HeartDiseasePrediction.ipynb

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      "name": "HeartDiseasePrediction.ipynb",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/vikiofficial/Heart-disease-predictor/blob/main/HeartDiseasePrediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "metadata": {
        "id": "AJgs32FY7jzu"
      },
      "source": [
        "**MODEL FOR PREDICTING CARDIOVASCULAR DISEASE USING MACHINE LEARNING ALGORITHMS**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x80kOZiU78bt"
      },
      "source": [
        "IMPORTING LIBRARIES:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0winxxpP8CNA"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "from sklearn.model_selection import KFold, StratifiedKFold, cross_val_score\n",
        "from sklearn import linear_model, tree, ensemble"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wd1X0e9J8e9R"
      },
      "source": [
        "LOADING THE DATA:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2K_DOUG_QiyH"
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      "source": [
        "**Dataset source:** https://archive.ics.uci.edu/ml/datasets/Heart+Disease\n",
        "\n",
        "**Content:** This database includes 76 attributes, but all published studies relate to the use of a subset of 14 of them. The **Cleveland database** is the only one used by ML researchers to date. One of the major tasks on this dataset is to predict based on the given attributes of a patient that whether that particular person has cardiovascular disease or not and other is the experimental task to diagnose and find out various insights from this dataset which could help in understanding the problem more.\n",
        "\n",
        "**Attributes information:**\n",
        "* age: The person's age in years\n",
        "* sex: The person's sex (1 = male, 0 = female)\n",
        "* cp: The chest pain experienced (Value 1: typical angina, Value 2: atypical angina, Value 3: non-anginal pain, Value 4: asymptomatic)\n",
        "* trestbps: The person's resting blood pressure (mm Hg on admission to the hospital)\n",
        "* chol: The person's cholesterol measurement in mg/dl\n",
        "* fbs: The person's fasting blood sugar (> 120 mg/dl, 1 = true; 0 = false)\n",
        "* restecg: Resting electrocardiographic measurement (0 = normal, 1 = having ST-T wave abnormality, 2 = showing probable or definite left ventricular hypertrophy by Estes' criteria)\n",
        "* thalach: The person's maximum heart rate achieved\n",
        "* exang: Exercise induced angina (1 = yes; 0 = no)\n",
        "* oldpeak: ST depression induced by exercise relative to rest ('ST' relates to positions on the ECG plot. See more here)\n",
        "* slope: the slope of the peak exercise ST segment (Value 1: upsloping, Value 2: flat, Value 3: downsloping)\n",
        "* ca: The number of major vessels (0-3)\n",
        "* thal: A blood disorder called thalassemia (3 = normal; 6 = fixed defect; 7 = reversable defect)\n",
        "* target: Heart disease (0 = no, 1 = yes)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ueBdPs5o8cWW"
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      "source": [
        "dataframe=pd.read_csv(\"/content/heart.csv\")"
      ],
      "execution_count": null,
      "outputs": []
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      "source": [
        "dataframe.head(10)"
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      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0   63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
              "1   37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
              "2   41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
              "3   56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
              "4   57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
              "5   57    1   0       140   192    0  ...      0      0.4      1   0     1       1\n",
              "6   56    0   1       140   294    0  ...      0      1.3      1   0     2       1\n",
              "7   44    1   1       120   263    0  ...      0      0.0      2   0     3       1\n",
              "8   52    1   2       172   199    1  ...      0      0.5      2   0     3       1\n",
              "9   57    1   2       150   168    0  ...      0      1.6      2   0     2       1\n",
              "\n",
              "[10 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gAhh9xQIVbRO"
      },
      "source": [
        "Our data has 3 types of data:\n",
        "* Continuous: which is quantitative data that can be measured\n",
        "* Ordinal Data: Categorical data that has a order to it (0,1,2,3, etc)\n",
        "* Binary Data: data whose unit can take on only two possible states ( 0 &1 )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ONyzvkb9Wpv"
      },
      "source": [
        "DATA ANALYSIS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7fTJ_WOa9ajz",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b430b66-f1f5-4d39-9bc5-e6f62231f923"
      },
      "source": [
        "dataframe.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    int64  \n",
            " 1   sex       303 non-null    int64  \n",
            " 2   cp        303 non-null    int64  \n",
            " 3   trestbps  303 non-null    int64  \n",
            " 4   chol      303 non-null    int64  \n",
            " 5   fbs       303 non-null    int64  \n",
            " 6   restecg   303 non-null    int64  \n",
            " 7   thalach   303 non-null    int64  \n",
            " 8   exang     303 non-null    int64  \n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slope     303 non-null    int64  \n",
            " 11  ca        303 non-null    int64  \n",
            " 12  thal      303 non-null    int64  \n",
            " 13  target    303 non-null    int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 33.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RoNNm7Hk-eJN",
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        },
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        "dataframe.shape"
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            "text/plain": [
              "(303, 14)"
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          "metadata": {},
          "execution_count": 5
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      "source": [
        "dataframe.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
              "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "N6rGwRH-DltM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3efe307a-b865-4370-d9ca-48a2cfc0f600"
      },
      "source": [
        "categorical_val = []\n",
        "continous_val = []\n",
        "for column in dataframe.columns:\n",
        "    print('==============================')\n",
        "    print(f\"{column} : {dataframe[column].unique()}\")\n",
        "    if len(dataframe[column].unique()) <= 10:\n",
        "        categorical_val.append(column)\n",
        "    else:\n",
        "        continous_val.append(column)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==============================\n",
            "age : [63 37 41 56 57 44 52 54 48 49 64 58 50 66 43 69 59 42 61 40 71 51 65 53\n",
            " 46 45 39 47 62 34 35 29 55 60 67 68 74 76 70 38 77]\n",
            "==============================\n",
            "sex : [1 0]\n",
            "==============================\n",
            "cp : [3 2 1 0]\n",
            "==============================\n",
            "trestbps : [145 130 120 140 172 150 110 135 160 105 125 142 155 104 138 128 108 134\n",
            " 122 115 118 100 124  94 112 102 152 101 132 148 178 129 180 136 126 106\n",
            " 156 170 146 117 200 165 174 192 144 123 154 114 164]\n",
            "==============================\n",
            "chol : [233 250 204 236 354 192 294 263 199 168 239 275 266 211 283 219 340 226\n",
            " 247 234 243 302 212 175 417 197 198 177 273 213 304 232 269 360 308 245\n",
            " 208 264 321 325 235 257 216 256 231 141 252 201 222 260 182 303 265 309\n",
            " 186 203 183 220 209 258 227 261 221 205 240 318 298 564 277 214 248 255\n",
            " 207 223 288 160 394 315 246 244 270 195 196 254 126 313 262 215 193 271\n",
            " 268 267 210 295 306 178 242 180 228 149 278 253 342 157 286 229 284 224\n",
            " 206 167 230 335 276 353 225 330 290 172 305 188 282 185 326 274 164 307\n",
            " 249 341 407 217 174 281 289 322 299 300 293 184 409 259 200 327 237 218\n",
            " 319 166 311 169 187 176 241 131]\n",
            "==============================\n",
            "fbs : [1 0]\n",
            "==============================\n",
            "restecg : [0 1 2]\n",
            "==============================\n",
            "thalach : [150 187 172 178 163 148 153 173 162 174 160 139 171 144 158 114 151 161\n",
            " 179 137 157 123 152 168 140 188 125 170 165 142 180 143 182 156 115 149\n",
            " 146 175 186 185 159 130 190 132 147 154 202 166 164 184 122 169 138 111\n",
            " 145 194 131 133 155 167 192 121  96 126 105 181 116 108 129 120 112 128\n",
            " 109 113  99 177 141 136  97 127 103 124  88 195 106  95 117  71 118 134\n",
            "  90]\n",
            "==============================\n",
            "exang : [0 1]\n",
            "==============================\n",
            "oldpeak : [2.3 3.5 1.4 0.8 0.6 0.4 1.3 0.  0.5 1.6 1.2 0.2 1.8 1.  2.6 1.5 3.  2.4\n",
            " 0.1 1.9 4.2 1.1 2.  0.7 0.3 0.9 3.6 3.1 3.2 2.5 2.2 2.8 3.4 6.2 4.  5.6\n",
            " 2.9 2.1 3.8 4.4]\n",
