05_Information_Gain_Mutual_Information_Classification_Regression.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JclSWYwRpfNl"
      },
      "source": [
        "# **Feature Selection-Information gain - mutual information In Classification**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone \"https://github.com/mohd-faizy/feature-engineering-hacks.git\" -q"
      ],
      "metadata": {
        "id": "HIVsgMifW4wo"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "R_DglhQmPxgS"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import os\n",
        "\n",
        "# plt.style.use('ggplot')\n",
        "# sns.set_theme(style='whitegrid')\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.chdir('/content/feature-engineering-hacks/_dataset')\n",
        "cwd = os.getcwd()\n",
        "print('Curent working directory is ', cwd)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ogwc_WBCXCuo",
        "outputId": "86e0821f-0fae-403e-8149-53060e28191b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Curent working directory is  /content/feature-engineering-hacks/_dataset\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qQZLLZIWXKJZ",
        "outputId": "41b46394-ad95-4202-a928-7c495caeac0f"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "diabetes.csv\t  pima-indians-diabetes.data.csv  titanic.csv\n",
            "housing_data.csv  santander.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NCy-9Ll-pfNp"
      },
      "source": [
        "### **Mutual Information**\n",
        "\n",
        "MI Estimate mutual information for a discrete target variable.\n",
        "\n",
        "**Mutual information** (MI) between two random variables is a **non-negative** value, which measures the dependency between the variables. It is equal to zero if and only if two random variables are independent, and higher values mean higher dependency.\n",
        "\n",
        "The function relies on nonparametric methods based on entropy estimation from `k-nearest` neighbors distances.\n",
        "\n",
        "**Inshort**\n",
        "\n",
        ">A quantity called mutual information measures the amount of information one can obtain from one random variable given another.\n",
        "\n",
        "The mutual information between two random variables `X` and `Y` can be stated formally as follows:\n",
        "\n",
        "$$I(X ; Y) = H(X) - H(X | Y)$$\n",
        "\n",
        "\n",
        "Where $I(X ; Y)$ is the mutual information for $X$ and $Y$, $H(X)$ is the entropy for $X$ and $H(X | Y)$ is the conditional entropy for $X$ given $Y$. The result has the units of bits."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wkq-ipJ-NFcz"
      },
      "source": [
        "**Mutual Information** is another metric used in feature selection to measure **the amount of information shared between a feature and the target variable in a dataset**.\n",
        "\n",
        "In feature selection, **we aim to select features that have a high mutual information score**, as they provide significant information for classifying instances.\n",
        "\n",
        "Calculate the mutual information between each feature and the target variable in the dataset.\n",
        "\n",
        "The mutual information measures the amount of shared information or dependency between the feature and the target variable. Higher mutual information indicates a stronger relationship between the feature and the target.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Il4F_B0F_-Q3"
      },
      "source": [
        "### **Information Gain**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z5quXPLwABfA"
      },
      "source": [
        "**Information gain** is a metric used in feature selection to measure the amount of useful information a feature provides for classifying instances in a dataset. It quantifies the reduction in entropy achieved by splitting the data based on a particular feature.\n",
        "\n",
        "In feature selection, we aim to select features that maximize information gain, as they contribute the most to improving the classification accuracy. Features with higher information gain have a stronger ability to discriminate between different classes or categories.\n",
        "\n",
        "Here's an explanation of information gain with respect to feature selection, along with a code example in Python:\n",
        "\n",
        "- Calculate the entropy of the target variable (class labels) before splitting the data.\n",
        "\n",
        "- For each feature, calculate the entropy of the target variable after splitting the data based on that feature.\n",
        "\n",
        "- Calculate the information gain by subtracting the weighted average of the entropies after splitting from the entropy before splitting.\n",
        "\n",
        "- Select the feature with the highest information gain as the most informative one.\n",
        "\n",
        "Here's an example code snippet that demonstrates the calculation of information gain for feature selection using the scikit-learn library:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_2Tn2rHHAPJn",
        "outputId": "692641cc-a57f-4b4f-ee45-9f74fec95688"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "petal length (cm): 0.9912223175926049\n",
            "petal width (cm): 0.9818639451723543\n",
            "sepal length (cm): 0.4812827527306831\n",
            "sepal width (cm): 0.2444656988926286\n"
          ]
        }
      ],
      "source": [
        "from sklearn.feature_selection import mutual_info_classif\n",
        "from sklearn.datasets import load_iris\n",
        "\n",
        "# Load the Iris dataset as an example\n",
        "data = load_iris()\n",
        "X = data.data       # Features\n",
        "y = data.target     # Target variable\n",
        "\n",
        "# Calculate the information gain using mutual information\n",
        "info_gain = mutual_info_classif(X, y)\n",
        "\n",
        "# Create a dictionary with feature names and their corresponding information gains\n",
        "feature_info_gain = {}\n",
        "for i, feature_name in enumerate(data.feature_names):\n",
        "    feature_info_gain[feature_name] = info_gain[i]\n",
        "\n",
        "# Sort the features based on their information gains in descending order\n",
        "sorted_features = sorted(feature_info_gain.items(), key=lambda x: x[1], reverse=True)\n",
        "\n",
        "# Print the features along with their information gains\n",
        "for feature, gain in sorted_features:\n",
        "    print(f\"{feature}: {gain}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SCeUFQMO0Vnf"
      },
      "source": [
        "### **Entropy**\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "872dIqPT0cZf"
      },
      "source": [
        "Entropy is a measure of impurity or disorder in a set of data. In the context of feature selection, entropy is used to quantify the amount of information provided by a feature in distinguishing different classes or categories within the dataset.\n",
        "\n",
        "When performing feature selection, we want to identify features that have the highest discriminatory power. In other words, we aim to select features that provide the most information in differentiating between different classes or categories.\n",
        "\n",
        "The entropy of a feature can be calculated using the formula:\n",
        "$$Entropy = -∑(p * log_{2}(p))$$\n",
        "\n",
        "\n",
        "\n",
        "Here's an example of calculating the entropy for a feature in Python:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C_ecR8kTNSJZ"
      },
      "source": [
        "[`np.unique`](https://numpy.org/doc/stable/reference/generated/numpy.unique.html)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pJ8RfDq0NANK",
        "outputId": "90822d01-90a6-4f1a-8a4a-8ae8cffa6041"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['a' 'b' 'c'] [0 1 3]\n"
          ]
        }
      ],
      "source": [
        "a = np.array(['a', 'b', 'b', 'c', 'a'])\n",
        "\n",
        "_, indices = np.unique(a, return_index=True)\n",
        "print(_, indices)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sqvRWhubOA7S",
        "outputId": "fc6be444-046d-41e1-bde9-f578d300cc69"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 2 3 4 6] [0 1 5 3 2]\n"
          ]
        }
      ],
      "source": [
        "b = np.array([1, 2, 6, 4, 2, 3, 2])\n",
        "\n",
        "_, indices = np.unique(b, return_index=True)\n",
        "print(_, indices)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f8RVUWSi93sE",
        "outputId": "5ff7105c-9c4e-450e-d077-fe00eacdd0d1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 2 3 4 5 6 7 8 9] [2 2 2 2 2 2 2 2 2 2]\n",
