BSIS Practicum Final Code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function seaborn.rcmod.set(*args, **kwargs)>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "# setting parameters for graphing format\n",
    "plt.rc('figure', figsize=(7,4))\n",
    "sns.set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data cleaning: passsing in dataset containg average food group intakes in kcal and dropping rows with null values\n",
    "#df_new to still have copy of non-manipulated datafrane\n",
    "df_kcal = pd.read_csv('Food_Supply_kcal_Data.csv', na_values = ['nan']).dropna()\n",
    "df_new =  pd.read_csv('Food_Supply_kcal_Data.csv', na_values = ['nan']).dropna()\n",
    "df_kg = pd.read_csv('Food_Supply_Quantity_kg_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Alcoholic Beverages</th>\n",
       "      <th>Animal Products</th>\n",
       "      <th>Animal fats</th>\n",
       "      <th>Aquatic Products, Other</th>\n",
       "      <th>Cereals - Excluding Beer</th>\n",
       "      <th>Eggs</th>\n",
       "      <th>Fish, Seafood</th>\n",
       "      <th>Fruits - Excluding Wine</th>\n",
       "      <th>Meat</th>\n",
       "      <th>...</th>\n",
       "      <th>Vegetable Oils</th>\n",
       "      <th>Vegetables</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>Undernourished</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>Population</th>\n",
       "      <th>Unit (all except Population)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>4.7774</td>\n",
       "      <td>0.8504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.1186</td>\n",
       "      <td>0.1501</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.4757</td>\n",
       "      <td>1.2006</td>\n",
       "      <td>...</td>\n",
       "      <td>2.3012</td>\n",
       "      <td>0.7504</td>\n",
       "      <td>4.5</td>\n",
       "      <td>29.8</td>\n",
       "      <td>0.142134</td>\n",
       "      <td>0.006186</td>\n",
       "      <td>0.123374</td>\n",
       "      <td>0.012574</td>\n",
       "      <td>38928000.0</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>0.9120</td>\n",
       "      <td>16.0930</td>\n",
       "      <td>1.0591</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.2107</td>\n",
       "      <td>0.8091</td>\n",
       "      <td>0.1471</td>\n",
       "      <td>3.8982</td>\n",
       "      <td>3.8688</td>\n",
       "      <td>...</td>\n",
       "      <td>2.8244</td>\n",
       "      <td>2.7508</td>\n",
       "      <td>22.3</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.967301</td>\n",
       "      <td>0.050951</td>\n",
       "      <td>1.792636</td>\n",
       "      <td>1.123714</td>\n",
       "      <td>2838000.0</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>0.0896</td>\n",
       "      <td>6.0326</td>\n",
       "      <td>0.1941</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0112</td>\n",
       "      <td>0.4181</td>\n",
       "      <td>0.1195</td>\n",
       "      <td>3.1805</td>\n",
       "      <td>1.2543</td>\n",
       "      <td>...</td>\n",
       "      <td>5.7638</td>\n",
       "      <td>2.0457</td>\n",
       "      <td>26.6</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.244897</td>\n",
       "      <td>0.006558</td>\n",
       "      <td>0.167572</td>\n",
       "      <td>0.070767</td>\n",
       "      <td>44357000.0</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1.9388</td>\n",
       "      <td>4.6927</td>\n",
       "      <td>0.2644</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.3521</td>\n",
       "      <td>0.0441</td>\n",
       "      <td>0.8372</td>\n",
       "      <td>2.3133</td>\n",
       "      <td>2.9302</td>\n",
       "      <td>...</td>\n",
       "      <td>4.2741</td>\n",
       "      <td>0.3525</td>\n",
       "      <td>6.8</td>\n",
       "      <td>25</td>\n",
       "      <td>0.061687</td>\n",
       "      <td>0.001461</td>\n",
       "      <td>0.056808</td>\n",
       "      <td>0.003419</td>\n",
       "      <td>32522000.0</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1.4354</td>\n",
       "      <td>14.9869</td>\n",
       "      <td>1.0650</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.7927</td>\n",
       "      <td>0.8643</td>\n",
       "      <td>0.2006</td>\n",
       "      <td>1.4663</td>\n",
       "      <td>9.4459</td>\n",
       "      <td>...</td>\n",
       "      <td>5.5410</td>\n",
       "      <td>0.8643</td>\n",
       "      <td>28.5</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.356147</td>\n",
       "      <td>0.108227</td>\n",
       "      <td>3.905192</td>\n",
       "      <td>0.342729</td>\n",
       "      <td>45377000.0</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Alcoholic Beverages  Animal Products  Animal fats  \\\n",
       "0  Afghanistan               0.0000           4.7774       0.8504   \n",
       "1      Albania               0.9120          16.0930       1.0591   \n",
       "2      Algeria               0.0896           6.0326       0.1941   \n",
       "3       Angola               1.9388           4.6927       0.2644   \n",
       "5    Argentina               1.4354          14.9869       1.0650   \n",
       "\n",
       "   Aquatic Products, Other  Cereals - Excluding Beer    Eggs  Fish, Seafood  \\\n",
       "0                      0.0                   37.1186  0.1501         0.0000   \n",
       "1                      0.0                   16.2107  0.8091         0.1471   \n",
       "2                      0.0                   25.0112  0.4181         0.1195   \n",
       "3                      0.0                   18.3521  0.0441         0.8372   \n",
       "5                      0.0                   16.7927  0.8643         0.2006   \n",
       "\n",
       "   Fruits - Excluding Wine    Meat  ...  Vegetable Oils  Vegetables  Obesity  \\\n",
       "0                   1.4757  1.2006  ...          2.3012      0.7504      4.5   \n",
       "1                   3.8982  3.8688  ...          2.8244      2.7508     22.3   \n",
       "2                   3.1805  1.2543  ...          5.7638      2.0457     26.6   \n",
       "3                   2.3133  2.9302  ...          4.2741      0.3525      6.8   \n",
       "5                   1.4663  9.4459  ...          5.5410      0.8643     28.5   \n",
       "\n",
       "   Undernourished  Confirmed    Deaths  Recovered    Active  Population  \\\n",
       "0            29.8   0.142134  0.006186   0.123374  0.012574  38928000.0   \n",
       "1             6.2   2.967301  0.050951   1.792636  1.123714   2838000.0   \n",
       "2             3.9   0.244897  0.006558   0.167572  0.070767  44357000.0   \n",
       "3              25   0.061687  0.001461   0.056808  0.003419  32522000.0   \n",
       "5             4.6   4.356147  0.108227   3.905192  0.342729  45377000.0   \n",
       "\n",
       "   Unit (all except Population)  \n",
       "0                             %  \n",
       "1                             %  \n",
       "2                             %  \n",
       "3                             %  \n",
       "5                             %  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See first 5 of df_kcal\n",
    "df_kcal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(154, 32)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kcal.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Updating Covid Rates of Dataset Using \"worldwide covid data.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Afghanistan' 'Albania' 'Algeria' 'Andorra' 'Angola' 'Anguilla'\n",
      " 'Antigua and Barbuda' 'Argentina' 'Armenia' 'Aruba' 'Australia' 'Austria'\n",
      " 'Azerbaijan' 'Bahamas' 'Bahrain' 'Bangladesh' 'Barbados' 'Belarus'\n",
      " 'Belgium' 'Belize' 'Benin' 'Bermuda' 'Bhutan' 'Bolivia'\n",
      " 'Bosnia and Herzegovina' 'Botswana' 'Brazil' 'Brunei' 'Bulgaria'\n",
      " 'Burkina Faso' 'Burundi' 'Cabo Verde' 'Cambodia' 'Cameroon' 'Canada'\n",
      " 'CAR' 'Caribbean Netherlands' 'Cayman Islands' 'Chad' 'Channel Islands'\n",
      " 'Chile' 'China' 'Colombia' 'Congo' 'Costa Rica' 'Croatia' 'Cuba'\n",
      " 'Curaçao' 'Cyprus' 'Czechia' 'Denmark' 'Djibouti' 'Dominica'\n",
      " 'Dominican Republic' 'DRC' 'Ecuador' 'Egypt' 'El Salvador'\n",
      " 'Equatorial Guinea' 'Eritrea' 'Estonia' 'Eswatini' 'Ethiopia'\n",
      " 'Faeroe Islands' 'Fiji' 'Finland' 'France' 'French Guiana' 'Gabon'\n",
      " 'Gambia' 'Georgia' 'Germany' 'Ghana' 'Gibraltar' 'Greece' 'Grenada'\n",
      " 'Guadeloupe' 'Guatemala' 'Guinea' 'Guinea-Bissau' 'Guyana' 'Haiti'\n",
      " 'Honduras' 'Hong Kong' 'Hungary' 'Iceland' 'India' 'Indonesia' 'Iran'\n",
      " 'Iraq' 'Ireland' 'Isle of Man' 'Israel' 'Italy' 'Ivory Coast' 'Jamaica'\n",
      " 'Japan' 'Jordan' 'Kazakhstan' 'Kenya' 'Kuwait' 'Kyrgyzstan' 'Laos'\n",
      " 'Latvia' 'Lebanon' 'Lesotho' 'Liberia' 'Libya' 'Liechtenstein'\n",
      " 'Lithuania' 'Luxembourg' 'Madagascar' 'Malawi' 'Malaysia' 'Maldives'\n",
      " 'Mali' 'Malta' 'Martinique' 'Mauritania' 'Mauritius' 'Mayotte' 'Mexico'\n",
      " 'Moldova' 'Monaco' 'Mongolia' 'Montenegro' 'Morocco' 'Mozambique'\n",
      " 'Myanmar' 'Namibia' 'Nepal' 'Netherlands' 'New Caledonia' 'New Zealand'\n",
      " 'Niger' 'Nigeria' 'North Macedonia' 'Norway' 'Oman' 'Pakistan'\n",
      " 'Palestine' 'Panama' 'Papua New Guinea' 'Paraguay' 'Philippines' 'Poland'\n",
      " 'Portugal' 'Qatar' 'Réunion' 'Romania' 'Russia' 'Rwanda' 'S. Korea'\n",
      " 'Saint Kitts and Nevis' 'Saint Lucia' 'Saint Martin' 'San Marino'\n",
      " 'Sao Tome and Principe' 'Saudi Arabia' 'Senegal' 'Serbia' 'Seychelles'\n",
      " 'Singapore' 'Sint Maarten' 'Slovakia' 'Slovenia' 'Somalia' 'South Africa'\n",
      " 'South Sudan' 'Spain' 'Sri Lanka' 'St. Barth' 'St. Vincent Grenadines'\n",
      " 'Sudan' 'Suriname' 'Sweden' 'Switzerland' 'Syria' 'Taiwan' 'Thailand'\n",
      " 'Timor-Leste' 'Togo' 'Trinidad and Tobago' 'Tunisia' 'Turkey'\n",
      " 'Turks and Caicos' 'UAE' 'Uganda' 'UK' 'Ukraine' 'Uruguay' 'USA'\n",
      " 'Uzbekistan' 'Venezuela' 'Vietnam' 'Yemen' 'Zambia' 'Zimbabwe']\n",
      "[  157015   196611   209111    16299    65033     1268     4136  5315989\n",
      "   334878    16210   200651  1079932   574799    22665   277421  1574636\n",
      "    23944   643372  1623560    29682    24846     5724     2633   530369\n",
      "   269904   194652 22019870    14701   676750    15514    20349    38331\n",
      "   119943   106190  1770106    11666     2714     6091     5105    16279\n",
      "  1747065    98524  5050255    18717   565952   576633   961042    17335\n",
      "   130591  2018069   458001    13501     5642   403438    57977   524353\n",
      "   351267   118041    13538     7188   218604    46491   370332     3209\n",
      "    52468   178527  7420237    45675    37158     9988   819182  5439263\n",
      "   130827     6996   894555     5876    55147   613950    30747     6434\n",
      "    37356    24950   377420    12411  1032215    17002 34526480  4253992\n",
      "  6088009  2076146   534594    10650  1341305  4932091    61591    90807\n",
      "  1726527   919173   965518   254781   413165   182953    64482   247758\n",
      "   659404    21731     5819   368987     4242   458298    86637    43672\n",
      "    61865  2591486    90821    16963    38842    44318    38699    18979\n",
      "    20860  3864278   358857     3567   378199   155011   948976   151492\n",
      "   518471   129105   819699  2488244    11954    10393     6851   213625\n",
      "   212375   245302   304509  1282510   428342   476129    34605   462331\n",
      "  2827820  3377698  1126318   242375    57173  1766987  9400835   100232\n",
      "   420950     2767    12909     3949     5790     3731   549556    73970\n",
      "  1238861    22831   253649     4561   621423   405477    22969  2930174\n",
      "    12680  5096538   558662     1595     5414    41822    50531  1192560\n",
      "   957764    47331    16539  2076135    19820    26210    66293   716298\n",
      "  8596410     3085   741570   127230  9932408  3353136   397990 48754985\n",
      "   191750   426014  1104835     9967   210073   133674]\n",
      "[  7305   3049   6026    130   1730      3    117 116382   7356    173\n",
      "   1968  12114   7657    671   1394  27958    211   4979  26669    564\n",
      "    161    106  19080  12309   2416 612842     96  27507    265     38\n",
      "    350   2909   1770  29526    101     21      4    175    100  38161\n",
      "   4636 128138    339   7264  10438   8295    177    591  32304   2820\n",
      "    186     35   4190   1104  33125  19991   3756    170     55   1754\n",
      "   1248   6693     12    695   1263 118555    323    274    342  11615\n",
      "  99954   1208     98  17517    200    746  15849    387    146    977\n",
      "    719  10388    213  33343     34 466147 143753 129177  23712   5609\n",
      "     66   8177 133247    703   2359  18349  11377  12577   5330   2464\n",
      "   2729    137   4007   8645    662    287   5403     61   6578    862\n",
      "    964   2304  30063    248    594    464    700    821    240    185\n",
      " 292524   8869     36   1958   2253  14764   1938  19034   3569  11509\n",
      "  19048    276     40    246   2974   7449   1002   4113  28668   4513\n",
      "   7354    486  16363  47482  81228  18353    611    379  55386 266579\n",
      "   1341   3328     28    279     56     93   8827   1885  11335    125\n",
      "    667     75  13985   5103   1324  89584    133  87832  14182      6\n",
      "     73   3103   1152  15094  11427   2709    848  20487    122    243\n",
      "   1977  25347  75235     23   2144   3250 144137  82318   6115 794965\n",
      "   1382   5088  23951   1942   3667   4699]\n",
      "[1.4032600e+05 1.8556900e+05 1.4341300e+05 1.5646000e+04 6.3123000e+04\n",
      " 1.1560000e+03 3.9920000e+03 5.1828210e+06 3.0892200e+05 1.5868000e+04\n",
      " 1.8523600e+05 9.1836600e+05 5.3949400e+05 2.1560000e+04 2.7577700e+05\n",
      " 1.5388550e+06 1.5487000e+04 6.2778900e+05 1.2663610e+06 2.7773000e+04\n",
      " 2.4546000e+04 5.5930000e+03 2.6200000e+03 4.8782400e+05 1.9221800e+05\n",
      " 1.9152900e+05 2.1230357e+07 1.4162000e+04 5.4272700e+05 1.5009000e+04\n",
      " 7.7300000e+02 3.7920000e+04 1.1632000e+05 1.0271600e+05 1.7164530e+06\n",
      " 6.8590000e+03 2.5470000e+03 2.0400000e+03 4.8740000e+03 1.4347000e+04\n",
      " 1.6436790e+06 9.2882000e+04 4.8909900e+06 1.2421000e+04 5.4047800e+05\n",
      " 5.3565400e+05 9.5132400e+05 1.7039000e+04 1.2411900e+05 1.7766030e+06\n",
      " 4.0311800e+05 1.3288000e+04 5.1990000e+03 3.9489100e+05 5.0930000e+04\n",
      " 4.4388000e+05 2.9265500e+05 1.0109200e+05 1.3274000e+04 6.9650000e+03\n",
      " 1.9360100e+05 4.5218000e+04 3.4694200e+05 2.8250000e+03 5.0855000e+04\n",
      " 4.6000000e+04 7.0622770e+06 1.1254000e+04 3.1282000e+04 9.6360000e+03\n",
      " 7.5965700e+05 4.6800000e+06 1.2888500e+05 6.2300000e+03 7.9764300e+05\n",
      " 5.6190000e+03 2.2500000e+03 5.9619000e+05 2.9708000e+04 6.1810000e+03\n",
      " 3.4566000e+04 2.0879000e+04 1.1934600e+05 1.2121000e+04 8.5131400e+05\n",
      " 1.5233000e+04 3.3946749e+07 4.1023230e+06 5.8070890e+06 2.0356300e+06\n",
      " 4.2412900e+05 1.0174000e+04 1.3270460e+06 4.6473300e+06 6.0574000e+04\n",
      " 6.1836000e+04 1.7069970e+06 8.6698900e+05 9.2761800e+05 2.4799200e+05\n",
      " 4.1045000e+05 1.7759900e+05 7.3390000e+03 2.2873200e+05 6.2751200e+05\n",
      " 1.3601000e+04 5.5230000e+03 3.3289200e+05 3.8070000e+03 4.2278500e+05\n",
      " 8.2443000e+04 4.1322000e+04 5.8755000e+04 2.4934370e+06 8.8576000e+04\n",
      " 1.5008000e+04 3.7078000e+04 1.0400000e+02 3.7021000e+04 1.8540000e+03\n",
      " 2.9640000e+03 3.2270270e+06 3.4104300e+05 3.4550000e+03 3.1325600e+05\n",
      " 1.4942700e+05 9.3114300e+05 1.4946000e+05 4.9178100e+05 1.2545200e+05\n",
      " 8.0089000e+05 2.0635680e+06 5.5000000e+01 5.5250000e+03 6.4460000e+03\n",
      " 2.0677800e+05 1.9663600e+05 8.8952000e+04 2.9992800e+05 1.2389800e+06\n",
      " 4.2124900e+05 4.6632000e+05 3.2938000e+04 4.4537800e+05 2.7616170e+06\n",
      " 2.9133200e+06 1.0621950e+06 2.3988800e+05 5.4801000e+04 1.6531790e+06\n",
      " 8.0896940e+06 4.5521000e+04 3.7856100e+05 2.7090000e+03 1.2488000e+04\n",
      " 1.3990000e+03 5.5190000e+03 3.6740000e+03 5.3867200e+05 7.2078000e+04\n",
      " 1.1630520e+06 2.2359000e+04 2.3485600e+05 4.4600000e+03 5.1268900e+05\n",
      " 3.5553900e+05 1.1714000e+04 2.8214810e+06 1.2169000e+04 4.9073440e+06\n",
      " 5.2752800e+05 4.6200000e+02 4.8780000e+03 3.2905000e+04 2.9556000e+04\n",
      " 1.1526090e+06 8.5041300e+05 2.8424000e+04 1.5580000e+04 1.9725530e+06\n",
      " 1.9691000e+04 2.5856000e+04 5.5621000e+04 6.9003300e+05 8.1208820e+06\n",
      " 2.9970000e+03 7.3633300e+05 9.7307000e+04 8.7913150e+06 2.7965970e+06\n",
      " 3.8994100e+05 3.8634642e+07 1.8830100e+05 4.1244000e+05 9.1027600e+05\n",
      " 6.7810000e+03 2.0631600e+05 1.2846500e+05]\n",
      "[9.384000e+03 7.993000e+03 5.967200e+04 5.230000e+02 1.800000e+02\n",
      " 1.090000e+02 2.700000e+01 1.678600e+04 1.860000e+04 1.690000e+02\n",
      " 1.344700e+04 1.494520e+05 2.764800e+04 4.340000e+02 2.500000e+02\n",
      " 7.823000e+03 8.246000e+03 1.060400e+04 3.305300e+05 1.345000e+03\n",
      " 1.390000e+02 2.500000e+01 1.000000e+01 2.346500e+04 6.537700e+04\n",
      " 7.070000e+02 1.766710e+05 4.430000e+02 1.065160e+05 2.400000e+02\n",
      " 1.953800e+04 6.100000e+01 7.140000e+02 1.704000e+03 2.412700e+04\n",
      " 4.706000e+03 1.460000e+02 4.047000e+03 5.600000e+01 1.832000e+03\n",
      " 6.522500e+04 1.006000e+03 3.112700e+04 5.957000e+03 1.821000e+04\n",
      " 3.054100e+04 1.423000e+03 1.190000e+02 5.881000e+03 2.091620e+05\n",
      " 5.206300e+04 4.080000e+02 4.357000e+03 5.943000e+03 4.734800e+04\n",
      " 3.862100e+04 1.319300e+04 9.400000e+01 1.680000e+02 2.324900e+04\n",
      " 1.669700e+04 3.720000e+02 9.180000e+02 1.312640e+05 2.394050e+05\n",
      " 3.409800e+04 5.602000e+03 4.791000e+04 6.593090e+05 7.340000e+02\n",
      " 6.680000e+02 7.939500e+04 5.700000e+01 5.215100e+04 1.911000e+03\n",
      " 6.520000e+02 1.070000e+02 1.813000e+03 3.352000e+03 2.476860e+05\n",
      " 7.700000e+01 1.475580e+05 1.735000e+03 1.135840e+05 7.916000e+03\n",
      " 1.517430e+05 1.680400e+04 1.048560e+05 4.100000e+02 6.082000e+03\n",
      " 1.515140e+05 3.140000e+02 2.661200e+04 1.181000e+03 4.080700e+04\n",
      " 2.532300e+04 1.459000e+03 2.510000e+02 2.625000e+03 5.700600e+04\n",
      " 1.501900e+04 2.324700e+04 7.468000e+03 9.000000e+00 3.069200e+04\n",
      " 3.740000e+02 2.893500e+04 3.332000e+03 1.386000e+03 8.060000e+02\n",
      " 6.798600e+04 1.997000e+03 1.361000e+03 1.300000e+03 4.351400e+04\n",
      " 8.570000e+02 1.688500e+04 1.771100e+04 3.447270e+05 8.945000e+03\n",
      " 7.600000e+01 6.298500e+04 3.331000e+03 3.069000e+03 7.656000e+03\n",
      " 8.400000e+01 7.300000e+03 4.056280e+05 1.162300e+04 4.828000e+03\n",
      " 1.590000e+02 3.873000e+03 8.290000e+03 1.553480e+05 4.680000e+02\n",
      " 1.486200e+04 2.580000e+03 2.455000e+03 5.900000e+02 1.872100e+04\n",
      " 3.831500e+05 4.577000e+04 1.876000e+03 1.993000e+03 5.842200e+04\n",
      " 1.044562e+06 5.337000e+04 3.906100e+04 3.000000e+01 1.420000e+02\n",
      " 2.494000e+03 1.780000e+02 1.000000e+00 2.057000e+03 7.000000e+00\n",
      " 6.447400e+04 3.470000e+02 1.812600e+04 2.600000e+01 9.474900e+04\n",
      " 4.483500e+04 9.931000e+03 1.910900e+04 3.780000e+02 1.013620e+05\n",
      " 1.695200e+04 1.127000e+03 4.630000e+02 5.814000e+03 1.982300e+04\n",
      " 2.485700e+04 9.592400e+04 1.619800e+04 1.110000e+02 8.309500e+04\n",
      " 8.695000e+03 4.002930e+05 6.500000e+01 3.093000e+03 2.667300e+04\n",
      " 9.969560e+05 4.742210e+05 1.934000e+03 9.325378e+06 2.067000e+03\n",
      " 8.486000e+03 1.706080e+05 1.244000e+03 9.000000e+01 5.100000e+02]\n",
      "[  3911  68424   4652 210481   1897  83481  41754 116140 112718 150935\n",
      "   7744 118963  55989  56880 155545   9431  83178  68118 139245  72812\n",
      "   1978  92385   3361  44590  82986  80516 102580  33162  98427    715\n",
      "   1643  67917   7039   3867  46335   2358 102176  91189    299  92447\n",
      "  90314     68  97804   3280 109706 141678  84920 105044 107079 187961\n",
      "  78687  13394  78106  36679    621  29106   3345  18072   9229   1989\n",
      " 164639  39504   3114  65328  57945  32151 113330 147613  16165   3978\n",
      " 205902  64633   4092 207738  86412  51886 137792  33411   2256   3165\n",
      "  47177   2152  37282   1637 107230  49378  24682  15327  71216  50057\n",
      " 106611 124330 143824  81741   2257  30481  13709  88861  50580   4598\n",
      "  94799  27414   8688 133406  97216  10033   1114  52704 110815 171685\n",
      " 135344   1523   3122  78667 164006    805  87644 118225   8030  14889\n",
      "  73985  29540  89248  90010 112840 246763  25293   4666   9441  49564\n",
      "  27447 144770  41319   2078    270   1002 101944  44762  57598   5654\n",
      "  81341 107987   3769  63732  25337  89383 110909  86322  63231  92711\n",
      "  64380   7481   8201  51506  69856  99768 170144  16596  15451   4262\n",
      " 142588 230165  42887 104723 113745 195003   1391  48555   1115 108947\n",
      "  25938 160803  48587    924  85078 117069 109551   2616    693  29641\n",
      "  14646   3066  47157  59739 100425  78210  73741   2665 145249  77318\n",
      " 114022 146103   5616  15041  11210    324  11000   8815]\n",
      "[ 182 1061  134 1679   50  198 1181 2543 2476 1611   76 1334  746 1684\n",
      "  782  167  733  527 2287 1384   13 1711    4 1604 3785  999 2855  217\n",
      " 4001   12    3  620  171   64  773   20  791   60   10  568 1973 2482\n",
      "   59 1408 2565 1073  485 3009  484  185  381 1839  190  575  116   15\n",
      " 1321 1060   56  244  768  227 1811 1044  119  136 2919 1188   38 2910\n",
      " 1692 1766 1864  862   28   72 1234   62 1026 3464   99  333  518 1511\n",
      "  572 1119  770  877 2208   26  792  146 1100  659   96  565  409   18\n",
      " 2158 1275  306   55  772 1594 2464 1347   34  913  448 1047 1867  170\n",
