Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/186008
Título: Enhancing product recategorization in the toys industry: a comprehensive approach integrating classification machine learning model and industry trend analysis
Autor: Ocariz, Amaia Jinrong Salazar Ochoa de
Orientador: Belo, Rodrigo
Palavras-chave: DRI – Directed Research Internship
KPI – Key Performance Indicator
EAN – European Article Number
ML – Machine Learning
MP – Marketplace
OPS – Order Product Sales
TTM – Trailing Twelve Months
AWS – Amazon Web Services
Data de Defesa: 24-Jan-2025
Resumo: This 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.
URI: http://hdl.handle.net/10362/186008
Designação: A 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 Economics
Aparece nas colecções:NSBE: Nova SBE - MA Dissertations

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54155_WP_AmaiaSalazarOchoadeOcariz.pdf1,15 MBAdobe PDFVer/Abrir


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