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Python Feature Engineering Cookbook

You're reading from  Python Feature Engineering Cookbook

Product type Book
Published in Jan 2020
Publisher Packt
ISBN-13 9781789806311
Pages 372 pages
Edition 1st Edition
Languages
Author (1):
Soledad Galli Soledad Galli
Profile icon Soledad Galli
Toc

Table of Contents (13) Chapters close

Preface 1. Foreseeing Variable Problems When Building ML Models 2. Imputing Missing Data 3. Encoding Categorical Variables 4. Transforming Numerical Variables 5. Performing Variable Discretization 6. Working with Outliers 7. Deriving Features from Dates and Time Variables 8. Performing Feature Scaling 9. Applying Mathematical Computations to Features 10. Creating Features with Transactional and Time Series Data 11. Extracting Features from Text Variables 12. Other Books You May Enjoy

Scaling to the maximum and minimum values

Scaling to the minimum and maximum values squeezes the values of the variables between 0 and 1. To implement this scaling technique, we need to subtract the minimum value from all the observations and divide the result by the value range, that is, the difference between the maximum and minimum values:

In this recipe, we will implement scaling on the minimum and maximum values by utilizing scikit-learn.

How to do it...

To begin, we will import the required packages, load the dataset, and prepare the train and test sets:

  1. Import pandas and the required scikit-learn classes and function:
import pandas as pd
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split...
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