Capstone.ipynb

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      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**EXTREME GRADIENT BOOSTING (XGB)**"
      ],
      "metadata": {
        "id": "DquIyjkm04zM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix, f1_score, recall_score, precision_score, roc_curve, roc_auc_score\n",
        "from sklearn.model_selection import cross_val_predict\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from sklearn.feature_selection import SelectPercentile\n",
        "from sklearn.feature_selection import chi2 , f_classif\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "from sklearn.metrics import mean_absolute_error,r2_score,mean_squared_error\n",
        "from sklearn.feature_selection import RFE\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "2Rf8rHIy4WKN"
      },
      "execution_count": 176,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv(\"/content/diabetes.csv\")"
      ],
      "metadata": {
        "id": "BZUWLVkV6Ker"
      },
      "execution_count": 177,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RmP1OOe9CLAT",
        "outputId": "e502792c-29cb-46f4-fac9-ad0877ec5d65"
      },
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 70591 entries, 0 to 70590\n",
            "Data columns (total 22 columns):\n",
            " #   Column                Non-Null Count  Dtype\n",
            "---  ------                --------------  -----\n",
            " 0   Diabetes_binary       70591 non-null  int64\n",
            " 1   HighBP                70591 non-null  int64\n",
            " 2   HighChol              70591 non-null  int64\n",
            " 3   CholCheck             70591 non-null  int64\n",
            " 4   BMI                   70591 non-null  int64\n",
            " 5   Smoker                70591 non-null  int64\n",
            " 6   Stroke                70591 non-null  int64\n",
            " 7   HeartDiseaseorAttack  70591 non-null  int64\n",
            " 8   PhysActivity          70591 non-null  int64\n",
            " 9   Fruits                70591 non-null  int64\n",
            " 10  Veggies               70591 non-null  int64\n",
            " 11  HvyAlcoholConsump     70591 non-null  int64\n",
            " 12  AnyHealthcare         70591 non-null  int64\n",
            " 13  NoDocbcCost           70591 non-null  int64\n",
            " 14  GenHlth               70591 non-null  int64\n",
            " 15  MentHlth              70591 non-null  int64\n",
            " 16  PhysHlth              70591 non-null  int64\n",
            " 17  DiffWalk              70591 non-null  int64\n",
            " 18  Sex                   70591 non-null  int64\n",
            " 19  Age                   70591 non-null  int64\n",
            " 20  Education             70591 non-null  int64\n",
            " 21  Income                70591 non-null  int64\n",
            "dtypes: int64(22)\n",
            "memory usage: 11.8 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()\n"
      ],
      "metadata": {
        "id": "fduiP_h108Ju",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9fa803db-3b78-4b1f-8c6c-f30d5ec25d27"
      },
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Diabetes_binary         0\n",
              "HighBP                  0\n",
              "HighChol                0\n",
              "CholCheck               0\n",
              "BMI                     0\n",
              "Smoker                  0\n",
              "Stroke                  0\n",
              "HeartDiseaseorAttack    0\n",
              "PhysActivity            0\n",
              "Fruits                  0\n",
              "Veggies                 0\n",
              "HvyAlcoholConsump       0\n",
              "AnyHealthcare           0\n",
              "NoDocbcCost             0\n",
              "GenHlth                 0\n",
              "MentHlth                0\n",
              "PhysHlth                0\n",
              "DiffWalk                0\n",
              "Sex                     0\n",
              "Age                     0\n",
              "Education               0\n",
              "Income                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.duplicated().sum()"
      ],
      "metadata": {
        "id": "ltAOJkW71ERd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7cb88302-ba0d-444c-8eea-8cb748f08498"
      },
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1633"
            ]
          },
          "metadata": {},
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "RxHFzrVr1HsM"
      },
      "execution_count": 181,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "id": "hVJSnHeK1KUE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a6ce07e-eb96-4e7d-f021-264ec65d15f1"
      },
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(68958, 22)"
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.dropna()"
      ],
      "metadata": {
        "id": "y3UR0VdwYPfn"
      },
      "execution_count": 183,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import IsolationForest\n",
        "model = IsolationForest()\n",
        "model.fit(data)\n",
        "data['anomaly']= model.predict(data)"
      ],
      "metadata": {
        "id": "Bn-kMJh01LCM"
      },
      "execution_count": 184,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "id": "HXh2_uqE1TOz",
        "colab": {
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          "height": 443
        },
        "outputId": "fab8c41e-8133-4976-f396-49f1d598cad7"
      },
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Diabetes_binary  HighBP  HighChol  CholCheck  BMI  Smoker  Stroke  \\\n",
              "0                    0       1         0          1   26       0       0   \n",
              "1                    0       1         1          1   26       1       1   \n",
              "2                    0       0         0          1   26       0       0   \n",
              "3                    0       1         1          1   28       1       0   \n",
              "4                    0       0         0          1   29       1       0   \n",
              "...                ...     ...       ...        ...  ...     ...     ...   \n",
              "70586                1       1         0          1   26       0       0   \n",
              "70587                1       1         1          1   28       0       0   \n",
              "70588                1       0         1          1   27       0       0   \n",
              "70589                1       1         1          1   28       1       0   \n",
              "70590                1       0         1          1   30       1       0   \n",
              "\n",
              "       HeartDiseaseorAttack  PhysActivity  Fruits  ...  NoDocbcCost  GenHlth  \\\n",
              "0                         0             1       0  ...            0        3   \n",
              "1                         0             0       1  ...            0        3   \n",
              "2                         0             1       1  ...            0        1   \n",
              "3                         0             1       1  ...            0        3   \n",
              "4                         0             1       1  ...            0        2   \n",
              "...                     ...           ...     ...  ...          ...      ...   \n",
              "70586                     0             1       1  ...            0        3   \n",
              "70587                     1             0       1  ...            1        4   \n",
              "70588                     0             1       1  ...            0        4   \n",
              "70589                     0             0       0  ...            0        5   \n",
              "70590                     0             0       1  ...            0        4   \n",
              "\n",
              "       MentHlth  PhysHlth  DiffWalk  Sex  Age  Education  Income  anomaly  \n",
              "0             5        30         0    1    4          6       8        1  \n",
              "1             0         0         0    1   12          6       8       -1  \n",
