Python Feature Engineering Cookbook

Python Feature Engineering Cookbook
Author: Soledad Galli
Publisher: Packt Publishing Ltd
Total Pages: 364
Release: 2020-01-22
Genre: Computers
ISBN: 1789807824


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Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries Key FeaturesDiscover solutions for feature generation, feature extraction, and feature selectionUncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasetsImplement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy librariesBook 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 learnSimplify your feature engineering pipelines with powerful Python packagesGet to grips with imputing missing valuesEncode categorical variables with a wide set of techniquesExtract insights from text quickly and effortlesslyDevelop features from transactional data and time series dataDerive new features by combining existing variablesUnderstand how to transform, discretize, and scale your variablesCreate informative variables from date and timeWho 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.

Python Feature Engineering Cookbook
Language: en
Pages: 364
Authors: Soledad Galli
Categories: Computers
Type: BOOK - Published: 2020-01-22 - Publisher: Packt Publishing Ltd

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Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries Key FeaturesDiscover
Python Feature Engineering Cookbook
Language: en
Pages: 372
Authors: Soledad Galli
Categories: Computers
Type: BOOK - Published: 2020-01-22 - Publisher:

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Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries Key Features Discover
Python Feature Engineering Cookbook - Second Edition
Language: en
Pages: 0
Authors: Soledad Galli
Categories:
Type: BOOK - Published: 2022-10-31 - Publisher:

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Create end-to-end, reproducible feature engineering pipelines that can be deployed into production using open-source Python libraries Key Features: Learn and im
Python Feature Engineering Cookbook
Language: en
Pages: 396
Authors: Soledad Galli
Categories: Computers
Type: BOOK - Published: 2024-08-30 - Publisher: Packt Publishing Ltd

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Leverage the power of Python to build real-world feature engineering and machine learning pipelines ready to be deployed to production Key Features Learn Craft
Machine Learning with Python Cookbook
Language: en
Pages: 305
Authors: Chris Albon
Categories: Computers
Type: BOOK - Published: 2018-03-09 - Publisher: "O'Reilly Media, Inc."

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This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re