Heart_Disease_PredictionModel.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "17ldm5O09V4ssVcC9lX1hpZHobt21pIqb",
      "authorship_tag": "ABX9TyN5d5tDWfjmsZ6C3a1UFTpY",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sneharawat1404/HeartBot-with-query-resolving-feature/blob/master/Heart_Disease_PredictionModel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Screenshot 2024-03-25 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)**Import Modules for Data Analysis **\n"
      ],
      "metadata": {
        "id": "Z6QqaeTmH03V"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SnLdlksmnzjw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce087248-d2b3-440b-c863-15ddaee5dde7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['.config', 'drive', 'sample_data']\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "import os\n",
        "print(os.listdir())\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "id": "-Z8LKLsAmBAk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Data Acquistion"
      ],
      "metadata": {
        "id": "5kzGf8_oIADP"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "y3JclQdqmjCC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"/content/heart.csv\")\n",
        "df.head(10)\n",
        "#df.info() #data analysis\n",
        "\n",
        "# df.isna().sum()\n",
        "#print(\"Since there are no null values and duplicates, our data is good to go for analysis and visualizations\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "wUuKzby2rYXE",
        "outputId": "0db1986b-4e6e-48e1-c14d-40fb4edd3c00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
              "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
              "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
              "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
              "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
              "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
              "5   57    1   0       140   192    0        1      148      0      0.4      1   \n",
              "6   56    0   1       140   294    0        0      153      0      1.3      1   \n",
              "7   44    1   1       120   263    0        1      173      0      0.0      2   \n",
              "8   52    1   2       172   199    1        1      162      0      0.5      2   \n",
              "9   57    1   2       150   168    0        1      174      0      1.6      2   \n",
              "\n",
              "   ca  thal  target  \n",
              "0   0     1       1  \n",
              "1   0     2       1  \n",
              "2   0     2       1  \n",
              "3   0     2       1  \n",
              "4   0     2       1  \n",
              "5   0     1       1  \n",
              "6   0     2       1  \n",
              "7   0     3       1  \n",
              "8   0     3       1  \n",
              "9   0     2       1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-40995794-d109-4d90-aa59-e806f32fc498\" 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>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>178</td>\n",
              "      <td>0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>57</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>140</td>\n",
              "      <td>192</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>148</td>\n",
              "      <td>0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>140</td>\n",
              "      <td>294</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>153</td>\n",
              "      <td>0</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>263</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>173</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>52</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>172</td>\n",
              "      <td>199</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>162</td>\n",
              "      <td>0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>57</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>150</td>\n",
              "      <td>168</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>174</td>\n",
              "      <td>0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    width: 32px;\n",
              "  }\n",
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              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "</div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"#print(\\\"Since there are no null values and duplicates, our data is good to go for analysis and visualizations\\\")\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 37,\n        \"max\": 63,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          63,\n          37,\n          44\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trestbps\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 120,\n        \"max\": 172,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          145,\n          130\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54,\n        \"min\": 168,\n        \"max\": 354,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          199,\n          250\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fbs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"restecg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thalach\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 148,\n        \"max\": 187,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          162,\n          187\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"exang\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oldpeak\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0426462061078583,\n        \"min\": 0.0,\n        \"max\": 3.5,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.5,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"slope\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ca\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. Data Filtering"
      ],
      "metadata": {
        "id": "-U0Ogl21Jemd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull()\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "DDf88k91JiwH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. Data transformation"
      ],
      "metadata": {
        "id": "ClLsC9BZKpP0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "csz5zl7TKr4R",
        "outputId": "ce95983b-0187-4c7f-d6bf-58c07eca9862"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    int64  \n",
            " 1   sex       303 non-null    int64  \n",
            " 2   cp        303 non-null    int64  \n",
            " 3   trestbps  303 non-null    int64  \n",
            " 4   chol      303 non-null    int64  \n",
            " 5   fbs       303 non-null    int64  \n",
            " 6   restecg   303 non-null    int64  \n",
            " 7   thalach   303 non-null    int64  \n",
            " 8   exang     303 non-null    int64  \n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slope     303 non-null    int64  \n",
            " 11  ca        303 non-null    int64  \n",
            " 12  thal      303 non-null    int64  \n",
            " 13  target    303 non-null    int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 33.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['sex'].unique()\n",
        "info = [\"age\",\"1: male, 0: female\",\"chest pain type, 1: typical angina, 2: atypical angina, 3: non-anginal pain, 4: asymptomatic\",\"resting blood pressure\",\" serum cholestoral in mg/dl\",\"fasting blood sugar > 120 mg/dl\",\"resting electrocardiographic results (values 0,1,2)\",\" maximum heart rate achieved\",\"exercise induced angina\",\"oldpeak = ST depression induced by exercise relative to rest\",\"the slope of the peak exercise ST segment\",\"number of major vessels (0-3) colored by flourosopy\",\"thal: 3 = normal; 6 = fixed defect; 7 = reversable defect\"]\n",
        "\n",
        "\n",
        "\n",
        "for i in range(len(info)):\n",
        "    print(df.columns[i]+\":\\t\\t\\t\"+info[i])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xtyq2CvlKoov",
        "outputId": "b38ddcf4-6ab7-4605-8b64-5eabfec1b57e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age:\t\t\tage\n",
            "sex:\t\t\t1: male, 0: female\n",
            "cp:\t\t\tchest pain type, 1: typical angina, 2: atypical angina, 3: non-anginal pain, 4: asymptomatic\n",
            "trestbps:\t\t\tresting blood pressure\n",
            "chol:\t\t\t serum cholestoral in mg/dl\n",
            "fbs:\t\t\tfasting blood sugar > 120 mg/dl\n",
            "restecg:\t\t\tresting electrocardiographic results (values 0,1,2)\n",
            "thalach:\t\t\t maximum heart rate achieved\n",
            "exang:\t\t\texercise induced angina\n",
            "oldpeak:\t\t\toldpeak = ST depression induced by exercise relative to rest\n",
            "slope:\t\t\tthe slope of the peak exercise ST segment\n",
            "ca:\t\t\tnumber of major vessels (0-3) colored by flourosopy\n",
            "thal:\t\t\tthal: 3 = normal; 6 = fixed defect; 7 = reversable defect\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "correlation b/w columns"
      ],
      "metadata": {
        "id": "zAa_U-2rRJlg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "correlation=df.corr()\n",
        "pd.DataFrame(correlation['target']).sort_values(by='target',ascending=False)"
      ],
      "metadata": {
        "id": "TZz_OFvuQ4E6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "3dc832b1-c16c-42b5-c804-9df710f93ce3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            target\n",
              "target    1.000000\n",
              "cp        0.433798\n",
              "thalach   0.421741\n",
              "slope     0.345877\n",
              "restecg   0.137230\n",
              "fbs      -0.028046\n",
              "chol     -0.085239\n",
              "trestbps -0.144931\n",
              "age      -0.225439\n",
              "sex      -0.280937\n",
              "thal     -0.344029\n",
              "ca       -0.391724\n",
              "oldpeak  -0.430696\n",
              "exang    -0.436757"
            ],
