Table of Contents

Imputing Missing Data

Encoding Categorical Variables

Transforming Numerical Variables

Performing Variable Discretization

Working with Outliers

Extracting Features from Date and Time

Performing Feature Scaling

Creating New Features

Extracting Features from Relational Data with Featuretools

Creating Features from Time Series with tsfresh

Extracting Features from Text Variables

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.