Badr Mohammed Almezaini and Muhammad Asif Khan
Keywords: WEKA, education data mining, machine learning, data mining, graduation project
Abstract: Educational institutions strive to monitor and develop student academic performance by difference means in order to prepare quality graduates. Educational data mining is increasingly becoming a latest trend which aids educational institutions to predict academic performance of students using machine learning techniques. Researchers have conducted research to predict student academic performance, but did not consider the prerequisite courses for final graduation project which is major culmination activity of an undergraduate degree program. As result students could not develop quality projects. In this current study we have used student data in three prerequisite courses required to begin graduation project. We have used data of three prerequisite courses and applied Naïve Bayes. J48 and Neural Network algorithms in WEKA to predict student performance in final graduation project. The accuracy and confusion matrix have been discussed and the results obtained in both the classifiers also elaborated. The results help students to focus and complete graduation projects with high quality
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