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Predictive Analysis of Absenteeism in MNCS Using Machine Learning Algorithm

  • Krittika Tewari
  • Shriya Vandita
  • Shruti JainEmail author
Conference paper
First Online:
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 597)

Abstract

Absenteeism has become a severe problem for many organizations. The problem posed in this paper was to build a predictive model to predict the absenteeism for MNCs by previously recorded data sets. This exercise not only leads to prevent or lower absenteeism but forecast future workforce requirements and suggests ways to meet those demands. For faster processing of massive data set, the data was analyzed efficiently so that we get the minimum response time and turn-around time, which is only possible when we use the right set of algorithms and by hard wiring of the program. Different machine learning algorithms are used in the paper that includes linear regression and support vector regression. By analyzing the results of each technique, we come across that the age parameter mainly affects the absenteeism that is linearly related to absenteeism.

Keywords

Absenteeism Machine learning Linear regression Support vector regression 
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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringJaypee University of Information TechnologySolanIndia

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