Attrition Analytics: Unveiling the Best Model for Predicting Employee Retention

Abstract

Organizations in all sectors are very concerned about employee attrition as it affects output, morale, and general performance. Keeping an even and productive workforce requires anticipating and controlling attrition. In this work, we investigate how well different machine learning models predict attrition among employees. We evaluate the effectiveness of Decision Tree, Random Forest, Logistic Regression, and Naive Bayes classifiers on a dataset that includes performance, job-related, and demographic characteristics of employees. GridSearchCV and other hyper parameter tweaking approaches are used to maximize model performance. Our findings provide important new information on how well various machine learning methods predict attrition. The study's conclusions extend the field of attrition analytics and offer insightful advice to businesses looking to improve retention tactics and reduce employee attrition.