Data Understanding_Features_Selection_Train_Model_Performance_Evaluation.ipynb

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        "<a href=\"https://colab.research.google.com/github/nermienkh/BMD302lab1_spring22/blob/master/Data%20Understanding_Features_Selection_Train_Model_Performance_Evaluation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "## **Dataset: Diabetes Health Indicators**\n",
        "Source: (https://www.kaggle.com/datasets/alexteboul/diabetes-health-indicators-dataset/code?datasetId=1703281&sortBy=voteCount)\n",
        "\n"
      ],
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      "source": [
        "#import library\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn import metrics"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#read data\n",
        "df = pd.read_csv('/content/drive/MyDrive/BMD212/diabetes_binary_health_indicators_BRFSS2015.csv')"
      ],
      "metadata": {
        "id": "Hg04LeRH0yF1"
      },
      "execution_count": 2,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "#print dataframe shape\n",
        "df.shape"
      ],
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        "id": "dY_febXK2-cu",
        "outputId": "91591bd8-c6fd-4c4c-df68-f9888f4df88b"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "(253680, 22)"
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          "metadata": {},
          "execution_count": 4
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    {
      "cell_type": "code",
      "source": [
        "#print some rows\n",
        "df.head()"
      ],
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            "text/plain": [
              "   Diabetes_binary  HighBP  HighChol  CholCheck   BMI  Smoker  Stroke  \\\n",
              "0              0.0     1.0       1.0        1.0  40.0     1.0     0.0   \n",
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              "3              0.0     1.0       0.0        1.0  27.0     0.0     0.0   \n",
              "4              0.0     1.0       1.0        1.0  24.0     0.0     0.0   \n",
              "\n",
              "   HeartDiseaseorAttack  PhysActivity  Fruits  ...  AnyHealthcare  \\\n",
              "0                   0.0           0.0     0.0  ...            1.0   \n",
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              "\n",
              "   NoDocbcCost  GenHlth  MentHlth  PhysHlth  DiffWalk  Sex   Age  Education  \\\n",
              "0          0.0      5.0      18.0      15.0       1.0  0.0   9.0        4.0   \n",
              "1          1.0      3.0       0.0       0.0       0.0  0.0   7.0        6.0   \n",
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              "4          0.0      2.0       3.0       0.0       0.0  0.0  11.0        5.0   \n",
              "\n",
              "   Income  \n",
              "0     3.0  \n",
              "1     1.0  \n",
              "2     8.0  \n",
              "3     6.0  \n",
              "4     4.0  \n",
              "\n",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 253680 entries, 0 to 253679\n",
            "Data columns (total 22 columns):\n",
            " #   Column                Non-Null Count   Dtype  \n",
            "---  ------                --------------   -----  \n",
            " 0   Diabetes_binary       253680 non-null  float64\n",
            " 1   HighBP                253680 non-null  float64\n",
            " 2   HighChol              253680 non-null  float64\n",
            " 3   CholCheck             253680 non-null  float64\n",
            " 4   BMI                   253680 non-null  float64\n",
            " 5   Smoker                253680 non-null  float64\n",
            " 6   Stroke                253680 non-null  float64\n",
            " 7   HeartDiseaseorAttack  253680 non-null  float64\n",
            " 8   PhysActivity          253680 non-null  float64\n",
            " 9   Fruits                253680 non-null  float64\n",
            " 10  Veggies               253680 non-null  float64\n",
            " 11  HvyAlcoholConsump     253680 non-null  float64\n",
            " 12  AnyHealthcare         253680 non-null  float64\n",
            " 13  NoDocbcCost           253680 non-null  float64\n",
            " 14  GenHlth               253680 non-null  float64\n",
            " 15  MentHlth              253680 non-null  float64\n",
            " 16  PhysHlth              253680 non-null  float64\n",
            " 17  DiffWalk              253680 non-null  float64\n",
            " 18  Sex                   253680 non-null  float64\n",
            " 19  Age                   253680 non-null  float64\n",
            " 20  Education             253680 non-null  float64\n",
            " 21  Income                253680 non-null  float64\n",
            "dtypes: float64(22)\n",
            "memory usage: 42.6 MB\n"
          ]
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      "source": [
        "df.describe()"
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              "       Diabetes_binary         HighBP       HighChol      CholCheck  \\\n",
              "count    253680.000000  253680.000000  253680.000000  253680.000000   \n",
              "mean          0.139333       0.429001       0.424121       0.962670   \n",
              "std           0.346294       0.494934       0.494210       0.189571   \n",
              "min           0.000000       0.000000       0.000000       0.000000   \n",
              "25%           0.000000       0.000000       0.000000       1.000000   \n",
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              "75%           0.000000       1.000000       1.000000       1.000000   \n",
              "max           1.000000       1.000000       1.000000       1.000000   \n",
              "\n",
              "                 BMI         Smoker         Stroke  HeartDiseaseorAttack  \\\n",
              "count  253680.000000  253680.000000  253680.000000         253680.000000   \n",
              "mean       28.382364       0.443169       0.040571              0.094186   \n",
              "std         6.608694       0.496761       0.197294              0.292087   \n",
              "min        12.000000       0.000000       0.000000              0.000000   \n",
              "25%        24.000000       0.000000       0.000000              0.000000   \n",
              "50%        27.000000       0.000000       0.000000              0.000000   \n",
              "75%        31.000000       1.000000       0.000000              0.000000   \n",
              "max        98.000000       1.000000       1.000000              1.000000   \n",
              "\n",
              "        PhysActivity         Fruits  ...  AnyHealthcare    NoDocbcCost  \\\n",
              "count  253680.000000  253680.000000  ...  253680.000000  253680.000000   \n",
              "mean        0.756544       0.634256  ...       0.951053       0.084177   \n",
              "std         0.429169       0.481639  ...       0.215759       0.277654   \n",
              "min         0.000000       0.000000  ...       0.000000       0.000000   \n",
              "25%         1.000000       0.000000  ...       1.000000       0.000000   \n",
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              "\n",
              "             GenHlth       MentHlth       PhysHlth       DiffWalk  \\\n",
              "count  253680.000000  253680.000000  253680.000000  253680.000000   \n",
              "mean        2.511392       3.184772       4.242081       0.168224   \n",
              "std         1.068477       7.412847       8.717951       0.374066   \n",
              "min         1.000000       0.000000       0.000000       0.000000   \n",
              "25%         2.000000       0.000000       0.000000       0.000000   \n",
              "50%         2.000000       0.000000       0.000000       0.000000   \n",
              "75%         3.000000       2.000000       3.000000       0.000000   \n",
              "max         5.000000      30.000000      30.000000       1.000000   \n",
              "\n",
              "                 Sex            Age      Education         Income  \n",
              "count  253680.000000  253680.000000  253680.000000  253680.000000  \n",
              "mean        0.440342       8.032119       5.050434       6.053875  \n",
              "std         0.496429       3.054220       0.985774       2.071148  \n",
              "min         0.000000       1.000000       1.000000       1.000000  \n",
              "25%         0.000000       6.000000       4.000000       5.000000  \n",
              "50%         0.000000       8.000000       5.000000       7.000000  \n",
              "75%         1.000000      10.000000       6.000000       8.000000  \n",
              "max         1.000000      13.000000       6.000000       8.000000  \n",
              "\n",
              "[8 rows x 22 columns]"
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Diabetes_binary</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",
              "      <th>Fruits</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",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>253680.000000</td>\n",
              "      <td>253680.000000</td>\n",
              "      <td>253680.000000</td>\n",
              "      <td>253680.000000</td>\n",
              "      <td>253680.000000</td>\n",
              "      <td>253680.000000</td>\n",
              "      <td>253680.000000</td>\n",
              "      <td>253680.000000</td>\n",
              "      <td>253680.000000</td>\n",
              "      <td>253680.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>253680.000000</td>\n",
              "      <td>253680.000000</td>\n",
              "      <td>253680.000000</td>\n",
              "      <td>253680.000000</td>\n",
              "      <td>253680.000000</td>\n",
              "      <td>253680.000000</td>\n",
              "      <td>253680.000000</td>\n",
              "      <td>253680.000000</td>\n",
              "      <td>253680.000000</td>\n",
              "      <td>253680.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.139333</td>\n",
              "      <td>0.429001</td>\n",
              "      <td>0.424121</td>\n",
              "      <td>0.962670</td>\n",
              "      <td>28.382364</td>\n",
              "      <td>0.443169</td>\n",
              "      <td>0.040571</td>\n",
              "      <td>0.094186</td>\n",
              "      <td>0.756544</td>\n",
              "      <td>0.634256</td>\n",
              "      <td>...</td>\n",
              "      <td>0.951053</td>\n",
              "      <td>0.084177</td>\n",
              "      <td>2.511392</td>\n",
              "      <td>3.184772</td>\n",
              "      <td>4.242081</td>\n",
              "      <td>0.168224</td>\n",
              "      <td>0.440342</td>\n",
              "      <td>8.032119</td>\n",
              "      <td>5.050434</td>\n",
              "      <td>6.053875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.346294</td>\n",
              "      <td>0.494934</td>\n",
              "      <td>0.494210</td>\n",
              "      <td>0.189571</td>\n",
              "      <td>6.608694</td>\n",
              "      <td>0.496761</td>\n",
              "      <td>0.197294</td>\n",
              "      <td>0.292087</td>\n",
              "      <td>0.429169</td>\n",
              "      <td>0.481639</td>\n",
              "      <td>...</td>\n",
              "      <td>0.215759</td>\n",
              "      <td>0.277654</td>\n",
              "      <td>1.068477</td>\n",
