diff --git "a/code_files/7-Ensemble Learning Project \342\200\223 Comparing Models on a Real Dataset/day7_ex.py" "b/code_files/7-Ensemble Learning Project \342\200\223 Comparing Models on a Real Dataset/day7_ex.py"
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+import pandas as pd
+from sklearn.preprocessing import LabelEncoder, StandardScaler
+from imblearn.over_sampling import SMOTE
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from xgboost import XGBClassifier
+from lightgbm import LGBMClassifier
+from sklearn.metrics import classification_report, roc_auc_score
+
+# Load Dataset
+df = pd.read_csv("Telco-Customer-Churn.csv")
+
+# Display dataset info and preview
+print("Dataset Info: \n")
+print(df.info())
+print("\n Class Distribution: \n")
+print(df['Churn'].value_counts())
+print("\n Sample Data:\n", df.head())
+
+# Handle missing values
+df['TotalCharges'] = pd.to_numeric(df['TotalCharges'], errors='coerce')
+df.fillna({'TotalCharges': df['TotalCharges'].median()}, inplace=True)
+
+# Encode categorical variables
+label_encoder = LabelEncoder()
+for column in df.select_dtypes(include=['object']).columns:
+    if column != 'Churn':
+        df[column] = label_encoder.fit_transform(df[column])
+        
+# Encode target variable
+df['Churn'] = label_encoder.fit_transform(df['Churn'])
+
+# Scale numerical features
+scaler = StandardScaler()
+numerical_features = ['tenure', 'MonthlyCharges', 'TotalCharges']
+df[numerical_features] = scaler.fit_transform(df[numerical_features])
+
+# Features and Target
+X = df.drop(columns=['Churn'])
+y = df['Churn']
+
+# Split dataset
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Apply SMOTE
+smote = SMOTE(random_state=42)
+X_train_resampled, y_train_resampled = smote.fit_resample(X_train, y_train)
+
+# Display class distribution after SMOTE
+print("\n Class Distribution after SMOTE: \n")
+print(pd.Series(y_train_resampled).value_counts())
+
+# Train Random Forest
+rf_model = RandomForestClassifier(random_state=42)
+rf_model.fit(X_train_resampled, y_train_resampled)
+y_pred_rf = rf_model.predict(X_test)
+roc_auc_rf = roc_auc_score(y_test, rf_model.predict_proba(X_test)[:, 1])
+
+# Train XGBoost
+xgb_model = XGBClassifier(eval_metric='logloss',random_state=42)
+xgb_model.fit(X_train_resampled, y_train_resampled)
+y_pred_xgb = xgb_model.predict(X_test)
+roc_auc_xgb = roc_auc_score(y_test, xgb_model.predict_proba(X_test)[:, 1])
+
+# Train LightGBM
+lgb_model = LGBMClassifier(random_state=42)
+lgb_model.fit(X_train_resampled, y_train_resampled)
+y_pred_lgb = lgb_model.predict(X_test)
+roc_auc_lgb = roc_auc_score(y_test, lgb_model.predict_proba(X_test)[:, 1])
+
+# Classification reports
+print("Random Forest Report:\n", classification_report(y_test, y_pred_rf))
+print("XGBoost Report: \n", classification_report(y_test, y_pred_xgb))
+print("LightGBM Report: \n", classification_report(y_test, y_pred_lgb))
+
+# ROC_AUC comparison
+print("ROC-AUC Scores: \n")
+print(f"Random Forest: {roc_auc_rf:.2f}")
+print(f"XGBoost: {roc_auc_xgb:.2f}")
+print(f"LightGBM: {roc_auc_lgb:.2f}")
+