diff --git a/docs/source/reference/ai/model-train-sklearn.rst b/docs/source/reference/ai/model-train-sklearn.rst
index 951625fcf..43485f7c4 100644
--- a/docs/source/reference/ai/model-train-sklearn.rst
+++ b/docs/source/reference/ai/model-train-sklearn.rst
@@ -6,21 +6,55 @@ Model Training with Sklearn
 1. Installation
 ---------------
 
-To use the `Sklearn framework <https://scikit-learn.org/stable/>`_, we need to install the extra sklearn dependency in your EvaDB virtual environment.
+To use the `Flaml Sklearn AutoML framework <https://microsoft.github.io/FLAML/docs/Examples/Integrate%20-%20Scikit-learn%20Pipeline/>`_, we need to install the extra Flaml dependency in your EvaDB virtual environment.
 
 .. code-block:: bash
-   
-   pip install evadb[sklearn]
+
+   pip install "flaml[automl]"
 
 2. Example Query
 ----------------
 
 .. code-block:: sql
 
-   CREATE OR REPLACE FUNCTION PredictHouseRent FROM
+   CREATE FUNCTION IF NOT EXISTS PredictRent FROM
    ( SELECT number_of_rooms, number_of_bathrooms, days_on_market, rental_price FROM HomeRentals )
    TYPE Sklearn
    PREDICT 'rental_price';
 
-In the above query, you are creating a new customized function by training a model from the ``HomeRentals`` table using the ``Sklearn`` framework.
-The ``rental_price`` column will be the target column for predication, while the rest columns from the ``SELECT`` query are the inputs. 
+In the above query, you are creating a new customized function by training a model from the ``HomeRentals`` table using the ``Flaml Sklearn`` framework.
+The ``rental_price`` column will be the target column for predication, while the rest columns from the ``SELECT`` query are the inputs.
+This shall run the ``Random Forest`` model by default.
+
+3. Model Training Parameters
+----------------------------
+
+.. list-table:: Available Parameters
+   :widths: 25 75
+
+   * - PREDICT (**required**)
+     - The name of the column we wish to predict.
+   * - MODEL
+     - The Sklearn models supported as of now are ``Random Forest``, ``Extra Trees Regressor`` and ``KNN``.
+       You can use ``rf`` for Random Forests, ``extra_tree`` for ExtraTrees Regressor, and ``kneighbor`` for KNN.
+   * - TIME_LIMIT
+     - Time limit to train the model in seconds. Default: 120.
+   * - TASK
+     - Specify whether you want to perform ``regression`` task or ``classification`` task.
+   * - METRIC
+     - Specify the metric that you want to use to train your model. For e.g. for training ``regression`` tasks you could
+       use the ``r2`` or ``RMSE`` metrics. For training ``classification`` tasks you could use the ``accuracy`` or ``f1_score`` metrics.
+       More information about the model metrics could be found `here <https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML#optimization-metric>`_
+
+Below are the example queries specifying the above parameters
+
+.. code-block:: sql
+
+   CREATE OR REPLACE FUNCTION PredictHouseRentSklearn FROM
+   ( SELECT number_of_rooms, number_of_bathrooms, days_on_market, rental_price FROM HomeRentals )
+   TYPE Sklearn
+   PREDICT 'rental_price'
+   MODEL 'extra_tree'
+   METRIC 'r2'
+   TASK 'regression'
+   TIME_LIMIT 180;
diff --git a/tutorials/17-home-rental-prediction.ipynb b/tutorials/17-home-rental-prediction.ipynb
index 8821bd569..a09fc41b8 100644
--- a/tutorials/17-home-rental-prediction.ipynb
+++ b/tutorials/17-home-rental-prediction.ipynb
@@ -49,7 +49,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "Z7PodOEEEDsQ",
-        "outputId": "83265c05-b542-431b-900d-bb39a9ad53c6"
+        "outputId": "fa0ff2bc-45a0-4a57-ab86-e02ecef102bb"
       },
       "outputs": [
         {
@@ -65,12 +65,12 @@
             "  libcommon-sense-perl libjson-perl libjson-xs-perl libtypes-serialiser-perl logrotate netbase\n",
             "  postgresql postgresql-14 postgresql-client-14 postgresql-client-common postgresql-common ssl-cert\n",
             "  sysstat\n",
-            "0 upgraded, 13 newly installed, 0 to remove and 19 not upgraded.\n",
+            "0 upgraded, 13 newly installed, 0 to remove and 11 not upgraded.\n",
             "Need to get 18.3 MB of archives.\n",
             "After this operation, 51.5 MB of additional disk space will be used.\n",
             "Preconfiguring packages ...\n",
             "Selecting previously unselected package logrotate.\n",
-            "(Reading database ... 120874 files and directories currently installed.)\n",
+            "(Reading database ... 120880 files and directories currently installed.)\n",
             "Preparing to unpack .../00-logrotate_3.19.0-1ubuntu1.1_amd64.deb ...\n",
             "Unpacking logrotate (3.19.0-1ubuntu1.1) ...\n",
             "Selecting previously unselected package netbase.\n",
@@ -183,13 +183,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": 2,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "UrlfWZOkEa4V",
-        "outputId": "477a18d7-93cf-432b-bbea-0115f9f48454"
+        "outputId": "0b174575-1a14-414c-cf5d-084bb94e9cef"
       },
       "outputs": [
         {
@@ -217,7 +217,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": 3,
       "metadata": {
