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R Data Analysis Cookbook, Second Edition

You're reading from   R Data Analysis Cookbook, Second Edition Customizable R Recipes for data mining, data visualization and time series analysis

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787124479
Length 560 pages
Edition 2nd Edition
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Authors (3):
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Kuntal Ganguly Kuntal Ganguly
Author Profile Icon Kuntal Ganguly
Kuntal Ganguly
Shanthi Viswanathan Shanthi Viswanathan
Author Profile Icon Shanthi Viswanathan
Shanthi Viswanathan
Viswa Viswanathan Viswa Viswanathan
Author Profile Icon Viswa Viswanathan
Viswa Viswanathan
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Toc

Table of Contents (14) Chapters Close

Preface 1. Acquire and Prepare the Ingredients - Your Data FREE CHAPTER 2. What's in There - Exploratory Data Analysis 3. Where Does It Belong? Classification 4. Give Me a Number - Regression 5. Can you Simplify That? Data Reduction Techniques 6. Lessons from History - Time Series Analysis 7. How does it look? - Advanced data visualization 8. This may also interest you - Building Recommendations 9. It's All About Your Connections - Social Network Analysis 10. Put Your Best Foot Forward - Document and Present Your Analysis 11. Work Smarter, Not Harder - Efficient and Elegant R Code 12. Where in the World? Geospatial Analysis 13. Playing Nice - Connecting to Other Systems

Introduction

With gigantic growth of information worldwide and the significant rise of users, companies nowadays are analyzing the past behavior of users to build intelligent applications to provide recommendations and choices of interest in terms of Relevant Job postings, Movies of Interest, Suggested Videos, Friends, or People You May Know, and so on. A Recommender System provides information or items that are likely to be of interest to a user in an automated fashion.

In this chapter, we will build, evaluate, and optimize three different categories of recommender systems: Content-based Recommenders, Collaborative Filtering, and Hybrid Recommenders.

The following illustration is indicative of their relations:

This chapter provides recipes for you to exploit all of these capabilities.

We will also build an image recognition system using deep learning and, finally, you will learn...

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