room_occupancy_prediction_testing_version_1.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from tensorflow.keras.metrics import AUC\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau"
      ]
    },
    {
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        "from google.colab import files\n",
        "uploaded = files.upload()"
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          "height": 76
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              "      Upload widget is only available when the cell has been executed in the\n",
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              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
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          "name": "stdout",
          "text": [
            "Saving Occupancy_Estimation.csv to Occupancy_Estimation.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import io\n",
        "data = pd.read_csv(io.BytesIO(uploaded['Occupancy_Estimation.csv']))"
      ],
      "metadata": {
        "id": "-fNY0cC-AEQ2"
      },
      "execution_count": 7,
      "outputs": []
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        "data.head()"
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              "         Date      Time  S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  \\\n",
              "0  2017/12/22  10:49:41    24.94    24.75    24.56    25.38       121   \n",
              "1  2017/12/22  10:50:12    24.94    24.75    24.56    25.44       121   \n",
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              "4  2017/12/22  10:51:44    25.00    24.75    24.56    25.44       121   \n",
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              "   S2_Light  S3_Light  S4_Light  S1_Sound  S2_Sound  S3_Sound  S4_Sound  \\\n",
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              "   S5_CO2  S5_CO2_Slope  S6_PIR  S7_PIR  Room_Occupancy_Count  \n",
              "0     390      0.769231       0       0                     1  \n",
              "1     390      0.646154       0       0                     1  \n",
              "2     390      0.519231       0       0                     1  \n",
              "3     390      0.388462       0       0                     1  \n",
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              "    .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",
              "    [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-69b37818-7e7b-47b7-b721-a97f38052d9f 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-69b37818-7e7b-47b7-b721-a97f38052d9f');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['temp'] = (data['S1_Temp']+data['S2_Temp']+data['S3_Temp']+data['S4_Temp'])/4\n",
        "data['light'] = (data['S1_Light']+data['S2_Light']+data['S3_Light']+data['S4_Light'])/4"
      ],
      "metadata": {
        "id": "86TAJxFGKDBt"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "hGuzhLlEK8tc",
        "outputId": "e8ba347d-8f27-4ef5-81bf-c3dcab0d12ae"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date      Time  S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  \\\n",
              "0  2017/12/22  10:49:41    24.94    24.75    24.56    25.38       121   \n",
              "1  2017/12/22  10:50:12    24.94    24.75    24.56    25.44       121   \n",
              "2  2017/12/22  10:50:42    25.00    24.75    24.50    25.44       121   \n",
              "3  2017/12/22  10:51:13    25.00    24.75    24.56    25.44       121   \n",
              "4  2017/12/22  10:51:44    25.00    24.75    24.56    25.44       121   \n",
              "\n",
              "   S2_Light  S3_Light  S4_Light  ...  S2_Sound  S3_Sound  S4_Sound  S5_CO2  \\\n",
              "0        34        53        40  ...      0.19      0.06      0.06     390   \n",
              "1        33        53        40  ...      0.05      0.06      0.06     390   \n",
              "2        34        53        40  ...      0.11      0.08      0.06     390   \n",
              "3        34        53        40  ...      0.10      0.10      0.09     390   \n",
              "4        34        54        40  ...      0.06      0.06      0.06     390   \n",
              "\n",
              "   S5_CO2_Slope  S6_PIR  S7_PIR  Room_Occupancy_Count     temp  light  \n",
              "0      0.769231       0       0                     1  24.9075  62.00  \n",
              "1      0.646154       0       0                     1  24.9225  61.75  \n",
              "2      0.519231       0       0                     1  24.9225  62.00  \n",
              "3      0.388462       0       0                     1  24.9375  62.00  \n",
              "4      0.253846       0       0                     1  24.9375  62.25  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>S1_Temp</th>\n",
              "      <th>S2_Temp</th>\n",
              "      <th>S3_Temp</th>\n",
              "      <th>S4_Temp</th>\n",
              "      <th>S1_Light</th>\n",
              "      <th>S2_Light</th>\n",
              "      <th>S3_Light</th>\n",
              "      <th>S4_Light</th>\n",
              "      <th>...</th>\n",
              "      <th>S2_Sound</th>\n",
              "      <th>S3_Sound</th>\n",
              "      <th>S4_Sound</th>\n",
              "      <th>S5_CO2</th>\n",
              "      <th>S5_CO2_Slope</th>\n",
              "      <th>S6_PIR</th>\n",
              "      <th>S7_PIR</th>\n",
              "      <th>Room_Occupancy_Count</th>\n",
              "      <th>temp</th>\n",
              "      <th>light</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>10:49:41</td>\n",
              "      <td>24.94</td>\n",
              "      <td>24.75</td>\n",
              "      <td>24.56</td>\n",
              "      <td>25.38</td>\n",
              "      <td>121</td>\n",
              "      <td>34</td>\n",
              "      <td>53</td>\n",
              "      <td>40</td>\n",
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              "      <td>0.19</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.06</td>\n",
              "      <td>390</td>\n",
              "      <td>0.769231</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>24.9075</td>\n",
              "      <td>62.00</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>10:50:12</td>\n",
              "      <td>24.94</td>\n",
              "      <td>24.75</td>\n",
              "      <td>24.56</td>\n",
              "      <td>25.44</td>\n",
              "      <td>121</td>\n",
              "      <td>33</td>\n",
              "      <td>53</td>\n",
              "      <td>40</td>\n",
              "      <td>...</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.06</td>\n",
              "      <td>390</td>\n",
              "      <td>0.646154</td>\n",
              "      <td>0</td>\n",
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              "      <td>24.9225</td>\n",
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              "      <th>2</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>10:50:42</td>\n",
              "      <td>25.00</td>\n",
              "      <td>24.75</td>\n",
              "      <td>24.50</td>\n",
              "      <td>25.44</td>\n",
              "      <td>121</td>\n",
              "      <td>34</td>\n",
              "      <td>53</td>\n",
              "      <td>40</td>\n",
              "      <td>...</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.06</td>\n",
              "      <td>390</td>\n",
              "      <td>0.519231</td>\n",
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              "      <td>24.9225</td>\n",
              "      <td>62.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>10:51:13</td>\n",
              "      <td>25.00</td>\n",
              "      <td>24.75</td>\n",
              "      <td>24.56</td>\n",
              "      <td>25.44</td>\n",
              "      <td>121</td>\n",
              "      <td>34</td>\n",
              "      <td>53</td>\n",
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              "      <td>2017/12/22</td>\n",
              "      <td>10:51:44</td>\n",
              "      <td>25.00</td>\n",
              "      <td>24.75</td>\n",
              "      <td>24.56</td>\n",
              "      <td>25.44</td>\n",
              "      <td>121</td>\n",
              "      <td>34</td>\n",
              "      <td>54</td>\n",
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              "      <td>0.06</td>\n",
              "      <td>0.06</td>\n",
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              "<p>5 rows × 21 columns</p>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
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              "    domScope.querySelector('#df-1affaece-1965-4aa8-a50d-ef38db2ca84d button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
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              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-362a6f31-71f5-422a-91ae-1d660cd3b734 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-362a6f31-71f5-422a-91ae-1d660cd3b734');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# or_operation = lambda row: row[selected_columns[0]] | row[selected_columns[1]]\n",
        "# selected_columns = [data['S6_PIR'],data['S7_PIR']]\n",
        "# data['PIR'] = data.apply(or_operation, axis=1)"
      ],
      "metadata": {
        "id": "pGvuzZ_7LIt8"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "CvkwJdXDMk1i",
        "outputId": "d204c2ab-eaa8-44d9-cba8-ae7b48599b68"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date      Time  S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  \\\n",
              "0  2017/12/22  10:49:41    24.94    24.75    24.56    25.38       121   \n",
              "1  2017/12/22  10:50:12    24.94    24.75    24.56    25.44       121   \n",
              "2  2017/12/22  10:50:42    25.00    24.75    24.50    25.44       121   \n",
              "3  2017/12/22  10:51:13    25.00    24.75    24.56    25.44       121   \n",
              "4  2017/12/22  10:51:44    25.00    24.75    24.56    25.44       121   \n",
              "\n",
              "   S2_Light  S3_Light  S4_Light  ...  S2_Sound  S3_Sound  S4_Sound  S5_CO2  \\\n",
              "0        34        53        40  ...      0.19      0.06      0.06     390   \n",
              "1        33        53        40  ...      0.05      0.06      0.06     390   \n",
              "2        34        53        40  ...      0.11      0.08      0.06     390   \n",
              "3        34        53        40  ...      0.10      0.10      0.09     390   \n",
              "4        34        54        40  ...      0.06      0.06      0.06     390   \n",
              "\n",
              "   S5_CO2_Slope  S6_PIR  S7_PIR  Room_Occupancy_Count     temp  light  \n",
              "0      0.769231       0       0                     1  24.9075  62.00  \n",
              "1      0.646154       0       0                     1  24.9225  61.75  \n",
              "2      0.519231       0       0                     1  24.9225  62.00  \n",
              "3      0.388462       0       0                     1  24.9375  62.00  \n",
              "4      0.253846       0       0                     1  24.9375  62.25  \n",
              "\n",
              "[5 rows x 21 columns]"
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              "                                                     [key], {});\n",
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          "metadata": {},
          "execution_count": 12
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    {
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      "source": [
        "data['pir'] = (data['S6_PIR']) | (data['S7_PIR'])"
      ],
      "metadata": {
        "id": "hFweg49eRoE0"
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      "execution_count": 13,
      "outputs": []
    },
    {
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      "source": [
        "data.head()"
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          "base_uri": "https://localhost:8080/",
          "height": 236
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        "id": "KENk_vGjR7dG",
        "outputId": "5d63ef0a-220c-4b74-89c3-7338c3709768"
      },
      "execution_count": 14,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date      Time  S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  \\\n",
              "0  2017/12/22  10:49:41    24.94    24.75    24.56    25.38       121   \n",
              "1  2017/12/22  10:50:12    24.94    24.75    24.56    25.44       121   \n",
              "2  2017/12/22  10:50:42    25.00    24.75    24.50    25.44       121   \n",
              "3  2017/12/22  10:51:13    25.00    24.75    24.56    25.44       121   \n",
              "4  2017/12/22  10:51:44    25.00    24.75    24.56    25.44       121   \n",
              "\n",
              "   S2_Light  S3_Light  S4_Light  ...  S3_Sound  S4_Sound  S5_CO2  \\\n",
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              "   S5_CO2_Slope  S6_PIR  S7_PIR  Room_Occupancy_Count     temp  light  pir  \n",
              "0      0.769231       0       0                     1  24.9075  62.00    0  \n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>24.9375</td>\n",
              "      <td>62.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>10:51:44</td>\n",
              "      <td>25.00</td>\n",
              "      <td>24.75</td>\n",
              "      <td>24.56</td>\n",
              "      <td>25.44</td>\n",
              "      <td>121</td>\n",
              "      <td>34</td>\n",
              "      <td>54</td>\n",
              "      <td>40</td>\n",
              "      <td>...</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.06</td>\n",
              "      <td>390</td>\n",
              "      <td>0.253846</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>24.9375</td>\n",
              "      <td>62.25</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 22 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f2c0f4b0-6b8d-441e-bed2-06cbb8dd5479')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-8db6150c-ee42-486e-94cd-9abf2b051300\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8db6150c-ee42-486e-94cd-9abf2b051300')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-8db6150c-ee42-486e-94cd-9abf2b051300 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-f2c0f4b0-6b8d-441e-bed2-06cbb8dd5479 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-f2c0f4b0-6b8d-441e-bed2-06cbb8dd5479');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "6UX-3LXNSCI0",
        "outputId": "8845c1bf-2ed8-492a-a6d0-d347a3339163"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Date      Time  S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  \\\n",
              "10124  2018/01/11  08:58:07    25.06    25.13    24.69    25.31         6   \n",
              "10125  2018/01/11  08:58:37    25.06    25.06    24.69    25.25         6   \n",
              "10126  2018/01/11  08:59:08    25.13    25.06    24.69    25.25         6   \n",
              "10127  2018/01/11  08:59:39    25.13    25.06    24.69    25.25         6   \n",
              "10128  2018/01/11  09:00:09    25.13    25.06    24.69    25.25         6   \n",
              "\n",
              "       S2_Light  S3_Light  S4_Light  ...  S3_Sound  S4_Sound  S5_CO2  \\\n",
              "10124         7        33        22  ...      0.06      0.08     345   \n",
              "10125         7        34        22  ...      0.05      0.08     345   \n",
              "10126         7        34        22  ...      0.06      0.08     345   \n",
              "10127         7        34        22  ...      0.10      0.08     345   \n",
              "10128         7        34        22  ...      0.06      0.08     345   \n",
              "\n",
              "       S5_CO2_Slope  S6_PIR  S7_PIR  Room_Occupancy_Count     temp  light  pir  \n",
              "10124           0.0       0       0                     0  25.0475  17.00    0  \n",
              "10125           0.0       0       0                     0  25.0150  17.25    0  \n",
              "10126           0.0       0       0                     0  25.0325  17.25    0  \n",
              "10127           0.0       0       0                     0  25.0325  17.25    0  \n",
              "10128           0.0       0       0                     0  25.0325  17.25    0  \n",
              "\n",
              "[5 rows x 22 columns]"
            ],
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              "\n",
              "\n",
              "  <div id=\"df-07f8c87c-3872-48b6-92aa-9d712e877722\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\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>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>S1_Temp</th>\n",
              "      <th>S2_Temp</th>\n",
              "      <th>S3_Temp</th>\n",
              "      <th>S4_Temp</th>\n",
              "      <th>S1_Light</th>\n",
              "      <th>S2_Light</th>\n",
              "      <th>S3_Light</th>\n",
              "      <th>S4_Light</th>\n",
              "      <th>...</th>\n",
              "      <th>S3_Sound</th>\n",
              "      <th>S4_Sound</th>\n",
              "      <th>S5_CO2</th>\n",
              "      <th>S5_CO2_Slope</th>\n",
              "      <th>S6_PIR</th>\n",
              "      <th>S7_PIR</th>\n",
              "      <th>Room_Occupancy_Count</th>\n",
              "      <th>temp</th>\n",
              "      <th>light</th>\n",
              "      <th>pir</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10124</th>\n",
              "      <td>2018/01/11</td>\n",
              "      <td>08:58:07</td>\n",
              "      <td>25.06</td>\n",
              "      <td>25.13</td>\n",
              "      <td>24.69</td>\n",
              "      <td>25.31</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>33</td>\n",
              "      <td>22</td>\n",
              "      <td>...</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.08</td>\n",
              "      <td>345</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.0475</td>\n",
              "      <td>17.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10125</th>\n",
              "      <td>2018/01/11</td>\n",
              "      <td>08:58:37</td>\n",
              "      <td>25.06</td>\n",
              "      <td>25.06</td>\n",
              "      <td>24.69</td>\n",
              "      <td>25.25</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>34</td>\n",
              "      <td>22</td>\n",
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              "      <td>0.05</td>\n",
              "      <td>0.08</td>\n",
              "      <td>345</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.0150</td>\n",
              "      <td>17.25</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10126</th>\n",
              "      <td>2018/01/11</td>\n",
              "      <td>08:59:08</td>\n",
              "      <td>25.13</td>\n",
              "      <td>25.06</td>\n",
              "      <td>24.69</td>\n",
              "      <td>25.25</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>34</td>\n",
              "      <td>22</td>\n",
              "      <td>...</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.08</td>\n",
              "      <td>345</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.0325</td>\n",
              "      <td>17.25</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10127</th>\n",
              "      <td>2018/01/11</td>\n",
              "      <td>08:59:39</td>\n",
              "      <td>25.13</td>\n",
              "      <td>25.06</td>\n",
              "      <td>24.69</td>\n",
              "      <td>25.25</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>34</td>\n",
              "      <td>22</td>\n",
              "      <td>...</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.08</td>\n",
              "      <td>345</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.0325</td>\n",
