Python code for the commented steps with the first as the example for second.

You can now build a scatterplot to visualize the data: As you remember, we also build a regular PCA model pca using the digits data - which results in as reduced_data_pca. Use this to replace reduced_data_rpca in above code block and visualize the results again. [ ] 1 \# complete the code below 2 \# you will use the same 'colors' list so you do not have to define it again 3 4 5 \# We are using the two components in the 'randomized_pca' model as x-axis and y-axis in the plot 6 \# Note that we are plotting the data points, not the original figures 7 \# The distance in the plot actually means the distance between the data points 8 \# i.e. two data points of class (0) should be close to each other 10 \# x-axis is the first component in -reduced_data_pca'model \# and the color is corresponding to class, e.g., 0 is 'black' \# x-axis is the second component in 'reduced_data_pca' model \# plot it out \# add legend as class names 18 19 \# add x-axis name as 'First Principal Component' 20 21 \# add y-axis name as 'Second Principal Component' 22 23 \# add title of plot as "PCA Scatter Plot" 24