diff --git a/V2_Extension_CSV_FIX__IoT_Child_Women_FBprophet.ipynb b/V2_Extension_CSV_FIX__IoT_Child_Women_FBprophet.ipynb
new file mode 100644
index 0000000..94467a0
--- /dev/null
+++ b/V2_Extension_CSV_FIX__IoT_Child_Women_FBprophet.ipynb
@@ -0,0 +1,3648 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Extension_CSV FIX_ IoT_Child_Women_FBprophet.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU",
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "fa2bf71192fc44ecb3908458e4bc546b": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HBoxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_3bf050d5c00344ecab24767ce32f740d",
+              "IPY_MODEL_574554a1af574d09be6b0e39b3490141",
+              "IPY_MODEL_a7a58b6194794f87be4672338c0807eb"
+            ],
+            "layout": "IPY_MODEL_b087def4ee074b2db4e66d79b78ac5c1"
+          }
+        },
+        "3bf050d5c00344ecab24767ce32f740d": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_fc2ab76afc7b482ca254dfb13030eb6a",
+            "placeholder": "​",
+            "style": "IPY_MODEL_fa280eaae7624cee9bab9915f4eed7d5",
+            "value": "100%"
+          }
+        },
+        "574554a1af574d09be6b0e39b3490141": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "FloatProgressModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "FloatProgressModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "ProgressView",
+            "bar_style": "success",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_00ada953b8fe490e8562f95d60650afe",
+            "max": 13,
+            "min": 0,
+            "orientation": "horizontal",
+            "style": "IPY_MODEL_18122f2b9890420a987757358aae62ae",
+            "value": 13
+          }
+        },
+        "a7a58b6194794f87be4672338c0807eb": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_ad1fea6aba2b412e95e6dbb76a33cb30",
+            "placeholder": "​",
+            "style": "IPY_MODEL_babd66c76706447390dc232508f15988",
+            "value": " 13/13 [00:37<00:00,  2.88s/it]"
+          }
+        },
+        "b087def4ee074b2db4e66d79b78ac5c1": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "fc2ab76afc7b482ca254dfb13030eb6a": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "fa280eaae7624cee9bab9915f4eed7d5": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "00ada953b8fe490e8562f95d60650afe": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "18122f2b9890420a987757358aae62ae": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "ProgressStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ProgressStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
+            "description_width": ""
+          }
+        },
+        "ad1fea6aba2b412e95e6dbb76a33cb30": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "babd66c76706447390dc232508f15988": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        }
+      }
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/amohsin87/Python_Tutorials/blob/master/V2_Extension_CSV_FIX__IoT_Child_Women_FBprophet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hCkDRSqBr0PS"
+      },
+      "source": [
+        "import pandas as pd \n",
+        "import numpy as np  \n",
+        "import matplotlib.pyplot as plt \n",
+        "import random\n",
+        "import seaborn as sns\n",
+        "from fbprophet import Prophet"
+      ],
+      "execution_count": 19,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "myRII3Jc5nMf"
+      },
+      "source": [
+        "#Load Data\n",
+        "df=pd.read_csv(\"23-year-old TEMP_HUMIDITY_GSR DAY 1-14.csv\")#you need to change file name for selected csv file\n",
+        "#df=pd.read_csv(\"23-year-old D1-14-data.csv\")\n",
+        "#df=pd.read_csv(\"23-year-old TEMP_HUMIDITY_GSR DAY 1-14.csv\")\n",
+        "\n"
+      ],
+      "execution_count": 20,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head(10)"
+      ],
+      "metadata": {
+        "id": "bhO1VZ6cf0hj",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 363
+        },
+        "outputId": "d2ec5516-9d5f-406c-96f0-a5eccdd69e57"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                created_at  entry_id  GSR(Stress)  Temp  Humidity\n",
+              "0  2021-11-02 22:10:52 +06         1          484  27.6       0.0\n",
+              "1  2021-11-02 22:11:08 +06         2          404  27.8      86.3\n",
+              "2  2021-11-02 22:11:24 +06         3          518  28.2      91.6\n",
+              "3  2021-11-02 22:11:40 +06         4          444  28.4      92.1\n",
+              "4  2021-11-02 22:11:55 +06         5          443  28.5      93.0\n",
+              "5  2021-11-02 22:12:11 +06         6          442  28.6      92.3\n",
+              "6  2021-11-02 22:12:27 +06         7          433  28.7      91.9\n",
+              "7  2021-11-02 22:12:43 +06         8          439  28.7      90.9\n",
+              "8  2021-11-02 22:12:59 +06         9          427  28.7      91.0\n",
+              "9  2021-11-02 22:13:15 +06        10          479  28.7      91.4"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7f6b5ff7-d01a-4878-883a-dabc45bb30f2\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>created_at</th>\n",
+              "      <th>entry_id</th>\n",
+              "      <th>GSR(Stress)</th>\n",
+              "      <th>Temp</th>\n",
+              "      <th>Humidity</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2021-11-02 22:10:52 +06</td>\n",
+              "      <td>1</td>\n",
+              "      <td>484</td>\n",
+              "      <td>27.6</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2021-11-02 22:11:08 +06</td>\n",
+              "      <td>2</td>\n",
+              "      <td>404</td>\n",
+              "      <td>27.8</td>\n",
+              "      <td>86.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2021-11-02 22:11:24 +06</td>\n",
+              "      <td>3</td>\n",
+              "      <td>518</td>\n",
+              "      <td>28.2</td>\n",
+              "      <td>91.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2021-11-02 22:11:40 +06</td>\n",
+              "      <td>4</td>\n",
+              "      <td>444</td>\n",
+              "      <td>28.4</td>\n",
+              "      <td>92.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2021-11-02 22:11:55 +06</td>\n",
+              "      <td>5</td>\n",
+              "      <td>443</td>\n",
+              "      <td>28.5</td>\n",
+              "      <td>93.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>2021-11-02 22:12:11 +06</td>\n",
+              "      <td>6</td>\n",
+              "      <td>442</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>92.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>2021-11-02 22:12:27 +06</td>\n",
+              "      <td>7</td>\n",
+              "      <td>433</td>\n",
+              "      <td>28.7</td>\n",
+              "      <td>91.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>2021-11-02 22:12:43 +06</td>\n",
+              "      <td>8</td>\n",
+              "      <td>439</td>\n",
+              "      <td>28.7</td>\n",
+              "      <td>90.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>2021-11-02 22:12:59 +06</td>\n",
+              "      <td>9</td>\n",
+              "      <td>427</td>\n",
+              "      <td>28.7</td>\n",
+              "      <td>91.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>2021-11-02 22:13:15 +06</td>\n",
+              "      <td>10</td>\n",
+              "      <td>479</td>\n",
+              "      <td>28.7</td>\n",
+              "      <td>91.4</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7f6b5ff7-d01a-4878-883a-dabc45bb30f2')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-7f6b5ff7-d01a-4878-883a-dabc45bb30f2 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-7f6b5ff7-d01a-4878-883a-dabc45bb30f2');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "G8T025KDvxHU",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 363
+        },
+        "outputId": "9d5e2efa-2020-4d06-d021-e80c890b5483"
+      },
+      "source": [
+        "df.tail(10)\n"
+      ],
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                    created_at  entry_id  GSR(Stress)  Temp  Humidity\n",
+              "408  2021-12-23T22:57:09+06:00        21          579  27.6      90.0\n",
+              "409  2021-12-23T22:57:25+06:00        22          526  27.5      89.3\n",
+              "410  2021-12-23T22:57:40+06:00        23          527  27.5      89.0\n",
+              "411  2021-12-23T22:57:56+06:00        24          528  27.5      89.0\n",
+              "412  2021-12-23T22:58:12+06:00        25          527  27.5      89.1\n",
+              "413  2021-12-23T22:58:28+06:00        26          386  27.4      89.8\n",
+              "414  2021-12-23T22:58:43+06:00        27          387  27.4      89.0\n",
+              "415  2021-12-23T22:58:59+06:00        28          383  27.3      88.3\n",
+              "416  2021-12-23T22:59:15+06:00        29          382  27.3      88.6\n",
+              "417  2021-12-23T22:59:30+06:00        30          381  27.2      88.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-176e3e91-8562-4471-a782-40cf87296ef7\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>created_at</th>\n",
+              "      <th>entry_id</th>\n",
+              "      <th>GSR(Stress)</th>\n",
+              "      <th>Temp</th>\n",
+              "      <th>Humidity</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>408</th>\n",
+              "      <td>2021-12-23T22:57:09+06:00</td>\n",
+              "      <td>21</td>\n",
+              "      <td>579</td>\n",
+              "      <td>27.6</td>\n",
+              "      <td>90.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>409</th>\n",
+              "      <td>2021-12-23T22:57:25+06:00</td>\n",
+              "      <td>22</td>\n",
+              "      <td>526</td>\n",
+              "      <td>27.5</td>\n",
+              "      <td>89.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>410</th>\n",
+              "      <td>2021-12-23T22:57:40+06:00</td>\n",
+              "      <td>23</td>\n",
+              "      <td>527</td>\n",
+              "      <td>27.5</td>\n",
+              "      <td>89.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>411</th>\n",
+              "      <td>2021-12-23T22:57:56+06:00</td>\n",
+              "      <td>24</td>\n",
+              "      <td>528</td>\n",
+              "      <td>27.5</td>\n",
+              "      <td>89.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>412</th>\n",
+              "      <td>2021-12-23T22:58:12+06:00</td>\n",
+              "      <td>25</td>\n",
+              "      <td>527</td>\n",
+              "      <td>27.5</td>\n",
+              "      <td>89.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>413</th>\n",
+              "      <td>2021-12-23T22:58:28+06:00</td>\n",
+              "      <td>26</td>\n",
+              "      <td>386</td>\n",
+              "      <td>27.4</td>\n",
+              "      <td>89.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>414</th>\n",