              "      <td>17.25</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10128</th>\n",
              "      <td>2018/01/11</td>\n",
              "      <td>09:00:09</td>\n",
              "      <td>25.13</td>\n",
              "      <td>25.06</td>\n",
              "      <td>24.69</td>\n",
              "      <td>25.25</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>34</td>\n",
              "      <td>22</td>\n",
              "      <td>...</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.08</td>\n",
              "      <td>345</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.0325</td>\n",
              "      <td>17.25</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 22 columns</p>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-8e1ac9cc-6953-44bb-bf46-105c9cc6fb8b\">\n",
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              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "        <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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "        displayQuickchartButton(document);\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-07f8c87c-3872-48b6-92aa-9d712e877722 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-07f8c87c-3872-48b6-92aa-9d712e877722');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "aqMX11nwSHnv",
        "outputId": "e7ecfd49-bc3a-4d24-c5ba-298f047d8ca6"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Date      Time  S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  \\\n",
              "0      2017/12/22  10:49:41    24.94    24.75    24.56    25.38       121   \n",
              "1      2017/12/22  10:50:12    24.94    24.75    24.56    25.44       121   \n",
              "2      2017/12/22  10:50:42    25.00    24.75    24.50    25.44       121   \n",
              "3      2017/12/22  10:51:13    25.00    24.75    24.56    25.44       121   \n",
              "4      2017/12/22  10:51:44    25.00    24.75    24.56    25.44       121   \n",
              "...           ...       ...      ...      ...      ...      ...       ...   \n",
              "10124  2018/01/11  08:58:07    25.06    25.13    24.69    25.31         6   \n",
              "10125  2018/01/11  08:58:37    25.06    25.06    24.69    25.25         6   \n",
              "10126  2018/01/11  08:59:08    25.13    25.06    24.69    25.25         6   \n",
              "10127  2018/01/11  08:59:39    25.13    25.06    24.69    25.25         6   \n",
              "10128  2018/01/11  09:00:09    25.13    25.06    24.69    25.25         6   \n",
              "\n",
              "       S2_Light  S3_Light  S4_Light  ...  S3_Sound  S4_Sound  S5_CO2  \\\n",
              "0            34        53        40  ...      0.06      0.06     390   \n",
              "1            33        53        40  ...      0.06      0.06     390   \n",
              "2            34        53        40  ...      0.08      0.06     390   \n",
              "3            34        53        40  ...      0.10      0.09     390   \n",
              "4            34        54        40  ...      0.06      0.06     390   \n",
              "...         ...       ...       ...  ...       ...       ...     ...   \n",
              "10124         7        33        22  ...      0.06      0.08     345   \n",
              "10125         7        34        22  ...      0.05      0.08     345   \n",
              "10126         7        34        22  ...      0.06      0.08     345   \n",
              "10127         7        34        22  ...      0.10      0.08     345   \n",
              "10128         7        34        22  ...      0.06      0.08     345   \n",
              "\n",
              "       S5_CO2_Slope  S6_PIR  S7_PIR  Room_Occupancy_Count     temp  light  pir  \n",
              "0          0.769231       0       0                     1  24.9075  62.00    0  \n",
              "1          0.646154       0       0                     1  24.9225  61.75    0  \n",
              "2          0.519231       0       0                     1  24.9225  62.00    0  \n",
              "3          0.388462       0       0                     1  24.9375  62.00    0  \n",
              "4          0.253846       0       0                     1  24.9375  62.25    0  \n",
              "...             ...     ...     ...                   ...      ...    ...  ...  \n",
              "10124      0.000000       0       0                     0  25.0475  17.00    0  \n",
              "10125      0.000000       0       0                     0  25.0150  17.25    0  \n",
              "10126      0.000000       0       0                     0  25.0325  17.25    0  \n",
              "10127      0.000000       0       0                     0  25.0325  17.25    0  \n",
              "10128      0.000000       0       0                     0  25.0325  17.25    0  \n",
              "\n",
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              "      <th>Date</th>\n",
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              "      <td>2017/12/22</td>\n",
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              "      <td>390</td>\n",
              "      <td>0.519231</td>\n",
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              "      <td>10:51:13</td>\n",
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              "      <td>10:51:44</td>\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
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              "            'suggestCharts', [key], {});\n",
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              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
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              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-f154c571-c26c-48a1-ade8-f68c658c4522 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
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              "\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",
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              "    }\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-b0a34368-89ec-4a0e-95e6-acd274b043f2 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-b0a34368-89ec-4a0e-95e6-acd274b043f2');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(300)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "CyFJqOodSVZT",
        "outputId": "8b464de4-3aa1-4aca-d9c6-a022ca15e725"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date      Time  S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  \\\n",
              "0    2017/12/22  10:49:41    24.94    24.75    24.56    25.38       121   \n",
              "1    2017/12/22  10:50:12    24.94    24.75    24.56    25.44       121   \n",
              "2    2017/12/22  10:50:42    25.00    24.75    24.50    25.44       121   \n",
              "3    2017/12/22  10:51:13    25.00    24.75    24.56    25.44       121   \n",
              "4    2017/12/22  10:51:44    25.00    24.75    24.56    25.44       121   \n",
              "..          ...       ...      ...      ...      ...      ...       ...   \n",
              "295  2017/12/22  13:49:25    26.06    27.06    26.00    26.38       164   \n",
              "296  2017/12/22  13:49:55    26.06    27.00    26.00    26.31       165   \n",
              "297  2017/12/22  13:50:26    26.06    27.00    26.00    26.31       164   \n",
              "298  2017/12/22  13:50:56    26.13    27.06    26.00    26.31       165   \n",
              "299  2017/12/22  13:51:27    26.06    27.13    26.00    26.31       165   \n",
              "\n",
              "     S2_Light  S3_Light  S4_Light  ...  S3_Sound  S4_Sound  S5_CO2  \\\n",
              "0          34        53        40  ...      0.06      0.06     390   \n",
              "1          33        53        40  ...      0.06      0.06     390   \n",
              "2          34        53        40  ...      0.08      0.06     390   \n",
              "3          34        53        40  ...      0.10      0.09     390   \n",
              "4          34        54        40  ...      0.06      0.06     390   \n",
              "..        ...       ...       ...  ...       ...       ...     ...   \n",
              "295       254       276        70  ...      0.64      0.08     910   \n",
              "296       254       275        70  ...      0.62      0.14     910   \n",
              "297       254       274        70  ...      0.22      0.11     910   \n",
              "298       254       274        71  ...      1.51      1.36     910   \n",
              "299       256       279        71  ...      0.85      0.25     910   \n",
              "\n",
              "     S5_CO2_Slope  S6_PIR  S7_PIR  Room_Occupancy_Count     temp   light  pir  \n",
              "0        0.769231       0       0                     1  24.9075   62.00    0  \n",
              "1        0.646154       0       0                     1  24.9225   61.75    0  \n",
              "2        0.519231       0       0                     1  24.9225   62.00    0  \n",
              "3        0.388462       0       0                     1  24.9375   62.00    0  \n",
              "4        0.253846       0       0                     1  24.9375   62.25    0  \n",
              "..            ...     ...     ...                   ...      ...     ...  ...  \n",
              "295      1.661538       1       1                     3  26.3750  191.00    1  \n",
              "296      1.553846       1       1                     3  26.3425  191.00    1  \n",
              "297      1.446154       1       1                     3  26.3425  190.50    1  \n",
              "298      1.392308       1       1                     3  26.3750  191.00    1  \n",
              "299      1.350000       1       1                     3  26.3750  192.75    1  \n",
              "\n",
              "[300 rows x 22 columns]"
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              "      <th></th>\n",
              "      <th>Date</th>\n",
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              "      <th>Room_Occupancy_Count</th>\n",
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              "      <td>390</td>\n",
              "      <td>0.519231</td>\n",
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              "      <td>10:51:13</td>\n",
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              "      <td>10:51:44</td>\n",
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              "      <th>296</th>\n",
              "      <td>2017/12/22</td>\n",
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              "    <tr>\n",
              "      <th>297</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:50:26</td>\n",
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              "      <td>27.00</td>\n",
              "      <td>26.00</td>\n",
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              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:50:56</td>\n",
              "      <td>26.13</td>\n",
              "      <td>27.06</td>\n",
              "      <td>26.00</td>\n",
              "      <td>26.31</td>\n",
              "      <td>165</td>\n",
              "      <td>254</td>\n",
              "      <td>274</td>\n",
              "      <td>71</td>\n",
              "      <td>...</td>\n",
              "      <td>1.51</td>\n",
              "      <td>1.36</td>\n",
              "      <td>910</td>\n",
              "      <td>1.392308</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.3750</td>\n",
              "      <td>191.00</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:51:27</td>\n",
              "      <td>26.06</td>\n",
              "      <td>27.13</td>\n",
              "      <td>26.00</td>\n",
              "      <td>26.31</td>\n",
              "      <td>165</td>\n",
              "      <td>256</td>\n",
              "      <td>279</td>\n",
              "      <td>71</td>\n",
              "      <td>...</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.25</td>\n",
              "      <td>910</td>\n",
              "      <td>1.350000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.3750</td>\n",
              "      <td>192.75</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>300 rows × 22 columns</p>\n",
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              "        const containerElement = document.querySelector('#' + key);\n",
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              "  quickchartButtonEl.style.display =\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-57dc0473-d6b3-407e-8479-9d0b9cbc313e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.iloc[200:250]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jAe-guKuSdBq",
        "outputId": "19ce4e5d-5069-4533-a05d-a112a781da25"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date      Time  S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  \\\n",
              "200  2017/12/22  12:34:52    25.75    28.69    25.31    26.06       159   \n",
              "201  2017/12/22  12:35:22    25.75    28.63    25.31    26.06       160   \n",
              "202  2017/12/22  12:35:53    25.81    28.81    25.31    26.06       160   \n",
              "203  2017/12/22  12:36:24    25.81    28.75    25.31    26.06       160   \n",
              "204  2017/12/22  12:36:54    25.81    28.88    25.31    26.06       160   \n",
              "205  2017/12/22  12:37:25    25.81    28.81    25.38    26.06       160   \n",
              "206  2017/12/22  12:37:56    25.81    28.81    25.38    26.13       160   \n",
              "207  2017/12/22  12:38:26    25.81    28.94    25.38    26.13       160   \n",
              "208  2017/12/22  12:38:57    25.81    28.94    25.31    26.13       160   \n",
              "209  2017/12/22  12:39:28    25.81    28.94    25.38    26.13       159   \n",
              "210  2017/12/22  12:39:58    25.81    29.00    25.38    26.06       159   \n",
              "211  2017/12/22  12:40:29    25.81    28.94    25.38    26.06       160   \n",
              "212  2017/12/22  12:40:59    25.81    28.94    25.38    26.06       160   \n",
              "213  2017/12/22  12:41:30    25.81    29.00    25.38    26.06       160   \n",
              "214  2017/12/22  12:42:01    25.81    28.94    25.38    26.06       159   \n",
              "215  2017/12/22  12:42:31    25.81    28.81    25.38    26.06       160   \n",
              "216  2017/12/22  13:08:03    25.94    28.19    25.50    26.19       163   \n",
              "217  2017/12/22  13:08:34    25.94    28.13    25.50    26.19       164   \n",
              "218  2017/12/22  13:09:04    25.94    28.13    25.50    26.19       163   \n",
              "219  2017/12/22  13:09:35    25.94    28.06    25.50    26.13       163   \n",
              "220  2017/12/22  13:10:06    25.94    28.13    25.50    26.19       163   \n",
              "221  2017/12/22  13:10:36    25.94    28.25    25.50    26.19       163   \n",
              "222  2017/12/22  13:11:07    25.94    28.25    25.50    26.13       162   \n",
              "223  2017/12/22  13:11:38    25.94    28.38    25.56    26.19       162   \n",
              "224  2017/12/22  13:12:08    25.94    28.19    25.50    26.13       162   \n",
              "225  2017/12/22  13:12:39    25.94    28.19    25.56    26.13       162   \n",
              "226  2017/12/22  13:13:09    25.94    28.25    25.56    26.13       162   \n",
              "227  2017/12/22  13:13:40    25.94    28.38    25.56    26.19       161   \n",
              "228  2017/12/22  13:14:11    25.94    28.25    25.56    26.13       162   \n",
              "229  2017/12/22  13:14:41    25.94    28.19    25.56    26.13       163   \n",
              "230  2017/12/22  13:15:12    25.94    28.13    25.56    26.19       162   \n",
              "231  2017/12/22  13:15:43    25.94    28.25    25.63    26.19       162   \n",
              "232  2017/12/22  13:16:13    25.94    28.00    25.63    26.19       163   \n",
              "233  2017/12/22  13:16:44    25.94    28.00    25.63    26.19       163   \n",
              "234  2017/12/22  13:17:15    26.00    27.94    25.63    26.19       164   \n",
              "235  2017/12/22  13:17:45    25.94    28.00    25.69    26.25       163   \n",
              "236  2017/12/22  13:18:16    26.00    28.00    25.69    26.19       164   \n",
              "237  2017/12/22  13:19:17    25.94    27.94    25.69    26.19       163   \n",
              "238  2017/12/22  13:19:48    26.00    27.81    25.69    26.19       163   \n",
              "239  2017/12/22  13:20:18    25.94    27.81    25.69    26.19       163   \n",
              "240  2017/12/22  13:20:49    25.94    27.75    25.69    26.19       163   \n",
              "241  2017/12/22  13:21:20    25.94    27.81    25.69    26.19       164   \n",
              "242  2017/12/22  13:21:50    26.00    27.75    25.69    26.19       163   \n",
              "243  2017/12/22  13:22:21    25.94    27.75    25.69    26.25       164   \n",
              "244  2017/12/22  13:22:52    26.00    27.88    25.75    26.25       164   \n",
              "245  2017/12/22  13:23:22    26.00    27.81    25.75    26.25       164   \n",
              "246  2017/12/22  13:23:53    26.00    27.81    25.75    26.19       165   \n",
              "247  2017/12/22  13:24:23    26.00    27.88    25.75    26.19       164   \n",
              "248  2017/12/22  13:24:54    26.00    27.88    25.75    26.25       165   \n",
              "249  2017/12/22  13:25:25    26.00    27.88    25.81    26.19       164   \n",
              "\n",
              "     S2_Light  S3_Light  S4_Light  ...  S3_Sound  S4_Sound  S5_CO2  \\\n",
              "200       245       216        65  ...      0.19      0.13     665   \n",
              "201       244       216        65  ...      0.19      0.22     665   \n",
              "202       244       223        67  ...      3.63      0.43     670   \n",
              "203       244       225        68  ...      1.32      0.26     680   \n",
              "204       244       224        67  ...      1.61      0.36     675   \n",
              "205       244       224        67  ...      0.96      0.65     675   \n",
              "206       243       215        66  ...      3.64      3.40     675   \n",
              "207       243       223        65  ...      3.65      1.03     675   \n",
              "208       243       222        66  ...      0.25      0.28     685   \n",
              "209       243       222        66  ...      3.65      0.27     690   \n",
              "210       243       224        67  ...      1.72      3.40     690   \n",
              "211       244       223        67  ...      1.86      0.50     695   \n",
              "212       244       223        67  ...      1.41      0.47     695   \n",
              "213       243       221        66  ...      0.58      0.23     700   \n",
              "214       246       221        66  ...      0.88      0.33     695   \n",
              "215       247       221        66  ...      0.88      0.21     695   \n",
              "216       249       229        70  ...      2.41      0.31     810   \n",
              "217       249       229        70  ...      2.56      0.47     810   \n",
              "218       249       228        70  ...      1.01      0.18     810   \n",
              "219       249       229        70  ...      0.25      3.40     815   \n",
              "220       248       229        70  ...      3.66      0.31     815   \n",
              "221       247       228        70  ...      0.80      0.28     815   \n",
              "222       247       228        70  ...      0.89      1.35     815   \n",
              "223       248       228        70  ...      3.66      0.31     820   \n",
              "224       247       228        70  ...      1.36      1.84     835   \n",
              "225       247       227        70  ...      3.66      2.43     840   \n",
              "226       249       227        70  ...      3.66      0.60     840   \n",
