Daily_Temperature_Forecast.ipynb

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  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "name": "Daily_Temperature_Forecast.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/BetikuOluwatobi/Daily-Tempearature-Forecast-Using-Recurrent-Neural-Networks/blob/main/Daily_Temperature_Forecast.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oyxUU1ywUJFt"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import os"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ElQdtVufUhS_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc2fb3d5-5c83-4dc6-bb7c-d58c7f2beaae"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bzBBH7vFUlf2"
      },
      "source": [
        "tf.keras.backend.clear_session()\n",
        "tf.random.set_seed(51)\n",
        "np.random.seed(51)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kfhFsD5OUB6P"
      },
      "source": [
        "## Mounting Drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HCC9RuFtuU97"
      },
      "source": [
        "data_names = os.listdir('/gdrive/MyDrive/Colab Notebooks/Tensorflow_in_practice/Mr_Kebou_Project/raw-data')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load multiple CSV files into a single pandas dataframe\n",
        "\n",
        "This code is a function that loads multiple CSV files located in a specific directory into a single pandas dataframe. The function takes two parameters: `dir`, which is the path to the directory where the CSV files are located, and `data_names`, which is a list of the names of the CSV files to load.\n",
        "\n",
        "The function first initializes an empty pandas dataframe called `df`. It then loops through each file in `data_names`, reads the file using the `csv` module, and extracts the data starting from the 16th row (excluding the header row). If the first row of the extracted data is empty, it is removed from the data.\n",
        "\n",
        "The extracted data is then converted into a new pandas dataframe called `df_new` with the first row as column names. The `df_new` dataframe is concatenated with the `df` dataframe along the row axis using `pd.concat()`, ignoring the original index values.\n",
        "\n",
        "Finally, the function returns the concatenated dataframe `df`.\n",
        "\n",
        "Note that the code assumes that all CSV files have the same format and the same number of columns. It also assumes that the CSV files have a header row. If these assumptions are not true, the code may need to be modified accordingly."
      ],
      "metadata": {
        "id": "DuIhZ22zTvj-"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xOvTfvrBtg2o"
      },
      "source": [
        "import csv\n",
        "path = '/gdrive/MyDrive/Colab Notebooks/Tensorflow_in_practice/Mr_Kebou_Project/raw-data/'\n",
        "def load_datas(dir=path,data_names = data_names):\n",
        "  df = pd.DataFrame()\n",
        "  for i, name in enumerate(data_names):\n",
        "    if i > 0:\n",
        "      the_file = open(path+name, 'r')\n",
        "      reader = csv.reader(the_file)\n",
        "      data = []\n",
        "      for i, row in enumerate(reader):\n",
        "          if i >= 15:\n",
        "            data.append(row)\n",
        "      if len(data[0]) < 1:\n",
        "        data = data[1:]\n",
        "      df_new = pd.DataFrame(data[1:],columns=data[0])\n",
        "      df = pd.concat([df,df_new],axis=0,ignore_index=True)\n",
        "  return df\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j-Xe_azc92Kn"
      },
      "source": [
        "df = load_datas(dir=path,data_names = data_names)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 713
        },
        "id": "K5nCAx7KULLj",
        "outputId": "d33c9d8d-de44-4d2f-8188-c118ac9564c7"
      },
      "source": [
        "df"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Date/Time  Year Month Day   Time Temp (°C) Temp Flag  \\\n",
              "0      2017-04-01 00:00  2017    04  01  00:00       0.6             \n",
              "1      2017-04-01 01:00  2017    04  01  01:00       0.6             \n",
              "2      2017-04-01 02:00  2017    04  01  02:00       0.7             \n",
              "3      2017-04-01 03:00  2017    04  01  03:00       0.6             \n",
              "4      2017-04-01 04:00  2017    04  01  04:00       0.7             \n",
              "...                 ...   ...   ...  ..    ...       ...       ...   \n",
              "40171  2017-09-30 19:00  2017    09  30  19:00       9.3             \n",
              "40172  2017-09-30 20:00  2017    09  30  20:00       7.7             \n",
              "40173  2017-09-30 21:00  2017    09  30  21:00       6.4             \n",
              "40174  2017-09-30 22:00  2017    09  30  22:00       4.2             \n",
              "40175  2017-09-30 23:00  2017    09  30  23:00       4.0             \n",
              "\n",
              "      Dew Point Temp (°C) Dew Point Temp Flag Rel Hum (%)  ... Wind Spd Flag  \\\n",
              "0                     0.1                              96  ...                 \n",
              "1                     0.1                              96  ...                 \n",
              "2                     0.1                              96  ...                 \n",
              "3                     0.0                              96  ...                 \n",
              "4                     0.1                              96  ...                 \n",
              "...                   ...                 ...         ...  ...           ...   \n",
              "40171                 4.0                              70  ...                 \n",
              "40172                 5.7                              87  ...                 \n",
              "40173                 5.4                              93  ...                 \n",
              "40174                 3.4                              95  ...                 \n",
              "40175                 3.4                              96  ...                 \n",
              "\n",
              "      Visibility (km) Visibility Flag Stn Press (kPa) Stn Press Flag Hmdx  \\\n",
              "0                                               99.91                       \n",
              "1                                               99.91                       \n",
              "2                                               99.94                       \n",
              "3                                               99.99                       \n",
              "4                                              100.06                       \n",
              "...               ...             ...             ...            ...  ...   \n",
              "40171                                          101.73                       \n",
              "40172                                          101.77                       \n",
              "40173                                          101.77                       \n",
              "40174                                          101.78                       \n",
              "40175                                          101.76                       \n",
              "\n",
              "      Hmdx Flag Wind Chill Wind Chill Flag Weather  \n",
              "0                                               NA  \n",
              "1                                               NA  \n",
              "2                                               NA  \n",
              "3                                               NA  \n",
              "4                                               NA  \n",
              "...         ...        ...             ...     ...  \n",
              "40171                                           NA  \n",
              "40172                                           NA  \n",
              "40173                                           NA  \n",
              "40174                                           NA  \n",
              "40175                                           NA  \n",
              "\n",
              "[40176 rows x 24 columns]"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date/Time</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Time</th>\n",
              "      <th>Temp (°C)</th>\n",
              "      <th>Temp Flag</th>\n",
              "      <th>Dew Point Temp (°C)</th>\n",
              "      <th>Dew Point Temp Flag</th>\n",
              "      <th>Rel Hum (%)</th>\n",
              "      <th>...</th>\n",
              "      <th>Wind Spd Flag</th>\n",
              "      <th>Visibility (km)</th>\n",
              "      <th>Visibility Flag</th>\n",
              "      <th>Stn Press (kPa)</th>\n",
              "      <th>Stn Press Flag</th>\n",
              "      <th>Hmdx</th>\n",
              "      <th>Hmdx Flag</th>\n",
              "      <th>Wind Chill</th>\n",
              "      <th>Wind Chill Flag</th>\n",
              "      <th>Weather</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2017-04-01 00:00</td>\n",
              "      <td>2017</td>\n",
              "      <td>04</td>\n",
              "      <td>01</td>\n",
              "      <td>00:00</td>\n",
              "      <td>0.6</td>\n",
              "      <td></td>\n",
              "      <td>0.1</td>\n",
              "      <td></td>\n",
              "      <td>96</td>\n",
              "      <td>...</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>99.91</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>NA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017-04-01 01:00</td>\n",
              "      <td>2017</td>\n",
              "      <td>04</td>\n",
              "      <td>01</td>\n",
              "      <td>01:00</td>\n",
              "      <td>0.6</td>\n",
              "      <td></td>\n",
              "      <td>0.1</td>\n",
              "      <td></td>\n",
              "      <td>96</td>\n",
              "      <td>...</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>99.91</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>NA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2017-04-01 02:00</td>\n",
              "      <td>2017</td>\n",
              "      <td>04</td>\n",
              "      <td>01</td>\n",
              "      <td>02:00</td>\n",
              "      <td>0.7</td>\n",
              "      <td></td>\n",
              "      <td>0.1</td>\n",
              "      <td></td>\n",
              "      <td>96</td>\n",
              "      <td>...</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>99.94</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>NA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2017-04-01 03:00</td>\n",
              "      <td>2017</td>\n",
              "      <td>04</td>\n",
              "      <td>01</td>\n",
              "      <td>03:00</td>\n",
              "      <td>0.6</td>\n",
              "      <td></td>\n",
              "      <td>0.0</td>\n",
              "      <td></td>\n",
              "      <td>96</td>\n",
              "      <td>...</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>99.99</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>NA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017-04-01 04:00</td>\n",
              "      <td>2017</td>\n",
              "      <td>04</td>\n",
              "      <td>01</td>\n",
              "      <td>04:00</td>\n",
              "      <td>0.7</td>\n",
              "      <td></td>\n",
              "      <td>0.1</td>\n",
              "      <td></td>\n",
              "      <td>96</td>\n",
              "      <td>...</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>100.06</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>NA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40171</th>\n",
              "      <td>2017-09-30 19:00</td>\n",
              "      <td>2017</td>\n",
              "      <td>09</td>\n",
              "      <td>30</td>\n",
              "      <td>19:00</td>\n",
              "      <td>9.3</td>\n",
              "      <td></td>\n",
              "      <td>4.0</td>\n",
              "      <td></td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>101.73</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>NA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40172</th>\n",
              "      <td>2017-09-30 20:00</td>\n",
              "      <td>2017</td>\n",
              "      <td>09</td>\n",
              "      <td>30</td>\n",
              "      <td>20:00</td>\n",
              "      <td>7.7</td>\n",
              "      <td></td>\n",
              "      <td>5.7</td>\n",
              "      <td></td>\n",
              "      <td>87</td>\n",
              "      <td>...</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>101.77</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>NA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40173</th>\n",
              "      <td>2017-09-30 21:00</td>\n",
              "      <td>2017</td>\n",
              "      <td>09</td>\n",
              "      <td>30</td>\n",
              "      <td>21:00</td>\n",
              "      <td>6.4</td>\n",
              "      <td></td>\n",
              "      <td>5.4</td>\n",
              "      <td></td>\n",
              "      <td>93</td>\n",
              "      <td>...</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>101.77</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>NA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40174</th>\n",
              "      <td>2017-09-30 22:00</td>\n",
              "      <td>2017</td>\n",
              "      <td>09</td>\n",
              "      <td>30</td>\n",
              "      <td>22:00</td>\n",
              "      <td>4.2</td>\n",
              "      <td></td>\n",
              "      <td>3.4</td>\n",
              "      <td></td>\n",
              "      <td>95</td>\n",
              "      <td>...</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>101.78</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>NA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40175</th>\n",
              "      <td>2017-09-30 23:00</td>\n",
              "      <td>2017</td>\n",
              "      <td>09</td>\n",
              "      <td>30</td>\n",
              "      <td>23:00</td>\n",
              "      <td>4.0</td>\n",
              "      <td></td>\n",
              "      <td>3.4</td>\n",
              "      <td></td>\n",
              "      <td>96</td>\n",
              "      <td>...</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>101.76</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>NA</td>\n",
              "    </tr>\n",
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              "<p>40176 rows × 24 columns</p>\n",
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              "\n",
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              "                                                     [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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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fePiafxQTUsQ"
      },
      "source": [
        "## Converting the date/time column to python datatime data type"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KTk4yXTk-gmK"
      },
      "source": [
        "df['Date/Time'] = pd.to_datetime(df['Date/Time'])"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DwtaCq4KTekd"
      },
      "source": [
        "### Sorting rows by the Date/Time column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "05_5qU8GAwoI"
      },
      "source": [
        "df = df.sort_values(by='Date/Time',ignore_index=True)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "DOlhs2yoNalw",
        "outputId": "2dc73720-1319-4254-e1b1-3e3611b3e668"
      },
      "source": [
        "pd.concat([df['Date/Time'],pd.Series(df['Temp (°C)'].values,name='Temp')],axis=1)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Date/Time  Temp\n",
              "0     2015-01-01 00:00:00  -5.9\n",
              "1     2015-01-01 01:00:00  -5.9\n",
              "2     2015-01-01 02:00:00  -6.1\n",
              "3     2015-01-01 03:00:00  -5.8\n",
              "4     2015-01-01 04:00:00  -5.7\n",
              "...                   ...   ...\n",
              "40171 2019-08-31 19:00:00  None\n",
              "40172 2019-08-31 20:00:00  None\n",
              "40173 2019-08-31 21:00:00  None\n",
              "40174 2019-08-31 22:00:00  None\n",
              "40175 2019-08-31 23:00:00  None\n",
              "\n",
              "[40176 rows x 2 columns]"
