Review the German Credit DataSet (Links to an external site.) (https://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data)) in the attachment. It has 1,000 observations. Train, test and validate a neural network with the first 980 observations and however many neurons in the hidden layer as you like.
Take a look at the data and remove a few attributes that you think do not help to determine the creditworthiness of a customer.
The last column is whether a customer is actually “good” or “bad” (i.e., their credit rating). See if you can improve the accuracy by changing various parameters, such as the number of neurons, and the number of layers. After you train, test your holdout 20 samples and report the results using the method below.
If your predictions are correct (good or bad) for each example, that counts as 0. If your prediction for a good customer is “bad” add 1 to your total. If your prediction for a bad customer is “good”, add 5 to your total. The lower the total the better your neural network