            "==============================\n",
            "slope : [0 2 1]\n",
            "==============================\n",
            "ca : [0 2 1 3 4]\n",
            "==============================\n",
            "thal : [1 2 3 0]\n",
            "==============================\n",
            "target : [1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2eWwXza_Em7K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b8d18d8-6cb4-419d-fde4-86ef6e7f1b0c"
      },
      "source": [
        "categorical_val"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal', 'target']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bCNbYL_T-7LC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2fc92711-9521-4f8a-eca8-81e44ed220dd"
      },
      "source": [
        "dataframe.nunique(axis=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age          41\n",
              "sex           2\n",
              "cp            4\n",
              "trestbps     49\n",
              "chol        152\n",
              "fbs           2\n",
              "restecg       3\n",
              "thalach      91\n",
              "exang         2\n",
              "oldpeak      40\n",
              "slope         3\n",
              "ca            5\n",
              "thal          4\n",
              "target        2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "comX6sHyVRE8"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nYJdgnO1_Np3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "9881a1c0-b8e5-4bb5-9c6c-860e365e0e85"
      },
      "source": [
        "pd.set_option(\"display.float\",\"{:.2f}\".format)\n",
        "dataframe.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-72e27b85-c68a-4239-8d0a-556c07ffca54\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>303.00</td>\n",
              "      <td>303.00</td>\n",
              "      <td>303.00</td>\n",
              "      <td>303.00</td>\n",
              "      <td>303.00</td>\n",
              "      <td>303.00</td>\n",
              "      <td>303.00</td>\n",
              "      <td>303.00</td>\n",
              "      <td>303.00</td>\n",
              "      <td>303.00</td>\n",
              "      <td>303.00</td>\n",
              "      <td>303.00</td>\n",
              "      <td>303.00</td>\n",
              "      <td>303.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.37</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.97</td>\n",
              "      <td>131.62</td>\n",
              "      <td>246.26</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.53</td>\n",
              "      <td>149.65</td>\n",
              "      <td>0.33</td>\n",
              "      <td>1.04</td>\n",
              "      <td>1.40</td>\n",
              "      <td>0.73</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.08</td>\n",
              "      <td>0.47</td>\n",
              "      <td>1.03</td>\n",
              "      <td>17.54</td>\n",
              "      <td>51.83</td>\n",
              "      <td>0.36</td>\n",
              "      <td>0.53</td>\n",
              "      <td>22.91</td>\n",
              "      <td>0.47</td>\n",
              "      <td>1.16</td>\n",
              "      <td>0.62</td>\n",
              "      <td>1.02</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>94.00</td>\n",
              "      <td>126.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>71.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>120.00</td>\n",
              "      <td>211.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>133.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>130.00</td>\n",
              "      <td>240.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>153.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.80</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>140.00</td>\n",
              "      <td>274.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>166.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.60</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>200.00</td>\n",
              "      <td>564.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>202.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>6.20</td>\n",
              "      <td>2.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>1.00</td>\n",
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              "  </tbody>\n",
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            ],
            "text/plain": [
              "         age    sex     cp  trestbps  ...  slope     ca   thal  target\n",
              "count 303.00 303.00 303.00    303.00  ... 303.00 303.00 303.00  303.00\n",
              "mean   54.37   0.68   0.97    131.62  ...   1.40   0.73   2.31    0.54\n",
              "std     9.08   0.47   1.03     17.54  ...   0.62   1.02   0.61    0.50\n",
              "min    29.00   0.00   0.00     94.00  ...   0.00   0.00   0.00    0.00\n",
              "25%    47.50   0.00   0.00    120.00  ...   1.00   0.00   2.00    0.00\n",
              "50%    55.00   1.00   1.00    130.00  ...   1.00   0.00   2.00    1.00\n",
              "75%    61.00   1.00   2.00    140.00  ...   2.00   1.00   3.00    1.00\n",
              "max    77.00   1.00   3.00    200.00  ...   2.00   4.00   3.00    1.00\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a09ph0ymV0Ct"
      },
      "source": [
        "The features described in the above data set are:\n",
        "\n",
        "1. Count: tells us the number of NoN-empty rows in a feature.\n",
        "\n",
        "2. Mean: tells us the mean value of that feature.\n",
        "\n",
        "3. Std: tells us the Standard Deviation Value of that feature.\n",
        "\n",
        "4. Min: tells us the minimum value of that feature.\n",
        "\n",
        "5. 25%, 50%, and 75%: are the percentile/quartile of each features.\n",
        "\n",
        "6. Max: tells us the maximum value of that feature."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C3mfae_xAj7z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00de16c1-f926-484d-a194-c3437794ca6f"
      },
      "source": [
        "#Checking for null values\n",
        "dataframe.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "cp          0\n",
              "trestbps    0\n",
              "chol        0\n",
              "fbs         0\n",
              "restecg     0\n",
              "thalach     0\n",
              "exang       0\n",
              "oldpeak     0\n",
              "slope       0\n",
              "ca          0\n",
              "thal        0\n",
              "target      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wFnhL5ZlUjep"
      },
      "source": [
        "Since there are no null values and duplicates, our data is good to go for analysis and visualizations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iUAJP27YBtlP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "087a1acb-a93b-4bd9-ac49-24ac1ee12d71"
      },
      "source": [
        "dataframe.target.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    165\n",
              "0    138\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-yZh_4ezCTP9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "outputId": "319be9cf-048c-41e3-9033-52b02b4be6a0"
      },
      "source": [
        "dataframe.target.value_counts().plot(kind=\"bar\",color=[\"blue\",\"orange\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f161f7a5110>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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viMaCAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xMowwOAlDo6T",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "6f7f58c9-833d-4e06-cc88-54dcffa8158c"
      },
      "source": [
        "correlation=dataframe.corr()\n",
        "pd.DataFrame(correlation['target']).sort_values(by='target',ascending=False)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-938b8e09-0932-4fa8-a8ef-f2a8143ac23d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cp</th>\n",
              "      <td>0.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thalach</th>\n",
              "      <td>0.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>slope</th>\n",
              "      <td>0.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>restecg</th>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fbs</th>\n",
              "      <td>-0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chol</th>\n",
              "      <td>-0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trestbps</th>\n",
              "      <td>-0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>-0.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>-0.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thal</th>\n",
              "      <td>-0.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ca</th>\n",
              "      <td>-0.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oldpeak</th>\n",
              "      <td>-0.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exang</th>\n",
              "      <td>-0.44</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-938b8e09-0932-4fa8-a8ef-f2a8143ac23d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-938b8e09-0932-4fa8-a8ef-f2a8143ac23d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-938b8e09-0932-4fa8-a8ef-f2a8143ac23d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          target\n",
              "target      1.00\n",
              "cp          0.43\n",
              "thalach     0.42\n",
              "slope       0.35\n",
              "restecg     0.14\n",
              "fbs        -0.03\n",
              "chol       -0.09\n",
