PINNs_S4&5.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%265.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> You know how we can use blocks to build all sorts of things, like a castle or a car? And we know how blocks work, like how they stack up and how they don't just float away.Well, PINNs are like super smart blocks for computers. But instead of building with blocks, they use special rules about how the world works, like how things move or how heat spreads. These rules are like the blocks!Just like we can use blocks and our knowledge of how they work to build lots of different things, PINNs use these special rules and build with them to understand all sorts of problems, like how water flows or how the air moves around a plane.And the really cool thing about PINNs is that they can learn and get better at building with these rule blocks over time, just like how you get better at building with blocks the more you practice!"
      ],
      "metadata": {
        "id": "fxev3SeNZVlb"
      }
    },
    {
      "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": null,
      "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": null,
      "metadata": {
        "id": "cooyKgwOzH1R"
      },
      "outputs": [],
      "source": [
        "# Load the dataset\n",
        "california_housing = fetch_california_housing(as_frame=True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "as_frame (default: False): If set to True, the function returns the dataset as a pandas DataFrame rather than as a NumPy array.\n",
        "\n"
      ],
      "metadata": {
        "id": "5kvjC1Slurwy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(california_housing.DESCR)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QVENGU5UaFym",
        "outputId": "bbbf74ef-e769-4edb-9dd9-424115a1d0b1"
      },
      "execution_count": null,
      "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": "99432f51-6508-4344-9b0c-17003b23b18e"
      },
      "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  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-804089b2-1094-4ff7-be35-cd20495901b6\" 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-804089b2-1094-4ff7-be35-cd20495901b6')\"\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-804089b2-1094-4ff7-be35-cd20495901b6 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-804089b2-1094-4ff7-be35-cd20495901b6');\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-43556ef9-0f30-48e3-aeaa-6f21080c2705\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-43556ef9-0f30-48e3-aeaa-6f21080c2705')\"\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-43556ef9-0f30-48e3-aeaa-6f21080c2705 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\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": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "california_housing.data.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HQ7Zp2_3aLjF",
        "outputId": "9338072b-9209-419e-8296-b7595532561e"
      },
      "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  \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-77ef4a27-e012-4664-beb1-df11ef7b23eb\" 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-77ef4a27-e012-4664-beb1-df11ef7b23eb')\"\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-77ef4a27-e012-4664-beb1-df11ef7b23eb 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-77ef4a27-e012-4664-beb1-df11ef7b23eb');\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-62801daa-5c92-4f9c-bfd7-c406c858e687\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-62801daa-5c92-4f9c-bfd7-c406c858e687')\"\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-62801daa-5c92-4f9c-bfd7-c406c858e687 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "california_housing.target.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "DYN7EkGwaMvs",
        "outputId": "ddedfdc2-938f-4088-8d67-c4671f022dc2"
      },
      "execution_count": null,
      "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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "california_housing.frame.info()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uz-xLBBOajlG",
        "outputId": "94985182-9fe2-46cc-d172-da6200a85ede"
      },
      "execution_count": null,
      "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": "2fab9e06-01e2-42a5-f560-b3d7ff3c8a05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 9 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "1. **Importing Library**: `import matplotlib.pyplot as plt` imports the `matplotlib.pyplot` module, which contains functions for creating various types of plots.\n",
        "\n",
        "2. **Creating Histograms**:\n",
        "   - `california_housing.frame.hist(...)`: This calls the `hist()` method on the `frame` of the `california_housing` dataset, which generates histograms for all numerical columns.\n",
        "   - `figsize=(12, 10)`: This sets the size of the figure to 12 inches wide and 10 inches tall.\n",
        "   - `bins=30`: Specifies that each histogram should be divided into 30 bins (intervals).\n",
        "   - `edgecolor=\"black\"`: This sets the color of the edges of the bins to black, making them more visually distinct.\n",
        "\n",
        "3. **Adjusting Subplots**:\n",
        "   - `plt.subplots_adjust(hspace=0.7, wspace=0.4)`: This adjusts the spacing of the subplots, where `hspace=0.7` controls the vertical space between rows of plots and `wspace=0.4` controls the horizontal space between columns of plots.\n",
        "\n"
      ],
      "metadata": {
        "id": "TTRT66WKwGW0"
      }
    },
    {
      "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": "4e9f6662-c64c-4c4f-f7ec-9b84482917a0"
      },
      "execution_count": null,
      "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-73d58b7f-22fc-476b-a792-9afa552deb17\" 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-73d58b7f-22fc-476b-a792-9afa552deb17')\"\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-73d58b7f-22fc-476b-a792-9afa552deb17 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-73d58b7f-22fc-476b-a792-9afa552deb17');\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-d9671fcb-4405-48aa-90eb-3b58bd23b282\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d9671fcb-4405-48aa-90eb-3b58bd23b282')\"\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-d9671fcb-4405-48aa-90eb-3b58bd23b282 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\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": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "1. **Defining Features of Interest**:\n",
        "   ```python\n",
        "   features_of_interest = [\"AveRooms\", \"AveBedrms\", \"AveOccup\", \"Population\"]\n",
        "   ```\n",
        "   - This line creates a list named `features_of_interest`, which includes four specific columns:\n",
        "     - `AveRooms`: Average number of rooms per dwelling.\n",
        "     - `AveBedrms`: Average number of bedrooms per dwelling.\n",
        "     - `AveOccup`: Average number of occupants per dwelling.\n",
        "     - `Population`: The population in the area.\n",
        "\n",
        "2. **Descriptive Statistics**:\n",
        "   ```python\n",
        "   california_housing.frame[features_of_interest].describe()\n",
        "   ```\n",
        "   - This line accesses the DataFrame of the California housing dataset and selects only the columns specified in `features_of_interest`.\n",
        "   - The `describe()` method is then called on this filtered DataFrame, which computes and returns descriptive statistics for the selected features. This typically includes:\n",
        "     - Count: Number of non-null entries.\n",
        "     - Mean: Average value.\n",
        "     - Standard Deviation (std): Measure of variation.\n",
        "     - Minimum (min): The smallest value.\n",
        "     - 25th, 50th (median), and 75th percentiles: Measures of central tendency and dispersion.\n",
        "     - Maximum (max): The largest value.\n",
        "\n",
        "In summary, this code filters the California housing dataset to focus on four specific features and then computes descriptive statistics for those features, providing a summary of their distributions.\n"
      ],
      "metadata": {
        "id": "Ijq2pfu90ayf"
      }
    },
    {
      "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": "a7385235-59d1-49fe-8c64-be20145d0175"
      },
      "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-98c72852-c722-4c93-bc28-4c03aed3874a\" 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-98c72852-c722-4c93-bc28-4c03aed3874a')\"\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-98c72852-c722-4c93-bc28-4c03aed3874a 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-98c72852-c722-4c93-bc28-4c03aed3874a');\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-a291adcd-86c6-4d68-8124-f4c14637f1d4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a291adcd-86c6-4d68-8124-f4c14637f1d4')\"\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-a291adcd-86c6-4d68-8124-f4c14637f1d4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\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": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "1. **Convert to DataFrame**:\n",
        "   ```python\n",
        "   df = pd.DataFrame(california_housing.data, columns=california_housing.feature_names)\n",
        "   ```\n",
        "   - This line creates a new DataFrame named `df` using the `pd.DataFrame()` constructor from the pandas library.\n",
        "   - `california_housing.data`: This contains the actual data values from the California housing dataset.\n",
        "   - `columns=california_housing.feature_names`: This assigns the column names to the DataFrame based on the names provided in the dataset.\n",
        "\n",
        "2. **Add Target Column**:\n",
        "   ```python\n",
        "   df['Target'] = california_housing.target\n",
        "   ```\n",
        "   - This line adds a new column named `Target` to the DataFrame `df`.\n",
        "   - `california_housing.target`: This contains the target variable (the house prices) associated with the dataset.\n",
