Normalization

Normalization aims to place numerical variables with different ranges onto a common scale, making model learning more balanced. Distance-based algorithms, neural networks, and optimization-driven models are often sensitive to scale differences. It is undesirable for a feature with large magnitude to dominate others purely because of its scale. For this reason, techniques such as min-max normalization are widely used. However, the decision to normalize should always consider the data distribution and the type of model being used.