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)

Clustering countries based on macroeconomic imbalances

In this section, we will develop an unsupervised model to visually detect macroeconomic problems in countries and also understand a little more about the main drivers of credit ratings. We will start by creating a cluster of the countries with macroeconomic problems. In the next chapter, we will move on to predicting the credit ratings based on these clusters.

Throughout this chapter, I've tried to make an effort to reiterate the code from the previous chapters.

Let's get started!

Data collection

As in the previous model, we need to collect the largest amount of data possible. First, we need the macroeconomic indicators of countries to analyze the macroeconomic...