PINNs_S4.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/fjadidi2001/DataScienceJourney/blob/master/PINNs_S4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train the model using NN methods"
      ],
      "metadata": {
        "id": "gOFQveqFZnrh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 1: Import Necessary Libraries\n"
      ],
      "metadata": {
        "id": "JF-TV3YTabFb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "N8qyNbUOylUX"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.datasets import fetch_california_housing\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers, models\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 2: Load and Prepare the Dataset\n"
      ],
      "metadata": {
        "id": "zFGx_juhalJb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "cooyKgwOzH1R"
      },
      "outputs": [],
      "source": [
        "# Load the dataset\n",
        "california_housing = fetch_california_housing(as_frame=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(california_housing.DESCR)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QVENGU5UaFym",
        "outputId": "8efa19e0-be53-4d86-a717-22b56197a4d5"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".. _california_housing_dataset:\n",
            "\n",
            "California Housing dataset\n",
            "--------------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 20640\n",
            "\n",
            "    :Number of Attributes: 8 numeric, predictive attributes and the target\n",
            "\n",
            "    :Attribute Information:\n",
            "        - MedInc        median income in block group\n",
            "        - HouseAge      median house age in block group\n",
            "        - AveRooms      average number of rooms per household\n",
            "        - AveBedrms     average number of bedrooms per household\n",
            "        - Population    block group population\n",
            "        - AveOccup      average number of household members\n",
            "        - Latitude      block group latitude\n",
            "        - Longitude     block group longitude\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "\n",
            "This dataset was obtained from the StatLib repository.\n",
            "https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html\n",
            "\n",
            "The target variable is the median house value for California districts,\n",
            "expressed in hundreds of thousands of dollars ($100,000).\n",
            "\n",
            "This dataset was derived from the 1990 U.S. census, using one row per census\n",
            "block group. A block group is the smallest geographical unit for which the U.S.\n",
            "Census Bureau publishes sample data (a block group typically has a population\n",
            "of 600 to 3,000 people).\n",
            "\n",
            "A household is a group of people residing within a home. Since the average\n",
            "number of rooms and bedrooms in this dataset are provided per household, these\n",
            "columns may take surprisingly large values for block groups with few households\n",
            "and many empty houses, such as vacation resorts.\n",
            "\n",
            "It can be downloaded/loaded using the\n",
            ":func:`sklearn.datasets.fetch_california_housing` function.\n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n",
            "      Statistics and Probability Letters, 33 (1997) 291-297\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "california_housing.frame.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_OvCqE6zaJQv",
        "outputId": "4cd6bab5-82df-4e17-cddc-e231e51971b4"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
              "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
              "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
              "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
              "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
              "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
              "\n",
              "   Longitude  MedHouseVal  \n",
              "0    -122.23        4.526  \n",
              "1    -122.22        3.585  \n",
              "2    -122.24        3.521  \n",
              "3    -122.25        3.413  \n",
              "4    -122.25        3.422  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a30debe4-45b2-491f-954c-77a6758ced25\" 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>MedInc</th>\n",
              "      <th>HouseAge</th>\n",
              "      <th>AveRooms</th>\n",
              "      <th>AveBedrms</th>\n",
              "      <th>Population</th>\n",
              "      <th>AveOccup</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>MedHouseVal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.3252</td>\n",
              "      <td>41.0</td>\n",
              "      <td>6.984127</td>\n",
              "      <td>1.023810</td>\n",
              "      <td>322.0</td>\n",
              "      <td>2.555556</td>\n",
              "      <td>37.88</td>\n",
              "      <td>-122.23</td>\n",
              "      <td>4.526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.3014</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6.238137</td>\n",
              "      <td>0.971880</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>2.109842</td>\n",
              "      <td>37.86</td>\n",
              "      <td>-122.22</td>\n",
              "      <td>3.585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.2574</td>\n",
              "      <td>52.0</td>\n",
              "      <td>8.288136</td>\n",
              "      <td>1.073446</td>\n",
              "      <td>496.0</td>\n",
              "      <td>2.802260</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.24</td>\n",
              "      <td>3.521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.6431</td>\n",
              "      <td>52.0</td>\n",
              "      <td>5.817352</td>\n",
              "      <td>1.073059</td>\n",
              "      <td>558.0</td>\n",
              "      <td>2.547945</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "      <td>3.413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.8462</td>\n",
              "      <td>52.0</td>\n",
              "      <td>6.281853</td>\n",
              "      <td>1.081081</td>\n",
              "      <td>565.0</td>\n",
              "      <td>2.181467</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "      <td>3.422</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-a30debe4-45b2-491f-954c-77a6758ced25')\"\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-a30debe4-45b2-491f-954c-77a6758ced25 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-a30debe4-45b2-491f-954c-77a6758ced25');\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-5bfab7e4-f405-4959-837b-bb9a5e5a8e4d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5bfab7e4-f405-4959-837b-bb9a5e5a8e4d')\"\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-5bfab7e4-f405-4959-837b-bb9a5e5a8e4d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"california_housing\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"MedInc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9218775476080674,\n        \"min\": 3.8462,\n        \"max\": 8.3252,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          8.3014,\n          3.8462,\n          7.2574\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HouseAge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.501851724856113,\n        \"min\": 21.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          41.0,\n          21.0,\n          52.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveRooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9705323807243326,\n        \"min\": 5.8173515981735155,\n        \"max\": 8.288135593220339,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6.238137082601054,\n          6.281853281853282,\n          8.288135593220339\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveBedrms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04661885487529508,\n        \"min\": 0.9718804920913884,\n        \"max\": 1.0810810810810811,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.9718804920913884,\n          1.0810810810810811,\n          1.073446327683616\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 862.3365352343596,\n        \"min\": 322.0,\n        \"max\": 2401.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2401.0,\n          565.0,\n          496.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveOccup\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2881316535489867,\n        \"min\": 2.109841827768014,\n        \"max\": 2.8022598870056497,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.109841827768014,\n          2.1814671814671813,\n          2.8022598870056497\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0130384048104057,\n        \"min\": 37.85,\n        \"max\": 37.88,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          37.88,\n          37.86,\n          37.85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.013038404810404884,\n        \"min\": -122.25,\n        \"max\": -122.22,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -122.22,\n          -122.25,\n          -122.23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MedHouseVal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4708973348830931,\n        \"min\": 3.413,\n        \"max\": 4.526,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.585,\n          3.422,\n          3.521\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "california_housing.data.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HQ7Zp2_3aLjF",
        "outputId": "b8d144bc-1e9c-4a1d-c8bd-96f167511821"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
              "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
              "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
              "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
              "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
              "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
              "\n",
              "   Longitude  \n",
              "0    -122.23  \n",
              "1    -122.22  \n",
              "2    -122.24  \n",
              "3    -122.25  \n",
              "4    -122.25  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d7022143-3813-4541-9051-4d22a9e83576\" 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>MedInc</th>\n",
              "      <th>HouseAge</th>\n",
              "      <th>AveRooms</th>\n",
              "      <th>AveBedrms</th>\n",
              "      <th>Population</th>\n",
              "      <th>AveOccup</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.3252</td>\n",
              "      <td>41.0</td>\n",
              "      <td>6.984127</td>\n",
              "      <td>1.023810</td>\n",
              "      <td>322.0</td>\n",
              "      <td>2.555556</td>\n",
              "      <td>37.88</td>\n",
              "      <td>-122.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.3014</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6.238137</td>\n",
              "      <td>0.971880</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>2.109842</td>\n",
              "      <td>37.86</td>\n",
              "      <td>-122.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.2574</td>\n",
              "      <td>52.0</td>\n",
              "      <td>8.288136</td>\n",
              "      <td>1.073446</td>\n",
              "      <td>496.0</td>\n",
              "      <td>2.802260</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.6431</td>\n",
              "      <td>52.0</td>\n",
              "      <td>5.817352</td>\n",
              "      <td>1.073059</td>\n",
              "      <td>558.0</td>\n",
              "      <td>2.547945</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.8462</td>\n",
              "      <td>52.0</td>\n",
              "      <td>6.281853</td>\n",
              "      <td>1.081081</td>\n",
              "      <td>565.0</td>\n",
