notebook/covid.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COVID19: EDA and forecasting\n",
    "## Data\n",
    "All the data in this notebook are extracted in from Kaggle with origin the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). \n",
    "Link: https://www.kaggle.com/c/covid19-global-forecasting-week-5/data?select=train.csv\n",
    "\n",
    "## Aims\n",
    "- **EAD**: I show several visualizations of the spreading of the COVID19 around the world. These visualizations include the evolution of deaths and cases for each country;\n",
    "- **forecasting**: several world countries have already passed the peak of cases and deaths.Other still not. Here, my aim to find the peak of deaths in Brazil. \n",
    "\n",
    "## Documentation and links\n",
    "The project is published at:\n",
    "- **Medium link:** https://medium.com/@operti.felipe/covid19-brazilians-pandemic-peak-forecasting-885373cf22d6\n",
    "- **Github:** https://github.com/felipeoperti/covid19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import data\n",
    "data = pd.read_csv(\"../data/train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>County</th>\n",
       "      <th>Province_State</th>\n",
       "      <th>Country_Region</th>\n",
       "      <th>Population</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Date</th>\n",
       "      <th>Target</th>\n",
       "      <th>TargetValue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>27657145</td>\n",
       "      <td>0.058359</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>ConfirmedCases</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>27657145</td>\n",
       "      <td>0.583587</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>27657145</td>\n",
       "      <td>0.058359</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>ConfirmedCases</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>27657145</td>\n",
       "      <td>0.583587</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>27657145</td>\n",
       "      <td>0.058359</td>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>ConfirmedCases</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id County Province_State Country_Region  Population    Weight        Date  \\\n",
       "0   1    NaN            NaN    Afghanistan    27657145  0.058359  2020-01-23   \n",
       "1   2    NaN            NaN    Afghanistan    27657145  0.583587  2020-01-23   \n",
       "2   3    NaN            NaN    Afghanistan    27657145  0.058359  2020-01-24   \n",
       "3   4    NaN            NaN    Afghanistan    27657145  0.583587  2020-01-24   \n",
       "4   5    NaN            NaN    Afghanistan    27657145  0.058359  2020-01-25   \n",
       "\n",
       "           Target  TargetValue  \n",
       "0  ConfirmedCases          0.0  \n",
       "1      Fatalities          0.0  \n",
       "2  ConfirmedCases          0.0  \n",
       "3      Fatalities          0.0  \n",
       "4  ConfirmedCases          0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(796490, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'County', 'Province_State', 'Country_Region', 'Population',\n",
       "       'Weight', 'Date', 'Target', 'TargetValue'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries = data[\"Country_Region\"].unique()\n",
    "targets = data[\"Target\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola',\n",
       "       'Antigua and Barbuda', 'Argentina', 'Armenia', 'Australia',\n",
       "       'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh',\n",
       "       'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan',\n",
       "       'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil',\n",
       "       'Brunei', 'Bulgaria', 'Burkina Faso', 'Burma', 'Burundi',\n",
       "       'Cabo Verde', 'Cambodia', 'Cameroon', 'Canada',\n",
       "       'Central African Republic', 'Chad', 'Chile', 'China', 'Colombia',\n",
       "       'Comoros', 'Congo (Brazzaville)', 'Congo (Kinshasa)', 'Costa Rica',\n",
       "       \"Cote d'Ivoire\", 'Croatia', 'Cuba', 'Cyprus', 'Czechia', 'Denmark',\n",
       "       'Diamond Princess', 'Djibouti', 'Dominica', 'Dominican Republic',\n",
       "       'Ecuador', 'Egypt', 'El Salvador', 'Equatorial Guinea', 'Eritrea',\n",
       "       'Estonia', 'Eswatini', 'Ethiopia', 'Fiji', 'Finland', 'France',\n",
       "       'Gabon', 'Gambia', 'Georgia', 'Germany', 'Ghana', 'Greece',\n",
       "       'Grenada', 'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana',\n",
       "       'Haiti', 'Holy See', 'Honduras', 'Hungary', 'Iceland', 'India',\n",
       "       'Indonesia', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy',\n",
       "       'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya',\n",
       "       'Korea, South', 'Kosovo', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia',\n",
       "       'Lebanon', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania',\n",
       "       'Luxembourg', 'MS Zaandam', 'Madagascar', 'Malawi', 'Malaysia',\n",
       "       'Maldives', 'Mali', 'Malta', 'Mauritania', 'Mauritius', 'Mexico',\n",
       "       'Moldova', 'Monaco', 'Mongolia', 'Montenegro', 'Morocco',\n",
       "       'Mozambique', 'Namibia', 'Nepal', 'Netherlands', 'New Zealand',\n",
       "       'Nicaragua', 'Niger', 'Nigeria', 'North Macedonia', 'Norway',\n",
       "       'Oman', 'Pakistan', 'Panama', 'Papua New Guinea', 'Paraguay',\n",
       "       'Peru', 'Philippines', 'Poland', 'Portugal', 'Qatar', 'Romania',\n",
       "       'Russia', 'Rwanda', 'Saint Kitts and Nevis', 'Saint Lucia',\n",
       "       'Saint Vincent and the Grenadines', 'San Marino',\n",
       "       'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia',\n",
       "       'Seychelles', 'Sierra Leone', 'Singapore', 'Slovakia', 'Slovenia',\n",
       "       'Somalia', 'South Africa', 'South Sudan', 'Spain', 'Sri Lanka',\n",
       "       'Sudan', 'Suriname', 'Sweden', 'Switzerland', 'Syria', 'Taiwan*',\n",
       "       'Tajikistan', 'Tanzania', 'Thailand', 'Timor-Leste', 'Togo',\n",
       "       'Trinidad and Tobago', 'Tunisia', 'Turkey', 'US', 'Uganda',\n",
       "       'Ukraine', 'United Arab Emirates', 'United Kingdom', 'Uruguay',\n",
       "       'Uzbekistan', 'Venezuela', 'Vietnam', 'West Bank and Gaza',\n",
       "       'Western Sahara', 'Yemen', 'Zambia', 'Zimbabwe'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional info (mean age and GDP) for several countries\n",
    "countries_dict = {\"Italy\":{\n",
    "                      \"mean_age\":45.5,\n",
    "                      \"GDP\":1988636},\n",
    "                  \"Brazil\":{\n",
    "                      \"mean_age\":32.6,\n",
    "                      \"GDP\":1847020},\n",
