Data Science Workshop 2 (Part 5): Data and Cluster Visualization (Continued)

As I was saying in the previous part, we will not get an interactive graphical interface if we use Google colab. That is why I am using regular Python on my computer.

When I use Python, my program needs to display the figure on a canvas. Unlike directly trying to display fig on the notebook like what we do on Google colab, we will use a method to display the plot on a graphical interface. The entire data and cluster visualization technique is demonstrated in the following YouTube video.

The Python code that I wrote in the video is as follows:

import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
import numpy as np
import matplotlib.cm as cm

data = np.array( 
   [[20, 25, 18],
   [3, 2, 3],
   [6, 4, 5],
   [24, 21, 19],
   [22, 24, 21],
   [18, 20, 21],
   [4, 2, 3],
   [1, 5, 2],
   [19, 20, 19],
   [23, 19, 22]]
)

km = KMeans(n_clusters=2).fit(data)
print(km.labels_)

fig=plt.figure()

ax = fig.add_subplot(111, projection='3d')

ax.scatter(data[:,0], data[:, 1], data[:, 2],
           c=km.labels_, cmap=cm.rainbow)

ax.set_xlabel("Feature 1", fontsize=16)
ax.set_ylabel("Feature 2", fontsize=16)
ax.set_zlabel("Feature 3", fontsize=16)

plt.show()

Please write the code in a file with .py extension and run it from the command prompt to view the interactive display. The video explains how to run the program.