Predictive Model of Postgraduate Student’s Dropout and Delay Using Machine Learning Algorithms

Neural networks and Logical Regression algorithm provide the best ways to classify data, but they are outperformed continuously by the Decision Tree in analyzing student performance. Therefore, many scholars have used the Decision Tree to predict student performance with greater success. This research analyzed postgraduate student degree outcomes using socioeconomic data to develop a prediction model, where Decision Tree recorded the highest accuracy of 92.79%, better than Logical Regression and Neural Network. For brevity, the Decision Tree was used to produce the prediction model. Based on the study findings, postgraduate students who delay or drop out at the university mostly lack sponsors or had decreased income. Besides, male students delay or drop out if they had financial issues more than their female counterparts. Age, money management skills, number of children, and health expenses are the other factors that contribute to higher dropout or delay at the university. Therefore, this study provides a reliable prediction model for degree outcomes, allowing personalized follow-up to improve graduation rates.