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

Our next challenge

We have dealt with some interesting applications of machine learning in the e-commerce domain in the last couple of chapters. For the next two chapters, our big challenge will be in the financial domain. We will be using data analysis and machine learning techniques to analyze financial data from a German bank. This data will contain a lot of information regarding customers of that bank. We will be analyzing that data in two stages which include descriptive and predictive analytics.

  • Descriptive: Here we will look closely at the data and its various attributes. We will perform descriptive analysis and visualizations to see the kind of features we are dealing with and how they might be related to credit risk. The data we will be dealing with here consists of labeled data already and we will be able to see how many customers were credit risks and how many weren't. We will also look closely at each feature in the data and understand its significance which will be useful in...