18: Multivariate_Time_Series_Modeling_LSTM.ipynb

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      "name": "Multivariate_Time_Series_Modeling_LSTM.ipynb",
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        "<a href=\"https://colab.research.google.com/github/gulabpatel/TimeSeries/blob/main/18%3A%20Multivariate_Time_Series_Modeling_LSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "Code walkthorugh video: https://www.youtube.com/watch?v=4FmVIpcwl4k"
      ],
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      }
    },
    {
      "cell_type": "code",
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        "id": "_GY3HgVB7T8D"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.preprocessing.sequence import TimeseriesGenerator\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
        "import tensorflow as tf\n",
        "\n",
        "mpl.rcParams['figure.figsize'] = (15, 15)\n",
        "mpl.rcParams['axes.grid'] = False"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lmNbWyCJ7n0a"
      },
      "source": [
        "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/00374/energydata_complete.csv')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
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          "base_uri": "https://localhost:8080/",
          "height": 835
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                      date  Appliances  lights         T1       RH_1  \\\n",
              "0      2016-01-11 17:00:00          60      30  19.890000  47.596667   \n",
              "1      2016-01-11 17:10:00          60      30  19.890000  46.693333   \n",
              "2      2016-01-11 17:20:00          50      30  19.890000  46.300000   \n",
              "3      2016-01-11 17:30:00          50      40  19.890000  46.066667   \n",
              "4      2016-01-11 17:40:00          60      40  19.890000  46.333333   \n",
              "...                    ...         ...     ...        ...        ...   \n",
              "19730  2016-05-27 17:20:00         100       0  25.566667  46.560000   \n",
              "19731  2016-05-27 17:30:00          90       0  25.500000  46.500000   \n",
              "19732  2016-05-27 17:40:00         270      10  25.500000  46.596667   \n",
              "19733  2016-05-27 17:50:00         420      10  25.500000  46.990000   \n",
              "19734  2016-05-27 18:00:00         430      10  25.500000  46.600000   \n",
              "\n",
              "              T2       RH_2         T3       RH_3         T4  ...         T9  \\\n",
              "0      19.200000  44.790000  19.790000  44.730000  19.000000  ...  17.033333   \n",
              "1      19.200000  44.722500  19.790000  44.790000  19.000000  ...  17.066667   \n",
              "2      19.200000  44.626667  19.790000  44.933333  18.926667  ...  17.000000   \n",
              "3      19.200000  44.590000  19.790000  45.000000  18.890000  ...  17.000000   \n",
              "4      19.200000  44.530000  19.790000  45.000000  18.890000  ...  17.000000   \n",
              "...          ...        ...        ...        ...        ...  ...        ...   \n",
              "19730  25.890000  42.025714  27.200000  41.163333  24.700000  ...  23.200000   \n",
              "19731  25.754000  42.080000  27.133333  41.223333  24.700000  ...  23.200000   \n",
              "19732  25.628571  42.768571  27.050000  41.690000  24.700000  ...  23.200000   \n",
              "19733  25.414000  43.036000  26.890000  41.290000  24.700000  ...  23.200000   \n",
              "19734  25.264286  42.971429  26.823333  41.156667  24.700000  ...  23.200000   \n",
              "\n",
              "          RH_9      T_out  Press_mm_hg     RH_out  Windspeed  Visibility  \\\n",
              "0      45.5300   6.600000        733.5  92.000000   7.000000   63.000000   \n",
              "1      45.5600   6.483333        733.6  92.000000   6.666667   59.166667   \n",
              "2      45.5000   6.366667        733.7  92.000000   6.333333   55.333333   \n",
              "3      45.4000   6.250000        733.8  92.000000   6.000000   51.500000   \n",
              "4      45.4000   6.133333        733.9  92.000000   5.666667   47.666667   \n",
              "...        ...        ...          ...        ...        ...         ...   \n",
              "19730  46.7900  22.733333        755.2  55.666667   3.333333   23.666667   \n",
              "19731  46.7900  22.600000        755.2  56.000000   3.500000   24.500000   \n",
              "19732  46.7900  22.466667        755.2  56.333333   3.666667   25.333333   \n",
              "19733  46.8175  22.333333        755.2  56.666667   3.833333   26.166667   \n",
              "19734  46.8450  22.200000        755.2  57.000000   4.000000   27.000000   \n",
              "\n",
              "       Tdewpoint        rv1        rv2  \n",
              "0       5.300000  13.275433  13.275433  \n",
              "1       5.200000  18.606195  18.606195  \n",
              "2       5.100000  28.642668  28.642668  \n",
              "3       5.000000  45.410389  45.410389  \n",
              "4       4.900000  10.084097  10.084097  \n",
              "...          ...        ...        ...  \n",
              "19730  13.333333  43.096812  43.096812  \n",
              "19731  13.300000  49.282940  49.282940  \n",
              "19732  13.266667  29.199117  29.199117  \n",
              "19733  13.233333   6.322784   6.322784  \n",
              "19734  13.200000  34.118851  34.118851  \n",
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>Appliances</th>\n",
              "      <th>lights</th>\n",
              "      <th>T1</th>\n",
              "      <th>RH_1</th>\n",
              "      <th>T2</th>\n",
              "      <th>RH_2</th>\n",
              "      <th>T3</th>\n",
              "      <th>RH_3</th>\n",
              "      <th>T4</th>\n",
              "      <th>...</th>\n",
              "      <th>T9</th>\n",
              "      <th>RH_9</th>\n",
              "      <th>T_out</th>\n",
              "      <th>Press_mm_hg</th>\n",
              "      <th>RH_out</th>\n",
              "      <th>Windspeed</th>\n",
              "      <th>Visibility</th>\n",
              "      <th>Tdewpoint</th>\n",
              "      <th>rv1</th>\n",
              "      <th>rv2</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-01-11 17:00:00</td>\n",
              "      <td>60</td>\n",
              "      <td>30</td>\n",
