Project Details

This project applies machine learning to detect fraudulent credit card transactions using an anonymized real-world dataset. A Random Forest classifier is trained on balanced transaction data and evaluated using precision, recall, F1-score, and confusion matrix analysis. The model achieves very high accuracy and precision while identifying key transaction features that contribute most to fraud detection, highlighting both the strengths and recall challenges of supervised fraud detection systems.