House pricing using neural network

Question

I wrote multilayer perceptron implementation (on Python) which is able to classify Iris dataset. It was trained by backpropagation algorithm and uses sigmoid actiovation functions on a hidden and output layers.

But now I want to change it to be able to approximate house price. (I have dataset of ~300 estates with prices and input parameters like rooms, location etc.)

Now output of my perceptron is in range [0;1]. But as far as I understand if I want to get resulting house price on the output neuron I need to change that activation function somehow right?

Can somebody help me? I'm new to neural networks Thanks in advance.

Answer 1

Assuming, for instance, that houses price between $1 and $1,000,000, then you can just map the 0...1 range to the final price range both for the training and for the testing. Just note that 300 estates is a fairly small data set.

To be precise, if a house is $500k, then the target training output becomes 0.5. You can basically divide by your maximum possible home value to get the target training amount. When you get the output value you multiple by the maximum home value to get the predicted price.

So, view the output of the neural network as the percentage of the total cost.