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The impact of machine learning models in the prediction of air cargo

Bibliographic Details
Main Author: Pinheiro, Diogo Alexandre Marujo
Publication Date: 2021
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/140151
Summary: Forecasting is key in the aviation industry. Airlines use software applications to make cargo predictions. This thesis explores the advantages of training Machine Learning models using data from an airline to produce cargo forecasts and it evaluates the quality of these forecasts in the context of the business problem. The dataset available is curated and fed into different regression models, using Python scripts. Results are benchmarked with the current model used by the airline. Most of the models trained in this thesis serve the airline with better cargo predictions than the current model does, at one hour before the scheduled departure time.
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spelling The impact of machine learning models in the prediction of air cargoOperations managementBusiness analysisDomínio/Área Científica::Ciências Sociais::Economia e GestãoForecasting is key in the aviation industry. Airlines use software applications to make cargo predictions. This thesis explores the advantages of training Machine Learning models using data from an airline to produce cargo forecasts and it evaluates the quality of these forecasts in the context of the business problem. The dataset available is curated and fed into different regression models, using Python scripts. Results are benchmarked with the current model used by the airline. Most of the models trained in this thesis serve the airline with better cargo predictions than the current model does, at one hour before the scheduled departure time.Brinca, PedroRodrigues, LuisRUNPinheiro, Diogo Alexandre Marujo2022-06-17T12:34:04Z2022-01-182021-12-172022-01-18T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/140151TID:202997324enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:02:31Zoai:run.unl.pt:10362/140151Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:33:25.919389Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv The impact of machine learning models in the prediction of air cargo
title The impact of machine learning models in the prediction of air cargo
spellingShingle The impact of machine learning models in the prediction of air cargo
Pinheiro, Diogo Alexandre Marujo
Operations management
Business analysis
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short The impact of machine learning models in the prediction of air cargo
title_full The impact of machine learning models in the prediction of air cargo
title_fullStr The impact of machine learning models in the prediction of air cargo
title_full_unstemmed The impact of machine learning models in the prediction of air cargo
title_sort The impact of machine learning models in the prediction of air cargo
author Pinheiro, Diogo Alexandre Marujo
author_facet Pinheiro, Diogo Alexandre Marujo
author_role author
dc.contributor.none.fl_str_mv Brinca, Pedro
Rodrigues, Luis
RUN
dc.contributor.author.fl_str_mv Pinheiro, Diogo Alexandre Marujo
dc.subject.por.fl_str_mv Operations management
Business analysis
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Operations management
Business analysis
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description Forecasting is key in the aviation industry. Airlines use software applications to make cargo predictions. This thesis explores the advantages of training Machine Learning models using data from an airline to produce cargo forecasts and it evaluates the quality of these forecasts in the context of the business problem. The dataset available is curated and fed into different regression models, using Python scripts. Results are benchmarked with the current model used by the airline. Most of the models trained in this thesis serve the airline with better cargo predictions than the current model does, at one hour before the scheduled departure time.
publishDate 2021
dc.date.none.fl_str_mv 2021-12-17
2022-06-17T12:34:04Z
2022-01-18
2022-01-18T00:00:00Z
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/10362/140151
TID:202997324
url http://hdl.handle.net/10362/140151
identifier_str_mv TID:202997324
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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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