khushi_csit.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6KDZkxSXuheU",
        "outputId": "d9b18e59-d0ee-4daa-c2d0-e8d1f7d7a4cf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "heart_data=pd.read_csv(\"/content/gdrive/MyDrive/heart 123.csv\")\n",
        "heart_data.head(15)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "id": "f7zU4h4JvMR0",
        "outputId": "ea300d2e-8f60-4e02-8081-68101df4cf27"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
              "0    40   M           ATA        140          289          0     Normal   \n",
              "1    49   F           NAP        160          180          0     Normal   \n",
              "2    37   M           ATA        130          283          0         ST   \n",
              "3    48   F           ASY        138          214          0     Normal   \n",
              "4    54   M           NAP        150          195          0     Normal   \n",
              "5    39   M           NAP        120          339          0     Normal   \n",
              "6    45   F           ATA        130          237          0     Normal   \n",
              "7    54   M           ATA        110          208          0     Normal   \n",
              "8    37   M           ASY        140          207          0     Normal   \n",
              "9    48   F           ATA        120          284          0     Normal   \n",
              "10   37   F           NAP        130          211          0     Normal   \n",
              "11   58   M           ATA        136          164          0         ST   \n",
              "12   39   M           ATA        120          204          0     Normal   \n",
              "13   49   M           ASY        140          234          0     Normal   \n",
              "14   42   F           NAP        115          211          0         ST   \n",
              "\n",
              "    MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
              "0     172              N      0.0       Up             0  \n",
              "1     156              N      1.0     Flat             1  \n",
              "2      98              N      0.0       Up             0  \n",
              "3     108              Y      1.5     Flat             1  \n",
              "4     122              N      0.0       Up             0  \n",
              "5     170              N      0.0       Up             0  \n",
              "6     170              N      0.0       Up             0  \n",
              "7     142              N      0.0       Up             0  \n",
              "8     130              Y      1.5     Flat             1  \n",
              "9     120              N      0.0       Up             0  \n",
              "10    142              N      0.0       Up             0  \n",
              "11     99              Y      2.0     Flat             1  \n",
              "12    145              N      0.0       Up             0  \n",
              "13    140              Y      1.0     Flat             1  \n",
              "14    137              N      0.0       Up             0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cbc66b71-2c38-4762-96c0-c7b61b7bc5fd\">\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>5</th>\n",
              "      <td>39</td>\n",
              "      <td>M</td>\n",
              "      <td>NAP</td>\n",
              "      <td>120</td>\n",
              "      <td>339</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>170</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>45</td>\n",
              "      <td>F</td>\n",
              "      <td>ATA</td>\n",
              "      <td>130</td>\n",
              "      <td>237</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>170</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>54</td>\n",
              "      <td>M</td>\n",
              "      <td>ATA</td>\n",
              "      <td>110</td>\n",
              "      <td>208</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>142</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>37</td>\n",
              "      <td>M</td>\n",
              "      <td>ASY</td>\n",
              "      <td>140</td>\n",
              "      <td>207</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>130</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>48</td>\n",