        "\n",
        "3. **Display the First Few Rows**:\n",
        "   ```python\n",
        "   df.head()\n",
        "   ```\n",
        "   - This function call retrieves and displays the first five rows of the DataFrame `df`.\n",
        "   - This is useful for getting a quick overview of the dataset, including the features and the target.\n",
        "\n",
        "In summary, the code creates a more user-friendly DataFrame from the California housing dataset, adds the target variable, and then shows the initial rows to allow for better visualization and inspection of the data.\n"
      ],
      "metadata": {
        "id": "nMTFFUBz1sxN"
      }
    },
    {
      "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": "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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "PsrDoOXvVj-W",
        "outputId": "f7f2cc17-f78e-4467-fd72-d3e008e52f27"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "1. **Importing Matplotlib**:\n",
        "   ```python\n",
        "   from matplotlib import pyplot as plt\n",
        "   ```\n",
        "   - This line imports the `pyplot` module from the `matplotlib` library, which is used for creating visualizations.\n",
        "\n",
        "2. **Creating Histogram**:\n",
        "   ```python\n",
        "   df['HouseAge'].plot(kind='hist', bins=20, title='HouseAge')\n",
        "   ```\n",
        "   - `df['HouseAge']`: This accesses the \"HouseAge\" column from the DataFrame `df`.\n",
        "   - `.plot(kind='hist', ...)`: This method is used to create a histogram.\n",
        "     - `kind='hist'`: Specifies that the plot type is a histogram.\n",
        "     - `bins=20`: Sets the number of bins (intervals) to 20, which determines how the data will be grouped.\n",
        "     - `title='HouseAge'`: Labels the histogram with the title \"HouseAge\".\n",
        "\n",
        "3. **Hiding Top and Right Spines**:\n",
        "   ```python\n",
        "   plt.gca().spines[['top', 'right',]].set_visible(False)\n",
        "   ```\n",
        "   - `plt.gca()`: Gets the current Axes instance on the current figure.\n",
        "   - `.spines[['top', 'right',]]`: Accesses the top and right spines (the lines that form the borders of the plot).\n",
        "   - `.set_visible(False)`: Hides the top and right spines to create a cleaner visual appearance for the histogram.\n",
        "\n",
        "In summary, this code snippet generates a histogram to show the distribution of house ages in the dataset and modifies the plot's appearance by removing the top and right borders for better aesthetics.\n"
      ],
      "metadata": {
        "id": "aulBUw1U179S"
      }
    },
    {
      "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": [
        "\n",
        "1. **Split the Data into Features and Target**:\n",
        "   ```python\n",
        "   X = df.drop('Target', axis=1)\n",
        "   y = df['Target']\n",
        "   ```\n",
        "   - `X = df.drop('Target', axis=1)`: This line creates a new DataFrame `X` that contains all columns from `df` except the \"Target\" column (which holds the house prices). The `axis=1` parameter indicates that a column is being dropped.\n",
        "   - `y = df['Target']`: This line creates a variable `y` which holds only the \"Target\" column, representing the dependent variable (house prices).\n",
        "\n",
        "2. **Split into Train and Test Sets**:\n",
        "   ```python\n",
        "   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "   ```\n",
        "   - This line uses the `train_test_split` function (typically imported from `sklearn.model_selection`) to divide the data into training and testing sets.\n",
        "   - `X_train, X_test`: These variables store the features for training and testing, respectively.\n",
        "   - `y_train, y_test`: These variables store the target values for training and testing, respectively.\n",
        "   - `test_size=0.2`: This specifies that 20% of the data will be used for testing, while the remaining 80% will be used for training.\n",
        "   - `random_state=42`: This parameter sets a seed for reproducibility, ensuring that every time this code is run, the same split of the data will be achieved.\n",
        "\n",
        "In summary, the code separates the dataset into features and target values, and it splits this data into training and testing sets to prepare for modeling.\n"
      ],
      "metadata": {
        "id": "fKi9KDbF2o1k"
      }
    },
    {
      "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": [
        "\n",
        "1. **Importing the StandardScaler**:\n",
        "   ```python\n",
        "   scaler = StandardScaler()\n",
        "   ```\n",
        "   - This line creates an instance of `StandardScaler`, which is a preprocessing tool from the `sklearn.preprocessing` module. It standardizes features by removing the mean and scaling to unit variance.\n",
        "\n",
        "2. **Fitting and Transforming the Training Data**:\n",
        "   ```python\n",
        "   X_train = scaler.fit_transform(X_train)\n",
        "   ```\n",
        "   - `scaler.fit_transform(X_train)`: This method first fits the `StandardScaler` to the training data (`X_train`) and then transforms it. The fitting process calculates the mean and standard deviation for each feature in the training data.\n",
        "   - The transformed data replaces the original `X_train`, resulting in scale-adjusted feature values that have a mean of 0 and a standard deviation of 1.\n",
        "\n",
        "3. **Transforming the Testing Data**:\n",
        "   ```python\n",
        "   X_test = scaler.transform(X_test)\n",
        "   ```\n",
        "   - `scaler.transform(X_test)`: This method is used to transform the testing data (`X_test`) using the same scaling parameters (mean and standard deviation) calculated from the training data. This ensures that the test data is scaled consistently with the training data.\n",
        "   - The transformed data replaces the original `X_test`.\n",
        "\n",
        "In summary, this code scales the features of both the training and testing datasets to have a mean of 0 and unit variance, which can improve the performance of machine learning algorithms.\n"
      ],
      "metadata": {
        "id": "hNEgipZ73W4I"
      }
    },
    {
      "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": "009bb0f0-ecfd-4c80-cc5a-4cec8bd5a893"
      },
      "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": "markdown",
      "source": [
        "\n",
        "1. **Create the Model**:\n",
        "   ```python\n",
        "   model = models.Sequential()\n",
        "   ```\n",
        "   - This line initializes a sequential model, which is a linear stack of layers. The Keras `Sequential` API allows you to build a neural network layer by layer.\n",
        "\n",
        "2. **Add Layers**:\n",
        "   ```python\n",
        "   model.add(layers.Dense(64, activation='relu', input_shape=(X_train.shape[1],)))\n",
        "   ```\n",
        "   - `model.add(layers.Dense(64, activation='relu', ...))`: This adds a dense (fully connected) layer with 64 neurons.\n",
        "     - `activation='relu'`: Specifies the ReLU (Rectified Linear Unit) activation function, which introduces non-linearity to the model.\n",
        "     - `input_shape=(X_train.shape[1],)`: Defines the shape of the input data. `X_train.shape[1]` represents the number of features in the training data.\n",
        "\n",
        "   ```python\n",
        "   model.add(layers.Dense(64, activation='relu'))\n",
        "   ```\n",
        "   - This adds another dense layer with 64 neurons and the same ReLU activation function, creating a second hidden layer.\n",
        "\n",
        "   ```python\n",
        "   model.add(layers.Dense(1))\n",
        "   ```\n",
        "   - This line adds the output layer with a single neuron. This is typical for regression tasks (predicting continuous values) like house prices.\n",
        "\n",
        "3. **Compile the Model**:\n",
        "   ```python\n",
        "   model.compile(optimizer='adam', loss='mse', metrics=['mae'])\n",
        "   ```\n",
        "   - This line configures the model for training.\n",
        "     - `optimizer='adam'`: Uses the Adam optimizer, which is an efficient stochastic gradient descent algorithm.\n",
        "     - `loss='mse'`: Sets the loss function to mean squared error (MSE), which measures the average squared difference between predicted and actual values—a common choice for regression problems.\n",
        "     - `metrics=['mae']`: Specifies that mean absolute error (MAE) should also be tracked during training as a performance metric.\n",
        "\n",
        "In summary, this code builds a neural network with two hidden layers, compiles it with the Adam optimizer and MSE loss function, and prepares it for training on regression tasks like predicting house prices.\n"
      ],
      "metadata": {
        "id": "-c-DE-aS4I4D"
      }
    },
    {
