Feature engineering is the process of selecting, manipulating, and transforming raw data into features that can be used in supervised learning. It involves creating new features or transforming existing features to improve the performance of a machine learning model. The goal is to improve model accuracy by providing more meaningful and relevant information. Feature engineering is a crucial step in preparing data for analysis and decision-making in various fields, such as finance, healthcare, marketing, and social sciences.

The feature engineering pipeline is the preprocessing steps that transform raw data into features that can be used in machine learning algorithms, such as predictive models. Feature engineering consists of creation, transformation, extraction, and selection of features, also known as variables, that are most conducive to creating an accurate ML algorithm. The steps required to engineer features include data extraction and cleansing and then feature creation and storage.

Feature engineering can be a time-consuming and error-prone process, as it requires domain expertise and often involves trial and error. However, it is a very important step in machine learning, and data scientists spend most of their time with data, making it important to make models accurate. Automated feature engineering has been available in some machine learning software since 2016. Deep learning algorithms may be used to process a large raw dataset without having to resort to feature engineering, but its important to note that deep learning algorithms still require careful preprocessing and cleaning of the input data.

In summary, feature engineering is the process of transforming raw data into features that are suitable for machine learning models. It involves selecting, extracting, and transforming the most relevant features from the available data to build more accurate and efficient machine learning models.