book

Python Feature Engineering Cookbook - Third Edition

Name: Python Feature Engineering Cookbook - Third Edition
Author: Soledad Galli
ISBN: 9781835883587

by Soledad Galli

August 2024

Intermediate to advanced

396 pages

9h 1m

English

Packt Publishing

Read now

Unlock full access

Python Feature Engineering Cookbook
ForewordContributorsAbout the authorAbout the reviewer
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesConventions usedSectionsGetting readyHow to do it…How it works…There’s more…See alsoGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Chapter 1: Imputing Missing Data
Technical requirementsRemoving observations with missing dataHow to do it...How it works...See alsoPerforming mean or median imputationHow to do it...How it works...Imputing categorical variablesHow to do it...How it works...Replacing missing values with an arbitrary numberHow to do it...How it works...Finding extreme values for imputationHow to do it...How it works...Marking imputed valuesHow to do it...How it works...There’s more…Implementing forward and backward fillHow to do it...How it works...Carrying out interpolationHow to do it...How it works...See alsoPerforming multivariate imputation by chained equationsHow to do it...How it works...See alsoEstimating missing data with nearest neighborsHow to do it...How it works...
Chapter 2: Encoding Categorical Variables
Technical requirementsCreating binary variables through one-hot encodingHow to do it...How it works...There’s more...Performing one-hot encoding of frequent categoriesHow to do it...How it works...There’s more...Replacing categories with counts or the frequency of observationsHow to do it...How it works...See alsoReplacing categories with ordinal numbersHow to do it...How it works...There’s more...Performing ordinal encoding based on the target valueHow to do it...How it works...See alsoImplementing target mean encodingHow to do it...How it works…There’s more…Encoding with Weight of EvidenceHow to do it...How it works...See alsoGrouping rare or infrequent categoriesHow to do it...How it works...Performing binary encodingHow to do it...How it works...
Chapter 3: Transforming Numerical Variables
Transforming variables with the logarithm functionGetting readyHow to do it...How it works...There’s more…Transforming variables with the reciprocal functionHow to do it...How it works...Using the square root to transform variablesHow to do it...How it works…Using power transformationsHow to do it...How it works...Performing Box-Cox transformationsHow to do it...How it works...There’s more…Performing Yeo-Johnson transformationsHow to do it...How it works...There’s more…
Chapter 4: Performing Variable Discretization
Technical requirementsPerforming equal-width discretizationHow to do it...How it works…See alsoImplementing equal-frequency discretizationHow to do it...How it works…Discretizing the variable into arbitrary intervalsHow to do it...How it works...Performing discretization with k-means clusteringHow to do it...How it works...See alsoImplementing feature binarizationGetting readyHow to do it...How it works…Using decision trees for discretizationHow to do it...How it works...There’s more...
Chapter 5: Working with Outliers
Technical requirementsVisualizing outliers with boxplots and the inter-quartile proximity ruleHow to do it...How it works…Finding outliers using the mean and standard deviationHow to do it...How it works…Using the median absolute deviation to find outliersHow to do it...How it works…Removing outliersHow to do it...How it works...See alsoBringing outliers back within acceptable limitsHow to do it...How it works...See alsoApplying winsorizationHow to do it...How it works...See also
Chapter 6: Extracting Features from Date and Time Variables
Technical requirementsExtracting features from dates with pandasGetting readyHow to do it...How it works...There’s more…See alsoExtracting features from time with pandasGetting readyHow to do it...How it works...There’s more…Capturing the elapsed time between datetime variablesHow to do it...How it works...There's more...See alsoWorking with time in different time zonesHow to do it...How it works...See alsoAutomating the datetime feature extraction with Feature-engineHow to do it...How it works...
Chapter 7: Performing Feature Scaling
Technical requirementsStandardizing the featuresGetting readyHow to do it...How it works...Scaling to the maximum and minimum valuesGetting readyHow to do it...How it works...Scaling with the median and quantilesHow to do it...How it works...Performing mean normalizationHow to do it...How it works…There’s more...Implementing maximum absolute scalingGetting readyHow to do it...There’s more...Scaling to vector unit lengthHow to do it...How it works...
Chapter 8: Creating New Features
Technical requirementsCombining features with mathematical functionsGetting readyHow to do it...How it works...See alsoComparing features to reference variablesHow to do it…How it works...See alsoPerforming polynomial expansionGetting readyHow to do it...How it works...There’s more...Combining features with decision treesHow to do it...How it works...See alsoCreating periodic features from cyclical variablesGetting readyHow to do it…How it works…Creating spline featuresGetting readyHow to do it…How it works…See also

Chapter 9: Extracting Features from Relational Data with Featuretools
Technical requirementsSetting up an entity set and creating features automaticallyGetting readyHow to do it...How it works...See alsoCreating features with general and cumulative operationsGetting readyHow to do it...How it works...Combining numerical featuresHow to do it...How it works...Extracting features from date and timeHow to do it...How it works...Extracting features from textGetting readyHow to do it...How it works...Creating features with aggregation primitivesGetting readyHow to do it...How it works...
Chapter 10: Creating Features from a Time Series with tsfresh
Technical requirementsExtracting hundreds of features automatically from a time seriesGetting readyHow to do it...How it works...See alsoAutomatically creating and selecting predictive features from time-series dataHow to do it...How it works...See alsoExtracting different features from different time seriesHow to do it...How it works...Creating a subset of features identified through feature selectionHow to do it...How it works...Embedding feature creation into a scikit-learn pipelineHow to do it...How it works...See also
Chapter 11: Extracting Features from Text Variables
Technical requirementsCounting characters, words, and vocabularyGetting readyHow to do it...How it works...There’s more...See alsoEstimating text complexity by counting sentencesGetting readyHow to do it...How it works...There’s more...Creating features with bag-of-words and n-gramsGetting readyHow to do it...How it works...See alsoImplementing term frequency-inverse document frequencyGetting readyHow to do it...How it works...See alsoCleaning and stemming text variablesGetting readyHow to do it...How it works...
Index
Why subscribe?
Other Books You May EnjoyPackt is searching for authors like youShare Your ThoughtsDownload a free PDF copy of this book

Content preview from Python Feature Engineering Cookbook - Third Edition

1 Imputing Missing Data

Missing data—meaning the absence of values for certain observations—is an unavoidable problem in most data sources. Some machine learning model implementations can handle missing data out of the box. To train other models, we must remove observations with missing data or transform them into permitted values.

The act of replacing missing data with their statistical estimates is called imputation. The goal of any imputation technique is to produce a complete dataset. There are multiple imputation methods. We select which one to use, depending on whether the data is missing at random, the proportion of missing values, and the machine learning model we intend to use. In this chapter, we will discuss several imputation methods. ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Publisher Resources

ISBN: 9781835883587

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills

Python Feature Engineering Cookbook - Third Edition

by Soledad Galli

1

Imputing Missing Data

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.