Kang Ni, Math/Econ major, Kehan Zhang, Econ/Stats/ major, and Alex Hong, spent ten weeks investigating a large collection of grocery store transaction data. They worked closely with Matt Harding Behavioral Economics and Healthy Food Choice Research Center. (BECR Center).
Project Results
The team used clustering techniques to identify distinct sets of products, a good first step towards developing a customer preference index.
Download the executive summary (PDF).
Disciplines Involved
L-R: Kang Ni, Alex Hong, Kehan Zhang
Project Team
Undergraduates: Alex Hong, Kehan Zhang, and Kang Ni
Client: Matt Harding, Director, BECR Center
Project Mentor: Ya Xue, Data Scientist, BECR Center
Social and environmental contexts are increasingly recognized as factors that impact health outcomes of patients. This team will have the opportunity to collaborate directly with clinicians and medical data in a real-world setting. They will examine the association between social determinants with risk prediction for hospital admissions, and to assess whether social determinants bias that risk in a systematic way. Applied methods will include machine learning, risk prediction, and assessment of bias. This Data+ project is sponsored by the Forge, Duke's center for actionable data science.
Project Leads: Shelly Rusincovitch, Ricardo Henao, Azalea Kim
Project Manager: Austin Talbot
Aaron Chai (Computer Sciece, Math) and Victoria Worsham (Economics, Math) spent ten weeks building tools to understand characteristics of successful oil and gas licenses in the North Sea. The team used data-scraping, merging, and OCR method to create a dataset containing license information and work obligations, and they also produced ArcGIS visualizations of license and well locations. They had the chance to consult frequently with analytics professionals at ExxonMobil.
Click here to read the Executive Summary
Project Lead: Kyle Bradbury
Project Manager: Artem Streltsov
Yueru Li (Math) and Jiacheng Fan (Economics, Finance) spent ten weeks investigating abnormal behavior by companies bidding for oil and gas rights in the Gulf of Mexico. Working with data provided by the Bureau of Ocean Energy Management and ExxonMobil, the team used outlier detection methods to automate the flagging of abnormal behavior, and then used statistical methods to examine various factors that might predict such behavior. They had the chance to consult frequently with analytics professionals at ExxonMobil.
Click here to read the Executive Summary
Project Lead: Kyle Bradbury
Project Manager: Hyeongyul Roh
