Analýza realitního trhu USA

The aim of this bachelor's thesis is to collect data on the residential real estate market in the United States through web scraping and to analyze it using exploratory data analysis and data mining techniques in order to identify patterns, relationships between attributes, and regional differences within the market. The theoretical part introduces the field of Knowledge Discovery in Databases (KDD) and explains key concepts, including its definition, historical development, main process steps, and the CRISP-DM methodology. It also explores the topic of web scraping, covering its definition, applications, and the techniques used, such as static HTML parsing and dynamic content extraction. The practical part follows the CRISP-DM framework and applies it to the analysis of the U.S. real estate market. It begins with business understanding, including the selection of a relevant data source and the choice of cities for extraction. Data is collected using web scraping and prepared through the Data Understanding and Data Preparation phases. Finally, exploratory data analysis (EDA) and modeling techniques are applied to uncover insights from the dataset.