Abstract
For higher education, it is crucial for the institution to forecast early student academic
performance in order to give students appropriate facilities for improving their academic records and
make it simple for the instructor to detect them. Numerous machine learning approaches, including knearest
neighbour, support vector machines, Naive Bayes classifiers, logistic regression, etc., have
been created to enhance student academic performance. In this paper, we propose a method for
predicting student performance using a machine learning neural network. The MATLAB toolbox is
used to develop this model, and comparison analysis is carried out utilising a variety of measurement
factors, including precision, recall, f-measure, and execution time