Main.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "ABB-HackFest\n",
        "\n",
        "* [Shareable Colab Link](https://colab.research.google.com/drive/1s4dtYvv_G3rV5ogJ9fMRsTv9OBCp6ufd?usp=sharing)\n"
      ],
      "metadata": {
        "id": "dWyRXYmYClJy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## How to run this notebook\n",
        "\n",
        "* Goto **Runtime** and select **Run All**\n",
        "  - Will take almost 2-5 mins depending on your internet speed.\n",
        "  - When execution is completed it show *0s completed* at the bottom.\n",
        "* Now unhide all cells to see all output\n",
        "  - Select **Edit** then **Select All Cells** option\n",
        "  - It will select all cells as a result everything is now higlighted.\n",
        "  - Select **View** and click on **Expand all sections**\n",
        "* Now everything is done.\n",
        "\n",
        "\n",
        "#### Extras:\n",
        "* Using college email ID for colab, results in extra memory and better runtime\n",
        "* If seeing third party cookies error, don't worry it's nothing, if you wish enable third party cookies for colab.\n",
        "* Any other issues email me at (aryan.2023y@gmail.com)\n",
        "\n",
        "### Task Done\n",
        "* Convert the data files to csv.\n",
        "* [x] Cleaning of Data\n",
        "\t- [x] Shape of Data\n",
        "\t- [x] Check for NAs\n",
        "\t- [x] Date format\n",
        "\t- [x] Check Data Info\n",
        "* [x] Country-wise monthly sales analysis.\n",
        "* [x] Yearly-sales revenue generated Country-wise.\n",
        "* [x] Predicted Dataset Generation\n",
        "\n",
        "## NOTE: Plotly Generated Graphs is NOT supported in HTML format.\n",
        "## Please prefer using the given colab link. (Linux cmds AHEAD :p)"
      ],
      "metadata": {
        "id": "x2lrBmyKFM_0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cloning github repository with data in it."
      ],
      "metadata": {
        "id": "0YPu9uW4Co3S"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "XXGlk1VZgT2n",
        "outputId": "423801b4-4b0b-43a5-dc68-23b972fe5c5d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rm: cannot remove 'abb-hackfest/': No such file or directory\n",
            "Cloning into 'abb-hackfest'...\n",
            "remote: Enumerating objects: 19, done.\u001b[K\n",
            "remote: Counting objects: 100% (19/19), done.\u001b[K\n",
            "remote: Compressing objects: 100% (15/15), done.\u001b[K\n",
            "remote: Total 19 (delta 3), reused 18 (delta 2), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (19/19), done.\n"
          ]
        }
      ],
      "source": [
        "!rm -r abb-hackfest/\n",
        "!git clone https://github.com/aa-ryan/abb-hackfest"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install plotly\n",
        "%pip install nbformat\n",
        "%pip install Prophet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IyGLsanxggP-",
        "outputId": "c821dd2e-7455-40a7-f45e-abfa5e2998ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\t\t\n",
        "import csv\t\t\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "import numpy as np\t\t\t\t\t\n",
        "import matplotlib.pyplot as plt\t\t \n",
        "import tensorflow as tf\t\t\t\t \n",
        "from tensorflow import keras\t\n",
        "import calendar\n",
        "import datetime as dt\n",
        "import prophet as Prophet"
      ],
      "metadata": {
        "id": "UkstQNKLg_Gr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('abb-hackfest/data.csv')\n",
        "data.head()\n",
        "print(list(data.columns))"
      ],
      "metadata": {
        "id": "CXB6wNxAhYJo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e56ba24-ba0a-4479-827e-52c12bc2da0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Country', 'Date', 'Value']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Converting each column to list for better usability."
      ],
      "metadata": {
        "id": "2Zez5LQ4C6iN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "jTijF-8LFlS1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Check NA values\n",
        "* None Found"
      ],
      "metadata": {
        "id": "2f7ExPNyFpV3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(data.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VOwmbdxUFnXc",
        "outputId": "4c77b1a1-f8bd-42be-955c-805108f18f9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Country    0\n",
            "Date       0\n",
            "Value      0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Seperate Year and Month from Date and convert month from integer to name\n",
        "##### Convert Date - string to DateTime\n",
        "##### Value from String to Integer"
      ],
      "metadata": {
        "id": "BDlcBbofH2-K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data[['Year','Month']] = data.Date.str.split('-',expand=True)\n",
        "data['Month'] = data['Month'].apply(lambda x: calendar.month_name[int(x)])\n",
        "data['Value'] = data['Value'].str.replace(',','').astype(int)"
      ],
      "metadata": {
        "id": "EmHLzBYcJBAr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#data['Date'] = pd.to_datetime(data['Date'], format='%Y-%m').dt.to_period('m')\n",
        "print(data.dtypes)\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "ZrXiIsyiO9z6",
        "outputId": "8238bdaf-b43e-4906-9ff9-210df6fc1bab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Country    object\n",
            "Date       object\n",
            "Value       int64\n",
            "Year       object\n",
            "Month      object\n",
            "dtype: object\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Country     Date     Value  Year     Month\n",
              "0    Canada  2019-01   2215572  2019   January\n",
              "1    Canada  2019-02   2090147  2019  February\n",
              "2    Canada  2019-03   6633213  2019     March\n",
              "3    Canada  2019-04   2067832  2019     April\n",
              "4    Canada  2019-05   2317593  2019       May\n",
              "..      ...      ...       ...   ...       ...\n",
              "195     USA  2021-12  20207040  2021  December\n",
              "196     USA  2022-01  24004887  2022   January\n",
              "197     USA  2022-02  25507001  2022  February\n",
              "198     USA  2022-03  35164285  2022     March\n",
              "199     USA  2022-04  24691715  2022     April\n",
              "\n",
              "[200 rows x 5 columns]"
            ],
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Country</th>\n",
              "      <th>Date</th>\n",
              "      <th>Value</th>\n",
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              "      <th>0</th>\n",