              "2             0        10         0    1   13          6       8        1  \n",
              "3             0         3         0    1   11          6       8        1  \n",
              "4             0         0         0    0    8          5       8        1  \n",
              "...         ...       ...       ...  ...  ...        ...     ...      ...  \n",
              "70586         0         4         0    0    6          5       1        1  \n",
              "70587         0         7         1    0   11          6       6       -1  \n",
              "70588        15         4         1    1    6          5       4       -1  \n",
              "70589         0        30         1    1    7          4       1       -1  \n",
              "70590        10        30         1    0    9          6       5       -1  \n",
              "\n",
              "[68958 rows x 23 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Diabetes_binary</th>\n",
              "      <th>HighBP</th>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
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          },
          "metadata": {},
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[data['anomaly']==-1]"
      ],
      "metadata": {
        "id": "JWAcdGk71UBU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "d93d5410-8c06-415d-8881-f85ec46a44b8"
      },
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       Diabetes_binary  HighBP  HighChol  CholCheck  BMI  Smoker  Stroke  \\\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70587</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70588</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>15</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70589</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70590</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>28617 rows × 23 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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",
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              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b6cb6643-a9e7-4ad2-9bf7-29347713f395');\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-d12c60af-bc4e-4b6c-8c9e-b439fd4fc2cc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d12c60af-bc4e-4b6c-8c9e-b439fd4fc2cc')\"\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",
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              "    <g>\n",
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              "    </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",
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              "    20% {\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",
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              "      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",
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              "\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",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d12c60af-bc4e-4b6c-8c9e-b439fd4fc2cc 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"
            }
          },
          "metadata": {},
          "execution_count": 186
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[data['anomaly']==-1].shape"
      ],
      "metadata": {
        "id": "UV00CTh91XTG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e6f07c6-bfc0-4fca-96d8-13c06ca2a9d5"
      },
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28617, 23)"
            ]
          },
          "metadata": {},
          "execution_count": 187
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop(data[data['anomaly']==-1].index,inplace = True)"
      ],
      "metadata": {
        "id": "sT-cCozj1cCf"
      },
      "execution_count": 188,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "id": "D5hZ5op_1dYR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48f91fb3-6ccb-4df3-9358-a75be302233d"
      },
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40341, 23)"
            ]
          },
          "metadata": {},
          "execution_count": 189
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data\n"
      ],
      "metadata": {
        "id": "kGF47xrK1f9i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "9cfc44c3-8a13-4e60-a62c-3e0ae607e4c5"
      },
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Diabetes_binary  HighBP  HighChol  CholCheck  BMI  Smoker  Stroke  \\\n",
              "0                    0       1         0          1   26       0       0   \n",
              "2                    0       0         0          1   26       0       0   \n",
              "3                    0       1         1          1   28       1       0   \n",
              "4                    0       0         0          1   29       1       0   \n",
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              "...                ...     ...       ...        ...  ...     ...     ...   \n",
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              "70586                1       1         0          1   26       0       0   \n",
              "\n",
              "       HeartDiseaseorAttack  PhysActivity  Fruits  ...  NoDocbcCost  GenHlth  \\\n",
              "0                         0             1       0  ...            0        3   \n",
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              "70580                     0             1       1  ...            0        3   \n",
              "70583                     0             1       1  ...            0        1   \n",
              "70586                     0             1       1  ...            0        3   \n",
              "\n",
              "       MentHlth  PhysHlth  DiffWalk  Sex  Age  Education  Income  anomaly  \n",
              "0             5        30         0    1    4          6       8        1  \n",
              "2             0        10         0    1   13          6       8        1  \n",
              "3             0         3         0    1   11          6       8        1  \n",
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              "70583         0         0         0    0   10          5       1        1  \n",
              "70586         0         4         0    0    6          5       1        1  \n",
              "\n",
              "[40341 rows x 23 columns]"
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>4</td>\n",
              "      <td>2</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>70578</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>25</td>\n",
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              "      <td>0</td>\n",
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              "      <td>3</td>\n",
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              "      <td>30</td>\n",
              "      <td>0</td>\n",
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              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70580</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>3</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>70583</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70586</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
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              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>40341 rows × 23 columns</p>\n",
              "</div>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    </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",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\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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              "    20% {\n",
              "      border-color: transparent;\n",
              "      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",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d36d9e7c-337b-480d-b356-9b3064145fc6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_294ff85b-1ae7-44e0-a591-814d240b4274\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_294ff85b-1ae7-44e0-a591-814d240b4274 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data');\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
