Kidney_Disease_Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "***Importing Libraries***"
      ],
      "metadata": {
        "id": "iyG5CcYBimNo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "QR6aIzn-LJwM"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "plt.style.use('fivethirtyeight')\n",
        "sns.set()\n",
        "plt.style.use('ggplot')\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Read Dataset And Information***"
      ],
      "metadata": {
        "id": "9tUosCh_i9mS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"/content/kidney_disease.csv\")"
      ],
      "metadata": {
        "id": "oLoBzuuPMjh8"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "CX-6qy2aMq_T",
        "outputId": "d0a26c6d-26b9-4ec6-df46-3fc08ef09479"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id   age     bp     sg   al   su       rbc        pc         pcc  \\\n",
              "0   0  48.0   80.0  1.020  1.0  0.0       NaN    normal  notpresent   \n",
              "1   1   7.0   50.0  1.020  4.0  0.0       NaN    normal  notpresent   \n",
              "2   2  62.0   80.0  1.010  2.0  3.0    normal    normal  notpresent   \n",
              "3   3  48.0   70.0  1.005  4.0  0.0    normal  abnormal     present   \n",
              "4   4  51.0   80.0  1.010  2.0  0.0    normal    normal  notpresent   \n",
              "5   5  60.0   90.0  1.015  3.0  0.0       NaN       NaN  notpresent   \n",
              "6   6  68.0   70.0  1.010  0.0  0.0       NaN    normal  notpresent   \n",
              "7   7  24.0    NaN  1.015  2.0  4.0    normal  abnormal  notpresent   \n",
              "8   8  52.0  100.0  1.015  3.0  0.0    normal  abnormal     present   \n",
              "9   9  53.0   90.0  1.020  2.0  0.0  abnormal  abnormal     present   \n",
              "\n",
              "           ba  ...  pcv     wc   rc  htn   dm  cad appet   pe  ane  \\\n",
              "0  notpresent  ...   44   7800  5.2  yes  yes   no  good   no   no   \n",
              "1  notpresent  ...   38   6000  NaN   no   no   no  good   no   no   \n",
              "2  notpresent  ...   31   7500  NaN   no  yes   no  poor   no  yes   \n",
              "3  notpresent  ...   32   6700  3.9  yes   no   no  poor  yes  yes   \n",
              "4  notpresent  ...   35   7300  4.6   no   no   no  good   no   no   \n",
              "5  notpresent  ...   39   7800  4.4  yes  yes   no  good  yes   no   \n",
              "6  notpresent  ...   36    NaN  NaN   no   no   no  good   no   no   \n",
              "7  notpresent  ...   44   6900    5   no  yes   no  good  yes   no   \n",
              "8  notpresent  ...   33   9600  4.0  yes  yes   no  good   no  yes   \n",
              "9  notpresent  ...   29  12100  3.7  yes  yes   no  poor   no  yes   \n",
              "\n",
              "  classification  \n",
              "0            ckd  \n",
              "1            ckd  \n",
              "2            ckd  \n",
              "3            ckd  \n",
              "4            ckd  \n",
              "5            ckd  \n",
              "6            ckd  \n",
              "7            ckd  \n",
              "8            ckd  \n",
              "9            ckd  \n",
              "\n",
              "[10 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4ea92d1c-3a3c-4395-abb2-296fa343aece\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>age</th>\n",
              "      <th>bp</th>\n",
              "      <th>sg</th>\n",
              "      <th>al</th>\n",
              "      <th>su</th>\n",
              "      <th>rbc</th>\n",
              "      <th>pc</th>\n",
              "      <th>pcc</th>\n",
              "      <th>ba</th>\n",
              "      <th>...</th>\n",
              "      <th>pcv</th>\n",
              "      <th>wc</th>\n",
              "      <th>rc</th>\n",
              "      <th>htn</th>\n",
              "      <th>dm</th>\n",
              "      <th>cad</th>\n",
              "      <th>appet</th>\n",
              "      <th>pe</th>\n",
              "      <th>ane</th>\n",
              "      <th>classification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>44</td>\n",
              "      <td>7800</td>\n",
              "      <td>5.2</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>38</td>\n",
              "      <td>6000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>62.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>31</td>\n",
              "      <td>7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>48.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.005</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>present</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>32</td>\n",
              "      <td>6700</td>\n",
              "      <td>3.9</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>51.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>35</td>\n",
              "      <td>7300</td>\n",
              "      <td>4.6</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>60.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>1.015</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>39</td>\n",
              "      <td>7800</td>\n",
              "      <td>4.4</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>68.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>36</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>24.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.015</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>44</td>\n",
              "      <td>6900</td>\n",
              "      <td>5</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>52.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>1.015</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>present</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>33</td>\n",
              "      <td>9600</td>\n",
              "      <td>4.0</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>53.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>present</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>29</td>\n",
              "      <td>12100</td>\n",
              "      <td>3.7</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 26 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ea92d1c-3a3c-4395-abb2-296fa343aece')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4ea92d1c-3a3c-4395-abb2-296fa343aece 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-4ea92d1c-3a3c-4395-abb2-296fa343aece');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-235af97a-c57e-4c21-be37-0868651b5dc8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-235af97a-c57e-4c21-be37-0868651b5dc8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-235af97a-c57e-4c21-be37-0868651b5dc8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['classification'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qestHYEcMwEV",
        "outputId": "6ffe67e3-55ff-4b59-a741-bb9ca7970193"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "classification\n",
              "ckd       248\n",
              "notckd    150\n",
              "ckd\\t       2\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-m7QUlZZMzOq",
        "outputId": "5e6ad34e-6a8a-44fb-a4b8-4d2477e0e9e8"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "w_olK4PuM1iL",
        "outputId": "0b1985cc-b1c5-42ac-9ed5-6cc456eebe9b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id   age    bp     sg   al   su     rbc        pc         pcc          ba  \\\n",
              "0   0  48.0  80.0  1.020  1.0  0.0     NaN    normal  notpresent  notpresent   \n",
              "1   1   7.0  50.0  1.020  4.0  0.0     NaN    normal  notpresent  notpresent   \n",
              "2   2  62.0  80.0  1.010  2.0  3.0  normal    normal  notpresent  notpresent   \n",
              "3   3  48.0  70.0  1.005  4.0  0.0  normal  abnormal     present  notpresent   \n",
              "4   4  51.0  80.0  1.010  2.0  0.0  normal    normal  notpresent  notpresent   \n",
              "\n",
              "   ...  pcv    wc   rc  htn   dm  cad appet   pe  ane classification  \n",
              "0  ...   44  7800  5.2  yes  yes   no  good   no   no            ckd  \n",
              "1  ...   38  6000  NaN   no   no   no  good   no   no            ckd  \n",
              "2  ...   31  7500  NaN   no  yes   no  poor   no  yes            ckd  \n",
              "3  ...   32  6700  3.9  yes   no   no  poor  yes  yes            ckd  \n",
              "4  ...   35  7300  4.6   no   no   no  good   no   no            ckd  \n",
              "\n",
              "[5 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f3d26b76-0961-41da-9360-cc2e82f50829\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>age</th>\n",
              "      <th>bp</th>\n",
              "      <th>sg</th>\n",
              "      <th>al</th>\n",
              "      <th>su</th>\n",
              "      <th>rbc</th>\n",
              "      <th>pc</th>\n",
              "      <th>pcc</th>\n",
              "      <th>ba</th>\n",
              "      <th>...</th>\n",
              "      <th>pcv</th>\n",
              "      <th>wc</th>\n",
              "      <th>rc</th>\n",
              "      <th>htn</th>\n",
              "      <th>dm</th>\n",
              "      <th>cad</th>\n",
              "      <th>appet</th>\n",
              "      <th>pe</th>\n",
              "      <th>ane</th>\n",
              "      <th>classification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>44</td>\n",
              "      <td>7800</td>\n",
              "      <td>5.2</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>38</td>\n",
              "      <td>6000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>62.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>31</td>\n",
              "      <td>7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>48.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.005</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>present</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>32</td>\n",
              "      <td>6700</td>\n",
              "      <td>3.9</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>51.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>35</td>\n",
              "      <td>7300</td>\n",
              "      <td>4.6</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f3d26b76-0961-41da-9360-cc2e82f50829')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f3d26b76-0961-41da-9360-cc2e82f50829 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-f3d26b76-0961-41da-9360-cc2e82f50829');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-75830f09-de38-4211-beb1-8617fd9e5a1a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-75830f09-de38-4211-beb1-8617fd9e5a1a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-75830f09-de38-4211-beb1-8617fd9e5a1a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns = ['id','age', 'blood_pressure', 'specific_gravity', 'albumin', 'sugar', 'red_blood_cells', 'pus_cell',\n",
        "              'pus_cell_clumps', 'bacteria', 'blood_glucose_random', 'blood_urea', 'serum_creatinine', 'sodium',\n",
        "              'potassium', 'haemoglobin', 'packed_cell_volume', 'white_blood_cell_count', 'red_blood_cell_count',\n",
        "              'hypertension', 'diabetes_mellitus', 'coronary_artery_disease', 'appetite', 'peda_edema',\n",
        "              'aanemia', 'class']"
      ],
      "metadata": {
        "id": "10zWhThcOivV"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "6E3QsiepOvya",
        "outputId": "1e7fdefa-12e3-475d-a18d-67882076b772"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id   age  blood_pressure  specific_gravity  albumin  sugar red_blood_cells  \\\n",
              "0   0  48.0            80.0             1.020      1.0    0.0             NaN   \n",
