Covid-19_predictor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "from matplotlib.cm import rainbow\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Other libraries\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Machine Learning\n",
    "from sklearn import neighbors\n",
    "from sklearn import linear_model\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>people_ID</th>\n",
       "      <td>5942</td>\n",
       "      <td>18664</td>\n",
       "      <td>5603</td>\n",
       "      <td>5649</td>\n",
       "      <td>5099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Designation</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>Mr</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>smt rekha prajapat</td>\n",
       "      <td>nirmal</td>\n",
       "      <td>pinky</td>\n",
       "      <td>pooja @aafrin</td>\n",
       "      <td>anjali</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Children</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation</th>\n",
       "      <td>Driver</td>\n",
       "      <td>Legal</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mode_transport</th>\n",
       "      <td>Public</td>\n",
       "      <td>Walk</td>\n",
       "      <td>Car</td>\n",
       "      <td>Car</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cases/1M</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deaths/1M</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comorbidity</th>\n",
       "      <td>Diabetes</td>\n",
       "      <td>Diabetes</td>\n",
       "      <td>Diabetes</td>\n",
       "      <td>None</td>\n",
       "      <td>Diabetes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>52</td>\n",
       "      <td>53</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coma score</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulmonary score</th>\n",
       "      <td>&lt;200</td>\n",
       "      <td>&lt;200</td>\n",
       "      <td>&lt;100</td>\n",
       "      <td>&lt;300</td>\n",
       "      <td>&lt;200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cardiological pressure</th>\n",
       "      <td>Stage-01</td>\n",
       "      <td>Stage-01</td>\n",
       "      <td>Stage-02</td>\n",
       "      <td>Elevated</td>\n",
       "      <td>Stage-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diuresis</th>\n",
       "      <td>388</td>\n",
       "      <td>409</td>\n",
       "      <td>440</td>\n",
       "      <td>206</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platelets</th>\n",
       "      <td>153</td>\n",
       "      <td>95</td>\n",
       "      <td>40</td>\n",
       "      <td>78</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HBB</th>\n",
       "      <td>196</td>\n",
       "      <td>138</td>\n",
       "      <td>166</td>\n",
       "      <td>83</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d-dimer</th>\n",
       "      <td>240</td>\n",
       "      <td>241</td>\n",
       "      <td>236</td>\n",
       "      <td>211</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heart rate</th>\n",
       "      <td>85</td>\n",
       "      <td>81</td>\n",
       "      <td>88</td>\n",
       "      <td>87</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDL cholesterol</th>\n",
       "      <td>53</td>\n",
       "      <td>61</td>\n",
       "      <td>47</td>\n",
       "      <td>52</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Charlson Index</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>13</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood Glucose</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insurance</th>\n",
       "      <td>3900000</td>\n",
       "      <td>1800000</td>\n",
       "      <td>5000000</td>\n",
       "      <td>3100000</td>\n",
       "      <td>2300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary</th>\n",
       "      <td>1300000</td>\n",
       "      <td>1300000</td>\n",
       "      <td>2000000</td>\n",
       "      <td>600000</td>\n",
       "      <td>1500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FT/month</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         0         1         2              3  \\\n",
       "people_ID                             5942     18664      5603           5649   \n",
       "Region                               Delhi     Delhi     Delhi          Delhi   \n",
       "Gender                              Female      Male    Female         Female   \n",
       "Designation                            Mrs        Mr       Mrs            Mrs   \n",
       "Name                    smt rekha prajapat    nirmal     pinky  pooja @aafrin   \n",
       "Married                                YES       YES       YES            YES   \n",
       "Children                                 2         2         2              2   \n",
       "Occupation                          Driver     Legal     Sales          Sales   \n",
       "Mode_transport                      Public      Walk       Car            Car   \n",
       "cases/1M                                 4         4         4              4   \n",
       "Deaths/1M                                1         1         1              1   \n",
       "comorbidity                       Diabetes  Diabetes  Diabetes           None   \n",
       "Age                                     52        53        35             31   \n",
       "Coma score                               3        11         9             12   \n",
       "Pulmonary score                       <200      <200      <100           <300   \n",
       "cardiological pressure            Stage-01  Stage-01  Stage-02       Elevated   \n",
       "Diuresis                               388       409       440            206   \n",
       "Platelets                              153        95        40             78   \n",
       "HBB                                    196       138       166             83   \n",
       "d-dimer                                240       241       236            211   \n",
       "Heart rate                              85        81        88             87   \n",
       "HDL cholesterol                         53        61        47             52   \n",
       "Charlson Index                          17         2        24             13   \n",
       "Blood Glucose                            3         5         3              6   \n",
       "Insurance                          3900000   1800000   5000000        3100000   \n",
       "salary                             1300000   1300000   2000000         600000   \n",
       "FT/month                                 1         1         2              2   \n",
       "\n",
       "                               4  \n",
       "people_ID                   5099  \n",
       "Region                     Delhi  \n",
       "Gender                    Female  \n",
       "Designation                  Mrs  \n",
       "Name                      anjali  \n",
       "Married                      YES  \n",
       "Children                       2  \n",
       "Occupation              Business  \n",
       "Mode_transport               Car  \n",
       "cases/1M                       4  \n",
       "Deaths/1M                      1  \n",
       "comorbidity             Diabetes  \n",
       "Age                           51  \n",
       "Coma score                     6  \n",
       "Pulmonary score             <200  \n",
       "cardiological pressure  Stage-01  \n",
       "Diuresis                     229  \n",
       "Platelets                    109  \n",
       "HBB                          207  \n",
       "d-dimer                      312  \n",
       "Heart rate                    94  \n",
       "HDL cholesterol               68  \n",
       "Charlson Index                39  \n",
       "Blood Glucose                  5  \n",
       "Insurance                2300000  \n",
       "salary                   1500000  \n",
       "FT/month                       1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data=pd.read_excel(\"Test_dataset.xlsx\")\n",
    "test_data.head().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_excel(\"Train_dataset.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>people_ID</th>\n",
       "      <td>14498.0</td>\n",
       "      <td>1.446996e+04</td>\n",
       "      <td>6.826922e+03</td>\n",
       "      <td>3887.0</td>\n",
       "      <td>7747.25</td>\n",
       "      <td>16467.5</td>\n",
       "      <td>20315.75</td>\n",
       "      <td>25212.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Children</th>\n",
       "      <td>14498.0</td>\n",
       "      <td>9.789626e-01</td>\n",
       "      <td>8.118332e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cases/1M</th>\n",
       "      <td>14498.0</td>\n",
       "      <td>5.886743e+00</td>\n",
       "      <td>4.651725e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deaths/1M</th>\n",
       "      <td>14498.0</td>\n",
       "      <td>1.342737e+00</td>\n",
       "      <td>9.535657e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>14498.0</td>\n",
       "      <td>4.208367e+01</td>\n",
       "      <td>1.532643e+01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>29.00</td>\n",
       "      <td>42.0</td>\n",
       "      <td>55.00</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coma score</th>\n",
       "      <td>14498.0</td>\n",
       "      <td>8.985860e+00</td>\n",
       "      <td>3.754762e+00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diuresis</th>\n",
       "      <td>14498.0</td>\n",
       "      <td>2.806576e+02</td>\n",
       "      <td>9.824068e+01</td>\n",
       "      <td>110.0</td>\n",
       "      <td>195.00</td>\n",
       "      <td>280.0</td>\n",
       "      <td>366.00</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platelets</th>\n",
       "      <td>14498.0</td>\n",
       "      <td>8.688647e+01</td>\n",
       "      <td>4.219445e+01</td>\n",
       "      <td>15.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>87.0</td>\n",
       "      <td>124.00</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HBB</th>\n",
       "      <td>14498.0</td>\n",
       "      <td>1.140830e+02</td>\n",
       "      <td>5.520797e+01</td>\n",
       "      <td>20.0</td>\n",
       "      <td>66.00</td>\n",
       "      <td>113.0</td>\n",
       "      <td>162.00</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d-dimer</th>\n",
       "      <td>14498.0</td>\n",
       "      <td>2.746673e+02</td>\n",
       "      <td>4.370616e+01</td>\n",
       "      <td>200.0</td>\n",
       "      <td>237.00</td>\n",
       "      <td>274.0</td>\n",
       "      <td>312.00</td>\n",
       "      <td>350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heart rate</th>\n",
       "      <td>14498.0</td>\n",
       "      <td>7.502173e+01</td>\n",
       "      <td>1.454968e+01</td>\n",
       "      <td>50.0</td>\n",
       "      <td>63.00</td>\n",
       "      <td>75.0</td>\n",
       "      <td>88.00</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDL cholesterol</th>\n",
       "      <td>14498.0</td>\n",
       "      <td>5.241792e+01</td>\n",
       "      <td>1.034122e+01</td>\n",
       "      <td>35.0</td>\n",
       "      <td>43.00</td>\n",
       "      <td>52.0</td>\n",
       "      <td>61.00</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Charlson Index</th>\n",
       "      <td>14498.0</td>\n",
       "      <td>1.999745e+01</td>\n",
       "      <td>1.188784e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood Glucose</th>\n",
       "      <td>14498.0</td>\n",
       "      <td>4.995724e+00</td>\n",
       "      <td>1.416303e+00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insurance</th>\n",
       "      <td>14498.0</td>\n",
       "      <td>2.654849e+06</td>\n",
       "      <td>1.375384e+06</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>1500000.00</td>\n",
       "      <td>2700000.0</td>\n",
       "      <td>3800000.00</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary</th>\n",
       "      <td>14498.0</td>\n",
       "      <td>1.300021e+06</td>\n",
       "      <td>6.055975e+05</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>800000.00</td>\n",
       "      <td>1300000.0</td>\n",
       "      <td>1800000.00</td>\n",
       "      <td>2300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FT/month</th>\n",
       "      <td>14498.0</td>\n",
       "      <td>1.002966e+00</td>\n",
       "      <td>8.189516e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count          mean           std       min         25%  \\\n",
       "people_ID        14498.0  1.446996e+04  6.826922e+03    3887.0     7747.25   \n",
       "Children         14498.0  9.789626e-01  8.118332e-01       0.0        0.00   \n",
       "cases/1M         14498.0  5.886743e+00  4.651725e+00       0.0        3.00   \n",
       "Deaths/1M        14498.0  1.342737e+00  9.535657e-01       0.0        1.00   \n",
       "Age              14498.0  4.208367e+01  1.532643e+01      16.0       29.00   \n",
       "Coma score       14498.0  8.985860e+00  3.754762e+00       3.0        6.00   \n",
       "Diuresis         14498.0  2.806576e+02  9.824068e+01     110.0      195.00   \n",
       "Platelets        14498.0  8.688647e+01  4.219445e+01      15.0       51.00   \n",
       "HBB              14498.0  1.140830e+02  5.520797e+01      20.0       66.00   \n",
       "d-dimer          14498.0  2.746673e+02  4.370616e+01     200.0      237.00   \n",
       "Heart rate       14498.0  7.502173e+01  1.454968e+01      50.0       63.00   \n",
       "HDL cholesterol  14498.0  5.241792e+01  1.034122e+01      35.0       43.00   \n",
       "Charlson Index   14498.0  1.999745e+01  1.188784e+01       0.0       10.00   \n",
       "Blood Glucose    14498.0  4.995724e+00  1.416303e+00       3.0        4.00   \n",
       "Insurance        14498.0  2.654849e+06  1.375384e+06  300000.0  1500000.00   \n",
       "salary           14498.0  1.300021e+06  6.055975e+05  300000.0   800000.00   \n",
       "FT/month         14498.0  1.002966e+00  8.189516e-01       0.0        0.00   \n",
       "\n",
       "                       50%         75%        max  \n",
       "people_ID          16467.5    20315.75    25212.0  \n",
       "Children               1.0        2.00        2.0  \n",
       "cases/1M               4.0       11.00       13.0  \n",
       "Deaths/1M              1.0        2.00        3.0  \n",
       "Age                   42.0       55.00       68.0  \n",
       "Coma score             9.0       12.00       15.0  \n",
       "Diuresis             280.0      366.00      450.0  \n",
       "Platelets             87.0      124.00      160.0  \n",
       "HBB                  113.0      162.00      210.0  \n",
       "d-dimer              274.0      312.00      350.0  \n",
       "Heart rate            75.0       88.00      100.0  \n",
       "HDL cholesterol       52.0       61.00       70.0  \n",
       "Charlson Index        20.0       30.00       40.0  \n",
       "Blood Glucose          5.0        6.00        7.0  \n",
       "Insurance        2700000.0  3800000.00  5000000.0  \n",
       "salary           1300000.0  1800000.00  2300000.0  \n",
       "FT/month               1.0        2.00        2.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10714 entries, 0 to 10713\n",
      "Data columns (total 28 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   people_ID               10714 non-null  int64  \n",
      " 1   Region                  10714 non-null  object \n",
      " 2   Gender                  10714 non-null  object \n",
      " 3   Designation             10714 non-null  object \n",
      " 4   Name                    10694 non-null  object \n",
      " 5   Married                 10714 non-null  object \n",
      " 6   Children                10403 non-null  float64\n",
      " 7   Occupation              9967 non-null   object \n",
      " 8   Mode_transport          10711 non-null  object \n",
      " 9   cases/1M                10714 non-null  int64  \n",
      " 10  Deaths/1M               10714 non-null  int64  \n",
      " 11  comorbidity             10488 non-null  object \n",
      " 12  Age                     10714 non-null  int64  \n",
      " 13  Coma score              10714 non-null  int64  \n",
      " 14  Pulmonary score         10714 non-null  object \n",
      " 15  cardiological pressure  10617 non-null  object \n",
      " 16  Diuresis                9998 non-null   float64\n",
      " 17  Platelets               9790 non-null   float64\n",
      " 18  HBB                     10698 non-null  float64\n",
      " 19  d-dimer                 9600 non-null   float64\n",
      " 20  Heart rate              9600 non-null   float64\n",
      " 21  HDL cholesterol         10679 non-null  float64\n",
      " 22  Charlson Index          10714 non-null  int64  \n",
      " 23  Blood Glucose           10714 non-null  int64  \n",
      " 24  Insurance               9624 non-null   float64\n",
      " 25  salary                  10714 non-null  int64  \n",
      " 26  FT/month                9991 non-null   float64\n",
      " 27  Infect_Prob             10714 non-null  float64\n",
      "dtypes: float64(10), int64(8), object(10)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>people_ID</th>\n",
       "      <th>Region</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Name</th>\n",
       "      <th>Married</th>\n",
       "      <th>Children</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Mode_transport</th>\n",
       "      <th>cases/1M</th>\n",
       "      <th>...</th>\n",
       "      <th>HBB</th>\n",
       "      <th>d-dimer</th>\n",
       "      <th>Heart rate</th>\n",
       "      <th>HDL cholesterol</th>\n",
       "      <th>Charlson Index</th>\n",
       "      <th>Blood Glucose</th>\n",
       "      <th>Insurance</th>\n",
       "      <th>salary</th>\n",
       "      <th>FT/month</th>\n",
       "      <th>Infect_Prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10709</th>\n",
       "      <td>22691</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mr</td>\n",
       "      <td>ashish</td>\n",
       "      <td>NO</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Business</td>\n",
       "      <td>Car</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>4000000.0</td>\n",
       "      <td>300000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.981760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10710</th>\n",
       "      <td>22692</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mr</td>\n",
       "      <td>mohd mujahid ansari</td>\n",
       "      <td>YES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Car</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>134.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>1500000.0</td>\n",
       "      <td>700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.148663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10711</th>\n",
       "      <td>22693</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mr</td>\n",
       "      <td>munnakumar</td>\n",
       "      <td>YES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Clerk</td>\n",
       "      <td>Public</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>151.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>3000000.0</td>\n",
       "      <td>2100000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>47.708533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10712</th>\n",
       "      <td>22694</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mr</td>\n",
       "      <td>prakash</td>\n",
       "      <td>YES</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Legal</td>\n",
       "      <td>Walk</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>110.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>2900000.0</td>\n",
       "      <td>700000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53.803565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10713</th>\n",
       "      <td>22695</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mr</td>\n",
       "      <td>mehboob mehandi</td>\n",
       "      <td>NO</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Business</td>\n",
       "      <td>Car</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>134.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2100000.0</td>\n",
       "      <td>1400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.342400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       people_ID     Region Gender Designation                 Name Married  \\\n",
       "10709      22691  Bengaluru   Male          Mr               ashish      NO   \n",
       "10710      22692  Bengaluru   Male          Mr  mohd mujahid ansari     YES   \n",
       "10711      22693  Bengaluru   Male          Mr           munnakumar     YES   \n",
       "10712      22694  Bengaluru   Male          Mr              prakash     YES   \n",
       "10713      22695  Bengaluru   Male          Mr      mehboob mehandi      NO   \n",
       "\n",
       "       Children     Occupation Mode_transport  cases/1M  ...    HBB d-dimer  \\\n",
       "10709       2.0       Business            Car         8  ...   24.0   209.0   \n",
       "10710       0.0  Manufacturing            Car         8  ...  134.0   211.0   \n",
       "10711       0.0          Clerk         Public         8  ...  151.0   296.0   \n",
       "10712       1.0          Legal           Walk         8  ...  110.0   253.0   \n",
       "10713       2.0       Business            Car         8  ...  134.0   201.0   \n",
       "\n",
       "       Heart rate  HDL cholesterol Charlson Index Blood Glucose  Insurance  \\\n",
       "10709        61.0             56.0             35             7  4000000.0   \n",
       "10710        65.0             47.0             35             7  1500000.0   \n",
       "10711        87.0             37.0             16             5  3000000.0   \n",
       "10712        56.0             70.0             28             5  2900000.0   \n",
       "10713        82.0             60.0              4             3  2100000.0   \n",
       "\n",
       "        salary  FT/month  Infect_Prob  \n",
       "10709   300000       0.0    41.981760  \n",
       "10710   700000       NaN    47.148663  \n",
       "10711  2100000       2.0    47.708533  \n",
       "10712   700000       1.0    53.803565  \n",
       "10713  1400000       0.0    68.342400  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.read_excel(\"Train_dataset.xlsx\")\n",
    "train_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>people_ID</th>\n",
       "      <td>10714.0</td>\n",
       "      <td>1.008489e+04</td>\n",
       "      <td>7.108664e+03</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.679250e+03</td>\n",
       "      <td>1.000350e+04</td>\n",
       "      <td>1.514175e+04</td>\n",
       "      <td>22695.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Children</th>\n",
       "      <td>10403.0</td>\n",
       "      <td>1.062770e+00</td>\n",
       "      <td>8.182223e-01</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cases/1M</th>\n",
       "      <td>10714.0</td>\n",
       "      <td>3.285888e+00</td>\n",
       "      <td>2.721274e+00</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>8.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deaths/1M</th>\n",
       "      <td>10714.0</td>\n",
       "      <td>5.232406e-01</td>\n",
       "      <td>8.543033e-01</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>10714.0</td>\n",
       "      <td>4.208372e+01</td>\n",
       "      <td>1.532340e+01</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>2.900000e+01</td>\n",
       "      <td>4.200000e+01</td>\n",
       "      <td>5.500000e+01</td>\n",
       "      <td>68.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coma score</th>\n",
       "      <td>10714.0</td>\n",
       "      <td>9.013067e+00</td>\n",
       "      <td>3.714970e+00</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>15.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diuresis</th>\n",
       "      <td>9998.0</td>\n",
       "      <td>2.788063e+02</td>\n",
       "      <td>9.798553e+01</td>\n",
       "      <td>110.0000</td>\n",
       "      <td>1.940000e+02</td>\n",
       "      <td>2.760000e+02</td>\n",
       "      <td>3.640000e+02</td>\n",
       "      <td>450.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platelets</th>\n",
       "      <td>9790.0</td>\n",
       "      <td>8.779581e+01</td>\n",
       "      <td>4.185110e+01</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>5.200000e+01</td>\n",
       "      <td>8.800000e+01</td>\n",
       "      <td>1.240000e+02</td>\n",
       "      <td>160.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HBB</th>\n",
       "      <td>10698.0</td>\n",
       "      <td>1.161710e+02</td>\n",
       "      <td>5.517881e+01</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>6.800000e+01</td>\n",
       "      <td>1.170000e+02</td>\n",
       "      <td>1.650000e+02</td>\n",
       "      <td>210.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d-dimer</th>\n",
       "      <td>9600.0</td>\n",
       "      <td>2.752923e+02</td>\n",
       "      <td>4.366969e+01</td>\n",
       "      <td>200.0000</td>\n",
       "      <td>2.370000e+02</td>\n",
       "      <td>2.760000e+02</td>\n",
       "      <td>3.130000e+02</td>\n",
       "      <td>350.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heart rate</th>\n",
       "      <td>9600.0</td>\n",
       "      <td>7.484740e+01</td>\n",
       "      <td>1.486910e+01</td>\n",
       "      <td>50.0000</td>\n",
       "      <td>6.200000e+01</td>\n",
       "      <td>7.500000e+01</td>\n",
       "      <td>8.800000e+01</td>\n",
       "      <td>100.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDL cholesterol</th>\n",
       "      <td>10679.0</td>\n",
       "      <td>5.263274e+01</td>\n",
       "      <td>1.043429e+01</td>\n",
       "      <td>35.0000</td>\n",
       "      <td>4.400000e+01</td>\n",
       "      <td>5.300000e+01</td>\n",
       "      <td>6.200000e+01</td>\n",
       "      <td>70.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Charlson Index</th>\n",
       "      <td>10714.0</td>\n",
       "      <td>2.002809e+01</td>\n",
       "      <td>1.182845e+01</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>40.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood Glucose</th>\n",
       "      <td>10714.0</td>\n",
       "      <td>5.015027e+00</td>\n",
       "      <td>1.399770e+00</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>7.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insurance</th>\n",
       "      <td>9624.0</td>\n",
       "      <td>2.644919e+06</td>\n",
       "      <td>1.383346e+06</td>\n",
       "      <td>300000.0000</td>\n",
       "      <td>1.400000e+06</td>\n",
       "      <td>2.600000e+06</td>\n",
       "      <td>3.800000e+06</td>\n",
       "      <td>5000000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary</th>\n",
       "      <td>10714.0</td>\n",
       "      <td>1.304713e+06</td>\n",
       "      <td>6.075947e+05</td>\n",
       "      <td>300000.0000</td>\n",
       "      <td>8.000000e+05</td>\n",
       "      <td>1.300000e+06</td>\n",
       "      <td>1.800000e+06</td>\n",
       "      <td>2300000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FT/month</th>\n",
       "      <td>9991.0</td>\n",
       "      <td>1.004604e+00</td>\n",
       "      <td>8.152567e-01</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infect_Prob</th>\n",
       "      <td>10714.0</td>\n",
       "      <td>5.228068e+01</td>\n",
       "      <td>1.067467e+01</td>\n",
       "      <td>29.2896</td>\n",
       "      <td>4.727743e+01</td>\n",
       "      <td>4.900839e+01</td>\n",
       "      <td>5.276231e+01</td>\n",
       "      <td>97.632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count          mean           std          min  \\\n",
       "people_ID        10714.0  1.008489e+04  7.108664e+03       1.0000   \n",
       "Children         10403.0  1.062770e+00  8.182223e-01       0.0000   \n",
       "cases/1M         10714.0  3.285888e+00  2.721274e+00       1.0000   \n",
       "Deaths/1M        10714.0  5.232406e-01  8.543033e-01       0.0000   \n",
       "Age              10714.0  4.208372e+01  1.532340e+01      16.0000   \n",
       "Coma score       10714.0  9.013067e+00  3.714970e+00       3.0000   \n",
       "Diuresis          9998.0  2.788063e+02  9.798553e+01     110.0000   \n",
       "Platelets         9790.0  8.779581e+01  4.185110e+01      15.0000   \n",
       "HBB              10698.0  1.161710e+02  5.517881e+01      20.0000   \n",
       "d-dimer           9600.0  2.752923e+02  4.366969e+01     200.0000   \n",
       "Heart rate        9600.0  7.484740e+01  1.486910e+01      50.0000   \n",
       "HDL cholesterol  10679.0  5.263274e+01  1.043429e+01      35.0000   \n",
       "Charlson Index   10714.0  2.002809e+01  1.182845e+01       0.0000   \n",
       "Blood Glucose    10714.0  5.015027e+00  1.399770e+00       3.0000   \n",
       "Insurance         9624.0  2.644919e+06  1.383346e+06  300000.0000   \n",
       "salary           10714.0  1.304713e+06  6.075947e+05  300000.0000   \n",
       "FT/month          9991.0  1.004604e+00  8.152567e-01       0.0000   \n",
       "Infect_Prob      10714.0  5.228068e+01  1.067467e+01      29.2896   \n",
       "\n",
       "                          25%           50%           75%          max  \n",
       "people_ID        2.679250e+03  1.000350e+04  1.514175e+04    22695.000  \n",
       "Children         0.000000e+00  1.000000e+00  2.000000e+00        2.000  \n",
       "cases/1M         2.000000e+00  2.000000e+00  4.000000e+00        8.000  \n",
       "Deaths/1M        0.000000e+00  0.000000e+00  1.000000e+00        2.000  \n",
       "Age              2.900000e+01  4.200000e+01  5.500000e+01       68.000  \n",
       "Coma score       6.000000e+00  9.000000e+00  1.200000e+01       15.000  \n",
       "Diuresis         1.940000e+02  2.760000e+02  3.640000e+02      450.000  \n",
       "Platelets        5.200000e+01  8.800000e+01  1.240000e+02      160.000  \n",
       "HBB              6.800000e+01  1.170000e+02  1.650000e+02      210.000  \n",
       "d-dimer          2.370000e+02  2.760000e+02  3.130000e+02      350.000  \n",
       "Heart rate       6.200000e+01  7.500000e+01  8.800000e+01      100.000  \n",
       "HDL cholesterol  4.400000e+01  5.300000e+01  6.200000e+01       70.000  \n",
       "Charlson Index   1.000000e+01  2.000000e+01  3.000000e+01       40.000  \n",
       "Blood Glucose    4.000000e+00  5.000000e+00  6.000000e+00        7.000  \n",
       "Insurance        1.400000e+06  2.600000e+06  3.800000e+06  5000000.000  \n",
       "salary           8.000000e+05  1.300000e+06  1.800000e+06  2300000.000  \n",
       "FT/month         0.000000e+00  1.000000e+00  2.000000e+00        2.000  \n",
       "Infect_Prob      4.727743e+01  4.900839e+01  5.276231e+01       97.632  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe().transpose()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "rcParams['figure.figsize'] = 30, 28\n",
    "plt.matshow(train_data.corr())\n",
    "plt.yticks(np.arange(train_data.shape[1]), train_data.columns)\n",
    "plt.xticks(np.arange(train_data.shape[1]), train_data.columns)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000015758469BC8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000015758528488>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000015758297588>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000015758011308>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000015757F77188>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000015758329A48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000015758BE4548>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000015758C0B2C8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000015758C0B4C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000157585B6388>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000015758622EC8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001575865DD48>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000015758696C08>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000157586CFA08>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000157587088C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000157587C1788>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001575887A608>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000157588F34C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000015758C0B948>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000015753B25C88>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 4320x8640 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = 60, 120\n",
    "train_data.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x157578c5608>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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xmmN74ITM3JmqMBxF9bod2HpWT2Kk5+3LwNvbslewcM/puq0n4o1tndE+RbUa7w68hLrBLKRl+s+kKtkAJ1AZ6CepVtPPde3zdW1bNy/muI4CyMydqBvm1q2yDHXDPYjK5A+vxXJ3qgD3uhhpdd+FKqg+niooPa2Ftx5waIuPtajMeHcqM7uCykA/Qd3w9qAqF3tQN6UXRQ2Xeknb/oupm363KZn5JKrw1onjW4FnZ+bftH3vDCt8B3Xj2YG6UazbtZ33UYW3dambz2ZUIQ+qkPGrdlyTga8CZ7f4up+64UFV8n/Vjv2h9r3jGmDvzNyFuln/W9e8PYBXZ+a+i/k+jSqYvQa4qyv+n9/COJMq+F5NpYOnMoZWgNgf+E8qLncHyMy/UIWKp1CFj62oQtVz275sDdzSegCG27H8jpGb8YWj4nEydT4OoBp33tv+vpeZ61KFi3WoQtWd7e9VmdkpzG/d9ndtqvB6UIvrucCC1op8e1t2Wjuen2Xmb6mb9KupAuf1VA/qP1Bp88OZuQ5VKTu+7e+7gLe0sLeiCim7tf3elUq/W7btHkLl549t0zekCmJT2/RHAZ/LzM2Bu6jC3eEtbl9EpZUDgaPasd0P3EQVXk+hCg3DVMPF1sDeVOPXPu04r275yTyq0v4sKq0fR11DT2jHsEFEbEZV9val0sa6wPNbvjGN6kW6PzM7ldy/tOvoOCoPGe1k4OjMHGtI219RhaDPUg1Fh1P5xf3APwOvpdLJBW35/6Kuk/WoSt3HI2LTNu+JVAPATtS5eyrVgHA6VZCEykNOo9LU11tcHd6OdRKVBn8CXNvidn2qceVT1Hm8mjo3W7btf74td0QLe1qL+63a9p7V4ncSdW4uohorOvlyZuaebZsPtv0+pMXHV6l8/s527j7Qwn15+/6nto1Ptm1v0dLioe0cHtyWX7/FZ3fPyR2Z+eS2jX1b+F+hKo2/p67dp7fvUI07f6RGOhxEpaG/o9LxYVRe8hWqMjqnHcujqLzsXuDN7bw81LY33PbnUuo6gEon32jztsrMLVs83UOd782odLhf13HQvm/b9nEWsGtE7E3l2b9qPVNvoyoF27bVFrDoCIm3tEr0LcAvM3Nu17yPtgrMu1p8H8BIQ0T3vnRGSXXuOYss0+XG9v+xVAPKplS8PblNPxX4TTv2x1EN2dOpa7OT391FxWvHlozk/++gGn++n5mfGCP8JwHvbj3BUNfW76h4vqut+03qfHw1MzemGmxeD7yJyqv/SDUw/hb4esubvg2c10YL7E6l392pSlRnpMx06pr+BJVnvqYrrm6i4veE9nldKh/YsJ3XJ1LlgX2pa/DVVH57MiMjYI6m0mhQeeP6VJlnLypNd+6PJ7dlX0eVy4bb9O77ZadBG+ravZO6j+1DXaeTqfQ/q31+RbvfnUWdz6Paup2K7Ttb3N8OvLbdk7Zrx/9y6jwf0KZPAdZu25tNlRFvphpc/6XFxY7U/felbTv/1tbNdrwAx2Xm7u2cdBpp57a4Pzgzn56Z76Mq+53HTw4GvpmZD1GVxHPa9L+m8sOdqfLQy6ge5WPaeYHKE9Zr+/b8FldPo673u9r8rYB/bfu8K5W3zgG+T+XX27X9X4fKpyZRjVlva9f00irFUOnoiq7vD7T8djYj97hZwO4R8cK2zLrApa0M+FOWc+SiVh4rxmuOG7t6br9KtZrtSPUUzKUKgY+JiBnAhpn507bsl6iCVsfXADJzNlWIHf2M2bOo1rC5VCayQUSs3+Z1MsM/Ub0XV1IF86dSre1voQornYLmTKoQCtW6PZY9qQIRVEH7bipDg7oRzKYKjg8BB7fwLwQeyUgh5aI6pBymKjAzqQx3mCoY7Nf2d6+27kZUheX1VAX00e24H6B6Bjo3tz2pHob7M/NuKsPt9q32/xJGCs5Tqd7VK6hC2kLDdlpF8MtUoac7DmZTN+VPUfH3xDbvl8AZmfkHqsA1mWpxhcqwZ1LX+dbtGH5J3Qi6h7/NAL7RWuA7lZiOH2bmHUv43rEf8Kqu+N+AKvg8gUpTc6nCw0/GWBcq7ucBD7U46O5tCSrOj6dumjtScTePujmu3bXsF6jGgvUZKSR37Nm2c2pm/oS6se/d/i6PiHOpgvcwlT6eSaWjD7XjembbLlSjzm8y85ft+5eouO/4AVWY+yYj5/7v2/QNqTT8+BaXk4EDW5rYi5Hr4zLgY63XfYvMnE8VbhZQaWtO29e9GRmSdwxV6fhy1/Thdhyd87M+lQa2bXH7b1Rh5tNt+pFUQ0zHvtQN/MLMvCkz/9SWv44q7N0KPCciXtz27ftU4fsC4I+ZeUW79m5o6+1ODWO+rZ2DM6jh1Ae18DoNAx1f6/q/UOV3jPzsKyxsM1pPGJUGD6F6yK6mRtD8gbquz6bO6VbUtXIJVdn6CyMF6ouptHYhdc1cSTV8/JqRwt5jqArdB4C3UdfBjzPzdqoQexrVoLAlVdi9jCpwzmjz/orq9bs5M6+hCq7/2bb9jRZf81t8DWfmENW49wiqUPjkto9btnXuav/3pBo6D6PO21pU+tmzEz+ZeWU73n+NiFe0sKCu7a+3YyAzf9Pi5DKqsnNZC7+TJ8FIvv5MqlC6AVXBAJjetnUD1SsFlUd3rte9qEbC41tcP5oqOL8eeA91P5lG3dOe27b9LEalDapRsNOAM4VKe1AjDTp52DNaHNxHVQimUZWKbvu1v8uoOO40Xo72AFVJ/w51Tt4zan5nGPKjqBE33T3Tb6MaQT5CNRjsyNg9zntRPdb3jZFPjvY96h75DqriuTMVZ0Ptvt4ZWXQfVRGbTp2Dw9q0B9vfI7u2+cvF5P9juaillY63UOf77PZ/va55nXvlRlRa+gKt0kZdkxdQFcbtqEaBp7X8cnvg5S1fm0VdG4+lzvle1PndkrpXfJJK03tk5g5UWWJb6rz+M3Wf3JnKM+7OzHMz8wIqLV5KVby+SOWFj2jT16KuuZszc25mzqPuw5uOkTd1x9vrgH+OiAepeH9kC/8iKu+YRTWe3k2dg4eo63Q+1chBmzZMVxmp5Rl/pEYFnAm8KyLeTjW6XQD8b4v3c1ucJXXNQ+V5U6hr6QBgXmbeRaXHs6gRKJsCn2vrbsrIsPdnRMSF7Zzsy0gj8RwqPXWcyMh5Pww4uTWy3ZGZnZ7733TdM66i8s/OSLlOl+hOwJx2P/pxO47HtXk3tfBvb3H0HOoesw0j1+4uwG+7wpnDyH16WXy0xcERVCNHx6nt/8P3uMxcQOWTnXL2cNdyX6XOn1YjVozXHDnq+93AVTnyjMdOmbnfWCsuZTujv0+ibiyd7W7eKoXQniuhbkRrUYUiqELc8Yw8Y9zJwKZQN+Ql6R4bchUL96Se3cLYpO3nZ7r2a6vMPKstN79rnaEW7uZtnVtbGCcA57b9v5IazrotdVM+Bng/VSC7dzH7NpZOuJ0woQoHf6QKkbsx0gLa7ZNUoa5T0QqqVfG41jvZ6Y3sbLs7nAXtJgKVAXfCvYqq9K1LxdnDL02hbsRnt9beA1i4otl9vGN9n08VfAN4cyf+qQrILW2ZHBUHi3Mro3rd23CraW39bOFc1cL4e+CyUel6PnWDfxJtaBwj+dxY56sTVx+lhrV3ehOnteWvZ6QFeXtGRkEs7dw/SMXVEDX8u5OOjqMKV6czEs+PpEZ77ERVUqcAZOb+jPRgXxAR+7flf9cVz++k0ustVKX0CmrI5AFUZWtKO56Hzw+VN3yiXR8vp66fzrV7F9ULOrphAha+jqBGRCygrov51PNZz26f96d6ALrX6Zy/seLuWmqEy4OtstctF/O5s2+jp3VsQDWSnEjXkFuqgLohMLMNCw1GhhneARzbyTOpoaYd8xmJpznAhzLzQSreO2nsM1Se9h6qgaH7WB/K8t/UObmearg4vus4gqpgjXX8D46ali1d7UIV5KdTBa3LGelF6jTiBdW4tj/Vw3IPda5Hn4tfAf9DXdObUXnQWHEc1DXWyZO+zMLXd3Yt913qHvA5qrev05g03BX+vYxcDwH8viuNnwi8tys/fnvb/r9TDQlHUQ2Mn2QkX9yQKoA/mqoYJtWo2B0nnbD+NTOnt7+pmfmvYxzrv3fdW/46M/+TRXUaqX5PVVTGHB3Tesh+wMIN0h0PUtdNp7A8PUZeWtR5jnpx6X20zrU3zKLX4ZR2XG/tOvYpVFr+f1Rv5o5UnHU3+HWnQag8ZpEXUDUP3yva0OotqXh8IlUZ/T2Vzrr3Nam0+Vqqgf3VmXksI48RrEVdy5e0dHc78I2ua/J5mfmLdo19jaoYnUmNyvhy28YlUc8gL2jh7kc1sCzoWmZ0o+ri8q2O7uW747dzrq6nKpKdfOJ31HDnX1K9jFsxcp42bce7DiNDhd9PVUw3YqThu5PvdParuyxxf4uDv6PKOkdQ18GHqbzuiBZn97X4pYV5H3VOFlDPj/8Ldd0eSJUffkbdq2ZR1/L/tF78z1GjE3ei7m/dcfVwOsjqvJlCXRuTW2Pc/oz0xMPCabU77Y6+bkfnh510OjRq+uep8/urrms3Ri23LOWTbp0ywbPbMXR0jnU8Y5qX9Xpe6kaGiAnztzqzYrzmeGyMvB3vEKp1cJPOtKi3/j2htQDeGRGdITCvpFpAOw5qy+9J3RzvYmFnUcOcaMvNGr0jbZ2jqQxviOqV7byFOCLiiRHxEkZuglDDasYym/asEJWhT2fhl5Z0empuA14QEVNbONtFRHclutv6VAX1Pqp36ExGnvGD6hl6S0RsSVXW5lC9NjNHbec84ICIWDsi1mNk2PKSzKCG/g5Tcd/JzO/uhN9a5M9m5Dnu2e3z71sBYzILZ+pLM0xVfuZSN9hrRh3LDEZeUnXoOLYLdSOY1v6/oaWz3ake78lt3w+mjm0G1YI/ltlUK/m6EXEEVbELamjdH6j0/HqqgLFJG954DbBZ1HPiHZOp1tk/UoWVTahCcieMSVTv7D5d02ZTBY5bop457aSbH1PxvhlA1LPenXnXUBWrv27fXwk82El/Y9igxdHp1PX53K5tRjtG6EpDEfGMzPxW1svebqMK0hcDfx0RT4h6KUznubIbGBkt8B9U78bujDROnEk7P1RBY7N2fcyg0jhRz/FuSPUcz2Gkh+3HVKWyc21t1Pb3yS3dH06NBPjHFo9QjS+Le5nKhdRzXJ23Tf8tNWLk31m0QAojPckHUT1BD8t6OdRdLb+CkbwCKp+4KWvI7AeoQv5vqEL5o6hC6alU+t2mTQM4IiImtyHUz2TkOcbF+QP1PP00qiepM+Li1VQB81kR8UiouIuIzmMPP6caIA6m8sK/p3otd2nLbdfWP7xtbw8WLXBtQOs9jojXUelqexZ9OeDstv0zqUbAYaox4rw2nYh4AtUTcwk1UmYSld7PBF7WdQxbMTKy5t42BP4VbT9Gu5RKo53HcvamrvFHUr2GnWtzA6rS29nXjbqupSuB10bE46i0ekaLl72oNPoNqoHoFkbKNPe2ecNUI2RQvbD3s/ALj35CPQe/Xju2/SLiUSzsTODwrmU2b8vczcgIEloYt2bmFxn7fkFbP6iKweihmrOpxxr2YOT9EN0vMTq+s0xETG8jtQ5g+f0c+Keu43p2O4b1ae8CYMm9WbtT18zXlrBMxwzq/v2KVmZ4CjVyYRoLV2o6z7BuSFWMD2v34Q2pBsTfteUebPs9lcqHOtfF9u1YtqYakM+jrrG9qdFeyUijyTwqjXQe1RmiGmduatNp12C0Yz2X6h3cm6qQT6YqZ50RJETEjrQRP915U+sNvZbqSV6blh/TKrMx8kb8KdTjDbe1fXkq1YM7nWogup+RxoSbqYbk2VT6n9q28yjqutyaGs3y47bs9u1793VHi9+O6VS56AzqHtd55Go+lV6exMg9cC2q4bHToHV7Oydjvqm6yz1UHnRy+/4cRp5jX1aXUxX+TqPLAyzaoNgJ66VUPrB+17V7KXUuOqMhOg1qo6/p5fXwPa7lj4cwUs6exEgcvYyu92Jo9eDLt9Ycv6BeOPAF6obzGSrz/XQb0jOFalG/iiqwHR/1Exe/ZuGhLndG/QzIBowUyLodDXw26iVVU6hMeZE3s2bmZRHxO2rYysupm9KLqGc+7m3rvZB6EdMRVIVhdKGxw/sAACAASURBVCUcqiXy+DZEZwGVkbyUKrAOU4XQt1MVkz8Dl7ab5G1t+x2dYd5btPA/TVW6Tqducr+jXjR0JXVD/jWVYW1C3RTmUT0fM7uO8eKI+B6VSf+WqkyMdQyjj+ebEfFSqvLbaWGcR/UAXU490/k/tAIr1YP3ZKqiMZ8advbwfiyjA9sxz6Baj8/omvcR6gUjb2XxQ52X5EVU2no6dWN5iCrY3UC1Ku9LnbcbqJvDIsMQM/PSiDiVSnP/St2o3k4Vun5Nxf1BVKF9iIrHe2k9R1E/ldR5mdF3qDT3d1ThozNU7Fiq0PEp6uZ+FdVQMEwVPm5kZLg+mXl11EsyPhIRH6Yq5b9r8x6IiMOoIehTqArr56jzuMjPC2Xm5RFxGVWIv7st836qwvln4D8j4kaq4tbxyagXu3RGNvwbVSi6nhpOt3Zb91xGXqBza1v3z1QF8AaqB+hE6lq5jyrQHUk1guzQ4nh6i7NrgJMz80/tWti7LfdV6vnjy9t2Z1OF5ouoAsrN1Lm9iMo7HqCrchY1TH33to8Xt+2dTTVMnZaZ323LHTs67qgXXl1