From 132438f29aa6eac2c362e0a035740f68a6dea807 Mon Sep 17 00:00:00 2001
From: Jineet Desai <jdesai61@gatech.edu>
Date: Tue, 24 Oct 2023 23:52:29 -0400
Subject: [PATCH] Adding colab notebook for XGBoost Regression and
 Classification

---
 tutorials/19-XGBoost-prediction.ipynb | 2829 +++++++++++++++++++++++++
 1 file changed, 2829 insertions(+)
 create mode 100644 tutorials/19-XGBoost-prediction.ipynb

diff --git a/tutorials/19-XGBoost-prediction.ipynb b/tutorials/19-XGBoost-prediction.ipynb
new file mode 100644
index 0000000000..e82b8440a1
--- /dev/null
+++ b/tutorials/19-XGBoost-prediction.ipynb
@@ -0,0 +1,2829 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Training Prediction Models Directly Within PostgreSQL Using XGBoost EvaDB\n",
+        "In this tutorial, we'll harness EvaDB's model training capabilities to predict home rental prices, showcasing how EvaDB seamlessly integrates AI into your PostgreSQL database.\n",
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/georgia-tech-db/eva/blob/staging/tutorials/17-home-rental-prediction.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\"/> Run on Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/georgia-tech-db/eva/blob/staging/tutorials/17-home-rental-prediction.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\"/> View source on GitHub</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/georgia-tech-db/eva/raw/staging/tutorials/17-home-rental-prediction.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /> Download notebook</a>\n",
+        "  </td>\n",
+        "</table><br><br>"
+      ],
+      "metadata": {
+        "id": "4o38TEFPWmZZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!apt -qq install postgresql\n",
+        "!service postgresql start"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0LgHQ_6J7sPs",
+        "outputId": "476e56e2-f79f-4baf-b9fb-bbec8c6c6a8a"
+      },
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "postgresql is already the newest version (14+238).\n",
+            "0 upgraded, 0 newly installed, 0 to remove and 18 not upgraded.\n",
+            " * Starting PostgreSQL 14 database server\n",
+            "   ...done.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Setup"
+      ],
+      "metadata": {
+        "id": "nsyZe8PmZYZ7"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Install and Launch the PostgreSQL Server\n",
+        "\n",
+        "To kick things off, we'll start by setting up the PostgreSQL database backend. If you already have a PostgreSQL server up and running, you can skip this step and proceed directly to [installing EvaDB](#install-evadb)."
+      ],
+      "metadata": {
+        "id": "i1sYj0bRZf6m"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Create User and Database"
+      ],
+      "metadata": {
+        "id": "FORYt3tWZpM1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!sudo -u postgres psql -c \"CREATE USER eva WITH SUPERUSER PASSWORD 'password'\"\n",
+        "!sudo -u postgres psql -c \"CREATE DATABASE evadb\""
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ngJzr2B77zF2",
+        "outputId": "572c6239-1c5e-4f9d-b1bf-cf6e557d5c3b"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "ERROR:  role \"eva\" already exists\n",
+            "ERROR:  database \"evadb\" already exists\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Prettify  Output"
+      ],
+      "metadata": {
+        "id": "uQh_rSIGZvNv"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import warnings\n",
+        "warnings.filterwarnings(\"ignore\")\n",
+        "\n",
+        "from IPython.core.display import display, HTML\n",
+        "def pretty_print(df):\n",
+        "    return display(HTML( df.to_html().replace(\"\\\\n\",\"<br>\")))"
+      ],
+      "metadata": {
+        "id": "T8sDqf9870dh"
+      },
+      "execution_count": 38,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Installing EvaDB and XGBoost dependencies\n",
+        "<a id='install_evadb'></a>\n",
+        "We install EvaDB along with the necessary PostgreSQL and XGBoost dependencies."
+      ],
+      "metadata": {
+        "id": "cgkNFXEnZ1jI"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "%pip install --quiet \"evadb[postgres,xgboost]\"\n",
+        "\n",
+        "import evadb\n",
+        "cursor = evadb.connect().cursor()"
+      ],
+      "metadata": {
+        "id": "Kf2xgkvr76bZ"
+      },
+      "execution_count": 39,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Load data into PostgresSQL"
+      ],
+      "metadata": {
+        "id": "gtzlHzn0aC3F"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Setting up a Data Source in EvaDB\n",
+        "To establish a direct connection between EvaDB and underlying database systems such as PostgreSQL, we will create a data source. This process entails supplying EvaDB with the connection credentials for the active PostgreSQL server."
