Random Forest approach optimized by the Grid Search process for predicting the dropout students

This study explores the application of Random Forest, a popular ensemble learning technique, in predicting student dropout behavior. The Random Forest algorithm is optimized using the Grid Search process to fine-tune its parameters, enhancing its ability to discern patterns indicative of dropout and non-dropout students. Dropout prediction in educational settings is of paramount importance for early intervention and support, aiming to mitigate the negative consequences of dropout on both individuals and society. By leveraging large-scale educational datasets containing diverse student attributes and academic performance metrics, the Random Forest model is trained to effectively classify students into dropout and non-dropout categories. Through rigorous evaluation and validation procedures, the optimized Random Forest model demonstrates promising performance in terms of predictive accuracy, sensitivity, and specificity. The findings suggest that Random Forest, empowered by parameter optimization through Grid Search, holds significant potential as a reliable tool for identifying students at risk of dropout, thereby enabling timely interventions to promote student retention and academic success. This research contributes to the advancement of predictive analytics in education and underscores the utility of machine learning algorithms in addressing critical challenges in student retention and success initiatives.