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Predictive Analytics Using Machine Learning Models on Undergraduate Students' Performance of the Federal University of Allied Health Sciences, Enugu, Nigeria in Introduction to Computing Science

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Ensemble models outperformed traditional classifiers, with Gradient Boosting achieving 87% accuracy and 0.91 ROC-AUC.
Feature selection methods identified key predictors such as midterm test scores, attendance rates, and parental education levels.
Supervised machine learning algorithms were applied to a dataset of 500 anonymised student records for performance prediction.
Integrating predictive analytics into educational frameworks may enable effective academic support and early warning systems.

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This study demonstrates how machine learning predicts academic performance in undergraduates, suggesting proactive strategies for intervention.

EdmondEdmond et al. (2025) studied this question.

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This study demonstrates how machine learning predicts academic performance in undergraduates, suggesting proactive strategies for intervention.