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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



Table of Contents
Chapter 1 Introduction
Chapter 2 Fancy Tricks with Simple Numbers
Chapter 3 Basic Feature Engineering for Text Data: Flatten and Filter
Chapter 4 The Effects of Feature Scaling: From Bag-of-Words to Tf-Idf
Chapter 5 Counts and Categorical Variables: Counting Eggs in the Age of Robotic Chickens
Chapter 6 Dimensionality Reduction: Squashing the Data Pancake with PCA
Chapter 7 Non-Linear Featurization and Model Stacking
Chapter 8 Automating the Featurizer: Image Feature Extraction and Deep Learning
Appendix A Linear Modeling and Linear Algebra Basics