Feature Scaling and Normalization - Lab

Introduction

In this lab, you'll practice your feature scaling and normalization skills!

Objectives

You will be able to:

• Identify if it is necessary to perform log transformations on a set of features
• Perform log transformations on different features of a dataset
• Determine if it is necessary to perform normalization/standardization for a specific model or set of data
• Compare the different standardization and normalization techniques
• Use standardization/normalization on features of a dataset

Back to our Boston Housing data

Let's import our Boston Housing data. Remember we categorized two variables ('RAD' and 'TAX') and deleted the 'NOX' (nitride oxide concentration) variable because it was highly correlated with two other features.

import pandas as pd
%matplotlib inline
from sklearn.datasets import load_boston
boston = load_boston()

boston_features = pd.DataFrame(boston.data, columns = boston.feature_names)

# First, create bins for RAD based on the values observed. 5 values will result in 4 bins
bins = [0, 3, 4 , 5, 24]
bins_rad = pd.cut(boston_features['RAD'], bins)
bins_rad = bins_rad.cat.as_unordered()

# First, create bins for TAX based on the values observed. 6 values will result in 5 bins
bins = [0, 250, 300, 360, 460, 712]
bins_tax = pd.cut(boston_features['TAX'], bins)
bins_tax = bins_tax.cat.as_unordered()

tax_dummy = pd.get_dummies(bins_tax, prefix='TAX', drop_first=True)
rad_dummy = pd.get_dummies(bins_rad, prefix='RAD', drop_first=True)
boston_features = boston_features.drop(['RAD', 'TAX'], axis=1)
boston_features = pd.concat([boston_features, rad_dummy, tax_dummy], axis=1)
boston_features = boston_features.drop('NOX', axis=1)

Look at the histograms for the continuous variables

# Your code here

Perform log transformations for the variables where it makes sense

Analyze the results in terms of how they improved the normality performance. What is the problem with the 'ZN' feature?

# Your code here

'ZN' has a lot of zeros (more than 50%!). Remember that this variable denoted: "proportion of residential land zoned for lots over 25,000 sq.ft.". It might have made sense to categorize this variable to "over 25,000 feet or not (binary variable 1/0). Now you have a zero-inflated variable which is cumbersome to work with.

# Your code here

Try different types of transformations on the continuous variables

Store your final features in a DataFrame features_final:

# Your code here

Summary

Great! You've now got some hands-on practice transforming data using log transforms, feature scaling, and normalization!