Add a 3D plot to visualise the K-mean algorithm

data_structures/arrays/sudoku_solver.py

@@ -172,7 +172,8 @@ def unitsolved(unit):
 
 def from_file(filename, sep="\n"):
     "Parse a file into a list of strings, separated by sep."
-    return open(filename).read().strip().split(sep)  # noqa: SIM115
+    with open(filename) as file:
+        return file.read().strip().split(sep)
 
 
 def random_puzzle(assignments=17):

machine_learning/k_means_clust.py

@@ -37,7 +37,13 @@
             heterogeneity,
             k
         )
-  5. Transfers Dataframe into excel format it must have feature called
+  5. 3D Plot of the labeled data points with centroids.
+        plot_kmeans(
+            X,
+            centroids,
+            cluster_assignment
+        )
+  6. Transfers Dataframe into excel format it must have feature called
       'Clust' with k means clustering numbers in it.
 """
 
@@ -126,6 +132,19 @@ def plot_heterogeneity(heterogeneity, k):
     plt.show()
 
 
+def plot_kmeans(data, centroids, cluster_assignment):
+    ax = plt.axes(projection="3d")
+    ax.scatter(data[:, 0], data[:, 1], data[:, 2], c=cluster_assignment, cmap="viridis")
+    ax.scatter(
+        centroids[:, 0], centroids[:, 1], centroids[:, 2], c="red", s=100, marker="x"
+    )
+    ax.set_xlabel("X")
+    ax.set_ylabel("Y")
+    ax.set_zlabel("Z")
+    ax.set_title("3D K-Means Clustering Visualization")
+    plt.show()
+
+
 def kmeans(
     data, k, initial_centroids, maxiter=500, record_heterogeneity=None, verbose=False
 ):
@@ -193,6 +212,7 @@ def kmeans(
         verbose=True,
     )
     plot_heterogeneity(heterogeneity, k)
+    plot_kmeans(dataset["data"], centroids, cluster_assignment)
 
 
 def report_generator(