06_ml_ensample_learning_students_dropout.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict Students Dropout - Ensemble Learning\n",
    "\n",
    "06_ml_ensample_learning_students_dropout.ipynb\n",
    "\n",
    "This notebook focuses on building and evaluating ensemble learning models to predict student dropout. The steps include reading the prepared data, implementing various ensemble techniques, and comparing their performance.\n",
    "\n",
    "Ensemble learning is a distinct and advanced technique that combines multiple models to improve prediction accuracy\n",
    "\n",
    "Steps\n",
    "1. Read in Prepared Data: Load the prepared data from the previously saved CSV file.\n",
    "2. Handle Missing Values: Handle any missing values in the dataset.\n",
    "3. Split Features and Target Variables: Separate the feature variables (X) and the target variable (y).\n",
    "4. Split Training and Testing Data: Split the dataset into training and testing sets to evaluate model performance.\n",
    "5. Implement Ensemble Learning Techniques: Construct and evaluate various ensemble learning models.\n",
    "6. Compare Model Performance: Compare the performance of different ensemble models to select the best one.\n",
    "7. Visualise Model Performance: Visualise the performance metrics of the ensemble models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier, VotingClassifier, BaggingClassifier, AdaBoostClassifier, GradientBoostingClassifier, StackingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, f1_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Read in Prepared Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data\n",
    "df_student = pd.read_csv('data/prep_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Handle Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handle Missing Values\n",
    "df_student.fillna(df_student.mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Split Features and Target Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define features and Target_encoded\n",
    "y = df_student['Target_encoded']\n",
    "X = df_student.drop(columns=['Target_encoded'])  # Replace 'Target_encoded' with your Target_encoded column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Split Training and Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Implement Ensemble Learning Techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression F1 Score on Training Data: 0.9091\n",
      "Logistic Regression F1 Score on Test Data: 0.8994\n",
      "Random Forest F1 Score on Training Data: 0.9845\n",
      "Random Forest F1 Score on Test Data: 0.9080\n",
      "SVC F1 Score on Training Data: 0.9383\n",
      "SVC F1 Score on Test Data: 0.9008\n",
      "XGBoost F1 Score on Training Data: 0.9319\n",
      "XGBoost F1 Score on Test Data: 0.9082\n",
      "Voting Classifier F1 Score on Training Data: 0.9429\n",
      "Voting Classifier F1 Score on Test Data: 0.9136\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create pipelines for different models with an imputer for missing values\n",
    "pipeline_lr = Pipeline([\n",
    "    ('imputer', SimpleImputer(strategy='mean')),\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('logistic', LogisticRegression(max_iter=2000, random_state=42))\n",
    "])\n",
    "\n",
    "pipeline_rf = Pipeline([\n",
    "    ('imputer', SimpleImputer(strategy='mean')),\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('random_forest', RandomForestClassifier(random_state=42))\n",
    "])\n",
    "\n",
    "pipeline_svc = Pipeline([\n",
    "    ('imputer', SimpleImputer(strategy='mean')),\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('svc', SVC(probability=True, random_state=42))\n",
    "])\n",
    "\n",
    "pipeline_xgb = Pipeline([\n",
    "    ('imputer', SimpleImputer(strategy='mean')),\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('xgb', XGBClassifier(eval_metric='mlogloss'))\n",
    "])\n",
    "\n",
    "# Define the hyperparameters to tune for each model\n",
    "param_grid_lr = {\n",
    "    'logistic__C': [0.01, 0.1, 1, 10, 100],  # Inverse of regularization strength\n",
    "    'logistic__solver': ['liblinear', 'lbfgs', 'newton-cg', 'saga']\n",
    "}\n",
    "\n",
    "param_grid_rf = {\n",
    "    'random_forest__n_estimators': [50, 100],  # Number of trees\n",
    "    'random_forest__max_depth': [None, 10, 20],\n",
    "    'random_forest__min_samples_split': [2, 5],\n",
    "    'random_forest__min_samples_leaf': [1, 2],\n",
    "}\n",
    "\n",
    "param_grid_svc = {\n",
    "    'svc__C': [0.1, 1, 10],\n",
    "    'svc__gamma': [1, 0.1, 0.01],\n",
    "    'svc__kernel': ['rbf']\n",
    "}\n",
    "\n",
    "param_grid_xgb = {\n",
    "    'xgb__n_estimators': [50, 100, 200],\n",
    "    'xgb__learning_rate': [0.01, 0.1, 0.2],\n",
    "    'xgb__max_depth': [3, 5, 7],\n",
    "    'xgb__subsample': [0.8, 1]\n",
    "}\n",
    "\n",
    "# Perform grid search with cross-validation for each model\n",
    "grid_search_lr = GridSearchCV(pipeline_lr, param_grid_lr, cv=3, scoring='accuracy', n_jobs=-1)\n",
    "grid_search_lr.fit(X_train, y_train)\n",
    "best_model_lr = grid_search_lr.best_estimator_\n",
    "\n",
    "grid_search_rf = GridSearchCV(pipeline_rf, param_grid_rf, cv=3, scoring='accuracy', n_jobs=-1)\n",
