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

Year 2023, , 107 - 124, 27.03.2023
https://doi.org/10.2339/politeknik.985534

Abstract

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 ≅0.81 and ≅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.

References

  • [1] Manyika J., Chui M., Bughin J., Dobbs R., Bisson P., and Marrs A., “Disruptive technologies: Advances that will transform life, business, and the global economy,” McKinsey Global Institute, (2013).
  • [2] Öztürk N., “İktisadi Kalkınmada Eğitimin Rolü,” Sosyoekonomi, 1:27–44, DOI:10.17233/se.86714, (2005).
  • [3] Schwab K., “The Fourth Industrial Revolution”, World Economic Forum, Geneva, Switzerland, (2016).
  • [4] Mosconi F., “The new European industrial policy: Global competitiveness and the manufacturing renaissance”, London, (2015).
  • [5] Russmann M., “Industry 4.0: World Economic Forum”, Bost. Consult. Gr., 1–20, (2015).
  • [6] Huimin M., “Strategic plan of ‘Made in China 2025’ and its implementation”, Anal. Impacts Ind. 4.0 Mod. Bus. Environ., 19: 1–23, (2018).
  • [7] Kurt R., “Industry 4.0 in Terms of Industrial Relations and Its Impacts on Labour Life”, Procedia Comput. Sci., 158: 590–601, (2019).
  • [8] Blinder A. S., “Education for the Third Industrial Revolution”, Princeton University, Department of Economics, Center for Economic Policy Studies,Working Papers, (2008).
  • [9] Pamuk N. S. and Soysal M., “Yeni Sanayi Devrimi Endüstri 4.0 Üzerine Bir İnceleme”, Verimlilik Dergisi, 1:41–66, (2018).
  • [10] Macurova P., Ludvik L., and Žwakova M., “The driving factors, risks and barriers of the industry 4.0 concept,” Journal of Applied Economic Sciences, vol. 12(7): 2003-2011, 2017.
  • [11] Weber E., “Industry 4.0 – job-producer or employment-destroyer?”, Institute for Employment Research, (2016).
  • [12] Kane G. C., Palmer D., Phillips A. N., and Kiron D., “Is Your Business Ready for a Digital Future?”, MIT Sloan Management Review, 56(4):7–44, (2015).
  • [13] Kleinert C., Matthes B., and Jacob M., “IAB Forschungsbericht 5/2008”, (2008).
  • [14] Özkan M., Al A., and Yavuz S., “Uluslararası Politik Ekonomi Açısından Dördüncü Sanayi-Endüstri Devrimi’nin Etkileri ve Türkiye”, Siyasal Bilimler Dergisi, 1–30, (2018).
  • [15] Bilim ve Sanayi Bakanlığı, “Mesleklerin Geleceği Araştırma Raporu”, (2018).
  • [16] Işık V., “Türkiye’de Genç İşsizliği ve Genç Nüfusta Atalet”, HAK-İŞ Uluslararası Emek ve Toplum Dergisi, 11:131–145, (2016).
  • [17] Yükseköğretim Kurulu Başkanlığı, “Geleceğin Meslekleri Çalışmaları Çalıştay Raporları”, (2019).
  • [18] Pejic-Bach M., Bertoncel T., Meško M., and Krstić Ž., “Text mining of industry 4.0 job advertisements” International Journal of Information Management, 50:416–431, (2020).
  • [19] De Mauro A., Greco M., Grimaldi M., and Ritala P., “Human resources for Big Data professions: A systematic classification of job roles and required skill sets”, Information Processing & Management, 54(5): 807–817, (2018).
  • [20] Frank M.R., Bessen J.E. , Brynjolfsson E., Cebrian M., Deming D.J., Feldman M., Groh M., Lobo J., Moro E., Wang D., Younk H. and Rahwana I., “Toward understanding the impact of artificial intelligence on labor,”, PNAS, 116(14):6531–6539, (2019).
