Analysis of Employee Attrition using Statistical and Machine Learning Approaches
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-48781-1_21
Reference19 articles.
1. Subramony, M., Holtom, B.C.: The long-term influence of service employee attrition on customer outcomes and pbrofits. J. Service Res. 15(4) 460–473 (2012). https://doi.org/10.1177/1094670512452792
2. Khalifa, N., Alnasheet, M., Kadhem, H.: Evaluating machine learning algorithms to detect employees’ attrition. In: 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC), Cairo, Egypt (2022). https://doi.org/10.1109/AIRC56195.2022.9836981
3. Seelam, S.R., Kumar, K.H., Supritha, M.S., Gnaneswar, G., Reddy, V.V.M.: Comparative study of predictive models to estimate employee attrition. In: Seventh International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India (2022). https://doi.org/10.1109/ICCES54183.2022.9835964
4. Sekaran, K., Shanmugam, S.: Interpreting the factors of employee attrition using explainable AI. In: International Conference on Decision Aid Sciences and Applications (DASA), Chiangrai, Thailand (2022). https://doi.org/10.1109/DASA54658.2022.9765067
5. Shankar, R.S., Piyadarshini, V., Neelima, P., Raminaidu, C.H.: Analyzing attrition and performance of an employee using machine learning techniques. In: Fifth International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India (2021). https://doi.org/10.1109/ICECA52323.2021.9676102
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