Прогнозирование результатов кинопроката с помощью машинного обучения

Abstract

The subject of the research is the results of distribution of Russian national films. The purpose of the study is to classify projects according to the principle of their success/failure at the box office and predict the characteristics of the box office. The objectives of the study are to create algorithms for selecting (classifying) potentially successful projects into an investment portfolio and predicting (regression) rental characteristics: the number of views, payback, viewer rating. The technique is based on the application of ensemble machine learning models. The empirical base of the study is the entire set of Russian national films in distribution from 2004 to April 2022 (N=1469) and from May 2022 to April 2023 (N=194). Achieved accuracy of 0.95 and 0.89 for two and four class classification and high performance ROC_AUC = 0.97 for two class model and 0.94 – 0.98 for four class model. More complex metamodels (superensembles) can achieve an accuracy of 0.97-0.98 for a two-class classification and 0.96 for a four-class one. Complex regression metamodels predict the absolute values of payback, fees, views with a coefficient of determination (R2) in the range of 0.97-0.98 using synthetic data. As a result, it became possible to form investment portfolios of film projects with an annual historical return of up to 139%. The scope of application is to ensure the selection of films for investment "portfolios of film projects" of state (Ministry of Culture, Cinema Fund) and private investors. Machine learning models can be adapted to the conditions of global and foreign markets by increasing the number of model features, expanding the arsenal of machine learning methods, including the analysis of texts, images, videos, and user data of social networks.