              "227       249       228        70  ...      1.03      0.33     845   \n",
              "228       249       228        70  ...      0.71      0.26     845   \n",
              "229       249       228        69  ...      0.80      0.10     845   \n",
              "230       246       227        70  ...      1.05      0.43     845   \n",
              "231       249       226        69  ...      1.13      0.32     840   \n",
              "232       250       226        70  ...      1.31      0.30     840   \n",
              "233       252       227        70  ...      0.14      0.07     845   \n",
              "234       251       228        70  ...      0.23      0.12     850   \n",
              "235       252       221        70  ...      2.00      0.43     855   \n",
              "236       252       225        70  ...      3.65      0.87     850   \n",
              "237       247       269        69  ...      3.01      0.81     850   \n",
              "238       249       267        69  ...      2.23      0.55     845   \n",
              "239       248       267        68  ...      2.23      0.28     845   \n",
              "240       251       266        70  ...      2.49      0.38     850   \n",
              "241       251       267        69  ...      3.19      0.92     855   \n",
              "242       252       267        70  ...      3.65      0.28     855   \n",
              "243       252       270        70  ...      0.88      0.29     860   \n",
              "244       253       261        70  ...      0.83      0.25     855   \n",
              "245       253       272        71  ...      0.83      0.18     860   \n",
              "246       254       274        72  ...      0.39      0.14     860   \n",
              "247       253       272        71  ...      0.61      0.23     860   \n",
              "248       254       272        70  ...      0.70      0.17     860   \n",
              "249       253       270        70  ...      0.07      0.07     860   \n",
              "\n",
              "     S5_CO2_Slope  S6_PIR  S7_PIR  Room_Occupancy_Count     temp   light  pir  \n",
              "200      0.707692       1       0                     2  26.4525  171.25    1  \n",
              "201      0.880769       1       1                     2  26.4375  171.25    1  \n",
              "202      1.038462       1       1                     3  26.4975  173.50    1  \n",
              "203      1.319231       1       1                     3  26.4825  174.25    1  \n",
              "204      1.430769       1       1                     3  26.5150  173.75    1  \n",
              "205      1.515385       1       1                     3  26.5150  173.75    1  \n",
              "206      1.623077       1       1                     3  26.5325  171.00    1  \n",
              "207      1.707692       1       1                     3  26.5650  172.75    1  \n",
              "208      1.811538       1       1                     3  26.5475  172.75    1  \n",
              "209      1.976923       1       1                     3  26.5650  172.50    1  \n",
              "210      2.057692       1       1                     3  26.5625  173.25    1  \n",
              "211      2.196154       1       1                     3  26.5475  173.50    1  \n",
              "212      2.296154       1       1                     3  26.5475  173.50    1  \n",
              "213      2.353846       1       0                     3  26.5625  172.50    1  \n",
              "214      2.319231       1       1                     3  26.5475  173.00    1  \n",
              "215      2.292308       1       1                     3  26.5150  173.50    1  \n",
              "216      3.288462       1       1                     3  26.4550  177.75    1  \n",
              "217      4.207692       1       1                     3  26.4400  178.00    1  \n",
              "218      4.953846       1       1                     3  26.4400  177.50    1  \n",
              "219      5.719231       1       1                     3  26.4075  177.75    1  \n",
              "220      6.361538       1       1                     3  26.4400  177.50    1  \n",
              "221      6.930769       1       1                     3  26.4700  177.00    1  \n",
              "222      7.380769       1       1                     3  26.4550  176.75    1  \n",
              "223      7.707692       0       1                     3  26.5175  177.00    1  \n",
              "224      8.096154       1       0                     3  26.4400  176.75    1  \n",
              "225      8.446154       1       1                     3  26.4550  176.50    1  \n",
              "226      8.661538       1       1                     3  26.4700  177.00    1  \n",
              "227      8.838462       1       1                     3  26.5175  177.00    1  \n",
              "228      8.980769       1       1                     3  26.4700  177.25    1  \n",
              "229      8.946154       1       1                     3  26.4550  177.25    1  \n",
              "230      8.780769       1       0                     3  26.4550  176.25    1  \n",
              "231      8.438462       1       1                     3  26.5025  176.50    1  \n",
              "232      7.969231       1       1                     3  26.4400  177.25    1  \n",
              "233      7.519231       0       0                     3  26.4400  178.00    0  \n",
              "234      7.042308       1       1                     3  26.4400  178.25    1  \n",
              "235      6.488462       1       1                     3  26.4700  176.50    1  \n",
              "236      5.811538       1       1                     3  26.4700  177.75    1  \n",
              "237      5.015385       1       1                     3  26.4400  187.00    1  \n",
              "238      4.103846       0       1                     3  26.4225  187.00    1  \n",
              "239      3.030769       1       1                     3  26.4075  186.50    1  \n",
              "240      1.888462       1       1                     3  26.3925  187.50    1  \n",
              "241      1.823077       1       1                     3  26.4075  187.75    1  \n",
              "242      1.723077       1       1                     3  26.4075  188.00    1  \n",
              "243      1.634615       1       1                     3  26.4075  189.00    1  \n",
              "244      1.511538       0       1                     3  26.4700  187.00    1  \n",
              "245      1.403846       1       1                     3  26.4525  190.00    1  \n",
              "246      1.261538       1       1                     3  26.4375  191.25    1  \n",
              "247      1.084615       1       1                     3  26.4550  190.00    1  \n",
              "248      0.923077       1       1                     3  26.4700  190.25    1  \n",
              "249      0.880769       0       1                     3  26.4700  189.25    1  \n",
              "\n",
              "[50 rows x 22 columns]"
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>S1_Temp</th>\n",
              "      <th>S2_Temp</th>\n",
              "      <th>S3_Temp</th>\n",
              "      <th>S4_Temp</th>\n",
              "      <th>S1_Light</th>\n",
              "      <th>S2_Light</th>\n",
              "      <th>S3_Light</th>\n",
              "      <th>S4_Light</th>\n",
              "      <th>...</th>\n",
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              "      <th>S4_Sound</th>\n",
              "      <th>S5_CO2</th>\n",
              "      <th>S5_CO2_Slope</th>\n",
              "      <th>S6_PIR</th>\n",
              "      <th>S7_PIR</th>\n",
              "      <th>Room_Occupancy_Count</th>\n",
              "      <th>temp</th>\n",
              "      <th>light</th>\n",
              "      <th>pir</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>12:34:52</td>\n",
              "      <td>25.75</td>\n",
              "      <td>28.69</td>\n",
              "      <td>25.31</td>\n",
              "      <td>26.06</td>\n",
              "      <td>159</td>\n",
              "      <td>245</td>\n",
              "      <td>216</td>\n",
              "      <td>65</td>\n",
              "      <td>...</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.13</td>\n",
              "      <td>665</td>\n",
              "      <td>0.707692</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>26.4525</td>\n",
              "      <td>171.25</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>12:35:22</td>\n",
              "      <td>25.75</td>\n",
              "      <td>28.63</td>\n",
              "      <td>25.31</td>\n",
              "      <td>26.06</td>\n",
              "      <td>160</td>\n",
              "      <td>244</td>\n",
              "      <td>216</td>\n",
              "      <td>65</td>\n",
              "      <td>...</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.22</td>\n",
              "      <td>665</td>\n",
              "      <td>0.880769</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26.4375</td>\n",
              "      <td>171.25</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>12:35:53</td>\n",
              "      <td>25.81</td>\n",
              "      <td>28.81</td>\n",
              "      <td>25.31</td>\n",
              "      <td>26.06</td>\n",
              "      <td>160</td>\n",
              "      <td>244</td>\n",
              "      <td>223</td>\n",
              "      <td>67</td>\n",
              "      <td>...</td>\n",
              "      <td>3.63</td>\n",
              "      <td>0.43</td>\n",
              "      <td>670</td>\n",
              "      <td>1.038462</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4975</td>\n",
              "      <td>173.50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>12:36:24</td>\n",
              "      <td>25.81</td>\n",
              "      <td>28.75</td>\n",
              "      <td>25.31</td>\n",
              "      <td>26.06</td>\n",
              "      <td>160</td>\n",
              "      <td>244</td>\n",
              "      <td>225</td>\n",
              "      <td>68</td>\n",
              "      <td>...</td>\n",
              "      <td>1.32</td>\n",
              "      <td>0.26</td>\n",
              "      <td>680</td>\n",
              "      <td>1.319231</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4825</td>\n",
              "      <td>174.25</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>204</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>12:36:54</td>\n",
              "      <td>25.81</td>\n",
              "      <td>28.88</td>\n",
              "      <td>25.31</td>\n",
              "      <td>26.06</td>\n",
              "      <td>160</td>\n",
              "      <td>244</td>\n",
              "      <td>224</td>\n",
              "      <td>67</td>\n",
              "      <td>...</td>\n",
              "      <td>1.61</td>\n",
              "      <td>0.36</td>\n",
              "      <td>675</td>\n",
              "      <td>1.430769</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.5150</td>\n",
              "      <td>173.75</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>205</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>12:37:25</td>\n",
              "      <td>25.81</td>\n",
              "      <td>28.81</td>\n",
              "      <td>25.38</td>\n",
              "      <td>26.06</td>\n",
              "      <td>160</td>\n",
              "      <td>244</td>\n",
              "      <td>224</td>\n",
              "      <td>67</td>\n",
              "      <td>...</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.65</td>\n",
              "      <td>675</td>\n",
              "      <td>1.515385</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.5150</td>\n",
              "      <td>173.75</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>206</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>12:37:56</td>\n",
              "      <td>25.81</td>\n",
              "      <td>28.81</td>\n",
              "      <td>25.38</td>\n",
              "      <td>26.13</td>\n",
              "      <td>160</td>\n",
              "      <td>243</td>\n",
              "      <td>215</td>\n",
              "      <td>66</td>\n",
              "      <td>...</td>\n",
              "      <td>3.64</td>\n",
              "      <td>3.40</td>\n",
              "      <td>675</td>\n",
              "      <td>1.623077</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.5325</td>\n",
              "      <td>171.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>207</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>12:38:26</td>\n",
              "      <td>25.81</td>\n",
              "      <td>28.94</td>\n",
              "      <td>25.38</td>\n",
              "      <td>26.13</td>\n",
              "      <td>160</td>\n",
              "      <td>243</td>\n",
              "      <td>223</td>\n",
              "      <td>65</td>\n",
              "      <td>...</td>\n",
              "      <td>3.65</td>\n",
              "      <td>1.03</td>\n",
              "      <td>675</td>\n",
              "      <td>1.707692</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.5650</td>\n",
              "      <td>172.75</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>208</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>12:38:57</td>\n",
              "      <td>25.81</td>\n",
              "      <td>28.94</td>\n",
              "      <td>25.31</td>\n",
              "      <td>26.13</td>\n",
              "      <td>160</td>\n",
              "      <td>243</td>\n",
              "      <td>222</td>\n",
              "      <td>66</td>\n",
              "      <td>...</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.28</td>\n",
              "      <td>685</td>\n",
              "      <td>1.811538</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.5475</td>\n",
              "      <td>172.75</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>209</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>12:39:28</td>\n",
              "      <td>25.81</td>\n",
              "      <td>28.94</td>\n",
              "      <td>25.38</td>\n",
              "      <td>26.13</td>\n",
              "      <td>159</td>\n",
              "      <td>243</td>\n",
              "      <td>222</td>\n",
              "      <td>66</td>\n",
              "      <td>...</td>\n",
              "      <td>3.65</td>\n",
              "      <td>0.27</td>\n",
              "      <td>690</td>\n",
              "      <td>1.976923</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.5650</td>\n",
              "      <td>172.50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>210</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>12:39:58</td>\n",
              "      <td>25.81</td>\n",
              "      <td>29.00</td>\n",
              "      <td>25.38</td>\n",
              "      <td>26.06</td>\n",
              "      <td>159</td>\n",
              "      <td>243</td>\n",
              "      <td>224</td>\n",
              "      <td>67</td>\n",
              "      <td>...</td>\n",
              "      <td>1.72</td>\n",
              "      <td>3.40</td>\n",
              "      <td>690</td>\n",
              "      <td>2.057692</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.5625</td>\n",
              "      <td>173.25</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>211</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>12:40:29</td>\n",
              "      <td>25.81</td>\n",
              "      <td>28.94</td>\n",
              "      <td>25.38</td>\n",
              "      <td>26.06</td>\n",
              "      <td>160</td>\n",
              "      <td>244</td>\n",
              "      <td>223</td>\n",
              "      <td>67</td>\n",
              "      <td>...</td>\n",
              "      <td>1.86</td>\n",
              "      <td>0.50</td>\n",
              "      <td>695</td>\n",
              "      <td>2.196154</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.5475</td>\n",
              "      <td>173.50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>12:40:59</td>\n",
              "      <td>25.81</td>\n",
              "      <td>28.94</td>\n",
              "      <td>25.38</td>\n",
              "      <td>26.06</td>\n",
              "      <td>160</td>\n",
              "      <td>244</td>\n",
              "      <td>223</td>\n",
              "      <td>67</td>\n",
              "      <td>...</td>\n",
              "      <td>1.41</td>\n",
              "      <td>0.47</td>\n",
              "      <td>695</td>\n",
              "      <td>2.296154</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.5475</td>\n",
              "      <td>173.50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>213</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>12:41:30</td>\n",
              "      <td>25.81</td>\n",
              "      <td>29.00</td>\n",
              "      <td>25.38</td>\n",
              "      <td>26.06</td>\n",
              "      <td>160</td>\n",
              "      <td>243</td>\n",
              "      <td>221</td>\n",
              "      <td>66</td>\n",
              "      <td>...</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.23</td>\n",
              "      <td>700</td>\n",
              "      <td>2.353846</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>26.5625</td>\n",
              "      <td>172.50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>12:42:01</td>\n",
              "      <td>25.81</td>\n",
              "      <td>28.94</td>\n",
              "      <td>25.38</td>\n",
              "      <td>26.06</td>\n",
              "      <td>159</td>\n",
              "      <td>246</td>\n",
              "      <td>221</td>\n",
              "      <td>66</td>\n",
              "      <td>...</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.33</td>\n",
              "      <td>695</td>\n",
              "      <td>2.319231</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.5475</td>\n",
              "      <td>173.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>215</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>12:42:31</td>\n",
              "      <td>25.81</td>\n",
              "      <td>28.81</td>\n",
              "      <td>25.38</td>\n",
              "      <td>26.06</td>\n",
              "      <td>160</td>\n",
              "      <td>247</td>\n",
              "      <td>221</td>\n",
              "      <td>66</td>\n",
              "      <td>...</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.21</td>\n",
              "      <td>695</td>\n",
              "      <td>2.292308</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.5150</td>\n",
              "      <td>173.50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>216</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:08:03</td>\n",
              "      <td>25.94</td>\n",
              "      <td>28.19</td>\n",
              "      <td>25.50</td>\n",
              "      <td>26.19</td>\n",
              "      <td>163</td>\n",
              "      <td>249</td>\n",
              "      <td>229</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>2.41</td>\n",
              "      <td>0.31</td>\n",
              "      <td>810</td>\n",
              "      <td>3.288462</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4550</td>\n",
              "      <td>177.75</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>217</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:08:34</td>\n",
              "      <td>25.94</td>\n",
              "      <td>28.13</td>\n",
              "      <td>25.50</td>\n",
              "      <td>26.19</td>\n",
              "      <td>164</td>\n",
              "      <td>249</td>\n",
              "      <td>229</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>2.56</td>\n",
              "      <td>0.47</td>\n",
              "      <td>810</td>\n",
              "      <td>4.207692</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4400</td>\n",
