forecast_COVID_19_Vaccines_ARIMA_FIX.ipynb

{
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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
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      "pygments_lexer": "ipython3",
      "version": "3.7.9"
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    "papermill": {
      "default_parameters": {},
      "duration": 1100.026267,
      "end_time": "2021-03-12T22:08:40.861761",
      "environment_variables": {},
      "exception": null,
      "input_path": "__notebook__.ipynb",
      "output_path": "__notebook__.ipynb",
      "parameters": {},
      "start_time": "2021-03-12T21:50:20.835494",
      "version": "2.2.2"
    },
    "colab": {
      "name": "forecast-COVID-19-Vaccines-ARIMA-FIX.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cO0dWQWbJIGP",
        "outputId": "e07e1e7f-36b1-4c57-f265-a7b0004dba59"
      },
      "source": [
        "!pip install pmdarima\n",
        "!pip install shap\n",
        "!pip install --upgrade mxnet~=1.7 gluonts\n",
        "!pip install python-utils\n",
        "!pip install plotly==4.1.0\n",
        "!pip install -U scikit-learn"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pmdarima in /usr/local/lib/python3.7/dist-packages (1.8.2)\n",
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            "Requirement already satisfied: statsmodels!=0.12.0,>=0.11 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (0.12.2)\n",
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            "Requirement already satisfied: setuptools!=50.0.0,>=38.6.0 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (57.0.0)\n",
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            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->pmdarima) (2018.9)\n",
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            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.7/dist-packages (from shap) (1.3.0)\n",
            "Requirement already satisfied: tqdm>4.25.0 in /usr/local/lib/python3.7/dist-packages (from shap) (4.41.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from shap) (1.4.1)\n",
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            "Requirement already satisfied: slicer==0.0.7 in /usr/local/lib/python3.7/dist-packages (from shap) (0.0.7)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from shap) (1.1.5)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.7/dist-packages (from shap) (0.51.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from shap) (1.19.5)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->shap) (2.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->shap) (1.0.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->shap) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->shap) (2018.9)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba->shap) (57.0.0)\n",
            "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba->shap) (0.34.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->shap) (1.15.0)\n",
            "Requirement already up-to-date: mxnet~=1.7 in /usr/local/lib/python3.7/dist-packages (1.8.0.post0)\n",
            "Requirement already up-to-date: gluonts in /usr/local/lib/python3.7/dist-packages (0.7.5)\n",
            "Requirement already satisfied, skipping upgrade: graphviz<0.9.0,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from mxnet~=1.7) (0.8.4)\n",
            "Requirement already satisfied, skipping upgrade: numpy<2.0.0,>1.16.0 in /usr/local/lib/python3.7/dist-packages (from mxnet~=1.7) (1.19.5)\n",
            "Requirement already satisfied, skipping upgrade: requests<3,>=2.20.0 in /usr/local/lib/python3.7/dist-packages (from mxnet~=1.7) (2.23.0)\n",
            "Requirement already satisfied, skipping upgrade: pandas~=1.0 in /usr/local/lib/python3.7/dist-packages (from gluonts) (1.1.5)\n",
            "Requirement already satisfied, skipping upgrade: toolz~=0.10 in /usr/local/lib/python3.7/dist-packages (from gluonts) (0.11.1)\n",
            "Requirement already satisfied, skipping upgrade: tqdm~=4.23 in /usr/local/lib/python3.7/dist-packages (from gluonts) (4.41.1)\n",
            "Requirement already satisfied, skipping upgrade: matplotlib~=3.0 in /usr/local/lib/python3.7/dist-packages (from gluonts) (3.2.2)\n",
            "Requirement already satisfied, skipping upgrade: holidays>=0.9 in /usr/local/lib/python3.7/dist-packages (from gluonts) (0.10.5.2)\n",
            "Requirement already satisfied, skipping upgrade: pydantic~=1.1 in /usr/local/lib/python3.7/dist-packages (from gluonts) (1.8.2)\n",
            "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.20.0->mxnet~=1.7) (2.10)\n",
            "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.20.0->mxnet~=1.7) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.20.0->mxnet~=1.7) (2020.12.5)\n",
