A machine learning web application that predicts Boston house prices using Decision Tree regression. The platform features a Flask backend with REST API endpoints, allowing users to input property features and receive instant price predictions. The application includes comprehensive data analysis capabilities and a modern, user-friendly interface.
Project Overview
Dataset / Input Data
Dataset Name / SourceBoston Housing Dataset
FormatCSV
Size / Info506 samples, 13 features
Model / Approach
Algorithm / Model
Decision Tree Regressor
Tools / Frameworks
Tech Stack
Python
Flask
scikit-learn
Pandas
REST API
Railway
Results / Output
Performance Metrics
R² Score78.8%
Dataset Size506 samples
Features13
Highlights / Improvements
- Achieved 78.8% R² accuracy on Boston housing data
- Production-ready Flask REST API
- Real-time price prediction interface
- Comprehensive data analysis dashboard
- Deployed on Railway for public access