              "      <td>Canada</td>\n",
              "      <td>2019-01</td>\n",
              "      <td>2215572</td>\n",
              "      <td>2019</td>\n",
              "      <td>January</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Canada</td>\n",
              "      <td>2019-02</td>\n",
              "      <td>2090147</td>\n",
              "      <td>2019</td>\n",
              "      <td>February</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Canada</td>\n",
              "      <td>2019-03</td>\n",
              "      <td>6633213</td>\n",
              "      <td>2019</td>\n",
              "      <td>March</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Canada</td>\n",
              "      <td>2019-04</td>\n",
              "      <td>2067832</td>\n",
              "      <td>2019</td>\n",
              "      <td>April</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Canada</td>\n",
              "      <td>2019-05</td>\n",
              "      <td>2317593</td>\n",
              "      <td>2019</td>\n",
              "      <td>May</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>USA</td>\n",
              "      <td>2021-12</td>\n",
              "      <td>20207040</td>\n",
              "      <td>2021</td>\n",
              "      <td>December</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>USA</td>\n",
              "      <td>2022-01</td>\n",
              "      <td>24004887</td>\n",
              "      <td>2022</td>\n",
              "      <td>January</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>USA</td>\n",
              "      <td>2022-02</td>\n",
              "      <td>25507001</td>\n",
              "      <td>2022</td>\n",
              "      <td>February</td>\n",
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              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>USA</td>\n",
              "      <td>2022-03</td>\n",
              "      <td>35164285</td>\n",
              "      <td>2022</td>\n",
              "      <td>March</td>\n",
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              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>USA</td>\n",
              "      <td>2022-04</td>\n",
              "      <td>24691715</td>\n",
              "      <td>2022</td>\n",
              "      <td>April</td>\n",
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              "<p>200 rows × 5 columns</p>\n",
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              "            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": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Data Visualisation"
      ],
      "metadata": {
        "id": "fyKkSVWzLhBK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Country Wise Monthly Sales Analysis"
      ],
      "metadata": {
        "id": "UE646Moyl_8t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "value_order_canada = data[data['Country']=='Canada']\n",
        "value_order_canada_2019 = value_order_canada[value_order_canada['Year'] == '2019']\n",
        "value_order_canada_2020 = value_order_canada[value_order_canada['Year'] == '2020']\n",
        "value_order_canada_2021 = value_order_canada[value_order_canada['Year'] == '2021']\n",
        "value_order_canada_2022 = value_order_canada[value_order_canada['Year'] == '2022']\n",
        "\n",
        "\n",
        "#print(value_order_canada_2019)\n",
        "\n",
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='2019',\n",
        "    y = value_order_canada_2019['Value'],\n",
        "    x = value_order_canada_2019['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='2020',\n",
        "    y = value_order_canada_2020['Value'],\n",
        "    x = value_order_canada_2020['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='2021',\n",
        "    y = value_order_canada_2021['Value'],\n",
        "    x = value_order_canada_2021['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='2022',\n",
        "    y = value_order_canada_2022['Value'],\n",
        "    x = value_order_canada_2022['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "\n",
        "fig.update_layout(title_text='Monthly Value Sold CANADA',\n",
        "                 template='plotly_dark')\n",
        "fig.update_xaxes(title_text='Months')\n",
        "fig.update_yaxes(title_text='Value')\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "fZ6I62YYjH0Q",
        "outputId": "59acf5fb-ce17-4801-9643-6cdc33609815"
      },
      "execution_count": null,
      "outputs": [
        {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "value_order_china = data[data['Country']=='China']\n",
        "value_order_china_2019 = value_order_china[value_order_china['Year'] == '2019']\n",
        "value_order_china_2020 = value_order_china[value_order_china['Year'] == '2020']\n",
        "value_order_china_2021 = value_order_china[value_order_china['Year'] == '2021']\n",
        "value_order_china_2022 = value_order_china[value_order_china['Year'] == '2022']\n",
        "\n",
        "\n",
        "#print(value_order_canada_2019)\n",
        "\n",
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='2019',\n",
        "    y = value_order_china_2019['Value'],\n",
        "    x = value_order_china_2019['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='2020',\n",
        "    y = value_order_china_2020['Value'],\n",
        "    x = value_order_china_2020['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='2021',\n",
        "    y = value_order_china_2021['Value'],\n",
        "    x = value_order_china_2021['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='2022',\n",
        "    y = value_order_china_2022['Value'],\n",
        "    x = value_order_china_2022['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "\n",
        "fig.update_layout(title_text='Monthly Value Sold CHINA',\n",
        "                 template='plotly_dark')\n",
        "fig.update_xaxes(title_text='Months')\n",
        "fig.update_yaxes(title_text='Value')\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "GfccsPa3jJMH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "b04e3b10-2a98-4f99-aa58-0ee2897924b4"
      },
      "execution_count": null,
      "outputs": [
        {
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Value Sold CHINA\"},\"xaxis\":{\"title\":{\"text\":\"Months\"}},\"yaxis\":{\"title\":{\"text\":\"Value\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
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              "</body>\n",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "value_order_Germany = data[data['Country']=='Germany']\n",
        "value_order_Germany_2019 = value_order_Germany[value_order_Germany['Year'] == '2019']\n",
        "value_order_Germany_2020 = value_order_Germany[value_order_Germany['Year'] == '2020']\n",
