COMPREHENSIVE VIEW ON CRAN PACKAGES- Robust Analysis of Data In eXtreme (RADIX)

RADIX101 has been designed with an intention to ease the search of suitable packages for specific Data Analysis in R. As RADIX is well known for its Source/Origin of Something, here it refers to a systematic beginning of Robust Data Analysis towards Extreme Statistical Computing in R.

Purpose of RADIX101:

Ø  Systematic Approach to any Data Analytics using R.

Ø  Comprehensive categorization of numerous CRAN Packages into Data Specific Desired Outcomes.

Ø  Platform on Wide range of specialised statistical packages under various Data Analytic stages.

  

COMPREHENSIVE VIEW ON CRAN PACKAGES- Robust Analysis of Data In eXtreme (RADIX)

RADIX101 categorises CRAN packages into 3 main stages:-

1.      Pre Modelling stage:- Various Analysis performed on Raw Data prior suitable models are applied. This stage comprises of :-

•   Data Visualisation – Different kinds of visualisation plots to understand the Data spread.

•   Data Statistics – Statistical Exploration of Raw Data.

•   Data Transformation- Suitable Transformation on Raw Data prior to models.

•   Missing Value – Imputations of Missing Values with suitable algorithms.

•   Outlier Detection – Analysis of Outliers in the Raw Data.

•   Feature Selection- Prior Model Selection of important Independent/Predictor variables.

•   Dimension Reduction- Reduction of Rows (Input Fields) and Columns (Independent Variables) by combination or merging using suitable algorithms.

2.              Modelling stage:- Various analytical models to study the Data to observe and predict suitable insights and outcomes.

•   Continuous Regression- Basic and Advanced models on Regression of Continuous Data.

•   Ordinal Regression- Basic and Advanced models on Regression of Ordinal Data.

•   Classification- Basic and Advanced models on Classification predictions.

•   Clustering- Basic and Advanced algorithms for suitable Clustering methods.

•   Time Series- Time Series Analysis includes AR, MA, ARMA, ARIMA, SARIMA.

•   Survival – Survival Analysis includes Cos Proportion Hazard models, Kaplan Meir Curves, Risk Prediction models etc.

•   Association/Conjoint- To study and analyse the Transaction Patterns.

•   Probabilistic Choice – Analysis of choices using Probabilistic/Discrete choice theory.

•   Fraud Analytics- To study the frauds using Benfords algorithm.

•   Miscellaneous Models- Models for Text Mining, Customer Relationship Management, Boosting, Bagging etc.

3.      Post-Modelling Stage:- Various Evaluation packages for validation of models along with ROC analysis.

•   General Model Validation- To study model accuracy for model comparisons and diagnostics

•   Regression Validation- Validation of Regression Models.

•   Classification Validation- Validation of Classification Models.

•   Clustering Validation- Validation of Clustering Models.

•   ROC analysis- Receiver Operating Characteristic (ROC) curves for complete empirical descriptions.

P.S. 

 R is an Open Source Software Programming Language used for Statistical Computing, Data Analysis and Graphical Visualisations. A numerous packages have been created which allow specialised statistical techniques to be performed on various data sets with different business objectives. There are more than 5,800 additional packages (a core set of packages are already pre-installed) and 120,000 functions (as on June 2014) available at Comprehensive R Archive Network (CRAN).These Packages provide wide range of statistical techniques like Regression Analysis, Classification Analysis, Clustering Analysis etc. using a varied number of models such as Neural Networks, Bayesian Techniques, Nearest Neighbour etc. with some specialised in certain domains like HealthCare, Bio-Sciences, Genetic Studies etc. Graphical User Interfaces of R software like Rattle, Deducer, RStudio, RGUI etc. are also widely used with specific design outcomes.