              "      <td>19.890000</td>\n",
              "      <td>47.596667</td>\n",
              "      <td>19.200000</td>\n",
              "      <td>44.790000</td>\n",
              "      <td>19.790000</td>\n",
              "      <td>44.730000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>17.033333</td>\n",
              "      <td>45.5300</td>\n",
              "      <td>6.600000</td>\n",
              "      <td>733.5</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>5.300000</td>\n",
              "      <td>13.275433</td>\n",
              "      <td>13.275433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-01-11 17:10:00</td>\n",
              "      <td>60</td>\n",
              "      <td>30</td>\n",
              "      <td>19.890000</td>\n",
              "      <td>46.693333</td>\n",
              "      <td>19.200000</td>\n",
              "      <td>44.722500</td>\n",
              "      <td>19.790000</td>\n",
              "      <td>44.790000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>17.066667</td>\n",
              "      <td>45.5600</td>\n",
              "      <td>6.483333</td>\n",
              "      <td>733.6</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>6.666667</td>\n",
              "      <td>59.166667</td>\n",
              "      <td>5.200000</td>\n",
              "      <td>18.606195</td>\n",
              "      <td>18.606195</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-01-11 17:20:00</td>\n",
              "      <td>50</td>\n",
              "      <td>30</td>\n",
              "      <td>19.890000</td>\n",
              "      <td>46.300000</td>\n",
              "      <td>19.200000</td>\n",
              "      <td>44.626667</td>\n",
              "      <td>19.790000</td>\n",
              "      <td>44.933333</td>\n",
              "      <td>18.926667</td>\n",
              "      <td>...</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>45.5000</td>\n",
              "      <td>6.366667</td>\n",
              "      <td>733.7</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>6.333333</td>\n",
              "      <td>55.333333</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>28.642668</td>\n",
              "      <td>28.642668</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-01-11 17:30:00</td>\n",
              "      <td>50</td>\n",
              "      <td>40</td>\n",
              "      <td>19.890000</td>\n",
              "      <td>46.066667</td>\n",
              "      <td>19.200000</td>\n",
              "      <td>44.590000</td>\n",
              "      <td>19.790000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>18.890000</td>\n",
              "      <td>...</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>45.4000</td>\n",
              "      <td>6.250000</td>\n",
              "      <td>733.8</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>51.500000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>45.410389</td>\n",
              "      <td>45.410389</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-01-11 17:40:00</td>\n",
              "      <td>60</td>\n",
              "      <td>40</td>\n",
              "      <td>19.890000</td>\n",
              "      <td>46.333333</td>\n",
              "      <td>19.200000</td>\n",
              "      <td>44.530000</td>\n",
              "      <td>19.790000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>18.890000</td>\n",
              "      <td>...</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>45.4000</td>\n",
              "      <td>6.133333</td>\n",
              "      <td>733.9</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>47.666667</td>\n",
              "      <td>4.900000</td>\n",
              "      <td>10.084097</td>\n",
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              "      <td>...</td>\n",
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              "      <th>19730</th>\n",
              "      <td>2016-05-27 17:20:00</td>\n",
              "      <td>100</td>\n",
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              "      <td>25.566667</td>\n",
              "      <td>46.560000</td>\n",
              "      <td>25.890000</td>\n",
              "      <td>42.025714</td>\n",
              "      <td>27.200000</td>\n",
              "      <td>41.163333</td>\n",
              "      <td>24.700000</td>\n",
              "      <td>...</td>\n",
              "      <td>23.200000</td>\n",
              "      <td>46.7900</td>\n",
              "      <td>22.733333</td>\n",
              "      <td>755.2</td>\n",
              "      <td>55.666667</td>\n",
              "      <td>3.333333</td>\n",
              "      <td>23.666667</td>\n",
              "      <td>13.333333</td>\n",
              "      <td>43.096812</td>\n",
              "      <td>43.096812</td>\n",
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              "    <tr>\n",
              "      <th>19731</th>\n",
              "      <td>2016-05-27 17:30:00</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>25.500000</td>\n",
              "      <td>46.500000</td>\n",
              "      <td>25.754000</td>\n",
              "      <td>42.080000</td>\n",
              "      <td>27.133333</td>\n",
              "      <td>41.223333</td>\n",
              "      <td>24.700000</td>\n",
              "      <td>...</td>\n",
              "      <td>23.200000</td>\n",
              "      <td>46.7900</td>\n",
              "      <td>22.600000</td>\n",
              "      <td>755.2</td>\n",
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              "      <td>3.500000</td>\n",
              "      <td>24.500000</td>\n",
              "      <td>13.300000</td>\n",
              "      <td>49.282940</td>\n",
              "      <td>49.282940</td>\n",
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              "    <tr>\n",
              "      <th>19732</th>\n",
              "      <td>2016-05-27 17:40:00</td>\n",
              "      <td>270</td>\n",
              "      <td>10</td>\n",
              "      <td>25.500000</td>\n",
              "      <td>46.596667</td>\n",
              "      <td>25.628571</td>\n",
              "      <td>42.768571</td>\n",
              "      <td>27.050000</td>\n",
              "      <td>41.690000</td>\n",
              "      <td>24.700000</td>\n",
              "      <td>...</td>\n",