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 14,\n  \"fields\": [\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.42165547633788675,\n        \"min\": -0.43675708335330315,\n        \"max\": 1.0,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          -0.28093657550176676,\n          -0.39172399235125244,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.corr()[\"target\"].abs().sort_values(ascending=False))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QRexfBuJWdv7",
        "outputId": "8a6a9891-7763-47c7-b3ae-b5e089e80bc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "target      1.000000\n",
            "exang       0.436757\n",
            "cp          0.433798\n",
            "oldpeak     0.430696\n",
            "thalach     0.421741\n",
            "ca          0.391724\n",
            "slope       0.345877\n",
            "thal        0.344029\n",
            "sex         0.280937\n",
            "age         0.225439\n",
            "trestbps    0.144931\n",
            "restecg     0.137230\n",
            "chol        0.085239\n",
            "fbs         0.028046\n",
            "Name: target, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Explatory data analysis"
      ],
      "metadata": {
        "id": "PQMBrBpCRSB-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ANALYSE TARGET VARIABLE"
      ],
      "metadata": {
        "id": "T8sfN8P_RaBz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = df[\"target\"]\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "target_temp = df.target.value_counts()\n",
        "\n",
        "print(target_temp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I9Smjj3QRd9v",
        "outputId": "7614c581-2246-48b1-a09c-13367769125b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    165\n",
            "0    138\n",
            "Name: target, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Percentage of patience without heart problems: \"+str(round(target_temp[0]*100/303,2)))\n",
        "print(\"Percentage of patience with heart problems: \"+str(round(target_temp[1]*100/303,2)))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aiZaVi4aR0K9",
        "outputId": "531319d9-440e-4189-ffa4-daf21f665d96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of patience without heart problems: 45.54\n",
            "Percentage of patience with heart problems: 54.46\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we shall find relationship between target and mostly risk factors/features which cause cardiovascular disease.\n",
        "\n"
      ],
      "metadata": {
        "id": "2J3TLGolXF-u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(data=df,x='sex',hue='target',palette='gist_rainbow')\n",
        "plt.title('Gender vs target')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "o1wtq9rTXH6b",
        "outputId": "cda491c9-e2b1-42ff-85af-d18b8dab5e14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Gender vs target')"
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=\"cp\",y=\"target\",data=df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "dqh9KRBxSKbv",
        "outputId": "fbe4ef19-721d-423d-eaa5-e28c831894e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='cp', ylabel='target'>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAGwCAYAAABVdURTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAfcElEQVR4nO3df3TV9X348VcSSMJvq5SgEAqrVOQgZIOSE6zbrFHa7iDs16G6Iyyj7NiSzhp/UoXMOQ3VlkErLcWJXc8pg81Tq0cd1qViTyeWGqCjzh/HKQ1HTYCyAaaTSJLvHx7T5ktEgkk+yTuPxzn3HO77fj7J63qPJ8/z+XzuvTltbW1tAQCQiNysBwAA6E7iBgBIirgBAJIibgCApIgbACAp4gYASIq4AQCSMijrAXpba2trvP766zFixIjIycnJehwA4BS0tbXF0aNH45xzzonc3JMfmxlwcfP6669HcXFx1mMAAKdh3759MX78+JNuM+DiZsSIERHxzn+ckSNHZjwNAHAqjhw5EsXFxe1/x09mwMXNu6eiRo4cKW4AoJ85lUtKXFAMACRF3AAASRE3AEBSxA0AkBRxAwAkRdwAAEkRNwBAUsQNAJAUcQMAJEXcAABJETcAQFLEDQCQFHEDACRlwH0rOKSura0tmpqa2u8PGzbslL5FFyAV4gYS09TUFPPnz2+//9BDD8Xw4cMznAigdzktBQAkRdwAAEkRNwBAUsQNAJAUcQMAJEXcAABJETcAQFLEDQCQFHEDACRF3AAASRE3AEBSxA0AkBRxAwAkRdwAAEkRNwBAUgZlPQAAp6+trS2ampra7w8bNixycnIynAiyJ24A+rGmpqaYP39++/2HHnoohg8fnuFEkD1xAwD9hCN1p0bcAEA/4UjdqXFBMQCQFHEDACRF3AAASRE3AEBSxA0AkBRxAwAkRdwAAEkRNwBAUsQNAJAUcQMAJEXcAABJETcAQFLEDQCQFHEDACRF3AAASRE3AEBSxA0AkBRxAwAkRdwAAEkZlPUA0BfNvOG7WY9w2nKON8eo37r/hys2R9ug/Mzm+aDq7l6U9QhAP+PIDQCQFHEDACRF3AAASck8btatWxcTJ06MwsLCKC0tjR07dpx0+zVr1sR5550XQ4YMieLi4rj22mvjrbfe6qVpAYC+LtO42bJlS1RVVUV1dXXs3LkzZsyYEXPnzo39+/d3uv2mTZvi5ptvjurq6nj++efjvvvuiy1btsSXv/zlXp4cAOirMo2b1atXx9KlS6OioiKmTp0a69evj6FDh8bGjRs73f7pp5+OCy+8MK688sqYOHFiXHbZZXHFFVe879EeAGDgyCxumpubo66uLsrLy38zTG5ulJeXx/bt2zvdZ86cOVFXV9ceM6+88ko89thj8ZnPfOY9f8+xY8fiyJEjHW4AQLoy+5ybgwcPRktLSxQVFXVYLyoqihdeeKHTfa688so4ePBgfOITn4i2trY4fvx4XH311Sc9LVVTUxO33XZbt84OAPRdmV9Q3BXbtm2LO++8M775zW/Gzp074/vf/348+uijcfvtt7/nPsuXL4/Dhw+33/bt29eLEwMAvS2zIzejR4+OvLy8aGxs7LDe2NgYY8eO7XSfFStWxFVXXRWf+9znIiLiggsuiKampvjrv/7ruOWWWyI398RWKygoiIKCgu5/AkAyfCJ13+ETqekOmR25yc/Pj5kzZ0ZtbW37Wmtra9TW1kZZWVmn+/z6178+IWDy8vIiIqKtra3nhgUA+o1Mv1uqqqoqFi9eHLNmzYrZs2fHmjVroqmpKSoqKiIiYtGiRTFu3LioqamJiIh58+bF6tWr43d/93ejtLQ0Xn755VixYkXMmzevPXIAgIEt07hZuHBhHDhwIFauXBkNDQ1RUlISW7dubb/IuL6+vsORmltvvTVycnLi1ltvjddeey0+/OEPx7x58+KOO+7I6ikAAH1M5t8KXllZGZWVlZ0+tm3btg73Bw0aFNXV1VFdXd0LkwEA/VG/ercUAMD7ETcAQFLEDQCQFHEDACRF3AAASRE3AEBSxA0AkBRxAwAkRdwAAEkRNwBAUsQNAJAUcQMAJEXcAABJETcAQFLEDQCQFHEDACRF3AAASRE3AEBSxA0AkBRxAwAkRdwAAEkRNwBAUsQNAJAUcQMAJEXcAABJETcAQFLEDQCQFHEDACRF3AAASRE3AEBSxA0AkBRxAwAkZVDWAwBw+tryBsfh6Vd0uA8DnbiBxPhjN8Dk5ETboPysp+hXZt7w3axHOG05x5tj1G/d/8MVm/v1619396Ie+bniBlLjjx0wwLnmBgBIirgBAJIibgCApIgbACAp4gYASIq4AQCSIm4AgKSIGwAgKeIGAEiKuAEAkiJuAICkiBsAICniBgBIirgBAJIibgCApIgbACAp4gYASIq4AQCSIm4AgKSIGwAgKeIGAEiKuAEAkiJuAICkiBsAICniBgBIirgBAJIibgCApIgbACAp4gYASIq4AQCSIm4AgKRkHjfr1q2LiRMnRmFhYZSWlsaOHTtOuv3//u//xrJly+Lss8+OgoKC+NjHPhaPPfZYL00LAPR1g7L85Vu2bImqqqpYv359lJaWxpo1a2Lu3Lnx4osvxpgxY07Yvrm5OS699NIYM2ZMPPDAAzFu3Lj45S9/GWeccUbvDw8A9EmZxs3q1atj6dKlUVFRERER69evj0cffTQ2btwYN9988wnbb9y4MQ4dOhRPP/10DB48OCIiJk6ceNLfcezYsTh27Fj7/SNHjnTfEwAA+pzMTks1NzdHXV1dlJeX/2aY3NwoLy+P7du3d7rPww8/HGVlZbFs2bIoKiqKadOmxZ133hktLS3v+Xtqampi1KhR7bfi4uJufy4AQN+RWdwcPHgwWlpaoqioqMN6UVFRNDQ0dLrPK6+8Eg888EC0tLTEY489FitWrIivfe1r8fd///fv+XuWL18ehw8fbr/t27evW58HANC3ZHpaqqtaW1tjzJgxsWHDhsjLy4uZM2fGa6+9FnfffXdUV1d3uk9BQUEUFBT08qQAQFYyi5vRo0dHXl5eNDY2dlhvbGyMsWPHdrrP2WefHYMHD468vLz2tfPPPz8aGhqiubk58vPze3RmAKDvy+y0VH5+fsycOTNqa2vb11pbW6O2tjbKyso63efCCy+Ml19+OVpbW9vXXnrppTj77LOFDQAQERl/zk1VVVXce++98U//9E/x/PPPx+c///loampqf/fUokWLYvny5e3bf/7zn49Dhw7FNddcEy+99FI8+uijceedd8ayZcuyegoAQB+T6TU3CxcujAMHDsTKlSujoaEhSkpKYuvWre0XGdfX10du7m/6q7i4OB5//PG49tprY/r06TFu3Li45ppr4qabbsrqKQAAfUzmFxRXVlZGZWVlp49t27bthLWysrJ45plnengqAKC/yvzrFwAAupO4AQCSIm4AgKSIGwAgKeIGAEiKuAEAkiJuAICkiBsAICniBgBIirgBAJIibgCApIgbACAp4gYASIq4AQCSIm4AgKSIGwAgKeIGAEiKuAEAktLluKmvr4+2trYT1tva2qK+vr5bhgIAOF1djptJkybFgQMHTlg/dOhQTJo0qVuGAgA4XV2Om7a2tsjJyTlh/c0334zCwsJuGQoA4HQNOtUNq6qqIiIiJycnVqxYEUOHDm1/rKWlJX76059GSUlJtw8IANAVpxw3u3btioh3jtzs2bMn8vPz2x/Lz8+PGTNmxPXXX9/9EwIAdMEpx82TTz4ZEREVFRWxdu3aGDlyZI8NBQBwurp8zc39998fI0eOjJdffjkef/zx+L//+7+IiE7fQQUA0Nu6HDeHDh2KSy65JD72sY/FZz7zmXjjjTciImLJkiVx3XXXdfuAAABd0eW4+dKXvhSDBw+O+vr6DhcVL1y4MLZu3dqtwwEAdNUpX3Pzrh/+8Ifx+OOPx/jx4zusT548OX75y19222AAAKejy0dumpqaOhyxedehQ4eioKCgW4YCADhdXY6biy66KL773e+238/JyYnW1ta466674uKLL+7W4QAAuqrLp6XuuuuuuOSSS+LZZ5+N5ubmuPHGG+O5556LQ4cOxX/8x3/0xIwAAKesy0dupk2bFi+99FJ84hOfiPnz50dTU1P8yZ/8SezatSs++tGP9sSMAACnrMtHbiIiRo0aFbfcckt3zwIA8IF1OW7+8z//s9P1nJycKCwsjAkTJriwGADITJfjpqSkpP1bwd/9VOLf/pbwwYMHx8KFC+Pb3/62bwkHAHpdl6+5efDBB2Py5MmxYcOG+PnPfx4///nPY8OGDXHeeefFpk2b4r777osf/ehHceutt/bEvAAAJ9XlIzd33HFHrF27NubOndu+dsEFF8T48eNjxYoVsWPHjhg2bFhcd9118dWvfrVbhwUAeD9dPnKzZ8+e+MhHPnLC+kc+8pHYs2dPRLxz6urd75wCAOhNXY6bKVOmxKpVq6K5ubl97e23345Vq1bFlClTIiLitddei6Kiou6bEgDgFHX5tNS6devi8ssvj/Hjx8f06dMj4p2jOS0tLfHII49ERMQrr7wSX/jCF7p3UgCAU9DluJkzZ068+uqr8b3vfS9eeumliIj48z//87jyyitjxIgRERFx1VVXde+UAACnqEtx8/bbb8eUKVPikUceiauvvrqnZgIAOtGWNzgOT7+iw31O1KW4GTx4cLz11ls9NQsAcDI5OdE2KD/rKfq8Ll9QvGzZsvjKV74Sx48f74l5AAA+kC5fc/Ozn/0samtr44c//GFccMEFMWzYsA6Pf//73++24QAAuqrLcXPGGWfEn/7pn/bELAAAH1iX4+b+++/viTkAALpFl6+5AQDoy7p85CYi4oEHHoh/+Zd/ifr6+g6fVBwRsXPnzm4ZDADgdHT5yM3Xv/71qKioiKKioti1a1fMnj07zjrrrHjllVfi05/+dE/MCABwyrocN9/85jdjw4YN8Y1vfCPy8/PjxhtvjCeeeCL+5m/+Jg4fPtwTMwIAnLIux019fX3MmTMnIiKGDBkSR48ejYh3vnLhn//5n7t3OgCALupy3IwdOzYOHToUERETJkyIZ555JiIiXn311Whra+ve6QAAuqjLcfPJT34yHn744YiIqKioiGuvvTYuvfTSWLhwYfzxH/9xtw8IANAVXX631C233BLjxo2LiHe+iuGss86Kp59+Oi6//PL41Kc+1e0DAgB0RZfj5txzz4033ngjxowZExERn/3sZ+Ozn/1s/OpXv4oxY8ZES0tLtw8JAHCqunxa6r2uq3nzzTejsLDwAw8EAPBBnPKRm6qqqoiIyMnJiZUrV8bQoUPbH2tpaYmf/vSnUVJS0u0DAgB0xSnHza5duyLinSM3e/bsifz8/PbH8vPzY8aMGXH99dd3/4QAAF1wynHz5JNPRsQ775Bau3ZtjBw5sseGAgA4Xb4VHABIim8FBwCSIm4AgKSIGwAgKeIGAEhKn4ibdevWxcSJE6OwsDBKS0tjx44dp7Tf5s2bIycnJxYsWNCzAwIA/UbmcbNly5aoqqqK6urq2LlzZ8yYMSPmzp0b+/fvP+l+e/fujeuvvz4uuuiiXpoUAOgPMo+b1atXx9KlS6OioiKmTp0a69evj6FDh8bGjRvfc5+Wlpb4i7/4i7jtttvid37nd3pxWgCgr8s0bpqbm6Ouri7Ky8vb13Jzc6O8vDy2b9/+nvv93d/9XYwZMyaWLFnyvr/j2LFjceTIkQ43ACBdmcbNwYMHo6WlJYqKijqsFxUVRUNDQ6f7/OQnP4n77rsv7r333lP6HTU1NTFq1Kj2W3Fx8QeeGwDouzI/LdUVR48ejauuuiruvffeGD169Cnts3z58jh8+HD7bd++fT08JQCQpS5//UJ3Gj16dOTl5UVjY2OH9cbGxhg7duwJ2//3f/937N27N+bNm9e+1traGhERgwYNihdffDE++tGPdtinoKAgCgoKemB6AKAvyvTITX5+fsycOTNqa2vb11pbW6O2tjbKyspO2H7KlCmxZ8+e2L17d/vt8ssvj4svvjh2797tlBMAkO2Rm4iIqqqqWLx4ccyaNStmz54da9asiaampqioqIiIiEWLFsW4ceOipqYmCgsLY9q0aR32P+OMMyIiTlgHAAamzONm4cKFceDAgVi5cmU0NDRESUlJbN26tf0i4/r6+sjN7VeXBgEAGco8biIiKisro7KystPHtm3bdtJ9v/Od73T/QABAv+WQCACQFHEDACRF3AAASRE3AEBSxA0AkBRxAwAkRdwAAEkRNwBAUsQNAJAUcQMAJEXcAABJETcAQFLEDQCQlD7xreD0rLa2tmhqamq/P2zYsMjJyclwIgDoOeJmAGhqaor58+e333/ooYdi+PDhGU4EAD3HaSkAICniBgBIirgBAJIibgCApIgbACAp4gYASIq4AQCSIm4AgKSIGwAgKeIGAEiKuAEAkiJuAICkiBsAICniBgBIirgBAJIibgCApIgbACAp4gYASIq4AQCSIm4AgKSIGwAgKeIGAEiKuAEAkiJuAICkiBsAICniBgBIyqCsB+gvZt7w3axHOG05x5tj1G/d/8MVm6NtUH5m83xQdXcvynoEAPowR24AgKSIGwAgKeIGAEiKuAEAkiJuAICkiBsAICniBgBIirgBAJIibgCApIgbACAp4gYASIq4AQCSIm4AgKSIGwAgKeIGAEiKuAEAkiJuAICkiBsAICniBgBIirgBAJIibgCApIgbACAp4gYASIq4AQCS0ifiZt26dTFx4sQoLCyM0tLS2LFjx3tue++998ZFF10UH/rQh+JDH/pQlJeXn3R7AGBgyTxutmzZElVVVVFdXR07d+6MGTNmxNy5c2P//v2dbr9t27a44oor4sknn4zt27dHcXFxXHbZZfHaa6/18uQAQF+UedysXr06li5dGhUVFTF16tRYv359DB06NDZu3Njp9t/73vfiC1/4QpSUlMSUKVPiH//xH6O1tTVqa2s73f7YsWNx5MiRDjcAIF2Zxk1zc3PU1dVFeXl5+1pubm6Ul5fH9u3bT+ln/PrXv4633347zjzzzE4fr6mpiVGjRrXfiouLu2V2AKBvyjRuDh48GC0tLVFUVNRhvaioKBoaGk7pZ9x0001xzjnndAik37Z8+fI4fPhw+23fvn0feG4AoO8alPUAH8SqVati8+bNsW3btigsLOx0m4KCgigoKOjlyQCArGQaN6NHj468vLxobGzssN7Y2Bhjx4496b5f/epXY9WqVfHv//7vMX369J4cEwDoRzI9LZWfnx8zZ87scDHwuxcHl5WVved+d911V9x+++2xdevWmDVrVm+M2q+15Q2Ow9OvaL+15Q3OeiQA6DGZn5aqqqqKxYsXx6xZs2L27NmxZs2aaGpqioqKioiIWLRoUYwbNy5qamoiIuIrX/lKrFy5MjZt2hQTJ05svzZn+PDhMXz48MyeR5+WkxNtg/KzngIAekXmcbNw4cI4cOBArFy5MhoaGqKkpCS2bt3afpFxfX195Ob+5gDTt771rWhubo4/+7M/6/Bzqqur42//9m97c3QAoA/KPG4iIiorK6OysrLTx7Zt29bh/t69e3t+IACg38r8Q/wAALqTuAEAkiJuAICkiBsAICniBgBIirgBAJIibgCApIgbACAp4gYASIq4AQCSIm4AgKSIGwAgKeIGAEiKuAEAkiJuAICkiBsAICniBgBIirgBAJIibgCApIgbACAp4gYASIq4AQCSIm4AgKSIGwAgKeIGAEiKuAEAkiJuAICkiBsAICniBgBIirgBAJIibgCApIgbACAp4gYASIq4AQCSIm4AgKSIGwAgKeIGAEiKuAEAkiJuAICkiBsAICniBgBIirgBAJIibgCApIgbACAp4gYASIq4AQCSIm4AgKSIGwAgKeIGAEiKuAEAkiJuAICkiBsAICniBgBIirgBAJIibgCApIgbACAp4gYASIq4AQCSIm4AgKSIGwAgKeIGAEiKuAEAkiJuAICkiBsAICniBgBISp+Im3Xr1sXEiROjsLAwSktLY8eOHSfd/l//9V9jypQpUVhYGBdccEE89thjvTQpANDXZR43W7Zsiaqqqqiuro6dO3fGjBkzYu7cubF///5Ot3/66afjiiuuiCVLlsSuXbtiwYIFsWDBgvjFL37Ry5MDAH1R5nGzevXqWLp0aVRUVMTUqVNj/fr1MXTo0Ni4cWOn269duzY+9alPxQ033BDnn39+3H777fF7v/d7cc899/Ty5ABAXzQoy1/e3NwcdXV1sXz58va13NzcKC8vj+3bt3e6z/bt26OqqqrD2ty5c+MHP/hBp9sfO3Ysjh071n7/8OHDERFx5MiRLs3acuz/urQ9Paerr93p8Hr3HV7vgcXrPbB05fV+d9u2trb33TbTuDl48GC0tLREUVFRh/WioqJ44YUXOt2noaGh0+0bGho63b6mpiZuu+22E9aLi4tPc2qyNuobV2c9Ar3I6z2weL0HltN5vY8ePRqjRo066TaZxk1vWL58eYcjPa2trXHo0KE466yzIicnJ8PJeteRI0eiuLg49u3bFyNHjsx6HHqY13tg8XoPLAP19W5ra4ujR4/GOeec877bZho3o0ePjry8vGhsbOyw3tjYGGPHju10n7Fjx3Zp+4KCgigoKOiwdsYZZ5z+0P3cyJEjB9T/DAOd13tg8XoPLAPx9X6/IzbvyvSC4vz8/Jg5c2bU1ta2r7W2tkZtbW2UlZV1uk9ZWVmH7SMinnjiiffcHgAYWDI/LVVVVRWLFy+OWbNmxezZs2PNmjXR1NQUFRUVERGxaNGiGDduXNTU1ERExDXXXBN/8Ad/EF/72tfij/7oj2Lz5s3x7LPPxoYNG7J8GgBAH5F53CxcuDAOHDgQK1eujIaGhigpKYmtW7e2XzRcX18fubm/OcA0Z86c2LRpU9x6663x5S9/OSZPnhw/+MEPYtq0aVk9hX6hoKAgqqurTzhFR5q83gOL13tg8Xq/v5y2U3lPFQBAP5H5h/gBAHQncQMAJEXcAABJETcAQFLEzQCxbt26mDhxYhQWFkZpaWns2LEj65HoAT/+8Y9j3rx5cc4550ROTs57fucaaaipqYmPf/zjMWLEiBgzZkwsWLAgXnzxxazHood861vfiunTp7d/eF9ZWVn827/9W9Zj9UniZgDYsmVLVFVVRXV1dezcuTNmzJgRc+fOjf3792c9Gt2sqakpZsyYEevWrct6FHrBU089FcuWLYtnnnkmnnjiiXj77bfjsssui6ampqxHoweMHz8+Vq1aFXV1dfHss8/GJz/5yZg/f34899xzWY/W53gr+ABQWloaH//4x+Oee+6JiHc+Bbq4uDi++MUvxs0335zxdPSUnJycePDBB2PBggVZj0IvOXDgQIwZMyaeeuqp+P3f//2sx6EXnHnmmXH33XfHkiVLsh6lT3HkJnHNzc1RV1cX5eXl7Wu5ublRXl4e27dvz3AyoLsdPnw4It75g0faWlpaYvPmzdHU1OTrhzqR+ScU07MOHjwYLS0t7Z/4/K6ioqJ44YUXMpoK6G6tra3xpS99KS688EKf2J6wPXv2RFlZWbz11lsxfPjwePDBB2Pq1KlZj9XniBuABCxbtix+8YtfxE9+8pOsR6EHnXfeebF79+44fPhwPPDAA7F48eJ46qmnBM7/R9wkbvTo0ZGXlxeNjY0d1hsbG2Ps2LEZTQV0p8rKynjkkUfixz/+cYwfPz7rcehB+fn5ce6550ZExMyZM+NnP/tZrF27Nr797W9nPFnf4pqbxOXn58fMmTOjtra2fa21tTVqa2udp4V+rq2tLSorK+PBBx+MH/3oRzFp0qSsR6KXtba2xrFjx7Ieo89x5GYAqKqqisWLF8esWbNi9uzZsWbNmmhqaoqKioqsR6Obvfnmm/Hyyy+333/11Vdj9+7dceaZZ8aECRMynIyesGzZsti0aVM89NBDMWLEiGhoaIiIiFGjRsWQIUMyno7utnz58vj0pz8dEyZMiKNHj8amTZti27Zt8fjjj2c9Wp/jreADxD333BN33313NDQ0RElJSXz961+P0tLSrMeim23bti0uvvjiE9YXL14c3/nOd3p/IHpUTk5Op+v3339//OVf/mXvDkOPW7JkSdTW1sYbb7wRo0aNiunTp8dNN90Ul156adaj9TniBgBIimtuAICkiBsAICniBgBIirgBAJIibgCApIgbACAp4gYASIq4AQCSIm4AgKSIGwAgKeIGAEiKuAH6pdbW1rjrrrvi3HPPjYKCgpgwYULccccdsXfv3sjJyYnNmzfHnDlzorCwMKZNmxZPPfVU1iMDvUTcAP3S8uXLY9WqVbFixYr4r//6r9i0aVMUFRW1P37DDTfEddddF7t27YqysrKYN29e/OpXv8pwYqC3+FZwoN85evRofPjDH4577rknPve5z3V4bO/evTFp0qRYtWpV3HTTTRERcfz48Zg0aVJ88YtfjBtvvDGLkYFe5MgN0O88//zzcezYsbjkkkvec5uysrL2fw8aNChmzZoVzz//fG+MB2RM3AD9zpAhQ7IeAejDxA3Q70yePDmGDBkStbW177nNM8880/7v48ePR11dXZx//vm9MR6QsUFZDwDQVYWFhXHTTTfFjTfeGPn5+XHhhRfGgQMH4rnnnms/VbVu3bqYPHlynH/++fEP//AP8T//8z/xV3/1VxlPDvQGcQP0SytWrIhBgwbFypUr4/XXX4+zzz47rr766vbHV61aFatWrYrdu3fHueeeGw8//HCMHj06w4mB3uLdUkBS3n231K5du6KkpCTrcYAMuOYGAEiKuAEAkuK0FACQFEduAICkiBsAICniBgBIirgBAJIibgCApIgbACAp4gYASIq4AQCS8v8A43OZYGkHo8sAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=\"fbs\",y=\"target\",data=df)"
      ],
      "metadata": {
        "id": "4NjOS7PAgq_b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "outputId": "2f5fb993-a3b9-4903-a8b6-d3e241e7359e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='fbs', ylabel='target'>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=\"restecg\",y=\"target\",data=df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "81F0jyifYjcE",
        "outputId": "f5cbb7ba-41ed-4046-d99d-1af8a438b6f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='restecg', ylabel='target'>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=\"exang\",y=\"target\",data=df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "t1DgwErAYo5E",
        "outputId": "2e23d6ab-af32-4166-fe1f-12487fecac4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='exang', ylabel='target'>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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3f//3uuuuu/SJT3zijI/f39+vvr6+uA0AAJgr4TM33//+97Vp0yaVl5fH9i1atEizZ8/W2rVr1dbWpkmTJunWW2/Vhg0bzvhYAwMDam9vV01NTWyf3W6X2+1Wa2vrkMd997vf1cyZM7VixQr913/91xmfw+fz6a677hrmqwMAAONdwmdu9u/frwsvvPC0/RdeeKH2798v6eRbV6d+c+pMuru7FQ6H5XQ64/Y7nU4FAoFBj3nhhRe0ZcsWPfzww8Oat6amRr29vbGts7NzWMcBAIDxKeG4WbBggdatW6eBgYHYvhMnTmjdunVasGCBJOm99947LVhGwtGjR/W1r31NDz/8sLKzs4d1jMPhUGZmZtwGAADMlfDbUg0NDfriF7+o2bNna/HixZJOns0Jh8P6+c9/Lkl68803ddNNN33kY2VnZyslJUXBYDBufzAYVE5Ozmnr33jjDb399tv6whe+ENsXiUROvpAJE3TgwAG+awcAgPNcwnGzZMkSvfXWW3rsscd08OBBSdK1116rv/u7v9OUKVMkSV/72teG9VhpaWkqKiqS3++PfZw7EonI7/dr1apVp61fsGBB7K2vU+68804dPXpUmzZtUl5eXqIvBwAAGCahuDlx4oQWLFign//857rxxhtHZACv16uqqioVFxerpKRE9fX1CoVC8ng8kqTKykrl5ubK5/MpPT1dF198cdzxU6dOlaTT9gMAgPNTQnGTmpqq//3f/x3RASoqKtTV1aXa2loFAgG5XC61tLTErtnp6OiQ3Z7wpUEAAOA8ZYue+ia+Ybrnnnt08OBB/fCHP9SECWf16w2W6uvrU1ZWlnp7e0f14uKi27Z+9CLgPNO+vtLqEQCMU4n8+51wnbz88svy+/169tlntWjRIk2aNCnu/ieeeCLRhwQAABgxCcfN1KlT9eUvf3k0ZgEAADhnCcfNj370o9GYAwAAYERwpS4AADDKWV0RvGPHDv3Hf/yHOjo64r6pWJL27t07IoMBAACcjYTP3Nx///3yeDxyOp3at2+fSkpKNGPGDL355pv6m7/5m9GYEQAAYNgSjpt/+7d/0+bNm/XAAw8oLS1Nt99+u5577jn98z//s3p7e0djRgAAgGFLOG46Ojq0ZMkSSdLEiRN19OhRSSd/cuHf//3fR3Y6AACABCUcNzk5Oerp6ZEkffzjH9d///d/S5LeeustJfh9gAAAACMu4bj53Oc+p5/97GeSJI/Ho1tuuUVXXXWVKioqdM0114z4gAAAAIlI+NNSd9xxh3JzcyVJK1eu1IwZM7Rnzx598Ytf1NVXXz3iAwIAACQi4biZO3euDh8+rJkzZ0qSrrvuOl133XX6/e9/r5kzZyocDo/4kAAAAMOV8NtSQ11Xc+zYMaWnp5/zQAAAAOdi2GduvF6vJMlms6m2tlYZGRmx+8LhsF566SW5XK4RHxAAACARw46bffv2STp55mb//v1KS0uL3ZeWlqbCwkKtWbNm5CcEAABIwLDjZteuXZJOfkJq06ZNyszMHLWhAAAAzha/Cg4AAIzCr4IDAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoyT8DcUAAFgtGo0qFArFbk+aNEk2m83CiZBMiBsAwLgTCoW0bNmy2O2nnnpKkydPtnAiJBPelgIAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABglKSIm4aGBuXn5ys9PV2lpaVqa2sbcu0TTzyh4uJiTZ06VZMmTZLL5dIjjzwyhtMCAIBkZnncNDc3y+v1qq6uTnv37lVhYaHKy8t15MiRQddPnz5dd9xxh1pbW/Xqq6/K4/HI4/Fo586dYzw5AABIRpbHzcaNG1VdXS2Px6OCggI1NjYqIyNDTU1Ng66/4oordM0112jhwoW66KKLtHr1ai1evFgvvPDCGE8OAACSkaVxMzAwoPb2drnd7tg+u90ut9ut1tbWjzw+Go3K7/frwIED+sxnPjPomv7+fvX19cVtAADAXJbGTXd3t8LhsJxOZ9x+p9OpQCAw5HG9vb2aPHmy0tLStHTpUj3wwAO66qqrBl3r8/mUlZUV2/Ly8kb0NQAAgORi+dtSZ2PKlCl65ZVX9PLLL+v73/++vF6vdu/ePejampoa9fb2xrbOzs6xHRYAAIwpS38VPDs7WykpKQoGg3H7g8GgcnJyhjzObrdr7ty5kiSXy6Xf/OY38vl8uuKKK05b63A45HA4RnRuAACQvCw9c5OWlqaioiL5/f7YvkgkIr/fr7KysmE/TiQSUX9//2iMCAAAxhlLz9xIktfrVVVVlYqLi1VSUqL6+nqFQiF5PB5JUmVlpXJzc+Xz+SSdvIamuLhYF110kfr7+/XMM8/okUce0UMPPWTlywAAAEnC8ripqKhQV1eXamtrFQgE5HK51NLSErvIuKOjQ3b7hyeYQqGQbrrpJr377ruaOHGiFixYoEcffVQVFRVWvQQAAJBEbNFoNGr1EGOpr69PWVlZ6u3tVWZm5qg9T9FtW0ftsYHxqn19pdUjwBDHjh3TsmXLYrefeuopTZ482cKJMNoS+fd7XH5aCgAAYCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKNMsHoAABhvim7bavUI5z3bnwaU9We3r1i7XdEJaZbNA6l9faXVI8Rw5gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGSYq4aWhoUH5+vtLT01VaWqq2trYh1z788MO67LLLNG3aNE2bNk1ut/uM6wEAwPnF8rhpbm6W1+tVXV2d9u7dq8LCQpWXl+vIkSODrt+9e7euv/567dq1S62trcrLy9Nf//Vf67333hvjyQEAQDKyPG42btyo6upqeTweFRQUqLGxURkZGWpqahp0/WOPPaabbrpJLpdLCxYs0A9/+ENFIhH5/f4xnhwAACQjS+NmYGBA7e3tcrvdsX12u11ut1utra3Deozjx4/rxIkTmj59+qD39/f3q6+vL24DAADmsjRuuru7FQ6H5XQ64/Y7nU4FAoFhPcY3v/lNXXDBBXGB9Od8Pp+ysrJiW15e3jnPDQAAkpflb0udi3Xr1mn79u168sknlZ6ePuiampoa9fb2xrbOzs4xnhIAAIylCVY+eXZ2tlJSUhQMBuP2B4NB5eTknPHYDRs2aN26dXr++ee1ePHiIdc5HA45HI4RmRcAACQ/S8/cpKWlqaioKO5i4FMXB5eVlQ153L333qu7775bLS0tKi4uHotRAQDAOGHpmRtJ8nq9qqqqUnFxsUpKSlRfX69QKCSPxyNJqqysVG5urnw+nyTpX/7lX1RbW6tt27YpPz8/dm3O5MmTNXnyZMteBwAASA6Wx01FRYW6urpUW1urQCAgl8ullpaW2EXGHR0dsts/PMH00EMPaWBgQH/7t38b9zh1dXX6zne+M5ajAwCAJGR53EjSqlWrtGrVqkHv2717d9ztt99+e/QHAgAA49a4/rQUAADA/0fcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjTLB6AAAAEhVNSVXv4uvjbgOnEDcAgPHHZlN0QprVUyBJ8bYUAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAolsdNQ0OD8vPzlZ6ertLSUrW1tQ259vXXX9eXv/xl5efny2azqb6+fuwGBQAA44KlcdPc3Cyv16u6ujrt3btXhYWFKi8v15EjRwZdf/z4cX3iE5/QunXrlJOTM8bTAgCA8cDSuNm4caOqq6vl8XhUUFCgxsZGZWRkqKmpadD1n/rUp7R+/Xpdd911cjgcw3qO/v5+9fX1xW0AAMBclsXNwMCA2tvb5Xa7PxzGbpfb7VZra+uIPY/P51NWVlZsy8vLG7HHBgAAyceyuOnu7lY4HJbT6Yzb73Q6FQgERux5ampq1NvbG9s6OztH7LEBAEDymWD1AKPN4XAM+y0sAAAw/ll25iY7O1spKSkKBoNx+4PBIBcLAwCAs2ZZ3KSlpamoqEh+vz+2LxKJyO/3q6yszKqxAADAOGfp21Jer1dVVVUqLi5WSUmJ6uvrFQqF5PF4JEmVlZXKzc2Vz+eTdPIi5P/5n/+J/e/33ntPr7zyiiZPnqy5c+da9joAAEDysDRuKioq1NXVpdraWgUCAblcLrW0tMQuMu7o6JDd/uHJpffff1+XXHJJ7PaGDRu0YcMGXX755dq9e/dYjw8AAJKQ5RcUr1q1SqtWrRr0vv8fLPn5+YpGo2MwFQAAGK8s//kFAACAkUTcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwChJETcNDQ3Kz89Xenq6SktL1dbWdsb1jz/+uBYsWKD09HQtWrRIzzzzzBhNCgAAkp3lcdPc3Cyv16u6ujrt3btXhYWFKi8v15EjRwZdv2fPHl1//fVasWKF9u3bp+XLl2v58uV67bXXxnhyAACQjCyPm40bN6q6uloej0cFBQVqbGxURkaGmpqaBl2/adMmXX311brtttu0cOFC3X333br00kv14IMPjvHkAAAgGU2w8skHBgbU3t6umpqa2D673S63263W1tZBj2ltbZXX643bV15erp/+9KeDru/v71d/f3/sdm9vrySpr6/vHKc/s3D/B6P6+MB4NNp/d2OFv2/gdKP9933q8aPR6EeutTRuuru7FQ6H5XQ64/Y7nU799re/HfSYQCAw6PpAIDDoep/Pp7vuuuu0/Xl5eWc5NYCzlfXAjVaPAGCUjNXf99GjR5WVlXXGNZbGzVioqamJO9MTiUTU09OjGTNmyGazWTgZxkJfX5/y8vLU2dmpzMxMq8cBMIL4+z6/RKNRHT16VBdccMFHrrU0brKzs5WSkqJgMBi3PxgMKicnZ9BjcnJyElrvcDjkcDji9k2dOvXsh8a4lJmZyf/5AYbi7/v88VFnbE6x9ILitLQ0FRUVye/3x/ZFIhH5/X6VlZUNekxZWVncekl67rnnhlwPAADOL5a/LeX1elVVVaXi4mKVlJSovr5eoVBIHo9HklRZWanc3Fz5fD5J0urVq3X55Zfrvvvu09KlS7V9+3b96le/0ubNm618GQAAIElYHjcVFRXq6upSbW2tAoGAXC6XWlpaYhcNd3R0yG7/8ATTkiVLtG3bNt1555369re/rXnz5umnP/2pLr74YqteApKYw+FQXV3daW9NAhj/+PvGUGzR4XymCgAAYJyw/Ev8AAAARhJxAwAAjELcAAAAoxA3AADAKMQNjNbQ0KD8/Hylp6ertLRUbW1tVo8E4Bz98pe/1Be+8AVdcMEFstlsQ/62IM5fxA2M1dzcLK/Xq7q6Ou3du1eFhYUqLy/XkSNHrB4NwDkIhUIqLCxUQ0OD1aMgSfFRcBirtLRUn/rUp/Tggw9KOvnt13l5efqnf/onfetb37J4OgAjwWaz6cknn9Ty5cutHgVJhDM3MNLAwIDa29vldrtj++x2u9xut1pbWy2cDAAw2ogbGKm7u1vhcDj2TdenOJ1OBQIBi6YCAIwF4gYAABiFuIGRsrOzlZKSomAwGLc/GAwqJyfHoqkAAGOBuIGR0tLSVFRUJL/fH9sXiUTk9/tVVlZm4WQAgNFm+a+CA6PF6/WqqqpKxcXFKikpUX19vUKhkDwej9WjATgHx44d06FDh2K333rrLb3yyiuaPn26Pv7xj1s4GZIFHwWH0R588EGtX79egUBALpdL999/v0pLS60eC8A52L17tz772c+etr+qqko//vGPx34gJB3iBgAAGIVrbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAFguUgkIp/Ppzlz5mjixIkqLCzUjh07FI1G5Xa7VV5erlM/g9fT06PZs2ertrZWkhQOh7VixYrYsfPnz9emTZviHv8f/uEftHz5cm3YsEGzZs3SjBkztHLlSp04cSK25vDhw1q6dKkmTpyoOXPmaNu2bcrPz1d9ff2Y/XcAMDImWD0AAPh8Pj366KNqbGzUvHnz9Mtf/lJf/epX9bGPfUw/+clPtGjRIt1///1avXq1brzxRuXm5sbiJhKJaPbs2Xr88cc1Y8YM7dmzR1//+tc1a9YsfeUrX4k9x65duzRr1izt2rVLhw4dUkVFhVwul6qrqyVJlZWV6u7u1u7du5Wamiqv16sjR45Y8t8DwLnhV8EBWKq/v1/Tp0/X888/r7Kystj+G264QcePH9e2bdv0+OOPq7KyUjfffLMeeOAB7du3T/PmzRvyMVetWqVAIKAdO3ZIOnnmZvfu3XrjjTeUkpIiSfrKV74iu92u7du367e//a0WLlyol19+WcXFxZKkQ4cOad68efrXf/1X3XzzzaP3HwDAiOPMDQBLHTp0SMePH9dVV10Vt39gYECXXHKJJOnaa6/Vk08+qXXr1umhhx46LWwaGhrU1NSkjo4OffDBBxoYGJDL5Ypb8xd/8RexsJGkWbNmaf/+/ZKkAwcOaMKECbr00ktj98+dO1fTpk0byZcKYIwQNwAsdezYMUnS008/rdzc3Lj7HA6HJOn48eNqb29XSkqKfve738Wt2b59u9asWaP77rtPZWVlmjJlitavX6+XXnopbl1qamrcbZvNpkgkMtIvB0ASIG4AWKqgoEAOh0MdHR26/PLLB11z6623ym636z//8z/1+c9/XkuXLtXnPvc5SdKLL76oJUuW6Kabboqtf+ONNxKaYf78+frTn/6kffv2qaioSNLJM0p/+MMfzvJVAbAScQPAUlOmTNGaNWt0yy23KBKJ6NOf/rR6e3v14osvKjMzU9nZ2WpqalJra6suvfRS3XbbbaqqqtKrr76qadOmad68edq6dat27typOXPm6JFHHtHLL7+sOXPmDHuGBQsWyO126+tf/7oeeughpaam6tZbb9XEiRNls9lG8dUDGA18FByA5e6++26tXbtWPp9PCxcu1NVXX62nn35a+fn5WrFihb7zne/Eroe566675HQ6deONN0qS/vEf/1Ff+tKXVFFRodLSUv3+97+PO4szXFu3bpXT6dRnPvMZXXPNNaqurtaUKVOUnp4+oq8VwOjj01IAMIh3331XeXl5ev7553XllVdaPQ6ABBA3ACDpF7/4hY4dO6ZFixbp8OHDuv322/Xee+/p4MGDp12MDCC5cc0NAEg6ceKEvv3tb+vNN9/UlClTtGTJEj322GOEDTAOceYGAAAYhQuKAQCAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEb5PwvE1a2F+ppjAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=\"slope\",y=\"target\",data=df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "RWsE3c9zYrp2",
        "outputId": "c635a9aa-5fd9-4ecc-8322-ffc896d33676"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='slope', ylabel='target'>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=\"thal\",y=\"target\",data=df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "48T--wLGYzw3",
        "outputId": "0d44e747-c039-4a68-cd39-d30d45f4d784"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='thal', ylabel='target'>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df.age,df.target).plot(kind=\"bar\",figsize=(20,6))\n",
        "plt.title('Age vs Heart Disease Frequency',size=25)\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "uGon95PKaKiM",
        "outputId": "7ecae2c0-892a-43c2-a743-ccf92168f94f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4. Feature Selection\n"
      ],
      "metadata": {
        "id": "ZGBQwgqJaSho"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_selection import chi2\n",
        "X = df.drop('target',axis=1)\n",
        "y = df['target']\n",
        "chi_scores = chi2(X,y)\n",
        "chi_scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RLARr75uaWB1",
        "outputId": "2b705f34-1936-4f25-f818-88e8bafccbbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 23.28662399,   7.57683451,  62.59809791,  14.8239245 ,\n",
              "         23.93639448,   0.20293368,   2.97827075, 188.32047169,\n",
              "         38.91437697,  72.64425301,   9.8040952 ,  66.44076512,\n",
              "          5.79185297]),\n",
              " array([1.39567266e-06, 5.91231763e-03, 2.53498179e-15, 1.18028642e-04,\n",
              "        9.95714828e-07, 6.52363208e-01, 8.43893904e-02, 7.39510166e-43,\n",
              "        4.42807388e-10, 1.55258340e-17, 1.74123685e-03, 3.60567663e-16,\n",
              "        1.61006098e-02]))"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p_values = pd.Series(chi_scores[1],index = X.columns)\n",
        "p_values.sort_values(ascending = False , inplace = True)\n",
        "p_values.plot.bar()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "IslCsJ4eaeMI",
        "outputId": "c38cfd02-4fbe-444b-b77b-22884cce7943"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_selection import SelectKBest\n",
        "chi2_selector = SelectKBest(chi2, k=2)\n",
        "X_kbest = chi2_selector.fit_transform(X, y)\n",
        "print(X_kbest)\n",
        "print('Original number of features:', X.shape)\n",
        "print('Reduced number of features:', X_kbest.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wqTBfryKajI5",
        "outputId": "9a87a24f-aee1-4bb7-d6ba-976602c8721d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1.50e+02 2.30e+00]\n",
            " [1.87e+02 3.50e+00]\n",
            " [1.72e+02 1.40e+00]\n",
            " [1.78e+02 8.00e-01]\n",
            " [1.63e+02 6.00e-01]\n",
            " [1.48e+02 4.00e-01]\n",
            " [1.53e+02 1.30e+00]\n",
            " [1.73e+02 0.00e+00]\n",
            " [1.62e+02 5.00e-01]\n",
            " [1.74e+02 1.60e+00]\n",
            " [1.60e+02 1.20e+00]\n",
            " [1.39e+02 2.00e-01]\n",
            " [1.71e+02 6.00e-01]\n",
            " [1.44e+02 1.80e+00]\n",
            " [1.62e+02 1.00e+00]\n",
            " [1.58e+02 1.60e+00]\n",
            " [1.72e+02 0.00e+00]\n",
            " [1.14e+02 2.60e+00]\n",
            " [1.71e+02 1.50e+00]\n",
            " [1.51e+02 1.80e+00]\n",
            " [1.61e+02 5.00e-01]\n",
            " [1.79e+02 4.00e-01]\n",
            " [1.78e+02 0.00e+00]\n",
            " [1.37e+02 1.00e+00]\n",
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            " [1.23e+02 6.00e-01]\n",
            " [1.57e+02 8.00e-01]\n",
            " [1.52e+02 1.20e+00]\n",
            " [1.68e+02 0.00e+00]\n",
            " [1.40e+02 4.00e-01]\n",
            " [1.88e+02 0.00e+00]\n",
            " [1.52e+02 5.00e-01]\n",
            " [1.25e+02 1.40e+00]\n",
            " [1.60e+02 1.40e+00]\n",
            " [1.70e+02 0.00e+00]\n",
            " [1.65e+02 1.60e+00]\n",
            " [1.48e+02 8.00e-01]\n",
            " [1.51e+02 8.00e-01]\n",
            " [1.42e+02 1.50e+00]\n",
            " [1.80e+02 2.00e-01]\n",
            " [1.48e+02 3.00e+00]\n",
            " [1.43e+02 4.00e-01]\n",
            " [1.82e+02 0.00e+00]\n",
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            " [1.65e+02 2.00e-01]\n",
            " [1.61e+02 1.40e+00]\n",
            " [1.66e+02 2.40e+00]\n",
            " [1.64e+02 0.00e+00]\n",
            " [1.84e+02 0.00e+00]\n",
            " [1.54e+02 6.00e-01]\n",
            " [1.79e+02 0.00e+00]\n",
            " [1.70e+02 0.00e+00]\n",
            " [1.60e+02 0.00e+00]\n",
            " [1.78e+02 1.20e+00]\n",
            " [1.22e+02 6.00e-01]\n",
            " [1.60e+02 1.60e+00]\n",
            " [1.51e+02 1.00e+00]\n",
            " [1.56e+02 0.00e+00]\n",
            " [1.58e+02 1.60e+00]\n",
            " [1.22e+02 1.00e+00]\n",
            " [1.75e+02 0.00e+00]\n",
            " [1.68e+02 0.00e+00]\n",
            " [1.69e+02 0.00e+00]\n",
            " [1.59e+02 0.00e+00]\n",
            " [1.38e+02 0.00e+00]\n",
            " [1.11e+02 0.00e+00]\n",
            " [1.57e+02 1.20e+00]\n",
            " [1.47e+02 1.00e-01]\n",
            " [1.62e+02 1.90e+00]\n",
            " [1.73e+02 0.00e+00]\n",
            " [1.78e+02 8.00e-01]\n",
            " [1.45e+02 4.20e+00]\n",
            " [1.79e+02 0.00e+00]\n",
            " [1.94e+02 8.00e-01]\n",
            " [1.63e+02 0.00e+00]\n",
            " [1.15e+02 1.50e+00]\n",
            " [1.31e+02 1.00e-01]\n",
            " [1.52e+02 2.00e-01]\n",
            " [1.62e+02 1.10e+00]\n",
            " [1.59e+02 0.00e+00]\n",
            " [1.54e+02 0.00e+00]\n",
            " [1.73e+02 2.00e-01]\n",
            " [1.33e+02 2.00e-01]\n",
            " [1.61e+02 0.00e+00]\n",
            " [1.55e+02 0.00e+00]\n",
            " [1.70e+02 0.00e+00]\n",
            " [1.68e+02 2.00e+00]\n",
            " [1.62e+02 1.90e+00]\n",
            " [1.72e+02 0.00e+00]\n",
            " [1.52e+02 0.00e+00]\n",
            " [1.22e+02 2.00e+00]\n",
            " [1.82e+02 0.00e+00]\n",
            " [1.72e+02 0.00e+00]\n",
            " [1.67e+02 0.00e+00]\n",
            " [1.79e+02 0.00e+00]\n",
            " [1.92e+02 7.00e-01]\n",
            " [1.43e+02 1.00e-01]\n",
            " [1.72e+02 0.00e+00]\n",
            " [1.69e+02 1.00e-01]\n",
            " [1.21e+02 2.00e-01]\n",
            " [1.63e+02 0.00e+00]\n",
            " [1.62e+02 0.00e+00]\n",
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            " [1.53e+02 0.00e+00]\n",
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            " [1.63e+02 0.00e+00]\n",
            " [9.60e+01 0.00e+00]\n",
            " [1.40e+02 0.00e+00]\n",
            " [1.26e+02 1.50e+00]\n",
            " [1.05e+02 2.00e-01]\n",
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            " [1.81e+02 1.20e+00]\n",
            " [1.73e+02 0.00e+00]\n",
            " [1.42e+02 3.00e-01]\n",
            " [1.16e+02 1.10e+00]\n",
            " [1.43e+02 0.00e+00]\n",
            " [1.49e+02 3.00e-01]\n",
            " [1.71e+02 9.00e-01]\n",
            " [1.69e+02 0.00e+00]\n",
            " [1.50e+02 0.00e+00]\n",
            " [1.38e+02 2.30e+00]\n",
            " [1.25e+02 1.60e+00]\n",
            " [1.55e+02 6.00e-01]\n",
            " [1.52e+02 0.00e+00]\n",
            " [1.52e+02 0.00e+00]\n",
            " [1.31e+02 6.00e-01]\n",
            " [1.79e+02 0.00e+00]\n",
            " [1.74e+02 0.00e+00]\n",
            " [1.44e+02 4.00e-01]\n",
            " [1.63e+02 0.00e+00]\n",
            " [1.69e+02 0.00e+00]\n",
            " [1.66e+02 1.20e+00]\n",
            " [1.82e+02 0.00e+00]\n",
            " [1.73e+02 0.00e+00]\n",
            " [1.73e+02 0.00e+00]\n",
            " [1.08e+02 1.50e+00]\n",
            " [1.29e+02 2.60e+00]\n",
            " [1.60e+02 3.60e+00]\n",
            " [1.47e+02 1.40e+00]\n",
            " [1.55e+02 3.10e+00]\n",
            " [1.42e+02 6.00e-01]\n",
            " [1.68e+02 1.00e+00]\n",
            " [1.60e+02 1.80e+00]\n",
            " [1.73e+02 3.20e+00]\n",
            " [1.32e+02 2.40e+00]\n",
            " [1.14e+02 2.00e+00]\n",
            " [1.60e+02 1.40e+00]\n",