Ind9Dxph/GHBS1Et0unsenslIg8P3qaHKG1EF5u9T+cP6VDp6MXVd7d/m3dP29UuZ+YeI2GGMcDtua8c/j0prG1IF1e9RaeukFjfTqUaLa9vnzlDt/6IKuB+kRi6sR1WabqDS74epXt75jHrLektX51C9T59v+3wXi1ZoPkf1TO9PVd73z8z5EfE56tqfRzWczW/781Dbt79QDR47UA1zCfw860Vjn6EKtA9R19J9XeEd1BpaOsP+n01VGKa1c3AB9QzjC6m0uRYjPclfB97Kwnn5aVSj7CaM9O6cS6Whzq8S3MhIL9J/tPi4k5Eepw+3+PlDS9v/R+U3P6IK9VAVj90YuY7IzLNapfznbZl7qOc8fxUR53dtazL1QseH2jIL3S+oxtZXUJW5eYy8awPqvN9F9YBvQd2jz2WUrnxyLnXPWWSZcfh7Kk5va/Hcadg4nnqe93zq3thtVruPdvLPS4AfRb34C+olWg9R57m7kesHVDlhFpV3LKDy8xdR5ZXzIuJeKv+4i+rhfRTV478Hdd86MTNvjYjTqev1O1S8P56Kz06D03+1bb+x7cOVbf0XUed/Iyqt3UqlmT+2fUwqP5hHPRPaKXPcSJWvDqcNs6ae/b2zrfNV4M1d11Dn2W5YNG/auu3P3VRa7gwX7zyf//h27I+h8uhOOngL1Xi/gDpfl7fPnZ70aYwM0f0QdT87iGqwWtCO63XUIyS/oB7ReT+VD21KpSWovOEiKq/dnCoLJVWRvIYqg7wy6gV5mwFXZOafI+KL1GilG6j8fUk/fXcvdf/7Wqs0bps1BHw8TqEeSZtH5TuLexv7PVQ+fDqVBq6jztd/U/fRn0ZEZ0j8+VTe88WoFxUemMv2nPEisl5y+04qfwrqcbfOr4zcCzwh6uep7mKk4VeriRgZdamJKiJm0t7ivILbOYd6C+votwWvFK1ifn9mdn5j9JDMfEE/wu6ViFgvM+9pxzKbGqJ06areL40tIu7JzPWWvuTqqbWOH5OZz1+GZU+kCpMXLG3ZXmqF/8dk5of6Ge54tJ6N51CNbLsBT8t63niNFvW7rjMy8z3t+2Rgamvo2YbqXdquDRHvddjH0t4k3uttS5oYon77+AWZ+cqokT6vyMyV9rN3g2KHnaflSd/ffFXvxjJ72szfXJKZ4/rd836xx1ir0q6MvNb+z4zdQ726OyHqJz/WpnqXrBRrtZCZr11F4X51VYQ7TtdQPTY3Ah8YkErxt6mRBft2TV4HODtq2HIAb1gZlWJJaqNM9qc9SpT1ywMOJe6JYCh9OrYX7DHWCmtDSv9h1OTzM/OosZaXJrI2rHfaqMmvzMwrxlpekiRNHBHxWepdHd0+lZknj7X8qrbDzmvnF7/3mKUvuJrYe6tf2WOsNVfLKFbLzELqtazfZ5UkSWsgO3YGlxVjSZIkSZqAEhj2h4Z6wliUJEmSJA00K8ZaqvZzSoZjOIZjOIZjOH0LZ006FsMxHMMxHK3+rBhrWfQrkzAcwzEcwzEcw+lnGIZjOIZjOKsiHK2GfMZYkiRJkiaoIWJV78IawZ9rGjAbbzQ5Z24xdVzr3PanITZ55ORxrXPNjZuMa3mAh+bfw9Rp641vnRnjT79Dd9/L5PXXHd9K0Z9wpt45/oxtwfx7mTJtnMezHBno8pyf5cmnH5p/L1PHezzLkY+t1vFGn44n+nU847dc4SzH7Wy1Pp5+hbNc1+nynJ/xnaDlu0YhJ4/vgBY8cC9T1h5/OI+b+ehxLX/bbbexySbjvzeOl+EYjuEsv0suueSezFx/pQfUQ9vvvHZ+/ntbrurdWGbP3OqX/lyTVg8zt5jKRWdusdLD2fPoI1d6GAA3P39BX8KJKcN9CWfz08bXaLG8cjkqRMtjeGp/wpn8YH/OT07qz/HEUH8aLIen9KmFuV/B9CcZLFcFfLn0Kd6yT+FM6lO6fmDG+Bpyl9eck9/al3Ak9U9EXLuq90GrjhVjSZIkSZqAMoOh9LVRvWAsSpIkSZIGmhVjSZIkSdJAs2IsSZIkSRpoPmMsSZIkSRPUsD/X1BP2GEuSJEmSBpoVY0mSJEnSQHMotSRJkiRNQAkM2dfZE8biKhARh0bEcSuyXkQcGxG/j4i5EXFdRHwrIh7f+72VJEmSpDWbFeOJ7ROZOSsztwVOBX4SEZus6p2SJEmSpInEivEYImJmRFwTEV+KiHkRcVpErBMRu0bETyPikog4MyI2bcvPiogL2rLfjohHtOnnRMQnI+JnEXFlRDxpjLA2iYhvRsTF7e9py7PPmXkqcBbwshU5dkmSJEkaNFaMF2974ITM3Bn4C3AU8BngwMzcFTgJ+GBb9svA29uyVwDv7drOupn5VOCNbZ3RPkX1/O4OvAQ4cQX2+VJgh9ETI+KIiJgTEXNu+9PQCmxekiRJ0uojGMpJE+ZvdebLtxbvxsw8v33+KvAuYEfghxEBMBm4JSJmABtm5k/bsl8CvtG1na8BZObsiNggIjYcFc6zgMe3bQJsEBHrZ+bdy7HPY/6IWWaeAJwAsNsT187l2K4kSZIkrbGsGC/e6Ark3cBVmblH98RWMR7PdkZ/nwTskZn3j38XF7ELMKcH25EkSZKkgbF692evWo+NiE4l+BDgAmCTzrSImBoRT8jMu4A7I2KvtuwrgZ92beegtvyewF1t+W5nAW/qfImIWcuzsxHxEmA/Wg+1JEmSpDVbAsNMmjB/qzN7jBfvF8CrI+ILwHXU88VnAp9uvcRTgE8CVwGvBo6PiHWAXwOHdW3nzoj4GbABcPgY4RwNfDYi5rVtzgZev4z7+JaIeAWwLnAlsG9m3ja+w5QkSZKkwWbFePGGM3N0BXUusPfoBTNzLvCUxWznm5n5zlHLnwKc0j7fTutVXppR6x0LHLss60mSJEmSFm/17s+WJEmSJGkls8d4DJl5A/UG6hXdzj7Ls15EHAb8w6jJ52fmUSu6T5IkSZLWHEM55g/TaJysGK+GMvNk4ORVvR+SJEmSNAgcSi1JkiRJGmj2GEuSJEnSBJQEQ/Z19oSxKEmSJEkaaFaMJUmSJEkDzaHUA+aaGzdhz6OPXOnhnPfpL6z0MAD2etPKPxaA7FMT0pR7FvQlnPmP6M+lP+X+4b6Es2Dt/ryNcdJQX4IhJ/XneKI/p4cYzr6EM2lBf8IZmtafDCGG+hRvfUrXw1P7k67veYxvZ5UkjZ8VY0mSJEmaoIb71YOzhjMWJUmSJEkDzYqxJEmSJGmgOZRakiRJkiagBH+uqUeMRUmSJEnSQLNiLEmSJEkaaFaMJUmSJEkDzWeMJUmSJGkCSoKh9Pfbe8Ee4x6LiKGImBsRV0bE9yNiw+XczmYRcVqv90+SJEmStDArxr13f2bOyswdgTuAo5ZnI5l5c2Ye2NtdkyRJkiSNZsV45fo5sDlARGwTET+IiEsi4tyI2KFr+gURcXFEvD8i7mnTZ0bEle3z2hFxckRcERGXRcQz2vRDI+JbbbvXRcRHVtFxSpIkSVoFhpk0Yf5WZ6v33k1gETEZeCbwvTbpBODNmbkrcAzwuTb9U8CnMnN34ObFbO4ogMzcCTgE+FJErN3mzQIOAnYCDoqILXp9LJIkSZK0JrNi3HvTI2Iu8CdgI+CHEbEe8FTgG23eF4BN2/J7AN9on/97MdvcE/gKQGZeA/wW2K7N+3Fm3pWZDwBXA1uOXjkijoiIOREx56H596zwAUqSJEnSmsSKce/dn5mzqArqWlRv7yTgz+3Z487f48axzSW9am5+1+chxnjTeGaekJm7ZeZuU6etN45gJUmSJGnNZ8V4JcnMu4CjqWHT9wO/iYiXAkR5Ylv0AuAl7fPBi9ncbODlbd3tgMcC166kXZckSZI0AWTCUE6aMH/LIiKeExHXRsT1EfGOMebPaL/+c3lEXBURh/UiLq0Yr0SZeRlwOVXhfTnwmoi4HLgKeEFb7B+Bt0bERdTw6rvG2NTngMkRcQVwKnBoZs4fYzlJkiRJmpDae5o+C+wPPB44JCIeP2qxo4CrM/OJwD7AxyNirRUNe5Fht1oxmbneqO8HdH19zhir/B54SmZmRBwMzGnr3QDs2D4/ABw6RlinAKd0fX/+Cu28JEmSJK06TwKuz8xfA0TE16kOxau7lklg/YgIYD3qJ3IXrGjAVoxXvV2B49qJ/TNw+CreH0mSJEkTQjC8xNcRTTibAzd2fb8JePKoZY6jfvnnZmB94KDMHF7RgK0Yr2KZeS7wxKUuKEmSJEkT28YRMafr+wmZeULX97Fq+Tnq+98Cc4F9gW2oXwE6NzP/siI7ZsVYkiRJktQPt2fmbkuYfxOwRdf3x1A9w90OAz6UmQlcHxG/AXYALlqRHfPlW5IkSZKk1cHFwLYRsVV7odbB1LDpbr8DngkQEY8Gtgd+vaIB22MsSZIkSRNQwjL/DNJEkJkLIuJNwJnAZOCkzLwqIl7f5h8PfAA4pf1iTwBvz8zbVzRsK8aSJEmSpNVCZp4BnDFq2vFdn28G9ut1uGtO84IkSZIkScvBHmNJkiRJmqCG7OvsCSvGA+ahGcnNz1/h379eqr3edORKDwPg3OO+0JdwHsqhvoSz75ve2Jdwpjww+q33K8eC6f3JqCct6M/xLPJjARNc/x5J6s/vKw5P7UswxFB/EsLwlP7EW0zuSzDEcH/ibep9fQlGkrSGsXlBkiRJkjTQ7DGWJEmSpAkoCYazPyOM1nT2GEuSJEmSBpoVY0mSJEnSQLNiLEmSJEkaaD5jLEmSJEkTlD/X1BvGoiRJkiRpoFkxliRJkiQNNIdS90hEPBr4BPAU4E7gQeAjmfntFdzuPsAxmfn8Fd5JSZIkSWuMBIbTvs5eMBZ7ICIC+A4wOzO3zsxdgYOBx6yCfbGxQ5IkSZLGwYpxb+wLPJiZx3cmZOZvM/MzETE5Ij4aERdHxLyIOBKqJzgizomI0yLimoj4r1bBJiKe06adB7y4s82IWDciTmrbuiwiXtCmHxoR34iI7wNn9fXIJUmSJGmCs3exN54AXLqYea8B7srM3SNiGnB+RHQqr7u0dW8GzgeeFhFzgC9Sle3rgVO7tvVu4CeZeXhEbAhcFBE/avP2AHbOzDt6eWCSJEmStKazYrwSRMRngT2p54x/C+wcEQe22TOAbdu8izLzprbOXGAmcA/wm8y8rk3/KnBEW3c/4O8i4pj2fW3gse3zDxdXKY6IIzrbmPzIDXt0lJIkSZJWrWCIWNU7sUawYtwbVwEv6XzJzKMiYmNgDvA74M2ZeWb3Cu2lWvO7Jg0xcj5yMeEE8JLMvHbUtp4M3Lu4ncvME4ATAKZt9ZjFbVuSJEmSBpLPGPfGT4C1I+INXdPWaf/PBN4QEVMBImK7iFh3Cdu6BtgqIrZp3w/pmncm8OauZ5F36cneS5IkSdIAs8e4BzIzI+KFwCci4p+A26ge3LcD36CGSF/aKrS3AS9cwrYeaEOfT4+I24HzgB3b7A8AnwTmtW3dAPgzTpIkSdIA8ueaeseKcY9k5i3UTzSN5V3tr9s57a+z/pu6Pv8A2GGMMO4Hjhxj+inAKePbY0mSJEkSOJRakiRJkjTgrBhLkiRJkgaaQ6klSZIkaYLy55p6wx5jSZIkSdJAs2IsSZIkSRpoDqWWJEmSpAkoM/y5ph4xFiVJkiRJA82KsSRJkiRpoDmUetBEElOGV3ow/RrR8VAO9SWcqTG5L+FoNdenlz7GcPYnoOzXAfUnGPoUbVo+Gf1JCP1K1pKkNYsVY0mSJEmaoIZ8xrgnjEVJkiRJ0kCzYixJkiRJGmgOpZYkSZKkCSiB4b69zGPNZo+xJEmSJGmgWTGWJEmSJA00K8aSJEmSpIHmM8aSJEmSNCGFP9fUIwMTixExFBFzI+KqiLg8It4aEct1/BFxRkRs2OP9mxkRL+v6vltEfLqXYUiSJEmSFjVIPcb3Z+YsgIh4FPDfwAzgvePdUGY+t8f7BjATeBm1X2TmHGDOSghHkiRJktRlYHqMu2XmrcARwJuiTI6Ij0bExRExLyKOBIiITSNidutpvjIi9mrTb4iIjdvn90TENRHxw4j4WkQc06afExEfjoiLIuKXXevOjIhzI+LS9vfUtlsfAvZqYb0lIvaJiP9t62wUEd9p+3ZBROzcph8bESe1sH4dEUf3Mx4lSZIkrToJDGdMmL/V2SD1GC8kM3/dhlI/CngBcFdm7h4R04DzI+Is4MXAmZn5wYiYDKzTvY2I2A14CbALFZeXApd0LTIlM58UEc+leqafBdwKPDszH4iIbYGvAbsB7wCOycznt23v07Wd9wGXZeYLI2Jf4MvArDZvB+AZwPrAtRHx+cx8qBdxJEmSJEmDYGArxk2n2WI/YOeIOLB9nwFsC1wMnBQRU4HvZObcUevvCXw3M+8HiIjvj5r/rfb/EmqoNMBU4LiImAUMAdstw37uSVXAycyfRMQjI2JGm3d6Zs4H5kfErcCjgZsWOsiII6geciZvPANJkiRJ0oiBrRhHxNZUxfRWqoL85sw8c4zl9gaeB3wlIj6amV/unr2UYOa3/0OMxPVbgD8CT6SGsj+wLLs7xrQcFcbocEYWzDwBOAFg2tab5+j5kiRJkjTIBvIZ44jYBDgeOC4zEzgTeEPrGSYitouIdSNiS+DWzPwi8J/A34za1HnAARGxdkSsR1Wgl2YGcEtmDgOvBCa36XdTw6HHMht4edu3fYDbM/Mvy3a0kiRJktZUQ0yaMH+rs0HqMZ4eEXOpocwLgK8A/9HmnUgNdb40IgK4DXghsA/wtoh4CLgHeFX3BjPz4oj4HnA58FvqLdJ3LWU/Pgd8MyJeCpwN3NumzwMWRMTlwCnAZV3rHAucHBHzgPuAV4/juCVJkiRJSzAwFePMnLyEecPAu9pfty+1v9HLz+z6+rHMPDYi1qF6dj/eltmna/nbac8YZ+Z1wM5d67+zTX8IeOaooM5p8+6gXhA2ej+OHfV9x7GOT5IkSZK0eANTMV6JToiIxwNrA1/KzEtX9Q5JkiRJWvMlq//PIE0UVoxXUGa+bFXvgyRJkiRp+a3eT0BLkiRJkrSSWTGWJEmSJA00h1JLkiRJ0gQ1bF9nTxiLkiRJkqSBZsVYkiRJkjTQHEotSZIkSRNQJgz5c009YcV4wEy9M9j8tKkrPZwp9yxY6WEA7PumN/YlnH4597Nf6Es4e7/xiL6EM3n+cF/Cycn9uSH0674T2Z9w6FM4MdyfgHJSn9LB5L4Ew+T5fYq3PpUEhvt0nW76Hz/rSzh87C39CUeS1BcOpZYkSZIkDTQrxpIkSZKkgeZQakmSJEmaoIZ9xrgn7DGWJEmSJA00K8aSJEmSpIHmUGpJkiRJmoCSYDjt6+wFY1GSJEmSNNCsGEuSJEmSBpoV4z6IiIyIj3d9PyYijl2FuyRJkiRJanzGuD/mAy+OiH/PzNtX9c5IkiRJWjMM4c819YI9xv2xADgBeMvoGRFxQERcGBGXRcSPIuLRbfqxEfGliDgrIm6IiBdHxEci4oqI+EFETG3L7RoRP42ISyLizIjYtL+HJkmSJEkTmxXj/vks8PKImDFq+nnAUzJzF+DrwD91zdsGeB7wAuCrwNmZuRNwP/C8Vjn+DHBgZu4KnAR8cOUehiRJkiStWRxK3SeZ+ZeI+DJwNFWx7XgMcGrr6V0L+E3XvP/LzIci4gpgMvCDNv0KYCawPbAj8MOIoC1zy+iwI+II4AiAadM37OFRSZIkSVpVEhhOh1L3gj3G/fVJ4DXAul3TPgMc13qCjwTW7po3HyAzh4GHMjPb9GGqUSOAqzJzVvvbKTP3Gx1oZp6Qmbtl5m5Tpq07erYkSZIkDTQrxn2UmXcA/0NVjjtmAL9vn189zk1eC2wSEXsARMTUiHjCCu+oJEmSJA0QK8b993Fg467vxwLfiIhzgXG9sTozHwQOBD4cEZcDc4Gn9mg/JUmSJGkg+IxxH2Tmel2f/wis0/X9u8B3x1jn2CVs49iuz3OBvXu6w5IkSZImgGA47evsBWNRkiRJkjTQrBhLkiRJkgaaQ6klSZIkaYIaxp9r6gV7jCVJkiRJA82KsSRJkiRpoFkxliRJkiQNNJ8xliRJkqQJKBOG0meMe8EeY0mSJEnSQLPHeOAEGSu/VWn+I/qTtKY8kH0Jp1/2fuMRfQln9udO6Es4ex11ZF/CmfRgf9LB8Fp9apHtVzDD/Ym3nNyfA4qhPh3PpDUrHfTjngAwqU/n5+6Dn9KXcCRJaxYrxpIkSZI0QQ2ng4B7wViUJEmSJA00K8aSJEmSpIFmxViSJEmSNNB8xliSJEmSJqAkGPbnmnrCHmNJkiRJ0kCzYixJkiRJGmgOpZYkSZKkCWq4Xz98v4azx3gFRURGxFe6vk+JiNsi4n9XcLubRcRp41znlIg4cEXClSRJkqRBY8V4xd0L7BgR09v3ZwO/H88GImLK6O+ZeXNmWsmVJEmSpJXMinFv/B/wvPb5EOBrnRkR8aSI+FlEXNb+b9+mHxoR34iI7wNnjfF9ZkRc2ZadHBEfjYiLI2JeRBzZpkdEHBcRV0fE6cCj+nnQkiRJkrQm8Bnj3vg68C9t+PTOwEnAXm3eNcDembkgIp4F/BvwkjZvD2DnzLwjIg4d9X1m1/ZfA9yVmbtHxDTg/Ig4C9gF2B7YCXg0cHULW5IkSdIaLsGfa+oRK8Y9kJnzWkX2EOCMUbNnAF+KiG2ptDu1a94PM/OOJXzv2A/Yuev54RnAtsDewNcycwi4OSJ+Mtb+RcQRwBEAa03fcDyHJkmSJElrPIdS9873gI/RNYy6+QBwdmbuCBwArN01795Ry47+3hHAmzNzVvvbKjPPavNyaTuWmSdk5m6ZudvUaest9UAkSZIkaZBYMe6dk4D3Z+YVo6bPYORlXIcu57bPBN4QEVMBImK7iFgXmA0c3J5B3hR4xnJuX5IkSdIENJyTJszf6syh1D2SmTcBnxpj1keoodRvBcYc6rwMTgRmApdGRAC3AS8Evg3sC1wB/BL46XJuX5IkSZIGlhXjFZSZi4xNzsxzgHPa558D23XNfk+bfgpwStc6o7/fAOzYPg8D72p/o71p+fdekiRJkrR692dLkiRJkrSS2WMsSZIkSRNRhj/X1CP2GEuSJEmSBpoVY0mSJEnSQHMotSRJkiRNQAkM41DqXrDHWJIkSZI00KwYS5IkSZIGmhVjSZIkSdJA8xnjQRMwPHXlP4cw5f7hlR4GwILpa1bbzuT5/Ym3vY46si/hnPvZL/QlnL3e1J/jIfsTTAz1J5xcsy4fhqf05xmryQ/2JyEsWHvNOp5+pet+HY8krS78uabeWMOKRZIkSZIkjY8VY0mSJEnSQHMotSRJkiRNQIlDqXvFHmNJkiRJ0kCzYixJkiRJGmhWjCVJkiRJA81njCVJkiRpgvIZ496wx1iSJEmSNNCsGC+HiPiriPh6RPwqIq6OiDMi4oiI+N/FLH9iRDy+fb4hIjYeY5ljI+KYlb3vkiRJkqSFOZR6nCIigG8DX8rMg9u0WcABi1snM1+7AuFNycwFy7u+JEmSpDVTEg6l7hF7jMfvGcBDmXl8Z0JmzgXOBdaLiNMi4pqI+K9WiSYizomI3UZvKCLeHRHXRsSPgO27pp8TEf8WET8F/iEido2In0bEJRFxZkRs2rXchyPiooj4ZUTstZKPXZIkSZLWOPYYj9+OwCWLmbcL8ATgZuB84GnAeWMtGBG7Age3daYAl47a7oaZ+fSImAr8FHhBZt4WEQcBHwQOb8tNycwnRcRzgfcCz1qRg5MkSZKkQWPFuLcuysybACJiLjCTxVSMgb2Ab2fmfW35742af2r7vz1VGf9h64CeDNzStdy32v9LWniLiIgjgCMA1lrnEct8MJIkSZI0CKwYj99VwIGLmTe/6/MQS4/fXMK8e9v/AK7KzD2WEuZiw8vME4ATANbbaIslhSlJkiRpAhnGZ4x7wWeMx+8nwLSIeF1nQkTsDjx9nNuZDbwoIqZHxPos/uVd1wKb/H/27jzMsqq89/j3100ztgEVNIpII4Iog600KIKAqDwakzjEKyCKaEwDKk4Ro/GqYK5RLl4ThTB0CIIExSgOqChEZFaGBppuwCGKqKgJ4gCCDN1d7/3j7NZjeap64PTuOrW/n+c5T+299trrXXtXdcFba629k+zRxJqVZMc16LckSZIkaQAT49VUVQW8GHhu87qmm4Cj6a0rXp12rqM3XXoRcA69h3cNqvcAvRHqY5Pc0NR/xhpfgCRJkiTpDziVeg1U1U+Blw049K99dd7Qt71v3/acvu3303uQ1vj29x23vwjYe7J6VXUHE6wxliRJkjQNFb6uaUgcMZYkSZIkdZqJsSRJkiRpSkjyvCTfSfK9JO+YoM6+SRYluSnJJcOI61RqSZIkSdI6l2Qm8C/AkN38PAAAIABJREFUc4HbgGuSnFtVN/fV2Qw4EXheVf0oySOGEdvEWJIkSZJGUDHt1hjvDnyvqm4BSHI28ELg5r46Lwc+W1U/Aqiq24cR2KnUkiRJkqSpYEvgx337tzVl/bYHHprk4iTXJjlkGIEdMZYkSZIktWHzJAv79hdU1YK+/UHD3zVufz1gV+DZwEbAN5NcWVXffTAdMzGWJEmSpBE1YlOp76iqeZMcvw3Yqm//McBPB9S5o6ruAe5JcinwZOBBJcZOpZYkSZIkTQXXANsl2SbJ+sCBwLnj6nwBeGaS9ZJsDDwN+NaDDeyIcddUMfOBsbUeZtmG7fzlasay8TMrRlvNbOm+PdDOfXvmGw5rJc5lJ5zSSpy9j5jfSpzl60+vfz8z71/7v3MAxma1c9/aijNjWSthSDvfHpZv0M59W7qxf/OXpFFVVcuSvAE4H5gJnFZVNyU5vDl+clV9K8lXgcXAGHBqVd34YGObGEuSJEmSpoSqOg84b1zZyeP2jwOOG2ZcE2NJkiRJGkFFRm2N8ZTlfCNJkiRJUqeZGEuSJEmSOs2p1JIkSZI0osqp1EPhiLEkSZIkqdNMjCVJkiRJneZUakmSJEkaUWM4lXoYOjtinOQxSb6Q5L+SfD/JR5Ksvw7786IkT+rbf1+S56yr/kiSJElSV3QyMU4S4LPA56tqO2B7YDbw/nXYrRcBv0uMq+o9VfW1ddgfSZIkSeqETibGwH7AfVX1MYCqWg68BXhNkk2SfCjJkiSLkxwJkGS3JN9IckOSq5M8JMmhSU5Y0WiSLyXZt9m+O8n/S3JdkguTbNGU/02Sa5p2zkmycZJnAH8JHJdkUZJtk5ye5KXNOc9Ocn3Tp9OSbNCU35rkmCbGkiQ7tHcLJUmSJGl66GpivCNwbX9BVd0F/Ah4LbAN8JSq2gU4q5li/SngTVX1ZOA5wL0ribEJcF1VPRW4BHhvU/7ZqtqtaedbwF9X1TeAc4GjqmpuVX1/RSNJNgROBw6oqp3prQs/oi/OHU2Mk4C3reZ9kCRJkjSiqmCsMjKfqayriXGAmqB8b+DkqloGUFW/BJ4A/KyqrmnK7lpxfBJj9JJpgH8H9mq2d0pyWZIlwMH0kvTJPAH4QVV9t9k/o+njCp9tvl4LzBnUQJL5SRYmWbjs/ntWEk6SJEmSuqWrifFNwLz+giR/AmzF4KR5okR6GX94DzecJOaK808H3tCM/h6zknNWxJ7M/c3X5UzwlPGqWlBV86pq3nobbLKS5iRJkiSpW7qaGF8IbJzkEIAkM4H/Ry9pvQA4PMl6zbGHAd8GHp1kt6bsIc3xW4G5SWYk2QrYvS/GDOClzfbLgcub7YcAP0syi96I8Qq/aY6N921gTpLHN/uvpDc1W5IkSVLHVWVkPlNZJxPjqirgxcD/SvJfwHeB+4C/B06lt9Z4cZIbgJdX1QPAAcDxTdl/0hvpvQL4AbAE+BBwXV+Ye4Adk1xL72Ff72vK3w1c1bTx7b76ZwNHNQ/Z2ravr/cBrwY+3Uy/HgNOHta9kCRJkqSuGzj1tguq6sfAX0xw+K3Np7/+NcDTB9Q9eEDZinPeTS8R7i87id6DssbXvYK+1zUBh/YduxB4yoBz5vRtLwT2nagvkiRJkqTBOjliLEmSJEnSCp0dMV7bqmr2uu6DJEmSpOls6r8GaVQ4YixJkiRJ6jQTY0mSJElSpzmVWpIkSZJG1FR/DdKocMRYkiRJktRpJsaSJEmSpE4zMZYkSZIkdZprjDsn1Iy1vw5hxvK1HqKnWorT0tKNtpaIjK3f1gW1E2bvI+a3EufSkxa0Eqet62nr53r5+tPrb7BZ3tIP9jRbMjbzgbbu21g7cSRpCijwdU1DMr3+b0WSJEmSpNVkYixJkiRJ6jSnUkuSJEnSKCqotpYWTnOOGEuSJEmSOs3EWJIkSZLUaSbGkiRJkqROc42xJEmSJI2osen2fr91xBFjSZIkSVKnmRhLkiRJkjptyifGSSrJmX376yX5eZIvrWY7tybZfA3i//3qnrMuJHlzko3XdT8kSZIktaOAqozMZyqb8okxcA+wU5KNmv3nAj9pMf7AxDg9U+L+JZkJvBkwMZYkSZKk1TQlErtV8BXgBc32QcAnVxxI8rAkn0+yOMmVSXZpyh+e5IIk1yc5BX6/Kj3JK5JcnWRRklOaxPKPJPkgsFFT76wkc5J8K8mJwHXAVklOSrIwyU1Jjuk799YkxyS5LsmSJDs05fs07S1q+vaQJPsmuTTJ55LcnOTkFUl3koOa829Mcmxf+3cneV+Sq4B3AY8GLkpy0TBuuCRJkiR1xagkxmcDBybZENgFuKrv2DHA9VW1C73R3Y835e8FLq+qpwDnAo8FSPJE4ABgz6qaCywHDh4UtKreAdxbVXOrakWdJwAfr6qnVNUPgXdV1bymX/usSMwbd1TVU4GTgLc1ZW8DXt/EfiZwb1O+O/C3wM7AtsBLkjwaOBbYD5gL7JbkRU39TYAbq+ppVfU+4KfAs6rqWeOvI8n8JnlfuPT+uwddqiRJkiR11ki8rqmqFieZQ2+0+Lxxh/cC/qqp9/VmpHhTYG/gJU35l5P8qqn/bGBX4JokABsBt69Gd35YVVf27b8syXx69/JRwJOAxc2xzzZfr13RF+AK4MNJzgI+W1W3Nf24uqpuAUjyyea6lgIXV9XPm/Kzmuv6PL2E/pxV6XBVLQAWAMx+6Fa1GtcqSZIkacoKY1N87e6oGInEuHEu8CFgX+DhfeWDfhJq3Nd+Ac6oqneuYT/u+V1DyTb0RoB3q6pfJTkd2LCv7v3N1+U097qqPpjky8CfAVcmec4EfS0GX9sK91XV8jW8BkmSJElSY1SmUgOcBryvqpaMK7+UZip0kn3pTV++a1z584GHNvUvBF6a5BHNsYcl2XqSuEuTzJrg2J/QS5TvTPJI4Pkru4gk21bVkqo6FlgI7NAc2j3JNs3a4gOAy+lNGd8nyebNOuiDgEsmaPo3wENWFl+SJEmS9IdGZsS4qm4DPjLg0NHAx5IsBn4LvKopPwb4ZJLr6CWTP2rauTnJ/wYuaJLQpcDrgR9OEHoBsLhp513j+nRDkuuBm4Bb6E2TXpk3J3kWvVHkm+k9WGwP4JvAB+mtMb4U+FxVjSV5J3ARvdHj86rqC5P08ytJfjZonbEkSZKk6adcKDkUUz4xrqrZA8ouBi5utn8JvHBAnV8A+/cVvaXv2KeAT61i/L8D/q6vaKdxxw+d4Lw5fdsL6U0Bp6qOHF+3WWP826o6YEA7nwA+MaB89rj944HjJ7oOSZIkSdJgozSVWpIkSZKkoZvyI8Ztad4HvMG44lcOWNM8dP0j4JIkSZKkdpkYN6rqaeu6D5IkSZK0OsrXNQ2FU6klSZIkSZ1mYixJkiRJ6jSnUkuSJEnSCKpyKvWwOGIsSZIkSeo0R4w7p8jytf8W8Joxvf5ylbF23pyetl7Q3tK3J8vbibN8/XYuaO8j5rcS59KTFrQSp63rCW39YLekpX8/YzPbCTSjpe9PW78PJElaE44YS5IkSZI6zRFjSZIkSRpRY64xHgpHjCVJkiRJnWZiLEmSJEnqNKdSS5IkSdKIqmn2jMt1xRFjSZIkSVKnmRhLkiRJkjrNxFiSJEmS1GkmxlNMkoOSvCvJDkm+meT+JG8bV6eSnNm3v16Snyf5Uvs9liRJkrSuVGVkPlOZifHU8zzgq8AvgTcCHxpQ5x5gpyQbNfvPBX7STvckSZIkaXoxMV6JJIckWZzkhiRnJvmLJFcluT7J15I8sqm3T5JFzef6JA9pyo9Kck3TxjFN2SZJvty0eWOSA5ryAHOB66rq9qq6Blg6Qde+Aryg2T4I+ORavA2SJEmSNG35uqZJJNkReBewZ1XdkeRhQAFPr6pK8lrg7cDfAm8DXl9VVySZDdyXZH9gO2B3IMC5SfYGtgB+WlUvaOJs2oR8CnBD1So9dP1s4D3N9OldgNOAZw7nyiVJkiRNdcXUn6I8Khwxntx+wGeq6g6Aqvol8Bjg/CRLgKOAHZu6VwAfTvJGYLOqWgbs33yuB64DdqCXKC8BnpPk2CTPrKo7mzaeR28keKWqajEwh95o8XmT1U0yP8nCJAuX3X/Pql25JEmSJHWEifHkQm+EuN/xwAlVtTNwGLAhQFV9EHgtsBFwZZIdmvM/UFVzm8/jq+rfquq7wK70EuQPJHlP0/b+wAWr0b9z6a1BnnQadVUtqKp5VTVvvQ02WY3mJUmSJGn6MzGe3IXAy5I8HKCZSr0pv3/Q1atWVEyybVUtqapjgYX0RofPB17TTK0myZZJHpHk0cBvq+rf6SW2T22mU69XVb9Yjf6dBryvqpY8uMuUJEmSpO5yjfEkquqmJO8HLkmynN6U6KOBTyf5CXAlsE1T/c1JngUsB24GvlJV9yd5IvDN3nO1uBt4BfB44LgkY/QernUEvSdLf21F7CR/Si/B/hNgLMmbgSdV1V19/bsN+Mjaun5JkiRJU9uqPJxIK2divBJVdQZwxrjiLwyod+QE53+EP05ev09vNPl3khwOnNp33n/TW888qM3ZA8ouBi4eVF+SJEmSNDET4ymiql67rvsgSZIkSV1kYixJkiRJo6jwdU1D4sO3JEmSJEmdZmIsSZIkSeo0E2NJkiRJUqe5xliSJEmSRpXvaxoKR4wlSZIkSZ1mYixJkiRJ6jSnUndNwth6a/+R7hlb6yEAqLb+tNPWY/BbmgqTsXYCtfX9mbGsrRvXTpi9j5jfSpxLT1rQSpx9Dmvnetr6eWvr91um2Z+ul6/fzj+gGUudUyipW3xd03BMs//sSpIkSZK0ekyMJUmSJEmdZmIsSZIkSeo01xhLkiRJ0ogqH60wFI4YS5IkSZI6zcRYkiRJktRpTqWWJEmSpBFU+LqmYXHEWJIkSZLUaZ1PjJMsT7IoyU1Jbkjy1iRrdF+SbJbkdX37+yb50mq28agkFzTbX03y6/FtJLk4yY+SpK/s80nuXpN+S5IkSVKXdT4xBu6tqrlVtSPwXODPgPeuYVubAa9baa3JPQ84v9k+DnjlBPV+DewJvYQceNSDjCtJkiRJnWRi3KeqbgfmA29Iz8wkxyW5JsniJIcBJJmd5MIk1yVZkuSFTRMfBLZtRqCPa8pmJ/lMkm8nOWvFKG+SDya5uWn3Q33deB7wlaY/FwK/maC7ZwMHNtsvAT47rPsgSZIkaQQUUBmdzxTmw7fGqapbmqnUjwBeCNxZVbsl2QC4opnm/GPgxVV1V5LNgSuTnAu8A9ipquZCbyo18BRgR+CnwBXAnkluBl4M7FBV1Yz4kmQm8ISqunkVunoh8K/NOQfSS+jfPaTbIEmSJEmd4YjxYCv+nLE/cEiSRcBVwMOB7Zrj/5hkMfA1YEvgkRO0dXVV3VZVY8AiYA5wF3AfcGqSlwC/beo+rYmzKpYDlwMHABtV1a0TXkwyP8nCJAuX3u8yZEmSJEnq54jxOEkeRy/pvJ1eAnxkVZ0/rs6hwBbArlW1NMmtwIYTNHl/3/ZyYL2qWpZkd+DZ9EZ73wDsBzwf+OpqdPds4HPA0ZNVqqoFwAKA2Q/bqlajfUmSJElTWPl/90PhiHGfJFsAJwMnVFXRewjWEUlmNce3T7IJsClwe5MUPwvYumniN8BDViHObGDTqjoPeDMwtzn0bHpTpFfVZcAHgE+uxjmSJEmSpD6OGMNGzVTpWcAy4Ezgw82xU+lNfb6ueWjWz4EXAWcBX0yykN706G8DVNUvklyR5EZ6D9D68gQxHwJ8IcmG9Eal39Ik5fdV1V0rKiW5DNiB3gO8bgP+un/0ukneP4QkSZIkaY11PjGuqpmTHBsD/r75jLfHBOe8fFzRxX3H3tBXvnt/pSSvAC4Y19YzJ4ix7wTlsweVS5IkSZIm1vnEeKqoqn9f132QJEmSNGJcYzwUrjGWJEmSJHWaibEkSZIkqdOcSi1JkiRJIylUZV13YlpwxFiSJEmS1GkmxpIkSZKkTjMxliRJkiR1mmuMJUmSJGlU+bqmoTAx7qIW1udnrK1/oS09bKCtMC3dt5o5vR7SMPP+sVbiLF+/nUk2aem/cPscNr+VOJecsqCVOHsf0c71VEtzrbK8nZ+DsfWm1++D6XY9kqR2OJVakiRJktRpjhhLkiRJ0igqfF3TkDhiLEmSJEnqNBNjSZIkSVKnmRhLkiRJkjrNNcaSJEmSNKp8XdNQOGIsSZIkSeo0E2NJkiRJUqeZGLcgya1JNh9QfniSQwaUz0lyY7M9L8lHm+19kzxj7fdYkiRJ0mjICH2mLtcYr2VJZk50rKpOXtn5VbUQWNjs7gvcDXxjKJ2TJEmSJHVrxDjJIUkWJ7khyZlJtk5yYVN2YZLHNvVOT3JSkouS3JJknySnJflWktP72jsoyZIkNyY5tq/87iTvS3IVsEdTfFSSq5vP45t6Ryd5W7O9a9OvbwKv72tr3yRfSjIHOBx4S5JFSZ6Z5AdJZjX1/qQZmZ61Nu+hJEmSJE03nUmMk+wIvAvYr6qeDLwJOAH4eFXtApwFfLTvlIcC+wFvAb4I/BOwI7BzkrlJHg0c29SZC+yW5EXNuZsAN1bV06rq8qbsrqravYn5zwO6+DHgjVW1x4BjVNWtwMnAP1XV3Kq6DLgYeEFT5UDgnKpauhq3RZIkSZI6rzOJMb0E9jNVdQdAVf2S3mjuJ5rjZwJ79dX/YlUVsAT4n6paUlVjwE3AHGA34OKq+nlVLaOXWO/dnLscOGdc/E/2ff2D5DfJpsBmVXVJX19WxanAq5vtV9NLrv9IkvlJFiZZuPT+u1exaUmSJElTXo3QZwrrUmIcVv7t6D9+f/N1rG97xf56TL56/L6qWj5J2+P7sSp9+yNVdQUwJ8k+wMyqunGCeguqal5VzZu1wezVDSNJkiRJ01qXEuMLgZcleThAkofRe4jVgc3xg4HLJzh3kKuAfZJs3jxg6yDgkknqH9D39Zv9B6rq18CdSVaMWB88QRu/AR4yruzj9EahB44WS5IkSZIm15mnUlfVTUneD1ySZDlwPfBG4LQkRwE/5/fTklelvZ8leSdwEb0R3/Oq6guTnLJB8zCuGfSS6PFe3fTlt8D5E7TxReAzSV4IHNmsMz4L+D/8fqq2JEmSpK6Y4lOUR0VnEmOAqjoDOGNc8X4D6h3at30rsNMExz7B79co958/e9z+nGbzmHHlR/dtXws8ue/w0U35xfQeskVVfRfYZVy4veitnf71+H5IkiRJklauU4nxdJPkeOD5wJ+t675IkiRJ0qgyMR5hVXXkuu6DJEmSJI06E2NJkiRJGkUF1GQvy9Gq6tJTqSVJkiRJ+iMmxpIkSZKkTnMqtSRJkiSNqPJ1TUPhiLEkSZIkaUpI8rwk30nyvSTvmKTebkmWJ3npMOKaGEuSJEmS1rkkM4F/ofdK2icBByV50gT1jgXOH1Zsp1J3TUHG1n6YGcvamdMxNquVML0n/rURZkY7TxXM8pa+P+u1cz1js3wa45qolv40uvcR81uJc+lJC1qJs/fr2rmeSku/D1r6/dbW7502/hsnSVprdge+V1W3ACQ5G3ghcPO4ekcC5wC7DSuwibEkSZIkjarRWmO8eZKFffsLqqr/L9tbAj/u278NeFp/A0m2BF4M7IeJsSRJkiRpxNxRVfMmOT5outT41P+fgb+rquUZ4uwqE2NJkiRJ0lRwG7BV3/5jgJ+OqzMPOLtJijcH/izJsqr6/IMJbGIsSZIkSaOqptWzVq4BtkuyDfAT4EDg5f0VqmqbFdtJTge+9GCTYjAxliRJkiRNAVW1LMkb6D1teiZwWlXdlOTw5vjJayu2ibEkSZIkaUqoqvOA88aVDUyIq+rQYcX1PcaSJEmSpE5zxFiSJEmSRlRb76Of7hwxnmKSvDhJJdlhXfdFkiRJkrrAxHjqOQi4nN4T2CRJkiRJa5mJ8RSSZDawJ/DXNIlxkhlJTkxyU5IvJTkvyUubY7smuSTJtUnOT/Koddh9SZIkSW2qEftMYSbGU8uLgK9W1XeBXyZ5KvASYA6wM/BaYA+AJLOA44GXVtWuwGnA+9dFpyVJkiRplPnwranlIOCfm+2zm/1ZwKeragz47yQXNcefAOwE/GcS6L3n62eDGk0yH5gPsP5Gm621zkuSJEnSKDIxniKSPBzYD9gpSdFLdAv43ESnADdV1R4ra7uqFgALAGY/dKspPolBkiRJktrlVOqp46XAx6tq66qaU1VbAT8A7gD+qllr/Ehg36b+d4AtkvxuanWSHddFxyVJkiStC4Eaoc8UZmI8dRzEH48OnwM8GrgNuBE4BbgKuLOqHqCXTB+b5AZgEfCM9rorSZIkSdODU6mniKrad0DZR6H3tOqquruZbn01sKQ5vgjYu81+SpIkSdJ0Y2I8Gr6UZDNgfeAfquq/13WHJEmSJE0BPkFoKEyMR8Cg0WRJkiRJ0nC4xliSJEmS1GmOGEuSJEnSqHIq9VA4YixJkiRJ6jQTY0mSJElSp5kYS5IkSZI6zTXGkiRJkjSqXGM8FCbGXdTCP57lG7QzGSHLp9dvgprZUpwZaSXOzAfa+f6MzWrnelr7eWvncshYO3GqpblJe79ufitxLj1xQStx9jrysFbiZHr9GpUkaY04lVqSJEmS1GmOGEuSJEnSKCqgWppqNs05YixJkiRJ6jQTY0mSJElSp5kYS5IkSZI6zTXGkiRJkjSifLvAcDhiLEmSJEnqNBNjSZIkSVKndSoxTvKnSc5O8v0kNyc5L8n267pfkiRJkrRGaoQ+U1hnEuMkAT4HXFxV21bVk4C/Bx65bnu2ZpK4PlySJEmShqAziTHwLGBpVZ28oqCqFlXVZek5LsmNSZYkOQAgyb5JLknyH0m+m+SDSQ5OcnVTb9um3l8kuSrJ9Um+luSPku0kOzbnLUqyOMl2Tfkhzf4NSc5syrZOcmFTfmGSxzblpyf5cJKLgGOTbJvkq0muTXJZkh3W/m2UJEmSpOmlS6OOOwHXTnDsJcBc4MnA5sA1SS5tjj0ZeCLwS+AW4NSq2j3Jm4AjgTcDlwNPr6pK8lrg7cDfjotxOPCRqjoryfrAzCQ7Au8C9qyqO5I8rKl7AvDxqjojyWuAjwIvao5tDzynqpYnuRA4vKr+K8nTgBOB/dbw/kiSJElSJ3UpMZ7MXsAnq2o58D9JLgF2A+4CrqmqnwEk+T5wQXPOEnqj0ACPAT6V5FHA+sAPBsT4JvCuJI8BPtsks/sBn6mqOwCq6pdN3T3oJesAZwL/t6+dTzdJ8WzgGcCne7PEAdhg0MUlmQ/MB1h/o81W5X5IkiRJUmd0aSr1TcCuExzLBOUA9/dtj/Xtj/H7PywcD5xQVTsDhwEbjm+kqj4B/CVwL3B+kxSHVVuG3l/nnubrDODXVTW37/PEgSdXLaiqeVU1b9YGs1chnCRJkiR1R5cS468DGyT5mxUFSXZLsg9wKXBAkplJtgD2Bq5ejbY3BX7SbL9qUIUkjwNuqaqPAucCuwAXAi9L8vCmzoqp1N8ADmy2D6Y3VfsPVNVdwA+S/K/m3CR58mr0WZIkSZJEhxLjqirgxcBzm9c13QQcDfyU3tOqFwM30Eug315V/70azR9Nb0rzZcAdE9Q5ALgxySJgB3priG8C3g9ckuQG4MNN3TcCr06yGHgl8KYJ2jwY+Ovm3JuAF65GnyVJkiSNuNTofKayTq0xrqqfAi+b4PBRzae//sXAxX37+w46VlVfAL6wktgfAD4woPwM4IxxZbcy4CFaVXXouP0fAM+bLK4kSZIkaXKdGTGWJEmSJGkQE2NJkiRJUqd1aiq1JEmSJE0rNdkLdrSqHDGWJEmSJHWaibEkSZIkqdOcSi1JkiRJo6iajx40R4wlSZIkSZ1mYixJkiRJ6jSnUndRCw+uy/J25nSMrTe9nsI38/6W5sK0dNuWbdhOoBnLWgnT2n0bm9lOoLT0p9G2fh9U2rlvex15WCtxLj/+lFbi7HPY/FbiVEs/b2Ozptd/FyRJ7TAxliRJkqRR5RrjoXAqtSRJkiSp00yMJUmSJEmd5lRqSZIkSRpRcSr1UDhiLEmSJEnqNBNjSZIkSVKnmRhLkiRJkjrNNcaSJEmSNKpcYzwUU2rEOMnyJIuS3Jjk00k2Xkn9i5PMa6t/kiRJkqTpZ0olxsC9VTW3qnYCHgAOX9cdWhVJZnYpriRJkiRNJ1MtMe53GfD4JPsm+dKKwiQnJDl0fOUkdyc5Nsm1Sb6WZPdmRPmWJH/Z1NkwyceSLElyfZJnNeWHJvlskq8m+a8k/7ev3ZOSLExyU5Jj+spvTfKeJJcD70hyXd+x7ZJcO6CPb0xyc5LFSc5uymb39Wlxkr9qyg9qym5Mcuy463xfkquAPZK8IsnVzUj7KSbLkiRJUofUCH2msCmZGCdZD3g+sGQ1TtsEuLiqdgV+A/wf4LnAi4H3NXVeD1BVOwMHAWck2bA5Nhc4ANgZOCDJVk35u6pqHrALsE+SXfpi3ldVe1XV+4E7k8xtyl8NnD6gj+8AnlJVu/D70fB3A3dW1c5N+deTPBo4Ftiv6dduSV7Ud503VtXTgF80fd6zquYCy4GDV/2WSZIkSZKmWmK8UZJFwELgR8C/rca5DwBfbbaXAJdU1dJme05TvhdwJkBVfRv4IbB9c+zCqrqzqu4Dbga2bspf1owGXw/sCDypL+an+rZPBV7djNgeAHxiQB8XA2cleQWwrCl7DvAvKypU1a+A3egl+T+vqmXAWcDeTZXlwDnN9rOBXYFrmvv2bOBx44Mmmd+Mei9cev/dA7olSZIkSd011Z5KfW8z8vk7SZbxhwn8hgy2tKpWDNCPAfcDVNVYMwINkEli39+3vRxYL8k2wNuA3arqV0lOHxf/nr7tc4D3Al8Hrq2qXwyI8QJ6Ce5fAu9OsmPTp/ETCybr531Vtbyv3hlV9c5J6lNVC4AFALMfutUUn8QgSZIkSe2aaiPGg/wQeFIB7RjWAAAgAElEQVSSDZJsSm9UdE1dSjPVOMn2wGOB70xS/0/oJb93JnkkvendAzUjzecDJwEfG388yQxgq6q6CHg7sBkwG7gAeENfvYcCV9Gbtr15MwJ9EHDJgLAXAi9N8ojm3Icl2XpAPUmSJEnTTGq0PlPZlE+Mq+rHwH/QTEOmN6V5TZ0IzEyyhN406EOr6v6JKlfVDU28m4DTgCtW0v5Z9EZ/LxhwbCbw703s64F/qqpf01sL/dDmIVs3AM+qqp8B7wQuAm4ArquqLwzo383A/wYuSLIY+E/gUSvpoyRJkiSpz5SaSl1Vsycofzu9Udbx5fsOOreqjh7UbjOqe+iAdk6n72FZVfXnfdt/VL8pnzOgeC/gtL6pzv31lzbHx5ffDbxqQPknGLBOefw9qqpP8YdrnSVJkiRJq2FKJcajLMnngG3pPUlakiRJkta+muzxRFpVJsZDUlUvXtd9kCRJkiStvim/xliSJEmSpLXJxFiSJEmS1GlOpZYkSZKkUTXFX4M0KhwxliRJkiR1momxJEmSJKnTnEotSZIkSSMqTqUeChPjrkk7rzqbsXztxwDIzHbitKVa+hdZaed9dzMfaOc3dcZaCdOaGdNssdDYeu38vLX1PwZtxdnnsPmtxLnklAWtxNnzTYe1EmfWPdPsF4IkqRVOpZYkSZIkdZqJsSRJkiSp05xKLUmSJEmjanqtwFpnHDGWJEmSJHWaibEkSZIkqdOcSi1JkiRJo6h8XdOwOGIsSZIkSeo0E2NJkiRJUqe1mhgnuTXJ5s32N1ZSd06SGx9ErPOSbLYG5x2a5IQ1jStJkiRJGi1rbY1xkvWqatlEx6vqGWsrdtP+n63N9gdJMrOqlrcUa9L7K0mSJKkDXGM8FCsdMU5ySJLFSW5IcmZT9hdJrkpyfZKvJXlkU350kgVJLgA+nuThSS5o6p0CpK/du5uvSXJckhuTLElywIA+bJjkY83x65M8qynfOMl/NP37VNOnec2x/tHpVb6GSe7D0UnOTPL1JP+V5G+a8n2TXJTkE8CSJDOb67mmiXlYU+9RSS5Nsqi51mc2dU/vu/a3NHUv7ruOzZPc2mwfmuTTSb4IXJBkkySnNbGuT/LClX0/JUmSJEl/aNIR4yQ7Au8C9qyqO5I8rDl0OfD0qqokrwXeDvxtc2xXYK+qujfJR4HLq+p9SV4AzB8Q5iXAXODJwObANUkuHVfn9QBVtXOSHeglhdsDrwN+VVW7JNkJWDSka5jILsDTgU2A65N8uSnfHdipqn6QZD5wZ1XtlmQD4IrmDwUvAc6vqvcnmQls3Fz3llW1U9PXVZn6vQewS1X9Msk/Al+vqtc0516d5GtVdc8qtCNJkiRJYuVTqfcDPlNVdwBU1S+b8scAn0ryKGB94Ad955xbVfc223vTSwipqi8n+dWAGHsBn2ymIP9PkkuA3YDF4+oc37Tz7SQ/BLZvyj/SlN+YZDF/bE2uYSJfaK7t3iQX0UuIfw1cXVUrzt8f2CXJS5v9TYHtgGuA05LMAj5fVYuS3AI8LsnxwJeBC1ahD//Zdw37A3+Z5G3N/obAY4Fv9Z/QJOvzAdbfeLWXXUuSJEmaqpxKPRQrm0odBt/q44ETqmpn4DB6CdkK40crV/atykqOT1ZnVc9d3WuYyPh2Vuz3X3OAI6tqbvPZpqouqKpL6f2h4CfAmUkOqapf0Rspv5jeqPipTRvL+P33Zny/xsf6q75Yj62qb42rT1UtqKp5VTVv1gazV+EyJUmSJKk7VpYYXwi8LMnDAfqmIW9KL8EDeNUk518KHNyc+3zgoRPUOaBZb7sFveTx6kna2Z7eqOh36E2HfllT/iRg57VwDf1e2Kx3fjiwL71R4PHOB45oRoZJsn2zFnhr4Paq+lfg34CnNmugZ1TVOcC7gac2bdxKb0o6wEuZ2PnAkUnSxHrKKl6HJEmSJKkx6VTqqropyfuBS5IsB64HDgWOBj6d5CfAlcA2EzRxDPDJJNcBlwA/GlDnc/TWzd5AbwT27VX130nm9NU5ETg5yRJ6o6mHVtX9SU4EzmimUF9Pb/r1nUO+hn5X05vy/FjgH6rqp02i3u9UYA5wXZOw/hx4Eb1E+qgkS4G7gUOALYGPJVnxB4p3Nl8/BPxHklcCX5+kP/8A/DOwuIl1K/Dnq3AdkiRJkqRGqkZ3UnrzEKtZVXVfkm3pjQ5vX1UPrIVYRwN3V9WHht12m2Y/bKva5dlvWutxZt7fzs/V2PqrMpt+dGSsnftWaee+tXU9GWslTGtq5rruwXCNzWzp5210/3M20Iyl7VzQJacsaCXOnm86rJU4s+5p5xfCpV96eytxJLUnybVVNW9d92N1bLjlVrX14W9d191YZd99z1un7D1ea+8xbsnGwEXNtOUAR6yNpFiSJEmSNH2NdGJcVb8BWvmLQ1Ud3UYcSZIkSVK7VvbwLUmSJEmSpjUTY0mSJElSp5kYS5IkSZI6zcRYkiRJktRpI/3wLUmSJEnqtGn2usJ1xRFjSZIkSVKnOWLcNVXMWL72/6w0NitrPQZAxtr5E1mlnesZm9lOnDZ+BgCyvJUwLN+gnfs284Fpdt/Wb+e+tSUt/Vy3pVr60/WebzqslThXfOSUVuLs/br5rcSRJE0vJsaSJEmSNIoKMr3+LrzOOJVakiRJktRpJsaSJEmSpE4zMZYkSZIkdZprjCVJkiRpVLnGeCgcMZYkSZIkdZqJsSRJkiSp05xKLUmSJEmjyqnUQ+GI8RpKsjzJoiQ3JbkhyVuTzGiOzUvy0bUc/9QkT1qbMSRJkiSpCxwxXnP3VtVcgCSPAD4BbAq8t6oWAgtXtaEkAVJVY6t6TlW9djX7K0mSJEkawBHjIaiq24H5wBvSs2+SLwEkOTrJ21bUTXJjkjnN51tJTgSuA7ZKclSSa5IsTnJMU3+TJF9uRqVvTHJAU35xMzI9M8npzbElSd7S/h2QJEmSpNHliPGQVNUtzVTqR6zGaU8AXl1Vr0uyP7AdsDsQ4NwkewNbAD+tqhcAJNl0XBtzgS2raqfm+GYP8lIkSZIkjYDetNN13YvpwRHj4cpq1v9hVV3ZbO/ffK6nN4K8A71EeQnwnCTHJnlmVd05ro1bgMclOT7J84C7/qhTyfwkC5MsXHb/PavZRUmSJEma3kyMhyTJ44DlwO3jDi3jD+/zhn3b/VlqgA9U1dzm8/iq+req+i6wK70E+QNJ3tPfeFX9CngycDHweuDU8X2rqgVVNa+q5q23wSZrdoGSJEmSNE05lXoIkmwBnAycUFXVe5bW79wK/HlT76nANhM0cz7wD0nOqqq7k2wJLKX3PfplVf17kruBQ8fF3hx4oKrOSfJ94PShXZgkSZKkqc2p1ENhYrzmNkqyCJhFb1T4TODDA+qdAxzS1L0G+O6gxqrqgiRPBL7ZJNZ3A68AHg8cl2SMXqJ8xLhTtwQ+tuJVUcA7H9RVSZIkSVLHmBivoaqaOcmxi+lNbaaq7qW3dniQncad9xHgI+PqfJ/eaPL4GPv27T51Zf2VJEmSJA3mGmNJkiRJUqc5YixJkiRJo6h8XdOwOGIsSZIkSeo0E2NJkiRJUqc5lVqSJEmSRpVTqYfCEWNJkiRJUqeZGEuSJEmSOs3EWJIkSZLUaa4x7piaGe7bdOZaj3P3Y7LWYwDM+m0rYah2LodHffgbrcT5zYFPbyXOzAfaWfSydOOW/saXsXbitGTG0na+P2PrtfMPaJp9exib1dLv0XvauXF7v25+K3EuPXFBK3HgqJbiSNJKuMZ4KBwxliRJkiR1momxJEmSJGlKSPK8JN9J8r0k7xhw/OAki5vPN5I8eRhxnUotSZIkSSMq02gqdZKZwL8AzwVuA65Jcm5V3dxX7QfAPlX1qyTPBxYAT3uwsR0xliRJkiRNBbsD36uqW6rqAeBs4IX9FarqG1X1q2b3SuAxwwhsYixJkiRJmgq2BH7ct39bUzaRvwa+MozATqWWJEmSJLVh8yQL+/YXVFX/6wQGvZJh4GTxJM+ilxjvNYyOmRhLkiRJ0qgarTXGd1TVvEmO3wZs1bf/GOCn4ysl2QU4FXh+Vf1iGB1zKrUkSZIkaSq4BtguyTZJ1gcOBM7tr5DkscBngVdW1XeHFdjEeDUkWZ5kUZIbk3w6ycZN+d0rOW+zJK9bxRhDa0uSJEmSRkVVLQPeAJwPfAv4j6q6KcnhSQ5vqr0HeDhwYpObLZygudViYrx67q2quVW1E/AAcPjKTmhsBgwrmR1mW5IkSZJGVY3YZ1Uuqeq8qtq+qratqvc3ZSdX1cnN9mur6qFNXjZ3JVOzV5mJ8Zq7DHh8f0GS2UkuTHJdkiVJVjxa/IPAts1fNI5r6h6V5JrmxdTHDAowQZ0/aCvJo5Jc2jeS/cy1dL2SJEmSNC358K01kGQ94PnAV8cdug94cVXdlWRz4Mok5wLvAHaqqrnN+fsD29F7T1eAc5PsXVWX9sUYWGdAW38LnF9V729eiL3xWrtwSZIkSZqGTIxXz0ZJFjXblwH/Nu54gH9sEtgxeu/ceuSAdvZvPtc3+7PpJcGXrkKdH41r6xrgtCSzgM9X1aJxx0kyH5gPsP4mD13JJUqSJElSt5gYr557V4zUTuBgYAtg16pamuRWYMMB9QJ8oKpOmaStgXWSzOnfr6pLm0T8BcCZSY6rqo+Pq7MAWACwyeZbjdYD3SVJkiRNKP7f/VC4xni4NgVub5LiZwFbN+W/AR7SV+984DVJZgMk2TLJI8a1NVGdP2grydZNzH+lN4L91LVwXZIkSZI0bTliPFxnAV9sHhm+CPg2QFX9IskVSW4EvlJVRyV5IvDNJAB3A68Abl/RUFVdMKhOVX2/vy3gRuCoJEubOoe0dbGSJEmSNB2YGK+Gqpo9WXlV3QHsMUGdl4/b/wjwkcliTFLn5eOKzlhZ3yVJkiRNQ06lHgqnUkuSJEmSOs3EWJIkSZLUaSbGkiRJkqROc42xJEmSJI0oX9c0HI4YS5IkSZI6zcRYkiRJktRpTqWWJEmSpFHlVOqhcMRYkiRJktRpjhh3zBPnPJKFH3vruu6GJvKht6zrHkjSiDtqXXdAkjSCTIwlSZIkaRQVTqUeEqdSS5IkSZI6zcRYkiRJktRpJsaSJEmSpE5zjbEkSZIkjaA0Hz14jhhLkiRJkjrNxFiSJEmS1GlOpZYkSZKkUeXrmobCEeMpIMnd4/YPTXJCs310kp8kWZTk20lOSjKjOXZ6kh/0HXvvuui/JEmSJI0yE+PR8E9VNRd4ErAzsE/fsaOaY3OBVyXZZl10UJIkSZJGlYnxaFkf2BD41YBjGzZf72mvO5IkSZI0+lxjPDVslGRR3/7DgHP79t+S5BXA1sBXqqq/7nFJ/jfweOCjVXX72u+uJEmSpKkgrjEeCkeMp4Z7q2ruig/wnnHHV0ylfgSwSZID+46tmEr9p8CzkzxjfONJ5idZmGThz3/+87V2EZIkSZI0ikyMR0hVLQW+Cuw94NjdwMXAXgOOLaiqeVU1b4sttljr/ZQkSZKkUWJiPEKSBHgG8P0Bx9YDnjbomCRJkqRpqkboM4WZGI+GtzRrkG+kty78xL5jxzXHFgNLgM+ug/5JkiRJ0sjy4VtTQFXNHrd/OnB6s300cPQE5x26VjsmSZIkSR3giLEkSZIkqdMcMZYkSZKkUTXF1+6OCkeMJUmSJEmdZmIsSZIkSeo0p1JLkiRJ0igqiFOph8IRY0mSJElSp5kYS5IkSZI6zcRYkiRJktRprjGWJEmSpFHlGuOhcMRYkiRJktRpJsaSJEmSpE5zKrUkSZIkjShf1zQcjhhLkiRJkjrNxFiSJEmS1GkmxpIkSZKkTnONsSRJkiSNKtcYD4UjxpIkSZKkTjMxHpIkRyd526rWSfK+JM9pp3eSJEmSpIk4lXodqar3DLvNJAFSVWPDbluSJEnS1OPrmobDEeMHIcm7knwnydeAJ6xOnSSnJ3lps31rkn9M8s0kC5M8Ncn5Sb6f5PC+c45Kck2SxUmOacrmJPlWkhOB64Ct1uY1S5IkSdJ044jxGkqyK3Ag8BR69/E64NrVrdPnx1W1R5J/Ak4H9gQ2BG4CTk6yP7AdsDsQ4NwkewM/opdwv7qqXjfMa5QkSZKkLjAxXnPPBD5XVb8FSHLuGtZZYcWxJcDsqvoN8Jsk9yXZDNi/+Vzf1JtNL1H+EfDDqrpyooaTzAfmAzz2sY9dxcuTJEmSpG4wMX5wxs/o3yjJomb75AnqTOT+5utY3/aK/fXojRJ/oKpO6T8pyRzgnkk7WbUAWAAwb948VyFIkiRJ00Hh65qGxDXGa+5S4MVJNkryEOAvgHuram7zOXmCOmvqfOA1SWYDJNkyySMe7EVIkiRJUtc5YryGquq6JJ8CFgE/BC5bkzr/v717DdfsLOsE//8bsBNJIBOIB4SYRsMZDLJhRBACIuKhBUdsVFAB24iieMShhVE8tNKDPYoircEWQaPgoCKDSGIDgQEJSQVCJQiKIiitNol4AIIYUnd/2Cuw2dRhV2UnVW+9v991vVet9azntFby5d7386x1GONd2PYuSd64+fLpfDDJY5Ncd6R9AgAAsPlpn6M9B25CGxsbs2fPnqM9DQAAOKa0vWxmNo72PA7Hp51++7nz//H9R3saO/aW877/mH3GllIDAACw1gTGAAAArDWBMQAAAGvNy7cAAABWUJPUK6N2hYwxAAAAa01gDAAAwFqzlBoAAGBVWUq9K2SMAQAAWGsCYwAAANaawBgAAIC1Zo8xAADAiurYZLwbZIwBAABYawJjAAAA1pql1AAAAKto4nNNu0TGGAAAgLUmMAYAAGCtCYwBAABYawLjLdp+cNv549o+Z5f6PrPtN+5CPz+8G/MBAABWX2d1fscygfFNoO3NkpyZ5JCBcdsTDlFFYAwAALCLBMY71Pb0tr/T9tLld/+l/L5t/7jtW5Z/77SUP67t/9v2/0tyYZJnJvnitpe3/b5tfZ/T9jVtfzPJFUvZS9te1vZtbc9dyp6Z5KSlj/OXsse2vWQp++UdBNYAAABs4XNNn+iktpdvOT8tycuW42cn+dmZeX3bM5JckOQuSd6R5IEz89G2D03yU0m+dmlzvyT3nJn3tz0nyQ/OzFcdYOz7Jrn7zPzlcv6Epd1JSS5t+zsz89S23zUzZydJ27skeXSS+8/MtW2fm+QxSV54wx8FAABwzDvGlyivCoHxJ/rw9UFnspn1TbKxnD40yV3bXn/5lm1PSXKrJC9oe1Y2/7e8+Zb+/mhm3r/DsS/ZEhQnyZPbfs1yfPskZyX5+21tviTJvbMZOCfJSUnet73jJeN8bpKcccYZO5wOAADAehAY79ynJLnfzHx4a2HbX0jympn5mrZnJrloy+UPHUb/H6u7ZJcfuox3TduLkpy4nzZN8oKZ+Y8H63hmzktyXpJsbGz4mxIAAMAW9hjv3IVJvuv6k7bXZ5ZvleR/LMePO0j7DyQ5ZYdj3SrJPyxB8Z2TfOGWa9e2vT4r/aokj2r76cucTmv7OTscAwAAgAiMD8eTk2y03dv2T5I8cSn/v5P8dNs3JDnYi6/2Jvlo27duf/nWfrwyyc3a7k3yE0ku3nLtvCR7254/M3+S5OlJLlzq/lGSzzrsOwMAAFbS0f4E0/HyuabOHOMzZFdtbGzMnj17jvY0AADgmNL2spnZOHTNY8ctbnP7udtXHSrnduy49AU/cMw+YxljAAAA1pqXbwEAAKwqC4B3hYwxAAAAa01gDAAAwFoTGAMAALDW7DEGAABYRSvwGaRVIWMMAADAWhMYAwAAsNYspQYAAFhVllLvChljAAAA1prAGAAAgLUmMAYAAGCt2WMMAACwghqfa9otMsYAAACsNYExAAAAa81SagAAgFU11lLvBhljAAAA1toxHRi3/eC288e1fc5y/Iy2/6Pt5W3f2fZ32951S92L2m4cwZiH1a7tOW1ffgTjnNr2Ow+33UH6O7PtlbvVHwAAwLo4pgPjHfjZmTl7Zs5K8uIkr257+tGe1A6dmuSwAuNuWvX/ZgAAAMeU4ybImpkXJ7kwyTfutE3bH2p7Rdu3tn3mlktf1/aStn/W9ouXuie2ff5S/y1tH7yf/m7R9lfbXrrUecRSfrelv8vb7m17VpJnJvncpexZS72nLG33tv2xpezMtm9v+9wkb05y+7bPanvlMpdHH+EjAwAAVlxndX7HsmP95Vsntb18y/lpSV52kPpvTnLnnXTc9suTPDLJ/z4z17Q9bcvlm83Mfdt+RZIfTfLQJE9Kkpm5R9s7J7mw7R23dfu0JK+emSe0PTXJJW3/e5InJnn2zJzf9lOTnJDkqUnuPjNnL/N5WJKzktw3m58ke1nbByb5qyR3SvL4mfnOtl+b5Owkn5/kNkkubfu6ndwzAAAAn+xYD4w/fH3gmGzuMU5ysP2/PYy+H5rk+TNzTZLMzPu3XPvd5d/Lkpy5HD8gyS8sdd/R9j1JtgfGD0vy1W1/cDk/MckZSd6Y5Gltb5fkd2fmne0nTfVhy+8ty/nJ2QyU/yrJe2bm4i3z+K2ZuS7J/2z72iT3SbL3QDfa9twk5ybJGWeccaBqAAAAa+lYD4wP172S7Nlh3SY5UEL/I8u/1+Xjz2gnQXeTfO3M/Om28re3fVOSr0xyQdv/kORd+2n70zPzy59Q2J6Z5EPb6h2WmTkvyXlJsrGxcYwvYgAAAHZkcuCIhsNy3OwxXpYYPyzJb+2wyYVJntD205b2px2i/uuSPGape8dsZoK3B8AXJPnuLungtvda/r1DknfNzM9ncyn4PZN8IMkp29o+oe3JS5vPbvvpB5jHo9uesLxo7IFJLtnZLQMAALDdqmeMv6/tY5PcIsmVSR4yM1dtuf4Hba9djt84M193/YWZeWXbs5PsafuvSV6R5IcPMtZzk/xS2yuSfDTJ42bmI9uWRP9Ekp9LsncJjt+d5KuSPDrJY5e5/F2SH5+Z97d9w/KJpT+cmae0vUuSNy59fjDJY7OZtd7q95LcL8lbs/n3oR+amb9bMssAAAAcps7Iva+TjY2N2bNnp6vNAQBgPbS9bGYO9j6jY87Jp91+7vFl33u0p7FjF7/oB4/ZZ7zqGWMAAIC11X1HewbHh+NmjzEAAAAcCYExAAAAa81SagAAgFXllVG7QsYYAACAtSYwBgAAYK0JjAEAAFhr9hgDAACsqNpjvCtkjAEAAFhrAmMAAADWmqXUAAAAq2iSjLXUu0HGGAAAgLUmMAYAAGCtCYwBAABYawJjAACAFdVZnd+O7qd9eNs/bfvnbZ+6n+tt+/PL9b1tv2A3nqPAGAAAgKOu7QlJfjHJlye5a5JvaHvXbdW+PMlZy+/cJP91N8YWGAMAAHAsuG+SP5+Zd83MvyZ5UZJHbKvziCQvnE0XJzm17Wfd0IFXKjBu+5ltX9T2L9r+SdtXtL1j23PavvwG9v2Mtj+4W3PdT//vbnubw6h/Ztsrb6z5AAAAx4FZod+hfXaSv95y/t6l7HDrHLaV+Y5x2yb5vSQvmJmvX8rOTvIZu9D3yjwHAACAFXWbtnu2nJ83M+dtOe9+2mwPqXdS57CtUsb4wUmunZlfur5gZi6fmf9/OT257UvavqPt+UsgnbY/0vbStle2PW9L+UVtf6rta5N8z9aB2j55yUjvbfuipey0ti9dyi5ue8+l/Bltf3Xp711tn3ywm1gywW9v+7y2b2t7YduTlmv3bvvWtm9M8qQtbU5o+6zlPva2/fal/Pvb/upyfI/lHj/thjxkAACAG8nVM7Ox5XfetuvvTXL7Lee3S/I3R1DnsK1SYHz3JJcd5Pq9knxvNjdp3yHJ/Zfy58zMfWbm7klOSvJVW9qcOjMPmpn/sq2vpya518zcM8kTl7IfS/KWpeyHk7xwS/07J/mybK6J/9G2Nz/EvZyV5Bdn5m5J/jHJ1y7lz0/y5Jm537b635rkn2bmPknuk+Tb2v7bJD+X5PPafs3S9ttn5ppDjA0AAHAsujTJWW3/bdtPTfL1SV62rc7Lknzz8nbqL8xmnPS3N3TgVQqMD+WSmXnvzOxLcnmSM5fyB7d9U9srkjwkyd22tHnxAfram+T8to9N8tGl7AFJfj1JZubVSW7d9lbLtT+YmY/MzNVJ3pdDL+/+y5m5fDm+LMmZS1+nzsxrl/Jf31L/Ydn8j395kjcluXWSs5Z7fdxS97Uz84b9Ddb23LZ72u656qqrDjE1AABgFTRH/xNMu/m5ppn5aJLvSnJBkrcn+e2ZeVvbJ7a9PmH5iiTvSvLnSZ6X5Dt341mu0t7atyV51EGuf2TL8XVJbtb2xCTPTbIxM3/d9hlJTtxS70MH6OsrkzwwyVcn+b/a3i0HX8v+SWMfZJ77q3/S0v+B/ndpku+emQv2c+2sJB9MctsDDbYsUTgvSTY2Nm7w+nsAAIAbw8y8IpvB79ayrdtpJ1u2ne6WVcoYvzrJv2n7bdcXtL1P2wcdpM31QfDVbU/OwQPr6/v8lCS3n5nXJPmhJKcmOTnJ65I8ZqlzTjbXx//zkdzI/szMPyb5p7YPWIoes+XyBUm+4/ol2subuG+xZJmfnc0g/tZtD3l/AAAAfKKVyRjPzCx7aX+u7VOT/EuSd2dzX/F+X889M//Y9nlJrljqXrqDoU5I8htL0NkkP7v084wkz2+7N8k1Sb7lht3Rfj0+ya+2vSabwfD1fiWbS8PfvLw87Kokj0zys0meOzN/1vZbk7ym7etm5n03wtwAAIBjyczmjxus40GulY2NjdmzZ8+hKwIAwBppe9nMbBzteRyOU0693Zx9zvccuuIx4vW//0PH7DNepaXUAAAAsOsExgAAAKy1ldljDAAAwCfayWeQODQZYwAAANaawBgAAIC1Zik1AADAqrKUelfIGAMAALDWBMYAAACsNYExAAAAa80eYwAAgBXlc027Q8YYAACAtSYwBgAAYK1ZSg0AALCKJsk+a6l3g4wxAOnKuUoAABMmSURBVAAAa01gDAAAwFoTGAMAALDW7DEGAABYVbYY74qVzhi3va7t5W3f2vbNbb9oKT+z7ZW7NMZFbTf2U36ztj/V9p3LHC5v+7Qt1z+4G+MDAABw41r1jPGHZ+bsJGn7ZUl+OsmDbqKxfzLJZya5x8z8S9tTkvzATTQ2AAAAu2SlM8bb3DLJP2wvbHti2+e3vaLtW9o++BDlJ7V9Udu9bV+c5KT99PlpSb4tyXfPzL8kycx8YGaesZ+657R9+Zbz57R93HJ8n7Z/vGS8L2l7ykHmdbelzuXL3M5ayh+7pfyX255wQx8kAACwGjqr8zuWrXrG+KS2lyc5MclnJXnIfuo8KUlm5h5t75zkwrZ3PEj5dyS5Zmbu2faeSd68nz4/L8lfzcwHjnTibT81yYuTPHpmLm17yyQfTvI9B5jXE5M8e2bOX9qe0PYuSR6d5P4zc23b5yZ5TJIXHum8AAAA1s2qZ4w/PDNnz8ydkzw8yQvbdludByT59SSZmXckeU+SOx6k/IFJfmMp35tk76Em0fbxS8b2r9vefodzv1OSv52ZS5ex/nlmPnqQeb0xyQ+3/T+TfM7MfDjJlyS5d5JLlz8QfEmSO+xnfue23dN2z1VXXbXD6QEAAKyHVQ+MP2Zm3pjkNklO33Zpe6B8qPLk0O92+/MkZyz7ijMzz1/2Ov9Tku1LmT+aT3zOJ24Zf3/j7HdeM/ObSb46m1nlC9o+ZKn7guWPA2fPzJ32t5x7Zs6bmY2Z2Tj99O2PBwAAWFkzq/M7hh03gfGy7PiEJH+/7dLrsrm8OMuS5DOS/OkOy++e5J7bx5qZa5L8tyTPaXviUveEJJ+6n6m9J8ld2/6btrfKZlY3Sd6R5LZt77O0P6XtzQ40r7Z3SPKumfn5JC9b5vWqJI9q++lL/dPafs5OnxkAAADHzx7jZDN7+i0zc9221dTPTfJLba/IZvb2cTPzkWU/7v7K/2uS57fdm+TyJJccYOynJfmJJFe2/UA2M7kvSPI3WyvNzF+3/e1sLsl+Z5K3LOX/2vbRSX6h7UlL+4ceZL6PTvLYttcm+bskPz4z72/79GzuQ/6UJNdmc+/0ew7/UQIAAKynzjGe0mZ3bWxszJ49e472NAAA4JjS9rKZ2Tja8zgcp9zqdnPv+3330Z7Gjr32gqces8941TPGAAAAa+tY/wzSqjhu9hgDAADAkRAYAwAAsNYspQYAAFhFk0N/aJYdkTEGAABgrQmMAQAAWGsCYwAAANaaPcYAAAArqEk6NhnvBhljAAAA1prAGAAAgLVmKTUAAMCq2ne0J3B8kDEGAABgrQmMAQAAWGsCYwAAANaaPcYAAAAryueadoeMMQAAAGtNYAwAAMBas5R6P9p+cGZOPtrzAAAAOKBZftxgMsY3sbYnHO05AAAA8HEC44Noe07bi9q+pO072p7ftsu1Z7b9k7Z72/7MUvZrbR+1pf0Ht/Tzmra/meSKpeylbS9r+7a2525t0/Y/tX1r24vbfsZS/hltf28pf2vbL1rKH9v2kraXt/1lgTcAAMDhERgf2r2SfG+Suya5Q5L7tz0tydckudvM3DPJT+6gn/smedrM3HU5f8LM3DvJRpInt731Un6LJBfPzOcneV2Sb1vKfz7Ja5fyL0jytrZ3SfLoJPefmbOTXJfkMTfsdgEAANaLPcaHdsnMvDdJ2l6e5MwkFyf5lyS/0vYPkrx8h/385ZbzJ7f9muX49knOSvL3Sf51S3+XJfnS5fghSb45SWbmuiT/1Pabktw7yaVLIvukJO/bPvCSkT43Sc4444wdTBUAADj2TeJzTbtCxvjQPrLl+LokN5uZj2YzA/w7SR6Z5JXL9Y9meabLkutP3dL2Q9cftD0nyUOT3G/JAL8lyYnL5WtnPvZ/93U5+B8vmuQFM3P28rvTzDxje6WZOW9mNmZm4/TTT9/BLQMAAKwPgfERaHtyklvNzCuyucz67OXSu7OZwU2SRyS5+QG6uFWSf5iZa9reOckX7mDYVyX5jmX8E9recil7VNtPX8pPa/s5R3BLAAAAa8tS6iNzSpLfb3tiNrO237eUP28pvySbQeuHDtD+lUme2HZvkj/N5tLsQ/meJOe1/dZsZpK/Y2be2PbpSS5s+ylJrk3ypCTvOcL7AgAAVkitpN4VHWvS18rGxsbs2bPnaE8DAACOKW0vm5mNoz2Pw3HLUz577vsFTzra09ixV73uacfsM7aUGgAAgLUmMAYAAGCt2WMMAACwqmyN3RUyxgAAAKw1gTEAAABrzVJqAACAVTRJ9x3tSRwfZIwBAABYawJjAAAA1prAGAAAgLVmjzEAAMCq8rmmXSFjDAAAwFoTGAMAALDWLKUGAABYVVZS7woZYwAAANaawBgAAIC1JjAGAABgrdljDAAAsKLqc027Qsb4GNH219o+6mjPAwAAYN0IjFdUW9l+AACAXSC4uhG1vUWS305yuyQnJPmJJHdK8u+SnJTkj5N8+8wnrn9o+yP7q9P2ouX8/kle3fZxSe44M9e2vWWSvUnOmplrb4LbAwAAjjZLqXeFjPGN6+FJ/mZmPn9m7p7klUmeMzP3Wc5PSvJV+2l3sDqnzsyDZubHklyU5CuX8q9P8juCYgAAgMMjML5xXZHkoW3/c9svnpl/SvLgtm9qe0WShyS5237aHazOi7cc/0qSxy/Hj0/y/P1Nou25bfe03XPVVVfd0HsCAAA4rgiMb0Qz82dJ7p3NAPmnlyXSz03yqJm5R5LnJTlxa5u2Jx6izoe29P+GJGe2fVCSE2bmygPM47yZ2ZiZjdNPP333bhAAAOA4IDC+EbW9bZJrZuY3kvxMki9YLl3d9uQk+3sL9Yk7qLPVC5P8Vg6QLQYAAI5Tk2TfCv2OYV6+deO6R5Jntd2X5Nok35HkkdnMIL87yaXbG8zMP7Z93sHqbHN+kp/MZnAMAADAYRIY34hm5oIkF2wr3pPk6fup+7gtx08/QJ1z9jPMA5K8ZGb+8YbMFQAAYF0JjFdY219I8uVJvuJozwUAALhpNZP6XNOuEBivsJn57qM9BwAAgFXn5VsAAACsNYExAAAAa81SagAAgFVlj/GukDEGAABgrQmMAQAAWGuWUgMAAKwqS6l3hYwxAAAAa01gDAAAwFoTGAMAALDW7DEGAABYRZNk39GexPFBxhgAAIC1JjAGAABgrVlKDQAAsKLqc027QsYYAACAtSYwBgAAYK0JjA+i7XVtL9/ye/yW439te8Vy/Myl/s3bXnYjzufUtt+55fycti+/scYDAABYB/YYH9yHZ+bsbWXPT5K2707y4Jm5esu1ByT54xtxPqcm+c4kz70RxwAAAFaFPca7QsZ4dz08yR+2PbPtO9r+Stsr257f9qFt39D2nW3vmyRtT2v70rZ7217c9p5L+TPa/mrbi9q+q+2Tl/6fmeRzlyz1s5ayk9u+ZBnv/La96W8bAABgdQmMD+6kLUunf28H9R+c5KLl+POSPDvJPZPcOck3ZjOj/INJfnip82NJ3jIz91zKXrilrzsn+bIk903yo21vnuSpSf5iZs6emacs9e6V5HuT3DXJHZLc/0huFAAAYF1ZSn1w+1tKvV9tb5vk/TNzzZK0/cuZuWK59rYkr5qZaXtFkjOXZg9I8rVJMjOvbnvrtrdarv3BzHwkyUfavi/JZxxg6Etm5r3LOJcvfb9+29zOTXJukpxxxhk7uR0AAOCYN5ZS7xIZ493z5Uku2HL+kS3H+7ac78vH/yCxv2XP1/+fvbX9dTnwHzEOWW9mzpuZjZnZOP300w/QDQAAwHoSGO+ehyf5w8Ns87okj0k23zCd5OqZ+eeD1P9AklOOaHYAAADsl6XUu6DtCUnOmpl3HGbTZyR5ftu9Sa5J8i0Hqzwzf7+8wOvKbAbhf3Ak8wUAAODjBMYHMTMnH+TamVtO75fk4i3X3p3k7lvOH7e/azPz/iSP2E/fz9h2vrWvb9xW/aIt177rQPMFAACOMxN7jHeJwHgXzMzrs+2FVwAAAKwGe4wBAABYazLGAAAAq2rf0Z7A8UHGGAAAgLUmMAYAAGCtCYwBAABYa/YYAwAArKj6XNOukDEGAABgrQmMAQAAWGuWUgMAAKwqS6l3hYwxAAAAa01gDAAAwFoTGAMAALDW7DEGAABYRZNknz3Gu0HGGAAAgLUmMAYAAGCtWUoNAACwksbnmnaJjDEAAABrba0D47Yf3EGdL277traXtz3pMPt/ZNu7HqLOr7X9y6X/N7e932GOcch7AAAAWHVtT2v7R23fufz7v+2nzu3bvqbt25c47nt20vdaB8Y79JgkPzMzZ8/Mhw+z7SOTHDQwXjxlZs5O8tQkv7z9YtsTDnNcAACA481Tk7xqZs5K8qrlfLuPJvmBmblLki9M8qRDJSsTgXGSpO05bS9q+5K272h7fjf9hyT/PsmPtD1/qfuUtpe23dv2x7b08c1L2Vvb/nrbL0ry1UmetWSDP3cHU3ldks9b+nt32x9p+/okX9f2G9pe0fbKtv952/z/y5JtflXb03fpsQAAAMe6mdX53XCPSPKC5fgF2UxEbnsc87cz8+bl+ANJ3p7ksw/VsZdvfdy9ktwtyd8keUOS+8/Mr7R9QJKXz8xL2j4syVlJ7pukSV7W9oFJ/j7J05Y2V7c9bWbe3/Zl17fd4Rz+XZIrtpz/y8w8oO1tk1yc5N5J/iHJhW0fOTMvTXKLJG+emR9o+yNJfjTJd23ttO25Sc5dTj/Y9k8P79HkNkmuPsw2R8I4xjGOcYxjnJtyDOMYxzjG2epON8EY3DCfMTN/m2wGwG0//WCV256ZzTjvTYfqWGD8cZfMzHuTpO3lSc5M8vptdR62/N6ynJ+czUD585O8ZGauTpKZef9hjv2stk9PclWSb91S/uLl3/skuWhmrlrmd36SByZ5aZJ9W+r9RpLf3d75zJyX5LzDnNPHtN0zMxtH2t44xjGOcYxjnGNxDOMYxzjG2T7OjT0Guc2253zeEqt8TNv/nuQz99P2aYczUNuTk/xOku+dmX8+VH2B8cd9ZMvxddn/s2mSn56ZT9gH3PbJSW7I2oCnHCCr/KEt4+6U97UDAMC6WK3PNV19qD9yzMxDD3St7f9s+1lLtvizkrzvAPVuns2g+PyZ+aTE4f7YY3x4LkjyhOWvD2n72Uv6/lVJ/n3bWy/lpy31P5DklF0Y901JHtT2NsuLuL4hyWuXa5+S5FHL8Tfmk7PcAAAAx4OXJfmW5fhbkvz+9gptm+S/JXn7zPw/O+1YYHwYZubCJL+Z5I1tr0jykiSnzMzbkvynJK9t+9Yk1/8HeFGSp7R9yw5fvnWgcf82yX9M8pokb83mnuLr/yf4UJK7tb0syUOS/PiRjnMQR7wM2zjGMY5xjGOcY3gM4xjHOMY5GuNw5J6Z5EvbvjPJly7naXvbtq9Y6tw/yTclecjyEuTL237FoTrurFbqHQAAgCS3OvEz54tu901Hexo79sq/+JnLbor94kfCHmMAAIBVNEn2SXTuBoHxTaTtL2Yzrb/Vs2fm+UdjPgAAAGwSGN9EZuZJR3sOAAAAfDKBMQAAwEqaZPYd7UkcF7yVGgAAgLUmMAYAAGCtCYwBAABYa/YYAwAArKrxuabdIGMMAADAWhMYAwAAsNYspQYAAFhFk2SfpdS7QcYYAACAtSYwBgAAYK0JjAEAAFhr9hgDAACsKp9r2hUyxgAAAKw1gTEAAABrzVJqAACAVWUp9a6QMQYAAGCtCYwBAABYawJjAAAA1po9xgAAACtp7DHeJTLGAAAArDWBMQAAAGvNUmoAAIBVNEn27TvaszguyBgDAACw1gTGAAAArDVLqQEAAFaVt1LvChljAAAA1prAGAAAgLUmMAYAAGCt2WMMAACwquwx3hUyxgAAAKw1gTEAAABrzVJqAACAlTTJPkupd4OMMQAAAGtNYAwAAMBaExgDAACw1uwxBgAAWEWTzOw72rM4LsgYAwAAsNYExgAAAKw1S6kBAABWlc817QoZYwAAANaawBgAAIC1JjAGAABgrdljDAAAsKrGHuPdIGMMAADAWhMYAwAAsNYspQYAAFhFM8m+fUd7FscFGWMAAADWmsAYAACAtSYwBgAAYK3ZYwwAALCqfK5pV8gYAwAAsNYExgAAAKw1S6kBAABW1Phc066QMQYAAGCtCYwBAABYawJjAAAA1po9xgAAACtpfK5pl8gYAwAAsNYExgAAAKw1S6kBAABW0STZZyn1bpAxBgAAYK0JjAEAAFhrAmMAAADWmj3GAAAAq2r2He0ZHBdkjAEAAFhrAmMAAADWmqXUAAAAK2iSjM817QoZYwAAANaawBgAAIC1JjAGAABgrdljDAAAsIpmfK5pl8gYAwAAsNYExgAAAKw1S6kBAABWlM817Q4ZYwAAANaawBgAAIC1JjAGAABgrdljDAAAsKp8rmlXyBgDAACw1gTGAAAArLXOeL03AADAqmn7yiS3OdrzOAxXz8zDj/Yk9kdgDAAAwFqzlBoAAIC1JjAGAABgrQmMAQAAWGsCYwAAANaawBgAAIC19r8A/2xlKTk49BQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = 30, 28\n",
    "plt.matshow(train_data.corr())\n",
    "plt.yticks(np.arange(train_data.shape[1]), train_data.columns)\n",
    "plt.xticks(np.arange(train_data.shape[1]), train_data.columns)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10714 entries, 0 to 10713\n",
      "Data columns (total 28 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   people_ID               10714 non-null  int64  \n",
      " 1   Region                  10714 non-null  object \n",
      " 2   Gender                  10714 non-null  object \n",
      " 3   Designation             10714 non-null  object \n",
      " 4   Name                    10694 non-null  object \n",
      " 5   Married                 10714 non-null  object \n",
      " 6   Children                10403 non-null  float64\n",
      " 7   Occupation              9967 non-null   object \n",
      " 8   Mode_transport          10711 non-null  object \n",
      " 9   cases/1M                10714 non-null  int64  \n",
      " 10  Deaths/1M               10714 non-null  int64  \n",
      " 11  comorbidity             10488 non-null  object \n",
      " 12  Age                     10714 non-null  int64  \n",
      " 13  Coma score              10714 non-null  int64  \n",
      " 14  Pulmonary score         10714 non-null  object \n",
      " 15  cardiological pressure  10617 non-null  object \n",
