Create end-to-end, reproducible feature engineering pipelines that can be deployed into production using open-source Python libraries
Key Features
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
Book DescriptionFeature engineering, the process of transforming variables and creating features, albeit time-consuming, ensures that your machine learning models perform seamlessly. This second edition of Python Feature Engineering Cookbook 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.
By the end of this Python 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.What you will learn
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
Who this book is forThis 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.
Create end-to-end, reproducible feature engineering pipelines that can be deployed into production using open-source Python libraries
Key Features
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
Book DescriptionFeature engineering, the process of transforming variables and creating features, albeit time-consuming, ensures that your machine learning models perform seamlessly. This second edition of Python Feature Engineering Cookbook 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.
By the end of this Python 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.What you will learn
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
Who this book is forThis 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.
Table of Contents
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.
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