Update iris to penguin

04 - Clustering.ipynb

@@ -148,7 +148,7 @@
         "4. Steps 2 and 3 are repeated until a stopping criteria is met. Typically, the algorithm terminates when each new iteration results in negligable movement of centroids and the clusters become static.\n",
         "5. When the clusters stop changing, the algorithm has *converged*, defining the locations of the clusters - note that the random starting point for the centroids means that re-running the algorithm could result in slightly different clusters, so training usually involves multiple iterations, reinitializing the centroids each time, and the model with the best WCSS is selected.\n",
         "\n",
-        "Let's try using K-Means on our iris data with a K value of 3."
+        "Let's try using K-Means on our penguin data with a K value of 3."
       ]
     },
     {
@@ -173,7 +173,7 @@
       "metadata": {},
       "cell_type": "markdown",
       "source": [
-        "Let's see those cluster assignments with the two-dimensional iris data points."
+        "Let's see those cluster assignments with the two-dimensional data points."
       ]
     },
     {
@@ -259,7 +259,7 @@
         "\n",
         "### Agglomerative Clustering\n",
         "\n",
-        "Let's see an example of clustering the iris data using an agglomerative clustering algorithm."
+        "Let's see an example of clustering the penguin data using an agglomerative clustering algorithm."
       ]
     },
     {