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-01 02:00:00</td>\n",
              "      <td>-6.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-01 03:00:00</td>\n",
              "      <td>-5.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-01 04:00:00</td>\n",
              "      <td>-5.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "    <tr>\n",
              "      <th>40171</th>\n",
              "      <td>2019-08-31 19:00:00</td>\n",
              "      <td>None</td>\n",
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              "      <th>40172</th>\n",
              "      <td>2019-08-31 20:00:00</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40173</th>\n",
              "      <td>2019-08-31 21:00:00</td>\n",
              "      <td>None</td>\n",
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              "    <tr>\n",
              "      <th>40174</th>\n",
              "      <td>2019-08-31 22:00:00</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40175</th>\n",
              "      <td>2019-08-31 23:00:00</td>\n",
              "      <td>None</td>\n",
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              "</table>\n",
              "<p>40176 rows × 2 columns</p>\n",
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              "\n",
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    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 886
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        "id": "IR5OJKZIUxn4",
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      "source": [
        "df"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Date/Time  Year Month Day   Time Temp (°C) Temp Flag  \\\n",
              "0     2015-01-01 00:00:00  2015    01  01  00:00      -5.9             \n",
              "1     2015-01-01 01:00:00  2015    01  01  01:00      -5.9             \n",
              "2     2015-01-01 02:00:00  2015    01  01  02:00      -6.1             \n",
              "3     2015-01-01 03:00:00  2015    01  01  03:00      -5.8             \n",
              "4     2015-01-01 04:00:00  2015    01  01  04:00      -5.7             \n",
              "...                   ...   ...   ...  ..    ...       ...       ...   \n",
              "40171 2019-08-31 19:00:00  2019    08  31  19:00      None      None   \n",
              "40172 2019-08-31 20:00:00  2019    08  31  20:00      None      None   \n",
              "40173 2019-08-31 21:00:00  2019    08  31  21:00      None      None   \n",
              "40174 2019-08-31 22:00:00  2019    08  31  22:00      None      None   \n",
              "40175 2019-08-31 23:00:00  2019    08  31  23:00      None      None   \n",
              "\n",
              "      Dew Point Temp (°C) Dew Point Temp Flag Rel Hum (%)  ... Wind Spd Flag  \\\n",
              "0                   -11.8                              63  ...                 \n",
              "1                   -13.6                              54  ...                 \n",
              "2                   -14.2                              53  ...                 \n",
              "3                   -13.2                              56  ...                 \n",
              "4                   -11.9                              62  ...                 \n",
              "...                   ...                 ...         ...  ...           ...   \n",
              "40171                None                None        None  ...          None   \n",
              "40172                None                None        None  ...          None   \n",
              "40173                None                None        None  ...          None   \n",
              "40174                None                None        None  ...          None   \n",
              "40175                None                None        None  ...          None   \n",
              "\n",
              "      Visibility (km) Visibility Flag Stn Press (kPa) Stn Press Flag  Hmdx  \\\n",
              "0                                              100.21                        \n",
              "1                                              100.19                        \n",
              "2                                              100.17                        \n",
              "3                                              100.14                        \n",
              "4                                              100.14                        \n",
              "...               ...             ...             ...            ...   ...   \n",
              "40171            None            None            None           None  None   \n",
              "40172            None            None            None           None  None   \n",
              "40173            None            None            None           None  None   \n",
              "40174            None            None            None           None  None   \n",
              "40175            None            None            None           None  None   \n",
              "\n",
              "      Hmdx Flag Wind Chill Wind Chill Flag Weather  \n",
              "0                      -14                      NA  \n",
              "1                      -13                      NA  \n",
              "2                      -14                      NA  \n",
              "3                      -14                      NA  \n",
              "4                      -13                      NA  \n",
              "...         ...        ...             ...     ...  \n",
              "40171      None       None            None    None  \n",
              "40172      None       None            None    None  \n",
              "40173      None       None            None    None  \n",
              "40174      None       None            None    None  \n",
              "40175      None       None            None    None  \n",
              "\n",
              "[40176 rows x 24 columns]"
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              "      <td>100.21</td>\n",
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              "      <td></td>\n",
              "      <td>-14</td>\n",
              "      <td></td>\n",
              "      <td>NA</td>\n",
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              "      <td>-5.9</td>\n",
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              "      <td>54</td>\n",
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              "      <td></td>\n",
              "      <td>NA</td>\n",
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              "      <th>2</th>\n",
              "      <td>2015-01-01 02:00:00</td>\n",
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              "      <td>-6.1</td>\n",
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              "      <td>53</td>\n",
              "      <td>...</td>\n",
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              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>100.17</td>\n",
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              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>-14</td>\n",
              "      <td></td>\n",
              "      <td>NA</td>\n",
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              "      <th>3</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",
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              "    <tr>\n",
              "      <th>40171</th>\n",
              "      <td>2019-08-31 19:00:00</td>\n",
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              "      <td>08</td>\n",
              "      <td>31</td>\n",
              "      <td>19:00</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40172</th>\n",
              "      <td>2019-08-31 20:00:00</td>\n",
              "      <td>2019</td>\n",
              "      <td>08</td>\n",
              "      <td>31</td>\n",
              "      <td>20:00</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40173</th>\n",
              "      <td>2019-08-31 21:00:00</td>\n",
              "      <td>2019</td>\n",
              "      <td>08</td>\n",
              "      <td>31</td>\n",
              "      <td>21:00</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40174</th>\n",
              "      <td>2019-08-31 22:00:00</td>\n",
              "      <td>2019</td>\n",
              "      <td>08</td>\n",
              "      <td>31</td>\n",
              "      <td>22:00</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40175</th>\n",
              "      <td>2019-08-31 23:00:00</td>\n",
              "      <td>2019</td>\n",
              "      <td>08</td>\n",
              "      <td>31</td>\n",
              "      <td>23:00</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>40176 rows × 24 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "D_5__Z8AU02D",
        "outputId": "97febe94-47bc-406e-dac7-83646be80486"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Date/Time  Year Month Day   Time Temp (°C) Temp Flag  \\\n",
              "40171 2019-08-31 19:00:00  2019    08  31  19:00      None      None   \n",
              "40172 2019-08-31 20:00:00  2019    08  31  20:00      None      None   \n",
              "40173 2019-08-31 21:00:00  2019    08  31  21:00      None      None   \n",
              "40174 2019-08-31 22:00:00  2019    08  31  22:00      None      None   \n",
              "40175 2019-08-31 23:00:00  2019    08  31  23:00      None      None   \n",
              "\n",
              "      Dew Point Temp (°C) Dew Point Temp Flag Rel Hum (%)  ... Wind Spd Flag  \\\n",
              "40171                None                None        None  ...          None   \n",
              "40172                None                None        None  ...          None   \n",
              "40173                None                None        None  ...          None   \n",
              "40174                None                None        None  ...          None   \n",
              "40175                None                None        None  ...          None   \n",
              "\n",
              "      Visibility (km) Visibility Flag Stn Press (kPa) Stn Press Flag  Hmdx  \\\n",
              "40171            None            None            None           None  None   \n",
              "40172            None            None            None           None  None   \n",
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              "40174            None            None            None           None  None   \n",
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              "\n",
              "      Hmdx Flag Wind Chill Wind Chill Flag Weather  \n",
              "40171      None       None            None    None  \n",
              "40172      None       None            None    None  \n",
              "40173      None       None            None    None  \n",
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              "[5 rows x 24 columns]"
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              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0XEDOrFBTMDh"
      },
      "source": [
        "### Dropping NA rows"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-2Ku9fH1Tnfw"
      },
      "source": [
        "## All rows after 2018 are NAs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WTxXQybtAwgt"
      },
      "source": [
        "df = df.dropna(axis=0)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iNGy-_G-TIxU"
      },
      "source": [
        "## Data Description"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D2ZVfAAKCbzv",
        "outputId": "4bb949f7-2f55-4f19-e2ed-5ccd99aebda6"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 39392 entries, 0 to 39935\n",
            "Data columns (total 24 columns):\n",
            " #   Column               Non-Null Count  Dtype         \n",
            "---  ------               --------------  -----         \n",
            " 0   Date/Time            39392 non-null  datetime64[ns]\n",
            " 1   Year                 39392 non-null  object        \n",
            " 2   Month                39392 non-null  object        \n",
            " 3   Day                  39392 non-null  object        \n",
            " 4   Time                 39392 non-null  object        \n",
            " 5   Temp (°C)            39392 non-null  object        \n",
            " 6   Temp Flag            39392 non-null  object        \n",
            " 7   Dew Point Temp (°C)  39392 non-null  object        \n",
            " 8   Dew Point Temp Flag  39392 non-null  object        \n",
            " 9   Rel Hum (%)          39392 non-null  object        \n",
            " 10  Rel Hum Flag         39392 non-null  object        \n",
            " 11  Wind Dir (10s deg)   39392 non-null  object        \n",
            " 12  Wind Dir Flag        39392 non-null  object        \n",
            " 13  Wind Spd (km/h)      39392 non-null  object        \n",
            " 14  Wind Spd Flag        39392 non-null  object        \n",
            " 15  Visibility (km)      39392 non-null  object        \n",
            " 16  Visibility Flag      39392 non-null  object        \n",
            " 17  Stn Press (kPa)      39392 non-null  object        \n",
            " 18  Stn Press Flag       39392 non-null  object        \n",
            " 19  Hmdx                 39392 non-null  object        \n",
            " 20  Hmdx Flag            39392 non-null  object        \n",
            " 21  Wind Chill           39392 non-null  object        \n",
            " 22  Wind Chill Flag      39392 non-null  object        \n",
            " 23  Weather              39392 non-null  object        \n",
            "dtypes: datetime64[ns](1), object(23)\n",
            "memory usage: 7.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "40tlICRtTC2T"
      },
      "source": [
        "## All values in weather column are NA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fGUM2ILYXFNn",
        "outputId": "fc9c37ac-fd22-4dff-8a74-68520ffbfff7"
      },
      "source": [
        "sum(df.Weather == 'NA')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "39392"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PqvjivJIS9O2"
      },
      "source": [
        "## Weather Column dropped no info given"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "48Jlhqi-EzOz"
      },
      "source": [
        "df = df.drop('Weather',axis=1)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RNGRqNPRS41l"
      },
      "source": [
        "## Columns with Missing Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bLP03C0fEzGf"
      },
      "source": [
        "columns = ['Temp Flag','Dew Point Temp Flag','Rel Hum Flag','Wind Dir Flag','Wind Spd Flag','Visibility (km)','Visibility Flag','Stn Press Flag','Hmdx','Hmdx Flag','Wind Chill Flag']"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2LGMN8CNS0xS"
      },
      "source": [
        "### Missing Values Check"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ep4Hj2zfGBSi",
        "outputId": "a39d9569-fce9-4634-87c2-3454ca9f4b61"
      },
      "source": [
        "(df[columns] == '').sum() #As is show most of this columns data are missing"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Temp Flag              39382\n",
              "Dew Point Temp Flag    39392\n",
              "Rel Hum Flag           39392\n",
              "Wind Dir Flag          39392\n",
              "Wind Spd Flag          39392\n",
              "Visibility (km)        39392\n",
              "Visibility Flag        39392\n",
              "Stn Press Flag         39392\n",
              "Hmdx                   33450\n",
              "Hmdx Flag              39392\n",
              "Wind Chill Flag        39392\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dagsoCeBScyl"
      },
      "source": [
        "## Dropping the columns with over 80 percent missing data values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D_ZaWC_LHc15",
        "outputId": "852741bc-2d07-4fe4-c262-302a3e0c74c9"
      },
      "source": [
        "(df[columns] == '').mean() * 100 #Over 80% data are missing for all so i will be dropping all columns"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Temp Flag               99.974614\n",
              "Dew Point Temp Flag    100.000000\n",
              "Rel Hum Flag           100.000000\n",
              "Wind Dir Flag          100.000000\n",
              "Wind Spd Flag          100.000000\n",
              "Visibility (km)        100.000000\n",
              "Visibility Flag        100.000000\n",
              "Stn Press Flag         100.000000\n",
