HOUSE PRICE PREDICTION USING DATA SCIENCE

Abstract

House price prediction has become an important research problem due to the increasing complexity of real estate markets and the growing availability of large datasets. Accurate estimation of property values helps buyers, sellers, investors, and financial institutions make informed decisions. Traditional property valuation methods mainly rely on manual comparison of historical transactions and expert judgment, which often leads to inconsistent and inaccurate pricing outcomes. With the rapid development of data science and machine learning techniques, predictive analytics has emerged as a powerful approach for modeling real estate price trends. This study presents a data-driven house price prediction system that uses machine learning algorithms to estimate property prices based on key housing attributes. The system utilizes a structured dataset containing features such as location, total area in square feet, number of bedrooms, number of bathrooms, and other relevant property characteristics. Data preprocessing techniques including data cleaning, handling missing values, encoding categorical variables, and removing outliers are applied to improve dataset quality. Feature engineering is performed to identify the most influential variables affecting house prices. The predictive model is implemented using the XGBoost Regressor algorithm due to its efficiency, scalability, and superior performance compared with traditional regression models. Model training and evaluation are conducted using appropriate performance metrics such as Mean Absolute Error and R-squared score to assess prediction accuracy. The proposed system is implemented as a webbased application that allows users to input housing parameters and obtain predicted property prices instantly. Experimental results demonstrate that the machine learning model significantly improves prediction accuracy and provides reliable price estimates for real estate decision making.