2_diabetes_prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RT6kyTQ12S7_"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier \n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, mean_squared_error\n",
        "from pandas_profiling import ProfileReport\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "from sklearn import tree\n",
        "import math\n",
        "import warnings\n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/diabetes_binary_health_indicators_BRFSS2015.csv.zip')"
      ],
      "metadata": {
        "id": "pocwiKI43fIq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "id": "qhVJY9115mbP",
        "outputId": "57de98c3-85f7-44c6-d497-de2591f7e541"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         count       mean       std   min   25%   50%   75%  \\\n",
              "Diabetes_binary       253680.0   0.139333  0.346294   0.0   0.0   0.0   0.0   \n",
              "HighBP                253680.0   0.429001  0.494934   0.0   0.0   0.0   1.0   \n",
              "HighChol              253680.0   0.424121  0.494210   0.0   0.0   0.0   1.0   \n",
              "CholCheck             253680.0   0.962670  0.189571   0.0   1.0   1.0   1.0   \n",
              "BMI                   253680.0  28.382364  6.608694  12.0  24.0  27.0  31.0   \n",
              "Smoker                253680.0   0.443169  0.496761   0.0   0.0   0.0   1.0   \n",
              "Stroke                253680.0   0.040571  0.197294   0.0   0.0   0.0   0.0   \n",
              "HeartDiseaseorAttack  253680.0   0.094186  0.292087   0.0   0.0   0.0   0.0   \n",
              "PhysActivity          253680.0   0.756544  0.429169   0.0   1.0   1.0   1.0   \n",
              "Fruits                253680.0   0.634256  0.481639   0.0   0.0   1.0   1.0   \n",
              "Veggies               253680.0   0.811420  0.391175   0.0   1.0   1.0   1.0   \n",
              "HvyAlcoholConsump     253680.0   0.056197  0.230302   0.0   0.0   0.0   0.0   \n",
              "AnyHealthcare         253680.0   0.951053  0.215759   0.0   1.0   1.0   1.0   \n",
              "NoDocbcCost           253680.0   0.084177  0.277654   0.0   0.0   0.0   0.0   \n",
              "GenHlth               253680.0   2.511392  1.068477   1.0   2.0   2.0   3.0   \n",
              "MentHlth              253680.0   3.184772  7.412847   0.0   0.0   0.0   2.0   \n",
              "PhysHlth              253680.0   4.242081  8.717951   0.0   0.0   0.0   3.0   \n",
              "DiffWalk              253680.0   0.168224  0.374066   0.0   0.0   0.0   0.0   \n",
              "Sex                   253680.0   0.440342  0.496429   0.0   0.0   0.0   1.0   \n",
              "Age                   253680.0   8.032119  3.054220   1.0   6.0   8.0  10.0   \n",
              "Education             253680.0   5.050434  0.985774   1.0   4.0   5.0   6.0   \n",
              "Income                253680.0   6.053875  2.071148   1.0   5.0   7.0   8.0   \n",
              "\n",
              "                       max  \n",
              "Diabetes_binary        1.0  \n",
              "HighBP                 1.0  \n",
              "HighChol               1.0  \n",
              "CholCheck              1.0  \n",
              "BMI                   98.0  \n",
              "Smoker                 1.0  \n",
              "Stroke                 1.0  \n",
              "HeartDiseaseorAttack   1.0  \n",
              "PhysActivity           1.0  \n",
              "Fruits                 1.0  \n",
              "Veggies                1.0  \n",
              "HvyAlcoholConsump      1.0  \n",
              "AnyHealthcare          1.0  \n",
              "NoDocbcCost            1.0  \n",
              "GenHlth                5.0  \n",
              "MentHlth              30.0  \n",
              "PhysHlth              30.0  \n",
              "DiffWalk               1.0  \n",
              "Sex                    1.0  \n",
              "Age                   13.0  \n",
              "Education              6.0  \n",
              "Income                 8.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8726ad28-9ac5-4bcb-a8c9-8f7b17c5489c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Diabetes_binary</th>\n",
              "      <td>253680.0</td>\n",
              "      <td>0.139333</td>\n",
              "      <td>0.346294</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HighBP</th>\n",
              "      <td>253680.0</td>\n",
              "      <td>0.429001</td>\n",
              "      <td>0.494934</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HighChol</th>\n",
              "      <td>253680.0</td>\n",
              "      <td>0.424121</td>\n",
              "      <td>0.494210</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CholCheck</th>\n",
              "      <td>253680.0</td>\n",
              "      <td>0.962670</td>\n",
              "      <td>0.189571</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BMI</th>\n",
              "      <td>253680.0</td>\n",
              "      <td>28.382364</td>\n",
              "      <td>6.608694</td>\n",
              "      <td>12.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>98.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Smoker</th>\n",
              "      <td>253680.0</td>\n",
              "      <td>0.443169</td>\n",
              "      <td>0.496761</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stroke</th>\n",
              "      <td>253680.0</td>\n",
              "      <td>0.040571</td>\n",
              "      <td>0.197294</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HeartDiseaseorAttack</th>\n",
              "      <td>253680.0</td>\n",
              "      <td>0.094186</td>\n",
              "      <td>0.292087</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PhysActivity</th>\n",
              "      <td>253680.0</td>\n",
              "      <td>0.756544</td>\n",
              "      <td>0.429169</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fruits</th>\n",
              "      <td>253680.0</td>\n",
              "      <td>0.634256</td>\n",
              "      <td>0.481639</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Veggies</th>\n",
              "      <td>253680.0</td>\n",
              "      <td>0.811420</td>\n",
              "      <td>0.391175</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HvyAlcoholConsump</th>\n",
              "      <td>253680.0</td>\n",
              "      <td>0.056197</td>\n",
              "      <td>0.230302</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AnyHealthcare</th>\n",
              "      <td>253680.0</td>\n",
              "      <td>0.951053</td>\n",
              "      <td>0.215759</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NoDocbcCost</th>\n",
              "      <td>253680.0</td>\n",
              "      <td>0.084177</td>\n",
              "      <td>0.277654</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GenHlth</th>\n",
              "      <td>253680.0</td>\n",
              "      <td>2.511392</td>\n",
              "      <td>1.068477</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MentHlth</th>\n",
              "      <td>253680.0</td>\n",
              "      <td>3.184772</td>\n",
              "      <td>7.412847</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PhysHlth</th>\n",
              "      <td>253680.0</td>\n",
              "      <td>4.242081</td>\n",
              "      <td>8.717951</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DiffWalk</th>\n",
              "      <td>253680.0</td>\n",
              "      <td>0.168224</td>\n",
              "      <td>0.374066</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>253680.0</td>\n",
              "      <td>0.440342</td>\n",
              "      <td>0.496429</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>253680.0</td>\n",
              "      <td>8.032119</td>\n",
              "      <td>3.054220</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Education</th>\n",
              "      <td>253680.0</td>\n",
              "      <td>5.050434</td>\n",
              "      <td>0.985774</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Income</th>\n",
              "      <td>253680.0</td>\n",
              "      <td>6.053875</td>\n",