              "trestbps   -0.14\n",
              "age        -0.23\n",
              "sex        -0.28\n",
              "thal       -0.34\n",
              "ca         -0.39\n",
              "oldpeak    -0.43\n",
              "exang      -0.44"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n6rI7RmWDqUD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "outputId": "9c38db88-622e-4c52-fcaa-2417eb52b074"
      },
      "source": [
        "correlation.plot(kind=\"box\",figsize=(20,10))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f161e352410>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKiNxiHtG96k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "outputId": "25a2f751-0adc-48eb-e86a-96990f3a7bff"
      },
      "source": [
        "plt.figure(figsize=(15,10))\n",
        "sns.heatmap(dataframe.corr(),linewidth=.01,annot=True,cmap=\"winter\")\n",
        "plt.show()\n",
        "plt.savefig('correlationfigure')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a7hMyT7_GuI3"
      },
      "source": [
        "From the above heatmap, we can understand that Chest pain(cp) and target have positive correlation. It means that whose has large risk of chest pain results in greater chance to have heart disease.\n",
        "In addition to chest pain, thalach,slope and restecg have positive correlation with target.\n",
        "\n",
        "Then, exercise induced angina(exang) and target has negative correlation which means when we exercise, heart requires more blood, but narrowed arteries slow down the blood flow.\n",
        "In addition to ca,oldpeak,thal have negative correlation with target."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kMJWMb-dSsbv"
      },
      "source": [
        "Now, we can find the relationship between target with each features with the help of histogram."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KOcwBryiTZEF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "outputId": "c5dba712-9513-40ff-b1ea-38d64c6e7ab3"
      },
      "source": [
        "\n",
        "dataframe.hist(figsize=(12,12))\n",
        "plt.savefig('featuresplot')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 16 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QfZesfdLTX83"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j_D5VuFaWuDK"
      },
      "source": [
        "Now, we shall find relationship between target and mostly risk factors/features which cause cardiovascular disease."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iEtarkp8GicP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "outputId": "782adcf0-a730-4401-8a9c-43f2478412fa"
      },
      "source": [
        "fig,ax=plt.subplots(figsize=(12,6))\n",
        "sns.barplot(y='target',x='cp',hue='cp',ax=ax,data=dataframe)\n",
        "plt.title('Chest Pain vs Heart Disease',size=25)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Chest Pain vs Heart Disease')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dlHT4FznYlgl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "14500422-e884-4084-d6a7-c9dab40f861e"
      },
      "source": [
        "sns.countplot(data=dataframe,x='sex',hue='target',palette='gist_rainbow')\n",
        "plt.title('Gender vs target')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Gender vs target')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "iAdkj6V_c0Ai"
      },
      "source": [
        "According to this dataset, males are more susceptible to get Heart Disease than females. Men experience heart attacks more than women and Sudden Heart Attacks are experienced by men between 70-90%."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CG4vHUhLcBm-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "37930218-ad41-4b09-91c5-92da0a6d6919"
      },
      "source": [
        "pd.crosstab(dataframe.age,dataframe.target).plot(kind=\"bar\",figsize=(20,6))\n",
        "plt.title('Age vs Heart Disease Frequency',size=25)\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vfKKIBjNg_Q6"
      },
      "source": [
        "From the above analysis, frequency of Heart Disease is very common in the seniors and adults which belong to the age group of 41 to 60. But it’s very rare among the age group of 19 to 30."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W9mQnby-hXXt"
      },
      "source": [
        "## **Feature** **selection**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_r6LD5EdY4_b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5c0e7ee0-2552-40c7-8f3b-2bdee082a728"
      },
      "source": [
        "from sklearn.feature_selection import chi2\n",
        "X = dataframe.drop('target',axis=1)\n",
        "y = dataframe['target']\n",
        "chi_scores = chi2(X,y)\n",
        "chi_scores"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 23.28662399,   7.57683451,  62.59809791,  14.8239245 ,\n",
              "         23.93639448,   0.20293368,   2.97827075, 188.32047169,\n",
              "         38.91437697,  72.64425301,   9.8040952 ,  66.44076512,\n",
              "          5.79185297]),\n",
              " array([1.39567266e-06, 5.91231763e-03, 2.53498179e-15, 1.18028642e-04,\n",
              "        9.95714828e-07, 6.52363208e-01, 8.43893904e-02, 7.39510166e-43,\n",
              "        4.42807388e-10, 1.55258340e-17, 1.74123685e-03, 3.60567663e-16,\n",
              "        1.61006098e-02]))"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n04BcOF-ZyoL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "197a3f54-c328-4bf7-841a-f3232ad12e30"
      },
      "source": [
        "p_values = pd.Series(chi_scores[1],index = X.columns)\n",
        "p_values.sort_values(ascending = False , inplace = True)\n",
        "p_values.plot.bar()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1612a19290>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "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": {
        "id": "1G-qjNOBcPab",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9896abc7-e8ff-4f66-eecd-5bf4465612df"
      },
      "source": [
        "from sklearn.feature_selection import SelectKBest\n",
        "chi2_selector = SelectKBest(chi2, k=2)\n",
        "X_kbest = chi2_selector.fit_transform(X, y)\n",
        "print(X_kbest)\n",
        "print('Original number of features:', X.shape)\n",
        "print('Reduced number of features:', X_kbest.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1.50e+02 2.30e+00]\n",
            " [1.87e+02 3.50e+00]\n",
            " [1.72e+02 1.40e+00]\n",
            " [1.78e+02 8.00e-01]\n",
            " [1.63e+02 6.00e-01]\n",
            " [1.48e+02 4.00e-01]\n",
            " [1.53e+02 1.30e+00]\n",
            " [1.73e+02 0.00e+00]\n",
            " [1.62e+02 5.00e-01]\n",
            " [1.74e+02 1.60e+00]\n",
            " [1.60e+02 1.20e+00]\n",
            " [1.39e+02 2.00e-01]\n",
            " [1.71e+02 6.00e-01]\n",
            " [1.44e+02 1.80e+00]\n",
            " [1.62e+02 1.00e+00]\n",
            " [1.58e+02 1.60e+00]\n",
            " [1.72e+02 0.00e+00]\n",
            " [1.14e+02 2.60e+00]\n",
            " [1.71e+02 1.50e+00]\n",
            " [1.51e+02 1.80e+00]\n",
            " [1.61e+02 5.00e-01]\n",
            " [1.79e+02 4.00e-01]\n",
            " [1.78e+02 0.00e+00]\n",
            " [1.37e+02 1.00e+00]\n",
            " [1.78e+02 1.40e+00]\n",
            " [1.62e+02 4.00e-01]\n",
            " [1.57e+02 1.60e+00]\n",
            " [1.23e+02 6.00e-01]\n",
            " [1.57e+02 8.00e-01]\n",
            " [1.52e+02 1.20e+00]\n",
            " [1.68e+02 0.00e+00]\n",
            " [1.40e+02 4.00e-01]\n",
            " [1.88e+02 0.00e+00]\n",
            " [1.52e+02 5.00e-01]\n",
            " [1.25e+02 1.40e+00]\n",
            " [1.60e+02 1.40e+00]\n",
            " [1.70e+02 0.00e+00]\n",
            " [1.65e+02 1.60e+00]\n",
            " [1.48e+02 8.00e-01]\n",
            " [1.51e+02 8.00e-01]\n",
            " [1.42e+02 1.50e+00]\n",
            " [1.80e+02 2.00e-01]\n",
            " [1.48e+02 3.00e+00]\n",
            " [1.43e+02 4.00e-01]\n",
            " [1.82e+02 0.00e+00]\n",
            " [1.72e+02 2.00e-01]\n",
            " [1.80e+02 0.00e+00]\n",
            " [1.56e+02 0.00e+00]\n",
            " [1.15e+02 0.00e+00]\n",
            " [1.60e+02 0.00e+00]\n",
            " [1.49e+02 5.00e-01]\n",
            " [1.51e+02 4.00e-01]\n",
            " [1.46e+02 1.80e+00]\n",
            " [1.75e+02 6.00e-01]\n",
            " [1.72e+02 0.00e+00]\n",
            " [1.58e+02 8.00e-01]\n",
            " [1.86e+02 0.00e+00]\n",
            " [1.85e+02 0.00e+00]\n",
            " [1.74e+02 0.00e+00]\n",
            " [1.59e+02 0.00e+00]\n",
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            " [1.56e+02 0.00e+00]\n",
            " [1.90e+02 0.00e+00]\n",
            " [1.32e+02 0.00e+00]\n",
            " [1.65e+02 0.00e+00]\n",
            " [1.82e+02 1.40e+00]\n",
            " [1.43e+02 1.20e+00]\n",
            " [1.75e+02 6.00e-01]\n",
            " [1.70e+02 0.00e+00]\n",
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            " [2.02e+02 0.00e+00]\n",
            " [1.86e+02 0.00e+00]\n",
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            " [1.61e+02 1.40e+00]\n",
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            " [1.60e+02 1.60e+00]\n",
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            " [1.68e+02 0.00e+00]\n",
            " [1.69e+02 0.00e+00]\n",
            " [1.59e+02 0.00e+00]\n",
            " [1.38e+02 0.00e+00]\n",
            " [1.11e+02 0.00e+00]\n",
            " [1.57e+02 1.20e+00]\n",
            " [1.47e+02 1.00e-01]\n",
            " [1.62e+02 1.90e+00]\n",
            " [1.73e+02 0.00e+00]\n",
