# Predicting Employee Attrition Using Machine Learning Approaches
###### Abstract  
Employee attrition refers to the natural reduction in the employees in an organization due to many unavoidable factors. Employee attrition results in a massive loss for an organization. The Society for Human Resource Management (SHRM) determines that USD 4129 is the average cost-per-hire for a new employee. According to recent stats, 57.3% is the attrition rate in the year 2021. A research study needs to be implemented to find the causes of employee attrition and a learning framework to predict employee attrition. This research study aimed to analyze the organizational factors that caused employee attrition and the prediction of employee attrition using machine learning techniques. The four machine learning techniques were applied in comparison. The proposed optimized Extra Trees Classifier (ETC) approach achieved an accuracy score of 93% for employee attrition prediction. The proposed approach outperformed recent state-of-the-art studies. The Employee Exploratory Data Analysis (EEDA) was applied to determine the factors that caused employee attrition. Our study revealed that the monthly income, hourly rate, job level, and age are the key factors that cause employee attrition. Our proposed approach and research findings help organizations overcome employee attrition by improving the factors that cause attrition. employee attrition; employee turnover; machine learning; attrition rate; organization analysis; employee attrition causes +
Footnote †: journal: _sciences_  
rate is calculated by dividing the number of employees who have left the company by the average number of employees over some time. The attrition rate helps us find the company's progress over a specific period. The employee attrition states [1] demonstrate that after six months of job duration, 1/3 of new employees leave the organization. The 3 to 4.5 million employees leave their job every month in the United States, according to the Job Openings and Labor Turnover Survey (JOLTS) [2]. The employee attrition rate is 57.3% in 2021 to the report of the Bureau of Labor Statistics [3]. The report also suggests that in many industries, the employee attrition rate is close to 19% [2]. The cost per hire of new employees is USD 4129 by SHRM [4]. Ninety percent of employee retention rate is considered suitable for a company, and the attrition rate must be less than 10%. Machine learning [5] in the field of Artificial Intelligence (AI) gives the ability to machines to learn from historical data and make future predictions. Currently, machine learning is a crucial component of the data science field. The goal of machine learning techniques is to achieve higher accuracy results than humans. The machine learning models are utilized for decision-making. The learning process of machines is automated. The refined data are fed to machines to train and obtain decisions from them for new data. The primary aim of machine learning models is to find the patterns in data and learn from them. The applications of machine learning for today's technology are growing daily. The key applications of machine learning cover a broader area of real-world domains. The typical real word problems such as image recognition [6], traffic prediction [7], speech recognition, text classification [8], social analysis, stock market trading, health care [9], e-commerce, agriculture, healthcare, and many more are solved by using machine learning techniques. The machine learning models are utilized for the prediction of employee attrition [10].