diff --git a/notebooks/Chapter_07.ipynb b/notebooks/Chapter_07.ipynb
index 18871e8..fc9b6f7 100644
--- a/notebooks/Chapter_07.ipynb
+++ b/notebooks/Chapter_07.ipynb
@@ -978,18 +978,18 @@
     "    bullish = df[predictions == True]\n",
     "    bullish_gains = bullish.sort_index().groupby(bullish.index.date).head(1)['gain']\n",
     "    bullish_gains.cumsum().plot(label='Bullish Only', legend=True)\n",
-    "    print(f'% of time with profit: {(bullish_gains.cumsum() > 0).mean():.3f}')\n",
+    "    print(f'Percentage of time with profit for bullish only: {(bullish_gains.cumsum() > 0).mean():.3f}')\n",
     "    print(f'Total Gains for bullish is {bullish_gains.sum():.3f}')\n",
     "    \n",
     "    bearish = df[predictions == False]\n",
     "    bearish_gains = bearish.sort_index().groupby(bearish.index.date).head(1)['gain']\n",
     "    bearish_gains.cumsum().plot(label='Bearish Only', legend=True)\n",
-    "    print(f'% of time with profit: {(bearish_gains.cumsum() > 0).mean():.3f}')\n",
+    "    print(f'Percentage of time with profit for bearish only: {(bearish_gains.cumsum() > 0).mean():.3f}')\n",
     "    print(f'Total Gains for bearish is {bearish_gains.sum():.3f}')\n",
     "    \n",
     "    gains = df.sort_index().groupby(df.index.date).head(1)['gain']\n",
     "    gains.cumsum().plot(label='All Predictions', legend=True)\n",
-    "    print(f'% of time with profit: {(gains.cumsum() > 0).mean():.3f}')\n",
+    "    print(f'Percentage of time with profit for all predictions: {(gains.cumsum() > 0).mean():.3f}')\n",
     "    print(f'Total Gains for all predictions is {gains.sum():.3f}')\n",
     "    \n",
     "    plt.title('Gains')\n",
@@ -1007,11 +1007,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "% of time with profit: 0.077\n",
+      "Percentage of time with profit for bullish only: 0.077\n",
       "Total Gains for bullish is -0.043\n",
-      "% of time with profit: 0.500\n",
+      "Percentage of time with profit for bearish only: 0.500\n",
       "Total Gains for bearish is -0.004\n",
-      "% of time with profit: 0.308\n",
+      "Percentage of time with profit for all predictions: 0.308\n",
       "Total Gains for all predictions is -0.021\n"
      ]
     },
@@ -1818,11 +1818,11 @@
       "\n",
       "Best params: {'classifier__criterion': 'entropy', 'classifier__max_depth': 10, 'classifier__max_features': None, 'classifier__min_samples_split': 3, 'select_from_model__estimator': LogisticRegression(random_state=0), 'select_from_model__threshold': 'median'}\n",
       "Overall took 263.71 seconds\n",
-      "% of time with profit: 0.000\n",
+      "Percentage of time with profit for bullish only: 0.000\n",
       "Total Gains for bullish is -0.038\n",
-      "% of time with profit: 1.000\n",
+      "Percentage of time with profit for bearish only: 1.000\n",
       "Total Gains for bearish is 0.017\n",
-      "% of time with profit: 0.923\n",
+      "Percentage of time with profit for all predictions: 0.923\n",
       "Total Gains for all predictions is 0.004\n"
      ]
     },
@@ -1924,11 +1924,11 @@
       "\n",
       "Best params: {'classifier__criterion': 'entropy', 'classifier__max_depth': 10, 'classifier__max_features': None, 'classifier__min_samples_split': 5, 'rfe__estimator': LogisticRegression(random_state=0), 'rfe__n_features_to_select': 0.8}\n",
       "Overall took 675.54 seconds\n",
-      "% of time with profit: 0.000\n",
+      "Percentage of time with profit for bullish only: 0.000\n",
       "Total Gains for bullish is -0.063\n",
-      "% of time with profit: 0.381\n",
+      "Percentage of time with profit for bearish only: 0.381\n",
       "Total Gains for bearish is -0.027\n",
-      "% of time with profit: 0.077\n",
+      "Percentage of time with profit for all predictions: 0.077\n",
       "Total Gains for all predictions is -0.100\n"
      ]
     },
@@ -4048,11 +4048,11 @@
       "\n",
       "Best params: {'classifier__criterion': 'entropy', 'classifier__max_depth': None, 'classifier__max_features': None, 'classifier__min_samples_split': 2, 'poly__degree': 2, 'select_from_model__estimator': RandomForestClassifier(n_estimators=20, random_state=0), 'select_from_model__threshold': 'mean'}\n",
       "Overall took 947.51 seconds\n",
-      "% of time with profit: 0.737\n",
+      "Percentage of time with profit for bullish only: 0.737\n",
       "Total Gains for bullish is 0.028\n",
-      "% of time with profit: 1.000\n",
+      "Percentage of time with profit for bearish only: 1.000\n",
       "Total Gains for bearish is 0.095\n",
-      "% of time with profit: 1.000\n",
+      "Percentage of time with profit for all predictions: 1.000\n",
       "Total Gains for all predictions is 0.158\n"
      ]
     },