        "value_order_Germany_2021 = value_order_Germany[value_order_Germany['Year'] == '2021']\n",
        "value_order_Germany_2022 = value_order_Germany[value_order_Germany['Year'] == '2022']\n",
        "\n",
        "\n",
        "#print(value_order_Germany_2019)\n",
        "\n",
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='2019',\n",
        "    y = value_order_Germany_2019['Value'],\n",
        "    x = value_order_Germany_2019['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='2020',\n",
        "    y = value_order_Germany_2020['Value'],\n",
        "    x = value_order_Germany_2020['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='2021',\n",
        "    y = value_order_Germany_2021['Value'],\n",
        "    x = value_order_Germany_2021['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='2022',\n",
        "    y = value_order_Germany_2022['Value'],\n",
        "    x = value_order_Germany_2022['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "\n",
        "fig.update_layout(title_text='Monthly Value Sold Germany',\n",
        "                 template='plotly_dark')\n",
        "fig.update_xaxes(title_text='Months')\n",
        "fig.update_yaxes(title_text='Value')\n",
        "\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "1eK10JmEi4MD",
        "outputId": "5eb2407c-457c-46be-e642-93f4ed058104"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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Value Sold Germany\"},\"xaxis\":{\"title\":{\"text\":\"Months\"}},\"yaxis\":{\"title\":{\"text\":\"Value\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
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              "</html>"
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "value_order_Italy = data[data['Country']=='Italy']\n",
        "value_order_Italy_2019 = value_order_Italy[value_order_Italy['Year'] == '2019']\n",
        "value_order_Italy_2020 = value_order_Italy[value_order_Italy['Year'] == '2020']\n",
        "value_order_Italy_2021 = value_order_Italy[value_order_Italy['Year'] == '2021']\n",
        "value_order_Italy_2022 = value_order_Italy[value_order_Italy['Year'] == '2022']\n",
        "\n",
        "\n",
        "#print(value_order_Italy_2019)\n",
        "\n",
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='2019',\n",
        "    y = value_order_Italy_2019['Value'],\n",
        "    x = value_order_Italy_2019['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='2020',\n",
        "    y = value_order_Italy_2020['Value'],\n",
        "    x = value_order_Italy_2020['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='2021',\n",
        "    y = value_order_Italy_2021['Value'],\n",
        "    x = value_order_Italy_2021['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='2022',\n",
        "    y = value_order_Italy_2022['Value'],\n",
        "    x = value_order_Italy_2022['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "\n",
        "fig.update_layout(title_text='Monthly Value Sold ITALY',\n",
        "                 template='plotly_dark')\n",
        "fig.update_xaxes(title_text='Months')\n",
        "fig.update_yaxes(title_text='Value')\n",
        "\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "oHKt5LvvjNFe",
        "outputId": "f0bb8463-5f2e-4489-b02c-224762418e0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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Value Sold ITALY\"},\"xaxis\":{\"title\":{\"text\":\"Months\"}},\"yaxis\":{\"title\":{\"text\":\"Value\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
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              "</body>\n",
              "</html>"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "value_order_USA = data[data['Country']=='USA']\n",
        "value_order_USA_2019 = value_order_USA[value_order_USA['Year'] == '2019']\n",
        "value_order_USA_2020 = value_order_USA[value_order_USA['Year'] == '2020']\n",
        "value_order_USA_2021 = value_order_USA[value_order_USA['Year'] == '2021']\n",
        "value_order_USA_2022 = value_order_USA[value_order_USA['Year'] == '2022']\n",
        "\n",
        "\n",
        "#print(value_order_USA_2019)\n",
        "\n",
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='2019',\n",
        "    y = value_order_USA_2019['Value'],\n",
        "    x = value_order_USA_2019['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='2020',\n",
        "    y = value_order_USA_2020['Value'],\n",
        "    x = value_order_USA_2020['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='2021',\n",
        "    y = value_order_USA_2021['Value'],\n",
        "    x = value_order_USA_2021['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='2022',\n",
        "    y = value_order_USA_2022['Value'],\n",
        "    x = value_order_USA_2022['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "\n",
        "fig.update_layout(title_text='Monthly Value Sold USA',\n",
        "                 template='plotly_dark')\n",
        "fig.update_xaxes(title_text='Months')\n",
        "fig.update_yaxes(title_text='Value')\n",
        "\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "FX81kqRnjaE5",
        "outputId": "f42f943e-d97e-4362-b97f-d9ffe9f45076"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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Value Sold USA\"},\"xaxis\":{\"title\":{\"text\":\"Months\"}},\"yaxis\":{\"title\":{\"text\":\"Value\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
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              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Year Wise Country Sales Value"
      ],
      "metadata": {
        "id": "Q0ggT9n5j47V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='Canada',\n",
        "    y = value_order_canada_2019['Value'],\n",
        "    x = value_order_canada_2019['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='China',\n",
        "    y = value_order_china_2019['Value'],\n",
        "    x = value_order_china_2019['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='Germany',\n",
        "    y = value_order_Germany_2019['Value'],\n",
        "    x = value_order_Germany_2019['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='Italy',\n",
        "    y = value_order_Italy_2019['Value'],\n",
        "    x = value_order_Italy_2019['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='USA',\n",
        "    y = value_order_USA_2019['Value'],\n",
        "    x = value_order_USA_2019['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.update_layout(title_text='Revenue Generated Year 2019',\n",
        "                 template='plotly_dark')\n",
        "fig.update_xaxes(title_text='Months')\n",
        "fig.update_yaxes(title_text='Value')\n",
        "\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "067gLIpVjeIO",