+      ],
+      "metadata": {
+        "id": "eHUbzlvXaZIC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "params = {\n",
+        "    \"user\": \"eva\",\n",
+        "    \"password\": \"password\",\n",
+        "    \"host\": \"localhost\",\n",
+        "    \"port\": \"5432\",\n",
+        "    \"database\": \"evadb\",\n",
+        "}\n",
+        "query = f\"CREATE DATABASE postgres_data WITH ENGINE = 'postgres', PARAMETERS = {params};\"\n",
+        "cursor.query(query).df()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 640
+        },
+        "id": "sE_Q8-mM795g",
+        "outputId": "8271de29-2cd6-4d33-ea5f-eaea9c12e28b"
+      },
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "10-25-2023 03:09:30 ERROR [plan_executor:plan_executor.py:execute_plan:0179] postgres_data already exists.\n",
+            "Traceback (most recent call last):\n",
+            "  File \"/usr/local/lib/python3.10/dist-packages/evadb/executor/plan_executor.py\", line 175, in execute_plan\n",
+            "    yield from output\n",
+            "  File \"/usr/local/lib/python3.10/dist-packages/evadb/executor/create_database_executor.py\", line 42, in exec\n",
+            "    raise ExecutorError(f\"{self.node.database_name} already exists.\")\n",
+            "evadb.executor.executor_utils.ExecutorError: postgres_data already exists.\n",
+            "ERROR:evadb.utils.logging_manager:postgres_data already exists.\n",
+            "Traceback (most recent call last):\n",
+            "  File \"/usr/local/lib/python3.10/dist-packages/evadb/executor/plan_executor.py\", line 175, in execute_plan\n",
+            "    yield from output\n",
+            "  File \"/usr/local/lib/python3.10/dist-packages/evadb/executor/create_database_executor.py\", line 42, in exec\n",
+            "    raise ExecutorError(f\"{self.node.database_name} already exists.\")\n",
+            "evadb.executor.executor_utils.ExecutorError: postgres_data already exists.\n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "ExecutorError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mExecutorError\u001b[0m                             Traceback (most recent call last)",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/evadb/executor/plan_executor.py\u001b[0m in \u001b[0;36mexecute_plan\u001b[0;34m(self, do_not_raise_exceptions, do_not_print_exceptions)\u001b[0m\n\u001b[1;32m    174\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 175\u001b[0;31m                 \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    176\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/evadb/executor/create_database_executor.py\u001b[0m in \u001b[0;36mexec\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     41\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mExecutorError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{self.node.database_name} already exists.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mExecutorError\u001b[0m: postgres_data already exists.",
+            "\nDuring handling of the above exception, another exception occurred:\n",
+            "\u001b[0;31mExecutorError\u001b[0m                             Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-40-28b234e6d6df>\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m }\n\u001b[1;32m      8\u001b[0m \u001b[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"CREATE DATABASE postgres_data WITH ENGINE = 'postgres', PARAMETERS = {params};\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/evadb/interfaces/relational/relation.py\u001b[0m in \u001b[0;36mdf\u001b[0;34m(self, drop_alias)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;36m2\u001b[0m      \u001b[0;36m5\u001b[0m     \u001b[0;36m6\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \"\"\"\n\u001b[0;32m--> 123\u001b[0;31m         \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdrop_alias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdrop_alias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    124\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframes\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"relation execute failed\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/evadb/interfaces/relational/relation.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, drop_alias)\u001b[0m\n\u001b[1;32m    139\u001b[0m             \u001b[0;34m>>\u001b[0m\u001b[0;34m>\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"SELECT * FROM MyTable;\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \"\"\"\n\u001b[0;32m--> 141\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexecute_statement\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_evadb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_query_node\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m         \u001b[0;31m# TODO: this is a dirty implementation. Ideally this should be done in the final projection.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdrop_alias\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/evadb/server/command_handler.py\u001b[0m in \u001b[0;36mexecute_statement\u001b[0;34m(evadb, stmt, do_not_raise_exceptions, do_not_print_exceptions, **kwargs)\u001b[0m\n\u001b[1;32m     51\u001b[0m     )\n\u001b[1;32m     52\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m         \u001b[0mbatch_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     54\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mBatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/evadb/executor/plan_executor.py\u001b[0m in \u001b[0;36mexecute_plan\u001b[0;34m(self, do_not_raise_exceptions, do_not_print_exceptions)\u001b[0m\n\u001b[1;32m    178\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mdo_not_print_exceptions\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                     \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mExecutorError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;31mExecutorError\u001b[0m: postgres_data already exists."
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Loading Home Property Sales Data from CSV into PostgreSQL\n",
+        "\n",
+        "In this step, we will import the [House Property Sales](https://www.kaggle.com/datasets/htagholdings/property-sales?resource=download) dataset into our PostgreSQL database. If you already have the data stored in PostgreSQL and are ready to proceed with the prediction model training, feel free to skip this section and head directly to the [model training process](#train-the-prediction-model)."