  • [21] Dawson N., Rizoiu M. A., Johnston B. and Williams M. A., “Predicting Skill Shortages in Labor Markets: A Machine Learning Approach”, 2020 IEEE International Conference on Big Data, 2:3052–3061, (2020).
  • [22] Boselli R., Cesarini M., Marrara S., Mercorio F., Pasi M.M.G. and Viviani M., “WoLMIS: a labor market intelligence system for classifying web job vacancies”, Journal of Intelligent Information Systems, 51:477–502, (2018).
  • [23] Papoutsoglou M., Ampatzoglou A., Mittas N., and Angelis L., “Extracting Knowledge from On-Line Sources for Software Engineering Labor Market: A Mapping Study”, IEEE Access, 7:157595-157613, (2019).
  • [24] Özköse H., “Yönetim Bilişim Sistemleri Alanının Türkiye ve Dünya’daki Bibliyometrik Analizi ve Haritası”, Gazi University, Enformatic Institute, (2017).
  • [25] Cover T. and Hart P., “Nearest Neighbor Pattern Classification”, IEEE Transactions on Information Theory, 13(1): 21–27, (1967).
  • [26] Berrar D., “Bayes’ theorem and naive bayes classifier”, Encyclopedia of Bioinformatics and Computational Biology ABC of Bioinformatics, 1–3:403–412, (2018).
  • [27] Breiman L., “Random forests”, Machine Learning, 45(1):5–32, (2001).
  • [28] Efron B., Hastie T.,Johnstone I. and Tibshirani R., “Least angle regression”, Annals of Statistics, 32(2): 407–499, (2004).
  • [29] statweb.stanford.edu/~tibs/lasso/simple.htm, “A simple explanation of the Lasso and Least Angle Regression”, (2015) .
  • [30] Zhang Z., Lai Z., Xu Y., Shao L., Wu J. and Xie S.G., “Discriminative Elastic-Net Regularized Linear Regression”, IEEE Transaction on Image Processing, 26(3):1466–1481, (2017).
  • [31] Zou H. and Hastie T., “Erratum: Regularization and variable selection via the elastic net”, Journal of the Royal Statistical Society. Series B: Statistical Methodology, 67(5):768, 2005.
  • [32] Ketkar N., “Stochastic Gradient Descent,”, Deep Learning with Python, 113–132, (2017).
  • [33] Rosenblatt F., “The perceptron: A probabilistic model for information storage and organization in the brain”, Psychological Review, 65(6): 386–408, (1958).
  • [34] Seifert J. W., “CRS Report for Congress Data Mining”, Reading, 1–16, (2004).
  • [35] www.baskent.edu.tr/~gmemis/courses/datamining/DM _1.pdf, “Veri̇ madenci̇li̇ği̇ 1”, (2019) [36] Wirth R., “,Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, 24959:29–39, (2000).
  • [37] Delen D. and Crossland M. D., “Seeding the survey and analysis of research literature with text mining”, Expert Systems with Applications, 34(3):1707–1720, (2008).
  • [38] Mecca G., Raunich S., and Pappalardo A., “A new algorithm for clustering search results,” Data & Knowledge Engineering, 62(3):504–522, (2007).
  • [39] Witten I. H., “Text mining: Practical handbook of internet computing”, Chapman & Hall/CRC Press, (2005).
  • [40] Iarrobino M., “The Evolution of Text Mining – Trends We’re Seeing Across R&D Organizations”, http://www.copyright.com/blog/trends-evolution-text-mining/, 2021.
  • [41] Gupta V. and Lehal G. S., “A Survey of Text Mining Techniques and Applications”, Journal of Emerging Technologies in Web Intelligence, 1(1): 60–76, (2009).
  • [42] Miner G. D., Elder J., and Nisbet R. A., “Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications”, Academic Press , (2012).
