Heart_Disease_Predictions.ipynb

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      "display_name": "Python 3",
      "language": "python",
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      "pygments_lexer": "ipython3",
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    "colab": {
      "name": "Heart Disease Predictions.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/subhomoy-roy-choudhury/python-projects/blob/master/Heart_Disease_Predictions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1434zCpO18Rz",
        "colab_type": "text"
      },
      "source": [
        "# Heart Disease Prediction\n",
        "\n",
        "In this machine learning project, I have collected the dataset from Kaggle (https://www.kaggle.com/ronitf/heart-disease-uci) and I will be using Machine Learning to predict whether any person is suffering from heart disease"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "57g10GjD18R6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import rcParams\n",
        "from matplotlib.cm import rainbow\n",
        "%matplotlib inline\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GCCvj5a918SQ",
        "colab_type": "text"
      },
      "source": [
        "Here we will be experimenting with 3 algorithms \n",
        "1. KNeighborsClassifier\n",
        "2. DecisionTreeClassifier\n",
        "3. RandomForestClassifier\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_mkHdRU18SS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sDGL4Zw02Hgb",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "3df9e862-c06b-45f7-87a3-12bdfbd5ad80"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded= files.upload()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-e1c512b5-8136-4b21-93a3-1b98e937cb7c\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-e1c512b5-8136-4b21-93a3-1b98e937cb7c\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving dataset.csv to dataset.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3aX3uC4918Sk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "64cf7ea4-6e2e-4889-e906-61cea663c8eb"
      },
      "source": [
        "df = pd.read_csv('dataset.csv')\n",
        "df"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
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              "      <th>thal</th>\n",
              "      <th>target</th>\n",
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              "      <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>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>140</td>\n",
              "      <td>241</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>123</td>\n",
              "      <td>1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>110</td>\n",
              "      <td>264</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>132</td>\n",
              "      <td>0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>68</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>144</td>\n",
              "      <td>193</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>141</td>\n",
              "      <td>0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>57</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>131</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>115</td>\n",
              "      <td>1</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>174</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>303 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0     63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
              "1     37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
              "2     41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
              "3     56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
              "4     57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
              "..   ...  ...  ..       ...   ...  ...  ...    ...      ...    ...  ..   ...     ...\n",
              "298   57    0   0       140   241    0  ...      1      0.2      1   0     3       0\n",
              "299   45    1   3       110   264    0  ...      0      1.2      1   0     3       0\n",
              "300   68    1   0       144   193    1  ...      0      3.4      1   2     3       0\n",
              "301   57    1   0       130   131    0  ...      1      1.2      1   1     3       0\n",
              "302   57    0   1       130   236    0  ...      0      0.0      1   1     2       0\n",
              "\n",
              "[303 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9zswKwas18S1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "d43fae64-e936-4866-ebde-146359b3c727"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DhipxJ0Q18TL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "5cc5b108-8a56-4224-9d56-457e6f3b72b6"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>count</th>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.366337</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.966997</td>\n",
              "      <td>131.623762</td>\n",
              "      <td>246.264026</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.528053</td>\n",
              "      <td>149.646865</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>2.313531</td>\n",
              "      <td>0.544554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.082101</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>1.032052</td>\n",
              "      <td>17.538143</td>\n",
              "      <td>51.830751</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>22.905161</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>0.612277</td>\n",
              "      <td>0.498835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>274.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age         sex          cp  ...          ca        thal      target\n",
              "count  303.000000  303.000000  303.000000  ...  303.000000  303.000000  303.000000\n",
              "mean    54.366337    0.683168    0.966997  ...    0.729373    2.313531    0.544554\n",
              "std      9.082101    0.466011    1.032052  ...    1.022606    0.612277    0.498835\n",
              "min     29.000000    0.000000    0.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     47.500000    0.000000    0.000000  ...    0.000000    2.000000    0.000000\n",
              "50%     55.000000    1.000000    1.000000  ...    0.000000    2.000000    1.000000\n",
