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Towards the Machine Learning Algorithms in Telecommunications Business Environment

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Information Systems (EMCIS 2020)

Abstract

We live in times where companies and individuals are dealing with extremely large amounts of data coming from all different kind of sources. This data includes a lot of very valuable information, which, most of the time, cannot be inferred at first sight. Therefore, in today’s businesses there is a growing necessity of discovering efficient and useful information out of the data that has been gathered. This is the reason why Machine Learning, a technology that has been developed since mid-20th century, is one of the biggest growing technologies in this last decade, being one of its most popular applications in the field of data. The paper presents an analysis what techniques are available for starting with a Data Science project, how easy they are to implement, and how they can be applied in a real world case. The data that was worked with for this project was gathered from a telecommunications company.

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References

  1. Alpaydin, E.: Introduction to Machine Learning. 4th edn. MIT Press, Cambridge (2020). ISBN 978-0-262-01243-0

    Google Scholar 

  2. Russell, S., Norvig, P.: Artificial Intelligence – A Modern Approach. Pearson, London (2009). ISBN 9789332543515

    Google Scholar 

  3. Urbanowicz, R.J., Moore, J.H.: Learning classifier systems: a complete introduction, review, and roadmap. J. Artif. Evol. Appl. 2009(1), 1–25 (2009). ISSN 1687-6229

    Google Scholar 

  4. Zhang, J., Zhan, Z.-H., Lin, Y., Chen, N., Gong, Y.-J., Zhong, J.-H. et al.: Evolutionary computation meets machine learning: a survey. Comput. Intell. Mag. 6(4), 68–75 (2011)

    Google Scholar 

  5. Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E., Gutierrez, J.B., et al.: A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques, University of Georgia, Athens (2017)

    Google Scholar 

  6. Rokach, L., Maimon, O.: Data Mining with Decision Trees: Theory and Applications. World Scientific Pub Co Inc., Singapore (2008). ISBN 978-981277171

    Google Scholar 

  7. Shalev-Shwartz, S., Ben-David, S.: 18. Decision Trees. Understanding Machine Learning. Cambridge University Press, Cambridge (2014)

    Google Scholar 

  8. Rodriguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation forest: a new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1619–1630 (2006)

    Article  Google Scholar 

  9. Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)

    Article  Google Scholar 

  10. Mason, L., Baxter, J., Bartlett, P.L., Frean, M.: Boosting algorithms as gradient descent. In: Solla, S.A., Leen, T.K., Müller, K. (eds.). Advances in Neural Information Processing Systems, vol. 12, pp. 512–518. MIT Press, Cambridge (1999)

    Google Scholar 

  11. Hastie, T., Tibshirani, R., Friedman, J.H.: 10. Boosting and Additive Trees. The Elements of Statistical Learning, 2nd edn., pp. 337–384. Springer, New York (2009). ISBN 978-0-387-84857-0

    Google Scholar 

  12. Hosmer, D.: Applied Logistic Regression. Wiley, New Jersey (2013). ISBN 978-0470582473

    Google Scholar 

  13. Harrell, F.E.: Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. Springer, New York (2010). ISBN 978-1-4419-2918-1

    Google Scholar 

  14. Telco Customer Churn, Kaggle. https://www.kaggle.com/blastchar/telco-customer-churn/data. Accessed 2020

  15. Package Overview, Pandas. https://pandas.pydata.org/docs/\\getting\_started/overview.html. Accessed 2020

  16. XGBoost Parameters, XGBoost. https://xgboost.readthedocs.io/en/latest/parameter.html. Accessed 2020

  17. Brownlee, J.: Recursive Feature Elimination (RFE) for Feature Selection in Python, Machine Learning Mastery. https://machinelearningmastery.com/rfe-feature-selection-in-python/. Accessed 2020

  18. Śniegula, A., Poniszewska-Marańda, A., Chomątek, Ł.: Towards the named entity recognition methods in biomedical field. In: Chatzigeorgiou, A., et al. (eds.) SOFSEM 2020. LNCS, vol. 12011, pp. 375–387. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-38919-2_31

    Chapter  Google Scholar 

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Correspondence to Aneta Poniszewska-Marańda .

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de Andrés, M.LO., Poniszewska-Marańda, A., Hernández Gómez, L.A. (2020). Towards the Machine Learning Algorithms in Telecommunications Business Environment. In: Themistocleous, M., Papadaki, M., Kamal, M.M. (eds) Information Systems. EMCIS 2020. Lecture Notes in Business Information Processing, vol 402. Springer, Cham. https://doi.org/10.1007/978-3-030-63396-7_6

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  • DOI: https://doi.org/10.1007/978-3-030-63396-7_6

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