There are several experienced and highly skilled employees considered to be assets in every organization; a good and flexible working environment is required to retain them. The perceived exit of well-skilled and highly experienced employees may result in financial losses, poor sales with customers’ dissatisfaction and produced a low turnover. It also led to low production and output. The existing methods of operation lack the merit in producing accurate and reliable results. It could not generalize well with testing datasets and results in the problem of model over-fitting. Little or no work has been done in the area of predicting the perceived employee tendency of leaving an organization using Support Vector Machine (SVM) and Naive Bayes (NB) algorithm. The implemention was done using Python (Spyder IDE) in ANACONDA. In this paper, a model which is capable of predicting the perceived employee tendency of exiting an organization was developed using the support vector machine and the Naive Bayesian machine learning algorithm. The adopted techniques improved the prediction accuracy and generalized well with testing datasets in overcoming the problem of over-fitting. It also reduced the sudden occurrence of experienced and skilled employees leaving an organization. We adopted the SVM and NB to effectively handle overlapping and reduce data misclassification errors that can work well with a limited number of the dataset. The proposed NB model was trained, successfully tested and evaluated using the same dataset in comparison with the SVM technique. The experimental results of NB model produced 100% prediction accuracy with a 0.0000 RMSE error value in comparison with the SVM which gave a 97.00% success rate and 0.0258 RMSE value.
Cite this paper
Emmanuel-Okereke, I. L. and Anigbogu, S. O. (2022). Predicting the Perceived Employee Tendency of Leaving an Organization Using SVM and Naive Bayes Techniques. Open Access Library Journal, 9, e8497. doi: http://dx.doi.org/10.4236/oalib.1108497.
Khera, S. and Divya, N. (2018) Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques. Vision, 23, 12-21.
https://doi.org/10.1177/0972262918821221
Pandiyan, P., Kannadasan, K. and Vinoth, R. (2013) Prospective Control in an Organization through Two Grade Systems. 4D International Journal of Multidisciplinary Research and Development, 1, 22-25.
Kalaivani, J., Vinoth, R. and Elangovan, S.R. (2014) Survival Time to Trace the threshold Grade Level in an Organization. International Journal of Multidisciplinary Research and Development, 1, 22-25.
Morrell, K., Loan-Clarke, J. and Wilkingson, A. (2004) The Role of Shocks in Employee Turnover. British Journal of Management, 15, 335-349.
https://doi.org/10.1111/j.1467-8551.2004.00423.x
Kannadasan, K., Pandiyan, P., Vinoth, R. and Saminathan, R. (2013) Time to Recruitment in an Organization through Three Parameter Generalized Exponential Model. Journal of Reliability and Statistical Studies, 6, 21-28.
Morrell, K. (2005) Towards a Typology of Nursing Turnover: The Role of Shocks in Nurses’ Decision to Leave. Journal of Advanced Nursing, 49, 315-322.
https://doi.org/10.1111/j.1365-2648.2004.03290.x
Kuwaiti, A.A., Raman, V., Subbarayalu, A.V., Palanivel, R.M. and Prabaharan, S. (2018) Predicting the Exit Time of Employees in an Organization Using Statistical Model. International Journal of Scientific and Technology Research (IJSTR), 5, 213-217.
Ramamurthy, K.N., Singh, M., Davis, M., Kevern, J.A., Klein, U. and Peran, M. (2015) Identifying Employees for Re-Skilling Using an Analytics-Based Approach. 2015 IEEE International Conference on Data Mining Workshop (ICDMW), Atlantic City, 14-17 November 2015, 345-354. https://doi.org/10.1109/ICDMW.2015.206
Singh, M., Varshney, K.R., Wang, J., Mojsilovic, A., Gill, A.R., Faur, P.I. and Ezry, R. (2012) An Analytics Approach for Proactively Combating Voluntary Attrition of Employees. 2012 IEEE 12th International Conference on Data Mining Workshops (ICDMW), Brussels, 10-13 December 2012, 317-323.
https://doi.org/10.1109/ICDMW.2012.136
Maharjan, R. (2011) Employee Churn Prediction Using Logistic Regression and Support Vector Machine Support Vector Machine. SJSU Scholar Works: Master Degree Work, 1-72.
Jayadi, R., Firmantyo, H.M., Dzaka, M.T.J., Sualdy, M. and Putra, A.M. (2019) Employee Performance Prediction Using Naive Bayes. International Journal of Advanced Trends in Computer Science and Engineering, 6, 3031-3035.
https://doi.org/10.30534/ijatcse/2019/59862019
Yahia, N.B., Hlel, J. and Colomo-Palacios, R. (2017) From Big Data to Deep Data to Su- pport People Analytics for Employee Attrition Prediction. IEEE Access, 34, 1-12.
Kamath, R.S., Jamsandekar, S.S. and Naik, P.G. (2019) Machine Learning Approach for Employee Attrition Analysis. International Journal of Trend in Scientific Research and Development (IJTSRD), 5, 62-67. https://doi.org/10.31142/ijtsrd23065
Alshehhi, K., Zawbaa, S.B. and Tariq, M.U. (2021) Employee Retention Prediction in Corporate Organizations Using Machine Learning Methods. Academic of Entrepreneurship Journal, 27, 1-16.
Senanayake, D., Muthugama, L., Mendis, L. and Madushanka, T. (2015) Customer Ch- urn Prediction: A Cognitive Approach. Internation Journal of Computer, Electrical, Automation, Control and Information Engineering, 9, 23-43.
Foley, A.E. (2019) Using Machine Learning to Predict Employee Resignation in the Swe- dish Armed Forces. Kth Royal Institute of Technology School of Industrial Engineering and Management, Stockholm, 1-85.
Panjasuchat, M. and Limpiyakorn, Y. (2020) Applying Reinforcement Learning for Cus- tomer Churn Prediction. Journal of Physics: Conference Series, 1619, 12015.
https://doi.org/10.1088/1742-6596/1619/1/012016
Bryant, P.C. and Allen, D.G. (2013) Compensation, Benefits and Employee Turnover: HR Strategies for Retaining Top Talent. Compensation and Benefits Review, 45, 171- 175. https://doi.org/10.1177/0886368713494342
Byun, H. and Lee, S.W. (2003) A Survey on Pattern Recognition Applications of Support Vector Machines. International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), 17, 459-486. https://doi.org/10.1142/S0218001403002460
Ramakrishnan, R., Bhattacharya, S. and Dhanya, P. (2018) Predict Employee Attrition by Using Predictive Analytics. Benchmarking: An International Journal, 26, 2-18.
https://doi.org/10.1108/BIJ-03-2018-0083