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Prediction of Graduate Students for Master Degree based on Their Past Performance using Decision Tree in Weka Environment

International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 74 - Number 11
Year of Publication: 2013
Jaimin N. Undavia
Prashant M. Dolia
Nikhil P. Shah

Jaimin N Undavia, Prashant M Dolia and Nikhil P Shah. Article: Prediction of Graduate Students for Master Degree based on Their Past Performance using Decision Tree in Weka Environment. International Journal of Computer Applications 74(11):23-29, July 2013. Full text available. BibTeX

	author = {Jaimin N. Undavia and Prashant M. Dolia and Nikhil P. Shah},
	title = {Article: Prediction of Graduate Students for Master Degree based on Their Past Performance using Decision Tree in Weka Environment},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {74},
	number = {11},
	pages = {23-29},
	month = {July},
	note = {Full text available}


For generating comprehensive and precise analysis, Decision Tree technique is found as most adequate technique. Usually decision trees are used in data mining to study historical data and on the basis of the data analysis and its rules, one can predict the result. Most of the higher education institutions are suffering from low percentage of result, placement and interest of the students. To address this issue, we have suggested one Decision Support System using decision tree which predicts the post graduation stream for the students on the basis of their past academic performance. Prediction of students' performance is a great concern to the higher education institutions. So, this paper covers all the parameters which have some influence in student's performance. In this investigation, a survey cum experimental methodology is adopted to generate the data store. Paper also discusses use of decision tree for the prediction. Decision tree algorithms are applied on Post Graduate students who are either pursuing or have completed. Academic history and social data are collected and used to design the model. This model is used for the prediction of students' performance.


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