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Home > Data > Machine Learning > Applied Supervised Learning with R
Applied Supervised Learning with R
Applied Supervised Learning with R

Applied Supervised Learning with R: Use machine learning libraries of R to build models that solve business problems and predict future trends

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Applied Supervised Learning with R

Chapter 1. R for Advanced Analytics

Note

Learning Objectives

By the end of this chapter, you will be able to:

  • Explain advanced R programming constructs

  • Print the summary statistics of a real-world dataset

  • Read data from CSV, text, and JSON files

  • Write R markdown files for code reproducibility

  • Explain R data structures such as data.frame, data.table, lists, arrays, and matrices

  • Implement the cbind, rbind, merge, reshape, aggregate, and apply functions

  • Use packages such as dplyr, plyr, caret, tm, and many more

  • Create visualizations using ggplot

Note

In this chapter, we will set the foundation for programming with R and understand the various syntax and data structures for advanced analytics.

Introduction

R was one of the early programming languages developed for statistical computing and data analysis with good support for visualization. With the rise of data science, R emerged as an undoubted choice of programming language among many data science practitioners. Since R was open-source and extremely powerful in building sophisticated statistical models, it quickly found adoption in both industry and academia.

Tools and software such as SAS and SPSS were only affordable by large corporations, and traditional programming languages such as C/C++ and Java were not suitable for performing complex data analysis and building model. Hence, the need for a much more straightforward, comprehensive, community-driven, cross-platform compatible, and flexible programming language was a necessity.

Though Python programming language is increasingly becoming popular in recent times because of its industry-wide adoption and robust production-grade implementation, R is still the choice of programming language for quick prototyping of advanced machine learning models. R has one of the most populous collection of packages (a collection of functions/methods for accomplishing a complicated procedure, which otherwise requires a lot of time and effort to implement). At the time of writing this book, the Comprehensive R Archive Network (CRAN), a network of FTP and web servers around the world that store identical, up-to-date, versions of code and documentation for R, has more than 13,000 packages.

While there are numerous books and online resources on learning the fundamentals of R, in this chapter, we will limit the scope only to cover the important topics in R programming that will be used extensively in many data science projects. We will use a real-world dataset from the UCI Machine Learning Repository to demonstrate the concepts. The material in this chapter will be useful for learners who are new to R Programming. The upcoming chapters in supervised learning concepts will borrow many of the implementations from this chapter.

Working with Real-World Datasets

There are plenty of open datasets available online these days. The following are some popular sources of open datasets:

  • Kaggle: A platform for hosting data science competitions. The official website is https://www.kaggle.com/.

  • UCI Machine Learning Repository: A collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. You can visit the official page via navigating to https://archive.ics.uci.edu/ml/index.php URL.

  • data.gov.in: Open Indian government data platform, which is available at https://data.gov.in/.

  • World Bank Open Data: Free and open access to global development data, which can be accessed from https://data.worldbank.org/.

Increasingly, many private and public organizations are willing to make their data available for public access. However, it is restricted to only complex datasets where the organization is looking for solutions to their data science problem through crowd-sourcing platforms such as Kaggle. There is no substitute for learning from data acquired internally in the organization as part of a job that offers all kinds of challenges in processing and analyzing.

Significant learning opportunity and challenge concerning data processing comes from the public data sources as well, as not all the data from these sources are clean and in a standard format. JSON, Excel, and XML are some other formats used along with CSV, though CSV is predominant. Each format needs a separate encoding and decoding method and hence a reader package in R. In our next section, we will discuss various data formats and how to process the available data in detail.

Throughout this chapter and in many others, we will use the direct marketing campaigns (phone calls) of a Portuguese banking institution dataset from UCI Machine Learning Repository. (https://archive.ics.uci.edu/ml/datasets/bank+marketing). The following table describes the fields in detail:

Figure 1.1: Portuguese banking institution dataset from UCI Machine Learning Repository (Part 1)

Figure 1.2: Portuguese banking institution dataset from UCI Machine Learning Repository (Part 2)

In the following exercise, we will download the bank.zip dataset as a ZIP file and unzip it using the unzip method.

