PREDICTION OF RESIDENTIAL HOUSE PRICES USING MACHINE LEARNING REGRESSION TECHNIQUES
DOI:
https://doi.org/10.69980/w4875467Keywords:
House Price Prediction, Machine Learning, Linear Regression, Real Estate Analytics, Regression Models, Data AnalysisAbstract
The precise forecasting of residential house prices is a significant issue in the real estate industry as it helps in informed decision making by buyers, sellers, and investors. The purpose of the study is to create a machine learning-based model to predict house prices on the basis of a structured housing data. The dataset in this study comprises 545 records with an array of properties, such as area, number of bedrooms, bathrooms, stories, and other housing characteristics. The dataset was prepared by data preprocessing methods like encoding categorical variables and feature selection, to use in modeling. Three machine learning regression algorithms - Linear Regression, Random Forest Regressor and Decision Tree Regressor - were deployed and compared. Evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and coefficient of determination (R2) were used to evaluate the performance of these models. Results show that the Linear Regression model performed better than the rest with R2 of around 0.65 which shows moderate predictive power. The results indicate that machine learning methods can be effective in predicting the prices of residential properties, and it can be used as a valuable tool in the analysis and decision-making of real estate.
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