diff --git a/CompressedTreeParser.ipynb b/CompressedTreeParser.ipynb
index 6435d62..3a84036 100644
--- a/CompressedTreeParser.ipynb
+++ b/CompressedTreeParser.ipynb
@@ -37,37 +37,71 @@
    "execution_count": 3,
    "id": "12ad454d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "here1\n",
+      "         protocl    src    dst    classfication\n",
+      "0              6  40234   5228            other\n",
+      "1              6  40234   5228            other\n",
+      "2              6    443  46330          Dropcam\n",
+      "3              6   3063    443            other\n",
+      "4              1      0      0   Netatmo Camera\n",
+      "...          ...    ...    ...              ...\n",
+      "2419339        6    443  47940          Dropcam\n",
+      "2419340        6  47940    443            other\n",
+      "2419341        6    443  47940          Dropcam\n",
+      "2419342        0      0      0  iHome PowerPlug\n",
+      "2419343        0      0      0            other\n",
+      "\n",
+      "[2419344 rows x 4 columns]\n",
+      "{8, 20}\n",
+      "{13}\n",
+      "[6, 40234, 5228]\n",
+      "other\n"
+     ]
+    }
+   ],
    "source": [
     "predict_Yt = []\n",
+    "index=0\n",
     "with open('compressed_tree.json', 'r') as file:\n",
     "    data = json.load(file)\n",
     "    classes = data[\"classes\"]\n",
     "    for x in X:\n",
     "        counter = 0\n",
     "        class_set = []\n",
+    "        paths_set = []\n",
     "        for feature in reversed(data['layers']): #Have to reverse this list due to structure of the data.csv file and how it aligns with the compressed tree layers\n",
     "            for node in data['layers'][feature]:\n",
     "                if node['min'] is None:\n",
-    "                    if x[counter] < node['max']:\n",
+    "                    if x[counter] <= node['max']:\n",
     "                        class_set.append(node['classes'])\n",
+    "                        paths_set.append(node[\"paths\"])\n",
     "                        break #is this an issue?\n",
     "                    else:\n",
     "                        continue\n",
     "                elif node['max'] is None:\n",
     "                    if node['min'] < x[counter]:\n",
     "                        class_set.append(node['classes'])\n",
+    "                        paths_set.append(node[\"paths\"])\n",
     "                        break #is this an issue?\n",
     "                    else:\n",
     "                        continue\n",
-    "                elif node['min'] < x[counter] and x[counter] < node['max']:\n",
+    "                elif node['min'] < x[counter] and x[counter] <= node['max']:\n",
     "                    class_set.append(node['classes'])\n",
+    "                    paths_set.append(node[\"paths\"])\n",
     "                    break #is this an issue?\n",
     "\n",
     "            counter += 1\n",
     "        result = set(class_set[0])\n",
+    "        paths = set(paths_set[0])\n",
     "        for s in class_set[1:]:\n",
     "            result.intersection_update(s)\n",
+    "        for s in paths_set[1:]:\n",
+    "            paths.intersection_update(s)\n",
     "\n",
     "        #predict_Yt.append(list(result))\n",
     "        #print(result)\n",
@@ -75,8 +109,26 @@
     "            prediction = list(result)[0]\n",
     "            pred_class = classes[prediction]\n",
     "            predict_Yt.append(pred_class)\n",
+    "        elif len(paths) == 1:\n",
+    "            print(\"here1\")\n",
+    "            print(pd.read_csv('data.csv'))\n",
+    "            print(result)\n",
+    "            print(paths)\n",
+    "            print(x)\n",
+    "            print(Y[index])\n",
+    "            break\n",
+    "            predict_Yt.append(None)\n",
     "        else:\n",
-    "            predict_Yt.append(None)"
+    "            print(\"here2\")\n",
+    "            print(pd.read_csv('data.csv'))\n",
+    "            print(result)\n",
+    "            print(paths)\n",
+    "            print(x)\n",
+    "            print(Y[index])\n",
+    "            break\n",
+    "            predict_Yt.append(None)\n",
+    "        \n",
+    "        index += 1"
    ]
   },
   {
@@ -86,10 +138,14 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.8448217242194891\n"
+     "ename": "IndexError",
+     "evalue": "list index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mIndexError\u001b[39m                                Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m correct = \u001b[32m0\u001b[39m\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(Y)):\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m     prediction = \u001b[43mpredict_Yt\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\n\u001b[32m      4\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m prediction != \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m Y[i] == prediction:\n\u001b[32m      5\u001b[39m         correct += \u001b[32m1\u001b[39m\n",
+      "\u001b[31mIndexError\u001b[39m: list index out of range"
      ]
     }
    ],