covid-vaccination-prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0s0q2o0N-cb9"
   },
   "source": [
    "## <center> Covid Vaccination Prediction <center> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IIiCKTZY-cb_"
   },
   "source": [
    "Importing required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "6nFWz030-cb_"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, PredefinedSplit\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "import xgboost as xgb\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rim9gQZk-ccA"
   },
   "source": [
    "Reading the covid dataset provided in https://github.com/owid/covid-19-data/tree/master/public/data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 590
    },
    "id": "ZST4c83Q-ccA",
    "outputId": "c19a5d65-6824-4272-e8ca-f1e849e9a789"
   },
   "outputs": [
    {
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       "  <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",
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       "      <th>positive_rate</th>\n",
       "      <th>tests_per_case</th>\n",
       "      <th>tests_units</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>people_vaccinated</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>new_vaccinations</th>\n",
       "      <th>new_vaccinations_smoothed</th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>people_vaccinated_per_hundred</th>\n",
       "      <th>people_fully_vaccinated_per_hundred</th>\n",
       "      <th>new_vaccinations_smoothed_per_million</th>\n",
       "      <th>stringency_index</th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>aged_65_older</th>\n",
       "      <th>aged_70_older</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</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",
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       "      <td>Afghanistan</td>\n",
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       "      <td>2020-02-26</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-27</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>ZWE</td>\n",
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       "      <td>Zimbabwe</td>\n",
       "      <td>2021-04-26</td>\n",
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       "      <td>16.0</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>1.077</td>\n",
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       "      <td>0.101</td>\n",
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       "      <td>0.116</td>\n",
       "      <td>0.020</td>\n",
       "      <td>49.6</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>411610.0</td>\n",
       "      <td>353834.0</td>\n",
       "      <td>57776.0</td>\n",
       "      <td>21592.0</td>\n",
       "      <td>13517.0</td>\n",
       "      <td>2.77</td>\n",
       "      <td>2.38</td>\n",
       "      <td>0.39</td>\n",
       "      <td>909.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85167</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-04-27</td>\n",
       "      <td>38164.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>41.286</td>\n",
       "      <td>1565.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.571</td>\n",
       "      <td>2567.731</td>\n",
       "      <td>4.171</td>\n",
       "      <td>2.778</td>\n",
       "      <td>105.296</td>\n",
       "      <td>0.336</td>\n",
       "      <td>0.106</td>\n",
       "      <td>1.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2355.0</td>\n",
       "      <td>486801.0</td>\n",
       "      <td>32.753</td>\n",
       "      <td>0.158</td>\n",
       "      <td>1703.0</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.024</td>\n",
       "      <td>41.2</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>433939.0</td>\n",
       "      <td>370676.0</td>\n",
       "      <td>63263.0</td>\n",
       "      <td>22329.0</td>\n",
       "      <td>15562.0</td>\n",
       "      <td>2.92</td>\n",
       "      <td>2.49</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1047.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85168</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-04-28</td>\n",
       "      <td>38191.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>30.143</td>\n",
       "      <td>1565.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.429</td>\n",
       "      <td>2569.548</td>\n",
       "      <td>1.817</td>\n",
       "      <td>2.028</td>\n",
       "      <td>105.296</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.096</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2025.0</td>\n",
       "      <td>488826.0</td>\n",
       "      <td>32.889</td>\n",
       "      <td>0.136</td>\n",
       "      <td>1716.0</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.018</td>\n",
       "      <td>56.9</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>458013.0</td>\n",
       "      <td>388021.0</td>\n",
       "      <td>69992.0</td>\n",
       "      <td>24074.0</td>\n",
       "      <td>17860.0</td>\n",
       "      <td>3.08</td>\n",
       "      <td>2.61</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85169</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-04-29</td>\n",
       "      <td>38235.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>31.000</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.714</td>\n",
       "      <td>2572.508</td>\n",
       "      <td>2.960</td>\n",
       "      <td>2.086</td>\n",
       "      <td>105.430</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.115</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>477597.0</td>\n",
       "      <td>400771.0</td>\n",
       "      <td>76826.0</td>\n",
       "      <td>19584.0</td>\n",
       "      <td>17971.0</td>\n",
       "      <td>3.21</td>\n",
       "      <td>2.70</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1209.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85170</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>38257.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>30.286</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.571</td>\n",
       "      <td>2573.988</td>\n",
       "      <td>1.480</td>\n",
       "      <td>2.038</td>\n",
       "      <td>105.430</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.106</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500342.0</td>\n",
       "      <td>414735.0</td>\n",
       "      <td>85607.0</td>\n",
       "      <td>22745.0</td>\n",
       "      <td>19194.0</td>\n",
       "      <td>3.37</td>\n",
       "      <td>2.79</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1291.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85171 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      iso_code continent  ... life_expectancy human_development_index\n",
       "0          AFG      Asia  ...           64.83                   0.511\n",
       "1          AFG      Asia  ...           64.83                   0.511\n",
       "2          AFG      Asia  ...           64.83                   0.511\n",
       "3          AFG      Asia  ...           64.83                   0.511\n",
       "4          AFG      Asia  ...           64.83                   0.511\n",
       "...        ...       ...  ...             ...                     ...\n",
       "85166      ZWE    Africa  ...           61.49                   0.571\n",
       "85167      ZWE    Africa  ...           61.49                   0.571\n",
       "85168      ZWE    Africa  ...           61.49                   0.571\n",
       "85169      ZWE    Africa  ...           61.49                   0.571\n",
       "85170      ZWE    Africa  ...           61.49                   0.571\n",
       "\n",
       "[85171 rows x 59 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('owid-covid-data.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Feq4XeGF-ccB"
   },
   "source": [
    "Information of dataset. Even it has 85171 examples, since the project aims to predict people_fully_vaccinated_per_hundred, we won't use most of the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "n2Fs4TIm-ccB",
    "outputId": "251d310d-6ac1-49c6-c2c0-b484ee647a45"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 85171 entries, 0 to 85170\n",
      "Data columns (total 59 columns):\n",
      " #   Column                                 Non-Null Count  Dtype  \n",
      "---  ------                                 --------------  -----  \n",
      " 0   iso_code                               85171 non-null  object \n",
      " 1   continent                              81060 non-null  object \n",
      " 2   location                               85171 non-null  object \n",
      " 3   date                                   85171 non-null  object \n",
      " 4   total_cases                            83072 non-null  float64\n",
      " 5   new_cases                              83070 non-null  float64\n",
      " 6   new_cases_smoothed                     82069 non-null  float64\n",
      " 7   total_deaths                           73408 non-null  float64\n",
      " 8   new_deaths                             73566 non-null  float64\n",
      " 9   new_deaths_smoothed                    82069 non-null  float64\n",
      " 10  total_cases_per_million                82623 non-null  float64\n",
      " 11  new_cases_per_million                  82621 non-null  float64\n",
      " 12  new_cases_smoothed_per_million         81625 non-null  float64\n",
      " 13  total_deaths_per_million               72972 non-null  float64\n",
      " 14  new_deaths_per_million                 73130 non-null  float64\n",
      " 15  new_deaths_smoothed_per_million        81625 non-null  float64\n",
      " 16  reproduction_rate                      68942 non-null  float64\n",
      " 17  icu_patients                           8684 non-null   float64\n",
      " 18  icu_patients_per_million               8684 non-null   float64\n",
      " 19  hosp_patients                          10814 non-null  float64\n",
      " 20  hosp_patients_per_million              10814 non-null  float64\n",
      " 21  weekly_icu_admissions                  789 non-null    float64\n",
      " 22  weekly_icu_admissions_per_million      789 non-null    float64\n",
      " 23  weekly_hosp_admissions                 1295 non-null   float64\n",
      " 24  weekly_hosp_admissions_per_million     1295 non-null   float64\n",
      " 25  new_tests                              38945 non-null  float64\n",
      " 26  total_tests                            38652 non-null  float64\n",
      " 27  total_tests_per_thousand               38652 non-null  float64\n",
      " 28  new_tests_per_thousand                 38945 non-null  float64\n",
      " 29  new_tests_smoothed                     44625 non-null  float64\n",
      " 30  new_tests_smoothed_per_thousand        44625 non-null  float64\n",
      " 31  positive_rate                          42904 non-null  float64\n",
      " 32  tests_per_case                         42311 non-null  float64\n",
      " 33  tests_units                            46079 non-null  object \n",
      " 34  total_vaccinations                     9374 non-null   float64\n",
      " 35  people_vaccinated                      8744 non-null   float64\n",
      " 36  people_fully_vaccinated                6431 non-null   float64\n",
      " 37  new_vaccinations                       7954 non-null   float64\n",
      " 38  new_vaccinations_smoothed              15092 non-null  float64\n",
      " 39  total_vaccinations_per_hundred         9374 non-null   float64\n",
      " 40  people_vaccinated_per_hundred          8744 non-null   float64\n",
      " 41  people_fully_vaccinated_per_hundred    6431 non-null   float64\n",
      " 42  new_vaccinations_smoothed_per_million  15092 non-null  float64\n",
      " 43  stringency_index                       72207 non-null  float64\n",
      " 44  population                             84622 non-null  float64\n",
      " 45  population_density                     79274 non-null  float64\n",
      " 46  median_age                             76706 non-null  float64\n",
      " 47  aged_65_older                          75830 non-null  float64\n",
      " 48  aged_70_older                          76276 non-null  float64\n",
      " 49  gdp_per_capita                         77046 non-null  float64\n",
      " 50  extreme_poverty                        52449 non-null  float64\n",
      " 51  cardiovasc_death_rate                  77634 non-null  float64\n",
      " 52  diabetes_prevalence                    78779 non-null  float64\n",
      " 53  female_smokers                         60828 non-null  float64\n",
      " 54  male_smokers                           59931 non-null  float64\n",
      " 55  handwashing_facilities                 39007 non-null  float64\n",
      " 56  hospital_beds_per_thousand             70847 non-null  float64\n",
      " 57  life_expectancy                        80833 non-null  float64\n",
      " 58  human_development_index                77517 non-null  float64\n",
      "dtypes: float64(54), object(5)\n",
      "memory usage: 38.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "maAkdLYF7Q8E"
   },
   "source": [
    "Data has so many inconsistent features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 304
    },
    "id": "FjK-Bg786wUM",
    "outputId": "3722eab8-3cfe-463f-c29b-0fdb69b2d203"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
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       "      <th>new_cases_smoothed_per_million</th>\n",
       "      <th>total_deaths_per_million</th>\n",
       "      <th>new_deaths_per_million</th>\n",
       "      <th>new_deaths_smoothed_per_million</th>\n",
       "      <th>reproduction_rate</th>\n",
       "      <th>icu_patients</th>\n",
       "      <th>icu_patients_per_million</th>\n",
       "      <th>hosp_patients</th>\n",
       "      <th>hosp_patients_per_million</th>\n",
       "      <th>weekly_icu_admissions</th>\n",
       "      <th>weekly_icu_admissions_per_million</th>\n",
       "      <th>weekly_hosp_admissions</th>\n",
       "      <th>weekly_hosp_admissions_per_million</th>\n",
       "      <th>new_tests</th>\n",
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       "      <th>total_tests_per_thousand</th>\n",
       "      <th>new_tests_per_thousand</th>\n",
       "      <th>new_tests_smoothed</th>\n",
       "      <th>new_tests_smoothed_per_thousand</th>\n",
       "      <th>positive_rate</th>\n",
       "      <th>tests_per_case</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>people_vaccinated</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>new_vaccinations</th>\n",
       "      <th>new_vaccinations_smoothed</th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>people_vaccinated_per_hundred</th>\n",
       "      <th>people_fully_vaccinated_per_hundred</th>\n",
       "      <th>new_vaccinations_smoothed_per_million</th>\n",
       "      <th>stringency_index</th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>aged_65_older</th>\n",
       "      <th>aged_70_older</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.307200e+04</td>\n",
       "      <td>83070.000000</td>\n",
       "      <td>82069.00000</td>\n",
       "      <td>7.340800e+04</td>\n",
       "      <td>73566.000000</td>\n",
       "      <td>82069.000000</td>\n",
       "      <td>82623.000000</td>\n",
       "      <td>82621.000000</td>\n",
       "      <td>81625.000000</td>\n",
       "      <td>72972.000000</td>\n",
       "      <td>73130.000000</td>\n",
       "      <td>81625.000000</td>\n",
       "      <td>68942.000000</td>\n",
       "      <td>8684.000000</td>\n",
       "      <td>8684.000000</td>\n",
       "      <td>10814.000000</td>\n",
       "      <td>10814.000000</td>\n",
       "      <td>789.000000</td>\n",
       "      <td>789.000000</td>\n",
       "      <td>1295.000000</td>\n",
       "      <td>1295.000000</td>\n",
       "      <td>3.894500e+04</td>\n",
       "      <td>3.865200e+04</td>\n",
       "      <td>38652.000000</td>\n",
       "      <td>38945.000000</td>\n",
       "      <td>4.462500e+04</td>\n",
       "      <td>44625.000000</td>\n",
       "      <td>42904.000000</td>\n",
       "      <td>42311.000000</td>\n",
       "      <td>9.374000e+03</td>\n",
       "      <td>8.744000e+03</td>\n",
       "      <td>6.431000e+03</td>\n",
       "      <td>7.954000e+03</td>\n",
       "      <td>1.509200e+04</td>\n",
       "      <td>9374.000000</td>\n",
       "      <td>8744.000000</td>\n",
       "      <td>6431.000000</td>\n",
       "      <td>15092.000000</td>\n",
       "      <td>72207.000000</td>\n",
       "      <td>8.462200e+04</td>\n",
       "      <td>79274.000000</td>\n",
       "      <td>76706.000000</td>\n",
       "      <td>75830.000000</td>\n",
       "      <td>76276.000000</td>\n",
       "      <td>77046.000000</td>\n",
       "      <td>52449.000000</td>\n",
       "      <td>77634.000000</td>\n",
       "      <td>78779.000000</td>\n",
       "      <td>60828.000000</td>\n",
       "      <td>59931.000000</td>\n",
       "      <td>39007.000000</td>\n",
       "      <td>70847.000000</td>\n",
       "      <td>80833.000000</td>\n",
       "      <td>77517.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.250083e+05</td>\n",
       "      <td>5808.325352</td>\n",
       "      <td>5780.94148</td>\n",
       "      <td>2.300409e+04</td>\n",
       "      <td>139.052606</td>\n",
       "      <td>122.992577</td>\n",
       "      <td>10062.094271</td>\n",
       "      <td>74.337193</td>\n",
       "      <td>74.307323</td>\n",
       "      <td>224.882585</td>\n",
       "      <td>1.507874</td>\n",
       "      <td>1.335129</td>\n",
       "      <td>1.019094</td>\n",
       "      <td>1087.808268</td>\n",
       "      <td>26.410840</td>\n",
       "      <td>4832.767339</td>\n",
       "      <td>173.771940</td>\n",
       "      <td>280.673146</td>\n",
       "      <td>21.121653</td>\n",
       "      <td>3981.037431</td>\n",
       "      <td>115.517820</td>\n",
       "      <td>4.401999e+04</td>\n",
       "      <td>5.963484e+06</td>\n",
       "      <td>227.476052</td>\n",
       "      <td>1.937240</td>\n",
       "      <td>4.141631e+04</td>\n",
       "      <td>1.775553</td>\n",
       "      <td>0.088971</td>\n",
       "      <td>159.455343</td>\n",
       "      <td>1.448260e+07</td>\n",
       "      <td>8.974136e+06</td>\n",
       "      <td>4.618760e+06</td>\n",
       "      <td>4.223566e+05</td>\n",
       "      <td>2.214247e+05</td>\n",
       "      <td>13.339040</td>\n",
       "      <td>9.359133</td>\n",
       "      <td>5.087038</td>\n",
       "      <td>2774.854360</td>\n",
       "      <td>58.740033</td>\n",
       "      <td>1.284213e+08</td>\n",
       "      <td>349.043245</td>\n",
       "      <td>30.522465</td>\n",
       "      <td>8.773478</td>\n",
       "      <td>5.557222</td>\n",
       "      <td>19138.882312</td>\n",
       "      <td>13.348729</td>\n",
       "      <td>257.783338</td>\n",
       "      <td>7.820936</td>\n",
       "      <td>10.520272</td>\n",
       "      <td>32.656970</td>\n",
       "      <td>50.916220</td>\n",
       "      <td>3.029584</td>\n",
       "      <td>73.165416</td>\n",
       "      <td>0.727123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.705959e+06</td>\n",
       "      <td>36285.123205</td>\n",
       "      <td>35533.79070</td>\n",
       "      <td>1.360297e+05</td>\n",
       "      <td>758.882229</td>\n",
       "      <td>693.391638</td>\n",
       "      <td>19295.833152</td>\n",
       "      <td>175.740570</td>\n",
       "      <td>149.026786</td>\n",
       "      <td>394.722350</td>\n",
       "      <td>3.979279</td>\n",
       "      <td>2.936500</td>\n",
       "      <td>0.356793</td>\n",
       "      <td>3036.371200</td>\n",
       "      <td>27.869382</td>\n",
       "      <td>12440.913787</td>\n",
       "      <td>216.378928</td>\n",
       "      <td>588.426814</td>\n",
       "      <td>37.119393</td>\n",
       "      <td>11632.511679</td>\n",
       "      <td>230.474544</td>\n",
       "      <td>2.280829e+05</td>\n",
       "      <td>2.702657e+07</td>\n",
       "      <td>495.353769</td>\n",
       "      <td>15.289039</td>\n",
       "      <td>1.484500e+05</td>\n",
       "      <td>4.810457</td>\n",
       "      <td>0.097594</td>\n",
       "      <td>864.928764</td>\n",
       "      <td>6.637651e+07</td>\n",
       "      <td>3.793750e+07</td>\n",
       "      <td>1.819767e+07</td>\n",
       "      <td>1.691168e+06</td>\n",
       "      <td>1.133430e+06</td>\n",
       "      <td>21.682342</td>\n",
       "      <td>13.808968</td>\n",
       "      <td>9.639009</td>\n",
       "      <td>4635.192312</td>\n",
       "      <td>21.663223</td>\n",
       "      <td>6.904393e+08</td>\n",
       "      <td>1699.215916</td>\n",
       "      <td>9.114990</td>\n",
       "      <td>6.224095</td>\n",
       "      <td>4.248993</td>\n",
       "      <td>19821.321091</td>\n",
       "      <td>19.942984</td>\n",
       "      <td>118.765619</td>\n",
       "      <td>3.977056</td>\n",
       "      <td>10.402965</td>\n",
       "      <td>13.475297</td>\n",
       "      <td>31.763233</td>\n",
       "      <td>2.463559</td>\n",
       "      <td>7.549526</td>\n",
       "      <td>0.150057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-74347.000000</td>\n",
       "      <td>-6223.00000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-1918.000000</td>\n",
       "      <td>-232.143000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>-2153.437000</td>\n",
       "      <td>-276.825000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>-76.445000</td>\n",
       "      <td>-10.921000</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.391720e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-23.010000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.090000e+02</td>\n",
       "      <td>0.137000</td>\n",
       "      <td>15.100000</td>\n",
       "      <td>1.144000</td>\n",
       "      <td>0.526000</td>\n",
       "      <td>661.240000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>79.370000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>1.188000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>53.280000</td>\n",
       "      <td>0.394000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.300000e+02</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.85700</td>\n",
       "      <td>4.500000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>202.464000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>1.153000</td>\n",
       "      <td>6.857000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.860000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>4.389000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>22.618000</td>\n",
       "      <td>7.935000</td>\n",
       "      <td>1.781000</td>\n",
       "      <td>59.310000</td>\n",
       "      <td>10.096500</td>\n",
       "      <td>1.433000e+03</td>\n",
       "      <td>1.246390e+05</td>\n",
       "      <td>11.192750</td>\n",
       "      <td>0.124000</td>\n",
       "      <td>1.467000e+03</td>\n",
       "      <td>0.123000</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>6.206025e+04</td>\n",
       "      <td>5.225850e+04</td>\n",
       "      <td>2.580700e+04</td>\n",
       "      <td>3.219750e+03</td>\n",
       "      <td>9.550000e+02</td>\n",
       "      <td>1.062500</td>\n",
