# Standardization with positive and negatives

I have a data set that has a few columns such as:

Total cost: mean = 3,000,000

Percent complete: mean = 50

final profit %: mean = 14

I know with such different orders of magnitude before I fit a linear regression I should standardize the data (using python and sklearn). The problem is there are negatives in this data that I need to keep so I don't know which type of standardization I should use? The only two I am familiar with are log transformations and StandardScaler both of which I think get rid of negatives.

• You can apply standardization to your samples regardless if they are positive or negative. The aim of standardization is to set your sample such that they have a 0 mean and variance 1. In the log case however you need strictly positive values.. – null Jun 25 '20 at 21:07

$$X_{Normalised} = \frac{X - \mu}{\sigma}$$
Here $$\mu$$ is your original mean and $$\sigma$$ is your standard deviation.