USE RSTUDIO FOR THIS TASK. PLEASE PROVIDE R CODE AND RESULTS IN SCREENSHOT. Please assign the corresponding number to the answer

data set : https://drive.google.com/file/d/1Ew9vpOb64ScoCwY1LPvPb-cjUX58QnRg/view?usp=sharing

Competitive Auctions on eBay.com. The file eBayAuctions.csv contains information on 1972 auctions that transacted on eBay.com during May-June 2004. The goal is to use these data to build a model that will classify auctions as competitive or noncompetitive. 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 task is to predict whether or not the auction will be competitive.

1. Data Preprocessing. Convert variable Duration into a categorical variable. Split the data into training (60%) and validation (40%) datasets.

Use CART method to answer the following questions. (Classification and regression trees (CART) are a set of techniques for classification and prediction. The technique is aimed at producing rules that predict the value of an outcome (target) variable from known values of predictor (explanatory) variables.)

2. Fit a classification tree using all predictors, using the best-pruned tree. To avoid overfitting, set the minimum number of records in a terminal node to 50 (in R: minbucket = 50). Also, set the maximum number of levels to be displayed at seven (in R: maxdepth = 7).Write down the results in terms of rules. (Note: If you had to slightly reduce the number of predictors due to software limitations, or for clarity of presentation, which would be a good variable to choose?) 3. Is this model practical for predicting the outcome of a new auction? 4. Describe the interesting and uninteresting information that these rules provide.

5. Find and examine the confusion matrix