Data Normalization

Data normalization is a crucial preprocessing step that transforms the features in a dataset to have a uniform scale, typically between 0 and 1 or with a mean of 0 and standard deviation of 1. This helps models converge faster during training and improves accuracy. Common normalization techniques include Min-Max scaling and Z-score normalization, each serving specific purposes depending on the dataset and the model being used.