Module 07: Feature Engineering
Learn to create and select the best features for your models.
What You'll Learn
- Feature Selection Techniques
- Feature Transformation
- Handling Categorical Variables
- Feature Scaling and Normalization
- Dimensionality Reduction (PCA)
- Creating New Features
ML for beginners curriculum map
| Topic | Where to study |
|---|---|
| ML-ready preprocessing and feature engineering | Guide |
| EDA and cleaning (before features) | EDA guide |
| Beginner ML path overview | Introduction to ML |
Topics Covered
1. Feature Selection
- Filter Methods: Statistical tests (Chi-square, correlation)
- Wrapper Methods: Forward/backward selection
- Embedded Methods: Lasso, tree-based importance
- Removing Redundant Features: Correlation analysis
2. Feature Transformation
- Log Transformation: Handle skewed distributions
- Power Transformation: Box-Cox transformation
- Binning: Convert continuous to categorical
- Polynomial Features: Create interaction terms
3. Categorical Variables
- One-Hot Encoding: Binary columns for each category
- Label Encoding: Numeric labels (for tree models)
- Target Encoding: Mean target per category
- Frequency Encoding: Count of category occurrences
4. Feature Scaling
- Standardization: Mean 0, Std 1 (Z-score)
- Normalization: Scale to [0, 1] range
- Robust Scaling: Using median and IQR
- When to scale, required for distance-based algorithms
5. Dimensionality Reduction
- Principal Component Analysis (PCA): Linear dimensionality reduction
- t-SNE: Non-linear visualization
- UMAP: Modern alternative to t-SNE
- When to use: High-dimensional data, visualization
6. Creating Features
- Domain Knowledge: Industry-specific features
- Temporal Features: Time-based features
- Interaction Features: Combinations of features
- Aggregation Features: Group statistics
Learning Objectives
By the end of this module, you should be able to:
- Select relevant features
- Transform features appropriately
- Handle categorical variables
- Apply dimensionality reduction
- Create domain-specific features
Projects
- Feature Engineering Challenge: Improve model with better features
- PCA Visualization: Visualize high-dimensional data
- Categorical Encoding Comparison: Compare encoding methods
Key Concepts
- Garbage In, Garbage Out: Good features = good models
- Domain Knowledge: Often more valuable than algorithms
- Feature Importance: Understand which features matter
- Curse of Dimensionality: Too many features can hurt
Documentation & Learning Resources
Official Documentation:
Free Courses:
- Feature Engineering (Kaggle Learn): Free comprehensive course
- Feature Engineering (Coursera): Free audit available
Tutorials:
- Feature Engineering Tutorial
- PCA Explained Simply
- Feature Selection Methods
- Categorical Encoding Guide
Video Tutorials:
Books:
- Feature Engineering Book: Check library access
Additional Resources:
- Advanced Topics →: Automated feature engineering, pipelines, advanced techniques
- Project Tutorial →: Step-by-step feature engineering project
- Quick Reference →: Quick lookup guide for feature engineering
Previous Module: 06-ensemble-methods
Next Module: 08-unsupervised-learning