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

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dc.contributor.author Zhou, Helper
dc.contributor.author Gumbo, Victor
dc.date.accessioned 2021-05-31T23:31:26Z
dc.date.available 2021-05-31T23:31:26Z
dc.date.issued 2021-05-31
dc.identifier.citation Zhou, H., & Gumbo, V. (2021). Supervised machine learning for predicting SMME sales: An evaluation of three algorithms. The African Journal of Information and Communication (AJIC), 27, 1-21. https://doi.org/10.23962/10539/31371 en_ZA
dc.identifier.issn 2077-7213 (online version)
dc.identifier.issn 2077-7205 (print version)
dc.identifier.uri https://hdl.handle.net/10539/31371
dc.identifier.uri https://doi.org/10.23962/10539/31371
dc.description.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. en_ZA
dc.language.iso en en_ZA
dc.publisher LINK Centre, University of the Witwatersrand (Wits), Johannesburg en_ZA
dc.rights This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence: https://creativecommons.org/licenses/by/4.0 en_ZA
dc.subject 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) en_ZA
dc.title Supervised Machine Learning for Predicting SMME Sales: An Evaluation of Three Algorithms en_ZA
dc.type Article en_ZA
dc.journal.title The African Journal of Information and Communication (AJIC) en_ZA
dc.description.librarian CA2021 en_ZA
dc.citation.doi https://doi.org/10.23962/10539/31371 en_ZA
dc.orcid.id Zhou: https://orcid.org/0000-0002-8492-7844 en_ZA
dc.orcid.id Gumbo: https://orcid.org/0000-0001-5219-9902 en_ZA
dc.journal.link http://www.wits.ac.za/linkcentre/ajic en_ZA
dc.journal.issue 27 en_ZA
dc.article.start-page 1 en_ZA
dc.article.end-page 21 en_ZA
dc.faculty Humanities en_ZA
dc.school School of Literature, Language and Media (SLLM) en_ZA


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  • AJIC Issue 27, 2021
    Articles on problematic internet use, Indigenous knowledge in vocational education, machine learning, scaling of innovation, institutional isomorphism, human–computer interaction for development, and scholarly publishing.

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