House Price Prediction using ML

This project focuses on developing a machine learning–based system to accurately estimate house prices. The model is trained using a dataset containing features such as square footage, number of bedrooms and bathrooms, location, age of the property, and other relevant factors.

Using algorithms like Linear Regression, Random Forest, or XGBoost, the system learns patterns from historical property data and predicts the price of new houses.

The project also includes data cleaning, feature engineering, model evaluation, and visualization of results. This helps users understand what features affect the price most and provides a reliable, automated way to estimate real-estate values.