    "                  \"US\":{\n",
    "                      \"mean_age\":38.1,\n",
    "                      \"GDP\":21439453},\n",
    "                  \"United Kingdom\":{\n",
    "                      \"mean_age\":40.5,\n",
    "                      \"GDP\":2743586},\n",
    "                  \"Portugal\":{\n",
    "                      \"mean_age\":42.2,\n",
    "                      \"GDP\":236408},\n",
    "                  \"Canada\":{\n",
    "                      \"mean_age\":42.2,\n",
    "                      \"GDP\":1730914},\n",
    "                  \"Austria\":{\n",
    "                      \"mean_age\":44.0,\n",
    "                      \"GDP\":447718},\n",
    "                  \"Belgium\":{\n",
    "                      \"mean_age\":41.4,\n",
    "                      \"GDP\":517609},\n",
    "                  \"Germany\":{\n",
    "                      \"mean_age\":47.1,\n",
    "                      \"GDP\":3863344},\n",
    "                  \"Greece\":{\n",
    "                      \"mean_age\":44.5,\n",
    "                      \"GDP\":214012},\n",
    "                  \"Finland\":{\n",
    "                      \"mean_age\":42.5,\n",
    "                      \"GDP\":269654}, \n",
    "                  \"France\":{\n",
    "                      \"mean_age\":41.4,\n",
    "                      \"GDP\":2707074},\n",
    "                  \"Netherlands\":{\n",
    "                      \"mean_age\":42.6,\n",
    "                      \"GDP\":902355},\n",
    "                  \"Russia\":{\n",
    "                      \"mean_age\":39.6,\n",
    "                      \"GDP\":1637892},\n",
    "                  \"Poland\":{\n",
    "                      \"mean_age\":40.7,\n",
    "                      \"GDP\":565854},\n",
    "                  \"Sweden\":{\n",
    "                      \"mean_age\":41.2,\n",
    "                      \"GDP\":528929},\n",
    "                  \"Spain\":{\n",
    "                      \"mean_age\":42.7,\n",
    "                      \"GDP\":1397870},\n",
    "                  \"Japan\":{\n",
    "                      \"mean_age\":47.3,\n",
    "                      \"GDP\":5154475},\n",
    "                  \"Korea, South\":{\n",
    "                      \"mean_age\":41.8,\n",
    "                      \"GDP\":1629532},\n",
    "                  \"China\":{\n",
    "                      \"mean_age\":37.4,\n",
    "                      \"GDP\":14140163\n",
    "                     \n",
    "                  }\n",
    "                  \n",
    "                }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove data from counties and province. I am only interested in Countries data\n",
    "data['Date'] =  pd.to_datetime(data['Date'], format='%Y-%m-%d')\n",
    "data = data[(data[\"County\"].isnull()) & (data[\"Province_State\"].isnull())] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.058359</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>ConfirmedCases</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <td>ConfirmedCases</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>Fatalities</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Zimbabwe</td>\n",
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       "      <td>2020-05-14</td>\n",
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       "    <tr>\n",
       "      <th>796486</th>\n",
       "      <td>969587</td>\n",
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       "      <td>Zimbabwe</td>\n",
       "      <td>14240168</td>\n",
       "      <td>0.060711</td>\n",
       "      <td>2020-05-15</td>\n",
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       "      <td>Zimbabwe</td>\n",
       "      <td>14240168</td>\n",
       "      <td>0.607106</td>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>Fatalities</td>\n",
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       "      <td>Zimbabwe</td>\n",
       "      <td>14240168</td>\n",
       "      <td>0.060711</td>\n",
       "      <td>2020-05-16</td>\n",
       "      <td>ConfirmedCases</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>14240168</td>\n",
       "      <td>0.607106</td>\n",
       "      <td>2020-05-16</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43010 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Id County Province_State Country_Region  Population    Weight  \\\n",
       "0            1    NaN            NaN    Afghanistan    27657145  0.058359   \n",
       "1            2    NaN            NaN    Afghanistan    27657145  0.583587   \n",
       "2            3    NaN            NaN    Afghanistan    27657145  0.058359   \n",
       "3            4    NaN            NaN    Afghanistan    27657145  0.583587   \n",
       "4            5    NaN            NaN    Afghanistan    27657145  0.058359   \n",
       "...        ...    ...            ...            ...         ...       ...   \n",
       "796485  969586    NaN            NaN       Zimbabwe    14240168  0.607106   \n",
       "796486  969587    NaN            NaN       Zimbabwe    14240168  0.060711   \n",
       "796487  969588    NaN            NaN       Zimbabwe    14240168  0.607106   \n",
       "796488  969589    NaN            NaN       Zimbabwe    14240168  0.060711   \n",
       "796489  969590    NaN            NaN       Zimbabwe    14240168  0.607106   \n",
       "\n",
       "             Date          Target  TargetValue  \n",
       "0      2020-01-23  ConfirmedCases          0.0  \n",
       "1      2020-01-23      Fatalities          0.0  \n",
       "2      2020-01-24  ConfirmedCases          0.0  \n",
       "3      2020-01-24      Fatalities          0.0  \n",
       "4      2020-01-25  ConfirmedCases          0.0  \n",
       "...           ...             ...          ...  \n",
       "796485 2020-05-14      Fatalities          0.0  \n",
       "796486 2020-05-15  ConfirmedCases          5.0  \n",
       "796487 2020-05-15      Fatalities          0.0  \n",
       "796488 2020-05-16  ConfirmedCases          0.0  \n",
       "796489 2020-05-16      Fatalities          0.0  \n",
       "\n",
       "[43010 rows x 9 columns]"
      ]
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataframe_country_target(data, country, target, countries_dict):\n",
    "    \"\"\"\n",
    "    This function create a dataframe for a country\n",
    "    Args:\n",
    "        - data: dataframe with all the data\n",
    "        - country: country to analyze\n",
    "        - targte: target to add. Could be \"Fatalities\" or \"ConfirmedCases\"\n",
    "        - countries_dict: dictionary with additional info\n",
    "    Return:\n",
