diff --git "a/_1209(Energy_Consumption).ipynb" "b/_1209(Energy_Consumption).ipynb"
new file mode 100644--- /dev/null
+++ "b/_1209(Energy_Consumption).ipynb"
@@ -0,0 +1,3571 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 60,
+      "metadata": {
+        "id": "xvhJR3JIk3OO"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "import os\n",
+        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
+        "  for filename in filenames:\n",
+        "    print(os.path.join(dirname, filename))\n",
+        "\n",
+        "    os.makedirs('/kaggle/working/', exist_ok=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "import warnings\n",
+        "\n",
+        "warnings.filterwarnings('ignore')"
+      ],
+      "metadata": {
+        "id": "t_ZGC30LlKeU"
+      },
+      "execution_count": 61,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "path = '/content/Energy_consumption_dataset.csv'\n",
+        "df = pd.read_csv(path)\n",
+        "df"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 444
+        },
+        "id": "g3MC37F7lVcQ",
+        "outputId": "87ca863b-4cde-4bf2-b5c2-050c389a6859"
+      },
+      "execution_count": 62,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      Month  Hour DayOfWeek Holiday  Temperature   Humidity  SquareFootage  \\\n",
+              "0         1     0  Saturday      No    25.139433  43.431581    1565.693999   \n",
+              "1         1     1  Saturday      No    27.731651  54.225919    1411.064918   \n",
+              "2         1     2  Saturday      No    28.704277  58.907658    1755.715009   \n",
+              "3         1     3  Saturday      No    20.080469  50.371637    1452.316318   \n",
+              "4         1     4  Saturday      No    23.097359  51.401421    1094.130359   \n",
+              "...     ...   ...       ...     ...          ...        ...            ...   \n",
+              "4995     12     6    Sunday     Yes    26.338718  52.580000    1563.567259   \n",
+              "4996     12    17    Monday      No    20.007565  42.765607    1999.982252   \n",
+              "4997     12    13  Thursday     Yes    26.226253  30.015975    1999.982252   \n",
+              "4998     12     8  Saturday     Yes    24.673206  50.223939    1240.811298   \n",
+              "4999     12     1  Saturday     Yes    25.802872  41.798829    1793.658686   \n",
+              "\n",
+              "      Occupancy HVACUsage LightingUsage  RenewableEnergy  EnergyConsumption  \n",
+              "0             5        On           Off         2.774699          75.364373  \n",
+              "1             1        On            On        21.831384          83.401855  \n",
+              "2             2       Off           Off         6.764672          78.270888  \n",
+              "3             1       Off            On         8.623447          56.519850  \n",
+              "4             9        On           Off         3.071969          70.811732  \n",
+              "...         ...       ...           ...              ...                ...  \n",
+              "4995          7        On            On        20.591717          70.270344  \n",
+              "4996          5       Off            On         7.536319          73.943071  \n",
+              "4997          5       Off            On        28.162193          85.784613  \n",
+              "4998          2        On            On        20.918483          63.784001  \n",
+              "4999          6       Off           Off         8.334079          53.263278  \n",
+              "\n",
+              "[5000 rows x 12 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-1b149705-432e-40e1-affa-5cf0f992a1d1\" 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>Month</th>\n",
+              "      <th>Hour</th>\n",
+              "      <th>DayOfWeek</th>\n",
+              "      <th>Holiday</th>\n",
+              "      <th>Temperature</th>\n",
+              "      <th>Humidity</th>\n",
+              "      <th>SquareFootage</th>\n",
+              "      <th>Occupancy</th>\n",
+              "      <th>HVACUsage</th>\n",
+              "      <th>LightingUsage</th>\n",
+              "      <th>RenewableEnergy</th>\n",
+              "      <th>EnergyConsumption</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Saturday</td>\n",
+              "      <td>No</td>\n",
+              "      <td>25.139433</td>\n",
+              "      <td>43.431581</td>\n",
+              "      <td>1565.693999</td>\n",
+              "      <td>5</td>\n",
+              "      <td>On</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>2.774699</td>\n",
+              "      <td>75.364373</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Saturday</td>\n",
+              "      <td>No</td>\n",
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+              "      <td>54.225919</td>\n",
+              "      <td>1411.064918</td>\n",
+              "      <td>1</td>\n",
+              "      <td>On</td>\n",
+              "      <td>On</td>\n",
+              "      <td>21.831384</td>\n",
+              "      <td>83.401855</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Saturday</td>\n",
+              "      <td>No</td>\n",
+              "      <td>28.704277</td>\n",
+              "      <td>58.907658</td>\n",
+              "      <td>1755.715009</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>6.764672</td>\n",
+              "      <td>78.270888</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Saturday</td>\n",
+              "      <td>No</td>\n",
+              "      <td>20.080469</td>\n",
+              "      <td>50.371637</td>\n",
+              "      <td>1452.316318</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>On</td>\n",
+              "      <td>8.623447</td>\n",
+              "      <td>56.519850</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1</td>\n",
+              "      <td>4</td>\n",
+              "      <td>Saturday</td>\n",
+              "      <td>No</td>\n",
+              "      <td>23.097359</td>\n",
+              "      <td>51.401421</td>\n",
+              "      <td>1094.130359</td>\n",
+              "      <td>9</td>\n",
+              "      <td>On</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>3.071969</td>\n",
+              "      <td>70.811732</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4995</th>\n",
+              "      <td>12</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Sunday</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>26.338718</td>\n",
+              "      <td>52.580000</td>\n",
+              "      <td>1563.567259</td>\n",
+              "      <td>7</td>\n",
+              "      <td>On</td>\n",
+              "      <td>On</td>\n",
+              "      <td>20.591717</td>\n",
+              "      <td>70.270344</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4996</th>\n",
+              "      <td>12</td>\n",
+              "      <td>17</td>\n",
+              "      <td>Monday</td>\n",
+              "      <td>No</td>\n",
+              "      <td>20.007565</td>\n",
+              "      <td>42.765607</td>\n",
+              "      <td>1999.982252</td>\n",
+              "      <td>5</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>On</td>\n",
+              "      <td>7.536319</td>\n",
+              "      <td>73.943071</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4997</th>\n",
+              "      <td>12</td>\n",
+              "      <td>13</td>\n",
+              "      <td>Thursday</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>26.226253</td>\n",
+              "      <td>30.015975</td>\n",
+              "      <td>1999.982252</td>\n",
+              "      <td>5</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>On</td>\n",
+              "      <td>28.162193</td>\n",
+              "      <td>85.784613</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4998</th>\n",
+              "      <td>12</td>\n",
+              "      <td>8</td>\n",
+              "      <td>Saturday</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>24.673206</td>\n",
+              "      <td>50.223939</td>\n",
+              "      <td>1240.811298</td>\n",
+              "      <td>2</td>\n",
+              "      <td>On</td>\n",
+              "      <td>On</td>\n",
+              "      <td>20.918483</td>\n",
+              "      <td>63.784001</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4999</th>\n",
+              "      <td>12</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Saturday</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>25.802872</td>\n",
+              "      <td>41.798829</td>\n",
+              "      <td>1793.658686</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>8.334079</td>\n",
+              "      <td>53.263278</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5000 rows × 12 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1b149705-432e-40e1-affa-5cf0f992a1d1')\"\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-1b149705-432e-40e1-affa-5cf0f992a1d1 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-1b149705-432e-40e1-affa-5cf0f992a1d1');\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",
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+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-cbf00457-6e4b-45e2-bcc4-7fad119d18d1\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cbf00457-6e4b-45e2-bcc4-7fad119d18d1')\"\n",
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+              "    </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",
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+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
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+              "    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",