              "      <td>23.200000</td>\n",
              "      <td>46.7900</td>\n",
              "      <td>22.466667</td>\n",
              "      <td>755.2</td>\n",
              "      <td>56.333333</td>\n",
              "      <td>3.666667</td>\n",
              "      <td>25.333333</td>\n",
              "      <td>13.266667</td>\n",
              "      <td>29.199117</td>\n",
              "      <td>29.199117</td>\n",
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              "      <th>19733</th>\n",
              "      <td>2016-05-27 17:50:00</td>\n",
              "      <td>420</td>\n",
              "      <td>10</td>\n",
              "      <td>25.500000</td>\n",
              "      <td>46.990000</td>\n",
              "      <td>25.414000</td>\n",
              "      <td>43.036000</td>\n",
              "      <td>26.890000</td>\n",
              "      <td>41.290000</td>\n",
              "      <td>24.700000</td>\n",
              "      <td>...</td>\n",
              "      <td>23.200000</td>\n",
              "      <td>46.8175</td>\n",
              "      <td>22.333333</td>\n",
              "      <td>755.2</td>\n",
              "      <td>56.666667</td>\n",
              "      <td>3.833333</td>\n",
              "      <td>26.166667</td>\n",
              "      <td>13.233333</td>\n",
              "      <td>6.322784</td>\n",
              "      <td>6.322784</td>\n",
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              "      <th>19734</th>\n",
              "      <td>2016-05-27 18:00:00</td>\n",
              "      <td>430</td>\n",
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              "      <td>22.200000</td>\n",
              "      <td>755.2</td>\n",
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              "      <td>4.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>13.200000</td>\n",
              "      <td>34.118851</td>\n",
              "      <td>34.118851</td>\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",
              "      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-efb8b0de-81be-4119-a3dd-5c588180b334 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-efb8b0de-81be-4119-a3dd-5c588180b334');\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": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3xYLENkY8Pdv"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "Attribute Information:\n",
        "\n",
        "date time year-month-day hour:minute:second\n",
        "Appliances, energy use in Wh\n",
        "lights, energy use of light fixtures in the house in Wh\n",
        "T1, Temperature in kitchen area, in Celsius\n",
        "RH_1, Humidity in kitchen area, in %\n",
        "T2, Temperature in living room area, in Celsius\n",
        "RH_2, Humidity in living room area, in %\n",
        "T3, Temperature in laundry room area\n",
        "RH_3, Humidity in laundry room area, in %\n",
        "T4, Temperature in office room, in Celsius\n",
        "RH_4, Humidity in office room, in %\n",
        "T5, Temperature in bathroom, in Celsius\n",
        "RH_5, Humidity in bathroom, in %\n",
        "T6, Temperature outside the building (north side), in Celsius\n",
        "RH_6, Humidity outside the building (north side), in %\n",
        "T7, Temperature in ironing room , in Celsius\n",
        "RH_7, Humidity in ironing room, in %\n",
        "T8, Temperature in teenager room 2, in Celsius\n",
        "RH_8, Humidity in teenager room 2, in %\n",
        "T9, Temperature in parents room, in Celsius\n",
        "RH_9, Humidity in parents room, in %\n",
        "To, Temperature outside (from Chievres weather station), in Celsius\n",
        "Pressure (from Chievres weather station), in mm Hg\n",
        "RH_out, Humidity outside (from Chievres weather station), in %\n",
        "Wind speed (from Chievres weather station), in m/s\n",
        "Visibility (from Chievres weather station), in km\n",
        "Tdewpoint (from Chievres weather station), Â°C\n",
        "rv1, Random variable 1, nondimensional\n",
        "rv2, Random variable 2, nondimensional\n",
        "\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e_gfpzvA0cJa",
        "outputId": "6e7213f0-857f-4221-dcbd-5c42ef746b5b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 19735 entries, 0 to 19734\n",
            "Data columns (total 29 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   date         19735 non-null  object \n",
            " 1   Appliances   19735 non-null  int64  \n",
            " 2   lights       19735 non-null  int64  \n",
            " 3   T1           19735 non-null  float64\n",
            " 4   RH_1         19735 non-null  float64\n",
            " 5   T2           19735 non-null  float64\n",
            " 6   RH_2         19735 non-null  float64\n",
            " 7   T3           19735 non-null  float64\n",
            " 8   RH_3         19735 non-null  float64\n",
            " 9   T4           19735 non-null  float64\n",
            " 10  RH_4         19735 non-null  float64\n",
            " 11  T5           19735 non-null  float64\n",
            " 12  RH_5         19735 non-null  float64\n",
            " 13  T6           19735 non-null  float64\n",
            " 14  RH_6         19735 non-null  float64\n",
            " 15  T7           19735 non-null  float64\n",
            " 16  RH_7         19735 non-null  float64\n",
            " 17  T8           19735 non-null  float64\n",
            " 18  RH_8         19735 non-null  float64\n",
            " 19  T9           19735 non-null  float64\n",
            " 20  RH_9         19735 non-null  float64\n",
            " 21  T_out        19735 non-null  float64\n",
            " 22  Press_mm_hg  19735 non-null  float64\n",
            " 23  RH_out       19735 non-null  float64\n",
            " 24  Windspeed    19735 non-null  float64\n",
            " 25  Visibility   19735 non-null  float64\n",
            " 26  Tdewpoint    19735 non-null  float64\n",
            " 27  rv1          19735 non-null  float64\n",
            " 28  rv2          19735 non-null  float64\n",
            "dtypes: float64(26), int64(2), object(1)\n",
            "memory usage: 4.4+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DX4Wjmy08aWn"
      },
      "source": [
        "df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6klgmpWq0TJZ",
        "outputId": "53bc939a-63ae-43a0-d2e9-238022a50c7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        }
      },
      "source": [