              "      <td>2.071148</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8726ad28-9ac5-4bcb-a8c9-8f7b17c5489c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8726ad28-9ac5-4bcb-a8c9-8f7b17c5489c 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-8726ad28-9ac5-4bcb-a8c9-8f7b17c5489c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# returns the top 5 rows of the dataset\n",
        "df.head() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "F6NQxAcW3-Yn",
        "outputId": "35d8df7b-2720-4f46-cc31-364b3b0b8971"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Diabetes_binary  HighBP  HighChol  CholCheck   BMI  Smoker  Stroke  \\\n",
              "0              0.0     1.0       1.0        1.0  40.0     1.0     0.0   \n",
              "1              0.0     0.0       0.0        0.0  25.0     1.0     0.0   \n",
              "2              0.0     1.0       1.0        1.0  28.0     0.0     0.0   \n",
              "3              0.0     1.0       0.0        1.0  27.0     0.0     0.0   \n",
              "4              0.0     1.0       1.0        1.0  24.0     0.0     0.0   \n",
              "\n",
              "   HeartDiseaseorAttack  PhysActivity  Fruits  ...  AnyHealthcare  \\\n",
              "0                   0.0           0.0     0.0  ...            1.0   \n",
              "1                   0.0           1.0     0.0  ...            0.0   \n",
              "2                   0.0           0.0     1.0  ...            1.0   \n",
              "3                   0.0           1.0     1.0  ...            1.0   \n",
              "4                   0.0           1.0     1.0  ...            1.0   \n",
              "\n",
              "   NoDocbcCost  GenHlth  MentHlth  PhysHlth  DiffWalk  Sex   Age  Education  \\\n",
              "0          0.0      5.0      18.0      15.0       1.0  0.0   9.0        4.0   \n",
              "1          1.0      3.0       0.0       0.0       0.0  0.0   7.0        6.0   \n",
              "2          1.0      5.0      30.0      30.0       1.0  0.0   9.0        4.0   \n",
              "3          0.0      2.0       0.0       0.0       0.0  0.0  11.0        3.0   \n",
              "4          0.0      2.0       3.0       0.0       0.0  0.0  11.0        5.0   \n",
              "\n",
              "   Income  \n",
              "0     3.0  \n",
              "1     1.0  \n",
              "2     8.0  \n",
              "3     6.0  \n",
              "4     4.0  \n",
              "\n",
              "[5 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-defb09bc-1496-4645-95e0-2246f19bd627\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Diabetes_binary</th>\n",
              "      <th>HighBP</th>\n",
              "      <th>HighChol</th>\n",
              "      <th>CholCheck</th>\n",
              "      <th>BMI</th>\n",
              "      <th>Smoker</th>\n",
              "      <th>Stroke</th>\n",
              "      <th>HeartDiseaseorAttack</th>\n",
              "      <th>PhysActivity</th>\n",
              "      <th>Fruits</th>\n",
              "      <th>...</th>\n",
              "      <th>AnyHealthcare</th>\n",
              "      <th>NoDocbcCost</th>\n",
              "      <th>GenHlth</th>\n",
              "      <th>MentHlth</th>\n",
              "      <th>PhysHlth</th>\n",
              "      <th>DiffWalk</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 22 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-defb09bc-1496-4645-95e0-2246f19bd627')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-defb09bc-1496-4645-95e0-2246f19bd627 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-defb09bc-1496-4645-95e0-2246f19bd627');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# returns the number of rows and columns of the dataset\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IO4XUwBX4DW_",
        "outputId": "f59c5a13-1d93-45c4-bc2a-e42f10adfde3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(253680, 22)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# returns the name of all columns\n",
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aziOAFhN4U1b",
        "outputId": "2d5e59ab-ff16-41cb-c057-9ad63db3b2fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Diabetes_binary', 'HighBP', 'HighChol', 'CholCheck', 'BMI', 'Smoker',\n",
              "       'Stroke', 'HeartDiseaseorAttack', 'PhysActivity', 'Fruits', 'Veggies',\n",
              "       'HvyAlcoholConsump', 'AnyHealthcare', 'NoDocbcCost', 'GenHlth',\n",
              "       'MentHlth', 'PhysHlth', 'DiffWalk', 'Sex', 'Age', 'Education',\n",
              "       'Income'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking the number of missing values in the daataset\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5NAgh8Hl4Yde",
        "outputId": "d2cb9c1b-8fb7-4ad9-c7fa-61ac80d11bb7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Diabetes_binary         0\n",
              "HighBP                  0\n",
              "HighChol                0\n",
              "CholCheck               0\n",
              "BMI                     0\n",
              "Smoker                  0\n",
              "Stroke                  0\n",
              "HeartDiseaseorAttack    0\n",
              "PhysActivity            0\n",
              "Fruits                  0\n",
              "Veggies                 0\n",
              "HvyAlcoholConsump       0\n",
              "AnyHealthcare           0\n",
              "NoDocbcCost             0\n",
              "GenHlth                 0\n",
              "MentHlth                0\n",
              "PhysHlth                0\n",
              "DiffWalk                0\n",
              "Sex                     0\n",
              "Age                     0\n",
              "Education               0\n",
              "Income                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Handling missing values \n",
        "# removing the rows having missing values\n",
        "df.dropna(inplace = True)"
      ],
      "metadata": {
        "id": "V7z-5ODI4b-b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking duplicates\n",
        "\n",
        "duplicate = df[df.duplicated()]\n",
        "print(\"Duplicate Rows : \",len(duplicate))\n",
        "duplicate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "wrFlBLV-4lo1",
        "outputId": "8f3f0ceb-5f18-4df8-f640-ee0cc124a59f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Duplicate Rows :  24206\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Diabetes_binary  HighBP  HighChol  CholCheck   BMI  Smoker  Stroke  \\\n",
              "1242                1.0     1.0       1.0        1.0  27.0     1.0     0.0   \n",
              "1563                0.0     0.0       0.0        1.0  21.0     1.0     0.0   \n",
              "2700                0.0     0.0       0.0        1.0  32.0     0.0     0.0   \n",
              "3160                0.0     0.0       0.0        1.0  21.0     0.0     0.0   \n",
              "3332                0.0     0.0       0.0        1.0  24.0     0.0     0.0   \n",
              "...                 ...     ...       ...        ...   ...     ...     ...   \n",
              "253492              1.0     1.0       1.0        1.0  33.0     0.0     0.0   \n",
              "253550              0.0     0.0       0.0        1.0  25.0     0.0     0.0   \n",
              "253563              0.0     0.0       1.0        1.0  24.0     1.0     0.0   \n",
              "253597              0.0     0.0       0.0        1.0  24.0     0.0     0.0   \n",
              "253638              0.0     0.0       0.0        1.0  24.0     0.0     0.0   \n",
              "\n",
              "        HeartDiseaseorAttack  PhysActivity  Fruits  ...  AnyHealthcare  \\\n",
              "1242                     0.0           0.0     0.0  ...            1.0   \n",
              "1563                     0.0           1.0     1.0  ...            1.0   \n",
              "2700                     0.0           1.0     1.0  ...            1.0   \n",
              "3160                     0.0           1.0     1.0  ...            1.0   \n",
              "3332                     0.0           1.0     1.0  ...            1.0   \n",
              "...                      ...           ...     ...  ...            ...   \n",