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+              "      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-cbf00457-6e4b-45e2-bcc4-7fad119d18d1 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_15f02057-b525-4cfe-9b1b-377dafdfe093\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\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-generate: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",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate: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",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\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",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_15f02057-b525-4cfe-9b1b-377dafdfe093 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df');\n",
+              "      }\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\": 5000,\n  \"fields\": [\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 12,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          11,\n          10,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hour\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 0,\n        \"max\": 23,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          8,\n          16,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DayOfWeek\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Saturday\",\n          \"Sunday\",\n          \"Thursday\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Holiday\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.041677552108525,\n        \"min\": 20.007565,\n        \"max\": 29.998671,\n        \"num_unique_values\": 4409,\n        \"samples\": [\n          26.366987947584217,\n          28.698193824921525\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.972690469128699,\n        \"min\": 30.01597450346074,\n        \"max\": 59.969085,\n        \"num_unique_values\": 4489,\n        \"samples\": [\n          43.88354637574899,\n          48.26307880103275\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SquareFootage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 293.14720927887083,\n        \"min\": 1000.5126606747408,\n        \"max\": 1999.982252131635,\n        \"num_unique_values\": 4710,\n        \"samples\": [\n          1050.579412110074,\n          1144.4227959771806\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HVACUsage\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Off\",\n          \"On\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LightingUsage\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"On\",\n          \"Off\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RenewableEnergy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.157037618534229,\n        \"min\": 0.006642,\n        \"max\": 29.96532733777335,\n        \"num_unique_values\": 4475,\n        \"samples\": [\n          10.737083256824402,\n          22.115806950483456\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EnergyConsumption\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.231573112556944,\n        \"min\": 53.263278,\n        \"max\": 99.20112,\n        \"num_unique_values\": 4937,\n        \"samples\": [\n          70.99960829697996,\n          87.28499908910176\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 62
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BbN9SakwldGk",
+        "outputId": "e4f86cec-17e4-4465-e5d7-6f0472afc92d"
+      },
+      "execution_count": 63,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 5000 entries, 0 to 4999\n",
+            "Data columns (total 12 columns):\n",
+            " #   Column             Non-Null Count  Dtype  \n",
+            "---  ------             --------------  -----  \n",
+            " 0   Month              5000 non-null   int64  \n",
+            " 1   Hour               5000 non-null   int64  \n",
+            " 2   DayOfWeek          5000 non-null   object \n",
+            " 3   Holiday            5000 non-null   object \n",
+            " 4   Temperature        5000 non-null   float64\n",
+            " 5   Humidity           5000 non-null   float64\n",
+            " 6   SquareFootage      5000 non-null   float64\n",
+            " 7   Occupancy          5000 non-null   int64  \n",
+            " 8   HVACUsage          5000 non-null   object \n",
+            " 9   LightingUsage      5000 non-null   object \n",
+            " 10  RenewableEnergy    5000 non-null   float64\n",
+            " 11  EnergyConsumption  5000 non-null   float64\n",
+            "dtypes: float64(5), int64(3), object(4)\n",
+            "memory usage: 468.9+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 320
+        },
+        "id": "k3wK_gQnle4s",
+        "outputId": "bfe230d3-52ae-4b73-d603-cb46508e6e13"
+      },
+      "execution_count": 64,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             Month         Hour  Temperature     Humidity  SquareFootage  \\\n",
+              "count  5000.000000  5000.000000  5000.000000  5000.000000    5000.000000   \n",
+              "mean      6.251200    11.556000    24.946823    45.478146    1507.403201   \n",
+              "std       3.588687     6.888306     3.041678     8.972690     293.147209   \n",
+              "min       1.000000     0.000000    20.007565    30.015975    1000.512661   \n",
+              "25%       3.000000     6.000000    22.453790    38.111104    1253.906598   \n",
+              "50%       6.000000    11.000000    24.831846    45.793124    1513.581105   \n",
+              "75%       9.000000    18.000000    27.427281    52.696651    1754.846457   \n",
+              "max      12.000000    23.000000    29.998671    59.969085    1999.982252   \n",
+              "\n",
+              "         Occupancy  RenewableEnergy  EnergyConsumption  \n",
+              "count  5000.000000      5000.000000        5000.000000  \n",
+              "mean      4.576000        15.201195          76.794919  \n",
+              "std       2.906185         9.157038           9.231573  \n",
+              "min       0.000000         0.006642          53.263278  \n",
+              "25%       2.000000         7.477928          70.419588  \n",
+              "50%       5.000000        15.343830          76.696267  \n",
+              "75%       7.000000        22.889997          83.246274  \n",
+              "max       9.000000        29.965327          99.201120  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e03d5a8e-6bb1-4876-bc74-37cffe6ca76f\" 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>Month</th>\n",
+              "      <th>Hour</th>\n",
+              "      <th>Temperature</th>\n",
+              "      <th>Humidity</th>\n",
+              "      <th>SquareFootage</th>\n",
+              "      <th>Occupancy</th>\n",
+              "      <th>RenewableEnergy</th>\n",
+              "      <th>EnergyConsumption</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.000000</td>\n",
+              "      <td>5000.000000</td>\n",
+              "      <td>5000.000000</td>\n",
+              "      <td>5000.000000</td>\n",
+              "      <td>5000.000000</td>\n",
+              "      <td>5000.000000</td>\n",
+              "      <td>5000.000000</td>\n",
+              "      <td>5000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>6.251200</td>\n",
+              "      <td>11.556000</td>\n",
+              "      <td>24.946823</td>\n",
+              "      <td>45.478146</td>\n",
+              "      <td>1507.403201</td>\n",
+              "      <td>4.576000</td>\n",
+              "      <td>15.201195</td>\n",
+              "      <td>76.794919</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>3.588687</td>\n",
+              "      <td>6.888306</td>\n",
+              "      <td>3.041678</td>\n",
+              "      <td>8.972690</td>\n",
+              "      <td>293.147209</td>\n",
+              "      <td>2.906185</td>\n",
+              "      <td>9.157038</td>\n",
+              "      <td>9.231573</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>20.007565</td>\n",
+              "      <td>30.015975</td>\n",
+              "      <td>1000.512661</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.006642</td>\n",
+              "      <td>53.263278</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>6.000000</td>\n",
+              "      <td>22.453790</td>\n",
+              "      <td>38.111104</td>\n",
+              "      <td>1253.906598</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>7.477928</td>\n",
+              "      <td>70.419588</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>6.000000</td>\n",
+              "      <td>11.000000</td>\n",
+              "      <td>24.831846</td>\n",
+              "      <td>45.793124</td>\n",
+              "      <td>1513.581105</td>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>15.343830</td>\n",
+              "      <td>76.696267</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>9.000000</td>\n",
+              "      <td>18.000000</td>\n",
+              "      <td>27.427281</td>\n",
+              "      <td>52.696651</td>\n",
+              "      <td>1754.846457</td>\n",
+              "      <td>7.000000</td>\n",
+              "      <td>22.889997</td>\n",
+              "      <td>83.246274</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>12.000000</td>\n",
+              "      <td>23.000000</td>\n",
+              "      <td>29.998671</td>\n",
+              "      <td>59.969085</td>\n",
+              "      <td>1999.982252</td>\n",
+              "      <td>9.000000</td>\n",
+              "      <td>29.965327</td>\n",