              "      <td>178.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>218</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:09:04</td>\n",
              "      <td>25.94</td>\n",
              "      <td>28.13</td>\n",
              "      <td>25.50</td>\n",
              "      <td>26.19</td>\n",
              "      <td>163</td>\n",
              "      <td>249</td>\n",
              "      <td>228</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>1.01</td>\n",
              "      <td>0.18</td>\n",
              "      <td>810</td>\n",
              "      <td>4.953846</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4400</td>\n",
              "      <td>177.50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>219</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:09:35</td>\n",
              "      <td>25.94</td>\n",
              "      <td>28.06</td>\n",
              "      <td>25.50</td>\n",
              "      <td>26.13</td>\n",
              "      <td>163</td>\n",
              "      <td>249</td>\n",
              "      <td>229</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>0.25</td>\n",
              "      <td>3.40</td>\n",
              "      <td>815</td>\n",
              "      <td>5.719231</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4075</td>\n",
              "      <td>177.75</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>220</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:10:06</td>\n",
              "      <td>25.94</td>\n",
              "      <td>28.13</td>\n",
              "      <td>25.50</td>\n",
              "      <td>26.19</td>\n",
              "      <td>163</td>\n",
              "      <td>248</td>\n",
              "      <td>229</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>3.66</td>\n",
              "      <td>0.31</td>\n",
              "      <td>815</td>\n",
              "      <td>6.361538</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4400</td>\n",
              "      <td>177.50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>221</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:10:36</td>\n",
              "      <td>25.94</td>\n",
              "      <td>28.25</td>\n",
              "      <td>25.50</td>\n",
              "      <td>26.19</td>\n",
              "      <td>163</td>\n",
              "      <td>247</td>\n",
              "      <td>228</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.28</td>\n",
              "      <td>815</td>\n",
              "      <td>6.930769</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4700</td>\n",
              "      <td>177.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:11:07</td>\n",
              "      <td>25.94</td>\n",
              "      <td>28.25</td>\n",
              "      <td>25.50</td>\n",
              "      <td>26.13</td>\n",
              "      <td>162</td>\n",
              "      <td>247</td>\n",
              "      <td>228</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>0.89</td>\n",
              "      <td>1.35</td>\n",
              "      <td>815</td>\n",
              "      <td>7.380769</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4550</td>\n",
              "      <td>176.75</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>223</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:11:38</td>\n",
              "      <td>25.94</td>\n",
              "      <td>28.38</td>\n",
              "      <td>25.56</td>\n",
              "      <td>26.19</td>\n",
              "      <td>162</td>\n",
              "      <td>248</td>\n",
              "      <td>228</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>3.66</td>\n",
              "      <td>0.31</td>\n",
              "      <td>820</td>\n",
              "      <td>7.707692</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.5175</td>\n",
              "      <td>177.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>224</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:12:08</td>\n",
              "      <td>25.94</td>\n",
              "      <td>28.19</td>\n",
              "      <td>25.50</td>\n",
              "      <td>26.13</td>\n",
              "      <td>162</td>\n",
              "      <td>247</td>\n",
              "      <td>228</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>1.36</td>\n",
              "      <td>1.84</td>\n",
              "      <td>835</td>\n",
              "      <td>8.096154</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4400</td>\n",
              "      <td>176.75</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>225</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:12:39</td>\n",
              "      <td>25.94</td>\n",
              "      <td>28.19</td>\n",
              "      <td>25.56</td>\n",
              "      <td>26.13</td>\n",
              "      <td>162</td>\n",
              "      <td>247</td>\n",
              "      <td>227</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>3.66</td>\n",
              "      <td>2.43</td>\n",
              "      <td>840</td>\n",
              "      <td>8.446154</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4550</td>\n",
              "      <td>176.50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>226</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:13:09</td>\n",
              "      <td>25.94</td>\n",
              "      <td>28.25</td>\n",
              "      <td>25.56</td>\n",
              "      <td>26.13</td>\n",
              "      <td>162</td>\n",
              "      <td>249</td>\n",
              "      <td>227</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>3.66</td>\n",
              "      <td>0.60</td>\n",
              "      <td>840</td>\n",
              "      <td>8.661538</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4700</td>\n",
              "      <td>177.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:13:40</td>\n",
              "      <td>25.94</td>\n",
              "      <td>28.38</td>\n",
              "      <td>25.56</td>\n",
              "      <td>26.19</td>\n",
              "      <td>161</td>\n",
              "      <td>249</td>\n",
              "      <td>228</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>1.03</td>\n",
              "      <td>0.33</td>\n",
              "      <td>845</td>\n",
              "      <td>8.838462</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.5175</td>\n",
              "      <td>177.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>228</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:14:11</td>\n",
              "      <td>25.94</td>\n",
              "      <td>28.25</td>\n",
              "      <td>25.56</td>\n",
              "      <td>26.13</td>\n",
              "      <td>162</td>\n",
              "      <td>249</td>\n",
              "      <td>228</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.26</td>\n",
              "      <td>845</td>\n",
              "      <td>8.980769</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4700</td>\n",
              "      <td>177.25</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>229</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:14:41</td>\n",
              "      <td>25.94</td>\n",
              "      <td>28.19</td>\n",
              "      <td>25.56</td>\n",
              "      <td>26.13</td>\n",
              "      <td>163</td>\n",
              "      <td>249</td>\n",
              "      <td>228</td>\n",
              "      <td>69</td>\n",
              "      <td>...</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.10</td>\n",
              "      <td>845</td>\n",
              "      <td>8.946154</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4550</td>\n",
              "      <td>177.25</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>230</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:15:12</td>\n",
              "      <td>25.94</td>\n",
              "      <td>28.13</td>\n",
              "      <td>25.56</td>\n",
              "      <td>26.19</td>\n",
              "      <td>162</td>\n",
              "      <td>246</td>\n",
              "      <td>227</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>1.05</td>\n",
              "      <td>0.43</td>\n",
              "      <td>845</td>\n",
              "      <td>8.780769</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4550</td>\n",
              "      <td>176.25</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>231</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:15:43</td>\n",
              "      <td>25.94</td>\n",
              "      <td>28.25</td>\n",
              "      <td>25.63</td>\n",
              "      <td>26.19</td>\n",
              "      <td>162</td>\n",
              "      <td>249</td>\n",
              "      <td>226</td>\n",
              "      <td>69</td>\n",
              "      <td>...</td>\n",
              "      <td>1.13</td>\n",
              "      <td>0.32</td>\n",
              "      <td>840</td>\n",
              "      <td>8.438462</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.5025</td>\n",
              "      <td>176.50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>232</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:16:13</td>\n",
              "      <td>25.94</td>\n",
              "      <td>28.00</td>\n",
              "      <td>25.63</td>\n",
              "      <td>26.19</td>\n",
              "      <td>163</td>\n",
              "      <td>250</td>\n",
              "      <td>226</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>1.31</td>\n",
              "      <td>0.30</td>\n",
              "      <td>840</td>\n",
              "      <td>7.969231</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4400</td>\n",
              "      <td>177.25</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>233</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:16:44</td>\n",
              "      <td>25.94</td>\n",
              "      <td>28.00</td>\n",
              "      <td>25.63</td>\n",
              "      <td>26.19</td>\n",
              "      <td>163</td>\n",
              "      <td>252</td>\n",
              "      <td>227</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.07</td>\n",
              "      <td>845</td>\n",
              "      <td>7.519231</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4400</td>\n",
              "      <td>178.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>234</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:17:15</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.94</td>\n",
              "      <td>25.63</td>\n",
              "      <td>26.19</td>\n",
              "      <td>164</td>\n",
              "      <td>251</td>\n",
              "      <td>228</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.12</td>\n",
              "      <td>850</td>\n",
              "      <td>7.042308</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4400</td>\n",
              "      <td>178.25</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>235</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:17:45</td>\n",
              "      <td>25.94</td>\n",
              "      <td>28.00</td>\n",
              "      <td>25.69</td>\n",
              "      <td>26.25</td>\n",
              "      <td>163</td>\n",
              "      <td>252</td>\n",
              "      <td>221</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>2.00</td>\n",
              "      <td>0.43</td>\n",
              "      <td>855</td>\n",
              "      <td>6.488462</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4700</td>\n",
              "      <td>176.50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>236</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:18:16</td>\n",
              "      <td>26.00</td>\n",
              "      <td>28.00</td>\n",
              "      <td>25.69</td>\n",
              "      <td>26.19</td>\n",
              "      <td>164</td>\n",
              "      <td>252</td>\n",
              "      <td>225</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>3.65</td>\n",
              "      <td>0.87</td>\n",
              "      <td>850</td>\n",
              "      <td>5.811538</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4700</td>\n",
              "      <td>177.75</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:19:17</td>\n",
              "      <td>25.94</td>\n",
              "      <td>27.94</td>\n",
              "      <td>25.69</td>\n",
              "      <td>26.19</td>\n",
              "      <td>163</td>\n",
              "      <td>247</td>\n",
              "      <td>269</td>\n",
              "      <td>69</td>\n",
              "      <td>...</td>\n",
              "      <td>3.01</td>\n",
              "      <td>0.81</td>\n",
              "      <td>850</td>\n",
              "      <td>5.015385</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4400</td>\n",
              "      <td>187.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>238</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:19:48</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.81</td>\n",
              "      <td>25.69</td>\n",
              "      <td>26.19</td>\n",
              "      <td>163</td>\n",
              "      <td>249</td>\n",
              "      <td>267</td>\n",
              "      <td>69</td>\n",
              "      <td>...</td>\n",
              "      <td>2.23</td>\n",
              "      <td>0.55</td>\n",
              "      <td>845</td>\n",
              "      <td>4.103846</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4225</td>\n",
              "      <td>187.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:20:18</td>\n",
              "      <td>25.94</td>\n",
              "      <td>27.81</td>\n",
              "      <td>25.69</td>\n",
              "      <td>26.19</td>\n",
              "      <td>163</td>\n",
              "      <td>248</td>\n",
              "      <td>267</td>\n",
              "      <td>68</td>\n",
              "      <td>...</td>\n",
              "      <td>2.23</td>\n",
              "      <td>0.28</td>\n",
              "      <td>845</td>\n",
              "      <td>3.030769</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4075</td>\n",
              "      <td>186.50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:20:49</td>\n",
              "      <td>25.94</td>\n",
              "      <td>27.75</td>\n",
              "      <td>25.69</td>\n",
              "      <td>26.19</td>\n",
              "      <td>163</td>\n",
              "      <td>251</td>\n",
              "      <td>266</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>2.49</td>\n",
              "      <td>0.38</td>\n",
              "      <td>850</td>\n",
              "      <td>1.888462</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.3925</td>\n",
              "      <td>187.50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:21:20</td>\n",
              "      <td>25.94</td>\n",
              "      <td>27.81</td>\n",
              "      <td>25.69</td>\n",
              "      <td>26.19</td>\n",
              "      <td>164</td>\n",
              "      <td>251</td>\n",
              "      <td>267</td>\n",
              "      <td>69</td>\n",
              "      <td>...</td>\n",
              "      <td>3.19</td>\n",
              "      <td>0.92</td>\n",
              "      <td>855</td>\n",
              "      <td>1.823077</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4075</td>\n",
              "      <td>187.75</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:21:50</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.75</td>\n",
              "      <td>25.69</td>\n",
              "      <td>26.19</td>\n",
              "      <td>163</td>\n",
              "      <td>252</td>\n",
              "      <td>267</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>3.65</td>\n",
              "      <td>0.28</td>\n",
              "      <td>855</td>\n",
              "      <td>1.723077</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4075</td>\n",
              "      <td>188.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>243</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:22:21</td>\n",
              "      <td>25.94</td>\n",
              "      <td>27.75</td>\n",
              "      <td>25.69</td>\n",
              "      <td>26.25</td>\n",
              "      <td>164</td>\n",
              "      <td>252</td>\n",
              "      <td>270</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.29</td>\n",
              "      <td>860</td>\n",
              "      <td>1.634615</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4075</td>\n",
              "      <td>189.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>244</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:22:52</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.88</td>\n",
              "      <td>25.75</td>\n",
              "      <td>26.25</td>\n",
              "      <td>164</td>\n",
              "      <td>253</td>\n",
              "      <td>261</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.25</td>\n",
              "      <td>855</td>\n",
              "      <td>1.511538</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4700</td>\n",
              "      <td>187.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>245</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:23:22</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.81</td>\n",
              "      <td>25.75</td>\n",
              "      <td>26.25</td>\n",
              "      <td>164</td>\n",
              "      <td>253</td>\n",
              "      <td>272</td>\n",
              "      <td>71</td>\n",
              "      <td>...</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.18</td>\n",
              "      <td>860</td>\n",
              "      <td>1.403846</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4525</td>\n",
              "      <td>190.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:23:53</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.81</td>\n",
              "      <td>25.75</td>\n",
              "      <td>26.19</td>\n",
              "      <td>165</td>\n",
              "      <td>254</td>\n",
              "      <td>274</td>\n",
              "      <td>72</td>\n",
              "      <td>...</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.14</td>\n",
              "      <td>860</td>\n",
              "      <td>1.261538</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4375</td>\n",
              "      <td>191.25</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>247</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:24:23</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.88</td>\n",
              "      <td>25.75</td>\n",
              "      <td>26.19</td>\n",
              "      <td>164</td>\n",
              "      <td>253</td>\n",
              "      <td>272</td>\n",
              "      <td>71</td>\n",
              "      <td>...</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.23</td>\n",
              "      <td>860</td>\n",
              "      <td>1.084615</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4550</td>\n",
              "      <td>190.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:24:54</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.88</td>\n",
              "      <td>25.75</td>\n",
              "      <td>26.25</td>\n",
              "      <td>165</td>\n",
              "      <td>254</td>\n",
              "      <td>272</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.17</td>\n",
              "      <td>860</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4700</td>\n",
              "      <td>190.25</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:25:25</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.88</td>\n",
              "      <td>25.81</td>\n",
              "      <td>26.19</td>\n",
