Supervised Machine Learning in SAS Viya: Development of a Supervised Machine Learning pipeline in SAS Viya for comparison with a pipeline developed in Python
Autor(a) principal: | |
---|---|
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 |