    "        A dataframe for the country with the target value e additional info\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    df_country = data[(data.Country_Region == country) & (data.Target == target)]\n",
    "    df_country = df_country.groupby([\"Country_Region\",\"Target\",\"Date\"]).agg({\"Population\":\"max\",\n",
    "                                                            \"TargetValue\":\"sum\"\n",
    "                                                           })\n",
    "    df_country = df_country.reset_index().set_index(\"Date\",drop=True)\n",
    "    df_country[\"mean_age\"] = countries_dict[country][\"mean_age\"]\n",
    "    df_country[\"GDP\"] = countries_dict[country][\"GDP\"]\n",
    "    df_country[\"Rate_over_population\"] =  df_country[\"TargetValue\"]*100000/df_country[\"Population\"]\n",
    "    return df_country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some example\n",
    "df_Italy = create_dataframe_country_target(data, \"Italy\", \"Fatalities\",countries_dict)\n",
    "df_Brazil = create_dataframe_country_target(data, \"Brazil\", \"Fatalities\",countries_dict)\n",
    "df_US = create_dataframe_country_target(data, \"US\", \"Fatalities\",countries_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "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>Country_Region</th>\n",
       "      <th>Target</th>\n",
       "      <th>Population</th>\n",
       "      <th>TargetValue</th>\n",
       "      <th>mean_age</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Rate_over_population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-13</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>7.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.002160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-14</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>7.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.002160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-15</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>9.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.002777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-16</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>22.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.006787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-17</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>23.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.007096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-18</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>10.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.003085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-19</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>82.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.025298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-20</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>44.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.013574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-21</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>63.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.019436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-22</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>119.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.036712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-23</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>125.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.038563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-24</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>154.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.047510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-25</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>236.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.072808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-26</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>267.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.082371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-27</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>370.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.114148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-28</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>445.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.137286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-29</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>441.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.136052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-30</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>511.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.157647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>895.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.276114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>883.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.272412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-02</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>1169.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.360645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-03</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>1161.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.358177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-04</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>1320.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.407230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-05</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.373911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-06</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>1164.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.359102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-07</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>1938.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.597887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-08</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.608685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-09</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>1783.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.550068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-10</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>2108.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.650333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-11</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>1876.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.578760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-12</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>1560.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.481271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-13</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>1509.