Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Cover of the book

2020 | OriginalPaper | Chapter

Predictive Analysis of Absenteeism in MNCS Using Machine Learning Algorithm

Authors : Krittika Tewari, Shriya Vandita, Shruti Jain

Published in: Proceedings of ICRIC 2019

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
next chapter IoT Based Healthcare Kit for Diabetic Foot Ulcer
Literature
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)
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)
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)CrossRef
4.
Jain, S.: Classification of protein kinase B using discrete wavelet transform. Int. J. Inf. Technol. 10(2), 211–216 (2018)
5.
Jain, S., Chauhan, D.S.: Mathematical analysis of receptors for survival proteins. Int. J. Pharma Bio Sci. 6(3), 164–176 (2015)
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)
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)
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)
9.
Jain, S.: Regression analysis on different mitogenic pathways. Netw. Biol. 6(2), 40–46 (2016)
10.
Jain, S.: System modeling of AkT using linear and robust regression analysis. Curr. Trends Biotechnol. Pharm. 12(2), 177–186 (2018)
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)CrossRef
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)CrossRef
Metadata
Title
Predictive Analysis of Absenteeism in MNCS Using Machine Learning Algorithm
Authors
Krittika Tewari
Shriya Vandita
Shruti Jain
Copyright Year
2020
Book
Proceedings of ICRIC 2019

Print ISBN: 978-3-030-29406-9

Electronic ISBN: 978-3-030-29407-6

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-29407-6_1

Premium Partner

    Image Credits