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

Chapter 6. Credit Risk Detection and Prediction – Predictive Analytics

In the previous chapter, we covered a lot of ground in the financial domain where we took up the challenge of detecting and predicting bank customers who could be potential credit risks. We now have a good idea about our main objective regarding credit risk analysis. Besides this, the substantial knowledge gained from descriptive analytics of the dataset and its features will be useful for predictive analytics, as we had mentioned earlier.

In this chapter, we will be journeying through the world of predictive analytics, which sits at the core of machine learning and data science. Predictive analytics encompasses several things which include classification algorithms, regression algorithms, domain knowledge, and business logic which are combined to build predictive models and derive useful insights from data. We had discussed various machine learning algorithms at the end of the previous chapter which would be applicable...