Hi! While working through Module 3: Machine Learning for Classification, I found the model-interpretation section especially useful.

One small addition that might be helpful for learners would be a short optional mention of SHAP (SHapley Additive exPlanations) after the logistic regression interpretation section.

The manual coefficient analysis is great for understanding how logistic regression works, but SHAP helped me connect that with:

• local explanations — why the model made a prediction for one customer
• global feature importance — which features matter most overall
• direction of impact — whether a feature pushes predictions toward or away from churn

For example:

• waterfall plot → explains one prediction
• bar plot → shows global feature importance
• beeswarm plot → shows both importance and direction

It could be presented as an optional “further exploration” section rather than a required part of the module.

I experimented with it in my own Module 3 notebook here:

https://github.com/besteekmen/ML-ZoomCamp-Outputs/blob/master/module3_classification/module3.ipynb

I found it especially helpful for comparing the manual logistic regression interpretation with a more general explainability approach.

Thanks for the course!