            " [1.78e+02 8.00e-01]\n",
            " [1.45e+02 4.20e+00]\n",
            " [1.79e+02 0.00e+00]\n",
            " [1.94e+02 8.00e-01]\n",
            " [1.63e+02 0.00e+00]\n",
            " [1.15e+02 1.50e+00]\n",
            " [1.31e+02 1.00e-01]\n",
            " [1.52e+02 2.00e-01]\n",
            " [1.62e+02 1.10e+00]\n",
            " [1.59e+02 0.00e+00]\n",
            " [1.54e+02 0.00e+00]\n",
            " [1.73e+02 2.00e-01]\n",
            " [1.33e+02 2.00e-01]\n",
            " [1.61e+02 0.00e+00]\n",
            " [1.55e+02 0.00e+00]\n",
            " [1.70e+02 0.00e+00]\n",
            " [1.68e+02 2.00e+00]\n",
            " [1.62e+02 1.90e+00]\n",
            " [1.72e+02 0.00e+00]\n",
            " [1.52e+02 0.00e+00]\n",
            " [1.22e+02 2.00e+00]\n",
            " [1.82e+02 0.00e+00]\n",
            " [1.72e+02 0.00e+00]\n",
            " [1.67e+02 0.00e+00]\n",
            " [1.79e+02 0.00e+00]\n",
            " [1.92e+02 7.00e-01]\n",
            " [1.43e+02 1.00e-01]\n",
            " [1.72e+02 0.00e+00]\n",
            " [1.69e+02 1.00e-01]\n",
            " [1.21e+02 2.00e-01]\n",
            " [1.63e+02 0.00e+00]\n",
            " [1.62e+02 0.00e+00]\n",
            " [1.62e+02 0.00e+00]\n",
            " [1.53e+02 0.00e+00]\n",
            " [1.63e+02 0.00e+00]\n",
            " [1.63e+02 0.00e+00]\n",
            " [9.60e+01 0.00e+00]\n",
            " [1.40e+02 0.00e+00]\n",
            " [1.26e+02 1.50e+00]\n",
            " [1.05e+02 2.00e-01]\n",
            " [1.57e+02 6.00e-01]\n",
            " [1.81e+02 1.20e+00]\n",
            " [1.73e+02 0.00e+00]\n",
            " [1.42e+02 3.00e-01]\n",
            " [1.16e+02 1.10e+00]\n",
            " [1.43e+02 0.00e+00]\n",
            " [1.49e+02 3.00e-01]\n",
            " [1.71e+02 9.00e-01]\n",
            " [1.69e+02 0.00e+00]\n",
            " [1.50e+02 0.00e+00]\n",
            " [1.38e+02 2.30e+00]\n",
            " [1.25e+02 1.60e+00]\n",
            " [1.55e+02 6.00e-01]\n",
            " [1.52e+02 0.00e+00]\n",
            " [1.52e+02 0.00e+00]\n",
            " [1.31e+02 6.00e-01]\n",
            " [1.79e+02 0.00e+00]\n",
            " [1.74e+02 0.00e+00]\n",
            " [1.44e+02 4.00e-01]\n",
            " [1.63e+02 0.00e+00]\n",
            " [1.69e+02 0.00e+00]\n",
            " [1.66e+02 1.20e+00]\n",
            " [1.82e+02 0.00e+00]\n",
            " [1.73e+02 0.00e+00]\n",
            " [1.73e+02 0.00e+00]\n",
            " [1.08e+02 1.50e+00]\n",
            " [1.29e+02 2.60e+00]\n",
            " [1.60e+02 3.60e+00]\n",
            " [1.47e+02 1.40e+00]\n",
            " [1.55e+02 3.10e+00]\n",
            " [1.42e+02 6.00e-01]\n",
            " [1.68e+02 1.00e+00]\n",
            " [1.60e+02 1.80e+00]\n",
            " [1.73e+02 3.20e+00]\n",
            " [1.32e+02 2.40e+00]\n",
            " [1.14e+02 2.00e+00]\n",
            " [1.60e+02 1.40e+00]\n",
            " [1.58e+02 0.00e+00]\n",
            " [1.20e+02 2.50e+00]\n",
            " [1.12e+02 6.00e-01]\n",
            " [1.32e+02 1.20e+00]\n",
            " [1.14e+02 1.00e+00]\n",
            " [1.69e+02 0.00e+00]\n",
            " [1.65e+02 2.50e+00]\n",
            " [1.28e+02 2.60e+00]\n",
            " [1.53e+02 0.00e+00]\n",
            " [1.44e+02 1.40e+00]\n",
            " [1.09e+02 2.20e+00]\n",
            " [1.63e+02 6.00e-01]\n",
            " [1.58e+02 0.00e+00]\n",
            " [1.42e+02 1.20e+00]\n",
            " [1.31e+02 2.20e+00]\n",
            " [1.13e+02 1.40e+00]\n",
            " [1.42e+02 2.80e+00]\n",
            " [1.55e+02 3.00e+00]\n",
            " [1.40e+02 3.40e+00]\n",
            " [1.47e+02 3.60e+00]\n",
            " [1.63e+02 2.00e-01]\n",
            " [9.90e+01 1.80e+00]\n",
            " [1.58e+02 6.00e-01]\n",
            " [1.77e+02 0.00e+00]\n",
            " [1.41e+02 2.80e+00]\n",
            " [1.11e+02 8.00e-01]\n",
            " [1.50e+02 1.60e+00]\n",
            " [1.45e+02 6.20e+00]\n",
            " [1.61e+02 0.00e+00]\n",
            " [1.42e+02 1.20e+00]\n",
            " [1.57e+02 2.60e+00]\n",
            " [1.39e+02 2.00e+00]\n",
            " [1.62e+02 0.00e+00]\n",
            " [1.50e+02 4.00e-01]\n",
            " [1.40e+02 3.60e+00]\n",
            " [1.40e+02 1.20e+00]\n",
            " [1.46e+02 1.00e+00]\n",
            " [1.44e+02 1.20e+00]\n",
            " [1.36e+02 3.00e+00]\n",
            " [9.70e+01 1.20e+00]\n",
            " [1.32e+02 1.80e+00]\n",
            " [1.27e+02 2.80e+00]\n",
            " [1.50e+02 0.00e+00]\n",
            " [1.54e+02 4.00e+00]\n",
            " [1.11e+02 5.60e+00]\n",
            " [1.74e+02 1.40e+00]\n",
            " [1.33e+02 4.00e+00]\n",
            " [1.26e+02 2.80e+00]\n",
            " [1.25e+02 2.60e+00]\n",
            " [1.03e+02 1.40e+00]\n",
            " [1.30e+02 1.60e+00]\n",
            " [1.59e+02 2.00e-01]\n",
            " [1.31e+02 1.80e+00]\n",
            " [1.52e+02 0.00e+00]\n",
            " [1.24e+02 1.00e+00]\n",
            " [1.45e+02 8.00e-01]\n",
            " [9.60e+01 2.20e+00]\n",
            " [1.09e+02 2.40e+00]\n",
            " [1.73e+02 1.60e+00]\n",
            " [1.71e+02 0.00e+00]\n",
            " [1.70e+02 1.20e+00]\n",
            " [1.62e+02 0.00e+00]\n",
            " [1.56e+02 0.00e+00]\n",
            " [1.12e+02 2.90e+00]\n",
            " [1.43e+02 0.00e+00]\n",
            " [1.32e+02 2.00e+00]\n",
            " [8.80e+01 1.20e+00]\n",
            " [1.05e+02 2.10e+00]\n",
            " [1.66e+02 5.00e-01]\n",
            " [1.50e+02 1.90e+00]\n",
            " [1.20e+02 0.00e+00]\n",
            " [1.95e+02 0.00e+00]\n",
            " [1.46e+02 2.00e+00]\n",
            " [1.22e+02 4.20e+00]\n",
            " [1.43e+02 1.00e-01]\n",
            " [1.06e+02 1.90e+00]\n",
            " [1.25e+02 9.00e-01]\n",
            " [1.25e+02 0.00e+00]\n",
            " [1.47e+02 0.00e+00]\n",
            " [1.30e+02 3.00e+00]\n",
            " [1.26e+02 9.00e-01]\n",
            " [1.54e+02 1.40e+00]\n",
            " [1.82e+02 3.80e+00]\n",
            " [1.65e+02 1.00e+00]\n",
            " [1.60e+02 0.00e+00]\n",
            " [9.50e+01 2.00e+00]\n",
            " [1.69e+02 1.80e+00]\n",
            " [1.08e+02 0.00e+00]\n",
            " [1.32e+02 1.00e-01]\n",
            " [1.17e+02 3.40e+00]\n",
            " [1.26e+02 8.00e-01]\n",
            " [1.16e+02 3.20e+00]\n",
            " [1.03e+02 1.60e+00]\n",
            " [1.44e+02 8.00e-01]\n",
            " [1.45e+02 2.60e+00]\n",
            " [7.10e+01 1.00e+00]\n",
            " [1.56e+02 1.00e-01]\n",
            " [1.18e+02 1.00e+00]\n",
            " [1.68e+02 1.00e+00]\n",
            " [1.05e+02 2.00e+00]\n",
            " [1.41e+02 3.00e-01]\n",
            " [1.52e+02 0.00e+00]\n",
            " [1.25e+02 3.60e+00]\n",
            " [1.25e+02 1.80e+00]\n",
            " [1.56e+02 1.00e+00]\n",
            " [1.34e+02 2.20e+00]\n",
            " [1.81e+02 0.00e+00]\n",
            " [1.38e+02 1.90e+00]\n",
            " [1.20e+02 1.80e+00]\n",
            " [1.62e+02 8.00e-01]\n",
            " [1.64e+02 0.00e+00]\n",
            " [1.43e+02 3.00e+00]\n",
            " [1.30e+02 2.00e+00]\n",
            " [1.61e+02 0.00e+00]\n",
            " [1.40e+02 4.40e+00]\n",
            " [1.46e+02 2.80e+00]\n",
            " [1.50e+02 8.00e-01]\n",
            " [1.44e+02 2.80e+00]\n",
            " [1.44e+02 4.00e+00]\n",
            " [1.36e+02 0.00e+00]\n",
            " [9.00e+01 1.00e+00]\n",
            " [1.23e+02 2.00e-01]\n",
            " [1.32e+02 1.20e+00]\n",
            " [1.41e+02 3.40e+00]\n",
            " [1.15e+02 1.20e+00]\n",
            " [1.74e+02 0.00e+00]]\n",
            "Original number of features: (303, 13)\n",
            "Reduced number of features: (303, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aV8nq2TXO9B8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "a1b951d9-b502-4ccb-94e4-402e1a7224e5"
      },
      "source": [
        "df = dataframe.dropna()\n",
        "df = df.drop(columns = [ 'slope', 'thal', 'fbs', 'restecg', 'exang','sex'])\n",
        "\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-80fd8084-8cb1-499d-9fa1-c9056905717d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>thalach</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>ca</th>\n",
              "      <th>target</th>\n",
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              "      <td>163</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-80fd8084-8cb1-499d-9fa1-c9056905717d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-80fd8084-8cb1-499d-9fa1-c9056905717d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-80fd8084-8cb1-499d-9fa1-c9056905717d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   age  cp  trestbps  chol  thalach  oldpeak  ca  target\n",
              "0   63   3       145   233      150     2.30   0       1\n",
              "1   37   2       130   250      187     3.50   0       1\n",
              "2   41   1       130   204      172     1.40   0       1\n",
              "3   56   1       120   236      178     0.80   0       1\n",
              "4   57   0       120   354      163     0.60   0       1"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RtmfLvR7xLRK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "fa5e7d1a-3fd4-4235-90ca-1c25163a2f43"
      },
      "source": [
        "correlat=df.corr()\n",
        "pd.DataFrame(correlat['target']).sort_values(by='target',ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-086abb5f-018c-4e4c-8294-2d65b0666ceb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cp</th>\n",
              "      <td>0.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thalach</th>\n",
