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

The sentiment analysis problem

Sentiment analysis is one of the most general text classification applications. The purpose of it is to analyze messages such as user reviews, and feedback from employees, in order to identify whether the underlying sentiment is positive, negative, or neutral.

Analyzing and reporting sentiment in texts allows businesses to quickly get a consolidated high-level insight without having to read each one of the comments received.

While it is possible to generate holistic sentiment based on the overall comments received, there is also an extended area called aspect-based sentiment analysis. It is focused on deriving sentiment based on each area of the service. For example, a customer that visited a restaurant when writing a review would generally cover areas such as ambience, food quality, service quality, and price. Though the feedback about each of...