              "Hmdx                    84.915719\n",
              "Hmdx Flag              100.000000\n",
              "Wind Chill Flag        100.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nGOS228CFvpm"
      },
      "source": [
        "df = df.drop(columns=columns,axis=1)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HCFN1YKUH2Ap"
      },
      "source": [
        "df = df.replace(r'^\\s*$', np.nan, regex=True)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Em_5BuaSVr3"
      },
      "source": [
        "## Boolean Check of Data with Missing Values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bVKBaVuvCcAC",
        "outputId": "d6dbee44-861d-465b-b2e1-1532a2683ca9"
      },
      "source": [
        "df.isna().any()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date/Time              False\n",
              "Year                   False\n",
              "Month                  False\n",
              "Day                    False\n",
              "Time                   False\n",
              "Temp (°C)               True\n",
              "Dew Point Temp (°C)     True\n",
              "Rel Hum (%)            False\n",
              "Wind Dir (10s deg)      True\n",
              "Wind Spd (km/h)        False\n",
              "Stn Press (kPa)         True\n",
              "Wind Chill              True\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UTzd-UjNSHZ7"
      },
      "source": [
        "## Percentage of Missing Values by column after dropping the first batch of missing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZkVb4QDgJKii",
        "outputId": "c778aa7c-8c1f-4535-e151-4fb91f968dfe"
      },
      "source": [
        "df.isna().mean() * 100 #72 percent of Wind Chill Data is missing thats too large so i will be dropping the column"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date/Time               0.000000\n",
              "Year                    0.000000\n",
              "Month                   0.000000\n",
              "Day                     0.000000\n",
              "Time                    0.000000\n",
              "Temp (°C)               0.025386\n",
              "Dew Point Temp (°C)     0.002539\n",
              "Rel Hum (%)             0.000000\n",
              "Wind Dir (10s deg)      0.545796\n",
              "Wind Spd (km/h)         0.000000\n",
              "Stn Press (kPa)         0.002539\n",
              "Wind Chill             74.240963\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YLGjoO8nSAs0"
      },
      "source": [
        "## Dropping Missing Columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1MpxNqEnTaZA"
      },
      "source": [
        "df = df.drop(['Wind Dir (10s deg)','Wind Chill'],axis=1)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RPJazL26R4mS"
      },
      "source": [
        "# Exploratory Data Analysis and Data Types Adjustment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xa2Z7WtYWP21"
      },
      "source": [
        "df_cols = ['Year','Month','Day','Temp (°C)','Dew Point Temp (°C)','Rel Hum (%)','Wind Spd (km/h)','Stn Press (kPa)']"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OPsBkBDRVJPG"
      },
      "source": [
        "df[df_cols] = df[df_cols].apply(pd.to_numeric)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jP2SMWUoXCHY",
        "outputId": "0e4da715-5d54-44fa-d153-42c1bdf2e475"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 39392 entries, 0 to 39935\n",
            "Data columns (total 10 columns):\n",
            " #   Column               Non-Null Count  Dtype         \n",
            "---  ------               --------------  -----         \n",
            " 0   Date/Time            39392 non-null  datetime64[ns]\n",
            " 1   Year                 39392 non-null  int64         \n",
            " 2   Month                39392 non-null  int64         \n",
            " 3   Day                  39392 non-null  int64         \n",
            " 4   Time                 39392 non-null  object        \n",
            " 5   Temp (°C)            39382 non-null  float64       \n",
            " 6   Dew Point Temp (°C)  39391 non-null  float64       \n",
            " 7   Rel Hum (%)          39392 non-null  int64         \n",
            " 8   Wind Spd (km/h)      39392 non-null  int64         \n",
            " 9   Stn Press (kPa)      39391 non-null  float64       \n",
            "dtypes: datetime64[ns](1), float64(3), int64(5), object(1)\n",
            "memory usage: 3.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VqsdCCDhav1K"
      },
      "source": [
        "# pd.get_dummies(pd.to_datetime(df['Time'],unit='ns'))"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "zENBFOmwa4xB",
        "outputId": "57b5533f-730d-45fc-dd84-b4e6c81f0b94"
      },
      "source": [
        "fig,ax = plt.subplots(figsize=(12,8))\n",
        "ax.plot(df['Date/Time'],df['Temp (°C)'],label='Temperature')\n",
        "ax.plot(df['Date/Time'],df['Dew Point Temp (°C)'],color='orange',label='Dew Point Temp')\n",
        "ax.set_title('Temperature VS Dew Point Correlation Over Time')\n",
        "ax.set_xlabel('Date/Time')\n",
        "ax.set_ylabel('Temp/Dew Point')\n",
        "ax.legend()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fa8436caef0>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mocbXqnZui68"
      },
      "source": [
        "df = df.set_index('Date/Time')"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wCPG-rgpnvrF"
      },
      "source": [
        "one_day = df['Temp (°C)'].iloc[0:24]"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bs1I5noXvGX5",
        "outputId": "c13918aa-b917-41d2-f344-4cc03944d598"
      },
      "source": [
        "one_day"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date/Time\n",
              "2015-01-01 00:00:00   -5.9\n",
              "2015-01-01 01:00:00   -5.9\n",
              "2015-01-01 02:00:00   -6.1\n",
              "2015-01-01 03:00:00   -5.8\n",
              "2015-01-01 04:00:00   -5.7\n",
              "2015-01-01 05:00:00   -5.3\n",
              "2015-01-01 06:00:00   -4.2\n",
              "2015-01-01 07:00:00   -3.8\n",
              "2015-01-01 08:00:00   -3.7\n",
              "2015-01-01 09:00:00   -3.2\n",
              "2015-01-01 10:00:00   -2.8\n",
              "2015-01-01 11:00:00   -3.5\n",
              "2015-01-01 12:00:00   -3.5\n",
              "2015-01-01 13:00:00   -3.4\n",
              "2015-01-01 14:00:00   -3.3\n",
              "2015-01-01 15:00:00   -2.7\n",
              "2015-01-01 16:00:00   -2.6\n",
              "2015-01-01 17:00:00   -2.7\n",
              "2015-01-01 18:00:00   -2.3\n",
              "2015-01-01 19:00:00   -2.4\n",
              "2015-01-01 20:00:00   -1.8\n",
              "2015-01-01 21:00:00   -1.3\n",
              "2015-01-01 22:00:00   -0.9\n",
              "2015-01-01 23:00:00   -0.9\n",
              "Name: Temp (°C), dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "45hyUbTdmGKK",
        "outputId": "ecb5f205-a4b5-482d-aee8-acf61c1e60f5"
      },
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "plt.plot(one_day)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fa8439c4be0>]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XlksI5TRs9X8"
      },
      "source": [
        "test = df['Temp (°C)'].rolling(24).mean()"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qY-ppGSWuYrb"
      },
      "source": [
        "test.dropna(inplace=True)"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "d6TSwTIdvt42",
        "outputId": "27818a67-8783-4be1-bf5b-f276c839228a"
      },
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "plt.plot(df['Temp (°C)'])"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fa848d83040>]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "kk8qDknDuXPi",
        "outputId": "fe54e656-ece4-4c92-f495-261f2e7c31ef"
      },
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "plt.plot(test)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fa8437e89a0>]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pjYCAT7CRS7l"
      },
      "source": [
        "### Categorizing the time column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 866
        },
        "id": "wt9kQS8qwmrj",
        "outputId": "944189fb-a5b8-4840-fb01-88f8e78ee05f"
      },
      "source": [
        "pd.get_dummies(df['Time'])"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     00:00  01:00  02:00  03:00  04:00  05:00  06:00  07:00  \\\n",
              "Date/Time                                                                     \n",
              "2015-01-01 00:00:00      1      0      0      0      0      0      0      0   \n",
              "2015-01-01 01:00:00      0      1      0      0      0      0      0      0   \n",
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          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x956Gsb4Q27d"
      },
      "source": [
        "### The Year and Time Columns are dropped"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lB9kht1PwQGN"
      },
      "source": [
        "df_train = df.drop(['Year','Time','Temp (°C)'],axis=1) #Dropping time for test case\n",
        "temp = df['Temp (°C)']\n",
        "df_train = pd.concat([df_train,temp],axis=1)"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pgEuQoExQwpf"
      },
      "source": [
        "## Data Description"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "54G5UsLNycPF",
        "outputId": "695fe66d-03d7-409e-f4f8-4ef73e9c9d99"
      },
      "source": [
        "df_train.info()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 39392 entries, 2015-01-01 00:00:00 to 2019-08-21 23:00:00\n",
            "Data columns (total 7 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   Month                39392 non-null  int64  \n",
            " 1   Day                  39392 non-null  int64  \n",
            " 2   Dew Point Temp (°C)  39391 non-null  float64\n",
            " 3   Rel Hum (%)          39392 non-null  int64  \n",
            " 4   Wind Spd (km/h)      39392 non-null  int64  \n",
            " 5   Stn Press (kPa)      39391 non-null  float64\n",
            " 6   Temp (°C)            39382 non-null  float64\n",
            "dtypes: float64(3), int64(4)\n",
            "memory usage: 2.4 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gMwr5o3nQfx2"
      },
      "source": [
        "### Filling missing column values with the mean of 6 hr lag temperature values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-RVuz2xUJ__r"
      },
      "source": [
        "df_train = df_train.fillna(df_train.rolling(6,min_periods=1).mean())"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3zGjMh7pQXET"
      },
      "source": [
        "### Cross-Checking If Missing Values are still Present"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dtcj34WWM_Ml",
        "outputId": "01103259-613a-48f6-ccd9-1f9b00ca7b98"
      },
      "source": [
        "df_train.isna().any()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Month                  False\n",
              "Day                    False\n",
              "Dew Point Temp (°C)    False\n",
              "Rel Hum (%)            False\n",
              "Wind Spd (km/h)        False\n",
              "Stn Press (kPa)        False\n",
              "Temp (°C)              False\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "la608WT3QLR3"
      },
      "source": [
        "### Splitting Data into Train Data and Validation Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hd5l1xXRV1hM"
      },
      "source": [
        "split_time = sum(df_train.index.year == 2015) + sum(df_train.index.year == 2016) + sum(df_train.index.year == 2017)\n",
        "time_train = df_train.index[:split_time]\n",
        "x_train = df_train.iloc[:split_time]\n",
        "time_valid = df_train.index[split_time:]\n",
        "x_valid = df_train.iloc[split_time:]\n",
        "\n",
        "window_size = 14\n",
        "batch_size = 120\n",
        "shuffle_buffer = 42"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GXMtiN2DQCAR"
      },
      "source": [
        "### Helper Function for preparing the dataset into a form acceptable by the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mbBiQ8LzVvGn"
      },
      "source": [
        "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n",
        "  dataset = tf.data.Dataset.from_tensor_slices(series)\n",
        "  dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n",
        "  dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n",
        "  dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:,:-1], window[-1,-1]))\n",
        "  dataset = dataset.batch(batch_size).prefetch(1)\n",
        "  return dataset"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sG1wM4LNP22u"
      },
      "source": [
        "### Dataset Preparation and Model Build"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lM2foMw2Wt9P",
        "outputId": "3368edd5-731c-46ba-b229-872066da58c0"
      },
      "source": [
        "tf.keras.backend.clear_session()\n",
        "dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer)\n",
        "dataset_test = windowed_dataset(x_valid, window_size, batch_size, shuffle_buffer)\n",
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Lambda(lambda x: x * 0.1,\n",
        "                      input_shape=[None,6]),\n",
        "  tf.keras.layers.Conv1D(filters=32, kernel_size=5,\n",
        "                      strides=1, padding=\"causal\",\n",
        "                      activation=\"relu\"),\n",
        "  tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64,activation='relu',return_sequences=True,input_shape=[None])),\n",
        "  tf.keras.layers.Dropout(0.3),\n",
        "  tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64,activation='relu', return_sequences=True)),\n",
        "  tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32,activation='relu')),\n",
        "  tf.keras.layers.Dropout(0.1),\n",
        "  tf.keras.layers.Dense(16,activation='relu'),\n",
        "  tf.keras.layers.Dense(1),\n",
        "  tf.keras.layers.Lambda(lambda x: x * 10)\n",
        "])\n",
        "\n",
        "# 4e-4\n",
        "# lr_schedule = tf.keras.callbacks.LearningRateScheduler(\n",
        "#     lambda epoch: 1e-10 * 10**(epoch / 20))\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=0.00034)\n",
        "\n",
        "# [tf.keras.metrics.RootMeanSquaredError()]\n",
        "# tf.keras.losses.Huber()\n",
        "\n",
        "model.compile(loss=tf.keras.losses.Huber(), optimizer=optimizer,metrics=[tf.keras.metrics.RootMeanSquaredError()])\n",
        "history = model.fit(dataset,validation_data=dataset_test,epochs=50)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "210/210 [==============================] - 33s 128ms/step - loss: 7.5776 - root_mean_squared_error: 10.0991 - val_loss: 9.0327 - val_root_mean_squared_error: 11.3960\n",
            "Epoch 2/50\n",
            "210/210 [==============================] - 26s 123ms/step - loss: 9.4422 - root_mean_squared_error: 11.7875 - val_loss: 7.8821 - val_root_mean_squared_error: 9.8274\n",
            "Epoch 3/50\n",
            "210/210 [==============================] - 27s 130ms/step - loss: 7.6504 - root_mean_squared_error: 9.8960 - val_loss: 7.0523 - val_root_mean_squared_error: 9.0137\n",
            "Epoch 4/50\n",
            "210/210 [==============================] - 26s 123ms/step - loss: 8.5900 - root_mean_squared_error: 52.3459 - val_loss: 5.6174 - val_root_mean_squared_error: 7.3093\n",
            "Epoch 5/50\n",
            "210/210 [==============================] - 26s 123ms/step - loss: 5.2284 - root_mean_squared_error: 7.2747 - val_loss: 5.0166 - val_root_mean_squared_error: 6.6677\n",
            "Epoch 6/50\n",
            "210/210 [==============================] - 26s 122ms/step - loss: 4.4875 - root_mean_squared_error: 6.2551 - val_loss: 4.4651 - val_root_mean_squared_error: 6.0578\n",
            "Epoch 7/50\n",
            "210/210 [==============================] - 26s 123ms/step - loss: 4.0103 - root_mean_squared_error: 5.6566 - val_loss: 4.0208 - val_root_mean_squared_error: 5.5256\n",
            "Epoch 8/50\n",
            "210/210 [==============================] - 26s 122ms/step - loss: 3.6790 - root_mean_squared_error: 5.2705 - val_loss: 3.6642 - val_root_mean_squared_error: 5.0985\n",
            "Epoch 9/50\n",
            "210/210 [==============================] - 28s 132ms/step - loss: 3.3945 - root_mean_squared_error: 4.9416 - val_loss: 3.4729 - val_root_mean_squared_error: 4.8651\n",
            "Epoch 10/50\n",
            "210/210 [==============================] - 26s 123ms/step - loss: 3.2135 - root_mean_squared_error: 4.7143 - val_loss: 3.3360 - val_root_mean_squared_error: 4.6830\n",
