Feature Scaling

Feature scaling is a technique used in data preprocessing to standardize the range of independent variables or features of a dataset. It is done to ensure that all the features contribute equally to the analysis and to prevent features with larger values from dominating the analysis. Feature scaling is important in machine learning algorithms that use distance-based metrics, such as k-nearest neighbors and support vector machines. There are two common methods of feature scaling: normalization and standardization. Normalization scales the values of a feature between 0 and 1, while standardization scales the values to have a mean of 0 and a standard deviation of 1.
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