Makine Öğrenimi Algoritması Kullanarak Kişisel Göstergelere Dayalı Çalışan Teşviklerinin Tahmini

Year 2024, Volume: 8 Issue: 2, 75 - 98

Yasmine Aya Ibrir , Mahmut Çavur

https://doi.org/10.33461/uybisbbd.1471499

Abstract

Terfi, çalışanın kendini geliştirmesi ve işin yükünü ve sorumluluğunu, kendisine yüklenen pozisyonla birlikte taşıma isteği için motive etmenin bir aracı olarak hareket eder. Geleneksel yöntemler ile yapılan terfilerin hakkaniyeti ve ölçülebilirliği nicel olarak ölçülemediği için farklı yöntemlere ihtiyaç duyulmaktadır. Son yıllarda şirketlerde bilgi sistemlerinin kullanımın yaygınlaşması ile çalışanlara ait performans bilgileri gibi birçok bilgi dijital ortamda tutulmaya başlandı. Yine ver bilimlerinin gelişmesi ve birçok alana uygulanması ile birlikte çalışanlara ait bu verilerin değerlendirmesinde makine öğrenmesi ve yapay zekâ algoritmalarının kullanımı yaygınlaştı. Bu çalışma, çeşitli özelliklere dayalı olarak bir kuruluş içindeki çalışanların terfilerini tahmin etmek için sağlam bir çerçeve oluşturmayı amaçlamaktadır. Bu özellikler, eğitim sayısını, önceki yıl derecelendirmelerini, hizmet süresini, kazanılan ödülleri ve ortalama eğitim puanını içermekle birlikte bunlarla sınırlı değildir. Çalışmanın amacı, kuruluşların bilinçli terfi kararları almaları için güvenli bir araç sağlamak ve bu çerçevenin diğer tahmin problemlerine genelleştirilebileceğini göstermektir. Deneysel sonuçlar XGBoost modelinin doğruluk açısından en verimli model olduğunu göstermektedir. XGBoost, %94 doğruluk ve ROC AUC, %94 duyarlılık ve %94 hassasiyetle bellek kullanımı verimliliği, doğruluk ve çalışma süresi açısından üstün bir algoritma olarak kabul edilmektedir.

Keywords

Çalışan Terfisi, Çalışan Terfi Tahmin Çerçevesi, XGBoost, Makine Öğrenimi, Denetimli Öğrenme.

Forecasting Employees’ Promotion Based on Personal Indicators by Using a Machine Learning Algorithm

Abstract

Promotion is a tool to motivate employees to improve themselves and take on the burden and responsibility of the position assigned to them. Due to the fairness and measurability of promotions conducted by traditional methods needing to be quantifiable, different methods are required. In recent years, with the widespread use of information systems in companies, much information, such as performance data of employees, has started to be stored digitally. Additionally, with the development of data sciences and their application in many fields, machine learning and artificial intelligence algorithms in evaluating this data have become widespread. This study aims to establish a robust framework to predict employee promotions based on various features. These features include but are not limited to the number of training sessions attended, previous year ratings, tenure, awards received, and average training scores. The study aims to provide organizations with a reliable tool to make informed promotion decisions and demonstrate that this framework can be generalized to other prediction problems. Experimental results show that the XGBoost model is the most efficient in terms of accuracy. XGBoost is considered a superior algorithm with 94% accuracy, 94% ROC AUC, 94% sensitivity, and 94% precision, excelling in memory usage efficiency, accuracy, and runtime.

Keywords

Employee Promotion, Employee Promotion Prediction, XGBoost, Machine Learning, Supervised Learning.

