The data are shown in Table 1 and the output in Table 2 is based on a subset of a dataset on cosmeticpurchases (Cosmetics.csv) at a large chain drugstore. The store wants to analyze associations among purchasesof these items for purposes of point-of-sale display, guidance to sales personnel in promoting cross-sales, andguidance for piloting an eventual time-of-purchase electronic recommender system to boost cross-sales.Consider first only the data shown in Table 1, given in binary matrix form.Table 1: Excerpt from data on cosmetics purchases in binary matrix formTasks1. Consider the results of the association rules analysis shown in Table 2.a. For the first row, explain the “confidence” output and how it is calculated.b. For the first row, explain the “support” output and how it is calculated.c. For the first row, explain the rule that is represented there in words.2. Now, use the complete dataset on the cosmetics purchases (in the file Cosmetics.csv). Using weka,apply association rules to these data (for apriori use min_support=0.1 and use_colnames=True, forassociation_rules use default parameters).a. Interpret the first three rules in the output in words.b. Reviewing the first couple of dozen rules, comment on their redundancy and how you would