  • [43] Lidy T. and Rauber A., “Classification and Clustering of Music for Novel Music Access Applications”, Machine Learning Techniques for Multimedia, Springer, 249:285, (2008).
  • [44] Kamikawa Y. and Kato T., “Development of liquid-crystalline folate derivatives: Effects of intermolecular hydrogen bonds at oligopeptide moieties”, Polymer Preprints, Japan, 55(2):2659– 2660, (2006).
  • [45] Agaoglu M., “Predicting Instructor Performance Using Data Mining Techniques in Higher Education”, IEEE Access, 4:550, (2016).
  • [46] medium.com/@datalabtr/naïve-bayes-algoritması -ve-r-uygulaması-4d321869d371, “Naïve Bayes Algoritması ve R Uygulaması”, (2019).
  • [47] Liu Y., Wang Y. and Zhang J., “New machine learning algorithm: Random forest”, Lecture Notes in Compuer. Science, 7473:246–252, 2012..
  • [48] Zou H. and Hastie T., “Regression Shrinkage and Selection via the Elastic Net, with Applications to Microarrays”, Journal of the Royal Statistical Society, Series B, 67(1):301–320, (2003).
  • [49] Rençber Ö. F. and Bağcı H., “Sermaye Yeterliliğini Etkileyen Değişkenlerin Elastik Net Regresyon Yöntemi İle Belirlenmesi,” OPUS Uluslararası Toplum Araştırmaları Dergisi, DOI: 10.26466/opus.561915, (2019).
  • [50] Shalev-Shwartz S. and Ben-David S., “Stochastic Gradient Descent”, Understanding Machine Learning, 150–166, (2014).
  • [51] Bottou L., “Stochastic gradient descent tricks”, Lecture Notes in Computer, 7700:421–436, (2012).
  • [52] Rumelhart D. E., Hinton G. E., and Williams R. J., “Learning internal representations by error propagation”, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press, 1:318–362, (1986).
  • [53] Chandra A. L., “Perceptron Learning Algorithm: A Graphical Explanation Of Why It Works.”, https://towardsdatascience.com/perceptron-learning-algorithm-d5db0deab975.
  • [54] Hu F. and Trivedi R. H., “Mapping hotel brand positioning and competitive landscapes by text-mining user-generated content”, International Journal of Hospitality Management, 84, (2020).
  • [55] Vanhala M., Lu C., Peltonen J., Sundqvist S., Nummenmaa J. and Järvelin K., “The usage of large data sets in online consumer behaviour: A bibliometric and computational text-mining–driven analysis of previous research”, Journal of Business Research, 106:46–59, (2020).
  • [56] Hassani H., Beneki C., Unger S., Mazinani M. T. and Yeganeg M. R. i, “Text mining in big data analytics”, Big Data and Cognitive Computing, 4(1):1–34, (2020).
  • [57] Xie X., Fu Y., Jin H., Zhao Y. and Cao W., “A novel text mining approach for scholar information extraction from web content in Chinese”, Future Generation Computer Systems, 111:859–872, (2020).
  • [58] Glen S., “Mean Absolute Percentage Error (MAPE).” https://www.statisticshowto.com/mean-absolute-percentage-error-mape/.
  • [59] Ohsaki M., Wang P., Matsuda K., Katagiri S., Watanabe H. and Ralescu A., “Confusion-matrix-based kernel logistic regression for imbalanced data classification”, IEEE Transactions on Knowledge and Data Engineering, 29(9):1806–1819, (2017).
  • [60] Özdemir D., Kılınç Ş., “Geleceğin Meslekleri Listesi”, 2019.
  • [61] Yüksek Öğretim Kurumu, “Geleceğin Meslekleri Çalışmaları”, Geleceğin Meslekleri, Mesleklerin Geleceği Çalıştayı, (2019).

