Combining Unsupervised and Supervised Learning for Credit Card Fraud Detection
By briar sauble
Briar is a senior at Millersville University from Hanover, Pennsylvania. He currently pursuing a Bachelor of Science in computer science, and began this project as an undergraduate thesis requirement for Millersville University's Honor's College. He is passionate about programming, data science, and cyber security, all topics which led to the exploration done within his current research. Briar's main drive for putting together his work was a desire to understand data mining algorithms and how they can be manipulated and simplified for real world applications. Similarly, the complexities of modern machine learning and its difficulty of interpretation made an incentive for him to create a better understanding through further study. His paper recognizes the limitations of current machine learning methods in order to make a combined approach that helps find cases of credit card fraud. Briar plans to pursue a career as a software engineer, desiring to put his work into use as a learning opportunity that furthers his experience in the field of computer science.