STUDENT PERFORMANCE PREDICTION

Student performance prediction has become an important application of Artificial Intelligence in education. Educational institutions are increasingly using data-driven approaches to analyze academic performance and identify students who require support. This study proposes an enhanced AI-based student performance prediction model using the Random Forest algorithm and multi-factor analysis. The model considers multiple academic and behavioral factors such as study hours, attendance, assignment completion, and previous grades. A dataset of student academic records was analyzed using machine learning techniques to build an accurate predictive model. The Random Forest algorithm was selected due to its robustness, high accuracy, and ability to handle complex data relationships. The results show that the proposed model improves prediction accuracy and provides valuable insights into the key factors affecting student performance. The findings of this study can help educators identify at-risk students early and implement appropriate interventions to improve learning outcomes.