Model
Gulf SST Storm Risk Model
Predicts whether to buy gas based on Gulf of Mexico sea surface temperature patterns that signal hurricane and refinery disruption risk.
Decision surface
The model's buy probability across its two most influential features. Every point was computed by calling model.predict_proba on the actual .joblib. Drag to rotate.
What the model pays attention to
Approach
I used 2 years of daily sea surface temperature data from NOAA's OISST dataset, spatially averaged across the Gulf of Mexico (18-30°N, 80-98°W). The causal hypothesis is that elevated Gulf SSTs fuel stronger hurricanes, which shut down Gulf Coast refineries — responsible for ~50% of US refining capacity — causing gas price spikes at the pump. Features include current SST, 7-day and 30-day rolling averages, the SST anomaly (deviation from the 30-day mean), the 7-day temperature trend, and a binary hurricane season flag (June–November). I chose GradientBoostingClassifier after comparing it against logistic regression and random forest via 5-fold cross-validation. The interaction between SST anomaly and hurricane season was the strongest signal — warm anomalies outside of storm season had little predictive power, which supports the underlying causal story.
Features
Built by
I have a background in sales, operations, and customer-facing leadership across tech, healthcare staffing, and mobility. I’m especially interested in AI, business strategy, and process improvement, and I enjoy finding practical ways to use technology to make teams more effective and help businesses run smarter.
Trained on
2024-01-01 to 2025-12-31