Day 11.ipynb

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        "id": "d05269b3"
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      "source": [
        "# **Imports**"
      ]
    },
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      "id": "b618a05f",
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        "tags": [],
        "id": "b618a05f"
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      "outputs": [],
      "source": [
        "import pickle\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import ConfusionMatrixDisplay , classification_report , accuracy_score\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mounting google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "RY3Q6a-0cP94",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6492e2ce-d831-4f41-e5cd-100cf8dade89"
      },
      "id": "RY3Q6a-0cP94",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "3dc1e454",
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          "iopub.status.idle": "2023-11-30T19:55:02.195545Z",
          "shell.execute_reply": "2023-11-30T19:55:02.194366Z"
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        "colab": {
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   male  age  education  currentSmoker  cigsPerDay  BPMeds  prevalentStroke  \\\n",
              "0     1   39        4.0              0         0.0     0.0                0   \n",
              "1     0   46        2.0              0         0.0     0.0                0   \n",
              "2     1   48        1.0              1        20.0     0.0                0   \n",
              "3     0   61        3.0              1        30.0     0.0                0   \n",
              "4     0   46        3.0              1        23.0     0.0                0   \n",
              "\n",
              "   prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose  \\\n",
              "0             0         0    195.0  106.0   70.0  26.97       80.0     77.0   \n",
              "1             0         0    250.0  121.0   81.0  28.73       95.0     76.0   \n",
              "2             0         0    245.0  127.5   80.0  25.34       75.0     70.0   \n",
              "3             1         0    225.0  150.0   95.0  28.58       65.0    103.0   \n",
              "4             0         0    285.0  130.0   84.0  23.10       85.0     85.0   \n",
              "\n",
              "   TenYearCHD  \n",
              "0           0  \n",
              "1           0  \n",
              "2           0  \n",
              "3           1  \n",
              "4           0  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>male</th>\n",
              "      <th>age</th>\n",
              "      <th>education</th>\n",
              "      <th>currentSmoker</th>\n",
              "      <th>cigsPerDay</th>\n",
              "      <th>BPMeds</th>\n",
              "      <th>prevalentStroke</th>\n",
              "      <th>prevalentHyp</th>\n",
              "      <th>diabetes</th>\n",
              "      <th>totChol</th>\n",
              "      <th>sysBP</th>\n",
              "      <th>diaBP</th>\n",
              "      <th>BMI</th>\n",
              "      <th>heartRate</th>\n",
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              "      <th>TenYearCHD</th>\n",
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              "  </thead>\n",
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              "      <td>81.0</td>\n",
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              "      <td>76.0</td>\n",
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              "      <td>20.0</td>\n",
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              "      <td>245.0</td>\n",
              "      <td>127.5</td>\n",
              "      <td>80.0</td>\n",
              "      <td>25.34</td>\n",
              "      <td>75.0</td>\n",
              "      <td>70.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>225.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>28.58</td>\n",
              "      <td>65.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>46</td>\n",
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              "      <td>285.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>23.10</td>\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4238,\n  \"fields\": [\n    {\n      \"column\": \"male\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 32,\n        \"max\": 70,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          34,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.019790689312203,\n        \"min\": 1.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"currentSmoker\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cigsPerDay\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.92009358782776,\n        \"min\": 0.0,\n        \"max\": 70.0,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          19.0,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BPMeds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16958356790200033,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prevalentStroke\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prevalentHyp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diabetes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"totChol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 44.590334318749456,\n        \"min\": 107.0,\n        \"max\": 696.0,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          311.0,\n          205.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sysBP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.038096643544584,\n        \"min\": 83.5,\n        \"max\": 295.0,\n        \"num_unique_values\": 234,\n        \"samples\": [\n          109.0,\n          184.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diaBP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.9108496002255,\n        \"min\": 48.0,\n        \"max\": 142.5,\n        \"num_unique_values\": 146,\n        \"samples\": [\n          106.0,\n          108.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.080111062409028,\n        \"min\": 15.54,\n        \"max\": 56.8,\n        \"num_unique_values\": 1363,\n        \"samples\": [\n          24.56,\n          19.87\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"heartRate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.0265963516126,\n        \"min\": 44.0,\n        \"max\": 143.0,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          85.0,\n          47.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.959998189237478,\n        \"min\": 40.0,\n        \"max\": 394.0,\n        \"num_unique_values\": 143,\n        \"samples\": [\n          394.0,\n          74.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TenYearCHD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "df = pd.read_csv('/content/drive/My Drive/Corizo/Class 11/framingham.csv')  # Read CSV file and load into \"data\" variable\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c0a10ac1",
