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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
2
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
4
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
5
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
6
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
7
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
8
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

Data preprocessing

In the data preprocessing step, we will be focusing on two things mainly: data type transformations and data normalization. Finally we will split the data into training and testing datasets for predictive modeling. You can access the code for this section in the data_preparation.R file. We will be using some utility functions, which are mentioned in the following code snippet. Remember to load them up in memory by running them in the R console:

## data type transformations - factoring
to.factors <- function(df, variables){
  for (variable in variables){
    df[[variable]] <- as.factor(df[[variable]])
  }
  return(df)
}

## normalizing - scaling
scale.features <- function(df, variables){
  for (variable in variables){
    df[[variable]] <- scale(df[[variable]], center=T, scale=T)
  }
  return(df)
}

The preceding functions operate on the data frame to transform the data. For data type transformations, we mainly perform factoring of the categorical variables,...

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