{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/125604"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/125604","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"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","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. 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The submission was exported from vireo on 2025-02-04 without embargo terms","The student, Leon Hounnou, accepted the attached license on 2024-07-10 at 15:53.","The student, Leon Hounnou, submitted this Dissertation for approval on 2024-07-10 at 15:56.","This Dissertation was approved for publication on 2024-07-12 at 14:01.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21040 on 2025-02-04 at 21:04:52","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. 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