Comparative study of supervised learning algorithms for student performance prediction

Abstract

With huge amount of data in diverse technological areas, and generating such kinds of data rapidly, it needs for proper usage; therefore, Data Mining has emerged. Data Mining can extract prominent knowledge from customary data that can attract attention of people to it which is meaningful information. Regarding this concept that data can be generated rapidly every day or even every moment, data need to take under process for offering better valuable information. Data of educational areas is more that belongs to students, and it's all right a good basis for commence of applying Data Mining. In this paper the focus is on how to use Data Mining techniques to discover information in student`s raw data and different algorithms such as KNN, Naïve Bayes, and Decision Tree are implemented.