              "      <td>7.412847</td>\n",
              "      <td>8.717951</td>\n",
              "      <td>0.374066</td>\n",
              "      <td>0.496429</td>\n",
              "      <td>3.054220</td>\n",
              "      <td>0.985774</td>\n",
              "      <td>2.071148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>7.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 22 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-79cea611-fe1d-46e8-93fb-f325676e5a63')\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "          document.querySelector('#df-79cea611-fe1d-46e8-93fb-f325676e5a63 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-79cea611-fe1d-46e8-93fb-f325676e5a63');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check Data Balance\n",
        "df[\"Diabetes_binary\"].value_counts().plot(kind = 'bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "IUOK7K5VO2kA",
        "outputId": "6a5b11ba-861d-4d3b-91bc-1285a309d7b6"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f017536ce90>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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HgH+vqmFnwToNPJPQ26rqLeDguPuQhvg68IGqemzmQJJ/HX07Zw/PJCRJXd64liR1GRKSpC5DQpLUZUhIkrr+H9Gq6fS0EzBTAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Compute Correlation Matrix\n",
        "Read more about it https://www.statology.org/how-to-read-a-correlation-matrix/\n",
        "<br>\n",
        "In short, correlation  is a measure of the linear association between two variables. It has a value between -1 and 1\n",
        "\n",
        "    -1 indicates a perfectly negative linear correlation between two variables\n",
        "    0 indicates no linear correlation between two variables\n",
        "    1 indicates a perfectly positive linear correlation between two variables\n",
        "\n",
        "\n",
        "**Note**: A high correlation value between a dependent variable (output) and an independent variable (feature) indicates that the independent variable is of very high significance in determining the output.\n",
        "<br>\n",
        "**Redundant Features**: Although some features are highly relevant to our target variable, they might be redundant. Any 2 independent variables are considered to be redundant if they are highly correlated. This causes unnecessary time and space wastage. Even the redundancy between 2 variables can be found using correlation.\n",
        "<br>\n",
        "**Note**: A high correlation between dependent and independent variables is desired whereas the high correlation between 2 independent variables is undesired.\n",
        "\n",
        "source:https://towardsdatascience.com/understanding-feature-extraction-using-correlation-matrix-and-scatter-plots-6c19e968a60c"
      ],
      "metadata": {
        "id": "Oz9SFivtq2Dm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "id": "_F0y3zrc4eUL",
        "outputId": "9b51c335-3dad-4550-af93-48a3c5c94f7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Diabetes_binary    HighBP  HighChol  CholCheck  \\\n",
              "Diabetes_binary              1.000000  0.263129  0.200276   0.064761   \n",
              "HighBP                       0.263129  1.000000  0.298199   0.098508   \n",
              "HighChol                     0.200276  0.298199  1.000000   0.085642   \n",
              "CholCheck                    0.064761  0.098508  0.085642   1.000000   \n",
              "BMI                          0.216843  0.213748  0.106722   0.034495   \n",
              "Smoker                       0.060789  0.096991  0.091299  -0.009929   \n",
              "Stroke                       0.105816  0.129575  0.092620   0.024158   \n",
              "HeartDiseaseorAttack         0.177282  0.209361  0.180765   0.044206   \n",
              "PhysActivity                -0.118133 -0.125267 -0.078046   0.004190   \n",
              "Fruits                      -0.040779 -0.040555 -0.040859   0.023849   \n",
              "Veggies                     -0.056584 -0.061266 -0.039874   0.006121   \n",
              "HvyAlcoholConsump           -0.057056 -0.003972 -0.011543  -0.023730   \n",
              "AnyHealthcare                0.016255  0.038425  0.042230   0.117626   \n",
              "NoDocbcCost                  0.031433  0.017358  0.013310  -0.058255   \n",
              "GenHlth                      0.293569  0.300530  0.208426   0.046589   \n",
              "MentHlth                     0.069315  0.056456  0.062069  -0.008366   \n",
              "PhysHlth                     0.171337  0.161212  0.121751   0.031775   \n",
              "DiffWalk                     0.218344  0.223618  0.144672   0.040585   \n",
              "Sex                          0.031430  0.052207  0.031205  -0.022115   \n",
              "Age                          0.177442  0.344452  0.272318   0.090321   \n",
              "Education                   -0.124456 -0.141358 -0.070802   0.001510   \n",
              "Income                      -0.163919 -0.171235 -0.085459   0.014259   \n",
              "\n",
              "                           BMI    Smoker    Stroke  HeartDiseaseorAttack  \\\n",
              "Diabetes_binary       0.216843  0.060789  0.105816              0.177282   \n",
              "HighBP                0.213748  0.096991  0.129575              0.209361   \n",
              "HighChol              0.106722  0.091299  0.092620              0.180765   \n",
              "CholCheck             0.034495 -0.009929  0.024158              0.044206   \n",
              "BMI                   1.000000  0.013804  0.020153              0.052904   \n",
              "Smoker                0.013804  1.000000  0.061173              0.114441   \n",
              "Stroke                0.020153  0.061173  1.000000              0.203002   \n",
              "HeartDiseaseorAttack  0.052904  0.114441  0.203002              1.000000   \n",
              "PhysActivity         -0.147294 -0.087401 -0.069151             -0.087299   \n",
              "Fruits               -0.087518 -0.077666 -0.013389             -0.019790   \n",
              "Veggies              -0.062275 -0.030678 -0.041124             -0.039167   \n",
              "HvyAlcoholConsump    -0.048736  0.101619 -0.016950             -0.028991   \n",
              "AnyHealthcare        -0.018471 -0.023251  0.008776              0.018734   \n",
              "NoDocbcCost           0.058206  0.048946  0.034804              0.031000   \n",
              "GenHlth               0.239185  0.163143  0.177942              0.258383   \n",
              "MentHlth              0.085310  0.092196  0.070172              0.064621   \n",
              "PhysHlth              0.121141  0.116460  0.148944              0.181698   \n",
              "DiffWalk              0.197078  0.122463  0.176567              0.212709   \n",
              "Sex                   0.042950  0.093662  0.002978              0.086096   \n",
              "Age                  -0.036618  0.120641  0.126974              0.221618   \n",
              "Education            -0.103932 -0.161955 -0.076009             -0.099600   \n",
              "Income               -0.100069 -0.123937 -0.128599             -0.141011   \n",
              "\n",
              "                      PhysActivity    Fruits  ...  AnyHealthcare  NoDocbcCost  \\\n",
              "Diabetes_binary          -0.118133 -0.040779  ...       0.016255     0.031433   \n",
              "HighBP                   -0.125267 -0.040555  ...       0.038425     0.017358   \n",
              "HighChol                 -0.078046 -0.040859  ...       0.042230     0.013310   \n",
              "CholCheck                 0.004190  0.023849  ...       0.117626    -0.058255   \n",
              "BMI                      -0.147294 -0.087518  ...      -0.018471     0.058206   \n",
              "Smoker                   -0.087401 -0.077666  ...      -0.023251     0.048946   \n",
              "Stroke                   -0.069151 -0.013389  ...       0.008776     0.034804   \n",
              "HeartDiseaseorAttack     -0.087299 -0.019790  ...       0.018734     0.031000   \n",
              "PhysActivity              1.000000  0.142756  ...       0.035505    -0.061638   \n",
              "Fruits                    0.142756  1.000000  ...       0.031544    -0.044243   \n",
              "Veggies                   0.153150  0.254342  ...       0.029584    -0.032232   \n",
              "HvyAlcoholConsump         0.012392 -0.035288  ...      -0.010488     0.004684   \n",
              "AnyHealthcare             0.035505  0.031544  ...       1.000000    -0.232532   \n",
              "NoDocbcCost              -0.061638 -0.044243  ...      -0.232532     1.000000   \n",
              "GenHlth                  -0.266186 -0.103854  ...      -0.040817     0.166397   \n",
              "MentHlth                 -0.125587 -0.068217  ...      -0.052707     0.192107   \n",
              "PhysHlth                 -0.219230 -0.044633  ...      -0.008276     0.148998   \n",
              "DiffWalk                 -0.253174 -0.048352  ...       0.007074     0.118447   \n",
              "Sex                       0.032482 -0.091175  ...      -0.019405    -0.044931   \n",
              "Age                      -0.092511  0.064547  ...       0.138046    -0.119777   \n",
              "Education                 0.199658  0.110187  ...       0.122514    -0.100701   \n",
              "Income                    0.198539  0.079929  ...       0.157999    -0.203182   \n",
              "\n",
              "                       GenHlth  MentHlth  PhysHlth  DiffWalk       Sex  \\\n",
              "Diabetes_binary       0.293569  0.069315  0.171337  0.218344  0.031430   \n",
              "HighBP                0.300530  0.056456  0.161212  0.223618  0.052207   \n",
              "HighChol              0.208426  0.062069  0.121751  0.144672  0.031205   \n",
              "CholCheck             0.046589 -0.008366  0.031775  0.040585 -0.022115   \n",
              "BMI                   0.239185  0.085310  0.121141  0.197078  0.042950   \n",
              "Smoker                0.163143  0.092196  0.116460  0.122463  0.093662   \n",
              "Stroke                0.177942  0.070172  0.148944  0.176567  0.002978   \n",
              "HeartDiseaseorAttack  0.258383  0.064621  0.181698  0.212709  0.086096   \n",
              "PhysActivity         -0.266186 -0.125587 -0.219230 -0.253174  0.032482   \n",
              "Fruits               -0.103854 -0.068217 -0.044633 -0.048352 -0.091175   \n",
              "Veggies              -0.123066 -0.058884 -0.064290 -0.080506 -0.064765   \n",
              "HvyAlcoholConsump    -0.036724  0.024716 -0.026415 -0.037668  0.005740   \n",
              "AnyHealthcare        -0.040817 -0.052707 -0.008276  0.007074 -0.019405   \n",
              "NoDocbcCost           0.166397  0.192107  0.148998  0.118447 -0.044931   \n",
              "GenHlth               1.000000  0.301674  0.524364  0.456920 -0.006091   \n",
              "MentHlth              0.301674  1.000000  0.353619  0.233688 -0.080705   \n",
              "PhysHlth              0.524364  0.353619  1.000000  0.478417 -0.043137   \n",
              "DiffWalk              0.456920  0.233688  0.478417  1.000000 -0.070299   \n",
              "Sex                  -0.006091 -0.080705 -0.043137 -0.070299  1.000000   \n",
              "Age                   0.152450 -0.092068  0.099130  0.204450 -0.027340   \n",
              "Education            -0.284912 -0.101830 -0.155093 -0.192642  0.019480   \n",
              "Income               -0.370014 -0.209806 -0.266799 -0.320124  0.127141   \n",
