Galli Python Feature Engineering Cookbook

A complete guide to crafting powerful features for your machine learning models
3. Auflage 2025
ISBN: 978-1-83588-359-4
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 396 Seiten

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.

Autoren/Hrsg.

Galli, Soledad

Weitere Mitwirkende

Molnar, Christoph

Fachgebiete

• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein

Über Autor(innen)

Galli Soledad :

Soledad Galli is a bestselling data science instructor, author, and open-source Python developer. As the leading instructor at Train in Data, she teaches intermediate and advanced courses in machine learning that have enrolled over 64,000 students worldwide and continue to receive positive reviews. Sole is also the developer and maintainer of the Python open-source library Feature-engine, which provides an extensive array of methods for feature engineering and selection. With extensive experience as a data scientist in finance and insurance sectors, Sole has developed and deployed machine learning models for assessing insurance claims, evaluating credit risk, and preventing fraud. She is a frequent speaker at podcasts, meetups, and webinars, sharing her expertise with the broader data science community.