Performing mean normalization

In mean normalization, we center the variable at 0 and rescale the distribution to the value range, so that its values lie between -1 and 1. This procedure involves subtracting the mean from each observation and then dividing the result by the difference between the minimum and maximum values, as shown here:

Note

Mean normalization is an alternative to standardization. In both cases, the variables are centered at 0. In mean normalization, the variance varies, while the values lie between -1 and 1. On the other hand, in standardization, the variance is set to 1 and the value range varies.

Mean normalization is a suitable alternative for models that need the variables to be centered at zero. However, it is sensitive to outliers and not a suitable option for sparse data, as it will destroy the sparse nature.

How to do it...

In this recipe, we will implement mean normalization with pandas:

1. Let’s import pandas and the required...