            }
          },
          "metadata": {},
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop(columns=['anomaly'], inplace=True)"
      ],
      "metadata": {
        "id": "u025YgFe1iuL"
      },
      "execution_count": 191,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "id": "VgpO422U1mJl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e35201f9-025e-4fe1-d115-081aae5ca3f0"
      },
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40341, 22)"
            ]
          },
          "metadata": {},
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = data.drop(['Diabetes_binary'],axis=1)\n",
        "y = data['Diabetes_binary']"
      ],
      "metadata": {
        "id": "S44Q6qin1o6U"
      },
      "execution_count": 193,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "3f7R_zANHjnz",
        "outputId": "2a81d5ef-0828-4006-8734-8830a43cf01d"
      },
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       Diabetes_binary  HighBP  HighChol  CholCheck  BMI  Smoker  Stroke  \\\n",
              "0                    0       1         0          1   26       0       0   \n",
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              "7                    0       0         0          1   31       1       0   \n",
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              "...                     ...           ...     ...  ...            ...   \n",
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              "70578                     0             1       0  ...              1   \n",
              "70580                     0             1       1  ...              1   \n",
              "70583                     0             1       1  ...              1   \n",
              "70586                     0             1       1  ...              1   \n",
              "\n",
              "       NoDocbcCost  GenHlth  MentHlth  PhysHlth  DiffWalk  Sex  Age  \\\n",
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              "70586            0        3         0         4         0    0    6   \n",
              "\n",
              "       Education  Income  \n",
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              "[40341 rows x 22 columns]"
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
            }
          },
          "metadata": {},
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Performing frequency analysis\n",
        "frequency = data['Diabetes_binary'].value_counts()\n",
        "\n",
        "print(\"Diabetes Status:\")\n",
        "print(frequency)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6qMfRzWXIxEV",
        "outputId": "f54d371e-9a78-40c6-b8e6-ce7a5d54ea3a"
      },
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Diabetes Status:\n",
            "0    23019\n",
            "1    17322\n",
            "Name: Diabetes_binary, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Performing frequency analysis\n",
        "frequency = data2['Diabetes_binary'].value_counts()\n",
        "\n",
        "print(\"Diabetes Status:\")\n",
        "print(frequency)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "10bi5vrOK7ZW",
        "outputId": "0ce18a85-6538-4a8e-b90d-b16286b60cd2"
      },
      "execution_count": 196,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Diabetes Status:\n",
            "0    16190\n",
            "1    16190\n",
            "Name: Diabetes_binary, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3yVh1Kz3LFhm",
        "outputId": "9101ef3c-cadb-4ee7-d4cc-8778c408fb84"
      },
      "execution_count": 197,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Diabetes_binary         0\n",
              "HighBP                  0\n",
              "HighChol                0\n",
              "CholCheck               0\n",
              "BMI                     0\n",
              "Smoker                  0\n",
              "Stroke                  0\n",
              "HeartDiseaseorAttack    0\n",
              "PhysActivity            0\n",
              "Fruits                  0\n",
              "Veggies                 0\n",
              "HvyAlcoholConsump       0\n",
              "AnyHealthcare           0\n",
              "NoDocbcCost             0\n",
              "GenHlth                 0\n",
              "MentHlth                0\n",
              "PhysHlth                0\n",
              "DiffWalk                0\n",
              "Sex                     0\n",
              "Age                     0\n",
              "Education               0\n",
              "Income                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = data.drop(['Diabetes_binary'],axis=1)\n",
        "y = data['Diabetes_binary']"
      ],
      "metadata": {
        "id": "YM335_jXmtXV"
      },
      "execution_count": 198,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(x)"
      ],
      "metadata": {
        "id": "Zk0_Y1xJ10B0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "8d71069e-bb61-47a5-e8b9-a751f0001154"
      },
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StandardScaler()"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_features = scaler.transform(x)\n",
        "x = pd.DataFrame(scaled_features,columns=data.columns[1:])\n",
        "x.head(10)"
      ],
      "metadata": {
        "id": "VCS4LYja14BA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "outputId": "10419db6-a56a-4c82-f178-a2730546fa2f"
      },
      "execution_count": 200,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     HighBP  HighChol  CholCheck       BMI    Smoker    Stroke  \\\n",
              "0  0.971623 -0.987363   0.122143 -0.481102 -0.850564 -0.055302   \n",
              "1 -1.029206 -0.987363   0.122143 -0.481102 -0.850564 -0.055302   \n",
              "2  0.971623  1.012798   0.122143 -0.141190  1.175691 -0.055302   \n",
              "3 -1.029206 -0.987363   0.122143  0.028766  1.175691 -0.055302   \n",
              "4 -1.029206 -0.987363   0.122143  0.368679  1.175691 -0.055302   \n",
              "5 -1.029206 -0.987363   0.122143  0.538635 -0.850564 -0.055302   \n",
              "6 -1.029206 -0.987363   0.122143 -0.311146  1.175691 -0.055302   \n",
              "7  0.971623  1.012798   0.122143 -0.821015  1.175691 -0.055302   \n",
              "8 -1.029206 -0.987363   0.122143 -1.330883 -0.850564 -0.055302   \n",
              "9  0.971623  1.012798   0.122143 -0.311146 -0.850564 -0.055302   \n",
              "\n",
              "   HeartDiseaseorAttack  PhysActivity    Fruits   Veggies  ...  AnyHealthcare  \\\n",
              "0             -0.253729      0.445956 -1.451791  0.368324  ...       0.057078   \n",
              "1             -0.253729      0.445956  0.688805  0.368324  ...       0.057078   \n",
              "2             -0.253729      0.445956  0.688805  0.368324  ...       0.057078   \n",
              "3             -0.253729      0.445956  0.688805  0.368324  ...       0.057078   \n",
              "4             -0.253729     -2.242374  0.688805  0.368324  ...       0.057078   \n",
              "5             -0.253729      0.445956  0.688805  0.368324  ...       0.057078   \n",
              "6             -0.253729     -2.242374  0.688805  0.368324  ...       0.057078   \n",
              "7              3.941218      0.445956  0.688805  0.368324  ...       0.057078   \n",
              "8             -0.253729      0.445956  0.688805  0.368324  ...       0.057078   \n",
              "9             -0.253729      0.445956  0.688805  0.368324  ...       0.057078   \n",
              "\n",
              "   NoDocbcCost   GenHlth  MentHlth  PhysHlth  DiffWalk       Sex       Age  \\\n",
              "0    -0.125344  0.637410  1.098212  5.858759 -0.309141  1.089594 -1.621697   \n",
              "1    -0.125344 -1.649986 -0.324409  1.703055 -0.309141  1.089594  1.584353   \n",
              "2    -0.125344  0.637410 -0.324409  0.248558 -0.309141  1.089594  0.871898   \n",
              "3    -0.125344 -0.506288 -0.324409 -0.374797 -0.309141 -0.917773 -0.196786   \n",
              "4    -0.125344  1.781108 -0.324409 -0.374797 -0.309141  1.089594 -0.909241   \n",