+      ],
+      "metadata": {
+        "id": "o0fkC5Z2ahK9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!mkdir -p content\n",
+        "!wget -nc -O /content/home_rentals.csv https://www.dropbox.com/scl/fi/gy2682i66a8l2tqsowm5x/home_rentals.csv?rlkey=e080k02rv5205h4ullfjdr8lw&raw=1"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iVJW8pPd8ERb",
+        "outputId": "1cd15039-0d1a-4124-f4b7-ee450d7530c6"
+      },
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "File ‘/content/home_rentals.csv’ already there; not retrieving.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cursor.query(\"\"\"\n",
+        "  USE postgres_data {\n",
+        "    CREATE TABLE IF NOT EXISTS home_rentals (\n",
+        "      number_of_rooms INT,\n",
+        "      number_of_bathrooms INT,\n",
+        "      sqft INT,\n",
+        "      location VARCHAR(128),\n",
+        "      days_on_market INT,\n",
+        "      initial_price INT,\n",
+        "      neighborhood VARCHAR(128),\n",
+        "      rental_price FLOAT\n",
+        "    )\n",
+        "  }\n",
+        "\"\"\").df()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "Gcjhxs8a8I1V",
+        "outputId": "e16b0410-5f9c-4b90-e5df-ab58b88813a1"
+      },
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    status\n",
+              "0  success"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e84cc7d9-7684-4e15-a29f-c2d5eeef323a\" 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>status</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>success</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-e84cc7d9-7684-4e15-a29f-c2d5eeef323a')\"\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-e84cc7d9-7684-4e15-a29f-c2d5eeef323a 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-e84cc7d9-7684-4e15-a29f-c2d5eeef323a');\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",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 42
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cursor.query(\"\"\"\n",
+        "  USE postgres_data {\n",
+        "    COPY home_rentals(number_of_rooms, number_of_bathrooms, sqft, location, days_on_market, initial_price, neighborhood, rental_price)\n",
+        "    FROM '/content/home_rentals.csv'\n",
+        "    DELIMITER ',' CSV HEADER\n",
+        "  }\n",
+        "\"\"\").df()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "SK6b1xdP9kIR",
+        "outputId": "b8d0705f-9ebe-43b5-f068-729dd869ce16"
+      },
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    status\n",
+              "0  success"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-19a5d954-6a0d-4fb1-af66-dfe0ed68f39b\" 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>status</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>success</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-19a5d954-6a0d-4fb1-af66-dfe0ed68f39b')\"\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-19a5d954-6a0d-4fb1-af66-dfe0ed68f39b 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-19a5d954-6a0d-4fb1-af66-dfe0ed68f39b');\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",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 43
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Preview the Data"
+      ],
+      "metadata": {
+        "id": "Mju7NS_sarR1"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Within the home_rentals table, there are 8 columns at our disposal. Our objective is to utilize the remaining 7 columns to make predictions for the rental_price."
+      ],
+      "metadata": {
+        "id": "jA1sc4eQaxzA"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cursor.query(\"SELECT * FROM postgres_data.home_rentals LIMIT 3;\").df()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        },
+        "id": "TRzw1K-b8qSO",
+        "outputId": "f72fa1a1-cc19-4eae-bcbf-f11fdcb457a6"
+      },
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   rental_price  number_of_bathrooms  sqft  initial_price  number_of_rooms  \\\n",
+              "0        2167.0                    1   674           2167                1   \n",
+              "1        1883.0                    1   554           1883                1   \n",
+              "2        2431.0                    1   529           2431                0   \n",
+              "\n",
+              "   days_on_market location neighborhood  \n",
+              "0               1     good     downtown  \n",
+              "1              19     poor     westbrae  \n",
+              "2               3    great   south_side  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-172da8b7-a21f-4445-8f06-f9080485284c\" 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>number_of_bathrooms</th>\n",
+              "      <th>sqft</th>\n",
+              "      <th>initial_price</th>\n",
+              "      <th>number_of_rooms</th>\n",
+              "      <th>days_on_market</th>\n",
+              "      <th>location</th>\n",
+              "      <th>neighborhood</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2167.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>674</td>\n",
+              "      <td>2167</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>good</td>\n",
+              "      <td>downtown</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1883.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>554</td>\n",
+              "      <td>1883</td>\n",
+              "      <td>1</td>\n",
+              "      <td>19</td>\n",
+              "      <td>poor</td>\n",
+              "      <td>westbrae</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2431.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>529</td>\n",
+              "      <td>2431</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>great</td>\n",
+              "      <td>south_side</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-172da8b7-a21f-4445-8f06-f9080485284c')\"\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-172da8b7-a21f-4445-8f06-f9080485284c 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-172da8b7-a21f-4445-8f06-f9080485284c');\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-53e72d76-d40f-4606-86e9-63475c715f6b\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-53e72d76-d40f-4606-86e9-63475c715f6b')\"\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",
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+            ]
+          },
+          "metadata": {},
+          "execution_count": 44
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Training Model\n",
+        "\n",
+        "Next, we employ EvaDB to facilitate the training of an ML model, which will enable us to predict `home rental prices`."
+      ],
+      "metadata": {
+        "id": "aQsTNqyha5Yi"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Train the prediction Model\n",
+        "For this purpose, we harness the capabilities of the [xgboost](https://xgboost.readthedocs.io/en/stable/) engine to train our prediction model. We employ the `Flaml` feature to automatically determine the optimal hyperparameters. Keep in mind that `TIME_LIMIT` specifies the time budget allocated for the training process. `METRIC` specifies the training error or accuracy you want to optimize on while training. `TASK` specifies whether you aim to perform classification or regression. In this example we shall use regression to predict home rental price."
