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.

Related Content you might be interested in

Current Title:

Small book image
R Machine Learning Projects
Dr. Sunil Kumar Chinnamgari
Jan, 2019 | 334 pages
Small book image
Mastering Machine Learning with R
Cory Lesmeister
Apr, 2017 | 420 pages
Small book image
R Data Analysis Projects
Gopi Subramanian
Nov, 2017 | 366 pages
Small book image
R Deep Learning Projects
Yuxi Hayden Liu | Pablo Maldonado
Feb, 2018 | 258 pages
Table of Contents (12 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Exploring the Machine Learning Landscape
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
2
Predicting Employee Attrition Using Ensemble Models
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
3
Implementing a Jokes Recommendation Engine
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
4
Sentiment Analysis of Amazon Reviews with NLP
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
5
Customer Segmentation Using Wholesale Data
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
6
Image Recognition Using Deep Neural Networks
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
7
Credit Card Fraud Detection Using Autoencoders
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
8
Automatic Prose Generation with Recurrent Neural Networks
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
9
Winning the Casino Slot Machines with Reinforcement Learning
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
10
The Road Ahead
The Road Ahead
11
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

Fundamental aspects of recommendation engines

While the basic intent of showing recommendations is to push sales, they actually serve just beyond the better sales concept. Highly personalized content is something recommendation engines are able to deliver. This essentially means that recommendation engines on a retail platform such as Amazon are able to offer the right content to the right customer at the right time through the right channel. It makes sense to provide personalized content; after all, there is no point in showing an irrelevant product to a customer. Also, with the lower attention spans of customers, businesses want to be able to maximize their selling opportunities by showing the right products and encouraging them to buy the right products. At a very high level, personalized content recommendation is achieved in AI in several ways:

  • Mapping similar products that...
Previous Section
End of Section 2
Next Section