Analýza finanční gramotnosti v EU

This thesis focuses on the application of machine learning models to analyze financial literacy across the European Union, with an emphasis on the predictive importance of socio-economic factors widely discussed in current literature. Using data from the 2023 survey conducted by the European Commission, a set of regression models was constructed to assess the significance of selected variables across defined EU regions. Due to the skewed distribution of the original scoring variable, an alternative synthetic variable was developed to better capture the true level of financial literacy in the population. The main contribution of this thesis lies in a comprehensive comparison of the predictive relevance of exogenous variables across various regions segmented by both economic and cultural metrics.