        "outputId": "dca9c8e2-f4f7-4de8-e48d-338be2b491c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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    },
    {
      "cell_type": "code",
      "source": [
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='Canada',\n",
        "    y = value_order_canada_2020['Value'],\n",
        "    x = value_order_canada_2020['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='China',\n",
        "    y = value_order_china_2020['Value'],\n",
        "    x = value_order_china_2020['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='Germany',\n",
        "    y = value_order_Germany_2020['Value'],\n",
        "    x = value_order_Germany_2020['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='Italy',\n",
        "    y = value_order_Italy_2020['Value'],\n",
        "    x = value_order_Italy_2020['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='USA',\n",
        "    y = value_order_USA_2020['Value'],\n",
        "    x = value_order_USA_2020['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.update_layout(title_text='Revenue Generated Year 2020',\n",
        "                 template='plotly_dark')\n",
        "fig.update_xaxes(title_text='Months')\n",
        "fig.update_yaxes(title_text='Value')\n",
        "\n",
        "fig.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "wkqXGEvZlntM",
        "outputId": "f893f3a4-9cea-47ad-d7e3-1917a92413e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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    },
    {
      "cell_type": "code",
      "source": [
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='Canada',\n",
        "    y = value_order_canada_2021['Value'],\n",
        "    x = value_order_canada_2021['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='China',\n",
        "    y = value_order_china_2021['Value'],\n",
        "    x = value_order_china_2021['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='Germany',\n",
        "    y = value_order_Germany_2021['Value'],\n",
        "    x = value_order_Germany_2021['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='Italy',\n",
        "    y = value_order_Italy_2021['Value'],\n",
        "    x = value_order_Italy_2021['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='USA',\n",
        "    y = value_order_USA_2021['Value'],\n",
        "    x = value_order_USA_2021['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.update_layout(title_text='Revenue Generated Year 2021',\n",
        "                 template='plotly_dark')\n",
        "fig.update_xaxes(title_text='Months')\n",
        "fig.update_yaxes(title_text='Value')\n",
        "\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "iYwb-R0AnsdN",
        "outputId": "0ca3285f-a9b7-4e62-c19c-6af8cafebf67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='Canada',\n",
        "    y = value_order_canada_2022['Value'],\n",
        "    x = value_order_canada_2022['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='China',\n",
        "    y = value_order_china_2022['Value'],\n",
        "    x = value_order_china_2022['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='Germany',\n",
        "    y = value_order_Germany_2022['Value'],\n",
        "    x = value_order_Germany_2022['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='Italy',\n",
        "    y = value_order_Italy_2022['Value'],\n",
        "    x = value_order_Italy_2022['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='USA',\n",
        "    y = value_order_USA_2022['Value'],\n",
        "    x = value_order_USA_2022['Month'],\n",
        "    connectgaps = True\n",
        "))\n",
        "\n",
        "fig.update_layout(title_text='Revenue Generated Year 2022',\n",
        "                 template='plotly_dark')\n",
        "fig.update_xaxes(title_text='Months')\n",
        "fig.update_yaxes(title_text='Value')\n",
        "\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "LSMM7decn7L_",
        "outputId": "91e3dac9-0fc2-4e3e-c6eb-f0f92bb43658"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "  Country     Date    Value  Year     Month\n",
              "0  Canada  2019-01  2215572  2019   January\n",
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      "source": [
        "# Prediction Making"
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      "metadata": {
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    {
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      "source": [
        "#### Creating a Pivot Table"
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        "id": "jLCSDomerXC5"
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      "source": [
        "pivot_table = data.pivot_table('Value', ['Date'], 'Country')\n",
        "new_df = pivot_table.reset_index()\n",
        "new_df.head()"
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              "Country     Date   Canada     China   Germany    Italy       USA\n",
              "0        2019-01  2215572   6788150  37362308  3405980  27164330\n",
              "1        2019-02  2090147   2946703  23448750  6455787  24954358\n",
              "2        2019-03  6633213  10384100  41479346  4865427  25969581\n",
              "3        2019-04  2067832   6349900  26591458  1503276  30632227\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Country</th>\n",
              "      <th>Date</th>\n",
              "      <th>Canada</th>\n",
              "      <th>China</th>\n",
              "      <th>Germany</th>\n",
              "      <th>Italy</th>\n",
              "      <th>USA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01</td>\n",
              "      <td>2215572</td>\n",
              "      <td>6788150</td>\n",
              "      <td>37362308</td>\n",
              "      <td>3405980</td>\n",
              "      <td>27164330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-02</td>\n",
              "      <td>2090147</td>\n",
              "      <td>2946703</td>\n",
              "      <td>23448750</td>\n",
              "      <td>6455787</td>\n",
              "      <td>24954358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-03</td>\n",
              "      <td>6633213</td>\n",
              "      <td>10384100</td>\n",
              "      <td>41479346</td>\n",
              "      <td>4865427</td>\n",
              "      <td>25969581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-04</td>\n",
              "      <td>2067832</td>\n",
              "      <td>6349900</td>\n",
              "      <td>26591458</td>\n",
              "      <td>1503276</td>\n",
              "      <td>30632227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-05</td>\n",
              "      <td>2317593</td>\n",
              "      <td>6910464</td>\n",