            "20\n",
            "[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]\n",
            "Entropy: 1.0\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "def calculate_entropy(feature):\n",
        "    _, counts = np.unique(feature, return_counts=True)\n",
        "    print(_, counts)\n",
        "    total_count = np.sum(counts)\n",
        "    print(total_count)\n",
        "    probabilities = counts / total_count\n",
        "    print(probabilities)\n",
        "    entropy = - np.sum(probabilities * np.log10(probabilities))\n",
        "    return entropy\n",
        "\n",
        "# Example feature data\n",
        "# feature_data = [0, 0, 1, 1, 0, 1, 1, 0, 1, 1]\n",
        "# feature_data = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0]  # Modified to have mostly one unique value\n",
        "feature_data = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]  # Repeated pattern\n",
        "\n",
        "# Calculate entropy\n",
        "entropy = calculate_entropy(feature_data)\n",
        "\n",
        "print(\"Entropy:\", entropy)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "13HBs06T-L93"
      },
      "source": [
        "Entropy measures the amount of disorder or randomness within a feature. A feature with entropy close to `0` indicates high purity and strong discriminatory power, **meaning it provides valuable information for classification**.\n",
        "\n",
        " On the other hand, an entropy value close to `1` suggests low purity and weak discriminatory power, indicating the **feature doesn't contribute much useful information for classification**.\n",
        "\n",
        " The goal of feature selection is to choose features with low entropy (`close to 0`) as they are more informative and helpful for classification tasks."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMVRCX92KPH3"
      },
      "source": [
        "### **Gini Impurity**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rtUGAzwhKVkP"
      },
      "source": [
        "Gini impurity is another metric used in feature selection to measure the impurity or disorder within a feature. It quantifies the probability of misclassifying a randomly chosen element in a dataset.\n",
        "\n",
        "In feature selection, we aim to select features that minimize Gini impurity, as they provide better discriminatory power for classification tasks.\n",
        "\n",
        "Here's an explanation of Gini impurity with respect to feature selection, along with a code example in Python:\n",
        "\n",
        "- Calculate the Gini impurity of the target variable (class labels) before splitting the data.\n",
        "\n",
        "- For each feature, calculate the Gini impurity of the target variable after splitting the data based on that feature.\n",
        "\n",
        "- Calculate the Gini impurity reduction by subtracting the weighted average of the Gini impurities after splitting from the Gini impurity before splitting.\n",
        "\n",
        "- Select the feature with the highest Gini impurity reduction as the most informative one.\n",
        "\n",
        "Here's an example code snippet that demonstrates the calculation of Gini impurity for feature selection using the scikit-learn library:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jnDXeInhKVKs",
        "outputId": "df0c6727-fc24-44a2-8d1b-36e1c5544e53"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sepal length (cm): 1.0\n",
            "sepal width (cm): 1.0\n",
            "petal length (cm): 1.0\n",
            "petal width (cm): 1.0\n"
          ]
        }
      ],
      "source": [
        "from sklearn.feature_selection import f_classif\n",
        "from sklearn.datasets import load_iris\n",
        "\n",
        "# Load the Iris dataset as an example\n",
        "data = load_iris()\n",
        "X = data.data  # Features\n",
        "y = data.target  # Target variable\n",
        "\n",
        "# Calculate the Gini impurity using the f_classif function\n",
        "gini_impurity = 1 - f_classif(X, y)[1]  # Gini impurity is calculated as (1 - p-value)\n",
        "\n",
        "# Create a dictionary with feature names and their corresponding Gini impurities\n",
        "feature_gini_impurity = {}\n",
        "for i, feature_name in enumerate(data.feature_names):\n",
        "    feature_gini_impurity[feature_name] = gini_impurity[i]\n",
        "\n",
        "# Sort the features based on their Gini impurities in descending order\n",
        "sorted_features = sorted(feature_gini_impurity.items(), key=lambda x: x[1], reverse=True)\n",
        "\n",
        "# Print the features along with their Gini impurities\n",
        "for feature, impurity in sorted_features:\n",
        "    print(f\"{feature}: {impurity}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "43vYBEvKMz9O"
      },
      "source": [
        "`f_classif` function from scikit-learn to calculate the `Gini impurity` for each feature in the Iris dataset. We subtract the p-value (obtained from the `f_classif` test) from 1 to obtain the Gini impurity."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C74VQYAlK4bJ"
      },
      "source": [
        "# **Mutual Information - Classification: Wine Datase**\n",
        "\n",
        "```python\n",
        "\n",
        "from sklearn.feature_selection import mutual_info_classif\n",
        "\n",
        "# Assuming you have your input features X and target variable y\n",
        "# X should be a 2D array-like object, and y should be a 1D array-like object\n",
        "\n",
        "# Compute the mutual information scores\n",
        "mi_scores = mutual_info_classif(X, y)\n",
        "\n",
        "# Print the mutual information scores for each feature\n",
        "for feature, score in zip(X.columns, mi_scores):\n",
        "    print(f\"Mutual information score for {feature}: {score}\")\n",
        "\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "Mc-WyzuKpfNs"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import os\n",
        "\n",
        "# plt.style.use('ggplot')\n",
        "# sns.set_theme(style='whitegrid')\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PSbkILN4pfNt",
        "outputId": "b7c6d38c-78ce-4dc6-b0b5-70aeb2d31d34"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Wine  Alcohol  Malic.acid   Ash   Acl   Mg  Phenols  Flavanoids  \\\n",
              "0     1    14.23        1.71  2.43  15.6  127     2.80        3.06   \n",
              "1     1    13.20        1.78  2.14  11.2  100     2.65        2.76   \n",
              "2     1    13.16        2.36  2.67  18.6  101     2.80        3.24   \n",
              "3     1    14.37        1.95  2.50  16.8  113     3.85        3.49   \n",
              "4     1    13.24        2.59  2.87  21.0  118     2.80        2.69   \n",
              "\n",
              "   Nonflavanoid.phenols  Proanth  Color.int   Hue    OD  Proline  \n",
              "0                  0.28     2.29       5.64  1.04  3.92     1065  \n",
              "1                  0.26     1.28       4.38  1.05  3.40     1050  \n",
              "2                  0.30     2.81       5.68  1.03  3.17     1185  \n",
              "3                  0.24     2.18       7.80  0.86  3.45     1480  \n",
              "4                  0.39     1.82       4.32  1.04  2.93      735  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c4dd0f2b-8110-406d-a933-ce944ff528c8\">\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",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Wine</th>\n",
              "      <th>Alcohol</th>\n",
              "      <th>Malic.acid</th>\n",
              "      <th>Ash</th>\n",
              "      <th>Acl</th>\n",
              "      <th>Mg</th>\n",
              "      <th>Phenols</th>\n",
              "      <th>Flavanoids</th>\n",
              "      <th>Nonflavanoid.phenols</th>\n",
              "      <th>Proanth</th>\n",
              "      <th>Color.int</th>\n",
              "      <th>Hue</th>\n",
              "      <th>OD</th>\n",
              "      <th>Proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>14.23</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2.43</td>\n",
              "      <td>15.6</td>\n",
              "      <td>127</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.06</td>\n",
              "      <td>0.28</td>\n",
              "      <td>2.29</td>\n",
              "      <td>5.64</td>\n",
              "      <td>1.04</td>\n",
              "      <td>3.92</td>\n",
              "      <td>1065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>13.20</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.14</td>\n",
