# Data Mining HW

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### Question Description

LabDecisionTrees.doc is assignment

Here if the excel file for the eBayAuctions

This is the XLMiner steps

Data Mining Review Questions / XLMiner Labs Chapter 9 – Classification and Regression Trees 1. Competitive Auctions on eBay.com. The file eBayAuctions.xls contains information on 1972 auctions transacted on eBay.com during May – 2004. The goal is to use these data to build a model that will classify competitive auctions from non-competitive 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/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 (textbook reference - 9.1). Data Preprocessing. Dummy variables for the categorical predictors have been created. These variable include Category (18 categories), Currency (USD, GPB, Euro), EndDay (Monday-Sunday), and Duration (1, 3, 5, 7, or 10 days). Now Split the data into training and validation sets 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? (Hint: Consider Closing Price) c. Based on the last tree, what can you conclude from these data about the chances of an auction obtaining at least two bids and its relationship to the auction settings set by the seller (i.e., duration, opening price, ending day, currency)? What strategy would you recommend to a seller that would most likely lead to a competitive auction?
Generic Steps for Using XLMiner Software 1. If necessary, create Dummy Variables using Data Utilities / Transform Categorical Data / Create Dummies 2. Partition Data into Training and Validation sets using Partition Data / Standard Partition (Note: The defaults are 60% Training and 40% Validation, but these values can be changed) 3. Add-in any NEW data records by typing or pasting the new records into a new Worksheet. (Tip: Copy/paste column headers from original data in order to match variable names) 4. Run desired XLMiner Data Mining technique 5. Analyze results generated in new tabs

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