Feature engineering refers to the process of selecting, creating, and transforming variables (features) from raw data

Feature engineering is crucial as the quality and relevance of features directly impact the accuracy of models

It involves data cleaning techniques, such as handling missing values, removing outliers, and correcting inconsistencies.

Feature engineering may involve creating new features by combining or transforming existing ones

Sometimes, feature engineering is used to reduce the dimensionality of the data.

Feature engineering techniques are used to handle categorical variables.

With advancements in automated feature engineering, machine learning frameworks and tools are now capable of automatically generating features based on the given data

Feature engineering is often an iterative process, where the initial set of features is refined and improved based on the model's performance and feedback.