diff --git a/Python Class Work 4 - ML.ipynb b/Python Class Work 4 - ML.ipynb
index 941f82d..7300371 100644
--- a/Python Class Work 4 - ML.ipynb	
+++ b/Python Class Work 4 - ML.ipynb	
@@ -1312,48 +1312,6 @@
         "print(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_testset, predTree))"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": 63,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Notice: You might need to uncomment and install the pydotplus and graphviz libraries if you have not installed these before\n",
-        "# !conda install -c conda-forge pydotplus -y\n",
-        "#!conda install -c conda-forge python-graphviz -y"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 64,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#from sklearn.externals.six import StringIO\n",
-        "#import pydotplus\n",
-        "#import matplotlib.image as mpimg\n",
-        "#from sklearn import tree\n",
-        "#%matplotlib inline "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 65,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#dot_data = StringIO()\n",
-        "#filename = \"drugtree.png\"\n",
-        "#featureNames = dfLabs.columns[0:5]\n",
-        "#targetNames = dfLabs[\"Drug\"].unique().tolist()\n",
-        "#out=tree.export_graphviz(drugTree,feature_names=featureNames, out_file=dot_data, class_names= np.unique(y_trainset), filled=True,  #special_characters=True,rotate=False)  \n",
-        "#graph = pydot.graph_from_dot_data(dot_data.getvalue())  \n",
-        "#graph.write_png(filename)\n",
-        "#img = mpimg.imread(filename)\n",
-        "#plt.figure(figsize=(100, 200))\n",
-        "#plt.imshow(img,interpolation='nearest')"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},