+              "      <td>99.201120</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-e03d5a8e-6bb1-4876-bc74-37cffe6ca76f')\"\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-e03d5a8e-6bb1-4876-bc74-37cffe6ca76f 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-e03d5a8e-6bb1-4876-bc74-37cffe6ca76f');\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-8d756767-a82a-4618-bdd9-32fa243d6ab8\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8d756767-a82a-4618-bdd9-32fa243d6ab8')\"\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-8d756767-a82a-4618-bdd9-32fa243d6ab8 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\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1765.7076529739375,\n        \"min\": 1.0,\n        \"max\": 5000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.2512,\n          6.0,\n          5000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hour\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1763.9203554029452,\n        \"min\": 0.0,\n        \"max\": 5000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          11.556,\n          11.0,\n          5000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1760.0732674339733,\n        \"min\": 3.041677552108525,\n        \"max\": 5000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          24.946822966670943,\n          24.83184649853723,\n          5000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1753.641262663482,\n        \"min\": 8.972690469128699,\n        \"max\": 5000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          45.47814600927568,\n          45.793123843725795,\n          5000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SquareFootage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1396.7794548534607,\n        \"min\": 293.14720927887083,\n        \"max\": 5000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1507.403201390195,\n          1513.581105066934,\n          5000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1766.229620591018,\n        \"min\": 0.0,\n        \"max\": 5000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.576,\n          5.0,\n          5000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RenewableEnergy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1762.738318555752,\n        \"min\": 0.006642,\n        \"max\": 5000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15.201194654945784,\n          15.343830459940168,\n          5000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EnergyConsumption\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1744.2922236564177,\n        \"min\": 9.231573112556944,\n        \"max\": 5000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          76.79491889460932,\n          76.69626692808049,\n          5000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 64
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.duplicated().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Zr2KsoY3lhCC",
+        "outputId": "f54b2c69-d6f3-4241-f03f-0494786f6cae"
+      },
+      "execution_count": 65,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 65
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.isnull().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 460
+        },
+        "id": "roQ1txVplj1W",
+        "outputId": "d853a3a5-f721-4688-a26c-109e926445e9"
+      },
+      "execution_count": 66,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Month                0\n",
+              "Hour                 0\n",
+              "DayOfWeek            0\n",
+              "Holiday              0\n",
+              "Temperature          0\n",
+              "Humidity             0\n",
+              "SquareFootage        0\n",
+              "Occupancy            0\n",
+              "HVACUsage            0\n",
+              "LightingUsage        0\n",
+              "RenewableEnergy      0\n",
+              "EnergyConsumption    0\n",
+              "dtype: int64"
+            ],
+            "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>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Month</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Hour</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>DayOfWeek</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Holiday</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Temperature</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Humidity</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>SquareFootage</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Occupancy</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>HVACUsage</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>LightingUsage</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>RenewableEnergy</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>EnergyConsumption</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 66
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "monthly_average_consumption = df.groupby('Month')['EnergyConsumption'].mean().reset_index() # Calculate the average energy consumption for each month\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "sns.barplot(x='Month', y='EnergyConsumption', data = monthly_average_consumption, palette='viridis') # Use the aggregated data for plotting\n",
+        "plt.title('Average Energy Consumption by Month', fontsize=16)\n",
+        "plt.xlabel('Month', fontsize=14)\n",
+        "plt.ylabel('Average Energy Consumption', fontsize=14)\n",
+        "plt.xticks(ticks=range(0, 12), labels=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 429
+        },
+        "id": "a0-xG7bNlmdM",
+        "outputId": "caf90251-bb65-4844-fb74-fab507f6d471"
+      },
+      "execution_count": 67,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "monthly_avg_consumption = df.groupby('Month')['EnergyConsumption'].mean().reset_index()\n",
+        "plt.plot(monthly_avg_consumption['Month'],\n",
+        "         monthly_avg_consumption['EnergyConsumption'],\n",
+        "         marker='o', color='b', label='Energy Consumption')\n",
+        "plt.title('Average Energy Consumption by Month', fontsize=16)\n",
+        "plt.xlabel('Month', fontsize=14)\n",
+        "plt.ylabel('Average Energy Consumption', fontsize=14)\n",
+        "plt.xticks(ticks=range(1, 13), labels=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])\n",
+        "plt.grid(alpha=0.5)\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 479
+        },
+        "id": "TtBKGQ5MmmRX",
+        "outputId": "4efc5e82-f780-4155-cc7c-7b66362224ff"
+      },
+      "execution_count": 68,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_season(month):\n",
+        "  if month in [3, 4, 5]:\n",
+        "    return 'Summer'\n",
+        "  elif month in [6, 7, 8]:\n",
+        "    return 'Monsoon'\n",
+        "  elif month in [9, 10, 11]:\n",
+        "    return 'Autumn'\n",
+        "  else:\n",
+        "    return 'Winter'\n",
+        "df['Season'] = df['Month'].apply(get_season)\n",
+        "df = df.drop('Month', axis=1)\n",
+        "columns = ['Season'] + [col for col in df.columns if col != 'Season']\n",
+        "df = df[columns]"
+      ],
+      "metadata": {
+        "id": "b_horAgYpEF8"
+      },
+      "execution_count": 69,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 226
+        },
+        "id": "Ww3dUDSmpvMX",
+        "outputId": "98337aec-cfcd-4bec-d5ec-0f8caf1e30f3"
+      },
+      "execution_count": 70,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Season  Hour DayOfWeek Holiday  Temperature   Humidity  SquareFootage  \\\n",
+              "0  Winter     0  Saturday      No    25.139433  43.431581    1565.693999   \n",
+              "1  Winter     1  Saturday      No    27.731651  54.225919    1411.064918   \n",
+              "2  Winter     2  Saturday      No    28.704277  58.907658    1755.715009   \n",
+              "3  Winter     3  Saturday      No    20.080469  50.371637    1452.316318   \n",
+              "4  Winter     4  Saturday      No    23.097359  51.401421    1094.130359   \n",
+              "\n",
+              "   Occupancy HVACUsage LightingUsage  RenewableEnergy  EnergyConsumption  \n",
+              "0          5        On           Off         2.774699          75.364373  \n",
+              "1          1        On            On        21.831384          83.401855  \n",
+              "2          2       Off           Off         6.764672          78.270888  \n",
+              "3          1       Off            On         8.623447          56.519850  \n",
+              "4          9        On           Off         3.071969          70.811732  "
+            ],
+            "text/html": [
+              "\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Season</th>\n",
+              "      <th>Hour</th>\n",
+              "      <th>DayOfWeek</th>\n",
+              "      <th>Holiday</th>\n",
+              "      <th>Temperature</th>\n",
+              "      <th>Humidity</th>\n",
+              "      <th>SquareFootage</th>\n",
+              "      <th>Occupancy</th>\n",
+              "      <th>HVACUsage</th>\n",
+              "      <th>LightingUsage</th>\n",
+              "      <th>RenewableEnergy</th>\n",
+              "      <th>EnergyConsumption</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Winter</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Saturday</td>\n",
+              "      <td>No</td>\n",
+              "      <td>25.139433</td>\n",
+              "      <td>43.431581</td>\n",
+              "      <td>1565.693999</td>\n",
+              "      <td>5</td>\n",
+              "      <td>On</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>2.774699</td>\n",
+              "      <td>75.364373</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Winter</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Saturday</td>\n",
+              "      <td>No</td>\n",
+              "      <td>27.731651</td>\n",
+              "      <td>54.225919</td>\n",
+              "      <td>1411.064918</td>\n",
+              "      <td>1</td>\n",
