👉 A normalization is a method used in statistics and machine learning to transform input data such as numerical values or categorical variables so that they can be processed more efficiently. These transformations are typically performed on the original data, which may include scaling or standardizing it, and then applied to the transformed data. Normalizations are often used in conjunction with other techniques, such as feature selection or dimensionality reduction, to improve model performance or reduce computational complexity. In machine learning, normalization is particularly useful when dealing with datasets
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What is the definition of Normalization? 🙋
👉 Normalization is a process used in data analysis to transform raw or unstructured data into more structured form, often by dividing it into meaningful categories. It is important for data scientists and analysts because it helps them understand and interpret complex data more effectively, enabling them to make informed decisions based on the information they have. Normalization can be achieved through various techniques such as standardizing, scaling, or transforming the dataset.
