A fraud detection classifier and investigation dashboard built on banking transaction data, combining SQL, Python, and interactive analytics to support faster fraud investigations and risk-based decisions.
💳 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
VAJRA -- a fake job posting detector
Analysis into a credit risk dataset and application of several supervised learning models to predict the binary variable on default status
End-to-end ML pipeline for imbalanced tabular data using Neural Networks and LightGBM with PR-AUC optimization, calibration, and stacking.
Multivariate anomaly detection on synthetic sensor telemetry — benchmarks Isolation Forest against LOF, One-Class SVM, and PCA reconstruction error, evaluated with PR-AUC and cost-based thresholding against labeled, categorized injected faults (spike, drift, stuck sensor, correlated failure).
Three classification models trained to predict failures of machines on the production line.
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