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      Determining success factors for project with supervised machine learning

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            Abstract

            Every year, enormous project failure rates plague business companies across the globe, costing millions of dollars for each failed project. It is critical to understand the factors that influence project success. The goal of this study is to empirically discover the factors that lead to a successful project. This study used data from 469 projects to use three distinct supervised machine learning methods for classification: a) The Support Vector Machine (SVM), 2) The Probit regression, and 3) the Logistic regression. Five factors have been chosen.: Support from the top management, Technical skills, Client’s acceptance, Communication, and Monitoring. The findings of the SVM study revealed that SVM could accurately predict successful and unsuccessful projects. The findings of Logistic and Probit regression revealed that project success is more likely if project managers get appropriate top-level support and if the project team has sufficient technical capabilities. This study also discovered that good communication and control improve the odds of a project's success. However, according to the results of this research, client acceptability is a minor success factor for a project.

            Content

            Author and article information

            Journal
            ScienceOpen Preprints
            ScienceOpen
            20 September 2021
            Affiliations
            [1 ] Aetherarcade
            Author notes
            Article
            10.14293/S2199-1006.1.SOR-.PPXBAU5.v1
            63c0d0fc-6799-4982-a7a4-15a4cf5ec24d

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .


            The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
            Management,Machine learning,Data analysis

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