Question: Given data set 'SampledSeeds.csv' sampled from the dataset 'seeds Data Set' and the attribute information can be found at here (https://archive.ics.uci.edu/ml/datasets/seeds). (a) Use KNN to

Given data set 'SampledSeeds.csv' sampled from the dataset 'seeds Data Set' and the attribute information can be found at here (https://archive.ics.uci.edu/ml/datasets/seeds).

(a) Use KNN to clssify the tuple with the values "16.17", "15.38", "0.8588", "5.762", "3.387", "4.286" and "5.703" for the attributes of ' area A', 'perimeter P', 'compactness C', 'length of kernel', 'width of kernel', 'asymmetry coefficient' and 'length of kernel groove' respectively. What would the class attribute value for this tuple be? When K=3, 5, 7 and Euclidean distance is in use.

(b) Use min max normalization method to normalized each attribute value into [0, 1] then redo part a). Is there difference in classification? Why?

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