Feature Scaler

Feature Scaler normalizes feature values to a common scale so that no single feature dominates the model due to its larger numerical range. It transforms all features to have comparable magnitudes.

This is a critical preprocessing step for distance-based algorithms like KNN, SVC, and SVR.

Always apply Feature Scaler before using distance-based models (KNN, SVC, SVR, K-Means). Without scaling, features with larger ranges (e.g., area in m²) will dominate over features with smaller ranges (e.g., compactness ratio 0–1). Tree-based models (Random Forest, Gradient Boosting) do not require scaling.