Week 3 Application Assignment

Week 3 Application Assignment of Predictive Modeling and Analytics

Question 1

Let’s reconsider the customer reward program dataset. In this exercise, you will complete a predictive modeling task where the target variable is binary. Using the following data file for this exercise:

The dataset also contains a column IndustryType, which is created based on the column Industry in the raw data. Note that Industry has many categories. The analyst who prepared the data chose to combine some categories, which resulted in the column IndustryType. IndustryType has five categories: Department, Discount, Grocery, Restaurants, Specialty. You can create a set of dummy variables based on IndustryType in XLMiner by using the Transform functions.

Part I.

Consider logistic regression models with Reward column as the target variable. Fit the model with two indicator variables, one indicating whether a retailer is a discount store (i.e., IndustryType is Discount), and the other indicating whether a retailer is a grocery store (i.e., IndustryType is Grocery). Report the coefficient estimates in the next three questions. [Hint: After you create the dummy variables, use them as Selected Variables (instead of Categorical Variables) in the first step of Logistic Regression.]

What is the estimated intercept coefficient?

1 point

0.5108

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-0.9627

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-0.7339

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Question 6

Part II.

Split the dataset into training and validation sets using a 60:40 split (set the seed for partitioning to 12345; this should be the default value if you have not changed it). [Hint: note that there two Partition buttons in XLMiner ribbon. You should use the Partition->Standard Partition in the Data Mining group.] Report the new coefficient estimates in the next three questions. Use the same two predictor variables as in Part I.

What is the estimated intercept coefficient (round the answer to 4 decimal places i.e. ) ?

1 point

0.6567

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-0.8390

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-1.1675

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Week 3 Application Assignment of Predictive Modeling and Analytics

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