              "      <td>11.2</td>\n",
              "      <td>100</td>\n",
              "      <td>2.65</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.28</td>\n",
              "      <td>4.38</td>\n",
              "      <td>1.05</td>\n",
              "      <td>3.40</td>\n",
              "      <td>1050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>13.16</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.67</td>\n",
              "      <td>18.6</td>\n",
              "      <td>101</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.24</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.81</td>\n",
              "      <td>5.68</td>\n",
              "      <td>1.03</td>\n",
              "      <td>3.17</td>\n",
              "      <td>1185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>14.37</td>\n",
              "      <td>1.95</td>\n",
              "      <td>2.50</td>\n",
              "      <td>16.8</td>\n",
              "      <td>113</td>\n",
              "      <td>3.85</td>\n",
              "      <td>3.49</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.18</td>\n",
              "      <td>7.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.45</td>\n",
              "      <td>1480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>13.24</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
              "      <td>118</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.82</td>\n",
              "      <td>4.32</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.93</td>\n",
              "      <td>735</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c4dd0f2b-8110-406d-a933-ce944ff528c8')\"\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",
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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",
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              "\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-c4dd0f2b-8110-406d-a933-ce944ff528c8 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-c4dd0f2b-8110-406d-a933-ce944ff528c8');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "wine_url = \"https://gist.githubusercontent.com/tijptjik/9408623/raw/b237fa5848349a14a14e5d4107dc7897c21951f5/wine.csv\"\n",
        "\n",
        "df = pd.read_csv(wine_url)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XghVL6P6pfNu",
        "outputId": "bd59e33a-17bc-4ca6-bb71-86e50c1aea7a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df['Wine'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vi6uFNwcpfNv",
        "outputId": "c0a6d137-44b6-4c6d-e749-410576824999"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 178 entries, 0 to 177\n",
            "Data columns (total 14 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   Wine                  178 non-null    int64  \n",
            " 1   Alcohol               178 non-null    float64\n",
            " 2   Malic.acid            178 non-null    float64\n",
            " 3   Ash                   178 non-null    float64\n",
            " 4   Acl                   178 non-null    float64\n",
            " 5   Mg                    178 non-null    int64  \n",
            " 6   Phenols               178 non-null    float64\n",
            " 7   Flavanoids            178 non-null    float64\n",
            " 8   Nonflavanoid.phenols  178 non-null    float64\n",
            " 9   Proanth               178 non-null    float64\n",
            " 10  Color.int             178 non-null    float64\n",
            " 11  Hue                   178 non-null    float64\n",
            " 12  OD                    178 non-null    float64\n",
            " 13  Proline               178 non-null    int64  \n",
            "dtypes: float64(11), int64(3)\n",
            "memory usage: 19.6 KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "IWcijJJXpfNw"
      },
      "outputs": [],
      "source": [
        "# dependent and independent varible\n",
        "X = df.drop(labels=['Wine'], axis=1)\n",
        "y = df['Wine']\n",
        "\n",
        "# Train test split to avoid overfitting\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test=train_test_split(X, y, test_size=0.3, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "W3tx7__kpfNw",
        "outputId": "f8a815b0-9387-411a-d22e-26d8e6784527"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Alcohol  Malic.acid   Ash   Acl   Mg  Phenols  Flavanoids  \\\n",
              "138    13.49        3.59  2.19  19.5   88     1.62        0.48   \n",
              "104    12.51        1.73  1.98  20.5   85     2.20        1.92   \n",
              "78     12.33        0.99  1.95  14.8  136     1.90        1.85   \n",
              "36     13.28        1.64  2.84  15.5  110     2.60        2.68   \n",
              "93     12.29        2.83  2.22  18.0   88     2.45        2.25   \n",
              "\n",
              "     Nonflavanoid.phenols  Proanth  Color.int   Hue    OD  Proline  \n",
              "138                  0.58     0.88       5.70  0.81  1.82      580  \n",
              "104                  0.32     1.48       2.94  1.04  3.57      672  \n",
              "78                   0.35     2.76       3.40  1.06  2.31      750  \n",
              "36                   0.34     1.36       4.60  1.09  2.78      880  \n",
              "93                   0.25     1.99       2.15  1.15  3.30      290  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a9245ed4-e5fa-487f-a66b-31b748ece79b\">\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",
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              "\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>Alcohol</th>\n",
              "      <th>Malic.acid</th>\n",
              "      <th>Ash</th>\n",
              "      <th>Acl</th>\n",
              "      <th>Mg</th>\n",
              "      <th>Phenols</th>\n",
              "      <th>Flavanoids</th>\n",
              "      <th>Nonflavanoid.phenols</th>\n",
              "      <th>Proanth</th>\n",
              "      <th>Color.int</th>\n",
              "      <th>Hue</th>\n",
              "      <th>OD</th>\n",
              "      <th>Proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>13.49</td>\n",
              "      <td>3.59</td>\n",
              "      <td>2.19</td>\n",
              "      <td>19.5</td>\n",
              "      <td>88</td>\n",
              "      <td>1.62</td>\n",
              "      <td>0.48</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.88</td>\n",
              "      <td>5.70</td>\n",
              "      <td>0.81</td>\n",
              "      <td>1.82</td>\n",
              "      <td>580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>12.51</td>\n",
              "      <td>1.73</td>\n",
              "      <td>1.98</td>\n",
              "      <td>20.5</td>\n",
              "      <td>85</td>\n",
              "      <td>2.20</td>\n",
              "      <td>1.92</td>\n",
              "      <td>0.32</td>\n",
              "      <td>1.48</td>\n",
              "      <td>2.94</td>\n",
              "      <td>1.04</td>\n",
              "      <td>3.57</td>\n",
              "      <td>672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>12.33</td>\n",
              "      <td>0.99</td>\n",
              "      <td>1.95</td>\n",
              "      <td>14.8</td>\n",
              "      <td>136</td>\n",
              "      <td>1.90</td>\n",
              "      <td>1.85</td>\n",
              "      <td>0.35</td>\n",
              "      <td>2.76</td>\n",
              "      <td>3.40</td>\n",
              "      <td>1.06</td>\n",
              "      <td>2.31</td>\n",
              "      <td>750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>13.28</td>\n",
              "      <td>1.64</td>\n",
              "      <td>2.84</td>\n",
              "      <td>15.5</td>\n",
              "      <td>110</td>\n",
              "      <td>2.60</td>\n",
              "      <td>2.68</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.36</td>\n",
              "      <td>4.60</td>\n",
              "      <td>1.09</td>\n",
              "      <td>2.78</td>\n",
              "      <td>880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>12.29</td>\n",
              "      <td>2.83</td>\n",
              "      <td>2.22</td>\n",
              "      <td>18.0</td>\n",
              "      <td>88</td>\n",
              "      <td>2.45</td>\n",
              "      <td>2.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.99</td>\n",
              "      <td>2.15</td>\n",
              "      <td>1.15</td>\n",