        "df.set_index('date')[['Appliances', 'lights','T_out', 'RH_1', 'Visibility']].plot(subplots=True)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f992d1ec110>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f992d25f110>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f992d1dca10>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f992d134e50>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f992d0fa1d0>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "03LtErv2z8fI"
      },
      "source": [
        "df_input=df[['Appliances','T_out', 'RH_1', 'Visibility']]"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZoZI-S5t760x",
        "outputId": "6c5704a5-cb29-483d-c19a-12dc1c7b48a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "source": [
        "df_input"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Appliances      T_out       RH_1  Visibility\n",
              "0              60   6.600000  47.596667   63.000000\n",
              "1              60   6.483333  46.693333   59.166667\n",
              "2              50   6.366667  46.300000   55.333333\n",
              "3              50   6.250000  46.066667   51.500000\n",
              "4              60   6.133333  46.333333   47.666667\n",
              "...           ...        ...        ...         ...\n",
              "19730         100  22.733333  46.560000   23.666667\n",
              "19731          90  22.600000  46.500000   24.500000\n",
              "19732         270  22.466667  46.596667   25.333333\n",
              "19733         420  22.333333  46.990000   26.166667\n",
              "19734         430  22.200000  46.600000   27.000000\n",
              "\n",
              "[19735 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b4c86aa3-aa77-4692-86a9-29d9378f3849\">\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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Appliances</th>\n",
              "      <th>T_out</th>\n",
              "      <th>RH_1</th>\n",
              "      <th>Visibility</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>60</td>\n",
              "      <td>6.600000</td>\n",
              "      <td>47.596667</td>\n",
              "      <td>63.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>60</td>\n",
              "      <td>6.483333</td>\n",
              "      <td>46.693333</td>\n",
              "      <td>59.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>50</td>\n",
              "      <td>6.366667</td>\n",
              "      <td>46.300000</td>\n",
              "      <td>55.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>50</td>\n",
              "      <td>6.250000</td>\n",
              "      <td>46.066667</td>\n",
              "      <td>51.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>60</td>\n",
              "      <td>6.133333</td>\n",
              "      <td>46.333333</td>\n",
              "      <td>47.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19730</th>\n",
              "      <td>100</td>\n",
              "      <td>22.733333</td>\n",
              "      <td>46.560000</td>\n",
              "      <td>23.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19731</th>\n",
              "      <td>90</td>\n",
              "      <td>22.600000</td>\n",
              "      <td>46.500000</td>\n",
              "      <td>24.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19732</th>\n",
              "      <td>270</td>\n",
              "      <td>22.466667</td>\n",
              "      <td>46.596667</td>\n",
              "      <td>25.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19733</th>\n",
              "      <td>420</td>\n",
              "      <td>22.333333</td>\n",
              "      <td>46.990000</td>\n",
              "      <td>26.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19734</th>\n",
              "      <td>430</td>\n",
              "      <td>22.200000</td>\n",
              "      <td>46.600000</td>\n",
              "      <td>27.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19735 rows × 4 columns</p>\n",
              "</div>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\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",
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              "  "
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          "metadata": {},
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      },
      "source": [
        "df_input.describe()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Appliances         T_out          RH_1    Visibility\n",
              "count  19735.000000  19735.000000  19735.000000  19735.000000\n",
              "mean      97.694958      7.411665     40.259739     38.330834\n",
              "std      102.524891      5.317409      3.979299     11.794719\n",
              "min       10.000000     -5.000000     27.023333      1.000000\n",
              "25%       50.000000      3.666667     37.333333     29.000000\n",
              "50%       60.000000      6.916667     39.656667     40.000000\n",
              "75%      100.000000     10.408333     43.066667     40.000000\n",
              "max     1080.000000     26.100000     63.360000     66.000000"
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              "      <th>Visibility</th>\n",
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              "      <th>count</th>\n",
              "      <td>19735.000000</td>\n",
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              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>97.694958</td>\n",
              "      <td>7.411665</td>\n",
              "      <td>40.259739</td>\n",
              "      <td>38.330834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>102.524891</td>\n",
              "      <td>5.317409</td>\n",
              "      <td>3.979299</td>\n",
              "      <td>11.794719</td>\n",
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              "      <th>min</th>\n",
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              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>3.666667</td>\n",
              "      <td>37.333333</td>\n",
              "      <td>29.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>6.916667</td>\n",
              "      <td>39.656667</td>\n",
              "      <td>40.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>100.000000</td>\n",
