Approach 2 - Devanshu Dalal.ipynb

{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "name": "MLSC.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "kXx1C9ZGkzSb"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import missingno as msno\n",
        "#import matplotlib.pyplot as plt % matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "3oep_JHOlJz8",
        "outputId": "5246eaca-90bf-4ab5-b7bd-9e1e797adcc3"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-20d3bf0a-5f51-47bb-aeb7-64fc781e371a\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-20d3bf0a-5f51-47bb-aeb7-64fc781e371a\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving heart.csv to heart.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "WEc5wZZUlO6Y",
        "outputId": "0e51d303-dad5-4701-87d2-b7edfb3013a9"
      },
      "source": [
        "import io\n",
        "df1 = pd.read_csv(io.BytesIO(uploaded['heart.csv']))\n",
        "df1"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-598538c0-c692-4df8-afc2-7008ad8345f9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>ChestPainType</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>RestingECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>ExerciseAngina</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>ST_Slope</th>\n",
              "      <th>HeartDisease</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40</td>\n",
              "      <td>M</td>\n",
              "      <td>ATA</td>\n",
              "      <td>140</td>\n",
              "      <td>289</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>172</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>F</td>\n",
              "      <td>NAP</td>\n",
              "      <td>160</td>\n",
              "      <td>180</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>156</td>\n",
              "      <td>N</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>M</td>\n",
              "      <td>ATA</td>\n",
              "      <td>130</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>ST</td>\n",
              "      <td>98</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48</td>\n",
              "      <td>F</td>\n",
              "      <td>ASY</td>\n",
              "      <td>138</td>\n",
              "      <td>214</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>108</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54</td>\n",
              "      <td>M</td>\n",
              "      <td>NAP</td>\n",
              "      <td>150</td>\n",
              "      <td>195</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>122</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>913</th>\n",
              "      <td>45</td>\n",
              "      <td>M</td>\n",
              "      <td>TA</td>\n",
              "      <td>110</td>\n",
              "      <td>264</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>132</td>\n",
              "      <td>N</td>\n",
              "      <td>1.2</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>914</th>\n",
              "      <td>68</td>\n",
              "      <td>M</td>\n",
              "      <td>ASY</td>\n",
              "      <td>144</td>\n",
              "      <td>193</td>\n",
              "      <td>1</td>\n",
              "      <td>Normal</td>\n",
              "      <td>141</td>\n",
              "      <td>N</td>\n",
              "      <td>3.4</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>915</th>\n",
              "      <td>57</td>\n",
              "      <td>M</td>\n",
              "      <td>ASY</td>\n",
              "      <td>130</td>\n",
              "      <td>131</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>115</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.2</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>916</th>\n",
              "      <td>57</td>\n",
              "      <td>F</td>\n",
              "      <td>ATA</td>\n",
              "      <td>130</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>LVH</td>\n",
              "      <td>174</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>917</th>\n",
              "      <td>38</td>\n",
              "      <td>M</td>\n",
              "      <td>NAP</td>\n",
              "      <td>138</td>\n",
              "      <td>175</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>173</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>918 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-598538c0-c692-4df8-afc2-7008ad8345f9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\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-598538c0-c692-4df8-afc2-7008ad8345f9 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-598538c0-c692-4df8-afc2-7008ad8345f9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     Age Sex ChestPainType  ...  Oldpeak  ST_Slope  HeartDisease\n",
              "0     40   M           ATA  ...      0.0        Up             0\n",
              "1     49   F           NAP  ...      1.0      Flat             1\n",