SUPPORT VECTOR MACHINE (SVM) - Detailed Example on Classification in R

R Code - Bank Subscription Marketing - Classification {SUPPORT VECTOR MACHINE}

 R Code  for Support Vector Machine (SVM)
Data Set:- Bank Marketing https://www.blogger.com/blogger.g?blogID=4359514443959080595#editor/target=post;postID=5426750358330739357
Source of Data Set:-  UCI Repository (http://archive.ics.uci.edu/ml/datasets/Bank+Marketing)
The Code includes the following:-
1. Data Exploration - Missing Values, Outliers
2. Data Visualisation
3.  Correlation Matrix
4. Data Partitioning
5. Package used "e1071"
6. Model Tuning and Plots
7. Confusion Matrix


## The data has been imported using Import Dataset option in R Environment
## The data set can be obtained from http://archive.ics.uci.edu/ml/datasets/Bank+Marketing
## DATASET UNDERSTANDING
head(bank_full) ## Displays first 6 rows for each variable
str(bank_full) ## Describes each variables
summary(bank_full) ## Provides basic statistical information of each variable
## DATA EXPLORATION - Check for Missing Data
## Option 1
is.na(bank_full) ## Displays True for a missing value
## Since it is a large dataset, graphical display of missing values will prove to be easier
##Option 2
require(Amelia)
missmap(bank_full,main="Missing Data - Bank Subscription", col=c("red","grey"),legend=FALSE)
## No red colour stripes are visible. hence no missing values.
##Option 3
summary(bank_full) ## displays missing values if any under every variable

## DATA VISUALISATION
## Use Box plots (Only for continuous variables)- To Check Ouliers
boxplot(bank_full$age~bank_full$subscribed, main=" AGE",ylab="age of customers",xlab="Subscribed")
boxplot(bank_full$balance~bank_full$subscribed, main=" BALANCE",ylab="Balance of customers",xlab="Subscribed")
boxplot(bank_full$lastday~bank_full$subscribed, main=" LAST DAY",ylab="Last day of contact",xlab="Subscribed")
boxplot(bank_full$lastduration~bank_full$subscribed, main="LAST DURATION",ylab="Last duration of contact",xlab="Subscribed")
boxplot(bank_full$numcontacts~bank_full$subscribed, main="NUM CONTACTS",ylab="number of contacts",xlab="Subscribed")
boxplot(bank_full$pdays~bank_full$subscribed, main=" Previous DAYS",ylab="Previous days of contact",xlab="Subscribed")
boxplot(bank_full$pcontacts~bank_full$subscribed, main=" Previous Contacts",ylab="Previous Contacts with customers",xlab="Subscribed")
## Though some outliers are observed in Previous contacts, NumContacts and LastDuration, they have not bee removed keeping their significance into consideration
## Use Histograms (For both continuous and categorical variables)
## These histograms provide details abpout Skewness, Normal Distribution etc
## Function to create histograms for continuous variables with normal curve
bank_Conthist<-function(VarName,NumBreaks,xlab,main,lengthxfit) ## xlab and main should be mentioned under quotes as they are characters
{
  hist(VarName,breaks=NumBreaks,col="yellow",xlab=xlab,main=main)
  xfit<-seq(min(VarName),max(VarName),length=lengthxfit)
  yfit<-dnorm(xfit,mean=mean(VarName),sd=sd(VarName))
  yfit<-yfit*diff(h$mids[1:2])*length(VarName)
  lines(xfit,yfit,col="red",lwd=3)
}
bank_Conthist(bank_full$age,10,"age of customers","AGE",30)
bank_Conthist(bank_full$balance,50,"Balance of customers","Balance",100)
## Balance is more skewed towards to Negative or Zero
bank_Conthist(bank_full$lastday,5,"Last Day of contact","LAst Day",10)
bank_Conthist(bank_full$lastduration,100,"LastDuration of COntact","Last Duration",10)
## Last Duration is more skewed towards 0 to 100 secs.
bank_Conthist(bank_full$numcontacts,30,"Number of Contacts","NUmContacts",20)
## NUmContacts are more skewed towards 1
bank_Conthist(bank_full$pdays,30,"Previous Days of contacts","PDays",20)
## Many were not contacted previously
bank_Conthist(bank_full$pcontacts,20,"Previous Contacts","PContacts",10)
## Since many were not contacted previously, therefore Pcontacts is 0

## Barplots for Categorical Variables
barplot(table(bank_full$job),col="red",main="JOB")
barplot(table(bank_full$marital),col="green",main="Marital")
barplot(table(bank_full$education),col="red",main="Education")
barplot(table(bank_full$creditdefault),col="red",main="Credit Default")
## Since Credit Default is highly skewed towards NO, this shall be removed from further analysis
bank_full[5]<-NULL
str(bank_full)
barplot(table(bank_full$housingloan),col="red",main="Housing Loan")
barplot(table(bank_full$personalloan),col="blue",main="Personal Loan")
barplot(table(bank_full$lastcommtype),col="red",main="Last communication type")
barplot(table(bank_full$lastmonth),col="violet",main="Last Month")
barplot(table(bank_full$poutcome),col="magenta",main="Previous Outcome")
## Correlation Matrix among input (or independent) continuous variables
bank_full.cont<-data.frame(bank_full$age,bank_full$balance,bank_full$lastday,bank_full$lastduration,bank_full$numcontacts,bank_full$pdays,bank_full$pcontacts)
str(bank_full.cont)
cor(bank_full.cont)
## It can be observed that No two variables are highly correlated

