SVR_NLD.ipynb

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   "outputs": [
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     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>...</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",
       "      <th>excess_mortality</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-05</td>\n",
       "      <td>39168.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>33.571</td>\n",
       "      <td>1605.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.571</td>\n",
       "      <td>2635.282</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-06</td>\n",
       "      <td>39189.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>35.000</td>\n",
       "      <td>1606.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.714</td>\n",
       "      <td>2636.695</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-07</td>\n",
       "      <td>39238.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>39.571</td>\n",
       "      <td>1611.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.429</td>\n",
       "      <td>2639.991</td>\n",
       "      <td>...</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",
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       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-08</td>\n",
       "      <td>39321.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>46.143</td>\n",
       "      <td>1617.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.571</td>\n",
       "      <td>2645.576</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-09</td>\n",
       "      <td>39432.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>57.286</td>\n",
       "      <td>1622.0</td>\n",
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       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-10</td>\n",
       "      <td>39496.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>57.714</td>\n",
       "      <td>1626.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.143</td>\n",
       "      <td>2657.350</td>\n",
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       "      <td>21.4</td>\n",
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       "      <td>1.82</td>\n",
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       "      <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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-11</td>\n",
       "      <td>39688.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>77.714</td>\n",
       "      <td>1629.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.429</td>\n",
       "      <td>2670.268</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-12</td>\n",
       "      <td>39852.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>97.714</td>\n",
       "      <td>1632.0</td>\n",
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       "      <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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-13</td>\n",
       "      <td>39959.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>110.000</td>\n",
       "      <td>1632.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.714</td>\n",
       "      <td>2688.501</td>\n",
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       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
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       "      <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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-14</td>\n",
       "      <td>40077.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>119.857</td>\n",
       "      <td>1635.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.429</td>\n",
       "      <td>2696.441</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-15</td>\n",
       "      <td>40318.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>142.429</td>\n",
       "      <td>1637.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.857</td>\n",
       "      <td>2712.655</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-16</td>\n",
       "      <td>40556.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>160.571</td>\n",
       "      <td>1640.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.571</td>\n",
       "      <td>2728.668</td>\n",
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       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-17</td>\n",
       "      <td>40927.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>204.429</td>\n",
       "      <td>1647.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.000</td>\n",
       "      <td>2753.630</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-18</td>\n",
       "      <td>41335.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>235.286</td>\n",
       "      <td>1656.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.857</td>\n",
       "      <td>2781.081</td>\n",
       "      <td>...</td>\n",
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       "      <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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-19</td>\n",
       "      <td>41628.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>253.714</td>\n",
       "      <td>1666.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.857</td>\n",
       "      <td>2800.794</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>41779.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>260.000</td>\n",
       "      <td>1672.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.714</td>\n",
       "      <td>2810.954</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-21</td>\n",
       "      <td>42195.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>302.571</td>\n",
       "      <td>1685.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.143</td>\n",
       "      <td>2838.943</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-22</td>\n",
       "      <td>42714.0</td>\n",
       "      <td>519.0</td>\n",
       "      <td>342.286</td>\n",