      " 16  Diuresis                9998 non-null   float64\n",
      " 17  Platelets               9790 non-null   float64\n",
      " 18  HBB                     10698 non-null  float64\n",
      " 19  d-dimer                 9600 non-null   float64\n",
      " 20  Heart rate              9600 non-null   float64\n",
      " 21  HDL cholesterol         10679 non-null  float64\n",
      " 22  Charlson Index          10714 non-null  int64  \n",
      " 23  Blood Glucose           10714 non-null  int64  \n",
      " 24  Insurance               9624 non-null   float64\n",
      " 25  salary                  10714 non-null  int64  \n",
      " 26  FT/month                9991 non-null   float64\n",
      " 27  Infect_Prob             10714 non-null  float64\n",
      "dtypes: float64(10), int64(8), object(10)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.get_dummies(train_data, columns = ['Gender','Married', 'Designation', 'Occupation', 'Mode_transport','comorbidity', 'Pulmonary score', 'cardiological pressure'])\n",
    "standardScaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.get_dummies(test_data, columns = ['Gender','Married', 'Designation', 'Occupation', 'Mode_transport','comorbidity', 'Pulmonary score', 'cardiological pressure'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_scale = ['Age', 'Children', 'cases/1M', 'Deaths/1M', 'Charlson Index','Blood Glucose','salary']\n",
    "train_data[columns_to_scale] = standardScaler.fit_transform(train_data[columns_to_scale])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data[columns_to_scale] = standardScaler.fit_transform(test_data[columns_to_scale])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data=train_data.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = test_data.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_X = test_data.drop(['Region','Name','Designation_Mr','Designation_Mrs'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train_data['Infect_Prob']\n",
    "X = train_data.drop(['Infect_Prob','Region','Name','Designation_Mr','Designation_Mrs'], axis = 1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.10, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>121</th>\n",
       "      <th>4401</th>\n",
       "      <th>2267</th>\n",
       "      <th>6014</th>\n",
       "      <th>3655</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>people_ID</th>\n",
       "      <td>1.220000e+02</td>\n",
       "      <td>9.048000e+03</td>\n",
       "      <td>2268.000000</td>\n",
       "      <td>1.312100e+04</td>\n",
       "      <td>3.656000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Children</th>\n",
       "      <td>1.145501e+00</td>\n",
       "      <td>-1.298940e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.298940e+00</td>\n",
       "      <td>-1.298940e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cases/1M</th>\n",
       "      <td>-4.725535e-01</td>\n",
       "      <td>1.732399e+00</td>\n",
       "      <td>-0.472554</td>\n",
       "      <td>-4.725535e-01</td>\n",
       "      <td>-8.400456e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deaths/1M</th>\n",
       "      <td>-6.125050e-01</td>\n",
       "      <td>1.728693e+00</td>\n",
       "      <td>-0.612505</td>\n",
       "      <td>-6.125050e-01</td>\n",
       "      <td>-6.125050e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-3.970403e-01</td>\n",
       "      <td>5.979879e-02</td>\n",
       "      <td>-1.441244</td>\n",
       "      <td>5.166379e-01</td>\n",
       "      <td>5.166379e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coma score</th>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>6.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diuresis</th>\n",
       "      <td>1.990000e+02</td>\n",
       "      <td>1.930000e+02</td>\n",
       "      <td>328.000000</td>\n",
       "      <td>1.480000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platelets</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.300000e+01</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>7.400000e+01</td>\n",
       "      <td>3.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HBB</th>\n",
       "      <td>1.900000e+02</td>\n",
       "      <td>3.700000e+01</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>1.700000e+02</td>\n",
       "      <td>1.660000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d-dimer</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.900000e+02</td>\n",
       "      <td>286.000000</td>\n",
       "      <td>3.310000e+02</td>\n",
       "      <td>2.750000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heart rate</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.600000e+01</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>8.700000e+01</td>\n",
       "      <td>5.900000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDL cholesterol</th>\n",
       "      <td>5.600000e+01</td>\n",
       "      <td>5.100000e+01</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>5.800000e+01</td>\n",
       "      <td>4.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Charlson Index</th>\n",
       "      <td>1.688543e+00</td>\n",
       "      <td>-5.096507e-01</td>\n",
       "      <td>-0.509651</td>\n",
       "      <td>1.667166e-01</td>\n",
       "      <td>-2.560130e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood Glucose</th>\n",
       "      <td>-1.073588e-02</td>\n",
       "      <td>-1.439609e+00</td>\n",
       "      <td>-1.439609</td>\n",
       "      <td>-7.251723e-01</td>\n",
       "      <td>-7.251723e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insurance</th>\n",
       "      <td>4.400000e+06</td>\n",
       "      <td>3.800000e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.700000e+06</td>\n",
       "      <td>4.900000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary</th>\n",
       "      <td>-1.159896e+00</td>\n",
       "      <td>8.151975e-01</td>\n",
       "      <td>-0.666122</td>\n",
       "      <td>-1.159896e+00</td>\n",
       "      <td>-1.159896e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FT/month</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender_Female</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender_Male</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married_NO</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married_YES</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation_Business</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation_Cleaner</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation_Clerk</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation_Driver</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation_Farmer</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation_Legal</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation_Manufacturing</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation_Researcher</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation_Sales</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mode_transport_Car</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mode_transport_Public</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mode_transport_Walk</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comorbidity_Coronary Heart Disease</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comorbidity_Diabetes</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comorbidity_Hypertension</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comorbidity_None</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulmonary score_&lt;100</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulmonary score_&lt;200</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulmonary score_&lt;300</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulmonary score_&lt;400</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cardiological pressure_Elevated</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cardiological pressure_Normal</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cardiological pressure_Stage-01</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cardiological pressure_Stage-02</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            121           4401         2267  \\\n",
       "people_ID                           1.220000e+02  9.048000e+03  2268.000000   \n",
       "Children                            1.145501e+00 -1.298940e+00     0.000000   \n",
       "cases/1M                           -4.725535e-01  1.732399e+00    -0.472554   \n",
       "Deaths/1M                          -6.125050e-01  1.728693e+00    -0.612505   \n",
       "Age                                -3.970403e-01  5.979879e-02    -1.441244   \n",
       "Coma score                          1.200000e+01  1.300000e+01     4.000000   \n",
       "Diuresis                            1.990000e+02  1.930000e+02   328.000000   \n",
       "Platelets                           0.000000e+00  6.300000e+01   135.000000   \n",
       "HBB                                 1.900000e+02  3.700000e+01   132.000000   \n",
       "d-dimer                             0.000000e+00  2.900000e+02   286.000000   \n",
       "Heart rate                          0.000000e+00  5.600000e+01    72.000000   \n",
       "HDL cholesterol                     5.600000e+01  5.100000e+01    42.000000   \n",
       "Charlson Index                      1.688543e+00 -5.096507e-01    -0.509651   \n",
       "Blood Glucose                      -1.073588e-02 -1.439609e+00    -1.439609   \n",
       "Insurance                           4.400000e+06  3.800000e+06     0.000000   \n",
       "salary                             -1.159896e+00  8.151975e-01    -0.666122   \n",
       "FT/month                            1.000000e+00  0.000000e+00     2.000000   \n",
       "Gender_Female                       1.000000e+00  1.000000e+00     1.000000   \n",
       "Gender_Male                         0.000000e+00  0.000000e+00     0.000000   \n",
       "Married_NO                          0.000000e+00  0.000000e+00     1.000000   \n",
       "Married_YES                         1.000000e+00  1.000000e+00     0.000000   \n",
       "Occupation_Business                 0.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Cleaner                  0.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Clerk                    0.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Driver                   0.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Farmer                   0.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Legal                    0.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Manufacturing            1.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Researcher               0.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Sales                    0.000000e+00  1.000000e+00     0.000000   \n",
       "Mode_transport_Car                  1.000000e+00  0.000000e+00     0.000000   \n",
       "Mode_transport_Public               0.000000e+00  1.000000e+00     0.000000   \n",
       "Mode_transport_Walk                 0.000000e+00  0.000000e+00     1.000000   \n",
       "comorbidity_Coronary Heart Disease  0.000000e+00  1.000000e+00     0.000000   \n",
       "comorbidity_Diabetes                0.000000e+00  0.000000e+00     0.000000   \n",
       "comorbidity_Hypertension            1.000000e+00  0.000000e+00     1.000000   \n",
       "comorbidity_None                    0.000000e+00  0.000000e+00     0.000000   \n",
       "Pulmonary score_<100                0.000000e+00  1.000000e+00     0.000000   \n",
       "Pulmonary score_<200                1.000000e+00  0.000000e+00     0.000000   \n",
       "Pulmonary score_<300                0.000000e+00  0.000000e+00     1.000000   \n",
       "Pulmonary score_<400                0.000000e+00  0.000000e+00     0.000000   \n",
       "cardiological pressure_Elevated     0.000000e+00  0.000000e+00     1.000000   \n",
       "cardiological pressure_Normal       0.000000e+00  0.000000e+00     0.000000   \n",
       "cardiological pressure_Stage-01     1.000000e+00  0.000000e+00     0.000000   \n",
       "cardiological pressure_Stage-02     0.000000e+00  1.000000e+00     0.000000   \n",
       "\n",
       "                                            6014          3655  \n",
       "people_ID                           1.312100e+04  3.656000e+03  \n",
       "Children                           -1.298940e+00 -1.298940e+00  \n",
       "cases/1M                           -4.725535e-01 -8.400456e-01  \n",
       "Deaths/1M                          -6.125050e-01 -6.125050e-01  \n",
       "Age                                 5.166379e-01  5.166379e-01  \n",
       "Coma score                          1.200000e+01  6.000000e+00  \n",
       "Diuresis                            1.480000e+02  4.420000e+02  \n",
       "Platelets                           7.400000e+01  3.500000e+01  \n",
       "HBB                                 1.700000e+02  1.660000e+02  \n",
       "d-dimer                             3.310000e+02  2.750000e+02  \n",
       "Heart rate                          8.700000e+01  5.900000e+01  \n",
       "HDL cholesterol                     5.800000e+01  4.200000e+01  \n",
       "Charlson Index                      1.667166e-01 -2.560130e-01  \n",
       "Blood Glucose                      -7.251723e-01 -7.251723e-01  \n",
       "Insurance                           2.700000e+06  4.900000e+06  \n",
       "salary                             -1.159896e+00 -1.159896e+00  \n",
       "FT/month                            1.000000e+00  1.000000e+00  \n",
       "Gender_Female                       0.000000e+00  1.000000e+00  \n",
       "Gender_Male                         1.000000e+00  0.000000e+00  \n",
       "Married_NO                          0.000000e+00  0.000000e+00  \n",
       "Married_YES                         1.000000e+00  1.000000e+00  \n",
       "Occupation_Business                 0.000000e+00  1.000000e+00  \n",
       "Occupation_Cleaner                  0.000000e+00  0.000000e+00  \n",
       "Occupation_Clerk                    0.000000e+00  0.000000e+00  \n",
       "Occupation_Driver                   0.000000e+00  0.000000e+00  \n",
       "Occupation_Farmer                   1.000000e+00  0.000000e+00  \n",
       "Occupation_Legal                    0.000000e+00  0.000000e+00  \n",
       "Occupation_Manufacturing            0.000000e+00  0.000000e+00  \n",
       "Occupation_Researcher               0.000000e+00  0.000000e+00  \n",
       "Occupation_Sales                    0.000000e+00  0.000000e+00  \n",
       "Mode_transport_Car                  0.000000e+00  0.000000e+00  \n",
       "Mode_transport_Public               1.000000e+00  1.000000e+00  \n",
       "Mode_transport_Walk                 0.000000e+00  0.000000e+00  \n",
       "comorbidity_Coronary Heart Disease  1.000000e+00  0.000000e+00  \n",
       "comorbidity_Diabetes                0.000000e+00  0.000000e+00  \n",
       "comorbidity_Hypertension            0.000000e+00  1.000000e+00  \n",
       "comorbidity_None                    0.000000e+00  0.000000e+00  \n",
       "Pulmonary score_<100                0.000000e+00  0.000000e+00  \n",
       "Pulmonary score_<200                0.000000e+00  1.000000e+00  \n",
       "Pulmonary score_<300                1.000000e+00  0.000000e+00  \n",
       "Pulmonary score_<400                0.000000e+00  0.000000e+00  \n",
       "cardiological pressure_Elevated     1.000000e+00  0.000000e+00  \n",
       "cardiological pressure_Normal       0.000000e+00  0.000000e+00  \n",
       "cardiological pressure_Stage-01     0.000000e+00  1.000000e+00  \n",
       "cardiological pressure_Stage-02     0.000000e+00  0.000000e+00  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9642"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cumulative explained variance')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x2016 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA().fit(X_train)\n",
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.xlim(0,7,1)\n",
    "plt.xlabel('Number of components')\n",
    "plt.ylabel('Cumulative explained variance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x7200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ranking = np.argsort(-rf_regressor.feature_importances_)\n",
    "f, ax = plt.subplots(figsize=(15, 100))\n",
    "sns.barplot(x=rf_regressor.feature_importances_[ranking], y=X_train.columns.values[ranking], orient='h')\n",
    "ax.set_xlabel(\"feature importance\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68.28684629663779"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=linear_model.LinearRegression()\n",
    "model.fit(X_train,y_train)\n",
    "y_predict=model.predict(X_test)\n",
    "max(y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_test=model.predict(X_test)\n",
    "y_Predict=list(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test error = 8.64602151786004 percent in Ridge Regression\n"
     ]
    }
   ],
   "source": [
    "error=0\n",
    "for i in range(len(y_test)):\n",
    "    error+=(abs(y_Predict[i]-Y_test[i])/y_Predict[i])\n",
    "test_error_ridge=error/len(Y_test)*100\n",
    "print(\"Test error = \"'{}'.format(test_error_ridge)+\" percent in Ridge Regression\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.model_selection import cross_val_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mutiple Linear Regression Accuracy:  0.250138284467735\n",
      "Cross-Predicted(KFold) Mutiple Linear Regression Accuracy:  0.2777879761063923\n"
     ]
    }
   ],
   "source": [
    "model = linear_model.LinearRegression()\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "#Predicting the SalePrices using test set \n",
    "y_pred_lr = model.predict(X_test)\n",
    "\n",
    "#Mutiple Linear Regression Accuracy with test set\n",
    "accuracy_lf = metrics.r2_score(y_test, y_pred_lr)\n",
    "print('Mutiple Linear Regression Accuracy: ', accuracy_lf)\n",
    "\n",
    "#Predicting the Corona using cross validation (KFold method)\n",
    "y_pred_kf_lr = cross_val_predict(model, X, y, cv=10 )\n",
    "\n",
    "#Mutiple Linear Regression Accuracy with cross validation (KFold method)\n",
    "accuracy_lf = metrics.r2_score(y, y_pred_kf_lr)\n",
    "print('Cross-Predicted(KFold) Mutiple Linear Regression Accuracy: ', accuracy_lf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4041</th>\n",
       "      <td>53.072883</td>\n",
       "      <td>52.377079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9305</th>\n",
       "      <td>52.406560</td>\n",
       "      <td>51.264990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10262</th>\n",
       "      <td>47.567603</td>\n",
       "      <td>50.575926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6057</th>\n",
       "      <td>48.914593</td>\n",
       "      <td>48.116691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7071</th>\n",
       "      <td>49.432939</td>\n",
       "      <td>49.728119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>93.726720</td>\n",
       "      <td>61.910886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1585</th>\n",
       "      <td>66.389760</td>\n",
       "      <td>63.950085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6293</th>\n",
       "      <td>50.282456</td>\n",
       "      <td>50.723859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4795</th>\n",
       "      <td>59.095554</td>\n",
       "      <td>54.802853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>51.394117</td>\n",
       "      <td>51.817719</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1072 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Actual  Predicted\n",
       "4041   53.072883  52.377079\n",
       "9305   52.406560  51.264990\n",
       "10262  47.567603  50.575926\n",
       "6057   48.914593  48.116691\n",