      "metadata": {
        "papermill": {
          "duration": 0.008974,
          "end_time": "2023-11-30T19:55:02.216455",
          "exception": false,
          "start_time": "2023-11-30T19:55:02.207481",
          "status": "completed"
        },
        "tags": [],
        "id": "c0a10ac1"
      },
      "source": [
        "# **Eplore the data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "c2b42683",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-30T19:55:02.237532Z",
          "iopub.status.busy": "2023-11-30T19:55:02.237095Z",
          "iopub.status.idle": "2023-11-30T19:55:02.243806Z",
          "shell.execute_reply": "2023-11-30T19:55:02.242747Z"
        },
        "papermill": {
          "duration": 0.01994,
          "end_time": "2023-11-30T19:55:02.245844",
          "exception": false,
          "start_time": "2023-11-30T19:55:02.225904",
          "status": "completed"
        },
        "tags": [],
        "id": "c2b42683",
        "outputId": "fbe83708-e7bc-41cc-86bf-391bab56bd36",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4238, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "33c4b450",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-30T19:55:02.266862Z",
          "iopub.status.busy": "2023-11-30T19:55:02.266151Z",
          "iopub.status.idle": "2023-11-30T19:55:02.291979Z",
          "shell.execute_reply": "2023-11-30T19:55:02.291118Z"
        },
        "papermill": {
          "duration": 0.039236,
          "end_time": "2023-11-30T19:55:02.294634",
          "exception": false,
          "start_time": "2023-11-30T19:55:02.255398",
          "status": "completed"
        },
        "tags": [],
        "id": "33c4b450",
        "outputId": "ab6853c5-6a7c-4659-914f-83ffa3e09f23",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4238 entries, 0 to 4237\n",
            "Data columns (total 16 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   male             4238 non-null   int64  \n",
            " 1   age              4238 non-null   int64  \n",
            " 2   education        4133 non-null   float64\n",
            " 3   currentSmoker    4238 non-null   int64  \n",
            " 4   cigsPerDay       4209 non-null   float64\n",
            " 5   BPMeds           4185 non-null   float64\n",
            " 6   prevalentStroke  4238 non-null   int64  \n",
            " 7   prevalentHyp     4238 non-null   int64  \n",
            " 8   diabetes         4238 non-null   int64  \n",
            " 9   totChol          4188 non-null   float64\n",
            " 10  sysBP            4238 non-null   float64\n",
            " 11  diaBP            4238 non-null   float64\n",
            " 12  BMI              4219 non-null   float64\n",
            " 13  heartRate        4237 non-null   float64\n",
            " 14  glucose          3850 non-null   float64\n",
            " 15  TenYearCHD       4238 non-null   int64  \n",
            "dtypes: float64(9), int64(7)\n",
            "memory usage: 529.9 KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "c25786a6",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-30T19:55:02.316560Z",
          "iopub.status.busy": "2023-11-30T19:55:02.316110Z",
          "iopub.status.idle": "2023-11-30T19:55:02.380026Z",
          "shell.execute_reply": "2023-11-30T19:55:02.378743Z"
        },
        "papermill": {
          "duration": 0.078185,
          "end_time": "2023-11-30T19:55:02.382649",
          "exception": false,
          "start_time": "2023-11-30T19:55:02.304464",
          "status": "completed"
        },
        "tags": [],
        "id": "c25786a6",
        "outputId": "a365248d-7908-44e4-fc80-76a3f8ba862d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              male          age    education  currentSmoker   cigsPerDay  \\\n",
              "count  4238.000000  4238.000000  4133.000000    4238.000000  4209.000000   \n",
              "mean      0.429212    49.584946     1.978950       0.494101     9.003089   \n",
              "std       0.495022     8.572160     1.019791       0.500024    11.920094   \n",
              "min       0.000000    32.000000     1.000000       0.000000     0.000000   \n",
              "25%       0.000000    42.000000     1.000000       0.000000     0.000000   \n",
              "50%       0.000000    49.000000     2.000000       0.000000     0.000000   \n",
              "75%       1.000000    56.000000     3.000000       1.000000    20.000000   \n",
              "max       1.000000    70.000000     4.000000       1.000000    70.000000   \n",
              "\n",
              "            BPMeds  prevalentStroke  prevalentHyp     diabetes      totChol  \\\n",
              "count  4185.000000      4238.000000   4238.000000  4238.000000  4188.000000   \n",
              "mean      0.029630         0.005899      0.310524     0.025720   236.721585   \n",
              "std       0.169584         0.076587      0.462763     0.158316    44.590334   \n",
              "min       0.000000         0.000000      0.000000     0.000000   107.000000   \n",
              "25%       0.000000         0.000000      0.000000     0.000000   206.000000   \n",
              "50%       0.000000         0.000000      0.000000     0.000000   234.000000   \n",
              "75%       0.000000         0.000000      1.000000     0.000000   263.000000   \n",
              "max       1.000000         1.000000      1.000000     1.000000   696.000000   \n",
              "\n",
              "             sysBP        diaBP          BMI    heartRate      glucose  \\\n",
