Abstract: Student dropout has been a perennial phenomenon in the higher education landscape. Conventional methods of analysing performance alone are not very effective for the early warning indicators of disengagement. This paper examines the use of four machine learning models: Logistic Regression, Decision Trees, Random Forest, and Support Vector Machine, on a data set of 1,200 students pursuing their higher education to determine the efficiency of models to predict student dropout.