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Predictive Analytics in Data Mining

Data warehouse tips by Burleson Consulting

This is an excerpt from Dr. Ham's premier book "Oracle Data Mining: Mining Gold from your Warehouse".

Predictive analytics concerns the prediction of future probabilities.  With predictive analytics, the data mining analyst takes the case dataset, identifies two key components, and voila a model is applied to the data.  Predictive analytics builds models automatically, by combining predictors based on your case dataset.  The results explain what attributes are important in predicting the outcome or target, and the probability that each case will meet the predicted target value. 

In contrast to the methods previously described in this text, using predictive analytics requires no decisions on the part of the data analyst in terms of picking an algorithm, adjusting sensitivity values or any other settings.  In essence, predictive analytics simplifies the process and fully automates data mining.

Oracle Predictive analytics is composed of the PREDICT and EXPLAIN wizards, and is based on Oracle 10G Release 2?s PL/SQL package DMBS_PREDICTIVE_ANALYTICS and DBMS_ODM.  Oracle provides an interface to this package with both ODMrand the predictive analytic spreadsheet add-in for MS Excel.  You can find the Predict and Explain wizards under the Data toolbar.  The Explain Wizard identifies attributes important for explaining the target attribute. 

The steps taken by the wizard include analyzing the input table, prepping the data, building the model, analyzing the model to identify important attributes, and creating a table with the attributes rank ordered in importance.  The output table lists the attributes sorted in decreasing order of importance for explaining the target values.  Importance is a number between 0 and 1, with 1 being most important.

After identifying the case dataset, Step 2 of the Explain Wizard asks you to select the attribute you wish to explain.  This is the target attribute, and for the Mining_Data_Build_V dataset the target is AFFINITY_CARD.  All that is left is to pick a name for the output table, and click Finish.

In the ?Explain Output? shown below, the top 10 ranking attributes for predicting whether a customer has an affinity card are HOUSEHOLD_SIZE, CUST_MARITAL_STATUS, YRS_RESIDENCE, Y_BOX_GAMES, EDUCATION, HOME_THEATER_PACKAGE, OCCUPATION, CUST_GENDER, AGE and BOOKKEEPING_APPLICATION.  The model built by predictive analytics sets the importance of the remaining columns at zero. 

The Predict Wizard assigns probabilities and predictions of the target value for every case in the dataset.  The Predict Wizard analyzes the input table, preps the data, builds the model, analyzes the model, and creates a table with three columns:  Case ID, Prediction of the target value, and the Probability of the prediction. 

Shown below is a portion of the Predict table showing the CUST_ID, PREDICTION and PROBABILITY of the OCCUPATION attribute for the Mining_Data_Build_V dataset.

The Predict Wizardis independent of the Explain Wizard.  You are not applying the Explain model to new data.  In other words, ?Explain? and ?Predict? are Data-Centric automated data mining, and since all supporting objects are linked to a data source the issue of matching the model to data is eliminated.  


For more tips and tricks for Oracle data warehouse analysis, see Dr. Ham's premier book "Oracle Data Mining: Mining Gold from your Warehouse"

You can buy it direct from the publisher for 30%-off:


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