Covid_Third_Wave_Forecasting/covid-19-forecasting-using-prophet.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>...</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>excess_mortality_cumulative_absolute</th>\n",
       "      <th>excess_mortality_cumulative</th>\n",
       "      <th>excess_mortality</th>\n",
       "      <th>excess_mortality_cumulative_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
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       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>165403</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-02-25</td>\n",
       "      <td>235467.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>348.143</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>1.000</td>\n",
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       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
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       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>165404</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-02-26</td>\n",
       "      <td>235803.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>368.429</td>\n",
       "      <td>5393.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
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       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>165405</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-02-27</td>\n",
       "      <td>235803.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>350.143</td>\n",
       "      <td>5393.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>165406</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-02-28</td>\n",
       "      <td>236380.0</td>\n",
       "      <td>577.0</td>\n",
       "      <td>401.286</td>\n",
       "      <td>5395.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.286</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
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       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>165407</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>236871.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>413.000</td>\n",
       "      <td>5395.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
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       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165408 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code continent     location        date  total_cases  new_cases  \\\n",
       "0           AFG      Asia  Afghanistan  2020-02-24          5.0        5.0   \n",
       "1           AFG      Asia  Afghanistan  2020-02-25          5.0        0.0   \n",
       "2           AFG      Asia  Afghanistan  2020-02-26          5.0        0.0   \n",
       "3           AFG      Asia  Afghanistan  2020-02-27          5.0        0.0   \n",
       "4           AFG      Asia  Afghanistan  2020-02-28          5.0        0.0   \n",
       "...         ...       ...          ...         ...          ...        ...   \n",
       "165403      ZWE    Africa     Zimbabwe  2022-02-25     235467.0      500.0   \n",
       "165404      ZWE    Africa     Zimbabwe  2022-02-26     235803.0      336.0   \n",
       "165405      ZWE    Africa     Zimbabwe  2022-02-27     235803.0        0.0   \n",
       "165406      ZWE    Africa     Zimbabwe  2022-02-28     236380.0      577.0   \n",
       "165407      ZWE    Africa     Zimbabwe  2022-03-01     236871.0      491.0   \n",
       "\n",
       "        new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  \\\n",
       "0                      NaN           NaN         NaN                  NaN   \n",
       "1                      NaN           NaN         NaN                  NaN   \n",
       "2                      NaN           NaN         NaN                  NaN   \n",
       "3                      NaN           NaN         NaN                  NaN   \n",
       "4                      NaN           NaN         NaN                  NaN   \n",
       "...                    ...           ...         ...                  ...   \n",
       "165403             348.143        5392.0         2.0                1.000   \n",
       "165404             368.429        5393.0         1.0                1.000   \n",
       "165405             350.143        5393.0         0.0                1.000   \n",
       "165406             401.286        5395.0         2.0                1.286   \n",
       "165407             413.000        5395.0         0.0                1.000   \n",
       "\n",
       "        ...  female_smokers  male_smokers  handwashing_facilities  \\\n",
       "0       ...             NaN           NaN                  37.746   \n",
       "1       ...             NaN           NaN                  37.746   \n",
       "2       ...             NaN           NaN                  37.746   \n",
       "3       ...             NaN           NaN                  37.746   \n",
       "4       ...             NaN           NaN                  37.746   \n",
       "...     ...             ...           ...                     ...   \n",
       "165403  ...             1.6          30.7                  36.791   \n",
       "165404  ...             1.6          30.7                  36.791   \n",
       "165405  ...             1.6          30.7                  36.791   \n",
       "165406  ...             1.6          30.7                  36.791   \n",
       "165407  ...             1.6          30.7                  36.791   \n",
       "\n",
       "        hospital_beds_per_thousand  life_expectancy  human_development_index  \\\n",
       "0                              0.5            64.83                    0.511   \n",
       "1                              0.5            64.83                    0.511   \n",
       "2                              0.5            64.83                    0.511   \n",
       "3                              0.5            64.83                    0.511   \n",
       "4                              0.5            64.83                    0.511   \n",
       "...                            ...              ...                      ...   \n",
       "165403                         1.7            61.49                    0.571   \n",
       "165404                         1.7            61.49                    0.571   \n",
       "165405                         1.7            61.49                    0.571   \n",
       "165406                         1.7            61.49                    0.571   \n",
       "165407                         1.7            61.49                    0.571   \n",
       "\n",
       "        excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
       "0                                        NaN                          NaN   \n",
       "1                                        NaN                          NaN   \n",
       "2                                        NaN                          NaN   \n",
       "3                                        NaN                          NaN   \n",
       "4                                        NaN                          NaN   \n",
