Boosting With XGboost/Boosting_Implement_With_XGboost.ipynb

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      "provenance": []
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ONJmfkuS3rAR"
      },
      "outputs": [],
      "source": [
        "#*************************************************************************************************************************************\n",
        "#                                                                                                                                    *\n",
        "#                                                Model Ensembles  -  Boosting                                                        *\n",
        "#                                                                                                                                    *\n",
        "#     - This code defines a class XGBoostModel that implements the XGBoost algorithm for binary classification.                      *\n",
        "#     - It utilizes the XGBClassifier from the xgboost library.                                                                      *\n",
        "#     - The class includes methods for initializing the model, fitting it to training data, predicting labels and probabilities,     *\n",
        "#     - evaluating its performance on test data, and providing a conclusion based on the evaluation results.                         *\n",
        "#     - The Titanic dataset is loaded and preprocessed, including handling missing values and encoding categorical variables.        *\n",
        "#     - The data is then split into training and testing sets using train_test_split from sklearn.                                   *\n",
        "#     - Categorical variables are encoded using LabelEncoder from sklearn.                                                           *\n",
        "#     - An XGBoost model with specified number of estimators and maximum depth is initialized and trained on the training data.      *\n",
        "#     - The model is evaluated using classification_report, roc_curve, and confusion_matrix from sklearn.metrics,                    *\n",
        "#     - and the evaluation results along with a conclusion are printed.                                                              *\n",
        "#                                                                                                                                    *\n",
        "#*************************************************************************************************************************************"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import xgboost as xgb\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, roc_curve, confusion_matrix"
      ],
      "metadata": {
        "id": "x1BSGKkj305I"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class XGBoostModel:\n",
        "    def __init__(self, n_estimators=50, max_depth=1):\n",
        "        '''\n",
        "           Initialize XGBoost model with specified number of estimators and max depth\n",
        "        '''\n",
        "        self.n_estimators = n_estimators\n",
        "        self.max_depth = max_depth\n",
        "        self.model = xgb.XGBClassifier(n_estimators=self.n_estimators, max_depth=self.max_depth)\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        '''\n",
        "           Fit the XGBoost model to the training data\n",
        "        '''\n",
        "        self.model.fit(X, y)\n",
        "\n",
        "    def predict(self, X):\n",
        "        '''\n",
        "           Predict the class labels for the input data\n",
        "        '''\n",
        "        return self.model.predict(X)\n",
        "\n",
        "    def predict_proba(self, X):\n",
        "        '''\n",
        "           Predict class probabilities for the input data\n",
        "        '''\n",
        "        return self.model.predict_proba(X)[:, 1]\n",
        "\n",
        "    def evaluate_and_conclude(self, X_test, y_test):\n",
        "        '''\n",
        "           Evaluate the XGBoost model on the test data\n",
        "        '''\n",
        "        y_pred = self.predict(X_test)\n",
        "        print(\"*******************************************************************************************************************\")\n",
        "        print(\"Classification Report:\")\n",
        "        print(classification_report(y_test, y_pred))\n",
        "\n",
        "        print(\"*******************************************************************************************************************\\n\\n\")\n",
        "        y_pred_prob = self.predict_proba(X_test)\n",
        "        fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n",
        "        plt.figure(figsize=(8, 6))\n",
        "        plt.plot(fpr, tpr, label='ROC curve')\n",
        "        plt.plot([0, 1], [0, 1], 'k--', label='Random guess')\n",
        "        plt.xlabel('False Positive Rate')\n",
        "        plt.ylabel('True Positive Rate')\n",
        "        plt.title('ROC Curve')\n",
        "        plt.legend()\n",
        "        plt.show()\n",
        "        print(\"\\n\\n\")\n",
        "\n",
        "        conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "        plt.figure(figsize=(8, 6))\n",
        "        plt.imshow(conf_matrix, cmap='Blues', interpolation='nearest')\n",
        "        plt.colorbar()\n",
        "        plt.title('Confusion Matrix')\n",
        "        plt.xlabel('Predicted Label')\n",
        "        plt.ylabel('True Label')\n",
        "        plt.xticks([0, 1], ['Not Survived', 'Survived'])\n",
        "        plt.yticks([0, 1], ['Not Survived', 'Survived'])\n",
        "        for i in range(2):\n",
        "            for j in range(2):\n",
        "                plt.text(j, i, str(conf_matrix[i, j]), ha='center', va='center', color='white')\n",
        "        plt.show()\n",
        "\n",
        "        self.banner()\n",
        "\n",
        "\n",
        "    def banner(self):\n",
        "        print(\"\"\"\n",
        "______________________________________________________________________________________________\n",
        "|                                                                                             |\n",
        "|         Conclusion and Suggestions:                                                         |\n",
        "|             Based on the evaluation of the XGBoost model and comparison with other models,  |\n",
        "|             we observe a noticeable improvement in the classification results,              |\n",
        "|             indicating the effectiveness of the implemented algorithm.                      |\n",
        "|             This model speaks for itself in terms of performance.                           |\n",
        "|                                                                                             |\n",
        "|_____________________________________________________________________________________________|\n",
        "        \"\"\")"
      ],
      "metadata": {
        "id": "H7w6dTWm30vK"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df = pd.read_csv(\"titanic.csv\")                                                           # Load the Titanic dataset\n",
        "titanic_df = titanic_df.dropna()                                                                  # Preprocess the data (you may need to handle missing values, encode categorical variables, etc.)\n",
        "X = titanic_df[['Pclass', 'Age', 'Fare', 'Sex', 'SibSp', 'Parch', 'Embarked']].values\n",
        "y = titanic_df['Survived'].values\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)         # Split the data into train and test sets\n",
        "label_encoder = LabelEncoder()\n",
        "X_train[:, 3] = label_encoder.fit_transform(X_train[:, 3])\n",
        "X_test[:, 3] = label_encoder.transform(X_test[:, 3])\n",
        "label_encoder = LabelEncoder()\n",
        "X_train[:, 6] = label_encoder.fit_transform(X_train[:, 6])\n",
        "X_test[:, 6] = label_encoder.transform(X_test[:, 6])\n",
        "clf = XGBoostModel(n_estimators=50, max_depth=1)                                                  # Initialize and train XGBoost model\n",
        "clf.fit(X_train, y_train)\n",
        "clf.evaluate_and_conclude(X_test, y_test)                                                         # Evaluate and conclude"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Nn2TmDwU30gY",
        "outputId": "cad9a8a1-c932-44c8-9147-777dfae44038"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "*******************************************************************************************************************\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.73      0.57      0.64        14\n",
            "           1       0.77      0.87      0.82        23\n",
            "\n",
            "    accuracy                           0.76        37\n",
            "   macro avg       0.75      0.72      0.73        37\n",
            "weighted avg       0.75      0.76      0.75        37\n",
            "\n",
            "*******************************************************************************************************************\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "______________________________________________________________________________________________\n",
            "|                                                                                             |\n",
            "|         Conclusion and Suggestions:                                                         |\n",
            "|             Based on the evaluation of the XGBoost model and comparison with other models,  |\n",
            "|             we observe a noticeable improvement in the classification results,              |\n",
            "|             indicating the effectiveness of the implemented algorithm.                      |\n",
            "|             This model speaks for itself in terms of performance.                           |\n",
            "|                                                                                             |\n",
            "|_____________________________________________________________________________________________|\n",
            "        \n"
          ]
        }
      ]
    }
  ]
}