              "75%     61.000000    1.000000    2.000000  ...    1.000000    3.000000    1.000000\n",
              "max     77.000000    1.000000    3.000000  ...    4.000000    3.000000    1.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a8f4S4XA18Tf",
        "colab_type": "text"
      },
      "source": [
        "## Feature Selection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2uB4xRos18Ti",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d7fe898c-2678-4f89-c3c2-3d529bfd8763"
      },
      "source": [
        "import seaborn as sns\n",
        "#get correlations of each features in dataset\n",
        "corrmat = df.corr()\n",
        "top_corr_features = corrmat.index\n",
        "plt.figure(figsize=(20,20))\n",
        "#plot heat map\n",
        "g=sns.heatmap(df[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "72Po-JED18Tu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "outputId": "197eaa96-e38b-4df1-de95-cec9de9ff3f8"
      },
      "source": [
        "df.hist()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fea51d82ac8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fea51d9bd68>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fea51d54fd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fea51d11278>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fea51cc64e0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fea51c7a748>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fea51cad9b0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fea51c60be0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fea51c60c50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fea51bd9128>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fea51b8a390>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fea51b3f5f8>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fea51af1860>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fea51b25ac8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fea51adbd30>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fea53be3cf8>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HkSzKaiF18UE",
        "colab_type": "text"
      },
      "source": [
        "It's always a good practice to work with a dataset where the target classes are of approximately equal size. Thus, let's check for the same."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BkAR2J_-18UJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "7cdb99a0-d952-4628-f40b-d7bbe29b3d1f"
      },
      "source": [
        "sns.set_style('whitegrid')\n",
        "sns.countplot(x='target',data=df,palette='RdBu_r')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fea51806c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RtWTOQZN18Uc",
        "colab_type": "text"
      },
      "source": [
        "### Data Processing\n",
        "\n",
        "After exploring the dataset, I observed that I need to convert some categorical variables into dummy variables and scale all the values before training the Machine Learning models.\n",
        "First, I'll use the `get_dummies` method to create dummy columns for categorical variables."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YVK0jgMxXNrJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "c9b10d3b-d996-4b9b-9523-554bc2811753"
      },
      "source": [
        "df"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>140</td>\n",
              "      <td>241</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>123</td>\n",
              "      <td>1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>110</td>\n",
              "      <td>264</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>132</td>\n",
              "      <td>0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>68</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>144</td>\n",
              "      <td>193</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>141</td>\n",
              "      <td>0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>57</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>131</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>115</td>\n",
              "      <td>1</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>174</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>303 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0     63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
              "1     37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
              "2     41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
              "3     56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
              "4     57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
              "..   ...  ...  ..       ...   ...  ...  ...    ...      ...    ...  ..   ...     ...\n",
              "298   57    0   0       140   241    0  ...      1      0.2      1   0     3       0\n",
              "299   45    1   3       110   264    0  ...      0      1.2      1   0     3       0\n",
              "300   68    1   0       144   193    1  ...      0      3.4      1   2     3       0\n",
              "301   57    1   0       130   131    0  ...      1      1.2      1   1     3       0\n",
              "302   57    0   1       130   236    0  ...      0      0.0      1   1     2       0\n",
              "\n",
              "[303 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9RCOPzu-18Ul",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "43e41ae8-78be-4345-e139-9f68b590974f"
      },
      "source": [
        "dataset = pd.get_dummies(df, columns = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal'])\n",
        "dataset"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>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>cp_2</th>\n",
              "      <th>cp_3</th>\n",
              "      <th>fbs_0</th>\n",
              "      <th>fbs_1</th>\n",
              "      <th>restecg_0</th>\n",
              "      <th>restecg_1</th>\n",
              "      <th>restecg_2</th>\n",
              "      <th>exang_0</th>\n",
              "      <th>exang_1</th>\n",
              "      <th>slope_0</th>\n",
              "      <th>slope_1</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>63</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>150</td>\n",
