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 the Amazon reviews dataset

We use the Amazon product reviews polarity dataset for the various projects in this chapter. It is an open dataset constructed and made available by Xiang Zhang. It is used as a text classification benchmark in the paper: Character-level Convolutional Networks for Text Classification and Advances in Neural Information Processing Systems 28, Xiang Zhang, Junbo Zhao, Yann LeCun, (NIPS 2015).

The Amazon reviews polarity dataset is constructed by taking review score 1 and 2 as negative, 4 and 5 as positive. Samples of score 3 are ignored. In the dataset, class 1 is the negative and class 2 is the positive. The dataset has 1,800,000 training samples and 200,000 testing samples.

The train.csv and test.csv files contains all the samples as comma-separated values. There are three columns in them, corresponding to class index (1 or 2), review title...