            " [1.58e+02 0.00e+00]\n",
            " [1.20e+02 2.50e+00]\n",
            " [1.12e+02 6.00e-01]\n",
            " [1.32e+02 1.20e+00]\n",
            " [1.14e+02 1.00e+00]\n",
            " [1.69e+02 0.00e+00]\n",
            " [1.65e+02 2.50e+00]\n",
            " [1.28e+02 2.60e+00]\n",
            " [1.53e+02 0.00e+00]\n",
            " [1.44e+02 1.40e+00]\n",
            " [1.09e+02 2.20e+00]\n",
            " [1.63e+02 6.00e-01]\n",
            " [1.58e+02 0.00e+00]\n",
            " [1.42e+02 1.20e+00]\n",
            " [1.31e+02 2.20e+00]\n",
            " [1.13e+02 1.40e+00]\n",
            " [1.42e+02 2.80e+00]\n",
            " [1.55e+02 3.00e+00]\n",
            " [1.40e+02 3.40e+00]\n",
            " [1.47e+02 3.60e+00]\n",
            " [1.63e+02 2.00e-01]\n",
            " [9.90e+01 1.80e+00]\n",
            " [1.58e+02 6.00e-01]\n",
            " [1.77e+02 0.00e+00]\n",
            " [1.41e+02 2.80e+00]\n",
            " [1.11e+02 8.00e-01]\n",
            " [1.50e+02 1.60e+00]\n",
            " [1.45e+02 6.20e+00]\n",
            " [1.61e+02 0.00e+00]\n",
            " [1.42e+02 1.20e+00]\n",
            " [1.57e+02 2.60e+00]\n",
            " [1.39e+02 2.00e+00]\n",
            " [1.62e+02 0.00e+00]\n",
            " [1.50e+02 4.00e-01]\n",
            " [1.40e+02 3.60e+00]\n",
            " [1.40e+02 1.20e+00]\n",
            " [1.46e+02 1.00e+00]\n",
            " [1.44e+02 1.20e+00]\n",
            " [1.36e+02 3.00e+00]\n",
            " [9.70e+01 1.20e+00]\n",
            " [1.32e+02 1.80e+00]\n",
            " [1.27e+02 2.80e+00]\n",
            " [1.50e+02 0.00e+00]\n",
            " [1.54e+02 4.00e+00]\n",
            " [1.11e+02 5.60e+00]\n",
            " [1.74e+02 1.40e+00]\n",
            " [1.33e+02 4.00e+00]\n",
            " [1.26e+02 2.80e+00]\n",
            " [1.25e+02 2.60e+00]\n",
            " [1.03e+02 1.40e+00]\n",
            " [1.30e+02 1.60e+00]\n",
            " [1.59e+02 2.00e-01]\n",
            " [1.31e+02 1.80e+00]\n",
            " [1.52e+02 0.00e+00]\n",
            " [1.24e+02 1.00e+00]\n",
            " [1.45e+02 8.00e-01]\n",
            " [9.60e+01 2.20e+00]\n",
            " [1.09e+02 2.40e+00]\n",
            " [1.73e+02 1.60e+00]\n",
            " [1.71e+02 0.00e+00]\n",
            " [1.70e+02 1.20e+00]\n",
            " [1.62e+02 0.00e+00]\n",
            " [1.56e+02 0.00e+00]\n",
            " [1.12e+02 2.90e+00]\n",
            " [1.43e+02 0.00e+00]\n",
            " [1.32e+02 2.00e+00]\n",
            " [8.80e+01 1.20e+00]\n",
            " [1.05e+02 2.10e+00]\n",
            " [1.66e+02 5.00e-01]\n",
            " [1.50e+02 1.90e+00]\n",
            " [1.20e+02 0.00e+00]\n",
            " [1.95e+02 0.00e+00]\n",
            " [1.46e+02 2.00e+00]\n",
            " [1.22e+02 4.20e+00]\n",
            " [1.43e+02 1.00e-01]\n",
            " [1.06e+02 1.90e+00]\n",
            " [1.25e+02 9.00e-01]\n",
            " [1.25e+02 0.00e+00]\n",
            " [1.47e+02 0.00e+00]\n",
            " [1.30e+02 3.00e+00]\n",
            " [1.26e+02 9.00e-01]\n",
            " [1.54e+02 1.40e+00]\n",
            " [1.82e+02 3.80e+00]\n",
            " [1.65e+02 1.00e+00]\n",
            " [1.60e+02 0.00e+00]\n",
            " [9.50e+01 2.00e+00]\n",
            " [1.69e+02 1.80e+00]\n",
            " [1.08e+02 0.00e+00]\n",
            " [1.32e+02 1.00e-01]\n",
            " [1.17e+02 3.40e+00]\n",
            " [1.26e+02 8.00e-01]\n",
            " [1.16e+02 3.20e+00]\n",
            " [1.03e+02 1.60e+00]\n",
            " [1.44e+02 8.00e-01]\n",
            " [1.45e+02 2.60e+00]\n",
            " [7.10e+01 1.00e+00]\n",
            " [1.56e+02 1.00e-01]\n",
            " [1.18e+02 1.00e+00]\n",
            " [1.68e+02 1.00e+00]\n",
            " [1.05e+02 2.00e+00]\n",
            " [1.41e+02 3.00e-01]\n",
            " [1.52e+02 0.00e+00]\n",
            " [1.25e+02 3.60e+00]\n",
            " [1.25e+02 1.80e+00]\n",
            " [1.56e+02 1.00e+00]\n",
            " [1.34e+02 2.20e+00]\n",
            " [1.81e+02 0.00e+00]\n",
            " [1.38e+02 1.90e+00]\n",
            " [1.20e+02 1.80e+00]\n",
            " [1.62e+02 8.00e-01]\n",
            " [1.64e+02 0.00e+00]\n",
            " [1.43e+02 3.00e+00]\n",
            " [1.30e+02 2.00e+00]\n",
            " [1.61e+02 0.00e+00]\n",
            " [1.40e+02 4.40e+00]\n",
            " [1.46e+02 2.80e+00]\n",
            " [1.50e+02 8.00e-01]\n",
            " [1.44e+02 2.80e+00]\n",
            " [1.44e+02 4.00e+00]\n",
            " [1.36e+02 0.00e+00]\n",
            " [9.00e+01 1.00e+00]\n",
            " [1.23e+02 2.00e-01]\n",
            " [1.32e+02 1.20e+00]\n",
            " [1.41e+02 3.40e+00]\n",
            " [1.15e+02 1.20e+00]\n",
            " [1.74e+02 0.00e+00]]\n",
            "Original number of features: (303, 13)\n",
            "Reduced number of features: (303, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe = df.dropna()\n",
        "dataframe = dataframe.drop(columns = [ 'slope', 'thal', 'fbs', 'restecg', 'exang','sex'])\n",
        "\n",
        "dataframe.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "q_lQOPTWal8d",
        "outputId": "652b61d1-a9af-4a3b-9045-5d8d7714d198"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  cp  trestbps  chol  thalach  oldpeak  ca  target\n",
              "0   63   3       145   233      150      2.3   0       1\n",
              "1   37   2       130   250      187      3.5   0       1\n",
              "2   41   1       130   204      172      1.4   0       1\n",
              "3   56   1       120   236      178      0.8   0       1\n",
              "4   57   0       120   354      163      0.6   0       1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6d460439-ff0a-4d2f-8058-f4fa74f23210\" 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>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>thalach</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>ca</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>3</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>150</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>187</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>172</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>178</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>163</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d460439-ff0a-4d2f-8058-f4fa74f23210')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6d460439-ff0a-4d2f-8058-f4fa74f23210 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-6d460439-ff0a-4d2f-8058-f4fa74f23210');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e3b2b0ea-a05e-4fbe-b227-425dc06f0bf0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e3b2b0ea-a05e-4fbe-b227-425dc06f0bf0')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e3b2b0ea-a05e-4fbe-b227-425dc06f0bf0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataframe",
              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 303,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 29,\n        \"max\": 77,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          46,\n          66,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          0,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trestbps\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 94,\n        \"max\": 200,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          104,\n          123,\n          114\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51,\n        \"min\": 126,\n        \"max\": 564,\n        \"num_unique_values\": 152,\n        \"samples\": [\n          277,\n          169,\n          242\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thalach\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 71,\n        \"max\": 202,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          159,\n          152,\n          194\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oldpeak\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1610750220686348,\n        \"min\": 0.0,\n        \"max\": 6.2,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          1.9,\n          3.0,\n          1.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ca\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correlat=dataframe.corr()\n",
        "pd.DataFrame(correlat['target']).sort_values(by='target',ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "1QeTO00ZfKGf",
        "outputId": "180113e2-c92a-419d-d412-89ede8a646e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            target\n",
              "target    1.000000\n",
              "cp        0.433798\n",
              "thalach   0.421741\n",
              "chol     -0.085239\n",
              "trestbps -0.144931\n",
              "age      -0.225439\n",
              "ca       -0.391724\n",
              "oldpeak  -0.430696"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e9d37090-c997-49e0-b41f-5390aa02a17f\" 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>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cp</th>\n",
              "      <td>0.433798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thalach</th>\n",
              "      <td>0.421741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chol</th>\n",
              "      <td>-0.085239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trestbps</th>\n",
              "      <td>-0.144931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>-0.225439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ca</th>\n",
              "      <td>-0.391724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oldpeak</th>\n",
              "      <td>-0.430696</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e9d37090-c997-49e0-b41f-5390aa02a17f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e9d37090-c997-49e0-b41f-5390aa02a17f 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-e9d37090-c997-49e0-b41f-5390aa02a17f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1fe75fb5-f44b-4e55-b012-ce7c665510e6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1fe75fb5-f44b-4e55-b012-ce7c665510e6')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1fe75fb5-f44b-4e55-b012-ce7c665510e6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4989126275205649,\n        \"min\": -0.4306960016873686,\n        \"max\": 1.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.43379826150689443,\n          -0.22543871587483838,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "sns.heatmap(dataframe.corr(),linewidths=0.3, linecolor='black',annot_kws={\"size\": 10},annot=True,cmap=\"winter\")\n",
