Machine Learning Methods as a Forecasting and Investment Instrument for Event Venues

This paper examines the application of machine learning methods to analyze and predict investment priorities in the business with events. Events create various opportunities for offering services to their organization, management and monitoring as a separate service or part of the tourism supply in relation to the possibilities for extra services and added value. In order to establish the relationship between event venues, their offering and attractiveness, entrepreneurs in the sector should conduct research and analyze consumer expectations for higher service value. For this purpose, survey data are used, which include various factors influencing investment decisions. The selected methods include approaches for data preprocessing, feature selection, and application of data classification algorithms such as K-NN, Decision Tree, Generalized Linear Model, and H2O Deep Learning. Key measures of model evaluation are calculated and analyzed, which indicate the accuracy of the predictions and their applicability. The most appropriate methods for researching empirical data from event venue owners are identified and potential solutions for future investment activities are provided. The study provides insights into the specific characteristics of contemporary events, their operational dynamics, and the way these influence strategic investment priorities. The empirical findings also reveal that venue infrastructure, interior renovation, and personalized service enhancements are among the strongest determinants shaping investment decisions.