Predicting Student Attrition in Kenyan Universities: A Comparative Analysis of Machine Learning Algorithms

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Logistic Regression emerged as the most effective model for predicting student attrition in Kenyan universities.
The analysis showed that Logistic Regression achieved the highest precision and F1 score compared to Decision Trees and Random Forest.
Despite hyperparameter tuning, Decision Trees and Random Forest had difficulty accurately predicting at-risk students for attrition.
Future work is suggested to optimize the predictive performance of machine learning models applied to student attrition.

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Research reveals Logistic Regression predicts student attrition effectively, suggesting improvements needed for Decision Trees and Random Forest.

Nyawira et al. (2025) studied this question.

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Research reveals Logistic Regression predicts student attrition effectively, suggesting improvements needed for Decision Trees and Random Forest.