Analysis of the results of the Russian agricultural census using machine learning methods

Abstract

The article provides a comparative analysis of the results of the Russian Agricultural Census of 2006 and 2016. As a result, there is a decrease in the number of agricultural producers, a decrease in the size of agricultural land and equipment in organizations. Against this background, one can see an increase in the concentration of production in both crop and livestock production. Machine learning models have been built to classify subsidy organizations using Python libraries. The accuracy of the constructed models was up to 86 %, which proves the possibility of their use. In the future, the use of machine learning methods will reduce the number of Russian agricultural census indicators and classify organizations with high accuracy according to qualitative characteristics.