Make notebook code match book examples more closely in chapter 2

02_end_to_end_machine_learning_project.ipynb

@@ -1154,16 +1154,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
-    "from sklearn.pipeline import FeatureUnion\n",
-    "\n",
     "num_attribs = list(housing_num)\n",
     "cat_attribs = [\"ocean_proximity\"]\n",
     "\n",
@@ -1177,7 +1173,20 @@
     "cat_pipeline = Pipeline([\n",
     "        ('selector', DataFrameSelector(cat_attribs)),\n",
     "        ('label_binarizer', LabelBinarizer()),\n",
-    "    ])\n",
+    "    ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import FeatureUnion\n",
     "\n",
     "full_pipeline = FeatureUnion(transformer_list=[\n",
     "        (\"num_pipeline\", num_pipeline),\n",
@@ -1219,7 +1228,7 @@
     "editable": true
    },
    "source": [
-    "# Prepare the data for Machine Learning algorithms"
+    "# Select and train a model "
    ]
   },
   {