       "7071   49.432939  49.728119\n",
       "...          ...        ...\n",
       "565    93.726720  61.910886\n",
       "1585   66.389760  63.950085\n",
       "6293   50.282456  50.723859\n",
       "4795   59.095554  54.802853\n",
       "396    51.394117  51.817719\n",
       "\n",
       "[1072 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred_lr})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = df.head(25)\n",
    "df1.plot(kind='bar',figsize=(16,10))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:  3.0min finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:    0.5s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Regression Accuracy:  30.25607518838509\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:  3.5min finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:  3.5min finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:  2.7min finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:  1.4min finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:  1.4min finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:  1.4min finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:  1.4min finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:  1.4min finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:  1.4min finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:  1.4min finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Predicted(KFold) Random Forest Regression Accuracy:  0.3182563967518206\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:    0.1s finished\n"
     ]
    }
   ],
   "source": [
    "rf_regressor = RandomForestRegressor(n_estimators = 1024 ,verbose=1,  random_state = 0)\n",
    "rf_regressor.fit(X_train,y_train)\n",
    "\n",
    "#Predicting the Covid-19 using test set \n",
    "y_pred_rf = rf_regressor.predict(X_test)\n",
    "\n",
    "#Random Forest Regression Accuracy with test set\n",
    "print('Random Forest Regression Accuracy: ', (rf_regressor.score(X_test,y_test)*100))\n",
    "\n",
    "#Predicting the Covid-19 using cross validation (KFold method)\n",
    "y_pred_rf = cross_val_predict(rf_regressor, X, y, cv=10 )\n",
    "\n",
    "#Random Forest Regression Accuracy with cross validation\n",
    "accuracy_rf = metrics.r2_score(y, y_pred_rf)\n",
    "print('Cross-Predicted(KFold) Random Forest Regression Accuracy: ', accuracy_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4041</th>\n",
       "      <td>53.072883</td>\n",
       "      <td>52.377079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9305</th>\n",
       "      <td>52.406560</td>\n",
       "      <td>51.264990</td>\n",
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       "    <tr>\n",
       "      <th>10262</th>\n",
       "      <td>47.567603</td>\n",
       "      <td>50.575926</td>\n",
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       "    <tr>\n",
       "      <th>6057</th>\n",
       "      <td>48.914593</td>\n",
       "      <td>48.116691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7071</th>\n",
       "      <td>49.432939</td>\n",
       "      <td>49.728119</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>565</th>\n",
       "      <td>93.726720</td>\n",
       "      <td>61.910886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1585</th>\n",
       "      <td>66.389760</td>\n",
       "      <td>63.950085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6293</th>\n",
       "      <td>50.282456</td>\n",
       "      <td>50.723859</td>\n",
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       "    <tr>\n",
       "      <th>4795</th>\n",
       "      <td>59.095554</td>\n",
       "      <td>54.802853</td>\n",
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       "      <th>396</th>\n",
       "      <td>51.394117</td>\n",
       "      <td>51.817719</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1072 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Actual  Predicted\n",
       "4041   53.072883  52.377079\n",
       "9305   52.406560  51.264990\n",
       "10262  47.567603  50.575926\n",
       "6057   48.914593  48.116691\n",
       "7071   49.432939  49.728119\n",
       "...          ...        ...\n",
       "565    93.726720  61.910886\n",
       "1585   66.389760  63.950085\n",
       "6293   50.282456  50.723859\n",
       "4795   59.095554  54.802853\n",
       "396    51.394117  51.817719\n",
       "\n",
       "[1072 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred_lr})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = df.head(25)\n",
    "df1.plot(kind='bar',figsize=(16,10))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1024 out of 1024 | elapsed:    2.0s finished\n"
     ]
    }
   ],
   "source": [
    "final_predictions = rf_regressor.predict(test_data_X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>121</th>\n",
       "      <th>4401</th>\n",
       "      <th>2267</th>\n",
       "      <th>6014</th>\n",
       "      <th>3655</th>\n",
       "      <th>4732</th>\n",
       "      <th>9335</th>\n",
       "      <th>1022</th>\n",
       "      <th>9581</th>\n",
       "      <th>6897</th>\n",
       "      <th>...</th>\n",
       "      <th>3468</th>\n",
       "      <th>6744</th>\n",
       "      <th>5874</th>\n",
       "      <th>4373</th>\n",
       "      <th>7891</th>\n",
       "      <th>9225</th>\n",
       "      <th>4859</th>\n",
       "      <th>3264</th>\n",
       "      <th>9845</th>\n",
       "      <th>2732</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>people_ID</th>\n",
       "      <td>1.220000e+02</td>\n",
       "      <td>9.048000e+03</td>\n",
       "      <td>2268.000000</td>\n",
       "      <td>1.312100e+04</td>\n",
       "      <td>3.656000e+03</td>\n",
       "      <td>9379.000000</td>\n",
       "      <td>1.827600e+04</td>\n",
       "      <td>1.023000e+03</td>\n",
       "      <td>21563.000000</td>\n",
       "      <td>1.400400e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>3.469000e+03</td>\n",
       "      <td>1.385100e+04</td>\n",
       "      <td>1.298100e+04</td>\n",
       "      <td>9.020000e+03</td>\n",
       "      <td>1.499800e+04</td>\n",
       "      <td>1.766700e+04</td>\n",
       "      <td>9.506000e+03</td>\n",
       "      <td>3.265000e+03</td>\n",
       "      <td>2.182700e+04</td>\n",
       "      <td>2.733000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Children</th>\n",
       "      <td>1.145501e+00</td>\n",
       "      <td>-1.298940e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.298940e+00</td>\n",
       "      <td>-1.298940e+00</td>\n",
       "      <td>-1.298940</td>\n",
       "      <td>1.145501e+00</td>\n",
       "      <td>1.145501e+00</td>\n",
       "      <td>-1.298940</td>\n",
       "      <td>1.145501e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.298940e+00</td>\n",
       "      <td>-7.671922e-02</td>\n",
       "      <td>-7.671922e-02</td>\n",
       "      <td>-7.671922e-02</td>\n",
       "      <td>-7.671922e-02</td>\n",
       "      <td>1.145501e+00</td>\n",
       "      <td>-1.298940e+00</td>\n",
       "      <td>-7.671922e-02</td>\n",
       "      <td>-7.671922e-02</td>\n",
       "      <td>-1.298940e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cases/1M</th>\n",
       "      <td>-4.725535e-01</td>\n",
       "      <td>1.732399e+00</td>\n",
       "      <td>-0.472554</td>\n",
       "      <td>-4.725535e-01</td>\n",
       "      <td>-8.400456e-01</td>\n",
       "      <td>1.732399</td>\n",
       "      <td>2.624306e-01</td>\n",
       "      <td>-4.725535e-01</td>\n",
       "      <td>1.732399</td>\n",
       "      <td>-4.725535e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.400456e-01</td>\n",
       "      <td>-4.725535e-01</td>\n",
       "      <td>-4.725535e-01</td>\n",
       "      <td>1.732399e+00</td>\n",
       "      <td>-4.725535e-01</td>\n",
       "      <td>2.624306e-01</td>\n",
       "      <td>1.732399e+00</td>\n",
       "      <td>-8.400456e-01</td>\n",
       "      <td>1.732399e+00</td>\n",
       "      <td>-8.400456e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deaths/1M</th>\n",
       "      <td>-6.125050e-01</td>\n",
       "      <td>1.728693e+00</td>\n",
       "      <td>-0.612505</td>\n",
       "      <td>-6.125050e-01</td>\n",
       "      <td>-6.125050e-01</td>\n",
       "      <td>1.728693</td>\n",
       "      <td>5.580941e-01</td>\n",
       "      <td>-6.125050e-01</td>\n",
       "      <td>1.728693</td>\n",
       "      <td>-6.125050e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.125050e-01</td>\n",
       "      <td>-6.125050e-01</td>\n",
       "      <td>-6.125050e-01</td>\n",
       "      <td>1.728693e+00</td>\n",
       "      <td>-6.125050e-01</td>\n",
       "      <td>5.580941e-01</td>\n",
       "      <td>1.728693e+00</td>\n",
       "      <td>-6.125050e-01</td>\n",
       "      <td>1.728693e+00</td>\n",
       "      <td>-6.125050e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-3.970403e-01</td>\n",
       "      <td>5.979879e-02</td>\n",
       "      <td>-1.441244</td>\n",
       "      <td>5.166379e-01</td>\n",
       "      <td>5.166379e-01</td>\n",
       "      <td>-1.310718</td>\n",
       "      <td>1.626104e+00</td>\n",
       "      <td>-4.623030e-01</td>\n",
       "      <td>-0.005464</td>\n",
       "      <td>-9.191421e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>3.861124e-01</td>\n",
       "      <td>-2.665149e-01</td>\n",
       "      <td>7.776888e-01</td>\n",
       "      <td>-7.233539e-01</td>\n",
       "      <td>-7.886167e-01</td>\n",
       "      <td>-5.463941e-03</td>\n",
       "      <td>-3.317776e-01</td>\n",
       "      <td>1.365053e+00</td>\n",
       "      <td>1.038740e+00</td>\n",
       "      <td>-4.623030e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coma score</th>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diuresis</th>\n",
       "      <td>1.990000e+02</td>\n",
       "      <td>1.930000e+02</td>\n",
       "      <td>328.000000</td>\n",
       "      <td>1.480000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.100000e+02</td>\n",
       "      <td>312.000000</td>\n",
       "      <td>1.930000e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>2.600000e+02</td>\n",
       "      <td>2.350000e+02</td>\n",
       "      <td>3.640000e+02</td>\n",
       "      <td>2.760000e+02</td>\n",
       "      <td>2.420000e+02</td>\n",
       "      <td>1.890000e+02</td>\n",
       "      <td>3.200000e+02</td>\n",
       "      <td>3.600000e+02</td>\n",
       "      <td>2.080000e+02</td>\n",
       "      <td>3.880000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platelets</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.300000e+01</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>7.400000e+01</td>\n",
       "      <td>3.500000e+01</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>6.700000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>9.500000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.120000e+02</td>\n",
       "      <td>1.360000e+02</td>\n",
       "      <td>8.600000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>9.000000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.200000e+02</td>\n",
       "      <td>5.400000e+01</td>\n",
       "      <td>6.100000e+01</td>\n",
       "      <td>6.900000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HBB</th>\n",
       "      <td>1.900000e+02</td>\n",
       "      <td>3.700000e+01</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>1.700000e+02</td>\n",
       "      <td>1.660000e+02</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>1.280000e+02</td>\n",
       "      <td>7.000000e+01</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>1.960000e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>8.800000e+01</td>\n",
       "      <td>3.900000e+01</td>\n",
       "      <td>8.700000e+01</td>\n",
       "      <td>1.050000e+02</td>\n",
       "      <td>1.920000e+02</td>\n",
       "      <td>2.800000e+01</td>\n",
       "      <td>8.200000e+01</td>\n",
       "      <td>1.820000e+02</td>\n",
       "      <td>1.150000e+02</td>\n",
       "      <td>3.900000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d-dimer</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.900000e+02</td>\n",
       "      <td>286.000000</td>\n",
       "      <td>3.310000e+02</td>\n",
       "      <td>2.750000e+02</td>\n",
       "      <td>213.000000</td>\n",
       "      <td>2.640000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>3.230000e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>2.390000e+02</td>\n",
       "      <td>3.360000e+02</td>\n",
       "      <td>2.250000e+02</td>\n",
       "      <td>3.470000e+02</td>\n",
       "      <td>3.290000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.060000e+02</td>\n",
       "      <td>2.920000e+02</td>\n",
       "      <td>2.640000e+02</td>\n",
       "      <td>2.750000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heart rate</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.600000e+01</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>8.700000e+01</td>\n",
       "      <td>5.900000e+01</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7.400000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>8.100000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>8.600000e+01</td>\n",
       "      <td>7.100000e+01</td>\n",
       "      <td>6.300000e+01</td>\n",
       "      <td>5.700000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.900000e+01</td>\n",
       "      <td>5.800000e+01</td>\n",
       "      <td>9.100000e+01</td>\n",
       "      <td>5.100000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDL cholesterol</th>\n",
       "      <td>5.600000e+01</td>\n",
       "      <td>5.100000e+01</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>5.800000e+01</td>\n",
       "      <td>4.200000e+01</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>7.000000e+01</td>\n",
       "      <td>4.700000e+01</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>4.100000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>4.100000e+01</td>\n",
       "      <td>4.600000e+01</td>\n",
       "      <td>4.400000e+01</td>\n",
       "      <td>4.700000e+01</td>\n",
       "      <td>5.500000e+01</td>\n",
       "      <td>5.400000e+01</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>3.600000e+01</td>\n",
       "      <td>6.500000e+01</td>\n",
       "      <td>4.100000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Charlson Index</th>\n",
       "      <td>1.688543e+00</td>\n",
       "      <td>-5.096507e-01</td>\n",
       "      <td>-0.509651</td>\n",
       "      <td>1.667166e-01</td>\n",
       "      <td>-2.560130e-01</td>\n",
       "      <td>1.181267</td>\n",
       "      <td>1.434905e+00</td>\n",
       "      <td>1.181267e+00</td>\n",
       "      <td>-0.002375</td>\n",
       "      <td>-4.251048e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.524202e+00</td>\n",
       "      <td>-6.787425e-01</td>\n",
       "      <td>1.688543e+00</td>\n",
       "      <td>3.358084e-01</td>\n",
       "      <td>2.512625e-01</td>\n",
       "      <td>-1.608748e+00</td>\n",
       "      <td>1.434905e+00</td>\n",
       "      <td>-3.405589e-01</td>\n",
       "      <td>1.096722e+00</td>\n",
       "      <td>-9.323802e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood Glucose</th>\n",
       "      <td>-1.073588e-02</td>\n",
       "      <td>-1.439609e+00</td>\n",
       "      <td>-1.439609</td>\n",
       "      <td>-7.251723e-01</td>\n",
       "      <td>-7.251723e-01</td>\n",
       "      <td>0.703701</td>\n",
       "      <td>-1.439609e+00</td>\n",
       "      <td>7.037006e-01</td>\n",
       "      <td>1.418137</td>\n",
       "      <td>-7.251723e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.418137e+00</td>\n",
       "      <td>7.037006e-01</td>\n",
       "      <td>-1.439609e+00</td>\n",
       "      <td>7.037006e-01</td>\n",
       "      <td>-7.251723e-01</td>\n",
       "      <td>-1.439609e+00</td>\n",
       "      <td>1.418137e+00</td>\n",
       "      <td>-7.251723e-01</td>\n",
       "      <td>-7.251723e-01</td>\n",
       "      <td>-1.073588e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insurance</th>\n",
       "      <td>4.400000e+06</td>\n",
       "      <td>3.800000e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.700000e+06</td>\n",
       "      <td>4.900000e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.100000e+06</td>\n",
       "      <td>2.200000e+06</td>\n",
       "      <td>900000.000000</td>\n",
       "      <td>2.400000e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>2.300000e+06</td>\n",
       "      <td>4.600000e+06</td>\n",
       "      <td>4.900000e+06</td>\n",
       "      <td>2.100000e+06</td>\n",
       "      <td>3.700000e+06</td>\n",
       "      <td>2.500000e+06</td>\n",
       "      <td>2.300000e+06</td>\n",
       "      <td>3.700000e+06</td>\n",
       "      <td>4.400000e+06</td>\n",
       "      <td>4.300000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary</th>\n",
       "      <td>-1.159896e+00</td>\n",
       "      <td>8.151975e-01</td>\n",
       "      <td>-0.666122</td>\n",
       "      <td>-1.159896e+00</td>\n",
       "      <td>-1.159896e+00</td>\n",
       "      <td>1.308971</td>\n",
       "      <td>-1.489078e+00</td>\n",
       "      <td>-3.369401e-01</td>\n",
       "      <td>-0.336940</td>\n",
       "      <td>1.638153e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.757933e-03</td>\n",
       "      <td>-3.369401e-01</td>\n",
       "      <td>3.214242e-01</td>\n",
       "      <td>-8.307134e-01</td>\n",
       "      <td>-1.324487e+00</td>\n",
       "      <td>4.860153e-01</td>\n",
       "      <td>-9.953045e-01</td>\n",
       "      <td>-1.159896e+00</td>\n",
       "      <td>6.506064e-01</td>\n",
       "      <td>-1.489078e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FT/month</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000e+00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender_Female</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e+00</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>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender_Male</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married_NO</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married_YES</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation_Business</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation_Cleaner</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</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>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation_Clerk</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation_Driver</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation_Farmer</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation_Legal</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</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>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation_Manufacturing</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation_Researcher</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</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>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation_Sales</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</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>0.000000e+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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mode_transport_Car</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+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>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mode_transport_Public</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</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>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mode_transport_Walk</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</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>1.000000e+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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comorbidity_Coronary Heart Disease</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comorbidity_Diabetes</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</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>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comorbidity_Hypertension</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comorbidity_None</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulmonary score_&lt;100</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulmonary score_&lt;200</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e+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>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulmonary score_&lt;300</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulmonary score_&lt;400</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</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>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cardiological pressure_Elevated</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cardiological pressure_Normal</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</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>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cardiological pressure_Stage-01</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e+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>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cardiological pressure_Stage-02</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45 rows × 9642 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            121           4401         2267  \\\n",
