library(MASS)
library(readr)

set.seed(0)

train <- read_csv("../input/train.csv")
test <- read_csv("../input/test.csv")

trainLabels <- as.factor(train[,1])

# Remove pixels that don't help from both train and test
allmisscols <- apply(train,2, function(x)all(x==0))
colswithallmiss <-names(allmisscols[allmisscols>0])
trainDataFrame <- train[, !names(train) %in% colswithallmiss]
testDataFrame <- test[, !names(test) %in% colswithallmiss]

#LDA
ldaModel <- lda(label ~ ., data = trainDataFrame)
summary(ldaModel)
predictedClass <- predict(ldaModel, testDataFrame[,1:ncol(testDataFrame)])$class
predictions <- data.frame(ImageId=1:nrow(test), Label=levels(trainLabels)[predictedClass])
write_csv(predictions, "lda_benchmark.csv")