+              "      <td>On</td>\n",
+              "      <td>On</td>\n",
+              "      <td>21.831384</td>\n",
+              "      <td>83.401855</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Winter</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Saturday</td>\n",
+              "      <td>No</td>\n",
+              "      <td>28.704277</td>\n",
+              "      <td>58.907658</td>\n",
+              "      <td>1755.715009</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>6.764672</td>\n",
+              "      <td>78.270888</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Winter</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Saturday</td>\n",
+              "      <td>No</td>\n",
+              "      <td>20.080469</td>\n",
+              "      <td>50.371637</td>\n",
+              "      <td>1452.316318</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>On</td>\n",
+              "      <td>8.623447</td>\n",
+              "      <td>56.519850</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Winter</td>\n",
+              "      <td>4</td>\n",
+              "      <td>Saturday</td>\n",
+              "      <td>No</td>\n",
+              "      <td>23.097359</td>\n",
+              "      <td>51.401421</td>\n",
+              "      <td>1094.130359</td>\n",
+              "      <td>9</td>\n",
+              "      <td>On</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>3.071969</td>\n",
+              "      <td>70.811732</td>\n",
+              "    </tr>\n",
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+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
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+              "\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",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-d103eab1-dbf8-4e8b-b080-62138ea0f08d');\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",
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+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
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+              "  [theme=dark] .colab-df-quickchart {\n",
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+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
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+              "    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",
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+              "      border-right-color: var(--fill-color);\n",
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+              "    }\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "      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-84cb0ce6-1c28-4648-9ea4-e03f15eba6db 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\": 5000,\n  \"fields\": [\n    {\n      \"column\": \"Season\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Summer\",\n          \"Autumn\",\n          \"Winter\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hour\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 0,\n        \"max\": 23,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          8,\n          16,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DayOfWeek\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Saturday\",\n          \"Sunday\",\n          \"Thursday\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Holiday\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.041677552108525,\n        \"min\": 20.007565,\n        \"max\": 29.998671,\n        \"num_unique_values\": 4409,\n        \"samples\": [\n          26.366987947584217,\n          28.698193824921525\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.972690469128699,\n        \"min\": 30.01597450346074,\n        \"max\": 59.969085,\n        \"num_unique_values\": 4489,\n        \"samples\": [\n          43.88354637574899,\n          48.26307880103275\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SquareFootage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 293.14720927887083,\n        \"min\": 1000.5126606747408,\n        \"max\": 1999.982252131635,\n        \"num_unique_values\": 4710,\n        \"samples\": [\n          1050.579412110074,\n          1144.4227959771806\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HVACUsage\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Off\",\n          \"On\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LightingUsage\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"On\",\n          \"Off\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RenewableEnergy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.157037618534229,\n        \"min\": 0.006642,\n        \"max\": 29.96532733777335,\n        \"num_unique_values\": 4475,\n        \"samples\": [\n          10.737083256824402,\n          22.115806950483456\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EnergyConsumption\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.231573112556944,\n        \"min\": 53.263278,\n        \"max\": 99.20112,\n        \"num_unique_values\": 4937,\n        \"samples\": [\n          70.99960829697996,\n          87.28499908910176\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 70
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['Season'].unique()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8xpMdJndpwtW",
+        "outputId": "bdb72253-ab23-4e00-a84a-deb4737067bf"
+      },
+      "execution_count": 71,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['Winter', 'Summer', 'Monsoon', 'Autumn'], dtype=object)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 71
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "season_avg_consumption = df.groupby('Season')['EnergyConsumption'].mean().reset_index()\n",
+        "\n",
+        "season_order = ['Summer', 'Monsoon', 'Autumn', 'Winter']\n",
+        "season_avg_consumption['Season'] = pd.Categorical(season_avg_consumption['Season'], categories = season_order, ordered=True)\n",
+        "season_avg_consumption = season_avg_consumption.sort_values('Season')\n",
+        "\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.plot(season_avg_consumption['Season'], season_avg_consumption['EnergyConsumption'], marker='o', linestyle='-', color='b')\n",
+        "plt.title('Average Energy Consumption by Season', fontsize=16)\n",
+        "plt.xlabel('Season', fontsize=14)\n",
+        "plt.ylabel('Average Energy Consumption', fontsize=14)\n",
+        "plt.grid(visible=True, linestyle='--', alpha=0.6)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 422
+        },
+        "id": "288q8iS9pz9S",
+        "outputId": "4d47d5ae-52a4-431f-a714-7a567b93cfce"
+      },
+      "execution_count": 72,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "hour_avg_consumption = df.groupby('Hour')['EnergyConsumption'].mean().reset_index()\n",
+        "\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.plot(hour_avg_consumption['Hour'], hour_avg_consumption['EnergyConsumption'], marker='o', linestyle='-', color='b')\n",
+        "plt.title('Average Energy Consumption by Hour of the Day', fontsize=16)\n",
+        "plt.xlabel('Hour of the Day', fontsize=14)\n",
+        "plt.ylabel('Average Energy Consumption', fontsize=14)\n",
+        "plt.xticks(range(0, 24))\n",
+        "plt.grid(visible=True, linestyle='--', alpha=0.6)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 422
+        },
+        "id": "-DqGDS1sq1-A",
+        "outputId": "20bff45f-be63-4bb6-c83e-e4955e8b79ad"
+      },
+      "execution_count": 73,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_time_of_day(hour):\n",
+        "  if 4 <= hour < 12:\n",
+        "    return 'Morning'\n",
+        "  elif 12 <= hour < 16:\n",
+        "    return 'Afternoon'\n",
+        "  elif 16 <= hour < 21:\n",
+        "    return 'Evening'\n",
+        "  else:\n",
+        "    return 'Night'\n",
+        "\n",
+        "df['TimeOfDay'] = df['Hour'].apply(get_time_of_day)\n",
+        "df = df.drop('Hour', axis=1)\n",
+        "columns = ['Season', 'TimeOfDay'] + [col for col in df.columns if col not in ['Season', 'TimeOfDay']]\n",
+        "df = df[columns]"
+      ],
+      "metadata": {
+        "id": "AVn9yoHNrrmt"
+      },
+      "execution_count": 74,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 226
+        },
+        "id": "j-J-4FUbsJaP",
+        "outputId": "f218bc27-ef22-4094-892b-c57c036b1330"
+      },
+      "execution_count": 75,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Season TimeOfDay DayOfWeek Holiday  Temperature   Humidity  SquareFootage  \\\n",
+              "0  Winter     Night  Saturday      No    25.139433  43.431581    1565.693999   \n",
+              "1  Winter     Night  Saturday      No    27.731651  54.225919    1411.064918   \n",
+              "2  Winter     Night  Saturday      No    28.704277  58.907658    1755.715009   \n",
+              "3  Winter     Night  Saturday      No    20.080469  50.371637    1452.316318   \n",
+              "4  Winter   Morning  Saturday      No    23.097359  51.401421    1094.130359   \n",
+              "\n",
+              "   Occupancy HVACUsage LightingUsage  RenewableEnergy  EnergyConsumption  \n",
+              "0          5        On           Off         2.774699          75.364373  \n",
+              "1          1        On            On        21.831384          83.401855  \n",
+              "2          2       Off           Off         6.764672          78.270888  \n",
+              "3          1       Off            On         8.623447          56.519850  \n",
+              "4          9        On           Off         3.071969          70.811732  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-6082ea7e-fcb6-48fd-af9c-63a07b78d20b\" 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",
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+              "\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>Season</th>\n",
