Feature Engineering

Feature Engineering is the process of using domain knowledge to select, modify, or create features from raw data that enhance the performance of machine learning algorithms. This practice involves transforming data into a format that better represents the underlying problem, enabling the model to learn more effectively. Feature engineering can include techniques such as normalization, encoding categorical variables, creating interaction features, and extracting relevant characteristics from complex data sources. The goal is to improve the predictive power of models by ensuring that they have access to the most informative and relevant aspects of the data. Proper feature engineering can significantly influence the success of machine learning projects, making it a crucial step in the data preparation phase.