              "      <td>0.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chol</th>\n",
              "      <td>-0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trestbps</th>\n",
              "      <td>-0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>-0.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ca</th>\n",
              "      <td>-0.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oldpeak</th>\n",
              "      <td>-0.43</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-086abb5f-018c-4e4c-8294-2d65b0666ceb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-086abb5f-018c-4e4c-8294-2d65b0666ceb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-086abb5f-018c-4e4c-8294-2d65b0666ceb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          target\n",
              "target      1.00\n",
              "cp          0.43\n",
              "thalach     0.42\n",
              "chol       -0.09\n",
              "trestbps   -0.14\n",
              "age        -0.23\n",
              "ca         -0.39\n",
              "oldpeak    -0.43"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hrhF0_atw-cE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "outputId": "95727883-f228-4b46-c516-67afaefd45a3"
      },
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "sns.heatmap(df.corr(),linewidths=0.3, linecolor='black',annot_kws={\"size\": 10},annot=True,cmap=\"winter\")\n",
        "plt.show()\n",
        "plt.savefig('correlationfigure')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9dRY9IzQEMYj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "outputId": "e1627af9-8f92-4f70-faa1-c2efac505b24"
      },
      "source": [
        "\n",
        "dataset = pd.get_dummies(dataframe, columns = categorical_val)\n",
        "dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-7265119a-6b90-4707-b711-3a07d9beb17a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>thalach</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>sex_0</th>\n",
              "      <th>sex_1</th>\n",
              "      <th>cp_0</th>\n",
              "      <th>cp_1</th>\n",
              "      <th>cp_2</th>\n",
              "      <th>cp_3</th>\n",
              "      <th>fbs_0</th>\n",
              "      <th>fbs_1</th>\n",
              "      <th>restecg_0</th>\n",
              "      <th>restecg_1</th>\n",
              "      <th>restecg_2</th>\n",
              "      <th>exang_0</th>\n",
              "      <th>exang_1</th>\n",
              "      <th>slope_0</th>\n",
              "      <th>slope_1</th>\n",
              "      <th>slope_2</th>\n",
              "      <th>ca_0</th>\n",
              "      <th>ca_1</th>\n",
              "      <th>ca_2</th>\n",
              "      <th>ca_3</th>\n",
              "      <th>ca_4</th>\n",
              "      <th>thal_0</th>\n",
              "      <th>thal_1</th>\n",
              "      <th>thal_2</th>\n",
              "      <th>thal_3</th>\n",
              "      <th>target_0</th>\n",
              "      <th>target_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>150</td>\n",
              "      <td>2.30</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>187</td>\n",
              "      <td>3.50</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>172</td>\n",
              "      <td>1.40</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>178</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>163</td>\n",
              "      <td>0.60</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7265119a-6b90-4707-b711-3a07d9beb17a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7265119a-6b90-4707-b711-3a07d9beb17a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7265119a-6b90-4707-b711-3a07d9beb17a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   age  trestbps  chol  thalach  ...  thal_2  thal_3  target_0  target_1\n",
              "0   63       145   233      150  ...       0       0         0         1\n",
              "1   37       130   250      187  ...       1       0         0         1\n",
              "2   41       130   204      172  ...       1       0         0         1\n",
              "3   56       120   236      178  ...       1       0         0         1\n",
              "4   57       120   354      163  ...       1       0         0         1\n",
              "\n",
              "[5 rows x 32 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sp5g2re-8Eaz"
      },
      "source": [
        "dataset = pd.get_dummies(dataframe, columns = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yUTuB1XJ7w8A"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "\n",
        "s_sc = StandardScaler()\n",
        "col_to_scale = ['age', 'trestbps', 'chol', 'thalach', 'oldpeak']\n",
        "dataset[col_to_scale] = s_sc.fit_transform(dataset[col_to_scale])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PnUv7N0w8SDD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "outputId": "3233606b-7885-4121-ce15-90b9d7fdc938"
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a126cf12-f697-4931-962e-75b3dad38cb8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>thalach</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>target</th>\n",
              "      <th>sex_0</th>\n",
              "      <th>sex_1</th>\n",
              "      <th>cp_0</th>\n",
              "      <th>cp_1</th>\n",
              "      <th>cp_2</th>\n",
              "      <th>cp_3</th>\n",
              "      <th>fbs_0</th>\n",
              "      <th>fbs_1</th>\n",
              "      <th>restecg_0</th>\n",
              "      <th>restecg_1</th>\n",
              "      <th>restecg_2</th>\n",
              "      <th>exang_0</th>\n",
              "      <th>exang_1</th>\n",
              "      <th>slope_0</th>\n",
              "      <th>slope_1</th>\n",
              "      <th>slope_2</th>\n",
              "      <th>ca_0</th>\n",
              "      <th>ca_1</th>\n",
              "      <th>ca_2</th>\n",
              "      <th>ca_3</th>\n",
              "      <th>ca_4</th>\n",
              "      <th>thal_0</th>\n",
              "      <th>thal_1</th>\n",
              "      <th>thal_2</th>\n",
              "      <th>thal_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.95</td>\n",
              "      <td>0.76</td>\n",
              "      <td>-0.26</td>\n",
              "      <td>0.02</td>\n",
              "      <td>1.09</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.92</td>\n",
              "      <td>-0.09</td>\n",
              "      <td>0.07</td>\n",
              "      <td>1.63</td>\n",
              "      <td>2.12</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.47</td>\n",
              "      <td>-0.09</td>\n",
              "      <td>-0.82</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.31</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.18</td>\n",
              "      <td>-0.66</td>\n",
              "      <td>-0.20</td>\n",
              "      <td>1.24</td>\n",
              "      <td>-0.21</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.29</td>\n",
              "      <td>-0.66</td>\n",
              "      <td>2.08</td>\n",
              "      <td>0.58</td>\n",
              "      <td>-0.38</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a126cf12-f697-4931-962e-75b3dad38cb8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
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              "      \n",
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              "      flex-wrap:wrap;\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a126cf12-f697-4931-962e-75b3dad38cb8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a126cf12-f697-4931-962e-75b3dad38cb8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    age  trestbps  chol  thalach  oldpeak  ...  ca_4  thal_0  thal_1  thal_2  thal_3\n",
              "0  0.95      0.76 -0.26     0.02     1.09  ...     0       0       1       0       0\n",
              "1 -1.92     -0.09  0.07     1.63     2.12  ...     0       0       0       1       0\n",
              "2 -1.47     -0.09 -0.82     0.98     0.31  ...     0       0       0       1       0\n",
              "3  0.18     -0.66 -0.20     1.24    -0.21  ...     0       0       0       1       0\n",
              "4  0.29     -0.66  2.08     0.58    -0.38  ...     0       0       0       1       0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vlzdNWwu8oDt"
      },
      "source": [
        "\n",
        "X= df.drop(['target'], axis=1)\n",
        "y= df['target']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0nYOOvMr8xX0"
      },
      "source": [
        "X_train, X_test,y_train, y_test=train_test_split(X,y,test_size=0.25,random_state=40)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lmp2cJG2kHwq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ddeadcf-1b78-4a99-b28a-60a9f39ead6c"
      },
      "source": [
        "print('X_train-', X_train.shape)\n",
        "print('X_test-',X_test.shape)\n",
        "print('y_train-', y_train.shape)\n",
        "print('y_test-', y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train- (227, 7)\n",
            "X_test- (76, 7)\n",
            "y_train- (227,)\n",
            "y_test- (76,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0T8flzha68bL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "9b051684-e3f4-4c51-a60d-6a774764d3ad"
      },
      "source": [
        "X_test.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
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              "</style>\n",
