Research Article
Machine Learning Techniques for Aspect Analysis of Employee Attrition
@INPROCEEDINGS{10.1007/978-3-031-35081-8_23, author={Anamika Hooda and Purva Garg and Nonita Sharma and Monika Mangla}, title={Machine Learning Techniques for Aspect Analysis of Employee Attrition}, proceedings={Intelligent Systems and Machine Learning. First EAI International Conference, ICISML 2022, Hyderabad, India, December 16-17, 2022, Proceedings, Part II}, proceedings_a={ICISML PART 2}, year={2023}, month={7}, keywords={Attrition Data Analytics SVM Logistic Regression Heatmap Correlation Matrix}, doi={10.1007/978-3-031-35081-8_23} }- Anamika Hooda
Purva Garg
Nonita Sharma
Monika Mangla
Year: 2023
Machine Learning Techniques for Aspect Analysis of Employee Attrition
ICISML PART 2
Springer
DOI: 10.1007/978-3-031-35081-8_23
Abstract
Employee attrition is the reduction in the employee workforce, which can be defined as the rate of employees leaving the company faster than the rate they are hired. Attrition may be for the whole establishment but sometimes it might be particular for a business field. This happens when there is intervention of technology that contribute in replacing the human workforce. There are several factors contributing to employee attrition, a few being age, number of years in the company, manager, technology change, etc. It is vital to understand the impact of these factors on employee attrition so that necessary action can be taken to avoid this. Thus, Machine learning technique is being used nowadays to inspect and predict the data of several real-life applications. After employing the models, authors performed the analysis on each of them using confusion matrix, F-1 score, recall, precision, etc., and found that the best model is SVM with an accuracy of 85.60%.
