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Home > Data > Data Science > Python Feature Engineering Cookbook - Third Edition


PYTHON FEATURE ENGINEERING COOKBOOK: A COMPLETE GUIDE TO CRAFTING POWERFUL
FEATURES FOR YOUR MACHINE LEARNING MODELS, THIRD EDITION

Profile Icon Soledad Galli
By Soledad Galli
$24.99 $35.99
Book Aug 2024 396 pages 3rd Edition
eBook
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Profile Icon Soledad Galli
By Soledad Galli
$24.99 $35.99
Book Aug 2024 396 pages 3rd Edition
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WHAT DO YOU GET WITH EBOOK?

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Table of content icon View table of contents Preview book icon Preview Book


PYTHON FEATURE ENGINEERING COOKBOOK - THIRD EDITION


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PAGE 1 OF 12

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KEY BENEFITS

 * Craft powerful features from tabular, transactional, and time-series data
 * Develop efficient and reproducible real-world feature engineering pipelines
 * Optimize data transformation and save valuable time
 * Purchase of the print or Kindle book includes a free PDF eBook


DESCRIPTION

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.


WHAT YOU WILL LEARN

 * 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


PRODUCT DETAILS

Country selected
Publication date, Length, Edition, Language, ISBN-13

Publication date : Aug 30, 2024
Length 396 pages
Edition : 3rd Edition
Language : English
ISBN-13 : 9781835883587
Category :
Data
Languages :
Python
Concepts :
Data Science


WHAT DO YOU GET WITH EBOOK?

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Product feature icon Access this title in our online reader with advanced
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PRODUCT DETAILS


Publication date : Aug 30, 2024
Length 396 pages
Edition : 3rd Edition
Language : English
ISBN-13 : 9781835883587
Category :
Data
Languages :
Python
Concepts :
Data Science


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TABLE OF CONTENTS

14 Chapters
Preface Chevron down icon Chevron up icon
Preface
Who this book is for
What this book covers
To get the most out of this book
Conventions used
Sections
Get in touch
Share Your Thoughts
1. Chapter 1: Imputing Missing Data Chevron down icon Chevron up icon
Chapter 1: Imputing Missing Data
Technical requirements
Removing observations with missing data
Performing mean or median imputation
Imputing categorical variables
Replacing missing values with an arbitrary number
Finding extreme values for imputation
Marking imputed values
Implementing forward and backward fill
Carrying out interpolation
Performing multivariate imputation by chained equations
Estimating missing data with nearest neighbors
2. Chapter 2: Encoding Categorical Variables Chevron down icon Chevron up icon
Chapter 2: Encoding Categorical Variables
Technical requirements
Creating binary variables through one-hot encoding
Performing one-hot encoding of frequent categories
Replacing categories with counts or the frequency of observations
Replacing categories with ordinal numbers
Performing ordinal encoding based on the target value
Implementing target mean encoding
Encoding with Weight of Evidence
Grouping rare or infrequent categories
Performing binary encoding
3. Chapter 3: Transforming Numerical Variables Chevron down icon Chevron up icon
Chapter 3: Transforming Numerical Variables
Transforming variables with the logarithm function
Transforming variables with the reciprocal function
Using the square root to transform variables
Using power transformations
Performing Box-Cox transformations
Performing Yeo-Johnson transformations
4. Chapter 4: Performing Variable Discretization Chevron down icon Chevron up
icon
Chapter 4: Performing Variable Discretization
Technical requirements
Performing equal-width discretization
Implementing equal-frequency discretization
Discretizing the variable into arbitrary intervals
Performing discretization with k-means clustering
Implementing feature binarization
Using decision trees for discretization
5. Chapter 5: Working with Outliers Chevron down icon Chevron up icon
Chapter 5: Working with Outliers
Technical requirements
Visualizing outliers with boxplots and the inter-quartile proximity rule
Finding outliers using the mean and standard deviation
Using the median absolute deviation to find outliers
Removing outliers
Bringing outliers back within acceptable limits
Applying winsorization
6. Chapter 6: Extracting Features from Date and Time Variables Chevron down icon
Chevron up icon
Chapter 6: Extracting Features from Date and Time Variables
Technical requirements
Extracting features from dates with pandas
Extracting features from time with pandas
Capturing the elapsed time between datetime variables
Working with time in different time zones
Automating the datetime feature extraction with Feature-engine
7. Chapter 7: Performing Feature Scaling Chevron down icon Chevron up icon
Chapter 7: Performing Feature Scaling
Technical requirements
Standardizing the features
Scaling to the maximum and minimum values
Scaling with the median and quantiles
Performing mean normalization
Implementing maximum absolute scaling
Scaling to vector unit length
8. Chapter 8: Creating New Features Chevron down icon Chevron up icon
Chapter 8: Creating New Features
Technical requirements
Combining features with mathematical functions
Comparing features to reference variables
Performing polynomial expansion
Combining features with decision trees
Creating periodic features from cyclical variables
Creating spline features
9. Chapter 9: Extracting Features from Relational Data with Featuretools Chevron
down icon Chevron up icon
Chapter 9: Extracting Features from Relational Data with Featuretools
Technical requirements
Setting up an entity set and creating features automatically
Creating features with general and cumulative operations
Combining numerical features
Extracting features from date and time
Extracting features from text
Creating features with aggregation primitives
10. Chapter 10: Creating Features from a Time Series with tsfresh Chevron down
icon Chevron up icon
Chapter 10: Creating Features from a Time Series with tsfresh
Technical requirements
Extracting hundreds of features automatically from a time series
Automatically creating and selecting predictive features from time-series data
Extracting different features from different time series
Creating a subset of features identified through feature selection
Embedding feature creation into a scikit-learn pipeline
11. Chapter 11: Extracting Features from Text Variables Chevron down icon
Chevron up icon
Chapter 11: Extracting Features from Text Variables
Technical requirements
Counting characters, words, and vocabulary
Estimating text complexity by counting sentences
Creating features with bag-of-words and n-grams
Implementing term frequency-inverse document frequency
Cleaning and stemming text variables
12. Index Chevron down icon Chevron up icon
Index
Why subscribe?
13. Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy
Packt is searching for authors like you
Share Your Thoughts
Download a free PDF copy of this book


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ABOUT THE AUTHOR

Profile icon Soledad Galli
LinkedIn icon
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.
Read more
See other products by Soledad Galli
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