       "      <td>1691.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.714</td>\n",
       "      <td>2873.862</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-23</td>\n",
       "      <td>43480.0</td>\n",
       "      <td>766.0</td>\n",
       "      <td>417.714</td>\n",
       "      <td>1692.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.429</td>\n",
       "      <td>2925.400</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>44306.0</td>\n",
       "      <td>826.0</td>\n",
       "      <td>482.714</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.857</td>\n",
       "      <td>2980.974</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          iso_code  location        date  total_cases  new_cases  \\\n",
       "continent                                                          \n",
       "Africa         ZWE  Zimbabwe  2021-06-05      39168.0       24.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-06      39189.0       21.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-07      39238.0       49.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-08      39321.0       83.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-09      39432.0      111.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-10      39496.0       64.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-11      39688.0      192.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-12      39852.0      164.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-13      39959.0      107.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-14      40077.0      118.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-15      40318.0      241.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-16      40556.0      238.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-17      40927.0      371.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-18      41335.0      408.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-19      41628.0      293.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-20      41779.0      151.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-21      42195.0      416.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-22      42714.0      519.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-23      43480.0      766.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-24      44306.0      826.0   \n",
       "\n",
       "           new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  \\\n",
       "continent                                                                      \n",
       "Africa                 33.571        1605.0         0.0                1.571   \n",
       "Africa                 35.000        1606.0         1.0                1.714   \n",
       "Africa                 39.571        1611.0         5.0                2.429   \n",
       "Africa                 46.143        1617.0         6.0                2.571   \n",
       "Africa                 57.286        1622.0         5.0                3.286   \n",
       "Africa                 57.714        1626.0         4.0                3.143   \n",
       "Africa                 77.714        1629.0         3.0                3.429   \n",
       "Africa                 97.714        1632.0         3.0                3.857   \n",
       "Africa                110.000        1632.0         0.0                3.714   \n",
       "Africa                119.857        1635.0         3.0                3.429   \n",
       "Africa                142.429        1637.0         2.0                2.857   \n",
       "Africa                160.571        1640.0         3.0                2.571   \n",
       "Africa                204.429        1647.0         7.0                3.000   \n",
       "Africa                235.286        1656.0         9.0                3.857   \n",
       "Africa                253.714        1666.0        10.0                4.857   \n",
       "Africa                260.000        1672.0         6.0                5.714   \n",
       "Africa                302.571        1685.0        13.0                7.143   \n",
       "Africa                342.286        1691.0         6.0                7.714   \n",
       "Africa                417.714        1692.0         1.0                7.429   \n",
       "Africa                482.714        1709.0        17.0                8.857   \n",
       "\n",
       "           total_cases_per_million  ...  extreme_poverty  \\\n",
       "continent                           ...                    \n",
       "Africa                    2635.282  ...             21.4   \n",
       "Africa                    2636.695  ...             21.4   \n",
       "Africa                    2639.991  ...             21.4   \n",
       "Africa                    2645.576  ...             21.4   \n",
       "Africa                    2653.044  ...             21.4   \n",
       "Africa                    2657.350  ...             21.4   \n",
       "Africa                    2670.268  ...             21.4   \n",
       "Africa                    2681.302  ...             21.4   \n",
       "Africa                    2688.501  ...             21.4   \n",
       "Africa                    2696.441  ...             21.4   \n",
       "Africa                    2712.655  ...             21.4   \n",
       "Africa                    2728.668  ...             21.4   \n",
       "Africa                    2753.630  ...             21.4   \n",
       "Africa                    2781.081  ...             21.4   \n",
       "Africa                    2800.794  ...             21.4   \n",
       "Africa                    2810.954  ...             21.4   \n",
       "Africa                    2838.943  ...             21.4   \n",
       "Africa                    2873.862  ...             21.4   \n",
       "Africa                    2925.400  ...             21.4   \n",
       "Africa                    2980.974  ...             21.4   \n",
       "\n",
       "           cardiovasc_death_rate  diabetes_prevalence  female_smokers  \\\n",
