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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

Challenges with social network data mining


Before we close the chapter, let us look at the different challenges posed by social networks to the process of data mining. The following points present a few arguments, questions, and challenges:

  • No doubt the data generated by social networks classifies as big data in every aspect. It has all the volume, velocity, and variety in it to overwhelm any system. Yet, interestingly, the challenge with such a huge source of data is the availability of enough granular data. If we zoom into our data sets and try to use data on a per user basis, we find that there isn't enough data to do some of the most common tasks, such as making recommendations!

  • Social networks such as Twitter handle millions of users creating and sharing tons of data every second. To keep their systems up and running at all times, they put limits upon the amount of data that can be tapped using their APIs (security is also a major reason behind these limits, though). These limits put...

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