Data preparation is the longest and the most complex phase of any ML project. The same was emphasized while discussing the CRISP-DM model, where we mentioned how the data preparation phase takes up about 60-70% of the overall time spent in a ML project.

Once we have our raw dataset preprocessed and wrangled, the next step is to make it usable for ML algorithms. Feature extraction is the process of deriving features from raw attributes. For instance, feature extraction while working with image data refers to the extraction of red, blue, and green channel information as features from raw pixel-level data.

On the same lines, feature engineering refers to the process of deriving additional features from existing ones using mathematical transformations. For instance, feature engineering would help us in deriving a feature such as annual income from a person's monthly income (based on use case requirements). Since both feature extraction and engineering help us transform raw datasets into usable forms the terms are used interchangeably by ML practioners.