Numerical_Feature_Selection.ipynb

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        "# Feature Selection\n",
        "\n",
        "**Feature selection** is the process of selecting a subset of relevant features from a larger set to improve model performance or reduce computational complexity. *Types of feature selection* refers to various methodologies or approaches used to select a subset of features from the original feature set. These methodologies can be broadly categorized into three main types:\n",
        "\n",
        "* Filter Methods\n",
        "* Wrapper Methods\n",
        "* Embedded Methods"
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        "# Numerical Feature Selection\n",
        "\n",
        "* **Filter Methods**\n",
        "\n",
        "\n",
        "  1. **Mutual Information**: Measures the relationship between two random variables; assesses the information gain by measuring the relationship of features with the target variable.\n",
        "\n",
        "  2. **Chi-Squared**: Measures the relationship between categorical independent variables and a categorical target variable; tests the association between two categorical variables.\n",
        "\n",
        "  3. **f_classif**: is a feature selection method commonly employed to evaluate the influence of features on a classification task, often utilizing ANOVA (Analysis of Variance). ANOVA is utilized to determine the statistical significance of the relationship between each feature and the target variable. High F-statistic values resulting from ANOVA imply a substantial association between the feature and the target, indicating its importance for classification. Conversely, low F-statistic values suggest a weaker impact. This technique plays a crucial role in feature selection processes aimed at enhancing model performance by selecting the most informative features.\n",
        "\n",
        "  4. **ROC_AUC**: Calculates the area under the Receiver Operating Characteristic (ROC) curve (AUC), evaluating classifier performance; a higher ROC AUC value indicates better classifier performance.\n",
        "\n",
        "  5. **Correlation**: Measures the linear relationship between features; assesses the correlation between features using statistical metrics such as the Pearson correlation coefficient."
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "# Import Necessary Librares"
      ],
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "pd.set_option(\"display.max_columns\", None)\n",
        "pd.set_option(\"display.max_rows\", None)\n",
        "pd.set_option(\"display.width\", 500)\n",
        "pd.set_option(\"display.float_format\", lambda x: \"%.4f\" % x)\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import f_classif\n",
        "from sklearn.feature_selection import chi2\n",
        "from sklearn.feature_selection import mutual_info_classif\n",
        "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import roc_auc_score, mean_squared_error"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Dataset"
      ],
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      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/datasets/diabetes.csv\")\n",
        "df.head()"
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              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin     BMI  DiabetesPedigreeFunction  Age  Outcome\n",
              "0            6      148             72             35        0 33.6000                    0.6270   50        1\n",
              "1            1       85             66             29        0 26.6000                    0.3510   31        0\n",
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              "3            1       89             66             23       94 28.1000                    0.1670   21        0\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151,\n          101,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86,\n          46,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7,\n          12,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 0,\n        \"max\": 846,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52,\n          41,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.884160320375446,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3313285950127749,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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        "dir = \"/content/drive/MyDrive/Colab Notebooks/datasets/diabetes.csv\"\n",
        "X, y = load_dataset(dir, drop_labels=[\"Outcome\"], target=\"Outcome\")"
      ],
      "metadata": {
        "id": "RFmSR49uAOX6"
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      "execution_count": 4,
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      "execution_count": 5,