      "  188  656 2236 2206  908  584 3587  394  347 1370  385 1108  954    8\n",
      "   14 3576  183  778  126  857 1668   53 2256  425 2150 1807  218  419\n",
      " 2906 1826  100   65  521 1510 1415 2733  249  248  109 1305 1260  113\n",
      " 1722 2560 2454   80 1484 1878  658  605  655   69 1940 1482 1307  150\n",
      "   36  292   90 1406 2114  879  583  213   68 2108 1898 1752 2382   40\n",
      "  180  243   63  192  310]\n",
      "[7.86248000e+05 1.37236000e+06 2.30861000e+05 1.93595000e+05\n",
      " 1.16618400e+06 5.13820000e+04 1.77820000e+04 2.59183910e+07\n",
      " 2.31855200e+06 1.77885000e+05 4.68296340e+07 1.07616106e+08\n",
      " 5.44937900e+06 1.65244000e+05 7.27022100e+06 1.07619790e+07\n",
      " 4.26098000e+05 1.02289360e+07 2.37820720e+07 3.38644000e+05\n",
      " 6.04310000e+05 6.35146000e+05 1.23177500e+06 2.56945100e+06\n",
      " 1.37149000e+06 1.85578200e+06 6.37761660e+07 5.92626000e+05\n",
      " 6.33927200e+06 2.26312000e+05 3.45742000e+05 2.11166000e+05\n",
      " 2.69650200e+06 1.75177400e+06 4.81485270e+07 6.02280000e+04\n",
      " 2.81700000e+04 1.39740000e+05 1.48082000e+05 1.05436700e+06\n",
      " 2.51260710e+07 1.60000000e+08 2.77446140e+07 1.88207000e+05\n",
      " 2.67462500e+06 3.36696500e+06 1.09474240e+07 3.25919000e+05\n",
      " 9.47713800e+06 4.28463890e+07 9.14115050e+07 2.44180000e+05\n",
      " 1.04184000e+05 2.40614500e+06 3.06299000e+05 1.99085000e+06\n",
      " 3.69336700e+06 1.40821400e+06 2.42340000e+05 2.36930000e+04\n",
      " 2.29650600e+06 3.82871000e+05 3.82302500e+06 5.45000000e+05\n",
      " 4.57742000e+05 7.80381000e+06 1.61863693e+08 4.44733000e+05\n",
      " 1.38725500e+06 1.21854000e+05 1.12650850e+07 8.19755290e+07\n",
      " 1.93340800e+06 4.08786000e+05 3.37812340e+07 8.64410000e+04\n",
      " 5.51264000e+05 3.06488800e+06 5.67389000e+05 1.05463000e+05\n",
      " 3.81997000e+05 1.32422000e+05 1.11365300e+06 2.71241600e+07\n",
      " 8.15787500e+06 1.22696700e+06 6.33489239e+08 5.25310790e+07\n",
      " 3.79969040e+07 1.62152010e+07 8.61474000e+06 1.33676000e+05\n",
      " 3.11880240e+07 1.14767310e+08 1.13110500e+06 6.64932000e+05\n",
      " 2.75519350e+07 1.17935620e+07 1.15750120e+07 2.80701400e+06\n",
      " 5.20048800e+06 1.81278400e+06 7.48091000e+05 5.26812800e+06\n",
      " 4.79557800e+06 2.54031000e+05 1.28246000e+05 1.88145800e+06\n",
      " 7.73680000e+04 6.01211200e+06 3.65770700e+06 2.49510000e+05\n",
      " 4.37392000e+05 3.67974820e+07 1.71944400e+06 4.79414000e+05\n",
      " 1.21145600e+06 4.16247000e+05 4.92666000e+05 3.58675000e+05\n",
      " 1.76919000e+05 1.17094810e+07 2.16460200e+06 5.49600000e+04\n",
      " 4.03004800e+06 8.89103000e+05 1.03431000e+07 9.68540000e+05\n",
      " 5.39580000e+06 7.78262000e+05 4.57696600e+06 1.92051140e+07\n",
      " 4.27560000e+04 4.71142200e+06 1.84421000e+05 3.44017200e+06\n",
      " 1.45021500e+06 8.46142300e+06 2.50000000e+07 2.17176370e+07\n",
      " 2.70410700e+06 4.18792300e+06 2.43396000e+05 1.93256100e+06\n",
      " 2.39244290e+07 2.37886240e+07 2.05305280e+07 2.95662500e+06\n",
      " 1.30360600e+06 1.57839640e+07 2.21400000e+08 3.37499700e+06\n",
      " 1.58040650e+07 5.56080000e+04 9.21250000e+04 5.43030000e+04\n",
      " 8.85120000e+04 1.46890000e+04 3.10783690e+07 8.65809000e+05\n",
      " 6.69307500e+06 2.15040000e+04 2.10987740e+07 6.20560000e+04\n",
      " 4.41416800e+06 1.89587100e+06 2.62138000e+05 1.91266990e+07\n",
      " 2.59491000e+05 6.62138580e+07 5.50080000e+06 3.83690000e+04\n",
      " 8.92970000e+04 2.38579000e+05 1.56763000e+05 1.35860950e+07\n",
      " 1.20480250e+07 1.03566000e+05 8.17627300e+06 1.49131350e+07\n",
      " 1.69501000e+05 5.72043000e+05 4.30812000e+05 3.14604300e+06\n",
      " 1.04799990e+08 9.47890000e+04 9.94020000e+07 1.92262800e+06\n",
      " 3.55369515e+08 1.55957370e+07 4.03309600e+06 7.43652091e+08\n",
      " 1.37791500e+06 3.35901400e+06 6.61254730e+07 2.65253000e+05\n",
      " 2.69130200e+06 1.56942300e+06]\n",
      "[1.9585000e+04 4.7760400e+05 5.1360000e+03 2.5000320e+06 3.4016000e+04\n",
      " 3.3828430e+06 1.7951500e+05 5.6624600e+05 7.8040800e+05 1.6563310e+06\n",
      " 1.8073200e+06 1.1854785e+07 5.3080600e+05 4.1469800e+05 4.0762820e+06\n",
      " 6.4454000e+04 1.4802060e+06 1.0830000e+06 2.0396680e+06 8.3071600e+05\n",
      " 4.8100000e+04 1.0251235e+07 1.5723090e+06 2.1602400e+05 4.2168300e+05\n",
      " 7.6762900e+05 2.9710300e+05 1.3368450e+06 9.2199000e+05 1.0430000e+04\n",
      " 2.7923000e+04 3.7415500e+05 1.5824100e+05 6.3787000e+04 1.2603590e+06\n",
      " 1.2176000e+04 1.0605380e+06 2.0920730e+06 8.6700000e+03 5.9876260e+06\n",
      " 1.2988850e+06 1.1116300e+05 5.3730400e+05 3.2981000e+04 5.1846000e+05\n",
      " 8.2725700e+05 9.6734400e+05 1.9749550e+06 7.7708210e+06 3.9906780e+06\n",
      " 1.5704898e+07 2.4225100e+05 1.4422930e+06 2.1875800e+05 3.2820000e+03\n",
      " 1.1050900e+05 3.5170000e+04 2.1559600e+05 1.6520800e+05 6.5550000e+03\n",
      " 1.7295880e+06 3.2533200e+05 3.2149000e+04 1.1095051e+07 5.0552600e+05\n",
      " 1.4054100e+06 2.4721670e+06 1.4372980e+06 6.0351600e+05 4.8531000e+04\n",
      " 2.8314820e+06 9.7408300e+05 6.0469000e+04 1.2138433e+07 3.2631930e+06\n",
      " 7.6328900e+05 1.3774060e+06 1.6678900e+05 4.1637000e+04 5.1887000e+04\n",
      " 4.8242200e+05 1.1420000e+04 1.1000700e+05 3.5776550e+06 8.4746400e+05\n",
      " 3.5634190e+06 4.5285900e+05 1.8927100e+05 4.4447600e+05 3.9096000e+05\n",
      " 1.7179830e+06 1.5605600e+06 3.3442020e+06 1.9020700e+06 4.1456000e+04\n",
      " 2.2319600e+05 2.1877400e+05 1.1401420e+06 6.0637600e+05 5.0654000e+04\n",
      " 1.1932330e+06 2.7163100e+05 1.0079000e+05 2.8366460e+06 7.0700900e+05\n",
      " 1.1728900e+05 2.4556000e+04 2.6873600e+05 2.0211080e+06 2.2522280e+06\n",
      " 5.7140510e+06 8.7000000e+03 2.2073000e+04 1.1170290e+06 3.1049950e+06\n",
      " 2.2764000e+04 2.7335590e+06 1.1104070e+06 1.0223000e+05 2.8137900e+05\n",
      " 6.2748400e+05 8.9511000e+04 5.3833500e+05 1.3868630e+06 1.2024160e+06\n",
      " 1.4153700e+06 2.7567900e+05 2.9829000e+04 9.8254000e+04 2.9877900e+05\n",
      " 1.5325700e+05 1.1173810e+06 1.4778500e+05 9.4188900e+05 7.2550000e+03\n",
      " 1.6134000e+04 6.9612900e+05 1.5440040e+06 4.7287350e+06 9.5736000e+04\n",
      " 5.1350300e+05 9.4983100e+05 2.6507000e+04 2.6640200e+05 2.1436200e+05\n",
      " 6.2951300e+05 2.0216490e+06 1.0530020e+06 1.4417290e+06 8.2816000e+05\n",
      " 1.5162150e+06 2.5190800e+05 3.0788800e+05 1.0351070e+06 4.9852800e+05\n",
      " 1.3719110e+06 2.6009990e+06 6.5339000e+04 8.7379600e+05 4.9885000e+04\n",
      " 7.7034700e+05 2.1678700e+05 3.5674080e+06 1.4248390e+06 8.0796400e+05\n",
      " 9.1176800e+05 1.5879000e+04 3.1694100e+05 2.2810000e+04 1.4154320e+06\n",
      " 2.5539800e+05 3.8682330e+06 8.0137300e+05 5.2700000e+03 2.6393800e+05\n",
      " 1.3336910e+06 1.3780780e+06 5.7250000e+03 3.4243700e+05 2.1291200e+05\n",
      " 1.2524900e+05 6.6921000e+04 3.0645500e+05 2.6237800e+05 1.2242910e+06\n",
      " 2.4030680e+06 9.8843750e+06 4.0270000e+04 5.1968320e+06 3.5961300e+05\n",
      " 1.1554580e+06 2.2284920e+06 4.0357000e+04 1.1859300e+05 6.7094200e+05\n",
      " 8.6300000e+03 1.4092000e+05 1.0350000e+05]\n",
      "[  40145225    2873425   44950745      77437   34283759      15189\n",
      "      99056   45772333    2970948     107397   25911097    9077862\n",
      "   10266229     398468    1783542  166970207     287864    9444999\n",
      "   11659774     407653   12563738      61958     783418   11894276\n",
      "    3252417    2417552  214660397     443302    6875643   21698358\n",
      "   12381877     564381   17040472   27462761   38202217    4946399\n",
      "      26562      66795   17079567     176091   19344337 1439323776\n",
      "   51636747    5706558    5158787    4070033   11316997     165026\n",
      "    1219580   10736618    5820573    1007962      72235   10999129\n",
      "   93336909   18015255  105014683    6531721    1466878    3614622\n",
      "    1327776    1176862  118915021      49121     905477    5552694\n",
      "   65474428     309423    2298620    2510837    3978512   84156579\n",
      "   31973413      33677   10352201     113248     400219   18375800\n",
      "   13626941    2032553     791831   11595710   10123442    7581547\n",
      "    9626219     344323 1398865061  277544004   85486948   41475312\n",
      "    5014450      85659    9326000   60338123   27284662    2979143\n",
      "  125937692   10343939   19088851   55415403    4358319    6673713\n",
      "    7422246    1857168    6782909    2165861    5222659    7001127\n",
      "      38280    2669406     640125   28678705   19815785   32942277\n",
      "     553767   21059783     443179     374860    4819214    1274704\n",
      "     281950  130815709    4020917      39629    3351626     628177\n",
      "   37518600   32470014   54917005    2604811   29864621   17187611\n",
      "     289313    5002100   25420762  213229123    2083255    5480181\n",
      "    5286826  226848065    5266001    4409124    9182373    7254299\n",
      "  111607800   37788938   10155338    2807805     904196   19059080\n",
      "  146021519   13397735   51330568      53722     184794      39582\n",
      "      34030     224811   35567074   17356069    8688395      99194\n",
      "    5914315      43553    5463321    2079336   16508250   60347867\n",
      "   11376123   46779973   21538123       9919     111430   45270164\n",
      "     593939   10186836    8742627   18090610   23876741   70043611\n",
      "    1353315    8548007    1405792   11990519   85600521      39445\n",
      "   10056478   47743722   68381950   43368091    3490475  333701877\n",
      "   34143146   28323827   98556168   30737797   19098078   15163536]\n",
      "[3.91117499e-01 6.84239192e+00 4.65200299e-01 2.10480778e+01\n",
      " 1.89690401e-01 8.34814669e+00 4.17541593e+00 1.16139787e+01\n",
      " 1.12717557e+01 1.50935315e+01 7.74382497e-01 1.18963254e+01\n",
      " 5.59893024e+00 5.68803517e+00 1.55544977e+01 9.43064052e-01\n",
      " 8.31781675e+00 6.81177415e+00 1.39244551e+01 7.28119258e+00\n",
      " 1.97759616e-01 9.23851641e+00 3.36091333e-01 4.45902718e+00\n",
      " 8.29856688e+00 8.05161585e+00 1.02580030e+01 3.31624942e+00\n",
      " 9.84271580e+00 7.14984977e-02 1.64345034e-01 6.79168859e+00\n",
      " 7.03871348e-01 3.86669061e-01 4.63351643e+00 2.35848341e-01\n",
      " 1.02176041e+01 9.11894603e+00 2.98895165e-02 9.24465191e+00\n",
      " 9.03140283e+00 6.84515893e-03 9.78035080e+00 3.27991059e-01\n",
      " 1.09706410e+01 1.41677721e+01 8.49202310e+00 1.05044054e+01\n",
      " 1.07078666e+01 1.87961330e+01 7.86865829e+00 1.33943542e+00\n",
      " 7.81061812e+00 3.66790861e+00 6.21158346e-02 2.91060548e+00\n",
      " 3.34493225e-01 1.80719599e+00 9.22912471e-01 1.98858968e-01\n",
      " 1.64639216e+01 3.95042070e+00 3.11425753e-01 6.53284746e+00\n",
      " 5.79451494e+00 3.21514206e+00 1.13330307e+01 1.47613461e+01\n",
      " 1.61653514e+00 3.97795635e-01 2.05901603e+01 6.46326534e+00\n",
      " 4.09174335e-01 2.07738219e+01 8.64120587e+00 5.18861260e+00\n",
      " 1.37792059e+01 3.34107903e+00 2.25633912e-01 3.16547711e-01\n",
      " 4.71767334e+00 2.15165781e-01 3.72817862e+00 1.63700100e-01\n",
      " 1.07229536e+01 4.93780549e+00 2.46817802e+00 1.53272704e+00\n",
      " 7.12156550e+00 5.00573932e+00 1.06610695e+01 1.24330193e+01\n",
      " 1.43824255e+01 8.17408755e+00 2.25734884e-01 3.04809135e+00\n",
      " 1.37093746e+00 8.88610229e+00 5.05802052e+00 4.59765672e-01\n",
      " 9.47991645e+00 2.74139748e+00 8.68766678e-01 1.33406348e+01\n",
      " 9.72155162e+00 1.00334232e+00 1.11418341e-01 5.27039432e+00\n",
      " 1.10815047e+01 1.71685386e+01 1.35343878e+01 1.52280237e-01\n",
      " 3.12200602e-01 7.86674825e+00 1.64005800e+01 8.05468888e-02\n",
      " 8.76440445e+00 1.18225471e+01 8.03014765e-01 1.48889468e+00\n",
      " 7.39847491e+00 2.95398621e+00 8.92475522e+00 9.00098413e+00\n",
      " 1.12840454e+01 2.46763253e+01 2.52934811e+00 4.66559700e-01\n",
      " 9.44099191e-01 4.95640567e+00 2.74471590e+00 1.44769625e+01\n",
      " 4.13185719e+00 2.07772735e-01 2.69504116e-02 1.00185658e-01\n",
      " 1.01943833e+01 4.47616602e+00 5.75976966e+00 5.65360785e-01\n",
      " 8.13410404e+00 1.07987210e+01 3.76863366e-01 6.37320022e+00\n",
      " 2.53371180e+00 8.93832476e+00 1.10908962e+01 8.63218778e+00\n",
      " 6.32307597e+00 9.27110333e+00 6.43797919e+00 7.48126456e-01\n",
      " 8.20076645e-01 5.15059007e+00 6.98561642e+00 9.97675711e+00\n",
      " 1.70143991e+01 1.65961630e+00 1.54512570e+00 4.26190977e-01\n",
      " 1.42588015e+01 2.30165131e+01 4.28872997e+00 1.04722981e+01\n",
      " 1.13744552e+01 1.95003116e+01 1.39136492e-01 4.85547236e+00\n",
      " 1.11461523e-01 1.08947006e+01 2.59382863e+00 1.60802500e+01\n",
      " 4.85865566e+00 9.23831422e-02 8.50777605e+00 1.17068735e+01\n",
      " 1.09551054e+01 2.61632969e-01 6.92682473e-02 2.96406049e+00\n",
      " 1.46455186e+00 3.06621181e-01 4.71570474e+00 5.97386986e+00\n",
      " 1.00424739e+01 7.82101661e+00 7.37405282e+00 2.66485298e-01\n",
      " 1.45248973e+01 7.73180447e+00 1.14021731e+01 1.46103419e+01\n",
      " 5.61606127e-01 1.50408347e+00 1.12102065e+00 3.24258762e-02\n",
      " 1.09996933e+00 8.81549000e-01]\n",
      "[1.81964356e-02 1.06110304e-01 1.34057845e-02 1.67878404e-01\n",
      " 5.04612111e-03 1.97511357e-02 1.18115006e-01 2.54262766e-01\n",
      " 2.47597736e-01 1.61084574e-01 7.59520139e-03 1.33445518e-01\n",
      " 7.45843484e-02 1.68394953e-01 7.81590790e-02 1.67443046e-02\n",
      " 7.32985021e-02 5.27157282e-02 2.28726560e-01 1.38352962e-01\n",
      " 1.28146575e-03 1.71083637e-01 3.82937334e-04 1.60413295e-01\n",
      " 3.78457006e-01 9.99358028e-02 2.85493742e-01 2.16556659e-02\n",
      " 4.00064401e-01 1.22129057e-03 3.06900157e-04 6.20148446e-02\n",
      " 1.70711234e-02 6.44509123e-03 7.72887081e-02 2.04188946e-03\n",
      " 7.90603117e-02 5.98847219e-03 1.02461614e-03 5.67888194e-02\n",
      " 1.97272204e-01 3.22095701e-04 2.48152735e-01 5.94053368e-03\n",
      " 1.40808295e-01 2.56459837e-01 7.32968295e-02 1.07255826e-01\n",
      " 4.84593057e-02 3.00876868e-01 4.84488383e-02 1.84530766e-02\n",
      " 4.84529660e-02 3.80939254e-02 1.18281183e-03 1.83871946e-01\n",
      " 1.90363856e-02 5.75039871e-02 1.15892392e-02 1.52159756e-03\n",
      " 1.32100595e-01 1.06044719e-01 5.62838903e-03 2.44294701e-02\n",
      " 7.67551246e-02 2.27457159e-02 1.81070692e-01 1.04387844e-01\n",
      " 1.19201956e-02 1.36209559e-02 2.91943320e-01 1.18771463e-01\n",
      " 3.77813904e-03 2.90999792e-01 1.69210393e-01 1.76603560e-01\n",
      " 1.86397947e-01 8.62493062e-02 2.83996240e-03 7.18308452e-03\n",
      " 1.23384914e-01 6.20056900e-03 1.02613321e-01 2.80945300e-03\n",
      " 3.46376911e-01 9.87444928e-03 3.33232285e-02 5.17946696e-02\n",
      " 1.51107278e-01 5.71713602e-02 1.11856734e-01 7.70496971e-02\n",
      " 8.76796054e-02 2.20833850e-01 2.57653916e-03 7.91838458e-02\n",
      " 1.45699033e-02 1.09987114e-01 6.58866267e-02 9.61826444e-03\n",
      " 5.65355588e-02 4.08917794e-02 1.84580247e-03 2.15758617e-01\n",
      " 1.27452690e-01 3.05652117e-02 5.49528506e-03 7.71732894e-02\n",
      " 1.59352142e-01 2.46421863e-01 1.34661199e-01 3.36137911e-03\n",
      " 1.16270943e-02 9.12596297e-02 4.47841782e-02 2.82054188e-03\n",
      " 1.04698102e-01 1.86736382e-01 1.70359731e-02 1.88279004e-02\n",
      " 6.56144707e-02 2.23615346e-01 2.20571576e-01 9.08425648e-02\n",
      " 5.84194060e-02 3.58656875e-01 3.93511485e-02 5.96858381e-03\n",
      " 3.46595740e-02 1.37015699e-01 3.85372378e-02 1.10824012e-01\n",
      " 9.53984093e-02 7.99664141e-04 9.67712927e-04 1.39474381e-03\n",
      " 3.57565444e-01 1.82840676e-02 7.77971509e-02 1.26375334e-02\n",
      " 8.57007053e-02 1.66790501e-01 5.29274949e-03 2.25562801e-01\n",
      " 4.25436215e-02 2.14951794e-01 1.80722690e-01 2.17607704e-02\n",
      " 4.19156908e-02 2.90601645e-01 1.82561448e-01 1.00091545e-02\n",
      " 6.48346615e-03 5.21201742e-02 1.50978928e-01 1.41478450e-01\n",
      " 2.73288275e-01 2.49098131e-02 2.48178976e-02 1.08607542e-02\n",
      " 1.30461380e-01 1.26015686e-01 1.12777219e-02 1.72203981e-01\n",
      " 2.55979834e-01 2.45414882e-01 8.02023231e-03 1.48446009e-01\n",
      " 1.16911535e-03 1.87755559e-01 6.58460350e-02 6.04899687e-02\n",
      " 6.55119806e-02 6.85440415e-03 1.93959312e-01 1.48171621e-01\n",
      " 1.30704421e-01 1.49746194e-02 3.55157347e-03 2.92489204e-02\n",
      " 9.01490045e-03 2.84276791e-03 1.40632469e-01 2.11392017e-01\n",
      " 8.78908202e-02 5.83090379e-02 2.13195912e-02 6.80717771e-03\n",
      " 2.10782231e-01 1.89812367e-01 1.75191056e-01 2.38226110e-01\n",
      " 4.04766450e-03 1.79636742e-02 2.43018783e-02 6.31795441e-03\n",
      " 1.92008850e-02 3.09888142e-02]\n",
      "[3.49545930e-01 6.45811184e+00 3.19044768e-01 2.02048117e+01\n",
      " 1.84119250e-01 7.61077095e+00 4.03004361e+00 1.13230431e+01\n",
      " 1.03980952e+01 1.47750868e+01 7.14890612e-01 1.01165451e+01\n",
      " 5.25503571e+00 5.41072307e+00 1.54623216e+01 9.21634481e-01\n",
      " 5.37997110e+00 6.64678736e+00 1.08609395e+01 6.81290215e+00\n",
      " 1.95371791e-01 9.02708286e+00 3.34431938e-01 4.10133412e+00\n",
      " 5.91000477e+00 7.92243559e+00 9.89020672e+00 3.19466188e+00\n",
      " 7.89347265e+00 6.91711327e-02 6.24299531e-03 6.71886545e+00\n",
      " 6.82610200e-01 3.74019204e-01 4.49307170e+00 1.38666533e-01\n",
      " 9.58888638e+00 3.05412082e+00 2.85370232e-02 8.14749192e+00\n",
      " 8.49695185e+00 6.45316930e-03 9.47191735e+00 2.17661855e-01\n",
      " 1.04768427e+01 1.31609252e+01 8.40615227e+00 1.03250397e+01\n",
      " 1.01771921e+01 1.65471380e+01 6.92574425e+00 1.31830367e+00\n",
      " 7.19734201e+00 3.59020246e+00 5.45657667e-02 2.46391183e+00\n",
      " 2.78680078e-01 1.54770848e+00 9.04915065e-01 1.92689581e-01\n",
      " 1.45808480e+01 3.84225168e+00 2.91756245e-01 5.75110442e+00\n",
      " 5.61637678e+00 8.28426706e-01 1.07863134e+01 3.63709227e+00\n",
      " 1.36090350e+00 3.83776406e-01 1.90939980e+01 5.56106255e+00\n",
      " 4.03100539e-01 1.84992725e+01 7.70505712e+00 4.96167703e+00\n",
      " 5.62192200e-01 3.24443017e+00 2.18009310e-01 3.04100311e-01\n",
      " 4.36532543e+00 1.80057970e-01 1.17890733e+00 1.59875023e-01\n",
      " 8.84370073e+00 4.42404370e+00 2.42673507e+00 1.47808021e+00\n",
      " 6.79295394e+00 4.90805229e+00 8.45813599e+00 1.18773275e+01\n",
      " 1.42295303e+01 7.70214546e+00 2.22007515e-01 2.07563047e+00\n",
      " 1.35542979e+00 8.38161362e+00 4.85947530e+00 4.47514566e-01\n",
      " 9.41762179e+00 2.66117227e+00 9.88784257e-02 1.23161717e+01\n",
      " 9.25136988e+00 6.27971971e-01 1.05750730e-01 4.75483447e+00\n",
      " 9.94514107e+00 1.58381677e+01 1.28792033e+01 1.44086004e-01\n",
      " 2.96506043e-01 7.56910945e+00 1.59951749e+01 7.12637922e-02\n",
      " 8.36637115e+00 2.77436910e-02 7.68195810e-01 1.45445531e-01\n",
      " 1.05125022e+00 2.46684976e+00 8.48172196e+00 8.71836282e+00\n",
      " 9.34638889e+00 2.37874039e+01 2.48181702e+00 4.60301619e-01\n",
      " 8.95498580e-01 4.81616517e+00 2.68173502e+00 1.20061363e+01\n",
      " 1.90105526e-02 1.10453609e-01 2.53572257e-02 9.69745582e-02\n",
      " 9.43888290e+00 1.62315807e+00 5.67312032e+00 5.46171730e-01\n",
      " 7.99940980e+00 1.05762505e+01 3.58709018e-01 6.13950431e+00\n",
      " 2.47439426e+00 7.70945190e+00 1.04594746e+01 8.54361325e+00\n",
      " 6.06074347e+00 8.67397062e+00 5.54006975e+00 3.39766386e-01\n",
      " 7.37496223e-01 5.04262686e+00 6.75779517e+00 3.53443484e+00\n",
      " 1.62180429e+01 1.63426167e+00 1.51452436e+00 4.15289891e-01\n",
      " 1.33862698e+01 2.25406779e+01 3.97097551e+00 1.02403968e+01\n",
      " 9.38420056e+00 1.70986796e+01 7.09584602e-02 4.67536160e+00\n",
      " 1.06969659e-01 1.04902668e+01 2.44927564e+00 4.65772759e+00\n",
      " 4.37763618e+00 7.26858423e-02 4.97626861e+00 1.13146908e+01\n",
      " 9.72720213e+00 1.57120186e-01 6.52517862e-02 2.81617834e+00\n",
      " 1.45501971e+00 3.02479865e-01 3.95655972e+00 5.75482179e+00\n",
      " 9.48695394e+00 7.59792116e+00 7.32197694e+00 2.03811090e-01\n",
      " 1.28561923e+01 6.44851303e+00 1.11715741e+01 1.15775921e+01\n",
      " 5.51504539e-01 1.45615916e+00 9.23611397e-01 2.20607872e-02\n",
      " 1.08029719e+00 8.47196854e-01]\n",
      "[2.33751337e-02 2.78169780e-01 1.32749746e-01 6.75387735e-01\n",
      " 5.25029942e-04 7.17624597e-01 2.72573090e-02 3.66728084e-02\n",
      " 6.26062792e-01 1.57360075e-01 5.18966835e-02 1.64633479e+00\n",
      " 2.69310182e-01 1.08917153e-01 1.40170515e-02 4.68526699e-03\n",
      " 2.86454715e+00 1.12271055e-01 2.83478908e+00 3.29937471e-01\n",
      " 1.10635863e-03 4.03499145e-02 1.27645778e-03 1.97279767e-01\n",
      " 2.01010510e+00 2.92444589e-02 8.23025591e-02 9.99318749e-02\n",
      " 1.54917875e+00 1.10607448e-03 1.57795139e-01 1.08083015e-02\n",
      " 4.19002478e-03 6.20476579e-03 6.31560205e-02 9.51399190e-02\n",
      " 5.49657405e-01 6.05883674e+00 3.27877165e-04 1.04037117e+00\n",
      " 3.37178783e-01 6.98939333e-05 6.02807144e-02 1.04388670e-01\n",
      " 3.52989957e-01 7.50387036e-01 1.25740070e-02 7.21098494e-02\n",
      " 4.82215189e-01 1.94811811e+00 8.94465201e-01 2.67867241e-03\n",
      " 5.64823147e-01 3.96122275e-02 6.36725607e-03 2.62821703e-01\n",
      " 3.67767620e-02 2.01983520e-01 6.40816755e-03 4.64778890e-03\n",
      " 1.75097306e+00 2.12429325e-03 1.40411193e-02 7.57313573e-01\n",
      " 1.01383028e-01 2.36396963e+00 3.65646570e-01 1.10198660e+01\n",
      " 2.43711444e-01 3.98273564e-04 1.20421906e+00 7.83431323e-01\n",
      " 2.29565733e-03 1.98354960e+00 7.66938354e-01 5.03320147e-02\n",
      " 1.30306157e+01 1.03995472e-02 4.78463949e-03 5.26431537e-03\n",
      " 2.28962998e-01 2.89072424e-02 2.44665796e+00 1.01562386e-03\n",
      " 1.53287599e+00 5.03887338e-01 8.11972528e-03 2.85216034e-03\n",
      " 1.77504290e-01 4.05156687e-02 2.09107679e+00 4.78642057e-01\n",
      " 6.52155265e-02 2.51108242e-01 1.15082972e-03 8.93277026e-01\n",
