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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

Concepts

Machine Learning

Author (1):

Soledad Galli

Table of Contents (13) Chapters

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

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Removing observations with missing data

Complete Case Analysis (CCA), also called list-wise deletion of cases, consists of discarding those observations where the values in any of the variables are missing. CCA can be applied to categorical and numerical variables. CCA is quick and easy to implement and has the advantage that it preserves the distribution of the variables, provided the data is missing at random and only a small proportion of the data is missing. However, if data is missing across many variables, CCA may lead to the removal of a big portion of the dataset.

How to do it...

Let's begin by loading pandas and the dataset:

First, we'll import the pandas library:

import pandas...

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Python Feature Engineering Cookbook

Published in: Jan 2020 Publisher: Packt ISBN-13: 9781789806311

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Authors (1)

Soledad Galli

Soledad Galli is a lead data scientist with more than 10 years of experience in world-class academic institutions and renowned businesses. She has researched, developed, and put into production machine learning models for insurance claims, credit risk assessment, and fraud prevention. Soledad received a Data Science Leaders' award in 2018 and was named one of LinkedIn's voices in data science and analytics in 2019. She is passionate about enabling people to step into and excel in data science, which is why she mentors data scientists and speaks at data science meetings regularly. She also teaches online courses on machine learning in a prestigious Massive Open Online Course platform, which have reached more than 10,000 students worldwide.

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