<![CDATA[This study examines the effectiveness of three machine learning algorithms—Random Forest Regression, Support Vector Regression, and Gradient Boosting—in predicting students’ academic grades based on lifestyle-related factors including study hours, sleep duration, social interaction, physical activity, and stress levels. Employing a quantitative experimental approach, model performance was evaluated using R², MSE, RMSE, and MAE, while SHAP analysis was applied to interpret feature importance. The results show that all models achieved reasonable predictive accuracy, with Gradient Boosting consistently outperforming the others across all metrics. Study duration was identified as the most influential predictor, whereas stress level and gender had minimal impact. These findings emphasize the importance of non-academic lifestyle factors in predicting academic achievement and provide insights for the development of data-driven, personalized decision support systems in education.]]>
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