Python Feature Engineering Cookbook: Over 70 recipes for creating,engineering,and transforming features to build machine learning models
Authors: Soledad Galli
ISBN-10: 1789806313
ISBN-13: 9781789806311
Publication Date 出版日期: 2020-01-22
Publisher finelybook 出版社: Packt
Print Length 页数: 372 pages
Book Description
By finelybook
Extract accurate information from data to train and improve machine learning models using NumPy,SciPy,pandas,and scikit-learn libraries
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.
What you will learn
Simplify your feature engineering pipelines with powerful Python packages
Get to grips with imputing missing values
Encode categorical variables with a wide set of techniques
Extract insights from text quickly and effortlessly
Develop features from transactional data and time series data
Derive new features by combining existing variables
Understand how to transform,discretize,and scale your variables
Create informative variables from date and time
Contents
Preface
Chapter 1: Foreseeing Variable Problems When Building ML Models
Chapter 2: Imputing Missing Data
Chapter 3: Encoding Categorical Variables
Chapter 4: Transforming Numerical Variables
Chapter 5: Performing Variable Discretization
Chapter 6: Working with Outliers
Chapter 7: Deriving Features from Dates and Time Variables
Chapter 8: Performing Feature Scaling
Chapter 9: Applying Mathematical Computations to Features
Chapter 10: Creating Features with Transactional and Time Series Data
Chapter 11: Extracting Features from Text Variables
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