              "5    -0.125344  0.637410 -0.324409 -0.374797 -0.309141 -0.917773 -1.977925   \n",
              "6    -0.125344  0.637410 -0.324409  0.871914 -0.309141  1.089594 -0.909241   \n",
              "7    -0.125344  0.637410 -0.324409  0.456344 -0.309141 -0.917773  1.228125   \n",
              "8    -0.125344 -1.649986 -0.324409 -0.374797 -0.309141  1.089594 -1.621697   \n",
              "9    -0.125344 -0.506288 -0.324409 -0.374797 -0.309141  1.089594 -0.553014   \n",
              "\n",
              "   Education    Income  \n",
              "0   0.966675  0.912823  \n",
              "1   0.966675  0.912823  \n",
              "2   0.966675  0.912823  \n",
              "3  -0.155369  0.912823  \n",
              "4  -1.277413 -1.888007  \n",
              "5   0.966675  0.912823  \n",
              "6  -1.277413 -1.327841  \n",
              "7  -1.277413 -0.207509  \n",
              "8   0.966675  0.912823  \n",
              "9   0.966675  0.912823  \n",
              "\n",
              "[10 rows x 21 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HighBP</th>\n",
              "      <th>HighChol</th>\n",
              "      <th>CholCheck</th>\n",
              "      <th>BMI</th>\n",
              "      <th>Smoker</th>\n",
              "      <th>Stroke</th>\n",
              "      <th>HeartDiseaseorAttack</th>\n",
              "      <th>PhysActivity</th>\n",
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              "      <th>Veggies</th>\n",
              "      <th>...</th>\n",
              "      <th>AnyHealthcare</th>\n",
              "      <th>NoDocbcCost</th>\n",
              "      <th>GenHlth</th>\n",
              "      <th>MentHlth</th>\n",
              "      <th>PhysHlth</th>\n",
              "      <th>DiffWalk</th>\n",
              "      <th>Sex</th>\n",
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              "      <th>Income</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.971623</td>\n",
              "      <td>-0.987363</td>\n",
              "      <td>0.122143</td>\n",
              "      <td>-0.481102</td>\n",
              "      <td>-0.850564</td>\n",
              "      <td>-0.055302</td>\n",
              "      <td>-0.253729</td>\n",
              "      <td>0.445956</td>\n",
              "      <td>-1.451791</td>\n",
              "      <td>0.368324</td>\n",
              "      <td>...</td>\n",
              "      <td>0.057078</td>\n",
              "      <td>-0.125344</td>\n",
              "      <td>0.637410</td>\n",
              "      <td>1.098212</td>\n",
              "      <td>5.858759</td>\n",
              "      <td>-0.309141</td>\n",
              "      <td>1.089594</td>\n",
              "      <td>-1.621697</td>\n",
              "      <td>0.966675</td>\n",
              "      <td>0.912823</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.029206</td>\n",
              "      <td>-0.987363</td>\n",
              "      <td>0.122143</td>\n",
              "      <td>-0.481102</td>\n",
              "      <td>-0.850564</td>\n",
              "      <td>-0.055302</td>\n",
              "      <td>-0.253729</td>\n",
              "      <td>0.445956</td>\n",
              "      <td>0.688805</td>\n",
              "      <td>0.368324</td>\n",
              "      <td>...</td>\n",
              "      <td>0.057078</td>\n",
              "      <td>-0.125344</td>\n",
              "      <td>-1.649986</td>\n",
              "      <td>-0.324409</td>\n",
              "      <td>1.703055</td>\n",
              "      <td>-0.309141</td>\n",
              "      <td>1.089594</td>\n",
              "      <td>1.584353</td>\n",
              "      <td>0.966675</td>\n",
              "      <td>0.912823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.971623</td>\n",
              "      <td>1.012798</td>\n",
              "      <td>0.122143</td>\n",
              "      <td>-0.141190</td>\n",
              "      <td>1.175691</td>\n",
              "      <td>-0.055302</td>\n",
              "      <td>-0.253729</td>\n",
              "      <td>0.445956</td>\n",
              "      <td>0.688805</td>\n",
              "      <td>0.368324</td>\n",
              "      <td>...</td>\n",
              "      <td>0.057078</td>\n",
              "      <td>-0.125344</td>\n",
              "      <td>0.637410</td>\n",
              "      <td>-0.324409</td>\n",
              "      <td>0.248558</td>\n",
              "      <td>-0.309141</td>\n",
              "      <td>1.089594</td>\n",
              "      <td>0.871898</td>\n",
              "      <td>0.966675</td>\n",
              "      <td>0.912823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-1.029206</td>\n",
              "      <td>-0.987363</td>\n",
              "      <td>0.122143</td>\n",
              "      <td>0.028766</td>\n",
              "      <td>1.175691</td>\n",
              "      <td>-0.055302</td>\n",
              "      <td>-0.253729</td>\n",
              "      <td>0.445956</td>\n",
              "      <td>0.688805</td>\n",
              "      <td>0.368324</td>\n",
              "      <td>...</td>\n",
              "      <td>0.057078</td>\n",
              "      <td>-0.125344</td>\n",
              "      <td>-0.506288</td>\n",
              "      <td>-0.324409</td>\n",
              "      <td>-0.374797</td>\n",
              "      <td>-0.309141</td>\n",
              "      <td>-0.917773</td>\n",
              "      <td>-0.196786</td>\n",
              "      <td>-0.155369</td>\n",
              "      <td>0.912823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.029206</td>\n",
              "      <td>-0.987363</td>\n",
              "      <td>0.122143</td>\n",
              "      <td>0.368679</td>\n",
              "      <td>1.175691</td>\n",
              "      <td>-0.055302</td>\n",
              "      <td>-0.253729</td>\n",
              "      <td>-2.242374</td>\n",
              "      <td>0.688805</td>\n",
              "      <td>0.368324</td>\n",
              "      <td>...</td>\n",
              "      <td>0.057078</td>\n",
              "      <td>-0.125344</td>\n",
              "      <td>1.781108</td>\n",
              "      <td>-0.324409</td>\n",
              "      <td>-0.374797</td>\n",
              "      <td>-0.309141</td>\n",
              "      <td>1.089594</td>\n",
              "      <td>-0.909241</td>\n",
              "      <td>-1.277413</td>\n",
              "      <td>-1.888007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-1.029206</td>\n",
              "      <td>-0.987363</td>\n",
              "      <td>0.122143</td>\n",
              "      <td>0.538635</td>\n",
              "      <td>-0.850564</td>\n",
              "      <td>-0.055302</td>\n",
              "      <td>-0.253729</td>\n",
              "      <td>0.445956</td>\n",
              "      <td>0.688805</td>\n",
              "      <td>0.368324</td>\n",
              "      <td>...</td>\n",
              "      <td>0.057078</td>\n",
              "      <td>-0.125344</td>\n",
              "      <td>0.637410</td>\n",
              "      <td>-0.324409</td>\n",
              "      <td>-0.374797</td>\n",
              "      <td>-0.309141</td>\n",
              "      <td>-0.917773</td>\n",
              "      <td>-1.977925</td>\n",
              "      <td>0.966675</td>\n",
              "      <td>0.912823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-1.029206</td>\n",
              "      <td>-0.987363</td>\n",
              "      <td>0.122143</td>\n",
              "      <td>-0.311146</td>\n",
              "      <td>1.175691</td>\n",
              "      <td>-0.055302</td>\n",
              "      <td>-0.253729</td>\n",
              "      <td>-2.242374</td>\n",
              "      <td>0.688805</td>\n",
              "      <td>0.368324</td>\n",
              "      <td>...</td>\n",
              "      <td>0.057078</td>\n",
              "      <td>-0.125344</td>\n",
              "      <td>0.637410</td>\n",
              "      <td>-0.324409</td>\n",
              "      <td>0.871914</td>\n",
              "      <td>-0.309141</td>\n",
