👉 Feature engineering is a critical step in the machine learning pipeline that involves transforming raw data into features that better represent the underlying problem to the predictive models, enhancing their performance. This process includes selecting relevant features, creating new ones through mathematical transformations or domain knowledge, and encoding categorical variables. It aims to improve model accuracy, reduce overfitting, and decrease training time by focusing on the most informative aspects of the data. Effective feature engineering requires a deep understanding of both the problem domain and the data, often involving exploratory data analysis, statistical methods, and iterative experimentation to identify the most impactful features.
