Economics and management of national economy

National projects and government programmes: functional algorithm for evaluating and modelling using the Data Science methodology

Economic Annals-XXI

Economic Annals-ХХI: Volume 183, Issue 5-6, Pages: 51-59

Citation information:
Astanakulov, O. (2020). National projects and government programmes: functional algorithm for evaluating and modelling using the Data Science methodology. Economic Annals-XXI, 183(5-6), 51-59. doi: https://doi.org/10.21003/ea.V183-05

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Olim Astanakulov
PhD (Economics),
Associate Professor,
Academy of Public Administration under President of Republic of Uzbekistan (APA)
45 Islam Karimov Str., Tashkent, 100066, Republic of Uzbekistan
astanakulov@gmail.com
ORCID ID: https://orcid.org/0000-0002-0536-1214

National projects and government programmes: functional algorithm for evaluating and modelling using the Data Science methodology

Abstract. Programme and target planning procedures in Russia have a lot of shortcomings, related to the selection of priority goals, establishment of criteria for evaluating the effectiveness of target programmes, as well as achievement of goals, development of a system of performance indicators, and so on. In addition, the problem of the lack of a high-quality theoretical and legislative framework for the transition to budget expenditures planning in accordance with the principles of result-oriented budgeting remains urgent.

The purpose of this paper is to develop a functional fuzzy computing algorithm for modelling the evaluation of government programmes using neural networks.

As a part of this work, we obtained stable results in the form of creating a neural network that can analyze government projects using a multi-criteria method, taking into account the root-mean-square error, with an accuracy of up to 95%. The analysis criteria cover all effective areas for predicting the correct use of the government projects by implementing them in the government systems.

Keywords: Support; Solution; System; Building; Model; Neural Network; Government Programme; Data Science

JEL Classіfіcatіon: C81; H52

Acknowledgements and Funding: The author received no direct funding for this research.

Contribution: The author contributed personally to this work.

DOI: https://doi.org/10.21003/ea.V183-05

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Received 19.02.2020
Received in revised form 20.03.2020
Accepted 26.03.2020
Available online 4.06.2020

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