A COMPARATIVE STUDY OF PREDICTING REAL ESTATE PRICES USING MACHINE LEARNING APPROACHES

Machine Learning engineering

Research into real estate price prediction using artificial intelligence has been the subject of several empirical studies. The purpose of this project is to identify the most accurate model for predicting real estate prices using artificial intelligence, based on the characteristics of the property and its locality. This study was conducted using an Indian real estate dataset, which includes 14,620 property sales samples and 23 features. To achieve this, various machine learning techniques were employed to compare selected supervised algorithms: Linear Regression,Ridge Regression, Lasso Regression, Support Vector Machine, Random Forest , K-Nearest Neighbors . The choice of the best model is based on the comparison of performance evaluation metrics such as: MAE, MSE, RMSE, R2 and R2 Cross-Validation. The prediction results showed that the optimized Random Forest algorithm provides the best overall performance, with lowest values of MAE, MSE, and RMSE, as well as high R² and R² cross-validation. It gives 87.44% prediction accuracy. This project also demonstrated the importance of taking into account the geographic context in the analysis and prediction of real estate prices.[1]

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