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Mastering Predictive Analytics with R, Second Edition

You're reading from   Mastering Predictive Analytics with R, Second Edition Machine learning techniques for advanced models

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787121393
Length 448 pages
Edition 2nd Edition
Languages
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Authors (2):
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James D. Miller James D. Miller
Author Profile Icon James D. Miller
James D. Miller
Rui Miguel Forte Rui Miguel Forte
Author Profile Icon Rui Miguel Forte
Rui Miguel Forte
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Table of Contents (16) Chapters Close

Preface 1. Gearing Up for Predictive Modeling FREE CHAPTER 2. Tidying Data and Measuring Performance 3. Linear Regression 4. Generalized Linear Models 5. Neural Networks 6. Support Vector Machines 7. Tree-Based Methods 8. Dimensionality Reduction 9. Ensemble Methods 10. Probabilistic Graphical Models 11. Topic Modeling 12. Recommendation Systems 13. Scaling Up 14. Deep Learning Index

Loading and pre-processing the data


Our first goal in building our recommender systems is to load the data in R, preprocess it, and convert it into a rating matrix. More precisely, in each case, we will be creating a realRatingMatrix object, which is the specific data structure that the recommenderlab package uses to store numerical ratings. We will start with the jester datasets. If we download and unzip the archive from the website, we'll see that the file jesterfinal151cols.csv contains the ratings. More specifically, each row in this file corresponds to the ratings made by a particular user, and each column corresponds to a particular joke.

The columns are comma-separated and there is no header row. In fact, the format is almost already a rating matrix, were it not for the fact that the first column is a special column and contains the total number of ratings made by a particular user. We will load this data into a data table using the function fread(), which is a fast implementation...

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