Summary  
This chapter covers how to transform categorical string features into numerical representations using StringIndexer and one-hot encoding to prepare data for model training.  

General domain of usage  
Machine learning pipelines

Master scalable data processing and analysis using PySpark, focusing on real-world e-commerce datasets. Learn to query, clean, transform, and prepare data for analytics and machine learning using Spark SQL and DataFrame APIs.

Comprehensive coverage of PySpark data processing techniques for e-commerce data, from querying to cleaning, feature engineering, and performance optimization.

Handling Categorical Features: StringIndexer and One-Hot Encoding