Python Feature Engineering Cookbook – Second Edition
- Paperback: 386 pages
- Publisher: WOW! eBook (October 31, 2022)
- Language: English
- ISBN-10: 1804611301
- ISBN-13: 978-1804611302
Python Feature Engineering Cookbook, 2nd Edition: Create end-to-end, reproducible feature engineering pipelines that can be deployed into production using open-source Python libraries
Feature engineering, the process of transforming variables and creating features, albeit time-consuming, ensures that your machine learning models perform seamlessly. This Python Feature Engineering Cookbook, Second Edition will take the struggle out of feature engineering by showing you how to use open source Python libraries to accelerate the process via a plethora of practical, hands-on recipes.
This updated edition begins by addressing fundamental data challenges such as missing data and categorical values, before moving on to strategies for dealing with skewed distributions and outliers. The concluding chapters show you how to develop new features from various types of data, including text, time series, and relational databases. With the help of numerous open source Python libraries, you’ll learn how to implement each feature engineering method in a performant, reproducible, and elegant manner.
- Impute missing data using various univariate and multivariate methods
- Encode categorical variables with one-hot, ordinal, and count encoding
- Handle highly cardinal categorical variables
- Transform, discretize, and scale your variables
- Create variables from date and time with pandas and Feature-engine
- Combine variables into new features
- Extract features from text as well as from transactional data with Featuretools
- Create features from time series data with tsfresh
By the end of this Python Feature Engineering Cookbook, 2nd Edition book, you will have the tools and expertise needed to confidently build end-to-end and reproducible feature engineering pipelines that can be deployed into production.
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