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Human Resource Working Prediction Based on Logistic Regression

    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
    Country/TerritoryIndia
    CityMangalore
    Period23-05-1924-05-19

    All Science Journal Classification (ASJC) codes

    • Control and Systems Engineering
    • General Computer Science

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