      "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": "iVBORw0KGgoAAAANSUhEUgAAAjIAAAGwCAYAAACzXI8XAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABwx0lEQVR4nO3dd3iT5foH8O+btE333lBoKWUVKMgeKhsRwXkABWSoKAIyRJCfIuBCj4qoeOCoQJ2AKCDKAWTJ3lD2ptACLaWU7jbNeH9/vE2adKRJmzRN+X6uK1eTN2+Su6E0d+/nfp5HEEVRBBEREZEDktk7ACIiIqKqYiJDREREDouJDBERETksJjJERETksJjIEBERkcNiIkNEREQOi4kMEREROSwnewdga1qtFrdu3YKXlxcEQbB3OERERGQGURSRk5OD8PBwyGQV113qfCJz69YtRERE2DsMIiIiqoLk5GTUr1+/wvvrfCLj5eUFQHojvL297RwNERERmSM7OxsRERH6z/GK1PlERjec5O3tzUSGiIjIwVTWFsJmXyIiInJYTGSIiIjIYTGRISIiIodV53tkiIioejQaDVQqlb3DoDrG2dkZcrm82s/DRIaIiMoliiJSU1ORmZlp71CojvL19UVoaGi11nljIkNEROXSJTHBwcFwd3fnoqJkNaIoIj8/H2lpaQCAsLCwKj8XExkiIipDo9Hok5iAgAB7h0N1kJubGwAgLS0NwcHBVR5mYrMvERGVoeuJcXd3t3MkVJfpfr6q04PFRIaIiCrE4SSyJWv8fDGRISIiIofFRIaIiIgcFhMZIiKiSkRGRmLhwoX2DoPKwUSmNlAVAFqtvaMgInJ4giCYvMydO7dKz3v48GGMGzeuWrH16NEDgiDgo48+KnPfwIEDK4xvxYoVkMvlmDBhQpn7/vnnnwq/19TU1GrF6yiYyNhbQSawoDmwaoS9IyEicngpKSn6y8KFC+Ht7W10bPr06fpzRVGEWq0263mDgoKsMoMrIiIC8fHxRsdu3ryJbdu2VbiWytKlSzFjxgysWLEChYWF5Z5z4cIFo+8zJSUFwcHB1Y7XETCRsbeMK0DBPeDGIXtHQkRkkiiKyC9S2+UiiqJZMYaGhuovPj4+EARBf/v8+fPw8vLCxo0b0a5dOygUCuzZswdXrlzB448/jpCQEHh6eqJDhw7YunWr0fOWHloSBAHfffcdnnzySbi7uyMmJgbr16+vNL7HHnsM6enp2Lt3r/7Y999/j379+pWbeCQmJmLfvn1488030aRJE6xZs6bc5w0ODjb63kNDQyGT3R8f8VwQz97UyuKvRfaNg4ioEgUqDVq8s9kur3323f5wd7HOR9abb76JTz/9FI0aNYKfnx+Sk5Px6KOP4oMPPoBCocAPP/yAQYMG4cKFC2jQoEGFzzNv3jz8+9//xieffIKvvvoKw4cPx/Xr1+Hv71/hY1xcXDB8+HAsX74c3bp1AwDEx8fj3//+d7nDSsuXL8fAgQPh4+ODESNGYOnSpXjuueeq/R7UJfdHulab6ROZ8suFRERkXe+++y769u2L6Oho+Pv7Iy4uDi+//DJatmyJmJgYvPfee4iOjq60wjJ69Gg8++yzaNy4MT788EPk5ubi0KHKq+tjx47Fr7/+iry8POzatQtZWVl47LHHypyn1WoRHx+PESOk1oNhw4Zhz549SExMLHNu/fr14enpqb/Exsaa+W44PlZk7E1TXInRKAFRBLj4FBHVUm7Ocpx9t7/dXtta2rdvb3Q7NzcXc+fOxYYNG5CSkgK1Wo2CggIkJSWZfJ7WrVvrr3t4eMDb21u/d5ApcXFxiImJwW+//YYdO3Zg5MiRcHIq+3G8ZcsW5OXl4dFHHwUABAYGom/fvli2bBnee+89o3N3794NLy8v/W1nZ+dK46grmMjYm64iAwAaFeDkYr9YiIhMEATBasM79uTh4WF0e/r06diyZQs+/fRTNG7cGG5ubnjmmWdQVGR6yL90siAIArRmzkAdO3Ysvv76a5w9e7bCKs7SpUuRkZGh35MIkKo0J0+exLx584x6YKKiouDr62vWa9c1jv8T6eiMEhklExkiohq2d+9ejB49Gk8++SQAqUJz7do1m77mc889h+nTpyMuLg4tWrQoc//du3fxxx9/YOXKlUbDRBqNBt27d8fff/+NRx55xKYxOgomMvamMUhk1EWAwn6hEBHdj2JiYrBmzRoMGjQIgiBg9uzZZldWqsrPzw8pKSkVDgH9+OOPCAgIwJAhQ8rsR/Too49i6dKlRolMWlpamanZAQEB98UQE5t97a10RYaIiGrUggUL4Ofnh65du2LQoEHo378/HnjgAZu/rq+vb5lhLp1ly5bhySefLHdTxaeffhrr169Henq6/ljTpk0RFhZmdDl69KjNYq9NBNHcyfkOKjs7Gz4+PsjKyoK3t7e9wynrwGJg05vS9dcSAP8ou4ZDRAQAhYWFSExMRFRUFFxdXe0dDtVRpn7OzP38ZkXG3gynXatZkSEiIrIEExl7M1wIj0NLREREFmEiY2+lm32JiIjIbHZNZHbt2oVBgwYhPDwcgiBg3bp1FZ77yiuvQBCEureNOpt9iYiIqsyuiUxeXh7i4uLw9ddfmzxv7dq1OHDgAMLDw2soshpkmMiwR4aIiMgidl1HZsCAARgwYIDJc27evIlJkyZh8+bNGDhwYA1FVoMMqzAaDi0RERFZolYviKfVajFy5Ei88cYbZm+ApVQqoVSWJAfZ2dm2Cs86DPtiWJEhIiKySK1u9v3444/h5OSE1157zezHzJ8/Hz4+PvpLRESEDSO0Ag2HloiIiKqq1iYyR48exRdffIH4+PhyVzasyKxZs5CVlaW/JCcn2zBKK2CzLxFRrdOjRw9MmTJFfzsyMrLSySaVTVoxl7We535RaxOZ3bt3Iy0tDQ0aNICTkxOcnJxw/fp1vP7664iMjKzwcQqFAt7e3kaXWo3NvkREVjNo0KAKN1PcvXs3BEHAyZMnLX7ew4cPY9y4cdUNz8jcuXPRpk2bMsdTUlIq7R+tLl2RoHnz5mXuW716NQRBKPeztqCgAP7+/ggMDDRq49CJjIyEIAhlLh999JEtvg0AtbhHZuTIkejTp4/Rsf79+2PkyJEYM2aMnaKyAcMGXzb7EhFVywsvvICnn34aN27cQP369Y3uW758Odq3b4/WrVtb/LxBQUHWCrFSoaGhNfI6Hh4eSEtLw/79+9GlSxf98aVLl6JBgwblPub3339HbGwsRFHEunXrMHTo0DLnvPvuu3jppZeMjnl5eVk3eAN2rcjk5uYiISEBCQkJAIDExEQkJCQgKSkJAQEBaNmypdHF2dkZoaGhaNq0qT3Dti5WZIiIrOaxxx5DUFAQ4uPjjY7n5uZi9erVeOGFF3D37l08++yzqFevHtzd3dGqVSusWLHC5POWHlq6dOkSHnroIbi6uqJFixbYsmVLmcfMnDkTTZo0gbu7Oxo1aoTZs2dDpVIBkCoi8+bNw4kTJ/RVC13MpYeWTp06hV69esHNzQ0BAQEYN24ccnNz9fePHj0aTzzxBD799FOEhYUhICAAEyZM0L9WRZycnPDcc89h2bJl+mM3btzAP//8g+eee67cxyxduhQjRozAiBEjsHTp0nLP8fLyQmhoqNGlos0xrcGuFZkjR46gZ8+e+tvTpk0DAIwaNarMD2GdZbjXEisyRFSbiSKgyrfPazu7A2b0Szo5OeH5559HfHw83nrrLX2P5erVq6HRaPDss88iNzcX7dq1w8yZM+Ht7Y0NGzZg5MiRiI6ORseOHSt9Da1Wi6eeegohISE4ePAgsrKyjPppdLy8vBAfH4/w8HCcOnUKL730Ery8vDBjxgwMHToUp0+fxqZNm7B161YAgI+PT5nnyMvLQ//+/dGlSxccPnwYaWlpePHFFzFx4kSjz8kdO3YgLCwMO3bswOXLlzF06FC0adOmTGWktLFjx6JHjx744osv4O7ujvj4eDzyyCMICQkpc+6VK1ewf/9+rFmzBqIoYurUqbh+/ToaNmxY6XtmS3ZNZHr06AFLNt++du2a7YKxFw2nXxORg1DlAx/aaWHS/7sFuJj3V/3YsWPxySefYOfOnejRowcAaVjp6aef1s9onT59uv583Vplv/76q1mJzNatW3H+/Hls3rxZv1Drhx9+WKav5e2339Zfj4yMxPTp07Fy5UrMmDEDbm5u8PT0hJOTk8mhpF9++QWFhYX44Ycf9FWNRYsWYdCgQfj444/1CYefnx8WLVoEuVyOZs2aYeDAgdi2bVuliUzbtm3RqFEj/Pbbbxg5ciTi4+OxYMECXL16tcy5y5Ytw4ABA+Dn5wdAavdYvnw55s6da3TezJkzjb53ANi4cSMefPBBk7FUVa1t9r1vcGiJiMiqmjVrhq5du+qHTC5fvozdu3fjhRdeAABoNBq89957aNWqFfz9/eHp6YnNmzcjKSnJrOc/d+4cIiIijFabN+wx0Vm1ahW6deuG0NBQeHp64u233zb7NQxfKy4uzmhoplu3btBqtbhw4YL+WGxsLORyuf52WFgY0tLSzHqNsWPHYvny5di5cyfy8vLw6KOPljlHo9Hg+++/x4gRI/THRowYgfj4eGi1WqNz33jjDX3biO7Svn17s79nS9XaZt/7hoa7XxORg3B2lyoj9nptC7zwwguYNGkSvv76ayxfvhzR0dF4+OGHAQCffPIJvvjiCyxcuBCtWrWCh4cHpkyZgqIi6w3v79+/H8OHD8e8efPQv39/+Pj4YOXKlfjss8+s9hqGnJ2djW4LglAmwajI8OHDMWPGDMydOxcjR46Ek1PZ1GDz5s24efNmmeZejUaDbdu2oW/fvvpjgYGBaNy4cRW+i6phImNvhj0yrMgQUW0mCGYP79jbkCFDMHnyZPzyyy/44YcfMH78eH2/zN69e/H444/rqwtarRYXL15EixYtzHru5s2bIzk5GSkpKQgLCwMAHDhwwOicffv2oWHDhnjrrbf0x65fv250jouLCzQaTaWvFR8fj7y8PH1VZu/evZDJZFab+OLv74/Bgwfj119/xZIlS8o9Z+nSpRg2bJjR9wMAH3zwAZYuXWqUyNQ0Di3Zm5rTr4mIrM3T0xNDhw7FrFmzkJKSgtGjR+vvi4mJwZYtW7Bv3z6cO3cOL7/8Mm7fvm32c/fp0wdNmjTBqFGjcOLECezevbvMB3xMTAySkpKwcuVKXLlyBV9++SXWrl1rdE5kZKR+tm56enq567IMHz4crq6uGDVqFE6fPo0dO3Zg0qRJGDlyZLkNuVUVHx+P9PR0NGvWrMx9d+7cwZ9//olRo0aVmU38/PPPY926dcjIyNCfn5OTg9TUVKOLLbcLYiJjb9yigIjIJl544QXcu3cP/fv3N+pnefvtt/HAAw+gf//+6NGjB0JDQ/HEE0+Y/bwymQxr165FQUEBOnbsiBdffBEffPCB0TmDBw/G1KlTMXHiRLRp0wb79u3D7Nmzjc55+umn8cgjj6Bnz54ICgoqdwq4u7s7Nm/ejIyMDHTo0AHPPPMMevfujUWLFln2ZlRCN7W7PLpG4969e5e5r3fv3nBzc8NPP/2kP/bOO+8gLCzM6DJjxgyrxmtIEC2ZNuSAsrOz4ePjg6ysrNq3yq8oAvN8S243fRR41vRaBkRENaGwsBCJiYmIioqCq6urvcOhOsrUz5m5n9+syNhT6QoMKzJEREQWYSJjT6VnKTGRISIisggTGXtSl2ru5fRrIiIiizCRsSdWZIiIiKqFiYw9lU5cOP2aiGqZOj4fhOzMGj9fTGTsic2+RFRL6VaKzc+30yaRdF/Q/XyVXpnYElzZ155KDy2xIkNEtYRcLoevr69+vx53d3f9yrhE1SWKIvLz85GWlgZfX1+jfaIsxUTGnko3+7IiQ0S1iG5XZnM3HySylK+vr8ndv83BRMaedPssyRVSdYaJDBHVIoIgICwsDMHBwVCpVPYOh+oYZ2fnalVidJjI2JNuKEnhBeQrOf2aiGoluVxulQ8cIltgs6896SowCi/pq6ZI2raAiIiIzMJExp40pRIZgA2/REREFmAiY0+6ioyrT9ljREREVCkmMvakS1pcPEuOsSJDRERkNiYy9qRLWpxdAVnxYkCsyBAREZmNiYw96ZIWJ1fASVF8rNB+8RARETkYJjL2pEtk5C7SBeDQEhERkQWYyNiTbtaSk8KgIsOhJSIiInMxkbEnVmSIiIiqhYmMPemSFqMeGVZkiIiIzMVExp50jb1OCmm/JYDbFBAREVmAiYw96Xa/lruwIkNERFQFTGTsic2+RERE1cJExp7UBokMm32JiIgsxkTGnvSzlliRISIiqgomMvakYUWGiIioOpjI2BObfYmIiKqFiYw9aQz2WuL0ayIiIosxkbEnfbMvKzJERERVwUTGntjsS0REVC1MZOxJv0UB91oiIiKqCrsmMrt27cKgQYMQHh4OQRCwbt06/X0qlQozZ85Eq1at4OHhgfDwcDz//PO4deuW/QK2NlZkiIiIqsWuiUxeXh7i4uLw9ddfl7kvPz8fx44dw+zZs3Hs2DGsWbMGFy5cwODBg+0QqY2oDZt9dRUZJjJERETmcrLniw8YMAADBgwo9z4fHx9s2bLF6NiiRYvQsWNHJCUloUGDBuU+TqlUQqksSQays7OtF7C1acpr9uXQEhERkbkcqkcmKysLgiDA19e3wnPmz58PHx8f/SUiIqLmArSEKBoPLXH6NRERkcUcJpEpLCzEzJkz8eyzz8Lb27vC82bNmoWsrCz9JTk5uQajtIBWDUCUrnP6NRERUZXYdWjJXCqVCkOGDIEoili8eLHJcxUKBRQKRQ1FVg3qwpLrTq5MZIiIiKqg1icyuiTm+vXr2L59u8lqjEMx7IWRc68lIiKiqqjViYwuibl06RJ27NiBgIAAe4dkPbpeGJkTIJOxIkNERFQFdk1kcnNzcfnyZf3txMREJCQkwN/fH2FhYXjmmWdw7Ngx/PXXX9BoNEhNTQUA+Pv7w8XFxV5hW4dho6/hVzb7EhERmc2uicyRI0fQs2dP/e1p06YBAEaNGoW5c+di/fr1AIA2bdoYPW7Hjh3o0aNHTYVpG/o1ZIoTGKfixIzTr4mIiMxm10SmR48eEEWxwvtN3efwNKUSGVZkiIiILOYw06/rHF3lRdfkyx4ZIiIiizGRsZfSFRkmMkRERBZjImMvunVk9ENLnH5NRERkKSYy9qIfWmJFhoiIqKqYyNhLRc2+WhWg1donJiIiIgfDRMZeyjT7GqyLw+ElIiIiszCRsRd9j4yr9FVusD8Up2ATERGZhYmMveiqLk6lpl8D7JMhIiIyExMZeym9RYEglAwzMZEhIiIyCxMZe9E3+xr0xuhX92WPDBERkTmYyNiLfq8l15JjTqzIEBERWYKJjL2UHloyvM5mXyIiIrMwkbGX0s2+hte5AzYREZFZmMjYCysyRERE1cZExl7UpVb2NbzOHhkiIiKzMJGxl9JbFBheZyJDRERkFiYy9qIfWipv+jUTGSIiInMwkbEXfbOvYUWGzb5ERESWYCJjL6X3WgJYkSEiIrIQExl7Kb37NcCKDBERkYWYyNhLec2+rMgQERFZhImMvZRbkdHNWiqs+XiIiIgcEBMZeymvR0afyHBoiYiIyBxMZOyFQ0tERETVxkTGXtjsS0REVG1MZOyFFRkiIqJqYyJjL+XutcSKDBERkSUsSmQ0Gg127dqFzMxMG4VzH+Hu10RERNVmUSIjl8vRr18/3Lt3z1bx3B+0GkDUSNfL3TSS06+JiIjMYfHQUsuWLXH16lVbxHL/MNzdutx1ZDi0REREZA6LE5n3338f06dPx19//YWUlBRkZ2cbXcgMhhUX7rVERERUZU6WPuDRRx8FAAwePBiCIOiPi6IIQRCg0WisF11dpdv5WpABcoN/Ajb7EhERWcTiRGbHjh22iOP+Ul6jr+FtVmSIiIjMYnEi8/DDD9sijvuLriLj5GJ8nBUZIiIii1icyABAZmYmli5dinPnzgEAYmNjMXbsWPj4+Fg1uDqrvH2WgJKKDGctERERmcXiZt8jR44gOjoan3/+OTIyMpCRkYEFCxYgOjoax44ds0WMdY9+e4JSQ0u6xIZDS0RERGaxOJGZOnUqBg8ejGvXrmHNmjVYs2YNEhMT8dhjj2HKlCkWPdeuXbswaNAghIeHQxAErFu3zuh+URTxzjvvICwsDG5ubujTpw8uXbpkaci1j357Ag4tERERVUeVKjIzZ86Ek1PJqJSTkxNmzJiBI0eOWPRceXl5iIuLw9dff13u/f/+97/x5ZdfYsmSJTh48CA8PDzQv39/FBY6+NALm32JiIiswuIeGW9vbyQlJaFZs2ZGx5OTk+Hl5WXRcw0YMAADBgwo9z5RFLFw4UK8/fbbePzxxwEAP/zwA0JCQrBu3ToMGzbM0tBrj/L2WQJYkSEiIrKQxRWZoUOH4oUXXsCqVauQnJyM5ORkrFy5Ei+++CKeffZZqwWWmJiI1NRU9OnTR3/Mx8cHnTp1wv79+yt8nFKprP2L9JW38zXAigwREZGFLK7IfPrppxAEAc8//zzUajUAwNnZGePHj8dHH31ktcBSU1MBACEhIUbHQ0JC9PeVZ/78+Zg3b57V4rAJfbNv6R6Z4kRGq5b2Y5LJazYuIiIiB2Px7tcHDhzA3Llzce/ePSQkJCAhIQEZGRn4/PPPoVAoKn8SG5s1axaysrL0l+TkZHuHVFZFFRnD22pWZYiIiCpjUUVGt/v1uXPnEBUVhVatWtkqLoSGhgIAbt++jbCwMP3x27dvo02bNhU+TqFQ1IqEyiT9OjIVDC0BxcmOe42FRERE5Ihq7e7XUVFRCA0NxbZt2/THsrOzcfDgQXTp0sXmr29TFa0jI3cuew4RERFVyOIeGd3u1++99x7atWsHDw8Po/u9vb3Nfq7c3FxcvnxZfzsxMREJCQnw9/dHgwYNMGXKFLz//vuIiYlBVFQUZs+ejfDwcDzxxBOWhl27VDS0JAhScqNRsuGXiIjIDHbd/frIkSPo2bOn/va0adMAAKNGjUJ8fDxmzJiBvLw8jBs3DpmZmejevTs2bdoEV1fXip7SMVTU7AtIyY1GyYoMERGRGey6+3WPHj0gimKF9wuCgHfffRfvvvuu1V6zVqhoryWgJLlhRYaIiKhSFiUyKpUK7777LpYsWYKYmBhbxVT3VbT7NVAy3MRZS0RERJWyqNnX2dkZJ0+etFUs94+KtigAmMgQERFZwOJZSyNGjMDSpUttEcv9o6JNIwGu7ktERGQBi3tk1Go1li1bhq1bt5Y7a2nBggVWC67O0u+1VE6PDPdbIiIiMpvFiczp06fxwAMPAAAuXrxodJ/hLCYywdTQEisyREREZrPrrKX7Fpt9iYiIrMLiHhlT0tLSrPl0dZfJioxu+jWHloiIiCpjdiLj7u6OO3fu6G8PHDgQKSkp+tul90QiE9QVrOxreIwVGSIiokqZncgUFhYaLV63a9cuFBQUGJ1janE7MlDRFgWGx5jIEBERVcqqQ0ts9jUTm32JiIiswqqJDJnJZLMvp18TERGZy+xERhAEo4pL6dtkAZN7LbEiQ0REZC6zp1+LoogmTZrok5fc3Fy0bdsWMplMfz+ZqbLdrwH2yBAREZnB7ERm+fLltozj/mKq2ZfTr4mIiMxmdiIzatQoW8Zxf2FFhoiIyCrY7GsPpnpkmMgQERGZjYlMTdNqAa1Kul7u0BKbfYmIiMzFRKamGfa+lDu0pJt+zUSGiIioMkxkapphpcVkRYbNvkRERJWpciJTVFSECxcuQK1WWzOeus+w0sJmXyIiomqxOJHJz8/HCy+8AHd3d8TGxiIpKQkAMGnSJHz00UdWD7DOMdyeoLwFBTn9moiIyGwWJzKzZs3CiRMn8M8//8DVtWTWTZ8+fbBq1SqrBlcn6bcnKGdYyfA4KzJERESVMnsdGZ1169Zh1apV6Ny5s9EWBbGxsbhy5YpVg6uT9BWZcoaVACYyREREFrC4InPnzh0EBweXOZ6Xl8e9l8xhag0ZgNOviYiILGBxItO+fXts2LBBf1uXvHz33Xfo0qWL9SKrq0ztfA0YVGTYI0NERFQZi4eWPvzwQwwYMABnz56FWq3GF198gbNnz2Lfvn3YuXOnLWKsWwybfcujb/ZlRYaIiKgyFldkunfvjoSEBKjVarRq1Qp///03goODsX//frRr184WMdYtZldkmMgQERFVxuKKDABER0fj22+/tXYs94dKe2Q4/ZqIiMhcFldk5HI50tLSyhy/e/cu5HK5VYKq0zhriYiIyGosTmREUSz3uFKphItLBR/OVKLSdWSKKzWiBtBw1WQiIiJTzB5a+vLLLwFIs5S+++47eHp66u/TaDTYtWsXmjVrZv0I6xpzm30BqeFXXqXRPyIiovuC2Z+Sn3/+OQCpIrNkyRKjYSQXFxdERkZiyZIl1o+wrtElMpWt7Ks718XD9jERERE5KLMTmcTERABAz549sWbNGvj5+dksqDpNU0kiI3MCIAAQ2fBLRERUCYvHLXbs2GGLOO4fuoXuKmr2FQQpyVEXsuGXiIioEhYnMmPHjjV5/7Jly6oczH2hsooMIPXPqAtZkSEiIqqExYnMvXv3jG6rVCqcPn0amZmZ6NWrl9UCq7P068iYSGScXAAlWJEhIiKqhMWJzNq1a8sc02q1GD9+PKKjo60SVJ2mH1oylcgUT8FmIkNERGSSxevIlPskMhmmTZumn9lkLRqNBrNnz0ZUVBTc3NwQHR2N9957r8K1bByCWUNL3G+JiIjIHFZbpOTKlStQq627gNvHH3+MxYsX4/vvv0dsbCyOHDmCMWPGwMfHB6+99ppVX6vGVNbsC3B1XyIiIjNZnMhMmzbN6LYoikhJScGGDRswatQoqwUGAPv27cPjjz+OgQMHAgAiIyOxYsUKHDp0yKqvU6Mq22sJ4H5LREREZrI4kTl+/LjRbZlMhqCgIHz22WeVzmiyVNeuXfHNN9/g4sWLaNKkCU6cOIE9e/ZgwYIFFT5GqVRCqSypZGRnZ1s1pmqrbPdrgBUZIiIiM9XqdWTefPNNZGdno1mzZpDL5dBoNPjggw8wfPjwCh8zf/58zJs3r8ZitFhlWxQArMgQERGZySrNvrby66+/4ueff8Yvv/yCY8eO4fvvv8enn36K77//vsLHzJo1C1lZWfpLcnJyDUZsBnOafVmRISIiMotZFZm2bdtCEASznvDYsWPVCsjQG2+8gTfffBPDhg0DALRq1QrXr1/H/PnzK+zHUSgUUChMJAn2VtleS4DB9OtC28dDRETkwMxKZJ544gkbh1G+/Px8yGTGRSO5XA6tVmuXeKyCQ0tERERWY1YiM2fOHFvHUa5Bgwbhgw8+QIMGDRAbG4vjx49jwYIFVm8qrlFs9iUiIrKaKq8jc/ToUZw7dw4AEBsbi7Zt21otKJ2vvvoKs2fPxquvvoq0tDSEh4fj5ZdfxjvvvGP116oxrMgQERFZjcWJTFpaGoYNG4Z//vkHvr6+AIDMzEz07NkTK1euRFBQkNWC8/LywsKFC7Fw4UKrPafd6XtkWJEhIiKqLotnLU2aNAk5OTk4c+YMMjIykJGRgdOnTyM7O9txV9utSfpZS+YsiMdEhoiIyBSLKzKbNm3C1q1b0bx5c/2xFi1a4Ouvv0a/fv2sGlydZNamkazIEBERmcPiioxWq4Wzs3OZ487Ozo49m6imaMwZWuLu10REROawOJHp1asXJk+ejFu3bumP3bx5E1OnTkXv3r2tGlydI4ola8Ow2ZeIiKjaLE5kFi1ahOzsbERGRiI6OhrR0dGIiopCdnY2vvrqK1vEWHdoVCXXubIvERFRtVncIxMREYFjx45h69atOH/+PACgefPm6NOnj9WDq3MMm3dNJTKsyBAREZmlSuvICIKAvn37om/fvgCk6ddkBrVBYsJmXyIiomqzeGjp448/xqpVq/S3hwwZgoCAANSrVw8nTpywanB1jq4/RuYEyEy89bokh9OviYiITLI4kVmyZAkiIiIAAFu2bMGWLVuwceNGDBgwAG+88YbVA6xTzFlDBiiZ0cSKDBERkUkWDy2lpqbqE5m//voLQ4YMQb9+/RAZGYlOnTpZPcA6Rb+GjImp1wCnXxMREZnJ4oqMn58fkpOTAUiL4+mafEVRhEajsW50dY2+ImOiPwZgsy8REZGZLK7IPPXUU3juuecQExODu3fvYsCAAQCA48ePo3HjxlYPsE7RbxhZWUWGzb5ERETmsDiR+fzzzxEZGYnk5GT8+9//hqenJwAgJSUFr776qtUDrFPUZvbI6Jt9WZEhIiIyxeJExtnZGdOnTy9zfOrUqVYJqE4zZ3sCw/tZkSEiIjKpSuvIXLhwAV999RXOnTsHQFoQb9KkSWjatKlVg6tzzNkw0vB+Tr8mIiIyyeJm399//x0tW7bE0aNHERcXh7i4OBw7dgwtW7bE77//bosY6w7dOjKVNfuyIkNERGQWiysyM2bMwKxZs/Duu+8aHZ8zZw5mzJiBp59+2mrB1Tm6npdKExmD6deiCAiCbeMiIiJyUBZXZFJSUvD888+XOT5ixAikpKRYJag6Sz9ryczp1xABrdqmIRERETkyixOZHj16YPfu3WWO79mzBw8++KBVgqqz9BUZM6dfAxxeIiIiMsGsoaX169frrw8ePBgzZ87E0aNH0blzZwDAgQMHsHr1asybN882UdYVuh4Zc5t9AU7BJiIiMkEQRVGs7CSZqQ0ODZ9MEGrd6r7Z2dnw8fFBVlYWvL297RvMrk+B7e8BbUcCjy8yfe48P0DUAtPOA95hNRMfERFRLWHu57dZFRmtVmu1wO5r5jb7AlJVRl3AKdhEREQmWNwjU5HMzEwsWlRJleF+Z26zL8Ap2ERERGaodiKzbds2PPfccwgLC8OcOXOsEVPdpTZzZV+AO2ATERGZoUqJTHJyMt59911ERUWhX79+EAQBa9euRWpqqrXjq1s0Zu61BHC/JSIiIjOYncioVCqsXr0a/fv3R9OmTZGQkIBPPvkEMpkMb731Fh555BE4OzvbMlbHp9+iwJyKDIeWiIiIKmP2yr716tVDs2bNMGLECKxcuRJ+fn4AgGeffdZmwdU5+oqMmc2+ho8hIiKiMsyuyKjVagiCAEEQIJfLbRlT3aVfR8aSigyHloiIiCpidiJz69YtjBs3DitWrEBoaCiefvpprF27FgL3ATKfLimxqEeGFRkiIqKKmJ3IuLq6Yvjw4di+fTtOnTqF5s2b47XXXoNarcYHH3yALVu21LrF8GodS4aW2CNDRERUqSrNWoqOjsb777+P69evY8OGDVAqlXjssccQEhJi7fjqFouafTn9moiIqDJmN/uWRyaTYcCAARgwYADu3LmDH3/80Vpx1U26Hhmzmn2Lkx0OLREREVXIaiv7BgUFYdq0adZ6urrJki0KdOew2ZeIiKhCVktkyAyWbFHAZl8iIqJKMZGpSVVq9mVFhoiIqCJMZGqSviJjRrMvKzJERESVYiJTk9QW7LXE6ddERESVsnjWkkajQXx8PLZt24a0tDRotVqj+7dv32614ADg5s2bmDlzJjZu3Ij8/Hw0btwYy5cvR/v27a36OjVC3+zL6ddERETWYHEiM3nyZMTHx2PgwIFo2bKlTVf2vXfvHrp164aePXti48aNCAoKwqVLl/T7PDkcNvsSERFZlcWJzMqVK/Hrr7/i0UcftUU8Rj7++GNERERg+fLl+mNRUVEmH6NUKqFUlnz4Z2dn2yw+i2jUgFi88jGbfYmIiKzC4h4ZFxcXNG7c2BaxlLF+/Xq0b98e//rXvxAcHIy2bdvi22+/NfmY+fPnw8fHR3+JiIiokVgrZVhZ4e7XREREVmFxIvP666/jiy++gCiKtojHyNWrV7F48WLExMRg8+bNGD9+PF577TV8//33FT5m1qxZyMrK0l+Sk5NtHqdZDHtdzBlaYkWGiIioUhYPLe3Zswc7duzAxo0bERsbC2dnZ6P716xZY7XgtFot2rdvjw8//BAA0LZtW5w+fRpLlizBqFGjyn2MQqGAQmFGolDTdI2+ggyQm/G2syJDRERUKYsTGV9fXzz55JO2iKWMsLAwtGjRwuhY8+bN8fvvv9fI61uVbp8lc6oxgMEWBUxkiIiIKmJxImPYeGtr3bp1w4ULF4yOXbx4EQ0bNqyxGKxGbcE+S4bnMZEhIiKqUK1eEG/q1Kk4cOAAPvzwQ1y+fBm//PILvvnmG0yYMMHeoVnOku0JAA4tERERmcHiigwA/Pbbb/j111+RlJSEoiLjZtRjx45ZJTAA6NChA9auXYtZs2bh3XffRVRUFBYuXIjhw4db7TVqjK4iY/bQEpt9iYiIKmNxRebLL7/EmDFjEBISguPHj6Njx44ICAjA1atXMWDAAKsH+Nhjj+HUqVMoLCzEuXPn8NJLL1n9NWqErkfGnFV9AVZkiIiIzGBxIvOf//wH33zzDb766iu4uLhgxowZ2LJlC1577TVkZWXZIsa6QWPBPksAKzJERERmsDiRSUpKQteuXQEAbm5uyMnJAQCMHDkSK1assG50dYl+aIkVGSIiImuxOJEJDQ1FRkYGAKBBgwY4cOAAACAxMbFGFslzWJY2+3LWEhERUaUsTmR69eqF9evXAwDGjBmDqVOnom/fvhg6dGiNrS/jkPQbRppZkWEiQ0REVCmLZy1988030Gq1AIAJEyYgICAA+/btw+DBg/Hyyy9bPcA6Q21hj4zh0JIoAjbcZZyIiMhRWZzIyGQyyGQlhZxhw4Zh2LBhVg2qTtIPLZlbkTE4T6My/3FERET3kSotiLd7926MGDECXbp0wc2bNwEAP/74I/bs2WPV4OoUS9eRMTyPDb9ERETlsjiR+f3339G/f3+4ubnh+PHjUCqlD9msrCz95o5UDv06MhY2+wKcgk1ERFQBixOZ999/H0uWLMG3335rtPN1t27drLqqb52jsXCvJZkcEOTFj2VFhoiIqDwWJzIXLlzAQw89VOa4j48PMjMzrRFT3aSftWRmIgMYzFwqtH48REREdUCV1pG5fPlymeN79uxBo0aNrBJUnaSvyFjQtKtPZDi0REREVB6LE5mXXnoJkydPxsGDByEIAm7duoWff/4Z06dPx/jx420RY92gq6pYUpHh6r5EREQmWTz9+s0334RWq0Xv3r2Rn5+Phx56CAqFAtOnT8ekSZNsEWPdoLZwZV+A+y0RERFVwuJERhAEvPXWW3jjjTdw+fJl5ObmokWLFvD09LRFfHWHpc2+ACsyRERElbA4kdFxcXFBixYtrBlL3VatZl8mMkREROUxO5EZO3asWectW7asysHUaWoLV/YFSvZlYiJDRERULrMTmfj4eDRs2BBt27blLtdVobFwryXDczm0REREVC6zE5nx48djxYoVSExMxJgxYzBixAj4+/vbMra6Rb9FgSXTr9nsS0REZIrZ06+//vprpKSkYMaMGfjzzz8RERGBIUOGYPPmzazQmENThVlLbPYlIiIyyaJ1ZBQKBZ599lls2bIFZ8+eRWxsLF599VVERkYiNzfXVjHWDVVZR8aJPTJERESmVGn3awCQyWQQBAGiKEKj0VgzprpJXZ3p1xxaIiIiKo9FiYxSqcSKFSvQt29fNGnSBKdOncKiRYuQlJTEdWQqU5WhJU6/JiIiMsnsZt9XX30VK1euREREBMaOHYsVK1YgMDDQlrHVLVVp9uX0ayIiIpPMTmSWLFmCBg0aoFGjRti5cyd27txZ7nlr1qyxWnB1iq5HxqKKDKdfExERmWJ2IvP8889DEARbxlK3VWWLAk6/JiIiMsmiBfGoGqqyRQGnXxMREZlU5VlLZAGtFtCqpOtVqsgwkSEiIioPE5maYFhRsajZl9OviYiITGEiUxMMKyoW7bXE6ddERESmMJGpCYYVFbmz+Y/j9GsiIiKTmMjUBMNGX0tmfnH6NRERkUlMZGqCugqr+gKcfk1ERFQJJjI1oSrbEwCcfk1ERFQJJjI1oSpryABs9iUiIqoEE5maoF/V14Kp10BJsy+nXxMREZWLiUxN0O2zxIoMERGRVTlUIvPRRx9BEARMmTLF3qFYRl2FfZYATr8mIiKqhMMkMocPH8Z///tftG7d2t6hWK6qzb6cfk1ERGSSQyQyubm5GD58OL799lv4+fnZOxzL6Zt9LeyR0Q8tsUeGiIioPA6RyEyYMAEDBw5Enz59Kj1XqVQiOzvb6GJ3VV1HRt/sy4oMERFReZzsHUBlVq5ciWPHjuHw4cNmnT9//nzMmzfPxlFZSD+0ZME+S0BJ4qMpAkTRslWBiYiI7gO1uiKTnJyMyZMn4+eff4arq3lJwKxZs5CVlaW/JCcn2zhKM+iGhiwdWjI8n1OwiYiIyqjVFZmjR48iLS0NDzzwgP6YRqPBrl27sGjRIiiVSsjlcqPHKBQKKBQWDuHYWpWbfQ3OVystfzwREVEdV6sTmd69e+PUqVNGx8aMGYNmzZph5syZZZKYWquqzb7yUokMERERGanViYyXlxdatmxpdMzDwwMBAQFljtdq6ir2yMhkgMwZ0KrY8EtERFSOWt0jU2foh5YsrMgAXN2XiIjIhFpdkSnPP//8Y+8QLKdv9q1Cjwv3WyIiIqoQKzI1QbfXUlWadVmRISIiqhATmZqgqeJeSwArMkRERCYwkakJ+llLrMgQERFZExOZmqCvyFSh2VfORMbafj54HXsvp9s7DCIisgImMjVB1yNTnYoMp19bxaXbOXhr7Wm8tuI4RFG0dzhERFRNTGRqQlU3jTR8DCsyVnEpLRcAcDevCHfz2HdEROTomMjUBDb71hqJ6Xn661fv5Jk4k4iIHAETmZrAZt9a45pRIpNrx0iIiMgamMjUBHU1VvZlRcaqrt/N11+/ms6KDBGRo2MiUxM0rMjUFol3WZEhIqpLmMjUBHV1emR0iUyh9eK5T+Uq1biTU5IQsiJDROT4mMjUBI0VZi1xaKnadP0xznIBAJB0Nx8qjdaeIRERUTUxkakJbPatFa4VDyu1rOcDV2cZ1FoRyRn5lTyKiIhqMyYyNYHNvrWCrtE3KtADUYGeADgFm4jI0TGRsTVRNBhacrX88azIWI1uDZmoAA80CvIAAFxNZ8MvEZEjc7J3AHWeRlVyXV6digwTmerS9chEBnroe2NYkSEicmxMZGzNcLZRtbYo4NBSdel6ZCIDPKDRSvssMZEhInJsTGRszbC3pSrNvpx+bRU5hSqk50r/FpGB7tAWbxjJKdhERI6NPTK2puttkTkDsiq83Zx+bRW6Rt9ATxd4uTrre2TSc5XILlSZeigREdViTGRsrTpryBg+js2+1aJr9I0MkBIYL1dnBHlJ7y2Hl4iIHBcTGVvTryFThUZfw8exIlMtho2+Oo2Kr3OrAiIix8VExtbUrMjUBon6Rl93/bFGQVxLhojI0TGRsTVNNfZZAkqafTn9ulp0PTKGFZloriVDROTwmMjYWnW2JwBKVgM2Z/r1noXA4u5Azu2qvVYddq1UjwyAkkXxWJEhInJYTGRsrTrbEwCWTb8+sBi4fQo4+0fVXquOyi5U4W6ebuq1YY+MNLSUmJ4HbfG6MkRE5FiYyNiaproVGTOnX2ffAnJTpes3DlXtteooXTUm0FMBT0XJ0kn1/dzgLBegVGtxK6vAXuEREVE1MJGxNXU19lkCzG/2vXW85Hrywaq9Vh2l32Mp0N3ouJNchgb+0jEOLxEROSYmMramb/a18fTrm8dKrmcmATmpVXu9Okjf6GvQH6NTMnOJDb9ERI6IiYyt6Xpbqju0VGlF5pjx7WQOL+mUt4aMTsku2KzIEBE5IiYytqaubkWmOJHRqgCttvxzRLFkaCn8Aekrh5f0dGvIRJWTyEQHci0ZIiJHxkSmirLyVdh0OhXJGfmmT9RUt0fGIAGqaC2Ze4lAwT1pGKr9GOkYKzJ6uopMwwD3MveVTMHm0BIRkSNiIlNFU39NwCs/HcXG0ymmT6z2FgUGQ1IVDS/p+mNCWwENu0nXUxK4GjCkhPNevrQppKkemVtZhcgvUtdobEREVH1MZKqoa3QAAGDv5bumT7TWFgVAxQ2/hsNK/o0A90Dp3JQTVXvNOuRa8bBSsJcCHgZTr3X8PVzg6+4snZteSXWNiIhqHSYyVdQ9JhAAcCgxA0XqCnpXgOqvIyMIJdWciios+kSmrXR+RCfpNvtk9IlMeY2+OrreGW5VQETkeJjIVFHTEC8EerqgQKXB8aR7FZ+oruZeS4DBfkvlVGS0GuBWgnS9XnGjb0QH6SsTmZI1ZMoZVtJpxIZfIiKHxUSmigRBQJdoqSqz93J6xSdqqjm0BBjst1RORSb9IqDKA5w9gMAm0jF9ReaQNKPpPqZv9A0s2+irw4ZfIiLHxUSmGro3Lu6TuWKiT6a6zb6A6R2wdY2+4W0Ambz4eltA5gTk3pYWx7uPXSteDM9URSa6Lq0lo1YCRXXg+yAiMlOtTmTmz5+PDh06wMvLC8HBwXjiiSdw4cIFe4el17W4IpOQnImcQlX5J1W32RcwXZHRLYQX3rbkmLMbEBYnXb/Pp2Gb0yNTsrpvHkRHrmCJIrDsEeDLtkB+hr2jISKqEbU6kdm5cycmTJiAAwcOYMuWLVCpVOjXrx/y8mrHX5wR/u5oGOAOjVbEocQKPjisMbQkN7G6r67RV9cfo1O/o/T1Pu6TycwvQmbx1Ovy1pDRaRjgDpkA5CrVuJPjwFPW085JiW3ubeDiJntHQ0RUI2p1IrNp0yaMHj0asbGxiIuLQ3x8PJKSknD06FF7h6anq8rsqahPRtfsW9VZS4A+CdKqSn3IqouA1FPSdcOKDABEMJHRNfqGeCvg7lJ26rWOwkmO+n5SonPFkRt+L28tuX5+g/3isJWMq8CxHype4ZqI7ku1OpEpLSsrCwDg7+9f4TlKpRLZ2dlGF1vq3lhKZPZVtJ6Mbq+lalRktMX9NW//fhSZ+QYzl9LOSDOZ3PwAvyjjB+kafm+fAZT3ZxOrfljJRH+Mjm4KdqIj98kYJjJXtgOqQvvFYgtrxwPrJwGnVts7EiKqRRwmkdFqtZgyZQq6deuGli1bVnje/Pnz4ePjo79ERETYNK4uxQvjXbidg7Sccj44dFOmq9Hsm5Ir9W1k5+bh078NeoRuGvTHCILxg3zqAd71AFFjtKGkVivi0u0cx+4FMZNugbvy9lgqzeFnLilzgaT90nUXL0CVDyTutG9M1pSbVlJdvLTZvrEQUa3iMInMhAkTcPr0aaxcudLkebNmzUJWVpb+kpycbNO4/D1cEBvuDQDYX97sJXX19lpKySpAYqbU5+ECFX4+mIRTN6TKVEmj7wPlP7ic4aXZf5xG38934aeDdX82kzmNvjr6hl9Hrchc2y0lzb4Ngbih0rG6NLx0aQuA4uT7yg5p/SQiIjhIIjNx4kT89ddf2LFjB+rXr2/yXIVCAW9vb6OLrXVrbGI9GU31dr/+8H/nUaCV+js6RHhCFKVkRKsVgZsVNPrqGK4nA2DH+TT8XJzArDx0HyQy6eYPLUUHOnhFRjes1LgP0PRR6frFTXWnn8Swebkgo2QRSCK679XqREYURUycOBFr167F9u3bERUVVfmD7KAkkblbdshG1yNThWbfA1fv4s8Tt6CClMgMbOEHDxc5EpIzsebQReDOOenEyioyNw4jM68QM38/qb/rzK1sXHHUD20ziKKo73eJNLEYno6uIpN8r8D0lhO1kSgWVywgJTKR3aXhpdzbRsOKDktdJPX8ACW9YFe22S8eIqpVanUiM2HCBPz000/45Zdf4OXlhdTUVKSmpqKgoMDeoRnpEOkHZ7mAm5kFuH631MaDVdyiQK3RYu76MwCA8ABfAIC3kxZT+0qr9274+29A1AJeYYB3WPlPEtJKGtIquIevf9uEtBwlGgV56De8XJ9wy6KYHElmvgrZhdJu1g39K6/IhHgr4OEih0YrIinDwYaXMq4CmdcBmTMQ9ZD0sxbTR7rvwv/sG5s1XN8LFOUCHsFAt9ekY5eZyBCRpFYnMosXL0ZWVhZ69OiBsLAw/WXVqlX2Ds2Iu4sTHmjgB6CcadhVXEfm54NJOJ+aA193ZzSPCNI/16iukWgS4okoZXHTb+lp14acXPTVmsyLeyETgAVD2uCZdtLw3J8nb9XZpt/E4v6YMB9XuLnIKz1fEAREFTf8OtwUbN2wUsMugEKqLOmHl87XgUTmYnFzb5N+UsUJAG4cBgqz7BcTEdUatTqREUWx3Mvo0aPtHVoZuuGlfVdKJTL6LQrMT2Tu5irxWfHspNf7NYXC1a34uYrgLJdh3uCWaC27AgC47dnC5HPlh7QDALQTLuLVHo3RJsIXfVuEQOEkw9U7eThzy7bT0+3Fkv4YHd3mkQ43BduwP0Ynpi8gyKXhx4yr9onLGkQRuLhRut7kEcC3ARAQI83Gu1qHZmURUZXV6kTGkZQkMnelRlwd/awl85t9P9l8AdmFarQI88ZzHRuU2WupS3QAurpKzbpLLvsYv54BURSxPClYeozLFbzWOwYA4OXqjF7NpON/nqybw0v6RMaMGUs6UY7Y8KsqBBJ3S9cNExk3PyCym3T9wsaaj8ta0i8B965Jyxc06iEda9xb+so+GSICExmriavvA0+FEzLzVTibYlDl0FhWkTl5IxOrjkhTxuc9Hgu5TDDYa6m436YgE8GqGwCAtbeD8dvRG+U+19rjN7HsupRgNdQmw0VVUoofFBcOAPjrREqFiZAjSyzuVYo0sTVBaSVryThQReb6XkBdIPVKBZeqztWF4SXdbKXI7oDCS7oeXZzIXN5+X+3unqdUY84fp7HnUgWriBPdp5jIWImTXIbOjaQVh/XTsDVqqSEXMKtHRqsV8c4fZyCKwJNt66FDZPEKxqV3vy7eXynbtT4y4YWPNp1HVr7xppUpWQWYs/4M7sIHmW7FiwLeOKK/v1ezYHgqnHAzswDHk+9V4Tuu3a5bsIaMTrQjriWja3pt3Lvsooi6RCZpn+NuIqnvj3mk5FhkN6lCk5UE3L1sk5fVakUs/ucK1h2/aZPnr4rlexPx/f7rmL76BNQaB5tZR2RDTGSsqMy+SxqDvZHMSGR+P3YDCcmZ8HCRY9aAZgaPLbX7dXEi49moA2KCPZGRV4TPtpSs+CuKImb8dhI5hWq0ifCFd0zxEIPBwniuznL0axECoO7NXjKcem3Oqr46unMz8oqMt4Kozcrrj9HxawiEtJSS6Ut/12xc1lBwr2S14ib9S467eAANukjXbTR7aePpVHy86TxeX30CN+7lV/4AG9NoRfxSvAZUanYhtp5Ls3NERLUHExkr6h4jJTKHr2VAqdYY71ZdydBSdqEKH286DwB4rXcMgr0NVgIuvft18dogsnoPYN7jsQCAnw5cx+mb0tDRL4eSsPtSOhROMnw2JA4y/Qq/h4xeUze8tOFUSpX+wrt0O6dW7hadkVeEnEI1BAFo4G/+0JKHwgmhxe+7Q8xcykwC0i9ITb2NepZ/TtMB0ldHXOX38japqTeoOeAXaXyfDftkNFoRC4r/MNBoRSzdk2j117DU9vNpuJVVsgXKzwev2zEaotqFiYwVxQR7IshLgUKVFseuZ5YkHoIMkFe8+zIAfLH1EtJzi9AoyANjupVa+M+p1NCSwYq+XaMDMSguHFoReOeP07h+Nw8fbJAWypv5SDNpuES3wu/No9JwV7HuMYHwdXdGem4RDly1bOjheNI9PPLFbgz4YjduZdaudX10WxOEebvC1bnyqdeGHGrPJV01on4HwM23/HN0w0uXtzneJpL6YaX+Ze/T9clc22P8B4MVrD1+E1fu5MFFLv16XHU4uczQbU378YCUuAxsFQZBAHZfSne82XVENsJExooEQdAvNrfvSrrBGjKm91m6dDsH3++7BgCYMygWLk6l/ll0iYy6SNo8L/sGAAEIiwMAvPVoc3i4yHEsKRPPLNmP/CINOkX5Y3TXSOlxwc2llV6LcoG0s/qndZbLMKCltJjenyfMH17SakXMXX8GGq2I9Fwlxv14BAVFtt37JlepxrrjN1Goqvx1Eos3i7SkP0ZHn8g4woeEqWElnfC2UiOwKk/aj8lRaNTA5eLVig37Y3RCYgHPUGlzTN3wkxUUqbVYuPUiAGBq3yZoHuaN/CINfrJjBeRaeh52XbwDQQBmPNIUPZpI60r9wqoMEQAmMlanm4a953J6ySwjEztfi6KIuX+egVorom+LEDxc/EvKiGGzr27H66Cm+lkcoT6umNxHmlp9J0cJDxc5Pv1XHGSy4uZPmRyo3166btAnAwCDi4eXNp5OkYbDzPDb0Rs4cSMLngonBHi44PTNbEz/7YTNFtcTRRFTViZgyqoETF2VUOnrVKXRV0e/lkxtH1pSF5Wso6IbZimPIDjm8NKNw1KPjJufVHEqTRCA6F7SdSv2yaw6kowb9woQ5KXA6K6RGPeQVB1dvveaWUm0LeiGkR5uEoSGAR4Y0bkhAGD10Rt2i4moNmEiY2W6RObkjSzk5hcPT5ho9P186yXsvXwXLk4yvPNYBYvbGU6/rmDH6zHdotAkRPoQfvuxFogo3RtisO+SoY5R/gj2UiC7UI3dFyuf1plVUNLLM7l3DBaPaAdnuYANJ1OwaLttZpBsPpOKreduA5CaMFcfKX+6uY6+0deCxfB0ovQVmdo3tJSYnoeMvOLk+MYhoCgHcA8AwtqYfmDTgdLXCxsdZxNJ3bTrxn0rHpbVJXBWSmQKVRos2n4JADCxZ2O4ucjxWOtwhPm4Ij1XaZcZTIUqDVYXL68wopOUwPRoGox6vm7IzFfhf6dSajwmotqGiYyV1fN1Q1SgBzRaEWeSihODChp9f9x/DV9uk35xzh0UWzb50DGsyNwqf8drZ7kMP7/YGSvHdcazHRuUfQ59w69xRUYuE/BYa6kqs96M4aUvtl7C3bwiRAd5YFTXSHSM8sd7j7cEAHy25SI2nU6t9DkskV2owjt/SHtONQuVKlBz/zxjsj/gWjUqMtHFFZlrd/OhqUXr6/x54hZ6ffYP+n2+C+m5ypJhpejegKyS/8ZRDwIunkBuKpBy3PbBWoOp/hidRj0BCEDaGSC7+h/oP+6/jtvZStTzdcOwjtKSBc5yGcYW96x9s/tqja+59NfJFGTmq1DP1w09ixexlMsEPNdJ+j/+0wEOLxExkbEBXZ/M2aTiKZLlVGQ2nEzBO8WbQk7pE6P/xVQuw+nXuqGlcvZYCvJSoHOjgPKfo157AIK0Smqu8dTNQXFSn8yWs7eRX6Qu+9hiF2/n4Pv91wAY9/IM69hA348z7dcEnEux3rYHn2y6gLQcJaICPbD21W7o3Mgf+UUaTF55HKpyZlqJoohr6ZYvhqdTz88NLk4yFKm1uHmvhpqYtVrg0tYK13rZfekOpv2aAFEE0nOVeHvtaYjm9MfoOClKqhe1dHG8fZfTsfFUijRseO+atLWCIDc9bOYRAIS3ka7rdseuolylGot3Stt+TO4dA4VTSZP4sI4R8FI44eqdPGw/X7PTnnVNvs91aiAtjllsSPsIOMkEHEvKxNk6us1IGVotsHI48OUDQI51/2Aix8ZExga6Fw8vnb9ZXJEplcjsu5xe3OsBDO/UAJOLtw6okK4ic+8akJ8OyJyk9UEs4eYrNf0CZaZht4nwRYS/GwpUGmyrYH0KURQx70+pwbdfixA8VKqX5+2BzdG9cSDyizR48fsjuJtb/ZkkR6/f0zdZfvBkS7i5yPH50DbwcXPGyRtZ+HzLxTKPuZtXhFylNPW6wgqXCXKZoE+ArtTU8NK2ecDPTwPf9gTy7hrddSI5Ey//eBQqjYjujQPhJBNw9Mw5CKmnAAimP+gN6YeXalcic/VOLsYsP4TnvjuI8T8fw8RfjkN5tnhLhQZdpB4ZU6KtMw172Z5EZOQVoVGgB556oJ7RfV6uznius/SHxje7am7fqpM3MnEiORPOcgFDO0QY3RfkpUD/lqEAYNdG5Bp1/Afg/F9AxhVg4wx7R0O1CBMZG+gSHQBBANIzi/9SMmj2PX0zC+N+PIoijRYDWobi3cdbQii9ImtpullPRcUfrCGxgLPpmVDl0jVNlhpeEgQBgyoZXtp0OlXfy/P2wLK9PE5yGRY91xaRAe64mVmA8T8dQ5G66v0YKo0W/7fmFEQReKZdff1ig2E+bvjoqVYAgMU7r+DAVeMPft0eS+E+bhZPvdbRNfzWyFYFFzYBexdK1+9dA1aN0E8nvpyWi9HLDyG/SIPujQOxdHR7TO4dg4flJwEAqpA4wCPQvNfRbSKZdhbIsP+6KDmFKnz4v3Pov3AXdly4A2e5ACeZgA2nUnBie/Hu9qaGlXT068nsALRVa3zNzC/Ct8UJypS+TeAkL/trcWy3KDjLBRy6loHjSTWzErZu2OjRVmEI9Cxb1dX1zKw7fhM5hfadHm5zObeBLe+U3D77R62tLlLNYyJjA77uLmgZ7gMXFA/TFFdkrt/Nw+jlh5CrVKNzI398PrSNUbm4QqU3nCzV6Gs23XoypSoyADC4jZTI7LxwB1kFxr8UC4o0eL94bZpXHmqEBhUM2fi6u+C7Ue3hpXDCoWsZmLP+dJVnMn2z6you3M6Bv4cL3nq0udF9A1qFYUj7+hBFYOqqBKM1Pqqyom9pNbaWzL3rwNqXpestngAU3tJ2An9OQUpmPkYtO4R7+Sq0ru+DJSPbQeEkx/ge0XjcQ/q3+F9BrPnvr7s/0LCrdN2Om0hqtSJWH0lGz0934ptdV6HSiOjZNAibpzyEX1/pgkbeIuLUpwAAWzVlh0/LqN9Bet8KMoCUhCrF9N9dV5GjVKNZqBceaxVW7jkh3q54vI1UqamJqkxWvkr/R4VullJpnRv5o3GwJ/KLNLVqKwWb2Px/QGGWtOREl4nSsf9NBwrvk2E1MomJjI10bRwAFxR/wDopkJZTiJFLDyE9twgtwrzxzfPtza8YlG4WrlfNRObW8ZKp4cWahXqjSYgnijRabD5jPP68ZOcV3MwsQLiPK8b3aGzyJRoHe+HLZ9tCEIAVh5Lxw37Ly97X7+bpm6DfHtgcfh5lp6/PGRSLyAB3pGQV4v/WntJ/oJc0+lo+rKTTqHjPJZsuOKZWAqtHA4WZQL12wFPfAv9aLlVNTvyCDYtn4mZmARoFemD56A7wVEgzd5wEEV0FqSLzw53GWHEo2fzX1C2OZ6fhpeNJ9/Dk4n1447eTSM+V+p6Wj+6A5WM6olGQJx5o4Ic/BhRBIahxTRuCF/+XhdnrTpteFkDuDEQ9BAAoOPc3ftx/DW/+fhK7Lt4xK6a0nELE770GAHi9X9OSJQvKMe6hRgCATWdS9ZU/W/nt2A0UqrRoFuqF9g3LH14TBAHD9U2/STZb/sDuLm0FTv8mLSw66Aug51vSSs/ZN4Ht79s7OqoFmMjYSO+wIjwu3wcAUAnOGL3sMJIy8tHA3x3xYzvA29XZ/CezVkUmIBpw85dmP6WcKHO3bnjJcHG85Ix8LClugnxrYAu4uVSefPVsFqzfK+rdv86WbKJpBlEU8dba01CqtejeOBBPtq1X7nkeCid8MaytfjhCtwN4SaNv1SsyumqOTYeW/n5bmkrv6gv8K176N27cB0X95gMAXlT+gKGex/HDCx0RYDiscPMY5MpMKJ08kSA2xvsbziLprpl7AenWk7luvImkVisiNasQB6/exeojyfjs7wuYvPI4Xog/jB0Xqt/ceju7ENNWJeDJ/+zDieRMeCqc8H+PNsPmKQ/pZ+LoeCVJvS4Z9aQZST8euI4h/z2AmxWsHl2o0uCUm7RG0uldazH7jzNYeTgZzy87hMkrj1e6hcZ/dlxBgUqDuAhf9GkebPLcJiFe6Nk0CKIIfLfHdlUZrVbUDyuN6NzQ5NDzUw/Uh5uzHBdu5+Do9bq3+SuK8oEN06TrnV6RJjm4uAOPLZSOHfoGSD5c4cOpmrJvGW02XFuZXjefLFeYBez5HB32/weCXPol+u2taJy9l41ATxf8MLYjgr0s7G8xrMg4uQFBzSo+1xRBkKZhX9wEfP+Y1DAc3kYq14a1waCWDfDZlovYd+Uu0nOVCPRU4IMN56BUa9GlUQAebRVq9ku99GAjnE/JwZrjNzHuhyN4rXcMRneLNJoNYkQUAUHAuoSb2HNZ2ifqgydN9w/FRfhiat8m+GTzBcxZfwYdIv1LKjLVSGSii4eWUrML8cjCXQj1cUWotytCvF0R5uOKkOLbod6u8HV3rrzHqbTTv0u/gAHgqW8AX+mvapVGi3Hn2+JhdX+McdqM+fgKsoKBgJ/BEEvxbCWXmN5olxmEQ4kZmL76BFaO62yymgAA8I8CglsAaWex5tfl2ICHcD0jH8kZ+VBW0M+07XwaxnaLwswBTSv+t6uAVitixeEkzP/feeQqpWHWf7WrjzceaVr+/wGtFrgobW75QJ9hWKZqgamrTuBEciYe+3I3Fg5ri4ebBEGrFXH4WgbWJdzEXydT4KP0wx4F0Fa4hA6hctQPC8UfCTfxR8It7DifhlmPNsfQ9hFl3p+bmQX6jRjf6NfUrH/HcQ9FY8eFO1h95Aam9mlinGRayb4rd5GYngdPhROeqCCR1/Fxc8bguHCsOpKMnw5cR/tIf6vHY1c7PwYyrwPe9YCe/1dyPLonEPcccOIX4M/XgHE7y/7BR9Vz5yKwrJ+0MGXbEcCAT6QkshZiImMtGhVwNB74Zz6QfxcCgLMurTEzZwhOFTaCh4sc8WM6VmltE6NZT2GtK923yaR2Y6Ql3QuzgJtHpEuxSLkCf3s2xKHCCFzaeB6Xmw/CpjOpkMsEzBncwqIPbEEQ8OFTrZCaXYh9V+5i/sbz+OVQEt4e2AJ9mgeXPFfuHWD9RODaHhS2Go4lxx8A4IXXesegoRnJyCsPR2PXxTs4mJiBySuP60v+VXqfi/m6uyCuvg9O3MjC+dQcnE/NqfBcT4UTHmjoh05R/ugY5Y/W9X1Mf+CnXwbWvyZd7z5V39Cq1Uo7lv9z4Q4OOz+Pp+op4XPzH2DFs8BL2wFvqVqmW7ZfiOmDzyLj0H/hLhy6loFlexPx4oONTH5fmflFOIb26IWzcLmyGdtUJQmxXCagnq8bGvi7o0GAOxr4uyM5Ix8/H0zCsr2JOHD1Lr56rq20d5cZrt/Nw8zfT+r38IqL8MW8wbFoE+Fb8YNSjgN5adKaNw27oZeTC/6a1B2v/nwMp25mYfTyQxjUOhxHr98zqtB4+kTgrtAAAYVJWN1fBTRvgzHdIjFrzSmcuZWNWWtOYc2xG/jwyVaICfHSP+6rbZdQpNGicyN/dGtcwbIFpXRuJP0bn7yRhR8PXMeUPk3MepwldNWYpx6opx9SNGVE54ZYdSQZ/zuVitmPKW2SXNnF7TPA/kXS9Uc/1a9krtfvfeDSZqmBfd+XwEPTaz7Guir7FvDTU1ISAwDHf5IqM/+KL5n9WosIYp0dWJVkZ2fDx8cHWVlZ8Pb2tv4LiKLUPLnlHeCu1NeBgBig77tYdLMxPt1yCS5yGZaP6aBf9ddihdnAR8XTLzuNBwZ8VL2YtVog46rUHJmSANxKAFJOAsoso9PuCT6YpRyD0M5DMHdwbBVfSsSa4zfx8abz+jL/gzGBmP1YCzTJPw78/pK0UFsxpeiErYre6PfSfDgHRZv1GrcyC/DIwl3ILpT+6pcJwLn3HrG4gmBIpdHi6p08pGQV4HZ2IVKzlEjNLkRqVgFSs5W4nV1YssquARcnGdpE+OoTmwca+MFD92FUlA981wdIOwNtg27IeGY1coukNUxWH0nG9/uvQy4T8N3z7dEz0hVY2k9aTyW0NTBmo9RX80k0ABGYdg7wDscvB5Pwf2tPwcVJhv+91h2Ng73KxCSKItYl3MT7f51D/fyz+EPxDgplbljbZzfqB/miob8Hwnxd4VzObJ2tZ2/jjd9O4F6+Cm7OcswbHIt/ta9fYVKr0YpYvjcRn/59AYUqLdyc5Xijf1OM6hpZeWP7jg+lv8CbDwaG/qg/XKjS4N2/zuqrJ4CUQD7aKhRPtK2HzlEBkG2aCRz6r5SoD1oIAFBrtIjfdw0LtlxEfpEGznIBLz8UjYm9GiMlqxB9FuyERivi9/Fd0K6h+ZWMP0/cwqQVx+Hv4YK9M3uZNdxqrpSsAnT/eAc0WhF/T30ITULK/nuW5/FFe3DiRhbeHNAMrzxs3v+bWk2rlaoBNw4DzQcBQ38q/7yTvwJrXpKq1uP3AYGme/jIDAWZwPJHpYUm/aOBPnOA/70B5N6WRgQe/USq0Fhaia4Ccz+/mchUx63jwN+zSzbjcw8AeswC2o0G5M7IyCvCu3+eweNt66FnU9Pj7yapi4D3i9dteepboPWQaodehigC9xJx7/IhrFz/F/rKjqKxTOqVKWr6OFwGLzB/qm85cpVq/GfHZXy3OxFqjRqTnddiknwNZBCBoGa41ORFZOz+Dp1k0vYHEGRAy2eAB6eZ9RfAhpMpmPCLtFhgfT837JnZq8qxmqtQpcGVO7k4nJiBQ9cycCjxnrTqrgG5TECTEC+IoohJOZ9joGY70kVvDFDOxx2UbeJcMCQOTz1QX7px7zrwbS9p7aCmA4HYJ6Rf2sGxwKtS/5Uoihi9/DB2XryD1vV9sGZ8V6Ppw1fv5GL2H6ex97I0TT0myB0b1OPgUpAGNHsMGPBvwMf08MXt7EJMXZWAfVek53isdRg+eLIVfNyM+7wu3c7BjN9P4nhSJgBpYciPnmpd4Sy3Mv77kNS79fh/gLbDy9z9R8JN7Difht7NQ9C3RYhxs/zFzcAvQ6RhusknjX7J3swswJw/TmNr8RpJDQPcEertioOJGejZNAjLx3Q0L75iao0WPT79BzfuFeC9J1piZAWziqpiwZaL+HLbJXSM8sevL3cx+3G/Hk7GjN9PooG/O/6Z3qPyYcba7vB3wIbXpc1uJx4qqUiWJorAT09L6whFPojUJ1bjqx2XceDqXUQGeCA23Bstwn0QG+6N+n5ulg8D16Drd/Nw6mYWIgM8EBPiWa0/xERRrNr3qiqU3s/rewDPEOCFv6XG6tw7wNpxJQtPthoCPLagbJXMypjIFLNZIvPXNODIUum6kyvQ+VWg+xTA1cd6r6EjisB7QYBWBUw6JjXt2tCQ/+5HQuJtTHRai4nOf0ImagD3QOkHt8Xj1Xrum9evIG/FGDQplJqN16Incnt9iGWH0pCYnoe3WmbiJawpWYIfkD5wH3y90tla01efwG9Hb6B3s2AsHV3ORoM2JooiEtPzcCgxA4cSM3AwMUM/BPIv+T/4xPkbaEQBI1T/h/1aqcLlqXCCh0IOHzdnvPhgIwxpb7zwGZIOAt8Pkhq0XX2lWU5dXwP6vac/JTWrEP0+34nsQjVe79sEk3rHQKnWYPE/V/CfHVdQpNFC4STDa71j8NKDjeBy4kfgr6mAqAGcPYAebwKdx0szgCqg0Yr4764r+Ozvi9BoRdTzdcOXz7ZBu4b+UGm0+O/OK/hy22UUabTwUjjh/wY2x7AOEeb/Ms2+BSxoDkAApl8EPC1M/IvygI8jAU0RMPFomb/MRVHE5jOpmLP+DG5nlySbf03qjpb1LP8/G783EXP/PIuGAe7Y/noP85ZRqIRKo0XXj7bjTo4SXz3bFoPiKvjwLkdBkQadPtyK7EI14sd0QI/q/OFkb9kpwNcdAWW21JfRaZzp8+9dg/ifLhBU+ZileRkrVA+Xe5qPmzNahHkjNtwbsfW8ERvug0aBHuWuG1RTCoo02Hg6BasOJ+NgYkkDvpNMQONgT7QI90aLMG/9V18XANk3AN+G0EKGlOxCXEvPw9X0PCTeyUNiei4S0/NwM7MAcfWlHsKu0QHm/T/UaoDfxkhr9Lh4AWP+J7Uy6O/XSmtebX9f+t3hHy0NNRmeY2VMZIrZLJHZ+Qmw432g9TCg19uAb0Tlj6mOw99JfS0Pvm7b1wGw8lAS3lxzCq3q+WDdkx6Qr39VGocGgNgnpfHqqlRnLm2R1k3JvwuNkwc+cXkFSzLa6e8O9lJg6+sPSzO6biUAuz8Dzv0JoPhHtFFPoNlAae2QkJZleoUKijRYcSgJPZoG6adQG8lLl0rVN49KwzRuvlLi6ar7Wuri5Frt8unNzAIknzuMDlv/BbmmEHc7vgFVt+nwUMjh4eJk3l/OJ1cDa14suf38eqCR8S/rdcdvYsqqBDjJBMwZHIvlexJxtbhf6KEmQXjv8VjjnqPU09JfvMkHpNtBzYGBnwGR3UyGcjzpHl5beRzJGQWQywS8+GAUdl9Mx9nibSl6NQvGB0+2RJiPW+Xfl6Gj8cCfk6WtNF6q4iq93w8CEncBj3wMdH6l3FNyClX47O+L+H7/NQxtH4GPnq7aL+H8IjW6frQdmfkqLB7+AAZUsP6MJXRVxUBPBfa92Uu/BYi55v15Bsv3XkOf5iH4blT7asdjN7+OAs6uk5YleGELIKu4MpFTqMJ3uxOh2v0FZsh+QqbogTeCv8NTD7XF7exCnLmVjTO3snEpLQcqTdmPOhcnGZqEeKJ5qDeahXmjeZiXlDC4m24c1mhF3MsvQnquEuk5RXBzkSE6yLPSxwFSUn3yRhZWHUnGnwm3kFPcCC8IQIswbyRn5OuHyeXQoLVwFV1kZ9FZdhYd5BfhBiWuyyKwQPU01qvaQ6xk8nGnKH+83q8pOkaZGD4VRWn46PC30gKuI37XL2tQRtIB4LexQPZNiHIFzse9iV/RD6O6RlWrN7E8TGSK2SyRKcoH0i+Uu+eRo9NqRWw8nYrOjfylxkG1Etj1CbB7gZSJuwdKH3qxT5j3hBoVsO1dqSEPAEJbAf/6HmrfKKw8LE33zSxQYfHwdnikZamZUXcuAHs+l8bCRYP1RJw9pApNREdpfZz6HaRF3wxfM/WU1KB247B0uWfhirZyhZTsuPlJF1eD67rjCi8AgjQUpkt6BKH4WPHX7e8Bdy9L+yI9t7ryTR7Lo+sfcfECZlwps+2FKIoY/9MxbDJYAyjIS4E5g1pgYKuw8v8i02qBEyuALbOB/OIVklsPk6o9Jioi2YUqvL32tNEq0L7uzpj7WAs8HuMM4c556d/tzvniywXpZ8jFQ7ooPKWGXt1tFw9pCm36BaDn28DDb1j+/gDAnoXA1jlATD9g+GqTpxYUaaBwklVrCOazvy/gq+2X0STEE8M6NICnq1NxhU36qqu2eSmc4aGQV/qX/7PfHMD+q3cxqVdjvN6vqcXxXE7LRZ8FOyETgN0ze6Ger4XJpJlUGi3u5CiRll2I3Jtn4XzrEAS5C2RuvpC7eUPu7gcndx8oPH3h4u4Ld1cXuLnI4SSTQS4TIBNQcYVAN0QoyIFx/1T4136hSoMf9l/D4n+u4F6+CnJosNljLhprrkCMfQrCv5Ybna9Ua3Dpdi7O3srGmVtZOHMrG2dTspFfVP4aRWE+rmgW6oWmod4QRRF3cpVIzy3CnRwl0nOVuJurhFxUI1q4hSbCDeTADQe0zeHh6Y3oIE9EB3uicZAnGgdLlzAfV9zLV2Ht8ZtYfSTZaAJBhL8b/tUuAs+0q49wbxeIKSeQdW47VJd3wufOEbhojJdX0IoCZIL0sX1W2xA/e4zA7ZAeiAryRFSgJ6ICPRDo6YKfDybhl4NJKCrek+7BmEBM69sEbRuUsy7R7s+k39EQgGeWAS2fKvd9yS9S48i1ezh2/iq6nn4bHYukxVU3aDoiu+9nePYh61ZnmMgUs3mz7/3k1nFgnUF1psUTQJcJ0i8dAPrKieGPlLpQ2kvoRvFaDx3HAX3fM9piIVepRnqO0nQ2f+8acGKVtL3CjSNlGpMBSE3W4W2ArBvFi/4Vlj0nqJn0V7+br1ThKsws/lrqIlZ9e4VyedcDXt4tbXRYFaIoVS38o4BGPco95W6uEgO+2I07uUqM7NwQ0/s3NW+9ovwMKdk6shyACCh8pCpjhxeM/xrWaqRz8+5AzEvDodMXsC