Neural Network Example  Sales Forecasting
Neural networks deal with data in a rows and columns format. For example, suppose you have a restaurant, and you are interested in predicting the restaurant's monthly sales. The following is an example of how to use a neural network to make such sales forecasts.
First, lay out in a tabular format in a word processor or spreadsheet the historical data you need, such as the season, number of special events scheduled in the town, the number of ads you have placed, and the cost of those ads.
These are the input variables which is what the neural network will use to make
predictions about total sales. Each month's data should be included in a row in the table. The input variables that affect your forecast of monthly sales are columns in the table. You can use any variables that you think affect sales.
One column, usually the last one, is designated as the output that you are
trying to predict.
Our neural networks "learn" to recognize patterns from
historical data (previous examples) presented as above. You will need more than
just a few historical examples  as a minimum, you will need 10 to 30 times as
many examples as you have input variables. After this learning, the neural networks are ready to make
new predictions for you. The neural networks build an internal formula that takes
the input variables and computes the total sales forecast. For new predictions, all
you have to do is show the neural network the new input variables. It will
show you the new neural network prediction for total sales. It's that easy!

