Evaluating the grain clustering

In the previous exercise, you observed from the inertia plot that 3 is a good number of clusters for the grain data. In fact, the grain samples come from a mix of 3 different grain varieties: "Kama", "Rosa" and "Canadian". In this exercise, cluster the grain samples into three clusters, and compare the clusters to the grain varieties using a cross-tabulation.

You have the array samples of grain samples, and a list varieties giving the grain variety for each sample. Pandas (pd) and KMeans have already been imported for you.

Create a KMeans model called model with 3 clusters.
Use the .fit_predict() method of model to fit it to samples and derive the cluster labels. Using .fit_predict() is the same as using .fit() followed by .predict().
Create a DataFrame df with two columns named 'labels' and 'varieties', using labels and varieties, respectively, for the column values. This has been done for you.
Use the pd.crosstab() function on df['labels'] and df['varieties'] to count the number of times each grain variety coincides with each cluster label. Assign the result to ct.
Hit submit to see the cross-tabulation!

Exercise

Evaluating the grain clustering

Instructions