Demystify Flight Data

Music City Center 

Flying can be stressful — but some airports make the experience a lot better than others. In this project, we set out to predict customer satisfaction scores (based on J.D. Power rankings) for major U.S. airports using a mix of airport operations data and local economic factors.

We gathered information on how many passengers airports serve, how often flights are delayed (both outbound and inbound), how often baggage gets lost, the average airfare, the local GDP, and even the region's average annual temperature. Using a blend of statistical modeling and machine learning tools, we explored how these factors connect to how travelers rate their airport experience. Additionally, visualization tools will be employed to identify trends and patterns in travel behavior.

By combining exploratory and inferential approaches, this study gives airport managers and planners a clearer roadmap for making travel a little less stressful — and maybe even a little more enjoyable — for millions of passengers each year.

analyzing consumers' travel habits

identify trends and patterns in travel behavior

classical regression methods and neural network techniques 

Section on Statistical Computing