Feature Engineering
Feature Engineering is a critical process in machine learning that involves using domain knowledge to select, modify, or create new features from raw data to enhance the performance of predictive models.
Effective feature engineering allows algorithms to better understand the underlying patterns in the data, leading to more accurate predictions. This process often involves transforming raw data into formats that are more suitable for models, such as normalizing scales, encoding categorical information, or creating interaction terms that reflect complex real-world behaviors.
In marketing, this could mean deriving features like customer lifetime value or engagement frequency from transactional data to better predict customer behavior.
