E-Commerce Shopper Behavior Analysis
ML pipeline predicting purchase intent from 12,330 sessions using clustering, SMOTE, and ensemble methods achieving 0.93 AUC.
ML pipeline predicting purchase intent from 12,330 sessions using clustering, SMOTE, and ensemble methods achieving 0.93 AUC.
End-to-end Telematics platform using ML to score driver risk with a real-time Streamlit dashboard achieving 0.98 ROC-AUC.
Undergraduate Honors Thesis using PyTorch, SMOTE, and Lipschitz bounds to achieve 97% accuracy in seizure detection.