       "continent                                                               \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "\n",
       "           male_smokers  handwashing_facilities  hospital_beds_per_thousand  \\\n",
       "continent                                                                     \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "\n",
       "           life_expectancy  human_development_index  excess_mortality  \n",
       "continent                                                              \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "\n",
       "[20 rows x 59 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "owid = pd.read_csv('./covid-19-data-master/public/data/owid-covid-data.csv', index_col='continent')\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.svm import SVR\n",
    "# owid\n",
    "\n",
    "df = owid.sort_values('date', ascending=True)\n",
    "df['date'] = pd.to_datetime(df['date'], format = '%Y-%m-%d')\n",
    "owid.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = owid.sort_values('date', ascending=True)\n",
    "df['date'] = pd.to_datetime(df['date'], format = '%Y-%m-%d')\n",
    "owid.tail(20)\n",
    "\n",
    "with plt.style.context('fivethirtyeight'):\n",
    "    df_filtered = df[df.iso_code=='NLD']\n",
    "    df_filtered.plot(x=\"date\", y=[\"new_cases\"])\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.title('Plotting new cases in NLD\\n', size=15);\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hamid/.local/lib/python3.8/site-packages/sklearn/utils/validation.py:73: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "svr_poly = SVR(kernel='poly', C=100, gamma='auto', degree=3, epsilon=.1, coef0=1)\n",
    "\n",
    "df_filtered = df[df.iso_code=='NLD']\n",
    "\n",
    "X_train = []\n",
    "for i, val in enumerate(df_filtered['date'].to_numpy()):\n",
    "    X_train.append(i)\n",
    "X_train = np.asarray(X_train)\n",
    "\n",
    "# print(X_train)\n",
    "\n",
    "# X_train = df_filtered['date'].to_numpy()\n",
    "y_train = df_filtered['new_cases'].to_numpy()\n",
    "\n",
    "X_train = X_train.reshape(-1,1)\n",
    "y_train = y_train.reshape(-1,1)\n",
    "\n",
    "# print(x, y)\n",
    "\n",
    "svr_rbf = SVR(kernel = 'rbf')\n",
    "# svr_rbf = SVR(kernel='poly')\n",
    "svr_rbf_predict = svr_rbf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [6.0000e+00],\n",
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       "       [1.0290e+03],\n",
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       "       [1.2210e+03],\n",
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       "       [1.1750e+03],\n",
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       "       [7.5000e+02],\n",
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       "       [7.1500e+02],\n",
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       "       [6.5500e+02],\n",
       "       [6.5600e+02],\n",
       "       [4.0000e+02],\n",
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       "       [3.3500e+02],\n",
       "       [1.9900e+02],\n",
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       "       [7.2200e+02]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_predict = svr_rbf_predict.predict(X_train)\n",
    "\n",
    "with plt.style.context('fivethirtyeight'):\n",
    "    df_filtered = df[df.iso_code=='NLD']\n",
    "#     df_filtered.plot(x=\"date\", y=[\"new_cases\"])\n",
    "    plt.plot(X_train, y_predict)\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.title('Plotting new cases in NLD\\n', size=15);\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = np.asarray(range(500,1000))\n",
    "X_test = X_test.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_predict = svr_rbf_predict.predict(X_test)\n",
    "\n",
    "with plt.style.context('fivethirtyeight'):\n",
    "    df_filtered = df[df.iso_code=='NLD']\n",
    "#     df_filtered.plot(x=\"date\", y=[\"new_cases\"])\n",
    "    plt.plot(X_test, y_predict)\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.title('Plotting new cases in NLD\\n', size=15);\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hamid/.local/lib/python3.8/site-packages/sklearn/utils/validation.py:73: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "# polynomial kernel\n",
    "\n",
    "# svr_rbf = SVR(kernel = 'rbf')\n",
    "svr_rbf = SVR(kernel='poly')\n",
    "svr_rbf_predict = svr_rbf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_predict = svr_rbf_predict.predict(X_train)\n",
    "\n",
    "with plt.style.context('fivethirtyeight'):\n",
    "    df_filtered = df[df.iso_code=='NLD']\n",
    "#     df_filtered.plot(x=\"date\", y=[\"new_cases\"])\n",
    "    plt.plot(X_train, y_predict)\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.title('Plotting new cases in NLD\\n', size=15);\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_predict = svr_rbf_predict.predict(X_test)\n",
    "\n",
    "with plt.style.context('fivethirtyeight'):\n",
    "    df_filtered = df[df.iso_code=='NLD']\n",
    "#     df_filtered.plot(x=\"date\", y=[\"new_cases\"])\n",
    "    plt.plot(X_test, y_predict)\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.title('Plotting new cases in NLD\\n', size=15);\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hamid/.local/lib/python3.8/site-packages/sklearn/utils/validation.py:73: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "svr_poly = SVR(kernel='poly', C=100, gamma='auto', degree=3, epsilon=.1, coef0=1)\n",
    "svr_poly_predict = svr_poly.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = svr_poly_predict.predict(X_train)\n",
    "\n",
    "with plt.style.context('fivethirtyeight'):\n",
    "    df_filtered = df[df.iso_code=='NLD']\n",
    "#     df_filtered.plot(x=\"date\", y=[\"new_cases\"])\n",
    "    plt.plot(X_train, y_predict)\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.title('Plotting new cases in NLD\\n', size=15);\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}