              "count  4238.000000  4238.000000  4219.000000  4237.000000  3850.000000   \n",
              "mean    132.352407    82.893464    25.802008    75.878924    81.966753   \n",
              "std      22.038097    11.910850     4.080111    12.026596    23.959998   \n",
              "min      83.500000    48.000000    15.540000    44.000000    40.000000   \n",
              "25%     117.000000    75.000000    23.070000    68.000000    71.000000   \n",
              "50%     128.000000    82.000000    25.400000    75.000000    78.000000   \n",
              "75%     144.000000    89.875000    28.040000    83.000000    87.000000   \n",
              "max     295.000000   142.500000    56.800000   143.000000   394.000000   \n",
              "\n",
              "        TenYearCHD  \n",
              "count  4238.000000  \n",
              "mean      0.151958  \n",
              "std       0.359023  \n",
              "min       0.000000  \n",
              "25%       0.000000  \n",
              "50%       0.000000  \n",
              "75%       0.000000  \n",
              "max       1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-43260dd0-3818-492b-9b3c-d73ead6050fc\" 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>male</th>\n",
              "      <th>age</th>\n",
              "      <th>education</th>\n",
              "      <th>currentSmoker</th>\n",
              "      <th>cigsPerDay</th>\n",
              "      <th>BPMeds</th>\n",
              "      <th>prevalentStroke</th>\n",
              "      <th>prevalentHyp</th>\n",
              "      <th>diabetes</th>\n",
              "      <th>totChol</th>\n",
              "      <th>sysBP</th>\n",
              "      <th>diaBP</th>\n",
              "      <th>BMI</th>\n",
              "      <th>heartRate</th>\n",
              "      <th>glucose</th>\n",
              "      <th>TenYearCHD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4238.000000</td>\n",
              "      <td>4238.000000</td>\n",
              "      <td>4133.000000</td>\n",
              "      <td>4238.000000</td>\n",
              "      <td>4209.000000</td>\n",
              "      <td>4185.000000</td>\n",
              "      <td>4238.000000</td>\n",
              "      <td>4238.000000</td>\n",
              "      <td>4238.000000</td>\n",
              "      <td>4188.000000</td>\n",
              "      <td>4238.000000</td>\n",
              "      <td>4238.000000</td>\n",
              "      <td>4219.000000</td>\n",
              "      <td>4237.000000</td>\n",
              "      <td>3850.000000</td>\n",
              "      <td>4238.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.429212</td>\n",
              "      <td>49.584946</td>\n",
              "      <td>1.978950</td>\n",
              "      <td>0.494101</td>\n",
              "      <td>9.003089</td>\n",
              "      <td>0.029630</td>\n",
              "      <td>0.005899</td>\n",
              "      <td>0.310524</td>\n",
              "      <td>0.025720</td>\n",
              "      <td>236.721585</td>\n",
              "      <td>132.352407</td>\n",
              "      <td>82.893464</td>\n",
              "      <td>25.802008</td>\n",
              "      <td>75.878924</td>\n",
              "      <td>81.966753</td>\n",
              "      <td>0.151958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.495022</td>\n",
              "      <td>8.572160</td>\n",
              "      <td>1.019791</td>\n",
              "      <td>0.500024</td>\n",
              "      <td>11.920094</td>\n",
              "      <td>0.169584</td>\n",
              "      <td>0.076587</td>\n",
              "      <td>0.462763</td>\n",
              "      <td>0.158316</td>\n",
              "      <td>44.590334</td>\n",
              "      <td>22.038097</td>\n",
              "      <td>11.910850</td>\n",
              "      <td>4.080111</td>\n",
              "      <td>12.026596</td>\n",
              "      <td>23.959998</td>\n",
              "      <td>0.359023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>107.000000</td>\n",
              "      <td>83.500000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>15.540000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>206.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>23.070000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>234.000000</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>25.400000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>263.000000</td>\n",
              "      <td>144.000000</td>\n",
              "      <td>89.875000</td>\n",
              "      <td>28.040000</td>\n",
              "      <td>83.000000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>696.000000</td>\n",
              "      <td>295.000000</td>\n",
              "      <td>142.500000</td>\n",
              "      <td>56.800000</td>\n",
              "      <td>143.000000</td>\n",
              "      <td>394.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-43260dd0-3818-492b-9b3c-d73ead6050fc 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-43260dd0-3818-492b-9b3c-d73ead6050fc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-16a99764-4bc3-43e3-b7b6-48815bc40a17\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-16a99764-4bc3-43e3-b7b6-48815bc40a17')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-16a99764-4bc3-43e3-b7b6-48815bc40a17 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"male\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1498.2116307917174,\n        \"min\": 0.0,\n        \"max\": 4238.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.42921189240207647,\n          1.0,\n          0.4950220855364925\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1482.955278652531,\n        \"min\": 8.572159925118484,\n        \"max\": 4238.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          49.58494572911751,\n          49.0,\n          4238.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1460.5295095025235,\n        \"min\": 1.0,\n        \"max\": 4133.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4133.0,\n          1.9789499153157513,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"currentSmoker\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1498.2081010291288,\n        \"min\": 0.0,\n        \"max\": 4238.