Customer Rating of Breakfast Cereals. The dataset Cereals.csv includes nutritional information, store display, and consumer ratings for 77 breakfast cereals. Data preprocessing. Remove all cereals with missing values.

1. Apply hierarchical clustering to the data using Euclidean distance to the normalized measurements. Compare the dendrograms from single linkage and complete link-age, and look at cluster centroids. Comment on the structure of the clusters and on their stability. (Him: To obtain cluster centroids for hierarchical clustering, compute the average values of each cluster members, using groupby with the cluster centers followed by mean: dataframe groupby (clusterlabel) mean(.)
2. Which method leads to the most insightful or meaningful clusters?
3. Choose one of the methods. How many clusters would you use? What distance is used for this cutof? (Look at the dendrogram.)
4. The elementary publie schools would like to choose a set of cereals to include in their daily cafeterias. Every day a different cereal is offered, but all cereals should support a healthy diet. For this goal, you are requested to find a cluster of "healthy cereals." Should the data be normalized? If not, how should they be used in the cluster analysis?