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            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin     BMI  DiabetesPedigreeFunction  Age\n",
              "0            6      148             72             35        0 33.6000                    0.6270   50\n",
              "1            1       85             66             29        0 26.6000                    0.3510   31\n",
              "2            8      183             64              0        0 23.3000                    0.6720   32\n",
              "3            1       89             66             23       94 28.1000                    0.1670   21\n",
              "4            0      137             40             35      168 43.1000                    2.2880   33"
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>6</td>\n",
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              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6000</td>\n",
              "      <td>0.6270</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6000</td>\n",
              "      <td>0.3510</td>\n",
              "      <td>31</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3000</td>\n",
              "      <td>0.6720</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1000</td>\n",
              "      <td>0.1670</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1000</td>\n",
              "      <td>2.2880</td>\n",
              "      <td>33</td>\n",
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              "  </tbody>\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c3b05f88-eb1e-4416-8f95-549ed904fb81')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\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-c3b05f88-eb1e-4416-8f95-549ed904fb81 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-c3b05f88-eb1e-4416-8f95-549ed904fb81');\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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              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-46a83fa9-a426-4a07-be83-c5088a32ed56\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-46a83fa9-a426-4a07-be83-c5088a32ed56')\"\n",
              "            title=\"Suggest charts\"\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151,\n          101,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86,\n          46,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7,\n          12,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 0,\n        \"max\": 846,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52,\n          41,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.884160320375446,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3313285950127749,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lz1tziD9AVKO",
        "outputId": "23c5622e-9b85-43f6-dfe7-a9f3fc4b1fe1"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    0\n",
              "2    1\n",
              "3    0\n",
              "4    1\n",
              "Name: Outcome, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fUSoqdQPoj2_",
        "outputId": "aede161c-8985-4719-d142-589f6567488d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-w3v3wzAon5W",
        "outputId": "fd040580-8b2e-40f8-b2ec-3dfbfb7f9516"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                 0\n",
              "Glucose                     0\n",
              "BloodPressure               0\n",
              "SkinThickness               0\n",
              "Insulin                     0\n",
              "BMI                         0\n",
              "DiabetesPedigreeFunction    0\n",
              "Age                         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "zero_columns = [col for col in X.columns if (X[col].min() == 0 and col not in [\"Pregnancies\"])]\n",
        "for col in zero_columns:\n",
        "  X[col] = np.where(X[col] == 0, np.nan, X[col])"
      ],
      "metadata": {
        "id": "hZz_pvXdornT"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XD2AwUPDorYb",
        "outputId": "61922d7f-9331-450b-e439-dfd976072a24"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                   0\n",
              "Glucose                       5\n",
              "BloodPressure                35\n",
              "SkinThickness               227\n",
              "Insulin                     374\n",
              "BMI                          11\n",
              "DiabetesPedigreeFunction      0\n",
              "Age                           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def fill_na_with_median(dataframe):\n",
        "  dataframe = dataframe.apply(lambda x: x.fillna(x.median()) if x.dtype not in [\"category\", \"object\", \"bool\"] else x, axis=0)\n",
        "  return dataframe"
      ],
      "metadata": {
