RunBuggy
Challenge Overview
RunBuggy revolutionized car shipping by providing an easy-to-use platform for car dealers, auctions, and individuals to ship and track vehicles across the U.S. However, repo yards—where repossessed cars were stored—presented challenges in ensuring timely, cost-effective shipping. The project focused on using data and insights to improve the customer experience by developing an algorithm or model to assess repo yards, creating transparency around their practices. Students collected data on repo sites, augmented it with proprietary data from RunBuggy and public sources, processed and cleaned the data, and developed visualizations to identify trends. They also created a statistical or ML model to score repo sites, similar to a "Yelp" review. This transparency enables better-informed decisions in the marketplace and improved outcomes for customers.
Challenge Mentor
- Pat Weinkam, Head of Data Science, Cofounder, RunBuggy
Student Team
- Leonardo "Leo" Bangayan, Data Science Major, Class of 2027
- Noah Golder, Data Science Major, Class of 2027
- Thuc Quyen "Alyssa" Le, Cognitive Science Major, Class of 2027
- Yuxing "Estella " Liu, Mathematics Major, Class of 2027
- Vaidehi Sinha, Computer Science Major, Class of 2026
- Richard Wang, Data Science Major, Class of 2028