Python feature engineering cookbook / Soledad Galli.

Format:

Book

Author/Creator:

Galli, Soledad, author.

Language:

English

Subjects (All):

Python (Computer program language).

Application software--Development.

Application software.

Machine learning.

Physical Description:

1 online resource (386 pages)

Edition:

Second edition.

Place of Publication:

Birmingham, England : Packt Publishing, Limited, [2022]

Biography/History:

Galli Soledad: Soledad Galli is a lead data scientist with more than 10 years of experience in world-class academic institutions and renowned businesses. She has researched, developed, and put into production machine learning models for insurance claims, credit risk assessment, and fraud prevention. Soledad received a Data Science Leaders' award in 2018 and was named one of LinkedIn's voices in data science and analytics in 2019. She is passionate about enabling people to step into and excel in data science, which is why she mentors data scientists and speaks at data science meetings regularly. She also teaches online courses on machine learning in a prestigious Massive Open Online Course platform, which have reached more than 10, 000 students worldwide.

Summary:

Python Feature Engineering Cookbook, Second Edition will give you the practice, tools, and techniques to streamline your feature engineering pipelines and simplify and improve the quality of your code. With more than 70 methods to transform or create variables, you will find solutions tailored to different datasets and machine learning models.

Contents:

Cover

Title Page

Copyright and Credits

Contributors

Table of Contents

Preface

Chapter 1: Imputing Missing Data

Technical requirements

Removing observations with missing data

How to do it...

How it works...

Performing mean or median imputation

Imputing categorical variables

Replacing missing values with an arbitrary number

Finding extreme values for imputation

Marking imputed values

Performing multivariate imputation by chained equations

See also

Estimating missing data with nearest neighbors

Chapter 2: Encoding Categorical Variables

Creating binary variables through one-hot encoding

There's more...

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

How it works…

There's more…

Encoding with the Weight of Evidence

Grouping rare or infrequent categories

Performing binary encoding

Chapter 3: Transforming Numerical Variables

Transforming variables with the logarithm function.

Getting ready

Transforming variables with the reciprocal function

Using the square root to transform variables

Using power transformations

Performing Box-Cox transformation

Performing Yeo-Johnson transformation

Chapter 4: Performing Variable Discretization

Performing equal-width discretization

Implementing equal-frequency discretization

Discretizing the variable into arbitrary intervals

Performing discretization with k-means clustering

Implementing feature binarization

Getting ready

Using decision trees for discretization

Chapter 5: Working with Outliers

Visualizing outliers with boxplots

Finding outliers using the mean and standard deviation

Finding outliers with the interquartile range proximity rule

Removing outliers

Capping or censoring outliers

Capping outliers using quantiles

Chapter 6: Extracting Features from Date and Time Variables

Extracting features from dates with pandas

How it works.

There's more…

Extracting features from time with pandas

Capturing the elapsed time between datetime variables

Working with time in different time zones

Automating feature extraction with Feature-engine

Chapter 7: Performing Feature Scaling

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

Chapter 8: Creating New Features

Combining features with mathematical functions

Comparing features to reference variables

How to do it…

Performing polynomial expansion

Combining features with decision trees

Creating periodic features from cyclical variables

Creating spline features

Chapter 9: Extracting Features from Relational Data with Featuretools

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

Chapter 10: Creating Features from a Time Series with tsfresh

Extracting features automatically from a time series

Creating and selecting features for a time series

Tailoring feature creation to different time series

Creating pre-selected features

Embedding feature creation in a scikit-learn pipeline

Chapter 11: Extracting Features from Text Variables

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

Index

About Packt

Other Books You May Enjoy.

Notes:

Includes index.

Description based on print version record.

ISBN:

9781523151547

1523151544

9781804615393

1804615390

OCLC:

1350412247