Feature Scaling

Feature scaling is a pre-processing step that transforms the features of a dataset to a similar scale, which is essential for many machine learning algorithms. Techniques like min-max scaling and standardization (z-score normalization) are commonly employed to ensure that features do not disproportionately influence the model due to differing magnitudes or ranges. This can improve convergence during training and lead to better model performance.