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

Data analysis and transformation

Now that we have processed our data, it is ready for analysis. We will be carrying out descriptive and exploratory analysis in this section, as mentioned earlier. We will analyze the different dataset attributes and talk about their significance, semantics, and relationship with the credit risk attribute. We will be using statistical functions, contingency tables, and visualizations to depict all of this.

Besides this, we will also be doing data transformation for some of the features in our dataset, namely the categorical variables. We will be doing this to combine the category classes which have similar semantics and remove the classes having very less proportion by merging them with a similar class. Some reasons for doing this include preventing the overfitting of our predictive models, which we will be building in Chapter 6, Credit Risk Detection and Prediction – Predictive Analytics, linking semantically similar classes together and also because modeling...