       "...                                      ...                          ...   \n",
       "165403                                   NaN                          NaN   \n",
       "165404                                   NaN                          NaN   \n",
       "165405                                   NaN                          NaN   \n",
       "165406                                   NaN                          NaN   \n",
       "165407                                   NaN                          NaN   \n",
       "\n",
       "        excess_mortality  excess_mortality_cumulative_per_million  \n",
       "0                    NaN                                      NaN  \n",
       "1                    NaN                                      NaN  \n",
       "2                    NaN                                      NaN  \n",
       "3                    NaN                                      NaN  \n",
       "4                    NaN                                      NaN  \n",
       "...                  ...                                      ...  \n",
       "165403               NaN                                      NaN  \n",
       "165404               NaN                                      NaN  \n",
       "165405               NaN                                      NaN  \n",
       "165406               NaN                                      NaN  \n",
       "165407               NaN                                      NaN  \n",
       "\n",
       "[165408 rows x 67 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "covid_word = pd.read_csv('../input/covidowid/owid-covid-data (1).csv')\n",
    "covid_word"
   ]
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    "Let's study our DataSet. As you can see, it consist of 159321 rows × 67 columns. There is some missing data are observed in the DataSet. We should be sure that Python recognized types of data correctly.\n"
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   "outputs": [
    {
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 165408 entries, 0 to 165407\n",
      "Data columns (total 67 columns):\n",
      " #   Column                                      Non-Null Count   Dtype  \n",
      "---  ------                                      --------------   -----  \n",
      " 0   iso_code                                    165408 non-null  object \n",
      " 1   continent                                   155504 non-null  object \n",
      " 2   location                                    165408 non-null  object \n",
      " 3   date                                        165408 non-null  object \n",
      " 4   total_cases                                 162378 non-null  float64\n",
      " 5   new_cases                                   162230 non-null  float64\n",
      " 6   new_cases_smoothed                          160247 non-null  float64\n",
      " 7   total_deaths                                144574 non-null  float64\n",
      " 8   new_deaths                                  144611 non-null  float64\n",
      " 9   new_deaths_smoothed                         142515 non-null  float64\n",
      " 10  total_cases_per_million                     161624 non-null  float64\n",
      " 11  new_cases_per_million                       161476 non-null  float64\n",
      " 12  new_cases_smoothed_per_million              159499 non-null  float64\n",
      " 13  total_deaths_per_million                    143833 non-null  float64\n",
      " 14  new_deaths_per_million                      143870 non-null  float64\n",
      " 15  new_deaths_smoothed_per_million             141780 non-null  float64\n",
      " 16  reproduction_rate                           125067 non-null  float64\n",
      " 17  icu_patients                                23307 non-null   float64\n",
      " 18  icu_patients_per_million                    23307 non-null   float64\n",
      " 19  hosp_patients                               24450 non-null   float64\n",
      " 20  hosp_patients_per_million                   24450 non-null   float64\n",
      " 21  weekly_icu_admissions                       5394 non-null    float64\n",
      " 22  weekly_icu_admissions_per_million           5394 non-null    float64\n",
      " 23  weekly_hosp_admissions                      10858 non-null   float64\n",
      " 24  weekly_hosp_admissions_per_million          10858 non-null   float64\n",
      " 25  new_tests                                   67006 non-null   float64\n",
      " 26  total_tests                                 68944 non-null   float64\n",
      " 27  total_tests_per_thousand                    68944 non-null   float64\n",
      " 28  new_tests_per_thousand                      67006 non-null   float64\n",
      " 29  new_tests_smoothed                          83658 non-null   float64\n",
      " 30  new_tests_smoothed_per_thousand             83658 non-null   float64\n",
      " 31  positive_rate                               78590 non-null   float64\n",
      " 32  tests_per_case                              78027 non-null   float64\n",
      " 33  tests_units                                 85981 non-null   object \n",
      " 34  total_vaccinations                          44858 non-null   float64\n",
      " 35  people_vaccinated                           42688 non-null   float64\n",
      " 36  people_fully_vaccinated                     40047 non-null   float64\n",
      " 37  total_boosters                              17241 non-null   float64\n",
      " 38  new_vaccinations                            37155 non-null   float64\n",
      " 39  new_vaccinations_smoothed                   83919 non-null   float64\n",
      " 40  total_vaccinations_per_hundred              44858 non-null   float64\n",
      " 41  people_vaccinated_per_hundred               42688 non-null   float64\n",
      " 42  people_fully_vaccinated_per_hundred         40047 non-null   float64\n",
      " 43  total_boosters_per_hundred                  17241 non-null   float64\n",
      " 44  new_vaccinations_smoothed_per_million       83919 non-null   float64\n",
      " 45  new_people_vaccinated_smoothed              82654 non-null   float64\n",
      " 46  new_people_vaccinated_smoothed_per_hundred  82654 non-null   float64\n",
      " 47  stringency_index                            129843 non-null  float64\n",
      " 48  population                                  164337 non-null  float64\n",
      " 49  population_density                          147109 non-null  float64\n",
      " 50  median_age                                  137068 non-null  float64\n",
      " 51  aged_65_older                               135582 non-null  float64\n",
      " 52  aged_70_older                               136333 non-null  float64\n",
      " 53  gdp_per_capita                              137737 non-null  float64\n",
      " 54  extreme_poverty                             90712 non-null   float64\n",
      " 55  cardiovasc_death_rate                       136019 non-null  float64\n",
      " 56  diabetes_prevalence                         143150 non-null  float64\n",