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.49410099103350635,\n          1.0,\n          0.5000241969070368\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cigsPerDay\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1482.6851658255594,\n        \"min\": 0.0,\n        \"max\": 4209.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4209.0,\n          9.003088619624615,\n          70.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BPMeds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1479.560410009123,\n        \"min\": 0.0,\n        \"max\": 4185.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.02962962962962963,\n          1.0,\n          0.16958356790200033\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prevalentStroke\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1498.3046354997873,\n        \"min\": 0.0,\n        \"max\": 4238.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.005899008966493629,\n          1.0,\n          0.07658717064585817\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prevalentHyp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1498.219254987568,\n        \"min\": 0.0,\n        \"max\": 4238.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.31052383199622463,\n          1.0,\n          0.4627626956737016\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diabetes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1498.2995058316505,\n        \"min\": 0.0,\n        \"max\": 4238.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.025719679093912224,\n          1.0,\n          0.15831642786899552\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"totChol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1403.9002477579238,\n        \"min\": 44.590334318749456,\n        \"max\": 4188.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          236.72158548233045,\n          234.0,\n          4188.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sysBP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1453.832095647962,\n        \"min\": 22.038096643544584,\n        \"max\": 4238.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          132.35240679565834,\n          128.0,\n          4238.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diaBP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1471.9456329708814,\n        \"min\": 11.9108496002255,\n        \"max\": 4238.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          82.89346389806512,\n          82.0,\n          4238.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1482.6891002296709,\n        \"min\": 4.080111062409028,\n        \"max\": 4219.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          25.80200758473572,\n          25.4,\n          4219.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"heartRate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1473.1708105984824,\n        \"min\": 12.0265963516126,\n        \"max\": 4237.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          75.87892376681614,\n          75.0,\n          4237.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1327.2109769547703,\n        \"min\": 23.959998189237478,\n        \"max\": 3850.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          81.96675324675324,\n          78.0,\n          3850.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TenYearCHD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1498.282992616441,\n        \"min\": 0.0,\n        \"max\": 4238.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.1519584709768759,\n          1.0,\n          0.35902299199027266\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d4ceee06",
      "metadata": {
        "papermill": {
          "duration": 0.010065,
          "end_time": "2023-11-30T19:55:02.402876",
          "exception": false,
          "start_time": "2023-11-30T19:55:02.392811",
          "status": "completed"
        },
        "tags": [],
        "id": "d4ceee06"
      },
      "source": [
        "# # **Data Preprocessing**"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0b2d23ab",
      "metadata": {
        "papermill": {
          "duration": 0.010203,
          "end_time": "2023-11-30T19:55:02.423154",
          "exception": false,
          "start_time": "2023-11-30T19:55:02.412951",
          "status": "completed"
        },
        "tags": [],
        "id": "0b2d23ab"
      },
      "source": [
        "# **Calculate missing values**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "63a65484",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-30T19:55:02.445900Z",
          "iopub.status.busy": "2023-11-30T19:55:02.444651Z",
          "iopub.status.idle": "2023-11-30T19:55:02.454870Z",
          "shell.execute_reply": "2023-11-30T19:55:02.453606Z"
        },
        "papermill": {
          "duration": 0.024045,
          "end_time": "2023-11-30T19:55:02.457393",
          "exception": false,
          "start_time": "2023-11-30T19:55:02.433348",
          "status": "completed"
        },
        "tags": [],
        "id": "63a65484"
      },
      "outputs": [],
      "source": [
        "df.drop(columns = 'education', inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "52ccb2ad",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-30T19:55:02.481400Z",
          "iopub.status.busy": "2023-11-30T19:55:02.480092Z",
          "iopub.status.idle": "2023-11-30T19:55:02.489600Z",
          "shell.execute_reply": "2023-11-30T19:55:02.488718Z"
        },
        "papermill": {
          "duration": 0.023772,
          "end_time": "2023-11-30T19:55:02.491766",
          "exception": false,
          "start_time": "2023-11-30T19:55:02.467994",
          "status": "completed"
        },
        "tags": [],
        "id": "52ccb2ad",
        "outputId": "0fcd0eb4-cfee-4c55-b1bc-b0b0a071d4f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "glucose            388\n",
              "BPMeds              53\n",
              "totChol             50\n",
              "cigsPerDay          29\n",
              "BMI                 19\n",
              "heartRate            1\n",
              "male                 0\n",
              "age                  0\n",
              "currentSmoker        0\n",
              "prevalentStroke      0\n",