            "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.20.0->mxnet~=1.7) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas~=1.0->gluonts) (2018.9)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas~=1.0->gluonts) (2.8.1)\n",
            "Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib~=3.0->gluonts) (2.4.7)\n",
            "Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib~=3.0->gluonts) (0.10.0)\n",
            "Requirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib~=3.0->gluonts) (1.3.1)\n",
            "Requirement already satisfied, skipping upgrade: korean-lunar-calendar in /usr/local/lib/python3.7/dist-packages (from holidays>=0.9->gluonts) (0.2.1)\n",
            "Requirement already satisfied, skipping upgrade: convertdate>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from holidays>=0.9->gluonts) (2.3.2)\n",
            "Requirement already satisfied, skipping upgrade: hijri-converter in /usr/local/lib/python3.7/dist-packages (from holidays>=0.9->gluonts) (2.1.1)\n",
            "Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.7/dist-packages (from holidays>=0.9->gluonts) (1.15.0)\n",
            "Requirement already satisfied, skipping upgrade: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from pydantic~=1.1->gluonts) (3.7.4.3)\n",
            "Requirement already satisfied, skipping upgrade: pymeeus<=1,>=0.3.13 in /usr/local/lib/python3.7/dist-packages (from convertdate>=2.3.0->holidays>=0.9->gluonts) (0.5.11)\n",
            "Requirement already satisfied: python-utils in /usr/local/lib/python3.7/dist-packages (2.5.6)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from python-utils) (1.15.0)\n",
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            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from plotly==4.1.0) (1.15.0)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly==4.1.0) (1.3.3)\n",
            "Requirement already up-to-date: scikit-learn in /usr/local/lib/python3.7/dist-packages (0.24.2)\n",
            "Requirement already satisfied, skipping upgrade: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.0.1)\n",
            "Requirement already satisfied, skipping upgrade: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (2.1.0)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.13.3 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.19.5)\n",
            "Requirement already satisfied, skipping upgrade: scipy>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.4.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R66IV-4sC1T5",
        "outputId": "f9023f60-cc60-4fde-e6ff-3da735345557"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "_kg_hide-input": true,
        "_kg_hide-output": true,
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "execution": {
          "iopub.execute_input": "2021-03-12T21:50:27.214010Z",
          "iopub.status.busy": "2021-03-12T21:50:27.213272Z",
          "iopub.status.idle": "2021-03-12T21:50:34.338132Z",
          "shell.execute_reply": "2021-03-12T21:50:34.339129Z"
        },
        "papermill": {
          "duration": 7.189289,
          "end_time": "2021-03-12T21:50:34.339471",
          "exception": false,
          "start_time": "2021-03-12T21:50:27.150182",
          "status": "completed"
        },
        "tags": [],
        "id": "deW4WpiuCg6f"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Data visualization\n",
        "import plotly.express as px\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns \n",
        "import folium\n",
        "\n",
        "# Statistics \n",
        "import scipy\n",
        "import statsmodels as sms\n",
        "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
        "from statsmodels.tsa.stattools import adfuller\n",
        "from statsmodels.tsa.arima.model import ARIMA\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import mean_absolute_percentage_error"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_kg_hide-input": true,
        "execution": {
          "iopub.execute_input": "2021-03-12T21:50:35.192673Z",
          "iopub.status.busy": "2021-03-12T21:50:35.187206Z",
          "iopub.status.idle": "2021-03-12T21:50:35.201112Z",
          "shell.execute_reply": "2021-03-12T21:50:35.200399Z"
        },
        "papermill": {
          "duration": 0.075169,
          "end_time": "2021-03-12T21:50:35.201259",
          "exception": false,
          "start_time": "2021-03-12T21:50:35.126090",
          "status": "completed"
        },
        "tags": [],
        "id": "2Cknc00bCg6o",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "40e85c3f-ba58-489c-8663-c9d33a6ea760"
      },
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/dataset/country_vaccinations.csv')\n",
        "data.tail(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>iso_code</th>\n",
              "      <th>date</th>\n",
              "      <th>total_vaccinations</th>\n",
              "      <th>people_vaccinated</th>\n",
              "      <th>people_fully_vaccinated</th>\n",