/hLDp43UUnzzQ4Z1wsmeVQVa/skRLdqkg9BSzpDji7AzOvlbtZqzXdyVGi28fbzd6Kw8NFDm83Z/i4OcPb1Rnebs7wdnOCt8IJnvIi/LLnApwFDf54tTNCPZ2k91urAbRq6SJqpOTTxUNK2t38ygwH6pKhkZ0b4l/t68NZLoOLqIRb3k245l6HS3YynLOvwynrOkQAhSFtkRXUDvd8WyNH44w8pRp5RWrkKtXIV2qQVaBCWk4h0nKUuJ2tREZWLhoVnkIf2TH0lh1DpOx2pd93juiGbLjjhhiES9p6uCjWxxWxPi6jPu7JfCEXpATHQyjEH3gdobiDTT7/ws6GryHQU4EgLwWCir8Geiqw90o6vtx2ST9EGB3kgdf7NcUj/qmQfddbep+e+7XSLS60WhFJGfk4n5qNsyk5OJ+SjXOp2UjOKL1mkYh6SEdTWTKaCcnFX5PQSEiBs1CSCClFZ+zXtsB2bRts17bFDbHkjwF3FzlUGq2+KqRwkmFAy1AMaxOEjorrkN04IFU6kg6W/d3m6gtEdkdBva645N4Gl5U+aHtrFRpeWAaZqnj18XrtpP+zjXoaVZFTsgqwaPtl/HokWf/avZoFY1rfJiVDqsd/Av6YIF03qGaKoojsAjXOp2Zj35W72H/lLo4n3zOobIkYK9+EWc4r4Aw10po/j+ChX5l8zy3FRKYYExkrK12dMZerDzB4EdBicPVj0Gqlv96TD0p/yScfLJkxZvSavlKlpn4HoH576T+7m2/lzy+K0r5WBfekDv6Ce9Kl0OC67j5lDgCxOHkr/mp4HaL0vfd6u+of0Ba4m6tEfpGmShtm4uZRqfdLt9R/cAvAI0gakstLk6o2lSV4ggzwi5ISxqCmUqN2YBOpclWUJ72vRl/zpPewKA8IjAHaPGd53DqiCHzWVJpd8fBMqUkRhlUyQF8pE0WgKMfg3zjT4N83s+TfW6OSkjlBLn01vC7IUaAB8tWACk5QiXIUiU4oEmVQik5QamXSRXSCCMAVRfAQCuEOJdxRCHdB+uqGIv1f2BZTeEsJjbs/4OaPm0Vu+DtRBS+hABFCGhoKtxEqVJ5cqkQ5zogNcVTbFEe0TXBE20TfjO6DXPSQJaCP/Bgelp2Et1BSIVDBCZddW0IDGRSaPLhpcuEu5sFDzINCt7K5CfdET1wU6+Oyth4ChSz0lx/BDTEQfZX/RgFMr7dVz9cNU/rE4Mm29UqqXX+/Dez7CvAKlyrGmqLii6rU9eLb+v+rWv11jUaDgiI1ClVqFBUVwb/oJlw1FSyOqfCRfsazbwFZSUZ3pTg3wG7hAazPa4kDmiZQwwndwoAXI++gq/MlKG4dkv7g0qrKPmfDrkDUg0Dkg9IwenlV3PwMqcp98L+AqvjfpGE3aeXjUkPEyRn5+Gr7Jfx+7CY0WulnrX9sCEb4n0e3I69BJmqwLWA4vvcYjbu5UtUpI6+o3OG4er5u6BIdgK7RAegSHYCw3LPA1nnAkB/M+/1qASYyxZjI2Mit48Dmt4BMgyXyhVJXdB8egU2lKXt+1pvdUUZ+hlSpST0hVT/qd5SaomvxLIVaSasBji6XysyF5VS9IEgfmh5BJRf/RlLiEtwMCGgMONtmSMMsa1+RhssclCjIIcicipMmg6+C7qusOAHLhL4CaoZc0Q1JCMF1bRCuiyFIFoPhDDXayS6io+wiQoSMMo9Jdw5DvnMA6uefgwwlf7Ro3QIhNOkHoekAaWG6imauqJVAYTa0BZlQ5qRDzLgK4c55yNIvQp5+HvKs6xDK+R6OPfgtznt2xp0cJe7kFiI9pwh3cpXScFZOIXzdXPDKw43wbKcGZWf2FOUB/+kMZCaVed5qkTlJv8dCWkgJfkis9NWnfklifOe8NDR2aYu0VpfBH3oaFy9o3IPgklnOitCeIUCDztJu7w06S8stmOgLKiM3TRp+P7xUmhQASJWZmH5S5dvZXer3c3ZHSr6AlQl3sPliFkJwD0ucP4ebUITfNQ/iddUrMPglrhfkpUDnRlLi0jU6AA383Wts9hcTmWJMZIiqIPeOtCeTs5vU2O0RLCUt7gHVW5DR1tIvSUmYqgBlhzpFGA17ungY9zu5+pa9LneRkjvdsI6oMbht8FX3F75WXfJXv+F1sXhIyNldWh3VxbP4ukfJcWd38/untBop0czPkDbMLP3VyU0ahvSLkipT7v6AIEAURai1IlQaLQQIcHWWSR9dWcnSsEZy8fDG7dMwSpSCWwBNHpG2uajXzrIP2oqoCqR/L8NeqnoPmJzQoPu4MvlBevsscHKVlPTJXaSfV7lL8cVZ+ipzlq7re9sM+twEGfSVO0EmJSsBMZatHFyQKU1VvrQFuPS3tISCTlAzKWGJ6Cx99Yu0zh9cWTeB3Z8Cx36QfvbMdM6jI9Y1+wx+3h4I9FQgwNMFgR7S1wBPl2pNA68uJjLFmMgQEVmoMFvqa8tJASK7Fw/TUZVotdKq1fn3pETNcFKCLdy7Bhz6VhpiVRVIw06qwuKvBYC6oPh4gTQz6alvpQb8WoiJTDEmMkRERI7H3M9v7n5NREREDouJDBERETksJjJERETksJjIEBERkcNiIkNEREQOi4kMEREROSwmMkREROSwmMgQERGRw3KIRObrr79GZGQkXF1d0alTJxw6dMjeIREREVEtUOsTmVWrVmHatGmYM2cOjh07hri4OPTv3x9paWn2Do2IiIjsrNZvUdCpUyd06NABixYtAgBotVpERERg0qRJePPNN8ucr1QqoVQq9bezs7MRERHBLQqIiIgcSJ3YoqCoqAhHjx5Fnz599MdkMhn69OmD/fv3l/uY+fPnw8fHR3+JiIioqXCJiIiohtXqRCY9PR0ajQYhISFGx0NCQpCamlruY2bNmoWsrCz9JTk5uSZCJSIiIjtwsncA1qZQKKBQKOwdBhEREdWAWp3IBAYGQi6X4/bt20bHb9++jdDQULOeQ9cClJ2dbfX4iIiIyDZ0n9uVtfLW6kTGxcUF7dq1w7Zt2/DEE08AkJp9t23bhokTJ5r1HDk5OQDAXhkiIiIHlJOTAx8fnwrvr9WJDABMmzYNo0aNQvv27dGxY0csXLgQeXl5GDNmjFmPDw8PR3JyMry8vCAIgtXi0s2GSk5O5myoGsD3u2bx/a55fM9rFt/vmlWV91sUReTk5CA8PNzkebU+kRk6dCju3LmDd955B6mpqWjTpg02bdpUpgG4IjKZDPXr17dZfN7e3vxPUIP4ftcsvt81j+95zeL7XbMsfb9NVWJ0an0iAwATJ040eyiJiIiI7h+1evo1ERERkSlMZKpIoVBgzpw5nOpdQ/h+1yy+3zWP73nN4vtds2z5ftf6LQqIiIiIKsKKDBERETksJjJERETksJjIEBERkcNiIkNEREQOi4lMFX399deIjIyEq6srOnXqhEOHDtk7pDph165dGDRoEMLDwyEIAtatW2d0vyiKeOeddxAWFgY3Nzf06dMHly5dsk+wdcD8+fPRoUMHeHl5ITg4GE888QQuXLhgdE5hYSEmTJiAgIAAeHp64umnny6z/xmZZ/HixWjdurV+UbAuXbpg48aN+vv5XtvORx99BEEQMGXKFP0xvt/WNXfuXAiCYHRp1qyZ/n5bvd9MZKpg1apVmDZtGubMmYNjx44hLi4O/fv3R1pamr1Dc3h5eXmIi4vD119/Xe79//73v/Hll19iyZIlOHjwIDw8PNC/f38UFhbWcKR1w86dOzFhwgQcOHAAW7ZsgUqlQr9+/ZCXl6c/Z+rUqfjzzz+xevVq7Ny5E7du3cJTTz1lx6gdV/369fHRRx/h6NGjOHLkCHr16oXHH38cZ86cAcD32lYOHz6M//73v2jdurXRcb7f1hcbG4uUlBT9Zc+ePfr7bPZ+i2Sxjh07ihMmTNDf1mg0Ynh4uDh//nw7RlX3ABDXrl2rv63VasXQ0FDxk08+0R/LzMwUFQqFuGLFCjtEWPekpaWJAMSdO3eKoii9v87OzuLq1av155w7d04EIO7fv99eYdYpfn5+4nfffcf32kZycnLEmJgYccuWLeLDDz8sTp48WRRF/mzbwpw5c8S4uLhy77Pl+82KjIWKiopw9OhR9OnTR39MJpOhT58+2L9/vx0jq/sSExORmppq9N77+PigU6dOfO+tJCsrCwDg7+8PADh69ChUKpXRe96sWTM0aNCA73k1aTQarFy5Enl5eejSpQvfaxuZMGECBg4caPS+AvzZtpVLly4hPDwcjRo1wvDhw5GUlATAtu+3Q+y1VJukp6dDo9GU2bQyJCQE58+ft1NU94fU1FQAKPe9191HVafVajFlyhR069YNLVu2BCC95y4uLvD19TU6l+951Z06dQpdunRBYWEhPD09sXbtWrRo0QIJCQl8r61s5cqVOHbsGA4fPlzmPv5sW1+nTp0QHx+Ppk2bIiUlBfPmzcODDz6I06dP2/T9ZiJDRACkv1xPnz5tNKZN1te0aVMkJCQgKysLv/32G0aNGoWdO3faO6w6Jzk5GZMnT8aWLVvg6upq73DuCwMGDNBfb926NTp16oSGDRvi119/hZubm81el0NLFgoMDIRcLi/TaX379m2EhobaKar7g+795XtvfRMnTsRff/2FHTt2oH79+vrjoaGhKCoqQmZmptH5fM+rzsXFBY0bN0a7du0wf/58xMXF4YsvvuB7bWVHjx5FWloaHnjgATg5OcHJyQk7d+7El19+CScnJ4SEhPD9tjFfX180adIEly9ftunPNxMZC7m4uKBdu3bYtm2b/phWq8W2bdvQpUsXO0ZW90VFRSE0NNTovc/OzsbBgwf53leRKIqYOHEi1q5di+3btyMqKsro/nbt2sHZ2dnoPb9w4QKSkpL4nluJVquFUqnke21lvXv3xqlTp5CQkKC/tG/fHsOHD9df5/ttW7m5ubhy5QrCwsJs+/NdrVbh+9TKlStFhUIhxsfHi2fPnhXHjRsn+vr6iqmpqfYOzeHl5OSIx48fF48fPy4CEBcsWCAeP35cvH79uiiKovjRRx+Jvr6+4h9//CGePHlSfPzxx8WoqCixoKDAzpE7pvHjx4s+Pj7iP//8I6akpOgv+fn5+nNeeeUVsUGDBuL27dvFI0eOiF26dBG7dOlix6gd15tvvinu3LlTTExMFE+ePCm++eaboiAI4t9//y2KIt9rWzOctSSKfL+t7fXXXxf/+ecfMTExUdy7d6/Yp08fMTAwUExLSxNF0XbvNxOZKvrqq6/EBg0aiC4uLmLHjh3FAwcO2DukOmHHjh0igDKXUaNGiaIoTcGePXu2GBISIioUCrF3797ihQsX7Bu0AyvvvQYgLl++XH9OQUGB+Oqrr4p+fn6iu7u7+OSTT4opKSn2C9qBjR07VmzYsKHo4uIiBgUFib1799YnMaLI99rWSicyfL+ta+jQoWJYWJjo4uIi1qtXTxw6dKh4+fJl/f22er8FURTF6tV0iIiIiOyDPTJERETksJjIEBERkcNiIkNEREQOi4kMEREROSwmMkREROSwmMgQERGRw2IiQ0RERA6LiQwRERE5LCYyRHTfEQQB69ats3cYRGQFTGSIqEaNHj0agiCUuTzyyCP2Do2IHJCTvQMgovvPI488guXLlxsdUygUdoqGiBwZKzJEVOMUCgVCQ0ONLn5+fgCkYZ/FixdjwIABcHNzQ6NGjfDbb78ZPf7UqVPo1asX3NzcEBAQgHHjxiE3N9fonGXLliE2NhYKhQJhYWGYOHGi0f3p6el48skn4e7ujpiYGKxfv9623zQR2QQTGSKqdWbPno2nn34aJ06cwPDhwzFs2DCcO3cOAJCXl4f+/fvDz88Phw8fxurVq7F161ajRGXx4sWYMGECxo0bh1OnTmH9+vVo3Lix0WvMmzcPQ4YMwcmTJ/Hoo49i+PDhyMjIqNHvk4isoNr7ZxMRWWDUqFGiXC4XPTw8jC4ffPCBKIqiCEB85ZVXjB7TqVMncfz48aIoiuI333wj+vn5ibm5ufr7N2zYIMpkMjE1NVUURVEMDw8X33rrrQpjACC+/fbb+tu5ubkiAHHjxo1W+z6JqGawR4aIalzPnj2xePFio2P+/v766126dDG6r0uXLkhISAAAnDt3DnFxcfDw8NDf361bN2i1Wly4cAGCIODWrVvo3bu3yRhat26tv+7h4QFvb2+kpaVV9VsiIjthIkNENc7Dw6PMUI+1uLm5mXWes7Oz0W1BEKDVam0REhHZEHtkiKjWOXDgQJnbzZs3BwA0b94cJ06cQF5env7+vXv3QiaToWnTpvDy8kJkZCS2bdtWozETkX2wIkNENU6pVCI1NdXomJOTEwIDAwEAq1evRvv27dG9e3f8/PPPOHToEJYuXQoAGD58OObMmYNRo0Zh7ty5uHPnDiZNmoSRI0ciJCQEADB37ly88sorCA4OxoABA5CTk4O9e/di0qRJNfuNEpHNMZEhohq3adMmhIWFGR1r2rQpzp8/D0CaUbRy5Uq8+uqrCAsLw4oVK9CiRQsAgLu7OzZv3ozJkyejQ4cOcHd3x9NPP40FCxbon2vUqFEoLCzE559/junTpyMwMBDPPPNMzX2DRFRjBFEURXsHQUSkIwgC1q5diyeeeMLeoRCRA2CPDBERETksJjJERETksNgjQ0S1Cke7icgSrMgQERGRw2IiQ0RERA6LiQwRERE5LCYyRERE5LCYyBAREZHDYiJDREREDouJDBERETksJjJERETksP4f/DRQYKDtgXgAAAAASUVORK5CYII=\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": [
        "1. numpy and pandas: These are fundamental libraries for data manipulation. numpy handles arrays and numerical operations, while pandas works with structured data like DataFrames.\n",
        "2. sklearn.datasets: fetch_california_housing loads a housing dataset from California.\n",
        "3. train_test_split: This function is used to split data into training and testing sets.\n",
        "4. StandardScaler: Used to normalize data by centering it to have a mean of 0 and a standard deviation of 1.\n",
        "5. tensorflow: A deep learning framework used to build and train neural networks.\n",
        "6. matplotlib.pyplot: Used for plotting graphs.\n",
        "7. tensorflow.keras.backend (K): Provides low-level operations like handling gradients"
      ],
      "metadata": {
        "id": "O_6WJTG6WLIV"
      }
    },
    {
      "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": [
        "1. fetch_california_housing(): Downloads the California housing dataset, a collection of house features and target prices.\n",
        "2. pd.DataFrame: Converts the dataset into a DataFrame for better readability.\n",
        "3. df['Target']: Adds a column for the target variable (house prices).\n",
        "4. df.head(): Displays the first few rows of the dataset."
      ],
      "metadata": {
        "id": "nQo5TG40l8WG"
      }
    },
    {
      "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": [
        "1. X = df.drop('Target', axis=1): Separates the feature columns from the target.\n",
        "2. y = df['Target']: The target column (housing prices).\n",
        "3. train_test_split(): Splits the data into training (80%) and testing (20%) sets. The random_state=42 ensures reproducibility.\n"
      ],
      "metadata": {
        "id": "_ymlynszmdHf"
      }
    },
    {
      "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": [
        "1. Feature scaling: Neural networks perform better when features are normalized.\n",
        " - fit_transform(): Fits the scaler on the training data and transforms it.\n",
        " - transform(): Uses the same transformation on the test data.\n"
      ],
      "metadata": {
        "id": "74aLTY3Km3cM"
      }
    },
    {
      "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 (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))  # 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"
      ],
      "metadata": {
        "id": "ZOncG8l2f8K9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- class PINN(tf.keras.Model): Defines a neural network (Physics-Informed Neural Network or PINN).\n",
        "  - self.hidden_layer_1/2: These are hidden layers with 64 neurons and ReLU activation. ReLU introduces non-linearity.\n",
        "  - self.output_layer: Output layer with 1 neuron since this is a regression problem (predicting housing prices).\n",
        "  - call(self, inputs): The forward pass of the network, where data moves through each layer.\n",
        "\n",
        "- train_step(self, data): Custom training loop, overriding the default one.\n",
        "  - GradientTape(): Tracks operations to compute gradients. Two tapes are used:\n",
        "    - The first computes the normal prediction loss.\n",
        "    - The second computes the constraint loss based on the derivative of the output with respect to input.\n",
        "- constraint_loss: This enforces a physics-based constraint on the derivative of the output.\n",