       "people_ID                           1.220000e+02  9.048000e+03  2268.000000   \n",
       "Children                            1.145501e+00 -1.298940e+00     0.000000   \n",
       "cases/1M                           -4.725535e-01  1.732399e+00    -0.472554   \n",
       "Deaths/1M                          -6.125050e-01  1.728693e+00    -0.612505   \n",
       "Age                                -3.970403e-01  5.979879e-02    -1.441244   \n",
       "Coma score                          1.200000e+01  1.300000e+01     4.000000   \n",
       "Diuresis                            1.990000e+02  1.930000e+02   328.000000   \n",
       "Platelets                           0.000000e+00  6.300000e+01   135.000000   \n",
       "HBB                                 1.900000e+02  3.700000e+01   132.000000   \n",
       "d-dimer                             0.000000e+00  2.900000e+02   286.000000   \n",
       "Heart rate                          0.000000e+00  5.600000e+01    72.000000   \n",
       "HDL cholesterol                     5.600000e+01  5.100000e+01    42.000000   \n",
       "Charlson Index                      1.688543e+00 -5.096507e-01    -0.509651   \n",
       "Blood Glucose                      -1.073588e-02 -1.439609e+00    -1.439609   \n",
       "Insurance                           4.400000e+06  3.800000e+06     0.000000   \n",
       "salary                             -1.159896e+00  8.151975e-01    -0.666122   \n",
       "FT/month                            1.000000e+00  0.000000e+00     2.000000   \n",
       "Gender_Female                       1.000000e+00  1.000000e+00     1.000000   \n",
       "Gender_Male                         0.000000e+00  0.000000e+00     0.000000   \n",
       "Married_NO                          0.000000e+00  0.000000e+00     1.000000   \n",
       "Married_YES                         1.000000e+00  1.000000e+00     0.000000   \n",
       "Occupation_Business                 0.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Cleaner                  0.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Clerk                    0.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Driver                   0.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Farmer                   0.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Legal                    0.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Manufacturing            1.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Researcher               0.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Sales                    0.000000e+00  1.000000e+00     0.000000   \n",
       "Mode_transport_Car                  1.000000e+00  0.000000e+00     0.000000   \n",
       "Mode_transport_Public               0.000000e+00  1.000000e+00     0.000000   \n",
       "Mode_transport_Walk                 0.000000e+00  0.000000e+00     1.000000   \n",
       "comorbidity_Coronary Heart Disease  0.000000e+00  1.000000e+00     0.000000   \n",
       "comorbidity_Diabetes                0.000000e+00  0.000000e+00     0.000000   \n",
       "comorbidity_Hypertension            1.000000e+00  0.000000e+00     1.000000   \n",
       "comorbidity_None                    0.000000e+00  0.000000e+00     0.000000   \n",
       "Pulmonary score_<100                0.000000e+00  1.000000e+00     0.000000   \n",
       "Pulmonary score_<200                1.000000e+00  0.000000e+00     0.000000   \n",
       "Pulmonary score_<300                0.000000e+00  0.000000e+00     1.000000   \n",
       "Pulmonary score_<400                0.000000e+00  0.000000e+00     0.000000   \n",
       "cardiological pressure_Elevated     0.000000e+00  0.000000e+00     1.000000   \n",
       "cardiological pressure_Normal       0.000000e+00  0.000000e+00     0.000000   \n",
       "cardiological pressure_Stage-01     1.000000e+00  0.000000e+00     0.000000   \n",
       "cardiological pressure_Stage-02     0.000000e+00  1.000000e+00     0.000000   \n",
       "\n",
       "                                            6014          3655         4732  \\\n",
       "people_ID                           1.312100e+04  3.656000e+03  9379.000000   \n",
       "Children                           -1.298940e+00 -1.298940e+00    -1.298940   \n",
       "cases/1M                           -4.725535e-01 -8.400456e-01     1.732399   \n",
       "Deaths/1M                          -6.125050e-01 -6.125050e-01     1.728693   \n",
       "Age                                 5.166379e-01  5.166379e-01    -1.310718   \n",
       "Coma score                          1.200000e+01  6.000000e+00     7.000000   \n",
       "Diuresis                            1.480000e+02  4.420000e+02   149.000000   \n",
       "Platelets                           7.400000e+01  3.500000e+01   124.000000   \n",
       "HBB                                 1.700000e+02  1.660000e+02   156.000000   \n",
       "d-dimer                             3.310000e+02  2.750000e+02   213.000000   \n",
       "Heart rate                          8.700000e+01  5.900000e+01    59.000000   \n",
       "HDL cholesterol                     5.800000e+01  4.200000e+01    49.000000   \n",
       "Charlson Index                      1.667166e-01 -2.560130e-01     1.181267   \n",
       "Blood Glucose                      -7.251723e-01 -7.251723e-01     0.703701   \n",
       "Insurance                           2.700000e+06  4.900000e+06     0.000000   \n",
       "salary                             -1.159896e+00 -1.159896e+00     1.308971   \n",
       "FT/month                            1.000000e+00  1.000000e+00     1.000000   \n",
       "Gender_Female                       0.000000e+00  1.000000e+00     1.000000   \n",
       "Gender_Male                         1.000000e+00  0.000000e+00     0.000000   \n",
       "Married_NO                          0.000000e+00  0.000000e+00     1.000000   \n",
       "Married_YES                         1.000000e+00  1.000000e+00     0.000000   \n",
       "Occupation_Business                 0.000000e+00  1.000000e+00     0.000000   \n",
       "Occupation_Cleaner                  0.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Clerk                    0.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Driver                   0.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Farmer                   1.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Legal                    0.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Manufacturing            0.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Researcher               0.000000e+00  0.000000e+00     0.000000   \n",
       "Occupation_Sales                    0.000000e+00  0.000000e+00     0.000000   \n",
       "Mode_transport_Car                  0.000000e+00  0.000000e+00     1.000000   \n",
       "Mode_transport_Public               1.000000e+00  1.000000e+00     0.000000   \n",
       "Mode_transport_Walk                 0.000000e+00  0.000000e+00     0.000000   \n",
       "comorbidity_Coronary Heart Disease  1.000000e+00  0.000000e+00     1.000000   \n",
       "comorbidity_Diabetes                0.000000e+00  0.000000e+00     0.000000   \n",
       "comorbidity_Hypertension            0.000000e+00  1.000000e+00     0.000000   \n",
       "comorbidity_None                    0.000000e+00  0.000000e+00     0.000000   \n",
       "Pulmonary score_<100                0.000000e+00  0.000000e+00     0.000000   \n",
       "Pulmonary score_<200                0.000000e+00  1.000000e+00     1.000000   \n",
       "Pulmonary score_<300                1.000000e+00  0.000000e+00     0.000000   \n",
       "Pulmonary score_<400                0.000000e+00  0.000000e+00     0.000000   \n",
       "cardiological pressure_Elevated     1.000000e+00  0.000000e+00     0.000000   \n",
       "cardiological pressure_Normal       0.000000e+00  0.000000e+00     0.000000   \n",
       "cardiological pressure_Stage-01     0.000000e+00  1.000000e+00     1.000000   \n",
       "cardiological pressure_Stage-02     0.000000e+00  0.000000e+00     0.000000   \n",
       "\n",
       "                                            9335          1022           9581  \\\n",
       "people_ID                           1.827600e+04  1.023000e+03   21563.000000   \n",
       "Children                            1.145501e+00  1.145501e+00      -1.298940   \n",
       "cases/1M                            2.624306e-01 -4.725535e-01       1.732399   \n",
       "Deaths/1M                           5.580941e-01 -6.125050e-01       1.728693   \n",
       "Age                                 1.626104e+00 -4.623030e-01      -0.005464   \n",
       "Coma score                          1.100000e+01  1.000000e+01      14.000000   \n",
       "Diuresis                            0.000000e+00  1.100000e+02     312.000000   \n",
       "Platelets                           6.700000e+01  0.000000e+00      84.000000   \n",
       "HBB                                 1.280000e+02  7.000000e+01      52.000000   \n",
       "d-dimer                             2.640000e+02  0.000000e+00     253.000000   \n",
       "Heart rate                          7.400000e+01  0.000000e+00      55.000000   \n",
       "HDL cholesterol                     7.000000e+01  4.700000e+01      37.000000   \n",
       "Charlson Index                      1.434905e+00  1.181267e+00      -0.002375   \n",
       "Blood Glucose                      -1.439609e+00  7.037006e-01       1.418137   \n",
       "Insurance                           2.100000e+06  2.200000e+06  900000.000000   \n",
       "salary                             -1.489078e+00 -3.369401e-01      -0.336940   \n",
       "FT/month                            2.000000e+00  1.000000e+00       0.000000   \n",
       "Gender_Female                       0.000000e+00  1.000000e+00       0.000000   \n",
       "Gender_Male                         1.000000e+00  0.000000e+00       1.000000   \n",
       "Married_NO                          0.000000e+00  0.000000e+00       0.000000   \n",
       "Married_YES                         1.000000e+00  1.000000e+00       1.000000   \n",
       "Occupation_Business                 0.000000e+00  0.000000e+00       0.000000   \n",
       "Occupation_Cleaner                  0.000000e+00  0.000000e+00       0.000000   \n",
       "Occupation_Clerk                    1.000000e+00  0.000000e+00       0.000000   \n",
       "Occupation_Driver                   0.000000e+00  0.000000e+00       0.000000   \n",
       "Occupation_Farmer                   0.000000e+00  0.000000e+00       0.000000   \n",
       "Occupation_Legal                    0.000000e+00  0.000000e+00       1.000000   \n",
       "Occupation_Manufacturing            0.000000e+00  1.000000e+00       0.000000   \n",
       "Occupation_Researcher               0.000000e+00  0.000000e+00       0.000000   \n",
       "Occupation_Sales                    0.000000e+00  0.000000e+00       0.000000   \n",
       "Mode_transport_Car                  0.000000e+00  0.000000e+00       0.000000   \n",
       "Mode_transport_Public               1.000000e+00  0.000000e+00       1.000000   \n",
       "Mode_transport_Walk                 0.000000e+00  1.000000e+00       0.000000   \n",
       "comorbidity_Coronary Heart Disease  0.000000e+00  0.000000e+00       0.000000   \n",
       "comorbidity_Diabetes                0.000000e+00  0.000000e+00       0.000000   \n",
       "comorbidity_Hypertension            0.000000e+00  0.000000e+00       0.000000   \n",
       "comorbidity_None                    1.000000e+00  1.000000e+00       1.000000   \n",
       "Pulmonary score_<100                0.000000e+00  0.000000e+00       0.000000   \n",
       "Pulmonary score_<200                0.000000e+00  1.000000e+00       0.000000   \n",
       "Pulmonary score_<300                0.000000e+00  0.000000e+00       0.000000   \n",
       "Pulmonary score_<400                1.000000e+00  0.000000e+00       1.000000   \n",
       "cardiological pressure_Elevated     0.000000e+00  0.000000e+00       0.000000   \n",
       "cardiological pressure_Normal       1.000000e+00  0.000000e+00       1.000000   \n",
       "cardiological pressure_Stage-01     0.000000e+00  1.000000e+00       0.000000   \n",
       "cardiological pressure_Stage-02     0.000000e+00  0.000000e+00       0.000000   \n",
       "\n",
       "                                            6897  ...          3468  \\\n",
       "people_ID                           1.400400e+04  ...  3.469000e+03   \n",
       "Children                            1.145501e+00  ... -1.298940e+00   \n",
       "cases/1M                           -4.725535e-01  ... -8.400456e-01   \n",
       "Deaths/1M                          -6.125050e-01  ... -6.125050e-01   \n",
       "Age                                -9.191421e-01  ...  3.861124e-01   \n",
       "Coma score                          6.000000e+00  ...  1.000000e+01   \n",
       "Diuresis                            1.930000e+02  ...  2.600000e+02   \n",
       "Platelets                           9.500000e+01  ...  1.120000e+02   \n",
       "HBB                                 1.960000e+02  ...  8.800000e+01   \n",
       "d-dimer                             3.230000e+02  ...  2.390000e+02   \n",
       "Heart rate                          8.100000e+01  ...  1.000000e+02   \n",
       "HDL cholesterol                     4.100000e+01  ...  4.100000e+01   \n",
       "Charlson Index                     -4.251048e-01  ... -1.524202e+00   \n",
       "Blood Glucose                      -7.251723e-01  ...  1.418137e+00   \n",
       "Insurance                           2.400000e+06  ...  2.300000e+06   \n",
       "salary                              1.638153e+00  ... -7.757933e-03   \n",
       "FT/month                            2.000000e+00  ...  0.000000e+00   \n",
       "Gender_Female                       0.000000e+00  ...  1.000000e+00   \n",
       "Gender_Male                         1.000000e+00  ...  0.000000e+00   \n",
       "Married_NO                          0.000000e+00  ...  0.000000e+00   \n",
       "Married_YES                         1.000000e+00  ...  1.000000e+00   \n",
       "Occupation_Business                 0.000000e+00  ...  0.000000e+00   \n",
       "Occupation_Cleaner                  0.000000e+00  ...  0.000000e+00   \n",
       "Occupation_Clerk                    0.000000e+00  ...  0.000000e+00   \n",
       "Occupation_Driver                   0.000000e+00  ...  1.000000e+00   \n",
       "Occupation_Farmer                   0.000000e+00  ...  0.000000e+00   \n",
       "Occupation_Legal                    1.000000e+00  ...  0.000000e+00   \n",
       "Occupation_Manufacturing            0.000000e+00  ...  0.000000e+00   \n",
       "Occupation_Researcher               0.000000e+00  ...  0.000000e+00   \n",
       "Occupation_Sales                    0.000000e+00  ...  0.000000e+00   \n",
       "Mode_transport_Car                  0.000000e+00  ...  1.000000e+00   \n",
       "Mode_transport_Public               1.000000e+00  ...  0.000000e+00   \n",
       "Mode_transport_Walk                 0.000000e+00  ...  0.000000e+00   \n",
       "comorbidity_Coronary Heart Disease  0.000000e+00  ...  1.000000e+00   \n",
       "comorbidity_Diabetes                0.000000e+00  ...  0.000000e+00   \n",
       "comorbidity_Hypertension            1.000000e+00  ...  0.000000e+00   \n",
       "comorbidity_None                    0.000000e+00  ...  0.000000e+00   \n",
       "Pulmonary score_<100                0.000000e+00  ...  0.000000e+00   \n",
       "Pulmonary score_<200                1.000000e+00  ...  1.000000e+00   \n",
       "Pulmonary score_<300                0.000000e+00  ...  0.000000e+00   \n",
       "Pulmonary score_<400                0.000000e+00  ...  0.000000e+00   \n",
       "cardiological pressure_Elevated     0.000000e+00  ...  0.000000e+00   \n",
       "cardiological pressure_Normal       0.000000e+00  ...  0.000000e+00   \n",
       "cardiological pressure_Stage-01     1.000000e+00  ...  1.000000e+00   \n",
       "cardiological pressure_Stage-02     0.000000e+00  ...  0.000000e+00   \n",
       "\n",
       "                                            6744          5874          4373  \\\n",
       "people_ID                           1.385100e+04  1.298100e+04  9.020000e+03   \n",
       "Children                           -7.671922e-02 -7.671922e-02 -7.671922e-02   \n",
       "cases/1M                           -4.725535e-01 -4.725535e-01  1.732399e+00   \n",
       "Deaths/1M                          -6.125050e-01 -6.125050e-01  1.728693e+00   \n",
       "Age                                -2.665149e-01  7.776888e-01 -7.233539e-01   \n",
       "Coma score                          5.000000e+00  1.500000e+01  4.000000e+00   \n",
       "Diuresis                            2.350000e+02  3.640000e+02  2.760000e+02   \n",
       "Platelets                           1.360000e+02  8.600000e+01  3.200000e+01   \n",
       "HBB                                 3.900000e+01  8.700000e+01  1.050000e+02   \n",
       "d-dimer                             3.360000e+02  2.250000e+02  3.470000e+02   \n",
       "Heart rate                          8.600000e+01  7.100000e+01  6.300000e+01   \n",
       "HDL cholesterol                     4.600000e+01  4.400000e+01  4.700000e+01   \n",
       "Charlson Index                     -6.787425e-01  1.688543e+00  3.358084e-01   \n",
       "Blood Glucose                       7.037006e-01 -1.439609e+00  7.037006e-01   \n",
       "Insurance                           4.600000e+06  4.900000e+06  2.100000e+06   \n",
       "salary                             -3.369401e-01  3.214242e-01 -8.307134e-01   \n",
       "FT/month                            1.000000e+00  1.000000e+00  2.000000e+00   \n",
       "Gender_Female                       0.000000e+00  0.000000e+00  1.000000e+00   \n",
       "Gender_Male                         1.000000e+00  1.000000e+00  0.000000e+00   \n",
