Having prepared and transformed existing data features in the previous chapters, we now shift focus to generating entirely new features. Constructing informative features is a significant step in improving model performance, as new features can capture complex relationships or domain-specific insights that might not be apparent from the original variables alone.

This chapter covers techniques for creating valuable features from your data:

• Interaction Features: Learn how to combine two or more features to capture their synergistic effects, for instance, by computing their product $f_{interaction} = f_1 \times f_2$.
• Polynomial Features: Generate higher-order terms (like $f_{squared} = f_1^2$) from numerical features to help models capture non-linear patterns.
• Date/Time Feature Extraction: Discover methods to break down timestamp data into useful components such as year, month, day of the week, or hour.
• Binning: Understand how to convert continuous numerical data into discrete categorical bins.
• Domain-Specific Engineering: Briefly discuss how understanding the problem domain guides the creation of relevant features.

We will implement these techniques using Python, primarily with Pandas for data manipulation and Scikit-learn for dedicated feature engineering tools.