      " 57  female_smokers                              105463 non-null  float64\n",
      " 58  male_smokers                                104016 non-null  float64\n",
      " 59  handwashing_facilities                      68189 non-null   float64\n",
      " 60  hospital_beds_per_thousand                  122981 non-null  float64\n",
      " 61  life_expectancy                             154405 non-null  float64\n",
      " 62  human_development_index                     135494 non-null  float64\n",
      " 63  excess_mortality_cumulative_absolute        5696 non-null    float64\n",
      " 64  excess_mortality_cumulative                 5696 non-null    float64\n",
      " 65  excess_mortality                            5696 non-null    float64\n",
      " 66  excess_mortality_cumulative_per_million     5696 non-null    float64\n",
      "dtypes: float64(62), object(5)\n",
      "memory usage: 84.6+ MB\n"
     ]
    }
   ],
   "source": [
    "covid_word.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b088a7dc",
   "metadata": {
    "id": "rR_i-AuyjolC",
    "papermill": {
     "duration": 0.026729,
     "end_time": "2022-03-03T03:32:56.357907",
     "exception": false,
     "start_time": "2022-03-03T03:32:56.331178",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As you can see, 66 columns of COVID-19 data were recognized correctly (float64). First 4 columns and tests_units were recognized as objects. Let's investigate them:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "272f4def",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T03:32:56.416574Z",
     "iopub.status.busy": "2022-03-03T03:32:56.415452Z",
     "iopub.status.idle": "2022-03-03T03:32:56.436356Z",
     "shell.execute_reply": "2022-03-03T03:32:56.436852Z",
     "shell.execute_reply.started": "2022-03-03T03:32:18.103920Z"
    },
    "id": "l9qfqY2SjolD",
    "papermill": {
     "duration": 0.052106,
     "end_time": "2022-03-03T03:32:56.437084",
     "exception": false,
     "start_time": "2022-03-03T03:32:56.384978",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>tests_units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165403</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>tests performed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165404</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>tests performed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165405</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>tests performed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165406</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165407</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165408 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code continent     location      tests_units\n",
       "0           AFG      Asia  Afghanistan              NaN\n",
       "1           AFG      Asia  Afghanistan              NaN\n",
       "2           AFG      Asia  Afghanistan              NaN\n",
       "3           AFG      Asia  Afghanistan              NaN\n",
       "4           AFG      Asia  Afghanistan              NaN\n",
       "...         ...       ...          ...              ...\n",
       "165403      ZWE    Africa     Zimbabwe  tests performed\n",
       "165404      ZWE    Africa     Zimbabwe  tests performed\n",
       "165405      ZWE    Africa     Zimbabwe  tests performed\n",
       "165406      ZWE    Africa     Zimbabwe              NaN\n",
       "165407      ZWE    Africa     Zimbabwe              NaN\n",
       "\n",
       "[165408 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fields = ['iso_code', 'continent', 'location', 'tests_units']\n",
    "covid_word[fields]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "013005ec",
   "metadata": {
    "id": "7GI9FShzjolE",
    "papermill": {
     "duration": 0.026216,
     "end_time": "2022-03-03T03:32:56.490676",
     "exception": false,
     "start_time": "2022-03-03T03:32:56.464460",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let's show information about the field date.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d09efd36",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T03:32:56.547117Z",
     "iopub.status.busy": "2022-03-03T03:32:56.546115Z",
     "iopub.status.idle": "2022-03-03T03:32:56.553365Z",
     "shell.execute_reply": "2022-03-03T03:32:56.553846Z",
     "shell.execute_reply.started": "2022-03-03T03:32:18.125300Z"
    },
    "id": "-hYrN453jolF",
    "papermill": {
     "duration": 0.036418,
     "end_time": "2022-03-03T03:32:56.554054",
     "exception": false,
     "start_time": "2022-03-03T03:32:56.517636",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         2020-02-24\n",
       "1         2020-02-25\n",
       "2         2020-02-26\n",
       "3         2020-02-27\n",
       "4         2020-02-28\n",
       "             ...    \n",
       "165403    2022-02-25\n",
       "165404    2022-02-26\n",
       "165405    2022-02-27\n",
       "165406    2022-02-28\n",
       "165407    2022-03-01\n",
       "Name: date, Length: 165408, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_word['date']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc65f7e7",
   "metadata": {
    "id": "OCAkVRmJjolG",
    "papermill": {
     "duration": 0.026296,
     "end_time": "2022-03-03T03:32:56.606756",
     "exception": false,
     "start_time": "2022-03-03T03:32:56.580460",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Сhange the data types of columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b4de397",
   "metadata": {
    "id": "YOwOer5pjolH",
    "papermill": {
     "duration": 0.027477,
     "end_time": "2022-03-03T03:32:56.661680",
     "exception": false,
     "start_time": "2022-03-03T03:32:56.634203",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As you can see, the columns: 'iso_code', 'continent', 'location', 'tests_units' have many repetitions and should be assigned to categorical fields **pandas.astype()**. \n",
    "\n",
    "The field 'data' should be converted into DataTime type **pandas.to_datetime**  \n",
    "To see the results we can use **pandas.describe()**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6a27e208",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T03:32:56.720827Z",
     "iopub.status.busy": "2022-03-03T03:32:56.719750Z",
     "iopub.status.idle": "2022-03-03T03:32:56.902697Z",
     "shell.execute_reply": "2022-03-03T03:32:56.901776Z",
     "shell.execute_reply.started": "2022-03-03T03:32:18.134446Z"
    },
    "id": "W8IQFt5ajolJ",
    "papermill": {
     "duration": 0.213051,
     "end_time": "2022-03-03T03:32:56.902859",
     "exception": false,
     "start_time": "2022-03-03T03:32:56.689808",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>tests_units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>165408</td>\n",
       "      <td>155504</td>\n",
       "      <td>165408</td>\n",
       "      <td>85981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>238</td>\n",
       "      <td>6</td>\n",
