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Solving business decision-making problems with an implementation of Azure machine learning

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dc.title Solving business decision-making problems with an implementation of Azure machine learning en
dc.contributor.author Beltrán-Prieto, Luis Antonio
dc.contributor.author Kuruppuge, Ravindra Hewa
dc.relation.ispartof 12th Annual International Bata Conference for Ph.D. Students and Young Researchers (DOKBAT)
dc.identifier.isbn 978-80-7454-592-4
dc.date.issued 2016
dc.citation.spage 43
dc.citation.epage 56
dc.event.title 12th Annual International Bata Conference for Ph.D. Students and Young Researchers (DOKBAT)
dc.event.location Zlín
utb.event.state-en Czech Republic
utb.event.state-cs Česká republika
dc.event.sdate 2016-04-28
dc.event.edate 2016-04-28
dc.type conferenceObject
dc.language.iso en
dc.publisher Tomas Bata University in Zlín
dc.identifier.doi 10.7441/dokbat.2016.05
dc.relation.uri http://dokbat.utb.cz/wp-content/uploads/DOKBAT2016.pdf
dc.subject Decision making en
dc.subject business models en
dc.subject Azure Machine Learning en
dc.subject Artificial Intelligence Algorithms en
dc.subject Mexico and Sri Lanka en
dc.description.abstract Business decision making is always risky and critical. The optimization of profit or cost is not guaranteed unless decisions are taken in the right time and the right way. Therefore, business decision-making is mostly supported by mathematical or statistical techniques. With the development of the technology, some business decisions-making models are developed to facilitate managers to take their decisions. The aim of this paper is to introduce a decision tree regression model built on the Azure Machine Learning platform and use it to predict and compare the performance of telecommunication industry between Mexico and Sri Lanka. Data related to telecommunication industry from both countries were collected from various reliable secondary sources. Data analysis was carried out in Azure Machine Learning. Results of the model indicated the ability of the model in terms of forecasting information, in this case, mobile cellphone subscriptions, which can be used by companies or the government to develop new technologies, offer new services or plan budgets. Results further reflected that managers of any business field can make predictions based on these models to make their decisions effectively at very high accuracy levels. However, other kind of projects can also be identified in order to test and apply these techniques in the solution of real-life problems, including those from the non-computer related fields of study. en
utb.faculty Faculty of Applied Informatics
utb.faculty Faculty of Management and Economics
dc.identifier.uri http://hdl.handle.net/10563/1008720
utb.identifier.rivid RIV/70883521:28120/16:43874871!RIV17-MSM-28120___
utb.identifier.obdid 43875552
utb.identifier.wok 000466741400005
utb.source d-wok
dc.date.accessioned 2019-08-07T12:05:26Z
dc.date.available 2019-08-07T12:05:26Z
dc.description.sponsorship Internal Grant Agency [IGA/CebiaTech/2016/007, IGA/FaME/2016/001]
utb.contributor.internalauthor Beltrán-Prieto, Luis Antonio
utb.contributor.internalauthor Kuruppuge, Ravindra Hewa
utb.fulltext.affiliation Luis Antonio Beltran Prieto, Ravindra Hewa Kuruppuge Tomas Bata University in Zlin, Faculty of Applied Informatics Mostni 4511,76005 Zlin, Czech Republic Email: luis.beltran@itcelaya.edu.mx orcid.org/0000-0002-8208-4206 Tomas Bata University in Zlin, Faculty of Management and Economics Mostni 5139,76001 Zlin, Czech Republic Email: kuruppuge@yahoo.com orcid.org/0000-0002-9456-4071
utb.fulltext.dates -
utb.fulltext.sponsorship Authors of this article are thankful to the Internal Grant Agency of projects IGA/CebiaTech/2016/007: Hybridization of Computational Intelligence Techniques with Applications and FaME TBU No. IGA/FaME/2016/001: Enhancing Business Performance through Employees’ Knowledge Sharing, for financial support to carry out this research.
utb.wos.affiliation [Prieto, Luis Antonio Beltran] Tomas Bata Univ, Fac Appl Informat, Mostni 4511, Zlin 76005, Czech Republic; [Kuruppuge, Ravindra Hewa] Tomas Bata Univ, Fac Management & Econ, Mostni 5139, Zlin 76001, Czech Republic
utb.fulltext.projects IGA/CebiaTech/2016/007
utb.fulltext.projects IGA/FaME/2016/001
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.faculty Faculty of Management and Economics
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