              "      <td>2.3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <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>37</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>187</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <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>41</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>172</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>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>56</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>178</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>163</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <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>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>57</td>\n",
              "      <td>140</td>\n",
              "      <td>241</td>\n",
              "      <td>123</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>45</td>\n",
              "      <td>110</td>\n",
              "      <td>264</td>\n",
              "      <td>132</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <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>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>68</td>\n",
              "      <td>144</td>\n",
              "      <td>193</td>\n",
              "      <td>141</td>\n",
              "      <td>3.4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>57</td>\n",
              "      <td>130</td>\n",
              "      <td>131</td>\n",
              "      <td>115</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>57</td>\n",
              "      <td>130</td>\n",
              "      <td>236</td>\n",
              "      <td>174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>303 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     age  trestbps  chol  thalach  ...  thal_0  thal_1  thal_2  thal_3\n",
              "0     63       145   233      150  ...       0       1       0       0\n",
              "1     37       130   250      187  ...       0       0       1       0\n",
              "2     41       130   204      172  ...       0       0       1       0\n",
              "3     56       120   236      178  ...       0       0       1       0\n",
              "4     57       120   354      163  ...       0       0       1       0\n",
              "..   ...       ...   ...      ...  ...     ...     ...     ...     ...\n",
              "298   57       140   241      123  ...       0       0       0       1\n",
              "299   45       110   264      132  ...       0       0       0       1\n",
              "300   68       144   193      141  ...       0       0       0       1\n",
              "301   57       130   131      115  ...       0       0       0       1\n",
              "302   57       130   236      174  ...       0       0       1       0\n",
              "\n",
              "[303 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oty83a8T18U0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "standardScaler = StandardScaler()\n",
        "columns_to_scale = ['age', 'trestbps', 'chol', 'thalach', 'oldpeak']\n",
        "dataset[columns_to_scale] = standardScaler.fit_transform(dataset[columns_to_scale])"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MY021Vun18VC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "f3e2046c-3c6d-4908-dd9a-deb522aa4519"
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>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>cp_2</th>\n",
              "      <th>cp_3</th>\n",
              "      <th>fbs_0</th>\n",
              "      <th>fbs_1</th>\n",
              "      <th>restecg_0</th>\n",
              "      <th>restecg_1</th>\n",
              "      <th>restecg_2</th>\n",
              "      <th>exang_0</th>\n",
              "      <th>exang_1</th>\n",
              "      <th>slope_0</th>\n",
              "      <th>slope_1</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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>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",
              "</div>"
            ],
            "text/plain": [
              "        age  trestbps      chol   thalach  ...  thal_0  thal_1  thal_2  thal_3\n",
              "0  0.952197  0.763956 -0.256334  0.015443  ...       0       1       0       0\n",
              "1 -1.915313 -0.092738  0.072199  1.633471  ...       0       0       1       0\n",
              "2 -1.474158 -0.092738 -0.816773  0.977514  ...       0       0       1       0\n",
              "3  0.180175 -0.663867 -0.198357  1.239897  ...       0       0       1       0\n",
              "4  0.290464 -0.663867  2.082050  0.583939  ...       0       0       1       0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kDC1p8Ds18VQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = dataset['target']\n",
        "X = dataset.drop(['target'], axis = 1)\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=100)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FVAop1Ex18Vj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "knn_scores = []\n",
        "for k in range(1,21):\n",
        "    knn_classifier = KNeighborsClassifier(n_neighbors = k)\n",
        "    score=cross_val_score(knn_classifier,X,y,cv=10)\n",
        "    knn_scores.append(score.mean())\n"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CxJjiH7u18Vr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "045eccee-3feb-4965-fe0e-e9cf2301b620"
      },
      "source": [
        "plt.plot([k for k in range(1, 21)], knn_scores, color = 'red')\n",
        "for i in range(1,21):\n",
        "    plt.text(i, knn_scores[i-1], (i, knn_scores[i-1]))\n",
        "plt.xticks([i for i in range(1, 21)])\n",
        "plt.xlabel('Number of Neighbors (K)')\n",
        "plt.ylabel('Scores')\n",
        "plt.title('K Neighbors Classifier scores for different K values')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'K Neighbors Classifier scores for different K values')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2CNHIbk718Vz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "knn_classifier = KNeighborsClassifier(n_neighbors = 12)\n",
        "score=cross_val_score(knn_classifier,X,y,cv=10)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iUEUMTru18WM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8b2e0ed7-6eab-4332-d9f8-4bf1a60680b8"
      },
      "source": [
