Add some section headers

02_end_to_end_machine_learning_project.ipynb

@@ -83,7 +83,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Get the data"
+    "# Get the Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download the Data"
    ]
   },
   {
@@ -132,6 +139,13 @@
     "    return pd.read_csv(csv_path)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Take a Quick Look at the Data Structure"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -182,6 +196,13 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a Test Set"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 10,
@@ -443,7 +464,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Discover and visualize the data to gain insights"
+    "# Discover and Visualize the Data to Gain Insights"
    ]
   },
   {
@@ -455,6 +476,13 @@
     "housing = strat_train_set.copy()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualizing Geographical Data"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 33,
@@ -540,6 +568,13 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Looking for Correlations"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 38,
@@ -585,6 +620,13 @@
     "save_fig(\"income_vs_house_value_scatterplot\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Experimenting with Attribute Combinations"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 42,
@@ -631,7 +673,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Prepare the data for Machine Learning algorithms"
+    "# Prepare the Data for Machine Learning Algorithms"
    ]
   },
   {
@@ -644,6 +686,29 @@
     "housing_labels = strat_train_set[\"median_house_value\"].copy()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Cleaning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the book 3 options are listed:\n",
+    "\n",
+    "```python\n",
+    "housing.dropna(subset=[\"total_bedrooms\"])    # option 1\n",
+    "housing.drop(\"total_bedrooms\", axis=1)       # option 2\n",
+    "median = housing[\"total_bedrooms\"].median()  # option 3\n",
+    "housing[\"total_bedrooms\"].fillna(median, inplace=True)\n",
+    "```\n",
+    "\n",
+    "To demonstrate each of them, let's create a copy of the housing dataset, but keeping only the rows that contain at least one null. Then it will be easier to visualize exactly what each option does:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 47,
@@ -815,6 +880,13 @@
     "housing_tr.head()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Handling Text and Categorical Attributes"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -910,6 +982,13 @@
     "cat_encoder.categories_"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Custom Transformers"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -985,6 +1064,13 @@
     "housing_extra_attribs.head()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Transformation Pipelines"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1154,7 +1240,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Select and train a model "
+    "# Select and Train a Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training and Evaluating on the Training Set"
    ]
   },
   {
@@ -1269,7 +1362,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Fine-tune your model"
+    "## Better Evaluation Using Cross-Validation"
    ]
   },
   {
@@ -1382,6 +1475,20 @@
     "svm_rmse"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fine-Tune Your Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Grid Search"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 99,
@@ -1457,6 +1564,13 @@
     "pd.DataFrame(grid_search.cv_results_)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Randomized Search"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 104,
@@ -1488,6 +1602,13 @@
     "    print(np.sqrt(-mean_score), params)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analyze the Best Models and Their Errors"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 106,
@@ -1512,6 +1633,13 @@
     "sorted(zip(feature_importances, attributes), reverse=True)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluate Your System on the Test Set"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 108,