References

• Aarshay (2020). XGBOOST parameters: XGBoost parameter tuning. Analytics Vidhya. Available at: https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/. (Accessed April 11, 2022).
• Bandyopadhyay, N. and Jadhav, A. (2021) ‘Churn Prediction of Employees Using Machine Learning Techniques.’, Technical Journal / Tehnicki Glasnik, 15(1), pp. 51–59. Available at: http://icproxy.khas.edu.tr/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=edb&AN=149158643&site=eds-live.
• Brownlee, J. (2022). Feature Importance and Feature Selection With XGBoost in Python. [online] Machine Learning Mastery. Available at: https://machinelearningmastery.com/feature-importance-and-feature-selection-with-xgboost-in-python/ (Accessed 14 April 2022).
• Chen, K.-Y., Hsu, Y.-L. and Wu, C.-C. (2012) Num 2 Fall 2012 1 The International Journal Of Organizational Innovation Volume 5 Number 2 Fall 2012 Information Regarding The International Journal Of Organizational Innovation 4 IJOI, The International Journal of Organizational Innovation. Available at: http://www.iaoiusa.org (Accessed: 1 March 2022).
• Chen, K.-Y., Hsu, Y.-L. and Wu, C.-C. (2012) Num 2 Fall 2012 1 The International Journal Of Organizational Innovation Volume 5 Number 2 Fall 2012 Information Regarding The International Journal Of Organizational Innovation 4 IJOI, The International Journal of Organizational Innovation. Available at: http://www.iaoiusa.org (Accessed: 1 March 2022).
• De Pater, I. E. et al. (2009) ‘Employees’ Challenging Job Experiences And Supervisors’ Evaluations Of Promotability’, Personnel Psychology, 62(2), pp. 297–325. doi: 10.1111/j.1744-6570.2009.01139.x.
• Faizankshaikh (2022). wns-analytics-wizard-2018/Rank 1: Siddharth3977 at master · analyticsvidhya/wns-analytics-wizard-2018. [online] GitHub. Available at: https://github.com/analyticsvidhya/wns-analytics-wizard-2018/tree/master/Rank%201:%20Siddharth3977 (Accessed 13 March 2022).
• Febrina, S. C. (2017) ‘Predicting Employee Performance by Leadership, Job Promotion, and Job Environmental in Banking Industry’, Jurnal Keuangan dan Perbankan, 21(4), pp. 641–649. doi: 10.26905/jkdp.v21i4.1630.
• Jain, P. K., Jain, M. and Pamula, R. (2020) ‘Explaining and predicting employees’ attrition: a machine learning approach’, SN Applied Sciences, 2(4). doi: 10.1007/s42452-020-2519-4.
• Jain, R. and Nayyar, A. (2018) ‘Predicting employee attrition using xgboost machine learning approach’, in Proceedings of the 2018 International Conference on System Modeling and Advancement in Research Trends, SMART 2018. (1)Department of Computer Science and Engineering (CSE), Bharati Vidyapeeth’s College of Engineering: Institute of Electrical and Electronics Engineers Inc., pp. 113–120. doi: 10.1109/SYSMART.2018.8746940.
• Jaiswal, Logistic regression in machine learning - javatpoint. www.javatpoint.com. Available at: https://www.javatpoint.com/logistic-regression-in-machine-learning (Accessed April 11, 2022).
• Jaiswal, Machine learning decision tree classification algorithm - javatpoint. www.javatpoint.com. Available at: https://www.javatpoint.com/machine-learning-decision-tree-classification-algorithm (Accessed April 11, 2022).
• Jaiswal, Machine learning random forest algorithm - javatpoint. www.javatpoint.com. Available at: https://www.javatpoint.com/machine-learning-random-forest-algorithm (Accessed April 11, 2022).
• Jantan, H. and Hamdan, A. (2010) ‘Applying Data Mining Classification Techniques for Employee’s Performance Prediction’, Knowledge …, pp. 601–607. Available at: http://www.kmice.cms.net.my/ProcKMICe/KMICe2010/Paper/PG601-607.pdf (Accessed: 29 November 2021).
• Li, M. G. T. et al. (2021) ‘Employee performance prediction using different supervised classifiers’, in Proceedings of the International Conference on Industrial Engineering and Operations Management, pp. 6870–6876.
• Liu, J. et al. (2019) ‘A data-driven analysis of employee promotion: The role of the position of organization’, in Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. National University of Defense Technology, College of Systems Engineering: Institute of Electrical and Electronics Engineers Inc., pp. 4056–4062. doi: 10.1109/SMC.2019.8914449.
• Long, Y. et al. (2018) ‘Prediction of employee promotion based on personal basic features and post features’, in ACM International Conference Proceeding Series. Association for Computing Machinery, pp. 5–10. doi: 10.1145/3224207.3224210.
• Machado, C. S. and Portela, M. (2021) ‘Age and Opportunities for Promotion’, SSRN Electronic Journal. doi: 10.2139/ssrn.2367639.
• Najafi-Zangeneh, S. et al. (2021) ‘An improved machine learning-based employees attrition prediction framework with emphasis on feature selection’, Mathematics, 9(11). doi: 10.3390/math9111226.
• Navlani, A., AdaBoost classifier algorithms using python Sklearn tutorial. DataCamp. Available at: https://www.datacamp.com/tutorial/adaboost-classifier-python (Accessed April 11, 2022).
• Punnoose, R. and Ajit, P. (2016) ‘Prediction of Employee Turnover in Organizations using Machine Learning Algorithms’, International Journal of Advanced Research in Artificial Intelligence, 5(9). doi: 10.14569/ijarai.2016.050904.
• SagarDhandare (2022). Feature Scaling In Data Science!. [online] Medium. Available at: https://medium.datadriveninvestor.com/feature-scaling-in-data-science-5b1e82492727 (Accessed 13 April 2022).
• Saradhi, V. V. and Palshikar, G. K. (2011) ‘Employee churn prediction’, Expert Systems with Applications, 38(3), pp. 1999–2006. doi: 10.1016/j.eswa.2010.07.134.
• Sarker, A. et al. (2018) Employee’s Performance Analysis and Prediction using K-Means Clustering & Decision Tree Algorithm Mawlana Bhashani Science and Technology University Employee’s Performance Analysis and Prediction using K-Means Clustering & Decision Tree Algorithm, Type: Double Blind Peer Reviewed International Research Journal Software & Data Engineering Global Journal of Computer Science and Technology: C.
• Tarbani (2021). Gradient boosting algorithm: How gradient boosting algorithm works. Analytics Vidhya. Available at: https://www.analyticsvidhya.com/blog/2021/04/how-the-gradient-boosting-algorithm-works/. (Accessed April 11, 2022).
• Yedida, R. et al. (2018) ‘Employee Attrition Prediction’, IJISET-International Journal of Innovative Science, Engineering & Technology, 7(9). Available at: www.ijiset.com (Accessed: 13 January 2022).