feature engineering

Feature engineering is the process of manipulating and transforming raw data into forms that are more valuable in a predictive model. Datasets offer many options for creating new features, and deciding which ones to create and retain is considered a craft by data scientists. For example, you might have repeated transactions for each customer in your dataset that are actually more informative to predictive models if used to calculate an average transaction amount for each customer. In addition to creating the new features, new labels must be added to make the engineered data understandable to users and meaningful when the model is assessed.