Solving Business Decision-Making Problems with an Implementation of Azure Machine Learning

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Solving Business Decision-Making Problems with an Implementation of Azure Machine Learning

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Solving Business Decision-Making Problems with an Implementation of Azure Machine Learning

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

AE - Řízení, správa a administrativa

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Conference Proceedings the 12th Annual International Bata Conference for Ph.D. Students and Young Researchers

ISBN

978-80-7454-592-4

ISSN

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e-ISSN

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Počet stran výsledku

13

Strana od-do

42 - 56

Název nakladatele

Univerzita Tomáše Bati ve Zlíně, Fakulta managementu a ekonomiky

Místo vydání

Zlín

Místo konání akce

Zlín

Datum konání akce

28. 4. 2016

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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