Exercise 1: Using the unzip Method for Unzipping a Downloaded File

In this exercise, we will write an R script to download the Portuguese Bank Direct Campaign dataset from UCI Machine Learning Repository and extract the content of the ZIP file in a given folder using the unzip function.

Preform these steps to complete the exercise:

  1. First, open R Studio on your system.

  2. Now, set the working directory of your choice using the following command:

    wd <- "<WORKING DIRECTORY>"
setwd(wd)

    Note

    R codes in this book are implemented using the R version 3.2.2.

  3. Download the ZIP file containing the datasets using the download.file() method:

    url <- "https://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank.zip"
destinationFileName <- "bank.zip"
download.file(url, destinationFileName,method = "auto", quiet=FALSE)

  4. Now, before we unzip the file in the working directory using the unzip() method, we need to choose a file and save its file path in R (for Windows) or specify the complete path:

    zipFile<-file.choose()

  5. Define the folder where the ZIP file is unzipped:

    outputDir <- wd

  6. Finally, unzip the ZIP file using the following command:

    unzip(zipFile, exdir=outputDir)

    The output is as follows:

    Figure 1.3: Unzipping the bank.zip file

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Page 1 of 15

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Key benefits

  • Study supervised learning algorithms by using real-world datasets
  • Fine-tune optimal parameters with hyperparameter optimization
  • Select the best algorithm using the model evaluation framework

Description

R provides excellent visualization features that are essential for exploring data before using it in automated learning. Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. The book demonstrates how you can add different regularization terms to avoid overfitting your model. By the end of this book, you will have gained the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs.

Who is this book for?

This book is specially designed for beginner and intermediate-level data analysts, data scientists, and data engineers who want to explore different methods of supervised machine learning and its use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the concepts covered in this book.

What you will learn

  • Develop analytical thinking to precisely identify a business problem
  • Wrangle data with dplyr, tidyr, and reshape2
  • Visualize data with ggplot2
  • Validate your supervised machine learning model using k-fold
  • Optimize hyperparameters with grid and random search, and Bayesian optimization
  • Deploy your model on Amazon Web Services (AWS) Lambda with plumber
  • Improve your model's performance with feature selection and dimensionality reduction
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Publication date : May 31, 2019
Length: 502 pages
Edition : 1st
Language : English
ISBN-13 : 9781838556334
Category :
Data
Languages :
R
Concepts :
Machine Learning

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ISBN-13 : 9781838556334
Category :
Data
Languages :
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Concepts :
Machine Learning