+              "      <th>TimeOfDay</th>\n",
+              "      <th>DayOfWeek</th>\n",
+              "      <th>Holiday</th>\n",
+              "      <th>Temperature</th>\n",
+              "      <th>Humidity</th>\n",
+              "      <th>SquareFootage</th>\n",
+              "      <th>Occupancy</th>\n",
+              "      <th>HVACUsage</th>\n",
+              "      <th>LightingUsage</th>\n",
+              "      <th>RenewableEnergy</th>\n",
+              "      <th>EnergyConsumption</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Winter</td>\n",
+              "      <td>Night</td>\n",
+              "      <td>Saturday</td>\n",
+              "      <td>No</td>\n",
+              "      <td>25.139433</td>\n",
+              "      <td>43.431581</td>\n",
+              "      <td>1565.693999</td>\n",
+              "      <td>5</td>\n",
+              "      <td>On</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>2.774699</td>\n",
+              "      <td>75.364373</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Winter</td>\n",
+              "      <td>Night</td>\n",
+              "      <td>Saturday</td>\n",
+              "      <td>No</td>\n",
+              "      <td>27.731651</td>\n",
+              "      <td>54.225919</td>\n",
+              "      <td>1411.064918</td>\n",
+              "      <td>1</td>\n",
+              "      <td>On</td>\n",
+              "      <td>On</td>\n",
+              "      <td>21.831384</td>\n",
+              "      <td>83.401855</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Winter</td>\n",
+              "      <td>Night</td>\n",
+              "      <td>Saturday</td>\n",
+              "      <td>No</td>\n",
+              "      <td>28.704277</td>\n",
+              "      <td>58.907658</td>\n",
+              "      <td>1755.715009</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>6.764672</td>\n",
+              "      <td>78.270888</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Winter</td>\n",
+              "      <td>Night</td>\n",
+              "      <td>Saturday</td>\n",
+              "      <td>No</td>\n",
+              "      <td>20.080469</td>\n",
+              "      <td>50.371637</td>\n",
+              "      <td>1452.316318</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>On</td>\n",
+              "      <td>8.623447</td>\n",
+              "      <td>56.519850</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Winter</td>\n",
+              "      <td>Morning</td>\n",
+              "      <td>Saturday</td>\n",
+              "      <td>No</td>\n",
+              "      <td>23.097359</td>\n",
+              "      <td>51.401421</td>\n",
+              "      <td>1094.130359</td>\n",
+              "      <td>9</td>\n",
+              "      <td>On</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>3.071969</td>\n",
+              "      <td>70.811732</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
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+              "\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",
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+              "      const buttonEl =\n",
+              "        document.querySelector('#df-6082ea7e-fcb6-48fd-af9c-63a07b78d20b 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-6082ea7e-fcb6-48fd-af9c-63a07b78d20b');\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",
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+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "\n",
+              "\n",
+              "<div id=\"df-79bb67c1-2cb7-40a7-ad91-3be9116cb753\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-79bb67c1-2cb7-40a7-ad91-3be9116cb753')\"\n",
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+              "    </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",
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+              "      --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",
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+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
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+              "    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",
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+              "      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-79bb67c1-2cb7-40a7-ad91-3be9116cb753 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\": 5000,\n  \"fields\": [\n    {\n      \"column\": \"Season\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Summer\",\n          \"Autumn\",\n          \"Winter\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TimeOfDay\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Morning\",\n          \"Evening\",\n          \"Night\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DayOfWeek\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Saturday\",\n          \"Sunday\",\n          \"Thursday\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Holiday\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.041677552108525,\n        \"min\": 20.007565,\n        \"max\": 29.998671,\n        \"num_unique_values\": 4409,\n        \"samples\": [\n          26.366987947584217,\n          28.698193824921525\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.972690469128699,\n        \"min\": 30.01597450346074,\n        \"max\": 59.969085,\n        \"num_unique_values\": 4489,\n        \"samples\": [\n          43.88354637574899,\n          48.26307880103275\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SquareFootage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 293.14720927887083,\n        \"min\": 1000.5126606747408,\n        \"max\": 1999.982252131635,\n        \"num_unique_values\": 4710,\n        \"samples\": [\n          1050.579412110074,\n          1144.4227959771806\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HVACUsage\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Off\",\n          \"On\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LightingUsage\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"On\",\n          \"Off\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RenewableEnergy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.157037618534229,\n        \"min\": 0.006642,\n        \"max\": 29.96532733777335,\n        \"num_unique_values\": 4475,\n        \"samples\": [\n          10.737083256824402,\n          22.115806950483456\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EnergyConsumption\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.231573112556944,\n        \"min\": 53.263278,\n        \"max\": 99.20112,\n        \"num_unique_values\": 4937,\n        \"samples\": [\n          70.99960829697996,\n          87.28499908910176\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 75
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['TimeOfDay'].unique()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Vrcbf-LdsLS2",
+        "outputId": "3fd38060-0615-4764-fda1-a585bd42181b"
+      },
+      "execution_count": 76,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['Night', 'Morning', 'Afternoon', 'Evening'], dtype=object)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 76
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dayofweek_avg_consumption = df.groupby('DayOfWeek')['EnergyConsumption'].mean().reset_index()\n",
+        "\n",
+        "# Sort the days of the week to ensure correct order\n",
+        "dayofweek_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
+        "dayofweek_avg_consumption['DayOfWeek'] = pd.Categorical(dayofweek_avg_consumption['DayOfWeek'], categories=dayofweek_order, ordered=True)\n",
+        "dayofweek_avg_consumption = dayofweek_avg_consumption.sort_values('DayOfWeek')\n",
+        "\n",
+        "# Bar plot\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.bar(dayofweek_avg_consumption['DayOfWeek'], dayofweek_avg_consumption['EnergyConsumption'])\n",
+        "plt.title('Average Energy Consumption by Day of the Week', fontsize=16)\n",
+        "plt.xlabel('Day of the Week', fontsize=14)\n",
+        "plt.ylabel('Average Energy Consumption', fontsize=14)\n",
+        "plt.grid(visible=True, linestyle='--', alpha=0.6)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 429
+        },
+        "id": "KqEqgvBMsOrC",
+        "outputId": "2fa11780-1a99-4972-e785-7762577dde23"
+      },
+      "execution_count": 77,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.drop('DayOfWeek', axis=1, inplace=True)"
+      ],
+      "metadata": {
+        "id": "x9zU_MQYyBen"
+      },
+      "execution_count": 78,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 226
+        },
+        "id": "isvy-RUEyFQ3",
+        "outputId": "649fbb30-56bd-4ea6-e4fb-9514f2c96a44"
+      },
+      "execution_count": 79,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Season TimeOfDay Holiday  Temperature   Humidity  SquareFootage  Occupancy  \\\n",
+              "0  Winter     Night      No    25.139433  43.431581    1565.693999          5   \n",
+              "1  Winter     Night      No    27.731651  54.225919    1411.064918          1   \n",
+              "2  Winter     Night      No    28.704277  58.907658    1755.715009          2   \n",
+              "3  Winter     Night      No    20.080469  50.371637    1452.316318          1   \n",
+              "4  Winter   Morning      No    23.097359  51.401421    1094.130359          9   \n",
+              "\n",
+              "  HVACUsage LightingUsage  RenewableEnergy  EnergyConsumption  \n",
+              "0        On           Off         2.774699          75.364373  \n",
+              "1        On            On        21.831384          83.401855  \n",
+              "2       Off           Off         6.764672          78.270888  \n",