              "      <td>21957328</td>\n",
              "      <td>2867991</td>\n",
              "      <td>32710666</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-78e5e997-b6c3-41b4-b638-50c066653c18')\"\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-78e5e997-b6c3-41b4-b638-50c066653c18 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-78e5e997-b6c3-41b4-b638-50c066653c18');\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": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "canada_df = pd.DataFrame([new_df.Date, new_df.Canada]).transpose()\n",
        "china_df = pd.DataFrame([new_df.Date, new_df.China]).transpose()\n",
        "germany_df = pd.DataFrame([new_df.Date, new_df.Germany]).transpose()\n",
        "italy_df = pd.DataFrame([new_df.Date, new_df.Italy]).transpose()\n",
        "usa_df = pd.DataFrame([new_df.Date, new_df.USA]).transpose()\n",
        "\n",
        "canada_df.columns = ['ds', 'y']\n",
        "canada_df['y'] = pd.to_numeric(canada_df['y'])\n",
        "canada_df['ds'] = pd.to_datetime(canada_df['ds'])\n",
        "\n",
        "\n",
        "china_df.columns = ['ds', 'y']\n",
        "china_df['y'] = pd.to_numeric(china_df['y'])\n",
        "china_df['ds'] = pd.to_datetime(china_df['ds'])\n",
        "\n",
        "germany_df.columns = ['ds', 'y']\n",
        "germany_df['y'] = pd.to_numeric(germany_df['y'])\n",
        "germany_df['ds'] = pd.to_datetime(germany_df['ds'])\n",
        "\n",
        "\n",
        "italy_df.columns = ['ds', 'y']\n",
        "italy_df['y'] = pd.to_numeric(italy_df['y'])\n",
        "italy_df['ds'] = pd.to_datetime(italy_df['ds'])\n",
        "\n",
        "\n",
        "usa_df.columns = ['ds', 'y']\n",
        "usa_df['y'] = pd.to_numeric(usa_df['y'])\n",
        "usa_df['ds'] = pd.to_datetime(usa_df['ds'])\n",
        "#print(canada_df.dtypes)"
      ],
      "metadata": {
        "id": "glBh9QF9zMQp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Insample Prediction\n",
        "* A dataframe with sample data dates"
      ],
      "metadata": {
        "id": "iHpVSXPZAAv7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "future = list()\n",
        "for i in range(1, 13):\n",
        "  date = '2019-%02d'%i\n",
        "  future.append([date])\n",
        "for i in range(1, 13):\n",
        "  date = '2020-%02d'%i\n",
        "  future.append([date])\n",
        "\n",
        "future = pd.DataFrame(future)\n",
        "future.columns = ['ds']\n",
        "future['ds'] = pd.to_datetime(future['ds'])\n"
      ],
      "metadata": {
        "id": "7bP9Xl-O8-fA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Insample Prediction for Canada"
      ],
      "metadata": {
        "id": "3R9AJMcpAfpH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "p_canada = Prophet.Prophet()\n",
        "model_canada = p_canada.fit(canada_df)\n",
        "canada_forecast = model_canada.predict(future)\n",
        "model_canada.plot(canada_forecast)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "id": "O8i59pHL8mRs",
        "outputId": "b6f37d65-5d70-4dc6-fbc8-3d675006afe0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnk9ytd1v/pxl840mi.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnk9ytd1v/gxpr9snf.json\n",
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.7/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=25198', 'data', 'file=/tmp/tmpnk9ytd1v/pxl840mi.json', 'init=/tmp/tmpnk9ytd1v/gxpr9snf.json', 'output', 'file=/tmp/tmpnk9ytd1v/prophet_model29hjndyw/prophet_model-20221127152227.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']\n",
            "15:22:27 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n",
            "15:22:28 - cmdstanpy - INFO - Chain [1] done processing\n",
            "INFO:cmdstanpy:Chain [1] done processing\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Insample Prediction for China\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "KercT4u-DCGl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "p_china = Prophet.Prophet()\n",
        "model_china = p_china.fit(china_df)\n",
        "china_forecast = model_china.predict(future)\n",
        "model_china.plot(china_forecast)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "id": "7hagtl_j3V2A",
        "outputId": "383619f3-850d-42ad-defa-511ca5f11e54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnk9ytd1v/ug9vukon.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnk9ytd1v/k5f14dht.json\n",
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.7/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=17212', 'data', 'file=/tmp/tmpnk9ytd1v/ug9vukon.json', 'init=/tmp/tmpnk9ytd1v/k5f14dht.json', 'output', 'file=/tmp/tmpnk9ytd1v/prophet_modelh185b5kx/prophet_model-20221127152228.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']\n",
            "15:22:28 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n",
            "15:22:29 - cmdstanpy - INFO - Chain [1] done processing\n",
            "INFO:cmdstanpy:Chain [1] done processing\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Insample Prediction for Germany"
      ],
      "metadata": {
        "id": "kgmhU829e_vP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "p_germany = Prophet.Prophet()\n",
        "model_germany = p_germany.fit(germany_df)\n",
        "germany_forecast = model_germany.predict(future)\n",
        "model_germany.plot(germany_forecast)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "id": "6uV4uLJjA_SR",
        "outputId": "3ca82ac6-496e-427c-9514-db342f2772cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnk9ytd1v/as58g55j.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnk9ytd1v/9rg1z0pm.json\n",
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.7/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=60203', 'data', 'file=/tmp/tmpnk9ytd1v/as58g55j.json', 'init=/tmp/tmpnk9ytd1v/9rg1z0pm.json', 'output', 'file=/tmp/tmpnk9ytd1v/prophet_modeltha63iz2/prophet_model-20221127152229.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']\n",
            "15:22:29 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n",
            "15:22:29 - cmdstanpy - INFO - Chain [1] done processing\n",
            "INFO:cmdstanpy:Chain [1] done processing\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Insample Prediction for Italy"
      ],
      "metadata": {
        "id": "XLZOHTy3fFL9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "p_italy = Prophet.Prophet()\n",
        "model_italy = p_italy.fit(italy_df)\n",
        "italy_forecast = model_italy.predict(future)\n",
        "model_italy.plot(italy_forecast)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "id": "dZeDI2f7D4TT",
        "outputId": "58c7a016-7a11-410b-9ca9-753be11d6d19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnk9ytd1v/7xlq0vmy.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnk9ytd1v/g704t4fx.json\n",