              "1   1   7.0            50.0             1.020      4.0    0.0             NaN   \n",
              "2   2  62.0            80.0             1.010      2.0    3.0          normal   \n",
              "3   3  48.0            70.0             1.005      4.0    0.0          normal   \n",
              "4   4  51.0            80.0             1.010      2.0    0.0          normal   \n",
              "\n",
              "   pus_cell pus_cell_clumps    bacteria  ...  packed_cell_volume  \\\n",
              "0    normal      notpresent  notpresent  ...                  44   \n",
              "1    normal      notpresent  notpresent  ...                  38   \n",
              "2    normal      notpresent  notpresent  ...                  31   \n",
              "3  abnormal         present  notpresent  ...                  32   \n",
              "4    normal      notpresent  notpresent  ...                  35   \n",
              "\n",
              "   white_blood_cell_count  red_blood_cell_count  hypertension  \\\n",
              "0                    7800                   5.2           yes   \n",
              "1                    6000                   NaN            no   \n",
              "2                    7500                   NaN            no   \n",
              "3                    6700                   3.9           yes   \n",
              "4                    7300                   4.6            no   \n",
              "\n",
              "   diabetes_mellitus  coronary_artery_disease appetite peda_edema aanemia  \\\n",
              "0                yes                       no     good         no      no   \n",
              "1                 no                       no     good         no      no   \n",
              "2                yes                       no     poor         no     yes   \n",
              "3                 no                       no     poor        yes     yes   \n",
              "4                 no                       no     good         no      no   \n",
              "\n",
              "  class  \n",
              "0   ckd  \n",
              "1   ckd  \n",
              "2   ckd  \n",
              "3   ckd  \n",
              "4   ckd  \n",
              "\n",
              "[5 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a48da656-b63a-4db0-b50d-026899713cd2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>age</th>\n",
              "      <th>blood_pressure</th>\n",
              "      <th>specific_gravity</th>\n",
              "      <th>albumin</th>\n",
              "      <th>sugar</th>\n",
              "      <th>red_blood_cells</th>\n",
              "      <th>pus_cell</th>\n",
              "      <th>pus_cell_clumps</th>\n",
              "      <th>bacteria</th>\n",
              "      <th>...</th>\n",
              "      <th>packed_cell_volume</th>\n",
              "      <th>white_blood_cell_count</th>\n",
              "      <th>red_blood_cell_count</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>diabetes_mellitus</th>\n",
              "      <th>coronary_artery_disease</th>\n",
              "      <th>appetite</th>\n",
              "      <th>peda_edema</th>\n",
              "      <th>aanemia</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>44</td>\n",
              "      <td>7800</td>\n",
              "      <td>5.2</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>38</td>\n",
              "      <td>6000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>62.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>31</td>\n",
              "      <td>7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>48.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.005</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>present</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>32</td>\n",
              "      <td>6700</td>\n",
              "      <td>3.9</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>51.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>35</td>\n",
              "      <td>7300</td>\n",
              "      <td>4.6</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a48da656-b63a-4db0-b50d-026899713cd2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a48da656-b63a-4db0-b50d-026899713cd2 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-a48da656-b63a-4db0-b50d-026899713cd2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-21407f6b-6321-4705-8b96-c79e0c3c7921\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-21407f6b-6321-4705-8b96-c79e0c3c7921')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-21407f6b-6321-4705-8b96-c79e0c3c7921 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "0obr_kMKOPLT",
        "outputId": "6d3e0007-8f83-40b4-90b8-7771bfca51e5"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               id         age  blood_pressure  specific_gravity     albumin  \\\n",
              "count  400.000000  391.000000      388.000000        353.000000  354.000000   \n",
              "mean   199.500000   51.483376       76.469072          1.017408    1.016949   \n",
              "std    115.614301   17.169714       13.683637          0.005717    1.352679   \n",
              "min      0.000000    2.000000       50.000000          1.005000    0.000000   \n",
              "25%     99.750000   42.000000       70.000000          1.010000    0.000000   \n",
              "50%    199.500000   55.000000       80.000000          1.020000    0.000000   \n",
              "75%    299.250000   64.500000       80.000000          1.020000    2.000000   \n",
              "max    399.000000   90.000000      180.000000          1.025000    5.000000   \n",
              "\n",
              "            sugar  blood_glucose_random  blood_urea  serum_creatinine  \\\n",
              "count  351.000000            356.000000  381.000000        383.000000   \n",
              "mean     0.450142            148.036517   57.425722          3.072454   \n",
              "std      1.099191             79.281714   50.503006          5.741126   \n",
              "min      0.000000             22.000000    1.500000          0.400000   \n",
              "25%      0.000000             99.000000   27.000000          0.900000   \n",
              "50%      0.000000            121.000000   42.000000          1.300000   \n",
              "75%      0.000000            163.000000   66.000000          2.800000   \n",
              "max      5.000000            490.000000  391.000000         76.000000   \n",
              "\n",
              "           sodium   potassium  haemoglobin  \n",
              "count  313.000000  312.000000   348.000000  \n",
              "mean   137.528754    4.627244    12.526437  \n",
              "std     10.408752    3.193904     2.912587  \n",
              "min      4.500000    2.500000     3.100000  \n",
              "25%    135.000000    3.800000    10.300000  \n",
              "50%    138.000000    4.400000    12.650000  \n",
              "75%    142.000000    4.900000    15.000000  \n",
              "max    163.000000   47.000000    17.800000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fa375c1d-18c7-40ec-ad94-10e0782bd373\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>age</th>\n",
              "      <th>blood_pressure</th>\n",
              "      <th>specific_gravity</th>\n",
              "      <th>albumin</th>\n",
              "      <th>sugar</th>\n",
              "      <th>blood_glucose_random</th>\n",
              "      <th>blood_urea</th>\n",
              "      <th>serum_creatinine</th>\n",
              "      <th>sodium</th>\n",
              "      <th>potassium</th>\n",
              "      <th>haemoglobin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>400.000000</td>\n",
              "      <td>391.000000</td>\n",
              "      <td>388.000000</td>\n",
              "      <td>353.000000</td>\n",
              "      <td>354.000000</td>\n",
              "      <td>351.000000</td>\n",
              "      <td>356.000000</td>\n",
              "      <td>381.000000</td>\n",
              "      <td>383.000000</td>\n",
              "      <td>313.000000</td>\n",
              "      <td>312.000000</td>\n",
              "      <td>348.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>199.500000</td>\n",
              "      <td>51.483376</td>\n",
              "      <td>76.469072</td>\n",
              "      <td>1.017408</td>\n",
              "      <td>1.016949</td>\n",
              "      <td>0.450142</td>\n",
              "      <td>148.036517</td>\n",
              "      <td>57.425722</td>\n",
              "      <td>3.072454</td>\n",
              "      <td>137.528754</td>\n",
              "      <td>4.627244</td>\n",
              "      <td>12.526437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>115.614301</td>\n",
              "      <td>17.169714</td>\n",
              "      <td>13.683637</td>\n",
              "      <td>0.005717</td>\n",
              "      <td>1.352679</td>\n",
              "      <td>1.099191</td>\n",
              "      <td>79.281714</td>\n",
              "      <td>50.503006</td>\n",
              "      <td>5.741126</td>\n",
              "      <td>10.408752</td>\n",
              "      <td>3.193904</td>\n",
              "      <td>2.912587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>1.005000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>3.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>99.750000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>1.010000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>135.000000</td>\n",
              "      <td>3.800000</td>\n",
              "      <td>10.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>199.500000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.020000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>138.000000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>12.650000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>299.250000</td>\n",
              "      <td>64.500000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.020000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>163.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>142.000000</td>\n",
              "      <td>4.900000</td>\n",
              "      <td>15.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>399.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>1.025000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>490.000000</td>\n",
