Projects
Selected work across ML, GenAI, and deployable systems
A collection of projects focused on retrieval-augmented generation, OCR-based document intelligence, regression systems, forecasting, and deployment-oriented backend engineering.
CivicAI
A full-stack AI-powered municipal by-law assistant that combines RAG, pgvector semantic retrieval, Redis and Celery background processing, S3-based document storage, and FastAPI backend services to deliver structured, citation-backed answers through a production-style architecture.
Belleville By-Law Assistant
An OCR-powered RAG assistant for municipal by-law documents built with Tesseract, MiniLM embeddings, FAISS retrieval, FastAPI, and Streamlit/Gradio interfaces, designed to turn scanned PDFs into citation-grounded legal question answering workflows.
Car Price Prediction
An end-to-end machine learning project covering exploratory analysis, feature engineering, model comparison, and deployment as a FastAPI microservice. Random Forest achieved the strongest performance, with R² of 0.908 and lower error than linear baselines.
Stock Price Prediction: ARIMA vs LSTM
A time-series forecasting project on Tesla stock data comparing ARIMA and LSTM models. LSTM significantly outperformed ARIMA, reducing RMSE from about 43.25 to 13.19 and better capturing volatile price movement.