Feature engineering is the process of creating, transforming, or selecting input variables (features) to improve the performance of machine learning models. It plays a critical role in the model development pipeline, especially for structured data tasks like classification, regression, or ranking.

The process includes techniques such as encoding categorical variables, normalizing or scaling numerical values, extracting date or time components, creating interaction terms, and generating domain-specific features based on expert knowledge. Good features can make simple models competitive, while poor features can limit the performance of even the most advanced algorithms.

Feature engineering also involves feature selection—identifying which features are relevant and discarding redundant or noisy ones. This can be done using statistical tests, model-based importance scores, or dimensionality reduction methods like PCA.

In modern workflows, feature engineering may be partially automated through tools like feature stores or AutoML platforms, but human insight is still key for understanding the data and crafting meaningful representations.