Abstract
The data mining models are an excellent tool to help companies that live from the sale of items they produce. With these models combined with Lean Production, it becomes easier to remove waste and optimize industrial production. This project is based on the phases of the methodology CRISP-DM. Several methods were applied to this data namely, average, mean and standard deviation, quartiles and Sturges rule. Classification Techniques were used in order to understand which model has the best probability of hitting the correct result. After performing the tests, model M1 was the one with the best chance to accomplish a great level of classification having 99.52% of accuracy.
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This work has been supported by Compete: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013.
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Bragança, R., Portela, F., Vale, A., Guimarães, T., Santos, M. (2017). Data Mining Classification Models for Industrial Planning. In: Singh, M., Gupta, P., Tyagi, V., Sharma, A., Ören, T., Grosky, W. (eds) Advances in Computing and Data Sciences. ICACDS 2016. Communications in Computer and Information Science, vol 721. Springer, Singapore. https://doi.org/10.1007/978-981-10-5427-3_60
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