        "- total_loss: Sum of the normal loss and the constraint loss.\n",
        "- optimizer.apply_gradients(): Updates the model's parameters based on the total loss.\n",
        "- compiled_metrics.update_state(): Updates metrics (e.g., MAE) after each training step."
      ],
      "metadata": {
        "id": "OSxPk957oPQw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 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'])"
      ],
      "metadata": {
        "id": "oSVawGoP6CnE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- PINN(): Instantiates the PINN model.\n",
        "- compile(): Configures the model with:\n",
        "    - adam: A popular optimization algorithm.\n",
        "    - loss='mse': The mean squared error loss function (used for regression).\n",
        "    - metrics=['mae']: Mean absolute error, another performance metric"
      ],
      "metadata": {
        "id": "No7xUiMa6Esj"
      }
    },
    {
      "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": [
        "- **fit()**: Trains the model.\n",
        "  - **epochs=50**: Runs the training for 50 epochs.\n",
        "  - **validation_split=0.2**: Uses 20% of the training data for validation during training.\n",
        "  - **batch_size=32:** Processes 32 samples per training step."
      ],
      "metadata": {
        "id": "cZX2zJLP-qXK"
      }
    },
    {
      "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": [
        "# Evaluates the model on the test data, returning loss and mean absolute error\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": [
        "- evaluate(): Evaluates the model on the test data, returning loss and mean absolute error.\n",
        "- print(f'Test MAE: {test_mae}'): Outputs the test MAE.\n"
      ],
      "metadata": {
        "id": "8ymvajY06nD8"
      }
    },
    {
      "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": 1,
      "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": 2,
      "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 + 0.1 * physics_loss  # Adjust weight of the 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": 3,
      "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": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This defines a physics-based constraint. It assumes that house prices should increase with median income (MedInc) and average rooms per household (AveRooms).\n",
        "The pseudo-target for Task 2 is computed as a function of these two features (scaled down by a factor of 10)."
      ],
      "metadata": {
        "id": "8lhXG0xJh7w5"
      }
    },
    {
      "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"
      ],
      "metadata": {
        "id": "gJ4PBgVqjONj"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        " apply the physics_constraint function to both the training and testing datasets to generate the pseudo-target for Task 2."
      ],
      "metadata": {
        "id": "Iw8vK8ywjO9C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 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": "508871e4-3afb-40dd-9802-03e2ffe253b6"
      },
      "execution_count": 6,
      "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[1m4s\u001b[0m 3ms/step - mae: 0.8176 - loss: 1.6783 - val_loss: 0.4606 - val_mae: 0.4754\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: 0.4487 - loss: 2.0574 - val_loss: 0.4425 - val_mae: 0.4553\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: 0.4411 - loss: 2.0454 - val_loss: 0.3606 - val_mae: 0.4228\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: 0.4233 - loss: 2.0821 - val_loss: 0.3504 - val_mae: 0.4106\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: 0.4097 - loss: 2.0618 - val_loss: 0.3333 - val_mae: 0.4021\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: 0.3951 - loss: 2.0654 - val_loss: 0.3283 - val_mae: 0.4080\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: 0.3964 - loss: 2.0895 - val_loss: 0.3432 - val_mae: 0.4012\n",
            "Epoch 8/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.3908 - loss: 2.0529 - val_loss: 0.3105 - val_mae: 0.3879\n",
            "Epoch 9/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - mae: 0.3883 - loss: 2.0758 - val_loss: 0.3108 - val_mae: 0.3866\n",
            "Epoch 10/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3890 - loss: 2.0789 - val_loss: 0.3111 - val_mae: 0.3865\n",
            "Epoch 11/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3792 - loss: 2.0615 - val_loss: 0.2974 - val_mae: 0.3759\n",
            "Epoch 12/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - mae: 0.3729 - loss: 2.0652 - val_loss: 0.3122 - val_mae: 0.3992\n",
            "Epoch 13/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - mae: 0.3747 - loss: 2.0648 - val_loss: 0.2980 - val_mae: 0.3779\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: 0.3752 - loss: 2.0712 - val_loss: 0.3041 - val_mae: 0.3879\n",
            "Epoch 15/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - mae: 0.3767 - loss: 2.0697 - val_loss: 0.2957 - val_mae: 0.3709\n",
            "Epoch 16/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - mae: 0.3744 - loss: 2.0713 - val_loss: 0.3075 - val_mae: 0.3973\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: 0.3690 - loss: 2.0730 - val_loss: 0.2923 - val_mae: 0.3752\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: 0.3705 - loss: 2.0578 - val_loss: 0.2922 - val_mae: 0.3702\n",
            "Epoch 19/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - mae: 0.3715 - loss: 2.0833 - val_loss: 0.2895 - val_mae: 0.3731\n",
            "Epoch 20/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - mae: 0.3633 - loss: 2.0750 - val_loss: 0.2922 - val_mae: 0.3679\n",
            "Epoch 21/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - mae: 0.3609 - loss: 2.0562 - val_loss: 0.3146 - val_mae: 0.3810\n",
            "Epoch 22/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - mae: 0.3618 - loss: 2.0835 - val_loss: 0.2891 - val_mae: 0.3717\n",
            "Epoch 23/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - mae: 0.3593 - loss: 2.0616 - val_loss: 0.2837 - val_mae: 0.3707\n",
            "Epoch 24/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - mae: 0.3575 - loss: 2.0553 - val_loss: 0.2816 - val_mae: 0.3634\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: 0.3541 - loss: 2.0727 - val_loss: 0.2976 - val_mae: 0.3663\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: 0.3525 - loss: 2.0744 - val_loss: 0.2850 - val_mae: 0.3674\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: 0.3639 - loss: 2.0816 - val_loss: 0.2910 - val_mae: 0.3651\n",
            "Epoch 28/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - mae: 0.3522 - loss: 2.0561 - val_loss: 0.2817 - val_mae: 0.3616\n",
            "Epoch 29/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - mae: 0.3501 - loss: 2.0583 - val_loss: 0.2756 - val_mae: 0.3577\n",
            "Epoch 30/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - mae: 0.3556 - loss: 2.0784 - val_loss: 0.2772 - val_mae: 0.3617\n",
            "Epoch 31/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3496 - loss: 2.0653 - val_loss: 0.2971 - val_mae: 0.3849\n",
            "Epoch 32/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - mae: 0.3500 - loss: 2.0606 - val_loss: 0.2819 - val_mae: 0.3708\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: 0.3431 - loss: 2.0739 - val_loss: 0.2876 - val_mae: 0.3718\n",
            "Epoch 34/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - mae: 0.3481 - loss: 2.0696 - val_loss: 0.2762 - val_mae: 0.3646\n",
            "Epoch 35/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.3436 - loss: 2.0699 - val_loss: 0.2720 - val_mae: 0.3603\n",
            "Epoch 36/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - mae: 0.3495 - loss: 2.0696 - val_loss: 0.2914 - val_mae: 0.3594\n",
            "Epoch 37/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - mae: 0.3435 - loss: 2.0747 - val_loss: 0.2771 - val_mae: 0.3519\n",
            "Epoch 38/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - mae: 0.3404 - loss: 2.0675 - val_loss: 0.2714 - val_mae: 0.3525\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: 0.3435 - loss: 2.0696 - val_loss: 0.2695 - 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: 0.3408 - loss: 2.0571 - val_loss: 0.2692 - val_mae: 0.3525\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: 0.3378 - loss: 2.0701 - val_loss: 0.2668 - val_mae: 0.3507\n",
            "Epoch 42/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - mae: 0.3363 - loss: 2.0643 - val_loss: 0.2655 - val_mae: 0.3480\n",
            "Epoch 43/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - mae: 0.3373 - loss: 2.0845 - val_loss: 0.2835 - val_mae: 0.3829\n",
            "Epoch 44/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - mae: 0.3378 - loss: 2.0783 - val_loss: 0.2708 - val_mae: 0.3424\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: 0.3371 - loss: 2.0697 - val_loss: 0.2793 - val_mae: 0.3481\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: 0.3320 - loss: 2.0634 - val_loss: 0.2696 - val_mae: 0.3445\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: 0.3310 - loss: 2.0589 - val_loss: 0.2696 - val_mae: 0.3522\n",
            "Epoch 48/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - mae: 0.3300 - loss: 2.0626 - val_loss: 0.2639 - val_mae: 0.3431\n",
            "Epoch 49/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.3263 - loss: 2.0612 - val_loss: 0.2714 - val_mae: 0.3434\n",
            "Epoch 50/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - mae: 0.3324 - loss: 2.0724 - val_loss: 0.2604 - val_mae: 0.3423\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Compile the model with the Adam optimizer, using mean squared error (MSE) as the loss function for both tasks and mean absolute error (MAE) as the evaluation metric.\n",
        "<br>\n",
        "\n",
        "Train the model for 50 epochs with a batch size of 32. The targets used are:\n",
        "- y_train: Actual house prices (Task 1).\n",
        "- physics_target_train: Physics-based pseudo-target (Task 2)."
