Employee churn prediction

Abstract

In the SHRM/Globoforce survey Using Recognition and Other Workplace Efforts to Engage Employees, 47 percent of HR professionals listed retention/turnover as the top workforce management challenge. Revolving workforces frequently result in higher training expenses, irregular production, low morale, and, as a result, lower or limited profitability. Therefore, it is necessary to focus on reducing turnover. In this study, we target to building a prediction model to predict employee churn using machine learning-individual classifier methods such as Decision Tree, Logistic Regression, SVM, KNN, MLP classifier and ensemble learning such as XGBoost, Random Forest, and Voting classifier. To evaluate the effectiveness of the proposed model, we perform extensive experiments on a public dataset of HR Analytics: Job Change of Data Scientist dataset. To understand more about the dataset before using it, we also explore the data by using different descriptive methods such as visualization, some statistical methods, etc. The findings will enable businesses to estimate their employees' churn rates and, as a result, lower their human resource costs