ML_LAB4_21171.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OEeVDAB-fsa_"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.tree import DecisionTreeClassifier #to build Decision Tree Classifier\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder   #to encode all the categorical and numerical values\n",
        "from sklearn import tree\n",
        "import matplotlib.pyplot as plt #to visualize the tree\n",
        "from sklearn.model_selection import train_test_split #to split the data into training and testing data\n",
        "from sklearn.tree import DecisionTreeClassifier,plot_tree\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score,classification_report  #to build a classification report"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WivkNw3PfsbD"
      },
      "source": [
        "A1. For the data table provided below, calculate the entropy associated with each attribute / feature at the root node. Using this information, identify the first feature that you’d select for constructing the decision tree. Use Information Gain as the impurity measure to identify the root node.‘buys_computer’ is the class label."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vRdRT_iVfsbF",
        "outputId": "d9ec1c84-bd80-483c-d4fc-7768ff264908"
      },
      "outputs": [
        {
          "data": {
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              "\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>age</th>\n",
              "      <th>income</th>\n",
              "      <th>student</th>\n",
              "      <th>credit_rating</th>\n",
              "      <th>buys_compter</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>&lt;=30</td>\n",
              "      <td>high</td>\n",
              "      <td>no</td>\n",
              "      <td>fair</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>&lt;=30</td>\n",
              "      <td>high</td>\n",
              "      <td>no</td>\n",
              "      <td>excellent</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31..40</td>\n",
              "      <td>high</td>\n",
              "      <td>no</td>\n",
              "      <td>fair</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>&gt;40</td>\n",
              "      <td>medium</td>\n",
              "      <td>no</td>\n",
              "      <td>fair</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>&gt;40</td>\n",
              "      <td>low</td>\n",
              "      <td>yes</td>\n",
              "      <td>fair</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>&gt;40</td>\n",
              "      <td>low</td>\n",
              "      <td>yes</td>\n",
              "      <td>excellent</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>31..40</td>\n",
              "      <td>low</td>\n",
              "      <td>yes</td>\n",
              "      <td>excellent</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>&lt;=30</td>\n",
              "      <td>medium</td>\n",
              "      <td>no</td>\n",
              "      <td>fair</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>&lt;=30</td>\n",
              "      <td>low</td>\n",
              "      <td>yes</td>\n",
              "      <td>fair</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>&gt;40</td>\n",
              "      <td>medium</td>\n",
              "      <td>yes</td>\n",
              "      <td>fair</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>&lt;=30</td>\n",
              "      <td>medium</td>\n",
              "      <td>yes</td>\n",
              "      <td>excellent</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>31..40</td>\n",
              "      <td>medium</td>\n",
              "      <td>no</td>\n",
              "      <td>excellent</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>31..40</td>\n",
              "      <td>high</td>\n",
              "      <td>yes</td>\n",
              "      <td>fair</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>&gt;40</td>\n",
              "      <td>medium</td>\n",
              "      <td>no</td>\n",
              "      <td>excellent</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       age  income student credit_rating buys_compter\n",
              "0     <=30    high      no          fair           no\n",
              "1     <=30    high      no     excellent           no\n",
              "2   31..40    high      no          fair          yes\n",
              "3      >40  medium      no          fair          yes\n",
              "4      >40     low     yes          fair          yes\n",
              "5      >40     low     yes     excellent           no\n",
              "6   31..40     low     yes     excellent          yes\n",
              "7     <=30  medium      no          fair           no\n",
              "8     <=30     low     yes          fair          yes\n",
              "9      >40  medium     yes          fair          yes\n",
              "10    <=30  medium     yes     excellent          yes\n",
              "11  31..40  medium      no     excellent          yes\n",
              "12  31..40    high     yes          fair          yes\n",
              "13     >40  medium      no     excellent           no"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data1 = pd.read_excel(r\"C:\\Amrita cse\\5th Semester\\Machine Learning\\Lab\\Lab 4\\lab4_data.xlsx\")\n",
        "df = pd.DataFrame(data1)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "66QXrgDNfsbG",
        "outputId": "c23a3108-fe26-4573-c042-d90d4ddf0966"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Root Entropy: 0.9402859586706311\n",
            "Entropy for age: 0.6935361388961918\n",
            "Entropy for income: 0.9110633930116763\n",
            "Entropy for student: 0.7884504573082896\n",
            "Entropy for credit_rating: 0.8921589282623617\n",
            "Entropy for buys_compter: 0.0\n"
          ]
        }
      ],
      "source": [
        "#CALCULATING ENTROPY OF EACH FEATURE\n",
        "def calculate_entropy(data):\n",
        "    # Count the occurrences of each class\n",
        "    classCounts = data['buys_compter'].value_counts()\n",
        "    # Calculate the entropy\n",
        "    entropy = 0\n",
        "    total_samples = len(data)\n",
        "    for count in classCounts:\n",
        "        probability = count / total_samples\n",
        "        entropy = entropy - probability * np.log2(probability)\n",
        "    return entropy\n",
        "\n",
        "def calculate_root_entropy(data, features):\n",
        "    root_entropy = calculate_entropy(data)\n",