              "      <td>3.30</td>\n",
              "      <td>290</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a9245ed4-e5fa-487f-a66b-31b748ece79b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\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",
              "      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-a9245ed4-e5fa-487f-a66b-31b748ece79b 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-a9245ed4-e5fa-487f-a66b-31b748ece79b');\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": 15
        }
      ],
      "source": [
        "X_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "psVpPhQspfNx",
        "outputId": "ee20acf7-9eeb-4392-9a3e-ee4f88e2aa5c",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.43219007, 0.2813662 , 0.10284627, 0.24919978, 0.24866228,\n",
              "       0.38678875, 0.70249515, 0.13239392, 0.39149403, 0.52277144,\n",
              "       0.60936507, 0.51527693, 0.54832643])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "from sklearn.feature_selection import mutual_info_classif\n",
        "\n",
        "# determine the mutual information\n",
        "mutual_info = mutual_info_classif(X_train, y_train)\n",
        "mutual_info"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I1Ji4bN9pfNx",
        "outputId": "5ba33ef1-1ea0-47a5-8ad8-91c571c08df6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Flavanoids              0.702495\n",
              "Hue                     0.609365\n",
              "Proline                 0.548326\n",
              "Color.int               0.522771\n",
              "OD                      0.515277\n",
              "Alcohol                 0.432190\n",
              "Proanth                 0.391494\n",
              "Phenols                 0.386789\n",
              "Malic.acid              0.281366\n",
              "Acl                     0.249200\n",
              "Mg                      0.248662\n",
              "Nonflavanoid.phenols    0.132394\n",
              "Ash                     0.102846\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "mutual_info = pd.Series(mutual_info)\n",
        "mutual_info.index = X_train.columns\n",
        "mutual_info.sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "BTmu8XrWpfNx",
        "outputId": "384848d3-f1a9-4ffa-b3d7-f4689eb6df97"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# the ordered mutual_info values per feature\n",
        "mutual_info_per_feat  = mutual_info.sort_values(ascending=False)\n",
        "\n",
        "# plot\n",
        "mutual_info_per_feat.plot.bar(figsize=(10, 5))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3LzwSkQEpfNy",
        "outputId": "ebe1283e-1a66-4842-ff74-a118ca7ebcc4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Flavanoids', 'Color.int', 'Hue', 'OD', 'Proline'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "from sklearn.feature_selection import SelectKBest\n",
        "\n",
        "#No we Will select the  top 5 important features\n",
        "sel_five_cols = SelectKBest(mutual_info_classif, k=5)\n",
        "sel_five_cols.fit(X_train, y_train)\n",
        "X_train.columns[sel_five_cols.get_support()]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sUP0gKhzU59W"
      },
      "source": [
        "# **Mutual Information - Regression: Housing Price Dataset**\n",
        "\n",
        "\n",
        "```python\n",
        "from sklearn.feature_selection import mutual_info_regression\n",
        "\n",
        "# Assuming you have your input features X and target variable y\n",
        "# X should be a 2D array-like object, and y should be a 1D array-like object\n",
        "\n",
        "# Compute the mutual information scores\n",
        "mi_scores = mutual_info_regression(X, y)\n",
        "\n",
        "# Print the mutual information scores for each feature\n",
        "for feature, score in zip(X.columns, mi_scores):\n",
        "    print(f\"Mutual information score for {feature}: {score}\")\n",
        "\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "-tDRDe8MU59X",
        "outputId": "b39c0a2a-705f-429a-a04c-3f65cb0ee816"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
              "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
              "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
              "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
              "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
              "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
              "\n",
              "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
              "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
              "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
              "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
              "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
              "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
              "\n",
              "  YrSold  SaleType  SaleCondition  SalePrice  \n",
              "0   2008        WD         Normal     208500  \n",
              "1   2007        WD         Normal     181500  \n",
              "2   2008        WD         Normal     223500  \n",
              "3   2006        WD        Abnorml     140000  \n",
              "4   2008        WD         Normal     250000  \n",
              "\n",
              "[5 rows x 81 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0f6e6ed0-032f-43f1-9711-e13cf570d18b\">\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",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>...</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>208500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>181500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>223500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>70</td>\n",
              "      <td>RL</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>WD</td>\n",
              "      <td>Abnorml</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>250000</td>\n",
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          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "url= \"/content/feature-engineering-hacks/_dataset/housing_data.csv\"\n",
        "\n",
        "housing_df = pd.read_csv(url)\n",
        "housing_df.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7zBed51AU59X",
        "outputId": "b085d59d-e2d1-4763-beb2-e9fffb24b145"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1460 entries, 0 to 1459\n",
            "Data columns (total 81 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Id             1460 non-null   int64  \n",
            " 1   MSSubClass     1460 non-null   int64  \n",
            " 2   MSZoning       1460 non-null   object \n",
            " 3   LotFrontage    1201 non-null   float64\n",
            " 4   LotArea        1460 non-null   int64  \n",
            " 5   Street         1460 non-null   object \n",
            " 6   Alley          91 non-null     object \n",
            " 7   LotShape       1460 non-null   object \n",
            " 8   LandContour    1460 non-null   object \n",
            " 9   Utilities      1460 non-null   object \n",
            " 10  LotConfig      1460 non-null   object \n",
            " 11  LandSlope      1460 non-null   object \n",
            " 12  Neighborhood   1460 non-null   object \n",
            " 13  Condition1     1460 non-null   object \n",
            " 14  Condition2     1460 non-null   object \n",
            " 15  BldgType       1460 non-null   object \n",
            " 16  HouseStyle     1460 non-null   object \n",
            " 17  OverallQual    1460 non-null   int64  \n",
            " 18  OverallCond    1460 non-null   int64  \n",
            " 19  YearBuilt      1460 non-null   int64  \n",
            " 20  YearRemodAdd   1460 non-null   int64  \n",
            " 21  RoofStyle      1460 non-null   object \n",
            " 22  RoofMatl       1460 non-null   object \n",
            " 23  Exterior1st    1460 non-null   object \n",
            " 24  Exterior2nd    1460 non-null   object \n",
            " 25  MasVnrType     1452 non-null   object \n",
            " 26  MasVnrArea     1452 non-null   float64\n",
            " 27  ExterQual      1460 non-null   object \n",
            " 28  ExterCond      1460 non-null   object \n",
            " 29  Foundation     1460 non-null   object \n",
            " 30  BsmtQual       1423 non-null   object \n",
            " 31  BsmtCond       1423 non-null   object \n",
            " 32  BsmtExposure   1422 non-null   object \n",
            " 33  BsmtFinType1   1423 non-null   object \n",
