answer this asap need to submit before 23rd

4. Given 1000 points (visualized below and see the attached data file). Notice that the X dimension has a much larger value range than that of Y. Perform K-means (K-2) clustering on this dataset and visualize it. Then do zscore normalization on the dataset and performe K-means again, and visualize it. Finally, do Min-Max normalization (e.g., nornamlze(x) =(x-min)/(max-min)) on the dataset and run K- means again normalization part. (20 pts) Data (note different axes scales -30 -20 -10 10 20 30 4. Given 1000 points (visualized below and see the attached data file). Notice that the X dimension has a much larger value range than that of Y. Perform K-means (K-2) clustering on this dataset and visualize it. Then do zscore normalization on the dataset and performe K-means again, and visualize it. Finally, do Min-Max normalization (e.g., nornamlze(x) =(x-min)/(max-min)) on the dataset and run K- means again normalization part. (20 pts) Data (note different axes scales -30 -20 -10 10 20 30