+      ],
+      "metadata": {
+        "id": "haHlsnpva-Xt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cursor.query(\"\"\"\n",
+        "  CREATE OR REPLACE FUNCTION PredictHouseRent FROM\n",
+        "  ( SELECT * FROM postgres_data.home_rentals )\n",
+        "  TYPE Xgboost\n",
+        "  PREDICT 'rental_price'\n",
+        "  METRIC 'rmse'\n",
+        "  TASK 'regression'\n",
+        "  TIME_LIMIT 180;\n",
+        "\"\"\").df()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "QjXDii0Q9oDi",
+        "outputId": "c82d1fa3-4179-4783-c6f8-5d32433f6e28"
+      },
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[flaml.automl.logger: 10-25 03:09:57] {1679} INFO - task = regression\n",
+            "[flaml.automl.logger: 10-25 03:09:57] {1690} INFO - Evaluation method: cv\n",
+            "[flaml.automl.logger: 10-25 03:09:57] {1788} INFO - Minimizing error metric: rmse\n",
+            "[flaml.automl.logger: 10-25 03:09:57] {1900} INFO - List of ML learners in AutoML Run: ['xgboost']\n",
+            "[flaml.automl.logger: 10-25 03:09:57] {2218} INFO - iteration 0, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:09:57] {2344} INFO - Estimated sufficient time budget=1949s. Estimated necessary time budget=2s.\n",
+            "[flaml.automl.logger: 10-25 03:09:57] {2391} INFO -  at 0.3s,\testimator xgboost's best error=873.9536,\tbest estimator xgboost's best error=873.9536\n",
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+            "[flaml.automl.logger: 10-25 03:11:03] {2391} INFO -  at 66.3s,\testimator xgboost's best error=5.6694,\tbest estimator xgboost's best error=5.6694\n",
+            "[flaml.automl.logger: 10-25 03:11:03] {2218} INFO - iteration 35, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:11:47] {2391} INFO -  at 110.2s,\testimator xgboost's best error=5.1787,\tbest estimator xgboost's best error=5.1787\n",
+            "[flaml.automl.logger: 10-25 03:11:47] {2218} INFO - iteration 36, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:11:55] {2391} INFO -  at 118.2s,\testimator xgboost's best error=5.1787,\tbest estimator xgboost's best error=5.1787\n",
+            "[flaml.automl.logger: 10-25 03:11:55] {2218} INFO - iteration 37, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:00] {2391} INFO -  at 183.9s,\testimator xgboost's best error=4.5479,\tbest estimator xgboost's best error=4.5479\n",
+            "[flaml.automl.logger: 10-25 03:13:15] {2627} INFO - retrain xgboost for 14.3s\n",
+            "[flaml.automl.logger: 10-25 03:13:15] {2630} INFO - retrained model: XGBRegressor(base_score=None, booster=None, callbacks=[],\n",
+            "             colsample_bylevel=0.9275689307086866, colsample_bynode=None,\n",
+            "             colsample_bytree=0.9639360747179713, device=None,\n",
+            "             early_stopping_rounds=None, enable_categorical=False,\n",
+            "             eval_metric=None, feature_types=None, gamma=None,\n",
+            "             grow_policy='lossguide', importance_type=None,\n",
+            "             interaction_constraints=None, learning_rate=0.15910240171256276,\n",
+            "             max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,\n",
+            "             max_delta_step=None, max_depth=0, max_leaves=460,\n",
+            "             min_child_weight=2.1211909508723865, missing=nan,\n",
+            "             monotone_constraints=None, multi_strategy=None, n_estimators=1264,\n",
+            "             n_jobs=-1, num_parallel_tree=None, random_state=None, ...)\n",
+            "[flaml.automl.logger: 10-25 03:13:15] {1930} INFO - fit succeeded\n",
+            "[flaml.automl.logger: 10-25 03:13:15] {1931} INFO - Time taken to find the best model: 183.9464476108551\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                        0\n",
+              "0  Function PredictHouseRent overwritten."
+            ],
+            "text/html": [
+              "\n",
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+              "\n",
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+              "  </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",
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+              "      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-06ef9218-66e7-4bf5-8086-af6bf6dad9ba 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-06ef9218-66e7-4bf5-8086-af6bf6dad9ba');\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",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Example training query using the `R2` metric"
+      ],
+      "metadata": {
+        "id": "HvBfd4wdeJTj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cursor.query(\"\"\"\n",
+        "  CREATE OR REPLACE FUNCTION PredictHouseRent FROM\n",
+        "  ( SELECT * FROM postgres_data.home_rentals )\n",
+        "  TYPE Xgboost\n",
+        "  PREDICT 'rental_price'\n",
+        "  METRIC 'r2'\n",
+        "  TASK 'regression'\n",
+        "  TIME_LIMIT 120;\n",
+        "\"\"\").df()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "hX2E1263eDgh",
+        "outputId": "05c06a93-38ae-4b56-c55e-8f3c16b34dbd"
+      },
+      "execution_count": 46,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[flaml.automl.logger: 10-25 03:13:21] {1679} INFO - task = regression\n",
+            "[flaml.automl.logger: 10-25 03:13:21] {1690} INFO - Evaluation method: cv\n",
+            "[flaml.automl.logger: 10-25 03:13:21] {1788} INFO - Minimizing error metric: 1-r2\n",
+            "[flaml.automl.logger: 10-25 03:13:21] {1900} INFO - List of ML learners in AutoML Run: ['xgboost']\n",