              "      <td>164</td>\n",
              "      <td>253</td>\n",
              "      <td>270</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.07</td>\n",
              "      <td>860</td>\n",
              "      <td>0.880769</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4700</td>\n",
              "      <td>189.25</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
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          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['room_occupancy_rate'] = (data['Room_Occupancy_Count'])/(6*4.6)"
      ],
      "metadata": {
        "id": "NxZpd2ukWm2Y"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.iloc[250:280]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "of9SfSlFXNZG",
        "outputId": "fb2e54f9-e067-4dab-ce36-e59c42deb42f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date      Time  S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  \\\n",
              "250  2017/12/22  13:25:55    26.00    27.94    25.81    26.19       164   \n",
              "251  2017/12/22  13:26:26    26.00    27.81    25.81    26.19       163   \n",
              "252  2017/12/22  13:26:57    26.00    27.81    25.81    26.19       164   \n",
              "253  2017/12/22  13:27:27    26.00    27.75    25.81    26.19       163   \n",
              "254  2017/12/22  13:27:58    26.00    27.69    25.81    26.19       163   \n",
              "255  2017/12/22  13:28:28    26.00    27.75    25.81    26.25       164   \n",
              "256  2017/12/22  13:28:59    26.00    27.75    25.81    26.25       162   \n",
              "257  2017/12/22  13:29:30    26.00    27.63    25.88    26.25       163   \n",
              "258  2017/12/22  13:30:00    26.00    27.63    25.88    26.19       164   \n",
              "259  2017/12/22  13:30:31    26.00    27.63    25.88    26.25       165   \n",
              "260  2017/12/22  13:31:02    26.06    27.56    25.88    26.19       165   \n",
              "261  2017/12/22  13:32:03    26.06    27.56    25.88    26.19       165   \n",
              "262  2017/12/22  13:32:34    26.00    27.50    25.88    26.31       165   \n",
              "263  2017/12/22  13:33:04    26.00    27.50    25.88    26.31       165   \n",
              "264  2017/12/22  13:33:35    26.00    27.38    25.94    26.25       165   \n",
              "265  2017/12/22  13:34:05    26.06    27.44    25.88    26.31       165   \n",
              "266  2017/12/22  13:34:36    26.06    27.50    25.88    26.25       165   \n",
              "267  2017/12/22  13:35:07    26.06    27.44    25.88    26.31       165   \n",
              "268  2017/12/22  13:35:37    26.00    27.38    25.88    26.31       165   \n",
              "269  2017/12/22  13:36:08    26.06    27.38    25.94    26.25       165   \n",
              "270  2017/12/22  13:36:39    26.06    27.38    25.94    26.31       165   \n",
              "271  2017/12/22  13:37:09    26.06    27.31    25.94    26.25       165   \n",
              "272  2017/12/22  13:37:40    26.06    27.31    25.94    26.31       165   \n",
              "273  2017/12/22  13:38:11    26.06    27.25    25.94    26.25       165   \n",
              "274  2017/12/22  13:38:41    26.06    27.31    25.88    26.31       164   \n",
              "275  2017/12/22  13:39:12    26.06    27.19    25.94    26.31       165   \n",
              "276  2017/12/22  13:39:42    26.06    27.19    25.88    26.31       165   \n",
              "277  2017/12/22  13:40:13    26.06    27.25    25.94    26.25       165   \n",
              "278  2017/12/22  13:40:44    26.06    27.25    25.94    26.25       165   \n",
              "279  2017/12/22  13:41:14    26.06    27.31    25.94    26.31       164   \n",
              "\n",
              "     S2_Light  S3_Light  S4_Light  ...  S4_Sound  S5_CO2  S5_CO2_Slope  \\\n",
              "250       252       268        70  ...      0.06     865      0.915385   \n",
              "251       252       268        70  ...      0.06     865      0.930769   \n",
              "252       252       268        70  ...      0.06     865      0.976923   \n",
              "253       252       266        69  ...      0.09     875      1.100000   \n",
              "254       252       267        70  ...      0.07     880      1.246154   \n",
              "255       251       267        70  ...      0.09     875      1.319231   \n",
              "256       247       254        69  ...      0.75     880      1.365385   \n",
              "257       253       268        70  ...      0.43     885      1.426923   \n",
              "258       253       269        70  ...      0.29     885      1.503846   \n",
              "259       254       270        71  ...      0.33     875      1.507692   \n",
              "260       254       272        73  ...      0.44     870      1.496154   \n",
              "261       254       274        73  ...      0.42     875      1.469231   \n",
              "262       254       280        72  ...      0.29     870      1.376923   \n",
              "263       254       275        70  ...      0.74     870      1.219231   \n",
              "264       254       275        70  ...      0.14     870      1.042308   \n",
              "265       254       276        71  ...      0.54     865      0.850000   \n",
              "266       254       276        71  ...      0.58     870      0.742308   \n",
              "267       254       275        72  ...      0.48     870      0.623077   \n",
              "268       254       278        73  ...      0.26     865      0.496154   \n",
              "269       255       278        72  ...      0.45     860      0.269231   \n",
              "270       254       278        72  ...      0.50     840     -0.096154   \n",
              "271       254       250        71  ...      0.22     845     -0.400000   \n",
              "272       254       275        71  ...      0.52     850     -0.646154   \n",
              "273       254       276        73  ...      0.58     860     -0.792308   \n",
              "274       247       279        72  ...      0.79     865     -0.892308   \n",
              "275       256       278        72  ...      0.83     870     -0.900000   \n",
              "276       254       278        72  ...      0.47     870     -0.911538   \n",
              "277       254       276        72  ...      0.55     885     -0.788462   \n",
              "278       257       279        72  ...      0.54     890     -0.534615   \n",
              "279       258       279        72  ...      0.18     895     -0.196154   \n",
              "\n",
              "     S6_PIR  S7_PIR  Room_Occupancy_Count     temp   light  pir  \\\n",
              "250       0       0                     3  26.4850  188.50    0   \n",
              "251       1       0                     3  26.4525  188.25    1   \n",
              "252       1       0                     3  26.4525  188.50    1   \n",
              "253       0       0                     3  26.4375  187.50    0   \n",
              "254       0       0                     3  26.4225  188.00    0   \n",
              "255       0       1                     3  26.4525  188.00    1   \n",
              "256       1       1                     3  26.4525  183.00    1   \n",
              "257       1       1                     2  26.4400  188.50    1   \n",
              "258       1       1                     2  26.4250  189.00    1   \n",
              "259       1       1                     2  26.4400  190.00    1   \n",
              "260       0       1                     2  26.4225  191.00    1   \n",
              "261       1       1                     2  26.4225  191.50    1   \n",
              "262       1       1                     2  26.4225  192.75    1   \n",
              "263       0       1                     2  26.4225  191.00    1   \n",
              "264       1       1                     2  26.3925  191.00    1   \n",
              "265       1       0                     2  26.4225  191.50    1   \n",
              "266       1       1                     2  26.4225  191.50    1   \n",
              "267       0       1                     2  26.4225  191.50    1   \n",
              "268       0       1                     2  26.3925  192.50    1   \n",
              "269       1       1                     2  26.4075  192.50    1   \n",
              "270       1       1                     2  26.4225  192.25    1   \n",
              "271       1       1                     2  26.3900  185.00    1   \n",
              "272       0       1                     2  26.4050  191.25    1   \n",
              "273       0       1                     2  26.3750  192.00    1   \n",
              "274       1       1                     3  26.3900  190.50    1   \n",
              "275       1       1                     3  26.3750  192.75    1   \n",
              "276       1       1                     3  26.3600  192.25    1   \n",
              "277       1       1                     3  26.3750  191.75    1   \n",
              "278       1       1                     2  26.3750  193.25    1   \n",
              "279       1       1                     2  26.4050  193.25    1   \n",
              "\n",
              "     room_occupancy_rate  \n",
              "250             0.108696  \n",
              "251             0.108696  \n",
              "252             0.108696  \n",
              "253             0.108696  \n",
              "254             0.108696  \n",
              "255             0.108696  \n",
              "256             0.108696  \n",
              "257             0.072464  \n",
              "258             0.072464  \n",
              "259             0.072464  \n",
              "260             0.072464  \n",
              "261             0.072464  \n",
              "262             0.072464  \n",
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              "270             0.072464  \n",
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              "272             0.072464  \n",
              "273             0.072464  \n",
              "274             0.108696  \n",
              "275             0.108696  \n",
              "276             0.108696  \n",
              "277             0.108696  \n",
              "278             0.072464  \n",
              "279             0.072464  \n",
              "\n",
              "[30 rows x 23 columns]"
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              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>S1_Temp</th>\n",
              "      <th>S2_Temp</th>\n",
              "      <th>S3_Temp</th>\n",
              "      <th>S4_Temp</th>\n",
              "      <th>S1_Light</th>\n",
              "      <th>S2_Light</th>\n",
              "      <th>S3_Light</th>\n",
              "      <th>S4_Light</th>\n",
              "      <th>...</th>\n",
              "      <th>S4_Sound</th>\n",
              "      <th>S5_CO2</th>\n",
              "      <th>S5_CO2_Slope</th>\n",
              "      <th>S6_PIR</th>\n",
              "      <th>S7_PIR</th>\n",
              "      <th>Room_Occupancy_Count</th>\n",
              "      <th>temp</th>\n",
              "      <th>light</th>\n",
              "      <th>pir</th>\n",
              "      <th>room_occupancy_rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:25:55</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.94</td>\n",
              "      <td>25.81</td>\n",
              "      <td>26.19</td>\n",
              "      <td>164</td>\n",
              "      <td>252</td>\n",
              "      <td>268</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>0.06</td>\n",
              "      <td>865</td>\n",
              "      <td>0.915385</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4850</td>\n",
              "      <td>188.50</td>\n",
              "      <td>0</td>\n",
              "      <td>0.108696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>251</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:26:26</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.81</td>\n",
              "      <td>25.81</td>\n",
              "      <td>26.19</td>\n",
              "      <td>163</td>\n",
              "      <td>252</td>\n",
              "      <td>268</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>0.06</td>\n",
              "      <td>865</td>\n",
              "      <td>0.930769</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4525</td>\n",
              "      <td>188.25</td>\n",
              "      <td>1</td>\n",
              "      <td>0.108696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>252</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:26:57</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.81</td>\n",
              "      <td>25.81</td>\n",
              "      <td>26.19</td>\n",
              "      <td>164</td>\n",
              "      <td>252</td>\n",
              "      <td>268</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>0.06</td>\n",
              "      <td>865</td>\n",
              "      <td>0.976923</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4525</td>\n",
              "      <td>188.50</td>\n",
              "      <td>1</td>\n",
              "      <td>0.108696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>253</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:27:27</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.75</td>\n",
              "      <td>25.81</td>\n",
              "      <td>26.19</td>\n",
              "      <td>163</td>\n",
              "      <td>252</td>\n",
              "      <td>266</td>\n",
              "      <td>69</td>\n",
              "      <td>...</td>\n",
              "      <td>0.09</td>\n",
              "      <td>875</td>\n",
              "      <td>1.100000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4375</td>\n",
              "      <td>187.50</td>\n",
              "      <td>0</td>\n",
              "      <td>0.108696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>254</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:27:58</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.69</td>\n",
              "      <td>25.81</td>\n",
              "      <td>26.19</td>\n",
              "      <td>163</td>\n",
              "      <td>252</td>\n",
              "      <td>267</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>0.07</td>\n",
              "      <td>880</td>\n",
              "      <td>1.246154</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4225</td>\n",
              "      <td>188.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.108696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>255</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:28:28</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.75</td>\n",
              "      <td>25.81</td>\n",
              "      <td>26.25</td>\n",
              "      <td>164</td>\n",
              "      <td>251</td>\n",
              "      <td>267</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>0.09</td>\n",
              "      <td>875</td>\n",
              "      <td>1.319231</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4525</td>\n",
              "      <td>188.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0.108696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>256</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:28:59</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.75</td>\n",
              "      <td>25.81</td>\n",
              "      <td>26.25</td>\n",
              "      <td>162</td>\n",
              "      <td>247</td>\n",
              "      <td>254</td>\n",
              "      <td>69</td>\n",
              "      <td>...</td>\n",
              "      <td>0.75</td>\n",
              "      <td>880</td>\n",
              "      <td>1.365385</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.4525</td>\n",
              "      <td>183.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0.108696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>257</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:29:30</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.63</td>\n",
              "      <td>25.88</td>\n",
              "      <td>26.25</td>\n",
              "      <td>163</td>\n",
              "      <td>253</td>\n",
              "      <td>268</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>0.43</td>\n",
              "      <td>885</td>\n",
              "      <td>1.426923</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26.4400</td>\n",
              "      <td>188.50</td>\n",
              "      <td>1</td>\n",
              "      <td>0.072464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>258</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:30:00</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.63</td>\n",
              "      <td>25.88</td>\n",
              "      <td>26.19</td>\n",
              "      <td>164</td>\n",
              "      <td>253</td>\n",
              "      <td>269</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>0.29</td>\n",
              "      <td>885</td>\n",
              "      <td>1.503846</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26.4250</td>\n",
              "      <td>189.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0.072464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:30:31</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.63</td>\n",
              "      <td>25.88</td>\n",
              "      <td>26.25</td>\n",
              "      <td>165</td>\n",
              "      <td>254</td>\n",
              "      <td>270</td>\n",
              "      <td>71</td>\n",
              "      <td>...</td>\n",
              "      <td>0.33</td>\n",
              "      <td>875</td>\n",
              "      <td>1.507692</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26.4400</td>\n",
              "      <td>190.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0.072464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>260</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:31:02</td>\n",
              "      <td>26.06</td>\n",
              "      <td>27.56</td>\n",
              "      <td>25.88</td>\n",
              "      <td>26.19</td>\n",
              "      <td>165</td>\n",
              "      <td>254</td>\n",
              "      <td>272</td>\n",
              "      <td>73</td>\n",
              "      <td>...</td>\n",
              "      <td>0.44</td>\n",
              "      <td>870</td>\n",
              "      <td>1.496154</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26.4225</td>\n",
              "      <td>191.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0.072464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>261</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:32:03</td>\n",
              "      <td>26.06</td>\n",
              "      <td>27.56</td>\n",
              "      <td>25.88</td>\n",
              "      <td>26.19</td>\n",
              "      <td>165</td>\n",
              "      <td>254</td>\n",
              "      <td>274</td>\n",
              "      <td>73</td>\n",
              "      <td>...</td>\n",
              "      <td>0.42</td>\n",
              "      <td>875</td>\n",
              "      <td>1.469231</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26.4225</td>\n",
              "      <td>191.50</td>\n",
              "      <td>1</td>\n",
              "      <td>0.072464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>262</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:32:34</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.50</td>\n",
              "      <td>25.88</td>\n",
              "      <td>26.31</td>\n",
              "      <td>165</td>\n",
              "      <td>254</td>\n",
              "      <td>280</td>\n",
              "      <td>72</td>\n",
              "      <td>...</td>\n",
              "      <td>0.29</td>\n",
              "      <td>870</td>\n",
