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Hands-On Predictive Analytics with Python

You're reading from   Hands-On Predictive Analytics with Python Master the complete predictive analytics process, from problem definition to model deployment

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789138719
Length 330 pages
Edition 1st Edition
Languages
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Author (1):
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Alvaro Fuentes Alvaro Fuentes
Author Profile Icon Alvaro Fuentes
Alvaro Fuentes
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Table of Contents (11) Chapters Close

Preface 1. The Predictive Analytics Process FREE CHAPTER 2. Problem Understanding and Data Preparation 3. Dataset Understanding – Exploratory Data Analysis 4. Predicting Numerical Values with Machine Learning 5. Predicting Categories with Machine Learning 6. Introducing Neural Nets for Predictive Analytics 7. Model Evaluation 8. Model Tuning and Improving Performance 9. Implementing a Model with Dash 10. Other Books You May Enjoy

Introduction to graphical multivariate EDA

As you may have guessed, multivariate EDA involves more than two variables. Here is where you will definitely get creative. A great deal of practice and effort is needed, to use the visualization and numerical techniques we've talked about so far in novel and interesting ways.

Common multivariate EDA techniques include:

  • Coloring a scatter plot by categorical feature
  • Using another categorical variable in the boxplots
  • Conditional or lattice plots: dividing the analysis by different categories
  • Parallel plots
  • Heatmaps
  • Principal component analysis and related plots

There are, of course, many others that we don't have the space to cover here. We will just provide a few examples of multivariate EDA to get you started.

For this section, we will go back to our credit card default dataset. Let's begin by importing the necessary...

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