Analysis of the Economic Efficiency of Locations in the Field of Trade and the Influence of External Factors on it
- Authors: Grineva N.V.1,2, Topyrkin A.D.1
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Affiliations:
- Russian Academy of National Economy and Public Administration under the President of the Russian Federation
- Financial University under the Government of the Russian Federation
- Issue: Vol 19, No 2 (2023)
- Pages: 184-196
- Section: Mathematical, Statistical and Instrumental Methods in Economics
- URL: https://journals.eco-vector.com/2541-8025/article/view/568526
- ID: 568526
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Abstract
The relevance of the article lies in the description of the process of data analysis and modeling for solving the placement problem. The main purpose of the research work is to solve the problem of location and assess the degree of influence of the geographical characteristics of locations on the indicators of the economic efficiency of the organization. The article defines the concepts of economic efficiency and profit, as well as how they are related to each other. A number of tasks are described in solving the placement problem. Questions regarding the geographic data used and the formation of the target variable are covered in detail, namely, the questions are answered. What? How? Why? What—what factors can be used to identify the potential of a location. How is the processing of data on store revenues to the final form of the target variable, why such transformations are needed. The process of correlation analysis and feature selection for the subsequent stage of modeling is shown. The course of building the model and assessing its accuracy is described. And also the analysis of the residuals for the best combinations was carried out using the methods of non-parametric statistics. The main tools in the process of solving these problems were the Python programming language and its libraries pandas, numpy, scikit-learn, xgboost, hyperopt, statsmodels, scipy, matplotlib, seaborn. The result of this research work is the constructed machine learning models to determine the economic potential of a location.
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About the authors
Natalia V. Grineva
Russian Academy of National Economy and Public Administration under the President of the Russian Federation; Financial University under the Government of the Russian Federation
Author for correspondence.
Email: ngrineva@fa.ru
ORCID iD: 0000-0001-7647-5967
Cand. Sci. (Econ.), Associate Professor; Associate Professor of the Department of data analysis and machine learning, Financial University under the Government of the Russian Federation; Associate Professor, Department of System Analysis, Russian Academy of National Economy and Public Administration under the President of the Russian Federation
Russian Federation, Moscow; MoscowAlexey D. Topyrkin
Russian Academy of National Economy and Public Administration under the President of the Russian Federation
Email: topyrkinalexei@yandex.ru
ORCID iD: 0009-0005-3862-2651
Russian Federation, Moscow
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