       "      <td>238</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>ARG</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>tests performed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>791</td>\n",
       "      <td>39197</td>\n",
       "      <td>791</td>\n",
       "      <td>62269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code continent   location      tests_units\n",
       "count    165408    155504     165408            85981\n",
       "unique      238         6        238                4\n",
       "top         ARG    Africa  Argentina  tests performed\n",
       "freq        791     39197        791            62269"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fields = ['iso_code', 'continent', 'location', 'tests_units']\n",
    "covid_word[fields] =covid_word[fields].astype('category')\n",
    "covid_word.loc[:, 'date'] = pd.to_datetime(covid_word['date'])\n",
    "covid_word[fields].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4321dae",
   "metadata": {
    "id": "WmHQN9iGjolK",
    "papermill": {
     "duration": 0.0276,
     "end_time": "2022-03-03T03:32:56.959897",
     "exception": false,
     "start_time": "2022-03-03T03:32:56.932297",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Rows filtering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edb79ef5",
   "metadata": {
    "id": "uaPESo_8jolL",
    "papermill": {
     "duration": 0.026191,
     "end_time": "2022-03-03T03:32:57.012671",
     "exception": false,
     "start_time": "2022-03-03T03:32:56.986480",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As we can see, the DataSet contains information about 6 continents and 238 countries.\n",
    "The field 'tests_units' consist of 4 categories. To show a list of countries, we can use **pandas.Series.cat.categories**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "04896044",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T03:32:57.072863Z",
     "iopub.status.busy": "2022-03-03T03:32:57.071832Z",
     "iopub.status.idle": "2022-03-03T03:32:57.078349Z",
     "shell.execute_reply": "2022-03-03T03:32:57.078917Z",
     "shell.execute_reply.started": "2022-03-03T03:32:18.244908Z"
    },
    "id": "-IgQLE9RjolL",
    "papermill": {
     "duration": 0.038104,
     "end_time": "2022-03-03T03:32:57.079118",
     "exception": false,
     "start_time": "2022-03-03T03:32:57.041014",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Afghanistan', 'Africa', 'Albania', 'Algeria', 'Andorra', 'Angola',\n",
       "       'Anguilla', 'Antigua and Barbuda', 'Argentina', 'Armenia',\n",
       "       ...\n",
       "       'Uzbekistan', 'Vanuatu', 'Vatican', 'Venezuela', 'Vietnam',\n",
       "       'Wallis and Futuna', 'World', 'Yemen', 'Zambia', 'Zimbabwe'],\n",
       "      dtype='object', length=238)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_word['location'].cat.categories"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e91823b4",
   "metadata": {
    "id": "80889yb9jolM",
    "papermill": {
     "duration": 0.027555,
     "end_time": "2022-03-03T03:32:57.135350",
     "exception": false,
     "start_time": "2022-03-03T03:32:57.107795",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let's investigate the dynamics of new cases of COVID-19 for a separate country. We will use India for saving models and further forecast. You can use your country. Let's use a pandas filter to do this.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d3b0576d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T03:32:57.194640Z",
     "iopub.status.busy": "2022-03-03T03:32:57.193610Z",
     "iopub.status.idle": "2022-03-03T03:32:57.237962Z",
     "shell.execute_reply": "2022-03-03T03:32:57.238518Z",
     "shell.execute_reply.started": "2022-03-03T03:32:18.251971Z"
    },
    "id": "CTqsGuIfjolN",
    "papermill": {
     "duration": 0.07523,
     "end_time": "2022-03-03T03:32:57.238701",
     "exception": false,
     "start_time": "2022-03-03T03:32:57.163471",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "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>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>...</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>excess_mortality_cumulative_absolute</th>\n",
       "      <th>excess_mortality_cumulative</th>\n",
       "      <th>excess_mortality</th>\n",
       "      <th>excess_mortality_cumulative_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69295</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69296</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69297</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69298</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69299</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70052</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>2022-02-25</td>\n",
       "      <td>42905844.0</td>\n",
       "      <td>11499.0</td>\n",
       "      <td>14762.714</td>\n",
       "      <td>513481.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>321.571</td>\n",
       "      <td>...</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70053</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>2022-02-26</td>\n",
       "      <td>42916117.0</td>\n",
       "      <td>10273.0</td>\n",
       "      <td>13377.714</td>\n",
       "      <td>513724.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>260.143</td>\n",
       "      <td>...</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70054</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>2022-02-27</td>\n",
       "      <td>42924130.0</td>\n",
       "      <td>8013.0</td>\n",
       "      <td>12229.429</td>\n",
       "      <td>513843.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>247.714</td>\n",
       "      <td>...</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70055</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>2022-02-28</td>\n",
       "      <td>42931045.0</td>\n",
       "      <td>6915.0</td>\n",
       "      <td>11302.286</td>\n",
       "      <td>514023.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>239.857</td>\n",
       "      <td>...</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70056</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>42938599.0</td>\n",
       "      <td>7554.0</td>\n",
       "      <td>10224.000</td>\n",
       "      <td>514246.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>232.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>762 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      iso_code continent location       date  total_cases  new_cases  \\\n",
       "69295      IND      Asia    India 2020-01-30          1.0        1.0   \n",
       "69296      IND      Asia    India 2020-01-31          1.0        0.0   \n",
       "69297      IND      Asia    India 2020-02-01          1.0        0.0   \n",
       "69298      IND      Asia    India 2020-02-02          2.0        1.0   \n",
       "69299      IND      Asia    India 2020-02-03          3.0        1.0   \n",
       "...        ...       ...      ...        ...          ...        ...   \n",
       "70052      IND      Asia    India 2022-02-25   42905844.0    11499.0   \n",
