Human Resource Working Prediction Based on Logistic Regression

Anusha Hegde, G. Poornalatha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A promising organization depends on the competitiveness and professional development of its employees. As an organization reaches new levels, the pressure on employees to achieve goals is in its peak. The work activity of the employee is highly related to the growth of the company. While setting these strategies, the business insights should recognize achievable target for the human force and the factors affecting the employee in achieving the given targets. The targets and deadlines cannot be met if the employees are not reporting to work and there is no suitable plan to overcome the loss. Hence, it is required to analyse and understand the working as well as the absence pattern of employee to minimize the possible loss to the company. In the present work, logistic regression is used to analyse these kinds of pattern to predict the absence of employees which enables the employer to take necessary actions and meet the deadlines in time.

Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence and Data Engineering - Select Proceedings of AIDE 2019
EditorsNiranjan N. Chiplunkar, Takanori Fukao
PublisherSpringer Gabler
Pages299-306
Number of pages8
ISBN (Print)9789811535130
DOIs
Publication statusPublished - 2021
EventInternational Conference on Artificial Intelligence and Data Engineering, AIDE 2019 - Mangalore, India
Duration: 23-05-201924-05-2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1133
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

ConferenceInternational Conference on Artificial Intelligence and Data Engineering, AIDE 2019
CountryIndia
CityMangalore
Period23-05-1924-05-19

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

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  • Cite this

    Hegde, A., & Poornalatha, G. (2021). Human Resource Working Prediction Based on Logistic Regression. In N. N. Chiplunkar, & T. Fukao (Eds.), Advances in Artificial Intelligence and Data Engineering - Select Proceedings of AIDE 2019 (pp. 299-306). (Advances in Intelligent Systems and Computing; Vol. 1133). Springer Gabler. https://doi.org/10.1007/978-981-15-3514-7_24