              "      <td>F</td>\n",
              "      <td>ATA</td>\n",
              "      <td>120</td>\n",
              "      <td>284</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>120</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>37</td>\n",
              "      <td>F</td>\n",
              "      <td>NAP</td>\n",
              "      <td>130</td>\n",
              "      <td>211</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>142</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>58</td>\n",
              "      <td>M</td>\n",
              "      <td>ATA</td>\n",
              "      <td>136</td>\n",
              "      <td>164</td>\n",
              "      <td>0</td>\n",
              "      <td>ST</td>\n",
              "      <td>99</td>\n",
              "      <td>Y</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>39</td>\n",
              "      <td>M</td>\n",
              "      <td>ATA</td>\n",
              "      <td>120</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>145</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>49</td>\n",
              "      <td>M</td>\n",
              "      <td>ASY</td>\n",
              "      <td>140</td>\n",
              "      <td>234</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>140</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>42</td>\n",
              "      <td>F</td>\n",
              "      <td>NAP</td>\n",
              "      <td>115</td>\n",
              "      <td>211</td>\n",
              "      <td>0</td>\n",
              "      <td>ST</td>\n",
              "      <td>137</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",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cbc66b71-2c38-4762-96c0-c7b61b7bc5fd')\"\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-cbc66b71-2c38-4762-96c0-c7b61b7bc5fd 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-cbc66b71-2c38-4762-96c0-c7b61b7bc5fd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#logistic regression"
      ],
      "metadata": {
        "id": "ERsp7r7L3MQR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "metadata": {
        "id": "6zZHAMtyv1yi"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "leb=LabelEncoder()"
      ],
      "metadata": {
        "id": "312cJNw7v3vM"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "heart_data.ChestPainType=leb.fit_transform(heart_data.ChestPainType)\n",
        "heart_data.RestingECG = leb.fit_transform(heart_data.RestingECG)\n",
        "heart_data.ExerciseAngina = leb.fit_transform(heart_data.ExerciseAngina)\n",
        "heart_data.ST_Slope = leb.fit_transform(heart_data.ST_Slope)\n",
        "heart_data.Sex=leb.fit_transform(heart_data.Sex)\n",
        "heart_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "2nKyw-K1xCkE",
        "outputId": "92adcf72-03c4-4437-c749-e01655f01199"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age  Sex  ChestPainType  RestingBP  Cholesterol  FastingBS  RestingECG  \\\n",
              "0   40    1              1        140          289          0           1   \n",
              "1   49    0              2        160          180          0           1   \n",
              "2   37    1              1        130          283          0           2   \n",
              "3   48    0              0        138          214          0           1   \n",
              "4   54    1              2        150          195          0           1   \n",
              "\n",
              "   MaxHR  ExerciseAngina  Oldpeak  ST_Slope  HeartDisease  \n",
              "0    172               0      0.0         2             0  \n",
              "1    156               0      1.0         1             1  \n",
              "2     98               0      0.0         2             0  \n",
              "3    108               1      1.5         1             1  \n",
              "4    122               0      0.0         2             0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-34a0c4b1-06c6-4e2c-9338-297f8c1076b4\">\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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>140</td>\n",
              "      <td>289</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>160</td>\n",
