<ul><li>Ensuring all features are on a comparable scale, preventing models from giving undue importance to any particular feature.</li><li>Speeding up convergence in optimization algorithms, as features on a similar scale lead to smoother and faster gradient descent.</li><li>Standardization is used to center features around the mean with unit variance, benefiting algorithms with different units or scales and outliers.</li><li>Feature scaling is crucial for algorithms like distance-based models (K-nearest neighbors (KNN) and support vector machines (SVM)) and gradient-based algorithms (neural networks) to ensure accurate and efficient model training.</li></ul>

Feature Scaling in Machine Learning and Data Science

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