Abstract
The presence of large amounts of data in information and analytical systems makes it necessary to study them using machine learning and artificial intelligence methods. These models require the definition of tuning parameters related to the specifics of the subject area. The article presents a Cox regression model to solve the problem of customer churn. Cox regression is recognized as a model with high accuracy of predictions in healthcare. Therefore, it is interesting to use the model in other industries. The paper presents the results and comparative analysis of calculations on the Cox model using three tools: Statistical Package for the Social Sciences, programming language R and Russian software – analytical platform Loginom. A distinctive feature of the developed probabilistic model is the determination of the risk of event occurrence in conditions of incomplete data, as well as the identification of indicators that have a significant impact on the degree of its manifestation.
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