Evaluation of Profession Predictions for Today and the Future with Machine Learning Methods: Emperical Evidence From Turkey

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dc.authoridErsöz, Süleyman/0000-0002-7534-6837
dc.authoridAteş, Volkan/0000-0002-2349-0140
dc.authoridİnal, Ali Fırat/0000-0001-7747-0746
dc.contributor.authorKaraahmetoglu, Ebru
dc.contributor.authorErsöz, Süleyman
dc.contributor.authorTürker, Ahmet Kürşat
dc.contributor.authorAteş, Volkan
dc.contributor.authorİnal, Ali Fırat
dc.date.accessioned2025-01-21T16:33:51Z
dc.date.available2025-01-21T16:33:51Z
dc.date.issued2023
dc.departmentKırıkkale Üniversitesi
dc.description.abstractFor the purpose of evaluating present and future trends of professions within the labor market, text mining approach could be an alternative to more traditional approaches such as employer surveys. Specifically, machine learning algorithms are used for making accurate predictions about the future directions of the professions which consequently will influence professional development of labour force. The aim of this study is to investigate the professions of the future and current in Turkey by the application of supervised learning algorithms and clustering methods to various Turkish data including documents belonging to Turkey's institutions. In this study, the popular professions were predicted with an accuracy rate between congruent to 0.81 and congruent to 0.93 thorough various machine learning algorithms. It was discovered that methodologically perceptron and stochastic gradient descent algorithms demonstrated superiority over other algorithms thanks to their intelligence functions. Furthermore, the analysis of current professions in Turkey revealed that the class of Professional occupations, Managers and Technicians and assistant professional members were popular, and according to the analysis of the future, information technology-based occupations will be important. Although limited Turkish data sources for the analysis of future, results with an accuracy of nearly 1 were produced.
dc.identifier.doi10.2339/politeknik.985534
dc.identifier.endpage124
dc.identifier.issn1302-0900
dc.identifier.issn2147-9429
dc.identifier.issue1
dc.identifier.startpage107
dc.identifier.trdizinid1236283
dc.identifier.urihttps://doi.org/10.2339/politeknik.985534
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay1236283
dc.identifier.urihttps://hdl.handle.net/20.500.12587/23871
dc.identifier.volume26
dc.identifier.wosWOS:001022165400010
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherGazi Univ
dc.relation.ispartofJournal of Polytechnic-Politeknik Dergisi
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectText mining; machine learning; professions; supervised learning
dc.titleEvaluation of Profession Predictions for Today and the Future with Machine Learning Methods: Emperical Evidence From Turkey
dc.typeArticle

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