Makine Öğrenmesi Yöntemleri İle Günümüz Ve Geleceğe Yönelik Meslek Tahminlerinin Değerlendirilmesi : Türkiye'den Ampirik Deliller

Year 2023, , 107 - 124, 27.03.2023
https://doi.org/10.2339/politeknik.985534

Abstract

İşgücü piyasasındaki mesleklerin mevcut ve gelecekteki eğilimlerini belirlemede metin madenciliği yaklaşımı, işveren anketleri gibi geleneksel yöntemlere alternatif olarak kullanılabilir. Teknik olarak, iş gücünün mesleki gelişimini etkileyecek mesleklerin, gelecekteki eğilimleri hakkında doğru tahminlerde bulunmak için makine öğrenme algoritmaları kullanılmaktadır. Bu çalışmanın amacı, Türkiye'deki kurumlara ait belgeler de dahil olmak üzere, çeşitli Türkçe verilere denetimli öğrenme algoritmaları ve kümeleme yöntemleri uygulanarak, Türkiye'deki geleceğin ve şimdiki mesleklerin araştırılmasıdır. Çalışmada, çeşitli makine öğrenme algoritmaları aracılığıyla ≅0.81 ve ≅0.93 arasında bir doğruluk oranıyla, popüler meslekler tahmin edilmiştir. Metodolojik olarak Perceptron ve Stokastik Gradyan İniş algoritmalarının, içerdiği zeka fonksiyonları sayesinde diğer algoritmalara göre üstünlük gösterdiği keşfedilmiştir. Ayrıca, Türkiye'deki mevcut mesleklerin analizi, "Profesyonel meslekler", "Yönetici" ve "Teknisyen ve meslek mensubu yardımcıları" sınıfının popüler olduğu ve gelecek analizine göre bilgi teknolojisi tabanlı mesleklerin önemli olacağı çıkarımı yapılmıştır. Geleceğin analizi için sınırlı Türkçe veri kaynakları olmasına rağmen, yaklaşık 1 doğrulukta sonuçlar üretilmiştir.

