Student dropout is a significant concern for university administrators, students and other stakeholders. Dropout is recognised as highly complex due to its multi-causality, which is expressed in the existing relationship in its explanatory variables associated with students, their socio-economic and academic conditions, and the characteristics of educational institutions. This article reports on a study that drew on university administrative data to build a profile of students at risk of dropout from 2008-2018. The study employed a data mining technique in which predictors were chosen based on their weight of evidence (WOE) and information value (IV). The selected predictors were then used to build a profile of students at risk of drop-out. The findings indicate that at-risk students fail more than four modules in a year with a participation average mark of 43% or less and have joined the university in the second academic year. It is suggested that universities put measures in place to control and prevent students who carry over four or more modules from adding modules to their registration until the failed modules are passed.