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

In Chapter 5, Credit Risk Detection and Prediction – Descriptive Analytics, we had analyzed the credit dataset from the German bank and performed several transformations already. We will be working on that transformed dataset in this chapter. We had saved the transformed dataset which you can check out by opening the credit_dataset_final.csv file. We will be doing all our analysis in R as usual. To load the data in memory, run the following code snippet:

> # load the dataset into data frame
> credit.df <- read.csv("credit_dataset_final.csv", header = TRUE, sep = ",")

This loads the dataset into a data frame which can now be readily accessed using the credit.df variable. Next, we will focus on data transformation and normalization.