R Machine Learning By Example

Raghav Bali, Dipanjan Sarkar

• Éditeur: Packt Publishing
• Année: 2016
• ISBN:
  9781784390846
• Édition: 1
• Sujet: Computer Science
• Sous-sujet: Data Modelling & Design

Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully

About This Book

• Get to grips with the concepts of machine learning through exciting real-world examples
• Visualize and solve complex problems by using power-packed R constructs and its robust packages for machine learning
• Learn to build your own machine learning system with this example-based practical guide

Who This Book Is For

If you are interested in mining useful information from data using state-of-the-art techniques to make data-driven decisions, this is a go-to guide for you. No prior experience with data science is required, although basic knowledge of R is highly desirable. Prior knowledge in machine learning would be helpful but is not necessary.

What You Will Learn

• Utilize the power of R to handle data extraction, manipulation, and exploration techniques
• Use R to visualize data spread across multiple dimensions and extract useful features
• Explore the underlying mathematical and logical concepts that drive machine learning algorithms
• Dive deep into the world of analytics to predict situations correctly
• Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action
• Write reusable code and build complete machine learning systems from the ground up
• Solve interesting real-world problems using machine learning and R as the journey unfolds
• Harness the power of robust and optimized R packages to work on projects that solve real-world problems in machine learning and data science

In Detail

Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to making machine learning give them data-driven insights to grow their business. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems.

This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems.

You'll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of machine learning, following current trends and cover major algorithms as well as popular R packages in detail. These projects have been neatly divided into six different chapters covering the worlds of e-commerce, finance, and social-media, which are at the very core of this data-driven revolution. Each of the projects will help you to understand, explore, visualize, and derive insights depending upon the domain and algorithms.

Through this book, you will learn to apply the concepts of machine learning to deal with data-related problems and solve them using the powerful yet simple language, R.

Style and approach

The book is an enticing journey that starts from the very basics to gradually pick up pace as the story unfolds. Each concept is first defined in the larger context of things succinctly, followed by a detailed explanation of their application. Each topic is explained with the help of a project that solves a real real-world problem involving hands-on work thus giving you a deep insight into the world of machine learning.

R Machine Learning By Example

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Table of Contents

R Machine Learning By Example

Credits

About the Authors

About the Reviewer

www.PacktPub.com

eBooks, discount offers, and more

Why subscribe?

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. Getting Started with R and Machine Learning

Delving into the basics of R

Using R as a scientific calculator

Operating on vectors

Special values

Data structures in R

Vectors

Creating vectors

Indexing and naming vectors

Arrays and matrices

Creating arrays and matrices

Names and dimensions

Matrix operations

Lists

Creating and indexing lists

Combining and converting lists

Data frames

Creating data frames

Operating on data frames

Working with functions

Built-in functions

User-defined functions

Passing functions as arguments

Controlling code flow

Working with if, if-else, and ifelse

Working with switch

Loops

Advanced constructs

lapply and sapply

apply

tapply

mapply

Next steps with R

Getting help

Handling packages

Machine learning basics

Machine learning – what does it really mean?

Machine learning – how is it used in the world?

Types of machine learning algorithms

Supervised machine learning algorithms

Unsupervised machine learning algorithms

Popular machine learning packages in R

Summary

2. Let's Help Machines Learn

Understanding machine learning

Algorithms in machine learning

Perceptron

Families of algorithms

Supervised learning algorithms

Linear regression

K-Nearest Neighbors (KNN)

Collecting and exploring data

Normalizing data

Creating training and test data sets

Learning from data/training the model

Evaluating the model

Unsupervised learning algorithms

Apriori algorithm

K-Means

Summary

3. Predicting Customer Shopping Trends with Market Basket Analysis

Detecting and predicting trends

Market basket analysis

What does market basket analysis actually mean?

Core concepts and definitions

Techniques used for analysis

Making data driven decisions

Evaluating a product contingency matrix

Getting the data

Analyzing and visualizing the data

Global recommendations

Advanced contingency matrices

Frequent itemset generation

Getting started

Data retrieval and transformation

Building an itemset association matrix

Creating a frequent itemsets generation workflow

Detecting shopping trends

Association rule mining

Loading dependencies and data

Exploratory analysis

Detecting and predicting shopping trends

Visualizing association rules

Summary

4. Building a Product Recommendation System

Understanding recommendation systems

Issues with recommendation systems

Collaborative filters

Core concepts and definitions

The collaborative filtering algorithm

Predictions

Recommendations

Similarity

Building a recommender engine

Matrix factorization

Implementation

Result interpretation

Production ready recommender engines

Extract, transform, and analyze

Model preparation and prediction

Model evaluation

Summary

5. Credit Risk Detection and Prediction – Descriptive Analytics

Types of analytics

Our next challenge

What is credit risk?

Getting the data

Data preprocessing

Dealing with missing values

Datatype conversions

Data analysis and transformation

Building analysis utilities

Analyzing the dataset

Saving the transformed dataset

Next steps

Feature sets

Machine learning algorithms

Summary

6. Credit Risk Detection and Prediction – Predictive Analytics

Predictive analytics

How to predict credit risk

Important concepts in predictive modeling

Preparing the data

Building predictive models

Evaluating predictive models

Getting the data

Data preprocessing

Feature selection

Modeling using logistic regression

Modeling using support vector machines

Modeling using decision trees

Modeling using random forests

Modeling using neural networks

Model comparison and selection

Summary

7. Social Media Analysis – Analyzing Twitter Data

Social networks (Twitter)

Data mining @social networks

Mining social network data

Data and visualization

Word clouds

Treemaps

Pixel-oriented maps

Other visualizations

Getting started with Twitter APIs

Overview

Registering the application

Connect/authenticate

Extracting sample tweets

Twitter data mining

Frequent words and associations

Popular devices

Hierarchical clustering

Topic modeling

Challenges with social network data mining

References

Summary

8. Sentiment Analysis of Twitter Data

Understanding Sentiment Analysis

Key concepts of sentiment analysis

Subjectivity

Sentiment polarity

Opinion summarization

Feature extraction

Approaches

Applications

Challenges

Sentiment analysis upon Tweets

Polarity analysis

Classification-based algorithms

Labeled dataset

Support Vector Machines

Ensemble methods

Boosting

Cross-validation

Summary

Index

R Machine Learning By Example

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