A Comparative Study on Student Academic Performance Prediction Using ID3 and C4.5 Classification Algorithms - Suneetha, Kandepi (International Journal of Computer Sciences and Engineering, 2020)
The ability to predict a student’s performance on a given concept is an important tool for the education institutions, as it allows them to understand the ability of students and derive important methods to enhance their knowledge levels. It is the responsibility of educational institutions to have an approximate prior knowledge of their students to predict their performance in future academics and to train them in various activities. It is used to identify bright students and also provides them an opportunity to pay attention to and improve the slow learners. For predicting the student academic performance a data mining technique under classification is used. I have analyzed the data set containing information about students, such as full name, Roll number, scores in board examinations of classes X and XII, Rank in Eamcet examinations, branch and admission type. ID3 and C4.5 classification algorithms are applied to predict the performance of newly admitted students in their future examinations. In this paper, the performance of ID3 and C4.5 algorithms are compared in terms of parameters like accuracy, error rate and the execution time and the experimental Results shown that C4.5 was found to be best in terms of execution time.