        "id": "ChBDEvSqo0aB"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = fill_na_with_median(X)"
      ],
      "metadata": {
        "id": "srISW_kOo0XM"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ppmJ75pmo0Ut",
        "outputId": "443ca3ec-b8d7-4ec4-885c-417c606ed86c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                 0\n",
              "Glucose                     0\n",
              "BloodPressure               0\n",
              "SkinThickness               0\n",
              "Insulin                     0\n",
              "BMI                         0\n",
              "DiabetesPedigreeFunction    0\n",
              "Age                         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Split Dataset"
      ],
      "metadata": {
        "id": "mJxzg150Sx4T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_train_test(X, y, test_size=0.33):\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=test_size, random_state=1)\n",
        "  return X_train, X_test, y_train, y_test"
      ],
      "metadata": {
        "id": "2Wgli_EzP2Cm"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = create_train_test(X, y)"
      ],
      "metadata": {
        "id": "J4bU46U6p6S-"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Filter Based Feature Selection: Anova"
      ],
      "metadata": {
        "id": "6kmNR58RS1mj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def filter_based_feature_selection(X_train, y_train, X_test, score_func):\n",
        "  model = SelectKBest(score_func=score_func, k='all')\n",
        "  model.fit(X_train, y_train)\n",
        "  X_train_model = model.transform(X_train)\n",
        "  X_test_model = model.transform(X_test)\n",
        "  return X_train_model, X_test_model, model"
      ],
      "metadata": {
        "id": "Kt2JGa_IAW2N"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_model, X_test_model, model = filter_based_feature_selection(X_train, y_train, X_test, score_func=f_classif)"
      ],
      "metadata": {
        "id": "qrejIhJJEKMc"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(model.scores_)):\n",
        "  print(f\"Feature: {i} Score: {model.scores_[i]}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_fzERl4VEK6I",
        "outputId": "38d0d1ea-1acd-46e1-f4f5-319e801fa30c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature: 0 Score: 16.527384507997283\n",
            "Feature: 1 Score: 134.74359786862397\n",
            "Feature: 2 Score: 10.491396711947141\n",
            "Feature: 3 Score: 18.947687279611863\n",
            "Feature: 4 Score: 28.832322500369134\n",
            "Feature: 5 Score: 62.97168216414612\n",
            "Feature: 6 Score: 13.37714228723036\n",
            "Feature: 7 Score: 25.12643971427903\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_results(model, plot=True):\n",
        "  output = pd.Series(model.scores_)\n",
        "  output = output.sort_values(ascending=False)\n",
        "  print(\"Features and Scores\")\n",
        "  for i, out in enumerate(output):\n",
        "    print(f\"Feature: {output.index[i]} Score: {out}\")\n",
        "\n",
        "  if plot:\n",
        "    plt.bar([i for i in range(len(model.scores_))], model.scores_)\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "XcsJMB_3EoRN"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_results(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "GiUroc-hGUFi",
        "outputId": "bcd36052-aacb-478e-d5c0-deac88e6e5bd"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Features and Scores\n",
            "Feature: 1 Score: 134.74359786862397\n",
            "Feature: 5 Score: 62.97168216414612\n",
            "Feature: 4 Score: 28.832322500369134\n",
            "Feature: 7 Score: 25.12643971427903\n",
            "Feature: 3 Score: 18.947687279611863\n",
            "Feature: 0 Score: 16.527384507997283\n",
            "Feature: 6 Score: 13.37714228723036\n",
            "Feature: 2 Score: 10.491396711947141\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def filter_based_FS(X, y, score_func, test_size=0.33, plot=True):\n",
        "\n",
        "  # Split Dataset\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=1)\n",
        "\n",
        "  # Filter Based Methods\n",
        "  model = SelectKBest(score_func=score_func, k='all')\n",
        "  model.fit(X_train, y_train)\n",
        "  X_train_model = model.transform(X_train)\n",
        "  X_test_model = model.transform(X_test)\n",
        "\n",
        "  # Plot Results\n",
        "  output = pd.Series(model.scores_)\n",
        "  output.index = X_train.columns\n",
        "  output = output.sort_values(ascending=False)\n",
        "  print(\"Features and Scores\")\n",
        "  for i, out in enumerate(output):\n",
        "    print(f\"Feature: {output.index[i]} Score: {out}\")\n",
