Machine Learning Based Student Academic Performance Prediction

Every educational system organizational goal is to provide a good and fruitful knowledge to the students. Now a days most of the educational institutions are spending most of their time and economy on finding out students' performance. By analyzing the performance, they identify certain cluster of the students for whom they must give extra bit of care and actions, so that they performance gets enhanced. Researchers have recently proposed several machine learning-based algorithms for predicting academic achievement. In this paper, Linear Regression algorithm and Random Forest algorithm are used to predict a student's academic achievement. On the basis of confusion matrix, accuracy, precision, recall, and F1 ranking, the performance of two algorithms was compared to that of existing algorithms. The Random Forest algorithm-based prediction is more accurate, according to the results report.