Imbalanced learning

Credit Card Fraud Detection

Fraud detection on the Kaggle credit card dataset, where 0.172% of transactions are fraud, comparing four classifiers with SMOTE and ADASYN resampling.

• Conducted fraud detection on the Kaggle dataset (284,807 transactions, only 492 fraud → 0.172%).
• Applied SMOTE and ADASYN to handle severe class imbalance.
• Implemented and compared Logistic Regression, Decision Tree, Random Forest, and XGBoost.
• Achieved ROC-AUC 0.9817 and Recall 0.735 with XGBoost on imbalanced data.
• Improved Recall to 0.816 with ADASYN while maintaining high ROC-AUC (0.9744), outperforming other models.
• Contributed to data preprocessing, resampling, model development, and evaluation, delivering a robust prototype for financial fraud detection.