        "plt.show()\n",
        "plt.savefig('correlationfigure')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "D5IsvnsVdR8z",
        "outputId": "f88e0324-df1d-463f-ff50-61fa9ee1d927"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas.core.groupby.generic import DataFrameGroupBy\n",
        "categorical_val = []\n",
        "continous_val = []\n",
        "for column in df.columns:\n",
        "    print('==============================')\n",
        "    print(f\"{column} : {df[column].unique()}\")\n",
        "    if len(df[column].unique()) <= 10:\n",
        "        categorical_val.append(column)\n",
        "    else:\n",
        "        continous_val.append(column)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AI_2hfrWS-2v",
        "outputId": "b4fc799d-f4e3-4a26-ab99-2da1e891a81b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==============================\n",
            "age : [63 37 41 56 57 44 52 54 48 49 64 58 50 66 43 69 59 42 61 40 71 51 65 53\n",
            " 46 45 39 47 62 34 35 29 55 60 67 68 74 76 70 38 77]\n",
            "==============================\n",
            "sex : [1 0]\n",
            "==============================\n",
            "cp : [3 2 1 0]\n",
            "==============================\n",
            "trestbps : [145 130 120 140 172 150 110 135 160 105 125 142 155 104 138 128 108 134\n",
            " 122 115 118 100 124  94 112 102 152 101 132 148 178 129 180 136 126 106\n",
            " 156 170 146 117 200 165 174 192 144 123 154 114 164]\n",
            "==============================\n",
            "chol : [233 250 204 236 354 192 294 263 199 168 239 275 266 211 283 219 340 226\n",
            " 247 234 243 302 212 175 417 197 198 177 273 213 304 232 269 360 308 245\n",
            " 208 264 321 325 235 257 216 256 231 141 252 201 222 260 182 303 265 309\n",
            " 186 203 183 220 209 258 227 261 221 205 240 318 298 564 277 214 248 255\n",
            " 207 223 288 160 394 315 246 244 270 195 196 254 126 313 262 215 193 271\n",
            " 268 267 210 295 306 178 242 180 228 149 278 253 342 157 286 229 284 224\n",
            " 206 167 230 335 276 353 225 330 290 172 305 188 282 185 326 274 164 307\n",
            " 249 341 407 217 174 281 289 322 299 300 293 184 409 259 200 327 237 218\n",
            " 319 166 311 169 187 176 241 131]\n",
            "==============================\n",
            "fbs : [1 0]\n",
            "==============================\n",
            "restecg : [0 1 2]\n",
            "==============================\n",
            "thalach : [150 187 172 178 163 148 153 173 162 174 160 139 171 144 158 114 151 161\n",
            " 179 137 157 123 152 168 140 188 125 170 165 142 180 143 182 156 115 149\n",
            " 146 175 186 185 159 130 190 132 147 154 202 166 164 184 122 169 138 111\n",
            " 145 194 131 133 155 167 192 121  96 126 105 181 116 108 129 120 112 128\n",
            " 109 113  99 177 141 136  97 127 103 124  88 195 106  95 117  71 118 134\n",
            "  90]\n",
            "==============================\n",
            "exang : [0 1]\n",
            "==============================\n",
            "oldpeak : [2.3 3.5 1.4 0.8 0.6 0.4 1.3 0.  0.5 1.6 1.2 0.2 1.8 1.  2.6 1.5 3.  2.4\n",
            " 0.1 1.9 4.2 1.1 2.  0.7 0.3 0.9 3.6 3.1 3.2 2.5 2.2 2.8 3.4 6.2 4.  5.6\n",
            " 2.9 2.1 3.8 4.4]\n",
            "==============================\n",
            "slope : [0 2 1]\n",
            "==============================\n",
            "ca : [0 2 1 3 4]\n",
            "==============================\n",
            "thal : [1 2 3 0]\n",
            "==============================\n",
            "target : [1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_val"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JXxZ1slbTPe4",
        "outputId": "79aa1648-ad53-4f88-8bfd-42cc26d5c1a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal', 'target']"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_dataset = pd.get_dummies(df, columns = categorical_val)\n",
        "df_dataset.head()\n"
      ],
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      },
      "execution_count": null,
      "outputs": [
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            "text/plain": [
              "   age  trestbps  chol  thalach  oldpeak  sex_0  sex_1  cp_0  cp_1  cp_2  ...  \\\n",
              "0   63       145   233      150      2.3      0      1     0     0     0  ...   \n",
              "1   37       130   250      187      3.5      0      1     0     0     1  ...   \n",
              "2   41       130   204      172      1.4      1      0     0     1     0  ...   \n",
              "3   56       120   236      178      0.8      0      1     0     1     0  ...   \n",
              "4   57       120   354      163      0.6      1      0     1     0     0  ...   \n",
              "\n",
              "   ca_1  ca_2  ca_3  ca_4  thal_0  thal_1  thal_2  thal_3  target_0  target_1  \n",
              "0     0     0     0     0       0       1       0       0         0         1  \n",
              "1     0     0     0     0       0       0       1       0         0         1  \n",
              "2     0     0     0     0       0       0       1       0         0         1  \n",
              "3     0     0     0     0       0       0       1       0         0         1  \n",
              "4     0     0     0     0       0       0       1       0         0         1  \n",
              "\n",
              "[5 rows x 32 columns]"
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_dataset"
            }
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_dataset = pd.get_dummies(df, columns = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal'])\n"
      ],
      "metadata": {
        "id": "tbJUdcynTaj1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#for continous value\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "\n",
        "s_sc = StandardScaler()\n",
        "col_to_scale = ['age', 'trestbps', 'chol', 'thalach', 'oldpeak']\n",
        "df_dataset[col_to_scale] = s_sc.fit_transform(df_dataset[col_to_scale])\n"
      ],
      "metadata": {
        "id": "MPxrb2-iRJMO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df_dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "kyTUwg0FRi4G",
        "outputId": "23c67bdf-b5fb-48c7-b5bd-c8c1e5bb9ee2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        age  trestbps      chol   thalach   oldpeak  target  sex_0  sex_1  \\\n",
              "0  0.952197  0.763956 -0.256334  0.015443  1.087338       1      0      1   \n",
              "1 -1.915313 -0.092738  0.072199  1.633471  2.122573       1      0      1   \n",
              "2 -1.474158 -0.092738 -0.816773  0.977514  0.310912       1      1      0   \n",
              "3  0.180175 -0.663867 -0.198357  1.239897 -0.206705       1      0      1   \n",
              "4  0.290464 -0.663867  2.082050  0.583939 -0.379244       1      1      0   \n",
              "\n",
              "   cp_0  cp_1  ...  slope_2  ca_0  ca_1  ca_2  ca_3  ca_4  thal_0  thal_1  \\\n",
              "0     0     0  ...        0     1     0     0     0     0       0       1   \n",
              "1     0     0  ...        0     1     0     0     0     0       0       0   \n",
              "2     0     1  ...        1     1     0     0     0     0       0       0   \n",
              "3     0     1  ...        1     1     0     0     0     0       0       0   \n",
              "4     1     0  ...        1     1     0     0     0     0       0       0   \n",
              "\n",
              "   thal_2  thal_3  \n",
              "0       0       0  \n",
              "1       1       0  \n",
              "2       1       0  \n",
              "3       1       0  \n",
              "4       1       0  \n",
              "\n",
              "[5 rows x 31 columns]"
            ],
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        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>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>thalach</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>target</th>\n",
              "      <th>sex_0</th>\n",
              "      <th>sex_1</th>\n",
              "      <th>cp_0</th>\n",
              "      <th>cp_1</th>\n",
              "      <th>...</th>\n",
              "      <th>slope_2</th>\n",
              "      <th>ca_0</th>\n",
              "      <th>ca_1</th>\n",
              "      <th>ca_2</th>\n",
              "      <th>ca_3</th>\n",
              "      <th>ca_4</th>\n",
              "      <th>thal_0</th>\n",
              "      <th>thal_1</th>\n",
              "      <th>thal_2</th>\n",
              "      <th>thal_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.952197</td>\n",
              "      <td>0.763956</td>\n",
              "      <td>-0.256334</td>\n",
              "      <td>0.015443</td>\n",
              "      <td>1.087338</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.915313</td>\n",
              "      <td>-0.092738</td>\n",
              "      <td>0.072199</td>\n",
              "      <td>1.633471</td>\n",
              "      <td>2.122573</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.474158</td>\n",
              "      <td>-0.092738</td>\n",
              "      <td>-0.816773</td>\n",
              "      <td>0.977514</td>\n",
              "      <td>0.310912</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.180175</td>\n",
              "      <td>-0.663867</td>\n",
              "      <td>-0.198357</td>\n",
              "      <td>1.239897</td>\n",
              "      <td>-0.206705</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.290464</td>\n",
              "      <td>-0.663867</td>\n",
              "      <td>2.082050</td>\n",
              "      <td>0.583939</td>\n",