              "      <td>2.181467</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</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-d7022143-3813-4541-9051-4d22a9e83576')\"\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-d7022143-3813-4541-9051-4d22a9e83576 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-d7022143-3813-4541-9051-4d22a9e83576');\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-730d18d3-aa5a-4da7-b9fc-f4a6ab9e6802\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-730d18d3-aa5a-4da7-b9fc-f4a6ab9e6802')\"\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-730d18d3-aa5a-4da7-b9fc-f4a6ab9e6802 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"california_housing\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"MedInc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9218775476080674,\n        \"min\": 3.8462,\n        \"max\": 8.3252,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          8.3014,\n          3.8462,\n          7.2574\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HouseAge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.501851724856113,\n        \"min\": 21.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          41.0,\n          21.0,\n          52.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveRooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9705323807243326,\n        \"min\": 5.8173515981735155,\n        \"max\": 8.288135593220339,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6.238137082601054,\n          6.281853281853282,\n          8.288135593220339\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveBedrms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04661885487529508,\n        \"min\": 0.9718804920913884,\n        \"max\": 1.0810810810810811,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.9718804920913884,\n          1.0810810810810811,\n          1.073446327683616\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 862.3365352343596,\n        \"min\": 322.0,\n        \"max\": 2401.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2401.0,\n          565.0,\n          496.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveOccup\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2881316535489867,\n        \"min\": 2.109841827768014,\n        \"max\": 2.8022598870056497,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.109841827768014,\n          2.1814671814671813,\n          2.8022598870056497\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0130384048104057,\n        \"min\": 37.85,\n        \"max\": 37.88,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          37.88,\n          37.86,\n          37.85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.013038404810404884,\n        \"min\": -122.25,\n        \"max\": -122.22,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -122.22,\n          -122.25,\n          -122.23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "california_housing.target.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "DYN7EkGwaMvs",
        "outputId": "be5ead58-100f-449f-fcac-aa99b811b20e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    4.526\n",
              "1    3.585\n",
              "2    3.521\n",
              "3    3.413\n",
              "4    3.422\n",
              "Name: MedHouseVal, dtype: float64"
            ],
            "text/html": [
              "<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>MedHouseVal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4.526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.422</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "california_housing.frame.info()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uz-xLBBOajlG",
        "outputId": "60fb7856-a507-4f34-b9ca-5b6cce4d3f94"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 20640 entries, 0 to 20639\n",
            "Data columns (total 9 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   MedInc       20640 non-null  float64\n",
            " 1   HouseAge     20640 non-null  float64\n",
            " 2   AveRooms     20640 non-null  float64\n",
            " 3   AveBedrms    20640 non-null  float64\n",
            " 4   Population   20640 non-null  float64\n",
            " 5   AveOccup     20640 non-null  float64\n",
            " 6   Latitude     20640 non-null  float64\n",
            " 7   Longitude    20640 non-null  float64\n",
            " 8   MedHouseVal  20640 non-null  float64\n",
            "dtypes: float64(9)\n",
            "memory usage: 1.4 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "california_housing.frame.hist(figsize=(12, 10), bins=30, edgecolor=\"black\")\n",
        "plt.subplots_adjust(hspace=0.7, wspace=0.4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 853
        },
        "id": "pAwlX0cuauu7",
        "outputId": "8eb8bb03-500d-4651-a2ca-bab75f3bf69f"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 9 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features_of_interest = [\"AveRooms\", \"AveBedrms\", \"AveOccup\", \"Population\"]\n",
        "california_housing.frame[features_of_interest].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "hlcBmzvbaxcN",
        "outputId": "c2c8b788-6424-4f71-c013-d096ff2a5855"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           AveRooms     AveBedrms      AveOccup    Population\n",
              "count  20640.000000  20640.000000  20640.000000  20640.000000\n",
              "mean       5.429000      1.096675      3.070655   1425.476744\n",
              "std        2.474173      0.473911     10.386050   1132.462122\n",
              "min        0.846154      0.333333      0.692308      3.000000\n",
              "25%        4.440716      1.006079      2.429741    787.000000\n",
              "50%        5.229129      1.048780      2.818116   1166.000000\n",
              "75%        6.052381      1.099526      3.282261   1725.000000\n",
              "max      141.909091     34.066667   1243.333333  35682.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4c6be125-fbe6-44b0-8115-8143b985920e\" 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>AveRooms</th>\n",
              "      <th>AveBedrms</th>\n",
              "      <th>AveOccup</th>\n",
              "      <th>Population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.429000</td>\n",
              "      <td>1.096675</td>\n",
              "      <td>3.070655</td>\n",
              "      <td>1425.476744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.474173</td>\n",
              "      <td>0.473911</td>\n",
              "      <td>10.386050</td>\n",
              "      <td>1132.462122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.846154</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.692308</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.440716</td>\n",
              "      <td>1.006079</td>\n",
              "      <td>2.429741</td>\n",
              "      <td>787.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.229129</td>\n",
              "      <td>1.048780</td>\n",
              "      <td>2.818116</td>\n",
              "      <td>1166.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.052381</td>\n",
              "      <td>1.099526</td>\n",
              "      <td>3.282261</td>\n",
              "      <td>1725.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>141.909091</td>\n",
              "      <td>34.066667</td>\n",
              "      <td>1243.333333</td>\n",
              "      <td>35682.000000</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-4c6be125-fbe6-44b0-8115-8143b985920e')\"\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-4c6be125-fbe6-44b0-8115-8143b985920e 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-4c6be125-fbe6-44b0-8115-8143b985920e');\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-cc8defe1-647c-4c0f-b98e-83a0e51936c9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cc8defe1-647c-4c0f-b98e-83a0e51936c9')\"\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-cc8defe1-647c-4c0f-b98e-83a0e51936c9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"california_housing\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"AveRooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7289.098256602403,\n        \"min\": 0.8461538461538461,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.428999742190376,\n          5.229128787878788,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveBedrms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7295.375141615938,\n        \"min\": 0.3333333333333333,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.096675149606208,\n          1.048780487804878,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveOccup\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7246.392673190071,\n        \"min\": 0.6923076923076923,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.0706551594363742,\n          2.818115654360196,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13192.258841737372,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1425.4767441860465,\n          1166.0,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Convert to DataFrame for better visualization\n",
        "df = pd.DataFrame(california_housing.data, columns=california_housing.feature_names)\n",
        "df['Target'] = california_housing.target\n",
        "\n",
        "# Display the first few rows of the dataset\n",
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "BGe9Ml3gZxg7",
        "outputId": "53e835cd-be81-410e-c4d3-7e075b71ae99"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
              "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
              "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
              "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
              "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
              "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
              "\n",
              "   Longitude  Target  \n",
              "0    -122.23   4.526  \n",
              "1    -122.22   3.585  \n",
              "2    -122.24   3.521  \n",
              "3    -122.25   3.413  \n",
              "4    -122.25   3.422  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4f385de4-3bb4-4ae7-891e-7e63f3934993\" 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>MedInc</th>\n",
              "      <th>HouseAge</th>\n",
              "      <th>AveRooms</th>\n",
              "      <th>AveBedrms</th>\n",
              "      <th>Population</th>\n",
              "      <th>AveOccup</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>Target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.3252</td>\n",
              "      <td>41.0</td>\n",
              "      <td>6.984127</td>\n",
              "      <td>1.023810</td>\n",
              "      <td>322.0</td>\n",
              "      <td>2.555556</td>\n",
              "      <td>37.88</td>\n",
              "      <td>-122.23</td>\n",
              "      <td>4.526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.3014</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6.238137</td>\n",
              "      <td>0.971880</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>2.109842</td>\n",
              "      <td>37.86</td>\n",
              "      <td>-122.22</td>\n",
              "      <td>3.585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.2574</td>\n",
              "      <td>52.0</td>\n",
              "      <td>8.288136</td>\n",
              "      <td>1.073446</td>\n",
              "      <td>496.0</td>\n",
              "      <td>2.802260</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.24</td>\n",
              "      <td>3.521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.6431</td>\n",
              "      <td>52.0</td>\n",
              "      <td>5.817352</td>\n",
              "      <td>1.073059</td>\n",
              "      <td>558.0</td>\n",
              "      <td>2.547945</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "      <td>3.413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.8462</td>\n",
              "      <td>52.0</td>\n",
              "      <td>6.281853</td>\n",
              "      <td>1.081081</td>\n",
              "      <td>565.0</td>\n",
              "      <td>2.181467</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "      <td>3.422</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-4f385de4-3bb4-4ae7-891e-7e63f3934993')\"\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-4f385de4-3bb4-4ae7-891e-7e63f3934993 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-4f385de4-3bb4-4ae7-891e-7e63f3934993');\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-ef86870f-ba8f-4d64-904f-ae632d5097e9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ef86870f-ba8f-4d64-904f-ae632d5097e9')\"\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-ef86870f-ba8f-4d64-904f-ae632d5097e9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 20640,\n  \"fields\": [\n    {\n      \"column\": \"MedInc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8998217179452688,\n        \"min\": 0.4999,\n        \"max\": 15.0001,\n        \"num_unique_values\": 12928,\n        \"samples\": [\n          5.0286,\n          2.0433,\n          6.1228\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HouseAge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.58555761211165,\n        \"min\": 1.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          35.0,\n          25.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveRooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4741731394243187,\n        \"min\": 0.8461538461538461,\n        \"max\": 141.9090909090909,\n        \"num_unique_values\": 19392,\n        \"samples\": [\n          6.111269614835948,\n          5.912820512820513,\n          5.7924528301886795\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveBedrms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.473910856795466,\n        \"min\": 0.3333333333333333,\n        \"max\": 34.06666666666667,\n        \"num_unique_values\": 14233,\n        \"samples\": [\n          0.9906542056074766,\n          1.112099644128114,\n          1.0398230088495575\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1132.462121765341,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 3888,\n        \"samples\": [\n          4169.0,\n          636.0,\n          3367.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveOccup\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.386049562213618,\n        \"min\": 0.6923076923076923,\n        \"max\": 1243.3333333333333,\n        \"num_unique_values\": 18841,\n        \"samples\": [\n          2.6939799331103678,\n          3.559375,\n          3.297082228116711\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1359523974571153,\n        \"min\": 32.54,\n        \"max\": 41.95,\n        \"num_unique_values\": 862,\n        \"samples\": [\n          33.7,\n          34.41,\n          38.24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0035317235025882,\n        \"min\": -124.35,\n        \"max\": -114.31,\n        \"num_unique_values\": 844,\n        \"samples\": [\n          -118.63,\n          -119.86,\n          -121.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1539561587441387,\n        \"min\": 0.14999,\n        \"max\": 5.00001,\n        \"num_unique_values\": 3842,\n        \"samples\": [\n          1.943,\n          3.79,\n          2.301\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "source": [
