#Lectura de la DB
MyData <- read.csv(file="C:/Users/56971/Desktop/Analisis de Datos/breast-cancer-wisconsin/breast-cancer-wisconsin/breast-cancer-wisconsin.csv", na.strings='?', header=TRUE, sep=",")

#Se eliminan las mediciones con datos NA
cancer <- na.omit(MyData)

smp_siz <- floor(0.75*nrow(cancer))  # creates a value for dividing the data into train and test. In this case the value is defined as 75% of the number of rows in the dataset

set.seed(123)

train_ind = sample(seq_len(nrow(cancer)),size = smp_siz)

train =cancer[train_ind,]
test=cancer[-train_ind,]

model <- naiveBayes(class ~., data = train)

results <- predict(model, newdata=test)

View(results)