CVD_Prediction.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "sns.set()\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "from scipy import stats\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "wgQ7mHVKTkq9"
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      "source": [
        "# Load data from a CSV file into a pandas DataFrame named 'df'\n",
        "\n",
        "df = pd.read_csv(\"data_cardiovascular_risk.csv\")"
      ]
    },
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              "   id  age  education sex is_smoking  cigsPerDay  BPMeds  prevalentStroke  \\\n",
              "0   0   64        2.0   F        YES         3.0     0.0                0   \n",
              "1   1   36        4.0   M         NO         0.0     0.0                0   \n",
              "2   2   46        1.0   F        YES        10.0     0.0                0   \n",
              "3   3   50        1.0   M        YES        20.0     0.0                0   \n",
              "4   4   64        1.0   F        YES        30.0     0.0                0   \n",
              "\n",
              "   prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose  \\\n",
              "0             0         0    221.0  148.0   85.0    NaN       90.0     80.0   \n",
              "1             1         0    212.0  168.0   98.0  29.77       72.0     75.0   \n",
              "2             0         0    250.0  116.0   71.0  20.35       88.0     94.0   \n",
              "3             1         0    233.0  158.0   88.0  28.26       68.0     94.0   \n",
              "4             0         0    241.0  136.5   85.0  26.42       70.0     77.0   \n",
              "\n",
              "   TenYearCHD  \n",
              "0           1  \n",
              "1           0  \n",
              "2           0  \n",
              "3           1  \n",
              "4           0  "
            ],
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\"number\",\n        \"std\": 22.29202954152076,\n        \"min\": 83.5,\n        \"max\": 295.0,\n        \"num_unique_values\": 226,\n        \"samples\": [\n          144.0,\n          197.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diaBP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.023581073495864,\n        \"min\": 48.0,\n        \"max\": 142.5,\n        \"num_unique_values\": 142,\n        \"samples\": [\n          142.5,\n          73.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.115448755360154,\n        \"min\": 15.96,\n        \"max\": 56.8,\n        \"num_unique_values\": 1259,\n        \"samples\": [\n          27.68,\n          30.76\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"heartRate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.971868473429426,\n        \"min\": 45.0,\n        \"max\": 143.0,\n        \"num_unique_values\": 68,\n        \"samples\": [\n          106.0,\n          58.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.244753474941984,\n        \"min\": 40.0,\n        \"max\": 394.0,\n        \"num_unique_values\": 132,\n        \"samples\": [\n          115.0,\n          53.0\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          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# Display the first 5 rows of the DataFrame 'df'\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5bUURzfPT9pj",
        "outputId": "a1b8d9b9-0d7b-4f3a-9f7c-d8f06a260c90"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3390, 17)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Retrieve the dimensions (number of rows, number of columns) of the DataFrame 'df'.\n",
        "\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jqKaewJ-VJ1Y",
        "outputId": "c1e09197-d1a4-4585-e829-24c98910a7e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3390 entries, 0 to 3389\n",
            "Data columns (total 17 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   id               3390 non-null   int64  \n",
            " 1   age              3390 non-null   int64  \n",
            " 2   education        3303 non-null   float64\n",
            " 3   sex              3390 non-null   object \n",
            " 4   is_smoking       3390 non-null   object \n",
            " 5   cigsPerDay       3368 non-null   float64\n",
            " 6   BPMeds           3346 non-null   float64\n",
            " 7   prevalentStroke  3390 non-null   int64  \n",
            " 8   prevalentHyp     3390 non-null   int64  \n",
            " 9   diabetes         3390 non-null   int64  \n",
            " 10  totChol          3352 non-null   float64\n",
            " 11  sysBP            3390 non-null   float64\n",
            " 12  diaBP            3390 non-null   float64\n",
            " 13  BMI              3376 non-null   float64\n",
            " 14  heartRate        3389 non-null   float64\n",
            " 15  glucose          3086 non-null   float64\n",
            " 16  TenYearCHD       3390 non-null   int64  \n",
            "dtypes: float64(9), int64(6), object(2)\n",
            "memory usage: 450.4+ KB\n"
          ]
        }
      ],
      "source": [
        "# Display a concise summary of the DataFrame 'df', including data types, non-null values, and memory usage.\n",
        "\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "cNJuEULLYTaz",
        "outputId": "e5ec78a0-c4a3-4766-8280-f1edf2381019"
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      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                  count         mean         std     min     25%      50%  \\\n",
              "id               3390.0  1694.500000  978.753033    0.00  847.25  1694.50   \n",
              "age              3390.0    49.542183    8.592878   32.00   42.00    49.00   \n",
              "education        3303.0     1.970936    1.019081    1.00    1.00     2.00   \n",
              "cigsPerDay       3368.0     9.069477   11.879078    0.00    0.00     0.00   \n",
              "BPMeds           3346.0     0.029886    0.170299    0.00    0.00     0.00   \n",
              "prevalentStroke  3390.0     0.006490    0.080309    0.00    0.00     0.00   \n",
              "prevalentHyp     3390.0     0.315339    0.464719    0.00    0.00     0.00   \n",
              "diabetes         3390.0     0.025664    0.158153    0.00    0.00     0.00   \n",
              "totChol          3352.0   237.074284   45.247430  107.00  206.00   234.00   \n",
              "sysBP            3390.0   132.601180   22.292030   83.50  117.00   128.50   \n",
              "diaBP            3390.0    82.883038   12.023581   48.00   74.50    82.00   \n",
              "BMI              3376.0    25.794964    4.115449   15.96   23.02    25.38   \n",
              "heartRate        3389.0    75.977279   11.971868   45.00   68.00    75.00   \n",
              "glucose          3086.0    82.086520   24.244753   40.00   71.00    78.00   \n",
              "TenYearCHD       3390.0     0.150737    0.357846    0.00    0.00     0.00   \n",
              "\n",
              "                     75%     max  \n",
              "id               2541.75  3389.0  \n",
              "age                56.00    70.0  \n",
              "education           3.00     4.0  \n",
              "cigsPerDay         20.00    70.0  \n",
              "BPMeds              0.00     1.0  \n",
              "prevalentStroke     0.00     1.0  \n",
              "prevalentHyp        1.00     1.0  \n",
              "diabetes            0.00     1.0  \n",
              "totChol           264.00   696.0  \n",
              "sysBP             144.00   295.0  \n",
              "diaBP              90.00   142.5  \n",
              "BMI                28.04    56.8  \n",
              "heartRate          83.00   143.0  \n",
              "glucose            87.00   394.0  \n",
              "TenYearCHD          0.00     1.0  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <td>3390.0</td>\n",
              "      <td>1694.500000</td>\n",
              "      <td>978.753033</td>\n",
              "      <td>0.00</td>\n",
              "      <td>847.25</td>\n",
              "      <td>1694.50</td>\n",
              "      <td>2541.75</td>\n",
              "      <td>3389.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>3390.0</td>\n",
              "      <td>49.542183</td>\n",
              "      <td>8.592878</td>\n",
              "      <td>32.00</td>\n",
              "      <td>42.00</td>\n",
              "      <td>49.00</td>\n",
              "      <td>56.00</td>\n",
              "      <td>70.0</td>\n",
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              "    <tr>\n",
              "      <th>education</th>\n",
