Country-level datasets for machine learning

Chien-Yu Shih

Diplomová práce

Anotace:

This thesis explores the landscape of datasets and datasets repositories available for machine learning applications at the country level, focused on economic indicators and demographic information. The thesis focuses on examining and defining various criteria for evaluating datasets, including update frequency, data licensing, community engagement, and documentation completeness. The research encompasses a comparative analysis of datasets and dataset repositories, considering factors such as the number of countries covered, organization type, and the frequency of updates. Additionally, it emphasizes the importance of data quality assessment.

Abstract:

This thesis explores the landscape of datasets and datasets repositories available for machine learning applications at the country level, focused on economic indicators and demographic information. The thesis focuses on examining and defining various criteria for evaluating datasets, including update frequency, data licensing, community engagement, and documentation completeness. The research encompasses a comparative analysis of datasets and dataset repositories, considering factors such as the number of countries covered, organization type, and the frequency of updates. Additionally, it emphasizes the importance of data quality assessment.

Klíčová slova

Data Analysis, Machine Learning, Data Quality

Keywords

Machine Learning, Data Quality, Data Analysis

Jazyk práce: angličtina

Datum vytvoření / odevzdání či podání práce: 24. 6. 2024

Obhajoba závěrečné práce

• Obhajoba proběhla 28. 8. 2024
• Vedoucí: Tomáš Kliegr
• Oponent: David Chudán

Vysoká škola ekonomická v Praze

Magisterský studijní program:
Information Systems Management