+              "      <td>2021-12-23T22:58:43+06:00</td>\n",
+              "      <td>27</td>\n",
+              "      <td>387</td>\n",
+              "      <td>27.4</td>\n",
+              "      <td>89.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>415</th>\n",
+              "      <td>2021-12-23T22:58:59+06:00</td>\n",
+              "      <td>28</td>\n",
+              "      <td>383</td>\n",
+              "      <td>27.3</td>\n",
+              "      <td>88.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>416</th>\n",
+              "      <td>2021-12-23T22:59:15+06:00</td>\n",
+              "      <td>29</td>\n",
+              "      <td>382</td>\n",
+              "      <td>27.3</td>\n",
+              "      <td>88.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>417</th>\n",
+              "      <td>2021-12-23T22:59:30+06:00</td>\n",
+              "      <td>30</td>\n",
+              "      <td>381</td>\n",
+              "      <td>27.2</td>\n",
+              "      <td>88.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-176e3e91-8562-4471-a782-40cf87296ef7')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-176e3e91-8562-4471-a782-40cf87296ef7 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-176e3e91-8562-4471-a782-40cf87296ef7');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "\n",
+        "def remove_timezone_label(data):\n",
+        "\n",
+        "    pos = data.index('+')\n",
+        "\n",
+        "    # Slice data to get rid of the tz, then strip leading/trailing whitespace\n",
+        "    data = (data[:pos]).strip()\n",
+        "\n",
+        "    return data\n",
+        "\n",
+        "\n",
+        "if __name__ == '__main__':\n",
+        "\n",
+        "    file_loc = \"23-year-old TEMP_HUMIDITY_GSR DAY 1-14.csv\"\n",
+        "\n",
+        "    df = pd.read_csv(file_loc)\n",
+        "\n",
+        "    # the apply() function is used to carry out a function on the data.\n",
+        "    df[\"created_at\"] = df[\"created_at\"].apply(remove_timezone_label)\n",
+        "\n",
+        "    df.to_csv(\"23-year-old D1-14-data.csv\", index=False)"
+      ],
+      "metadata": {
+        "id": "MoNVxOAlVn2O"
+      },
+      "execution_count": 24,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Wim5iY4r5nPS",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "outputId": "a6dc6dde-ce24-4439-e88a-ac1e434c758e"
+      },
+      "source": [
+        "df = df.drop(['entry_id'], axis=1)\n",
+        "\n",
+        "#df = df.drop(['GSR(Stress)'], axis=1)\n",
+        "df = df.drop(['Temp'], axis=1)\n",
+        "df = df.drop(['Humidity'], axis=1)\n",
+        "\n",
+        "\n",
+        "df.head()"
+      ],
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "            created_at  GSR(Stress)\n",
+              "0  2021-11-02 22:10:52          484\n",
+              "1  2021-11-02 22:11:08          404\n",
+              "2  2021-11-02 22:11:24          518\n",
+              "3  2021-11-02 22:11:40          444\n",
+              "4  2021-11-02 22:11:55          443"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-60cf905d-1bc9-40b2-880b-f8ff702f11df\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>created_at</th>\n",
+              "      <th>GSR(Stress)</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2021-11-02 22:10:52</td>\n",
+              "      <td>484</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2021-11-02 22:11:08</td>\n",
+              "      <td>404</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2021-11-02 22:11:24</td>\n",
+              "      <td>518</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2021-11-02 22:11:40</td>\n",
+              "      <td>444</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2021-11-02 22:11:55</td>\n",
+              "      <td>443</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-60cf905d-1bc9-40b2-880b-f8ff702f11df')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-60cf905d-1bc9-40b2-880b-f8ff702f11df button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-60cf905d-1bc9-40b2-880b-f8ff702f11df');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "GXQnZ6Z82Y0p"
+      },
+      "source": [
+        "# Extract all Data Like Year MOnth Day Time etc\n",
+        "#dataset = df\n",
+        "#dataset[\"Month\"] = pd.to_datetime(df[\"Timeline\"]).dt.month\n",
+        "#dataset[\"Year\"] = pd.to_datetime(df[\"Timeline\"]).dt.year\n",
+        "#dataset[\"Date\"] = pd.to_datetime(df[\"Date\"]).dt.date\n",
+        "#dataset[\"Time\"] = pd.to_datetime(df[\"Timeline\"]).dt.time\n",
+        "#dataset[\"Week\"] = pd.to_datetime(df[\"Date\"]).dt.week\n",
+        "#dataset[\"Day\"] = pd.to_datetime(df[\"Date\"]).dt.day_name()\n",
+        "#dataset = df.set_index(\"Timeline\")\n",
+        "#dataset.index = pd.to_datetime(dataset.index)\n",
+        "#dataset\n",
+        "#dataset.head(5)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "a6iQEiPh6LTK"
+      },
+      "source": [
+        "#df = df.drop(['Timeline'], axis=1)\n",
+        "#df = df.drop(['alarm'], axis=1)\n",
+        "#df = df.drop(['cng'], axis=1)\n",
+        "#df.head()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IBZYq2-85qBT"
+      },
+      "source": [
+        "#sns.lineplot(x=df.index, y=\"temp\", data=df);\n",
+        "#sns.lineplot(x=df.index, y=\"Time\", data=df);\n",
+        "#sns.lineplot(x=Time, y=\"temp\", data=df);"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "LA1sS4qn3jPn"
+      },
+      "source": [
+        "#df = df.drop(['Timeline'], axis=1)\n",
+        "#df = df.drop(['Month'], axis=1)\n",
+        "#df = df.drop(['Year'], axis=1)\n",
+        "#df = df.drop(['co'], axis=1)\n",
+        "\n",
+        "\n",
+        "\n",
+        "df.head()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5YTy1aGJlaP5",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "outputId": "e6f703bd-49cc-496a-9006-35a76ddb507e"
+      },
+      "source": [
+        "df.tail()"
+      ],
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "              created_at  GSR(Stress)\n",
+              "413  2021-12-23T22:58:28          386\n",
+              "414  2021-12-23T22:58:43          387\n",
+              "415  2021-12-23T22:58:59          383\n",
+              "416  2021-12-23T22:59:15          382\n",
+              "417  2021-12-23T22:59:30          381"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-31b4dc8a-5664-44ff-a463-debabeee33a0\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>created_at</th>\n",
+              "      <th>GSR(Stress)</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>413</th>\n",
+              "      <td>2021-12-23T22:58:28</td>\n",
+              "      <td>386</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>414</th>\n",
+              "      <td>2021-12-23T22:58:43</td>\n",
+              "      <td>387</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>415</th>\n",
+              "      <td>2021-12-23T22:58:59</td>\n",
+              "      <td>383</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>416</th>\n",
+              "      <td>2021-12-23T22:59:15</td>\n",
+              "      <td>382</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>417</th>\n",
+              "      <td>2021-12-23T22:59:30</td>\n",
+              "      <td>381</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-31b4dc8a-5664-44ff-a463-debabeee33a0')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-31b4dc8a-5664-44ff-a463-debabeee33a0 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-31b4dc8a-5664-44ff-a463-debabeee33a0');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Um-1Dh1Y5nSV",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "28d1d2ee-4dc8-4291-d603-04c9841369ee"
+      },
+      "source": [
+        "df.isnull().sum()"
+      ],
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "created_at     0\n",
+              "GSR(Stress)    0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "JIC4i_fClQJT",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3f09bcf8-8c6e-4ccf-998a-44d60782d6d2"
+      },
+      "source": [
+        "df.shape"
+      ],
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(418, 2)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "iLlonGFG9D8P"
+      },
+      "source": [
+        "#conversion_rate = 85.33\n",
+        "#df['price'] = conversion_rate * df['price']\n",
+        "#df.head()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "32g_V_6f5nbd",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 374
+        },
+        "outputId": "cad4e853-f0f7-4f04-d389-ff5076af8fc9"
+      },
+      "source": [
+        "df.plot(figsize=(15, 6))\n",
+        "plt.show()"
+      ],
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1080x432 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zfLd6kYf4enH",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "outputId": "7929e558-19ca-4166-e1e7-75480a669aa9"
+      },
+      "source": [
+        "df.columns = ['ds', 'y'] #required by fbprophet\n",
+        "df.head()"
+      ],
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                    ds    y\n",
+              "0  2021-11-02 22:10:52  484\n",
+              "1  2021-11-02 22:11:08  404\n",
+              "2  2021-11-02 22:11:24  518\n",
+              "3  2021-11-02 22:11:40  444\n",
+              "4  2021-11-02 22:11:55  443"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4d934fcf-cdc2-4a37-82a1-698f04e163c5\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ds</th>\n",
+              "      <th>y</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2021-11-02 22:10:52</td>\n",
+              "      <td>484</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2021-11-02 22:11:08</td>\n",
+              "      <td>404</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2021-11-02 22:11:24</td>\n",
+              "      <td>518</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2021-11-02 22:11:40</td>\n",
+              "      <td>444</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2021-11-02 22:11:55</td>\n",
+              "      <td>443</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d934fcf-cdc2-4a37-82a1-698f04e163c5')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-4d934fcf-cdc2-4a37-82a1-698f04e163c5 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-4d934fcf-cdc2-4a37-82a1-698f04e163c5');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8CpTCSq85VNQ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "outputId": "bc602116-eaa2-47b6-fa39-51a5c246d52b"
+      },
+      "source": [
+        "df['ds'] = pd.to_datetime(df['ds'])\n",
+        "df.head()"
+      ],
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                   ds    y\n",
+              "0 2021-11-02 22:10:52  484\n",
+              "1 2021-11-02 22:11:08  404\n",
+              "2 2021-11-02 22:11:24  518\n",
+              "3 2021-11-02 22:11:40  444\n",
+              "4 2021-11-02 22:11:55  443"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-2f4867bd-9d84-421d-bdf8-afaba76c2073\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ds</th>\n",