              "      <td>3303.0</td>\n",
              "      <td>1.970936</td>\n",
              "      <td>1.019081</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.00</td>\n",
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              "      <td>4.0</td>\n",
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              "      <th>cigsPerDay</th>\n",
              "      <td>3368.0</td>\n",
              "      <td>9.069477</td>\n",
              "      <td>11.879078</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>20.00</td>\n",
              "      <td>70.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BPMeds</th>\n",
              "      <td>3346.0</td>\n",
              "      <td>0.029886</td>\n",
              "      <td>0.170299</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prevalentStroke</th>\n",
              "      <td>3390.0</td>\n",
              "      <td>0.006490</td>\n",
              "      <td>0.080309</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prevalentHyp</th>\n",
              "      <td>3390.0</td>\n",
              "      <td>0.315339</td>\n",
              "      <td>0.464719</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>diabetes</th>\n",
              "      <td>3390.0</td>\n",
              "      <td>0.025664</td>\n",
              "      <td>0.158153</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>totChol</th>\n",
              "      <td>3352.0</td>\n",
              "      <td>237.074284</td>\n",
              "      <td>45.247430</td>\n",
              "      <td>107.00</td>\n",
              "      <td>206.00</td>\n",
              "      <td>234.00</td>\n",
              "      <td>264.00</td>\n",
              "      <td>696.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sysBP</th>\n",
              "      <td>3390.0</td>\n",
              "      <td>132.601180</td>\n",
              "      <td>22.292030</td>\n",
              "      <td>83.50</td>\n",
              "      <td>117.00</td>\n",
              "      <td>128.50</td>\n",
              "      <td>144.00</td>\n",
              "      <td>295.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>diaBP</th>\n",
              "      <td>3390.0</td>\n",
              "      <td>82.883038</td>\n",
              "      <td>12.023581</td>\n",
              "      <td>48.00</td>\n",
              "      <td>74.50</td>\n",
              "      <td>82.00</td>\n",
              "      <td>90.00</td>\n",
              "      <td>142.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BMI</th>\n",
              "      <td>3376.0</td>\n",
              "      <td>25.794964</td>\n",
              "      <td>4.115449</td>\n",
              "      <td>15.96</td>\n",
              "      <td>23.02</td>\n",
              "      <td>25.38</td>\n",
              "      <td>28.04</td>\n",
              "      <td>56.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>heartRate</th>\n",
              "      <td>3389.0</td>\n",
              "      <td>75.977279</td>\n",
              "      <td>11.971868</td>\n",
              "      <td>45.00</td>\n",
              "      <td>68.00</td>\n",
              "      <td>75.00</td>\n",
              "      <td>83.00</td>\n",
              "      <td>143.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>glucose</th>\n",
              "      <td>3086.0</td>\n",
              "      <td>82.086520</td>\n",
              "      <td>24.244753</td>\n",
              "      <td>40.00</td>\n",
              "      <td>71.00</td>\n",
              "      <td>78.00</td>\n",
              "      <td>87.00</td>\n",
              "      <td>394.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TenYearCHD</th>\n",
              "      <td>3390.0</td>\n",
              "      <td>0.150737</td>\n",
              "      <td>0.357846</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
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              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    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-7dfa2222-acf8-478a-8052-77b80b788c48 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\": 15,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.69743143385122,\n        \"min\": 3086.0,\n        \"max\": 3390.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3303.0,\n          3376.0,\n          3390.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 429.8048363358407,\n        \"min\": 0.006489675516224189,\n        \"max\": 1694.5,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          132.60117994100295,\n          25.7949644549763,\n          1694.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 250.39673383608834,\n        \"min\": 0.08030854328226925,\n        \"max\": 978.7530332009194,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          22.29202954152076,\n          4.115448755360154,\n          978.7530332009194\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.20687221402545,\n        \"min\": 0.0,\n        \"max\": 107.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          45.0,\n          32.0,\n          48.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 215.69580586910774,\n        \"min\": 0.0,\n        \"max\": 847.25,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          68.0,\n          42.0,\n          117.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 430.12725604008335,\n        \"min\": 0.0,\n        \"max\": 1694.5,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          75.0,\n          49.0,\n          128.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 646.085277717158,\n        \"min\": 0.0,\n        \"max\": 2541.75,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          83.0,\n          28.04,\n          2541.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 862.6195521284487,\n        \"min\": 1.0,\n        \"max\": 3389.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          143.0,\n          70.0,\n          295.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Generate descriptive statistics for the numerical columns in the DataFrame 'df'.\n",
        "\n",
        "df.describe().T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r1j6rC41ZUMv"
      },
      "outputs": [],
      "source": [
        "# Removing the columns not having any significance with target variable\n",
        "\n",
        "df = df.drop(['id','education'],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ak2Djs-mcAnL",
        "outputId": "3751f7d3-e169-4ff9-dbe9-0f8406ab9016"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3390, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F5HNX-RHc2Sz",
        "outputId": "c5aaca50-1662-410d-9ef1-b8fa87a29108"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Categorical variables: 2\n",
            "Integer/Float variables: 5\n"
          ]
        }
      ],
      "source": [
        "# Checking numerical/categorical veriables count\n",
        "\n",
        "obj = (df.dtypes == 'object')\n",
        "object_cols = list(obj[obj].index)\n",
        "print(\"Categorical variables:\",len(object_cols))\n",
        "\n",
        "int_ = (df.dtypes == 'int')\n",
        "num_cols = list(int_[int_].index)\n",
        "print(\"Integer/Float variables:\",len(num_cols))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WyIPZ4YJhviL",
        "outputId": "40512658-b05d-4878-88c2-443b1f5026a7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex\n",
              "F    1923\n",
              "M    1467\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Checking the count of each unique value\n",
        "\n",
        "df['sex'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['is_smoking'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ArCTTukv00U3",
        "outputId": "7cddbacd-ec3d-44f9-abd0-12071af5b31d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "is_smoking\n",
              "NO     1703\n",
              "YES    1687\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "orpQElkRd7ll"
      },
      "outputs": [],
      "source": [
        "# Convert categorical to numeric\n",
        "\n",
        "df['is_smoking'] = np.where(df['is_smoking']=='YES',1,0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NNxoHzp2khUs",
        "outputId": "4b5e2e9e-5167-42af-ccca-443e226787fd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "is_smoking\n",
              "0    1703\n",
              "1    1687\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "df['is_smoking'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F9vfXlHUlV0e"
      },
      "outputs": [],
      "source": [
        "# Convert categorical to numeric\n",
