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203.6.4 Building SVM model in R

Building a SVM model in R.

We discussed the SVM algorithm in our last post. In this post we will try to build a SVM classification model in R.

LAB: First SVM Learning Problem

  • Dataset: Fraud Transaction/Transactions_sample.csv
  • Draw a classification graph that shows all the classes
  • Build a SVM classifier
  • Draw the classifier on the data plots
  • Predict the (Fraud vs not-Fraud) class for the data points Total_Amount=11000, Tr_Count_week=15 & Total_Amount=2000, Tr_Count_week=4
  • Download the complete Dataset: Fraud Transaction/Transaction.csv
  • Draw a classification graph that shows all the classes
  • Build a SVM classifier
  • Draw the classifier on the data plots

Solution

#SVM Building needs e1071 package
library(e1071)

#Converting the output into factor, otherwise SVM will fit a regression model
Transactions_sample$Fraud_id<-factor(Transactions_sample$Fraud_id) 
head(Transactions_sample)
##      id Total_Amount Tr_Count_week Fraud_id
## 1 16078      7294.60          4.79        0
## 2 41365      7659.53          2.45        0
## 3 11666      8259.29         10.77        0
## 4 11824     11630.25         15.29        1
## 5 36414     12286.63         22.18        1
## 6    90     12783.34         16.34        1
#SVM Model building
svm_model <- svm(Fraud_id~Total_Amount+Tr_Count_week, data=Transactions_sample)
summary(svm_model)
## 
## Call:
## svm(formula = Fraud_id ~ Total_Amount + Tr_Count_week, data = Transactions_sample)
## 
## 
## Parameters:
##    SVM-Type:  C-classification 
##  SVM-Kernel:  radial 
##        cost:  1 
##       gamma:  0.5 
## 
## Number of Support Vectors:  12
## 
##  ( 6 6 )
## 
## 
## Number of Classes:  2 
## 
## Levels: 
##  0 1
#Plotting SVM Clasification graph o the data
ggplot(Transactions_sample)+geom_point(aes(x=Total_Amount,y=Tr_Count_week,color=factor(Fraud_id),shape=factor(Fraud_id)),size=5)

#Data With SVM model
plot(svm_model, Transactions_sample,Tr_Count_week~Total_Amount ) #x2~x1

#Prediction in SVM
new_data1<-data.frame(Total_Amount=11000, Tr_Count_week=15)
p1<-predict(svm_model, new_data1)
p1
## 1 
## 1 
## Levels: 0 1
new_data2<-data.frame(Total_Amount=2000, Tr_Count_week=4)
p2<-predict(svm_model, new_data2)
p2
## 1 
## 0 
## Levels: 0 1
#SVM on overall data
Transactions<- read.csv("C:\\Amrita\\Datavedi\\Fraud Transaction\\Transaction.csv")
dim(Transactions)
## [1] 45000     4
#Converting the output into factor, otherwise SVM will fit a regression model
svm_model_1 <- svm(Fraud_id~Total_Amount+Tr_Count_week, type="C", data=Transactions)
summary(svm_model_1)
## 
## Call:
## svm(formula = Fraud_id ~ Total_Amount + Tr_Count_week, data = Transactions, 
##     type = "C")
## 
## 
## Parameters:
##    SVM-Type:  C-classification 
##  SVM-Kernel:  radial 
##        cost:  1 
##       gamma:  0.5 
## 
## Number of Support Vectors:  44
## 
##  ( 21 23 )
## 
## 
## Number of Classes:  2 
## 
## Levels: 
##  0 1
#Plotting SVM Clasification graph
plot(svm_model_1, Transactions,Tr_Count_week~Total_Amount ) 

 

The next post is about the Non-linear Decision Boundary.

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