              "      <th>daily_vaccinations_raw</th>\n",
              "      <th>daily_vaccinations</th>\n",
              "      <th>total_vaccinations_per_hundred</th>\n",
              "      <th>people_vaccinated_per_hundred</th>\n",
              "      <th>people_fully_vaccinated_per_hundred</th>\n",
              "      <th>daily_vaccinations_per_million</th>\n",
              "      <th>vaccines</th>\n",
              "      <th>source_name</th>\n",
              "      <th>source_website</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>Zimbabwe</td>\n",
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              "      <td>2.92</td>\n",
              "      <td>2.49</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1047.0</td>\n",
              "      <td>Sinopharm/Beijing</td>\n",
              "      <td>Ministry of Health</td>\n",
              "      <td>https://twitter.com/MoHCCZim/status/1390390738...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15657</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>2021-04-28</td>\n",
              "      <td>458013.0</td>\n",
              "      <td>388021.0</td>\n",
              "      <td>69992.0</td>\n",
              "      <td>24074.0</td>\n",
              "      <td>17860.0</td>\n",
              "      <td>3.08</td>\n",
              "      <td>2.61</td>\n",
              "      <td>0.47</td>\n",
              "      <td>1202.0</td>\n",
              "      <td>Sinopharm/Beijing</td>\n",
              "      <td>Ministry of Health</td>\n",
              "      <td>https://twitter.com/MoHCCZim/status/1390390738...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15658</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>2021-04-29</td>\n",
              "      <td>477597.0</td>\n",
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              "      <td>19584.0</td>\n",
              "      <td>17971.0</td>\n",
              "      <td>3.21</td>\n",
              "      <td>2.70</td>\n",
              "      <td>0.52</td>\n",
              "      <td>1209.0</td>\n",
              "      <td>Sinopharm/Beijing</td>\n",
              "      <td>Ministry of Health</td>\n",
              "      <td>https://twitter.com/MoHCCZim/status/1390390738...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15659</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>2021-04-30</td>\n",
              "      <td>500342.0</td>\n",
              "      <td>414735.0</td>\n",
              "      <td>85607.0</td>\n",
              "      <td>22745.0</td>\n",
              "      <td>19194.0</td>\n",
              "      <td>3.37</td>\n",
              "      <td>2.79</td>\n",
              "      <td>0.58</td>\n",
              "      <td>1291.0</td>\n",
              "      <td>Sinopharm/Beijing</td>\n",
              "      <td>Ministry of Health</td>\n",
              "      <td>https://twitter.com/MoHCCZim/status/1390390738...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15660</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>2021-05-01</td>\n",
              "      <td>520299.0</td>\n",
              "      <td>428135.0</td>\n",
              "      <td>92164.0</td>\n",
              "      <td>19957.0</td>\n",
              "      <td>21171.0</td>\n",
              "      <td>3.50</td>\n",
              "      <td>2.88</td>\n",
              "      <td>0.62</td>\n",
              "      <td>1424.0</td>\n",
              "      <td>Sinopharm/Beijing</td>\n",
              "      <td>Ministry of Health</td>\n",
              "      <td>https://twitter.com/MoHCCZim/status/1390390738...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15661</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>2021-05-02</td>\n",
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              "      <td>430068.0</td>\n",
              "      <td>94131.0</td>\n",
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              "      <td>3.53</td>\n",
              "      <td>2.89</td>\n",
              "      <td>0.63</td>\n",
              "      <td>1290.0</td>\n",
              "      <td>Sinopharm/Beijing</td>\n",
              "      <td>Ministry of Health</td>\n",
              "      <td>https://twitter.com/MoHCCZim/status/1390390738...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15662</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>2021-05-03</td>\n",
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              "      <td>3.62</td>\n",
              "      <td>2.95</td>\n",
              "      <td>0.67</td>\n",
              "      <td>1210.0</td>\n",
              "      <td>Sinopharm/Beijing</td>\n",
              "      <td>Ministry of Health</td>\n",
              "      <td>https://twitter.com/MoHCCZim/status/1390390738...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15663</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>2021-05-04</td>\n",
              "      <td>559777.0</td>\n",