        "\n",
        "df['sex'] = np.where(df['sex']=='F',1,0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F-2tcNHUnMCS",
        "outputId": "98eca7ec-01ae-4c85-8e33-14cd4b1cf3b8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex\n",
              "1    1923\n",
              "0    1467\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "df['sex'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "iO_MWoLJnQ9G",
        "outputId": "a597c507-a0d8-4e28-c4b2-768f8dde0f68"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  sex  is_smoking  cigsPerDay  BPMeds  prevalentStroke  prevalentHyp  \\\n",
              "0   64    1           1         3.0     0.0                0             0   \n",
              "1   36    0           0         0.0     0.0                0             1   \n",
              "\n",
              "   diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose  TenYearCHD  \n",
              "0         0    221.0  148.0   85.0    NaN       90.0     80.0           1  \n",
              "1         0    212.0  168.0   98.0  29.77       72.0     75.0           0  "
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      margin-bottom: 4px;\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        const element = document.querySelector('#df-a228c686-3ec9-470d-a2a7-d71d35927c3a');\n",
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              "                                                    [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",
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              "  [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-306d0bf5-4537-4461-8692-67c432473e74 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",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3390,\n  \"fields\": [\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          67,\n          69,\n          61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_smoking\",\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\": \"cigsPerDay\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.879077681778632,\n        \"min\": 0.0,\n        \"max\": 70.0,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          12.0,\n          50.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BPMeds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17029944333964092,\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\": 45.24742978889774,\n        \"min\": 107.0,\n        \"max\": 696.0,\n        \"num_unique_values\": 240,\n        \"samples\": [\n          279.0,\n          238.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sysBP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.29202954152076,\n        \"min\": 83.5,\n        \"max\": 295.0,\n        \"num_unique_values\": 226,\n        \"samples\": [\n          144.0,\n          197.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diaBP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.023581073495864,\n        \"min\": 48.0,\n        \"max\": 142.5,\n        \"num_unique_values\": 142,\n        \"samples\": [\n          142.5,\n          73.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.115448755360154,\n        \"min\": 15.96,\n        \"max\": 56.8,\n        \"num_unique_values\": 1259,\n        \"samples\": [\n          27.68,\n          30.76\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"heartRate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.971868473429426,\n        \"min\": 45.0,\n        \"max\": 143.0,\n        \"num_unique_values\": 68,\n        \"samples\": [\n          106.0,\n          58.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.244753474941984,\n        \"min\": 40.0,\n        \"max\": 394.0,\n        \"num_unique_values\": 132,\n        \"samples\": [\n          115.0,\n          53.0\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          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "df.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YLguCDDbnWRW",
        "outputId": "7e26b096-1cbf-4c9a-d510-aeebfbf84c9d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3390 entries, 0 to 3389\n",
            "Data columns (total 15 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   age              3390 non-null   int64  \n",
            " 1   sex              3390 non-null   int64  \n",
            " 2   is_smoking       3390 non-null   int64  \n",
            " 3   cigsPerDay       3368 non-null   float64\n",
            " 4   BPMeds           3346 non-null   float64\n",
            " 5   prevalentStroke  3390 non-null   int64  \n",
            " 6   prevalentHyp     3390 non-null   int64  \n",
            " 7   diabetes         3390 non-null   int64  \n",
            " 8   totChol          3352 non-null   float64\n",
            " 9   sysBP            3390 non-null   float64\n",
            " 10  diaBP            3390 non-null   float64\n",
            " 11  BMI              3376 non-null   float64\n",
            " 12  heartRate        3389 non-null   float64\n",
            " 13  glucose          3086 non-null   float64\n",
            " 14  TenYearCHD       3390 non-null   int64  \n",
            "dtypes: float64(8), int64(7)\n",
            "memory usage: 397.4 KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "59SGi9hmsRzS",
        "outputId": "74949686-4cb4-4914-a402-2a85fea64e9b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                  0\n",
              "sex                  0\n",
              "is_smoking           0\n",
              "cigsPerDay          22\n",
              "BPMeds              44\n",
              "prevalentStroke      0\n",
              "prevalentHyp         0\n",
              "diabetes             0\n",
              "totChol             38\n",
              "sysBP                0\n",
              "diaBP                0\n",
              "BMI                 14\n",
              "heartRate            1\n",
              "glucose            304\n",
              "TenYearCHD           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# Count the number of missing values in each column of the DataFrame 'df'.\n",
        "\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BSHOfyJ9sqZz",
        "outputId": "74aa1636-3811-44e0-a655-6bf3b6410e38"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# Identify duplicate rows in the DataFrame 'df' and printing it\n",
        "\n",
        "df.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZTwQDrm76tpt",
        "outputId": "5e09d815-4291-48e5-efbf-cd7231c6b097"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                0.000000\n",
              "sex                0.000000\n",
              "is_smoking         0.000000\n",
              "cigsPerDay         0.648968\n",
              "BPMeds             1.297935\n",
              "prevalentStroke    0.000000\n",
              "prevalentHyp       0.000000\n",
              "diabetes           0.000000\n",
              "totChol            1.120944\n",
              "sysBP              0.000000\n",
              "diaBP              0.000000\n",
              "BMI                0.412979\n",
              "heartRate          0.029499\n",
              "glucose            8.967552\n",
              "TenYearCHD         0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# Percentage of missing values in each column of the DataFrame\n",
        "\n",
        "df.isnull().sum()/len(df)*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "k3swGp5Ns6qU",
        "outputId": "645602db-7803-4fc7-cee0-373cb6604222"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Heatmap displaying columns having null values\n",
        "\n",
        "sns.heatmap(df.isnull())\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "vB8OPi4Kuw5k",
        "outputId": "df852c37-4825-47e7-a16e-83f4a60f82ee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age  sex  is_smoking  cigsPerDay  BPMeds  prevalentStroke  prevalentHyp  \\\n",
              "1388   64    0           1        18.0     0.0                0             1   \n",
              "\n",
              "      diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose  TenYearCHD  \n",
              "1388         0    219.0  172.5   75.0  29.29        NaN     91.0           1  "
            ],
