Forecasting Demand in the Pharmaceutical Industry Using Machine Learning

Detalhes bibliográficos
Autor(a) principal: Ascensão, João Aires Lancastre de Sousa Cabral de
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/10362/159901
Resumo: Internship Report presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Data Science for Marketing
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spelling Forecasting Demand in the Pharmaceutical Industry Using Machine LearningDemand ForecastingPharmaceutical SalesMachine LearningSDG 3 - Good health and well-beingSDG 4 - Quality educationSDG 8 - Decent work and economic growthSDG 9 - Industry, innovation and infrastructureSDG 12 - Responsible production and consumptionDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoInternship Report presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Data Science for MarketingThis study delves into the exploitation of three machine learning models, namely the Extreme Gradient Boosting (XGBoost), the Long Short-Term Memory (LSTM), and the novel Prophet algorithm, to surpass the challenge of demand forecast within the pharmaceutical industry. Following the CRISP-DM framework, we enabled accurate sales forecasting by studying, treating, transforming, and training a dataset containing historical sales data from a major Portuguese pharmaceutical company. Our findings align with the literature, underlying the robustness of the XGBoost and the inefficacy of the LSTM for the delineated task, considering the singularities of the provided data. Furthermore, this research highlights the potential of the Prophet for both its effectiveness and efficiency. This endeavor allowed us to reinforce the literature’s conviction of the need for product-specific forecasting, showcasing that no single model achieves the best accuracy for all drugs.Gonçalves, Rui Alexandre HenriquesRUNAscensão, João Aires Lancastre de Sousa Cabral de2023-11-13T18:18:54Z2023-10-242023-10-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/159901TID:203385853enginfo: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:RCAAP2024-03-11T05:42:24Zoai:run.unl.pt:10362/159901Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:57:45.954619Repositório 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 Forecasting Demand in the Pharmaceutical Industry Using Machine Learning
title Forecasting Demand in the Pharmaceutical Industry Using Machine Learning
spellingShingle Forecasting Demand in the Pharmaceutical Industry Using Machine Learning
Ascensão, João Aires Lancastre de Sousa Cabral de
Demand Forecasting
Pharmaceutical Sales
Machine Learning
SDG 3 - Good health and well-being
SDG 4 - Quality education
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
SDG 12 - Responsible production and consumption
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Forecasting Demand in the Pharmaceutical Industry Using Machine Learning
title_full Forecasting Demand in the Pharmaceutical Industry Using Machine Learning
title_fullStr Forecasting Demand in the Pharmaceutical Industry Using Machine Learning
title_full_unstemmed Forecasting Demand in the Pharmaceutical Industry Using Machine Learning
title_sort Forecasting Demand in the Pharmaceutical Industry Using Machine Learning
author Ascensão, João Aires Lancastre de Sousa Cabral de
author_facet Ascensão, João Aires Lancastre de Sousa Cabral de
author_role author
dc.contributor.none.fl_str_mv Gonçalves, Rui Alexandre Henriques
RUN
dc.contributor.author.fl_str_mv Ascensão, João Aires Lancastre de Sousa Cabral de
dc.subject.por.fl_str_mv Demand Forecasting
Pharmaceutical Sales
Machine Learning
SDG 3 - Good health and well-being
SDG 4 - Quality education
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
SDG 12 - Responsible production and consumption
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Demand Forecasting
Pharmaceutical Sales
Machine Learning
SDG 3 - Good health and well-being
SDG 4 - Quality education
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
SDG 12 - Responsible production and consumption
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 Driven Marketing, specialization in Data Science for Marketing
publishDate 2023
dc.date.none.fl_str_mv 2023-11-13T18:18:54Z
2023-10-24
2023-10-24T00:00:00Z
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/10362/159901
TID:203385853
url http://hdl.handle.net/10362/159901
identifier_str_mv TID:203385853
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
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