            " 34  BsmtFinSF1     1460 non-null   int64  \n",
            " 35  BsmtFinType2   1422 non-null   object \n",
            " 36  BsmtFinSF2     1460 non-null   int64  \n",
            " 37  BsmtUnfSF      1460 non-null   int64  \n",
            " 38  TotalBsmtSF    1460 non-null   int64  \n",
            " 39  Heating        1460 non-null   object \n",
            " 40  HeatingQC      1460 non-null   object \n",
            " 41  CentralAir     1460 non-null   object \n",
            " 42  Electrical     1459 non-null   object \n",
            " 43  1stFlrSF       1460 non-null   int64  \n",
            " 44  2ndFlrSF       1460 non-null   int64  \n",
            " 45  LowQualFinSF   1460 non-null   int64  \n",
            " 46  GrLivArea      1460 non-null   int64  \n",
            " 47  BsmtFullBath   1460 non-null   int64  \n",
            " 48  BsmtHalfBath   1460 non-null   int64  \n",
            " 49  FullBath       1460 non-null   int64  \n",
            " 50  HalfBath       1460 non-null   int64  \n",
            " 51  BedroomAbvGr   1460 non-null   int64  \n",
            " 52  KitchenAbvGr   1460 non-null   int64  \n",
            " 53  KitchenQual    1460 non-null   object \n",
            " 54  TotRmsAbvGrd   1460 non-null   int64  \n",
            " 55  Functional     1460 non-null   object \n",
            " 56  Fireplaces     1460 non-null   int64  \n",
            " 57  FireplaceQu    770 non-null    object \n",
            " 58  GarageType     1379 non-null   object \n",
            " 59  GarageYrBlt    1379 non-null   float64\n",
            " 60  GarageFinish   1379 non-null   object \n",
            " 61  GarageCars     1460 non-null   int64  \n",
            " 62  GarageArea     1460 non-null   int64  \n",
            " 63  GarageQual     1379 non-null   object \n",
            " 64  GarageCond     1379 non-null   object \n",
            " 65  PavedDrive     1460 non-null   object \n",
            " 66  WoodDeckSF     1460 non-null   int64  \n",
            " 67  OpenPorchSF    1460 non-null   int64  \n",
            " 68  EnclosedPorch  1460 non-null   int64  \n",
            " 69  3SsnPorch      1460 non-null   int64  \n",
            " 70  ScreenPorch    1460 non-null   int64  \n",
            " 71  PoolArea       1460 non-null   int64  \n",
            " 72  PoolQC         7 non-null      object \n",
            " 73  Fence          281 non-null    object \n",
            " 74  MiscFeature    54 non-null     object \n",
            " 75  MiscVal        1460 non-null   int64  \n",
            " 76  MoSold         1460 non-null   int64  \n",
            " 77  YrSold         1460 non-null   int64  \n",
            " 78  SaleType       1460 non-null   object \n",
            " 79  SaleCondition  1460 non-null   object \n",
            " 80  SalePrice      1460 non-null   int64  \n",
            "dtypes: float64(3), int64(35), object(43)\n",
            "memory usage: 924.0+ KB\n"
          ]
        }
      ],
      "source": [
        "housing_df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gN72Ujx3U59Y"
      },
      "source": [
        "taking only the integer and float datatypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_kAb8JwRU59Y",
        "outputId": "926e3274-aa2e-40f5-e00b-92827b9a87ee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                 0\n",
              "MSSubClass         0\n",
              "MSZoning           0\n",
              "LotFrontage      259\n",
              "LotArea            0\n",
              "                ... \n",
              "MoSold             0\n",
              "YrSold             0\n",
              "SaleType           0\n",
              "SaleCondition      0\n",
              "SalePrice          0\n",
              "Length: 81, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "housing_df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
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        "id": "ZXdd8qqFU59Y",
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              "        Id  MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  \\\n",
              "0        1          60         65.0     8450            7            5   \n",
              "1        2          20         80.0     9600            6            8   \n",
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              "4        5          60         84.0    14260            8            5   \n",
              "...    ...         ...          ...      ...          ...          ...   \n",
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              "1458  1459          20         68.0     9717            5            6   \n",
              "1459  1460          20         75.0     9937            5            6   \n",
              "\n",
              "      YearBuilt  YearRemodAdd  MasVnrArea  BsmtFinSF1  ...  WoodDeckSF  \\\n",
              "0          2003          2003       196.0         706  ...           0   \n",
              "1          1976          1976         0.0         978  ...         298   \n",
              "2          2001          2002       162.0         486  ...           0   \n",
              "3          1915          1970         0.0         216  ...           0   \n",
              "4          2000          2000       350.0         655  ...         192   \n",
              "...         ...           ...         ...         ...  ...         ...   \n",
              "1455       1999          2000         0.0           0  ...           0   \n",
              "1456       1978          1988       119.0         790  ...         349   \n",
              "1457       1941          2006         0.0         275  ...           0   \n",
              "1458       1950          1996         0.0          49  ...         366   \n",
              "1459       1965          1965         0.0         830  ...         736   \n",
              "\n",
              "      OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  \\\n",
              "0              61              0          0            0         0        0   \n",
              "1               0              0          0            0         0        0   \n",
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              "4              84              0          0            0         0        0   \n",
              "...           ...            ...        ...          ...       ...      ...   \n",
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              "1458            0            112          0            0         0        0   \n",
              "1459           68              0          0            0         0        0   \n",
              "\n",
              "      MoSold  YrSold  SalePrice  \n",
              "0          2    2008     208500  \n",
              "1          5    2007     181500  \n",
              "2          9    2008     223500  \n",
              "3          2    2006     140000  \n",
              "4         12    2008     250000  \n",
              "...      ...     ...        ...  \n",
              "1455       8    2007     175000  \n",
              "1456       2    2010     210000  \n",
              "1457       5    2010     266500  \n",
              "1458       4    2010     142125  \n",
              "1459       6    2008     147500  \n",
              "\n",
              "[1460 rows x 38 columns]"
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              "      <td>1976</td>\n",
              "      <td>1976</td>\n",
              "      <td>0.0</td>\n",
              "      <td>978</td>\n",
              "      <td>...</td>\n",
              "      <td>298</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>181500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>60</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2001</td>\n",
              "      <td>2002</td>\n",
              "      <td>162.0</td>\n",
              "      <td>486</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>223500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>70</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1915</td>\n",
              "      <td>1970</td>\n",
              "      <td>0.0</td>\n",
              "      <td>216</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>272</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>140000</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>60</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>350.0</td>\n",
              "      <td>655</td>\n",
              "      <td>...</td>\n",
              "      <td>192</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "    <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>1455</th>\n",