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              "      <th>age</th>\n",
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              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3856e82a-ba07-4178-a96d-6b78c341e5fe');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     age  cp  trestbps  chol  thalach  oldpeak  ca\n",
              "18    43   0       150   247      171     1.50   0\n",
              "112   64   2       140   313      133     0.20   0\n",
              "102   63   1       140   195      179     0.00   2\n",
              "275   52   0       125   212      168     1.00   2\n",
              "92    52   2       138   223      169     0.00   4"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "blUZNIknjMvI"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gwu0h1sbY-zC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d78368a8-6b17-4599-f684-68fafc5df054"
      },
      "source": [
        "param_grid_lr = {\n",
        "    'max_iter': [20, 50, 100, 200, 500, 1000],                      \n",
        "    'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],   \n",
        "    'class_weight': ['balanced']                                    \n",
        "}\n",
        "logModel_grid = GridSearchCV(estimator=LogisticRegression(random_state=1234), param_grid=param_grid_lr, verbose=1, cv=10, n_jobs=-1)\n",
        "logModel_grid.fit(X_train, y_train)\n",
        "print(logModel_grid.best_estimator_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 30 candidates, totalling 300 fits\n",
            "LogisticRegression(class_weight='balanced', max_iter=20, random_state=1234,\n",
            "                   solver='newton-cg')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/optimize.py:212: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
            "  ConvergenceWarning,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HTwpGpaxZPau",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "outputId": "fc6fb12d-294b-4ae6-b534-d03750a321d9"
      },
      "source": [
        "lr=LogisticRegression(C=1.0, class_weight='balanced', dual=False,\n",
        "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
        "                   max_iter=100, multi_class='auto', n_jobs=None, penalty='l2',\n",
        "                   random_state=1234, solver='lbfgs', tol=0.0001, verbose=0,\n",
        "                   warm_start=False)\n",
        "model1=lr.fit(X_train,y_train)\n",
        "prediction1=model1.predict(X_test)\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm=confusion_matrix(y_test,prediction1)\n",
        "cm\n",
        "sns.heatmap(cm, annot=True,cmap='winter',linewidths=0.3, linecolor='black',annot_kws={\"size\": 20})\n",
        "TP=cm[0][0]\n",
        "TN=cm[1][1]\n",
        "FN=cm[1][0]\n",
        "FP=cm[0][1]\n",
        "\n",
        "print('Testing Accuracy for Logistic Regression:',(TP+TN)/(TP+TN+FN+FP))\n",
        "print('Testing Sensitivity for Logistic Regression:',(TP/(TP+FN)))\n",
        "print('Testing Specificity for Logistic Regression:',(TN/(TN+FP)))\n",
        "print('Testing Precision for Logistic Regression:',(TP/(TP+FP)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing Accuracy for Logistic Regression: 0.8157894736842105\n",
            "Testing Sensitivity for Logistic Regression: 0.7631578947368421\n",
            "Testing Specificity for Logistic Regression: 0.868421052631579\n",
            "Testing Precision for Logistic Regression: 0.8529411764705882\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "98glGGtd9Tcj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "b0f9c60a-8ad7-47b9-c4bf-af1c052fc9b7"
      },
      "source": [
        "\n",
        "for c in range(4,6):\n",
        "  lr=LogisticRegression(C=c)\n",
        "  model1=lr.fit(X_train,y_train)\n",
        "  prediction1=model1.predict(X_test)\n",
        "  from sklearn.metrics import confusion_matrix\n",
        "  cm=confusion_matrix(y_test,prediction1)\n",
        "  cm\n",
        "  sns.heatmap(cm, annot=True,cmap='winter')\n",
        "  TP=cm[0][0]\n",
        "  TN=cm[1][1]\n",
        "  FN=cm[1][0]\n",
        "  FP=cm[0][1]\n",
        "\n",
        "  print('Testing Accuracy for Logistic Regression:',(TP+TN)/(TP+TN+FN+FP))\n",
        "  print('Testing Sensitivity for Logistic Regression:',(TP/(TP+FN)))\n",
        "  print('Testing Specificity for Logistic Regression:',(TN/(TN+FP)))\n",
        "  print('Testing Precision for Logistic Regression:',(TP/(TP+FP)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing Accuracy for Logistic Regression: 0.8026315789473685\n",
            "Testing Sensitivity for Logistic Regression: 0.7567567567567568\n",
            "Testing Specificity for Logistic Regression: 0.8461538461538461\n",
            "Testing Precision for Logistic Regression: 0.8235294117647058\n",
            "Testing Accuracy for Logistic Regression: 0.8026315789473685\n",
            "Testing Sensitivity for Logistic Regression: 0.7567567567567568\n",
            "Testing Specificity for Logistic Regression: 0.8461538461538461\n",
            "Testing Precision for Logistic Regression: 0.8235294117647058\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ei4UnW0Z-Tuq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cbbb4622-7392-4e14-e17f-71a93b3edf54"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, prediction1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.82      0.79        34\n",
            "           1       0.85      0.79      0.81        42\n",
            "\n",
            "    accuracy                           0.80        76\n",
            "   macro avg       0.80      0.80      0.80        76\n",
            "weighted avg       0.81      0.80      0.80        76\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jIj6KsWyTRcU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "outputId": "e39c8d46-0983-4e33-f3d6-d42f0ec4f1da"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "test_score = accuracy_score(y_test,model1.predict(X_test) ) * 100\n",
        "train_score = accuracy_score(y_train, model1.predict(X_train)) * 100\n",
        "\n",
        "results_df = pd.DataFrame(data=[[\"Logistic Regression\", train_score, test_score]], \n",
        "                          columns=['Logistic Regression', 'Training Results %', 'Testing Results %'])\n",
        "results_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "  <div id=\"df-82278864-8321-4e6a-aaa4-92b91f61d9a8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Logistic Regression</th>\n",
              "      <th>Training Results %</th>\n",
              "      <th>Testing Results %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>79.74</td>\n",
              "      <td>80.26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-82278864-8321-4e6a-aaa4-92b91f61d9a8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-82278864-8321-4e6a-aaa4-92b91f61d9a8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-82278864-8321-4e6a-aaa4-92b91f61d9a8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Logistic Regression  Training Results %  Testing Results %\n",
              "0  Logistic Regression               79.74              80.26"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9RCHHxwsehFG"
      },
      "source": [
        "randomized search cv for dt:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RUYRDd4AgV4m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1aea4646-0cb7-4dd6-f28c-b5d95fb124b6"
      },
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "parameters={\n",
        "    'criterion':('gini','entropy'),\n",
        "    'max_depth':(3,5,7,9,10),\n",
        "    'max_features':('auto','sqrt','log2'),\n",
        "    'min_samples_split': (2,4,6)\n",
        "}\n",
        "dt_grid = RandomizedSearchCV(DecisionTreeClassifier(), param_distributions = parameters, cv=10, verbose=True)\n",
        "dt_grid.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n",
              "                   param_distributions={'criterion': ('gini', 'entropy'),\n",
              "                                        'max_depth': (3, 5, 7, 9, 10),\n",
              "                                        'max_features': ('auto', 'sqrt',\n",
              "                                                         'log2'),\n",
              "                                        'min_samples_split': (2, 4, 6)},\n",
              "                   verbose=True)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jfp9r1QQibJI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5677c978-4abb-4158-c7e0-f5b1e852dde7"
      },
      "source": [
        "dt_grid.best_estimator_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', max_depth=7, max_features='auto',\n",
              "                       min_samples_split=6)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TQybMH_hNEbc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "75bf8994-8a32-4091-c363-24430145ff98"
      },
      "source": [
        "!pip install graphviz"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (0.10.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J29xjAwZNI95",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d96a5a8e-738e-4c4b-cb2f-1b36cc3cb75f"
      },
      "source": [
        "!pip install pyparsing\n",
        "!pip install pydotplus"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pyparsing in /usr/local/lib/python3.7/dist-packages (3.0.6)\n",
            "Requirement already satisfied: pydotplus in /usr/local/lib/python3.7/dist-packages (2.0.2)\n",
            "Requirement already satisfied: pyparsing>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from pydotplus) (3.0.6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gqww3MXdI45f"
