This study evaluates the performance of various machine learning models in predicting primary school dropout risks by analyzing student-related data such as attendance, academic performance, and socio-economic factors. By comparing multiple algorithms, the research identifies the most effective approaches for early detection of at-risk students. The model’s predictive insights help educators implement targeted interventions, improve student support systems, and reduce dropout rates. This data-driven method strengthens decision-making and supports long-term academic success.
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International Journal of Innovation Studies (Scopus Q2)
2024
https://ijistudies.com/index.php/ijis/article/view/125