+              "      <th>y</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2021-11-02 22:10:52</td>\n",
+              "      <td>484</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2021-11-02 22:11:08</td>\n",
+              "      <td>404</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2021-11-02 22:11:24</td>\n",
+              "      <td>518</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2021-11-02 22:11:40</td>\n",
+              "      <td>444</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2021-11-02 22:11:55</td>\n",
+              "      <td>443</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2f4867bd-9d84-421d-bdf8-afaba76c2073')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-2f4867bd-9d84-421d-bdf8-afaba76c2073 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-2f4867bd-9d84-421d-bdf8-afaba76c2073');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 31
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Bp3Aq0M7-PFa",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "outputId": "e323b735-a3e2-4b45-c629-672c2abd836c"
+      },
+      "source": [
+        "df = df.sort_values(by='ds')\n",
+        "df"
+      ],
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                     ds    y\n",
+              "0   2021-11-02 22:10:52  484\n",
+              "1   2021-11-02 22:11:08  404\n",
+              "2   2021-11-02 22:11:24  518\n",
+              "3   2021-11-02 22:11:40  444\n",
+              "4   2021-11-02 22:11:55  443\n",
+              "..                  ...  ...\n",
+              "413 2021-12-23 22:58:28  386\n",
+              "414 2021-12-23 22:58:43  387\n",
+              "415 2021-12-23 22:58:59  383\n",
+              "416 2021-12-23 22:59:15  382\n",
+              "417 2021-12-23 22:59:30  381\n",
+              "\n",
+              "[418 rows x 2 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8b57ba17-c00e-4859-9547-c6a11e77ef59\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ds</th>\n",
+              "      <th>y</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2021-11-02 22:10:52</td>\n",
+              "      <td>484</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2021-11-02 22:11:08</td>\n",
+              "      <td>404</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2021-11-02 22:11:24</td>\n",
+              "      <td>518</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2021-11-02 22:11:40</td>\n",
+              "      <td>444</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2021-11-02 22:11:55</td>\n",
+              "      <td>443</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>413</th>\n",
+              "      <td>2021-12-23 22:58:28</td>\n",
+              "      <td>386</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>414</th>\n",
+              "      <td>2021-12-23 22:58:43</td>\n",
+              "      <td>387</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>415</th>\n",
+              "      <td>2021-12-23 22:58:59</td>\n",
+              "      <td>383</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>416</th>\n",
+              "      <td>2021-12-23 22:59:15</td>\n",
+              "      <td>382</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>417</th>\n",
+              "      <td>2021-12-23 22:59:30</td>\n",
+              "      <td>381</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>418 rows × 2 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8b57ba17-c00e-4859-9547-c6a11e77ef59')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-8b57ba17-c00e-4859-9547-c6a11e77ef59 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-8b57ba17-c00e-4859-9547-c6a11e77ef59');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 32
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "jhrDrYhB50Zc",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "bb9079e4-0655-421f-f302-90b15e1e90e0"
+      },
+      "source": [
+        "dir(Prophet)"
+      ],
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['__class__',\n",
+              " '__delattr__',\n",
+              " '__dict__',\n",
+              " '__dir__',\n",
+              " '__doc__',\n",
+              " '__eq__',\n",
+              " '__format__',\n",
+              " '__ge__',\n",
+              " '__getattribute__',\n",
+              " '__gt__',\n",
+              " '__hash__',\n",
+              " '__init__',\n",
+              " '__init_subclass__',\n",
+              " '__le__',\n",
+              " '__lt__',\n",
+              " '__module__',\n",
+              " '__ne__',\n",
+              " '__new__',\n",
+              " '__reduce__',\n",
+              " '__reduce_ex__',\n",
+              " '__repr__',\n",
+              " '__setattr__',\n",
+              " '__sizeof__',\n",
+              " '__str__',\n",
+              " '__subclasshook__',\n",
+              " '__weakref__',\n",
+              " '_load_stan_backend',\n",
+              " 'add_country_holidays',\n",
+              " 'add_group_component',\n",
+              " 'add_regressor',\n",
+              " 'add_seasonality',\n",
+              " 'construct_holiday_dataframe',\n",
+              " 'fit',\n",
+              " 'flat_growth_init',\n",
+              " 'flat_trend',\n",
+              " 'fourier_series',\n",
+              " 'initialize_scales',\n",
+              " 'linear_growth_init',\n",
+              " 'logistic_growth_init',\n",
+              " 'make_all_seasonality_features',\n",
+              " 'make_future_dataframe',\n",
+              " 'make_holiday_features',\n",
+              " 'make_seasonality_features',\n",
+              " 'parse_seasonality_args',\n",
+              " 'percentile',\n",
+              " 'piecewise_linear',\n",
+              " 'piecewise_logistic',\n",
+              " 'plot',\n",
+              " 'plot_components',\n",
+              " 'predict',\n",
+              " 'predict_seasonal_components',\n",
+              " 'predict_trend',\n",
+              " 'predict_uncertainty',\n",
+              " 'predictive_samples',\n",
+              " 'regressor_column_matrix',\n",
+              " 'sample_model',\n",
+              " 'sample_posterior_predictive',\n",
+              " 'sample_predictive_trend',\n",
+              " 'set_auto_seasonalities',\n",
+              " 'set_changepoints',\n",
+              " 'setup_dataframe',\n",
+              " 'validate_column_name',\n",
+              " 'validate_inputs']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "K_odgvCq6Im-"
+      },
+      "source": [
+        "model = Prophet()"
+      ],
+      "execution_count": 34,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "rbJpEaZH6Ip3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a2ea0679-7a86-4ee9-e70c-4d2b20d015ed"
+      },
+      "source": [
+        "df.columns"
+      ],
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['ds', 'y'], dtype='object')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 35
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "vNMY39mN6Is0",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f74fedb3-f8de-40b1-a69f-fbfcd85989ac"
+      },
+      "source": [
+        "model.fit(df)"
+      ],
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<fbprophet.forecaster.Prophet at 0x7fc4e3488190>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "9VZn9dWl6Ivy",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b11a1890-6d89-4d8c-df64-5b0b4d60ee21"
+      },
+      "source": [
+        "model.component_modes"
+      ],
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'additive': ['weekly',\n",
+              "  'daily',\n",
+              "  'additive_terms',\n",
+              "  'extra_regressors_additive',\n",
+              "  'holidays'],\n",
+              " 'multiplicative': ['multiplicative_terms', 'extra_regressors_multiplicative']}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "dFy6IwZW-6Ch",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "outputId": "f28cdd93-6fb6-48ed-d781-0122c25904dd"
+      },
+      "source": [
+        "df.tail()"
+      ],
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                     ds    y\n",
+              "413 2021-12-23 22:58:28  386\n",
+              "414 2021-12-23 22:58:43  387\n",
+              "415 2021-12-23 22:58:59  383\n",
+              "416 2021-12-23 22:59:15  382\n",
+              "417 2021-12-23 22:59:30  381"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4e7b45a3-02d7-4d9e-90a9-059a0098445c\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ds</th>\n",
+              "      <th>y</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>413</th>\n",
+              "      <td>2021-12-23 22:58:28</td>\n",
+              "      <td>386</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>414</th>\n",
+              "      <td>2021-12-23 22:58:43</td>\n",
+              "      <td>387</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>415</th>\n",
+              "      <td>2021-12-23 22:58:59</td>\n",
+              "      <td>383</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>416</th>\n",
+              "      <td>2021-12-23 22:59:15</td>\n",
+              "      <td>382</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>417</th>\n",
+              "      <td>2021-12-23 22:59:30</td>\n",
+              "      <td>381</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4e7b45a3-02d7-4d9e-90a9-059a0098445c')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-4e7b45a3-02d7-4d9e-90a9-059a0098445c button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-4e7b45a3-02d7-4d9e-90a9-059a0098445c');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 38
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "VH8pYuUB6Iy6"
+      },
+      "source": [
+        "future_dates = model.make_future_dataframe(periods=180)"
+      ],
+      "execution_count": 39,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "NZf4ebXM-84Z",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "outputId": "a3dc1327-268c-446c-90c8-a72d4ad46786"
+      },
+      "source": [
+        "future_dates.tail()"
+      ],
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                     ds\n",
+              "593 2022-06-17 22:59:30\n",
+              "594 2022-06-18 22:59:30\n",
+              "595 2022-06-19 22:59:30\n",
+              "596 2022-06-20 22:59:30\n",
+              "597 2022-06-21 22:59:30"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4de7fe8a-6a8f-4ff5-8f69-29082fe9cc47\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ds</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>593</th>\n",
+              "      <td>2022-06-17 22:59:30</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>594</th>\n",
+              "      <td>2022-06-18 22:59:30</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>595</th>\n",
+              "      <td>2022-06-19 22:59:30</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>596</th>\n",
+              "      <td>2022-06-20 22:59:30</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>597</th>\n",
