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

Understanding customer segmentation

Customer segmentation, or market segmentation, at a basic level, is the partitioning of a broad range of potential customers in a given market into specific subgroups of customers, where each of the subgroups contains customers that share certain similarities. The following diagram depicts the formal definition of customer segmentation where customers are identified into three groups:

Illustration depicting customer segmentation definition

Customer segmentation needs the organizations to gather data about customers and analyze it to identify patterns that can be used to determine subgroups. The segmentation of customers could be achieved through multiple data points related to customers. The following are some of the data points:

  • Demographics: This data point includes race, ethnicity, age, gender, religion, level of education, income, life...