        "\n",
        "  if plot:\n",
        "    plt.bar(output.index, model.scores_)\n",
        "    plt.xticks(rotation='vertical')\n",
        "    plt.title(f\"Feature Importance for Score Function: {score_func.__name__}\")\n",
        "    plt.xlabel(\"Feature Index\")\n",
        "    plt.ylabel(\"Score\")\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "r4ljhbLwLGej"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filter_based_FS(X, y, score_func=f_classif)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        },
        "id": "V_-hxpYMUoff",
        "outputId": "cdd4afe3-9a26-4d59-fb03-61e31e88defe"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Features and Scores\n",
            "Feature: Glucose Score: 134.74359786862397\n",
            "Feature: BMI Score: 62.97168216414612\n",
            "Feature: Insulin Score: 28.832322500369134\n",
            "Feature: Age Score: 25.12643971427903\n",
            "Feature: SkinThickness Score: 18.947687279611863\n",
            "Feature: Pregnancies Score: 16.527384507997283\n",
            "Feature: DiabetesPedigreeFunction Score: 13.37714228723036\n",
            "Feature: BloodPressure Score: 10.491396711947141\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Filter Based Feature Selection: Anova + Chi2"
      ],
      "metadata": {
        "id": "9ZVd1dGuuEei"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def filter_based_FS(X, y, score_funcs, test_size=0.33, plot=True):\n",
        "\n",
        "    # Split Dataset\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=1)\n",
        "\n",
        "    for score_func in score_funcs:\n",
        "        # Filter Based Methods\n",
        "        model = SelectKBest(score_func=score_func, k='all')\n",
        "        model.fit(X_train, y_train)\n",
        "        X_train_model = model.transform(X_train)\n",
        "        X_test_model = model.transform(X_test)\n",
        "\n",
        "        # Plot Results\n",
        "        output = pd.Series(model.scores_)\n",
        "        output.index = X_train.columns\n",
        "        output = output.sort_values(ascending=False)\n",
        "        print(f\"Features and Scores for Score Function: {score_func.__name__}\")\n",
        "        for i, out in enumerate(output):\n",
        "            print(f\"Feature: {output.index[i]} Score: {out}\")\n",
        "\n",
        "        if plot:\n",
        "            plt.figure(figsize=(8, 6))\n",
        "            plt.xticks(rotation='vertical')\n",
        "            plt.bar(output.index, model.scores_)\n",
        "            plt.title(f\"Feature Importance for Score Function: {score_func.__name__}\")\n",
        "            plt.xlabel(\"Feature Index\")\n",
        "            plt.ylabel(\"Score\")\n",
        "            plt.show()"
      ],
      "metadata": {
        "id": "OSdK3laXUymW"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filter_based_FS(X, y, score_funcs=[f_classif, chi2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5havCxm-V1NI",
        "outputId": "61413579-bc64-4404-9af7-dce1f50cda78"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Features and Scores for Score Function: f_classif\n",
            "Feature: Glucose Score: 134.74359786862397\n",
            "Feature: BMI Score: 62.97168216414612\n",
            "Feature: Insulin Score: 28.832322500369134\n",
            "Feature: Age Score: 25.12643971427903\n",
            "Feature: SkinThickness Score: 18.947687279611863\n",
            "Feature: Pregnancies Score: 16.527384507997283\n",
            "Feature: DiabetesPedigreeFunction Score: 13.37714228723036\n",
            "Feature: BloodPressure Score: 10.491396711947141\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Features and Scores for Score Function: chi2\n",
            "Feature: Insulin Score: 1298.345701541465\n",
            "Feature: Glucose Score: 796.8783257889012\n",
            "Feature: Age Score: 104.4052129225523\n",
            "Feature: BMI Score: 84.05393926773033\n",
            "Feature: SkinThickness Score: 48.74634550625772\n",
            "Feature: Pregnancies Score: 47.63612386967638\n",
            "Feature: BloodPressure Score: 23.519474095243357\n",
            "Feature: DiabetesPedigreeFunction Score: 3.0894038172177227\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def filter_based_FS(X, y, score_funcs, test_size=0.33, plot=True):\n",
        "\n",
        "    # Split Dataset\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=1)\n",
        "\n",
        "    plt.figure(figsize=(15, 5))\n",
        "    for idx, score_func in enumerate(score_funcs, start=1):\n",
        "        # Filter Based Methods\n",
        "        model = SelectKBest(score_func=score_func, k='all')\n",
        "        model.fit(X_train, y_train)\n",
