2026· EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA· Vol 5/13, pp. 225-232· 0 citations
TL;DR
It is shown that the effectiveness of neural network models in business depends not only on algorithmic accuracy, but also on data quality, interpretability, and proper integration into managerial processes.
Abstract
The article examines the mathematical foundations of neural networks and machine learning in the context of modern business analytics. It explains the role of linear algebra, probability theory, mathematical statistics, and optimization methods in the construction and training of intelligent models. Particular attention is given to the practical application of such solutions in demand forecasting, customer segmentation, churn detection, recommender systems, and anomaly detection. It is shown that the effectiveness of neural network models in business depends not only on algorithmic accuracy, but also on data quality, interpretability, and proper integration into managerial processes. The paper also identifies the key limitations and prospects of using machine learning in the analytical activities of companies.
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