              "prevalentHyp         0\n",
              "diabetes             0\n",
              "sysBP                0\n",
              "diaBP                0\n",
              "TenYearCHD           0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>glucose</th>\n",
              "      <td>388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BPMeds</th>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>totChol</th>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cigsPerDay</th>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BMI</th>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>heartRate</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>currentSmoker</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prevalentStroke</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prevalentHyp</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>diabetes</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sysBP</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>diaBP</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TenYearCHD</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "df.isna().sum().sort_values(ascending = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "ab943591",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-30T19:55:02.514423Z",
          "iopub.status.busy": "2023-11-30T19:55:02.513919Z",
          "iopub.status.idle": "2023-11-30T19:55:02.519644Z",
          "shell.execute_reply": "2023-11-30T19:55:02.518797Z"
        },
        "papermill": {
          "duration": 0.019586,
          "end_time": "2023-11-30T19:55:02.521707",
          "exception": false,
          "start_time": "2023-11-30T19:55:02.502121",
          "status": "completed"
        },
        "tags": [],
        "id": "ab943591"
      },
      "outputs": [],
      "source": [
        "#filled missing values in columns with relevant values\n",
        "df['glucose'].fillna(df['glucose'].mean(), inplace=True)\n",
        "df['BPMeds'].fillna(df['BPMeds'].mode()[0], inplace=True)\n",
        "df['cigsPerDay'].fillna(df['cigsPerDay'].mode()[0], inplace=True)\n",
        "df['totChol'].fillna(df['totChol'].mean(), inplace=True)\n",
        "df['BMI'].fillna(df['BMI'].mean(), inplace=True)\n",
        "df['heartRate'].fillna(df['heartRate'].mean(), inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "53db7eb4",
      "metadata": {
        "papermill": {
          "duration": 0.009887,
          "end_time": "2023-11-30T19:55:02.563123",
          "exception": false,
          "start_time": "2023-11-30T19:55:02.553236",
          "status": "completed"
        },
        "tags": [],
        "id": "53db7eb4"
      },
      "source": [
        "# **Data distribution**"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "061abf32",
      "metadata": {
        "papermill": {
          "duration": 0.009883,
          "end_time": "2023-11-30T19:55:02.583425",
          "exception": false,
          "start_time": "2023-11-30T19:55:02.573542",
          "status": "completed"
        },
        "tags": [],
        "id": "061abf32"
      },
      "source": [
        "**Plot the distribution for all features.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "fde978e6",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-30T19:55:02.606739Z",
          "iopub.status.busy": "2023-11-30T19:55:02.605798Z",
          "iopub.status.idle": "2023-11-30T19:55:07.634721Z",
          "shell.execute_reply": "2023-11-30T19:55:07.633637Z"
        },
        "papermill": {
          "duration": 5.044125,
          "end_time": "2023-11-30T19:55:07.637732",
          "exception": false,
          "start_time": "2023-11-30T19:55:02.593607",
          "status": "completed"
        },
        "tags": [],
        "id": "fde978e6",
        "outputId": "bf429323-184b-46f0-b5bc-75c3777ae5ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x2000 with 16 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "df.hist(figsize=(16, 20), bins=40, xlabelsize=6, ylabelsize=6);"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4226969f",
      "metadata": {
        "papermill": {
          "duration": 0.011422,
          "end_time": "2023-11-30T19:55:07.660806",
          "exception": false,
          "start_time": "2023-11-30T19:55:07.649384",
          "status": "completed"
        },
        "tags": [],
        "id": "4226969f"
      },
      "source": [
        "# **Outliers**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "6c4a4161",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-30T19:55:07.688140Z",
          "iopub.status.busy": "2023-11-30T19:55:07.687477Z",
          "iopub.status.idle": "2023-11-30T19:55:16.054180Z",
          "shell.execute_reply": "2023-11-30T19:55:16.052947Z"
        },
        "papermill": {
          "duration": 8.38482,
          "end_time": "2023-11-30T19:55:16.058972",
          "exception": false,
          "start_time": "2023-11-30T19:55:07.674152",
          "status": "completed"
        },
        "tags": [],
        "id": "6c4a4161",
        "outputId": "10d0eb9c-0d06-43c1-c967-02b1342c4825",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x4000 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, axes = plt.subplots(nrows=(len(df.columns) + 1) // 2, ncols=2, figsize=(16, 40))\n",
        "for idx,column in enumerate(df):\n",
        "    row_idx = idx // 2\n",
        "    col_idx = idx % 2\n",
        "    sns.boxenplot( x='TenYearCHD' , y= column , data=df, ax=axes[row_idx, col_idx])\n",
        "    plt.xlabel('TenYearCHD')\n",
        "    plt.ylabel(column)\n",
        "    plt.title(f\"{column} Distribution\")\n",
        "    plt.tight_layout();"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "08b1f168",
      "metadata": {
        "papermill": {
          "duration": 0.014231,
          "end_time": "2023-11-30T19:55:16.087966",
          "exception": false,
          "start_time": "2023-11-30T19:55:16.073735",
          "status": "completed"
        },
        "tags": [],
        "id": "08b1f168"
      },
      "source": [
        "**There is not a lot of outliers**"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "74c1630e",
      "metadata": {
        "papermill": {
          "duration": 0.014116,
          "end_time": "2023-11-30T19:55:16.116697",
          "exception": false,