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.465537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-14</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>2303.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.710492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-15</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>2494.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.769417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-16</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>4591.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>1.416357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-17</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>3857.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>1.189912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-18</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>1891.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.583387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-19</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.616089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-20</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>1433.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.442091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-21</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>2350.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.724992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-22</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>2178.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.671929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-23</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>3329.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>1.027021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-24</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.615472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-25</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>1806.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.557164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-26</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>1126.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.347379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-27</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>1378.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.425123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-28</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>2096.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.646631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-29</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>2612.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.805821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-30</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>2029.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.625961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-01</th>\n",
       "      <td>US</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>324141489</td>\n",
       "      <td>1947.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>21439453</td>\n",
       "      <td>0.600664</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country_Region      Target  Population  TargetValue  mean_age  \\\n",
       "Date                                                                       \n",
       "2020-03-13             US  Fatalities   324141489          7.0      38.1   \n",
       "2020-03-14             US  Fatalities   324141489          7.0      38.1   \n",
       "2020-03-15             US  Fatalities   324141489          9.0      38.1   \n",
       "2020-03-16             US  Fatalities   324141489         22.0      38.1   \n",
       "2020-03-17             US  Fatalities   324141489         23.0      38.1   \n",
       "2020-03-18             US  Fatalities   324141489         10.0      38.1   \n",
       "2020-03-19             US  Fatalities   324141489         82.0      38.1   \n",
       "2020-03-20             US  Fatalities   324141489         44.0      38.1   \n",
       "2020-03-21             US  Fatalities   324141489         63.0      38.1   \n",
       "2020-03-22             US  Fatalities   324141489        119.0      38.1   \n",
       "2020-03-23             US  Fatalities   324141489        125.0      38.1   \n",
       "2020-03-24             US  Fatalities   324141489        154.0      38.1   \n",
       "2020-03-25             US  Fatalities   324141489        236.0      38.1   \n",
       "2020-03-26             US  Fatalities   324141489        267.0      38.1   \n",
       "2020-03-27             US  Fatalities   324141489        370.0      38.1   \n",
       "2020-03-28             US  Fatalities   324141489        445.0      38.1   \n",
       "2020-03-29             US  Fatalities   324141489        441.0      38.1   \n",
       "2020-03-30             US  Fatalities   324141489        511.0      38.1   \n",
       "2020-03-31             US  Fatalities   324141489        895.0      38.1   \n",
       "2020-04-01             US  Fatalities   324141489        883.0      38.1   \n",
       "2020-04-02             US  Fatalities   324141489       1169.0      38.1   \n",
       "2020-04-03             US  Fatalities   324141489       1161.0      38.1   \n",
       "2020-04-04             US  Fatalities   324141489       1320.0      38.1   \n",
       "2020-04-05             US  Fatalities   324141489       1212.0      38.1   \n",
       "2020-04-06             US  Fatalities   324141489       1164.0      38.1   \n",
       "2020-04-07             US  Fatalities   324141489       1938.0      38.1   \n",
       "2020-04-08             US  Fatalities   324141489       1973.0      38.1   \n",
       "2020-04-09             US  Fatalities   324141489       1783.0      38.1   \n",
       "2020-04-10             US  Fatalities   324141489       2108.0      38.1   \n",
       "2020-04-11             US  Fatalities   324141489       1876.0      38.1   \n",
       "2020-04-12             US  Fatalities   324141489       1560.0      38.1   \n",
       "2020-04-13             US  Fatalities   324141489       1509.0      38.1   \n",
       "2020-04-14             US  Fatalities   324141489       2303.0      38.1   \n",
       "2020-04-15             US  Fatalities   324141489       2494.0      38.1   \n",
       "2020-04-16             US  Fatalities   324141489       4591.0      38.1   \n",
       "2020-04-17             US  Fatalities   324141489       3857.0      38.1   \n",
       "2020-04-18             US  Fatalities   324141489       1891.0      38.1   \n",
       "2020-04-19             US  Fatalities   324141489       1997.0      38.1   \n",
