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Machine Learning with R Quick Start Guide

You're reading from Machine Learning with R Quick Start Guide A beginner's guide to implementing machine learning techniques from scratch using R 3.5

Product type Paperback

Published in Mar 2019

Publisher Packt

ISBN-13 9781838644338

Length 250 pages

Edition 1st Edition

Languages

R

Tools

RStudio

Concepts

Machine Learning

Author (1):

Iván Pastor Sanz

View More author details

Table of Contents (9) Chapters

Preface

1. R Fundamentals for Machine Learning

2. Predicting Failures of Banks - Data Collection FREE CHAPTER

3. Predicting Failures of Banks - Descriptive Analysis

4. Predicting Failures of Banks - Univariate Analysis

5. Predicting Failures of Banks - Multivariate Analysis

6. Visualizing Economic Problems in the European Union

7. Sovereign Crisis - NLP and Topic Modeling

8. Other Books You May Enjoy

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Summary

In this opening chapter, we established the purpose of this book. Now that you are clear on the basics of R and its concepts, we will move on to develop two main predictive models. We will cover all the necessary steps: data collection, data analysis, and feature selection, and different algorithms will be described in a practical manner.

In the next chapter, we will start solving programming and collect the necessary data to start modeling development.

The rest of the chapter is locked

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Authors (1)

Sanz

Sanz

Ivn Pastor Sanz is a lead data scientist and machine learning enthusiast with extensive experience in finance, risk management, and credit risk modeling. Ivn has always endeavored to find solutions to make banking more comprehensible, accessible, and fair. Thus, in his thesis to obtain his PhD in economics, Ivn tried to identify the origins of the 2008 financial crisis and suggest ways to avoid a similar crisis in the future.

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