This is the code repository for Python Feature Engineering Cookbook, published by Packt.
Over 70 recipes for creating, engineering, and transforming features to build machine learning models
Feature engineering is invaluable for developing and enriching your machine learning models. In this book, you will work with the best Python tools to streamline your feature engineering pipelines, feature engineering techniques and simplify and improve the quality of your code.
This book covers the following exciting features:
- 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
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
def get_first_cabin(row):
try:
return row.split()[0]
except:
return np.nan
Following is what you need for this book: 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.
With the following software and hardware list you can run all code files present in the book (Chapter 1-11).
| Chapter | Software required | OS required |
|---|---|---|
| 1 - 11 | Python 3.5+, Anaconda Distibution, IDE(personal preference) | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
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Python Machine Learning Cookbook - Second Edition [Packt] [Amazon]
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Python Machine Learning By Example - Second Edition [Packt] [Amazon]
Soledad Galli is a lead data scientist with more than 10 years of experience in world-class academic institutions and renowned businesses. She has researched, developed, and put into production machine learning models for insurance claims, credit risk assessment, and fraud prevention. Soledad received a Data Science Leaders' award in 2018 and was named one of LinkedIn's voices in data science and analytics in 2019. She is passionate about enabling people to step into and excel in data science, which is why she mentors data scientists and speaks at data science meetings regularly. She also teaches online courses on machine learning in a prestigious Massive Open Online Course platform, which have reached more than 10,000 students worldwide.
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Simply click on the link to claim your free PDF.