<p>Data <a target="_blank" href="https://www.almabetter.com/bytes/tutorials/data-science/normalization-in-machine-learning">normalization in machine learning</a> refers to the process of rescaling the features of a dataset to a standard scale. This ensures that each feature contributes equally to the analysis and prevents certain features from dominating due to their larger scale. Common normalization techniques include Min-Max scaling, where values are scaled to a range (e.g., 0 to 1), and Z-score standardization, which transforms features to have zero mean and unit variance. Normalization helps improve the performance and stability of machine learning algorithms by reducing the influence of different scales and making the data more consistent and interpretable across features.</p>


Data [normalization in machine learning](https://www.almabetter.com/bytes/tutorials/data-science/normalization-in-machine-learning) refers to the process of rescaling the features of a dataset to a standard scale. This ensures that each feature contributes equally to the analysis and prevents certain features from dominating due to their larger scale. Common normalization techniques include Min-Max scaling, where values are scaled to a range (e.g., 0 to 1), and Z-score standardization, which transforms features to have zero mean and unit variance. Normalization helps improve the performance and stability of machine learning algorithms by reducing the influence of different scales and making the data more consistent and interpretable across features.

Data Normalization in Machine Learning