diff --git a/ds1001_final/notebooks/Final_Project_Notebook.ipynb b/ds1001_final/notebooks/Final_Project_Notebook.ipynb
index 8d102d2..3a5881f 100644
--- a/ds1001_final/notebooks/Final_Project_Notebook.ipynb
+++ b/ds1001_final/notebooks/Final_Project_Notebook.ipynb
@@ -256,7 +256,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Scatterplot between two variables\n",
+    "# Scatterplot between two continuous variables\n",
     "plt.figure(figsize=(10, 6))\n",
     "sns.scatterplot(x='Variable1', y='Variable2', data=\"xx\")  # Replace 'Variable1' and 'Variable2' with your column names\n",
     "plt.title('Scatterplot of Variable1 vs Variable2')\n",
@@ -324,7 +324,7 @@
    "source": [
     "# Divide the dataset into features and target\n",
     "target = \"xx\"['TargetVariable']  # Replace 'TargetVariable' with your actual target column name and \"xx\" with your dataframe name\n",
-    "features = \"xx\".drop(columns=[target])\n"
+    "features = \"xx\".drop(columns=['TargetVariable']) # Drop the target column from features\n"
    ]
   },
   {
@@ -454,7 +454,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mf_race.by_group #What do the results show? Change the mf_race with each subgroup and report the findings. "
+    "mf_race.by_group #What do the results show? Change the mf_race with each subgroup and report the findings. This means\n",
+    "# you should run this cell multiple times, once for each of the levels in the race variable."
    ]
   },
   {
@@ -464,7 +465,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mf_gender.by_group #What do the results show?"
+    "mf_gender.by_group #What do the results show? There's only two groups here so we don't need to change anything. \n",
+    "# in the metric frame."
    ]
   },
   {
@@ -475,7 +477,8 @@
    "outputs": [],
    "source": [
     "# Derived fairness metrics. Be sure you understand the scale and meaning of these. Here we are calculating the \n",
-    "# two fairness ratios using the gender_m feature. What do the results show, is the model more or less fair with this grouping?\n",
+    "# two fairness ratios using the gender_m feature, which is bi-variate. What do the results show, is the model more or \n",
+    "# less fair with this grouping?\n",
     "\n",
     "dpr_gender = fairlearn.metrics.demographic_parity_ratio(y_test, y_pred, sensitive_features=X_test['gender_m'])\n",
     "print(\"Demographic Parity ratio:\\t\", dpr_gender)\n",
@@ -501,6 +504,17 @@
     "eodds_race = fairlearn.metrics.equalized_odds_ratio(y_test, y_pred, sensitive_features=X_test.filter(regex=\"race.*\"))\n",
     "print(\"Equalized Odds ratio:\\t\\t\", eodds_race)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "767ebbe9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git commit -m \"Insert Message Here\" # This will commit your changes to git. \n",
+    "!git push # This will push your changes to back to your remote repository on GitHub."
+   ]
   }
  ],
  "metadata": {