Authors - Albert Manamela, Tevin Moodley Abstract - Student retention is critical for academic quality and institutional effectiveness, especially in programs where foundational natural science courses such as mathematics, physics, and chemistry strongly influence progression and pose significant challenges. Early dropout identification in these contexts requires predictive models that are both accurate and interpretable. This study proposes an interpretable machine learning framework for student dropout prediction using academic, financial, and demographic data. It combines cost-sensitive XGBoost with Shapley Additive exPlanations (SHAP), addressing class imbalance without synthetic oversampling to preserve authentic performance patterns. Using a benchmark dataset from the Polytechnic Institute of Portalegre, the model achieved strong performance (Accuracy = 89.6%, F1 = 0.834, AUC-ROC = 0.934). SHAP analyses identified academic engagement, tuition payment status, and scholarship access as key predictors. The findings support transparent early-warning systems and inform policies to improve retention, strengthen support in science-based learning environments, and promote equitable student outcomes.
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