    "grid_search_rf.fit(X_train, y_train)\n",
    "best_model_rf = grid_search_rf.best_estimator_\n",
    "\n",
    "grid_search_svc = GridSearchCV(pipeline_svc, param_grid_svc, cv=3, scoring='accuracy', n_jobs=-1)\n",
    "grid_search_svc.fit(X_train, y_train)\n",
    "best_model_svc = grid_search_svc.best_estimator_\n",
    "\n",
    "grid_search_xgb = GridSearchCV(pipeline_xgb, param_grid_xgb, cv=3, scoring='accuracy', n_jobs=-1)\n",
    "grid_search_xgb.fit(X_train, y_train)\n",
    "best_model_xgb = grid_search_xgb.best_estimator_\n",
    "\n",
    "# Define the VotingClassifier with all models\n",
    "voting_clf = VotingClassifier(estimators=[\n",
    "    ('lr', best_model_lr),\n",
    "    ('rf', best_model_rf),\n",
    "    ('svc', best_model_svc),\n",
    "    ('xgb', best_model_xgb)\n",
    "], voting='soft')\n",
    "\n",
    "voting_clf.fit(X_train, y_train)\n",
    "\n",
    "# Evaluate the models on training and test data\n",
    "models = {\n",
    "    'Logistic Regression': best_model_lr,\n",
    "    'Random Forest': best_model_rf,\n",
    "    'SVC': best_model_svc,\n",
    "    'XGBoost': best_model_xgb,\n",
    "    'Voting Classifier': voting_clf\n",
    "}\n",
    "\n",
    "f1_scores_train = {}\n",
    "f1_scores_test = {}\n",
    "\n",
    "for model_name, model in models.items():\n",
    "    y_train_pred = model.predict(X_train)\n",
    "    y_test_pred = model.predict(X_test)\n",
    "    f1_scores_train[model_name] = f1_score(y_train, y_train_pred, average='weighted')\n",
    "    f1_scores_test[model_name] = f1_score(y_test, y_test_pred, average='weighted')\n",
    "    print(f\"{model_name} F1 Score on Training Data: {f1_scores_train[model_name]:.4f}\")\n",
    "    print(f\"{model_name} F1 Score on Test Data: {f1_scores_test[model_name]:.4f}\")\n",
    "\n",
    "# Visualization of model comparison\n",
    "def barplot_models(model_scores, title):\n",
    "    model_names = list(model_scores.keys())\n",
    "    f1_scores = list(model_scores.values())\n",
    "    plt.figure(figsize=(9, 7))\n",
    "    sns.set_palette(\"YlGnBu\")\n",
    "    bars = sns.barplot(x=model_names, y=f1_scores)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(\"Model\")\n",
    "    plt.ylabel(\"F1 Score\")\n",
    "    plt.ylim(0, 1)\n",
    "    for bar, f1_score in zip(bars.patches, f1_scores):\n",
    "        plt.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.02, f\"{f1_score:.2f}\", ha=\"center\", va=\"bottom\")\n",
    "    plt.show()\n",
    "\n",
    "barplot_models(model_scores=f1_scores_train, title=\"Model Comparison Plot Training\")\n",
    "barplot_models(model_scores=f1_scores_test, title=\"Model Comparison Plot Test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/youssef/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/youssef/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/youssef/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/youssef/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/youssef/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/youssef/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/youssef/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/youssef/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/youssef/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/youssef/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/youssef/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "# Base models\n",
    "logisticRegr = LogisticRegression(max_iter=1000)\n",
    "rf_model = RandomForestClassifier(random_state=42)\n",
    "svc_model = SVC(random_state=42, probability=True)\n",
    "\n",
    "# Voting Classifier\n",
    "voting_clf = VotingClassifier(estimators=[\n",
    "    ('lr', logisticRegr),\n",
    "    ('rf', rf_model),\n",
    "    ('svc', svc_model)\n",
    "], voting='soft')\n",
    "voting_clf.fit(X_train, y_train)\n",
    "y_pred_voting = voting_clf.predict(X_test)\n",
    "\n",
    "# Bagging Classifier\n",
    "bagging_clf = BaggingClassifier(estimator=logisticRegr, n_estimators=10, random_state=42)\n",
    "bagging_clf.fit(X_train, y_train)\n",
    "y_pred_bagging = bagging_clf.predict(X_test)\n",
    "\n",
    "# AdaBoost Classifier\n",
    "adaboost_clf = AdaBoostClassifier(estimator=logisticRegr, n_estimators=50, random_state=42)\n",
    "adaboost_clf.fit(X_train, y_train)\n",
    "y_pred_adaboost = adaboost_clf.predict(X_test)\n",
    "\n",
    "# Gradient Boosting Classifier\n",
    "gb_clf = GradientBoostingClassifier(n_estimators=100, random_state=42)\n",
    "gb_clf.fit(X_train, y_train)\n",
    "y_pred_gb = gb_clf.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Voting Classifier - Accuracy: 0.91%\n",
      "Voting Classifier - F1 Score: 0.91\n",
      "Voting Classifier - Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.83      0.88       277\n",
      "           1       0.90      0.96      0.93       449\n",
      "\n",
      "    accuracy                           0.91       726\n",
      "   macro avg       0.92      0.89      0.90       726\n",
      "weighted avg       0.91      0.91      0.91       726\n",
      "\n",
      "Voting Classifier - Confusion Matrix:\n",
      " [[229  48]\n",
      " [ 17 432]]\n",
      "Bagging Classifier - Accuracy: 0.89%\n",
      "Bagging Classifier - F1 Score: 0.89\n",
