IN PYTHON PLEASE!!!!
All needed documents are attached
Problem 15.1: University Rankings
A.
Remove all records with missing measurements from the dataset.
B.
For all the continuous measurements, run hierarchical clustering using complete linkage and Euclidean distance. Make sure to normalize the measurements. From the dendrogram: How many clusters seem reasonable for describing these data?
C.
Compare the summary statistics for each cluster and describe each cluster in this context (e.g., “Universities with high tuition, low acceptance rate…”). (Hint: To obtain cluster statistics for hierarchical clustering, use the pandas method groupby(clusterlabel) together with methods such as mean or median.)
D.
Use the categorical measurements that were not used in the analysis (State and Private/Public) to characterize the different clusters. Is there any relationship between the clusters and the categorical information?
E.
What other external information can explain the contents of some or all of these clusters?
F.
Consider Tufts University, which is missing some information. Compute the Euclidean distance of this record from each of the clusters that you found above (using only the measurements that you have). Which cluster is it closest to? Impute the missing values for Tufts by taking the average of the cluster on those measurements.
THANK YOU!!!!
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Jun 18 2022| 10:00 AM |
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