References

  • [1] Manyika J., Chui M., Bughin J., Dobbs R., Bisson P., and Marrs A., “Disruptive technologies: Advances that will transform life, business, and the global economy,” McKinsey Global Institute, (2013).
  • [2] Öztürk N., “İktisadi Kalkınmada Eğitimin Rolü,” Sosyoekonomi, 1:27–44, DOI:10.17233/se.86714, (2005).
  • [3] Schwab K., “The Fourth Industrial Revolution”, World Economic Forum, Geneva, Switzerland, (2016).
  • [4] Mosconi F., “The new European industrial policy: Global competitiveness and the manufacturing renaissance”, London, (2015).
  • [5] Russmann M., “Industry 4.0: World Economic Forum”, Bost. Consult. Gr., 1–20, (2015).
  • [6] Huimin M., “Strategic plan of ‘Made in China 2025’ and its implementation”, Anal. Impacts Ind. 4.0 Mod. Bus. Environ., 19: 1–23, (2018).
  • [7] Kurt R., “Industry 4.0 in Terms of Industrial Relations and Its Impacts on Labour Life”, Procedia Comput. Sci., 158: 590–601, (2019).
  • [8] Blinder A. S., “Education for the Third Industrial Revolution”, Princeton University, Department of Economics, Center for Economic Policy Studies,Working Papers, (2008).
  • [9] Pamuk N. S. and Soysal M., “Yeni Sanayi Devrimi Endüstri 4.0 Üzerine Bir İnceleme”, Verimlilik Dergisi, 1:41–66, (2018).
  • [10] Macurova P., Ludvik L., and Žwakova M., “The driving factors, risks and barriers of the industry 4.0 concept,” Journal of Applied Economic Sciences, vol. 12(7): 2003-2011, 2017.
  • [11] Weber E., “Industry 4.0 – job-producer or employment-destroyer?”, Institute for Employment Research, (2016).
  • [12] Kane G. C., Palmer D., Phillips A. N., and Kiron D., “Is Your Business Ready for a Digital Future?”, MIT Sloan Management Review, 56(4):7–44, (2015).
  • [13] Kleinert C., Matthes B., and Jacob M., “IAB Forschungsbericht 5/2008”, (2008).
  • [14] Özkan M., Al A., and Yavuz S., “Uluslararası Politik Ekonomi Açısından Dördüncü Sanayi-Endüstri Devrimi’nin Etkileri ve Türkiye”, Siyasal Bilimler Dergisi, 1–30, (2018).
  • [15] Bilim ve Sanayi Bakanlığı, “Mesleklerin Geleceği Araştırma Raporu”, (2018).
  • [16] Işık V., “Türkiye’de Genç İşsizliği ve Genç Nüfusta Atalet”, HAK-İŞ Uluslararası Emek ve Toplum Dergisi, 11:131–145, (2016).
  • [17] Yükseköğretim Kurulu Başkanlığı, “Geleceğin Meslekleri Çalışmaları Çalıştay Raporları”, (2019).
  • [18] Pejic-Bach M., Bertoncel T., Meško M., and Krstić Ž., “Text mining of industry 4.0 job advertisements” International Journal of Information Management, 50:416–431, (2020).
  • [19] De Mauro A., Greco M., Grimaldi M., and Ritala P., “Human resources for Big Data professions: A systematic classification of job roles and required skill sets”, Information Processing & Management, 54(5): 807–817, (2018).
  • [20] Frank M.R., Bessen J.E. , Brynjolfsson E., Cebrian M., Deming D.J., Feldman M., Groh M., Lobo J., Moro E., Wang D., Younk H. and Rahwana I., “Toward understanding the impact of artificial intelligence on labor,”, PNAS, 116(14):6531–6539, (2019).
  • [21] Dawson N., Rizoiu M. A., Johnston B. and Williams M. A., “Predicting Skill Shortages in Labor Markets: A Machine Learning Approach”, 2020 IEEE International Conference on Big Data, 2:3052–3061, (2020).
  • [22] Boselli R., Cesarini M., Marrara S., Mercorio F., Pasi M.M.G. and Viviani M., “WoLMIS: a labor market intelligence system for classifying web job vacancies”, Journal of Intelligent Information Systems, 51:477–502, (2018).
  • [23] Papoutsoglou M., Ampatzoglou A., Mittas N., and Angelis L., “Extracting Knowledge from On-Line Sources for Software Engineering Labor Market: A Mapping Study”, IEEE Access, 7:157595-157613, (2019).
  • [24] Özköse H., “Yönetim Bilişim Sistemleri Alanının Türkiye ve Dünya’daki Bibliyometrik Analizi ve Haritası”, Gazi University, Enformatic Institute, (2017).
  • [25] Cover T. and Hart P., “Nearest Neighbor Pattern Classification”, IEEE Transactions on Information Theory, 13(1): 21–27, (1967).
  • [26] Berrar D., “Bayes’ theorem and naive bayes classifier”, Encyclopedia of Bioinformatics and Computational Biology ABC of Bioinformatics, 1–3:403–412, (2018).
  • [27] Breiman L., “Random forests”, Machine Learning, 45(1):5–32, (2001).
  • [28] Efron B., Hastie T.,Johnstone I. and Tibshirani R., “Least angle regression”, Annals of Statistics, 32(2): 407–499, (2004).
  • [29] statweb.stanford.edu/~tibs/lasso/simple.htm, “A simple explanation of the Lasso and Least Angle Regression”, (2015) .
  • [30] Zhang Z., Lai Z., Xu Y., Shao L., Wu J. and Xie S.G., “Discriminative Elastic-Net Regularized Linear Regression”, IEEE Transaction on Image Processing, 26(3):1466–1481, (2017).
  • [31] Zou H. and Hastie T., “Erratum: Regularization and variable selection via the elastic net”, Journal of the Royal Statistical Society. Series B: Statistical Methodology, 67(5):768, 2005.
