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

How to predict credit risk

If you remember our main objective from the previous chapter, we were dealing with customer data from a German bank. We will quickly recap our main problem scenario to refresh your memory. These bank customers are potential candidates who ask for credit loans from the bank with the stipulation that they make monthly payments with some interest on the amount to repay the credit amount. In a perfect world there would be credit loans dished out freely and people would pay them back without issues. Unfortunately, we are not living in a utopian world, and so there will be customers who will default on their credit loans and be unable to repay the amount, causing huge losses to the bank. Therefore, credit risk analysis is one of the crucial areas which banks focus on where they analyze detailed information pertaining to customers and their credit history.

Now coming back to the main question, for predicting credit risk, we need to analyze the dataset pertaining to customers...