              "253492                   0.0           1.0     1.0  ...            1.0   \n",
              "253550                   0.0           1.0     1.0  ...            1.0   \n",
              "253563                   0.0           1.0     1.0  ...            1.0   \n",
              "253597                   0.0           1.0     1.0  ...            1.0   \n",
              "253638                   0.0           1.0     1.0  ...            1.0   \n",
              "\n",
              "        NoDocbcCost  GenHlth  MentHlth  PhysHlth  DiffWalk  Sex   Age  \\\n",
              "1242            0.0      5.0       0.0      30.0       1.0  0.0  10.0   \n",
              "1563            0.0      1.0       0.0       0.0       0.0  0.0   4.0   \n",
              "2700            0.0      2.0       0.0       0.0       0.0  0.0   5.0   \n",
              "3160            0.0      1.0       0.0       0.0       0.0  0.0   4.0   \n",
              "3332            0.0      1.0       0.0       0.0       0.0  1.0   9.0   \n",
              "...             ...      ...       ...       ...       ...  ...   ...   \n",
              "253492          0.0      3.0       0.0       0.0       0.0  1.0   9.0   \n",
              "253550          0.0      1.0       0.0       0.0       0.0  0.0   7.0   \n",
              "253563          0.0      2.0       0.0       0.0       0.0  1.0   8.0   \n",
              "253597          0.0      2.0       0.0       0.0       0.0  0.0   5.0   \n",
              "253638          0.0      2.0       0.0       0.0       0.0  1.0   1.0   \n",
              "\n",
              "        Education  Income  \n",
              "1242          4.0     5.0  \n",
              "1563          6.0     8.0  \n",
              "2700          6.0     8.0  \n",
              "3160          6.0     8.0  \n",
              "3332          6.0     8.0  \n",
              "...           ...     ...  \n",
              "253492        6.0     6.0  \n",
              "253550        6.0     8.0  \n",
              "253563        6.0     8.0  \n",
              "253597        6.0     8.0  \n",
              "253638        4.0     6.0  \n",
              "\n",
              "[24206 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-75fa42e1-f835-41f2-ae9d-a2dc30c203fa\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Diabetes_binary</th>\n",
              "      <th>HighBP</th>\n",
              "      <th>HighChol</th>\n",
              "      <th>CholCheck</th>\n",
              "      <th>BMI</th>\n",
              "      <th>Smoker</th>\n",
              "      <th>Stroke</th>\n",
              "      <th>HeartDiseaseorAttack</th>\n",
              "      <th>PhysActivity</th>\n",
              "      <th>Fruits</th>\n",
              "      <th>...</th>\n",
              "      <th>AnyHealthcare</th>\n",
              "      <th>NoDocbcCost</th>\n",
              "      <th>GenHlth</th>\n",
              "      <th>MentHlth</th>\n",
              "      <th>PhysHlth</th>\n",
              "      <th>DiffWalk</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1242</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1563</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2700</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3160</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3332</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>8.0</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",
              "      <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>253492</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>253550</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>253563</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>253597</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>253638</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>24206 rows × 22 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-75fa42e1-f835-41f2-ae9d-a2dc30c203fa')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-75fa42e1-f835-41f2-ae9d-a2dc30c203fa 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-75fa42e1-f835-41f2-ae9d-a2dc30c203fa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing duplicate rows from the dataset\n",
        "df.drop_duplicates(inplace = True)"
      ],
      "metadata": {
        "id": "dh2lFNbG4oTf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "duplicate = df[df.duplicated()]\n",
        "print(\"Duplicate Rows : \", len(duplicate))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oGA53FHl4u0a",
        "outputId": "024be639-e78c-4409-be2d-4fecd43e93f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Duplicate Rows :  0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# in our dataset the label is diabetes column\n",
        "\n",
        "# This will return the label distribution count  \n",
        "df['Diabetes_binary'].value_counts()"
      ],
      "metadata": {
        "id": "Cz_JOAm34x0X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting the label distribution\n",
        "df['Diabetes_binary'].value_counts().plot(kind = 'bar', title = 'Label Distribution')"
      ],
      "metadata": {
        "id": "-ao-2XU149X_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "outputId": "5bb352fe-fe04-475f-e378-eb66a03a3b62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f51b027f790>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# over sampling of the dataset to get a balanced dataset\n",
        "class_0 = df[df['Diabetes_binary'] == 0]\n",
        "class_1 = df[df['Diabetes_binary'] == 1]\n",
        "\n",
        "# over sampling of the minority class 1\n",
        "class_1_over = class_1.sample(len(class_0), replace=True)\n",
        "\n",
        "# Creating a new dataframe with over sampled class 1 df and class 0 df\n",
        "df_new = pd.concat([class_1_over, class_0], axis=0)\n",
        "\n",
        "# plotting the new label distribution\n",
        "df_new['Diabetes_binary'].value_counts().plot(kind='bar', title='Label Distribution after Oversampling')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "tQBgIuMcivN_",
        "outputId": "2b46792d-cb36-4d86-b5ed-d70334f7ad86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f51b01153d0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df_new.drop('Diabetes_binary', axis = 1) # features\n",
        "y = df_new[['Diabetes_binary']] # labels\n",
        "\n",
        "# splitting the features and labels into train and test with test size = 20% and train size = 80%\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "        X, y, test_size=0.2, random_state=10)"
      ],
      "metadata": {
        "id": "NVGBACVPi0SD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initializing the model\n",
        "model_1 = RandomForestClassifier(n_estimators = 300, criterion = 'entropy',min_samples_split=10, random_state=0)\n",
        "\n",
        "# fitting the model on the train data\n",
        "model_1.fit(X_train, y_train)\n",
        "\n",
        "# predicting values on test data\n",
        "predictions = model_1.predict(X_test)"
      ],
      "metadata": {
        "id": "IAHwX1EHi--J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a new dataframe with true and predicted values\n",
        "pred_df = pd.DataFrame()\n",
        "pred_df['True values'] = y_test\n",
        "pred_df['Predicted values'] = predictions\n",
        "pred_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "AYv-kVBjjL0v",
        "outputId": "0321b395-7a8f-4894-ed24-4335e062d127"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        True values  Predicted values\n",
              "73626           0.0               0.0\n",
              "94946           1.0               1.0\n",
              "99292           0.0               0.0\n",
              "59038           0.0               0.0\n",
              "79699           0.0               0.0\n",
              "...             ...               ...\n",
              "227119          0.0               0.0\n",
              "200007          1.0               1.0\n",
              "248419          1.0               1.0\n",
              "18062           1.0               1.0\n",
              "240058          0.0               0.0\n",
              "\n",
              "[77751 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c783f93a-4ca2-4d31-94fe-29caee6039a6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>True values</th>\n",