+              "      <td>2022-06-21 22:59:30</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4de7fe8a-6a8f-4ff5-8f69-29082fe9cc47')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-4de7fe8a-6a8f-4ff5-8f69-29082fe9cc47 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-4de7fe8a-6a8f-4ff5-8f69-29082fe9cc47');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 40
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "HALPg6Pe7ie2",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 444
+        },
+        "outputId": "910923e0-bec7-4059-b6f8-63bcf5fd6f1d"
+      },
+      "source": [
+        "pred = model.predict(future_dates)\n",
+        "pred.head()"
+      ],
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                   ds       trend  yhat_lower  yhat_upper  trend_lower  \\\n",
+              "0 2021-11-02 22:10:52  796.226190  423.204781  543.844242   796.226190   \n",
+              "1 2021-11-02 22:11:08  796.226749  422.262543  539.295499   796.226749   \n",
+              "2 2021-11-02 22:11:24  796.227308  420.627833  536.349886   796.227308   \n",
+              "3 2021-11-02 22:11:40  796.227867  420.038709  539.150525   796.227867   \n",
+              "4 2021-11-02 22:11:55  796.228391  417.040953  539.357434   796.228391   \n",
+              "\n",
+              "   trend_upper  additive_terms  additive_terms_lower  additive_terms_upper  \\\n",
+              "0   796.226190     -311.893440           -311.893440           -311.893440   \n",
+              "1   796.226749     -313.083939           -313.083939           -313.083939   \n",
+              "2   796.227308     -314.276086           -314.276086           -314.276086   \n",
+              "3   796.227867     -315.469865           -315.469865           -315.469865   \n",
+              "4   796.228391     -316.590500           -316.590500           -316.590500   \n",
+              "\n",
+              "        daily  daily_lower  daily_upper     weekly  weekly_lower  \\\n",
+              "0 -227.843968  -227.843968  -227.843968 -84.049472    -84.049472   \n",
+              "1 -228.740364  -228.740364  -228.740364 -84.343575    -84.343575   \n",
+              "2 -229.638403  -229.638403  -229.638403 -84.637683    -84.637683   \n",
+              "3 -230.538070  -230.538070  -230.538070 -84.931795    -84.931795   \n",
+              "4 -231.382971  -231.382971  -231.382971 -85.207529    -85.207529   \n",
+              "\n",
+              "   weekly_upper  multiplicative_terms  multiplicative_terms_lower  \\\n",
+              "0    -84.049472                   0.0                         0.0   \n",
+              "1    -84.343575                   0.0                         0.0   \n",
+              "2    -84.637683                   0.0                         0.0   \n",
+              "3    -84.931795                   0.0                         0.0   \n",
+              "4    -85.207529                   0.0                         0.0   \n",
+              "\n",
+              "   multiplicative_terms_upper        yhat  \n",
+              "0                         0.0  484.332750  \n",
+              "1                         0.0  483.142810  \n",
+              "2                         0.0  481.951222  \n",
+              "3                         0.0  480.758001  \n",
+              "4                         0.0  479.637890  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-30421961-7efa-4eb3-9a58-8012d863bf3e\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ds</th>\n",
+              "      <th>trend</th>\n",
+              "      <th>yhat_lower</th>\n",
+              "      <th>yhat_upper</th>\n",
+              "      <th>trend_lower</th>\n",
+              "      <th>trend_upper</th>\n",
+              "      <th>additive_terms</th>\n",
+              "      <th>additive_terms_lower</th>\n",
+              "      <th>additive_terms_upper</th>\n",
+              "      <th>daily</th>\n",
+              "      <th>daily_lower</th>\n",
+              "      <th>daily_upper</th>\n",
+              "      <th>weekly</th>\n",
+              "      <th>weekly_lower</th>\n",
+              "      <th>weekly_upper</th>\n",
+              "      <th>multiplicative_terms</th>\n",
+              "      <th>multiplicative_terms_lower</th>\n",
+              "      <th>multiplicative_terms_upper</th>\n",
+              "      <th>yhat</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2021-11-02 22:10:52</td>\n",
+              "      <td>796.226190</td>\n",
+              "      <td>423.204781</td>\n",
+              "      <td>543.844242</td>\n",
+              "      <td>796.226190</td>\n",
+              "      <td>796.226190</td>\n",
+              "      <td>-311.893440</td>\n",
+              "      <td>-311.893440</td>\n",
+              "      <td>-311.893440</td>\n",
+              "      <td>-227.843968</td>\n",
+              "      <td>-227.843968</td>\n",
+              "      <td>-227.843968</td>\n",
+              "      <td>-84.049472</td>\n",
+              "      <td>-84.049472</td>\n",
+              "      <td>-84.049472</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>484.332750</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2021-11-02 22:11:08</td>\n",
+              "      <td>796.226749</td>\n",
+              "      <td>422.262543</td>\n",
+              "      <td>539.295499</td>\n",
+              "      <td>796.226749</td>\n",
+              "      <td>796.226749</td>\n",
+              "      <td>-313.083939</td>\n",
+              "      <td>-313.083939</td>\n",
+              "      <td>-313.083939</td>\n",
+              "      <td>-228.740364</td>\n",
+              "      <td>-228.740364</td>\n",
+              "      <td>-228.740364</td>\n",
+              "      <td>-84.343575</td>\n",
+              "      <td>-84.343575</td>\n",
+              "      <td>-84.343575</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>483.142810</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2021-11-02 22:11:24</td>\n",
+              "      <td>796.227308</td>\n",
+              "      <td>420.627833</td>\n",
+              "      <td>536.349886</td>\n",
+              "      <td>796.227308</td>\n",
+              "      <td>796.227308</td>\n",
+              "      <td>-314.276086</td>\n",
+              "      <td>-314.276086</td>\n",
+              "      <td>-314.276086</td>\n",
+              "      <td>-229.638403</td>\n",
+              "      <td>-229.638403</td>\n",
+              "      <td>-229.638403</td>\n",
+              "      <td>-84.637683</td>\n",
+              "      <td>-84.637683</td>\n",
+              "      <td>-84.637683</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>481.951222</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2021-11-02 22:11:40</td>\n",
+              "      <td>796.227867</td>\n",
+              "      <td>420.038709</td>\n",
+              "      <td>539.150525</td>\n",
+              "      <td>796.227867</td>\n",
+              "      <td>796.227867</td>\n",
+              "      <td>-315.469865</td>\n",
+              "      <td>-315.469865</td>\n",
+              "      <td>-315.469865</td>\n",
+              "      <td>-230.538070</td>\n",
+              "      <td>-230.538070</td>\n",
+              "      <td>-230.538070</td>\n",
+              "      <td>-84.931795</td>\n",
+              "      <td>-84.931795</td>\n",
+              "      <td>-84.931795</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>480.758001</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2021-11-02 22:11:55</td>\n",
+              "      <td>796.228391</td>\n",
+              "      <td>417.040953</td>\n",
+              "      <td>539.357434</td>\n",
+              "      <td>796.228391</td>\n",
+              "      <td>796.228391</td>\n",
+              "      <td>-316.590500</td>\n",
+              "      <td>-316.590500</td>\n",
+              "      <td>-316.590500</td>\n",
+              "      <td>-231.382971</td>\n",
+              "      <td>-231.382971</td>\n",
+              "      <td>-231.382971</td>\n",
+              "      <td>-85.207529</td>\n",
+              "      <td>-85.207529</td>\n",
+              "      <td>-85.207529</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>479.637890</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-30421961-7efa-4eb3-9a58-8012d863bf3e')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-30421961-7efa-4eb3-9a58-8012d863bf3e button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-30421961-7efa-4eb3-9a58-8012d863bf3e');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 41
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ruMW5cjaqYnl",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "outputId": "bab06fc7-69e3-4134-acc4-59e293c452df"
+      },
+      "source": [
+        "pred = pred[['ds', 'yhat']]\n",
+        "pred"
+      ],
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                     ds         yhat\n",
+              "0   2021-11-02 22:10:52   484.332750\n",
+              "1   2021-11-02 22:11:08   483.142810\n",
+              "2   2021-11-02 22:11:24   481.951222\n",
+              "3   2021-11-02 22:11:40   480.758001\n",
+              "4   2021-11-02 22:11:55   479.637890\n",
+              "..                  ...          ...\n",
+              "593 2022-06-17 22:59:30  1178.182890\n",
+              "594 2022-06-18 22:59:30  2730.911749\n",
+              "595 2022-06-19 22:59:30  -115.072387\n",
+              "596 2022-06-20 22:59:30   972.474090\n",
+              "597 2022-06-21 22:59:30   957.808061\n",
+              "\n",
+              "[598 rows x 2 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-26cd7dbe-f188-4c2a-89e4-2ba743d5cd10\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ds</th>\n",
+              "      <th>yhat</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2021-11-02 22:10:52</td>\n",
+              "      <td>484.332750</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2021-11-02 22:11:08</td>\n",
+              "      <td>483.142810</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2021-11-02 22:11:24</td>\n",
+              "      <td>481.951222</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2021-11-02 22:11:40</td>\n",
+              "      <td>480.758001</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2021-11-02 22:11:55</td>\n",
+              "      <td>479.637890</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>593</th>\n",
+              "      <td>2022-06-17 22:59:30</td>\n",
+              "      <td>1178.182890</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>594</th>\n",
+              "      <td>2022-06-18 22:59:30</td>\n",
+              "      <td>2730.911749</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>595</th>\n",
+              "      <td>2022-06-19 22:59:30</td>\n",
+              "      <td>-115.072387</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>596</th>\n",
+              "      <td>2022-06-20 22:59:30</td>\n",
+              "      <td>972.474090</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>597</th>\n",
+              "      <td>2022-06-21 22:59:30</td>\n",
