PYTHON FOR DATA PROCESSING AND SIMULATION OF FINANCIAL AND ECONOMIC INDICATORS

Authors

  • Tetiana Chupilko University of Customs and Finance
  • Yulia Ulianovska University of Customs and Finance
  • Mykola Mormul University of Customs and Finance
  • Anastasia Lagoda University of Customs and Finance

DOI:

https://doi.org/10.31649/1999-9941-2021-51-2-68-77

Keywords:

Python, data processing, modeling, forecasting, regression model

Abstract

The article considers aspects of efficient data processing. Much attention is paid to the problems that arise in modeling and forecasting data and the role of research in decision-making. The stages of work with data and features that are inherent in each stage are determined. A special place in the work is described by the possibilities of software data processing using Python, which is becoming increasingly popular due to simplicity, flexibility, open source, ease of working with data in various formats, as well as many developed packages that facilitate fast and efficient information processing. NumPy, Pandas, which provide data structures and functions that make working with structured data simple and fast, the most popular tool for data visualization Matplotlib, packages for various computational tasks SciPy, Statsmodels, as well as a package focused on machine learning Scikit-learn.  An example of using Python for customs tasks is given. The authors have created a program for calculation, which uses the above packages. Regression models are being built to analyze the replenishment of the state budget of Ukraine with revenues from customs authorities at the expense of import and export duties. The analysis of models on the basis of econometric methods of modeling is carried out and forecast estimates of receipts are calculated.

Author Biographies

Tetiana Chupilko, University of Customs and Finance

PhD in engineering, associate professor

Yulia Ulianovska, University of Customs and Finance

PhD in engineering, associate professor

Mykola Mormul, University of Customs and Finance

PhD in engineering, associate professor

Anastasia Lagoda, University of Customs and Finance

student

References

U. Makkyny, Python y analiz dannykh. M., Rossia: DMK Press, 2020, 540 p.

S. Devy, M. Arno, A. Mokhamed, Osnovy Data Science i BigData. Python y nauka o dannkh. Peter-burg, Rossia: Pyter, 2017, 336 p.

T. A. Chupilko, "Aktualni problemy vysokoefektyvnoi obrobky danykh. Modeliuvannia pokaznykiv za dopomohoiu movy prohramuvannia Python," u Aktualni napriamy rozvytku tekhnichnoho ta vyrob-nychoho potentsialu natsionalnoi ekonomiky. Dnipro: Porohy, 2021, рр. 151−163.

T. A. Chupilko, "Bazovyi instrumentarii u suchasnykh tekhnolohiiakh kompiuternoi biznes-analityky," in Mizhnar. Nauk. Konf. Innovatsiini tekhnolohii, modeli uprav-linnia kiberbezpekoiu ITMK-2020, Dnipro, 2020, t. 2, рр. 53−54.

T. A. Chupilko, "Kompiuterni tekhnolohii ta ekonomiko-matematychni metody v upravlinni biznes-protsesamy na pidpryiemstvi," in Mizhnar. Nauk. Konf. Innovatsiini tekhnolohii, modeli upravlinnia kiberbezpekoiu ITMK-2020, Dnipro, 2020, t. 1, pp. 26−28.

Ministerstvo finansiv Ukrainu. [Online]. Available: http://mof.gov.ua. Accessed on: August 20, 2021.

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Abstract views: 735

Published

2021-10-21

How to Cite

[1]
T. Chupilko, Y. Ulianovska, M. Mormul, and A. . Lagoda, “PYTHON FOR DATA PROCESSING AND SIMULATION OF FINANCIAL AND ECONOMIC INDICATORS”, ІТКІ, vol. 51, no. 2, pp. 68–77, Oct. 2021.

Issue

Section

Mathematical modeling and computational methods

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