              "      <td>1456</td>\n",
              "      <td>60</td>\n",
              "      <td>62.0</td>\n",
              "      <td>7917</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>1999</td>\n",
              "      <td>2000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>2007</td>\n",
              "      <td>175000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1456</th>\n",
              "      <td>1457</td>\n",
              "      <td>20</td>\n",
              "      <td>85.0</td>\n",
              "      <td>13175</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1978</td>\n",
              "      <td>1988</td>\n",
              "      <td>119.0</td>\n",
              "      <td>790</td>\n",
              "      <td>...</td>\n",
              "      <td>349</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2010</td>\n",
              "      <td>210000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1457</th>\n",
              "      <td>1458</td>\n",
              "      <td>70</td>\n",
              "      <td>66.0</td>\n",
              "      <td>9042</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>1941</td>\n",
              "      <td>2006</td>\n",
              "      <td>0.0</td>\n",
              "      <td>275</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2500</td>\n",
              "      <td>5</td>\n",
              "      <td>2010</td>\n",
              "      <td>266500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458</th>\n",
              "      <td>1459</td>\n",
              "      <td>20</td>\n",
              "      <td>68.0</td>\n",
              "      <td>9717</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>1950</td>\n",
              "      <td>1996</td>\n",
              "      <td>0.0</td>\n",
              "      <td>49</td>\n",
              "      <td>...</td>\n",
              "      <td>366</td>\n",
              "      <td>0</td>\n",
              "      <td>112</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2010</td>\n",
              "      <td>142125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1459</th>\n",
              "      <td>1460</td>\n",
              "      <td>20</td>\n",
              "      <td>75.0</td>\n",
              "      <td>9937</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>1965</td>\n",
              "      <td>1965</td>\n",
              "      <td>0.0</td>\n",
              "      <td>830</td>\n",
              "      <td>...</td>\n",
              "      <td>736</td>\n",
              "      <td>68</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2008</td>\n",
              "      <td>147500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1460 rows × 38 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-17a91fb9-bc2a-46c2-8681-761e6370c2da')\"\n",
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            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "num_lst = [\"int16\", \"int32\", \"int64\", \"float16\", \"float32\", \"float64\"]\n",
        "num_cols = list(housing_df.select_dtypes(include=num_lst).columns)\n",
        "housing_df = housing_df[num_cols]\n",
        "housing_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fqU9xVhCU59Y",
        "outputId": "79258c67-a74e-49b4-f825-349afad3e2b2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                 0\n",
              "MSSubClass         0\n",
              "LotFrontage      259\n",
              "LotArea            0\n",
              "OverallQual        0\n",
              "OverallCond        0\n",
              "YearBuilt          0\n",
              "YearRemodAdd       0\n",
              "MasVnrArea         8\n",
              "BsmtFinSF1         0\n",
              "BsmtFinSF2         0\n",
              "BsmtUnfSF          0\n",
              "TotalBsmtSF        0\n",
              "1stFlrSF           0\n",
              "2ndFlrSF           0\n",
              "LowQualFinSF       0\n",
              "GrLivArea          0\n",
              "BsmtFullBath       0\n",
              "BsmtHalfBath       0\n",
              "FullBath           0\n",
              "HalfBath           0\n",
              "BedroomAbvGr       0\n",
              "KitchenAbvGr       0\n",
              "TotRmsAbvGrd       0\n",
              "Fireplaces         0\n",
              "GarageYrBlt       81\n",
              "GarageCars         0\n",
              "GarageArea         0\n",
              "WoodDeckSF         0\n",
              "OpenPorchSF        0\n",
              "EnclosedPorch      0\n",
              "3SsnPorch          0\n",
              "ScreenPorch        0\n",
              "PoolArea           0\n",
              "MiscVal            0\n",
              "MoSold             0\n",
              "YrSold             0\n",
              "SalePrice          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "housing_df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "16kuJ681U59Z",
        "outputId": "290e8aae-5b0c-4f2f-e5b4-3675441b5fcc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
              "0             60         65.0     8450            7            5       2003   \n",
              "1             20         80.0     9600            6            8       1976   \n",
              "2             60         68.0    11250            7            5       2001   \n",
              "3             70         60.0     9550            7            5       1915   \n",
              "4             60         84.0    14260            8            5       2000   \n",
              "...          ...          ...      ...          ...          ...        ...   \n",
              "1455          60         62.0     7917            6            5       1999   \n",
              "1456          20         85.0    13175            6            6       1978   \n",
              "1457          70         66.0     9042            7            9       1941   \n",
              "1458          20         68.0     9717            5            6       1950   \n",
              "1459          20         75.0     9937            5            6       1965   \n",
              "\n",
              "      YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  ...  WoodDeckSF  \\\n",
              "0             2003       196.0         706           0  ...           0   \n",
              "1             1976         0.0         978           0  ...         298   \n",
              "2             2002       162.0         486           0  ...           0   \n",
              "3             1970         0.0         216           0  ...           0   \n",
              "4             2000       350.0         655           0  ...         192   \n",
              "...            ...         ...         ...         ...  ...         ...   \n",
              "1455          2000         0.0           0           0  ...           0   \n",
              "1456          1988       119.0         790         163  ...         349   \n",
              "1457          2006         0.0         275           0  ...           0   \n",
              "1458          1996         0.0          49        1029  ...         366   \n",
              "1459          1965         0.0         830         290  ...         736   \n",
              "\n",
              "      OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  \\\n",
              "0              61              0          0            0         0        0   \n",
              "1               0              0          0            0         0        0   \n",
              "2              42              0          0            0         0        0   \n",
              "3              35            272          0            0         0        0   \n",
              "4              84              0          0            0         0        0   \n",
              "...           ...            ...        ...          ...       ...      ...   \n",
              "1455           40              0          0            0         0        0   \n",
              "1456            0              0          0            0         0        0   \n",
              "1457           60              0          0            0         0     2500   \n",
              "1458            0            112          0            0         0        0   \n",
              "1459           68              0          0            0         0        0   \n",
              "\n",
              "      MoSold  YrSold  SalePrice  \n",
              "0          2    2008     208500  \n",
              "1          5    2007     181500  \n",