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              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0482c2fc-d629-4d54-a18a-bedd464c52bf 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-0482c2fc-d629-4d54-a18a-bedd464c52bf');\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>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[df['heartRate']\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 64,\n        \"max\": 64,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          64\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_smoking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cigsPerDay\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 18.0,\n        \"max\": 18.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          18.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BPMeds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prevalentStroke\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prevalentHyp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diabetes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"totChol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 219.0,\n        \"max\": 219.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          219.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sysBP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 172.5,\n        \"max\": 172.5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          172.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diaBP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 75.0,\n        \"max\": 75.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          75.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 29.29,\n        \"max\": 29.29,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          29.29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"heartRate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 91.0,\n        \"max\": 91.0,\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TenYearCHD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "df[df['heartRate'].isnull()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KGe8k96izf4h"
      },
      "outputs": [],
      "source": [
        "# Dropping null values from column 'heartRate'\n",
        "\n",
        "df.dropna(subset=['heartRate'], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j1LJyRir8iTa"
      },
      "outputs": [],
      "source": [
        "# Treating missing values using mean method\n",
        "\n",
        "df.fillna(df.mean(), inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4gupXvBONGYf"
      },
      "outputs": [],
      "source": [
        "# # Treating missing values using KNN Imputer\n",
        "\n",
        "# from sklearn.impute import KNNImputer\n",
        "# imputer = KNNImputer()\n",
        "# df_imputed = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)\n",
        "# print(df_imputed.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ERUkuVCQlRdY",
        "outputId": "7148ced0-e1ff-44d8-9377-7dad461234e9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                0\n",
              "sex                0\n",
              "is_smoking         0\n",
              "cigsPerDay         0\n",
              "BPMeds             0\n",
              "prevalentStroke    0\n",
              "prevalentHyp       0\n",
              "diabetes           0\n",
              "totChol            0\n",
              "sysBP              0\n",
              "diaBP              0\n",
              "BMI                0\n",
              "heartRate          0\n",
              "glucose            0\n",
              "TenYearCHD         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 899
        },
        "id": "Qbc2OzjyKhMQ",
        "outputId": "a8f02ce0-d0c6-4cce-c7c0-a2322c63c9e0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1200 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# This visualization is useful for understanding the relationships between different features in the dataset.\n",
        "# High positive correlations (close to 1) indicate strong positive linear relationships, while high negative correlations (close to -1)\n",
        "# indicate strong negative linear relationships. A correlation coefficient close to 0 suggests no linear relationship between the variables.\n",
        "\n",
        "plt.figure(figsize=(20,12))\n",
        "cor_matrix = df.drop('TenYearCHD', axis=1).corr()\n",
        "sns.heatmap(cor_matrix, cmap='plasma',vmin=-1, vmax=1 , annot = True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "558auLdjH7le"
      },
      "outputs": [],
      "source": [
        "# Dropping cigsPerDay column since is_smoking and cigsPerDay have high correlation and has same effect on target variable\n",
        "\n",
        "df.drop('cigsPerDay',axis=1,inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vbcAHBLiqMGO"
      },
      "source": [
        "# Bivariate Analysis\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "vP8n-CAHLBEn",
        "outputId": "cc0a35c1-485c-4bc4-9e77-658055de44ed"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='sex', ylabel='TenYearCHD'>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Males are more prone to CHD\n",
        "\n",
        "sns.barplot(x=\"sex\",y=\"TenYearCHD\",data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "8bGSy3rPqgfM",
        "outputId": "1bd6785c-977e-417a-e43a-1ee57d169bf6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='is_smoking', ylabel='TenYearCHD'>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Smokers having more chances of having CHD\n",
        "\n",
        "sns.barplot(x=\"is_smoking\",y=\"TenYearCHD\",data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "1QrELG85D4rH",
        "outputId": "fe161909-9a67-4ccc-d86e-b7c58eb6e075"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='age', ylabel='TenYearCHD'>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# With increasing age there are more chances of having CHD\n",
        "\n",
        "sns.lineplot(x=\"age\", y=\"TenYearCHD\", data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "3yczTMrTEjEp",
        "outputId": "476871a0-3315-4ca7-d9b6-0b65fe0f7280"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='age', ylabel='glucose'>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# # With increasing age Glucose level is at a higher scale\n",
        "\n",
        "sns.lineplot(x=\"age\", y=\"glucose\", data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "iyiXrI2oEw6L",
        "outputId": "f424b076-fecb-4b3b-d36c-823383401cd4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='age', ylabel='is_smoking'>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Young people are\n",
        "\n",
        "sns.lineplot(x=\"age\", y=\"is_smoking\", data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xfyIQB6vot7R",
        "outputId": "68b80267-5c3f-4d3f-87dc-cedab4bbc142"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['age', 'sex', 'is_smoking', 'BPMeds', 'prevalentStroke', 'prevalentHyp', 'diabetes', 'totChol', 'sysBP', 'diaBP', 'BMI', 'heartRate', 'glucose']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "# Excluding target variable\n",
        "\n",
        "cols = list(df.iloc[:, :-1])\n",
        "print(cols)\n",
        "\n",
        "len(cols)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 997
        },
        "id": "EAv4cmsNrdGh",
        "outputId": "3ec34e03-fdc5-4189-d98b-e580dcc2818a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1400 with 15 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Checking on outliers\n",
        "\n",
        "f,ax=plt.subplots(3,5,figsize=(18,14),dpi=100)\n",
        "\n",
        "ind=0\n",
        "try:\n",
        "    for i in range(3):\n",
        "        for j in range(5):\n",
        "            ax[i,j].boxplot(x=df[cols[ind]])\n",
        "            ax[i,j].set_title(cols[ind])\n",
        "            ind+=1\n",
        "        plt.tight_layout()\n",
        "except:\n",
        "    pass\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kCWugAVtt7Xg",
        "outputId": "54f94b99-6ea5-4054-e130-8b3167c0fc73"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Skewness Values:\n",
            "prevalentStroke    12.295764\n",
            "glucose             6.439474\n",
            "diabetes            6.001020\n",
            "BPMeds              5.559597\n",
            "TenYearCHD          1.955922\n",
            "sysBP               1.177224\n",
            "BMI                 1.025120\n",
            "totChol             0.945535\n",
            "prevalentHyp        0.796196\n",
            "diaBP               0.717706\n",
            "heartRate           0.676490\n",
            "age                 0.226129\n",
            "is_smoking          0.010037\n",
            "sex                -0.272303\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Calculate skewness for each feature\n",
        "from scipy import stats\n",
        "skew_values = df.skew().sort_values(ascending=False)\n",
        "\n",