              "      <th>Predicted values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>73626</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94946</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99292</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59038</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79699</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227119</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200007</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248419</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18062</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240058</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>77751 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c783f93a-4ca2-4d31-94fe-29caee6039a6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c783f93a-4ca2-4d31-94fe-29caee6039a6 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-c783f93a-4ca2-4d31-94fe-29caee6039a6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation\n",
        "\n",
        "# calculating the accuracy of the model\n",
        "accuracies = {}\n",
        "accuracy_1 = accuracy_score(y_test, predictions)\n",
        "accuracies['Random Forest Classifier'] = accuracy_1\n",
        "\n",
        "# calculating the classification report \n",
        "classificationreport = classification_report(y_test, predictions) \n",
        "\n",
        "# calculating the mse \n",
        "mse = mean_squared_error(y_test, predictions)\n",
        "\n",
        "# calculating the rmse \n",
        "rmse = math.sqrt(mse)\n",
        "print('\\nAccuracy score of Random Forest Classifier : ' + str(round(accuracy_1*100, 2)))\n",
        "print(\"\\n\"+\"*\"*50)\n",
        "print('\\nClassification_report : ')\n",
        "print(classificationreport)\n",
        "print(\"\\n\"+\"*\"*50)\n",
        "print('\\nMean squared error : '+ str(mse))\n",
        "print(\"\\n\"+\"*\"*50)\n",
        "print('\\nRoot mean squared error : '+ str(rmse))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7WLhs0E5jTYS",
        "outputId": "ae8d5093-1678-4a65-f83a-14e35229a780"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy score of Random Forest Classifier : 90.47\n",
            "\n",
            "**************************************************\n",
            "\n",
            "Classification_report : \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.95      0.85      0.90     38705\n",
            "         1.0       0.87      0.96      0.91     39046\n",
            "\n",
            "    accuracy                           0.90     77751\n",
            "   macro avg       0.91      0.90      0.90     77751\n",
            "weighted avg       0.91      0.90      0.90     77751\n",
            "\n",
            "\n",
            "**************************************************\n",
            "\n",
            "Mean squared error : 0.09531710203084205\n",
            "\n",
            "**************************************************\n",
            "\n",
            "Root mean squared error : 0.30873467902204\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " print('Confusion Matrix for Random Forest Classifier')\n",
        "\n",
        "# calculating and plotting the confusion matrix\n",
        "cm1 = confusion_matrix(y_test,predictions)\n",
        "plot_confusion_matrix(conf_mat=cm1,show_absolute=True,\n",
        "                                show_normed=True,\n",
        "                                colorbar=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "8eG1AFWYkcxH",
        "outputId": "c9480a59-f54f-48c0-efe1-3e69d5c4933d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix for Random Forest Classifier\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initializing the model\n",
        "model_2 = LogisticRegression()\n",
        "\n",
        "# fitting the model on the train data\n",
        "model_2.fit(X_train, y_train)\n",
        "\n",
        "# predicting values on test data\n",
        "predictions = model_2.predict(X_test)"
      ],
      "metadata": {
        "id": "u2uX_L57kctj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a new dataframe with true and predicted values\n",
        "pred_df = pd.DataFrame()\n",
        "pred_df['True values'] = y_test\n",
        "pred_df['Predicted values'] = predictions\n",
        "pred_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "_5zrgwxNkcqx",
        "outputId": "c79a1881-551d-4ca5-a296-c3fec0189988"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        True values  Predicted values\n",
              "73626           0.0               0.0\n",
              "94946           1.0               0.0\n",
              "99292           0.0               0.0\n",
              "59038           0.0               0.0\n",
              "79699           0.0               0.0\n",
              "...             ...               ...\n",
              "227119          0.0               0.0\n",
              "200007          1.0               1.0\n",
              "248419          1.0               1.0\n",
              "18062           1.0               1.0\n",
              "240058          0.0               0.0\n",
              "\n",
              "[77751 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5ff13406-8087-44d9-a0df-40def513a60d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>True values</th>\n",
              "      <th>Predicted values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>73626</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94946</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99292</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59038</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79699</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227119</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200007</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248419</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18062</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240058</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>77751 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5ff13406-8087-44d9-a0df-40def513a60d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5ff13406-8087-44d9-a0df-40def513a60d 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-5ff13406-8087-44d9-a0df-40def513a60d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation\n",
        "\n",
        "# calculating the accuracy of the model\n",
        "accuracy_2 = accuracy_score(y_test, predictions)\n",
        "accuracies['Logistic Regression'] = accuracy_2\n",
        "# calculating the classification report \n",
        "classificationreport = classification_report(y_test, predictions) \n",
        "\n",
        "# calculating the mse \n",
        "mse = mean_squared_error(y_test, predictions)\n",
        "\n",
        "# calculating the rmse \n",
        "rmse = math.sqrt(mse)\n",
        "print('\\nAccuracy score of Logistic Regression : ' + str(round(accuracy_2*100, 2)))\n",
        "print(\"\\n\"+\"*\"*50)\n",
        "print('\\nClassification_report : ')\n",
        "print(classificationreport)\n",
        "print(\"\\n\"+\"*\"*50)\n",
        "print('\\nMean squared error : '+ str(mse))\n",
        "print(\"\\n\"+\"*\"*50)\n",
        "print('\\nRoot mean squared error : '+ str(rmse))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_etpa9Gakcn-",
        "outputId": "34b0088f-ad68-49f3-9965-37576c7ee04e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy score of Logistic Regression : 72.78\n",
            "\n",
            "**************************************************\n",
            "\n",
            "Classification_report : \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.74      0.70      0.72     38705\n",
            "         1.0       0.72      0.76      0.74     39046\n",
            "\n",
            "    accuracy                           0.73     77751\n",
            "   macro avg       0.73      0.73      0.73     77751\n",
            "weighted avg       0.73      0.73      0.73     77751\n",
            "\n",
            "\n",
            "**************************************************\n",
            "\n",
            "Mean squared error : 0.272240871500045\n",
            "\n",