              "      <td>1.376923</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26.4225</td>\n",
              "      <td>192.75</td>\n",
              "      <td>1</td>\n",
              "      <td>0.072464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>263</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:33:04</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.50</td>\n",
              "      <td>25.88</td>\n",
              "      <td>26.31</td>\n",
              "      <td>165</td>\n",
              "      <td>254</td>\n",
              "      <td>275</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>0.74</td>\n",
              "      <td>870</td>\n",
              "      <td>1.219231</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26.4225</td>\n",
              "      <td>191.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0.072464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>264</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:33:35</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.38</td>\n",
              "      <td>25.94</td>\n",
              "      <td>26.25</td>\n",
              "      <td>165</td>\n",
              "      <td>254</td>\n",
              "      <td>275</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>0.14</td>\n",
              "      <td>870</td>\n",
              "      <td>1.042308</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26.3925</td>\n",
              "      <td>191.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0.072464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>265</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:34:05</td>\n",
              "      <td>26.06</td>\n",
              "      <td>27.44</td>\n",
              "      <td>25.88</td>\n",
              "      <td>26.31</td>\n",
              "      <td>165</td>\n",
              "      <td>254</td>\n",
              "      <td>276</td>\n",
              "      <td>71</td>\n",
              "      <td>...</td>\n",
              "      <td>0.54</td>\n",
              "      <td>865</td>\n",
              "      <td>0.850000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>26.4225</td>\n",
              "      <td>191.50</td>\n",
              "      <td>1</td>\n",
              "      <td>0.072464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>266</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:34:36</td>\n",
              "      <td>26.06</td>\n",
              "      <td>27.50</td>\n",
              "      <td>25.88</td>\n",
              "      <td>26.25</td>\n",
              "      <td>165</td>\n",
              "      <td>254</td>\n",
              "      <td>276</td>\n",
              "      <td>71</td>\n",
              "      <td>...</td>\n",
              "      <td>0.58</td>\n",
              "      <td>870</td>\n",
              "      <td>0.742308</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26.4225</td>\n",
              "      <td>191.50</td>\n",
              "      <td>1</td>\n",
              "      <td>0.072464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>267</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:35:07</td>\n",
              "      <td>26.06</td>\n",
              "      <td>27.44</td>\n",
              "      <td>25.88</td>\n",
              "      <td>26.31</td>\n",
              "      <td>165</td>\n",
              "      <td>254</td>\n",
              "      <td>275</td>\n",
              "      <td>72</td>\n",
              "      <td>...</td>\n",
              "      <td>0.48</td>\n",
              "      <td>870</td>\n",
              "      <td>0.623077</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26.4225</td>\n",
              "      <td>191.50</td>\n",
              "      <td>1</td>\n",
              "      <td>0.072464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>268</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:35:37</td>\n",
              "      <td>26.00</td>\n",
              "      <td>27.38</td>\n",
              "      <td>25.88</td>\n",
              "      <td>26.31</td>\n",
              "      <td>165</td>\n",
              "      <td>254</td>\n",
              "      <td>278</td>\n",
              "      <td>73</td>\n",
              "      <td>...</td>\n",
              "      <td>0.26</td>\n",
              "      <td>865</td>\n",
              "      <td>0.496154</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26.3925</td>\n",
              "      <td>192.50</td>\n",
              "      <td>1</td>\n",
              "      <td>0.072464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>269</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:36:08</td>\n",
              "      <td>26.06</td>\n",
              "      <td>27.38</td>\n",
              "      <td>25.94</td>\n",
              "      <td>26.25</td>\n",
              "      <td>165</td>\n",
              "      <td>255</td>\n",
              "      <td>278</td>\n",
              "      <td>72</td>\n",
              "      <td>...</td>\n",
              "      <td>0.45</td>\n",
              "      <td>860</td>\n",
              "      <td>0.269231</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26.4075</td>\n",
              "      <td>192.50</td>\n",
              "      <td>1</td>\n",
              "      <td>0.072464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:36:39</td>\n",
              "      <td>26.06</td>\n",
              "      <td>27.38</td>\n",
              "      <td>25.94</td>\n",
              "      <td>26.31</td>\n",
              "      <td>165</td>\n",
              "      <td>254</td>\n",
              "      <td>278</td>\n",
              "      <td>72</td>\n",
              "      <td>...</td>\n",
              "      <td>0.50</td>\n",
              "      <td>840</td>\n",
              "      <td>-0.096154</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26.4225</td>\n",
              "      <td>192.25</td>\n",
              "      <td>1</td>\n",
              "      <td>0.072464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:37:09</td>\n",
              "      <td>26.06</td>\n",
              "      <td>27.31</td>\n",
              "      <td>25.94</td>\n",
              "      <td>26.25</td>\n",
              "      <td>165</td>\n",
              "      <td>254</td>\n",
              "      <td>250</td>\n",
              "      <td>71</td>\n",
              "      <td>...</td>\n",
              "      <td>0.22</td>\n",
              "      <td>845</td>\n",
              "      <td>-0.400000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26.3900</td>\n",
              "      <td>185.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0.072464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>272</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:37:40</td>\n",
              "      <td>26.06</td>\n",
              "      <td>27.31</td>\n",
              "      <td>25.94</td>\n",
              "      <td>26.31</td>\n",
              "      <td>165</td>\n",
              "      <td>254</td>\n",
              "      <td>275</td>\n",
              "      <td>71</td>\n",
              "      <td>...</td>\n",
              "      <td>0.52</td>\n",
              "      <td>850</td>\n",
              "      <td>-0.646154</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26.4050</td>\n",
              "      <td>191.25</td>\n",
              "      <td>1</td>\n",
              "      <td>0.072464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:38:11</td>\n",
              "      <td>26.06</td>\n",
              "      <td>27.25</td>\n",
              "      <td>25.94</td>\n",
              "      <td>26.25</td>\n",
              "      <td>165</td>\n",
              "      <td>254</td>\n",
              "      <td>276</td>\n",
              "      <td>73</td>\n",
              "      <td>...</td>\n",
              "      <td>0.58</td>\n",
              "      <td>860</td>\n",
              "      <td>-0.792308</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26.3750</td>\n",
              "      <td>192.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0.072464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>274</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:38:41</td>\n",
              "      <td>26.06</td>\n",
              "      <td>27.31</td>\n",
              "      <td>25.88</td>\n",
              "      <td>26.31</td>\n",
              "      <td>164</td>\n",
              "      <td>247</td>\n",
              "      <td>279</td>\n",
              "      <td>72</td>\n",
              "      <td>...</td>\n",
              "      <td>0.79</td>\n",
              "      <td>865</td>\n",
              "      <td>-0.892308</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.3900</td>\n",
              "      <td>190.50</td>\n",
              "      <td>1</td>\n",
              "      <td>0.108696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>275</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:39:12</td>\n",
              "      <td>26.06</td>\n",
              "      <td>27.19</td>\n",
              "      <td>25.94</td>\n",
              "      <td>26.31</td>\n",
              "      <td>165</td>\n",
              "      <td>256</td>\n",
              "      <td>278</td>\n",
              "      <td>72</td>\n",
              "      <td>...</td>\n",
              "      <td>0.83</td>\n",
              "      <td>870</td>\n",
              "      <td>-0.900000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.3750</td>\n",
              "      <td>192.75</td>\n",
              "      <td>1</td>\n",
              "      <td>0.108696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>276</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:39:42</td>\n",
              "      <td>26.06</td>\n",
              "      <td>27.19</td>\n",
              "      <td>25.88</td>\n",
              "      <td>26.31</td>\n",
              "      <td>165</td>\n",
              "      <td>254</td>\n",
              "      <td>278</td>\n",
              "      <td>72</td>\n",
              "      <td>...</td>\n",
              "      <td>0.47</td>\n",
              "      <td>870</td>\n",
              "      <td>-0.911538</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.3600</td>\n",
              "      <td>192.25</td>\n",
              "      <td>1</td>\n",
              "      <td>0.108696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>277</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:40:13</td>\n",
              "      <td>26.06</td>\n",
              "      <td>27.25</td>\n",
              "      <td>25.94</td>\n",
              "      <td>26.25</td>\n",
              "      <td>165</td>\n",
              "      <td>254</td>\n",
              "      <td>276</td>\n",
              "      <td>72</td>\n",
              "      <td>...</td>\n",
              "      <td>0.55</td>\n",
              "      <td>885</td>\n",
              "      <td>-0.788462</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.3750</td>\n",
              "      <td>191.75</td>\n",
              "      <td>1</td>\n",
              "      <td>0.108696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>278</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:40:44</td>\n",
              "      <td>26.06</td>\n",
              "      <td>27.25</td>\n",
              "      <td>25.94</td>\n",
              "      <td>26.25</td>\n",
              "      <td>165</td>\n",
              "      <td>257</td>\n",
              "      <td>279</td>\n",
              "      <td>72</td>\n",
              "      <td>...</td>\n",
              "      <td>0.54</td>\n",
              "      <td>890</td>\n",
              "      <td>-0.534615</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26.3750</td>\n",
              "      <td>193.25</td>\n",
              "      <td>1</td>\n",
              "      <td>0.072464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>279</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>13:41:14</td>\n",
              "      <td>26.06</td>\n",
              "      <td>27.31</td>\n",
              "      <td>25.94</td>\n",
              "      <td>26.31</td>\n",
              "      <td>164</td>\n",
              "      <td>258</td>\n",
              "      <td>279</td>\n",
              "      <td>72</td>\n",
              "      <td>...</td>\n",
              "      <td>0.18</td>\n",
              "      <td>895</td>\n",
              "      <td>-0.196154</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26.4050</td>\n",
              "      <td>193.25</td>\n",
              "      <td>1</td>\n",
              "      <td>0.072464</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>30 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a70db155-48a0-46e2-bbf0-048c83bf50c9')\"\n",
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              "              title=\"Suggest charts.\"\n",
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              "\n",
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              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
              "      <script>\n",
              "\n",
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              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a70db155-48a0-46e2-bbf0-048c83bf50c9 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-a70db155-48a0-46e2-bbf0-048c83bf50c9');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "zm1bj_7hYcc2",
        "outputId": "752d394a-7733-4d39-d8f2-9cdee6c54980"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date      Time  S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  \\\n",
              "0  2017/12/22  10:49:41    24.94    24.75    24.56    25.38       121   \n",
              "1  2017/12/22  10:50:12    24.94    24.75    24.56    25.44       121   \n",
              "2  2017/12/22  10:50:42    25.00    24.75    24.50    25.44       121   \n",
              "3  2017/12/22  10:51:13    25.00    24.75    24.56    25.44       121   \n",
              "4  2017/12/22  10:51:44    25.00    24.75    24.56    25.44       121   \n",
              "\n",
              "   S2_Light  S3_Light  S4_Light  ...  S4_Sound  S5_CO2  S5_CO2_Slope  S6_PIR  \\\n",
              "0        34        53        40  ...      0.06     390      0.769231       0   \n",
              "1        33        53        40  ...      0.06     390      0.646154       0   \n",
              "2        34        53        40  ...      0.06     390      0.519231       0   \n",
              "3        34        53        40  ...      0.09     390      0.388462       0   \n",
              "4        34        54        40  ...      0.06     390      0.253846       0   \n",
              "\n",
              "   S7_PIR  Room_Occupancy_Count     temp  light  pir  room_occupancy_rate  \n",
              "0       0                     1  24.9075  62.00    0             0.036232  \n",
              "1       0                     1  24.9225  61.75    0             0.036232  \n",
              "2       0                     1  24.9225  62.00    0             0.036232  \n",
              "3       0                     1  24.9375  62.00    0             0.036232  \n",
              "4       0                     1  24.9375  62.25    0             0.036232  \n",
              "\n",
              "[5 rows x 23 columns]"
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              "      <th></th>\n",
              "      <th>Date</th>\n",
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              "      <td>0.519231</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>24.9225</td>\n",
              "      <td>62.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.036232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>10:51:13</td>\n",
              "      <td>25.00</td>\n",
              "      <td>24.75</td>\n",
              "      <td>24.56</td>\n",
              "      <td>25.44</td>\n",
              "      <td>121</td>\n",
              "      <td>34</td>\n",
              "      <td>53</td>\n",
              "      <td>40</td>\n",
              "      <td>...</td>\n",
              "      <td>0.09</td>\n",
              "      <td>390</td>\n",
              "      <td>0.388462</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>24.9375</td>\n",
              "      <td>62.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.036232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>10:51:44</td>\n",
              "      <td>25.00</td>\n",
              "      <td>24.75</td>\n",
              "      <td>24.56</td>\n",
              "      <td>25.44</td>\n",
              "      <td>121</td>\n",
              "      <td>34</td>\n",
              "      <td>54</td>\n",
              "      <td>40</td>\n",
              "      <td>...</td>\n",
              "      <td>0.06</td>\n",
              "      <td>390</td>\n",
              "      <td>0.253846</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>24.9375</td>\n",
              "      <td>62.25</td>\n",
              "      <td>0</td>\n",
              "      <td>0.036232</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ce9ea637-9351-44af-b9a6-8923a4bcd280')\"\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 0 24 24\"\n",
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              "  </svg>\n",
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              "\n",
              "\n",
              "\n",
              "    <div id=\"df-04873e45-5f0f-4de6-a8ac-ca35338c1733\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-04873e45-5f0f-4de6-a8ac-ca35338c1733')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    fill: #1967D2;\n",
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              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-04873e45-5f0f-4de6-a8ac-ca35338c1733 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-ce9ea637-9351-44af-b9a6-8923a4bcd280 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-ce9ea637-9351-44af-b9a6-8923a4bcd280');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['room_occupancy_rate'] = np.where(data['room_occupancy_rate'] > 0.08, 'high', (np.where(data['room_occupancy_rate']> 0.05, 'medium', (np.where(data['room_occupancy_rate']>0.009, 'low', 'none')))))\n",
        "\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "Tbek7XlsYg55",
        "outputId": "b9e8246a-b170-4b1f-b44d-b87f97f28731"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date      Time  S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  \\\n",
              "0  2017/12/22  10:49:41    24.94    24.75    24.56    25.38       121   \n",
              "1  2017/12/22  10:50:12    24.94    24.75    24.56    25.44       121   \n",
              "2  2017/12/22  10:50:42    25.00    24.75    24.50    25.44       121   \n",
              "3  2017/12/22  10:51:13    25.00    24.75    24.56    25.44       121   \n",
              "4  2017/12/22  10:51:44    25.00    24.75    24.56    25.44       121   \n",
              "\n",
              "   S2_Light  S3_Light  S4_Light  ...  S4_Sound  S5_CO2  S5_CO2_Slope  S6_PIR  \\\n",
              "0        34        53        40  ...      0.06     390      0.769231       0   \n",
              "1        33        53        40  ...      0.06     390      0.646154       0   \n",
              "2        34        53        40  ...      0.06     390      0.519231       0   \n",
              "3        34        53        40  ...      0.09     390      0.388462       0   \n",
              "4        34        54        40  ...      0.06     390      0.253846       0   \n",
              "\n",
              "   S7_PIR  Room_Occupancy_Count     temp  light  pir  room_occupancy_rate  \n",
              "0       0                     1  24.9075  62.00    0                  low  \n",
              "1       0                     1  24.9225  61.75    0                  low  \n",
              "2       0                     1  24.9225  62.00    0                  low  \n",
              "3       0                     1  24.9375  62.00    0                  low  \n",
              "4       0                     1  24.9375  62.25    0                  low  \n",
