STUDY OF THE EFFICIENCY OF CLASSIFICATION METHODS FOR IDENTIFYING FRAUDIOUS BANKING TRANSACTIONS
The article discusses the study of the work of classification methods in the identification of fraudulent banking transactions and a comparison of the results obtained. Processed initial data. Several experiments were carried out to train models on various training samples obtained because of data balancing using the Python programming language. The results of model predictions are evaluated by several selected metrics.