        "# Print skewness values and identify skewed features\n",
        "print(\"Skewness Values:\")\n",
        "print(skew_values)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Splitting Data into Dependent and Independent Variable"
      ],
      "metadata": {
        "id": "9bzpw9EU16S0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kvxJ8gFic_wQ"
      },
      "outputs": [],
      "source": [
        "# excluding Target\n",
        "\n",
        "x = df.iloc[:, :-1]\n",
        "\n",
        "# Taking Target variable as y\n",
        "\n",
        "y = df['TenYearCHD']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "fUk-no5ednnG",
        "outputId": "e5eaab0a-fc5d-4855-fa1c-303b65adad06"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  sex  is_smoking  BPMeds  prevalentStroke  prevalentHyp  diabetes  \\\n",
              "0   64    1           1     0.0                0             0         0   \n",
              "1   36    0           0     0.0                0             1         0   \n",
              "2   46    1           1     0.0                0             0         0   \n",
              "3   50    0           1     0.0                0             1         0   \n",
              "4   64    1           1     0.0                0             0         0   \n",
              "\n",
              "   totChol  sysBP  diaBP        BMI  heartRate  glucose  \n",
              "0    221.0  148.0   85.0  25.793929       90.0     80.0  \n",
              "1    212.0  168.0   98.0  29.770000       72.0     75.0  \n",
              "2    250.0  116.0   71.0  20.350000       88.0     94.0  \n",
              "3    233.0  158.0   88.0  28.260000       68.0     94.0  \n",
              "4    241.0  136.5   85.0  26.420000       70.0     77.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1aa7ac6a-1b78-40b9-bfe1-ca12c700d906\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>is_smoking</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>64</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>221.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>25.793929</td>\n",
              "      <td>90.0</td>\n",
              "      <td>80.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>36</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>212.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>29.770000</td>\n",
              "      <td>72.0</td>\n",
              "      <td>75.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>250.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>20.350000</td>\n",
              "      <td>88.0</td>\n",
              "      <td>94.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>233.0</td>\n",
              "      <td>158.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>28.260000</td>\n",
              "      <td>68.0</td>\n",
              "      <td>94.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>64</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>241.0</td>\n",
              "      <td>136.5</td>\n",
              "      <td>85.0</td>\n",
              "      <td>26.420000</td>\n",
              "      <td>70.0</td>\n",
              "      <td>77.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1aa7ac6a-1b78-40b9-bfe1-ca12c700d906')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1aa7ac6a-1b78-40b9-bfe1-ca12c700d906 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-1aa7ac6a-1b78-40b9-bfe1-ca12c700d906');\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-4d83d5eb-d2e9-4084-bc76-b6cb66e9907a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4d83d5eb-d2e9-4084-bc76-b6cb66e9907a')\"\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-4d83d5eb-d2e9-4084-bc76-b6cb66e9907a 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",
              "variable_name": "x",
              "summary": "{\n  \"name\": \"x\",\n  \"rows\": 3389,\n  \"fields\": [\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          67,\n          69,\n          61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_smoking\",\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\": \"BPMeds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16921450532658544,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          0.029895366218236172\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\": 44.998608551398135,\n        \"min\": 107.0,\n        \"max\": 696.0,\n        \"num_unique_values\": 241,\n        \"samples\": [\n          245.0,\n          238.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sysBP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.284776158579216,\n        \"min\": 83.5,\n        \"max\": 295.0,\n        \"num_unique_values\": 226,\n        \"samples\": [\n          144.0,\n          197.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diaBP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.024592496107566,\n        \"min\": 48.0,\n        \"max\": 142.5,\n        \"num_unique_values\": 142,\n        \"samples\": [\n          142.5,\n          73.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.1071064949908935,\n        \"min\": 15.96,\n        \"max\": 56.8,\n        \"num_unique_values\": 1260,\n        \"samples\": [\n          30.8,\n          22.99\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"heartRate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.971868473429426,\n        \"min\": 45.0,\n        \"max\": 143.0,\n        \"num_unique_values\": 68,\n        \"samples\": [\n          106.0,\n          58.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.134714197185563,\n        \"min\": 40.0,\n        \"max\": 394.0,\n        \"num_unique_values\": 133,\n        \"samples\": [\n          102.0,\n          124.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "x.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0zYWFwg8dupk",
        "outputId": "27f1f7a8-8ee3-4de3-f812-387115a9e08c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    0\n",
              "2    0\n",
              "3    1\n",
              "4    0\n",
              "Name: TenYearCHD, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "y.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pTh1rPq6dyOl"
      },
      "outputs": [],
      "source": [
        "#Splitting into Train and Test\n",
        "\n",
        "x_train , x_test , y_train , y_test = train_test_split(x, y,test_size=0.2,random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g_zWRYMweKjr",
        "outputId": "51121b3f-50b3-4d59-f8f9-04f3672ebdda"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TenYearCHD\n",
              "0    85.09775\n",
              "1    14.90225\n",
              "Name: count, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "# Calculate the percentage distribution of classes in the y_train set to identify class imbalance\n",
        "\n",
        "y_train.value_counts() * 100 / len(x_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-tyyuVRXfgvO",
        "outputId": "31fb80ee-48ec-4064-9c7b-c0b117487b0d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TenYearCHD\n",
              "0    2307\n",
              "1     404\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "y_train.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "HwIfm6ENgbzg",
        "outputId": "b68614ec-a26b-44ff-b780-18fdeabb5f92"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='TenYearCHD', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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uateuXZNMDAAAIBj5fWkoISGhMecBAADQLDTovtpHH32k9evXq6KiQk6n0+c38CwWiz744IMGTRAAACCY+B1Pb7zxhl566SW1a9dOffv2VWxsbGPOCwAAICj5HU8rV65U//79tXz5coWGhjbmnAAAAIKW3w/JdDqdGjJkCOEEAABuKX7HU58+fVReXt6YcwEAAAh6fsfTnDlztH37dm3evLkx5wMAABDU/F7zlJWVpdraWv3zP/+z5syZox/96EeyWr1bzGKx6L333mvwJAEAAIKF3/HUtm1btW3bVl27dm3M+QAAAAQ1v+Np1apVjTkPAACAZsHvNU8AAAC3Ir+vPP33f//3dY275557/D0FAABA0PE7niZMmCCLxfKD4w4dOuTvKQAAAIJOg54w/pfq6upUWVmptWvXyuVyaebMmQ2aHAAAQLDxO54SEhKuuW/kyJF65JFH9MknnygpKcnfUwAAAASdJlkwbrVaNXToUK1bt64pDg8AABAwTfbbdg6HQ1VVVU11eAAAgIDw+7bdV199Ve92p9OpvXv3qqCgQD/72c/8nhgAAEAw8jueBg4ceM3ftnO73YqLi9PcuXP9nhgAAEAw8juenn/+eZ94slgsstvt6tKli3r06NHgyQEAAAQbv+Np5MiRjTkPAACAZsHveLral19+qcrKSklSx44dueoEAABuWg2Kpw8++EDz5s3zhNMVnTp1Uk5OjtLS0ho0OQAAgGDjdzzt2rVL06dP149//GNlZ2ere/fukqRjx45p7dq1mjZtmpYuXarU1NRGmywAAECg+R1Pr7/+umJjY/X222+rZcuWnu1paWn6xS9+oUceeURLliwhngAAwE3F74dkHj58WH/7t3/rFU5XtGzZUg8//LAOHz7coMkBAAAEG7/jKTw8XA6H45r7HQ6HwsPD/T08AABAUPI7nhITE7Vy5UqVlpb67Pvss8+0atUqPhQYAADcdPxe8/RP//RPGjt2rB555BH17dtX0dHRkqTy8nLt379f7dq106xZsxptogAAAMHA7ytPnTt31nvvvacJEybI4XCosLBQhYWFcjgcevTRR/Xuu++qU6dOjTlXAACAgPP7ylNtba3Cw8OVm5ur3Nxcn/3V1dWqra1VSEijPIcTAAAgKPh95elf/uVfNHbs2GvuHzdunObNm+fv4QEAAIKS3/H00UcfaciQIdfcP2TIEBUXF/t7eAAAgKDkdzydPXtWHTp0uOb+22+/XWfOnPH38AAAAEHJ73hq27atysvLr7n/2LFjat26tb+HBwAACEp+x9N9992n1atX6+DBgz77Pv/8c61du5aPZgEAADcdv38VbsaMGfroo480atQoDRw4UD169JAkHT16VDt37lRUVJRmzJjRaBMFAAAIBn7HU4cOHbRhwwa99NJL2rFjh7Zv3y5Jat26tR566CFlZ2d/75ooAACA5qhBD2G6/fbb9eKLL8rtduvcuXOSpKioKFkslkaZHAAAQLBplCdYWiwWtWvXrjEOBQAAENT8XjAOAABwKwqqeDpx4oTy8vI0YsQI9e7dW8OGDat33Lp16zRkyBD16dNHw4cP186dO33GVFVVKTc3VwkJCYqPj9f06dN19uxZn3H79u3TmDFj1LdvXw0YMEDLly+X2+1u9PcGAABuDkEVT0ePHtWuXbvUtWtXde/evd4x77//vp555hmlp6crPz9fcXFxmjp1qj799FOvcVlZWdqzZ4/mzJmjBQsWqLy8XJMnT1Ztba1nzIkTJ5SZman27dtr2bJlmjhxohYtWqQ333yzKd8mAABoxoLqU3sHDhyoQYMGSZJycnJ04MABnzGLFi3S0KFDlZWVJUnq37+/jhw5oiVLlig/P1+SVFpaqt27d6ugoEApKSmSpOjoaGVkZGjbtm3KyMiQJBUUFCgyMlIvv/yywsLClJSUpHPnzmnp0qWaMGGCwsLCbsC7BgAAzUlQXXmyWr9/OhUVFTp+/LjS09O9tmdkZKikpEQXL16UJBUXF8tutys5OdkzJiYmRr169fL6vL3i4mKlpaV5RVJGRoacTqdKS0sb4y0BAICbTFDF0w8pKyuTdPkq0tW6d++uS5cuqaKiwjMuOjra55EJMTExnmPU1NTo1KlTiomJ8RljsVg84wAAAK4WVLftfojD4ZAk2e12r+1Xvr6y3+l0qk2bNj6vj4iI8NwKrKqqqvdYYWFhatGihedY/goJaboutdmaVfMCNxQ/HwCaWrOKp+bCarUoMrJVoKcB3JLs9haBngKAm1yziqeIiAhJl68atW/f3rPd6XR67bfb7Tp9+rTP6x0Oh2fMlStTV65AXXHx4kVduHDBM84fLpdbTmeN36//ITablb8ggGtwOi+ors4V6GkAaGbs9hbXfeW6WcXTlfVJZWVlXmuVysrKFBoaqs6dO3vGlZSUyO12e617Ki8vV8+ePSVJLVu21B133OGztqm8vFxut9tnLZSp2lr+8AYCoa7Oxc8fgCbVrBYHdO7cWd26dVNRUZHX9sLCQiUlJXl+ay41NVUOh0MlJSWeMeXl5Tp48KBSU1M921JTU7Vjxw5dunTJ61h2u13x8fFN/G4AAEBzFFRXni5cuKBdu3ZJkiorK1VdXe0JpYSEBEVFRWnatGmaNWuWunTposTERBUWFmr//v166623PMeJj49XSkqKcnNzNXv2bIWHh2vhwoWKjY3V4MGDPeMyMzO1efNmzZw5U+PGjdORI0dUUFCg7OxsnvEEAADqZXEH0WeRnDx5UmlpafXuW7lypRITEyVd/niW/Px8ffXVV4qOjtYTTzyhAQMGeI2vqqrSCy+8oO3bt6u2tlYpKSl6+umn1aFDB69x+/bt07x583To0CFFRUVp/Pjxmjx5ss9jDkzU1bl07tx5v1//Q0JCrIqMbKXcVwt1vPKbJjsP0Jx06xip52dk6JtvznPbDoCxqKhW173mKaji6WZBPAE3HvEEoCFM4qlZrXkCAAAINOIJAADAAPEEAABggHgCAAAwQDwBAAAYIJ4AAAAMEE8AAAAGiCcAAAADxBMAAIAB4gkAAMAA8QQAAGCAeAIAADBAPAEAABggngAAAAwQTwAAAAaIJwAAAAPEEwAAgAHiCQAAwADxBAAAYIB4AgAAMEA8AQAAGCCeAAAADBBPAAAABognAAAAA8QTAACAAeIJAADAAPEEAABggHgCAAAwQDwBAAAYIJ4AAAAMEE8AAAAGiCcAAAADxBMAAIAB4gkAAMAA8QQAAGCAeAIAADBAPAEAABggngAAAAwQTwAAAAaIJwAAAAPEEwAAgAHiCQAAwADxBAAAYIB4AgAAMEA8AQAAGCCeAAAADBBPAAAABognAAAAA8QTAACAAeIJAADAAPEEAABggHgCAAAwQDwBAAAYIJ4AAAAMEE8AAAAGiCcAAAADxBMAAIAB4gkAAMAA8QQAAGCAeAIAADBAPAEAABggngAAAAwQTwAAAAaaXTxt3LhRsbGxPv8sWLDAa9y6des0ZMgQ9enTR8OHD9fOnTt9jlVVVaXc3FwlJCQoPj5e06dP19mzZ2/UWwEAAM1QSKAn4K833nhDbdq08XzdoUMHz7+///77euaZZ/TYY4+pf//+Kiws1NSpU/X2228rLi7OMy4rK0tffvml5syZo/DwcL3yyiuaPHmyNmzYoJCQZvutAQAATajZFsKdd96pqKioevctWrRIQ4cOVVZWliSpf//+OnLkiJYsWaL8/HxJUmlpqXbv3q2CggKlpKRIkqKjo5WRkaFt27YpIyPjhrwPAADQvDS723Y/pKKiQsePH1d6errX9oyMDJWUlOjixYuSpOLiYtntdiUnJ3vGxMTEqFevXiouLr6hcwYAAM1Hs42nYcOGqVevXkpLS9OyZctUV1cnSSorK5N0+SrS1bp3765Lly6poqLCMy46OloWi8VrXExMjOcYAAAAf6nZ3bZr3769pk2bprvvvlsWi0UffvihXnnlFZ05c0Z5eXlyOBySJLvd7vW6K19f2e90Or3WTF0RERGhAwcONHieISFN16U2W7NtXqDJ8fMBoKk1u3i67777dN9993m+TklJUXh4uH73u9/pscceC+DM/j+r1aLIyFaBngZwS7LbWwR6CgBucs0unuqTnp6uN998U4cOHVJERISky48haN++vWeM0+mUJM9+u92u06dP+xzL4XB4xvjL5XLL6axp0DG+j81m5S8I4Bqczguqq3MFehoAmhm7vcV1X7m+KeLpajExMZIur2m68u9Xvg4NDVXnzp0940pKSuR2u73WPZWXl6tnz54NnkdtLX94A4FQV+fi5w9Ak7opFgcUFhbKZrOpd+/e6ty5s7p166aioiKfMUlJSQoLC5MkpaamyuFwqKSkxDOmvLxcBw8eVGpq6g2dPwAAaD6a3ZWnzMxMJSYmKjY2VpK0Y8cOrV27Vo8++qjnNt20adM0a9YsdenSRYmJiSosLNT+/fv11ltveY4THx+vlJQU5ebmavbs2QoPD9fChQsVGxurwYMHB+S9AQCA4Nfs4ik6OlobNmzQ6dOn5XK51K1bN+Xm5mrChAmeMcOGDdOFCxeUn5+v5cuXKzo6WosXL1Z8fLzXsV555RW98MILysvLU21trVJSUvT000/zdHEAAHBNFrfb7Q70JG42dXUunTt3vsmOHxJiVWRkK+W+Wqjjld802XmA5qRbx0g9PyND33xznjVPAIxFRbW67gXjN8WaJwAAgBuFeAIAADBAPAEAABggngAAAAwQTwAAAAaIJwAAAAPEEwAAgAGeBgkAQcZqtchqtfzwQOAW4nK55XIFx6MpiScACCJWq0Vt27a87of1AbeKujqXvv22JigCingCgCBitVpks1m15J09qjzrCPR0gKDQ8fYIPT4uWVarhXgCANSv8qyDj18CghTXhQEAAAwQTwAAAAaIJwAAAAPEEwAAgAHiCQAAwADxBAAAYIB4AgAAMEA8AQAAGCCeAAAADBBPAAAABognAAAAA8QTAACAAeIJAADAAPEEAABggHgCAAAwQDwBAAAYIJ4AAAAMEE8AAAAGiCcAAAADxBMAAIAB4gkAAMAA8QQAAGCAeAIAADBAPAEAABggngAAAAwQTwAAAAaIJwAAAAPEEwAAgAHiCQAAwADxBAAAYIB4AgAAMEA8AQAAGCCeAAAADBBPAAAABognAAAAA8QTAACAAeIJAADAAPEEAABggHgCAAAwQDwBAAAYIJ4AAAAMEE8AAAAGiCcAAAADxBMAAIAB4gkAAMAA8QQAAGCAeAIAADBAPAEAABggngAAAAwQTwAAAAaIJwAAAAPEEwAAgAHiCQAAwMAtH0/Hjh3TpEmTFBcXp+TkZM2fP18XL14M9LQAAECQCgn0BALJ4XBo4sSJ6tatm1577TWdOXNG8+bN03fffae8vLxATw8AAAShWzqeVq9erfPnz2vx4sVq27atJKmurk5z587VlClT1KFDh8BOEAAABJ1b+rZdcXGxkpKSPOEkSenp6XK5XNqzZ0/gJgYAAILWLX3lqaysTH/3d3/ntc1ut6t9+/YqKyvz+7hWq0VRUa0aOr1rslgu/+fszIGqq3M12XmA5sRmu/z/BSMiWsjtDvBkGoCfb8DXjfj5tlot1z32lo4np9Mpu93usz0iIkIOh8Pv41osFtls1/9fgr8iWt/W5OcAmhur9ea4oM7PN+ArWH6+g2MWAAAAzcQtHU92u11VVVU+2x0OhyIiIgIwIwAAEOxu6XiKiYnxWdtUVVWlr7/+WjExMQGaFQAACGa3dDylpqbq448/ltPp9GwrKiqS1WpVcnJyAGcGAACClcXtbs6/l9IwDodDQ4cOVXR0tKZMmeJ5SOZDDz3EQzIBAEC9bul4ki5/PMtzzz2n0tJStWrVSiNGjFB2drbCwsICPTUAABCEbvl4AgAAMHFLr3kCAAAwRTwBAAAYIJ4AAAAMEE8AAAAGiCcAAAADxBMAAIAB4gnww7FjxzRp0iTFxcUpOTlZ8+fP18WLFwM9LQCN4MSJE8rLy9OIESPUu3dvDRs2LNBTQpAJCfQEgObG4XBo4sSJ6tatm1577TXPk+m/++47nkwP3ASOHj2qXbt26e6775bL5RKPQ8RfIp4AQ6tXr9b58+e1ePFitW3bVpJUV1enuXPnasqUKerQoUNgJwigQQYOHKhBgwZJknJycnTgwIEAzwjBhtt2gKHi4mIlJSV5wkmS0tPT5XK5tGfPnsBNDECjsFr5qxHfj/+FAIbKysoUExPjtc1ut6t9+/YqKysL0KwAADcK8QQYcjqdstvtPtsjIiLkcDgCMCMAwI1EPAEAABggngBDdrtdVVVVPtsdDociIiICMCMAwI1EPAGGYmJifNY2VVVV6euvv/ZZCwUAuPkQT4Ch1NRUffzxx3I6nZ5tRUVFslqtSk5ODuDMAAA3As95AgyNHTtWq1at0uOPP64pU6bozJkzmj9/vsaOHcsznoCbwIULF7Rr1y5JUmVlpaqrq1VUVCRJSkhIUFRUVCCnhyBgcfPoVMDYsWPH9Nxzz6m0tFStWrXSiBEjlJ2drbCwsEBPDUADnTx5UmlpafXuW7lypRITE2/wjBBsiCcAAAADrHkCAAAwQDwBAAAYIJ4AAAAMEE8AAAAGiCcAAAADxBMAAIAB4gkAAMAA8QQAAGCAj2cBcEPExsZe17jGeoJzXl6eNm7cqI0bN6pnz55e+2pra/Xwww/r/Pnz2rJli1q2bNng812v6upqrVixQtu2bVNFRYXq6urUpUsX3X///Xr00Uc9H/GTk5OjrVu3qrS0tN7jxMbGavz48crLy5Pk+1TskJAQtW7dWtHR0UpISNDYsWP14x//uOnfIHALIJ4A3BDz58/3+vrdd9/Vnj17fLZ37969Uc43a9Ysffjhh/r1r3+tf//3f5fFYvHsW7FihY4cOaLly5ff0HCqqKjQL3/5S506dUoPPvigxowZo9DQUB0+fFjr16/XBx98oK1btzboHMOGDVNqaqrcbrccDof++Mc/6ne/+51Wrlypf/3Xf9XQoUMb6d0Aty7iCcANMWLECK+vP/vsM+3Zs8dne2Ox2+3Kzc1Vdna21q5dqzFjxkiSvvrqKy1ZskTp6em6//77m+TcV6utrZXL5ZLVatXUqVP15z//WStXrtTPfvYzr3HZ2dnKz89v8Pl69+7t8z2trKzU3//932v27Nnq3r27/vqv/7rB5wFuZax5AhA0XC6XVqxYoaFDh6pPnz669957lZeXJ4fD4TVu4MCBmjJlivbu3auf//zn6tOnj9LS0rRp0yavcRkZGbr//vv10ksv6c9//rMk6bnnnlNISIieeuopSdKZM2f05JNP6t5779Vdd92loUOHav369V7HuXjxol599VWNHDlSP/3pTxUXF6dHHnlE//Vf/+U17uTJk4qNjVVBQYFWrFihQYMGqU+fPjp27Ji2bdumL774Qo899phPOElS69atlZ2d3dBvYb06duyoefPm6dKlS40SaMCtjitPAIJGXl6efv/732vkyJGaMGGCTp48qbffflsHDx7UO++8o9DQUM/YEydOaMaMGfr5z3+uhx9+WBs2bFBOTo7uvPNO/eQnP/GM+/Wvf61hw4bp+eefV3p6uj788EPNnTtX7du315/+9CeNHj1aFotF48ePV1RUlIqLi/XUU0+purpav/zlLyVdXqe0bt06DRs2TKNGjdL58+e1fv16/epXv9K6devUq1cvr/exceNG/d///Z9Gjx6tsLAwRUREaMeOHZJ8r8D9kHPnzvn53fQWHx+vLl266OOPP26U4wG3MuIJQFDYu3ev1q1bpwULFuihhx7ybE9MTNSvfvUrFRUVeW0vLy/X22+/7bmKc+U23MaNGzV79mzPuI4dO2ratGl68cUXtXv3bvXr189zC2/hwoWqq6vT5s2bFRkZKUkaN26cnnjiCS1evFhjx47VbbfdpoiICH344YcKCwvzHHf06NFKT0/XqlWr9Pzzz3u9l9OnT2v79u2KiorybCsrK1ObNm10xx13XPf3pKamRklJSdc9/of85Cc/0Y4dO1RdXa3WrVs32nGBWw3xBCAoFBUVqU2bNkpOTva62nLnnXeqZcuW+sMf/uAVTz169PC6/RUVFaXo6GhVVFT4HHvixInatGmTjh49qmeffVYWi0Vut1vbtm1Tenq63G631zlTUlL0/vvv6/PPP9dPf/pT2Ww22Ww2SZdvLTqdTrlcLt111106ePCgz/kGDx7sFU7S5atXrVq1MvqehIeHa+nSpfXumzRpktGxJHkWx58/f554AhqAeAIQFE6cOKGqqqprXmm5smbpivqu4ERERPisj5Ikm82m3r17q6KiwnNL79y5c3I6nVqzZo3WrFlT7zmvDqrf//73evPNN1VeXq5Lly55tnfq1MnndfVta926db1h931sNpvuvfdeo9d8n5qaGkkyjjgA3ognAEHB5XKpXbt2WrBgQb37//JKzpUrQQ05nyQNHz5cDz/8cL1jrjyb6t1331VOTo4GDRqkzMxMtWvXTjabTcuWLas3iG677TafbTExMTp48KBOnTpldOuuMR09elTt2rXjqhPQQMQTgKDQpUsXlZSUqF+/fvXGR2OLiopSq1at5HK5fvDqztatW9W5c2ctXrzY63lRixYtuu7zDRgwQFu2bNF7772nKVOm+D1vf5WWlup///d/NXz48Bt+buBmw6MKAASF9PR01dXV6fXXX/fZV1tbK6fT2ajns9lsGjJkiLZu3aojR4747L/6lt2Vq1xut9uz7bPPPtOnn3563ecbMmSIevbsqaVLl9b71PDq6motXLjQ4B1cv8rKSuXk5Cg0NFSZmZlNcg7gVsKVJwBBISEhQWPGjNGyZct06NAhJScnKzQ0VMePH1dRUZGeeuopPfjgg416zpkzZ+oPf/iDRo8erVGjRqlHjx5yOBz6/PPPVVJSok8++USS9MADD2jbtm16/PHH9cADD+jkyZNavXq1evTo4VlH9ENCQ0O1ePFiTZo0Sb/4xS/04IMPql+/fgoNDdXRo0e1ZcsW2e32Bj/r6eDBg3r33XfldrvldDr1xz/+Udu2bZPFYtH8+fN5QCbQCIgnAEHj2Wef1V133aXVq1dr4cKFstls6tixo4YPH65+/fo1+vn+6q/+SuvWrdOSJUu0fft2vfPOO2rbtq169OihWbNmecaNHDlSf/rTn7RmzRrt3r1bPXr00L/927+pqKjIE1jXo2vXrtq0aZNWrFih7du3a8eOHXK5XOratatGjRqlCRMmNPg9bdmyRVu2bPF8tl3Xrl01ceJEPtsOaEQW99XXoQEAAPC9WPMEAABggHgCAAAwQDwBAAAYIJ4AAAAMEE8AAAAGiCcAAAADxBMAAIAB4gkAAMAA8QQAAGCAeAIAADBAPAEAABggngAAAAwQTwAAAAb+H+Z4SP1XwatXAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot the count of each unique value in y_train using seaborn's countplot\n",
        "\n",
        "sns.countplot(x=y_train,data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o_2RgmJgiH7w"
      },
      "outputs": [],
      "source": [
        "# Applying smote to treat imbalance data\n",
        "from imblearn.over_sampling import SMOTE\n",
        "smote = SMOTE()\n",
        "x_train_resampled, y_train_resampled = smote.fit_resample(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sRvW9duHQyTM",
