Feature engineering in data science involves the process of selecting, transforming, and creating new features from raw data to improve model performance. This step is crucial for making machine learning models more effective, as the quality of input features directly impacts the accuracy and predictive power of the model. Techniques include handling missing data, encoding categorical variables, normalizing or scaling numerical features, creating interaction terms, and extracting domain-specific features. Effective feature engineering requires domain knowledge, creativity, and an understanding of the model’s behavior, and it often involves iterative testing and refinement to achieve optimal results.
Feature Engineering in Data Science: A Complete Guide