        "# @title HouseAge\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "df['HouseAge'].plot(kind='hist', bins=20, title='HouseAge')\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ],
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkQAAAGzCAYAAADOnwhmAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA0pElEQVR4nO3de1xVdb7/8fdW2HgJQUTYUAjkNS94wZGYUdPBEZXjaDkzmZpYpFODXSQbZcYUbU6QTqSVk+OZ1DpROs5kzWg54iXtQqYYmtaQmMlUgOaFLZSIsH5/9HOddtjFLZsNrNfz8ViPB+v7/e61Pus7Pmbes9Z37W0zDMMQAACAhbXwdgEAAADeRiACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyAC4HFr1qyRzWbT3r17L9k/bNgw9e7du4GrunKvvPKKbDabwsPDVVtb6+1yAFwBAhEAuCknJ0dRUVEqKSnR9u3bvV0OgCtAIAIAN1RWVurll19WWlqa+vfvr5ycHG+XBOAKEIgANDoXLlzQQw89pM6dO8vPz09RUVH63e9+p6qqKpdxNptNGRkZdT4fFRWladOmmfvV1dVauHChunbtqlatWqlDhw4aPHiwcnNzXT7373//W7/4xS8UFBSkVq1aaeDAgfrHP/5xyRo3bNigL7/8Ur/85S81ceJEvfjiizp37lydcV9++aXuueceBQcHy9/fXz//+c/16aefXrL2Tz/9VLfffrtCQ0Pl5+enXr16adWqVT9s0gBcEQIRgAZTXl6uzz//vM5WXV3tMu6OO+7Q/PnzNWDAAD322GO64YYblJmZqYkTJ7p13oyMDC1cuFDDhw/Xk08+qd///vfq1KmT9u3bZ445dOiQrr/+en3wwQeaO3euHn30UbVt21bjx4/Xhg0b6hwzJydHw4cPl8Ph0MSJE3X27Fn985//rDNu2rRpeuKJJzRmzBg98sgjat26tZKSkuqMKysr0/XXX6+tW7dq5syZWrZsmbp06aKUlBQtXbrUresGcBkMAPCw1atXG5K+c+vVq5dhGIZRUFBgSDLuuOMOl2PMnj3bkGRs377dbJNkLFiwoM75IiMjjeTkZHO/b9++RlJS0nfWmJCQYPTp08c4d+6c2VZbW2v8+Mc/Nrp27eoytqyszPDx8TH+53/+x2z78Y9/bIwbN85lXH5+viHJuO+++1zap02bVqf2lJQUIywszPj8889dxk6cONEICAgwvvjii++sH8CV4Q4RgAazfPly5ebm1tliYmLMMa+88ookKS0tzeWz999/vyRp06ZNl33ewMBAHTp0SIcPH75k/6lTp7R9+3b96le/0tmzZ807VydPnlRiYqIOHz6sTz/91By/du1atWjRQhMmTDDbbrnlFr366qs6ffq02bZ582ZJ0m9+8xuX8919990u+4Zh6O9//7vGjh0rwzBc7p4lJiaqvLzc5W4WgPrn4+0CAFjHoEGDNHDgwDrt7du31+effy5JOnbsmFq0aKEuXbq4jHE4HAoMDNSxY8cu+7yLFi3SuHHj1K1bN/Xu3VujRo3SrbfeagaxoqIiGYahBx98UA8++OAlj3H8+HFdffXVkqTnnntOgwYN0smTJ3Xy5ElJUv/+/XX+/HmtX79eM2bMcLmW6Ohol2N989pOnDihM2fOaOXKlVq5cuW3nh+A5xCIADRKNpvN7c/W1NS47A8dOlRHjhzRyy+/rC1btugvf/mLHnvsMa1YsUJ33HGH+R1Cs2fPVmJi4iWPeTHEHD58WHv27JEkde3atc64nJwcMxD9UBfPP2XKFCUnJ19yzNfvogGofwQiAI1KZGSkamtrdfjwYV133XVme1lZmc6cOaPIyEizrX379jpz5ozL58+fP6+SkpI6xw0KCtJtt92m2267TRUVFRo6dKgyMjJ0xx136Nprr5Uk+fr6asSIEd9ZX05Ojnx9ffW///u/atmypUvfG2+8occff1zFxcXq1KmTeS1Hjx51CU9FRUUun+vYsaP8/f1VU1PzvecH4BmsIQLQqIwZM0aS6rxZlZ2dLUkub2h17txZu3btchm3cuXKOneILj7Wuuiqq65Sly5dzNf4Q0JCNGzYMP35z3++ZJg6ceKE+XdOTo6GDBmim2++Wb/4xS9ctgceeECS9MILL0iSebfpT3/6k8vxnnjiCZf9li1basKECfr73/+ugwcPfuf5AXgGd4gANCp9+/ZVcnKyVq5cqTNnzuiGG27QO++8o2eeeUbjx4/X8OHDzbF33HGH7rzzTk2YMEE/+9nPtH//fv3rX/9ScHCwyzF79uypYcOGKTY2VkFBQdq7d6/+9re/aebMmeaY5cuXa/DgwerTp4+mT5+ua6+9VmVlZcrLy9Mnn3yi/fv3a/fu3SoqKnL53NddffXVGjBggHJycjRnzhzFxsZqwoQJWrp0qU6ePKnrr79eO3fu1IcffijJ9bFgVlaWduzYobi4OE2fPl09e/bUqVOntG/fPm3dulWnTp2qz2kG8E1efssNgAVcfO1+z549l+y/4YYbzNfuDcMwqqurjYULFxrR0dGGr6+vERERYaSnp7u8Em8YhlFTU2PMmTPHCA4ONtq0aWMkJiYaRUVFdV67/8Mf/mAMGjTICAwMNFq3bm306NHD+O///m/j/PnzLsc7cuSIMXXqVMPhcBi+vr7G1VdfbfzXf/2X8be//c0wDMO4++67DUnGkSNHvvVaMzIyDEnG/v37DcMwjMrKSiM1NdUICgoyrrrqKmP8+PFGYWGhIcnIyspy+WxZWZmRmppqREREGL6+vobD4TASEhKMlStXfv8kA7giNsMwDO9GMgCwloKCAvXv31/PPfecJk+e7O1yAIg1RADgUV9++WWdtqVLl6pFixYaOnSoFyoCcCmsIQIAD1q8eLHy8/M1fPhw+fj46NVXX9Wrr76qGTNmKCIiwtvlAfj/eGQGAB6Um5urhQsX6v3331dFRYU6deqkW2+9Vb///e/l48P/JwUaCwIRAACwPNYQAQAAyyMQAQAAyyMQ/QCGYcjpdIqniwAANE8Eoh/g7NmzCggI0NmzZ71dCgAA8AACEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwfbxcAALh8UXM3eezYH2cleezYQGPFHSIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5Xg1Eu3bt0tixYxUeHi6bzaaXXnrJpd9ms11yW7JkiTkmKiqqTn9WVpbLcQ4cOKAhQ4aoVatWioiI0OLFixvi8gAAQBPh1UBUWVmpvn37avny5ZfsLykpcdlWrVolm82mCRMmuIxbtGiRy7i7777b7HM6nRo5cqQiIyOVn5+vJUuWKCMjQytXrvTotQEAgKbDx5snHz16tEaPHv2t/Q6Hw2X/5Zdf1vDhw3Xttde6tPv7+9cZe1FOTo7Onz+vVatWyW63q1evXiooKFB2drZmzJhx5RcBAACavCazhqisrEybNm1SSkpKnb6srCx16NBB/fv315IlS3ThwgWzLy8vT0OHDpXdbjfbEhMTVVhYqNOnT1/yXFVVVXI6nS4bAABovrx6h+hyPPPMM/L399dNN93k0n7PPfdowIABCgoK0ltvvaX09HSVlJQoOztbklRaWqro6GiXz4SGhpp97du3r3OuzMxMLVy40ENXAgAAGpsmE4hWrVqlyZMnq1WrVi7taWlp5t8xMTGy2+369a9/rczMTPn5+bl1rvT0dJfjOp1ORUREuFc4AABo9JpEIHr99ddVWFiodevWfe/YuLg4XbhwQR9//LG6d+8uh8OhsrIylzEX979t3ZGfn5/bYQoAADQ9TWIN0dNPP63Y2Fj17dv3e8cWFBSoRYsWCgkJkSTFx8dr165dqq6uNsfk5uaqe/ful3xcBgAArMergaiiokIFBQUqKCiQJB09elQFBQUqLi42xzidTq1fv1533HFHnc/n5eVp6dKl2r9/vz766CPl5ORo1qxZmjJlihl2Jk2aJLvdrpSUFB06dEjr1q3TsmXLXB6JAQAAa/PqI7O9e/dq+PDh5v7FkJKcnKw1a9ZIktauXSvDMHTLLbfU+byfn5/Wrl2rjIwMVVVVKTo6WrNmzXIJOwEBAdqyZYtSU1MVGxur4OBgzZ8/n1fuAQCAyWYYhuHtIho7p9OpgIAAlZeXq127dt4uBwAUNXeTx479cVaSx44NNFZNYg0RAACAJxGIAACA5RGIAACA5TWJ7yECGjtPredgLQcANAzuEAEAAMvjDhEAwAVvsMGKuEMEAAAsj0AEAAAsj0AEAAAsjzVEAJoM3uYD4CncIQIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJbn1UC0a9cujR07VuHh4bLZbHrppZdc+qdNmyabzeayjRo1ymXMqVOnNHnyZLVr106BgYFKSUlRRUWFy5gDBw5oyJAhatWqlSIiIrR48WJPXxoAAGhCvBqIKisr1bdvXy1fvvxbx4waNUolJSXm9sILL7j0T548WYcOHVJubq42btyoXbt2acaMGWa/0+nUyJEjFRkZqfz8fC1ZskQZGRlauXKlx64LAAA0LT7ePPno0aM1evTo7xzj5+cnh8Nxyb4PPvhAmzdv1p49ezRw4EBJ0hNPPKExY8boj3/8o8LDw5WTk6Pz589r1apVstvt6tWrlwoKCpSdne0SnAAAgHU1+jVEr732mkJCQtS9e3fdddddOnnypNmXl5enwMBAMwxJ0ogRI9SiRQvt3r3bHDN06FDZ7XZzTGJiogoLC3X69OlLnrOqqkpOp9NlAwAAzVejDkSjRo3Ss88+q23btumRRx7Rzp07NXr0aNXU1EiSSktLFRIS4vIZHx8fBQUFqbS01BwTGhrqMubi/sUx35SZmamAgABzi4iIqO9LAwAAjYhXH5l9n4kTJ5p/9+nTRzExMercubNee+01JSQkeOy86enpSktLM/edTiehCACAZqxR3yH6pmuvvVbBwcEqKiqSJDkcDh0/ftxlzIULF3Tq1Clz3ZHD4VBZWZnLmIv737Y2yc/PT+3atXPZAABA89WkAtEnn3yikydPKiwsTJIUHx+vM2fOKD8/3xyzfft21dbWKi4uzhyza9cuVVdXm2Nyc3PVvXt3tW/fvmEvAAAANEpeDUQVFRUqKChQQUGBJOno0aMqKChQcXGxKioq9MADD+jtt9/Wxx9/rG3btmncuHHq0qWLEhMTJUnXXXedRo0apenTp+udd97Rm2++qZkzZ2rixIkKDw+XJE2aNEl2u10pKSk6dOiQ1q1bp2XLlrk8EgMAANbm1UC0d+9e9e/fX/3795ckpaWlqX///po/f75atmypAwcO6Oc//7m6deumlJQUxcbG6vXXX5efn595jJycHPXo0UMJCQkaM2aMBg8e7PIdQwEBAdqyZYuOHj2q2NhY3X///Zo/fz6v3AMAAJNXF1UPGzZMhmF8a/+//vWv7z1GUFCQnn/++e8cExMTo9dff/2y6wMAANbQpNYQAQAAeEKjfu0eAAA0HlFzN3ns2B9nJXns2D8Ed4gAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDl8do9AMtrzq8SA/hhCESARXkqBBAAADRFPDIDAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACW5+PtAgAA1hE1d5NHjvtxVpJHjgvr4A4RAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPK++ZbZr1y4tWbJE+fn5Kikp0YYNGzR+/HhJUnV1tebNm6dXXnlFH330kQICAjRixAhlZWUpPDzcPEZUVJSOHTvmctzMzEzNnTvX3D9w4IBSU1O1Z88edezYUXfffbd++9vfNsg1AlfCU2/kAABcefUOUWVlpfr27avly5fX6fviiy+0b98+Pfjgg9q3b59efPFFFRYW6uc//3mdsYsWLVJJSYm53X333Waf0+nUyJEjFRkZqfz8fC1ZskQZGRlauXKlR68NAAA0HV69QzR69GiNHj36kn0BAQHKzc11aXvyySc1aNAgFRcXq1OnTma7v7+/HA7HJY+Tk5Oj8+fPa9WqVbLb7erVq5cKCgqUnZ2tGTNm1N/FAACAJqtJrSEqLy+XzWZTYGCgS3tWVpY6dOig/v37a8mSJbpw4YLZl5eXp6FDh8put5ttiYmJKiws1OnTpy95nqqqKjmdTpcNAAA0X03mm6rPnTunOXPm6JZbblG7du3M9nvuuUcDBgxQUFCQ3nrrLaWnp6ukpETZ2dmSpNLSUkVHR7scKzQ01Oxr3759nXNlZmZq4cKFHrwaAADQmDSJQFRdXa1f/epXMgxDTz31lEtfWlqa+XdMTIzsdrt+/etfKzMzU35+fm6dLz093eW4TqdTERER7hUPAAAavUYfiC6GoWPHjmn79u0ud4cuJS4uThcuXNDHH3+s7t27y+FwqKyszGXMxf1vW3fk5+fndpgCAABNT6NeQ3QxDB0+fFhbt25Vhw4dvvczBQUFatGihUJCQiRJ8fHx2rVrl6qrq80xubm56t69+yUflwEAAOvx6h2iiooKFRUVmftHjx5VQUGBgoKCFBYWpl/84hfat2+fNm7cqJqaGpWWlkqSgoKCZLfblZeXp927d2v48OHy9/dXXl6eZs2apSlTpphhZ9KkSVq4cKFSUlI0Z84cHTx4UMuWLdNjjz3mlWsGAACNj1cD0d69ezV8+HBz/+K6neTkZGVkZOgf//iHJKlfv34un9uxY4eGDRsmPz8/rV27VhkZGaqqqlJ0dLRmzZrlsv4nICBAW7ZsUWpqqmJjYxUcHKz58+fzyj0AADB5NRANGzZMhmF8a/939UnSgAED9Pbbb3/veWJiYvT6669fdn0AAMAaGvUaIgAAgIZAIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJbnViD66KOP6rsOAAAAr3ErEHXp0kXDhw/Xc889p3PnztV3TQAAAA3KrUC0b98+xcTEKC0tTQ6HQ7/+9a/1zjvv1HdtAAAADcKtQNSvXz8tW7ZMn332mVatWqWSkhINHjxYvXv3VnZ2tk6cOFHfdQIAAHjMFS2q9vHx0U033aT169frkUceUVFRkWbPnq2IiAhNnTpVJSUl9VUnAACAx1xRINq7d69+85vfKCwsTNnZ2Zo9e7aOHDmi3NxcffbZZxo3blx91QkAAOAxPu58KDs7W6tXr1ZhYaHGjBmjZ599VmPGjFGLFl/lq+joaK1Zs0ZRUVH1WSsAAIBHuBWInnrqKd1+++2aNm2awsLCLjkmJCRETz/99BUVBwAA0BDcCkSHDx/+3jF2u13JycnuHB4AAKBBubWGaPXq1Vq/fn2d9vXr1+uZZ5654qIAAAAakluBKDMzU8HBwXXaQ0JC9PDDD19xUQAAAA3JrUBUXFys6OjoOu2RkZEqLi6+4qIAAAAakluBKCQkRAcOHKjTvn//fnXo0OGKiwIAAGhIbgWiW265Rffcc4927Nihmpoa1dTUaPv27br33ns1ceLE+q4RAADAo9x6y+yhhx7Sxx9/rISEBPn4fHWI2tpaTZ06lTVEAIBmJWruJo8c9+OsJI8cF+5xKxDZ7XatW7dODz30kPbv36/WrVurT58+ioyMrO/6AAAAPM6tQHRRt27d1K1bt/qqBQAAwCvcCkQ1NTVas2aNtm3bpuPHj6u2ttalf/v27fVSHAAAQENwKxDde++9WrNmjZKSktS7d2/ZbLb6rgsAAKDBuBWI1q5dq7/+9a8aM2ZMfdcDAADQ4Nx67d5ut6tLly71XQsAAIBXuBWI7r//fi1btkyGYdR3PQAAAA3OrUdmb7zxhnbs2KFXX31VvXr1kq+vr0v/iy++WC/FAQAANAS3AlFgYKBuvPHG+q4FAADAK9wKRKtXr67vOgAAALzGrTVEknThwgVt3bpVf/7zn3X27FlJ0meffaaKiooffIxdu3Zp7NixCg8Pl81m00svveTSbxiG5s+fr7CwMLVu3VojRozQ4cOHXcacOnVKkydPVrt27RQYGKiUlJQ6NRw4cEBDhgxRq1atFBERocWLF7t30QAAoFlyKxAdO3ZMffr00bhx45SamqoTJ05Ikh555BHNnj37Bx+nsrJSffv21fLlyy/Zv3jxYj3++ONasWKFdu/erbZt2yoxMVHnzp0zx0yePFmHDh1Sbm6uNm7cqF27dmnGjBlmv9Pp1MiRIxUZGan8/HwtWbJEGRkZWrlypTuXDgAAmiG3v5hx4MCB2r9/vzp06GC233jjjZo+ffoPPs7o0aM1evToS/YZhqGlS5dq3rx5GjdunCTp2WefVWhoqF566SVNnDhRH3zwgTZv3qw9e/Zo4MCBkqQnnnhCY8aM0R//+EeFh4crJydH58+f16pVq2S329WrVy8VFBQoOzvbJTgBAADrcusO0euvv6558+bJbre7tEdFRenTTz+tl8KOHj2q0tJSjRgxwmwLCAhQXFyc8vLyJEl5eXkKDAw0w5AkjRgxQi1atNDu3bvNMUOHDnWpNTExUYWFhTp9+vQlz11VVSWn0+myAQCA5sutQFRbW6uampo67Z988on8/f2vuChJKi0tlSSFhoa6tIeGhpp9paWlCgkJcen38fFRUFCQy5hLHePr5/imzMxMBQQEmFtERMSVXxAAAGi03ApEI0eO1NKlS819m82miooKLViwoFn8nEd6errKy8vN7T//+Y+3SwIAAB7k1hqiRx99VImJierZs6fOnTunSZMm6fDhwwoODtYLL7xQL4U5HA5JUllZmcLCwsz2srIy9evXzxxz/Phxl89duHBBp06dMj/vcDhUVlbmMubi/sUx3+Tn5yc/P796uQ4AAND4uXWH6JprrtH+/fv1u9/9TrNmzVL//v2VlZWld999t84jLHdFR0fL4XBo27ZtZpvT6dTu3bsVHx8vSYqPj9eZM2eUn59vjtm+fbtqa2sVFxdnjtm1a5eqq6vNMbm5uerevbvat29fL7UCAICmza07RNJXa3WmTJlyRSevqKhQUVGRuX/06FEVFBQoKChInTp10n333ac//OEP6tq1q6Kjo/Xggw8qPDxc48ePlyRdd911GjVqlKZPn64VK1aourpaM2fO1MSJExUeHi5JmjRpkhYuXKiUlBTNmTNHBw8e1LJly/TYY49dUe0AAKD5cCsQPfvss9/ZP3Xq1B90nL1792r48OHmflpamiQpOTlZa9as0W9/+1tVVlZqxowZOnPmjAYPHqzNmzerVatW5mdycnI0c+ZMJSQkqEWLFpowYYIef/xxsz8gIEBbtmxRamqqYmNjFRwcrPnz5/PKPQAAMLn9PURfV11drS+++EJ2u11t2rT5wYFo2LBhMgzjW/ttNpsWLVqkRYsWfeuYoKAgPf/88995npiYGL3++us/qCYAAGA9bq0hOn36tMtWUVGhwsJCDR48uN4WVQMAADQUt3/L7Ju6du2qrKysOnePAAAAGrt6C0TSVwutP/vss/o8JAAAgMe5tYboH//4h8u+YRgqKSnRk08+qZ/85Cf1UhgAAEBDcSsQXXzt/SKbzaaOHTvqpz/9qR599NH6qAsAAKDBuBWIamtr67sOAGiWouZu8nYJAH6Ael1DBAAA0BS5dYfo4hco/hDZ2dnunAIAAKDBuBWI3n33Xb377ruqrq5W9+7dJUkffvihWrZsqQEDBpjjbDZb/VQJAADgQW4ForFjx8rf31/PPPOM+QOpp0+f1m233aYhQ4bo/vvvr9ciAQAAPMmtNUSPPvqoMjMzXX4tvn379vrDH/7AW2YAAKDJcSsQOZ1OnThxok77iRMndPbs2SsuCgAAoCG5FYhuvPFG3XbbbXrxxRf1ySef6JNPPtHf//53paSk6KabbqrvGgEAADzKrTVEK1as0OzZszVp0iRVV1d/dSAfH6WkpGjJkiX1WiAAAICnuRWI2rRpoz/96U9asmSJjhw5Iknq3Lmz2rZtW6/FAQAANIQr+mLGkpISlZSUqGvXrmrbtq0Mw6ivugAAABqMW4Ho5MmTSkhIULdu3TRmzBiVlJRIklJSUnjlHgAANDluBaJZs2bJ19dXxcXFatOmjdl+8803a/PmzfVWHAAAQENwaw3Rli1b9K9//UvXXHONS3vXrl117NixeikMAACgobh1h6iystLlztBFp06dkp+f3xUXBQAA0JDcCkRDhgzRs88+a+7bbDbV1tZq8eLFGj58eL0VBwAA0BDcemS2ePFiJSQkaO/evTp//rx++9vf6tChQzp16pTefPPN+q4RAADAo9wKRL1799aHH36oJ598Uv7+/qqoqNBNN92k1NRUhYWF1XeNAJqQqLmbvF0CAFy2yw5E1dXVGjVqlFasWKHf//73nqgJAACgQV32GiJfX18dOHDAE7UAAAB4hVuLqqdMmaKnn366vmsBAADwCrfWEF24cEGrVq3S1q1bFRsbW+c3zLKzs+ulOAAAgIZwWYHoo48+UlRUlA4ePKgBAwZIkj788EOXMTabrf6qAwAAaACXFYi6du2qkpIS7dixQ9JXP9Xx+OOPKzQ01CPFAQAANITLWkP0zV+zf/XVV1VZWVmvBQEAADQ0txZVX/TNgAQAANAUXVYgstlsddYIsWYIAAA0dZe1hsgwDE2bNs38Addz587pzjvvrPOW2Ysvvlh/FQIAAHjYZQWi5ORkl/0pU6bUazEAAADecFmBaPXq1Z6qAwAAwGuuaFE1AABAc9DoA1FUVJS5mPvrW2pqqiRp2LBhdfruvPNOl2MUFxcrKSlJbdq0UUhIiB544AFduHDBG5cDAAAaIbd+uqMh7dmzRzU1Neb+wYMH9bOf/Uy//OUvzbbp06dr0aJF5n6bNm3Mv2tqapSUlCSHw6G33npLJSUlmjp1qnx9ffXwww83zEUAAIBGrdEHoo4dO7rsZ2VlqXPnzrrhhhvMtjZt2sjhcFzy81u2bNH777+vrVu3KjQ0VP369dNDDz2kOXPmKCMjQ3a73aP1AwCAxq/RPzL7uvPnz+u5557T7bff7vL9Rzk5OQoODlbv3r2Vnp6uL774wuzLy8tTnz59XH5eJDExUU6nU4cOHbrkeaqqquR0Ol02AADQfDX6O0Rf99JLL+nMmTOaNm2a2TZp0iRFRkYqPDxcBw4c0Jw5c1RYWGh+F1JpaWmd31q7uF9aWnrJ82RmZmrhwoWeuQgAANDoNKlA9PTTT2v06NEKDw8322bMmGH+3adPH4WFhSkhIUFHjhxR586d3TpPenq60tLSzH2n06mIiAj3CwcAAI1akwlEx44d09atW7/3W7Dj4uIkSUVFRercubMcDofeeecdlzFlZWWS9K3rjvz8/Mxv4wYAAM1fk1lDtHr1aoWEhCgpKek7xxUUFEiSwsLCJEnx8fF67733dPz4cXNMbm6u2rVrp549e3qsXgAA0HQ0iTtEtbW1Wr16tZKTk+Xj838lHzlyRM8//7zGjBmjDh066MCBA5o1a5aGDh2qmJgYSdLIkSPVs2dP3XrrrVq8eLFKS0s1b948paamchcIAABIaiKBaOvWrSouLtbtt9/u0m6327V161YtXbpUlZWVioiI0IQJEzRv3jxzTMuWLbVx40bdddddio+PV9u2bZWcnOzyvUVoXKLmbvLIcT/O+u67iwAA62oSgWjkyJEyDKNOe0REhHbu3Pm9n4+MjNQrr7ziidIAAEAz0GTWEAEAAHgKgQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFiej7cLAADgSkXN3eTtEtDEEYiaMU/+F8THWUkeOzYAAA2NR2YAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDy+OkOWAa/dQSgMeHnlRoX7hABAADLIxABAADLa9SBKCMjQzabzWXr0aOH2X/u3DmlpqaqQ4cOuuqqqzRhwgSVlZW5HKO4uFhJSUlq06aNQkJC9MADD+jChQsNfSkAAKARa/RriHr16qWtW7ea+z4+/1fyrFmztGnTJq1fv14BAQGaOXOmbrrpJr355puSpJqaGiUlJcnhcOitt95SSUmJpk6dKl9fXz388MMNfi0AAKBxavSByMfHRw6Ho057eXm5nn76aT3//PP66U9/KklavXq1rrvuOr399tu6/vrrtWXLFr3//vvaunWrQkND1a9fPz300EOaM2eOMjIyZLfbG/pyAADwOF4iuXyN+pGZJB0+fFjh4eG69tprNXnyZBUXF0uS8vPzVV1drREjRphje/TooU6dOikvL0+SlJeXpz59+ig0NNQck5iYKKfTqUOHDn3rOauqquR0Ol