decision tree.R

#Decision Trees

# train and test set are still loaded in
str(train)
str(test)

library(rpart)
# Build the decision tree
my_tree_two <- rpart(Survived ~ Pclass + Sex + Age + SibSp + Parch + Fare + Embarked, 
                     data = train, 
                     method ="class")

# Visualize the decision tree using plot() and text()
plot(my_tree_two)
text(my_tree_two)

# Load in the packages to create a fancified version of your tree
library(rattle)
library(rpart.plot)
library(RColorBrewer)

# Time to plot your fancy tree
fancyRpartPlot(my_tree_two)

#Predict & submit to kaggle
# Make your prediction using the test set
my_prediction <- predict(my_tree_two, test, type = "class") 

# Create a data frame with two columns: PassengerId & Survived. Survived contains your predictions
my_solution <- data.frame(PassengerId = test$PassengerId, Survived = my_prediction)

# Check that your data frame has 418 entries
nrow(my_solution)

# Write your solution to a csv file with the name my_solution.csv
write.csv(my_solution, file="my_solution.csv" , row.names=FALSE)