Python Feature Engineering Cookbook, Third Edition
eBook Details:
- Paperback: 396 pages
- Publisher: WOW! eBook; 3rd edition (August 30, 2024)
- Language: English
- ISBN-10: 1835883583
- ISBN-13: 978-1835883594
eBook Description:
Python Feature Engineering Cookbook, Third Edition: A complete guide to crafting powerful features for your machine learning models. Leverage the power of Python to build real-world feature engineering and machine learning pipelines ready to be deployed to production.
Streamline data preprocessing and feature engineering in your machine learning project with this Python Feature Engineering Cookbook, 3rd Edition to make your data preparation more efficient.
This Python Feature Engineering Cookbook, Third Edition 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 Python Feature Engineering Cookbook, 3rd Edition 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.
- 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
By the end of Python Feature Engineering Cookbook, Third Edition, 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.