Predicting Student Performance Using Random Forest Algorithm

Accurate prediction of student performance is a significant step toward enhancing educational outcomes through data-driven decision-making. With the growth of educational data mining (EDM), machine learning (ML) algorithms have emerged as effective tools for analyzing student data to uncover hidden insights. This paper presents a comprehensive study on using the Random Forest (RF) algorithm to predict student academic performance. By utilizing various features such as demographics, prior grades, attendance, socioeconomic status, and extracurricular involvement, the Random Forest classifier delivers high prediction accuracy while offering interpretability through feature importance rankings. This study further discusses the model training, performance evaluation, graphical representations, and implications of integrating such systems into educational settings. Additionally, limitations and future directions for improvement are explored, emphasizing the broader potential of ML in transforming the education sector.