Key Takeaways for Students

EDA before everything.

The insight that Ideal cut diamonds have lower median prices than Premium could only be discovered through exploration. A model built without this understanding would confuse your team.

Start with baselines.

Linear regression’s R2 of 0.85 is already pretty good. Random Forest getting to 0.97 tells you the relationship is non-linear — worth knowing.

Business context makes metrics meaningful.

An MAE of $540 on a $500 diamond is terrible (108% error). On a $12,000 diamond, it’s excellent (4.5% error). Always interpret model metrics in the context of your actual use case.

Fine tuning is optimization, not magic.

GridSearchCV might gain you another 1–2% on R2. The real gains come from clean data and good feature engineering — not hyperparameter wizardry.

The workflow is iterative.

If evaluation reveals your model struggles with very large carat diamonds, go back to EDA, engineer a new feature (like carat_squared), retrain, and re-evaluate. The feedback loop is real.