        "outputId": "c90e2c6f-c892-4127-c358-1c2c9addd070"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TenYearCHD\n",
              "0    2307\n",
              "1    2307\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "y_train_resampled.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Building Logistic Regression Model\n",
        "\n",
        "logit_model = LogisticRegression(solver='liblinear')\n",
        "logit_model.fit(x_train_resampled, y_train_resampled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "GCdIM1531IoI",
        "outputId": "7a05419e-545e-4589-ae30-1cd0dd310e7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(solver='liblinear')"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</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(solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the trained logistic regression model 'logit_model' to predict the target variable for the training set 'x_train'.\n",
        "\n",
        "y_pred_train = logit_model.predict(x_train_resampled)\n",
        "\n",
        "# # Use the trained logistic regression model 'logit_model' to predict the target variable for the testing set 'x_test'.\n",
        "\n",
        "y_pred_test = logit_model.predict(x_test)"
      ],
      "metadata": {
        "id": "4ph_DAeI1OUx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def model_report(model):\n",
        "    # Use the provided model to predict the target variable for the training set.\n",
        "    model_pred_train = model.predict(x_train_resampled)\n",
        "\n",
        "    # Use the provided model to predict the target variable for the testing set.\n",
        "    model_pred_test = model.predict(x_test)\n",
        "\n",
        "    # Print the classification report for the predictions on the training set.\n",
        "    print(\"Classification Report Train: \")\n",
        "    print(classification_report(y_train_resampled, model_pred_train))\n",
        "\n",
        "    # Print the classification report for the predictions on the testing set.\n",
        "    print(\"Classification Report Test: \")\n",
        "    print(classification_report(y_test, model_pred_test))\n",
        "\n",
        "    # Print the accuracy score for the predictions on the training set.\n",
        "    print(\"Accuracy Score Train: \", accuracy_score(y_train_resampled,model_pred_train))\n",
        "\n",
        "    # Print the accuracy score for the predictions on the testing set.\n",
        "    print(\"Accuracy Score Test: \", accuracy_score(y_test,model_pred_test))\n",
        "\n",
        "    # Print the confusion matrix for the predictions on the training set.\n",
        "    print(\"Confusion Matrix Train: \")\n",
        "    print(confusion_matrix(y_train_resampled,model_pred_train))\n",
        "\n",
        "    # Print the confusion matrix for the predictions on the testing set.\n",
        "    print(\"Confusion Matrix Test: \")\n",
        "    print(confusion_matrix(y_test,model_pred_test))"
      ],
      "metadata": {
        "id": "5Xn3jI0i1Sw5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_report(logit_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wyegST2k2Gr2",
        "outputId": "834d6dc4-1117-4e32-9d4a-b19f0d73e4e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report Train: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.73      0.71      0.72      2307\n",
            "           1       0.72      0.74      0.73      2307\n",
            "\n",
            "    accuracy                           0.73      4614\n",
            "   macro avg       0.73      0.73      0.73      4614\n",
            "weighted avg       0.73      0.73      0.73      4614\n",
            "\n",
            "Classification Report Test: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.75      0.81       572\n",
            "           1       0.24      0.42      0.31       106\n",
            "\n",
            "    accuracy                           0.70       678\n",
            "   macro avg       0.56      0.59      0.56       678\n",
            "weighted avg       0.78      0.70      0.73       678\n",
            "\n",
            "Accuracy Score Train:  0.7267013437364542\n",
            "Accuracy Score Test:  0.700589970501475\n",
            "Confusion Matrix Train: \n",
            "[[1647  660]\n",
            " [ 601 1706]]\n",
            "Confusion Matrix Test: \n",
            "[[430 142]\n",
            " [ 61  45]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Building Random Forest Model\n",
        "\n",
        "rf = RandomForestClassifier(n_estimators=500,criterion='entropy',max_depth=8,min_samples_split=5)\n",
        "rf.fit(x_train_resampled, y_train_resampled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "gd61aFkI2Z1L",
        "outputId": "76b73ca1-1466-4cc2-9aef-63223e6c58f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(criterion='entropy', max_depth=8, min_samples_split=5,\n",
              "                       n_estimators=500)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, max_depth=8, min_samples_split=5,\n",
              "                       n_estimators=500)</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, max_depth=8, min_samples_split=5,\n",
              "                       n_estimators=500)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the trained Random Forest model 'rf' to predict the target variable for the training set 'x_train'.\n",
        "\n",
        "y_pred_train = rf.predict(x_train_resampled)\n",
        "\n",
        "# Use the trained Random Forest model 'rf' to predict the target variable for the test set 'x_test'.\n",
        "\n",
        "y_pred_test = rf.predict(x_test)"
      ],
      "metadata": {
        "id": "svWZGeGX2evc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_report(rf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xX5p6Top2lOP",
        "outputId": "1b2e07ff-0eb2-4356-d250-20c23578137f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report Train: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.85      0.85      2307\n",
            "           1       0.85      0.84      0.84      2307\n",
            "\n",
            "    accuracy                           0.84      4614\n",
            "   macro avg       0.84      0.84      0.84      4614\n",
            "weighted avg       0.84      0.84      0.84      4614\n",
            "\n",
            "Classification Report Test: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.84      0.86       572\n",
            "           1       0.29      0.35      0.32       106\n",
            "\n",
            "    accuracy                           0.76       678\n",
            "   macro avg       0.58      0.59      0.59       678\n",
            "weighted avg       0.78      0.76      0.77       678\n",
            "\n",
            "Accuracy Score Train:  0.8446033810143043\n",
            "Accuracy Score Test:  0.7640117994100295\n",
            "Confusion Matrix Train: \n",
            "[[1958  349]\n",
            " [ 368 1939]]\n",
            "Confusion Matrix Test: \n",
            "[[481  91]\n",
            " [ 69  37]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "svm_clf = SVC(C=12,kernel='poly')\n",
        "svm_clf.fit(x_train_resampled, y_train_resampled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "CK6E6fv72wnC",
        "outputId": "7958cf23-ec3d-4b0b-c3de-69b132e961ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=12, kernel='poly')"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=12, kernel=&#x27;poly&#x27;)</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=12, kernel=&#x27;poly&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_report(svm_clf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZE0miNbV25iC",
        "outputId": "445d54ab-d737-49ec-85d3-c10416eb9dd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report Train: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.68      0.74      0.71      2307\n",
            "           1       0.72      0.66      0.69      2307\n",
            "\n",
            "    accuracy                           0.70      4614\n",
            "   macro avg       0.70      0.70      0.70      4614\n",
            "weighted avg       0.70      0.70      0.70      4614\n",
            "\n",
            "Classification Report Test: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.78      0.84       572\n",
            "           1       0.30      0.49      0.37       106\n",
            "\n",
            "    accuracy                           0.74       678\n",
            "   macro avg       0.59      0.64      0.60       678\n",