  • [32] Ketkar N., “Stochastic Gradient Descent,”, Deep Learning with Python, 113–132, (2017).
  • [33] Rosenblatt F., “The perceptron: A probabilistic model for information storage and organization in the brain”, Psychological Review, 65(6): 386–408, (1958).
  • [34] Seifert J. W., “CRS Report for Congress Data Mining”, Reading, 1–16, (2004).
  • [35] www.baskent.edu.tr/~gmemis/courses/datamining/DM _1.pdf, “Veri̇ madenci̇li̇ği̇ 1”, (2019) [36] Wirth R., “,Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, 24959:29–39, (2000).
  • [37] Delen D. and Crossland M. D., “Seeding the survey and analysis of research literature with text mining”, Expert Systems with Applications, 34(3):1707–1720, (2008).
  • [38] Mecca G., Raunich S., and Pappalardo A., “A new algorithm for clustering search results,” Data & Knowledge Engineering, 62(3):504–522, (2007).
  • [39] Witten I. H., “Text mining: Practical handbook of internet computing”, Chapman & Hall/CRC Press, (2005).
  • [40] Iarrobino M., “The Evolution of Text Mining – Trends We’re Seeing Across R&D Organizations”, http://www.copyright.com/blog/trends-evolution-text-mining/, 2021.
  • [41] Gupta V. and Lehal G. S., “A Survey of Text Mining Techniques and Applications”, Journal of Emerging Technologies in Web Intelligence, 1(1): 60–76, (2009).
  • [42] Miner G. D., Elder J., and Nisbet R. A., “Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications”, Academic Press , (2012).
  • [43] Lidy T. and Rauber A., “Classification and Clustering of Music for Novel Music Access Applications”, Machine Learning Techniques for Multimedia, Springer, 249:285, (2008).
  • [44] Kamikawa Y. and Kato T., “Development of liquid-crystalline folate derivatives: Effects of intermolecular hydrogen bonds at oligopeptide moieties”, Polymer Preprints, Japan, 55(2):2659– 2660, (2006).
  • [45] Agaoglu M., “Predicting Instructor Performance Using Data Mining Techniques in Higher Education”, IEEE Access, 4:550, (2016).
  • [46] medium.com/@datalabtr/naïve-bayes-algoritması -ve-r-uygulaması-4d321869d371, “Naïve Bayes Algoritması ve R Uygulaması”, (2019).
  • [47] Liu Y., Wang Y. and Zhang J., “New machine learning algorithm: Random forest”, Lecture Notes in Compuer. Science, 7473:246–252, 2012..
  • [48] Zou H. and Hastie T., “Regression Shrinkage and Selection via the Elastic Net, with Applications to Microarrays”, Journal of the Royal Statistical Society, Series B, 67(1):301–320, (2003).
  • [49] Rençber Ö. F. and Bağcı H., “Sermaye Yeterliliğini Etkileyen Değişkenlerin Elastik Net Regresyon Yöntemi İle Belirlenmesi,” OPUS Uluslararası Toplum Araştırmaları Dergisi, DOI: 10.26466/opus.561915, (2019).
  • [50] Shalev-Shwartz S. and Ben-David S., “Stochastic Gradient Descent”, Understanding Machine Learning, 150–166, (2014).
  • [51] Bottou L., “Stochastic gradient descent tricks”, Lecture Notes in Computer, 7700:421–436, (2012).
  • [52] Rumelhart D. E., Hinton G. E., and Williams R. J., “Learning internal representations by error propagation”, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press, 1:318–362, (1986).
  • [53] Chandra A. L., “Perceptron Learning Algorithm: A Graphical Explanation Of Why It Works.”, https://towardsdatascience.com/perceptron-learning-algorithm-d5db0deab975.
  • [54] Hu F. and Trivedi R. H., “Mapping hotel brand positioning and competitive landscapes by text-mining user-generated content”, International Journal of Hospitality Management, 84, (2020).
  • [55] Vanhala M., Lu C., Peltonen J., Sundqvist S., Nummenmaa J. and Järvelin K., “The usage of large data sets in online consumer behaviour: A bibliometric and computational text-mining–driven analysis of previous research”, Journal of Business Research, 106:46–59, (2020).
  • [56] Hassani H., Beneki C., Unger S., Mazinani M. T. and Yeganeg M. R. i, “Text mining in big data analytics”, Big Data and Cognitive Computing, 4(1):1–34, (2020).
  • [57] Xie X., Fu Y., Jin H., Zhao Y. and Cao W., “A novel text mining approach for scholar information extraction from web content in Chinese”, Future Generation Computer Systems, 111:859–872, (2020).
  • [58] Glen S., “Mean Absolute Percentage Error (MAPE).” https://www.statisticshowto.com/mean-absolute-percentage-error-mape/.
  • [59] Ohsaki M., Wang P., Matsuda K., Katagiri S., Watanabe H. and Ralescu A., “Confusion-matrix-based kernel logistic regression for imbalanced data classification”, IEEE Transactions on Knowledge and Data Engineering, 29(9):1806–1819, (2017).
  • [60] Özdemir D., Kılınç Ş., “Geleceğin Meslekleri Listesi”, 2019.
  • [61] Yüksek Öğretim Kurumu, “Geleceğin Meslekleri Çalışmaları”, Geleceğin Meslekleri, Mesleklerin Geleceği Çalıştayı, (2019).
There are 60 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Ebru Karaahmetoğlu 0000-0003-4381-7865

