A community-driven app for tracking and comparing the prices of everyday goods across cities. Built with Python, SQLite, Pandas, and Streamlit, it lets users log, visualize, and analyze item price trends, inflation, and cost-of-living differences with clear charts and full local data privacy.

An enterprise-grade NLP + Streamlit + SQL platform for analyzing customer feedback. Performs automated sentiment detection, stores labeled reviews in SQLite, and delivers real-time dashboards with probability insights to support business, marketing, and product optimization decisions.

Machine learning model to predict NYC taxi fares with rush hour comparison and inflation tracking (2016-2025). Random Forest model achieving $1.79 RMSE.