              "      <td>-0.379244</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-669c9487-1ae5-4947-a0e9-7d44f3ffbc5f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-669c9487-1ae5-4947-a0e9-7d44f3ffbc5f 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-669c9487-1ae5-4947-a0e9-7d44f3ffbc5f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ee0b4e95-f5a5-4dad-82fb-a5ec58af0869\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ee0b4e95-f5a5-4dad-82fb-a5ec58af0869')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ee0b4e95-f5a5-4dad-82fb-a5ec58af0869 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_dataset"
            }
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "X= dataframe.drop(['target'], axis=1)\n",
        "y= dataframe['target']"
      ],
      "metadata": {
        "id": "fpikOof1xyJQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_G_NkT49gr2m",
        "outputId": "f9e88200-8ae8-4a78-dd61-efef0a46baa0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  cp  trestbps  chol  thalach  oldpeak  ca  target\n",
              "0   63   3       145   233      150      2.3   0       1\n",
              "1   37   2       130   250      187      3.5   0       1\n",
              "2   41   1       130   204      172      1.4   0       1\n",
              "3   56   1       120   236      178      0.8   0       1\n",
              "4   57   0       120   354      163      0.6   0       1"
            ],
            "text/html": [
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              "\n",
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              "\n",
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              "    border-radius: 50%;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "  }\n",
              "</style>\n",
              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 303,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 29,\n        \"max\": 77,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          46,\n          66,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          0,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trestbps\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 94,\n        \"max\": 200,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          104,\n          123,\n          114\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51,\n        \"min\": 126,\n        \"max\": 564,\n        \"num_unique_values\": 152,\n        \"samples\": [\n          277,\n          169,\n          242\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thalach\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 71,\n        \"max\": 202,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          159,\n          152,\n          194\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oldpeak\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1610750220686348,\n        \"min\": 0.0,\n        \"max\": 6.2,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          1.9,\n          3.0,\n          1.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ca\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test,y_train, y_test=train_test_split(X,y,test_size=0.25,random_state=40)"
      ],
      "metadata": {
        "id": "o38tHrIagr6Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('X_train-', X_train.shape)\n",
        "print('X_test-',X_test.shape)\n",
        "print('y_train-', y_train.shape)\n",
        "print('y_test-', y_test.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Abp7XtIMgwFa",
        "outputId": "2aa1f817-0b3c-426d-c0b2-fa6d688539f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train- (227, 7)\n",
            "X_test- (76, 7)\n",
            "y_train- (227,)\n",
            "y_test- (76,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Fitting"
      ],
      "metadata": {
        "id": "7glTH6NZg_JM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "PlXWN06Qg9Jw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Logistic Regression\n"
      ],
      "metadata": {
        "id": "B0c6FlUciXQz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "lr = LogisticRegression()\n",
        "\n",
        "lr.fit(X_train,y_train)\n",
        "\n",
        "Y_pred_lr = lr.predict(X_test)"
      ],
      "metadata": {
        "id": "M7fly0I3h_oA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred_lr.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zb6SMIFtiHRN",
        "outputId": "55335baa-f021-449b-a85d-28e6c417a1df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(76,)"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_lr = round(accuracy_score(Y_pred_lr,y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using Logistic Regression is: \"+str(score_lr)+\" %\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LWou51U1iJZ9",
        "outputId": "f79cbea3-beae-4690-a6fc-0d275fc63b55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy score achieved using Logistic Regression is: 81.58 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Naive Bayes"
      ],
      "metadata": {
        "id": "XwZhT3cLijI1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "nb = GaussianNB()\n",
        "\n",
        "nb.fit(X_train,y_train)\n",
        "\n",
        "Y_pred_nb = nb.predict(X_test)\n",
        "Y_pred_nb.shape\n",
        "score_nb = round(accuracy_score(Y_pred_nb,y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using Naive Bayes is: \"+str(score_nb)+\" %\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nIuOpmRUiagb",
        "outputId": "a9cf0b49-ee4e-4cdf-cda9-976997abe5dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy score achieved using Naive Bayes is: 84.21 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "XxLuRTIUjT1D"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "rZ6trFPKjVfS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# max_accuracy = 0\n",
        "\n",
        "\n",
        "# for x in range(200):\n",
        "#     dt = DecisionTreeClassifier(random_state=x)\n",
        "#     dt.fit(X_train,y_train)\n",
        "#     Y_pred_dt = dt.predict(X_test)\n",
        "#     current_accuracy = round(accuracy_score(Y_pred_dt,y_test)*100,2)\n",
        "#     if(current_accuracy>max_accuracy):\n",
        "#         max_accuracy = current_accuracy\n",
        "#         best_x = x\n",
        "\n",
        "# #print(max_accuracy)\n",
        "# #print(best_x)\n",
        "\n",
        "\n",
        "# dt = DecisionTreeClassifier(random_state=best_x)\n",
        "# dt.fit(X_train,y_train)\n",
        "# Y_pred_dt = dt.predict(X_test)\n",
        "# score_dt = round(accuracy_score(Y_pred_dt,y_test)*100,2)\n",
        "\n",
        "# print(\"The accuracy score achieved using Decision Tree is: \"+str(score_dt)+\" %\")"
      ],
      "metadata": {
        "id": "XJ7KVmZqioXB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Random Forest classifier"
      ],
      "metadata": {
        "id": "T3oXxw8Wlsp1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "max_accuracy = 0\n",
        "\n",
        "\n",
        "for x in range(2000):\n",
        "    rf = RandomForestClassifier(random_state=x)\n",
        "    rf.fit(X_train,y_train)\n",
        "    Y_pred_rf = rf.predict(X_test)\n",
        "    current_accuracy = round(accuracy_score(Y_pred_rf,y_test)*100,2)\n",
        "    if(current_accuracy>max_accuracy):\n",
        "        max_accuracy = current_accuracy\n",
        "        best_x = x\n",
        "\n",
        "#print(max_accuracy)\n",
        "#print(best_x)\n",
        "\n",
        "rf = RandomForestClassifier(random_state=best_x)\n",
        "rf.fit(X_train,y_train)\n",
        "Y_pred_rf = rf.predict(X_test)\n",
        "score_rf = round(accuracy_score(Y_pred_rf,y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using Random forest classifier is: \"+str(score_rf)+\" %\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t_pnuoUki1iM",
        "outputId": "42a0bf2e-737e-4fd3-e157-7e764a094394"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy score achieved using Random forest classifier is: 85.53 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Decision tree\n"
      ],
      "metadata": {
        "id": "nEUh720HxKoi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. randomized search cv for dt:"
      ],
      "metadata": {
        "id": "0_ro036XxNJP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "parameters={\n",
        "    'criterion':('gini','entropy'),\n",
        "    'max_depth':(3,5,7,9,10),\n",
        "    'max_features':('auto','sqrt','log2'),\n",
        "    'min_samples_split': (2,4,6)\n",
        "}\n",
        "dt_grid = RandomizedSearchCV(DecisionTreeClassifier(), param_distributions = parameters, cv=10, verbose=True)\n",
        "dt_grid.fit(X_train,y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        },
        "id": "pPAFsMylid6Z",
        "outputId": "c70bc177-e6db-4928-e6cc-684cf88a0895"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n",
              "                   param_distributions={'criterion': ('gini', 'entropy'),\n",
              "                                        'max_depth': (3, 5, 7, 9, 10),\n",
              "                                        'max_features': ('auto', 'sqrt',\n",
              "                                                         'log2'),\n",
              "                                        'min_samples_split': (2, 4, 6)},\n",
              "                   verbose=True)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n",
              "                   param_distributions={&#x27;criterion&#x27;: (&#x27;gini&#x27;, &#x27;entropy&#x27;),\n",
              "                                        &#x27;max_depth&#x27;: (3, 5, 7, 9, 10),\n",
              "                                        &#x27;max_features&#x27;: (&#x27;auto&#x27;, &#x27;sqrt&#x27;,\n",
              "                                                         &#x27;log2&#x27;),\n",
              "                                        &#x27;min_samples_split&#x27;: (2, 4, 6)},\n",
              "                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n",
