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English Information

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Authors
# Name
1 José Augusto Filho(augustocam95@gmail.com)
2 José Monteiro(jose.monteiro@lsbd.ufc.br)

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Reference
# Reference
1 Alcalá-Fdez, J., Sanchez, L., Garcia, S., del Jesus, M. J., Ventura, S., Garrell, J. M., Otero,J., Romero, C., Bacardit, J., Rivas, V. M., et al. (2009). Keel: a software tool to assess evolutionary algorithms for data mining problems.Soft Computing, 13(3):307–318.
2 Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae,V., Prettenhofer, P., Gramfort, A., Grobler, J., et al. (2013). Api design for machine learning software: experiences from the scikit-learn project.arXiv preprint ar-Xiv:1309.0238.
3 Chertchom, P. (2018). A comparison study between data mining tools over regressionmethods: Recommendation for smes. In2018 5th International Conference on Busi-ness and Industrial Research (ICBIR), pages 46–50. IEEE.
4 DEMŠAR, Janez et al. Orange: data mining toolbox in Python. the Journal of machine Learning research, v. 14, n. 1, p. 2349-2353, 2013.
5 Filho, J. A. C., Monteiro, J. M., Mattos, C. L. C., and Nobre, J. S. (2021). A practicalguide to support predictive tasks in data science. In Filipe, J., Smialek, M., Brodsky,A., and Hammoudi, S., editors,Proceedings of the 23rd International Conference onEnterprise Information Systems, ICEIS 2021, Online Streaming, April 26-28, 2021,Volume 1, pages 248–255. SCITEPRESS.
6 Grinberg, M. (2018).Flask web development: developing web applications with python.”O’Reilly Media, Inc.”.
7 Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009).The weka data mining software: an update.ACM SIGKDD explorations newsletter,11(1):10–18.
8 Hasim, N. and Haris, N. A. (2015). A study of open-source data mining tools for forecasting. InProceedings of the 9th International Conference on Ubiquitous information management and Communication, pages 1–4.
9 James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013).An introduction to statisticallearning, volume 112. Springer.
10 Jovic, A., Brkic, K., and Bogunovic, N. (2014). An overview of free software toolsfor general data mining. In2014 37th International Convention on Information andCommunication Technology, Electronics and Microelectronics (MIPRO), pages 1112–1117. IEEE.
11 Olorisade, B. K., Brereton, P., and Andras, P. (2017).Reproducibility in machinelearning-based studies: An example of text mining.
12 Ozdemir, S. (2016).Principles of data science. Packt Publishing Ltd.
13 Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel,M., Prettenhofer, P., Weiss, R., Dubourg, V., et al. (2011). Scikit-learn: Machinelearning in python.the Journal of machine Learning research, 12:2825–2830.
14 Provost, F. and Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision-making.Big data, 1(1):51–59.
15 Ramamohan, Y., Vasantharao, K., Chakravarti, C. K., Ratnam, A., et al. (2012). A studyof data mining tools in knowledge discovery process.International Journal of SoftComputing and Engineering (IJSCE) ISSN, 2(3):2231–2307.
16 Sandve, G. K., Nekrutenko, A., Taylor, J., and Hovig, E. (2013). Ten simple rules forreproducible computational research.PLoS Comput Biol, 9(10):e1003285.