       "70053      IND      Asia    India 2022-02-26   42916117.0    10273.0   \n",
       "70054      IND      Asia    India 2022-02-27   42924130.0     8013.0   \n",
       "70055      IND      Asia    India 2022-02-28   42931045.0     6915.0   \n",
       "70056      IND      Asia    India 2022-03-01   42938599.0     7554.0   \n",
       "\n",
       "       new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  ...  \\\n",
       "69295                 NaN           NaN         NaN                  NaN  ...   \n",
       "69296                 NaN           NaN         NaN                  NaN  ...   \n",
       "69297                 NaN           NaN         NaN                  NaN  ...   \n",
       "69298                 NaN           NaN         NaN                  NaN  ...   \n",
       "69299                 NaN           NaN         NaN                  NaN  ...   \n",
       "...                   ...           ...         ...                  ...  ...   \n",
       "70052           14762.714      513481.0       255.0              321.571  ...   \n",
       "70053           13377.714      513724.0       243.0              260.143  ...   \n",
       "70054           12229.429      513843.0       119.0              247.714  ...   \n",
       "70055           11302.286      514023.0       180.0              239.857  ...   \n",
       "70056           10224.000      514246.0       223.0              232.000  ...   \n",
       "\n",
       "       female_smokers  male_smokers  handwashing_facilities  \\\n",
       "69295             1.9          20.6                   59.55   \n",
       "69296             1.9          20.6                   59.55   \n",
       "69297             1.9          20.6                   59.55   \n",
       "69298             1.9          20.6                   59.55   \n",
       "69299             1.9          20.6                   59.55   \n",
       "...               ...           ...                     ...   \n",
       "70052             1.9          20.6                   59.55   \n",
       "70053             1.9          20.6                   59.55   \n",
       "70054             1.9          20.6                   59.55   \n",
       "70055             1.9          20.6                   59.55   \n",
       "70056             1.9          20.6                   59.55   \n",
       "\n",
       "       hospital_beds_per_thousand  life_expectancy  human_development_index  \\\n",
       "69295                        0.53            69.66                    0.645   \n",
       "69296                        0.53            69.66                    0.645   \n",
       "69297                        0.53            69.66                    0.645   \n",
       "69298                        0.53            69.66                    0.645   \n",
       "69299                        0.53            69.66                    0.645   \n",
       "...                           ...              ...                      ...   \n",
       "70052                        0.53            69.66                    0.645   \n",
       "70053                        0.53            69.66                    0.645   \n",
       "70054                        0.53            69.66                    0.645   \n",
       "70055                        0.53            69.66                    0.645   \n",
       "70056                        0.53            69.66                    0.645   \n",
       "\n",
       "       excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
       "69295                                   NaN                          NaN   \n",
       "69296                                   NaN                          NaN   \n",
       "69297                                   NaN                          NaN   \n",
       "69298                                   NaN                          NaN   \n",
       "69299                                   NaN                          NaN   \n",
       "...                                     ...                          ...   \n",
       "70052                                   NaN                          NaN   \n",
       "70053                                   NaN                          NaN   \n",
       "70054                                   NaN                          NaN   \n",
       "70055                                   NaN                          NaN   \n",
       "70056                                   NaN                          NaN   \n",
       "\n",
       "       excess_mortality  excess_mortality_cumulative_per_million  \n",
       "69295               NaN                                      NaN  \n",
       "69296               NaN                                      NaN  \n",
       "69297               NaN                                      NaN  \n",
       "69298               NaN                                      NaN  \n",
       "69299               NaN                                      NaN  \n",
       "...                 ...                                      ...  \n",
       "70052               NaN                                      NaN  \n",
       "70053               NaN                                      NaN  \n",
       "70054               NaN                                      NaN  \n",
       "70055               NaN                                      NaN  \n",
       "70056               NaN                                      NaN  \n",
       "\n",
       "[762 rows x 67 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "c_covid = covid_word[covid_word['location'] == \"India\"]\n",
    "c_covid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fadcd23",
   "metadata": {
    "id": "orrIf1ImjolQ",
    "papermill": {
     "duration": 0.028596,
     "end_time": "2022-03-03T03:32:57.295396",
     "exception": false,
     "start_time": "2022-03-03T03:32:57.266800",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Elimination of missing data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aca745b",
   "metadata": {
    "id": "OyOgB9-NjolR",
    "papermill": {
     "duration": 0.027895,
     "end_time": "2022-03-03T03:32:57.351091",
     "exception": false,
     "start_time": "2022-03-03T03:32:57.323196",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As you can see, there are large fluctuations in the new cases plot. Let's try to make a forecast for these waves.\n",
    "First of all, we should delete missing data using **pandas.DataFrame.dropna()**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6c333f6",
   "metadata": {
    "id": "9GwadSLqjolS",
    "papermill": {
     "duration": 0.028496,
     "end_time": "2022-03-03T03:32:57.407983",
     "exception": false,
     "start_time": "2022-03-03T03:32:57.379487",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "723b2aa7",
   "metadata": {
    "id": "0g93hJUJjolT",
    "papermill": {
     "duration": 0.027647,
     "end_time": "2022-03-03T03:32:57.464321",
     "exception": false,
     "start_time": "2022-03-03T03:32:57.436674",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Data transformation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17c1b32a",
