Supervised Machine Learning in SAS Viya: Development of a Supervised Machine Learning pipeline in SAS Viya for comparison with a pipeline developed in Python

Detalhes bibliográficos
Autor(a) principal: Neves, Guilherme Luís Ataíde
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/148544
Resumo: Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
id RCAP_5684913665972a61efd3940d4bf0df0a
oai_identifier_str oai:run.unl.pt:10362/148544
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Supervised Machine Learning in SAS Viya: Development of a Supervised Machine Learning pipeline in SAS Viya for comparison with a pipeline developed in PythonSupervised Machine LearningBinary ClassificationPredictive ModelsSAS ViyaPythonDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThis internship report details the development of a supervised ML pipeline in SAS Viya, a cloud-based environment composed of several solutions for importing, managing and transforming data and building and deploying predictive models into production environments. As a practical case study, this report showcases the SAS Viya features and capabilities which can be offered to the end-user. A comparison with a similar supervised ML pipeline in Python was made, to highlight both tools’ advantages and disadvantages. Thus, analytical tasks were employed, to demonstrate which different supervised ML techniques can be used in each technology. Furthermore, it was shown that, depending on the experience and knowledge of the end-user, both SAS Viya and Jupyter Notebook/Python are able to produce satisfactory results, being the latter more suited to data scientists with some experience in programming and ML. At the same time, SAS Viya fits more for employees who are getting started in the ML field, due to its point-and-click user interface. On the other hand, building a supervised ML pipeline in SAS Viya can be more straightforward than in Jupyter Notebook/Python, since the code is already developed and the process automatized, while pipeline templates are made available to the user. However, due to its open-source nature, Python has more supervised ML techniques available to be used in Jupyter Notebook. This report shows that these two solutions can complement each other, as SAS Viya offers good visualizations for data exploration, while Jupyter Notebook/Python can be dedicated to data transformation and predictive models’ development.Henriques, Roberto André PereiraRUNNeves, Guilherme Luís Ataíde2023-02-02T14:55:34Z2023-01-232023-01-23T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/148544TID:203212142enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:30:11Zoai:run.unl.pt:10362/148544Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:53:24.668763Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Supervised Machine Learning in SAS Viya: Development of a Supervised Machine Learning pipeline in SAS Viya for comparison with a pipeline developed in Python
title Supervised Machine Learning in SAS Viya: Development of a Supervised Machine Learning pipeline in SAS Viya for comparison with a pipeline developed in Python
spellingShingle Supervised Machine Learning in SAS Viya: Development of a Supervised Machine Learning pipeline in SAS Viya for comparison with a pipeline developed in Python
Neves, Guilherme Luís Ataíde
Supervised Machine Learning
Binary Classification
Predictive Models
SAS Viya
Python
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Supervised Machine Learning in SAS Viya: Development of a Supervised Machine Learning pipeline in SAS Viya for comparison with a pipeline developed in Python
title_full Supervised Machine Learning in SAS Viya: Development of a Supervised Machine Learning pipeline in SAS Viya for comparison with a pipeline developed in Python
title_fullStr Supervised Machine Learning in SAS Viya: Development of a Supervised Machine Learning pipeline in SAS Viya for comparison with a pipeline developed in Python
title_full_unstemmed Supervised Machine Learning in SAS Viya: Development of a Supervised Machine Learning pipeline in SAS Viya for comparison with a pipeline developed in Python
title_sort Supervised Machine Learning in SAS Viya: Development of a Supervised Machine Learning pipeline in SAS Viya for comparison with a pipeline developed in Python
author Neves, Guilherme Luís Ataíde
author_facet Neves, Guilherme Luís Ataíde
author_role author
dc.contributor.none.fl_str_mv Henriques, Roberto André Pereira
RUN
dc.contributor.author.fl_str_mv Neves, Guilherme Luís Ataíde
dc.subject.por.fl_str_mv Supervised Machine Learning
Binary Classification
Predictive Models
SAS Viya
Python
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Supervised Machine Learning
Binary Classification
Predictive Models
SAS Viya
Python
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
description Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
publishDate 2023
dc.date.none.fl_str_mv 2023-02-02T14:55:34Z
2023-01-23
2023-01-23T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/148544
TID:203212142
url http://hdl.handle.net/10362/148544
identifier_str_mv TID:203212142
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
_version_ 1799138124574490624

Registros relacionados