A Support Vector Machine Approach to Analyzing Aftermarket Ticket Sales

Abstract

Support Vector Machines (SVMs) are used to linearly separate & classify data. If our data is not linearly separable, we use a kernel function to map data to a suitable space where linear classification is possible. We apply this idea to the aftermarket resale of tickets, using various features (e.g. price, quality of seating, # of days till event, etc.) to determine whether an offering of tickets will sell or not. We present the proposed model, some results from the analysis and summarize the work of this research project.