        "score.mean()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8448387096774195"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3vrksEi0ecEb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "ef59e45d-244b-4cc1-9787-e9562760bfe1"
      },
      "source": [
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "knn_classifier.fit(X_train,y_train)\n",
        "predict_random=knn_classifier.predict(X_test)\n",
        "print(classification_report(y_test,predict_random))\n",
        "print(confusion_matrix(y_test,predict_random))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.82      0.87        33\n",
            "           1       0.81      0.93      0.87        28\n",
            "\n",
            "    accuracy                           0.87        61\n",
            "   macro avg       0.87      0.87      0.87        61\n",
            "weighted avg       0.88      0.87      0.87        61\n",
            "\n",
            "[[27  6]\n",
            " [ 2 26]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zo3DSs6A18Wd",
        "colab_type": "text"
      },
      "source": [
        "## Random Forest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8YPC08yS18We",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6yOPMePF18Ws",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "randomforest_classifier= RandomForestClassifier(n_estimators=10)\n",
        "\n",
        "score=cross_val_score(randomforest_classifier,X,y,cv=10)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tns_PF8B18W7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c06bb7e1-5c57-475b-b326-c9e66da11012"
      },
      "source": [
        "score.mean()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8349462365591398"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZBN2iltodYFc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "e3e39c1e-01fd-4f00-b92e-e90e4c2cd7a3"
      },
      "source": [
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "randomforest_classifier.fit(X_train,y_train)\n",
        "predict_random=randomforest_classifier.predict(X_test)\n",
        "print(classification_report(y_test,predict_random))\n",
        "print(confusion_matrix(y_test,predict_random))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.88      0.89        33\n",
            "           1       0.86      0.89      0.88        28\n",
            "\n",
            "    accuracy                           0.89        61\n",
            "   macro avg       0.88      0.89      0.88        61\n",
            "weighted avg       0.89      0.89      0.89        61\n",
            "\n",
            "[[29  4]\n",
            " [ 3 25]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tcw7nN4zZWWd",
        "colab_type": "text"
      },
      "source": [
        "# Decision Tree Classifier\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dU0vjjvva28R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=100)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CTfMEl-C18Xt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "da6de570-de50-4163-a8c3-75326ef4cf2e"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "decision=DecisionTreeClassifier()\n",
        "score1=cross_val_score(decision,X,y,cv=10)\n",
        "score1.mean()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7580645161290323"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k542MR7S18X6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "d8b3429c-b437-4019-efca-51f9e7dfb6e9"
      },
      "source": [
        "decision.fit(X_train,y_train)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "13ZLQUvlcFV6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predict_decision=decision.predict(X_test)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jAXUM6MycWZA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "82f228a2-51c9-473d-c9d3-ff11d6343933"
      },
      "source": [
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "print(classification_report(y_test,predict_decision))\n",
        "print(confusion_matrix(y_test,predict_decision))"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.70      0.75        33\n",
            "           1       0.70      0.82      0.75        28\n",
            "\n",
            "    accuracy                           0.75        61\n",
            "   macro avg       0.76      0.76      0.75        61\n",
            "weighted avg       0.76      0.75      0.75        61\n",
            "\n",
            "[[23 10]\n",
            " [ 5 23]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FVvyuPdrglF6",
        "colab_type": "text"
      },
      "source": [
        "# Analysis by dropping excess values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_gJ3ZeefBoD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "bb61e307-8a92-4460-9cad-f768b8872305"
      },
      "source": [
        "dataset_new = pd.get_dummies(df, columns = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal'],drop_first=True)\n",
        "dataset_new"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>thalach</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>target</th>\n",
              "      <th>sex_1</th>\n",
              "      <th>cp_1</th>\n",
              "      <th>cp_2</th>\n",
              "      <th>cp_3</th>\n",
              "      <th>fbs_1</th>\n",
              "      <th>restecg_1</th>\n",
              "      <th>restecg_2</th>\n",
              "      <th>exang_1</th>\n",
              "      <th>slope_1</th>\n",
              "      <th>slope_2</th>\n",
              "      <th>ca_1</th>\n",
              "      <th>ca_2</th>\n",
              "      <th>ca_3</th>\n",
              "      <th>ca_4</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>63</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>150</td>\n",
              "      <td>2.3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>187</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>172</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>178</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>163</td>\n",
              "      <td>0.6</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>57</td>\n",
              "      <td>140</td>\n",
              "      <td>241</td>\n",
              "      <td>123</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>45</td>\n",
              "      <td>110</td>\n",
              "      <td>264</td>\n",
              "      <td>132</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>68</td>\n",
              "      <td>144</td>\n",