            "**************************************************\n",
            "\n",
            "Root mean squared error : 0.5217670663237044\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Confusion Matrix for Logistic Regression')\n",
        "\n",
        "# calculating and plotting the confusion matrix\n",
        "cm1 = confusion_matrix(y_test,predictions)\n",
        "plot_confusion_matrix(conf_mat=cm1,show_absolute=True,\n",
        "                                show_normed=True,\n",
        "                                colorbar=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "2UUwnAEXkck-",
        "outputId": "39f8c834-483b-45fd-baef-a6b9257af7e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix for Logistic Regression\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initializing the model\n",
        "\n",
        "model_3 = DecisionTreeClassifier(max_depth = 3)\n",
        "\n",
        "# fitting the model on the train data\n",
        "model_3.fit(X_train, y_train)\n",
        "\n",
        "# predicting values on test data\n",
        "predictions = model_3.predict(X_test)"
      ],
      "metadata": {
        "id": "Ldf5s1Eckb-6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting decision tree classifier\n",
        "plt.figure(figsize = (20,10))\n",
        "tree.plot_tree(model_3)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "id": "Eg1A3pummxqj",
        "outputId": "59caef18-b458-4306-ded6-031b9b4cde6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a new dataframe with true and predicted values\n",
        "pred_df = pd.DataFrame()\n",
        "pred_df['True values'] = y_test\n",
        "pred_df['Predicted values'] = predictions\n",
        "pred_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ThhWzzQEm0V5",
        "outputId": "29fe8e25-4b87-4876-c4a8-ebe7ef86a729"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        True values  Predicted values\n",
              "73626           0.0               0.0\n",
              "94946           1.0               0.0\n",
              "99292           0.0               0.0\n",
              "59038           0.0               0.0\n",
              "79699           0.0               0.0\n",
              "...             ...               ...\n",
              "227119          0.0               0.0\n",
              "200007          1.0               1.0\n",
              "248419          1.0               1.0\n",
              "18062           1.0               1.0\n",
              "240058          0.0               0.0\n",
              "\n",
              "[77751 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-00e011a3-3d46-4375-b0c7-66b80e69f156\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>True values</th>\n",
              "      <th>Predicted values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>73626</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94946</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99292</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59038</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79699</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227119</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200007</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248419</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18062</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240058</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>77751 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-00e011a3-3d46-4375-b0c7-66b80e69f156')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-00e011a3-3d46-4375-b0c7-66b80e69f156 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-00e011a3-3d46-4375-b0c7-66b80e69f156');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation\n",
        "\n",
        "# calculating the accuracy of the model\n",
        "accuracy_3 = accuracy_score(y_test, predictions)\n",
        "accuracies['Decision Tree Classifier'] = accuracy_3\n",
        "\n",
        "# calculating the classification report \n",
        "classificationreport = classification_report(y_test, predictions) \n",
        "\n",
        "# calculating the mse \n",
        "mse = mean_squared_error(y_test, predictions)\n",
        "\n",
        "# calculating the rmse \n",
        "rmse = math.sqrt(mse)\n",
        "print('\\nAccuracy score of Decision Tree Classifier : ' + str(round(accuracy_3*100, 2)))\n",
        "print(\"\\n\"+\"*\"*50)\n",
        "print('\\nClassification_report : ')\n",
        "print(classificationreport)\n",
        "print(\"\\n\"+\"*\"*50)\n",
        "print('\\nMean squared error : '+ str(mse))\n",
        "print(\"\\n\"+\"*\"*50)\n",
        "print('\\nRoot mean squared error : '+ str(rmse))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e4WSJLXQm4z_",
        "outputId": "1c07ab01-2851-49bb-d23c-9970ecc7bb0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy score of Decision Tree Classifier : 70.06\n",
            "\n",
            "**************************************************\n",
            "\n",
            "Classification_report : \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.70      0.71      0.70     38705\n",
            "         1.0       0.71      0.69      0.70     39046\n",
            "\n",
            "    accuracy                           0.70     77751\n",
            "   macro avg       0.70      0.70      0.70     77751\n",
            "weighted avg       0.70      0.70      0.70     77751\n",
            "\n",
            "\n",
            "**************************************************\n",
            "\n",
            "Mean squared error : 0.2994045092667618\n",
            "\n",
            "**************************************************\n",
            "\n",
            "Root mean squared error : 0.5471786812977657\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Confusion Matrix for Decision Tree Classifier')\n",
        "\n",
        "# calculating and plotting the confusion matrix\n",
        "cm1 = confusion_matrix(y_test,predictions)\n",
        "plot_confusion_matrix(conf_mat=cm1,show_absolute=True,\n",
        "                                show_normed=True,\n",
        "                                colorbar=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "meHeN2wOnA1C",
        "outputId": "3e417abe-0807-4467-d4af-c1fa5641c70d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix for Decision Tree Classifier\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initializing the model\n",
        "\n",
        "model_4 = KNeighborsClassifier(n_neighbors = 5, p = 2)\n",
        "\n",
        "# fitting the model on the train data\n",
        "model_4.fit(X_train, y_train)\n",
        "\n",
        "# predicting values on test data\n",
        "predictions = model_4.predict(X_test)"
      ],
      "metadata": {
        "id": "AwENWcTbnE3h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a new dataframe with true and predicted values\n",
        "pred_df = pd.DataFrame()\n",
        "pred_df['True values'] = y_test\n",
        "pred_df['Predicted values'] = predictions\n",
        "pred_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "wobdLDPOnILE",
        "outputId": "c3fcaf9c-98ad-44f2-88f9-d67c6b3cebc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        True values  Predicted values\n",
              "73626           0.0               0.0\n",
              "94946           1.0               1.0\n",
              "99292           0.0               0.0\n",
              "59038           0.0               0.0\n",
              "79699           0.0               0.0\n",
              "...             ...               ...\n",
              "227119          0.0               0.0\n",
              "200007          1.0               1.0\n",
              "248419          1.0               1.0\n",
              "18062           1.0               1.0\n",
              "240058          0.0               0.0\n",
              "\n",
              "[77751 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fe2eedfb-2b66-4230-96d1-e2d18edc7508\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>True values</th>\n",
