Enhanced Ticketing System: Data-Driven Fraud Detection and Risk Assessment for Fair Event Access

Complete my research proposal paper, The primary purpose of the proposed research is to develop and implement a
data-driven strategy to combat ticket scalping on major ticketing platforms,
such as Ticketmaster, with a particular focus on leveraging data analysis
techniques. The research will employ a rigorous data analysis approach to
assess the effectiveness of current technological measures against scalping and
to propose enhancements for mitigating scalping activities. The research design
will center on quantitative methods, utilizing ticketing data and other
relevant datasets to analyze patterns of scalping behavior and identify key
factors contributing to scalping incidents. The planned method of data
collection will involve gathering comprehensive ticketing data, including
purchase history, pricing information, and transactional details, from relevant
ticketing platforms. The primary method of data analysis will include
statistical analysis, machine learning algorithms, and data mining techniques
to uncover insights into scalping activities and develop predictive models for
identifying and preventing scalping incidents. Key data variables to be
considered include ticket purchase frequency, pricing trends, geographic
distribution of purchases, and anomalies in purchasing patterns. The chosen
research design emphasizes a data-centric approach to understanding and
addressing the challenges posed by ticket scalping, aiming to provide
actionable insights and recommendations for improving the fairness and
integrity of the ticketing process. attached is the template that contains the instuctions