Eman Jamil@emanjamil358

Real-Time Fraud Detection System is a machine learning system that detects potentially fraudulent financial transactions in real time. It uses a tuned Random Forest Classifier trained on the PaySim synthetic financial transactions dataset (~30,000 rows), with engineered features such as origin/destination balance changes, large-transaction flags, and one-hot encoded transaction types. The model is served through a FastAPI REST API (/predict endpoint) deployed as a serverless function on Vercel, and a lightweight HTML/CSS/JavaScript frontend lets users enter transaction details (type, amount, step, origin/destination balances) and instantly see whether a transaction is predicted as Legitimate or Fraudulent. On the validation side, the model reaches 99.85% accuracy, 100% precision, and 47% recall on the held-out test split — tuned to minimize false positives (flagging legitimate transactions as fraud) while still catching a meaningful share of fraud cases. The project also implements a governed ML lifecycle workflow (retrain → validate → promote → verify → rollback) enforced through IBM Bob, so no model artifact ever reaches production without passing a hard validation gate.

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