Feature scaling is an important technique in Machine Learning and it is one of the most important steps during the preprocessing of data before creating a machine learning model. The reason to perform features scaling is to ensure one feature doesn’t dominate others.

The two most important scaling techniques are standardization and normalization.

Standardization is a scaling technique where the values are centered around the mean with a unit standard deviation. This means that the mean of the attribute becomes zero and the resultant distribution has a unit standard deviation.

Here‟s the formula for standardization:

X’ = X – *mu* / *sigma*

where *mu *is the mean of the feature values and *sigma *is the standard deviation of the feature values. Note that in this case, the values are not restricted to a particular range.

Normalization is a scaling technique in which values are shifted and rescaled so that they end up ranging between 0 and 1. It is also known as Min-Max scaling.

Here‟s the formula for normalization:

X’ = X – X*min* / X*max *– X*min*

Here, X*max* and X*min* are the maximum and the minimum values of the feature respectively. When the value of X is the minimum value in the column, the numerator will be 0, and hence X’ is 0.

On the other hand, when the value of X is the maximum value in the column, the numerator is equal to the denominator and thus the value of X’ is 1. If the value of X is between the minimum and the maximum value, then the value of X’ is between 0 and 1

Normalization is good to use when you know that the distribution of your data does not follow a Gaussian distribution. This can be useful in algorithms that do not assume any distribution of the data like K-Nearest Neighbors and Neural Networks. Standardization, on the other hand, can be helpful in cases where the data follows a Gaussian distribution. However, this does not have to be necessarily true. Also, unlike normalization, standardization does not have a bounding range. So, even if you have outliers in your data, they will not be affected by standardization.