Decision trees for loss prediction in retail - case of Pingo Doce
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2020 |
| Tipo de documento: | Dissertação |
| Idioma: | eng |
| Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| Texto Completo: | http://hdl.handle.net/10362/100957 |
Resumo: | Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence |
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Decision trees for loss prediction in retail - case of Pingo DoceMachine LearningRetailLossClassification AlgorithmClassification TreeDecision TreeProject Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe use of data mining as a way of solving problems from the widest range of areas with the main purpose of gaining competitive advantage is rising, specially in retail, an extremely competitive sector that requires an even bigger advantage. Additionally, food loss, beyond representing a huge waste of resources, can also be considered a major issue to the retail sector due to the financial losses originated from it. Thus, I proposed to help Pingo Doce, a well-known Portuguese retail company, to solve their food loss issue which, despite being the major cause of a huge drop in the company’s profits, has never been solved till this day. Therefore, this project focuses on the development of a classification algorithm that will allow to predict future significant losses in several fruits sold in certain Pingo Doce stores. To do so I applied a Decision Tree algorithm that, due to its representation in the form of if-then rules, will help to identify the main features that lead to a higher number of losses, namely the period of the year and the category to which each fruit belongs, among others. The dataset provided by the company contains variables that measure the quantity and value of sales, stocks, identified and unidentified losses, over a one-year period, and regarding 81 different fruits and 20 stores from all over the country. Additionally, I created new variables such as the criminality rate of the municipality and the climate class of each store, as well as the seasons and the day of the week in which each observation occurred. All these variables allowed me to create four different datasets that originated four different Classification Trees. The results show that, using a dataset with no information regarding stocks and sales, containing only variables that describe the characteristics of the stores, products and periods of time, as well as the value of product sold per unit of measurement, i.e. the price per unit of measurement of each fruit, it is possible to create a Decision Tree that reaches an accuracy of 74% and correctly predicts 82% of the observations that represent significant losses. The algorithm obtained allowed to identify the variables that are more prone to originate significant losses, namely: the day of the week, the fruit’s category, the season of the year, the position of that week in the respective month and the price at which the product is being sold.Henriques, Roberto André PereiraRUNHenriques, Mariana Bonito2020-07-16T18:36:40Z2020-07-062020-07-06T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/100957TID:202501191enginfo: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:RCAAP2024-03-11T04:47:18Zoai:run.unl.pt:10362/100957Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:39:28.273982Repositório 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 |
Decision trees for loss prediction in retail - case of Pingo Doce |
| title |
Decision trees for loss prediction in retail - case of Pingo Doce |
| spellingShingle |
Decision trees for loss prediction in retail - case of Pingo Doce Henriques, Mariana Bonito Machine Learning Retail Loss Classification Algorithm Classification Tree Decision Tree |
| title_short |
Decision trees for loss prediction in retail - case of Pingo Doce |
| title_full |
Decision trees for loss prediction in retail - case of Pingo Doce |
| title_fullStr |
Decision trees for loss prediction in retail - case of Pingo Doce |
| title_full_unstemmed |
Decision trees for loss prediction in retail - case of Pingo Doce |
| title_sort |
Decision trees for loss prediction in retail - case of Pingo Doce |
| author |
Henriques, Mariana Bonito |
| author_facet |
Henriques, Mariana Bonito |
| author_role |
author |
| dc.contributor.none.fl_str_mv |
Henriques, Roberto André Pereira RUN |
| dc.contributor.author.fl_str_mv |
Henriques, Mariana Bonito |
| dc.subject.por.fl_str_mv |
Machine Learning Retail Loss Classification Algorithm Classification Tree Decision Tree |
| topic |
Machine Learning Retail Loss Classification Algorithm Classification Tree Decision Tree |
| description |
Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence |
| publishDate |
2020 |
| dc.date.none.fl_str_mv |
2020-07-16T18:36:40Z 2020-07-06 2020-07-06T00:00:00Z |
| dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
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masterThesis |
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publishedVersion |
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http://hdl.handle.net/10362/100957 TID:202501191 |
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http://hdl.handle.net/10362/100957 |
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TID:202501191 |
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eng |
| language |
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:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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