              "2     37   M           ATA  ...      0.0        Up             0\n",
              "3     48   F           ASY  ...      1.5      Flat             1\n",
              "4     54   M           NAP  ...      0.0        Up             0\n",
              "..   ...  ..           ...  ...      ...       ...           ...\n",
              "913   45   M            TA  ...      1.2      Flat             1\n",
              "914   68   M           ASY  ...      3.4      Flat             1\n",
              "915   57   M           ASY  ...      1.2      Flat             1\n",
              "916   57   F           ATA  ...      0.0      Flat             1\n",
              "917   38   M           NAP  ...      0.0        Up             0\n",
              "\n",
              "[918 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "6XwcDUO_yY5h",
        "outputId": "c71bb416-3936-46c6-d4fa-ec92067dfc01"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d778cdec-5612-48f5-9a2f-b412ca028baa\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>HeartDisease</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>53.510893</td>\n",
              "      <td>132.396514</td>\n",
              "      <td>198.799564</td>\n",
              "      <td>0.233115</td>\n",
              "      <td>136.809368</td>\n",
              "      <td>0.887364</td>\n",
              "      <td>0.553377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.432617</td>\n",
              "      <td>18.514154</td>\n",
              "      <td>109.384145</td>\n",
              "      <td>0.423046</td>\n",
              "      <td>25.460334</td>\n",
              "      <td>1.066570</td>\n",
              "      <td>0.497414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>-2.600000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>173.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>54.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>223.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>138.000000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>267.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>603.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d778cdec-5612-48f5-9a2f-b412ca028baa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              Age   RestingBP  ...     Oldpeak  HeartDisease\n",
              "count  918.000000  918.000000  ...  918.000000    918.000000\n",
              "mean    53.510893  132.396514  ...    0.887364      0.553377\n",
              "std      9.432617   18.514154  ...    1.066570      0.497414\n",
              "min     28.000000    0.000000  ...   -2.600000      0.000000\n",
              "25%     47.000000  120.000000  ...    0.000000      0.000000\n",
              "50%     54.000000  130.000000  ...    0.600000      1.000000\n",
              "75%     60.000000  140.000000  ...    1.500000      1.000000\n",
              "max     77.000000  200.000000  ...    6.200000      1.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xxa_A7LeycOc",
        "outputId": "110f4c71-bc0a-45dc-83e3-da14b4bee09a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 918 entries, 0 to 917\n",
            "Data columns (total 12 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   Age             918 non-null    int64  \n",
            " 1   Sex             918 non-null    object \n",
            " 2   ChestPainType   918 non-null    object \n",
            " 3   RestingBP       918 non-null    int64  \n",
            " 4   Cholesterol     918 non-null    int64  \n",
            " 5   FastingBS       918 non-null    int64  \n",
            " 6   RestingECG      918 non-null    object \n",
            " 7   MaxHR           918 non-null    int64  \n",
            " 8   ExerciseAngina  918 non-null    object \n",
            " 9   Oldpeak         918 non-null    float64\n",
            " 10  ST_Slope        918 non-null    object \n",
            " 11  HeartDisease    918 non-null    int64  \n",
            "dtypes: float64(1), int64(6), object(5)\n",
            "memory usage: 86.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2frq8hOlW4w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        },
        "outputId": "3e7bc69d-cdec-4df1-f587-4a53d9845a1e"
      },
      "source": [
        "tc = df1.corr()\n",
        "sns.heatmap(tc, annot = True, cmap ='plasma',\n",
        "            linecolor ='black', linewidths = 1)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0fe75f8090>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "msno.bar(df1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 705
        },
        "id": "j_r1-9JvyJbP",
        "outputId": "7e281b1a-ac80-4c04-8fcd-867dd8cf0081"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0fe6d4d950>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.get_dummies(df1) "
      ],
      "metadata": {
        "id": "FfbaGhVQxaFv"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Szc2s9gsz-n",
        "outputId": "d69d3917-1265-433f-ab16-3eb661bd8fe4"
      },
      "source": [
        "corr_matrix=df1.corr()\n",
