diff --git a/ml/cc/exercises/linear_regression_taxi.ipynb b/ml/cc/exercises/linear_regression_taxi.ipynb
index 38d3d4b..1f54b63 100644
--- a/ml/cc/exercises/linear_regression_taxi.ipynb
+++ b/ml/cc/exercises/linear_regression_taxi.ipynb
@@ -264,7 +264,7 @@
         "print(\"How many cab companies are in the dataset? \t\tAnswer: {number}\".format(number = num_unique_companies))\n",
         "\n",
         "# What is the most frequent payment type?\n",
-        "most_freq_payment_type = training_df['PAYMENT_TYPE'].value_counts().idxmax()\n",
+        "most_freq_payment_type = training_df['PAYMENT_TYPE'].mode()[0]\n",
         "print(\"What is the most frequent payment type? \t\tAnswer: {type}\".format(type = most_freq_payment_type))\n",
         "\n",
         "# Are any features missing data?\n",
@@ -526,7 +526,8 @@
         "  epochs = 20\n",
         "  batch_size = 50\n",
         "\n",
-        "it takes about 5 epochs for the training run to converge to the final model.\n",
+        "it takes about 6 epochs for the training run to approach convergence (RMSE\n",
+        "under 4).\n",
         "\"\"\"\n",
         "print(answer)\n",
         "\n",
@@ -534,6 +535,7 @@
         "# -----------------------------------------------------------------------------\n",
         "answer = '''\n",
         "It appears from the model plot that the model fits the sample data fairly well.\n",
+        "Specificlly, the final RMSE of $3.82 represents around 16% of the mean FARE.\n",
         "'''\n",
         "print(answer)"
       ],
@@ -618,21 +620,20 @@
         "# How did lowering the learning rate impact your ability to train the model?\n",
         "# -----------------------------------------------------------------------------\n",
         "answer = '''\n",
-        "When the learning rate is too small, it may take longer for the loss curve to\n",
-        "converge. With a small learning rate the loss curve decreases slowly, but does\n",
-        "not show a dramatic drop or leveling off. With a small learning rate you could\n",
-        "increase the number of epochs so that your model will eventually converge, but\n",
-        "it will take longer.\n",
+        "When the learning rate is too small, it takes longer for the loss curve to\n",
+        "converge. The loss curve decreases slowly, but does not show a dramatic drop or\n",
+        "leveling off. You could increase the number of epochs so that your model will\n",
+        "eventually converge, but it will take longer.\n",
         "'''\n",
         "print(answer)\n",
         "\n",
         "# Did changing the batch size effect your training results?\n",
         "# -----------------------------------------------------------------------------\n",
         "answer = '''\n",
-        "Increasing the batch size makes each epoch run faster, but as with the smaller\n",
-        "learning rate, the model does not converge with just 20 epochs. If you have\n",
-        "time, try increasing the number of epochs and eventually you should see the\n",
-        "model converge.\n",
+        "Increasing the batch size makes each epoch run faster, but this implies less\n",
+        "learning iterations per epoch. As with the smaller learning rate, the model\n",
+        "does not converge with just 20 epochs. If you try increasing the number of\n",
+        "epochs, eventually you should see the model converge.\n",
         "'''\n",
         "print(answer)"
       ],