COVID-19 Data Analysis

This thesis analyses COVID-19 data. The dataset used covers data from all over the world. The main aim of this thesis is to analyse the data with the use of a clustering algorithm. This thesis consists of five parts. In the first part we explain what knowledge discovery in databases is and the tasks it solves, different areas of application and finally CRISP-DM which is the methodology used in this thesis. In the second part we explain different knowledge discovery methods including clustering which is used in the analysis part, as well as the environment and libraries used. In the third part we compare the COVID-19 pandemic with previous pandemics. In the fourth part we set the goals, clean and prepare the data and visualize the data to get a better understanding of it. In the final, fifth part, we create clustering models with different levels of granularity and by taking data for the entire world and then only for Europe. We also visualize the results and explain the visible patterns.