Students’ Academic Performance Prediction Model Using Machine Learning

Abstract

Providing quality education to students is the main objective of higher education institutions. The need of identifying students with weak performances has been a rising problem and most teachers have relied on calculating the average of exam grades. The main objective of our project is to predict and identify the students who might fail in semester examinations. This would prove helpful for teachers in providing additional assistance to such students. The data which was analyzed consisted of students’ transcript data that included their CGPA and grades in all courses which were taken from a university. The machine learning algorithms which were used in this research include; Naïve Bayes classifier, Neural Network, Support Vector Machine, and Decision Tree classifier. A comparative analysis has been performed on the obtained accuracy results of the algorithms used. This research shows that machine learning proves useful in predictions, but there is a lot more work to be done using this technology.