Методи машинного навчання для аналізу фінансових витрат у мобільних застосунках

Abstract

This work is devoted to the application of machine learning methods for the analysis of personal financial expenses in mobile applications. Classification and clustering algorithms that enable automated categorization of transactions and identification of spending patterns are considered. The effectiveness of decision trees, random forests, support vector machines, LSTM networks, and k-means clustering is analyzed in the context of processing real-world financial data. A comparative analysis of the methods is presented. The practical significance of the proposed approaches for improving expense tracking accuracy is substantiated.