       "      <td>0.897500</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>44.440000</td>\n",
       "      <td>2.416664e+06</td>\n",
       "      <td>36.253000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>3.441000</td>\n",
       "      <td>2.043000</td>\n",
       "      <td>4466.507000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>167.295000</td>\n",
       "      <td>5.290000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>20.859000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>67.920000</td>\n",
       "      <td>0.602000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.042250e+04</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>85.57100</td>\n",
       "      <td>3.150000e+02</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.286000</td>\n",
       "      <td>1383.968000</td>\n",
       "      <td>7.643000</td>\n",
       "      <td>10.047000</td>\n",
       "      <td>39.351500</td>\n",
       "      <td>0.121000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>16.364500</td>\n",
       "      <td>679.000000</td>\n",
       "      <td>81.541500</td>\n",
       "      <td>46.677000</td>\n",
       "      <td>8.937000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>44.249000</td>\n",
       "      <td>5.446000e+03</td>\n",
       "      <td>6.207335e+05</td>\n",
       "      <td>55.466000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>5.620000e+03</td>\n",
       "      <td>0.559000</td>\n",
       "      <td>0.053000</td>\n",
       "      <td>18.200000</td>\n",
       "      <td>4.803760e+05</td>\n",
       "      <td>3.548135e+05</td>\n",
       "      <td>2.178900e+05</td>\n",
       "      <td>1.943150e+04</td>\n",
       "      <td>6.845000e+03</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>3.785000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1361.000000</td>\n",
       "      <td>61.110000</td>\n",
       "      <td>1.009927e+07</td>\n",
       "      <td>82.805000</td>\n",
       "      <td>29.700000</td>\n",
       "      <td>6.378000</td>\n",
       "      <td>3.871000</td>\n",
       "      <td>12951.839000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>242.648000</td>\n",
       "      <td>7.110000</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>31.400000</td>\n",
       "      <td>49.839000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>74.530000</td>\n",
       "      <td>0.748000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.105940e+05</td>\n",
       "      <td>764.000000</td>\n",
       "      <td>792.85700</td>\n",
       "      <td>3.045000e+03</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>13.571000</td>\n",
       "      <td>9898.760000</td>\n",
       "      <td>67.692000</td>\n",
       "      <td>75.369000</td>\n",
       "      <td>240.628250</td>\n",
       "      <td>1.282000</td>\n",
       "      <td>1.212000</td>\n",
       "      <td>1.187500</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>41.033000</td>\n",
       "      <td>2806.750000</td>\n",
       "      <td>257.343000</td>\n",
       "      <td>223.444000</td>\n",
       "      <td>22.764000</td>\n",
       "      <td>1977.885000</td>\n",
       "      <td>130.191000</td>\n",
       "      <td>2.157900e+04</td>\n",
       "      <td>2.582847e+06</td>\n",
       "      <td>224.889750</td>\n",
       "      <td>1.773000</td>\n",
       "      <td>2.357700e+04</td>\n",
       "      <td>1.834000</td>\n",
       "      <td>0.128000</td>\n",
       "      <td>57.200000</td>\n",
       "      <td>2.997519e+06</td>\n",
       "      <td>2.108087e+06</td>\n",
       "      <td>1.320154e+06</td>\n",
       "      <td>1.117732e+05</td>\n",
       "      <td>3.775875e+04</td>\n",
       "      <td>16.327500</td>\n",
       "      <td>11.730000</td>\n",
       "      <td>5.520000</td>\n",
       "      <td>3485.500000</td>\n",
       "      <td>75.930000</td>\n",
       "      <td>3.691056e+07</td>\n",
       "      <td>208.354000</td>\n",
       "      <td>39.100000</td>\n",
       "      <td>14.178000</td>\n",
       "      <td>8.678000</td>\n",
       "      <td>27216.445000</td>\n",
       "      <td>21.200000</td>\n",
       "      <td>329.635000</td>\n",
       "      <td>10.080000</td>\n",
       "      <td>19.300000</td>\n",
       "      <td>41.100000</td>\n",
       "      <td>83.241000</td>\n",
       "      <td>3.861000</td>\n",
       "      <td>78.730000</td>\n",
       "      <td>0.848000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.513995e+08</td>\n",
       "      <td>905992.000000</td>\n",
       "      <td>826374.28600</td>\n",
       "      <td>3.180238e+06</td>\n",
       "      <td>17906.000000</td>\n",
       "      <td>14435.143000</td>\n",
       "      <td>171254.773000</td>\n",
       "      <td>8652.658000</td>\n",
       "      <td>2648.773000</td>\n",
       "      <td>2850.828000</td>\n",
       "      <td>218.329000</td>\n",
       "      <td>63.140000</td>\n",
       "      <td>5.760000</td>\n",
       "      <td>30028.000000</td>\n",
       "      <td>192.642000</td>\n",
       "      <td>129800.000000</td>\n",
       "      <td>1532.573000</td>\n",
       "      <td>4037.019000</td>\n",
       "      <td>279.130000</td>\n",
       "      <td>116374.000000</td>\n",
       "      <td>2656.911000</td>\n",
       "      <td>3.202280e+07</td>\n",
       "      <td>4.135027e+08</td>\n",
       "      <td>6233.953000</td>\n",
       "      <td>2827.217000</td>\n",
       "      <td>4.594014e+06</td>\n",
       "      <td>405.595000</td>\n",
       "      <td>0.742000</td>\n",
       "      <td>44258.700000</td>\n",
       "      <td>1.134333e+09</td>\n",
       "      <td>5.952349e+08</td>\n",
       "      <td>2.680025e+08</td>\n",
       "      <td>2.462373e+07</td>\n",
       "      <td>1.966495e+07</td>\n",
       "      <td>208.760000</td>\n",
       "      <td>111.290000</td>\n",
       "      <td>97.470000</td>\n",
       "      <td>118759.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>7.794799e+09</td>\n",
       "      <td>20546.766000</td>\n",
       "      <td>48.200000</td>\n",
       "      <td>27.049000</td>\n",
       "      <td>18.493000</td>\n",
       "      <td>116935.600000</td>\n",
       "      <td>77.600000</td>\n",
       "      <td>724.417000</td>\n",
       "      <td>30.530000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>78.100000</td>\n",
       "      <td>98.999000</td>\n",
       "      <td>13.800000</td>\n",
       "      <td>86.750000</td>\n",
       "      <td>0.957000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_cases      new_cases  ...  life_expectancy  human_development_index\n",
       "count  8.307200e+04   83070.000000  ...     80833.000000             77517.000000\n",
       "mean   8.250083e+05    5808.325352  ...        73.165416                 0.727123\n",
       "std    5.705959e+06   36285.123205  ...         7.549526                 0.150057\n",
       "min    1.000000e+00  -74347.000000  ...        53.280000                 0.394000\n",
       "25%    9.300000e+02       2.000000  ...        67.920000                 0.602000\n",
       "50%    1.042250e+04      70.000000  ...        74.530000                 0.748000\n",
       "75%    1.105940e+05     764.000000  ...        78.730000                 0.848000\n",
       "max    1.513995e+08  905992.000000  ...        86.750000                 0.957000\n",
       "\n",
       "[8 rows x 54 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CaeB2PBzCAvy"
   },
   "source": [
    "Looking at the new cases per million values of some of the biggest countries of the Europe. Their means are close but United Kingdom has a lot of outliers from the days which the pandemic was going bad. Germany has been the most consistent one out of this countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 285
    },
    "id": "2OG9XLho6wWz",
    "outputId": "46707fab-3b5d-4374-8e50-29434ad7b73b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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S0TJJpwLgKOBVd7/JzAYDTwFpP0E7/KVkjQMO6MmmTZtYtcrZseNMVq1yNm/eTFFRESUlhZkOT1oxd+5cJk+ezJtvvsnAgQMZP348w4cPz3RY0g65+HnbF5LJO8BOgq4q3H2pmX1A0DI5xMwK3L3ezAqAvsAagpZJc3WRZVvLZNOmTZSVGTfeeCM33ngjEPTPVle7vunu45rbXOmjjz7JqYlv2SJbP28JLZN/revgWP6Fu38ALCAc/wjP0ioFqoHXCBaYJLx+1d03uPv65uo6MvZ9Tc+eB7BixQpKS0vJz8+ntLSUFStW0LPnAZkOTVqhzZUk7iIlEzNLd9K5GhhnZn8FHgdGuPumsPw6M6sGrgvvJz6nubqctHnzZvLyoLERGhoaaGwMlsHevHlzpkOTVlRXO4MHn7Zb2eDBp+XU5koSb612c4VdSFvNrMjd03JaUHh68ef2Ur4CGNzMc5qty1WNjQ0UFvaka9eu5Ofn07VrV3r0KGTLluwcDMwmZWXG0qUvM2RI+a6ypUtfzqlVZyXeWm1xuHs9QZdT7/SHI8k64YQT6d69OwDdu3fnhBNOzHBEEkXTqrOLFy9kx44du1adrajInbWdJN6iDsD/DJhvZncD7xLMLwHA3V9IR2DSPosWvUhRURGNjY3U1KxLeoKVdAztRSNxF3UG/Kpmqhrd/ajUhtQh+pGF80z69CmioaHhX8rz8/OpqdmUgYikPTTPJN40z6QF7n5k6sOSVGtKJKWlpXzwwQcceOCBrF+/fq8JRvY9VVXzqKyctmv1goqKMWqZSGxEPkvLzDqZ2Rlm9o3wfncz656+0KQ9vva1C+nVKxje6tWrN1/72oUZjkiiaJpnMmXKVLZt28aUKVOZMuU2qqrmZTo0kUiinhp8HMEg/APAQ2HxZ4GH0xSXtNPzz/+O2tpaGhsbqa2t5fnnf5fpkCQCzTORuIvaMpkFTHD3AcCOsOxFYEhaopJ2KSoqYsuWj9i+fRt5eXls376NLVs+oqioKNOhSSs0z0TiLmoyOQZ4LLzdtLJvLbB/OoKS9unWrRuFhYV06dIVgC5dulJYWEi3bt0yHJm0pmmeSSLNM5E4iZpMVgMnJRaY2anAW6kOSNqvpqaGO+6Ytts8kzvumEZNTU2GI5PWaJ6JxF3UU4OHEoyV3AvcCEwmWL7kSnd/Nq0Rpkc/YnxqcK5v0JOtdDZXdsjVU4Mj72diZicCVwJHEKzO+4C7/ymlkXacfsQ4mTQnceXZYcOGUlU1n4qKazX5LWY0zyTecjWZRF6C3t1fBb6b0sgkpRJnUTddK5GISEeI2s3VmWD3w+EE+4a8R7C672R335bWCNOjH1nYMkmUzd+Osp1aJvGWzZ+9VLRMZgEGjAbeJujqGgccAlyeqkBFRCSeoiaTrwKfCvcYAfibmS0lOJtLyUREJMdFPTW4BthzssL+wLrUhiMiInEUtWUyB/h/ZnYPwRL0hwHXAI+a2eebHqTl6EVEclPUZPKd8HrcHuVX87/b5TYCcVyOXkREkqQl6EVEJGmRl6AXERFpjpKJiIgkTclERESSpmQiIiJJi7rTYomZ9QhvF5jZt8zsMjNTMhJJkaqqeZSXD6agoIDy8sHasldiJWoymA8cHd6eDIwBrgf+Ox1BieQa7QEvcRc1mZQBr4W3LwHOBT4PXJyOoERyjfaAl7iLmkzqgc5mdhyw2d3fATYBPdIWmUgO0R7wEndRk8nTwC8IVg9+PCz7NLA2HUGJ5BrtAS9xF3U5lW8DlwE7CNbpAjgQuCWVwZjZxPA1j3P3N8zsM8B9BItKrgYucff14WObrROJm4qKMVx22XA++WQbO3fuYL/9OrH//l2ZOrUy06GJRBKpZeLu2939fmA2UBKW/d7dH2/5mdGZ2SDgMwT7pRCeKfYYcI27lwELgTtbqxOJo2XLllJbW0txcTF5eXkUFxdTW1vLsmVLMx2aSCRRTw0uMrP/C2wj2MMEM/uKmd2eiiDMrAswExiVUHwSsM3dF4f37wW+HqFOJHbmzJnNsGEX0bt3b/Ly8ujduzfDhl3EnDmzMx2aSCRRx0zuBTYT7LBYF5a9DHwjRXHcCjzm7qsTyg4nbKUAuPsHQL6Z9WqlTiR26uq289RTv2LFijdpaGhgxYo3eeqpX1FXtz3ToYlEEnXM5AtAX3ffYWaNAO6+wcxKkw3AzE4DTga+n+xrtVW4l3HWKikpzHQI0gbbt29n1KhR3HHHHYwdO5ZZs2YBOo5xlIvHLGoy2Uww4L5rZ0UzO5zU7LT4WWAgsMrMAA4FngGmE7SEmt7vQKDB3f9pZu80V9eWN964cSsNDY3J/wT7qA0btmQ6BGmj+++/n1mzZlFQULCrTMcxfrL1mOXn5zX7JTxqN9eDwBNmdiZBd9JpBIPx9yYbnLvf6e593b2fu/cj2Mnxi8BUYH8zGxI+9GqgaTrwn1qoE4mt+vr63a5F4iJqMvkh8HOCQfJOwMPA/wB3pyku3L0BGAHMMrOVBC2Y77dWJxJnI0dewaZNmxg58opMhyLSJnmNjdnbzdOCfsCqbO7mKi3tyfr1H2U6DImotLRns3U6jvGSzZ+9hG6uIwnm9+0Sacwk7N5a7e6rzKwPQUulARjr7jWpDVdEROImajfXTwjW5wL4MUFXVwNwfzqCEslV+fkFu12LxEXUs7kOcfd3zGw/gsHxpvkm76UtMpEc1NBQv9u1SFxEbZl8ZGYHEQx0/83dt4blndITloiIxEnUZHIPsAz4GcEZXQCnAyvSEZRIrvriF89lw4YNfPGL52Y6FJE2idTN5e4/NLMngXp3/3tYvJZgNWERSYFDDz2MBQteoKSkhM6du3DooYfx7rtrMh2WSCSR93B39+qERNJ0/6/pCUsk96xd+y7jx0+ktraW8eMnsnbtu5kOSSSySPNMzKwnwT4jnyVYViWvqc7dD09XcGnUD80zkX1I3769yM/Pp6Ghcdd+Jvn5eTQ0NPDee21aJUgyLJs/ey3NM2nLqcGDCFb37QVcB7wD3JWyKEVy2GWXXc7OnTspLi4GoLi4mJ07d3LZZZdnODKRaKK2TNYDA919o5ltcvciMzsEeMrdB6U9ytTrh1omkiHl5YNZseLNdj9/wICBLFyoTbP2Vdn82Ut6BjxBC2ZzeHurmR1AsGJw/xTFKJIzWksE2fzPSLJX1GTyOsF4yfPAIoJur61AdZriEhGRGIk6ZnIl/9uk+R7wCVAEXJqGmEREJGaizjP5R8Lt9Wh+iYiIJIjUMjGz6Wb273uU/buZVaYnLBERiZOo3VzDgVf2KPsT8M3UhiMiInEUNZk07uWxBW14voiIZLGoyWARcLuZ5QOE17eE5SIikuOinhr8PWA+sM7M3gYOJ5hncn66AhMRkfiIejbXu2Y2CDgVOAxYA/zR3RvSGZyIiMRD1JYJYeL4Q3gRERHZRQPoIiKStMgtExGRbFdWdjibNm1K+nVKS3u2+7lFRUVUV7+TdAwdTclERCS0adOmpBfZLCkpZMOGLe1+fjKJKJOizoAfbWbHpzsYERGJp6gtk5OBG82skGBuyYvh5c/unp0bgoiISGSRWibufqm7H0Gw22IVcCzBcvQfpjE2ERGJichjJmZmBHuafA44nWAvkxfTE5aIiMRJpGRiZu8DW4BfAo8C33H39o8w7f7avYE5wKeAOmBl+PobzOwzwH3A/gT7qVwSLoFPS3VxpzNKRCRuorZMfg2cAXwVKAZ6mdmL7r42BTE0Aj9y998DmNlU4E4zuxJ4DBjp7ovN7AfAncDl4dpge61LQTwZpzNKRCRuoo6ZXOnuAwi6uZ4D/h1YbmZvJRuAu/+zKZGE/gAcAZwEbHP3xWH5vcDXw9st1YmISAeLPAPezE4k2NfkEoJ9TGqBP6YymLDFMYqgJXQ48HZTnbt/AOSbWa9W6kREpINFHTP5ENgMLCT4R3+juyfdKtmLe4CtwAzga2l4/d307t0j3W/RbiUlhRl/jVTEIO2j333m6LPXPlHHTE5099XpDMTMpgFHA+e7e4OZvUPQ3dVUfyDQ4O7/bKmuLe+5ceNWGhr2zWkyyYx3QPJjJqmIQdpPv/vM0Wevefn5ec1+CY86ZrLazAaY2c1mNgMgvP9vqQjQzKYQjIN81d23h8V/AvY3syHh/auBeRHqRESkg0VdTuUigpnvhwCXhsU9gB8nG4CZHQOMBfoCL5nZa2b2ZLjk/QhglpmtJBj8/z7sWg5/r3UiItLxonZz3Qqc5e6vm9k3wrLXgaTX63L35UBeM3UvAce1tU5ERDpW1LO5SoG/hLcbE673zQEHERHpUFGTyZ8IupUSXUyKTw0WEZF4itrNNRp41syuALqb2TNAGXBO2iITEZHYiJRM3H2FmQ0AhgLzgTXAfHffms7gREQkHiKvGuzuHwO/SGMsIiISUy0mEzNbQMuD7I3u/oXUhiQiInHTWsvksWbKDyEYR+mW2nBE4m1f2D4AtIWAdLwWk4m7P5R4P9x7ZCxwJfBzgvknIhLaF7YPAG0hIB0v6kKPPYGbgGsJBuAHufvf0xmYiIjER2tjJvsDFcCNwO+BIeGMdRERkV1aa5msJpjY+CPgFeAgMzso8QHu/kJ6QhMRkbhoLZl8QnA216hm6huBo1IakYiIxE5rA/D9OigOERGJscjb9oqIiDRHyURERJKmZCIiIklTMhERkaQpmYiISNKUTEREJGlKJiIikrTI+5mIiGS7p6/7AlvuH5nUayS3RGcQQxwpmeyD9Actkhnn3vN8xld9Pre0J+tvTiqEjFAy2QfpD1pE4kZjJiIikjQlExERSZqSiYiIJE1jJiIptC+cPNEUh0hHUjIRSaF94eQJ0AkU0vFinUzMrAyYDfQGNgKXuvvKzEYlIpJ7Yp1MgHuBme7+mJldAtwHfD7DMaVEaWnPjL5/UVFRRt9fROIltsnEzEqBQcDZYdFcYIaZlbj7hsxFlrxku0kgSEapeB1pu0x/EQB9GUhGpo9fXI9dbJMJcBiw1t3rAdy93szeC8sjJZPevXukMbzMKykpzHQIOaexsbHVxxx77LEsX7683e9xzDHH8MYbb7T7+dK8KMevNXl5eSl5nbiJczJJ2saNW2loyN6DnuwgrqTHggUvt1gfZQBexzYzyssHs2LFm60+Li8vr9m6AQMGsnDh0lSG1WHy8/Oa/RIe52SyBjjEzArCVkkB0DcsFxFJuShJIBVn48VRbCctuvt64DVgeFg0HHg17uMlIiJxFOeWCcDVwGwzmwB8CFya4XhERHJSrJOJu68ABmc6DhGRXBfbbi4REdl3KJmIiEjSlExERCRpSiYiIpI0JRMREUmakomIiCRNyURERJIW63kmuSrq+kAtrX4a5/WBRGTfo2QSQ1ofSET2NermEhGRpCmZiIhI0pRMREQkaUomIiKSNCUTERFJmpKJiIgkTclERESSlqvzTAoA8vPzMh1HWmX7z5fNdOziLVuPX8LPVbBnXV5jY2PHRrNvGAIsynQQIiIxdQawOLEgV5NJF+AUYB1Qn+FYRETiogA4GFgGbE+syNVkIiIiKaQBeBERSZqSiYiIJE3JREREkqZkIiIiSVMyERGRpCmZiIhI0pRMREQkabm6nEqbmVkjUOjuWxPKPgBOdvfVrTz3t8B17v53MxsJvOTu1e2I4Ragh7uP2UvdamCou79hZt2AJ4H3gG8D9wGz3T1ls/7N7BHgFXefkarXjAsz6wSMB4YDO8PLSmCCu/8tk7FJejR9voCTifj5zbXPiJJJB3D38xLujgQ+ANqcTKIwsyLgNwQzVK9390aChCKp81OgGzDY3TeZWR5wHmBApGRiZvu5+840xijpMZI0fn7jTMkkRcJvLo8CZxMsNzCt6RtJwreaUwi+2Uw3s9uBMe7+nJn9F3ABwfFYC1zp7jVmdgDwEHAsUAOsAd5vIYxSYA7wa3efmBDb78N45offlrYBZcBhwMvAZe7eaGaHhD9DH+DvQB7wjLvPSKg7GFgNNCS8/kHAvcCnwudMdfdHE372x4AvAIcA3w/j/CbQC7jc3Re2+gveR5jZ0cDXgEPdfRNAmLB/E9Z3BiYDnyVYtucvwCh33xr+7ncSJJ1CM6sA7gb+CHwG2AGMACYSHPM1wGU3+DMAAAeVSURBVDB3rzWzLwC3A10J/k4mu/vj4Xv+nuDLw2lAX+AX7v59MzsF+Km7H5sQ/+thPC+l5zeU1c5kj88vwefxJ0B3gmNzv7tXJj7JzLoCq4BB7r4uLJsO1Lj7lA6MP600ZpJa3dz9NOBzwJ1m1iOx0t1/CrwCjHb3E8JEcgnBP+HPuPsg4LfAf4dPmQB85O4DgAsJ/kG15BfAU4mJpBnHEnyTPgY4CTgrLJ8OLHD3Y4Dr9ni/6cBCd/80cO1e6t5w938Dzgl/9mMT6ruEv5cLgAeAHe5+KjAOiNuH6URgpbt/2Ez9fwKb3f1Udz+eoKtxbEL9CcCX3P2E8P6ngZnufhxBYn8GuCH8PdcTdKUB/BkY4u4nEhyvaWZWnPC6hwPlYXzfNrOj3X0ZsNXMPgtgZmcADUok7baAPT6/BF+szgo/u6cCV5nZwMQnufs2YDZwFUD4f+Fi4MEOjD3tlEySl7i42eMA4RjKh8ChEZ7/FYJ/Dn82s9eAa4B+Yd2ZBC0T3P0DoKqV1/ot8A0z69vK437l7tvcvY7gn9SnEt7vp+H7vQ08n/CcMwn/+N39H3vUnUUwLkP4zeu34eOb/Dy8/jNB91DT/T8B/VuJdZ9mZp82s9fMrNrM7iY4npeEZa+F9z+V8JRfunttwn1399fC238GXnP3d8P7ib+fEuCXZvYGQcLpRdDCaTLP3RvcfTPwZsJ7Tge+G96+BpiZ7M8su+kGPGRmfwWWELQMj9/L42YC3zKz/YBLgGfdfX3HhZl+6uaKbgPQG9gKQZ83cEBY3mRbwu16ov1+84Db3f3hFMT4I+B8YIGZnenu7zXzuPbEmYxtAO5eb2aJ798R751qrwJHm1mRu28KB9xPMLNrCbpA8oDvuvsLzTx/6x739zwWe97fP7w9C/g1QbdXo5lVE3SrNPc6Tb/XecAdZnYiQYK/PMoPKZFNIeiCHunuO83sWXY/LgC4+xozewX4D4KkflXHhpl+aplE9zvgOwn3rwL+4O4ft/F1PiJIQk1+DXy3qcvCzLqYWdM3mxeAb4XlvQn66lvk7ncQNKkXRGih7On3wGXh+x0GfD6hLjGWIwnGQJo8B1wZ1vUh6EJr7p9prLn7SuB/gAfCMa0m3cPrXwM3mNn+AGZWuGe3RzsVAavDRHI2EVt07r4DeDiM62ft+HuV3e35+S0C1oSJ5FiCfT6acw9QSdDN+3IaY8wIJZPoKoB+ZvaXsPviXILB0ra6H5gQdoOc5e5zgJ8BL5rZXwi6Nk4PH3sbUGxmK4AngEgD1eGg3qO0PaF8DzjbzJYTfBP+I7A5oe5MM/sbMIMg8TQZDRwfxv874PvuvrwN7xs3I4EVwDIzW25miwnGnqYDdwKvh3V/IdhAKBXJ5PsE4ySvAV8nGNiP6kGCkx9mpSCOXLfb55fgpIgrw2N9Cy18Rt39RYIW5E86ItCOpv1MZJfw2/SO8FtW0wY4X3B3z3BokoTwJI/h7v7lTMeSy8IW/RKgfza2EOPWXy3pdTTwaDhvohMwSYkk3szsGYLB+K9kOpZcZma3EoxX3ZiNiQTUMhERkRTQmImIiCRNyURERJKmZCIiIklTMhGJOTM7w8x0ooRklAbgRSIys28CNwADgC3AawQLLi5O43s2Ake7+1vpeg+RVFDLRCQCM7uBYPbyFOAggoUVf0KwPEYm49Lp/bJPUMtEpBXhsilrgW+5+7y91HcBfkgwMx2C1Zv/y923h5uhfdvdhyQ8fldrI1yWvpZgcc9ygv1QvhlupLaQYHmOjwkWFL2CYMnzxwiW5rieYMWBh4DH3P3Q8PX7hvXlBGuB3eXu08O6UwmSYBnwCcESKzek4NckOU4tE5HWnUaweN+TzdSPJ9iP5ASCFWNPBX7Qhte/GJgEFANvEeyHgruXh/XHu3sPd29abbkPwarBR7DHgoFmlg88RbCkyyEEa6hVmNkXw4fcDdzt7j0JJjP+og1xijRLTWSR1vUGPmhhZ8T/Q7At83oAM5tEsCT/zRFf/0l3/2P43J8BP27l8Q3ARHffHj4nse4UoMTdbw3v/8PMHiBIWM8QbMDV38wODLc1+EPEGEVapGQi0rqNwIEtbLXbF3g74f7bYVlUNQm3PwZ6NPfA0IZww6W9OQLoa2abEsoKgEXh7SuAW4EVZraKYMmc+W2IVWSvlExEWvcysB34KvDLvdS/R/BPvGml5MPDMgjGQ7o1PTBcoj9ZLQ10rgFWufvRe6sMl9AfHnaHDSPYcKv3Hht2ibSZkolIK9x9s5lNAGaa2U7gWYLuorMINpyaC/zAzJYR/KOfQDBIDsHYxTFmdgLBsvW3tPHt3weOIhhLieKPwBYz+y+CJfHrCJbA39/dl4UrCD/j7hsSWi8NbYxJ5F9oAF4kAnf/b4I5Jj8g2F1zDXAt8CuCPS1eIdhj5K8E2+/eHj6vmqBb6TlgJcH+Jm1xCzDbzDaZ2ddbe7C71wNDCU4GWAV8QLCfSdOGTl8ClpvZVoLB+Ivd/ZM2xiTyL3RqsIiIJE0tExERSZqSiYiIJE3JREREkqZkIiIiSVMyERGRpCmZiIhI0pRMREQkaUomIiKSNCUTERFJ2v8HYib82O7LaBMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot([data[data['location']=='United Kingdom']['new_cases_per_million'], \n",
    "             data[data['location']=='Germany']['new_cases_per_million'], data[data['location']=='Italy']['new_cases_per_million']], labels=['United Kingdom','Germany','Italy'])\n",
    "plt.xlabel('Countries')\n",
    "plt.ylabel('New cases per million')\n",
    "plt.savefig('newcases.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NRR5rSV6JTZI"
   },
   "source": [
    "Total vaccination percentages of Turkey, United States, France and Japan. United States' vaccination percentage increased way more faster than the other 3 countries. Turkey and France seems to have vaccinated near amounts of their populations. Japan seems like they are slow at vaccinations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "sXQzwpg86wZj",
    "outputId": "3d98fae9-caf9-4be5-9676-13be7f65eaea"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(data[data['location']=='Turkey']['date'], data[data['location']=='Turkey']['total_vaccinations_per_hundred'], color='red')\n",
    "ax.plot(data[data['location']=='United States']['date'], data[data['location']=='United States']['total_vaccinations_per_hundred'], color='blue')\n",
    "ax.plot(data[data['location']=='France']['date'], data[data['location']=='France']['total_vaccinations_per_hundred'], color='magenta')\n",
    "ax.plot(data[data['location']=='Japan']['date'], data[data['location']=='Japan']['total_vaccinations_per_hundred'], color='black')\n",
    "plt.xlabel('Date')\n",
    "ax.set_xticks([])\n",
    "plt.ylabel('Total vaccinations percentage')\n",
    "plt.legend(['Turkey','US','France','Japan'])\n",
    "plt.savefig(\"total_vac.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QxXGXB7ZOJoF"
   },
   "source": [
    "Death count of the world due to coronavirus. Unfortunately, more than 3 milion people have died. The amount of deaths increased exponentially."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 355
    },
    "id": "GuLkFg7-FK6J",
    "outputId": "da476a3e-9d12-4eca-a20a-4a19ed921d9e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(data[data['location']=='World']['date'], data[data['location']=='World']['total_deaths'])\n",
    "ax.set_xticks([x for x in range(1,data[data['location']=='World']['date'].shape[0],100)])\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylim((0, 3500000))\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Total Deaths(in millions)')\n",
    "plt.title('Total Deaths in World')\n",
    "plt.savefig('world.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0QRq5RxsQTp3"
   },
   "source": [
    "Looking at the total cases per million for 5 continents. Africa and Asia seems to have way less cases than Europe and American continents. The reason may be the emptyness of the information or people's request for a test. Also, Asia has more than half of the worlds population, which may lead to better results than the other continents, in the case of good protection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "XfLqvuSJLBSd",
    "outputId": "f72dbb17-7464-4c75-88be-7b9b829556be"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(data[data['location']=='Africa']['date'], data[data['location']=='Africa']['total_cases_per_million'], color='red')\n",
    "ax.plot(data[data['location']=='Europe']['date'], data[data['location']=='Europe']['total_cases_per_million'], color='blue')\n",
    "ax.plot(data[data['location']=='Asia']['date'], data[data['location']=='Asia']['total_cases_per_million'], color='magenta')\n",
    "ax.plot(data[data['location']=='North America']['date'], data[data['location']=='North America']['total_cases_per_million'], color='black')\n",
    "ax.plot(data[data['location']=='South America']['date'], data[data['location']=='South America']['total_cases_per_million'], color='green')\n",
    "plt.xlabel('Date')\n",
    "ax.set_xticks([])\n",
    "plt.ylabel('Total cases per million')\n",
    "plt.legend(['Africa','Europe','Asia','North America','South America'])\n",
    "plt.savefig(\"continents.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0s883cI0-ccC"
   },
   "source": [
    "Dropping some features.\n",
    "\n",
    "\n",
    "iso_code(3 letter codes of countries) is dropped because the location feature already provides the country name.\n",
    "\n",
    "smoothed versions of cases,deaths,tests and vaccinations are dropped because they are closely correlated with normal versions.\n",
    "\n",
    "weekly_icu_admission and weekly_hosp_admissions are dropped because the amount of data in these columnns are small.\n",
    "\n",
    "aged_70_older is dropped because the aged_65_older feature is closely correlated with that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 590
    },
    "id": "RNJoGqn0-ccC",
    "outputId": "e75c27ce-6af7-4660-a130-9add4085dc6e"
   },
   "outputs": [
    {
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.026</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.511</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.33</td>\n",
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       "      <td>54.422</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1495.0</td>\n",
       "      <td>484446.0</td>\n",
       "      <td>32.594</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.020</td>\n",
       "      <td>49.6</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>411610.0</td>\n",
       "      <td>353834.0</td>\n",
       "      <td>57776.0</td>\n",
       "      <td>21592.0</td>\n",
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       "      <td>0.39</td>\n",
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       "      <td>Africa</td>\n",
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       "      <td>38164.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1565.0</td>\n",
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       "      <td>2567.731</td>\n",
       "      <td>4.171</td>\n",
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       "      <td>0.336</td>\n",
       "      <td>1.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.158</td>\n",
       "      <td>0.024</td>\n",
       "      <td>41.2</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>433939.0</td>\n",
       "      <td>370676.0</td>\n",
       "      <td>63263.0</td>\n",
       "      <td>22329.0</td>\n",
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       "      <td>0.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
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       "    <tr>\n",
       "      <th>85168</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
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       "      <td>38191.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1565.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.136</td>\n",
       "      <td>0.018</td>\n",
       "      <td>56.9</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>458013.0</td>\n",
