Comparison of Classification Techniques on Data Mining
International Journal of Emerging Technology and Innovative Engineering, Volume 5, Issue 5, May 2019
6 Pages Posted: 22 Apr 2019
Date Written: April 19, 2019
Educational Data Mining is applying mining techniques to learning-related data. Predicting student performance is the complicated one because of the huge amount of records in learning field. Now a day there is lack of existing survey to get the clear view about predictions. There are two factors involve in this process such as attributes for prediction and prediction methods. The core aim of this paper is to predict the student’s performance by using the idea of mining methods. In this paper, we compared the accuracy percentage with different data mining methods such as Decision Tree, Neural Network, Naive Bayes, K-Nearest Neighbor, and Support Vector Machine. Among these techniques, Decision Tree and Neural Network provide the best accuracy.
Keywords: Classification Technique, Educational Data Mining, Decision Tree, Neural Network
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