New update:
I understand PCA components ensure we select variables responsible for high variance, but I would like to know how to extract key variables responsible only for high variance through PCA components.
Ideally, a simple example would help.
This is my code:
#Implementing PCA for visualizing after Kmeans clustering
`# Interpret 3 cluster solution
model3=KMeans(n_clusters=3)
model3.fit(clus_train)
clusassign=model3.predict(clus_train)
# plot clusters
'''The new variables, called canonical variables, are ordered in terms of the proportion of variance and the clustering variables that is accounted for by each of the canonical variables. So the first canonical variable will count for the largest proportion of the variance. The second canonical variable will account for the next largest proportion of variance, and so on. Usually, the majority of the variance in the clustering variables will be accounted for by the first couple of canonical variables and those are the variables that we can plot. '''
from sklearn.decomposition import PCA
pca_2 = PCA(2) # Selecting 2 components
plot_columns = pca_2.fit_transform(clus_train)
plt.scatter(x=plot_columns[:,0], y=plot_columns[:,1], c=model3.labels_,)
Observations are more spread out indicating less correlation among the observations and higher within cluster variance.
plt.xlabel('Canonical variable 1')
plt.ylabel('Canonical variable 2')
plt.title('Scatterplot of Canonical Variables for 3 Clusters')
plt.show()`
