Assignment_3_WEKA_Solution.pdf - ITS632 Assignment 3(WEKA \u2013 Due November 22nd 2020h at 11:59 Instructor Charles Edeki Submit the assignment in the

# Assignment_3_WEKA_Solution.pdf - ITS632 Assignment 3(WEKA...

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ITS632Assignment 3 (WEKA) – Due November 22nd, 2020hat 11:59Instructor: Charles EdekiSubmit the assignment in the Assignment 31 drop box.Total points is 100 (Each question is 20 points).1.Choose the area of your preference, whatever you would like to describe in a dataset and explain using data mining. For example: actresses/actors, food, movies, sports, music bands, or anything you want. Create a data file in .arff format containing about 20 entries, each described by about 4 attributes, with the last attribute containing your preference (class attribute), e.g. @relation food @attribute calories numeric @attribute taste {sweet, sour, bitter, salty} @attribute course {appetizer, main, dessert, drink} @attribute vegetarian {yes, no} @attribute like_it {yes, no} @data 100, sweet, dessert, yes, yes%icecream 80, bitter, drink, yes, yes%beer 2, sweet, dessert,yes, no%cake Compare 3 algorithms for classification of your data: decision trees, a classification or an association rule learner, and naive Bayes. For each algorithm check what the error is (which algorithm can explain your personal liking the best), and observe the generated rules (do they tell you anything interesting?). 2.Use the following learning schemes to compare the training set and 10-fold stratified cross-validation scores of the labor data (in labor_neg_nominal.arff): k-nearest neighbours (IBk) with decision trees (j48.J48) k-nearest neighbours (IBk) with decision trees j48.J48 with option -M 3, so that each leaf has at least 3 instances.