MultivariateTimeSeriesModelingusingLSTM.ipynb

{
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      "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'] = (16, 8)\n",
        "mpl.rcParams['axes.grid'] = False\n",
        "\n",
        "frame = pd.read_csv('/content/energydatacomplete.csv')"
      ]
    },
    {
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      "execution_count": 2,
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              "      <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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              "      <th>0</th>\n",
              "      <td>2016-01-11 17:00:00</td>\n",
              "      <td>60</td>\n",
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              "      <td>19.890000</td>\n",
              "      <td>47.596667</td>\n",
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              "      <td>44.790000</td>\n",
              "      <td>19.790000</td>\n",
              "      <td>44.730000</td>\n",
              "      <td>19.000000</td>\n",
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              "      <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",
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              "    <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",
              "    </tr>\n",
              "    <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",
              "      <td>10.084097</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>19730</th>\n",
              "      <td>2016-05-27 17:20:00</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <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",
              "      <td>56.000000</td>\n",
              "      <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",
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              "      <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",
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              "      <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",
              "      <td>10</td>\n",
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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",
              "[19735 rows x 29 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "frame"
      ]
    },
    {
      "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",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e_gfpzvA0cJa",
        "outputId": "0c34dd8e-ad83-45dc-d533-c48909c7be14"
      },
      "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"
          ]
        }
      ],
      "source": [
        "frame.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "DX4Wjmy08aWn"
      },
      "outputs": [],
      "source": [
        "frame['date'] = pd.to_datetime(frame['date'], infer_datetime_format=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "6klgmpWq0TJZ",
        "outputId": "f4de6049-e686-4ddd-e796-4d3cc5360054"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "frame.set_index('date')[['Appliances', 'lights','T_out', 'RH_1', 'Visibility']].plot(subplots=True, color=[\"seagreen\", \"black\", \"red\", \"blue\", \"orange\"], linewidth=2.0)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "03LtErv2z8fI"
      },
      "outputs": [],
      "source": [
        "frame_input=frame[['Appliances','T_out', 'RH_1', 'Visibility']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "ZoZI-S5t760x",
        "outputId": "9efde1f8-abeb-45be-c1cd-278c1297005f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e7b0b561-5ec9-4c41-adcf-5a695a404331\">\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",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e7b0b561-5ec9-4c41-adcf-5a695a404331')\"\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",
              "  <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-e7b0b561-5ec9-4c41-adcf-5a695a404331 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-e7b0b561-5ec9-4c41-adcf-5a695a404331');\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",
              "  "
            ],
            "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]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "frame_input"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "s3QqtUCsyKLa",
        "outputId": "a7065949-998a-4a69-9953-a6d7404c8424"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b2c50526-1936-4a9e-bb1f-a10449dce317\">\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",
              "\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>count</th>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "    </tr>\n",
              "    <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>-5.000000</td>\n",
              "      <td>27.023333</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <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",
              "    </tr>\n",
              "    <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",
              "    </tr>\n",
              "    <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",
              "      <td>66.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b2c50526-1936-4a9e-bb1f-a10449dce317')\"\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",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      width: 32px;\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\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",
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              "\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-b2c50526-1936-4a9e-bb1f-a10449dce317');\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",
              "  "
            ],
            "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"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "frame_input.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "_p2XsCLfyW4C",
        "outputId": "b3b23a17-1702-4728-c6d2-adb1f778ecf1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-2d5bd793-c6d8-49a3-9d9c-66caf2eced04\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<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",
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              "  </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",
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              "      <th>314</th>\n",
              "      <td>520</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>41.530000</td>\n",
              "      <td>36.333333</td>\n",
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              "    <tr>\n",
              "      <th>392</th>\n",
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              "      <td>2.033333</td>\n",
              "      <td>43.433333</td>\n",
              "      <td>40.000000</td>\n",