       "Married_NO                          0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "Married_YES                         1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "Occupation_Business                 0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "Occupation_Cleaner                  1.000000e+00  0.000000e+00  0.000000e+00   \n",
       "Occupation_Clerk                    0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "Occupation_Driver                   0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "Occupation_Farmer                   0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "Occupation_Legal                    0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "Occupation_Manufacturing            0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "Occupation_Researcher               0.000000e+00  0.000000e+00  1.000000e+00   \n",
       "Occupation_Sales                    0.000000e+00  1.000000e+00  0.000000e+00   \n",
       "Mode_transport_Car                  1.000000e+00  1.000000e+00  0.000000e+00   \n",
       "Mode_transport_Public               0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "Mode_transport_Walk                 0.000000e+00  0.000000e+00  1.000000e+00   \n",
       "comorbidity_Coronary Heart Disease  1.000000e+00  1.000000e+00  0.000000e+00   \n",
       "comorbidity_Diabetes                0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "comorbidity_Hypertension            0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "comorbidity_None                    0.000000e+00  0.000000e+00  1.000000e+00   \n",
       "Pulmonary score_<100                0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "Pulmonary score_<200                0.000000e+00  1.000000e+00  0.000000e+00   \n",
       "Pulmonary score_<300                0.000000e+00  0.000000e+00  1.000000e+00   \n",
       "Pulmonary score_<400                1.000000e+00  0.000000e+00  0.000000e+00   \n",
       "cardiological pressure_Elevated     0.000000e+00  0.000000e+00  1.000000e+00   \n",
       "cardiological pressure_Normal       1.000000e+00  0.000000e+00  0.000000e+00   \n",
       "cardiological pressure_Stage-01     0.000000e+00  1.000000e+00  0.000000e+00   \n",
       "cardiological pressure_Stage-02     0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "\n",
       "                                            7891          9225          4859  \\\n",
       "people_ID                           1.499800e+04  1.766700e+04  9.506000e+03   \n",
       "Children                           -7.671922e-02  1.145501e+00 -1.298940e+00   \n",
       "cases/1M                           -4.725535e-01  2.624306e-01  1.732399e+00   \n",
       "Deaths/1M                          -6.125050e-01  5.580941e-01  1.728693e+00   \n",
       "Age                                -7.886167e-01 -5.463941e-03 -3.317776e-01   \n",
       "Coma score                          7.000000e+00  1.500000e+01  7.000000e+00   \n",
       "Diuresis                            2.420000e+02  1.890000e+02  3.200000e+02   \n",
       "Platelets                           9.000000e+01  0.000000e+00  1.200000e+02   \n",
       "HBB                                 1.920000e+02  2.800000e+01  8.200000e+01   \n",
       "d-dimer                             3.290000e+02  0.000000e+00  2.060000e+02   \n",
       "Heart rate                          5.700000e+01  0.000000e+00  9.900000e+01   \n",
       "HDL cholesterol                     5.500000e+01  5.400000e+01  4.000000e+01   \n",
       "Charlson Index                      2.512625e-01 -1.608748e+00  1.434905e+00   \n",
       "Blood Glucose                      -7.251723e-01 -1.439609e+00  1.418137e+00   \n",
       "Insurance                           3.700000e+06  2.500000e+06  2.300000e+06   \n",
       "salary                             -1.324487e+00  4.860153e-01 -9.953045e-01   \n",
       "FT/month                            0.000000e+00  2.000000e+00  0.000000e+00   \n",
       "Gender_Female                       0.000000e+00  0.000000e+00  1.000000e+00   \n",
       "Gender_Male                         1.000000e+00  1.000000e+00  0.000000e+00   \n",
       "Married_NO                          0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "Married_YES                         1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "Occupation_Business                 0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "Occupation_Cleaner                  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "Occupation_Clerk                    0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "Occupation_Driver                   0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "Occupation_Farmer                   0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "Occupation_Legal                    0.000000e+00  0.000000e+00  1.000000e+00   \n",
       "Occupation_Manufacturing            1.000000e+00  1.000000e+00  0.000000e+00   \n",
       "Occupation_Researcher               0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "Occupation_Sales                    0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "Mode_transport_Car                  1.000000e+00  0.000000e+00  0.000000e+00   \n",
       "Mode_transport_Public               0.000000e+00  0.000000e+00  1.000000e+00   \n",
       "Mode_transport_Walk                 0.000000e+00  1.000000e+00  0.000000e+00   \n",
       "comorbidity_Coronary Heart Disease  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "comorbidity_Diabetes                0.000000e+00  0.000000e+00  1.000000e+00   \n",
       "comorbidity_Hypertension            0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "comorbidity_None                    1.000000e+00  1.000000e+00  0.000000e+00   \n",
       "Pulmonary score_<100                0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "Pulmonary score_<200                0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "Pulmonary score_<300                1.000000e+00  1.000000e+00  0.000000e+00   \n",
       "Pulmonary score_<400                0.000000e+00  0.000000e+00  1.000000e+00   \n",
       "cardiological pressure_Elevated     1.000000e+00  1.000000e+00  0.000000e+00   \n",
       "cardiological pressure_Normal       0.000000e+00  0.000000e+00  1.000000e+00   \n",
       "cardiological pressure_Stage-01     0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "cardiological pressure_Stage-02     0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "\n",
       "                                            3264          9845          2732  \n",
       "people_ID                           3.265000e+03  2.182700e+04  2.733000e+03  \n",
       "Children                           -7.671922e-02 -7.671922e-02 -1.298940e+00  \n",
       "cases/1M                           -8.400456e-01  1.732399e+00 -8.400456e-01  \n",
       "Deaths/1M                          -6.125050e-01  1.728693e+00 -6.125050e-01  \n",
       "Age                                 1.365053e+00  1.038740e+00 -4.623030e-01  \n",
       "Coma score                          4.000000e+00  3.000000e+00  7.000000e+00  \n",
       "Diuresis                            3.600000e+02  2.080000e+02  3.880000e+02  \n",
       "Platelets                           5.400000e+01  6.100000e+01  6.900000e+01  \n",
       "HBB                                 1.820000e+02  1.150000e+02  3.900000e+01  \n",
       "d-dimer                             2.920000e+02  2.640000e+02  2.750000e+02  \n",
       "Heart rate                          5.800000e+01  9.100000e+01  5.100000e+01  \n",
       "HDL cholesterol                     3.600000e+01  6.500000e+01  4.100000e+01  \n",
       "Charlson Index                     -3.405589e-01  1.096722e+00 -9.323802e-01  \n",
       "Blood Glucose                      -7.251723e-01 -7.251723e-01 -1.073588e-02  \n",
       "Insurance                           3.700000e+06  4.400000e+06  4.300000e+06  \n",
       "salary                             -1.159896e+00  6.506064e-01 -1.489078e+00  \n",
       "FT/month                            0.000000e+00  1.000000e+00  0.000000e+00  \n",
       "Gender_Female                       1.000000e+00  0.000000e+00  1.000000e+00  \n",
       "Gender_Male                         0.000000e+00  1.000000e+00  0.000000e+00  \n",
       "Married_NO                          0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "Married_YES                         1.000000e+00  1.000000e+00  1.000000e+00  \n",
       "Occupation_Business                 0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "Occupation_Cleaner                  0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "Occupation_Clerk                    0.000000e+00  1.000000e+00  0.000000e+00  \n",
       "Occupation_Driver                   0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "Occupation_Farmer                   0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "Occupation_Legal                    0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "Occupation_Manufacturing            0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "Occupation_Researcher               0.000000e+00  0.000000e+00  1.000000e+00  \n",
       "Occupation_Sales                    1.000000e+00  0.000000e+00  0.000000e+00  \n",
       "Mode_transport_Car                  0.000000e+00  0.000000e+00  1.000000e+00  \n",
       "Mode_transport_Public               0.000000e+00  1.000000e+00  0.000000e+00  \n",
       "Mode_transport_Walk                 1.000000e+00  0.000000e+00  0.000000e+00  \n",
       "comorbidity_Coronary Heart Disease  0.000000e+00  0.000000e+00  1.000000e+00  \n",
       "comorbidity_Diabetes                0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "comorbidity_Hypertension            1.000000e+00  1.000000e+00  0.000000e+00  \n",
       "comorbidity_None                    0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "Pulmonary score_<100                0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "Pulmonary score_<200                0.000000e+00  0.000000e+00  1.000000e+00  \n",
       "Pulmonary score_<300                0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "Pulmonary score_<400                1.000000e+00  1.000000e+00  0.000000e+00  \n",
       "cardiological pressure_Elevated     0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "cardiological pressure_Normal       1.000000e+00  1.000000e+00  0.000000e+00  \n",
       "cardiological pressure_Stage-01     0.000000e+00  0.000000e+00  1.000000e+00  \n",
       "cardiological pressure_Stage-02     0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "\n",
       "[45 rows x 9642 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>people_ID</th>\n",
       "      <th>Children</th>\n",
       "      <th>cases/1M</th>\n",
       "      <th>Deaths/1M</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14498 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       people_ID  Children  cases/1M  Deaths/1M       Age  Coma score  \\\n",
       "0           5942  1.257737 -0.405615  -0.359439  0.647031           3   \n",
       "1          18664  1.257737 -0.405615  -0.359439  0.712280          11   \n",
       "2           5603  1.257737 -0.405615  -0.359439 -0.462202           9   \n",
       "3           5649  1.257737 -0.405615  -0.359439 -0.723199          12   \n",
       "4           5099  1.257737 -0.405615  -0.359439  0.581782           6   \n",
       "...          ...       ...       ...        ...       ...         ...   \n",
       "14493      23737 -1.205908  1.099255   0.689293  0.842778           5   \n",
       "14494      11171 -1.205908  1.099255   0.689293  0.125039           4   \n",
       "14495      23243 -1.205908  1.099255   0.689293  0.255537           6   \n",
       "14496      11199 -1.205908  1.099255   0.689293 -0.527451           5   \n",
       "14497      10787 -1.205908  1.099255   0.689293 -0.984195           4   \n",
       "\n",
       "       Diuresis  Platelets  HBB  d-dimer  ...  comorbidity_Hypertension  \\\n",
       "0           388        153  196      240  ...                         0   \n",
       "1           409         95  138      241  ...                         0   \n",
       "2           440         40  166      236  ...                         0   \n",
       "3           206         78   83      211  ...                         0   \n",
       "4           229        109  207      312  ...                         0   \n",
       "...         ...        ...  ...      ...  ...                       ...   \n",
       "14493       417         16   36      346  ...                         0   \n",
       "14494       199         22   29      278  ...                         0   \n",
       "14495       161         15   28      294  ...                         0   \n",
       "14496       174         31   24      227  ...                         0   \n",
       "14497       126         16   63      282  ...                         0   \n",
       "\n",
       "       comorbidity_None  Pulmonary score_<100  Pulmonary score_<200  \\\n",
       "0                     0                     0                     1   \n",
       "1                     0                     0                     1   \n",
       "2                     0                     1                     0   \n",
       "3                     1                     0                     0   \n",
       "4                     0                     0                     1   \n",
       "...                 ...                   ...                   ...   \n",
       "14493                 0                     0                     0   \n",
       "14494                 1                     0                     0   \n",
       "14495                 1                     0                     0   \n",
       "14496                 0                     0                     1   \n",
       "14497                 0                     0                     1   \n",
       "\n",
       "       Pulmonary score_<300  Pulmonary score_<400  \\\n",
       "0                         0                     0   \n",
       "1                         0                     0   \n",
       "2                         0                     0   \n",
       "3                         1                     0   \n",
       "4                         0                     0   \n",
       "...                     ...                   ...   \n",
       "14493                     0                     1   \n",
       "14494                     1                     0   \n",
       "14495                     1                     0   \n",
       "14496                     0                     0   \n",
       "14497                     0                     0   \n",
       "\n",
       "       cardiological pressure_Elevated  cardiological pressure_Normal  \\\n",
       "0                                    0                              0   \n",
       "1                                    0                              0   \n",
       "2                                    0                              0   \n",
       "3                                    1                              0   \n",
       "4                                    0                              0   \n",
       "...                                ...                            ...   \n",
       "14493                                0                              1   \n",
       "14494                                1                              0   \n",
       "14495                                1                              0   \n",
       "14496                                0                              0   \n",
       "14497                                0                              0   \n",
       "\n",
       "       cardiological pressure_Stage-01  cardiological pressure_Stage-02  \n",
       "0                                    1                                0  \n",
       "1                                    1                                0  \n",
       "2                                    0                                1  \n",
       "3                                    0                                0  \n",
       "4                                    1                                0  \n",
       "...                                ...                              ...  \n",
       "14493                                0                                0  \n",
       "14494                                0                                0  \n",
       "14495                                0                                0  \n",
       "14496                                1                                0  \n",
       "14497                                1                                0  \n",
       "\n",
       "[14498 rows x 45 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "</div>"
      ],
      "text/plain": [
       "       people_ID  Predicted %\n",
       "0           5942    50.428785\n",
       "1          18664    50.511225\n",
       "2           5603    50.265391\n",
       "3           5649    50.185280\n",
       "4           5099    50.307205\n",
       "...          ...          ...\n",
       "14493      23737    45.378146\n",
       "14494      11171    45.505668\n",
       "14495      23243    45.391811\n",
       "14496      11199    45.197001\n",
       "14497      10787    45.283673\n",
       "\n",
       "[14498 rows x 2 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PREDICTIONS = df = pd.DataFrame({'people_ID': test_data_X['people_ID'], 'Predicted %': final_predictions})\n",
    "PREDICTIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "PREDICTIONS.to_csv(\"prediction20thMarch\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FInal Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>people_ID</th>\n",
       "      <th>Predicted %</th>\n",
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       "</table>\n",
       "<p>14498 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       people_ID  Predicted %\n",
       "0           5942    50.428785\n",
       "1          18664    50.511225\n",
       "2           5603    50.265391\n",
       "3           5649    50.185280\n",
       "4           5099    50.307205\n",
       "...          ...          ...\n",
       "14493      23737    45.378146\n",
       "14494      11171    45.505668\n",
       "14495      23243    45.391811\n",
       "14496      11199    45.197001\n",
       "14497      10787    45.283673\n",
       "\n",
       "[14498 rows x 2 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = pd.read_csv(\"prediction20thMarch\")\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "warning: LF will be replaced by CRLF in Covid-19_predictor.ipynb.\n",
      "The file will have its original line endings in your working directory\n",
      "warning: LF will be replaced by CRLF in .ipynb_checkpoints/Covid-19_predictor-checkpoint.ipynb.\n",
      "The file will have its original line endings in your working directory\n"
     ]
    }
   ],
   "source": [
    "!git add *\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[master 072b276] task 1 done using RandomForest\n",
      " 3 files changed, 17176 insertions(+), 371 deletions(-)\n",
      " create mode 100644 .ipynb_checkpoints/Covid-19_predictor-checkpoint.ipynb\n",
      " create mode 100644 prediction20thMarch\n"
     ]
    }
   ],
   "source": [
    "!git commit -m \"task 1 done using RandomForest\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "To https://github.com/shubham2637/COVID-19-predictions.git\n",
      "   fb91988..072b276  master -> master\n"
     ]
    }
   ],
   "source": [
    "!git push origin master"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
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