      "Bagging Classifier - Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.82      0.85       277\n",
      "           1       0.89      0.93      0.91       449\n",
      "\n",
      "    accuracy                           0.89       726\n",
      "   macro avg       0.89      0.87      0.88       726\n",
      "weighted avg       0.89      0.89      0.89       726\n",
      "\n",
      "Bagging Classifier - Confusion Matrix:\n",
      " [[226  51]\n",
      " [ 30 419]]\n",
      "AdaBoost Classifier - Accuracy: 0.88%\n",
      "AdaBoost Classifier - F1 Score: 0.88\n",
      "AdaBoost Classifier - Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.80      0.84       277\n",
      "           1       0.88      0.93      0.91       449\n",
      "\n",
      "    accuracy                           0.88       726\n",
      "   macro avg       0.88      0.87      0.87       726\n",
      "weighted avg       0.88      0.88      0.88       726\n",
      "\n",
      "AdaBoost Classifier - Confusion Matrix:\n",
      " [[221  56]\n",
      " [ 30 419]]\n",
      "Gradient Boosting Classifier - Accuracy: 0.89%\n",
      "Gradient Boosting Classifier - F1 Score: 0.89\n",
      "Gradient Boosting Classifier - Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.83      0.86       277\n",
      "           1       0.90      0.93      0.92       449\n",
      "\n",
      "    accuracy                           0.89       726\n",
      "   macro avg       0.89      0.88      0.89       726\n",
      "weighted avg       0.89      0.89      0.89       726\n",
      "\n",
      "Gradient Boosting Classifier - Confusion Matrix:\n",
      " [[230  47]\n",
      " [ 30 419]]\n",
      "                          Model  Accuracy  F1 Score\n",
      "0             Voting Classifier  0.910468  0.909309\n",
      "1            Bagging Classifier  0.888430  0.887507\n",
      "2           AdaBoost Classifier  0.881543  0.880293\n",
      "3  Gradient Boosting Classifier  0.893939  0.893246\n"
     ]
    }
   ],
   "source": [
    "# Function to evaluate model performance\n",
    "def evaluate_model_performance(y_test, y_pred, model_name):\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    f1 = f1_score(y_test, y_pred, average='weighted')\n",
    "    print(f\"{model_name} - Accuracy: {accuracy:.2f}%\")\n",
    "    print(f\"{model_name} - F1 Score: {f1:.2f}\")\n",
    "    print(f\"{model_name} - Classification Report:\\n\", classification_report(y_test, y_pred))\n",
    "    print(f\"{model_name} - Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred))\n",
    "    return accuracy, f1\n",
    "\n",
    "# Evaluate Voting Classifier\n",
    "voting_accuracy, voting_f1 = evaluate_model_performance(y_test, y_pred_voting, \"Voting Classifier\")\n",
    "\n",
    "# Evaluate Bagging Classifier\n",
    "bagging_accuracy, bagging_f1 = evaluate_model_performance(y_test, y_pred_bagging, \"Bagging Classifier\")\n",
    "\n",
    "# Evaluate AdaBoost Classifier\n",
    "adaboost_accuracy, adaboost_f1 = evaluate_model_performance(y_test, y_pred_adaboost, \"AdaBoost Classifier\")\n",
    "\n",
    "# Evaluate Gradient Boosting Classifier\n",
    "gb_accuracy, gb_f1 = evaluate_model_performance(y_test, y_pred_gb, \"Gradient Boosting Classifier\")\n",
    "\n",
    "# Compare Ensemble Model Performance\n",
    "ensemble_performance = {\n",
    "    'Model': ['Voting Classifier', 'Bagging Classifier', 'AdaBoost Classifier', 'Gradient Boosting Classifier'],\n",
    "    'Accuracy': [voting_accuracy, bagging_accuracy, adaboost_accuracy, gb_accuracy],\n",
    "    'F1 Score': [voting_f1, bagging_f1, adaboost_f1, gb_f1]\n",
    "}\n",
    "\n",
    "performance_df = pd.DataFrame(ensemble_performance)\n",
    "print(performance_df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6: Evaluate and Compare Ensemble Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stacking Classifier F1 Score on Training Data: 0.9458\n",
      "Stacking Classifier F1 Score on Test Data: 0.9071\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define base learners\n",
    "base_learners = [\n",
    "    ('lr', best_model_lr),\n",
    "    ('rf', best_model_rf),\n",
    "    ('svc', best_model_svc),\n",
    "    ('xgb', best_model_xgb)\n",
    "]\n",
    "\n",
    "# Define meta-learner\n",
    "meta_learner = LogisticRegression()\n",
    "\n",
    "# Create stacking classifier\n",
    "stacking_clf = StackingClassifier(estimators=base_learners, final_estimator=meta_learner, cv=3)\n",
    "\n",
    "# Fit stacking classifier\n",
    "stacking_clf.fit(X_train, y_train)\n",
    "\n",
    "# Evaluate the stacking classifier\n",
    "y_train_pred_stacking = stacking_clf.predict(X_train)\n",
    "y_test_pred_stacking = stacking_clf.predict(X_test)\n",
    "\n",
    "# Calculate F1 scores\n",
    "f1_score_train_stacking = f1_score(y_train, y_train_pred_stacking, average='weighted')\n",
    "f1_score_test_stacking = f1_score(y_test, y_test_pred_stacking, average='weighted')\n",
    "\n",
    "print(f\"Stacking Classifier F1 Score on Training Data: {f1_score_train_stacking:.4f}\")\n",
    "print(f\"Stacking Classifier F1 Score on Test Data: {f1_score_test_stacking:.4f}\")\n",
    "\n",
    "# Calculate confusion matrix for train and test predictions\n",