              "2          9    2008     223500  \n",
              "3          2    2006     140000  \n",
              "4         12    2008     250000  \n",
              "...      ...     ...        ...  \n",
              "1455       8    2007     175000  \n",
              "1456       2    2010     210000  \n",
              "1457       5    2010     266500  \n",
              "1458       4    2010     142125  \n",
              "1459       6    2008     147500  \n",
              "\n",
              "[1460 rows x 37 columns]"
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              "      <td>...</td>\n",
              "      <td>298</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>181500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>60</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2001</td>\n",
              "      <td>2002</td>\n",
              "      <td>162.0</td>\n",
              "      <td>486</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>223500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>70</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1915</td>\n",
              "      <td>1970</td>\n",
              "      <td>0.0</td>\n",
              "      <td>216</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>272</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>60</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>350.0</td>\n",
              "      <td>655</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>192</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "    <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>1455</th>\n",
              "      <td>60</td>\n",
              "      <td>62.0</td>\n",
              "      <td>7917</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>1999</td>\n",
              "      <td>2000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>2007</td>\n",
              "      <td>175000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1456</th>\n",
              "      <td>20</td>\n",
              "      <td>85.0</td>\n",
              "      <td>13175</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1978</td>\n",
              "      <td>1988</td>\n",
              "      <td>119.0</td>\n",
              "      <td>790</td>\n",
              "      <td>163</td>\n",
              "      <td>...</td>\n",
              "      <td>349</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2010</td>\n",
              "      <td>210000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1457</th>\n",
              "      <td>70</td>\n",
              "      <td>66.0</td>\n",
              "      <td>9042</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>1941</td>\n",
              "      <td>2006</td>\n",
              "      <td>0.0</td>\n",
              "      <td>275</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2500</td>\n",
              "      <td>5</td>\n",
              "      <td>2010</td>\n",
              "      <td>266500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458</th>\n",
              "      <td>20</td>\n",
              "      <td>68.0</td>\n",
              "      <td>9717</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>1950</td>\n",
              "      <td>1996</td>\n",
              "      <td>0.0</td>\n",
              "      <td>49</td>\n",
              "      <td>1029</td>\n",
              "      <td>...</td>\n",
              "      <td>366</td>\n",
              "      <td>0</td>\n",
              "      <td>112</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2010</td>\n",
              "      <td>142125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1459</th>\n",
              "      <td>20</td>\n",
              "      <td>75.0</td>\n",
              "      <td>9937</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>1965</td>\n",
              "      <td>1965</td>\n",
              "      <td>0.0</td>\n",
              "      <td>830</td>\n",
              "      <td>290</td>\n",
              "      <td>...</td>\n",
              "      <td>736</td>\n",
              "      <td>68</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2008</td>\n",
              "      <td>147500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1460 rows × 37 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-25a189c8-88f2-400b-a8b2-688be3ea6130')\"\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",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-25a189c8-88f2-400b-a8b2-688be3ea6130 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-25a189c8-88f2-400b-a8b2-688be3ea6130');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "# droping the id column\n",
        "housing_df = housing_df.drop('Id', axis=1)\n",
        "housing_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oi7Gjr7GU59Z"
      },
      "source": [
        "Regression models are commonly used in scenarios where the prediction target is a continuous value, such as predicting housing prices, stock market prices, or in this case, the sales price of a product.\n",
        "\n",
        " so this is a regression problem since the output feature is `SalesPrice`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "KQ6j74B5U59Z"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Performing the train_train_split to avoid the overfitting\n",
        "X = housing_df.drop(labels=['SalePrice'], axis=1)\n",
        "y = housing_df[\"SalePrice\"]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
        "                                                    test_size=0.3,\n",
        "                                                    random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-jM835WZU59u",
        "outputId": "03207e66-5976-4f2a-95f9-292c7965d601"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MSSubClass         0\n",
              "LotFrontage      190\n",
              "LotArea            0\n",
              "OverallQual        0\n",
              "OverallCond        0\n",
              "YearBuilt          0\n",
              "YearRemodAdd       0\n",
              "MasVnrArea         3\n",
              "BsmtFinSF1         0\n",
              "BsmtFinSF2         0\n",
              "BsmtUnfSF          0\n",
              "TotalBsmtSF        0\n",
              "1stFlrSF           0\n",
              "2ndFlrSF           0\n",
              "LowQualFinSF       0\n",
              "GrLivArea          0\n",
              "BsmtFullBath       0\n",
              "BsmtHalfBath       0\n",
              "FullBath           0\n",
              "HalfBath           0\n",
              "BedroomAbvGr       0\n",
              "KitchenAbvGr       0\n",
              "TotRmsAbvGrd       0\n",
              "Fireplaces         0\n",
              "GarageYrBlt       54\n",
              "GarageCars         0\n",
              "GarageArea         0\n",
              "WoodDeckSF         0\n",
              "OpenPorchSF        0\n",
              "EnclosedPorch      0\n",
              "3SsnPorch          0\n",
              "ScreenPorch        0\n",
              "PoolArea           0\n",
              "MiscVal            0\n",
              "MoSold             0\n",
              "YrSold             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "X_train.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gWmj0s07U59v",
        "outputId": "70c881dc-5aa3-43bf-dd27-6a3bc4580789"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2.93700954e-01, 1.95479610e-01, 1.52345970e-01, 5.30071703e-01,\n",
              "       1.05665820e-01, 3.19521953e-01, 2.55651777e-01, 8.52054745e-02,\n",
              "       1.37707008e-01, 1.12030623e-02, 1.50193596e-01, 3.15843339e-01,\n",
              "       2.87619878e-01, 2.19659627e-01, 1.63864840e-04, 4.51163296e-01,\n",
              "       3.11248766e-02, 8.31241870e-04, 2.44983710e-01, 6.57203744e-02,\n",
              "       4.57500369e-02, 2.95791843e-02, 1.58854017e-01, 1.57008466e-01,\n",
              "       2.69941142e-01, 3.60055345e-01, 3.42128170e-01, 7.79819453e-02,\n",
              "       1.50016662e-01, 2.71544470e-02, 0.00000000e+00, 1.38667287e-02,\n",
              "       6.51434352e-03, 0.00000000e+00, 3.64075290e-03, 1.97421775e-02])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "from sklearn.feature_selection import mutual_info_regression\n",
        "\n",
        "# Fill missing values with the mean\n",
        "X_train_filled = X_train.fillna(X_train.mean())\n",
        "\n",
        "# Determine the mutual information\n",
        "mutual_info = mutual_info_regression(X_train_filled, y_train)\n",
        "mutual_info\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sVZx55ASU59v",
