Approach 1 - Manomay Jamble.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "HeartMLSC.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Umbqpf-k7nl6"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j4A3bEs-8YFw"
      },
      "source": [
        "data = pd.read_csv('/content/heart.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "xV6xljP28c4t",
        "outputId": "84bdebde-7d14-497f-cd4b-6ae08044fc5c"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": null,
      "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>ChestPainType</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>RestingECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>ExerciseAngina</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>ST_Slope</th>\n",
              "      <th>HeartDisease</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40</td>\n",
              "      <td>M</td>\n",
              "      <td>ATA</td>\n",
              "      <td>140</td>\n",
              "      <td>289</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>172</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>F</td>\n",
              "      <td>NAP</td>\n",
              "      <td>160</td>\n",
              "      <td>180</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>156</td>\n",
              "      <td>N</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>M</td>\n",
              "      <td>ATA</td>\n",
              "      <td>130</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>ST</td>\n",
              "      <td>98</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48</td>\n",
              "      <td>F</td>\n",
              "      <td>ASY</td>\n",
              "      <td>138</td>\n",
              "      <td>214</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>108</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54</td>\n",
              "      <td>M</td>\n",
              "      <td>NAP</td>\n",
              "      <td>150</td>\n",
              "      <td>195</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>122</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age Sex ChestPainType  ...  Oldpeak  ST_Slope  HeartDisease\n",
              "0   40   M           ATA  ...      0.0        Up             0\n",
              "1   49   F           NAP  ...      1.0      Flat             1\n",
              "2   37   M           ATA  ...      0.0        Up             0\n",
              "3   48   F           ASY  ...      1.5      Flat             1\n",
              "4   54   M           NAP  ...      0.0        Up             0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gz3EGfIsX0QG",
        "outputId": "46ffc547-db74-4b3e-d6ce-906e24a3025d"
      },
      "source": [
        "data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age               0\n",
              "Sex               0\n",
              "ChestPainType     0\n",
              "RestingBP         0\n",
              "Cholesterol       0\n",
              "FastingBS         0\n",
              "RestingECG        0\n",
              "MaxHR             0\n",
              "ExerciseAngina    0\n",
              "Oldpeak           0\n",
              "ST_Slope          0\n",
              "HeartDisease      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sQwLoysCX6QH",
        "outputId": "0d63ee38-63df-4212-edae-5889134a2c11"
      },
      "source": [
        "data.ChestPainType.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['ATA', 'NAP', 'ASY', 'TA'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BpbvkHFNZkPg",
        "outputId": "ed3c6d67-09df-4cd9-f433-eb115e498fba"
      },
      "source": [
        "data.RestingECG.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Normal', 'ST', 'LVH'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mYesQcvpZoos",
        "outputId": "af361add-3b31-4ed7-eab9-7b6f568b9e80"
      },
      "source": [
        "data.ST_Slope.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Up', 'Flat', 'Down'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_RznS63BZtt4",
        "outputId": "19705273-1084-461f-a608-f25f7fe95744"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 918 entries, 0 to 917\n",
            "Data columns (total 12 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   Age             918 non-null    int64  \n",
            " 1   Sex             918 non-null    object \n",
            " 2   ChestPainType   918 non-null    object \n",
            " 3   RestingBP       918 non-null    int64  \n",
            " 4   Cholesterol     918 non-null    int64  \n",
            " 5   FastingBS       918 non-null    int64  \n",
            " 6   RestingECG      918 non-null    object \n",
            " 7   MaxHR           918 non-null    int64  \n",
            " 8   ExerciseAngina  918 non-null    object \n",
            " 9   Oldpeak         918 non-null    float64\n",
            " 10  ST_Slope        918 non-null    object \n",
            " 11  HeartDisease    918 non-null    int64  \n",
            "dtypes: float64(1), int64(6), object(5)\n",
            "memory usage: 86.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "0W7MXGYBaFpm",
        "outputId": "01605b64-cb91-4648-fe33-52d981f93398"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": null,
      "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>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>HeartDisease</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>53.510893</td>\n",
              "      <td>132.396514</td>\n",
              "      <td>198.799564</td>\n",
              "      <td>0.233115</td>\n",
              "      <td>136.809368</td>\n",
              "      <td>0.887364</td>\n",
              "      <td>0.553377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.432617</td>\n",