            "Epoch 11/50\n",
            "210/210 [==============================] - 26s 123ms/step - loss: 3.0699 - root_mean_squared_error: 4.5308 - val_loss: 3.3192 - val_root_mean_squared_error: 4.6469\n",
            "Epoch 12/50\n",
            "210/210 [==============================] - 26s 123ms/step - loss: 2.9457 - root_mean_squared_error: 4.3676 - val_loss: 3.3219 - val_root_mean_squared_error: 4.6361\n",
            "Epoch 13/50\n",
            "210/210 [==============================] - 26s 122ms/step - loss: 2.8629 - root_mean_squared_error: 4.2614 - val_loss: 3.3239 - val_root_mean_squared_error: 4.6340\n",
            "Epoch 14/50\n",
            "210/210 [==============================] - 26s 122ms/step - loss: 2.7682 - root_mean_squared_error: 4.1453 - val_loss: 3.2724 - val_root_mean_squared_error: 4.5665\n",
            "Epoch 15/50\n",
            "210/210 [==============================] - 26s 123ms/step - loss: 2.7093 - root_mean_squared_error: 4.0661 - val_loss: 3.2404 - val_root_mean_squared_error: 4.5423\n",
            "Epoch 16/50\n",
            "210/210 [==============================] - 26s 124ms/step - loss: 2.6262 - root_mean_squared_error: 3.9538 - val_loss: 3.1662 - val_root_mean_squared_error: 4.4775\n",
            "Epoch 17/50\n",
            "210/210 [==============================] - 26s 124ms/step - loss: 2.5293 - root_mean_squared_error: 3.8337 - val_loss: 3.0559 - val_root_mean_squared_error: 4.3616\n",
            "Epoch 18/50\n",
            "210/210 [==============================] - 26s 124ms/step - loss: 2.4671 - root_mean_squared_error: 3.7475 - val_loss: 3.0261 - val_root_mean_squared_error: 4.3325\n",
            "Epoch 19/50\n",
            "210/210 [==============================] - 26s 123ms/step - loss: 2.4094 - root_mean_squared_error: 3.6931 - val_loss: 2.9583 - val_root_mean_squared_error: 4.2766\n",
            "Epoch 20/50\n",
            "210/210 [==============================] - 26s 123ms/step - loss: 2.3433 - root_mean_squared_error: 3.6079 - val_loss: 2.8567 - val_root_mean_squared_error: 4.1268\n",
            "Epoch 21/50\n",
            "210/210 [==============================] - 26s 124ms/step - loss: 2.1734 - root_mean_squared_error: 3.3721 - val_loss: 2.7646 - val_root_mean_squared_error: 4.0089\n",
            "Epoch 22/50\n",
            "210/210 [==============================] - 26s 121ms/step - loss: 2.0976 - root_mean_squared_error: 3.2968 - val_loss: 2.7053 - val_root_mean_squared_error: 3.9451\n",
            "Epoch 23/50\n",
            "210/210 [==============================] - 26s 122ms/step - loss: 1.9891 - root_mean_squared_error: 3.1465 - val_loss: 2.6165 - val_root_mean_squared_error: 3.8241\n",
            "Epoch 24/50\n",
            "210/210 [==============================] - 26s 123ms/step - loss: 1.9019 - root_mean_squared_error: 3.0405 - val_loss: 2.6409 - val_root_mean_squared_error: 3.8320\n",
            "Epoch 25/50\n",
            "210/210 [==============================] - 26s 124ms/step - loss: 1.8252 - root_mean_squared_error: 2.9484 - val_loss: 2.3865 - val_root_mean_squared_error: 3.5514\n",
            "Epoch 26/50\n",
            "210/210 [==============================] - 26s 123ms/step - loss: 1.7045 - root_mean_squared_error: 2.7969 - val_loss: 2.3130 - val_root_mean_squared_error: 3.4438\n",
            "Epoch 27/50\n",
            "210/210 [==============================] - 26s 122ms/step - loss: 1.5708 - root_mean_squared_error: 2.6422 - val_loss: 2.0179 - val_root_mean_squared_error: 3.0777\n",
            "Epoch 28/50\n",
            "210/210 [==============================] - 25s 120ms/step - loss: 1.4995 - root_mean_squared_error: 2.5685 - val_loss: 1.7245 - val_root_mean_squared_error: 2.7165\n",
            "Epoch 29/50\n",
            "210/210 [==============================] - 26s 123ms/step - loss: 1.3671 - root_mean_squared_error: 2.3876 - val_loss: 1.5236 - val_root_mean_squared_error: 2.5219\n",
            "Epoch 30/50\n",
            "210/210 [==============================] - 26s 122ms/step - loss: 1.3316 - root_mean_squared_error: 2.3691 - val_loss: 1.4548 - val_root_mean_squared_error: 2.4157\n",
            "Epoch 31/50\n",
            "210/210 [==============================] - 26s 123ms/step - loss: 1.2285 - root_mean_squared_error: 2.1973 - val_loss: 1.2644 - val_root_mean_squared_error: 2.1946\n",
            "Epoch 32/50\n",
            "210/210 [==============================] - 26s 122ms/step - loss: 1.1474 - root_mean_squared_error: 2.1053 - val_loss: 1.2455 - val_root_mean_squared_error: 2.1721\n",
            "Epoch 33/50\n",
            "210/210 [==============================] - 26s 124ms/step - loss: 1.1008 - root_mean_squared_error: 2.0256 - val_loss: 1.0107 - val_root_mean_squared_error: 1.8694\n",
            "Epoch 34/50\n",
            "210/210 [==============================] - 26s 121ms/step - loss: 1.0034 - root_mean_squared_error: 1.8963 - val_loss: 0.8713 - val_root_mean_squared_error: 1.7026\n",
            "Epoch 35/50\n",
            "210/210 [==============================] - 25s 120ms/step - loss: 0.9370 - root_mean_squared_error: 1.8101 - val_loss: 0.8221 - val_root_mean_squared_error: 1.6475\n",
            "Epoch 36/50\n",
            "210/210 [==============================] - 25s 120ms/step - loss: 0.8843 - root_mean_squared_error: 1.7489 - val_loss: 0.6647 - val_root_mean_squared_error: 1.4249\n",
            "Epoch 37/50\n",
            "210/210 [==============================] - 25s 121ms/step - loss: 0.8104 - root_mean_squared_error: 1.6252 - val_loss: 0.7818 - val_root_mean_squared_error: 1.5851\n",
            "Epoch 38/50\n",
            "210/210 [==============================] - 25s 119ms/step - loss: 0.7701 - root_mean_squared_error: 1.5838 - val_loss: 0.7843 - val_root_mean_squared_error: 1.5300\n",
            "Epoch 39/50\n",
            "210/210 [==============================] - 25s 120ms/step - loss: 0.7013 - root_mean_squared_error: 1.4689 - val_loss: 0.7742 - val_root_mean_squared_error: 1.5264\n",
            "Epoch 40/50\n",
            "210/210 [==============================] - 26s 121ms/step - loss: 0.6689 - root_mean_squared_error: 1.4354 - val_loss: 0.7270 - val_root_mean_squared_error: 1.4419\n",
            "Epoch 41/50\n",
            "210/210 [==============================] - 26s 122ms/step - loss: 0.6039 - root_mean_squared_error: 1.3387 - val_loss: 1.0353 - val_root_mean_squared_error: 1.8217\n",
            "Epoch 42/50\n",
            "210/210 [==============================] - 25s 120ms/step - loss: 0.5516 - root_mean_squared_error: 1.2547 - val_loss: 0.9483 - val_root_mean_squared_error: 1.7020\n",
            "Epoch 43/50\n",
            "210/210 [==============================] - 25s 121ms/step - loss: 0.5214 - root_mean_squared_error: 1.2332 - val_loss: 0.9163 - val_root_mean_squared_error: 1.7189\n",
            "Epoch 44/50\n",
            "210/210 [==============================] - 25s 120ms/step - loss: 0.4891 - root_mean_squared_error: 1.1708 - val_loss: 0.8170 - val_root_mean_squared_error: 1.4764\n",
            "Epoch 45/50\n",
            "210/210 [==============================] - 25s 120ms/step - loss: 0.4833 - root_mean_squared_error: 1.1807 - val_loss: 0.8778 - val_root_mean_squared_error: 1.6906\n",
            "Epoch 46/50\n",
            "210/210 [==============================] - 26s 126ms/step - loss: 0.4417 - root_mean_squared_error: 1.0908 - val_loss: 1.1922 - val_root_mean_squared_error: 2.0978\n",
            "Epoch 47/50\n",
            "210/210 [==============================] - 25s 120ms/step - loss: 0.3949 - root_mean_squared_error: 1.0300 - val_loss: 1.1527 - val_root_mean_squared_error: 2.0671\n",
            "Epoch 48/50\n",
            "210/210 [==============================] - 25s 121ms/step - loss: 0.3665 - root_mean_squared_error: 0.9828 - val_loss: 0.7848 - val_root_mean_squared_error: 1.5431\n",
            "Epoch 49/50\n",
            "210/210 [==============================] - 26s 125ms/step - loss: 0.3759 - root_mean_squared_error: 0.9944 - val_loss: 1.3602 - val_root_mean_squared_error: 2.4054\n",
            "Epoch 50/50\n",
            "210/210 [==============================] - 25s 121ms/step - loss: 0.3603 - root_mean_squared_error: 0.9709 - val_loss: 1.2170 - val_root_mean_squared_error: 2.2104\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j2AbJF0zPtd5"
      },
      "source": [
        "### Model Evaluation for Multi-Variate Approach"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "id": "mA2wGIWlnxeA",
        "outputId": "c041fe3b-d471-4e21-daef-74c612dbfcab"
      },
      "source": [
        "epochs =  range(50)\n",
        "plt.figure(figsize=(12,8))\n",
        "plt.title('Epochs VS Huber Loss')\n",
        "plt.plot(epochs, history.history[\"val_loss\"],color='b',label='val_loss')\n",
        "plt.plot(epochs, history.history[\"loss\"],color='r',label='train_loss')\n",
        "plt.legend()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fa82ebd3dc0>"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "id": "oy_UjMUQcu_D",
        "outputId": "99c870dd-1b12-46ae-da41-bc84a4942c94"
      },
      "source": [
        "epochs =  range(50)\n",
        "plt.figure(figsize=(12,8))\n",
        "plt.title('Epochs VS Root Mean Squared Error')\n",
        "plt.plot(epochs, history.history[\"val_root_mean_squared_error\"],color='b',label='val_error')\n",
        "plt.plot(epochs, history.history[\"root_mean_squared_error\"],color='r',label='train_error')\n",
        "plt.legend()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fa82ec1c610>"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BhXuucqyW-nd"
      },
      "source": [
        "### Saving the model and weights"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XPfeJdEgHtP-"
      },
      "source": [
        "model.save_weights('/gdrive/MyDrive/Colab Notebooks/Tensorflow_in_practice/Mr_Kebou_Project/models/conv_net_lstm_weights_epochs_50')"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KooRlkFkIhRf",
        "outputId": "4eb36983-f441-4064-f73e-206762bf2cee"
      },
      "source": [
        "model.save('/gdrive/MyDrive/Colab Notebooks/Tensorflow_in_practice/Mr_Kebou_Project/models/conv_net_lstm_model_epochs_50')"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _update_step_xla while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cwt-6Y3qBOtX"
      },
      "source": [
        "### Load Saved Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dNZR4fbnCCDs",
        "outputId": "8dbfebba-3a38-4bb1-a70b-bfc84bdb4563"
      },
      "source": [
        "new_model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Lambda(lambda x: x * 0.1,\n",
        "                      input_shape=[None,6]),\n",
        "  tf.keras.layers.Conv1D(filters=32, kernel_size=5,\n",
        "                      strides=1, padding=\"causal\",\n",
        "                      activation=\"relu\"),\n",
        "  tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64,activation='relu',return_sequences=True,input_shape=[None])),\n",
        "  tf.keras.layers.Dropout(0.3),\n",
        "  tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64,activation='relu', return_sequences=True)),\n",
        "  tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32,activation='relu')),\n",
        "  tf.keras.layers.Dropout(0.1),\n",
        "  tf.keras.layers.Dense(16,activation='relu'),\n",
        "  tf.keras.layers.Dense(1),\n",
        "  tf.keras.layers.Lambda(lambda x: x * 10)\n",
        "])\n",
        "\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=0.00034)\n",
        "\n",
        "new_model.compile(loss=tf.keras.losses.Huber(), optimizer=optimizer,metrics=[tf.keras.metrics.RootMeanSquaredError()])\n",
        "\n",
        "# Check its architecture\n",
        "new_model.summary()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lambda_2 (Lambda)           (None, None, 6)           0         \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, None, 32)          992       \n",
            "                                                                 \n",
            " bidirectional_3 (Bidirectio  (None, None, 128)        49664     \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, None, 128)         0         \n",
            "                                                                 \n",
            " bidirectional_4 (Bidirectio  (None, None, 128)        98816     \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " bidirectional_5 (Bidirectio  (None, 64)               41216     \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 16)                1040      \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 17        \n",
            "                                                                 \n",
            " lambda_3 (Lambda)           (None, 1)                 0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 191,745\n",
            "Trainable params: 191,745\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ufaC7RVYyir"
      },
      "source": [
        "new_model.load_weights(\"/gdrive/MyDrive/Colab Notebooks/Tensorflow_in_practice/Mr_Kebou_Project/models/model.h5\")"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VWtuIC34vdQs",
        "outputId": "f0ddf28a-83cf-4dca-bdfd-b226198e2344"
      },
      "source": [
        "# serialize model to JSON\n",
        "model_json = new_model.to_json()\n",
        "with open(\"/gdrive/MyDrive/Colab Notebooks/Tensorflow_in_practice/Mr_Kebou_Project/models/model.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json)\n",
        "# serialize weights to HDF5\n",
        "new_model.save_weights(\"/gdrive/MyDrive/Colab Notebooks/Tensorflow_in_practice/Mr_Kebou_Project/models/model.h5\")\n",
        "print(\"Saved model to disk\")"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved model to disk\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_pRzVzH9wHp2",
        "outputId": "bbb905be-022d-4ad1-ce59-90a7503c45f2"
      },
      "source": [
        "# load json and create model\n",
        "from tensorflow.keras.models import model_from_json\n",
        "json_file = open('/gdrive/MyDrive/Colab Notebooks/Tensorflow_in_practice/Mr_Kebou_Project/models/model.json', 'r')\n",
        "loaded_model_json = json_file.read()\n",
        "json_file.close()\n",
        "loaded_model = model_from_json(loaded_model_json)\n",
        "# load weights into new model\n",
        "loaded_model.load_weights(\"/gdrive/MyDrive/Colab Notebooks/Tensorflow_in_practice/Mr_Kebou_Project/models/model.h5\")\n",
        "print(\"Loaded model from disk\")"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded model from disk\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RF6wiDLWw3c8",
        "outputId": "5c42bc56-1075-4bb9-87f4-eafb920f1684"
      },
      "source": [
        "loaded_model.summary()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lambda_2 (Lambda)           (None, None, 6)           0         \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, None, 32)          992       \n",
            "                                                                 \n",
            " bidirectional_3 (Bidirectio  (None, None, 128)        49664     \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, None, 128)         0         \n",
            "                                                                 \n",
            " bidirectional_4 (Bidirectio  (None, None, 128)        98816     \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " bidirectional_5 (Bidirectio  (None, 64)               41216     \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 16)                1040      \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 17        \n",
            "                                                                 \n",
            " lambda_3 (Lambda)           (None, 1)                 0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 191,745\n",
            "Trainable params: 191,745\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L7hK62AGnFmO"
      },
      "source": [
        "Train Model for one Time Lag"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k9A_NCeLaGbs",
        "outputId": "837a543d-3afb-4e9f-aa1e-0521fcc21800"
      },
      "source": [
        "tf.keras.backend.clear_session()\n",
        "dataset = windowed_dataset(x_train, 0, batch_size, shuffle_buffer)\n",
        "dataset_test = windowed_dataset(x_valid, 0, batch_size, shuffle_buffer)\n",
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Lambda(lambda x: x * 0.1,\n",
        "                      input_shape=[None,6]),\n",
        "  tf.keras.layers.Conv1D(filters=32, kernel_size=5,\n",
        "                      strides=1, padding=\"causal\",\n",