       "2020-04-20             US  Fatalities   324141489       1433.0      38.1   \n",
       "2020-04-21             US  Fatalities   324141489       2350.0      38.1   \n",
       "2020-04-22             US  Fatalities   324141489       2178.0      38.1   \n",
       "2020-04-23             US  Fatalities   324141489       3329.0      38.1   \n",
       "2020-04-24             US  Fatalities   324141489       1995.0      38.1   \n",
       "2020-04-25             US  Fatalities   324141489       1806.0      38.1   \n",
       "2020-04-26             US  Fatalities   324141489       1126.0      38.1   \n",
       "2020-04-27             US  Fatalities   324141489       1378.0      38.1   \n",
       "2020-04-28             US  Fatalities   324141489       2096.0      38.1   \n",
       "2020-04-29             US  Fatalities   324141489       2612.0      38.1   \n",
       "2020-04-30             US  Fatalities   324141489       2029.0      38.1   \n",
       "2020-05-01             US  Fatalities   324141489       1947.0      38.1   \n",
       "\n",
       "                 GDP  Rate_over_population  \n",
       "Date                                        \n",
       "2020-03-13  21439453              0.002160  \n",
       "2020-03-14  21439453              0.002160  \n",
       "2020-03-15  21439453              0.002777  \n",
       "2020-03-16  21439453              0.006787  \n",
       "2020-03-17  21439453              0.007096  \n",
       "2020-03-18  21439453              0.003085  \n",
       "2020-03-19  21439453              0.025298  \n",
       "2020-03-20  21439453              0.013574  \n",
       "2020-03-21  21439453              0.019436  \n",
       "2020-03-22  21439453              0.036712  \n",
       "2020-03-23  21439453              0.038563  \n",
       "2020-03-24  21439453              0.047510  \n",
       "2020-03-25  21439453              0.072808  \n",
       "2020-03-26  21439453              0.082371  \n",
       "2020-03-27  21439453              0.114148  \n",
       "2020-03-28  21439453              0.137286  \n",
       "2020-03-29  21439453              0.136052  \n",
       "2020-03-30  21439453              0.157647  \n",
       "2020-03-31  21439453              0.276114  \n",
       "2020-04-01  21439453              0.272412  \n",
       "2020-04-02  21439453              0.360645  \n",
       "2020-04-03  21439453              0.358177  \n",
       "2020-04-04  21439453              0.407230  \n",
       "2020-04-05  21439453              0.373911  \n",
       "2020-04-06  21439453              0.359102  \n",
       "2020-04-07  21439453              0.597887  \n",
       "2020-04-08  21439453              0.608685  \n",
       "2020-04-09  21439453              0.550068  \n",
       "2020-04-10  21439453              0.650333  \n",
       "2020-04-11  21439453              0.578760  \n",
       "2020-04-12  21439453              0.481271  \n",
       "2020-04-13  21439453              0.465537  \n",
       "2020-04-14  21439453              0.710492  \n",
       "2020-04-15  21439453              0.769417  \n",
       "2020-04-16  21439453              1.416357  \n",
       "2020-04-17  21439453              1.189912  \n",
       "2020-04-18  21439453              0.583387  \n",
       "2020-04-19  21439453              0.616089  \n",
       "2020-04-20  21439453              0.442091  \n",
       "2020-04-21  21439453              0.724992  \n",
       "2020-04-22  21439453              0.671929  \n",
       "2020-04-23  21439453              1.027021  \n",
       "2020-04-24  21439453              0.615472  \n",
       "2020-04-25  21439453              0.557164  \n",
       "2020-04-26  21439453              0.347379  \n",
       "2020-04-27  21439453              0.425123  \n",
       "2020-04-28  21439453              0.646631  \n",
       "2020-04-29  21439453              0.805821  \n",
       "2020-04-30  21439453              0.625961  \n",
       "2020-05-01  21439453              0.600664  "
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   "metadata": {},
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country_Region</th>\n",
       "      <th>Target</th>\n",
       "      <th>Population</th>\n",
       "      <th>TargetValue</th>\n",
       "      <th>mean_age</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Rate_over_population</th>\n",
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       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>206135893</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.6</td>\n",
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       "      <th>2020-01-24</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>206135893</td>\n",
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       "      <td>1847020</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>206135893</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>206135893</td>\n",
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       "      <td>32.6</td>\n",
       "      <td>1847020</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-27</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Fatalities</td>\n",
       "      <td>206135893</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.6</td>\n",
       "      <td>1847020</td>\n",
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       "           Country_Region      Target  Population  TargetValue  mean_age  \\\n",
       "Date                                                                       \n",
       "2020-01-23         Brazil  Fatalities   206135893          0.0      32.6   \n",
       "2020-01-24         Brazil  Fatalities   206135893          0.0      32.6   \n",
       "2020-01-25         Brazil  Fatalities   206135893          0.0      32.6   \n",
       "2020-01-26         Brazil  Fatalities   206135893          0.0      32.6   \n",
       "2020-01-27         Brazil  Fatalities   206135893          0.0      32.6   \n",
       "\n",
       "                GDP  Rate_over_population  \n",
       "Date                                       \n",
       "2020-01-23  1847020                   0.0  \n",
       "2020-01-24  1847020                   0.0  \n",
       "2020-01-25  1847020                   0.0  \n",
       "2020-01-26  1847020                   0.0  \n",
       "2020-01-27  1847020                   0.0  "
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "# Plot deaths for each country\n",
    "import plotly.express as px\n",
    "fig = px.line()\n",
    "for country in list(countries_dict.keys()):\n",
    "    df = create_dataframe_country_target(data, country, \"Fatalities\",countries_dict)\n",
    "    fig.add_scatter(x=df.reset_index()['Date'], \n",
    "                y=df.reset_index()['TargetValue'],\n",