              "      <td>180</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>156</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>98</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>138</td>\n",
              "      <td>214</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>108</td>\n",
              "      <td>1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>150</td>\n",
              "      <td>195</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>122</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-34a0c4b1-06c6-4e2c-9338-297f8c1076b4')\"\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-34a0c4b1-06c6-4e2c-9338-297f8c1076b4 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-34a0c4b1-06c6-4e2c-9338-297f8c1076b4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart_data.HeartDisease.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9WXpQcT9yi6g",
        "outputId": "3bc2a29d-6051-4e3f-caf1-1e459740c3b9"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    508\n",
              "0    410\n",
              "Name: HeartDisease, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=\"HeartDisease\", data=heart_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "nyoZB_pDynPb",
        "outputId": "44082321-dc0a-4b4f-9a5a-c0e7fc3390ee"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c7fd36670>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=\"Sex\",data=heart_data)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "0oIcQyBTy9K8",
        "outputId": "acaf7879-925d-4135-836d-d445e17915ff"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAQxUlEQVR4nO3dfazeZ13H8feHPYiirhs71toWO7UZmQ8b42QOn6IuKJtKFwMTfFidjcc/plHj09RElGCC8QGZmiWNAzqiwEBx1SxiLQgxOvRM5oBNsuPCbGu3nrExgQVMzdc/7utc3HSn3d1uv/s+7Xm/kl/u67p+1/073yXNPvld9+8hVYUkSQDPmXUBkqS1w1CQJHWGgiSpMxQkSZ2hIEnqzp51Ac/EhRdeWNu2bZt1GZJ0Wrn77rsfraq51fad1qGwbds2FhcXZ12GJJ1Wkjx0vH0uH0mSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJK60/qOZulM9l+v/cZZl6A16AW/8eFBj++ZgiSpMxQkSZ2hIEnqBguFJBcnuWds+58kP5fkgiT7kjzQPs9v85Pk5iRLSe5NcvlQtUmSVjdYKFTVx6rqsqq6DHgx8CTwbuAmYH9VbQf2tz7A1cD2ti0AtwxVmyRpddNaProK+M+qegjYAexp43uAa1t7B3BbjdwFbEiyaUr1SZKYXii8Cnhba2+sqsOt/TCwsbU3AwfGvnOwjX2BJAtJFpMsLi8vD1WvJK1Lg4dCknOBlwPvPHZfVRVQJ3O8qtpdVfNVNT83t+rb5CRJp2gaZwpXA/9WVY+0/iMry0Lt80gbPwRsHfveljYmSZqSaYTCq/n80hHAXmBna+8E7hgbv75dhXQl8MTYMpMkaQoGfcxFkucBLwV+amz49cDtSXYBDwHXtfE7gWuAJUZXKt0wZG2SpKcaNBSq6jPA848Z+wSjq5GOnVvAjUPWI0k6Me9oliR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJ3aChkGRDkncl+Y8k9yd5SZILkuxL8kD7PL/NTZKbkywluTfJ5UPWJkl6qqHPFN4I/G1VvRC4FLgfuAnYX1Xbgf2tD3A1sL1tC8AtA9cmSTrGYKGQ5DzgO4BbAarqf6vqk8AOYE+btge4trV3ALfVyF3AhiSbhqpPkvRUQ54pXAQsA29O8qEkf5rkecDGqjrc5jwMbGztzcCBse8fbGNfIMlCksUki8vLywOWL0nrz5ChcDZwOXBLVb0I+AyfXyoCoKoKqJM5aFXtrqr5qpqfm5t71oqVJA0bCgeBg1X1wdZ/F6OQeGRlWah9Hmn7DwFbx76/pY1JkqZksFCoqoeBA0kubkNXAfcBe4GdbWwncEdr7wWub1chXQk8MbbMJEmagrMHPv7PAH+W5FzgQeAGRkF0e5JdwEPAdW3uncA1wBLwZJsrSZqiQUOhqu4B5lfZddUqcwu4cch6JEkn5h3NkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSd2goZDk40k+nOSeJItt7IIk+5I80D7Pb+NJcnOSpST3Jrl8yNokSU81jTOF76qqy6pqvvVvAvZX1XZgf+sDXA1sb9sCcMsUapMkjZnF8tEOYE9r7wGuHRu/rUbuAjYk2TSD+iRp3Ro6FAr4uyR3J1loYxur6nBrPwxsbO3NwIGx7x5sY18gyUKSxSSLy8vLQ9UtSevS2QMf/9uq6lCSrwD2JfmP8Z1VVUnqZA5YVbuB3QDz8/Mn9V1J0okNeqZQVYfa5xHg3cAVwCMry0Lt80ibfgjYOvb1LW1MkjQlg4VCkucl+bKVNvA9wEeAvcDONm0ncEdr7wWub1chXQk8MbbMJEmagiGXjzYC706y8nf+vKr+Nsm/Arcn2QU8BFzX5t8JXAMsAU8CNwxYmyRpFYOFQlU9CFy6yvgngKtWGS/gxqHqkSQ9Pe9oliR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdROFQpL9k4xJkk5vJ3wdZ5LnAl8CXJjkfCBt15cDmweuTZI0ZU93pvBTwN3AC9vnynYH8MeT/IEkZyX5UJK/af2LknwwyVKSdyQ5t41/Uesvtf3bTu0/SZJ0qk4YClX1xqq6CPjFqvqaqrqobZdW1UShAPwscP9Y/3eAN1TV1wGPA7va+C7g8Tb+hjZPkjRFE/2mUFV/lORbkvxwkutXtqf7XpItwPcBf9r6Ab4beFebsge4trV3tD5t/1VtviRpSk74m8KKJG8Fvha4B/i/NlzAbU/z1T8Efhn4stZ/PvDJqjra+gf5/G8Tm4EDAFV1NMkTbf6jk9QoSXrmJgoFYB64pKpq0gMn+X7gSFXdneQ7T6W44xx3AVgAeMELXvBsHVaSxOT3KXwE+MqTPPa3Ai9P8nHg7YyWjd4IbEiyEkZbgEOtfQjYCtD2nwd84tiDVtXuqpqvqvm5ubmTLEmSdCKThsKFwH1J3pNk78p2oi9U1a9W1Zaq2ga8CnhvVf0I8D7gFW3aTkZXMgHsbX3a/veezJmJJOmZm3T56Defxb/5K8Dbk7wO+BBwaxu/FXhrkiXgMUZBIkmaoolCoare/0z+SFX9A/APrf0gcMUqcz4LvPKZ/B1J0jMz6dVHn2J0tRHAucA5wGeq6suHKkySNH2TnimsXFK6cq/BDuDKoYqSJM3GST8ltUb+CvjeAeqRJM3QpMtHPzjWfQ6j+xY+O0hFkqSZmfTqox8Yax8FPs5oCUmSdAaZ9DeFG4YuRJI