Boston_House_Pricing_Prediction_with_GCP.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOxKs/PyOPPJ4by142GqqmA",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Harshal12355/Boston-House-Price-Prediction/blob/main/Boston_House_Pricing_Prediction_with_GCP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BXoJBxq0RDhE",
        "outputId": "c6b62d55-3e95-497a-b28c-a894d2dc9797"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3.0.0dev,>=2.14.1->google-cloud-bigquery) (5.3.3)\n",
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            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.25.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install google-cloud-bigquery\n",
        "!pip install matplotlib\n",
        "!pip install numpy"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scikit-learn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "slfKLmFrRMga",
        "outputId": "894b5b64-8b1c-4c3a-f088-f1f397e0b402"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.11.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip uninstall -y pandas\n",
        "!pip install google-cloud-bigquery\n",
        "!pip install pandas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PMzHmtk2SwQn",
        "outputId": "5a82d7be-808a-41db-cf63-c79e591274b0"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found existing installation: pandas 2.2.2\n",
            "Uninstalling pandas-2.2.2:\n",
            "  Successfully uninstalled pandas-2.2.2\n",
            "Requirement already satisfied: google-cloud-bigquery in /usr/local/lib/python3.10/dist-packages (3.21.0)\n",
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            "Requirement already satisfied: google-auth<3.0.0dev,>=2.14.1 in /usr/local/lib/python3.10/dist-packages (from google-cloud-bigquery) (2.27.0)\n",
            "Requirement already satisfied: google-cloud-core<3.0.0dev,>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from google-cloud-bigquery) (2.3.3)\n",
            "Requirement already satisfied: google-resumable-media<3.0dev,>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from google-cloud-bigquery) (2.7.1)\n",
            "Requirement already satisfied: packaging>=20.0.0 in /usr/local/lib/python3.10/dist-packages (from google-cloud-bigquery) (24.1)\n",
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            "Requirement already satisfied: protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0.dev0,>=3.19.5 in /usr/local/lib/python3.10/dist-packages (from google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.1->google-cloud-bigquery) (3.20.3)\n",
            "Requirement already satisfied: grpcio<2.0dev,>=1.33.2 in /usr/local/lib/python3.10/dist-packages (from google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.1->google-cloud-bigquery) (1.64.1)\n",
            "Requirement already satisfied: grpcio-status<2.0.dev0,>=1.33.2 in /usr/local/lib/python3.10/dist-packages (from google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.1->google-cloud-bigquery) (1.48.2)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3.0.0dev,>=2.14.1->google-cloud-bigquery) (5.3.3)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3.0.0dev,>=2.14.1->google-cloud-bigquery) (0.4.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3.0.0dev,>=2.14.1->google-cloud-bigquery) (4.9)\n",
            "Requirement already satisfied: google-crc32c<2.0dev,>=1.0 in /usr/local/lib/python3.10/dist-packages (from google-resumable-media<3.0dev,>=0.6.0->google-cloud-bigquery) (1.5.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil<3.0dev,>=2.7.2->google-cloud-bigquery) (1.16.0)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0dev,>=2.21.0->google-cloud-bigquery) (3.7)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0dev,>=2.21.0->google-cloud-bigquery) (2024.7.4)\n",
            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3.0.0dev,>=2.14.1->google-cloud-bigquery) (0.6.0)\n",
            "Collecting pandas\n",
            "  Using cached pandas-2.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.0 MB)\n",
            "Requirement already satisfied: numpy>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.25.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
            "Installing collected packages: pandas\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pandas<2.2.2dev0,>=2.0, but you have pandas 2.2.2 which is incompatible.\n",
            "google-colab 1.0.0 requires pandas==2.0.3, but you have pandas 2.2.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed pandas-2.2.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import auth\n",
        "auth.authenticate_user()"
      ],
      "metadata": {
        "id": "cu0bTOCTR4s9"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.cloud import bigquery\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "# Set your project ID\n",
        "project_id = 'round-bounty-429122-g7'  # replace with your project ID\n",
        "\n",
        "# Initialize BigQuery client with the project ID\n",
        "client = bigquery.Client(project=project_id)\n",
        "\n",
        "query = \"\"\"\n",
        "SELECT * FROM `round-bounty-429122-g7.Boston_House_Pricing.HousePrices` LIMIT 1000\n",
        "\"\"\"\n",
        "df = client.query(query).to_dataframe()\n"
      ],
      "metadata": {
        "id": "txFbc7J_RgAr"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "JvQnFrPdTaHQ",
        "outputId": "651bca7b-80ec-49f1-a160-f22faae341f8"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
              "0    0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296.0   \n",
              "1    0.02187  60.0   2.93     0  0.401  6.800   9.9  6.2196    1  265.0   \n",
              "2    0.01439  60.0   2.93     0  0.401  6.604  18.8  6.2196    1  265.0   \n",
              "3    0.04819  80.0   3.64     0  0.392  6.108  32.0  9.2203    1  315.0   \n",
              "4    0.03548  80.0   3.64     0  0.392  5.876  19.1  9.2203    1  315.0   \n",
