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.

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Table of Contents (9 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
R Fundamentals for Machine Learning
R Fundamentals for Machine Learning
R and RStudio installation
Some basic commands
Objects, special cases, and basic operators in R
Controlling code flow
All about R packages
Taking further steps
Summary
2
Predicting Failures of Banks - Data Collection
Predicting Failures of Banks - Data Collection
Collecting financial data
Collecting the target variable
Structuring data
Summary
3
Predicting Failures of Banks - Descriptive Analysis
Predicting Failures of Banks - Descriptive Analysis
Data overview
Implementing descriptive analysis
Summary
4
Predicting Failures of Banks - Univariate Analysis
Predicting Failures of Banks - Univariate Analysis
Feature selection algorithm
Filter methods
Wrapper methods
Embedded methods
Dimensionality reduction
Summary
5
Predicting Failures of Banks - Multivariate Analysis
Predicting Failures of Banks - Multivariate Analysis
Logistic regression
Regularized methods
Testing a random forest model
Gradient boosting
Deep learning in neural networks
Support vector machines
Ensembles
Automatic machine learning
Summary
6
Visualizing Economic Problems in the European Union
Visualizing Economic Problems in the European Union
A general overview of economic problems in countries
Clustering countries based on macroeconomic imbalances
Summary
7
Sovereign Crisis - NLP and Topic Modeling
Sovereign Crisis - NLP and Topic Modeling
Predicting country ratings using macroeconomic information
Implementing decision trees
Predicting sovereign ratings using European country reports
Summary
8
Other Books You May Enjoy
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Predicting sovereign ratings using European country reports

According to the model based on macroeconomic information described in the Predicting country ratings using macroeconomic information section, decision trees can be considered a good alternative approach to predicting sovereign ratings.

Nevertheless, qualitative information represents an important and low transparent part of the rating assignment. In this section, we propose a model using only qualitative information based on the so-called country reports, published by the European Commission.

These reports, mainly published at the end of February, contain an annual analysis of the economic and social challenges for the EU member states.

For example, at the following link, we can download the country reports published in 2018, https://ec.europa.eu/info/publications/2018-european-semester-country-reports_en. For all...

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