        "                      activation=\"relu\"),\n",
        "  tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64,activation='relu',return_sequences=True,input_shape=[None])),\n",
        "  tf.keras.layers.Dropout(0.3),\n",
        "  tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64,activation='relu', return_sequences=True)),\n",
        "  tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32,activation='relu')),\n",
        "  tf.keras.layers.Dropout(0.1),\n",
        "  tf.keras.layers.Dense(16,activation='relu'),\n",
        "  tf.keras.layers.Dense(1),\n",
        "  tf.keras.layers.Lambda(lambda x: x * 10)\n",
        "])\n",
        "\n",
        "# 4e-4\n",
        "# lr_schedule = tf.keras.callbacks.LearningRateScheduler(\n",
        "#     lambda epoch: 1e-10 * 10**(epoch / 20))\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=0.00034)\n",
        "\n",
        "# [tf.keras.metrics.RootMeanSquaredError()]\n",
        "# tf.keras.losses.Huber()\n",
        "\n",
        "model.compile(loss=tf.keras.losses.Huber(), optimizer=optimizer,metrics=[tf.keras.metrics.RootMeanSquaredError()])\n",
        "history = model.fit(dataset,validation_data=dataset_test,epochs=23)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/23\n",
            "210/210 [==============================] - 13s 30ms/step - loss: 10.7736 - root_mean_squared_error: 13.5133 - val_loss: 9.0574 - val_root_mean_squared_error: 11.5410\n",
            "Epoch 2/23\n",
            "210/210 [==============================] - 7s 33ms/step - loss: 9.6096 - root_mean_squared_error: 12.1288 - val_loss: 7.4748 - val_root_mean_squared_error: 9.5332\n",
            "Epoch 3/23\n",
            "210/210 [==============================] - 6s 27ms/step - loss: 7.4802 - root_mean_squared_error: 9.8115 - val_loss: 6.6522 - val_root_mean_squared_error: 8.6949\n",
            "Epoch 4/23\n",
            "210/210 [==============================] - 6s 27ms/step - loss: 5.7517 - root_mean_squared_error: 7.9332 - val_loss: 5.6174 - val_root_mean_squared_error: 7.3872\n",
            "Epoch 5/23\n",
            "210/210 [==============================] - 8s 38ms/step - loss: 3.7862 - root_mean_squared_error: 5.5356 - val_loss: 4.4273 - val_root_mean_squared_error: 5.9476\n",
            "Epoch 6/23\n",
            "210/210 [==============================] - 7s 32ms/step - loss: 3.0345 - root_mean_squared_error: 4.5275 - val_loss: 3.5094 - val_root_mean_squared_error: 5.2862\n",
            "Epoch 7/23\n",
            "210/210 [==============================] - 5s 26ms/step - loss: 2.5687 - root_mean_squared_error: 4.0077 - val_loss: 2.9321 - val_root_mean_squared_error: 4.6061\n",
            "Epoch 8/23\n",
            "210/210 [==============================] - 6s 30ms/step - loss: 2.0216 - root_mean_squared_error: 3.3236 - val_loss: 2.7605 - val_root_mean_squared_error: 4.3181\n",
            "Epoch 9/23\n",
            "210/210 [==============================] - 6s 26ms/step - loss: 1.7559 - root_mean_squared_error: 2.9525 - val_loss: 2.6931 - val_root_mean_squared_error: 4.1314\n",
            "Epoch 10/23\n",
            "210/210 [==============================] - 8s 38ms/step - loss: 1.5569 - root_mean_squared_error: 2.6675 - val_loss: 2.6941 - val_root_mean_squared_error: 4.0948\n",
            "Epoch 11/23\n",
            "210/210 [==============================] - 7s 32ms/step - loss: 1.4421 - root_mean_squared_error: 2.5013 - val_loss: 2.4723 - val_root_mean_squared_error: 3.7436\n",
            "Epoch 12/23\n",
            "210/210 [==============================] - 6s 27ms/step - loss: 1.2408 - root_mean_squared_error: 2.2311 - val_loss: 2.0995 - val_root_mean_squared_error: 3.2463\n",
            "Epoch 13/23\n",
            "210/210 [==============================] - 7s 32ms/step - loss: 0.9637 - root_mean_squared_error: 1.8809 - val_loss: 1.4127 - val_root_mean_squared_error: 2.3281\n",
            "Epoch 14/23\n",
            "210/210 [==============================] - 7s 33ms/step - loss: 0.8151 - root_mean_squared_error: 1.7083 - val_loss: 1.1443 - val_root_mean_squared_error: 2.0105\n",
            "Epoch 15/23\n",
            "210/210 [==============================] - 7s 32ms/step - loss: 0.7357 - root_mean_squared_error: 1.5880 - val_loss: 1.1017 - val_root_mean_squared_error: 1.9551\n",
            "Epoch 16/23\n",
            "210/210 [==============================] - 7s 31ms/step - loss: 0.6701 - root_mean_squared_error: 1.5052 - val_loss: 1.1141 - val_root_mean_squared_error: 1.9664\n",
            "Epoch 17/23\n",
            "210/210 [==============================] - 7s 31ms/step - loss: 0.6142 - root_mean_squared_error: 1.4353 - val_loss: 1.2208 - val_root_mean_squared_error: 2.0780\n",
            "Epoch 18/23\n",
            "210/210 [==============================] - 8s 36ms/step - loss: 0.5464 - root_mean_squared_error: 1.3312 - val_loss: 1.2885 - val_root_mean_squared_error: 2.1544\n",
            "Epoch 19/23\n",
            "210/210 [==============================] - 8s 38ms/step - loss: 0.5013 - root_mean_squared_error: 1.2556 - val_loss: 1.2359 - val_root_mean_squared_error: 2.0935\n",
            "Epoch 20/23\n",
            "210/210 [==============================] - 5s 26ms/step - loss: 0.4688 - root_mean_squared_error: 1.2073 - val_loss: 1.3390 - val_root_mean_squared_error: 2.2351\n",
            "Epoch 21/23\n",
            "210/210 [==============================] - 7s 32ms/step - loss: 0.4335 - root_mean_squared_error: 1.1564 - val_loss: 1.4492 - val_root_mean_squared_error: 2.3876\n",
            "Epoch 22/23\n",
            "210/210 [==============================] - 5s 26ms/step - loss: 0.3947 - root_mean_squared_error: 1.1047 - val_loss: 1.5032 - val_root_mean_squared_error: 2.4498\n",
            "Epoch 23/23\n",
            "210/210 [==============================] - 6s 31ms/step - loss: 0.3607 - root_mean_squared_error: 1.0342 - val_loss: 1.6642 - val_root_mean_squared_error: 2.6597\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "id": "5ApiHRk1cELb",
        "outputId": "734382a7-5b9d-4bd7-eb5a-9e7dc8a6c281"
      },
      "source": [
        "epochs =  range(23)\n",
        "plt.figure(figsize=(12,8))\n",
        "plt.title('Epochs VS Huber Loss')\n",
        "plt.plot(epochs, history.history[\"val_loss\"],color='b',label='val_loss')\n",
        "plt.plot(epochs, history.history[\"loss\"],color='r',label='train_loss')\n",
        "plt.legend()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fa832f93760>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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WrFhh79ixo71EiRL24OBge4kSJew9e/a079q1y+l6AQC+xWa3Z3EsGwAAbrJv3z6VK1dOb775poYPH251OQAAAGkwJxoAAAAAACcRogEAAAAAcBIhGgAAAAAAJzEnGgAAAAAAJ9ETDQAAAACAkwjRAAAAAAA4KcjqAq6VlJSkw4cPKyIiQjabzepyAAAAAAA+zm6369y5cypRooQCAjLva/a4EH348GFFR0dbXQYAAAAAwM8cPHhQpUqVyvQajwvRERERkkzxkZGRFlcDAAAAAPB1sbGxio6OTs6jmfG4EO0Ywh0ZGUmIBgAAAADkGmemFLOwGAAAAAAATiJEAwAAAADgJEI0AAAAAABO8rg50QAAAADgaRITE3XlyhWry0AOBAcHX3f7KmcQogEAAAAgA3a7XUePHtXZs2etLgU5FBAQoHLlyik4ODhH9yFEAwAAAEAGHAG6aNGiCg8Pd2r1ZniepKQkHT58WEeOHFHp0qVz9OdIiAYAAACAdCQmJiYH6EKFClldDnKoSJEiOnz4sBISEpQnT55s34eFxQAAAAAgHY450OHh4RZXAldwDONOTEzM0X0I0QAAAACQCYZw+wZX/TkSogEAAAAAcBIhGgAAAACQStmyZfXOO+84da3NZtPChQvdWo8nIUQDAAAAAOAkQjQAAAAAAE4iRAMAAACAD/nwww9VokQJJSUlpTrfsWNHDRgwQH///bc6duyoYsWKKV++fKpbt66+++47lz3/t99+01133aWwsDAVKlRIDz74oM6fP5/8/sqVK1WvXj3lzZtX+fPnV+PGjbV//35J0tatW3XnnXcqIiJCkZGRuvXWW7Vx40aX1eYKhGgAAAAAcJLdLl24YM1htztXY/fu3XXq1Cn98MMPyedOnz6tpUuXqlevXjp//rzuvvturVixQr/++qtat26t9u3b68CBAzn+/blw4YJatWqlAgUKaMOGDZo7d66+++47DRkyRJKUkJCgTp06qWnTptq2bZvWrl2rBx98MHnl7F69eqlUqVLasGGDNm3apKeffjpHezq7Q5DVBQAAAACAt7h4UcqXz5pnnz8v5c17/esKFCigNm3aaMaMGWrWrJkkad68eSpcuLDuvPNOBQQEqFatWsnXv/zyy1qwYIEWL16cHHaza8aMGbp8+bKmTZumvP8WO27cOLVv316jR49Wnjx5FBMTo3bt2qlChQqSpKpVqyZ//sCBA/rvf/+rKlWqSJIqVqyYo3rcgZ5oAAAAAPAxvXr10hdffKG4uDhJ0vTp09WjRw8FBATo/PnzGj58uKpWrar8+fMrX7582r59u0t6ordv365atWolB2hJaty4sZKSkrRz504VLFhQ/fv3V6tWrdS+fXu9++67OnLkSPK1w4YN0wMPPKDmzZvr9ddf199//53jmlyNEA0AAAAATgoPNz3CVhzh4c7X2b59e9ntdi1ZskQHDx7U6tWr1atXL0nS8OHDtWDBAr322mtavXq1tmzZoho1aig+Pt5Nv2upTZ48WWvXrlWjRo00e/ZsVapUSevWrZMkjRw5Un/88Yfatm2r77//XtWqVdOCBQtypS5nMZwbAAAAAJxkszk3pNpqoaGh6tKli6ZPn66//vpLlStX1i233CJJWrNmjfr376/OnTtLks6fP699+/a55LlVq1bVlClTdOHCheTe6DVr1iggIECVK1dOvu7mm2/WzTffrGeeeUYNGzbUjBkz1KBBA0lSpUqVVKlSJT3xxBPq2bOnJk+enFyrJ6AnGgAAAAB8UK9evbRkyRJ9+umnyb3QkplnPH/+fG3ZskVbt27Vfffdl2Yl75w8MzQ0VP369dPvv/+uH374QUOHDlWfPn1UrFgx7d27V88884zWrl2r/fv369tvv9Xu3btVtWpVXbp0SUOGDNHKlSu1f/9+rVmzRhs2bEg1Z9oT0BMNAAAAAD7orrvuUsGCBbVz507dd999yefHjh2rAQMGqFGjRipcuLCeeuopxcbGuuSZ4eHhWrZsmR577DHVrVtX4eHh6tq1q8aOHZv8/o4dOzR16lSdOnVKN9xwgwYPHqyHHnpICQkJOnXqlPr27atjx46pcOHC6tKli1588UWX1OYqNrvd2YXSc0dsbKyioqIUExOjyMhIq8sBAAAA4KcuX76svXv3qly5cgoNDbW6HORQZn+eWcmhDOcGAAAAAMBJhOiciItzfsdzAAAAAPAy06dPV758+dI9qlevbnV5lmBOdHZ9/7308MPS889LffpYXQ0AAAAAuFyHDh1Uv379dN/LkydPLlfjGQjR2fXLL9Lu3dKTT0rt2kkFClhdEQAAAAC4VEREhCIiIqwuw6MwnDu7hg2TqlWTTpyQnn3W6moAAAAAALmAEJ1dwcHShAmmPWmStH69tfUAAAAAANyOEJ0TTZtK/fqZxcUeflhKSLC6IgAAAACAGxGic+rNN8186C1bpPHjra4GAAAAAOBGhOicKlJEev11037+eenwYWvrAQAAAAC4DSHaFR54QGrQQDp3TnriCaurAQAAAACXKVu2rN555x2X3GvlypWy2Ww6e/asS+5nBUK0KwQESBMnmtc5c6Rly6yuCAAAAIAfu+OOO/T444+75F4bNmzQgw8+6JJ7+QJCtKvUri09+qhpDx4sXbpkaTkAAAAAkBG73a4EJxdGLlKkiMLDw91ckfcgRLvSSy9JJUtKf/+dMk8aAAAAAHJR//799eOPP+rdd9+VzWaTzWbTlClTZLPZ9M033+jWW29VSEiIfvrpJ/3999/q2LGjihUrpnz58qlu3br67rvvUt3v2uHcNptNH3/8sTp37qzw8HBVrFhRixcvzna9X3zxhapXr66QkBCVLVtWY8aMSfX+hAkTVLFiRYWGhqpYsWLq1q1b8nvz5s1TjRo1FBYWpkKFCql58+a6cOFCtmtxBiHalSIiJMc31+uvS7t2WVoOAAAAABez26ULF6w57HanSnz33XfVsGFD/ec//9GRI0d05MgRRUdHS5Kefvppvf7669q+fbtq1qyp8+fP6+6779aKFSv066+/qnXr1mrfvr0OHDiQ6TNefPFF3XPPPdq2bZvuvvtu9erVS6dPn87yb+emTZt0zz33qEePHvrtt980cuRIPf/885oyZYokaePGjXr00Uf10ksvaefOnVq6dKluv/12SdKRI0fUs2dPDRgwQNu3b9fKlSvVpUsX2Z38fcquILfe3R917Sq1bi0tXWqGdX/7rWSzWV0VAAAAAFe4eFHKl8+aZ58/L+XNe93LoqKiFBwcrPDwcBUvXlyStGPHDknSSy+9pBYtWiRfW7BgQdWqVSv565dfflkLFizQ4sWLNWTIkAyf0b9/f/Xs2VOS9Nprr+m9997TL7/8otatW2fplzR27Fg1a9ZMzz//vCSpUqVK+vPPP/Xmm2+qf//+OnDggPLmzat27dopIiJCZcqU0c033yzJhOiEhAR16dJFZcqUkSTVqFEjS8/PDnqiXc1mk8aNk0JDpe++k2bPtroiAAAAAJAk1alTJ9XX58+f1/Dhw1W1alXlz59f+fLl0/bt26/bE12zZs3kdt68eRUZGanjx49nuZ7t27ercePGqc41btxYu3fvVmJiolq0aKEyZcqofPny6tOnj6ZPn66LFy9KkmrVqqVmzZqpRo0a6t69uz766COdOXMmyzVkFSHaHSpUkJ591rSfeEKKibG2HgAAAACuER5ueoStOFywuFfea3qyhw8frgULFui1117T6tWrtWXLFtWoUUPx8fGZ3idPnjypvrbZbEpKSspxfdeKiIjQ5s2bNXPmTN1www164YUXVKtWLZ09e1aBgYFavny5vvnmG1WrVk3vv/++KleurL1797q8jqsRot3lf/+TKlWSjh6V/h2aAAAAAMDL2WxmSLUVRxamiQYHBysxMfG6161Zs0b9+/dX586dVaNGDRUvXlz79u3LwW9Q1lStWlVr1qxJU1OlSpUUGBgoSQoKClLz5s31xhtvaNu2bdq3b5++//57SSa8N27cWC+++KJ+/fVXBQcHa8GCBW6tmTnR7hISIk2YIDVvLo0fL/XrJ916q9VVAQAAAPADZcuW1fr167Vv3z7ly5cvw17iihUrav78+Wrfvr1sNpuef/55t/QoZ+TJJ59U3bp19fLLL+vee+/V2rVrNW7cOE2YMEGS9NVXX2nPnj26/fbbVaBAAX399ddKSkpS5cqVtX79eq1YsUItW7ZU0aJFtX79ep04cUJVq1Z1a830RLtTs2bSffdJSUnSww9LTvwkCAAAAAByavjw4QoMDFS1atVUpEiRDOc4jx07VgUKFFCjRo3Uvn17tWrVSrfcckuu1XnLLbdozpw5mjVrlm666Sa98MILeumll9S/f39JUv78+TV//nzdddddqlq1qj744APNnDlT1atXV2RkpFatWqW7775blSpV0nPPPacxY8aoTZs2bq3ZZnf3+t9ZFBsbq6ioKMXExCgyMtLqcnLu6FGpShUzL3r8eOmRR6yuCAAAAIATLl++rL1796pcuXIKDQ21uhzkUGZ/nlnJofREu1vx4tKrr5r2s8+aUA0AAAAA8EqE6Nzw8MNSnTqmN3r4cKurAQAAAAC3ePjhh5UvX750j4cfftjq8lyC4dy5ZeNGqV49yW6XVqyQ7rrL6ooAAAAAZILh3Fl3/PhxxcbGpvteZGSkihYtmssVpXDVcG5W584tdepIgwdL48ZJgwZJ27aZFbwBAAAAwEcULVrU0qCcGxjOnZteecXMkd61S3rzTaurAQAAAABkESE6N0VFSWPHmvarr0p79lhbDwAAAIDr8rAZsMgmV/05EqJzW48eZv/oy5elIUPMHGkAAAAAHidPnjySpIsXL1pcCVwhPj5ekhQYGJij+zAnOrfZbNKECVKNGtI330jz50tdu1pdFQAAAIBrBAYGKn/+/Dp+/LgkKTw8XDabzeKqkB1JSUk6ceKEwsPDFRSUsxhMiLZCpUrSU09JL78sPfaY1LKlFBFhdVUAAAAArlG8eHFJSg7S8F4BAQEqXbp0jn8QwhZXVrl0SbrpJjMv+oknUuZKAwAAAPA4iYmJunLlitVlIAeCg4MVEJD+jOas5FBCtJWWLpXatJECA6VNm6RatayuCAAAAAD8TlZyKAuLWal1a6l7dykx0ewdnZRkdUUAAAAAgEwQoq329ttSvnzS2rXSJ59YXQ0AAAAAIBOEaKuVLGkWGJPMYmMnTlhbDwAAAAAgQ4RoTzBkiFS7tnTmjPS//1ldDQAAAAAgA4RoTxAUJE2caPaQnjJFWrXK6ooAAAAAAOkgRHuKBg2kBx807UGDpPh4a+sBAAAAAKRBiPYko0ZJRYpIf/5pFhwDAAAAAHgUQrQnKVBAeust037pJWn/fmvrAQAAAACkkuUQvWrVKrVv314lSpSQzWbTwoULU71vt9v1wgsv6IYbblBYWJiaN2+u3bt3u6pe39enj9S0qXTxovToo1ZXAwAAAAC4SpZD9IULF1SrVi2NHz8+3fffeOMNvffee/rggw+0fv165c2bV61atdLly5dzXKxfsNmkCRPMYmOLF0uLFlldEQAAAADgXza73W7P9odtNi1YsECdOnWSZHqhS5QooSeffFLDhw+XJMXExKhYsWKaMmWKevTocd17xsbGKioqSjExMYqMjMxuad7vmWek11+XSpc2c6Tz5rW6IgAAAADwSVnJoS6dE713714dPXpUzZs3Tz4XFRWl+vXra+3ata58lO97/nmpbFnpwAEzPxoAAAAAYDmXhuijR49KkooVK5bqfLFixZLfu1ZcXJxiY2NTHZAUHi69/75pjx0r/f67tfUAAAAAAKxfnXvUqFGKiopKPqKjo60uyXO0ayd16iQlJEiPPCJlf+Q9AAAAAMAFXBqiixcvLkk6duxYqvPHjh1Lfu9azzzzjGJiYpKPgwcPurIk7/fuu6ZXevVqaepUq6sBAAAAAL/m0hBdrlw5FS9eXCtWrEg+Fxsbq/Xr16thw4bpfiYkJESRkZGpDlyldGlp5EjT/u9/pVOnLC0HAAAAAPxZlkP0+fPntWXLFm3ZskWSWUxsy5YtOnDggGw2mx5//HG98sorWrx4sX777Tf17dtXJUqUSF7BG9nw+OPSTTdJJ09KTz9tdTUAAAAA4LeyvMXVypUrdeedd6Y5369fP02ZMkV2u10jRozQhx9+qLNnz+q2227ThAkTVKlSJafuzxZXGfjpJ6lJE9Nes0Zq1MjaegAAAADAR2Qlh+Zon2h3IERnYuBA6dNPpZo1pU2bpKAgqysCAAAAAK9n2T7RcLPRo6WCBaVt26T33rO6GgAAAADwO4Rob1K4sPTGG6Y9YoT0zz/W1gMAAAAAfoYQ7W3uv9/Mhz5/3iw4BgAAAADINYRobxMQIE2cKAUGSl98IX39tdUVAQAAAIDfIER7o5o1pSeeMO0hQ6SLF62tBwAAAAD8BCHaW40YIUVHS3v3Sq+9ZnU1AAAAAOAXCNHeKl8+6d13TfuNN6QdO6ytBwAAAAD8ACHam3XqJLVtK125Ij3yiORZW34DAAAAgM8hRHszm016/30pLEz64QdpxgyrKwIAAAAAn0aI9nblyknPPWfaw4ZJZ85YWw8AAAAA+DBCtC8YPlyqWlU6flz6v/+zuhoAAAAA8FmEaF8QHCxNmGDaH3wg/fKLtfUAAAAAgI8iRPuKO+6Q+vQxi4sNGiQlJlpdEQAAAAD4HEK0L3nrLSl/fmnz5pSeaQAAAACAyxCifUnRotKoUab93HNSbKy19QAAAACAjyFE+5oHH5QqVTIB+ssvra4GAAAAAHwKIdrXBARIPXqY9pw51tYCAAAAAD6GEO2L7rnHvC5dKsXEWFsLAAAAAPgQQrQvql5dqlZNio+XFi+2uhoAAAAA8BmEaF/l6I1mSDcAAAAAuAwh2ld1725ely2Tzp61tBQAAAAA8BWEaF9VrZoZ1n3lirRokdXVAAAAAIBPIET7MoZ0AwAAAIBLEaJ9mWNI97ffSmfOWFsLAAAAAPgAQrQvq1pVqlFDSkiQFi60uhoAAAAA8HqEaF/HkG4AAAAAcBlCtK9zDOn+7jvp9GlrawEAAAAAL0eI9nWVK0u1ajGkGwAAAABcgBDtDxjSDQAAAAAuQYj2B1cP6T51ytpaAAAAAMCLEaL9QcWKUu3aUmKitGCB1dUAAAAAgNciRPsLhnQDAAAAQI4Rov2FY0j3999LJ05YWwsAAAAAeClCtL+48UbpllsY0g0AAAAAOUCI9icM6QYAAACAHCFE+xPHkO4ffpCOH7e2FgAAAADwQoRof1K+vFSnjpSUJM2fb3U1AAAAAOB1CNH+xjGke+5ca+sAAAAAAC9EiPY3jiHdK1dKx45ZWgoAAAAAeBtCtL8pW1aqV48h3QAAAACQDYRof8Qq3QAAAACQLYRof9Stm3n98Ufp6FFrawEAAAAAL0KI9kdlykj160t2u/TFF1ZXAwAAAABegxCdTUeOSP/3f9L331tdSTYxpBsAAAAAsowQnU1vvim99po0apTVlWSTY0j36tXS4cPW1gIAAAAAXoIQnU2PPioFBEjffSdt3Wp1NdlQurTUsCFDugEAAAAgCwjR2VS2bMqWy2PGWFpK9jGkGwAAAACyhBCdA8OHm9eZM6V//rG2lmxxDOn+6Sfp0CFrawEAAAAAL0CIzoE6daTbb5cSEqT337e6mmwoVUpq3Ni0GdINAAAAANdFiM4hR2/0pEnSuXPW1pItDOkGAAAAAKcRonOobVupcmUpJkb65BOrq8mGrl0lm01as8ZLx6QDAAAAQO4hROdQQIA0bJhpv/OOGdrtVUqWlG67zbTnzbO2FgAAAADwcIRoF+jTRypSRNq/30unFjuWGWdINwAAAABkihDtAmFh0uDBpv3WW2brZa/iGNK9dq104IDV1QAAAACAxyJEu8gjj0ihodLGjdLq1VZXk0UlSkhNmpg2Q7oBAAAAIEOEaBcpUkTq18+033rL2lqyhVW6AQAAAOC6CNEu9MQTZlT0l19KO3ZYXU0WOYZ0r18v7dtndTUAAAAA4JEI0S5UubLUoYNpv/22tbVkWfHiUtOmps2QbgAAAABIFyHaxZ580rxOnSodP25tLVnGkG4AAAAAyBQh2sVuu02qV0+Ki5MmTLC6mizq0sVsfL1hA0O6AQAAACAdhGgXs9lSeqPHj5cuXrS2niwpVky64w7TnjvX0lIAAAAAwBMRot2gSxepbFnp5Elp2jSrq8kihnQDAAAAQIYI0W4QFCQ9/rhpjx0rJSVZWk7WOIZ0b9wo7dljdTUAAAAA4FEI0W4yYICUP7+0e7fZ8sprFCki3XmnaTOkGwAAAABSIUS7SUSE9PDDpj1mjLW1ZBlDugEAAAAgXYRoNxo6VMqTR1q9Wlq/3upqsqBzZykwUNq8WfrrL6urAQAAAACPQYh2oxIlpPvuM22v6o0uUkS66y7TZkg3AAAAACQjRLvZsGHm9YsvpL17ra0lSxjSDQAAAABpEKLdrGZNqWVLs0L3O+9YXU0WOIZ0b9ki7dpldTUAAAAA4BEI0blg+HDz+skn0pkz1tbitEKFpObNTZsh3QAAAAAgiRCdK5o3Nz3SFy5IkyZZXU0WOIZ0E6IBAAAAQBIhOlfYbNKTT5r2e+9J8fHW1uO0Tp2koCBp61Zp506rqwEAAAAAyxGic0mPHma17iNHpJkzra7GSQULSi1amDa90QAAAABAiM4twcHSo4+a9pgxkt1ubT1OY5VuAAAAAEhGiM5FDz0k5csn/fabtHy51dU4qWNHKU8eU/T27VZXAwAAAACWIkTnovz5pYEDTfuttywtxXkFCjCkGwAAAAD+RYjOZY8/LgUEmJ7orVutrsZJDOkGAAAAAEmE6FxXtqzUrZtpjx1raSnOcwzp/uMPcwAAAACAnyJEW2D4cPM6Y4Z06JC1tTglf36pVSvTZkg3AAAAAD9GiLZA3brS7bdLCQnS++9bXY2Trh7S7TVLiwMAAACAaxGiLfLkk+b1gw+kc+esrcUpHTqYfbq2b2dINwAAAAC/RYi2SLt2UqVKUkyM9OmnVlfjhKgoqXVr02ZINwAAAAA/RYi2SECANGyYab/9thna7fEY0g0AAADAzxGiLdS3r1S4sLR/v/TFF1ZX44T27aWQEGnHDun3362uBgAAAAByHSHaQmFh0pAhpj1mjBd07kZGpgzpZs9oAAAAAH6IEG2xRx6RQkOlDRuk1autrsYJDOkGAAAA4McI0RYrUkTq18+0x4yxthanOIZ079olbdtmdTUAAAAAkKsI0R7giSfM6+LF0s6d1tZyXRER0t13mzZDugEAAAD4GUK0B6hc2WzDLEljx1pbi1MY0g0AAADATxGiPcTw4eZ12jTp+HFra7mudu3MRO6//pK2bLG6GgAAAADINYRoD3HbbVLdutLly9KECVZXcx358klt25o2Q7oBAAAA+BFCtIew2VJ6o8ePly5dsrae62JINwAAAAA/RIj2IF26SGXKSCdPmmHdHq1tW7PR9Z490q+/Wl0NAAAAAOQKQrQHCQpKWal77FgpKcnaejKVN6+ZGy0xpBsAAACA3yBEe5gBA6T8+c02zF99ZXU118GQbgAAAAB+hhDtYSIipIceMu233rK2luu6+24pPFzau1fatMnqagAAAADA7QjRHmjoUClPHmn1aumXX6yuJhPh4QzpBgAAAOBXXB6iExMT9fzzz6tcuXIKCwtThQoV9PLLL8vOcF+nlSwp9exp2mPGWFvLdTGkGwAAAIAfcXmIHj16tCZOnKhx48Zp+/btGj16tN544w29//77rn6UT3vySfM6b54ZLe2x2rQxi4zt3y9t2GB1NQAAAADgVi4P0T///LM6duyotm3bqmzZsurWrZtatmypXzx6XLLnqVlTatnSrND97rtWV5OJ8HCpfXvTZkg3AAAAAB/n8hDdqFEjrVixQrt27ZIkbd26VT/99JPatGmT7vVxcXGKjY1NdcBw9EZ//LF05oy1tWSKId0AAAAA/ITLQ/TTTz+tHj16qEqVKsqTJ49uvvlmPf744+rVq1e6148aNUpRUVHJR3R0tKtL8lotWkg1akgXLkgffmh1NZlo3VrKl086eFBav97qagAAAADAbVweoufMmaPp06drxowZ2rx5s6ZOnaq33npLU6dOTff6Z555RjExMcnHwYMHXV2S17LZUnqj331Xio+3tp4MhYVJHTqYNkO6AQAAAPgwm93Fy2ZHR0fr6aef1uDBg5PPvfLKK/r888