              "      <td>452191.0</td>\n",
              "      <td>107586.0</td>\n",
              "      <td>22261.0</td>\n",
              "      <td>17977.0</td>\n",
              "      <td>3.77</td>\n",
              "      <td>3.04</td>\n",
              "      <td>0.72</td>\n",
              "      <td>1210.0</td>\n",
              "      <td>Sinopharm/Beijing</td>\n",
              "      <td>Ministry of Health</td>\n",
              "      <td>https://twitter.com/MoHCCZim/status/1390390738...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15664</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>2021-05-05</td>\n",
              "      <td>576233.0</td>\n",
              "      <td>461023.0</td>\n",
              "      <td>115210.0</td>\n",
              "      <td>16456.0</td>\n",
              "      <td>16889.0</td>\n",
              "      <td>3.88</td>\n",
              "      <td>3.10</td>\n",
              "      <td>0.78</td>\n",
              "      <td>1136.0</td>\n",
              "      <td>Sinopharm/Beijing</td>\n",
              "      <td>Ministry of Health</td>\n",
              "      <td>https://twitter.com/MoHCCZim/status/1390390738...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15665</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>2021-05-06</td>\n",
              "      <td>607355.0</td>\n",
              "      <td>478174.0</td>\n",
              "      <td>129181.0</td>\n",
              "      <td>31122.0</td>\n",
              "      <td>18537.0</td>\n",
              "      <td>4.09</td>\n",
              "      <td>3.22</td>\n",
              "      <td>0.87</td>\n",
              "      <td>1247.0</td>\n",
              "      <td>Sinopharm/Beijing</td>\n",
              "      <td>Ministry of Health</td>\n",
              "      <td>https://twitter.com/MoHCCZim/status/1390390738...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        country  ...                                     source_website\n",
              "15656  Zimbabwe  ...  https://twitter.com/MoHCCZim/status/1390390738...\n",
              "15657  Zimbabwe  ...  https://twitter.com/MoHCCZim/status/1390390738...\n",
              "15658  Zimbabwe  ...  https://twitter.com/MoHCCZim/status/1390390738...\n",
              "15659  Zimbabwe  ...  https://twitter.com/MoHCCZim/status/1390390738...\n",
              "15660  Zimbabwe  ...  https://twitter.com/MoHCCZim/status/1390390738...\n",
              "15661  Zimbabwe  ...  https://twitter.com/MoHCCZim/status/1390390738...\n",
              "15662  Zimbabwe  ...  https://twitter.com/MoHCCZim/status/1390390738...\n",
              "15663  Zimbabwe  ...  https://twitter.com/MoHCCZim/status/1390390738...\n",
              "15664  Zimbabwe  ...  https://twitter.com/MoHCCZim/status/1390390738...\n",
              "15665  Zimbabwe  ...  https://twitter.com/MoHCCZim/status/1390390738...\n",
              "\n",
              "[10 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7lIL3gS2SP8G"
      },
      "source": [
        "data[\"date\"] = pd.to_datetime(data[\"date\"], format = '%Y-%m-%d')\n",
        "data = data.replace([np.inf, -np.inf], np.nan)\n",
        "data = data.fillna(0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "FvU3p9UuL-s1",
        "outputId": "8d36b99a-d18e-435f-9903-ff9916c423ba"
      },
      "source": [
        "plt.figure(figsize = (8,3))\n",
        "plt.subplot(121); plot_acf(data[data['vaccines']=='Sinopharm/Beijing, Sinopharm/Wuhan, Sinovac']['total_vaccinations'], lags = 40, ax = plt.gca(), color = 'green')\n",
        "plt.subplot(122); plot_pacf(data[data['vaccines']=='Sinopharm/Beijing, Sinopharm/Wuhan, Sinovac']['total_vaccinations'], lags = 40,ax = plt.gca(), color = 'green')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "22khDc4IMCea",
        "outputId": "f74786f6-57bf-4a58-abea-4d18f3cc454c"
      },
      "source": [
        "result = adfuller(data[data['vaccines']=='Sinopharm/Beijing, Sinopharm/Wuhan, Sinovac']['total_vaccinations'].dropna())\n",
        "print(\"ADF Statistics \",result[0])\n",
        "print(\"p-value\",result[1])\n",
        "print(\"critical values\",result[5])\n",
        "if result[1]<=0.05:\n",
        "    print(\"fail to reject null hypothese h1 , it mean data is stationary\")\n",
        "else:\n",
        "    print(\"Reject the null hypotheise , it mean data is not stationary\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ADF Statistics  3.0526736998311965\n",
            "p-value 1.0\n",
            "critical values 4584.850222121779\n",
            "Reject the null hypotheise , it mean data is not stationary\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "M5TACYJYMIp6",
        "outputId": "358dba86-e5a4-4194-c2f3-1cc8b3598f65"
      },
      "source": [
        "data['diff1'] = data[data['vaccines']=='Sinopharm/Beijing, Sinopharm/Wuhan, Sinovac']['total_vaccinations'].diff()\n",