   "metadata": {
    "id": "kXxia8tOjolT",
    "papermill": {
     "duration": 0.028356,
     "end_time": "2022-03-03T03:32:57.520447",
     "exception": false,
     "start_time": "2022-03-03T03:32:57.492091",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    " To check for dependencies, it is necessary to perform a correlation analysis between them. This requires:\n",
    "\n",
    "1.  duplicating of data and moving it vertically down for a certain number of days (lag)\n",
    "2.  deleting the missing data at the beginning and end **pandas.DataFrame.shift()**\n",
    "3.  calculating the correlation coefficient between the obtained series.\n",
    "\n",
    "It is convenient to create a separate function for lag:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10e4180c",
   "metadata": {
    "id": "TsFjwyuLjolV",
    "papermill": {
     "duration": 0.027337,
     "end_time": "2022-03-03T03:32:57.575504",
     "exception": false,
     "start_time": "2022-03-03T03:32:57.548167",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e450efb5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T03:32:57.638521Z",
     "iopub.status.busy": "2022-03-03T03:32:57.637544Z",
     "iopub.status.idle": "2022-03-03T03:32:57.958259Z",
     "shell.execute_reply": "2022-03-03T03:32:57.958861Z",
     "shell.execute_reply.started": "2022-03-03T03:32:18.290026Z"
    },
    "papermill": {
     "duration": 0.356,
     "end_time": "2022-03-03T03:32:57.959074",
     "exception": false,
     "start_time": "2022-03-03T03:32:57.603074",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fields = ['new_cases', 'date']\n",
    "c_covid[fields].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "049f22dd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T03:32:58.022574Z",
     "iopub.status.busy": "2022-03-03T03:32:58.021497Z",
     "iopub.status.idle": "2022-03-03T03:32:58.037966Z",
     "shell.execute_reply": "2022-03-03T03:32:58.038595Z",
     "shell.execute_reply.started": "2022-03-03T03:32:18.471113Z"
    },
    "papermill": {
     "duration": 0.05003,
     "end_time": "2022-03-03T03:32:58.038774",
     "exception": false,
     "start_time": "2022-03-03T03:32:57.988744",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "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>new_cases</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69295</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-01-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69296</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69297</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69298</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-02-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69299</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-02-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70052</th>\n",
       "      <td>11499.0</td>\n",
       "      <td>2022-02-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70053</th>\n",
       "      <td>10273.0</td>\n",
       "      <td>2022-02-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70054</th>\n",
       "      <td>8013.0</td>\n",
       "      <td>2022-02-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70055</th>\n",
       "      <td>6915.0</td>\n",
       "      <td>2022-02-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70056</th>\n",
       "      <td>7554.0</td>\n",
       "      <td>2022-03-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>762 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       new_cases       date\n",
       "69295        1.0 2020-01-30\n",
       "69296        0.0 2020-01-31\n",
       "69297        0.0 2020-02-01\n",
       "69298        1.0 2020-02-02\n",
       "69299        1.0 2020-02-03\n",
       "...          ...        ...\n",
       "70052    11499.0 2022-02-25\n",
       "70053    10273.0 2022-02-26\n",
       "70054     8013.0 2022-02-27\n",
       "70055     6915.0 2022-02-28\n",
       "70056     7554.0 2022-03-01\n",
       "\n",
       "[762 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_covid = c_covid[fields].dropna()\n",
    "c_covid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f35a648b",
   "metadata": {
    "papermill": {
     "duration": 0.030366,
     "end_time": "2022-03-03T03:32:58.099074",
     "exception": false,
     "start_time": "2022-03-03T03:32:58.068708",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5737ebb5",
   "metadata": {
    "id": "zEWzS05xjolX",
    "papermill": {
     "duration": 0.030396,
     "end_time": "2022-03-03T03:32:58.159672",
     "exception": false,
     "start_time": "2022-03-03T03:32:58.129276",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let's create a target DataSet.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "09732f4b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T03:32:58.224957Z",
     "iopub.status.busy": "2022-03-03T03:32:58.223797Z",
     "iopub.status.idle": "2022-03-03T03:32:58.238882Z",
     "shell.execute_reply": "2022-03-03T03:32:58.239391Z",
     "shell.execute_reply.started": "2022-03-03T03:32:18.487286Z"
    },
    "id": "x0dTv-hAjolY",
    "papermill": {
     "duration": 0.050054,
     "end_time": "2022-03-03T03:32:58.239590",
     "exception": false,
     "start_time": "2022-03-03T03:32:58.189536",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "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",
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       "      <th>new_cases</th>\n",
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       "      <th>69297</th>\n",
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       "      <th>69298</th>\n",
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       "      <th>69299</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-02-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70052</th>\n",
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       "      <th>70053</th>\n",
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       "      <td>2022-02-28</td>\n",
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       "      <th>70056</th>\n",
       "      <td>7554.0</td>\n",
       "      <td>2022-03-01</td>\n",
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       "</table>\n",
       "<p>762 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       new_cases       date\n",
       "69295        1.0 2020-01-30\n",
       "69296        0.0 2020-01-31\n",
       "69297        0.0 2020-02-01\n",
       "69298        1.0 2020-02-02\n",
       "69299        1.0 2020-02-03\n",
       "...          ...        ...\n",
       "70052    11499.0 2022-02-25\n",
       "70053    10273.0 2022-02-26\n",
       "70054     8013.0 2022-02-27\n",
       "70055     6915.0 2022-02-28\n",
       "70056     7554.0 2022-03-01\n",
       "\n",
       "[762 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_dataset = c_covid[['new_cases','date']]\n",
    "y_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9510aaf0",
   "metadata": {
    "papermill": {
     "duration": 0.031415,
