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
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Summary

In this chapter, we have learned some initial important steps to prepare and understand our data. How many variables are available in our dataset? What kind of information do we have? Are there some missing values in the data? How can I treat missing values and outliers? I hope you can now answer these questions.

Moreover, in this chapter, we also learned how to split our data to train and validate our forthcoming predictive model. In the next chapter, we will advance one step ahead, performing a univariate analysis on this data, which means analyzing whether variables are useful for predicting bank failures.

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End of Chapter 3
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