Predictive Analysis of Absenteeism in MNCS Using Machine Learning Algorithm

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


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.


Absenteeism Machine learning Linear regression Support vector regression 


  1. 1.
    Delen, D., Zaim, H., Kuzey, C., Zaim, S.: A comparative analysis of machine learning systems for measuring the impact of knowledge management practices. Decis Support Syst 54(2), 1150–1160 (2013)Google Scholar
  2. 2.
    Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques, p. 560. Morgan Kaufmann Publishers, San Francisco (2005)Google Scholar
  3. 3.
    Faber, F.A., Hutchison, L., Huang, B., Gilmer, J., Schoenholz, S.S., Dahl, G.E., Vinyals, O., Kearnes, S., Riley, P.F., von Lilienfeld, O.A.: Prediction errors of molecular machine learning models lower than hybrid DFT error. J. Chem. Theory Comput. 13(11), 5255–5264 (2017)CrossRefGoogle Scholar
  4. 4.
    Jain, S.: Classification of protein kinase B using discrete wavelet transform. Int. J. Inf. Technol. 10(2), 211–216 (2018)Google Scholar
  5. 5.
    Jain, S., Chauhan, D.S.: Mathematical analysis of receptors for survival proteins. Int. J. Pharma Bio Sci. 6(3), 164–176 (2015)Google Scholar
  6. 6.
    Bhusri, S., Jain, S., Virmani, J.: Classification of breast lesions using the difference of statistical features. Res. J. Pharm., Biol. Chem. Sci. (RJPBCS), 1366 (2016)Google Scholar
  7. 7.
    Rana, S., Jain, S., Virmani, J.: SVM-based characterization of focal kidney lesions from B-mode ultrasound images. Res. J. Pharm., Biol. Chem. Sci. (RJPBCS) 7(4), 83 (2016)Google Scholar
  8. 8.
    Sharma, S., Jain, S., Bhusri, S.: Two class classification of breast lesions using statistical and transform domain features. J. Glob. Pharma Technol. 9(7), 18–24 (2017)Google Scholar
  9. 9.
    Jain, S.: Regression analysis on different mitogenic pathways. Netw. Biol. 6(2), 40–46 (2016)Google Scholar
  10. 10.
    Jain, S.: System modeling of AkT using linear and robust regression analysis. Curr. Trends Biotechnol. Pharm. 12(2), 177–186 (2018)Google Scholar
  11. 11.
    Zhang, L., Tan, J., Han, D., Zhu, H.: From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov. Today 22(11), 1680–1685 (2017)CrossRefGoogle Scholar
  12. 12.
    Borchers, M.R., Chang, Y.M., Proudfoot, K.L., Wadsworth, B.A., Stone, A.E., Bewley, J.M.: Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle. J. Dairy Sci. 100(7), 5664–5674 (2017)CrossRefGoogle Scholar

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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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