Machine Learning Approach to Predicting Attrition Among Employees at Work-Reference-Cited by-同舟云学术

Machine Learning Approach to Predicting Attrition Among Employees at Work

Published:2022 Issue: Volume: Page:285-294
ISSN:2367-3370
Container-title:Artificial Intelligence Trends in Systems
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Author:

Bhatta Sudipta,Zaman Isfaf Uz,Raisa Nuzhat,Fahim Shazzadul Islam,Momen Sifat

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-09076-9_27

Reference14 articles.

1. Absenteeism at work data set. https://archive.ics.uci.edu/ml/datasets/Absenteeism+at+work?fbclid=IwAR1Nv_i1jNKAbaQhjph51L6AFudIeL9X_kNm02V0ZKi_XH9QuyqX3TBofcc

2. Sklearn feature selection variancethreshold. https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.VarianceThreshold.html

3. Sklearn stratified train-test split. https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html

4. Alduayj, S.S., Rajpoot, K.: Predicting employee attrition using machine learning. In: 2018 International Conference on Innovations in Information Technology (IIT), pp. 93–98. IEEE (2018)

5. Alhashmi, S.M.: Towards understanding employee attrition using a decision tree approach. In: 2019 International Conference on Digitization (ICD), pp. 44–47. IEEE (2019)

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