Fraud Detection

Developed a robust end-to-end machine learning pipeline to detect fraudulent credit card transactions. The project involved handling highly imbalanced datasets using techniques like SMOTE (Synthetic Minority Over-sampling Technique). I implemented and compared multiple classifiers, including Random Forest and XGBoost, achieving high recall to ensure maximum fraud detection while maintaining a low false-alarm rate