       "      <td>388021.0</td>\n",
       "      <td>69992.0</td>\n",
       "      <td>24074.0</td>\n",
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       "      <td>0.47</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1.00</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>400771.0</td>\n",
       "      <td>76826.0</td>\n",
       "      <td>19584.0</td>\n",
       "      <td>3.21</td>\n",
       "      <td>2.70</td>\n",
       "      <td>0.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85170</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>38257.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2573.988</td>\n",
       "      <td>1.480</td>\n",
       "      <td>105.430</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500342.0</td>\n",
       "      <td>414735.0</td>\n",
       "      <td>85607.0</td>\n",
       "      <td>22745.0</td>\n",
       "      <td>3.37</td>\n",
       "      <td>2.79</td>\n",
       "      <td>0.58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85171 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      continent     location  ... life_expectancy  human_development_index\n",
       "0          Asia  Afghanistan  ...           64.83                    0.511\n",
       "1          Asia  Afghanistan  ...           64.83                    0.511\n",
       "2          Asia  Afghanistan  ...           64.83                    0.511\n",
       "3          Asia  Afghanistan  ...           64.83                    0.511\n",
       "4          Asia  Afghanistan  ...           64.83                    0.511\n",
       "...         ...          ...  ...             ...                      ...\n",
       "85166    Africa     Zimbabwe  ...           61.49                    0.571\n",
       "85167    Africa     Zimbabwe  ...           61.49                    0.571\n",
       "85168    Africa     Zimbabwe  ...           61.49                    0.571\n",
       "85169    Africa     Zimbabwe  ...           61.49                    0.571\n",
       "85170    Africa     Zimbabwe  ...           61.49                    0.571\n",
       "\n",
       "[85171 rows x 45 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(labels=['iso_code','new_deaths_smoothed','new_cases_smoothed','new_cases_smoothed_per_million',\n",
    "                   'new_deaths_smoothed_per_million', 'new_tests_smoothed', 'new_tests_smoothed_per_thousand',\n",
    "                      'new_vaccinations_smoothed','new_vaccinations_smoothed_per_million', 'aged_70_older',\n",
    "                  'weekly_icu_admissions','weekly_icu_admissions_per_million','weekly_hosp_admissions', \n",
    "                  'weekly_hosp_admissions_per_million'], axis=1,inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bpYpIkKm-ccC"
   },
   "source": [
    "In the info(), it seems like there are null values even in continent column. When we look at this values, we see that this rows are from the whole continents and and the whole world. These are irrelevant for our goal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 590
    },
    "id": "9NL3owTR-ccD",
    "outputId": "a084bf7a-64d5-496e-cada-a0623ac14801"
   },
   "outputs": [
    {
     "data": {
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       "      <th>continent</th>\n",
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       "      <td>15469.207</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>6.434</td>\n",
       "      <td>34.635</td>\n",
       "      <td>60.13</td>\n",
       "      <td>2.705</td>\n",
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       "      <td>0.737</td>\n",
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       "    <tr>\n",
       "      <th>83968</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.134333e+09</td>\n",
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       "      <td>22012766.0</td>\n",
       "      <td>14.55</td>\n",
       "      <td>7.64</td>\n",
       "      <td>3.44</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>58.045</td>\n",
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       "      <td>15469.207</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>34.635</td>\n",
       "      <td>60.13</td>\n",
       "      <td>2.705</td>\n",
       "      <td>72.58</td>\n",
       "      <td>0.737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4111 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      continent location  ... life_expectancy  human_development_index\n",
       "432         NaN   Africa  ...             NaN                      NaN\n",
       "433         NaN   Africa  ...             NaN                      NaN\n",
       "434         NaN   Africa  ...             NaN                      NaN\n",
       "435         NaN   Africa  ...             NaN                      NaN\n",
       "436         NaN   Africa  ...             NaN                      NaN\n",
       "...         ...      ...  ...             ...                      ...\n",
       "83964       NaN    World  ...           72.58                    0.737\n",
       "83965       NaN    World  ...           72.58                    0.737\n",
       "83966       NaN    World  ...           72.58                    0.737\n",
       "83967       NaN    World  ...           72.58                    0.737\n",
       "83968       NaN    World  ...           72.58                    0.737\n",
       "\n",
       "[4111 rows x 45 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['continent'].isnull()==True]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p-t7Ctm6-ccD"
   },
   "source": [
    "Also there are null values in population column. When we look, we see that all of this examples are from Northern Cyprus. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 590
    },
    "id": "cWNMGNZy-ccD",
    "outputId": "71be1aa0-3ee0-48eb-dabb-5d0e5564ff88"
   },
   "outputs": [
    {
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       "      <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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57044</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Northern Cyprus</td>\n",
       "      <td>2021-04-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107365.0</td>\n",
       "      <td>58812.0</td>\n",
       "      <td>48553.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.09</td>\n",
       "      <td>15.39</td>\n",
       "      <td>12.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57045</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Northern Cyprus</td>\n",
       "      <td>2021-04-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57046</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Northern Cyprus</td>\n",
       "      <td>2021-04-21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57047</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Northern Cyprus</td>\n",
       "      <td>2021-04-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57048</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Northern Cyprus</td>\n",
       "      <td>2021-04-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>109490.0</td>\n",
       "      <td>60642.0</td>\n",
       "      <td>48848.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.65</td>\n",
       "      <td>15.87</td>\n",
       "      <td>12.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>100 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      continent         location  ... life_expectancy  human_development_index\n",
       "56949      Asia  Northern Cyprus  ...             NaN                      NaN\n",
       "56950      Asia  Northern Cyprus  ...             NaN                      NaN\n",
       "56951      Asia  Northern Cyprus  ...             NaN                      NaN\n",
       "56952      Asia  Northern Cyprus  ...             NaN                      NaN\n",
       "56953      Asia  Northern Cyprus  ...             NaN                      NaN\n",
       "...         ...              ...  ...             ...                      ...\n",
       "57044      Asia  Northern Cyprus  ...             NaN                      NaN\n",
       "57045      Asia  Northern Cyprus  ...             NaN                      NaN\n",
       "57046      Asia  Northern Cyprus  ...             NaN                      NaN\n",
       "57047      Asia  Northern Cyprus  ...             NaN                      NaN\n",
       "57048      Asia  Northern Cyprus  ...             NaN                      NaN\n",
       "\n",
       "[100 rows x 45 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna(subset=['continent'],inplace=True)\n",
    "data[data['population'].isnull()==True]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OLdLRMjB-ccD"
   },
   "source": [
    "Dropping the data from Northern Cyprus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PUNNmkif-ccE",
    "outputId": "850fb2c3-5cbc-4649-cd88-d00d14f73358"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 80960 entries, 0 to 85170\n",
      "Data columns (total 45 columns):\n",
      " #   Column                               Non-Null Count  Dtype  \n",
      "---  ------                               --------------  -----  \n",
      " 0   continent                            80960 non-null  object \n",
      " 1   location                             80960 non-null  object \n",
      " 2   date                                 80960 non-null  object \n",
      " 3   total_cases                          78966 non-null  float64\n",
      " 4   new_cases                            78959 non-null  float64\n",
      " 5   total_deaths                         69469 non-null  float64\n",
      " 6   new_deaths                           69468 non-null  float64\n",
      " 7   total_cases_per_million              78966 non-null  float64\n",
      " 8   new_cases_per_million                78959 non-null  float64\n",
      " 9   total_deaths_per_million             69469 non-null  float64\n",
      " 10  new_deaths_per_million               69468 non-null  float64\n",
      " 11  reproduction_rate                    68479 non-null  float64\n",
      " 12  icu_patients                         8684 non-null   float64\n",
      " 13  icu_patients_per_million             8684 non-null   float64\n",
      " 14  hosp_patients                        10814 non-null  float64\n",
      " 15  hosp_patients_per_million            10814 non-null  float64\n",
      " 16  new_tests                            38945 non-null  float64\n",
      " 17  total_tests                          38652 non-null  float64\n",
      " 18  total_tests_per_thousand             38652 non-null  float64\n",
      " 19  new_tests_per_thousand               38945 non-null  float64\n",
      " 20  positive_rate                        42904 non-null  float64\n",
      " 21  tests_per_case                       42311 non-null  float64\n",
      " 22  tests_units                          46079 non-null  object \n",
      " 23  total_vaccinations                   8395 non-null   float64\n",
      " 24  people_vaccinated                    7768 non-null   float64\n",
      " 25  people_fully_vaccinated              5577 non-null   float64\n",
      " 26  new_vaccinations                     6998 non-null   float64\n",
      " 27  total_vaccinations_per_hundred       8395 non-null   float64\n",
      " 28  people_vaccinated_per_hundred        7768 non-null   float64\n",
      " 29  people_fully_vaccinated_per_hundred  5577 non-null   float64\n",
      " 30  stringency_index                     72207 non-null  float64\n",
      " 31  population                           80960 non-null  float64\n",
      " 32  population_density                   78809 non-null  float64\n",
      " 33  median_age                           76241 non-null  float64\n",
      " 34  aged_65_older                        75365 non-null  float64\n",
      " 35  gdp_per_capita                       76581 non-null  float64\n",
      " 36  extreme_poverty                      51984 non-null  float64\n",
      " 37  cardiovasc_death_rate                77169 non-null  float64\n",
      " 38  diabetes_prevalence                  78314 non-null  float64\n",
      " 39  female_smokers                       60363 non-null  float64\n",
      " 40  male_smokers                         59466 non-null  float64\n",
      " 41  handwashing_facilities               38542 non-null  float64\n",
      " 42  hospital_beds_per_thousand           70382 non-null  float64\n",
      " 43  life_expectancy                      80368 non-null  float64\n",
      " 44  human_development_index              77052 non-null  float64\n",
      "dtypes: float64(41), object(4)\n",
      "memory usage: 28.4+ MB\n"
     ]
    }
   ],
   "source": [
    "index_of_ncyprus =data[data['location'] == 'Northern Cyprus'].index\n",
    "data.drop(index_of_ncyprus, inplace=True)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n8H8LOkS-ccE"
   },
   "source": [
    "Since our goal is to predict people_fully_vaccinated_per_hundred, we need to see the date which this feature is filled for the first time.\n",
    "\n",
    "When we look, we see that first data of people_fully_vaccinated_per_hundred is from Germany and it is at 27 December, 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
    },
    "id": "A2sYoswO-ccE",
    "outputId": "d0855cfe-c17e-4efd-bfdd-2d19ac341fcc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>29871</th>\n",
       "      <td>Germany</td>\n",
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       "      <th>29874</th>\n",
       "      <td>Germany</td>\n",
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       "      <td>Germany</td>\n",
       "      <td>2020-12-31</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>72963</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>2021-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78906</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>2021-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35020</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>2021-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61409</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>2021-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85170</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-04-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5577 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          location        date\n",
       "29871      Germany  2020-12-27\n",
       "29872      Germany  2020-12-28\n",
       "29873      Germany  2020-12-29\n",
       "29874      Germany  2020-12-30\n",
       "29875      Germany  2020-12-31\n",
       "...            ...         ...\n",
       "72963    Sri Lanka  2021-04-30\n",
       "78906       Turkey  2021-04-30\n",
       "35020      Iceland  2021-04-30\n",
       "61409  Philippines  2021-04-30\n",
       "85170     Zimbabwe  2021-04-30\n",
       "\n",
       "[5577 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['people_fully_vaccinated_per_hundred'].isnull()==False][['location','date']].sort_values('date',ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5cIgVjbS-ccE"
   },
   "source": [
    "But when we look at the date which the people_vaccinated and total_vaccinations datas are provided together for the first time, we see that day is 15 December, 2020.\n",
    "\n",
    "Since \"fully vaccinated\" means someone who took 2 doses of vaccine, we can fill the data before 27 December, with the formula:\n",
    "\n",
    "$$\n",
    "people\\_fully\\_vaccinated=total\\_vaccinations - people\\_vaccinated\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
    },
    "id": "l0Kpfnlr-ccF",
    "outputId": "b25358d6-4639-48eb-ea59-97ad86ef4dc2"
   },
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>63439</th>\n",
       "      <td>2020-12-15</td>\n",
       "      <td>Russia</td>\n",
       "      <td>28500.0</td>\n",
       "      <td>28500.0</td>\n",
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       "    <tr>\n",
       "      <th>38050</th>\n",
       "      <td>2020-12-19</td>\n",
       "      <td>Israel</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81076</th>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>United States</td>\n",
       "      <td>556208.0</td>\n",
       "      <td>556208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38051</th>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>Israel</td>\n",
       "      <td>7436.0</td>\n",
       "      <td>7436.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81077</th>\n",
       "      <td>2020-12-21</td>\n",
       "      <td>United States</td>\n",
       "      <td>614117.0</td>\n",
       "      <td>614117.0</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>67548</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>Serbia</td>\n",
       "      <td>2026571.0</td>\n",
       "      <td>3514605.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34591</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>4023137.0</td>\n",
       "      <td>5971865.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6631</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>693423.0</td>\n",
       "      <td>1235170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5788</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>966132.0</td>\n",
       "      <td>1488218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85170</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>414735.0</td>\n",
       "      <td>500342.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7692 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date       location  people_vaccinated  total_vaccinations\n",
       "63439  2020-12-15         Russia            28500.0             28500.0\n",
       "38050  2020-12-19         Israel               61.0                61.0\n",
       "81076  2020-12-20  United States           556208.0            556208.0\n",
       "38051  2020-12-20         Israel             7436.0              7436.0\n",
       "81077  2020-12-21  United States           614117.0            614117.0\n",
       "...           ...            ...                ...                 ...\n",
       "67548  2021-04-30         Serbia          2026571.0           3514605.0\n",
       "34591  2021-04-30        Hungary          4023137.0           5971865.0\n",
       "6631   2021-04-30        Bahrain           693423.0           1235170.0\n",
       "5788   2021-04-30     Azerbaijan           966132.0           1488218.0\n",
       "85170  2021-04-30       Zimbabwe           414735.0            500342.0\n",
       "\n",
       "[7692 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['people_vaccinated'].isnull()==False) & (data['total_vaccinations'].isnull()==False)].sort_values('date',ascending=True)[['date','location','people_vaccinated','total_vaccinations']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xerpchgv-ccF"
   },
   "source": [
    "Because we have information about vaccine since the 15th of December, we drop the data before that date.\n",
    "\n",
    "Also since the dataset already has the population and per million/per thousand/per hundred versions of some features, we can drop the original features, such as total cases and new cases.\n",
    "\n",
    "Now we have 27412 examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Js1hi4D--ccF",
    "outputId": "9051cd02-8898-4f67-abf4-b3ab11a771a2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 27412 entries, 295 to 85170\n",
      "Data columns (total 36 columns):\n",
      " #   Column                               Non-Null Count  Dtype  \n",
      "---  ------                               --------------  -----  \n",
      " 0   continent                            27412 non-null  object \n",
      " 1   location                             27412 non-null  object \n",
      " 2   date                                 27412 non-null  object \n",
      " 3   total_cases_per_million              25993 non-null  float64\n",
      " 4   new_cases_per_million                25993 non-null  float64\n",
      " 5   total_deaths_per_million             24494 non-null  float64\n",
      " 6   new_deaths_per_million               24494 non-null  float64\n",
      " 7   reproduction_rate                    24526 non-null  float64\n",
      " 8   icu_patients_per_million             2940 non-null   float64\n",
      " 9   hosp_patients_per_million            3636 non-null   float64\n",
      " 10  total_tests_per_thousand             12489 non-null  float64\n",
      " 11  new_tests_per_thousand               12665 non-null  float64\n",
      " 12  positive_rate                        14590 non-null  float64\n",
      " 13  tests_units                          15389 non-null  object \n",
      " 14  total_vaccinations                   8394 non-null   float64\n",
      " 15  people_vaccinated                    7768 non-null   float64\n",
      " 16  people_fully_vaccinated              5577 non-null   float64\n",
      " 17  new_vaccinations                     6998 non-null   float64\n",
      " 18  total_vaccinations_per_hundred       8394 non-null   float64\n",
      " 19  people_vaccinated_per_hundred        7768 non-null   float64\n",
      " 20  people_fully_vaccinated_per_hundred  5577 non-null   float64\n",
      " 21  stringency_index                     22826 non-null  float64\n",
      " 22  population                           27412 non-null  float64\n",
      " 23  population_density                   26401 non-null  float64\n",
      " 24  median_age                           24990 non-null  float64\n",
      " 25  aged_65_older                        24716 non-null  float64\n",
      " 26  gdp_per_capita                       25335 non-null  float64\n",
      " 27  extreme_poverty                      16951 non-null  float64\n",
      " 28  cardiovasc_death_rate                25384 non-null  float64\n",
      " 29  diabetes_prevalence                  25928 non-null  float64\n",
      " 30  female_smokers                       19475 non-null  float64\n",
      " 31  male_smokers                         19201 non-null  float64\n",
      " 32  handwashing_facilities               12878 non-null  float64\n",
      " 33  hospital_beds_per_thousand           22763 non-null  float64\n",
      " 34  life_expectancy                      27096 non-null  float64\n",
      " 35  human_development_index              25254 non-null  float64\n",
      "dtypes: float64(32), object(4)\n",
      "memory usage: 7.7+ MB\n"
     ]
    }
   ],
   "source": [
    "vaccine=data[data['date'] >= '2020-12-15']\n",
    "vaccine.drop(['total_cases','new_cases','total_deaths','new_deaths','icu_patients','hosp_patients','new_tests',\n",
    "                 'total_tests','tests_per_case'],axis=1, inplace=True)\n",
    "vaccine.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8yrgSOm4-ccF"
   },
   "source": [
    "By executing the rows below, it fills the empty people_fully_vaccinated rows, if the total_vaccinations and people_vaccinated values are provided. After that, it fills the per_hundred version of the people fully vaccinated using the population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "M5eN-Ax_-ccG"
   },
   "outputs": [],
   "source": [
    "vaccine['people_fully_vaccinated'] = vaccine.apply(\n",
    "    lambda row: row.total_vaccinations - row.people_vaccinated \n",
    "    if np.isnan(row.people_fully_vaccinated) else row.people_fully_vaccinated, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "HG_0YOND-ccG"
   },