              "      <td>193</td>\n",
              "      <td>141</td>\n",
              "      <td>3.4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>57</td>\n",
              "      <td>130</td>\n",
              "      <td>131</td>\n",
              "      <td>115</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>57</td>\n",
              "      <td>130</td>\n",
              "      <td>236</td>\n",
              "      <td>174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>303 rows × 23 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     age  trestbps  chol  thalach  oldpeak  ...  ca_3  ca_4  thal_1  thal_2  thal_3\n",
              "0     63       145   233      150      2.3  ...     0     0       1       0       0\n",
              "1     37       130   250      187      3.5  ...     0     0       0       1       0\n",
              "2     41       130   204      172      1.4  ...     0     0       0       1       0\n",
              "3     56       120   236      178      0.8  ...     0     0       0       1       0\n",
              "4     57       120   354      163      0.6  ...     0     0       0       1       0\n",
              "..   ...       ...   ...      ...      ...  ...   ...   ...     ...     ...     ...\n",
              "298   57       140   241      123      0.2  ...     0     0       0       0       1\n",
              "299   45       110   264      132      1.2  ...     0     0       0       0       1\n",
              "300   68       144   193      141      3.4  ...     0     0       0       0       1\n",
              "301   57       130   131      115      1.2  ...     0     0       0       0       1\n",
              "302   57       130   236      174      0.0  ...     0     0       0       1       0\n",
              "\n",
              "[303 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oNDjT_sLgtj8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "outputId": "017ccd21-13f4-4e04-9432-92a90077c88d"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "standardScaler = StandardScaler()\n",
        "columns_to_scale = ['age', 'trestbps', 'chol', 'thalach', 'oldpeak']\n",
        "dataset_new[columns_to_scale] = standardScaler.fit_transform(dataset[columns_to_scale])\n",
        "dataset_new.head(10)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>thalach</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>target</th>\n",
              "      <th>sex_1</th>\n",
              "      <th>cp_1</th>\n",
              "      <th>cp_2</th>\n",
              "      <th>cp_3</th>\n",
              "      <th>fbs_1</th>\n",
              "      <th>restecg_1</th>\n",
              "      <th>restecg_2</th>\n",
              "      <th>exang_1</th>\n",
              "      <th>slope_1</th>\n",
              "      <th>slope_2</th>\n",
              "      <th>ca_1</th>\n",
              "      <th>ca_2</th>\n",
              "      <th>ca_3</th>\n",
              "      <th>ca_4</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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",
              "      <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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <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>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>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",
              "    </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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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",
              "    </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>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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.290464</td>\n",
              "      <td>0.478391</td>\n",
              "      <td>-1.048678</td>\n",
              "      <td>-0.072018</td>\n",
              "      <td>-0.551783</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>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>6</th>\n",
              "      <td>0.180175</td>\n",
              "      <td>0.478391</td>\n",
              "      <td>0.922521</td>\n",
              "      <td>0.146634</td>\n",
              "      <td>0.224643</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>7</th>\n",
              "      <td>-1.143291</td>\n",
              "      <td>-0.663867</td>\n",
              "      <td>0.323431</td>\n",
              "      <td>1.021244</td>\n",
              "      <td>-0.896862</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>-0.260980</td>\n",
              "      <td>2.306004</td>\n",
              "      <td>-0.913400</td>\n",
              "      <td>0.540209</td>\n",
              "      <td>-0.465514</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.290464</td>\n",
              "      <td>1.049520</td>\n",
              "      <td>-1.512490</td>\n",
              "      <td>1.064975</td>\n",
              "      <td>0.483451</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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",
              "</div>"
            ],
            "text/plain": [
              "        age  trestbps      chol   thalach  ...  ca_4  thal_1  thal_2  thal_3\n",
              "0  0.952197  0.763956 -0.256334  0.015443  ...     0       1       0       0\n",
              "1 -1.915313 -0.092738  0.072199  1.633471  ...     0       0       1       0\n",
              "2 -1.474158 -0.092738 -0.816773  0.977514  ...     0       0       1       0\n",
              "3  0.180175 -0.663867 -0.198357  1.239897  ...     0       0       1       0\n",
              "4  0.290464 -0.663867  2.082050  0.583939  ...     0       0       1       0\n",
              "5  0.290464  0.478391 -1.048678 -0.072018  ...     0       1       0       0\n",
              "6  0.180175  0.478391  0.922521  0.146634  ...     0       0       1       0\n",
              "7 -1.143291 -0.663867  0.323431  1.021244  ...     0       0       0       1\n",
              "8 -0.260980  2.306004 -0.913400  0.540209  ...     0       0       0       1\n",
              "9  0.290464  1.049520 -1.512490  1.064975  ...     0       0       1       0\n",
              "\n",
              "[10 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MkiRBu1_g-SI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_new = dataset_new['target']\n",
        "X_new= dataset_new.drop(['target'], axis = 1)\n",
        "X_new_train,X_new_test,y_new_train,y_new_test = train_test_split(X_new,y_new,test_size=0.2,random_state=100)"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LMrDEyOGhOeo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "5ba25140-3c24-445e-8c1e-9e0577faad7e"
      },
      "source": [
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "random=RandomForestClassifier(n_estimators=10)\n",
        "random.fit(X_new_train,y_new_train)\n",
        "predict_new_random=random.predict(X_new_test)\n",
        "print(classification_report(y_new_test,predict_new_random))\n",
        "print(confusion_matrix(y_new_test,predict_new_random))"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.76      0.83        33\n",
            "           1       0.76      0.93      0.84        28\n",
            "\n",
            "    accuracy                           0.84        61\n",
            "   macro avg       0.85      0.84      0.84        61\n",
            "weighted avg       0.85      0.84      0.84        61\n",
            "\n",
            "[[25  8]\n",
            " [ 2 26]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "druA8BfnkFqY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "1b4045b3-d510-4bab-ab88-17b31cff7b7f"
      },
      "source": [
        "import seaborn as sns\n",
        "cf_matrix=confusion_matrix(y_new_test,predict_new_random)\n",
        "sns.heatmap(cf_matrix, annot=True, cmap='Blues')"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fea516124a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7t1lwNDqAkHj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b883bce7-55e5-4a1e-d2c7-6398e05a427e"
      },
      "source": [
        "import tensorflow as tf\n",
        "ann = tf.keras.models.Sequential()\n",
        "ann.add(tf.keras.layers.Dense(units=8, activation='sigmoid'))\n",
        "ann.add(tf.keras.layers.Dense(units=6, activation='sigmoid'))\n",
        "ann.add(tf.keras.layers.Dense(units=1))\n",
        "ann.compile(optimizer = 'adam', loss = 'mean_squared_error')\n",
        "ann.fit(X_new_train, y_new_train, batch_size = 20, epochs = 100)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "WARNING:tensorflow:Layer dense is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
            "\n",
            "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
            "\n",
            "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
            "\n",
            "13/13 [==============================] - 0s 2ms/step - loss: 0.2616\n",
            "Epoch 2/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.2488\n",
            "Epoch 3/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.2403\n",
            "Epoch 4/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.2358\n",
            "Epoch 5/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.2313\n",
            "Epoch 6/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.2278\n",
            "Epoch 7/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.2232\n",
            "Epoch 8/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.2194\n",
            "Epoch 9/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.2156\n",
            "Epoch 10/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.2114\n",
            "Epoch 11/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.2076\n",
            "Epoch 12/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.2039\n",
            "Epoch 13/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.2001\n",
            "Epoch 14/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1960\n",
            "Epoch 15/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1924\n",
            "Epoch 16/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1889\n",
            "Epoch 17/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1855\n",
            "Epoch 18/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1816\n",
            "Epoch 19/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1775\n",
            "Epoch 20/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1742\n",
            "Epoch 21/100\n",
            "13/13 [==============================] - 0s 2ms/step - loss: 0.1715\n",
            "Epoch 22/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1676\n",
            "Epoch 23/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1642\n",
            "Epoch 24/100\n",
            "13/13 [==============================] - 0s 2ms/step - loss: 0.1613\n",
            "Epoch 25/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1594\n",
            "Epoch 26/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1566\n",
            "Epoch 27/100\n",
            "13/13 [==============================] - 0s 2ms/step - loss: 0.1541\n",
            "Epoch 28/100\n",
            "13/13 [==============================] - 0s 2ms/step - loss: 0.1510\n",
            "Epoch 29/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1485\n",
            "Epoch 30/100\n",
            "13/13 [==============================] - 0s 2ms/step - loss: 0.1469\n",
            "Epoch 31/100\n",
            "13/13 [==============================] - 0s 3ms/step - loss: 0.1445\n",
            "Epoch 32/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1422\n",
            "Epoch 33/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1403\n",
            "Epoch 34/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1385\n",
            "Epoch 35/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1366\n",
            "Epoch 36/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1358\n",
            "Epoch 37/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1345\n",
            "Epoch 38/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1336\n",
            "Epoch 39/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1321\n",
            "Epoch 40/100\n",
            "13/13 [==============================] - 0s 2ms/step - loss: 0.1307\n",
            "Epoch 41/100\n",
            "13/13 [==============================] - 0s 2ms/step - loss: 0.1297\n",
            "Epoch 42/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1286\n",
            "Epoch 43/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1279\n",
            "Epoch 44/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1269\n",
            "Epoch 45/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1267\n",