              "      <td>10.408333</td>\n",
              "      <td>43.066667</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1080.000000</td>\n",
              "      <td>26.100000</td>\n",
              "      <td>63.360000</td>\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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      },
      "source": [
        "df_input[df[\"Appliances\"] > 500]"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       Appliances      T_out       RH_1  Visibility\n",
              "11            580   5.983333  46.396667   40.000000\n",
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              "...           ...        ...        ...         ...\n",
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              "19587         520  20.900000  43.366667   40.000000\n",
              "19684         580  15.900000  47.226667   61.000000\n",
              "19685         660  16.100000  47.693333   61.000000\n",
              "19695         620  19.100000  48.326667   64.000000\n",
              "\n",
              "[288 rows x 4 columns]"
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              "      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-77ab2e1e-eb5e-4a04-9d14-33a39a5c84fc 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-77ab2e1e-eb5e-4a04-9d14-33a39a5c84fc');\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",
      "metadata": {
        "id": "_p2XsCLfyW4C",
        "outputId": "ff1045a8-f393-4180-c98d-40351667fa66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "source": [
        "#other way to do the above query\n",
        "df_input.query(\"Appliances > 500\")"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Appliances      T_out       RH_1  Visibility\n",
              "11            580   5.983333  46.396667   40.000000\n",
              "314           520   2.900000  41.530000   36.333333\n",
              "392           550   2.033333  43.433333   40.000000\n",
              "393           690   2.100000  43.766667   40.000000\n",
              "394           620   2.166667  43.693333   40.000000\n",
              "...           ...        ...        ...         ...\n",
              "19586         600  20.833333  44.066667   40.000000\n",
              "19587         520  20.900000  43.366667   40.000000\n",
              "19684         580  15.900000  47.226667   61.000000\n",
              "19685         660  16.100000  47.693333   61.000000\n",
              "19695         620  19.100000  48.326667   64.000000\n",
              "\n",
              "[288 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3dc4731d-b333-492a-a770-dcd396821645\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Appliances</th>\n",
              "      <th>T_out</th>\n",
              "      <th>RH_1</th>\n",
              "      <th>Visibility</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>580</td>\n",
              "      <td>5.983333</td>\n",
              "      <td>46.396667</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>314</th>\n",
              "      <td>520</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>41.530000</td>\n",
              "      <td>36.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>550</td>\n",
              "      <td>2.033333</td>\n",
              "      <td>43.433333</td>\n",
              "      <td>40.000000</td>\n",
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              "      <th>393</th>\n",
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              "      <td>2.100000</td>\n",
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              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>620</td>\n",
              "      <td>2.166667</td>\n",
              "      <td>43.693333</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "    <tr>\n",
              "      <th>19586</th>\n",
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              "      <td>44.066667</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19587</th>\n",
              "      <td>520</td>\n",
              "      <td>20.900000</td>\n",
              "      <td>43.366667</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19684</th>\n",
              "      <td>580</td>\n",
              "      <td>15.900000</td>\n",
              "      <td>47.226667</td>\n",
              "      <td>61.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19685</th>\n",
              "      <td>660</td>\n",
              "      <td>16.100000</td>\n",
              "      <td>47.693333</td>\n",
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              "    <tr>\n",
              "      <th>19695</th>\n",
              "      <td>620</td>\n",
              "      <td>19.100000</td>\n",
              "      <td>48.326667</td>\n",
              "      <td>64.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>288 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3dc4731d-b333-492a-a770-dcd396821645')\"\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",
              "  <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",
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              "      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-3dc4731d-b333-492a-a770-dcd396821645 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-3dc4731d-b333-492a-a770-dcd396821645');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdnkSpWaENYQ"
      },
      "source": [
        "#MinMaxScaler is robust to outlier\n",
        "scaler = MinMaxScaler()\n",
        "data_scaled = scaler.fit_transform(df_input)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rCY48qWVESHY",
        "outputId": "2c94d77d-6632-4bec-8541-ee00f3b50605",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data_scaled"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.04672897, 0.37299035, 0.56618659, 0.95384615],\n",
              "       [0.04672897, 0.36923901, 0.54132648, 0.89487179],\n",
              "       [0.03738318, 0.36548767, 0.53050179, 0.83589744],\n",
              "       ...,\n",
              "       [0.24299065, 0.88317256, 0.53866618, 0.37435897],\n",
              "       [0.38317757, 0.87888532, 0.54949087, 0.38717949],\n",
              "       [0.39252336, 0.87459807, 0.53875791, 0.4       ]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IEZuMY99zfLU"
      },
      "source": [
        "features=data_scaled\n",