    "conf_matrix_train = confusion_matrix(y_train, y_train_pred_stacking)\n",
    "conf_matrix_test = confusion_matrix(y_test, y_test_pred_stacking)\n",
    "\n",
    "# Plot the confusion matrices\n",
    "def plot_confusion_matrix(conf_matrix, title):\n",
    "    plt.figure(figsize=(10, 7))\n",
    "    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('Actual')\n",
    "    plt.show()\n",
    "\n",
    "plot_confusion_matrix(conf_matrix_train, \"Confusion Matrix for Training Data\")\n",
    "plot_confusion_matrix(conf_matrix_test, \"Confusion Matrix for Test Data\")\n",
    "\n",
    "# Visualization of F1 scores\n",
    "def plot_f1_scores(f1_train, f1_test, title):\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    plt.bar(['Training F1 Score', 'Test F1 Score'], [f1_train, f1_test], color=['blue', 'green'])\n",
    "    plt.ylim(0, 1)\n",
    "    plt.title(title)\n",
    "    plt.ylabel('F1 Score')\n",
    "    for i, v in enumerate([f1_train, f1_test]):\n",
    "        plt.text(i, v + 0.02, f\"{v:.4f}\", ha='center', va='bottom')\n",
    "    plt.show()\n",
    "\n",
    "plot_f1_scores(f1_score_train_stacking, f1_score_test_stacking, \"Stacking Classifier F1 Scores\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/youssef/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boosting Classifier F1 Score on Training Data: 0.9315\n",
      "Boosting Classifier F1 Score on Test Data: 0.9174\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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eePvttxESEoKjR4/ixRdfhImJCXJycrB//3507NgR77zzzmMvpzYnT55UhsHc3FwUFxdj/fr1AID27durjaV50CeffIIDBw4gKChIeZnthQsX8M0336CgoADz589X9l24cCEGDhyIHj16YMqUKXByckJWVha2b9+ONWvWAADi4uLQt29f9O7dG++//z7kcjkSEhJw/PhxrF279qFHcFq0aIHY2FjMnDkT58+fV95z5urVqzh8+DBMTEwQExODY8eO4d1338XQoUPxwgsvQC6XY/fu3Th27BhmzJjxuJuUGgpdjjAlehyariKxsLAQXbp0EfHx8eLevXsq/QsKCsTEiROFvb29MDAwEM7OziIqKkqt3927d8X06dOFs7OzMDQ0FPb29uKdd94RN27cUPZJS0sTr732mnB2dhZGRkbCyspK+Pn5iS1btqjMa+fOnaJr167CyMhIABCjR49Wqb36VSQdOnRQW8/qV0YIIcTx48fFyy+/LBQKhbC0tBTjxo0TK1asEADEH3/8odX2y8zMFKNHjxZOTk5CLpcLExMT0bVrVzF79myRl5enUlf1q0iSkpJEmzZthJGRkWjZsqWIi4sTS5cuVVknbbbRihUrRO/evYWtra2Qy+XCwcFBDBs2TBw7dkzZ53GuInmwXi8vL2FiYiKMjY2Fq6urCA4OFkePHlVZT03bvyZVV5Fcu3ZNq36aHpquMHrQb7/9JsLDw0Xnzp2FpaWl0NfXF02bNhX9+vUTKSkpav3T0tJEYGCgsLCwEEZGRsLV1VVMmTJFpc++fftEnz59lNuiR48e4scff1TpU9P2rbJ582bRu3dvYW5uLoyMjISzs7N44403xM6dO4UQQly9elWMGTNGtG3bVpiYmAhTU1PRqVMn8eWXX4ry8vJa15meHTIhqt0Zh4gapLfffhtr165FQUEB5HK5rsshouccT5EQNUCxsbFwcHBAy5Ytcfv2bWzduhXfffcdZs2axXBBRE8FBgyiBsjQ0BDz58/H5cuXUV5ejhdeeAHx8fFP/Ie+iIhqwlMkREREJDmdXqb666+/YuDAgXBwcIBMJtN4HX11e/fuhYeHBxQKBVq2bMnrqYmIiJ5COg0Yd+7cQefOnVVu6VybCxcuoH///vD19UVGRgY+/PBDTJ48GRs2bKjnSomIiKgunppTJDKZDJs2bcLgwYNr7DN9+nRs2bIFp06dUrZNnDgRf/zxB9LS0p5AlURERKSNBjXIMy0tTfkbCFUCAgKwdOlSlJWVwdDQUG2akpISlTvbVVZW4vr167CysmowtwUmIiJ6GgghcOvWLTg4ODz0pn4NKmDk5uaq/aCTra0tysvLkZ+fr/FHfOLi4hATE/OkSiQiInrmZWdnP/S3jBpUwADUf3yn6gxPTUcjoqKiEBkZqXxeWFgIJycnZGdnw9zcvP4KJSIiesYUFRXB0dERZmZmD+3boAKGnZ2d2i9G5uXlwcDAQOPPXAP3f3BK068ompubM2A8YQkJCZg/fz5ycnLQoUMHLFiwAL6+vjX2X7hwIb755htcvHgRTk5OmDlzJoKDg5Wvb9y4EZ9++in+/vtvlJWV4YUXXsDUqVOVvxcC3L9Saf78+UhPT0dOTo7GcT5jxozBihUrVNq8vLzw22+/SbPiRETPGG2GGDSogOHt7a32K487duyAp6enxvEX9PRITk5GREQEEhIS0LNnTyxevBiBgYE4efIknJyc1PonJiYiKioK3377Lbp164bDhw8jNDQUTZo0Uf4Il6WlJWbOnIm2bdtCLpdj69atCAkJgY2NDQICAgD8/yuVQkJCMGTIkBrr69evH5YtW6Z8zrthEhE9Jt39DIoQt27dEhkZGSIjI0MAEPHx8SIjI0NcunRJCCHEjBkzxKhRo5T9z58/Lxo1aiSmTJkiTp48KZYuXSoMDQ3F+vXrtV5mYWGhACAKCwslXx+qWffu3cXEiRNV2tq2bStmzJihsb+3t7d4//33Vdree+890bNnz1qX07VrVzFr1iyNrwEQmzZtUmsfPXq0GDRoUK3zJSKiun2H6vQ+GEePHkXXrl3RtWtXAEBkZCS6du2K2bNnAwBycnKQlZWl7O/i4oKUlBTs2bMHXbp0wdy5c/Gf//yn1v8zJd0rLS1Fenq62hVA/v7+OHjwoMZpSkpKoFAoVNqMjY1x+PBhlJWVqfUXQmDXrl04ffo0XnzxxTrXuGfPHtjY2KB169YIDQ1FXl5enedBRET/n05PkfTq1Us5SFOT5cuXq7X5+fnh999/r8eqSGr5+fmoqKjQeAVQ9TE1VQICAvDdd99h8ODBcHd3R3p6OpKSklBWVqZyxVBhYSGaNWuGkpIS6OvrIyEhAX379q1TfYGBgRg6dCicnZ1x4cIFfPTRR+jTpw/S09M1jt8hIqKH0+kRDHq+aLoCqKaBQh999BECAwPRo0cPGBoaYtCgQRgzZgwAQF9fX9nPzMwMmZmZOHLkCD755BNERkZiz549daorKCgIAwYMgJubGwYOHIiff/4ZZ86cwU8//VSn+RA9KxISEuDi4gKFQgEPDw/s27ev1v4LFy5Eu3btYGxsjDZt2mDlypUqr584cQJDhgxBixYtIJPJsGDBArV5VL1W/REeHq5xmRMmTKhxXvR0YMCgemdtbQ19fX2NVwBVP6pRxdjYGElJSSguLsbFixeRlZWFFi1awMzMDNbW1sp+enp6aNWqFbp06YKpU6fijTfeQFxc3GPVa29vD2dnZ5w9e/ax5kPUEFUNyJ45cyYyMjLg6+uLwMBAldPVD6oakD1nzhycOHECMTExCA8PVxmQX1xcjJYtW+Lf//437OzsNM7nyJEjyMnJUT5SU1MBAEOHDlXru3nzZhw6dAgODg4SrDHVFwYMqndyuRweHh7KD4wqqamp8PHxqXVaQ0NDNG/eHPr6+vj+++/xyiuv1Hr3OCGEyp1bH0VBQQGys7M13riN6FkXHx+PcePGYfz48WjXrh0WLFgAR0dHJCYmauy/atUqTJgwAUFBQWjZsiWGDx+OcePG4bPPPlP26datG+bPn4/hw4fXeNqxadOmsLOzUz62bt0KV1dX+Pn5qfS7cuUK3n33XaxZs4ZXDz7lGtRlqtRwRUZGYtSoUfD09IS3tzeWLFmCrKwsTJw4EcD9G6JduXJFeWj1zJkzOHz4MLy8vHDjxg3Ex8fj+PHjKveriIuLg6enJ1xdXVFaWoqUlBSsXLlS5YPw9u3b+Pvvv5XPL1y4gMzMTFhaWsLJyQm3b9/GnDlzMGTIENjb2+PixYv48MMPYW1tjddee+0JbR2ip0PVgOwZM2aotD/OgOxHCQGlpaVYvXo1IiMjVU6jVlZWYtSoUZg2bRo6dOhQ5/nSk8WAQU9EUFAQCgoKEBsbi5ycHLi5uSElJQXOzs4A1K8YqqiowBdffIHTp0/D0NAQvXv3xsGDB9GiRQtlnzt37iAsLAyXL1+GsbEx2rZti9WrVyMoKEjZ5+jRo+jdu7fyedVdXUePHo3ly5dDX18ff/75J1auXImbN2/C3t4evXv3RnJyslZ3qiN6ltTngOy62Lx5M27evKkcd1Xls88+g4GBASZPnlznedKTx4BBT0xYWBjCwsI0vlb9iqF27dohIyOj1vl9/PHH+Pjjj2vt87ArlYyNjbF9+/Za50H0vKnrgOzc3Fz06NEDQgjY2tpizJgxmDdvnsqA7LpYunQpAgMDVcZYpKen46uvvsLvv//OH6psIDgGg4iIANTvgGxtXbp0CTt37sT48eNV2vft24e8