              "      <td>1.089594</td>\n",
              "      <td>-0.909241</td>\n",
              "      <td>-1.277413</td>\n",
              "      <td>-1.327841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.971623</td>\n",
              "      <td>1.012798</td>\n",
              "      <td>0.122143</td>\n",
              "      <td>-0.821015</td>\n",
              "      <td>1.175691</td>\n",
              "      <td>-0.055302</td>\n",
              "      <td>3.941218</td>\n",
              "      <td>0.445956</td>\n",
              "      <td>0.688805</td>\n",
              "      <td>0.368324</td>\n",
              "      <td>...</td>\n",
              "      <td>0.057078</td>\n",
              "      <td>-0.125344</td>\n",
              "      <td>0.637410</td>\n",
              "      <td>-0.324409</td>\n",
              "      <td>0.456344</td>\n",
              "      <td>-0.309141</td>\n",
              "      <td>-0.917773</td>\n",
              "      <td>1.228125</td>\n",
              "      <td>-1.277413</td>\n",
              "      <td>-0.207509</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>-1.029206</td>\n",
              "      <td>-0.987363</td>\n",
              "      <td>0.122143</td>\n",
              "      <td>-1.330883</td>\n",
              "      <td>-0.850564</td>\n",
              "      <td>-0.055302</td>\n",
              "      <td>-0.253729</td>\n",
              "      <td>0.445956</td>\n",
              "      <td>0.688805</td>\n",
              "      <td>0.368324</td>\n",
              "      <td>...</td>\n",
              "      <td>0.057078</td>\n",
              "      <td>-0.125344</td>\n",
              "      <td>-1.649986</td>\n",
              "      <td>-0.324409</td>\n",
              "      <td>-0.374797</td>\n",
              "      <td>-0.309141</td>\n",
              "      <td>1.089594</td>\n",
              "      <td>-1.621697</td>\n",
              "      <td>0.966675</td>\n",
              "      <td>0.912823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.971623</td>\n",
              "      <td>1.012798</td>\n",
              "      <td>0.122143</td>\n",
              "      <td>-0.311146</td>\n",
              "      <td>-0.850564</td>\n",
              "      <td>-0.055302</td>\n",
              "      <td>-0.253729</td>\n",
              "      <td>0.445956</td>\n",
              "      <td>0.688805</td>\n",
              "      <td>0.368324</td>\n",
              "      <td>...</td>\n",
              "      <td>0.057078</td>\n",
              "      <td>-0.125344</td>\n",
              "      <td>-0.506288</td>\n",
              "      <td>-0.324409</td>\n",
              "      <td>-0.374797</td>\n",
              "      <td>-0.309141</td>\n",
              "      <td>1.089594</td>\n",
              "      <td>-0.553014</td>\n",
              "      <td>0.966675</td>\n",
              "      <td>0.912823</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 21 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13231f83-c93e-45a4-86a3-4e1628916512')\"\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-13231f83-c93e-45a4-86a3-4e1628916512 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-13231f83-c93e-45a4-86a3-4e1628916512');\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-c342e60c-6fae-4928-a847-c87241ea8db3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c342e60c-6fae-4928-a847-c87241ea8db3')\"\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-c342e60c-6fae-4928-a847-c87241ea8db3 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"
            }
          },
          "metadata": {},
          "execution_count": 200
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train , x_test , y_train , y_test = train_test_split(x,y , test_size=0.2, random_state=0, shuffle =True)"
      ],
      "metadata": {
        "id": "JJ9esuTF14st"
      },
      "execution_count": 201,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#data2['Diabetes_binary'].value_counts()"
      ],
      "metadata": {
        "id": "4bjWRfUL2FnM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "497c79b3-d680-48a9-8e28-2e352e88dd8e"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    16190\n",
              "1    16190\n",
              "Name: Diabetes_binary, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Resampling using SMOOTE"
      ],
      "metadata": {
        "id": "c1SlAg9Z2mAk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.combine import SMOTEENN\n",
        "sm = SMOTEENN()\n",
        "x_resampled, y_resampled = sm.fit_resample(x,y)"
      ],
      "metadata": {
        "id": "_G2CjkRs2o-F"
      },
      "execution_count": 202,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xre_train,xre_test,yre_train,yre_test = train_test_split(x_resampled, y_resampled, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "rTI6rzr93WRu"
      },
      "execution_count": 203,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter training set for Diabetes_binary=0 and count samples\n",
        "num_train_diabetes_0 = sum(y == 0 for y in yre_train)\n",
        "\n",
        "# Filter training set for Diabetes_binary=1 and count samples\n",
        "num_train_diabetes_1 = sum(y == 1 for y in yre_train)\n",
        "\n",
        "# Filter testing set for Diabetes_binary=0 and count samples\n",
        "num_test_diabetes_0 = sum(y == 0 for y in yre_test)\n",
        "\n",
        "# Filter testing set for Diabetes_binary=1 and count samples\n",
        "num_test_diabetes_1 = sum(y == 1 for y in yre_test)\n",
        "\n",
        "print(\"Training set:\")\n",
        "print(\"Number of data points with Diabetes_binary=0:\", num_train_diabetes_0)\n",
        "print(\"Number of data points with Diabetes_binary=1:\", num_train_diabetes_1)\n",
        "\n",
        "print(\"Testing set:\")\n",
        "print(\"Number of data points with Diabetes_binary=0:\", num_test_diabetes_0)\n",
        "print(\"Number of data points with Diabetes_binary=1:\", num_test_diabetes_1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GFd-p-SDkovp",
        "outputId": "0aad3883-5633-4b55-f40a-74a40f531531"
      },
      "execution_count": 204,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set:\n",
            "Number of data points with Diabetes_binary=0: 8360\n",
            "Number of data points with Diabetes_binary=1: 8632\n",
            "Testing set:\n",
            "Number of data points with Diabetes_binary=0: 2094\n",
            "Number of data points with Diabetes_binary=1: 2154\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xgb= XGBClassifier(max_depth=20)\n",
        "xgb.fit(xre_train,yre_train)\n",
        "\n",
        "y_pred_train_xgb = xgb.predict(xre_train)\n",
        "acc_train_xgb = accuracy_score(yre_train, y_pred_train_xgb)\n",
        "\n",
        "y_pred_test_xgb = xgb.predict(xre_test)\n",
        "acc_test_xgb = accuracy_score(yre_test, y_pred_test_xgb)\n",
        "print(\"Training accuracy:\")\n",
        "print(acc_train_xgb)\n",
        "print(\"Testing accuracy\")\n",
        "print(acc_test_xgb)\n",
        "\n",
        "print(classification_report(yre_test, y_pred_test_xgb))\n",
        "\n",
        "print('Precision: %.2f' % precision_score(yre_test, y_pred_test_xgb,average=\"micro\"))\n",
        "print('Recall: %.2f' % recall_score(yre_test, y_pred_test_xgb,average=\"micro\"))\n",
        "print('F-measure: %.2f' % f1_score(yre_test, y_pred_test_xgb,average=\"micro\"))\n",
        "\n",
        "# calculating and plotting the confusion matrix\n",
        "cm1 = confusion_matrix(yre_test,y_pred_test_xgb)\n",
        "plot_confusion_matrix(conf_mat=cm1,show_absolute=True,\n",
        "                                show_normed=True,\n",
        "\n",
        "\n",
        "                                colorbar=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "cDmL45Ub3W9C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 744
        },
        "outputId": "2882441b-6303-4214-9888-05c03419a062"
      },
      "execution_count": 205,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy:\n",
            "1.0\n",
            "Testing accuracy\n",
