The ability to timely predict the academic performance tendency of postgraduate students is very important in MSc programs and useful for tutors. The scope of this research is to investigate which is the most efficient machine learning technique in predicting the final grade of Ionian University Informatics postgraduate students. Consequently, five academic courses are chosen, each constituting an individual dataset, and six well-known classification algorithms are experimented with. Furthermore, the datasets are enriched with demographic, in-term performance and in-class behaviour features. The small size of the datasets and the imbalance in the distribution of class values are the main research challenges of the present work. Several techniques, like resampling and feature selection, are employed to address these issues, for the first time in a performance prediction application. Naïve Bayes and 1-NN achieved the best prediction results, which are very satisfactory compared to those of similar approaches.