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.7/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=84653', 'data', 'file=/tmp/tmpnk9ytd1v/7xlq0vmy.json', 'init=/tmp/tmpnk9ytd1v/g704t4fx.json', 'output', 'file=/tmp/tmpnk9ytd1v/prophet_model0w3099pf/prophet_model-20221127152230.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']\n",
            "15:22:30 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n",
            "15:22:30 - cmdstanpy - INFO - Chain [1] done processing\n",
            "INFO:cmdstanpy:Chain [1] done processing\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Insample Prediction for USA"
      ],
      "metadata": {
        "id": "Kg3k6UxVfJZw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "p_usa = Prophet.Prophet()\n",
        "model_usa = p_usa.fit(usa_df)\n",
        "usa_forecast = model_usa.predict(future)\n",
        "model_usa.plot(usa_forecast)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "id": "goReExDyEEC8",
        "outputId": "83a9d61b-eca2-494d-b6fd-8806d61eceac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnk9ytd1v/zl6rj8kq.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnk9ytd1v/e2o7t11o.json\n",
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.7/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=22358', 'data', 'file=/tmp/tmpnk9ytd1v/zl6rj8kq.json', 'init=/tmp/tmpnk9ytd1v/e2o7t11o.json', 'output', 'file=/tmp/tmpnk9ytd1v/prophet_modelrqo3tb7v/prophet_model-20221127152231.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']\n",
            "15:22:31 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n",
            "15:22:31 - cmdstanpy - INFO - Chain [1] done processing\n",
            "INFO:cmdstanpy:Chain [1] done processing\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Outsample Prediction"
      ],
      "metadata": {
        "id": "OjP2TZBLEe-8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "future = list()\n",
        "for i in range(5, 13):\n",
        "  date = '2022-%02d'%i\n",
        "  future.append([date])\n",
        "for i in range(1, 8):\n",
        "  date = '2023-%02d'%i\n",
        "  future.append([date])\n",
        "\n",
        "print(future)\n",
        "\n",
        "future = pd.DataFrame(future)\n",
        "future.columns = ['ds']\n",
        "future['ds'] = pd.to_datetime(future['ds'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mVOr4JCTEOPv",
        "outputId": "d9a264eb-602f-47d7-f684-86acbcf798fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['2022-05'], ['2022-06'], ['2022-07'], ['2022-08'], ['2022-09'], ['2022-10'], ['2022-11'], ['2022-12'], ['2023-01'], ['2023-02'], ['2023-03'], ['2023-04'], ['2023-05'], ['2023-06'], ['2023-07']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Outsample Prediction for Canada"
      ],
      "metadata": {
        "id": "JjCiF1ouE99L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pout_canada = Prophet.Prophet()\n",
        "model_canada = pout_canada.fit(canada_df)\n",
        "canada_forecast = model_canada.predict(future)\n",
        "model_canada.plot(canada_forecast)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "id": "fAWGuxLVE0mA",
        "outputId": "93bb9bfc-c116-4256-8962-4a1186837fc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnk9ytd1v/rnd6wupa.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnk9ytd1v/i155251b.json\n",
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.7/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=66071', 'data', 'file=/tmp/tmpnk9ytd1v/rnd6wupa.json', 'init=/tmp/tmpnk9ytd1v/i155251b.json', 'output', 'file=/tmp/tmpnk9ytd1v/prophet_modelwitudz4g/prophet_model-20221127152232.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']\n",
            "15:22:32 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n",
            "15:22:32 - cmdstanpy - INFO - Chain [1] done processing\n",
            "INFO:cmdstanpy:Chain [1] done processing\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Outsample prediction for China"
      ],
      "metadata": {
        "id": "LTzyocZtF8f1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pout_china = Prophet.Prophet()\n",
        "model_china = pout_china.fit(china_df)\n",
        "china_forecast = model_china.predict(future)\n",
        "model_china.plot(china_forecast)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "id": "liHjjmVZFToJ",
        "outputId": "2f476074-ecbc-4f77-82bf-6045b509c6a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnk9ytd1v/_3v5orsi.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnk9ytd1v/sszu1856.json\n",
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.7/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=70502', 'data', 'file=/tmp/tmpnk9ytd1v/_3v5orsi.json', 'init=/tmp/tmpnk9ytd1v/sszu1856.json', 'output', 'file=/tmp/tmpnk9ytd1v/prophet_modelpiq02hz2/prophet_model-20221127152233.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']\n",
            "15:22:33 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n",
            "15:22:33 - cmdstanpy - INFO - Chain [1] done processing\n",
            "INFO:cmdstanpy:Chain [1] done processing\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Outsample prediction for Germany"
      ],
      "metadata": {
        "id": "zCH8yP2-GbMl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pout_germany = Prophet.Prophet()\n",
        "model_germany = pout_germany.fit(germany_df)\n",
        "germany_forecast = model_germany.predict(future)\n",
        "model_germany.plot(germany_forecast)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "id": "3oBNSmHMGCKD",
        "outputId": "a3cefaee-ed1a-4266-b05c-35aa7cde87c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnk9ytd1v/65400bxx.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnk9ytd1v/drk6c1qb.json\n",
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.7/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=50746', 'data', 'file=/tmp/tmpnk9ytd1v/65400bxx.json', 'init=/tmp/tmpnk9ytd1v/drk6c1qb.json', 'output', 'file=/tmp/tmpnk9ytd1v/prophet_model4x9s0wbt/prophet_model-20221127152234.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']\n",
            "15:22:34 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n",
            "15:22:34 - cmdstanpy - INFO - Chain [1] done processing\n",
            "INFO:cmdstanpy:Chain [1] done processing\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Outsample prediction for Italy"
      ],
      "metadata": {
        "id": "mHkFzlRfGzLh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pout_italy = Prophet.Prophet()\n",
        "model_italy = pout_italy.fit(italy_df)\n",
        "italy_forecast = model_italy.predict(future)\n",
        "model_italy.plot(italy_forecast)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "id": "huH9VyFMGgf1",