+            "[flaml.automl.logger: 10-25 03:13:21] {2218} INFO - iteration 0, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:21] {2344} INFO - Estimated sufficient time budget=1994s. Estimated necessary time budget=2s.\n",
+            "[flaml.automl.logger: 10-25 03:13:21] {2391} INFO -  at 0.3s,\testimator xgboost's best error=0.4579,\tbest estimator xgboost's best error=0.4579\n",
+            "[flaml.automl.logger: 10-25 03:13:21] {2218} INFO - iteration 1, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:22] {2391} INFO -  at 0.5s,\testimator xgboost's best error=0.4579,\tbest estimator xgboost's best error=0.4579\n",
+            "[flaml.automl.logger: 10-25 03:13:22] {2218} INFO - iteration 2, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:22] {2391} INFO -  at 0.7s,\testimator xgboost's best error=0.1174,\tbest estimator xgboost's best error=0.1174\n",
+            "[flaml.automl.logger: 10-25 03:13:22] {2218} INFO - iteration 3, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:22] {2391} INFO -  at 0.9s,\testimator xgboost's best error=0.0171,\tbest estimator xgboost's best error=0.0171\n",
+            "[flaml.automl.logger: 10-25 03:13:22] {2218} INFO - iteration 4, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:22] {2391} INFO -  at 1.1s,\testimator xgboost's best error=0.0171,\tbest estimator xgboost's best error=0.0171\n",
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+            "[flaml.automl.logger: 10-25 03:13:22] {2391} INFO -  at 1.3s,\testimator xgboost's best error=0.0171,\tbest estimator xgboost's best error=0.0171\n",
+            "[flaml.automl.logger: 10-25 03:13:22] {2218} INFO - iteration 6, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:23] {2391} INFO -  at 1.6s,\testimator xgboost's best error=0.0049,\tbest estimator xgboost's best error=0.0049\n",
+            "[flaml.automl.logger: 10-25 03:13:23] {2218} INFO - iteration 7, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:23] {2391} INFO -  at 1.8s,\testimator xgboost's best error=0.0049,\tbest estimator xgboost's best error=0.0049\n",
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+            "[flaml.automl.logger: 10-25 03:13:23] {2391} INFO -  at 1.9s,\testimator xgboost's best error=0.0049,\tbest estimator xgboost's best error=0.0049\n",
+            "[flaml.automl.logger: 10-25 03:13:23] {2218} INFO - iteration 9, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:23] {2391} INFO -  at 2.1s,\testimator xgboost's best error=0.0009,\tbest estimator xgboost's best error=0.0009\n",
+            "[flaml.automl.logger: 10-25 03:13:23] {2218} INFO - iteration 10, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:23] {2391} INFO -  at 2.3s,\testimator xgboost's best error=0.0009,\tbest estimator xgboost's best error=0.0009\n",
+            "[flaml.automl.logger: 10-25 03:13:23] {2218} INFO - iteration 11, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:23] {2391} INFO -  at 2.4s,\testimator xgboost's best error=0.0009,\tbest estimator xgboost's best error=0.0009\n",
+            "[flaml.automl.logger: 10-25 03:13:23] {2218} INFO - iteration 12, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:24] {2391} INFO -  at 2.7s,\testimator xgboost's best error=0.0003,\tbest estimator xgboost's best error=0.0003\n",
+            "[flaml.automl.logger: 10-25 03:13:24] {2218} INFO - iteration 13, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:24] {2391} INFO -  at 2.8s,\testimator xgboost's best error=0.0003,\tbest estimator xgboost's best error=0.0003\n",
+            "[flaml.automl.logger: 10-25 03:13:24] {2218} INFO - iteration 14, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:24] {2391} INFO -  at 3.2s,\testimator xgboost's best error=0.0003,\tbest estimator xgboost's best error=0.0003\n",
+            "[flaml.automl.logger: 10-25 03:13:24] {2218} INFO - iteration 15, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:24] {2391} INFO -  at 3.3s,\testimator xgboost's best error=0.0003,\tbest estimator xgboost's best error=0.0003\n",
+            "[flaml.automl.logger: 10-25 03:13:24] {2218} INFO - iteration 16, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:25] {2391} INFO -  at 3.6s,\testimator xgboost's best error=0.0003,\tbest estimator xgboost's best error=0.0003\n",
+            "[flaml.automl.logger: 10-25 03:13:25] {2218} INFO - iteration 17, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:25] {2391} INFO -  at 3.8s,\testimator xgboost's best error=0.0003,\tbest estimator xgboost's best error=0.0003\n",
+            "[flaml.automl.logger: 10-25 03:13:25] {2218} INFO - iteration 18, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:25] {2391} INFO -  at 4.2s,\testimator xgboost's best error=0.0001,\tbest estimator xgboost's best error=0.0001\n",
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+            "[flaml.automl.logger: 10-25 03:13:27] {2391} INFO -  at 6.5s,\testimator xgboost's best error=0.0001,\tbest estimator xgboost's best error=0.0001\n",
+            "[flaml.automl.logger: 10-25 03:13:27] {2218} INFO - iteration 22, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:29] {2391} INFO -  at 7.7s,\testimator xgboost's best error=0.0001,\tbest estimator xgboost's best error=0.0001\n",
+            "[flaml.automl.logger: 10-25 03:13:29] {2218} INFO - iteration 23, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:29] {2391} INFO -  at 8.4s,\testimator xgboost's best error=0.0001,\tbest estimator xgboost's best error=0.0001\n",
+            "[flaml.automl.logger: 10-25 03:13:29] {2218} INFO - iteration 24, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:31] {2391} INFO -  at 9.6s,\testimator xgboost's best error=0.0001,\tbest estimator xgboost's best error=0.0001\n",
+            "[flaml.automl.logger: 10-25 03:13:31] {2218} INFO - iteration 25, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:31] {2391} INFO -  at 9.9s,\testimator xgboost's best error=0.0001,\tbest estimator xgboost's best error=0.0001\n",
