Excel with XLMINER problems

9.1 Competitive Auctions on eBay.com. The file eBayAuctions.xls contains information on 1972 auctions transacted on eBay.com during May-June 2004. The goal is to use these data to build a model that will classify competitive auctions from noncompetitive ones. A competitive auction is defined as an auction with at least two bids placed on the item auctioned. The data include variables that describe the item (auction category), the seller (his/her eBay rating), and the auction terms that the seller selected (auction duration, opening price, currency, day-of-week of auction close). In addition, we have the price at which the auction closed. The goal is to predict whether or not the auction will be competitive.

Data Preprocessing. Create dummy variables for the categorical predictors. These include Category (18 categories), Currency (USD, GBP, Euro), EndDay (Monday-Sunday), and Duration (1, 3, 5, 7, or 10 days). Split the data into training and validation datasets using a 60% : 40% ratio.

a. Fit a classification tree using all predictors, using the best pruned tree. To avoid overfitting, set the minimum number of observations in a leaf node to 50. Also, set the maximum number of levels to be displayed at seven (the maximum allowed in XLminer). To remain within the limitation of 30 predictors, combine some of the categories of categorical predictors. Write down the results in terms of rules.

b. Is this model practical for predicting the outcome of a new auction?