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

Hounnou, Leon

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

Description

Title

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

Author(s)

Hounnou, Leon

Issue Date

2024-07-12

Doctoral Committee Chair(s)

• Okumu, Moses
• McNamara, Paul E

Committee Member(s)

• He, Jing Rui
• Ansong, David

Department of Study

Illinois Informatics Institute

Discipline

Informatics

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

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

Abstract

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.

Graduation Semester

2024-08

Type of Resource

Thesis

Handle URL

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

Copyright and License Information

Copyright 2024 Leon Hounnou

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