      " 9.37765320e-04 3.94501553e-01 1.32658587e-01 2.63284199e-03\n",
      " 5.75910116e-03 3.93334265e-02 7.68042450e-01 8.08704436e-01\n",
      " 3.42729056e-01 3.44805138e-01 1.72326012e-04 4.38386563e-01\n",
      " 9.77011494e-01 1.08394901e+00 5.20523335e-01 4.83285420e-03\n",
      " 4.06746440e-03 2.06379177e-01 3.60620983e-01 6.46255472e-03\n",
      " 2.93335199e-01 1.16080670e+01 1.77829829e-02 1.32462125e+00\n",
      " 6.28161021e+00 2.63521104e-01 2.22461692e-01 1.91778748e-01\n",
      " 1.87923712e+00 5.30264559e-01 8.17994275e-03 2.89497873e-04\n",
      " 1.39410370e-02 3.22480211e-03 2.44436385e-02 2.36000221e+00\n",
      " 4.01744823e+00 9.65194618e-02 6.25472989e-04 1.81635601e-03\n",
      " 3.97934962e-01 2.83472389e+00 8.85219222e-03 6.55152161e-03\n",
      " 4.89935342e-02 5.56799945e-02 1.28615991e-02 8.13310838e-03\n",
      " 1.67739172e-02 1.01392106e+00 4.50698933e-01 6.68137567e-02\n",
      " 2.20416812e-01 3.06531060e-01 7.15347989e-01 3.98350915e-01\n",
      " 7.60969565e-02 5.58430438e-02 7.68423217e-02 6.30084382e+00\n",
      " 5.23067881e-01 4.44818092e-04 5.78343892e-03 4.03317134e-05\n",
      " 7.42070313e-01 3.49819546e-01 3.06476743e-01 5.96973802e-02\n",
      " 1.73427481e+00 2.15621718e+00 6.01577999e-02 3.16647480e-02\n",
      " 3.32274888e-03 2.16678193e-01 7.87069514e-02 1.13620325e+01\n",
      " 4.15507493e-01 1.28428958e-02 3.33754813e+00 2.44010996e-01\n",
      " 1.09719882e+00 8.95381637e-02 4.64887566e-04 1.18633233e-01\n",
      " 5.17248386e-04 1.29854830e-03 6.18512554e-01 7.65604892e-03\n",
      " 4.67629163e-01 1.64786411e-01 3.07562946e-02 5.58670311e-02\n",
      " 1.45792274e+00 1.09347907e+00 5.54079316e-02 2.79452369e+00\n",
      " 6.05392368e-03 2.99606406e-02 1.73107380e-01 4.04713454e-03\n",
      " 4.71251610e-04 3.36333161e-03]\n"
     ]
    }
   ],
   "source": [
    "#Reading in Updated data for confirmed, death, recovered, and active percentages\n",
    "ud = pd.read_csv('worldwide covid data.csv',  na_values = ['nan']).dropna()\n",
    "ud['Confirmed%'] = (ud['Total Cases']/ud['Population']) * 100\n",
    "ud['Death%'] = (ud['Total Deaths']/ud['Population']) * 100\n",
    "ud['Recovered%'] = (ud['Total Recovered']/ud['Population']) * 100\n",
    "ud['Active%'] = (ud['Active Cases']/ud['Population']) * 100\n",
    "#To find null values\n",
    "for i in ud.columns:\n",
    "    print(ud[i].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Editing names of df_kcal to match the updated dataframe names\n",
    "df_kcal.loc[df_kcal.Country == 'Central African Republic', 'Country'] = 'CAR'\n",
    "df_kcal.loc[df_kcal.Country == 'Iran (Islamic Republic of)', 'Country'] = 'Iran'\n",
    "df_kcal.loc[df_kcal.Country == \"Lao People's Democratic Republic'\", 'Country'] = 'Laos'\n",
    "df_kcal.loc[df_kcal.Country == 'Republic of Moldova', 'Country'] = 'Moldova'\n",
    "df_kcal.loc[df_kcal.Country == 'Russian Federation', 'Country'] = 'Russia' \n",
    "df_kcal.loc[df_kcal.Country == 'Saint Vincent and the Grenadines', 'Country'] = 'St. Vincent Grenadines'\n",
    "df_kcal.loc[df_kcal.Country == 'Taiwan*', 'Country'] = 'Taiwan'\n",
    "df_kcal.loc[df_kcal.Country == 'United Arab Emirates', 'Country'] = 'UAE'\n",
    "df_kcal.loc[df_kcal.Country == 'United Kingdom', 'Country'] = 'UK'\n",
    "df_kcal.loc[df_kcal.Country == 'United Republic of Tanzania', 'Country'] = 'Tanzania'\n",
    "df_kcal.loc[df_kcal.Country == 'United States of America', 'Country'] = 'USA'\n",
    "df_kcal.loc[df_kcal.Country == 'Venezuela (Bolivarian Republic of)', 'Country'] = 'Venezuela'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Alcoholic Beverages</th>\n",
       "      <th>Animal Products</th>\n",
       "      <th>Animal fats</th>\n",
       "      <th>Aquatic Products, Other</th>\n",
       "      <th>Cereals - Excluding Beer</th>\n",
       "      <th>Eggs</th>\n",
       "      <th>Fish, Seafood</th>\n",
       "      <th>Fruits - Excluding Wine</th>\n",
       "      <th>Meat</th>\n",
       "      <th>...</th>\n",
       "      <th>Vegetable Oils</th>\n",
       "      <th>Vegetables</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>Undernourished</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>Population</th>\n",
       "      <th>Unit (all except Population)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>4.7774</td>\n",
       "      <td>0.8504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.1186</td>\n",
       "      <td>0.1501</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.4757</td>\n",
       "      <td>1.2006</td>\n",
       "      <td>...</td>\n",
       "      <td>2.3012</td>\n",
       "      <td>0.7504</td>\n",
       "      <td>4.5</td>\n",
       "      <td>29.8</td>\n",
       "      <td>0.391117</td>\n",
       "      <td>0.018196</td>\n",
       "      <td>0.349546</td>\n",
       "      <td>0.023375</td>\n",
       "      <td>40145225.0</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>0.9120</td>\n",
       "      <td>16.0930</td>\n",
       "      <td>1.0591</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.2107</td>\n",
       "      <td>0.8091</td>\n",
       "      <td>0.1471</td>\n",
       "      <td>3.8982</td>\n",
       "      <td>3.8688</td>\n",
       "      <td>...</td>\n",
       "      <td>2.8244</td>\n",
       "      <td>2.7508</td>\n",
       "      <td>22.3</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.842392</td>\n",
       "      <td>0.106110</td>\n",
       "      <td>6.458112</td>\n",
       "      <td>0.278170</td>\n",
       "      <td>2873425.0</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>0.0896</td>\n",
       "      <td>6.0326</td>\n",
       "      <td>0.1941</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0112</td>\n",
       "      <td>0.4181</td>\n",
       "      <td>0.1195</td>\n",
       "      <td>3.1805</td>\n",
       "      <td>1.2543</td>\n",
       "      <td>...</td>\n",
       "      <td>5.7638</td>\n",
       "      <td>2.0457</td>\n",
       "      <td>26.6</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.465200</td>\n",
       "      <td>0.013406</td>\n",
       "      <td>0.319045</td>\n",
       "      <td>0.132750</td>\n",
       "      <td>44950745.0</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1.9388</td>\n",
       "      <td>4.6927</td>\n",
       "      <td>0.2644</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.3521</td>\n",
       "      <td>0.0441</td>\n",
       "      <td>0.8372</td>\n",
       "      <td>2.3133</td>\n",
       "      <td>2.9302</td>\n",
       "      <td>...</td>\n",
       "      <td>4.2741</td>\n",
       "      <td>0.3525</td>\n",
       "      <td>6.8</td>\n",
       "      <td>25</td>\n",
       "      <td>0.189690</td>\n",
       "      <td>0.005046</td>\n",
       "      <td>0.184119</td>\n",
       "      <td>0.000525</td>\n",
       "      <td>34283759.0</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1.4354</td>\n",
       "      <td>14.9869</td>\n",
       "      <td>1.0650</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.7927</td>\n",
       "      <td>0.8643</td>\n",
       "      <td>0.2006</td>\n",
       "      <td>1.4663</td>\n",
       "      <td>9.4459</td>\n",
       "      <td>...</td>\n",
       "      <td>5.5410</td>\n",
       "      <td>0.8643</td>\n",
       "      <td>28.5</td>\n",
       "      <td>4.6</td>\n",
       "      <td>11.613979</td>\n",
       "      <td>0.254263</td>\n",
       "      <td>11.323043</td>\n",
       "      <td>0.036673</td>\n",
       "      <td>45772333.0</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>0.8454</td>\n",
       "      <td>7.2303</td>\n",
       "      <td>0.6007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.3126</td>\n",
       "      <td>0.2892</td>\n",
       "      <td>0.4449</td>\n",
       "      <td>2.3804</td>\n",
       "      <td>3.1368</td>\n",
       "      <td>...</td>\n",
       "      <td>7.5417</td>\n",
       "      <td>0.6674</td>\n",
       "      <td>25.2</td>\n",
       "      <td>21.2</td>\n",
       "      <td>1.504083</td>\n",
       "      <td>0.017964</td>\n",
       "      <td>1.456159</td>\n",
       "      <td>0.029961</td>\n",
       "      <td>28323827.0</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>0.7150</td>\n",
       "      <td>10.9806</td>\n",
       "      <td>0.9363</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.9833</td>\n",
       "      <td>0.2894</td>\n",
       "      <td>1.0385</td>\n",
       "      <td>1.8046</td>\n",
       "      <td>7.8311</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3279</td>\n",
       "      <td>1.9578</td>\n",
       "      <td>2.1</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1.121021</td>\n",
       "      <td>0.024302</td>\n",
       "      <td>0.923611</td>\n",
       "      <td>0.173107</td>\n",
       "      <td>98556168.0</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.4667</td>\n",
       "      <td>0.3394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0727</td>\n",
       "      <td>0.1455</td>\n",
       "      <td>0.1697</td>\n",
       "      <td>1.1879</td>\n",
       "      <td>2.0121</td>\n",
       "      <td>...</td>\n",
       "      <td>3.9515</td>\n",
       "      <td>0.3636</td>\n",
       "      <td>14.1</td>\n",
       "      <td>38.9</td>\n",
       "      <td>0.032426</td>\n",
       "      <td>0.006318</td>\n",
       "      <td>0.022061</td>\n",
       "      <td>0.004047</td>\n",
       "      <td>30737797.0</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1.1925</td>\n",
       "      <td>3.3043</td>\n",
       "      <td>0.3230</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.5528</td>\n",
       "      <td>0.1988</td>\n",
       "      <td>0.5714</td>\n",
       "      <td>0.2236</td>\n",
       "      <td>1.5155</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0062</td>\n",
       "      <td>0.4472</td>\n",
       "      <td>6.5</td>\n",
       "      <td>46.7</td>\n",
       "      <td>1.099969</td>\n",
       "      <td>0.019201</td>\n",
       "      <td>1.080297</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>19098078.0</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1.4269</td>\n",
       "      <td>3.9356</td>\n",
       "      <td>0.6904</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.8044</td>\n",
       "      <td>0.1381</td>\n",
       "      <td>0.1611</td>\n",
       "      <td>0.4373</td>\n",
       "      <td>1.7491</td>\n",
       "      <td>...</td>\n",
       "      <td>6.2601</td>\n",
       "      <td>0.2532</td>\n",
       "      <td>12.3</td>\n",
       "      <td>51.3</td>\n",
       "      <td>0.881549</td>\n",
       "      <td>0.030989</td>\n",
       "      <td>0.847197</td>\n",
       "      <td>0.003363</td>\n",
       "      <td>15163536.0</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country  Alcoholic Beverages  Animal Products  Animal fats  \\\n",
       "0    Afghanistan               0.0000           4.7774       0.8504   \n",
       "1        Albania               0.9120          16.0930       1.0591   \n",
       "2        Algeria               0.0896           6.0326       0.1941   \n",
       "3         Angola               1.9388           4.6927       0.2644   \n",
       "5      Argentina               1.4354          14.9869       1.0650   \n",
       "..           ...                  ...              ...          ...   \n",
       "165    Venezuela               0.8454           7.2303       0.6007   \n",
       "166      Vietnam               0.7150          10.9806       0.9363   \n",
       "167        Yemen               0.0000           3.4667       0.3394   \n",
       "168       Zambia               1.1925           3.3043       0.3230   \n",
       "169     Zimbabwe               1.4269           3.9356       0.6904   \n",
       "\n",
       "     Aquatic Products, Other  Cereals - Excluding Beer    Eggs  Fish, Seafood  \\\n",
       "0                        0.0                   37.1186  0.1501         0.0000   \n",
       "1                        0.0                   16.2107  0.8091         0.1471   \n",
       "2                        0.0                   25.0112  0.4181         0.1195   \n",
       "3                        0.0                   18.3521  0.0441         0.8372   \n",
       "5                        0.0                   16.7927  0.8643         0.2006   \n",
       "..                       ...                       ...     ...            ...   \n",
       "165                      0.0                   21.3126  0.2892         0.4449   \n",
       "166                      0.0                   26.9833  0.2894         1.0385   \n",
       "167                      0.0                   32.0727  0.1455         0.1697   \n",
       "168                      0.0                   31.5528  0.1988         0.5714   \n",
       "169                      0.0                   29.8044  0.1381         0.1611   \n",
       "\n",
       "     Fruits - Excluding Wine    Meat  ...  Vegetable Oils  Vegetables  \\\n",
       "0                     1.4757  1.2006  ...          2.3012      0.7504   \n",
       "1                     3.8982  3.8688  ...          2.8244      2.7508   \n",
       "2                     3.1805  1.2543  ...          5.7638      2.0457   \n",
       "3                     2.3133  2.9302  ...          4.2741      0.3525   \n",
       "5                     1.4663  9.4459  ...          5.5410      0.8643   \n",
       "..                       ...     ...  ...             ...         ...   \n",
       "165                   2.3804  3.1368  ...          7.5417      0.6674   \n",
       "166                   1.8046  7.8311  ...          1.3279      1.9578   \n",
       "167                   1.1879  2.0121  ...          3.9515      0.3636   \n",
       "168                   0.2236  1.5155  ...          3.0062      0.4472   \n",
       "169                   0.4373  1.7491  ...          6.2601      0.2532   \n",
       "\n",
       "     Obesity  Undernourished  Confirmed    Deaths  Recovered    Active  \\\n",
       "0        4.5            29.8   0.391117  0.018196   0.349546  0.023375   \n",
       "1       22.3             6.2   6.842392  0.106110   6.458112  0.278170   \n",
       "2       26.6             3.9   0.465200  0.013406   0.319045  0.132750   \n",
       "3        6.8              25   0.189690  0.005046   0.184119  0.000525   \n",
       "5       28.5             4.6  11.613979  0.254263  11.323043  0.036673   \n",
       "..       ...             ...        ...       ...        ...       ...   \n",
       "165     25.2            21.2   1.504083  0.017964   1.456159  0.029961   \n",
       "166      2.1             9.3   1.121021  0.024302   0.923611  0.173107   \n",
       "167     14.1            38.9   0.032426  0.006318   0.022061  0.004047   \n",
       "168      6.5            46.7   1.099969  0.019201   1.080297  0.000471   \n",
       "169     12.3            51.3   0.881549  0.030989   0.847197  0.003363   \n",
       "\n",
       "     Population  Unit (all except Population)  \n",
       "0    40145225.0                             %  \n",
       "1     2873425.0                             %  \n",
       "2    44950745.0                             %  \n",
       "3    34283759.0                             %  \n",
       "5    45772333.0                             %  \n",
       "..          ...                           ...  \n",
       "165  28323827.0                             %  \n",
       "166  98556168.0                             %  \n",
       "167  30737797.0                             %  \n",
       "168  19098078.0                             %  \n",
       "169  15163536.0                             %  \n",
       "\n",
       "[154 rows x 32 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#writing function that updates value of confirmed, death, recovered, and active percentages along with population of main dataframe if updated dataframe contains the respective country\n",
    "ud_c = ud.Country.tolist()\n",
    "def contains(value):\n",
    "    if value in ud_c:\n",
    "        #ensuring the code is no longer changing number of entries in recovered and Active\n",
    "#         print(df_kcal.describe().at[\"count\", \"Recovered\"], \":\", value)\n",
    "        df_kcal.loc[df_kcal.Country == value, ['Confirmed', 'Deaths', 'Recovered', 'Active', 'Population']] = [ud.loc[ud.Country == value, ['Confirmed%']], ud.loc[ud.Country == value, ['Death%']], ud.loc[ud.Country == value, ['Recovered%']], ud.loc[ud.Country == value, ['Active%']], ud.loc[ud.Country == value, ['Population']]]\n",
    "#The function above is being applied to every country in the main dataframe\n",
    "df_kcal.Country.apply(contains)\n",
    "#checking to see if main dataframe has been updated as expected\n",
    "df_kcal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>Alcoholic Beverages</th>\n",
       "      <th>Animal Products</th>\n",
       "      <th>Animal fats</th>\n",
       "      <th>Aquatic Products, Other</th>\n",
       "      <th>Cereals - Excluding Beer</th>\n",
       "      <th>Eggs</th>\n",
       "      <th>Fish, Seafood</th>\n",
       "      <th>Fruits - Excluding Wine</th>\n",
       "      <th>Meat</th>\n",
       "      <th>Milk - Excluding Butter</th>\n",
       "      <th>...</th>\n",
       "      <th>Treenuts</th>\n",
       "      <th>Vegetal Products</th>\n",
       "      <th>Vegetable Oils</th>\n",
       "      <th>Vegetables</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>1.540000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.310975</td>\n",
       "      <td>9.050435</td>\n",
       "      <td>1.265275</td>\n",
       "      <td>0.003076</td>\n",
       "      <td>20.575647</td>\n",
       "      <td>0.421891</td>\n",
       "      <td>0.592167</td>\n",
       "      <td>2.008186</td>\n",
       "      <td>3.684662</td>\n",
       "      <td>2.938984</td>\n",
       "      <td>...</td>\n",
       "      <td>0.266022</td>\n",
       "      <td>40.950944</td>\n",
       "      <td>4.875444</td>\n",
       "      <td>1.066180</td>\n",
       "      <td>18.449351</td>\n",
       "      <td>5.510239</td>\n",
       "      <td>0.092164</td>\n",
       "      <td>4.968231</td>\n",
       "      <td>0.449843</td>\n",
       "      <td>4.873200e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.080327</td>\n",
       "      <td>4.793536</td>\n",
       "      <td>1.316687</td>\n",
       "      <td>0.032440</td>\n",
       "      <td>6.506307</td>\n",
       "      <td>0.310935</td>\n",
       "      <td>0.550383</td>\n",
       "      <td>1.455394</td>\n",
       "      <td>2.116294</td>\n",
       "      <td>2.066412</td>\n",
       "      <td>...</td>\n",
       "      <td>0.293467</td>\n",
       "      <td>4.793145</td>\n",
       "      <td>2.184836</td>\n",
       "      <td>0.641302</td>\n",
       "      <td>9.519483</td>\n",
       "      <td>5.386185</td>\n",
       "      <td>0.097782</td>\n",
       "      <td>4.961672</td>\n",
       "      <td>0.737322</td>\n",
       "      <td>1.660749e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.623700</td>\n",
       "      <td>0.040800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.956500</td>\n",
       "      <td>0.018800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.147100</td>\n",
       "      <td>0.298000</td>\n",
       "      <td>0.116900</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.708900</td>\n",
       "      <td>0.932500</td>\n",
       "      <td>0.095700</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>0.000312</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000306</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.223500e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.361300</td>\n",
       "      <td>4.810425</td>\n",
       "      <td>0.336850</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.338125</td>\n",
       "      <td>0.130675</td>\n",
       "      <td>0.231125</td>\n",
       "      <td>1.184600</td>\n",
       "      <td>1.918425</td>\n",
       "      <td>1.069250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043175</td>\n",
       "      <td>36.931350</td>\n",
       "      <td>3.075900</td>\n",
       "      <td>0.589400</td>\n",
       "      <td>8.250000</td>\n",
       "      <td>0.465540</td>\n",
       "      <td>0.010222</td>\n",
       "      <td>0.406148</td>\n",
       "      <td>0.006212</td>\n",
       "      <td>3.386338e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.194250</td>\n",
       "      <td>8.730650</td>\n",
       "      <td>0.763300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.060950</td>\n",
       "      <td>0.393550</td>\n",
       "      <td>0.442200</td>\n",
       "      <td>1.679950</td>\n",
       "      <td>3.482050</td>\n",
       "      <td>2.675300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.176050</td>\n",
       "      <td>41.273700</td>\n",
       "      <td>4.638450</td>\n",
       "      <td>0.989000</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>4.204724</td>\n",
       "      <td>0.056853</td>\n",
       "      <td>3.716227</td>\n",
       "      <td>0.088721</td>\n",
       "      <td>1.034807e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.027450</td>\n",
       "      <td>13.073450</td>\n",
       "      <td>1.869675</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.970375</td>\n",
       "      <td>0.629950</td>\n",
       "      <td>0.834400</td>\n",
       "      <td>2.383025</td>\n",
       "      <td>5.042525</td>\n",
       "      <td>4.376425</td>\n",
       "      <td>...</td>\n",
       "      <td>0.396200</td>\n",
       "      <td>45.201875</td>\n",
       "      <td>6.519100</td>\n",
       "      <td>1.334775</td>\n",
       "      <td>25.700000</td>\n",
       "      <td>9.427713</td>\n",
       "      <td>0.146331</td>\n",
       "      <td>8.445140</td>\n",
       "      <td>0.498469</td>\n",
       "      <td>3.424861e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.157400</td>\n",
       "      <td>22.291100</td>\n",
       "      <td>7.800700</td>\n",
