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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

A more structured approach to predictive analytics

We near to finally draw conclusions from our predictive analyses, so I think it is the right moment to make you clear that the one we have followed here is a simplified approach to such kind of analyses.

We already talked before about the difference between training and testing dataset, let me place this into the context of predictive analysis.

When you estimate a model for the purpose of gaining prediction about the future, you are basically assuming that the relative importance of variables and all the other circumstances observed in your estimation sample will be found as well in future data and that therefore we can draw prediction about the future from past data.

What if this is not true? In this case, we would have a model able to describe the past but not to predict the future.

In a similar way, if we specify our model in a way which is too much related to the actual data available within our estimation sample, we are going to incur in...