        "target=data_scaled[:,0]"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Ag5wZCs9ZFY",
        "outputId": "bbfac66d-dae0-45c2-92f6-7a0e2c5331e6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "TimeseriesGenerator(features, target, length=2, sampling_rate=1, batch_size=1)[0]"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[[0.04672897, 0.37299035, 0.56618659, 0.95384615],\n",
              "         [0.04672897, 0.36923901, 0.54132648, 0.89487179]]]),\n",
              " array([0.03738318]))"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l3D7t9UA4Juf"
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(features, target, test_size=0.20, random_state=123, shuffle = False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DLv-nf0rFlLw",
        "outputId": "ee0ed89d-73c6-40cf-b786-86cb583ea8ef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "x_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15788, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ez0G67QwK_aW",
        "outputId": "1cf1116c-5570-4de7-e288-21e40561c21d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "x_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3947, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XAR9vd-n1MIK"
      },
      "source": [
        "win_length=720 #Five days of data (24*60/10)*5 = 720\n",
        "batch_size=32  \n",
        "num_features=4\n",
        "train_generator = TimeseriesGenerator(x_train, y_train, length=win_length, sampling_rate=1, batch_size=batch_size)\n",
        "test_generator = TimeseriesGenerator(x_test, y_test, length=win_length, sampling_rate=1, batch_size=batch_size)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YW4H0bXV2NwA",
        "outputId": "382f7853-fb32-45cc-c503-b96a12c96b74",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "train_generator[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[[0.04672897, 0.37299035, 0.56618659, 0.95384615],\n",
              "         [0.04672897, 0.36923901, 0.54132648, 0.89487179],\n",
              "         [0.03738318, 0.36548767, 0.53050179, 0.83589744],\n",
              "         ...,\n",
              "         [0.06542056, 0.25241158, 0.31584258, 0.79230769],\n",
              "         [0.05607477, 0.23901393, 0.31309054, 0.72820513],\n",
              "         [0.06542056, 0.22561629, 0.31107238, 0.66410256]],\n",
              " \n",
              "        [[0.04672897, 0.36923901, 0.54132648, 0.89487179],\n",
              "         [0.03738318, 0.36548767, 0.53050179, 0.83589744],\n",
              "         [0.03738318, 0.36173633, 0.52408036, 0.77692308],\n",
              "         ...,\n",
              "         [0.05607477, 0.23901393, 0.31309054, 0.72820513],\n",
              "         [0.06542056, 0.22561629, 0.31107238, 0.66410256],\n",
              "         [0.06542056, 0.21221865, 0.30923768, 0.6       ]],\n",
              " \n",
              "        [[0.03738318, 0.36548767, 0.53050179, 0.83589744],\n",
              "         [0.03738318, 0.36173633, 0.52408036, 0.77692308],\n",
              "         [0.04672897, 0.35798499, 0.53141914, 0.71794872],\n",
              "         ...,\n",
              "         [0.06542056, 0.22561629, 0.31107238, 0.66410256],\n",
              "         [0.06542056, 0.21221865, 0.30923768, 0.6       ],\n",
              "         [0.13084112, 0.2170418 , 0.3055683 , 0.6       ]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[0.08411215, 0.34297964, 0.50389872, 0.6       ],\n",
              "         [0.09345794, 0.34083601, 0.49821117, 0.6       ],\n",
              "         [0.36448598, 0.34244373, 0.49380791, 0.6       ],\n",
              "         ...,\n",
              "         [0.08411215, 0.17470525, 0.32134667, 0.24615385],\n",
              "         [0.09345794, 0.17524116, 0.31749381, 0.26923077],\n",
              "         [0.09345794, 0.17577706, 0.31400789, 0.29230769]],\n",
              " \n",
              "        [[0.09345794, 0.34083601, 0.49821117, 0.6       ],\n",
              "         [0.36448598, 0.34244373, 0.49380791, 0.6       ],\n",
              "         [0.36448598, 0.34405145, 0.48830383, 0.6       ],\n",
              "         ...,\n",
              "         [0.09345794, 0.17524116, 0.31749381, 0.26923077],\n",
              "         [0.09345794, 0.17577706, 0.31400789, 0.29230769],\n",
              "         [0.1682243 , 0.17631297, 0.31208146, 0.31538462]],\n",
              " \n",
              "        [[0.36448598, 0.34244373, 0.49380791, 0.6       ],\n",
              "         [0.36448598, 0.34405145, 0.48830383, 0.6       ],\n",
              "         [0.35514019, 0.34565916, 0.48261627, 0.6       ],\n",
              "         ...,\n",
              "         [0.09345794, 0.17577706, 0.31400789, 0.29230769],\n",
              "         [0.1682243 , 0.17631297, 0.31208146, 0.31538462],\n",
              "         [0.41121495, 0.17684887, 0.3100633 , 0.33846154]]]),\n",
              " array([0.06542056, 0.13084112, 0.34579439, 0.26168224, 0.24299065,\n",
              "        0.38317757, 0.25233645, 0.27102804, 0.31775701, 0.24299065,\n",
              "        0.51401869, 1.        , 0.69158879, 0.35514019, 0.31775701,\n",
              "        0.30841121, 0.26168224, 0.20560748, 0.10280374, 0.09345794,\n",
              "        0.08411215, 0.09345794, 0.11214953, 0.12149533, 0.09345794,\n",
              "        0.08411215, 0.08411215, 0.09345794, 0.09345794, 0.1682243 ,\n",
              "        0.41121495, 0.18691589]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P8AvmYYcNyOR"
      },
      "source": [
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.LSTM(128, input_shape= (win_length, num_features), return_sequences=True))\n",
        "model.add(tf.keras.layers.LeakyReLU(alpha=0.5)) \n",
        "model.add(tf.keras.layers.LSTM(128, return_sequences=True))\n",
        "model.add(tf.keras.layers.LeakyReLU(alpha=0.5)) \n",
        "model.add(tf.keras.layers.Dropout(0.3)) \n",
        "model.add(tf.keras.layers.LSTM(64, return_sequences=False))\n",
        "model.add(tf.keras.layers.Dropout(0.3)) \n",
        "model.add(tf.keras.layers.Dense(1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2dQ-DlXiLswo",
        "outputId": "33dcfe6e-cf9c-424c-d2ef-c1aa47f7f7c9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm (LSTM)                  (None, 720, 128)          68096     \n",
            "_________________________________________________________________\n",
            "leaky_re_lu (LeakyReLU)      (None, 720, 128)          0         \n",
