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Python Feature Engineering Cookbook

You're reading from   Python Feature Engineering Cookbook Over 70 recipes for creating, engineering, and transforming features to build machine learning models

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781789806311
Length 372 pages
Edition 1st Edition
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Author (1):
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Soledad Galli Soledad Galli
Author Profile Icon Soledad Galli
Soledad Galli
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Table of Contents (13) Chapters Close

Preface 1. Foreseeing Variable Problems When Building ML Models 2. Imputing Missing Data FREE CHAPTER 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

Using power transformations on numerical variables

Exponential or power functions are mathematical transformations that follow , where lambda can be any exponent. The square and cube root transformations are special cases of power transformations where lambda is 1/2 or 1/3, respectively. In practice, we try different lambdas to determine which one offers the best transformation. In this recipe, we will carry out power transformations using NumPy, scikit-learn, and Feature-engine.

How to do it...

Let's begin by importing the libraries and getting the dataset ready:

  1. Import the required Python libraries and classes:
import numpy as np
import pandas as pd
from sklearn.datasets import load_boston
from sklearn.preprocessing...
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