0e5O+ZGdLkncnOdK2v2gPu5MknUEm/aH5zYzuOP6qtv11G5MknUEmDYW5qnpzVR1t21sAHzwkSWeYSUPhE0l+tL1F7awkP8oqD6uTJJ3eJg2FnwCuAx4GDjN6YN2PD1STJGlGJr0k9bXAzqp6HCDJBcDvMQoLSdIZYtIzhW9aCQSAqnoMeNEwJUmSZmXSUHhOkvNXOu1MYdKzDEnSaWLS/7H/PvDPSd7Z+q8EfnuYkiRJszLpHc23JVlk9EpNgB+sqvuGK0uSNAsTLwG1EDAIJOkMdtKPzpYknbkMBUlSZyhIkrrBQiHJc5P8S5J/T/LRJL/Vxi9K8sEkS0nekeTcNv5Frb/U9m8bqjZJ0uqGPFP4HPDdVXUpcBnwsiRXAr8DvKGqvg54HNjV5u8CHm/jb2jzJElTNFgotHc5f7p1z2lbMbqs9V1tfA9wbWvvaH3a/quSZKj6JElPNehvCu2JqvcAR4B9wH8Cn6yqo23KQWBza28GDgC0/U8Az1/lmAtJFpMsLi8vD1m+JK07g4ZCVf1fVV0GbAGuAF74LBxzd1XNV9X83JyvdJCkZ9NUrj6qqk8C7wNeAmxIsnLT3BbgUGsfArYCtP3n4TsbJGmqhrz6aC7Jhtb+YuClwP2MwuEVbdpO4I7W3tv6tP3vraoaqj5J0lMN+aTTTcCeJGcxCp/bq+pvktwHvD3J64APAbe2+bcCb02yBDwGvGrA2iRJqxgsFKrqXlZ550JVPcjo94Vjxz/L6OmrkqQZ8Y5mSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpG6wUEiyNcn7ktyX5KNJfraNX5BkX5IH2uf5bTxJbk6ylOTeJJcPVZskaXVDnikcBX6hqi4BrgRuTHIJcBOwv6q2A/tbH+BqYHvbFoBbBqxNkrSKwUKhqg5X1b+19qeA+4HNwA5gT5u2B7i2tXcAt9XIXcCGJJuGqk+S9FRT+U0hyTbgRcAHgY1VdbjtehjY2NqbgQNjXzvYxo491kKSxSSLy8vLg9UsSevR4KGQ5EuBvwB+rqr+Z3xfVRVQJ3O8qtpdVfNVNT83N/csVipJOnvIgyc5h1Eg/FlV/WUbfiTJpqo63JaHjrTxQ8DWsa9vaWODevEv3Tb0n9Bp6O7fvX7WJUgzMeTVRwFuBe6vqj8Y27UX2NnaO4E7xsavb1chXQk8MbbMJEmagiHPFL4V+DHgw0nuaWO/BrweuD3JLuAh4Lq2707gGmAJeBK4YcDaJEmrGCwUquofgRxn91WrzC/gxqHqkSQ9Pe9oliR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJ3WChkORNSY4k+cjY2AVJ9iV5oH2e38aT5OYkS0nuTXL5UHVJko5vyDOFtwAvO2bsJmB/VW0H9rc+wNXA9rYtALcMWJck6TgGC4Wq+gDw2DHDO4A9rb0HuHZs/LYauQvYkGTTULVJklY37d8UNlbV4dZ+GNjY2puBA2PzDraxp0iykGQxyeLy8vJwlUrSOjSzH5qrqoA6he/trqr5qpqfm5sboDJJWr+mHQqPrCwLtc8jbfwQsHVs3pY2JkmaommHwl5gZ2vvBO4YG7++XYV0JfDE2DKTJGlKzh7qwEneBnwncGGSg8BrgNcDtyfZBTwEXNem3wlcAywBTwI3DFWXJOn4BguFqnr1cXZdtcrcAm4cqhZJ0mS8o1mS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpW1OhkORlST6WZCnJTbOuR5LWmzUTCknOAv4EuBq4BHh1kktmW5UkrS9rJhSAK4Clqnqwqv4XeDuwY8Y1SdK6cvasCxizGTgw1j8IfPOxk5IsAAut++kkH5tCbevFhcCjsy5iLcjv7Zx1CfpC/ttc8Zo8G0f56uPtWEuhMJGq2g3snnUdZ6Iki1U1P+s6pGP5b3N61tLy0SFg61h/SxuTJE3JWgqFfwW2J7koybnAq4C9M65JktaVNbN8VFVHk/w08B7gLOBNVfXRGZe13rgsp7XKf5tTkqqadQ2SpDViLS0fSZJmzFCQJHWGgny8iNasJG9KciTJR2Zdy3phKKxzPl5Ea9xbgJfNuoj1xFCQjxfRmlVVHwAem3Ud64mhoNUeL7J5RrVImjFDQZLUGQry8SKSOkNBPl5EUmcorHNVdRRYebzI/cDtPl5Ea0WStwH/DFyc5GCSXbOu6UznYy4kSZ1nCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAXpFCX59SQfTXJvknuSfPOsa5KeqTXzOk7pdJLkJcD3A5dX1eeSXAicO+OypGfMMwXp1GwCHq2qzwFU1aNV9d9JXpzk/UnuTvKeJJuSnNfeV3ExjG7ISvKTM61eOg5vXpNOQZIvBf4R+BLg74F3AP8EvB/YUVXLSX4I+N6q+okkLwVeC7wR+PGq8h0BWpNcPpJOQVV9OsmLgW8HvotRKLwO+AZgXxKAs4DDbf6+JK9k9EKjS2dStDQBzxSkZ0GSVwA3As+tqpessv85jM4itgHXVNWHp1uhNBl/U5BOQZKLk2wfG7qM0QMF59qP0CQ5J8nXt/0/3/b/MPDmJOdMtWBpQp4pSKegLR39EbABOAosAQuM3kdxM3Aeo+XZPwQ+APwVcEVVfSrJHwCfqqrXzKJ26UQMBUlS5/KRJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpO7/AUEiWl51OMYZAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart_data[\"Age\"].plot.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "4FC9cikBzEDo",
        "outputId": "485a04b9-e83b-4f0d-ebc6-416e4cdf5a1a"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c7fcbd880>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=\"RestingECG\", data=heart_data)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "q3M3_lKizJAl",
        "outputId": "e8da012c-2334-410e-f05f-fc172d80af68"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart_data.isnull()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "f3IR6wdrzVbz",
        "outputId": "047f136b-f703-452b-8c35-10060e5db9bd"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Age    Sex  ChestPainType  RestingBP  Cholesterol  FastingBS  \\\n",
              "0    False  False          False      False        False      False   \n",
              "1    False  False          False      False        False      False   \n",
              "2    False  False          False      False        False      False   \n",
              "3    False  False          False      False        False      False   \n",
              "4    False  False          False      False        False      False   \n",
              "..     ...    ...            ...        ...          ...        ...   \n",
              "913  False  False          False      False        False      False   \n",
              "914  False  False          False      False        False      False   \n",