            "_________________________________________________________________\n",
            "lstm_1 (LSTM)                (None, 720, 128)          131584    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_1 (LeakyReLU)    (None, 720, 128)          0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 720, 128)          0         \n",
            "_________________________________________________________________\n",
            "lstm_2 (LSTM)                (None, 64)                49408     \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 249,153\n",
            "Trainable params: 249,153\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JHnl-d8824y0",
        "outputId": "e1ef7fff-0e0d-495e-8f57-8b3020f4d560",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss',\n",
        "                                                    patience=2,\n",
        "                                                    mode='min')\n",
        "\n",
        "model.compile(loss=tf.losses.MeanSquaredError(),\n",
        "              optimizer=tf.optimizers.Adam(),\n",
        "              metrics=[tf.metrics.MeanAbsoluteError()])\n",
        "\n",
        "history = model.fit_generator(train_generator, epochs=50,\n",
        "                    validation_data=test_generator,\n",
        "                    shuffle=False,\n",
        "                    callbacks=[early_stopping])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "471/471 [==============================] - 40s 85ms/step - loss: 0.0040 - mean_absolute_error: 0.0313 - val_loss: 0.0032 - val_mean_absolute_error: 0.0268\n",
            "Epoch 3/50\n",
            "471/471 [==============================] - 40s 85ms/step - loss: 0.0040 - mean_absolute_error: 0.0309 - val_loss: 0.0032 - val_mean_absolute_error: 0.0277\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G4q1O2Hw5tXI",
        "outputId": "9aa3cee0-1992-4af0-d757-3ad2bf607d63",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model.evaluate_generator(test_generator, verbose=0)  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.0031727079767733812, 0.02770618349313736]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wx6HG0ZjXodP"
      },
      "source": [
        "predictions=model.predict_generator(test_generator)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kEoR4K3pYDnR",
        "outputId": "abaf1dd4-9ad4-44da-811c-7379d5c5de0a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "predictions.shape[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3227"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GMC2ewo0YRDW",
        "outputId": "36c940b6-e072-4eb2-a1c5-934c8cd84a10",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "predictions"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.06806677],\n",
              "       [0.09536017],\n",
              "       [0.12105477],\n",
              "       ...,\n",
              "       [0.08679415],\n",
              "       [0.26628438],\n",
              "       [0.2688685 ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 45
        }
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      "metadata": {
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        "outputId": "3ea491bf-3745-4d4c-bcfe-f562909a82ab",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "y_test"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.3364486 , 0.54205607, 0.28971963, ..., 0.24299065, 0.38317757,\n",
              "       0.39252336])"
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          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
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        "outputId": "1de126d9-023f-4127-a8e6-58b910636388",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "x_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.3364486 , 0.34297964, 0.30923768, 0.6       ],\n",
              "       [0.54205607, 0.34726688, 0.3055683 , 0.6       ],\n",
              "       [0.28971963, 0.35155413, 0.30455921, 0.6       ],\n",
              "       ...,\n",
              "       [0.24299065, 0.88317256, 0.53866618, 0.37435897],\n",
              "       [0.38317757, 0.87888532, 0.54949087, 0.38717949],\n",
              "       [0.39252336, 0.87459807, 0.53875791, 0.4       ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
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    {
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      "metadata": {
        "id": "3YhtkAbq7hGf",
        "outputId": "de92f723-30bb-46fc-a754-90bf18a24cf3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
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      "source": [
        "x_test[:,1:][win_length:]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.45337621, 0.20851298, 0.6       ],\n",
              "       [0.47106109, 0.18282726, 0.6       ],\n",
              "       [0.48874598, 0.18466196, 0.6       ],\n",
              "       ...,\n",
              "       [0.88317256, 0.53866618, 0.37435897],\n",
              "       [0.87888532, 0.54949087, 0.38717949],\n",
              "       [0.87459807, 0.53875791, 0.4       ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qR04JJlC6yaM"
      },
      "source": [
        "df_pred=pd.concat([pd.DataFrame(predictions), pd.DataFrame(x_test[:,1:][win_length:])],axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Fu_2Z9DR7rh",
        "outputId": "f19f6c81-ffb7-4abd-c30a-0fa1211fa4ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
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      "source": [
        "df_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "      <td>0.453376</td>\n",
              "      <td>0.208513</td>\n",
              "      <td>0.600000</td>\n",
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              "      <th>1</th>\n",
              "      <td>0.095360</td>\n",
              "      <td>0.471061</td>\n",
              "      <td>0.182827</td>\n",
              "      <td>0.600000</td>\n",
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              "      <td>0.488746</td>\n",
              "      <td>0.184662</td>\n",
              "      <td>0.600000</td>\n",
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              "      <th>3223</th>\n",
              "      <td>0.102214</td>\n",
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              "      <td>0.536006</td>\n",
              "      <td>0.361538</td>\n",
