Abstract

Students’ academic performance is critical for educational institutions because strategic programs can be planned in improving or maintaining students’ performance during their period of studies in the institutions. The academic performance in this study is measured by their cumulative grade point average (CGPA) upon graduating. This study presents the work of data mining in predicting the drop out feature of students. This study applies decision tree technique to choose the best prediction and analysis. The list of students who are predicted as likely to drop out from college by data mining is then turned over to teachers and management for direct or indirect intervention.

How to Cite
QUADRI, Mr. M. N.; KALYANKAR, Dr. N.V.. Drop Out Feature of Student Data for Academic Performance Using Decision Tree Techniques. Global Journal of Computer Science and Technology, [S.l.], apr. 2010. ISSN 0975-4172. Available at: <https://computerresearch.org/index.php/computer/article/view/891>. Date accessed: 17 sep. 2021.