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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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Profile Icon Karthik Ramasubramanian Profile Icon Jojo Moolayil
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Paperback May 2019 502 pages 1st Edition
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Arrow left icon
Profile Icon Karthik Ramasubramanian Profile Icon Jojo Moolayil
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Paperback May 2019 502 pages 1st Edition
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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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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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Table of Contents

9 Chapters
R for Advanced Analytics Chevron down icon Chevron up icon
Exploratory Analysis of Data Chevron down icon Chevron up icon
Introduction to Supervised Learning Chevron down icon Chevron up icon
Regression Chevron down icon Chevron up icon
Classification Chevron down icon Chevron up icon
Feature Selection and Dimensionality Reduction Chevron down icon Chevron up icon
Model Improvements Chevron down icon Chevron up icon
Model Deployment Chevron down icon Chevron up icon
Capstone Project - Based on Research Papers Chevron down icon Chevron up icon
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