02AADQfDXqQBQXF6c1a9Zo8+bNeuqpp3T06FENGTJEZ8+eVWlpqex2uwIDA10+ExoaqtLSUklSaWmpSxi62H+x79tkZmYqICDA3CIiIur3wgAAQKPSqB+ZjR492vw7JiZGcXFxioyM1F//+le1bt3aY+dNT09XWlqaue90OglFAAA0Y436DtE3BQYGqlu3bioqKpLD4dD58+d15swZlzFlZWXmmiOHw1HnrbOL+5dal3SRn5+f2rVr57IBAIDmq0kFooqKCh05ckRhYWGKjY2Vr6+vtm3bZvYXFhaquLhY8fHxkqT4+Hi99957On78uDkmNzdX7dq1U8+ePRu8fgAA0Dg16kdms2fP1tixYxUZGanPPvtMCxYsUMuWLXXLLbcoICBAKSkpSktLU1BQkNq1a6e7775b8fHxuv766yVJI0eOVM+ePXXrrbdq8eLFKi0t1bx585Samio/Pz8vXx0AAGgsGnUg+uSTT3TLLbfo5MmT6tixowYPHqy3335bHTt2lCQ99thjatGihSZMmKCqqiolJibqT3/6k/n5li1bauPGjbrrrrsUHx+vtm3bKjk5WYsWLfLWJQEAgEbIZhiG4e0iGjun06mAgACVl5c3qfVETfWHA/n+DACwHm//IG2TWkMEAADgCQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeT7eLgBNU9TcTd4uAQCAesMdIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHmNOhBlZmbqRz/6kfz9/RUSEqLx48ersLDQZcywYcNks9lctjvvvNNlTHFxsZKSktSmTRuFhITogQce0IULFxryUgAAQCPm4+0CvsvOnTuVmpqqH/3oR7pw4YJ+97vfaeTIkXr//ffVtm1bc9z06dO1aNEic79Nmzbm3zU1NUpKSpLD4dBbb72lkpISTZ06Vb6+vnr44Ycb9HoAAEDj1KgD0ebNm13216xZo5CQEOXn52vo0KFme5s2beRwOC55jC1btuj999/X1q1bFRoaqn79+umhhx7SnDlzlJGRIbvd7tFrAAAAjV+jfmT2TeXl5ZKkoKAgl/acnBwFBwerd+/eSk9P1xdffGH25eXlqU+fPgoNDTXbEhMT5XQ6dejQoUuep6qqSk6n02UDAADNV6O+Q/R1tbW1uu+++/STn/xEvXv3NtsnTZqkyMhIhYeH68CBA5ozZ44KCwv14osvSpJKS0tdwpAkc7+0tPSS58rMzNTChQs9dCUAAKCxaTKBKDU1VQcPHtQbb7zh0j5jxgzz7z59+igsLEwJCQk6cuSIOnfu7Na50tPTlZaWZu47nU5FRES4VzgAAGj0msQjs5kzZ2rjxo3asWOHrrnmmu8cGxcXJ0kqKiqSJDkcDpWVlbmMubj/beuO/Pz81K5dO5cNAAA0X406EBmGoZkzZ2rDhg3avn27oqOjv/czBQUFkqSwsDBJUnx8vN577z0dP37cHJObm6t27dqpZ8+eHqkbAAA0LY36kVlqaqqef/55vfzyy/L39zfX/AQEBKh169Y6cuSInn/+eY0ZM0YdOnTQgQMHNGvWLA0dOlQxMTGSpJEjR6pnz5669dZbtXjxYpWWlmrevHlKTU2Vn5+fNy8PAAA0Eo36DtFTTz2l8vJyDRs2TGFhYea2bt06SZLdbtfWrVs1cuRI9ejRQ/fff78mTJigf/7zn+YxWrZsqY0bN6ply5aKj4/XlClTNHXqVJfvLQIAANbWqO8QGYbxnf0RERHauXPn9x4nMjJSr7zySn2VBQAAmplGfYcIAACgIRCIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5fl4uwBIUXM3ebsEAAAsjTtEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8iwViJYvX66oqCi1atVKcXFxeuedd7xdEgAAaAQsE4jWrVuntLQ0LViwQPv27VPfvn2VmJio48ePe7s0AADgZZYJRNnZ2Zo+fbpuu+029ezZUytWrFCbNm20atUqb5cGAAC8zMfbBTSE8+fPKz8/X+np6WZbixYtNGLECOXl5dUZX1VVpaqqKnO/vLxckuR0Oj1SX23VFx45LgAATYWn/jdWkvz9/WWz2b5zjCUC0eeff66amhqFhoa6tIeGhurf//53nfGZmZlauHBhnfaIiAiP1QgAgJUFLPXcscvLy9WuXbvvHGOJQHS50tPTlZaWZu7X1tbq1KlT6tChw/cmzK9zOp2KiIjQf/7zn+/9DwLuYY49i/n1LObX85hjz2oq8+vv7/+9YywRiIKDg9WyZUuVlZW5tJeVlcnhcNQZ7+fnJz8/P5e2wMBAt8/frl27Rv0PpTlgjj2L+fUs5tfzmGPPag7za4lF1Xa7XbGxsdq2bZvZVltbq23btik+Pt6LlQEAgMbAEneIJCktLU3JyckaOHCgBg0apKVLl6qyslK33Xabt0sDAABeZplAdPPNN+vEiROaP3++SktL1a9fP23evLnOQuv65OfnpwULFtR5/Ib6wxx7FvPrWcyv5zHHntWc5tdmGIbh7SIAAAC8yRJriAAAAL4LgQgAAFgegQgAAFgegQgAAFgegQgAAFgegciDli9frqioKLVq1UpxcXF65513vF1Sk7Rr1y6NHTtW4eHhstlseumll1z6DcPQ/PnzFRYWptatW2vEiBE6fPiwd4ptgjIzM/WjH/1I/v7+CgkJ0fjx41VYWOgy5ty5c0pNTVWHDh101VVXacKECXW++R3f7qmnnlJMTIz5bb7x8fF69dVXzX7mt35lZWXJZrPpvvvuM9uYY/dlZGTIZrO5bD169DD7m8vcEog8ZN26dUpLS9OCBQu0b98+9e3bV4mJiTp+/Li3S2tyKisr1bdvXy1fvvyS/YsXL9bjjz+uFStWaPfu3Wrbtq0SExN17ty5Bq60adq5c6dSU1P19ttvKzc3V9XV1Ro5cqQqKyvNMbNmzdI///lPrV+/Xjt37tRnn32mm266yYtVNy3XXHONsrKylJ+fr7179+qnP/2pxo0bp0OHDklifuvTnj179Oc//1kxMTEu7czxlenVq5dKSkrM7Y033jD7ms3cGvCIQYMGGampqeZ+TU2NER4ebmRmZnqxqqZPkrFhwwZzv7a21nA4HMaSJUvMtjNnzhh+fn7GCy+84IUKm77jx48bkoydO3cahvHVfPr6+hrr1683x3zwwQeGJCMvL89bZTZ57du3N/7yl78wv/Xo7NmzRteuXY3c3FzjhhtuMO69917DMPg3fKUWLFhg9O3b95J9zWluuUPkAefPn1d+fr5GjBhhtrVo0UIjRoxQXl6eFytrfo4eParS0lKXuQ4ICFBcXBxz7aby8nJJUlBQkCQpPz9f1dXVLnPco0cPderUiTl2Q01NjdauXavKykrFx8czv/UoNTVVSUlJLnMp8W+4Phw+fFjh4eG69tprNXnyZBUXF0tqXnNrmZ/uaEiff/65ampq6vwsSGhoqP797397qarmqbS0VJIuOdcX+/DD1dbW6r777tNPfvIT9e7dW9JXc2y32xUYGOgyljm+PO+9957i4+N17tw5XXXVVdqwYYN69uypgoIC5rcerF27Vvv27dOePXvq9PFv+MrExcVpzZo16t69u0pKSrRw4UINGTJEBw8ebFZzSyACYEpNTdXBgwdd1gegfnTv3l0FBQUqLy/X3/72NyUnJ2vnzp3eLqtZ+M9//qN7771Xubm5atWqlbfLaXZGjx5t/h0TE6O4uDhFRkbqr3/9q1q3bu3FyuoXj8w8IDg4WC1btqyzyr6srEwOh8NLVTVPF+eTub5yM2fO1MaNG7Vjxw5dc801ZrvD4dD58+d15swZl/HM8eWx2+3q0qWLYmNjlZmZqb59+2rZsmXMbz3Iz8/X8ePHNWDAAPn4+MjHx0c7d+7U448/Lh8fH4WGhjLH9SgwMFDdunVTUVFRs/r3SyDyALvdrtjYWG3bts1sq62t1bZt2xQfH+/Fypqf6OhoORwOl7l2Op3avXs3c/0DGYahmTNnasOGDdq+fbuio6Nd+mNjY+Xr6+syx4WFhSouLmaOr0Btba2qqqqY33qQkJCg9957TwUFBeY2cOBATZ482fybOa4/FRUVOnLkiMLCwprXv19vr+purtauXWv4+fkZa9asMd5//31jxowZRmBgoFFaWurt0pqcs2fPGu+++67x7rvvGpKM7Oxs49133zWOHTtmGIZhZGVlGYGBgcbLL79sHDhwwBg3bpwRHR1tfPnll16uvGm46667jICAAOO1114zSkpKzO2LL74wx9x5551Gp06djO3btxt79+414uPjjfj4eC9W3bTMnTvX2Llzp3H06FHjwIEDxty5cw2bzWZs2bLFMAzm1xO+/paZYTDHV+L+++83XnvtNePo0aPGm2++aYwYMcIIDg42jh8/bhhG85lbApEHPfHEE0anTp0Mu91uDBo0yHj77be9XVKTtGPHDkNSnS05OdkwjK9evX/wwQeN0NBQw8/Pz0hISDAKCwu9W3QTcqm5lWSsXr3aHPPll18av/nNb4z27dsbbdq0MW688UajpKTEe0U3MbfffrsRGRlp2O12o2PHjkZCQoIZhgyD+fWEbwYi5th9N998sxEWFmbY7Xbj6quvNm6++WajqKjI7G8uc2szDMPwzr0pAACAxoE1RAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPL+Hxmc4jJgUR4BAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "PsrDoOXvVj-W",
        "outputId": "9e6138f6-8982-4312-afb2-b709533fb3e1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 3: Split the Data into Features and Target\n"
      ],
      "metadata": {
        "id": "47XjpAXca1qo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z3xms_MWzLhy"
      },
      "outputs": [],
      "source": [
        "# Split the data into features (X) and target (y)\n",
        "X = df.drop('Target', axis=1)\n",
        "y = df['Target']\n",
        "\n",
        "# Split into train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 4: Scale the Features\n"
      ],
      "metadata": {
        "id": "eEIj0mSZa-xp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t5jJnq33zNgr"
      },
      "outputs": [],
      "source": [
        "# Scale the features\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 5: Create and Compile the Model\n"
      ],
      "metadata": {
        "id": "6D7l7ZLYbPz7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tPSp946HzR17",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f5e0d3d2-c272-4726-e6d0-4606ed456584"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        }
      ],
      "source": [
        "# Create the model\n",
        "model = models.Sequential()\n",
        "\n",
        "# Add layers\n",
        "model.add(layers.Dense(64, activation='relu', input_shape=(X_train.shape[1],)))\n",
        "model.add(layers.Dense(64, activation='relu'))\n",
        "model.add(layers.Dense(1))\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='mse', metrics=['mae'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "yx39sOn6zTr4",
        "outputId": "b952db16-825f-4e4a-b487-cc1e994fcf1b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │             \u001b[38;5;34m576\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │           \u001b[38;5;34m4,160\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_9 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m65\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,160</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m4,801\u001b[0m (18.75 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,801</span> (18.75 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m4,801\u001b[0m (18.75 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,801</span> (18.75 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "model.summary()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 6: Train the Model\n"
      ],
      "metadata": {
        "id": "Iumq1qPIcBqE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "50K_2WxpzVTL",
        "outputId": "5ecc27c0-078e-4303-80ad-6a9d71251bf3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - loss: 186.0295 - mae: 5.9350 - val_loss: 1.1235 - val_mae: 0.8085\n",
            "Epoch 2/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 1.4192 - mae: 0.8967 - val_loss: 1.4410 - val_mae: 1.0228\n",
            "Epoch 3/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 1.5823 - mae: 0.8757 - val_loss: 304.2381 - val_mae: 14.6544\n",
            "Epoch 4/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 49.8039 - mae: 3.8392 - val_loss: 1.0459 - val_mae: 0.8242\n",
            "Epoch 5/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 3.3670 - mae: 1.1110 - val_loss: 12.1808 - val_mae: 2.6942\n",
            "Epoch 6/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 39.9122 - mae: 3.1880 - val_loss: 1.4928 - val_mae: 0.9825\n",
            "Epoch 7/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 1.4654 - mae: 0.8492 - val_loss: 1.2882 - val_mae: 0.9060\n",
            "Epoch 8/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 17.7572 - mae: 1.9040 - val_loss: 0.7290 - val_mae: 0.6488\n",
            "Epoch 9/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 8.2461 - mae: 1.4364 - val_loss: 0.8207 - val_mae: 0.6495\n",
            "Epoch 10/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.9102 - mae: 0.7232 - val_loss: 1.4278 - val_mae: 0.9352\n",
            "Epoch 11/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 2.8450 - mae: 0.9856 - val_loss: 0.8714 - val_mae: 0.7501\n",
            "Epoch 12/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 2.1861 - mae: 0.9625 - val_loss: 0.7257 - val_mae: 0.6634\n",
            "Epoch 13/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 89.7102 - mae: 3.3487 - val_loss: 0.9747 - val_mae: 0.7481\n",
            "Epoch 14/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 1.5079 - mae: 0.8953 - val_loss: 0.8113 - val_mae: 0.7239\n",
            "Epoch 15/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 1.5669 - mae: 0.8509 - val_loss: 0.7768 - val_mae: 0.6972\n",
            "Epoch 16/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 1.2493 - mae: 0.7895 - val_loss: 0.7694 - val_mae: 0.6955\n",
            "Epoch 17/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 3.4742 - mae: 1.0986 - val_loss: 2.8128 - val_mae: 1.2029\n",
            "Epoch 18/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 3.1238 - mae: 0.9540 - val_loss: 1.1185 - val_mae: 0.7457\n",
            "Epoch 19/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.8687 - mae: 0.7038 - val_loss: 7.9516 - val_mae: 2.1151\n",
            "Epoch 20/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 2.3600 - mae: 1.0228 - val_loss: 1.0935 - val_mae: 0.8593\n",
            "Epoch 21/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 1.7002 - mae: 0.8555 - val_loss: 0.7625 - val_mae: 0.6251\n",
            "Epoch 22/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.9256 - mae: 0.7275 - val_loss: 0.7069 - val_mae: 0.6589\n",
            "Epoch 23/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 2.7532 - mae: 1.0239 - val_loss: 1.0323 - val_mae: 0.7228\n",
            "Epoch 24/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 3.8688 - mae: 1.1570 - val_loss: 0.7898 - val_mae: 0.7045\n",
            "Epoch 25/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 2.1637 - mae: 0.9665 - val_loss: 6.0441 - val_mae: 2.0043\n",
            "Epoch 26/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 7.2451 - mae: 1.4979 - val_loss: 0.6623 - val_mae: 0.6138\n",
            "Epoch 27/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 1.1717 - mae: 0.7793 - val_loss: 0.6512 - val_mae: 0.6131\n",
            "Epoch 28/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 21.2245 - mae: 1.4760 - val_loss: 0.7955 - val_mae: 0.6394\n",
            "Epoch 29/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 1.3509 - mae: 0.7744 - val_loss: 0.6860 - val_mae: 0.6346\n",
            "Epoch 30/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.8184 - mae: 0.6689 - val_loss: 0.7497 - val_mae: 0.6265\n",
            "Epoch 31/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7316 - mae: 0.6546 - val_loss: 0.6695 - val_mae: 0.6436\n",
            "Epoch 32/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 4.6408 - mae: 0.8569 - val_loss: 0.7488 - val_mae: 0.6492\n",
            "Epoch 33/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7157 - mae: 0.6518 - val_loss: 0.7119 - val_mae: 0.6206\n",
            "Epoch 34/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.8509 - mae: 0.6955 - val_loss: 0.7159 - val_mae: 0.6725\n",
            "Epoch 35/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6951 - mae: 0.6310 - val_loss: 0.6397 - val_mae: 0.5835\n",
            "Epoch 36/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.8161 - mae: 0.6624 - val_loss: 0.7654 - val_mae: 0.6997\n",
            "Epoch 37/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.8254 - mae: 0.6841 - val_loss: 0.7233 - val_mae: 0.6765\n",
            "Epoch 38/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.7831 - mae: 0.6616 - val_loss: 0.7353 - val_mae: 0.6124\n",
            "Epoch 39/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7292 - mae: 0.6403 - val_loss: 2.6485 - val_mae: 1.2279\n",
            "Epoch 40/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.9732 - mae: 0.7271 - val_loss: 0.6842 - val_mae: 0.6205\n",
            "Epoch 41/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.8906 - mae: 0.6856 - val_loss: 0.6825 - val_mae: 0.6294\n",
            "Epoch 42/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.8224 - mae: 0.6558 - val_loss: 0.6587 - val_mae: 0.6314\n",
            "Epoch 43/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.9468 - mae: 0.6916 - val_loss: 0.7651 - val_mae: 0.6996\n",
            "Epoch 44/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7631 - mae: 0.6571 - val_loss: 0.6564 - val_mae: 0.5868\n",