+              "3       Off            On         8.623447          56.519850  \n",
+              "4        On           Off         3.071969          70.811732  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-f323c160-ed16-483d-b0c5-db04b1dcfb49\" 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>Season</th>\n",
+              "      <th>TimeOfDay</th>\n",
+              "      <th>Holiday</th>\n",
+              "      <th>Temperature</th>\n",
+              "      <th>Humidity</th>\n",
+              "      <th>SquareFootage</th>\n",
+              "      <th>Occupancy</th>\n",
+              "      <th>HVACUsage</th>\n",
+              "      <th>LightingUsage</th>\n",
+              "      <th>RenewableEnergy</th>\n",
+              "      <th>EnergyConsumption</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Winter</td>\n",
+              "      <td>Night</td>\n",
+              "      <td>No</td>\n",
+              "      <td>25.139433</td>\n",
+              "      <td>43.431581</td>\n",
+              "      <td>1565.693999</td>\n",
+              "      <td>5</td>\n",
+              "      <td>On</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>2.774699</td>\n",
+              "      <td>75.364373</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Winter</td>\n",
+              "      <td>Night</td>\n",
+              "      <td>No</td>\n",
+              "      <td>27.731651</td>\n",
+              "      <td>54.225919</td>\n",
+              "      <td>1411.064918</td>\n",
+              "      <td>1</td>\n",
+              "      <td>On</td>\n",
+              "      <td>On</td>\n",
+              "      <td>21.831384</td>\n",
+              "      <td>83.401855</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Winter</td>\n",
+              "      <td>Night</td>\n",
+              "      <td>No</td>\n",
+              "      <td>28.704277</td>\n",
+              "      <td>58.907658</td>\n",
+              "      <td>1755.715009</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>6.764672</td>\n",
+              "      <td>78.270888</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Winter</td>\n",
+              "      <td>Night</td>\n",
+              "      <td>No</td>\n",
+              "      <td>20.080469</td>\n",
+              "      <td>50.371637</td>\n",
+              "      <td>1452.316318</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>On</td>\n",
+              "      <td>8.623447</td>\n",
+              "      <td>56.519850</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Winter</td>\n",
+              "      <td>Morning</td>\n",
+              "      <td>No</td>\n",
+              "      <td>23.097359</td>\n",
+              "      <td>51.401421</td>\n",
+              "      <td>1094.130359</td>\n",
+              "      <td>9</td>\n",
+              "      <td>On</td>\n",
+              "      <td>Off</td>\n",
+              "      <td>3.071969</td>\n",
+              "      <td>70.811732</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
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+              "  <style>\n",
+              "    .colab-df-container {\n",
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+              "      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",
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+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-f323c160-ed16-483d-b0c5-db04b1dcfb49 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-f323c160-ed16-483d-b0c5-db04b1dcfb49');\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",
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+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "\n",
+              "\n",
+              "<div id=\"df-b855e65a-2606-4391-941e-b74a621fbafb\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b855e65a-2606-4391-941e-b74a621fbafb')\"\n",
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+              "    </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-b855e65a-2606-4391-941e-b74a621fbafb 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\": 5000,\n  \"fields\": [\n    {\n      \"column\": \"Season\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Summer\",\n          \"Autumn\",\n          \"Winter\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TimeOfDay\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Morning\",\n          \"Evening\",\n          \"Night\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Holiday\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.041677552108525,\n        \"min\": 20.007565,\n        \"max\": 29.998671,\n        \"num_unique_values\": 4409,\n        \"samples\": [\n          26.366987947584217,\n          28.698193824921525\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.972690469128699,\n        \"min\": 30.01597450346074,\n        \"max\": 59.969085,\n        \"num_unique_values\": 4489,\n        \"samples\": [\n          43.88354637574899,\n          48.26307880103275\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SquareFootage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 293.14720927887083,\n        \"min\": 1000.5126606747408,\n        \"max\": 1999.982252131635,\n        \"num_unique_values\": 4710,\n        \"samples\": [\n          1050.579412110074,\n          1144.4227959771806\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HVACUsage\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Off\",\n          \"On\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LightingUsage\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"On\",\n          \"Off\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RenewableEnergy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.157037618534229,\n        \"min\": 0.006642,\n        \"max\": 29.96532733777335,\n        \"num_unique_values\": 4475,\n        \"samples\": [\n          10.737083256824402,\n          22.115806950483456\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EnergyConsumption\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.231573112556944,\n        \"min\": 53.263278,\n        \"max\": 99.20112,\n        \"num_unique_values\": 4937,\n        \"samples\": [\n          70.99960829697996,\n          87.28499908910176\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 79
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OvMuEqxCyHZS",
+        "outputId": "7277583e-bbab-4f21-9442-7cc37f288d0c"
+      },
+      "execution_count": 80,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(5000, 11)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 80
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DcEdw77ryJNF",
+        "outputId": "13958d29-33b2-4557-d88d-05f1769ac2e4"
+      },
+      "execution_count": 81,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 5000 entries, 0 to 4999\n",
+            "Data columns (total 11 columns):\n",
+            " #   Column             Non-Null Count  Dtype  \n",
+            "---  ------             --------------  -----  \n",
+            " 0   Season             5000 non-null   object \n",
+            " 1   TimeOfDay          5000 non-null   object \n",
+            " 2   Holiday            5000 non-null   object \n",
+            " 3   Temperature        5000 non-null   float64\n",
+            " 4   Humidity           5000 non-null   float64\n",
+            " 5   SquareFootage      5000 non-null   float64\n",
+            " 6   Occupancy          5000 non-null   int64  \n",
+            " 7   HVACUsage          5000 non-null   object \n",
+            " 8   LightingUsage      5000 non-null   object \n",
+            " 9   RenewableEnergy    5000 non-null   float64\n",
+            " 10  EnergyConsumption  5000 non-null   float64\n",
+            "dtypes: float64(5), int64(1), object(5)\n",
+            "memory usage: 429.8+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "categorical_features = ['Season', 'TimeOfDay', 'Holiday', 'HVACUsage', 'LightingUsage']\n",
+        "df = pd.get_dummies(df, columns=categorical_features, drop_first=True)\n",
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 226
+        },
+        "id": "Detp5B6RyK4d",
+        "outputId": "53e85e7b-51ad-4605-a243-d142386be310"
+      },
+      "execution_count": 82,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Temperature   Humidity  SquareFootage  Occupancy  RenewableEnergy  \\\n",
+              "0    25.139433  43.431581    1565.693999          5         2.774699   \n",
+              "1    27.731651  54.225919    1411.064918          1        21.831384   \n",
+              "2    28.704277  58.907658    1755.715009          2         6.764672   \n",
+              "3    20.080469  50.371637    1452.316318          1         8.623447   \n",
+              "4    23.097359  51.401421    1094.130359          9         3.071969   \n",
+              "\n",
+              "   EnergyConsumption  Season_Monsoon  Season_Summer  Season_Winter  \\\n",
+              "0          75.364373           False          False           True   \n",
+              "1          83.401855           False          False           True   \n",
+              "2          78.270888           False          False           True   \n",
+              "3          56.519850           False          False           True   \n",
+              "4          70.811732           False          False           True   \n",
+              "\n",
+              "   TimeOfDay_Evening  TimeOfDay_Morning  TimeOfDay_Night  Holiday_Yes  \\\n",
+              "0              False              False             True        False   \n",
+              "1              False              False             True        False   \n",
+              "2              False              False             True        False   \n",
+              "3              False              False             True        False   \n",
