Projects & Research
Voltage Anomaly Detection (Schneider Electric)
Conducted a large-scale statistical analysis of voltage behavior across 200+ corporations to define normal operating ranges. Applied feature engineering, unsupervised learning algorithms (GMM, DBSCAN, K-Means, PCA), and developed a fully automated, scalable ML pipeline using Python (OOP) for anomaly detection and voltage profiling.
The solution optimized operational efficiency, reduced costs, and enabled automated technician dispatch through intelligent voltage anomaly identification.
Note: Project details are protected under NDA and not open-sourced.
Gradient Descent: Insights into Behavior and Optimization Challenges
Manually implemented and visualized gradient descent optimization paths across various cost functions to investigate convergence behaviors under different initialization strategies and learning rates. Explored phenomena like slow convergence, divergence, oscillations, and local minima through a series of four structured case studies.
This project highlights practical limitations of gradient-based optimization and provides valuable insights for debugging, hyperparameter tuning, and model optimization in machine learning workflows.