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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

Transforming variables with the reciprocal function

The reciprocal function, defined as 1/x, is a strong transformation with a very drastic effect on the variable distribution. It isn't defined for the value 0, but it can be applied to negative numbers. In this recipe, we will implement the reciprocal transformation using NumPy, scikit-learn, and Feature-engine and compare its effect with a diagnostic function.

How to do it...

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

  1. Import the required Python libraries, methods, and classes:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.stats as stats
from sklearn.datasets import load_boston
from sklearn.preprocessing...
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