Summary  
This chapter covers how to use VectorAssembler to combine multiple input columns into a single feature vector column for use in pipeline-based modeling.

General domain of usage  
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.

Building Feature Vectors with VectorAssembler