Predicting Employee Attrition using Explainable Machine Learning Algorithm

Abstract

Attrition is one of the most critical problems with employees that our society faces today. The most significant loss for a business is when employees leave. Almost every business today gives careful thought to how to keep their employees. But they can't figure out why people leave their jobs. Employee attrition is the loss of employees that happens naturally in a company because of things that can't be changed. Attrition, how hard it is for the organizers to keep workers, and suggestions for keeping them. Attrition is the gradual loss of staff members because of things that can't be helped, like people quitting for personal or professional reasons. When employees are let go, an organization loses a lot of money. Most of the time, employers can't do anything about the fact that more people are leaving their jobs than are being hired. The Society for Human Resource Management (SHRM) says that the average cost to hire a new employee is USD 4,129, based on recent data. In 2021, the rate of people leaving was expected to be 57.3%. A research study needs to be done to determine why employees leave and to develop a way to predict employee turnover (Raza et al., 2022). This study aims to look at organizational factors that lead to employees leaving and predict the rate of employees leaving using machine learning techniques that are easy to understand. The other goal of this study is to find out what makes employees leave the most. In the past few years, researchers have looked at different machine-learning algorithms and found out why they work the way they do. Our study found that monthly income, hourly wage, job level, job satisfaction, and age are why employees leave their jobs (Raza et al., 2022). Our suggested strategy and research results assist organizations in identifying the actual reasons why employees leave their jobs. A company can also stop employees from leaving by addressing why they leave. Many workers also quit their jobs for various hidden reasons, such as not feeling secure in their jobs, not being able to move up in their careers, wanting to try something new, wanting to make more money, having problems with their bosses, or other personal reasons. This study assists in understanding the challenges employers face in retaining employees and the factors contributing to employee attrition. Key-Word: Machine learning, explainable machine learning algorithm, employee, attrition, LIME, SHAP, organization, SVM, LR are all important word.