Please help me on this coding hw. Thanks soooo much!

d. present a confusion matrix for both models.

f. present a lift chart with both models in it, and the base case line.

10.4 Competitive Auctions on eBay.com. The file eBayAuctions.csv contains infor- mation on 1972 auctions transacted on eBay.com during May-June 2004. The goal is to use these data to build a model that will distinguish competitive auctions from noncompetitive ones. A competitive auction is defined as an auction with at least two bids placed on the item being auctioned. The data include variables that describe the item (auction category), the seller (his or 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 an auction of interest 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). a. Create pivot tables for the mean of the binary outcome (Competitive?) as a function of the various categorical variables (use the original variables, not the dummies). Use the information in the tables to reduce the number of dummies that will be used in the model. For example, categories that appear most similar with respect to the distribution of competitive auctions could be combined. 10.4 Competitive Auctions on eBay.com. The file eBayAuctions.csv contains infor- mation on 1972 auctions transacted on eBay.com during May-June 2004. The goal is to use these data to build a model that will distinguish competitive auctions from noncompetitive ones. A competitive auction is defined as an auction with at least two bids placed on the item being auctioned. The data include variables that describe the item (auction category), the seller (his or 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 an auction of interest 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). a. Create pivot tables for the mean of the binary outcome (Competitive?) as a function of the various categorical variables (use the original variables, not the dummies). Use the information in the tables to reduce the number of dummies that will be used in the model. For example, categories that appear most similar with respect to the distribution of competitive auctions could be combined