+              "4              False               True            False        False   \n",
+              "\n",
+              "   HVACUsage_On  LightingUsage_On  \n",
+              "0          True             False  \n",
+              "1          True              True  \n",
+              "2         False             False  \n",
+              "3         False              True  \n",
+              "4          True             False  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-032c6a51-3343-4ca7-b783-9ebc587d6fad\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Temperature</th>\n",
+              "      <th>Humidity</th>\n",
+              "      <th>SquareFootage</th>\n",
+              "      <th>Occupancy</th>\n",
+              "      <th>RenewableEnergy</th>\n",
+              "      <th>EnergyConsumption</th>\n",
+              "      <th>Season_Monsoon</th>\n",
+              "      <th>Season_Summer</th>\n",
+              "      <th>Season_Winter</th>\n",
+              "      <th>TimeOfDay_Evening</th>\n",
+              "      <th>TimeOfDay_Morning</th>\n",
+              "      <th>TimeOfDay_Night</th>\n",
+              "      <th>Holiday_Yes</th>\n",
+              "      <th>HVACUsage_On</th>\n",
+              "      <th>LightingUsage_On</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>25.139433</td>\n",
+              "      <td>43.431581</td>\n",
+              "      <td>1565.693999</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2.774699</td>\n",
+              "      <td>75.364373</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>27.731651</td>\n",
+              "      <td>54.225919</td>\n",
+              "      <td>1411.064918</td>\n",
+              "      <td>1</td>\n",
+              "      <td>21.831384</td>\n",
+              "      <td>83.401855</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>28.704277</td>\n",
+              "      <td>58.907658</td>\n",
+              "      <td>1755.715009</td>\n",
+              "      <td>2</td>\n",
+              "      <td>6.764672</td>\n",
+              "      <td>78.270888</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>20.080469</td>\n",
+              "      <td>50.371637</td>\n",
+              "      <td>1452.316318</td>\n",
+              "      <td>1</td>\n",
+              "      <td>8.623447</td>\n",
+              "      <td>56.519850</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>23.097359</td>\n",
+              "      <td>51.401421</td>\n",
+              "      <td>1094.130359</td>\n",
+              "      <td>9</td>\n",
+              "      <td>3.071969</td>\n",
+              "      <td>70.811732</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</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-032c6a51-3343-4ca7-b783-9ebc587d6fad')\"\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-032c6a51-3343-4ca7-b783-9ebc587d6fad 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-032c6a51-3343-4ca7-b783-9ebc587d6fad');\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-67450e87-4d74-44db-92ec-4e524e85126b\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-67450e87-4d74-44db-92ec-4e524e85126b')\"\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-67450e87-4d74-44db-92ec-4e524e85126b 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\": 5000,\n  \"fields\": [\n    {\n      \"column\": \"Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.041677552108525,\n        \"min\": 20.007565,\n        \"max\": 29.998671,\n        \"num_unique_values\": 4409,\n        \"samples\": [\n          26.366987947584217,\n          28.698193824921525,\n          28.137735960826884\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.972690469128699,\n        \"min\": 30.01597450346074,\n        \"max\": 59.969085,\n        \"num_unique_values\": 4489,\n        \"samples\": [\n          43.88354637574899,\n          48.26307880103275,\n          38.34388808313577\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SquareFootage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 293.14720927887083,\n        \"min\": 1000.5126606747408,\n        \"max\": 1999.982252131635,\n        \"num_unique_values\": 4710,\n        \"samples\": [\n          1050.579412110074,\n          1144.4227959771806,\n          1608.293515257484\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3,\n          1,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RenewableEnergy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.157037618534229,\n        \"min\": 0.006642,\n        \"max\": 29.96532733777335,\n        \"num_unique_values\": 4475,\n        \"samples\": [\n          10.737083256824402,\n          22.115806950483456,\n          16.726565526610706\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EnergyConsumption\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.231573112556944,\n        \"min\": 53.263278,\n        \"max\": 99.20112,\n        \"num_unique_values\": 4937,\n        \"samples\": [\n          70.99960829697996,\n          87.28499908910176,\n          74.11609389205148\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Season_Monsoon\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Season_Summer\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Season_Winter\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TimeOfDay_Evening\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TimeOfDay_Morning\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TimeOfDay_Night\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Holiday_Yes\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HVACUsage_On\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LightingUsage_On\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 82
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gwDziULfyc9J",
+        "outputId": "28339e36-24ca-4d7c-814b-fa2a930bf10b"
+      },
+      "execution_count": 83,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(5000, 15)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 83
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "xXV2SQVqzBpy"
+      },
+      "execution_count": 83,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X = df.drop(columns=['EnergyConsumption'])\n",
+        "y = df['EnergyConsumption']"
+      ],
+      "metadata": {
+        "id": "GebzK3aEyfeO"
+      },
+      "execution_count": 84,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
+      ],
+      "metadata": {
+        "id": "uxRha1FOymDj"
+      },
+      "execution_count": 85,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_IheMs36ywqQ",
+        "outputId": "1dbf8dbb-2692-45f9-93ca-5135c8fe0297"
+      },
+      "execution_count": 86,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(4000, 14)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 86
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ],
+      "metadata": {
+        "id": "WPDZjvR-yyoM"
+      },
+      "execution_count": 87,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train_scaled"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TCl4GVBSy-va",
+        "outputId": "06c41072-777e-4361-f610-147f8cdb1491"
+      },
+      "execution_count": 88,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[0.38356624, 0.16583629, 0.78710351, ..., 1.        , 0.        ,\n",
+              "        1.        ],\n",
+              "       [0.03026714, 0.67376719, 1.        , ..., 1.        , 1.        ,\n",
+              "        1.        ],\n",
+              "       [0.05697615, 0.34603388, 0.39311902, ..., 1.        , 1.        ,\n",
+              "        1.        ],\n",
+              "       ...,\n",
+              "       [0.87394221, 0.35380599, 0.58082363, ..., 0.        , 1.        ,\n",
+              "        1.        ],\n",
+              "       [0.60222829, 1.        , 0.88663545, ..., 1.        , 1.        ,\n",
+              "        1.        ],\n",
+              "       [0.31837734, 0.96334379, 0.8799872 , ..., 0.        , 1.        ,\n",
+              "        0.        ]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 88
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import tensorflow\n",
+        "from tensorflow import keras\n",
+        "from tensorflow.keras import Sequential\n",
+        "from tensorflow.keras.layers import *"
+      ],
+      "metadata": {
+        "id": "ZuW8_-2JzDLo"
+      },
+      "execution_count": 89,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = Sequential()\n",
+        "model.add(Dense(32, activation='relu', input_dim = 14))\n",
+        "model.add(Dropout(0.01))\n",
+        "model.add(Dense(8, activation='relu'))\n",
+        "model.add(Dense(4, activation='relu'))\n",
+        "model.add(Dense(1, activation='linear'))"
+      ],
+      "metadata": {
+        "id": "u7WE5MNPzPUm"
+      },
+      "execution_count": 90,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from tensorflow.keras.optimizers import RMSprop\n",
+        "from tensorflow.keras.losses import Huber\n",
+        "model.compile(optimizer=RMSprop(learning_rate=0.001),\n",
+        "              loss=Huber(),\n",
+        "              metrics=['mae'])"
+      ],
+      "metadata": {
+        "id": "iTP-gPXrznZd"
+      },
+      "execution_count": 91,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.summary()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 304
+        },
+        "id": "wA_N8hnOz193",
+        "outputId": "17209c08-7ee5-4f69-a5c3-84fd7eb1f522"
+      },
+      "execution_count": 92,