              "\n",
              "[5 rows x 23 columns]"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>S1_Temp</th>\n",
              "      <th>S2_Temp</th>\n",
              "      <th>S3_Temp</th>\n",
              "      <th>S4_Temp</th>\n",
              "      <th>S1_Light</th>\n",
              "      <th>S2_Light</th>\n",
              "      <th>S3_Light</th>\n",
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              "      <th>S5_CO2</th>\n",
              "      <th>S5_CO2_Slope</th>\n",
              "      <th>S6_PIR</th>\n",
              "      <th>S7_PIR</th>\n",
              "      <th>Room_Occupancy_Count</th>\n",
              "      <th>temp</th>\n",
              "      <th>light</th>\n",
              "      <th>pir</th>\n",
              "      <th>room_occupancy_rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>10:49:41</td>\n",
              "      <td>24.94</td>\n",
              "      <td>24.75</td>\n",
              "      <td>24.56</td>\n",
              "      <td>25.38</td>\n",
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              "      <td>53</td>\n",
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              "      <td>0</td>\n",
              "      <td>low</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>10:50:12</td>\n",
              "      <td>24.94</td>\n",
              "      <td>24.75</td>\n",
              "      <td>24.56</td>\n",
              "      <td>25.44</td>\n",
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              "      <td>53</td>\n",
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              "      <td>low</td>\n",
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              "    <tr>\n",
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              "      <td>10:50:42</td>\n",
              "      <td>25.00</td>\n",
              "      <td>24.75</td>\n",
              "      <td>24.50</td>\n",
              "      <td>25.44</td>\n",
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              "      <td>34</td>\n",
              "      <td>53</td>\n",
              "      <td>40</td>\n",
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              "      <td>0</td>\n",
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              "      <td>24.9225</td>\n",
              "      <td>62.00</td>\n",
              "      <td>0</td>\n",
              "      <td>low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>10:51:13</td>\n",
              "      <td>25.00</td>\n",
              "      <td>24.75</td>\n",
              "      <td>24.56</td>\n",
              "      <td>25.44</td>\n",
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              "      <td>34</td>\n",
              "      <td>53</td>\n",
              "      <td>40</td>\n",
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              "      <td>0.09</td>\n",
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              "      <td>62.00</td>\n",
              "      <td>0</td>\n",
              "      <td>low</td>\n",
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              "      <td>10:51:44</td>\n",
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              "      <td>54</td>\n",
              "      <td>40</td>\n",
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              "      <td>0</td>\n",
              "      <td>low</td>\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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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              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
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              "  quickchartButtonEl.style.display =\n",
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              "      display:flex;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8240485e-15aa-4366-a716-ba1d9e016f6e 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-8240485e-15aa-4366-a716-ba1d9e016f6e');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils import to_categorical\n",
        "data['room_occupancy_rate_int'] = np.where(data['room_occupancy_rate'] == 'high', 0, (np.where(data['room_occupancy_rate']== 'medium', 1, (np.where(data['room_occupancy_rate'] == 'low' ,2,3)))))\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "s85YcHxe4nCv",
        "outputId": "044d9b39-70b9-4819-c324-601930ae29dc"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date      Time  S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  \\\n",
              "0  2017/12/22  10:49:41    24.94    24.75    24.56    25.38       121   \n",
              "1  2017/12/22  10:50:12    24.94    24.75    24.56    25.44       121   \n",
              "2  2017/12/22  10:50:42    25.00    24.75    24.50    25.44       121   \n",
              "3  2017/12/22  10:51:13    25.00    24.75    24.56    25.44       121   \n",
              "4  2017/12/22  10:51:44    25.00    24.75    24.56    25.44       121   \n",
              "\n",
              "   S2_Light  S3_Light  S4_Light  ...  S5_CO2  S5_CO2_Slope  S6_PIR  S7_PIR  \\\n",
              "0        34        53        40  ...     390      0.769231       0       0   \n",
              "1        33        53        40  ...     390      0.646154       0       0   \n",
              "2        34        53        40  ...     390      0.519231       0       0   \n",
              "3        34        53        40  ...     390      0.388462       0       0   \n",
              "4        34        54        40  ...     390      0.253846       0       0   \n",
              "\n",
              "   Room_Occupancy_Count     temp  light  pir  room_occupancy_rate  \\\n",
              "0                     1  24.9075  62.00    0                  low   \n",
              "1                     1  24.9225  61.75    0                  low   \n",
              "2                     1  24.9225  62.00    0                  low   \n",
              "3                     1  24.9375  62.00    0                  low   \n",
              "4                     1  24.9375  62.25    0                  low   \n",
              "\n",
              "   room_occupancy_rate_int  \n",
              "0                        2  \n",
              "1                        2  \n",
              "2                        2  \n",
              "3                        2  \n",
              "4                        2  \n",
              "\n",
              "[5 rows x 24 columns]"
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              "      <td>2017/12/22</td>\n",
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              "      <td>10:51:13</td>\n",
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              "      </button>\n",
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              "\n",
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              "\n",
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              "}\n",
              "\n",
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              "\n",
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              "\n",
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              "      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-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-dde7182e-06b4-420d-a07d-d2e03009ee28 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-dde7182e-06b4-420d-a07d-d2e03009ee28');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temp = np.array(data['room_occupancy_rate_int'].values.reshape(1,-1)[0])\n",
        "print(temp)\n",
        "room_occupancy_rate_one_hot= to_categorical(temp)\n",
        "# print(np.array(data['room_occupancy_rate_int'].values))\n",
        "one_hot_encoded_df = pd.DataFrame(room_occupancy_rate_one_hot, columns=['high','medium','low','none'])\n",
        "print(one_hot_encoded_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "687q3LoI-xcx",
        "outputId": "85214203-2941-49d0-ae6c-9133a3ebb8ed"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2 2 2 ... 3 3 3]\n",
            "       high  medium  low  none\n",
            "0       0.0     0.0  1.0   0.0\n",
            "1       0.0     0.0  1.0   0.0\n",
            "2       0.0     0.0  1.0   0.0\n",
            "3       0.0     0.0  1.0   0.0\n",
            "4       0.0     0.0  1.0   0.0\n",
            "...     ...     ...  ...   ...\n",
            "10124   0.0     0.0  0.0   1.0\n",
            "10125   0.0     0.0  0.0   1.0\n",
            "10126   0.0     0.0  0.0   1.0\n",
            "10127   0.0     0.0  0.0   1.0\n",
            "10128   0.0     0.0  0.0   1.0\n",
            "\n",
            "[10129 rows x 4 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([data, one_hot_encoded_df], axis=1)\n",
        "df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "id": "duewj6okbtET",
        "outputId": "703d3625-9123-4e9b-8f8d-d385799b8833"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date      Time  S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  \\\n",
              "0  2017/12/22  10:49:41    24.94    24.75    24.56    25.38       121   \n",
              "1  2017/12/22  10:50:12    24.94    24.75    24.56    25.44       121   \n",
              "2  2017/12/22  10:50:42    25.00    24.75    24.50    25.44       121   \n",
              "3  2017/12/22  10:51:13    25.00    24.75    24.56    25.44       121   \n",
              "4  2017/12/22  10:51:44    25.00    24.75    24.56    25.44       121   \n",
              "5  2017/12/22  10:52:14    25.00    24.81    24.56    25.44       121   \n",
              "6  2017/12/22  10:52:45    25.00    24.75    24.56    25.44       120   \n",
              "7  2017/12/22  10:53:15    25.00    24.81    24.56    25.44       121   \n",
              "8  2017/12/22  10:53:46    25.00    24.81    24.56    25.50       122   \n",
              "9  2017/12/22  10:54:17    25.00    24.81    24.56    25.50       101   \n",
              "\n",
              "   S2_Light  S3_Light  S4_Light  ...  Room_Occupancy_Count     temp  light  \\\n",
              "0        34        53        40  ...                     1  24.9075  62.00   \n",
              "1        33        53        40  ...                     1  24.9225  61.75   \n",
              "2        34        53        40  ...                     1  24.9225  62.00   \n",
              "3        34        53        40  ...                     1  24.9375  62.00   \n",
              "4        34        54        40  ...                     1  24.9375  62.25   \n",
              "5        34        54        40  ...                     1  24.9525  62.25   \n",
              "6        34        54        40  ...                     1  24.9375  62.00   \n",
              "7        34        54        41  ...                     1  24.9525  62.50   \n",
              "8        35        56        43  ...                     1  24.9675  64.00   \n",
              "9        34        57        43  ...                     1  24.9675  58.75   \n",
              "\n",
              "   pir  room_occupancy_rate  room_occupancy_rate_int  high  medium  low  none  \n",
              "0    0                  low                        2   0.0     0.0  1.0   0.0  \n",
              "1    0                  low                        2   0.0     0.0  1.0   0.0  \n",
              "2    0                  low                        2   0.0     0.0  1.0   0.0  \n",
              "3    0                  low                        2   0.0     0.0  1.0   0.0  \n",
              "4    0                  low                        2   0.0     0.0  1.0   0.0  \n",
              "5    0                  low                        2   0.0     0.0  1.0   0.0  \n",
              "6    1                  low                        2   0.0     0.0  1.0   0.0  \n",
              "7    0                  low                        2   0.0     0.0  1.0   0.0  \n",
              "8    0                  low                        2   0.0     0.0  1.0   0.0  \n",
              "9    1                  low                        2   0.0     0.0  1.0   0.0  \n",
              "\n",
              "[10 rows x 28 columns]"
            ],
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>S1_Temp</th>\n",
              "      <th>S2_Temp</th>\n",
              "      <th>S3_Temp</th>\n",
              "      <th>S4_Temp</th>\n",
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              "      <th>S2_Light</th>\n",
              "      <th>S3_Light</th>\n",
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              "      <th>Room_Occupancy_Count</th>\n",
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              "      <td>2017/12/22</td>\n",
              "      <td>10:49:41</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>10:50:12</td>\n",
              "      <td>24.94</td>\n",
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              "      <td>24.56</td>\n",
              "      <td>25.44</td>\n",
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              "      <td>53</td>\n",
              "      <td>40</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>24.9225</td>\n",
              "      <td>61.75</td>\n",
              "      <td>0</td>\n",
              "      <td>low</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>10:50:42</td>\n",
              "      <td>25.00</td>\n",
              "      <td>24.75</td>\n",
              "      <td>24.50</td>\n",
              "      <td>25.44</td>\n",
              "      <td>121</td>\n",
              "      <td>34</td>\n",
              "      <td>53</td>\n",
              "      <td>40</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>24.9225</td>\n",
              "      <td>62.00</td>\n",
              "      <td>0</td>\n",
              "      <td>low</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>10:51:13</td>\n",
              "      <td>25.00</td>\n",
              "      <td>24.75</td>\n",
              "      <td>24.56</td>\n",
              "      <td>25.44</td>\n",
              "      <td>121</td>\n",
              "      <td>34</td>\n",
              "      <td>53</td>\n",
              "      <td>40</td>\n",
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              "      <td>24.9375</td>\n",
              "      <td>62.00</td>\n",
              "      <td>0</td>\n",
              "      <td>low</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>10:51:44</td>\n",
              "      <td>25.00</td>\n",
              "      <td>24.75</td>\n",
              "      <td>24.56</td>\n",
              "      <td>25.44</td>\n",
              "      <td>121</td>\n",
              "      <td>34</td>\n",
              "      <td>54</td>\n",
              "      <td>40</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>24.9375</td>\n",
              "      <td>62.25</td>\n",
              "      <td>0</td>\n",
              "      <td>low</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>10:52:14</td>\n",
              "      <td>25.00</td>\n",
              "      <td>24.81</td>\n",
              "      <td>24.56</td>\n",
              "      <td>25.44</td>\n",
              "      <td>121</td>\n",
              "      <td>34</td>\n",
              "      <td>54</td>\n",
              "      <td>40</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>24.9525</td>\n",
              "      <td>62.25</td>\n",
              "      <td>0</td>\n",
              "      <td>low</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>10:52:45</td>\n",
              "      <td>25.00</td>\n",
              "      <td>24.75</td>\n",
              "      <td>24.56</td>\n",
              "      <td>25.44</td>\n",
              "      <td>120</td>\n",
              "      <td>34</td>\n",
              "      <td>54</td>\n",
              "      <td>40</td>\n",
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              "      <td>24.9375</td>\n",
              "      <td>62.00</td>\n",
              "      <td>1</td>\n",
              "      <td>low</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>10:53:15</td>\n",
              "      <td>25.00</td>\n",
              "      <td>24.81</td>\n",
              "      <td>24.56</td>\n",
              "      <td>25.44</td>\n",
              "      <td>121</td>\n",
              "      <td>34</td>\n",
              "      <td>54</td>\n",
              "      <td>41</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>24.9525</td>\n",
              "      <td>62.50</td>\n",
              "      <td>0</td>\n",
              "      <td>low</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>10:53:46</td>\n",
              "      <td>25.00</td>\n",
              "      <td>24.81</td>\n",
              "      <td>24.56</td>\n",
              "      <td>25.50</td>\n",
              "      <td>122</td>\n",
              "      <td>35</td>\n",
              "      <td>56</td>\n",
              "      <td>43</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>24.9675</td>\n",
              "      <td>64.00</td>\n",
              "      <td>0</td>\n",
              "      <td>low</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2017/12/22</td>\n",
              "      <td>10:54:17</td>\n",
              "      <td>25.00</td>\n",
              "      <td>24.81</td>\n",
              "      <td>24.56</td>\n",
              "      <td>25.50</td>\n",
              "      <td>101</td>\n",
              "      <td>34</td>\n",
              "      <td>57</td>\n",
              "      <td>43</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>24.9675</td>\n",
              "      <td>58.75</td>\n",
              "      <td>1</td>\n",
              "      <td>low</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "  </tbody>\n",
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              "<p>10 rows × 28 columns</p>\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "cols_to_use = ['temp', 'light','S5_CO2','pir','high', 'medium', 'low','none']\n",
        "\n",
        "df = df[cols_to_use]\n",
        "\n",
        "df.head()"
      ],
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        "id": "ftTvAFanvb_b",
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      "execution_count": 26,
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              "      temp  light  S5_CO2  pir  high  medium  low  none\n",
              "0  24.9075  62.00     390    0   0.0     0.0  1.0   0.0\n",
              "1  24.9225  61.75     390    0   0.0     0.0  1.0   0.0\n",
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          "metadata": {},
          "execution_count": 26
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    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
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        "id": "fRTxek8e1RDX",
        "outputId": "330a56a9-8c47-407d-b493-35218cfa7208"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10129 entries, 0 to 10128\n",
            "Data columns (total 24 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   Date                     10129 non-null  object \n",
            " 1   Time                     10129 non-null  object \n",
            " 2   S1_Temp                  10129 non-null  float64\n",
            " 3   S2_Temp                  10129 non-null  float64\n",
            " 4   S3_Temp                  10129 non-null  float64\n",
            " 5   S4_Temp                  10129 non-null  float64\n",
            " 6   S1_Light                 10129 non-null  int64  \n",
            " 7   S2_Light                 10129 non-null  int64  \n",
            " 8   S3_Light                 10129 non-null  int64  \n",
            " 9   S4_Light                 10129 non-null  int64  \n",
            " 10  S1_Sound                 10129 non-null  float64\n",
            " 11  S2_Sound                 10129 non-null  float64\n",
            " 12  S3_Sound                 10129 non-null  float64\n",
            " 13  S4_Sound                 10129 non-null  float64\n",
            " 14  S5_CO2                   10129 non-null  int64  \n",
