Python Feature Engineering Cookbook: Over 70 recipes for creating, engineering, and transforming features to build machine learning models, Edition 2
About this ebook
- Learn and implement feature engineering best practices
- Reinforce your learning with the help of multiple hands-on recipes
- Build end-to-end feature engineering pipelines that are performant and reproducible
- 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
This book is for machine learning and data science students and professionals, as well as software engineers working on machine learning model deployment, who want to learn more about how to transform their data and create new features to train machine learning models in a better way.
About the author
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.
