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

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

doi:10.1007/978-3-030-59194-6_1

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

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

^*Corresponding author for this work

PUCE Esmeraldas

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Systems and Information Sciences - Proceedings of ICCIS 2020
Editors	Miguel Botto-Tobar, Willian Zamora, Johnny Larrea Plúa, José Bazurto Roldan, Alex Santamaría Philco
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	3-15
Number of pages	13
ISBN (Print)	9783030591939
DOIs	https://doi.org/10.1007/978-3-030-59194-6_1
State	Published - 2021
Event	1st International Conference on Systems and Information Sciences, ICCIS 2020 - Manta, Ecuador Duration: 27 Jul 2020 → 29 Jul 2020

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1273 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	1st International Conference on Systems and Information Sciences, ICCIS 2020
Country/Territory	Ecuador
City	Manta
Period	27/07/20 → 29/07/20

Bibliographical note

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

Keywords

GUI
Machine learning
Matlab
Python
Supervised learning
Unsupervised learning

Access to Document

10.1007/978-3-030-59194-6_1

Cite this

Pico-Valencia, P., Vinueza-Celi, O., & Holgado-Terriza, J. A. (2021). Bringing Machine Learning Predictive Models Based on Machine Learning Closer to Non-technical Users. In M. Botto-Tobar, W. Zamora, J. Larrea Plúa, J. Bazurto Roldan, & A. Santamaría Philco (Eds.), Systems and Information Sciences - Proceedings of ICCIS 2020 (pp. 3-15). (Advances in Intelligent Systems and Computing; Vol. 1273 AISC). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59194-6_1

Pico-Valencia, Pablo ; Vinueza-Celi, Oscar ; Holgado-Terriza, Juan A. / Bringing Machine Learning Predictive Models Based on Machine Learning Closer to Non-technical Users. Systems and Information Sciences - Proceedings of ICCIS 2020. editor / Miguel Botto-Tobar ; Willian Zamora ; Johnny Larrea Plúa ; José Bazurto Roldan ; Alex Santamaría Philco. Springer Science and Business Media Deutschland GmbH, 2021. pp. 3-15 (Advances in Intelligent Systems and Computing).

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title = "Bringing Machine Learning Predictive Models Based on Machine Learning Closer to Non-technical Users",

abstract = "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.",

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author = "Pablo Pico-Valencia and Oscar Vinueza-Celi and Holgado-Terriza, {Juan A.}",

note = "Publisher Copyright: {\textcopyright} 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.; 1st International Conference on Systems and Information Sciences, ICCIS 2020 ; Conference date: 27-07-2020 Through 29-07-2020",

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Pico-Valencia, P, Vinueza-Celi, O & Holgado-Terriza, JA 2021, Bringing Machine Learning Predictive Models Based on Machine Learning Closer to Non-technical Users. in M Botto-Tobar, W Zamora, J Larrea Plúa, J Bazurto Roldan & A Santamaría Philco (eds), Systems and Information Sciences - Proceedings of ICCIS 2020. Advances in Intelligent Systems and Computing, vol. 1273 AISC, Springer Science and Business Media Deutschland GmbH, pp. 3-15, 1st International Conference on Systems and Information Sciences, ICCIS 2020, Manta, Ecuador, 27/07/20. https://doi.org/10.1007/978-3-030-59194-6_1

Bringing Machine Learning Predictive Models Based on Machine Learning Closer to Non-technical Users. / Pico-Valencia, Pablo; Vinueza-Celi, Oscar; Holgado-Terriza, Juan A.
Systems and Information Sciences - Proceedings of ICCIS 2020. ed. / Miguel Botto-Tobar; Willian Zamora; Johnny Larrea Plúa; José Bazurto Roldan; Alex Santamaría Philco. Springer Science and Business Media Deutschland GmbH, 2021. p. 3-15 (Advances in Intelligent Systems and Computing; Vol. 1273 AISC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Bringing Machine Learning Predictive Models Based on Machine Learning Closer to Non-technical Users

AU - Pico-Valencia, Pablo

AU - Vinueza-Celi, Oscar

AU - Holgado-Terriza, Juan A.

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A2 - Zamora, Willian

A2 - Larrea Plúa, Johnny

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Pico-Valencia P, Vinueza-Celi O, Holgado-Terriza JA. Bringing Machine Learning Predictive Models Based on Machine Learning Closer to Non-technical Users. In Botto-Tobar M, Zamora W, Larrea Plúa J, Bazurto Roldan J, Santamaría Philco A, editors, Systems and Information Sciences - Proceedings of ICCIS 2020. Springer Science and Business Media Deutschland GmbH. 2021. p. 3-15. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-59194-6_1

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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