What is feature scaling and why is it important?

Feature scaling is the process of normalizing the range of independent variables. Many ML algorithms are sensitive to the scale of features — algorithms that use distance metrics (KNN, SVM, K-Means) or gradient descent (neural networks, linear regression) perform poorly when features have very different scales. Min-Max Normalization scales features to [0, 1]; Standardization (Z-score) transforms features to have mean 0 and standard deviation 1. Tree-based models (Random Forest, XGBoost) are generally scale-invariant.