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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

Predictive analytics


We had already discussed a fair bit about predictive analytics in the previous chapter to give you a general overview of what it means. We will be discussing it in more detail in this section. Predictive analytics can be defined as a subset of the machine learning universe, which encompasses a wide variety of supervised learning algorithms based on data science, statistics, and mathematical formulae which enable us to build predictive models using these algorithms and data which has already been collected. These models enable us to make predictions of what might happen in the future based on past observations. Combining this with domain knowledge, expertise, and business logic enables analysts to make data driven decisions using these predictions, which is the ultimate outcome of predictive analytics.

The data we are talking about here is data which has already been observed in the past and has been collected over a period of time for analysis. This data is often known...

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