              "      <td>18.514154</td>\n",
              "      <td>109.384145</td>\n",
              "      <td>0.423046</td>\n",
              "      <td>25.460334</td>\n",
              "      <td>1.066570</td>\n",
              "      <td>0.497414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>-2.600000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>173.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>54.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>223.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>138.000000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>267.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>603.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Age   RestingBP  ...     Oldpeak  HeartDisease\n",
              "count  918.000000  918.000000  ...  918.000000    918.000000\n",
              "mean    53.510893  132.396514  ...    0.887364      0.553377\n",
              "std      9.432617   18.514154  ...    1.066570      0.497414\n",
              "min     28.000000    0.000000  ...   -2.600000      0.000000\n",
              "25%     47.000000  120.000000  ...    0.000000      0.000000\n",
              "50%     54.000000  130.000000  ...    0.600000      1.000000\n",
              "75%     60.000000  140.000000  ...    1.500000      1.000000\n",
              "max     77.000000  200.000000  ...    6.200000      1.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IDJQVqfrf-_H"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "6Uiypt-mgB0z",
        "outputId": "669d8615-e5db-43a4-90cc-e6714031f82c"
      },
      "source": [
        "plt.scatter(data[['Cholesterol']], data[['Age']])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f7fa553be90>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "Hz2CrCaym1Cf",
        "outputId": "9fb01383-5181-4a6d-8e1c-387b6ebb84a5"
      },
      "source": [
        "plt.scatter(data['RestingBP'], data['Age'])\n",
        "# data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f7f9f3a29d0>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "9_s9dxXHCd3g",
        "outputId": "1d4e4565-ec60-4442-bb6d-bce5b8c9bc8c"
      },
      "source": [
        "plt.scatter(data['Cholesterol'], data[['Age']])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f7f9ee82910>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0I4nz6xegSnG",
        "outputId": "9e03ff46-b4a3-4fc4-b85f-f16298db37ce"
      },
      "source": [
        "# data.drop(data.index[data['Cholesterol'] == 0], inplace = True)\n",
        "data.drop(data.index[data['RestingBP'] == 0], inplace = True)\n",
        "data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(917, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JjpduF0YnxTP",
        "outputId": "cc94aafd-368a-496d-9c4a-1c4521ad1152"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 917 entries, 0 to 917\n",
            "Data columns (total 12 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   Age             917 non-null    int64  \n",
            " 1   Sex             917 non-null    object \n",
            " 2   ChestPainType   917 non-null    object \n",
            " 3   RestingBP       917 non-null    int64  \n",
            " 4   Cholesterol     917 non-null    int64  \n",
            " 5   FastingBS       917 non-null    int64  \n",
            " 6   RestingECG      917 non-null    object \n",
            " 7   MaxHR           917 non-null    int64  \n",
            " 8   ExerciseAngina  917 non-null    object \n",
            " 9   Oldpeak         917 non-null    float64\n",
            " 10  ST_Slope        917 non-null    object \n",
            " 11  HeartDisease    917 non-null    int64  \n",
            "dtypes: float64(1), int64(6), object(5)\n",
            "memory usage: 93.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lcgp-S1mkx_i"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I-JzgMtulKNL"
      },
      "source": [
        "le = LabelEncoder()\n",
        "data['ChestPainType'] = le.fit_transform(data['ChestPainType'])\n",
        "data['RestingECG'] = le.fit_transform(data['RestingECG'])\n",
        "data['ST_Slope'] = le.fit_transform(data['ST_Slope'])\n",
        "data['Sex'] = le.fit_transform(data['Sex'])\n",
        "data['ExerciseAngina'] = le.fit_transform(data['ExerciseAngina'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "V9sr4AGglnGe",
        "outputId": "4ccd746c-6c01-4dd1-c86d-1a15675073d2"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": null,
      "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>ChestPainType</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>RestingECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>ExerciseAngina</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>ST_Slope</th>\n",
              "      <th>HeartDisease</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>140</td>\n",
              "      <td>289</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>160</td>\n",
              "      <td>180</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>156</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>98</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>138</td>\n",
              "      <td>214</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>108</td>\n",
              "      <td>1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>150</td>\n",
              "      <td>195</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>122</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  Sex  ChestPainType  ...  Oldpeak  ST_Slope  HeartDisease\n",
              "0   40    1              1  ...      0.0         2             0\n",
              "1   49    0              2  ...      1.0         1             1\n",
