AI Credit Card Fraud Detection

Built an applied fraud-detection workflow for anonymized card transaction data, with preprocessing, class-imbalance correction, model comparison, and security-focused evaluation metrics.

The project compares logistic regression, random forest, XGBoost, and neural models using recall, precision, F1 score, and ROC-AUC so the result is judged by fraud-catching usefulness instead of accuracy alone.