POVERTY PREDICTION MODEL USING ARTIFICIAL INTELLIGENCE TECHNOLOGIES

Abstract

This scientific work studies the issue of creating a poverty prediction model based on artificial intelligence (AI) technologies. Today, there is a growing need for accurate and fast analytical tools to identify poverty and develop effective measures to reduce it. The article discusses methods for analyzing data based on various artificial intelligence methods - in particular, machine learning algorithms (decision tree, random forest, neural networks) and determining the probable poverty level based on them. The models are based on factors such as income, education level, employment status, and place of residence. The results of the study are important for early detection of poverty, planning social assistance measures, and optimizing economic policy. This model can also be a useful tool for making decisions aimed at reducing social inequality.