+            "[flaml.automl.logger: 10-25 03:13:31] {2218} INFO - iteration 26, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:34] {2391} INFO -  at 12.9s,\testimator xgboost's best error=0.0001,\tbest estimator xgboost's best error=0.0001\n",
+            "[flaml.automl.logger: 10-25 03:13:34] {2218} INFO - iteration 27, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:38] {2391} INFO -  at 16.8s,\testimator xgboost's best error=0.0000,\tbest estimator xgboost's best error=0.0000\n",
+            "[flaml.automl.logger: 10-25 03:13:38] {2218} INFO - iteration 28, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:40] {2391} INFO -  at 19.4s,\testimator xgboost's best error=0.0000,\tbest estimator xgboost's best error=0.0000\n",
+            "[flaml.automl.logger: 10-25 03:13:40] {2218} INFO - iteration 29, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:41] {2391} INFO -  at 20.1s,\testimator xgboost's best error=0.0000,\tbest estimator xgboost's best error=0.0000\n",
+            "[flaml.automl.logger: 10-25 03:13:41] {2218} INFO - iteration 30, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:13:56] {2391} INFO -  at 34.6s,\testimator xgboost's best error=0.0000,\tbest estimator xgboost's best error=0.0000\n",
+            "[flaml.automl.logger: 10-25 03:13:56] {2218} INFO - iteration 31, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:14:02] {2391} INFO -  at 41.0s,\testimator xgboost's best error=0.0000,\tbest estimator xgboost's best error=0.0000\n",
+            "[flaml.automl.logger: 10-25 03:14:02] {2218} INFO - iteration 32, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:14:10] {2391} INFO -  at 48.7s,\testimator xgboost's best error=0.0000,\tbest estimator xgboost's best error=0.0000\n",
+            "[flaml.automl.logger: 10-25 03:14:10] {2218} INFO - iteration 33, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:14:21] {2391} INFO -  at 60.4s,\testimator xgboost's best error=0.0000,\tbest estimator xgboost's best error=0.0000\n",
+            "[flaml.automl.logger: 10-25 03:14:21] {2218} INFO - iteration 34, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:14:29] {2391} INFO -  at 68.1s,\testimator xgboost's best error=0.0000,\tbest estimator xgboost's best error=0.0000\n",
+            "[flaml.automl.logger: 10-25 03:14:29] {2218} INFO - iteration 35, current learner xgboost\n",
+            "[flaml.automl.logger: 10-25 03:15:13] {2391} INFO -  at 112.3s,\testimator xgboost's best error=0.0000,\tbest estimator xgboost's best error=0.0000\n",
+            "[flaml.automl.logger: 10-25 03:15:23] {2627} INFO - retrain xgboost for 9.3s\n",
+            "[flaml.automl.logger: 10-25 03:15:23] {2630} INFO - retrained model: XGBRegressor(base_score=None, booster=None, callbacks=[],\n",
+            "             colsample_bylevel=0.9025517915891937, colsample_bynode=None,\n",
+            "             colsample_bytree=0.8696723552623628, device=None,\n",
+            "             early_stopping_rounds=None, enable_categorical=False,\n",
+            "             eval_metric=None, feature_types=None, gamma=None,\n",
+            "             grow_policy='lossguide', importance_type=None,\n",
+            "             interaction_constraints=None, learning_rate=0.1823033077636653,\n",
+            "             max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,\n",
+            "             max_delta_step=None, max_depth=0, max_leaves=142,\n",
+            "             min_child_weight=14.890670283201343, missing=nan,\n",
+            "             monotone_constraints=None, multi_strategy=None, n_estimators=2015,\n",
+            "             n_jobs=-1, num_parallel_tree=None, random_state=None, ...)\n",
+            "[flaml.automl.logger: 10-25 03:15:23] {1930} INFO - fit succeeded\n",
+            "[flaml.automl.logger: 10-25 03:15:23] {1931} INFO - Time taken to find the best model: 112.30713248252869\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                        0\n",
+              "0  Function PredictHouseRent overwritten."
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-2de5d7a5-08e4-4dea-8d96-5f2d9661d673\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
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+              "        vertical-align: top;\n",
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+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Function PredictHouseRent overwritten.</td>\n",
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+              "  </tbody>\n",
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+              "    <div class=\"colab-df-buttons\">\n",
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+              "      fill: #174EA6;\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\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",
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+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-2de5d7a5-08e4-4dea-8d96-5f2d9661d673 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-2de5d7a5-08e4-4dea-8d96-5f2d9661d673');\n",
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+              "          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",
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+              "        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",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 46
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Utilizing the Prediction Model\n",
+        "Following the model training, we proceed to employ the `PredictHouseRent`` model to make predictions for home rental prices."
