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Modelling and Data Analysis
2019. Vol. 9, no. 4, 57–66
https://doi.org/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

OpenAlex citations: 0

OpenAlex trends: Big Data and Business Intelligence, Economic and Technological Systems Analysis, Advanced Research in Systems and Signal Processing

Information about the work in OpenAlex

Number of citations: 0

Topics

Big Data and Business Intelligence

This cluster of papers explores the impact of big data analytics on business performance, with a focus on supply chain management, predictive analytics, data science, and decision support systems. It delves into the challenges and opportunities of leveraging big data for firm performance and sustainability, as well as the integration of business intelligence and knowledge management. The research also examines the role of big data in innovation, risk mitigation, and operational transparency within organizations.

Number of works: 191588  |  Total number of citations: 1263296

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Economic and Technological Systems Analysis

This cluster of papers covers a wide range of topics related to digital transformation, innovation management, and the integration of information technologies in various domains. It includes research on quality management, cyber-physical systems, big data, sustainability, neural networks, environmental safety, and project management.

Number of works: 36814  |  Total number of citations: 64075

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Advanced Research in Systems and Signal Processing

This cluster of papers focuses on the integration of cyber, physical, and social systems, with an emphasis on decision making, urban computing, autodyne sensors, machine learning, data mining, transportation systems, information management, parallel computing, and infrastructure development.

Number of works: 31317  |  Total number of citations: 164598

Topic detailsв OpenAlex

Work details in OpenAlex

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

  1. Dubes R.C., Jain A.K. Algorithms for Clustering Data. Englewood Cliffs: Prentice Hall, 1988.
  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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