completed 2 clustering parts of unsupervised learning section

python/Unsupervised Learning/Clustering/helper_functions.py

@@ -0,0 +1,41 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from sklearn.cluster import KMeans
+from sklearn.datasets import make_blobs
+
+# Generate Question 1 Data
+X, y = make_blobs(n_samples=500, n_features=3, centers=4, random_state=5)
+
+
+def plot_q1_data():
+    fig = plt.figure()
+    ax = Axes3D(fig)
+    ax.scatter(X[:, 0], X[:, 1], X[:, 2]);
+
+
+# Generate Question 2 Data
+Z, y = make_blobs(n_samples=500, n_features=5, centers=2, random_state=42)
+
+
+def plot_q2_data():
+    fig = plt.figure()
+    plt.scatter(Z[:, 0], Z[:, 1]);
+
+
+# Generate Question 3 Data
+T, y = make_blobs(n_samples=500, n_features=5, centers=8, random_state=5)
+
+
+def plot_q3_data():
+    fig = plt.figure()
+    ax = Axes3D(fig)
+    ax.scatter(T[:, 1], T[:, 3], T[:, 4]);
+
+# Plot data for Question 4
+
+
+def plot_q4_data():
+    fig = plt.figure()
+    ax = Axes3D(fig)
+    ax.scatter(T[:, 1], T[:, 2], T[:, 3]);