        "data['diff1'] = data['diff1'].fillna(data['diff1'].mean())\n",
        "ax = sns.lineplot(x = data['date'], y = data['diff1'], color='blue')\n",
        "sns.lineplot(x = data['date'], y = data['diff1'].mean(), color='salmon')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Nonseasonal diff. series of 1nd order')\n",
        "plt.show()\n",
        "result = adfuller(data.diff1)\n",
        "print(\"ADF Statistics \",result[0])\n",
        "print(\"p-value\",result[1])\n",
        "print(\"critical values\",result[5])\n",
        "if result[1]<=0.05:\n",
        "    print(\"fail to reject null hypothese h1 , it mean data is stationary\")\n",
        "else:\n",
        "    print(\"Reject the null hypotheise , it mean data is not stationary\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "ADF Statistics  -12.436264519383535\n",
            "p-value 3.841062696179127e-23\n",
            "critical values 478709.8660042889\n",
            "fail to reject null hypothese h1 , it mean data is stationary\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "U12cXMEIMKc-",
        "outputId": "ae431552-7bc9-463a-e37e-9e55f7b28b1b"
      },
      "source": [
        "plt.figure(figsize = (8,3))\n",
        "plt.subplot(121); plot_acf(data['diff1'], lags = 40, ax = plt.gca(), color = 'green')\n",
        "plt.subplot(122); plot_pacf(data['diff1'], lags = 40,ax = plt.gca(), color = 'green')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "rIdJCUXFEZca",
        "outputId": "b870e092-df2d-4ae8-c21f-cf788e465b8c"
      },
      "source": [
        "data.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>country</th>\n",
              "      <th>iso_code</th>\n",
              "      <th>date</th>\n",
              "      <th>total_vaccinations</th>\n",
              "      <th>people_vaccinated</th>\n",
              "      <th>people_fully_vaccinated</th>\n",
              "      <th>daily_vaccinations_raw</th>\n",
              "      <th>daily_vaccinations</th>\n",
              "      <th>total_vaccinations_per_hundred</th>\n",
              "      <th>people_vaccinated_per_hundred</th>\n",
              "      <th>people_fully_vaccinated_per_hundred</th>\n",
              "      <th>daily_vaccinations_per_million</th>\n",
              "      <th>vaccines</th>\n",
              "      <th>source_name</th>\n",
              "      <th>source_website</th>\n",
              "      <th>diff1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>15661</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>2021-05-02</td>\n",
              "      <td>524199.0</td>\n",
              "      <td>430068.0</td>\n",
              "      <td>94131.0</td>\n",
              "      <td>3900.0</td>\n",
              "      <td>19169.0</td>\n",
              "      <td>3.53</td>\n",
              "      <td>2.89</td>\n",
              "      <td>0.63</td>\n",
              "      <td>1290.0</td>\n",
              "      <td>Sinopharm/Beijing</td>\n",
              "      <td>Ministry of Health</td>\n",
              "      <td>https://twitter.com/MoHCCZim/status/1390390738...</td>\n",
              "      <td>2.086155e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15662</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>2021-05-03</td>\n",
              "      <td>537516.0</td>\n",
              "      <td>437751.0</td>\n",
              "      <td>99765.0</td>\n",
              "      <td>13317.0</td>\n",
              "      <td>17987.0</td>\n",
              "      <td>3.62</td>\n",
              "      <td>2.95</td>\n",
              "      <td>0.67</td>\n",
              "      <td>1210.0</td>\n",
              "      <td>Sinopharm/Beijing</td>\n",
              "      <td>Ministry of Health</td>\n",
              "      <td>https://twitter.com/MoHCCZim/status/1390390738...</td>\n",
              "      <td>2.086155e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15663</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>2021-05-04</td>\n",
              "      <td>559777.0</td>\n",
              "      <td>452191.0</td>\n",
              "      <td>107586.0</td>\n",
              "      <td>22261.0</td>\n",
              "      <td>17977.0</td>\n",
              "      <td>3.77</td>\n",
              "      <td>3.04</td>\n",
              "      <td>0.72</td>\n",
              "      <td>1210.0</td>\n",
              "      <td>Sinopharm/Beijing</td>\n",
              "      <td>Ministry of Health</td>\n",
              "      <td>https://twitter.com/MoHCCZim/status/1390390738...</td>\n",
              "      <td>2.086155e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15664</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>2021-05-05</td>\n",
              "      <td>576233.0</td>\n",
              "      <td>461023.0</td>\n",
              "      <td>115210.0</td>\n",
              "      <td>16456.0</td>\n",
              "      <td>16889.0</td>\n",
              "      <td>3.88</td>\n",
