IJSRP, Volume 10, Issue 8, August 2020 Edition [ISSN 2250-3153]
Onoja Emmanuel Oche, Suleiman Muhammad Nasir, Abdullahi, Maimuna Ibrahim
Predicting students’ performance over a given period of time is one of the greatest challenges faced by the academic sector in this present time. Data mining techniques could be used for this kind of job. In this study, data mining techniques is applied on data collected from students and academic office of Federal Polytechnic Nasarawa State in other to predict students’ performances. WEKA data mining tool was used with implementation of six (6) classifiers namely; J48 decision tree algorithm, Bayesian Network, Navive Bayes, IBk OneR and JRip algorithm. Result shows that Bayes registered accuracy of 72%, BayesNet registered accuracy of 74%, J48 registered accuracy approximately 70 %, while OneR, IBK and JR classifiers produced classification accuracy of 63, 69 and 70% respectively.