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A supermarket is offering a new line of organic products

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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, these exercises concentrate on the binary variable TargetBuy. a. Create a new diagram named Organics. 3.5 Autonomous Tree Growth Options (Self-Study) 3-103 b. Define the data set AAEM.ORGANICS as a data source for the project. 1) Set the model roles for the analysis variables as shown above. 2) Examine the distribution of the target variable. What is the proportion of individuals who purchased organic products? 3) 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. 4) As noted above, only TargetBuy will be used for this analysis and should have a role of Target. Can TargetAmt be used as an input for a model used to predict TargetBuy? Why or why not? 5) Finish the Organics data source definition. c. Add the AAEM.ORGANICS data source to the Organics diagram workspace. d. 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. e. Add a Decision Tree node to the workspace and connect it to the Data Partition node. f. Create a decision tree model autonomously. Use average square error as the model assessment statistic. 1) How many leaves are in the optimal tree? 2) Which variable was used for the first split? What were the competing splits for this first split? g. Add a second Decision Tree node to the diagram and connect it to the Data Partition node. 1) In the Properties panel of the new Decision Tree node, change the maximum number of branches from a node to 3 to enable three-way splits. 2) Create a decision tree model. Use average square error as the model assessment statistic. 3) How many leaves are in the optimal tree? h. Based on average square error, which of the decision tree models appears to be better?