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 based on an association-rule mining technique

Association-rule mining, or market-basket analysis, is a very popular data mining technique used in the retail industry to identify the products that need to be kept together so as to encourage cross sales. An interesting aspect behind this algorithm is that historical invoices are mined to identify the products that are bought together.

There are several off-the-shelf algorithms available to perform market-basket analysis. Some of them are Apriori, equivalence class transformation (ECLAT), and frequent pattern growth (FP-growth). We will learn to solve our problem of recommending jokes to users through applying the Apriori algorithm on the Jester jokes dataset. We will now learn the theoretical aspects that underpin the Apriori algorithm.