         "id": "EZf65ZkcFIX7"
       },
@@ -244,11 +244,108 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 10,
+      "execution_count": 5,
       "metadata": {
-        "id": "NoAykveeElqm"
+        "id": "NoAykveeElqm",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "53c6fa5f-a891-4983-ae29-2a4d2ca0844b"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m578.7/578.7 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m137.6/137.6 kB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m111.6/111.6 kB\u001b[0m \u001b[31m12.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 kB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m42.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.2/295.2 kB\u001b[0m \u001b[31m26.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.7/98.7 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m682.2/682.2 kB\u001b[0m \u001b[31m46.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m80.8/80.8 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.2/519.2 kB\u001b[0m \u001b[31m38.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m280.2/280.2 kB\u001b[0m \u001b[31m24.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.1/98.1 kB\u001b[0m \u001b[31m11.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m232.0/232.0 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m897.5/897.5 kB\u001b[0m \u001b[31m56.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m59.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.5/20.5 MB\u001b[0m \u001b[31m63.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.5/92.5 MB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m100.0/100.0 kB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.8/154.8 kB\u001b[0m \u001b[31m14.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.5/62.5 MB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m58.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.6/58.6 MB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m120.5/120.5 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.3/4.3 MB\u001b[0m \u001b[31m72.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m51.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.3/3.3 MB\u001b[0m \u001b[31m66.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.0/54.0 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m307.2/307.2 kB\u001b[0m \u001b[31m27.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.1/53.1 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.7/117.7 kB\u001b[0m \u001b[31m13.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.7/8.7 MB\u001b[0m \u001b[31m95.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.8/4.8 MB\u001b[0m \u001b[31m92.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m201.4/201.4 kB\u001b[0m \u001b[31m19.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m60.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m128.2/128.2 kB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.7/59.7 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m70.0/70.0 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.9/92.9 kB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.4/58.4 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.4/62.4 MB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.5/62.5 MB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.9/56.9 MB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.9/56.9 MB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.9/56.9 MB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.9/56.9 MB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.2/56.2 MB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.2/56.2 MB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m51.1/51.1 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.6/11.6 MB\u001b[0m \u001b[31m87.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m548.2/548.2 kB\u001b[0m \u001b[31m30.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.8/81.8 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[33mWARNING: ray 2.4.0 does not provide the extra 'client'\u001b[0m\u001b[33m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m468.9/468.9 kB\u001b[0m \u001b[31m34.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.0/67.0 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Building wheel for deepspeed (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Building wheel for gpustat (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Building wheel for GPUtil (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Building wheel for ludwig (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "lida 0.0.10 requires kaleido, which is not installed.\n",
+            "lida 0.0.10 requires python-multipart, which is not installed.\n",
+            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
+            "llmx 0.0.15a0 requires openai, which is not installed.\n",
+            "llmx 0.0.15a0 requires tiktoken, which is not installed.\n",
+            "distributed 2023.8.1 requires dask==2023.8.1, but you have dask 2023.3.2 which is incompatible.\n",