              "915  False  False          False      False        False      False   \n",
              "916  False  False          False      False        False      False   \n",
              "917  False  False          False      False        False      False   \n",
              "\n",
              "     RestingECG  MaxHR  ExerciseAngina  Oldpeak  ST_Slope  HeartDisease  \n",
              "0         False  False           False    False     False         False  \n",
              "1         False  False           False    False     False         False  \n",
              "2         False  False           False    False     False         False  \n",
              "3         False  False           False    False     False         False  \n",
              "4         False  False           False    False     False         False  \n",
              "..          ...    ...             ...      ...       ...           ...  \n",
              "913       False  False           False    False     False         False  \n",
              "914       False  False           False    False     False         False  \n",
              "915       False  False           False    False     False         False  \n",
              "916       False  False           False    False     False         False  \n",
              "917       False  False           False    False     False         False  \n",
              "\n",
              "[918 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0428cb02-c004-4ed5-887f-f0a6945a2225\">\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>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</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>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>914</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>915</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>916</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>917</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</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-0428cb02-c004-4ed5-887f-f0a6945a2225')\"\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-0428cb02-c004-4ed5-887f-f0a6945a2225 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-0428cb02-c004-4ed5-887f-f0a6945a2225');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yn7PEYF4zbEW",
        "outputId": "3b94e492-1090-48a8-ddde-0436f53a2d1f"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age               0\n",
              "Sex               0\n",
              "ChestPainType     0\n",
              "RestingBP         0\n",
              "Cholesterol       0\n",
              "FastingBS         0\n",
              "RestingECG        0\n",
              "MaxHR             0\n",
              "ExerciseAngina    0\n",
              "Oldpeak           0\n",
              "ST_Slope          0\n",
              "HeartDisease      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(heart_data.isnull(), yticklabels=False,cmap=\"viridis\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "_AGZ_At6zhqp",
        "outputId": "2ba4c824-9d02-4874-aabf-e36164adb12c"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c7fc01f70>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = heart_data.HeartDisease.values\n",
        "X = heart_data.drop(['HeartDisease'], axis = 1)\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TIl91sQMzoiY",
        "outputId": "5794cf06-cb2d-4de1-cd90-60afb0521b55"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0,\n",
              "       0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
              "       1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0,\n",
              "       0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0,\n",
              "       1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0,\n",
              "       0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "       1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
              "       0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
              "       1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0,\n",
              "       1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1,\n",
              "       1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