       "      <td>0.400700</td>\n",
       "      <td>37.526500</td>\n",
       "      <td>1.446100</td>\n",
       "      <td>4.418300</td>\n",
       "      <td>8.854000</td>\n",
       "      <td>10.567400</td>\n",
       "      <td>9.944100</td>\n",
       "      <td>...</td>\n",
       "      <td>1.421000</td>\n",
       "      <td>48.386400</td>\n",
       "      <td>10.383900</td>\n",
       "      <td>3.226900</td>\n",
       "      <td>45.500000</td>\n",
       "      <td>24.676325</td>\n",
       "      <td>0.400064</td>\n",
       "      <td>23.787404</td>\n",
       "      <td>3.337548</td>\n",
       "      <td>1.439324e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Alcoholic Beverages  Animal Products  Animal fats  \\\n",
       "count           154.000000       154.000000   154.000000   \n",
       "mean              1.310975         9.050435     1.265275   \n",
       "std               1.080327         4.793536     1.316687   \n",
       "min               0.000000         1.623700     0.040800   \n",
       "25%               0.361300         4.810425     0.336850   \n",
       "50%               1.194250         8.730650     0.763300   \n",
       "75%               2.027450        13.073450     1.869675   \n",
       "max               5.157400        22.291100     7.800700   \n",
       "\n",
       "       Aquatic Products, Other  Cereals - Excluding Beer        Eggs  \\\n",
       "count               154.000000                154.000000  154.000000   \n",
       "mean                  0.003076                 20.575647    0.421891   \n",
       "std                   0.032440                  6.506307    0.310935   \n",
       "min                   0.000000                  8.956500    0.018800   \n",
       "25%                   0.000000                 15.338125    0.130675   \n",
       "50%                   0.000000                 20.060950    0.393550   \n",
       "75%                   0.000000                 24.970375    0.629950   \n",
       "max                   0.400700                 37.526500    1.446100   \n",
       "\n",
       "       Fish, Seafood  Fruits - Excluding Wine        Meat  \\\n",
       "count     154.000000               154.000000  154.000000   \n",
       "mean        0.592167                 2.008186    3.684662   \n",
       "std         0.550383                 1.455394    2.116294   \n",
       "min         0.000000                 0.147100    0.298000   \n",
       "25%         0.231125                 1.184600    1.918425   \n",
       "50%         0.442200                 1.679950    3.482050   \n",
       "75%         0.834400                 2.383025    5.042525   \n",
       "max         4.418300                 8.854000   10.567400   \n",
       "\n",
       "       Milk - Excluding Butter  ...    Treenuts  Vegetal Products  \\\n",
       "count               154.000000  ...  154.000000        154.000000   \n",
       "mean                  2.938984  ...    0.266022         40.950944   \n",
       "std                   2.066412  ...    0.293467          4.793145   \n",
       "min                   0.116900  ...    0.000000         27.708900   \n",
       "25%                   1.069250  ...    0.043175         36.931350   \n",
       "50%                   2.675300  ...    0.176050         41.273700   \n",
       "75%                   4.376425  ...    0.396200         45.201875   \n",
       "max                   9.944100  ...    1.421000         48.386400   \n",
       "\n",
       "       Vegetable Oils  Vegetables     Obesity   Confirmed      Deaths  \\\n",
       "count      154.000000  154.000000  154.000000  154.000000  154.000000   \n",
       "mean         4.875444    1.066180   18.449351    5.510239    0.092164   \n",
       "std          2.184836    0.641302    9.519483    5.386185    0.097782   \n",
       "min          0.932500    0.095700    2.100000    0.000312    0.000000   \n",
       "25%          3.075900    0.589400    8.250000    0.465540    0.010222   \n",
       "50%          4.638450    0.989000   21.300000    4.204724    0.056853   \n",
       "75%          6.519100    1.334775   25.700000    9.427713    0.146331   \n",
       "max         10.383900    3.226900   45.500000   24.676325    0.400064   \n",
       "\n",
       "        Recovered      Active    Population  \n",
       "count  154.000000  154.000000  1.540000e+02  \n",
       "mean     4.968231    0.449843  4.873200e+07  \n",
       "std      4.961672    0.737322  1.660749e+08  \n",
       "min      0.000306    0.000000  7.223500e+04  \n",
       "25%      0.406148    0.006212  3.386338e+06  \n",
       "50%      3.716227    0.088721  1.034807e+07  \n",
       "75%      8.445140    0.498469  3.424861e+07  \n",
       "max     23.787404    3.337548  1.439324e+09  \n",
       "\n",
       "[8 rows x 29 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kcal.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Alcoholic Beverages</th>\n",
       "      <th>Animal Products</th>\n",
       "      <th>Animal fats</th>\n",
       "      <th>Aquatic Products, Other</th>\n",
       "      <th>Cereals - Excluding Beer</th>\n",
       "      <th>Eggs</th>\n",
       "      <th>Fish, Seafood</th>\n",
       "      <th>Fruits - Excluding Wine</th>\n",
       "      <th>Meat</th>\n",
       "      <th>...</th>\n",
       "      <th>Vegetable Oils</th>\n",
       "      <th>Vegetables</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>Undernourished</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>Population</th>\n",
       "      <th>Unit (all except Population)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>4.7774</td>\n",
       "      <td>0.8504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.1186</td>\n",
       "      <td>0.1501</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.4757</td>\n",
       "      <td>1.2006</td>\n",
       "      <td>...</td>\n",
       "      <td>2.3012</td>\n",
       "      <td>0.7504</td>\n",
       "      <td>4.5</td>\n",
       "      <td>29.8</td>\n",
       "      <td>0.142134</td>\n",
       "      <td>0.006186</td>\n",
       "      <td>0.123374</td>\n",
       "      <td>0.012574</td>\n",
       "      <td>38928000.0</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>0.9120</td>\n",
       "      <td>16.0930</td>\n",
       "      <td>1.0591</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.2107</td>\n",
       "      <td>0.8091</td>\n",
       "      <td>0.1471</td>\n",
       "      <td>3.8982</td>\n",
       "      <td>3.8688</td>\n",
       "      <td>...</td>\n",
       "      <td>2.8244</td>\n",
       "      <td>2.7508</td>\n",
       "      <td>22.3</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.967301</td>\n",
       "      <td>0.050951</td>\n",
       "      <td>1.792636</td>\n",
       "      <td>1.123714</td>\n",
       "      <td>2838000.0</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>0.0896</td>\n",
       "      <td>6.0326</td>\n",
       "      <td>0.1941</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0112</td>\n",
       "      <td>0.4181</td>\n",
       "      <td>0.1195</td>\n",
       "      <td>3.1805</td>\n",
       "      <td>1.2543</td>\n",
       "      <td>...</td>\n",
       "      <td>5.7638</td>\n",
       "      <td>2.0457</td>\n",
       "      <td>26.6</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.244897</td>\n",
       "      <td>0.006558</td>\n",
       "      <td>0.167572</td>\n",
       "      <td>0.070767</td>\n",
       "      <td>44357000.0</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1.9388</td>\n",
       "      <td>4.6927</td>\n",
       "      <td>0.2644</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.3521</td>\n",
       "      <td>0.0441</td>\n",
       "      <td>0.8372</td>\n",
       "      <td>2.3133</td>\n",
       "      <td>2.9302</td>\n",
       "      <td>...</td>\n",
       "      <td>4.2741</td>\n",
       "      <td>0.3525</td>\n",
       "      <td>6.8</td>\n",
       "      <td>25</td>\n",
       "      <td>0.061687</td>\n",
       "      <td>0.001461</td>\n",
       "      <td>0.056808</td>\n",
       "      <td>0.003419</td>\n",
       "      <td>32522000.0</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1.4354</td>\n",
       "      <td>14.9869</td>\n",
       "      <td>1.0650</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.7927</td>\n",
       "      <td>0.8643</td>\n",
       "      <td>0.2006</td>\n",
       "      <td>1.4663</td>\n",
       "      <td>9.4459</td>\n",
       "      <td>...</td>\n",
       "      <td>5.5410</td>\n",
       "      <td>0.8643</td>\n",
       "      <td>28.5</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.356147</td>\n",
       "      <td>0.108227</td>\n",
       "      <td>3.905192</td>\n",
       "      <td>0.342729</td>\n",
       "      <td>45377000.0</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Alcoholic Beverages  Animal Products  Animal fats  \\\n",
       "0  Afghanistan               0.0000           4.7774       0.8504   \n",
       "1      Albania               0.9120          16.0930       1.0591   \n",
       "2      Algeria               0.0896           6.0326       0.1941   \n",
       "3       Angola               1.9388           4.6927       0.2644   \n",
       "5    Argentina               1.4354          14.9869       1.0650   \n",
       "\n",
       "   Aquatic Products, Other  Cereals - Excluding Beer    Eggs  Fish, Seafood  \\\n",
       "0                      0.0                   37.1186  0.1501         0.0000   \n",
       "1                      0.0                   16.2107  0.8091         0.1471   \n",
       "2                      0.0                   25.0112  0.4181         0.1195   \n",
       "3                      0.0                   18.3521  0.0441         0.8372   \n",
       "5                      0.0                   16.7927  0.8643         0.2006   \n",
       "\n",
       "   Fruits - Excluding Wine    Meat  ...  Vegetable Oils  Vegetables  Obesity  \\\n",
       "0                   1.4757  1.2006  ...          2.3012      0.7504      4.5   \n",
       "1                   3.8982  3.8688  ...          2.8244      2.7508     22.3   \n",
       "2                   3.1805  1.2543  ...          5.7638      2.0457     26.6   \n",
       "3                   2.3133  2.9302  ...          4.2741      0.3525      6.8   \n",
       "5                   1.4663  9.4459  ...          5.5410      0.8643     28.5   \n",
       "\n",
       "   Undernourished  Confirmed    Deaths  Recovered    Active  Population  \\\n",
       "0            29.8   0.142134  0.006186   0.123374  0.012574  38928000.0   \n",
       "1             6.2   2.967301  0.050951   1.792636  1.123714   2838000.0   \n",
       "2             3.9   0.244897  0.006558   0.167572  0.070767  44357000.0   \n",
       "3              25   0.061687  0.001461   0.056808  0.003419  32522000.0   \n",
       "5             4.6   4.356147  0.108227   3.905192  0.342729  45377000.0   \n",
       "\n",
       "   Unit (all except Population)  \n",
       "0                             %  \n",
       "1                             %  \n",
       "2                             %  \n",
       "3                             %  \n",
       "5                             %  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Confirming modifications by comparing to original main dataframe\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing Impact of Various Columns on Recovery% to Death% Ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Assigning levels for meat consumption based on quantiles\n",
    "quant_meat = df_kcal['Meat'].quantile(3/4)\n",
    "criteria = [df_kcal['Meat'] < quant_meat, df_kcal['Meat'] >= quant_meat]\n",
    "values = [0, 1]\n",
    "df_kcal['Meat_Consumption'] = np.select(criteria, values, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Assigning levels for obesity rates based on quantiles\n",
    "quant_obesity = df_kcal['Obesity'].quantile(3/4)\n",
    "criteria = [df_kcal['Obesity'] < quant_obesity, df_kcal['Obesity'] >= quant_obesity]\n",
    "values = [0, 1]\n",
    "df_kcal['Obesity_Level'] = np.select(criteria, values, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Assigning levels for vegetable consumption based on quantiles\n",
    "quantile1 = df_kcal['Vegetables'].quantile(1/2)\n",
    "criteria = [df_kcal['Vegetables'] < quantile1, df_kcal['Vegetables'] >= quantile1]\n",
    "values = [0, 1]\n",
    "df_kcal['Vegetable_Consumption'] = np.select(criteria, values, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 463.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking relationship between meat consumption and Recovery to Death Ratio\n",
    "sns.lmplot(x=\"Deaths\",y='Recovered', hue='Meat_Consumption', data=df_kcal)\n",
    "plt.title(\"Relationship Between Recovery To Deaths Among High and Low Meat Eaters\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 488.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking relationship between vegetable consumption and Recovery to Death Ratio\n",
    "sns.lmplot(x=\"Deaths\",y='Recovered', hue='Vegetable_Consumption', data=df_kcal)\n",
    "plt.title(\"Relationship Between Recovery To Deaths Among High and Low Vegetable Eaters\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 437.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking relationship between obesity and Recovery to Death Ratio\n",
    "sns.lmplot(x=\"Deaths\",y='Recovered', hue='Obesity_Level', data=df_kcal)\n",
    "plt.title(\"Relationship Between Recovery To Deaths Among Countries With High and Low Obesity\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x1fe2e55a6d0>,\n",
       "  <matplotlib.lines.Line2D at 0x1fe2e55aa30>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1fe2e55ad90>,\n",
       "  <matplotlib.lines.Line2D at 0x1fe2e566130>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x1fe2e55a370>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1fe2e566490>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1fe2e5667f0>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df_kcal.Obesity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating Required Columns and Preprocessing Steps for ML models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prof mentioned that high death rate accompanied by a high recovery rate is expected because more confirmed cases means more deaths and recoveries\n",
    "#So decided to observe correlation between food group and ratio of recovery to death\n",
    "sum_column2 = df_kcal['Recovered']/df_kcal['Deaths']\n",
    "df_kcal['Ratio of Recovery to Death'] = sum_column2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "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>Alcoholic Beverages</th>\n",
       "      <th>Animal Products</th>\n",
       "      <th>Animal fats</th>\n",
       "      <th>Aquatic Products, Other</th>\n",
       "      <th>Cereals - Excluding Beer</th>\n",
       "      <th>Eggs</th>\n",
       "      <th>Fish, Seafood</th>\n",
       "      <th>Fruits - Excluding Wine</th>\n",
       "      <th>Meat</th>\n",
       "      <th>Milk - Excluding Butter</th>\n",
       "      <th>...</th>\n",
       "      <th>Vegetal Products</th>\n",
       "      <th>Vegetable Oils</th>\n",
       "      <th>Vegetables</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>Population</th>\n",
       "      <th>Ratio of Recovery to Death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>1.540000e+02</td>\n",
       "      <td>154.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.310975</td>\n",
       "      <td>9.050435</td>\n",
       "      <td>1.265275</td>\n",
       "      <td>0.003076</td>\n",
       "      <td>20.575647</td>\n",
       "      <td>0.421891</td>\n",
       "      <td>0.592167</td>\n",
       "      <td>2.008186</td>\n",
       "      <td>3.684662</td>\n",
       "      <td>2.938984</td>\n",
       "      <td>...</td>\n",
       "      <td>40.950944</td>\n",
       "      <td>4.875444</td>\n",
       "      <td>1.066180</td>\n",
       "      <td>18.449351</td>\n",
       "      <td>5.510239</td>\n",
       "      <td>0.092164</td>\n",
       "      <td>4.968231</td>\n",
       "      <td>0.449843</td>\n",
       "      <td>4.873200e+07</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.080327</td>\n",
       "      <td>4.793536</td>\n",
       "      <td>1.316687</td>\n",
       "      <td>0.032440</td>\n",
       "      <td>6.506307</td>\n",
       "      <td>0.310935</td>\n",
       "      <td>0.550383</td>\n",
       "      <td>1.455394</td>\n",
       "      <td>2.116294</td>\n",
       "      <td>2.066412</td>\n",
       "      <td>...</td>\n",
       "      <td>4.793145</td>\n",
       "      <td>2.184836</td>\n",
       "      <td>0.641302</td>\n",
       "      <td>9.519483</td>\n",
       "      <td>5.386185</td>\n",
       "      <td>0.097782</td>\n",
       "      <td>4.961672</td>\n",
       "      <td>0.737322</td>\n",
       "      <td>1.660749e+08</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.623700</td>\n",
       "      <td>0.040800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.956500</td>\n",
       "      <td>0.018800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.147100</td>\n",
       "      <td>0.298000</td>\n",
       "      <td>0.116900</td>\n",
       "      <td>...</td>\n",
       "      <td>27.708900</td>\n",
       "      <td>0.932500</td>\n",
       "      <td>0.095700</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>0.000312</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000306</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.223500e+04</td>\n",
       "      <td>3.491761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.361300</td>\n",
       "      <td>4.810425</td>\n",
       "      <td>0.336850</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.338125</td>\n",
       "      <td>0.130675</td>\n",
       "      <td>0.231125</td>\n",
       "      <td>1.184600</td>\n",
       "      <td>1.918425</td>\n",
       "      <td>1.069250</td>\n",
       "      <td>...</td>\n",
       "      <td>36.931350</td>\n",
       "      <td>3.075900</td>\n",
       "      <td>0.589400</td>\n",
       "      <td>8.250000</td>\n",
       "      <td>0.465540</td>\n",
       "      <td>0.010222</td>\n",
       "      <td>0.406148</td>\n",
       "      <td>0.006212</td>\n",
       "      <td>3.386338e+06</td>\n",
       "      <td>32.107916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.194250</td>\n",
       "      <td>8.730650</td>\n",
       "      <td>0.763300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.060950</td>\n",
       "      <td>0.393550</td>\n",
       "      <td>0.442200</td>\n",
       "      <td>1.679950</td>\n",
       "      <td>3.482050</td>\n",
       "      <td>2.675300</td>\n",
       "      <td>...</td>\n",
       "      <td>41.273700</td>\n",
       "      <td>4.638450</td>\n",
       "      <td>0.989000</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>4.204724</td>\n",
       "      <td>0.056853</td>\n",
       "      <td>3.716227</td>\n",
       "      <td>0.088721</td>\n",
       "      <td>1.034807e+07</td>\n",
       "      <td>56.450311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.027450</td>\n",
       "      <td>13.073450</td>\n",
       "      <td>1.869675</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.970375</td>\n",
       "      <td>0.629950</td>\n",
       "      <td>0.834400</td>\n",
       "      <td>2.383025</td>\n",
       "      <td>5.042525</td>\n",
       "      <td>4.376425</td>\n",
       "      <td>...</td>\n",
       "      <td>45.201875</td>\n",
       "      <td>6.519100</td>\n",
       "      <td>1.334775</td>\n",
       "      <td>25.700000</td>\n",
       "      <td>9.427713</td>\n",
       "      <td>0.146331</td>\n",
       "      <td>8.445140</td>\n",
       "      <td>0.498469</td>\n",
       "      <td>3.424861e+07</td>\n",
       "      <td>78.736622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.157400</td>\n",
       "      <td>22.291100</td>\n",
       "      <td>7.800700</td>\n",
       "      <td>0.400700</td>\n",
       "      <td>37.526500</td>\n",
       "      <td>1.446100</td>\n",
       "      <td>4.418300</td>\n",
       "      <td>8.854000</td>\n",
       "      <td>10.567400</td>\n",
       "      <td>9.944100</td>\n",
       "      <td>...</td>\n",
       "      <td>48.386400</td>\n",
       "      <td>10.383900</td>\n",
       "      <td>3.226900</td>\n",
       "      <td>45.500000</td>\n",
       "      <td>24.676325</td>\n",
       "      <td>0.400064</td>\n",
       "      <td>23.787404</td>\n",
       "      <td>3.337548</td>\n",
       "      <td>1.439324e+09</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Alcoholic Beverages  Animal Products  Animal fats  \\\n",
       "count           154.000000       154.000000   154.000000   \n",
       "mean              1.310975         9.050435     1.265275   \n",
       "std               1.080327         4.793536     1.316687   \n",
       "min               0.000000         1.623700     0.040800   \n",
       "25%               0.361300         4.810425     0.336850   \n",
       "50%               1.194250         8.730650     0.763300   \n",
       "75%               2.027450        13.073450     1.869675   \n",
       "max               5.157400        22.291100     7.800700   \n",
       "\n",
       "       Aquatic Products, Other  Cereals - Excluding Beer        Eggs  \\\n",
       "count               154.000000                154.000000  154.000000   \n",
       "mean                  0.003076                 20.575647    0.421891   \n",
       "std                   0.032440                  6.506307    0.310935   \n",
       "min                   0.000000                  8.956500    0.018800   \n",
       "25%                   0.000000                 15.338125    0.130675   \n",
       "50%                   0.000000                 20.060950    0.393550   \n",
       "75%                   0.000000                 24.970375    0.629950   \n",
       "max                   0.400700                 37.526500    1.446100   \n",