              "\n",
              "                           Age  Education    Income  \n",
              "Diabetes_binary       0.177442  -0.124456 -0.163919  \n",
              "HighBP                0.344452  -0.141358 -0.171235  \n",
              "HighChol              0.272318  -0.070802 -0.085459  \n",
              "CholCheck             0.090321   0.001510  0.014259  \n",
              "BMI                  -0.036618  -0.103932 -0.100069  \n",
              "Smoker                0.120641  -0.161955 -0.123937  \n",
              "Stroke                0.126974  -0.076009 -0.128599  \n",
              "HeartDiseaseorAttack  0.221618  -0.099600 -0.141011  \n",
              "PhysActivity         -0.092511   0.199658  0.198539  \n",
              "Fruits                0.064547   0.110187  0.079929  \n",
              "Veggies              -0.009771   0.154329  0.151087  \n",
              "HvyAlcoholConsump    -0.034578   0.023997  0.053619  \n",
              "AnyHealthcare         0.138046   0.122514  0.157999  \n",
              "NoDocbcCost          -0.119777  -0.100701 -0.203182  \n",
              "GenHlth               0.152450  -0.284912 -0.370014  \n",
              "MentHlth             -0.092068  -0.101830 -0.209806  \n",
              "PhysHlth              0.099130  -0.155093 -0.266799  \n",
              "DiffWalk              0.204450  -0.192642 -0.320124  \n",
              "Sex                  -0.027340   0.019480  0.127141  \n",
              "Age                   1.000000  -0.101901 -0.127775  \n",
              "Education            -0.101901   1.000000  0.449106  \n",
              "Income               -0.127775   0.449106  1.000000  \n",
              "\n",
              "[22 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1f1fb463-b8b3-4672-8cba-8f8cbd70815f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Diabetes_binary</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",
              "      <th>Fruits</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",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Diabetes_binary</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.263129</td>\n",
              "      <td>0.200276</td>\n",
              "      <td>0.064761</td>\n",
              "      <td>0.216843</td>\n",
              "      <td>0.060789</td>\n",
              "      <td>0.105816</td>\n",
              "      <td>0.177282</td>\n",
              "      <td>-0.118133</td>\n",
              "      <td>-0.040779</td>\n",
              "      <td>...</td>\n",
              "      <td>0.016255</td>\n",
              "      <td>0.031433</td>\n",
              "      <td>0.293569</td>\n",
              "      <td>0.069315</td>\n",
              "      <td>0.171337</td>\n",
              "      <td>0.218344</td>\n",
              "      <td>0.031430</td>\n",
              "      <td>0.177442</td>\n",
              "      <td>-0.124456</td>\n",
              "      <td>-0.163919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HighBP</th>\n",
              "      <td>0.263129</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.298199</td>\n",
              "      <td>0.098508</td>\n",
              "      <td>0.213748</td>\n",
              "      <td>0.096991</td>\n",
              "      <td>0.129575</td>\n",
              "      <td>0.209361</td>\n",
              "      <td>-0.125267</td>\n",
              "      <td>-0.040555</td>\n",
              "      <td>...</td>\n",
              "      <td>0.038425</td>\n",
              "      <td>0.017358</td>\n",
              "      <td>0.300530</td>\n",
              "      <td>0.056456</td>\n",
              "      <td>0.161212</td>\n",
              "      <td>0.223618</td>\n",
              "      <td>0.052207</td>\n",
              "      <td>0.344452</td>\n",
              "      <td>-0.141358</td>\n",
              "      <td>-0.171235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HighChol</th>\n",
              "      <td>0.200276</td>\n",
              "      <td>0.298199</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.085642</td>\n",
              "      <td>0.106722</td>\n",
              "      <td>0.091299</td>\n",
              "      <td>0.092620</td>\n",
              "      <td>0.180765</td>\n",
              "      <td>-0.078046</td>\n",
              "      <td>-0.040859</td>\n",
              "      <td>...</td>\n",
              "      <td>0.042230</td>\n",
              "      <td>0.013310</td>\n",
              "      <td>0.208426</td>\n",
              "      <td>0.062069</td>\n",
              "      <td>0.121751</td>\n",
              "      <td>0.144672</td>\n",
              "      <td>0.031205</td>\n",
              "      <td>0.272318</td>\n",
              "      <td>-0.070802</td>\n",
              "      <td>-0.085459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CholCheck</th>\n",
              "      <td>0.064761</td>\n",
              "      <td>0.098508</td>\n",
              "      <td>0.085642</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.034495</td>\n",
              "      <td>-0.009929</td>\n",
              "      <td>0.024158</td>\n",
              "      <td>0.044206</td>\n",
              "      <td>0.004190</td>\n",
              "      <td>0.023849</td>\n",
              "      <td>...</td>\n",
              "      <td>0.117626</td>\n",
              "      <td>-0.058255</td>\n",
              "      <td>0.046589</td>\n",
              "      <td>-0.008366</td>\n",
              "      <td>0.031775</td>\n",
              "      <td>0.040585</td>\n",
              "      <td>-0.022115</td>\n",
              "      <td>0.090321</td>\n",
              "      <td>0.001510</td>\n",
              "      <td>0.014259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BMI</th>\n",
              "      <td>0.216843</td>\n",
              "      <td>0.213748</td>\n",
              "      <td>0.106722</td>\n",
              "      <td>0.034495</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.013804</td>\n",
              "      <td>0.020153</td>\n",
              "      <td>0.052904</td>\n",
              "      <td>-0.147294</td>\n",
              "      <td>-0.087518</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.018471</td>\n",
              "      <td>0.058206</td>\n",
              "      <td>0.239185</td>\n",
              "      <td>0.085310</td>\n",
              "      <td>0.121141</td>\n",
              "      <td>0.197078</td>\n",
              "      <td>0.042950</td>\n",
              "      <td>-0.036618</td>\n",
              "      <td>-0.103932</td>\n",
              "      <td>-0.100069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Smoker</th>\n",
              "      <td>0.060789</td>\n",
              "      <td>0.096991</td>\n",
              "      <td>0.091299</td>\n",
              "      <td>-0.009929</td>\n",
              "      <td>0.013804</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.061173</td>\n",
              "      <td>0.114441</td>\n",
              "      <td>-0.087401</td>\n",
              "      <td>-0.077666</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.023251</td>\n",
              "      <td>0.048946</td>\n",
              "      <td>0.163143</td>\n",
              "      <td>0.092196</td>\n",
              "      <td>0.116460</td>\n",
              "      <td>0.122463</td>\n",
              "      <td>0.093662</td>\n",
              "      <td>0.120641</td>\n",
              "      <td>-0.161955</td>\n",
              "      <td>-0.123937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stroke</th>\n",
              "      <td>0.105816</td>\n",
              "      <td>0.129575</td>\n",
              "      <td>0.092620</td>\n",
              "      <td>0.024158</td>\n",
              "      <td>0.020153</td>\n",
              "      <td>0.061173</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.203002</td>\n",
              "      <td>-0.069151</td>\n",
              "      <td>-0.013389</td>\n",
              "      <td>...</td>\n",
              "      <td>0.008776</td>\n",
              "      <td>0.034804</td>\n",
              "      <td>0.177942</td>\n",
              "      <td>0.070172</td>\n",
              "      <td>0.148944</td>\n",
              "      <td>0.176567</td>\n",
              "      <td>0.002978</td>\n",
              "      <td>0.126974</td>\n",
              "      <td>-0.076009</td>\n",
              "      <td>-0.128599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HeartDiseaseorAttack</th>\n",
              "      <td>0.177282</td>\n",
              "      <td>0.209361</td>\n",
              "      <td>0.180765</td>\n",
              "      <td>0.044206</td>\n",
              "      <td>0.052904</td>\n",
              "      <td>0.114441</td>\n",
              "      <td>0.203002</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.087299</td>\n",
              "      <td>-0.019790</td>\n",
              "      <td>...</td>\n",
              "      <td>0.018734</td>\n",
              "      <td>0.031000</td>\n",
              "      <td>0.258383</td>\n",
              "      <td>0.064621</td>\n",
              "      <td>0.181698</td>\n",
              "      <td>0.212709</td>\n",
              "      <td>0.086096</td>\n",
              "      <td>0.221618</td>\n",
              "      <td>-0.099600</td>\n",
              "      <td>-0.141011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PhysActivity</th>\n",
              "      <td>-0.118133</td>\n",
              "      <td>-0.125267</td>\n",
              "      <td>-0.078046</td>\n",
              "      <td>0.004190</td>\n",
              "      <td>-0.147294</td>\n",
              "      <td>-0.087401</td>\n",
              "      <td>-0.069151</td>\n",
              "      <td>-0.087299</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.142756</td>\n",
              "      <td>...</td>\n",
              "      <td>0.035505</td>\n",
              "      <td>-0.061638</td>\n",
              "      <td>-0.266186</td>\n",
              "      <td>-0.125587</td>\n",
              "      <td>-0.219230</td>\n",
              "      <td>-0.253174</td>\n",
              "      <td>0.032482</td>\n",
              "      <td>-0.092511</td>\n",
              "      <td>0.199658</td>\n",
              "      <td>0.198539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fruits</th>\n",
              "      <td>-0.040779</td>\n",
              "      <td>-0.040555</td>\n",
              "      <td>-0.040859</td>\n",
              "      <td>0.023849</td>\n",
              "      <td>-0.087518</td>\n",
              "      <td>-0.077666</td>\n",
              "      <td>-0.013389</td>\n",
              "      <td>-0.019790</td>\n",
              "      <td>0.142756</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.031544</td>\n",
              "      <td>-0.044243</td>\n",
              "      <td>-0.103854</td>\n",
              "      <td>-0.068217</td>\n",
              "      <td>-0.044633</td>\n",
              "      <td>-0.048352</td>\n",
              "      <td>-0.091175</td>\n",
              "      <td>0.064547</td>\n",
              "      <td>0.110187</td>\n",
              "      <td>0.079929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Veggies</th>\n",
              "      <td>-0.056584</td>\n",
              "      <td>-0.061266</td>\n",
              "      <td>-0.039874</td>\n",
              "      <td>0.006121</td>\n",
              "      <td>-0.062275</td>\n",
              "      <td>-0.030678</td>\n",
              "      <td>-0.041124</td>\n",
