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Employee Attrition Analysis Using Predictive Techniques

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Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1 ( ICTIS 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 83))

Abstract

Employee churn is an unsolicited aftermath of our blooming economy. Attrition may be defined as voluntary or involuntary resignation of a serving employee from an organization. Employee churn can incur a colossal cost to the firm. However, furtherance to prediction and control over attrition can give quality results. Earmarking the risk of attrition, the management can take required steps to retain the high valued talent. Workforce Analytics can be applied to reduce the overall business risk by predicting the employee churn. Predictive Analytics is the field of study that employs statistical analysis, data mining techniques and machine learning to predict the future events with accuracy based on past and current situation. The paper presents a framework for predicting the employee attrition with respect to voluntary termination employing predictive analytics.

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Author information

Authors and Affiliations

  1. Department of CSE and IT, Manipal University Jaipur, Jaipur, India

    Devesh Kumar Srivastava & Priyanka Nair

Authors
  1. Devesh Kumar Srivastava
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  2. Priyanka Nair
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Corresponding author

Correspondence to Priyanka Nair .

Editor information

Editors and Affiliations

  1. Department of CSE, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Sabar Institute of Technology for Girls , Ahmedabad, Gujarat, India

    Amit Joshi

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Srivastava, D.K., Nair, P. (2018). Employee Attrition Analysis Using Predictive Techniques. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1. ICTIS 2017. Smart Innovation, Systems and Technologies, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-319-63673-3_35

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  • DOI: https://doi.org/10.1007/978-3-319-63673-3_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63672-6

  • Online ISBN: 978-3-319-63673-3

  • eBook Packages: EngineeringEngineering (R0)

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