 ## Partitioning Data into Train and Test datasets in 70:30
library(caret)
set.seed(1234567)
train2<-createDataPartition(bank_full$subscribed,p=0.7,list=FALSE)
train<-bank_full[train2,]
test<-bank_full[-train2,]
write.table(bank_full,"bank1.csv",sep=",")
str(train)
str(test)


## CLASSIFICATION USING SUPPORT VECTOR MACHINE(SVM) Model
 

install.packages("e1071")
library(e1071)
## TUNING to perform cross-validation to know about optimum cost parameter for SVM modelling. Tune() performs only ten-fold cross validation here.
bank_full.tune<-tune(svm,subscribed~.,data=bank_full,kernel="linear",ranges=list(cost=c(0.01,0.1,1,5,10,100)))
## Diffferent kernels such as Polynomial, Radial Basis and Sigmoid can also be used for tuning.
summary(bank_full.tune)
bestmodal.tune<-bank_full.tune$best.model
summary(bestmodal.tune)
##We get cost=0.01 as optimum one with kernel = linear.
##In the following example, Polynomial function is being used
train.svm<-svm(subscribed~.,train,kernel="polynomial",cost=0.01,scale=TRUE,degree=3,gamma=1)
summary(train.svm)
plot(train.svm,train)  ## Not possible for large data-sets
## Testing data using the trained model
test.svm<-predict(train.svm,test)
## Misclassification Table
table(predict=test.svm,truth=test$subscribed)

K NEAREST NEIGHBOUR (KNN) model - Detailed Solved Example of Classification in R

R Code - Bank Subscription Marketing - Classification {K Nearest Neighbour}

 R Code  for K Nearest Neighbour (KNN)
Data Set:- Bank Marketing https://www.blogger.com/blogger.g?blogID=4359514443959080595#editor/target=post;postID=5426750358330739357
Source of Data Set:-  UCI Repository (http://archive.ics.uci.edu/ml/datasets/Bank+Marketing)
The Code includes the following:-
1. Data Exploration - Missing Values, Outliers
2. Data Visualisation
3.  Correlation Matrix
4. Data Partitioning
5. Package used "KKNN"
6. Plots
7. Confusion Matrix


## The data has been imported using Import Dataset option in R Environment
## The data set can be obtained from http://archive.ics.uci.edu/ml/datasets/Bank+Marketing
## DATASET UNDERSTANDING
head(bank_full) ## Displays first 6 rows for each variable
str(bank_full) ## Describes each variables
summary(bank_full) ## Provides basic statistical information of each variable
## DATA EXPLORATION - Check for Missing Data
## Option 1
is.na(bank_full) ## Displays True for a missing value
## Since it is a large dataset, graphical display of missing values will prove to be easier
##Option 2
require(Amelia)
missmap(bank_full,main="Missing Data - Bank Subscription", col=c("red","grey"),legend=FALSE)
## No red colour stripes are visible. hence no missing values.
##Option 3
summary(bank_full) ## displays missing values if any under every variable

