diff --git a/Part 4 - Clustering/Section 24 - K-Means Clustering/k-means.py b/Part 4 - Clustering/Section 24 - K-Means Clustering/k-means.py
index 1d0d2b1..f87ead7 100644
--- a/Part 4 - Clustering/Section 24 - K-Means Clustering/k-means.py	
+++ b/Part 4 - Clustering/Section 24 - K-Means Clustering/k-means.py	
@@ -26,11 +26,11 @@
 y_kmeans = kmeans.fit_predict(X)
 
 # Visualizing the clusters
-plt.scatter(X[y_kmeans ==0, 0], X[y_kmeans == 0, 1], s = 50, c= 'red', label = 'Cluster 1')
-plt.scatter(X[y_kmeans ==1, 0], X[y_kmeans == 1, 1], s = 50, c= 'yellow', label = 'Cluster 2')
-plt.scatter(X[y_kmeans ==2, 0], X[y_kmeans == 2, 1], s = 50, c= 'green', label = 'Cluster 3')
-plt.scatter(X[y_kmeans ==3, 0], X[y_kmeans == 3, 1], s = 50, c= 'cyan', label = 'Cluster 4')
-plt.scatter(X[y_kmeans ==4, 0], X[y_kmeans == 4, 1], s = 50, c= 'magenta', label = 'Cluster 5')
+plt.scatter(X[y_kmeans ==0, 0], X[y_kmeans == 0, 1], s = 50, c= 'red', label = 'Cluster 1: Careful')
+plt.scatter(X[y_kmeans ==1, 0], X[y_kmeans == 1, 1], s = 50, c= 'yellow', label = 'Cluster 2: Standard')
+plt.scatter(X[y_kmeans ==2, 0], X[y_kmeans == 2, 1], s = 50, c= 'green', label = 'Cluster 3: Target')
+plt.scatter(X[y_kmeans ==3, 0], X[y_kmeans == 3, 1], s = 50, c= 'cyan', label = 'Cluster 4L Careless')
+plt.scatter(X[y_kmeans ==4, 0], X[y_kmeans == 4, 1], s = 50, c= 'magenta', label = 'Cluster 5: Sensible')
 plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], s = 150, c= 'black', label = 'Centroids')
 plt.title('Clusters of clients')
 plt.xlabel('Annual Income (k$)')