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Statistical Application Development with R and Python

You're reading from Statistical Application Development with R and Python Develop applications using data processing, statistical models, and CART

Product type Paperback

Published in Aug 2017

Publisher

ISBN-13 9781788621199

Length 432 pages

Edition 2nd Edition

Languages

Python

Concepts

Application Development

Author (1):

Prabhanjan Narayanachar Tattar

View More author details

Table of Contents (12) Chapters

Preface

1. Data Characteristics FREE CHAPTER

2. Import/Export Data

3. Data Visualization

4. Exploratory Analysis

5. Statistical Inference

6. Linear Regression Analysis

7. Logistic Regression Model

8. Regression Models with Regularization

9. Classification and Regression Trees

10. CART and Beyond

Index

Visualization techniques for categorical data

In Chapter 1, Data Characteristics, we came across many variables whose outcomes are categorical in nature. Gender, Car_Model, Minor_Problems, Major_Problems, and Satisfaction_Rating are examples of categorical data. In a software product development cycle, various issues or bugs are raised at different severity levels such as Minor and Show Stopper. Visualization methods for the categorical data require special attention and techniques, and the goal of this section is to aid the reader with some useful graphical tools.

In this section, we will mainly focus on the dataset related to bugs, which are of primary concern for any software engineer. The source of the datasets is http://bug.inf.usi.ch/ and the reader is advised to check the website before proceeding further in this section. We will begin with the software system Eclipse JDT Core, and the details for this system may be found at http://www.eclipse.org/jdt/core/index.php. The files for...

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