            "Epoch 46/100\n",
            "13/13 [==============================] - 0s 2ms/step - loss: 0.1256\n",
            "Epoch 47/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1249\n",
            "Epoch 48/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1246\n",
            "Epoch 49/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1243\n",
            "Epoch 50/100\n",
            "13/13 [==============================] - 0s 2ms/step - loss: 0.1231\n",
            "Epoch 51/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1228\n",
            "Epoch 52/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1223\n",
            "Epoch 53/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1218\n",
            "Epoch 54/100\n",
            "13/13 [==============================] - 0s 2ms/step - loss: 0.1212\n",
            "Epoch 55/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1205\n",
            "Epoch 56/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1202\n",
            "Epoch 57/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1195\n",
            "Epoch 58/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1197\n",
            "Epoch 59/100\n",
            "13/13 [==============================] - 0s 2ms/step - loss: 0.1195\n",
            "Epoch 60/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1183\n",
            "Epoch 61/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1179\n",
            "Epoch 62/100\n",
            "13/13 [==============================] - 0s 2ms/step - loss: 0.1175\n",
            "Epoch 63/100\n",
            "13/13 [==============================] - 0s 2ms/step - loss: 0.1172\n",
            "Epoch 64/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1167\n",
            "Epoch 65/100\n",
            "13/13 [==============================] - 0s 2ms/step - loss: 0.1166\n",
            "Epoch 66/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1164\n",
            "Epoch 67/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1164\n",
            "Epoch 68/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1156\n",
            "Epoch 69/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1156\n",
            "Epoch 70/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1147\n",
            "Epoch 71/100\n",
            "13/13 [==============================] - 0s 2ms/step - loss: 0.1146\n",
            "Epoch 72/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1144\n",
            "Epoch 73/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1138\n",
            "Epoch 74/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1145\n",
            "Epoch 75/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1138\n",
            "Epoch 76/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1137\n",
            "Epoch 77/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1139\n",
            "Epoch 78/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1127\n",
            "Epoch 79/100\n",
            "13/13 [==============================] - 0s 2ms/step - loss: 0.1126\n",
            "Epoch 80/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1125\n",
            "Epoch 81/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1121\n",
            "Epoch 82/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1119\n",
            "Epoch 83/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1114\n",
            "Epoch 84/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1112\n",
            "Epoch 85/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1108\n",
            "Epoch 86/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1108\n",
            "Epoch 87/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1112\n",
            "Epoch 88/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1109\n",
            "Epoch 89/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1103\n",
            "Epoch 90/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1099\n",
            "Epoch 91/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1099\n",
            "Epoch 92/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1096\n",
            "Epoch 93/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1096\n",
            "Epoch 94/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1100\n",
            "Epoch 95/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1094\n",
            "Epoch 96/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1091\n",
            "Epoch 97/100\n",
            "13/13 [==============================] - 0s 2ms/step - loss: 0.1088\n",
            "Epoch 98/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1088\n",
            "Epoch 99/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1086\n",
            "Epoch 100/100\n",
            "13/13 [==============================] - 0s 1ms/step - loss: 0.1083\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fea0cdab748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KDsGJcVoB5hf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred=ann.predict(X_new_test)\n",
        "y_pred=(y_new_test>0.5)\n"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IWXOhiYzB8hY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "e6e79855-9738-413c-a808-7416989bb44e"
      },
      "source": [
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "print(confusion_matrix(y_new_test,y_pred))\n",
        "print(classification_report(y_new_test,y_pred))"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[33  0]\n",
            " [ 0 28]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        33\n",
            "           1       1.00      1.00      1.00        28\n",
            "\n",
            "    accuracy                           1.00        61\n",
            "   macro avg       1.00      1.00      1.00        61\n",
            "weighted avg       1.00      1.00      1.00        61\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8XovryBtCXO0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "c0f166e6-42f0-492e-b3de-6e6d4b88930b"
      },
      "source": [
        "import seaborn as sns\n",
        "cf_matrix=confusion_matrix(y_new_test,y_pred)\n",
        "sns.heatmap(cf_matrix, annot=True, cmap='Blues')"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fea09f41ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}