Dynamic Feature Engineering Automation Framework

Create a modular Python library that automatically generates and selects optimal features from raw datasets using advanced techniques like polynomial feature expansion, interaction terms, and automated feature selection algorithms. Integrate recursive feature elimination, mutual information ranking, and machine learning-based importance scoring. Design the framework to support multiple input data types and provide explainable feature importance visualizations.