\n",
            "age                       0\n",
            "hypertension              0\n",
            "heart_disease             0\n",
            "ever_married              0\n",
            "work_type                 0\n",
            "Residence_type            0\n",
            "avg_glucose_level         0\n",
            "bmi                       0\n",
            "stroke                    0\n",
            "work_type_Private         0\n",
            "work_type_Selfemployed    0\n",
            "work_type_Govtjob         0\n",
            "work_type_Never           0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Explore dataset again, after pre-processing of data\n",
        "description = data.describe()\n",
        "description\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "N4LhjN9-kzBE",
        "outputId": "2a40e5f7-922b-45fa-e7bd-221248606097"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            gender          age  hypertension  heart_disease  ever_married  \\\n",
              "count  4419.000000  4419.000000   4419.000000    4419.000000   4419.000000   \n",
              "mean      0.603530    48.898846      0.112016       0.062231      0.758543   \n",
              "std       0.489219    18.701545      0.315422       0.241602      0.428015   \n",
              "min       0.000000     7.000000      0.000000       0.000000      0.000000   \n",
              "25%       0.000000    34.000000      0.000000       0.000000      1.000000   \n",
              "50%       1.000000    50.000000      0.000000       0.000000      1.000000   \n",
              "75%       1.000000    63.000000      0.000000       0.000000      1.000000   \n",
              "max       1.000000    82.000000      1.000000       1.000000      1.000000   \n",
              "\n",
              "       Residence_type  avg_glucose_level          bmi       stroke  \\\n",
              "count     4419.000000        4419.000000  4419.000000  4419.000000   \n",
              "mean         0.508712         107.994956    30.204945     0.055895   \n",
              "std          0.499981          47.270453     7.017585     0.229745   \n",
              "min          0.000000          55.120000    11.300000     0.000000   \n",
              "25%          0.000000          77.460000    25.400000     0.000000   \n",
              "50%          1.000000          92.160000    29.050000     0.000000   \n",
              "75%          1.000000         115.885000    33.700000     0.000000   \n",
              "max          1.000000         271.740000    71.900000     1.000000   \n",
              "\n",
              "       work_type_Private  work_type_Selfemployed  work_type_Govtjob  \\\n",
              "count        4419.000000             4419.000000        4419.000000   \n",
              "mean            0.661009                0.185336           0.148676   \n",
              "std             0.473420                0.388614           0.355809   \n",
              "min             0.000000                0.000000           0.000000   \n",
              "25%             0.000000                0.000000           0.000000   \n",
              "50%             1.000000                0.000000           0.000000   \n",
              "75%             1.000000                0.000000           0.000000   \n",
              "max             1.000000                1.000000           1.000000   \n",
              "\n",
              "       work_type_Never  \n",
              "count      4419.000000  \n",
              "mean          0.004979  \n",
              "std           0.070391  \n",
              "min           0.000000  \n",
              "25%           0.000000  \n",
              "50%           0.000000  \n",
              "75%           0.000000  \n",
              "max           1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0d41c9b4-b319-413b-95f0-b60e16a239aa\" 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>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>stroke</th>\n",
              "      <th>work_type_Private</th>\n",
              "      <th>work_type_Selfemployed</th>\n",
              "      <th>work_type_Govtjob</th>\n",
              "      <th>work_type_Never</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4419.000000</td>\n",
              "      <td>4419.000000</td>\n",
              "      <td>4419.000000</td>\n",
              "      <td>4419.000000</td>\n",
              "      <td>4419.000000</td>\n",
              "      <td>4419.000000</td>\n",
              "      <td>4419.000000</td>\n",
              "      <td>4419.000000</td>\n",
              "      <td>4419.000000</td>\n",
              "      <td>4419.000000</td>\n",
              "      <td>4419.000000</td>\n",
              "      <td>4419.000000</td>\n",
              "      <td>4419.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.603530</td>\n",
              "      <td>48.898846</td>\n",
              "      <td>0.112016</td>\n",
              "      <td>0.062231</td>\n",
              "      <td>0.758543</td>\n",
              "      <td>0.508712</td>\n",
              "      <td>107.994956</td>\n",
              "      <td>30.204945</td>\n",
              "      <td>0.055895</td>\n",
              "      <td>0.661009</td>\n",
              "      <td>0.185336</td>\n",
              "      <td>0.148676</td>\n",
              "      <td>0.004979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.489219</td>\n",
              "      <td>18.701545</td>\n",
              "      <td>0.315422</td>\n",
              "      <td>0.241602</td>\n",
              "      <td>0.428015</td>\n",
              "      <td>0.499981</td>\n",
              "      <td>47.270453</td>\n",
              "      <td>7.017585</td>\n",
              "      <td>0.229745</td>\n",
              "      <td>0.473420</td>\n",
              "      <td>0.388614</td>\n",
              "      <td>0.355809</td>\n",
              "      <td>0.070391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>55.120000</td>\n",
              "      <td>11.300000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>77.460000</td>\n",
              "      <td>25.400000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>92.160000</td>\n",
              "      <td>29.050000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>115.885000</td>\n",
              "      <td>33.700000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>271.740000</td>\n",
              "      <td>71.900000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0d41c9b4-b319-413b-95f0-b60e16a239aa')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-0d41c9b4-b319-413b-95f0-b60e16a239aa 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-0d41c9b4-b319-413b-95f0-b60e16a239aa');\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",
              "\n",
              "\n",
              "<div id=\"df-f2761ecb-6491-4f2d-af42-21e4eab9eb4e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f2761ecb-6491-4f2d-af42-21e4eab9eb4e')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f2761ecb-6491-4f2d-af42-21e4eab9eb4e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation matrix to quantify relationships (this might not be super helpful for us, since we have so many categorical variables. i think we should just fix for collinearity using L1 or L2\n",
        "corr_matrix = data.corr()\n",
        "sns.heatmap(corr_matrix, annot=True, fmt=\".1f\")\n",
        "# For the continuous variables, they don't seem to be heavily correlated with each other"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612
        },
        "id": "j0CIrm3fNNsP",
        "outputId": "54c35c14-0be7-4771-b365-1f9925375366"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}