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     "start_time": "2022-03-03T03:32:58.269342",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Phophet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8eade4e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T03:32:58.366639Z",
     "iopub.status.busy": "2022-03-03T03:32:58.365603Z",
     "iopub.status.idle": "2022-03-03T03:32:58.774697Z",
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from fbprophet import Prophet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2e1b6311",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T03:32:58.843614Z",
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    },
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     "exception": false,
     "start_time": "2022-03-03T03:32:58.807468",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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      "text/plain": [
       "         y         ds\n",
       "69295  1.0 2020-01-30\n",
       "69296  0.0 2020-01-31\n",
       "69297  0.0 2020-02-01\n",
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       "69299  1.0 2020-02-03"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb_confirm = y_dataset.rename(columns={'date': 'ds',\n",
    "                        'new_cases': 'y'})\n",
    "\n",
    "fb_confirm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b8fa8101",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T03:32:58.920554Z",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial log joint probability = -22.9903\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x7f8aaf62e0d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
      "      75       1106.92    0.00716648       119.438   9.592e-05       0.001      135  LS failed, Hessian reset \n",
      "      99        1107.7    0.00718274       81.9743           1           1      162   \n",
      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
      "     123       1107.93   0.000161125       59.3405   2.446e-06       0.001      236  LS failed, Hessian reset \n",
      "     151       1108.02   0.000684647       66.2539   8.095e-06       0.001      319  LS failed, Hessian reset \n",
      "     199       1108.07   9.90012e-06       80.4482           1           1      386   \n",
      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
      "     267       1108.79    0.00124393       70.5795   1.457e-05       0.001      523  LS failed, Hessian reset \n",
      "     299       1108.88   2.96698e-05       77.1269      0.4355      0.4355      566   \n",
      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
      "     340       1108.89   1.16277e-05       71.1033    1.69e-07       0.001      672  LS failed, Hessian reset \n",
      "     399       1108.92   0.000638132        55.737           1           1      745   \n",
      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
      "     499       1108.94   2.47934e-05       76.7466      0.4312      0.4312      887   \n",
      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
      "     528       1109.22     0.0012164       92.0166   8.896e-06       0.001      963  LS failed, Hessian reset \n",
      "     599       1109.39    6.6822e-05       78.0442           1           1     1052   \n",
      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
      "     614       1109.41   3.50497e-05       60.2326   4.867e-07       0.001     1111  LS failed, Hessian reset \n",
      "     686       1109.42   2.39407e-09       68.1267    0.007011           1     1212   \n",
      "Optimization terminated normally: \n",
      "  Convergence detected: absolute parameter change was below tolerance\n"
     ]
    }
   ],
   "source": [
    "# evaluate prophet time series forecasting model on hold out dataset\n",
    "from pandas import read_csv\n",
    "from pandas import to_datetime\n",
    "from pandas import DataFrame\n",
    "from fbprophet import Prophet\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from matplotlib import pyplot\n",
    "# load data\n",
    "df=fb_confirm\n",
    "m = Prophet()\n",
    "m.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5a44f50f",
   "metadata": {
    "execution": {
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   "outputs": [
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       "      <td>104929.759958</td>\n",
       "      <td>-13684.591868</td>\n",
       "      <td>-13684.591868</td>\n",
       "      <td>-13684.591868</td>\n",
       "      <td>1915.727835</td>\n",
       "      <td>1915.727835</td>\n",
       "      <td>1915.727835</td>\n",
       "      <td>-15600.319703</td>\n",
       "      <td>-15600.319703</td>\n",
       "      <td>-15600.319703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91165.190009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>2022-03-13</td>\n",
       "      <td>105036.454435</td>\n",
       "      <td>19016.333562</td>\n",
       "      <td>159192.060561</td>\n",
       "      <td>104944.218224</td>\n",
       "      <td>105143.907949</td>\n",
       "      <td>-16055.020556</td>\n",
       "      <td>-16055.020556</td>\n",
       "      <td>-16055.020556</td>\n",
       "      <td>-949.055733</td>\n",
       "      <td>-949.055733</td>\n",
       "      <td>-949.055733</td>\n",
       "      <td>-15105.964824</td>\n",
       "      <td>-15105.964824</td>\n",
       "      <td>-15105.964824</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88981.433878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>2022-03-14</td>\n",
       "      <td>105223.126993</td>\n",
       "      <td>11232.425505</td>\n",
       "      <td>152522.241572</td>\n",
       "      <td>105112.341075</td>\n",
       "      <td>105354.394963</td>\n",
       "      <td>-22271.709388</td>\n",
       "      <td>-22271.709388</td>\n",
       "      <td>-22271.709388</td>\n",
       "      <td>-7571.645684</td>\n",
       "      <td>-7571.645684</td>\n",
       "      <td>-7571.645684</td>\n",
       "      <td>-14700.063704</td>\n",
       "      <td>-14700.063704</td>\n",
       "      <td>-14700.063704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82951.417604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>2022-03-15</td>\n",
       "      <td>105409.799551</td>\n",
       "      <td>13324.390482</td>\n",
       "      <td>159087.474927</td>\n",
       "      <td>105271.711741</td>\n",
       "      <td>105568.255429</td>\n",
       "      <td>-16037.878801</td>\n",
       "      <td>-16037.878801</td>\n",
       "      <td>-16037.878801</td>\n",
       "      <td>-1679.415918</td>\n",
       "      <td>-1679.415918</td>\n",
       "      <td>-1679.415918</td>\n",
       "      <td>-14358.462883</td>\n",
       "      <td>-14358.462883</td>\n",
       "      <td>-14358.462883</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89371.920749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>776 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds          trend    yhat_lower     yhat_upper    trend_lower  \\\n",
       "0   2020-01-30  -27677.118292 -60408.357087   82281.084313  -27677.118292   \n",
