Project Overview This project predicts house prices based on various features like area, location, number of rooms, and amenities. It demonstrates a complete machine learning pipeline—from data preprocessing to model deployment—making it ideal for learners and developers aiming to build real-world ML applications. 🚀 Features End-to-end ML pipeline using Scikit-learn Data cleaning, encoding, scaling, and feature engineering Comparison of Linear, Lasso, and Ridge Regression models Evaluation using R² Score, MAE, and MSE Interactive web app built with Streamlit / Flask Modular codebase with app.py Professional UI with sidebar navigation and result placement Optional deployment on GitHub git clone https://github.com/imukeshkumarprajapat/house_price_prediction live>>https://housepriceprediction-4gaxewvefhgtlvhfp6cpqv.streamlit.app/