Employee Churn Rate Prediction and Performance Using Machine Learning
Aniket Tambde1, Dilip Motwani2

1Aniket Tambde, Department of Computer Engineering, Vidyalankar Institute of Technology, Mumbai (Maharashtra), India.
2Prof. Dilip Motwani, Department of Computer Engineering, Vidyalankar Institute of Technology, Mumbai (Maharashtra), India.
Manuscript received on 12 October 2019 | Revised Manuscript received on 21 October 2019 | Manuscript Published on 02 November 2019 | PP: 824-826 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B11340982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1134.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: A person working for an organization is the vital resource which is known as an employee. If one of them leaves company suddenly, this could affect and cost massive amount to respective company. And recruitment would consume not only time and money but also the newly joined person needs some time for making particular business cost-effective. This model will help to predict rate at which employees are quitting jobs based on obtained analytic data accessible and use different machine learning algorithms to decrease prediction error. Personalized or individual employee’s prediction is different with respect to environment they are working in. While it has become apparent that employee churn prediction responds differently to salary, depending on their location, lifestyle, and environment, the linked knowledge and understanding remain fragmented. In this paper, we aim to design expert prediction system to deal with problems associated with lack of knowledge of employee behavior, to aware organizations about the importance of employee, to prevent unnecessary employee churn, and to improve growth of both separately.
Keywords: Employee Churn, Prediction Error, Machine Learning Algorithms.
Scope of the Article: Machine Learning