       "\n",
       "       Fish, Seafood  Fruits - Excluding Wine        Meat  \\\n",
       "count     154.000000               154.000000  154.000000   \n",
       "mean        0.592167                 2.008186    3.684662   \n",
       "std         0.550383                 1.455394    2.116294   \n",
       "min         0.000000                 0.147100    0.298000   \n",
       "25%         0.231125                 1.184600    1.918425   \n",
       "50%         0.442200                 1.679950    3.482050   \n",
       "75%         0.834400                 2.383025    5.042525   \n",
       "max         4.418300                 8.854000   10.567400   \n",
       "\n",
       "       Milk - Excluding Butter  ...  Vegetal Products  Vegetable Oils  \\\n",
       "count               154.000000  ...        154.000000      154.000000   \n",
       "mean                  2.938984  ...         40.950944        4.875444   \n",
       "std                   2.066412  ...          4.793145        2.184836   \n",
       "min                   0.116900  ...         27.708900        0.932500   \n",
       "25%                   1.069250  ...         36.931350        3.075900   \n",
       "50%                   2.675300  ...         41.273700        4.638450   \n",
       "75%                   4.376425  ...         45.201875        6.519100   \n",
       "max                   9.944100  ...         48.386400       10.383900   \n",
       "\n",
       "       Vegetables     Obesity   Confirmed      Deaths   Recovered      Active  \\\n",
       "count  154.000000  154.000000  154.000000  154.000000  154.000000  154.000000   \n",
       "mean     1.066180   18.449351    5.510239    0.092164    4.968231    0.449843   \n",
       "std      0.641302    9.519483    5.386185    0.097782    4.961672    0.737322   \n",
       "min      0.095700    2.100000    0.000312    0.000000    0.000306    0.000000   \n",
       "25%      0.589400    8.250000    0.465540    0.010222    0.406148    0.006212   \n",
       "50%      0.989000   21.300000    4.204724    0.056853    3.716227    0.088721   \n",
       "75%      1.334775   25.700000    9.427713    0.146331    8.445140    0.498469   \n",
       "max      3.226900   45.500000   24.676325    0.400064   23.787404    3.337548   \n",
       "\n",
       "         Population  Ratio of Recovery to Death  \n",
       "count  1.540000e+02                  154.000000  \n",
       "mean   4.873200e+07                         inf  \n",
       "std    1.660749e+08                         NaN  \n",
       "min    7.223500e+04                    3.491761  \n",
       "25%    3.386338e+06                   32.107916  \n",
       "50%    1.034807e+07                   56.450311  \n",
       "75%    3.424861e+07                   78.736622  \n",
       "max    1.439324e+09                         inf  \n",
       "\n",
       "[8 rows x 30 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for issues in data\n",
    "df_kcal.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 33)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Ratio of recovery to death has some undefined and some values = 0 for stats, so need to drop rows where recovery or death = 0\n",
    "df_kcal = df_kcal.drop(df_kcal[(df_kcal.Recovered == 0) | (df_kcal.Deaths == 0) | (df_kcal.Confirmed == 0)].index)\n",
    "df_kcal.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>Alcoholic Beverages</th>\n",
       "      <th>Animal Products</th>\n",
       "      <th>Animal fats</th>\n",
       "      <th>Aquatic Products, Other</th>\n",
       "      <th>Cereals - Excluding Beer</th>\n",
       "      <th>Eggs</th>\n",
       "      <th>Fish, Seafood</th>\n",
       "      <th>Fruits - Excluding Wine</th>\n",
       "      <th>Meat</th>\n",
       "      <th>Milk - Excluding Butter</th>\n",
       "      <th>...</th>\n",
       "      <th>Vegetal Products</th>\n",
       "      <th>Vegetable Oils</th>\n",
       "      <th>Vegetables</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>Population</th>\n",
       "      <th>Ratio of Recovery to Death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>1.500000e+02</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.331336</td>\n",
       "      <td>9.092725</td>\n",
       "      <td>1.272763</td>\n",
       "      <td>0.002934</td>\n",
       "      <td>20.644697</td>\n",
       "      <td>0.429543</td>\n",
       "      <td>0.573934</td>\n",
       "      <td>1.985351</td>\n",
       "      <td>3.669779</td>\n",
       "      <td>2.998377</td>\n",
       "      <td>...</td>\n",
       "      <td>40.908687</td>\n",
       "      <td>4.943734</td>\n",
       "      <td>1.073469</td>\n",
       "      <td>18.314667</td>\n",
       "      <td>5.657150</td>\n",
       "      <td>0.094622</td>\n",
       "      <td>5.100696</td>\n",
       "      <td>0.461832</td>\n",
       "      <td>4.997500e+07</td>\n",
       "      <td>67.007175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.085828</td>\n",
       "      <td>4.812964</td>\n",
       "      <td>1.329959</td>\n",
       "      <td>0.032774</td>\n",
       "      <td>6.472334</td>\n",
       "      <td>0.311389</td>\n",
       "      <td>0.543156</td>\n",
       "      <td>1.453913</td>\n",
       "      <td>2.107009</td>\n",
       "      <td>2.059498</td>\n",
       "      <td>...</td>\n",
       "      <td>4.812327</td>\n",
       "      <td>2.169599</td>\n",
       "      <td>0.637078</td>\n",
       "      <td>9.306917</td>\n",
       "      <td>5.380827</td>\n",
       "      <td>0.097897</td>\n",
       "      <td>4.959738</td>\n",
       "      <td>0.743416</td>\n",
       "      <td>1.681106e+08</td>\n",
       "      <td>59.821187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.623700</td>\n",
       "      <td>0.040800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.956500</td>\n",
       "      <td>0.018800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.147100</td>\n",
       "      <td>0.298000</td>\n",
       "      <td>0.116900</td>\n",
       "      <td>...</td>\n",
       "      <td>27.708900</td>\n",
       "      <td>0.932500</td>\n",
       "      <td>0.095700</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>0.000852</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.000306</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>7.223500e+04</td>\n",
       "      <td>3.491761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.375500</td>\n",
       "      <td>4.895300</td>\n",
       "      <td>0.326550</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.338125</td>\n",
       "      <td>0.138300</td>\n",
       "      <td>0.228950</td>\n",
       "      <td>1.184600</td>\n",
       "      <td>1.918425</td>\n",
       "      <td>1.108550</td>\n",
       "      <td>...</td>\n",
       "      <td>36.931350</td>\n",
       "      <td>3.224100</td>\n",
       "      <td>0.601400</td>\n",
       "      <td>8.250000</td>\n",
       "      <td>0.599988</td>\n",
       "      <td>0.012100</td>\n",
       "      <td>0.481769</td>\n",
       "      <td>0.006828</td>\n",
       "      <td>4.001392e+06</td>\n",
       "      <td>31.152323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.214400</td>\n",
       "      <td>8.798450</td>\n",
       "      <td>0.774200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.244050</td>\n",
       "      <td>0.403750</td>\n",
       "      <td>0.427900</td>\n",
       "      <td>1.667250</td>\n",
       "      <td>3.482050</td>\n",
       "      <td>2.869250</td>\n",
       "      <td>...</td>\n",
       "      <td>41.205900</td>\n",
       "      <td>4.687100</td>\n",
       "      <td>0.995250</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>4.595935</td>\n",
       "      <td>0.057962</td>\n",
       "      <td>4.028947</td>\n",
       "      <td>0.098951</td>\n",
       "      <td>1.086787e+07</td>\n",
       "      <td>55.456803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.039750</td>\n",
       "      <td>13.073450</td>\n",
       "      <td>1.869675</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.970375</td>\n",
       "      <td>0.632800</td>\n",
       "      <td>0.822275</td>\n",
       "      <td>2.370700</td>\n",
       "      <td>5.042525</td>\n",
       "      <td>4.516450</td>\n",
       "      <td>...</td>\n",
       "      <td>45.104700</td>\n",
       "      <td>6.599950</td>\n",
       "      <td>1.334775</td>\n",
       "      <td>25.700000</td>\n",
       "      <td>9.661143</td>\n",
       "      <td>0.148377</td>\n",
       "      <td>8.620012</td>\n",
       "      <td>0.516364</td>\n",
       "      <td>3.524625e+07</td>\n",
       "      <td>76.322902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.157400</td>\n",
       "      <td>22.291100</td>\n",
       "      <td>7.800700</td>\n",
       "      <td>0.400700</td>\n",
       "      <td>37.526500</td>\n",
       "      <td>1.446100</td>\n",
       "      <td>4.418300</td>\n",
       "      <td>8.854000</td>\n",
       "      <td>10.567400</td>\n",
       "      <td>9.944100</td>\n",
       "      <td>...</td>\n",
       "      <td>48.386400</td>\n",
       "      <td>10.383900</td>\n",
       "      <td>3.226900</td>\n",
       "      <td>37.300000</td>\n",
       "      <td>24.676325</td>\n",
       "      <td>0.400064</td>\n",
       "      <td>23.787404</td>\n",
       "      <td>3.337548</td>\n",
       "      <td>1.439324e+09</td>\n",
       "      <td>448.029412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Alcoholic Beverages  Animal Products  Animal fats  \\\n",
       "count           150.000000       150.000000   150.000000   \n",
       "mean              1.331336         9.092725     1.272763   \n",
       "std               1.085828         4.812964     1.329959   \n",
       "min               0.000000         1.623700     0.040800   \n",
       "25%               0.375500         4.895300     0.326550   \n",
       "50%               1.214400         8.798450     0.774200   \n",
       "75%               2.039750        13.073450     1.869675   \n",
       "max               5.157400        22.291100     7.800700   \n",
       "\n",
       "       Aquatic Products, Other  Cereals - Excluding Beer        Eggs  \\\n",
       "count               150.000000                150.000000  150.000000   \n",
       "mean                  0.002934                 20.644697    0.429543   \n",
       "std                   0.032774                  6.472334    0.311389   \n",
       "min                   0.000000                  8.956500    0.018800   \n",
       "25%                   0.000000                 15.338125    0.138300   \n",
       "50%                   0.000000                 20.244050    0.403750   \n",
       "75%                   0.000000                 24.970375    0.632800   \n",
       "max                   0.400700                 37.526500    1.446100   \n",
       "\n",
       "       Fish, Seafood  Fruits - Excluding Wine        Meat  \\\n",
       "count     150.000000               150.000000  150.000000   \n",
       "mean        0.573934                 1.985351    3.669779   \n",
       "std         0.543156                 1.453913    2.107009   \n",
       "min         0.000000                 0.147100    0.298000   \n",
       "25%         0.228950                 1.184600    1.918425   \n",
       "50%         0.427900                 1.667250    3.482050   \n",
       "75%         0.822275                 2.370700    5.042525   \n",
       "max         4.418300                 8.854000   10.567400   \n",
       "\n",
       "       Milk - Excluding Butter  ...  Vegetal Products  Vegetable Oils  \\\n",
       "count               150.000000  ...        150.000000      150.000000   \n",
       "mean                  2.998377  ...         40.908687        4.943734   \n",
       "std                   2.059498  ...          4.812327        2.169599   \n",
       "min                   0.116900  ...         27.708900        0.932500   \n",
       "25%                   1.108550  ...         36.931350        3.224100   \n",
       "50%                   2.869250  ...         41.205900        4.687100   \n",
       "75%                   4.516450  ...         45.104700        6.599950   \n",
       "max                   9.944100  ...         48.386400       10.383900   \n",
       "\n",
       "       Vegetables     Obesity   Confirmed      Deaths   Recovered      Active  \\\n",
       "count  150.000000  150.000000  150.000000  150.000000  150.000000  150.000000   \n",
       "mean     1.073469   18.314667    5.657150    0.094622    5.100696    0.461832   \n",
       "std      0.637078    9.306917    5.380827    0.097897    4.959738    0.743416   \n",
       "min      0.095700    2.100000    0.000852    0.000035    0.000306    0.000040   \n",
       "25%      0.601400    8.250000    0.599988    0.012100    0.481769    0.006828   \n",
       "50%      0.995250   21.300000    4.595935    0.057962    4.028947    0.098951   \n",
       "75%      1.334775   25.700000    9.661143    0.148377    8.620012    0.516364   \n",
       "max      3.226900   37.300000   24.676325    0.400064   23.787404    3.337548   \n",
       "\n",
       "         Population  Ratio of Recovery to Death  \n",
       "count  1.500000e+02                  150.000000  \n",
       "mean   4.997500e+07                   67.007175  \n",
       "std    1.681106e+08                   59.821187  \n",
       "min    7.223500e+04                    3.491761  \n",
       "25%    4.001392e+06                   31.152323  \n",
       "50%    1.086787e+07                   55.456803  \n",
       "75%    3.524625e+07                   76.322902  \n",
       "max    1.439324e+09                  448.029412  \n",
       "\n",
       "[8 rows x 30 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checkimg to confirm that issues have been resolved\n",
    "df_kcal.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x1fe3290e6a0>,\n",
       "  <matplotlib.lines.Line2D at 0x1fe3290ea30>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1fe3290ed90>,\n",
       "  <matplotlib.lines.Line2D at 0x1fe32918130>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x1fe3290e340>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1fe32918490>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1fe329187f0>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking for Outliers\n",
    "plt.boxplot(df_kcal['Ratio of Recovery to Death'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55.98217017795897"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dropping outlier rows\n",
    "df_kcal = df_kcal.drop(df_kcal[df_kcal['Ratio of Recovery to Death'] > 180].index)\n",
    "#Making sure the difference is not too big\n",
    "(df_kcal['Ratio of Recovery to Death'].max() - df_kcal['Ratio of Recovery to Death'].min())/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x1fe32962640>,\n",
       "  <matplotlib.lines.Line2D at 0x1fe329629a0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1fe32962d00>,\n",
       "  <matplotlib.lines.Line2D at 0x1fe3296d0a0>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x1fe329622e0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1fe3296d400>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1fe3296d760>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Rechecking for improvement in outliers\n",
    "plt.boxplot(df_kcal['Ratio of Recovery to Death'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating Required Y Column #1: Levels for Recovery% : Death% Ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Alcoholic Beverages</th>\n",
       "      <th>Animal Products</th>\n",
       "      <th>Animal fats</th>\n",
       "      <th>Aquatic Products, Other</th>\n",
       "      <th>Cereals - Excluding Beer</th>\n",
       "      <th>Eggs</th>\n",
       "      <th>Fish, Seafood</th>\n",
       "      <th>Fruits - Excluding Wine</th>\n",
       "      <th>Meat</th>\n",
       "      <th>...</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>Population</th>\n",
       "      <th>Unit (all except Population)</th>\n",
       "      <th>Number Confirmed Cases</th>\n",
       "      <th>Ratio of Recovery to Death</th>\n",
       "      <th>Meat_Consumption</th>\n",
       "      <th>Obesity_Level</th>\n",
       "      <th>Vegetable_Consumption</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>4.7774</td>\n",
       "      <td>0.8504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.1186</td>\n",
       "      <td>0.1501</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.4757</td>\n",
       "      <td>1.2006</td>\n",
       "      <td>...</td>\n",
       "      <td>0.349546</td>\n",
       "      <td>0.023375</td>\n",
       "      <td>40145225.0</td>\n",
       "      <td>%</td>\n",
       "      <td>157015.0</td>\n",
       "      <td>19.209582</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>0.9120</td>\n",
       "      <td>16.0930</td>\n",
       "      <td>1.0591</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.2107</td>\n",
       "      <td>0.8091</td>\n",
       "      <td>0.1471</td>\n",
       "      <td>3.8982</td>\n",
       "      <td>3.8688</td>\n",
       "      <td>...</td>\n",
       "      <td>6.458112</td>\n",
       "      <td>0.278170</td>\n",
       "      <td>2873425.0</td>\n",
       "      <td>%</td>\n",
       "      <td>196611.0</td>\n",
       "      <td>60.862250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>0.0896</td>\n",
       "      <td>6.0326</td>\n",
       "      <td>0.1941</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0112</td>\n",
       "      <td>0.4181</td>\n",
       "      <td>0.1195</td>\n",
       "      <td>3.1805</td>\n",
       "      <td>1.2543</td>\n",
       "      <td>...</td>\n",
       "      <td>0.319045</td>\n",
       "      <td>0.132750</td>\n",
       "      <td>44950745.0</td>\n",
       "      <td>%</td>\n",
       "      <td>209111.0</td>\n",
       "      <td>23.799038</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1.9388</td>\n",
       "      <td>4.6927</td>\n",
       "      <td>0.2644</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.3521</td>\n",
       "      <td>0.0441</td>\n",
       "      <td>0.8372</td>\n",
       "      <td>2.3133</td>\n",
       "      <td>2.9302</td>\n",
       "      <td>...</td>\n",
       "      <td>0.184119</td>\n",
       "      <td>0.000525</td>\n",
       "      <td>34283759.0</td>\n",
       "      <td>%</td>\n",
       "      <td>65033.0</td>\n",
       "      <td>36.487283</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1.4354</td>\n",
       "      <td>14.9869</td>\n",
       "      <td>1.0650</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.7927</td>\n",
       "      <td>0.8643</td>\n",
       "      <td>0.2006</td>\n",
       "      <td>1.4663</td>\n",
       "      <td>9.4459</td>\n",
       "      <td>...</td>\n",
       "      <td>11.323043</td>\n",
       "      <td>0.036673</td>\n",
       "      <td>45772333.0</td>\n",
       "      <td>%</td>\n",
       "      <td>5315989.0</td>\n",
       "      <td>44.532840</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>0.8454</td>\n",
       "      <td>7.2303</td>\n",
       "      <td>0.6007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.3126</td>\n",
       "      <td>0.2892</td>\n",
       "      <td>0.4449</td>\n",
       "      <td>2.3804</td>\n",
       "      <td>3.1368</td>\n",
       "      <td>...</td>\n",
       "      <td>1.456159</td>\n",
       "      <td>0.029961</td>\n",
       "      <td>28323827.0</td>\n",
       "      <td>%</td>\n",
       "      <td>426014.0</td>\n",
       "      <td>81.061321</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>0.7150</td>\n",
       "      <td>10.9806</td>\n",
       "      <td>0.9363</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.9833</td>\n",
       "      <td>0.2894</td>\n",
       "      <td>1.0385</td>\n",
       "      <td>1.8046</td>\n",
       "      <td>7.8311</td>\n",
       "      <td>...</td>\n",
       "      <td>0.923611</td>\n",
       "      <td>0.173107</td>\n",
       "      <td>98556168.0</td>\n",
       "      <td>%</td>\n",
       "      <td>1104835.0</td>\n",
       "      <td>38.005762</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.4667</td>\n",
       "      <td>0.3394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0727</td>\n",
       "      <td>0.1455</td>\n",
       "      <td>0.1697</td>\n",
       "      <td>1.1879</td>\n",
       "      <td>2.0121</td>\n",
       "      <td>...</td>\n",
       "      <td>0.022061</td>\n",
       "      <td>0.004047</td>\n",
       "      <td>30737797.0</td>\n",
       "      <td>%</td>\n",
       "      <td>9967.0</td>\n",
       "      <td>3.491761</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1.1925</td>\n",
       "      <td>3.3043</td>\n",
       "      <td>0.3230</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.5528</td>\n",
       "      <td>0.1988</td>\n",
       "      <td>0.5714</td>\n",
       "      <td>0.2236</td>\n",
       "      <td>1.5155</td>\n",
       "      <td>...</td>\n",
       "      <td>1.080297</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>19098078.0</td>\n",
       "      <td>%</td>\n",
       "      <td>210073.0</td>\n",
       "      <td>56.262885</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1.4269</td>\n",
       "      <td>3.9356</td>\n",
       "      <td>0.6904</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.8044</td>\n",
       "      <td>0.1381</td>\n",
       "      <td>0.1611</td>\n",
       "      <td>0.4373</td>\n",
       "      <td>1.7491</td>\n",
       "      <td>...</td>\n",
       "      <td>0.847197</td>\n",
       "      <td>0.003363</td>\n",
       "      <td>15163536.0</td>\n",
       "      <td>%</td>\n",
       "      <td>133674.0</td>\n",
       "      <td>27.338795</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country  Alcoholic Beverages  Animal Products  Animal fats  \\\n",
       "0    Afghanistan               0.0000           4.7774       0.8504   \n",
