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R Data Mining
R Data Mining

R Data Mining: Implement data mining techniques through practical use cases and real-world datasets

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R Data Mining

A First Primer on Data Mining Analysing Your Bank Account Data

It should be now clear to you why R is worth investing your time in: it is a powerful language, plugin-ready, data visualization-friendly, and all the other adjectives you can derive from the previous chapter. Wouldn't it be great to taste a bit of all of those powerhouses?

That is what this chapter is all about—letting you experiment with discovering insights from your data with R. 

We are going to do this with your own data, in particular, your banking data. We are going to discover and model your expenditure habits, employing the power of R. After reading this chapter, apart from being even more enthusiastic about reading the remaining chapters, you will be able to do the following:

  • Summarize your data with functions provided by dplyr
  • Answer questions regarding your finance habits...

Acquiring and preparing your banking data

The first step in getting this accomplished is to download our banking data from our bank's website. Obviously, I am not going to describe how to do this for every bank's website, and you should look on your own bank's website, probably within the account movements section.

If you don't have a bank account, or your bank doesn't let you download your data, you shouldn't despair, just look at the additional material provided together with the book, and you will find a folder named data containing, among other things, an XLS file named banking. You can safely use this for your experiments.

Data model

Let's have a closer look at how our...

Summarizing your data with pivot-like tables

When moving to R, one of the common questions that arises is this, how do I produce a pivot table with R? Purists of the language will probably be horrified at this question, but we do not have to be too fussy: pivot tables are an effective and convenient way to summarize and show data, and are therefore relevant to be able to perform the same summarization in our beloved language.

As you might be guessing, yes, it is actually possible to perform the same kind of summarization, even if it is not called a pivot table. But before getting into detail, let's discuss the concept. What is a pivot table? 

We define with this concept a summary of a given detailed dataset, showing descriptive statistics of attributes stored within the dataset, aggregated by keys composed from other attributes of the same dataset.

To be clear, let&apos...

Visualizing your data with ggplot2

It is beyond the scope of this book to provide a comprehensive and exhaustive explanation of the data visualization principles and techniques, but in the remaining sections of this chapter, we are going to learn the basic elements of this powerful discipline and how to apply them to our data through the means of the ggplot2 package.

Basic data visualization principles

As is often the case, when dealing with data visualization we should start from the final objective to work out the best way to accomplish it. The main objective of data visualization is to effectively communicate an insight contained within a given set of data. We can elaborate a bit more on this. The point here...

Further references

  • On the way our brain visualizes: Information Visualization by  Ware, Colin. (2012) , 3rd Edition. Morgan Kaufmann
  • http://www.datavizcatalogue.com for quite an exhaustive catalogue of data visualization techniques and how to employ them
  • Interaction of Color, Joseph Alber, one of the greatest books on the theory of colour 
  • ggplot2: Elegant Graphics for Data Analysis, by Hadley Wickham, Springer-Verlag

Summary

Can you feel your backpack becoming heavy? This chapter was a big boost for your R knowledge: nearly 30 pages earlier you were only just aware of how to print "Hello World" with R, and now you have discovered useful insights from your real banking data.

We have learned the following:

  • Installing additional packages in the base version of R 
  • Importing data into your R environment
  • Creating pivot tables in R
  • Discovering and showing information through data visualization techniques
  • Plotting data with ggplot2

I am tempted to accelerate further in the next chapter, immediately showing you how to implement data mining algorithms with the powerful weapon we have at our disposal. But, we have to be prudent and firmly cover the foundations to let you soundly build upon them. In the next chapter, we'll learn how to organize and conduct a data mining project through...

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Key benefits

  • Understand the basics of data mining and why R is a perfect tool for it.
  • Manipulate your data using popular R packages such as ggplot2, dplyr, and so on to gather valuable business insights from it.
  • Apply effective data mining models to perform regression and classification tasks.

Description

R is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R. It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques. While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data. Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets.

Who is this book for?

If you are a budding data scientist, or a data analyst with a basic knowledge of R, and want to get into the intricacies of data mining in a practical manner, this is the book for you. No previous experience of data mining is required.

What you will learn

  • Master relevant packages such as dplyr, ggplot2 and so on for data mining
  • Learn how to effectively organize a data mining project through the CRISP-DM methodology
  • Implement data cleaning and validation tasks to get your data ready for data mining activities
  • Execute Exploratory Data Analysis both the numerical and the graphical way
  • Develop simple and multiple regression models along with logistic regression
  • Apply basic ensemble learning techniques to join together results from different data mining models
  • Perform text mining analysis from unstructured pdf files and textual data
  • Produce reports to effectively communicate objectives, methods, and insights of your analyses
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Publication date : Nov 29, 2017
Length: 442 pages
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Language : English
ISBN-13 : 9781787124462
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Concepts :
Tools :

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Table of Contents

15 Chapters
Why to Choose R for Your Data Mining and Where to Start Chevron down icon Chevron up icon
A First Primer on Data Mining Analysing Your Bank Account Data Chevron down icon Chevron up icon
The Data Mining Process - CRISP-DM Methodology Chevron down icon Chevron up icon
Keeping the House Clean – The Data Mining Architecture Chevron down icon Chevron up icon
How to Address a Data Mining Problem – Data Cleaning and Validation Chevron down icon Chevron up icon
Looking into Your Data Eyes – Exploratory Data Analysis Chevron down icon Chevron up icon
Our First Guess – a Linear Regression Chevron down icon Chevron up icon
A Gentle Introduction to Model Performance Evaluation Chevron down icon Chevron up icon
Don't Give up – Power up Your Regression Including Multiple Variables Chevron down icon Chevron up icon
A Different Outlook to Problems with Classification Models Chevron down icon Chevron up icon
The Final Clash – Random Forests and Ensemble Learning Chevron down icon Chevron up icon
Looking for the Culprit – Text Data Mining with R Chevron down icon Chevron up icon
Sharing Your Stories with Your Stakeholders through R Markdown Chevron down icon Chevron up icon
Epilogue Chevron down icon Chevron up icon
Dealing with Dates, Relative Paths and Functions Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Full star icon 5
(2 Ratings)
5 star 100%
4 star 0%
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1 star 0%
KRL Feb 11, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great description of classical methods but also a detailled presentation of sequence analysis. Cool 3D graphics tools. I recommend.
Amazon Verified review Amazon
Chris H Feb 03, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
There are countless books out there that attempt to start the reader along the learning curve with data science/machine learning tools. This is one of the very best that I have seen as an introduction to the field. It is exceedingly clear in its presentation and takes great care to explain why each step or manipulation is done.
Amazon Verified review Amazon
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