California House Price Prediction

This project involved training a regression model to predict California house prices based on 2,000 housing data samples. Key features analyzed included the number of bedrooms, bathrooms, year built, and square footage. Through careful data preprocessing, feature selection, and model tuning, I built a predictive model that provides accurate price estimates. To make the model accessible and user-friendly, I deployed it to a web application where users can input property details and receive real-time predictions. This project highlights my expertise in regression modeling, feature engineering, and web deployment, demonstrating my ability to create practical, data-driven solutions for real-world applications.