              "2   37    1              1  ...      0.0         2             0\n",
              "3   48    0              0  ...      1.5         1             1\n",
              "4   54    1              2  ...      0.0         2             0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder"
      ],
      "metadata": {
        "id": "osykifB1xvzL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "enc = OneHotEncoder(handle_unknown='ignore')\n",
        "enc_data = pd.DataFrame(enc.fit_transform(data[['ChestPainType']]).toarray())\n",
        "data = data.join(enc_data)"
      ],
      "metadata": {
        "id": "qWL_Yg1Tx0Tj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "Q7qZJQt3yM2Q",
        "outputId": "ba72b8ed-6f59-4c2e-a7e1-bd2a5b8afcf1"
      },
      "execution_count": null,
      "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>ChestPainType</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>RestingECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>ExerciseAngina</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>ST_Slope</th>\n",
              "      <th>HeartDisease</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>140</td>\n",
              "      <td>289</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>160</td>\n",
              "      <td>180</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>156</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>98</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>138</td>\n",
              "      <td>214</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>108</td>\n",
              "      <td>1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>150</td>\n",
              "      <td>195</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>122</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  Sex  ChestPainType  RestingBP  ...    0    1    2    3\n",
              "0   40    1              1        140  ...  0.0  1.0  0.0  0.0\n",
              "1   49    0              2        160  ...  0.0  0.0  1.0  0.0\n",
              "2   37    1              1        130  ...  0.0  1.0  0.0  0.0\n",
              "3   48    0              0        138  ...  1.0  0.0  0.0  0.0\n",
              "4   54    1              2        150  ...  0.0  0.0  1.0  0.0\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0BJUs1wxAIM1",
        "outputId": "c2df0d0c-79a6-4b75-dd1d-15b9195bc474"
      },
      "source": [
        "data.Cholesterol.mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "236.08803215942953"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hhpjunkdjTBz"
      },
      "source": [
        "x = data.drop(columns=data[['HeartDisease']])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ip8d-4DsjIM1"
      },
      "source": [
        "y = data['HeartDisease']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zo3Eqvuajdwy"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xH-6WlwCjxYo"
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IHVcBZ_mksn2",
        "outputId": "0da6a14b-91c7-41bc-b526-24590f6ab832"
      },
      "source": [
        "model = DecisionTreeClassifier(criterion='entropy', random_state=1)\n",
        "model.fit(x_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', random_state=1)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nkpiRYyqos_8",
        "outputId": "fea5abc5-e14c-4a81-bcbf-d8eb1f812a93"
      },
      "source": [
        "model.predict(x_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0,\n",
              "       1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1,\n",
              "       1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1,\n",
              "       1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1,\n",
              "       1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1,\n",
              "       0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
              "       0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0,\n",
              "       1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
              "       0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0,\n",
              "       1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1,\n",
              "       1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0,\n",
              "       1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0,\n",
              "       0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8My2fiofpXAB",
        "outputId": "988bffce-190e-4506-a497-9792cddf24d2"
      },
      "source": [
        "model.score(x_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.822463768115942"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3hySfkk_qDLZ"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1bo91XTkqlep"
      },
      "source": [
        "modelrf = RandomForestClassifier(random_state=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7BpOpxa7qvZk",
        "outputId": "715c691a-13eb-4df5-cdf2-ec8538d47c59"
      },
      "source": [
        "modelrf.fit(x_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=1)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1bLHon_kq0Gt",
        "outputId": "d175f388-7846-438d-afa5-921b7d412a37"
      },
      "source": [
        "modelrf.score(x_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8768115942028986"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AtzKv8_MBVBn"
      },
      "source": [
        "Score before replacing cholesterol 0 values with mean = dtree: around 82, rf: 85\n",
        "\n",
        "After = dtree: 84, rf: 86.9\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "NQ4xBYXT2B8G",
        "outputId": "d15bf7ba-cd9e-45d3-f19f-0595128086f2"
      },
      "execution_count": null,
