Analyzing Student Performance for Early Intervention: A Binary Classification Study

This study investigates the classification of pass/fail outcomes of students without using the G3 characteristics, which stands for final grades. The dataset includes a variety of characteristics, including the socioeconomic variables. This study intends to create reliable machine-learning models to predict student outcomes based on certain characteristics. This will help to get important insights into early intervention techniques and enable support systems in educational environments. The proposed supervised learning method involves training and evaluating ensemble models of multiple classification algorithms through the application of cross-validation techniques. The outcomes show that the proposed method produces good classification accuracy rates, proving that the machine learning techniques are effective in forecasting student success irrespective of final grades. The study’s insights can help educational stakeholders to support atrisk students and raise overall academic attainment.