Book Image

Machine Learning with R Quick Start Guide

By : Iván Pastor Sanz
Book Image

Machine Learning with R Quick Start Guide

By: Iván Pastor Sanz

Overview of this book

Machine Learning with R Quick Start Guide takes you on a data-driven journey that starts with the very basics of R and machine learning. It gradually builds upon core concepts so you can handle the varied complexities of data and understand each stage of the machine learning pipeline. From data collection to implementing Natural Language Processing (NLP), this book covers it all. You will implement key machine learning algorithms to understand how they are used to build smart models. You will cover tasks such as clustering, logistic regressions, random forests, support vector machines, and more. Furthermore, you will also look at more advanced aspects such as training neural networks and topic modeling. By the end of the book, you will be able to apply the concepts of machine learning, deal with data-related problems, and solve them using the powerful yet simple language that is R.
Table of Contents (9 chapters)

Sovereign Crisis - NLP and Topic Modeling

Continuing with the detection of economic problems in European countries, in this chapter, we will try to replicate country ratings, provided by Standard & Poor's, using both quantitative and qualitative information.

This chapter is an interesting real-case application, because we will use some basic text-mining techniques to replicate Standard & Poor's credit ratings. For this purpose, we will use the country reports issued by the European Commission for the European member states.

We will perform a text-mining process to extract combinations of words or useful terms to predict sovereign ratings.

The following topics will be covered in this chapter:

  • Predicting country ratings using macroeconomic information
  • Implementing decision trees
  • Predicting sovereign ratings using European country reports
...