+            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.8.0 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Downloading: \"http://ml.cs.tsinghua.edu.cn/~chenxi/pytorch-models/mnist-b07bb66b.pth\" to /root/.cache/torch/hub/checkpoints/mnist-b07bb66b.pth\n",
+            "100%|██████████| 1.03M/1.03M [00:01<00:00, 757kB/s] \n",
+            "Downloading: \"https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth\" to /root/.cache/torch/hub/checkpoints/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth\n"
+          ]
+        }
+      ],
       "source": [
         "%pip install --quiet \"evadb[postgres,xgboost,ludwig]\"\n",
         "\n",
@@ -277,14 +374,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 11,
+      "execution_count": 6,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 80
+          "height": 78
         },
         "id": "IsP6rLZ2Ftxo",
-        "outputId": "84a41fb6-5f1f-4d7c-e219-f08a2e73b088"
+        "outputId": "a7e679d3-a5ea-45cb-b033-f3692317a3f5"
       },
       "outputs": [
         {
@@ -296,7 +393,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-91ffca60-bfcf-4e4b-ad03-1c7ff0f85bc6\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-2edbe5b1-30e1-4b84-91d1-7bd6d28cf433\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -329,7 +426,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-91ffca60-bfcf-4e4b-ad03-1c7ff0f85bc6')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2edbe5b1-30e1-4b84-91d1-7bd6d28cf433')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -381,12 +478,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-91ffca60-bfcf-4e4b-ad03-1c7ff0f85bc6 button.colab-df-convert');\n",
+              "        document.querySelector('#df-2edbe5b1-30e1-4b84-91d1-7bd6d28cf433 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-91ffca60-bfcf-4e4b-ad03-1c7ff0f85bc6');\n",
+              "        const element = document.querySelector('#df-2edbe5b1-30e1-4b84-91d1-7bd6d28cf433');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -410,7 +507,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 11
+          "execution_count": 6
         }
       ],
       "source": [
@@ -438,34 +535,34 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": 7,
       "metadata": {
         "id": "msbHcP_xJpFV",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "0eca67d7-e820-42ed-941e-9ca38ceade76"
+        "outputId": "f6d8d6a2-c36d-4fb3-acf1-837ee5da3b47"
       },
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "--2023-10-26 18:33:10--  https://www.dropbox.com/scl/fi/gy2682i66a8l2tqsowm5x/home_rentals.csv?rlkey=e080k02rv5205h4ullfjdr8lw\n",
-            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.3.18, 2620:100:6018:18::a27d:312\n",
-            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.3.18|:443... connected.\n",
+            "--2023-11-28 19:28:27--  https://www.dropbox.com/scl/fi/gy2682i66a8l2tqsowm5x/home_rentals.csv?rlkey=e080k02rv5205h4ullfjdr8lw\n",
+            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.5.18, 2620:100:601d:18::a27d:512\n",
+            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.5.18|:443... connected.\n",
             "HTTP request sent, awaiting response... 302 Found\n",
-            "Location: https://uc8d990fb5d1437b8351f26de88a.dl.dropboxusercontent.com/cd/0/inline/CGVkpQp8MQys_mL2iZ4WZL0mLTIryv1kHm9c6YlkGomsP279V5incJvOz05jfx5oqY0zKLKoMNcksLTrXBuk3MgOYSnfeHIjuKAzyWM5fZKfHS1qfV9_8EQsi--9jZx53LFu_MvgxiKWMys7t4DrWF_1/file# [following]\n",
-            "--2023-10-26 18:33:11--  https://uc8d990fb5d1437b8351f26de88a.dl.dropboxusercontent.com/cd/0/inline/CGVkpQp8MQys_mL2iZ4WZL0mLTIryv1kHm9c6YlkGomsP279V5incJvOz05jfx5oqY0zKLKoMNcksLTrXBuk3MgOYSnfeHIjuKAzyWM5fZKfHS1qfV9_8EQsi--9jZx53LFu_MvgxiKWMys7t4DrWF_1/file\n",
-            "Resolving uc8d990fb5d1437b8351f26de88a.dl.dropboxusercontent.com (uc8d990fb5d1437b8351f26de88a.dl.dropboxusercontent.com)... 162.125.3.15, 2620:100:601b:15::a27d:80f\n",
-            "Connecting to uc8d990fb5d1437b8351f26de88a.dl.dropboxusercontent.com (uc8d990fb5d1437b8351f26de88a.dl.dropboxusercontent.com)|162.125.3.15|:443... connected.\n",
+            "Location: https://ucdac3aa9e9d3c29ba317cdba528.dl.dropboxusercontent.com/cd/0/inline/CIZBjRk_219UjBC3q8uyjzPSHGVgfxu9ZtCf_WESgWXjOAd_foWNWFvmDso2nw60coSSOeaZ3tOqD8nTdEDbRY6ohlvIm9yJIi9K2DrWPIx8uQvMqiaO2v_76rb3IEERLR399gA2D8NkCgA7aBIpNU62/file# [following]\n",
+            "--2023-11-28 19:28:28--  https://ucdac3aa9e9d3c29ba317cdba528.dl.dropboxusercontent.com/cd/0/inline/CIZBjRk_219UjBC3q8uyjzPSHGVgfxu9ZtCf_WESgWXjOAd_foWNWFvmDso2nw60coSSOeaZ3tOqD8nTdEDbRY6ohlvIm9yJIi9K2DrWPIx8uQvMqiaO2v_76rb3IEERLR399gA2D8NkCgA7aBIpNU62/file\n",
+            "Resolving ucdac3aa9e9d3c29ba317cdba528.dl.dropboxusercontent.com (ucdac3aa9e9d3c29ba317cdba528.dl.dropboxusercontent.com)... 162.125.5.15, 2620:100:601d:15::a27d:50f\n",
+            "Connecting to ucdac3aa9e9d3c29ba317cdba528.dl.dropboxusercontent.com (ucdac3aa9e9d3c29ba317cdba528.dl.dropboxusercontent.com)|162.125.5.15|:443... connected.\n",
             "HTTP request sent, awaiting response... 200 OK\n",
             "Length: 204532 (200K) [text/plain]\n",
             "Saving to: ‘/content/home_rentals.csv’\n",