              "      <td>391.000000</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>163.000000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>17.800000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fa375c1d-18c7-40ec-ad94-10e0782bd373')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fa375c1d-18c7-40ec-ad94-10e0782bd373 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-fa375c1d-18c7-40ec-ad94-10e0782bd373');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-28a271e1-6c25-4e82-90c6-dbf2e57dd2bc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-28a271e1-6c25-4e82-90c6-dbf2e57dd2bc')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-28a271e1-6c25-4e82-90c6-dbf2e57dd2bc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 143.94335370781954,\n        \"min\": 0.0,\n        \"max\": 400.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          400.0,\n          199.5,\n          299.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 124.96066286828237,\n        \"min\": 2.0,\n        \"max\": 391.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          51.48337595907928,\n          55.0,\n          391.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"blood_pressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 119.0212751707383,\n        \"min\": 13.68363749352526,\n        \"max\": 388.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          388.0,\n          76.46907216494846,\n          80.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"specific_gravity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 124.4965948725244,\n        \"min\": 0.0057166169743763555,\n        \"max\": 353.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          353.0,\n          1.0174079320113314,\n          1.02\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"albumin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 124.69573145448318,\n        \"min\": 0.0,\n        \"max\": 354.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          354.0,\n          1.0169491525423728,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sugar\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 123.77815413979741,\n        \"min\": 0.0,\n        \"max\": 351.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.45014245014245013,\n          5.0,\n          1.099191251885407\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"blood_glucose_random\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 157.36822219060494,\n        \"min\": 22.0,\n        \"max\": 490.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          148.0365168539326,\n          121.0,\n          356.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"blood_urea\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 161.06812991816776,\n        \"min\": 1.5,\n        \"max\": 391.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          57.425721784776904,\n          42.0,\n          381.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"serum_creatinine\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 133.3772918788655,\n        \"min\": 0.4,\n        \"max\": 383.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.072454308093995,\n          1.3,\n          383.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sodium\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 96.30199646834795,\n        \"min\": 4.5,\n        \"max\": 313.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          137.52875399361022,\n          138.0,\n          313.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"potassium\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 107.81460981356975,\n        \"min\": 2.5,\n        \"max\": 312.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.62724358974359,\n          4.4,\n          312.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"haemoglobin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 119.40085837792167,\n        \"min\": 2.9125866088267642,\n        \"max\": 348.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          12.526436781609195,\n          12.649999999999999,\n          348.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s4CeQSYYOJsI",
        "outputId": "b2109c29-2345-483a-dcc5-9004f9ba9523"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 400 entries, 0 to 399\n",
            "Data columns (total 26 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   id                       400 non-null    int64  \n",
            " 1   age                      391 non-null    float64\n",
            " 2   blood_pressure           388 non-null    float64\n",
            " 3   specific_gravity         353 non-null    float64\n",
            " 4   albumin                  354 non-null    float64\n",
            " 5   sugar                    351 non-null    float64\n",
            " 6   red_blood_cells          248 non-null    object \n",
            " 7   pus_cell                 335 non-null    object \n",
            " 8   pus_cell_clumps          396 non-null    object \n",
            " 9   bacteria                 396 non-null    object \n",
            " 10  blood_glucose_random     356 non-null    float64\n",
            " 11  blood_urea               381 non-null    float64\n",
            " 12  serum_creatinine         383 non-null    float64\n",
            " 13  sodium                   313 non-null    float64\n",
            " 14  potassium                312 non-null    float64\n",
            " 15  haemoglobin              348 non-null    float64\n",
            " 16  packed_cell_volume       330 non-null    object \n",
            " 17  white_blood_cell_count   295 non-null    object \n",
            " 18  red_blood_cell_count     270 non-null    object \n",
            " 19  hypertension             398 non-null    object \n",
            " 20  diabetes_mellitus        398 non-null    object \n",
            " 21  coronary_artery_disease  398 non-null    object \n",
            " 22  appetite                 399 non-null    object \n",
            " 23  peda_edema               399 non-null    object \n",
            " 24  aanemia                  399 non-null    object \n",
            " 25  class                    400 non-null    object \n",
            "dtypes: float64(11), int64(1), object(14)\n",
            "memory usage: 81.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['packed_cell_volume'] = pd.to_numeric(df['packed_cell_volume'], errors='coerce')\n",
        "df['white_blood_cell_count'] = pd.to_numeric(df['white_blood_cell_count'], errors='coerce')\n",
        "df['red_blood_cell_count'] = pd.to_numeric(df['red_blood_cell_count'], errors='coerce')"
      ],
      "metadata": {
        "id": "L1XDkKs5OUhQ"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N0aMxgeBO4vE",
        "outputId": "36cdc73a-d9be-4434-87cf-6809c7d79b2a"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 400 entries, 0 to 399\n",
            "Data columns (total 26 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   id                       400 non-null    int64  \n",
            " 1   age                      391 non-null    float64\n",
            " 2   blood_pressure           388 non-null    float64\n",
            " 3   specific_gravity         353 non-null    float64\n",
            " 4   albumin                  354 non-null    float64\n",
            " 5   sugar                    351 non-null    float64\n",
            " 6   red_blood_cells          248 non-null    object \n",
            " 7   pus_cell                 335 non-null    object \n",
            " 8   pus_cell_clumps          396 non-null    object \n",
            " 9   bacteria                 396 non-null    object \n",
            " 10  blood_glucose_random     356 non-null    float64\n",
            " 11  blood_urea               381 non-null    float64\n",
            " 12  serum_creatinine         383 non-null    float64\n",
            " 13  sodium                   313 non-null    float64\n",
            " 14  potassium                312 non-null    float64\n",
            " 15  haemoglobin              348 non-null    float64\n",
            " 16  packed_cell_volume       329 non-null    float64\n",
            " 17  white_blood_cell_count   294 non-null    float64\n",
            " 18  red_blood_cell_count     269 non-null    float64\n",
            " 19  hypertension             398 non-null    object \n",
            " 20  diabetes_mellitus        398 non-null    object \n",
            " 21  coronary_artery_disease  398 non-null    object \n",
            " 22  appetite                 399 non-null    object \n",
            " 23  peda_edema               399 non-null    object \n",
            " 24  aanemia                  399 non-null    object \n",
            " 25  class                    400 non-null    object \n",
            "dtypes: float64(14), int64(1), object(11)\n",
            "memory usage: 81.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iEvYd2qvO6K0",
        "outputId": "0632b230-cd9b-4039-cc6b-5853d50b9707"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', 'age', 'blood_pressure', 'specific_gravity', 'albumin', 'sugar',\n",
              "       'red_blood_cells', 'pus_cell', 'pus_cell_clumps', 'bacteria',\n",
              "       'blood_glucose_random', 'blood_urea', 'serum_creatinine', 'sodium',\n",
              "       'potassium', 'haemoglobin', 'packed_cell_volume',\n",
              "       'white_blood_cell_count', 'red_blood_cell_count', 'hypertension',\n",
              "       'diabetes_mellitus', 'coronary_artery_disease', 'appetite',\n",
              "       'peda_edema', 'aanemia', 'class'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols = [col for col in df.columns if df[col].dtype == 'object']\n",
        "num_cols = [col for col in df.columns if df[col].dtype != 'object']"
      ],
      "metadata": {
        "id": "tgu7AaiNO_IJ"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zdUh6cElPAuk",
        "outputId": "4707e1b8-b8a8-4c58-fa29-78651e2c5687"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['red_blood_cells',\n",
              " 'pus_cell',\n",
              " 'pus_cell_clumps',\n",
              " 'bacteria',\n",
              " 'hypertension',\n",
              " 'diabetes_mellitus',\n",
              " 'coronary_artery_disease',\n",
              " 'appetite',\n",
              " 'peda_edema',\n",
              " 'aanemia',\n",
              " 'class']"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_cols"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z-C2BlbRPEUs",
        "outputId": "305af348-fd44-4d5d-c3ca-a70634de565b"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['id',\n",
              " 'age',\n",
              " 'blood_pressure',\n",
              " 'specific_gravity',\n",
              " 'albumin',\n",
              " 'sugar',\n",
              " 'blood_glucose_random',\n",
              " 'blood_urea',\n",
              " 'serum_creatinine',\n",
              " 'sodium',\n",
              " 'potassium',\n",
              " 'haemoglobin',\n",
              " 'packed_cell_volume',\n",
              " 'white_blood_cell_count',\n",
              " 'red_blood_cell_count']"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in cat_cols:\n",
        "    print(f\"{col} has {df[col].unique()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QwSKJ8haPHLG",
        "outputId": "d9777717-f5cf-4479-a270-ebde80a2f88f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "red_blood_cells has [nan 'normal' 'abnormal']\n",
            "pus_cell has ['normal' 'abnormal' nan]\n",
            "pus_cell_clumps has ['notpresent' 'present' nan]\n",
            "bacteria has ['notpresent' 'present' nan]\n",
            "hypertension has ['yes' 'no' nan]\n",
            "diabetes_mellitus has ['yes' 'no' ' yes' '\\tno' '\\tyes' nan]\n",
            "coronary_artery_disease has ['no' 'yes' '\\tno' nan]\n",
            "appetite has ['good' 'poor' nan]\n",
            "peda_edema has ['no' 'yes' nan]\n",
            "aanemia has ['no' 'yes' nan]\n",
            "class has ['ckd' 'ckd\\t' 'notckd']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['diabetes_mellitus'].replace(to_replace = {'\\tno':'no', '\\tyes': 'yes', ' yes':'yes'}, inplace=True)\n",
        "df['coronary_artery_disease'] = df['coronary_artery_disease'].replace(to_replace = '\\tno', value = 'no')\n",
        "df['class'] = df['class'].replace(to_replace={'ckd\\t':'ckd', 'notckd': 'not ckd'})"
      ],
      "metadata": {
        "id": "SxGY7qWtPLD-"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cols = ['diabetes_mellitus', 'coronary_artery_disease', 'class']\n",
        "for col in cols:\n",
        "    print(f\"{col} has {df[col].unique()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tgwJPhrVPM_i",
        "outputId": "497f399e-409f-4795-c607-9cfeae385afe"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "diabetes_mellitus has ['yes' 'no' nan]\n",
            "coronary_artery_disease has ['no' 'yes' nan]\n",
            "class has ['ckd' 'not ckd']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['class'] = df['class'].map({'ckd':0, 'not ckd': 1})\n",