      "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>ChestPainType</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>RestingECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>ExerciseAngina</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>ST_Slope</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.055670</td>\n",
              "      <td>-0.077439</td>\n",
              "      <td>0.263084</td>\n",
              "      <td>0.006513</td>\n",
              "      <td>0.198170</td>\n",
              "      <td>-0.007487</td>\n",
              "      <td>-0.382280</td>\n",
              "      <td>0.216017</td>\n",
              "      <td>0.258563</td>\n",
              "      <td>-0.268217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>0.055670</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.127408</td>\n",
              "      <td>0.009427</td>\n",
              "      <td>-0.150955</td>\n",
              "      <td>0.120424</td>\n",
              "      <td>0.071553</td>\n",
              "      <td>-0.189668</td>\n",
              "      <td>0.191226</td>\n",
              "      <td>0.105444</td>\n",
              "      <td>-0.150408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ChestPainType</th>\n",
              "      <td>-0.077439</td>\n",
              "      <td>-0.127408</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.011020</td>\n",
              "      <td>-0.045796</td>\n",
              "      <td>-0.072461</td>\n",
              "      <td>-0.072625</td>\n",
              "      <td>0.288465</td>\n",
              "      <td>-0.354027</td>\n",
              "      <td>-0.178367</td>\n",
              "      <td>0.214582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RestingBP</th>\n",
              "      <td>0.263084</td>\n",
              "      <td>0.009427</td>\n",
              "      <td>-0.011020</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.096541</td>\n",
              "      <td>0.067823</td>\n",
              "      <td>0.023455</td>\n",
              "      <td>-0.109693</td>\n",
              "      <td>0.153064</td>\n",
              "      <td>0.174252</td>\n",
              "      <td>-0.082155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cholesterol</th>\n",
              "      <td>0.006513</td>\n",
              "      <td>-0.150955</td>\n",
              "      <td>-0.045796</td>\n",
              "      <td>0.096541</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.062391</td>\n",
              "      <td>-0.120645</td>\n",
              "      <td>0.073198</td>\n",
              "      <td>0.046727</td>\n",
              "      <td>0.059568</td>\n",
              "      <td>-0.013531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FastingBS</th>\n",
              "      <td>0.198170</td>\n",
              "      <td>0.120424</td>\n",
              "      <td>-0.072461</td>\n",
              "      <td>0.067823</td>\n",
              "      <td>-0.062391</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.087074</td>\n",
              "      <td>-0.131067</td>\n",
              "      <td>0.059988</td>\n",
              "      <td>0.053062</td>\n",
              "      <td>-0.176196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RestingECG</th>\n",
              "      <td>-0.007487</td>\n",
              "      <td>0.071553</td>\n",
              "      <td>-0.072625</td>\n",
              "      <td>0.023455</td>\n",
              "      <td>-0.120645</td>\n",
              "      <td>0.087074</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.179339</td>\n",
              "      <td>0.077545</td>\n",
              "      <td>-0.020452</td>\n",
              "      <td>-0.006768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MaxHR</th>\n",
              "      <td>-0.382280</td>\n",
              "      <td>-0.189668</td>\n",
              "      <td>0.288465</td>\n",
              "      <td>-0.109693</td>\n",
              "      <td>0.073198</td>\n",
              "      <td>-0.131067</td>\n",
              "      <td>-0.179339</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.370023</td>\n",
              "      <td>-0.161213</td>\n",
              "      <td>0.344047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExerciseAngina</th>\n",
              "      <td>0.216017</td>\n",
              "      <td>0.191226</td>\n",
              "      <td>-0.354027</td>\n",
              "      <td>0.153064</td>\n",
              "      <td>0.046727</td>\n",
              "      <td>0.059988</td>\n",
              "      <td>0.077545</td>\n",
              "      <td>-0.370023</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.409494</td>\n",
              "      <td>-0.429483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oldpeak</th>\n",
              "      <td>0.258563</td>\n",
              "      <td>0.105444</td>\n",
              "      <td>-0.178367</td>\n",
              "      <td>0.174252</td>\n",
              "      <td>0.059568</td>\n",
              "      <td>0.053062</td>\n",
              "      <td>-0.020452</td>\n",
              "      <td>-0.161213</td>\n",
              "      <td>0.409494</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.501735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ST_Slope</th>\n",
              "      <td>-0.268217</td>\n",
              "      <td>-0.150408</td>\n",
              "      <td>0.214582</td>\n",
              "      <td>-0.082155</td>\n",
              "      <td>-0.013531</td>\n",
              "      <td>-0.176196</td>\n",
              "      <td>-0.006768</td>\n",
              "      <td>0.344047</td>\n",
              "      <td>-0.429483</td>\n",
