Deliver and close

Step 1: Send findings to Luciana

Send the findings summary to Luciana. Include a covering message that leads with her priorities: which barrels to focus her tasting on, what production factors drive quality, and how confident the model is.

Keep the covering message short. She is busy. The findings summary has the detail. The message should tell her what to expect and invite questions.

Step 2: Read Luciana's response

Luciana is direct. She responds to the confusion matrix translation specifically -- "so if the model says Reserve, it's right 8 out of 10 times. I can work with that." She asks about the feature importances with genuine curiosity, especially about fermentation temperature and altitude.

Then she asks a follow-up: "What about predicting the actual score, not just Reserve or not? If I knew a barrel was likely to score 88, I might blend it differently."

Step 3: Handle the scope extension

Luciana's question changes the problem. Predicting Reserve-or-not is classification. Predicting the actual score (a number from 1 to 100) is regression -- the kind of modeling you did in P2 for Wanjiku's veterinary data.

These are different problems. Different evaluation metrics (RMSE vs precision/recall). Different model behavior. Different communication (a predicted score vs a binary flag). A model that is good at classifying Reserve might not be good at predicting exact scores, and vice versa.

Respond professionally. You can either:

• Take it on briefly as a follow-up analysis if the data supports it
• Explain why it would be a separate analysis and recommend it as a next step

Either response is valid. The key is recognizing that this is a problem type change, not just a variation of what you already built.

Step 4: Write a decision record

Write a decision record documenting the most consequential analytical decision in this project. Strong candidates:

• The shift from accuracy to recall as the primary metric, driven by Luciana's priorities
• The threshold tuning rationale and the trade-off it represents
• The proxy feature removal -- why a feature that improved the model had to go
• The model selection trade-off between performance and interpretability

Pick one. Document what the decision was, what alternatives existed, what you chose, and why. The decision record is for your future self and anyone who inherits this work.

Step 5: Commit and push

Direct AI to commit the work to Git with a meaningful commit message and push to GitHub. The repository should contain:

• The Jupyter notebook with the analysis
• The findings summary
• The completed methodology memo
• The decision record

Verify the push succeeded.