title: Feature-wise Normalization of Predictor Variables in R
body: <p>So I want to fit a neural network later to predict the continous numeric variable Outstate in the College dataset from the ISLR packagage as a function of the 17 other (predictor) variables in the dataset. Before I fit the neural network I need to do feature-wise normalization of only the predictor variables. Since there are 17 predictors I don't want to have to explicitly type out every variable I wanna standardize. Is there any feasible way of doing this in R? The scale function scales all variables in the dataset from what I understand.</p>
  
  <pre><code>library(ISLR)
  
  set.seed(1)
  College$Private = as.numeric(College$Private)
  train.ind = sample(1:nrow(College),0.5*nrow(College))
  college.train = College[train.ind,]
  college.test = College[-train.ind,]
  </code></pre>