        "df['class'] = pd.to_numeric(df['class'], errors = 'coerce')"
      ],
      "metadata": {
        "id": "0lMjZnBGPPwM"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cols = ['diabetes_mellitus', 'coronary_artery_disease', 'class']\n",
        "for col in cols:\n",
        "    print(f\"{col} has {df[col].unique()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "merTLrjrPS3Q",
        "outputId": "beb27db3-fa4e-44ca-8892-97b967b26c61"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "diabetes_mellitus has ['yes' 'no' nan]\n",
            "coronary_artery_disease has ['no' 'yes' nan]\n",
            "class has [0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Exploratory Data Analysis (EDA)***"
      ],
      "metadata": {
        "id": "F4ovuiT_jFjO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (20, 15))\n",
        "plotnumber = 1\n",
        "\n",
        "for column in num_cols:\n",
        "    if plotnumber <= 14:\n",
        "        ax = plt.subplot(3, 5, plotnumber)\n",
        "        sns.distplot(df[column])\n",
        "        plt.xlabel(column)\n",
        "\n",
        "    plotnumber += 1\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "So5Okrp1PVm4",
        "outputId": "fa59f407-bbd3-4a3d-84b3-28aa440faec1"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 14 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (20, 15))\n",
        "plotnumber = 1\n",
        "\n",
        "for column in cat_cols:\n",
        "    if plotnumber <= 14:\n",
        "        ax = plt.subplot(3, 5, plotnumber)\n",
        "        sns.countplot(df[column] ,palette = 'rocket')\n",
        "        plt.xlabel(column)\n",
        "\n",
        "    plotnumber += 1\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WzuQ440pPheE",
        "outputId": "17ac5060-615d-4eb2-97df-cb942e3374e9"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 11 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def voilin(col):\n",
        "    fig  = px.violin(df, y=col, x='class', color='class', box=True, template='plotly_dark')\n",
        "    return fig.show()\n",
        "\n",
        "def kde(col):\n",
        "    grid = sns.FacetGrid(df, hue='class', height = 6, aspect = 2)\n",
        "    grid.map(sns.kdeplot, col)\n",
        "    grid.add_legend()\n",
        "\n",
        "def scatter_plot(col1, col2):\n",
        "    fig  = px.scatter(df, x=col1, y=col2, color=\"class\",  template='plotly_dark')\n",
        "    return fig.show()"
      ],
      "metadata": {
        "id": "MP8SotcxP7oI"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kde('red_blood_cell_count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "OGLyJaKiQAiS",
        "outputId": "7089a2ef-270a-4551-fe25-9bd1553a6239"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1264.62x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Data Preprocessing***"
      ],
      "metadata": {
        "id": "097cnRqojY23"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rBHNJ8FVQC3y",
        "outputId": "b0270161-9a03-465c-a19a-45ee76774896"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "red_blood_cells            152\n",
              "red_blood_cell_count       131\n",
              "white_blood_cell_count     106\n",
              "potassium                   88\n",
              "sodium                      87\n",
              "packed_cell_volume          71\n",
              "pus_cell                    65\n",
              "haemoglobin                 52\n",
              "sugar                       49\n",
              "specific_gravity            47\n",
              "albumin                     46\n",
              "blood_glucose_random        44\n",
              "blood_urea                  19\n",
              "serum_creatinine            17\n",
              "blood_pressure              12\n",
              "age                          9\n",
              "bacteria                     4\n",
              "pus_cell_clumps              4\n",
              "hypertension                 2\n",
              "diabetes_mellitus            2\n",
              "coronary_artery_disease      2\n",
              "aanemia                      1\n",
              "appetite                     1\n",
              "peda_edema                   1\n",
              "id                           0\n",
              "class                        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[num_cols].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hbCAxLcKQINH",
        "outputId": "a6ba7d55-4d79-4f4a-ca8c-944d4992b902"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                          0\n",
              "age                         9\n",
              "blood_pressure             12\n",
              "specific_gravity           47\n",
              "albumin                    46\n",
              "sugar                      49\n",
              "blood_glucose_random       44\n",
              "blood_urea                 19\n",
              "serum_creatinine           17\n",
              "sodium                     87\n",
              "potassium                  88\n",
              "haemoglobin                52\n",
              "packed_cell_volume         71\n",
              "white_blood_cell_count    106\n",
              "red_blood_cell_count      131\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[cat_cols].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GobCZQX9QLPd",
        "outputId": "f56d1d99-d6d6-4e24-b6cd-f4cb4d777bac"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "red_blood_cells            152\n",
              "pus_cell                    65\n",
              "pus_cell_clumps              4\n",
              "bacteria                     4\n",
              "hypertension                 2\n",
              "diabetes_mellitus            2\n",
              "coronary_artery_disease      2\n",
              "appetite                     1\n",
              "peda_edema                   1\n",
              "aanemia                      1\n",
              "class                        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "MH2Mb2pjQOL5",
        "outputId": "3c9cdc81-2fad-4ea7-f692-7df519c37cce"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id   age  blood_pressure  specific_gravity  albumin  sugar red_blood_cells  \\\n",
              "0   0  48.0            80.0             1.020      1.0    0.0             NaN   \n",
              "1   1   7.0            50.0             1.020      4.0    0.0             NaN   \n",
              "2   2  62.0            80.0             1.010      2.0    3.0          normal   \n",
              "3   3  48.0            70.0             1.005      4.0    0.0          normal   \n",
              "4   4  51.0            80.0             1.010      2.0    0.0          normal   \n",
              "\n",
              "   pus_cell pus_cell_clumps    bacteria  ...  packed_cell_volume  \\\n",
              "0    normal      notpresent  notpresent  ...                44.0   \n",
              "1    normal      notpresent  notpresent  ...                38.0   \n",
              "2    normal      notpresent  notpresent  ...                31.0   \n",
              "3  abnormal         present  notpresent  ...                32.0   \n",
              "4    normal      notpresent  notpresent  ...                35.0   \n",
              "\n",
              "   white_blood_cell_count  red_blood_cell_count  hypertension  \\\n",
              "0                  7800.0                   5.2           yes   \n",
              "1                  6000.0                   NaN            no   \n",
              "2                  7500.0                   NaN            no   \n",
              "3                  6700.0                   3.9           yes   \n",
              "4                  7300.0                   4.6            no   \n",
              "\n",
              "   diabetes_mellitus  coronary_artery_disease  appetite  peda_edema  aanemia  \\\n",
              "0                yes                       no      good          no       no   \n",
              "1                 no                       no      good          no       no   \n",
              "2                yes                       no      poor          no      yes   \n",
              "3                 no                       no      poor         yes      yes   \n",
              "4                 no                       no      good          no       no   \n",
              "\n",
              "  class  \n",
              "0     0  \n",
              "1     0  \n",
              "2     0  \n",
              "3     0  \n",
              "4     0  \n",
              "\n",
              "[5 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-44d2ad30-fb83-47c0-be7f-1ba9064e59c8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>age</th>\n",
              "      <th>blood_pressure</th>\n",
              "      <th>specific_gravity</th>\n",
              "      <th>albumin</th>\n",
              "      <th>sugar</th>\n",
              "      <th>red_blood_cells</th>\n",
              "      <th>pus_cell</th>\n",
              "      <th>pus_cell_clumps</th>\n",
              "      <th>bacteria</th>\n",
              "      <th>...</th>\n",
              "      <th>packed_cell_volume</th>\n",
              "      <th>white_blood_cell_count</th>\n",
              "      <th>red_blood_cell_count</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>diabetes_mellitus</th>\n",
              "      <th>coronary_artery_disease</th>\n",
              "      <th>appetite</th>\n",
              "      <th>peda_edema</th>\n",
              "      <th>aanemia</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>44.0</td>\n",
              "      <td>7800.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>38.0</td>\n",
              "      <td>6000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>62.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>31.0</td>\n",
              "      <td>7500.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>48.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.005</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>present</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>32.0</td>\n",
              "      <td>6700.0</td>\n",
              "      <td>3.9</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>51.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>35.0</td>\n",
              "      <td>7300.0</td>\n",
              "      <td>4.6</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44d2ad30-fb83-47c0-be7f-1ba9064e59c8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-44d2ad30-fb83-47c0-be7f-1ba9064e59c8 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-44d2ad30-fb83-47c0-be7f-1ba9064e59c8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4ecf259c-4f64-4db4-9042-e416d83e538c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4ecf259c-4f64-4db4-9042-e416d83e538c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4ecf259c-4f64-4db4-9042-e416d83e538c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def random_sampling(feature):\n",
        "    random_sample = df[feature].dropna().sample(df[feature].isna().sum())\n",
        "    random_sample.index = df[df[feature].isnull()].index\n",
        "    df.loc[df[feature].isnull(), feature] = random_sample\n",
        "\n",
        "def impute_mode(feature):\n",
        "    mode = df[feature].mode()[0]\n",
        "    df[feature] = df[feature].fillna(mode)"
      ],
      "metadata": {
        "id": "Urc3gtLjQROv"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col in num_cols:\n",
        "    random_sampling(col)"
      ],
      "metadata": {
