Marketing Issue 11 (2025)

MACHINE LEARNING MODELS FOR PREDICTING HS CODE: PROSPECTS AND EFFECTIVENESS OF USE

Муратова Шохиста Ниматуллаевна

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Abstract

The article examines various machine learning models for predicting GN FEA codes based on product descriptions entered into customs declarations. GN FEA codes are widely used by all customs services due to a number of advantages, including a more convenient and simplified approach to calculating duties and preventing potential revenue loss. This study is based on a cross -industry process to develop a data mining methodology. The results demonstrate that machine learning models are effective tools for predicting GN FEA codes based on input data. 38

machine learningGN FEA codespredictive modelscustoms servicesrevenue loss preventiontradeмашинное обучениекод ТН ВЭДпрогнозные моделитаможенные службы

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APA 7 
Муратова Шохиста Ниматуллаевна (2025). MACHINE LEARNING MODELS FOR PREDICTING HS CODE: PROSPECTS AND EFFECTIVENESS OF USE. Marketing, (11).
GOST R 7.0.5 
Муратова Шохиста Ниматуллаевна MACHINE LEARNING MODELS FOR PREDICTING HS CODE: PROSPECTS AND EFFECTIVENESS OF USE // Marketing. 2025. № 11.
BibTeX 
@article{ниматуллаевна2025,
  author  = {Муратова Шохиста Ниматуллаевна},
  title   = {MACHINE LEARNING MODELS FOR PREDICTING HS CODE: PROSPECTS AND EFFECTIVENESS OF USE},
  journal = {Marketing},
  year    = {2025},
  number  = {11}
}
RIS 
TY  - JOUR
AU  - Муратова Шохиста Ниматуллаевна
TI  - MACHINE LEARNING MODELS FOR PREDICTING HS CODE: PROSPECTS AND EFFECTIVENESS OF USE
JO  - Marketing
PY  - 2025
IS  - 11
ER  -