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Table of Contents

9 Chapters
R for Advanced Analytics Chevron down icon Chevron up icon
R for Advanced Analytics
Introduction
Working with Real-World Datasets
Reading Data from Various Data Formats
Write R Markdown Files for Code Reproducibility
Data Structures in R
DataFrame
Data Processing and Transformation
The Apply Family of Functions
Useful Packages
Data Visualization
Line Charts
Histogram
Boxplot
Summary
Exploratory Analysis of Data Chevron down icon Chevron up icon
Exploratory Analysis of Data
Introduction
Defining the Problem Statement
Understanding the Science Behind EDA
Exploratory Data Analysis
Univariate Analysis
Exploring Categorical Features
Bivariate Analysis
Studying the Relationship between Two Numeric Variables
Studying the Relationship between a Categorical and a Numeric Variable
Studying the Relationship Between Two Categorical Variables
Multivariate Analysis
Validating Insights Using Statistical Tests
Categorical Dependent and Numeric/Continuous Independent Variables
Categorical Dependent and Categorical Independent Variables
Summary
Introduction to Supervised Learning Chevron down icon Chevron up icon
Introduction to Supervised Learning
Introduction
Summary of the Beijing PM2.5 Dataset
Regression and Classification Problems
Machine Learning Workflow
Regression
Exploratory Data Analysis (EDA)
Classification
Evaluation Metrics
Summary
Regression Chevron down icon Chevron up icon
Regression
Introduction
Linear Regression
Model Diagnostics
Residual versus Fitted Plot
Normal Q-Q Plot
Scale-Location Plot
Residual versus Leverage
Improving the Model
Quantile Regression
Polynomial Regression
Ridge Regression
LASSO Regression
Elastic Net Regression
Poisson Regression
Cox Proportional-Hazards Regression Model
NCCTG Lung Cancer Data
Summary
Classification Chevron down icon Chevron up icon
Classification
Introduction
Getting Started with the Use Case
Classification Techniques for Supervised Learning
Logistic Regression
How Does Logistic Regression Work?
Evaluating Classification Models
What Metric Should You Choose?
Evaluating Logistic Regression
Decision Trees
XGBoost
Deep Neural Networks
Choosing the Right Model for Your Use Case
Summary
Feature Selection and Dimensionality Reduction Chevron down icon Chevron up icon
Feature Selection and Dimensionality Reduction
Introduction
Feature Engineering
One-Hot Encoding
Log Transformation
Feature Selection
Highly Correlated Variables
Feature Reduction
Variable Clustering
Linear Discriminant Analysis for Feature Reduction
Summary
Model Improvements Chevron down icon Chevron up icon
Model Improvements
Introduction
Bias-Variance Trade-off
Underfitting and Overfitting
Defining a Sample Use Case
Cross-Validation
Holdout Approach/Validation
K-Fold Cross-Validation
Hold-One-Out Validation
Hyperparameter Optimization
Grid Search Optimization
Random Search Optimization
Bayesian Optimization
Summary
Model Deployment Chevron down icon Chevron up icon
Model Deployment
Introduction
What is an API?
Introduction to plumber
A Brief History of the Pre-Docker Era
Docker
Amazon Web Services
Introducing AWS SageMaker
What is Amazon Lambda?
What is Amazon API Gateway?
Building Serverless ML Applications
Deleting All Cloud Resources to Stop Billing
Summary
Capstone Project - Based on Research Papers Chevron down icon Chevron up icon
Capstone Project - Based on Research Papers
Introduction
Exploring Research Work
The mlr Package
Problem Design from the Research Paper
Features in Scene Dataset
Implementing Multilabel Classifier Using the mlr and OpenML Packages
Constructing a Learner
Predictions
Summary

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About the authors

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Profile icon Karthik Ramasubramanian
Karthik Ramasubramanian
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Karthik Ramasubramanian completed his M.Sc. in Theoretical Computer Science at PSG College of Technology, India, where he pioneered the application of machine learning, data mining, and fuzzy logic in his research work on computer and network security. He has over seven years' experience of leading data science and business analytics in retail, Fast-Moving Consumer Goods, e-commerce, information technology, and the hospitality industry for multinational companies and unicorn start-ups. He is a researcher and a problem solver with diverse experience of the data science life cycle, starting from data problem discovery to creating data science proof of concepts and products for various industry use cases. In his leadership roles, Karthik has been instrumental in solving many ROI-driven business problems via data science solutions. He has mentored and trained hundreds of professionals and students globally in data science through various online platforms and university engagement programs. He has also developed intelligent chatbots based on deep learning models that understand human-like interactions, customer segmentation models, recommendation systems, and many natural language processing models. He is an author of the book Machine Learning Using R, published by Apress, a publishing house of Springer Business+Science Media. The book was a big success with more than 50,000 online downloads and hardcover sales. The book was subsequently published as a second edition with extended chapters on Deep Learning and Time Series Modeling.
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Profile icon Jojo Moolayil
Jojo Moolayil
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Jojo Moolayil is an artificial intelligence, deep learning, machine learning, and decision science professional with over six years of industrial experience. He is the author of Learn Keras for Deep Neural Networks, published by Apress, and Smarter Decisions – The Intersection of IoT and Decision Science, published by Packt Publishing. He has worked with several industry leaders on high-impact, critical data science and machine learning projects across multiple verticals. He is currently associated with Amazon Web Services as a research scientist in Canada. Apart from writing books on AI, decision science, and the internet of things, Jojo has been a technical reviewer for various books in the same fields published by Apress and Packt Publishing.
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  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela