Summary  
This chapter explains how to implement logistic regression in code for binary classification by modeling probabilities with the sigmoid function, optimizing parameters via gradient descent, and making predictions based on a probability threshold.

General domain of usage  
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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.

Classification: Logistic Regression