International Journal of Advanced Research in Computer and Communication Engineering

Abstract: In the field of educational data mining, it has become more and more crucial to accurately forecast student performance in order to facilitate early interventions and enhance academic results. In order to predict academic accomplishment, this study uses a dataset of 6000 students (student-scores-6k.csv) that includes factors including study hours, attendance, extracurricular activities, part-time employment, and gender. We used and compared two machine learning algorithms: Random Forest Regressor and Linear Regression. When compared to Linear Regression (R2 = 0.62, RMSE = 8.5), the Random Forest model performed better (R2 = 0.82, RMSE = 5.1). Gender had no bearing on student progress, while weekly self-study hours and absence days were the most significant indicators, according to feature importance analysis. In addition to offering educators and policymakers useful insights for creating interventions that support academic performance, the study shows that non-linear models are more adept at capturing the complexity of educational data.

Keywords: Student Performance, Machine Learning, Regression, Random Forest, Educational Data Mining, Predictive Analytics

Downloads: | DOI: 10.17148/IJARCCE.2025.141068