Book Image

R Machine Learning By Example

By : Raghav Bali
Book Image

R Machine Learning By Example

By: Raghav Bali

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.

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R Machine Learning By Example
Raghav Bali
Mar, 2016 | 340 pages
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Table of Contents (15 chapters)
R Machine Learning By Example
R Machine Learning By Example
Credits
Credits
About the Authors
About the Authors
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Getting Started with R and Machine Learning
Getting Started with R and Machine Learning
Delving into the basics of R
Data structures in R
Working with functions
Controlling code flow
Advanced constructs
Next steps with R
Machine learning basics
Summary
2
Let's Help Machines Learn
Let's Help Machines Learn
Understanding machine learning
Algorithms in machine learning
Families of algorithms
Summary
3
Predicting Customer Shopping Trends with Market Basket Analysis
Predicting Customer Shopping Trends with Market Basket Analysis
Detecting and predicting trends
Market basket analysis
Evaluating a product contingency matrix
Frequent itemset generation
Association rule mining
Summary
4
Building a Product Recommendation System
Building a Product Recommendation System
Understanding recommendation systems
Issues with recommendation systems
Collaborative filters
Building a recommender engine
Production ready recommender engines
Summary
5
Credit Risk Detection and Prediction – Descriptive Analytics
Credit Risk Detection and Prediction – Descriptive Analytics
Types of analytics
Our next challenge
What is credit risk?
Getting the data
Data preprocessing
Data analysis and transformation
Next steps
Summary
6
Credit Risk Detection and Prediction – Predictive Analytics
Credit Risk Detection and Prediction – Predictive Analytics
Predictive analytics
How to predict credit risk
Important concepts in predictive modeling
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 Media Analysis – Analyzing Twitter Data
Social networks (Twitter)
Data mining @social networks
Getting started with Twitter APIs
Twitter data mining
Challenges with social network data mining
References
Summary
8
Sentiment Analysis of Twitter Data
Sentiment Analysis of Twitter Data
Understanding Sentiment Analysis
Sentiment analysis upon Tweets
Summary
Index
Index

Types of analytics

Before we start tackling our next challenge, it will be useful to get an idea of the different types of analytics which broadly encompass the data science domain. We use a variety of data mining and machine learning techniques to solve different data problems. However, depending on the mechanism of the technique and its end result, we can broadly classify analytics into four different types which are explained next:

  • Descriptive analytics: This is what we use when we have some data to analyze. We start with looking at the different attributes of the data, extract meaningful features, and use statistics and visualizations to understand what has already happened. The main aim of descriptive analytics is to get a broad idea of what kind of data we are dealing with and summarize what has happened in the past. Above almost 80% of all analytics in businesses today are descriptive.

  • Diagnostic analytics: This is sometimes clubbed together with descriptive analytics. Here the main...

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