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R Machine Learning By Example

You're reading from  R Machine Learning By Example

Product type Book
Published in Mar 2016
Publisher
ISBN-13 9781784390846
Pages 340 pages
Edition 1st Edition
Languages
Author (1):
Mr. Raghav Bali Mr. Raghav Bali
Author Profile Icon Mr. Raghav Bali
Mr. Raghav Bali
Toc

Table of Contents (15) Chapters Close

R Machine Learning By Example
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
1. Getting Started with R and Machine Learning 2. Let's Help Machines Learn 3. Predicting Customer Shopping Trends with Market Basket Analysis 4. Building a Product Recommendation System 5. Credit Risk Detection and Prediction – Descriptive Analytics 6. Credit Risk Detection and Prediction – Predictive Analytics 7. Social Media Analysis – Analyzing Twitter Data 8. Sentiment Analysis of Twitter Data Index

Data mining @social networks


We have traveled quite a distance so far through the chapters of this book, understanding various concepts and learning some amazing algorithms. We have even worked on projects that have applications in our daily lives. In short, we have done data mining without using the term explicitly. Let us now take this opportunity to formally define data mining.

Mining, in the classical sense of the word, refers to the extraction of useful minerals from the Earth (such as coal mining). Put in the context of the information age, mining refers to the extraction of useful information from large pools of data. Thus, if we look carefully, Knowledge Mining or Knowledge Discovery from Data (KDD) seems to be a better representation than the term data mining. As is the case with many keywords, short and sweet catches the attention. Thus, you may find in many places the terms Knowledge Discovery from Data and data mining being used interchangeably, which is rightly so. The process...

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