Student’s Academic Performance Prediction in Academic using Data Mining Techniques

Data Mining has adopted by many areas like education, telecommunication, retail management etc. to resolve their business problems. Due to features likes classification, clustering and association rule mining, it becomes imperative. In this paper, for building predictive classification models algorithms like Naive-Bayes, Decision Tree, Random-Forest, JRip, and ZeroR are implemented on student academic performance dataset. In our implementation results, we found that school, as well as study-time, also affect the final student grade. Classification algorithms like One Rule, Joint Reserve Intelligence Program and Decision Tree have more than 80.00 % accuracy for predicting student result, and they perform equally well.