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Comparative Analysis of Machine Learning Techniques via Data Mining in a Railroad Company

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Proceedings of the 11th International Conference on Production Research – Americas (ICPR 2022)

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Abstract

This study aimed to carry out a comparative analysis of machine learning techniques via data mining in a company in the railway segment located in the state of Paraná, Brazil. To achieve the goal of reducing emissions of polluting gases that impact future climate changes, information was first collected regarding fuel consumption over the years from 2006 to 2020. Then, data mining techniques were applied to identify patterns, clean and prepare the collected base, then the machine learning techniques of K-Nearest Neighbors (KNN), Random Forest and Support Vector Machine (SVM) were applied. The selection of the best technique was based on the smallest error in the test set. As a result, it was possible to identify that the KNN was the technique with the lowest error compared to the others analyzed, being the one chosen to make future forecasts for the company under study.

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Author information

Authors and Affiliations

  1. Pontifical Catholic University of Paraná, Curitiba, PR, 80215-901, Brazil

    Marcelo Carneiro Gonçalves, Elpidio Oscar Benitez Nara, Isabel Martinski dos Santos, Isabela Boscardin Mateus & Lucas Montanari Bento do Amaral

Authors
  1. Marcelo Carneiro Gonçalves
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  2. Elpidio Oscar Benitez Nara
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  3. Isabel Martinski dos Santos
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  4. Isabela Boscardin Mateus
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  5. Lucas Montanari Bento do Amaral
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Corresponding author

Correspondence to Marcelo Carneiro Gonçalves .

Editor information

Editors and Affiliations

  1. Industrial and Systems Engineering Graduate Program (PPGEPS), Pontifícia Universidade Catolica do Parana, Curitiba, Brazil

    Fernando Deschamps

  2. Industrial and Systems Engineering Graduate Program (PPGEPS), Universidade Tecnologica Federal do Parana, Curitiba, Paraná, Brazil

    Edson Pinheiro de Lima

  3. Industrial and Systems Engineering Graduate Program (PPGEPS), Universidade Tecnologica Federal do Parana, Curitiba, Paraná, Brazil

    Sérgio E. Gouvêa da Costa

  4. Industrial and Systems Engineering Graduate Program (PPGEPS), Universidade Tecnologica Federal do Parana, Curitiba, Paraná, Brazil

    Marcelo G. Trentin

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Gonçalves, M.C., Nara, E.O.B., Santos, I.M.d., Mateus, I.B., do Amaral, L.M.B. (2023). Comparative Analysis of Machine Learning Techniques via Data Mining in a Railroad Company. In: Deschamps, F., Pinheiro de Lima, E., Gouvêa da Costa, S.E., G. Trentin, M. (eds) Proceedings of the 11th International Conference on Production Research – Americas. ICPR 2022. Springer, Cham. https://doi.org/10.1007/978-3-031-36121-0_83

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  • DOI: https://doi.org/10.1007/978-3-031-36121-0_83

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36120-3

  • Online ISBN: 978-3-031-36121-0

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