              "      <td>-0.039167</td>\n",
              "      <td>0.153150</td>\n",
              "      <td>0.254342</td>\n",
              "      <td>...</td>\n",
              "      <td>0.029584</td>\n",
              "      <td>-0.032232</td>\n",
              "      <td>-0.123066</td>\n",
              "      <td>-0.058884</td>\n",
              "      <td>-0.064290</td>\n",
              "      <td>-0.080506</td>\n",
              "      <td>-0.064765</td>\n",
              "      <td>-0.009771</td>\n",
              "      <td>0.154329</td>\n",
              "      <td>0.151087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HvyAlcoholConsump</th>\n",
              "      <td>-0.057056</td>\n",
              "      <td>-0.003972</td>\n",
              "      <td>-0.011543</td>\n",
              "      <td>-0.023730</td>\n",
              "      <td>-0.048736</td>\n",
              "      <td>0.101619</td>\n",
              "      <td>-0.016950</td>\n",
              "      <td>-0.028991</td>\n",
              "      <td>0.012392</td>\n",
              "      <td>-0.035288</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.010488</td>\n",
              "      <td>0.004684</td>\n",
              "      <td>-0.036724</td>\n",
              "      <td>0.024716</td>\n",
              "      <td>-0.026415</td>\n",
              "      <td>-0.037668</td>\n",
              "      <td>0.005740</td>\n",
              "      <td>-0.034578</td>\n",
              "      <td>0.023997</td>\n",
              "      <td>0.053619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AnyHealthcare</th>\n",
              "      <td>0.016255</td>\n",
              "      <td>0.038425</td>\n",
              "      <td>0.042230</td>\n",
              "      <td>0.117626</td>\n",
              "      <td>-0.018471</td>\n",
              "      <td>-0.023251</td>\n",
              "      <td>0.008776</td>\n",
              "      <td>0.018734</td>\n",
              "      <td>0.035505</td>\n",
              "      <td>0.031544</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.232532</td>\n",
              "      <td>-0.040817</td>\n",
              "      <td>-0.052707</td>\n",
              "      <td>-0.008276</td>\n",
              "      <td>0.007074</td>\n",
              "      <td>-0.019405</td>\n",
              "      <td>0.138046</td>\n",
              "      <td>0.122514</td>\n",
              "      <td>0.157999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NoDocbcCost</th>\n",
              "      <td>0.031433</td>\n",
              "      <td>0.017358</td>\n",
              "      <td>0.013310</td>\n",
              "      <td>-0.058255</td>\n",
              "      <td>0.058206</td>\n",
              "      <td>0.048946</td>\n",
              "      <td>0.034804</td>\n",
              "      <td>0.031000</td>\n",
              "      <td>-0.061638</td>\n",
              "      <td>-0.044243</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.232532</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.166397</td>\n",
              "      <td>0.192107</td>\n",
              "      <td>0.148998</td>\n",
              "      <td>0.118447</td>\n",
              "      <td>-0.044931</td>\n",
              "      <td>-0.119777</td>\n",
              "      <td>-0.100701</td>\n",
              "      <td>-0.203182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GenHlth</th>\n",
              "      <td>0.293569</td>\n",
              "      <td>0.300530</td>\n",
              "      <td>0.208426</td>\n",
              "      <td>0.046589</td>\n",
              "      <td>0.239185</td>\n",
              "      <td>0.163143</td>\n",
              "      <td>0.177942</td>\n",
              "      <td>0.258383</td>\n",
              "      <td>-0.266186</td>\n",
              "      <td>-0.103854</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.040817</td>\n",
              "      <td>0.166397</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.301674</td>\n",
              "      <td>0.524364</td>\n",
              "      <td>0.456920</td>\n",
              "      <td>-0.006091</td>\n",
              "      <td>0.152450</td>\n",
              "      <td>-0.284912</td>\n",
              "      <td>-0.370014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MentHlth</th>\n",
              "      <td>0.069315</td>\n",
              "      <td>0.056456</td>\n",
              "      <td>0.062069</td>\n",
              "      <td>-0.008366</td>\n",
              "      <td>0.085310</td>\n",
              "      <td>0.092196</td>\n",
              "      <td>0.070172</td>\n",
              "      <td>0.064621</td>\n",
              "      <td>-0.125587</td>\n",
              "      <td>-0.068217</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.052707</td>\n",
              "      <td>0.192107</td>\n",
              "      <td>0.301674</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.353619</td>\n",
              "      <td>0.233688</td>\n",
              "      <td>-0.080705</td>\n",
              "      <td>-0.092068</td>\n",
              "      <td>-0.101830</td>\n",
              "      <td>-0.209806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PhysHlth</th>\n",
              "      <td>0.171337</td>\n",
              "      <td>0.161212</td>\n",
              "      <td>0.121751</td>\n",
              "      <td>0.031775</td>\n",
              "      <td>0.121141</td>\n",
              "      <td>0.116460</td>\n",
              "      <td>0.148944</td>\n",
              "      <td>0.181698</td>\n",
              "      <td>-0.219230</td>\n",
              "      <td>-0.044633</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.008276</td>\n",
              "      <td>0.148998</td>\n",
              "      <td>0.524364</td>\n",
              "      <td>0.353619</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.478417</td>\n",
              "      <td>-0.043137</td>\n",
              "      <td>0.099130</td>\n",
              "      <td>-0.155093</td>\n",
              "      <td>-0.266799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DiffWalk</th>\n",
              "      <td>0.218344</td>\n",
              "      <td>0.223618</td>\n",
              "      <td>0.144672</td>\n",
              "      <td>0.040585</td>\n",
              "      <td>0.197078</td>\n",
              "      <td>0.122463</td>\n",
              "      <td>0.176567</td>\n",
              "      <td>0.212709</td>\n",
              "      <td>-0.253174</td>\n",
              "      <td>-0.048352</td>\n",
              "      <td>...</td>\n",
              "      <td>0.007074</td>\n",
              "      <td>0.118447</td>\n",
              "      <td>0.456920</td>\n",
              "      <td>0.233688</td>\n",
              "      <td>0.478417</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.070299</td>\n",
              "      <td>0.204450</td>\n",
              "      <td>-0.192642</td>\n",
              "      <td>-0.320124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>0.031430</td>\n",
              "      <td>0.052207</td>\n",
              "      <td>0.031205</td>\n",
              "      <td>-0.022115</td>\n",
              "      <td>0.042950</td>\n",
              "      <td>0.093662</td>\n",
              "      <td>0.002978</td>\n",
              "      <td>0.086096</td>\n",
              "      <td>0.032482</td>\n",
              "      <td>-0.091175</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.019405</td>\n",
              "      <td>-0.044931</td>\n",
              "      <td>-0.006091</td>\n",
              "      <td>-0.080705</td>\n",
              "      <td>-0.043137</td>\n",
              "      <td>-0.070299</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.027340</td>\n",
              "      <td>0.019480</td>\n",
              "      <td>0.127141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0.177442</td>\n",
              "      <td>0.344452</td>\n",
              "      <td>0.272318</td>\n",
              "      <td>0.090321</td>\n",
              "      <td>-0.036618</td>\n",
              "      <td>0.120641</td>\n",
              "      <td>0.126974</td>\n",
              "      <td>0.221618</td>\n",
              "      <td>-0.092511</td>\n",
              "      <td>0.064547</td>\n",
              "      <td>...</td>\n",
              "      <td>0.138046</td>\n",
              "      <td>-0.119777</td>\n",
              "      <td>0.152450</td>\n",
              "      <td>-0.092068</td>\n",
              "      <td>0.099130</td>\n",
              "      <td>0.204450</td>\n",
              "      <td>-0.027340</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.101901</td>\n",
              "      <td>-0.127775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Education</th>\n",
              "      <td>-0.124456</td>\n",
              "      <td>-0.141358</td>\n",
              "      <td>-0.070802</td>\n",
              "      <td>0.001510</td>\n",
              "      <td>-0.103932</td>\n",
              "      <td>-0.161955</td>\n",
              "      <td>-0.076009</td>\n",
              "      <td>-0.099600</td>\n",
              "      <td>0.199658</td>\n",
              "      <td>0.110187</td>\n",
              "      <td>...</td>\n",
              "      <td>0.122514</td>\n",
              "      <td>-0.100701</td>\n",
              "      <td>-0.284912</td>\n",
              "      <td>-0.101830</td>\n",
              "      <td>-0.155093</td>\n",
              "      <td>-0.192642</td>\n",
              "      <td>0.019480</td>\n",
              "      <td>-0.101901</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.449106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Income</th>\n",
              "      <td>-0.163919</td>\n",
              "      <td>-0.171235</td>\n",
              "      <td>-0.085459</td>\n",
              "      <td>0.014259</td>\n",
              "      <td>-0.100069</td>\n",
              "      <td>-0.123937</td>\n",
              "      <td>-0.128599</td>\n",
              "      <td>-0.141011</td>\n",
              "      <td>0.198539</td>\n",
              "      <td>0.079929</td>\n",
              "      <td>...</td>\n",
              "      <td>0.157999</td>\n",
              "      <td>-0.203182</td>\n",
              "      <td>-0.370014</td>\n",
              "      <td>-0.209806</td>\n",
              "      <td>-0.266799</td>\n",
              "      <td>-0.320124</td>\n",
              "      <td>0.127141</td>\n",
              "      <td>-0.127775</td>\n",
              "      <td>0.449106</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>22 rows × 22 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1f1fb463-b8b3-4672-8cba-8f8cbd70815f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1f1fb463-b8b3-4672-8cba-8f8cbd70815f 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-1f1fb463-b8b3-4672-8cba-8f8cbd70815f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#heatmap correlation\n",
        "plt.figure(figsize = (10,6))\n",
        "sns.heatmap(df.corr())\n",
        "plt.title(\"Pearson Correlation\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "O3axh5084Yon",
        "outputId": "ff5128db-c6a3-4205-e53c-eadcf0db46c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature selection \n",
        "is the process of reducing the number of input variables when developing a predictive model.\n",
        "\n",
        "It is desirable to reduce the number of input variables to both reduce the computational cost of modeling and, in some cases, to improve the performance of the model."