              "      <td>-0.501735</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Age       Sex  ...   Oldpeak  ST_Slope\n",
              "Age             1.000000  0.055670  ...  0.258563 -0.268217\n",
              "Sex             0.055670  1.000000  ...  0.105444 -0.150408\n",
              "ChestPainType  -0.077439 -0.127408  ... -0.178367  0.214582\n",
              "RestingBP       0.263084  0.009427  ...  0.174252 -0.082155\n",
              "Cholesterol     0.006513 -0.150955  ...  0.059568 -0.013531\n",
              "FastingBS       0.198170  0.120424  ...  0.053062 -0.176196\n",
              "RestingECG     -0.007487  0.071553  ... -0.020452 -0.006768\n",
              "MaxHR          -0.382280 -0.189668  ... -0.161213  0.344047\n",
              "ExerciseAngina  0.216017  0.191226  ...  0.409494 -0.429483\n",
              "Oldpeak         0.258563  0.105444  ...  1.000000 -0.501735\n",
              "ST_Slope       -0.268217 -0.150408  ... -0.501735  1.000000\n",
              "\n",
              "[11 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x['Age'], x['Cholesterol'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "ZpOL2hbx03yT",
        "outputId": "3e5dc09e-5a04-47dc-8ee0-47628380e47a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f7f9ee03290>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_data = pd.read_csv('/content/heart.csv')\n",
        "df_pred_chol = new_data[new_data.Cholesterol == 0]\n",
        "df_pred_chol.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "DkjBBE8N2e8S",
        "outputId": "afb9262b-1710-4a2b-bcd1-f698d34073aa"
      },
      "execution_count": null,
      "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>ChestPainType</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>RestingECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>ExerciseAngina</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>ST_Slope</th>\n",
              "      <th>HeartDisease</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>293</th>\n",
              "      <td>65</td>\n",
              "      <td>M</td>\n",
              "      <td>ASY</td>\n",
              "      <td>115</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>93</td>\n",
              "      <td>Y</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>294</th>\n",
              "      <td>32</td>\n",
              "      <td>M</td>\n",
              "      <td>TA</td>\n",
              "      <td>95</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Normal</td>\n",
              "      <td>127</td>\n",
              "      <td>N</td>\n",
              "      <td>0.7</td>\n",
              "      <td>Up</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>295</th>\n",
              "      <td>61</td>\n",
              "      <td>M</td>\n",
              "      <td>ASY</td>\n",
              "      <td>105</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Normal</td>\n",
              "      <td>110</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Up</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>50</td>\n",
              "      <td>M</td>\n",
              "      <td>ASY</td>\n",
              "      <td>145</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Normal</td>\n",
              "      <td>139</td>\n",
              "      <td>Y</td>\n",
              "      <td>0.7</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>297</th>\n",
              "      <td>57</td>\n",
              "      <td>M</td>\n",
              "      <td>ASY</td>\n",
              "      <td>110</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>ST</td>\n",
              "      <td>131</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.4</td>\n",
              "      <td>Up</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Age Sex ChestPainType  ...  Oldpeak  ST_Slope  HeartDisease\n",
              "293   65   M           ASY  ...      0.0      Flat             1\n",
              "294   32   M            TA  ...      0.7        Up             1\n",
              "295   61   M           ASY  ...      1.5        Up             1\n",
              "296   50   M           ASY  ...      0.7      Flat             1\n",
              "297   57   M           ASY  ...      1.4        Up             1\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_data['ChestPainType'] = le.fit_transform(new_data['ChestPainType'])\n",
        "new_data['RestingECG'] = le.fit_transform(new_data['RestingECG'])\n",
        "new_data['ST_Slope'] = le.fit_transform(new_data['ST_Slope'])\n",
        "new_data['Sex'] = le.fit_transform(new_data['Sex'])\n",
        "new_data['ExerciseAngina'] = le.fit_transform(new_data['ExerciseAngina'])"
      ],
      "metadata": {
        "id": "QSc4hJn-SsSO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dum_new_data = pd.get_dummies(new_data, columns=[\"ChestPainType\", \"RestingECG\", \"ST_Slope\", \"Sex\", \"ExerciseAngina\"], prefix=[\"CPT_Type\", \"RECG_Type\", \"ST_Slope\", \"Sex\", \"ExerciseAngina\"] )"
      ],
      "metadata": {
        "id": "yCI_djQPyh3O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dum_new_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "qP2UTaNLyrKG",
        "outputId": "1a7e1dad-7169-4461-d007-f3ad33ce766d"
      },
      "execution_count": null,
      "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>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>HeartDisease</th>\n",
              "      <th>CPT_Type_0</th>\n",
              "      <th>CPT_Type_1</th>\n",
              "      <th>CPT_Type_2</th>\n",
              "      <th>CPT_Type_3</th>\n",
              "      <th>RECG_Type_0</th>\n",
              "      <th>RECG_Type_1</th>\n",
              "      <th>RECG_Type_2</th>\n",
              "      <th>ST_Slope_0</th>\n",
