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 this chapter, we learned about the concept of segmentation and its association with clustering, an ML unsupervised learning technique. We made use of the wholesale dataset available from the UCI repository and implemented clustering using the k-means, DIANA, and AGNES algorithms. During the course of this chapter, we also studied various aspects related to clustering, such as tendency to cluster, distance, linkage measures, and methods to identify the right number of clusters, and measuring the output of clustering. We also explored making use of the clustering output for customer-segmentation purposes.

Can computers see and identify objects and living creatures like humans do? Let's explore the answer to this question in the next chapter.