Harnessing the power of machine learning, Bayesian neural networks, and spatial analysis in modeling a predictive system, credit risk, and organizational performance across continents

In a world increasingly driven by data and technological advancements, addressing pressing global issues requires innovative research approaches. This thesis aligns with this ideal by exploring the potential of Machine Learning, Bayesian Neural Networks, and Spatial Analysis in Education, International Development, and Finance. The work unfolds over three distinct chapters, each tackling urgent issues and striving for transformative solutions. The first chapter focuses on early graders' literacy outcomes in South Africa, designing a predictive system to identify at-risk students and facilitate timely interventions. The second chapter employs Spatial Analysis techniques to understand the organizational dynamics of Area Stakeholder Panels (ASPs) in Malawi, and evaluates the impact of agricultural interventions on smallholder farmers. The third chapter highlights the use of Bayesian Neural Networks in credit risk prediction in the United States, demonstrating the importance of Bayesian inference in accounting for uncertainties. Collectively, these three chapters underscore the interdisciplinary nature of contemporary research, transcending geographical and sectoral boundaries. They emphasize the transformative power of data-driven insights in various fields, highlighting the potential of advanced analytical techniques to drive positive change. This study reinforces the importance of informed decision-making and proactive strategies for addressing global challenges. It contributes to a more inclusive and globally informed future, ensuring that knowledge is harnessed to address critical issues and drive meaningful change.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Informatics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Hounnou, Leon

Contributors dc:contributor

• Okumu, Moses
• McNamara, Paul E
• He, Jing Rui
• Ansong, David

• Predictive System
• Machine Learning
• Bayesian Analysis
• Spatial Econometrics.

Statement dc:rights

• Copyright 2024 Leon Hounnou

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/125604

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F125604