SMART ESTATE: Intelligent Real Estate Price Prediction and Investment Recommendation System

Abstract: This paper presents a data-driven framework for intelligent real estate price prediction and investment recommendation using machine learning techniques. Unlike conventional valuation methods that rely on manual appraisals and subjective judgments, the proposed system leverages structured datasets containing attributes such as property size, location, area, and the number of rooms. The model integrates Linear Regression and Random Forest algorithms to enhance prediction accuracy and provide reliable valuation insights. Additionally, the system offers investment recommendations based on predictive analysis, thereby assisting buyers and investors in informed decision-making. Comparative evaluation of the models demonstrates that the Random Forest approach outperforms Linear Regression in terms of accuracy and stability. The results indicate that the proposed Smart Estate system can significantly improve transparency, minimize pricing bias, and modernize real estate transactions in the Indian market.

Keywords: Real Estate · Machine Learning · Price Prediction · Investment Recommendation · Regression Models