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You're reading from R Data Mining Implement data mining techniques through practical use cases and real-world datasets

Product type Paperback

Published in Nov 2017

Publisher Packt

ISBN-13 9781787124462

Length 442 pages

Edition 1st Edition

Languages

Tools

ggplot

Concepts

Data Mining

Author (1):

Andrea Cirillo

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Table of Contents (16) Chapters

Preface

1. Why to Choose R for Your Data Mining and Where to Start

2. A First Primer on Data Mining Analysing Your Bank Account Data FREE CHAPTER

3. The Data Mining Process - CRISP-DM Methodology

4. Keeping the House Clean – The Data Mining Architecture

5. How to Address a Data Mining Problem – Data Cleaning and Validation

6. Looking into Your Data Eyes – Exploratory Data Analysis

7. Our First Guess – a Linear Regression

8. A Gentle Introduction to Model Performance Evaluation

9. Don't Give up – Power up Your Regression Including Multiple Variables

10. A Different Outlook to Problems with Classification Models

11. The Final Clash – Random Forests and Ensemble Learning

12. Looking for the Culprit – Text Data Mining with R

13. Sharing Your Stories with Your Stakeholders through R Markdown

14. Epilogue

15. Dealing with Dates, Relative Paths and Functions

Business understanding

This is an often underestimated phase, and we should look at it carefully since its role is decisive for all of the remaining phases. Within the business understanding phase, we fundamentally answer the following two questions:

What are the objectives of the business where the data mining problem is coming from?
What are the data mining goals for this project?

Giving the wrong answer to either one of these two questions will result in producing results not relevant for the business, or not solving the data mining problem at its core.

The first step in this phase is understanding your client's needs and objectives, since those objectives will become the objectives of the project. Within this phase, we gather information through the means of interviews and technical literature, finally defining a project plan and clearly stating a data mining goal and how we plan to reach it.

The project plan should not be considered an unchangeable one, since the following phases will naturally...