+              "      <td>957.808061</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>598 rows × 2 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-26cd7dbe-f188-4c2a-89e4-2ba743d5cd10')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-26cd7dbe-f188-4c2a-89e4-2ba743d5cd10 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-26cd7dbe-f188-4c2a-89e4-2ba743d5cd10');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 42
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "43-7pUnS7ih5"
+      },
+      "source": [
+        "#model.plot(pred)\n",
+        "#model.plot(df.columns) "
+      ],
+      "execution_count": 43,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zp3vNawF7ilW"
+      },
+      "source": [
+        "#model.plot_components(pred)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "RLiKogXC7i4D",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 273,
+          "referenced_widgets": [
+            "fa2bf71192fc44ecb3908458e4bc546b",
+            "3bf050d5c00344ecab24767ce32f740d",
+            "574554a1af574d09be6b0e39b3490141",
+            "a7a58b6194794f87be4672338c0807eb",
+            "b087def4ee074b2db4e66d79b78ac5c1",
+            "fc2ab76afc7b482ca254dfb13030eb6a",
+            "fa280eaae7624cee9bab9915f4eed7d5",
+            "00ada953b8fe490e8562f95d60650afe",
+            "18122f2b9890420a987757358aae62ae",
+            "ad1fea6aba2b412e95e6dbb76a33cb30",
+            "babd66c76706447390dc232508f15988"
+          ]
+        },
+        "outputId": "3c944ff7-8bbc-4b7a-c036-412500275cd4"
+      },
+      "source": [
+        "from fbprophet.diagnostics import cross_validation\n",
+        "#df_cv = cross_validation(model, initial='80 min', period='45 min', horizon='9 min')\n",
+        "#df_cv = cross_validation(model, initial=' 14 day', period='7 day ', horizon='14 day')\n",
+        "#df_cv = cross_validation(model, initial='150 min', period='90 min', horizon='150 min')\n",
+        "#df_cv = cross_validation(model, initial='20 min', period='15 min', horizon='30 min')\n",
+        "#df_cv = cross_validation(model, initial='20 min', period='60 min', horizon= '30 min')#mq4 home\n",
+        "df_cv = cross_validation(model, initial='60 min', period='30 min', horizon= '5 min')#Flame\n",
+        "#df_cv = cross_validation(model, initial='90 min', period='60 min', horizon= '30 min')\n",
+        "#df_cv = cross_validation(model, initial='365 days', period='90 days', horizon='180 days')\n",
+        "df_cv.head()"
+      ],
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "INFO:fbprophet:Making 13 forecasts with cutoffs between 2021-11-07 12:09:31 and 2021-12-23 22:54:30\n",
+            "WARNING:fbprophet:Seasonality has period of 7 days which is larger than initial window. Consider increasing initial.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/13 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "fa2bf71192fc44ecb3908458e4bc546b"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                   ds        yhat  yhat_lower  yhat_upper    y  \\\n",
+              "0 2021-11-07 12:09:47  450.389227  419.459742  481.720563  465   \n",
+              "1 2021-11-07 12:10:02  448.266804  417.166429  477.429951  461   \n",
+              "2 2021-11-07 12:10:19  445.860619  415.632101  477.544060  460   \n",
+              "3 2021-11-07 12:10:35  443.595245  411.660419  473.682473  449   \n",
+              "4 2021-11-07 12:10:52  441.187533  412.513843  471.649011  450   \n",
+              "\n",
+              "               cutoff  \n",
+              "0 2021-11-07 12:09:31  \n",
+              "1 2021-11-07 12:09:31  \n",
+              "2 2021-11-07 12:09:31  \n",
+              "3 2021-11-07 12:09:31  \n",
+              "4 2021-11-07 12:09:31  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-635dd322-3945-496e-9c42-a0609caff893\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ds</th>\n",
+              "      <th>yhat</th>\n",
+              "      <th>yhat_lower</th>\n",
+              "      <th>yhat_upper</th>\n",
+              "      <th>y</th>\n",
+              "      <th>cutoff</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2021-11-07 12:09:47</td>\n",
+              "      <td>450.389227</td>\n",
+              "      <td>419.459742</td>\n",
+              "      <td>481.720563</td>\n",
+              "      <td>465</td>\n",
+              "      <td>2021-11-07 12:09:31</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2021-11-07 12:10:02</td>\n",
+              "      <td>448.266804</td>\n",
+              "      <td>417.166429</td>\n",
+              "      <td>477.429951</td>\n",
+              "      <td>461</td>\n",
+              "      <td>2021-11-07 12:09:31</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2021-11-07 12:10:19</td>\n",
+              "      <td>445.860619</td>\n",
+              "      <td>415.632101</td>\n",
+              "      <td>477.544060</td>\n",
+              "      <td>460</td>\n",
+              "      <td>2021-11-07 12:09:31</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2021-11-07 12:10:35</td>\n",
+              "      <td>443.595245</td>\n",
+              "      <td>411.660419</td>\n",
+              "      <td>473.682473</td>\n",
+              "      <td>449</td>\n",
+              "      <td>2021-11-07 12:09:31</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2021-11-07 12:10:52</td>\n",
+              "      <td>441.187533</td>\n",
+              "      <td>412.513843</td>\n",
+              "      <td>471.649011</td>\n",
+              "      <td>450</td>\n",
+              "      <td>2021-11-07 12:09:31</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-635dd322-3945-496e-9c42-a0609caff893')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-635dd322-3945-496e-9c42-a0609caff893 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-635dd322-3945-496e-9c42-a0609caff893');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 44
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "81_NE5a36I13",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "outputId": "37c5a121-d2a6-4461-ebe9-4daeffb2c1ad"
+      },
+      "source": [
+        "\n",
+        "\n",
+        "from fbprophet.diagnostics import performance_metrics\n",
+        "df_p = performance_metrics(df_cv)\n",
+        "df_p.head()"
+      ],
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          horizon          mse       rmse        mae      mape     mdape  \\\n",
+              "0 0 days 00:00:31  1800.501320  42.432315  28.071474  0.050989  0.025062   \n",
+              "1 0 days 00:00:32  1996.273828  44.679680  29.082079  0.052476  0.025062   \n",
+              "2 0 days 00:00:34  1993.766057  44.651608  29.342394  0.052884  0.027749   \n",
+              "3 0 days 00:00:35  1967.764739  44.359494  28.835106  0.052097  0.027621   \n",
+              "4 0 days 00:00:36  1975.701511  44.448864  29.084801  0.052549  0.027749   \n",
+              "\n",
+              "   coverage  \n",
+              "0  0.826087  \n",
+              "1  0.826087  \n",
+              "2  0.826087  \n",
+              "3  0.826087  \n",
+              "4  0.826087  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-274b3a2a-f050-4539-9bee-ce5662bb0fe1\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>horizon</th>\n",
+              "      <th>mse</th>\n",
+              "      <th>rmse</th>\n",
+              "      <th>mae</th>\n",
+              "      <th>mape</th>\n",
+              "      <th>mdape</th>\n",
+              "      <th>coverage</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0 days 00:00:31</td>\n",
+              "      <td>1800.501320</td>\n",
+              "      <td>42.432315</td>\n",
+              "      <td>28.071474</td>\n",
+              "      <td>0.050989</td>\n",
+              "      <td>0.025062</td>\n",
+              "      <td>0.826087</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0 days 00:00:32</td>\n",
+              "      <td>1996.273828</td>\n",
+              "      <td>44.679680</td>\n",
+              "      <td>29.082079</td>\n",
+              "      <td>0.052476</td>\n",
+              "      <td>0.025062</td>\n",
+              "      <td>0.826087</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0 days 00:00:34</td>\n",
+              "      <td>1993.766057</td>\n",
+              "      <td>44.651608</td>\n",
+              "      <td>29.342394</td>\n",
+              "      <td>0.052884</td>\n",
+              "      <td>0.027749</td>\n",
+              "      <td>0.826087</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0 days 00:00:35</td>\n",
+              "      <td>1967.764739</td>\n",
+              "      <td>44.359494</td>\n",
+              "      <td>28.835106</td>\n",
+              "      <td>0.052097</td>\n",
+              "      <td>0.027621</td>\n",
+              "      <td>0.826087</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0 days 00:00:36</td>\n",
+              "      <td>1975.701511</td>\n",
+              "      <td>44.448864</td>\n",
+              "      <td>29.084801</td>\n",
+              "      <td>0.052549</td>\n",
+              "      <td>0.027749</td>\n",
+              "      <td>0.826087</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-274b3a2a-f050-4539-9bee-ce5662bb0fe1')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-274b3a2a-f050-4539-9bee-ce5662bb0fe1 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-274b3a2a-f050-4539-9bee-ce5662bb0fe1');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "VkfYtdsC6I5F",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 500
+        },
+        "outputId": "13dc0926-7807-4ce1-95ad-8bccf35ed6c4"
+      },
+      "source": [
+        "from fbprophet.plot import plot_cross_validation_metric\n",
+        "fig = plot_cross_validation_metric(df_cv, metric='mse')\n",
+        "\n",
+        "#sns.distplot(df[\"demand\"])\n",
+        "#plt.title(\"Load Consumption\")\n",
+        "#plt.plot(df[['demand','forecast']])\n",
+        "plt.xlabel(\"Duration (Minutes)\",{\"Size\":16})\n",
+        "plt.ylabel(\"MSE (a.u)\",{\"Size\":16})\n",
+        "plt.title(\"MSE\",{\"Size\":16})\n",
+        "#plt.legend()\n",
+        "plt.show()"
+      ],
+      "execution_count": 46,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/fbprophet/plot.py:526: FutureWarning: casting timedelta64[ns] values to int64 with .astype(...) is deprecated and will raise in a future version. Use .view(...) instead.\n",
+            "  x_plt = df_none['horizon'].astype('timedelta64[ns]').astype(np.int64) / float(dt_conversions[i])\n",
+            "/usr/local/lib/python3.7/dist-packages/fbprophet/plot.py:527: FutureWarning: casting timedelta64[ns] values to int64 with .astype(...) is deprecated and will raise in a future version. Use .view(...) instead.\n",
+            "  x_plt_h = df_h['horizon'].astype('timedelta64[ns]').astype(np.int64) / float(dt_conversions[i])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "M7DD2VrBDe57",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 500
+        },
+        "outputId": "c751f76a-45e6-4c8f-a7dc-9c5d9c09cb00"
+      },
+      "source": [
+        "from fbprophet.plot import plot_cross_validation_metric\n",
+        "fig = plot_cross_validation_metric(df_cv, metric='rmse')\n",
