Abstract
Employee attrition is a serious issue pertaining in most organizations. Employees quit their jobs and their positions and are never being replaced or their job roles, at times shifting the workload over to the shoulders of existing employees. Employee attrition, employee turnover, and employee retention need to be looked over from various perspectives. Therefore, we have taken the IBM employee dataset, which is analyzed for inferring the various insights using machine learning and deep learning methods. Here for the analysis of data, we have used deep learning methodologies and machine learning techniques to gather more valuable insights than using the traditional methods. This analysis helped rigorously for the analysis of employee attrition and retention, and in fact more about turnover. This paper opens up brand new insights about employee attrition, employee retention, and employee turnover.
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References
Hoffman M, Tadelis S (2021) People management skills, employee attrition, and manager rewards: an empirical analysis. J Polit Econ 129(1):243–285
Bennett N et al (1993) A firm-level analysis of employee attrition. Group Organ Manage 18(4):482–499
Kraut AI (1975) Predicting turnover of employees from measured job attitudes. Organ Behav Hum Perform 13(2):233–243
Srivastava DK, Nair P (2017) Employee attrition analysis using predictive techniques. In: International conference on information and communication technology for intelligent systems. Springer, Cham
Adhikari A (2009) Factors affecting employee attrition: a multiple regression approach. IUP J Manage Res 8(5):38
Yiu L, Saner R (2014) Talent attrition and retention: strategic challenges for Indian industries in the next decade. Elite Res J Account Bus Manage 2(1):1–9
Mishra S, Mishra D (2013) Review of literature on factors influencing attrition and retention. Int J Organ Behav Manage Perspect 2(3):435
Frye A et al (2018) Employee attrition: what makes an employee quit? SMU Data Sci Rev 1(1):9
Karumuri V, Singareddi S (2014) Employee attrition and retention: a theoretical perspective. Asia Pac J Res I(XIII)
Alduayj SS, Rajpoot K (2018) Predicting employee attrition using machine learning. In: 2018 International conference on innovations in information technology (IIT). IEEE
Fallucchi F et al (2020) Predicting employee attrition using machine learning techniques. Computers 9(4):86
Chowdhury AH, Malakar S, Seal DB, Goswami S (2022) Understanding employee attrition using machine learning techniques. In: Data management, analytics and innovation. Springer, Singapore, pp 101–109
Khan EA, Hayat Khan SM (2019) Factors affecting employee attrition and predictive modelling using IBM HR data. J Comput Theor Nanosci 16(8):3379–3383
Yang S, Ravikumar P, Shi T (2020) IBM employee attrition analysis. arXiv preprint arXiv:2012.01286
Ponnuru S et al (2020) Employee attrition prediction using logistic regression. Int J Res Appl Sci Eng Technol 8:2871–2875
Shankar RS et al (2018) Prediction of employee attrition using datamining. In: 2018 IEEE international conference on system, computation, automation and networking (ICSCA). IEEE
Ozdemir F et al (2020) Assessing employee attrition using classifications algorithms. In: Proceedings of the 2020 the 4th international conference on information system and data mining
Goswami BK, Jha S (2012) Attrition issues and retention challenges of employees. Int J Sci Eng Res 3(4):1–6
Steel RP, Ovalle NK (1984) A review and meta-analysis of research on the relationship between behavioral intentions and employee turnover. J Appl Psychol 69(4):673
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Pulari, S.R., Punitha, A., Raja Varshni Meenachi, S., Vasudevan, S. (2023). A Comparative Study of Employee Attrition Analysis Using Machine Learning and Deep Learning Techniques. In: Ranganathan, G., Fernando, X., Rocha, Á. (eds) Inventive Communication and Computational Technologies. Lecture Notes in Networks and Systems, vol 383. Springer, Singapore. https://doi.org/10.1007/978-981-19-4960-9_1
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DOI: https://doi.org/10.1007/978-981-19-4960-9_1
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