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Textbook in PDF format

Key Features:
Craft powerful features from tabular, transactional, and time-series data
Develop efficient and reproducible real-world feature engineering pipelines
Optimize data transformation and save valuable time
Book Description:
Streamline data preprocessing and feature engineering in your machine learning project with this third edition of the Python Feature Engineering Cookbook to make your data preparation more efficient.
This guide addresses common challenges, such as imputing missing values and encoding categorical variables using practical solutions and open source Python libraries.
You’ll learn advanced techniques for transforming numerical variables, discretizing variables, and dealing with outliers. Each chapter offers step-by-step instructions and real-world examples, helping you understand when and how to apply various transformations for well-prepared data.
The book explores feature extraction from complex data types such as dates, times, and text. You’ll see how to create new features through mathematical operations and decision trees and use advanced tools like Featuretools and tsfresh to extract features from relational data and time series.
By the end, you’ll be ready to build reproducible feature engineering pipelines that can be easily deployed into production, optimizing data preprocessing workflows and enhancing machine learning model performance.
What you will learn:
Discover multiple methods to impute missing data effectively
Encode categorical variables while tackling high cardinality
Find out how to properly transform, discretize, and scale your variables
Automate feature extraction from date and time data
Combine variables strategically to create new and powerful features
Extract features from transactional data and time series
Learn methods to extract meaningful features from text data
Who this book is for:
If you're a machine learning or data science enthusiast who wants to learn more about feature engineering, data preprocessing, and how to optimize these tasks, this book is for you. If you already know the basics of feature engineering and are looking to learn more advanced methods to craft powerful features, this book will help you. You should have basic knowledge of Python programming and machine learning to get started.
Table of Contents:
Imputing Missing Data
Encoding Categorical Variables
Transforming Numerical Variables
Performing Variable Discretization
Working with Outliers
Extracting Features from Date and Time Variables
Performing Feature Scaling
Creating New Features
Extracting Features from Relational Data with Featuretools
Creating Features from a Time Series with tsfresh
Extracting Features from Text Variables