diff --git a/Experimental Mouse Data Analysis.ipynb b/Experimental Mouse Data Analysis.ipynb
index b008341..4be1df4 100644
--- a/Experimental Mouse Data Analysis.ipynb	
+++ b/Experimental Mouse Data Analysis.ipynb	
@@ -1118,13 +1118,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 238,
+   "execution_count": 279,
    "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.cluster import KMeans\n",
     "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn import *"
+    "from sklearn.metrics import *"
    ]
   },
   {
@@ -1171,7 +1171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 241,
+   "execution_count": 308,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1180,24 +1180,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 242,
+   "execution_count": 309,
    "metadata": {},
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x18faa5a10d0>"
+       "<matplotlib.collections.PathCollection at 0x18fb058dfa0>"
       ]
      },
      "metadata": {},
-     "execution_count": 242
+     "execution_count": 309
     },
     {
      "output_type": "display_data",
      "data": {
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      },
      "metadata": {
@@ -1213,7 +1213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 243,
+   "execution_count": 310,
    "metadata": {},
    "outputs": [
     {
@@ -1233,24 +1233,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 247,
+   "execution_count": 311,
    "metadata": {},
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x18fafa766d0>"
+       "<matplotlib.collections.PathCollection at 0x18fb08475b0>"
       ]
      },
      "metadata": {},
-     "execution_count": 247
+     "execution_count": 311
     },
     {
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
      },
      "metadata": {
@@ -1266,14 +1266,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 245,
+   "execution_count": 312,
    "metadata": {},
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "0.3164176254356233\n"
+      "0.3164176254356233\n0.5427350983278696\n"
      ]
     }
    ],
@@ -1281,12 +1281,14 @@
     "ari_kmeans = adjusted_rand_score(colour_list, kmeans.labels_)\n",
     "print(ari_kmeans)\n",
     "#acc_score = accuracy_score(ground_truth, kmeans.labels_)\n",
-    "#print(acc_score)"
+    "#print(acc_score)\n",
+    "c_score = metrics.completeness_score(colour_list, kmeans.labels_)\n",
+    "print(c_score)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 278,
+   "execution_count": 313,
    "metadata": {},
    "outputs": [
     {
@@ -1294,55 +1296,105 @@
      "name": "stdout",
      "text": [
       "0.3164176254356233\n",
+      "0.5427350983278696\n",
       "0.2979396262577863\n",
+      "0.5299947088746245\n",
       "0.3164176254356233\n",
+      "0.5427350983278695\n",
       "0.2979396262577863\n",
+      "0.5299947088746244\n",
       "0.3164176254356233\n",
+      "0.5427350983278695\n",
       "0.2183488987664186\n",
+      "0.4731471416677808\n",
       "0.2522602375465343\n",
+      "0.5216642869861097\n",
       "0.2979396262577863\n",
+      "0.5299947088746245\n",
       "0.3164176254356233\n",
+      "0.5427350983278695\n",
       "0.3164176254356233\n",
+      "0.5427350983278695\n",
       "0.24191229331416247\n",
+      "0.4793723140812728\n",
       "0.3164176254356233\n",
+      "0.5427350983278695\n",
       "0.3164176254356233\n",
+      "0.5427350983278696\n",
       "0.317518583096265\n",
+      "0.5842337767212318\n",
       "0.2522602375465343\n",
+      "0.5216642869861099\n",
       "0.25466574503801714\n",
+      "0.4909772064840389\n",
       "0.2981540420114577\n",
+      "0.5763457410914344\n",
       "0.2979396262577863\n",
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       "0.2183488987664186\n",
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       "0.3164176254356233\n",
+      "0.5427350983278696\n",
       "0.3164176254356233\n",
+      "0.5427350983278695\n",
       "0.3164176254356233\n",
+      "0.5427350983278696\n",
       "0.2522602375465343\n",
+      "0.5216642869861097\n",
       "0.24191229331416247\n",
+      "0.4793723140812727\n",
       "0.24191229331416247\n",
+      "0.4793723140812728\n",
       "0.3010007322431047\n",
