An Experimental Analysis of Work-Life Balance Among The Employees using Machine Learning Classifiers
  IJCTT-book-cover
 		          
 
© 2021 by IJCTT Journal
Volume-69 Issue-4
Year of Publication : 2021
Authors : K. Radha, M. Rohith
DOI :  10.14445/22312803/IJCTT-V69I4P108

How to Cite?

K. Radha, M. Rohith, "An Experimental Analysis of Work-Life Balance Among The Employees using Machine Learning Classifiers," International Journal of Computer Trends and Technology, vol. 69, no. 4, pp. 39-48, 2021. Crossref, https://doi.org/10.14445/22312803/IJCTT-V69I4P108

Abstract
Researchers today have found out the importance of Artificial Intelligence, and Machine Learning in our daily lives, as well as they can be used to improve the quality of our lives as well as the cities and nations alike. An example of this is that it is currently speculated that ML can provide ways to relieve workers as it can predict effective working schedules and patterns which increase the efficiency of the workers. Ultimately this is leading to a Work-Life balance for the workers. But how is this possible? It is practically possible with the Machine Learning algorithms to predict, calculate the factors affecting the feelings of the worker’s work-life balance. In order to actually do this, a sizable amount of 12,756 people’s data has been taken under consideration. Upon analyzing the data and calculating under various factors, we have found out the correlation of various factors and WLB(Work-Life Balance in short). There are some factors that have to be taken into serious consideration as they play a major role in WLB. We have trained 80% of our data with Random Forest Classifier, SVM, and Naïve Bayes algorithms. Upon testing, the algorithms predict the WLB with 71.5% as the best accuracy.

Keywords
Machine Learning Algorithms, Naïve Bayes, Random Forest Classifier, SVM, Work-Life-Balance.

Reference

[1] Pawlicka A, Pawlicki M, Tomaszewska R,Chora? M, Gerlach R., Innovative machine learning approach and evaluation campaign for predicting the subjective feeling of work-life balance among employees. PLoS ONE 15(5)(2020) e0232771. pone.0232771,May 15 2020,pp.1-18.
[2] Wilkins J. How Industry 4.0 Is Improving Peoples’ Work-Life Balance. In: IMPO. (2018).
[3] Meenaksh SP, Subrahmanyam V, Ravichandran K. The Importance of Work-Life-Balance. IOSR J Bus Manag. (2013) 14: 31–35.
[4] A. Rohit Hebbar, S. H. Patil, S. B. Rajeshwari and S. S. M. Saqquaf., Comparison of Machine Learning Techniques to Predict the Attrition Rate of the Employees., 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India, (2018) 934-938, doi: 10.1109/RTEICT42901.2018.9012243.
[5] Francesca Fallucchi , Marco Coladangelo, Romeo Giuliano ,and Ernesto William De Luca., Predicting Employee Attrition Using Machine Learning Techniques, CoComputers 9(2020), 86 1- 17, Computers 2020, 9, 86; doi:10.3390/computers9040086
[6] Sandeep Kumar Gupta, Reznik Nadia P., Esra SIPAHI, Sayonara de Fátima Teston and Abrham Fantaw., Analysis of the Effect of Compensation on Twitter Based on Job, Talent Development & Excellence,12(3)(2020) 3289 – 3314.
[7] Boushey, H.; Glynn, S.J. There Are Significant Business Costs to Replacing Employees. Cent. Am. Prog. (2012) 16, 1–9.
[8] Martin, L. How to retain motivated employees in their jobs? Econ. Ind. Democr. 34 (2018) 25–41.
[9] Wood, S.; Van Veldhoven, M.; Croon, M.; de Menezes, L.M. Enriched job design, high involvement management and organizational performance: The mediating roles of job satisfaction and wellbeing. Hum. Relat. 65(2012) 419–446.
[10] Zelenski, J.M.; Murphy, S.A.; Jenkins, D.A. The happy-productive worker thesis revisited. J. Happiness Stud. 9(2008) 521–537.
[11] Clark, A.E.What reallymatters in a job? Hedonicmeasurement using quit data. Labour Econ. 8(2001) 223–242.
[12] Clark, A.E.; Georgellis, Y.; Sanfey, P. Job satisfaction, wage changes, and quits: Evidence from Germany. Res. Labor Econ. 17(1998) 95–121.
[13] Delfgaauw, J. The effect of job satisfaction on job search: Not just whether, but also where. Labour Econ. 14(2007) 299–317.
[14] Green, F. Well-being, job satisfaction and labour mobility. Labour Econ. 17(2010) 897–903.
[15] Kristensen, N.; Westergaard-Nielsen, N. Job satisfaction and quits – which job characteristics matters most? Dan. Econ. J. 144(2006) 230–249.
[16] Marchington, M.;Wilkinson, A.; Donnelly, R.; Kynighou, A. Human Resource Management atWork; Kogan Page Publishers: London, UK, 22(2016).
[17] Van Reenen, J. Human resource management and productivity. In Handbook of Labor Economics; Elsevier: Amsterdam, The Netherlands, (2011).
[18] Deepak, K.D.; Guthrie, J.; Wright, P. Human Resource Management and Labor Productivity: Does Industry Matter? Acad. Manag. J. 48(2005) 135–145.
[19] Gordini, N.; Veglio, V. Customers churn prediction and marketing retention strategies. An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce industry. Ind. Mark. Manag. 62(2016) 100–107.
[20] Keramati, A.; Jafari-Marandi, R.; Aliannejadi, M.; Ahmadian, I.; Mozaffari, M.; Abbasi, U. Improved churn prediction in telecommunication industry using data mining techniques. Appl. Soft Comput. 24(2014) 994 1012.
[21] Alao, D.; Adeyemo, A. Analyzing employee attrition using decision tree algorithms. Comput. Inf. Syst. Dev. Inf. Allied Res. J. 4(2013) 17–28. [22] Nagadevara, V. Early Prediction of Employee Attrition in Software Companies-Application of Data Mining Techniques. Res. Pract. Hum. Resour. Manag. 16(2008) 2032. 28.
[23] Rombaut, E.; Guerry, M.A. Predicting voluntary turnover through Human Resources database analysis. Manag. Res. Rev. 41(2018) 96–112.
[24] Usha, P.; Balaji, N. Analysing Employee attrition using machine learning. Karpagam J. Comput. Sci. 13(2019) 277–282