Supervised Machine Learning for Predicting SMME Sales: An Evaluation of Three Algorithms




Supervised machine learning, Algorithms, Sales predictive modelling, Ordinary least squares (OLS), Least absolute shrinkage and selection operator (LASSO), Artificial neural networks (ANNs), Small, medium and micro enterprises (SMMEs)


The emergence of machine learning algorithms presents the opportunity for a variety of stakeholders to perform advanced predictive analytics and to make informed decisions. However, to date there have been few studies in developing countries that evaluate the performance of such algorithms—with the result that pertinent stakeholders lack an informed basis for selecting appropriate techniques for modelling tasks. This study aims to address this gap by evaluating the performance of three machine learning techniques: ordinary least squares (OLS), least absolute shrinkage and selection operator (LASSO), and artificial neural networks (ANNs). These techniques are evaluated in respect of their ability to perform predictive modelling of the sales performance of small, medium and micro enterprises (SMMEs) engaged in manufacturing. The evaluation finds that the ANNs algorithm’s performance is far superior to that of the other two techniques, OLS and LASSO, in predicting the SMMEs’ sales performance.


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How to Cite

Zhou, H. and Gumbo, V. (2021) “Supervised Machine Learning for Predicting SMME Sales: An Evaluation of Three Algorithms”, The African Journal of Information and Communication (AJIC). South Africa, (27). doi: 10.23962/10539/31371.



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