R Data Mining

R Data Mining

Overview of this book

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.

Title Page

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

Free Chapter

Why to Choose R for Your Data Mining and Where to Start

What is R?

A bit of history

R's points of strength

Installing R and writing R code

Possible alternatives to write and run R code

R foundational notions

R's weaknesses and how to overcome them

Further references

Summary

A First Primer on Data Mining Analysing Your Bank Account Data

Acquiring and preparing your banking data

Summarizing your data with pivot-like tables

Visualizing your data with ggplot2

Further references

Summary

The Data Mining Process - CRISP-DM Methodology

The Crisp-DM methodology data mining cycle

Business understanding

Summary

Keeping the House Clean – The Data Mining Architecture

A general overview

Data sources

Databases and data warehouses

The data mining engine

User interface

How to build a data mining architecture in R

Further references

Summary

How to Address a Data Mining Problem – Data Cleaning and Validation

On a quiet day

Data cleaning

Further references

Summary

Looking into Your Data Eyes – Exploratory Data Analysis

Introducing summary EDA

Graphical EDA

Further references

Summary

Our First Guess – a Linear Regression

Defining a data modelling strategy

Applying linear regression to our data

Further references

Summary

A Gentle Introduction to Model Performance Evaluation

Defining model performance

Measuring performance in regression models

Measuring the performance in classification problems

A final general warning – training versus test datasets

Further references

Summary

Don't Give up – Power up Your Regression Including Multiple Variables

Moving from simple to multiple linear regression

Dimensionality reduction

Fitting a multiple linear model with R

Further references

Summary

A Different Outlook to Problems with Classification Models

What is classification and why do we need it?

Logistic regression

Support vector machines

References

Summary

The Final Clash – Random Forests and Ensemble Learning

Random forest

Ensemble learning

Applying estimated models on new data

A more structured approach to predictive analytics

Applying the majority vote ensemble technique on predicted data

Further references

Summary

Looking for the Culprit – Text Data Mining with R

Extracting data from a PDF file in R

Sentiment analysis

Developing wordclouds from text

Looking for context in text – analyzing document n-grams

Performing network analysis on textual data

Further references

Summary

Sharing Your Stories with Your Stakeholders through R Markdown

Principles of a good data mining report

Set up an rmarkdown report

Develop an R markdown report in RStudio

Rendering and sharing an R markdown report

Further references

Summary

Epilogue

Dealing with Dates, Relative Paths and Functions

Dealing with dates in R

Working directories and relative paths in R

Conditional statements

Customer Reviews

5 star

4 star

3 star

2 star

1 star

Fitting a multiple linear model with R

It is now time to apply all that we have seen until now to our data. First of all, we are going to fit our model, applying the previously introduced lm() function. This will not require too much time, and will directly lead us to model assumptions validation, both on multicollinearity and residual behavior. We will finally, for the best possible model, apply both stepwise regression and principal component regression.

Model fitting

Let us define the dataset we are going to employ for our modeling activity. We will employ clean_casted_stored_data_validated_complete, removing the default_flag and the customer_code first because it is actually meaningless as an explanatory variable:

clean_casted_stored_data_validated_complete %>% 
(-default_flag) %>% 
(-customer_code) -> training_data

And we are ready now to fit our model:

multiple_regression <- lm(as.numeric(default_numeric)~., data= training_data)

You should have already noticed the small point...

R Data Mining

R Data Mining

Overview of this book

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