his one uses the NARX model to predict the forthcoming house price in months of 2017.
To execute this code run main.m in MATLAB. It will open a GUI and proceed further as desire.
To predict the house price, we need a dataset which can train the neural network. This dataset must be large enough to train the network so that overfitting of results can be avoided. We have used the dataset obtained from London data store. it contains the data form year 1995-2015. This is categorised as • ID (Transaction ID) • Date (Date processed, Month of transaction, Year of transaction, etc) • Transaction Price • Property classification (Type, Build, Tenure) • Address information (Postcode, Local authority, Full address, Borough, Ward, etc) These variables are further divided as dependent variables and independent variables for the NN training. Out of these dependent variables will be the input for training and independent variable will act as target.
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