    "                mode='lines',   name=country)\n",
    "fig.update_layout(\n",
    "    xaxis_title=\"Time\",\n",
    "    yaxis_title=\"N. of deaths\",\n",
    "    font=dict(\n",
    "        family=\"Courier New, monospace\",\n",
    "        size=8,\n",
    "        color=\"#7f7f7f\"\n",
    "    )\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
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      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "# Plot deaths over 100K inhabitants for each country\n",
    "import plotly.express as px\n",
    "fig = px.line()\n",
    "for country in list(countries_dict.keys()):\n",
    "    df = create_dataframe_country_target(data, country, \"Fatalities\",countries_dict)\n",
    "    fig.add_scatter(x=df.reset_index()['Date'], \n",
    "                y=df.reset_index()['Rate_over_population'],\n",
    "                mode='lines',   name=country)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def create_training_df(df_country,n_days,target,population=True,mean_age=True,gdp=True):\n",
    "    \"\"\"\n",
    "    This function create a dataframe for training the models based in the number of lookback days.\n",
    "    Args:\n",
    "        - df_country: dataframe of the country\n",
    "        - n_days: number of days as lookback\n",
    "        - target: target column\n",
    "        - population: if True population data will be used\n",
    "        - mean_age: if True mean age data will be used\n",
    "        - gdp: if True gdp data will be used\n",
    "    Return:\n",
    "        A dataframe that could be trained in a machine learning model\n",
    "    \"\"\"\n",
    "    df_country = df_country.sort_index().reset_index(drop=False)\n",
    "    columns = [\"Target\",\"Date\"]+list(np.arange(0,n_days,1))\n",
    "    df_train = pd.DataFrame(columns=columns)\n",
    "    j=0    \n",
    "    for i in range(n_days,df_country.shape[0]):\n",
    "        df_train = df_train.append({'Target': df_country.loc[i,target],\n",
    "                                    'Date': df_country.loc[i,\"Date\"]\n",
    "                                   }, ignore_index=True)\n",
    "        for day in range(n_days):\n",
    "            df_train.loc[j,day] = df_country.loc[i-n_days+day,target]\n",
    "        j=j+1    \n",
    "    if population:\n",
    "        df_train[\"Population\"] = df_country[\"Population\"].max()\n",
    "    if mean_age:\n",
    "        df_train[\"mean_age\"] = df_country[\"mean_age\"].max()\n",
    "    if gdp:\n",
    "        df_train[\"GDP\"] = df_country[\"GDP\"].max()\n",
    "    return df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Italy', 'US', 'United Kingdom', 'Portugal', 'Canada', 'Austria', 'Belgium', 'Germany', 'Greece', 'Finland', 'France', 'Netherlands', 'Russia', 'Poland', 'Sweden', 'Spain', 'Japan', 'Korea, South', 'China']\n"
     ]
    }
   ],
   "source": [
    "# Prepare the training dataset using several countries. Brazil will be used as test.\n",
    "# I decided to use 30 days to predict the number of death at day 31\n",
    "countries_to_train = list(countries_dict.keys())\n",
    "countries_to_train.remove(\"Brazil\")\n",
    "print(countries_to_train)\n",
    "target = \"Fatalities\"\n",
    "n_days = 30\n",
    "target_deaths = \"Rate_over_population\"\n",
    "def create_training_countries(countries_to_train, data, target, target_deaths, n_days):\n",
    "    \"\"\"\n",
    "    This function create the training dataset for several countries\n",
    "    Args:\n",
    "        - countries_to_train: list with countries to use\n",
    "        - data: original data\n",
    "        - target: target to use. Ex: \"Fatalities\"\n",
    "        - target_deaths: target of deaths to use\n",
    "        - n_days: number of days to lookback\n",
    "    Return:\n",
    "        A dataframe with the traing data using several countries\n",
    "    \"\"\"\n",
    "    df_train = create_training_df(create_dataframe_country_target(data, countries_to_train[0], target,countries_dict),n_days, target=target_deaths)\n",
    "    for i in range(1, len(countries_to_train)):\n",
    "        df_train = pd.concat([df_train,\n",
    "                         create_training_df(create_dataframe_country_target(data, \n",
    "                                                                            countries_to_train[i], \n",
    "                                                                            target,\n",
    "                                                                            countries_dict),\n",
    "                                            n_days,\n",
    "                                            target=target_deaths)])\n",
    "    return df_train\n",
    "df_train = create_training_countries(countries_to_train, data, target, target_deaths, n_days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Target</th>\n",
       "      <th>Date</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001648</td>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001648</td>\n",
       "      <td>0.001648</td>\n",
       "      <td>60665551</td>\n",
       "      <td>45.5</td>\n",
       "      <td>1988636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.006594</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001648</td>\n",
       "      <td>0.001648</td>\n",
       "      <td>0.001648</td>\n",
       "      <td>60665551</td>\n",
       "      <td>45.5</td>\n",
       "      <td>1988636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.004945</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001648</td>\n",
       "      <td>0.001648</td>\n",
       "      <td>0.001648</td>\n",
       "      <td>0.006594</td>\n",
       "      <td>60665551</td>\n",
       "      <td>45.5</td>\n",
       "      <td>1988636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.003297</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001648</td>\n",
       "      <td>0.001648</td>\n",
       "      <td>0.001648</td>\n",
       "      <td>0.006594</td>\n",
       "      <td>0.004945</td>\n",
       "      <td>60665551</td>\n",
       "      <td>45.5</td>\n",
       "      <td>1988636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Target       Date    0    1    2    3    4    5    6    7  ...   23   24  \\\n",
       "0  0.001648 2020-02-22  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "1  0.001648 2020-02-23  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2  0.006594 2020-02-24  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "3  0.004945 2020-02-25  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "4  0.003297 2020-02-26  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "\n",
       "         25        26        27        28        29  Population  mean_age  \\\n",