       "1   2020-01-31  -27481.916948 -63057.448720   75332.696934  -27481.916948   \n",
       "2   2020-02-01  -27286.715604 -68538.772594   71486.111165  -27286.715604   \n",
       "3   2020-02-02  -27091.514260 -83707.550713   63081.390984  -27091.514260   \n",
       "4   2020-02-03  -26896.312917 -95921.516267   48665.523337  -26896.312917   \n",
       "..         ...            ...           ...            ...            ...   \n",
       "771 2022-03-11  104663.109319  24684.725787  159604.462084  104618.751946   \n",
       "772 2022-03-12  104849.781877  18425.640275  163161.410220  104778.115144   \n",
       "773 2022-03-13  105036.454435  19016.333562  159192.060561  104944.218224   \n",
       "774 2022-03-14  105223.126993  11232.425505  152522.241572  105112.341075   \n",
       "775 2022-03-15  105409.799551  13324.390482  159087.474927  105271.711741   \n",
       "\n",
       "       trend_upper  additive_terms  additive_terms_lower  \\\n",
       "0    -27677.118292    40562.542242          40562.542242   \n",
       "1    -27481.916948    33936.110914          33936.110914   \n",
       "2    -27286.715604    27434.724765          27434.724765   \n",
       "3    -27091.514260    18100.425045          18100.425045   \n",
       "4    -26896.312917     4931.540053           4931.540053   \n",
       "..             ...             ...                   ...   \n",
       "771  104719.860859   -14085.153697         -14085.153697   \n",
       "772  104929.759958   -13684.591868         -13684.591868   \n",
       "773  105143.907949   -16055.020556         -16055.020556   \n",
       "774  105354.394963   -22271.709388         -22271.709388   \n",
       "775  105568.255429   -16037.878801         -16037.878801   \n",
       "\n",
       "     additive_terms_upper       weekly  weekly_lower  weekly_upper  \\\n",
       "0            40562.542242  2716.970665   2716.970665   2716.970665   \n",
       "1            33936.110914  2119.832241   2119.832241   2119.832241   \n",
       "2            27434.724765  1915.727835   1915.727835   1915.727835   \n",
       "3            18100.425045  -949.055733   -949.055733   -949.055733   \n",
       "4             4931.540053 -7571.645684  -7571.645684  -7571.645684   \n",
       "..                    ...          ...           ...           ...   \n",
       "771         -14085.153697  2119.832241   2119.832241   2119.832241   \n",
       "772         -13684.591868  1915.727835   1915.727835   1915.727835   \n",
       "773         -16055.020556  -949.055733   -949.055733   -949.055733   \n",
       "774         -22271.709388 -7571.645684  -7571.645684  -7571.645684   \n",
       "775         -16037.878801 -1679.415918  -1679.415918  -1679.415918   \n",
       "\n",
       "           yearly  yearly_lower  yearly_upper  multiplicative_terms  \\\n",
       "0    37845.571577  37845.571577  37845.571577                   0.0   \n",
       "1    31816.278673  31816.278673  31816.278673                   0.0   \n",
       "2    25518.996930  25518.996930  25518.996930                   0.0   \n",
       "3    19049.480777  19049.480777  19049.480777                   0.0   \n",
       "4    12503.185737  12503.185737  12503.185737                   0.0   \n",
       "..            ...           ...           ...                   ...   \n",
       "771 -16204.985939 -16204.985939 -16204.985939                   0.0   \n",
       "772 -15600.319703 -15600.319703 -15600.319703                   0.0   \n",
       "773 -15105.964824 -15105.964824 -15105.964824                   0.0   \n",
       "774 -14700.063704 -14700.063704 -14700.063704                   0.0   \n",
       "775 -14358.462883 -14358.462883 -14358.462883                   0.0   \n",
       "\n",
       "     multiplicative_terms_lower  multiplicative_terms_upper          yhat  \n",
       "0                           0.0                         0.0  12885.423950  \n",
       "1                           0.0                         0.0   6454.193966  \n",
       "2                           0.0                         0.0    148.009161  \n",
       "3                           0.0                         0.0  -8991.089216  \n",
       "4                           0.0                         0.0 -21964.772864  \n",
       "..                          ...                         ...           ...  \n",
       "771                         0.0                         0.0  90577.955621  \n",
       "772                         0.0                         0.0  91165.190009  \n",
       "773                         0.0                         0.0  88981.433878  \n",
       "774                         0.0                         0.0  82951.417604  \n",
       "775                         0.0                         0.0  89371.920749  \n",
       "\n",
       "[776 rows x 19 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future = m.make_future_dataframe(periods= 14, freq='D')\n",
    "forecast = m.predict(future)\n",
    "forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper', 'trend', 'trend_lower', 'trend_upper']].tail()\n",
    "forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a1f17afc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T03:33:06.381819Z",
     "iopub.status.busy": "2022-03-03T03:33:06.380982Z",
     "iopub.status.idle": "2022-03-03T03:33:06.663664Z",
     "shell.execute_reply": "2022-03-03T03:33:06.664458Z",
     "shell.execute_reply.started": "2022-03-03T03:32:20.811820Z"
    },
    "papermill": {
     "duration": 0.335355,
     "end_time": "2022-03-03T03:33:06.664697",
     "exception": false,
     "start_time": "2022-03-03T03:33:06.329342",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = m.plot(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4a95fe64",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T03:33:06.745689Z",
     "iopub.status.busy": "2022-03-03T03:33:06.744150Z",
     "iopub.status.idle": "2022-03-03T03:33:07.563275Z",
     "shell.execute_reply": "2022-03-03T03:33:07.563787Z",
     "shell.execute_reply.started": "2022-03-03T03:32:21.050332Z"
    },
    "papermill": {
     "duration": 0.860016,
     "end_time": "2022-03-03T03:33:07.563965",
     "exception": false,
     "start_time": "2022-03-03T03:33:06.703949",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = m.plot_components(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3fde36fc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-03T03:33:07.644588Z",
     "iopub.status.busy": "2022-03-03T03:33:07.643742Z",
     "iopub.status.idle": "2022-03-03T03:33:08.003974Z",
     "shell.execute_reply": "2022-03-03T03:33:08.004509Z",
     "shell.execute_reply.started": "2022-03-03T03:32:21.704610Z"
    },
    "papermill": {
     "duration": 0.402045,
     "end_time": "2022-03-03T03:33:08.004688",
     "exception": false,
     "start_time": "2022-03-03T03:33:07.602643",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from fbprophet.plot import add_changepoints_to_plot\n",
    "fig = m.plot(forecast)\n",
    "a = add_changepoints_to_plot(fig.gca(), m, forecast)"
   ]
  }
 ],
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