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Smart Grid AI System: A real-time energy monitoring and forecasting dashboard. Features live electricity demand tracking (EIA API), machine learning load prediction using Random Forest, and grid anomaly detection via Isolation Forest. Built with Flask, scikit-learn, and Chart.js.
End-to-end credit card fraud detection pipeline with preprocessing, feature scaling, and class imbalance handling via undersampling and SMOTE. Trains Logistic Regression and Decision Tree models, evaluates with key metrics, and exports the final model using joblib for manual transaction classification.
Mahalanobis scoring stage of PaDiM anomaly detection, implemented four ways - PyTorch, C++17 CPU, hand-written CUDA (naive + PBCR), cuBLAS - behind a fixed artifact boundary, with a 1e-4 numerical agreement gate on every reported timing.