Publicação
Data mining in retail sector
| Resumo: | The retailsectorisoneofPortugal’smostrelevanteconomicactivitiesbecausein2021it was the sector that employed the most Portuguese people and the second largest contributor t gross fixed capital formation. Despite this,in the same year it was the third sector with th most accidents at work.There fore, this master’s thesis aims to apply data mining techniques to improve work accidents prevention using internal and external data from a Portuguese retail company. The company provide dinternal data on stores, accidents and employees, which was the nintegrated with weather information collected via anexternal API. Th correlation analysis was applied separating the data by store and by district and idemonstrated a weak correlation between the features studied and the occurrence of accidents at work. Further more, ML models were trained using the same features with the intention of classifying the data between occurrence(1) ornon-occurrence(0) ofaccidents, also separating by store and by district while comparing 8ML algorithms. Another categorization of stores was testedusing a clustering algorithm along with a number of clusters optimizing method.The stores were then dividedin to clusters so that the same correlation analysis and ML classification models could be implemented for comparison. The correlation analysis per-cluster yielded no different results from the previous ones. On the other hand, the classificationa lgorithms trained by cluster performed better,with the Multilayer Perceptron algorithm obtaining Recall = 0.7959. |
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| Autores principais: | Borges, Lucas D. |
| Assunto: | Data mining Retail Work accidents |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| Resumo: | The retailsectorisoneofPortugal’smostrelevanteconomicactivitiesbecausein2021it was the sector that employed the most Portuguese people and the second largest contributor t gross fixed capital formation. Despite this,in the same year it was the third sector with th most accidents at work.There fore, this master’s thesis aims to apply data mining techniques to improve work accidents prevention using internal and external data from a Portuguese retail company. The company provide dinternal data on stores, accidents and employees, which was the nintegrated with weather information collected via anexternal API. Th correlation analysis was applied separating the data by store and by district and idemonstrated a weak correlation between the features studied and the occurrence of accidents at work. Further more, ML models were trained using the same features with the intention of classifying the data between occurrence(1) ornon-occurrence(0) ofaccidents, also separating by store and by district while comparing 8ML algorithms. Another categorization of stores was testedusing a clustering algorithm along with a number of clusters optimizing method.The stores were then dividedin to clusters so that the same correlation analysis and ML classification models could be implemented for comparison. The correlation analysis per-cluster yielded no different results from the previous ones. On the other hand, the classificationa lgorithms trained by cluster performed better,with the Multilayer Perceptron algorithm obtaining Recall = 0.7959. |
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