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


In all the ensembles we have learned about so far, we have manipulated the dataset in certain ways and exposed subsets of the data for model building. However, in stacking, we are not going to do anything with the dataset; instead we are going to apply a different technique that involves using multiple ML algorithms instead. In stacking, we build multiple models with various ML algorithms. Each algorithm possesses a unique way of learning the characteristics of data and the final stacked model indirectly incorporates all those unique ways of learning. Stacking gets the combined power of several ML algorithms through getting the final prediction by means of voting or averaging as we do in other types of ensembles.

Building attrition prediction model with stacking