            "Epoch 45/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 1.2197 - mae: 0.7933 - val_loss: 0.7197 - val_mae: 0.6768\n",
            "Epoch 46/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7872 - mae: 0.6507 - val_loss: 1.3885 - val_mae: 0.7747\n",
            "Epoch 47/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 2.9158 - mae: 1.0908 - val_loss: 0.6601 - val_mae: 0.5758\n",
            "Epoch 48/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6362 - mae: 0.6006 - val_loss: 0.5756 - val_mae: 0.5613\n",
            "Epoch 49/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.7317 - mae: 0.6399 - val_loss: 1.5938 - val_mae: 0.9906\n",
            "Epoch 50/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 1.8325 - mae: 0.8705 - val_loss: 0.7166 - val_mae: 0.7039\n"
          ]
        }
      ],
      "source": [
        "# Train the model\n",
        "history = model.fit(X_train, y_train, epochs=50, validation_split=0.2, batch_size=32)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "93yjUJBO17eU",
        "outputId": "cf69470b-a95a-4666-9758-636c108a296a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot training history\n",
        "plt.plot(history.history['mae'], label='Train MAE')\n",
        "plt.plot(history.history['val_mae'], label='Validation MAE')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Mean Absolute Error')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wHXD1Jfl2gfl"
      },
      "source": [
        "\n",
        "- **Data Preparation:** We loaded the California Housing dataset, split it into training and test sets, and scaled the features.\n",
        "- **Model Creation:** We built a simple feedforward neural network with two hidden layers.\n",
        "- **Training:** We trained the model for 50 epochs and monitored its performance using validation data.\n",
        "- **Evaluation:** We evaluated the model on the test set and visualized the results to understand its performance.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 7: Evaluate the Model\n"
      ],
      "metadata": {
        "id": "Wd1oUFskcUKY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model on the test set\n",
        "test_loss, test_mae = model.evaluate(X_test, y_test)\n",
        "print(f'Test MAE: {test_mae}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BHHKv_CwcSx1",
        "outputId": "09f1d4af-d4d2-4b9b-f942-b45cd92324e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6996 - mae: 0.6984\n",
            "Test MAE: 0.7011523842811584\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 8: Make Predictions\n"
      ],
      "metadata": {
        "id": "xGbShfAtcz4-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions on the test set\n",
        "y_pred = model.predict(X_test)\n",
        "print(y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1jJ5Ompzck8K",
        "outputId": "2ba5e71f-410e-4c9f-cf00-073a491a8e1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n",
            "[[1.1569897]\n",
            " [1.9981943]\n",
            " [3.1375022]\n",
            " ...\n",
            " [4.579888 ]\n",
            " [2.094378 ]\n",
            " [2.1077695]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train the California Housing dataset with a basic PINN structure"
      ],
      "metadata": {
        "id": "9wm5uyIde1Jm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 1: Import Necessary Libraries\n"
      ],
      "metadata": {
        "id": "BOzVpKSzfUUQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.datasets import fetch_california_housing\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers, models\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras import backend as K\n"
      ],
      "metadata": {
        "id": "W6IbeIq_-FBS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 2: Load and Prepare the Dataset\n"
      ],
      "metadata": {
        "id": "B3Dp0eyBfeE8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset\n",
        "california = fetch_california_housing()\n",
        "\n",
        "# Convert to DataFrame for better visualization\n",
        "df = pd.DataFrame(california.data, columns=california.feature_names)\n",
        "df['Target'] = california.target\n",
        "\n",
        "# Display the first few rows of the dataset\n",
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "y4HM9dVFfa3Q",
        "outputId": "fe4de215-362d-41ce-fbbb-451d1b3bab97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
              "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
              "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
              "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
              "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
              "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
              "\n",
              "   Longitude  Target  \n",
              "0    -122.23   4.526  \n",
              "1    -122.22   3.585  \n",
              "2    -122.24   3.521  \n",
              "3    -122.25   3.413  \n",
              "4    -122.25   3.422  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3b3c892a-c423-4891-9929-32be5db89f1a\" 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>MedInc</th>\n",
              "      <th>HouseAge</th>\n",
              "      <th>AveRooms</th>\n",
              "      <th>AveBedrms</th>\n",
              "      <th>Population</th>\n",
              "      <th>AveOccup</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>Target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.3252</td>\n",
              "      <td>41.0</td>\n",
              "      <td>6.984127</td>\n",
              "      <td>1.023810</td>\n",
              "      <td>322.0</td>\n",
              "      <td>2.555556</td>\n",
              "      <td>37.88</td>\n",
              "      <td>-122.23</td>\n",
              "      <td>4.526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.3014</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6.238137</td>\n",
              "      <td>0.971880</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>2.109842</td>\n",
              "      <td>37.86</td>\n",
              "      <td>-122.22</td>\n",
              "      <td>3.585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.2574</td>\n",
              "      <td>52.0</td>\n",
              "      <td>8.288136</td>\n",
              "      <td>1.073446</td>\n",
              "      <td>496.0</td>\n",
              "      <td>2.802260</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.24</td>\n",
              "      <td>3.521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.6431</td>\n",
              "      <td>52.0</td>\n",
              "      <td>5.817352</td>\n",
              "      <td>1.073059</td>\n",
              "      <td>558.0</td>\n",
              "      <td>2.547945</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "      <td>3.413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.8462</td>\n",
              "      <td>52.0</td>\n",
              "      <td>6.281853</td>\n",
              "      <td>1.081081</td>\n",
              "      <td>565.0</td>\n",
              "      <td>2.181467</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "      <td>3.422</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-3b3c892a-c423-4891-9929-32be5db89f1a')\"\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-3b3c892a-c423-4891-9929-32be5db89f1a 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-3b3c892a-c423-4891-9929-32be5db89f1a');\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-8922db3c-802d-445f-9a73-eb7f6aba4d31\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8922db3c-802d-445f-9a73-eb7f6aba4d31')\"\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-8922db3c-802d-445f-9a73-eb7f6aba4d31 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 20640,\n  \"fields\": [\n    {\n      \"column\": \"MedInc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8998217179452688,\n        \"min\": 0.4999,\n        \"max\": 15.0001,\n        \"num_unique_values\": 12928,\n        \"samples\": [\n          5.0286,\n          2.0433,\n          6.1228\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HouseAge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.58555761211165,\n        \"min\": 1.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          35.0,\n          25.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveRooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4741731394243187,\n        \"min\": 0.8461538461538461,\n        \"max\": 141.9090909090909,\n        \"num_unique_values\": 19392,\n        \"samples\": [\n          6.111269614835948,\n          5.912820512820513,\n          5.7924528301886795\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveBedrms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.473910856795466,\n        \"min\": 0.3333333333333333,\n        \"max\": 34.06666666666667,\n        \"num_unique_values\": 14233,\n        \"samples\": [\n          0.9906542056074766,\n          1.112099644128114,\n          1.0398230088495575\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1132.462121765341,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 3888,\n        \"samples\": [\n          4169.0,\n          636.0,\n          3367.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveOccup\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.386049562213618,\n        \"min\": 0.6923076923076923,\n        \"max\": 1243.3333333333333,\n        \"num_unique_values\": 18841,\n        \"samples\": [\n          2.6939799331103678,\n          3.559375,\n          3.297082228116711\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1359523974571153,\n        \"min\": 32.54,\n        \"max\": 41.95,\n        \"num_unique_values\": 862,\n        \"samples\": [\n          33.7,\n          34.41,\n          38.24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0035317235025882,\n        \"min\": -124.35,\n        \"max\": -114.31,\n        \"num_unique_values\": 844,\n        \"samples\": [\n          -118.63,\n          -119.86,\n          -121.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1539561587441387,\n        \"min\": 0.14999,\n        \"max\": 5.00001,\n        \"num_unique_values\": 3842,\n        \"samples\": [\n          1.943,\n          3.79,\n          2.301\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 3: Split the Data into Features and Target\n"
      ],
      "metadata": {
        "id": "-n7U4ApUfngf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into features (X) and target (y)\n",
        "X = df.drop('Target', axis=1)\n",
        "y = df['Target']\n",
        "\n",
        "# Split into train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "LuuPQrgbfkzf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 4: Scale the Features\n"
      ],
      "metadata": {
        "id": "WT_xr4Knfwc5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Scale the features\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n"
      ],
      "metadata": {
        "id": "TRx8hfAVfuK9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 5: Define the PINN Model\n"
      ],
      "metadata": {
        "id": "WpL3paFxgLFu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the PINN model\n",
        "class PINN(tf.keras.Model):\n",
        "    def __init__(self):\n",
        "        super(PINN, self).__init__()\n",
        "        self.hidden_layer_1 = layers.Dense(64, activation='relu')\n",
        "        self.hidden_layer_2 = layers.Dense(64, activation='relu')\n",
        "        self.output_layer = layers.Dense(1)\n",
        "\n",
        "    def call(self, inputs):\n",
        "        x = self.hidden_layer_1(inputs)\n",
        "        x = self.hidden_layer_2(x)\n",
        "        return self.output_layer(x)\n",
        "\n",
        "    def train_step(self, data):\n",
        "        X, y = data\n",
        "\n",
        "        with tf.GradientTape() as tape:\n",
        "            y_pred = self(X, training=True)\n",
        "            loss = self.compiled_loss(y, y_pred)\n",
        "\n",
        "            # Implement the PINN constraint (Example: derivative constraint)\n",
        "            with tf.GradientTape() as tape2:\n",
        "                tape2.watch(X)\n",
        "                y_pred = self(X, training=True)\n",
        "            gradients = tape2.gradient(y_pred, X)\n",
        "            constraint_loss = K.mean(tf.square(gradients[:, 0] - 1.0))  # Example constraint\n",
        "\n",
        "            total_loss = loss + constraint_loss\n",
        "\n",
        "        trainable_vars = self.trainable_variables\n",
        "        gradients = tape.gradient(total_loss, trainable_vars)\n",
        "        self.optimizer.apply_gradients(zip(gradients, trainable_vars))\n",
        "\n",
        "        self.compiled_metrics.update_state(y, y_pred)\n",
        "        return {m.name: m.result() for m in self.metrics}\n",
        "\n",
        "# Instantiate the model\n",
        "pinn_model = PINN()\n",
        "\n",
        "# Compile the model with a custom training step\n",
        "pinn_model.compile(optimizer='adam', loss='mse', metrics=['mae'])\n"
      ],
      "metadata": {
        "id": "ZOncG8l2f8K9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 6: Train the PINN Model\n"
      ],
      "metadata": {
        "id": "S7yfoFjqgjic"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the PINN model\n",
