The sample project contains several data sets and sample modeler flows. If you don't already have the sample project, then refer to the Tutorials topic to create the sample project. Then follow these steps to open the sample project:

1. In watsonx, from the Navigation menu , choose Projects > View all Projects.
2. Click SPSS Modeler Project.
3. Click the Assets tab to see the data sets and modeler flows.

Check your progress

The following image shows the project Assets tab. You are now ready to work with the sample modeler flow associated with this tutorial.

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Making Offers to Customers - Self-Learning modeler flow includes several nodes. Follow these steps to examine the Data Asset and Filler nodes:

1. From the Assets tab, open the Making Offers to Customers - Self-Learning modeler flow, and wait for the canvas to load.
2. Double-click the pm_customer_train1.csv node. This node is a Data Asset node that points to the pm_customer_train1.csv file in the project.
3. Review the File format properties.
4. Optional: Click Preview data to see the full data set.
5. Double-click the Filler node.
   Filler nodes are used to replace field values and change storage. You can choose to replace values based on a specified CLEM condition, such as @BLANK(FIELD). Alternatively, you can choose to replace all blanks or null values with a specific value. Filler nodes are often used with the Type node to replace missing values.
   In the Fill in fields section, you can specify the fields from the dataset whose values you want to be examined and replaced. In this case, the campaign column is specified together with an Always selection under the Replace section.
6. Click Cancel.

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The following image shows the Filler node. You are now ready to examine the Type and Reclassify nodes.

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Follow these steps to examine the Type and Reclassify nodes:

1. Double-click the Type node to see its properties.
   With the Type node, you can indicate the types of fields you're using and how they're used to predict the outcomes. Notice, that the role for the campaign and response fields are set to Target. The specified targets are the fields on which you want to base your predictions. The Measure is set to Flag for the response field.
2. Click Read values.

   

3. Click Save.

   Because the campaign field data shows as a list of numbers (1, 2, 3, and 4), you can reclassify the fields to have more meaningful titles.

4. Double-click the Reclassify node to see its properties.
5. In the Reclassify Into section, select Existing field.
6. Next, in the Reclassify Field section, select campaign.
7. Click Get values. The campaign values are added to the ORIGINAL VALUE column.
8. Notice, that in the Values section in the NEW VALUE column, there are four rows:
   • Mortgage
   • Car loan
   • Savings
   • Pension
9. Click Save.

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The following image shows the Reclassify node. You are now ready to explore the SLRM node.

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Follow these steps to explore the SLRM node:

1. Double-click the campaign (SLRM) node to view its properties.
2. Select campaign for the Target field, and response for the Target response field.
3. Under Model Options, for Maximum number of predictions per record, reduce the number to 2. This means that for each customer there are two offers identified that have the highest probability of being accepted.
4. Make sure Take account of model reliability is selected.
5. Click Save.

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The following image shows the flow. You are now ready to generate the model.

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Follow these steps to generate the model:

1. Hover over the campaign (SLRM) node, and click the Run icon .
2. In the Outputs and models pane, click the campaign model to view the results.

   The initial view shows the estimated accuracy of the predictions for each offer. You can also click Predictor Importance to see the relative importance of each predictor in estimating the model, or click Association With Response to show the correlation of each predictor with the target variable.

   

3. To switch between each of the four offers for which there are predictions, use the View menu.
4. Return to the flow.

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The following image shows the flow. You are now ready to experiment with other Data Asset nodes.

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Follow these steps to experiment with other Data Asset nodes:

1. Disconnect the pm_customer_train1.csv node from the Filler node.
2. Connect the pm_customer_train2.csv node to the Filler node.
3. Double-click the campaign (SLRM) node, and under the Build Options make sure that Continue training existing model is selected.
4. Click Save.
5. Run the flow to regenerate the model nugget. Hover over the campaign (SLRM) node, and click the Run icon .
6. In the Outputs and models pane, click the campaign model to view the results. The model now shows the revised estimates of accuracy of the predictions for each offer.

   

7. Disconnect the pm_customer_train2.csv node from the Filler node.
8. Connect the pm_customer_train3.csv node to the Filler node.
9. Run the flow to regenerate the model nugget. Hover over the campaign (SLRM) node, and click the Run icon .
10. In the Outputs and models pane, click the campaign model to view the results.

    

    The model now shows the final estimated accuracy of the predictions for each offer. As you can see, the average accuracy fell slightly as you added the additional data sources. However, this fluctuation is a minimal amount and might be attributed to slight anomalies within the available data.

11. Hover over the Table node, and click the Run icon .
12. In the Outputs and models pane, click the Table model to view the results.

    

The predictions in the table show which offers a customer is most likely to accept and the confidence that they accept that offer, depending on each customer's details. For example, in the first row, there is only a 13.2% confidence rating (denoted by the value 0.132 in the $SC-campaign-1 column) that a customer who previously took out a car loan accepts a pension if offered one. However, the second and third lines show two more customers who also took out a car loan; in their cases, there is a 95.7% confidence that they, and other customers with similar histories, open a savings account if offered one, and over 80% confidence that they accept a pension.

Explanations of the mathematical foundations of the modeling methods that are used in SPSS Modeler are available in the SPSS Modeler Algorithms Guide.

These results are based on the training data only. To assess how well the model generalizes to other data in the real world, you use a Partition node to hold out a subset of records for purposes of testing and validation.

Check your progress

The following image shows the flow.

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