             "\n",
-            "/content/home_renta 100%[===================>] 199.74K  --.-KB/s    in 0.04s   \n",
+            "/content/home_renta 100%[===================>] 199.74K  --.-KB/s    in 0.09s   \n",
             "\n",
-            "2023-10-26 18:33:11 (4.91 MB/s) - ‘/content/home_rentals.csv’ saved [204532/204532]\n",
+            "2023-11-28 19:28:29 (2.28 MB/s) - ‘/content/home_rentals.csv’ saved [204532/204532]\n",
             "\n"
           ]
         }
@@ -477,14 +574,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 13,
+      "execution_count": 8,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 80
+          "height": 78
         },
         "id": "MhYfovbzNB-k",
-        "outputId": "3b453405-f0dd-47d9-b867-3d552cdeebff"
+        "outputId": "a4a82b50-de95-4256-c2b9-e29ebd15fa1c"
       },
       "outputs": [
         {
@@ -496,7 +593,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-856ca5aa-70e5-4093-bef1-aad5bf9fea1a\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-40f0a90f-6829-49de-af81-a08ca0f7bfe7\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -529,7 +626,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-856ca5aa-70e5-4093-bef1-aad5bf9fea1a')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-40f0a90f-6829-49de-af81-a08ca0f7bfe7')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -581,12 +678,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-856ca5aa-70e5-4093-bef1-aad5bf9fea1a button.colab-df-convert');\n",
+              "        document.querySelector('#df-40f0a90f-6829-49de-af81-a08ca0f7bfe7 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-856ca5aa-70e5-4093-bef1-aad5bf9fea1a');\n",
+              "        const element = document.querySelector('#df-40f0a90f-6829-49de-af81-a08ca0f7bfe7');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -610,7 +707,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 13
+          "execution_count": 8
         }
       ],
       "source": [
@@ -632,14 +729,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 14,
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 80
+          "height": 78
         },
         "id": "OH8Fxn0SNYO3",
-        "outputId": "06825af6-9feb-4949-ee7a-73891f4487c4"
+        "outputId": "4d0e01bd-c0df-44ff-d1e1-a70eec33f45f"
       },
       "outputs": [
         {
@@ -651,7 +748,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-5a3961cf-cc34-4e8f-83a3-41ceaca92b1b\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-b00bb3f2-4c9b-4afc-9155-0b7020917d53\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -684,7 +781,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5a3961cf-cc34-4e8f-83a3-41ceaca92b1b')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b00bb3f2-4c9b-4afc-9155-0b7020917d53')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -736,12 +833,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-5a3961cf-cc34-4e8f-83a3-41ceaca92b1b button.colab-df-convert');\n",
+              "        document.querySelector('#df-b00bb3f2-4c9b-4afc-9155-0b7020917d53 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-5a3961cf-cc34-4e8f-83a3-41ceaca92b1b');\n",
+              "        const element = document.querySelector('#df-b00bb3f2-4c9b-4afc-9155-0b7020917d53');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -765,7 +862,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 14
+          "execution_count": 9
         }
       ],
       "source": [
@@ -798,14 +895,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 17,
+      "execution_count": 10,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 142
+          "height": 140
         },
         "id": "20_IEby7N1Pe",
-        "outputId": "9fd8bcfb-2be7-4f59-e4c2-812c91a6a325"
+        "outputId": "900b2c43-2846-466b-b942-bfdbfbab2615"
       },
       "outputs": [
         {
@@ -824,7 +921,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-e2385a39-e37b-4af2-8541-796a2d8eb033\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-364b72b8-8894-45c1-839f-b112be266ae4\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -893,7 +990,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e2385a39-e37b-4af2-8541-796a2d8eb033')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-364b72b8-8894-45c1-839f-b112be266ae4')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -945,12 +1042,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-e2385a39-e37b-4af2-8541-796a2d8eb033 button.colab-df-convert');\n",
+              "        document.querySelector('#df-364b72b8-8894-45c1-839f-b112be266ae4 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-e2385a39-e37b-4af2-8541-796a2d8eb033');\n",
+              "        const element = document.querySelector('#df-364b72b8-8894-45c1-839f-b112be266ae4');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -970,9 +1067,9 @@
               "  </div>\n",
               "\n",
               "\n",
-              "<div id=\"df-07ed7fd5-a749-4f4b-98c4-d96ad24c0ff1\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-07ed7fd5-a749-4f4b-98c4-d96ad24c0ff1')\"\n",
-              "            title=\"Suggest charts.\"\n",