              "      <th>Predicted values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>73626</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94946</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99292</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59038</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79699</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227119</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200007</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248419</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18062</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240058</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>77751 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe2eedfb-2b66-4230-96d1-e2d18edc7508')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fe2eedfb-2b66-4230-96d1-e2d18edc7508 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-fe2eedfb-2b66-4230-96d1-e2d18edc7508');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation\n",
        "\n",
        "# calculating the accuracy of the model\n",
        "accuracy_4 = accuracy_score(y_test, predictions)\n",
        "accuracies['KNeighborsClassifier'] = accuracy_4\n",
        "  \n",
        "# calculating the classification report \n",
        "classificationreport = classification_report(y_test, predictions)  \n",
        "\n",
        "# calculating the mse\n",
        "mse = mean_squared_error(y_test, predictions) \n",
        "\n",
        "# calculating the rmse \n",
        "rmse = math.sqrt(mse)\n",
        "print('\\nAccuracy score of K - Nearest Neighbors : ' + str(round(accuracy_4*100, 2)))\n",
        "print(\"\\n\"+\"*\"*50)\n",
        "print('\\nClassification_report : ')\n",
        "print(classificationreport)\n",
        "print(\"\\n\"+\"*\"*50)\n",
        "print('\\nMean squared error : '+ str(mse))\n",
        "print(\"\\n\"+\"*\"*50)\n",
        "print('\\nRoot mean squared error : '+ str(rmse))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WMayAtUKnRyO",
        "outputId": "58efa001-4f4b-470a-ee7e-636ae43bf2a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy score of K - Nearest Neighbors : 82.02\n",
            "\n",
            "**************************************************\n",
            "\n",
            "Classification_report : \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.93      0.69      0.79     38705\n",
            "         1.0       0.76      0.94      0.84     39046\n",
            "\n",
            "    accuracy                           0.82     77751\n",
            "   macro avg       0.84      0.82      0.82     77751\n",
            "weighted avg       0.84      0.82      0.82     77751\n",
            "\n",
            "\n",
            "**************************************************\n",
            "\n",
            "Mean squared error : 0.17975331506990264\n",
            "\n",
            "**************************************************\n",
            "\n",
            "Root mean squared error : 0.4239732480592409\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Confusion Matrix for K - Nearest Neighbors')\n",
        "\n",
        "# calculating and plotting the confusion matrix\n",
        "cm1 = confusion_matrix(y_test,predictions)\n",
        "plot_confusion_matrix(conf_mat=cm1,show_absolute=True,\n",
        "                                show_normed=True,\n",
        "                                colorbar=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "OoM5kwUUnTYC",
        "outputId": "db388200-fe02-42b3-e0d1-efab1cf42b34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix for K - Nearest Neighbors\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEGCAYAAADyuIefAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3dfVxUVf7A8c9lAEWeUQEf0LIwWXw2NRQx0RGVCEQRtdp0dW3VMrWtNB/zec1cS39ZRutamYkoYGKKUoJoalmKmlamJKgMyaOiMDDM7w9ygkVlRhl58PvuNb3g3HPPPUflyzn33nOOotfr9QghhADAoqYrIIQQtYkERSGEKEeCohBClCNBUQghypGgKIQQ5VjWdAXK23fwW65cL63paggTeDRuVNNVECawKC2he7cu91TGexticbRraFTeHh3b4OnpeU/Xu99qVVC8cr2UeV/n1XQ1hAnefe6Rmq6CMIHdjcv3XIajXUPGL/rCqLzffzb5nq93v9WqoCiEqCOU+nvnTYKiEMI0igIWqpquhdlIUBRCmE5RaroGZiNBUQhhOhk+CyHETYr0FIUQogLpKQohxB8U6nVPsf6GeyGEmfzx9NmYzx0UFRUxfPhwnn76aQIDA3n33XcBmDFjBv7+/gQHBxMcHMzp06cB0Ov1LFq0CLVaTVBQEKdOnTKUFR0dzcCBAxk4cCDR0dGG9JMnTxIUFIRarWbRokUYs1Ki9BSFEKarhuGztbU1GzZswNbWluLiYkaPHo2fnx8Ar732GoMGDaqQPykpidTUVOLj4zl+/Djz589ny5Yt5ObmsmbNGrZu3YqiKISGhuLv74+joyPz589n4cKFdOrUib///e8kJSXRt2/fO9ZLeopCCBP98aDFmM+dSlEUbG1tASgpKaGkpATlDuckJCQQEhKCoih07tyZ/Px8MjMzSU5Opnfv3jg5OeHo6Ejv3r3Zv38/mZmZXLt2jc6dO6MoCiEhISQkJFTZOgmKQgjTKJT1FI34ZGdnExoaavhs3ry5QlE6nY7g4GB69epFr1696NSpEwD//ve/CQoKYsmSJWi1WgA0Gg3u7u6Gc93d3dFoNJXS3dzcbpl+M39VZPgshDCdkcNnFxcXtm3bdtvjKpWK2NhY8vPzmTx5Mj///DPTp0+nadOmFBcXM2fOHNatW8eLL75YXTWvkvQUhRAmUkClMu5jJAcHB3r27Mn+/ftxdXVFURSsra0JDQ3lxIkTQFkPMCMjw3BORkYGbm5uldI1Gs0t02/mr4oERSGEaW6+knOP9xSzs7PJz88HoLCwkIMHD9KmTRsyMzOBsqfNe/fuNSw95u/vT0xMDHq9nmPHjmFvb4+rqyu+vr4kJyeTl5dHXl4eycnJ+Pr64urqip2dHceOHUOv1xMTE0P//v2rbJ4Mn4UQJlKq5elzZmYmM2bMQKfTodfrGTRoEP369eOvf/0rOTk56PV62rVrx5tvvglA3759SUxMRK1WY2Njw5IlSwBwcnJi0qRJDB8+HIDJkyfj5OQEwLx585g5cyaFhYX4+fkZnm7fsXW1aYvTqL2HZT3FOubd57rWdBWECexuXKZnlw73VMbG2K8Zv+aIUXm/f/dpvLy87ul695v0FIUQppNpfkIIUU49nuYnQVEIYRpZZFYIIf6HDJ+FEOImWU9RCCEqkp6iEEL84ebc53pKgqIQwkTyoEUIISqSe4pCCHFT9Uzzq60kKAohTFPP92iRoCiEMNmdVsiu6yQoCiFMJkFRCCHKUSwkKAohBFDWS5SeohBClCNBUQghypGgKIQQ5dXfmChBUQhhOukpCiHEHxRFwcJCZrQIIYSB9BSFEKK8+hsTqb99YCGE2dx8V7Gqz50UFRUxfPhwnn76aQIDA3n33XcBSEtLIywsDLVazdSpU9FqtQBotVqmTp2KWq0mLCyM9PR0Q1kffPABarWagIAA9u/fb0hPSkoiICAAtVrNunXrjGqbBEUhhGmU6gmK1tbWbNiwge3btxMTE8P+/fs5duwYK1asYMyYMezZswcHBweioqIA2LJlCw4ODuzZs4cxY8awYsUKAM6ePUtcXBxxcXFERETw5ptvotPp0Ol0LFiwgIiICOLi4tixYwdnz56tsnkSFIUQJlMsFKM+dyxDUbC1tQWgpKSEkpISFEXh0KFDBAQEADB06FASEhIA+Oqrrxg6dCgAAQEBfPPNN+j1ehISEggMDMTa2hoPDw9at25NSkoKKSkptG7dGg8PD6ytrQkMDDSUdSdyT1EIYRIF46f5ZWdnExoaavg+PDyc8PBww/c6nY7Q0FAuXLjA6NGj8fDwwMHBAUvLstDk7u6ORqMBQKPR0KxZMwAsLS2xt7cnJycHjUZDp06dDGW6ubkZznF3d6+QnpKSUmWdJSgKIUxmbFB0cXFh27Zttz2uUqmIjY0lPz+fyZMnc+7cueqq4l2ToGgkd8cGLB3egcZ21uj1sOXbdD795gIAo5/wYNQTrSgt1ZP00++8vfsXLC0UFgz1xqu5PSoLhe0/XCYi6TwAz/q0Ynj3lihA1HfpfHLwguE6typL3J23Zk3h8L49OLk0IeKLspvvibti+XjNW1w49zNrIuN5rH1nAEqKi3l7zlR++fEEpboSBgSPYPSEqQAc2Z/Ae0tmUVqqY/DwZxn195cB+OHQfj5YPo+S4mI8vTvyz0XvoLJ8AH6klOp/JcfBwYGePXty7Ngx8vPzKSkpwdLSkoyMDNzc3ICynt7ly5dxd3enpKSEq1ev4uzsjJubGxkZGYayNBqN4Zzbpd+J3FM0UkmpnuVf/sTT7xxk1PuHGfWEB480taXHw874e7kSuvogwe8eZH3ybwAEtHfDylJh6OpvGPHeIUb0aElzp4Y86mrH8O4tGbn2EKFrvqHvY01p5WIDcNuyxN0JCBnJ0nWfV0h7yNOL+av/S4fHfSqkJ+7eTrFWS8T2JN6L2kvc5o/JuHgBnU7H6oUzWLLucz764gBfx0Xz29mfKC0tZfnMF5n99odEfLEft+YexMdUvFZ9Vh0PWrKzs8nPzwegsLCQgwcP8sgjj9CzZ092794NQHR0NP7+/gD4+/sTHR0NwO7du3niiSdQFAV/f3/i4uLQarWkpaWRmppKx44d6dChA6mpqaSlpaHVaomLizOUdScPwK+16nHlqpYrV8teDbiu1XHu9wJcHRowvHtLIpLOU6zTA5BdUJZHDzSytkRlodDAUkWxrpSCohLat3QkJS2XwuJSAL5LzWGAtxv/2Z9KeE+PW5Yl7k7H7r3IuHihQlrrR9reMq+iKBTeuI6upISiwkIsraxoZGvPTynf07zVQzT3eAiAJ4eEcOCrLxni0hhLK2taPvwIAN169WXTuncYPPxZs7ap1qiGjmJmZiYzZsxAp9Oh1+sZNGgQ/fr149FHH2XatGmsWrUKLy8vwsLCABg+fDivvvoqarUaR0dH/v3vfwPg6enJ4MGDGTJkCCqVirlz56JSle02OHfuXMaPH49Op2PYsGF4enpWWS8JinehuVNDvJrZk5Kexz8Ht6XbQ868rPakqETHii9/5uTFfOJPaujn1ZR9M/rS0ErF8p1nyLtRwlnNNV5WP4qjjRVFJTr6tG3CqYtlvy0fatLolmUJ8/MbGMTBhC8Z4deeosIb/GPGQhycnLmSeRlX9xaGfE3dmnMm5SiOzo3RlZTw08ljPNa+M0nxX5CZcakGW3D/KFTPNL927doRExNTKd3Dw8PwGk55DRo0MLzL+L8mTpzIxIkTK6X37duXvn37mlQvCYomamStYtXoziyL+4mCIh0qCwscbawY9f5hOrR04O2RnQh4ez8dWjpSWgr9liXiYGPFx3/vzjdnszn3ewEfJaXy4dhu3NDqOHP5KqWlZT3D25UlzO/Mie+xUKnYnHiCq/m5THs2iK4+frfNrygKs99ex9plsynWaunW60lUqgfkbpQZ7inWJhIUTWBpobBqdCfijl9m74+ZAGjyCtl7quzx/4n0fEr1epwbWRHYyZ3kX65QUqonu0DLDxdy8W7hQHrODbYdvci2oxcBeFn9KJr8ojuWlXO9uAZa+2D5asdWuvv6Y2llhXPjpnh37cHPJ4/h6t6CzIyLhny/ay7R2K3stZC/dOnOqk93APDdga9J/+3XGql7jai/MVEetJhiQag35zIL2HDgzwcgCacz6dHGBYDWjRthpbIg53oxl3ML6flHuo2Vik4ejpz/vQAAF1trAJo5NmSAtxtxxy/fsSxhfq7NWnLscFmv/Mb1Ak4fP0qrNp481qELF387z+X03yjWatm3M4Ze/QYBkJP1OwBabRGbI1YTFD6mpqp/31XHg5bayqw9xaSkJBYvXkxpaSlhYWFMmDDBnJczq66tnQju0pyfMq6y9cUnAFgVf5booxdZGOpNzJReFOtKmbX1JACbDqexKNSb2Cm9UBSIPnqJnzXXys4b3QmnRlaU6PQs2n6aq4UlALctS9ydxa9M4PiRA+TlZjPyyY48/+Jr2Ds6s2bxTPKys5j1j9E80s6bf0VsIXj033hr1hTGPeWLHj0BQ0fR5jFvAF6avZQZ40dQWlrKoNBRPOTZDoDI//wfh/fFU1paStDIMXR5ok9NNve+qqsBzxiKXq/Xm6NgnU5HQEAA69evx83NjeHDh7Ny5UoeffTR254Ttfcw877OM0d1hJm8+1zXmq6CMIHdjcv07NLhnsrYvPsbXt+ZZVTeL//xCF5eXvd0vfvNbMPnu513KISo3RTFuHnPdXUbVLMFRY1GU2ne4c35iOVt3ryZ0NBQQkND0RddM1d1hBDVSO4pmlH5CeJRew8DMnwWorarqwHPGGbrKd5pPmJd1sDSgv+OfxwLBYK7NGfntN7snNab4C7Nb3vO6Cc8+GJqb2Kn9OKVgLI36q1UCotCvYl+yYdtL/rQ/WFnQ/6Isd