From Global to Local in the Sneakers Universe: A Data Science Approach

Autores
Perdomo, Luciano; Ordinez, Leonardo
Año de publicación
2021
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In Argentina there was a great growth of e-commerce due to the COVID-19 pandemic. With the aim of helping local companies to understand the market and help them in decision making, data were obtained from online shoe sales sites and with them Machine Learning models were implemented to make price predictions in sneakers. It was concluded that higher-tier companies have greater competitive advantage over lower-tier companies. Nonetheless, the cost-effective methodology used would aid local companies scale up.
Workshop: WBDMD - Base de Datos y Minería de Datos
Red de Universidades con Carreras en Informática
Materia
Ciencias Informáticas
E-commerce
Machine learning
Linear Regression
Random Forest
LGBM Regressor
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/130352
Acceder
id SEDICI_a83fe44923d72d5d1fbfe84ba8c4c9de
oai_identifier_str oai:sedici.unlp.edu.ar:10915/130352
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling From Global to Local in the Sneakers Universe: A Data Science ApproachPerdomo, LucianoOrdinez, LeonardoCiencias InformáticasE-commerceMachine learningLinear RegressionRandom ForestLGBM RegressorIn Argentina there was a great growth of e-commerce due to the COVID-19 pandemic. With the aim of helping local companies to understand the market and help them in decision making, data were obtained from online shoe sales sites and with them Machine Learning models were implemented to make price predictions in sneakers. It was concluded that higher-tier companies have greater competitive advantage over lower-tier companies. Nonetheless, the cost-effective methodology used would aid local companies scale up.Workshop: WBDMD - Base de Datos y Minería de DatosRed de Universidades con Carreras en Informática2021-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf333-342http://sedici.unlp.edu.ar/handle/10915/130352enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-633-574-4info:eu-repo/semantics/reference/hdl/10915/129809info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T17:13:46Zoai:sedici.unlp.edu.ar:10915/130352Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 17:13:46.466SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv From Global to Local in the Sneakers Universe: A Data Science Approach
title From Global to Local in the Sneakers Universe: A Data Science Approach
spellingShingle From Global to Local in the Sneakers Universe: A Data Science Approach
Perdomo, Luciano
Ciencias Informáticas
E-commerce
Machine learning
Linear Regression
Random Forest
LGBM Regressor
title_short From Global to Local in the Sneakers Universe: A Data Science Approach
title_full From Global to Local in the Sneakers Universe: A Data Science Approach
title_fullStr From Global to Local in the Sneakers Universe: A Data Science Approach
title_full_unstemmed From Global to Local in the Sneakers Universe: A Data Science Approach
title_sort From Global to Local in the Sneakers Universe: A Data Science Approach
dc.creator.none.fl_str_mv Perdomo, Luciano
Ordinez, Leonardo
author Perdomo, Luciano
author_facet Perdomo, Luciano
Ordinez, Leonardo
author_role author
author2 Ordinez, Leonardo
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
E-commerce
Machine learning
Linear Regression
Random Forest
LGBM Regressor
topic Ciencias Informáticas
E-commerce
Machine learning
Linear Regression
Random Forest
LGBM Regressor
dc.description.none.fl_txt_mv In Argentina there was a great growth of e-commerce due to the COVID-19 pandemic. With the aim of helping local companies to understand the market and help them in decision making, data were obtained from online shoe sales sites and with them Machine Learning models were implemented to make price predictions in sneakers. It was concluded that higher-tier companies have greater competitive advantage over lower-tier companies. Nonetheless, the cost-effective methodology used would aid local companies scale up.
Workshop: WBDMD - Base de Datos y Minería de Datos
Red de Universidades con Carreras en Informática
description In Argentina there was a great growth of e-commerce due to the COVID-19 pandemic. With the aim of helping local companies to understand the market and help them in decision making, data were obtained from online shoe sales sites and with them Machine Learning models were implemented to make price predictions in sneakers. It was concluded that higher-tier companies have greater competitive advantage over lower-tier companies. Nonetheless, the cost-effective methodology used would aid local companies scale up.
publishDate 2021
dc.date.none.fl_str_mv 2021-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/130352
url http://sedici.unlp.edu.ar/handle/10915/130352
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-987-633-574-4
info:eu-repo/semantics/reference/hdl/10915/129809
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
333-342
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1846783511292280832
score 12.982451

Publicaciones similares