        "id": "NXi5hb3gQT7r"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[num_cols].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZXiTRVA0QVz-",
        "outputId": "f55e7e1b-e54f-42c7-8a25-3c02331e376a"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                        0\n",
              "age                       0\n",
              "blood_pressure            0\n",
              "specific_gravity          0\n",
              "albumin                   0\n",
              "sugar                     0\n",
              "blood_glucose_random      0\n",
              "blood_urea                0\n",
              "serum_creatinine          0\n",
              "sodium                    0\n",
              "potassium                 0\n",
              "haemoglobin               0\n",
              "packed_cell_volume        0\n",
              "white_blood_cell_count    0\n",
              "red_blood_cell_count      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random_sampling('red_blood_cells')\n",
        "random_sampling('pus_cell')\n",
        "\n",
        "for col in cat_cols:\n",
        "    impute_mode(col)"
      ],
      "metadata": {
        "id": "MB44Ei_CQZn5"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[cat_cols].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3dV8b4ljQcNw",
        "outputId": "cf7511eb-f6f3-4c9a-8126-fa8fcd03d8ff"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "red_blood_cells            0\n",
              "pus_cell                   0\n",
              "pus_cell_clumps            0\n",
              "bacteria                   0\n",
              "hypertension               0\n",
              "diabetes_mellitus          0\n",
              "coronary_artery_disease    0\n",
              "appetite                   0\n",
              "peda_edema                 0\n",
              "aanemia                    0\n",
              "class                      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Feature Encoding***"
      ],
      "metadata": {
        "id": "go49B_pbjn09"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for col in cat_cols:\n",
        "    print(f\"{col} has {df[col].nunique()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eVt1mq7JQsqG",
        "outputId": "64489070-d67f-422a-87fe-0e01e0e6e915"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "red_blood_cells has 2\n",
            "pus_cell has 2\n",
            "pus_cell_clumps has 2\n",
            "bacteria has 2\n",
            "hypertension has 2\n",
            "diabetes_mellitus has 2\n",
            "coronary_artery_disease has 2\n",
            "appetite has 2\n",
            "peda_edema has 2\n",
            "aanemia has 2\n",
            "class has 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "le = LabelEncoder()\n",
        "\n",
        "for col in cat_cols:\n",
        "    df[col] = le.fit_transform(df[col])"
      ],
      "metadata": {
        "id": "U8lY6IMMQvPl"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "wPoeUQNRQxf_",
        "outputId": "ea1c905c-f0f4-44ad-a5db-f643cce8cfe2"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id   age  blood_pressure  specific_gravity  albumin  sugar  \\\n",
              "0   0  48.0            80.0             1.020      1.0    0.0   \n",
              "1   1   7.0            50.0             1.020      4.0    0.0   \n",
              "2   2  62.0            80.0             1.010      2.0    3.0   \n",
              "3   3  48.0            70.0             1.005      4.0    0.0   \n",
              "4   4  51.0            80.0             1.010      2.0    0.0   \n",
              "\n",
              "   red_blood_cells  pus_cell  pus_cell_clumps  bacteria  ...  \\\n",
              "0                1         1                0         0  ...   \n",
              "1                1         1                0         0  ...   \n",
              "2                1         1                0         0  ...   \n",
              "3                1         0                1         0  ...   \n",
              "4                1         1                0         0  ...   \n",
              "\n",
              "   packed_cell_volume  white_blood_cell_count  red_blood_cell_count  \\\n",
              "0                44.0                  7800.0                   5.2   \n",
              "1                38.0                  6000.0                   4.4   \n",
              "2                31.0                  7500.0                   6.4   \n",
              "3                32.0                  6700.0                   3.9   \n",
              "4                35.0                  7300.0                   4.6   \n",
              "\n",
              "   hypertension  diabetes_mellitus  coronary_artery_disease  appetite  \\\n",
              "0             1                  1                        0         0   \n",
              "1             0                  0                        0         0   \n",
              "2             0                  1                        0         1   \n",
              "3             1                  0                        0         1   \n",
              "4             0                  0                        0         0   \n",
              "\n",
              "   peda_edema  aanemia  class  \n",
              "0           0        0      0  \n",
              "1           0        0      0  \n",
              "2           0        1      0  \n",
              "3           1        1      0  \n",
              "4           0        0      0  \n",
              "\n",
              "[5 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-520becfc-aaf9-45cc-9fd3-e1fd5304dd28\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>age</th>\n",
              "      <th>blood_pressure</th>\n",
              "      <th>specific_gravity</th>\n",
              "      <th>albumin</th>\n",
              "      <th>sugar</th>\n",
              "      <th>red_blood_cells</th>\n",
              "      <th>pus_cell</th>\n",
              "      <th>pus_cell_clumps</th>\n",
              "      <th>bacteria</th>\n",
              "      <th>...</th>\n",
              "      <th>packed_cell_volume</th>\n",
              "      <th>white_blood_cell_count</th>\n",
              "      <th>red_blood_cell_count</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>diabetes_mellitus</th>\n",
              "      <th>coronary_artery_disease</th>\n",
              "      <th>appetite</th>\n",
              "      <th>peda_edema</th>\n",
              "      <th>aanemia</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>44.0</td>\n",
              "      <td>7800.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>38.0</td>\n",
              "      <td>6000.0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>62.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>31.0</td>\n",
              "      <td>7500.0</td>\n",
              "      <td>6.4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>48.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.005</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>32.0</td>\n",
              "      <td>6700.0</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>51.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>35.0</td>\n",
              "      <td>7300.0</td>\n",
              "      <td>4.6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-520becfc-aaf9-45cc-9fd3-e1fd5304dd28')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-520becfc-aaf9-45cc-9fd3-e1fd5304dd28 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-520becfc-aaf9-45cc-9fd3-e1fd5304dd28');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5614e242-0523-4f36-b7b1-08770ec5eab1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5614e242-0523-4f36-b7b1-08770ec5eab1')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5614e242-0523-4f36-b7b1-08770ec5eab1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Model Building***"
      ],
      "metadata": {
        "id": "QapM6mUBjt-b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop('class', axis = 1)\n",
        "y = df['class']"
      ],
      "metadata": {
        "id": "SNAriv1jQzYN"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "MO_yMtb2Q0y-",
        "outputId": "d93bc0b7-c633-4f1c-a5ee-20c0cdc46d5d"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id   age  blood_pressure  specific_gravity  albumin  sugar  \\\n",
              "0      0  48.0            80.0             1.020      1.0    0.0   \n",
              "1      1   7.0            50.0             1.020      4.0    0.0   \n",
              "2      2  62.0            80.0             1.010      2.0    3.0   \n",
              "3      3  48.0            70.0             1.005      4.0    0.0   \n",
              "4      4  51.0            80.0             1.010      2.0    0.0   \n",
              "..   ...   ...             ...               ...      ...    ...   \n",
              "395  395  55.0            80.0             1.020      0.0    0.0   \n",
              "396  396  42.0            70.0             1.025      0.0    0.0   \n",
              "397  397  12.0            80.0             1.020      0.0    0.0   \n",
              "398  398  17.0            60.0             1.025      0.0    0.0   \n",
              "399  399  58.0            80.0             1.025      0.0    0.0   \n",
              "\n",
              "     red_blood_cells  pus_cell  pus_cell_clumps  bacteria  ...  haemoglobin  \\\n",
              "0                  1         1                0         0  ...         15.4   \n",
              "1                  1         1                0         0  ...         11.3   \n",
              "2                  1         1                0         0  ...          9.6   \n",
              "3                  1         0                1         0  ...         11.2   \n",
              "4                  1         1                0         0  ...         11.6   \n",
              "..               ...       ...              ...       ...  ...          ...   \n",
              "395                1         1                0         0  ...         15.7   \n",
              "396                1         1                0         0  ...         16.5   \n",
              "397                1         1                0         0  ...         15.8   \n",
              "398                1         1                0         0  ...         14.2   \n",
              "399                1         1                0         0  ...         15.8   \n",
              "\n",
              "     packed_cell_volume  white_blood_cell_count  red_blood_cell_count  \\\n",
              "0                  44.0                  7800.0                   5.2   \n",
              "1                  38.0                  6000.0                   4.4   \n",
              "2                  31.0                  7500.0                   6.4   \n",
              "3                  32.0                  6700.0                   3.9   \n",
              "4                  35.0                  7300.0                   4.6   \n",
              "..                  ...                     ...                   ...   \n",
              "395                47.0                  6700.0                   4.9   \n",
              "396                54.0                  7800.0                   6.2   \n",
              "397                49.0                  6600.0                   5.4   \n",
              "398                51.0                  7200.0                   5.9   \n",
              "399                53.0                  6800.0                   6.1   \n",
              "\n",
              "     hypertension  diabetes_mellitus  coronary_artery_disease  appetite  \\\n",
