Thursday, February 3, 2022 - 09:00 to 10:00
SCHOOL OF COMPUTER SCIENCE
The School of Computer Science is pleased to present...
MSc Thesis Proposal by: Ahmed Shafeek
Date: Thursday February 3, 2022
Time: 9:00am – 10:00am
Meeting URL: https://us06web.zoom.us/j/87540713915?from=addon
Passcode: If interested in attending this event, contact the Graduate Secretary at email@example.com with sufficient notice before the event to obtain the passcode.
The literature review we conducted pointed to that decision trees are the most successful approach in modeling financial performance and credit scoring. Comparative studies showed tree-based methods’ superiority over deep learning and other neural networks for tabular and categorical datasets. We will investigate applying Tree-Based Methods to find the feature importance and accurately predict financial performance. We chose CatBoost platform due to its efficiency, simplicity, and accuracy. We were given access to a proprietary US-based dataset, to mine and analyze, on the condition of namelessness. Profitability, risk, and bottom lines are all non-factors for this specific group of people. Predictions will not be used to refuse loans higher risk population, but rather to provide them with services and training that can better prepare them for success; a humanitarian approach to a financial problem.
Keywords: Financial Performance Prediction, Credit Scoring, Delinquency Rate, Decision Trees, Tree-based Methods, CatBoost
MSc Thesis Committee:
Internal Reader: Dr. Ziad Kobti
External Reader: Dr. Hoda ElMaraghy
Advisor: Dr. Alioune Ngom
MSc Thesis Proposal Announcement
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