+1Y8eO634+NjZWUVFRiomJUWRkpCtL80rx8VK5ctLhw9KUKVK/flZXlIFFi6ROnaToaGnfPimA3dMAAAAAeIes5FCXJ52LFy8q4JoAFRgYqKSkJFc/yi8EB0uPPmraY8Z48JTjVq2kiAiGdAMAAADwaS4P0e3bt9err76qJUuWaN++fVqwYIHGjh2rzp07u/pRfuPBB80uUr/9Ji1fbnU1GQgNlTp2NG2GdAMAAADwUS4P0e+//766deumRx55RFWrVtXw4cP10EMP6eWXX3b1o/xGgQLSAw+Y9pgx1taSqe7dzevcuWZvLgAAAADwMS6fE51TzIlO39690o03mmy6davZR9rjXL4sFSsmxcZKP/0kNW5sdUUAAAAAcF2WzomGe5QrJ3XrZtpjx1pbS4YY0g0AAADAxxGivYhju6sZM6RDh6ytJUP33GNeGdINAAAAwAcRor1IvXpSkybSlSvS++9bXU0GWrSQoqKkI0ekNWusrgYAAAAAXIoQ7WWGDzevkyZJ585ZW0u6QkLMftESQ7oBAAAA+BxCtJdp106qVEk6e1b69FOrq8mAY0j3vHlSYqK1tQAAAACACxGivUxAgDRsmGm/846UkGBpOelr3lzKn186etSs0g0AAAAAPoIQ7YX69pUKF5b27ZPmz7e6mnQEB0udO5s2Q7oBAAAA+BBCtBcKC5MGDzbtt96SPGun7385hnR/8QVDugEAAAD4DEK0l3rkEbMt84YNHjpiulkzqUAB6dgxafVqq6sBAAAAAJcgRHupokXNsG7J9EZ7nDx5GNINAAAAwOcQor2YY4GxL7+Udu60tpZ0XT2k2yNXQAMAAACArCFEe7HKlaX27c2c6LfftrqadNx1l1SwoHT8uLRqldXVAAAAAECOEaK93PDh5nXqVOnECWtrSSNPHqlLF9NmSDcAAAAAH0CI9nJNmkh160qXL0sTJlhdTToY0g0AAADAhxCivZzNJj35pGmPGyddumRtPWnceadUqJB08qS0cqXV1QAAAABAjhCifUDXrlKZMianfvaZ1dVcIyjIFCgxpBsAAACA1yNE+4CgIOnxx017zBgpKcnSctJyDOmeP1+6csXaWgAAAAAgBwjRPmLgQCkqStq1S/rqK6uruUbTplKRItKpU9IPP1hdDQAAAABkGyHaR0RESA8/bNpjxlhbSxpXD+meO9faWgAAAAAgBwjRPmToUJNXV62SNmywupprdO9uXhnSDQAAAMCLEaJ9SMmS0n33mbbH9UbffrtUtKh0+rT0/fdWVwMAAAAA2UKI9jHDhpnXuXOlr7+2tpZUgoKkbt1Me8oUS0sBAAAAgOwiRPuYWrXMImNJSWZR7F9/tbqiqwwcaF6/+EI6dszaWgAAAAAgGwjRPmjCBOmuu6QLF6R27aSDB62u6F+33CI1aGDmRH/yidXVAAAAAECWEaJ9UHCw6eytXl06fFi6+24pJsbqqv71yCPm9YMPpMREa2sBAAAAgCwiRPuo/PmlJUuk4sWl3383i2N7xKLY3btLhQqZ7vElS6yuBgAAAACyhBDtw8qUkb76SgoPl5YvN/tI2+0WFxUaKg0YYNoTJlhbCwAAAABkESHax916qzR7thQQIH36qfTaa1ZXJOmhhySbTVq2TPrrL6urAQAAAACnEaL9QLt20vvvm/Zzz0nTp1tbjypUkFq3Nu1Jk6ytBQAAAACygBDtJx55RBo+3LQHDJB+/NHaepIXGPv0U+nSJWtrAQAAAAAnEaL9yOjRUteuUny81KmTtH27hcW0aWMmbZ8+Lc2ZY2EhAAAAAOA8QrQfCQiQPvtMathQOnvWbH117JhFxQQGmrnREguMAQAAAPAahGg/ExYmLVpkpiXv2yd16CBdvGhRMQMHSnnySL/8Im3caFERAAAAAOA8QrQfKlJE+vprqWBBk1979ZISEy0opGhRs2+0JE2caEEBAAAAAJA1hGg/VamStHixFBIiLVwoPfmkRYU4FhibOVM6c8aiIgAAAADAOYRoP9a4sTR1qmm/+645cl2jRlLNmmaFbkcxAAAAAOChCNF+7t57pddfN+0nnjDzpXOVzZbSGz1hgpSUlMsFAAAAAIDzCNHQ//5nFsq226WePc086VzVq5cUESHt3i19/30uPxwAAAAAnEeIhmw2adw4s3XzpUtS+/bS3r25WEC+fFLfvqbNdlcAAAAAPBghGpKkoCBp9mypdm3p+HGzh3SurvM1aJB5XbxY+uefXHwwAAAAADiPEI1kERHSV19JpUpJO3ZInTtLcXG59PDq1aWmTc1eWx99lEsPBQAAAICsIUQjlZIlpSVLTKD+8Udp4EAzVzpXOBYY+/BD6cqVXHooAAAAADiPEI00ataUvvjCDPGePl164YVcenCnTlLx4tLRo2bzagAAAADwMIRopKtFC2nSJNN+5RXp009z4aHBwdIDD5g2C4wBAAAA8ECEaGRowADpuedM+6GHpOXLc+GhDz4oBQRIK1dKf/6ZCw8EAAAAAOcRopGpl16S7rtPSkiQunWTfvvNzQ+MjpY6dDDtDz5w88MAAAAAIGsI0ciUzWaGcjdtKsXGmq2vDh9280MdC4xNnSqdP+/mhwEAAACA8wjRuK6QEGnBAqlKFbOFc9u20rlzbnxgs2ZSxYomtc+Y4cYHAQAAAEDWEKLhlAIFpK+/looWlbZskXr0MEO83SIgQBo0yLTHj8/FPbYAAAAAIHOEaDitXDnpyy+lsDATqIcOdWO+7ddPCg2Vtm2T1q5100MAAAAAIGsI0ciSevXMCGubzaz79dZbbnpQwYJSz56mzXZXAAAAADwEIRpZ1qmT9Pbbpv2//0lz5rjpQY4FxubOlU6ccNNDAAAAAMB5hGhky2OPmUOS+vaV1qxxw0Pq1JHq1pXi480S4QAAAABgMUI0sm3MGNMrHRcndewo7d7thoc4eqM/+EBKTHTDAwAAAADAeYRoZFtgoDR9uuksPnVKatPGDaOu773XLA2+b5+0dKmLbw4AAAAAWUOIRo6Eh5sVu8uWlf7+2/RIX7rkwgeEhUn332/aLDAGAAAAwGKEaORYsWJmy6v8+c1uVP36SUlJLnzAww+b12++kfbudeGNAQAAACBrCNFwiapVpYULpTx5zGLaTz/twptXrCi1bGk2pZ40yYU3BgAAAICsIUTDZZo2lSZPNu0335QmTnThzR0LjH3yiXT5sgtvDAAAAADOI0TDpXr1kl55xbSHDJGWLHHRjdu2laKjpZMnpXnzXHRTAAAAAMgaQjRc7tlnpQEDzLzoe++VNm92wU2DgqSHHjJtFhgDAAAAYBFCNFzOZjPbOrdoIV24YDqRDxxwwY0HDjRheu1aacsWF9wQAAAAALKGEA23cCwwVqOGdPSodPfdUkxMDm9avLjUtatpu3TCNQAAAAA4hxANt4mKMnOiS5SQ/vjD5N/4+Bze1LHA2OefuyCVAwAAAEDWEKLhVtHRJkjnyyetWGGmNdvtObhhkyZS9erSxYvStGkuqxMAAAAAnEGIhtvVri3NmSMFBkpTpkhvv52Dm9lsKb3REybkMJEDAAAAQNYQopEr2rSR3n3XtJ95Rvr99xzcrHdv07W9Y4e0cqUrygMAAAAApxCikWseeURq397Mi+7dOwfzoyMjzQ0ktrsCAAAAkKsI0cg1Npv00UdS4cLS1q3SyJE5uNmgQeZ14ULp8GEXVAcAAAAA10eIRq4qVkyaNMm0R4+Wfv45mzeqWVO67TYpIUH6+GOX1QcAAAAAmSFEI9d16SL17SslJUl9+kjnz2fzRo4FxiZNkq5ccVl9AAAAAJARQjQs8d57UunS0p490vDh2bxJly5S0aJmOPeXX7q0PgAAAABIDyEaloiKMttdSaYj+euvs3GTkBDpgQdMmwXGAAAAAOQCQjQsc+ed0hNPmPbAgdLJk9m4yYMPmhXLVqyQdu50aX0AAAAAcC1CNCz16qtS1arS0aNmwW27PYs3KFNGatfOtD/4wOX1AQAAAMDVCNGwVFiY9PnnUlCQNG+eNH16Nm7iWGBs8mTpwgWX1gcAAAAAVyNEw3K33CKNGGHaQ4ZIBw9m8QYtW0rly0sxMdKsWS6vDwAAAAAcCNHwCE8/LTVoYHLw/feb7a+cFhBgxoJL0vjx2RgTDgAAAADOIUTDIwQFSdOmSeHhZo2wceOyeIP77zerdf/6q/TLL26pEQAAAAAI0fAYFStKb75p2k89JW3fnoUPFyok3XuvaU+c6PLaAAAAAEAiRMPDDBoktWolXb4s9e0rXbmShQ87FhibNUs6dcot9QEAAADwb4RoeBSbTfr0U6lAAWnjRrMFltPq1TOrlMXFmZW6AQAAAMDFCNHwOCVKpIzIfuWVLExxttlSeqMnTszi6mQAAAAAcH2EaHike++VevSQEhOlPn2kixed/GDPnlJUlLRnj/Ttt26tEQAAAID/IUTDY40fb3qld+0yC405JTzcrNQtSRMmuK02AAAAAP6JEA2PVbBgytTmceOk5cud/ODDD5vXJUuk/fvdUhsAAAAA/0SIhkdr2VIaPNi0779fOnPGiQ9Vriw1a2bmRH/4oVvrAwAAAOBfCNHweG+8IVWqJB06JA0Z4uSHHAuMffyxWa0bAAAAAFyAEA2PFx4uTZsmBQZKM2ZIc+Y48aEOHcyE6uPHpfnz3V4jAAAAAP9AiIZXqF9fevZZ0374Yenw4et8IChIeugh02aBMQAAAAAuQoiG13j+eenWW8286IEDJbv9Oh944AHTff3TT9Jvv+VKjQAAAAB8GyEaXiNPHumzz6TQUGnpUmnSpOt8oEQJqXNn05440e31AQAAAPB9hGh4lapVpddfN+0nn5R2777OBxwLjH32mRQb69baAAAAAPg+QjS8ztCh0l13SRcvSn37SgkJmVx8xx1SlSrS+fPS55/nVokAAAAAfBQhGl4nIECaPFmKjJTWrZNGj87kYpstpTd6wgQnJlIDAAAAQMYI0fBKpUtL48aZ9siR0q+/ZnJx375mn6w//pBWr86N8gAAAAD4KEI0vFbv3lLXrmY4d+/e0uXLGVwYFSX16mXabHcFAAAAIAfcEqIPHTqk3r17q1ChQgoLC1ONGjW0ceNGdzwKfsxmkz74QCpWTPrzT+n//i+Tix1DuufPl44ezZX6AAAAAPgel4foM2fOqHHjxsqTJ4+++eYb/fnnnxozZowKFCjg6kcBKlxY+uQT0377bWnlygwurF1bathQunIl5QMAAAAAkEU2u921Ky09/fTTWrNmjVZnc+5pbGysoqKiFBMTo8jISFeWBh/24IPSRx+ZudK//WYWHUvj88+lPn2k6Ghpzx4pKCjX6wQAAADgebKSQ13eE7148WLVqVNH3bt3V9GiRXXzzTfro48+yvD6uLg4xcbGpjqArBo7VipfXjpwQHrssQwu6tbNdF0fPCgtWZKr9QEAAADwDS4P0Xv27NHEiRNVsWJFLVu2TIMGDdKjjz6qqVOnpnv9qFGjFBUVlXxER0e7uiT4gXz5pGnTzPZXU6ZICxemc1FoqDRwoGmzwBgAAACAbHD5cO7g4GDVqVNHP//8c/K5Rx99VBs2bNDatWvTXB8XF6e4uLjkr2NjYxUdHc1wbmTLM89Ir79uOpx//90sOpbK3r1ShQpmv+jdu6Ubb7SkTgAAAACew9Lh3DfccIOqVauW6lzVqlV14MCBdK8PCQlRZGRkqgPIrpEjpZo1pZMnpf/8x2TlVMqVk9q0Me0PPsjt8gAAAAB4OZeH6MaNG2vnzp2pzu3atUtlypRx9aOANEJCzPphwcHSl19Kkyenc5Fju6tPP5UuXcrV+gAAAAB4N5eH6CeeeELr1q3Ta6+9pr/++kszZszQhx9+qMGDB7v6UUC6atSQXnnFtB97zIzgTqV1a6lsWenMGWn27NwuDwAAAIAXc3mIrlu3rhYsWKCZM2fqpptu0ssvv6x33nlHvXr1cvWjgAwNGyY1aSKdPy/16yclJl71ZmCg9PDDps0CYwAAAACywOULi+UU+0TDVfbuNfOjz5+X3nhD+u9/r3rzxAmpVCkpPl7asEGqU8eyOgEAAABYy9KFxQBPUa6c9M47pv3cc9K2bVe9WaSI1L27aU+cmNulAQAAAPBShGj4tAEDpPbtTYdznz7SVbuppSwwNmOGmR8NAAAAANdBiIZPs9mkjz4yHc/btpktsJI1bCjVqiVdvixNmWJRhQAAAAC8CSEaPq9YMWnSJNN+4w1pzZp/37DZUnqjJ06UkpIsqQ8AAACA9yBEwy907mxW6U5Kkvr2lc6d+/eN++6TIiOl3bulFSssrREAAACA5yNEw2+8+65UurS0Z4/05JP/nsyXz6Rrie2uAAAAAFwXIRp+IypKmjo1ZZ70kiX/vjFokHn98kvp2DHL6gMAAADg+QjR8Ct33CE98YRpDxwonTwpqWpVqV49KTFRmjXLyvIAAAAAeDhCNPzOq69K1aqZTueHH5bsdpn9ryTps88srQ0AAACAZyNEw++Ehkqffy4FBUlffGHa6tHDnNi0Sdq+3eoSAQAAAHgoQjT80s03p+wZPWSIdOBiYalNG3OC3mgAAAAAGSBEw2899ZTUoIEUGyvdf7+U1OvfId3Tp7NnNAAAAIB0EaLht4KCTKdzeLj0/ffShIPtzRLeBw5Iq1ZZXR4AAAAAD0SIhl+78UZpzBjT/t8LoTrXprv5Yto064oCAAAA4LEI0fB7Dz0kNW0qXbokvX6orzk5b5508aK1hQEAAADwOIRo+D2bTRo/XgoMlEatbqyLxcpK585JixdbXRoAAAAAD0OIBiRVry49/rhkV4Amx/c2J1mlGwAAAMA1CNHAv0aMkG64QXr3zL+rdC9bJh07Zm1RAAAAADwKIRr4V0SEWWRstyppg62elJgozZpldVkAAAAAPAghGrhKjx7SHXdIU+3/9kYzpBsAAADAVQjRwFVsNmncOGleYA9dUZC0aZO0fbvVZQEAAADwEIRo4BrVq0u9Hy+sb9RGkpQwmd5oAAAAAAYhGkjHiBHSkgJmSPeFD6dLSUkWVwQAAADAExCigXREREh3vd1eZxWlqJgDOjpnldUlAQAAAPAAhGggA/f0DdVPN3SXJP3+FEO6AQAAABCigQzZbFL1UWZId90D87R0wSWLKwIAAABgNUI0kIlyfW7T6cgyilKsvh60WJcvW10RAAAAACsRooHMBAQo74O9JUmtjk3TW29ZXA8AAAAASxGigesIecAM6W6lZfrolWPat8/aegAAAABYhxANXE/lyrLXq6cgJapT3Cw9/rjVBQEAAACwCiEacIKtj+mN7qvPtGiR9PXXFhcEAAAAwBKEaMAZPXpIQUG6VZtURdv16KNikTEAAADADxGiAWcULiy1aSNJeiTfZ/r7b+nNNy2uCQAAAECuI0QDzvp3SPeA0OmyKUmvvSbt3WtxTQAAAAByFSEacFb79lJUlPKePKDHaq/S5cvSE09YXRQAAACA3ESIBpwVGip17y5JeqH8ZwoKkhYtkpYssbguAAAAALmGEA1kxb9Dugt8N0//HXJJklhkDAAAAPAjhGggK267TSpTRoqN1fO1FqtECWnPHhYZAwAAAPwFIRrIioAAqXdvSVLYvM80dqw5zSJjAAAAgH8gRANZ9e+Qbi1dqnvuOK677jLDuR9/3NKqAAAAAOQCQjSQVZUrS3XrSomJss2epXHjpKAgafFiFhkDAAAAfB0hGsgOR2/0Z5+patWUra5YZAwAAADwbYRoIDt69DDdzxs3Stu36/nnpZIlzSJjb7xhdXEAAAAA3IUQDWRHkSJSmzam/dlnioiQxowxX44axSJjAAAAgK8iRAPZ5RjSPX26lJSke+4Ri4wBAAAAPo4QDWRX+/ZSVJR04IC0apVsNqVaZOyrr6wuEAAAAICrEaKB7AoNlbp3N+3PPpOkNIuMXbpkUW0AAAAA3IIQDeSEY0j3vHnJidmxyNjevSwyBgAAAPgaQjSQE7fdJpUpI8XGmjHckiIipLFjzduvv25W7AYAAADgGwjRQE4EBEi9e5v2v0O6JTPKu1kzFhkDAAAAfA0hGsgpx5DupUul48clSTab9P77ZpGxL79kkTEAAADAVxCigZyqXFmqW1dKTJRmzUo+XbWqNGyYabPIGAAAAOAbCNGAKzh6o68a0i2xyBgAAADgawjRgCv06GHGbm/cKO3YkXw6X76URcZGjWKRMQAAAMDbEaIBVyhSRGrd2rSv6Y12LDIWFyc99pgFtQEAAABwGUI04CqOId2ffy4lJSWfttmkceOkPHnMAmNffmlRfQAAAAByjBANuEr79lJkpHTggLRqVaq3qlSRnnjCtB97jEXGAAAAAG9FiAZcJSxMuuce075mSLeUepGx0aNzuTYAAAAALkGIBlzJMaR73rw03c358klvv23ar7/OImMAAACANyJEA650221SmTJSbKy0eHGat7t1k5o3Z5ExAAAAwFsRogFXCgiQevc27XSGdNts0vvvs8gYAAAA4K0I0YCrOYZ0L10qHT+e5u0qVaRhw0z70UdZZAwAAADwJoRowNUqV5bq1pUSE6VZs9K95LnnpFKlpH37WGQMAAAA8CaEaMAdHL3R6QzplswiY2PHmvbrr0t//51LdQEAAADIEUI04A49ekhBQdLGjdKOHelewiJjAAAAgPchRAPuUKSI1Lq1aWfQG331ImNLlrDIGAAAAOANCNGAuziGdH/+uZSUlO4lLDIGAAAAeBdCNOAu7dtLkZHSgQPS6tUZXnb1ImOvv5575QEAAADIOkI04C5hYVL37qadwZBuySwy9vbbpj16NIuMAQAAAJ6MEA24k2NI99y5mY7V7tpVatHCLDL26KOS3Z5L9QEAAADIEkI04E5NmkilS0uxsZmuHHb1ImNff80iYwAAAICnIkQD7hQQkNIbPW1appdWriw9+aRpP/YYi4wBAAAAnogQDbibI0QvXSodP57ppc89J0VHs8gYAAAA4KkI0YC7Va4s1a0rJSZKs2ZlemnevNLYsaY9erT011+5UB8AAAAApxGigdzg6I3OZJVuh6sXGXvsMRYZAwAAADwJIRrIDT16SEFB0saN0o4dmV7KImMAAACA5yJEA7mhSBGpdWvTdqI3+upFxh59VLp40Y21AQAAAHAaIRrILY4h3Z9/LiUlXfdyxyJj+/dLjz/u3tIAAAAAOIcQDeSW9u2lyEjpwAFp9errXp43r/Txx2Z490cfSZMn50KNAAAAADJFiAZyS1iY1L27aTsxpFuSWraUXnzRtB95RPr1VzfVBgAAAMAphGggNzmGdM+dK1265NRH/u//pLZtpcuXzcrdp0+7sT4AAAAAmSJEA7mpSROpdGkpNtbpZbcDAkzHdfny0t69Joc7MaUaAAAAgBsQooHcFBAg9e5t2k4O6ZakAgWkL76QQkPNtlevvOKm+gAAAABkihAN5DbHkO6lS6UTJ5z+WO3a0sSJpj1ypPk4AAAAgNxFiAZyW5UqUp06UkKCNGtWlj7av7/00EOS3S7dd5+0b59bKgQAAACQAUI0YAVHb/S0aVn+6LvvSnXrSmfOSN26mQXHAAAAAOQOQjRghR49pKAgaeNGaceOLH00JESaN08qVEjatEkaOtRNNQIAAABIgxANWKFoUal1a9POwgJjDqVLSzNnSjab9PHH0iefuLg+AAAAAOkiRANWcQzp/vzzbO1Z1aKF9PLLpj14sOmVBgAAAOBehGjAKu3bS5GR0oED0urV2brFM8+Y28TFmfnRp0+7uEYAAAAAqRCiAauEhUndu5t2NoZ0S2bb6WnTpAoVzErdvXplq1MbAAAAgJMI0YCVHEO6586VLl3K1i3y55e++MJk8qVLpZdecl15AAAAAFIjRANWatLErBIWGyt9+WW2b1OrlvTBB6b90kvSN9+4qD4AAAAAqRCiASsFBEi9e5t2Nod0O/TtKz38sGS3m2Hde/e6oD4AAAAAqRCiAas5hnQvXSqdOJGjW73zjlSvnnTmjFloLJsjxAEAAABkgBANWK1KFalOHSkhQZo1K0e3CgmR5s2TCheWNm+WhgxxUY0AAAAAJOVCiH799ddls9n0+OOPu/tRgPdy9EbncEi3JEVHSzNnmpHin34qffxxjm8JAAAA4F9uDdEbNmzQpEmTVLNmTXc+BvB+PXpIgYHShg3Szp05vl3z5tIrr5j2kCHSpk05viUAAAAAuTFEnz9/Xr169dJHH32kAgUKuOsxgG8oWlRq3dq0XdAbLUlPPSV16CDFxUldu0qnTrnktgAAAIBfc1uIHjx4sNq2bavmzZu76xGAb3EM6f78cykpKce3CwiQpk6VKlSQ9u83K3YnJub4tgAAAIBfc0uInjVrljZv3qxRo0Zd99q4uDjFxsamOgC/1KGDFBlpEu/q1S65Zf780vz5UliYtGyZ2UMaAAAAQPa5PEQfPHhQjz32mKZPn67Q0NDrXj9q1ChFRUUlH9HR0a4uCfAOYWFS9+6m7aIh3ZJUs6b04Yem/dJL0pIlLrs1AAAA4Hdsdrvd7sobLly4UJ07d1ZgYGDyucTERNlsNgUEBCguLi7Ve3FxcYqLi0v+OjY2VtHR0YqJiVFkZKQrSwM8348/SnfcYXqkjx41wdpFBg+WJkwwvdObNknly7vs1gAAAIBXi42NVVRUlFM51OUh+ty5c9q/f3+qc/fff7+qVKmip556SjfddFOmn89K8YDPSUqSypWTDhyQZs+W7rnHZbeOi5OaNpXWr5dq15Z+/tmlGR0AAADwWlnJoS4fzh0REaGbbrop1ZE3b14VKlTougEa8HsBAVLv3qbtwiHdkhQSIs2bJxUpIm3ZYnqmXfsjNAAAAMD3uXWfaADZ4File+lS6cQJl966VClp1iyT1SdPlj7+2KW3BwAAAHxeroTolStX6p133smNRwHer0oVqU4dKSHBJF4Xu+su6dVXTXvIEGnDBpc/AgAAAPBZ9EQDnsjRG+3iId0OTz0ldeokxcdL3bpJJ0+65TEAAACAzyFEA56oRw8pMNB0E+/c6fLb22zSlClSxYpmDbNevaTERJc/BgAAAPA5hGjAExUtKrVubdpu6o2OipK++MKs0P3tt9LIkW55DAAAAOBTCNGAp3IM6f78c7P1lRvUqCF99JFpv/KK9NVXbnkMAAAA4DMI0YCn6tBBioyU9u+XFi5022N69TILjEkmt+/Z47ZHAQAAAF6PEA14qrAwacAA0+7dW1q92m2PGjNGathQOntW6tJFunjRbY8CAAAAvBohGvBko0dLbdtKly6ZVzftRxUcLM2ZIxUpIm3dKj3yiGS3u+VRAAAAgFcjRAOeLDhYmjtXuvNO6dw5s9jY77+75VGlSkmzZ0sBAdLUqdKHH7rlMQAAAIBXI0QDni4sTFq0SGrQQDp9WmreXNq92y2PuvNOadQo0370Ubd1fAMAAABeixANeIOICOnrr6XataVjx6RmzcyCY27w3/9KnTtL8fFS167SyZNueQwAAADglQjRgLcoUEBatkyqUkU6eND0SB854vLH2GzS5MlSpUrmMT17SomJLn8MAAAA4JUI0YA3KVpU+u47qVw56a+/pBYt3NJVHBUlzZ8vhYebx40Y4fJHAAAAAF6JEA14m5IlpRUrpBIlpD/+MIuNxcS4/DHVq0sff2zar74qffmlyx8BAAAAeB1CNOCNypUzQbpIEWnTJqldO+nCBZc/pmdPaehQ0+7Tx3R+AwAAAP6MEA14qypVpG+/lfLnl376yawGdvmyyx/z1ltSo0ams7trV+niRZc/AgAAAPAahGjAm9WuLX3zjZQ3r7R8udSjh3TliksfERwszZljpmNv2yYNGiTZ7S59BAAAAOA1CNGAt2vQwExYDg01+0n36+fy5bRLlpRmz5YCA6Vp06RJk1x6ewAAAMBrEKIBX3DnndK8eVJQkDRzpvTwwy7vLr7jDun110370Uel9etdensAAADAKxCiAV/Rtq00Y4YUEGCW1R42zOVB+sknzbzoK1ekbt2kEydcensAAADA4xGiAV/Svbv06aem/c47Lt/g2WYzt69cWfrnH5PbDx926SMAAAAAj0aIBnxNv37SuHGm/fLL0htvuPT2kZHSF19IBQpIGzZIdepI69a59BEAAACAxyJEA75o8OCUCcxPPSVNmODS21evbgL0TTdJR45ITZtKn3zi0kcAAAAAHokQDfiqp56S/u//THvwYLOstgtVqCCtXSt16SLFx0sPPCANGeLyHbYAAAAAj0KIBnzZyy9Ljz1m2vffb8Zhu1C+fNLcudJLL5mvx4+XWrRgwTEAAAD4LkI04MtsNuntt6WBA6WkJKlnT+nrr136iIAA6fnnzRbVERHSjz+aedK//urSxwAAAAAegRAN+DqbTZo0SerRw4y17tpVWrnS5Y/p0MHsHV2xonTggNS4sdmyGgAAAPAlhGjAHwQGmjnR7dtLly+b1/XrXf6YqlWlX36R2rSRLl2S7rvPTM1OTHT5owAAAABLEKIBf5EnjzRnjtSsmXT+vNS6tbR1q8sfkz+/9OWX0tNPm6/feMPsJ33mjMsfBQAAAOQ6QjTgT0JDzeTlRo2ks2fNKmA7drj8MYGB0qhR0uzZUni4tGyZVK+e9McfLn8UAAAAkKsI0YC/yZtXWrJEuuUWs4x28+bS3r1uedQ990g//yyVKSP99ZfUoIG0cKFbHgUAAADkCkI04I/y5zfdw9WqSYcOmSB96JBbHlWrlrRxo3TnnWYUeefO0siRZrFwAAAAwNsQogF/VbiwtHy5VKGCtGePCdJu2uC5cGGT2R991Hz94otmkfBz59zyOAAAAMBtCNGAPytRQlqxQipVysyNbtnSzJV2gzx5pHfflT79VAoONsO6GzQww7wBAAAAb0GIBvxdmTImSBctKm3ZYvanOn/ebY+7/35p1SqT3//8U6pbV1q61G2PAwAAAFyKEA1AqlTJDO0uUEBat07q2NHsJ+0m9eubedING5qO77ZtzVZYdrvbHgkAAAC4BCEagFGzpukSzpdP+v57qVs3KT7ebY+74Qbphx+kBx4wi4w99ZTUq5d08aLbHgkAAADkGCEaQIp69cz2V2Fh5rVPHykx0W2PCwmRPvxQGj9eCgqSZs6UGjeW9u932yMBAACAHCFEA0jt9tulBQvMSmBz5qR0FbuJzSY98oiZll2kiJmWXaeOtHKl2x4JAAAAZBshGkBarVpJs2ZJgYHSlCnSY4+5fcLy7bebedK33CKdPGl23Bo3jnnSAAAA8CyEaADp69LFBGibzaTZ//s/tz+ydGlp9WqpZ08zinzoUNMRHhfn9kcDAAAATiFEA8hY797SxImmPWqU9Nprbn9keLg0fbr05ptSQIDZV/qOO6TDh93+aAAAAOC6CNEAMvfQQybRSqY3+r333P5Im00aPlz6+mspf36z61adOuYVAAAAsBIhGsD1DR8ujRhh2o89ZrqHc0GrVtKGDVL16tKRI1LTprn2aAAAACBdhGgAzhkxQho2zLQfeECaPTtXHnvjjdLatVLnzmbb6oEDzVzpK1dy5fEAAABAKoRoAM6x2aS33jLDu+12M1967txceXREhDRvnvTii+brceOkFi2kEydy5fEAAABAMkI0AOfZbNKECVKvXlJCgnTPPSZMnzzp9kcHBEgvvCAtXCjlyyf9+KOZJ/3rr25/NAAAAJCMEA0gawICzNZXw4eb9vTpUrVq0pw5ubKpc8eO0vr1Zpj3gQNS48bSzJlufywAAAAgiRANIDuCgsyK3T//bFb9OnFCuvdes7f0kSNuf3y1atIvv0itW0uXLkn33Sc99ZTZWxoAAABwJ0I0gOyrX1/atMmMsw4KMmOtq1WTJk92e690gQLSV1+Z8CxJb7whtW0rnTnj1scCAADAzxGiAeRMSIhZ8WvTJunWW6WzZ6UBA8z+VPv2ufXRgYHS66+b4dxhYdKyZVK9etIff7j1sQAAAPBjhGgArlGzprRunTR6tBQaKi1fLt10k1lKOynJrY/u0UNas0YqXVr66y+pdm2pYUPTS/3VV/ROAwAAwHVsdnsurASUBbGxsYqKilJMTIwiIyOtLgdAduzaZfaSXr3afN24sfTJJ1Llym597IkTZn70d9+lPm+zSTVqSLffLjVpYo4bbnBrKQAAAPAiWcmhhGgA7pGUJH3wgekOPn/eDPseOdKs6h0U5LbH2u1mFPmqVSbDr1ol7d6d9robb0wJ1bffLpUrZ8I2AAAA/A8hGoDn2L9feughM2FZkm65Rfr0U6lWrVwr4ehRE6gdoXrbtrTrnpUokTpUV6tmdvACAACA7yNEA/Asdrs0bZr0xBNmgnJQkPT009Jzz5ke6lx29qyZQ+0I1Rs3SleupL6mYEHptttSQvXNN0t58uR6qQAAAMgFhGgAnunoUWnwYGn+fPN1tWpmrnSDBpaWdfGitH59Sqheu9acu1revGaxMkeorl/frAgOAAAA70eIBuDZ5s0zYfr4cTMR+fHHpZdfNknVA1y5Im3enDIEfPXqtCt858kj1a2bEqobN5aioqypFwAAADlDiAbg+U6dkoYNM8O8Jal8eenjj6U777S2rnQkJUl//ml6qR0Llh0+nPoam81M83aE6iZNpGLFrKkXAAAAWUOIBuA9vvnGLDx28KD5+sEHpTfe8OhuXbtd2rMnZfj36tVmf+prVaqUOlSXLcsK4AAAAJ6IEA3Au8TGmoXGJk40X5csabbHatfO2rqy4MiR1CuA//Zb2hXAS5Y0W2sVL272qS5ePO1RuLAUGGjNrwEAAMBfEaIBeKcff5QeeCClW/e++6R33zXJ0sucOZN2BfCEhOt/LjBQKlo0/YB9bfDOl4+ebQAAAFcgRAPwXhcvSiNGSGPHmsnIRYpI778v3XOPVyfGCxfMYmWHDplFyo8eNb3XjvbRo9KJE2l7rzMTHp55r7bjvaJF2Z4LAAAgM4RoAN5vwwZpwADp99/N1x07ShMmSCVKWFuXGyUkmCB9bbhOL3CfP5+1excunHnP9tV/3V79r4Kjnd653GjnySPdcgvbiQEAAPciRAPwDfHx0muvmePKFbPY2Nix0v33e3WvtCucPy8dO5Z+4L46dB87JiUmWl1tzoSHS61bm5+jtG0rFSpkdUUAAMDXEKIB+JbffpMGDjS905LUooX04YdmuWtkKinJ7CaWUY+249z58yk/l7j65xPpta/3viuvPX3a1OgQGGhWOu/Y0RzlymX+6wcAAHAGIRqA70lIkN55R3r+eenyZSlvXmnUKGnwYCkgwOrq4CZ2u/Trr9KiRdLChdK2banfr1nThOlOnaSbb/b7AQoAACCbCNEAfNfu3WYF71WrzNeNG0uffCJVrmxtXcgVe/dKixebQL16deqh6tHRKT3UTZuymBoAAHAeIRqAb0tKkiZNkv73PzMOOSREGjlSGj5cCgqyujrkklOnpCVLTC/10qVmYXeH/Pmlu+82PdStW0sREVZVCQAAvAEhGoB/OHBAeughk6Aks4zzJ59ItWtbWhZy36VL0ooVJlAvXiwdP57yXnCwdNddJlB36GBWJQcAALgaIRqA/7Dbpc8/lx57TDpzxvREP/209MILjOf1U4mJ0vr1Zsj3okXSrl2p369fP2XYd9WqzKMGAACEaAD+6NgxacgQad4883XjxtLs2VLJktbWBcvt2JESqNetS/1exYopC5M1aGBW/wYAAP6HEA3Af82daxYei42VihSRZs6UmjWzuip4iCNHpC+/NKF6xQqzFblDkSJS+/YmUDdvLoWFWVUlAADIbYRoAP7t77+lbt2kLVvMWN2XXpKefZatsJDKuXPSsmUmUC9ZIp09m/JeeLjUqpXppW7XTipUyKoqAQBAbiBEA8ClS9Kjj0off2y+bt3azJ0mDSEdV66YXdMc+1EfPJjyXkCA1KSJ6aHu2FEqV86qKgEAgLsQogHAYepUadAgE6qjo81w7/r1ra4KHsxuN4MYHIF669bU79eoYQJ1z55mYTIAAOD9CNEAcLVt28zw7t27zYrdY8dKgwezLDOcsm+fCdSLFpne6sREc95mk3r1MluUV6hgZYUAACCnspJDmSAIwPfVrClt3GiC9JUr0tChphvx3DmrK4MXKFvW7KD2/fdm/+lp08w8acfualWqSA8/LB06ZHWlAAAgNxCiAfiHyEhpzhzpnXfMXtKzZ0t160q//251ZfAiBQtKffqYFb43bZLatJESEqRJk0xv9JNPSidOWF0lAABwJ0I0AP9hs5kuxVWrpFKlpJ07pXr1pM8+s7oyeKFbbpG+/tp8OzVpIsXFmZkC5ctLL7wgxcRYXSEAAHAHQjQA/9OwobR5s9SypVlwrG9fMx738mWrK4MXatJE+vFHaelS6dZbpfPnpZdfNqt4jx4tXbxodYUAAMCVCNEA/FORIqYbceRI00M9aZLUuLG0Z4/VlcEL2WxmX+kNG6QvvjCrdp85Iz39tBnmPW6c6akGAADejxANwH8FBkojRpguxMKFTe/0rbdKixdbXRm8lM0mdeki/fab2V2tXDnp6FGzll3lytLkyWYONQAA8F6EaABo2dIE6IYNpbNnpY4dpaeeIu0g2wIDzSyBHTukCROkG26Q9u+XBgyQbrrJbFeelGR1lQAAIDsI0QAgSdHR0sqV0uOPm6/feENq1kw6csTKquDlgoOlQYOkv/+W3nxTKlTIrGd3zz1m0MPXX5utsgAAgPcgRAOAQ3Cw9PbbppswIsIsu3zzzSZcAzkQFiYNH26m3I8cab69tmyR2raVbrvNLEwGAAC8AyEaAK7VrZu0caNUo4Z07JjpkR41ivG3yLHISDMNf+9e6b//lUJDpZ9/lu64w8wq2LDB6goBAMD1EKIBID2VKknr1kn9+pnw/OyzUocO0unTVlcGH1CokJkx8Pff0iOPSHnySMuXm23Lu3SR/vjD6goBAEBGCNEAkJHwcLOc8scfSyEh0pIl0i23mF5qwAVKlJDGjzfzpPv1kwICpAULzCCIPn1MyAYAAJ6FEA0AmbHZpIEDTa90hQpmieXGjaWJE1kRCi5Trpw0ZYr0++9mNoHdLn3+uVSlivTww9KhQ1ZXCAAAHAjRAOCM2rVND3SnTlJ8vBmD26ePdP681ZXBh1Stata127RJatPG7LI2aZL5+c2TT0onTlhdIQAAIEQDgLPy55fmz5feestsBDx9upnEun271ZXBx9xyi9n+avVqqUkTKS5OGjtWKl9eeuEFKSbG6goBAPBfhGgAyAqbzXQJrlwp3XCDCdB160ozZ1pdGXyQY/urpUvNvtLnz0svv2yGf48eLV28aHWFAAD4H0I0AGTHbbdJv/4q3XWXdOGCdN990uDBpssQcCGbTWrVymx/9cUXZsj3mTPS00+bYd7jxvFtBwBAbiJEA0B2FSsmffut9Nxz5usJE0y43rfP0rLgm2w2s/3Vb79J06aZ3uijR6WhQ6XKlc3CZAkJVlcJAIDvI0QDQE4EBprxtV9/LRUsaBYfu+UWsx0W4AaBgWZNux07zM9tbrjBLBp///3STTeZhclYOB4AAPchRAOAK7RpI23ebBYaO3NGatdO+r//o2sQbhMcLA0aZPaSfvNNqVAhs9/0PfeY3umkJKsrBADANxGiAcBVypSRVq2ShgwxX7/2mtSypXTsmLV1waeFhUnDh0t79piZBTabNH689OCDUmKi1dUBAOB7XB6iR40apbp16yoiIkJFixZVp06dtHPnTlc/BgA8U0iI9P770qxZUr580g8/SDffbMI14EaRkWZmwdSpUkCA9MknUt++DIYAAMDVXB6if/zxRw0ePFjr1q3T8uXLdeXKFbVs2VIXLlxw9aMAwHPde69ZTrl6denIEbOK95tvMlkVbtenj/kZTlCQNGOG+VaMj7e6KgAAfIfNbnfv/9GdOHFCRYsW1Y8//qjbb7/9utfHxsYqKipKMTExioyMdGdpAOB+Fy5IDz0kTZ9uvq5aVerXT+rdWypZ0tra4NO+/FLq1s0E6LvvlubNM0O/AQBAWlnJoW6fEx0TEyNJKliwYLrvx8XFKTY2NtUBAD4jb17ps8+kDz6QwsOl7dvNBr+lS5vNf2fOlC5dsrpK+KD27aWvvjLB+euvzVp3DAoDACDn3NoTnZSUpA4dOujs2bP66aef0r1m5MiRevHFF9OcpycagM+JiTH7D02dKl39d2JkpFlSuV8/qXFjszIU4CKrVklt20rnz5tvryVLpKgoq6sCAMCzZKUn2q0hetCgQfrmm2/0008/qVSpUuleExcXp7i4uOSvY2NjFR0dTYgG4Nv+/luaNs0c+/alnK9QwawG1bevVLasVdXBx6xfL7VuLZ09K9WpIy1bZrY1BwAAhkeE6CFDhmjRokVatWqVypUr5/TnmBMNwK8kJZmuwqlTTS/11eNt77jD9E5362ZW+gZyYMsWqUUL6eRJqWZNaflyqWhRq6sCAMAzWBqi7Xa7hg4dqgULFmjlypWqWLFilj5PiAbgty5ckObPN4H6++9TVvIOD5e6dpX69zfBOsDty1nAR/3xh9S8uXT0qFSlivTdd6xvBwCAZHGIfuSRRzRjxgwtWrRIlStXTj4fFRWlMCeWBSVEA4CkAwfMgmRTp0q7d6ecL13a7GHUr5+UxR9SApL5dmrWTDp4UCpf3vy8pkwZq6sCAMBaloZoWwYL4kyePFn9+/e/7ucJ0QBwFbtdWrfOhOlZs8ziZA4NG5re6XvukfLnt6pCeKF9+0yQ3rNHio42QfrGG62uCgAA63jEnOjsIkQDQAYuX5YWLTKBetkyM59akkJCpE6dTO90ixZSUJClZcI7HDpkgvTOndINN5ih3dWqWV0VAADWIEQDgK87ckSaPl2aMsVMdHUoXlzq3dsE6ptusqw8eIdjx8zPXX77TSpc2Cw2Vru21VUBAJD7CNEA4C/sdmnzZtM7PWOGdOpUynu33mrCdM+eJiEB6Th9WmrVStq40cwKWLZMqlfP6qoAAMhdhGgA8Efx8dLXX5ve6SVLpIQEcz5PHqltWzN/uk0bKTjYyirhgWJipLvvln7+WYqIMN8+TZpYXRUAALmHEA0A/u7ECWnmTNNDvXlzyvnChaX77jM91DffLGWwGCT8z/nzUseOZpGxsDBp8WKzHRYAAP6AEA0ASPH77yZMf/aZmQTrcNNNpne6Vy8zlxp+79IlsyX5N9+Y9ermzZPatbO6KgAA3I8QDQBIKyFB+vZbE6gXLZLi4sz5wEDprrvMUO9WraSqVemh9mNxcWYa/YIFZqH3mTOlbt2srgoAAPciRAMAMnfmjDRnjpk/vW5d6veio02YbtXKjOdlD2q/c+WKGfE/c6YUEGB+7tK7t9VVAQDgPoRoAIDzdu2SvvpKWrpUWrUqpYdaMr3U9eubQN26tVnxOzDQulqRaxITpQcflD791AxMmDRJ+s9/rK4KAAD3IEQDALLn4kXpxx/NPkfLlkk7dqR+v2BBs7Fw69ZSy5ZSiRLW1IlckZQkPfqoNH68+fqdd6THHrO0JAAA3IIQDQBwjf37UwL1d99JsbGp369RI6WX+rbbzGpU8Cl2u/TUU9Kbb5qvR42Snn7a2poAAN4lMdHzB7IRogEArpeQIK1fb4Z9L1smbdxoEpZDeLh0xx0mULdqJVWsyAJlPsJul1580RyS9Pzzps0fLwAgPUePSmvXmuPnn81GIcePS8HBVleWMUI0AMD9Tp6Uli9P6ak+ejT1+2XLpvRS33WXxN/pXm/06JRe6CefNL3TBGkA8G8JCSYk//yzOdaulfbsSXvdunVmmRVPRYgGAOQuu13ati0lUK9ebZZ4dggKkho2TOmlvvlms+wzvM7775t50pL0yCPma/4oAcB/nD5tArGjl3n9eunChdTX2GzSTTdJjRqZf/4bNZJuvNGzf/BKiAYAWOv8eWnlShOoly6V/vor9ftFipiFyVq1Mq/FillSJrLn44/Nyt12u3T//dJHH3n+XDcAQNYlJUk7d6buZd6+Pe11kZFSgwYmLDdqJNWrJ0VF5X69OUGIBgB4lj17UnqpV6wwIftqtWun9FI3auTZk6YgSZo+3ewlnZgo9eghTZsm5cljdVUAgJw4d0765ZeUXua1a6WzZ9NeV6lSSg9zo0ZS1are/8NUQjQAwHPFx5t/lR2hevPm1O/ny2fmUDt6qStU8OzxX37siy+knj3NyP1OnaRZs1igHQC8hd0u7d2bupd52zbT+3y1sDDTs+wYmt2ggRlQ5msI0QAA73HsWOoFyk6cSP1+RIRUpYr5MffVR/nyZq41LLVkidS1qxQXZwYTzJ9v/ocLAOBZLl+WNm1KCc0//2xWzL5W6dIpPcyNGkk1a/rHSCNCNADAOyUlSVu2pATqn39OvUDZ1YKDzXiya8N15cpSaGiulu3vVqyQOnSQLl6U7rxTWrzYDCgAAFjn0KHUvcybN6f9JzVPHunWW1OGZjdsKJUsaU29ViNEAwB8Q3y89Pff0p9/mpVMHMeOHdKlS+l/xmYzvdTXhuuqVb1vlRMv8tNP0t13m/l0DRtK33zDbzcAuJvdbpYZOXnS7DS5YUNKcD54MO31xYql9DA3bGgCND93NgjRAADflpQkHTiQNlxv3y6dOZPx50qUSD9cFyvGvGsX2LDBTGU/c8b8j9myZVKhQlZXBQDeISnJLOJ16pQ5Tp5M/ZpRO6MBWwEBZij21aG5XDn+ucsIIRoA4J/sdjPBa/v2tAH78OGMP5c/v1StWtpwXaYMmyBn0datUvPm5n/ubrpJ+u47djAD4H8SEsx+ytcLwFe3T59Ou6iXs0JCzGJfNWqkhOa6dc2yInAOIRoAgGvFxJhh4NeG6717M/6/lrAwM8f62oB9441sw5WJ7dulZs2kI0fMtPUVK6RSpayuCgByJjbW7Jm8f//1e4nT2xbKWfnySYULm5E8hQo51w4Pp4c5pwjRAAA46/JladeutL3Xu3aZOdnpCQoyY+RatzZHw4asFH6Nv/4yQfrAATN88PvvpbJlra4KADKXmGj+3tq50/zc9erXI0eyfr8CBZwPwoULSwULslWgVQjRAADkVEKC6aV2hOqrA/b586mvjYoyY5hbtzaTgqOjranZw+zfb4L033+bnuglS8zPHgDAaufOpR+Ud+0yW/ZlpHhxqUIFM3T6eqG4QAF+vupNCNEAALiL3W6WPF25Ulq6VPr2WzN272rVq6f0Ujdp4tfdCocPm58vbN9uvm7cWOrfX7rnHol/5gG4k2MNyvTCcmbLZDh2UKxcWapSJeW1UiV2HfBlhGgAAHJLYqK0aZMJ1EuXSuvXp55jHR5uNk9u3Vpq08Z0YfiZEyek//xH+vLLlN+asDCpSxcTqO+6i/XbAGTfuXOmBzm9XuXLlzP+XLFiqUOy47VMGSkwMPfqh2cgRAMAYJXTp6Xly1NC9dGjqd+/8caUXuo77pDy5rWkTCscPix9/rk0ZUpKz7RkRr/362eOG2+0rDwAHiwpyQwCSq9X+dChjD8XHGz+Xrk2LFeubDZmABwI0QAAeAK7Xdq2LSVQ//STmWvtEBws3X57SqiuVs0vlle1282e0lOmSDNnpl7F9rbbUoZ7szUL4H/OnpV27zbHzp0pYXnXLunSpYw/V7Ro2h7lypXNgobMS4YzCNEAAHii2Fjphx9MoP7mG7Py1tWio1MCdbNmfjH57vJlafFiafJkM73cMdw7PFzq2tUE6jvuYLg34EtiY01I/uuvlMDsOE6ezPhzefKYXuX0wnKBArlXP3wTIRoAAE9nt5suFkcv9cqVqZeEDQyUGjVKCdW1a/t8kjx0KGW4944dKedLl04Z7u2HU8oBr3T+fPohefdu6fjxzD9bvLhUsaJZyOvqsFyuHL3KcB9CNAAA3ubiRWnVqpRQvXNn6veLFjXbZ7VuLbVsafZR8VF2u/TLLynDvWNiUt67/XbTO92tG8O94VnsdtPDevSo2U/46NHUh5Sy9dG1R8GC5jUszNpfQ1ZdupRxUL7enspFipigfO1x4438tw1rEKIBAPB2e/dKy5aZQL1iReq9qW02qW7dlF7qevV8dinZS5ekRYtMoP72WxNUJDPcu1s3E6ibNvX5TnpYKD5eOnYsdSC+NiQ7vs5sJWhnhIWlH64zCt2OvYjd+Z//5cvSnj3pB+V//sn8s4UKZRyU/WC2CrwMIRoAAF8SHy+tWZPSS71tW+r3CxSQWrQwgbpVK6lECWvqdLN//kkZ7n11R32ZMinDvcuXt6w8eBG7XTpzJuNgfHX72m3grycyUrrhBjMk+epDMov3nzqV9jh9OvWag1lhs5lVprMSvAsVMhsDONYxjI9PHZSv7l0+cCDlh1fpyZ8//aBcsSLzlOFdCNEAAPiyw4dTeqm//Tb18tY2m1mU7IEHpE6dpJAQq6p0G7vdbMc9ebI0a5YZQuvQtGnKcO98+SwrERa5fDml1zij3mLHER/v/H2DglIH4mtDsuPrYsXMKImsstvNXsfpBWxHyE7v/NXf+1kVHGzCdHCw2Trq6u3trxURkXFQLlTILzYVgB8gRAMA4C8SEsx+UY5e6l9+SXmvYEGpd29p4ECpZk3ranSjS5ekhQtN7/Ty5Sk9Znnzpgz3vv12hnv7koQEad8+s+XRtcfBg1m7V4EC1w/GxYub/5Q88XvoyhXTo57V8J3eDxDy5s04KBcpQlCG7yNEAwDgr/buNV20kyennrBYt64J0z17mvGmPuiff6TPPjOBeteulPPlypmh3n37mjY8n91uepR37TJD968Oyn//bcJjRoKDnQvGxYpJoaG592vyFHa7dOFCSsC+dMn8d1G8OEEZ/o0QDQCAv0tMNEO9P/nEbMTsSB1hYdI995hAfdttPvl/zXa7tHatCdOzZ6ce8nrHHaZ3umtXhnt7Asd+wY6AfHVgPncu48+FhqZsgeQ4Klc2C1YVLuyT39YA3IwQDQAAUhw/brpoP/lE2r495XylStKAAaab1rHykY+5eDFluPd336Ue7t29u3T//eZnCZ44VNdXOBatunbo9c6dKVs/pcdmk8qWNeH46rBcqZIUHc2fGQDXIkQDAIC07HZp3ToTpmfNMmM6JbM/Trt2pne6TRuzipIPOnhQmjbNBOq//ko5X7iw2YY7KirlyJ8/46+vbkdEEOYksyjV4cNph17v2mVmGCQmZvzZokVT9yY72uXL++dwawDWIEQDAIDMnTsnzZljAvXatSnnb7jBjHceMMCMjfVBdrv0888pw70zGzZ8PTabmWLuTODO6L3w8Nwdfmy3m1B75YpZpCs7rydOpA7Mu3ebXv+M5M2btje5cmUzJDt//lz7pQNAhgjRAADAeX/+acL0tGnSyZMp55s2NVtlde1q5lL7oIsXpR07zC5hMTEphzNfZ2WLpMwEBqYfuKOiTOBNSMh+2E3vNbv7ETvz6yhfPv3h1yVKME8ZgGcjRAMAgKyLj5e+/FL6+GOzD7XjfxGioqT77jOB+pZbrK3Rg1y+nH7Izkogz2yYc24LCjJHnjzXf42KSjv8ulw58x4AeCNCNAAAyJmDB814508/NZvyOtSubeZO9+plNtlFttntpic8o5AdG2t6b68Xap0Nvpm9BgbSUwzAvxGiAQCAayQlST/8YHqn589PGcMcEiJ16WJ6p++4g9W1AABejRANAABc7/Rpafp0E6i3bUs5X66cWYisf3+pVCnLygMAILsI0QAAwH3sdmnTJrMY2YwZZtyxZHqjW7c2w73btZOCg62tEwAAJ2UlhzL2CgAAZI3NJtWpI02cKB05Ylb1vv12M/T766/Nat6lSkn//a9Z+hoAAB9CTzQAAHCN3bvNQmRTpkhHj6acb9TI9E537CgVKmRZeQAAZITh3AAAwDoJCaZH+pNPpCVLUu/jVL68VLeuVK+eeb3lFilvXutqBQBAhGgAAOApjhyRpk6VPvtM+vPPtO8HBEjVqqWE6rp1pRo1mE8NAMhVhGgAAOB5zp6VNm6UNmwwxy+/SIcOpb0uJESqVSt1sK5cmW20AABuQ4gGAADe4ciRlEDtCNdnzqS9LiLCLGbmCNX16knR0WaRMwAAcogQDQAAvJPdLu3ZkzpUb94sXbyY9tqiRVOH6rp1pcKFc79mAIDXI0QDAADfkZBg5lM7QvWGDdK2beb8tcqWTR2sb7nF9GIDAJAJQjQAAPBtly5JW7emHgq+c2fa62w2qWrV1L3VNWuaedcAAPyLEA0AAPxPTIy0aVPqoeAHD6a9LjjYBGlHqK5dWypdWipQgDnWAOCnCNEAAACSdPRo6mHgv/winT6d/rXh4VKpUmbBsqtfr24TtAHAJxGiAQAA0mO3S3v3pg7Vf/4pnTrl3OevDdrphW6CNgB4HUI0AABAVly6ZPasPnhQ+ueflNer2ydPOncvgjYAeJ2s5NCgXKoJAADAc4WFSTfeaI6MOBu0L16Udu0yR0YcQTu9gE3QBgCPRogGAABwRnaC9tUBOztBu3hxcxQrlvlrWJjrf70AgHQRogEAAFwlK0E7vYB9bdDes8cc1xMR4VzgLlaM7b0AIIcI0QAAALkpK0H76FHp2LHMX+PipHPnzLF79/Wfnz9/6nCdUeAuWlTKk8dlv2wA8BWEaAAAAE/jTNCWzGrjsbHOhe1jx6QrV6SzZ82xY8f16yhUKP2QXbSoVLhw6iMqijncAPwCIRoAAMBb2WwmvEZFSZUrZ36t3S6dOeNc2D52TEpMNFt/nTol/fHH9WsJCkobrK93hIcTvAF4HUI0AACAP7DZpIIFzVG1aubXJiVJp0+bUJ1e0D5xwszbdhznz0sJCSnXOys0NGuhu1Ah8xkAsBAhGgAAAKkFBKQE15tuuv71ly+bHuurg/W1x7XBOy7OfM6xirmz8uXLPGg75nk7hp8TugG4GCEaAAAAORMaKpUsaQ5n2O3ShQuZh+70jsRE0+t9/ry0b59zzypQICVU33BD6pB99deFCjG0HIBTCNEAAADIXTab6VHOl08qW9a5z9jtUkxM5iH7+PGUIedHj0rx8WYe+Jkz0vbtmd8/T57UK5ZfHbCvDd/syw34NUI0AAAAPJ/NZrbnyp//+quWSykLqTkC9ZEjKe1rvz51yqxc7uzQ8qgo53q3Cxc2Q+MB+BRCNAAAAHzP1QupVauW+bXx8al7sDML3Zcvmx7xmBhp587M7xsYmNK7XbiwGVru+EHA1Ud650NCcvxbAMA9CNEAAADwb8HBUnS0OTJz9b7cmfVsO1YwT0yUDh82R1aFhmYtdF99PirKDE8H4BaEaAAAAMAZWdmX+8oVE6QdAfv0aensWTPE/OzZ1MfV52JiTFi/fNl87siR7NWaN6/zgdsxPz1v3tTtvHkZjg6kgxANAAAAuFqePFKJEubIiqQk6dy564ftjM6dO2fuc+GCOQ4dytmvIzw8bbjOaTs8nHAOr0aIBgAAADxFQEBKb3d2JCSY3uysBHBH4HZsH3bhgukNl6SLF81x4kSOf2mphIdfP3BHRJj21a8Ztek1Ry4iRAMAAAC+IijI7HldqFD272G3S5cupQ3W17av9356114bzl0pq8H7eu8HEZWQPr4zAAAAAKSw2UxPcXi4VKSI6+6bWTi/9ty5cylfO9rnzqVtnztnhsBLKT3qrhISkn7wjoxMOSIiUn+d3hERwUJvPoYQDQAAAMD93BHOHYuwZRa0rxfEr23Hx5t7x8WZ4+TJnNcZFpa14J1RUA8LM7+PsBQhGgAAAIB3stlMsAwLk4oWdc094+MzD+KxsaYdG3v94/Jlc89Ll8xx7FjOagsMzDxsO3rLr+45v/a4+nxICKE8GwjRAAAAAOAQHJzzeeUOV644H7gzC+fnzple98REsyDcmTM5r00yoTwroduZ86GhPh/MCdEAAAAA4A558kgFC5ojJ5KSzHzvzIJ3TEzKPPJrj6vnmJ8/b3rFJRPKY2LM4SpXB/Orj+nTpZIlXfccCxGiAQAAAMCTBQSkDNd2RRBNTEy7gFtmoduZ9xyrrbsjmHsYQjQAAAAA+JOr51a7iiOYZxS6Cxd23bMsRogGAAAAAOSMO4K5hwqwugAAAAAAALwFIRoAAAAAACcRogEAAAAAcBIhGgAAAAAAJxGiAQAAAABwEiEaAAAAAAAnEaIBAAAAAHASIRoAAAAAACcRogEAAAAAcBIhGgAAAAAAJxGiAQAAAABwEiEaAAAAAAAnuS1Ejx8/XmXLllVoaKjq16+vX375xV2PAgAAAAAgV7glRM+ePVvDhg3TiBEjtHnzZtWqVUutWrXS8ePH3fE4AAAAAAByhVtC9NixY/Wf//xH999/v6pVq6YPPvhA4eHh+vTTT93xOAAAAAAAcoXLQ3R8fLw2bdqk5s2bpzwkIEDNmzfX2rVrXf04AAAAAAByTZCrb3jy5EklJiaqWLFiqc4XK1ZMO3bsSHN9XFyc4uLikr+OjY11dUkAAAAAALiE5atzjxo1SlFRUclHdHS01SUBAAAAAJAul4fowoULKzAwUMeOHUt1/tixYypevHia65955hnFxMQkHwcPHnR1SQAAAAAAuITLQ3RwcLBuvfVWrVixIvlcUlKSVqxYoYYNG6a5PiQkRJGRkakOAAAAAAA8kcvnREvSsGHD1K9fP9WpU0f16tXTO++8owsXLuj+++93x+MAAAAAAMgVbgnR9957r06cOKEXXnhBR48eVe3atbV06dI0i40BAAAAAOBNbHa73W51EVeLjY1VVFSUYmJiGNoNAAAAAHC7rORQy1fnBgAAAADAW7hlOHdOODrG2S8aAAAAAJAbHPnTmYHaHheiz507J0nsFw0AAAAAyFXnzp1TVFRUptd43JzopKQkHT58WBEREbLZbFaXk6nY2FhFR0fr4MGDzN+G3+H7H/6M73/4M77/4c/4/vdddrtd586dU4kSJRQQkPmsZ4/riQ4ICFCpUqWsLiNL2N8a/ozvf/gzvv/hz/j+hz/j+983Xa8H2oGFxQAAAAAAcBIhGgAAAAAAJxGicyAkJEQjRoxQSEiI1aUAuY7vf/gzvv/hz/j+hz/j+x+SBy4sBgAAAACAp6InGgAAAAAAJxGiAQAAAABwEiEaAAAAAAAnEaIBAAAAAHASITqbxo8fr7Jlyyo0NFT169fXL7/8YnVJgNuNHDlSNpst1VGlShWrywLcZtWqVWrfvr1KlCghm82mhQsXpnrfbrfrhRde0A033KCwsDA1b95cu3fvtqZYwMWu9/3fv3//NP8mtG7d2ppiARcaNWqU6tatq4iICBUtWlSdOnXSzp07U11z+fJlDR48WIUKFVK+fPnUtWtXHTt2zKKKkdsI0dkwe/ZsDRs2TCNGjNDmzZtVq1YttWrVSsePH7e6NMDtqlevriNHjiQfP/30k9UlAW5z4cIF1apVS+PHj0/3/TfeeEPvvfeePvjgA61fv1558+ZVq1atdPny5VyuFHC9633/S1Lr1q1T/Zswc+bMXKwQcI8ff/xRgwcP1rp167R8+XJduXJFLVu21IULF5KveeKJJ/Tll19q7ty5+vHHH3X48GF16dLFwqqRm9jiKhvq16+vunXraty4cZKkpKQkRUdHa+jQoXr66actrg5wn5EjR2rhwoXasmWL1aUAuc5ms2nBggXq1KmTJNMLXaJECT355JMaPny4JCkmJkbFihXTlClT1KNHDwurBVzr2u9/yfREnz17Nk0PNeBrTpw4oaJFi+rHH3/U7bffrpiYGBUpUkQzZsxQt27dJEk7duxQ1apVtXbtWjVo0MDiiuFu9ERnUXx8vDZt2qTmzZsnnwsICFDz5s21du1aCysDcsfu3btVokQJlS9fXr169dKBAwesLgmwxN69e3X06NFU/x5ERUWpfv36/HsAv7Fy5UoVLVpUlStX1qBBg3Tq1CmrSwJcLiYmRpJUsGBBSdKmTZt05cqVVH//V6lSRaVLl+bvfz9BiM6ikydPKjExUcWKFUt1vlixYjp69KhFVQG5o379+poyZYqWLl2qiRMnau/evWrSpInOnTtndWlArnP8nc+/B/BXrVu31rRp07RixQqNHj1aP/74o9q0aaPExESrSwNcJikpSY8//rgaN26sm266SZL5+z84OFj58+dPdS1///uPIKsLAOA92rRpk9yuWbOm6tevrzJlymjOnDkaOHCghZUBAHLb1VMWatSooZo1a6pChQpauXKlmjVrZmFlgOsMHjxYv//+O2vAIBV6orOocOHCCgwMTLP63rFjx1S8eHGLqgKskT9/flWqVEl//fWX1aUAuc7xdz7/HgBG+fLlVbhwYf5NgM8YMmSIvvrqK/3ww/+3d/8qrQRxFMcPhKwoaMiCuFFJWNAniARSC1pZiEWwCiI2GkGC2AURfADBB7CzFR8gptUubcAgiKiNIBIVUuRnFwj3InP1xkXy/cA2u1OcYpjhsP9qmp6e7p4PgkDtdlvPz88941n/Bwcl+h95nqdsNqtqtdo91+l0VK1Wlc/nI0wG/LxWq6Vms6lUKhV1FODHhWGoIAh69oOXlxddXV2xH2Ag3d3d6enpiT0Bv56ZqVQq6ezsTBcXFwrDsOd6NptVPB7vWf8bjYZub29Z/wcEj3N/QblcVrFY1NzcnHK5nI6OjvT6+qq1tbWoowF9tbu7q6WlJWUyGd3f32t/f1+xWEyrq6tRRwP6otVq9dxVu7m5Ub1el+/7SqfT2tnZ0eHhoWZnZxWGoSqViiYnJ3u+YAz8Vp/Nf9/3dXBwoJWVFQVBoGazqb29Pc3MzGhxcTHC1MD3bW1t6fT0VOfn5xodHe2+55xIJDQ8PKxEIqH19XWVy2X5vq+xsTFtb28rn8/zZe5BYfiS4+NjS6fT5nme5XI5u7y8jDoS0HeFQsFSqZR5nmdTU1NWKBTs+vo66lhA39RqNZP0x1EsFs3MrNPpWKVSsYmJCRsaGrL5+XlrNBrRhgb+k8/m/9vbmy0sLNj4+LjF43HLZDK2sbFhj4+PUccGvu1v816SnZycdMe8v7/b5uamJZNJGxkZseXlZXt4eIguNH4U/4kGAAAAAMAR70QDAAAAAOCIEg0AAAAAgCNKNAAAAAAAjijRAAAAAAA4okQDAAAAAOCIEg0AAAAAgCNKNAAAAAAAjijRAAAAAAA4okQDAAAAAOCIEg0AAAAAgCNKNAAAAAAAjijRAAAAAAA4+gDvi2/YvGif5wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zommqjoVNg93"
      },
      "source": [
        "# model.save('/gdrive/MyDrive/Colab Notebooks/Tensorflow_in_practice/Mr_Kebou_Project/models/hourly_lstm_weights_epochs_23')"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E4AANwD-Nee-"
      },
      "source": [
        "## Model Forecast And Prediction Section"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kK4u1dczj8jD"
      },
      "source": [
        "series = x_valid.values[:,:-1]\n",
        "y_valid = x_valid.values[:,-1]"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Fwjm-L7FXIm"
      },
      "source": [
        "def model_forecast(model, series, window_size):\n",
        "    ds = tf.data.Dataset.from_tensor_slices(series)\n",
        "    ds = ds.window(window_size + 1, shift=1, drop_remainder=True)\n",
        "    ds = ds.flat_map(lambda w: w.batch(window_size + 1))\n",
        "    ds = ds.batch(32).prefetch(1)\n",
        "    forecast = model.predict(ds)\n",
        "    return forecast"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qs2FXmQoh400",
        "outputId": "21e56ae5-4076-451e-8e7f-70db383167cb"
      },
      "source": [
        "model_forecast(model, series[0][np.newaxis,...], 0)[0][0]"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 38ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-19.559116"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pjHskRt7XsUm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff1d528b-2121-4aa0-b0fe-3dcd55586cce"
      },
      "source": [
        "rnn_forecast = model_forecast(model, series, 0)\n",
        "rnn_forecast = rnn_forecast[:,0]"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "446/446 [==============================] - 3s 7ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "id": "8lpy2wzvgUur",
        "outputId": "04c8343a-e07a-4176-fe65-147032ad95c7"
      },
      "source": [
        "fig,ax = plt.subplots(figsize=(10, 6))\n",
        "ax.set_title('Model Forecast VS Actual Forecast')\n",
        "ax.plot(time_valid[window_size:], y_valid[window_size:],label='Actual Forecast')\n",
        "ax.plot(time_valid[window_size:], rnn_forecast[window_size:],label='RNN Forecast')\n",
        "ax.set_xlabel('Data/Time')\n",
        "ax.set_ylabel('\tTemp (°C)')\n",
        "ax.legend()"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fa833db4730>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Glyph 9 (\t) missing from current font.\n",
            "  func(*args, **kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 9 (\t) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u5v2TReyMoS_"
      },
      "source": [
        "## Looking at the prediction plot of the data above the RNN(LSTM) model performs well in forecasting future unseen temperature values, The actual future temperature value plot is in Blue while the RNN(LSTM) model Forecast Predictions is in orange"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qfgG1Wr7WazN"
      },
      "source": [
        "# plt.semilogx(history.history[\"lr\"], history.history[\"loss\"])\n",
        "# # plt.ylim(0,4)\n",
        "# # plt.axis([1e-10, 1e-4, 0, 30])"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YYiDTySVNoXi"
      },
      "source": [
        "## Trying Out the Univariate approach to see if better forecast can be generated"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OiSZvS5aN1QH"
      },
      "source": [
        "### Using the temperature data only for model training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1yF7-HFqDpUJ"
      },
      "source": [
        "split_time = sum(test.index.year == 2015) + sum(test.index.year == 2016) + sum(test.index.year == 2017)\n",
        "time_train = test.index[:split_time]\n",
        "x_train = test.iloc[:split_time]\n",
        "time_valid = test.index[split_time:]\n",
        "x_valid = test.iloc[split_time:]\n",
        "\n",
        "window_size =14\n",
        "batch_size = 32\n",
        "shuffle_buffer = 11"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CZh4KDlXN8pM"
      },
      "source": [
        "#### Helper Function for preparing the data to a form acceptable by the model together with model building and training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UsYT5pUlWPGy",
        "outputId": "48f630f2-4acb-41d8-86ba-be60c8750be8"
      },
      "source": [
        "def window_dataset(series, window_size, batch_size, shuffle_buffer):\n",
        "  dataset = tf.data.Dataset.from_tensor_slices(series)\n",
        "  dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n",
        "  dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n",
        "  dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1]))\n",