       "0  0.000000  0.000000  0.000000  0.000000  0.001648    60665551      45.5   \n",
       "1  0.000000  0.000000  0.000000  0.001648  0.001648    60665551      45.5   \n",
       "2  0.000000  0.000000  0.001648  0.001648  0.001648    60665551      45.5   \n",
       "3  0.000000  0.001648  0.001648  0.001648  0.006594    60665551      45.5   \n",
       "4  0.001648  0.001648  0.001648  0.006594  0.004945    60665551      45.5   \n",
       "\n",
       "       GDP  \n",
       "0  1988636  \n",
       "1  1988636  \n",
       "2  1988636  \n",
       "3  1988636  \n",
       "4  1988636  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Models\n",
    "from sklearn.linear_model import Ridge\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn import linear_model\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "\n",
    "def rfr(X,y):\n",
    "    \"\"\"\n",
    "    Random forest regression model\n",
    "    Args:\n",
    "        - X: features\n",
    "        - y: target\n",
    "    Return:\n",
    "        Trained model\n",
    "    \n",
    "    \"\"\"\n",
    "    param_grid = { \n",
    "        'rf__n_estimators': [100],\n",
    "        'rf__max_depth' : [2,3,4,5,6,7,8],\n",
    "        'rf__min_samples_leaf':[3,5]\n",
    "    }    \n",
    "    pipeline = Pipeline([\n",
    "        ('rf', RandomForestRegressor())\n",
    "    ])\n",
    "    \n",
    "    CV_regr = GridSearchCV(pipeline, param_grid=param_grid, cv=5)\n",
    "    CV_regr.fit(X, y)\n",
    "    return CV_regr\n",
    "\n",
    "def ridge(X,y):    \n",
    "    \"\"\"\n",
    "    Linear Ridge regression model\n",
    "    Args:\n",
    "        - X: features\n",
    "        - y: target\n",
    "    Return:\n",
    "        Trained model\n",
    "    \n",
    "    \"\"\"\n",
    "    parameters = {'ridge__alpha':[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
    "                  'ridge__solver':['auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga'],\n",
    "                  'ridge__fit_intercept':[True,False],\n",
    "                 }\n",
    "    pipeline = Pipeline([\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('ridge', Ridge())\n",
    "    ])\n",
    "\n",
    "    CV_regr = GridSearchCV(pipeline,  param_grid=parameters, cv=5)\n",
    "    CV_regr.fit(X,y)\n",
    "    return CV_regr\n",
    "    \n",
    "def lasso(X,y):    \n",
    "    \"\"\"\n",
    "    Linear Lasso regression model\n",
    "    Args:\n",
    "        - X: features\n",
    "        - y: target\n",
    "    Return:\n",
    "        Trained model\n",
    "    \n",
    "    \"\"\"\n",
    "    parameters = {'lasso__alpha':[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
    "                  'lasso__fit_intercept':[True,False],\n",
    "                  'lasso__normalize':[True, False],\n",
    "                  'lasso__positive':[True, False],\n",
    "                  'lasso__selection':['cyclic', 'random']\n",
    "                 }\n",
    "    pipeline = Pipeline([\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('lasso', Lasso())\n",
    "    ])\n",
    "\n",
    "    CV_regr = GridSearchCV(pipeline,  param_grid=parameters, cv=5)\n",
    "    CV_regr.fit(X,y)\n",
    "    return CV_regr\n",
    " \n",
    "def kernel_ridge_regr(X,y):    \n",
    "    \"\"\"\n",
    "    Not-linear Ridge regression model\n",
    "    Args:\n",
    "        - X: features\n",
    "        - y: target\n",
    "    Return:\n",
    "        Trained model\n",
    "    \n",
    "    \"\"\"\n",
    "    parameters = {'kr__alpha':[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
    "                  'kr__gamma':[None,0.1,1,2.0],\n",
    "                  'kr__kernel':['rbf','linear','polynomial'],\n",
    "                  'kr__degree':[1,2,3,4,5]\n",
    "                 }\n",
    "    pipeline = Pipeline([\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('kr', KernelRidge())\n",
    "    ])\n",
    "\n",
    "    CV_regr = GridSearchCV(pipeline,  param_grid=parameters, cv=5)\n",
    "    CV_regr.fit(X,y)\n",
    "    return CV_regr\n",
    " \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply_model(model,X,y):\n",
    "    \"\"\"\n",
    "    This function apply the model choosed\n",
    "    Args:\n",
    "        - model: model to apply\n",
    "        - X: features\n",
    "        - y: target\n",
    "    Returns:\n",
    "        Model trained\n",
    "    \"\"\"\n",
    "    \n",
    "    if model == \"RFR\":\n",
    "        CV_regr = rfr(X,y)\n",
    "    elif model == \"ridge\":\n",
    "        CV_regr = ridge(X,y)\n",
    "    elif model == \"lasso\":\n",
    "        CV_regr = ridge(X,y)  \n",
    "    elif model == \"kernel_ridge\":\n",
    "        CV_regr = kernel_ridge_regr(X,y)          \n",
    "    return CV_regr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=7.607e-18): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=2.29631e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=2.33866e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=1.69587e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=1.62092e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=5.68546e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=4.71702e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=4.46282e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=3.30269e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=5.64646e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=7.22455e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=6.03492e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=3.24956e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=7.57005e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=9.76701e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=7.46969e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=4.08904e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=9.53378e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=8.34105e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=7.28692e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=5.06404e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=9.80952e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=5.80068e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=8.39214e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=6.67124e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=9.52156e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=7.69438e-17): result may not be accurate.\n",
      "\n",
      "/home/felipe/PycharmProjects/covid19/venv/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning:\n",
      "\n",
      "Ill-conditioned matrix (rcond=7.48947e-17): result may not be accurate.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Apply model\n",
