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


Congratulations on staying until the end of this chapter! You have learnt several important things by now which we have covered in this chapter. You now have an idea about one of the most important areas in the financial domain, that is, Credit Risk analysis. Besides this, you also gained significant domain knowledge about how banks analyze customers for their credit ratings and what kind of attributes and features are considered by them. Descriptive and exploratory analysis of the dataset also gave you an insight into how to start working from scratch when you just have a problem to solve and a dataset given to you! You now know how to perform feature engineering, build beautiful publication quality visualizations using ggplot2, and perform statistical tests to check feature associations. Finally, we wrapped up our discussion by talking about feature sets and gave a brief introduction to several supervised machine learning algorithms which will help us in the next step of predicting...