              "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
              "501  2.81838   0.0  18.10     0  0.532  5.762  40.3  4.0983   24  666.0   \n",
              "502  2.37857   0.0  18.10     0  0.583  5.871  41.9  3.7240   24  666.0   \n",
              "503  3.67367   0.0  18.10     0  0.583  6.312  51.9  3.9917   24  666.0   \n",
              "504  5.69175   0.0  18.10     0  0.583  6.114  79.8  3.5459   24  666.0   \n",
              "505  4.83567   0.0  18.10     0  0.583  5.905  53.2  3.1523   24  666.0   \n",
              "\n",
              "     PTRATIO       B  LSTAT  MEDV  \n",
              "0       15.3  396.90   4.98  24.0  \n",
              "1       15.6  393.37   5.03  31.1  \n",
              "2       15.6  376.70   4.38  29.1  \n",
              "3       16.4  392.89   6.57  21.9  \n",
              "4       16.4  395.18   9.25  20.9  \n",
              "..       ...     ...    ...   ...  \n",
              "501     20.2  392.92  10.42  21.8  \n",
              "502     20.2  370.73  13.34  20.6  \n",
              "503     20.2  388.62  10.58  21.2  \n",
              "504     20.2  392.68  14.98  19.1  \n",
              "505     20.2  388.22  11.45  20.6  \n",
              "\n",
              "[506 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-270f7e3c-52aa-40c4-93fe-4802c5fc302b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>MEDV</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>4.0900</td>\n",
              "      <td>1</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.02187</td>\n",
              "      <td>60.0</td>\n",
              "      <td>2.93</td>\n",
              "      <td>0</td>\n",
              "      <td>0.401</td>\n",
              "      <td>6.800</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6.2196</td>\n",
              "      <td>1</td>\n",
              "      <td>265.0</td>\n",
              "      <td>15.6</td>\n",
              "      <td>393.37</td>\n",
              "      <td>5.03</td>\n",
              "      <td>31.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.01439</td>\n",
              "      <td>60.0</td>\n",
              "      <td>2.93</td>\n",
              "      <td>0</td>\n",
              "      <td>0.401</td>\n",
              "      <td>6.604</td>\n",
              "      <td>18.8</td>\n",
              "      <td>6.2196</td>\n",
              "      <td>1</td>\n",
              "      <td>265.0</td>\n",
              "      <td>15.6</td>\n",
              "      <td>376.70</td>\n",
              "      <td>4.38</td>\n",
              "      <td>29.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.04819</td>\n",
              "      <td>80.0</td>\n",
              "      <td>3.64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.392</td>\n",
              "      <td>6.108</td>\n",
              "      <td>32.0</td>\n",
              "      <td>9.2203</td>\n",
              "      <td>1</td>\n",
              "      <td>315.0</td>\n",
              "      <td>16.4</td>\n",
              "      <td>392.89</td>\n",
              "      <td>6.57</td>\n",
              "      <td>21.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.03548</td>\n",
              "      <td>80.0</td>\n",
              "      <td>3.64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.392</td>\n",
              "      <td>5.876</td>\n",
              "      <td>19.1</td>\n",
              "      <td>9.2203</td>\n",
              "      <td>1</td>\n",
              "      <td>315.0</td>\n",
              "      <td>16.4</td>\n",
              "      <td>395.18</td>\n",
              "      <td>9.25</td>\n",
              "      <td>20.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>501</th>\n",
              "      <td>2.81838</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.10</td>\n",
              "      <td>0</td>\n",
              "      <td>0.532</td>\n",
              "      <td>5.762</td>\n",
              "      <td>40.3</td>\n",
              "      <td>4.0983</td>\n",
              "      <td>24</td>\n",
              "      <td>666.0</td>\n",
              "      <td>20.2</td>\n",
              "      <td>392.92</td>\n",
              "      <td>10.42</td>\n",
              "      <td>21.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>502</th>\n",
              "      <td>2.37857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.10</td>\n",
              "      <td>0</td>\n",
              "      <td>0.583</td>\n",
              "      <td>5.871</td>\n",
              "      <td>41.9</td>\n",
              "      <td>3.7240</td>\n",
              "      <td>24</td>\n",
              "      <td>666.0</td>\n",
              "      <td>20.2</td>\n",
              "      <td>370.73</td>\n",
              "      <td>13.34</td>\n",
              "      <td>20.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>503</th>\n",
              "      <td>3.67367</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.10</td>\n",
              "      <td>0</td>\n",
              "      <td>0.583</td>\n",
              "      <td>6.312</td>\n",
              "      <td>51.9</td>\n",
              "      <td>3.9917</td>\n",
              "      <td>24</td>\n",
              "      <td>666.0</td>\n",
              "      <td>20.2</td>\n",
              "      <td>388.62</td>\n",
              "      <td>10.58</td>\n",
              "      <td>21.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>504</th>\n",
              "      <td>5.69175</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.10</td>\n",
              "      <td>0</td>\n",
              "      <td>0.583</td>\n",
              "      <td>6.114</td>\n",
              "      <td>79.8</td>\n",
              "      <td>3.5459</td>\n",
              "      <td>24</td>\n",
              "      <td>666.0</td>\n",
              "      <td>20.2</td>\n",
              "      <td>392.68</td>\n",
