gistfile1.txt

train = read.csv("train.csv", header = TRUE)

response <- train$TARGET
train <-train[-c(1,371)]

#Remove no variance predictors

zero_var = nearZeroVar(train, names=TRUE, freqCut = 95/5,uniqueCut = 10,saveMetrics = TRUE)
train = train[,-which(zero_var$zeroVar)]

train_cat_names = list()
train_num_names = list()

#loop through training data by column / predictor variable

for (i in (1:length(train))){
  if (all(train[,c(i)] == floor(train[,c(i)]))){
    train_cat_names[length(train_cat_names)+1]=(names(train[c(i)]))
  }else{
    train_num_names[length(train_num_names)+1]=(names(train[c(i)]))
  }
}

idx <- match(train_cat_names, names(train))
train_cat = train[,idx]
train_num = train[,-idx]

#change categorical variables to factors

for (j in (1:length(train_cat))){
  train_cat[,c(j)] = as.factor(train_cat[,c(j)])
}

#normalize continuous variables

preproc = preProcess(train_num,method = c("center", "scale"))
train_standardized <- predict(preproc, train_num)


train_standardized = cbind(train_num,train_cat,response)

I am trying to run this code, I need packages names that are used to implements this code.