Quick Start-10gR2 Data Mining CD BUYERS

8/8/2019 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.
mailto:[email protected]:[email protected]


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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.

Quick Start-10gR2 Data Mining CD BUYERS

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