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Python Feature Engineering Cookbook

You're reading from   Python Feature Engineering Cookbook A complete guide to crafting powerful features for your machine learning models

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835883587
Length 396 pages
Edition 3rd 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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Toc

Table of Contents (14) Chapters Close

Preface 1. Chapter 1: Imputing Missing Data 2. Chapter 2: Encoding Categorical Variables FREE CHAPTER 3. Chapter 3: Transforming Numerical Variables 4. Chapter 4: Performing Variable Discretization 5. Chapter 5: Working with Outliers 6. Chapter 6: Extracting Features from Date and Time Variables 7. Chapter 7: Performing Feature Scaling 8. Chapter 8: Creating New Features 9. Chapter 9: Extracting Features from Relational Data with Featuretools 10. Chapter 10: Creating Features from a Time Series with tsfresh 11. Chapter 11: Extracting Features from Text Variables 12. Index 13. Other Books You May Enjoy

Transforming variables with the reciprocal function

The reciprocal function is defined as 1/x. It is often useful when we have ratios – that is, values resulting from the division of two variables. Examples of this are population density – that is, people per area – and, as we will see in this recipe, house occupancy – that is, the number of occupants per house.

The reciprocal transformation is not defined for the 0 value, and although it is defined for negative values, it is mainly useful for transforming positive variables.

In this recipe, we will implement the reciprocal transformation using NumPy, scikit-learn, and Feature-engine, and compare its effect on variable distribution using histograms and a Q-Q plot.

How to do it...

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

  1. Import the required Python libraries and data:
    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    import scipy.stats...
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