              "      <th>ST_Slope_1</th>\n",
              "      <th>ST_Slope_2</th>\n",
              "      <th>Sex_0</th>\n",
              "      <th>Sex_1</th>\n",
              "      <th>ExerciseAngina_0</th>\n",
              "      <th>ExerciseAngina_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40</td>\n",
              "      <td>140</td>\n",
              "      <td>289</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>160</td>\n",
              "      <td>180</td>\n",
              "      <td>0</td>\n",
              "      <td>156</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>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>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>130</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>98</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48</td>\n",
              "      <td>138</td>\n",
              "      <td>214</td>\n",
              "      <td>0</td>\n",
              "      <td>108</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54</td>\n",
              "      <td>150</td>\n",
              "      <td>195</td>\n",
              "      <td>0</td>\n",
              "      <td>122</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  RestingBP  Cholesterol  ...  Sex_1  ExerciseAngina_0  ExerciseAngina_1\n",
              "0   40        140          289  ...      1                 1                 0\n",
              "1   49        160          180  ...      0                 1                 0\n",
              "2   37        130          283  ...      1                 1                 0\n",
              "3   48        138          214  ...      0                 0                 1\n",
              "4   54        150          195  ...      1                 1                 0\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_pred_chol = new_data[new_data.Cholesterol != 0]\n",
        "df_pred_chol.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "LrHQUORT2ABt",
        "outputId": "1133e98b-7b56-4b4b-d92a-921b1f2e162b"
      },
      "execution_count": null,
      "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>ChestPainType</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>RestingECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>ExerciseAngina</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>ST_Slope</th>\n",
              "      <th>HeartDisease</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>140</td>\n",
              "      <td>289</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>160</td>\n",
              "      <td>180</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>156</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>98</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>138</td>\n",
              "      <td>214</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>108</td>\n",
              "      <td>1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>150</td>\n",
              "      <td>195</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>122</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  Sex  ChestPainType  ...  Oldpeak  ST_Slope  HeartDisease\n",
              "0   40    1              1  ...      0.0         2             0\n",
              "1   49    0              2  ...      1.0         1             1\n",
              "2   37    1              1  ...      0.0         2             0\n",
              "3   48    0              0  ...      1.5         1             1\n",
              "4   54    1              2  ...      0.0         2             0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_chol = df_pred_chol[['Age']]\n",
        "test_chol.head()"
      ],
      "metadata": {
        "id": "T5_djvmO3EwU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "72722642-ff55-40e9-9e9a-226d06999dce"
      },
      "execution_count": null,
      "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age\n",
              "0   40\n",
              "1   49\n",
              "2   37\n",
              "3   48\n",
              "4   54"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_chol = df_pred_chol[['Age', 'Cholesterol']]"
      ],
      "metadata": {
        "id": "Nl9wUrip3kZ2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_chol.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HmU8aAwi3-SC",
        "outputId": "a3acc2bc-3035-495f-c8e1-4449cf2b588a"
      },
      "execution_count": null,
      "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>Cholesterol</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40</td>\n",
              "      <td>289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48</td>\n",
              "      <td>214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54</td>\n",
              "      <td>195</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  Cholesterol\n",
              "0   40          289\n",
              "1   49          180\n",
              "2   37          283\n",
              "3   48          214\n",
              "4   54          195"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_chol.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "AIP0A8-r10aM",
        "outputId": "00ec7a88-e0c5-4b72-a6d6-facff749ea89"
      },
      "execution_count": null,
      "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>Cholesterol</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>746.000000</td>\n",
              "      <td>746.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>52.882038</td>\n",
              "      <td>244.635389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.505888</td>\n",
              "      <td>59.153524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>28.000000</td>\n",