            "0.9595103578154426\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.96      0.96      2094\n",
            "           1       0.96      0.96      0.96      2154\n",
            "\n",
            "    accuracy                           0.96      4248\n",
            "   macro avg       0.96      0.96      0.96      4248\n",
            "weighted avg       0.96      0.96      0.96      4248\n",
            "\n",
            "Precision: 0.96\n",
            "Recall: 0.96\n",
            "F-measure: 0.96\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "LOG"
      ],
      "metadata": {
        "id": "UXKKrIu4JBup"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import GaussianNB, CategoricalNB\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, precision_score, recall_score, f1_score\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Logistic Regression\n",
        "log_reg = LogisticRegression()\n",
        "log_reg.fit(xre_train, yre_train)\n",
        "\n",
        "y_pred_train_logreg = log_reg.predict(xre_train)\n",
        "acc_train_logreg = accuracy_score(yre_train, y_pred_train_logreg)\n",
        "\n",
        "y_pred_test_logreg = log_reg.predict(xre_test)\n",
        "acc_test_logreg = accuracy_score(yre_test, y_pred_test_logreg)\n",
        "\n",
        "print(\"Logistic Regression:\")\n",
        "print(\"Training Accuracy:\", acc_train_logreg)\n",
        "print(\"Test Accuracy:\", acc_test_logreg)\n",
        "\n",
        "# calculating and plotting the confusion matrix\n",
        "cm_logreg = confusion_matrix(yre_test, y_pred_test_logreg)\n",
        "plot_confusion_matrix(conf_mat=cm_logreg, show_absolute=True,\n",
        "                      show_normed=True, colorbar=True)\n",
        "plt.title(\"Confusion Matrix - Logistic Regression\")\n",
        "plt.show()\n",
        "\n",
        "print(classification_report(yre_test, y_pred_test_xgb))\n",
        "\n",
        "print('Precision: %.2f' % precision_score(yre_test, y_pred_test_xgb,average=\"micro\"))\n",
        "print('Recall: %.2f' % recall_score(yre_test, y_pred_test_xgb,average=\"micro\"))\n",
        "print('F-measure: %.2f' % f1_score(yre_test, y_pred_test_xgb,average=\"micro\"))\n",
        "\n",
        "# calculating and plotting the confusion matrix\n",
        "cm1 = confusion_matrix(yre_test,y_pred_test_xgb)\n",
        "plot_confusion_matrix(conf_mat=cm1,show_absolute=True,\n",
        "                                show_normed=True,\n",
        "\n",
        "\n",
        "                                colorbar=True)\n",
        "plt.show()\n",
        "\n",
        "# Decision Trees\n",
        "dt = DecisionTreeClassifier(max_depth=20)\n",
        "dt.fit(xre_train, yre_train)\n",
        "\n",
        "y_pred_train_dt = dt.predict(xre_train)\n",
        "acc_train_dt = accuracy_score(yre_train, y_pred_train_dt)\n",
        "\n",
        "y_pred_test_dt = dt.predict(xre_test)\n",
        "acc_test_dt = accuracy_score(yre_test, y_pred_test_dt)\n",
        "\n",
        "print(\"\\nDecision Trees:\")\n",
        "print(\"Training Accuracy:\", acc_train_dt)\n",
        "print(\"Test Accuracy:\", acc_test_dt)\n",
        "\n",
        "# calculating and plotting the confusion matrix\n",
        "cm_dt = confusion_matrix(yre_test, y_pred_test_dt)\n",
        "plot_confusion_matrix(conf_mat=cm_dt, show_absolute=True,\n",
        "                      show_normed=True, colorbar=True)\n",
        "plt.title(\"Confusion Matrix - Decision Trees\")\n",
        "plt.show()\n",
        "\n",
        "# Random Forest\n",
        "rf = RandomForestClassifier(max_depth=20)\n",
        "rf.fit(xre_train, yre_train)\n",
        "\n",
        "y_pred_train_rf = rf.predict(xre_train)\n",
        "acc_train_rf = accuracy_score(yre_train, y_pred_train_rf)\n",
        "\n",
        "y_pred_test_rf = rf.predict(xre_test)\n",
        "acc_test_rf = accuracy_score(yre_test, y_pred_test_rf)\n",
        "\n",
        "print(\"\\nRandom Forest:\")\n",
        "print(\"Training Accuracy:\", acc_train_rf)\n",
        "print(\"Test Accuracy:\", acc_test_rf)\n",
        "\n",
        "# calculating and plotting the confusion matrix\n",
        "cm_rf = confusion_matrix(yre_test, y_pred_test_rf)\n",
        "plot_confusion_matrix(conf_mat=cm_rf, show_absolute=True,\n",
        "                      show_normed=True, colorbar=True)\n",
        "plt.title(\"Confusion Matrix - Random Forest\")\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "# Naive Bayes\n",
        "nb = GaussianNB()\n",
        "nb.fit(xre_train, yre_train)\n",
        "\n",
        "y_pred_train_nb = nb.predict(xre_train)\n",
        "acc_train_nb = accuracy_score(yre_train, y_pred_train_nb)\n",
        "\n",
        "y_pred_test_nb = nb.predict(xre_test)\n",
        "acc_test_nb = accuracy_score(yre_test, y_pred_test_nb)\n",
        "\n",
        "print(\"\\nNaive Bayes:\")\n",
        "print(\"Training Accuracy:\", acc_train_nb)\n",
        "print(\"Test Accuracy:\", acc_test_nb)\n",
        "\n",
        "# calculating and plotting the confusion matrix\n",
        "cm_nb = confusion_matrix(yre_test, y_pred_test_nb)\n",
        "plot_confusion_matrix(conf_mat=cm_nb, show_absolute=True,\n",
        "                      show_normed=True, colorbar=True)\n",
        "plt.title(\"Confusion Matrix - Naive Bayes\")\n",
        "plt.show()\n",
        "\n",
        "# K-Nearest Neighbors (K-NN)\n",
        "knn = KNeighborsClassifier()\n",
        "knn.fit(xre_train, yre_train)\n",
        "\n",
        "y_pred_train_knn = knn.predict(xre_train)\n",
        "acc_train_knn = accuracy_score(yre_train, y_pred_train_knn)\n",
        "\n",
        "y_pred_test_knn = knn.predict(xre_test)\n",
        "acc_test_knn = accuracy_score(yre_test, y_pred_test_knn)\n",
        "\n",
        "print(\"\\nK-Nearest Neighbors:\")\n",
        "print(\"Training Accuracy:\", acc_train_knn)\n",
        "print(\"Test Accuracy:\", acc_test_knn)\n",
        "\n",
        "# calculating and plotting the confusion matrix\n",
        "cm_knn = confusion_matrix(yre_test, y_pred_test_knn)\n",
        "plot_confusion_matrix(conf_mat=cm_knn, show_absolute=True,\n",
        "                      show_normed=True, colorbar=True)\n",
        "plt.title(\"Confusion Matrix - K-Nearest Neighbors\")\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "k0nSXUa-JC01",
        "outputId": "6e3c70c9-0c8c-4189-d1f1-7775cec1d7e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression:\n",
            "Training Accuracy: 0.9490649606299213\n",
            "Test Accuracy: 0.9473554735547356\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Decision Trees:\n",
            "Training Accuracy: 0.999753937007874\n",
            "Test Accuracy: 0.9387453874538746\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Random Forest:\n",
            "Training Accuracy: 0.9999384842519685\n",
            "Test Accuracy: 0.9685116851168512\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Naive Bayes:\n",
            "Training Accuracy: 0.8153912401574803\n",
            "Test Accuracy: 0.8051660516605166\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "K-Nearest Neighbors:\n",
            "Training Accuracy: 0.9791461614173228\n",
            "Test Accuracy: 0.9630996309963099\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "LOGISTICS REGRESSION"
      ],
      "metadata": {
        "id": "ncLscnjijvyI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, precision_score, recall_score, f1_score\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Logistic Regression\n",
        "log_reg = LogisticRegression()\n",
        "log_reg.fit(xre_train, yre_train)\n",
        "\n",
        "# Predictions on training set\n",
        "y_pred_train_logreg = log_reg.predict(xre_train)\n",
        "acc_train_logreg = accuracy_score(yre_train, y_pred_train_logreg)\n",
        "\n",
        "# Predictions on testing set\n",
        "y_pred_test_logreg = log_reg.predict(xre_test)\n",
        "acc_test_logreg = accuracy_score(yre_test, y_pred_test_logreg)\n",
        "\n",
        "# Print accuracies\n",
        "print(\"Logistic Regression:\")\n",
        "print(\"Training Accuracy:\", acc_train_logreg)\n",
        "print(\"Test Accuracy:\", acc_test_logreg)\n",