+              "<div id=\"df-d29e7837-6ac3-4b18-aff2-0a7e1c8d5d68\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d29e7837-6ac3-4b18-aff2-0a7e1c8d5d68')\"\n",
+              "            title=\"Suggest charts\"\n",
               "            style=\"display:none;\">\n",
               "\n",
               "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
@@ -1090,7 +1187,7 @@
               "    }\n",
               "    (() => {\n",
               "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-07ed7fd5-a749-4f4b-98c4-d96ad24c0ff1 button');\n",
+              "        document.querySelector('#df-d29e7837-6ac3-4b18-aff2-0a7e1c8d5d68 button');\n",
               "      quickchartButtonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "    })();\n",
@@ -1101,7 +1198,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 17
+          "execution_count": 10
         }
       ],
       "source": [
@@ -1177,7 +1274,7 @@
         "id": "7Zl8nJfaSglz",
         "outputId": "ad6fd61c-4fd5-4b6d-a982-79408803a765"
       },
-      "execution_count": 19,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -1591,45 +1688,170 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "AhItlDfBUHOo"
-      },
       "source": [
-        "### Utilizing the Prediction Model\n",
-        "Following the model training, we proceed to employ the `PredictHouseRent`` model to make predictions for home rental prices."
-      ]
+        "### Train the prediction Model using Sklearn\n",
+        "With Sklearn Flaml integration, we harness the capabilities of the [Sklearn](https://scikit-learn.org/stable/) engine to train our prediction model. We employ the `Flaml` AutoML model to automatically determine the optimal hyperparameters. We can utilize `TIME_LIMIT` to specify the time budget allocated for the training process. Similarly you can use the `METRIC` parameter to determine the accuracy/error metric for training the model. `TASK` parameter can be used to specify whether you want to perform a `classification` task or `regression` task. Additionally you can use the `MODEL` parameter to specify the Sklearn ML model that you would like to use for training. You can set the `MODEL` parameter to with `rf`, `extra_tree` or `kneighbor`."
+      ],
+      "metadata": {
+        "id": "Nd5SERJ2aEj8"
+      }
     },
     {
       "cell_type": "code",
-      "execution_count": 20,
+      "source": [
+        "cursor.query(\"\"\"\n",
+        "  CREATE OR REPLACE FUNCTION PredictHouseRent FROM\n",
+        "  ( SELECT * FROM postgres_data.home_rentals )\n",
+        "  TYPE Sklearn\n",
+        "  PREDICT 'rental_price'\n",
+        "  TIME_LIMIT 180\n",
+        "  METRIC 'rmse'\n",
+        "  TASK 'regression'\n",
+        "  MODEL 'rf';\n",
+        "\"\"\").df()"
+      ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 359
+          "height": 1000
         },
-        "id": "_7m-QQG5U3_C",
-        "outputId": "f8e77484-c2ce-4100-8edf-e8237799fc67"
+        "id": "ACZaNB2ebzTn",
+        "outputId": "97c9a31e-75d9-44ae-d961-e80b252b12de"
       },
+      "execution_count": 11,
       "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[flaml.automl.logger: 11-28 19:41:22] {1679} INFO - task = regression\n",
+            "[flaml.automl.logger: 11-28 19:41:22] {1690} INFO - Evaluation method: cv\n",
+            "[flaml.automl.logger: 11-28 19:41:22] {1788} INFO - Minimizing error metric: rmse\n",
+            "[flaml.automl.logger: 11-28 19:41:22] {1900} INFO - List of ML learners in AutoML Run: ['rf']\n",
+            "[flaml.automl.logger: 11-28 19:41:22] {2218} INFO - iteration 0, current learner rf\n",
+            "[flaml.automl.logger: 11-28 19:41:23] {2344} INFO - Estimated sufficient time budget=3889s. Estimated necessary time budget=4s.\n",
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+            "[flaml.automl.logger: 11-28 19:44:13] {2627} INFO - retrain rf for 3.2s\n",
+            "[flaml.automl.logger: 11-28 19:44:13] {2630} INFO - retrained model: RandomForestRegressor(max_features=0.9525825118696016, max_leaf_nodes=2109,\n",
+            "                      n_estimators=264, n_jobs=-1, random_state=12032022)\n",
+            "[flaml.automl.logger: 11-28 19:44:13] {1930} INFO - fit succeeded\n",
+            "[flaml.automl.logger: 11-28 19:44:13] {1931} INFO - Time taken to find the best model: 109.49768424034119\n"
+          ]
+        },
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "   rental_price\n",
-              "0   2166.502930\n",
-              "1   1881.248047\n",
-              "2   2429.649170\n",
-              "3   5504.289062\n",
-              "4   2272.817139\n",
-              "5   4125.753906\n",
-              "6   2222.618164\n",
-              "7   2100.471191\n",
-              "8   3870.793945\n",
-              "9   2043.048096"
+              "                                                  0\n",
+              "0  Function PredictHouseRent added to the database.\n",
+              "1               Validation Score: 5.499365480177278\n",
+              "2                          Training time: 171 secs."