              "      <td>85.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>46.000000</td>\n",
              "      <td>207.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>54.000000</td>\n",
              "      <td>237.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>59.000000</td>\n",
              "      <td>275.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>603.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Age  Cholesterol\n",
              "count  746.000000   746.000000\n",
              "mean    52.882038   244.635389\n",
              "std      9.505888    59.153524\n",
              "min     28.000000    85.000000\n",
              "25%     46.000000   207.250000\n",
              "50%     54.000000   237.000000\n",
              "75%     59.000000   275.000000\n",
              "max     77.000000   603.000000"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "x3njTbh04BZS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LinearRegression()"
      ],
      "metadata": {
        "id": "Cq_uI1uy4Ht_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr.fit(train_chol[['Age']], train_chol[['Cholesterol']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LvJzA2iB4UQZ",
        "outputId": "d84c59f3-5059-4317-afd6-b479c22452c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_chol = lr.predict(test_chol)"
      ],
      "metadata": {
        "id": "WG-wuMaX4nmt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# new_data['Cholesterol'] = new_data['Cholesterol'].replace(0, predicted_chol)"
      ],
      "metadata": {
        "id": "xh0h7FOw42mX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dum_new_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GiNiYgoH5pmi",
        "outputId": "9ad965eb-4c33-4c14-bf58-145fb58b98cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(918, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "count = 0\n",
        "for i in range(0, 918):\n",
        "  if dum_new_data['Cholesterol'][i] == 0:\n",
        "    dum_new_data['Cholesterol'][i] = predicted_chol[count]\n",
        "    count+=1"
      ],
      "metadata": {
        "id": "UJTnsybL5iT9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(new_data['Age'], new_data['Cholesterol'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "86ALnlt0SASN",
        "outputId": "2e6c4de4-d972-427f-c847-d41ae1ffacaf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f217f57b4d0>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dum_new_data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "nj59iJqi2s1m",
        "outputId": "1f57f57e-f710-4bc1-beef-b5331b13e84f"
      },
      "execution_count": null,
      "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>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>HeartDisease</th>\n",
              "      <th>CPT_Type_0</th>\n",
              "      <th>CPT_Type_1</th>\n",
              "      <th>CPT_Type_2</th>\n",
              "      <th>CPT_Type_3</th>\n",
              "      <th>RECG_Type_0</th>\n",
              "      <th>RECG_Type_1</th>\n",
              "      <th>RECG_Type_2</th>\n",
              "      <th>ST_Slope_0</th>\n",
              "      <th>ST_Slope_1</th>\n",
              "      <th>ST_Slope_2</th>\n",
              "      <th>Sex_0</th>\n",
              "      <th>Sex_1</th>\n",
              "      <th>ExerciseAngina_0</th>\n",
              "      <th>ExerciseAngina_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>53.510893</td>\n",
              "      <td>132.396514</td>\n",
              "      <td>244.178649</td>\n",
              "      <td>0.233115</td>\n",
              "      <td>136.809368</td>\n",
              "      <td>0.887364</td>\n",
              "      <td>0.553377</td>\n",
              "      <td>0.540305</td>\n",
              "      <td>0.188453</td>\n",
              "      <td>0.221133</td>\n",
              "      <td>0.050109</td>\n",
              "      <td>0.204793</td>\n",
              "      <td>0.601307</td>\n",
              "      <td>0.193900</td>\n",
              "      <td>0.068627</td>\n",
              "      <td>0.501089</td>\n",
              "      <td>0.430283</td>\n",
              "      <td>0.210240</td>\n",
              "      <td>0.789760</td>\n",
              "      <td>0.595861</td>\n",
              "      <td>0.404139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.432617</td>\n",
              "      <td>18.514154</td>\n",
              "      <td>53.341646</td>\n",
              "      <td>0.423046</td>\n",
              "      <td>25.460334</td>\n",
              "      <td>1.066570</td>\n",
              "      <td>0.497414</td>\n",
              "      <td>0.498645</td>\n",
              "      <td>0.391287</td>\n",
              "      <td>0.415236</td>\n",
              "      <td>0.218289</td>\n",
              "      <td>0.403770</td>\n",
              "      <td>0.489896</td>\n",
              "      <td>0.395567</td>\n",
              "      <td>0.252957</td>\n",
              "      <td>0.500271</td>\n",
              "      <td>0.495386</td>\n",
              "      <td>0.407701</td>\n",
              "      <td>0.407701</td>\n",
              "      <td>0.490992</td>\n",
              "      <td>0.490992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>-2.600000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>214.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>54.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>241.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>138.000000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>267.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>603.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Age   RestingBP  ...  ExerciseAngina_0  ExerciseAngina_1\n",