        "history = pinn_model.fit(X_train, y_train, epochs=50, validation_split=0.2, batch_size=32)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ac-HcukZgRmr",
        "outputId": "316b0644-a3e9-4581-a20e-420c97e8e12d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/trainer.py:607: UserWarning: `model.compiled_loss()` is deprecated. Instead, use `model.compute_loss(x, y, y_pred, sample_weight, training)`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/trainer.py:582: UserWarning: `model.compiled_metrics()` is deprecated. Instead, use e.g.:\n",
            "```\n",
            "for metric in self.metrics:\n",
            "    metric.update_state(y, y_pred)\n",
            "```\n",
            "\n",
            "  return self._compiled_metrics_update_state(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4ms/step - mae: 0.7957 - loss: 1.6932 - val_loss: 0.4690 - val_mae: 0.4901\n",
            "Epoch 2/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - mae: 0.4697 - loss: 2.0376 - val_loss: 0.4316 - val_mae: 0.4627\n",
            "Epoch 3/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.4381 - loss: 2.0556 - val_loss: 0.4113 - val_mae: 0.4490\n",
            "Epoch 4/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - mae: 0.4409 - loss: 2.0550 - val_loss: 0.4013 - val_mae: 0.4486\n",
            "Epoch 5/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.4322 - loss: 2.0619 - val_loss: 0.4220 - val_mae: 0.4502\n",
            "Epoch 6/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.4217 - loss: 2.0656 - val_loss: 0.3925 - val_mae: 0.4374\n",
            "Epoch 7/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.4219 - loss: 2.0585 - val_loss: 0.4003 - val_mae: 0.4320\n",
            "Epoch 8/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.4173 - loss: 2.0474 - val_loss: 0.3817 - val_mae: 0.4294\n",
            "Epoch 9/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.4173 - loss: 2.0734 - val_loss: 0.3834 - val_mae: 0.4237\n",
            "Epoch 10/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.4103 - loss: 2.0405 - val_loss: 0.3860 - val_mae: 0.4265\n",
            "Epoch 11/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.4135 - loss: 2.0627 - val_loss: 0.3733 - val_mae: 0.4265\n",
            "Epoch 12/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.4093 - loss: 2.0658 - val_loss: 0.3761 - val_mae: 0.4289\n",
            "Epoch 13/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.4087 - loss: 2.0732 - val_loss: 0.3843 - val_mae: 0.4346\n",
            "Epoch 14/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.4038 - loss: 2.0488 - val_loss: 0.3577 - val_mae: 0.4171\n",
            "Epoch 15/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.4060 - loss: 2.0633 - val_loss: 0.3720 - val_mae: 0.4203\n",
            "Epoch 16/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - mae: 0.4005 - loss: 2.0772 - val_loss: 0.3633 - val_mae: 0.4192\n",
            "Epoch 17/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - mae: 0.3982 - loss: 2.0657 - val_loss: 0.3760 - val_mae: 0.4243\n",
            "Epoch 18/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3993 - loss: 2.0633 - val_loss: 0.3672 - val_mae: 0.4232\n",
            "Epoch 19/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3996 - loss: 2.0672 - val_loss: 0.3983 - val_mae: 0.4297\n",
            "Epoch 20/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.4028 - loss: 2.0650 - val_loss: 0.3880 - val_mae: 0.4207\n",
            "Epoch 21/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3911 - loss: 2.0574 - val_loss: 0.3547 - val_mae: 0.4127\n",
            "Epoch 22/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3941 - loss: 2.0709 - val_loss: 0.3603 - val_mae: 0.4152\n",
            "Epoch 23/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3969 - loss: 2.0591 - val_loss: 0.3499 - val_mae: 0.4075\n",
            "Epoch 24/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3906 - loss: 2.0619 - val_loss: 0.3505 - val_mae: 0.4122\n",
            "Epoch 25/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3890 - loss: 2.0466 - val_loss: 0.3607 - val_mae: 0.4300\n",
            "Epoch 26/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.3908 - loss: 2.0754 - val_loss: 0.3631 - val_mae: 0.4134\n",
            "Epoch 27/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - mae: 0.3958 - loss: 2.0586 - val_loss: 0.3479 - val_mae: 0.4173\n",
            "Epoch 28/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3880 - loss: 2.0529 - val_loss: 0.3431 - val_mae: 0.4066\n",
            "Epoch 29/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3901 - loss: 2.0674 - val_loss: 0.3512 - val_mae: 0.4093\n",
            "Epoch 30/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3861 - loss: 2.0720 - val_loss: 0.3445 - val_mae: 0.4091\n",
            "Epoch 31/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3998 - loss: 2.0689 - val_loss: 0.3475 - val_mae: 0.4076\n",
            "Epoch 32/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3816 - loss: 2.0670 - val_loss: 0.3566 - val_mae: 0.4291\n",
            "Epoch 33/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3893 - loss: 2.0723 - val_loss: 0.3438 - val_mae: 0.3996\n",
            "Epoch 34/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3808 - loss: 2.0572 - val_loss: 0.3437 - val_mae: 0.4040\n",
            "Epoch 35/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3893 - loss: 2.0840 - val_loss: 0.3437 - val_mae: 0.4098\n",
            "Epoch 36/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3848 - loss: 2.0557 - val_loss: 0.3375 - val_mae: 0.4020\n",
            "Epoch 37/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.3768 - loss: 2.0598 - val_loss: 0.3503 - val_mae: 0.4092\n",
            "Epoch 38/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - mae: 0.3847 - loss: 2.0753 - val_loss: 0.3389 - val_mae: 0.4068\n",
            "Epoch 39/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3838 - loss: 2.0712 - val_loss: 0.3508 - val_mae: 0.4021\n",
            "Epoch 40/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3783 - loss: 2.0615 - val_loss: 0.3409 - val_mae: 0.4071\n",
            "Epoch 41/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3807 - loss: 2.0772 - val_loss: 0.3372 - val_mae: 0.4107\n",
            "Epoch 42/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3859 - loss: 2.0665 - val_loss: 0.3315 - val_mae: 0.3925\n",
            "Epoch 43/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3778 - loss: 2.0617 - val_loss: 0.3419 - val_mae: 0.4063\n",
            "Epoch 44/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3782 - loss: 2.0641 - val_loss: 0.3287 - val_mae: 0.3940\n",
            "Epoch 45/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3816 - loss: 2.0649 - val_loss: 0.3269 - val_mae: 0.4019\n",
            "Epoch 46/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3808 - loss: 2.0623 - val_loss: 0.3271 - val_mae: 0.3982\n",
            "Epoch 47/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3786 - loss: 2.0752 - val_loss: 0.3380 - val_mae: 0.4080\n",
            "Epoch 48/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - mae: 0.3840 - loss: 2.0555 - val_loss: 0.3359 - val_mae: 0.4021\n",
            "Epoch 49/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - mae: 0.3791 - loss: 2.0668 - val_loss: 0.3311 - val_mae: 0.4025\n",
            "Epoch 50/50\n",
            "\u001b[1m413/413\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3796 - loss: 2.0755 - val_loss: 0.3272 - val_mae: 0.4022\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 7: Plot Training History\n"
      ],
      "metadata": {
        "id": "upZ1qxGmgriV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot training history\n",
        "plt.plot(history.history['mae'], label='Train MAE')\n",
        "plt.plot(history.history['val_mae'], label='Validation MAE')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Mean Absolute Error')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "p8BFZSQpgn-6",
        "outputId": "034ba844-b3d7-40d9-a70f-d888edc5a3e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 8: Evaluate the PINN Model\n"
      ],
      "metadata": {
        "id": "HGmImTolg8ml"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model on the test set\n",
        "test_loss, test_mae = pinn_model.evaluate(X_test, y_test)\n",
        "print(f'Test MAE: {test_mae}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D0eiS2BDg3ZS",
        "outputId": "b6a027fa-a71a-426a-a50d-59c05bb6d9ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.3151 - mae: 0.3906\n",
            "Test MAE: 0.3958660364151001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 9: Make Predictions\n"
      ],
      "metadata": {
        "id": "3PFul5rLhL-g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions on the test set\n",
        "y_pred = pinn_model.predict(X_test)\n",
        "print(y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zEU6FgE6hCOW",
        "outputId": "bbe19242-a67f-4cc7-9a45-449b461c0dbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n",
            "[[0.6765418]\n",
            " [1.4998381]\n",
            " [4.589984 ]\n",
            " ...\n",
            " [4.828855 ]\n",
            " [0.6701598]\n",
            " [1.7663598]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Implement Physics-Informed Multi-Task Learning with TensorFlow on the California Housing dataset"
      ],
      "metadata": {
        "id": "bCIBxSQyi_Ig"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 1: Import Necessary Libraries\n"
      ],
      "metadata": {
        "id": "VYEW7h5ljXIM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.datasets import fetch_california_housing\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers, models\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras import backend as K\n"
      ],
      "metadata": {
        "id": "kegE7_Y7jUMo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 2: Load and Prepare the Dataset\n"
      ],
      "metadata": {
        "id": "Oi10A6sOjiBF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset\n",
        "california = fetch_california_housing()\n",
        "\n",
        "# Convert to DataFrame for better visualization\n",
        "df = pd.DataFrame(california.data, columns=california.feature_names)\n",
        "df['Target'] = california.target\n",
        "\n",
        "# Split the data into features (X) and target (y)\n",
        "X = df.drop('Target', axis=1)\n",
        "y = df['Target']\n",
        "\n",
        "# Split into train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Scale the features\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n"
      ],
      "metadata": {
        "id": "2geppQx2jc1c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 3: Define the Physics-Informed Multi-Task Model\n"
      ],
      "metadata": {
        "id": "unRqQ1aikWbp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class PIMTL(tf.keras.Model):\n",
        "    def __init__(self):\n",
        "        super(PIMTL, self).__init__()\n",
        "        # Shared layers for both tasks\n",
        "        self.shared_layer_1 = layers.Dense(64, activation='relu')\n",
        "        self.shared_layer_2 = layers.Dense(64, activation='relu')\n",
        "\n",
        "        # Output for predicting house prices (Task 1)\n",
        "        self.price_output = layers.Dense(1, name=\"price_output\")\n",
        "\n",
        "        # Output for physics-informed constraint (Task 2)\n",
        "        self.physics_output = layers.Dense(1, name=\"physics_output\")\n",
        "\n",
        "    def call(self, inputs):\n",
        "        # Forward pass through shared layers\n",
        "        x = self.shared_layer_1(inputs)\n",
        "        x = self.shared_layer_2(x)\n",
        "\n",
        "        # Task 1: Predict house prices\n",
        "        price_pred = self.price_output(x)\n",
        "\n",
        "        # Task 2: Output for physics-informed constraint\n",
        "        physics_pred = self.physics_output(x)\n",
        "\n",
        "        return price_pred, physics_pred\n",
        "\n",
        "    def train_step(self, data):\n",
        "        X, y = data\n",
        "        y_true, physics_target = y\n",
        "\n",
        "        with tf.GradientTape() as tape:\n",
        "            y_pred, physics_pred = self(X, training=True)\n",
        "\n",
        "            # Loss for price prediction (Task 1)\n",
        "            price_loss = self.compiled_loss(y_true, y_pred)\n",
        "\n",
        "            # Loss for physics-informed task (Task 2)\n",
        "            physics_loss = self.compiled_loss(physics_target, physics_pred)\n",
        "\n",
        "            # Combine losses (weighted sum)\n",
        "            total_loss = price_loss + physics_loss\n",
        "\n",
        "        # Compute gradients and update weights\n",
        "        trainable_vars = self.trainable_variables\n",
        "        gradients = tape.gradient(total_loss, trainable_vars)\n",
        "        self.optimizer.apply_gradients(zip(gradients, trainable_vars))\n",
        "\n",
        "        # Update metrics\n",
        "        self.compiled_metrics.update_state(y_true, y_pred)\n",
        "        return {m.name: m.result() for m in self.metrics}\n"
      ],
      "metadata": {
        "id": "-_rA7bl_kSC2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 4: Define Physics Constraints\n"
      ],
      "metadata": {
        "id": "KlysMLjUkt9r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def physics_constraint(X):\n",
        "    \"\"\"\n",
        "    Define a simple physics-based relationship for task 2.\n",
        "    We assume that house prices (Target) should increase with both\n",
        "    median income (MedInc) and average rooms per household (AveRooms).\n",
        "    \"\"\"\n",
        "    # Get MedInc and AveRooms from the features\n",
        "    MedInc = X[:, 0]\n",
        "    AveRooms = X[:, 2]\n",
        "\n",
        "    # Construct a pseudo-target where we enforce that the price should\n",
        "    # increase with MedInc and AveRooms\n",
        "    pseudo_target = MedInc * AveRooms / 10  # Scaling down for simplicity\n",
        "\n",
        "    return pseudo_target\n"
      ],
      "metadata": {
        "id": "68Vi-W6Ekr1N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 5: Compile and Train the Model with Named Outputs\n"
      ],
      "metadata": {
        "id": "ketspkctnFpq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare the physics target for Task 2\n",
        "physics_target_train = physics_constraint(X_train)\n",
        "physics_target_test = physics_constraint(X_test)\n",
        "\n",
        "# Compile the model\n",
        "pimtl_model = PIMTL()\n",
        "pimtl_model.compile(optimizer='adam', loss='mse', metrics=['mae'])\n",
        "\n",
        "# Train the model\n",
        "history = pimtl_model.fit(\n",
        "    X_train,\n",
        "    [y_train, physics_target_train],  # Two targets: price and physics constraint\n",
        "    epochs=50,\n",
        "    validation_data=(X_test, [y_test, physics_target_test]),\n",
        "    batch_size=32\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "735kdOlvlbU8",
        "outputId": "606eabc4-efdd-4d19-d063-d85d4ddbf24b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/trainer.py:607: UserWarning: `model.compiled_loss()` is deprecated. Instead, use `model.compute_loss(x, y, y_pred, sample_weight, training)`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/trainer.py:582: UserWarning: `model.compiled_metrics()` is deprecated. Instead, use e.g.:\n",
            "```\n",
            "for metric in self.metrics:\n",
            "    metric.update_state(y, y_pred)\n",
            "```\n",
            "\n",
            "  return self._compiled_metrics_update_state(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - mae: 0.8143 - loss: 1.7136 - val_loss: 0.4164 - val_mae: 0.4631\n",
            "Epoch 2/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.4482 - loss: 2.0713 - val_loss: 0.3855 - val_mae: 0.4288\n",
            "Epoch 3/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.4352 - loss: 2.0684 - val_loss: 0.3578 - val_mae: 0.4202\n",
            "Epoch 4/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.4143 - loss: 2.0621 - val_loss: 0.3484 - val_mae: 0.4193\n",
            "Epoch 5/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.4110 - loss: 2.0686 - val_loss: 0.3407 - val_mae: 0.4029\n",
            "Epoch 6/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3998 - loss: 2.0656 - val_loss: 0.3244 - val_mae: 0.3929\n",
            "Epoch 7/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.4015 - loss: 2.0786 - val_loss: 0.3172 - val_mae: 0.3891\n",
            "Epoch 8/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3833 - loss: 2.0620 - val_loss: 0.3418 - val_mae: 0.4210\n",
            "Epoch 9/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3899 - loss: 2.0665 - val_loss: 0.3186 - val_mae: 0.3869\n",
            "Epoch 10/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.3836 - loss: 2.0659 - val_loss: 0.3053 - val_mae: 0.3806\n",
            "Epoch 11/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - mae: 0.3828 - loss: 2.0756 - val_loss: 0.3049 - val_mae: 0.3823\n",
            "Epoch 12/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3743 - loss: 2.0591 - val_loss: 0.3024 - val_mae: 0.3884\n",
            "Epoch 13/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3776 - loss: 2.0828 - val_loss: 0.3009 - val_mae: 0.3697\n",
            "Epoch 14/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3734 - loss: 2.0788 - val_loss: 0.2927 - val_mae: 0.3682\n",
            "Epoch 15/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3692 - loss: 2.0613 - val_loss: 0.2917 - val_mae: 0.3748\n",
            "Epoch 16/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3724 - loss: 2.0828 - val_loss: 0.2934 - val_mae: 0.3773\n",
            "Epoch 17/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3624 - loss: 2.0617 - val_loss: 0.2990 - val_mae: 0.3671\n",
            "Epoch 18/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3623 - loss: 2.0772 - val_loss: 0.2824 - val_mae: 0.3652\n",
            "Epoch 19/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3574 - loss: 2.0620 - val_loss: 0.2901 - val_mae: 0.3726\n",
            "Epoch 20/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - mae: 0.3536 - loss: 2.0678 - val_loss: 0.2944 - val_mae: 0.3876\n",
            "Epoch 21/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.3600 - loss: 2.0845 - val_loss: 0.2853 - val_mae: 0.3721\n",
            "Epoch 22/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3568 - loss: 2.0589 - val_loss: 0.2928 - val_mae: 0.3756\n",
            "Epoch 23/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3582 - loss: 2.0611 - val_loss: 0.2899 - val_mae: 0.3732\n",
            "Epoch 24/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3475 - loss: 2.0739 - val_loss: 0.2822 - val_mae: 0.3582\n",
            "Epoch 25/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3532 - loss: 2.0706 - val_loss: 0.2762 - val_mae: 0.3544\n",
            "Epoch 26/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3598 - loss: 2.0653 - val_loss: 0.2760 - val_mae: 0.3583\n",
            "Epoch 27/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3547 - loss: 2.0686 - val_loss: 0.2768 - val_mae: 0.3605\n",
            "Epoch 28/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3509 - loss: 2.0643 - val_loss: 0.2868 - val_mae: 0.3799\n",
            "Epoch 29/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3461 - loss: 2.0863 - val_loss: 0.2885 - val_mae: 0.3703\n",
            "Epoch 30/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - mae: 0.3510 - loss: 2.0707 - val_loss: 0.2727 - val_mae: 0.3545\n",
            "Epoch 31/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - mae: 0.3473 - loss: 2.0724 - val_loss: 0.2707 - val_mae: 0.3491\n",
            "Epoch 32/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3459 - loss: 2.0762 - val_loss: 0.2779 - val_mae: 0.3651\n",
            "Epoch 33/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3387 - loss: 2.0714 - val_loss: 0.2713 - val_mae: 0.3611\n",
            "Epoch 34/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3450 - loss: 2.0834 - val_loss: 0.2726 - val_mae: 0.3619\n",
            "Epoch 35/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3462 - loss: 2.0686 - val_loss: 0.2723 - val_mae: 0.3526\n",
            "Epoch 36/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3459 - loss: 2.0767 - val_loss: 0.2717 - val_mae: 0.3562\n",
            "Epoch 37/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3414 - loss: 2.0639 - val_loss: 0.2719 - val_mae: 0.3559\n",
            "Epoch 38/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3383 - loss: 2.0628 - val_loss: 0.2627 - val_mae: 0.3451\n",
            "Epoch 39/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - mae: 0.3364 - loss: 2.0919 - val_loss: 0.2760 - val_mae: 0.3512\n",
            "Epoch 40/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - mae: 0.3380 - loss: 2.0631 - val_loss: 0.2619 - val_mae: 0.3469\n",
            "Epoch 41/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3421 - loss: 2.0788 - val_loss: 0.2695 - val_mae: 0.3539\n",
            "Epoch 42/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3369 - loss: 2.0667 - val_loss: 0.2702 - val_mae: 0.3464\n",
            "Epoch 43/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3366 - loss: 2.0671 - val_loss: 0.2767 - val_mae: 0.3645\n",
            "Epoch 44/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3377 - loss: 2.0764 - val_loss: 0.2634 - val_mae: 0.3485\n",
            "Epoch 45/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3383 - loss: 2.0893 - val_loss: 0.2741 - val_mae: 0.3492\n",
            "Epoch 46/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3335 - loss: 2.0684 - val_loss: 0.2626 - val_mae: 0.3454\n",
            "Epoch 47/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3356 - loss: 2.0695 - val_loss: 0.2628 - val_mae: 0.3462\n",
            "Epoch 48/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.3427 - loss: 2.0901 - val_loss: 0.2710 - val_mae: 0.3602\n",
            "Epoch 49/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - mae: 0.3347 - loss: 2.0861 - val_loss: 0.2659 - val_mae: 0.3444\n",
            "Epoch 50/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3349 - loss: 2.0565 - val_loss: 0.2621 - val_mae: 0.3466\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 6: Plot Training History\n"
      ],
      "metadata": {
        "id": "pGY8O0BGmr8_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the available keys in the history to check the metric names\n",
        "print(history.history.keys())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aS8bqRiEpYUV",
        "outputId": "a2138c42-56d3-4895-95ee-c0a2c39d101d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['loss', 'mae', 'val_loss', 'val_mae'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot training history\n",
        "plt.figure(figsize=(12, 5))\n",
        "\n",
        "# Plot loss\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(history.history['loss'], label='Train Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.title('Loss History')\n",
        "\n",
        "# Plot MAE\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(history.history['mae'], label='Train MAE')\n",
        "plt.plot(history.history['val_mae'], label='Validation MAE')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Mean Absolute Error')\n",
        "plt.legend()\n",
        "plt.title('MAE History')\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "w53CP1ekmAMM",
        "outputId": "d6bacf13-ed07-455c-f48d-868bcdee527e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 7: Evaluate the Model\n"
      ],
      "metadata": {
        "id": "gIbcBsAAmpmR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model on test data\n",
        "test_loss, test_mae = pimtl_model.evaluate(X_test, [y_test, physics_target_test])\n",
        "\n",
        "print(f'Test Loss: {test_loss}')\n",
        "print(f'Test MAE: {test_mae}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "slc9yZFfmoFh",
        "outputId": "c50cfb03-c42d-4e95-a1c8-b310bcdaa431"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.2539 - mae: 0.3426\n",
            "Test Loss: 0.2620503604412079\n",
            "Test MAE: 0.34663113951683044\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Implementing Physics-Informed Kolmogorov-Arnold Networks (PIKAR)"
      ],
      "metadata": {
        "id": "Ebsh03YIwB-L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 1: Load and Preprocess the California Housing Dataset\n"
      ],
      "metadata": {
        "id": "xRViZJn6CjqV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WRRg1XSu2V5p"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from sklearn.datasets import fetch_california_housing\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import numpy as np\n",
        "\n",
        "# Load the California Housing dataset\n",
        "data = fetch_california_housing()\n",
        "\n",
        "# Split the data into training and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)\n",
        "\n",
        "# Standardize the dataset\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Convert targets to float32 for TensorFlow compatibility\n",
        "y_train = y_train.astype(np.float32)\n",
        "y_test = y_test.astype(np.float32)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 2: Build a PIKAN-Inspired Neural Network\n"
      ],
      "metadata": {
        "id": "mA5xeCMjCuum"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras import layers, models\n",
        "\n",
        "# Define the PIKAN-inspired network structure\n",
        "def create_pikan_model(input_shape):\n",
        "    # Input layer\n",
        "    inputs = layers.Input(shape=input_shape)\n",
        "\n",
        "    # Subnet 1\n",
        "    x1 = layers.Dense(64, activation='relu')(inputs)\n",
        "    x1 = layers.Dense(32, activation='relu')(x1)\n",
        "    x1 = layers.Dense(1)(x1)\n",
        "\n",
        "    # Subnet 2\n",
        "    x2 = layers.Dense(64, activation='relu')(inputs)\n",
        "    x2 = layers.Dense(32, activation='relu')(x2)\n",
        "    x2 = layers.Dense(1)(x2)\n",
        "\n",
        "    # Subnet 3\n",
        "    x3 = layers.Dense(64, activation='relu')(inputs)\n",
        "    x3 = layers.Dense(32, activation='relu')(x3)\n",
