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Modelling and Data Analysis
2020. Vol. 10, no. 4, 41–50
https://doi.org/10.17759/mda.2020100404
ISSN: 2219-3758 / 2311-9454 (online)

Using Machine Learning Methods to Solve Problems of Forecasting Demand for New Products in the Internet Marketplace

 
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Abstract

The work is aimed at researching the possibility of using machine learning methods to build models for forecasting demand for new products in the online store Ozon. ru. Approaches to the solution that were not previously used in a specific task are proposed for consideration. Data on sales history and storage of goods at Ozon.ru are used as a sample. There is a description and analysis of the approximate loss of the Ozon.ru website, the data used, the process of building a base model, and the results obtained. It describes the metrics used to evaluate the prediction results and makes a comparative analysis between the prediction results of the built model and the results of heuristically selected values.

General Information

Keywords: demand forecasting, new products, encoding, gradient busting, regression, preprocessing, data processing, machine learning

Journal rubric: Data Analysis

OpenAlex citations: 1

OpenAlex trends: Data Quality and Management, Big Data and Business Intelligence, Economic and Technological Systems Analysis

Information about the work in OpenAlex

Number of citations: 1

Topics

Data Quality and Management

This cluster of papers focuses on data quality assessment, improvement, and related techniques such as record linkage, data cleaning, entity resolution, information quality benchmarks, and privacy-preserving record linkage. It also covers topics like name disambiguation, data integration, and the challenges of data quality in the era of big data.

Number of works: 66672  |  Total number of citations: 419023

Topic detailsв OpenAlex

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

Topic detailsв OpenAlex

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

Topic detailsв OpenAlex

Work details in OpenAlex

DOI: https://doi.org/10.17759/mda.2020100404

Published

For citation: Osin, A.A., Fomin, A.K., Sologub, G.B., Vinogradov, V.I. (2020). Using Machine Learning Methods to Solve Problems of Forecasting Demand for New Products in the Internet Marketplace. Modelling and Data Analysis, 10(4), 41–50. (In Russ.). https://doi.org/10.17759/mda.2020100404

© Osin A.A., Fomin A.K., Sologub G.B., Vinogradov V.I., 2020

License: CC BY-NC 4.0

References

  1. Bisong E. Introduction to Scikit-learn // Building Machine Learning and Deep Learning Models on Google Cloud Platform 2019. P. 215–229.
  2. Cerda P., Varoquaux G., Kégl B. Similarity encoding for learning with dirty categorical variables // Machine Learning. 2018. P. 1477–1494.
  3. Ke G. et al. Lightgbm: A highly efficient gradient boosting decision tree // Advances in neural information processing systems. 2017. P. 3146–3154.
  4. Redell N. Shapley Decomposition of R-Squared in Machine Learning Models // arXiv preprint arXiv:1908.09718. 2019.
  5. Botchkarev, Alexei. “Performance metrics (error measures) in machine learning regression, forecasting and prognostics: Properties and typology.” // arXiv preprint arXiv:1809.03006.
  6. Al Daoud E. Comparison between XGBoost, LightGBM and CatBoost Using a Home Credit Dataset // International Journal of Computer and Information Engineering. 2019. P. 6–10.

Information About the Authors

Artem A. Osin, Moscow Aviation Institute (National Research University), Moscow, Russian Federation, ORCID: https://orcid.org/0000-0002-2664-1370, e-mail: artemosin1@yandex.ru

Artem K. Fomin, Moscow Aviation Institute (National Research University), Moscow, Russian Federation, ORCID: https://orcid.org/0000-0003-3545-4435, e-mail: artem.fomin@outlook.com

Gleb B. Sologub, Candidate of Science (Physics and Matematics), Associate Professor of the Department of Mathematical Cybernetics of Institute of Information Technologies and Applied Mathematics, Moscow Aviation Institute (National Research University), Moscow, Russian Federation, ORCID: https://orcid.org/0000-0002-5657-4826, e-mail: glebsologub@ya.ru

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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