Week 9: Feature Engineering

Learning Objectives

By the end of this week, students will be able to:

• Apply scaling and normalization to numerical features
• Encode categorical variables using one-hot and ordinal encoding
• Handle missing data with imputation strategies
• Build a scikit-learn Pipeline to chain preprocessing and modeling steps

Perspectival Reading

Reading: TBD

Reflection Questions

1. Feature engineering requires domain knowledge — whose knowledge counts, and who is excluded from this process?
2. Imputation fills in missing values with estimates. What assumptions does a chosen strategy make about why data is missing?
3. When you encode a variable like gender or ethnicity, what are you doing to how the model treats those groups?

Slides

View slides

Notebook Demo

Open in Google Colab (link TBD)

Lab Assignment

Week 9 Lab — GitHub Classroom (link TBD)