Book Image

R Machine Learning Projects

By : Dr. Sunil Kumar Chinnamgari
Book Image

R Machine Learning Projects

By: Dr. Sunil Kumar Chinnamgari

Overview of this book

R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You’ll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine. By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations.
Table of Contents (12 chapters)
The Road Ahead

Building a recommendation system with a user-based collaborative filtering technique

The Jokes recommendation system we built earlier, with item-based filtering, uses the powerful recommenderlab library available in R. In this implementation of the user-based collaborative filtering (UBCF) approach, we make use of the same library.

The following diagram shows the working principle of UBCF:

Example depicting working principle of user based collaborative filter

To understand the concept better, let's discuss the preceding diagram in detail. Let's assume that there are three users: X,Y, and Z. In UBCF, users X and Z are very similar as both of them like strawberries and watermelons. User X also likes grapes and oranges. So a user-based collaborative filter recommends grapes and oranges to user Z. The idea is that similar people tend to like similar things.

The primary...