Student Academic Performance Prediction Based on Random Forest and Support Vector Machine
TL;DR
The results show that both random forest and support vector machine achieved high accuracy, but RF demonstrated better recall for identifying at-risk students, making it more suitable for early intervention systems.
Abstract
: Amid the global acceleration of digital transformation in education, achieving precision teaching and improving student learning outcomes have become central concerns in the educational sector. This study explores the application of machine learning models — Random Forest and Support Vector Machine — in predicting student academic performance. Using an open-source dataset from Kaggle, the research selects 13 behavioral and demographic indicators, including study hours, mental health, attendance, and lifestyle habits. The results show that both random forest and support vector machine achieved high accuracy, but RF demonstrated better recall for identifying at-risk students, making it more suitable for early intervention systems. Feature importance analysis reveals that daily study time and mental health ratings are the most influential predictors. RF integrates a wider range of behavioral traits, while SVM relies more heavily on entertainment-related variables, leading to lower robustness. Visualization of prediction results enhances interpretability and supports data-driven educational decisions. The study concludes by emphasizing the need to incorporate broader psychological and social factors in future models to improve prediction generalization and provide actionable insights for personalized teaching strategies.