MultipleLinearRegression/PolynomialRegression.R at master · anupomr/MultipleLinearRegression · GitHub

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# Polynomial Regression

# Imporing the dataset
dataset =  read.csv('Position_Salaries.csv')
dataset = dataset[2:3]

# Fitting Linear Regression to the dataset
lin_reg =lm(formula = Salary ~ .,
            data = dataset)

# Fitting polynomial Regression to the dataset
dataset$Level2= dataset$Level^2
dataset$Level3= dataset$Level^3
dataset$Level4= dataset$Level^4
poly_reg =lm(formula = Salary ~ .,
            data = dataset)
# in console summary(lin_reg)


# Visualising the Linear Regression result
#install.packages('ggplot2')
library(ggplot2)
ggplot() +
  geom_point(aes(x = dataset$Level, y = dataset$Salary),
             colour = 'red') +
  geom_line(aes(x = dataset$Level, y = predict(lin_reg, newdata = dataset)),
            colour = 'blue') +
  ggtitle('Linear Regression') +
  xlab('Level') +
  ylab('Salary')

# Visualising the polynomial Regression result
ggplot() +
  geom_point(aes(x = dataset$Level, y = dataset$Salary),
             colour = 'red') +
  geom_line(aes(x = dataset$Level, y = predict(poly_reg, newdata = dataset)),
            colour = 'blue') +
  ggtitle('Polynomial Regression') +
  xlab('Level') +
  ylab('Salary')

# Predicting a new result with linear regression
y_pred = predict(lin_reg, data.frame(Level = 6.5))


# Predicting a new result with polynomial regression
y_pred = predict(poly_reg, data.frame(Level = 6.5,
                                      Level2 =6.5^2,
                                      Level3 = 6.5^3,
                                      Level4 = 6.5^4))