diff --git a/02_end_to_end_machine_learning_project.ipynb b/02_end_to_end_machine_learning_project.ipynb
index 02c3bf9..9971e97 100644
--- a/02_end_to_end_machine_learning_project.ipynb
+++ b/02_end_to_end_machine_learning_project.ipynb
@@ -59,7 +59,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It also requires Scikit-Learn ≥ 1.0:"
+    "It also requires Scikit-Learn ≥ 1.0.1:"
    ]
   },
   {
@@ -200,7 +200,6 @@
    "source": [
     "# Not in the book\n",
     "\n",
-    "# Where to save the figures\n",
     "IMAGES_PATH = Path() / \"images\" / \"end_to_end_project\"\n",
     "IMAGES_PATH.mkdir(parents=True, exist_ok=True)\n",
     "\n",
@@ -220,7 +219,7 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "housing.hist(bins=50, figsize=(12, 8))\n",
-    "save_fig(\"attribute_histogram_plots\")  # save_fig() lines are never in the book\n",
+    "save_fig(\"attribute_histogram_plots\")  # not in the book\n",
     "plt.show()"
    ]
   },
@@ -418,7 +417,7 @@
     "housing[\"income_cat\"].value_counts().sort_index().plot.bar(rot=0, grid=True)\n",
     "plt.xlabel(\"Income category\")\n",
     "plt.ylabel(\"Number of districts\")\n",
-    "save_fig(\"housing_income_cat_bar_plot\")\n",
+    "save_fig(\"housing_income_cat_bar_plot\")  # not in the book\n",
     "plt.show()"
    ]
   },
@@ -556,7 +555,7 @@
    "outputs": [],
    "source": [
     "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", grid=True)\n",
-    "save_fig(\"bad_visualization_plot\")\n",
+    "save_fig(\"bad_visualization_plot\")  # not in the book\n",
     "plt.show()"
    ]
   },
@@ -567,7 +566,7 @@
    "outputs": [],
    "source": [
     "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", grid=True, alpha=0.2)\n",
-    "save_fig(\"better_visualization_plot\")\n",
+    "save_fig(\"better_visualization_plot\")  # not in the book\n",
     "plt.show()"
    ]
   },
@@ -579,9 +578,9 @@
    "source": [
     "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", grid=True,\n",
     "             s=housing[\"population\"] / 100, label=\"population\",\n",
-    "             c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
+    "             c=\"median_house_value\", cmap=\"jet\", colorbar=True,\n",
     "             legend=True, sharex=False, figsize=(10, 7))\n",
-    "save_fig(\"housing_prices_scatterplot\")\n",
+    "save_fig(\"housing_prices_scatterplot\")  # not in the book\n",
     "plt.show()"
    ]
   },
@@ -622,6 +621,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# not in the book\n",
+    "\n",
     "housing_renamed = housing.rename(columns={\n",
     "    \"latitude\": \"Latitude\", \"longitude\": \"Longitude\",\n",
     "    \"population\": \"Population\",\n",
@@ -629,7 +630,7 @@
     "housing_renamed.plot(\n",
     "             kind=\"scatter\", x=\"Longitude\", y=\"Latitude\",\n",
     "             s=housing_renamed[\"Population\"] / 100, label=\"Population\",\n",
-    "             c=\"Median house value (ᴜsᴅ)\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
+    "             c=\"Median house value (ᴜsᴅ)\", cmap=\"jet\", colorbar=True,\n",
     "             legend=True, sharex=False, figsize=(10, 7))\n",
     "\n",
     "california_img = plt.imread(IMAGES_PATH / filename)\n",
@@ -677,7 +678,7 @@
     "attributes = [\"median_house_value\", \"median_income\", \"total_rooms\",\n",
     "              \"housing_median_age\"]\n",
     "scatter_matrix(housing[attributes], figsize=(12, 8))\n",
-    "save_fig(\"scatter_matrix_plot\")\n",
+    "save_fig(\"scatter_matrix_plot\")  # not in the book\n",
     "plt.show()"
    ]
   },
@@ -689,7 +690,7 @@
    "source": [
     "housing.plot(kind=\"scatter\", x=\"median_income\", y=\"median_house_value\",\n",
     "             alpha=0.1, grid=True)\n",
-    "save_fig(\"income_vs_house_value_scatterplot\")\n",
+    "save_fig(\"income_vs_house_value_scatterplot\")  # not in the book\n",
     "plt.show()"
    ]
   },
@@ -1183,10 +1184,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "output_df = pd.DataFrame(cat_encoder.transform(df_test_unknown),\n",
+    "df_output = pd.DataFrame(cat_encoder.transform(df_test_unknown),\n",
     "                         columns=cat_encoder.get_feature_names_out(),\n",
-    "                         index=df_test_unknown.index)\n",
-    "output_df"
+    "                         index=df_test_unknown.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_output"
    ]
   },
   {
@@ -1198,7 +1207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1210,7 +1219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1222,7 +1231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1246,7 +1255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1267,7 +1276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1278,11 +1287,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Not in the book\n",
+    "\n",
     "ages = np.linspace(housing[\"housing_median_age\"].min(),\n",
     "                   housing[\"housing_median_age\"].max(),\n",
     "                   500).reshape(-1, 1)\n",
@@ -1311,7 +1321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1325,13 +1335,21 @@
     "some_new_data = housing[[\"median_income\"]].iloc[:5]  # pretend this is new data\n",
     "\n",
     "scaled_predictions = model.predict(some_new_data)\n",
-    "predictions = target_scaler.inverse_transform(scaled_predictions)\n",
+    "predictions = target_scaler.inverse_transform(scaled_predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "predictions"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1345,7 +1363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1368,7 +1386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1380,32 +1398,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [],
    "source": [
     "rbf_transformer = FunctionTransformer(rbf_kernel,\n",
     "                                      kw_args=dict(Y=[[35.]], gamma=0.1))\n",
-    "age_simil_35 = rbf_transformer.transform(housing[[\"housing_median_age\"]])\n",
+    "age_simil_35 = rbf_transformer.transform(housing[[\"housing_median_age\"]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "age_simil_35"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [],
    "source": [
     "sf_coords = 37.7749, -122.41\n",
     "sf_transformer = FunctionTransformer(rbf_kernel,\n",
