Machine Learning for Predicting Student Dropout

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This research explores dropout challenges within the CPUT context, addressing the problem, aims, and objectives through ML predictive analytics. A systematic literature review identifies academic, financial, and engagement predictors, while examining algorithms and ethical considerations. Findings reveal key predictors and gaps, leading to the development of a CPUT framework focused on governance, data, and explainable AI. The study concludes with actionable recommendations for responsible...