      ],
      "metadata": {
        "id": "w14GS8NI3fVl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Select Features Automatically:<br>\n",
        "1- Statistical tests can be used to select those features that have the strongest relationship with the output variable.<br>\n",
        "2- The Recursive Feature Elimination (RFE) method is a feature selection approach. It works by recursively removing attributes and building a model on those attributes that remain. It uses the model accuracy to identify which attributes (and combination of attributes) contribute the most to predicting the target attribute"
      ],
      "metadata": {
        "id": "t9MkuZuBCKJo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Dataset Preperation\n",
        "#seprate dataset from target \n",
        "dataset=df.iloc[:, 1:]"
      ],
      "metadata": {
        "id": "fZIIkIX_8Aht"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "jxjk2BlKECzG",
        "outputId": "e3f9306d-5bb0-4733-a653-038585f5a018"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        HighBP  HighChol  CholCheck   BMI  Smoker  Stroke  \\\n",
              "0          1.0       1.0        1.0  40.0     1.0     0.0   \n",
              "1          0.0       0.0        0.0  25.0     1.0     0.0   \n",
              "2          1.0       1.0        1.0  28.0     0.0     0.0   \n",
              "3          1.0       0.0        1.0  27.0     0.0     0.0   \n",
              "4          1.0       1.0        1.0  24.0     0.0     0.0   \n",
              "...        ...       ...        ...   ...     ...     ...   \n",
              "253675     1.0       1.0        1.0  45.0     0.0     0.0   \n",
              "253676     1.0       1.0        1.0  18.0     0.0     0.0   \n",
              "253677     0.0       0.0        1.0  28.0     0.0     0.0   \n",
              "253678     1.0       0.0        1.0  23.0     0.0     0.0   \n",
              "253679     1.0       1.0        1.0  25.0     0.0     0.0   \n",
              "\n",
              "        HeartDiseaseorAttack  PhysActivity  Fruits  Veggies  ...  \\\n",
              "0                        0.0           0.0     0.0      1.0  ...   \n",
              "1                        0.0           1.0     0.0      0.0  ...   \n",
              "2                        0.0           0.0     1.0      0.0  ...   \n",
              "3                        0.0           1.0     1.0      1.0  ...   \n",
              "4                        0.0           1.0     1.0      1.0  ...   \n",
              "...                      ...           ...     ...      ...  ...   \n",
              "253675                   0.0           0.0     1.0      1.0  ...   \n",
              "253676                   0.0           0.0     0.0      0.0  ...   \n",
              "253677                   0.0           1.0     1.0      0.0  ...   \n",
              "253678                   0.0           0.0     1.0      1.0  ...   \n",
              "253679                   1.0           1.0     1.0      0.0  ...   \n",
              "\n",
              "        AnyHealthcare  NoDocbcCost  GenHlth  MentHlth  PhysHlth  DiffWalk  \\\n",
              "0                 1.0          0.0      5.0      18.0      15.0       1.0   \n",
              "1                 0.0          1.0      3.0       0.0       0.0       0.0   \n",
              "2                 1.0          1.0      5.0      30.0      30.0       1.0   \n",
              "3                 1.0          0.0      2.0       0.0       0.0       0.0   \n",
              "4                 1.0          0.0      2.0       3.0       0.0       0.0   \n",
              "...               ...          ...      ...       ...       ...       ...   \n",
              "253675            1.0          0.0      3.0       0.0       5.0       0.0   \n",
              "253676            1.0          0.0      4.0       0.0       0.0       1.0   \n",
              "253677            1.0          0.0      1.0       0.0       0.0       0.0   \n",
              "253678            1.0          0.0      3.0       0.0       0.0       0.0   \n",
              "253679            1.0          0.0      2.0       0.0       0.0       0.0   \n",
              "\n",
              "        Sex   Age  Education  Income  \n",
              "0       0.0   9.0        4.0     3.0  \n",
              "1       0.0   7.0        6.0     1.0  \n",
              "2       0.0   9.0        4.0     8.0  \n",
              "3       0.0  11.0        3.0     6.0  \n",
              "4       0.0  11.0        5.0     4.0  \n",
              "...     ...   ...        ...     ...  \n",
              "253675  1.0   5.0        6.0     7.0  \n",
              "253676  0.0  11.0        2.0     4.0  \n",
              "253677  0.0   2.0        5.0     2.0  \n",
              "253678  1.0   7.0        5.0     1.0  \n",
              "253679  0.0   9.0        6.0     2.0  \n",
              "\n",
              "[253680 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4778753c-68dd-4d3e-8f5d-928415df00f5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "        vertical-align: top;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "      <th>Fruits</th>\n",
              "      <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",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
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              "      <td>28.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>9.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.0</td>\n",
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              "    <tr>\n",
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              "      <td>2.0</td>\n",
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              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>253675</th>\n",
              "      <td>1.0</td>\n",
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              "      <td>6.0</td>\n",
              "      <td>7.0</td>\n",
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              "    <tr>\n",
              "      <th>253676</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>18.0</td>\n",
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              "      <td>1.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>253680 rows × 21 columns</p>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4778753c-68dd-4d3e-8f5d-928415df00f5');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target=df.iloc[:,0]"
      ],
      "metadata": {
        "id": "uf3ZWQ8VEEz1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j517SlT_ESPu",
        "outputId": "b784f376-f9d1-40b5-f29c-6f2a241651ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         0.0\n",
              "1         0.0\n",
              "2         0.0\n",
              "3         0.0\n",
              "4         0.0\n",
              "         ... \n",
              "253675    0.0\n",
              "253676    1.0\n",
              "253677    0.0\n",
              "253678    0.0\n",
              "253679    1.0\n",
              "Name: Diabetes_binary, Length: 253680, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import set_printoptions\n",
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import f_classif #Compute the ANOVA F-value for the provided sample.\n",
        "# feature extraction\n",
        "test = SelectKBest(score_func=f_classif, k=5)\n",
        "fit = test.fit(dataset, target)\n",
        "# summarize scores\n",
        "set_printoptions(precision=3)\n",
