Explain how normalization of features is better while preprocessing a model?
Normalization refers to rescaling real valued numeric attributes into the range 0 and 1.
It is useful to scale the input attributes for a model that depends on the magnitude of values, such as distance measures algorithm used in models like k-nearest neighbors or clustering and in the preparation of coefficients in regression.
It works on the following formula
The below graph shows the difference between original and normalized data.
