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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

Understanding recommendation systems

Every individual in unique, the way we do things is what defines us uniquely. We eat, walk, talk, and even shop in a very unique way. Since the focus of this chapter is e-commerce, we will focus mostly on our shopping behaviors. We will utilize each customer's unique behavior to provide a personalized shopping experience.

To accomplish the task of providing a personalized shopping experience, we need a system to understand and model our customers. Recommendation engines are the systems which learn about customer preferences, choices, and so on, to recommend new products which are closer to what the user might have purchased themselves, thus providing a personalized experience. The options presented by such systems would have a high probability of the customer purchasing them.

Let us try to formally define a recommendation system.

Recommendation systems (or recommender engines) are a class of information filtering systems which analyze the input data...

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