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Mastering Predictive Analytics with R

Mastering Predictive Analytics with R

3.9 (18)
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Mastering Predictive Analytics with R

Mastering Predictive Analytics with R

3.9 (18)

Overview of this book

This book is intended for the budding data scientist, predictive modeler, or quantitative analyst with only a basic exposure to R and statistics. It is also designed to be a reference for experienced professionals wanting to brush up on the details of a particular type of predictive model. Mastering Predictive Analytics with R assumes familiarity with only the fundamentals of R, such as the main data types, simple functions, and how to move data around. No prior experience with machine learning or predictive modeling is assumed, however you should have a basic understanding of statistics and calculus at a high school level.
Table of Contents (13 chapters)
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12
Index

R and Big Data


Before we dive deep into building a few recommender systems using real-world data sets, we'll take a short detour and spend some time thinking about Big Data. Many real-world recommender systems arise out of the analysis of massive data sets. Examples include the product recommendation engine of amazon.com and the movie recommendation engine of Netflix.

Most, if not all, of the data sets that we have looked at in this book have been relatively small in size and have been chosen quite intentionally in order for the reader to be able to follow along with the examples and not have to worry about having access to powerful computing resources. These days, the field of predictive analytics, as well as the related fields of machine learning, data science, and data analysis in general, is heavily concerned with the importance of handling Big Data.

Note

The term Big Data has become a buzzword that has entered everyday conversation and as an inevitable result, we often encounter uses that...

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