Paper Key : IRJ************736
Author: Abhishek Sawalkar,Sumeet Suryawanshi,Samir Pathan
Date Published: 01 Dec 2022
Abstract
Employee turnover is the measure of the total number of employees who leave the company over a specific time frame. Along with the fast economic and industrial growth development, employee turnover has gradually become popular in recent years. Employee turnover has been identified as a key issue for organizations because of its adverse impact on workplace productivity and long-term growth strategies. It is considered a serious challenge for organizations and companies. Organizations need to strategize to reduce the turnover goals of the workers to have a competitive advantage over other organizations. Furthermore, Organizations need to grasp the major factors of employee turnover, and then take relevant measures to deal with this problem. This paper studies the machine learning algorithms used to predict employee turnover. Some of the popular algorithms used for this prediction are Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, and Logistic Regression. Organizations can use this predictive analysis to measure the number of employees that needs to be hired in place of the ones that are leaving.