Dynamic Behavioral Segmentation with Machine Learning Preparation

Design a SQL query that prepares customer data for machine learning-based behavioral segmentation. The query should extract and normalize features like interaction frequency, monetary value, product preferences, and temporal patterns. Implement feature engineering techniques such as min-max scaling, z-score normalization, and one-hot encoding directly within SQL, outputting a clean dataset ready for clustering algorithms.