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              "      <th>3224</th>\n",
              "      <td>0.086794</td>\n",
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              "             0         0         1         2\n",
              "0     0.068067  0.453376  0.208513  0.600000\n",
              "1     0.095360  0.471061  0.182827  0.600000\n",
              "2     0.121055  0.488746  0.184662  0.600000\n",
              "3     0.347120  0.506431  0.193996  0.600000\n",
              "4     0.151283  0.524116  0.181818  0.600000\n",
              "...        ...       ...       ...       ...\n",
              "3222  0.085864  0.891747  0.537657  0.348718\n",
              "3223  0.102214  0.887460  0.536006  0.361538\n",
              "3224  0.086794  0.883173  0.538666  0.374359\n",
              "3225  0.266284  0.878885  0.549491  0.387179\n",
              "3226  0.268869  0.874598  0.538758  0.400000\n",
              "\n",
              "[3227 rows x 4 columns]"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "noykl7lh9hmT"
      },
      "source": [
        "rev_trans=scaler.inverse_transform(df_pred)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D2vcu4tBAf4n",
        "outputId": "7341d586-94f3-4f47-ef6d-25f6403ed31c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "rev_trans"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 82.83144213,   9.1       ,  34.6       ,  40.        ],\n",
              "       [112.03538701,   9.65      ,  33.66666667,  40.        ],\n",
              "       [139.52860236,  10.2       ,  33.73333333,  40.        ],\n",
              "       ...,\n",
              "       [102.86974356,  22.46666667,  46.59666667,  25.33333333],\n",
              "       [294.92428273,  22.33333333,  46.99      ,  26.16666667],\n",
              "       [297.68930137,  22.2       ,  46.6       ,  27.        ]])"
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            "tags": []
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      "outputs": [
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          "output_type": "execute_result",
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              "-3227"
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          "execution_count": 53
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    {
      "cell_type": "code",
      "metadata": {
        "id": "5eopbjZZBRUs"
      },
      "source": [
        "df_final=df_input[predictions.shape[0]*-1:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iEgc_FyMBjSO",
        "outputId": "7bece969-9481-4e0e-fbe7-d5ce4b494412",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df_final.count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Appliances    3227\n",
              "T_out         3227\n",
              "RH_1          3227\n",
              "Visibility    3227\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 55
        }
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      "metadata": {
        "id": "D1VdgBdB-aWE",
        "outputId": "f984ecd4-0d05-4e20-976e-e04482a29721",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df_final['App_Pred']=rev_trans[:,0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ym90OS9J_-6O",
        "outputId": "7f5c1914-e32e-43d3-fa6a-c18fde831ba0",
        "colab": {
          "base_uri": "https://localhost:8080/",
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      "source": [
        "df_final"
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      "execution_count": null,
      "outputs": [
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19730</th>\n",
              "      <td>100</td>\n",
              "      <td>22.733333</td>\n",
              "      <td>46.560000</td>\n",
              "      <td>23.666667</td>\n",
              "      <td>101.874257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19731</th>\n",
              "      <td>90</td>\n",
              "      <td>22.600000</td>\n",
              "      <td>46.500000</td>\n",
              "      <td>24.500000</td>\n",
              "      <td>119.369308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19732</th>\n",
              "      <td>270</td>\n",
              "      <td>22.466667</td>\n",
              "      <td>46.596667</td>\n",
              "      <td>25.333333</td>\n",
              "      <td>102.869744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19733</th>\n",
              "      <td>420</td>\n",
              "      <td>22.333333</td>\n",
              "      <td>46.990000</td>\n",
              "      <td>26.166667</td>\n",
              "      <td>294.924283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19734</th>\n",
              "      <td>430</td>\n",
              "      <td>22.200000</td>\n",
              "      <td>46.600000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>297.689301</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3227 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Appliances      T_out       RH_1  Visibility    App_Pred\n",
              "16508          90   9.100000  34.600000   40.000000   82.831442\n",
              "16509         120   9.650000  33.666667   40.000000  112.035387\n",
              "16510         390  10.200000  33.733333   40.000000  139.528602\n",
              "16511         240  10.750000  34.072500   40.000000  381.418641\n",
              "16512         120  11.300000  33.630000   40.000000  171.873156\n",
              "...           ...        ...        ...         ...         ...\n",
              "19730         100  22.733333  46.560000   23.666667  101.874257\n",
              "19731          90  22.600000  46.500000   24.500000  119.369308\n",
              "19732         270  22.466667  46.596667   25.333333  102.869744\n",
              "19733         420  22.333333  46.990000   26.166667  294.924283\n",
              "19734         430  22.200000  46.600000   27.000000  297.689301\n",
              "\n",
              "[3227 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ig7S76aZApyp",
        "outputId": "d1a0b462-b102-4aff-b41c-477570d70fce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "df_final[['Appliances','App_Pred']].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f926630af60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qdw_xNzgO7c3"
      },
      "source": [
        "For more help visit https://www.youtube.com/watch?v=4FmVIpcwl4k"
      ]
    }
  ]
}