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

Abstract

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.

The accuracy of artificial neural networks and logit models in predicting the companies’ financial distress

“…Logistic regression, also known as the logit model, is a binomial regression model that is widely used in many fields. This model has been used many times in the past for distress prediction because of its simplicity in implementation and interpretation of results Zhou & Gumbo, 2021;Zizi et al, 2021). The formal expression of the model is:…”