+      ],
+      "metadata": {
+        "id": "clg31P6vbpvc"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cursor.query(\"SELECT PredictHouseRent(*) FROM postgres_data.home_rentals LIMIT 10;\").df()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 359
+        },
+        "id": "9ZovWvhSBhAV",
+        "outputId": "1adb172a-6b27-4f3f-cabe-2622c858d9a3"
+      },
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   rental_price\n",
+              "0   2152.748779\n",
+              "1   1940.956787\n",
+              "2   2438.577881\n",
+              "3   5532.644043\n",
+              "4   2270.967529\n",
+              "5   4169.572754\n",
+              "6   2207.939941\n",
+              "7   2101.080566\n",
+              "8   3873.550049\n",
+              "9   2027.248047"
+            ],
+            "text/html": [
+              "\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>rental_price</th>\n",
+              "    </tr>\n",
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+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2152.748779</td>\n",
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+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1940.956787</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2438.577881</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>5532.644043</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2270.967529</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>4169.572754</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>2207.939941</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>2101.080566</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>3873.550049</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>2027.248047</td>\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\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",
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+              "        document.querySelector('#df-5fab4122-4127-4c15-865a-b1040aee84ca 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-5fab4122-4127-4c15-865a-b1040aee84ca');\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",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
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+              "      --bg-color: #3B4455;\n",
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+              "\n",
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+              "      spin 1s steps(1) infinite;\n",
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+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 47
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "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`."
+      ],
+      "metadata": {
+        "id": "aLr0TmExbvdC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cursor.query(\"\"\"\n",
+        "  SELECT rental_price, predicted_rental_price FROM postgres_data.home_rentals\n",
+        "  JOIN LATERAL PredictHouseRent(*) AS Predicted(predicted_rental_price) LIMIT 10;\n",
+        "\"\"\").df()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 359
+        },
+        "id": "lKx3_ExZBoK6",
+        "outputId": "0e874f2e-3156-4cd5-b12f-8b7416b8eece"
+      },
+      "execution_count": 48,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   rental_price  predicted_rental_price\n",
+              "0      2167.000             2166.888184\n",
+              "1      1883.000             1882.995605\n",
+              "2      2431.000             2430.979492\n",
+              "3      5510.000             5510.027832\n",
+              "4      2272.000             2272.018066\n",
+              "5      4123.812             4123.840820\n",
+              "6      2224.000             2223.957275\n",
+              "7      2104.000             2103.984131\n",
+              "8      3861.000             3860.960449\n",
+              "9      2041.000             2041.064087"
+            ],
+            "text/html": [
+              "\n",
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+              "<style scoped>\n",
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+              "</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>2166.888184</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1883.000</td>\n",
+              "      <td>1882.995605</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2431.000</td>\n",
+              "      <td>2430.979492</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>5510.000</td>\n",
+              "      <td>5510.027832</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2272.000</td>\n",
+              "      <td>2272.018066</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>4123.812</td>\n",
+              "      <td>4123.840820</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>2224.000</td>\n",
+              "      <td>2223.957275</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>2104.000</td>\n",
+              "      <td>2103.984131</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>3861.000</td>\n",
+              "      <td>3860.960449</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>2041.000</td>\n",
+              "      <td>2041.064087</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
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+              "      fill: #D2E3FC;\n",
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+              "\n",
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+              "      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-c999ae2b-3533-4376-8a7d-ba2a8638d4fc 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-c999ae2b-3533-4376-8a7d-ba2a8638d4fc');\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-8fe74d2a-0b58-48c8-83f6-03419ffc68b9\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8fe74d2a-0b58-48c8-83f6-03419ffc68b9')\"\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-8fe74d2a-0b58-48c8-83f6-03419ffc68b9 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": 48
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Training Classification Models using EVADB\n",
+        "\n",
+        "Next, we employ EvaDB to facilitate the training of Classification ML models, which will enable us to predict `leave_or_not` i.e. a variable depicting whether an employee will leave the current company or not based on several parameters."
+      ],
+      "metadata": {
+        "id": "ZDVPDGqjfrch"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Loading Employee Data from CSV into PostgreSQL\n",
+        "\n",
+        "In this step, we will import the [Employee Data](https://www.kaggle.com/datasets/tawfikelmetwally/employee-dataset) dataset into our PostgreSQL database. If you already have the data stored in PostgreSQL and are ready to proceed with the prediction model training, feel free to skip this section and head directly to the [model training process](#train-the-prediction-model)."
