Python feature engineering cookbook :: over 70 recipes for creating, engineering, and transforming features to build machine learning models /
"Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries Key Features Discover solutions for feature generation, feature extraction, and feature selection Uncover the end-to-end feature engineering process across...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
2020.
|
| Schlagworte: | |
| Online-Zugang: | DE-862 DE-863 |
| Zusammenfassung: | "Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries Key Features Discover solutions for feature generation, feature extraction, and feature selection Uncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasets Implement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy libraries Book Description 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 Who this book is for This book is for machine learning professionals, AI engineers, data scientists, and NLP and reinforcement learning engineers who want to optimize and enrich their machine learning models with the best features. Knowledge of machine learning and Python coding will assist you with understanding the concepts covered in this book."--EBook Central |
| Beschreibung: | 1 online resource (1 volume) : illustrations |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 9781789807820 1789807824 |
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| 505 | 0 | |a Preface -- Foreseeing Variable Problems When Building ML Models -- Imputing Missing Data -- Encoding Categorical Variables -- Transforming Numerical Variables -- Performing Variable Discretization -- Working with Outliers -- Deriving Features from Dates and Time Variables -- Performing Feature Scaling -- Applying Mathematical Computations to Features -- Creating Features with Transactional and Time Series Data -- Extracting Features from Text Variables | |
| 520 | |a "Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries Key Features Discover solutions for feature generation, feature extraction, and feature selection Uncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasets Implement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy libraries Book Description 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 Who this book is for This book is for machine learning professionals, AI engineers, data scientists, and NLP and reinforcement learning engineers who want to optimize and enrich their machine learning models with the best features. Knowledge of machine learning and Python coding will assist you with understanding the concepts covered in this book."--EBook Central | ||
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| contents | Preface -- Foreseeing Variable Problems When Building ML Models -- Imputing Missing Data -- Encoding Categorical Variables -- Transforming Numerical Variables -- Performing Variable Discretization -- Working with Outliers -- Deriving Features from Dates and Time Variables -- Performing Feature Scaling -- Applying Mathematical Computations to Features -- Creating Features with Transactional and Time Series Data -- Extracting Features from Text Variables |
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| spelling | Galli, Soledad, author. Python feature engineering cookbook : over 70 recipes for creating, engineering, and transforming features to build machine learning models / Soledad Galli. Birmingham, UK : Packt Publishing, 2020. 1 online resource (1 volume) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from title page (Safari, viewed June 26, 2020). Includes bibliographical references. Preface -- Foreseeing Variable Problems When Building ML Models -- Imputing Missing Data -- Encoding Categorical Variables -- Transforming Numerical Variables -- Performing Variable Discretization -- Working with Outliers -- Deriving Features from Dates and Time Variables -- Performing Feature Scaling -- Applying Mathematical Computations to Features -- Creating Features with Transactional and Time Series Data -- Extracting Features from Text Variables "Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries Key Features Discover solutions for feature generation, feature extraction, and feature selection Uncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasets Implement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy libraries Book Description 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 Who this book is for This book is for machine learning professionals, AI engineers, data scientists, and NLP and reinforcement learning engineers who want to optimize and enrich their machine learning models with the best features. Knowledge of machine learning and Python coding will assist you with understanding the concepts covered in this book."--EBook Central Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Python (Langage de programmation) Logiciels d'application Développement. Apprentissage automatique. Data capture & analysis. bicssc Data mining. bicssc Information architecture. bicssc Database design & theory. bicssc Computers Data Processing. bisacsh Computers Database Management Data Mining. bisacsh Computers Data Modeling & Design. bisacsh Application software Development fast Machine learning fast Python (Computer program language) fast has work: Python Feature Engineering Cookbook (Work) https://id.oclc.org/worldcat/entity/E39PCYkpmKbTx9mXKTt4QRdcw3 https://id.oclc.org/worldcat/ontology/hasWork Print version: Galli, Soledad. Python Feature Engineering Cookbook : Over 70 Recipes for Creating, Engineering, and Transforming Features to Build Machine Learning Models. Birmingham : Packt Publishing, Limited, ©2020 9781789806311 |
| spellingShingle | Galli, Soledad Python feature engineering cookbook : over 70 recipes for creating, engineering, and transforming features to build machine learning models / Preface -- Foreseeing Variable Problems When Building ML Models -- Imputing Missing Data -- Encoding Categorical Variables -- Transforming Numerical Variables -- Performing Variable Discretization -- Working with Outliers -- Deriving Features from Dates and Time Variables -- Performing Feature Scaling -- Applying Mathematical Computations to Features -- Creating Features with Transactional and Time Series Data -- Extracting Features from Text Variables Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Python (Langage de programmation) Logiciels d'application Développement. Apprentissage automatique. Data capture & analysis. bicssc Data mining. bicssc Information architecture. bicssc Database design & theory. bicssc Computers Data Processing. bisacsh Computers Database Management Data Mining. bisacsh Computers Data Modeling & Design. bisacsh Application software Development fast Machine learning fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh96008834 http://id.loc.gov/authorities/subjects/sh95009362 http://id.loc.gov/authorities/subjects/sh85079324 |
| title | Python feature engineering cookbook : over 70 recipes for creating, engineering, and transforming features to build machine learning models / |
| title_auth | Python feature engineering cookbook : over 70 recipes for creating, engineering, and transforming features to build machine learning models / |
| title_exact_search | Python feature engineering cookbook : over 70 recipes for creating, engineering, and transforming features to build machine learning models / |
| title_full | Python feature engineering cookbook : over 70 recipes for creating, engineering, and transforming features to build machine learning models / Soledad Galli. |
| title_fullStr | Python feature engineering cookbook : over 70 recipes for creating, engineering, and transforming features to build machine learning models / Soledad Galli. |
| title_full_unstemmed | Python feature engineering cookbook : over 70 recipes for creating, engineering, and transforming features to build machine learning models / Soledad Galli. |
| title_short | Python feature engineering cookbook : |
| title_sort | python feature engineering cookbook over 70 recipes for creating engineering and transforming features to build machine learning models |
| title_sub | over 70 recipes for creating, engineering, and transforming features to build machine learning models / |
| topic | Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Python (Langage de programmation) Logiciels d'application Développement. Apprentissage automatique. Data capture & analysis. bicssc Data mining. bicssc Information architecture. bicssc Database design & theory. bicssc Computers Data Processing. bisacsh Computers Database Management Data Mining. bisacsh Computers Data Modeling & Design. bisacsh Application software Development fast Machine learning fast Python (Computer program language) fast |
| topic_facet | Python (Computer program language) Application software Development. Machine learning. Python (Langage de programmation) Logiciels d'application Développement. Apprentissage automatique. Data capture & analysis. Data mining. Information architecture. Database design & theory. Computers Data Processing. Computers Database Management Data Mining. Computers Data Modeling & Design. Application software Development Machine learning |
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