Comparative Study on Marks Prediction using Data Mining and Classification Algorithms

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Bhrigu Kapur
Nakin Ahluwalia
Sathyaraj R


Today that collecting data has been easy more than ever in almost all aspects of life, but the collected data is of no use if it can’t be efficiently utilised for the betterment of the society. Every year thousands of students graduate from our education system which people believe is not as optimal as it could be and there has been a considerable research on how to improve it. In light of this the primary purpose of this paper is to look at and compare well performing algorithms such as Naïve Bayes , decision tree (J48), Random Forest, Naïve Bayes Multiple Nominal, K-star and IBk. Data that we have to gauge students’ potential based on various indicators like previous performances and in other cases their background to give a comparative account on what method is the best in achieving that end. The benefits from this are not limited to the students but help us evolve the system and gain knowledge into what method is the most efficient. All educational institutions whether public or private can design curriculum and the method of teaching based on what is the most effective.
Keywords: Prediction, classification, student, marks, GPA, data mining, educational data mining, performance


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