Online Solution Based on Machine Learning for IT Project Management in Software Factory Companies

Augusto Hayashida Marchinares, Ciro Rodriguez Rodriguez

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

Project Portfolio Management is relevant for the growth of companies since it favors planning. Project Portfolio Management manages the resources to plan, control, and execute projects and obtain the strategic objectives of the organizations. In Project Portfolio Management, a large amount of data is forged, important for planning new projects in companies; therefore, the need arises to create models that help process and interpret the data. In this context, Machine Learning is presented as a technological enabler that allows a system, by itself and in an automated way, to learn to discover trends, patterns, and relationships between data; it is an engine of digital transformation of business and that organizations are embracing. Therefore, this article aims to compile and review proposals made to implement machine learning in the management of the project portfolio and apply algorithms that allow the development of models that help in the management and evaluation of projects to be developed in a Software Factory. The CRISP-DM methodology is applied to process the data of costs, times, and types of Projects; the Python programming language is used, the dataset corresponds to a Software Factory. The results validate the models implemented using Machine Learning algorithms, such as regression and decision trees, and thereby obtain the best model for predictions, establishing the correlation between variables and the benefit to be achieved. It is concluded, the implementation of Machine Learning improves the IT Project Portfolio Management, helping to identify which projects are more profitable and beneficial.

Idioma originalInglés
Título de la publicación alojadaProceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas150-154
Número de páginas5
ISBN (versión digital)9781728176956
DOI
EstadoPublicada - 22 set. 2021
Evento13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021 - Lima, Perú
Duración: 22 set. 202123 set. 2021

Serie de la publicación

NombreProceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021

Conferencia

Conferencia13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021
País/TerritorioPerú
CiudadLima
Período22/09/2123/09/21

Nota bibliográfica

Publisher Copyright:
© 2021 IEEE.

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