              "0               1                  1                        0         0   \n",
              "1               0                  0                        0         0   \n",
              "2               0                  1                        0         1   \n",
              "3               1                  0                        0         1   \n",
              "4               0                  0                        0         0   \n",
              "..            ...                ...                      ...       ...   \n",
              "395             0                  0                        0         0   \n",
              "396             0                  0                        0         0   \n",
              "397             0                  0                        0         0   \n",
              "398             0                  0                        0         0   \n",
              "399             0                  0                        0         0   \n",
              "\n",
              "     peda_edema  aanemia  \n",
              "0             0        0  \n",
              "1             0        0  \n",
              "2             0        1  \n",
              "3             1        1  \n",
              "4             0        0  \n",
              "..          ...      ...  \n",
              "395           0        0  \n",
              "396           0        0  \n",
              "397           0        0  \n",
              "398           0        0  \n",
              "399           0        0  \n",
              "\n",
              "[400 rows x 25 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d9b52548-a46a-4106-a101-aef37d09a77e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>age</th>\n",
              "      <th>blood_pressure</th>\n",
              "      <th>specific_gravity</th>\n",
              "      <th>albumin</th>\n",
              "      <th>sugar</th>\n",
              "      <th>red_blood_cells</th>\n",
              "      <th>pus_cell</th>\n",
              "      <th>pus_cell_clumps</th>\n",
              "      <th>bacteria</th>\n",
              "      <th>...</th>\n",
              "      <th>haemoglobin</th>\n",
              "      <th>packed_cell_volume</th>\n",
              "      <th>white_blood_cell_count</th>\n",
              "      <th>red_blood_cell_count</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>diabetes_mellitus</th>\n",
              "      <th>coronary_artery_disease</th>\n",
              "      <th>appetite</th>\n",
              "      <th>peda_edema</th>\n",
              "      <th>aanemia</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>15.4</td>\n",
              "      <td>44.0</td>\n",
              "      <td>7800.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>11.3</td>\n",
              "      <td>38.0</td>\n",
              "      <td>6000.0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>62.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>9.6</td>\n",
              "      <td>31.0</td>\n",
              "      <td>7500.0</td>\n",
              "      <td>6.4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>48.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.005</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>11.2</td>\n",
              "      <td>32.0</td>\n",
              "      <td>6700.0</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>51.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>11.6</td>\n",
              "      <td>35.0</td>\n",
              "      <td>7300.0</td>\n",
              "      <td>4.6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>395</th>\n",
              "      <td>395</td>\n",
              "      <td>55.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>15.7</td>\n",
              "      <td>47.0</td>\n",
              "      <td>6700.0</td>\n",
              "      <td>4.9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>396</td>\n",
              "      <td>42.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>16.5</td>\n",
              "      <td>54.0</td>\n",
              "      <td>7800.0</td>\n",
              "      <td>6.2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>397</td>\n",
              "      <td>12.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>15.8</td>\n",
              "      <td>49.0</td>\n",
              "      <td>6600.0</td>\n",
              "      <td>5.4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>398</th>\n",
              "      <td>398</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>1.025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>14.2</td>\n",
              "      <td>51.0</td>\n",
              "      <td>7200.0</td>\n",
              "      <td>5.9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>399</td>\n",
              "      <td>58.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>15.8</td>\n",
              "      <td>53.0</td>\n",
              "      <td>6800.0</td>\n",
              "      <td>6.1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 25 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d9b52548-a46a-4106-a101-aef37d09a77e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d9b52548-a46a-4106-a101-aef37d09a77e 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-d9b52548-a46a-4106-a101-aef37d09a77e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a349d3d5-12d1-41c8-8db8-21a9699094c4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a349d3d5-12d1-41c8-8db8-21a9699094c4')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a349d3d5-12d1-41c8-8db8-21a9699094c4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_14d38315-6a75-40b2-9537-256277a6b188\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_14d38315-6a75-40b2-9537-256277a6b188 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('X');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5NnBSVDYQ5Vw",
        "outputId": "72e33d0b-b47e-41f4-e986-e9b4ef92b16a"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      0\n",
              "1      0\n",
              "2      0\n",
              "3      0\n",
              "4      0\n",
              "      ..\n",
              "395    1\n",
              "396    1\n",
              "397    1\n",
              "398    1\n",
              "399    1\n",
              "Name: class, Length: 400, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Train Test Split***"
      ],
      "metadata": {
        "id": "P6sX_zlakAYf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train,X_test, y_train, y_test =  train_test_split(X,y, test_size = 0.2, random_state = 0)"
      ],
      "metadata": {
        "id": "TLaLn2rcQ9H6"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
      ],
      "metadata": {
        "id": "yDDxnQLVRVc-"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***KNN***"
      ],
      "metadata": {
        "id": "VMQy6XS5lBEy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "knn = KNeighborsClassifier()\n",
        "knn.fit(X_train, y_train)\n",
        "\n",
        "knn_acc = accuracy_score(y_test, knn.predict(X_test))\n",
        "print(f\"Training Accuracy of KNN is {accuracy_score(y_train, knn.predict(X_train))}\")\n",
        "print(f\"Testing Accuracy of KNN is {accuracy_score(y_test, knn.predict(X_test))}\")\n",
        "\n",
        "print(f\"Confusion Matrix of KNN is \\n {confusion_matrix(y_test, knn.predict(X_test))}\\n\")\n",
        "print(f\"Classification Report of KNN is \\n{classification_report(y_test, knn.predict(X_test))}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QgHEVzqRk_ud",
        "outputId": "d3bc76aa-cc5d-47a1-caa6-c2b89a968139"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy of KNN is 0.94375\n",
            "Testing Accuracy of KNN is 0.8875\n",
            "Confusion Matrix of KNN is \n",
            " [[45  7]\n",
            " [ 2 26]]\n",
            "\n",
            "Classification Report of KNN is \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.87      0.91        52\n",
            "           1       0.79      0.93      0.85        28\n",
            "\n",
            "    accuracy                           0.89        80\n",
            "   macro avg       0.87      0.90      0.88        80\n",
            "weighted avg       0.90      0.89      0.89        80\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Decision Tree***"
      ],
      "metadata": {
        "id": "ZpMsPpE3j5yd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "dtc = DecisionTreeClassifier()\n",
        "dtc.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "F7nhiv8zRDAU",
        "outputId": "44af4552-fcd7-4127-9553-98c530c76c97"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dtc_acc = accuracy_score(y_test, dtc.predict(X_test))\n",
        "print(f\"Training Accuracy of DTC is {accuracy_score(y_train, dtc.predict(X_train))}\")\n",
        "print(f\"Testing Accuracy of DTC is {accuracy_score(y_test, dtc.predict(X_test))}\")\n",
        "\n",
        "print(f\"Confusion Matrix of DTC is \\n {confusion_matrix(y_test, dtc.predict(X_test))}\\n\")\n",
        "print(f\"Classification Report of DTC is \\n{classification_report(y_test, dtc.predict(X_test))}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CQLQX4oTRFx0",
        "outputId": "51de7f26-61fb-482a-e0c5-027e5912c28c"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy of DTC is 1.0\n",
            "Testing Accuracy of DTC is 1.0\n",
            "Confusion Matrix of DTC is \n",
            " [[52  0]\n",
            " [ 0 28]]\n",
            "\n",
            "Classification Report of DTC is \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        52\n",
            "           1       1.00      1.00      1.00        28\n",
            "\n",
            "    accuracy                           1.00        80\n",
            "   macro avg       1.00      1.00      1.00        80\n",
            "weighted avg       1.00      1.00      1.00        80\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Hyper Parameter Tuning***"
      ],
      "metadata": {
        "id": "04cRt8_nmedq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "GRID_PARAMETER = {\n",
        "    'criterion':['gini','entropy'],\n",
        "    'max_depth':[3,5,7,10],\n",
        "    'splitter':['best','random'],\n",
        "    'min_samples_leaf':[1,2,3,5,7],\n",
        "    'min_samples_split':[1,2,3,5,7],\n",
        "    'max_features':['auto', 'sqrt', 'log2']\n",
        "}\n",
        "\n",
        "grid_search_dtc = GridSearchCV(dtc, GRID_PARAMETER, cv=5, n_jobs=-1, verbose = 1)\n",
        "grid_search_dtc.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        },
        "id": "f6WaMuSqmcU7",
        "outputId": "32148812-326e-44ce-9910-9aa8920d6cc5"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 1200 candidates, totalling 6000 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
              "             param_grid={'criterion': ['gini', 'entropy'],\n",
              "                         'max_depth': [3, 5, 7, 10],\n",
              "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
              "                         'min_samples_leaf': [1, 2, 3, 5, 7],\n",
              "                         'min_samples_split': [1, 2, 3, 5, 7],\n",
              "                         'splitter': ['best', 'random']},\n",
              "             verbose=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
              "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
              "                         &#x27;max_depth&#x27;: [3, 5, 7, 10],\n",
              "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
              "                         &#x27;min_samples_leaf&#x27;: [1, 2, 3, 5, 7],\n",
