Week 4 covered the second cluster of EDA—techniques for handling high dimensions and class imbalance. Today we knit four days into one workflow, review holistically, and highlight exam traps.
| Day | Topic | Essentials |
|---|---|---|
| 1 | Dimensionality reduction | Curse of dimensionality, PCA (variance preservation, standardization mandatory), t-SNE (visualization only) |
| 2 | Feature selection | Filter, wrapper, embedded; feature importance; multicollinearity (VIF) |
| 3 | Data visualization | Distribution, correlation charts; QuickSight; Anscombe's lesson |
| 4 | Class imbalance | Accuracy paradox; SMOTE, undersampling; class weights; PR-AUC |