Book Image

Python Feature Engineering Cookbook

By : Soledad Galli
Book Image

Python Feature Engineering Cookbook

By: Soledad Galli

Overview of this book

Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code. Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you’ll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You’ll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains. By the end of this book, you’ll have discovered tips and practical solutions to all of your feature engineering problems.

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Table of Contents (13 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Sections
Get in touch
1
Foreseeing Variable Problems When Building ML Models
Foreseeing Variable Problems When Building ML Models
Technical requirements
Identifying numerical and categorical variables
Quantifying missing data
Determining cardinality in categorical variables
Pinpointing rare categories in categorical variables
Identifying a linear relationship
Identifying a normal distribution
Distinguishing variable distribution
Highlighting outliers
Comparing feature magnitude
Free Chapter
2
Imputing Missing Data
Imputing Missing Data
Technical requirements
Removing observations with missing data
Performing mean or median imputation
Implementing mode or frequent category imputation
Replacing missing values with an arbitrary number
Capturing missing values in a bespoke category
Replacing missing values with a value at the end of the distribution
Implementing random sample imputation
Adding a missing value indicator variable
Performing multivariate imputation by chained equations
Assembling an imputation pipeline with scikit-learn
Assembling an imputation pipeline with Feature-engine
3
Encoding Categorical Variables
Encoding Categorical Variables
Technical requirements
Creating binary variables through one-hot encoding
Performing one-hot encoding of frequent categories
Replacing categories with ordinal numbers
Replacing categories with counts or frequency of observations
Encoding with integers in an ordered manner
Encoding with the mean of the target
Encoding with the Weight of Evidence
Grouping rare or infrequent categories
Performing binary encoding
Performing feature hashing
4
Transforming Numerical Variables
Transforming Numerical Variables
Technical requirements
Transforming variables with the logarithm
Transforming variables with the reciprocal function
Using square and cube root to transform variables
Using power transformations on numerical variables
Performing Box-Cox transformation on numerical variables
Performing Yeo-Johnson transformation on numerical variables
5
Performing Variable Discretization
Performing Variable Discretization
Technical requirements
Dividing the variable into intervals of equal width
Sorting the variable values in intervals of equal frequency
Performing discretization followed by categorical encoding
Allocating the variable values in arbitrary intervals
Performing discretization with k-means clustering
Using decision trees for discretization
6
Working with Outliers
Working with Outliers
Technical requirements
Trimming outliers from the dataset
Performing winsorization
Capping the variable at arbitrary maximum and minimum values
Performing zero-coding – capping the variable at zero
7
Deriving Features from Dates and Time Variables
Deriving Features from Dates and Time Variables
Technical requirements
Extracting date and time parts from a datetime variable
Deriving representations of the year and month
Creating representations of day and week
Extracting time parts from a time variable
Capturing the elapsed time between datetime variables
Working with time in different time zones
8
Performing Feature Scaling
Performing Feature Scaling
Technical requirements
Standardizing the features
Performing mean normalization
Scaling to the maximum and minimum values
Implementing maximum absolute scaling
Scaling with the median and quantiles
Scaling to vector unit length
9
Applying Mathematical Computations to Features
Applying Mathematical Computations to Features
Technical requirements
Combining multiple features with statistical operations
Combining pairs of features with mathematical functions
Performing polynomial expansion
Deriving new features with decision trees
Carrying out PCA
10
Creating Features with Transactional and Time Series Data
Creating Features with Transactional and Time Series Data
Technical requirements
Aggregating transactions with mathematical operations
Aggregating transactions in a time window
Determining the number of local maxima and minima
Deriving time elapsed between time-stamped events
Creating features from transactions with Featuretools
11
Extracting Features from Text Variables
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
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Combining multiple features with statistical operations

New features can be created by performing mathematical and statistical operations over existing variables. We previously mentioned that we can calculate the total debt by summing up the debt across individual financial products:

Total debt = car loan debt + credit card debt + mortgage debt

We can also derive other insightful features using alternative statistical operations. For example, we can determine the maximum debt of a customer across financial products, the minimum time they spent surfing one page of our website, or the mean time they spent reading an article of our magazine:

maximum debt = max(car loan balance, credit card balance, mortgage balance)

minimum time on page = min(time on homepage, time on about page, time on the contact us page)

mean time reading article = (time on article 1 + time on article 2...

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