    "CV_regr = apply_model(\"kernel_ridge\",df_train.drop([\"Target\",\"Date\"],axis=1), df_train[\"Target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create base for test\n",
    "df_Brazil_test = create_training_df(create_dataframe_country_target(data, \"Brazil\", target,countries_dict),n_days,target=\"Rate_over_population\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "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>Target</th>\n",
       "      <th>Date</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>...</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>Population</th>\n",
       "      <th>mean_age</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-02-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>206135893</td>\n",
       "      <td>32.6</td>\n",
       "      <td>1847020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "   Target       Date    0    1    2    3    4    5    6    7  ...   23   24  \\\n",
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       "\n",
       "    25   26   27   28   29  Population  mean_age      GDP  \n",
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       "[5 rows x 35 columns]"
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     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_Brazil_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prediction for the test data\n",
    "df_Brazil_test[\"Prediction\"] = CV_regr.predict(df_Brazil_test.drop([\"Target\",\"Date\"],axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
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  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
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   "source": [
    "# Show prediction and grand true\n",
    "fig = px.line()\n",
    "fig.add_scatter(y=df_Brazil_test['Prediction'],\n",
    "                mode='lines',   name=\"Prediction\")\n",
    "fig.add_scatter(y=df_Brazil_test['Target'],\n",
    "                mode='lines',   name=\"Target\")\n",
    "fig.update_layout(\n",
    "    xaxis_title=\"Time\",\n",
    "    yaxis_title=\"N. of deaths\",\n",
    "    font=dict(\n",
    "        family=\"Courier New, monospace\",\n",
    "        size=11,\n",
    "        color=\"#7f7f7f\"\n",
    "    )\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "def roll_predictions(day_to_roll,start_row,df_test,noise,CV_regr):\n",
    "    \"\"\"\n",
    "    This function extend the prediction for several days after. It uses the result of the prediction for the\n",
    "    next prediction\n",
    "    Args:\n",
    "        - day_to_roll: number of days to predict\n",
    "        - start_row: day where we start the prediction\n",
    "        - df_test: dataframe with the data\n",
    "        - noise: additional noise in the result\n",
    "        - CV_regr: trained model\n",
    "    Returns:\n",
    "        Dataframe with the the result of the prediction and the comparison\n",
    "    \"\"\"\n",
    "    cols = list(df_test.columns)\n",
    "    cols.remove(\"Target\")\n",
    "    cols.remove(\"Population\")\n",
    "    cols.remove(\"mean_age\")\n",
    "    cols.remove(\"GDP\")\n",
    "    cols.remove(\"Date\")\n",
    "    df_test_to_compare = df_test.copy()\n",
    "    df_test = df_test.drop(\"Date\",axis=1).reset_index(drop=True)\n",
    "    row = df_test.iloc[start_row,1:len(df_test.columns)].to_frame().transpose()\n",
    "    preds = []\n",
    "    for day in range(day_to_roll):\n",
    "        prediction = CV_regr.predict(row)\n",
    "        prediction = prediction[0]+ random.uniform(0, 1)*noise\n",
    "        preds.append(prediction)\n",
    "        for i in range(len(cols)-1):\n",
    "            row[cols[i]] =row[cols[i+1]] \n",
    "        row[len(cols)-1] = prediction     \n",
    "        \n",
    "    predicted = pd.DataFrame(np.column_stack((list(np.arange(start_row,day_to_roll+start_row,1)),preds)),\n",
    "                        columns=[\"Day\",\"Target_predicted\"])\n",
    "    real = df_test_to_compare[[\"Target\",\"Date\"]].reset_index(drop=False)\n",
    "    real.columns = [ \"Day\",\"Target_real\",\"Date\"]\n",
    "    df_comparison = pd.merge(real,predicted,on=\"Day\",how=\"outer\")\n",
    "    df_date_to_fill = pd.Series(pd.date_range(start=df_comparison[\"Date\"].max(),periods=df_comparison[\"Date\"].isnull().sum()+1)).iloc[1:]\n",
    "    df_exists = df_comparison[df_comparison[\"Date\"].isnull()==False][\"Date\"]\n",
    "    df_comparison[\"Date\"] = df_exists.append(df_date_to_fill).values\n",
    "    \n",
    "    return df_comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "# Calculate the prediction starting prediction from the 22th of April\n",
    "df_test = create_training_df(create_dataframe_country_target(data, \"Brazil\", target,countries_dict),n_days,target=\"Rate_over_population\")\n",
    "day_to_roll=100\n",
    "start_row=60\n",
    "noise=0.00\n",
    "df_comparison = roll_predictions(day_to_roll,start_row,df_test,noise,CV_regr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results\n",
    "fig = px.line()\n",
    "fig.add_scatter(x=df_comparison[\"Date\"],\n",
    "                y=df_comparison['Target_real'],\n",
    "                mode='lines',   name=\"Real\")\n",
    "fig.add_scatter(x=df_comparison[\"Date\"],\n",
    "                y=df_comparison['Target_predicted'],\n",
    "                mode='lines',   name=\"Predicted\")\n",
    "fig.update_layout(\n",
    "    xaxis_title=\"Time\",\n",
    "    yaxis_title=\"N. of deaths\",\n",
    "    font=dict(\n",
    "        family=\"Courier New, monospace\",\n",
    "        size=11,\n",
    "        color=\"#7f7f7f\"\n",
    "    )\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06252680629083622"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the RMSE of the prediction\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "true = df_comparison.dropna()['Target_predicted']\n",
    "pred = df_comparison.dropna()['Target_real']\n",
    "rmse = sqrt(mean_squared_error(true, pred))\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Day                                 110\n",
       "Target_real                         NaN\n",
       "Date                2020-06-11 00:00:00\n",
       "Target_predicted                0.53835\n",
       "Name: 110, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the peak of deaths in Brazil\n",
    "df_comparison.iloc[df_comparison[\"Target_predicted\"].argmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2020-06-11'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comparison.iloc[df_comparison[\"Target_predicted\"].argmax()][\"Date\"].strftime(\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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