              "       1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
              "       1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
              "       1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
              "       1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1,\n",
              "       1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
              "       1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0,\n",
              "       1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
              "       1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
              "       0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
              "       0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1,\n",
              "       0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0,\n",
              "       1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0,\n",
              "       1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1,\n",
              "       0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0,\n",
              "       1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,\n",
              "       0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1,\n",
              "       1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
              "       1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n",
              "       1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0,\n",
              "       1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1,\n",
              "       0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1,\n",
              "       0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test= train_test_split(X,y,test_size=0.2,random_state=15)\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "clf = LogisticRegression().fit(X_train,y_train)\n",
        "y_pred = clf.predict(X_test)\n",
        "from sklearn.metrics import confusion_matrix\n",
        "confusion_matrix(y_test,y_pred)\n",
        "from sklearn.metrics import accuracy_score\n",
        "accuracy=accuracy_score(y_test,y_pred)\n",
        "accuracy\n",
        "from sklearn.metrics import accuracy_score,recall_score,precision_score,f1_score\n",
        "\n",
        "\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn import preprocessing\n",
        "\n",
        "iris = load_iris()\n",
        "print(iris.data.shape)\n",
        "\n",
        "X = iris.data\n",
        "y = iris.target\n",
        "\n",
        "normalized_X = preprocessing.normalize(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F5C2n_oYzyXi",
        "outputId": "4c1f51fa-7255-4a78-fa28-7afce51fd5ed"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(150, 4)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy Score : ' + str(accuracy_score(y_test,y_pred)))\n",
        "print('Precision Score : ' + str(precision_score(y_test,y_pred)))\n",
        "print('Recall Score : ' + str(recall_score(y_test,y_pred)))\n",
        "print('F1 Score : ' + str(f1_score(y_test,y_pred)))\n",
        "from sklearn.metrics import confusion_matrix\n",
        "print('Confusion Matrix : \\n' + str(confusion_matrix(y_test,y_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sn_aJqUO0tf6",
        "outputId": "28e096e6-1d12-4fb3-e20c-4795c531d0e6"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score : 0.8369565217391305\n",
            "Precision Score : 0.8392857142857143\n",
            "Recall Score : 0.8867924528301887\n",
            "F1 Score : 0.8623853211009174\n",
            "Confusion Matrix : \n",
            "[[60 18]\n",
            " [12 94]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#knn"
      ],
      "metadata": {
        "id": "R6ERP8Go00ti"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "heart_data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "LO6-tgru02z4",
        "outputId": "0dffe138-1eb6-423d-d457-505ef99207a2"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Age         Sex  ChestPainType   RestingBP  Cholesterol  \\\n",
              "count  918.000000  918.000000     918.000000  918.000000   918.000000   \n",
              "mean    53.510893    0.789760       0.781046  132.396514   198.799564   \n",
              "std      9.432617    0.407701       0.956519   18.514154   109.384145   \n",