              "                   param_distributions={&#x27;criterion&#x27;: (&#x27;gini&#x27;, &#x27;entropy&#x27;),\n",
              "                                        &#x27;max_depth&#x27;: (3, 5, 7, 9, 10),\n",
              "                                        &#x27;max_features&#x27;: (&#x27;auto&#x27;, &#x27;sqrt&#x27;,\n",
              "                                                         &#x27;log2&#x27;),\n",
              "                                        &#x27;min_samples_split&#x27;: (2, 4, 6)},\n",
              "                   verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "dt_grid.best_estimator_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "WcNXrkLaxZfL",
        "outputId": "6d243e4f-5b11-4c5a-c361-b439b8282f4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', max_depth=3, max_features='sqrt',\n",
              "                       min_samples_split=6)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3, max_features=&#x27;sqrt&#x27;,\n",
              "                       min_samples_split=6)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3, max_features=&#x27;sqrt&#x27;,\n",
              "                       min_samples_split=6)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install graphviz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "66y870nryz7q",
        "outputId": "bf665306-fedc-41c2-b50c-5c8ad636accb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (0.20.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pyparsing\n",
        "!pip install pydotplus"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N2x6lpPry3en",
        "outputId": "461a92f5-9b1a-47d0-e086-14f4b4ca61eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pyparsing in /usr/local/lib/python3.10/dist-packages (3.1.2)\n",
            "Requirement already satisfied: pydotplus in /usr/local/lib/python3.10/dist-packages (2.0.2)\n",
            "Requirement already satisfied: pyparsing>=2.0.1 in /usr/local/lib/python3.10/dist-packages (from pydotplus) (3.1.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kf =KFold(n_splits=10, shuffle=True, random_state=42)\n",
        "\n",
        "cnt = 1\n",
        "for train_index, test_index in kf.split(X, y):\n",
        "    print(f'Fold:{cnt}, Train set: {len(train_index)}, Test set:{len(test_index)}')\n",
        "    cnt += 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dhJujjKOzszD",
        "outputId": "ec6b14d1-d648-40fb-ac69-5f631e622579"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fold:1, Train set: 272, Test set:31\n",
            "Fold:2, Train set: 272, Test set:31\n",
            "Fold:3, Train set: 272, Test set:31\n",
            "Fold:4, Train set: 273, Test set:30\n",
            "Fold:5, Train set: 273, Test set:30\n",
            "Fold:6, Train set: 273, Test set:30\n",
            "Fold:7, Train set: 273, Test set:30\n",
            "Fold:8, Train set: 273, Test set:30\n",
            "Fold:9, Train set: 273, Test set:30\n",
            "Fold:10, Train set: 273, Test set:30\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def rmse(score):\n",
        "    rmse = np.sqrt(-score)\n",
        "    print(f'rmse= {\"{:.2f}\".format(rmse)}')"
      ],
      "metadata": {
        "id": "1Y_Id4aTy-HF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score = cross_val_score(tree.DecisionTreeRegressor(random_state= 42), X, y, cv=kf, scoring=\"neg_mean_squared_error\")\n",
        "print(f'Scores for each fold: {score}')\n",
        "rmse(score.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sWGaUM-NzAnC",
        "outputId": "537a364b-3b33-462a-e112-b4b337e58d4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scores for each fold: [-0.35483871 -0.22580645 -0.35483871 -0.4        -0.26666667 -0.33333333\n",
            " -0.33333333 -0.26666667 -0.2        -0.2       ]\n",
            "rmse= 0.54\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_depth = [1,2,3,4,5,6,7,8,9,10]\n",
        "\n",
        "for val in max_depth:\n",
        "    score = cross_val_score(tree.DecisionTreeRegressor(max_depth= val, random_state= 42), X, y, cv= kf, scoring=\"neg_mean_squared_error\")\n",
        "    print(f'For max depth: {val}')\n",
        "    rmse(score.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pXTeIaIkzDXi",
        "outputId": "3108553a-1d0a-4e35-b3c3-8c3f5424961a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For max depth: 1\n",
            "rmse= 0.43\n",
            "For max depth: 2\n",
            "rmse= 0.41\n",
            "For max depth: 3\n",
            "rmse= 0.42\n",
            "For max depth: 4\n",
            "rmse= 0.43\n",
            "For max depth: 5\n",
            "rmse= 0.46\n",
            "For max depth: 6\n",
            "rmse= 0.49\n",
            "For max depth: 7\n",
            "rmse= 0.50\n",
            "For max depth: 8\n",
            "rmse= 0.53\n",
            "For max depth: 9\n",
            "rmse= 0.53\n",
            "For max depth: 10\n",
            "rmse= 0.54\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = cross_val_score(tree.DecisionTreeClassifier(random_state= 42), X, y, cv= kf, scoring=\"accuracy\")\n",
        "print(f'Scores for each fold are: {score}')\n",
        "print(f'Average score: {\"{:.2f}\".format(score.mean())}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9XzAiaASzL5G",
        "outputId": "7d85ec06-6b7b-4f0c-e077-cf76c494dbda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scores for each fold are: [0.64516129 0.77419355 0.64516129 0.6        0.73333333 0.66666667\n",
            " 0.66666667 0.73333333 0.83333333 0.8       ]\n",
            "Average score: 0.71\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def run_cross_validation_on_trees(X, y, tree_depths, cv=10, scoring='accuracy'):\n",
        "    cv_scores_list = []\n",
        "    cv_scores_std = []\n",
        "    cv_scores_mean = []\n",
        "    accuracy_scores = []\n",
        "    for depth in tree_depths:\n",
        "        tree_model = DecisionTreeClassifier(max_depth=depth)\n",
        "        cv_scores = cross_val_score(tree_model, X, y, cv=cv, scoring=scoring)\n",
        "        cv_scores_list.append(cv_scores)\n",
        "        cv_scores_mean.append(cv_scores.mean())\n",
        "        cv_scores_std.append(cv_scores.std())\n",
        "        accuracy_scores.append(tree_model.fit(X, y).score(X, y))\n",
        "    cv_scores_mean = np.array(cv_scores_mean)\n",
        "    cv_scores_std = np.array(cv_scores_std)\n",
        "    accuracy_scores = np.array(accuracy_scores)\n",
        "    return cv_scores_mean, cv_scores_std, accuracy_scores\n",
        "\n",
        "def plot_cross_validation_on_trees(depths, cv_scores_mean, cv_scores_std, accuracy_scores, title):\n",
        "    fig, ax = plt.subplots(1,1, figsize=(25,5))\n",
        "    ax.plot(depths, cv_scores_mean, '-o', label='mean cross-validation accuracy', alpha=0.9)\n",
        "    ax.fill_between(depths, cv_scores_mean-2*cv_scores_std, cv_scores_mean+2*cv_scores_std, alpha=0.2)\n",
        "    ylim = plt.ylim()\n",
        "    ax.plot(depths, accuracy_scores, '-*', label='train accuracy', alpha=0.9)\n",
        "    ax.set_title(title, fontsize=16)\n",
        "    ax.set_xlabel('Tree depth', fontsize=14)\n",
        "    ax.set_ylabel('Accuracy', fontsize=14)\n",
        "    ax.set_ylim(ylim)\n",
        "    ax.set_xticks(depths)\n",
        "    ax.legend()\n",
        "\n",
        "sm_tree_depths = range(1,20)\n",
        "sm_cv_scores_mean, sm_cv_scores_std, sm_accuracy_scores = run_cross_validation_on_trees(X_train, y_train, sm_tree_depths)\n",
        "\n",
        "plot_cross_validation_on_trees(sm_tree_depths, sm_cv_scores_mean, sm_cv_scores_std, sm_accuracy_scores,\n",
        "                               'Accuracy per decision tree depth on training data')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "SHdEv2G3zOGc",
        "outputId": "56a1395b-cfd2-4392-f932-53ac9f1b2099"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tree_model = DecisionTreeClassifier(max_depth=5,criterion='entropy')\n",
        "cv_scores = cross_val_score(tree_model, X, y, cv=10, scoring='accuracy')\n",
        "m=tree_model.fit(X, y)\n",
        "prediction=m.predict(X_test)\n",
        "cm= confusion_matrix(y_test,prediction)\n",
        "sns.heatmap(cm, annot=True,cmap='winter',linewidths=0.3, linecolor='black',annot_kws={\"size\": 20})\n",
        "print(classification_report(y_test, prediction))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "kwJNmLEnzUKz",
        "outputId": "2e7cd71f-d43f-4f05-9baf-b6d65d9b991f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.88      0.91        34\n",
            "           1       0.91      0.95      0.93        42\n",
            "\n",
            "    accuracy                           0.92        76\n",
            "   macro avg       0.92      0.92      0.92        76\n",
            "weighted avg       0.92      0.92      0.92        76\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "TP=cm[0][0]\n",
        "TN=cm[1][1]\n",
        "FN=cm[1][0]\n",
        "FP=cm[0][1]\n",
        "print('Testing Accuracy for Decision Tree:',(TP+TN)/(TP+TN+FN+FP))\n",
        "print('Testing Sensitivity for Decision Tree:',(TP/(TP+FN)))\n",
        "print('Testing Specificity for Decision Tree:',(TN/(TN+FP)))\n",
        "print('Testing Precision for Decision Tree:',(TP/(TP+FP)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FFg5rXHEzXhv",
        "outputId": "f131a851-b0a1-4f39-fc0d-0299412c509c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing Accuracy for Decision Tree: 0.9210526315789473\n",
            "Testing Sensitivity for Decision Tree: 0.9375\n",
            "Testing Specificity for Decision Tree: 0.9090909090909091\n",
            "Testing Precision for Decision Tree: 0.8823529411764706\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Random Forest Classifier show highest accuarcy thus used for prediction"
      ],
      "metadata": {
        "id": "LYNIkwexJhrb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Random Forest Classifier show highest accuarcy thus used for prediction**"
      ],
      "metadata": {
        "id": "w_-8Nli4JVTt"
      }
    }
  ]
}