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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 logarithm

The logarithm function is commonly used to transform variables. It has a strong effect on the shape of the variable distribution and can only be applied to positive variables. In this recipe, we will learn how to perform logarithmic transformation using NumPy, scikit-learn, and Feature-engine. We will also create a diagnostic plot function to evaluate the effect of the transformation on the variable distribution.

How to do it...

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

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