A thesis on utilizing machine learning models to predict material hardship
This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Applied Economics, 2023.
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Brac University
2024
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| Acesso em linha: | http://hdl.handle.net/10361/22944 |
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10361-229442024-05-27T21:04:59Z A thesis on utilizing machine learning models to predict material hardship Das, Tirtha Quadria, Taufiq Hasan Department of Economics and Social Sciences, Brac University Material hardship Logistic regression Vector machine model Social equity Machine-learning Regression analysis--Data processing This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Applied Economics, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 36-38). This thesis addresses the concern of material hardship, defined by a paucity in resources necessary to fulfil fundamental needs, especially affecting children and families. Drawing insights from studies on the determinants of material hardship, including low income, unemployment, single parenthood, and financial literacy, the research employs a comprehensive methodology. It incorporates the integration of machine learning techniques to enhance the predictive capacity of identifying at-risk populations. The methodology advocates for a holistic approach, incorporating the transformative potential of machine learning techniques such as Logistic Regression, Non-Linear Support Vector Machine Model, Decision Tree etc. This paper highlights the transformative potential of machine learning in proficiently analyzing extensive datasets to recognize complex patterns. The positive correlation established between higher financial literacy, bill paying tendency, management of finances and improved economic outcomes stresses the potential impact of targeted financial education initiatives. Moreover, the research emphasizes the need for a proactive stance, advocating for the development of predictive models using historical evidence to anticipate and address material hardship in a timely manner. The inferences emphasize the necessity for targeted interventions and proactive measures, promoting social equity, resilience, and contributing to broader poverty reduction strategies Tirtha Das M. in Economics 2024-05-27T10:32:48Z 2024-05-27T10:32:48Z ©2023 2023-12 Thesis ID 22275001 http://hdl.handle.net/10361/22944 en Brac University theses are protected by copyright. This may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 45 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Material hardship Logistic regression Vector machine model Social equity Machine-learning Regression analysis--Data processing |
| spellingShingle |
Material hardship Logistic regression Vector machine model Social equity Machine-learning Regression analysis--Data processing Das, Tirtha A thesis on utilizing machine learning models to predict material hardship |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Applied Economics, 2023. |
| author2 |
Quadria, Taufiq Hasan |
| author_facet |
Quadria, Taufiq Hasan Das, Tirtha |
| format |
Thesis |
| author |
Das, Tirtha |
| author_sort |
Das, Tirtha |
| title |
A thesis on utilizing machine learning models to predict material hardship |
| title_short |
A thesis on utilizing machine learning models to predict material hardship |
| title_full |
A thesis on utilizing machine learning models to predict material hardship |
| title_fullStr |
A thesis on utilizing machine learning models to predict material hardship |
| title_full_unstemmed |
A thesis on utilizing machine learning models to predict material hardship |
| title_sort |
thesis on utilizing machine learning models to predict material hardship |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/22944 |
| work_keys_str_mv |
AT dastirtha athesisonutilizingmachinelearningmodelstopredictmaterialhardship AT dastirtha thesisonutilizingmachinelearningmodelstopredictmaterialhardship |
| _version_ |
1814309816104910848 |