Model
WTI Gas Timing Model
Predicts whether consumers should buy gas now based on recent WTI crude oil price levels and short-term trends.
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 daily WTI crude oil spot prices from the EIA API to train a logistic regression classifier. I engineered features that capture current crude price, short-term average price, recent 7-day change, and a 30-day trend. The target is whether the average crude oil price over the following week is higher than the current price, which serves as a practical proxy for upcoming upward pressure on retail gas prices. I chose logistic regression because it is simple, interpretable, and works well for binary classification while supporting both predict and predict_proba for the team pipeline.
Features
Built by
Business Analytics graduate student at Arizona State University with a background in data analysis and reporting automation. I am interested in building practical machine learning solutions that connect economic signals to real consumer decisions, especially in energy and pricing trends.
Trained on
1986-02-12 to 2005-10-18