              "                         &#x27;min_samples_split&#x27;: [1, 2, 3, 5, 7],\n",
              "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]},\n",
              "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
              "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
              "                         &#x27;max_depth&#x27;: [3, 5, 7, 10],\n",
              "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
              "                         &#x27;min_samples_leaf&#x27;: [1, 2, 3, 5, 7],\n",
              "                         &#x27;min_samples_split&#x27;: [1, 2, 3, 5, 7],\n",
              "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]},\n",
              "             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(grid_search_dtc.best_params_)\n",
        "print(grid_search_dtc.best_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o5PPPcVpmonM",
        "outputId": "4bd5c967-be8c-4e98-abb9-0f06423294d7"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'criterion': 'entropy', 'max_depth': 5, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 7, 'splitter': 'best'}\n",
            "1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dtc = grid_search_dtc.best_estimator_\n",
        "\n",
        "dtc_acc = accuracy_score(y_test, dtc.predict(X_test))\n",
        "print(f\"Training Accuracy of DTC is {accuracy_score(y_train, dtc.predict(X_train))}\")\n",
        "print(f\"Testing Accuracy of DTC is {accuracy_score(y_test, dtc.predict(X_test))}\")\n",
        "\n",
        "print(f\"Confusion Matrix of DTC is \\n {confusion_matrix(y_test, dtc.predict(X_test))}\\n\")\n",
        "print(f\"Classification Report of DTC is \\n{classification_report(y_test, dtc.predict(X_test))}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kxqATC8Smsd0",
        "outputId": "f2f20cc7-d7e4-4351-d08c-fae7ba110612"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy of DTC is 1.0\n",
            "Testing Accuracy of DTC is 1.0\n",
            "Confusion Matrix of DTC is \n",
            " [[52  0]\n",
            " [ 0 28]]\n",
            "\n",
            "Classification Report of DTC is \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        52\n",
            "           1       1.00      1.00      1.00        28\n",
            "\n",
            "    accuracy                           1.00        80\n",
            "   macro avg       1.00      1.00      1.00        80\n",
            "weighted avg       1.00      1.00      1.00        80\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Random Forest Classifier***"
      ],
      "metadata": {
        "id": "eMrHN6HsmytX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rand_clf = RandomForestClassifier(criterion = \"gini\", max_depth = 10, max_features=\"sqrt\", min_samples_leaf= 1, min_samples_split= 7, n_estimators = 400)\n",
        "rand_clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "21v42TlVmvoZ",
        "outputId": "fe17e57e-14e2-4af2-a09f-cf3c7fa1af37"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=10, min_samples_split=7, n_estimators=400)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10, min_samples_split=7, n_estimators=400)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=10, min_samples_split=7, n_estimators=400)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rand_clf_acc = accuracy_score(y_test, rand_clf.predict(X_test))\n",
        "print(f\"Training Accuracy of Random Forest is {accuracy_score(y_train, rand_clf.predict(X_train))}\")\n",
        "print(f\"Testing Accuracy of Random Forest is {accuracy_score(y_test, rand_clf.predict(X_test))}\")\n",
        "\n",
        "print(f\"Confusion Matrix of Random Forest is \\n {confusion_matrix(y_test, rand_clf.predict(X_test))}\\n\")\n",
        "print(f\"Classification Report of Random Forest is \\n{classification_report(y_test, rand_clf.predict(X_test))}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HxvrdC2Ym5RB",
        "outputId": "c49bd075-c32a-4b00-e2cb-4106490f0c31"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy of Random Forest is 1.0\n",
            "Testing Accuracy of Random Forest is 1.0\n",
            "Confusion Matrix of Random Forest is \n",
            " [[52  0]\n",
            " [ 0 28]]\n",
            "\n",
            "Classification Report of Random Forest is \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        52\n",
            "           1       1.00      1.00      1.00        28\n",
            "\n",
            "    accuracy                           1.00        80\n",
            "   macro avg       1.00      1.00      1.00        80\n",
            "weighted avg       1.00      1.00      1.00        80\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***XgBoost***"
      ],
      "metadata": {
        "id": "jTqOBI9Rm-nW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier\n",
        "xgb = XGBClassifier(objective=\"binary:logistic\", learning_rate = 0.001, max_depth = 10, n_estimators = 100)\n",
        "xgb.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "MnHVXmzfm74-",
        "outputId": "b9c53596-e52a-4a26-e150-010822dcac9b"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.001, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=10, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.001, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=10, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.001, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=10, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_acc = accuracy_score(y_test, xgb.predict(X_test))\n",
        "print(f\"Training Accuracy of XGB is {accuracy_score(y_train, xgb.predict(X_train))}\")\n",
        "print(f\"Testing Accuracy of XGB is {accuracy_score(y_test, xgb.predict(X_test))}\")\n",
        "\n",
        "print(f\"Confusion Matrix of XGB is \\n {confusion_matrix(y_test, xgb.predict(X_test))}\\n\")\n",
        "print(f\"Classification Report of XGB is \\n{classification_report(y_test, xgb.predict(X_test))}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "--GavV-AnDPo",
        "outputId": "c079c0a3-e40b-4b34-a265-1a08c0aa0ac3"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy of XGB is 0.61875\n",
            "Testing Accuracy of XGB is 0.65\n",
            "Confusion Matrix of XGB is \n",
            " [[52  0]\n",
            " [28  0]]\n",
            "\n",
            "Classification Report of XGB is \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.65      1.00      0.79        52\n",
            "           1       0.00      0.00      0.00        28\n",
            "\n",
            "    accuracy                           0.65        80\n",
            "   macro avg       0.33      0.50      0.39        80\n",
            "weighted avg       0.42      0.65      0.51        80\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "lr = LogisticRegression()\n",
        "lr.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "YUgUlghYnGcS",
        "outputId": "8bb31a89-29e2-4c69-c813-14d48f70af82"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr_acc = accuracy_score(y_test, lr.predict(X_test))\n",
        "print(f\"Training Accuracy of LR is {accuracy_score(y_train, lr.predict(X_train))}\")\n",
        "print(f\"Testing Accuracy of LR is {accuracy_score(y_test, lr.predict(X_test))}\")\n",
        "\n",
        "print(f\"Confusion Matrix of LR is \\n {confusion_matrix(y_test, lr.predict(X_test))}\\n\")\n",
        "print(f\"Classification Report of LR is \\n{classification_report(y_test, lr.predict(X_test))}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eNVZShwFnIia",
        "outputId": "ffbf7614-4cf2-4c20-c7bf-15b4eac3e4f7"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy of LR is 1.0\n",
            "Testing Accuracy of LR is 1.0\n",
            "Confusion Matrix of LR is \n",
            " [[52  0]\n",
            " [ 0 28]]\n",
            "\n",
            "Classification Report of LR is \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        52\n",
            "           1       1.00      1.00      1.00        28\n",
            "\n",
            "    accuracy                           1.00        80\n",
            "   macro avg       1.00      1.00      1.00        80\n",
            "weighted avg       1.00      1.00      1.00        80\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***SVM***"
      ],
      "metadata": {
        "id": "CIsid3jXnKqV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "svm  = SVC(probability=True)\n",
        "\n",
        "parameter = {\n",
        "    'gamma':[0.0001, 0.001, 0.01, 0.1],\n",
        "    'C':[0.01, 0.05, 0.5, 0.1, 1, 10, 15, 20]\n",
        "}\n",
        "\n",
        "grid_search = GridSearchCV(svm, parameter)\n",
        "grid_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "ajQOc8R3nNKu",
        "outputId": "e4b29856-7da2-4524-c0e1-5b4ad93b9877"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(estimator=SVC(probability=True),\n",
              "             param_grid={'C': [0.01, 0.05, 0.5, 0.1, 1, 10, 15, 20],\n",
              "                         'gamma': [0.0001, 0.001, 0.01, 0.1]})"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=SVC(probability=True),\n",
              "             param_grid={&#x27;C&#x27;: [0.01, 0.05, 0.5, 0.1, 1, 10, 15, 20],\n",
              "                         &#x27;gamma&#x27;: [0.0001, 0.001, 0.01, 0.1]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=SVC(probability=True),\n",
              "             param_grid={&#x27;C&#x27;: [0.01, 0.05, 0.5, 0.1, 1, 10, 15, 20],\n",
              "                         &#x27;gamma&#x27;: [0.0001, 0.001, 0.01, 0.1]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(probability=True)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(probability=True)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(grid_search.best_params_)\n",
        "print(grid_search.best_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3VFavHZvnRFa",
        "outputId": "9b99b152-f133-4f64-8422-b8702825a4f7"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'C': 10, 'gamma': 0.0001}\n",
            "0.9375\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svm  = SVC(gamma = 0.0001, C  = 15, probability=True)\n",
        "svm.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "V1-Dv6s3nTBH",
        "outputId": "ee210ae1-4166-4ce6-915e-13a69dd6882d"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=15, gamma=0.0001, probability=True)"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=15, gamma=0.0001, probability=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=15, gamma=0.0001, probability=True)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svm_acc = accuracy_score(y_test, svm.predict(X_test))\n",
        "print(f\"Training Accuracy of SVC is {accuracy_score(y_train, svm.predict(X_train))}\")\n",
        "print(f\"Testing Accuracy of SVC is {accuracy_score(y_test, svm.predict(X_test))}\")\n",
        "\n",
        "print(f\"Confusion Matrix of SVC is \\n {confusion_matrix(y_test, svm.predict(X_test))}\\n\")\n",
        "print(f\"Classification Report of SVC is \\n{classification_report(y_test, svm.predict(X_test))}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dFNRd53MnU3N",
        "outputId": "06150aca-f9eb-4e1a-aed4-8394a29cc68e"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy of SVC is 1.0\n",
            "Testing Accuracy of SVC is 0.9375\n",
            "Confusion Matrix of SVC is \n",
            " [[50  2]\n",
            " [ 3 25]]\n",
            "\n",
            "Classification Report of SVC is \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.96      0.95        52\n",
            "           1       0.93      0.89      0.91        28\n",
            "\n",
            "    accuracy                           0.94        80\n",
