Predicting personnel appointment in the Nigerian army using performance records and machine learning techniques

Professionalism and discipline in the Nigerian armed forces have been negatively impacted due to a lack of structured methods of promotion, appointment, and succession in the rank and file of military officers. This lacuna is an attribution of the socio-cultural diversities in Nigeria dispensed through nepotism, favouritism, and ethnicity. Thus, validates the need for pellucid techniques for personnel appointment at the higher echelon based on merit. This paper aims to promote professionalism in the armed forces through a model of seamless human resource processing of enthroning a seamless and transparent culture of succession based on personnel performance records. Supervised learning techniques are adopted for this research given labelled data of 10, 000 records of officers from the rank of major general eligible for appointment as the chief of army staff from the year 1990 to 2002. Relevant features were extracted from the dataset during pre-processing to filter noise, and resampled using sci-kit random over sampler to generate augmented data to balance the target class in order to eliminate algorithmic bias toward the underrepresented class. Three classification algorithms were used comparatively for modelling. The result obtained in terms of accuracy is Logistic regression 84%, decision tree 92%, and random forest 92%. The findings in this research show that our best model random forest will be 92% correct every time prediction is made with a 95 AUC score signifying 95% correctness in distinguishing between the two target classes. This research is the first of its kind and gives room for further improvement with a larger dataset