        "outputId": "d90cf138-6105-4cff-b227-891fbe7775a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnk9ytd1v/vw9iryoo.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnk9ytd1v/5dznva3p.json\n",
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.7/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=24285', 'data', 'file=/tmp/tmpnk9ytd1v/vw9iryoo.json', 'init=/tmp/tmpnk9ytd1v/5dznva3p.json', 'output', 'file=/tmp/tmpnk9ytd1v/prophet_modelpbubsx34/prophet_model-20221127152235.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']\n",
            "15:22:35 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n",
            "15:22:35 - cmdstanpy - INFO - Chain [1] done processing\n",
            "INFO:cmdstanpy:Chain [1] done processing\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Outsample prediction for USA"
      ],
      "metadata": {
        "id": "W_fadPSyG-Xa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pout_usa = Prophet.Prophet()\n",
        "model_usa = pout_usa.fit(usa_df)\n",
        "usa_forecast = model_usa.predict(future)\n",
        "model_usa.plot(usa_forecast)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "id": "ZAjcGQgeG3Fo",
        "outputId": "a6840470-3179-4233-960f-e8ed4156db9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnk9ytd1v/ks91yj79.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpnk9ytd1v/zvf09mh5.json\n",
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.7/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=84103', 'data', 'file=/tmp/tmpnk9ytd1v/ks91yj79.json', 'init=/tmp/tmpnk9ytd1v/zvf09mh5.json', 'output', 'file=/tmp/tmpnk9ytd1v/prophet_modelvz5mw6fs/prophet_model-20221127152235.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']\n",
            "15:22:35 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n",
            "15:22:36 - cmdstanpy - INFO - Chain [1] done processing\n",
            "INFO:cmdstanpy:Chain [1] done processing\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluation of Model"
      ],
      "metadata": {
        "id": "6yK7hZ1NICro"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_log_error"
      ],
      "metadata": {
        "id": "ShNbOyOfIzF-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Canada"
      ],
      "metadata": {
        "id": "8s_7_CjDIJum"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#print(canada_df)\n",
        "\n",
        "## Calculation of RMSLE between expected and predicted value\n",
        "y_true = canada_df['y'][-15:].values\n",
        "y_pred = canada_forecast['yhat'].values\n",
        "#print(y_true.shape, y_pred.shape)\n",
        "rmsle = mean_squared_log_error(y_true, y_pred)\n",
        "\n",
        "print(\"RMSLE: {}\".format(rmsle) )\n",
        "plt.plot(y_true, label='Actual')\n",
        "plt.plot(y_pred, label='Predicted')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "LNVFSkwOHDvh",
        "outputId": "77585813-d057-4719-c6e7-c509761b94d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSLE: 0.18556062230205417\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Calculation of RMSLE between expected and predicted value\n",
        "y_true = china_df['y'][-15:].values\n",
        "y_pred = china_forecast['yhat'].values\n",
        "print(y_true.shape, y_pred.shape)\n",
        "rmsle = mean_squared_log_error(y_true, y_pred)\n",
        "\n",
        "print(\"RMSLE: {}\".format(rmsle) )\n",
        "plt.plot(y_true, label='Actual')\n",
        "plt.plot(y_pred, label='Predicted')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "D9uoD3w8IUfx",
        "outputId": "bfeb19ce-50ce-4c37-a5e9-9fda24170361"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(15,) (15,)\n",
            "RMSLE: 0.08855656425263261\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Calculation of RMSLE between expected and predicted value\n",
        "y_true = germany_df['y'][-15:].values\n",
        "y_pred = germany_forecast['yhat'].values\n",
        "print(y_true.shape, y_pred.shape)\n",
        "rmsle = mean_squared_log_error(y_true, y_pred)\n",
        "\n",
        "print(\"RMSLE: {}\".format(rmsle) )\n",
        "plt.plot(y_true, label='Actual')\n",
        "plt.plot(y_pred, label='Predicted')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "wl3DToG_R4kl",
        "outputId": "192ee3b7-77b8-4ab9-8d10-98119461790c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(15,) (15,)\n",
            "RMSLE: 0.08850648257862732\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Calculation of RMSLE between expected and predicted value\n",
        "y_true = italy_df['y'][-15:].values\n",
        "y_pred = italy_forecast['yhat'].values\n",
        "print(y_true.shape, y_pred.shape)\n",
        "rmsle = mean_squared_log_error(y_true, y_pred)\n",
        "\n",
        "print(\"RMSLE: {}\".format(rmsle) )\n",
        "plt.plot(y_true, label='Actual')\n",
        "plt.plot(y_pred, label='Predicted')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "qo4_UELwWqQl",
        "outputId": "59637483-f2e6-41a2-b2cc-0b52225f2513"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(15,) (15,)\n",
            "RMSLE: 0.3258658342836568\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Calculation of RMSLE between expected and predicted value\n",
        "y_true = usa_df['y'][-15:].values\n",
        "y_pred = usa_forecast['yhat'].values\n",
        "print(y_true.shape, y_pred.shape)\n",
        "rmsle = mean_squared_log_error(y_true, y_pred)\n",
        "\n",
        "print(\"RMSLE: {}\".format(rmsle) )\n",
        "plt.plot(y_true, label='Actual')\n",
        "plt.plot(y_pred, label='Predicted')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "3XJTH1p0WuLV",
        "outputId": "82f7dd96-9b9f-4d08-cc23-e5eca6e5b6de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(15,) (15,)\n",
            "RMSLE: 0.129013280383453\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from functools import reduce\n",
        "df_list = [canada_forecast[['ds', 'yhat']], china_forecast[['ds', 'yhat']], germany_forecast[['ds', 'yhat']], italy_forecast[['ds', 'yhat']], usa_forecast[['ds', 'yhat']]]\n",
        "df_merged = reduce(lambda left, right: pd.merge(left, right, on=['ds'], how='outer'), df_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BkKhBJR4WyQE",
        "outputId": "737b97fe-d2d6-4f8c-c482-88e56342a38f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: FutureWarning:\n",
            "\n",
            "Passing 'suffixes' which cause duplicate columns {'yhat_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_merged.columns = ['Date', 'Canada', 'China', 'Germany', 'Italy', 'USA']\n"
      ],
      "metadata": {
        "id": "6hmiaXwUbu8j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas.api.types import is_period_dtype\n",
        "\n",
        "if not is_period_dtype(df_merged.dtypes['Date']):\n",