   "outputs": [],
   "source": [
    "vaccine['people_fully_vaccinated_per_hundred'] = vaccine.apply(\n",
    "    lambda row: (row.people_fully_vaccinated / row.population)*100\n",
    "    if np.isnan(row.people_fully_vaccinated_per_hundred) else row.people_fully_vaccinated_per_hundred, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DrGn7aI0-ccG"
   },
   "source": [
    "The count of non-null examples in people_fully_vaccinated_per_hundred column increased from 5577 to 7695."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MT8SSFs3-ccG",
    "outputId": "a97e8909-91d7-4fda-d3de-a59821bf3f06"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 27412 entries, 295 to 85170\n",
      "Data columns (total 36 columns):\n",
      " #   Column                               Non-Null Count  Dtype  \n",
      "---  ------                               --------------  -----  \n",
      " 0   continent                            27412 non-null  object \n",
      " 1   location                             27412 non-null  object \n",
      " 2   date                                 27412 non-null  object \n",
      " 3   total_cases_per_million              25993 non-null  float64\n",
      " 4   new_cases_per_million                25993 non-null  float64\n",
      " 5   total_deaths_per_million             24494 non-null  float64\n",
      " 6   new_deaths_per_million               24494 non-null  float64\n",
      " 7   reproduction_rate                    24526 non-null  float64\n",
      " 8   icu_patients_per_million             2940 non-null   float64\n",
      " 9   hosp_patients_per_million            3636 non-null   float64\n",
      " 10  total_tests_per_thousand             12489 non-null  float64\n",
      " 11  new_tests_per_thousand               12665 non-null  float64\n",
      " 12  positive_rate                        14590 non-null  float64\n",
      " 13  tests_units                          15389 non-null  object \n",
      " 14  total_vaccinations                   8394 non-null   float64\n",
      " 15  people_vaccinated                    7768 non-null   float64\n",
      " 16  people_fully_vaccinated              7695 non-null   float64\n",
      " 17  new_vaccinations                     6998 non-null   float64\n",
      " 18  total_vaccinations_per_hundred       8394 non-null   float64\n",
      " 19  people_vaccinated_per_hundred        7768 non-null   float64\n",
      " 20  people_fully_vaccinated_per_hundred  7695 non-null   float64\n",
      " 21  stringency_index                     22826 non-null  float64\n",
      " 22  population                           27412 non-null  float64\n",
      " 23  population_density                   26401 non-null  float64\n",
      " 24  median_age                           24990 non-null  float64\n",
      " 25  aged_65_older                        24716 non-null  float64\n",
      " 26  gdp_per_capita                       25335 non-null  float64\n",
      " 27  extreme_poverty                      16951 non-null  float64\n",
      " 28  cardiovasc_death_rate                25384 non-null  float64\n",
      " 29  diabetes_prevalence                  25928 non-null  float64\n",
      " 30  female_smokers                       19475 non-null  float64\n",
      " 31  male_smokers                         19201 non-null  float64\n",
      " 32  handwashing_facilities               12878 non-null  float64\n",
      " 33  hospital_beds_per_thousand           22763 non-null  float64\n",
      " 34  life_expectancy                      27096 non-null  float64\n",
      " 35  human_development_index              25254 non-null  float64\n",
      "dtypes: float64(32), object(4)\n",
      "memory usage: 7.7+ MB\n"
     ]
    }
   ],
   "source": [
    "vaccine.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jUV89Voi-ccH"
   },
   "source": [
    "It still has lots of null values, we can fill some of them with backfill method. But we need to separate the countries from each other to execute this operation correctly. By using backfill limit as 1, we don't fill consecutive null values, which may lead the model to give wrong results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "V8zXvwZi-ccH"
   },
   "outputs": [],
   "source": [
    "for country in vaccine['location'].unique():\n",
    "    country_vaccine = vaccine[vaccine['location'] == country]\n",
    "    country_vaccine.bfill(axis='rows',limit=1,inplace=True)\n",
    "    vaccine[vaccine['location'] == country] = country_vaccine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D7YtLCxm-ccH"
   },
   "source": [
    "We can also drop the vaccination features because we have their per hundred versions.\n",
    "\n",
    "people_fully_vaccinated_per_hundred has 8966 non-null values now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RJxD1fQ_-ccH",
    "outputId": "fedeedf0-b4b4-494c-ccbf-00f171ce5980"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 27412 entries, 295 to 85170\n",
      "Data columns (total 33 columns):\n",
      " #   Column                               Non-Null Count  Dtype  \n",
      "---  ------                               --------------  -----  \n",
      " 0   continent                            27412 non-null  object \n",
      " 1   location                             27412 non-null  object \n",
      " 2   date                                 27412 non-null  object \n",
      " 3   total_cases_per_million              25993 non-null  float64\n",
      " 4   new_cases_per_million                25993 non-null  float64\n",
      " 5   total_deaths_per_million             24503 non-null  float64\n",
      " 6   new_deaths_per_million               24503 non-null  float64\n",
      " 7   reproduction_rate                    24530 non-null  float64\n",
      " 8   icu_patients_per_million             2979 non-null   float64\n",
      " 9   hosp_patients_per_million            3704 non-null   float64\n",
      " 10  total_tests_per_thousand             13185 non-null  float64\n",
      " 11  new_tests_per_thousand               13030 non-null  float64\n",
      " 12  positive_rate                        14637 non-null  float64\n",
      " 13  tests_units                          15399 non-null  object \n",
      " 14  new_vaccinations                     7546 non-null   float64\n",
      " 15  total_vaccinations_per_hundred       9770 non-null   float64\n",
      " 16  people_vaccinated_per_hundred        9050 non-null   float64\n",
      " 17  people_fully_vaccinated_per_hundred  8966 non-null   float64\n",
      " 18  stringency_index                     22829 non-null  float64\n",
      " 19  population                           27412 non-null  float64\n",
      " 20  population_density                   26401 non-null  float64\n",
      " 21  median_age                           24990 non-null  float64\n",
      " 22  aged_65_older                        24716 non-null  float64\n",
      " 23  gdp_per_capita                       25335 non-null  float64\n",
      " 24  extreme_poverty                      16951 non-null  float64\n",
      " 25  cardiovasc_death_rate                25384 non-null  float64\n",
      " 26  diabetes_prevalence                  25928 non-null  float64\n",
      " 27  female_smokers                       19475 non-null  float64\n",
      " 28  male_smokers                         19201 non-null  float64\n",
      " 29  handwashing_facilities               12878 non-null  float64\n",
      " 30  hospital_beds_per_thousand           22763 non-null  float64\n",
      " 31  life_expectancy                      27096 non-null  float64\n",
      " 32  human_development_index              25254 non-null  float64\n",
      "dtypes: float64(29), object(4)\n",
      "memory usage: 7.1+ MB\n"
     ]
    }
   ],
   "source": [
    "vaccine.drop(['total_vaccinations','people_vaccinated','people_fully_vaccinated'],axis=1,inplace=True)\n",
    "vaccine.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TmdSKk7h-ccI"
   },
   "source": [
    "Since we filled enough, we can drop the examples with null people_fully_vaccinated_per_hundred."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 590
    },
    "id": "etRyC3Ue-ccI",
    "outputId": "c13584cb-0ce2-4e5f-9c84-b817df09c176"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>total_deaths_per_million</th>\n",
       "      <th>new_deaths_per_million</th>\n",
       "      <th>reproduction_rate</th>\n",
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       "      <th>hosp_patients_per_million</th>\n",
       "      <th>total_tests_per_thousand</th>\n",
       "      <th>new_tests_per_thousand</th>\n",
       "      <th>positive_rate</th>\n",
       "      <th>tests_units</th>\n",
       "      <th>new_vaccinations</th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>people_vaccinated_per_hundred</th>\n",
       "      <th>people_fully_vaccinated_per_hundred</th>\n",
       "      <th>stringency_index</th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>aged_65_older</th>\n",
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       "      <th>extreme_poverty</th>\n",
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       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
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       "  <tbody>\n",
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       "      <th>363</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-02-21</td>\n",
       "      <td>1428.368</td>\n",
       "      <td>0.617</td>\n",
       "      <td>62.474</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.04</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>18.6</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</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",
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       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-02-22</td>\n",
       "      <td>1428.702</td>\n",
       "      <td>0.334</td>\n",
       "      <td>62.499</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.04</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>18.6</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-02-27</td>\n",
       "      <td>1431.014</td>\n",
       "      <td>0.283</td>\n",
       "      <td>62.756</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.04</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>18.6</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>1431.194</td>\n",
       "      <td>0.180</td>\n",
       "      <td>62.756</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.04</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>18.6</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-03-15</td>\n",
       "      <td>1438.155</td>\n",
       "      <td>0.000</td>\n",
       "      <td>63.167</td>\n",
       "      <td>0.051</td>\n",
       "      <td>1.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.04</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>18.6</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</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",
       "    </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",
       "      <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>85166</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-04-26</td>\n",
       "      <td>2563.560</td>\n",
       "      <td>1.077</td>\n",
       "      <td>104.959</td>\n",
       "      <td>0.202</td>\n",
       "      <td>1.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.594</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.020</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>21592.0</td>\n",
       "      <td>2.77</td>\n",
       "      <td>2.38</td>\n",
       "      <td>0.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85167</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-04-27</td>\n",
       "      <td>2567.731</td>\n",
       "      <td>4.171</td>\n",
       "      <td>105.296</td>\n",
       "      <td>0.336</td>\n",
       "      <td>1.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.753</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.024</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>22329.0</td>\n",
       "      <td>2.92</td>\n",
       "      <td>2.49</td>\n",
       "      <td>0.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85168</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-04-28</td>\n",
       "      <td>2569.548</td>\n",
       "      <td>1.817</td>\n",
       "      <td>105.296</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.889</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.018</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>24074.0</td>\n",
       "      <td>3.08</td>\n",
       "      <td>2.61</td>\n",
       "      <td>0.47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85169</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-04-29</td>\n",
       "      <td>2572.508</td>\n",
       "      <td>2.960</td>\n",
       "      <td>105.430</td>\n",
       "      <td>0.135</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19584.0</td>\n",
       "      <td>3.21</td>\n",
       "      <td>2.70</td>\n",
       "      <td>0.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85170</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>2573.988</td>\n",
       "      <td>1.480</td>\n",
       "      <td>105.430</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22745.0</td>\n",
       "      <td>3.37</td>\n",
       "      <td>2.79</td>\n",
       "      <td>0.58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8966 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      continent     location  ... life_expectancy  human_development_index\n",
       "363        Asia  Afghanistan  ...           64.83                    0.511\n",
       "364        Asia  Afghanistan  ...           64.83                    0.511\n",
       "369        Asia  Afghanistan  ...           64.83                    0.511\n",
       "370        Asia  Afghanistan  ...           64.83                    0.511\n",
       "385        Asia  Afghanistan  ...           64.83                    0.511\n",
       "...         ...          ...  ...             ...                      ...\n",
       "85166    Africa     Zimbabwe  ...           61.49                    0.571\n",
       "85167    Africa     Zimbabwe  ...           61.49                    0.571\n",
       "85168    Africa     Zimbabwe  ...           61.49                    0.571\n",
       "85169    Africa     Zimbabwe  ...           61.49                    0.571\n",
       "85170    Africa     Zimbabwe  ...           61.49                    0.571\n",
       "\n",
       "[8966 rows x 33 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine.dropna(axis=0,subset=['people_fully_vaccinated_per_hundred'],inplace=True)\n",
    "vaccine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DAiw--J_-ccI",
    "outputId": "637a44f0-d650-4f6a-a982-dd93bfbe86dc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8966 entries, 363 to 85170\n",
      "Data columns (total 33 columns):\n",
      " #   Column                               Non-Null Count  Dtype  \n",
      "---  ------                               --------------  -----  \n",
      " 0   continent                            8966 non-null   object \n",
      " 1   location                             8966 non-null   object \n",
      " 2   date                                 8966 non-null   object \n",
      " 3   total_cases_per_million              8363 non-null   float64\n",
      " 4   new_cases_per_million                8363 non-null   float64\n",
      " 5   total_deaths_per_million             8278 non-null   float64\n",
      " 6   new_deaths_per_million               8278 non-null   float64\n",
      " 7   reproduction_rate                    8271 non-null   float64\n",
      " 8   icu_patients_per_million             2349 non-null   float64\n",
      " 9   hosp_patients_per_million            2898 non-null   float64\n",
      " 10  total_tests_per_thousand             6269 non-null   float64\n",
      " 11  new_tests_per_thousand               6199 non-null   float64\n",
      " 12  positive_rate                        6690 non-null   float64\n",
      " 13  tests_units                          6965 non-null   object \n",
      " 14  new_vaccinations                     6958 non-null   float64\n",
      " 15  total_vaccinations_per_hundred       8966 non-null   float64\n",
      " 16  people_vaccinated_per_hundred        8964 non-null   float64\n",
      " 17  people_fully_vaccinated_per_hundred  8966 non-null   float64\n",
      " 18  stringency_index                     7621 non-null   float64\n",
      " 19  population                           8966 non-null   float64\n",
      " 20  population_density                   8827 non-null   float64\n",
      " 21  median_age                           8241 non-null   float64\n",
      " 22  aged_65_older                        8209 non-null   float64\n",
      " 23  gdp_per_capita                       8376 non-null   float64\n",
      " 24  extreme_poverty                      6003 non-null   float64\n",
      " 25  cardiovasc_death_rate                8202 non-null   float64\n",
      " 26  diabetes_prevalence                  8555 non-null   float64\n",
      " 27  female_smokers                       7499 non-null   float64\n",
      " 28  male_smokers                         7405 non-null   float64\n",
      " 29  handwashing_facilities               2549 non-null   float64\n",
      " 30  hospital_beds_per_thousand           7922 non-null   float64\n",
      " 31  life_expectancy                      8901 non-null   float64\n",
      " 32  human_development_index              8329 non-null   float64\n",
      "dtypes: float64(29), object(4)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "vaccine.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GjPzXOfE-ccI"
   },
   "source": [
    "handwashing_facilities, hosp_patients_per_million ,icu_patients_per_million columns still have lots of null values. We need to fill +- 6000 rows to use them in model, which is why we dropping them. The others can be filled because they have more non-null values and require much less filling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "z76QMMAW-ccJ"
   },
   "outputs": [],
   "source": [
    "vaccine.drop(axis=1, labels=['handwashing_facilities','hosp_patients_per_million','icu_patients_per_million'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KpxxIWVm-ccJ"
   },
   "source": [
    "The remaining null values filled with mean of the each column.\n",
    "\n",
    "test_units is dropped because of its irrelevancy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kUxEb5pZ-ccJ",
    "outputId": "6d08931b-b39d-4571-ceab-9093ec31c1b0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8966 entries, 363 to 85170\n",
      "Data columns (total 29 columns):\n",
      " #   Column                               Non-Null Count  Dtype  \n",
      "---  ------                               --------------  -----  \n",
      " 0   continent                            8966 non-null   object \n",
      " 1   location                             8966 non-null   object \n",
      " 2   date                                 8966 non-null   object \n",
      " 3   total_cases_per_million              8966 non-null   float64\n",
      " 4   new_cases_per_million                8966 non-null   float64\n",
      " 5   total_deaths_per_million             8966 non-null   float64\n",
      " 6   new_deaths_per_million               8966 non-null   float64\n",
      " 7   reproduction_rate                    8966 non-null   float64\n",
      " 8   total_tests_per_thousand             8966 non-null   float64\n",
      " 9   new_tests_per_thousand               8966 non-null   float64\n",
      " 10  positive_rate                        8966 non-null   float64\n",
      " 11  new_vaccinations                     8966 non-null   float64\n",
      " 12  total_vaccinations_per_hundred       8966 non-null   float64\n",
      " 13  people_vaccinated_per_hundred        8966 non-null   float64\n",
      " 14  people_fully_vaccinated_per_hundred  8966 non-null   float64\n",
      " 15  stringency_index                     8966 non-null   float64\n",
      " 16  population                           8966 non-null   float64\n",
      " 17  population_density                   8966 non-null   float64\n",
      " 18  median_age                           8966 non-null   float64\n",
      " 19  aged_65_older                        8966 non-null   float64\n",
      " 20  gdp_per_capita                       8966 non-null   float64\n",
      " 21  extreme_poverty                      8966 non-null   float64\n",
      " 22  cardiovasc_death_rate                8966 non-null   float64\n",
      " 23  diabetes_prevalence                  8966 non-null   float64\n",
      " 24  female_smokers                       8966 non-null   float64\n",
      " 25  male_smokers                         8966 non-null   float64\n",
      " 26  hospital_beds_per_thousand           8966 non-null   float64\n",
      " 27  life_expectancy                      8966 non-null   float64\n",
      " 28  human_development_index              8966 non-null   float64\n",
      "dtypes: float64(26), object(3)\n",
      "memory usage: 2.1+ MB\n"
     ]
    }
   ],
   "source": [
    "vaccine=vaccine.fillna(vaccine.mean())\n",
    "vaccine.drop(\"tests_units\",axis=1,inplace=True)\n",
    "vaccine.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1HkIyKEAGPIM"
   },
   "source": [
    "reseting indexes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 590
    },
    "id": "NiYNfSue0KZK",
    "outputId": "f464ba1b-8dc2-4a6c-f65b-6222137c090e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>total_deaths_per_million</th>\n",
       "      <th>new_deaths_per_million</th>\n",
       "      <th>reproduction_rate</th>\n",
       "      <th>total_tests_per_thousand</th>\n",
       "      <th>new_tests_per_thousand</th>\n",
       "      <th>positive_rate</th>\n",
       "      <th>new_vaccinations</th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>people_vaccinated_per_hundred</th>\n",
       "      <th>people_fully_vaccinated_per_hundred</th>\n",
       "      <th>stringency_index</th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>aged_65_older</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-02-21</td>\n",
       "      <td>1428.368</td>\n",
       "      <td>0.617</td>\n",
       "      <td>62.474</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.78000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.096123</td>\n",
       "      <td>109852.842196</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.040000</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>18.6</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>5.2326</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>15.633991</td>\n",
       "      <td>32.75549</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-02-22</td>\n",
       "      <td>1428.702</td>\n",
       "      <td>0.334</td>\n",
       "      <td>62.499</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.80000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.096123</td>\n",
       "      <td>109852.842196</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.040000</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>18.6</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>5.2326</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>15.633991</td>\n",
