Merge d9a7fba into e71de51

SuperCowPowers · Sep 4, 2018 · 889b54a · 889b54a
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diff --git a/notebooks/Clustering_Picking_K.ipynb b/notebooks/Clustering_Picking_K.ipynb
@@ -366,6 +366,7 @@
    "metadata": {},
    "source": [
     "<div style=\"float: right; margin: 10px 40px 10px 40px\"><img src=\"images/transformers.png\" width=\"200px\"></div>\n",
+    "\n",
     "## Transformers\n",
     "**We'll now use the Scikit-Learn tranformer class to convert the Pandas DataFrame to a numpy ndarray (matrix). The transformer class takes care of many low-level details**\n",
     "* Applies 'one-hot' encoding for the Categorical fields\n",
@@ -439,7 +440,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div style=\"float: right; margin: 10px 40px 10px 40px\"><img src=\"images/silhouette.jpg\" width=\"250px\"></div>\n",
+    "<div style=\"float: right; margin: 10px 40px 10px 40px\"><img src=\"images/silhouette.jpg\" width=\"150px\"></div>\n",
+    "\n",
     "# Silhouette Scoring\n",
     "\"The silhouette value is a measure of how similar an object is to its own cluster (cohesion) compared to other clusters (separation). The silhouette ranges from -1 to 1, where a high value indicates that the object is well matched to its own cluster and poorly matched to neighboring clusters. If most objects have a high value, then the clustering configuration is appropriate. If many points have a low or negative value, then the clustering configuration may have too many or too few clusters.\"\n",
     "- https://en.wikipedia.org/wiki/Silhouette_(clustering)"
@@ -734,7 +736,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.6.6"
   }
  },
  "nbformat": 4,

diff --git a/notebooks/images/silhouette.jpg b/notebooks/images/silhouette.jpg