        "  df_merged['Date'] = df_merged['Date'].dt.to_period('M')\n",
        "\n",
        "## convert float to int\n",
        "m = df_merged.select_dtypes(np.number)\n",
        "df_merged[m.columns] = m.round().astype('Int64')"
      ],
      "metadata": {
        "id": "d4pKMcQrcGMZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_merged"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "y26hJgNDdYsT",
        "outputId": "4c6c7ca8-ed40-4de8-b75b-acd131385daa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Date   Canada     China   Germany    Italy       USA\n",
              "0   2022-05  1717071   9411402  35288611  3994917  19290972\n",
              "1   2022-06  1847119  11394118  36704457  6085226  16213312\n",
              "2   2022-07  1691548   7638364  44605249  3643464  19206717\n",
              "3   2022-08  2019688   8557129  42809508  2174046  19172167\n",
              "4   2022-09  2089607  12140717  49343413  4766562  19320429\n",
              "5   2022-10  1946539   8285965  55787326  3464936  20289179\n",
              "6   2022-11  1683905   8562230  40594015  4534843  17957680\n",
              "7   2022-12  1276151  10839569  34114246  4988636  18241378\n",
              "8   2023-01  1866681   7755563  49084936  5811350  15470307\n",
              "9   2023-02  1596642   5970709  39819685  7734966  14766072\n",
              "10  2023-03  4535980  14646741  57793570  5544292  22982734\n",
              "11  2023-04  1260761   8398225  44226583  3750208  21141822\n",
              "12  2023-05  1402386   9173027  39376577  4436543  18469363\n",
              "13  2023-06  1417270  12929612  41112009  6361758  11697118\n",
              "14  2023-07  1466534   7664651  49802628  3772612  15690925"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Canada</th>\n",
              "      <th>China</th>\n",
              "      <th>Germany</th>\n",
              "      <th>Italy</th>\n",
              "      <th>USA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022-05</td>\n",
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              "      <td>9411402</td>\n",
              "      <td>35288611</td>\n",
              "      <td>3994917</td>\n",
              "      <td>19290972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-06</td>\n",
              "      <td>1847119</td>\n",
              "      <td>11394118</td>\n",
              "      <td>36704457</td>\n",
              "      <td>6085226</td>\n",
              "      <td>16213312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-07</td>\n",
              "      <td>1691548</td>\n",
              "      <td>7638364</td>\n",
              "      <td>44605249</td>\n",
              "      <td>3643464</td>\n",
              "      <td>19206717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-08</td>\n",
              "      <td>2019688</td>\n",
              "      <td>8557129</td>\n",
              "      <td>42809508</td>\n",
              "      <td>2174046</td>\n",
              "      <td>19172167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-09</td>\n",
              "      <td>2089607</td>\n",
              "      <td>12140717</td>\n",
              "      <td>49343413</td>\n",
              "      <td>4766562</td>\n",
              "      <td>19320429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2022-10</td>\n",
              "      <td>1946539</td>\n",
              "      <td>8285965</td>\n",
              "      <td>55787326</td>\n",
              "      <td>3464936</td>\n",
              "      <td>20289179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2022-11</td>\n",
              "      <td>1683905</td>\n",
              "      <td>8562230</td>\n",
              "      <td>40594015</td>\n",
              "      <td>4534843</td>\n",
              "      <td>17957680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2022-12</td>\n",
              "      <td>1276151</td>\n",
              "      <td>10839569</td>\n",
              "      <td>34114246</td>\n",
              "      <td>4988636</td>\n",
              "      <td>18241378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2023-01</td>\n",
              "      <td>1866681</td>\n",
              "      <td>7755563</td>\n",
              "      <td>49084936</td>\n",
              "      <td>5811350</td>\n",
              "      <td>15470307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2023-02</td>\n",
              "      <td>1596642</td>\n",
              "      <td>5970709</td>\n",
              "      <td>39819685</td>\n",
              "      <td>7734966</td>\n",
              "      <td>14766072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2023-03</td>\n",
              "      <td>4535980</td>\n",
              "      <td>14646741</td>\n",
              "      <td>57793570</td>\n",
              "      <td>5544292</td>\n",
              "      <td>22982734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2023-04</td>\n",
              "      <td>1260761</td>\n",
              "      <td>8398225</td>\n",
              "      <td>44226583</td>\n",
              "      <td>3750208</td>\n",
              "      <td>21141822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2023-05</td>\n",
              "      <td>1402386</td>\n",
              "      <td>9173027</td>\n",
              "      <td>39376577</td>\n",
              "      <td>4436543</td>\n",
              "      <td>18469363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2023-06</td>\n",
              "      <td>1417270</td>\n",
              "      <td>12929612</td>\n",
              "      <td>41112009</td>\n",
              "      <td>6361758</td>\n",
              "      <td>11697118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2023-07</td>\n",
              "      <td>1466534</td>\n",
              "      <td>7664651</td>\n",
              "      <td>49802628</td>\n",
              "      <td>3772612</td>\n",
              "      <td>15690925</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f37cfa1c-be19-401e-9338-8aa613534708')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </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-f37cfa1c-be19-401e-9338-8aa613534708 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-f37cfa1c-be19-401e-9338-8aa613534708');\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",
      "source": [
        "df_merged.to_csv('Aryan_DSATM_1DT19CS023_submission.csv',index=False)"
      ],
      "metadata": {
        "id": "vPiDW0aNeaki"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### Conversion to Notebook to HTML\n",
        "## https://stackoverflow.com/questions/53460051/convert-ipynb-notebook-to-html-in-google-colab\n"
      ],
      "metadata": {
        "id": "eo41ubllg1gP"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}