Please use this identifier to cite or link to this item:
https://hdl.handle.net/10321/3596
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhou, Helper | en_US |
dc.contributor.author | Gumbo, Victor | en_US |
dc.date.accessioned | 2021-07-22T09:53:46Z | - |
dc.date.available | 2021-07-22T09:53:46Z | - |
dc.date.issued | 2021-05-31 | - |
dc.identifier.citation | 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. (27): 1-21. doi:10.23962/10539/31371 | en_US |
dc.identifier.issn | 2077-7205 | - |
dc.identifier.issn | 2077-7213 (Online) | - |
dc.identifier.uri | https://hdl.handle.net/10321/3596 | - |
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_US |
dc.format.extent | 21 p | en_US |
dc.language.iso | en | en_US |
dc.publisher | Wits School of Literature, Language and Media (SLLM) | en_US |
dc.relation.ispartof | The African Journal of Information and Communication; Issue 27 | en_US |
dc.subject | Supervised machine learning | en_US |
dc.subject | Algorithms | en_US |
dc.subject | Sales predictive modelling | en_US |
dc.subject | Ordinary least squares (OLS) | en_US |
dc.subject | Least absolute shrinkage and selection operator (LASSO) | en_US |
dc.subject | Artificial neural networks (ANNs) | en_US |
dc.subject | Small, medium and micro enterprises (SMMEs) | en_US |
dc.title | Supervised machine learning for predicting SMME sales : an evaluation of three algorithms | en_US |
dc.type | Article | en_US |
dc.date.updated | 2021-07-21T13:29:25Z | - |
dc.identifier.doi | 10.23962/10539/31371 | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.languageiso639-1 | en | - |
item.openairetype | Article | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
Appears in Collections: | Research Publications (Management Sciences) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Zhou-Gumbo 2021 Supervised machine learning for predicting SMME sales.pdf | Published version | 434.6 kB | Adobe PDF | View/Open |
Page view(s)
331
checked on Dec 22, 2024
Download(s)
40
checked on Dec 22, 2024
Google ScholarTM
Check
Altmetric
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.