ABSTRACT: House prices are influenced by many factors such as location, area, number of rooms, age of the property, amenities and local market conditions. Because these factors interact with each other, estimating a fair property price can be difficult for buyers, sellers and real-estate professionals. This paper presents a machine-learning based framework for house price prediction and real-estate analytics. The study explains how a structured property dataset can be cleaned, explored and transformed into useful features before applying regression algorithms. Linear Regression, Decision Tree Regression, Random Forest Regression and Gradient Boosting are considered as candidate models. Model performance can be evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and R². The paper also discusses how the prediction model can be converted into a practical web or mobile application. The numerical charts included here are explicitly marked as illustrative examples; they are not claimed as measured results from a collected dataset. The proposed framework provides a practical foundation for using machine learning in real-estate analytics.
KEYWORDS: House Price Prediction, Machine Learning, Real Estate Analytics, Regression, Random Forest, Decision Tree, Property Valuation, Data Analytics
HOUSE PRICE PREDICTION AND REAL ESTATE ANALYTICS USING MACHINE LEARNING
MISS. RUCHI BANDEKAR