        "\n",
        "# Classification report for Logistic Regression\n",
        "print(\"Classification Report - Logistic Regression:\")\n",
        "print(classification_report(yre_test, y_pred_test_logreg))\n",
        "\n",
        "# Precision, Recall, and F-measure for Logistic Regression\n",
        "print('Precision: %.2f' % precision_score(yre_test, y_pred_test_logreg, average=\"micro\"))\n",
        "print('Recall: %.2f' % recall_score(yre_test, y_pred_test_logreg, average=\"micro\"))\n",
        "print('F-measure: %.2f' % f1_score(yre_test, y_pred_test_logreg, average=\"micro\"))\n",
        "\n",
        "# Calculating and plotting the confusion matrix for Logistic Regression\n",
        "cm_logreg = confusion_matrix(yre_test, y_pred_test_logreg)\n",
        "plot_confusion_matrix(conf_mat=cm_logreg, show_absolute=True, show_normed=True, colorbar=True)\n",
        "plt.title(\"Confusion Matrix - Logistic Regression\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 750
        },
        "id": "z7O0egDLjx3X",
        "outputId": "6e7e23b2-351d-4d4e-ee9a-d3bd33a4e957"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression:\n",
            "Training Accuracy: 0.9436680951493696\n",
            "Test Accuracy: 0.9438648782911078\n",
            "Classification Report - Logistic Regression:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.94      0.94      1987\n",
            "           1       0.94      0.95      0.94      2039\n",
            "\n",
            "    accuracy                           0.94      4026\n",
            "   macro avg       0.94      0.94      0.94      4026\n",
            "weighted avg       0.94      0.94      0.94      4026\n",
            "\n",
            "Precision: 0.94\n",
            "Recall: 0.94\n",
            "F-measure: 0.94\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**RANDOM FOREST**"
      ],
      "metadata": {
        "id": "5rzy83ynqfqC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Random Forest\n",
        "rf = RandomForestClassifier(max_depth=20)\n",
        "rf.fit(xre_train, yre_train)\n",
        "\n",
        "# Predictions on training set\n",
        "y_pred_train_rf = rf.predict(xre_train)\n",
        "acc_train_rf = accuracy_score(yre_train, y_pred_train_rf)\n",
        "\n",
        "# Predictions on testing set\n",
        "y_pred_test_rf = rf.predict(xre_test)\n",
        "acc_test_rf = accuracy_score(yre_test, y_pred_test_rf)\n",
        "\n",
        "# Print accuracies\n",
        "print(\"\\nRandom Forest:\")\n",
        "print(\"Training Accuracy:\", acc_train_rf)\n",
        "print(\"Test Accuracy:\", acc_test_rf)\n",
        "\n",
        "# Classification report for Random Forest\n",
        "print(\"Classification Report - Random Forest:\")\n",
        "print(classification_report(yre_test, y_pred_test_rf))\n",
        "\n",
        "# calculating and plotting the confusion matrix\n",
        "cm_rf = confusion_matrix(yre_test, y_pred_test_rf)\n",
        "plot_confusion_matrix(conf_mat=cm_rf, show_absolute=True,\n",
        "                      show_normed=True, colorbar=True)\n",
        "plt.title(\"Confusion Matrix - Random Forest\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 715
        },
        "id": "pljPy2zHqgug",
        "outputId": "e82a33d1-9e66-4a59-def0-1ce0150b68c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Random Forest:\n",
            "Training Accuracy: 1.0\n",
            "Test Accuracy: 0.9719014626635873\n",
            "Classification Report - Random Forest:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.97      0.97      1356\n",
            "           1       0.97      0.97      0.97      1242\n",
            "\n",
            "    accuracy                           0.97      2598\n",
            "   macro avg       0.97      0.97      0.97      2598\n",
            "weighted avg       0.97      0.97      0.97      2598\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "DECISION TREE"
      ],
      "metadata": {
        "id": "JSW4YqKEcLIH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# Decision Trees\n",
        "dt = DecisionTreeClassifier(max_depth=20)\n",
        "dt.fit(xre_train, yre_train)\n",
        "\n",
        "# Predictions on training set\n",
        "y_pred_train_dt = dt.predict(xre_train)\n",
        "acc_train_dt = accuracy_score(yre_train, y_pred_train_dt)\n",
        "\n",
        "# Predictions on testing set\n",
        "y_pred_test_dt = dt.predict(xre_test)\n",
        "acc_test_dt = accuracy_score(yre_test, y_pred_test_dt)\n",
        "\n",
        "# Print accuracies\n",
        "print(\"\\nDecision Trees:\")\n",
        "print(\"Training Accuracy:\", acc_train_dt)\n",
        "print(\"Test Accuracy:\", acc_test_dt)\n",
        "\n",
        "# Classification report for Decision Trees\n",
        "print(\"Classification Report - Decision Trees:\")\n",
        "print(classification_report(yre_test, y_pred_test_dt))\n",
        "\n",
        "# calculating and plotting the confusion matrix\n",
        "cm_dt = confusion_matrix(yre_test, y_pred_test_dt)\n",
        "plot_confusion_matrix(conf_mat=cm_dt, show_absolute=True,\n",
        "                      show_normed=True, colorbar=True)\n",
        "plt.title(\"Confusion Matrix - Decision Trees\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 715
        },
        "id": "YNG-XxPBrJlB",
        "outputId": "b57092c1-4599-474f-e0fb-cab0ace2617f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Decision Trees:\n",
            "Training Accuracy: 1.0\n",
            "Test Accuracy: 0.949576597382602\n",
            "Classification Report - Decision Trees:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.95      0.95      1356\n",
            "           1       0.94      0.95      0.95      1242\n",
            "\n",
            "    accuracy                           0.95      2598\n",
            "   macro avg       0.95      0.95      0.95      2598\n",
            "weighted avg       0.95      0.95      0.95      2598\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "NAIVE BAYES"
      ],
      "metadata": {
        "id": "YBWRvlVxrsN0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "# Naive Bayes\n",
        "nb = GaussianNB()\n",
        "nb.fit(xre_train, yre_train)\n",
        "\n",
        "# Predictions on training set\n",
        "y_pred_train_nb = nb.predict(xre_train)\n",
        "acc_train_nb = accuracy_score(yre_train, y_pred_train_nb)\n",
        "\n",
        "# Predictions on testing set\n",
        "y_pred_test_nb = nb.predict(xre_test)\n",
        "acc_test_nb = accuracy_score(yre_test, y_pred_test_nb)\n",
        "\n",
        "# Print accuracies\n",
        "print(\"\\nNaive Bayes:\")\n",
        "print(\"Training Accuracy:\", acc_train_nb)\n",
        "print(\"Test Accuracy:\", acc_test_nb)\n",
        "\n",
        "# Classification report for Naive Bayes\n",
        "print(\"Classification Report - Naive Bayes:\")\n",
        "print(classification_report(yre_test, y_pred_test_nb))\n",
        "\n",
        "# calculating and plotting the confusion matrix\n",
        "cm_nb = confusion_matrix(yre_test, y_pred_test_nb)\n",
        "plot_confusion_matrix(conf_mat=cm_nb, show_absolute=True,\n",
        "                      show_normed=True, colorbar=True)\n",
        "plt.title(\"Confusion Matrix - Naive Bayes\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 715
        },
        "id": "gzUZZZ-SrtyZ",
        "outputId": "787f5c74-56e9-4145-ab9b-1ad5bf6eb108"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Naive Bayes:\n",
            "Training Accuracy: 0.9244320369657297\n",
            "Test Accuracy: 0.9257120862201693\n",
            "Classification Report - Naive Bayes:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.92      0.93      1356\n",
            "           1       0.91      0.93      0.92      1242\n",
            "\n",
            "    accuracy                           0.93      2598\n",
            "   macro avg       0.93      0.93      0.93      2598\n",
            "weighted avg       0.93      0.93      0.93      2598\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "KNN"
