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R Machine Learning Projects

By : Dr. Sunil Kumar Chinnamgari

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Buy this Book

R Machine Learning Projects

1 (1)

By: Dr. Sunil Kumar Chinnamgari

Buy this Book

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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

Exploring the Machine Learning Landscape

ML versus software engineering

Types of ML methods

ML terminology – a quick review

ML project pipeline

Learning paradigm

Datasets

Summary

Predicting Employee Attrition Using Ensemble Models

Philosophy behind ensembling

Getting started

Understanding the attrition problem and the dataset

K-nearest neighbors model for benchmarking the performance

Bagging

Randomization with random forests

Boosting

Stacking

Summary

Implementing a Jokes Recommendation Engine

Fundamental aspects of recommendation engines

Getting started

Understanding the Jokes recommendation problem and the dataset

Building a recommendation system with an item-based collaborative filtering technique

Building a recommendation system with a user-based collaborative filtering technique

Building a recommendation system based on an association-rule mining technique

Content-based recommendation engine

Building a hybrid recommendation system for Jokes recommendations

Summary

References

Sentiment Analysis of Amazon Reviews with NLP

The sentiment analysis problem

Getting started

Understanding the Amazon reviews dataset

Building a text sentiment classifier with the BoW approach

Understanding word embedding

Building a text sentiment classifier with pretrained word2vec word embedding based on Reuters news corpus

Building a text sentiment classifier with GloVe word embedding

Building a text sentiment classifier with fastText

Summary

Customer Segmentation Using Wholesale Data

Understanding customer segmentation

Understanding the wholesale customer dataset and the segmentation problem

Identifying the customer segments in wholesale customer data using k-means clustering

Identifying the customer segments in the wholesale customer data using DIANA

Identifying the customer segments in the wholesale customers data using AGNES

Summary

Image Recognition Using Deep Neural Networks

Technical requirements

Understanding computer vision

Achieving computer vision with deep learning

Introduction to the MXNet framework

Understanding the MNIST dataset

Implementing a deep learning network for handwritten digit recognition

Implementing computer vision with pretrained models

Summary

Credit Card Fraud Detection Using Autoencoders

Machine learning in credit card fraud detection

Autoencoders explained

The credit card fraud dataset

Building AEs with the H2O library in R

Summary

Automatic Prose Generation with Recurrent Neural Networks

Understanding language models

Exploring recurrent neural networks

Backpropagation through time

Problems and solutions to gradients in RNN

Building an automated prose generator with an RNN

Summary

Winning the Casino Slot Machines with Reinforcement Learning

Understanding RL

Multi-arm bandit – real-world use cases

Solving the MABP with UCB and Thompson sampling algorithms

Summary

The Road Ahead

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Understanding the Jokes recommendation problem and the dataset

Dr. Ken Goldberg and his colleagues, Theresa Roeder, Dhruv Gupta, and Chris Perkins, introduced a dataset to the world through their paper Eigentaste: A Constant Time Collaborative Filtering Algorithm, which is pretty popular in the recommender-systems domain. The dataset is named the Jester's jokes dataset. To create it, a number of users are presented with several jokes and they are asked to rate them. The ratings provided by the users for the various jokes formed the dataset. The data in this dataset is collected between April 1999 and May 2003. The following are the attributes of the dataset: