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)
10
The Road Ahead

Content-based recommendation engine

A recommendation engine that is solely based on the explicit or implicit feedback received from customers is termed as content-based recommendation system. Explicit feedback is the customer's expression of the interest through filling in a survey about preferences or rating jokes of interest or opting for newsletters related to the joke or adding the joke on the watchlist, and so on. Implicit feedback is more of a mellowed-out approach where a customer visits a page, clicks on a joke link, or just spends time reading a joke review on an e-commerce page. Based on the feedback received, similar jokes are recommended to the customers. It may be noted that content-based recommendations do not take into consideration the preferences and feedback of other customers in the system; instead, it is purely based on the personalized feedback from the...