      ],
      "metadata": {
        "id": "yQ8x0YSZjXOq"
      }
    },
    {
      "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": "05af3f1b-b30d-4a0a-a273-8ba50deabd3c"
      },
      "execution_count": 8,
      "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": 393
        },
        "id": "w53CP1ekmAMM",
        "outputId": "4acd3d23-f83b-4fa0-f963-cb8f47f5202a"
      },
      "execution_count": 7,
      "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": "0670ad47-d818-4290-9333-aa612871adb0"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2520 - mae: 0.3402\n",
            "Test Loss: 0.26041126251220703\n",
            "Test MAE: 0.34226322174072266\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The result you obtained shows that your model achieved the following:\n",
        "\n",
        "- **Test Loss**: `0.2604` — This represents the overall loss of the model on the test set. Since you're working with a mean squared error (MSE) loss, this value indicates how well the model predicts both house prices and adheres to the physics-based constraint.\n",
        "  \n",
        "- **Test MAE (Mean Absolute Error)**: `0.3423` — This reflects the average difference between predicted and actual house prices on the test set. A lower MAE indicates better accuracy in predicting house prices.\n",
        "\n",
        "### Interpretation:\n",
        "1. **Test Loss**: Since this loss is a combination of two objectives (house price prediction and the physics-informed constraint), it’s not directly indicative of performance on house prices alone. It accounts for both tasks, meaning it reflects how well the model balances these two goals.\n",
        "   \n",
        "2. **Test MAE**: This metric is more indicative of your model's performance on the main task — house price prediction. An MAE of `0.34` means that, on average, your model's predictions are off by `0.34` units in the house price. Given the scaled data, this can be a relatively small error, but it depends on the actual scale of the targets (house prices).\n",
        "\n",
        "### Next Steps:\n",
        "\n",
        "1. **Check Scaling**:\n",
        "   - Verify the range of the house price target (`y_train` and `y_test`). If the data is scaled, the MAE will also reflect the scaled version. If needed, you can reverse the scaling on the target values and recompute the error in actual house price units.\n",
        "\n",
        "2. **Adjust Physics Constraint Weight**:\n",
        "   - You might want to experiment with the weight of the physics constraint (currently set to `0.1`). If you care more about house price prediction accuracy, reducing this weight may lead to better performance on that task.\n",
        "   \n",
        "3. **Model Architecture**:\n",
        "   - Consider adding more layers or increasing the number of neurons in the shared layers. Alternatively, using techniques like dropout or batch normalization might improve generalization.\n",
        "   \n",
        "4. **Tuning the Hyperparameters**:\n",
        "   - Tune the learning rate, batch size, and number of epochs. This might help the model converge better and improve performance.\n",
        "\n",
        "### Example of Experimentation:\n",
        "- **Reducing Physics Loss Weight**:\n",
        "    ```python\n",
        "    total_loss = price_loss + 0.05 * physics_loss  # Reduce the physics constraint weight\n",
        "    ```\n",
        "\n",
        "- **Increase Layers or Units**:\n",
        "    ```python\n",
        "    self.shared_layer_1 = layers.Dense(128, activation='relu')  # Increase units\n",
        "    self.shared_layer_2 = layers.Dense(128, activation='relu')  # Increase units\n",
        "    ```\n",
        "\n",
        "### Conclusion:\n",
        "Your model is working and yielding reasonable results, but you can continue to tune it by experimenting with different weights for the physics constraint, adjusting the model architecture, and possibly improving the data preprocessing."
      ],
      "metadata": {
        "id": "FQnWhhwT8RvM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Summary of What This Code Does:\n",
        "\n",
        "1. **Multi-task Learning**: The model simultaneously learns two tasks:\n",
        "    - Task 1: Predict house prices based on input features.\n",
        "    - Task 2: Satisfy a physics constraint where house prices increase with certain features (median income and average rooms).\n",
        "   \n",
        "2. **Shared Architecture**: Both tasks share a common set of hidden layers to extract useful features, but have separate outputs.\n",
        "\n",
        "3. **Custom Training Loop**: The model is trained using a custom training step that combines the losses from both tasks and updates the weights accordingly.\n",
        "\n",
        "4. **Physics-Informed Constraint**: By incorporating domain knowledge (house prices should increase with income and number of rooms), the model aims to generalize better.\n",
        "\n",
        "This approach can be useful when you want to inject domain-specific constraints into machine learning models, potentially improving their performance and interpretability."
      ],
      "metadata": {
        "id": "h8AcPSZYjp6i"
      }
    },
    {
      "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": 10,
      "metadata": {
        "id": "WRRg1XSu2V5p"
      },
      "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",
        "\n",
        "# Load the dataset\n",
        "california = fetch_california_housing()\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"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 2: Build a PIKAN-Inspired Neural Network\n"
      ],
      "metadata": {
        "id": "mA5xeCMjCuum"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class KANN(tf.keras.Model):\n",
        "    def __init__(self, n_features, n_hidden=64, n_decompose=10):\n",
        "        super(KANN, self).__init__()\n",
        "\n",
        "        # Layers to approximate each univariate function\n",
        "        self.univariate_layers = []\n",
        "        for _ in range(n_decompose):\n",
        "            self.univariate_layers.append(\n",
        "                layers.Dense(n_hidden, activation='relu')\n",
        "            )\n",
        "\n",
        "        # Output layer for the combined sum of univariate approximations\n",
        "        self.output_layer = layers.Dense(1, name=\"output_layer\")\n",
        "\n",
        "    def call(self, inputs):\n",
        "        # Compute the univariate approximations for each input\n",
        "        decomposition_sum = 0\n",
        "        for i, layer in enumerate(self.univariate_layers):\n",
        "            decomposition_sum += layer(inputs[:, i:i+1])  # Apply to each feature separately\n",
        "\n",
        "        # Final prediction is the sum of univariate approximations\n",
        "        return self.output_layer(decomposition_sum)\n"
      ],
      "metadata": {
        "id": "UdCtP9fN8LSe"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def physics_constraint(X):\n",
        "    \"\"\"\n",
        "    Define a physics-based relationship for the task.\n",
        "    We assume that house prices should increase with both\n",
        "    median income (MedInc) and average rooms per household (AveRooms).\n",
        "    \"\"\"\n",
        "    MedInc = X[:, 0]\n",
        "    AveRooms = X[:, 2]\n",
        "\n",
        "    # Simple constraint: price should increase with MedInc and AveRooms\n",
        "    pseudo_target = MedInc * AveRooms / 10  # Scaling down for simplicity\n",
        "\n",
        "    return pseudo_target\n"
      ],
      "metadata": {
        "id": "ZPv8DRMtBZQf"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class PIKANN(KANN):\n",
        "    def __init__(self, n_features, n_hidden=64, n_decompose=10):\n",
        "        super(PIKANN, self).__init__(n_features, n_hidden, n_decompose)\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 = self(X, training=True)\n",
        "\n",
        "            # Loss for price prediction (Task 1)\n",
        "            price_loss = self.compiled_loss(y_true, y_pred)\n",
        "\n",
        "            # Physics constraint loss (Task 2)\n",
        "            physics_loss = self.compiled_loss(physics_target, physics_constraint(X))\n",
        "\n",
        "            # Combine losses (weighted sum)\n",
        "            total_loss = price_loss + 0.1 * physics_loss  # Weight can be tuned\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": "5aa1ya9UBe8z"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 3: Train the Network\n"
      ],
      "metadata": {
        "id": "mQ6PoDEnC7S_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare the physics target\n",
        "physics_target_train = physics_constraint(X_train)\n",
        "physics_target_test = physics_constraint(X_test)\n",
        "\n",
        "# Compile the model\n",
        "kann_model = PIKANN(n_features=X_train.shape[1])\n",
        "kann_model.compile(optimizer='adam', loss='mse', metrics=['mae'])\n",
        "\n",
        "# Train the model\n",
        "history = kann_model.fit(\n",
        "    X_train,\n",
        "    [y_train, physics_target_train],  # Two targets: house price and physics constraint\n",
        "    epochs=50,\n",
        "    validation_data=(X_test, [y_test, physics_target_test]),\n",
        "    batch_size=32\n",
        ")\n",
        "\n",
        "# Evaluate the model\n",
        "test_loss, test_mae = kann_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": "LBjYITuG8Vjf",
        "outputId": "2cae4ad7-d155-4597-af25-e0768661c5ef"
      },
      "execution_count": 14,
      "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[1m6s\u001b[0m 4ms/step - mae: 0.7089 - loss: 1.9295 - val_loss: 0.4806 - val_mae: 0.4995\n",
            "Epoch 2/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.5002 - loss: 2.0863 - val_loss: 0.4475 - val_mae: 0.4871\n",
            "Epoch 3/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - mae: 0.4760 - loss: 2.0586 - val_loss: 0.4541 - val_mae: 0.4846\n",
            "Epoch 4/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - mae: 0.4663 - loss: 2.0613 - val_loss: 0.4246 - val_mae: 0.4849\n",
            "Epoch 5/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.4639 - loss: 2.0825 - val_loss: 0.4165 - val_mae: 0.4785\n",
            "Epoch 6/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.4571 - loss: 2.0761 - val_loss: 0.4217 - val_mae: 0.4861\n",
            "Epoch 7/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.4666 - loss: 2.0815 - val_loss: 0.4031 - val_mae: 0.4637\n",
            "Epoch 8/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.4619 - loss: 2.0875 - val_loss: 0.4144 - val_mae: 0.4572\n",
            "Epoch 9/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.4526 - loss: 2.0862 - val_loss: 0.5037 - val_mae: 0.4889\n",
            "Epoch 10/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 6ms/step - mae: 0.4520 - loss: 2.0758 - val_loss: 0.4015 - val_mae: 0.4486\n",
            "Epoch 11/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4ms/step - mae: 0.4539 - loss: 2.0866 - val_loss: 0.3976 - val_mae: 0.4573\n",
            "Epoch 12/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.4536 - loss: 2.0852 - val_loss: 0.4035 - val_mae: 0.4553\n",
            "Epoch 13/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - mae: 0.4508 - loss: 2.0715 - val_loss: 0.3992 - val_mae: 0.4524\n",
            "Epoch 14/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - mae: 0.4459 - loss: 2.0667 - val_loss: 0.4009 - val_mae: 0.4708\n",
            "Epoch 15/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - mae: 0.4449 - loss: 2.0685 - val_loss: 0.3928 - val_mae: 0.4558\n",
            "Epoch 16/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4ms/step - mae: 0.4539 - loss: 2.0711 - val_loss: 0.3917 - val_mae: 0.4499\n",
            "Epoch 17/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - mae: 0.4488 - loss: 2.0646 - val_loss: 0.4029 - val_mae: 0.4657\n",
            "Epoch 18/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.4512 - loss: 2.0861 - val_loss: 0.3906 - val_mae: 0.4548\n",
            "Epoch 19/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - mae: 0.4457 - loss: 2.0813 - val_loss: 0.4004 - val_mae: 0.4711\n",
            "Epoch 20/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - mae: 0.4440 - loss: 2.0809 - val_loss: 0.3930 - val_mae: 0.4514\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: 0.4452 - loss: 2.0571 - val_loss: 0.3970 - val_mae: 0.4468\n",
            "Epoch 22/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - mae: 0.4346 - loss: 2.0718 - val_loss: 0.3893 - val_mae: 0.4516\n",
            "Epoch 23/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - mae: 0.4465 - loss: 2.0727 - val_loss: 0.3890 - val_mae: 0.4542\n",
            "Epoch 24/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - mae: 0.4436 - loss: 2.0560 - val_loss: 0.3919 - val_mae: 0.4588\n",
            "Epoch 25/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - mae: 0.4444 - loss: 2.0713 - val_loss: 0.4023 - val_mae: 0.4492\n",
            "Epoch 26/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.4409 - loss: 2.0677 - val_loss: 0.3961 - val_mae: 0.4589\n",
            "Epoch 27/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - mae: 0.4480 - loss: 2.0687 - val_loss: 0.3985 - val_mae: 0.4442\n",
            "Epoch 28/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.4438 - loss: 2.0694 - val_loss: 0.3976 - val_mae: 0.4481\n",
            "Epoch 29/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - mae: 0.4372 - loss: 2.0620 - val_loss: 0.3935 - val_mae: 0.4626\n",
            "Epoch 30/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - mae: 0.4466 - loss: 2.0651 - val_loss: 0.3897 - val_mae: 0.4439\n",
            "Epoch 31/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - mae: 0.4407 - loss: 2.0654 - val_loss: 0.3883 - val_mae: 0.4489\n",
            "Epoch 32/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.4466 - loss: 2.0638 - val_loss: 0.3863 - val_mae: 0.4446\n",
            "Epoch 33/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - mae: 0.4354 - loss: 2.0741 - val_loss: 0.4001 - val_mae: 0.4538\n",
            "Epoch 34/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.4421 - loss: 2.0743 - val_loss: 0.3916 - val_mae: 0.4510\n",
            "Epoch 35/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.4463 - loss: 2.0816 - val_loss: 0.3930 - val_mae: 0.4427\n",
            "Epoch 36/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 7ms/step - mae: 0.4405 - loss: 2.0708 - val_loss: 0.3890 - val_mae: 0.4438\n",
            "Epoch 37/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.4414 - loss: 2.0694 - val_loss: 0.3985 - val_mae: 0.4513\n",
            "Epoch 38/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - mae: 0.4462 - loss: 2.0732 - val_loss: 0.3905 - val_mae: 0.4431\n",
            "Epoch 39/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - mae: 0.4414 - loss: 2.0717 - val_loss: 0.3850 - val_mae: 0.4436\n",
            "Epoch 40/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.4399 - loss: 2.0764 - val_loss: 0.3903 - val_mae: 0.4485\n",
            "Epoch 41/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - mae: 0.4427 - loss: 2.0817 - val_loss: 0.3951 - val_mae: 0.4668\n",
            "Epoch 42/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - mae: 0.4402 - loss: 2.0742 - val_loss: 0.3866 - val_mae: 0.4497\n",
            "Epoch 43/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - mae: 0.4460 - loss: 2.0898 - val_loss: 0.3901 - val_mae: 0.4578\n",
            "Epoch 44/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - mae: 0.4387 - loss: 2.0507 - val_loss: 0.3900 - val_mae: 0.4592\n",
            "Epoch 45/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - mae: 0.4359 - loss: 2.0601 - val_loss: 0.4006 - val_mae: 0.4540\n",
            "Epoch 46/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - mae: 0.4389 - loss: 2.0691 - val_loss: 0.3882 - val_mae: 0.4421\n",
            "Epoch 47/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - mae: 0.4393 - loss: 2.0553 - val_loss: 0.3868 - val_mae: 0.4471\n",
            "Epoch 48/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - mae: 0.4348 - loss: 2.0586 - val_loss: 0.3833 - val_mae: 0.4455\n",
            "Epoch 49/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - mae: 0.4369 - loss: 2.0710 - val_loss: 0.3892 - val_mae: 0.4542\n",
            "Epoch 50/50\n",
            "\u001b[1m516/516\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - mae: 0.4432 - loss: 2.0891 - val_loss: 0.3832 - val_mae: 0.4414\n",
            "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3797 - mae: 0.4384\n",
            "Test Loss: 0.38317635655403137\n",
            "Test MAE: 0.441402405500412\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 = kann_model.evaluate(X_test, [y_test, physics_target_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": 395
        },
        "id": "j2cdUD8DCCLw",
        "outputId": "fea13075-2c08-48c2-a9d5-804371b98438"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.3797 - mae: 0.4384\n",
            "Test Loss: 0.3832\n",
            "Test MAE: 0.4414\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x500 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "1. **Model Evaluation**:\n",
        "   - `test_loss, test_mae = kann_model.evaluate(X_test, [y_test, physics_target_test])`: Evaluates the model on the test set using both the target house prices (`y_test`) and the physics constraint targets (`physics_target_test`).\n",
        "   - The loss and MAE values printed will reflect how well the model performs on unseen data.\n",
        "\n",
        "2. **Plotting Loss**:\n",
        "   - The loss plot shows how the training and validation loss evolve over the epochs. If the model is converging, you should see the losses decreasing and the gap between training and validation losses closing.\n",
        "\n",
        "3. **Plotting MAE**:\n",
        "   - The Mean Absolute Error (MAE) plot shows the average prediction error during training and validation. Like the loss plot, the MAE should also decrease over time, reflecting improvement in model accuracy.\n",
        "\n",
        "### What to Expect:\n",
        "- **Loss Plot**: Ideally, the validation loss should decrease and be close to the training loss. If there's a large gap, it might indicate overfitting.\n",
        "- **MAE Plot**: A decreasing MAE with a stable validation curve suggests the model is learning effectively and generalizing well.\n",
        "\n",
        "By monitoring these metrics, you can determine if the Physics-Informed Kolmogorov-Arnold Network is training effectively and if there are any signs of overfitting or underfitting."
      ],
      "metadata": {
        "id": "NqQVTev8CNxg"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4NXPncwTCSQr"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": [],
      "authorship_tag": "ABX9TyNeXRWAvF38BirW7Z1uT9++",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}