+        "plt.xlabel(\"Duration (Minutes)\",{\"Size\":16})\n",
+        "plt.ylabel(\"RMSE (a.u)\",{\"Size\":16})\n",
+        "plt.title(\"RMSE\",{\"Size\":16})\n",
+        "#plt.legend()\n",
+        "plt.show()"
+      ],
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/fbprophet/plot.py:526: FutureWarning: casting timedelta64[ns] values to int64 with .astype(...) is deprecated and will raise in a future version. Use .view(...) instead.\n",
+            "  x_plt = df_none['horizon'].astype('timedelta64[ns]').astype(np.int64) / float(dt_conversions[i])\n",
+            "/usr/local/lib/python3.7/dist-packages/fbprophet/plot.py:527: FutureWarning: casting timedelta64[ns] values to int64 with .astype(...) is deprecated and will raise in a future version. Use .view(...) instead.\n",
+            "  x_plt_h = df_h['horizon'].astype('timedelta64[ns]').astype(np.int64) / float(dt_conversions[i])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5Pdvwopl8dge"
+      },
+      "source": [
+        "#from google.colab import drive\n",
+        "#drive.mount('/content/drive')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wxkjqFYaDe9q",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 500
+        },
+        "outputId": "8522c419-5f56-47dc-cf18-33a9d17dd089"
+      },
+      "source": [
+        "from fbprophet.plot import plot_cross_validation_metric\n",
+        "fig = plot_cross_validation_metric(df_cv, metric='mae')\n",
+        "plt.xlabel(\"Duration (Minutes)\",{\"Size\":16})\n",
+        "plt.ylabel(\"MAE (a.u)\",{\"Size\":16})\n",
+        "plt.title(\"MAE\",{\"Size\":16})\n",
+        "#plt.legend()\n",
+        "plt.show()"
+      ],
+      "execution_count": 48,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/fbprophet/plot.py:526: FutureWarning: casting timedelta64[ns] values to int64 with .astype(...) is deprecated and will raise in a future version. Use .view(...) instead.\n",
+            "  x_plt = df_none['horizon'].astype('timedelta64[ns]').astype(np.int64) / float(dt_conversions[i])\n",
+            "/usr/local/lib/python3.7/dist-packages/fbprophet/plot.py:527: FutureWarning: casting timedelta64[ns] values to int64 with .astype(...) is deprecated and will raise in a future version. Use .view(...) instead.\n",
+            "  x_plt_h = df_h['horizon'].astype('timedelta64[ns]').astype(np.int64) / float(dt_conversions[i])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "y0YT8e7EDfBE",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 500
+        },
+        "outputId": "2ce9e327-85a9-419c-eacb-ec8814305c8b"
+      },
+      "source": [
+        "from fbprophet.plot import plot_cross_validation_metric\n",
+        "fig = plot_cross_validation_metric(df_cv, metric='mape')\n",
+        "plt.xlabel(\"Duration (Minutes)\",{\"Size\":16})\n",
+        "plt.ylabel(\"MAPE (a.u)\",{\"Size\":16})\n",
+        "plt.title(\"MAPE\",{\"Size\":16})\n",
+        "#plt.legend()\n",
+        "plt.show()"
+      ],
+      "execution_count": 49,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/fbprophet/plot.py:526: FutureWarning: casting timedelta64[ns] values to int64 with .astype(...) is deprecated and will raise in a future version. Use .view(...) instead.\n",
+            "  x_plt = df_none['horizon'].astype('timedelta64[ns]').astype(np.int64) / float(dt_conversions[i])\n",
+            "/usr/local/lib/python3.7/dist-packages/fbprophet/plot.py:527: FutureWarning: casting timedelta64[ns] values to int64 with .astype(...) is deprecated and will raise in a future version. Use .view(...) instead.\n",
+            "  x_plt_h = df_h['horizon'].astype('timedelta64[ns]').astype(np.int64) / float(dt_conversions[i])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "DZbcCJxwDrjs",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 500
+        },
+        "outputId": "86b4d6c7-b1b6-425e-8fcc-c2ea499363c8"
+      },
+      "source": [
+        "from fbprophet.plot import plot_cross_validation_metric\n",
+        "fig = plot_cross_validation_metric(df_cv, metric='mdape')\n",
+        "plt.xlabel(\"Duration (Minutes)\",{\"Size\":16})\n",
+        "plt.ylabel(\"MDAPE (a.u)\",{\"Size\":16})\n",
+        "plt.title(\"MDAPE\",{\"Size\":16})\n",
+        "#plt.legend()\n",
+        "plt.show()"
+      ],
+      "execution_count": 50,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/fbprophet/plot.py:526: FutureWarning: casting timedelta64[ns] values to int64 with .astype(...) is deprecated and will raise in a future version. Use .view(...) instead.\n",
+            "  x_plt = df_none['horizon'].astype('timedelta64[ns]').astype(np.int64) / float(dt_conversions[i])\n",
+            "/usr/local/lib/python3.7/dist-packages/fbprophet/plot.py:527: FutureWarning: casting timedelta64[ns] values to int64 with .astype(...) is deprecated and will raise in a future version. Use .view(...) instead.\n",
+            "  x_plt_h = df_h['horizon'].astype('timedelta64[ns]').astype(np.int64) / float(dt_conversions[i])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmYAAAGKCAYAAACmQo3uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzde3gTZd438G/SUyhtE06FkkKh1CLljK2Aui0giBSfrpwEPICLWnTxsJ59Hl8RkX1kV1f0XXSXiq8grqCiLu4CRVBaVldABHQVlXJoaXqgFJrQAmmbZN4/YkIPaY4zk2ny/VxXLmgymbnnzj0zv7lPoxIEQQARERERBZ062AkgIiIiIjsGZkREREQKwcCMiIiISCEYmBEREREpBAMzIiIiIoVgYEZERESkEAzMiKhTWbduHVQqFVQqFY4ePdru8+LiYufnu3btAgAsW7bM+Z5KpUJ8fDyuuOIK3HrrrdixY4fb7d1zzz1QqVR4+OGHPabHse6RI0di9erVsFgszuUmTJjQarmWr9/97ncB5AgRhZLIYCeAiMgf8fHx2LBhA55//vlW769fvx7x8fGor69v950vvvgCERERuHjxIk6ePInNmzfjxhtvxO23347169dDrW59r3rp0iW8//77AIB3330XL774IiIjXZ82P/jgAyQnJ+P8+fP44IMP8MADD6CmpgbLly93LjNixAisWbOm3XeTkpJ83n8iCk0MzIioU5o5cybeeecdLF++HCqVCoA9kNq8eTNmzZqFdevWtfvO2LFjWwVWd911F1atWoVHHnkEo0aNwqOPPtpq+b///e84f/48cnNzsW3bNhQWFuKmm25ymZ5Ro0YhLS0NAHDDDTfg2LFjePXVV1sFZvHx8Rg3blygu05EIYxNmUTUKd1xxx0oKyvDF1984Xzv448/hs1mw6xZs7xez8MPP4zRo0fj1VdfbffZ+vXr0a1bN6xbtw5dunTB+vXrvV5vVlYWzp8/j5qaGq+/Q0TEwIyIOqWUlBRkZ2djw4YNzvfefvttzJgxA3FxcT6ta9q0aSgvL8epU6ec71VWVmLXrl2YO3cuevXqhZtvvhn/+Mc/UFdX59U6T548iYiIiHZpsVgs7V58Mh4ROTAwI6JOa8GCBfjggw9gNptRVVWFXbt2YcGCBT6vp3///gCAqqoq53vvvPMOrFarc30LFy5EY2Mj3nvvPZfrsFqtsFgsqKurw5o1a/DRRx/hpptuQmxsrHOZL7/8ElFRUe1eH374oc9pJqLQxD5mRNRpzZkzB/fffz/+8Y9/oKysDH369MH111+PPXv2+LQeR42Vo68aYG/GvOKKKzB+/HgAwOTJk9G3b1+sX78e9957b7t1XHnllc7/q9Vq3HbbbXjllVdaLTNy5EisXbu23XcHDRrkU3qJKHQxMCOiTis+Ph4333wzNmzYgNLSUtx2223tRlZ6o7y8HMDl0ZEHDhzAkSNH8OSTT8JoNDqXmzlzJlavXo2jR48iPT291To+/vhjJCcnIz4+HikpKdBoNO22ExcXh8zMTJ/TR0Thg4EZEXVqCxYswPTp02Gz2bBx40a/1rFt2zb0798f/fr1AwBnJ/8//OEP+MMf/tBu+bfffhsrVqxo9d6wYcOcozKJiPzFwIyIOrUpU6bglltugU6nw9ChQ33+/qpVq3D48GFns2NTUxM2btyIsWPHYuXKle2Wf/jhh53zp7Vs+iQiEgMDMyLq1CIiIryuKdu3bx8iIiJgNptx4sQJbN68Gdu3b8fChQvx4IMPAgC2bt2Ks2fP4k9/+hMmTJjQbh2LFy/Gfffdh6KiIkycONGntNbX12Pv3r3t3u/WrRsGDx7s07qIKDQxMCOisHHdddcBAGJjY5GUlISrr74ahYWFmDp1qnMZx5MD5syZ43Id8+fPxyOPPIL169f7HJh99913zsEELU2fPh3//Oc/fVoXEYUmlcAJdIiIiIgUgfOYERERESkEAzMiIiIihWBgRkRERKQQDMyIiIiIFIKBGREREZFChMR0GT179sSAAQMkW/+FCxfQtWtXydYfjpin4mOeiov5KT7mqfiYp+KTI09LS0tRW1vr8rOQCMwGDBiAAwcOSLb+oqIilxNNkv+Yp+JjnoqL+Sk+5qn4mKfikyNP3T0zl02ZRERERArBwIyIiIhIIRiYERERESkEAzMiIiIihWBgRkRERKQQDMyIiIiIFIKBGREREZFCMDAjIiIiUggGZkREREQKwcCMiIiISCEYmBEREREBMJlMMBqNMJlMQUsDAzMiIiIKeyaTCcXFxaiurkZxcXHQgjMGZkRERBT2jEYjbDYbNBoNbDYbjEZjUNLBwIyIiIjCnk6ng1qthtlshlqthk6nC0o6GJgRERFR2NNqtcjJyUGfPn2Qk5MDrVYblHQwMCMiIiKCPTjT6XRBC8oABmZEREREisHAjIiIiEghGJgRERERKQQDMyIiIiKFYGBGREREpBAMzIiIiIgUgoEZERERkUIwMCMiIiJSCAZmRERERArBwIyIiIhIIRiYERERESmErIHZokWLkJiYiGHDhrn8/G9/+xtGjBiB4cOH45prrsG3334rZ/KIiIiIgkrWwOzOO+9EYWFhh58PHDgQxcXF+M9//oNnnnkG+fn5MqaOiIiIKLgi5dxYdnY2SktLO/z8mmuucf5/3LhxMBgMMqSKiIiISBkU28fszTffxLRp04KdDCIiIiLZyFpj5q3du3fjzTffxBdffNHhMgUFBSgoKAAAGAwGFBUVSZaehoYGSdcfjpin4mOeiov5KT7mqfiYp+ILdp4qLjD77rvvcPfdd2P79u3o0aNHh8vl5+c7+6BlZmZiwoQJkqWpqKhI0vWHI+ap+Jin4mJ+io95Kj7mqfiCnaeKaso8deoUZs6ciQ0bNiA9PT3YySEiIiKSlaw1ZvPnz0dRURFqa2uRnJyM5557Ds3NzQCAe++9F8uXL8fZs2fx29/+1p64yEgcOHBAziQSERERBY2sgdnGjRvdfr527VqsXbtWptQQERERKYuimjKJiIiIwhkDMyIiIiKFYGBGREREpBAMzIiIiIgUgoEZERERkUIwMCMiIiJSCAZmRERERArBwIyIiIhIIRiYERERESkEAzMiIiIihWBgRkRERKQQDMyIiIiIFIKBGREREZFCMDAjIiIiUggGZkREREQKwcCMiIiISCEYmBEREREpBAMzIiIiIoVgYEZERESkEAzMiIiIiBSCgRkRERGRQjAwIyIiIlIIBmZERERECsHAjIiIiEghGJgRERERKQQDMyIiIiKFYGBGREREpBAMzIiIiIgUgoEZERERkUIwMCMiIiJSCAZmRERERArBwIyIiIhIIRiYERERESkEAzMiIiIihWBgRkRERKQQDMyIiIiIFIKBGREREZFCMDAjIiIiUghZA7NFixYhMTERw4YNc/m5IAh48MEHkZaWhhEjRuDgwYNyJo+IiIgoqGQNzO68804UFhZ2+Pn27dtRUlKCkpISFBQU4L777pMxdURERETBJWtglp2dje7du3f4+ZYtW7BgwQKoVCqMGzcORqMRVVVVMqaQiIiIKHgig52AlioqKtCvXz/n38nJyaioqEBSUlK7ZQsKClBQUAAAMBgMKCoqkixdDQ0Nkq4/HDFPxcc8FRfzU3zMU/ExT8UX7DxVVGDmi/z8fOTn5wMAMjMzMWHCBMm2VVRUJOn6wxHzVHzMU3ExP8XHPBUf81R8wc5TRY3K1Ov1KC8vd/5tMBig1+uDmCIiIiIi+SgqMMvLy8Pbb78NQRCwd+9eaLVal82YRERERKFI1qbM+fPno6ioCLW1tUhOTsZzzz2H5uZmAMC9996L3NxcbNu2DWlpaYiNjcVbb70lZ/KIiIiIgkrWwGzjxo1uP1epVHjttddkSg0RERGRsiiqKZOIiIgonDEwIyIiIlIIBmZERERECsHAjIiIiEghGJgRERERKQQDMyIiIiKFYGBGREREpBAMzIiIiIgUgoEZERERkUIwMCMiIiJSCAZmRERERArBwIyIiIhIIRiYERERESkEAzMiIiIihWBgRkRERKQQDMyIiIiIFIKBGREREZFCMDAjIiIiUggGZkREREQKwcCMiIiISCEYmBEREREpBAMzIiIiIoVgYEZERESkEAzMiIiIiBSCgRkRERGRQjAwIyIiIlIIBmZERERECsHAjIiIiEghGJgRERERKQQDMyIiIiKFiPT1C9XV1aisrMSlS5fQs2dPDBw4ENHR0VKkjYiIiCiseBWYHThwAGvXrsWOHTtw6tSpVp9FR0cjKysL8+fPx+233474+HhJEkpEREQU6twGZgcOHMBjjz2GPXv2YPjw4bjpppswevRo9OrVC126dMG5c+dw8uRJ7Nu3D0899RSeeuopPPHEE3j00Ueh0Wjk2gciIiKikOA2MMvJycE999yDv/zlLxgyZIjbFZnNZmzZsgV//OMfYbPZ8Mwzz4iaUCIiIqJQ5zYwO378OPr06ePVijQaDebOnYu5c+fi9OnToiSOiIiIKJy4HZXpbVDWVu/evf36HhEREVE443QZRERERArh9XQZAwcOhEql6vBzlUqF48ePe1xPYWEhHnroIVitVtx999146qmnWn1+6tQpLFy4EEajEVarFStXrkRubq63ySQiIqIQYzKZYDQaodPpoNVqg50cSXkdmOXk5LQLzM6ePYt///vfiIuLw6RJkzyuw2q1YsmSJdi5cyeSk5ORlZWFvLw8ZGRkOJdZsWIFbrnlFtx33304cuQIcnNzUVpa6v0eERERUcgwmUwoLi6GzWaDWq1GTk5OSAdnXgdm69atc/m+0WjEjTfeiMmTJ3tcx/79+5GWlobU1FQAwLx587Bly5ZWgZlKpcL58+cB2H+Mvn37eptEIiIiCjFGoxE2mw06nQ5GoxFGozGkAzOVIAhCoCv58MMP8fTTT+Onn35yu9zmzZtRWFiItWvXAgA2bNiAffv2YfXq1c5lqqqqcMMNN6Curg4XLlzArl27cNVVV7VbV0FBAQoKCgAABoMBmzZtCnQ3OtTQ0IC4uDjJ1h+OmKfiY56Ki/kpPuap+MIhT81mM8rKyiAIAlQqFVJSUiSdK1WOPH3sscdw4MABl5/5/EgmVzQaDQwGgxirwsaNG3HnnXfi0UcfxVdffYU77rgD33//PdTq1uMU8vPzkZ+fDwDIzMzEhAkTRNm+K0VFRZKuPxwxT8XHPBUX81N8zFPxBTNP5ez3Jee2gl1OAwrMLBYLvv/+eyxbtgxDhw71uLxer0d5ebnzb4PBAL1e32qZN998E4WFhQCA8ePHw2w2o7a2FomJiYEklYiIiEQid78vrVYb0s2XLXk9XYZarUZERESrV0xMDK666iocO3YMq1at8riOrKwslJSU4OTJk2hqasKmTZuQl5fXapn+/fvjs88+AwD8+OOPMJvN6NWrl4+7RURERFJp2e/LZrPBaDQGO0khw+sas6VLl7YblanRaJCSkoJp06Z5FclGRkZi9erVmDp1KqxWKxYtWoShQ4di6dKlyMzMRF5eHv70pz/hnnvuwapVq6BSqbBu3Tq303QQERGRvHQ6HdRqNYxGI9RqNXQ6XbCTFDK8DsyWLVsmygZzc3PbzUu2fPly5/8zMjLw5ZdfirItIiIiEp9Wq0VOTk7YzC0mJ1E6/xMREVF4Cad+X3IS5ZFM77zzDt5++20xVkVEREQUtkSpMfvNb34Dm82GBQsWiLE6IiIiorAkSmD22WefQYR5aomIiIjCmiiBWXZ2thirISIiIgprovQxIyIiIqLA+VRj1tTUhO3bt+Pnn3+G2Wxu9ZlKpcIzzzwjauKIiIiIwonXgVllZSWuu+46lJaWQqVSOfuUtZz8lYEZERERkf+8bsp8/PHH0atXL5w6dQqCIGDfvn04ceIEnn76aaSlpeHEiRNSppOIiIgo5HldY/avf/0LL730Evr27QvA/uzMAQMGYPny5bBarXjwwQexZcsWyRJKREREFOq8rjE7e/Ys+vbtC7Vaja5du6Kurs752aRJk1BUVCRF+oiIiIjChteBWXJyMmprawEAgwYNwqeffur8bP/+/dBoNOKnjoiIiCiMeN2UOXHiRBQXF+Pmm2/G4sWLsWTJEhw+fBhRUVHYsWMHFi9eLGU6iYiIiEKe14HZihUrcO7cOQDAfffdB4vFgvfeew8XL17EE088gaVLl0qWSCIiIqJw4HVg1rNnT/Ts2dP59wMPPIAHHnhAkkQRERERhSPO/E9ERESkEG4DswcffBDV1dU+rfCjjz7Cpk2bAkoUERERUThyG5iVlpYiNTUVc+fOxSeffOLsY9aSzWbD4cOH8fzzz2Pw4MG477770L17d8kSTERERBSq3PYx++STT7Bnzx689NJLmDVrFmw2G/r27YtevXohJiYGdXV1KC8vh9lsRlJSEu655x48/PDDSEhIkCv9RERERCHDY+f/7OxsZGdno6qqCp9++in27duHyspKmM1mpKam4rbbbkN2djZ+9atfQa1mlzUiIiIif3k9KjMpKQkLFy7EwoULpUwPERER+clkMsFoNEKn00Gr1QY7OeQHrwMzIiIiUi6TyYTi4mLYbDao1Wrk5OQwOOuE2PZIREQUAoxGI2w2G3Q6HWw2G4xGY7CTRH5gYEZERBQCdDod1Go1jEYj1Go1dDpdsJNEfmBTJhERUQjQarXIyclhH7NOjoEZERFRiNBqtQzIOjk2ZRIREREphNvA7JFHHkF5eXmr9z766COYTKZW7x09ehR5eXnip46IiIgojLgNzF599VVUVVU5/7ZarZgzZw6OHz/earm6ujps3bpVmhQSERERhQm3gZkgCF69R0RERNTZOSbobdsyKCf2MSMiIiKfmUwmlJWVyRLEyLEtxwS91dXVKC4uDlpwxlGZRERE5BM5nzIg17YcE/RqNBrnBL3BGOHqscZMpVJ59R4RERF1TM4aJqnJ+ZQBubblmKDXbDYHdYJejzVm+fn5iI+Pb/XeXXfdhbi4OOff9fX14qeMiIgoRITacyzlfMqAXNtyTNC7e/fuoP4+bgOz7OzsdrVjOTk57ZbT6XTIzs4WN2VEREQhomWtj9FolKyZzNF5XeqZ/+V8yoDc2wr2UxPcBmZFRUUyJYOIiCh06XQ6WCwWGAwGaDQaSWp95K6Vk/MpA+H0RAOvR2XW1tbCbDZLmRYiIiLyk5z9vkg6bgMzq9WKZcuWoVu3bujduzcSEhIwa9asgH7swsJCDB48GGlpaVi5cqXLZd5//31kZGRg6NChuPXWW/3eFhERkRIYjUZERkYiOTkZkZGRkgRNcvb7Ium4bcr861//iuXLl2PChAnIysrCiRMn8PHHHyMhIQFvvfWWzxuzWq1YsmQJdu7cieTkZGRlZSEvLw8ZGRnOZUpKSvDCCy/gyy+/RLdu3VBTU+P7XhEREXlJjn5ZcgRNcvbFIum4DczeeOMN3HPPPVizZo3zvTVr1uD+++/HmjVrEB0d7dPG9u/fj7S0NKSmpgIA5s2bhy1btrQKzN544w0sWbIE3bp1AwAkJib6tA0iIiJvydUvS66gKZz6YoUqt02ZJ06cwJw5c1q9N3fuXFitVpSVlfm8sYqKCvTr18/5d3JyMioqKlotc/ToURw9ehTXXnstxo0bh8LCQp+3Q0RE5A05+2VptVqkpKQwcCK33NaYNTQ0ICEhodV7jjnNpJq7zGKxoKSkBEVFRTAYDMjOzsZ//vOfdtW+BQUFKCgoAAAYDAZJR5A2NDRwhKrImKfiY56Ki/kpPiXmqdlsxunTp1FdXQ2VSoUff/wRJ0+eDHayvKbEPO3sgp2nHieYraiowIkTJ5x/W61W5/ttgyVHE2VH9Ho9ysvLnX8bDAbo9fpWyyQnJ2Ps2LGIiorCwIEDkZ6ejpKSEmRlZbVaLj8/H/n5+QCAzMxMTJgwwdOu+K2oqEjS9Ycj5qn4mKfiYn6KT6l5KtfcX1JQap52ZsHOU4+B2ezZs12+f/PNN7d7zxG0dSQrKwslJSU4efIk9Ho9Nm3ahHfffbfdejdu3Ijf/OY3qK2txdGjRz0GfERERP5ivyxSEreBmT8jL91uLDISq1evxtSpU2G1WrFo0SIMHToUS5cuRWZmJvLy8jB16lR8+umnyMjIQEREBF588UX06NFD1HQQERERKZHbwGzhwoWibzA3Nxe5ubmt3lu+fLnz/yqVCi+//DJefvll0bdNREREpGRez/zvTnFxMRYtWiTGqoiIiIjClt+B2bFjx7B06VIMHDgQEydOxPvvvy9muoiIKIyZTCaUlZXBZDIFOylEsvLY+b8lk8mE9957D+vXr8fevXsBACNHjsRTTz2F+fPnS5JAIiIKL3I/jJtISTzWmNlsNmzbtg1z585FUlIS7r33XpSVlWHJkiUAgFdeeQWLFy9uN98ZERGRP/gwbgpnbmvMHn30Ubz77ruoqamBRqPBjBkzsHDhQkyePBnnz5/H6tWr5UonERGFCT6Mm8KZ28Bs1apVUKlUyM3Nxbp161pNW6FSqSRPHBERhR8+jJvCmdumzLvuugvx8fHYunUrBg8ejPvvvx/79++XK21ERBSm+FxJClduA7M33ngD1dXV+Nvf/obMzEysWbMG48ePx5AhQ/CHP/yBtWZEREREIvLY+V+j0WD+/PkoLCzEqVOn8MILLyAiIgIrV66EIAh46qmn8M4778BsNsuRXiIiIqKQ5dM8ZklJSXjiiSfw/fffY//+/ViyZAlKSkqwYMECJCUlSZVGIiIiorDg9wSzmZmZ+POf/4zKykp8+OGHfLo9ERERUYB8mmDWYDCgqqoKKpUKSUlJ0Ov1iIqKwowZMzBjxgyp0khEREQUFryqMVu1ahUGDBiAlJQUjBs3DmPHjkX//v0xcOBAzmVGRBQm+JgkIul5rDFbuHAhNmzYgNGjR+P2229H//79IQgCysvLsXXrVjz00EM4dOgQ3nzzTTnSS0REQcDHJBHJw21g9umnn2LDhg1YtWoVHnrooXafr1ixAqtWrcJjjz2G+fPnY/LkyZIllIiIgqflY5KMRiOMRiMDMyIJuG3KXL9+PaZNm+YyKHN4+OGHceONN2LdunVip42IiBSCj0kikofbwOzrr7/G7NmzPa5k9uzZfCIAEVEIczwmafTo0WzGJJKQ26bM6upqDBw40ONKUlNTUVVVJVqiiIhIebRaLQMyIom5rTFraGhAbGysx5VoNBpcvHhRtEQRERERhSOPozJtNhtsNpvbZaxWq2gJIiIiIgpXHgOza6+9Vo50EBEREYU9t4HZs88+K1c6iIiIiMIeAzMiIiIihfD7IeZEREREJC63NWaff/65TyubNGlSQIkhIiIiCmduA7PJkydDpVIBAARBcLmMSqWCIAhQqVQcnUlEREQUAI+jMuPj4zFr1izMmjULXbt2lSNNRERERGHJbWBWVFSE9evXY/Pmzfjggw8wY8YMLFy4kE2WRERERBJw2/k/Ozsbb775Jk6fPo2//vWvqKmpwdSpU9G/f3/893//N3788Ue50klEREQU8rwalanRaHDrrbdi+/btOHXqFB566CFs27YNw4YNw/333y91GomIiIjCgs/TZfTo0QMDBgzAgAEDoFKpUFdXJ0W6iIiIiMKO14HZl19+iXvvvRdJSUlYuHAh4uLisHXrVmzYsEHK9BFRCyaTCWVlZTCZTMFOChERScBt5/9jx45hw4YNeOedd1BaWors7Gy89NJLmDNnDuLi4uRKIxHBHpQVFxfDZrNBrVYjJycHWq022MkiIiIRuQ3M0tPTkZCQgJkzZ2Lt2rVISUkBANTU1KCmpqbd8qmpqdKkkohgNBphs9mg0+lgNBphNBoZmBERhRiP85idP38e69atw/r16z2ujBPMEklHp9NBrVbDaDRCrVZDp9MFO0lERCQyt4HZW2+9JVc6iMgDrVaLnJwcGI1G6HQ61pYREYUgt4HZwoUL5UoHEXlBq9UyICMiCmE+T5dBRERERNKQPTArLCzE4MGDkZaWhpUrV3a43IcffgiVSoUDBw7ImDoiIiKi4JE1MLNarViyZAm2b9+OI0eOYOPGjThy5Ei75err6/Hqq69i7NixciaPiIiIKKhkDcz279+PtLQ0pKamIjo6GvPmzcOWLVvaLffMM8/gySefhEajkTN5REREREHlcboMMVVUVKBfv37Ov5OTk7Fv375Wyxw8eBDl5eWYPn06XnzxxQ7XVVBQgIKCAgCAwWBAUVGRJGkGgIaGBknXH46Yp+Jjnoor0Pw0m80wm83QaDS8yfwFy6j4mKfiC3aeyhqYeWKz2fDII49g3bp1HpfNz89Hfn4+ACAzMxMTJkyQLF1FRUWSrj8cMU/FxzwVVyD56XhKAwA0NTVh/PjxHE0LllEpME/FF+w8lbUpU6/Xo7y83Pm3wWCAXq93/l1fX4/vv/8eEyZMwIABA7B3717k5eVxAAARdSotn9Jgs9lgNBqDnSQi6iRkDcyysrJQUlKCkydPoqmpCZs2bUJeXp7zc61Wi9raWpSWlqK0tBTjxo3DJ598gszMTDmTSUQUEDmf0sAH2xOFFlmbMiMjI7F69WpMnToVVqsVixYtwtChQ7F06VJkZma2CtKIiDoruZ7SwAfbE4Ue2fuY5ebmIjc3t9V7y5cvd7ksOzQSUWclx1Ma+GB7otDDmf+JiDopPtieKPQoalQmEZGUTCZTSD0Eng+2Jwo9DMyIKCyEan8sPtieKLSwKZOIwgKnsCCizoCBGRGFBfbHIqLOgE2ZRBQW2B+LiDoDBmZEFDbYH4uIlI5NmUQi4QzsREQUKNaYEYkgVEf8ERGRvFhjRiQCjvgjIiIxMDAjEgFH/BERkRjYlEkkAo74IyIiMTAwIxIJR/wREVGg2JRJREREpBAMzCgscCoLIqLOK5zO4WzKpJDHqSw6B5PJxD56RNROuJ3DWWNGIY9TWSif48R76NAhFBcXh8VdMRF5J9zO4QzMKORxKgvlC7cTLxF5L9zO4WzKpJDHqSyUL9xOvERSC6WuAXKewx35ZjKZgpZvDMwoLHAqC2Vj8EwknlDskyXHOdyRb9XV1SguLg5avrEpk4gUQavVIiUlpZGeJzQAACAASURBVNNfQIjckWN0odxdA0JlxKQj3zQaTVC7VLDGjIiISAZy1WTJ2TUglGrnHPlmNpuD2qWCNWZEREQykKsmy9E1YPTo0ZIHSqE0cMeRb3369AlqgMkaMyIKqY7CREolZ02WXP1qQ23gjlarDfp5kIEZUZgLpaYIIiULxUEuobhPwcbAjEIGa33807Ipwmg0wmg0SpZ//I1IieQsl6E4QjwU9ymYGJhRSAjFWh+5LhZyNUWE4m8UrkIpwGa5JKVhYEYhQc5aHznIebGQqyki1H6jcBVqgQzLJSkNR2WSqII1n02odUCVe6STHHOIhdpvFK5CaRQewHJJysMaMxJNMO+kQ60DaiheLELtNwpXoVY2WS5JaRiYkWiC3SQgZwdUqfvYhOrFgp2EO79QLJssl6QkDMxINKF2J90RuWoGebEgpWLZJJIOAzMSTSjeSbsS7JpBIiIKXQzMSFSu7qRDaWg9ED41g0REJD8GZiSpUBtaD4RPzSAREcmPgRlJKlSb/djHhoiIpMB5zEhSbPYjIiLynuyBWWFhIQYPHoy0tDSsXLmy3ecvv/wyMjIyMGLECFx//fUoKyuTO4khKxiTvzqa/UaPHh0SzZjhJlgTBhMRhStZmzKtViuWLFmCnTt3Ijk5GVlZWcjLy0NGRoZzmdGjR+PAgQOIjY3FX/7yFzzxxBN477335ExmSAr25K8MyDqfUOwfSESkdLLWmO3fvx9paWlITU1FdHQ05s2bhy1btrRaZuLEiYiNjQUAjBs3DgaDQc4khqxQe4wKSY9lpnOQo1YzFGpOQ2EfKDzIWmNWUVGBfv36Of9OTk7Gvn37Olz+zTffxLRp0+RIWshjXy/yFcuM8klZq+mY5iYiIgIHDx6UreZUiul13OVTqE3nQ52fYkdlvvPOOzhw4ACKi4tdfl5QUICCggIAgMFgQFFRkWRpaWhokHT9comOjobZbEZ0dDS++uormM1maDQaaDQa2dMSKnmqJFLkacsyc+jQIVHXrXRKLKNms7nVcWs0GlFdXQ2NRgOz2Yzdu3eLEkCbzWaUlZVBEAQ0NzdDrVaja9euAW+jbZ623Z+W21WpVEhJSRHl/NRRPkm1PTkpsZx2dsHOU1kDM71ej/LycuffBoMBer2+3XK7du3C73//exQXFyMmJsbluvLz85Gfnw8AyMzMxIQJEyRJMwAUFRVJun65Oe4eAaCpqQnjx4+X/U4x1PJUCZin4lJafro6bgG0qgkaM2YMrFZrwLU/ZWVlMJvN0Ol0qKmpgcViQWxsbMA1Zi3z1NX+GI1G53aNRiOGDBmClJQUv/fDoaMas5b7Keb25KS0ctrZmUwm7N69G6NHjw5aDaqsgVlWVhZKSkpw8uRJ6PV6bNq0Ce+++26rZQ4dOoTFixejsLAQiYmJciZPMaSuWg/VucWIQpmr4zYlJcU52bGYTY4tm7E1Go1oAZ+n/ZGq+byjSaHZXE8tOQL46upqFBcXB23Ak6yBWWRkJFavXo2pU6fCarVi0aJFGDp0KJYuXYrMzEzk5eXh8ccfR0NDA+bMmQMA6N+/Pz755BM5kxlUcoyE48mIqPPp6Lh1jHouKysT7YZLjqdbuNofKbfranQ4n+JBLTluFjQajXPAU8gHZgCQm5uL3NzcVu8tX77c+f9du3bJnSRFkaM2iycjos7H03Er9g2X1NPcdLQ/ck+vw+l8yMFxDJnN5qBWWii283+4kqs2iycjInmJ0UXB3XHbGW+4eB4iJXEcQ7t37w7qvI0MzBSmM55ciZRISdMgyDVZLwMdosBotdqgnzMYmCkQT65EgVHaUws44Iak4Lj5MJlMLE8hhA8x9xJnjSbqPJT21AIOuCGxtR1BGMrXpnC7/rLGzAtms1lRd99E5J7SAiF2USCxdTSCUElN+GJQWu23HBiYecFsNgMAmyGIOgklBkLsokBicjWCMBSDmHDsBsCmTC9oNBpF3X0TkWdarRYpKSmSn8TDrZmFlMFx89GnTx9nAKa0JnwxKK32Ww6sMfOCRqNxPi5EKXffRBR8oVhDQZ1H2xGEoRjEKLH2W2oMzLwkVTNEqPUHIAoF3o52C8dmFlIuuYMYua5f4dYNgIFZEPFum0h5fHleXijWUFDnJlcQE6rXL4PB4HwlJycHJQ3sYxZEodgfgKizczXarSOOGorRo0eHzIUp2Nhnr3MIxeuXwWDAxx9/3OrfYGCNWRDxbptIeXx9Xl64NbNIKVRrYUKR3NcvOZpNKysrYbVaERkZCavVisrKyqDUmjEwC6Jw7NRIpHRKeV5eOGKfvc5DzuuXfAG7DkePdkNk5EUkJRmRkJAgwTY8Y2AWZLzbplDVmQe2KOF5eeEo1FsROvMx4dB2H6QOyIxGIy5cuCBawG61AqdOAT/9BPz8s/1fx/+rq9MBpOOaa/6D3/72O3Tp0kXcHfISAzMiEh2bpMgfodSK0DaAkfKYkCvgk/O4brkti8UCAD4F7A0NlwOvlgFYSQnwy5zxAIBu3YArrwSmTQNSUi6hqek/iI0tQXx8fNBuDBiYEZHo2CRF/gqFVgRXAYxUx4TUjwxsGfTJeVy33VZ6ejq6du3aKvgUBMBgaF3r5fh/RcXldanVwMCB9gDshhuAwYPt/7/ySqBnT0ClcizZBSbTYOzeXRnUm0kGZkQkulBvkgpXcjbFyVkLJPZ2XAUwUh0TUj4y0GQy4fPP9+Dbb7ujufk0UlNTceJE31+CwL64eLEnDh+2L9ujB5CeDvTq1TLQ8Z9Wq8OpU1rU1jYiMVGN2NgEFBZGo6wsBqWl9uDr6FHgwoXL30lIsAddkybZgy5HAJaWBsTEBJ4muTAwIwphwerTEkpNUv4I1QAmGM1YUm5Lqu3odDpYLBYYDAZoNBrnbyPFMaHRaNDU1CTJTZDBYMKKFVk4eLBPi3d7uv2OVmsP0AYPtv/b8tW1q/vtCQLwn/8A778PvP++FiUlE9G7twU7dtTg3nubsGNHMlQqAf362TBkiBrZ2a0DsD59AgsKfZnDUEoMzIhaCIXOuQ7B7ucVCk1S/jCZTNi5cyfMZjM0Gg2mTJnS6QMYQN7maSm3FaymOUCaY+LYsURs2jQCp0/bEBERAQCwWpsQEREBtToioHWfPZuM2lrgrru+w7Bh5zBmzFWIj49vt5wgADU19qbEo0ftrz17gHfeab2cXt86UHMEb2YzsHmzPSD76Sd70+OkScBddwHPPhuJmTN74cSJKMyZU45Zsw5j3LgRSElJCWjfXHE1hyEDM/JJKAURShDsQMZVegL5fcW46MhRxgwGAyorK5GQkIAuXbpIsi05j5WKigpUVVUhKioKdXV1qKio6JQBTEsmkwkXLlyAxWKRtHna8TvZgwrxm/3aHuNjxoyRZDv//KcZr702Hg88UI7IyOpWv8tXXwFPPw00Nga+HYsF2L9/DJKTgfHjgebmJpw5cwaCYK856tWrF6Kiov1ev1qtxvz5DRg1SgudLgVabeugrOVxNWaMFjfe2Pr7Fy8Cx45dDtYcgdv77wN1dW23BeTkAA89BMycCSQm2t9PSAB++9soDB58DrfcchDR0SrJukY4ajrPnz+PuLg4dv4n3ygtiAgF3lzkOlOzUaB9WhxpMJvNsFgsyM7OFn2yRccM21arFZcuXUL//v0RHx8v+oi1lqO7MjIykJCQAKvV6vZ3FAT/tyn88mUhkJV4QafT4dKlSzhz5oxkF5KW+QcA6enp0Ov1Xv0+vhwvju00NQn47rveKC+fhPJyNSIjo/DGG1Fuv6vVApMnA7/+tX2UXUfaHuMWixWRkZPwySdWlJbGetxOSzfeCCxZ0vq98nLgkUeAzZt7AwB0ukbce28NIiIiUFZWBoulG2bNss+NNXSo15tya/78Uygo6I+4OKCsrAqHDh1y7t/o0aNFqFmK++XVmjfnqNhYYMQI+6uts2cvB2oWC3DTTfamyLbuvdcepI0aFYnIyFFhURHBwKyTkvNOOVxq5TwFMp2t2SjQPi1GoxFmsxl1dXVobGzEnj17MH36dFH3ubKyEoIgQKPR4OLFiwDgdROCt2WzZfPE0aNH0dDQgPr6evTu3RsajQbZ2Tk4d06Lb78FDh+G81+j8Rq8/jpw662+7ZNer0ffvn3R0NCALl26SDpJZX19PWpqamC1WnHuXDM+/rgZ9fVAfLy9piEhof3/4+LstRMOjY322ouWr3PnAKMRaG4G6uqsMBgGokuXLrh0yYxvvklC166ey4DZbEZpaSVUKhuiohowaFA0KiqScfCg6+Xr6qw4ejQdu3cPwJkzGmg09n5E3vQZ+vpr4G9/s1/Y//Y3ezOYKy2P8YsXo/Hww0n4+9+jERUFDBkCRHp5RTQagX/+Exg7FsjMtOfhyy8DK1bYA/rnnwe++aYJn302AK+8EoODB7+BxWLD738/HufOxWPfPhVGjvRuW54UFZ1AXFz/dvsn9aCbQM9RPXoA11xjf7mjUgGzZgFAwi8v6RiNRkRGRiIhIQGRkZFsyiTfyHEAhlutXEeBjK+THIoRzCphVKOjWr+xsRExMTGSnKj69u0LlUoFs9kM1S9XYG/215eyqdPpcO5cLN56axCuv74aiYmxWL9+BGpq+qO5WY3q6njU1+OXbdv7vIwfD3z33SXcdls0Vq0CfJlncto0LX7962F4/XUDdu8ehj/+MQo2mxXl5RHo2xcYNcr+Gj3a/m9Skvfrdrh4Efj0U+CNNzQoLs7HxYsxEATvH33cMjD7pSLMje6/vHylATCkzXtpHreTkVGDO+7Yh/vuG4S0NM81tCaTCXV1Rhw92gMPPRSHyZOB//N/gKVL2wdajmO8qOgi/vu/e6OiQo3//V/g/vvtQau3zp8HrrgC+N3v7Nt68EH7/FgzZgCrVgEpKcAXX0Tj738HtmyJQVqaDe++m4mDB3vj978/i5Eje3i/MR/IOegmFB/J5Ovj2KTCwKyTkuMADMe5qNp2zvV1kkOxgllvf9+WJyux06LVapGdnY09e/YgMjLSObpMTMnJyZgxYwZ+/LEG99+fgbKyaMTEqHDddSpMmQJMmQJkZLQfaeVL2dRqtdi16xp88UUcTp3qjuHDa1BUNBjp6bWIjbVi3rxmZGXFYNQoe/NSbKz9e599dhj//ncOdu/2fn/q64H/+R9gzZpElJVdicREI3r1akT//o244YZYGAzAN98AH3xw+Tu9e18O1tLSWgdNbZnNwM6dwI4dwKVLgFbbDVdc8SN0OiMiI2249dYBmD69Py5csAcP9fX2fx2v+nqgpsaMS5fMiInRQKPRQKOxN/85Xt27X/5/9C/dk0wmE0wmE7RaLXr00CIqyvPoN3v52wOLRYDNFolrr70W3333HX71q191+J2qqgp8800xIiMjceSICb16xbsts23L+Gef5eDpp7V4/nng448BVy3vNpsWn3+uhV4P/Otf9iDcVwkJ9lqxxYvtE5OmpwOFhcDUqZeXufZa4MorrfjznxPQv/8YfPllMvLyjmPJEvejGgMl16CbUHwkk1Iex8bArBOT+gBUQq1NsHkzyaG75QMJZj39vm1PVtHRrTv5ipGW5ORkTJ8+XdKTb3JyMp57LhnHj9trHi5cAHbvBrZts3+elGTvPzRliv3fpCTfyuYPP5zHBx/E4corz+Lnn7vj1KlumDfvAl544QK6ddNBq3U9wVFEhIBnngGeecb7fREEYPnyc3j+eR2uueYnzJz5GZKSuv3SBHx5OaMR+O474NAhe7Pp4cP2prDmZs/b0OuBRYvstTMpKQbs2/ctBEGASqXCddelODtNu+LvBS4hQYt+/eyz1585U+ZVWejRQ4vrr89uVXaOH7fCXctuXZ0FsbGxXpfZtmW8udmIt97SYvJk4PXX7U2yrixYALz0kvv+aJ7cdRewf//lmrO282SdP2/CtGnH8MYbw3HuXB/MmlWHN97oGVI3t3IFgXJWEijhcWwMzAIQ6v2vwn0uKqB9AOCq03PLchDMPh7mls8ZcZF2X9LiriZODIJgr705d85eg7R2LfDYY8CLL15epqzMXju0c6c9SNuwwf7+sGHAlClaXH319YiKqkeXLnH4/vv2nZMdVq1SQxCAZ58twe7dETh1aiBefTUKiYniD7dXqYDf/S4CGRnbIQgXYLF0Q3Z2drsyo9MB2dn2l0NNjQnHjtUjISGhw35pajXQt+/lWjWTSYf4+HhnoOXp9wrkAudPUOfrhdvXMtvR8rfdZn95w9/zeESEvdx2xGg0Iju7HHl59c6O+N26iV/mlEDqa6GreeFCGQMzP4VL/ysp7ogcB7GjaURKgZ4wPAWnrsqBlMGsuyCwbY2Zv4F1y326eNGGdetGYvfugbDZVAAEAIFP6y0IrUc9pqQAy5a1XiYlBbj7bvvLZrPXKjkCtddfB1at6grAw4yVAIA4XH/9KajVpzBs2Glcf/1J7N2rkbQ55IYbrvMp300mE/buted5ba335xNff+NAgnU5