Python Feature Engineering Cookbook: A complete guide to crafting powerful features for your machine learning models
1146070718
Python Feature Engineering Cookbook: A complete guide to crafting powerful features for your machine learning models
35.99 In Stock
Python Feature Engineering Cookbook: A complete guide to crafting powerful features for your machine learning models

Python Feature Engineering Cookbook: A complete guide to crafting powerful features for your machine learning models

Python Feature Engineering Cookbook: A complete guide to crafting powerful features for your machine learning models

Python Feature Engineering Cookbook: A complete guide to crafting powerful features for your machine learning models

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$35.99 

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Product Details

ISBN-13: 9781835883594
Publisher: Packt Publishing
Publication date: 08/30/2024
Sold by: Barnes & Noble
Format: eBook
Pages: 396
File size: 14 MB
Note: This product may take a few minutes to download.

About the Author

Soledad Galli is a bestselling data science instructor, author, and open-source Python developer. As the leading instructor at Train in Data, she teaches intermediate and advanced courses in machine learning that have enrolled over 64,000 students worldwide and continue to receive positive reviews. Sole is also the developer and maintainer of the Python open-source library Feature-engine, which provides an extensive array of methods for feature engineering and selection. With extensive experience as a data scientist in finance and insurance sectors, Sole has developed and deployed machine learning models for assessing insurance claims, evaluating credit risk, and preventing fraud. She is a frequent speaker at podcasts, meetups, and webinars, sharing her expertise with the broader data science community.

Table of Contents

Table of Contents
  1. Imputing Missing Data
  2. Encoding Categorical Variables
  3. Transforming Numerical Variables
  4. Performing Variable Discretization
  5. Working with Outliers
  6. Extracting Features from Date and Time Variables
  7. Performing Feature Scaling
  8. Creating New Features
  9. Extracting Features from Relational Data with Featuretools
  10. Creating Features from a Time Series with tsfresh
  11. Extracting Features from Text Variables
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