diff --git a/homemade/k_means/__pycache__/k_means.cpython-37.pyc b/homemade/k_means/__pycache__/k_means.cpython-37.pyc
new file mode 100644
index 0000000..01698fe
Binary files /dev/null and b/homemade/k_means/__pycache__/k_means.cpython-37.pyc differ
diff --git a/homemade/k_means/k_means.py b/homemade/k_means/k_means.py
index 4933568..17bcd1b 100644
--- a/homemade/k_means/k_means.py
+++ b/homemade/k_means/k_means.py
@@ -83,10 +83,13 @@ def centroids_find_closest(data, centroids):
         # We need to return the following variables correctly.
         closest_centroids_ids = np.zeros((num_examples, 1))
 
-        # Go over every example, find its closest centroid, and store
-        # the index inside closest_centroids_ids at the appropriate location.
-        # Concretely, closest_centroids_ids(i) should contain the index of the centroid
-        # closest to example i. Hence, it should be a value in the range 1...num_centroids.
+        """
+        Go over every example, find its closest centroid, and store
+        the index inside closest_centroids_ids at the appropriate location.
+        Concretely, closest_centroids_ids(i) should contain the index of the centroid
+        closest to example i. Hence, it should be a value in the range 1...num_centroids.
+        """
+
         for example_index in range(num_examples):
             distances = np.zeros((num_centroids, 1))
             for centroid_index in range(num_centroids):
diff --git a/homemade/k_means/k_means_example.py b/homemade/k_means/k_means_example.py
new file mode 100644
index 0000000..7d80cb5
--- /dev/null
+++ b/homemade/k_means/k_means_example.py
@@ -0,0 +1,29 @@
+from k_means import KMeans
+import pandas as pd
+
+# Load data.
+data = pd.read_csv('homemade-machine-learning\data\iris.csv')
+
+# Get total number of Iris examples.
+num_examples = data.shape[0]
+
+# Get features.
+x_train = data[['petal_length', 'petal_width']].values.reshape((num_examples, 2))
+
+# Set K-Means parameters.
+
+# Clusters act as the cassifications for our data.
+num_clusters = 3
+
+# Max number of iterations, acts as a stopping condition.
+max_iterations = 50
+
+# Load KMeans.
+k_means = KMeans(x_train, num_clusters)
+
+# Train / fit K-Means.
+(centroids, closest_centroids_ids) = k_means.train(max_iterations)
+
+print(centroids)
+
+# There is no prediction module?
diff --git a/homemade/linear_regression/linear_regression.py b/homemade/linear_regression/linear_regression.py
index f01e600..cc6db50 100644
--- a/homemade/linear_regression/linear_regression.py
+++ b/homemade/linear_regression/linear_regression.py
@@ -1,4 +1,4 @@
-"""Linear Regression Module"""
+#Linear Regression Module
 
 # Import dependencies.
 import numpy as np
@@ -6,7 +6,7 @@
 
 
 class LinearRegression:
-    """Linear Regression Class"""
+    #Linear Regression Class
 
     def __init__(self, data, labels, polynomial_degree=0, sinusoid_degree=0):
         """Linear regression constructor.