# Normalizing Observations

## Preliminaries

# Load libraries from sklearn.preprocessing import Normalizer import numpy as np

## Create Feature Matrix

# Create feature matrix X = np.array([[0.5, 0.5], [1.1, 3.4], [1.5, 20.2], [1.63, 34.4], [10.9, 3.3]])

## Normalize Observations

`Normalizer`

rescales the values on individual observations to have unit norm (the sum of their lengths is one).

# Create normalizer normalizer = Normalizer(norm='l2') # Transform feature matrix normalizer.transform(X)

array([[ 0.70710678, 0.70710678], [ 0.30782029, 0.95144452], [ 0.07405353, 0.99725427], [ 0.04733062, 0.99887928], [ 0.95709822, 0.28976368]])