          "start_time": "2023-11-30T19:55:16.102581",
          "status": "completed"
        },
        "tags": [],
        "id": "74c1630e"
      },
      "source": [
        "# **Multicollinearity**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "9821d2c7",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-30T19:55:16.147433Z",
          "iopub.status.busy": "2023-11-30T19:55:16.146986Z",
          "iopub.status.idle": "2023-11-30T19:55:17.456447Z",
          "shell.execute_reply": "2023-11-30T19:55:17.455271Z"
        },
        "papermill": {
          "duration": 1.329245,
          "end_time": "2023-11-30T19:55:17.460452",
          "exception": false,
          "start_time": "2023-11-30T19:55:16.131207",
          "status": "completed"
        },
        "tags": [],
        "id": "9821d2c7",
        "outputId": "bcd44003-7512-49cd-85c1-c712c1fb4d40",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x2000 with 2 Axes>"
            ],
            "image/png": 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W17xPF2pYj3c1edEEs/+DF19rrcZPNzI9d/zHdrG1tVWDJ+opJCJYTs5OOnH0T00a/YmyjFlq93qb2/iJ3Lu2fPeTZk6cra79O5m25fCeozR58YRct+WhPYc17u2Jeqnri6pap7I2r9ui9/q9r3Gz/2falqkpaSpbobTqNKqpyaM/zfV9D+09ohE9R+mZ9i3Uqc+rKlSokE4c/VM2NswvdKsWzFqkZQtWauCIfirh66OZU2apb7eBmrVshuzz2Kf+sG6jpoz7VL0H91CZyDJaOn+5+r4+UHNWzpS7h7uKexfTsu8WmdX5etk3Wjh7iarVrnYnmnXfWfzFUq1c+JX6Du8lH18ffTF1jga+8bamL/nkuv26T8d/ph6D3lDpyAgtn79Sg954WzOWTzP9TgkrE6pHmjRQcZ9iSkxI1Jxp8zSw29ua/dUMFSqUfQHo0rkrtGzuCnXq+apKR0YoNTVVF85euFNNv2f9vP5XzflogTr0ba/QciFas2id3uv1gcYt+J9cPSz7YEf2HtXHw6bq+S6tVLl2RW399meNGzBRYz4fIf+QnMR+hRpR6jK4o+m5rZ2d2XrG9h0vHz8fDfm4vwrbF9aaRd/q/b7jNWHJ+3LL5eIoWPrl+22aP2mxXunTViFlg7V28XqN7T1BYxeMlKt7btvumKYMn6bnXmupirXK6+fvtmnCwMl6d+ZQ+QdnJ/vnfbxYB3YeVNe3O8irhJf2btuvL8bPk7uXmyrXqai46ATFRcfrhW6t5BtUUtHnYzTr/bmKj05Qj5Fd7/RHcF+Z+/l8LZm/TENGDlRJ35KaNmm6enXpo3krZ8ve3j7XOqkpqQqNCNFTLZ7QwF5DLF9PTdWRg0f1ymvtFRoeqsTLiZrwv4/Uv8dAzVz42e1u0j3vbu0bnD9zQaP6/k9Pv/iU3hrRU1eSrmj6h7M0pv/7mjDn/dv4iQC4V9jcaEFXV9cbfkBa+9sGvT3rfa3cutbaoTyQjEajVs5fpRc6tFat+jUUHBakfsN7K+ZSrH7a+HOe9ZbPXanHWzRW42aPqlRwgHoM6iZ7B3utW/WdJCkwNFBD3x+kGnWrq6R/CVWsVkEvv95Ov27epsyMTEnSpm9/VFBYkNp2fkG+/iVVvkqUOvZ8RV8t+UZXkq/ckfbfz7Ys/1kPPV5ZVR6rJO9SxfV096dU2N5OO9btyrV86/7PqEbTaioZUkLF/Yup5ZvNZDQa9cfu43c48gfP3m/2qXTDCEU0CJe7n7vqdKwt28K2OrzhSK7lS5YroaBqgXL3c5OLj4sin4iUR4CHLhzOPllTuEhhPTGkiUJqBsutpJu8w4ur9is1FX08WknRSXeyafcVo9Go5fNXqG3HF1S7fk2FhAer/4i+ir4Uoy0bf8qz3tJ5y/VEi8f1+NOPKTC4lN4c3F32DvZau8p89L29g708vDxMD6eiOaNHmzRvrDf6dlWFKuVV0q+EHn2yoRo3fVQ//rD1trX3XmY0GrVq4ddq/eqzqlGvmoLCAtX7nR6KjY7Vz5u25Vlv5fyv1Lj5o3q0aUMFBPur24DXZO9gr++++kGSlJyUrO++/F4d3nxZFR6KUmiZEL059A0d3HNYh/YeNlvX6qVrlZSUrJZt8k7eu3q4yt3L3fSwsbnhn5v3pVXzv9JjzRupUdNHFBDsr9cHdJa9g73WX/v8/+2rhatVuUZFtXzpafkH+altlxcUXDpI3yxeIyn7/+DLhd/ouVefMf0f9Hqnu2Kj4/TLv/4PHIs4mm0LB0cH02s+vt5q1PQRBYUHqniJYqpe9yHVb/ywDuw6ePs+jHvcqgVf67GnG6ph0wbyD/ZT1wGdZO9QWN9/tSHX8l8tyt6WLV5qJv8gP7Xp8ryCI4K1eklO/6DBE3XVuuOzKv9QVJ7vO/PDL/Tkc030TPvmCgj2l2+pkqrTqJbsCtvlWQf/zWg0aun8FXqpUxvVaVBLIeHBGvhu/+zj34a8j0NL5i7Tky2bqMnTjyswpJR6D+4pBwd7rV6ZffwrVKiQPL08zB4/btiqBo/WU5EilhdS4fqMRqNWzF+lFzu0Vq36Na/1695SzKVYbb1Ov27Z3BVq0uJxU7+u56A3ZO/goHWrvjWVebJlE5WvHCmfkt4KKxOql19vp0sXLunC2eyZTBIvJ+qLKXPUb0RvPdKkvkr6l1BwWJBq1qtx29t9r/tm4Vo90qye6j9VV35BvurQ72UVti+sjV9vzrX8msXfqkL1KDVt84R8A0vquc7PKCgiUOuWrTcrZ2dnJzdPN9OjqEvO78rL8Yk6f+qCnn7pSZUKDVAJfx+90LWV0lKv6tTxM7e1vfeTNQu/U/2mD6vuk3XkG1RSr/RtK3uHwtr89ZZcy3+7ZL3KV4/Uky8+Lt/Aknq2U3MFhpfS+mU5v3OO7jumh5vUUpnKpVWshJceebqeAkL89MeBE5Ik/2Bf9Rz1uirXqShv3+IqV6WMnu3cQru2/m46z4KbZzQatXjuEr3c6SXVbfCwQsNDNHTUYEVfitHmH3LfnpJU8+Eaeq17J9VrWDfX14s6F9XEaePVsPEjKhUUcG00/5s6dOCwzp/jQov/crf2Df44dFxZmVlq2+UFlfDzUUjpYLVo20wnjvypjIyM2/eBALhn3PCZnc8///yGH4C1nT9zQbExcapcvaJpmZOzk0pHRujgnkO51klPT9fRQ8dUuVpOHRsbG1WqVlEH9uZeR8o+AVvEqYhp2tL0q+kWJ3YK29vratpVHT14LP+NgjLSM3T26FmFVsqZVsXGxkYhlYJ18uDpG1pHelq6MjOyVMSZEzm3U2ZGpqKPR8s3Kme6RYONQb5RJXXx6MX/rG80GnVm71klnEuQTxmfPMtdvXJVMmQnGZE/586cV2x0nCpXr2RaVtTZSWUiS+vAntwTCunp6Tpy8KhZHRsbG1WuXsmizvdrNqjFI8+pQ6vXNP3jmUpNSb1uPMlJyXJ2db6FFt2/Lpy9oLiYeFWsljPNl1NRJ0WUC7NI+P0tPT1dxw79oYoP5UwvZWNjo4oPlTfVOXbwuDIyMszW6x/op2I+Xjq0Nyfxf/L4KS2YsVi93+khw3VGQvVo21svNXlVQ954Rwd+f7CTUtmf/3GLz7/CQ1F5brNDe4+oQrXyZssq16ho2hYXzl5UXEy8WRmnok4KLxemw3vNL9RY9sVKtWn0snq27aPlc1Zd92Tc2VPntPOX3YqsXO6m2/kgSE/P0B+Hjqt8tZyE39/b8t+f+98O7z1ikSCsVKOCDu89esPvGx+boCP7j8rVw1X9Ow5R+8c7aXCXYTqwO+/fprgx2ce/WFX51/GvbGRpHdhzINc66enpOnzwiKpUz5lOzMbGRlWqV86zzuEDR3Ts8B96ovnjBduAB8T5M+fz3a+rlEu/7mAe/bqUlFSt+/I7+fh6q5hP9tR8O3/ZrSxjlqIvxqjDM6/pxSbtNLL/GF08fynXdSBbRnqGThz+U5FVc44nNjY2inyonI7uy70/fHTfMUU+ZH78KV890qL8gV2H9NoTb6j38/014/1ZSkzIuZjQ2bWoSgaU0OY1W5WakqbMjEx9v2qDXNxdFBQRWHANvI9lpGfozyN/qVzVnFsY2djYqFzVMjq2P/cLcY/tO65yVc2njo2qXk5H9/1heh4WGaqdW35X7KU4GY1GHdh5SOdPXVBUtbx/c6QkX5GjkwO3h7kFZ8+cU0x0rKrWqGpaVtS5qMpGldG+3/cV6HslJyXLYDDI2dlylijkuJv7BiGlg2WwMWj9VxuUmZmp5KRkbVi9SRWqRXHbAwCSbvEeifmVlpamtLQ084VZRonpeVBAYmPiJMk0Zdvf3DzcFBsTn2udy/HZ92X795Qn7p5uOvVn7kmqhLgEzZ++UE1a5pwYqFqzslYu+FIb1m5S3UfrKC4mTvM+W5AdV3Rc/hoESdKVy1eUlWW0mMK0qJuTLp2KvqF1rJ35nVw8nRVSKe97PODWpV5OlTHLaDGFqaOro+LPJuRZ7+qVq5rXZYEyMzJlY2Oj2h1qya987ve/ybiaoW3zf1NIrRASibcg7tr+0v1f+0t3TzfF5bHPSri2v7So4+GmU3+eMj1/5PEG8i5RXJ7FPHX86Al99tFMnfrztIaPs7yPniTt//2ANn63WaMnjsh/g+5jcdeOX/+e8sbNw03xMblvq8vxidnHtlyOh6f/OnNtvXGytbM13evwn2X+/v9Iv5qusUPG69Ue7VXcp5jOn7G82tjDy13dBrymsDKhSk9P17pV6zWwy1CN+/w9hZbO+55I97Ocz99ym535K/fREfEx8blsL1fFxcZLyvnO5lrmH79xnnruCYWUDlJRl6I6tOewZk+Zr7joOHXo9bJZvX4dBumPwyeUfjVdjVs8qhdfa33T7XwQJP79O/Ffn7urh5tO/3U21zrZ29L1X+XNt9N/uXDtu7bosyV6ucdLCgoP1IbVmzT0jRH6aP44s3vf4ObERmdPz+zhYX4PV3dPd1Nf4t8S4hKUlZmVa52T/zj+/dPqlWuzR2xUJEmfHzn9un995v84Rv3b3/26f9/nN7tfZ76dvlz8taZ/9LlSU1LlV8pP700eJbtr02WeO3NOxiyjFsxcrNf7dJaTs5NmTZmtAa8P0aeLJpnKwdzfx75/T23u6uGqs3+dy7VOfEyCxbSZru6uio/J6TdUqB6lh+pVUfGSxXTh9EUt+nSp/tf7A42YNlQ2hWxkMBg06KN+Gjdgol5t9JoMNga5uLtowPg+ZiMXkbfEhKRr2858W7h4uOjsX+dzrRMfm9u2c1FCbM62a9frBc0cO1s9W/RVoUKFZLAxqEO/dipdMTz3OOITtXLW12rQNPcRcbgxsdExkiQPT/P9p4enh2Jjcr9FQX6kpaVpyoef6NEmDc1mn4Glu7lv4OPrrREfv63/DRqvKe99qqzMLJWOCtfQCYPz3V7cZ8jbPPDynUhcunSpFi9erJMnT+rq1atmr+3cufO6dceMGaPhw4ebLwxylkK4Xxny54fVGzRx9GTT83cnDrvt75mcdEVv9xyugOAAvdT5RdPyKjUrq2PPV/TR6MkaO3Sc7Ozs1Kbj89q3a/91R3Dg9tu06Eft2bhPHce+zHRgdyk7Bzu1HNtCGanpOrP3rH6Z/aucizurZDnzE6VZGVn6fsIGGY1SnY61rBTtvWn96h/04aiPTM9Hf3T7knZPPfOE6e/gsCB5enmoT5cBOnvqrEr6lzQre+LYn3q713C169xGVWtWuW0x3Us2rN2kyWNy7pk27EPrdeJmTZ4r/yA/NWhSL88yfqV85VcqJ/FfpnxpnT99XqsWfK23hve8E2HiH5q3aWr6