+      ],
+      "metadata": {
+        "id": "ODq5QPC3gp5U"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!mkdir -p content\n",
+        "!wget -nc -O /content/Employee.csv https://drive.google.com/file/d/1R4ij5Ww6bOGwLJrbBStzcaPRhAJ-fn72/view?usp=share_link"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MlJ4adTuiDUF",
+        "outputId": "f5f7c292-954b-4a0c-8b1f-44d3ea1f7102"
+      },
+      "execution_count": 49,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "File ‘/content/Employee.csv’ already there; not retrieving.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cursor.query(\"\"\"\n",
+        "  USE postgres_data {\n",
+        "    CREATE TABLE IF NOT EXISTS Employee (\n",
+        "      education VARCHAR(128),\n",
+        "      joining_year INTEGER,\n",
+        "      city VARCHAR(128),\n",
+        "      payment_tier INTEGER,\n",
+        "      age INTEGER,\n",
+        "      gender VARCHAR(128),\n",
+        "      ever_benched VARCHAR(128),\n",
+        "      experience_in_current_domain INTEGER,\n",
+        "      leave_or_not INTEGER\n",
+        "    )\n",
+        "  }\n",
+        "\"\"\").df()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "g_KW1uc2iNdv",
+        "outputId": "e8daf7bc-bab6-4a76-d0ca-7f65d22ec38d"
+      },
+      "execution_count": 55,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    status\n",
+              "0  success"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c3dcb513-5df9-4446-9867-ee4423aa5f7d\" 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>status</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>success</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-c3dcb513-5df9-4446-9867-ee4423aa5f7d')\"\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-c3dcb513-5df9-4446-9867-ee4423aa5f7d 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-c3dcb513-5df9-4446-9867-ee4423aa5f7d');\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",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 55
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cursor.query(\"\"\"\n",
+        "  USE postgres_data {\n",
+        "    COPY Employee(education, joining_year, city, payment_tier, age, gender, ever_benched, experience_in_current_domain, leave_or_not)\n",
+        "    FROM '/content/Employee.csv'\n",
+        "    DELIMITER ',' CSV HEADER\n",
+        "  }\n",
+        "\"\"\").df()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "xcnQHGN31b7z",
+        "outputId": "8d94312d-1871-49e1-94a2-9ee042367621"
+      },
+      "execution_count": 60,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    status\n",
+              "0  success"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-282391a9-a74e-452f-add6-701d442e0a2e\" 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>status</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>success</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-282391a9-a74e-452f-add6-701d442e0a2e')\"\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-282391a9-a74e-452f-add6-701d442e0a2e 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-282391a9-a74e-452f-add6-701d442e0a2e');\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",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 60
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cursor.query(\"SELECT * FROM postgres_data.employee LIMIT 3;\").df()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        },
+        "id": "Ugj7wEsa43-K",
+        "outputId": "fb20a3b6-fdb3-4b0e-8a30-0ff0c6b3f0a8"
+      },
+      "execution_count": 62,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   leave_or_not  joining_year  payment_tier  age  \\\n",
+              "0             0          2017             3   34   \n",
+              "1             1          2013             1   28   \n",
+              "2             0          2014             3   38   \n",
+              "\n",
+              "   experience_in_current_domain  gender       city ever_benched  education  \n",
+              "0                             0    Male  Bangalore           No  Bachelors  \n",
+              "1                             3  Female       Pune           No  Bachelors  \n",
+              "2                             2  Female  New Delhi           No  Bachelors  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-3db2672d-ca93-4b02-b764-4e76eeef7e8c\" 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>leave_or_not</th>\n",
+              "      <th>joining_year</th>\n",
+              "      <th>payment_tier</th>\n",
+              "      <th>age</th>\n",
+              "      <th>experience_in_current_domain</th>\n",
+              "      <th>gender</th>\n",
+              "      <th>city</th>\n",
+              "      <th>ever_benched</th>\n",
+              "      <th>education</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>2017</td>\n",
+              "      <td>3</td>\n",
+              "      <td>34</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Bangalore</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Bachelors</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>2013</td>\n",
+              "      <td>1</td>\n",
+              "      <td>28</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>Pune</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Bachelors</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0</td>\n",
+              "      <td>2014</td>\n",
+              "      <td>3</td>\n",
+              "      <td>38</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>New Delhi</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Bachelors</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-3db2672d-ca93-4b02-b764-4e76eeef7e8c')\"\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",
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+              "  </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-3db2672d-ca93-4b02-b764-4e76eeef7e8c 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-3db2672d-ca93-4b02-b764-4e76eeef7e8c');\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-49199930-6c44-44ab-a505-263d6565c7f4\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-49199930-6c44-44ab-a505-263d6565c7f4')\"\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-49199930-6c44-44ab-a505-263d6565c7f4 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": 62
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Train the prediction Model\n",
+        "Train the XGBoost AutoML model for classification using the `accuracy` metric"
+      ],
+      "metadata": {
+        "id": "1AtF1AtT4OC0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cursor.query(\"\"\"\n",
+        "  CREATE FUNCTION IF NOT EXISTS PredictEmployee FROM\n",
+        "    ( SELECT payment_tier, age, gender, experience_in_current_domain, leave_or_not FROM postgres_data.employee )\n",
+        "    TYPE XGBoost\n",
+        "    PREDICT 'leave_or_not'\n",
+        "    TIME_LIMIT 180\n",
+        "    METRIC 'f1'\n",
+        "    TASK 'classification';\n",
+        "\"\"\").df()"
+      ],
+      "metadata": {
+        "id": "NUbo47cG33cp"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Utilizing the Prediction Model\n",
+        "Following the model training, we proceed to employ the `PredictEmployee` model to make predictions for whether the employee will leave or not."
+      ],
+      "metadata": {
+        "id": "Fc2eLIEB61Pj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cursor.query(\"SELECT PredictEmployee(payment_tier, age, gender, experience_in_current_domain, leave_or_not) FROM postgres_data.employee LIMIT 10;\").df()"
+      ],
+      "metadata": {
+        "id": "MzSDNiSt6vGb"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file