Using Neural Networks when target variable has huge deviation I have a dataset of 10 features which are in the small range between 0.5 and 1 exactly, while the target variable is between 75 and 9000 with a standard deviation of 350, now I need to use a Neural Network to perform regression on the data but I'm struggling with finding a suitable architecture to solve the issue I'm facing with discrepancy between the ranges of the input and output

If the output layer has a linear activation function this distribution shouldn't be a problem, but you can always just rescale the target variable by dividing by 350 or 9000 (to have a SD or a maximum of 1). To obtain predictions on the original scale, you can then just multiply them.
