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How to Normalize the data in Python
Two of the best ways to normalize the input data in python with sklearn
In this post, I’ll try to explain how to Normalize the data in the simplest way possible.
What is Normalization?
Normalization is the process of scaling down data(in simple words). Usually while normalizing we change the scale of the data to fall between 0–1.
What is the need for Normalization?
Machine learning models or Artificial neural networks often benefit substantially from input normalization. It also makes it easier to understand the importance of each feature later, when looking at the model weights.
Normally, we need a preparation that aims to facilitate the network optimization process and maximize the probability of obtaining good results.
Normalization also makes the training process less sensitive to the scale of the features. This results in getting better coefficients after training.
This process of making features more suitable for training by rescaling is called feature scaling.
The formula for Normalization is given below :