       "1        Albania               0.9120          16.0930       1.0591   \n",
       "2        Algeria               0.0896           6.0326       0.1941   \n",
       "3         Angola               1.9388           4.6927       0.2644   \n",
       "5      Argentina               1.4354          14.9869       1.0650   \n",
       "..           ...                  ...              ...          ...   \n",
       "165    Venezuela               0.8454           7.2303       0.6007   \n",
       "166      Vietnam               0.7150          10.9806       0.9363   \n",
       "167        Yemen               0.0000           3.4667       0.3394   \n",
       "168       Zambia               1.1925           3.3043       0.3230   \n",
       "169     Zimbabwe               1.4269           3.9356       0.6904   \n",
       "\n",
       "     Aquatic Products, Other  Cereals - Excluding Beer    Eggs  Fish, Seafood  \\\n",
       "0                        0.0                   37.1186  0.1501         0.0000   \n",
       "1                        0.0                   16.2107  0.8091         0.1471   \n",
       "2                        0.0                   25.0112  0.4181         0.1195   \n",
       "3                        0.0                   18.3521  0.0441         0.8372   \n",
       "5                        0.0                   16.7927  0.8643         0.2006   \n",
       "..                       ...                       ...     ...            ...   \n",
       "165                      0.0                   21.3126  0.2892         0.4449   \n",
       "166                      0.0                   26.9833  0.2894         1.0385   \n",
       "167                      0.0                   32.0727  0.1455         0.1697   \n",
       "168                      0.0                   31.5528  0.1988         0.5714   \n",
       "169                      0.0                   29.8044  0.1381         0.1611   \n",
       "\n",
       "     Fruits - Excluding Wine    Meat  ...  Recovered    Active  Population  \\\n",
       "0                     1.4757  1.2006  ...   0.349546  0.023375  40145225.0   \n",
       "1                     3.8982  3.8688  ...   6.458112  0.278170   2873425.0   \n",
       "2                     3.1805  1.2543  ...   0.319045  0.132750  44950745.0   \n",
       "3                     2.3133  2.9302  ...   0.184119  0.000525  34283759.0   \n",
       "5                     1.4663  9.4459  ...  11.323043  0.036673  45772333.0   \n",
       "..                       ...     ...  ...        ...       ...         ...   \n",
       "165                   2.3804  3.1368  ...   1.456159  0.029961  28323827.0   \n",
       "166                   1.8046  7.8311  ...   0.923611  0.173107  98556168.0   \n",
       "167                   1.1879  2.0121  ...   0.022061  0.004047  30737797.0   \n",
       "168                   0.2236  1.5155  ...   1.080297  0.000471  19098078.0   \n",
       "169                   0.4373  1.7491  ...   0.847197  0.003363  15163536.0   \n",
       "\n",
       "     Unit (all except Population)  Number Confirmed Cases  \\\n",
       "0                               %                157015.0   \n",
       "1                               %                196611.0   \n",
       "2                               %                209111.0   \n",
       "3                               %                 65033.0   \n",
       "5                               %               5315989.0   \n",
       "..                            ...                     ...   \n",
       "165                             %                426014.0   \n",
       "166                             %               1104835.0   \n",
       "167                             %                  9967.0   \n",
       "168                             %                210073.0   \n",
       "169                             %                133674.0   \n",
       "\n",
       "     Ratio of Recovery to Death  Meat_Consumption  Obesity_Level  \\\n",
       "0                     19.209582                 0              0   \n",
       "1                     60.862250                 0              0   \n",
       "2                     23.799038                 0              1   \n",
       "3                     36.487283                 0              0   \n",
       "5                     44.532840                 1              1   \n",
       "..                          ...               ...            ...   \n",
       "165                   81.061321                 0              0   \n",
       "166                   38.005762                 1              0   \n",
       "167                    3.491761                 0              0   \n",
       "168                   56.262885                 0              0   \n",
       "169                   27.338795                 0              0   \n",
       "\n",
       "     Vegetable_Consumption  y  \n",
       "0                        0  0  \n",
       "1                        1  1  \n",
       "2                        1  0  \n",
       "3                        0  0  \n",
       "5                        0  1  \n",
       "..                     ... ..  \n",
       "165                      0  2  \n",
       "166                      1  1  \n",
       "167                      0  0  \n",
       "168                      0  1  \n",
       "169                      0  0  \n",
       "\n",
       "[146 rows x 38 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import scale\n",
    "# determining all independent variables\n",
    "x = scale(df_kcal.iloc[:, 1:24])\n",
    "#Dividing \"Ratio of Recovery to Death\" values into 3 levels based on quantiles\n",
    "#to determine 3 categories, instead of doing equal ranges, do it by distribution with quantile = 0.33: df.field_A.quantile(0.33), to get = number of observations in each group\n",
    "q1 = df_kcal['Ratio of Recovery to Death'].quantile(1/3)\n",
    "q2 = df_kcal['Ratio of Recovery to Death'].quantile(2/3)\n",
    "\n",
    "maximum = df_kcal['Ratio of Recovery to Death'].max()\n",
    "#Splits into 3 respective groups and assigns them to 0, 1, 2 and puts them in a new variable called y\n",
    "criteria = [df_kcal['Ratio of Recovery to Death'].between(0, q1), df_kcal['Ratio of Recovery to Death'].between(q1, q2), df_kcal['Ratio of Recovery to Death'].between(q2, maximum + 1)]\n",
    "values = [0, 1, 2]\n",
    "df_kcal['y'] = np.select(criteria, values, 0)\n",
    "df_kcal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.cluster import KMeans\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import sklearn.metrics as sm\n",
    "from sklearn import datasets\n",
    "from sklearn.metrics import confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code to create a Kmeans cluster model with 3 clusters\n",
    "clustering = KMeans(n_clusters = 3, random_state = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3, random_state=5)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fits independent variable (inputs) to model\n",
    "clustering.fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting color theme for clusters\n",
    "color_theme = color_theme = np.array(['darkgray', 'lightsalmon', 'powderblue'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating Required Y Column #2: Levels for Confirmed% "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Alcoholic Beverages</th>\n",
       "      <th>Animal Products</th>\n",
       "      <th>Animal fats</th>\n",
       "      <th>Aquatic Products, Other</th>\n",
       "      <th>Cereals - Excluding Beer</th>\n",
       "      <th>Eggs</th>\n",
       "      <th>Fish, Seafood</th>\n",
       "      <th>Fruits - Excluding Wine</th>\n",
       "      <th>Meat</th>\n",
       "      <th>...</th>\n",
       "      <th>Active</th>\n",
       "      <th>Population</th>\n",
       "      <th>Unit (all except Population)</th>\n",
       "      <th>Number Confirmed Cases</th>\n",
       "      <th>Ratio of Recovery to Death</th>\n",
       "      <th>Meat_Consumption</th>\n",
       "      <th>Obesity_Level</th>\n",
       "      <th>Vegetable_Consumption</th>\n",
       "      <th>y</th>\n",
       "      <th>y_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>4.7774</td>\n",
       "      <td>0.8504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.1186</td>\n",
       "      <td>0.1501</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.4757</td>\n",
       "      <td>1.2006</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023375</td>\n",
       "      <td>40145225.0</td>\n",
       "      <td>%</td>\n",
       "      <td>157015.0</td>\n",
       "      <td>19.209582</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>0.9120</td>\n",
       "      <td>16.0930</td>\n",
       "      <td>1.0591</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.2107</td>\n",
       "      <td>0.8091</td>\n",
       "      <td>0.1471</td>\n",
       "      <td>3.8982</td>\n",
       "      <td>3.8688</td>\n",
       "      <td>...</td>\n",
       "      <td>0.278170</td>\n",
       "      <td>2873425.0</td>\n",
       "      <td>%</td>\n",
       "      <td>196611.0</td>\n",
       "      <td>60.862250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>0.0896</td>\n",
       "      <td>6.0326</td>\n",
       "      <td>0.1941</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0112</td>\n",
       "      <td>0.4181</td>\n",
       "      <td>0.1195</td>\n",
       "      <td>3.1805</td>\n",
       "      <td>1.2543</td>\n",
       "      <td>...</td>\n",
       "      <td>0.132750</td>\n",
       "      <td>44950745.0</td>\n",
       "      <td>%</td>\n",
       "      <td>209111.0</td>\n",
       "      <td>23.799038</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1.9388</td>\n",
       "      <td>4.6927</td>\n",
       "      <td>0.2644</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.3521</td>\n",
       "      <td>0.0441</td>\n",
       "      <td>0.8372</td>\n",
       "      <td>2.3133</td>\n",
       "      <td>2.9302</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000525</td>\n",
       "      <td>34283759.0</td>\n",
       "      <td>%</td>\n",
       "      <td>65033.0</td>\n",
       "      <td>36.487283</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1.4354</td>\n",
       "      <td>14.9869</td>\n",
       "      <td>1.0650</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.7927</td>\n",
       "      <td>0.8643</td>\n",
       "      <td>0.2006</td>\n",
       "      <td>1.4663</td>\n",
       "      <td>9.4459</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036673</td>\n",
       "      <td>45772333.0</td>\n",
       "      <td>%</td>\n",
       "      <td>5315989.0</td>\n",
       "      <td>44.532840</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>0.8454</td>\n",
       "      <td>7.2303</td>\n",
       "      <td>0.6007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.3126</td>\n",
       "      <td>0.2892</td>\n",
       "      <td>0.4449</td>\n",
       "      <td>2.3804</td>\n",
       "      <td>3.1368</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029961</td>\n",
       "      <td>28323827.0</td>\n",
       "      <td>%</td>\n",
       "      <td>426014.0</td>\n",
       "      <td>81.061321</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>0.7150</td>\n",
       "      <td>10.9806</td>\n",
       "      <td>0.9363</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.9833</td>\n",
       "      <td>0.2894</td>\n",
       "      <td>1.0385</td>\n",
       "      <td>1.8046</td>\n",
       "      <td>7.8311</td>\n",
       "      <td>...</td>\n",
       "      <td>0.173107</td>\n",
       "      <td>98556168.0</td>\n",
       "      <td>%</td>\n",
       "      <td>1104835.0</td>\n",
       "      <td>38.005762</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.4667</td>\n",
       "      <td>0.3394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0727</td>\n",
       "      <td>0.1455</td>\n",
       "      <td>0.1697</td>\n",
       "      <td>1.1879</td>\n",
       "      <td>2.0121</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004047</td>\n",
       "      <td>30737797.0</td>\n",
       "      <td>%</td>\n",
       "      <td>9967.0</td>\n",
       "      <td>3.491761</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1.1925</td>\n",
       "      <td>3.3043</td>\n",
       "      <td>0.3230</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.5528</td>\n",
       "      <td>0.1988</td>\n",
       "      <td>0.5714</td>\n",
       "      <td>0.2236</td>\n",
       "      <td>1.5155</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>19098078.0</td>\n",
       "      <td>%</td>\n",
       "      <td>210073.0</td>\n",
       "      <td>56.262885</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1.4269</td>\n",
       "      <td>3.9356</td>\n",
       "      <td>0.6904</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.8044</td>\n",
       "      <td>0.1381</td>\n",
       "      <td>0.1611</td>\n",
       "      <td>0.4373</td>\n",
       "      <td>1.7491</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003363</td>\n",
       "      <td>15163536.0</td>\n",
       "      <td>%</td>\n",
       "      <td>133674.0</td>\n",
       "      <td>27.338795</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country  Alcoholic Beverages  Animal Products  Animal fats  \\\n",
       "0    Afghanistan               0.0000           4.7774       0.8504   \n",
       "1        Albania               0.9120          16.0930       1.0591   \n",
       "2        Algeria               0.0896           6.0326       0.1941   \n",
       "3         Angola               1.9388           4.6927       0.2644   \n",
       "5      Argentina               1.4354          14.9869       1.0650   \n",
       "..           ...                  ...              ...          ...   \n",
       "165    Venezuela               0.8454           7.2303       0.6007   \n",
       "166      Vietnam               0.7150          10.9806       0.9363   \n",
       "167        Yemen               0.0000           3.4667       0.3394   \n",
       "168       Zambia               1.1925           3.3043       0.3230   \n",
       "169     Zimbabwe               1.4269           3.9356       0.6904   \n",
       "\n",
       "     Aquatic Products, Other  Cereals - Excluding Beer    Eggs  Fish, Seafood  \\\n",
       "0                        0.0                   37.1186  0.1501         0.0000   \n",
       "1                        0.0                   16.2107  0.8091         0.1471   \n",
       "2                        0.0                   25.0112  0.4181         0.1195   \n",
       "3                        0.0                   18.3521  0.0441         0.8372   \n",
       "5                        0.0                   16.7927  0.8643         0.2006   \n",
       "..                       ...                       ...     ...            ...   \n",
       "165                      0.0                   21.3126  0.2892         0.4449   \n",
       "166                      0.0                   26.9833  0.2894         1.0385   \n",
       "167                      0.0                   32.0727  0.1455         0.1697   \n",
       "168                      0.0                   31.5528  0.1988         0.5714   \n",
       "169                      0.0                   29.8044  0.1381         0.1611   \n",
       "\n",
       "     Fruits - Excluding Wine    Meat  ...    Active  Population  \\\n",
       "0                     1.4757  1.2006  ...  0.023375  40145225.0   \n",
       "1                     3.8982  3.8688  ...  0.278170   2873425.0   \n",
       "2                     3.1805  1.2543  ...  0.132750  44950745.0   \n",
       "3                     2.3133  2.9302  ...  0.000525  34283759.0   \n",
       "5                     1.4663  9.4459  ...  0.036673  45772333.0   \n",
       "..                       ...     ...  ...       ...         ...   \n",
       "165                   2.3804  3.1368  ...  0.029961  28323827.0   \n",
       "166                   1.8046  7.8311  ...  0.173107  98556168.0   \n",
       "167                   1.1879  2.0121  ...  0.004047  30737797.0   \n",
       "168                   0.2236  1.5155  ...  0.000471  19098078.0   \n",
       "169                   0.4373  1.7491  ...  0.003363  15163536.0   \n",
       "\n",
       "     Unit (all except Population)  Number Confirmed Cases  \\\n",
       "0                               %                157015.0   \n",
       "1                               %                196611.0   \n",
       "2                               %                209111.0   \n",
       "3                               %                 65033.0   \n",
       "5                               %               5315989.0   \n",
       "..                            ...                     ...   \n",
       "165                             %                426014.0   \n",
       "166                             %               1104835.0   \n",
       "167                             %                  9967.0   \n",
       "168                             %                210073.0   \n",
       "169                             %                133674.0   \n",
       "\n",
       "     Ratio of Recovery to Death  Meat_Consumption  Obesity_Level  \\\n",
       "0                     19.209582                 0              0   \n",
       "1                     60.862250                 0              0   \n",
       "2                     23.799038                 0              1   \n",
       "3                     36.487283                 0              0   \n",
       "5                     44.532840                 1              1   \n",
       "..                          ...               ...            ...   \n",
       "165                   81.061321                 0              0   \n",
       "166                   38.005762                 1              0   \n",
       "167                    3.491761                 0              0   \n",
       "168                   56.262885                 0              0   \n",
       "169                   27.338795                 0              0   \n",
       "\n",
       "     Vegetable_Consumption  y  y_2  \n",
       "0                        0  0    0  \n",
       "1                        1  1    1  \n",
       "2                        1  0    0  \n",
       "3                        0  0    0  \n",
       "5                        0  1    2  \n",
       "..                     ... ..  ...  \n",
       "165                      0  2    1  \n",
       "166                      1  1    0  \n",
       "167                      0  0    0  \n",
       "168                      0  1    0  \n",
       "169                      0  0    0  \n",
       "\n",
       "[146 rows x 39 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dividing \"Confirmed\"% values into 3 levels based on quantiles\n",
    "g1 = df_kcal['Confirmed'].quantile(1/3)\n",
    "g2 = df_kcal['Confirmed'].quantile(2/3)\n",
    "maximum = df_kcal['Confirmed'].max()\n",
    "#Splits into 3 respective groups and assigns them to 0, 1, 2 and puts them in a new variable called y\n",
    "criteria = [df_kcal['Confirmed'].between(0, g1), df_kcal['Confirmed'].between(g1, g2), df_kcal['Confirmed'].between(g2, maximum + 1)]\n",
    "values = [0, 1, 2]\n",
    "df_kcal['y_2'] = np.select(criteria, values, 0)\n",
