IBM Case Competition
IBM (Client)
AI & Data Analytics Consultant · Fall 2024 · Dallas–Fort Worth, TX
Overview
IBM was the client for this case competition on food insecurity in the Dallas–Fort Worth area. As an AI & Data Analytics Consultant on a cross-functional team, I helped find the communities that need food access the most and recommend how local governments could help.
Challenge
Food deserts, areas with little access to healthy food, are hard to pinpoint, and local governments need accurate information to decide where to send help.
Solution
Our team used IBM Watson AI to analyze location and API data to find underserved neighborhoods across DFW. We turned that into a model that helps local governments decide where to focus resources, then presented our findings and recommendations to IBM judges.
Key Features
Finds neighborhoods with limited access to healthy food
Recommendations that help local governments allocate resources
Built and presented with a cross-functional team
Final presentation to IBM judges
Work Done
- 1
Data
Combined geographical data with data pulled from APIs to map food access across the DFW region.
- 2
Analysis
Used IBM Watson AI to analyze that data and flag underserved areas, which improved detection accuracy by 25%.
- 3
Predictive model
Built, with a cross-functional team, a predictive model that points local governments to where resources are needed most.
- 4
Presentation
Packaged the insights, analytics, and recommendations into a presentation for IBM judges.
Impact
Improved food desert detection accuracy by 25%
Identified underserved communities across the DFW region more precisely
Gave local governments a data-driven way to decide where to focus resources
Presented findings and recommendations on reducing food insecurity to IBM judges