Süleyman Ersöz 0000-0002-7534-6837

Ahmet Kürşad Türker 0000-0001-6686-9241

Volkan Ateş 0000-0002-2349-0140

Ali Firat İnal 0000-0001-7747-0746

Publication Date March 27, 2023
Submission Date August 20, 2021
Published in Issue Year 2023

Cite

APA Karaahmetoğlu, E., Ersöz, S., Türker, A. K., Ateş, V., et al. (2023). Evaluation of Profession Predictions for Today and the Future with Machine Learning Methods : Emperical Evidence From Turkey. Politeknik Dergisi, 26(1), 107-124. https://doi.org/10.2339/politeknik.985534
AMA Karaahmetoğlu E, Ersöz S, Türker AK, Ateş V, İnal AF. Evaluation of Profession Predictions for Today and the Future with Machine Learning Methods : Emperical Evidence From Turkey. Politeknik Dergisi. March 2023;26(1):107-124. doi:10.2339/politeknik.985534
Chicago Karaahmetoğlu, Ebru, Süleyman Ersöz, Ahmet Kürşad Türker, Volkan Ateş, and Ali Firat İnal. “Evaluation of Profession Predictions for Today and the Future With Machine Learning Methods : Emperical Evidence From Turkey”. Politeknik Dergisi 26, no. 1 (March 2023): 107-24. https://doi.org/10.2339/politeknik.985534.
EndNote Karaahmetoğlu E, Ersöz S, Türker AK, Ateş V, İnal AF (March 1, 2023) Evaluation of Profession Predictions for Today and the Future with Machine Learning Methods : Emperical Evidence From Turkey. Politeknik Dergisi 26 1 107–124.
IEEE E. Karaahmetoğlu, S. Ersöz, A. K. Türker, V. Ateş, and A. F. İnal, “Evaluation of Profession Predictions for Today and the Future with Machine Learning Methods : Emperical Evidence From Turkey”, Politeknik Dergisi, vol. 26, no. 1, pp. 107–124, 2023, doi: 10.2339/politeknik.985534.
ISNAD Karaahmetoğlu, Ebru et al. “Evaluation of Profession Predictions for Today and the Future With Machine Learning Methods : Emperical Evidence From Turkey”. Politeknik Dergisi 26/1 (March 2023), 107-124. https://doi.org/10.2339/politeknik.985534.
JAMA Karaahmetoğlu E, Ersöz S, Türker AK, Ateş V, İnal AF. Evaluation of Profession Predictions for Today and the Future with Machine Learning Methods : Emperical Evidence From Turkey. Politeknik Dergisi. 2023;26:107–124.
MLA Karaahmetoğlu, Ebru et al. “Evaluation of Profession Predictions for Today and the Future With Machine Learning Methods : Emperical Evidence From Turkey”. Politeknik Dergisi, vol. 26, no. 1, 2023, pp. 107-24, doi:10.2339/politeknik.985534.
Vancouver Karaahmetoğlu E, Ersöz S, Türker AK, Ateş V, İnal AF. Evaluation of Profession Predictions for Today and the Future with Machine Learning Methods : Emperical Evidence From Turkey. Politeknik Dergisi. 2023;26(1):107-24.
 
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