## DATA VISUALISATION
## Use Box plots (Only for continuous variables)- To Check Ouliers
boxplot(bank_full$age~bank_full$subscribed, main=" AGE",ylab="age of customers",xlab="Subscribed")
boxplot(bank_full$balance~bank_full$subscribed, main=" BALANCE",ylab="Balance of customers",xlab="Subscribed")
boxplot(bank_full$lastday~bank_full$subscribed, main=" LAST DAY",ylab="Last day of contact",xlab="Subscribed")
boxplot(bank_full$lastduration~bank_full$subscribed, main="LAST DURATION",ylab="Last duration of contact",xlab="Subscribed")
boxplot(bank_full$numcontacts~bank_full$subscribed, main="NUM CONTACTS",ylab="number of contacts",xlab="Subscribed")
boxplot(bank_full$pdays~bank_full$subscribed, main=" Previous DAYS",ylab="Previous days of contact",xlab="Subscribed")
boxplot(bank_full$pcontacts~bank_full$subscribed, main=" Previous Contacts",ylab="Previous Contacts with customers",xlab="Subscribed")
## Though some outliers are observed in Previous contacts, NumContacts and LastDuration, they have not bee removed keeping their significance into consideration
## Use Histograms (For both continuous and categorical variables)
## These histograms provide details abpout Skewness, Normal Distribution etc
## Function to create histograms for continuous variables with normal curve
bank_Conthist<-function(VarName,NumBreaks,xlab,main,lengthxfit) ## xlab and main should be mentioned under quotes as they are characters
{
  hist(VarName,breaks=NumBreaks,col="yellow",xlab=xlab,main=main)
  xfit<-seq(min(VarName),max(VarName),length=lengthxfit)
  yfit<-dnorm(xfit,mean=mean(VarName),sd=sd(VarName))
  yfit<-yfit*diff(h$mids[1:2])*length(VarName)
  lines(xfit,yfit,col="red",lwd=3)
}
bank_Conthist(bank_full$age,10,"age of customers","AGE",30)
bank_Conthist(bank_full$balance,50,"Balance of customers","Balance",100)
## Balance is more skewed towards to Negative or Zero
bank_Conthist(bank_full$lastday,5,"Last Day of contact","LAst Day",10)
bank_Conthist(bank_full$lastduration,100,"LastDuration of COntact","Last Duration",10)
## Last Duration is more skewed towards 0 to 100 secs.
bank_Conthist(bank_full$numcontacts,30,"Number of Contacts","NUmContacts",20)
## NUmContacts are more skewed towards 1
bank_Conthist(bank_full$pdays,30,"Previous Days of contacts","PDays",20)
## Many were not contacted previously
bank_Conthist(bank_full$pcontacts,20,"Previous Contacts","PContacts",10)
## Since many were not contacted previously, therefore Pcontacts is 0

## Barplots for Categorical Variables
barplot(table(bank_full$job),col="red",main="JOB")
barplot(table(bank_full$marital),col="green",main="Marital")
barplot(table(bank_full$education),col="red",main="Education")
barplot(table(bank_full$creditdefault),col="red",main="Credit Default")
## Since Credit Default is highly skewed towards NO, this shall be removed from further analysis
bank_full[5]<-NULL
str(bank_full)
barplot(table(bank_full$housingloan),col="red",main="Housing Loan")
barplot(table(bank_full$personalloan),col="blue",main="Personal Loan")
barplot(table(bank_full$lastcommtype),col="red",main="Last communication type")
barplot(table(bank_full$lastmonth),col="violet",main="Last Month")
barplot(table(bank_full$poutcome),col="magenta",main="Previous Outcome")
## Correlation Matrix among input (or independent) continuous variables
bank_full.cont<-data.frame(bank_full$age,bank_full$balance,bank_full$lastday,bank_full$lastduration,bank_full$numcontacts,bank_full$pdays,bank_full$pcontacts)
str(bank_full.cont)
cor(bank_full.cont)
## It can be observed that No two variables are highly correlated