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              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>690</td>\n",
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              "      <td>43.766667</td>\n",
              "      <td>40.000000</td>\n",
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              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>620</td>\n",
              "      <td>2.166667</td>\n",
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              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>19586</th>\n",
              "      <td>600</td>\n",
              "      <td>20.833333</td>\n",
              "      <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",
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              "    <tr>\n",
              "      <th>19685</th>\n",
              "      <td>660</td>\n",
              "      <td>16.100000</td>\n",
              "      <td>47.693333</td>\n",
              "      <td>61.000000</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-2d5bd793-c6d8-49a3-9d9c-66caf2eced04')\"\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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              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </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",
              "      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-2d5bd793-c6d8-49a3-9d9c-66caf2eced04 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-2d5bd793-c6d8-49a3-9d9c-66caf2eced04');\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",
              "  "
            ],
            "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]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "frame_input.query(\"Appliances > 500\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "gdnkSpWaENYQ"
      },
      "outputs": [],
      "source": [
        "scaler = MinMaxScaler()\n",
        "data_scaled = scaler.fit_transform(frame_input)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rCY48qWVESHY",
        "outputId": "b016414e-d9ab-4b24-e7d4-f3b09254b168"
      },
      "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": 11
        }
      ],
      "source": [
        "data_scaled"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "IEZuMY99zfLU"
      },
      "outputs": [],
      "source": [
        "features=data_scaled\n",
        "target=data_scaled[:,0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Ag5wZCs9ZFY",
        "outputId": "041dd77b-a8d9-4b70-f058-d27b41f5adcd"
      },
      "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": 13
        }
      ],
      "source": [
        "TimeseriesGenerator(features, target, length=2, sampling_rate=1, batch_size=1)[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "l3D7t9UA4Juf"
      },
      "outputs": [],
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(features, target, test_size=0.20, random_state=123, shuffle = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DLv-nf0rFlLw",
        "outputId": "d5e2a32a-0187-496f-adfa-e70f0b6777f2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15788, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "x_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ez0G67QwK_aW",
        "outputId": "8cde1e50-0af6-4cde-d9ba-3ff88b8ed8ed"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3947, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "x_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "XAR9vd-n1MIK"
      },
      "outputs": [],
      "source": [
        "win_length=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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YW4H0bXV2NwA",
        "outputId": "07e06e86-0272-40a6-e1ac-cfa6b82279a4"
      },
      "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": {},
          "execution_count": 18
        }
      ],
      "source": [
        "train_generator[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "P8AvmYYcNyOR"
      },
      "outputs": [],
      "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))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2dQ-DlXiLswo",
        "outputId": "eaa18c34-c67c-475f-e453-b01654be9b3d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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",
            "=================================================================\n",
            "Total params: 249,153\n",
            "Trainable params: 249,153\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JHnl-d8824y0",
        "outputId": "bf40cb03-0017-4dc2-a056-ccda0421e1ec"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  \"\"\"\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "471/471 [==============================] - 166s 331ms/step - loss: 0.0086 - mean_absolute_error: 0.0516 - val_loss: 0.0056 - val_mean_absolute_error: 0.0440\n",
            "Epoch 2/50\n",
            "471/471 [==============================] - 155s 328ms/step - loss: 0.0067 - mean_absolute_error: 0.0432 - val_loss: 0.0044 - val_mean_absolute_error: 0.0362\n",
            "Epoch 3/50\n",
            "471/471 [==============================] - 155s 328ms/step - loss: 0.0048 - mean_absolute_error: 0.0348 - val_loss: 0.0033 - val_mean_absolute_error: 0.0282\n",
            "Epoch 4/50\n",
            "471/471 [==============================] - 154s 328ms/step - loss: 0.0042 - mean_absolute_error: 0.0320 - val_loss: 0.0033 - val_mean_absolute_error: 0.0292\n",
            "Epoch 5/50\n",
            "471/471 [==============================] - 154s 328ms/step - loss: 0.0041 - mean_absolute_error: 0.0314 - val_loss: 0.0032 - val_mean_absolute_error: 0.0272\n",
            "Epoch 6/50\n",
            "471/471 [==============================] - 155s 330ms/step - loss: 0.0040 - mean_absolute_error: 0.0311 - val_loss: 0.0032 - val_mean_absolute_error: 0.0278\n",
            "Epoch 7/50\n",
            "471/471 [==============================] - 154s 328ms/step - loss: 0.0040 - mean_absolute_error: 0.0310 - val_loss: 0.0032 - val_mean_absolute_error: 0.0280\n"
          ]
        }
      ],
      "source": [
        "early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=2, mode='min')\n",
        "\n",
        "model.compile(loss=tf.losses.MeanSquaredError(),optimizer=tf.optimizers.Adam(), metrics=[tf.metrics.MeanAbsoluteError()])\n",
        "\n",
        "history = model.fit_generator(train_generator, epochs=50,validation_data=test_generator, shuffle=False, callbacks=[early_stopping])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G4q1O2Hw5tXI",
        "outputId": "65af5cfa-fa4a-4e06-d991-ac2561627c34"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: UserWarning: `Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.0031915782019495964, 0.0279859509319067]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "model.evaluate_generator(test_generator, verbose=0)  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wx6HG0ZjXodP"
