Should we scale the data if our response(Y) is numeric, a large number, and 99% of other variables are dummy variables? Our response variable is something like Sales which should be very large(Mean at the million level), and one predictor is also numeric but with mean at ten thousand level. The other predictors are all dummy variables.
From my perspective, there are too many dummy variables which mean linear model will not perform well lm(Y~ x1 + Dummies). The reason why I thought about neural network is that I really need make a good fit and make a decent prediction about the Sales.
Should I standardize the whole dataset and then build the neural network model?

Standardizing would be advisable. Neural networks usually do not behave well with large numbers due to disproportionate gradient steps.
