svr.r

## Prepare Scatter Plot with Best Fit Line

#Set Working Directory using setwd("")

#Read Data
data=read.csv("SVM.csv", header=T)
head(data)

#Scatter Plot
plot(data, main ="Scatter Plot")

#Linear Model
model=lm(Y~X,data)
abline(model)





## Prepare Scatter Plot with Predicted Points

#Scatter Plot
plot(data, pch=16)

#Predict Y using Linear Model
predY <- predict(model, data)

#Overlay Predictions on Scatter Plot
points(data$X, predY, col = "blue", pch=16)





## Calulate Root Mean Square Error (RMSE) for Linear Model

#Install Package
install.packages("hydroGOF")

#Load Library
library(hydroGOF)

#Calculate RMSE 
RMSE=rmse(predY,data$Y)





## Fit SVR Model and Prepare Scatter Plot

#Install Package
install.packges("e1071")

#Load Library
library(e1071)

#Scatter Plot
plot(data)

#Regression with SVM
modelsvm=svm(Y~X,data)

#Predict using SVM regression
predYsvm <- predict(modelsvm, data)

##Overlay SVM Predictions on Scatter Plot
points(data$X, predYsvm, col = "red", pch=16)





## Calculate parameters of the SVR Model
#Find value of W
W=t(modelsvm$coefs) %*% modelsvm$SV

#Find value of b
b=modelsvm$rho  




## RMSE for SVR Model

#Calculate RMSE 
RMSEsvm=rmse(predYsvm,data$Y)





##  Optimising SVR Model and Selecting Best Model

#Tune the above SVM model
OptModelsvm=tune(svm, Y~X, data=data,ranges=list(elsilon=seq(0,1,0.1), cost=1:100))

#Print optimum value of parameters
print(OptModelsvm)

#Plot the perfrormance of SVM Regression model
plot(OptModelsvm)

#Find out the best model
BstModel=OptModelsvm$best.model

#Predict Y using best model
PredYBst=predict(BstModel,data)

#Calculate RMSE of the best model 
RMSEBst=rmse(PredYBst,data$Y)





## Calculate parameters of the Best SVR Model
#Find value of W
W=t(BstModel$coefs) %*% BstModel$SV

#Find value of b
b=BstModel$rho  



## Plotting SVR Model and Tuned Model in same plot

plot(data, pch=16)
points(data$X, predYsvm, col = "blue", pch=3)
points(data$X, PredYBst, col = "red", pch=4)
points(data$X, predYsvm, col = "blue", pch=3, type="l")
points(data$X, PredYBst, col = "red", pch=4, type="l")

Hello, could you please upload the data SVM.csv?