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R Machine Learning By Example

You're reading from   R Machine Learning By Example Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully

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Product type Paperback
Published in Mar 2016
Publisher
ISBN-13 9781784390846
Length 340 pages
Edition 1st Edition
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Author (1):
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Raghav Bali Raghav Bali
Author Profile Icon Raghav Bali
Raghav Bali
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Table of Contents (10) Chapters Close

Preface 1. Getting Started with R and Machine Learning FREE CHAPTER 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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