This repository contains resources, notebooks, and insights I’ve developed while studying and building projects focused on model explainability. Using libraries like ELI5 and SHAP, I’ve explored how to interpret and communicate machine learning predictions — a skill I believe is critical for driving real impact in business. In many cases, it’s not just about making accurate predictions — it’s the ability to explain them that truly makes the difference.
- Feature Importance
- Permutation Feature Importance
- Partial Dependence Plots (PDP)
- ELI5
- SHAP Values And more!
- Python
- Pandas
- Scikit-learn
- Matplotlib
- ELI5
- SHAP library