ai183Z9Abx6lPI+HS4tDuFwL5cTAzE/h2P9KDHLOrOxqmgRvh/m35C44dVUOUlJSAtqn5mbgiy+ArVuBU6eAJ58ErrrKvj+rVh3Bjh0psFovoXv3HgCmwWJpRmRkFM6fN+G119qlHgkJWvz61/bh6I7+U+449ikysgeefjoDpaU9cOONVYiPP48+fXpDqw38AqNS2ac46N7d/rrmGvezgqvVwJgx9teTT9r7Vx040PphxO62lZGhhcEwAACQmJgo6zHrzc1BIOcTX26eAglk5Ao0fL0ZDOTmUcrzuKfBRHL0y5JjO3JcCx2jJZOTkyU/duWsOOgIAzM/dXSSCvXmzUC1nLpA6pmVXU2TEB0dLep8XGJdrM6cAbZvtw/B37HD3lE7OtoerHz4ob3Jq65Og2+/HY+ePRuRkHAJly7ZEB0dA8A+91J9fYzLSSu//hp49137uvLygHnz7J2UO3p2nGOfCgqSUFbWHU888SXGjz/zy91wH3T0c3lT9l0tc/lE6P0x06UL4KYPuQtadO06GNXV1a1+K09p9ud4djVgJDIy0m1tgpy1K/4GMqHYtcGR7zU1NbBYLM6Z88XibjBRZ3zUlCtylF25jg8+kqmTc3WSkvJgCJWAT87hyI5t1dbW/tIsdxHnz58XdT4uV+XgyBGgqsrzd61We+fhf/7T/q8g2Du233ILMH26vaO7xQI89xywdy+g0UTgzjt/QG7uMcTEqJCTkwPA7Nz2oUOHMGHCBJfb2bMH2LTJ/tiTjRvtfZxmzrQHaRMntp5WQKvVIj7+amzbloj58y/gf/5nmFcBl6ey72oZAO1mYvc08au32h4zbX+rtttum2az2ezX8dyyBsFgMACAxzv9zhL0yNXZuyUpz31arRZjxoxxjjw+ePAg4uPdjwQNhFwtLXJ3lg+0OdnTd+U6PuSsOHCHgVkA2p6kpDoYgtWGL8UJ0XGAyTEc2bGtiooKfP311zCZTJLMx+UoByaTCS+9VIvHH/d+OLxKBWRl2YOv6dPtc1u1nYZg1SrH/yJhMiXDaIxzGVi07WPmEBFhD74mTgRWrwZ27bIHaR98APy//wf06gWkpl5e3mq14ORJLWJirJg69QsA45GSkgKTyYSyMtcj8rwp+66WAeB8r6amBnv27EFsbGzA5byjY6blMVtWVuY2zY7BFL4ezy3v7jUajXPfPd2IBCPo8VdnegKGJ1ar1aeRoI50Kbk5WKzteLuf/pZdX35fOY4PxyADPpIphEh10Mlx99P2AJQ7GPT2BODPCbFr164YO3YsDh48KMp8XIJg7/tlsdhnGu/aFaisNOG558qwdu1wjBpVg6efBgTBhri4OMTFdTxiMD3dPo+Vt9wFFm1HZboSFWWfd2naNGDNGnvz6YcfArW1l5e5dKkZ/fufx+zZNUhIMDsDKHflwZuy39EyjvcsFgsiIyNFKefeHDOe0qzRaNDU1OTz8eyqZk7pNWG+6AxPwPDlPOHreTuQvqty1fyIsR05fmdvf99QaTHyFgMzEUlVpSv1XZbJZMLOnTthNpuh0Wgwbtw4VFZWwmw2i9JRuuV+NTUBW7fuxdmzNbhwYStycrIRHx/v1Qmg5YlCpVLjmmty0KWLFs3NQFOTvdO847EzKpV9+c8/L8aBA72wd68eXbveDEEQEB0dhXffdV27JAj2DuUXLthfFy/aX1br5WXOnQNMpst/x8UBFks8zOYRGDnSiIcf/heamhoRGxuL5mY1RozoeH987VvVkqdRmZ5oNPa5sGbMaJuuJhQX72s1BYOnE6g3Zb+jZRzvRURE4ODBg6KUc2+OGU9p1mg0GD9+vF/Hc9u7e6kvJv5euPz5nq/BUiBTr/hz7vM1oPD1vN2276rZbMaxY8d8GkkrR3AR6HbkqBDw5vcNxo1AdHQ0mzJDSSBVui2DoylTpjhrroxGoyh9b0wmEyoqKgCg1R1eeXkFjhwxISoqGk1NdTh1ah+io2NQVmbD3r0D8d13fZCWFoERI+yzsGdk2CdV9CYOKC+vx5/+VIsDBxJhMsWgoSEagH167IgIK9RqAWq1GjZbLlQqFQTB/rerWcUFIR4223TYbCpYrR1Pjx4VZW++E4R4WK03wWJRIz6+Eb16qREV5fkhxRqNvRYsPt5emxUb27oPVny8/aG8Gg1QXW1/mc3N6N//awweXAuzudFjzY8YJ5u2F5RDhw759P2WaXHXF8uRLm8CHU/74GqZlu/Fx8d7fYF0F1R4e7H1lGY5mxcDCa78KUv+fs+XYMnTJMie+HOz609A4cvv3LLvqkqlQs+ePWE2m4N2EZeKHM2u3vy+LQPh2tpaVFRUSJbPERERqKqqwoULF9DY2Cj6YBBvMTDzg7uRZf4GThUVFaisrER0dDTOnTvnDKBcBWv+pnnnzp04fPgCvvnmSsTHV0Cr1eDHH2Nw+PBgXLiQ4fJ7Go0VeXlWnD0bicJCYN06X7ccj4iIrsjKqsOIEWcwaFAcGhrKcPp0PWy2boiJ6QKdTouKiko45shKT09HvIsH1zU2NqO8/NQvD0UGrrgiBfHxGkRH24OxyEj7aMazZ+3zXjmWT0urw9ixp3H99e0n+hRPDEym4V7X/Ih1Nxpo4OBNXyzHduRqghErqOhsfbb8DdT9LUv+fs+XstB2G940t7vanpT9uHw9dzv2v6KiAkeOHAn6cxUBafsDB/uYd/T7Onr0KFQqFY4cOeLXtEfeOH/+PARBQEREBARBwPnz50XfhjcYmPnIm5Fl/la1Oh7i7Pi3oqICVVVViIqKQl1dHUpKStCrVy+/DhLHSdFo7IXCwrEAgLg4G0aOBObPtwD4FhbLJVitArp0iUJMTBQiIoD770/DoEGXZyE/dw7Yv78BR46YodHEItbDxFhNTRcRE/MvaLWXnHlTX5+Ejz76Cn369IFGo0FaWhq+//44oqOj0dTUhLFjeyIlxdUThWNgMiW2OFFoPOy1Y/lo6HSDAaDDzuu+ats84/h/Ssrlmb0rKyvRt29fl9uSqxOwJ75cnJUU6MjRzCKnQPbH37IUSBn0tiwE0tzub7DhS0DhOJ+bzWZYLBavp9Jx7L9erw963yd/Rw97w9PvLEffL61Wi4yMDJjNZllqJ9VqtfMVLAzMfNTyBFpTU4Off/4ZcXFxbk+q3hRevV6PpKQkZ+2YXq9HRUUFhF+mR7darThy5AgSEhL8Ovh0Oh00Gg3S04/h//7fPyMpKalFDVwMDIZE55BxtVrdokNr60fDRESY0NRUjNRUb08CsTCZxrVrKktPT8eQIUOcF4Njx445q6sDGbnmqlnO0SQs1snL0zxVAHDw4EHYbDZUV1e7HH7vz92oFCdBx4VTqnmcpKKUwLatQEbrBfLImbS0NADwqSZBjhoRf5vbAz1evQ0cHTesdXV1aGxs9HkqHV9vVqQ4hjsaPSx10NRR9xsp6PV6HDt2TPLaSb1ej759+zpvqvV6vSTb8YSBmY9aXshOnz4NAM7I2tVFwtsTjFarxZQpU9p1ku3VqxesViu6dOniPGH7U0PgWL+rPmZA+yHjXbt2ddveH2j/DY1G45yCwdGHLtBqY3d5LWYNS8t1uZqnCoBX2/LlpN7RvgX6TEutVt55nMQiVzOLLxe3YExr03ab3l5IWu5Xy1peb78XEREh2nxzrvhzvPoTiDgC4sbGRkmm0mmbPikCGVejh+Uoi21bdKTs+yVns+qUKVOwe/duTJw4MWjnQdkDs8LCQjz00EOwWq24++678dRTT7X6vLGxEQsWLMA333yDHj164L333sOAAQPkTmaHHAXk559/BnD58S7p6eno2rVru0LjuCOLjo72WAXrqnYnKioKI0eOREJCglcj1jx1iHakqS1XNRCu1iVm/42Oap58GeHUkruTuZg1LC3X1dE8VWLW5phMJvz8888wm81ISEhwdoAFvJvHzBN/5nFSAl/6o/lzQve1iSiQ4N9o9O+RM/4GMIEMFjCbzTh9+jR69+4Njcb9M0f97fzvz3nGn33SarXIzs523phI+YBsV/2IxQrM2o4e9jRHn1isVisEQYDNZhN93W3J1ZVCq9UGtWkakDkws1qtWLJkCXbu3Ink5GRkZWUhLy8PGRmXO56/+eab6NatG44dO4ZNmzbhySefxHvvvSdnMj3SarUYPLj14106akKIiIjA6dOnIQgCVCqVV01FbU+2Xbt2RXJysscRa55OTu4+b3tHArjuN+dP/42O0uOp5snXAyMiIgIXL15EU1NTuxOsmHdcrvKq7XrF2lbLi2FlZSUqKysRERGBI0eOAGhdM+dPx2ogOM2CnWGCUl8nmA0kH+XsJ+ZvAOn4XnR09C/TznieUqDttrwto74er4EExcnJyZg+fbos5bFtP2KxtAxaTCYTLly4AIvFIukxnZBg7+bS3NyMiIgI598UOFkDs/379yMtLQ2pv0wzPm/ePGzZsqVVYLZlyxYsW7YMADB7D3sMZgAAE8BJREFU9mzcf//9zqBGSbw9cVitVvTu3dvZsd3ackKsDnR0svV0x+Dp5OTp85brd3fH5Uv/DXfb86bmyVsmk8k5gazFYsGYMWNc1hiKddJtuy6ptuXIw8TERFy8eBHNzc3o16+f8wIXyDxmLdMq5+OAOsMEpYDvE8wGko/+ftef7wUaBJrNZqhUKpc3QJ625UsZ9eUYCvTmQo7aGFf9iMXW8tgCgPT0dMlGMFqtVvTt29enaxt5RyU4epfLYPPmzSgsLMTatWsBABs2bMC+ffuwevVq5zLDhg1DYWGhc2TMoEGDsG/fPvTs2foxNwUFBSgoKAAAGAwGbNq0SbJ0NzQ0uJ293R2z2YyysjJncJmSkuIMQjx9z3EAe7O8N9vyJS3+ptvbdTjytOV+Or7jyz47GI1GVFdXQ6PRwGw2o0+fPorpEB6IlnnoONna53lTOfsGOfLMYrH4XU7lJOdvFUg5bmhoQGRkpN9lUsn8Ob+0/J5jzkFvvt9yW1KWUX/3SU5SpLHl9amzHFtKF8g131uPPfYYDhw44PKzTtv5Pz8/H/n5+QCAzMxMTHDx8GaxFBUVBbR+OR8n4WlbvnZmDjTdHa0j0Dx1tZ1gPE9UDh1NzdF2/8TOU6ko9XFfbXWW/OxMmKfia5mnneXYUrpgl1NZAzO9Xo/y8nLn3waDoV11rmOZ5ORkWCwWmEwm9OjRQ85kik6uTovebMuXtIiRbjk7bMrZHCcnT82mnY3cv5Wcxx9RMPHYCg2yBmZZWVkoKSnByZMnodfrsWnTJrz77rutlsnLy8P69esxfvx4bN68GZMmTVJc/zJSJp4kOg/+VkTS4LHV+ckamEVGRmL16tWYOnUqrFYrFi1ahKFDh2Lp0qXIzMxEXl4e7rrrLtxxxx1IS0tD9+7dJe07RkRERKQksvcxy83NRW5ubqv3li9f7vy/RqPBBx98IHeyiIiIiIIueA+DIiIiIqJWGJgRERERKQQDMyIiIiKFYGBGREREpBAMzIiIiIgUgoEZERERkUIwMCMiIiJSCAZmRERERArBwIyIiIhIIVSCIAjBTkSgevbsiQEDBki2/jNnzqBXr16SrT8cMU/FxzwVF/NTfMxT8TFPxSdHnpaWlqK2ttblZyERmEktMzMTBw4cCHYyQgrzVHzMU3ExP8XHPBUf81R8wc5TNmUSERERKQQDMyIiIiKFiFi2bNmyYCeiM7jqqquCnYSQwzwVH/NUXMxP8TFPxcc8FV8w85R9zIiIiIgUgk2ZRERERArBwMyNwsJCDB48GGlpaVi5cmWwk9NpDRgwAMOHD8eoUaOQmZkJADh37hymTJmCK664AlOmTEFdXV2QU6lsixYtQmJiIoYNG+Z8r6M8FAQBDz74INLS0jBixAgcPHgwWMlWNFd5umzZMuj1eowaNQqjRo3Ctm3bnJ+98MILSEtLw+DBg7Fjx45gJFnRysvLMXHiRGRkZGDo0KF49dVXAbCcBqKjPGU59Z/ZbMbVV1+NkSNHYujQoXj22WcBACdPnsTYsWORlpaGuXPnoqmpCQDQ2NiIuXPnIi0tDWPHjkVpaan0iRTIJYvFIqSmpgrHjx8XGhsbhREjRgg//PBDsJPVKaWkpAhnzpxp9d7jjz8uvPDCC4IgCMILL7wgPPHEE8FIWqdRXFwsfPPNN8LQoUOd73WUh1u3bhVuvPFGwWazCV999ZVw9dVXByXNSucqT5999lnhxRdfbLfsDz/8IIwYMUIwm83CiRMnhNTUVMFisciZXMWrrKwUvvnmG0EQBOH8+fPCFVdcIfzwww8spwHoKE9ZTv1ns9mE+vp6QRAEoampSbj66quFr776SpgzZ46wceNGQRAEYfHixcLrr78uCIIgvPbaa8LixYsFQRCEjRs3CrfccovkaWSNWQf279+PtLQ0pKamIjo6GvPmzcOWLVuCnayQsWXLFixcuBAAsHDhQvz9738PcoqULTs7G927d2/1Xkd5uGXLFixYsAAqlQrjxo2D0WhEVVWV7GlWOld52pEtW7Zg3rx5iImJwcCBA5GWlob9+/dLnMLOJSkpCWPGjAEAxMfHY8iQIaioqGA5DUBHedoRllPPVCoV4uLiAADNzc1obm6GSqXC559/jtmzZwNoX04d5Xf27Nn47LPPIEjcNZ+BWQcqKirQr18/59/JycluDwjqmEqlwg033ICrrroKBQUFAIDTp08jKSkJANCnTx+cPn06mEnslDrKQ5bdwKxevRojRozAokWLnM1uzFPflJaW4tChQxg7dizLqUha5inAchoIq9WKUaNGITExEVOmTMGgQYOg0+kQGRkJoHW+tczTyMhIaLVanD17VtL0MTAjyX3xxRc4ePAgtm/fjtdeew179uxp9blKpYJKpQpS6kID81Ac9913H44fP47Dhw8jKSkJjz76aLCT1Ok0NDRg1qxZeOWVV5CQkNDqM5ZT/7TNU5bTwERERODw4cMwGAzYv38/fvrpp2AnqRUGZh3Q6/UoLy93/m0wGKDX64OYos7LkW+JiYmYMWMG9u/fj969ezubLaqqqpCYmBjMJHZKHeUhy67/evfujYiICKjVatxzzz3OZiDmqXeam5sxa9Ys3HbbbZg5cyYAltNAdZSnLKeB0+l0mDhxIr766isYjUZYLBYArfOtZZ5aLBaYTCb06NFD0nQxMOtAVlYWSkpKcPLkSTQ1NWHTpk3Iy8sLdrI6nQsXLqC+vt75/08//RTDhg1DXl4e1q9fDwBYv349fv3rXwczmZ1SR3mYl5eHt99+G4IgYO/evdBqtc6mJHKvZR+njz/+2DliMy8vD5s2bUJjYyNOnjyJkpISXH311cFKpiIJgoC77roLQ4YMwSOPPOJ8n+XUfx3lKcup/86cOQOj0QgAuHTpEnbu3IkhQ4Zg4sSJ2Lx5M4D25dRRfjdv3oxJkyZJX+sr+fCCTmzr1q3CFVdcIaSmpgorVqwIdnI6pePHjwsjRowQRowYIWRkZDjzsba2Vpg0aZKQlpYmXH/99cLZs2eDnFJlmzdvntCnTx8hMjJS0Ov1wtq1azvMQ5vNJvz2t78VUlNThWHDhglff/11kFOvTK7y9PbbbxeGDRsmDB8+XPiv//ovobKy0rn8ihUrhNTUVCE9PV3Ytm1bEFOuTP/6178EAMLw4cOFkSNHCiNHjhS2bt3KchqAjvKU5dR/3377rTBq1Chh+PDhwtChQ4XnnntOEAT7tSorK0sYNGiQMHv2bMFsNguCIAiXLl0SZs+eLQwaNEjIysoSjh8/LnkaOfM/ERERkUKwKZP+f3v3HxN1/Qdw/HnCcaegptAFZkgoSD8GeqY71imXgt5EBdZhWRdBucVq03luulQER43pCrN0/hGYIW3yQzMFupgjEursl9Hyj7Is1GFUGLYVIeHu+we7z9fzc/wQMUhej+3+eX/e936/7nUbvPZ+vz/3EUIIIcQIIYWZEEIIIcQIIYWZEEIIIcQIIYWZEEIIIcQIIYWZEEIIIcQIIYWZEEIIIcQIIYWZEKPQ/v37lcfjaDQaAgMDiYiIIC0tjfLy8lv+kN6+NDU1kZeXx++//666ptFoyMvL+/eDAgoLC4mNjfXKjSd/mzZtUvV3u91ERkai0Wiw2+1Ke319PRqNhvr6+lsWa185HAput5vZs2ezY8eOWzK+EKOZFGZCjGIVFRW4XC5qamrIz89Hp9OxatUqkpKS+Pvvv4clpqamJrZt2+azqHC5XKxevfpfj+ny5cu8/PLLbN26VfWr3+PHj+edd95RFbMNDQ00NzcTGBjo1W40GnG5XBiNxlsWb185HAoajYatW7dSUFBwy+YQYrSSwkyIUWzWrFmYTCYSEhJ46qmnOHjwIOXl5dTV1bFhw4YhmcPtdtPV1TUkY5lMJqZOnTokY92I4uJiAgICSEtLU11LTU3lwoULfPTRR17tJSUlJCQkEBIS4tU+YcIETCaT6gHf/zUrVqxAr9dTVFQ03KEIcVuRwkwI4eXRRx8lJSWFN998k46ODqD37TfPlmhzc7PSFhERgd1uZ9++fcTExBAQEEB1dTUAubm5GI1GJkyYQEhICAsXLuTkyZNe42VlZQEQFRWlbBV6xve1lel0OomPj2fs2LFMnDiR1NRUvvvuO68+FosFs9nM8ePHMRqNjBs3jgcffJB33313QDkpKipi5cqV+Pn5qa6Fh4djsVg4cOCA0tbZ2UllZSUZGRmq/r5yOdD4MjMziYiIUI1psViwWCxA/zns7u6moKCAmJgYdDodU6ZMYf369XR2dirjdXd3k5OTw/Tp09Hr9YSEhGA2m2lsbFT6+Pn5kZ6eLoWZEENMCjMhhMrSpUu5cuUKX3zxxaDe/+GHH1JYWEhubi5Op5PY2FgAWlpaWLduHe+99x779+/HYDCwYMECvvnmGwCSk5PZsmUL8P9tVpfL1evDrZ1OJ8nJyQQFBVFWVsbevXs5ffo0ZrOZlpYWr75nz55l7dq1OBwODh8+TFhYGOnp6fzwww99fpZz587x7bffMn/+/F77ZGRkUFlZqRQ3R44c4Z9//sFmsw0sYTcR3/X6y6Hdbuell17iiSeeoLq6mhdffJHi4mKefPJJZYzt27ezc+dO1qxZwwcffMBbb73FokWLVNuWCxYs4Pvvv+fHH3+8oRiFEL3zH+4AhBAjT3h4OAA///zzoN7f3t7Ol19+SWhoqFf7tasrV69exWq18sADD1BUVMSuXbu48847mT59OtCzzTpjxow+59myZQuRkZG8//77+Pv3/DmLj48nOjqaV199lcLCQqVvW1sbJ06cICoqCug56xUWFkZ5ebnPw/senhW9uLi4XvvYbDZeeOEFjhw5wuOPP05JSQmpqamMHz++z/ivNdj4rtdXDhsaGigrK+Ptt99WVvMSExOZPHkydrudpqYmZs2ahcvlYvHixaxdu1Z57/Lly1VzzZ49G+jJUWRk5IBjFEL0TlbMhBAqnoPs1x90HyiTyaQqygCOHz/OI488QnBwMP7+/mi1Ws6cOaPaehyIv/76i1OnTvHYY48pRRnAvffey8MPP6w68xUVFaUUPQAGgwGDwcD58+f7nOfixYtAT8HTm6CgINLS0jhw4ACtra3U1tb63Mbsy2DjuxFOp5OAgABsNhvd3d3Ka/HixQCcOHECgLlz51JTU8PmzZtpbGzs9YygJyeeHAkhbp4UZkIIlQsXLgD0uoXYH1/vO3XqFEuXLiUoKIji4mJOnjzJ559/TlxcnNf5poFqb2/H7Xb7nCs0NFS17TZ58mRVP51O1+/cnus6na7PfhkZGdTW1rJz504MBgOJiYn9fYQhie9G/Prrr3R1dREYGIhWq1VeBoMBgEuXLgGwadMmtm3bxtGjR5k/fz7BwcFkZWXR1tbmNd7YsWMBhu0OXiFuR7KVKYRQqa6uRq/XM2fOHAD0ej2AauXE84/8er5W2g4dOoS/vz+HDx9Gq9Uq7e3t7dxxxx03HOOkSZPQaDS0traqrrW2tvosdAYjODgY6InTU4j4kpiYiMFg4JVXXsHhcPi8UeBm6fV6n6tXly5dUuLsS3BwMHq9noaGBp/Xp0yZAoBWq2Xjxo1s3LiR1tZWqqqqcDgcdHR0UFZWpvT3FL/X33kqhBg8WTETQng5dOgQR48eJTs7m3HjxgEwbdo0AE6fPu3V13O35UB0dHTg5+fnVbTV1dWptuo8K1P9rcIEBgYyZ84cKioquHr1qtJ+7tw5PvnkE+UuxZsVExMD0O8B9zFjxpCTk8Py5ct55plnhmTu602bNo1ffvmF3377TWk7e/asaiu4txxarVY6Ozv5448/eOihh1QvT2F2rdDQUFavXk1iYqLq+//pp58AmDlz5pB8PiGErJgJMao1NTXR1tZGV1cX58+fp6qqioqKCpKSkigoKFD6hYWFkZCQQEFBASEhIRgMBkpLS2/objyr1cprr71GZmYmWVlZnDlzhvz8fO6++26vfvfffz8Ae/bs4emnn0ar1RIbG0tAQIBqzPz8fJKTk1m2bBnPP/88f/75J7m5uUycOJH169cPMive5s2bh06n47PPPsNsNvfZNzs7m+zs7CGZ15f09HRycnKw2+04HA7a2tqU7+RaveXQYrGwatUqbDYbDoeDefPmMWbMGJqbm6mpqWH79u1ER0eTkpJCXFwcRqORSZMm8dVXX+F0Onnuuee85vn000/RarWYTKZb9pmFGG1kxUyIUSw9PZ34+HiWLFnC5s2buXLlCgcPHsTpdCrblx6lpaWYTCbWrFlDZmYm4eHhys8yDMSSJUt4/fXX+fjjj1m2bBn79u2jpKREdedlXFwceXl5HDt2DLPZzNy5c3s9XG61Wqmuruby5cusXLmS7Oxs7rvvPhobG32u/gyGXq8nJSWFY8eODcl4N2PGjBlUVlbS0tJCamoqO3bsoLCwkOjoaK9+feWwtLSUvLw8KisrSUlJwWazsXv3bqKiorjrrruAnp/BqK2t5dlnn8VqtbJ37142bNigegRTVVUVK1asUFZWhRA3T+MezofiCSHEf0B9fT0LFy6kublZ+SmR0e7ixYvcc8891NbWsmjRouEOR4jbhhRmQggxAElJScycOZPdu3cPdygjwrp16/j666+pq6sb7lCEuK3IVqYQQgzAG2+8wdSpU1UPKx+N3G43oaGh7NmzZ7hDEeK2IytmQgghhBAjhKyYCSGEEEKMEFKYCSGEEEKMEFKYCSGEEEKMEFKYCSGEEEKMEFKYCSGEEEKMEP8DJsqh1m0tn4MAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file