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

Building a hybrid recommendation system for Jokes recommendations

We see that both content-based filtering and collaborative filtering have their strengths and weaknesses. To overcome the issues, organizations build recommender systems that combine two or more technique and they are termed hybrid recommendation models. An example of this is a combination of content-based, IBCF, UBCF, and model-based recommender engine. This takes into account all the possible aspects that contribute to making the most relevant recommendation to the user. The following diagram shows an example approach followed in hybrid recommendation engines:

Sample approach to hybrid recommendation engine

We need to note that there is no standard approach to achieving a hybrid recommendation engine. In order to combine recommendations, here are some suggested strategies:

  • Voting: Apply voting among the recommendation...