        "    x3 = layers.Dense(1)(x3)\n",
        "\n",
        "    # Combine the subnets using a sum operation\n",
        "    combined = layers.Add()([x1, x2, x3])\n",
        "\n",
        "    # Output layer\n",
        "    outputs = layers.Dense(1)(combined)\n",
        "\n",
        "    # Create the model\n",
        "    model = models.Model(inputs=inputs, outputs=outputs)\n",
        "    return model\n",
        "\n",
        "# Instantiate the model\n",
        "input_shape = X_train.shape[1:]\n",
        "pikan_model = create_pikan_model(input_shape)\n",
        "\n",
        "# Compile the model\n",
        "pikan_model.compile(optimizer='adam', loss='mse', metrics=['mae'])\n",
        "\n",
        "# Display the model architecture\n",
        "pikan_model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "UdCtP9fN8LSe",
        "outputId": "6ba8a68c-8f50-4bf0-a43a-b3b37f0be466"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"functional\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m       Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to          \u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)              │              \u001b[38;5;34m0\u001b[0m │ -                      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │            \u001b[38;5;34m576\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │            \u001b[38;5;34m576\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │            \u001b[38;5;34m576\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │          \u001b[38;5;34m2,080\u001b[0m │ dense[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │          \u001b[38;5;34m2,080\u001b[0m │ dense_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │          \u001b[38;5;34m2,080\u001b[0m │ dense_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m33\u001b[0m │ dense_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m33\u001b[0m │ dense_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m33\u001b[0m │ dense_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ add (\u001b[38;5;33mAdd\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │              \u001b[38;5;34m0\u001b[0m │ dense_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],         │\n",
              "│                           │                        │                │ dense_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],         │\n",
              "│                           │                        │                │ dense_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_9 (\u001b[38;5;33mDense\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │              \u001b[38;5;34m2\u001b[0m │ add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)              </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">        Param # </span>┃<span style=\"font-weight: bold\"> Connected to           </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)              │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │            <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span> │ input_layer[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │            <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span> │ input_layer[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │            <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span> │ input_layer[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │          <span style=\"color: #00af00; text-decoration-color: #00af00\">2,080</span> │ dense[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]            │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │          <span style=\"color: #00af00; text-decoration-color: #00af00\">2,080</span> │ dense_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │          <span style=\"color: #00af00; text-decoration-color: #00af00\">2,080</span> │ dense_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span> │ dense_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span> │ dense_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span> │ dense_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],         │\n",
              "│                           │                        │                │ dense_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],         │\n",
              "│                           │                        │                │ dense_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │              <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> │ add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,069\u001b[0m (31.52 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">8,069</span> (31.52 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,069\u001b[0m (31.52 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">8,069</span> (31.52 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 3: Train the Network\n"
      ],
      "metadata": {
        "id": "mQ6PoDEnC7S_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "history = pikan_model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=50, batch_size=32)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LBjYITuG8Vjf",
        "outputId": "3b28c25a-a3d4-451b-ac38-4f802acbf252"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 6ms/step - loss: 0.8737 - mae: 0.6366 - val_loss: 0.8829 - val_mae: 0.4807\n",
            "Epoch 2/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - loss: 0.4007 - mae: 0.4400 - val_loss: 0.3850 - val_mae: 0.4224\n",
            "Epoch 3/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - loss: 0.3892 - mae: 0.4223 - val_loss: 0.3652 - val_mae: 0.4343\n",
            "Epoch 4/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4ms/step - loss: 0.3290 - mae: 0.4014 - val_loss: 0.3365 - val_mae: 0.4057\n",
            "Epoch 5/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.3216 - mae: 0.3969 - val_loss: 0.3192 - val_mae: 0.3857\n",
            "Epoch 6/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - loss: 0.3071 - mae: 0.3874 - val_loss: 0.3124 - val_mae: 0.3960\n",
            "Epoch 7/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.3214 - mae: 0.3950 - val_loss: 0.3215 - val_mae: 0.4070\n",
            "Epoch 8/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.3204 - mae: 0.3798 - val_loss: 0.3107 - val_mae: 0.3869\n",
            "Epoch 9/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.3090 - mae: 0.3808 - val_loss: 0.2958 - val_mae: 0.3666\n",
            "Epoch 10/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.2843 - mae: 0.3690 - val_loss: 0.2969 - val_mae: 0.3697\n",
            "Epoch 11/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3012 - mae: 0.3722 - val_loss: 0.3013 - val_mae: 0.3879\n",
            "Epoch 12/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2763 - mae: 0.3655 - val_loss: 0.2889 - val_mae: 0.3682\n",
            "Epoch 13/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3404 - mae: 0.3673 - val_loss: 0.3034 - val_mae: 0.3694\n",
            "Epoch 14/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2776 - mae: 0.3581 - val_loss: 0.3758 - val_mae: 0.3788\n",
            "Epoch 15/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2730 - mae: 0.3584 - val_loss: 0.2999 - val_mae: 0.3730\n",
            "Epoch 16/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2636 - mae: 0.3536 - val_loss: 0.3154 - val_mae: 0.3655\n",
            "Epoch 17/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2684 - mae: 0.3538 - val_loss: 0.2885 - val_mae: 0.3736\n",
            "Epoch 18/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.2636 - mae: 0.3489 - val_loss: 0.2999 - val_mae: 0.3633\n",
            "Epoch 19/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.2681 - mae: 0.3498 - val_loss: 0.2834 - val_mae: 0.3572\n",
            "Epoch 20/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2596 - mae: 0.3469 - val_loss: 0.2797 - val_mae: 0.3663\n",
            "Epoch 21/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2449 - mae: 0.3379 - val_loss: 0.2781 - val_mae: 0.3558\n",
            "Epoch 22/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2503 - mae: 0.3429 - val_loss: 0.2978 - val_mae: 0.3825\n",
            "Epoch 23/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2454 - mae: 0.3384 - val_loss: 0.2833 - val_mae: 0.3610\n",
            "Epoch 24/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2570 - mae: 0.3412 - val_loss: 0.2806 - val_mae: 0.3529\n",
            "Epoch 25/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2407 - mae: 0.3346 - val_loss: 0.2931 - val_mae: 0.3739\n",
            "Epoch 26/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2390 - mae: 0.3375 - val_loss: 0.2862 - val_mae: 0.3698\n",
            "Epoch 27/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.2511 - mae: 0.3423 - val_loss: 0.2741 - val_mae: 0.3502\n",
            "Epoch 28/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.2527 - mae: 0.3427 - val_loss: 0.2913 - val_mae: 0.3591\n",
            "Epoch 29/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.2382 - mae: 0.3340 - val_loss: 0.2786 - val_mae: 0.3627\n",
            "Epoch 30/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.2419 - mae: 0.3344 - val_loss: 0.2727 - val_mae: 0.3612\n",
            "Epoch 31/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.2428 - mae: 0.3351 - val_loss: 0.2741 - val_mae: 0.3567\n",
            "Epoch 32/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.2420 - mae: 0.3357 - val_loss: 0.2751 - val_mae: 0.3538\n",
            "Epoch 33/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.2352 - mae: 0.3330 - val_loss: 0.2747 - val_mae: 0.3510\n",
            "Epoch 34/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - loss: 0.2431 - mae: 0.3286 - val_loss: 0.3104 - val_mae: 0.3609\n",
            "Epoch 35/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2419 - mae: 0.3322 - val_loss: 0.2746 - val_mae: 0.3513\n",
            "Epoch 36/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2657 - mae: 0.3360 - val_loss: 0.2827 - val_mae: 0.3583\n",
            "Epoch 37/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2318 - mae: 0.3295 - val_loss: 0.2747 - val_mae: 0.3476\n",
            "Epoch 38/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2317 - mae: 0.3299 - val_loss: 0.2672 - val_mae: 0.3465\n",
            "Epoch 39/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2282 - mae: 0.3232 - val_loss: 0.2653 - val_mae: 0.3468\n",
            "Epoch 40/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.2280 - mae: 0.3241 - val_loss: 0.2769 - val_mae: 0.3436\n",
            "Epoch 41/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.2371 - mae: 0.3316 - val_loss: 0.3087 - val_mae: 0.3601\n",
            "Epoch 42/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.2567 - mae: 0.3345 - val_loss: 0.2760 - val_mae: 0.3464\n",
            "Epoch 43/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.2309 - mae: 0.3240 - val_loss: 0.2748 - val_mae: 0.3469\n",
            "Epoch 44/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2275 - mae: 0.3298 - val_loss: 0.2850 - val_mae: 0.3577\n",
            "Epoch 45/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2303 - mae: 0.3276 - val_loss: 0.2751 - val_mae: 0.3447\n",
            "Epoch 46/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2273 - mae: 0.3235 - val_loss: 0.2676 - val_mae: 0.3480\n",
            "Epoch 47/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.2221 - mae: 0.3223 - val_loss: 0.2788 - val_mae: 0.3544\n",
            "Epoch 48/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.2238 - mae: 0.3233 - val_loss: 0.2662 - val_mae: 0.3465\n",
            "Epoch 49/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2303 - mae: 0.3294 - val_loss: 0.2700 - val_mae: 0.3552\n",
            "Epoch 50/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.2221 - mae: 0.3234 - val_loss: 0.2761 - val_mae: 0.3453\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 4: Evaluate and Visualize the Results\n"
      ],
      "metadata": {
        "id": "E07gYdnwDD-c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "test_loss, test_mae = pikan_model.evaluate(X_test, y_test)\n",
        "\n",
        "print(f\"Test Loss: {test_loss:.4f}\")\n",
        "print(f\"Test MAE: {test_mae:.4f}\")\n",
        "\n",
        "# Plot training & validation loss values\n",
        "plt.figure(figsize=(14, 5))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(history.history['loss'], label='Train Loss')\n",
        "plt.plot(history.history['val_loss'], label='Val Loss')\n",
        "plt.title('Model Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "# Plot training & validation MAE values\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(history.history['mae'], label='Train MAE')\n",
        "plt.plot(history.history['val_mae'], label='Val MAE')\n",
        "plt.title('Model MAE')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('MAE')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "j2cdUD8DCCLw",
        "outputId": "2acbcaaa-2e78-46b6-dc36-15669fcd795c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.2700 - mae: 0.3460\n",
            "Test Loss: 0.2761\n",
            "Test MAE: 0.3453\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x500 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyMOedC7LWgKozPQlaKWMqWq",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}