Рақамли технологияларнинг назарий ва амалий масалалари Volume 8 Issue 1 (2025) · pp. 158-164

Information model of professional guidance: analysis of competencies using the k-NN method

Шодмонов, Д.А.

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Abstract

The article is devoted to the development of an information model of vocational guidance, focused on the analysis of competencies using the k-nearest neighbors (k-NN) method. In this work, the method is used to assess the level of development of various professional skills and competencies among students, as well as to determine the compliance of these competencies with the requirements of specific professions. Carries out the process of collecting and processing data on competencies, as well as building models that help predict suitable professional activities for each person. Particular attention is paid to the possibilities of using k-NN to create personalized recommendations for choosing a profession based on existing functions and interests.

профессиональная ориентацияпрогнозированиеметодk-ближайших соседейкомпетентностьинформационная модельпрофессиональная деятельностьклассификациямодель специалистаумение

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Cite

APA 7
Шодмонов, Д.А. (2025). Information model of professional guidance: analysis of competencies using the k-NN method. Рақамли технологияларнинг назарий ва амалий масалалари, 8(1), 158-164.
GOST R 7.0.5
Шодмонов, Д.А. Information model of professional guidance: analysis of competencies using the k-NN method // Рақамли технологияларнинг назарий ва амалий масалалари. 2025. Т. 8. № 1. С. 158-164.
BibTeX
@article{д.а.2025,
  author  = {Шодмонов, Д.А.},
  title   = {Information model of professional guidance: analysis of competencies using the k-NN method},
  journal = {Рақамли технологияларнинг назарий ва амалий масалалари},
  year    = {2025},
  volume  = {8},
  number  = {1},
  pages   = {158-164}
}
RIS
TY  - JOUR
AU  - Шодмонов, Д.А.
TI  - Information model of professional guidance: analysis of competencies using the k-NN method
JO  - Рақамли технологияларнинг назарий ва амалий масалалари
PY  - 2025
VL  - 8
IS  - 1
SP  - 158
EP  - 164
ER  -