ISM425 Business Analyties Homework 4 Please allocate sufficient time for all homework assignments as they usually take time to complete. Start early on assignments. If you have doubts, please email the instructor Please subemit your answers in a word (or PDF) file, together with your Excel workbook. Competitive auctions on eBay.com: The file eBayAuction.xlsx contains information on 1972 auctions that transacted on eBay.com during May-June in 2004. The goal is to use these data in order to build a model that will classify competitive auctions from non-competitive ones. A competitive auction is defined as an auction with at least 2 bids placed on the auctioned item. The data include variables that describe the auctioned item (auction category), the seller (his/her eBay rating) and the auction terms that the seller selected (auction duration, opening price, curreney, day-of-week of auction close) In addition, we have the price that the auction closed at. The goal is to predict whether the auction will be competitive or not. Data pre-processing: 1. Open eBayAuctions.xls. Save it as eBay Auctions LastnameFirstname.xlsx 2. Because the XLMiner decision trees tool cannot handle categorical variables directly, so you will need to create dummy variables for the categorical predictors. These include Category (11 categories), Currency (USD, nonUS), and EndDay (Weckend, Week). 3, Split the data into training and validation datasets using a 60%-40% ratio. Analysis: Fit a classification tree. Use Competitive as the output variable and the rest of variables as predictors. Make sure to exclude one durnmy variable from each group of dunmy variables 4. ay Weckend). To avoid overfitting, set the minimum number of records in a leaf node to 50. Also, set the maximum number of levels to be displayed at 7, select the best pruned tree. a. Report the best pruned decision tree (copy and paste the tree diagram) b. Report validation data scoring-summary report (Using best pruned tree) c. List the predictors selected by the decision tree. d. Describe the rules. For example, if variable1 0 AND variable2 -2, class-o. 5. Are the rules practical for predicting the outcome of a new auction? Explain why (Hint: are you able to use the rules to classify a new auction before the auction ends? Do you know the ISM425 Business Analyties Homework 4 Please allocate sufficient time for all homework assignments as they usually take time to complete. Start early on assignments. If you have doubts, please email the instructor Please subemit your answers in a word (or PDF) file, together with your Excel workbook. Competitive auctions on eBay.com: The file eBayAuction.xlsx contains information on 1972 auctions that transacted on eBay.com during May-June in 2004. The goal is to use these data in order to build a model that will classify competitive auctions from non-competitive ones. A competitive auction is defined as an auction with at least 2 bids placed on the auctioned item. The data include variables that describe the auctioned item (auction category), the seller (his/her eBay rating) and the auction terms that the seller selected (auction duration, opening price, curreney, day-of-week of auction close) In addition, we have the price that the auction closed at. The goal is to predict whether the auction will be competitive or not. Data pre-processing: 1. Open eBayAuctions.xls. Save it as eBay Auctions LastnameFirstname.xlsx 2. Because the XLMiner decision trees tool cannot handle categorical variables directly, so you will need to create dummy variables for the categorical predictors. These include Category (11 categories), Currency (USD, nonUS), and EndDay (Weckend, Week). 3, Split the data into training and validation datasets using a 60%-40% ratio. Analysis: Fit a classification tree. Use Competitive as the output variable and the rest of variables as predictors. Make sure to exclude one durnmy variable from each group of dunmy variables 4. ay Weckend). To avoid overfitting, set the minimum number of records in a leaf node to 50. Also, set the maximum number of levels to be displayed at 7, select the best pruned tree. a. Report the best pruned decision tree (copy and paste the tree diagram) b. Report validation data scoring-summary report (Using best pruned tree) c. List the predictors selected by the decision tree. d. Describe the rules. For example, if variable1 0 AND variable2 -2, class-o. 5. Are the rules practical for predicting the outcome of a new auction? Explain why (Hint: are you able to use the rules to classify a new auction before the auction ends? Do you know the