      ],
      "metadata": {
        "id": "ZfQu9WELsTUs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "# K-Nearest Neighbors (K-NN)\n",
        "knn = KNeighborsClassifier()\n",
        "knn.fit(xre_train, yre_train)\n",
        "\n",
        "# Predictions on training set\n",
        "y_pred_train_knn = knn.predict(xre_train)\n",
        "acc_train_knn = accuracy_score(yre_train, y_pred_train_knn)\n",
        "\n",
        "# Predictions on testing set\n",
        "y_pred_test_knn = knn.predict(xre_test)\n",
        "acc_test_knn = accuracy_score(yre_test, y_pred_test_knn)\n",
        "\n",
        "# Print accuracies\n",
        "print(\"\\nK-Nearest Neighbors:\")\n",
        "print(\"Training Accuracy:\", acc_train_knn)\n",
        "print(\"Test Accuracy:\", acc_test_knn)\n",
        "\n",
        "# Classification report for K-Nearest Neighbors\n",
        "print(\"Classification Report - K-Nearest Neighbors:\")\n",
        "print(classification_report(yre_test, y_pred_test_knn))\n",
        "\n",
        "# calculating and plotting the confusion matrix\n",
        "cm_knn = confusion_matrix(yre_test, y_pred_test_knn)\n",
        "plot_confusion_matrix(conf_mat=cm_knn, show_absolute=True,\n",
        "                      show_normed=True, colorbar=True)\n",
        "plt.title(\"Confusion Matrix - K-Nearest Neighbors\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 715
        },
        "id": "EJBwQF74sVBX",
        "outputId": "6550d04e-e6d1-4c90-da7c-0d770046bb35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "K-Nearest Neighbors:\n",
            "Training Accuracy: 0.9801694262610705\n",
            "Test Accuracy: 0.972671285604311\n",
            "Classification Report - K-Nearest Neighbors:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.97      0.97      1356\n",
            "           1       0.97      0.97      0.97      1242\n",
            "\n",
            "    accuracy                           0.97      2598\n",
            "   macro avg       0.97      0.97      0.97      2598\n",
            "weighted avg       0.97      0.97      0.97      2598\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Support Vector Machine (SVM)\n",
        "svm = SVC()\n",
        "svm.fit(xre_train, yre_train)\n",
        "\n",
        "y_pred_train_svm = svm.predict(xre_train)\n",
        "acc_train_svm = accuracy_score(yre_train, y_pred_train_svm)\n",
        "\n",
        "y_pred_test_svm = svm.predict(xre_test)\n",
        "acc_test_svm = accuracy_score(yre_test, y_pred_test_svm)\n",
        "\n",
        "print(\"\\nSupport Vector Machine:\")\n",
        "print(\"Training Accuracy:\", acc_train_svm)\n",
        "print(\"Test Accuracy:\", acc_test_svm)\n",
        "\n",
        "# calculating and plotting the confusion matrix\n",
        "cm_svm = confusion_matrix(yre_test, y_pred_test_svm)\n",
        "plot_confusion_matrix(conf_mat=cm_svm, show_absolute=True,\n",
        "                      show_normed=True, colorbar=True)\n",
        "plt.title(\"Confusion Matrix - Support Vector Machine\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "6NKiswZjcMsD",
        "outputId": "aa540089-c56b-4ccf-c10f-846282325192"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Support Vector Machine:\n",
            "Training Accuracy: 0.9627214566929134\n",
            "Test Accuracy: 0.9571955719557196\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**NEURAL NETWORK**"
      ],
      "metadata": {
        "id": "vnnTqNR2tTl6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "# Neural Network\n",
        "mlp = MLPClassifier()\n",
        "mlp.fit(xre_train, yre_train)\n",
        "\n",
        "# Predictions on training set\n",
        "y_pred_train_mlp = mlp.predict(xre_train)\n",
        "acc_train_mlp = accuracy_score(yre_train, y_pred_train_mlp)\n",
        "\n",
        "# Predictions on testing set\n",
        "y_pred_test_mlp = mlp.predict(xre_test)\n",
        "acc_test_mlp = accuracy_score(yre_test, y_pred_test_mlp)\n",
        "\n",
        "# Print accuracies\n",
        "print(\"\\nNeural Network:\")\n",
        "print(\"Training Accuracy:\", acc_train_mlp)\n",
        "print(\"Test Accuracy:\", acc_test_mlp)\n",
        "\n",
        "# Classification report for Neural Network\n",
        "print(\"Classification Report - Neural Network:\")\n",
        "print(classification_report(yre_test, y_pred_test_mlp))\n",
        "\n",
        "# calculating and plotting the confusion matrix\n",
        "cm_mlp = confusion_matrix(yre_test, y_pred_test_mlp)\n",
        "plot_confusion_matrix(conf_mat=cm_mlp, show_absolute=True,\n",
        "                      show_normed=True, colorbar=True)\n",
        "plt.title(\"Confusion Matrix - Neural Network\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 715
        },
        "id": "nxckeeefdyqe",
        "outputId": "09567e5d-793b-4caa-fa8b-42401350b5fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Neural Network:\n",
            "Training Accuracy: 0.9922025413939161\n",
            "Test Accuracy: 0.9757505773672055\n",
            "Classification Report - Neural Network:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.97      0.98      1356\n",
            "           1       0.97      0.98      0.97      1242\n",
            "\n",
            "    accuracy                           0.98      2598\n",
            "   macro avg       0.98      0.98      0.98      2598\n",
            "weighted avg       0.98      0.98      0.98      2598\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import svm\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming you have defined other classifiers (log_reg, dt, rf, nb, knn, mlp) earlier\n",
        "\n",
        "# Create SVM classifier\n",
        "svm_classifier = svm.SVC()\n",
        "\n",
        "# Ensemble Method (Voting Classifier)\n",
        "ensemble = VotingClassifier(estimators=[\n",
        "    ('log_reg', log_reg),\n",
        "    ('dt', dt),\n",
        "    ('rf', rf),\n",
        "    ('svm', svm_classifier),\n",
        "    ('nb', nb),\n",
        "    ('knn', knn),\n",
        "    ('mlp', mlp)\n",
        "], voting='hard')\n",
        "\n",
        "ensemble.fit(xre_train, yre_train)\n",
        "\n",
        "# Predictions on training set\n",
        "y_pred_train_ensemble = ensemble.predict(xre_train)\n",
        "acc_train_ensemble = accuracy_score(yre_train, y_pred_train_ensemble)\n",
        "\n",
        "# Predictions on testing set\n",
        "y_pred_test_ensemble = ensemble.predict(xre_test)\n",
        "acc_test_ensemble = accuracy_score(yre_test, y_pred_test_ensemble)\n",
        "\n",
        "# Print accuracies\n",
        "print(\"\\nEnsemble Method:\")\n",
        "print(\"Training Accuracy:\", acc_train_ensemble)\n",
        "print(\"Test Accuracy:\", acc_test_ensemble)\n",
        "\n",
        "# Classification report for Ensemble Method\n",
        "print(\"Classification Report - Ensemble Method:\")\n",
        "print(classification_report(yre_test, y_pred_test_ensemble))\n",
        "\n",
        "# calculating and plotting the confusion matrix\n",
        "cm_ensemble = confusion_matrix(yre_test, y_pred_test_ensemble)\n",
        "plot_confusion_matrix(conf_mat=cm_ensemble, show_absolute=True,\n",
        "                      show_normed=True, colorbar=True)\n",
        "plt.title(\"Confusion Matrix - Ensemble Method\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 715
        },
        "id": "DGSEUaGtkejk",
        "outputId": "907b227f-c59b-43ac-d052-1532d86afbf7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Ensemble Method:\n",
            "Training Accuracy: 0.9881594147092799\n",
            "Test Accuracy: 0.9742109314857583\n",
            "Classification Report - Ensemble Method:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.97      0.98      1356\n",
            "           1       0.97      0.98      0.97      1242\n",
            "\n",
            "    accuracy                           0.97      2598\n",
            "   macro avg       0.97      0.97      0.97      2598\n",
            "weighted avg       0.97      0.97      0.97      2598\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}