        "corr_matrix['HeartDisease'].sort_values(ascending=False)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "HeartDisease         1.000000\n",
              "ST_Slope_Flat        0.554134\n",
              "ChestPainType_ASY    0.516716\n",
              "ExerciseAngina_Y     0.494282\n",
              "Oldpeak              0.403951\n",
              "Sex_M                0.305445\n",
              "Age                  0.282039\n",
              "FastingBS            0.267291\n",
              "ST_Slope_Down        0.122527\n",
              "RestingBP            0.107589\n",
              "RestingECG_ST        0.102527\n",
              "RestingECG_LVH       0.010670\n",
              "ChestPainType_TA    -0.054790\n",
              "RestingECG_Normal   -0.091580\n",
              "ChestPainType_NAP   -0.212964\n",
              "Cholesterol         -0.232741\n",
              "Sex_F               -0.305445\n",
              "MaxHR               -0.400421\n",
              "ChestPainType_ATA   -0.401924\n",
              "ExerciseAngina_N    -0.494282\n",
              "ST_Slope_Up         -0.622164\n",
              "Name: HeartDisease, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tc = df1.corr()\n",
        "sns.heatmap(tc, annot = True, cmap ='plasma',\n",
        "            linecolor ='black', linewidths = 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "GMDhLcCAxCGJ",
        "outputId": "f3811c1a-622e-4455-e69d-f32893a722ea"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0fddd952d0>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "msno.bar(df1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "id": "Qksd_rE4xyMn",
        "outputId": "d24a72c0-0e51-48b4-84c4-64288db27068"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0fddc7f650>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xJsELK-9W3aW",
        "outputId": "879028cb-bc2d-4fe0-cac3-3a0f7d9d9e88"
      },
      "source": [
        "pd.value_counts(df1['Cholesterol'])"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      172\n",
              "254     11\n",
              "223     10\n",
              "220     10\n",
              "230      9\n",
              "      ... \n",
              "392      1\n",
              "316      1\n",
              "153      1\n",
              "466      1\n",
              "131      1\n",
              "Name: Cholesterol, Length: 222, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.value_counts(df1['RestingBP'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eCzQzlxFzu1k",
        "outputId": "b6347f18-a662-457f-f557-f3c62854bc28"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "120    132\n",
              "130    118\n",
              "140    107\n",
              "110     58\n",
              "150     55\n",
              "      ... \n",
              "185      1\n",
              "98       1\n",
              "92       1\n",
              "113      1\n",
              "164      1\n",
              "Name: RestingBP, Length: 67, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AJm8wGcsXSK0"
      },
      "source": [
        "for i in range(len(df1['Cholesterol'])):\n",
        "  if(df1['Cholesterol'][i]==0 or df1['RestingBP'][i]==0):\n",
        "    df1=df1.drop(i)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "0AAxlKBIl5I0",
        "outputId": "16668214-e407-473c-92fe-84da9a67f00c"
      },
      "source": [
        "df1"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-52b29b41-ab17-4e86-8f75-5d70b6622b4b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>HeartDisease</th>\n",
              "      <th>Sex_F</th>\n",
              "      <th>Sex_M</th>\n",
              "      <th>ChestPainType_ASY</th>\n",
              "      <th>ChestPainType_ATA</th>\n",
              "      <th>ChestPainType_NAP</th>\n",
              "      <th>ChestPainType_TA</th>\n",
              "      <th>RestingECG_LVH</th>\n",
              "      <th>RestingECG_Normal</th>\n",
              "      <th>RestingECG_ST</th>\n",
              "      <th>ExerciseAngina_N</th>\n",
              "      <th>ExerciseAngina_Y</th>\n",
              "      <th>ST_Slope_Down</th>\n",
              "      <th>ST_Slope_Flat</th>\n",
              "      <th>ST_Slope_Up</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40</td>\n",
              "      <td>140</td>\n",
              "      <td>289</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>160</td>\n",
              "      <td>180</td>\n",
              "      <td>0</td>\n",
              "      <td>156</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>130</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>98</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48</td>\n",
              "      <td>138</td>\n",
              "      <td>214</td>\n",
              "      <td>0</td>\n",
              "      <td>108</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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>4</th>\n",
              "      <td>54</td>\n",
              "      <td>150</td>\n",
              "      <td>195</td>\n",
