Real Estate Rental Price Prediction using ML

This project develops a machine learning model that predicts rental prices for real-estate properties based on features such as location, square-foot area, number of rooms, property age, amenities, and local market trends.

The workflow includes data cleaning, feature engineering, outlier handling, categorical encoding, model training using Linear Regression, Random Forest, or Gradient Boosting, and evaluation using RMSE and R² score.

This system helps owners, renters, and property agencies estimate fair rental prices accurately and make data-driven decisions.