Implementing Classification Techniques of Data Mining in Creating Model for Predicting Academic Marketing
Sheila A. Abaya, Bobby D. Gerardo, Bartolome T. Tanguilig
Journal of Scientific Research and Reports · pp. 494–500 · Published 3 Jun 2015
10.9734/JSRR/2015/16940Abstract
The education domain is one of the business areas with abundant data. Nowadays, most of tertiary educational institutions have dilemmas in identifying probable secondary schools which are considered as feeders for enrollment. The data mining technique of classification has been used in this research to easily identify the target secondary schools for enrollment. With these techniques, higher educational institutions may lessen the marketing cost by filtering which of these secondary schools are considered enrollment contributors. The techniques of ID3, C4.5, BayesNet and Naïve Bayes were used in this research implemented on WEKA 3.6.0 toolkit [1]. Based on the experimental results, C4.5 outperformed ID3, BayesNet and Naïve Bayes in determining the best classification technique to identify the targeted secondary schools qualified for enrollment in tertiary level. The model created can aid in education management’s decision making process in terms of student recruitment.
References (9)
- 1 A Tutorial on Learning with Bayesian Networks [DOI]
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- 4 Introduction to the special section on educational data mining [DOI]
- 5 Survey on Classification Algorithms for Data Mining:(Comparison and Evaluation)
- 6 International Journal on Computer Science and Engineering [DOI]
- 7 Computer Engineering and Intelligent Systems [DOI]
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