vDw4OTnBwMAABgYGuHTpEqZOnapyZJOeHgwYREQE4MkOyK7JsmXLYGNjgwEDBqi0jxo1CseOHUNmZqby4eDggGnTpvEo5FOKp0iIiEipPgZkl5aW4uTJk8p/X7lyBZmZmTA1NUWrVq2U/SorK7Fs2TKMHj0aBgaqX09WVlZqP2ppaGgIOzs7tGnTpl62BT0eBgwiIlKqjwHZ//zzj/InIQDg888/x+effw4/Pz+VG+Pt3LkTWVlZGDt2bL2vJ9U/mahtBNwzqKioCBYWFigsLOTPtRMREdVBXb5DOQaDiIiIJMeAQURERJJjwCAiIiLJcZCnhHjvF3pePF8jt4joUfAIBhEREUmOAYOIiIgkx4BBREREkmPAICIiIslxkCcRPVdkMRyNTc8PEa27Edk8gkFERESSY8AgIiIiyTFgEBERkeQYMIiIiEhyDBhEREQkOQYMIiIikhwDBhEREUmOAYOIiIgkx4BBREREkmPAICIiIskxYBAREZHkGDCIiIhIcgwYREREJDkGDCIiIpIcAwYRERFJjgGDiIiIJMeAQURERJJjwCAiIiLJMWAQERGR5BgwiIiISHIMGERERCQ5BgwiIiKSHAMGERERSY4Bg4iIiCTHgEFERESSY8AgIiIiyTFgEBERkeQYMIiIiEhyDBhEREQkOQYMIiIikpzOA0ZCQgJcXFygUCjg4eGBffv21dp/zZo16Ny5Mxo1agR7e3uEhISgoKDgCVVLRERE2tBpwEhOTkZERARmzpyJjIwM+Pr6IjAwEFlZWRr779+/H8HBwRg3bhxOnDiB//3vfzhy5AjGjx//hCsnIiKi2ug0YMTHx2PcuHEYP3482rVrhwULFsDR0RGJiYka+//2229o0aIFJk+eDBcXF/zrX//ChAkTcPTo0SdcOREREdVGZwGjtLQU6enp8Pf3V2n39/fHwYMHNU7j4+ODy5cvIyUlBUIIXL16FevXr8eAAQOeRMlERESkJZ0FjPz8fFRUVMDW1lal3dbWFrm5uRqn8fHxwZo1axAUFAS5XA47Ozs0btwYX3/9dY3LKSkpQVFRkcqDiIiI6pfOB3nKZDKV50IItbYqJ0+exOTJkzF79mykp6dj27ZtuHDhAiZOnFjj/OPi4mBhYaF8ODo6Slo/ERERqdNZwLC2toa+vr7a0Yq8vDy1oxpV4uLi0LNnT0ybNg2dOnVCQEAAEhISkJSUhJycHI3TREVFobCwUPnIzs6WfF2IiIhIlc4Chlwuh4eHB1JTU1XaU1NT4ePjo3Ga4uJi6Omplqyvrw/g/pEPTYyMjGBubq7yICIiovql01MkkZGR+O6775CUlIRTp05hypQpyMrKUp7yiIqKQnBwsLL/wIEDsXHjRiQmJuL8+fM4cOAAJk+ejO7du8PBwUFXq0FERETVGOhy4UFBQSgoKEBsbCxycnLg5uaGlJQUODs7AwBycnJU7okxZswY3Lp1C9988w2mTp2Kxo0bo0+fPvjss890tQpERESkgUzUdG7hGVVUVAQLCwsUFhZKfrqkhrGpRM+chvypIYvhjkrPDxEt7c5al+9QnV9FQkRERM8eBgwiIiKSHAMGERERSY4Bg4iIiCTHgEFERESSY8AgIiIiyTFgEBERkeQYMIiIiEhyDBhEREQkOQYMIiIikhwDBhEREUmOAYOIiIgkx4BBREREkmPAICIiIskxYBAREZHkGDCIiIhIcgwYREREJDkGDCIiIpIcAwYRERFJjgGDiIiIJMeAQURERJJjwCAiIiLJMWAQERGR5BgwiIiISHIMGERERCQ5BgwiIiKSHAMGERERSY4Bg4iIiCTHgEFERESSY8AgIiIiyTFgEBERkeQYMIiIiEhyDBhEREQkOQYMIiIikhwDBhEREUmOAYOIiIgkx4BBREREkmPAICIiIskxYBAREZHkGDCIiIhIcgwYREREJDkGDCIiIpIcAwYRERFJjgGDiIiIJMeAQURERJJjwCAiIiLJMWAQERGR5BgwiIiISHIMGERERCQ5BgwiIiKSHAMGERERSU7nASMhIQEuLi5QKBTw8PDAvn37au1fUlKCmTNnwtnZGUZGRnB1dUVSUtITqpaIiIi0YaDLhScnJyMiIgIJCQno2bMnFi9ejMDAQJw8eRJOTk4apxk2bBiuXr2KpUuXolWrVsjLy0N5efkTrpyIiIhqIxNCCF0t3MvLC+7u7khMTFS2tWvXDoMHD0ZcXJxa/23btmH48OE4f/48LC0tH2mZRUVFsLCwQGFhIczNzR+5dk1kMklnR/TU0t2nxuOTxXBHpeeHiJZ2Z63Ld6jOTpGUlpYiPT0d/v7+Ku3+/v44ePCgxmm2bNkCT09PzJs3D82aNUPr1q3x/vvv4+7duzUup6SkBEVFRSoPIiIiql86O0WSn5+PiooK2NraqrTb2toiNzdX4zTnz5/H/v37oVAosGnTJuTn5yMsLAzXr1+vcRxGXFwcYmJiJK+fiIiIaqbzQZ6yaucVhBBqbVUqKyshk8mwZs0adO/eHf3790d8fDyWL19e41GMqKgoFBYWKh/Z2dmSrwMRERGpeqSAUV5ejp07d2Lx4sW4desWAOCff/7B7du3tZ6HtbU19PX11Y5W5OXlqR3VqGJvb49mzZrBwsJC2dauXTsIIXD58mWN0xgZGcHc3FzlQURERPWrzgHj0qVL6NixIwYNGoTw8HBcu3YNADBv3jy8//77Ws9HLpfDw8MDqampKu2pqanw8fHROE3Pnj3VgsyZM2egp6eH5s2b13VViIiIqJ7UOWC899578PT0xI0bN2BsbKxsf+2117Br1646zSsyMhLfffcdkpKScOrUKUyZMgVZWVmYOHEigPunN4KDg5X9R4wYASsrK4SEhODkyZP49ddfMW3aNIwdO1alFiIiItKtOg/y3L9/Pw4cOAC5XK7S7uzsjCtXrtRpXkFBQSgoKEBsbCxycnLg5uaGlJQUODs7AwBycnKQlZWl7G9qaorU1FRMmjQJnp6esLKywrBhw/Dxxx/XdTWIiIioHtU5YFRWVqKiokKt/fLlyzAzM6tzAWFhYQgLC9P42vLly9Xa2rZtq3ZahYiIiJ4udT5F0rdvXyxYsED5XCaT4fbt24iOjkb//v2lrI2IiIgaqDofwYiPj0efPn3Qvn173Lt3DyNGjMDZs2dhbW2NtWvX1keNRERE1MDUOWA0a9YMmZmZ+P7775Geno7KykqMGzcOb731FgdaEhEREYA6BoyysjK0adMGW7duRUhICEJCQuqrLiIiImrA6jQGw9DQECUlJTXeaZOIiIgIeIRBnpMmTcJnn33Gn0gnIiKiGtV5DMahQ4ewa9cu7NixAx07doSJiYnK6xs3bpSsOCIiImqY6hwwGjdujCFDhtRHLURERPSMqHPAWLZsWX3UQURERM+QOgeMKteuXcPp06chk8nQunVrNG3aVMq6iIiIqAGr8yDPO3fuYOzYsbC3t8eLL74IX19fODg4YNy4cSguLq6PGomIiKiBqXPAiIyMxN69e/Hjjz/i5s2buHnzJn744Qfs3bsXU6dOrY8aiYiIqIGp8ymSDRs2YP369ejVq5eyrX///jA2NsawYcOQmJgoZX1ERETUANX5CEZxcTFsbW3V2m1sbHiKhIiIiAA8QsDw9vZGdHQ07t27p2y7e/cuYmJi4O3tLWlxRERE1DDV+RTJV199hX79+qF58+bo3LkzZDIZMjMzoVAosH379vqokYiIiBqYOgcMNzc3nD17FqtXr8Zff/0FIQSGDx/OX1MlIiIipUe6D4axsTFCQ0OlroWIiIieEXUegxEXF4ekpCS19qSkJHz22WeSFEVEREQNW50DxuLFi9G2bVu19g4dOmDRokWSFEVEREQNW50DRm5uLuzt7dXamzZtipycHEmKIiIiooatzgHD0dERBw4cUGs/cOAAHBwcJCmKiIiIGrY6D/IcP348IiIiUFZWhj59+gAAdu3ahQ8++IC3CiciIiIAjxAwPvjgA1y/fh1hYWEoLS0FACgUCkyfPh1RUVGSF0hEREQNj0wIIR5lwtu3b+PUqVMwNjbGCy+8ACMjI6lrqxdFRUWwsLBAYWEhzM3NJZ23TCbp7IieWo/2qfF0kMVwR6Xnh4iWdmety3doncdgVDE1NUW3bt1gZmaGc+fOobKy8lFnRURERM8YrQPGihUrsGDBApW2t99+Gy1btkTHjh3h5uaG7OxsqesjIiKiBkjrgLFo0SJYWFgon2/btg3Lli3DypUrceTIETRu3BgxMTH1UiQRERE1LFoP8jxz5gw8PT2Vz3/44Qe8+uqreOuttwAAn376KUJCQqSvkIiIiBocrY9g3L17V2VAx8GDB/Hiiy8qn7ds2RK5ubnSVkdEREQNktYBw9nZGenp6QCA/Px8nDhxAv/617+Ur+fm5qqcQiEiIqLnl9anSIKDgxEeHo4TJ05g9+7daNu2LTw8PJSvHzx4EG5ubvVSJBERETUsWgeM6dOno7i4GBs3boSdnR3+97//qbx+4MABvPnmm5IXSERERA3PI99oq6HijbaIHl9D/tTgjbboedIgb7RFREREVBMGDCIiIpIcAwYRERFJjgGDiIiIJMeAQURERJKTLGBkZ2dj7NixUs2OiIiIGjDJAsb169exYsUKqWZHREREDZjWN9rasmVLra+fP3/+sYshIiKiZ4PWAWPw4MGQyWSo7b5cMt5pioiIiFCHUyT29vbYsGEDKisrNT5+//33+qyTiIiIGhCtA4aHh0etIeJhRzeIiIjo+aH1KZJp06bhzp07Nb7eqlUr/PLLL5IURURERA2b1gHD19e31tdNTEzg5+f32AURERFRw6f1KZLz58/zFAgRERFpReuA8cILL+DatWvK50FBQbh69Wq9FEVEREQNm9YBo/rRi5SUlFrHZBAREdHzi79FQkRERJLTOmDIZDK1G2lJcWOthIQEuLi4QKFQwMPDA/v27dNqugMHDsDAwABdunR57BqIiIhIWlpfRSKEwJgxY2BkZAQAuHfvHiZOnAgTExOVfhs3btR64cnJyYiIiEBCQgJ69uyJxYsXIzAwECdPnoSTk1ON0xUWFiI4OBgvvfQSx4EQERE9hWRCy0tDQkJCtJrhsmXLtF64l5cX3N3dkZiYqGxr164dBg8ejLi4uBqnGz58OF544QXo6+tj8+bNyMzM1HqZRUVFsLCwQGFhIczNzbWeThu8Uzo9LxryBWWyGO6o9PwQ0dLurHX5DtX6CEZdgoM2SktLkZ6ejhkzZqi0+/v74+DBg7XWce7cOaxevRoff/zxQ5dTUlKCkpIS5fOioqJHL5qIiIi0orNBnvn5+aioqICtra1Ku62tLXJzczVOc/bsWcyYMQNr1qyBgYF22SguLg4WFhbKh6Oj42PXTkRERLXT+VUk1QeKCiE0Dh6tqKjAiBEjEBMTg9atW2s9/6ioKBQWFiof2dnZj10zERER1U7rUyRSs7a2hr6+vtrRiry8PLWjGgBw69YtHD16FBkZGXj33XcBAJWVlRBCwMDAADt27ECfPn3UpjMyMlIOTCUiIqInQ2dHMORyOTw8PJCamqrSnpqaCh8fH7X+5ubm+PPPP5GZmal8TJw4EW3atEFmZia8vLyeVOlERET0EDo7ggEAkZGRGDVqFDw9PeHt7Y0lS5YgKysLEydOBHD/9MaVK1ewcuVK6Onpwc3NTWV6GxsbKBQKtXYiIiLSLZ0GjKCgIBQUFCA2NhY5OTlwc3NDSkoKnJ2dAQA5OTnIysrSZYlERET0CLS+D8azgvfBIHp8DflTg/fBoOeJLu+DofOrSIiIiOjZw4BBREREkmPAICIiIskxYBAREZHkGDCIiIhIcgwYREREJDkGDCIiIpIcAwYRERFJjgGDiIiIJMeAQURERJJjwCAiIiLJMWAQERGR5BgwiIiISHIMGERERCQ5BgwiIiKSHAMGERERSY4Bg4iIiCTHgEFERESSY8AgIiIiyTFgEBERkeQYMIiIiEhyDBhEREQkOQYMIiIikhwDBhEREUmOAYOIiIgkx4BBREREkmPAICIiIskxYBAREZHkGDCIiIhIcgwYREREJDkGDCIiIpIcAwYRERFJjgGDiIiIJMeAQURERJJjwCAiIiLJMWAQERGR5BgwiIiISHIMGERERCQ5BgwiIiKSHAMGERERSY4Bg4iIiCTHgEFERESSY8AgIiIiyTFgEBERkeQYMIiIiEhyDBhEREQkOQYMIiIikhwDBhEREUmOAYOIiIgkx4BBREREktN5wEhISICLiwsUCgU8PDywb9++Gvtu3LgRffv2RdOmTWFubg5vb29s3779CVZLRERE2tBpwEhOTkZERARmzpyJjIwM+Pr6IjAwEFlZWRr7//rrr+jbty9SUlKQnp6O3r17Y+DAgcjIyHjClRMREVFtZEIIoauFe3l5wd3dHYmJicq2du3aYfDgwYiLi9NqHh06dEBQUBBmz56tVf+ioiJYWFigsLAQ5ubmj1R3TWQySWdH9NTS3afG45PFcEel54eIlnZnrct3qM6OYJSWliI9PR3+/v4q7f7+/jh48KBW86isrMStW7dgaWlZHyUSERHRIzLQ1YLz8/NRUVEBW1tblXZbW1vk5uZqNY8vvvgCd+7cwbBhw2rsU1JSgpKSEuXzoqKiRyuYiIiItKbzQZ6yaucVhBBqbZqsXbsWc+bMQXJyMmxsbGrsFxcXBwsLC+XD0dHxsWsmIiKi2uksYFhbW0NfX1/taEVeXp7aUY3qkpOTMW7cOKxbtw4vv/xyrX2joqJQWFiofGRnZz927URERFQ7nQUMuVwODw8PpKamqrSnpqbCx8enxunWrl2LMWPG4L///S8GDBjw0OUYGRnB3Nxc5UFERET1S2djMAAgMjISo0aNgqenJ7y9vbFkyRJkZWVh4sSJAO4ffbhy5QpWrlwJ4H64CA4OxldffYUePXooj34YGxvDwsJCZ+tBREREqnQaMIKCglBQUIDY2Fjk5OTAzc0NKSkpcHZ2BgDk5OSo3BNj8eLFKC8vR3h4OMLDw5Xto0ePxvLly590+URERFQDnd4HQxd4Hwyix9eQPzV4Hwx6njyX98EgIiKiZxcDBhEREUmOAYOIiIgkx4BBREREkmPAICIiIskxYBAREZHkGDCIiIhIcgwYREREJDkGDCIiIpIcAwYRERFJjgGDiIiIJMeAQURERJJjwCAiIiLJMWAQERGR5BgwiIiISHIMGERERCQ5BgwiIiKSHAMGERERSY4Bg4iIiCTHgEFERESSY8AgIiIiyTFgEBERkeQYMIiIiEhyDBhEREQkOQYMIiIikhwDBhEREUmOAYOIiIgkx4BBREREkmPAICIiIskxYBAREZHkGDCIiIhIcgwYREREJDkGDCIiIpIcAwYRERFJjgGDiIiIJMeAQURERJJjwCAiIiLJMWAQERGR5BgwiIiISHIMGERERCQ5BgwiIiKSHAMGERERSY4Bg4iIiCTHgEFERESSY8AgIiIiyTFgEBERkeQYMIiIiEhyDBhEREQkOQYMIiIikhwDBhEREUmOAYOIiIgkp/OAkZCQABcXFygUCnh4eGDfvn219t+7dy88PDygUCjQsmVLLFq06AlVSkRERNrSacBITk5GREQEZs6ciYyMDPj6+iIwMBBZWVka+1+4cAH9+/eHr68vMjIy8OGHH2Ly5MnYsGHDE66ciIiIaiMTQghdLdzLywvu7u5ITExUtrVr1w6DBw9GXFycWv/p06djy5YtOHXqlLJt4sSJ+OOPP5CWlqbVMouKimBhYYHCwkKYm5s//ko8QCaTdHZETy3dfWo8PlkMd1R6fohoaXfWunyHGki65DooLS1Feno6ZsyYodLu7++PgwcPapwmLS0N/v7+Km0BAQFYunQpysrKYGhoqDZNSUkJSkpKlM8LCwsB3N9IRPRoGvTuc0/XBRA9OVJ/11XNT5tjEzoLGPn5+aioqICtra1Ku62tLXJzczVOk5ubq7F/eXk58vPzYW9vrzZNXFwcYmJi1NodHR0fo3qi55uFha4rICJtWPy7fnbWW7duweIhHwQ6CxhVZNXOKwgh1Noe1l9Te5WoqChERkYqn1dWVuL69euwsrKqdTn09CsqKoKjoyOys7MlP91FRNLhvvrsEELg1q1bcHBweGhfnQUMa2tr6Ovrqx2tyMvLUztKUcXOzk5jfwMDA1hZWWmcxsjICEZGRiptjRs3fvTC6aljbm7ODy2iBoD76rPhYUcuqujsKhK5XA4PDw+kpqaqtKempsLHx0fjNN7e3mr9d+zYAU9PT43jL4iIiEg3dHqZamRkJL777jskJSXh1KlTmDJlCrKysjBx4kQA909vBAcHK/tPnDgRly5dQmRkJE6dOoWkpCQsXboU77//vq5WgYiIiDTQ6RiMoKAgFBQUIDY2Fjk5OXBzc0NKSgqcnZ0BADk5OSr3xHBxcUFKSgqmTJmChQsXwsHBAf/5z38wZMgQXa0C6ZCRkRGio6PVToER0dOF++rzSaf3wSAiIqJnk85vFU5ERETPHgYMIiIikhwDBhEREUmOAYO01qtXL0RERGjd/+LFi5DJZMjMzKy3moiI6OnEgPEMkslktT7GjBnzSPPduHEj5s6dq3V/R0dH5dVB9akqyFR/jBw5Utnnvffeg4eHB4yMjNClSxet5puRkYFXXnkFNjY2UCgUaNGiBYKCgpCfn19Pa0L0cPW1fwNAixYtsGDBAq36VV9u8+bNla8vWbIEvXr1grm5OWQyGW7evPnQeebl5WHChAlwcnKCkZER7OzsEBAQoPUPWdLTR+e3Cifp5eTkKP+dnJyM2bNn4/Tp08o2Y2Njlf41/VBcdZaWlnWqQ19fH3Z2dnWa5nHs3LkTHTp0UD5/cD2FEBg7diwOHTqEY8eOPXReeXl5ePnllzFw4EBs374djRs3xoULF7BlyxYUFxfXS/2A9n8Len7Vdf+uL7GxsQgNDVU+19fXV/67uLgY/fr1Q79+/RAVFaXV/IYMGYKysjKsWLECLVu2xNWrV7Fr1y5cv35d8tqrlJaWQi6X19v8n3uCnmnLli0TFhYWyucXLlwQAERycrLw8/MTRkZGIikpSeTn54vhw4eLZs2aCWNjY+Hm5ib++9//qszLz89PvPfee8rnzs7O4pNPPhEhISHC1NRUODo6isWLF6stKyMjQwghxC+//CIAiJ07dwoPDw9hbGwsvL29xV9//aWynLlz54qmTZsKU1NTMW7cODF9+nTRuXPnGtex+nJqEx0dXeu8qmzatEkYGBiIsrKyWvsdP35c9O/fX5iZmQlTU1Pxr3/9S/z9999CCCEqKipETEyMaNasmZDL5aJz587i559/Vqu7+t9CCCGSkpJE27ZthZGRkWjTpo1YuHDhQ2um50/1/VsIIbZs2SLc3d2FkZGRcHFxEXPmzFF5H0dHRwtHR0chl8uFvb29mDRpkhDi/v4NQOVRE2dnZ/Hll18+tL6qff7GjRu19rtx44YAIPbs2fPQfqGhocLGxkYYGRmJDh06iB9//FH5+vr160X79u2FXC4Xzs7O4vPPP1ere+7cuWL06NHC3NxcBAcHCyGEOHDggPD19RUKhUI0b95cTJo0Sdy+ffuh60e1Y8B4xtUUMFq0aCE2bNggzp8/L65cuSIuX74s5s+fLzIyMsS5c+fEf/7zH6Gvry9+++035bSaAoalpaVYuHChOHv2rIiLixN6enri1KlTKsuqHjC8vLzEnj17xIkTJ4Svr6/w8fFRznP16tVCoVCIpKQkcfr0aRETEyPMzc2feMBIS0sTAMS6detEZWWlxj6XL18WlpaW4vXXXxdHjhwRp0+fFklJScrAFB8fL8zNzcXatWvFX3/9JT744ANhaGgozpw5o1J39b/FkiVLhL29vbJtw4YNwtLSUixfvvyhddPzpfr+vW3bNmFubi6WL18uzp07J3bs2CFatGgh5syZI4QQ4n//+58wNzcXKSkp4tKlS+LQoUNiyZIlQgghCgoKRPPmzUVsbKzIyckROTk5NS5X6oBRVlYmTE1NRUREhLh3757GPhUVFaJHjx6iQ4cOYseOHeLcuXPixx9/FCkpKUIIIY4ePSr09PREbGysOH36tFi2bJkwNjYWy5YtU6nb3NxczJ8/X5w9e1acPXtWHDt2TJiamoovv/xSnDlzRhw4cEB07dpVjBkz5qHrR7VjwHjG1RQwFixY8NBp+/fvL6ZOnap8rilgjBw5Uvm8srJS2NjYiMTERJVlaTqCUeWnn34SAMTdu3eFEEJ4eXmJ8PBwlTp69uypVcAwNjYWJiYmysfvv/+u1lfbgCGEEB9++KEwMDAQlpaWol+/fmLevHkiNzdX+XpUVJRwcXERpaWlGqd3cHAQn3zyiUpbt27dRFhYmErd1f8Wjo6OakeP5s6dK7y9vbWqm54f1fdvX19f8emnn6r0WbVqlbC3txdCCPHFF1+I1q1b1/ie1TY4ODs7C7lcrrK/ffXVV2r9tA0YQtw/+tCkSROhUCiEj4+PiIqKEn/88Yfy9e3btws9PT1x+vRpjdOPGDFC9O3bV6Vt2rRpon379ip1Dx48WKXPqFGjxNtvv63Stm/fPqGnp6f8XKJHw0GezylPT0+V5xUVFfjkk0/QqVMnWFlZwdTUFDt