Summary  
This chapter covers techniques for selecting the most relevant variables and engineering new input features—such as filter methods, wrapper methods, polynomial features, and interaction terms—to improve regression model performance.

General domain of usage  
Predictive modeling

Master machine learning techniques using PySpark and real estate data. Learn to build, evaluate, and optimize ML pipelines for classification, regression, and clustering tasks using Spark MLlib.

Comprehensive guide to machine learning with PySpark using real estate data, covering classification, regression, clustering, and pipeline optimization.

Feature Selection and Engineering for Regression