        "    feature_entropies = {}\n",
        "    for feature in features:\n",
        "        unique_values = data[feature].unique()\n",
        "        feature_entropy = 0\n",
        "        for value in unique_values:\n",
        "            subset = data[data[feature] == value]\n",
        "            feature_entropy += (len(subset) / len(data)) * calculate_entropy(subset)\n",
        "        feature_entropies[feature] = feature_entropy\n",
        "\n",
        "    return root_entropy, feature_entropies\n",
        "# Specify the list of features (attributes)\n",
        "features = ['age', 'income', 'student','credit_rating','buys_compter']\n",
        "# Calculate root entropy and entropies for each feature\n",
        "root_entropy, feature_entropies = calculate_root_entropy(df, features)\n",
        "# Print the results\n",
        "print(f\"Root Entropy: {root_entropy}\")\n",
        "for feature, entropy in feature_entropies.items():\n",
        "    print(f\"Entropy for {feature}: {entropy}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RZV-7NWpfsbH",
        "outputId": "76803fd2-cee4-4d16-f6cc-a29a016851bd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Information Gain for age: 0.2467\n",
            "Information Gain for income: 0.0292\n",
            "Information Gain for student: 0.1518\n",
            "Information Gain for credit_rating: 0.0481\n",
            "Information Gain for buys_compter: 0.9403\n",
            "The root node is: buys_compter with information gain 0.9403\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Function to calculate entropy\n",
        "def calculate_entropy(data):\n",
        "    class_counts = data['buys_compter'].value_counts()\n",
        "    entropy = 0\n",
        "    total_samples = len(data)\n",
        "    for count in class_counts:\n",
        "        probability = count / total_samples\n",
        "        entropy -= probability * np.log2(probability)\n",
        "    return entropy\n",
        "\n",
        "# Function to calculate information gain\n",
        "def calculate_information_gain(root_entropy, feature_entropy):\n",
        "    information_gain = root_entropy - feature_entropy\n",
        "    return information_gain\n",
        "\n",
        "df = pd.DataFrame(data1)\n",
        "\n",
        "# Calculate root entropy\n",
        "root_entropy = calculate_entropy(df)\n",
        "\n",
        "# Calculate feature entropies\n",
        "features = ['age', 'income', 'student', 'credit_rating']\n",
        "information_gains = {}\n",
        "\n",
        "for feature in features:\n",
        "    unique_values = df[feature].unique()\n",
        "    feature_entropy = 0\n",
        "    for value in unique_values:\n",
        "        subset = df[df[feature] == value]\n",
        "        feature_entropy += (len(subset) / len(df)) * calculate_entropy(subset)\n",
        "    feature_entropies[feature] = feature_entropy\n",
        "    information_gains[feature] = calculate_information_gain(root_entropy, feature_entropy)\n",
        "\n",
        "# Calculate information gain for each feature\n",
        "information_gains = {feature: calculate_information_gain(root_entropy, entropy) for feature, entropy in feature_entropies.items()}\n",
        "\n",
        "# Print the information gains\n",
        "for feature, gain in information_gains.items():\n",
        "    print(f\"Information Gain for {feature}: {gain:.4f}\")\n",
        "\n",
        "# Select the feature with the highest information gain as the root node\n",
        "root_node = max(information_gains, key=information_gains.get)\n",
        "\n",
        "print(f\"The root node is: {root_node} with information gain {information_gains[root_node]:.4f}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zvJFJBTpfsbI"
      },
      "source": [
        "A2. Create a Decision Tree for the above data. Get the depth of the constructed tree"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "se_LtjWGfsbI",
        "outputId": "b8333ef3-1d00-4a39-b59c-991bea0c9dd0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The accuracy of train model is:  100.0 %\n",
            "The accuracy of test model is:  33.33333333333333 %\n",
            "The depth of the tree is:  5\n"
          ]
        }
      ],
      "source": [
        "#splitting the data into features and target variables\n",
        "#splitting the dataset into features is known as independent variables\n",
        "#target variables is known as variable or label\n",
        "#this is typically used to model training and model evaluation that helps in the predictions of the model\n",
        "X = df.drop('buys_compter',axis=1) #axis=1 is to access the columns of the dataset\n",
        "y = df['buys_compter']\n",
        "\n",
        "xEncoded = pd.get_dummies(X,columns=['age', 'income', 'student', 'credit_rating']) #pd.get_dummies is used to do one-hot encoding in python\n",
        "#here one-hot encoding is performed to convert the categorical values into binary values\n",
        "\n",
        "#splitting of code into train and set using train_test_split\n",
        "X_train,X_test,y_train,y_test = train_test_split(xEncoded,y,test_size=0.2)\n",
        "\n",
        "#creating and fitting the decsion tree classifier. Using the training values to make the decision tree\n",
        "model = DecisionTreeClassifier()\n",
        "model.fit(X_train,y_train)\n",
        "\n",
        "\n",
        "RequiredAccuracyTrain = model.score(X_train,y_train)\n",
        "print(\"The accuracy of train model is: \",RequiredAccuracyTrain * 100,\"%\" )\n",
        "\n",
        "RequiredAccuracyTest = model.score(X_test,y_test)\n",
        "print(\"The accuracy of test model is: \",RequiredAccuracyTest * 100,\"%\" )\n",
        "\n",
        "print(\"The depth of the tree is: \",model.get_depth())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yw740hJGfsbK"
      },
      "source": [
        "A3. Visualize the constructed tree with plot_tree() command."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bkhrsWdQfsbK",
        "outputId": "b762be97-5d3d-49ae-e91c-16ce6cf6b1f2"
      },
      "outputs": [
        {
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            "text/plain": [
              "<Figure size 9000x4000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(90,40))\n",
        "plot_tree(model,filled=True)\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "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": "3.10.5"
    },
    "orig_nbformat": 4,
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}