    "df_kcal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### K means Clustering for Checking Match with Confirmed% Levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'K-Means Classification')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEICAYAAAAKrmAuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAABpJklEQVR4nO39d5hc13Xgi/7WOZW7q3NGziABkCAJggHMQRIlWaJsSbZlW84avRmnufbM+Pm9e8e+d+6beXOf7bn3jZM89kgey5IsWzlLFEkwggRJgCASkWOjc3fldM6+f+zq7uruSp2rG/v3ff0BXefUPqurzjpr77VXEKUUBoPBYDDUGtZyC2AwGAwGQzGMgTIYDAZDTWIMlMFgMBhqEmOgDAaDwVCTGANlMBgMhprEGCiDwWAw1CTGQM0CEdkoIkpEPMtw7Ysi8sQCjqdEZOtCjTdt7J8TkR8U/H5ARM6ISExEnhaR74rILy7Cdf9SRP7nhR7XcHMhIp8Vkf+wiOPHRGRz/v9BEfmmiIyJyJen684CXvNBETm90OMuNjVnoETkZ0TkkIjERaQ///9/KSKy3LKVI3/Tjf+4IpIs+P3nZjnWvBVERLpF5G9EpFdEoiJySkT+SETq5jNuNSilPq+Uek/BS/8r8F+VUvVKqa8ppZ5SSn1uPtcQkV8SkRenXffTSqn/bT7jGiaZPinK6+aIiDxc5FwlIn2FkzcR8eR1uKaSLUXzWyLyTv45czVvHPYsxfXzenA+/+tHgU6gVSn1sSK6MyemT0CVUi8opXbMd9ylpqYMlIj8LvB/Av8H0IX+4j4NHAB8Jd5jL5mAZcjfdPVKqXrgMvATBa99fvy8pVh9iUgL8AoQBO5TSoWBJ4EmYMtiX78IG4Djy3BdwwKRX/H+GfABpdTzJU4bBZ4q+P39wMgiizYX/k/gt4HfAlqA7cDXgA8sgywbgHeVUrlluHbto5SqiR+gEYgDP1XhvM8CfwF8J3/+E8AtwHNoBTkOfKjg/OeAXyv4/ZeAFwt+V2gjeAatTH8GSP6YDfz/gEHgPPCv8ud7Ksh4EXgi//9HgKvAvwNuAP9jugwFcmwFPgVkgQwQA75ZMObvAW8DY8CXgECJ6/8H4BhglZFRAVvz//8A8BYQAa4Af1hwXgD4e2Ao//m+DnQWfJbngShwAfi56Z8xcA5wgWT+7/EX+U5+HTiZH+cEcGf+9d/Pv3/89Y/kX78FSAFOfszRgnvjP0wb9ywwDHwD6Knmezc/U+/j/D05COyrcD/9v4EvF7z2T8D/C1AFrzUCfwP0Atfy96qdP7YF+HH+XhsEPg80TZOnqA4AbcC38vfoMPBCsfsf2Ja/b/aX+Vsm7iOgOT/uQP4++RawtuDcUjqwFXg+L+cg8KUiuv5HaD3P5u/jX2Xm82kX8MP839QH/EH+9f3oSeho/rP8r4Avf+xg/hrx/Lg/Tf45VDBuuWfmZ/P68O3833UI2LIs9+ByK0HBh/I+IEflh/9n81/6AfQKMIx+CP0BepX1WP5D3ZE//zkqG6hvoVcX6/M34vvyxz4NnALWoWdazzI3A5UD/r/oh3NwugyFN+10BZk25mtAT16Wk8CnS1z/VeCPKshYeL1HgD35z/O2vCI8nT/2L4BvAiG0wb4LaADq0AZt/HPuBnaV+IwnPo/p3wnwMfSD6m5A0Iq7oeBYT16un0YrXHexa0z/3PL3wSBwZ/5z//8DB6v53s3PlO/tn/P3w+1V3E+78+c25X/68q+pgvO+BvxV/v7pyN/T/yJ/bCt6pe8H2tEP2v9SjQ4A/xH4S8Cb/3mQIhMOtE5fqvC3FN5HrcBP5e//MPBl4Gv5Y+V04Ato42yhJ3kPlNC9PwT+vuDYxH2dv14v8Lv5McLAPfljdwH3Ah5gY/6z+J1i1yjQ8av5/3sp/8z8LNog7s+P/3ngi8txD9aSi68NGFQFS10ReVlERvP7OQ8VnPt1pdRLSikX2AvUA/9JKZVRSv0Y/eD52Vlc+z8ppUaVUpfRRmhv/vWPoxXkilJqGK0Ec8EF/r1SKq2USs5xDID/Syl1PS/LNwvknE4r+sauCqXUc0qpY0opVyn1Nlq5xvcZsvnxtiqlHKXUG0qpSP6YC+wWkaBSqlcpNRc33q8B/1kp9brSnFVKXcrL9eX83+sqpb6EXu3sr3LcnwP+Vin1plIqDfw/gftEZGPBOaW+d8MkT6InPMeqODeFvi9/GvgZ9Ko1NX5QRDrRLsDfUUrFlVL9wJ/mzyX/3f8wrycDwJ8weR+OU0oHsmgDsUEplVV6z6XY3tdsdWNIKfXPSqmEUioK/O/TZCqlA1m0+65HKZVSSr3I7PkgcEMp9cf5MaJKqUN5ud5QSr2qlMoppS6ijf6MvcES3EvlZ+ZXlFKv5Z/Hn2eZdKOWDNQQ0Fa4R6OUul8p1ZQ/VijrlYL/9wBX8sZqnEvAmllc+0bB/xPoL29i7GnjzoUBpVSq8mkVKSXndIbQyloVInKPiDwrIgMiMoaeZbblD/8P4PvAF0Xkuoj8ZxHxKqXi6AfRp4FeEfm2iOyc7R+EXp2eKyHXJ0XkSH6SMoqejbcVO7cIPRR8X0qpGPpzKbwvqv08b2Y+jd6j+W/jgUoicrwgAOjBaef/HfDJ/M/fTTu2AT177y34Tv8KvZJCRDpE5Isick1EImjX8vTvu9R39n+gVwU/EJHzIvL7Jf6e2epGSET+SkQu5WU6CDSJiF1BB/4t2iPwWv7z+pVqr1lAOd3YLiLfEpEbebn+P8xONyo9M2tCN2rJQL0CpIEPV3Fu4czoOrBORAr/lvVotxFot1Co4FjXLGTqRd8khePOhekzuSkyich0mYrN/GbDj4CPTPtMyvEP6NnuOqVUI9pVIgD52egfKaVuBe5Hz+o+mT/2faXUk2iFPwX89RxkvUKRwA0R2ZAf7zfQEU5NwDvjclH5M7qOfiCOj1eHnj1fK/kOQzH6gcfRLrM/B1BK7VKTAUAvTDv/BfT90AlMXzVcQet4m1KqKf/ToJTalT/+H9Hf621KqQbg55n8vsuSX138rlJqM/ATwP8kIo8XOfUZYK2I7KtmXLR7bQfatdYAjHtyxvWjqA4opW4opX5dKdWDdpP/ucw+raOobuT5i/z1tuXl+gOq/Kyo/MysGWrGQCmlRtGbhn8uIh8VkXoRsURkL9rXW4pD6Af+vxURr4g8gr5Bv5g/fgT4yfxMaCt6I7Ja/hH4LRFZKyLN6E37heAosEtE9opIAO2HLqQP2DyP8f8EvU/0ufyDHhFZIyJ/IiK3FTk/DAwrpVIish/4xPgBEXlURPbkoyUjaNeFIyKdIvKh/IM/jd6MdeYg638Dfk9E7sqH/27Ny1yHflgN5OX4ZfQKapw+9IOmaHQn2uj+cv4z9qNnmIfy7hDDLFBKXUfvU7xPRP60wrkKrX8fmu5iU0r1Aj8A/lhEGvL6vaUgbD1MPuhFRNYA/6ZaGUXkg/l7R9D3qUOR+1EpdQZtaL8gIo+IiE9EAqJD6Ivpdxgd4DOaj4799wXXLKkDIvIxEVmbP3UEfS/PVj++BXSJyO+IiF9EwiJyT4FcESCWX7X9P6a9t9wzpNIzs2aoGQMFoJT6z8D/hF4e96M/5L9CR8C9XOI9GeBDaN/2IPrm+6RS6lT+lD9FR8r0AZ9D+1Or5a/R7q2jwJvAV2b3FxVHKfUuOjfoR+h9lekzzb8Bbs27Qb42h/GH0audLHBIRKLomeMY2g0ynX8J/K/58/4XtGEepwsdjRVBb8Q+j3a9WOjZ5XX0hurD+XFmK+uX0X79f0Bv1H4NaFFKnQD+GL2y7kMHcbxU8NYfo6OPbojIYJFxnwH+Z/Qmfy96Jvozs5XPoFFKXUEbqY+KSNm9WKXU8TL7kZ9Eb8yfQD+4/4lJl9sfoYNaxtARZLPRt21ofYqh75k/V0o9V+Lc30JHvf0ZOortHPAR9J7WdP4LOrBpEL0X972CY+V04G607sXQ3onfVkpdmMXfQ37P60m08biBflY8mj/8e+iJZBT9nPrStLf/IXqCOioiH582bqVnZs0gqug+osFgMBgMy0tNraAMBoPBYBjHGCiDwWAw1CTGQBkMBoOhJjEGymAwGAw1yZK2jWhra1MbN25cyksaDAvKG2+8MaiUal9uOcYxOmVYDZTSqyU1UBs3buTw4cNLeUmDYUERkblWE1kUjE4ZVgOl9Mq4+AwGg8FQkxgDZTAYDIaaxBgog8FgMNQkxkAZDAaDoSZZ0iAJg2ExiGVznI3EGEpl8NkWm8N19IQC5LtDGAyG2RIdhuMvwY1z4AvCjv2w6Taw7CUVwxgow4pmIJnm0MAwrsr338g5jGXGuBpPsr+92Rgpg2G2DFyGH/x3cHIw3jJqpBfOvQVP/OKSGinj4jOsWJRSHB4cxVFTm0M5SjGQytCbTC+bbAbDikQpOPglyGUmjRNALqsN1/mjSyqOMVCGFctwOotbohq/oxQXo/EllshgWOGM3IBUovixXBZOH1pScYyBMqxYsq5b9nimwnGDwTCNTArKucXTyaWTBWOgDCuYRp+35ArKAtoD/qUVyGBY6TR3gVui8a9Y0DWfRt+zxxgow4ol6LHpDgWK3sSWCJvDdUsuk8GwovEHYeudYHtnHrNt2P3AkopjovgMK5o72pqwhka5Fk9hiaAAv2Wxr72JoGdpQ2INhlXB/g/qqKNzb+qIPaXAF4CHfhoa2pZUFGOgDCsaW4Q725rZ1ewQyeTw2haNXo8JLzcY5oplw30fhjue1OHlXj+0rim/N7VIGANlWBX4bZv2oFkxGQwLRiAE3VuWVQSzB2UwGAyGmsQYKIPBYDDUJMZAGQwGg6EmMQbKYDAYDDWJMVAGg8FgqEmMgTIYDAZDTWIMlMFgMBhqEmOgDAaDwVCTGANlMBgMhprEGCiDwWAw1CTGQBkMBoOhJjEGymAwGAw1iTFQBoPBYKhJjIEyGAwGQ01iDJTBYDAYahJjoAwGg8FQkxgDZTAYDIaaxBgog8FgMNQkxkAZDAaDoSYxBspgMBgMNYkxUAaDwWCoSYyBMhgMBkNNYgyUwWAwGGqSigZKRNaJyLMiclJEjovIb+dfbxGRH4rImfy/zYsvrsGw8jE6ZTBURzUrqBzwu0qpW4B7gX8lIrcCvw88o5TaBjyT/91gMFTG6JTBUAUVDZRSqlcp9Wb+/1HgJLAG+DDwufxpnwOeXiQZDYZVhdEpg6E6ZrUHJSIbgTuAQ0CnUqoXtMIBHSXe8ykROSwihwcGBuYprsGwujA6ZTCUpmoDJSL1wD8Dv6OUilT7PqXUZ5RS+5RS+9rb2+cio8GwKjE6ZTCUpyoDJSJetCJ9Xin1lfzLfSLSnT/eDfQvjogGw+rD6JTBUBlPpRNERIC/AU4qpf6k4NA3gF8E/lP+368vioSGFUki53AuEmMglcEjwsZwiLV1QSyR5RZt2TE6ZZgLqZzD+WicG8k0tggb6oOsqw9hr2KdqmiggAPALwDHRORI/rU/QCvRP4rIrwKXgY8tioSGFcdoOstLfUM4SqHyr0WGx7gcS3J/Z4sxUkanDLMkksnywo0hXKVw869FR3JciiV4oLMN21qdOlXRQCmlXgRK/fWPL6w4hpWOUoo3BkfIKTXldUfBaCbL5ViCjeG6ZZKuNjA6ZZgtbw2NFdEpRSST43w0zrbG+mWSbHExlSQMC0os55B03KLHHKW4EE0ssUQGw8ommXOIZLJFj7nAxdjq1SljoAwLStZ1Sy4N9HFV5qjBYJhO1nXLusVzbvEJ4WrAGCjDghL2enApboQEaA14l1Ygg2GFU+f1lNAoTbPft2SyLDXGQBkWFK9lsak+hF1kwmeJsK1hdfrKDYbFwhZha0Nd0Wg9W2DHKt1/guqi+AyGWbGruQEFXIwmJlwTtiXc1dpEg8+soAyG2bKjsR5HKc5H41h5J7olcEdr06peQRkDZVhwRIQ9LY3saAwzlsnisYQmnxcx4eUGw5wQEXY1N7C9sZ7RdBbbEppvAp0yBsqwaPhsi/agf7nFMBhWDV7r5tIpswdlMBgMhprEGCiDwWAw1CTGQBkMBoOhJjEGymAwGAw1iTFQBoPBYKhJjIEyGAwGQ01iwswNU1BKEc3mAF22aLXnWRgMi45SMDYArgtN7WDZyy3RisEYKMME1+NJ3h6JkMsXdLUFdjc3sK4+tMySGQwrlGtn4OWvQiYBiDZOd70Xtt+93JKtCIyBMgAwkEzz5tAoTkFVSkfB0eExPJZFdyiwfMIZDCuRgcvw7OfBmdYq47Vvg23DljuXR64VhNmDMgBwfDQyxTiN4yg4PhJZeoEMhpXOmz+caZxAv/bGD7Trz1AWY6AMKKUYy+RKHk/knFXdc8ZgWBQGrpQ+lklCMrp0sqxQjIvPgIhgobtzlqJcw7RqiGdzXIolSOQcGn1e1teH8NtmfmRYxdie4iso0KsnzzyrkMdH4d3DEBmE5i7Ytg+Cq6v1hjFQBgB66gJci6eKNkbrCPrnZaAuRuMcG4mgFCigN5ni9FiM+zpaaA2s3lYBhpuczbfDu6+D68w81rYWfPPY173wNrz0z9rQuQ5cOQnHnoNHfx56ts593BrDTGENANza1IDPsqbcEAJ4LWFPc8Ocx41ncxwbieDmjROAq8BRikMDw7jGD29Yrdz+mF7RFIaViwVeP9z39NzHTUa1cXJyk8bPyUEuq4Mysul5iV1LGANlACDosXm0p43NDXUEbYugbbEpHOLR7nbqvHNfaF+KJUruBSsFN5KpOY9tMNQ0gTr4id+E3Q9BfTOEGnR4+Yd+E5o65j7u2bfKH790fO5j1xjGxWeYwG/b7GpuYNc8VkzTieecom5DAFcpUjkTfGFYxfiDcMcT+mehiI3oFVMxcllIrJ6oW7OCMiwqjT4vVontKxGhfh6rM4PhpqSlCzze4sc8PmhsX1p5FhHzdDBMYTid4Xw0TiLr0ODzsKWhjrC3hDJUwYb6IO+OxaDIOspvW7SbIAnDamfoOpx8WZc7auqAWw5oIzNXNu+FN75f/JjHC+t2zn3sGsMYKMMEZ8ZinB6LTiTsjmayXI0nuaO1iTV1wTmN6bdt7u1o5lD/CAqdc2WJ4LMs7u9oMbX+DKubU4fg8HfBzelN16FrcOEY3PNBHRY+F7x+eM+vwI8+p4MknBxYHvD69OurqNafMVAGAGLZHKfGorgFCx2FriTx1tAoHUE/XmtuHuG2gJ/3reukL5Ei6biEvR7aAz5jnAyrm/gYHP7O1P0ipXRu1KFvwtqdc89balsLH/99uHoaYqPQ0Ao922COOlqrGANlAOBymWg7gN5EivXzKBpri9Azx1WYwbAiOX+0mGd7kotvwy33z318y4b1t879/SuA1WVuDXMm5bhlou0g45hoO4NhVqTj2rVXDCcHqfjSyrMCMQbKAECr34ddwuNmidDon3ughMFwU9K2tnQ5I48PWtcsrTwrEGOgDACsqQtgF9kTEiBoW7T5TbSdwTAr1t2iAxqm65UI+EOwdsfyyLWCMAbKAIDHsnigq406j40tgkcEW3Qe04GuVhPQYDDMFtsDT30KGtp1+LfXD7YXmjrhfb++qqLtFouKQRIi8rfAB4F+pdTu/Gt/CPw6MJA/7Q+UUt9ZLCENS0PY6+HxnnZGM1mSjkO9x0ODz7j2FhqjUzcR4Rb48G/B8HVdASLcAi09yy3ViqGaFdRngfcVef1PlVJ78z9GkVYJIkKz30dPKGiM0+LxWYxO3TyI6P2mDbuNcZolFQ2UUuogMLwEsqwqlFKMZrL0J9OknCLl9g03LUan5ohSMHwDrp3ROUaGVc988qB+Q0Q+CRwGflcpNVLsJBH5FPApgPXr18/jciuH0XSW1wdGSLsugi6K2h0KcEdrE3apwnQGg9Gp0oz261YSiTEQW4dv92yDBz+m93YMq5K5Bkn8BbAF2Av0An9c6kSl1GeUUvuUUvva21dPEcNSpHIOL/UNkXAcHKXIKYWLTnR9Y3B0ucUz1C5Gp0qRTsJ3P6M7x+aykE3pPKLrZ7TRMqxa5mSglFJ9SilHKeUCfw3sX1ixlg+lFAOpNFfjScYyJdo1l+FCLFG0CZ8L9CVTJHIlEvcMNzWrWadQCvou6soKQ9coW7KkGGfeKN5ewslB/yW9ujKsSubk4hORbqVUb/7XjwDvLJxIy8doOsurA8M4rspXVVCEvV7u7WjGb1cXEjqQTFOq5oIlwmg6S8hjKkwZprJadYqRPnjm7yCdyL+gdCTb478IdY3VjXHjnK5fVwyxYODK/BoAGmqWasLMvwA8ArSJyFXg3wOPiMhedKWpi8C/WDwRl4aM4/JS3xC5abO7sUyWV/qGebi7rapcIJ9dflE614KrhtXDzaJTZNPwvb+GTHLq66MD8P2/gY/8jjYwlQjUlT4mopsCGlYlFQ2UUupni7z8N4sgy7JyKZagWDU6BcRyDiOZLC1VVFPYFA4xmMrgFHFjiECr6X9003Oz6BTnjxavRadcSEah9zz0bK08zrZ9cPGd4qsopWDNtvnLaqhJzHQ+z3A6M6XVxFQUkSr3ozoCfjqD/illgwSwBfa1NWOZigyGm4WBKzqooRhODoZ7ix+bTscG2Hz71Lp2YumqDA9+TP9rWJWYzZA8gTJ7TIJUdN1NnCvCvrYmepMpLkQSpF2XFp+XLY31hE17c8PNRKhBl/Nxi+QB2p7yrrtCROC+p3Vtu5OvQCICbWtg14PQ3LmgIhtqC/PEzLMxHOJyPFFyFdUZDFQ9lojQEwrSEzK+ccNNzLa74MSLxY8pNbteRiK6lfkqamduqIxx8eVp9HnZ3lA/peWEds0Jd7c3F630XY5Ivl36QCqNmm1YrcGwGgi3wJ3v1S64cf0pdM35qp/0ATA2CBfehutni6/KDKsOs4IqYEdTmI6gnwvROImcS5PPy+aG0KzCwjOOy6GBYUYzWSy0Utoi7O9orirIwmBYVdx6P3RthJOvQnQIWrph5326RXm1ZFLw3D/onCextLGzbHjkE9C1adFENyw/xkBNo9nvo3kehuTVfm2cFExEBeaU4pW+YZ5Y0151PlWt4ChFbyJFIudQ57HpChXvG2UwlKSlBw785Nzf//wXdaLv9FXTM5+DD/821DfPS7wlx8nBlVPaYNc1aVenxwR6FMMYqAVkLJMlks0WbZ3uKsXFaIIdTeEll2uuDKczvNo/jFLayNoiWMNj3N/RSpPpsGtYCiJD0HehuEvPdXXQxN3vX3q55spwL/zgb7WRcrLa3fnq1+GJX9TRioYpmD2oBUSXRiq+unCB4fTsSyctF1nX5ZW+YbKumkhedpQi6ype7h/CKR2TbzAsHCM3Sjf2cx3ov7y08swHJ6eNUzoBuYwOFMlldELzDz+rXZmGKZgV1ALis6wS5kmTchyUUovenTaRy3FqNEpvIo0COgI+djaFZ9Xf6Xo8hSq6FgRXwfVEinX1JkrRsMgE6srX7kvHtaFa7O60iQgceUYnDLsOdG+GO56YXX+nKyeL1xQEQMH5I7Dz3oWQdtVgVlALSEfQTznbE8vmODK8uH1sErkcz/UOciWeIqeU3kNKpjl4Y2hWxW8j2RxOieeCoxSx7MpZDRpWMB3ry7fTiI/BwX9cXBkSEfjmf4Wzb+YrqWfh6mn4zmd04Ea1jA3qFVMxcllT9LYIxkAtIJYId7c1l1xFucDVWJJkbvFCZE+MRMkWcb85SvH2LIxjncemVOsqWzAFbw1Lg1jw6Cd0Ym8xXAeuntKtOBaLt5/TLT/UtDLQThZe+Xr149Q3Ta2GUYjt0WH5hikYA7XAtAf9NJcJIBCBoXSJWdQC0Jss7cceSWfJuaVqrU9lbV0QKWlqhZ66WeawGAxzpX09dFeo2dd7bvGuf/HYTOM0TmRQ1xWshvW7KLVHDQJb7piLdKsaY6AWAV+ZiuWClFyZLAgVYheqDW3w2RZ3tzdhCxPyWqJzuu5pbzZV2Q1LS7mK5ePJv4tFpUldtUnDXh88/gt6FTUur+3R/3/4Z6ov/XQTYfw0i8C6+hADJSqaKxQdgcVrUd0a8DGQKr5CU8CRoTHuamuqqmhtZzDAk2s6uBJLEsvmCHs9rKsPVV2X0GBYMLbcAZeOF9/DUS6s3bF41+7Zqq9dbHrnOvDy1+Cxny/thiykaxN89N/CubdgtA8a2vTfFqxfaKlXBeZJswh0B/00+bxTyiaB3ru5takBzyKuPm5taphx3UL6kilOjcaqHs9v22xtrGdvWxNbGuuNcTIsD12b9cN9+krJ44XbH1vc1cfex8sn0vZdgNe/U/14/qCusHH/R2D3g8Y4lcGsoObAcDrD6dEoo5ksXstiY32ITQ11ExUWRIT7O1s4F4lzIRon6yrCXg87mupnVXR2LjT5vdzf0cqLfUNF3XmOggvRODub6k3rD0PtMHQNjj6r85q8Pti+D265fzKoQAQe/Tk4fQhOvALpGITb4PZHZ1d0di40dcD7fg2++9fFe1I5OR3ht+99pYMgDHPCGKhZci2e5K2h0YkQ7IzrcGosyvVkigc6Wyce+pYI2xrr2da49LOjlgpNEV2lyLruiiu7ZFilXDsDz34+nyOkdG7T0WfhwjF4/6cnVy+WrY3WLfcvvYyta8CyoNR2kwgkYyYSb4Ex/ppZ4CrFkaGxGflBjtLVy6/Fk8XfuAz4y7nixLSeN9QIyoWX/im/MilQLCenyxydeWPZRJtBsEyZMtcBf2jpZLlJME+pWVAuPNxRcClWOwZqc7iuaLSgBawLBae49xyl6E+m6U2kSDvVhaEbDAvCcC9kS+iVk4Wzh5dWnnLceqD4XpRl6z5Vhe1DXEeHvl8+oVdWhjlhXHyzwK3Q16lY1N5ysaWhjqFUhsH0ZDShLUK912Z3S8PEeVdjCY4ORyZ+d5ViYzjE7uaGRS/JZDCQy1K2/EqpygvLwfZ9cOO8TgzO5d2RHh/UNcJ9H5k87/JJvSocfx44jm5Zf9+HF78k0yrDGKhZ0OzzlTRSFtAZrC58XClF0nGwxSrvipsHlgj3dDQzlM5wLZ5CKUV3XYCOgH/C8Aym0hwZnumyvBRL4BGLW5rDZByXRM7Bb1sEPUa5DAtMS3fpPCPLhjVVho8rF+IRvcJZrIg+seChn4bBK3D+qDaua3fo1dO44Rm6Bge/NDOY4sLb2pjd80FdFDY6pOWsa1ocWVcJxkDNAp9tsTEc4lIsMeOhblvCpnB5H7RSigvRBKfGorhKoZTu5HtHWyNh78InGooIbQE/bSXyrk6PxYrW23MUnIvGieey9CbSWCK4StHs93FXW5MxVIaFw+uHXQ/o1vC5aQ912wO7DlQe48xhePMH2lWoXGju1CHcsynkWi0iurJF+/rix99+rnhBWCcLZ17Xlcsvvq0NmutAczc89HETXFECswc1S3Y3N7AlrEPKbREsoNnn5aGutopRcWcjcU6M6lp5jtK1+UYyWQ72DhWtz9eXTPF87yDfvNzL9670cXI0Sm4B21yMlWn/4SjF9UQaF90LykXvwb1wY7CmXJmGVcDex2HPI9pYeXzaMLV0w1OfqrzCOP0aHPoWpOLaCLgODF3XIeGRoRmnDyTTHMzr1Hev9HFiJEK2yvJfVTF4lZL1WlwXLhzVBiyb1v8OXYXv/KVptVECs4KaJSLCLc0NbG8ME8/l8FrVub4cV+VXLMULuZ6LxNjd0jjx2oVonOMjkYkVTlq5nB2L0ZdM8VBX24LkMHksi6xTukxLMTXLuIpr8STr603EkmGBEIHbHtErqeiwNlR1jRXfhuvolVPR3KSsXs088FMTL12NJaa4tDPK5VwkTm8ixcPd7XgWogaZL6irnxdDuTOVarwn1Lk3lyd8vsYxK6g5YltCg89btbtrLJstWSZSAb3J9MTvOdfl+Eh0hvvNRbfsuBaf32zLVYq+ZIpmn3fWN8B4xJ/BsODYHp0UW41xAr1CKlUHTym49u7Er65SHB2OFNWppONwOZaYm8wTAzlw/azOl6qm5FEhuXz7DsMMzApqibBEyhZqtQtWRIOpTElj5ig4PRalK+SfUy7TQDLN64MjKKWVdrpzwxJd0FYVOTaOd1Gr3RoMVWLZ5ZsZFrjchyukiFwZ6GOzrwsCc/AM9F+CH38e3Jx24zkOump5XjbbMxmpOH2fbRyf6Q5QDLOCWiIavR48JdxyFrC+brJacyWPeDzn8Mz1gVn3lYpncxwaGJlo4154Ha8IzT4vOxrreaS7rWRXAFuEdca9Z6gFwi0QKpE8a9mwee/Er5W2Td1UHL72p9rFOBsSEd2uPR3P7yvlE44F7apsWwu7H4IP/XbpVgIeH2zdN7vr3iQYA7VEKGBHU/2MD9wCgh6bjeEQWdfl7eEx3hwYIVdBo9KOy+HBEdQsAhbOReMlw+QdpbirrYntjWHqvR52FSk6awv0hAI0z6J1vMGwWLhAYv+HUNMLyFq2rvqw6wFyrss7wxEO9Q+X1CnLydI9cA7SCXj2Hyq31yjk9GvFz1dKv37f0zoIJNwM931oZrFb26urpfdU6Hd1k2JcfIuMUopz0TinR2MotKHSD37BI8L6+iDbGusRhOd7B0jknIorqHGG01m+f7Wffe1NJUPJCxlJZ0tO4iwRItkcIY/NcDpLxnVZWxckksmRdBwCtsWWhnrWhAImgdew7FyM6ohYV4UJ3/GT3HrxNVrHrmPZHr1y2vMIri/IizcGiWZzJXVKXBePk2PT9WP6hZFe+Mf/CA9+HNZsqyzIwGXt2is6uMDIDR2ROHgVoiOw+Tbd+j06DIF6XdV8y97yyco3McZALTJnI/EZ0XuO0kbqwa5W6rz6K7gUTZB03KqN0zhp1+XV/hEe6W6j3lv+6wyUSQrWhlN4qW+Y0UwWRykErTfr6kLc3mIqSxhqg4vROO+MRCd0arS+nZd3fwBb4KGuNhryK/zeeJJY1mF6Zoa4LqJclAhtI1fZe+Y5/NmCwKN0Qhevff+noaWrvDChRqbsN025kOi9pR99Fm5c1GHlAlgeXYH9gY/qArSGkphPZxHJum7J0HJXKd4dm6zRdTWenHN+kZsPU6/EpnDdlGCMQryWcC2eYCQzWRpJAa6Cq/HE/KOcDIYFQDkOJ0ciJdI1mNLr7FoJnTpw5Cs8ceh/8NRL/437j32TUKpIy3Y3B8eerSzQjv2lo/YsSxumGxcn96aU0v+/fAJOvlx5/JscY6AWAaUU5yNxfnClr6TRUUB/Kl3w+9yTXxU68q8S7QEf6+uDU4yUBXhE2NfWzLVEasZsE7Tin4nE5yyfwbAgnDlM4ht/hlMqEg4YTBfq1Ews16E50kcwE8dbLH9q4s1K96aqRPs67aazvUxEFlm2Dnx4+BO6ekSpPK3jL1Ye/ybHuPgWgQvRBCdGIyVbx4xTaCh6QsG8a21u16ym062IsKe5gTWhIBeicdKOS2vAx8ZwqGKUU6pMQm8hSimG01lyyqXJ51u0WoOGm4yzb8Khb2HZPlTJJAywmKpTA6nM3CufVBv6fed7YN0tcOoQJMa00dpxj268WKzs0ThVVjlXSjGSyZJ1XRp9XgI3UR+3igZKRP4W+CDQr5TanX+tBfgSsBG4CHxcKTWyeGKuHFylODU6M8l2OhZMqcawvj7IuUicpOPMei1li7CxvroCmSJCa8BH67Smho5bfg1XjVL0J9O8MTgysQpzlWJdfYjbWhpM994CjE7NEuXCm98HJ0vQyVKfHCVS3zbjNK1Tk+kaPXUB3h2LEs9N6pRr2Yw2dNISuVH+mrZXG5lqaV+nfwpx3cmae8UIVtbZwVSaw4OjOHmlcpWipy7A3tamku761UQ109vPAu+b9trvA88opbYBz+R/N6ArPVSasVkCIY/N5oLish7L4qHuNtbUBbDQzoKAbbElHCra12kcW4RWv4+1dfNL9LMt0X2iSlxja0N5ZYpksrw2MEImn2M1nmd1JZ7g+EiJ0i83L5/F6FT1JKKQnuy19vDhL7H73efxpSf3RS3Ab9tsbZjsYG2L8FB3G+vqgvkEdPBZFok73jszNL0Qjxfa1sC2eeYmWVbpPSrbq/tLlSGezfFq/whpx52iU9fjKY4Ojc1PthVCxRWUUuqgiGyc9vKHgUfy//8c8Bzw7xZSsJXKQDJdNhJPgG0N9WxpqMMzLYLHb1vc1dbMHa0KVylsEUSE7lCQ4yMRRjLalx20LSwRArbFxnDdgoV+725pIJLNESkwspbAmlCADRWSc98tGQwCF2MJdjaFTRffPEanZsmNC1NWIRaKzdePsWbgLC/c8VMo28Pa9k62NtTPcHV7LYs72prYqxpxJnSqE+p/DQ5/F/ouAUqXV7I8ugXGjv2w6baF6d10x5O6KePA5XwVCdEGa+0OuPWBsm89Gymet+iiA0B2NYcrFqhe6cx1D6pTKdULoJTqFZGOUieKyKeATwGsX1+iRP0iEs/mGE5n8VhCe8BftiDkeDuMc5E4Kcch6LHZ1lDH+vpQVQbAVYpTY+X9ytsb69nZVKZ1NDonqdAl1hrw8VB3G+5E6PfiLO09lsV9Hc2cGI1yPZFCKegI+tnaWE/GdfFZVslrD5epjG4hRLM5Wvy+kufMh0TO4WosQdpVtPi9dIcCK9GluGJ0KplzGEpnsAQ6Av4ZE61ClFJcjiU4E4mTzDkEPDZbw3VsDFenUyhXG5JpCBDIJnnytc/DLffC9vI5SyIytZJL21p4369PJtku1uTJ9uA88Utc6r3OpVSGnNi0+71sa22mLp3QbeJLXHsonSmbtziWydERXBwDlcw5XI0nSTkOzX4f3aHAsrgUFz1IQin1GeAzAPv27VuyPg2OUrwxMMqNZGrKl7w1XMeugo6yhbwxqM8f3z+K5xyOjUQYy+a4raDSeCnK1fsC7YbYUsFVVvb9i3yDZF2XF/qGiGcnk4WvJVJcS6R05RZL2N5Yz+Zw3YyHi9cSkqXqdqLwLdID4FwkxonRKOTbl1yKCcdHIjzQ1UZolfatWi6dcpXiyNAYkb4rbLnyFuFoPyP+etyd99G57baiyaZvD0e4UhDuncg5HB+NMJzJcFdbc+WLDt8o31VXBG57dK5/0qLnITmu4sW+IaKOjeMJ4sskaT/6YwKDF1AiiMcLux/UPzJVFl+5yTSLVxPzUjTO2yORCZ2yY0neGY7wQFdrxVzLhWau306fiHQD5P/tXziRFoa3h8fonWacAM5G47wxMHPveTSdnWKcxnGU7jAbz5aJxpk4V5VtC1/n9dS0m+tcJE68RCULhW61cXI0yqnRmXkjm8KhGaWRxgnZnkW5sYfTGU6ORnHVZP1CRylSjsuh/lnWVFt+al6nToxGcS4e44E3/4k1fe8STozQPnKF1kNfIfnSV2ecH8vmuBxPzHD9Ogp6EynGMmXCvPMo1ykfDRtqWLwOugvAxVicaDaHo8Bycjz05pfpHjyPrRzEzUEmCUefhde+PeO95fIWfZbQtAglxyKZLMdGIjN0Ku26vNI/PKvSagvBXJ+W3wB+Mf//XwS+vjDiLAxZ1+VKLFny+NVEisg05biWSJaMvFN5hSqHUoozY7GS+08Wei+nlrkcSxTNgyrEUdrIT2/ytr4+RLPPVzTH6q72poUXFm1Qi31nCr36nf4d1zg1rVOOq7g8OsYdJ3+Ex81hFUz9PG4O38Wjuqp3Ab15N3HR8ZTeR6nEG7lA6UmfZcOGXVX/DcvBxehk9+21/e/izySw1LSnhJOFdw9DcurErycUoD3gmzLxE3Twx7725kVx9V+IzuwWPk7acRkp48pfDCoaKBH5AvAKsENErorIrwL/CXhSRM4AT+Z/rxli2VyZTAnN5WkGrNyDWVG5wvhgOsNopvQqy7aEjeHanekBFQvUjmMhDE1LDLZEuK+zhb2tjbT6fTR6PWxpqOOxnnYaF6m4bKzMqlbQ7qRaZCXqVMJx6By6hCrxUBQnpx+yBbiqfOpCJZ0aSWe4kc1xctO95KypK3AXdCRehUi45aZw9dgzcBZPqbp9lgW956e8JCLsb2/mztYm2gI+GrweNodDPNbTvmj7udEKnqL4EutUNVF8P1vi0OMLLMuC4betirlE6Wm5CZ1BP5diM90RoOvmdVQoxno9niobXr65vq7mk1Zb/D76qmxGWGw/zBJhbV2QtQWtQxYax1VcSyQZTGXKulMVijpvbe5BrUidsizsXBopVREcdMuJAtqDfs5E4iV0SugMltep3oR2uV9YezuO5eGWi4fw5tKgFCMN3UTv/gCbqm1uuEy0Bnxci+utBlfK3I8iRffDRISeuiA9i6hTODm4dByun6WhdSvDofaSydBLrVOrspJEyOOhzmMTK2HtLZhR/bs94CPs9RDJZKfM7Cz0g7vJX34VUG6uaAHeGjdOADsbwwym0hWTjBXQWsUMLpFzyLou9R4P9gJs6CZyOQ7eGCLnqrKTAQHCXi9hr2kLslD4bEtHvp0pfty1vVjdW6a81uzz0uL3MpTKzNCpBq+Htgr3UOE3fLlnF5e7byWQiZOzvOS8fnbW1Zd8b62wvbGe3kQaRymudO6gfeQqHreIm8x1qmu5ER/TOWHhZt1var4kIvCdv9IFcnMZNl2/wOV9H8cpkicWtK0lb7WzKg0UwD3tzTzTO1j0mNeyWBuaOiMREQ50tvDOiI460o9TYUN9kFubi0f9FdIdCnC11CpKdLh2rdPk97K/vYW3hkbJlKisbgvsbg6XNTiRTJY3BkeJZXMTnYQ3h0Pc0hSel9/8tQGdtFiM8XrStgh+22J/exURYoZZsWPjVsZOdNE02outJid/ChDbA1vvnHK+iHBPRwvHRyJ5l7rWjbV1QXY3V66O3xUM5PdE1PiApPzaKNkCncHa3tMFPVG6r6OFNwZHGWjfQvTqWzTEBrELPTgeL9z+OPjKrJIiQ/DCl3U7EMvW4fHb74Z975tfvtbBf9RGKr8vFk6OsPf0j3lrx+NgWbhiYYvgs4R7O1qWvKPBqjVQ9T4vT/S080r/MPGco/OHgHqvh/3tzUUfsB7LYm9rE3taGsk4Lj7bKhlFk3Vd+pJ6ZtTm99ER8BddgdmiFS28xOGZc6Uj6Oc9azqIZHNkHP03XoknyLmKsNfDzqYwXWWCPVI5hxduDE3sZ4274c5HtatnTxXh+sWIZXNl95xCHpu1dUGafF46g37TGmQRCHpsPO/9JVLPfYlg/3lcy8ZSCqlrQh79RNEHrC3CbS2N7GpuqKhTZNNw7V39b8cGWhraaPZ7GS6yAmsL+Ct6NWqF1oCPJ9e0E83myLX/Kpx+WReRzaSgoQ32PgYbdpceIJ2A7/xlvpqGmqzv9+7rkE3BgZ+am2CJCAxemTBO46ztP0P7yBWubbqbzC0HaMzr1HLkFq6Mp+YcqfN6eGJNB4lcjljWIeixCHu9KKW4FE1wJhIjkXPw2xabw3VsaajDEsEWIVgkh2YkneFyLEl/MkWiYCYv6BXUfR3NnBqNcTmexFUKjwibG+rY0Vj7rohCRGQisKE96Gd3ibyxYpwv0bXXyVeU2NEYrqqw7XSSOQcp1XcH/R1USoA2zB+vP4j3vb8E8TGssQEI1kOz7pl0LZ7k1FiUeNbBZ1lsDIfY1liPXUanGL4BZ14nPXCNXm+YK123EMrE2fLOF2gKN3Lvwz/LyWhqYn/YFmFTOLTivmsR0X2qfF6480n9Uy3vvp7PBZueA5OFC2/rahWh6nV0gkREr76KFLT1Z1NsvnoU7ptekWtpWdUGapyQx0PIM/mnHh+NcjE6GaKcclxOjUUZTKWLLmOVUrwzEskryczxFXA9kcJvW9zW2sjulgacvIG62WbyfWVKPVkIo5nsnNyd9V5P2aAIs9+0xNQ16p88p0ejnInEJvQj7bqcGYsxkErzQGdrcT048iN450WUk8OPYq3loWfwLC/d9jQv3v40t509yPpXv87uBz/GruYwuZtUp7h6unRVdMvWZZTKrcBKUd9SupAtQGP77MdcYGp/536BSeYcLhTJn3EVDKWzDBWpBtGfSnMpVjpPapxL0QQ518USwVumLNBqplI5lLmWSwl6bFoDvqKxRbYI2xprO4R/NZNx3Hwtxqmvu8BYJseNYpGh/ZfgnRfBySL5lYHHzeHNZbjn+HdxLA9Htz5M5uq7kE4gN7FOVQyGKFf4thyBEKzbqWsQFhtzz8NzG3cBuekM1I1kilJJUo5SRZMHz5cIlS1GssQm/s3CxjIVJSyBlnnsG+xra6bR59UFP9GGyQL2NIdpXqS8EENl+pLpYlWOAK1TV4sl5J58peiqQABvLkVz5AaiXHrbt+oAgZuZbft0A8RSTIuenBX3/yS0r9UGybJ1wIbl0T2u5jPuAnFTuPgKUYpS2xgARUodKUarrEigYNFqzq0U1tYFuRhNEMlObb5oC9zR2jSvGbDPtnioq5WRTJaRdAaPZdEdDMxpT2s6o+ks56Nx4rkcjT4vm8N1S153bKWiUyxK7w9Od826SpEeGyJYUhGFQCbOaLiDjO3V+1w3M+tugfb1etVZ2J3X9sK9T5duOV8NXr8umjt4VVd29/pg/a0LUz5q5IaeiIwNQHMn7LwPmkrWQC7KTaeB7UE/jFJUl2wRugsi1FyleOnGEJlK9X/yNPo8C5aMm3Fcsq5L0GPPK3rGUYp4NofX0tFTrlL47cVzlVgiHOhq5WI0zoWojv5r8XvZ3hhekKgrEaHF71vQTPozYzFOj002mRxJZ7kcS3BXW/OU+8FQnPaAv2SNNluEnoLPUCnFK/3D9IRa2TByA6vIjqUol2ioRbdnJwf1C5QykEnqSLhQw/we6k4OosP6Ye7xa6MRrJ9R7HXBsCx44pO6UsepV/Tf0Nqji+R2LFA1+7a1+mehOP0avP4dvcelXBi4Amffgvs+DFvuqHqYm85Ahb0euoMBepOpKeWNLKDOY9NVsIHfG09WXc9NgP3tLfOWL57NcWRojOF0Bsm7srY21rG9oX5WRkXlO/ueiyYANfHwHW+EuKu5gTWLlJ1ui7CloZ4tDbU/841lc5wai065FxR6Jf3G4CjvW9tRtp2EQe8PrqsPcTU+NYho/F4rrILQl0wxnMqQXHM763pPYk2r6eiKxVh9O/FgI/XJMVrvWoDiGvExePmrcOP8ZM7QLffB3idmV81cKXjnIBx7XuchOTlA6TH9IbjrvbN6+M4Ky4ad9+ifWic+Bq9/e6oLV7nguPDK12DN9qpXaDedgQK4s62J06MxHRI9njwYCrK7JZ88eP0svPE9eoZ76RKL6+1beXvbQ+S8xWfTYY+H+zpbiofRzoKM43LwxhCZcaXNz0rPjMXJuYpdVSQMj3N8JMLFIlGHCr1P9tbQKMCiGamVwqVYomRBU9DldtZVaNZogNtbGgh6LM6NxXFQoKC7LsBtzY3Yoms3FjbdjIeaOHzre9l38gfaQahcQIiFmji86720qCx3b96MBOZ5f2bT8O2/gFRM69N41NqJlyGVgPufrn6st5/VBio3bdLqOrrQ6ytf1w/l7XfPT+aVzvkjZbZRBC4eg533VjXUTWmgLBFuaQ6zo6merOvisQqSBy8d1xnbTlZvxCuXNfnEtR/f/bNkffphNd7baWM4NCWEfT5cjMXJTa90jHbTnY/G2VakY2gxMo7LhWiibDFOR8E7IxF6Fqgb70ol5TgldcnNtxkwVEZE2NEYZnuDbm5ZqFP9yTSvDQzPmCz1tW3ie/f/Cp1DF/FlU0TC7TR1b+JAY/3C7f+de0sns06fhThZfWzvY9XlEOUyxY3T9DHf/L6uqLEQ3XhXKskYlCqK62QhFS9+rAg3te/CEsFv25PGSbnw6jembkSiW0x7c2m2XXlryuubG+oWzDgB9CbSJauqWwgjmfINEccZzWSr2rfKuu5NH3XY4vOViTqURavEvlqRaTqllOLo8FjJFA3H9nK9YxsX1+xhtLGLLU0LaJwALp8obVQsC/ouVjfO8I3q9pgcB8aKl1i7aWhbWzrq0OODlu6qh1pdKyil9PLx2HMQHYFgGHY9ANv3VXdzjQ6U7N45vpI6seUAtsCG+hABe2FnSZXqqVoVm4hUN844SlV/7mplbX2Qk2PRomkEQduqWND0ZqA3keL0WJRYdrzqSohN4bqqJkEpxyXlVG7RYIvuf7SQEz49cJkJhkj1wRIez8xVWDGUurlXT6B7dL3+nfzEoHBTUsAfhLU7qh5qda2g3vwBvPwVGOnThiY6BIe/Awe/vGCX8IiwrbGe3bPYD6qWDfWhsomsrYHqHpYtfl/JvJRC6r2eBTeyKw2vZfFAZyvBfI04jwi26IjMA6UqINxEvDsW443BUcYyORylSOQcTo7GeK1/pKruqtXEv3pE2BKu547WpnnLO4Otd5aezbtu9bk+zd3gqyKiM1AHDa3Vy7casT3w1K9DuEV/9l6//rehTYe0z8KAr54VVHxUb3xO933msnD1pI7zrxRG2diuP9wiqyhl2Xg37eGpdZ2LVjRxTV2Q89E40UxuRsHZ21oaqr6uJcIdrU28MThS0rViC9zeWtu9dJaKBp+XJ9d0MJzOknQcwl6Pce2hO6ieHo3O2Mt0lGIwnWEwldFpG2UI2hZ+yyrpSu4JBrirvWnxCpGu26lDsgevzcwhuvup6ltWiOiirD/++xlbAFPGvP8jVDU7XO00tMFH/rUuRhsd0Ua7dc2sP5vVY6Aun8Sl+JJQ5bLIhaOVDZRlwf4P6pDUwptQBPH68O55aPY3XyoO774GV9/VyrBtn068G7isDSeil8Rt67BFeLCzjbORGBdjOoeo0acriE/vX1WJ7lCAA52tnB6LMZzO6ACm/Iy3PejnlqaweQgXICJVr1BvFvqSqXwZopn3vOO6XIknKxooyVczP1xksuQR4dbm8KyNU9Z1uRRN0JtMYYuwvj5Ed9DPWDbH9UQKFHSF/LT6fYhlw5O/DCdfhlOHIJOApk7d3mLNtlldl56t8P5PwdFn9d6V6+g9JwE6NsIdT0D7utmNuZoR0QnG7XPP1ZJqlukLxb59+9Thw4crnzgHnGMH4a0fYheJggPINrTjTUa0j3jNdn0zlSqGeOUkHP4+RAcB0T7T/R+YfcLg2KAuk+9kJ3MCbC/Y+X4u4ys1jw+6NsGjP2f81zWOiLyhlNq33HKMs5g6dXEsyjtDI0Wb1wHUqxwpy4sC2gI+bm0K64rdRehPpjk+EploKd4e8LOnpWHWARGJXI6DvUNklTsRUGShc+/cgnw/W4Rmn5d7O1vmXP/RsHSU0qtVs4Lqb15Lu1gzepsA5Cwvff4m1kYG9AuXjuu+M+//tC7BMZ11t+ifXFavquZqNF74R93zpdAT72RnughyGeg9r8NYb3t0btcyGBaYttGrKIonW9u5DE2D57natRPQ9fgGUxkOdLYUrYvYEfTTEWzHUbqZ+Fxdem8NjZFx3Sl7Wy4zyyk5SjGcznByJDqrdjGG2mLVGKhouAOrsYe2sWtTulU6YhEPNjDYtIa1A+P9qpU2Cq99C977q6UH9UybDcbH9D7X9TO6zMn2/bD59uIGLDYKo31Ut02MNlonXq7OQEWH4dSrMHRdr+puuVf7dw2GBaQ+OUZXdJC+1g1TVlHiOvgzCTqHLk4YKNBG4ehwhEe620qOOWM1k4xp99vVU7pI6bZ9sPWOotF3GcdlOJWpVqNw0T3IdjVX7uSczDlciMYZTmcJeiw2hesWtJyWYW6sGgPV4PXw5p4PsPPsC6y7cQoAQdHbupl3ttzPjktF3CB9F7XrrZpQ0+Eb8L3P6PPHDeDIDThzGN7zKzPHSCdKNgMrSTqhXZDllOnqaXjuC3ql6DrQf1GH1u99DHY/VP21DIZKNHdx15t/x4nUPi6u2Q0olFh0Dl1i95kXuLBmz4y3RDLZic65FRl3gecykzo12q/3bJ/61Izou6zrakMzi20JVylySuEto1ODqTSv5qMSXYC0Dq3fHK7j1kWI1jVUz6oxUJ1BP7bXy9vbHuKdLQ8QyMRJe4M4thdvLs26/neLvEtVf7O/8CVdNqWQXFavYt49rFcxhTS06n2m2RCsL2+cchl4/otTXYRK6d+PPKPdktU0GUtE8tcLm4gjQ2k6NmCFGth9/iVuufgqKV8IXzaF18mStb1c7r51xlsEcKtd47z0z8Vd4GMDcOKlGd6EoMeuMhNwkvHUgVK4SvHawMiMPDhHwflogu5QoKpWLinHQSlde/BmT01YSFZNHpSIcKCzlbpsElEuaW8IAfyZBAeOfBVPsdDQps6ZbrxiRIa0W60YThbePTTzda9fJwhX20zM9sKuB8ufc+VU6WOuq1tDl6P3HHz1T+Gf/xi+8sfw1T/Re3EGQzFEtHfA68d2HepS0Qnj9MptP0HGN7NOXsBj46+mAGsyBkPXKOoCd3JF72VLhM0NdSUrf0zHFtgcritrMAZS6ZJzVEcpLkYTZa8xlMrw4+sD/PBqPz+63s8Pr/VzvVj/K8OcWDUrKIA6r4fHAzmGj/yImK+eYDpG+8jViY6dU7C9cPf7qxs4myrvrsukir++7ynIZuD8UR25B3rF09Shk4nH3RqWpUPNb72/vBypWOkWzcqdXBkVo+8iPPM/pq6+osPw7D/A479QE83JFpOs6xLL5vBZFnWmz1P1hBrgsV+AH/73iXsv6Q8TqZ+5UrcFdjdV3u8BJnWq1P083VuRZ2djPTnX5WI0MRFo4SpFo8/LWDY7YWwsgY6Anx1N5Svqp53y671yVTBGM1le6R+aDJ9XuhDzG/lCzD2rvBBzznWJ5lv5LFbvtFWnqbJ2B62XT9B68dhkGLfYoBxd7khEBxbs/2D1D+WG9tLuOpHSPVksGw78pA5pH7gyGU5ue/T+1dXTgOhkwmoaeTV3l1Zq21s+3+Dw94onGDpZeP278KHfqHz9FYirFMeGI1yOJ7DQoch1Hg/72ppKhkQbptG1CXbcA2deh1yWhsQwB97+Bke3PkQk3IYgBDw2u5vCdFf7UK5rKu9eLpGzKCLsaWlke2M9w+kslkBbwI8tQjSb5UYijUK7/KvJ89PnFDdRFpR1750ciRRNhHcVHB+N0r1KCzErpTg+GuViNI4gKBRB2+autuYF6flWyKozUIjobO5Nt8HpQzpRtmsz7NivH+5KzezQGRmERFTvGxWrbOz16T2mk6/OfMhbHthTIfIu1KBXSIU0d+mf2dC5Eeqb9Oby9HB629ZlXUDvjb1zULtJsintyhy8Wnrc0RvVB4usMI4MjXE9kcRVk3sj0WyOF28M8fiadvw3eamnqtn/Ad1p9fQhSERobl/PI5vWkwk04KLwW1P3XuLZHEnHoc7jKd6GxvbooJ63n5upU7YX9pbvA+W3bbpDU8cNe72EG2f3gGz0eWnwehnNZGeYKRFhY1h3L3CV4mwkzoVonKyrqPfaRDKlA6BSOYeMq/BX649cQbwzEuHSRCsf/anFcg4v9Q3xaE87oXm2HSpk9T2RQBupnq36pxxD1+GZv9O9XJD8+7bBwz89swTKHe+Z9I2PGzqPFx74GNQ16uRey9ZGpFTtr/niOnDf07riemRIyyuAN6DddL6AlvF7f61D3MddkuWMEzDxt68yUo7DtXiyaNsRRykuRBPsbAovuVwrlq5N+qeA6Xd6JJPl1f7hidJGArT6fdzd3jwzsm/Pw9rLceKlyVQNsbTXobFDh54jWqeqLUk0W5wc9wbhVWUTyTlIPgzDEuGejmYCto1Sipf7hhnNZCZWTGNljBPox/ZqLMScdV0uxhJFuy44SnEuEmNPy8KVUFudBqoaIoPwrT9ncnmfj+i7dlr729//6annj5dBuv1xvbnr9UHLGjjyI3j27ycVTLmw7/16xbZQuC689UOd+wTaULX06Bys5i7o3DBZrf3iMRjrn114e/eWVVnBYiSt245MT+IEnSPTl0wbA7WApHIOz/UOTlmJKGAwneHFviEe7W6b6vISgTvfo1dSQ9f0qqp1DbzzAhz80uQ96braTb7rgYUTVrl69Xb8RXxK8ZDrEFl7K5E9j+Ova6At4JuQ9UYyzWgmW7KuZTGafF68q7AT82gmO+Eqn45CVwxZSFbfJ1gl2We/gCq1PTp4DYavFz/mD+qVWft6OPUKnHxFG4NsWv/ksrrU/NXTCyfsK1/T18llJnNGBq/q1tOtPVNbiZx9s3xTtSmInpnu/8DCyVpDlAsvBvCuxinuMvJq/3DJgINYNsdQukQ/M19AT5I6NuhurO88P1WnnCy89SO48PbCCXv4e3DsYF5ntU41XH6HtT/6DO3iTDGkV2LJou1YSmGLcNsqLcTskaIhZ5PHF9go35QG6sJYFM/ojdI5FcqF/ivlB3FdbSBKBR689aP5iqmJj+kowBnXUTp68OzUJoplV04en96P8wW0W3Dz7fDBf1Vd7tQKpDXgK/kd2yJsNK3cF4yrsQRj2dL3ngKGSxmoiZMUHPlx8QnWQupUOqELxxbTqVxmRtpIuTg/W4Q2vxevpfOtekIBHu5upWmVBuA0+bx4SkzsbGFiz26huOlcfBnH5eTQCBsrneitsI+UTpRsbgjojPiFoP+Sdi8W3UjJwpUTU5OE1++C4d7ihlO58PDPQuDmeDBbItzZ1jSjkrYt0Or30h2qor+PoSI51+WtobGK51Us2urkIFkmVSI6pO/hapqPlmPgSr5gcxGD6uTg8skpScLdwQCDqXRRF59SijvbmosHgqxCRIS7Wps4NDCMqyY3SHQPNS/rFji0/qZbQfUmU7gIaW+Fh/S6W8of9/jKV6FYqE1d21MygCHj8XOpeQNnx2IMptK6gdz2u8DnZ0aLBNurQ4VvEuM0TlcowINdbfSEAgRti0avhz0tjdzT0bIqQ4CXg2r3HdZUmhBYdnnj4/HO3zhB2WjVrO3lcusmzo7F6E9qnVpTF8RnzaxiYQmsrQveNMZpnPagn4