              "      <td>0</td>\n",
              "      <td>122</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <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>913</th>\n",
              "      <td>45</td>\n",
              "      <td>110</td>\n",
              "      <td>264</td>\n",
              "      <td>0</td>\n",
              "      <td>132</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>914</th>\n",
              "      <td>68</td>\n",
              "      <td>144</td>\n",
              "      <td>193</td>\n",
              "      <td>1</td>\n",
              "      <td>141</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>915</th>\n",
              "      <td>57</td>\n",
              "      <td>130</td>\n",
              "      <td>131</td>\n",
              "      <td>0</td>\n",
              "      <td>115</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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>1</td>\n",
              "      <td>0</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>916</th>\n",
              "      <td>57</td>\n",
              "      <td>130</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>917</th>\n",
              "      <td>38</td>\n",
              "      <td>138</td>\n",
              "      <td>175</td>\n",
              "      <td>0</td>\n",
              "      <td>173</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>746 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-52b29b41-ab17-4e86-8f75-5d70b6622b4b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-52b29b41-ab17-4e86-8f75-5d70b6622b4b 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-52b29b41-ab17-4e86-8f75-5d70b6622b4b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     Age  RestingBP  Cholesterol  ...  ST_Slope_Down  ST_Slope_Flat  ST_Slope_Up\n",
              "0     40        140          289  ...              0              0            1\n",
              "1     49        160          180  ...              0              1            0\n",
              "2     37        130          283  ...              0              0            1\n",
              "3     48        138          214  ...              0              1            0\n",
              "4     54        150          195  ...              0              0            1\n",
              "..   ...        ...          ...  ...            ...            ...          ...\n",
              "913   45        110          264  ...              0              1            0\n",
              "914   68        144          193  ...              0              1            0\n",
              "915   57        130          131  ...              0              1            0\n",
              "916   57        130          236  ...              0              1            0\n",
              "917   38        138          175  ...              0              0            1\n",
              "\n",
              "[746 rows x 21 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4M37s4lul6cG"
      },
      "source": [
        "x=df1['HeartDisease']"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8kCEMnvImH0I"
      },
      "source": [
        "df1=df1.drop(['HeartDisease'],axis=1)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "a=df1.columns"
      ],
      "metadata": {
        "id": "m6wwWMReBsfQ"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# fit scaler on training data\n",
        "norm = MinMaxScaler().fit(df1)\n",
        "\n",
        "# transform training data\n",
        "df = norm.transform(df1)"
      ],
      "metadata": {
        "id": "r05KhTSmB6Gs"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "xc_FIiqtCc3M",
        "outputId": "92703eea-b7b2-4ac6-e8b0-7cf0f059ae84"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-64fe3f51-0973-4b10-8587-a7e8a531df51\">\n",
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              "<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>Age</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>Sex_F</th>\n",
              "      <th>Sex_M</th>\n",
              "      <th>ChestPainType_ASY</th>\n",
              "      <th>ChestPainType_ATA</th>\n",
              "      <th>ChestPainType_NAP</th>\n",
              "      <th>ChestPainType_TA</th>\n",
              "      <th>RestingECG_LVH</th>\n",
              "      <th>RestingECG_Normal</th>\n",
              "      <th>RestingECG_ST</th>\n",
              "      <th>ExerciseAngina_N</th>\n",
              "      <th>ExerciseAngina_Y</th>\n",
              "      <th>ST_Slope_Down</th>\n",
              "      <th>ST_Slope_Flat</th>\n",
              "      <th>ST_Slope_Up</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48</td>\n",
              "      <td>138</td>\n",
              "      <td>214</td>\n",
              "      <td>0</td>\n",
              "      <td>108</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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>4</th>\n",
              "      <td>54</td>\n",
              "      <td>150</td>\n",