            "weighted avg       0.80      0.74      0.76       678\n",
            "\n",
            "Accuracy Score Train:  0.699826614651062\n",
            "Accuracy Score Test:  0.7389380530973452\n",
            "Confusion Matrix Train: \n",
            "[[1707  600]\n",
            " [ 785 1522]]\n",
            "Confusion Matrix Test: \n",
            "[[449 123]\n",
            " [ 54  52]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# using cross validation approach to check overfitting\n",
        "from sklearn.model_selection import cross_val_score\n",
        "training = cross_val_score(svm_clf,x_train_resampled, y_train_resampled, cv=10)\n",
        "print(\"All 10 accuracy\", training)\n",
        "print(\"Avg Training Acc\", training.mean())\n",
        "print(\"Max Training Acc\", training.max())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S94Wz-Dw3LPE",
        "outputId": "62911c71-0b70-4f19-e63e-6aa80ae94949"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "All 10 accuracy [0.65584416 0.67965368 0.69480519 0.71861472 0.68112798 0.70715835\n",
            " 0.72234273 0.67895879 0.68112798 0.68763557]\n",
            "Avg Training Acc 0.6907269158896058\n",
            "Max Training Acc 0.7223427331887202\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize an XGBoost classifier\n",
        "from xgboost import XGBClassifier\n",
        "model = XGBClassifier()\n",
        "model.fit(x_train_resampled, y_train_resampled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "hSbYobLb36uB",
        "outputId": "86fcc474-530e-4d67-ce3b-6b32597cfed9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_report(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CJIHeiVr4vrD",
        "outputId": "959b9f54-dff9-43d6-8ddf-fe4ead9b5b7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report Train: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      1.00      1.00      2307\n",
            "           1       1.00      0.99      0.99      2307\n",
            "\n",
            "    accuracy                           1.00      4614\n",
            "   macro avg       1.00      1.00      1.00      4614\n",
            "weighted avg       1.00      1.00      1.00      4614\n",
            "\n",
            "Classification Report Test: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.92      0.89       572\n",
            "           1       0.29      0.18      0.22       106\n",
            "\n",
            "    accuracy                           0.80       678\n",
            "   macro avg       0.57      0.55      0.55       678\n",
            "weighted avg       0.77      0.80      0.78       678\n",
            "\n",
            "Accuracy Score Train:  0.9950151712180321\n",
            "Accuracy Score Test:  0.8023598820058997\n",
            "Confusion Matrix Train: \n",
            "[[2303    4]\n",
            " [  19 2288]]\n",
            "Confusion Matrix Test: \n",
            "[[525  47]\n",
            " [ 87  19]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bSKt3F3DoaN6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Ensemble methods\n",
        "# Voting\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "model1 = LogisticRegression(random_state=1)\n",
        "model2 = DecisionTreeClassifier(random_state=1)\n",
        "model = VotingClassifier(estimators=[('lr', model1), ('dt', model2)], voting='hard')\n",
        "model.fit(x_train_resampled,y_train_resampled)\n",
        "model.score(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zel1RuU98_yT",
        "outputId": "21ca8b95-b39c-46cf-8936-a38b2db66d94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7861356932153393"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Implementing Stacking with the final layer being logistic regression\n",
        "from sklearn.ensemble import StackingClassifier\n",
        "\n",
        "decisiontree =  DecisionTreeClassifier()\n",
        "randomforest = RandomForestClassifier()\n",
        "knn =  KNeighborsClassifier()\n",
        "xgb = XGBClassifier()\n",
        "\n",
        "#list of the estimator\n",
        "\n",
        "classificationres = [('decisiontree',decisiontree),('randomforest',randomforest),('knn',knn),('xgb',xgb)]\n",
        "\n",
        "# Logistic Regression\n",
        "\n",
        "lr = LogisticRegression()\n",
        "stack_model = StackingClassifier( estimators = classificationres,final_estimator = lr)\n",
        "\n",
        "# Fit the stacking model with training data\n",
        "stack_model.fit(x_train_resampled, y_train_resampled)\n",
        "\n",
        "score = cross_val_score(stack_model,x_test, y_test,cv = 5,scoring = 'accuracy')\n",
        "\n",
        "# Printing the final accuracy of the stacked model\n",
        "\n",
        "print(\"The accuracy score is:\",score.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hPFLcJcFDLmb",
        "outputId": "96e40cf1-6255-4c6d-fc32-9933298a418d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy score is: 0.8436601307189543\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_resampled.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V3jH_yvfN6zE",
        "outputId": "e4684eb7-4b82-4fb4-a678-3881a38079ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'sex', 'is_smoking', 'BPMeds', 'prevalentStroke', 'prevalentHyp',\n",
              "       'diabetes', 'totChol', 'sysBP', 'diaBP', 'BMI', 'heartRate', 'glucose'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Taking Stacking classifier as our model for prediction\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "RAoNZmUsLlnA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# User input function\n",
        "def predict_cvd():\n",
        "    # Get user input\n",
        "    age = float(input(\"Enter age: \"))\n",
        "    sex = int(input(\"Enter sex (1 for female, 0 for male): \"))\n",
        "    is_smoking = int(input(\"Is the person smoking? (0 for no, 1 for yes): \"))\n",
        "    b_pmeds = int(input(\"Is the person on blood pressure medications? (0 for no, 1 for yes): \"))\n",
        "    prevalent_stroke = int(input(\"Has the person had a prevalent stroke? (0 for no, 1 for yes): \"))\n",
        "    prevalent_hyp = int(input(\"Has the person had prevalent hypertension? (0 for no, 1 for yes): \"))\n",
        "    diabetes = int(input(\"Does the person have diabetes? (0 for no, 1 for yes): \"))\n",
        "    tot_chol = float(input(\"Enter total cholesterol level: \"))\n",
        "    sys_bp = float(input(\"Enter systolic blood pressure: \"))\n",
        "    dia_bp = float(input(\"Enter diastolic blood pressure: \"))\n",
        "    bmi = float(input(\"Enter BMI: \"))\n",
        "    heart_rate = float(input(\"Enter heart rate: \"))\n",
        "    glucose = float(input(\"Enter glucose level: \"))\n",
        "\n",
        "    # Convert user input into a numpy array for prediction\n",
        "    user_input = np.array([[age, sex, is_smoking, b_pmeds, prevalent_stroke, prevalent_hyp,\n",
        "                            diabetes, tot_chol, sys_bp, dia_bp, bmi, heart_rate, glucose]])\n",
        "\n",
        "    # Make prediction using the trained model\n",
        "    prediction = stack_model.predict(user_input)\n",
        "\n",
        "    # Interpret the prediction\n",
        "    if prediction == 1:\n",
        "        print(\"The person is predicted to have cardiovascular disease (CVD).\")\n",
        "    else:\n",
        "        print(\"The person is predicted to be normal without CVD.\")\n",
        "\n",
        "# Call the user input function for prediction\n",
        "predict_cvd()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v237b1M9WV23",
        "outputId": "aaaf62a8-be97-411f-845f-d9180c855cfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter age: 40\n",
            "Enter sex (1 for female, 0 for male): 0\n",
            "Is the person smoking? (0 for no, 1 for yes): 1\n",
            "Is the person on blood pressure medications? (0 for no, 1 for yes): 1\n",
            "Has the person had a prevalent stroke? (0 for no, 1 for yes): 1\n",
            "Has the person had prevalent hypertension? (0 for no, 1 for yes): 1\n",
            "Does the person have diabetes? (0 for no, 1 for yes): 1\n",
            "Enter total cholesterol level: 300\n",
            "Enter systolic blood pressure: 180\n",
            "Enter diastolic blood pressure: 110\n",
            "Enter BMI: 24\n",
            "Enter heart rate: 100\n",
            "Enter glucose level: 200\n",
            "The person is predicted to have cardiovascular disease (CVD).\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}