Download - Quick Start-10gR2 Data Mining CD BUYERS
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QuickStart: Oracle Data Mining
Release 10gR2 [email protected] January 2006
Identifying and Understanding Your MostValuable Customers
Oracle Data Mining (ODM) is powerful data
mining software embedded in the Oracle
Database.
Know More
ODM helps you find information & new
insights hidden in the data
Do More
With ODM, you can build models &
applications simultaneously using Oracle Data
Miner and ODMs Java and PL/SQL APIs.
Application developers can integrate the
models into enterprise applications that
automate and integrate data mining.
Spend Less
ODMs total cost of ownership is less than the
other major competitors.
Understanding the Business Problem
1. A major bank is looking to target and better understandcustomers who purchase 6-Month Certificates of Deposits. The
data is about customers, their demographic information, bank
balances, previous responses to 6-Month CDs and other data.They want to mine the data to find the factors most associated
with customers who purchase 6-Month Certificates of Deposits
and hope to build a predictive model to predict which customers
have the highest likelihood to respond.
2. Using CD_BUYERS data, examine the main ODM GUI and
briefly pull down each menu and review its purpose:a. File, View, Data, Models, Tools, Help
3. Show the data table and explain some of the attributes e.g. AGE,AVE_CHECKING_BAL, SAVINGS_BAL,
MARITAL_STATUS, RELATIONSHIP, etc.
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4. Right-mouse click on CD_BUYERS and select Show SummarySingle-Record and review the data.
a. Click on AGE and display the histogram
b. Click on CD_BUYERS. In this data population, 24% of
the customers have purchased 6-Month CDs in the past,but we want to mine the data to understand what factors
contribute to both groups of customers.
c. Click on other attributes e.g. AVE_CHECKING_BAL,MARITAL_STATUS, RELATIONSHIP as possiblyrelated attributes.
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Identify Attributes with the Strongest Relationships
5. Lets first mine the data to identify the attributes that are mostclosely relatedto our business problemfinding customers who
purchase 6-Month CDs. From the top menu bar, select Activityand then select Build.
a. In the drop down menu, change Function Type from
Classification to Attribute Importance.
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b. In Step 2, accept all the defaults and pick CUST_ID as
the unique idenfier.c. In Step 3, pick CD_BUYER as the Target attribute.
ODMs Mining Activity Guides provide
guidance for users who are new to data
mining. In this Data Usage step, ODM has
examined the attributes found in the table and
suggests default data usages. The user can
override them at any time.
d. In Step 4, either provide a unique name for the Mining
Activity or accept the default name and click Nexte. At the Finish Step, notice that the Mining Activity defaultis set to Run upon Finish. Advanced users may
override the default settings.
f. Click Finish.
6. Oracle Data Miner automatically runs the Activity Guides
sequence of analytical steps and produces the mining results.Notice that Oracle Data Miner has taken care of all analytical
options applying reasonable defaults that advanced users can
review, change and rerun.
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7. In the final step, Build, click on Results to view graphical
and tabular view of the attributes that are most related to the
target attribute CD_BUYERS.
Clicking on the Results output of the Activity
Guide displays the attributes that have the
strongest relationships with the target
attribute.
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Understanding Your Most Valuable Customers
8. Now, lets use Oracle Data Mining to better understand the
customers who have purchased 6-Month CDs. Well use ODMs
Decision Trees to develop detailed profiles of customers whohave and have not purchased 6-Month CDs in the past.
9. From the main menu, click on Activity Guide and then selectBuild.
a. In Step 1, select Classification for the Function Type and
pick Decision Tree for the Algorithm.
b. Select CD_BUYERS for the data table and select
CUST_ID as the Unique Identifier.
c. Select CD_BUYER for the target attributed. Accept all defaults (1 in the Prefer a Target Value drop
down menu).
i. Accept default name or optionally, rename it.e. At the Finish step, accept all the defaults, but review the
Advanced Settings and understand that Oracle DataMiner has made reasonable default selections for the user.Advanced users can override them to their own
preferences.
f. Click Finish and see that Oracle Data Minerautomatically runs the mining steps.
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10. When ODM has completed running the Activity Guide,remember that Oracle Data Miner has provided reasonable
defaults for all analytical settings and then click on Test
Metric and then click on Result. Notice that ODM hasindicated that the model is generally considered to be good.
You can interact with the Tree display results
to gain a better understanding about the
relationships that ODM has found.
11. To view more detailed profiles of customers, in the Build step
of the Activity Guide, click on Result.
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a. Use the + icons to expand and collapse the decision treesrules to gain a better understanding of the various profiles
of customers who purchase, and dont purchase 6-Month
CDs. You can use these more detailed insights to createtargeted marketing campaigns.
Predicting Who Will Be Your Most ValuableCustomers
12. Now, lets use our ODM model to predict who will be our most
valuable customers (people who purchase 6-Month CDs). From
the top menu, select Activity and then select Apply.a. In Step 1, select the Classification Model that you created
previously (CD_BUYERS_TREE3_BA).
b. In Step 2, select the table to which you want to Apply theODM mining model, CD_BUYERS_APPLY, in this
case.
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c. In Step 3, select the Supplemental Attributes that youwant included in the output table that will be the result of
applying the ODM mining model. Make sure that you
include the unique identifier, CUST_ID, so you can
match the predictions with each customer.
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d. In Step 4, select the Specific Target Value option and
select 1 to predict the likelihood that a customer willpurchase 6-Month CDs.
e. Give the model a unique name that you can locate later,
like CD_BUYERS_TREE3_APPLY_AA.f. At the Finish Step, click Finish to have ODM apply the
model to the data and make predictions.
13. When the Activity Guide has finished, click on the Apply
Result to view the scored list of customers. You may want toincrease the default Fetch Size to 1,000. Then click on the top
of the Prob_1 column to sort the customers in their likelihood
to purchase 6-Month CDs. Notice that a detailed rule isavailable that describes the profile of each customer.
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14. Congratulations! You have successfully mined your data to find
insights and make predictions.