Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/186008
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Campo DCValorIdioma
dc.contributor.advisorBelo, Rodrigo-
dc.contributor.authorOcariz, Amaia Jinrong Salazar Ochoa de-
dc.date.accessioned2025-08-04T14:28:55Z-
dc.date.available2025-08-04T14:28:55Z-
dc.date.issued2025-01-24-
dc.date.submitted2024-01-11-
dc.identifier.urihttp://hdl.handle.net/10362/186008-
dc.description.abstractThis project encompasses a complete business analysis of the retail toys industry in a renowned company of this sector, which was conducted,first,with the development of a machine learning model which served to correctly categorize toys products into eleven predefined categories and, secondly, by performing a deep analysis at different granularity levels regarding the exanimated toys items, which served to analyse the most important pillars trends regarding the retailing business model, these being: pricing trends, availability trends, selection analysis and search trends. This industry analysis was accomplished by the usage of different automatized data pulling techniques such as complex SQL queries and python programs, in addition to the usage of data visualization methods.pt_PT
dc.language.isoengpt_PT
dc.relationUID/ECO/00124/2013pt_PT
dc.rightsopenAccesspt_PT
dc.subjectDRI – Directed Research Internshippt_PT
dc.subjectKPI – Key Performance Indicatorpt_PT
dc.subjectEAN – European Article Numberpt_PT
dc.subjectML – Machine Learningpt_PT
dc.subjectMP – Marketplacept_PT
dc.subjectOPS – Order Product Salespt_PT
dc.subjectTTM – Trailing Twelve Monthspt_PT
dc.subjectAWS – Amazon Web Servicespt_PT
dc.titleEnhancing product recategorization in the toys industry: a comprehensive approach integrating classification machine learning model and industry trend analysispt_PT
dc.typemasterThesispt_PT
thesis.degree.nameA Work Project, presented as part of the requirements for the Award of a Master’s degree in Business Analytics from the Nova School of Business and Economicspt_PT
dc.identifier.tid203962745pt_PT
dc.subject.fosDomínio/Área Científica::Ciências Sociais::Economia e Gestãopt_PT
Aparece nas colecções:NSBE: Nova SBE - MA Dissertations

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