💳 Payment Fraud Detection ML Model — XGBoost + SMOTE on 10,000 PaySim transactions (1.12% fraud rate). PR-AUC 1.00 · Zero false negatives · dest_balance_zeroed top feature (45.25%). Class imbalance handled via SMOTE. Python · XGBoost · imblearn

End-to-end ML pipeline for imbalanced tabular data using Neural Networks and LightGBM with PR-AUC optimization, calibration, and stacking.

Three classification models trained to predict failures of machines on the production line.

Analysis into a credit risk dataset and application of several supervised learning models to predict the binary variable on default status