In this example, a logistic regression is performed on a data set containing bank marketing information to predict whether or not a customer subscribed for a term deposit.

The logistic regression, using the 1010data function `g_logreg(G;S;Y;XX;Z)`, is applied to
the Bank Marketing Data Set, which contains information related to a campaign
by a Portuguese banking institution to get its customers to subscribe for a term deposit.

The logistic regression uses the following 10 variables in that data set as
predictors:

`age``duration``previous``empvarrate``housing``default``loan``poutcome``job``marital`

As a response, the column `y` is used, which is
`yes` if a customer has subscribed for a term deposit.

This analysis will follow the following steps:

- Prepare the data by creating dummy variables for each of the categorial columns (since we cannot use textual data to build our model).
- Divide the data into a training set and a test set.
- Run the logistic regression on the training data set based on the continuous variables in the original data set and the dummy variables that we created.
- Obtain the predicted probability that a customer has subscribed for a term deposit.
- Create a cumulative gains chart and calculate the area under the curve (AUC) for the test data.
- Obtain the model coefficients.
- Chart the logistic curve for both the training and test data.