-    "                                   kw_args=dict(Y=[sf_coords], gamma=0.1))\n",
-    "sf_simil = sf_transformer.transform(housing[[\"latitude\", \"longitude\"]])\n",
+    "                                     kw_args=dict(Y=[sf_coords], gamma=0.1))\n",
+    "sf_simil = sf_transformer.transform(housing[[\"latitude\", \"longitude\"]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "sf_simil"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1415,7 +1449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1441,7 +1475,7 @@
     "            X = X - self.mean_\n",
     "        return X / self.scale_\n",
     "    \n",
-    "    # Not in the book (left as an exercise):\n",
+    "    # not in the book (left as an exercise):\n",
     "    def inverse_transform(self, X):\n",
     "        check_is_fitted(self)\n",
     "        X = check_array(X)\n",
@@ -1449,7 +1483,7 @@
     "        X = X * self.scale_\n",
     "        return X + self.mean_ if self.with_mean else X\n",
     "    \n",
-    "    # Not in the book (left as an exercise):\n",
+    "    # not in the book (left as an exercise):\n",
     "    def get_feature_names_out(self, names=None):\n",
     "        return names or getattr(self, \"feature_names_in_\",\n",
     "                               [f\"x{i}\" for i in range(self.n_features_in_)])   "
@@ -1464,7 +1498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1488,7 +1522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1514,7 +1548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1525,7 +1559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1534,11 +1568,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Not in the book\n",
+    "\n",
     "housing_renamed = housing.rename(columns={\n",
     "    \"latitude\": \"Latitude\", \"longitude\": \"Longitude\",\n",
     "    \"population\": \"Population\",\n",
@@ -1548,7 +1583,7 @@
     "housing_renamed.plot(kind=\"scatter\", x=\"Longitude\", y=\"Latitude\", grid=True,\n",
     "                     s=housing_renamed[\"Population\"] / 100, label=\"Population\",\n",
     "                     c=\"Max cluster similarity\",\n",
-    "                     cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
+    "                     cmap=\"jet\", colorbar=True,\n",
     "                     legend=True, sharex=False, figsize=(10, 7))\n",
     "plt.plot(cluster_simil.kmeans_.cluster_centers_[:, 1],\n",
     "         cluster_simil.kmeans_.cluster_centers_[:, 0],\n",
@@ -1575,7 +1610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1589,7 +1624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1600,7 +1635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1613,7 +1648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1623,7 +1658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1670,52 +1705,16 @@
     "monkey_patch_get_signature_names_out()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "housing_num_prepared_df = pd.DataFrame(\n",
-    "    housing_num_prepared, columns=num_pipeline.get_feature_names_out(),\n",
-    "    index=housing_num.index)\n",
-    "housing_num_prepared_df.head(2)  # Not in the book"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num_pipeline.steps"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num_pipeline[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num_pipeline[:-1]"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 110,
    "metadata": {},
    "outputs": [],
    "source": [
-    "num_pipeline.named_steps[\"simpleimputer\"]"
+    "df_housing_num_prepared = pd.DataFrame(\n",
+    "    housing_num_prepared, columns=num_pipeline.get_feature_names_out(),\n",
+    "    index=housing_num.index)\n",
+    "df_housing_num_prepared.head(2)  # not in the book"
    ]
   },
   {
@@ -1724,7 +1723,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "num_pipeline.set_params(simpleimputer__strategy=\"median\")"
+    "num_pipeline.steps"
    ]
   },
   {
@@ -1732,6 +1731,42 @@
    "execution_count": 112,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "num_pipeline[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_pipeline[:-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_pipeline.named_steps[\"simpleimputer\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_pipeline.set_params(simpleimputer__strategy=\"median\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from sklearn.compose import ColumnTransformer\n",
     "\n",
@@ -1751,7 +1786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1765,7 +1800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1774,7 +1809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1787,7 +1822,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1824,7 +1859,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1834,7 +1869,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1857,7 +1892,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1876,12 +1911,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [],
    "source": [
     "housing_predictions = lin_reg.predict(housing)\n",
-    "housing_predictions[:5].round(-2)"
+    "housing_predictions[:5].round(-2)  # -2 = rounded to the nearest hundred"
    ]
   },
   {
@@ -1893,7 +1928,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1902,7 +1937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1913,7 +1948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1926,7 +1961,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1938,7 +1973,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1957,7 +1992,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1969,7 +2004,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1978,7 +2013,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1997,7 +2032,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2011,7 +2046,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2027,7 +2062,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2068,7 +2103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2098,7 +2133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2115,7 +2150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2124,7 +2159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2140,18 +2175,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [],
    "source": [
     "cv_res = pd.DataFrame(grid_search.cv_results_)\n",
     "cv_res.sort_values(by=\"mean_test_score\", ascending=False, inplace=True)\n",
+    "\n",
+    "# not in the book\n",
     "cv_res = cv_res[[\"param_preprocessing__geo__n_clusters\",\n",
     "                 \"param_random_forest__max_features\", \"split0_test_score\",\n",
     "                 \"split1_test_score\", \"split2_test_score\", \"mean_test_score\"]]\n",
     "score_cols = [\"split0\", \"split1\", \"split2\", \"mean_test_rmse\"]\n",
     "cv_res.columns = [\"n_clusters\", \"max_features\"] + score_cols\n",
     "cv_res[score_cols] = -cv_res[score_cols].round().astype(np.int64)\n",
+    "\n",
     "cv_res.head()"
    ]
   },
@@ -2171,7 +2209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2179,9 +2217,16 @@
     "from sklearn.model_selection import HalvingRandomSearchCV"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Try 30 (`n_iter` × `cv`) random combinations of hyperparameters:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2191,7 +2236,6 @@
     "param_distribs = {'preprocessing__geo__n_clusters': randint(low=3, high=50),\n",
     "                  'random_forest__max_features': randint(low=2, high=20)}\n",
     "\n",
-    "# try 30 (n_iter × cv) random combinations of hyperparameters\n",
     "rnd_search = RandomizedSearchCV(\n",
     "    full_pipeline, param_distributions=param_distribs, n_iter=10, cv=3,\n",
     "    scoring='neg_root_mean_squared_error', random_state=42)\n",
@@ -2201,7 +2245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2238,7 +2282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 144,
    "metadata": {
     "tags": []
    },
@@ -2299,7 +2343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 145,
    "metadata": {
     "tags": []
    },
@@ -2362,18 +2406,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 146,
    "metadata": {},
    "outputs": [],
    "source": [
-    "final_model = rnd_search.best_estimator_\n",
+    "final_model = rnd_search.best_estimator_  # includes preprocessing\n",
     "feature_importances = final_model[\"random_forest\"].feature_importances_\n",
     "feature_importances.round(2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 147,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2391,7 +2435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 148,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2400,16 +2444,8 @@
     "\n",
     "final_predictions = final_model.predict(X_test)\n",
     "\n",
-    "final_rmse = mean_squared_error(y_test, final_predictions, squared=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "final_rmse"
+    "final_rmse = mean_squared_error(y_test, final_predictions, squared=False)\n",
+    "print(final_rmse)"
    ]
   },
   {
@@ -2421,7 +2457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 149,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2443,7 +2479,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 150,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2464,7 +2500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 151,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2490,7 +2526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 152,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2508,21 +2544,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 153,
    "metadata": {},
    "outputs": [],
    "source": [
     "import joblib\n",
+    "\n",
+    "# not in the book\n",
     "from sklearn.cluster import KMeans\n",
     "from sklearn.base import BaseEstimator, TransformerMixin\n",
     "from sklearn.metrics.pairwise import rbf_kernel\n",
     "\n",
-    "#class ClusterSimilarity(BaseEstimator, TransformerMixin):\n",
-    "#    [...]\n",
-    "\n",
     "def column_ratio(X):\n",
     "    return X[:, [0]] / X[:, [1]]\n",
     "\n",
+    "#class ClusterSimilarity(BaseEstimator, TransformerMixin):\n",
+    "#    [...]\n",
+    "\n",
     "final_model_reloaded = joblib.load(\"my_california_housing_model.pkl\")\n",
     "\n",
     "new_data = housing.iloc[:5]  # pretend these are new districts\n",
@@ -2531,7 +2569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 154,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2568,7 +2606,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 155,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2598,7 +2636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 156,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2615,7 +2653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 157,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2652,7 +2690,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 158,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2686,7 +2724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 159,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2703,7 +2741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 160,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2726,7 +2764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 161,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2766,7 +2804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 162,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2784,7 +2822,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 163,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2826,7 +2864,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 164,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2871,7 +2909,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 165,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2887,7 +2925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 166,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2906,7 +2944,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 167,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2922,7 +2960,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 168,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2938,7 +2976,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 169,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2952,7 +2990,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 170,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2987,7 +3025,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 171,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3009,7 +3047,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 172,
    "metadata": {},
    "outputs": [],
    "source": [