REAL ESTATE PRICE PREDICTION USING MACHINE LEARNING

Abstract

The real estate industry's notorious lack of transparency and volatile housing prices have spurred research projects to enhance housing price predictions. These initiatives employ a combination of regression techniques, emphasizing the weighted average of these methods to yield more accurate outcomes. They also suggest the utilization of real-time neighborhood data from Google Maps to enhance real-world valuations. With the real estate market's competitiveness and its reliance on factors like physical condition, concepts, and location, these projects strive to forecast residential prices based on customer financial needs, studying historical market trends and forthcoming developments. Multiple regression techniques such as Linear Regression, Lasso Regression, Forest Regression aiming to optimize house price predictions and assist lower- and middle-class individuals with financial parameter-based price estimations. Overall, these projects leverage data science and machine learning to address real estate pricing challenges, seeking to enhance transparency and decision-making for both buyers and investors. Key Words: Real Estate, Linear Regression, Lasso Regression, Forest Regression, Decision-making.