Decison_Tree_CarAcceptability.R

install.packages("caret")
library("caret")
library("rpart.plot")

data_url <- c('https://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data')
download.file(url= data_url, destfile = "car.data")

car_df <- read.csv("car.data", sep = ',', header = FALSE)

str(car_df)
head(car_df)

set.seed(3033)

intrain <- createDataPartition( y = car_df$V7, p= 0.7, list = FALSE)
training <- car_df[intrain,]
testing <- car_df[-intrain,]

dim(training)
dim(testing)

anyNA(car_df)

summary(car_df)

trctrl <- trainControl(method = "repeatedcv", number = 10, repeats = 3)
set.seed(3324)
dtre_fit <- train(V7 ~., data = training, method= "rpart", parms= list(split = "information"), tuneLength =10)

dtre_fit

prp(dtre_fit$finalModel, box.palette = "Reds", tweak = 1.2)

testing[1,]

predict(dtre_fit, newdata = testing[1,])

test_pred <- predict(dtre_fit, newdata = testing)

confusionMatrix(test_pred, testing$V7)

set.seed(3323)

dtre_fit_gini <- train(V7 ~., data = training, method= "rpart", parms= list(split = "gini"), trControl=trctrl, tuneLength =10)

dtre_fit_gini

prp(dtre_fit_gini$finalModel, box.palette = "Blues", tweak = 1.2)

test_pred_gini <- predict(dtre_fit_gini, newdata = testing)
confusionMatrix(test_pred_gini, testing$V7)