Hybrid models of predictive analytics in the creative industry

D.S. Kazakova

Abstract


The main content of the article includes an analysis of modern publications, examples of successful application of hybrid models, and a discussion of their role in new directions of the creative sphere, such as video games and virtual reality. The novelty of the article lies in identifying the prospects for using hybrid models to optimize processes and improve forecast accuracy in the creative industry and investment markets. The results obtained show that hybrid models can significantly improve business processes, enhance risk assessment, and predict project success. Recommendations for future research include the development of new algorithms and model integration. This article will be useful for data analytics specialists, investors, and creative  industry professionals, providing them with tools to make informed decisions and  achieve commercial success. The purpose of this article is to explore the  current achievements and prospects of applying hybrid models of predictive analytics in the creative industries as investment objects. Hybrid models  combining machine learning methods and statistical analysis demonstrate high accuracy and reliability of forecasts. The article discusses the key advantages of these models, their successful application in such industries as cinema, music, and digital art, as well as their potential in assessing investment risks and selecting profitable projects. The main content of the article includes an analysis of modern publications, examples of successful application of hybrid models, and a discussion of their role in new directions of the creative sphere,  such as video games and virtual reality. The novelty of the article lies in  identifying the prospects for using hybrid models to optimize processes and  improve forecast accuracy in the creative industry and investment markets. The results obtained show that hybrid models can significantly improve business processes, enhance risk assessment, and predict project  success. Recommendations for future research include the development of new algorithms and model integration. This article will be useful for data analytics
specialists, investors, and creative industry professionals, providing them with tools to make informed decisions and achieve commercial success.


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