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Machine Learning Based Smart House Price Prediction System

V. MAGESWARI, MCA.,

(Assistant Professor, Master of Computer Applications)

S. SANJAI,MCA.,

Christ College of Engineering and Technology

Moolakulam, Oulgaret Municipality, Puducherry – 605010.

Abstract

Accurate house price estimation is essential for real estate decision-making, loan approval, investment planning, and market analysis. Traditional valuation methods are often subjective and depend on human experience, which may lead to inconsistent pricing and delayed decisions. Machine learning provides a reliable and data-driven solution for house price prediction by learning patterns from historical datasets and producing accurate price estimates for new houses [1], [2]. This paper presents a Machine Learning Based Smart House Price Prediction System that predicts house prices using real estate attributes such as location, total area (sqft), number of bedrooms (BHK), bathrooms, balconies, area type, and availability. Data preprocessing methods including missing value handling, categorical encoding, feature scaling, and outlier removal were performed to improve model performance [1]. Multiple regression models including Linear Regression, Ridge, Lasso, Support Vector Regression (SVR), and XGBoost Regression were trained and evaluated using standard metrics such as RMSE and R² score [7], [8]. Recent studies show that ensemble boosting models such as XGBoost often produce superior prediction accuracy in house price estimation due to their ability to capture complex non-linear relationships [3], [4]. The final trained model was deployed using a Flask-based web application, enabling users to enter house details and obtain real-time predicted prices. This system supports users by providing transparent, automated, and consistent house price estimation.

Keywords

House price prediction, machine learning, regression models, real estate analytics, data preprocessing, feature engineering, Linear Regression, SVR, XGBoost, RMSE, Flask web application [1], [3], [7]