        "        X_train_model = model.transform(X_train)\n",
        "        X_test_model = model.transform(X_test)\n",
        "\n",
        "        # Plot Results\n",
        "        output = pd.Series(model.scores_)\n",
        "        output.index = X_train.columns\n",
        "        output = output.sort_values(ascending=False)\n",
        "\n",
        "        print(f\"Features and Scores for Score Function: {score_func.__name__}\")\n",
        "        feature_scores = {\"Feature\": [], \"Score\": []}\n",
        "        for i, out in enumerate(output):\n",
        "            print(f\"Feature: {output.index[i]} Score: {out}\")\n",
        "            feature_scores[\"Feature\"].append(output.index[i])\n",
        "            feature_scores[\"Score\"].append(out)\n",
        "        print(100*\"#\")\n",
        "\n",
        "        # Plot subplot\n",
        "        plt.subplot(1, len(score_funcs), idx)\n",
        "        plt.xticks(rotation='vertical')\n",
        "        plt.bar(output.index, model.scores_)\n",
        "        plt.title(f\"Feature Importance for Score Function: {score_func.__name__}\")\n",
        "        plt.xlabel(\"Feature Index\")\n",
        "        plt.ylabel(\"Score\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n"
      ],
      "metadata": {
        "id": "4CM7akM2V9Oa"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Filter Based Feature Selection: Anova + Chi2 + Mutual_Information"
      ],
      "metadata": {
        "id": "RrYvAdGruNLC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "filter_based_FS(X, y, score_funcs=[f_classif, chi2, mutual_info_classif])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "uCEwXn5kWqTG",
        "outputId": "91a48050-e79d-45cc-b76c-bbfcb4b8dff8"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Features and Scores for Score Function: f_classif\n",
            "Feature: Glucose Score: 134.74359786862397\n",
            "Feature: BMI Score: 62.97168216414612\n",
            "Feature: Insulin Score: 28.832322500369134\n",
            "Feature: Age Score: 25.12643971427903\n",
            "Feature: SkinThickness Score: 18.947687279611863\n",
            "Feature: Pregnancies Score: 16.527384507997283\n",
            "Feature: DiabetesPedigreeFunction Score: 13.37714228723036\n",
            "Feature: BloodPressure Score: 10.491396711947141\n",
            "####################################################################################################\n",
            "Features and Scores for Score Function: chi2\n",
            "Feature: Insulin Score: 1298.345701541465\n",
            "Feature: Glucose Score: 796.8783257889012\n",
            "Feature: Age Score: 104.4052129225523\n",
            "Feature: BMI Score: 84.05393926773033\n",
            "Feature: SkinThickness Score: 48.74634550625772\n",
            "Feature: Pregnancies Score: 47.63612386967638\n",
            "Feature: BloodPressure Score: 23.519474095243357\n",
            "Feature: DiabetesPedigreeFunction Score: 3.0894038172177227\n",
            "####################################################################################################\n",
            "Features and Scores for Score Function: mutual_info_classif\n",
            "Feature: Glucose Score: 0.09714810165546384\n",
            "Feature: BMI Score: 0.0713283534701239\n",
            "Feature: SkinThickness Score: 0.04916548955259836\n",
            "Feature: Insulin Score: 0.04133927486706446\n",
            "Feature: BloodPressure Score: 0.03799279285073043\n",
            "Feature: Age Score: 0.03649865795457674\n",
            "Feature: DiabetesPedigreeFunction Score: 0.01623369709865119\n",
            "Feature: Pregnancies Score: 0.00011661288803566094\n",
            "####################################################################################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ROC_AUC\n",
        "\n",
        "ROC AUC (Receiver Operating Characteristic Area Under the Curve) is a metric used to measure the performance of a classification model. The ROC curve illustrates the relationship between sensitivity (true positive rate) and (1 - false positive rate). The area under the ROC curve (AUC) is a measure of the model's classification ability. ROC AUC ranges from 0 to 1, where a value closer to 1 indicates a model with excellent classification ability, while a value closer to 0.5 suggests that the model performs no better than random classification. Therefore, in feature selection methods, ROC AUC can be used to assess the contribution of each feature to the model's classification performance. The ROC AUC values of features can help evaluate how adding or removing different features affects the model's performance, with higher ROC AUC values suggesting that a particular feature may enhance the model's performance."
