Analyzing The Employee Turnover by Using Decision Tree Algorithm

Abstract

In knowledge-based organizations, employee turnover is a significant challenge. Frequently, a company's competitive advantage can be traced back to the tacit knowledge of its employees, which is lost when those employees depart. If a company is sincere about remaining ahead of the competition, it must do everything possible to prevent its employees from leaving in droves. To comprehend the factors contributing to employee turnover in enterprises, this article examines employee turnover. The worker was assigned to one of several predetermined attrition categories primarily based on employee demographic and employment history information. Python was used to develop decision tree models and rule sets. A predictive model was constructed using the results of the developed rule sets and decision tree models to predict future employee turnover cases. Also proposed was a software application framework to implement the study's recommendations.

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