              "min     28.000000    0.000000       0.000000    0.000000     0.000000   \n",
              "25%     47.000000    1.000000       0.000000  120.000000   173.250000   \n",
              "50%     54.000000    1.000000       0.000000  130.000000   223.000000   \n",
              "75%     60.000000    1.000000       2.000000  140.000000   267.000000   \n",
              "max     77.000000    1.000000       3.000000  200.000000   603.000000   \n",
              "\n",
              "        FastingBS  RestingECG       MaxHR  ExerciseAngina     Oldpeak  \\\n",
              "count  918.000000  918.000000  918.000000      918.000000  918.000000   \n",
              "mean     0.233115    0.989107  136.809368        0.404139    0.887364   \n",
              "std      0.423046    0.631671   25.460334        0.490992    1.066570   \n",
              "min      0.000000    0.000000   60.000000        0.000000   -2.600000   \n",
              "25%      0.000000    1.000000  120.000000        0.000000    0.000000   \n",
              "50%      0.000000    1.000000  138.000000        0.000000    0.600000   \n",
              "75%      0.000000    1.000000  156.000000        1.000000    1.500000   \n",
              "max      1.000000    2.000000  202.000000        1.000000    6.200000   \n",
              "\n",
              "         ST_Slope  HeartDisease  \n",
              "count  918.000000    918.000000  \n",
              "mean     1.361656      0.553377  \n",
              "std      0.607056      0.497414  \n",
              "min      0.000000      0.000000  \n",
              "25%      1.000000      0.000000  \n",
              "50%      1.000000      1.000000  \n",
              "75%      2.000000      1.000000  \n",
              "max      2.000000      1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b901ff82-fc21-4bc7-aa1c-273031f2ff47\">\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>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",
              "      <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>0.789760</td>\n",
              "      <td>0.781046</td>\n",
              "      <td>132.396514</td>\n",
              "      <td>198.799564</td>\n",
              "      <td>0.233115</td>\n",
              "      <td>0.989107</td>\n",
              "      <td>136.809368</td>\n",
              "      <td>0.404139</td>\n",
              "      <td>0.887364</td>\n",
              "      <td>1.361656</td>\n",
              "      <td>0.553377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.432617</td>\n",
              "      <td>0.407701</td>\n",
              "      <td>0.956519</td>\n",
              "      <td>18.514154</td>\n",
              "      <td>109.384145</td>\n",
              "      <td>0.423046</td>\n",
              "      <td>0.631671</td>\n",
              "      <td>25.460334</td>\n",
              "      <td>0.490992</td>\n",
              "      <td>1.066570</td>\n",
              "      <td>0.607056</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>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-2.600000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>173.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>54.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>223.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>138.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>267.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>603.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b901ff82-fc21-4bc7-aa1c-273031f2ff47')\"\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-b901ff82-fc21-4bc7-aa1c-273031f2ff47 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-b901ff82-fc21-4bc7-aa1c-273031f2ff47');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = heart_data.HeartDisease.values\n",
        "x = heart_data.drop(['HeartDisease'], axis = 1)\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=2)"
      ],
      "metadata": {
        "id": "Xq8R48ok1cWU"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import recall_score"
      ],
      "metadata": {
        "id": "ltjfiUoS2y3J"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a_score=[]\n",
        "for k in range(1,18,2):\n",
        "  knn=KNeighborsClassifier(n_neighbors=k)\n",
