# Building the flow #

Figure 1\. Example flow to classify customers using binomial logistic regression

![Example flow to classify customers using binomial logistic regression](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/tut_churn.png)

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1.  Add a Data Asset node that points to  telco\.csv\.
2.  Add a Type node, double\-click it to open its properties, and make sure all measurement levels are set correctly\. For example, most fields with values of `0` and `1` can be regarded as flags, but certain fields, such as gender, are more accurately viewed as a nominal field with two values\.
    
    Figure 2. Measurement levels
    
    ![Measurement levels](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/tut_churn_measurement.png)
3.  Set the measurement level for the `churn` field to  Flag, and set the role to  Target\. Leave the role for all other fields set to  Input\.
4.  Add a Feature Selection modeling node to the Type node\. You can use a Feature Selection node to remove predictors or data that don't add any useful information about the predictor/target relationship\.
5.  Run the flow\. Right\-click the resulting model nugget and select View Model\. You'll see a list of the most important fields\.
6.  Add a Filter node after the Type node\. Not all of the data in the  telco\.csv data file will be useful in predicting churn\. You can use the filter to only select data considered to be important for use as a predictor (the fields marked as  Important in the model generated in the previous step)\.
7.  Double\-click the Filter node to open its properties, select the option  Retain the selected fields (all other fields are filtered), and add the following important fields from the Feature Selection model nugget:
    
        tenure
        age
        address
        income
        ed
        employ
        equip
        callcard
        wireless
        longmon
        tollmon
        equipmon
        cardmon
        wiremon
        longten
        tollten
        cardten
        voice
        pager
        internet
        callwait
        confer
        ebill
        loglong
        logtoll
        lninc
        custcat
        churn
8.  Add a Data Audit output node after the Filter node\. Right\-click the node and run it, then open the output that was added to the  Outputs pane\.
9.  Look at the  % Complete column, which lets you identify any fields with large amounts of missing data\. In this case, the only field you need to amend is `logtoll`, which is less than 50% complete\.
10. Close the output, and add a Filler node after the Filter node\. Double\-click the node to open its properties, click  Add Columns, and select the `logtoll` field\.
11. Under  Replace, select  Blank and null values\. Click  Save to close the node properties\.
12. Right\-click the Filler node you just created and select  Create supernode\. Double\-click the supernode and change its name to  Missing Value Imputation\.
13. Add a Logistic node after the Filler node\. Double\-click the node to open its properties\. Under  Model Settings, select the  Binomial procedure and the  Forwards Stepwise method\.
    
    Figure 3. Choosing model settings
    
    ![Choosing model settings](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/tut_churn_model.png)
14. Under  Expert Options, select  Expert\.
    
    Figure 4. Choosing expert options
    
    ![Choosing expert options](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/tut_churn_expert.png)
15. Click  Output to open the display settings\. Select  At each step,  Iteration history, and  Parameter estimates, then click  OK\.
    
    Figure 5. Choosing expert options
    
    ![Choosing expert options](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/tut_churn_expert_output.png)

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