Predicting Employee Attrition along with Identifying High Risk Employees using Big Data and Machine Learning

Abstract

"It takes a lot of time and energy to build a great employee and only a second to lose one." Employee turnover is a perennial challenge faced by all the major companies across the globe, performance of a company is directly proportional to the quality of employees retained by them. Whenever a good employee quits the organization it leads to financial losses, gaps in company's execution capability, re-recruiting costs and loss of productivity. The success of a company lies not only in impeding the attrition rate but also in retaining the right talent. According to NASSCOM, the global employee churn rate as of 2019 is 18-20 percent, which is what makes it necessary to alleviate the business risks associated with the turnover using statistical analysis. This research aims to foresee potential attrition (specifically in the B.P.O. sector) by mining turnover trends amongst employees and use supervised classification techniques to cluster out vulnerable employees".