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Machine Learning for Student Retention by Andres is a document available to read on EtoBox.

This study investigates the use of machine learning algorithms to predict student retention at Mindanao State University, utilizing historical academic and sociodemographic data over a decade. Among ten evaluated algorithms, Extreme Gradient Boosting (XGBoost) achieved the highest accuracy and lowest error rates, suggesting its effectiveness for early intervention strategies in higher education. The research emphasizes the importance of integrating educational data science into curricula to enhance real-wor

Author

Andres

Language

EN