              "      <td>14.98</td>\n",
              "      <td>19.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>505</th>\n",
              "      <td>4.83567</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.10</td>\n",
              "      <td>0</td>\n",
              "      <td>0.583</td>\n",
              "      <td>5.905</td>\n",
              "      <td>53.2</td>\n",
              "      <td>3.1523</td>\n",
              "      <td>24</td>\n",
              "      <td>666.0</td>\n",
              "      <td>20.2</td>\n",
              "      <td>388.22</td>\n",
              "      <td>11.45</td>\n",
              "      <td>20.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>506 rows × 14 columns</p>\n",
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              "\n",
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\"min\": 0.46,\n        \"max\": 27.74,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          2.97,\n          15.04,\n          1.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CHAS\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NOX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11587767566755595,\n        \"min\": 0.385,\n        \"max\": 0.871,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          0.413,\n          0.538\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RM\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7026171434153233,\n        \"min\": 3.561,\n        \"max\": 8.78,\n        \"num_unique_values\": 446,\n        \"samples\": [\n          5.913,\n          7.016\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28.148861406903617,\n        \"min\": 2.9,\n        \"max\": 100.0,\n        \"num_unique_values\": 356,\n        \"samples\": [\n          26.3,\n          38.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DIS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.105710126627611,\n        \"min\": 1.1296,\n        \"max\": 12.1265,\n        \"num_unique_values\": 412,\n        \"samples\": [\n          7.9809,\n          4.429\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RAD\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TAX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 168.53711605495903,\n        \"min\": 187.0,\n        \"max\": 711.0,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          384.0,\n          293.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PTRATIO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1649455237144406,\n        \"min\": 12.6,\n        \"max\": 22.0,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          16.6,\n          16.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"B\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 91.29486438415783,\n        \"min\": 0.32,\n        \"max\": 396.9,\n        \"num_unique_values\": 357,\n        \"samples\": [\n          338.63,\n          393.24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LSTAT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.14106151134857,\n        \"min\": 1.73,\n        \"max\": 37.97,\n        \"num_unique_values\": 455,\n        \"samples\": [\n          13.59,\n          4.08\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MEDV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.197104087379817,\n        \"min\": 5.0,\n        \"max\": 50.0,\n        \"num_unique_values\": 229,\n        \"samples\": [\n          11.0,\n          35.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Making training and testing sets"
      ],
      "metadata": {
        "id": "TfzHopYcr5Lp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "JdInVJh7Tg_Z"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scaling and setting X as my input variables\n",
        "scaler = StandardScaler()\n",
        "X = scaler.fit_transform(df.drop('MEDV', axis=1))\n",
        "\n",
        "# Target variable\n",
        "y = df['MEDV']  # replace with your target variable\n"
      ],
      "metadata": {
        "id": "TlJsPgL3sH2p"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "GA-sToxqr4hY"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Running the Model"
      ],
      "metadata": {
        "id": "ch4oN9iurtDR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Linear Regression"
      ],
      "metadata": {
        "id": "gk3bNC4LsOSB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize and train the model\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print(f'Mean Squared Error: {mse}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7i26xeMUguVN",
        "outputId": "3841af60-0e77-4c8b-e76c-f2ce289ce5b5"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 22.383899250824907\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Decision Tree"
      ],
      "metadata": {
        "id": "6W5r-rJbsQ6x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "model_dt = DecisionTreeRegressor()\n",
        "model_dt.fit(X_train, y_train)\n",
        "\n",
        "y_pred_dt = model_dt.predict(X_test)\n",
        "mse_dt = mean_squared_error(y_test, y_pred_dt)\n",
        "print(f'Mean Squared Error: {mse_dt}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6cX9y63IsIma",
        "outputId": "2c48a11c-c3fb-4a65-a8c7-6413c8986d94"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 25.180588235294113\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Random Forest"
      ],
      "metadata": {
        "id": "ee1dAijesYhp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "model_rf = RandomForestRegressor()\n",
        "model_rf.fit(X_train, y_train)\n",
        "\n",
        "y_pred_rf = model_rf.predict(X_test)\n",
        "mse_rf = mean_squared_error(y_test, y_pred_rf)\n",
        "print(f'Mean Squared Error: {mse_rf}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b9nMID5asVg2",
        "outputId": "c2dbf00e-ebfa-40f6-8902-ba3853b4aed3"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 11.296373715686272\n"
          ]
        }
      ]
    }
  ]
}