Export Ready — 

Supervised Machine Learning Algorithms in Predicting Damaged Cargo: A Portuguese Logistics & Transportation Company Case Study

Bibliographic Details
Main Author: Vale, Alice Lourenço
Publication Date: 2024
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/166562
Summary: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
id RCAP_5a57a3f38b28255adc2ff1fa918e01a6
oai_identifier_str oai:run.unl.pt:10362/166562
network_acronym_str RCAP
network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository_id_str https://opendoar.ac.uk/repository/7160
spelling Supervised Machine Learning Algorithms in Predicting Damaged Cargo: A Portuguese Logistics & Transportation Company Case StudySupervised Machine LearningImbalanced Binary ClassificationPredictive AnalyticsDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThis dissertation aims to predict the likelihood of units and products getting damaged during their shipment process, using supervised machine learning classifiers. Conducted as a case study within a Portuguese transportation company, the research employs real-world data to address several gaps in the prediction of damaged cargo literature. The primary gap addressed involves incorporating sampling techniques to overcome imbalanced datasets issues, testing different classifiers with recent data from a verified business source, and investigating cargo incidents from freight road transportation within the Portuguese context. To achieve these objectives, the research applies Synthetic Minority Over-sampling Technique (SMOTE), Adaptive Synthetic (ADASYN), Random Over Sampling (ROS), and Random Under Sampling (RUS) techniques, evaluating their performance against the highly imbalanced original data. Furthermore, the predictive performance of machine learning classifiers, including Random Forest, Logistic Regression, Gradient Boosting, and K-Nearest Neighbors, is assessed, and compared. The findings highlight the superiority of the Random Forest model over other classifiers, with a combination of ROS and RUS proving to be the most effective resampling technique. Notably, when testing the model's performance with imbalanced data, the recall score surpassed 0.7, aligning with the real-world context objective of minimizing misclassification costs. Additionally, the research identifies features with significant influence on the likelihood of cargo suffering damage, providing valuable insights for optimizing logistics operations. Overall, this dissertation presents a practical application of handling imbalanced datasets to deepen understanding of business challenges, contributing to advancements in the prediction of damaged cargo literature.Jardim, João Bruno Morais de SousaRUNVale, Alice Lourenço2024-04-24T09:25:01Z2024-04-192024-04-19T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/166562TID:203591640enginfo: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:RCAAP2024-05-22T18:20:41Zoai:run.unl.pt:10362/166562Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:51:17.992389Repositórios 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 Supervised Machine Learning Algorithms in Predicting Damaged Cargo: A Portuguese Logistics & Transportation Company Case Study
title Supervised Machine Learning Algorithms in Predicting Damaged Cargo: A Portuguese Logistics & Transportation Company Case Study
spellingShingle Supervised Machine Learning Algorithms in Predicting Damaged Cargo: A Portuguese Logistics & Transportation Company Case Study
Vale, Alice Lourenço
Supervised Machine Learning
Imbalanced Binary Classification
Predictive Analytics
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Supervised Machine Learning Algorithms in Predicting Damaged Cargo: A Portuguese Logistics & Transportation Company Case Study
title_full Supervised Machine Learning Algorithms in Predicting Damaged Cargo: A Portuguese Logistics & Transportation Company Case Study
title_fullStr Supervised Machine Learning Algorithms in Predicting Damaged Cargo: A Portuguese Logistics & Transportation Company Case Study
title_full_unstemmed Supervised Machine Learning Algorithms in Predicting Damaged Cargo: A Portuguese Logistics & Transportation Company Case Study
title_sort Supervised Machine Learning Algorithms in Predicting Damaged Cargo: A Portuguese Logistics & Transportation Company Case Study
author Vale, Alice Lourenço
author_facet Vale, Alice Lourenço
author_role author
dc.contributor.none.fl_str_mv Jardim, João Bruno Morais de Sousa
RUN
dc.contributor.author.fl_str_mv Vale, Alice Lourenço
dc.subject.por.fl_str_mv Supervised Machine Learning
Imbalanced Binary Classification
Predictive Analytics
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Supervised Machine Learning
Imbalanced Binary Classification
Predictive Analytics
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
publishDate 2024
dc.date.none.fl_str_mv 2024-04-24T09:25:01Z
2024-04-19
2024-04-19T00:00:00Z
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/10362/166562
TID:203591640
url http://hdl.handle.net/10362/166562
identifier_str_mv TID:203591640
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
instacron_str RCAAP
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
_version_ 1833597013867888640

Similar Items