Predicting Employee Attrition Using Machine Learning Algorithms
- Title
- Predicting Employee Attrition Using Machine Learning Algorithms
- Creator
- George S.; Lakshmi K.A.; Thomas K.T.
- Description
- Employees are considered the foundation of any organization. Due to their importance, the Human resources department implements various policies to sustain them. Yet the attrition rate in any organization is increasing yearly. The attrition rate signifies the number of employees who leaves a firm without being replaced. It is regarded as a well-known issue that requires the administration to make the best choices to retain highly competent staff. It is interesting to note that artificial intelligence is frequently used as a successful technique for foreseeing such an issue. This review paper aims to study the different machine learning approaches that predict employee attrition and factors influencing an employee to attrite from an organization. A Hybrid model comprising the various ensemble models is proposed to predict attrition at its earliest. The forecasted attrition model aids in not only taking preventive action but also in improving recruiting choices and rewarding top performers who contribute to the company's success. 2022 IEEE.
- Source
- Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022, pp. 700-705.
- Date
- 2022-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- ADASYN; Employee attrition; Ensemble model; Extra Tree Classifier; SMOTE; XGBoost
- Coverage
- George S., Christ University, Lavasa, Pune, India; Lakshmi K.A., Christ University, Lavasa, Pune, India; Thomas K.T., Christ University, Lavasa, Pune, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-166547436-8
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
George S.; Lakshmi K.A.; Thomas K.T., “Predicting Employee Attrition Using Machine Learning Algorithms,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/20120.