      },
      "source": [
        "k-fold implementation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hJNDbM8kIx_H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f5ebf521-73cf-4606-d05a-b5510270e420"
      },
      "source": [
        "kf =KFold(n_splits=10, shuffle=True, random_state=42)\n",
        "\n",
        "cnt = 1\n",
        "for train_index, test_index in kf.split(X, y):\n",
        "    print(f'Fold:{cnt}, Train set: {len(train_index)}, Test set:{len(test_index)}')\n",
        "    cnt += 1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fold:1, Train set: 272, Test set:31\n",
            "Fold:2, Train set: 272, Test set:31\n",
            "Fold:3, Train set: 272, Test set:31\n",
            "Fold:4, Train set: 273, Test set:30\n",
            "Fold:5, Train set: 273, Test set:30\n",
            "Fold:6, Train set: 273, Test set:30\n",
            "Fold:7, Train set: 273, Test set:30\n",
            "Fold:8, Train set: 273, Test set:30\n",
            "Fold:9, Train set: 273, Test set:30\n",
            "Fold:10, Train set: 273, Test set:30\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-0kixO5zI2_J"
      },
      "source": [
        "def rmse(score):\n",
        "    rmse = np.sqrt(-score)\n",
        "    print(f'rmse= {\"{:.2f}\".format(rmse)}')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xsw9lO1LI8eQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7843eae4-7941-48e2-89d1-4bb3d8e55813"
      },
      "source": [
        "score = cross_val_score(tree.DecisionTreeRegressor(random_state= 42), X, y, cv=kf, scoring=\"neg_mean_squared_error\")\n",
        "print(f'Scores for each fold: {score}')\n",
        "rmse(score.mean())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scores for each fold: [-0.35483871 -0.22580645 -0.35483871 -0.4        -0.26666667 -0.33333333\n",
            " -0.33333333 -0.26666667 -0.2        -0.2       ]\n",
            "rmse= 0.54\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eum0UgrLJE7F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40e71441-1ba1-47cb-b31c-ee687ea74430"
      },
      "source": [
        "max_depth = [1,2,3,4,5,6,7,8,9,10]\n",
        "\n",
        "for val in max_depth:\n",
        "    score = cross_val_score(tree.DecisionTreeRegressor(max_depth= val, random_state= 42), X, y, cv= kf, scoring=\"neg_mean_squared_error\")\n",
        "    print(f'For max depth: {val}')\n",
        "    rmse(score.mean())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For max depth: 1\n",
            "rmse= 0.43\n",
            "For max depth: 2\n",
            "rmse= 0.41\n",
            "For max depth: 3\n",
            "rmse= 0.42\n",
            "For max depth: 4\n",
            "rmse= 0.43\n",
            "For max depth: 5\n",
            "rmse= 0.46\n",
            "For max depth: 6\n",
            "rmse= 0.49\n",
            "For max depth: 7\n",
            "rmse= 0.50\n",
            "For max depth: 8\n",
            "rmse= 0.53\n",
            "For max depth: 9\n",
            "rmse= 0.53\n",
            "For max depth: 10\n",
            "rmse= 0.54\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qLKnrKTWJMKY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28a64082-a81f-40da-ec3c-195411d77ac7"
      },
      "source": [
        "score = cross_val_score(tree.DecisionTreeClassifier(random_state= 42), X, y, cv= kf, scoring=\"accuracy\")\n",
        "print(f'Scores for each fold are: {score}')\n",
        "print(f'Average score: {\"{:.2f}\".format(score.mean())}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scores for each fold are: [0.64516129 0.77419355 0.64516129 0.6        0.73333333 0.66666667\n",
            " 0.66666667 0.73333333 0.83333333 0.8       ]\n",
            "Average score: 0.71\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ogkbcE4qJ67E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "outputId": "3bbf30ac-14f6-4ab3-a25d-1509a4c5bc35"
      },
      "source": [
        "def run_cross_validation_on_trees(X, y, tree_depths, cv=10, scoring='accuracy'):\n",
        "    cv_scores_list = []\n",
        "    cv_scores_std = []\n",
        "    cv_scores_mean = []\n",
        "    accuracy_scores = []\n",
        "    for depth in tree_depths:\n",
        "        tree_model = DecisionTreeClassifier(max_depth=depth)\n",
        "        cv_scores = cross_val_score(tree_model, X, y, cv=cv, scoring=scoring)\n",
        "        cv_scores_list.append(cv_scores)\n",
        "        cv_scores_mean.append(cv_scores.mean())\n",
        "        cv_scores_std.append(cv_scores.std())\n",
        "        accuracy_scores.append(tree_model.fit(X, y).score(X, y))\n",
        "    cv_scores_mean = np.array(cv_scores_mean)\n",
        "    cv_scores_std = np.array(cv_scores_std)\n",
        "    accuracy_scores = np.array(accuracy_scores)\n",
        "    return cv_scores_mean, cv_scores_std, accuracy_scores\n",
        "\n",
        "def plot_cross_validation_on_trees(depths, cv_scores_mean, cv_scores_std, accuracy_scores, title):\n",
        "    fig, ax = plt.subplots(1,1, figsize=(25,5))\n",
        "    ax.plot(depths, cv_scores_mean, '-o', label='mean cross-validation accuracy', alpha=0.9)\n",
        "    ax.fill_between(depths, cv_scores_mean-2*cv_scores_std, cv_scores_mean+2*cv_scores_std, alpha=0.2)\n",
        "    ylim = plt.ylim()\n",
        "    ax.plot(depths, accuracy_scores, '-*', label='train accuracy', alpha=0.9)\n",
        "    ax.set_title(title, fontsize=16)\n",
        "    ax.set_xlabel('Tree depth', fontsize=14)\n",
        "    ax.set_ylabel('Accuracy', fontsize=14)\n",
        "    ax.set_ylim(ylim)\n",
        "    ax.set_xticks(depths)\n",
        "    ax.legend()\n",
        "\n",
        "sm_tree_depths = range(1,20)\n",
        "sm_cv_scores_mean, sm_cv_scores_std, sm_accuracy_scores = run_cross_validation_on_trees(X_train, y_train, sm_tree_depths)\n",
        "\n",
        "plot_cross_validation_on_trees(sm_tree_depths, sm_cv_scores_mean, sm_cv_scores_std, sm_accuracy_scores, \n",
        "                               'Accuracy per decision tree depth on training data')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oxndg4sEmekU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "outputId": "c13024c9-0ca9-4c7a-eed3-6adaa47ca74e"
      },
      "source": [
        "\n",
        "tree_model = DecisionTreeClassifier(max_depth=5,criterion='entropy')\n",
        "cv_scores = cross_val_score(tree_model, X, y, cv=10, scoring='accuracy')\n",
        "m=tree_model.fit(X, y)\n",
        "prediction=m.predict(X_test)\n",
        "cm= confusion_matrix(y_test,prediction)\n",
        "sns.heatmap(cm, annot=True,cmap='winter',linewidths=0.3, linecolor='black',annot_kws={\"size\": 20})\n",
        "print(classification_report(y_test, prediction))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.88      0.91        34\n",
            "           1       0.91      0.95      0.93        42\n",
            "\n",
            "    accuracy                           0.92        76\n",
            "   macro avg       0.92      0.92      0.92        76\n",
            "weighted avg       0.92      0.92      0.92        76\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a6d17bsCoDZB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e871235e-11f4-4e89-bc0e-565e7ec84f48"
      },
      "source": [
        "TP=cm[0][0]\n",
        "TN=cm[1][1]\n",
        "FN=cm[1][0]\n",
        "FP=cm[0][1]\n",
        "print('Testing Accuracy for Decision Tree:',(TP+TN)/(TP+TN+FN+FP))\n",
        "print('Testing Sensitivity for Decision Tree:',(TP/(TP+FN)))\n",
        "print('Testing Specificity for Decision Tree:',(TN/(TN+FP)))\n",
        "print('Testing Precision for Decision Tree:',(TP/(TP+FP)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing Accuracy for Decision Tree: 0.9210526315789473\n",
            "Testing Sensitivity for Decision Tree: 0.9375\n",
            "Testing Specificity for Decision Tree: 0.9090909090909091\n",
            "Testing Precision for Decision Tree: 0.8823529411764706\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8_7jtjoQI8SZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d044ef1d-9310-4f0e-b227-e1b53dd4d0c0"
      },
      "source": [
        "idx_max = sm_cv_scores_mean.argmax()\n",
        "sm_best_tree_depth = sm_tree_depths[idx_max]\n",
        "sm_best_tree_cv_score = sm_cv_scores_mean[idx_max]\n",
        "sm_best_tree_cv_score_std = sm_cv_scores_std[idx_max]\n",
        "print('The depth-{} tree achieves the best mean cross-validation accuracy {} on training dataset'.format(\n",
        "      sm_best_tree_depth, round(sm_best_tree_cv_score*100,5)))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The depth-7 tree achieves the best mean cross-validation accuracy 77.98419 on training dataset\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lYTZg6QJllhs"
      },
      "source": [
        "predictiontree=tree_model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ZTF0Pt0-Vwj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "outputId": "3dcee15a-059c-499d-8501-b165c442d4d3"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "dtclas=DecisionTreeClassifier()\n",
        "modeldt=dtclas.fit(X_train,y_train)\n",
        "predictiondt=modeldt.predict(X_test)\n",
        "cmdt= confusion_matrix(y_test,predictiondt)\n",
        "sns.heatmap(cmdt, annot=True,cmap='winter')\n",
        "print(classification_report(y_test, predictiondt))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.67      0.71      0.69        34\n",
            "           1       0.75      0.71      0.73        42\n",
