Intelligent Algorithms for Evaluating Economic Indicators Based on Machine Learning Models

This study focuses on the development of intelligent algorithms for evaluating economic indicators based on artificial intelligence models. In particular, the research addresses the intellectual assessment of the balance between supply and demand in sales, considered as key economic indicators. The process of predicting delays in product sales, viewed as an imbalance between supply and demand, is thoroughly examined. To this end, the problem is formulated as a classification task, and solutions are proposed using machine learning models of artificial intelligence. Specifically, intelligent algorithms based on logistic regression, decision trees, and the K-Nearest Neighbors (K-NN) methods have been developed for evaluating economic indicators. Experimental results demonstrating the effectiveness of the proposed approaches are presented.