      ],
      "metadata": {
        "id": "XKTF43IFKabW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def feature_roc_auc_scores(X, y, roc_auc_value=0.6, test_size=0.33, plot=True ):\n",
        "    # Split Dataset\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=1)\n",
        "\n",
        "    # Calculate ROC AUC scores for each feature\n",
        "    roc_values = []\n",
        "    for feature in X_train.columns:\n",
        "        clf = DecisionTreeClassifier()\n",
        "        clf.fit(X_train[feature].fillna(0).to_frame(), y_train)\n",
        "        y_scored = clf.predict_proba(X_test[feature].fillna(0).to_frame())\n",
        "        roc_values.append(roc_auc_score(y_test, y_scored[:, 1]))\n",
        "\n",
        "    # Add variable names and order for clearer visualization\n",
        "    roc_values = pd.Series(roc_values)\n",
        "    roc_values.index = X_train.columns\n",
        "    roc_values_sorted = roc_values.sort_values(ascending=False)\n",
        "\n",
        "    # Plotting\n",
        "    roc_values_sorted.plot.bar(figsize=(20, 8))\n",
        "    plt.xlabel('Features')\n",
        "    plt.ylabel('ROC AUC')\n",
        "    plt.title('ROC AUC Scores for Each Feature')\n",
        "    plt.axhline(roc_auc_value, color='red', linestyle='--', label='Random Guess')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "    # Count features with ROC AUC > roc_auc_value\n",
        "    num_features_above_random = (roc_values_sorted > roc_auc_value).sum()\n",
        "    print(f'Number of features with ROC AUC > {roc_auc_value}: {num_features_above_random}')\n",
        "\n",
        "    print(\"Features with ROC AUC > {:.2f}:\".format(roc_auc_value))\n",
        "    for feature, roc in roc_values_sorted.items():\n",
        "        if roc > roc_auc_value:\n",
        "            print(f\"{feature}: {roc}\")"
      ],
      "metadata": {
        "id": "5uKMxOK-PSJo"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature_roc_auc_scores(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 860
        },
        "id": "oebcTjP2oYtd",
        "outputId": "519a6590-6ae6-4403-e0f4-9a27a7728101"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of features with ROC AUC > 0.6: 3\n",
            "Features with ROC AUC > 0.60:\n",
            "Glucose: 0.730072463768116\n",
            "Age: 0.666935050993022\n",
            "Pregnancies: 0.6474100912506708\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Correlation\n",
        "\n",
        "Measures the linear relationship between features; assesses the correlation between features using statistical metrics such as the Pearson correlation coefficient."
      ],
      "metadata": {
        "id": "qtqWIwyZrQhy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def feature_selection_correlation(X, y, test_size=0.33, threshold=0.8):\n",
        "\n",
        "    # Split Dataset\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=1)\n",
        "\n",
        "    # Build a Dataframe with Correlation between Features\n",
        "    corr_matrix = X_train.corr()\n",
        "    # Take absolute values of correlated coefficients\n",
        "    corr_matrix = corr_matrix.abs().unstack()\n",
        "    corr_matrix = corr_matrix.sort_values(ascending=False)\n",
        "    # Take only features with correlation above threshold\n",
        "    corr_matrix = corr_matrix[(corr_matrix >= threshold) & (corr_matrix < 1)]\n",
        "    corr_matrix = pd.DataFrame(corr_matrix).reset_index()\n",
        "    corr_matrix.columns = ['feature1', 'feature2', 'Correlation']\n",
        "\n",
        "    # Get groups of features that are correlated amongs themselves\n",
        "    grouped_features = []\n",
        "    correlated_groups = []\n",
        "\n",
        "    for feature in corr_matrix.feature1.unique():\n",
        "        if feature not in grouped_features:\n",
        "            # Find all features correlated to a single feature\n",
        "            correlated_block = corr_matrix[corr_matrix.feature1 == feature]\n",
        "            grouped_features = grouped_features + list(correlated_block.feature2.unique()) + [feature]\n",