1waFjjv6/qjaLCG0x/7ml0Oh3xMZ/zfEAPng/ocdtpeevfWcrfg/vywtAneX1cGFcyy/4dXzj3Cy+NHMzgji2I/M//GfIXa7VMezYIXUnJfWlPbaAoxn3qIrMFxbudd1jbhXZrwd5Tmdg3tGSifxtGvX+YkWsPM9G/zS0D2e3mMw9/vCUAQ1d/w/j1R3l18GOGf0RfHLvMyCc87lub6rtdWz/DVx1IwdV8Pv6/FazevJs1kfF8/H8ruJqXWyn/iHEv8mFsIh9E7+OJJ9V8+l7ZYqb2jk5MnrWEsL9NqpDfytqaLj5+7Puy8uyMeqmaFpmtrcwWFC0tLQ3zDocMGcLgwYONmndY2wV2asZXpzPp7dmEb85mkXejhPzCEr45m4Vv2yaV8t9uPvMjrrYcPpdtSLtaWEz7Fg4AfH06kyEdm92nFtV/CTu20qv/YL478DXdevXFwckZe0cnuvXqy7fJX1XKb2tnb/j6xo3r3HxT2blxU9p16ILK0qrSOb37Dybhi8pT0+ojBbCwUIz61EVmHaPdzbzD2sxKpeDhYsOl3EIGtm9ARl6h4ZgmvwhXhwaVzrndfOafMq7Sr11TdqZk4O7YkL80d8DdsSEn0vPJLyzBWqXgaGNF3g15efteFGu1XE5Pxb1FK5J2f0FT9z9vczR1a84VzeVbnvefVYvZExuJrZ0DKzZEV3mdhzy9+OnksWqrd21XRzuBRpEZLSZwamRNfqFp943Kz2d+e9fPvD2ybIXgbUcvockvInJST2YEPsaxC7noSv98ZTS7QHvLICtMk5ebjZ29o8nn/W3qLDZ9fRz/oGHEbvyoyvwqlQpLKyuuFzwAb1AoxvUS62pPUYKiCYqKdTSwLPsjy8wvwt2xoeGYm0MDMv+Yw1ze7eYz60r1/GvnTwxbc4iXPj2GfUMrfrty3XCetaWKomKdmVtU/zVo0BBtUdnfSxO3ZvxebiWb3zWXaOJ259sU/Z8azv74HUZdq1irxdq6/v8iU5AHLeIP+YUlWCgK1pYWHPjlCr0ebYJDQ0scGlrS69EmHPjlSqVzbjefuaGVBTZWZWu++Tzigq5Uz69/zI0GaGJvzcXcwkrlCdPYOzpRWqpDW1TI4737cfTAPq7m5XI1L5ejB/bxeO9+lc5JT/1zYYeDX32JR5vbz8K6KS8nG0dnFyytKt9vrI/q84MWee/DRAfPXqFraycO/ZrN+/t+ZfOksnnQa7/+lbwbZUPrN4f+hcgj6Zy6mH/b+cwuttasG9ONUr2ezPwiZkSdMFzDu4UDKRfyKgynxd3r1utJThw9TLdefXlm4nQmj1AD8OykV3BwKnsV6u3ZU3lq5Bgea9+ZiJULST//K4qFBW7NWzJ1ftnT5+zfNUwKU3P92lUUCwu2ffwBH+04gK2dPcePJNOzr7rG2ni/1dF4ZxSzzX2+G3Vh7rNXc3v+2qs1M6PMt1jDjMDH+Pr074an07VZXZj7/Mup42zd8AEzlr9ntmvMf2kM46fPMazEXVtVx9znrQmHWZBk3L3Tz0c2r3Nzn6WnaKLTl65y5Hw2FgqYqyN3VnOtTgTEusLTuxOdevZGp9MZlqmvTsVaLb36D671AbE61eeeogTFuxB91LzLzkd9d7HqTMIkg4c9Y7ayraytGRgSXnXGeqSu3i80hgRFIYTJ6nFMlKAohDBRHX6ybAwJikIIk9x8T7G+kqAohDBZXZ2tYgx5eVsIYbLqeHn78uXLPPfccwwZMoTAwEA2bNgAwOrVq+nTpw/BwcEEBweTmJhoOOeDDz5ArVYTEBDA/v1/bv+blJREQEAAarWadevWGdLT0tIICwtDrVYzdepUtFptlW2TnqIQwjTVNIVPpVIxY8YMvL29uXbtGsOGDaN3794AjBkzhnHjxlXIf/bsWeLi4oiLi0Oj0TB27Fh2794NwIIFCyrsB+Xv78+jjz7KihUrGDNmDIGBgcydO5eoqChGjx59x3pJT1EIYZKye4r33lN0dXXF27tsx0Q7OzvatGlzyy1LbkpISCAwMBBra2s8PDxo3bo1KSkpt90PSq/Xc+jQIQICAgAYOnSoUftESVAUQpjM2AUhsrOzDXswhYaGsnnz5luWl56ezunTp+nUqWwVqY0bNxIUFMTMmTPJyyub5Xa7fZ9ul56Tk4ODgwOWlmUDYnd39zsG3Ztk+CyEMJmxD1pcXFzYtm3bHfMUFBQwZcoU3njjDezs7Bg1ahSTJk1CURTeeecdli1bxtKlS6uj2kaRnqIQwjRGDp2NeZexuLiYKVOmEBQUxMCBAwFo0qQJKpUKCwsLwsLCOHGibLGU2+37dLt0Z2dn8vPzKflj75yMjAyj9omSoCiEMEl13VPU6/XMmjWLNm3aMHbsWEN6Zmam4eu9e/catjHx9/cnLi4OrVZLWloaqampdOzY8bb7QSmKQs+ePQ0PY6Kjo43aJ0qGz0IIk1XH0+ejR48SGxtL27ZtCQ4OBmD69Ons2LGDM2fOANCiRQsWLFgAgKenJ4MHD2bIkCGoVCrmzp1rWODj5n5QOp2OYcOGGQLpq6++yrRp01i1ahVeXl6EhYVV3TZZOkzci7qwdJj4U3UsHRaz71tWHTNu76C1Ac6ydJgQov6TaX5CCPEHRanf0/wkKAohTGZRj7uKEhSFECarxzFRgqIQwlSynqIQQhgoCtTjW4oSFIUQpnsgH7R06dLF0EW++Sqjoijo9XoUReH777+/PzUUQtQ6Cg9gUPzhhx/uZz2EEHWEQv0ePhs19/m7775j69atQNlSQGlpaWatlBCidquuBSFqoyqD4po1a4iIiDAs8V1cXMyrr75q9ooJIWopI9dSrKMxseqguGfPHtauXYuNjQ1QtnxPQUGB2SsmhKidFBQsFOM+dVGVT5+trKwqdIWvX79u9koJIWq3B/Lp802DBw9m7ty55OfnExkZydatWxkxYsT9qJsQopaqo51Ao1QZFMeNG8eBAwewtbXl/PnzTJkyxbDjlhDiwVP28nb9jYpGvbzdtm1bCgsLURSFtm3bmrtOQoharv6GRCMetGzZsoWwsDD27NnD7t27CQ8PJyoq6n7UTQhRS9XnV3Kq7ClGREQQHR2Ns7MzADk5OYwcOZLhw4ebvXJCiNqpHj9nqTooOjs7Y2tra/je1tbWECCFEA8ehQf06fP69esBaNWqFSNGjKB///4oikJCQgKPPfbYfaugEKKWqcNDY2PcNijefEG7VatWtGrVypDev39/89dKCFGr1eOO4u2D4osvvng/6yGEqCNu7vtcX1V5TzE7O5sPP/yQs2fPUlRUZEj/+OOPzVoxIUTtVR0h8fLly7z22mtkZWWhKAojRozg+eefJzc3l2nTpnHx4kVatGjBqlWrcHR0RK/Xs3jxYhITE2nYsCHLli3D29sbKNvofu3atQBMnDiRoUOHAnDy5ElmzpxJYWEhffv2ZdasWVUG9CpfyfnnP/9JmzZtSE9P58UXX6RFixZ06HBv+8YKIeo2lYVi1OeOZahUzJgxg507d7J582Y+++wzzp49y7p16/Dx8SE+Ph4fHx/DYjRJSUmkpqYSHx/PwoULmT9/PgC5ubmsWbOGyMhItmzZwpo1a8jLK9s/fv78+SxcuJD4+HhSU1NJSkqqsm1VBsXc3FzCwsKwtLSkR48eLF26lEOHDlVZsBCifipbAefe31N0dXU19PTs7Oxo06YNGo2GhIQEQkJCAAgJCWHv3r0AhnRFUejcuTP5+flkZmaSnJxM7969cXJywtHRkd69e7N//34yMzO5du0anTt3RlEUQkJCSEhIqLJ9VQ6fLS0tDQ3Yt28frq6uhigshHgwGXtLMTs7m9DQUMP34eHhhIeHV8qXnp7O6dOn6dSpE1lZWbi6ugLQtGlTsrKyANBoNLi7uxvOcXd3R6PRVEp3c3O7ZfrN/FWpMihOnDiRq1ev8vrrr7Nw4UIKCgqYOXNmlQULIeovY+c+u7i4sG3btjvmKSgoYMqUKbzxxhvY2dlVOFYTM2OqDIr9+vUDwN7enk8++cTsFRJC1H7VFaeKi4uZMmUKQUFBDBw4EIDGjRuTmZmJq6srmZmZuLi4AGU9wIyMDMO5GRkZuLm54ebmxpEjRwzpGo2GHj163DZ/VW4bFBcuXHjHCD179uwqCzeVV3MHjr7Zs9rLFebj3F1e3apLImYH0bPLvT0oVapp32e9Xs+sWbNo06YNY8eONaT7+/sTExPDhAkTiImJMbwb7e/vz6effkpgYCDHjx/H3t4eV1dXfH19WblypeG2XnJyMtOnT8fJyQk7OzuOHTtGp06diImJ4bnnnquyXrcNiu3bt7/XNgsh6iMFVEYHRf1tjxw9epTY2Fjatm1LcHAwANOnT2fChAlMnTqVqKgomjdvzqpVqwDo27cviYmJqNVqbGxsWLJkCQBOTk5MmjTJsB7D5MmTcXJyAmDevHmGV3L8/Pzw8/Orunn6m/uX1gKnfjzNI229aroawgTSU6xbImYH8cywgHsqI/6bH9iZaWNU3hfa6vHyqls/00atpyiEEOU9kNP8hBDidoy/p1hrBqJGk6AohDCJQv3uKVY5o+X8+fM8//zzPPXUUwCcOXOG9957z+wVE0LUUg/6vs9z5szhlVdeMcxsadeuHTt37jR7xYQQtZMCWCqKUZ+6qMrh840bN+jYsWOFNJVKZbYKCSFqvzoa74xi1HYEFy5cMNxY3bVrF02bNjV7xYQQtdcDvcXpvHnzmDNnDufOnaNPnz60bNmSt956637UTQhRC5UtMlvTtTCfKoOih4cH//3vf7l+/TqlpaWVJmwLIR4wSv1++lxlUFyzZs0t02W7AiEeTApVLyBbl1UZFBs1amT4uqioiH379tGmTRuzVkoIUbvV45hYdVD829/+VuH7cePGMW7cOLNVSAhR+ynVsktL7WTyjJYbN25UWKNMCPFgqe8zWqoMikFBQYavS0tLyc7OZvLkyWatlBCiFnvQH7S8//77f2a2tKRx48aG2S1CiAfTA7vvs06nY9y4cezatet+1UcIUcspgKrKCcJ11x2bplKpePjhh7l06dL9qo8Qog6wUBSjPnVRlePg/Px8AgMD6dixIzY2f662W35YLYR4cCgP+j3Fl19++X7UQwhRh9TRTqBRqrwzkJiYSI8ePSp8EhMT70fdhBC1lAWKUZ+6qMqgePDgwUppSUlJZqmMEKJuqM+LzN52+PzZZ5+xadMm0tLSKryrWFBQQNeuXe9L5YQQtY+CgmU9vql426AYFBSEn58fK1eu5JVXXjGk29raGvZUFUI8gKqxFzhz5kz27dtH48aN2bFjBwCrV68mMjISFxcXoGwv6L59+wLwwQcfEBUVhYWFBbNnz6ZPnz5A2eh18eLFlJaWEhYWxoQJEwBIS0tj+vTp5Obm4u3tzfLly7G2tr5jnW47fLa3t6dly5asXLmSFi1aGD4SEIV4sJVN86ueV3JCQ0OJiIiolD5mzBhiY2OJjY01BMSzZ88SFxdHXFwcERERvPnmm+h0OnQ6HQsWLCAiIoK4uDh27NjB2bNnAVixYgVjxoxhz549ODg4EBUVVWWd6vErmEIIc6mue4rdu3fH0dHRqGsmJCQQGBiItbU1Hh4etG7dmpSUFFJSUmjdujUeHh5YW1sTGBhIQkICer2eQ4cOERAQAMDQoUNJSEio8joyX08IYTJje1PZ2dmEhoYavg8PDyc8PLzK8zZu3EhMTAzt27dnxowZODo6otFo6NSpkyGPm5sbGo0GAHd39wrpKSkp5OTk4ODgYJiW7O7ubsh/JxIUhRAmuTl8NoaLiwvbtm0zqfxRo0YxadIkFEXhnXfeYdmyZSxduvQuanp3ZPgshDCNYt5pfk2aNEGlUmFhYUFYWBgnTpwAynqA5Zct1Gg0uLm53Tbd2dmZ/Px8SkpKAMjIyMDNza3K60tQFEKYTDHyczcyMzMNX+/duxdPT08A/P39iYuLQ6vVkpaWRmpqKh07dqRDhw6kpqaSlpaGVqslLi4Of39/FEWhZ8+e7N69G4Do6Gj8/f2rvL4Mn4UQJqnO3fymT5/OkSNHyMnJwc/Pj5deeokjR45w5swZAFq0aMGCBQsA8PT0ZPDgwQwZMgSVSsXcuXMNe9DPnTuX8ePHo9PpGDZsmCGQvvrqq0ybNo1Vq1bh5eVFWFhY1e3T6/X66mnevTv1440XwEIAABfUSURBVGkeaetV09UQJnDuLhuY1SURs4N4ZljAPZVx4OhxLlg0MSpv54b5eHnVrZ9p6SkKIUxWn++7SVAUQphEoe6ulWgMCYpCCJM9sNsRCCHErcjwWQghblKkpyiEEAb38g5iXSBBUQhhMpX0FIUQ4k/1OCZKUBRCmKZs+Fx/o6IERSGEyaSnKCpIS0tj/Ni/kpmpQVEU/jZuAi9OeZmtUVtYvHA+Z06fZv/BI3R7/HHDOSdSUnhx0gtcvZqPhWJB8qFvKS4uZsCTfQx5Ll5MZ+ToZ1mxclVNNKveaWBtyd6PpmJtbYmlSkX03h9Y9P5OAOZPDiJU3QWdrpQPo/bz3qZEHOwa8p9Fz+PRzBlLlYpVHyfwyfZD+D3uyfJ/DjOU+9hDbvx1xnq+2JdC3+5tWTptKNZWKn44ncY/3tyITldaU02+T+ruTn3GkKB4FywtLVm2/G26dO3K1atX6dWzG/0HqPH2bs/nkdt4cdILFfKXlJTwt+ef5aP/fkLHTp3IysrCysqKhg0bcvjoMUO+Xj26ETI09H8vJ+5SkbaEQRPepeCGFktLC776z3TiD/zIYw+709LdiU5DF6LX62nqbAfACyP8OHMug+FTP6CJsx3Ho+fw+c5vSfruF54YuQwAZ4dGnNw+j72HTqMoChELnmPwC6s5eyGTORMDeTaoJxtivqnJZt8X9bmnWJ/fwTSbZs2a0eWPHQ3t7e1p186LS5cu0s7Li7aPPVYp/9498bTv0JGOf6wa3LhxY8PqHjf98vPPZP6eSW/fPpXOF3ev4IYWACtLFZaWKvR6PRPCfFmy7kturoXye841APSAnW0DAGxtGpCTd52S/+n1DR3QhfgDP3KjsJjGTrZoi0s4e6FsqauvDp0hpH/n+9SymqOYeT3FmiZB8R79lprKsWM/0L1Hz9vm+eXnn1EUhaAhAfh078rbK5ZXyrMl8nOGh4XX65dia4KFhcKhz2dwIWEZXx06w7cnf+Phlk0ZPrAbyRtfI2bNRB5p1RSA9z9PpN3D7pyLX8x3W97gn29F8b+LSIUFdCVy11EAruRcw9JSRde/tAJg6IDOtHRzvr8NrCEWinGfukiGz/fg2rVrjBoxjLfeXoWDg8Nt85XoSjh4MJnkb76lUaNGDB7Yn65du9HPv78hz5bIz/lo/Sf3o9oPlNJSPU+MXIajnQ2bV/6dvzzSjAbWlhRpi/F9ZjnB/p34YN4zDBi3CnUvL1J+SmfQhHdp49GEuLUvciD8V64WFALg3sQBb8/m7PnmR0P5f52xnuWvhJbdv/zmDLrS+n4/sUx9fvosPcW7VFxczKgRwwgf9UyV9wFbtGiJr68fTZo0oVGjRgwaPIQffvjecDzl+HFKSkro2q2buav9wMq7doPE735mYK+/cFGTQ0zCcQBivzpOe88WADz39BPEflWWfi7tCqkXs3jsoT+Xrx+m7sr2r1IoKfkz8B1OOc+Acavo89wKkr8/y9nfMqnvbi4yWx27+dVGEhTvgl6v5x9/H8dj7bx4edr0KvOrBwZw6uQJrl+/TklJCfuTEvHy+ovheOTmTYwIH2XOKj+Qmjjb4WhnA0DDBlb079mOn1I1fzw1LluZuU83T8M9wbSMHJ7sUXZP2NXFnrYPuXH+4hVDeSMGdSNy13cVrnHzIY21lSWvjFHzYVSy2dtVGyhG/lcXmW34PHPmTPbt20fjxo3ZsWOHuS5TIw4eOMBnGz+hffsO9OxWdmP9zUVLKCoqYvrUl7jy+++EBgfSsVNnvti5G2dnZ6ZMnY6vT3cURSFg0BAGDwk0lLc1KpKY7Ttrqjn1lnsTBz5c8BwqCwssLBS27vmeL/ef5OAPv7J+yfO89Iw/BTeKmLjgMwCWfbiLdW8+y7eRb6AoMOudWLJyCwBo1cyFlu7O7D96tsI1pj0/gMF92mNhofDhlv0kfvvzfW9nTair9wuNYbbtCL79tuz+2euvv250UJTtCOoe2Y6gbqmO7Qi+/eEERXYtjcrbuCSjzm1HYLbhc/fu3XF0dDRX8UKIGmTO3fxqWo3fU9y8eTOhoaGEhoaSk5Nd09URQlTFyHcU6+p7ijX+Sk54eDjh4eFA2fBZCFH71c1wZ5waD4pCiDqoHkfFGh8+1zU3btxA7d8XnU7Hpx9voL2XJ+29PPn04w23zJ+dnU3gIDXtvTwJHKQmJyfHcCwpcR89u3Wmaydv1P59AdBqtQzo50dJScl9ac+DoGEDK+IjXsbCQuGZoJ6ciJ3Lidi5PBN061lIHdq2YN+GV/g28g2iVr2AvW3DCsc93J35/cDbTH2u7OV7K0sVez6aikr1YPw4KVTfNL+ZM2fi4+PDU089ZUjLzc1l7NixDBw4kLFjx5KXlweUvQq3aNEi1Go1QUFBnDp1ynBOdHQ0AwcOZODAgURHRxvST548SVBQEGq1mkWLFlWaoXQrZvtbnD59OiNHjuT8+fP4+fmxZcsWc13qvtqw/j8Eh4SSl5fH4kVvknTgMPsPHmHxojcrBLybVixfxpP+/Tl5+hee9O/PiuVlCwvk5uby8kuT2BK9ne+Pn2Lj52V/PtbW1vTz78+WyM33tV312fPBPsQmHMfRzoZZEwbj99wK+jz7FrMmDMbJ3qZS/rVzRzP73Vi6j1jC9q+PM+35/hWO/+uVUOIP/PkDWVyi4+vDPxE2sKvZ21JbVNeDltDQUCIiIiqkrVu3Dh8fH+Lj4/Hx8WHdunUAJCUlkZqaSnx8PAsXLmT+/PlA2c/SmjVriIyMZMuWLaxZs8YQSOfPn8/ChQuJj48nNTWVpKSkKutktqC4cuVKkpOTOXXqFElJSYSFhZnrUvfV55s2EvR0MHvid9O/vxoXFxecnZ3p319N/O5dlfLv+CKWZ597HoBnn3ueL7bHALB502cEh4TSqlXZvFlXV1fDOUFPh7B508b70JoHw8ghj/PFvhTUvbxIOHSGnPzr5F69QcKhMwzs/ZdK+R9t5UryH+8j/u8iD0FPdiT1YhY//ppR4Zwv9qUQPqS7eRtSm1RTVLzVWyoJCQmEhIQAEBISwt69eyukK4pC586dyc/PJzMzk+TkZHr37o2TkxOOjo707t2b/fv3k5mZybVr1+jcuTOKohASEkJCQkKVdXow+vvVRKvVknr+HK0feohLly7S0sPDcKxFy5ZcunSx0jmZGg3NmjUDwN3dnUyNBoBffvmZ3JwcBvZ/kl49urHxk48N53i3b8/R7741c2seDFaWKh5q0YQLl7Np3tSJdM2fvfmLmbk0b+pU6ZzT5y4T9GRHAELVXQ2LPNjaWPPKWDWLP6j8ov2ps5fo5t3KTK2ofYyd0ZKdnW14uyQ0NJTNm6seAWVlZRk6CU2bNiUrKwsAjUaDu7u7IZ+7uzsajaZSupub2y3Tb+avijxoMcGVK1dwdKr8Q2QsRVEMq+CUlJTw/fdH+TI+gRs3bvBkHx969HwCz7ZtUalUWFlbc/XqVezt7aur+g+kJs525F29btI5L8zfyNuvDWfG3wcRl3gCbbEOgNn/CGT1p18ZliMrr7RUT3GxDrtGDbh2vaha6l5bKYb/Vc3FxYVt27bd/bXK/czcLxIUTWBjY0NhYdmKKc2bt2B/4j7DsYvp6fTp+2Slc1zd3Lh8+TLNmjXj8uXLNP3jN2CLli1p3Lgxtra22Nra4uvrR0rKcTzbtgVAW1REw4YNK5UnTHOjUEvDBlYAXPo9lz7dPA3HWrg6sf/oL5XO+TlVQ9Ck/wPKhtKD+3gD0L19a4YO6MziqSE42ttQWqqnUFvM+5vL7lNZW1lSqC02d5NqBXOGqcaNG5OZmYmrqyuZmZm4uLgAZT3AjIw/b1tkZGTg5uaGm5sbR44cMaRrNBp69Ohx2/xVkeGzCZydndHpdBQWFqIeGMDevfHk5OSQk5PD3r3xqAdWnj4V+NTTfPpJ2ZPpTz/ZwFNBwQAEBQVz8EAyJSUlXL9+nW+/PUy7dmXTobKysmjcpAlWVlb3r3H1VO7VG6gsLGhgbcmeg6cZ4NMOJ3sbnOxtGODTjj0HK78be3ORB0VRmPH3AMMiDwPGraJd4DzaBc5jzcZ9vPVRvCEgujjakpV7rcIKOvWW8mcPrqrP3fD39ycmpuzee0xMDP3796+QrtfrOXbsGPb29ri6uuLr60tycjJ5eXnk5eWRnJyMr68vrq6u2NnZcezYMfR6fYWy7kR6iiYaMGAgBw8k499/ADPfmIOvT9nN9TdmzTX8Rps4YTzjJ/yDbo8/zj9fm8Gzo0awYf1HtGrVmk83RQLQzssLdcAgunftiIWFBWPGjse7fXsAEvd9zaDBgbeugDDZ3kOn6dXlEb4+/BNLP9xF8qevAbBk3S5y8suG1u/NHU1EVDLf/3iBEYMe54VwPwBivzrGx7GHqrxG3+6e7Eo+VWW++qK6RrTTp0/nyJEj5OTk4Ofnx0svvcSECROYOnUqUVFRNG/enFWryvYs6tu3L4mJiajVamxsbFiyZAkATk5OTJo0ieHDhwMwefJknP64zTVv3jxmzpxJYWEhfn5++Pn5Vd02cy0IcTfqwoIQP3z/Pavf+Tf/2WC+BWHDw0JZtHiZYShdm9WFBSE6t2vJS8/4M27Ox1VnvkufrxjP7He3G5Yhq62qY0GIo8dPYunc2qi81gXpsiBEfdela1f6PtkPnU5nlvK1Wi1PPx1SJwJiXXHsTDqJ3/2MhZnWu7KyVLF9X0qtD4jVqh6vCCHD57vw/Ni/ma1sa2trnnnur2Yr/0FlzBD4bhWX6Phsx5GqM9YTZfGujkY8I0hQFEKYrI4ugGMUCYpCCJNJUBRCCIO6u/+KMSQoCiFMU4d36jOGBEUhhEnq8INlo0hQFEKYrh5HRQmKQgiTGbv/Sl2c9ChBUQhhsnrcUZSgKIS4C/U4KkpQFEKYTF7JEUKIPyjIKzlCCFFBPY6JEhSFECb6Y5HZ+kqCohDCZPU4JkpQFEKYrh7HRAmKQoi7UI+jogRFIYRJZJFZIYT4H3JPUQghyjHTdje1ggRFIYSJ6vfiYRIUhRCmqcZFZv39/bG1tcXCwgKVSsW2bdvIzc1l2rRpXLx4kRYtWrBq1SocHR3R6/UsXryYxMREGjZsyLJly/D29gYgOjqatWvXAjBx4kSGDh1613WSLU6FECYxdndTY+Pmhg0biI2NZdu2bQCsW7cOHx8f4uPj8fHxYd26dQAkJSWRmppKfHw8CxcuZP78+QDk5uayZs0aIiMj2bJlC2vWrCEvL++u2ydBUQhhMkUx7nM3EhISCAkJASAkJIS9e/dWSFcUhc6dO5Ofn09mZibJycn07t0bJycnHB0d6d27N/v377/rtsnwWQhhMmOn+WVnZxMaGmr4Pjw8nPDw8Ap5xo0bh6IohmNZWVm4uroC0LRpU7KysgDQaDS4u7sbznN3d0ej0VRKd3NzQ6PR3HXbJCgKIUxmbCfQxcXFMCy+lU2bNuHm5kZWVhZjx46lTZs2Fa+jKPd9nrUMn4UQJquu4bObmxsAjRs3Rq1Wk5KSQuPGjcnMzAQgMzMTFxcXQ96MjAzDuRkZGbi5uVVK12g0hnLvhgRFIYRJbs5oMea/O7l+/TrXrl0zfH3gwAE8PT3x9/cnJiYGgJiYGPr37w9gSNfr9Rw7dgx7e3tcXV3x9fUlOTmZvLw88vLySE5OxtfX967bJ8NnIYTpqmFEm5WVxeTJkwHQ6XQ89dRT+Pn50aFDB6ZOnUpUVBTNmzdn1apVAPTt25fExETUajU2NjYsWbIEACcnJyZNmsTw4cMBmDx5Mk5OTnddL0Wv1+vvsW3V5tSPp3mkrVdNV0OYwLn7izVdBWGCiNlBPDMs4J7KOHHyR5o91NaovL+n/YKXV936mZaeohDCNIrxW5zWRRIUhRAmq8cxUR60CCFEedJTFEKYRHbzE0KI/yGLzAohRDnSUxRCiHIkKAohhEHVs1XqMgmKQgiT3MuyYHWBBEUhhMnqcUyUoCiEuAv1OCpKUBRCmEym+d0nxdoizv9yuqarUe2ys7MNa8LVN99/Nrmmq2AW9fXvrKio6J7LsLayNPrn1NKyVoUYo9SqVXLqq9DQ0DuuPixqH/k7e3DJ3GchhChHgqIQQpQjQfE++N/dy0TtJ39nDy65pyiEEOVIT1EIIcqRoCiEEOVIUDSjpKQkAgICUKvVrFu3rqarI4wwc+ZMfHx8eOqpp2q6KqKGSFA0E51Ox4IFC4iIiCAuLo4dO3Zw9uzZmq6WqEJoaCgRERE1XQ1RgyQomklKSgqtW7fGw8MDa2trAgMDSUhIqOlqiSp0794dR0fHmq6GqEESFM1Eo9Hg7u5u+N7NzQ2NRlODNRJCGEOCohBClCNB0Uzc3NzIyMgwfK/RaHBzc6vBGgkhjCFB0Uw6dOhAamoqaWlpaLVa4uLi8Pf3r+lqCSGqIDNazCgxMZElS5ag0+kYNmwYEydOrOkqiSpMnz6dI0eOkJOTQ+PGjXnppZcICwur6WqJ+0iCohBClCPDZyGEKEeCohBClCNBUQghypGgKIQQ5UhQFEKIciQoPqAOHz7MCy+8AEBCQsIdV/HJz89n48aNJl9j9erVfPTRR0anlzdjxgx27dpl9LXS09NlZRtRLSQo1jM6nc7kc/r378+ECRNuezw/P59NmzbdS7WEqDPq3qasD6j09HTGjx+Pt7c3P/74I56envzrX//CxsYGf39/Bg8ezMGDBxk/fjyOjo6sXr0arVaLh4cHS5cuxdbWlqSkJJYsWYKNjQ3dunUzlL1t2zZOnjzJ3LlzuXLlCvPmzSMtLQ2A+fPn88knn3DhwgWCg4Pp1asXr7/+OhEREXz55ZdotVrUajVTpkwBYO3atcTExODi4kKzZs3w9va+Y7siIyPZvHkzxcXFtG7dmuXLl2NjYwPAwYMHWbduHQUFBcyYMYN+/fqh0+lYsWIFR44cQavV8swzzzBy5Egz/amLB5H0FOuQ8+fPM3r0aL788ktsbW357LPPDMecnJyIjo7Gx8eHtWvXsn79eqKjo2nfvj3r16+nqKiIOXPm8P7777Nt2zZ+//33W15j0aJFdO/ene3btxMdHY2npyevvPIKrVq1IjY2ltdff53k5GR+++03oqKiiI2N5dSpU3z77becPHmSnTt3EhMTw4cffsiJEyeqbJNarWbr1q1s376dNm3aEBUVZTh28eJFoqKi+OCDD5g3bx5FRUVERUVhb2/P1q1b2bp1K5GRkYYALkR1kJ5iHdKsWTNDD+/pp5/mk08+Ydy4cQAMGTIEgOPHj3P27FlGjRoFQHFxMZ07d+bcuXO0bNmShx56yHB+ZGRkpWscOnSI5cuXA6BSqbC3tycvL69CngMHDnDgwAFCQkIAuH79OqmpqRQUFDBgwABDT8+Yud6//PILq1at4urVqxQUFODr62s4NnjwYCwsLHjooYfw8PDg3LlzHDhwgJ9++ondu3cDcPXqVX777TdDu4S4VxIU6xBFUW77/c1ApNfr6d27NytXrqyQ9/Tp09VWD71ez4QJEyoNW//73/+aXNaMGTN47733aNeuHdu2bePIkSOGY7dqr16vZ/bs2fTp06fCsfT0dJOvLcStyPC5Drl06RI//PADADt27KhwX/Cmzp078/333/Pbb78BZb248+fP06ZNGy5evMiFCxcAiIuLu+U1fHx8DMNynU7H1atXsbW1paCgwJDH19eXrVu3GtI0Gg1ZWVl0796dvXv3UlhYyLVr1/j666+rbFNBQQFNmzaluLiYL774osKxXbt2UVpayoULF0hLS+Phhx/G19eXTZs2UVxcDJTdUrh+/XqV1xHCWNJTrEMefvhhNm7cyBtvvMGjjz5qGCKX5+LiwtKlS5k+fTparRaAqVOn8vDDD7NgwQImTJhgeNBSPtDdNGvWLObMmcPWrVuxsLBg/vz5dOnSha5du/LUU0/Rp08fXn/9dX799VdDT7FRo0a89dZbeHt7M2TIEIKDg3FxcaFDhw5Vtunll18mLCwMFxcXOnXqVKFOzZo1Y/jw4RQUFPDmm2/SoEEDwsLCuHjxIqGhoej1epydnXnvvffu9o9UiEpklZw6Ij09nX/84x/s2LGjpqsiRL0mw2chhChHeopCCFGO9BSFEKIcCYpCCFGOBEUhhChHgqIQQpQjQVEIIcr5fwB2BiAWV7FWAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "colors = [\"purple\", \"green\", \"orange\", \"magenta\"]\n",
        "plt.figure(figsize = (15,6))\n",
        "plt.grid(True)\n",
        "sns.set_style(\"whitegrid\")\n",
        "sns.barplot(x=list(accuracies.keys()), y=list(accuracies.values()), palette=colors)\n",
        "plt.title('Comparing accuracy of the four models', fontsize = 20)\n",
        "plt.xticks(fontsize = 12)\n",
        "plt.yticks(fontsize = 12)\n",
        "plt.ylabel(\"Accuracy %\")\n",
        "plt.xlabel(\"Algorithms\")\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "id": "n6yEIsE0nTUZ",
        "outputId": "dc7c2158-6a4c-44ae-f748-4778c81274b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_accuracy = pd.DataFrame(accuracies.items(), columns=['Model', 'Accuracy'])\n",
        "df_accuracy.sort_values(by= ['Accuracy'], inplace= True, ascending= False)\n",
        "df_accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "Xu9fa_v6nTRY",
        "outputId": "14e6543f-4ae0-42d8-cee1-9f039639cfa6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Model  Accuracy\n",
              "0  Random Forest Classifier  0.904683\n",
              "3      KNeighborsClassifier  0.820247\n",
              "1       Logistic Regression  0.727759\n",
              "2  Decision Tree Classifier  0.700595"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6675f149-c05c-40ef-8f46-a0f8f783eca3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>0.904683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>KNeighborsClassifier</td>\n",
              "      <td>0.820247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.727759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>0.700595</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6675f149-c05c-40ef-8f46-a0f8f783eca3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6675f149-c05c-40ef-8f46-a0f8f783eca3 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-6675f149-c05c-40ef-8f46-a0f8f783eca3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Summary**\n",
        "* Used oversampling method for imbalanced classification data.\n",
        "* Random Forest Classifier models performs best fit for our dataset.\n",
        "* Random Forest Classifer has the lowest Mean squared error : 0.095 and Root mean squared error : 0.308"
      ],
      "metadata": {
        "id": "2mJK2nldv_Dc"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "O3rUyJMwv1PA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}