            "\n",
            "    accuracy                           0.71        76\n",
            "   macro avg       0.71      0.71      0.71        76\n",
            "weighted avg       0.71      0.71      0.71        76\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GjoepKTWWrQT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f5a2da82-ec20-45ff-d735-58feb1d88188"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rfc=RandomForestClassifier(random_state=42)\n",
        "param_grid = { \n",
        "    'n_estimators': [200, 500],\n",
        "    'max_features': ['auto', 'sqrt', 'log2'],\n",
        "    'max_depth' : [4,5,6,7,8],\n",
        "    'criterion' :['gini', 'entropy']\n",
        "}\n",
        "CV_rfc = GridSearchCV(estimator=rfc, param_grid=param_grid, cv= 5)\n",
        "CV_rfc.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
              "             param_grid={'criterion': ['gini', 'entropy'],\n",
              "                         'max_depth': [4, 5, 6, 7, 8],\n",
              "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
              "                         'n_estimators': [200, 500]})"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MwYoDn1xW3qL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa30b1b2-c3d7-4852-f3b4-799e3fd4d184"
      },
      "source": [
        "CV_rfc.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'criterion': 'entropy',\n",
              " 'max_depth': 8,\n",
              " 'max_features': 'auto',\n",
              " 'n_estimators': 500}"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UUocLR24XUid",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96368d97-5ffe-4bbb-94de-cb2e47522c47"
      },
      "source": [
        "rfc1=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
        "                                              class_weight=None,\n",
        "                                              criterion='entropy', max_depth=8,\n",
        "                                              max_features='auto',\n",
        "                                              max_leaf_nodes=None,\n",
        "                                              max_samples=None,\n",
        "                                              min_impurity_decrease=0.0,\n",
        "                              \n",
        "                                              min_samples_leaf=1,\n",
        "                                              min_samples_split=2,\n",
        "                                              min_weight_fraction_leaf=0.0,\n",
        "                                              n_estimators=500, n_jobs=None,\n",
        "                                              oob_score=False, random_state=42,\n",
        "                                              verbose=0, warm_start=False)\n",
        "rfc1.fit(X_train, y_train)\n",
        "pred=rfc1.predict(X_test)\n",
        "print(\"Accuracy for Random Forest on CV data: \",accuracy_score(y_test,pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy for Random Forest on CV data:  0.8026315789473685\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0xJbq6Eg-7ZP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "outputId": "df68fca0-fa5f-4e8b-b216-47ef935310fd"
      },
      "source": [
        "\n",
        "rfc=RandomForestClassifier(n_estimators=500,criterion='entropy',max_depth=8,min_samples_split=5)\n",
        "model3 = rfc.fit(X_train, y_train)\n",
        "prediction3 = model3.predict(X_test)\n",
        "cm3=confusion_matrix(y_test, prediction3)\n",
        "sns.heatmap(cm3, annot=True,cmap='winter',linewidths=0.3, linecolor='black',annot_kws={\"size\": 20})\n",
        "TP=cm3[0][0]\n",
        "TN=cm3[1][1]\n",
        "FN=cm3[1][0]\n",
        "FP=cm3[0][1]\n",
        "print(round(accuracy_score(prediction3,y_test)*100,2))\n",
        "print('Testing Accuracy for Random Forest:',(TP+TN)/(TP+TN+FN+FP))\n",
        "print('Testing Sensitivity for Random Forest:',(TP/(TP+FN)))\n",
        "print('Testing Specificity for Random Forest:',(TN/(TN+FP)))\n",
        "print('Testing Precision for Random Forest:',(TP/(TP+FP)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "80.26\n",
            "Testing Accuracy for Random Forest: 0.8026315789473685\n",
            "Testing Sensitivity for Random Forest: 0.7714285714285715\n",
            "Testing Specificity for Random Forest: 0.8292682926829268\n",
            "Testing Precision for Random Forest: 0.7941176470588235\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AKqkwGK__XfJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e67ea30-1d07-4f76-9413-22627556e654"
      },
      "source": [
        "print(classification_report(y_test, prediction3))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.79      0.78        34\n",
            "           1       0.83      0.81      0.82        42\n",
            "\n",
            "    accuracy                           0.80        76\n",
            "   macro avg       0.80      0.80      0.80        76\n",
            "weighted avg       0.80      0.80      0.80        76\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0okB4ETSlU_D"
      },
      "source": [
        "SVM ALGORITHM\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iXvtvaSZ_Z9V"
      },
      "source": [
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import cross_val_score, GridSearchCV"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I-tKAVH9_ghB"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "for temp in range(1,100,5):\n",
        "  svm=SVC(C=temp,kernel='linear')\n",
        " \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nTe0yeLeEqOj"
      },
      "source": [
        "  svm=SVC(C=12,kernel='linear')\n",
        "  model4=svm.fit(X_train,y_train)\n",
        "  prediction4=model4.predict(X_test)\n",
        "  cm4= confusion_matrix(y_test,prediction4)\n",
        "  sns.heatmap(cm4, annot=True,cmap='winter',linewidths=0.3, linecolor='black',annot_kws={\"size\": 20})\n",
        "  TP=cm4[0][0]\n",
        "  TN=cm4[1][1]\n",
        "  FN=cm4[1][0]\n",
        "  FP=cm4[0][1]\n",
        "  \n",
        "  print('Testing Accuracy for SVM:',(TP+TN)/(TP+TN+FN+FP))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n0WUcrw2ACVq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38a14364-72d6-4312-f948-b0b494d9a824"
      },
      "source": [
        "print(classification_report(y_test, prediction4))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.82      0.80        34\n",
            "           1       0.85      0.81      0.83        42\n",
            "\n",
            "    accuracy                           0.82        76\n",
            "   macro avg       0.81      0.82      0.81        76\n",
            "weighted avg       0.82      0.82      0.82        76\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umspAtQjRF-y"
      },
      "source": [
        "PREDICTION MODEL:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2mZd65FePTjZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62107cfe-f43c-4c85-e502-8c89d867c4a4"
      },
      "source": [
        "input=(63,1,3,145,200,150,98)\n",
        "input_as_numpy=np.asarray(input)\n",
        "input_reshaped=input_as_numpy.reshape(1,-1)\n",
        "pre1=tree_model.predict(input_reshaped)\n",
        "print(pre1)\n",
        "a1 = accuracy_score(pre1,model1.predict(input_reshaped)) * 100\n",
        "print(a1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n",
            "100.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:446: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:446: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V1COvIbnmQMI"
      },
      "source": [
        "import pickle\n",
        "pickle.dump(model4, open('heart_disease_detector.pkl', 'wb'))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kkF5xjpkGXtE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8dfedffb-2695-4d08-e134-019ef2a84793"
      },
      "source": [
        "from flask import Flask, request, render_template\n",
        "mod = pickle.load(open('heart_disease_detector.pkl', 'rb')) \n",
        "\n",
        "app = Flask(__name__,template_folder='home')\n",
        "\n",
        "@app.route('/')\n",
        "@app.route('/home')\n",
        "def home():\n",
        "    return render_template('home.html')\n",
        "\n",
        "@app.route('/predict', methods =['POST'])\n",
        "def predict():\n",
        "    \n",
        "    features = [float(i) for i in request.form.values()]\n",
        "    array_features = [np.array(features)]\n",
        "\n",
        "    prediction = mod.predict(array_features)\n",
        "    \n",
        "    output = prediction\n",
        "\n",
        "    if output == 1:\n",
        "        return render_template('home.html', \n",
        "                               result = 'The patient is not likely to have heart disease!')\n",
        "    else:\n",
        "        return render_template('home.html', \n",
        "                               result = 'The patient is likely to have heart disease!')\n",
        "\n",
        "if __name__ == '__main__':\n",
        "\n",
        "    app.run()\n",
        "  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " * Serving Flask app \"__main__\" (lazy loading)\n",
            " * Environment: production\n",
            "\u001b[31m   WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
            "\u001b[2m   Use a production WSGI server instead.\u001b[0m\n",
            " * Debug mode: off\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\n"
          ]
        }
      ]
    }
  ]
}