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

Predicting Employee Attrition Using Ensemble Models

If you reviewed the recent machine learning competitions, one key observation I am sure you would make is that the recipes of all three winning entries in most of the competitions include very good feature engineering, along with well-tuned ensemble models. One conclusion I derive from this observation is that good feature engineering and building well-performing models are two areas that should be given equal emphasis in order to deliver successful machine learning solutions.

While feature engineering most times is something that is dependent on the creativity and domain expertise of the person building the model, building a well-performing model is something that can be achieved through a philosophy called ensembling. Machine learning practitioners often use ensembling techniques to beat the performance benchmarks yielded by...