Boston House Predictor
About this Project
🏠 Boston House Predictor is a user-friendly web application that offers instant predictions of house prices based on Boston housing data. Built with HTML, CSS, and JavaScript, it delivers a clean, responsive UI deployed via GitHub Pages. Users input parameters to get real-time price estimates, view insights through charts, and enjoy seamless experience across devices. ⚡ Ideal for anyone curious about housing markets or learning applied ML in web tools.
Key Features
- •Real-time house price prediction using trained machine learning model
- •Interactive web interface with intuitive parameter input forms
- •Dynamic charts and visualizations showing price trends and factors
- •Responsive design that works seamlessly across all devices
- •Fast prediction results with detailed breakdown of contributing factors
- •Educational tooltips explaining housing market variables
- •Clean, modern UI with smooth animations and transitions
Challenges & Solutions
The main challenges were converting a Python ML model to work in a browser environment, optimizing model performance for real-time web predictions, creating meaningful visualizations for housing data, and ensuring the interface remained educational while being user-friendly for non-technical users.
What I Learned
This project enhanced my skills in client-side machine learning implementation, data visualization with JavaScript charting libraries, responsive web design principles, and bridging the gap between complex ML models and accessible user interfaces. I also learned about housing market dynamics and feature importance in real estate.