              "      <td>195</td>\n",
              "      <td>0</td>\n",
              "      <td>122</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <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>913</th>\n",
              "      <td>45</td>\n",
              "      <td>110</td>\n",
              "      <td>264</td>\n",
              "      <td>0</td>\n",
              "      <td>132</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>914</th>\n",
              "      <td>68</td>\n",
              "      <td>144</td>\n",
              "      <td>193</td>\n",
              "      <td>1</td>\n",
              "      <td>141</td>\n",
              "      <td>3.4</td>\n",
              "      <td>0</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>915</th>\n",
              "      <td>57</td>\n",
              "      <td>130</td>\n",
              "      <td>131</td>\n",
              "      <td>0</td>\n",
              "      <td>115</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0</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>1</td>\n",
              "      <td>0</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>916</th>\n",
              "      <td>57</td>\n",
              "      <td>130</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>917</th>\n",
              "      <td>38</td>\n",
              "      <td>138</td>\n",
              "      <td>175</td>\n",
              "      <td>0</td>\n",
              "      <td>173</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>746 rows × 20 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-64fe3f51-0973-4b10-8587-a7e8a531df51')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-64fe3f51-0973-4b10-8587-a7e8a531df51 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-64fe3f51-0973-4b10-8587-a7e8a531df51');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     Age  RestingBP  Cholesterol  ...  ST_Slope_Down  ST_Slope_Flat  ST_Slope_Up\n",
              "0     40        140          289  ...              0              0            1\n",
              "1     49        160          180  ...              0              1            0\n",
              "2     37        130          283  ...              0              0            1\n",
              "3     48        138          214  ...              0              1            0\n",
              "4     54        150          195  ...              0              0            1\n",
              "..   ...        ...          ...  ...            ...            ...          ...\n",
              "913   45        110          264  ...              0              1            0\n",
              "914   68        144          193  ...              0              1            0\n",
              "915   57        130          131  ...              0              1            0\n",
              "916   57        130          236  ...              0              1            0\n",
              "917   38        138          175  ...              0              0            1\n",
              "\n",
              "[746 rows x 20 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rows, cols = (20, 2)\n",
        "b = [[0 for i in range(cols)] for j in range(rows)]"
      ],
      "metadata": {
        "id": "mbuTn-nQHBy1"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(a)):\n",
        "  b[i][0]=min(df1[a[i]])\n",
        "  b[i][1]=max(df1[a[i]])\n",
        "  print(b[i])\n",
        "print(b)  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hZIMCAltFzsY",
        "outputId": "1ff432df-b312-48ad-9fa0-ccad9b82771c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[28, 77]\n",
            "[92, 200]\n",
            "[85, 603]\n",
            "[0, 1]\n",
            "[69, 202]\n",
            "[-0.1, 6.2]\n",
            "[0, 1]\n",
            "[0, 1]\n",
            "[0, 1]\n",
            "[0, 1]\n",
            "[0, 1]\n",
            "[0, 1]\n",
            "[0, 1]\n",
            "[0, 1]\n",
            "[0, 1]\n",
            "[0, 1]\n",
            "[0, 1]\n",
            "[0, 1]\n",
            "[0, 1]\n",
            "[0, 1]\n",
            "[[28, 77], [92, 200], [85, 603], [0, 1], [69, 202], [-0.1, 6.2], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1=df1.astype(float)\n",
        "for i in range(len(a)):\n",
        "  for j in df1.index:\n",
        "    df1[a[i]][j]=(float(df1[a[i]][j])-float(b[i][0]))/((float(b[i][1])-float(b[i][0])))"
      ],
      "metadata": {
        "id": "T355nO88Htw3"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HHZDBzbzmauf"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x1, test_set = train_test_split(df1,test_size=0.1,random_state=40)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b5VZGo2mnIjZ"
      },
      "source": [
        "y1, test_setx1 = train_test_split(x,test_size=0.1,random_state=40)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NzolUOinhGF"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "model = RandomForestClassifier(n_estimators = 200 , random_state = 42)\n",