      },
      "outputs": [],
      "source": [
        "predictions=model.predict_generator(test_generator)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kEoR4K3pYDnR",
        "outputId": "baf8664d-af4f-4784-fcd3-6c4aa023ab38"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3227"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "predictions.shape[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GMC2ewo0YRDW"
      },
      "outputs": [],
      "source": [
        "predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dzQB2PtOYeMy",
        "outputId": "f28ed33c-5dde-44e3-c1e6-1e1845fea7e9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.3364486 , 0.54205607, 0.28971963, ..., 0.24299065, 0.38317757,\n",
              "       0.39252336])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "y_test"
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      "execution_count": 27,
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              "array([[0.3364486 , 0.34297964, 0.30923768, 0.6       ],\n",
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              "       [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       ]])"
            ]
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          "metadata": {},
          "execution_count": 27
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        "x_test"
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    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
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              "array([[0.45337621, 0.20851298, 0.6       ],\n",
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              "       ...,\n",
              "       [0.88317256, 0.53866618, 0.37435897],\n",
              "       [0.87888532, 0.54949087, 0.38717949],\n",
              "       [0.87459807, 0.53875791, 0.4       ]])"
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      "source": [
        "x_test[:,1:][win_length:]"
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    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "qR04JJlC6yaM"
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      "outputs": [],
      "source": [
        "frame_pred=pd.concat([pd.DataFrame(predictions), pd.DataFrame(x_test[:,1:][win_length:])],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
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        "outputId": "9e65dd08-0e50-4f88-a214-c6e278a93af9"
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              "0     0.068826  0.453376  0.208513  0.600000\n",
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              "2     0.113293  0.488746  0.184662  0.600000\n",
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              "...        ...       ...       ...       ...\n",
              "3222  0.090446  0.891747  0.537657  0.348718\n",
              "3223  0.103653  0.887460  0.536006  0.361538\n",
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              "\n",
              "[3227 rows x 4 columns]"
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        "rev_trans=scaler.inverse_transform(frame_pred)"
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      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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          "data": {
            "text/plain": [
              "array([[ 83.64361793,   9.1       ,  34.6       ,  40.        ],\n",
              "       [107.6844424 ,   9.65      ,  33.66666667,  40.        ],\n",
              "       [131.22370087,  10.2       ,  33.73333333,  40.        ],\n",
              "       ...,\n",
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        "frame_final=frame_input[predictions.shape[0]*-1:]"
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      "execution_count": 34,
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              "Appliances    3227\n",
              "T_out         3227\n",
              "RH_1          3227\n",
              "Visibility    3227\n",
              "dtype: int64"
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      "metadata": {
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          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/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"
          ]
        }
      ],
      "source": [
        "frame_final['App_Pred']=rev_trans[:,0]"
      ]
    },
    {
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      "execution_count": 36,
      "metadata": {
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        "id": "Ym90OS9J_-6O",
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              "       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",
              "      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-0372d0a0-6fcf-4a8d-a2ec-4ae9a6bba4cf 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-0372d0a0-6fcf-4a8d-a2ec-4ae9a6bba4cf');\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",
              "  "
            ],
            "text/plain": [
              "       Appliances      T_out       RH_1  Visibility    App_Pred\n",
              "16508          90   9.100000  34.600000   40.000000   83.643618\n",
              "16509         120   9.650000  33.666667   40.000000  107.684442\n",
              "16510         390  10.200000  33.733333   40.000000  131.223701\n",
              "16511         240  10.750000  34.072500   40.000000  369.623218\n",
              "16512         120  11.300000  33.630000   40.000000  169.498373\n",
              "...           ...        ...        ...         ...         ...\n",
              "19730         100  22.733333  46.560000   23.666667  106.777199\n",
              "19731          90  22.600000  46.500000   24.500000  120.909171\n",
              "19732         270  22.466667  46.596667   25.333333  106.800223\n",
              "19733         420  22.333333  46.990000   26.166667  277.925903\n",
              "19734         430  22.200000  46.600000   27.000000  303.483662\n",
              "\n",
              "[3227 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "frame_final"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "Ig7S76aZApyp",
        "outputId": "053e35a8-d38a-4ec4-9a9e-1945e9e66033"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f11b64408d0>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "frame_final[['Appliances','App_Pred']].plot(color=[\"seagreen\", \"black\"], linewidth=2.0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "grtp4xctGwDY"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Multivariate_Time_Series_Modeling_using_LSTM.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}