Analyzing Student’s Academic Performance Based on Data Mining Approach
Kalaivani. S , Priyadharshini. B , Selva Nalini
Career building is the most cherished part of every engineering student. For an engineering graduate it is necessary to have immense knowledge in their domain to get placed in a reputed company. Data Mining is used to gain knowledge, find the hidden information and also this system applies data mining techniques to the academic dataset. The Academic data includes the Internal (CCET 1, CCET2 and CCET3) marks and the Assignment marks. The final semester marks are predicted from the analyzed result of each student. In order to increase the accuracy, this system introduces reweight enhanced boosting algorithm.
Data mining, Balanced Boosting, Educational data mining, Re-weight enhanced boosting, Ada-boosting
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[Kalaivani. S , Priyadharshini. B , Selva Nalini (2017) Analyzing Student’s Academic Performance Based on Data Mining Approach IJIRCST Vol-5 Issue-1 Page No-184-197] (ISSN 2347 - 5552). www.ijircst.org
Information Technology, Dr Mahalingam College of Engineering and Technology, Pollachi, India, 9487443674.