        "print(fit.scores_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sKPuAXqiEi4S",
        "outputId": "38a59745-6c14-4e75-9135-85c198646f4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[18870.366 10600.351  1068.397 12516.719   940.878  2872.607  8231.555\n",
            "  3590.29    422.555   814.826   828.525    67.047   250.886 23924.565\n",
            "  1224.701  7672.268 12699.342   250.842  8246.866  3991.111  7004.371]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LPsR9JTHE4uP",
        "outputId": "3f8bbfbb-3e40-4363-90a3-af84078f4dd2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['HighBP', 'HighChol', 'CholCheck', 'BMI', 'Smoker', 'Stroke',\n",
              "       'HeartDiseaseorAttack', 'PhysActivity', 'Fruits', 'Veggies',\n",
              "       'HvyAlcoholConsump', 'AnyHealthcare', 'NoDocbcCost', 'GenHlth',\n",
              "       'MentHlth', 'PhysHlth', 'DiffWalk', 'Sex', 'Age', 'Education',\n",
              "       'Income'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ranked_features=pd.DataFrame(fit.scores_,index=dataset.columns)\n",
        "ranked_features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "XHVkFj8zFelt",
        "outputId": "6227603f-8683-4526-8dea-8cc8037f23b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 0\n",
              "HighBP                18870.365816\n",
              "HighChol              10600.350806\n",
              "CholCheck              1068.396844\n",
              "BMI                   12516.718642\n",
              "Smoker                  940.878480\n",
              "Stroke                 2872.607437\n",
              "HeartDiseaseorAttack   8231.555129\n",
              "PhysActivity           3590.290347\n",
              "Fruits                  422.555361\n",
              "Veggies                 814.826113\n",
              "HvyAlcoholConsump       828.524705\n",
              "AnyHealthcare            67.046943\n",
              "NoDocbcCost             250.886466\n",
              "GenHlth               23924.564885\n",
              "MentHlth               1224.700591\n",
              "PhysHlth               7672.267690\n",
              "DiffWalk              12699.341579\n",
              "Sex                     250.842280\n",
              "Age                    8246.866284\n",
              "Education              3991.111142\n",
              "Income                 7004.370724"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e918f793-ddcf-424f-a683-2007d24c7027\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "      <th>0</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>HighBP</th>\n",
              "      <td>18870.365816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HighChol</th>\n",
              "      <td>10600.350806</td>\n",
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              "      <th>CholCheck</th>\n",
              "      <td>1068.396844</td>\n",
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              "    <tr>\n",
              "      <th>BMI</th>\n",
              "      <td>12516.718642</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Smoker</th>\n",
              "      <td>940.878480</td>\n",
              "    </tr>\n",
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              "      <th>Stroke</th>\n",
              "      <td>2872.607437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HeartDiseaseorAttack</th>\n",
              "      <td>8231.555129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PhysActivity</th>\n",
              "      <td>3590.290347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fruits</th>\n",
              "      <td>422.555361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Veggies</th>\n",
              "      <td>814.826113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HvyAlcoholConsump</th>\n",
              "      <td>828.524705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AnyHealthcare</th>\n",
              "      <td>67.046943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NoDocbcCost</th>\n",
              "      <td>250.886466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GenHlth</th>\n",
              "      <td>23924.564885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MentHlth</th>\n",
              "      <td>1224.700591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PhysHlth</th>\n",
              "      <td>7672.267690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DiffWalk</th>\n",
              "      <td>12699.341579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>250.842280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>8246.866284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Education</th>\n",
              "      <td>3991.111142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Income</th>\n",
              "      <td>7004.370724</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e918f793-ddcf-424f-a683-2007d24c7027')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
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              "          document.querySelector('#df-e918f793-ddcf-424f-a683-2007d24c7027 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-e918f793-ddcf-424f-a683-2007d24c7027');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ranked_features.iloc[:,0].nlargest(5).index #to be used later"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2e0TDPerGBdl",
        "outputId": "633ee212-538a-4fee-aa2c-ab21bf9f8862"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['GenHlth', 'HighBP', 'DiffWalk', 'BMI', 'HighChol'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2- The Recursive Feature Elimination (RFE) method "
      ],
      "metadata": {
        "id": "YGQYYtaNH0zC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_selection import RFE\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "# create a base classifier used to evaluate a subset of attributes\n",
        "model =  LogisticRegression(solver='lbfgs')\n",
        "# create the RFE model and select 5 attributes\n",
        "rfe = RFE(model, n_features_to_select= 5)\n",
        "rfe = rfe.fit(dataset, target)\n",
        "# summarize the selection of the attributes\n",
        "print(rfe.support_)\n",
        "print(rfe.ranking_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YcIJyQD7H41m",
        "outputId": "94a6fc00-6c71-4620-a41f-a7721c1914c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ True  True False False False False  True False False False  True False\n",
            " False  True False False False False False False False]\n",
            "[ 1  1  9  8 12  7  1 11 13 14  1  5 15  1 17 16  2  3  4  6 10]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rfe.support_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NXWUiiR-IdTj",
        "outputId": "a1a3bb9c-e5d0-4449-c49d-f2f2fb44a689"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True,  True, False, False, False, False,  True, False, False,\n",
              "       False,  True, False, False,  True, False, False, False, False,\n",
              "       False, False, False])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(rfe.support_,index=dataset.columns)[pd.DataFrame(rfe.support_,index=dataset.columns).iloc[:,0]==True]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "SF1Sr0FzKiau",
        "outputId": "4a06bea4-4ddb-4501-cab0-83b8ac540f7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         0\n",
              "HighBP                True\n",
              "HighChol              True\n",
              "HeartDiseaseorAttack  True\n",
