Machine learning models for predicting hs code: prospects and effectiveness of use

Муратова Шохиста Ниматуллаевна

Marketing · 2025-yil

Annotatsiya

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

Maqola ma’lumotlari
MualliflarМуратова Шохиста Ниматуллаевна
JurnalMarketing
NashriyotТошкент давлат иқтисодиёт университети
ISSN3060-4621
Nashr sanasi2025-11-29
Son11
TilIngliz
Ilmiy sohaIqtisodiyot
DOI10.67668/mj/2025iss11/617
Indeksga qo‘shilgan2026-10-18

DOI: 10.67668/mj/2025iss11/617 · Maqolaning asl sahifasi

Kalit so‘zlar

machine learning, GN FEA codes, predictive models, customs services, revenue loss prevention, trade, машинное обучение, код ТН ВЭД, прогнозные модели, таможенные службы, предотвращение потери доходов, торговля, mashinaviy o‘qitish, TIF TN kodi, prognoz modellar i, bojxona xizmati, daromad yo‘qotishlarining oldini olish, savdo

Ilmiy soha

Iqtisodiyot

Iqtibos keltirish

APAМуратова Шохиста Ниматуллаевна (2025). MACHINE LEARNING MODELS FOR PREDICTING HS CODE: PROSPECTS AND EFFECTIVENESS OF USE. Marketing, (11). https://doi.org/10.67668/mj/2025iss11/617
GOST (OAK)Муратова Шохиста Ниматуллаевна MACHINE LEARNING MODELS FOR PREDICTING HS CODE: PROSPECTS AND EFFECTIVENESS OF USE // Marketing. – 2025. – № 11. – DOI: 10.67668/mj/2025iss11/617.
MLAМуратова Шохиста Ниматуллаевна. “MACHINE LEARNING MODELS FOR PREDICTING HS CODE: PROSPECTS AND EFFECTIVENESS OF USE.” Marketing, (11), 2025. https://doi.org/10.67668/mj/2025iss11/617
ChicagoМуратова Шохиста Ниматуллаевна. “MACHINE LEARNING MODELS FOR PREDICTING HS CODE: PROSPECTS AND EFFECTIVENESS OF USE.” Marketing, (11) (2025). https://doi.org/10.67668/mj/2025iss11/617
HarvardМуратова Шохиста Ниматуллаевна (2025) ‘MACHINE LEARNING MODELS FOR PREDICTING HS CODE: PROSPECTS AND EFFECTIVENESS OF USE’, Marketing, (11). https://doi.org/10.67668/mj/2025iss11/617
BibTeX
@article{article2025,
  title = {MACHINE LEARNING MODELS FOR PREDICTING HS CODE: PROSPECTS AND EFFECTIVENESS OF USE},
  author = {Муратова Шохиста Ниматуллаевна},
  journal = {Marketing},
  year = {2025},
  number = {11},
  doi = {10.67668/mj/2025iss11/617},
  url = {https://doi.org/10.67668/mj/2025iss11/617}
}

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