EFFICIENT MACHINE LEARNING ALGORITHMS FOR STUDENT’S ACADEMIC PERFORMANCE PREDICTION

Predicting students’ academic performance is one of the crucial issues in the academic field. Several methods and practices have been applied for educational improvement since there is a lot of academic information related to students. In this paper, our model for predicting students’ academic performance based on academic and demographic factors is developed to predict the final course grade at early stages. By applying several machine learning (ML) algorithms, Linear Regression (LR), Support Vector Machine (SVM), and Random Forest (RF). This model to the data of male students of Technical college. The dataset contains 890 instances for 199 students. The result showed that the prediction’s Mean Absolute Percentage Error (MAPE) reached 0.04% and the academic factors had a higher impact on student’s academic performance than the demographic factors.