Predicting University's Students Performance Based on Machine Learning Techniques
Dindar Mikaeel Ahmed, Adnan Mohsin Abdulazeez, Diyar Qader Zeebaree, Falah Y. H. Ahmed
Abstract
Machine learning algorithms have been used in many fields, like economics, medicine, etc. Education data mining is one of the areas concerned with exploring patterns of data in an educational environment. One of the most important uses is to predict students' performance to improve the existing educational situation. It can be considered as one of the data mining sciences. The ability to predict in advance in many areas has many benefits. In the case of learning, it enables us to know students' levels in advance and identify students who need special attention. This paper proposes using the algorithm (GBDT) which is a machine learning technology used for regression, classification, and ranking tasks, and is part of the Boosting method family to predict university students' performance in final exams. It compares the proposed system's performance with selected machine learning algorithms (Support vector machine, Logistic Regression, Naive Bayes, Gradient Boosted Trees).