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-f82ac135-a21f-43f4-bcf9-b9f02ec6870c\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-1c8fcf72-860e-4e74-9c31-ebf116f549cf\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -1648,49 +1870,21 @@
               "  <thead>\n",
               "    <tr style=\"text-align: right;\">\n",
               "      <th></th>\n",
-              "      <th>rental_price</th>\n",
+              "      <th>0</th>\n",
               "    </tr>\n",
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>0</th>\n",
-              "      <td>2166.502930</td>\n",
+              "      <td>Function PredictHouseRent added to the database.</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1</th>\n",
-              "      <td>1881.248047</td>\n",
+              "      <td>Validation Score: 5.499365480177278</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>2</th>\n",
-              "      <td>2429.649170</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>5504.289062</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>2272.817139</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>4125.753906</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>6</th>\n",
-              "      <td>2222.618164</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>7</th>\n",
-              "      <td>2100.471191</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>8</th>\n",
-              "      <td>3870.793945</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>9</th>\n",
-              "      <td>2043.048096</td>\n",
+              "      <td>Training time: 171 secs.</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
@@ -1698,7 +1892,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f82ac135-a21f-43f4-bcf9-b9f02ec6870c')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1c8fcf72-860e-4e74-9c31-ebf116f549cf')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -1750,12 +1944,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-f82ac135-a21f-43f4-bcf9-b9f02ec6870c button.colab-df-convert');\n",
+              "        document.querySelector('#df-1c8fcf72-860e-4e74-9c31-ebf116f549cf button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-f82ac135-a21f-43f4-bcf9-b9f02ec6870c');\n",
+              "        const element = document.querySelector('#df-1c8fcf72-860e-4e74-9c31-ebf116f549cf');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -1775,9 +1969,9 @@
               "  </div>\n",
               "\n",
               "\n",
-              "<div id=\"df-dcbfc23d-c817-49db-9222-3db946a2c42c\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dcbfc23d-c817-49db-9222-3db946a2c42c')\"\n",
-              "            title=\"Suggest charts.\"\n",
+              "<div id=\"df-3c446f4f-0d9f-49a5-8160-132de1748f96\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3c446f4f-0d9f-49a5-8160-132de1748f96')\"\n",
+              "            title=\"Suggest charts\"\n",
               "            style=\"display:none;\">\n",
               "\n",
               "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
@@ -1895,7 +2089,7 @@
               "    }\n",
               "    (() => {\n",
               "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-dcbfc23d-c817-49db-9222-3db946a2c42c button');\n",
+              "        document.querySelector('#df-3c446f4f-0d9f-49a5-8160-132de1748f96 button');\n",
               "      quickchartButtonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "    })();\n",
@@ -1906,53 +2100,126 @@
             ]
           },
           "metadata": {},
-          "execution_count": 20
+          "execution_count": 11
         }
-      ],
-      "source": [
-        "cursor.query(\"SELECT PredictHouseRent(*) FROM postgres_data.home_rentals LIMIT 10;\").df()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bRuPU8hVc3nv"
-      },
-      "source": [
-        "We have the option to utilize a `LATERAL JOIN` to compare the actual rental prices in the `home_rentals` dataset with the predicted rental prices generated by the trained model, `PredictHouseRent`."
