A machine learning system that analyzes financial transactions and predicts fraudulent behavior in real time using engineered behavioral features and trained classification models.
This system processes raw transaction logs, extracts meaningful behavior-based features, and applies trained ML models to detect anomalies that indicate possible fraud attempts.
✅ Processed 2,000+ labeled financial transactions
✅ Engineered advanced behavioral & transactional features
✅ Trained multiple ML models (Random Forest, XGBoost, Logistic Regression)
✅ Achieved 87% fraud detection accuracy on validation set
| Layer | Technology |
|---|---|
| Language | Python |
| ML Libraries | scikit-learn, XGBoost |
| Data Tools | pandas, NumPy |
| Visualization | matplotlib / seaborn (optional) |