Predictive Human Resource Analytics Using Data Mining Classification Techniques
The turnover ratio of employees in organization is the most important concerns as employees switching of organization/ job leaves huge gape and affects the performance of that organization. Among many, job satisfaction is the prime reason of employees to quit/switch, which is also directly related to human resource management (HRM) practices of the organization. It is always difficult and sometime beyond the control of human resource (HR) department to retained their well-trained and skilled employees but Data mining can play role to predict those employees who are expected to quit/leave an organization such that the HR department can device intervention strategy or look for alternative. In this paper, we focus on similar problem, where we use data mining techniques such as J48, Naive Bayes, and Logistic Regression predict employees who will leave the organization. Our data consists of different indicator values and some other important features such as number of projects, supervisor evaluation score and experience. We show that J48 perform well with accuracy 98.84% and TP rate 0.984%. Conventional statistical analysis has been used in literature to identify important factors affecting employees satisfaction but there is not agreed set. We also apply data mining techniques to identify such factors using two approaches such as Bayesian Network and IR. Finally, we provide a decision tree based model for decision makers that can easily stimulate employees satisfaction level for better retention policy.
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