Suggested scenarios for using the Microsoft Neural Network algorithm include the following:
Marketing and promotion analysis, such as measuring the success of a direct mail promotion or a radio advertising campaign
Predicting stock movement, currency fluctuation, or other highly fluid financial information from historical data
Analyzing manufacturing and industrial processes
Text mining
Any prediction model that analyzes complex relationships between many inputs and relatively fewer outputs
Data Required for Neural Network Models
A neural network model must contain a key column, one or more input columns, and one or more predictable columns.
Data mining models that use the Microsoft Neural Network algorithm are heavily influenced by the values that you specify for the parameters that are available to the algorithm.
The parameters define how data is sampled, how data is distributed or expected to be distributed in each column, and when feature selection is invoked to limit the values that are used in the final model.
Creating Predictions
After the model has been processed, you can use the network and the weights stored within each node to make predictions. A neural network model supports regression, association, and classification analysis, Therefore, the meaning of each prediction might be different. You can also query the model itself, to review the correlations that were found and retrieve related statistics. For examples of how to create queries against a neural network model, see Neural Network Model Query Examples.
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