       "      <td>32.75549</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-02-27</td>\n",
       "      <td>1431.014</td>\n",
       "      <td>0.283</td>\n",
       "      <td>62.756</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.87000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.096123</td>\n",
       "      <td>109852.842196</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.040000</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>18.6</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>5.2326</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>15.633991</td>\n",
       "      <td>32.75549</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>1431.194</td>\n",
       "      <td>0.180</td>\n",
       "      <td>62.756</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.90000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.096123</td>\n",
       "      <td>109852.842196</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.040000</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>18.6</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>5.2326</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>15.633991</td>\n",
       "      <td>32.75549</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-03-15</td>\n",
       "      <td>1438.155</td>\n",
       "      <td>0.000</td>\n",
       "      <td>63.167</td>\n",
       "      <td>0.051</td>\n",
       "      <td>1.09000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.096123</td>\n",
       "      <td>109852.842196</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.040000</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>18.6</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>5.2326</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>15.633991</td>\n",
       "      <td>32.75549</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</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",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8961</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-04-26</td>\n",
       "      <td>2563.560</td>\n",
       "      <td>1.077</td>\n",
       "      <td>104.959</td>\n",
       "      <td>0.202</td>\n",
       "      <td>1.01000</td>\n",
       "      <td>32.594000</td>\n",
       "      <td>0.101000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>21592.000000</td>\n",
       "      <td>2.77</td>\n",
       "      <td>2.38</td>\n",
       "      <td>0.39</td>\n",
       "      <td>64.158802</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4000</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>30.70000</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8962</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-04-27</td>\n",
       "      <td>2567.731</td>\n",
       "      <td>4.171</td>\n",
       "      <td>105.296</td>\n",
       "      <td>0.336</td>\n",
       "      <td>1.01000</td>\n",
       "      <td>32.753000</td>\n",
       "      <td>0.158000</td>\n",
       "      <td>0.024000</td>\n",
       "      <td>22329.000000</td>\n",
       "      <td>2.92</td>\n",
       "      <td>2.49</td>\n",
       "      <td>0.43</td>\n",
       "      <td>64.158802</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4000</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>30.70000</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8963</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-04-28</td>\n",
       "      <td>2569.548</td>\n",
       "      <td>1.817</td>\n",
       "      <td>105.296</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>32.889000</td>\n",
       "      <td>0.136000</td>\n",
       "      <td>0.018000</td>\n",
       "      <td>24074.000000</td>\n",
       "      <td>3.08</td>\n",
       "      <td>2.61</td>\n",
       "      <td>0.47</td>\n",
       "      <td>64.158802</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4000</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>30.70000</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8964</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-04-29</td>\n",
       "      <td>2572.508</td>\n",
       "      <td>2.960</td>\n",
       "      <td>105.430</td>\n",
       "      <td>0.135</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.096123</td>\n",
       "      <td>19584.000000</td>\n",
       "      <td>3.21</td>\n",
       "      <td>2.70</td>\n",
       "      <td>0.52</td>\n",
       "      <td>64.158802</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4000</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>30.70000</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8965</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>2573.988</td>\n",
       "      <td>1.480</td>\n",
       "      <td>105.430</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.01015</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.096123</td>\n",
       "      <td>22745.000000</td>\n",
       "      <td>3.37</td>\n",
       "      <td>2.79</td>\n",
       "      <td>0.58</td>\n",
       "      <td>64.158802</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4000</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>30.70000</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8966 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     continent     location  ... life_expectancy  human_development_index\n",
       "0         Asia  Afghanistan  ...           64.83                    0.511\n",
       "1         Asia  Afghanistan  ...           64.83                    0.511\n",
       "2         Asia  Afghanistan  ...           64.83                    0.511\n",
       "3         Asia  Afghanistan  ...           64.83                    0.511\n",
       "4         Asia  Afghanistan  ...           64.83                    0.511\n",
       "...        ...          ...  ...             ...                      ...\n",
       "8961    Africa     Zimbabwe  ...           61.49                    0.571\n",
       "8962    Africa     Zimbabwe  ...           61.49                    0.571\n",
       "8963    Africa     Zimbabwe  ...           61.49                    0.571\n",
       "8964    Africa     Zimbabwe  ...           61.49                    0.571\n",
       "8965    Africa     Zimbabwe  ...           61.49                    0.571\n",
       "\n",
       "[8966 rows x 29 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine.reset_index(drop=True, inplace=True)\n",
    "vaccine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X7p3bzaH-ccK"
   },
   "source": [
    "The heatmap of correlations shows that there are some features with very high correlation. To get rid of this dependency, I dropped; total_vaccinations_per_hundred, people_vaccinated_per_hundred , median_age ,human_development_index and total_deaths_per_million."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "tEnyUWG_-ccK",
    "outputId": "fa4a15a9-965e-4a50-f857-c2f243a0cb15"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax=plt.subplots(figsize=(30,30))\n",
    "sns.heatmap(vaccine.corr(), annot=True, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 590
    },
    "id": "4rQN1BYc-ccJ",
    "outputId": "6fea756c-892e-4cc5-c7ee-dea8d3ef536c"
   },
   "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>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>new_deaths_per_million</th>\n",
       "      <th>reproduction_rate</th>\n",
       "      <th>total_tests_per_thousand</th>\n",
       "      <th>new_tests_per_thousand</th>\n",
       "      <th>positive_rate</th>\n",
       "      <th>new_vaccinations</th>\n",
       "      <th>stringency_index</th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>aged_65_older</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-02-21</td>\n",
       "      <td>1428.368</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.78000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.096123</td>\n",
       "      <td>109852.842196</td>\n",
       "      <td>12.040000</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>5.2326</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>15.633991</td>\n",
       "      <td>32.75549</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-02-22</td>\n",
       "      <td>1428.702</td>\n",
       "      <td>0.334</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.80000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.096123</td>\n",
       "      <td>109852.842196</td>\n",
       "      <td>12.040000</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>5.2326</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>15.633991</td>\n",
       "      <td>32.75549</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-02-27</td>\n",
       "      <td>1431.014</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.87000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.096123</td>\n",
       "      <td>109852.842196</td>\n",
       "      <td>12.040000</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>5.2326</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>15.633991</td>\n",
       "      <td>32.75549</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>1431.194</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.90000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.096123</td>\n",
       "      <td>109852.842196</td>\n",
       "      <td>12.040000</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>5.2326</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>15.633991</td>\n",
       "      <td>32.75549</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-03-15</td>\n",
       "      <td>1438.155</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.051</td>\n",
       "      <td>1.09000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.096123</td>\n",
       "      <td>109852.842196</td>\n",
       "      <td>12.040000</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>5.2326</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>15.633991</td>\n",
       "      <td>32.75549</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8961</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-04-26</td>\n",
       "      <td>2563.560</td>\n",
       "      <td>1.077</td>\n",
       "      <td>0.202</td>\n",
       "      <td>1.01000</td>\n",
       "      <td>32.594000</td>\n",
       "      <td>0.101000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>21592.000000</td>\n",
       "      <td>64.158802</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4000</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>30.70000</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8962</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-04-27</td>\n",
       "      <td>2567.731</td>\n",
       "      <td>4.171</td>\n",
       "      <td>0.336</td>\n",
       "      <td>1.01000</td>\n",
       "      <td>32.753000</td>\n",
       "      <td>0.158000</td>\n",
       "      <td>0.024000</td>\n",
       "      <td>22329.000000</td>\n",
       "      <td>64.158802</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4000</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>30.70000</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8963</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-04-28</td>\n",
       "      <td>2569.548</td>\n",
       "      <td>1.817</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>32.889000</td>\n",
       "      <td>0.136000</td>\n",
       "      <td>0.018000</td>\n",
       "      <td>24074.000000</td>\n",
       "      <td>64.158802</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4000</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>30.70000</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8964</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-04-29</td>\n",
       "      <td>2572.508</td>\n",
       "      <td>2.960</td>\n",
       "      <td>0.135</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.096123</td>\n",
       "      <td>19584.000000</td>\n",
       "      <td>64.158802</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4000</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>30.70000</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8965</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>2573.988</td>\n",
       "      <td>1.480</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.01015</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.096123</td>\n",
       "      <td>22745.000000</td>\n",
       "      <td>64.158802</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4000</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>30.70000</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8966 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     continent     location  ... hospital_beds_per_thousand  life_expectancy\n",
       "0         Asia  Afghanistan  ...                        0.5            64.83\n",
       "1         Asia  Afghanistan  ...                        0.5            64.83\n",
       "2         Asia  Afghanistan  ...                        0.5            64.83\n",
       "3         Asia  Afghanistan  ...                        0.5            64.83\n",
       "4         Asia  Afghanistan  ...                        0.5            64.83\n",
       "...        ...          ...  ...                        ...              ...\n",
       "8961    Africa     Zimbabwe  ...                        1.7            61.49\n",
       "8962    Africa     Zimbabwe  ...                        1.7            61.49\n",
       "8963    Africa     Zimbabwe  ...                        1.7            61.49\n",
       "8964    Africa     Zimbabwe  ...                        1.7            61.49\n",
       "8965    Africa     Zimbabwe  ...                        1.7            61.49\n",
       "\n",
       "[8966 rows x 23 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = vaccine['people_fully_vaccinated_per_hundred']\n",
    "X = vaccine.drop('people_fully_vaccinated_per_hundred',axis=1)\n",
    "X.drop([\"people_vaccinated_per_hundred\",'median_age','human_development_index',\n",
    "        'total_deaths_per_million',\"total_vaccinations_per_hundred\"],axis=1,inplace=True)\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IxcUWdyv-ccK"
   },
   "source": [
    "In order to feed categorical data to a machine learning model, we need to encode it. Continent, Location and Date columns are encoded. In order to match countries after the prediction, X is copied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "id": "Eh08jUR6-ccK",
    "outputId": "fa46fb6d-fc66-412d-d312-ba1ad9c741f9"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>new_deaths_per_million</th>\n",
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       "      <th>new_tests_per_thousand</th>\n",
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       "      <th>new_vaccinations</th>\n",
       "      <th>stringency_index</th>\n",
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       "      <th>aged_65_older</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
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       "      <td>66</td>\n",
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       "      <td>0.78000</td>\n",
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       "      <td>38928341.0</td>\n",
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       "      <td>5.2326</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>15.633991</td>\n",
       "      <td>32.75549</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>1428.702</td>\n",
       "      <td>0.334</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.80000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.096123</td>\n",
       "      <td>109852.842196</td>\n",
       "      <td>12.040000</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>5.2326</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>15.633991</td>\n",
       "      <td>32.75549</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>1431.014</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.87000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.096123</td>\n",
       "      <td>109852.842196</td>\n",
       "      <td>12.040000</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>5.2326</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>15.633991</td>\n",
       "      <td>32.75549</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>1431.194</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.90000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.096123</td>\n",
       "      <td>109852.842196</td>\n",
       "      <td>12.040000</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>5.2326</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>15.633991</td>\n",
       "      <td>32.75549</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>1438.155</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.051</td>\n",
       "      <td>1.09000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.096123</td>\n",
       "      <td>109852.842196</td>\n",
       "      <td>12.040000</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>5.2326</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>15.633991</td>\n",
       "      <td>32.75549</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8961</th>\n",
       "      <td>0</td>\n",
       "      <td>183</td>\n",
       "      <td>130</td>\n",
       "      <td>2563.560</td>\n",
       "      <td>1.077</td>\n",
       "      <td>0.202</td>\n",
       "      <td>1.01000</td>\n",
       "      <td>32.594000</td>\n",
       "      <td>0.101000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>21592.000000</td>\n",
       "      <td>64.158802</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4000</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>30.70000</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8962</th>\n",
       "      <td>0</td>\n",
       "      <td>183</td>\n",
       "      <td>131</td>\n",
       "      <td>2567.731</td>\n",
       "      <td>4.171</td>\n",
       "      <td>0.336</td>\n",
       "      <td>1.01000</td>\n",
       "      <td>32.753000</td>\n",
       "      <td>0.158000</td>\n",
       "      <td>0.024000</td>\n",
       "      <td>22329.000000</td>\n",
       "      <td>64.158802</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4000</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>30.70000</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8963</th>\n",
       "      <td>0</td>\n",
       "      <td>183</td>\n",
       "      <td>132</td>\n",
       "      <td>2569.548</td>\n",
       "      <td>1.817</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>32.889000</td>\n",
       "      <td>0.136000</td>\n",
       "      <td>0.018000</td>\n",
       "      <td>24074.000000</td>\n",
       "      <td>64.158802</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4000</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>30.70000</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8964</th>\n",
       "      <td>0</td>\n",
       "      <td>183</td>\n",
       "      <td>133</td>\n",
       "      <td>2572.508</td>\n",
       "      <td>2.960</td>\n",
       "      <td>0.135</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.096123</td>\n",
       "      <td>19584.000000</td>\n",
       "      <td>64.158802</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4000</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>30.70000</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8965</th>\n",
       "      <td>0</td>\n",
       "      <td>183</td>\n",
       "      <td>134</td>\n",
       "      <td>2573.988</td>\n",
       "      <td>1.480</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.01015</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.096123</td>\n",
       "      <td>22745.000000</td>\n",
       "      <td>64.158802</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4000</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>30.70000</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8966 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      continent  location  ...  hospital_beds_per_thousand  life_expectancy\n",
       "0             1         0  ...                         0.5            64.83\n",
       "1             1         0  ...                         0.5            64.83\n",
       "2             1         0  ...                         0.5            64.83\n",
       "3             1         0  ...                         0.5            64.83\n",
       "4             1         0  ...                         0.5            64.83\n",
       "...         ...       ...  ...                         ...              ...\n",
       "8961          0       183  ...                         1.7            61.49\n",
       "8962          0       183  ...                         1.7            61.49\n",
       "8963          0       183  ...                         1.7            61.49\n",
       "8964          0       183  ...                         1.7            61.49\n",
       "8965          0       183  ...                         1.7            61.49\n",
       "\n",
       "[8966 rows x 23 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder = LabelEncoder()\n",
    "X_transformed=X.copy()\n",
    "X_transformed['continent'] = encoder.fit_transform(X_transformed['continent'])\n",
    "X_transformed['date'] = encoder.fit_transform(X_transformed['date'])\n",
    "X_transformed['location'] = encoder.fit_transform(X_transformed['location'])\n",
    "X_transformed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gteQ-QNh-ccL"
   },
   "source": [
    "Since the dataset is time-series, if we want to make a prediction at some day t, the data should be trained with the days before t, not with the data after it. In order to split the data efficently, we store the number of examples of each country with the encoded location. In the for loop, it checks that, if there are 5 or more examples from the country the current country. If the answer is true, it carries the last 20% of the data to the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "kEiCR9Sq-ccL"
   },
   "outputs": [],
   "source": [
    "countries = dict(X_transformed['location'].value_counts())\n",
    "X_test=pd.DataFrame(columns=X_transformed.columns)\n",
    "y_test=pd.Series()\n",
    "for k,v in countries.items():\n",
    "    if v >= 5:\n",
    "        add = X_transformed[X_transformed['location']==k].sort_values('date',ascending=True)[-(v//5):]\n",
    "        X_transformed.drop(add.index,axis=0,inplace=True)\n",
    "        X_test=X_test.append(add)\n",
    "        y_test=y_test.append(y.loc[add.index])\n",
    "        y.drop(add.index,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zPr63iRkHGfY"
   },
   "source": [
    "Shapes of the sets after the split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "o3JRgqct-ccL",
    "outputId": "1346205e-ddfc-4a4e-ef41-d0e746ba39c3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " X shape: (7251, 23)\n",
      " X_test shape: (1715, 23)\n",
      " y shape: (7251,)\n",
      " y_test shape: (1715,)\n"
     ]
    }
   ],
   "source": [
    "print(f' X shape: {X_transformed.shape}')\n",
    "print(f' X_test shape: {X_test.shape}')\n",
    "print(f' y shape: {y.shape}')\n",
    "print(f' y_test shape: {y_test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "wLlfV5W556LK"
   },
   "outputs": [],
   "source": [
    "X_transformed.reset_index(drop=True, inplace=True)\n",
    "y.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VHuf9vNKHKHB"
   },
   "source": [
    "In order to do cross validation at time-series, we need to pass a predefined split. \n",
