Prediction of Job Satisfaction from the Employee Using Ensemble Method

Abstract

In an organization, employees are the major and important resources and may quit the job unpredictably which may produce immense cost. In general, the employee attitude and their effort are influenced by their personality traits but the job satisfaction may result for an individual observations from an organization based on the environment conditions. Meanwhile, the hiring of new employee may consume time and cost. Similarly, recently hired employee may need to put certain efforts for being productive. The job satisfaction of the employee is one of the factor for leaving out from the organization. The employee attrition prediction and its reasons to leave the organization required to be performed from Human Resource Management (HRM) perspective. This kind of prediction has to be progressed from HRM for analyzing the best and experienced employee's reason for leaving their organization using various data mining technique but the exact prediction is not obtained. This can be analyzed by seeing some experienced and best employee leaving their organization. Therefore, this paper has attempted for developing an ensemble model which assist in providing an accurate prediction of the employee attrition based on the HR analytics dataset. The proposed research work focus in analyzing the job satisfaction mentioned by the employee in the “Employee Attrition” has been considered by predicting the dataset using Weighed Average Mechanism (WAM) in ensemble method with Logistic Regression (LR). Moreover, the performance evaluation of proposed ensemble method attaints the higher accuracy of 98.2% which outperforms the other three existing methods for analyzing the better prediction of job satisfaction from the employees.