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

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# Multiple Linear Regrassion

# Importing the dataset
dataset = read.csv('50_Startups.csv')

# Encoding categorical data
dataset$State = factor(dataset$State,
                       levels = c('New York', 'California', 'Florida'),
                       labels = c(1, 2, 3))

# Spliting the dataset into training set and test set
# install.packages('caTools')
library(caTools)
set.seed(123)
split = sample.split(dataset$Profit, SplitRatio = 0.8)
training_set = subset(dataset, split == TRUE)
test_set = subset(dataset, split == FALSE)

# Fitting Multiple Linear Regression to the training set
regressor = lm(formula = Profit ~ .,
               data = training_set)
#Predicting the test set results
y_pred = predict(regressor, newdata = test_set)

# Building the optimal model uning of Backgroung eleminiation
regressor = lm(formula = Profit ~ R.D.Spend + Administration + Marketing.Spend + State,
                  data = dataset)
summary(regressor)

regressor = lm(formula = Profit ~ R.D.Spend + Administration + Marketing.Spend,
               data = dataset)
summary(regressor)

regressor = lm(formula = Profit ~ R.D.Spend,
               data = dataset)
summary(regressor)