Data Normalization in Machine Learning

Data normalization in machine learning involves transforming numerical features to a standard scale to ensure fair contribution to model training. Techniques like Min-Max Scaling and Z-score Standardization are commonly used. Normalization enhances model convergence, prevents dominant features, and improves algorithm performance, especially in distance-based and gradient-descent algorithms. However, its necessity depends on the algorithm and dataset characteristics.