diff --git a/code_files/6- Handling Imbalanced Data/day6_ex.py b/code_files/6- Handling Imbalanced Data/day6_ex.py
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+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import classification_report, roc_auc_score
+from imblearn.over_sampling import SMOTE
+
+# Load dataset
+url = "https://storage.googleapis.com/download.tensorflow.org/data/creditcard.csv"
+df = pd.read_csv(url)
+
+# Explore dataset
+print("Dataset Info:\n")
+print(df.info())
+print("\n Class Distribution:\n")
+print(df["Class"].value_counts())
+
+# SPlit dataset
+X = df.drop(columns=['Class'])
+y = df['Class']
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train Random Forest
+rf_model = RandomForestClassifier(random_state=42, class_weight="balanced")
+rf_model.fit(X_train, y_train)
+
+# Predict and evaluate
+y_pred = rf_model.predict(X_test)
+print("\n CLassification Report:\n")
+print(classification_report(y_test, y_pred))
+
+roc_auc = roc_auc_score(y_test, rf_model.predict_proba(X_test)[:,1])
+print(f"ROC-AUC: {roc_auc:.2f}")
+
+# Apply SMOTE
+smote = SMOTE(random_state=42)
+X_resampled, y_resampled = smote.fit_resample(X_train, y_train)
+
+# Display new class distribution
+print("\n Class Distribution After SMOTE: \n")
+print(pd.Series(y_resampled).value_counts())
+
+# Train Random Forest on resampled data
+rf_model_smote = RandomForestClassifier(random_state=42)
+rf_model_smote.fit(X_resampled, y_resampled)
+
+# Predict and evaluate
+y_pred_smote = rf_model_smote.predict(X_test)
+print("\n CLassification Report (SMOTE):\n")
+print(classification_report(y_test, y_pred_smote))
+
+roc_auc_smote = roc_auc_score(y_test, rf_model_smote.predict_proba(X_test)[:,1])
+print(f"ROC-AUC (SMOTE): {roc_auc_smote:.2f}")