#importing data

Salary = read.csv(choose.files())

#Removing Positin as it is already taken as levels

Salary$Position = NULL

#fitting Salary to liner regression

linReg = lm(formula=Salary~Level, data = Salary)

predict(linReg,newdata = 6.5)

#fitting Salary to Ploynomial regression

Salary$Level2 = Salary$Level^2

Salary$Level3 = Salary$Level^3

Salary$Level4 = Salary$Level^4

polyReg = lm(formula=Salary~.,

data = Salary)

#Visualising Linear Regression results

library(ggplot2)

ggplot()+

geom_point(aes(x=Salary$Level, y=Salary$Salary), color="red")+

geom_line(aes(x=Salary$Level, y=predict(linReg, newdata = Salary)), color = 'blue')

#Visualising Polynomial Regression results

ggplot()+

geom_point(aes(x=Salary$Level, y=Salary$Salary), color="red")+

geom_line(aes(x=Salary$Level, y=predict(polyReg, newdata = Salary)), color = 'blue')

#Predicting the value with linear regreesion model

y_pred = predict(linReg, newdata = data.frame(Level = 6.5))

#Predicting the value with polynomial regreesion model

y_pred_poly = predict(polyReg, newdata = data.frame(Level = 6.5,

Level2 = 6.5^2,

Level3 = 6.5^3,

Level4 = 6.5^4))