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R Machine Learning By Example

R Machine Learning By Example

By : Raghav Bali
4.6 (14)
Buy this Book
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R Machine Learning By Example

R Machine Learning By Example

4.6 (14)
By: Raghav Bali
Buy this Book

Overview of this book

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.
Table of Contents (10 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
Free Chapter
1
1. Getting Started with R and Machine Learning
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1. Getting Started with R and Machine Learning
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Delving into the basics of R
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Data structures in R
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Working with functions
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Controlling code flow
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Advanced constructs
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Next steps with R
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Machine learning basics
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Summary
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2. Let's Help Machines Learn
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2. Let's Help Machines Learn
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Understanding machine learning
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Algorithms in machine learning
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Families of algorithms
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Summary
3
3. Predicting Customer Shopping Trends with Market Basket Analysis
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3. Predicting Customer Shopping Trends with Market Basket Analysis
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Detecting and predicting trends
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Market basket analysis
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Evaluating a product contingency matrix
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Frequent itemset generation
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Association rule mining
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Summary
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4. Building a Product Recommendation System
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4. Building a Product Recommendation System
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Understanding recommendation systems
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Issues with recommendation systems
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Collaborative filters
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Building a recommender engine
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Production ready recommender engines
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Summary
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5. Credit Risk Detection and Prediction – Descriptive Analytics
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5. Credit Risk Detection and Prediction – Descriptive Analytics
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Types of analytics
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Our next challenge
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What is credit risk?
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Getting the data
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Data preprocessing
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Data analysis and transformation
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Next steps
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Summary
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6. Credit Risk Detection and Prediction – Predictive Analytics
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6. Credit Risk Detection and Prediction – Predictive Analytics
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Predictive analytics
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How to predict credit risk
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Important concepts in predictive modeling
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Getting the data
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Data preprocessing
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Feature selection
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Modeling using logistic regression
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Modeling using support vector machines
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Modeling using decision trees
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Modeling using random forests
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Modeling using neural networks
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Model comparison and selection
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Summary
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7. Social Media Analysis – Analyzing Twitter Data
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7. Social Media Analysis – Analyzing Twitter Data
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Social networks (Twitter)
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Data mining @social networks
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Getting started with Twitter APIs
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Twitter data mining
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Challenges with social network data mining
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References
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Summary
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8. Sentiment Analysis of Twitter Data
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8. Sentiment Analysis of Twitter Data
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Understanding Sentiment Analysis
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Sentiment analysis upon Tweets
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Summary
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Index
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Index

Chapter 1. Getting Started with R and Machine Learning

This introductory chapter will get you started with the basics of R which include various constructs, useful data structures, loops and vectorization. If you are already an R wizard, you can skim through these sections and dive right into the next part which talks about what machine learning actually represents as a domain and the main areas it encompasses. We will also talk about different machine learning techniques and algorithms used in each area. Finally, we will conclude by looking at some of the most popular machine learning packages in R, some of which we will be using in the subsequent chapters.

If you are a data or machine learning enthusiast, surely you would have heard by now that being a data scientist is referred to as the sexiest job of the 21st century by Harvard Business Review.

Note

Reference: https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/

There is a huge demand in the current market for data scientists, primarily because their main job is to gather crucial insights and information from both unstructured and structured data to help their business and organization grow strategically.

Some of you might be wondering how machine learning or R relate to all this! Well, to be a successful data scientist, one of the major tools you need in your toolbox is a powerful language capable of performing complex statistical calculations and working with various types of data and building models which help you get previously unknown insights and R is the perfect language for that! Machine learning forms the foundation of the skills you need to build to become a data analyst or data scientist, this includes using various techniques to build models to get insights from data.

This book will provide you with some of the essential tools you need to be well versed with both R and machine learning by not only looking at concepts but also applying those concepts in real-world examples. Enough talk; now let's get started on our journey into the world of machine learning with R!

In this chapter, we will cover the following aspects:

  • Delving into the basics of R
  • Understanding the data structures in R
  • Working with functions
  • Controlling code flow
  • Taking further steps with R
  • Understanding machine learning basics
  • Familiarizing yourself with popular machine learning packages in R
End of Section 1
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