## CLASSIFICATION USING K Nearest Neighbour (KNN) Model
install.packages("kknn")
library(kknn)
## Prior running the KNN model, the dataset has top be transformed to Numeric or integral as shown below
## One cannot use directly as.numeric() to convert factors to numeric as it has limitations.
## First, Lets convert factors having character levels to numeric levels
str(bank_full)
bank_full_transform<-bank_full
bank_full_transform$marital=factor(bank_full_transform$marital,levels=c("single","married","divorced"),labels=c(1,2,3))
bank_full_transform$job=factor(bank_full_transform$job,levels=c("admin.","blue-collar","entrepreneur","housemaid","management","retired","self-employed","services","student","technician","unemployed","unknown"),labels=c(1,2,3,4,5,6,7,8,9,10,11,12))
bank_full_transform$education=factor(bank_full_transform$education,levels=c("primary","secondary","tertiary","unknown"),labels=c(1,2,3,4))
bank_full_transform$housingloan=factor(bank_full_transform$housingloan,levels=c("no","yes"),labels=c(1,2))
bank_full_transform$personalloan=factor(bank_full_transform$personalloan,levels=c("no","yes"),labels=c(1,2))
bank_full_transform$subscribed=factor(bank_full_transform$subscribed,levels=c("no","yes"),labels=c(1,2))
bank_full_transform$lastcommtype=factor(bank_full_transform$lastcommtype,levels=c("cellular","telephone","unknown"),labels=c(1,2,3))
bank_full_transform$poutcome=factor(bank_full_transform$poutcome,levels=c("failure","other","success","unknown"),labels=c(1,2,3,4))
bank_full_transform$lastmonth=factor(bank_full_transform$lastmonth,levels=c("apr","aug","dec","feb","jan","jul","jun","mar","may","nov","oct","sep"),labels=c(1,2,3,4,5,6,7,8,9,10,11,12))
str(bank_full_transform)
## Now convert these numerical factors into numeric
bank_full_transform$subscribed<-as.numeric(as.character(bank_full_transform$subscribed))
bank_full_transform$job<-as.numeric(as.character(bank_full_transform$job))
bank_full_transform$marital<-as.numeric(as.character(bank_full_transform$marital))
bank_full_transform$education<-as.numeric(as.character(bank_full_transform$education))
bank_full_transform$personalloan<-as.numeric(as.character(bank_full_transform$personalloan))
bank_full_transform$housingloan<-as.numeric(as.character(bank_full_transform$housingloan))
bank_full_transform$lastcommtype<-as.numeric(as.character(bank_full_transform$lastcommtype))
bank_full_transform$lastmonth<-as.numeric(as.character(bank_full_transform$lastmonth))
bank_full_transform$poutcome<-as.numeric(as.character(bank_full_transform$poutcome))
bank_full_transform$age<-as.numeric(as.character(bank_full_transform$age))
bank_full_transform$balance<-as.numeric(as.character(bank_full_transform$balance))
bank_full_transform$lastday<-as.numeric(as.character(bank_full_transform$lastday))
bank_full_transform$lastduration<-as.numeric(as.character(bank_full_transform$lastduration))
bank_full_transform$numcontacts<-as.numeric(as.character(bank_full_transform$numcontacts))
bank_full_transform$pdays<-as.numeric(as.character(bank_full_transform$pdays))
bank_full_transform$pcontacts<-as.numeric(as.character(bank_full_transform$pcontacts))
str(bank_full_transform)
## Now all the variables are wither intergers or numeric
## Partitioning Data into Train and Test datasets in 70:30
library(caret)
set.seed(1234567)
train2<-createDataPartition(bank_full_transform$subscribed,p=0.7,list=FALSE)
train<-bank_full_transform[train2,]
test<-bank_full_transform[-train2,]
write.table(bank_full_transform,"bank1.csv",sep=",")
str(train)
str(test)
## Remember to check for missing values
which(is.na(train))
which(is.na(test))
## Spectral Clustering based on K Nearest Neighbour Graph for train Dataset
x<-specClust(train[,1:15],centers=NULL,nn=10,method="symmetric",gmax=NULL)
##Other methods like random-walk or NULL can also be used
summary(x)
x ## To know the number of clusters
plot(x)
table[train[,15],x$cluster]

## Train the Dataset (Train) using KKNN method via leave-one-out crossvalidation
train.kknn<-train.kknn(subscribed~.,train,kmax=15,distance=1.5,kernel=c("rectangular","triangular","epanechnikov","biweight","triweight","cos","inv","gaussian","optimal"),ykernal=NULL,scale=TRUE)
## For better optimisation of your model, one can choose suitable kernal, vary the distance (which is a parameter of miniskowi) and kmax
plot(train.kknn)

## Cross validation procedure to test prediction accuracy
## Output is mean and SD of miclssification error. Hence, lower the values are much better for the model
simulation(subscribed~.,train,runs=10,kernel="triangular",k=10,train=TRUE)
## Any kernel can be used for beter modeling purposes

## For Test Data set - Accuracy prediction
train.kknn=kknn(subscribed~.,train=train,test=test,k=10,distance=1,kernel="gaussian",ykernel=NULL,scale=TRUE,kknn.dist(learn=train,valid=test,k=10,distance=1))
## misclassification table
fit=fitted(train.kknn)
table(predict(train.kknn,train),train$subscribed)
pairs(train[,1:15],pch=train$subscribed,col=c("green","red")[(train$subscribed!=fit)+1])
 

PLOT of the Trained Model using different kernels. 
Kernel having least mean squared error is selected for training the data set