            "   macro avg       0.93      0.93      0.93        80\n",
            "weighted avg       0.94      0.94      0.94        80\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Gradient Boosting***"
      ],
      "metadata": {
        "id": "hbznxZNcndDq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "gbc = GradientBoostingClassifier()\n",
        "\n",
        "PARAMETERS = {\n",
        "    'loss': ['log_loss', 'exponential'],\n",
        "    'learning_rate':[0.001, 0.1, 1, 10],\n",
        "    'n_estimators':[100,150,180, 200]\n",
        "}\n",
        "grid_search_gbc = GridSearchCV(gbc, PARAMETERS, cv=5, n_jobs=-1, verbose= 1)\n",
        "grid_search_gbc.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        },
        "id": "JGzjRkn2nawW",
        "outputId": "4fe78997-75b4-4bfa-d9e6-84f64af7df18"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 32 candidates, totalling 160 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=GradientBoostingClassifier(), n_jobs=-1,\n",
              "             param_grid={'learning_rate': [0.001, 0.1, 1, 10],\n",
              "                         'loss': ['log_loss', 'exponential'],\n",
              "                         'n_estimators': [100, 150, 180, 200]},\n",
              "             verbose=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=GradientBoostingClassifier(), n_jobs=-1,\n",
              "             param_grid={&#x27;learning_rate&#x27;: [0.001, 0.1, 1, 10],\n",
              "                         &#x27;loss&#x27;: [&#x27;log_loss&#x27;, &#x27;exponential&#x27;],\n",
              "                         &#x27;n_estimators&#x27;: [100, 150, 180, 200]},\n",
              "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=GradientBoostingClassifier(), n_jobs=-1,\n",
              "             param_grid={&#x27;learning_rate&#x27;: [0.001, 0.1, 1, 10],\n",
              "                         &#x27;loss&#x27;: [&#x27;log_loss&#x27;, &#x27;exponential&#x27;],\n",
              "                         &#x27;n_estimators&#x27;: [100, 150, 180, 200]},\n",
              "             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(grid_search_gbc.best_params_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "euuOTo3cnjVi",
        "outputId": "9a811025-37d0-49d1-f3fe-9a6d4b2d55df"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'learning_rate': 0.1, 'loss': 'log_loss', 'n_estimators': 100}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(grid_search_gbc.best_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l5tmDwhUnlB3",
        "outputId": "690cb417-b3a2-4a1a-d255-04825f60af41"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.990625\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gbc = GradientBoostingClassifier(learning_rate= 0.1, loss = 'log_loss', n_estimators = 100)\n",
        "gbc.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "UBv3xu2Snmwi",
        "outputId": "165f0d4d-67e9-4cea-e3cb-94de6afed1eb"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gbc_acc = accuracy_score(y_test, gbc.predict(X_test))\n",
        "print(f\"Training Accuracy of GBC is {accuracy_score(y_train, gbc.predict(X_train))}\")\n",
        "print(f\"Testing Accuracy of GBC is {accuracy_score(y_test, gbc.predict(X_test))}\")\n",
        "\n",
        "print(f\"Confusion Matrix of GBC is \\n {confusion_matrix(y_test, gbc.predict(X_test))}\\n\")\n",
        "print(f\"Classification Report of GBC is \\n{classification_report(y_test, gbc.predict(X_test))}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VswGyoxwnpXQ",
        "outputId": "d8026704-1623-48fe-f80f-5a9373f05bcd"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy of GBC is 1.0\n",
            "Testing Accuracy of GBC is 1.0\n",
            "Confusion Matrix of GBC is \n",
            " [[52  0]\n",
            " [ 0 28]]\n",
            "\n",
            "Classification Report of GBC is \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        52\n",
            "           1       1.00      1.00      1.00        28\n",
            "\n",
            "    accuracy                           1.00        80\n",
            "   macro avg       1.00      1.00      1.00        80\n",
            "weighted avg       1.00      1.00      1.00        80\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Model Comparision***"
      ],
      "metadata": {
        "id": "JZjXP01koOI_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "models = pd.DataFrame({\n",
        "    'Model':['Logistic Regression', 'KNN', 'SVM', 'DT', 'Random Forest Classifier', 'XgBoost','Gradient Boosting'],\n",
        "    'Score':[lr_acc, knn_acc, svm_acc, dtc_acc, rand_clf_acc, xgb_acc, gbc_acc]\n",
        "})\n",
        "\n",
        "models.sort_values(by='Score', ascending = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "b_UnwBv0ntb6",
        "outputId": "b0e85bc8-54c5-4d42-905f-d447d42e52b2"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Model   Score\n",
              "0       Logistic Regression  1.0000\n",
              "3                        DT  1.0000\n",
              "4  Random Forest Classifier  1.0000\n",
              "6         Gradient Boosting  1.0000\n",
              "2                       SVM  0.9375\n",
              "1                       KNN  0.8875\n",
              "5                   XgBoost  0.6500"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-15625e99-ad9c-4388-bcdb-1878e7ba8e3f\" 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>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>DT</td>\n",
              "      <td>1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Gradient Boosting</td>\n",
              "      <td>1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SVM</td>\n",
              "      <td>0.9375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>KNN</td>\n",
              "      <td>0.8875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>XgBoost</td>\n",
              "      <td>0.6500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-15625e99-ad9c-4388-bcdb-1878e7ba8e3f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-15625e99-ad9c-4388-bcdb-1878e7ba8e3f 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-15625e99-ad9c-4388-bcdb-1878e7ba8e3f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1877c6da-cb35-402b-a22a-304a2ece29db\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1877c6da-cb35-402b-a22a-304a2ece29db')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1877c6da-cb35-402b-a22a-304a2ece29db button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"models\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Logistic Regression\",\n          \"DT\",\n          \"KNN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12889756915215017,\n        \"min\": 0.65,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.9375,\n          0.65,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "model = dtc\n",
        "pickle.dump(model, open(\"kindey.pkl\", 'wb'))"
      ],
      "metadata": {
        "id": "U2VqlzXKnxPh"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "plt.figure(figsize=(8,5))\n",
        "models = [\n",
        "{\n",
        "    'label': 'LR',\n",
        "    'model': lr,\n",
        "},\n",
        "{\n",
        "    'label': 'DT',\n",
        "    'model': dtc,\n",
        "},\n",
        "{\n",
        "    'label': 'SVM',\n",
        "    'model': svm,\n",
        "},\n",
        "{\n",
        "    'label': 'KNN',\n",
        "    'model': knn,\n",
        "},\n",
        "{\n",
        "    'label': 'XGBoost',\n",
        "    'model': xgb,\n",
        "},\n",
        "{\n",
        "    'label': 'RF',\n",
        "    'model': rand_clf,\n",
        "},\n",
        "{\n",
        "    'label': 'GBDT',\n",
        "    'model': gbc,\n",
        "}\n",
        "]\n",
        "for m in models:\n",
        "    model = m['model']\n",
        "    model.fit(X_train, y_train)\n",
        "    y_pred=model.predict(X_test)\n",
        "    fpr1, tpr1, thresholds = metrics.roc_curve(y_test, model.predict_proba(X_test)[:,1])\n",
        "    auc = metrics.roc_auc_score(y_test,model.predict(X_test))\n",
        "    plt.plot(fpr1, tpr1, label='%s - ROC (area = %0.2f)' % (m['label'], auc))\n",
        "\n",
        "plt.plot([0, 1], [0, 1],'r--')\n",
        "plt.xlim([-0.01, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('1 - Specificity (False Positive Rate)', fontsize=12)\n",
        "plt.ylabel('Sensitivity (True Positive Rate)', fontsize=12)\n",
        "plt.title('ROC - Kidney Disease Prediction', fontsize=12)\n",
        "plt.legend(loc=\"lower right\", fontsize=12)\n",
        "plt.savefig(\"roc_kidney.jpeg\", format='jpeg', dpi=400, bbox_inches='tight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "ZBYWjJGqn9cA",
        "outputId": "ab31894a-15d8-4f0c-955e-2a7ff9fefa2d"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "models = [\n",
        "{\n",
        "    'label': 'LR',\n",
        "    'model': lr,\n",
        "},\n",
        "{\n",
        "    'label': 'DT',\n",
        "    'model': dtc,\n",
        "},\n",
        "{\n",
        "    'label': 'SVM',\n",
        "    'model': svm,\n",
        "},\n",
        "{\n",
        "    'label': 'KNN',\n",
        "    'model': knn,\n",
        "},\n",
        "{\n",
        "    'label': 'XGBoost',\n",
        "    'model': xgb,\n",
        "},\n",
        "{\n",
        "    'label': 'RF',\n",
        "    'model': rand_clf,\n",
        "},\n",
        "{\n",
        "    'label': 'GBDT',\n",
        "    'model': gbc,\n",
        "}\n",
        "]\n",
        "\n",
        "means_roc = []\n",
        "means_accuracy = [100*round(lr_acc,4), 100*round(dtc_acc,4), 100*round(svm_acc,4), 100*round(knn_acc,4), 100*round(xgb_acc,4),\n",
        "                  100*round(rand_clf_acc,4), 100*round(gbc_acc,4)]\n",
        "\n",
        "for m in models:\n",
        "    model = m['model']\n",
        "    model.fit(X_train, y_train)\n",
        "    y_pred=model.predict(X_test)\n",
        "    fpr1, tpr1, thresholds = metrics.roc_curve(y_test, model.predict_proba(X_test)[:,1])\n",
        "    auc = metrics.roc_auc_score(y_test,model.predict(X_test))\n",
        "    auc = 100*round(auc,4)\n",
        "    means_roc.append(auc)\n",
        "\n",
        "print(means_accuracy)\n",
        "print(means_roc)\n",
        "\n",
        "\n",
        "n_groups = 7\n",
        "means_accuracy = tuple(means_accuracy)\n",
        "means_roc = tuple(means_roc)\n",
        "\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(8,5))\n",
        "index = np.arange(n_groups)\n",
        "bar_width = 0.35\n",
        "opacity = 0.8\n",
        "\n",
        "rects1 = plt.bar(index, means_accuracy, bar_width,\n",
        "alpha=opacity,\n",
        "color='mediumpurple',\n",
        "label='Accuracy (%)')\n",
        "\n",
        "rects2 = plt.bar(index + bar_width, means_roc, bar_width,\n",
        "alpha=opacity,\n",
        "color='rebeccapurple',\n",
        "label='ROC (%)')\n",
        "\n",
        "plt.xlim([-1, 8])\n",
        "plt.ylim([45, 104])\n",
        "\n",
        "plt.title('Performance Evaluation - Kidney Disease Prediction', fontsize=12)\n",
        "plt.xticks(index, ('   LR', '   DT', '   SVM', '   KNN', 'XGBoost' , '   RF', '   GBDT'), rotation=40, ha='center', fontsize=12)\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "plt.savefig(\"PE_kidney.jpeg\", format='jpeg', dpi=400, bbox_inches='tight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        },
        "id": "kgJDssPNoDp7",
        "outputId": "ba2d96f4-3784-410d-9a19-dc2f718cf8dc"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[100.0, 100.0, 93.75, 88.75, 65.0, 100.0, 100.0]\n",
            "[100.0, 100.0, 92.72, 89.7, 50.0, 98.21, 100.0]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}