diff --git a/ProgrammingAssignment1.ipynb b/ProgrammingAssignment1.ipynb
index a28af7c59fd2c9d0368f765cac5a5ff26aa32017..437e0e7386261858a7b2fe0074cedb4f4b31430e 100644
--- a/ProgrammingAssignment1.ipynb
+++ b/ProgrammingAssignment1.ipynb
@@ -183,28 +183,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'my_model' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-3-e365162558f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfinal_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmy_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_indices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mthreshold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Calculate accuracy and generalization error with confidence interval here. For now, We will consider a data point as predicted in the positive class if more than 0.5 of its k-neighbors are positive.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'my_model' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "final_labels = my_model.predict(my_model.test_indices)\n",
     "\n",
-    "# Calculate accuracy and generalization error with confidence interval here. \n",
     "# For now, We will consider a data point as predicted in the positive class if more than 0.5 \n",
     "# of its k-neighbors are positive.\n",
-    "threshold = 0.5"
+    "threshold = 0.5\n",
+    "# Calculate accuracy and generalization error with confidence interval here."
    ]
   },
   {
@@ -214,7 +202,7 @@
     " ### Plotting a learning curve\n",
     " \n",
     "A learning curve shows how error changes as the training set size increases. For more information, see [learning curves](https://www.dataquest.io/blog/learning-curves-machine-learning/).\n",
-    "We'll plot the error values for training and validation data while varying the size of the training set."
+    "We'll plot the error values for training and validation data while varying the size of the training set. Report a good size for training set for which there is a good balance between bias and variance."
    ]
   },
   {
@@ -267,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [