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

Identifying the customer segments in the wholesale customers data using AGNES

AGNES is the reverse of DIANA in the sense that it follows a bottom-up approach to clustering the dataset. The following diagram illustrates the working principle of the AGNES algorithm for clustering:

Working of agglomerative hierarchical clustering algorithm

Except for the bottom-up approach followed by AGNES, the implementation details behind the algorithm are the same as for DIANA; therefore, we won't repeat the discussion of the concepts here. The following code block clusters our wholesale dataset into three clusters with AGNES; it also creates a visualization of the clusters thus formed:

# setting the working directory to a folder where dataset is located
# reading the dataset to cust_data dataframe
cust_data = read.csv(file='Wholesale_customers_...