Abstract
The student performance evaluation has been always a complex task and suffers with a lot of biasing at university levels. This work focuses on the evaluation of student performance using various machine learning techniques. In this paper, initially dataset is pre-processed for removing redundancy and noises. Then K-nearest neighbour, Support Vector machine, decision trees and Naive Bayes algorithms are applied to model an expert system which will evaluate the performance of the students. The student’s data of GLA university is considered which consists of 500 records. Four important features such as Attendance, quiz, assignment and terms marks are taken for training and testing. Above mentioned machine learning techniques are compared in terms of accuracy rate. These algorithms classify the data into three classes like excellent, good and improve.
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