The impact of machine learning models in the prediction of air cargo
| Main Author: | |
|---|---|
| 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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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 |
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info:eu-repo/semantics/masterThesis |
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masterThesis |
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publishedVersion |
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http://hdl.handle.net/10362/140151 TID:202997324 |
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http://hdl.handle.net/10362/140151 |
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TID:202997324 |
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eng |
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eng |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia instacron:RCAAP |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia |
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