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Modelling and Data Analysis
2019. Vol. 9, no. 4, 57–66
doi:10.17759/mda.2019090404
ISSN: 2219-3758 / 2311-9454 (online)

The Tasks of Analysis and Forecasting the Activities of IT Companies Using Machine Learning Methods

 
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Abstract

The article describes applying machine learning methods for improving the efficiency of business processes when working with clients in an IT company. Two models of machine learning are considered: clustering the customer base and revenue forecasting.

General Information

Keywords: Machine Learning, IT Company, customer base segmentation, forecasting

Journal rubric: Data Analysis

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2019090404

Acknowledgements. The authors are grateful to G.F. Artamonov, General Director of OVIONT INFORM, for the data provided.

Published

For citation: Alekseychuk, A.S., Vinogradov, V.I. (2019). The Tasks of Analysis and Forecasting the Activities of IT Companies Using Machine Learning Methods. Modelling and Data Analysis, 9(4), 57–66. (In Russ.). https://doi.org/10.17759/mda.2019090404

© Alekseychuk A.S., Vinogradov V.I., 2019

License: CC BY-NC 4.0

References

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  2. Shlens J. A tutorial on principal component analysis. Institute for Nonlinear Science, UCSD, 2005.
  3. Rui Xu, Wunsch D. Survey of clustering algorithms. IEEE Transactions on Neural Networks, vol. 16, no. 3, 2005. pp. 645–678.
  4. Wang L., Leckie C., Ramamohanarao K., Bezdek J. Automatically Determining the Number of Clusters in Unlabeled Data Sets. IEEE Transactions on Knowledge and Data Engineering, vol. 21, 2009. p. 335–350.
  5. Rousseeuw Peter J. Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis. Computational and Applied Mathematics, vol. 20, 1987. p. 53–65.
  6. Lukashin Yu.P. Adaptivnye metody kratkosrochnogo prognozirovaniya vremennyh ryadov. [Adaptive methods of short-term forecasting of time series.] – M.: Finansy i statistika, 2003. (In Russ., Abstr. in Engl.)

Information About the Authors

Andrey S. Alekseychuk, Candidate of Science (Physics and Matematics), Associate Professor, Department of Mathematical Cybernetics, Moscow Aviation Institute (National Research University) (MAI), Associate Professor of the Department of Digital Education, Moscow State University of Psychology and education, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0003-4167-8347, e-mail: alexejchuk@gmail.com

Vladimir I. Vinogradov, Candidate of Science (Physics and Matematics), Associate Professor, Department of Mathematical Cybernetics, Moscow Aviation Institute (National Research University), Moscow, Russian Federation, ORCID: https://orcid.org/0000-0003-3773-9653, e-mail: vvinogradov@inbox.ru

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