              "HvyAlcoholConsump     True\n",
              "GenHlth               True"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cc4e8935-653d-4460-be17-e818882d02c8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>HighBP</th>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HighChol</th>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HeartDiseaseorAttack</th>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HvyAlcoholConsump</th>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GenHlth</th>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cc4e8935-653d-4460-be17-e818882d02c8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-cc4e8935-653d-4460-be17-e818882d02c8 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-cc4e8935-653d-4460-be17-e818882d02c8');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Split Dataset"
      ],
      "metadata": {
        "id": "w17jF8gy_Fbi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#split data\n",
        "X_train, X_test, y_train, y_test = train_test_split(dataset, target, test_size = 0.5, random_state = 0)\n",
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XtK3HXxU_EWP",
        "outputId": "21fbf6fd-6796-46b3-888f-f58801256761"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(126840, 21)\n",
            "(126840,)\n",
            "(126840, 21)\n",
            "(126840,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Random Forest Model(Learning Model:Classification)\n"
      ],
      "metadata": {
        "id": "X1aB4hrH-gzn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1- All Features"
      ],
      "metadata": {
        "id": "FnsvCrm-Eprz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#build model\n",
        "rf = RandomForestClassifier(random_state = 1, max_features = 'sqrt', n_jobs = 1, verbose = 1)\n",
        "rf.fit(X_train, y_train)\n",
        "rf.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4_bVm6d4Kswy",
        "outputId": "b446fa89-e6ba-4a09-a164-cdd4123c20a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:   14.5s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    4.2s finished\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8591374960580258"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#prediction\n",
        "y_pred = rf.predict(X_test)\n",
        "print(y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lBLNOAz0BXL4",
        "outputId": "342031d1-e18d-4b44-d6e2-fbf1994c5f33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 0. 0. ... 0. 0. 1.]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    4.3s finished\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#accurcy \n",
        "acc = np.sum(np.equal(y_test, y_pred)) / len(y_test)\n",
        "print(\"acc=\",acc*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cQsrF0NXDBNp",
        "outputId": "c17b2f32-a33e-4d5c-faac-91c4c88f4901"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "acc= 85.91374960580258\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Confusion Matrix:\n"
      ],
      "metadata": {
        "id": "AAdWhhkJPDHn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#confusion matrix\n",
        "matrix = metrics.confusion_matrix(y_test, y_pred)\n",
        "print(matrix)\n",
        "\n",
        "#heatmap matrix\n",
        "plt.figure(figsize = (8,6))\n",
        "sns.heatmap(matrix, annot = True, fmt = \".0f\", cmap = 'viridis')\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.xlabel(\"Prediction\")\n",
        "plt.ylabel(\"Actual\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "xmib17l0Bfwa",
        "outputId": "86fde6be-a4b9-423d-c861-91ff959943cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[106071   2908]\n",
            " [ 14959   2902]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ],
            "image/png": 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ETAAm1DVNLPIEgMycGRH/CzwLvA7cDNwHvJyZrX/ZzQBGF49HA9OLfRdGxHxgeNF+V9156veZvlj7hzrzWrpr+LoFGNVG+5rFOnWDHbeFXxdD07/+feV5a/v9D8HChfD6vyrXkt/9LnjvhvDMDJjxPLz5Fky6FT5SNwdm8p9gh61h4MCefy3SMed9kWcfm8l1p99UbRsyYhUAIoL9TvgEN/3iZgD+euNUdthnW96xwgDWWGd1Ro9dk8fvaWL2sy+x0YfGMnDwCgB8YMf38uyjM3r+xahdXZ3olZkTM3OLumVi/fEjYiiVynVdKtm0IpXh54bTXZXyUcAtEfEEtb8e1gbWAw7vpnP2Kcd8F+75G7w8H3b4JBx+IHz+M5WPRF37Wxi1Bpz+ncq2/7EO/OeWsOdBEP0qH31a/92Vdd88Cj5/bOUjUXvtBmPXrZ1j0q1wyGcWPe+Lc2DvL1QmgPXrB5dcW5nR7TVnLU/v2XZDPrb/h3ly2jP8/P7KxwwuOOFyRo9dkz2+VJlbc+cN9zD5wj8C8MwjM7j9mr9y3sOn07ywhTMPP4+WlhYeu6eJO667i7Pv+yHNC5v5vweeZtLEP5T2utS2Hrh5yEeBpzLzRYCIuB7YFhgSEQOKankMMLPYfiawFjCjGO5eFZhT196qfp8ltS+TyOyeUeKI6EdlnL21tJ8J3JuZzR3Z3+Fr9Qa7jNq07C5Iy8WUlmu6LTk/fdeELv17f8VWE9vtW0R8CLgA+CCV4euLgKnA9sB1mXllRPwcmJaZZ0fEYcB7M/PQiNgX2CszPxUR7wEup5Jto4BbgLFUpvD8A9iJIuuAz2Tmw8v6Wrrtgy2Z2cKiY++SJP2b7r55SGbeHRHXAvcDC4EHqMxf+i1wZUScXLSdX+xyPnBpRDQBc6nMuCYzH46Iq4FHiuMc1lpoRsThwGQqM7sv6Ewgg3f0kiSVrCfufZ2ZJwInLtb8JJWqd/Ft/wXsvYTjfB/4fhvtk4BJXe2noSxJKlVvuytXVxjKkqRS+S1RNd5mU5KkBmGlLEkqlZVyjaEsSSqVoVxjKEuSSmUo1xjKkqRSOfu6xolekiQ1CCtlSVKpHL6uMZQlSaUylGsMZUlSqQzlGq8pS5LUIKyUJUmlslKuMZQlSaVKQ7nKUJYklcrPKdcYypKkUjl8XeNEL0mSGoSVsiSpVF5TrjGUJUmlcvi6xlCWJJXKSrnGUJYklcpKucaJXpIkNQgrZUlSqTLL7kHjMJQlSaXy5iE1hrIkqVRO9KrxmrIkSQ3CSlmSVCpnX9cYypKkUjnRq8ZQliSVymvKNYayJKlUhnKNE70kSWoQVsqSpFI50avGUJYklcqJXjWGsiSpVF5TrjGUJUmlMpRrnOglSVKDsFKWJJXKS8o1hrIkqVQOX9cYypKkclkqV3lNWZKkBmGlLEkqlcPXNYayJKlU3jykxlCWJJXKSrnGUJYklctQrnKilyRJDcJKWZJUKq8p1xjKkqRyGcpVhrIkqVRO9KoxlCVJ5bJSrnKilyRJDcJKWZJUKoevawxlSVK5HL6uMpQlSSWzUm7lNWVJkhqEoSxJKld2cemAiBgSEddGxGMR8WhEbB0RwyJiSkQ8UfwcWmwbEXFGRDRFxLSI2KzuOAcU2z8REQfUtW8eEQ8V+5wREZ0q/w1lSVK5eiCUgZ8Cv8/MDYFNgUeB44FbMnMscEvxHGBXYGyxTADOAYiIYcCJwIeALYETW4O82OaQuv3GLdN7UDCUJUnlyujashQRsSqwPXA+QGa+mZkvA+OBi4vNLgb2LB6PBy7JiruAIRGxJrALMCUz52bmPGAKMK5Yt0pm3pWZCVxSd6xlYihLkkqV2bUlIiZExNS6ZcJip1gXeBG4MCIeiIjzImJFYGRmPl9sMwsYWTweDUyv239G0dZe+4w22peZs68lSW9rmTkRmNjOJgOAzYAjMvPuiPgptaHq1mNkRJT+4SwrZUlSubr/mvIMYEZm3l08v5ZKSL9QDD1T/JxdrJ8JrFW3/5iirb32MW20LzNDWZJUrm6+ppyZs4DpEbFB0bQT8AhwI9A6g/oA4NfF4xuB/YtZ2FsB84th7snAzhExtJjgtTMwuVj3SkRsVcy63r/uWMvE4WtJUql6aND4COCyiFgBeBI4kEphenVEHAw8A3yq2HYSsBvQBCwotiUz50bE94B7i+1Oysy5xeMvARcBg4HfFcsyW2IoR8SZtDMwkJlf7swJJUlaRA+Ecmb+DdiijVU7tbFtAoct4TgXABe00T4V2KSL3Wy3Up7a1YNLkqSOW2IoZ+bFS1onSdJy47dEVS31mnJEjACOAzYGBrW2Z+aO3dgvSVJfUfoHkRpHR2ZfX0bldmTrAt8FnqZ2kVuSpK7pmdtsvi10JJSHZ+b5wFuZ+afMPAiwSpYkaTnryEei3ip+Ph8RuwPPAcO6r0uSpD6ll1W7XdGRUD65uJn3McCZwCrA0d3aK0lS3+FEr6qlhnJm3lQ8nA98pHu7I0nqa8q/43Tj6Mjs6wtpY3ChuLYsSVLXGMpVHRm+vqnu8SDg/1G5rixJkpajjgxfX1f/PCKuAO7sth5JktRHdeYLKcYCqy/vjixu9x0/2d2nkHrAE2V3QGp4XlOu6cg15VdZdMR/FpU7fEmS1HXOvq7qyPD1yj3REUlSH2WlXLXUO3pFxC0daZMkSV3T3vcpDwLeCawWEUOB1vGFVYDRPdA3SVJfYKVc1d7w9ReAo4BRwH3UQvkV4Gfd3C9JUh/hRK+a9r5P+afATyPiiMw8swf7JEnqSwzlqo58S1RLRAxpfRIRQyPiS93YJ0mS+qSOhPIhmfly65PMnAcc0n1dkiT1KX6fclVHbh7SPyIiMxMgIvoDK3RvtyRJfYXXlGs6Esq/B66KiF8Uz78A/K77uiRJ6lO8eUhVR0L5OGACcGjxfBqwRrf1SJLUt1gpVy31mnJmtgB3A08DWwI7Ao92b7ckSep72rt5yPrAp4vlJeAqgMz8SM90TZLUF3hNuaa94evHgDuAj2dmE0BEHN0jvZIk9R2GclV7w9d7Ac8Df4yIcyNiJ2p39ZIkabmI7NrSmywxlDPzV5m5L7Ah8Ecqt9xcPSLOiYide6qDkiT1FR2Z6PVaZl6emf8FjAEewO9TliQtL948pKojd/Sqysx5mTkxM3fqrg5JkvoYQ7mqI59TliSp2/S268JdsUyVsiRJ6j6GsiRJDcLha0lSuRy+rjKUJUml8ppyjaEsSSqXoVxlKEuSymUoVznRS5KkBmGlLEkqldeUawxlSVK5DOUqQ1mSVCor5RqvKUuS1CCslCVJ5bJSrjKUJUnlMpSrDGVJUqm8plxjKEuSymUoVznRS5KkBmGlLEkql5VylaEsSSqV15RrDGVJUrkM5SpDWZJUKivlGid6SZLUIKyUJUnlslKuMpQlSeUylKscvpYklSq6uHToHBH9I+KBiLipeL5uRNwdEU0RcVVErFC0DyyeNxXr16k7xteL9scjYpe69nFFW1NEHN+V98JQliT1BUcCj9Y9PxU4PTPXA+YBBxftBwPzivbTi+2IiI2BfYH3AOOAs4ug7w+cBewKbAx8uti2UwxlSVK5sovLUkTEGGB34LzieQA7AtcWm1wM7Fk8Hl88p1i/U7H9eODKzHwjM58CmoAti6UpM5/MzDeBK4ttO8VQliSVKrJrSwf8BPga0FI8Hw68nJkLi+czgNHF49HAdIBi/fxi+2r7Yvssqb1TDGVJUrm6