A Comparative Study on Student Academic Performance Prediction using Classification Algorithm

Abstract: Data mining is the process of mining the exact data from a large amount of database In other words it can be said that data mining is mining the knowledge from data. A database is collection of inter related data. Normally, the performance of the student is based on the training provided by the teacher and the facilities provided by the institution. J48 classifies the simple c4.5 decision tree for classification. The binary tree is a decision tree approach is most useful in classification problem. The current system uses Naive Bayes, C4.5 and ID3 algorithm for analyzing the performance of the students is used for classifying the students into high and low category. This paper provides comparative study of predicting student level performance using classification algorithm such as Naive Bayes, C4.5 and ID3.

Keywords: Data Mining, Classification algorithm, Naive Bayes, C4.5, ID3 algorithm and Random Forest algorithm.