SAS enterprise miner

SAS enterprise miner

SEIS 632 Data Analytics and Visualization

Assignment 1

Due: On canvas by midnight 2/17/20

A supermarket is offering a new line of organic products. The supermarket’s management wants to determine which

customers are likely to purchase these products.

The supermarket has a customer loyalty program. As an initial buyer incentive plan, the supermarket provided

coupons for the organic products to all of the loyalty program participants and collected data that includes whether

these customers purchased any of the organic products.

The ORGANICS data set contains 13 variables and over 22,000 observations. The variables in the data set are shown

below with the appropriate roles and levels:

Name Model

Role

Measurement

Level

Description

ID ID Nominal Customer loyalty identification number

DemAffl Input Interval Affluence grade on a scale from 1 to 30

DemAge Input Interval Age, in years

DemCluster Rejected Nominal Type of residential neighborhood

DemClusterGroup Input Nominal Neighborhood group

DemGender Input Nominal M = male, F = female, U = unknown

DemRegion Input Nominal Geographic region

DemTVReg Input Nominal Television region

PromClass Input Nominal Loyalty status: tin, silver, gold, or platinum

PromSpend Input Interval Total amount spent

PromTime Input Interval Time as loyalty card member

TargetBuy Target Binary Organics purchased? 1 = Yes, 0 = No

TargetAmt Rejected Interval Number of organic products purchased

Although two target variables are listed, the target variable of interest to us is the binary variable TargetBuy.

Create a new diagram named Organics.

Define the data set AAEM.ORGANICS as a data source for the project.

Set the model roles for the analysis variables as shown above.

Examine the distribution of the target variable.

Question 1: What is the proportion of individuals who purchased organic products?

The variable DemClusterGroup contains collapsed levels of the variable DemCluster. Presume that, based on previous experience, you believe that DemClusterGroup is sufficient for this type of modeling effort. Set the

model role for DemCluster to Rejected.

As noted above, only TargetBuy will be used for this analysis and should have a role of Target. Set the role for TargetAmt to Rejected.

Finish the Organics data source definition.

Add the AAEM.ORGANICS data source to the Organics diagram workspace.

Add a Data Partition node to the diagram and connect it to the Data Source node. Assign 50% of the data for training and 50% for validation.

Add a Decision Tree node to the workspace and connect it to the Data Partition node.

Create a decision tree model autonomously. Use Decision as the model assessment statistic.

Question 2: How many leaves are in the optimal tree?

Question 3: Which variable was used for the first split?

Question 4: What were the competing splits for this first split? That is, based on the logworth what were

the other top attributes at the second, third, and fourth place.

Add a second Decision Tree node to the diagram and connect it to the Data Partition node.

Create a decision tree model autonomously. Use average square error as the model assessment statistic.

Question 5: How many leaves are in the optimal tree?

Question 6: Which variable was used for the first split?

Question 7: What were the competing splits for this first split?

Submission:

1) Answers to the questions asked above.

2) In the diagram, as a last node, add a Reporter node from the Utility tab. Change the Nodes property of the Reporter

node to All. Now right click on the Reporter node and select Run. This will generate a pdf.

You should submit the above 2 items on canvas.