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Machine Learning with R Quick Start Guide
Machine Learning with R Quick Start Guide

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

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Machine Learning with R Quick Start Guide

Predicting Failures of Banks - Data Collection

In each model development, we will need to obtain enough data to build the model. It is very common to read the expression garbage in, garbage out, which relates to the fact that if you develop a model with bad data, the resulting model will be also bad.

Especially in machine learning applications, what we expect is to have a huge amount of data, although in many cases that's not the case. Regardless of the amount of information available, the quality of this data is the most important issue.

Moreover, as a developer, it is important to have structured data, because it can be immediately manipulated. However, data is commonly found in an unstructured form, meaning that it takes a lot of time to process and prepare for development. Many people consider machine learning applications to only be based on the use of new algorithms...

Collecting financial data

We will obtain our data from the Federal Deposit Insurance Corporation (FDIC) website (https://www.fdic.gov/). The FDIC is an independent agency led by the US Congress with an aim to maintain the confidence of the people and the stability of the financial system.

Why FDIC?

FDIC provides deposit insurance to depositors in US commercial banks and savings institutions. Thus, if a US bank fails and closes, the FDIC guarantees that the depositors do not lose their savings. Up to US$250,000 is guaranteed.

The FDIC also examines and supervises certain financial institutions. These institutions are obliged to periodically report detailed information about their financial statements related to the following...

Collecting the target variable

We need to determine whether or not a bank has failed in the past – this will be our target. This information is also available on the FDIC website at https://www.fdic.gov/bank/individual/failed/banklist.html.

The website includes banks that have failed since October 2000, which covers all our dataset:

Let's see the steps to achieve this:

  1. Download this information into a .csv file:
download.file("https://www.fdic.gov/bank/individual/failed/banklist.csv", "failed_banks.csv",method="auto", quiet=FALSE, mode = "wb", cacheOK = TRUE)

Even this list is updated periodically, as historical information does not change, but the results are still replicable. Anyway, the file used in the development is also available in the data repository of this book.

  1. Now, upload the downloaded file into R, as follows...

Structuring data

After having acquired our target variable and knowing our dataset, we can now move on to the actual data collection based on our target. Here, we will try acquiring the data of the bank according to different years as described in the Collecting the target variable section.

To do this, we create a new variable extracting only the year when a bank went bankrupt, and then we count the number of banks by year:

failed_banks$year<-as.numeric(format(failed_banks$Closing.Date, "%Y"))

Failed_by_Year<-as.data.frame(table(failed_banks$year))
colnames(Failed_by_Year)<-c("year","Number_of_banks")

print(Failed_by_Year)
## year Number_of_banks
## 1 2000 2
## 2 2001 4
## 3 2002 11
## 4 2003 3
## 5 2004 4
## 6 2007 3
## 7 2008 25
...

Summary

In this chapter, we have started collecting the data needed to develop our model to predict bank failures. In this case, we have downloaded a large amount of data and we have structured it. Moreover, we have created our target variable. At the end of this chapter, you should have learned that data collection is the first and one of the most important steps in the model development. When you deal with your own problems, take time to understand the problem and then think about what kind of data you need and how to obtain it. In the next chapter, we will do a descriptive analysis of the data that we have acquired.

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Key benefits

  • Use R 3.5 to implement real-world examples in machine learning
  • Implement key machine learning algorithms to understand the working mechanism of smart models
  • Create end-to-end machine learning pipelines using modern libraries from the R ecosystem

Description

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.

Who is this book for?

This book is for graduate students, aspiring data scientists, and data analysts who wish to enter the field of machine learning and are looking to implement machine learning techniques and methodologies from scratch using R 3.5. A working knowledge of the R programming language is expected.

What you will learn

  • Introduce yourself to the basics of machine learning with R 3.5
  • Get to grips with R techniques for cleaning and preparing your data for analysis and visualize your results
  • Learn to build predictive models with the help of various machine learning techniques
  • Use R to visualize data spread across multiple dimensions and extract useful features
  • Use interactive data analysis with R to get insights into data
  • Implement supervised and unsupervised learning, and NLP using R libraries
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Table of Contents

8 Chapters
R Fundamentals for Machine Learning Chevron down icon Chevron up icon
Predicting Failures of Banks - Data Collection Chevron down icon Chevron up icon
Predicting Failures of Banks - Descriptive Analysis Chevron down icon Chevron up icon
Predicting Failures of Banks - Univariate Analysis Chevron down icon Chevron up icon
Predicting Failures of Banks - Multivariate Analysis Chevron down icon Chevron up icon
Visualizing Economic Problems in the European Union Chevron down icon Chevron up icon
Sovereign Crisis - NLP and Topic Modeling Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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Joaq_32 Apr 22, 2019
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Aunque no me ha dado tiempo a leerlo entero, los casos y el código mostrado parecen muy fiables a los problemas que me he encontrado yo a la hora de desarrollar aplicaciones de machine learning. Buena compra
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