R Data Mining

Mine valuable insights from your data using popular tools and techniques in R
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R Data Mining

Andrea Cirillo

Mine valuable insights from your data using popular tools and techniques in R

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Book Details

ISBN 139781787124462
Paperback442 pages

Book 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.

Table of Contents

Chapter 1: 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
Chapter 2: 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
Chapter 3: The Data Mining Process - CRISP-DM Methodology
The Crisp-DM methodology data mining cycle 
Business understanding
Data understanding
Data preparation
Modelling
Evaluation
Deployment
Summary
Chapter 4: 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
Chapter 5: How to Address a Data Mining Problem – Data Cleaning and Validation
On a quiet day
Data cleaning
Further references
Summary
Chapter 6: Looking into Your Data Eyes – Exploratory Data Analysis
Introducing summary EDA
Graphical EDA
Further references
Summary
Chapter 7: Our First Guess – a Linear Regression
Defining a data modelling strategy
Applying linear regression to our data
Further references
Summary
Chapter 8: 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
Chapter 9: 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
Chapter 10: A Different Outlook to Problems with Classification Models
What is classification and why do we need it?
Logistic regression
Support vector machines
References
Summary
Chapter 11: 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
Chapter 12: 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
Chapter 13: 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
Chapter 14: Epilogue
Chapter 15: Dealing with Dates, Relative Paths and Functions
Dealing with dates in R
Working directories and relative paths in R
Conditional statements

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

Authors

Table of Contents

Chapter 1: 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
Chapter 2: 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
Chapter 3: The Data Mining Process - CRISP-DM Methodology
The Crisp-DM methodology data mining cycle 
Business understanding
Data understanding
Data preparation
Modelling
Evaluation
Deployment
Summary
Chapter 4: 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
Chapter 5: How to Address a Data Mining Problem – Data Cleaning and Validation
On a quiet day
Data cleaning
Further references
Summary
Chapter 6: Looking into Your Data Eyes – Exploratory Data Analysis
Introducing summary EDA
Graphical EDA
Further references
Summary
Chapter 7: Our First Guess – a Linear Regression
Defining a data modelling strategy
Applying linear regression to our data
Further references
Summary
Chapter 8: 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
Chapter 9: 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
Chapter 10: A Different Outlook to Problems with Classification Models
What is classification and why do we need it?
Logistic regression
Support vector machines
References
Summary
Chapter 11: 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
Chapter 12: 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
Chapter 13: 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
Chapter 14: Epilogue
Chapter 15: Dealing with Dates, Relative Paths and Functions
Dealing with dates in R
Working directories and relative paths in R
Conditional statements

Book Details

ISBN 139781787124462
Paperback442 pages
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