Presenting a model to reduce students' academic drop by using analytical comparison of machine learning algorithms in data mining (case study of Shahed University)

This research aims to find factors that predict undergraduate student educational performance. To achieve this goal, the study follows the CRISP-DM method. This study used various classification algorithms to predict the total GPA. The data used in this research are records of undergraduate students from 2012 in Shahed University. We used 1,468 data records in data mining. We used the Rapidminer 9.9 tool for modelling. This study also considers four feature selection techniques. This study used K-fold cross-validation to split the data. This study introduced the best model for predicting students' academic performance. In two-class modelling, we get better results and higher accuracy than four-class modelling. This research found the random forest algorithm best for predicting students' performance. It achieved 94.17% accuracy with two classes. The random forest results show a higher chance of success in students with a higher 1st semester GPA.