    "\n",
    "test_fold is a list that holds, which example will be in which split's validation. -1 means always using it for training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gdy_XKN4k77w",
    "outputId": "9b7ddc39-8371-4a18-f1d2-0c7102f5d47d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7251\n"
     ]
    }
   ],
   "source": [
    "countries = dict(X_transformed['location'].value_counts())\n",
    "test_fold=[-1]*X_transformed.shape[0]\n",
    "print(len(test_fold))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OAgI5wksHv6W"
   },
   "source": [
    "Using the same ratio, %80 training and %20 validation for countries with more than or equal to 5 examples, we can use 1378 examples in validation set. Since I want to use a 5-fold cross validation, 275-276 will be the number of examples in each fold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kbh4SAE-3a_v",
    "outputId": "dbc7ec84-b8f4-4f17-8ce4-6ecbc27f517b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1378\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "275"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j=0\n",
    "for i in countries.values():\n",
    "    if i>=5:\n",
    "        j=j+(i//5)\n",
    "print(j)\n",
    "j//5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4RPJdKoOI5bt"
   },
   "source": [
    "m is for checking the number of examples in split, fold for checking the total number of splits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "BQh18kianqKm"
   },
   "outputs": [],
   "source": [
    "fold=0\n",
    "m=0\n",
    "for k,v in countries.items():\n",
    "    if v >= 5:\n",
    "        if m<276:\n",
    "            validate = X_transformed[X_transformed['location']==k].sort_values('date',ascending=True)[-(v//5):].index\n",
    "            for i in validate:\n",
    "                test_fold[i]=fold\n",
    "            m+=len(validate)\n",
    "        else:\n",
    "            m=0\n",
    "            fold+=1\n",
    "cv=PredefinedSplit(test_fold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SEcZ01IuJKU1"
   },
   "source": [
    "Standardization of the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "3EdaKVb3-ccL"
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_transformed = scaler.fit_transform(X_transformed)\n",
    "X_scaled_test=scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EF9csqn-JOdQ"
   },
   "source": [
    "Initialization of Regression models that will be used. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "HBITBZU8-ccL"
   },
   "outputs": [],
   "source": [
    "lr = LinearRegression()\n",
    "nn = MLPRegressor(random_state=0)\n",
    "dt = DecisionTreeRegressor(random_state=0)\n",
    "rf = RandomForestRegressor(random_state=0)\n",
    "xg = xgb.XGBRegressor(random_state=0, objective='reg:squarederror')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OxptSgf3J8Kk"
   },
   "source": [
    "A function to automize tuning.\n",
    "\n",
    "For Linear Regression, there are no parameters to tune. It is directly passed into *cross_val_score* with the predefined split of cv.\n",
    "\n",
    "Other models are tuned with some parameters and *GridCVSearch*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "vpjgYoi_-ccM"
   },
   "outputs": [],
   "source": [
    "def tune(model):\n",
    "    if model==lr:\n",
    "        return cross_val_score(model, X_transformed, y, cv=cv).mean()\n",
    "    if model==nn:\n",
    "        parameters={\n",
    "            'hidden_layer_sizes':[(30,),(50,)],\n",
    "            'learning_rate_init': np.arange(0.01, 0.13, 0.02)\n",
    "        }\n",
    "    if model==dt:\n",
    "        parameters={\n",
    "            'max_depth': [5,10],\n",
    "            'min_samples_split':[2,3,4,5]\n",
    "        }\n",
    "    if model==rf:\n",
    "        parameters={\n",
    "            'n_estimators': [100, 300, 500],\n",
    "            'max_depth': [5,10],\n",
    "            'min_samples_split':[2,3,4]  \n",
    "        }\n",
    "    if model==xg:\n",
    "        parameters = {\n",
    "            'n_estimators': [100, 300, 500, 700],\n",
    "            'learning_rate': np.arange(0.1, 0.55, 0.2),\n",
    "            'max_depth': [5,10]\n",
    "        }\n",
    "    gs = GridSearchCV(model, parameters, cv=cv, verbose=1, n_jobs=-1).fit(X_transformed, y)\n",
    "    return pd.DataFrame(gs.cv_results_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kuDUmGVoKpgy"
   },
   "source": [
    "The cross validation score of Linear Regression is very bad."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7NXK_bgd-ccM",
    "outputId": "b33fde15-036f-4165-faed-b24d9f8b8f5b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression score --> 0.04740418912549453\n"
     ]
    }
   ],
   "source": [
    "print(f\"Linear Regression score --> {tune(lr)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aWW6DyrIKuxu"
   },
   "source": [
    "Let's see what is returns for other examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 893
    },
    "id": "JaXNaHRg-ccM",
    "outputId": "59724458-9ec0-456d-c516-a2c4d1b7e321"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   38.6s\n",
      "[Parallel(n_jobs=-1)]: Done  60 out of  60 | elapsed:   46.3s finished\n"
     ]
    },
    {
     "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_hidden_layer_sizes</th>\n",
       "      <th>param_learning_rate_init</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.895059</td>\n",
       "      <td>0.556235</td>\n",
       "      <td>0.001390</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>(30,)</td>\n",
       "      <td>0.01</td>\n",
       "      <td>{'hidden_layer_sizes': (30,), 'learning_rate_i...</td>\n",
       "      <td>0.897496</td>\n",
       "      <td>0.846131</td>\n",
       "      <td>0.774932</td>\n",
       "      <td>0.856722</td>\n",
       "      <td>0.771690</td>\n",
       "      <td>0.829394</td>\n",
       "      <td>0.048909</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.682114</td>\n",
       "      <td>0.365671</td>\n",
       "      <td>0.001293</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>(30,)</td>\n",
       "      <td>0.03</td>\n",
       "      <td>{'hidden_layer_sizes': (30,), 'learning_rate_i...</td>\n",
       "      <td>0.902514</td>\n",
       "      <td>0.830273</td>\n",
       "      <td>0.653407</td>\n",
       "      <td>0.822338</td>\n",
       "      <td>0.733997</td>\n",
       "      <td>0.788506</td>\n",
       "      <td>0.086151</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.547315</td>\n",
       "      <td>0.156326</td>\n",
       "      <td>0.001321</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>(30,)</td>\n",
       "      <td>0.05</td>\n",
       "      <td>{'hidden_layer_sizes': (30,), 'learning_rate_i...</td>\n",
       "      <td>0.902898</td>\n",
       "      <td>0.808612</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.819991</td>\n",
       "      <td>0.644821</td>\n",
       "      <td>0.758341</td>\n",
       "      <td>0.110043</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.356418</td>\n",
       "      <td>0.195193</td>\n",
       "      <td>0.001274</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>(30,)</td>\n",
       "      <td>0.07</td>\n",
       "      <td>{'hidden_layer_sizes': (30,), 'learning_rate_i...</td>\n",
       "      <td>0.893594</td>\n",
       "      <td>0.762333</td>\n",
       "      <td>0.688505</td>\n",
       "      <td>0.785566</td>\n",
       "      <td>0.753339</td>\n",
       "      <td>0.776667</td>\n",
       "      <td>0.066743</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.053483</td>\n",
       "      <td>0.181074</td>\n",
       "      <td>0.001198</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>(30,)</td>\n",
       "      <td>0.09</td>\n",
       "      <td>{'hidden_layer_sizes': (30,), 'learning_rate_i...</td>\n",
       "      <td>0.872220</td>\n",
       "      <td>0.753487</td>\n",
       "      <td>0.602386</td>\n",
       "      <td>0.718139</td>\n",
       "      <td>0.595922</td>\n",
       "      <td>0.708431</td>\n",
       "      <td>0.102815</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.932999</td>\n",
       "      <td>0.166416</td>\n",
       "      <td>0.001294</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>(30,)</td>\n",
       "      <td>0.11</td>\n",
       "      <td>{'hidden_layer_sizes': (30,), 'learning_rate_i...</td>\n",
       "      <td>0.857203</td>\n",
       "      <td>0.760303</td>\n",
       "      <td>0.518370</td>\n",
       "      <td>0.768504</td>\n",
       "      <td>0.060739</td>\n",
       "      <td>0.593024</td>\n",
       "      <td>0.288952</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.184273</td>\n",
       "      <td>0.383070</td>\n",
       "      <td>0.001269</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>(50,)</td>\n",
       "      <td>0.01</td>\n",
       "      <td>{'hidden_layer_sizes': (50,), 'learning_rate_i...</td>\n",
       "      <td>0.900930</td>\n",
       "      <td>0.840610</td>\n",
       "      <td>0.797947</td>\n",
       "      <td>0.880826</td>\n",
       "      <td>0.622095</td>\n",
       "      <td>0.808482</td>\n",
       "      <td>0.099663</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.619449</td>\n",
       "      <td>0.407251</td>\n",
       "      <td>0.001299</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>(50,)</td>\n",
       "      <td>0.03</td>\n",
       "      <td>{'hidden_layer_sizes': (50,), 'learning_rate_i...</td>\n",
       "      <td>0.876556</td>\n",
       "      <td>0.818488</td>\n",
       "      <td>0.746994</td>\n",
       "      <td>0.823212</td>\n",
       "      <td>0.668906</td>\n",
       "      <td>0.786831</td>\n",
       "      <td>0.071930</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.321576</td>\n",
       "      <td>0.270277</td>\n",
       "      <td>0.001242</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>(50,)</td>\n",
       "      <td>0.05</td>\n",
       "      <td>{'hidden_layer_sizes': (50,), 'learning_rate_i...</td>\n",
       "      <td>0.894043</td>\n",
       "      <td>0.811973</td>\n",
       "      <td>0.713313</td>\n",
       "      <td>0.830152</td>\n",
       "      <td>0.595809</td>\n",
       "      <td>0.769058</td>\n",
       "      <td>0.104229</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.135019</td>\n",
       "      <td>0.206926</td>\n",
       "      <td>0.001288</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>(50,)</td>\n",
       "      <td>0.07</td>\n",
       "      <td>{'hidden_layer_sizes': (50,), 'learning_rate_i...</td>\n",
       "      <td>0.884063</td>\n",
       "      <td>0.791186</td>\n",
       "      <td>0.741609</td>\n",
       "      <td>0.839825</td>\n",
       "      <td>0.564780</td>\n",
       "      <td>0.764293</td>\n",
       "      <td>0.110538</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.958200</td>\n",
       "      <td>0.130042</td>\n",
       "      <td>0.001232</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>(50,)</td>\n",
       "      <td>0.09</td>\n",
       "      <td>{'hidden_layer_sizes': (50,), 'learning_rate_i...</td>\n",
       "      <td>0.728330</td>\n",
       "      <td>0.741414</td>\n",
       "      <td>0.721037</td>\n",
       "      <td>0.709985</td>\n",
       "      <td>0.668632</td>\n",
       "      <td>0.713880</td>\n",
       "      <td>0.024822</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.273189</td>\n",
       "      <td>0.289999</td>\n",
       "      <td>0.001182</td>\n",
       "      <td>0.000208</td>\n",
       "      <td>(50,)</td>\n",
       "      <td>0.11</td>\n",
       "      <td>{'hidden_layer_sizes': (50,), 'learning_rate_i...</td>\n",
       "      <td>0.890925</td>\n",
       "      <td>0.712990</td>\n",
       "      <td>0.775838</td>\n",
       "      <td>0.807258</td>\n",
       "      <td>0.642675</td>\n",
       "      <td>0.765937</td>\n",
       "      <td>0.084172</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  ...  std_test_score  rank_test_score\n",
       "0        2.895059      0.556235  ...        0.048909                1\n",
       "1        1.682114      0.365671  ...        0.086151                3\n",
       "2        1.547315      0.156326  ...        0.110043                9\n",
       "3        1.356418      0.195193  ...        0.066743                5\n",
       "4        1.053483      0.181074  ...        0.102815               11\n",
       "5        0.932999      0.166416  ...        0.288952               12\n",
       "6        2.184273      0.383070  ...        0.099663                2\n",
       "7        1.619449      0.407251  ...        0.071930                4\n",
       "8        1.321576      0.270277  ...        0.104229                6\n",
       "9        1.135019      0.206926  ...        0.110538                8\n",
       "10       0.958200      0.130042  ...        0.024822               10\n",
       "11       1.273189      0.289999  ...        0.084172                7\n",
       "\n",
       "[12 rows x 15 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=tune(nn)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wVp21G2xK-6H"
   },
   "source": [
    "We can directly see the best estimator with it'S rank test score.\n",
    "\n",
    "Way better than Linear Regression for this problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 130
    },
    "id": "KTKS-cT5faPb",
    "outputId": "8e9b1a40-7a2a-4409-c120-88fdaa7cb184"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_hidden_layer_sizes</th>\n",
       "      <th>param_learning_rate_init</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.895059</td>\n",
       "      <td>0.556235</td>\n",
       "      <td>0.00139</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>(30,)</td>\n",
       "      <td>0.01</td>\n",
       "      <td>{'hidden_layer_sizes': (30,), 'learning_rate_i...</td>\n",
       "      <td>0.897496</td>\n",
       "      <td>0.846131</td>\n",
       "      <td>0.774932</td>\n",
       "      <td>0.856722</td>\n",
       "      <td>0.77169</td>\n",
       "      <td>0.829394</td>\n",
       "      <td>0.048909</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  ...  std_test_score  rank_test_score\n",
       "0       2.895059      0.556235  ...        0.048909                1\n",
       "\n",
       "[1 rows x 15 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['rank_test_score']==1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P9TWFosOLVCH"
   },
   "source": [
    "Creating a dataframe for best parameters of each model.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 77
    },
    "id": "iF0PJU6ye9kK",
    "outputId": "338ed50d-465d-4552-a591-5eb56dbe2d7d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
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       "        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>params</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'hidden_layer_sizes': (30,), 'learning_rate_i...</td>\n",
       "      <td>0.829394</td>\n",
       "      <td>2.895059</td>\n",
       "      <td>MLPRegressor(activation='relu', alpha=0.0001, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              params  ...                                              model\n",
       "0  {'hidden_layer_sizes': (30,), 'learning_rate_i...  ...  MLPRegressor(activation='relu', alpha=0.0001, ...\n",
       "\n",
       "[1 rows x 4 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['rank_test_score']==1][['params','mean_test_score','mean_fit_time']]\n",
    "df['model']=str(nn)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mrndQL5_Ldiz"
   },
   "source": [
    "Calling the remaining models and storing their best model in the df. This takes at least 15 minutes to run on GoogleColab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H4FIv5jR-ccN",
    "outputId": "eacc48d6-ab4e-4bde-bb00-749d2ae19330"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  40 out of  40 | elapsed:    1.6s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 18 candidates, totalling 90 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:  3.7min\n",
      "[Parallel(n_jobs=-1)]: Done  90 out of  90 | elapsed: 10.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:  2.9min\n",
      "[Parallel(n_jobs=-1)]: Done 120 out of 120 | elapsed:  6.6min finished\n"
     ]
    }
   ],
   "source": [
    "for i in [dt,rf,xg]:\n",
    "    model = tune(i)\n",
    "    best = model[model['rank_test_score']==1][['params','mean_test_score','mean_fit_time']]\n",
    "    best['model']=str(i)\n",
    "    df = df.append(best)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MD6NBO9ULs2u"
   },
   "source": [
    "XGBRegressor is clearly the best model to use, followed by Neural Network and RandomForestRegression(works very slower than NeuralNetwork). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "id": "30Lzoan9-ccN",
    "outputId": "49cff198-0302-43a9-9fc5-5c3a87895bde"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>params</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'hidden_layer_sizes': (30,), 'learning_rate_i...</td>\n",
       "      <td>0.829394</td>\n",
       "      <td>2.895059</td>\n",
       "      <td>MLPRegressor(activation='relu', alpha=0.0001, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'max_depth': 10, 'min_samples_split': 2}</td>\n",
       "      <td>0.693566</td>\n",
       "      <td>0.094040</td>\n",
       "      <td>DecisionTreeRegressor(ccp_alpha=0.0, criterion...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>{'max_depth': 10, 'min_samples_split': 2, 'n_e...</td>\n",
       "      <td>0.827313</td>\n",
       "      <td>29.009831</td>\n",
       "      <td>RandomForestRegressor(bootstrap=True, ccp_alph...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'learning_rate': 0.1, 'max_depth': 5, 'n_esti...</td>\n",
       "      <td>0.894918</td>\n",
       "      <td>8.942499</td>\n",
       "      <td>XGBRegressor(base_score=0.5, booster='gbtree',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.047404</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LinearRegression(copy_X=True, fit_intercept=Tr...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               params  ...                                              model\n",
       "0   {'hidden_layer_sizes': (30,), 'learning_rate_i...  ...  MLPRegressor(activation='relu', alpha=0.0001, ...\n",
       "4           {'max_depth': 10, 'min_samples_split': 2}  ...  DecisionTreeRegressor(ccp_alpha=0.0, criterion...\n",
       "11  {'max_depth': 10, 'min_samples_split': 2, 'n_e...  ...  RandomForestRegressor(bootstrap=True, ccp_alph...\n",
       "3   {'learning_rate': 0.1, 'max_depth': 5, 'n_esti...  ...  XGBRegressor(base_score=0.5, booster='gbtree',...\n",
       "0                                                 NaN  ...  LinearRegression(copy_X=True, fit_intercept=Tr...\n",
       "\n",
       "[5 rows x 4 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg = pd.DataFrame(data=[[np.nan, tune(lr),np.nan, str(lr)]],columns=df.columns)\n",
    "df = df.append(linreg)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hSJd84M4MOgS"
   },
   "source": [
    "Checking the best model's parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vi1VV2VaA1ny",
    "outputId": "685693af-4a9a-4b02-c40b-992233a93050"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 700}"
      ]
     },
     "execution_count": 44,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[3,'params']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3SKCC5mRMR6J"
   },
   "source": [
    "Initializing the selected model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "KIFmZC3nC8Fl"
   },
   "outputs": [],
   "source": [
    "model=xgb.XGBRegressor(max_depth=5,learning_rate=0.1, n_estimators=700, random_state=0, objective='reg:squarederror')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aRALsUgYMZSk"
   },
   "source": [
    "Fitting the model and predicting test set. $R^2$ of it is very good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VAIuxrcOEI1p",
    "outputId": "53b0f8d5-7fc2-4cd2-eccf-6998e50c7e8f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score of the model ---> 0.9225629753101191\n",
      "Mean squared error of the model ---> 15.231172084139294\n"
     ]
    }
   ],
   "source": [
    "model.fit(X_transformed, y)\n",
    "prediction = model.predict(X_scaled_test)\n",
    "print(f\"R2 score of the model ---> {r2_score(y_test, prediction)}\")\n",
    "print(f\"Mean squared error of the model ---> {mean_squared_error(y_test, prediction)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bihb4FnEMxSY"
   },
   "source": [
    "Comparing actual rates and the predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 594
    },
    "id": "zsWryhFeFMhQ",
    "outputId": "d63722c2-d406-4717-de55-ace35e246264"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.plot(y_test.values, color='red')\n",
    "plt.plot(prediction, color='blue')\n",
    "plt.legend(['Actual','Prediction'])\n",
    "plt.savefig(\"predictions.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BNsGCcdlNjUo"
   },
   "source": [
    "Same lines as subgraphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 261
    },
    "id": "BnLjf1LMJZH_",
    "outputId": "249189e1-e347-4ac8-c436-56fe0b959c87"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame({'actual':y_test.values, 'prediction':prediction}).plot(subplots=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1hA5zVmU3THL"
   },
   "source": [
    "checking the worst prediction. idxmax gives the index of the maximum element."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nfEtOmW8lVOe",
    "outputId": "469eba72-4b87-48ed-a032-f80d180dd2ad"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maximum error-->813.6695618818699\n",
      "index of it--->7356\n"
     ]
    }
   ],
   "source": [
    "mse = np.square(y_test-prediction)\n",
    "print(f\"maximum error-->{mse.max()}\\nindex of it--->{mse.idxmax()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v4TCNUQR3epT"
   },
   "source": [
    "Looking at the features of the worst prediction.\n",
    "It has the location code 147. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Q4ymRVMnoSww",
    "outputId": "5f8cd475-0023-4866-a3c8-9037d8657039"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent                            0\n",
       "location                           147\n",
       "date                               129\n",
       "total_cases_per_million        53731.9\n",
       "new_cases_per_million          793.167\n",
       "new_deaths_per_million               0\n",
       "reproduction_rate                 1.21\n",
       "total_tests_per_thousand       678.502\n",
       "new_tests_per_thousand         4.95169\n",
       "positive_rate                 0.096123\n",
       "new_vaccinations                109853\n",
       "stringency_index                 43.52\n",
       "population                       98340\n",
       "population_density             208.354\n",
       "aged_65_older                    8.606\n",
       "gdp_per_capita                 26382.3\n",
       "extreme_poverty                    1.1\n",
       "cardiovasc_death_rate          242.648\n",
       "diabetes_prevalence              10.55\n",
       "female_smokers                     7.1\n",
       "male_smokers                      35.7\n",
       "hospital_beds_per_thousand         3.6\n",
       "life_expectancy                   73.4\n",
