How to predict housing price using Neural Network Toolbox?

I would like to predict housing prices using inputs such as distance away from subway, numbers of rooms etc.. which of the 4 wizards ( fitting tool, pattern recognition tool, clustering tool, time series tool) should i use?

Thank You WT Lim

Answer 1

[Greg Heath]

Use the fitting tool with fitnet

Also, see

 help house_dataset
 doc  house_dataset
 help fitnet
 doc  fitnet

 [ x,t ] = house_dataset;  
 [ I N ] = size(x)        % [ 13 506 ]
 [ O N ] = size(t)        % [ 1 506 ]
 MSE00   = mean(var(t',1)) % 84.42
 net     = fitnet;

 rng('default')
 [net tr y e ] = train(net,x,t); % e=t-y
 NMSE          = mse(e)/MSE00    % normalized MSE = 0.071101
 R2            = 1-NMSE          % Rsquare (See Wikipedia) = 0.9289

% ~ 93% of the target variance is modeled by the net. % Obtain details from the training record

 tr = tr

Hope this helps

Thank you for formally accepting my answer

Greg

[Greg Heath]

Better solutions probably can be obtained by using

 1. A different number of hidden nodes
 2. A different set of random initial weigts

[WT]

Thank you for replying. Can i still use fitting tool with fitnet when the inputs are dependent on time?