Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering.

Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples.

You’ll examine:

- Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms
- Natural text techniques: bag-of-words, n-grams, and phrase detection
- Frequency-based filtering and feature scaling for eliminating uninformative features
- Encoding techniques of categorical variables, including feature hashing and bin-counting
- Model-based feature engineering with principal component analysis
- The concept of model stacking, using k-means as a featurization technique
- Image feature extraction with manual and deep-learning techniques

- Preface
- 1. The Machine Learning Pipeline
- 2. Fancy Tricks with Simple Numbers
- 3. Text Data: Flattening, Filtering, and Chunking
- 4. The Effects of Feature Scaling: From Bag-of-Words to Tf-Idf
- 5. Categorical Variables: Counting Eggs in the Age of Robotic Chickens
- 6. Dimensionality Reduction: Squashing the Data Pancake with PCA
- 7. Nonlinear Featurization via K-Means Model Stacking
- 8. Automating the Featurizer: Image Feature Extraction and Deep Learning
- 9. Back to the Feature: Building an Academic Paper Recommender
- A. Linear Modeling and Linear Algebra Basics
- Index