STUDENT ACADEMIC PERFORMANCE PREDICTION USING SUPPORT VECTOR MACHINE

This paper investigates the relationship between students' preadmission academic profile and final academic performance. Data Sample of students in one of the Federal Polytechnic in south West part of Nigeria was used. The preadmission academic profile used for this study is the 'O' level grades(terminal high school results).The academic performance is defined using student's Grade Point Average(GPA). This research focused on using data mining technique to develop a model for predicting student performance based on 'O' level results and their first 3 semester at each semester. Data preprocessing was done to remove the results of rusticated and expelled student .Results obtained by comparing SVM with other ML techniques such as KNN,Decision trees, linear Regression shows that SVM outperforms other ML algorithms. The parameters of the SVM algorithm(kernel) was also tuned to improve its accuracy and result obtained shows that the RBF kernel with penalty(C=100) performs best.SVM and RBF gave the highest training accuracy of 94% and 97% predicting accuracy which outperforms other state of the art ML technique like KNN,decision trees etc