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       "0.2979396262577863\n",
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       "0.2522602375465343\n",
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       "0.3164176254356233\n",
+      "0.5427350983278695\n",
       "0.3164176254356233\n",
+      "0.5427350983278696\n",
       "0.2979396262577863\n",
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       "0.3164176254356233\n",
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       "0.3164176254356233\n",
+      "0.5427350983278696\n",
       "0.25466574503801714\n",
+      "0.5098941084715454\n",
       "0.3164176254356233\n",
+      "0.5427350983278695\n",
       "0.24315652261104986\n",
+      "0.5044336508279111\n",
       "0.3164176254356233\n",
+      "0.5427350983278696\n",
       "0.3164176254356233\n",
+      "0.5427350983278696\n",
       "0.2721311475409836\n",
+      "0.5430666005771546\n",
       "0.25466574503801714\n",
+      "0.4909772064840388\n",
       "0.2522602375465343\n",
+      "0.5216642869861099\n",
       "0.2979396262577863\n",
+      "0.5299947088746243\n",
       "0.3164176254356233\n",
+      "0.5427350983278696\n",
       "0.2979396262577863\n",
+      "0.5299947088746244\n",
       "0.3164176254356233\n",
+      "0.5427350983278695\n",
       "0.3164176254356233\n",
+      "0.5427350983278696\n",
       "0.2522602375465343\n",
+      "0.5216642869861097\n",
       "0.2979396262577863\n",
+      "0.5299947088746244\n",
+      "0.3164176254356233\n",
+      "0.5427350983278695\n",
       "0.3164176254356233\n",
-      "0.3164176254356233\n"
+      "0.5427350983278695\n"
      ]
     }
    ],
@@ -1352,15 +1404,47 @@
     "    kmeans = KMeans(init=\"random\",n_clusters=6,n_init=10, max_iter=300, random_state=i*12+1)\n",
     "    kmeans.fit(features)\n",
     "    ari_kmeans = adjusted_rand_score(colour_list, kmeans.labels_)\n",
-    "    print(ari_kmeans)"
+    "    print(ari_kmeans)\n",
+    "    c_score = metrics.completeness_score(colour_list, kmeans.labels_)\n",
+    "    print(c_score)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 320,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0.186480707936279\n0.32129820368474604\n0.16574030596851375\n0.24430063365576732\n0.22599736236274487\n0.28985598502257726\n0.23169710363478055\n0.2965313718255232\n0.21281215608478468\n0.23282149174131248\n0.31374114936393316\n0.1777214647695583\n0.24066938419061198\n0.17003674668492721\n0.2342058236244116\n0.375013898773401\n0.3349236381454569\n0.287136086274843\n0.33876792499972336\n0.28334616554756303\n0.21375625978513302\n0.21448732609637042\n0.1740265679695563\n0.23384339618103658\n0.26129923243607434\n0.1732955016583188\n0.22599736236274495\n0.21815132854445318\n0.2999045361451192\n0.192000164802131\n0.2560706136987088\n0.19563327289829416\n0.2641931670028276\n0.24684024799731796\n0.20844798167738407\n0.2737224780508776\n0.2611189480298907\n0.24934896798694917\n0.1320011935534592\n0.29639734632815334\n0.34101670121278715\n0.16618053415126452\n0.2990709861670738\n0.26177221360175246\n0.18339031168967446\n0.2561187312385306\n0.19058493856711242\n0.2048636385996851\n0.17246195168027342\n0.15422841107113155\nMean of Completeness Score =  0.23966643830951084\n"
+     ]
+    }
+   ],
+   "source": [
+    "from random import randint\n",
+    "apple_pie = []\n",
+    "for i in np.arange(50):\n",
+    "    # Nah this isn't good because it's still clustering based on closeness. \n",
+    "    #kmeans = KMeans(init=\"random\",n_clusters=6,n_init=1, max_iter=1, random_state=i*12+1)\n",
+    "    #kmeans.fit(features)\n",
+    "\n",
+    "    # Use rand int to generate a list\n",
+    "    rand_array = []\n",
+    "    for j in np.arange(37):\n",
+    "        rand_array.append(randint(0,5))\n",
+    "\n",
+    "    # Confirms ari = 0 for random\n",
+    "    ari_kmeans = adjusted_rand_score(rand_array, kmeans.labels_)\n",
+    "    print(ari_kmeans)\n",
+    "    #\n",
+    "    c_score = metrics.completeness_score(rand_array, kmeans.labels_)\n",
+    "    apple_pie.append(c_score)\n",
+    "    print(c_score)\n",
+    "\n",
+    "print('Mean of Completeness Score = ', np.mean(apple_pie))"
+   ]
   }
  ],
  "metadata": {