27FC5VbsmnTp1Uv5bJpPBzs4OeXl5Wk9jb28PAMppTp8+je7du6v0r/68JsnJycjMzFQ+2rdvr9V0Nfnkk0+Qm5uLRYsWoX379li0aBHatm2LP//8EwCQmZkJX19fjWMmioqK8M8//6Bnz54q7T179sSpU6dU2h78W1y7dg3Z2dkYN24cTE1NlY+PP/4Y586de6z1oWdfeno6YmNjVd47oaGhyMnJQXFxMYYOHYq7d++iZcuWCA0NxaZNm1BeXv5Iy5o2bZrK/vbg70Y9iiFDhuCff/7Bli1bEBAQgD179sDd3R3Lly8HcH9/a968OVq3bq1x+lOnTmnc386ePYuKigplW/XPvvT0dCxfvlxlmwUEBKCyshIXLlx4rHV63nGQ53PKxMRE5fkXX3yBL7/8EgsWLEDHjh1hYmKCiIgIlJaW1jqf6l+uMpkMlZWVWk8jk8kAQGWaqrYqQsu72Ts6OqJVq1Za9dWWlZUVhg4diqFDhyIuLg5du3bF559/jhUrVmg1mE7TulRve/BvUbUdvv32W3h5ean0e3AQHZEmlZWViImJweuvv672mkKhgKOjI06fPo3U1FTs3LkTYWFhmD9/Pvbu3VvnwcXW1taS728KhQJ9+/ZF3759MXv2bIwfPx7R0dEYM2bMQ/c3TfuWps+O6p99lZWVmDBhAiZPnqzW18nJ6RHWgqowYBAAYN++fRg0aJDy0s7KykqcPXsW7dq1e6J1tGnTBocPH8aoUaOUbUePHn2iNdRELpfD1dUVd+7cAXD/SMyKFSs0Xvlhbm4OBwcH7N+/Hy+++KKy/eDBg7UekbG1tUWzZs1w/vx5vPXWW/WzIvTMcnd3x+nTp2v94jc2Nsarr76KV199FeHh4cqjcu7u7pDL5Sr/t69r7du3x+bNmwHc398uX76MM2fOaDyK0b59e+zfv1+l7eDBg2jdunWt4dzd3R0nTpyQPCwRAwb9n1atWmHDhg04ePAgmjRpgvj4eOTm5j7xgDFp0iSEhobC09MTPj4+SE5OxrFjx9CyZcvHmu/ff/+N27dvIzc3F3fv3lXe/Kt9+/YaL1PbunUrvv/+ewwfPhytW7eGEAI//vgjUlJSsGzZMgDAu+++i6+//hrDhw9HVFQULCws8Ntvv6F79+5o06YNpk2bhujoaLi6uqJLly5YtmwZMjMzsWbNmlprnTNnDiZPngxzc3MEBgaipKQER48exY0bNxAZGflY24GebbNnz8Yrr7wCR0dHDB06FHp6ejh27Bj+/PNPfPzxx1i+fDkqKirg5eWFRo0aYdWqVTA2Nlb+gnWLFi3w66+/Yvjw4TAyMoK1tfUj1ZGbm4vc3Fz8/fffAIA///wTZmZmcHJy0ni5e0FBAYYOHYqxY8eiU6dOMDMzw9GjRzFv3jwMGjQIAODn54cXX3wRQ4YMQXx8PFq1aoW//voLMpkM/fr1w9SpU9GtWzfMnTsXQUFBSEtLwzfffIOEhIRaa50+fTp69OiB8PBwhIaGwsTEBKdOnUJqaiq+/vrrR1p/+j86HQFC9a6mQZ7Vr7goKCgQgwYNEqampsLGxkbMmjVLBAcHi0GDBin7aBrkWX1AWOfOnUV0dLTGZWka8JWRkSEAiAsXLijbYmNjhbW1tTA1NRVjx44VkydPFj169KhxHbW5ikTTJXjVl/ugc+fOidDQUNG6dWthbGwsGjduLLp166YyIl0IIf744w/h7+8vGjVqJMzMzISvr684d+6cEEL1MlVDQ8MaL1PVVPeaNWtEly5dhFwuF02aNBEvvvii2LhxY43rR88nTZepbtu2Tfj4+AhjY2Nhbm4uunfvrrxSZNOmTcLLy0uYm5sLExMT0aNHD5VB12lpaaJTp07CyMjosS5TjY6O1ri/Vd9/qty7d0/MmDFDuLu7CwsLC9GoUSPRpk0bMWvWLFFcXKzsV1BQIEJCQoSVlZVQKBTCzc1NbN26Vfl61WWqhoaGwsnJScyfP1+rug8fPiz69u0rTE1NhYmJiejUqZPaAG2qO/5cOz31+vbtCzs7O6xatUrXpRARkZZ4ioSeKsXFxVi0aBECAgKgr6+PtWvXYufOnUhNTdV1aUREVAc8gkFPlbt372LgwIH4/fffUVJSgjZt2mDWrFkaR8UTEdHTiwGDiIiIJMcbbREREZHkGDCIiIhIcgwYREREJDkGDCIiIpIcAwYRERFJjgGDiIiIJMeAQURERJJjwCAiIiLJMWAQERGR5P4fhd1OFFkJTHkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boosting Classifier F1 Score on Training Data: 0.9315\n",
      "Boosting Classifier F1 Score on Test Data: 0.9174\n"
     ]
    }
   ],
   "source": [
    "# Create an AdaBoost classifier\n",
    "boosting_clf = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1), n_estimators=100, random_state=42)\n",
    "\n",
    "# Fit boosting classifier\n",
    "boosting_clf.fit(X_train, y_train)\n",
    "\n",
    "# Evaluate the boosting classifier\n",
    "y_train_pred_boosting = boosting_clf.predict(X_train)\n",
    "y_test_pred_boosting = boosting_clf.predict(X_test)\n",
    "\n",
    "# Calculate F1 scores\n",
    "f1_score_train_boosting = f1_score(y_train, y_train_pred_boosting)\n",
    "f1_score_test_boosting = f1_score(y_test, y_test_pred_boosting)\n",
    "\n",
    "print(f\"Boosting Classifier F1 Score on Training Data: {f1_score_train_boosting:.4f}\")\n",
    "print(f\"Boosting Classifier F1 Score on Test Data: {f1_score_test_boosting:.4f}\")\n",
    "\n",
    "\n",
    "# Calculate confusion matrix for train and test predictions\n",
    "conf_matrix_train = confusion_matrix(y_train, y_train_pred_boosting)\n",
    "conf_matrix_test = confusion_matrix(y_test, y_test_pred_boosting)\n",
    "\n",
    "# Plot the confusion matrices\n",
    "def plot_confusion_matrix(conf_matrix, title):\n",
    "    plt.figure(figsize=(10, 7))\n",
    "    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('Actual')\n",
    "    plt.show()\n",
    "\n",
    "plot_confusion_matrix(conf_matrix_train, \"Confusion Matrix for Training Data\")\n",
    "plot_confusion_matrix(conf_matrix_test, \"Confusion Matrix for Test Data\")\n",
    "\n",
    "# Visualization of F1 scores\n",
    "def plot_f1_scores(f1_train, f1_test, title):\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    plt.bar(['Training F1 Score', 'Test F1 Score'], [f1_train, f1_test], color=['blue', 'green'])\n",
    "    plt.ylim(0, 1)\n",
    "    plt.title(title)\n",
    "    plt.ylabel('F1 Score')\n",
    "    for i, v in enumerate([f1_train, f1_test]):\n",
    "        plt.text(i, v + 0.02, f\"{v:.4f}\", ha='center', va='bottom')\n",
    "    plt.show()\n",
    "\n",
    "plot_f1_scores(f1_score_train_boosting, f1_score_test_boosting, \"Boosting Classifier F1 Scores\")\n",
    "\n",
    "print(f\"Boosting Classifier F1 Score on Training Data: {f1_score_train_boosting:.4f}\")\n",
    "print(f\"Boosting Classifier F1 Score on Test Data: {f1_score_test_boosting:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Voting Classifier - Accuracy: 0.91%\n",
      "Voting Classifier - F1 Score: 0.91\n",
      "Voting Classifier - Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.83      0.88       277\n",
      "           1       0.90      0.96      0.93       449\n",
      "\n",
      "    accuracy                           0.91       726\n",
      "   macro avg       0.92      0.89      0.90       726\n",
      "weighted avg       0.91      0.91      0.91       726\n",
      "\n",
      "Voting Classifier - Confusion Matrix:\n",
