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.
Table of Contents (15 chapters)
R Machine Learning By Example
About the Authors
About the Reviewer

Getting the data

The first step in our data analysis pipeline is to get the dataset. We have actually cleaned the data and provided meaningful names to the data attributes and you can check that out by opening the german_credit_dataset.csv file. You can also get the actual dataset from the source which is from the Department of Statistics, University of Munich through the following URL:

You can download the data and then run the following commands by firing up R in the same directory with the data file, to get a feel of the data we will be dealing with in the following sections:

> # load in the data and attach the data frame
> credit.df <- read.csv("german_credit_dataset.csv", header = TRUE, sep = ",") 
> # class should be data.frame
> class(credit.df)
[1] "data.frame"
> # get a quick peek at the data
> head(credit.df)

The following figure shows the first six rows of the data. Each column indicates...