Flask app for tracking expenses securely. Features authentication, transaction management, and insightful analytics for informed financial decisions.

A containerized analytics engine with multivariate anomaly detection (LOF, One Class SVM, deep autoencoder), forecasting, drift analysis, and semantic clustering via REST APIs, delivering results in ∼3s on 10,000+ transactions.

Analyzing Personal Expenses - This project uses Faker library to simulate an expense tracker, storing monthly data in a SQL database. A Streamlit app visualizes spending insights across categories like bills, groceries, and subscriptions for a year.