Feature engineering, the process of transforming variables and creating features, albeit time-consuming, ensures that your machine learning models perform seamlessly. This second edition of Python Feature Engineering Cookbook will take the struggle out of feature engineering by showing you how to use open source Python libraries to accelerate the process via a plethora of practical, hands-on recipes.

This updated edition begins by addressing fundamental data challenges such as missing data and categorical values, before moving on to strategies for dealing with skewed distributions and outliers. The concluding chapters show you how to develop new features from various types of data, including text, time series, and relational databases. With the help of numerous open source Python libraries, you'll learn how to implement each feature engineering method in a performant, reproducible, and elegant manner.

By the end of this Python book, you will have the tools and expertise needed to confidently build end-to-end and reproducible feature engineering pipelines that can be deployed into production.

Osoby które kupowały "Python Feature Engineering Cookbook - Second Edition", wybierały także:

• Windows Media Center. Domowe centrum rozrywki 66,67 zÅ‚, (8,00 zÅ‚ -88%)
• Ruby on Rails. Ćwiczenia 18,75 zÅ‚, (3,00 zÅ‚ -84%)
• DevOps w praktyce. Kurs video. Jenkins, Ansible, Terraform i Docker 190,00 zÅ‚, (39,90 zÅ‚ -79%)
• Przywództwo w Å›wiecie VUCA. Jak być skutecznym liderem w niepewnym Å›rodowisku 58,64 zÅ‚, (12,90 zÅ‚ -78%)
• Scrum. O zwinnym zarzÄ…dzaniu projektami. Wydanie II rozszerzone 58,64 zÅ‚, (12,90 zÅ‚ -78%)

Spis treści

Python Feature Engineering Cookbook. Over 70 recipes for creating, engineering, and transforming features to build machine learning models - Second Edition eBook -- spis treści

• 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
• 7. Performing Feature Scaling
• 8. Creating New Features
• 9. Extracting Features from Relational Data with Featuretools
• 10. Creating Features from Time Series with tsfresh
• 11. Extracting Features from Text Variables