Which of the following best defines feature engineering?

Feature engineering is a critical step in the data analytics and machine learning process that involves selecting, modifying, or creating features from raw data to improve the performance of predictive models. The correct definition captures the essence of this process, emphasizing the transformation of data into meaningful inputs that can enhance modeling efforts.

In detail, feature engineering may include techniques such as scaling, encoding categorical variables, and creating new features based on existing ones (e.g., combining or decomposing attributes). By thoughtfully selecting and modifying features, data scientists are able to improve the model's ability to learn underlying patterns in the data.

The other options do not accurately capture the purpose of feature engineering. Data storage is concerned with how data is retained and managed but does not address the transformation of data for better insight. Visualizing data trends pertains to representing data graphically, which is important for analysis but separate from the process of feature engineering. Deploying algorithms for analysis refers to implementing models using existing data but does not involve the preliminarily necessary task of preparing and optimizing features that feed into those algorithms.