            " 15  S5_CO2_Slope             10129 non-null  float64\n",
            " 16  S6_PIR                   10129 non-null  int64  \n",
            " 17  S7_PIR                   10129 non-null  int64  \n",
            " 18  Room_Occupancy_Count     10129 non-null  int64  \n",
            " 19  temp                     10129 non-null  float64\n",
            " 20  light                    10129 non-null  float64\n",
            " 21  pir                      10129 non-null  int64  \n",
            " 22  room_occupancy_rate      10129 non-null  object \n",
            " 23  room_occupancy_rate_int  10129 non-null  int64  \n",
            "dtypes: float64(11), int64(10), object(3)\n",
            "memory usage: 1.9+ MB\n"
          ]
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      "source": [
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      "metadata": {
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      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10129 entries, 0 to 10128\n",
            "Data columns (total 8 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   temp    10129 non-null  float64\n",
            " 1   light   10129 non-null  float64\n",
            " 2   S5_CO2  10129 non-null  int64  \n",
            " 3   pir     10129 non-null  int64  \n",
            " 4   high    10129 non-null  float32\n",
            " 5   medium  10129 non-null  float32\n",
            " 6   low     10129 non-null  float32\n",
            " 7   none    10129 non-null  float32\n",
            "dtypes: float32(4), float64(2), int64(2)\n",
            "memory usage: 474.9 KB\n"
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    {
      "cell_type": "code",
      "source": [
        "df['co2'] = df['S5_CO2']\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "v90j_okE1fOs",
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      "execution_count": 29,
      "outputs": [
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              "      temp  light  S5_CO2  pir  high  medium  low  none  co2\n",
              "0  24.9075  62.00     390    0   0.0     0.0  1.0   0.0  390\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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              "\n",
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              "    <div id=\"df-ad778367-e290-479a-b0ca-879409aec1ce\">\n",
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              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-ad778367-e290-479a-b0ca-879409aec1ce button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-33261fa5-60df-48c0-831b-687c7fedc144 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-33261fa5-60df-48c0-831b-687c7fedc144');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "N6OC5ytm4l2R",
        "outputId": "36aec10b-bb93-44f6-bc8a-f1a9c210033f"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      temp  light  S5_CO2  pir  high  medium  low  none  co2\n",
              "0  24.9075  62.00     390    0   0.0     0.0  1.0   0.0  390\n",
              "1  24.9225  61.75     390    0   0.0     0.0  1.0   0.0  390\n",
              "2  24.9225  62.00     390    0   0.0     0.0  1.0   0.0  390\n",
              "3  24.9375  62.00     390    0   0.0     0.0  1.0   0.0  390\n",
              "4  24.9375  62.25     390    0   0.0     0.0  1.0   0.0  390"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-c1472c37-12cc-4737-99e5-45cafbdb8293\">\n",
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              "      <th></th>\n",
              "      <th>temp</th>\n",
              "      <th>light</th>\n",
              "      <th>S5_CO2</th>\n",
              "      <th>pir</th>\n",
              "      <th>high</th>\n",
              "      <th>medium</th>\n",
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              "      <th>0</th>\n",
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              "      <td>390</td>\n",
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              "      <td>390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>24.9225</td>\n",
              "      <td>61.75</td>\n",
              "      <td>390</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>390</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>24.9225</td>\n",
              "      <td>62.00</td>\n",
              "      <td>390</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>24.9375</td>\n",
              "      <td>62.00</td>\n",
              "      <td>390</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>390</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24.9375</td>\n",
              "      <td>62.25</td>\n",
              "      <td>390</td>\n",
              "      <td>0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>390</td>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-fcca8994-5e94-471a-8f50-c77dfc7d9eed button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-c1472c37-12cc-4737-99e5-45cafbdb8293 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-c1472c37-12cc-4737-99e5-45cafbdb8293');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Y = df['high','medium','low']\n",
        "X = df.drop(['high','medium','low','none'], axis=1)\n"
      ],
      "metadata": {
        "id": "SY4AoTC34rsT"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y = df.drop(['temp','light','pir','co2','S5_CO2'], axis=1)"
      ],
      "metadata": {
        "id": "lf9nn09a8RjN"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "VYdYru5B8bPY",
        "outputId": "48416174-2462-4b41-858d-4c007c114deb"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               temp         light           pir           co2\n",
              "count  10129.000000  10129.000000  10129.000000  10129.000000\n",
              "mean      25.452704     24.732525      0.118274    460.860401\n",
              "std        0.399201     43.811460      0.322949    199.964940\n",
              "min       24.890000      0.000000      0.000000    345.000000\n",
              "25%       25.127500      0.000000      0.000000    355.000000\n",
              "50%       25.362500      0.000000      0.000000    360.000000\n",
              "75%       25.612500     30.500000      0.000000    465.000000\n",
              "max       26.800000    193.750000      1.000000   1270.000000"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-fec43134-781f-4979-ad6f-d54747cdbb5c\">\n",
              "    <div 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",
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              "\n",
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              "        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>temp</th>\n",
              "      <th>light</th>\n",
              "      <th>pir</th>\n",
              "      <th>co2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10129.000000</td>\n",
              "      <td>10129.000000</td>\n",
              "      <td>10129.000000</td>\n",
              "      <td>10129.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>25.452704</td>\n",
              "      <td>24.732525</td>\n",
              "      <td>0.118274</td>\n",
              "      <td>460.860401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.399201</td>\n",
              "      <td>43.811460</td>\n",
              "      <td>0.322949</td>\n",
              "      <td>199.964940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>24.890000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>345.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>25.127500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>355.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>25.362500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>360.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>25.612500</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>465.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>26.800000</td>\n",
              "      <td>193.750000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1270.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fec43134-781f-4979-ad6f-d54747cdbb5c')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-21d86385-3019-4d84-98bb-0b6d2b083344\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-21d86385-3019-4d84-98bb-0b6d2b083344')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-21d86385-3019-4d84-98bb-0b6d2b083344 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-fec43134-781f-4979-ad6f-d54747cdbb5c 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-fec43134-781f-4979-ad6f-d54747cdbb5c');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "TKb38EO38ikA",
        "outputId": "3bf905eb-7136-459b-daf5-47fb55ac136f"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               high        medium           low          none\n",
              "count  10129.000000  10129.000000  10129.000000  10129.000000\n",
              "mean       0.068516      0.073847      0.045315      0.812321\n",
              "std        0.252642      0.261535      0.208005      0.390475\n",
              "min        0.000000      0.000000      0.000000      0.000000\n",
              "25%        0.000000      0.000000      0.000000      1.000000\n",
              "50%        0.000000      0.000000      0.000000      1.000000\n",
              "75%        0.000000      0.000000      0.000000      1.000000\n",
              "max        1.000000      1.000000      1.000000      1.000000"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-49a50c65-d504-4562-9e2a-8a94b62a49f9\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "      <th></th>\n",
              "      <th>high</th>\n",
              "      <th>medium</th>\n",
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              "      <th>none</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10129.000000</td>\n",
              "      <td>10129.000000</td>\n",
              "      <td>10129.000000</td>\n",
              "      <td>10129.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.068516</td>\n",
              "      <td>0.073847</td>\n",
              "      <td>0.045315</td>\n",
              "      <td>0.812321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.252642</td>\n",
              "      <td>0.261535</td>\n",
              "      <td>0.208005</td>\n",
              "      <td>0.390475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-49a50c65-d504-4562-9e2a-8a94b62a49f9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </g>\n",
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              "\n",
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              "    display: none;\n",
              "    fill: #1967D2;\n",
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              "\n",
              "  .colab-df-quickchart: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",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
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              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
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              "    domScope.querySelector('#df-f5177959-ebc3-4d58-bafc-0919d6595cce button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\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-49a50c65-d504-4562-9e2a-8a94b62a49f9 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-49a50c65-d504-4562-9e2a-8a94b62a49f9');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y.iloc[1000:1100]"
      ],
      "metadata": {
        "id": "ovZCF7-48rQG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.options.display.max_rows = 100\n",
        "df"
      ],
      "metadata": {
        "id": "ELDqH_HG-FAj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QyxBwsxDJYcR"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y.head()"
      ],
      "metadata": {
        "id": "LtpFqYfpJbmt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(X['temp'], bins=20, edgecolor='black')\n",
        "\n",
        "plt.xlabel('Value')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Temperature Distribution')\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "TE79eHprK6eg",
        "outputId": "638c8956-4b56-4f43-ce2a-297a40cbda20"
      },
      "execution_count": 58,
      "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": [
        "plt.hist(X['light'], bins=20, edgecolor='black')\n",
        "\n",
        "plt.xlabel('Value')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Light Intensity Distribution')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "H1-FsLPtMNoK",
        "outputId": "ec6afb10-c324-4486-a385-d04d815da6a5"
      },
      "execution_count": 47,
      "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": [
        "plt.hist(X['co2'], bins=20, edgecolor='black')\n",
        "\n",
        "plt.xlabel('Value')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('CO2 Distribution')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "eJT4SmUyMdv8",
        "outputId": "5732e6ee-bf5c-40be-d799-80949f97d911"
      },
      "execution_count": 48,
      "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": [
        "plt.figure(figsize = (10,10))\n",
        "sns.clustermap(df.corr(), annot = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TXWwokfPMsVK",
        "outputId": "4f78d6ed-a783-4877-ecdd-eab7599026e2"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.matrix.ClusterGrid at 0x785c0d561900>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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=42)"
      ],
      "metadata": {
        "id": "te73UrcHV2CO"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential([\n",
        "                    Dense(200, activation = \"swish\", input_shape = (X.shape[1],)),\n",
        "                    Dropout(0.5),\n",
        "                    Dense(100, activation = \"swish\"),\n",
        "                    Dropout(0.5),\n",
        "                    Dense(50,activation = \"swish\"),\n",
        "                    Dropout(0.5),\n",
        "                    Dense(4, activation = \"softmax\")\n",
        "])\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MKmNSEvtWMey",
        "outputId": "40a83374-fee9-48a5-d5a3-2b7cf13360c4"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_12 (Dense)            (None, 200)               1200      \n",
            "                                                                 \n",
            " dropout_9 (Dropout)         (None, 200)               0         \n",
            "                                                                 \n",
            " dense_13 (Dense)            (None, 100)               20100     \n",
            "                                                                 \n",
            " dropout_10 (Dropout)        (None, 100)               0         \n",
            "                                                                 \n",
            " dense_14 (Dense)            (None, 50)                5050      \n",
            "                                                                 \n",
            " dropout_11 (Dropout)        (None, 50)                0         \n",
            "                                                                 \n",
            " dense_15 (Dense)            (None, 4)                 204       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 26,554\n",
            "Trainable params: 26,554\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer = \"adam\",\n",
        "              loss = \"categorical_crossentropy\",\n",
        "              metrics = AUC())"
      ],
      "metadata": {
        "id": "Z-DhMa09bGxj"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y9_RLclebKm0",
        "outputId": "d117bda4-4340-44b2-d2b6-f668d22b4970"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "254/254 [==============================] - 3s 8ms/step - loss: 12.6776 - auc_3: 0.7607 - val_loss: 0.8082 - val_auc_3: 0.9304\n",
            "Epoch 2/10\n",
            "254/254 [==============================] - 1s 4ms/step - loss: 1.3411 - auc_3: 0.8499 - val_loss: 0.8414 - val_auc_3: 0.8408\n",
            "Epoch 3/10\n",
            "254/254 [==============================] - 1s 3ms/step - loss: 0.8103 - auc_3: 0.8894 - val_loss: 0.7305 - val_auc_3: 0.8761\n",
            "Epoch 4/10\n",
            "254/254 [==============================] - 1s 4ms/step - loss: 0.7214 - auc_3: 0.8927 - val_loss: 0.7193 - val_auc_3: 0.8741\n",
            "Epoch 5/10\n",
            "254/254 [==============================] - 1s 4ms/step - loss: 0.6994 - auc_3: 0.8925 - val_loss: 0.7191 - val_auc_3: 0.8761\n",
            "Epoch 6/10\n",
            "254/254 [==============================] - 1s 4ms/step - loss: 0.6793 - auc_3: 0.8960 - val_loss: 0.7059 - val_auc_3: 0.8825\n",
            "Epoch 7/10\n",
            "254/254 [==============================] - 1s 4ms/step - loss: 0.6802 - auc_3: 0.8986 - val_loss: 0.6684 - val_auc_3: 0.8913\n",
            "Epoch 8/10\n",
            "254/254 [==============================] - 1s 4ms/step - loss: 0.6472 - auc_3: 0.9097 - val_loss: 0.5899 - val_auc_3: 0.9509\n",
            "Epoch 9/10\n",
            "254/254 [==============================] - 1s 4ms/step - loss: 0.6191 - auc_3: 0.9218 - val_loss: 0.4320 - val_auc_3: 0.9730\n",
            "Epoch 10/10\n",
            "254/254 [==============================] - 1s 4ms/step - loss: 0.5431 - auc_3: 0.9457 - val_loss: 0.3735 - val_auc_3: 0.9778\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Jo-mGbscn07",
        "outputId": "5ee18638-05f5-4c11-f284-4a9aceed4813"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x785c052b2530>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "J5P7_Y68dHBU",
        "outputId": "956606a1-a7ca-498c-fc8f-27d0e4b964a4"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      temp  light  S5_CO2  pir  co2\n",
              "0  24.9075  62.00     390    0  390\n",
              "1  24.9225  61.75     390    0  390\n",
              "2  24.9225  62.00     390    0  390\n",
              "3  24.9375  62.00     390    0  390\n",
              "4  24.9375  62.25     390    0  390"
            ],
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              "\n",
              "\n",
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              "      <th>0</th>\n",
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              "      <td>62.00</td>\n",
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              "              title=\"Suggest charts.\"\n",
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              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "\n",
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              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
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              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
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              "\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2d567091-f4ac-4c21-9971-ac730d265e15 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-2d567091-f4ac-4c21-9971-ac730d265e15');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction = model.predict([[24, 70, 500, 1, 500]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AwvRMGvVkMrN",
        "outputId": "0c24df60-7030-46eb-8aaa-f539a53d859a"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 41ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3W_jydp8mQaO",
        "outputId": "ab431e81-20f2-4556-cea1-89f96b3bc993"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.16488461 0.24064535 0.1501614  0.4443086 ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "with open('model_pkl', 'wb') as files:\n",
        "    pickle.dump(model, files)"
      ],
      "metadata": {
        "id": "vbJ0LidPpBz5"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "files.download('model_pkl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "MnwTwot8tE9j",
        "outputId": "2b818f97-c535-4059-ec0d-e54c4a2f3e50"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_50a045bd-93eb-42c4-9e56-793020dd1819\", \"model_pkl\", 358259)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}