Feature Engineering & Selection

#Learning Objectives

• Implement numerical transforms (standardization, min-max scaling, log transform, binning) and explain when each is appropriate
• Build one-hot, label, and target encoding for categorical features and identify the data leakage risk in target encoding
• Construct a TF-IDF vectorizer from scratch and explain why it outperforms raw word counts for text classification
• Apply filter-based feature selection (variance threshold, correlation, mutual information) to reduce dimensionality