diff --git a/02_end_to_end_machine_learning_project.ipynb b/02_end_to_end_machine_learning_project.ipynb
index 96ff4c5..74806ab 100644
--- a/02_end_to_end_machine_learning_project.ipynb
+++ b/02_end_to_end_machine_learning_project.ipynb
@@ -70,7 +70,7 @@
     "\n",
     "# Ignore useless warnings (see SciPy issue #5998)\n",
     "import warnings\n",
-    "warnings.filterwarnings(action=\"ignore\", module=\"scipy\", message=\"internal gelsd\")"
+    "warnings.filterwarnings(action=\"ignore\", message=\"^internal gelsd\")"
    ]
   },
   {
@@ -983,14 +983,21 @@
     "**Warning**: earlier versions of the book applied different transformations to different columns using a solution based on a `DataFrameSelector` transformer and a `FeatureUnion` (see below). It is now preferable to use the `ColumnTransformer` class that will be introduced in Scikit-Learn 0.20. For now we import it from `future_encoders.py`, but when Scikit-Learn 0.20 is released, you can import it from `sklearn.compose` instead:"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from future_encoders import ColumnTransformer"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from future_encoders import ColumnTransformer\n",
-    "\n",
     "num_attribs = list(housing_num)\n",
     "cat_attribs = [\"ocean_proximity\"]\n",
     "\n",