The document emphasizes the importance of normalizing data in machine learning to improve model accuracy and runtime. It explains normalization as the process of scaling data to a common scale and highlights its impact on various models. Additionally, it provides a normalization recipe using the Iris dataset and mentions resources like scikit-learn.

1. HELP YOUR DATA BE
NORMAL

2. DAMIAN MINGLE
CHIEF DATA SCIENTIST
@DamianMingle

3. GET THE FULL STORY
bit.ly/UseSciKitNow

4. Want faster model run times and
better accuracy?
Try Normalizing Your Data

5. What’s Normal Anyway?
 Often stated as “scaling individual samples to have unit norm” or “scale
input vectors individually to unit norm (vector length).
 Adjusting values measured on different scales to a notionally common
scale

6. Why Normalization Matters
 In truth, not all machine learning models are sensitive to magnitude.
 Data on the same scale can help machine learning models learn (think k-
nearest neighbors and coefficients in regression)

7. Power in SciKit Learn
 Preprocessing
 Clustering
 Regression
 Classification
 Dimensionality Reduction
 Model Selection
Power of SciKit Learn

8. Let’s Look at ML Recipe
Normalization

9. The Imports
from sklearn.datasets import load_iris
from sklearn import preprocessing

10. Separate Features from Target
iris = load_iris()
print(iris.data.shape)
X = iris.data
y = iris.target

11. Normalize the Features
normalized_X = preprocessing.normalize(X)

12. Normalization Recipe
# Normalize the data attributes for the Iris dataset. from
sklearn.datasets import load_iris
from sklearn import preprocessing
# load the iris dataset iris = load_iris()
print(iris.data.shape)
# separate the data from the target attributes
X = iris.data
y = iris.target
# normalize the data attributes
normalized_X = preprocessing.normalize(X)

13. HELP YOUR DATA BE
NORMAL

14. DAMIAN MINGLE
CHIEF DATA SCIENTIST
@DamianMingle

15. GET THE FULL STORY
bit.ly/UseSciKitNow

16. Resources
 Society of Data Scientists
 SciKit Learn