Evaluatıon Of Machıne Learnıng Models For Employee Churn Predıctıon

  • Ramesh Cheripelli, P.V Ajıtha


The aim of this paper is to study a new prediction method for the churn problem in Information Technology Sectors. For this end, a logistic regression model is built, which integrates a machine learning algorithm logistic regression model from statistics and data analytics. First, we have to classify churn and non-churn employees utilizing the logistic regression model to, and then the organisation can do the needful to retain them. At last, we present the outcomes of a simulative assessment and prove that the presented method is conducive to analysing the churn problem in human resource analytics

Index Terms: Logistic regression, Churn problem, Machine learning, HR Analytics, ERM(Employee Relationship Management)    

How to Cite
Ramesh Cheripelli, P.V Ajıtha. (2020). Evaluatıon Of Machıne Learnıng Models For Employee Churn Predıctıon. International Journal of Advanced Science and Technology, 29(7s), 4392-4398. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/22988