Machine Learning based Student Academic Performance Prediction

The primary objective of educational institutions is to ensure students receive a highquality education. In modern times, a significant portion of their resources and efforts are directed towards assessing student performance. Through thorough analysis, they can pinpoint groups of students who may require additional support and interventions to improve their academic outcomes. Recently, researchers have introduced various machine learning techniques to forecast academic success. This study employs Linear Regression and Random Forest algorithms to anticipate students' academic achievements. Evaluating the models using metrics such as confusion matrix, accuracy, precision, recall, and F1 score reveals that the Random Forest algorithm outperforms others in predicting academic performance, as indicated by the findings.