        "from sklearn.neighbors import KNeighborsClassifier  \n",
        "model2 = KNeighborsClassifier(n_neighbors=5, metric='minkowski', p=2 )  "
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-CXjiw7RnqQ0",
        "outputId": "07a173c8-e340-499f-a38e-343be8967acb"
      },
      "source": [
        "model.fit(x1,y1)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=200, random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kMaFTp8OoPhO"
      },
      "source": [
        "y_pred=model.predict(test_set)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9T61pUvXn5II",
        "outputId": "61fa7da2-3316-4c8e-b679-60540340f589"
      },
      "source": [
        "from sklearn import metrics\n",
        "# Model Accuracy, how often is the classifier correct?\n",
        "print(\"Accuracy:\",metrics.accuracy_score(test_setx1, y_pred))\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score\n",
        "cm7 = confusion_matrix(test_setx1, y_pred)\n",
        "cm7"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8666666666666667\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[29,  3],\n",
              "       [ 7, 36]])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KBditv0Iogqw"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "model1 = LogisticRegression()"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8GqZdzdpPNa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5819996-af5d-47ee-9a70-0fbfa53b0816"
      },
      "source": [
        "model1.fit(x1,y1)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zf_4IozSrnGP"
      },
      "source": [
        "y_pred=model1.predict(test_set)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WNV0LfeRrrck",
        "outputId": "9b557cd5-f477-458b-ac09-ea3bb44d74aa"
      },
      "source": [
        "print(\"Accuracy:\",metrics.accuracy_score(test_setx1, y_pred))\n",
        "cm7 = confusion_matrix(test_setx1, y_pred)\n",
        "cm7"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.88\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[29,  3],\n",
              "       [ 6, 37]])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3MSdQEwxryWG",
        "outputId": "1836d124-ad94-4d92-88a6-6c7a1eb2f0d4"
      },
      "source": [
        "model2.fit(x1,y1)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gu2EaXNWsHM1"
      },
      "source": [
        "y_pred=model2.predict(test_set)"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zkUlWoztsI_v",
        "outputId": "ad4e4de6-912c-4fcd-8547-72589e24ac42"
      },
      "source": [
        "print(\"Accuracy:\",metrics.accuracy_score(test_setx1, y_pred))\n",
        "cm7 = confusion_matrix(test_setx1, y_pred)\n",
        "cm7"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.92\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[30,  2],\n",
              "       [ 4, 39]])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LaIt1s0isL-l"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LSkQ2TN_t8cj"
      },
      "source": [
        "model3 = DecisionTreeClassifier(criterion=\"entropy\", max_depth=4)"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "igYvAgspuYgZ",
        "outputId": "3495146b-6f39-42b7-9835-932c7dfcdf84"
      },
      "source": [
        "model3.fit(x1,y1)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', max_depth=4)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6y5BzBlGub1V"
      },
      "source": [
        "y_pred=model3.predict(test_set)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eFtoo5wHuejE",
        "outputId": "1481fc4b-3057-42e0-c354-b64097e3c206"
      },
      "source": [
        "print(\"Accuracy:\",metrics.accuracy_score(test_setx1, y_pred))\n",
        "cm7 = confusion_matrix(test_setx1, y_pred)\n",
        "cm7"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.84\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[31,  1],\n",
              "       [11, 32]])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rUQOnv0fuhpD"
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB  "
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2DGUitYau588"
      },
      "source": [
        "model4 = GaussianNB()  "
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mgacWYZlu9jG",
        "outputId": "cd8994f3-238a-483c-8819-eea6cfd90e4e"
      },
      "source": [
        "model4.fit(x1,y1)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zGeqHjgJvBt2"
      },
      "source": [
        "y_pred=model4.predict(test_set)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ViGLaXeFvE4t",
        "outputId": "fc92e360-f56f-4222-d969-2149e3b4310f"
      },
      "source": [
        "print(\"Accuracy:\",metrics.accuracy_score(test_setx1, y_pred))\n",
        "cm7 = confusion_matrix(test_setx1, y_pred)\n",
        "cm7"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9066666666666666\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[30,  2],\n",
              "       [ 5, 38]])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.DataFrame(columns=a,index=[0])\n",
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "llCg6ALTJu1v",