        "  knn.fit(x_train,y_train)\n",
        "  y_pred=knn.predict(x_test)\n",
        "  accuracy=accuracy_score(y_test,y_pred)\n",
        "  a_score.append(accuracy)\n",
        "\n",
        "mse=[1 - x for x in a_score]"
      ],
      "metadata": {
        "id": "MaVit9ok244q"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max=a_score[0]\n",
        "for j in a_score:\n",
        "  if(j>max):\n",
        "    max=j\n",
        "print(max)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "INgkJXWe26rv",
        "outputId": "4dfab8a0-aec4-46f2-a786-fb5df19cbb6c"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7355072463768116\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#decision tree"
      ],
      "metadata": {
        "id": "P_S2siQE3BnY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "X0xsRb-43EH3"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "heart_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VWCHasEY4fHw",
        "outputId": "5f443039-4fcc-46d3-d2bc-c0d41dd22021"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(918, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = heart_data[['HeartDisease']].copy()\n",
        "x = heart_data.drop(['HeartDisease'], axis = 1)\n",
        "x.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Aa3sfvQA5QCU",
        "outputId": "4639b975-fd31-4daf-eb44-eda186d49648"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Age', 'Sex', 'ChestPainType', 'RestingBP', 'Cholesterol', 'FastingBS',\n",
              "       'RestingECG', 'MaxHR', 'ExerciseAngina', 'Oldpeak', 'ST_Slope'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9xA573ca85HE",
        "outputId": "6ddd8d02-a43f-4783-b7cf-1eb582b85337"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['HeartDisease'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=2)"
      ],
      "metadata": {
        "id": "ucuSQFAd9mNK"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "heart_classifier=DecisionTreeClassifier(max_leaf_nodes=10,random_state=0)\n",
        "heart_classifier.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uv5fgpVj-UzO",
        "outputId": "256e1672-58d6-4c13-a778-60eb1139d213"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_leaf_nodes=10, random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted=heart_classifier.predict(x_test)"
      ],
      "metadata": {
        "id": "-l83xIgZAY01"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test,y_predicted)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZIBQaAnsBs4i",
        "outputId": "6377c1b4-22e3-4bcb-8a25-ffd6b05a1b5e"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "83.33333333333334"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,y_predicted)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7GQrZrIQCJfe",
        "outputId": "bab3127b-6330-4e91-978e-455efca8d031"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[103,  30],\n",
              "       [ 16, 127]])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#random forest classifier"
      ],
      "metadata": {
        "id": "_x1OeTBiCnVu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels=heart_data.pop(\"HeartDisease\")"
      ],
      "metadata": {
        "id": "KXIKQ42ND3bg"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(heart_data,labels,test_size=0.3)"
      ],
      "metadata": {
        "id": "MGyPPpJEEfAN"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rf = RandomForestClassifier()\n",
        "rf.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hw757DeAFOGA",
        "outputId": "a1af5972-a2e1-4281-a793-5b542aed6f6f"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred= rf.predict(x_test)"
      ],
      "metadata": {
        "id": "HUsO1ag3F2pP"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import  roc_curve,auc\n",
        "false_positive_rate,true_positive_rate,thresholds=roc_curve(y_test,y_pred)\n",
        "roc_auc=auc(false_positive_rate,true_positive_rate)\n",
        "roc_auc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QMTTU8Y2GZxf",
        "outputId": "5f39e861-c6d2-4eea-c405-b5073bc3e2cd"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8755820799657443"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nhOAAOX6GwyM"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}