        "  dataset = dataset.batch(batch_size).prefetch(1)\n",
        "  return dataset\n",
        "\n",
        "dataset = window_dataset(x_train, window_size, batch_size, shuffle_buffer)\n",
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=-1),\n",
        "                    input_shape=[None]),\n",
        "  tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32,activation='relu', return_sequences=True)),\n",
        "  tf.keras.layers.Dropout(0.35),\n",
        "  tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32,activation='relu',return_sequences=True)),\n",
        "  tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(16,activation='relu')),\n",
        "  tf.keras.layers.Dropout(0.2),\n",
        "  tf.keras.layers.Dense(8,activation='relu'),\n",
        "  tf.keras.layers.Dense(1)\n",
        "  # tf.keras.layers.Lambda(lambda x: x * 100.0)\n",
        "])\n",
        "\n",
        "# lr_schedule = tf.keras.callbacks.LearningRateScheduler(\n",
        "#     lambda epoch: 1e-8 * 10**(epoch / 20))\n",
        "optimizer = optimizer = tf.keras.optimizers.Adam(learning_rate=4e-4)\n",
        "model.compile(loss=tf.keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[tf.keras.metrics.RootMeanSquaredError()])\n",
        "# Train model\n",
        "history = model.fit(dataset, epochs=100) #callbacks=[lr_schedule])"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "779/779 [==============================] - 27s 26ms/step - loss: 4.5065 - root_mean_squared_error: 8.3979\n",
            "Epoch 2/100\n",
            "779/779 [==============================] - 22s 28ms/step - loss: 2.3803 - root_mean_squared_error: 4.3534\n",
            "Epoch 3/100\n",
            "779/779 [==============================] - 20s 25ms/step - loss: 1.9383 - root_mean_squared_error: 3.6587\n",
            "Epoch 4/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 1.6025 - root_mean_squared_error: 3.1708\n",
            "Epoch 5/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 1.4557 - root_mean_squared_error: 2.9622\n",
            "Epoch 6/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 1.6457 - root_mean_squared_error: 3.4559\n",
            "Epoch 7/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 1.8585 - root_mean_squared_error: 3.9484\n",
            "Epoch 8/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 1.8576 - root_mean_squared_error: 3.7003\n",
            "Epoch 9/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 1.8077 - root_mean_squared_error: 3.5923\n",
            "Epoch 10/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 1.6227 - root_mean_squared_error: 3.2579\n",
            "Epoch 11/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 1.2436 - root_mean_squared_error: 2.5160\n",
            "Epoch 12/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 1.1164 - root_mean_squared_error: 2.3333\n",
            "Epoch 13/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.9749 - root_mean_squared_error: 2.1307\n",
            "Epoch 14/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.9401 - root_mean_squared_error: 2.0646\n",
            "Epoch 15/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.8678 - root_mean_squared_error: 1.9725\n",
            "Epoch 16/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.8145 - root_mean_squared_error: 1.9364\n",
            "Epoch 17/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.8250 - root_mean_squared_error: 2.0535\n",
            "Epoch 18/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.7694 - root_mean_squared_error: 1.8483\n",
            "Epoch 19/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.6534 - root_mean_squared_error: 1.6526\n",
            "Epoch 20/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.5283 - root_mean_squared_error: 1.4523\n",
            "Epoch 21/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.4774 - root_mean_squared_error: 1.4330\n",
            "Epoch 22/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.3954 - root_mean_squared_error: 1.2756\n",
            "Epoch 23/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.3742 - root_mean_squared_error: 1.2631\n",
            "Epoch 24/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.3648 - root_mean_squared_error: 1.2317\n",
            "Epoch 25/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.3357 - root_mean_squared_error: 1.1743\n",
            "Epoch 26/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.3093 - root_mean_squared_error: 1.1795\n",
            "Epoch 27/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.3032 - root_mean_squared_error: 1.1113\n",
            "Epoch 28/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.2886 - root_mean_squared_error: 1.1231\n",
            "Epoch 29/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.2726 - root_mean_squared_error: 1.0690\n",
            "Epoch 30/100\n",
            "779/779 [==============================] - 21s 26ms/step - loss: 0.2581 - root_mean_squared_error: 1.0808\n",
            "Epoch 31/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.2317 - root_mean_squared_error: 0.9907\n",
            "Epoch 32/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.2269 - root_mean_squared_error: 1.0353\n",
            "Epoch 33/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.2209 - root_mean_squared_error: 1.0223\n",
            "Epoch 34/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.2346 - root_mean_squared_error: 1.0410\n",
            "Epoch 35/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.2113 - root_mean_squared_error: 0.9918\n",
            "Epoch 36/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.2110 - root_mean_squared_error: 0.9587\n",
            "Epoch 37/100\n",
            "779/779 [==============================] - 20s 25ms/step - loss: 0.1962 - root_mean_squared_error: 0.9313\n",
            "Epoch 38/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.1922 - root_mean_squared_error: 0.9363\n",
            "Epoch 39/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.1884 - root_mean_squared_error: 0.9250\n",
            "Epoch 40/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.1790 - root_mean_squared_error: 0.9599\n",
            "Epoch 41/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.1880 - root_mean_squared_error: 0.9311\n",
            "Epoch 42/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.1791 - root_mean_squared_error: 0.9394\n",
            "Epoch 43/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.1703 - root_mean_squared_error: 0.8844\n",
            "Epoch 44/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.1644 - root_mean_squared_error: 0.8691\n",
            "Epoch 45/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.1614 - root_mean_squared_error: 0.8600\n",
            "Epoch 46/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.1655 - root_mean_squared_error: 0.8672\n",
            "Epoch 47/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.1597 - root_mean_squared_error: 0.8441\n",
            "Epoch 48/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.1493 - root_mean_squared_error: 0.7970\n",
            "Epoch 49/100\n",
            "779/779 [==============================] - 21s 28ms/step - loss: 0.1524 - root_mean_squared_error: 0.8444\n",
            "Epoch 50/100\n",
            "779/779 [==============================] - 20s 25ms/step - loss: 0.1460 - root_mean_squared_error: 0.8258\n",
            "Epoch 51/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.1497 - root_mean_squared_error: 0.8439\n",
            "Epoch 52/100\n",
            "779/779 [==============================] - 20s 25ms/step - loss: 0.1580 - root_mean_squared_error: 0.8889\n",
            "Epoch 53/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.1389 - root_mean_squared_error: 0.8162\n",
            "Epoch 54/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.1418 - root_mean_squared_error: 0.8141\n",
            "Epoch 55/100\n",
            "779/779 [==============================] - 22s 28ms/step - loss: 0.1414 - root_mean_squared_error: 0.8185\n",
            "Epoch 56/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.1398 - root_mean_squared_error: 0.8527\n",
            "Epoch 57/100\n",
            "779/779 [==============================] - 20s 25ms/step - loss: 0.1331 - root_mean_squared_error: 0.7883\n",
            "Epoch 58/100\n",
            "779/779 [==============================] - 22s 28ms/step - loss: 0.1238 - root_mean_squared_error: 0.7416\n",
            "Epoch 59/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.1410 - root_mean_squared_error: 0.8585\n",
            "Epoch 60/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.1312 - root_mean_squared_error: 0.8053\n",
            "Epoch 61/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.1296 - root_mean_squared_error: 0.7971\n",
            "Epoch 62/100\n",
            "779/779 [==============================] - 21s 26ms/step - loss: 0.1300 - root_mean_squared_error: 0.8224\n",
            "Epoch 63/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.1296 - root_mean_squared_error: 0.7973\n",
            "Epoch 64/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.1278 - root_mean_squared_error: 0.7979\n",
            "Epoch 65/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.1227 - root_mean_squared_error: 0.7965\n",
            "Epoch 66/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.1276 - root_mean_squared_error: 0.8108\n",
            "Epoch 67/100\n",
            "779/779 [==============================] - 21s 28ms/step - loss: 0.1163 - root_mean_squared_error: 0.7003\n",
            "Epoch 68/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.1117 - root_mean_squared_error: 0.7170\n",
            "Epoch 69/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.1187 - root_mean_squared_error: 0.7310\n",
            "Epoch 70/100\n",
            "779/779 [==============================] - 21s 28ms/step - loss: 0.1152 - root_mean_squared_error: 0.7026\n",
            "Epoch 71/100\n",
            "779/779 [==============================] - 20s 25ms/step - loss: 0.1102 - root_mean_squared_error: 0.6918\n",
            "Epoch 72/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.1081 - root_mean_squared_error: 0.6909\n",
            "Epoch 73/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.1086 - root_mean_squared_error: 0.6686\n",
            "Epoch 74/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.1117 - root_mean_squared_error: 0.6657\n",
            "Epoch 75/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.1052 - root_mean_squared_error: 0.6495\n",
            "Epoch 76/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.0998 - root_mean_squared_error: 0.6004\n",
            "Epoch 77/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.0960 - root_mean_squared_error: 0.6023\n",
            "Epoch 78/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.1047 - root_mean_squared_error: 0.6417\n",
            "Epoch 79/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.1036 - root_mean_squared_error: 0.6399\n",
            "Epoch 80/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.1010 - root_mean_squared_error: 0.6089\n",
            "Epoch 81/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.1047 - root_mean_squared_error: 0.6169\n",
            "Epoch 82/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.0955 - root_mean_squared_error: 0.5747\n",
            "Epoch 83/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.0939 - root_mean_squared_error: 0.5672\n",
            "Epoch 84/100\n",
            "779/779 [==============================] - 22s 28ms/step - loss: 0.0895 - root_mean_squared_error: 0.5695\n",
            "Epoch 85/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.0956 - root_mean_squared_error: 0.5743\n",
            "Epoch 86/100\n",
            "779/779 [==============================] - 21s 28ms/step - loss: 0.0930 - root_mean_squared_error: 0.5793\n",
            "Epoch 87/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.0928 - root_mean_squared_error: 0.5674\n",
            "Epoch 88/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.0939 - root_mean_squared_error: 0.5583\n",
            "Epoch 89/100\n",
            "779/779 [==============================] - 21s 26ms/step - loss: 0.0954 - root_mean_squared_error: 0.5829\n",
            "Epoch 90/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.0954 - root_mean_squared_error: 0.5697\n",
            "Epoch 91/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.0862 - root_mean_squared_error: 0.5432\n",
            "Epoch 92/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.0965 - root_mean_squared_error: 0.5507\n",
            "Epoch 93/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.0850 - root_mean_squared_error: 0.5321\n",
            "Epoch 94/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.0910 - root_mean_squared_error: 0.5517\n",
            "Epoch 95/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.0737 - root_mean_squared_error: 0.4977\n",
            "Epoch 96/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.0932 - root_mean_squared_error: 0.5475\n",
            "Epoch 97/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.0809 - root_mean_squared_error: 0.5157\n",
            "Epoch 98/100\n",
            "779/779 [==============================] - 21s 27ms/step - loss: 0.0870 - root_mean_squared_error: 0.5237\n",
            "Epoch 99/100\n",
            "779/779 [==============================] - 20s 26ms/step - loss: 0.0800 - root_mean_squared_error: 0.5070\n",
            "Epoch 100/100\n",
            "779/779 [==============================] - 21s 28ms/step - loss: 0.0784 - root_mean_squared_error: 0.5015\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xddk4Q67Ogp_"
      },
      "source": [
        "### Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 693
        },
        "id": "eS672AhKMSm4",
        "outputId": "140a713e-6753-4ab4-b238-391692445109"
      },
      "source": [
        "epochs =  range(100)\n",
        "plt.figure(figsize=(12,8))\n",
        "plt.plot(epochs, history.history[\"root_mean_squared_error\"],color='r')"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fa82f9dace0>]"
            ]
          },
          "metadata": {},
          "execution_count": 98
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZOohKJjFOqWs"
      },
      "source": [
        "### Forecast and Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eKPR3MDGTe5d"
      },
      "source": [
        "def model_forecast(model, series, window_size):\n",
        "    ds = tf.data.Dataset.from_tensor_slices(series)\n",
        "    ds = ds.window(window_size + 1, shift=1, drop_remainder=True)\n",
        "    ds = ds.flat_map(lambda w: w.batch(window_size + 1))\n",
        "    ds = ds.batch(32).prefetch(1)\n",
        "    forecast = model.predict(ds)\n",
        "    return forecast"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M10B1UfwTetd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83a0eade-1840-466d-b320-2f68fe4cb6e2"
      },
      "source": [
        "rnn_forecast = model_forecast(model, x_valid.values[..., np.newaxis], window_size)\n",
        "rnn_forecast = rnn_forecast[:,0]"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "445/445 [==============================] - 4s 8ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "id": "A-UWOAalMSL7",
        "outputId": "a905631a-1872-48c8-e558-bd704a6354bc"
      },
      "source": [
        "fig,ax = plt.subplots(figsize=(10, 6))\n",
        "ax.set_title('Model Forecast VS Actual Forecast')\n",
        "ax.plot(time_valid[window_size:], x_valid.values[window_size:],label='Actual Forecast')\n",
        "ax.plot(time_valid[window_size:], rnn_forecast,label='RNN Forecast')\n",
        "ax.set_xlabel('Data/Time')\n",
        "ax.set_ylabel('\tTemp (°C)')\n",
        "ax.legend()"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fa82fb7e5f0>"
            ]
          },
          "metadata": {},
          "execution_count": 104
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Glyph 9 (\t) missing from current font.\n",
            "  func(*args, **kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 9 (\t) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x0cO-xO-Oyeu"
      },
      "source": [
        "#### Looking at the plot, it is clear that the Temperature alone is sufficient to forecast future temperature as the data is auto-correlated and the model can generate patterns from it,however the Predictions are not as optimal as that of the multi-variate approach"
      ]
    }
  ]
}