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 21,
+      "source": [
+        "cursor.query(\"\"\"\n",
+        "  CREATE OR REPLACE FUNCTION PredictHouseRent FROM\n",
+        "  ( SELECT * FROM postgres_data.home_rentals )\n",
+        "  TYPE Sklearn\n",
+        "  PREDICT 'rental_price'\n",
+        "  TIME_LIMIT 30\n",
+        "  METRIC 'r2'\n",
+        "  TASK 'regression'\n",
+        "  MODEL 'extra_tree';\n",
+        "\"\"\").df()"
+      ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 359
+          "height": 1000
         },
-        "id": "1rDikEWodBdX",
-        "outputId": "be1eccf7-1c0a-439a-b7b0-1f25a1949b4b"
+        "id": "NvoGmEv9d7AW",
+        "outputId": "cfb62446-dcc4-4486-be9c-a3ada49524ea"
       },
+      "execution_count": 17,
       "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[flaml.automl.logger: 11-28 19:52:18] {1679} INFO - task = regression\n",
+            "[flaml.automl.logger: 11-28 19:52:18] {1690} INFO - Evaluation method: cv\n",
+            "[flaml.automl.logger: 11-28 19:52:18] {1788} INFO - Minimizing error metric: 1-r2\n",
+            "[flaml.automl.logger: 11-28 19:52:18] {1900} INFO - List of ML learners in AutoML Run: ['extra_tree']\n",
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+            "[flaml.automl.logger: 11-28 19:52:18] {2344} INFO - Estimated sufficient time budget=2913s. Estimated necessary time budget=3s.\n",
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+            "[flaml.automl.logger: 11-28 19:52:44] {2218} INFO - iteration 31, current learner extra_tree\n",
+            "[flaml.automl.logger: 11-28 19:52:47] {2391} INFO -  at 29.7s,\testimator extra_tree's best error=0.0000,\tbest estimator extra_tree's best error=0.0000\n",
+            "[flaml.automl.logger: 11-28 19:52:48] {2627} INFO - retrain extra_tree for 0.9s\n",
+            "[flaml.automl.logger: 11-28 19:52:48] {2630} INFO - retrained model: ExtraTreesRegressor(max_leaf_nodes=2516, n_estimators=64, n_jobs=-1,\n",
+            "                    random_state=12032022)\n",
+            "[flaml.automl.logger: 11-28 19:52:48] {1930} INFO - fit succeeded\n",
+            "[flaml.automl.logger: 11-28 19:52:48] {1931} INFO - Time taken to find the best model: 26.15423846244812\n"
+          ]
+        },
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "   rental_price  predicted_rental_price\n",
-              "0      2167.000             2167.564697\n",
-              "1      1883.000             1881.478516\n",
-              "2      2431.000             2430.222656\n",
-              "3      5510.000             5506.156738\n",
-              "4      2272.000             2274.904297\n",
-              "5      4123.812             4123.654785\n",
-              "6      2224.000             2224.599609\n",
-              "7      2104.000             2101.194824\n",
-              "8      3861.000             3866.042725\n",
-              "9      2041.000             2043.027222"
+              "                                                  0\n",
+              "0  Function PredictHouseRent added to the database.\n",
+              "1           Validation Score: 6.991127421329679e-06\n",
+              "2                           Training time: 30 secs."
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-1b6ccf35-5cc5-41e4-9351-d031bd07bca8\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-49db9fc3-7571-40dc-899e-eda358280bc3\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -1971,60 +2238,21 @@
               "  <thead>\n",
               "    <tr style=\"text-align: right;\">\n",
               "      <th></th>\n",
-              "      <th>rental_price</th>\n",
-              "      <th>predicted_rental_price</th>\n",
+              "      <th>0</th>\n",
               "    </tr>\n",
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>0</th>\n",
-              "      <td>2167.000</td>\n",
-              "      <td>2167.564697</td>\n",
+              "      <td>Function PredictHouseRent added to the database.</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1</th>\n",
-              "      <td>1883.000</td>\n",
-              "      <td>1881.478516</td>\n",
+              "      <td>Validation Score: 6.991127421329679e-06</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>2</th>\n",
-              "      <td>2431.000</td>\n",
-              "      <td>2430.222656</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>5510.000</td>\n",
-              "      <td>5506.156738</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>2272.000</td>\n",
-              "      <td>2274.904297</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>4123.812</td>\n",
-              "      <td>4123.654785</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>6</th>\n",
-              "      <td>2224.000</td>\n",
-              "      <td>2224.599609</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>7</th>\n",
-              "      <td>2104.000</td>\n",
-              "      <td>2101.194824</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>8</th>\n",
-              "      <td>3861.000</td>\n",
-              "      <td>3866.042725</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>9</th>\n",
-              "      <td>2041.000</td>\n",
-              "      <td>2043.027222</td>\n",
+              "      <td>Training time: 30 secs.</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
@@ -2032,7 +2260,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1b6ccf35-5cc5-41e4-9351-d031bd07bca8')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-49db9fc3-7571-40dc-899e-eda358280bc3')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -2084,12 +2312,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-1b6ccf35-5cc5-41e4-9351-d031bd07bca8 button.colab-df-convert');\n",
+              "        document.querySelector('#df-49db9fc3-7571-40dc-899e-eda358280bc3 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-1b6ccf35-5cc5-41e4-9351-d031bd07bca8');\n",
+              "        const element = document.querySelector('#df-49db9fc3-7571-40dc-899e-eda358280bc3');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -2109,9 +2337,9 @@
               "  </div>\n",
               "\n",
               "\n",
-              "<div id=\"df-15c9f7ba-1df9-48a2-aae4-dfe52f9f6376\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-15c9f7ba-1df9-48a2-aae4-dfe52f9f6376')\"\n",
-              "            title=\"Suggest charts.\"\n",
