Real-time scoring with nested predictive models for missing value imputation

Let's suppose that you work at a bank that has been on a acquision spree acquiring several smaller banks.  That's great, but now you need to reach out to these new customers with proactive marketing programs to keep them as customers with your new, larger mega-bank.  Suppose that you have already used Oracle Data Mining's in-database data mining functions to build an attrition model, attrition_model  and now you want to reach out to the customers who are most likely to leave.  The problem is introduced by the fact that not all of the acquired banks were consistent about the information they gathered and stored and you predictive model relies heavily on several key attributes, including most importantly, annual income.  

So what do you do?  Easy!  You build another model, estim_income and nest that model inside the ODM  attrition_model  when scoring.  

See this example where we select the 10 customers who are most likely to attrite based solely on: age, gender, annual_income, and zipcode.  In addition, since annual_income is often missing, perform null/missing value imputation for the annual_income attribute using all of the customer demographics.

SELECT * FROM (
  SELECT cust_name, cust_contact_info,
         rank() over (ORDER BY
     PREDICTION_PROBABILITY(attrition_model, 'attrite'
       USING age, gender, zipcode,
         NVL(annual_income,
             PREDICTION(estim_income USING *))
           as annual_income) DESC) as cust_rank
    FROM customers)
WHERE cust_rank < 11;   

As the first model scores, the second model estim_income performs Oracle's null/missing value imputation for the annual_income attribute using all of the customer demographics/  

VoilĂ ! A complex multi-model problem made easy with Oracle Data Mining and its 12 in-database data mining functions combined with the power of SQL and the Oracle Database.
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