WClHxISImFq3TGg9dER8HJidmff15EvqLGdfS5LK1cXZ15k5EZi4hNXbAntExG7AIGAV4KfAkIgYUFTDY4CZxfYzgbWAGRExAFgVmFPX3qp+nyW1LzMrZUlSr5WZX8/MMZm5DpWJWrdm5n7AH4FPFpsdAPy6eHxj8Zxi/a2ZmUX7vsXs7HWBscA9wL3A2GI29wrFOW7sbH+tlCVJpSrpNpvHAVdGxMnAA8D5Rfv5wKUR0QTMpRKyZObDEXE18AiwEDgsM5sBIuJwYDLQH7ggMx/ubKcMZUlSuXoolDPzNuC24vGTVGZOL77Nv4C9l7D/94Hvt9E+CZi0PPpoKEuSSuUXUtQYypKkchnKVU70kiSpQVgpS5JK5fB1jaEsSSqXoVxlKEuSymUoV3lNWZKkBmGlLEkqldeUawxlSVK5DOUqQ1mSVKpIU7mVoSxJKpeZXOVEL0mSGoSVsiSpVE70qjGUJUnlMpSrDGVJUqmslGsMZUlSuQzlKid6SZLUIKyUJUmlcvi6xlCWJJXLUK4ylCVJpbJSrvGasiRJDcJKWZJULu99XWUoS5JK5fB1jaEsSSqXoVxlKEuSShUtZfegcTjRS5KkBmGl3AscffJebPnhDXl57mt8cfxPF1m313//J4d8bTf22eZkXnl5ASutMoijT/4Ea641jDffWMjp37yeZ5peAOCiKV9lwWtv0NLSQvPCFo781NkArLvBGhxx4p4MeucKzJ45jx9+7WoWvPZGj79O9R0jxgznaxcfztCRQ8hMJp37B244YxLvft+7OPKcCQxeaRCznp7NKZ89gwWvvg7AvsfvybiDdqKluYWzj7yAqTc/uMTjqME4fF1lKPcCU264nxsvu4tjT9l7kfbV1liVzbZZjxeem1dt22fCDvzfY8/zvS9fxph1R3DYt/bg6wedX11//H+fxysvL1jkOEedtBfnnfY7Hpr6FDvvtTmfOGg7Lj3zD937otSnNS9s5hfHXkLTA08xeKVBnD31VO6bMo2vnHsoE796KdNuf4RdDvwIe391Dy7+9lWsvdEYdthnWw7Z5GiGjxrGqVO+xYEbHLnE4zz76IyyX6LqONGrxuHrXuDv9z3Nq/MX/Fv7F47bnfN/9PtF/gpd+z9W58G7nwRgxlMvMnLUEIYMX6nd449eZzUemvoUAPf/pYn/3HmT5dd5qQ1zZ71M0wOV37nX//kvnn10JquNHsaY9Ucx7fZHALh/yjS222srALYZvwW3XfVn3npzIbOens1zTbPYYMv1lngcNZjMri29iKHcS22140a8NPsVnnp81iLtTz4+i20/ujEA6793DKuPGsJqI1cBIDP5/nkHcsY1h7Hr3h+s7vNM0wtsvdNGAGy3yyastsaqPfQqJBj5rhGs94F1eezuJ3j64elsM77yu7n93lszYq3hAKw2ejgvTp9T3efFmXP/LXzrj6PGEtm1pTfp8VCOiAPbWTchIqZGxNTp8x7oyW71KgMHvYN9JuzApWdO+bd115z7J1ZcZTA/u/5w9thva/7v0edpaan8Vh/72Ykc8cmz+NYXLuLjn96KTTZfB4DTv3k9H993K8645jAGrziQhW819+TLUR82aMVBfPvaYznn6AtZ8Orr/Ojgs9nji7tw1r2nMnjlQSx8c2GnjiM1qjKuKX8XuLCtFZk5EZgIsOvG3+hlf//0nDXXGsYao4dy9g1fBmC1katw5nWHc9Q+ZzPvpX9y+gnXVbe9aMpXmTV9LgBzZr8CwPy5r/GXWx5hg/eN4e/3Pc2Mp17khEMq/8lGv2s4W26/QQ+/IvVF/Qf058Rrj+HWy+/gzhvuAWD6489x/LiTARg9dk0+tNvmALw0c061agYYMXoYL82cu8TjqMH4r31Vt4RyRExb0ipgZHecUzVPP/ECn97uB9XnF035Kl/e+yxeeXkBK648iDf+9RYL32pm3Ce34KGpT7HgtTcYOPgd9Ivg9QVvMnDwO9hsm/W4/JxbAVh12IrMn/saEcG+h36ESVf7D5u63zHnfZFnH5vJdaffVG0bMmIVXn7xFSKC/U74BDf94mYA/nrjVL5+2ZFc9+ObGD5qGKPHrsnj9zQt8ThqLL1tCLoruqtSHgnsAsxbrD2Av3TTOfus407bh/dtuS6rDFmRS289jkt/9gduvv6+Nrdd690jOOZ/9oZMnmmazU++Vamahw5fiW+d8VkA+g/ox22/fZD77qxce9tht035+GcqE2r+MuXhJR5bWl7es+2GfGz/D/PktGf4+f2nAXDBCZczeuya7PGlXQC484Z7mHzhHwF45pEZ3H7NXznv4dNpXtjCmYefR0tLyxKPc8/vvDzWUHrZZK2uiOyGNyMizgcuzMw721h3eWZ+ZmnHcPhavcHCxzD4W64AAAfPSURBVJxUpN5hSss10V3H3m7P07r07/0dv/pqt/Wtp3VLpZyZB7ezbqmBLEnqOxy+rvHmIZKkchnKVYayJKlUVso1hrIkqVwtpnIr7+glSVKDsFKWJJXLQrnKUJYklcpryjWGsiSpXN48pMpQliSVykq5xolekiQ1CCtlSVK5rJSrDGVJUqnCa8pVhrIkqVwtZXegcXhNWZKkBmGlLEkqlcPXNYayJKlcZnKVoSxJKpeVcpWhLEkqlTcPqXGilyRJDcJQliSVK7Nry1JExFoR8ceIeCQiHo6II4v2YRExJSKeKH4OLdojIs6IiKaImBYRm9Ud64Bi+yci4oC69s0j4qFinzMiIjrzVhjKkqRSRUvXlg5YCByTmRsDWwGHRcTGwPHALZk5FrileA6wKzC2WCYA50AlxIETgQ8BWwIntgZ5sc0hdfuN68x7YShLksrVzZVyZj6fmfcXj18FHgVGA+OBi4vNLgb2LB6PBy7JiruAIRGxJrALMCUz52bmPGAKMK5Yt0pm3pWZCVxSd6xlYihLkvqMiFgH+ABwNzAyM58vVs0CRhaPRwPT63abUbS11z6jjfZlZihLksqVXVsiYkJETK1bJrR1mohYCbgOOCozX1mkC5UKt/R54H4kSpJUqq7e0SszJwIT2z1HxDuoBPJlmXl90fxCRKyZmc8XQ9Czi/aZwFp1u48p2mYCOyzWflvRPqaN7ZeZlbIkqVzdP/s6gPOBRzPzx3WrbgRaZ1AfAPy6rn3/Yhb2VsD8Yph7MrBzRAwtJnjtDEwu1r0SEVsV59q/7ljLxEpZklSu7v+WqG2BzwEPRcTfirZvAKcAV0fEwcAzwKeKdZOA3YAmYAFwIEBmzo2I7wH3FtudlJlzi8dfAi4CBgO/K5ZlZihLknq1zLwTWNLnhndqY/sEDlvCsS4ALmijfSqwSRe6CRjKkqSS+S1RNYayJKlchnKVoSxJKpehXGUoS5LK1f0Tvd42/EiUJEkNwkpZklQqJ3rVGMqSpHIZylWGsiSpXIZyldeUJUlqEFbKkqRyWSlXGcqSpHL5kagqQ1mSVCpnX9cYypKkchnKVU70kiSpQVgpS5LK1WKl3MpQliSVy+HrKkNZklQuQ7nKUJYklctQrnKilyRJDcJKWZJULid6VRnKkqRypbf0amUoS5LK5TXlKq8pS5LUIKyUJUnl8ppylaEsSSqXw9dVhrIkqVyGcpWhLEkql6Fc5UQvSZIahJWyJKlcLX5OuZWhLEkql8PXVYayJKlchnKVoSxJKpefU65yopckSQ3CSlmSVKr0CymqDGVJUrkcvq4ylCVJ5XKiV5XXlCVJahBWypKkcnnzkCpDWZJULoevqwxlSVKp0kq5ylCWJJXLSrnKiV6SJDUIK2VJUrn8nHKVoSxJKpd39KoylCVJpUor5SpDWZJULivlKid6SZLUIKyUJUmlcvi6xlCWJJXL4euqSD+03WdFxITMnFh2P6Su8ndZvYXXlPu2CWV3QFpO/F1Wr2AoS5LUIAxlSZIahKHct3kNTr2Fv8vqFZzoJUlSg7BSliSpQRjKfVREjIuIxyOiKSKOL7s/UmdExAURMTsi/l52X6TlwVDugyKiP3AWsCuwMfDpiNi43F5JnXIRMK7sTkjLi6HcN20JNGXmk5n5JnAlML7kPknLLDNvB+aW3Q9peTGU+6bRwPS65zOKNklSiQxlSZIahKHcN80E1qp7PqZokySVyFDum+4FxkbEuhGxArAvcGPJfZKkPs9Q7oMycyFwODAZeBS4OjMfLrdX0rKLiCuAvwIbRMSMiDi47D5JXeEdvSRJahBWypIkNQhDWZKkBmEoS5LUIAxlSZIahKEsSVKDMJTV50VEc0T8LSL+HhHXRMQ7u3CsiyLik8Xj89r7oo+I2CEitql7fmhE7N/Zc0t6+zOUJXg9M9+fmZsAbwKH1q+MiAGdOWhmfj4zH2lnkx2Aaihn5s8z85LOnEtS72AoS4u6A1ivqGLviIgbgUcion9EnBYR90bEtIj4AkBU/Kz4buo/AKu3HigibouILYrH4yLi/oh4MCJuiYh1qIT/0UWVvl1EfCciji22f39E3FWc64aIGFp3zFMj4p6I+EdEbNej746kbtWpCkDqjYqKeFfg90XTZsAmmflUREwA5mfmByNiIPDniLgZ+ACwAZXvpR4JPAJcsNhxRwDnAtsXxxqWmXMj4ufAPzPzf4vtdqrb7RLgiMz8U0ScBJwIHFWsG5CZW0bEbkX7R5f3eyGpHIayBIMj4m/F4zuA86kMK9+TmU8V7TsD72u9XgysCowFtgeuyMxm4LmIuLWN428F3N56rMxs9/t/I2JVYEhm/qlouhi4pm6T64uf9wHrdOwlSno7MJSl4ppyfUNEALxW30Slcp282Ha7dX/3/s0bxc9m/H9Y6lW8pix1zGTgixHxDoCIWD8iVgRuB/YprjmvCXykjX3vAraPiHWLfYcV7a8CKy++cWbOB+bVXS/+HPCnxbeT1Pv4V7bUMedRGSq+Pypl9IvAnsANwI5UriU/S+UbixaRmS8W16Svj4h+wGzgY8BvgGsjYjxwxGK7HQD8vPh41pPAgd3xoiQ1Fr8lSpKkBuHwtSRJDcJQliSpQRjKkiQ1CENZkqQGYShLktQgDGVJkhqEoSxJUoMwlCVJahD/H8U8BnNFYRwFAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#True Positive/all\n",
        "(106071+2908)/(106071+2908+14959+2902)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u6uRG1UdOZuE",
        "outputId": "64ba9226-9dd6-4bf3-d456-1145e6b5d294"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8591847997477137"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2- Selected Features (Scored)"
      ],
      "metadata": {
        "id": "1e06JtNxE8sO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf.fit(X_train[ranked_features.iloc[:,0].nlargest(5).index], y_train)\n",
        "rf.score(X_test[ranked_features.iloc[:,0].nlargest(5).index], y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Y-9BzQmFnSj",
        "outputId": "656fffff-ceaa-4483-edb7-2efeae1b40cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    4.9s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    2.0s finished\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8619678334910122"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "3- Selected Features (Recursive)\n"
      ],
      "metadata": {
        "id": "9VjjRmS5E83v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf.fit(X_train[X_train.columns[rfe.support_]], y_train)\n",
        "rf.score(X_test[X_test.columns[rfe.support_]], y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qvhq6T85Bv6M",
        "outputId": "12bb1b0b-e570-4a1f-dc9c-dd23646b7c76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    2.9s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    1.2s finished\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8605723746452223"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Conclusion:\n",
        "### One must always remember that more number of features does not imply better accuracy. More features may lead to a decline in the accuracy if they contain any irrelevant features creating unrequired noise in our model."
      ],
      "metadata": {
        "id": "8I0fWUETwXqL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Assignment:\n",
        "Extract the following features from the given 15 ECG samples to generate a feature matrix:\n",
        "\n",
        "1.  max\n",
        "2.  min\n",
        "3.  mean\n",
        "4.  median\n",
        "5.  Variance\n",
        "6.  skewness\n",
        "7.  kurtosis\n",
        "\n",
        "Tip: you can use built-in functions for calculations\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "6oca_xqkFrNF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "_izBF0yDTcpC"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}