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Modelling and Data Analysis
2020. Vol. 10, no. 4, 31–40
https://doi.org/10.17759/mda.2020100403
ISSN: 2219-3758 / 2311-9454 (online)

Using Machine Learning Methods to Solve Problems of Forecasting the Amount and Probability of Purchase Based on E-Commerce Data

 
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Abstract

The study is aimed at investigating the possibility of using machine learning methods to build models for predicting the probability of purchase and the amount of purchase by online store customers. As a sample, we used data of users transactions of the site ponpare.jp in the period from 01.07.2011 to 23.06.2012. The description and comparative analysis of the most common methods for solving similar problems are given. The metrics used to measure the results in the case of forecasting the fact and amount of the purchase are being described. The results obtained make it clear that within the framework of the problem of predicting the probability of a purchase, gradient boosting, namely its implementation of LGBMClassifier, shows the most accurate estimate. For the problem of predicting the amount of a customer’s purchase, using gradient boosting also gave the best results.

General Information

Keywords: probability and purchase amount forecast, classification, regression, data analysis, data processing, machine learning

Journal rubric: Data Analysis

OpenAlex citations: 4

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

Information about the work in OpenAlex

Number of citations: 4

Topics

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

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

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

Work details in OpenAlex

DOI: https://doi.org/10.17759/mda.2020100403

Published

For citation: Mamiev, O.A., Finogenov, N.A., Sologub, G.B. (2020). Using Machine Learning Methods to Solve Problems of Forecasting the Amount and Probability of Purchase Based on E-Commerce Data. Modelling and Data Analysis, 10(4), 31–40. (In Russ.). https://doi.org/10.17759/mda.2020100403

© Mamiev O.A., Finogenov N.A., Sologub G.B., 2020

License: CC BY-NC 4.0

References

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  3. Kuhn M, Johnson K. Applied predictive modeling // New York: Springer. 2013.
  4. Glasbey, C.A. An analysis of histogram-based thresholding algorithms // CVGIP: Graphical models and image processing. 1993. P. 532–537.
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  6. Yang S, Zhang H. Comparison of several data mining methods in credit card default prediction // Intelligent Information Management. 2018. P. 115.
  7. Wu, H., Jiao, H., Yu, Y., Li, Z., Peng, Z., Liu, L. and Zeng, Z. Influence factors and regression model of urban housing prices based on internet open access data // Sustainability. 2018. P. 1676.
  8. Liu, L., Ji, M. and Buchroithner, M. Combining partial least squares and the gradient-boosting method for soil property retrieval using visible near-infrared shortwave infrared spectra // Remote Sensing. 2017. P. 1299.
  9. Wu, J.Y. Housing Price prediction Using Support Vector Regression. 2017.
  10. Limsombunchai, V. House price prediction: hedonic price model vs. artificial neural network // In New Zealand agricultural and resource economics society conference. 2004. P. 25–26.
  11. Li, J.Z. Monthly Housing Rent Forecast Based on LightGBM (Light Gradient Boosting) Model // International Journal of Intelligent Information and Management Science, 2018.

Information About the Authors

Oleg A. Mamiev, Moscow Aviation Institute (National Research University), Moscow, Russian Federation, ORCID: https://orcid.org/0000-0003-1137-4019, e-mail: olegios@mail.ru

Nikita A. Finogenov, Moscow Aviation Institute (National Research University), Moscow, Russian Federation, ORCID: https://orcid.org/0000-0001-7680-9496, e-mail: finogenov.nik@gmail.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

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