+              "<div id=\"df-919b7838-e84e-42e9-9da3-97cda6a181a1\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-919b7838-e84e-42e9-9da3-97cda6a181a1')\"\n",
+              "            title=\"Suggest charts\"\n",
               "            style=\"display:none;\">\n",
               "\n",
               "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
@@ -2229,7 +2457,7 @@
               "    }\n",
               "    (() => {\n",
               "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-15c9f7ba-1df9-48a2-aae4-dfe52f9f6376 button');\n",
+              "        document.querySelector('#df-919b7838-e84e-42e9-9da3-97cda6a181a1 button');\n",
               "      quickchartButtonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "    })();\n",
@@ -2240,7 +2468,662 @@
             ]
           },
           "metadata": {},
-          "execution_count": 21
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AhItlDfBUHOo"
+      },
+      "source": [
+        "### Utilizing the Prediction Model\n",
+        "Following the model training, we proceed to employ the `PredictHouseRent`` model to make predictions for home rental prices."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 352
+        },
+        "id": "_7m-QQG5U3_C",
+        "outputId": "a91ce295-fa21-4f0a-a53c-c0fee402bfc8"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   rental_price\n",
+              "0   2167.179067\n",
+              "1   1881.860347\n",
+              "2   2430.833594\n",
+              "3   5510.144792\n",
+              "4   2272.339955\n",
+              "5   4126.598719\n",
+              "6   2224.262202\n",
+              "7   2104.066761\n",
+              "8   3860.835156\n",
+              "9   2040.849091"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d1d12708-0db5-45c8-adfb-b084987eb044\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>rental_price</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2167.179067</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1881.860347</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2430.833594</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>5510.144792</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2272.339955</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>4126.598719</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>2224.262202</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>2104.066761</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>3860.835156</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>2040.849091</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d1d12708-0db5-45c8-adfb-b084987eb044')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-d1d12708-0db5-45c8-adfb-b084987eb044 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-d1d12708-0db5-45c8-adfb-b084987eb044');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-40fb2926-7cb3-47c0-8d1e-7873377cd758\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-40fb2926-7cb3-47c0-8d1e-7873377cd758')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-40fb2926-7cb3-47c0-8d1e-7873377cd758 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ],
+      "source": [
+        "cursor.query(\"SELECT PredictHouseRent(*) FROM postgres_data.home_rentals LIMIT 10;\").df()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bRuPU8hVc3nv"
+      },
+      "source": [
+        "We have the option to utilize a `LATERAL JOIN` to compare the actual rental prices in the `home_rentals` dataset with the predicted rental prices generated by the trained model, `PredictHouseRent`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 352
+        },
+        "id": "1rDikEWodBdX",
+        "outputId": "39207488-3256-4423-d2e2-59ba56656aec"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   rental_price  predicted_rental_price\n",
+              "0      2167.000             2167.179067\n",
+              "1      1883.000             1883.452487\n",
+              "2      2431.000             2430.646094\n",
+              "3      5510.000             5509.533854\n",
+              "4      2272.000             2272.023586\n",
+              "5      4123.812             4123.744156\n",
+              "6      2224.000             2224.262202\n",
+              "7      2104.000             2103.969496\n",
+              "8      3861.000             3861.523438\n",
+              "9      2041.000             2040.849091"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-f4184088-c522-41ed-89ea-32368c04fa1d\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>rental_price</th>\n",
+              "      <th>predicted_rental_price</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2167.000</td>\n",
+              "      <td>2167.179067</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1883.000</td>\n",
+              "      <td>1883.452487</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2431.000</td>\n",
+              "      <td>2430.646094</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>5510.000</td>\n",
+              "      <td>5509.533854</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2272.000</td>\n",
+              "      <td>2272.023586</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>4123.812</td>\n",
+              "      <td>4123.744156</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>2224.000</td>\n",
+              "      <td>2224.262202</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>2104.000</td>\n",
+              "      <td>2103.969496</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>3861.000</td>\n",
+              "      <td>3861.523438</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>2041.000</td>\n",
+              "      <td>2040.849091</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f4184088-c522-41ed-89ea-32368c04fa1d')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-f4184088-c522-41ed-89ea-32368c04fa1d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-f4184088-c522-41ed-89ea-32368c04fa1d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-e72b3a5e-b186-4052-a96f-8bda5b5b00ff\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e72b3a5e-b186-4052-a96f-8bda5b5b00ff')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-e72b3a5e-b186-4052-a96f-8bda5b5b00ff button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
         }
       ],
       "source": [
@@ -2279,4 +3162,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}
\ No newline at end of file