Credit Card Fraud Detection

Overview

Built a robust machine learning system to detect fraudulent credit card transactions in real-time with high precision and recall.

Methodology

Implemented an ensemble approach combining Random Forest and XGBoost algorithms. Used SMOTE to handle class imbalance and feature engineering to improve model performance.

Key Findings

• Achieved 99.2% accuracy with 0.95 precision and 0.91 recall on fraudulent transactions
• Feature importance analysis revealed unusual transaction timing as a key indicator
• Model reduced false positives by 35% compared to previous rule-based system

Data Visualization