original shape( reduced shape( we can now plot the first two principal components (figure - )in[ ]plot first vs second principal componentcolored by class plt figure(figsize=( )mglearn discrete_scatter(x_pca[: ]x_pca[: ]cancer targetplt legend(cancer target_namesloc="best"plt gca(set_aspect("equal"plt xlabel("first principal component"plt ylabel("second principal component"figure - two-dimensional scatter plot of the breast cancer dataset using the first two principal components it is important to note that pca is an unsupervised methodand does not use any class information when finding the rotation it simply looks at the correlations in the data for the scatter plot shown herewe plotted the first principal component against the dimensionality reductionfeature extractionand manifold learning