Evaluatıon Of Machıne Learnıng Models For Employee Churn Predıctıon

  • Ramesh Cheripelli, P.V Ajıtha

Abstract

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)    

Published
2020-06-06
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