R data mining : implement data mining techniques through practical use cases and real-world datasets / Andrea Cirillo.

Format:

Book

Author/Creator:

Cirillo, Andrea, author.

Language:

English

Subjects (All):

R (Computer program language).

Data mining.

Physical Description:

1 online resource (1 volume) : illustrations

Edition:

1st edition

Place of Publication:

Birmingham, England ; Mumbai, [India] : Packt Publishing, 2017.

System Details:

text file

Summary:

Mine valuable insights from your data using popular tools and techniques in R About This Book 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. Who This Book Is 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 In Detail 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 w...

Contents:

Cover

Copyright

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

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

Open source inside

Plugin ready

Data visualization friendly

Installing R and writing R code

Downloading R

R installation for Windows and macOS

R installation for Linux OS

Main components of a base R installation

Possible alternatives to write and run R code

RStudio (all OSs)

The Jupyter Notebook (all OSs)

Visual Studio (Windows users only)

R foundational notions

A preliminary R session

Executing R interactively through the R console

Creating an R script

Executing an R script

Vectors

Lists

Creating lists

Subsetting lists

Data frames

Functions

R's weaknesses and how to overcome them

Learning R effectively and minimizing the effort

The tidyverse

Leveraging the R community to learn R

Where to find the R community

Engaging with the community to learn R

Handling large datasets with R

Further references

Summary

Chapter 2: A First Primer on Data Mining Analysing Your Bank Account Data

Acquiring and preparing your banking data

Data model

Summarizing your data with pivot-like tables

A gentle introduction to the pipe operator

An even more gentle introduction to the dplyr package

Installing the necessary packages and loading your data into R

Installing and loading the necessary packages

Importing your data into R

Defining the monthly and daily sum of expenses

Visualizing your data with ggplot2

Basic data visualization principles

Less but better

Not every chart is good for your message

Scatter plot

Line chart

Bar plot

Other advanced charts.

Colors have to be chosen carefully

A bit of theory - chromatic circle, hue, and luminosity

Visualizing your data with ggplot

One more gentle introduction - the grammar of graphics

A layered grammar of graphics - ggplot2

Visualizing your banking movements with ggplot2

Visualizing the number of movements per day of the week

Chapter 3: The Data Mining Process - CRISP-DM Methodology

The Crisp-DM methodology data mining cycle

Business understanding

Data understanding

Data collection

How to perform data collection with R

Data import from TXT and CSV files

Data import from different types of format already structured as tables

Data import from unstructured sources

Data description

How to perform data description with R

Data exploration

What to use in R to perform this task

The summary() function

Box plot

Histograms

Data preparation

Modelling

Defining a data modeling strategy

How similar problems were solved in the past

Emerging techniques

Classification of modeling problems

How to perform data modeling with R

Evaluation

Clustering evaluation

Classification evaluation

Regression evaluation

How to judge the adequacy of a model's performance

Deployment

Deployment plan development

Maintenance plan development

Chapter 4: Keeping the House Clean - The Data Mining Architecture

A general overview

Data sources

Types of data sources

Unstructured data sources

Structured data sources

Key issues of data sources

Databases and data warehouses

The third wheel - the data mart

One-level database

Two-level database

Three-level database

Technologies

SQL

MongoDB

Hadoop

The data mining engine

The interpreter.

The interface between the engine and the data warehouse

The data mining algorithms

User interface

Clarity

Clarity and mystery

Clarity and simplicity

Efficiency

Consistency

Syntax highlight

Auto-completion

How to build a data mining architecture in R

The data warehouse

The interface between the engine and the data warehouse

The user interface

Chapter 5: How to Address a Data Mining Problem - Data Cleaning and Validation

On a quiet day

Data cleaning

Tidy data

Analysing the structure of our data

The str function

The describe function

head, tail, and View functions

Evaluating your data tidiness

Every row is a record

Every column shows an attribute

Every table represents an observational unit

Tidying our data

The tidyr package

Long versus wide data

The spread function

The gather function

The separate function

Applying tidyr to our dataset

Validating our data

Fitness for use

Conformance to standards

Data quality controls

Consistency checks

Data type checks

Logical checks

Domain checks

Uniqueness checks

Performing data validation on our data

Data type checks with str()

The final touch - data merging

left_join function

moving beyond left_join

Chapter 6: Looking into Your Data Eyes - Exploratory Data Analysis

Introducing summary EDA

Describing the population distribution

Quartiles and Median

Mean

The mean and phenomenon going on within sub populations

The mean being biased by outlier values

Computing the mean of our population

Variance

Standard deviation

Skewness

Measuring the relationship between variables

Correlation.

The Pearson correlation coefficient

Distance correlation

Weaknesses of summary EDA - the Anscombe quartet

Graphical EDA

Visualizing a variable distribution

Histogram

Reporting date histogram

Geographical area histogram

Cash flow histogram

Boxplot

Checking for outliers

Visualizing relationships between variables

Scatterplots

Adding title, subtitle, and caption to the plot

Setting axis and legend

Adding explicative text to the plot

Final touches on colors

Chapter 7: Our First Guess - a Linear Regression

Defining a data modelling strategy

Data modelling notions

Supervised learning

Unsupervised learning

The modeling strategy

Applying linear regression to our data

The intuition behind linear regression

The math behind the linear regression

Ordinary least squares technique

Model requirements - what to look for before applying the model

Residuals' uncorrelation

Residuals' homoscedasticity

How to apply linear regression in R

Fitting the linear regression model

Validating model assumption

Visualizing fitted values

Preparing the data for visualization

Developing the data visualization

Chapter 8: A Gentle Introduction to Model Performance Evaluation

Defining model performance

Fitting versus interpretability

Making predictions with models

Measuring performance in regression models

Mean squared error

R-squared

R-squared meaning and interpretation

R-squared computation in R

Adjusted R-squared

R-squared misconceptions

The R-squared doesn't measure the goodness of fit

A low R-squared doesn't mean your model is not statistically significant

Measuring the performance in classification problems

The confusion matrix

Confusion matrix in R

Accuracy.

How to compute accuracy in R

Sensitivity

How to compute sensitivity in R

Specificity

How to compute specificity in R

How to choose the right performance statistics

A final general warning - training versus test datasets

Chapter 9: Don't Give up - Power up Your Regression Including Multiple Variables

Moving from simple to multiple linear regression

Notation

Assumptions

Variables' collinearity

Tolerance

Variance inflation factors

Addressing collinearity

Dimensionality reduction

Stepwise regression

Backward stepwise regression

From the full model to the n-1 model

Forward stepwise regression

Double direction stepwise regression

Principal component regression

Fitting a multiple linear model with R

Model fitting

Variable assumptions validation

Residual assumptions validation

Linear model cheat sheet

Chapter 10: A Different Outlook to Problems with Classification Models

What is classification and why do we need it?

Linear regression limitations for categorical variables

Common classification algorithms and models

Logistic regression

The intuition behind logistic regression

The logistic function estimates a response variable enclosed within an upper and lower bound

The logistic function estimates the probability of an observation pertaining to one of the two available categories

The math behind logistic regression

Maximum likelihood estimator

Model assumptions

Absence of multicollinearity between variables

Linear relationship between explanatory variables and log odds

Large enough sample size

How to apply logistic regression in R

Fitting the model

Reading the glm() estimation output.

The level of statistical significance of the association between the explanatory variable and the response variable.

Notes:

Includes bibliographical references at the end of each chapters and index.

Description based on online resource; title from PDF title page (EBC, viewed December 29, 2017).

ISBN:

9781787129238

1787129233

OCLC:

1018480584