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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

Types of analytics


Before we start tackling our next challenge, it will be useful to get an idea of the different types of analytics which broadly encompass the data science domain. We use a variety of data mining and machine learning techniques to solve different data problems. However, depending on the mechanism of the technique and its end result, we can broadly classify analytics into four different types which are explained next:

  • Descriptive analytics: This is what we use when we have some data to analyze. We start with looking at the different attributes of the data, extract meaningful features, and use statistics and visualizations to understand what has already happened. The main aim of descriptive analytics is to get a broad idea of what kind of data we are dealing with and summarize what has happened in the past. Above almost 80% of all analytics in businesses today are descriptive.

  • Diagnostic analytics: This is sometimes clubbed together with descriptive analytics. Here the main...

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