Мodeling of security indicators of the national economy and data processing using Python

Abstract

The article considers various aspects of effective data processing, outlines the stages involved and features of each stage. Problems arising during data processing and effective means of their modeling and forecasting are analyzed. The use of several packages NumPy, Pandas, Matplotlib, SciPy, Statsmodels, Scikit-learn is considered. An example of how Python can be used for customs-related tasks is given, taking into account the country’s economic security indicators. The authors built a series of regression models to analyze the contribution of customs revenues from import and export duties to the state budget of Ukraine. A calculation program has been developed using the above packages. At the same time, the Python programming language was used, using NumPy, Statsmodels, Matplotlib and Xlrd libraries. Preliminary data preparation was carried out in Excel. The obtained results are analyzed. The article demonstrates the possibility of software data processing. Complete statistics could not be obtained from the official website to enable analysis of large sets of similar data. However, problems encountered when modeling based on the aggregated data provided will be reproduced similarly. In addition, the lists contain a warning that more data should be available for an accurate calculation. If the volume of data is insufficient, it is necessary to use adjusted estimates, which is also known from the theory of mathematical statistics. The possibility of using the results of modeling financial and economic indicators for making management decisions is indicated. It is possible to build various single-factor and multi-factor: linear and non-linear models, and thus get a complete picture of what processes are taking place in the industry, in particular, at customs, and identify positive and negative phenomena. Based on the results of modeling, it is possible to obtain a scientifically based forecast and make appropriate management decisions.