Bringing Machine Learning Predictive Models Based on Machine Learning Closer to Non-technical Users

Pablo Pico-Valencia, Oscar Vinueza-Celi, Juan A. Holgado-Terriza

Producción científica: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

1 Cita (Scopus)


Today, data science has positioned as an area of interest for decision makers in many organizations. Advances in Machine Learning (ML) allow training predictive models based on the analysis of datasets in multiple domains such as: business, medicine, marketing, among others. These models are able to learn and predict future behaviors which helps in the decision-making process. However, many of the ML tools such as Python, Matlab, R Suite, and even their libraries, require that every action must be performed as a sequence of commands by means of scripts. These software packages require extensive technical knowledge of statistics, artificial intelligence, algorithms and computer programming that generally only computer engineers are skilled at. In this research we propose the development of a process complemented with the assistance of a set of user graphic interfaces (GUIs) to help non-sophisticated users to train and test ML models without writing scripts. A tool compatible with Python and Matlab was developed with a set of GUIs adapted to professionals of the business area that generally require to apply ML models in their jobs, but they do not have time to learn programming.

Idioma originalInglés
Título de la publicación alojadaSystems and Information Sciences - Proceedings of ICCIS 2020
EditoresMiguel Botto-Tobar, Willian Zamora, Johnny Larrea Plúa, José Bazurto Roldan, Alex Santamaría Philco
EditorialSpringer Science and Business Media Deutschland GmbH
Número de páginas13
ISBN (versión impresa)9783030591939
EstadoPublicada - 2021
Evento1st International Conference on Systems and Information Sciences, ICCIS 2020 - Manta, Ecuador
Duración: 27 jul. 202029 jul. 2020

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing
Volumen1273 AISC
ISSN (versión impresa)2194-5357
ISSN (versión digital)2194-5365


Conferencia1st International Conference on Systems and Information Sciences, ICCIS 2020

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Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

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