The Estimation of Students' Academic Success by Data Mining Methods


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Goker H., Bülbül H. İ., Irmak E.

12th International Conference on Machine Learning and Applications (ICMLA), Florida, United States Of America, 4 - 07 December 2013, pp.535-539 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/icmla.2013.173
  • City: Florida
  • Country: United States Of America
  • Page Numbers: pp.535-539
  • Keywords: data mining, classification, feature selection, weka, data warehouse, naive bayes, PERFORMANCE
  • Gazi University Affiliated: Yes

Abstract

Data mining is a process of getting out useful information from data stacks. One of the most common application areas is to use classification of algorithms that estimate the future events by past experiences. In this context, in order to predict future events, a data warehouse is created by using the background of students which includes demographic, personal, school, and course information of students. On this data warehouse by using classification algorithms, new applications which can make inferences for the future could be developed.