• Must have some python programming experience.

• Aspiring and practicing data analysts or ML beginners who have trained basic models and now want to interpret and explain why a model is making predictions. Some familiarity with scikit-learn, regression/classification, and data manipulation with Pandas is expected.

Course Outline:

1. Why Feature Importance Matters?
   • Motivation: trust, transparency, accountability in ML
   • Use cases in business, healthcare, finance
   • Different types of feature importance (built-in, permutation, SHAP)

2. Introduction to SHAP Values
   • What are SHAP values? (Game theory roots simplified)
   • Benefits over traditional feature importance
   • Model-agnostic vs. model-specific SHAP
   • Visual intuition with simple examples
   • Hands-on: Visualize SHAP values
3. SHAP for Regression
   • Load and train a regression model (e.g., RandomForestRegressor)
   • Use “shap.Explainer” to compute SHAP values
   • Visualize: Summary Plot, Waterfall, etc.
   • Hands-on: Interpret feature effects on housing dataset
4. SHAP for Classification
   • Train a classifier (e.g., Random Forest Classifier or XGBoost)
   • Compute and visualize SHAP values for classification
   • Class-wise explanations: understanding prediction drivers
   • Feature importance and model interpretability
   • Hands-on: Explore feature impacts on classification decisions

Training material provided: Yes (Digital format)

Hands-on Lab: Instructions will be provided to install Jupyter notebook and other required python libraries. Students can opt to use ‘Google Colaboratory’ if they do not want to install these tools