Data Mining of selected sustainability indicators in Africa

Vallary Cherop Pkania, BSc

Master's thesis

Abstract:

This thesis explores the application of data mining techniques to analyze selected sustainability indicators in Africa. With the continent facing unique environmental, social and economic challenges, understanding and managing sustainability is of paramount importance. The study focuses on identifying key sustainability indicators relevant to African contexts and utilizing exploratory factor analysis and cluster analysis to extract insights from the dataset. The research methodology involved mining of dataset from world bank on world development indicators pertaining to environmental, social and economic dimensions of sustainability in African countries. This dataset encompasses indicators such as carbon emissions, renewable energy consumption, biodiversity loss, poverty rates, education levels and economic growth. Data mining techniques such as Principal component analysis and clustering were applied to uncover patterns, trends and relationships within the data. The findings of this study provide invaluable insights into the current state of sustainability in Africa and highlights the areas of concern and opportunities for improvements. Through data mining, significant correlation between different sustainability indicators is identified, shedding light on complex interdependencies and facilitating informed decision making. Moreover, the research contributes to the development of predictive models for assessing future sustainability trends and scenarios in Africa. By harnessing the power of data mining, this study aims to enhance our understanding of sustainability dynamics and support efforts towards achieving a more resilient, equitable and environmentally sustainable future for the continent.

Keywords

Sustainability, Sustainable development, Data mining, Africa, Social indicators, Economic indicators, Environmental indicators, Sustainable Development Goals(SDGs), Multivariate analysis, Economic development

Language used: English

Date on which the thesis was submitted / produced: 1. 4. 2024

Thesis defence

• Supervisor: Ing. Tomáš Hlavsa, Ph.D.
• Reader: Michal Kadlec, externi

Czech University of Life Sciences Prague

Faculty of Economics and Management

Master programme:
Informatics