Summary  
This chapter explains techniques for transforming numerical values into a consistent scale or distribution—such as min-max scaling and z-score normalization—to ensure features have comparable magnitudes before further processing.

General domain of usage  
Data preprocessing for machine learning

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.

Scaling and Normalization