Modelling and Data Analysis
2020. Vol. 10, no. 4, 41–50
doi: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
Abstract
General Information
Keywords: demand forecasting, new products, encoding, gradient busting, regression, preprocessing, data processing, machine learning
Journal rubric: Data Analysis
DOI: https://doi.org/10.17759/mda.2020100404
For citation: Osin A.A., Fomin A.K., Sologub G.B., Vinogradov V.I. Using Machine Learning Methods to Solve Problems of Forecasting Demand for New Products in the Internet Marketplace. Modelirovanie i analiz dannikh = Modelling and Data Analysis, 2020. Vol. 10, no. 4, pp. 41–50. DOI: 10.17759/mda.2020100404. (In Russ., аbstr. in Engl.)
References
- Bisong E. Introduction to Scikit-learn // Building Machine Learning and Deep Learning Models on Google Cloud Platform 2019. P. 215–229.
- Cerda P., Varoquaux G., Kégl B. Similarity encoding for learning with dirty categorical variables // Machine Learning. 2018. P. 1477–1494.
- Ke G. et al. Lightgbm: A highly efficient gradient boosting decision tree // Advances in neural information processing systems. 2017. P. 3146–3154.
- Redell N. Shapley Decomposition of R-Squared in Machine Learning Models // arXiv preprint arXiv:1908.09718. 2019.
- Botchkarev, Alexei. “Performance metrics (error measures) in machine learning regression, forecasting and prognostics: Properties and typology.” // arXiv preprint arXiv:1809.03006.
- 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
Metrics
Views
Total: 538
Previous month: 10
Current month: 11
Downloads
Total: 376
Previous month: 14
Current month: 7