        "outputId": "dc246aa6-e33e-4dd8-eba1-31111392994b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OverallQual      0.530072\n",
              "GrLivArea        0.451163\n",
              "GarageCars       0.360055\n",
              "GarageArea       0.342128\n",
              "YearBuilt        0.319522\n",
              "TotalBsmtSF      0.315843\n",
              "MSSubClass       0.293701\n",
              "1stFlrSF         0.287620\n",
              "GarageYrBlt      0.269941\n",
              "YearRemodAdd     0.255652\n",
              "FullBath         0.244984\n",
              "2ndFlrSF         0.219660\n",
              "LotFrontage      0.195480\n",
              "TotRmsAbvGrd     0.158854\n",
              "Fireplaces       0.157008\n",
              "LotArea          0.152346\n",
              "BsmtUnfSF        0.150194\n",
              "OpenPorchSF      0.150017\n",
              "BsmtFinSF1       0.137707\n",
              "OverallCond      0.105666\n",
              "MasVnrArea       0.085205\n",
              "WoodDeckSF       0.077982\n",
              "HalfBath         0.065720\n",
              "BedroomAbvGr     0.045750\n",
              "BsmtFullBath     0.031125\n",
              "KitchenAbvGr     0.029579\n",
              "EnclosedPorch    0.027154\n",
              "YrSold           0.019742\n",
              "ScreenPorch      0.013867\n",
              "BsmtFinSF2       0.011203\n",
              "PoolArea         0.006514\n",
              "MoSold           0.003641\n",
              "BsmtHalfBath     0.000831\n",
              "LowQualFinSF     0.000164\n",
              "MiscVal          0.000000\n",
              "3SsnPorch        0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "# Convert mutual_info to a Pandas Series\n",
        "mutual_info = pd.Series(mutual_info)\n",
        "\n",
        "# Assign feature names as the index\n",
        "mutual_info.index = X_train.columns\n",
        "\n",
        "# Sort mutual_info values in ascending order\n",
        "mutual_info.sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "XOuIrTNqU59w",
        "outputId": "abebd2cf-f80e-4923-bd43-943e13e2deeb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# ploting the mutual_info\n",
        "plt.figure(figsize=(15, 5))\n",
        "mutual_info.sort_values(ascending=False).plot(kind='bar')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_E58HWHEU59w",
        "outputId": "5870d08f-5e48-4483-c561-baf6b934cbe9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['MSSubClass', 'OverallQual', 'YearBuilt', 'TotalBsmtSF', 'GrLivArea',\n",
              "       'GarageCars', 'GarageArea'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "# Importing the necessary modules for feature selection\n",
        "from sklearn.feature_selection import SelectPercentile\n",
        "from sklearn.feature_selection import mutual_info_regression\n",
        "\n",
        "# Initializing the feature selector object using mutual_info_regression as the scoring function and setting the percentile to 20\n",
        "selector = SelectPercentile(mutual_info_regression, percentile=20)\n",
        "\n",
        "# Fitting the selector object to the training data to identify the top features based on mutual information\n",
        "# X_train is filled with the mean values to handle missing data\n",
        "selector.fit(X_train.fillna(X_train.mean()), y_train)\n",
        "\n",
        "# Retrieving the column names of the selected features identified by the selector object\n",
        "X_train.columns[selector.get_support()]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h07YQJLmU59w"
      },
      "source": [
        "Selecting the top `20 percentile` means we want to choose the subset of features that are among the top 20% most informative or relevant for predicting the target variable. It is a way to prioritize the features that have the highest mutual information scores or statistical relevance with the target variable in the context of feature selection."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vP2sf4hRpfNy"
      },
      "source": [
        "#### **Difference Between Information Gain And Mutual Information**\n",
        "\n",
        "\n",
        "$$I(X ; Y) = H(X) - H(X | Y)$$\n",
        "and\n",
        "$$IG(S, a) = H(S) - H(S | a)$$\n",
        "\n",
        "As such, mutual information is sometimes used as a synonym for\n",
        "information gain. Technically, they calculate the same quantity\n",
        "if applied to the same data.\n",
        "\n",
        "\n",
        "Mutual information $(I(X; Y))$ measures the amount of shared information between two variables, `X` and `Y`. It tells us how much knowing the value of one variable reduces uncertainty about the other variable.\n",
        "\n",
        "Information gain $(IG(S, a))$ is used in machine learning and decision trees to assess the usefulness of a feature (attribute) in predicting a target variable. It quantifies the reduction in uncertainty about the target variable achieved by splitting the data based on a specific feature.\n",
        "\n",
        "While mutual information and information gain may yield the same result when applied to the same data, they serve different purposes. Mutual information focuses on measuring the dependence between variables, while information gain is used for feature selection in machine learning."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0d5DglEwpfNz"
      },
      "source": [
        "### **Comparion of Univariate And Mutual Information**\n",
        "\n",
        "Comparison of `F-test` and `Mutual Information`?\n",
        "\n",
        "The `F-test` and `Mutual Information` are both **statistical measures** used to `analyze relationships between variables`, but they have different applications and interpretations.\n",
        "\n",
        "- The `F-test` is used to compare the variances between two or more groups or samples. It determines whether the means of these groups are significantly different from each other. The F-test calculates the ratio of the variances between groups and within groups, and based on this ratio, it determines the statistical significance of the differences. The F-test is commonly used in analysis of variance (ANOVA) to test for differences in means across multiple groups.\n",
        "\n",
        "- On the other hand, `Mutual Information` measures the dependence or information shared between two variables. It quantifies the reduction in uncertainty about one variable when the value of the other variable is known. Mutual Information is often used in feature selection and information theory. It helps identify the relevance or amount of information one variable provides about another.\n",
        "\n",
        "\n",
        "\n",
        "|     | F-test| Mutual Information |\n",
        "| --- | --------------------------------------------- | ---------------------------------------------- |\n",
        "| `Use` | Compares variances between groups              | Measures dependence between variables           |\n",
        "| `Application` | Testing differences in means across groups   | Feature selection, information theory           |\n",
        "| `Interpretation` | Determines statistical significance of differences | Quantifies shared information or dependence |\n",
        "| `Focus` | Group comparisons                              | Information content and relevance between variables |\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "z8rF87WsLwFb",
        "outputId": "8aa6bc64-60dc-448a-c4a3-8dec367b468b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x300 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.feature_selection import f_regression, mutual_info_regression\n",
        "\n",
        "np.random.seed(0)\n",
        "X = np.random.rand(1000, 3)\n",
        "y = X[:, 0] + np.sin(6 * np.pi * X[:, 1]) + 0.1 * np.random.randn(1000)\n",
        "\n",
        "f_test, _ = f_regression(X, y)\n",
        "f_test /= np.max(f_test)\n",
        "\n",
        "mi = mutual_info_regression(X, y)\n",
        "mi /= np.max(mi)\n",
        "\n",
        "plt.figure(figsize=(10, 3))\n",
        "for i in range(3):\n",
        "    plt.subplot(1, 3, i + 1)\n",
        "    plt.scatter(X[:, i], y, edgecolor=\"black\", s=20)\n",
        "    plt.xlabel(\"$x_{}$\".format(i + 1), fontsize=14)\n",
        "    if i == 0:\n",
        "        plt.ylabel(\"$y$\", fontsize=14)\n",
        "    plt.title(\"F-test={:.2f}, MI={:.2f}\".format(f_test[i], mi[i]), fontsize=16)\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.13"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}