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


To recollect, we were using a class-imbalanced dataset to build the attrition model. Using techniques to resolve the class imbalance prior to model building is another key aspect of getting better model performance measurements. We used bagging, randomization, boosting, and stacking to implement and predict the attrition model. We were able to accomplish 91% accuracy just by using the features that were readily available in the models. Feature engineering is a crucial aspect whose role cannot be ignored in ML models. This may be one other path to explore to improve model performance further.

In the next chapter, we will explore the secret recipe of recommending products or content through building a personalized recommendation engines. I am all set to implement a project to recommend jokes. Turn to the next chapter to continue the journey of learning.