        "\n",
        "            # Append block of features to the list\n",
        "            correlated_groups.append(correlated_block)\n",
        "\n",
        "    if len(correlated_groups) == 0:\n",
        "        print('No correlated feature groups found above the threshold of {}.'.format(threshold))\n",
        "        return None, None\n",
        "\n",
        "    print('Found {} correlated feature groups'.format(len(correlated_groups)))\n",
        "    print('out of {} total features.'.format(X_train.shape[1]))\n",
        "\n",
        "    # Visualize Correlated Feature Groups\n",
        "    for group in correlated_groups:\n",
        "        print(group)\n",
        "        print('\\n')\n",
        "\n",
        "    # Random Forest Classifier for Feature Importance\n",
        "    features = list(group.feature2.unique())\n",
        "    rfc = RandomForestClassifier(n_estimators=20, random_state=101, max_depth=4)\n",
        "    rfc.fit(X_train[features].fillna(0), y_train)\n",
        "\n",
        "    # Get Feature Importance using RFC\n",
        "    importance = pd.concat([pd.Series(features), pd.Series(rfc.feature_importances_)], axis=1)\n",
        "    importance.columns = ['feature', 'importance']\n",
        "    importance_sorted = importance.sort_values(by='importance', ascending=False)\n",
        "\n",
        "    # Plot Feature Importance\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    plt.bar(importance_sorted['feature'], importance_sorted['importance'], color='skyblue')\n",
        "    plt.xlabel('Feature Name')\n",
        "    plt.ylabel('Importance')\n",
        "    plt.title('Feature Importance')\n",
        "    plt.xticks(rotation=90)\n",
        "    plt.show()\n",
        "\n",
        "    return correlated_groups, importance_sorted"
      ],
      "metadata": {
        "id": "DuUeXE8ssMd1"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "correlated_groups, importance_sorted = feature_selection_correlation(X, y, threshold=0.9)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7oDEd4gkxYrx",
        "outputId": "ab9e291d-e1e0-4cf7-b535-c2e0fbdcc915"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No correlated feature groups found above the threshold of 0.9.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def high_correlated_cols(X, corr_th=0.9, plot=True, remove_correlated_variables=False):\n",
        "    num_cols = [col for col in X.columns if X[col].dtypes in [\"uint8\", \"int64\", \"float64\"]]\n",
        "    corr = X[num_cols].corr()\n",
        "    corr_matrix = corr.abs()\n",
        "    upper_triangle_matrix = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n",
        "    drop_list = [col for col in upper_triangle_matrix.columns if any(upper_triangle_matrix[col] > corr_th)]\n",
        "\n",
        "    if drop_list == []:\n",
        "        print(\"############## After Correlation Analysis, You Don't Need to Remove Variables ##############\")\n",
        "\n",
        "    if plot:\n",
        "        sns.set(rc={'figure.figsize': (6, 3)})\n",
        "        sns.heatmap(corr, cmap=\"RdBu\")\n",
        "        plt.show()\n",
        "\n",
        "    if remove_correlated_variables:\n",
        "      new_df = X.drop(drop_list, axis=1)\n",
        "      sns.set(rc={'figure.figsize': (6,3)})\n",
        "      sns.heatmap(new_df.corr(), cmap=\"RdBu\", annot=True, fmt=\".2f\")\n",
        "      plt.show()\n",
        "\n",
        "    return drop_list, new_df"
      ],
      "metadata": {
        "id": "bhCmz_vC0V-U"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drop_list, new_df = high_correlated_cols(X, remove_correlated_variables=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 975
        },
        "id": "VzadGN0ficWl",
        "outputId": "48f08815-8312-4426-9814-0a1e22dc3b10"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "############## After Correlation Analysis, You Don't Need to Remove Variables ##############\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eYkkllYNj_ci"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}