Porovnání implementací automatického strojového učení na reálných datech

The topic of the thesis is a comparison of implementations of automated machine learning on real data. It meets today's pressing need for a better yet relatively simple way to analyse data. The aim of the thesis is to perform regression analysis using different implementations of automated machine learning on real competition data from the Kaggle platform, which organises competitions in the field of data science, and compare the results of these implementations. The analytical part of the work provides an overview of the current state of research in the field of automated machine learning with a short introduction to machine learning issues. There are also introduced possible environments for using automated machine learning, both programming libraries and ready-made solutions with a user interface. As section of the practical part of the work, the analysis of competition data from an online platform for organising competitions in the field of data analysis and machine learning was carried out. The analysis is done in a number of ways, including using Python libraries for automated machine learning and multiple cloud platforms. At the end, the created models are summarised, the individual results are evaluated and compared.