<![CDATA[Advances in machine learning drive its application in analyzing complex educational data. In international education, housing rent (Rent_USD) is a critical cost-of-living component showing significant variation across regions. These variations are influenced by geography, local economics, and educational environments, requiring systematic data modeling. This study aims to model Rent_USD using the CRISP-DM framework: Business Understanding, Data Understanding, Data Preparation, Modeling, and Evaluation. Three algorithms were employed: Decision Tree as the baseline, Random Forest as a comparison, and XGBoost as the primary model. To enhance performance, hyperparameter tuning was conducted via GridSearchCV. Model evaluation utilized Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R2). The experimental results demonstrate that the XGBoost algorithm delivers the most superior performance, achieving the lowest RMSE of 93.27 USD and an R2 of 0.96. This performance outperforms Random Forest (RMSE: 114.87, R2: 0.94) and Decision Tree (RMSE: 157.16, R2: 0.89). Furthermore, feature importance analysis revealed crucial findings: the Living Cost Index and Tuition Fee are the most dominant factors influencing Rent_USD variations, contributing 58.32% and 32.94% respectively. This research provides an empirical overview of machine learning applications in modeling international education costs and serves as a vital reference for future studies regarding educational data management and predictive analytics in global student mobility.]]>