        "outputId": "0542d176-c930-455c-e971-d28b65b024af"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Age', 'RestingBP', 'Cholesterol', 'FastingBS', 'MaxHR', 'Oldpeak',\n",
              "       'Sex_F', 'Sex_M', 'ChestPainType_ASY', 'ChestPainType_ATA',\n",
              "       'ChestPainType_NAP', 'ChestPainType_TA', 'RestingECG_LVH',\n",
              "       'RestingECG_Normal', 'RestingECG_ST', 'ExerciseAngina_N',\n",
              "       'ExerciseAngina_Y', 'ST_Slope_Down', 'ST_Slope_Flat', 'ST_Slope_Up'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(a)):\n",
        "  df[a[i]][0]=0"
      ],
      "metadata": {
        "id": "MA2wYhR8bSJK"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predict():\n",
        "  df[a[0]][0]=int(input(\"Enter your age\"))\n",
        "  df[a[1]][0]=int(input(\"Enter your RestingBP\"))\n",
        "  df[a[2]][0]=int(input(\"Enter your Cholesterol\"))\n",
        "  df[a[3]][0]=int(input(\"Enter your FastingBS\"))\n",
        "  df[a[4]][0]=int(input(\"Enter your MaxHR\"))\n",
        "  df[a[5]][0]=int(input(\"Enter your Oldpeak\"))\n",
        "  a1=input(\"Enter your gender\")\n",
        "  a1=a1.lower()\n",
        "  if(a1==\"male\"):\n",
        "    df[a[6]][0]=1\n",
        "  else:\n",
        "    df[a[7]][0]=1  \n",
        "  a1=input(\"Enter your ChestPainType\")  \n",
        "  a1=a1.lower()\n",
        "  if(a1==\"asy\"):\n",
        "    df[a[8]][0]=1\n",
        "  elif(a1==\"ata\"):\n",
        "    df[a[9]][0]=1  \n",
        "  elif(a1==\"nap\"):\n",
        "    df[a[10]][0]=1  \n",
        "  else:\n",
        "    df[a[11]][0]=1  \n",
        "  a1=input(\"Enter your RestingECG Type\")\n",
        "  a1=a1.lower()\n",
        "  if(a1==\"lvh\"):\n",
        "    df[a[12]][0]=1\n",
        "  elif(a1==\"normal\"):\n",
        "    df[a[13]][0]=1\n",
        "  else:\n",
        "    df[a[14]][0]=1\n",
        "  a1=input(\"Enter your ExerciseAngina\")\n",
        "  a1=a1.lower()\n",
        "  if(a1==\"n\"):\n",
        "    df[a[15]][0]=1\n",
        "  else:\n",
        "    df[a[16]][0]=1\n",
        "  a1=input(\"Enter your ST_Slope\")\n",
        "  a1=a1.lower()\n",
        "  if(a1==\"down\"):\n",
        "    df[a[17]][0]=1\n",
        "  elif(a1==\"flat\"):\n",
        "    df[a[18]][0]=1\n",
        "  else:\n",
        "    df[a[19]][0]=1   \n",
        "  for i in range(len(a)):\n",
        "    df[a[i]][0]=(float(df[a[i]][0])-float(b[i][0]))/((float(b[i][1])-float(b[i][0])))\n",
        "  return model2.predict(df)[0]  "
      ],
      "metadata": {
        "id": "bsJ7vNEgbT4J"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predict()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hI89CTq4bSrs",
        "outputId": "4f81732d-dfb3-499c-ea19-eea47a3298cc"
      },
      "execution_count": 52,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your age19\n",
            "Enter your RestingBP120\n",
            "Enter your Cholesterol180\n",
            "Enter your FastingBS0\n",
            "Enter your MaxHR120\n",
            "Enter your Oldpeak1\n",
            "Enter your gendermale\n",
            "Enter your ChestPainTypeasy'\n",
            "Enter your RestingECG Typenormal\n",
            "Enter your ExerciseAnginan\n",
            "Enter your ST_Slopeflat\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "tsX2rxPOhkC1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "outputId": "b758b9bc-e290-4051-9f5e-04c1834eba9c"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-4f986a57-fd9f-43f7-b6f2-e6800497ebe0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>Sex_F</th>\n",
              "      <th>Sex_M</th>\n",
              "      <th>ChestPainType_ASY</th>\n",
              "      <th>ChestPainType_ATA</th>\n",
              "      <th>ChestPainType_NAP</th>\n",
              "      <th>ChestPainType_TA</th>\n",
              "      <th>RestingECG_LVH</th>\n",
              "      <th>RestingECG_Normal</th>\n",
              "      <th>RestingECG_ST</th>\n",
              "      <th>ExerciseAngina_N</th>\n",
              "      <th>ExerciseAngina_Y</th>\n",
              "      <th>ST_Slope_Down</th>\n",
              "      <th>ST_Slope_Flat</th>\n",
              "      <th>ST_Slope_Up</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.183673</td>\n",
              "      <td>0.259259</td>\n",
              "      <td>0.183398</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4f986a57-fd9f-43f7-b6f2-e6800497ebe0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4f986a57-fd9f-43f7-b6f2-e6800497ebe0 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-4f986a57-fd9f-43f7-b6f2-e6800497ebe0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        Age RestingBP Cholesterol  ... ST_Slope_Down ST_Slope_Flat ST_Slope_Up\n",
              "0 -0.183673  0.259259    0.183398  ...           0.0           1.0         0.0\n",
              "\n",
              "[1 rows x 20 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "CNbuqHctiAcY"
      },
      "execution_count": 51,
      "outputs": []
    }
  ]
}