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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

Dataset Understanding – Exploratory Data Analysis

In this chapter, we introduce and explain the main techniques of exploratory data analysis (EDA). We start by explaining the general goal of this stage of the predictive analytics process, and discuss how we accomplish this.

A natural and common way to classify EDA techniques is by the number of variables involved in the analysis—one, two, or more than two. Hence, this chapter has sections on univariate, bivariate, and multivariate analysis. Within the univariate and bivariate types of analysis, we have different numerical and graphical techniques that depend on the type of feature we are working with.

In this chapter, we use the diamond prices dataset to introduce and illustrate the main techniques of univariate and bivariate EDA. We will provide examples of how to produce the main visualizations used in analytics...

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