Prediction of House Prices Using Machine Learning

Abstract

This study investigates the application of machine learning techniques, specifically regression models alongside the Random Forest algorithm, for the predictive analysis of housing prices. Leveraging a comprehensive dataset comprising various housing attributes, the research focuses on preprocessing steps encompassing data cleaning, normalization, and feature engineering to enhance model performance. Regression models including linear regression, ridge regression, and lasso regression, as well as the Random Forest algorithm, are trained and evaluated using rigorous cross-validation techniques to ensure robustness and accuracy."These evaluation measures, including the square root of the average squared variances between predicted and actual values and the mean of the absolute variances between predicted and actual values, are used for a comprehensive performance evaluation".Results demonstrate that the Random Forest algorithm outperforms traditional regression models, showcasing higher accuracy and resilience in handling complex, non-linear relationships among housing features. These findings underscore the significance of leveraging machine learning, particularly the Random Forest approach, in effectively predicting housing prices, providing valuable insights for stakeholders in the real estate domain for informed decision-making processes.