Evaluation of Machine Learning Models for Employee Churn Prediction

Authors

  • Ramesh Cheripelli
  • P. V. Ajitha

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.

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Published

2020-03-12

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Section

Articles