       "Name: 7356, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.loc[7356]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i1V5wLov3qYJ"
   },
   "source": [
    "By looking at the label encoder, we can access the country with code 147, it's Seychelles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "86v2Q9XdvhDY",
    "outputId": "fe9c2a89-ee66-411c-fb4f-e1c1c02bbe03"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'Seychelles'"
      ]
     },
     "execution_count": 51,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.classes_[147]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PahnvSwe3ztE"
   },
   "source": [
    "It predicted only the half of the actual value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yJtd2jSj01be",
    "outputId": "afc898b4-595f-4e74-d43a-8daf0b53ba10"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "actual---> 58.21\n",
      "prediction --->29.68510627746582\n"
     ]
    }
   ],
   "source": [
    "worst=mse.argmax()\n",
    "print(f'actual---> {y_test.iloc[worst]}\\nprediction --->{prediction[worst]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_mCSt0C436Og"
   },
   "source": [
    "I think because of the death values are empty and  some of the features(total tests, new tests, etc.) seems like filled with mean value, it got huge error when predicting this country. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 590
    },
    "id": "wwmSJWcA0XjT",
    "outputId": "41412aa1-b15e-4e2c-8603-80bed652a4d1"
   },
   "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>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>new_deaths_per_million</th>\n",
       "      <th>total_tests_per_thousand</th>\n",
       "      <th>new_tests_per_thousand</th>\n",
       "      <th>people_fully_vaccinated_per_hundred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7299</th>\n",
       "      <td>Seychelles</td>\n",
       "      <td>2021-02-01</td>\n",
       "      <td>12436.445</td>\n",
       "      <td>183.038</td>\n",
       "      <td>10.169</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7300</th>\n",
       "      <td>Seychelles</td>\n",
       "      <td>2021-02-02</td>\n",
       "      <td>12639.821</td>\n",
       "      <td>203.376</td>\n",
       "      <td>0.000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7301</th>\n",
       "      <td>Seychelles</td>\n",
       "      <td>2021-02-03</td>\n",
       "      <td>13005.898</td>\n",
       "      <td>366.077</td>\n",
       "      <td>0.000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7302</th>\n",
       "      <td>Seychelles</td>\n",
       "      <td>2021-02-05</td>\n",
       "      <td>13107.586</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>1.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7303</th>\n",
       "      <td>Seychelles</td>\n",
       "      <td>2021-02-06</td>\n",
       "      <td>15293.878</td>\n",
       "      <td>2186.292</td>\n",
       "      <td>10.169</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>1.61</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7356</th>\n",
       "      <td>Seychelles</td>\n",
       "      <td>2021-04-25</td>\n",
       "      <td>53731.950</td>\n",
       "      <td>793.167</td>\n",
       "      <td>0.000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>58.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7357</th>\n",
       "      <td>Seychelles</td>\n",
       "      <td>2021-04-26</td>\n",
       "      <td>54443.767</td>\n",
       "      <td>711.816</td>\n",
       "      <td>0.000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>58.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7358</th>\n",
       "      <td>Seychelles</td>\n",
       "      <td>2021-04-27</td>\n",
       "      <td>54443.767</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>59.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7359</th>\n",
       "      <td>Seychelles</td>\n",
       "      <td>2021-04-28</td>\n",
       "      <td>55480.984</td>\n",
       "      <td>1037.218</td>\n",
       "      <td>0.000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>59.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7360</th>\n",
       "      <td>Seychelles</td>\n",
       "      <td>2021-04-29</td>\n",
       "      <td>56528.371</td>\n",
       "      <td>1047.387</td>\n",
       "      <td>0.000</td>\n",
       "      <td>678.501932</td>\n",
       "      <td>4.951688</td>\n",
       "      <td>60.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        location  ... people_fully_vaccinated_per_hundred\n",
       "7299  Seychelles  ...                                0.00\n",
       "7300  Seychelles  ...                                0.00\n",
       "7301  Seychelles  ...                                0.01\n",
       "7302  Seychelles  ...                                1.61\n",
       "7303  Seychelles  ...                                1.61\n",
       "...          ...  ...                                 ...\n",
       "7356  Seychelles  ...                               58.21\n",
       "7357  Seychelles  ...                               58.21\n",
       "7358  Seychelles  ...                               59.48\n",
       "7359  Seychelles  ...                               59.48\n",
       "7360  Seychelles  ...                               60.16\n",
       "\n",
       "[62 rows x 8 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine[(vaccine['location']=='Seychelles') & (vaccine['date']>='2021-02-01')][['location','date','total_cases_per_million','new_cases_per_million','new_deaths_per_million','total_tests_per_thousand','new_tests_per_thousand','people_fully_vaccinated_per_hundred']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W3oJlG0PNr_6"
   },
   "source": [
    "Checking Turkey.\n",
    "\n",
    "Prediction for Turkey seems perfect when rounded to 2 decimals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
    },
    "id": "V5rHGzOaTXWw",
    "outputId": "5f50e590-d1d1-48ec-e766-51783efd29f2"
   },
   "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>date</th>\n",
       "      <th>actual</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8291</th>\n",
       "      <td>2021-02-11</td>\n",
       "      <td>0.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8292</th>\n",
       "      <td>2021-02-12</td>\n",
       "      <td>0.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8293</th>\n",
       "      <td>2021-02-13</td>\n",
       "      <td>0.54</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8294</th>\n",
       "      <td>2021-02-14</td>\n",
       "      <td>0.58</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8295</th>\n",
       "      <td>2021-02-15</td>\n",
       "      <td>0.68</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8365</th>\n",
       "      <td>2021-04-26</td>\n",
       "      <td>9.61</td>\n",
       "      <td>9.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8366</th>\n",
       "      <td>2021-04-27</td>\n",
       "      <td>9.84</td>\n",
       "      <td>9.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8367</th>\n",
       "      <td>2021-04-28</td>\n",
       "      <td>10.22</td>\n",
       "      <td>10.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8368</th>\n",
       "      <td>2021-04-29</td>\n",
       "      <td>10.58</td>\n",
       "      <td>10.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8369</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>10.80</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  actual prediction\n",
       "8291  2021-02-11    0.40        NaN\n",
       "8292  2021-02-12    0.40        NaN\n",
       "8293  2021-02-13    0.54        NaN\n",
       "8294  2021-02-14    0.58        NaN\n",
       "8295  2021-02-15    0.68        NaN\n",
       "...          ...     ...        ...\n",
       "8365  2021-04-26    9.61       9.61\n",
       "8366  2021-04-27    9.84       9.84\n",
       "8367  2021-04-28   10.22      10.22\n",
       "8368  2021-04-29   10.58      10.58\n",
       "8369  2021-04-30   10.80       10.8\n",
       "\n",
       "[79 rows x 3 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turkey = pd.DataFrame(columns=['date','actual','prediction'])\n",
    "turkey[['date','actual']] = vaccine[vaccine['location']=='Turkey'].sort_values('date',ascending=True)[['date','people_fully_vaccinated_per_hundred']]\n",
    "for i in turkey.index.values[-15:]:\n",
    "  turkey.loc[i,'prediction']=y_test[i]\n",
    "turkey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 675
    },
    "id": "bF14OI4LbKgl",
    "outputId": "888e086d-5039-4122-d237-2ff7432a2bdb"
   },
   "outputs": [
    {
     "data": {
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333wjb3jDq2loaOSUU07l4Ycf4rTTzuCyy97KBz7w56TTacrKyvjUpz7LjBnNXHTRS3jLWy6lrq6er3zlSt7xjnfxiU98lB//+CpOP/0MWltnA7Bs2TE8//kv5M1vfj0NDY2ce+55PPjgqiJ/Y0mSJEmSjlwDQyPcfM9GbrxnA8PDGZ5z8hxeft4Smuoqil3aES+Vze7virUnCyF0AItjjF3jxhqBtTHGphBCCuiKMdYVrtT9WgysbW/vedJlX9u2rWf27EWHuZSJKS1NMzJyeHuXT8Wf03TX0lJHW1t3scvQEcC5IHAeKMd5IHAeKMd5IHAeTNTIaIY7HtzCtXeupatvmNOXt/Dq85cyp7mm2KUdkkLPg3Q6RXNzLcASYN3495KuSLoOuCaE8AlyPZHmAx/KjwOcu++JJUmSJEmSjgSZbJZ7V+/gp3c8wY7OfpYvaOS9r1nGsnkNxS5tykkaJL0T+CjwVWAusBW4Cvin/PtPABdPdnGSJEmSJEkT8ci6Dq667XHWb+tmfksNH3jdSZy4tJlUKlXs0qakREFSjHEA+GD+sb/3t01mUZIkSZIkSROxvaOPH96yhgfW7KS5voI/vfh4zl05m3TaAGkikq5IIoRQDgRgJrDnpx5jvCXBsf8GvIZcT6MTY4x/zI8vB74JNAPtwOUxxscOof4DymazpotPI0lvLEmSJEmSppregWGuu2sdv7pvE6WlaV5z/lIuOnMBZaUlxS5tWkgUJIUQng38CKgA6oEuoA7YCCxNcIqrgc8Dv95n/CvAf8YYvxNCeDO5S+dekKz0AystLae3t4uamnrDpP3IZrP09nZRWlpe7FIkSZIkSZoUo5kMt63awjV3rqW3f5jnnDyHVz1nKQ213oltMiVdkfRZ4NMxxs+GEDpjjDNCCB8B+pIcHGO8EyCEsGcshDALOA24MD/0feCLIYSWGGNb0i+wP01NLXR2ttHTs2sipzms0uk0mczhu2tbaWk5TU0th+3zJEmSJEkqlD880c4Pb1nDlp29HLewkUsvOJaFrYf7xvJHh6RB0nJyK4rG+ySwFvi3Z/jZC4DNMcZRgBjjaAhhS358QkFSSUkpM2fOmcgpDjtv4ShJkiRJ0qHZsrOXH96yhj880c6sxire8+oTOfXYmV6dVEBJg6Td5C5p2wVsDSGsINfTqLZQhR2K5uYjoowJa2kxLZXzQHs5FwTOA+U4DwTOA+U4DwTOA4Cu3iG+//PV/Ow366gqL+HtL1vJJc9eclT1QSrWPEgaJP0UeCnwPeBK4FZgGPjxBD57IzAvhFCSX41UAszNjx+S9vYeMpmp3TzaFUkC54H2ci4InAfKcR4InAfKcR4InAcjoxluuX8z1965lv6hEZ53yjxe8Zwl1FeXs6szUfedaaHQ8yCdTh1w0U6iICnG+IFx2/8WQribXLPtm55pUTHGHSGEB4A3AN/JP6+aaH8kSZIkSZI0fWSzWdZu7eaeR7ZzzyPb2dUzxMrFTfzJBccyv2V6XKE0lSS9a9sXYozvG3s9rnn254APHPDAcccDrwZmA78MIbTHGFcC7wK+mW/c3QlcfuhfQZIkSZIkTSfZbJZNbb3c88h2fvvwdnbuHqC0JMWJS5s5/5R5nLh0hn2QiiTppW1vBd63n/HLSBAk5UOopxwfY1wNnJ2wBkmSJEmSNI1t6+jjnoe389tHtrO1vY90KsWKxU28/LwlnLZ8JtWVZcUu8aj3tEFSCOHtY/uN2x6zFNhZkKokSZIkSdJRYefufn73yA5++8h2NmzvIQUsX9DIC89YwOmhhfrq8mKXqHEOtiLpsvxz+bhtgCywHXhLIYqSJEmSJEnTUyaTZeOOHh5Z38n9j7axZvNuAJbOrefSC47lzONm0VRXUeQqdSBPGyTFGJ8PEEL4eIzxw4enJEmSJEmSNF1kslk2t/Wyen0nqzd0Ejfsom9wBID5LbW85vylnHl8K7Maq4pcqZJIete2DwOEEGYBtfu890QB6pIkSZIkSVNQNptla3sfqzd08sj6XHDU0z8MQEtjJaeHFo5b1MRxC5tceTQFJb1r24uAK4E5+7yVBUomuyhJkiRJknR4ZbNZMtksmUyW0UzuOZNlz/ZoJvOUsfH7btrZw+p8cLS7dwiAGfUVnLSsmePzwVFzQ2WRv6UmKuld274E/DPwzRhjfwHrkSRJkiTpqJXJZuntH6arb5iu3qG9j74hdvcOkUqn6esf2hviZJ8a6GSy2XHhz7jnffcdt18mk/vsiWqoLc+FRouaOG5hIy2NVaRSqUn4yehIkTRIagK+GmOc+KySJEmSJGmaGs1kGBzKMDg8mnsMjT5pe2Dc657+J4dFu/uG6OkbZjTz1P/1LkmnqK0uo7aqnGw2SzqVoiSdIp1+8nN5aZp0Or1nLJ2CkpI06RTj9k1Tktp7TCqdO//4c6bTqSftk86/v+/r8fvPbKhk9oxqg6NpLmmQ9HXgbeQub5MkSZIkadrKZrN7gp7e/hF6+of3PHr7h+nOP48fH8iHRCOjmcSfU1qSor6mnPrqchrrKlg4u46G/Ov6mr2PhppyqitLSadStLTU0dbWXcBvLz29pEHSOcD7QggfBLaNfyPG+NxJr0qSJEmSdNR4+kuu9l7CxSFeI5MFhoZH6R8coW9wJPc8kHvuHxylb9x4/7j3ewdGnjYQqqoopbaqlNqqMmqry5jdXE11RSkVZSVUlJc86bmyrITy8tzzk94vK6G8LO3qHU05SYOk/8o/JEmSJEl6kuGRUXrGrdwZW62z78qd8dvDI5k9YVExeqiUl6apqiilqqKU6src84z6SqorSqipLKO2qoyaqtzz2HZdVRnVlaWUlqSLULF0ZEgUJMUYv1noQiRJkiRJhTc4PEpPXz7QGRgX7uTHBodHGR7NMDKSYXgks3d7dOx1lpGR0T3bwyOZp129U1FWQm1V6Z5QprmhkpqqMirKSnL9efbt9bNvn549vXl4Rqt3xgKjsbCoqqKU6grDIOmZShQkhRBSwBXAG4CZMcaTQgjPBWbHGK8qZIGSJEmSpP3LZrMMDI3S1Tf+7l57Gzh39w/T0zdET/8IvQN7VwIdSGV5CZXlJZSVpiktSVNWmn+UpKkpL6OsZO/rstI0pfntyopS6qqeuoqntqqUstKSw/gTkVRoSS9t+yfgQuBzwFfyY5uAzwIGSZIkSZI0QaOZzJ6+Pf35Pj57+voMjpAhxda2nj23gh8Li4YOEAzVVpVRV50LdmY2VLJ4Tt24gKcsf/lW6ZMu3XKVjqSDSRokvRU4Nca4M4Tw5fzYWmBpQaqSJEmSpCNIJpPds6LnyXfwGmF4ZHQ/DaLZ0yA6k9nbC2jseXA4Q9/gMP2Do3saPA8Ojz5tDekU1Fbn7ujVUFNGa1PD3jt7Vefu7DX2uq66jJK0oZCkyZc0SCoBevLbY33QaseNSZIkSdKU0zcwTNuuAXbs6qdtV3/ucrC+4SeFRr39w/QNjBy0IfRYH58D9foZ/7qirISqilIaayv29Oyprih9SvPn6opSqipzzwvnN9HR7v+CSSqupEHSz4DPhBD+Evb0TPpn4LpCFSZJkiRJE5XJZOnoHqBt1wBt+bBoR2f/nu3egZEn7Z9rDL23v8/Mhsqn3LVr3z5A5WVp0qlUwW/jXpL2NvGSii9pkPRXwDeB3UAZuZVINwOXF6guSZIkSTokPf3DrN3axdotXazb1s3W9l527h5gNLN3LVFJOkVzQyUtjVUsmVNPS2NV/pEbq6pI+r9IknR0SvRvyRhjF/CqEMIsYBGwMca4raCVSZIkSdIBDA6Nsn57dy44yj/adg0AkALmzKxhQWsdp4dZtDRWMisfGDXVV9g7SJImIFGQFEK4CFgXY3wU2JEfC8DCGOMvClifJEmSpKPcyGiGzW29TwqNNu/sJZtfaNRcX8GSOfU879R5LJldz6LZda4skqQCSfpv1/8EnrvPWHd+fPmkViRJkiTpqDI4NMrOrgE6ugZo3z1Ae1f+sTs31tk9RCafGtVUlrJkbj2nHtvCkrn1LJlTT0NNeZG/gSQdPZIGSbNijFv3GdsKzJ7keiRJkiRNM6OZDO27B9jW0ce2jn527u7fExh1dA3S0z/8pP3TqRRNdRU0N1SyfEEjzQ2VzJtZy5K59bQ0VBa8qbUk6cCSBklPhBBeEGO8ZdzY84C1k1+SJEmSpKkmm83S1TfMtvZetnf2s62jj+0dfWzr6GNHZ/+TGl5XlJXQ3FBJc30lS+fU79meUV/JzIZKGmrL7WMkSUeopEHSR4GfhhC+DjwOLAPeln9IkiRJmuYy2Sw9/cPs6h6ks3uQzp5BdnUPsmNXP9va+9je2Uf/4Oie/UtLUrQ2VTOnuYZTjp3J7KZqZjdX09pUTV11mauKJGmKSnrXtmvyDbffDlwMbAReFGP8XSGLkyRJkvTMZbNZslkYzWTJZLK55+y47UyW0bHXoxl6+ofHhURDe8Kizu5BdvUMPmlVEeTujjajvpLZM6o4d+VsWmdUM2dGNa0zqmmurySdNiySpOnmoEFSCKEEeBRYEWN8V+FLkiRJknQoBoZGWLu1m8c372bN5t2s3dpF38DIU4KfQ1FRVkJjXQVNteUsX9BAY10FjbUVNNVW0FSXe9TXlFNa4iVoknQ0OWiQFGMcDSGMApXAYOFLkiRJknQg2WyWtt0De0KjxzfvZtOO3j13NZvTXM3Jy2bm+wylSKdTpFOpvdvpvdslqSeP1VSV7QmKKstLvPxMkvQUSXskfQ64KoTwL8AmYM+fNmKMTxSiMEmSJEkwODzKQ0+0c99DW/cER119ubucVZSXsHROPRefu4hl8xpYOree2qqyIlcsSZrOkgZJX8w/X7jPeBYombxyJEmSpKNDJpuluy/fvHpcL6LOnlw/orHXvQMje46Z1VTFCUubWTavgWVz65nfUmsfIknSYZW02bYXPkuSJEmHKJvN0tE1yMa2Hjbt6GFTWw/tXQPs6h5kV8/QfptX19eU01hXwcyGKo6d30hjXQUrl81kZl059dXlxfkikiTlJV2RBEAIYQEwL8Z4d4HqkSRJkqakgaERNrf17g2NdvSwsa2X/sG9K4qa6yuZ1VTF8gVNNNaV7+lHlGtqXZHva/TUv+G2tNTR1tZ9OL+OJEn7lShICiEsBL4PnELucrbaEMJrgRfHGK+YaBEhhEuAfyb3R5gU8LEY408nel5JkiSpEPoGRogbO1m/rZtNbb1s2tHDjl39e96vLC9h/qxazl7RyoKWGubPqmXezFqqKw/p77iSJB1xkv6X7KvADcBzgPb82C+Af59oASGEFPBt4Dkxxj+GEE4C7gohXB1jzEz0/JIkSdJEjYxmeGJLFw+t7eDhdR08sbWLbBZSKWhtqmbh7DrOO3E282fVsqClluaGSu94JkmalpIGSWcBF8cYMyGELECMcXcIoWGS6sgAY+dqBLYaIkmSJKlYstksW9r7eHhdBw+v7WD1xl0MDo2SSsGSOfVcfO5iVi5uYvGceirKvPeMJOnokTRI2g4cAzw6NhBCWAFsmGgBMcZsCOH1wDUhhF6gDnjpRM8rSZIkHYrdvUN7gqOH13fS2T0IwKzGKs5dOZuVi5s4blETNZVlRa5UkqTiSWWz2YPuFEJ4O/BB4F+BzwPvBP4e+GSM8bsTKSCEUArcBPxjjPGuEMJ55PoxrYgx9hzk8MXA2ol8viRJko4+fQPDrN3SxeObdvH45t2s2bSLDdtyzaxrq8o4+dgWTlmee8xurilytZIkFc0SYN34gURBEkAI4RXkAqRFwEbgKzHGqydaUQjhDOBbMcYV48YeAS6PMf7uIIcvBta2t/eQyST7Hkcq78QhcB5oL+eCwHmgHOfBxPX0D7N+ezcbtnWzfns367f3sKOjj7HfHutrylnUWsfyBQ2sWDyDRa11pNNHVn8j54HAeaAc54Gg8PMgnU7R3FwL+wmSEt82IsZ4DXDNpFaWswmYH0IIMcYYQjgeaAUeL8BnSZIkaYroHxzh8S27GRgcPaTjMtks2zr62LC9h/XbumnvGtjzXnN9JYtm13HuylYWtdaxaHYdjbUVk126JEnTVuIgKX952xuAucAW4AfAlTHGCS0FijFuCyG8G/hxCGGswSXjC+QAACAASURBVPbbY4wdEzmvJEmSppbBoVEe27yL1et3sXpDJ+u2dpNJuHp+Xylg1oxqls2r5wWnz2NRax0LW+uorbK/kSRJE5EoSAohfBp4BfA5YD25y9v+GgjA3060iHyfpQn1WpIkSdLUMjQ8yuObd/PIhlxwtHZLF6OZLCXpFEvm1PPScxcSFjbRUF1+aCdO5VYeVVUk/pupJElKKOl/Xd8KnBZj3DQ2EEK4HrifSQiSJEmSNP0Nj2R4YstuVm/Yxer1nTy+pYuR0QypFCyeXc9FZy3g+IVNHDO/gcpyQyBJko5ESf8L3Z1/7DvWNbnlSJIkaboYGc2wbls3q9d38sj6Th7fvJuhkQwpYEFrLS84bR7HLWpi+fxGqisNjiRJmgqS/hf7c8BPQwifJNccewHwN8BnQwhLx3aKMT4x+SVKkiRpKhjNZNiwvScXHG3o5LGNuxkczjXKnt9Sw3NPnpsLjhY02qtIkqQpKmmQ9Pn88/P3Gb8A+EJ+OwuUTEZRkiRJOvJlslk2bu9h9YZOVq/v5NFNu+jP32FtTnM1zzpxNscvbGL5wkbqD7XPkSRJOiIlCpJijOlCFyJJkqQjXyab5eG1Hfz691t5eF0HvQMjAMxqquLM41o5flETxy1spKG2osiVSpKkQvBidEmSJB3U7t4h7vz9Fm5/YAs7dw9QW1XGqce2cNyiRo5b2MSM+spilyhJkg4DgyRJkiTtVzabZfX6Tm59YAurHm1jNJPluIWNvOb8ZZy2vIWyUhetS5J0tDFIkiRJ0pN09w1x1x+2cfsDm9ne2U9NZSkXnD6f80+Zy5zmmmKXJ0mSisggSZIkSWSzWR7btJvbHtjMvat3MDKa5Zj5DbzsvMWcEWZRXuY9VSRJ0iEESSGEMuAcYG6M8YchhBqAGGNvoYqTJElSYfUODPO/f9zG7Q9sYcvOXqoqSjn/5Hmcf+pc5rfUFrs8SZJ0hEkUJIUQTgSuBQaB+cAPgfOBtwB/UrDqJEmSNOmy2SxPbOnitlWbuWf1DoZHMiyZU8/bXnIcZx3fSkW5q48kSdL+JV2R9GXgIzHGb4cQOvNjtwNfK0xZkiRJmmz9gyPc/dA2bl21hU1tPVSUl3DeCbM5/5R5LJpdV+zyJEnSFJA0SFoJfCe/nYXcJW0hhKqCVCVJkqRJs3ZrF7c/sJnfPryDweFRFrbWcvmLA2cf30pVhS0zJUlSckl/c1gHnA7cOzYQQjgLWFOAmiRJkjRBA0Mj/Pbh7dz2wBbWb+umvDTNWStaed4p81gyp45UKlXsEiVJ0hSUNEj6B+CGEMJXgPIQwoeAdwHvKFhlkiRJOmSbdvRw66rN/OahbQwMjTKvpYY3Xbicc1e2Ul1ZVuzyJEnSFJcoSIoxXh9CeDG54Oh2YBHw6hjjfYUsTpIkScl09w3xk9sf59cPbqWkJM2Zx83i+afOY9m8elcfSZKkSZP4ovgY4yrgzwtYiyRJkg5RJpPl9gc289M7nmBgaJSLzlrAxecuprbK1UeSJGnyHTBICiH8U5ITxBg/MnnlSJIkKanHN+/mOzc/yvrt3Ry3sJE3XRSYN7Om2GVJkqRp7OlWJC1IcHx2sgqRJElSMl29Q/z4tse58w9baawt512vWMmZx83yEjZJklRwBwySYoxvO5yFSJIk6ellMlluXbWZ/7njCQaHR3nx2Qt52bMWU1WRuFuBJEnShCT+rSOEcCzwemAusAW4Ksb4WKEKkyRJ0l5rNu3mOzdHNuzo4fhFTbzpwuXM9TI2SZJ0mKWT7BRCeCOwCjgJ6AVOBO7Pj0uSJKlAOrsH+Pr1D/Mv37mP7v5h3v3KE/jrS08xRJIkSUWRdEXSx4GXxhjvGBsIITwH+DbwvUIUJkmSdDQbHslw26rNXHvXWgaGRnnpOYu45FmLqCz3MjZJklQ8SX8TqQN+s8/Y3YB/CpMkSZpEwyOj3PHgVn5293o6uwc5dXkLrz1/KXOa/bVLkiQVX9Ig6TPAv4QQ/iHGOBBCqAI+lh+XJEnSBA0Oj3L7A1u48bfr2d0zxPL5Dfzpxcfz3DMWsnNnT7HLkyRJApIHSX8OzAbeH0LoBJqAFLA1hPDusZ1ijAsnv0RJkqTpa3BolFtXbeamezbQ1TvEcQsbeefLVnLcoiYAUqlUkSuUJEnaK2mQ9OaCViFJknSUGRga4Zb7N3PTbzfQ0z/MisVNvPyVJ7B8QWOxS5MkSTqgREFSjPH2QhciSZJ0NOgfHOFX923i5t9tpKd/mBOWzODl5y3hmPkNxS5NkiTpoBIFSSGEMuDDwGXAXGALuTu2fSLGOFS48iRJkqaHvoFhfnnfJn7xu430Doxw0rJmXnbeYpbNNUCSJElTR9JL2z4NnAW8C1gPLAL+AagH/rIwpUmSJE1tfQPD/P7xdu5/tI0/PNHB4PAopxwzk5c/ezGLZ9cXuzxJkqRDljRIeh1wcoyxPf86hhDuBx5kEoKkEEIl8FnghcAA8JsY459N9LySJEmHW2f3IA881sb9j7axesMuRjNZGmrKOXdlK+efMo9Fs+uKXaIkSdIzljRIOtDtQibrNiKfJhcgLY8xZkMIrZN0XkmSpILb2t7L/Y+2seqxnTyxpQuA1qYqLjpzAacub2Hp3HrS3n1NkiRNA0mDpB8B14UQPgZsIHdp24eBqyZaQAihFrgcmB9jzALEGLdP9LySJEmFkslmWbu1i1WP7uT+R9vY1tEHwOLZdbz6uUs5dXkLc5urSRkeSZKkaSaVzWYPulMIoZxccPRGcs22NwM/AD4eYxycSAEhhJOBn+Yfzwd6gA/HGO9McPhiYO1EPl+SJB3dOroG6OgaoG9gmN7+YXr7R/ZuD+S2e/qHc2MDI/T2D9PVO0Rv/zDpdIoTlzVz7glzOGvlHFqaqor9dSRJkibTEmDd+IFEQVIhhRBOA+4D3hRj/F4I4WzgOuCYGGPXQQ5fDKxtb+8hkynu95iolpY62tq6i12Gisx5oDHOBYHzoFCy2Syb2npZ9Wiuj9GGHT0H3LeivITqilKqKkr3PFdVlFBdWcYx8+o5adlMaqvKClqv80DgPFCO80DgPFBOoedBOp2iubkW9hMkJb20jRDChcClwKwY48tCCGcA9THGWyZY3wZgBPg+QIzxtyGEncBy4N4JnluSJIlMJsuazbvzfYzaaNs1QApYNr+B1z1/GbObqvMhUSnVlXsDo5J0utilS5IkHVESBUkhhPcC7wf+C3htfrgf+ALwrIkUEGPcGUK4FbgQuDmEsByYBayZyHklSdLRbXhklIfWdbLq0TYeWLOT7r5hSktSrFg8g5ees4hTjm2hoaa82GVKkiRNKUlXJH0AuCDGuC6E8Hf5sdVAmKQ63gVcGUL4d2AYuCzGuGuSzi1Jko4SfQPDPPh4O6sebeMPT3QwODxKVUUJJy2byanHzuTEpc1UVSRekC1JkqR9JP1Nqg7YmN8ea0ZUBgxNRhExxieA503GuSRJ0tEnm81y+4Nb+OGv1jA4PEpDTTnnrmzltOUthIVNlJV6iZokSdJkSBok3QF8EPjEuLH3AbdOekWSJEmHoKt3iG/cuJoH1uxkxeImXvmcpSydW086lSp2aZIkSdNO0iDpvcB1IYR3AHUhhAh0A5cUrDJJkqSDeHDNTv77Z4/QNzjKpRccywvPmG+AJEmSVECJgqQY49YQwpnAmcAicpe53RNjzBSyOEmSpP0ZHB7lqlvWcOuqzcxvqeWv37CC+S21xS5LkiRp2juUbpNpcn2RAEoA/9wnSZIOu7Vbu/jadQ+zraOPF5+1kFc9d6k9kCRJkg6TREFSCOEk4GqgAtgMzAcGQgivijE+WMD6JEmSAMhksvzs7vVcc+da6mvK+ZtLT+H4xTOKXZYkSdJRJemKpCuB/wQ+E2PMhhBSwF/mx08vVHGSJEkAbbv6+dr1D7Nm027OOn4Wl70oUFNZdvADJUmSNKmSBknLgc/FGLMA+TDp88BHC1WYJElSNpvlf/+4je/+4lFSKXjHy1ZwzopWUjbUliRJKoqkQdLPgJcD/zNu7GXADZNekSRJEtDTP8y3fh65d/UOli9o5IpLjmdmQ1Wxy5IkSTqqJQ2SSoAfhBDuI3fHtgXkLmm7JoTwrbGdYoyXT36JkiTpaDI4NMrtD27hxrvX09M/zOuet4wXnbWQdNpVSJIkScWWNEj6Y/4x5mHg55NfjiRJOlr1DQzzq/s384vfbaSnf5iwoJFLLziWRbPril2aJEmS8hIFSTHGjxW6EEmSdHTq6h3iF/du5Jb7N9E/OMpJy5q55NzFHDO/odilSZIkaR9JVyRJkiRNqo6uAW767QbueHALwyMZTj9uFpecu4iFra5AkiRJOlIZJEmSpMNqe2cfP/vNev73j9sAOGdlKy89ZxFzmmuKXJkkSZIOxiBJkiQdFpt29HD9b9bxu9U7KEmnOf+Uubz47IXeiU2SJGkKMUiSJEkFtatnkG//PLLqsZ1UlJfw4rMWctGZC2iorSh2aZIkSTpEBwySQghvT3KCGOOVk1eOJEmaTh5a28HXrnuIgeFRXvHsJVxw+nxqq8qKXZYkSZKeoadbkXTZuO0UcB6wDdgILABagbsAgyRJkvQko5kM19y5lhv+dz1zZtbwN688gXkz7YEkSZI01R0wSIoxPn9sO4TwH8DVMcbPjRt7P7CssOVJkqSpprN7kK9e+xCPbtzFs0+aw5suXE5FWUmxy5IkSdIkSNoj6c3AzH3GvgjsBN43qRVJkqQp649PtPO16x9mcHiUKy45nmedMKfYJUmSJGkSJQ2StgEvB/5n3NjLgB2TXpEkSZpyRjMZrv71Wm74zXrmtdTw7lecwFwvZZMkSZp2kgZJ7wN+EkL4G3I9khYCK4DXFaowSZI0NXR0DfDVax/isU27ee7Jc3jDC72UTZIkabpKFCTFGH8RQlgKvASYC9wA3BBjbC9kcZIk6cj2+8fb+a/rH2Z4JMM7XraCc1fOLnZJkiRJKqCkK5KIMe4MIdwGzIsx3l24kiRJ0pFuZDTD//z6CW68ewPzW2p59ytXMqfZS9kkSZKmu0RBUghhIfB94BQgC9SGEF4LvDjGeEUB65MkSUeYjq4BvnLNQ6zZvJvnnTKXSy84lnIvZZMkSToqJF2R9FVyl7M9Bxi7nO0XwL8XoihJknRkui/u4Bs3rmYkk+XPXr6Cc1Z4KZskSdLRJGmQdBZwcYwxE0LIAsQYd4cQGgpXmiRJOlL0D47w/V89xp2/38qi2XW88+UrmT2juthlSZIk6TBLGiRtB44BHh0bCCGsADYUoihJknTkWLNpN1+7/iF27h7gkmct4uXnLaG0JF3ssiRJklQESYOkfwOuDyH8K1AaQngD8PfAJwtWmSRJKqqR0QzX3bWO63+zjub6Sv7ujaexfEFjscuSJElSESUKkmKMV4YQ2oF3AhuBy4F/iDFeXcjiJElScWzr6ONr1z3E2q3dnHfCbN544XKqKhLf7FWSJEnTVNK7tp0dY7wGuGaf8bNijPcUpDJJknTYZbNZbn9wCz/41WOUlaR59ytP4MzjZhW7LEmSJB0hkv5p8RdA/X7GbwJmTFYxIYR/BD4KnBhj/ONknVeSJB1cV+8Q37hxNQ+s2cmKxU386cUraKqrKHZZkiRJOoI8bZAUQkgDKSAVQkjlt8csA0Ymq5AQwmnAOcD6yTqnJElK5sE1O/nvnz1C3+Aol15wLC88Yz7pVOrgB0qSJOmocrAVSSNAdtz2eBngE5NRRAihAvhP4A3AbZNxTkmSdHCDw6Ncdcsabl21mfktNfz1pSuZP6u22GVJkiTpCHWwIGkJuVVItwPPHTeeBdpijP2TVMc/Ad+JMa4LIRzywc3N0+MX3paWumKXoCOA80BjnAuCws2D4ZFRbr9/E1f96jG27uzllecv47KXHE95WUlBPk8T478PBM4D5TgPBM4D5RRrHjxtkBRjHLvMbFGhCgghnAucAXzwmZ6jvb2HTCZ78B2PYC0tdbS1dRe7DBWZ80BjnAuCwsyDnv5hbl21mVvu28Tu3qH8KqRTWLF4Brt39U3qZ2ly+O8DgfNAOc4DgfNAOYWeB+l06oCLdhLfxzeE8HLgfGAm43olxRgvn2B95wPHA2vzq5HmAz8PIbwtxnjzBM8tSZKA7R193HzvRu76/VaGRjKcsGQGV5y1kBWLm0jZC0mSJEkJJQqS8ndTexfwA+B1wFeBNwI/nGgBMcZPAp8c91nrgEu8a5skSROTzWZ5bNNufn7PBh54bCclJSnOWTGbi85awPyW6XFZuCRJkg6vpCuS3g5cGGP8Y36l0F+GEL4PfLiAtUmSpGdgNJPhvtjGz+/ZwNqt3dRUlnLxsxZzwWnzaKitKHZ5kiRJmsKSBkmN41YIDYUQymKM94QQzp/sgmKMiyf7nJIkHQ36B0f49YNb+MW9m2jvGqC1qYrLLlrOs06cQ4VNtCVJkjQJkgZJj4cQVsYYHwL+CLw7hNAJdBauNEmSNGZoeJRdPYN0dg/S2TPIru6hfV7ntkczWZbPb+CNLzyWk4+dSdr+R5IkSZpESYOkDwPN+e0PAd8FaoE/L0RRkiQdrbr6hnhkXSdxQydd/SNs7+hlV/cgvQMjT9m3oqyExroKmmrLOWZ+A011FZwRZrFkTn0RKpckSdLRIFGQFGP82bjt3wLHFKwiSZKOIkPDozy2aTcPrevg4bUdbNjRA0BVRSnzZ9Uyq7GK5QsaaaqtoKmugsa6ChprK2iqraCqosQ7rkmSJOmwSroiiRBCAxDIrUTaI8Z4y2QXJUnSdJXJZtm4vYeH13Xw0LoOHtu0m+GRDCXpFMfMa+BVz13KysUzWDy7jtbWetrauotdsiRJkrRHoiAphPBW4D+BHqBv3FtZYOnklyVJ0vTR0TXAH9d28PC6Dh5e10lP/zAA82bW8LxT5rFySRPLFzRSWZ747zuSJElSUST9jfUTwGtjjDcWshhJkqaLHZ193Bfb+N3qHazblltV1FBTzolLm1mxuIkVi2fQVFdR5ColSZKkQ5M0SCoFbi5kIZIkTXXbO/r43eod3Bt3sGF7rtfR4tl1vOb8pZy8bCbzWmrsaSRJkqQpLWmQ9CngwyGEf44xZgpZkCRJU8nW9t5ceLS6jU1tufBo2dx6Xv/8YzgjtDCzsarIFUqSJEmTJ2mQ9JfAbOBvQwjt49+IMS6c9KokSTpCZbNZtuzMhUf3xTY27+wF4Jj5DbzhgmM5PbQwo76yyFVKkiRJhZE0SHpzQauQJOkIk81m6e4fZlt7H9s7+tjW2cf2jn427ehhx65+UsDyBY286cLlnLa8xX5HkiRJOiokCpJijLcXuhBJkophcGiU7Z19bOvIB0Yd/Xu2+wZH9uxXkk4xq6mKeS01vOisBZy2vIWGWsMjSZIkHV0OGCSFEP5vjPET+e1/OtB+McaPFKIwSZImQ//gCO1dA3R0DdC+e4D2rkHa92wP0Nk9+KT9m+sraJ1RzdkrW5ndVE3rjGpmN1fTXF9BSTpdpG8hSZIkHRmebkXS/HHbCwpdiCRJz0Q2m6VtVz8btvewMx8OjYVEHV0D9A6MPGn/knSKGfUVNNdXsmJxE7OaqpkzIxcYzWqqoqKspEjfRJIkSTryHTBIijG+e9z22w5POZIkHdhoJsO29j42bO9h/fZu1m/rZsOObvoHR/fsU1VRSnM+KDp2fgPN9ZU0N1Qyo76S5vpKGmrLSadSRfwWkiRJ0tSVqEdSCOFy4IEY4+/HjZ0MnBRj/HahipMkHb2GRzJs2dmbC4y2d7NhWzcbd/QwNJIBoLw0zYJZtZyzcjaLWutY2FrLrMZqqiuT3kdCkiRJ0qFK+tv2PwOn7DO2EbgWMEiS/n/27jxK8rKw9/+7qvd1uqenZ99nmIdVFgE1IoqGYBA1xhiDV4y4RE2UmHsvxhjU4MIl/jAa1Bhu1IgiKlGvRNEEc13BsEXjDdsDzMbs0zPTPb1vVd/fH1XdNOMs36Grpmum369z6tS3nqr61qfnPHoOn/N8n6+kI0qShKGRHP1Do/QPjdM/NMbA0Bj9E4/hMfoHC8d9g6Ps2DtILp8A0FBXxfL5Lbzo7CWTpdHCjkb3LJIkSZKOsbRFUivQe8DYfqCttHEkSZVoZDRHT/8IPf0jDI3mGB3LMTyaY2Qsx8iU5+Gxp96b+pmBoTEGhscni6EDZYCmhhqaGmpobqhm3pwGnrVmHisWtrBiQTPz2hq8HE2SJEmqAGmLpIeBVwO3TRl7FfBIyRNJko6ZfD6hd3CU7r4RevpG6C6WRU+9Lrw3NDJ+2PNkMxnqaquoq8lSV1tNXU2W+poqmhtrmFdTXyyIamiqLzw3N9TQ3PjUcWNdNdmsRZEkSZJU6dIWSX8OfC+E8FpgPbAWeAlwabmCSZJKZzyXZ+e+Qbbu7mdLVz9bdw+wfU8/3X2j5JOnrxLKZjLMaa6lrbmOhXMbOWV5O20thddtLXU01lVTV1NVeNQWnqurMmRcMSRJkiSd8FIVSTHGu0IIpwOvA5YB9wF/GmPcUs5wkqSjkyQJvQOjk2XRlt39bO3qZ/uegcnLyqqyGRbPa+KkZW3Mm1NPe3PdZEnU3lJHa2Otq4MkSZIkHVTau7bVATtijNdPGasJIdTFGEfKlk6SdFi9A6M8tqWHJ7btnyyN+gbHJt9vb6ljaWczp6+ey7LOZpbOb2bh3Eaqq9ykWpIkSdLRS3tp2w+A9wD3TBl7NnA98KISZ5IkHUSSJOzZP8xjW3p4fGsPj23Zz859gwDUVGdZ2tnEWWvnsXR+82Rp1NxQM8OpJUmSJJ1I0hZJZwD3HjB2H3BmaeNIkibkk4TtewZ4fEsPj23dz2NbeujuKywCbayr5qSlc3jBsxZx0rI2Vi5scZWRJEmSpLJLWyTtBxYAO6eMLQAGSp5IkmapsfEcm3f288S2/Wze3c+D6/cwMFy4W1pbcy3rlrVx0tI21i1rY0lnE1k3t5YkSZJ0jKUtkr4J3BpCuArYAKwB/ga4rVzBJOlE1903wvpt+3li237Wb9vP5l19jOcKG2IvntfE2es6Wbe0jXXL5tDZ1uBd0SRJkiTNuLRF0l8CH6dwOVsdMAz8I/C+MuWSpBPKeC7Plt39PLF1P+u3F8qjfb2Fy9RqqrOsXNjCxecuY82SOaxZMoe1Kzvo6uqb4dSSJEmS9HSpiqQY4zDwJyGEdwLzgD0xxqSsySTpODY6luPxrft5ePM+1m/dz8adfYyN5wGY21rHmsVzuOS8Qmm0fEGz+xtJkiRJOi6kXZE0obn4aAkhABBj3FDqUJJ0vMknCVt29fPQpn08tHEfj2/dz3guT1U2w4qFLbzorCWsXTqHNYtbmdtaP9NxJUmSJOkZSVUkhRBOBb5C4S5tCZApPgNUlSeaJFW2vfuHeWjTPh7etI+HN3XTPzQGwJLOJl58zhJOXTmXsKyNulr/b1KSJEnSiSHtiqS/A34EXARsBFYC/wv4eXliSVLlGRoZ59HN3YVVR5u62bVvEIA5TbWcsbqD01a1c+rKubQ1181wUkmSJEkqj7RF0pnAxTHGsRBCJsa4P4RwNfAgcMt0AoQQOoAvU7gT3CjwOPC2GGPXdM4rSdMxnsuzrWuAjTt62bCjl407etm+Z4AkgdqaLGFZOxedtZhTV81lybwm76gmSZIkaVZIWyQNAzXAGLAnhLAc6AY6SpAhAT4WY/wxQAjh/wOuB95cgnNL0hHlk4Td3UNs3NHLxu29bNzZy5O7+ic3x25uqGHVolaeva6Tk5e3s2bJHGqq3RxbkiRJ0uyTtkj6GfD7wBeBbwDfB0aAH043QIxxH/DjKUP3AO+Y7nklzU5JkjCeyzM2nmcslzA2nmNsPM94LimMjecYzyUMjYyzeVcfm3b0snFHH4Mj40BhtdHKBS1cdPYSVi9uZdWiVubNqXfFkSRJkiQBmSRJjvypKUIIWeB1QAvwpRjjQKnCFM99J/DPMcYbU3xlJYU9myTNEvl8ws69A6zfup/123pYv20/T+7sY3h0nNGxPOO5fOpzZbMZVi5q5aRlbaxb3s665e0sm99MVZWrjSRJkiQJWAVsmjqQ9q5tZ8UY/xMgxphnmvsiHcangH7g00fzpb17+8nnj64QqzSdnS10dfXNdAzNMOfB0+XyeXbsHWTzzj6e3NXP5l19PLmrj+HRHABV2QxLO5s5ZUUbjXU11FRnqa7KUFOdpaa6ipqqDNXV2cLrqmzx/cJzbXUVizoaqa15+h3V9u0rWTc+Lc4FgfNABc4DgfNABc4DgfNABeWeB9lsho6O5oO+l/bStjtDCF3AV4FbY4wbShVuQgjhBuAk4OXFskrSLLO7e5BHn+xh884+Nu/qY8vup/Ypqq3OsmxBM79x+kKWL2hhxYIWlnQ2Ue3qIUmSJEk6ZtIWSYuAlwKXA/8ZQngIuBX4eoxx93RDhBCuA54NvCzGODLd80k6PvQPjfHo5m4e2rSPhzbuY8/+YQAa6qpYUdynaMWCFpYvbGHR3EayWfcpkiRJkqSZlKpIijHmgDuAO0IIDcArKWyIfQNQN50AIYTTgL8AHgN+HkIA2BhjfNV0ziup8oyN51m/bT8PbdrHw5v2sWlHHwlQX1vFycvbueT85Zy6sp0FcxvJurm1JEmSJFWctCuSAAgh1AOXAa8FzqVwN7dpiTE+BPhfjNIJKEkStnUNFFYcbdrHY1t6GB3Lk81kWL2klVdcsIrTVs5l1eIWqrJeoiZJkiRJlS7tZtuXUrhT2yuAh4GvAe+IMe4sYzZJx5l8krB1dz+Pb93PY1t6iFt66B0YBWDh3EZecMZiTl3VzsnL22moO6oeW5IkSZJUAdL+l9wNFPZE+mCMcX0Z80g6jozn8mza0cdjW3t4bEsPj2/dz9DIOABzW+s4dUU7p6xo57RVc5nbWj/DaSVJkiRJ05V2j6RTyx1EUuUbHh1n/fZeHnuyh8e3JemDAwAAIABJREFU9rBhey+jxbuqLepo5LyT57Nu2RzWLWtj3pyGGU4rSZIkSSo1ry2R9GtGx3Ls6h5i175Bdu4bZNe+QbbtGeDJXf3kk4RMBpYvaOGFZy1h3bI5nLS0jdam2pmOLUmSJEkqM4skaZbK5xP29A5PlkUThdGufYPs7R152mfbW+pYOLeRS5+3nHVL21izZI57HEmSJEnSLOR/CUonsCRJ6Bsce1pRNHHc1TPEeC6Z/GxDXRUL5zayblkbC+Y2srD4mN/eQH2t/1chSZIkSUp/17YzY4y/KncYSc/MyGiOXd2DBxRGQ+zcNzi5+TVAdVWG+e2FguistfOeVhi1NNaQyWRm8K+QJEmSJFW6tMsM/i2EsB34MvCVGOOOMmaSZrUkSRgaydE/PEb/4Bj9Q2MMDBWeD3wMDI3ROzhKT//o087R0VrHgrmNPPe0BSxsb2RhRyML5jYyr7WebNaySJIkSZL0zKQtkhYBLwNeD/xVCOHnwJeAb8UYB8sVTjqeTBRAvYOj9A6M0jc4yvBojpGxHCPF5+HRHKNjOYanjBXezzMyNs7YeELf4Ci5fHLQ38gATQ01NDXU0NxQTXtLHcsXtNDZ3sCiuYWyaH57A3U1Vcf2j5ckSZIkzQqpiqQY4zhwO3B7CGEO8BrgPcBnQwj/B7gpxnh3+WJKMyeXz7O7e4ie/kJB1DswSu/gKPsHnv66d2CM8Vz+sOeqq6mirraKuprslOMqWhtrqautoq21gSoSmhtqaJ4sjJ56NNZVu6JIkiRJkjRjjmoH3RBCM/A7wB8AS4GvAU8CXwkh3BFj/JPSR5SOnbHxHFu7Bti8q48nd/WzeWcfW7v6GRt/ekGUzWRoaaphTmMtrU21LJ7XRGtTLa2NtcxpKoy1NNZQX1dNfU2hLKqpyZI9wh5EnZ0tdHX1lfNPlCRJkiTpGUu72fbLgCuA3wbuBj4HfDvGOFx8/zMUCiWLJB03hkfH2bK7UBY9uaufzbv62L5nYPKysoa6alYsaOais5ewbH4zc1vqCmVRUy1NDTVHLIUkSZIkSTrRpF2RdD2FPZH+7GAbbccY94UQ3l3SZFIJjefybNndzxPb9rNxey+bd/Wxc+8gEzsRtTTWsGJhC89a08GKBS0sX9hC55x672ImSZIkSdIUafdIOiPFZz43/ThSafQOjLJ+236e2L6f9dt62bSjl9Hi5WntLXWsXNjCc05ZwPIFLaxY2EJbc62lkSRJkiRJR3DIIimE8KE0J4gxfqB0caSjl8vn2dY1UCiOthWKo909QwBUZTMsX9DCC89awpolraxdMoe5rfUznFiSJEmSpOPT4VYkLTtmKaSU+ofG2L5ngO17B9i+Z4Ctu/vZuLOPkdEcAK1NtaxdMocXnr2YtUvmsGJBC7U1VTOcWpIkSZKkE8Mhi6QY45XHMog0Vd/gaKEw2jPA9j2Dk8XR/oHRyc/U1mRZ3NHE809fyNolc1izZA7z3NdIkiRJkqSyOdylbavTnCDGuKF0cTSbDI+O09UzTFfPEF09Q+zuHppcbdQ3ODb5ufraKhbPa+KM1R0sntfE4nmNLO5oYu6ceu+cJkmSJEnSMXS4S9ueABLgcP+lngBeN6SDSpKEnv7RyaKoq2eI3RPH3UP0TimLABrrqlk0r5GzT5rH4o6mYmnURHtLnauMJEmSJEmqAIe7tC17LIPo+JQkCX2DY+zcN8jOfYPsmnjuHmJPz9DkndIAMhmY21JPZ1s9Z500j862hqc9mhtqZvAvkSRJkiRJR3K4FUnSpJHRHLu6Bw8ojIbYuW+QoZHxyc9VV2WY397IgvYGzlg9d7Ikmt/WQMeceqqr7CclSZIkSTpepSqSQgg/o3AZ26+JMV5Y0kQqmyRJ6B0YpX94nKGRwmNw6nHxMTQyztDwU2P9Q2P09I8+7VwdrXUsmNvIc09bwMK5jSyc28iCuY3Ma60nm/UyNEmSJEmSTkRpVyR97oDXC4E3A7eUNo5KqXdwlE07etm4o4+NO3rZsL2X/qGxQ34+m8nQUFdFY301DXXVNNZV09nWwMqFrXS2N7CoWBbNb2+grsatsSRJkiRJmm1SFUkxxpsPHAshfBP4R+BDpQ6lozc8Os7mnX2TpdHGHb3s2T8MFHZLXzyviTPXdrB8QQutjbWFomhKYdRYV01tTdZNrSVJkiRJ0iFNZ4+kbcCzShVkNsvl8/z8/21n957+o/re8GiOzbsKxdH2PQMkxYsPO1rrWbW4lYvOWcLqRa0sX9BCQ53bYUmSJEmSpOlJu0fSmw4YagR+F7in5IlmoW1dA1z/pfsni6Cj0dxQw6pFrTx7XSerFrWyalErrU21pQ8pSZIkSZJmvbTLVK444PUA8HPgE6WNMzstX9DCzR+8hB07e4/qe9VVWdqaa70cTZIkSZIkHROHLJJCCO+MMX66+PKtMcYnjlGmWam9pZ7x4UNvhC1JkiRJkjTTsod576NTjn9R7iCSJEmSJEmqbIe7tG1DCOHjwENAzUH2SQIgxviF6YYIIawDbgY6gL3AG2KMj0/3vJIkSZIkSSqdwxVJrwXeA1wO1PDr+yQBJMC0iyTg74HPxBhvCSG8HrgJeHEJzitJkiRJkqQSOWSRFGN8DHgLQAjh/8YYX1KOACGE+cA5wMXFoa8Cnw4hdMYYu8rxm5IkSZIkSTp6h9sjaVK5SqSiZcC2GGOu+Fs5YHtxXJIkSZIkSRXicJe2HTc6OppnOkJJdHa2zHQEVQDngSY4FwTOAxU4DwTOAxU4DwTOAxXM1DyohCJpC7AkhFAVY8yFEKqAxcXxI6kC6O4eIJ9Pypmx7Do6mtm7t3+mY2iGOQ80wbkgcB6owHkgcB6owHkgcB6ooNzzIJvN0N7eBMXeZapMksx8ARNC+DHwuSmbbb85xnhRiq9eAPysrOEkSZIkSZJmpxcAd00dqJQi6WTgZqAd6AbeEGOMKb5aB5wH7ABy5UsoSZIkSZI0a1QBi4D7gZGpb1REkSRJkiRJkqTKl+qubZIkSZIkSZJFkiRJkiRJklKxSJIkSZIkSVIqFkmSJEmSJElKxSJJkiRJkiRJqVgkSZIkSZIkKRWLJEmSJEmSJKVikSRJkiRJkqRULJIkSZIkSZKUikWSJEmSJEmSUrFIkiRJkiRJUioWSZIkSZIkSUrFIkmSJEmSJEmpWCRJkiRJkiQpFYskSZIkSZIkpWKRJEmSJEmSpFQskiRJkiRJkpSKRZIkSZIkSZJSsUiSJEmSJElSKhZJkiRJkiRJSsUiSZIkSZIkSalYJEmSJEmSJCkViyRJkiRJkiSlYpEkSZIkSZKkVCySJEmSJEmSlIpFkiRJkiRJklKxSJIkSZIkSVIqFkmSJEmSJElKxSJJkiRJkiRJqVgkSZIkSZIkKRWLJEmSJEmSJKVikSRJkiRJkqRULJIkSZIkSZKUikWSJEmSJEmSUrFIkiRJkiRJUioWSZIkSZIkSUrFIkmSJEmSJEmpWCRJkiRJkiQpFYskSZIkSZIkpWKRJEmSJEmSpFQskiRJkiRJkpRK9UwHmKY64DxgB5Cb4SySJEmSJEkngipgEXA/MDL1jeO9SDoP+NlMh5AkSZIkSToBvQC4a+rA8V4k7QDo7h4gn09mOsu0dHQ0s3dv/0zH0AxzHmiCc0HgPFCB80DgPFCB80DgPFBBuedBNpuhvb0Jir3LVMd7kZQDyOeT475IAk6Iv0HT5zzQBOeCwHmgAueBwHmgAueBwHmggmM0D35tGyE325YkSZIkSVIqFkmSJEmSJElKxSJJkiRJkiRJqRzveyQdVC43Tnd3F+PjozMdJbXdu7Pk8/mZjjEt2WwVDQ3NNDfPIZPJzHQcSZIkSZJUYqmLpBDCxcAfAPNjjC8PIZwLtMYYf1i2dM9Qd3cX9fWNNDUtPG4KjerqLOPjx2+RlCQJudw4fX09dHd3MXfu/JmOJEmSJEmSSizVpW0hhHcBnwUeBy4sDg8BHylTrmkZHx+lqan1uCmRTgSZTIbq6hra2joYHR2e6TiSJEmSJKkM0u6R9G7gN2OM1wMTy2YeBUJZUpWAJdLMyGSygLeilCRJkiTNDkmSkNuzmZEH/g/j2x6e6Thll7ZIagG2FI8nWoIa4PjZhOgE9M53/hF33/0zAK6//sP86le/POznf/GLB7jvvnsmX+/Z08W73vW2smaUJEmSJOlEkyQJua6NjNx7GwNf/3MGv/VBRn/5z+T3PjnT0cou7R5JPwXeC3x0ythVwI9KnmgWGx8fp7r6me1//t73vv+In/nlL/+DoaEhzj//uQDMm9fJpz510zP6PUmSJEmSZpMkSch3bWBsw/2Mb3yApG8PZKqoWnIK1We9jOoVZ5NtaJ3pmGWXtrV4F/CdEMJbgZYQQgT6gMvKluwEcsEF53LllW/lZz/7CSMjw7ztbX/Ci170kqe9d889d3P++c/jda+7gk996hOsX/84o6OjnH32ubzrXX9GVVUVGzdu4LrrrmVoaIg1a9YwOvrUgrB3vvOPuPzyK3j+819Af38/N974cR599GEymSxnnnkWr3zlq7n99m+Rz+d54IH7eMlLfovf/M3f4i1vuYI77vi/ANxzz8+56aZPk8/naWtr5+qr38fSpcv4xS8e4MYb/4ZTTz2Nhx76LyDDtddex8qVq2bin1OSJEmSpGMiSfLkd61nbOMDjG+4n2RgH2SrqFpyGjXnvJLqFWeTqW+e6ZjHVKoiKca4I4RwHnA+sJzCZW73xRiPi9uMjT12N2Pxp2U5d024kJp1zz/i57LZLF/84q08+eQm3v72N3PmmWfT3j4XgLq6Ov7xH29hfDzP9dd/mLPOOof3vvf95PN5rr32Gu644595xStexYc//AFe85o/4Ld/+zIefPC/+OM/fvNBf+vGGz9OQ0MDX/ziV8lms/T09NDW1sYrX/m7DA0N8c53vhuAHTu2T36nu3sfH/nIB/jUp/43q1at5rvf/TbXXnsN//APNwOwceN63ve+D/Ce9/wlN9/8eW6++fN88IMVude6JEmSJEnTktuzibF4V2Hl0WAPZKupWno6Nee9muoVZ5Gpa5rpiDMm9XVUMcYEuLf40FG67LJXArB8+UrWrQs89NB/ccEFLwTgt3/7qYVdd931Ux555CG+9rWvADA8PMz8+QsYGOhn48b1XHLJpQCcfvoZrF699qC/9fOf/4zPfe4WstnCFlhtbW1HzPfQQw+yZs06Vq1aDcCll76Cj3/8rxkcHCjmXsG6dScDcNppZ0zuzSRJkiRJ0oki37OTkfu/wfjGB6Cqhuplz6J69blULz+LTG3DTMerCKmKpBDCFg5+K64RYCvwLeCzMcbxEmYrmZp1z0+1amimNDQ0TnmVcN11N7BkydKnfWZgoP/YhjpAbW3d5HE2myWXy81gGkmSJEmSSic/2MPof9zO2KM/gaoaas95JbXPeqnl0UGkvWvbjUA3cC3wFuBDwF7gH4GvU9h4+7pyBDxR3HHHPwOwZcuTPP545LTTzjjo557//Au55ZabJ4uanp4etm/fRlNTM6tXr+UHP/gXAB5++EE2bHjioOf4jd94AV/96pdIkmTyHABNTU2HLKROO+0M1q9/jM2bNwHw/e9/l5NOCjQ2zt7lepIkSZKkE1syOsTI/d9k4GvvYezRn1Jz6kU0/cHHqDv3VZZIh5D20rY3AhfHGCc31QkhfB+4M8Z4WgjhR8C/Ae8pfcQTQy6X48orX8fw8DBXX/2+yf2RDvSnf/o/+Lu/u5E3vvFyMpkMNTW1XHXV/2Dx4iVcc821XHfdtdxyyxdZvXotJ5986kHP8a53/XduvPHjXHHFa6mqquLss8/h3e++mgsvvIj3ve9q3vjG101utj2hvb2da675ENde+5fkcjna2tr5wAc+XJZ/C0mSJEmSZlKSG2Ps4R8y+ovvkIz0U73mOdSd92qyrfNnOlrFy0ysWjmcEMI+YGWMsXfKWBuwMcbYHkLIAL0xxpbyRT2olcDGvXv7yeef+jt27tzMwoUrjnGUQ7vggnO5886f0tjYeMjPVFdnGR8/LvYuP6JK+/c/nnR2ttDV1TfTMVQBnAsC54EKnAcC54EKnAcC58F0JUme8SfuYeT+b5L076VqyWnUnf8aqjpXznS0o1LueZDNZujoaAZYBWya+l7aFUnfAW4PIXyUwp5IS4G/KI4DPO/AE0uSJEmSJFWCJEnIbfkvRu7/J/J7t5Cdt4L6C6+keunpMx3tuJO2SHob8FfATcBiYAdwG4W9kgA2AC8rdbgTxV13PTDTESRJkiRJmpVyuzcwcu9t5HY8Sqalk/oXv53qNeeTyaTdNlpTpSqSYozDwHuLj4O9v7OUoSRJkiRJkqYj37+Pkfv+ifEn/p1MfQt1v/F6ak55EZmqtGtqdDCp//VCCLVAAOYBmYnxGOMPU3z3BuDVFPY0OiPG+GBxfB1wM9BB4S5wb4gxPn4U+Q8pSRIymcyRP6iSSpI8U6aHJEmSJEnHVDI2wuivvsfor74P5Kk96zJqz3qZd2ErkVRFUgjhAuCfgDqgFegFWoAtwOoUp/g28LfAzw4Y/3vgMzHGW0IIr6dw6dyL00U/tOrqWgYGemlqarVMOkaSJCGXG6evr5va2vqZjiNJkiRJmmWSJM/44//OyP3fIBnopnr1+dQ95zVkWzpnOtoJJe2KpE8AH4sxfiKE0B1jnBtC+AAwmObLMca7AEIIk2MhhPnAOcDFxaGvAp8OIXTGGLvS/gEH097eSXd3F/39PdM5zTGVzWbJ54/vu7Zls1U0NDTT3DxnpqNIkiRJkmaR3M7HGf73W8l3bSTbuYr6l7yD6oXrZjrWCSltkbSOwoqiqa4HNgI3PMPfXgZsizHmAGKMuRDC9uL4tIqkqqpq5s1bNJ1THHPewlGSJEmSpKOT79vDyL23Mb7hPjKNbdS/6K1Un/Q8N9Iuo7RF0n4Kl7T1ADtCCKdS2NOouVzBjkZHR0XEmLbOzpaZjqAK4DzQBOeCwHmgAueBwHmgAueBwHkAkB8Zoufn36L/3u9AJkPbBa+h7Xm/Q3YWbbUyU/MgbZH0LeBS4FbgC8CPgDHgG9P47S3AkhBCVXE1UhWwuDh+VPbu7SefT6YRZea5IkngPNBTnAsC54EKnAcC54EKnAcC50GSzzP+2F2M3P9NkqH9VK99HnXn/x655g727h+jUFWc+Mo9D7LZzCEX7aQqkmKM755yfEMI4R4Km23/yzMNFWPcHUL4T+By4Jbi8y+nuz+SJEmSJEk6seR6tjP+xL2Mrb+HZP8usvPX0HDJVVTNXzPT0WadtHdtuzHGeNXE6ymbZ38SePchvzjl+8DvAguBfwsh7I0xnga8Hbi5uHF3N/CGo/8TJEmSJEnSiSbf28XYhnsZX38v+b1bgAxVi0+m5tzfpXr1+d6lfYakvbTtjcBVBxm/ghRFUrGE+rXvxxgfBZ6TMoMkSZIkSTqB5Qe6Gd9wH2Pr7yW/ewMA2QVrqfuN/0b16vPINrbNcEIdtkgKIbxp4nNTjiesBvaUJZUkSZIkSZoV8kO9jG98gPH195Lb8RiQkO1YQe35v0/NmvPItnTOdERNcaQVSVcUn2unHAMkwC7gD8sRSpIkSZIknZiSJCHp3c349kcY3/gAuW0PQ5In27aI2mf/DjVrzifbtmimY+oQDlskxRgvAgghfCTGeM2xiSRJkiRJkk4k+b4uctsfZXz7I+S2P0oysA+ATEsntWdeSvWa55Cdu9R9j44Dae/adg1ACGE+0HzAexvKkEuSJEmSJB2n8gPd5LY/wvi2R8jteISkr7AzTqa+harFJ1O1+DKqFp9Mds4iy6PjTNq7tl0CfAE4cG1ZAlSVOpQkSZIkSZpZSZKHJA/5PORzkORJis/k85DkIJ8nKT7n920trDra8QjJ/l2Fk9Q1Ub3oZKrOuISqxaeQbV9icXScS3vXtr8DPgzcHGMcKmMeSZIkSZJmtSQ3TjK0n2Sor/jcS35oP8lgL13VeYYGRwolzqFKnqQ4NqXkYfI5TzL19cR3p3xn8j2Sow9f00DVonVUn3JRoTjqWEYmky35v5FmTtoiqR24Kcb4DGaRJEmSJEmzR5LkYXyUZGz4qeexEZLxkacfD/dPFkXJUC/J4H7yQ70wOnjwE1fXka9vJJ9kIJuFTBWZ4nPhdRayWTITr6tryGTqIFtVKHMyWchWHea7VZDJFsefek226qmxic8feM5MlmzLPLLzVpDJeuHSiSxtkfR54EoKl7dJkiRJknRCS5I8jA4Vyp6R/sLzoY5H+klGh2FsmGR8BMZH0/9QbQOZhjlkG1rJzl1KVcMcMo2tZBrmkGloJdvw1HGmpo7Ozha6uvrK94dLR5C2SHoucFUI4b3AzqlvxBgvLHkqSZIkSdKslCQJJMnTLsUinyvu13O0F8kkhRVBo0Mko4Mko0NQfJ58PTI4eTwxzsgAychA4bcPJpMhU9dMpq4J6pvJNHWQndtIproOaurIVNeRqamfPKam+Lq6jszUsdpGMtW10/43k46ltEXS54oPSZIkSZKmZfjuLzO+4f4D9vCZsoHzsVJdWyhzahsLK4PqGsm2zCNT10SmvrlQFtU3k6lvKh63kKlvLnzWfX80S6UqkmKMN5c7iCRJkiRpdqhacFJhY+cp+/NkpuzHM7GHz+R+PdkqyGQK7x+lTFUN1DaSqWskU9swpTiqJ5NNu7ZC0oRU/6sJIWSAtwCXA/NijM8KIVwILIwx3lbOgJIkSZKkE0vN2udSs/a5Mx1D0jOQts79EPBm4H8Dy4tjW4E/L0coSZIkSZIkVZ60RdIbgctijF8DJnY32wisLkcoSZIkSZIkVZ60RVIV0F88niiSmqeMSZIkSZIk6QSXtkj6HvA3IYQ6mNwz6cPAd8oVTJIkSZIkSZUlbZH034FFwH5gDoWVSCtwjyRJkiRJkqRZI9Vd22KMvcCrQgjzKRRIW2KMO8uaTJIkSZIkSRUlVZEUQvgtYFOM8TFgd3EsAMtjjD8oYz5JkiRJkiRViLSXtn0G6DtgrK84LkmSJEmSpFkgbZE0P8a444CxHcDCEueRJEmSJElShUpbJG0IIbz4gLEXARtLG0eSJEmSJEmVKtUeScBfAd8KIXweWA+sAa4sPiRJkiRJkjQLpFqRFGO8HfgtoAl4WfH5kuK4JEmSJEmSZoEjrkgKIVQBjwGnxhjfXv5IkiRJkiRJqkRHXJEUY8wBOaC+/HEkSZIkSZJUqdLukfRJ4LYQwnXAViCZeCPGuKEcwSRJkiRJklRZ0hZJny4+X3zAeAJUlS6OJEmSJEmSKlWqIinGmGpTbkmSJEmSJJ24jqogCiEsCyE8t1xhJEmSJEmSVLlSrUgKISwHvgqcReFytuYQwu8BL40xvmW6IUIIlwEfBjLFx7Uxxm9N97ySJEmSJEkqnbQrkm4C7gBagLHi2A/49T2TjloIIQN8GbgixngWcAVwcwjBy+kkSZIkSZIqSNqy5nzg+hhjnuId22KM+4E5JcqRn3KuNmBH8bckSZIkSZJUIdIWSbuAtVMHQginAk9ON0CMMQF+H7g9hLAZ+DbwhumeV5IkSZIkSaWVSZLkiB8KIbwJeC/wv4C/Bd4GvI/CKqWvTCdACKEa+BfggzHGu0MIz6ewH9OpMcb+I3x9JbBxOr8vSZIkSZKkg1oFbJo6kGqz7RjjF0IIeykUSFuAPwTeH2P8dglCnQUsjjHeXfytu0MIA8ApwP1pTrB3bz/5/JELsUrW2dlCV1ffTMfQDHMeaIJzQeA8UIHzQOA8UIHzQOA8UEG550E2m6Gjo/mg76UqkgBijLcDt5cq1BRbgaUhhBBjjCGEU4AFwPoy/JYkSZIkSZKeodRFUvHytsuBxcB24GvAF4p7HD1jMcadIYR3AN8IIUxssP2mGOO+6ZxXkiRJkiRJpZWqSAohfAx4JfBJYDOwAvifQADeM90QxX2WprXXkiRJkiRJksor7YqkNwLnxBi3TgyEEL4L/IISFEmSJEmSJEmqfNmUn+srPg4c6y1tHEmSJEmSJFWqtCuSPgl8K4RwPYXNsZcBVwOfCCGsnvhQjHFD6SNKkiRJkiSpEqQtkv62+HzRAeMvAW4sHidAVSlCSZIkSZIkqfKkKpJijGkvgZMkSZIkSdIJyoJIkiRJkiRJqVgkSZIkSZIkKRWLJEmSJEmSJKVikSRJkiRJkqRU0t61jRBCDfBcYHGM8eshhCaAGONAucJJkiRJkiSpcqRakRRCOAN4DPgH4PPF4RcCXyhTLkmSJEmSJFWYtJe2fRb4QIzxZGCsOPYT4IKypJIkSZIkSVLFSVsknQbcUjxOYPKStoZyhJIkSZIkSVLlSVskbQKePXUghHA+8ESpA0mSJEmSJKkypd1s+/3AHSGEvwdqQwh/AbwdeGvZkkmSJEmSJKmipFqRFGP8LvBSoJPC3kgrgN+NMd5ZxmySJEmSJEmqIGlXJBFj/CXwx2XMIkmSJEmSpAp2yCIphPChNCeIMX6gdHEkSZIkSZJUqQ63ImlZiu8npQoiSZIkSZKkynbIIinGeOWxDCJJkiRJkqTKlnqPpBDCScDvA4uB7cBtMcbHyxVMkiRJkiRJlSXVXdtCCK8Dfgk8CxgAzgB+URyXJEmSJEnSLJB2RdJHgEtjjD+dGAghvAD4MnBrOYJJkiRJkiSpsqRakQS0AP9+wNg9QFNp40iSJEmSJKlSpS2S/ga4LoRQDxBCaAA+WhyXJEmSJEnSLJD20rY/BhYCfxpC6AbagQywI4TwjokPxRiXlz6iJEmSJEmSKkHaIun1ZU0hSZIkSZKkipeqSIox/qTcQSRJkiRJklTZUhVJIYQa4BrgCmAxsJ3CHds+GmMcLV88SZIkSZIkVYq0l7Z9DDgfeDuwGVgBvB9oBf6sPNEkSZIkSZJUSdIWSa8Bzowx7i2+jiGEXwC/ogRFUvFucJ8AfhMYBv49xviyssC2AAAecklEQVRH0z2vJEmSJEmSSidtkZQ5yvGj9TEKBdK6GGMSQlhQovNKkiRJkiSpRNIWSf8EfCeEcC3wJIVL264BbptugBBCM/AGYGmMMQGIMe6a7nklSZIkSZJUWpkkSY74oRBCLYXi6HUUNtveBnwN+EiMcWQ6AUIIZwLfKj4uAvqBa2KMd6X4+kpg43R+X5IkSZIkSQe1Ctg0dSBVkVROIYRzgP8A/luM8dYQwnOA7wBrY4y9R/j6SmDj3r395PMz+3dMV2dnC11dfTMdQzPMeaAJzgWB80AFzgOB80AFzgOB80AF5Z4H2WyGjo5mOEiRlE17khDCxSGEz4cQvlN8fW4I4cUlyPckMA58FSDGeC+wB1hXgnNLkiRJkiSpRFIVSSGEdwGfBR4HLiwODwEfmW6AGOMe4EfAxcXfWgfMB56Y7rklSZIkSZJUOmlXJL0b+M0Y4/VAvjj2KBBKlOPtwPtCCP9FYe+lK2KMPSU6tyRJkiRJkkog7V3bWoAtxeOJzYhqgNFShIgxbgBeVIpzSZIkSZIkqTzSrkj6KfDeA8auonBJmiRJkiRJkmaBtCuS3gV8J4TwVqAlhBCBPuCysiWTJEmSJElSRUlVJMUYd4QQzgPOA1ZQuMztvhhj/vDflCRJkiRJ0oki7aVtE5+tKR5XAZnSx5EkSZIkSVKlSrUiKYTwLODbQB2wDVgKDIcQXhVj/FUZ80mSJEmSJKlCpF2R9AXgM8DSGOP5wBLg08VxSZIkSZIkzQJpi6R1wCdjjAlA8flvgZPKFUySJEmSJEmVJW2R9D3gFQeMvRy4o7RxJEmSJEmSVKlS7ZFEYXPtr4UQ/oPCHduWAc8Gbg8hfGniQzHGN5Q+oiRJkiRJkipB2iLpweJjwsPAv5Y+jiRJkiRJkipVqiIpxnhtuYNIkiRJkiSpsqXdI0mSJEmSJEmznEWSJEmSJEmSUrFIkiRJkiRJUioWSZIkSZIkSUrlkJtthxDelOYEMcYvlC6OJEmSJEmSKtXh7tp2xZTjDPB8YCewBVgGLADuBiySJEmSJEmSZoFDFkkxxosmjkMInwK+HWP85JSxPwXWlDeeJEmSJEmSKsXhViRN9Xpg3gFjnwb2AFeVNJEkSZIkSZIqUtrNtncCrzhg7OXA7tLGkSRJkiRJUqVKuyLpKuCbIYSrKeyRtBw4FXhNuYJJkiRJkiSpsqRakRRj/AGwGvgs8Ivi8+oY451lzCZJkiRJkqQKknZFEjHGPSGEHwNLYoz3lC+SJEmSJEmSKlGqIimEsBz4KnAWkADNIYTfA14aY3xLGfNJkiRJkiSpQqTdbPsm4A6gBRgrjv0AuLgcoSRJkiRJklR50hZJ5wPXxxjzFFYkEWPcD8wpVzBJkiRJkiRVlrRF0i5g7dSBEMKpwJMlTyRJkiRJkqSKlLZIugH4bgjhSqA6hHA58HXgr8uWTJIkSZIkSRUlVZEUY/wCcDXwGmAL8Abg/THGr5QxmyRJkiRJkipI2ru2PSfGeDtw+wHj58cY7ytLMkmSJEmSJFWUtJe2/eAQ4/9SqiAAIYQPhhCSEMLppTyvJEmSJEmSpu+wK5JCCFkgA2RCCJni8YQ1wHipgoQQzgGeC2wu1TklSZIkSZJUOke6tG0cSKYcT5UHPlqKECGEOuAzwOXAj0txTkmSJEmSJJXWkYqkVRRWIf0EuHDKeAJ0xRiHSpTjQ8AtMcZNIYSj/nJHR3OJYsyszs6WmY6gCuA80ATngsB5oALngcB5oALngcB5oIKZmgeHLZJijBOXma0oV4AQwvOAc4H3PtNz7N3bTz6fHPmDFayzs4Wurr6ZjqEZ5jzQBOeCwHmgAueBwHmgAueBwHmggnLPg2w2c8hFO6nu2gYQQngF8EJgHlP2SooxvmGa+V4InAJsLK5GWgr8awjhyhjjndM8tyRJkiRJkkok1V3bQggfBG4qfv41wF7gEqBnugFijNfHGBfHGFfGGFcCW4FLLJEkSZIkSZIqS6oiCXgTcHGM8c+A0eLzy4GV5QomSZIkSZKkypL20ra2GOODxePREEJNjPG+EMILSx2ouCpJkiRJkiRJFSbtiqT1IYTTiscPAu8IIVwBdJcnliRJkiRJkipN2hVJ1wAdxeO/AL4CNAN/XI5QkiRJkiRJqjypiqQY4/emHN8LrC1bIkmSJEmSJFWktCuSCCHMAQKFlUiTYow/LHUoSZIkSZIkVZ5URVII4Y3AZ4B+YHDKWwmwuvSxJEmSJEmSVGnSrkj6KPB7McbvlzOMJEmSJEmSKlfau7ZVA3eWM4gkSZIkSZIqW9oi6a+Ba0IIaT8vSZIkSZKkE0zaS9v+DFgIvCeEsHfqGzHG5SVPJUmSJEmSpIqTtkh6fVlTSJIkSZIkqeKlKpJijD8pdxBJkiRJkiRVtkMWSSGEv4wxfrR4/KFDfS7G+IFyBJMkSZIkSVJlOdyKpKVTjpeVO4gkSZIkSZIq2yGLpBjjO6YcX3ls4kiSJEmSJKlSZdN8KITwhhDCsw4YOzOEcEV5YkmSJEmSJKnSpCqSgA8DWw4Y2wJ8pLRxJEmSJEmSVKnSFkmtQO8BY/uBttLGkSRJkiRJUqVKWyQ9DLz6gLFXAY+UNo4kSZIkSZIq1eHu2jbVnwPfCyG8FlgPrAVeAlxarmCSJEmSJEmqLKlWJMUY7wJOB+4HmoD7gNNjjHeXMZskSZIkSZIqSKoVSSGEOmBHjPH6KWM1IYS6GONI2dJJkiRJkiSpYqTdI+kHwLMPGHs28K+ljSNJkiRJkqRKlbZIOgO494Cx+4AzSxtHkiRJkiRJlSptkbQfWHDA2AJgoLRxJEmSJEmSVKnS3rXtm8CtIYSrgA3AGuBvgNvKFUySJEmSJEmVJe2KpL8EHqFwOVsfcA8QgfeVKZckSZIkSZIqTKoVSTHGYeBPQgjvBOYBe2KMSVmTSZIkSZIkqaKkvbRtQnPx0RJCACDGuKHUoSRJkiRJklR5UhVJIYRTga9QuEtbAmSKzwBV5YkmSZIkSZKkSpJ2j6S/A34EzAV6gXbgJuAPy5RLkiRJkiRJFSbtpW1nAhfHGMdCCJkY4/4QwtXAg8At0wkQQugAvkzhTnCjwOPA22KMXdM5ryRJkiRJkkor7YqkYaCmeLwnhLC8+N2OEmRIgI/FGEOM8QxgPXB9Cc4rSZIkSZKkEkq7IulnwO8DXwS+AXwfGAF+ON0AMcZ9wI+nDN0DvGO655UkSZIkSVJpZZIkOfKnpgghZIHXAS3Al2KMA6UKUzz3ncA/xxhvTPGVlcDGUv2+JEmSJEmSJq0CNk0dSHvXtrNijP8JEGPMM819kQ7jU0A/8Omj+dLevf3k80dXiFWazs4Wurr6ZjqGZpjzQBOcCwLngQqcBwLngQqcBwLngQrKPQ+y2QwdHc0HfS/tpW13hhC6gK8Ct8YYN5Qq3IQQwg3AScDLi2WVJEmSJEmSKkjaImkR8FLgcuA/QwgPAbcCX48x7p5uiBDCdcCzgZfFGEemez5JkiRJkiSVXqoiKcaYA+4A7gghNACvpLAh9g1A3XQChBBOA/4CeAz4eQgBYGOM8VXTOa8kSZIkSZJKK+2KJABCCPXAZcBrgXMp3M1tWmKMDwGZ6Z5HkiRJkiRJ5ZV2s+1LKdyp7RXAw8DXgHfEGHeWMZskSZIkSZIqSNoVSTdQ2BPpgzHG9WXMI0mSJEmSpAqVdo+kU8sdRJIkSZIkSZUtO9MBJEmS9P+3d+/BdpXlAcafQ8KlAnITBkUTJhpe7BQVUamitmhhRMrYgdpguXgpeBmvVWS0HWlBHaHVkQpYpVrCxQtYVFQcR3vRobRWFESn1ZcoCDRckoLcFCEcdv/41sLjJjlskrPWt3f285vJhL3WyeE9zDM5Z3+s9S1JkqTJ4EKSJEmSJEmSRuJCkiRJkiRJkkYy0kJSRDy960EkSZIkSZI03kZ9ats/R8RNwPnApzLz5g5nkiRJkiRJ0hga9da2xwMnAfsDqyLi6xFxdEQ8prvRJEmSJEmSNE5GWkjKzAcy85LMfDmwB3ARcCJwa0ScFxEHdDmkJEmSJEmS6ntUm21HxHbAHwFHAk8EPgusAj4VEWct/HiSJEmSJEkaFyPtkRQRhwLHAIcAlwOfAL6Ymb9qzp8F3AC8saM5JUmSJEmSVNmom22fCpwH/Pn6NtrOzNsj4m0LOpkkSZIkSZLGykgLSZm5zwgf84lNH0eSJEmSJEnjaoMLSRFxyiifIDNPWrhxJEmSJEmSNK7muyLpSb1NIUmSJEmSpLG3wYWkzHx1n4NIkiRJkiRpvM13a9uyUT5BZl67cONIkiRJkiRpXM13a9tPgAEwM8/HDIBFCzqRJEmSJEmSxtJ8t7Zt0ecgkiRJkiRJGm8uFkmSJEmSJGkk893a9pCIuIxyG9vDZOYLF3QiSZIkSZIkjaWRFpKATwy93h34M+CChR1HkiRJkiRJ42qkhaTMPHf4WERcDJwDnLLQQ0mSJEmSJGn8bMoeSauBpy3UIJIkSZIkSRpvo+6R9JqhQ48BDge+veATSZIkSZIkaSyNukfSMUOvfwH8B/DhhR1HkiRJkiRJ42qDC0kR8abMPLN5eXxm/qSnmSRJkiRJkjSG5tsj6f1z/vnKrgeRJEmSJEnSeJvv1rZrI+JDwH8DW65nnyQAMvMfN3WIiNgLOBfYBbgNODYzV23q55UkSZIkSdLCmW8haQVwIvAKYEsevk8SwADY5IUk4GPAWZl5QUQcDXwceNECfF5JkiRJkiQtkA0uJGXmNcBxABHxL5n54i4GiIjdgGcCBzWHPgOcGRG7ZubaLv6dkiRJkiRJevTm2yPpIV0tIjWeBKzOzNnm3zUL3NQclyRJkiRJ0piY79a2ibHLLtvVHmFB7Lrr9rVH0BiwA7VsQWAHKuxAYAcq7EBgBypqdTAOC0k3AntExKLMnI2IRcATmuMjue22e3jwwUFnA/Zh1123Z+3au2uPocrsQC1bENiBCjsQ2IEKOxDYgYquO9hii5kNXrRTfSEpM9dExPcpm3pf0Px+1Yj7Iy2C8gVuDjaXr0Obxg7UsgWBHaiwA4EdqLADgR2o6LKDOZ970fC5mcGg/pU8EbE3cC6wE/Bz4NjMzBH+6POBy7qcTZIkSZIkaUq9APj3uQfGYiFpE2wNPBu4GZitPIskSZIkSdLmYBHweOAK4L65JyZ9IUmSJEmSJEk92aL2AJIkSZIkSZoMLiRJkiRJkiRpJC4kSZIkSZIkaSQuJEmSJEmSJGkkLiRJkiRJkiRpJC4kSZIkSZIkaSQuJEmSJEmSJGkkLiRJkiRJkiRpJC4kSZIkSZIkaSSLaw8wzSLipMw8pfYcqisiXp2Z59SeQ92LiB2A9wAPAqcAbwCOBn4IvCUzb684nnpiB2pFxJbA6ygtnA0cDhxFaeG9mXlfxfHUo4g4AFgK/Gtm3jLn+Csz89x6k6lPdiCAiFgCPBH43tzvAxFxUGZ+o95k0q95RVJdx9UeQGPh5NoDqDdnU/7e3RH4ErAn8FrgJuD0emOpZ3ag1hnA7wOHAucDK4BPA8uAD9YbS32KiLcDK4Ejgasj4vA5p99aZSj1zg4EEBFHAd8DPgZcExHPnXP6tDpTSQ/nFUkdi4jvbODUDLBbn7Oonoi4aAOnZoCd+5xFVT01M1dExCLgVuCgzJxt/p64uvJs6o8dqHVAZu4TEVsDa4DdM/PeiPgC8N3Ks6k/rwb2y8y7ImJv4IsR8djMXEn5OUHTwQ4E8E7gGZm5OiJ+H/hsRByfmV/HDjRGXEjq3l7AK4BfDh2fAS7sfxxVcijwNuD+oeMzwIH9j6NKHgBoFg1uzMzZ5vUgIh6sO5p6ZAdqrQPIzPsi4trMvLd5fX9EzNYdTT0aZOZdAJn544g4EPhGs8A4qDuaemQHApjJzNUAmfnNiDgEuDQi3owdaIy4kNS9K4G7MvPy4RMRMbyooM3XVcD3M/OK4RMR8d4K86iO2YjYJjN/lZn7tgcjYtuaQ6l3dqDWICIWZ+YDwEvbgxGxFbCo3ljq2f0RsXu7J05m3hwRLwK+DiyvO5p6ZAcCICJ2zMw7ADLzfyLiYOBreBeDxoh7JHWv3UB1fZb2OYiqeiNw4wbOPb/PQVTVYTRXowzZCXhHz7OonsNorkQZYgfT5zU0C0aZefOc43vgXhjT5BTgSXMPZOYa4MXAWVUmUg12ICh75z1t7oHMXAUcBLjRtsbGzGDgFXKSJEmSJEl6ZN7aVlFEnJSZp9SeQ/3wka6KiG0omyiuoDzWFcqVahcBH2z3R9H0ioizM/O1tedQf5on9CwFLs3Mq+ccf3dmfqDeZOpTRBxN+d7QXpHSfm+4IDP9v75TzvcM08X3DJoE3tpW13G1B1A/fKSrGucAS4BXUvY7WA68qjm2stpUGicvqT2A+hMRpwGvB3YHvhoRb5tz+uV1plLfIuIjlK0QVgLHN79WAkdRbnORfM8wJXzPoEnhFUkdax7nvD4zwG59zqKqfKSroDSw19CxtcDxEXFNjYHUv4hYs4FTM8COfc6i6g4F9s3MdRHxPuCSiNghM0/G7w3T5JDMHN5M+YqI+AKQNQZS/3zPoIbvGTQRXEjq3l7AK4BfDh2fAS7sfxxV4iNdBeVpXcsy89q5ByPiyYCPfZ8eM5TNU+9cz/GHPeFTm7fMXNf8vqZ5Ms+Xm9tg/d4wRSJi58y8fejwTvjGcZr4nkHgewZNCBeSunclcFdmPuzNQUTcX2Ee1eEjXQVwInB5RFwBXN8c2xN4FuC+ONPju8DjMvMHwyciYnWFeVTPnRHx5Mz8KUBm3h0RhwBfAfapO5p69DeUW1i+wG9+b3gZ8N5aQ6l3vmcQ+J5BE8I9krp3NPDDDZxb2ucgqspHuorM/DLwFOA84Lrm17nA8uacpsPLgG+t70RmPqfnWVTXCcDWcw80m+6/FPfCmBqZ+Q/AC4AbKD8bLqUsKP1ec07TwfcMAt8zaELMDAZeIde3iNg2M39Rew7VZQcCiIjlmbmq9hyqyw7UsgWBPyOosAOBHWg8eUVSHZfVHkBjwQ4E7nugwg7UsgWBPyOosAOBHWgMuZBUhxsnCuxAhR0I7EC/ZgsCO1BhBwI70BhyIamO62oPoLFgBwKf0qXCDtSyBYE/I6iwA4EdaAy5R5IkSZI0RiJiu8y8p/YcqssOBHag8bS49gDTICKOojxt4dLMvHrO8Xdn5gfqTaY+2YEiYgfgPcCDlKdyvIFfP6XlLZl5e8Xx1BM7UMsWNI8fAMtqD6Hq7EBgBxpDXpHUsYg4DXgecBVwBPC3mXl6c+7KzHxmzfnUDzsQQERcCKwGtgOeAvwIOI/SxO6ZeWzF8dQTO1DLFgQQEWvWc3hn4HaAzNyt34lUgx0I7ECTwyuSuncosG9mrouI9wGXRMQOmXkybpw2TexAAE/NzBURsQi4FTgoM2cj4jvA1Y/wZ7X5sAO1bEFQrja4DjgVeIDyc8FlwPNrDqXe2YHADjQh3Gy7B5m5rvl9DXAwcGBEfADwcrApYgei/EBAZs4CNza/k5kDyq0tmg52oJYtiMz8A8rC4UrgsZn5M2BdZl6fmdfXnE39sQOBHWhyuJDUvTsj4snti8y8GzgEeA6wT7Wp1Dc7EMBsRGwDkJn7tgcjYtt6I6kCO1DLFgRAZp4JvAY4IyL+Gn9Gn0p2ILADTQaj7N4JwDZzD2TmvcBLgbdWmUg12IEADqO5AmHITsA7ep5F9diBWragh2TmKuBA4JeUDdc1hexAYAcaf262LUmSJEmSpJG42XbHmkvWTwCOBJ7YHL4RuAj4YHNVijZzdiB4qIN3Aiuwg6llB2rZgsAOVNiBwA40Oby1rXvnAEuBVwLLm1+vApZQNlHTdLADQelgCXYw7exALVsQ2IEKOxDYgSaEVyR1b7/M3Gvo2Frg+Ii4psZAqsIOBHagwg7UsgWBHaiwA4EdaEJ4RVL3ZiNi2fDB5glePtp3etiBwA5U2IFatiCwAxV2ILADTQivSOreicDlEXEFcH1zbE/gWcBraw2l3tmBwA5U2IFatiCwAxV2ILADTQif2taDiNgWOIRybyvADcDXMvOeelOpb3YgsAMVdqCWLQjsQIUdCOxAk8GFpAoiYnlmrqo9h+qyA4EdqLADtWxBYAcq7EBgBxpP7pFUx4W1B9BYsAOBHaiwA7VsQWAHKuxAYAcaQy4k1TFTewCNBTsQ2IEKO1DLFgR2oMIOBHagMeRCUh2X1x5AY8EOBHagwg7UsgWBHaiwA4EdaAy5R5IkSZIkSZJG4hVJFUXE2bVnUH12ILADFXagli0I7ECFHQjsQOPFhaS6XlJ7AI0FOxDYgQo7UMsWBHagwg4EdqAxsrj2AJu7iFizgVMzwI59zqJ67EBgByrsQC1bENiBCjsQ2IEmhwtJ3ZsBXgzcuZ7jbpw2PexAYAcq7EAtWxDYgQo7ENiBJoQLSd37LvC4zPzB8ImIWF1hHtVhBwI7UGEHatmCwA5U2IHADjQhfGpbxyJiK2A2M2drz6J67EBgByrsQC1bENiBCjsQ2IEmhwtJkiRJkiRJGom3tnUsIrYGTgCWApdk5qVzzp2RmW+uNpx6YwcCO1BhB2rZgsAOVNiBwA40ObaoPcAU+CiwD/Bj4LSIOH3OuQPqjKQK7EBgByrsQC1bENiBCjsQ2IEmhFckde/Zmfk0gIj4e+AzEfFJ4DjK7vuaDnYgsAMVdqCWLQjsQIUdCOxAE8Irkrr30GJdZt4LHAFsC5yP//2niR0I7ECFHahlCwI7UGEHAjvQhDDG7t0SEU9vXzQ78P8pMAB+p9pU6psdCOxAhR2oZQsCO1BhBwI70IRwIal7rwPumHsgMx8EjgUOqzKRarADgR2osAO1bEFgByrsQGAHmhAzg8Gg9gxTIyJ2BMjMOx7pY7X5sgOBHaiwA7VsQWAHKuxAYAcaby4kdSwiHgecBvwJZYO0GWAW+BzwrsxcW3E89cQOBHagwg7UsgWBHaiwA4EdaHJ4a1v3LgCuBfbMzO0yc1tgGXBdc07TwQ4EdqDCDtSyBYEdqLADgR1oQnhFUsci4seZufcGzmVmRt8zqX92ILADFXagli0I7ECFHQjsQJPDK5K696uIeO7wwYh4HnBfhXlUhx0I7ECFHahlCwI7UGEHAjvQhFhce4Ap8Hrg/Ii4F7i+ObYnsA1l931NBzsQ2IEKO1DLFgR2oMIOBHagCeGtbT2IiBlgP2BJc+gG4HuZ6X/8KWIHAjtQYQdq2YLADlTYgcAONBm8IqljEbELcCqwFPhSZp4559zFmXlEteHUGzsQ2IEKO1DLFgR2oMIOBHagyeEeSd37OPBz4GPAyyLi8xHRLuAtqzeWemYHAjtQYQdq2YLADlTYgcAONCFcSOre8sw8MTM/DxwM3Ax8JSK2qTyX+mUHAjtQYQdq2YLADlTYgcAONCFcSOreVu0/ZOYgM98I/BC4lLJpmqaDHQjsQIUdqGULAjtQYQcCO9CEcCGpe9dGxAvnHsjMdwL/BexVZyRVYAcCO1BhB2rZgsAOVNiBwA40IVxI6t4xlFXk35CZfwHs0/84qsQOBHagwg7UsgWBHaiwA4EdaELMDAY+RVCSJEmSJEmPzCuSJEmSJEmSNBIXkiRJkiRJkjQSF5IkSZIWSESsjIj31Z5DkiSpKy4kSZIk9SwivhkRx9WeQ5Ik6dFyIUmSJEmSJEkj8altkiRJGyki9gU+CSwHvgoMgJ8AHwLOB/YHFgOXA6/PzP+NiPcD7wLWAQ8AKzPzTRGxN3AGsB+wFnhPZl7U85ckSZI0L69IkiRJ2ggRsRXwRcqC0c7A54AjmtNbAOcAS4ElwL3AmQCZ+ZfAZcCbMnO7ZhFpW+AbwKeB3YAjgY9GxG/39xVJkiQ9ssW1B5AkSZpQvwtsCZyemQPgnyLi7QCZeRtwcfuBzVVI/zbP5/pD4GeZeU7z+qqIuBh4OXByF8NLkiRtDBeSJEmSNs4TgNXNIlLreoCIeAzwYeAlwE7Nue0jYlFmzq7ncy0F9o+IO+YcW0y52kmSJGlsuJAkSZK0cW4G9oiImTmLSUuAnwLvAALYPzNviYhnAFcBM83HDW9SeSPwrcw8qIe5JUmSNpoLSZIkSRvnPymbZb8lIj4KHAY8h3IL2/aUfZHuiIidgb8a+rO3AsvmvP4KcGpEHAN8tjn2DOCezPxRd1+CJEnSo+Nm25IkSRshM+8HDgdeBdwOrAA+35w+Hfgt4P+AbwNfG/rjfwf8cUT8PCI+kpl3AwdTNtm+CbgFOA3YuuMvQ5Ik6VGZGQyGr6yWJEmSJEmSHs4rkiRJkiRJkjQSF5IkSZIkSZI0EheSJEmSJEmSNBIXkiRJkiRJkjQSF5IkSZIkSZI0EheSJEmSJEmSNBIXkiRJkiRJkjQSF5IkSZIkSZI0EheSJEmSJEmSNJL/ByuImdDwenhGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "turkey.plot(x='date',y=['actual','prediction'], figsize=(20,10),\n",
    "            ylabel='fully vaccinated people percentage',rot=90, title=\"Turkey in vaccinations\",subplots=True,sharex=True,sharey=True)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "[batuhan_seremet_118200054_cmpe407_project].ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}