      " [[229  48]\n",
      " [ 17 432]]\n",
      "Bagging Classifier - Accuracy: 0.89%\n",
      "Bagging Classifier - F1 Score: 0.89\n",
      "Bagging Classifier - Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.82      0.85       277\n",
      "           1       0.89      0.93      0.91       449\n",
      "\n",
      "    accuracy                           0.89       726\n",
      "   macro avg       0.89      0.87      0.88       726\n",
      "weighted avg       0.89      0.89      0.89       726\n",
      "\n",
      "Bagging Classifier - Confusion Matrix:\n",
      " [[226  51]\n",
      " [ 30 419]]\n",
      "AdaBoost Classifier - Accuracy: 0.88%\n",
      "AdaBoost Classifier - F1 Score: 0.88\n",
      "AdaBoost Classifier - Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.80      0.84       277\n",
      "           1       0.88      0.93      0.91       449\n",
      "\n",
      "    accuracy                           0.88       726\n",
      "   macro avg       0.88      0.87      0.87       726\n",
      "weighted avg       0.88      0.88      0.88       726\n",
      "\n",
      "AdaBoost Classifier - Confusion Matrix:\n",
      " [[221  56]\n",
      " [ 30 419]]\n",
      "Gradient Boosting Classifier - Accuracy: 0.89%\n",
      "Gradient Boosting Classifier - F1 Score: 0.89\n",
      "Gradient Boosting Classifier - Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.83      0.86       277\n",
      "           1       0.90      0.93      0.92       449\n",
      "\n",
      "    accuracy                           0.89       726\n",
      "   macro avg       0.89      0.88      0.89       726\n",
      "weighted avg       0.89      0.89      0.89       726\n",
      "\n",
      "Gradient Boosting Classifier - Confusion Matrix:\n",
      " [[230  47]\n",
      " [ 30 419]]\n",
      "                          Model  Accuracy  F1 Score\n",
      "0             Voting Classifier  0.910468  0.909309\n",
      "1            Bagging Classifier  0.888430  0.887507\n",
      "2           AdaBoost Classifier  0.881543  0.880293\n",
      "3  Gradient Boosting Classifier  0.893939  0.893246\n"
     ]
    }
   ],
   "source": [
    "# Function to evaluate model performance\n",
    "def evaluate_model_performance(y_test, y_pred, model_name):\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    f1 = f1_score(y_test, y_pred, average='weighted')\n",
    "    print(f\"{model_name} - Accuracy: {accuracy:.2f}%\")\n",
    "    print(f\"{model_name} - F1 Score: {f1:.2f}\")\n",
    "    print(f\"{model_name} - Classification Report:\\n\", classification_report(y_test, y_pred))\n",
    "    print(f\"{model_name} - Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred))\n",
    "    return accuracy, f1\n",
    "\n",
    "# Evaluate Voting Classifier\n",
    "voting_accuracy, voting_f1 = evaluate_model_performance(y_test, y_pred_voting, \"Voting Classifier\")\n",
    "\n",
    "# Evaluate Bagging Classifier\n",
    "bagging_accuracy, bagging_f1 = evaluate_model_performance(y_test, y_pred_bagging, \"Bagging Classifier\")\n",
    "\n",
    "# Evaluate AdaBoost Classifier\n",
    "adaboost_accuracy, adaboost_f1 = evaluate_model_performance(y_test, y_pred_adaboost, \"AdaBoost Classifier\")\n",
    "\n",
    "# Evaluate Gradient Boosting Classifier\n",
    "gb_accuracy, gb_f1 = evaluate_model_performance(y_test, y_pred_gb, \"Gradient Boosting Classifier\")\n",
    "\n",
    "# Compare Ensemble Model Performance\n",
    "ensemble_performance = {\n",
    "    'Model': ['Voting Classifier', 'Bagging Classifier', 'AdaBoost Classifier', 'Gradient Boosting Classifier'],\n",
    "    'Accuracy': [voting_accuracy, bagging_accuracy, adaboost_accuracy, gb_accuracy],\n",
    "    'F1 Score': [voting_f1, bagging_f1, adaboost_f1, gb_f1]\n",
    "}\n",
    "\n",
    "performance_df = pd.DataFrame(ensemble_performance)\n",
    "print(performance_df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7: Visualise Ensemble Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Accuracy\n",
    "plt.figure(figsize=(10, 5))\n",
    "bars = sns.barplot(x='Model', y='Accuracy', data=performance_df, palette='viridis')\n",
    "plt.title('Ensemble Model Comparison - Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Model')\n",
    "plt.ylim(0, 1)\n",
    "for bar in bars.patches:\n",
    "    bars.annotate(f'{bar.get_height()*100:.2f}%',\n",
    "                  (bar.get_x() + bar.get_width() / 2, bar.get_height()),\n",
    "                  ha='center', va='center', size=12, xytext=(0, 8),\n",
    "                  textcoords='offset points')\n",
    "plt.show()\n",
    "\n",
    "# Plot F1 Score\n",
    "plt.figure(figsize=(10, 5))\n",
    "bars = sns.barplot(x='Model', y='F1 Score', data=performance_df, palette='viridis')\n",
    "plt.title('Ensemble Model Comparison - F1 Score')\n",
    "plt.ylabel('F1 Score')\n",
    "plt.xlabel('Model')\n",
    "plt.ylim(0, 1)\n",
    "for bar in bars.patches:\n",
    "    bars.annotate(f'{bar.get_height()*100:.2f}%',\n",
    "                  (bar.get_x() + bar.get_width() / 2, bar.get_height()),\n",
    "                  ha='center', va='center', size=12, xytext=(0, 8),\n",
    "                  textcoords='offset points')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}