Systems, proof, trade-offs.

I built an end-to-end ML system forecasting NYC taxi/rideshare demand across 263 zones, cutting prediction error (RMSE) by 43% over a baseline Random Forest and serving live predictions through a REST API and dashboard.

I built a personal analytics platform that processes a 71K+ event Spotify streaming-history export through a 4-stage Python ETL pipeline and serves insights via a FastAPI backend and React/MUI dashboard.

I built a citation-backed RAG portal that unifies fragmented multi-payer insurance policies so clinicians can answer drug coverage and prior-authorization questions without digging through 50-500 page PDFs.

I built an LLM-powered conversational AI assistant that provides personalized expense insights using retrieval-augmented generation and tool-based reasoning.

I built an LSTM-based deep learning model that classifies news headlines with 93.28% accuracy on a dataset of 400K+ headlines.

I developed a personalized recommendation system for movies, music, and books using multiple algorithms including collaborative filtering, content-based filtering, and clustering.

I built an end-to-end Android music streaming app with API integration and intelligent caching that reduced API calls by 70%.