              "      <td>3.10</td>\n",
              "      <td>0.78</td>\n",
              "      <td>1136.0</td>\n",
              "      <td>Sinopharm/Beijing</td>\n",
              "      <td>Ministry of Health</td>\n",
              "      <td>https://twitter.com/MoHCCZim/status/1390390738...</td>\n",
              "      <td>2.086155e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15665</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>2021-05-06</td>\n",
              "      <td>607355.0</td>\n",
              "      <td>478174.0</td>\n",
              "      <td>129181.0</td>\n",
              "      <td>31122.0</td>\n",
              "      <td>18537.0</td>\n",
              "      <td>4.09</td>\n",
              "      <td>3.22</td>\n",
              "      <td>0.87</td>\n",
              "      <td>1247.0</td>\n",
              "      <td>Sinopharm/Beijing</td>\n",
              "      <td>Ministry of Health</td>\n",
              "      <td>https://twitter.com/MoHCCZim/status/1390390738...</td>\n",
              "      <td>2.086155e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        country  ...         diff1\n",
              "15661  Zimbabwe  ...  2.086155e+06\n",
              "15662  Zimbabwe  ...  2.086155e+06\n",
              "15663  Zimbabwe  ...  2.086155e+06\n",
              "15664  Zimbabwe  ...  2.086155e+06\n",
              "15665  Zimbabwe  ...  2.086155e+06\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oSUi7BbtUfEO"
      },
      "source": [
        "# ARIMA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "id": "mZMyWNZG2mmL",
        "outputId": "417fd360-bb99-4070-d92c-ad20b220d9ba"
      },
      "source": [
        "train = data['total_vaccinations'][:100]\n",
        "test = data['total_vaccinations'][100:]\n",
        "model = ARIMA(data['total_vaccinations'], order = (0,1,1))\n",
        "model = model.fit()\n",
        "start = len(train)\n",
        "end = len(train)+len(test)-1\n",
        "pred = model.predict(start=start, end=end, typ = 'levels')\n",
        "print(pred.tail(10))\n",
        "plt.figure(figsize=(12,5), dpi=100)\n",
        "ax = sns.lineplot(x=data['date'], y = pred, label='forecast')\n",
        "sns.lineplot(x=data['date'], y = data['total_vaccinations'], label='actual', ax=ax)\n",
        "plt.title('Forecast vs Actuals')\n",
        "plt.legend(loc='upper left', fontsize=8)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "15656    406555.739860\n",
            "15657    428516.222326\n",
            "15658    452171.676995\n",
            "15659    472561.957670\n",
            "15660    494840.646525\n",
            "15661    515257.416561\n",
            "15662    522428.275156\n",
            "15663    534528.138912\n",
            "15664    554776.902879\n",
            "15665    571983.993715\n",
            "Name: predicted_mean, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iqNNs8ms9Gzj",
        "outputId": "63247793-7b0a-4ef5-d71f-f7b7511e0ab5"
      },
      "source": [
        "mean_squared_error(test, pred, squared = False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4533622.574622663"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4twx-BWs9Npi",
        "outputId": "28424e01-8249-4f64-8c05-81955a934489"
      },
      "source": [
        "pred.mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3182782.711671191"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AWBC5wKR9Q2a",
        "outputId": "ef19287b-7174-46b3-da61-09d41d5c5329"
      },
      "source": [
        "mean_absolute_percentage_error(test,pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.173199716185583e+20"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tH9uzjQV9WKi",
        "outputId": "1b5a5b40-2637-4f7a-88ff-3b2c4f28218e"
      },
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_score(test, pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9291889675312034"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LjNZ8QmK9aMV",
        "outputId": "fed4aec0-1b3f-4d4e-dbd4-b26fd4f63652"
      },
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "print(mean_absolute_error(test, pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "445328.60153364454\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IZywVA3l9dDS",
        "outputId": "a7d309f2-0ff9-410d-bf9d-d5d75a1bd104"
      },
      "source": [
        "from sklearn.metrics import median_absolute_error\n",
        "print(median_absolute_error(test, pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "12824.36844128089\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xjA-JiFI9f9p",
        "outputId": "0b93122b-57f6-4cdf-c8db-d17adda817d7"
      },
      "source": [
        "from math import sqrt\n",
        "rmse = sqrt(mean_squared_error(test, pred))\n",
        "print(rmse)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4533622.574622663\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}