Predicting Student Performance Based On Clustering And Classification

In today’s world the education field is growing, developing widely and becoming one of the most crucial industries. The data available in the educational field can be studied using educational data mining so that the unseen knowledge can be obtained from it. In this paper, various data mining approaches like Clustering, classification and regression our used to predict the students’ performance in examination in advance, so that necessary measures can be taken to improvise on their performance to score better marks. A hybrid approach of Enhanced K-strange points clustering algorithm and Naïve Bayes classification algorithm is presented implemented and compared it with existing hybrid approach which is K-means clustering algorithm and Decision tree. Finally, to predict student performance, multiple linear regression is used. The results obtained after the implementation may be useful for instructor as well as students. This work will help in taking appropriate decision to improve student’s performance.