Employee Attrition, Job Involvement, and Work Life Balance Prediction Using Machine Learning Classifier Models
- Title
- Employee Attrition, Job Involvement, and Work Life Balance Prediction Using Machine Learning Classifier Models
- Creator
- Kumar R.; Tople A.
- Description
- Employee performance is an integral part organizational success, for which Talent management is highly required, and the motivating factors of employee depend on employee performance. Certain variables have been observed as outliers, but none of those variables were operated or predicted. This paper aims at creating predictive models for the employee attrition by using classifier models for attrition rate, Job Involvement, and Work Life Balance. Job Involvement is specifically linked to the employee intentions to turn around that is minimal turnover rate. So, getting justifiable solution, this paper states the novel and accurate classification models. The Ridge Classifier model is the first one it has been used to classify IBM employee attrition, and it gave an accuracy of 92.7%. Random Forest had the highest accuracy for predicting Job Involvement, with accuracy rate of 62.3%. Similarly, Logistic Regression has been the model selected to predict Work Life Balance, and it has a 64.8% accuracy rate, making it an acceptable classification model. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
- Source
- Lecture Notes in Networks and Systems, Vol-726 LNNS, pp. 907-915.
- Date
- 2023-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Attrition rate; Control variables; CRISP-DM method; Job Involvement; Low-code library approach; Machine learning; PyCaret; Ridge classifiers
- Coverage
- Kumar R., Christ (Deemed to be University), Lavasa, Pune, India; Tople A., Analytics and Insights at Tata Consultancy Services, Pune, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-981993715-8
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Kumar R.; Tople A., “Employee Attrition, Job Involvement, and Work Life Balance Prediction Using Machine Learning Classifier Models,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19869.