              "count  918.000000  918.000000  ...        918.000000        918.000000\n",
              "mean    53.510893  132.396514  ...          0.595861          0.404139\n",
              "std      9.432617   18.514154  ...          0.490992          0.490992\n",
              "min     28.000000    0.000000  ...          0.000000          0.000000\n",
              "25%     47.000000  120.000000  ...          0.000000          0.000000\n",
              "50%     54.000000  130.000000  ...          1.000000          0.000000\n",
              "75%     60.000000  140.000000  ...          1.000000          1.000000\n",
              "max     77.000000  200.000000  ...          1.000000          1.000000\n",
              "\n",
              "[8 rows x 21 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_new = dum_new_data.drop(columns=['HeartDisease'])"
      ],
      "metadata": {
        "id": "vcLQCiiXR7HH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_new = dum_new_data['HeartDisease']"
      ],
      "metadata": {
        "id": "_UFfUBOqSYxT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_new.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "Q54pAFC2SelM",
        "outputId": "7353c821-bc38-49dc-9d58-31fb3191ff69"
      },
      "execution_count": null,
      "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>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>CPT_Type_0</th>\n",
              "      <th>CPT_Type_1</th>\n",
              "      <th>CPT_Type_2</th>\n",
              "      <th>CPT_Type_3</th>\n",
              "      <th>RECG_Type_0</th>\n",
              "      <th>RECG_Type_1</th>\n",
              "      <th>RECG_Type_2</th>\n",
              "      <th>ST_Slope_0</th>\n",
              "      <th>ST_Slope_1</th>\n",
              "      <th>ST_Slope_2</th>\n",
              "      <th>Sex_0</th>\n",
              "      <th>Sex_1</th>\n",
              "      <th>ExerciseAngina_0</th>\n",
              "      <th>ExerciseAngina_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40</td>\n",
              "      <td>140</td>\n",
              "      <td>289</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>160</td>\n",
              "      <td>180</td>\n",
              "      <td>0</td>\n",
              "      <td>156</td>\n",
              "      <td>1.0</td>\n",
              "      <td>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>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>130</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>98</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48</td>\n",
              "      <td>138</td>\n",
              "      <td>214</td>\n",
              "      <td>0</td>\n",
              "      <td>108</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54</td>\n",
              "      <td>150</td>\n",
              "      <td>195</td>\n",
              "      <td>0</td>\n",
              "      <td>122</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  RestingBP  Cholesterol  ...  Sex_1  ExerciseAngina_0  ExerciseAngina_1\n",
              "0   40        140          289  ...      1                 1                 0\n",
              "1   49        160          180  ...      0                 1                 0\n",
              "2   37        130          283  ...      1                 1                 0\n",
              "3   48        138          214  ...      0                 0                 1\n",
              "4   54        150          195  ...      1                 1                 0\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_trainn, x_testt, y_trainn, y_testt = train_test_split(x_new, y_new, test_size=0.3)"
      ],
      "metadata": {
        "id": "qH5GFrY96tXO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "dt = DecisionTreeClassifier(criterion='entropy')\n",
        "dt.fit(x_trainn, y_trainn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0J4e09BiTB5D",
        "outputId": "fb20ae20-990d-4312-e0c9-52b7cfa6e4bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy')"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt.score(x_testt, y_testt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G2Bvz_TyTU_V",
        "outputId": "c7b9d9c4-8dcb-48d8-fbfb-8d5315d587ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7862318840579711"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rf = RandomForestClassifier(criterion='entropy')\n",
        "rf.fit(x_trainn, y_trainn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M94v6XQ6TdCw",
        "outputId": "7d107f65-272d-4593-ccd3-52c9219e0554"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(criterion='entropy')"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf.score(x_testt, y_testt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NMnx0IdKTzG7",
        "outputId": "41bedb60-461c-4b25-ee03-f03242e71c96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8405797101449275"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf1 = RandomForestClassifier(criterion='entropy', random_state=1, n_estimators=250, n_jobs=-1)\n",
        "rf1.fit(x_trainn, y_trainn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ALJtkt4TUj62",
        "outputId": "1e4df06a-b191-43e5-a252-bae74c317ece"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(criterion='entropy', n_estimators=250, n_jobs=-1,\n",
              "                       random_state=1)"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf1.score(x_testt, y_testt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gr59Eu8RU7WO",
        "outputId": "7861f7fd-2c78-49ce-9894-1e034a995d67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.855072463768116"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "vcVgSL583Iaq"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}