📞 +91-7667918914 | ✉️ ijarcce@gmail.com
IJARCCE Logo
International Journal of Advanced Research in Computer and Communication Engineering A monthly Peer-reviewed & Refereed journal
ISSN Online 2278-1021ISSN Print 2319-5940Since 2012
IJARCCE adheres to the suggestive parameters outlined by the University Grants Commission (UGC) for peer-reviewed journals, upholding high standards of research quality, ethical publishing, and academic excellence.
← Back to VOLUME 4, ISSUE 11, NOVEMBER 2015

Students’ Academic Failure Prediction Using Data Mining

Lumbini P. Khobragade, Prof. Pravin Mahadik

DOI: 10.17148/IJARCCE.2015.41165

Abstract: This paper proposes to apply Data Mining Techniques to predict the students� failure on real time data of school or graduating students. Experiment attempts the detection of students� failure to improve their academic performance and to prevent them dropping out. Research has been done on assessing students� failure based on various attributes. In this experiment, 11 best attributes has been selected. Different approaches have been applied to resolve the problem of high dimensionality and using classification algorithm on Engineering students� previous and present education information to generate the model and this model can be used to detect students� academic failure. The results are compared and presented.



Keywords: Educational Data Mining (EDM), Academic failure, Classification, Prediction, Decision tree, Induction Rule.

How to Cite:

[1] Lumbini P. Khobragade, Prof. Pravin Mahadik, “Students’ Academic Failure Prediction Using Data Mining,” International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2015.41165