Feature Engineering is the process of transforming raw data into meaningful inputs, or features, that machine learning models can use effectively. It may involve selecting useful variables, combining existing data, handling missing values, or creating new measurements that better represent the patterns within a dataset.
For example, a system predicting equipment failure could combine temperature, vibration, operating hours, and maintenance history into features that capture changes in equipment condition. Effective feature engineering can improve model accuracy, reduce unnecessary data, and help machine learning systems identify important patterns more reliably.
