Forecasting Condominium Prices in Bangkok through Machine Learning Techniques

Understanding Thailand is condominium price trends is crucial but challenging. It has implications for investors, homeowners, and real estate professionals. This study explores how accurately predicting these price fluctuations can impact investment strategies. To achieve this we employ machine learning techniques. Evaluate algorithms like Multiple Linear Regression, Gradient Boosting, XGBoost Random Forest and others to forecast condominium prices in Bangkok. The results highlight the effectiveness of XGBoost in predicting price trends with an R2 score of 0.742142. Other models also show varying levels of performance. These findings contribute to optimizing investment strategies in Thailand is real estate market by reducing risks and maximizing returns They provide a foundation for research and practical applications.