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    Evaluation of Profession Predictions for Today and the Future with Machine Learning Methods: Emperical Evidence From Turkey
    (Gazi Univ, 2023) Karaahmetoglu, Ebru; Ersöz, Süleyman; Türker, Ahmet Kürşat; Ateş, Volkan; İnal, Ali Fırat
    For 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.