+      "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_4 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │             \u001b[38;5;34m480\u001b[0m │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)                   │             \u001b[38;5;34m264\u001b[0m │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m)                   │              \u001b[38;5;34m36\u001b[0m │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m5\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_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span> │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">264</span> │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">36</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\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span> │\n",
+              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m785\u001b[0m (3.07 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\">785</span> (3.07 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m785\u001b[0m (3.07 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\">785</span> (3.07 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "history = model.fit(X_train_scaled, y_train,\n",
+        "                    validation_data =(X_test_scaled, y_test),\n",
+        "                    epochs = 30,\n",
+        "                    batch_size = 32,\n",
+        "                    verbose = 1)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9WadRSfjz4YD",
+        "outputId": "4694cb94-f24e-4386-d02d-e359b41eda7a"
+      },
+      "execution_count": 93,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 76.2554 - mae: 76.7554 - val_loss: 76.0193 - val_mae: 76.5193\n",
+            "Epoch 2/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 76.0257 - mae: 76.5257 - val_loss: 75.8943 - val_mae: 76.3943\n",
+            "Epoch 3/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 75.9287 - mae: 76.4287 - val_loss: 75.7693 - val_mae: 76.2693\n",
+            "Epoch 4/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 75.8031 - mae: 76.3031 - val_loss: 75.6443 - val_mae: 76.1443\n",
+            "Epoch 5/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 75.7360 - mae: 76.2360 - val_loss: 75.5193 - val_mae: 76.0193\n",
+            "Epoch 6/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 75.8284 - mae: 76.3284 - val_loss: 75.3943 - val_mae: 75.8943\n",
+            "Epoch 7/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 75.4253 - mae: 75.9253 - val_loss: 75.2693 - val_mae: 75.7693\n",
+            "Epoch 8/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 75.3435 - mae: 75.8435 - val_loss: 75.1443 - val_mae: 75.6443\n",
+            "Epoch 9/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 75.2513 - mae: 75.7513 - val_loss: 75.0193 - val_mae: 75.5193\n",
+            "Epoch 10/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 75.2630 - mae: 75.7630 - val_loss: 74.8943 - val_mae: 75.3943\n",
+            "Epoch 11/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 75.2109 - mae: 75.7109 - val_loss: 74.7693 - val_mae: 75.2693\n",
+            "Epoch 12/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 74.9506 - mae: 75.4506 - val_loss: 74.6443 - val_mae: 75.1443\n",
+            "Epoch 13/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 74.6581 - mae: 75.1581 - val_loss: 74.5193 - val_mae: 75.0193\n",
+            "Epoch 14/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 74.7003 - mae: 75.2003 - val_loss: 74.3943 - val_mae: 74.8943\n",
+            "Epoch 15/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 74.6224 - mae: 75.1224 - val_loss: 74.2693 - val_mae: 74.7693\n",
+            "Epoch 16/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 74.3579 - mae: 74.8579 - val_loss: 74.1443 - val_mae: 74.6443\n",
+            "Epoch 17/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 74.4014 - mae: 74.9014 - val_loss: 74.0193 - val_mae: 74.5193\n",
+            "Epoch 18/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 74.1839 - mae: 74.6839 - val_loss: 73.8943 - val_mae: 74.3943\n",
+            "Epoch 19/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 73.8309 - mae: 74.3309 - val_loss: 73.7693 - val_mae: 74.2693\n",
+            "Epoch 20/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 73.8182 - mae: 74.3182 - val_loss: 73.6443 - val_mae: 74.1443\n",
+            "Epoch 21/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 73.8813 - mae: 74.3813 - val_loss: 73.5193 - val_mae: 74.0193\n",
+            "Epoch 22/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 73.7011 - mae: 74.2011 - val_loss: 73.3943 - val_mae: 73.8943\n",
+            "Epoch 23/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 73.4792 - mae: 73.9792 - val_loss: 73.2693 - val_mae: 73.7693\n",
+            "Epoch 24/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 73.6872 - mae: 74.1872 - val_loss: 73.1443 - val_mae: 73.6443\n",
+            "Epoch 25/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 73.1501 - mae: 73.6501 - val_loss: 73.0194 - val_mae: 73.5194\n",
+            "Epoch 26/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 72.8854 - mae: 73.3854 - val_loss: 72.8944 - val_mae: 73.3944\n",
+            "Epoch 27/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 72.8901 - mae: 73.3901 - val_loss: 72.7694 - val_mae: 73.2694\n",
+            "Epoch 28/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 72.8610 - mae: 73.3610 - val_loss: 72.6444 - val_mae: 73.1444\n",
+            "Epoch 29/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 72.8297 - mae: 73.3297 - val_loss: 72.5194 - val_mae: 73.0194\n",
+            "Epoch 30/30\n",
+            "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 72.6377 - mae: 73.1377 - val_loss: 72.3944 - val_mae: 72.8944\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "plt.plot(history.history['loss'], label='Training Loss')\n",
+        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
+        "plt.title('Model Loss Over Epochs')\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('Loss')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 509
+        },
+        "id": "NyPcnPBi0GV_",
+        "outputId": "625cfbc6-3b25-4a99-efcd-6aab5857b152"
+      },
+      "execution_count": 94,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "test_loss, test_mae = model.evaluate(X_test_scaled, y_test)\n",
+        "print(f\"Test Loss: {test_loss}\")\n",
+        "print(f\"Test MAE: {test_mae}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8RamM_P_0dVV",
+        "outputId": "39eb415f-2102-49fd-81d9-3c8d8093bfa4"
+      },
+      "execution_count": 95,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 72.0646 - mae: 72.5646\n",
+            "Test Loss: 72.3944091796875\n",
+            "Test MAE: 72.8944091796875\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred = model.predict(X_test_scaled)\n",
+        "\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.hist(y_test, bins=30, alpha=0.5, label='True Energy Consumption')\n",
+        "plt.hist(y_pred, bins=30, alpha=0.5, label='Predicted Energy Consumption')\n",
+        "plt.xlabel('Energy Consumption')\n",
+        "plt.ylabel('Frequency')\n",
+        "plt.title('Distribution of True vs Predicted Energy Consumption')\n",
+        "plt.legend(loc='upper right')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 435
+        },
+        "id": "8SitjvPI0mUs",
+        "outputId": "95ca4595-6700-4190-9a9e-e7d5a91c26cf"
+      },
+      "execution_count": 96,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "zbqINQhh14KA"
+      },
+      "execution_count": 96,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, explained_variance_score\n",
+        "import numpy as np\n",
+        "mae = mean_absolute_error(y_test, y_pred)\n",
+        "mse = mean_squared_error(y_test, y_pred)\n",
+        "rmse = np.sqrt(mse)\n",
+        "r2 = r2_score(y_test, y_pred)\n",
+        "explained_variance = explained_variance_score(y_test, y_pred)\n",
+        "\n",
+        "print(f'Mean Absolute Error (MAE: {mae}')\n",
+        "print(f'Mean Squared Error (MSE): {mse}')\n",
+        "print(f'Root Mean Squared Error (RMSE): {rmse}')\n",
+        "print(f'Root Mean Squared Error (RMSE): {rmse}')\n",
+        "print(f'R² Score: {r2}')\n",
+        "print(f'Explained Variance Score: {explained_variance}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "efg9dIe-1D41",
+        "outputId": "37d07d41-cb01-4557-db13-a0d846d50a26"
+      },
+      "execution_count": 97,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean Absolute Error (MAE: 72.89439823995266\n",
+            "Mean Squared Error (MSE): 5400.434998380947\n",
+            "Root Mean Squared Error (RMSE): 73.4876520129807\n",
+            "Root Mean Squared Error (RMSE): 73.4876520129807\n",
+            "R² Score: -61.187114871129566\n",
+            "Explained Variance Score: 0.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.save('/kaggle/working/model.keras')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 321
+        },
+        "id": "Q5XGKvLC24U_",
+        "outputId": "e8348088-a081-47a4-9ff7-64066a19af9b"
+      },
+      "execution_count": 98,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "FileNotFoundError",
+          "evalue": "[Errno 2] No such file or directory: '/kaggle/working/model.keras'",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-98-9bc25d85ce89>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/kaggle/working/model.keras'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    120\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;31m# `keras.config.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py\u001b[0m in \u001b[0;36msave_model\u001b[0;34m(model, filepath, weights_format, zipped)\u001b[0m\n\u001b[1;32m    139\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip_filepath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m                 \u001b[0m_save_model_to_fileobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights_format\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/kaggle/working/model.keras'"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file