OygsULZ2tpoyZpradWtjdgzsO7q3UpJTdOLoX5r18WytmOutZ9o1t0LEyI3RaJQkPdaikRo2bSBJCo4I0p7t+/T9Vxv0UrcXr1cd//Dd6u81buQE0/P3Php5298zLTVN69f8oHaduO/ojfp+9QZNHD3J9HzkxHdu6/s1bNJAVWpUUkx0nJbOWaaRA8ZowswPVNi+sIxGozIyMvR639dUtWb2KNSBo/vr+cfa6vff9qhqLX6v3Em1Hs2ZUjYgxF8Bof56s1VfHdh1UJFVy8loNOrzD2bL1d1Fw6YOUmH7wvrhy036oN+HGjnjHbl7uVkv+Afct0t/0LH9x9XrvTfk5eOpw78f1Rfj58nNy02RD5U1K5uSnKIP+n4k38CSatGBe6bfjHXffKuxI8aZnn8w+X+3/T0z0jP0dp9hMhqN6jvkrdv+fsi//+obxEXHadKoT/TIE/VVt3FtpSSnav60hfrfgA80YtJQGQyczwQedPlKJH700UcaPHiwXn75Za1atUqvvPKK/vjjD/3222/q1q3bf9YfOHCgevfubbbMtUWZPEoD/61GveqKiIowPU+/mi5Jio+Nl2cxD9Py+Nh4hYQH5boOFzcX2RSyUfy/rhKPi4mXu5f5FVxXkq9ocPehcnRy1LAPBsvWzvyr9EzbFmrZprlio2NV1LmoLpy7qJmTvlAJ37ynaMR/K+JSRDY2BiXFm98PLyk+Wc7u159C48elW7Vp8Ra9OqadSgSzHW43BxcHGWwMSklIMVuekpCiIm55TytrsDHI1Sf7ohLPQE/Fn4nX7pW/myUSszKytH7CD0q6lKQnhzZhNOJNqlWvhspEljY9T0/PvhgoLjZensU8TcvjYuIVEpH7yF3Xa/vLv6+CNNWJjbe44vWfSkdlv++ZfyUS/zz+l/p0GaAnWzZR246cFP9b9YerKaJczpXaOce2BHl4mR/bgvI8tjlnH9v+ta3iY+NNIzXcPd2VkZ6hpMRks1GJ2WWyt+ee7Xv11x8n1eyHZ7NfzM5t6MXH2qv1K8+qTefnc33/8HJh2r/7wZ3eNOfzNx+JHR8bbzEDwt/cPN1y2V4JphHAf2+T+Nh4efzj90l8bIKCwwPzjCWiXLgyMzN14dxFs4RvMe/sKfwCgv2VlZWlyaM/UfM2TVWoEFOJ/ZPz378T/7VtEmLjLUZn/y17Wyb8q3yCxSip6/n7N6h/kJ/Zcr9AX126cGOzMSBb7Xo1/3X8y96nxsbG/ev4F6fQiNxHUbu6u8qmkI1iY81HwsXFxOV6/Nu0frPSUtPU+KlHC6IJD4Sa9aqrdK79ujizfl1cbLxCwnP/nfJ3v+7fo3/jYsz3m1L2NKlOzk7yDfBVmagItazfWls3/KQGj9c3HWtLBQeYyru5u8rFzYXpTa/j72NfQi77v3+Pwvmbm6erEuIum5ePS5CbZ+7lJcnbt7ic3Zx1/vRFRVYtp/07DmjnT7s1fd1UFXHK7m906Buofb/t1+bVW/R0u6dusWX3P2fXote2nfm2uBx7Oc9t4eaR27a7bBqRejXtqpZMW643R3dTxVrZUy8GhPrrr6MntXrBOrNEYsqVVI19a4Icizio5+huTKd4k+rUr6NyUTmf59Vr+8/YmDh5FfMyLY+NiVVYROgtv19GeoaG9B2m8+cu6OPpExiNeAPu5r7BN0vXqkjRInqlx0umMr2H99SrTV/T4X1HVToq9xHEAB4cN3yPxH+aMmWKpk2bpo8//liFCxdWv3799N1336lHjx5KSMh7yrq/2dvby8XFxezB8FjciiJOReTrX9L0KBUcIA9Pd+3atttUJjnpig7tO6wy5Uvnug47OzuFlQ7Vrt9+Ny3LysrS7t9+V9monDrJSVc0qNvbsrOz1fDxb6uwfe5JDIPBIM9inrJ3sNeGtZtUzLvYAzu1W0GxtbNVybCSOrb7hGlZVlaW/th9XAFl/PKst3nJFv0wf7NeHtlWfuG5T5OJglXItpC8gr10Zm/O9EXGLKPO7jur4mHFb3g9RqNRWf+Yt//vJOLlcwl64u3H5eDscJ3ayE0RpyLyDShpepQKLiUPL3ftNNtfJuvgvkMqWz73i3zs7OwUXibMbB+blZWlXdt251lHkv44nH2vlH8mwf7840+91bm/HnuqkTq88fItte1+U8TJUSX9S5geAcH+cvd00+7f9pjKXEm6osP7j5qddP0nOzs7hZYO0e//qJOVlaXft+8x1QktEyxbW1uzMqf/OqNL56NNHcZB/+unj+aN00dzsx/dB3eVJP3v01F68tm87/t1/MgJi5O2D5Lszz9Yv/+217QsKytLe7bvzXOblY4K155/lJek3b/+btoW3iWLy93TzWydV5Ku6Mj+o4q4Tgf/+NETsrGxkZt73idljVlZyszINI2CQw47O1uFlA7Wnt9y7imUlZWlPb/ty/Nzj4gK157t/9qW2/YoIirsht+3eIli8ijmrjP/mj717Mlzpvu44cYUcSoivwBf0yMwuJQ8vDy089ddpjLJSck6sO+QypYvm+s67OzsFFEm3KxOVlaWdmzblWudb1auVa16NS2mG0Pe8u7X5fTRbrRft/u33aZl2f263SoTlXsdSTIaJRlzkpflKmRv09N/5dzm4nJCoi7HX1bxEjf+e/ZBY2tnq6CIQO3bkXPf0KysLO3ffkBhkbknL8IiQ7V/u/l9Rvdu259neUmKuRirpIQkU4IrLTX74jibf42aMdgYOK7dIFs7WwWGl9KBHTkXgWVlZWn/jkMKLZd74j40Mlj7t5tfNLbvtwMKi8w+95GZkanMjEyL0Uw2NjZm2yUlOUVje42XrW0h9frfGypszwxCN8vJqYj8AvxMj6CQQHl6eWj7rztMZZKTknVg70FFVoi8pff6O4l46q/TmjjtQ7m65f37Ejnu5r5BWupV2fzr3LxNoey0gdGYdROtBHC/ytflPSdPnlStWtlTyTk6OioxMVGS9NJLL6lGjRqaNGnS9ao/EJwciijUN9D0PMjHXxVCyir2crxOXcr9PiooOAaDQc1ffFoLZiySb4CvfEp664upc+VZzEO16tc0levfZZBqNaipp1tnD/Fv2ba5Phj2ocLLhCkiMlwr5q9SakqqHmvWSFJOEjEtNU393u2jK8kpupKcPeLK1d3FdPX+ktnLVLVmFRlsDNr6w09aPGupBr/Xn6v7C0CdljW19IMV8gsrKb8IX21d8Yuupqar8mOVJElL3l8uF08XNX41e5ttWrxF6+dsUOv+z8jd202Jsdn7q8KOhWXvaG+1djwIop6M1KYpm1UsxEvFQopp3+p9Sk/LUHj97B+zGyZtkpNHEVV78SFJ0u4Vv8srxEsu3s7KTM/SqV2ndPTHY6rTobaka0nED79X9IkYNe73qIxZRl2JvyJJsi9qr0K2fL/yw2AwqOWLLTRv+gL5BZSUT0kffT51tryKeapO/ZxpY/u8NkB1GtRS8+ezpxh6tk1L/W/YBwovG6bS5SK0bP4KpaakqnGzxyRJZ0+d1fdrN6h67WpycXPW8aMnNGXcNJWvHGUaQXDi2J/q81p/Va1ZRa3atjTdr8qmkI3c3N3u7AdxDzAYDHr6+ae0aOZS+fqXkHdJb839ZIE8vDxUs141U7lBrw9TzfrV1fS57Kllm7/YVB8O/1hhZUIVXi5MqxZ+pdSUNDV66hFJklNRJz3arKGmT/hczi5FVcSpiD75YLpKR0WYOrQl/MxHcl+Oz96X+gf5mUYxrlrwlbxLeisg2F9Xr17Vt6vWa8/2fRrxUe73xHxQPP1iU00YPkmhZUIUXi5UXy78RqkpaWr4VPY0lR8O+0gexT3Vvlv21IdNn39Cg14bphXzvtRDtato87dbdOzgcXUb1EVS9v9Bs+ef1OKZy1TSv4S8SxbXvE8WysPLXTWu/R8c2nNYh/cfVfkqkXJ0ctShvYc148NZqvf4wyrqkj2Cf+PazbItZKtSoQGyK2ynYwf+0Owp81Xn0VqMAMjD0y88pYkjJiu0TLDCyobqq4WrlZqapoZP1ZckTXhnkjyLeZimG23a+gkN7vKOVs77SlVrV9aP323VHwf/0OsDO5vWmZiQpEsXohV7KXv/d/ZawtDd003unm7Zv2nbNNPCzxYrKCxQQeGB+uGbjTrz1xn1G2M+wwtujsFg0LMvttCc6fPlF+CrEr4lNGPKrOzjX4PapnK9X+urOg1qq+XzzSVJrdo+ozFDxyqibLjKREZo6bXjX5OnG5ut//TJM9qzc6/e+3jUnWzWfcdgMKjFi09r/oyF8r32O2XW1DnyLOah2v/o1/XrMki1/9Gve6ZtC70/bLzCyoSpdGS4ll/r1zVulj069Nzpc9r47Y+qUrOS3NxcdelitBbNWqLCDoX1UJ3s36Z+pXxVs14NTflgmt4c/IaKOBXRzElfyD/QTxWrlr/zH8Y95MnnH9fUkZ8puHSQQssGa82idUpLTVO9px6WJE0Z8anci7nrha7PSZKaPPeYRrw+Rl/PX6NKtSro5/W/6vihE+rU/xVJUuqVVC2buVLV6leVm6erLpy5qPmTF8nbr7gqVI+SlJ2MdHJ20tSRn6nlK09fm9p0oy6evaRKtSpY54O4BzV5/lFNGzVTQaVLKbhMkNYtXq+0lDTVfTJ7v/jJuzPkXsxNrbs8I0l6rFUjjX7jfa1esE4Va5XXL+u36cShP/V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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "corr = df.drop(columns= 'TenYearCHD').corr()\n",
        "fig , ax = plt.subplots(figsize=(25 , 20))\n",
        "sns.heatmap(corr ,annot= True , ax=ax , cmap= 'Greens');"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8f111952",
      "metadata": {
        "papermill": {
          "duration": 0.01774,
          "end_time": "2023-11-30T19:55:17.496950",
          "exception": false,
          "start_time": "2023-11-30T19:55:17.479210",
          "status": "completed"
        },
        "tags": [],
        "id": "8f111952"
      },
      "source": [
        "# **Splitting data for train and test**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "d39c2ba2",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-30T19:55:17.533556Z",
          "iopub.status.busy": "2023-11-30T19:55:17.533119Z",
          "iopub.status.idle": "2023-11-30T19:55:17.545305Z",
          "shell.execute_reply": "2023-11-30T19:55:17.544046Z"
        },
        "papermill": {
          "duration": 0.033358,
          "end_time": "2023-11-30T19:55:17.547718",
          "exception": false,
          "start_time": "2023-11-30T19:55:17.514360",
          "status": "completed"
        },
        "tags": [],
        "id": "d39c2ba2",
        "outputId": "75b9a1db-ca35-4c7f-fdc4-2c8eed13e3e3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape: (3390, 14)\n",
            "y_train shape: (3390,)\n",
            "X_test shape: (848, 14)\n",
            "y_test shape: (848,)\n"
          ]
        }
      ],
      "source": [
        "X = df.drop(columns=['TenYearCHD'])\n",
