Feature Engineering and Selection
Feature engineering involves transforming raw data into meaningful representations that improve the performance of machine learning models. This step includes:
- Feature Extraction – Creating new variables from existing ones (e.g., extracting time-based features from a timestamp).
- Feature Scaling – Standardizing or normalizing numerical features to ensure consistent model performance.
- Feature Encoding – Converting categorical variables into numerical representations using methods like One-Hot Encoding or Label Encoding.
- Dimensionality Reduction – Removing irrelevant or redundant features using techniques like PCA (Principal Component Analysis) or LDA (Linear Discriminant Analysis).
- Feature Selection – Identifying the most relevant features through statistical tests, correlation analysis, or automated techniques like Recursive Feature Elimination (RFE).
Efficient feature engineering significantly impacts model accuracy and generalization, making it an essential step in any Data Science pipeline.
