PROJECTS

An end-to-end data-driven web application designed to make property buying decisions smarter. Built with Streamlit, it integrates interactive visualizations—including sector-level geomaps, price distribution charts, and feature word clouds—with a machine learning–powered price prediction model and a content-based recommender system that suggests similar properties based on user preferences. Users can explore market trends, compare sectors, and get personalized property recommendations, making it a valuable tool for buyers, sellers, and real estate analysts.

A personalized movie recommendation system built using content-based filtering. By analyzing features like genres, keywords, cast, and movie overviews, it identifies patterns in your preferences and recommends similar titles. Powered by vector space modeling and cosine similarity, the system offers intelligent recommendations straight from the TMDB 5000 Movie Dataset.