        "target = df['TenYearCHD']\n",
        "\n",
        "X_train , X_test , y_train , y_test = train_test_split(X ,target ,test_size=0.2 , random_state=42 )\n",
        "print(\"X_train shape:\", X_train.shape)\n",
        "print(\"y_train shape:\", y_train.shape)\n",
        "print(\"X_test shape:\", X_test.shape)\n",
        "print(\"y_test shape:\", y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "52f1bc8a",
      "metadata": {
        "papermill": {
          "duration": 0.01787,
          "end_time": "2023-11-30T19:55:17.669349",
          "exception": false,
          "start_time": "2023-11-30T19:55:17.651479",
          "status": "completed"
        },
        "tags": [],
        "id": "52f1bc8a"
      },
      "source": [
        "# **Logistic regression model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "61813138",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-30T19:55:17.707980Z",
          "iopub.status.busy": "2023-11-30T19:55:17.707589Z",
          "iopub.status.idle": "2023-11-30T19:55:17.788096Z",
          "shell.execute_reply": "2023-11-30T19:55:17.786355Z"
        },
        "papermill": {
          "duration": 0.104836,
          "end_time": "2023-11-30T19:55:17.792684",
          "exception": false,
          "start_time": "2023-11-30T19:55:17.687848",
          "status": "completed"
        },
        "tags": [],
        "id": "61813138",
        "outputId": "544ddf16-82dd-44c1-de81-f3bffda8c071",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "# LR_model = make_pipeline(\n",
        "#     SimpleImputer(strategy='mean'),\n",
        "#     MinMaxScaler(),\n",
        "#     LogisticRegression()\n",
        "# )\n",
        "# LR_model.fit(X_train,y_train)\n",
        "\n",
        "# Applying MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Initializing and fitting the Logistic Regression model\n",
        "LR_model = LogisticRegression()\n",
        "LR_model.fit(X_train_scaled, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "b4ad874e",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-30T19:55:17.877842Z",
          "iopub.status.busy": "2023-11-30T19:55:17.877105Z",
          "iopub.status.idle": "2023-11-30T19:55:17.903068Z",
          "shell.execute_reply": "2023-11-30T19:55:17.901606Z"
        },
        "papermill": {
          "duration": 0.073298,
          "end_time": "2023-11-30T19:55:17.907155",
          "exception": false,
          "start_time": "2023-11-30T19:55:17.833857",
          "status": "completed"
        },
        "tags": [],
        "id": "b4ad874e",
        "outputId": "d997e941-f154-46ba-ea02-250c28f6eb6e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy :  0.8540\n",
            "Testing accuracy :  0.8573\n"
          ]
        }
      ],
      "source": [
        "# get predictions for training and testing data\n",
        "y_training_pred = LR_model.predict(X_train_scaled)\n",
        "y_testing_pred = LR_model.predict(X_test_scaled)\n",
        "\n",
        "#get accuracy for training and testing\n",
        "training_acc = accuracy_score(y_train, y_training_pred)\n",
        "testing_acc = accuracy_score(y_test, y_testing_pred)\n",
        "\n",
        "print(f\"Training accuracy : {training_acc: .4f}\")\n",
        "print(f\"Testing accuracy : {testing_acc: .4f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "932dc585",
      "metadata": {
        "papermill": {
          "duration": 0.040683,
          "end_time": "2023-11-30T19:55:17.988385",
          "exception": false,
          "start_time": "2023-11-30T19:55:17.947702",
          "status": "completed"
        },
        "tags": [],
        "id": "932dc585"
      },
      "source": [
        "# **Confusion matrix**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "88ac06e6",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-30T19:55:18.046303Z",
          "iopub.status.busy": "2023-11-30T19:55:18.045878Z",
          "iopub.status.idle": "2023-11-30T19:55:18.369314Z",
          "shell.execute_reply": "2023-11-30T19:55:18.368083Z"
        },
        "papermill": {
          "duration": 0.347977,
          "end_time": "2023-11-30T19:55:18.372075",
          "exception": false,
          "start_time": "2023-11-30T19:55:18.024098",
          "status": "completed"
        },
        "tags": [],
        "id": "88ac06e6",
        "outputId": "e7cb5acc-a29e-4dbe-8450-adfd5c5a65b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but LogisticRegression was fitted without feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7cbaba66cc40>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ConfusionMatrixDisplay.from_estimator(LR_model,X_test,y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "46097f6b",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-11-30T19:55:18.413886Z",
          "iopub.status.busy": "2023-11-30T19:55:18.413480Z",
          "iopub.status.idle": "2023-11-30T19:55:18.444606Z",
          "shell.execute_reply": "2023-11-30T19:55:18.443142Z"
        },
        "papermill": {
          "duration": 0.05689,
          "end_time": "2023-11-30T19:55:18.448951",
          "exception": false,
          "start_time": "2023-11-30T19:55:18.392061",
          "status": "completed"
        },
        "tags": [],
        "id": "46097f6b",
        "outputId": "c7304682-f5ae-4a3c-a7bb-0597cc5240b5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00       724\n",
            "           1       0.15      1.00      0.26       124\n",
            "\n",
            "    accuracy                           0.15       848\n",
            "   macro avg       0.07      0.50      0.13       848\n",
            "weighted avg       0.02      0.15      0.04       848\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but LogisticRegression was fitted without feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test , LR_model.predict(X_test)))"
      ]
    }
  ],
  "metadata": {
    "kaggle": {
      "accelerator": "none",
      "dataSources": [
        {
          "datasetId": 222487,
          "sourceId": 478477,
          "sourceType": "datasetVersion"
        }
      ],
      "dockerImageVersionId": 30587,
      "isGpuEnabled": false,
      "isInternetEnabled": true,
      "language": "python",
      "sourceType": "notebook"
    },
    "kernelspec": {
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