mtcars

#Sample dataset
?mtcars
#importing dataset in R
data(mtcars)
head(mtcars)
#Structure of the dataset
str(mtcars)
#Summary of the dataset
summary(mtcars)
#Plots between 2 variables
plot(mtcars$cyl,mtcars$mpg)
plot(mtcars$mpg,mtcars$cyl)
mtcars$cyl=as.factor(mtcars$cyl)
hist(mtcars$mpg)
#Plotting 2 variables using ggplot2 package
library(ggplot2)
ggplot(mtcars,aes(cyl,mpg))+geom_point()
ggplot(mtcars,aes(factor(cyl),mpg))+geom_bar(stat="identity")
ggplot(mtcars,aes(factor(cyl),mpg,fill=factor(am)))+
  geom_bar(stat="identity")
#Histogram of mpg(Target variable)
ggplot(mtcars,aes(mpg))+geom_histogram()
ggplot(mtcars,aes(factor(cyl),mpg,fill=factor(am)))+
  geom_bar(stat="identity",position = "dodge")
ggplot(mtcars,aes(qsec,mpg))+geom_point()
ggplot(mtcars,aes(factor(vs),mpg))+geom_bar(stat ="identity")
ggplot(mtcars,aes(qsec,mpg))+geom_point()
ggplot(mtcars,aes(factor(am),mpg))+geom_bar(stat = "identity")
ggplot(mtcars,aes(factor(gear),mpg))+geom_bar(stat ="identity")
head(mtcars)
ggplot(mtcars,aes(factor(vs),mpg))+geom_bar(stat ="identity")
#Multiple linear regression for the model
master=lm(mpg~ cyl+disp+hp+gear+wt+carb,mtcars)
summary(master)
#Prediction
mypred=predict(master,mtcars)
head(mypred)
head(mtcars)
#Root mean square error
myrmse= sqrt(mean((mtcars$mpg - mypred)^2))
myrmse
#Multiple linear regression for relevant input variables
master1=lm(mpg~wt+hp,mtcars)
summary(master1)
mypred1=predict(master1,mtcars)
#Root mean square error of the model
myrmse1= sqrt(mean((mtcars$mpg - mypred1)^2))
myrmse1