# Find the cluster centers
centers = clusterer.cluster_centers_
# Predict the cluster for each transformed sample data point
sample_preds = clusterer.predict(pca_samples)
# Calculate the mean silhouette coefficient for the number of clusters chosen
score = silhouette_score(reduced_data, clusterer.labels_, metric='euclidean')
print "%d clusters: %f" % (k, score)
clusterer = KMeans(n_clusters=2, random_state=10).fit(reduced_data)
preds = clusterer.predict(reduced_data)
centers = clusterer.cluster_centers_
sample_preds = clusterer.predict(pca_samples)
# Display the results of the clustering from implementation
vs.cluster_results(reduced_data, preds, centers, pca_samples)
# Inverse transform the centers
log_centers = pca.inverse_transform(centers)
# Exponentiate the centers
true_centers = np.exp(log_centers)
# Display the true centers
segments = ['Segment {}'.format(i) for i in range(0,len(centers))]
true_centers = pd.DataFrame(np.round(true_centers), columns = data.keys())
true_centers.index = segments
display(true_centers)
true_centers = true_centers.append(data.describe().ix['50%'])
true_centers.plot(kind = 'bar', figsize = (16, 4))
score, preds, clusterer, centers, sample_preds = best_score, best_preds, best_clusterer, best_centers, best_samples print "The best score is", best_score, # ### Question 7 # *Report the silhouette score for several cluster numbers you tried. Of these, which number of clusters has the best silhouette score?* # **Answer:** According to the tests, having 2 clusters produced the best score of 0.426. Other numbers were in the range of 0.3. # ### Cluster Visualization # Once you've chosen the optimal number of clusters for your clustering algorithm using the scoring metric above, you can now visualize the results by executing the code block below. Note that, for experimentation purposes, you are welcome to adjust the number of clusters for your clustering algorithm to see various visualizations. The final visualization provided should, however, correspond with the optimal number of clusters. # In[22]: # Display the results of the clustering from implementation vs.cluster_results(reduced_data, best_preds, best_centers, pca_samples) # ### Implementation: Data Recovery # Each cluster present in the visualization above has a central point. These centers (or means) are not specifically data points from the data, but rather the *averages* of all the data points predicted in the respective clusters. For the problem of creating customer segments, a cluster's center point corresponds to *the average customer of that segment*. Since the data is currently reduced in dimension and scaled by a logarithm, we can recover the representative customer spending from these data points by applying the inverse transformations. # # In the code block below, you will need to implement the following: # - Apply the inverse transform to `centers` using `pca.inverse_transform` and assign the new centers to `log_centers`. # - Apply the inverse function of `np.log` to `log_centers` using `np.exp` and assign the true centers to `true_centers`. # # In[23]: # TODO: Inverse transform the centers log_centers = pca.inverse_transform(centers) # TODO: Exponentiate the centers
