**R Code for RANDOM FOREST**

**Data Set:- Bank Marketing**

**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. RANDOM FOREST - Detailed**

**6. R Package- randomForest**

## 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

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)

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")

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)

{

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)

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")

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")

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)

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)*

train1<-createDataPartition(bank_full$subscribed,p=0.7,list=FALSE)

train<-bank_full[train1,]

test<-bank_full[-train1,]

train1<-createDataPartition(bank_full$subscribed,p=0.7,list=FALSE)

train<-bank_full[train1,]

test<-bank_full[-train1,]

## Without Any Transformations on the Variables, Random Forest Model has been Applied.

##

**RANDOM FOREST FOR CLASSIFICATION**library(randomForest)

## Tune Random Forest to obtain optimal mtry (No. of independent or predictor variables to be sampled at each split) parameter

tuneRF(train[,1:15],train$subscribed,stepFactor=2,improve=0.01,plot=TRUE,doBest=FALSE)

## optimal mtry can be used while training the model, but this has been excluded for this dataset

## Train data set for training the random forest model-- Ensure that your systme is equiped with higher memory space to run random forests

train.RandomForest=randomForest(subscribed~.,data=train,ntree=500,proximity=TRUE,importance=TRUE)

## Independent Variable Importance

importance(train.RandomForest,type=1)

## Independent Variable Importance Plot

varImpPlot(train.RandomForest,sort=TRUE,type=1)

## Independent or Predictor Variables actually used in the Random Forest model

varUsed(train.RandomForest,by.tree=FALSE,count=TRUE)

##size of tree in Random Forest

a<-treesize(train.RandomForest,terminal=TRUE)

hist(a) ## Histogram to see the importance of each variable

## To compute oulying measures or outliers based on proximity matrix

b=outlier(train.RandomForest)

## graphical plot of outlier

plot(b,type='h',col=c("red","blue")[as.numeric(train$subscribed)])

## MultiDimensional scaling plot for trained model

MDSplot(train.RandomForest,train$subscribed,k=2,palette=rep(1,2),pch=as.numeric(train$subscribed))

## Partial Dependence Plot to observe the marginal effect of a variable on the classification class probabilities

## Lets observe the partial dependence of age on YES category class

partialPlot(train.RandomForest,train,age,"yes")

## Predict on the TEST data using the trained Random Forest Model

test.RandomForest=predict(train.RandomForest,test,predict.all=TRUE,proximity=TRUE,nodes=TRUE)

## Table showing the actual subscribed as per data and predicted subscribed by the trained model

table(observed=test$subscribed,predicted=test.RandomForest)

##Nodes Matrix

str(attr(test.RandomForest,"nodes"))

## confusion Matrix

table(test.RandomForest,test$subscribed)

## Plot margin of predictions on test dataset--- Here, positive margin means correct classification

margin(test.RandomForest)

plot(margin(test.RandomForest,sort=TRUE))

## Plot error rates or MSE(mean square error) for test dataset

plot(test.RandomForest,type=1)

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