Predicting School Dropout Risk Using Machine Learning Models: A Comparative Study of Random Forest, Gradient Boosting, and Neural Network

Dropping out of school is a serious challenge in the education system that negatively impacts individual and social development. Early identification of students at risk of dropping out of school is crucial to prevent its long-term impact. This study aims to develop and compare a model for predicting the risk of dropping out of school using a machine learning approach. The three models compared in the study were Random Forest, Gradient Boosting, and Neural Network, with data covering 1000 students and features such as socioeconomic status, academic performance, parental engagement, distance to school, and educational resources. The results of the evaluation showed that the Random Forest model performed best with an accuracy of 93%, followed by Neural Network (92%) and Gradient Boosting (90%). The feature importance analysis revealed that socioeconomic status, parental involvement, and academic achievement were the dominant factors in predicting the risk of dropping out. These findings demonstrate the potential of applying machine learning as an early warning system for more targeted interventions in improving student retention. Further research is recommended to include psychological variables and longitudinal data as well as develop information technology-based systems for real implementation in schools.