e62lhTp3WqwethV1MDBzpbF7yv17xWUCJyEYgCDpCrpTYEgM5WP/a89l0rF3q2EQp3snVshN7WjWy4cWKGhVaAbN9fuXum16cj/q6dnmmobI+uIjFOJqndcaGG2QcjdG8pmoN1uXM7R7c/htgW7mgUS4Q6j839na34P/Av9b5V7zmt0JYNux/UUVM3IY0+L3e3z7JVyjJR6zqVcVzORmJciet9mXafl+7hS2ztv0raF+Jy5w5UEQPQHfATqNTO3bJg4x64+PbMe96yYcudE79mXZes6xKw7dk/FDs2FH35ettm3rzlSbDsCZ0K2BYHOlt5qLuVo0Nj9CfT+eBZYXO4jp0VEoFXKw0+L3e1Lb5OLYSL71Gl1OACjLOwJKPwzf+qXXHjN/uFo7QBbYBjeVAIjgiiVD7dTIjUtdBU7YP8vg/rSMBMatKl5vFCuBX2PAKxEd38sO9i3lBYOmJpz0PVzwS9fl3x4vXvTFxjrL6Nt7c/ilvwIHCUIprN8frACA90tcITv6jlyqZ0zb1VGKW3iqlJnco4Ls/1DpDKV18IJcfY9dJX8ebSrHGyOJaH3Wdf4OXbPsRIU8+U9+5srjJa8u73a7d2MjZVp0KNcOeTpHIOR4bHGEimEdFB4ZvDIXZWW8EC9ATy3qfh5a9MXCMWbOTNW56cUrV9vMX9oYFhHulu556OFrKuS85V+G1r8boAGyZYvXtQb/0IUokZCa3jt5Qn73/Oic3hW58ka/tJBMM8fuSrEKpSmUIN8PTvwJk34NIxnbS79U6dJJzLwrf/YrJCOY6eEx99VmfkJ2PaSG3YBXc8qXOpSrFjP4Rb4OgzMNLPmU334FgzvzoFjGQyxLM5Xc7HF6i8EjQYquTdsdiEcUIp7nv7GwTScaz8TsS4Tt3/9jd49u5PkAjqpHcLqi8vFaiDD/0mnHsLLhwFBDbvhS17yVkeDl4fmCIDwNlInCvxJGnXRRC68h2jy15z821az4/+GIauc37j3bgldCqWzTGWydKYDx333nQbI8vHfA2UAn4gIgr4K6XUZ6afICKfAj4FsH793Fv/zpqLx4o2L5yOKEVDbJCBlvU8+PY3sfc8OLvVhi8Auw7on0JOvKRDWKe7/9yc7hU1zvkjOiT9J36jvJEqSDyOXh+AEhFTFkIslzP15lYuNatTV+PJiU3xlrFeApnEhHGaIp9y2Xj9GCe2HMACtjbWz66rrdcPO+/VPwVcicZnNCsEHT80nhgMimuJFH3JNI90t5XXg65N0KX7wY3dGESli6dnCEIsm6uqdJJhYZnvXOCAUupO4CngX4nIjIZESqnPKKX2KaX2tbcvYThzqSKU07CVw7arR3no7W8Q3H4n3Hqg8ptKMTao85AuHYdLJ6pLllX5cPGjz1Z9mXKlRMbrYhlWLDWrU4WmoT45ilIKx7K52rGd0+vv4mrHNhzLxlYujbFBLIHNDXXsbJz7Pk08m+NyLMG1eJLriVTVybI5pTgxGq36OnVl9scUGJ1aJuY1zVZKXc//2y8iXwX2AwcXQrB507FBBwlUQgTPhlvh3g/NPfIul4XnvzAZlICUD0GfjnLh0jtw/9NVnb6loY6BEmGvdR6PKYK6gqllnWrz++lN6sr9iUADow2dvLbr/SgRHNuD7eR4e9vD3HvsW9S3dvO+tZ1zrqbgKsUbg6PcSKaQfAGi3CySZQFuJEp0GSjC5nAd1xPJojrlsyyaF7gIqqE65ryCEpE6EQmP/x94D/DOQgk2b+58T3W9mJTShSnnExb+8le1cXJy+WoPaapu9T6O6+j8pUL3XwnaAn62NtRjyeSemi2C37LYv0Ki1QwzqXWd2tk06aobbFrDa7s/QM7rx/H4QCwcj4+sN8Art/0Evl33zavUz7HhCDcSKVylgxVma5xAa+BYJksiV9mT0eT3srMpPE2ndLWRezuaTUrCMjGfFVQn8NX8F+cB/kEp9b0FkWohaFsLT3wSXv4axIZL5yzZHm0Ywi1zu04qrl16xZL+AH27V6Fcuawu8uo6ulDmgx8r24JjZ1OYNXUBLseSpB2XtoCPNaEgtinfs5KpaZ1q8Hm5v7OFI0NjxLI5ciUmgMr2ct1Tz1x3x3Kuy+V4omhu+mxQwIs3hiZarNzZ1lS2wsPWhnq6ggEuxxKkHJcWv5e1dcEFL99jqJ45Gyil1Hng9gWUZeHp2gwf+de6TfqlEhNR19UZ6rPByU2GjY8NaCNXykB5/TqjL12py6aabNI2fB2++1fw9L+e2RqkgLDXy65m43pYLawEnWrx+3isp52jQ6NcjBW/px2xiJUpe1T0PUphoSsVxHNOyYKkoN0+HkvIFCupPY3xlVc0m+OlG0M82tNGqMx+U73Xw63NRVruGJaF1R/qJQIt3bp0f7GgBdujm6dVw6Xj8Ob3dSdasXS1iVvuK7N6AvyhfDuPosJRcnWVy+litHc8WZ1sBsMS0uDzYkuqaBFVWyhrBArpS6Y4PhIlms0h6EaD2xrqi1agH8dnWWSriNCdjqMUZ8fiq7aQ62rk5li7bruLGaV/xrEsWF+hrBHoXKcXvqz7MCmlXXGXjsNzX9CJucXGtz26tlixuni2B7bsLb335eYWtiK6wbCArC1bc01YU1c5/+5aPMnrAyNE86sthW5t8erAMI0+b1GNtYCsUkWDGSx0OaVSVewV0J9a2HYQhsVlZa+gnJzONzr1qk6IbWiF2x+DDbunnhcM6z2dF/5x8n0eLyDw+CcrB1O4Dhz+TomK4knYcKteJeWyU7Pf29bD0NXiKzcnB/2Xy1/Xs0hN2gyGErhKcSEa51wkTtp1CXlstjeEWVs3tX25Nx+Q89rACAqFq/TKCYR72psrBkgopThWomV6Lt+xNuE45Fx34hxbhCafh9ESzQJddMX0co6/WeVjGZadlWuglAs/+pyuTDxuFEb64IV/0vlItz0y9fwNu6Dj38D5tyA6ogMQNu+trtLCcG/pIAvX0RF8P/m7cPYNuH4WfEHYtg9Q8OznS49brvilxwvb764sm8GwQCileH1gZEoKQyzrcHR4jEg2y65pezMdQT/vWdPBlXiCWNah3muzri40s3NuEWI5h1yJPSS90snwRE87V2JJ+lJpPCKsrw/htYSX+4ZLjisieC1wilg+W2CDabGyoli5BuraGd20b/qqxsnqpNft+2dW7g7Ww64Hy4+r8sEKtl1dmDoAog3drQdg0+1w5jC8+xrUNxUt9ApoF9+mPdDUCc9/Kb/KGp8qenXH3E17qry+wTB/htNZBlKZGasaRynOR+JsDtfNqNztsy22NFROxM26LhYyEWVaaR0j6OZ3mxrq6PF7uHDhAufOjhAMhaC5eHSrBfSEAnQE/BwaGJmyP2ajAyCMgVpZrFwDdf5I6WRYy9JVxrfcMbsxL7wNb/5ANwkUoHsr3PMTOsii1ErH9ugKzKBXT8/+vTZyTk6XTFKu/rewsoWIXmVtv0d36H3q17VRHbiiDd3Oe7SBNQVeDUvI1XiZzrGiW9VsDtfNaszeRIrjIxESOX3/twV87GlppN5j47Wk6EpH0IYGYHBwkIMHD+qqFY7udGu3R/Bs3IKa5q7zWMKWhjoCts1DXa2cHosxlMrgsXRzyk3hOpOGscJYuQaqXBmh8SCG2fDu6/DatydXZAq4fga+/efwod/SbdFf+frUFZtY2tDccj9kM9qdV9gNdEIGV++DpeLaOK3dCfd8UBsngNY18NjPz05eg2GBKRc5hyrf/qwY1+JJ3hoanbIiG0hlONg7yKM9bdzW0sgb05pJgk6O3dZQj+M4vPDCC+QKEm2VUuT6e8F1qNu2k3TeTdgR9HNbSwOBfEmihhXUYsVQmpVroNbv0iuWoqsopfsoVYvrwBvfm+kuVEobnhMvwb6ntDF68wcw2qdXNxv3wF3v1a7Ec28VHxtAbN0s8Jb79IqrTMM0g2G56AoFuJYoHjqOQHugQpfpAsoFQThK8e5ojL1tTexvb+HESISxfJh5dyjAruYGAh6ba9eu6SacRXCGBuhsbWbXnj1YIib4YZWy8p6USumghLF+/aB3clOrltte2Lgb6mcxexq5AaWS/sbDyfc9Bet26h/X1SuhQqVIlAgnBx0yHh8zrS8MNYlSiqF0htF0Bo/IDANlCXQE/LOq8RivEATRm0yzF73y6Qi2TxiiwkjBRCKBW2IPVylFIh6fVzklQ+2zsgxUOgHf/xudKJvL5Pd48kogljYY2+6Cuz8wu3GlQjmi6ftPxZSisR1sX74O3zQ8Pmjpmp1MBsMSkHVdXukbJpLN4Sg1GbyQTuOOjSA+H2u7u7h9lu4yPU5pnZq+3ilW666xsRHLsooaKdu2aWpqmpVMhpXHypp+vPhPurTQuFvPdZhQAuXq30+9qmvapeLVj9vUlc+LKoLlgc1VVJ9Zu0O3gS+G5JuuGQw1xtHhMcYy2YlV07hJUR4PTmSUzJlTnHnxeZ47c4FUrvp93ZDHxl8iyEegqkTe9vZ2/H5/UeMlImzevLlqeQwrk5VjoJJR7dqrJvhh4DJ85y9nNix0HbhyEt55Ac4fnQxosCy450Mzw8rF0vtLt9xX+ZqWDe/9Vd1l1+PTv3v8et/qyV9eGPfe8A04/F1dAPfiser6TU15/3V47Vs6Yfnsm1MDOgw3HVnXpTeeKlqUVWwbe816PBu34Nmyg5G+Gzx7pXdGIIXruvT29nLq1CkuXrxINqvvKRHh9tbGfPLuVLyWxbYqekSJCI888gihUAiPx4NlWXg8HrxeLw888ACBwPx1KprNcWIkwpHBUa7EEqWjGEsQyWR5ZzjCGwMjXIjGyZVKKzHMCSm1CbkY7Nu3Tx0+fHhubx64Aj/875MFVavh/p/MlzlCr7y+/zc66MHJ6v0rEXjsF3RnTdAG8I3v6we57dWt2+94smzB1hm4LvSehcigrvG3dsf8w8WVgte/De8ensyX8vh0e+ynPqWNYqX3H/4unH5t6vu9Pnj/p2e3X3eTIyJvKKX2Lbcc48xHp6LZLAd7h0q2sijcF1KOnhjuaW1iS964xONxnn32WTKZDI7jYNs2Sinuv/9+uru7ARhKZTgxGmEkncUSHQRxa1PDjHyqciil6O/vJxKJEAwG6e7uxl6ABoInR6KcjcZQSq8cbRG8lvBgV2tVtQRPjUY5E4lNbF/bosd4oKuNsOloPStK6dXK+RTrGsGZZej4kR9pA+W62jgVFm0ddxM+83fwU/9Gr5S6t8AH/+X85LQsWLNd/ywUV07q5N/CIIxcBuI5OPiP8L5fK//+a+/qxOHp73eyupbgfP9mw4okYNtlQ8sLXWuSNz6nx2JsaaxHKcXzzz9PMpmcMGTj4eAvv/wyTz31FKFQiNaAjwe72uYlp4jQ2dlJZ2fnvMYpZCCZ5lw0PiU2ylEKx1G81j/CIz3lOxUPpTKcLTBO+v16jEP9wzze0256SC0AK8fFF2qAzo1TI+cqkYzp6Llr7+qVUzGU0lXDLx7T0XqZ6rtwLhnHXyzujlMuDF7Rf2M5TrxU4v1Kh8xHBhdGTsOKwmtZdIUCSJXNNUWEnFJEszkGBwdJpVJFw8CVUrx79izX40mux5NknNpze52LxEu682K53EQB21Kcj8ZLtp9POS5jJeoFGmbHyllBATz0cfjKn0C2SiNie3SwRGSwdEuM8dJIHp/evXUduP1x2PPQgok9b8p12bVsSIzpFeac3u/RIfIN85vlGlYme1sbGYxESYtMRsWKlJz9WyKkHYdIJFIyR8l1Xc7e6OdavuGmqxRbG+rY2RSumVVFvEzAhyAkc05ZN128TJdeAZKOQxOmV9t8WTkrKNB7Ljv2U7mSVx7l6k65dY0V9oGUDg/PpvUezdEf6xVVrdBUxt3gOFBfoRtwcxclPzM3l28XYrgZ8VoWm3JJnLOncXqvolJJ3LERVJn8o3qvh1AoVN7YBALk8q3aXeBcNM6lWGJx/og50OArbXxcdDX1cjR6i7cDmXz/ypr71yory0ABbL1TF3KthGXD5jt09Ny6naVr6RXDycKRZ+Yu40Kz++HihWstG9ZuqxzEsfvB4tUrLFvvu5VbfRlWPevXrUNFRnGuXMTpv4FML7KcR9DVJgK2TWdnZ+lABbGwO7qnvOQoOD0WW2DJ587Whrqi1ScEaPH5KgZJbGmoo1hZPwGafF4TJLFA1L6Bio3AWz+Cg1/WeVCvfVvvR0kZI2VZk/XuQD/cn/ikduONP+grRdbV0r5M92a444l8maR8iw5PvuL5gY9Wfn/7Ov1Z2J6CEHivLoL74McXX35DbREfg6M/xj34Za6+8kPefP31iRWR23cdd6gfUNqFNx7Jhy51dEdrEwCWZfHQQw/h9XonDJVlWdo4bdyMFZxp5FKOO+sw7sWi2e9jT3MYKx95J+h/w15PVTX8Gnxe7mhtmojcswrev9/UAFwwatPMZ1IQHYLr5+DoM/novWk3tlh6/6SuUVdxaFur3XP1zTps3LJ1AdgrJ3WIuuvohNz2tTpgoL4R3n5+Zq7UON4aK0u06wGd7Hv5uA746NigDU+1Pv1t+3T9wssntCuzfZ3+zGpkT8CwuGSzWWKxGL6+89Qd/gZRX5hXg5uIhmxobUWa27FGhlBDAwRHh6hXDu2bNkMoRND20Brw4clluXTpIr2JFGOBehyPh8a77qUpNkouFiUUCnHeX4/yFW+0aUltzYg3hOvoDgW5nkiRdV2a/V5a/b6q98nW1AXpCPrpTaTIuoomn5cWv7dm9tlWA7VloJwcHPqWbiqIoJxs6d0m5eqf5i549BOTr7uOTmS9cHRmUu/wNV3D74GP6np9kWG4dGxmzybLk284WGME63XR2bniD07mhRluClzX5ejRo5w/f17HAHk8+O74BATrcC0Li8lwcqlvgO61hPqv89CB+yfGUErx1ltvcf78eVzyZcGUwu5eR2TtehL1TexZt4EN4RDO0BiXY4kZyb8CrKsL1tzD22dbbAzPvUeU17JYb3pMLRq1NKGBg1/SxsnJQTnjVMjl41MrKhx5Rgc4lKo44WTh1a9ro7T/gxBqmrq/4/HpoITbH5vHH2Iw1AaHDx/m/PnzOI5DTgTPrjtw68Io20amReuJbYPXx0hdA5nMZFrG6dOnuXDhgq6J5zpad5TCuXEVd2gAR8E7IxEcpbi1OUyd1zNlf8cWoc5jz+jIazBUonZWUJEhna9UpHyPAjLY2Lh4iuVspFMQqtfvPflK6ari4zg5GOnVfZg+/Ju6UeGFY3rvavNe3R6+MKjAdeHM63DiZZ3s29AGtz0K62+Z159sMCwmyWSSy5cvTxRbtTu6wLJ0ZQilIJfTvxcEO4hlQVMLyXQan8+HUopTp07hFEuSd11yVy9ht+lw8pF0hraAn4e727geT3I1nkKhWFsXZE1dcIrRUkpxJZ7kbCRGMucS8thsb6ynJxSouVWWYfmoHQPVf4liodDn7BaOe7rI5IMiOt0od2WvEVIFRmg8YiYZq66rmsikW8/j0+68Ui49peD5L+aNZ/6aQ9fghS/BbY/Ankeq+vMMhqVmaGhoSjVwq7EFsW1yg/04Vy7AeN28hka8G7cigXwDTRQev95HymazUxoGziA9mZM4WfJHWFcfYl0Z19eRobEpvaci2RxvDY0xmsmalZZhgtpx8XlmVgI/ZbdzxNtDyvLiioUrFr1WA9/27+SU3cao+Ml6g7ooK+g9llJBD9Np6a58DkDfRbj+7sxVWS6f4JusndBZg6EQj8czJZlWOQ65/l6cC2cgk9GTL6VQY6Nk3jmCm0zgppJYjkPI650Yoyx5T4MCWvzVJaaOZrJcS8xsL+8oxflInEQ5g2i4qagdA7VmG4WRejmE495OnOnh5CIosXjb28MP/Dv4unc7h157TVdR9vph7fbyOU+2F+58T/VdbQurnk9HBK6eqm4cg2GJaW+fWg/O6e/FuXxhZlAQgJMj+/YbZI+9SfrI67z88suk02ksy2L9+vXF3W4iWJ1d2CJsb6jHU2XzwGvxZMkyQQroTdRguTHDslA7BsrrJ37bEyj0TTpshcoHSeQ72roKrly5wnPPPadni/c+rUPPi6zICIbhvg/Bznurl8spUcMP9Ax0ti0vDIYlwrZt7t61k/GoOzU6XLpz9Diui8q30Hj22WdxXZe9e/dSX18/dTVlWUhdPXVrN7KrOcy2xrqq5SqXC6WgpPEy3HzUzB5Uf38/z5++gdd/K51ulCYnVWUJSx1KG4lE6O/v1xWPP/zbOpLv8nEdMr5pD3Rs0hXLZ7sBu3YnXD45Wf18Ol2maZqhNklFhon3n6dt+zbGcoLT34sbq84l7bou8Xic69evs3btWt7znvdw7do1Ll++jGVZrF23no6uLvwee9ZBDZ1BP5djM118oPev2gIlGn8abjpqwkD19/fz/PPPo5QiY3m5Is1csZmVMXEch76+Pm2gPF5dEmnrnfMXbv2tupJFfHRq6LrtgZ6tkC+IaTDUEsPpDC8PxXHW7AXbRpTCbmnHfeu1qlf9juPQ29vL2rVrsW2b9evXs379+nnL1hHwU+exiWVzU/KlLNFlgpp9psiqQbPsLj7HcXjxxRenVkbOu+9mS9loo7lie+D9/0L3d7I8k+WStu2Dh3924a9nMMwTpRSH+oZxLM9E3UoRwbJtPBu3zmqsxdApEeGBrla6QoGJEkGWwNpQkHs7WkyYuWGCZV9BXb9+fcGUIBRapIzuQB089vOQSUIqAaFw8T0ug6EGGEimSOeySJFAIKu1DTu1Hqf/hg7+KUy5KMJi6ZTXsri7vZms65J2XAK2VXWQheHmYdnviCNHjlR/slL5EkczfdciQmPjIlfl9gWhodUYJ0NNc+z48SmlK1U6hTM6jBvX+09WYxNkM9hr1uPbdz9WzzqdpD4NEaG5eXELn3oti3qvxxgnQ1GWdQU1NjZGMpmcxTsUBzIXGJZ6Tno7prgB7XwLAIPhZiaVSDB27QpWWxcqlyN79iQqEtEbPEqBz4/VrPuHOVcvgVJ4123ECQTJnX93ylgiQk9Pz3L8GQYDMM8VlIi8T0ROi8hZEfn92b7/xz/+cfUnK0WPE2GNG2On08/WnG6HYVkWtm1z4MABXe7fYFjBzFenjv3om+QyWdxohOypd1CRMe11cPI19FJJ3N5rE+1mxKe9AVZLG3bPunEZsG2b+++/v3KirsGwiMz57hMRG/gz4EngKvC6iHxDKXWi2jGy2Qo186ZxX/YSAF5cOiXFcEsLra2tbNu2jfr6Ck37DIYaZyF06krSAfGQe/fERKWI4uT7PLXqKFSxbTxNzTSm4jQ3N7N9+3bC4fC8/h6DYb7MZ3q0HzirlDoPICJfBD4MVKVMajaNy5TiruxVxmtKuEDLmvU88fATsxLYYKhx5qVTALlxLSkT+ACAbWOvWY+Vj/JTStHW2MgDu3fORW6DYVGYj09sDXCl4Per+demICKfEpHDInJ4YGBg9ldRioDKssUZnnzNsgnueWD2YxkMtc3S6BQgdfV48i49AAvFLe2tcxrLYFgs5mOgiiUrzFgWKaU+o5Tap5Ta197ePvlmEbze6hLy7spenRhc2R6sXQ9WX+zVYFg5zEunANqCnmJvmXYVwbNmw/hgWMDGhnpaTQUHQ40xHwN1FVhX8Pta4PpsBnj44YcrnrO+p5M1W3dC6xpkwy7kyV+GO5+cnaQGw8pg3jp178OPI+Xsk2XRsX4DW7o7afJ56QoFuLejhT2mxYWhBpnPHtTrwDYR2QRcA34G+ET5t0ylpaWFJ554goMHD07p4GlZFk1NTezZs8eEjhtuJuatU6HGJj7wvvfy8rM/ZDjtTqRi2F4vjc3N3LpzJz1dXQsuuMGwGMzZQCmlciLyG8D3ARv4W6XU8dmO09LSwtNPPz1XMQyGVcNC6VSosYknnv7YgstnMCw180pyUEp9B/jOAsliMNz0GJ0yGCYxma0Gg8FgqEmMgTIYDAZDTWIMlMFgMBhqEmOgDAaDwVCTyKxKDs33YiIDwKUyp7QBg0skzlyodfmg9mVc6fJtUEq1lzm+pBidWhJqXcbVIF9RvVpSA1UJETmslNq33HKUotblg9qX0ci3tNT631Pr8kHty7ia5TMuPoPBYDDUJMZAGQwGg6EmqTUD9ZnlFqACtS4f1L6MRr6lpdb/nlqXD2pfxlUrX03tQRkMBoPBME6traAMBoPBYACMgTIYDAZDjbIsBkpE3icip0XkrIj8fpHjIiL/V/742yJyZ43Jt1NEXhGRtIj83lLKVqV8P5f/3N4WkZdF5PYalPHDefmO5LvDLmmL5EryFZx3t4g4IvLRpZRvthidWnT5jE7NU76C86rXKaXUkv6g2wicAzYDPuAocOu0c94PfBfdYfRe4FCNydcB3A3878Dv1eDndz/QnP//U0v5+c1Cxnom90BvA07VknwF5/0YXV38o0v5GS7C5210an7yGZ2ap3wF51WtU8uxgtoPnFVKnVdKZYAvAh+eds6Hgb9TmleBJhFZqh7vFeVTSvUrpV4Hsksk02zle1kpNZL/9VV0Z9ZakzGm8ncsUEfFPuVLK1+e3wT+GehfQtnmgtGpxZfP6NQ85cszK51aDgO1BrhS8PvV/GuzPWexWM5rV8Ns5ftV9Mx5KalKRhH5iIicAr4N/MoSyQZVyCcia4CPAH+5hHLNFaNT88Po1PxZFJ1aDgMlRV6bbumrOWexWM5rV0PV8onIo2hl+neLKlGRSxd5bYaMSqmvKqV2Ak8D/9tiC1VANfL9F+DfKaWcxRdn3hidmh9Gp+bPoujUvDrqzpGrwLqC39cC1+dwzmKxnNeuhqrkE5HbgP8GPKWUGloi2caZ1WeolDooIltEpE0ptRRFL6uRbx/wRREBXezy/SKSU0p9bQnkmy1Gp+aH0an5szg6tVSbaAWbZB7gPLCJyc20XdPO+QBTN3RfqyX5Cs79Q5Z+Q7eaz289cBa4f6m/31nIuJXJDd07gWvjv9eCfNPO/yy1HSRhdGrxPz+jUwv0HefPr0qnlnwFpZTKichvAN9HR3T8rVLquIh8On/8L9ERHu9H3xAJ4JdrST4R6QIOAw2AKyK/g45YidSCfMD/ArQCf56freTUElY7rlLGnwI+KSJZIAn8tMrfuTUi34rB6NTiy4fRqYWQb9aYUkcGg8FgqElMJQmDwWAw1CTGQBkMBoOhJjEGymAwGAw1iTFQBoPBYKhJjIEyGAwGQ01iDJTBYDAYahJjoAwGg8FQk/zfjiIT0uvxE9sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Clustering subplot\n",
    "plt.subplot(1,2,1)\n",
    "plt.scatter(x=df_kcal['Deaths'],y=df_kcal['Recovered'],c=color_theme[df_kcal.y_2], s=50)\n",
    "plt.title('Ground Truth Classification')\n",
    "# Clustered scatter plot with color scheme based on K-means cluster predicted species \n",
    "plt.subplot(1,2,2)\n",
    "plt.scatter(x=df_kcal['Deaths'],y=df_kcal['Recovered'],c=color_theme[clustering.labels_], s=50)\n",
    "plt.title('K-Means Classification')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Recovered%')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Kmeans made accurate prediction however miscolored the clusters\n",
    "#Will fix by making 0(gray) -> 0, 1(orange) -> 2, and 2(blue) -> 1\n",
    "relabel = np.choose(clustering.labels_,[0,2,1]).astype(np.int64)\n",
    "plt.subplot(1,2,1)\n",
    "plt.scatter(x=df_kcal['Deaths'],y=df_kcal['Recovered'],c=color_theme[df_kcal.y_2], s=50)\n",
    "plt.xlabel('Death%')\n",
    "plt.ylabel('Recovered%')\n",
    "plt.title('Ground Truth Classification')\n",
    "plt.subplot(1,2,2)\n",
    "plt.scatter(x=df_kcal['Deaths'],y=df_kcal['Recovered'],c=color_theme[relabel], s=50)\n",
    "plt.title('K-Means Classification')\n",
    "plt.xlabel('Death%')\n",
    "plt.ylabel('Recovered%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.73      0.77        49\n",
      "           1       0.57      0.69      0.62        48\n",
      "           2       0.75      0.67      0.71        49\n",
      "\n",
      "    accuracy                           0.70       146\n",
      "   macro avg       0.71      0.70      0.70       146\n",
      "weighted avg       0.71      0.70      0.70       146\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Classification Report\n",
    "y_actual = pd.DataFrame(df_kcal['y_2'])\n",
    "print(classification_report(y_actual, relabel))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### K means Clustering for Checking Match with Recovery%:Death% Levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'K-Means Classification')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Clustering subplot\n",
    "plt.subplot(1,2,1)\n",
    "plt.scatter(x=df_kcal['Deaths'],y=df_kcal['Recovered'],c=color_theme[df_kcal.y], s=50)\n",
    "plt.title('Ground Truth Classification')\n",
    "# Clustered scatter plot with color scheme based on K-means cluster predicted species \n",
    "plt.subplot(1,2,2)\n",
    "plt.scatter(x=df_kcal['Deaths'],y=df_kcal['Recovered'],c=color_theme[clustering.labels_], s=50)\n",
    "plt.title('K-Means Classification')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Recovered%')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Kmeans made accurate prediction however miscolored the clusters\n",
    "#Will fix by making 0(gray) -> 0, 1(orange) -> 2, and 2(blue) -> 1\n",
    "relabel = np.choose(clustering.labels_,[0,2,1]).astype(np.int64)\n",
    "plt.subplot(1,2,1)\n",
    "plt.scatter(x=df_kcal['Deaths'],y=df_kcal['Recovered'],c=color_theme[df_kcal.y], s=50)\n",
    "plt.xlabel('Death%')\n",
    "plt.ylabel('Recovered%')\n",
    "plt.title('Ground Truth Classification')\n",
    "plt.subplot(1,2,2)\n",
    "plt.scatter(x=df_kcal['Deaths'],y=df_kcal['Recovered'],c=color_theme[relabel], s=50)\n",
    "plt.title('K-Means Classification')\n",
    "plt.xlabel('Death%')\n",
    "plt.ylabel('Recovered%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.43      0.39      0.41        49\n",
      "           1       0.28      0.33      0.30        48\n",
      "           2       0.43      0.39      0.41        49\n",
      "\n",
      "    accuracy                           0.37       146\n",
      "   macro avg       0.38      0.37      0.37       146\n",
      "weighted avg       0.38      0.37      0.37       146\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Classification Report\n",
    "y_actual = pd.DataFrame(df_kcal['y'])\n",
    "print(classification_report(y_actual, relabel))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import tree\n",
    "from itertools import repeat\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest Classifier for Confirmed% Levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7136363636363635\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted Result</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual result</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted Result  0  1  2\n",
       "Actual result            \n",
       "0                 2  1  1\n",
       "1                 2  5  1\n",
       "2                 0  5  5"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating Average Accuracy\n",
    "predictions = 0\n",
    "avg_score = 0.0\n",
    "ntimes = 30\n",
    "clf = 0\n",
    "X = df_kcal.iloc[:, 1:24]\n",
    "y = df_kcal.y_2\n",
    "for i in repeat(None, ntimes):\n",
    "\n",
    "    # train model with 80% of the data\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15)\n",
    "\n",
    "    # prediction\n",
    "    # Creating a random forest classifier\n",
    "    clf = RandomForestClassifier(n_jobs = 2, random_state = 0, n_estimators = 40)\n",
    "    # Training Classifier\n",
    "    clf.fit(X_train, y_train)\n",
    "    predictions = clf.predict(X_test)\n",
    "    # compute model accuracy\n",
    "    avg_score += accuracy_score(y_test, predictions)\n",
    "\n",
    "avg_score/= ntimes\n",
    "print(avg_score)\n",
    "#Confusion Matrix (for most recent model)\n",
    "array = pd.crosstab(y_test, predictions, rownames = ['Actual result'], colnames = ['Predicted Result'])\n",
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Predicted Result', ylabel='Actual result'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Colored Confusion Matrix\n",
    "sns.heatmap(array,cmap=\"Greens\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest Classifier for Recovery% : Death% Levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3075757575757575\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted Result</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual result</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted Result  0  1  2\n",
       "Actual result            \n",
       "0                 3  5  1\n",
       "1                 1  0  5\n",
       "2                 3  2  2"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating Average Accuracy\n",
    "predictions = 0\n",
    "avg_score = 0.0\n",
    "ntimes = 30\n",
    "clf = 0\n",
    "X = df_kcal.iloc[:, 1:24]\n",
    "y = df_kcal.y\n",
    "for i in repeat(None, ntimes):\n",
    "\n",
    "    # train model with 80% of the data\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15)\n",
    "\n",
    "    # prediction\n",
    "    # Creating a random forest classifier\n",
    "    clf = RandomForestClassifier(n_jobs = 2, random_state = 0, n_estimators = 40)\n",
    "    # Training Classifier\n",
    "    clf.fit(X_train, y_train)\n",
    "    predictions = clf.predict(X_test)\n",
    "    # compute model accuracy\n",
    "    avg_score += accuracy_score(y_test, predictions)\n",
    "\n",
    "avg_score/= ntimes\n",
    "print(avg_score)\n",
    "#Confusion Matrix (for most recent model)\n",
    "array = pd.crosstab(y_test, predictions, rownames = ['Actual result'], colnames = ['Predicted Result'])\n",
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Predicted Result', ylabel='Actual result'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Colored Confusion Matrix\n",
    "sns.heatmap(array, cmap = \"Reds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing influence of animal product consumption on Infection (Confirmed%) rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6155555555555555\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted Result</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual result</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted Result  0  1  2\n",
       "Actual result            \n",
       "0                 3  4  1\n",
       "1                 5  7  1\n",
       "2                 0  6  3"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating Average Accuracy\n",
    "predictions = 0\n",
    "avg_score = 0.0\n",
    "ntimes = 30\n",
    "clf = 0\n",
    "X = df_kcal[['Animal Products', 'Animal fats', 'Meat']]\n",
    "y = df_kcal.y_2\n",
    "for i in repeat(None, ntimes):\n",
    "\n",
    "    # train model with 80% of the data\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "    # prediction\n",
    "    # Creating a random forest classifier\n",
    "    clf = RandomForestClassifier(n_jobs = 2, random_state = 0, n_estimators = 40)\n",
    "    # Training Classifier\n",
    "    clf.fit(X_train, y_train)\n",
    "    predictions = clf.predict(X_test)\n",
    "    # compute model accuracy\n",
    "    avg_score += accuracy_score(y_test, predictions)\n",
    "\n",
    "avg_score/= ntimes\n",
    "print(avg_score)\n",
    "#Confusion Matrix (for most recent model)\n",
    "array = pd.crosstab(y_test, predictions, rownames = ['Actual result'], colnames = ['Predicted Result'])\n",
    "array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing influence of  plant-based consumption on Infection (Confirmed%) rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.643939393939394\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted Result</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual result</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted Result  0  1  2\n",
       "Actual result            \n",
       "0                 5  0  2\n",
       "1                 2  4  0\n",
       "2                 0  2  7"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating Average Accuracy\n",
    "predictions = 0\n",
    "avg_score = 0.0\n",
    "ntimes = 30\n",
    "clf = 0\n",
    "X = df_kcal[['Fruits - Excluding Wine', 'Pulses', 'Spices', 'Vegetables', 'Treenuts', 'Vegetal Products', 'Starchy Roots']]\n",
    "y = df_kcal.y_2\n",
    "for i in repeat(None, ntimes):\n",
    "\n",
    "    # train model with 80% of the data\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15)\n",
    "\n",
    "    # prediction\n",
    "    # Creating a random forest classifier\n",
    "    clf = RandomForestClassifier(n_jobs = 2, random_state = 0, n_estimators = 40)\n",
    "    # Training Classifier\n",
    "    clf.fit(X_train, y_train)\n",
    "    predictions = clf.predict(X_test)\n",
    "    # compute model accuracy\n",
    "    avg_score += accuracy_score(y_test, predictions)\n",
    "\n",
    "avg_score/= ntimes\n",
    "print(avg_score)\n",
    "#Confusion Matrix (for most recent model)\n",
    "array = pd.crosstab(y_test, predictions, rownames = ['Actual result'], colnames = ['Predicted Result'])\n",
    "array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing influence of obesity and malnourishment rates on Infection (Confirmed%) rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['29.8', '6.2', '3.9', '25', '4.6', '4.3', '<2.5', '14.7', '7.5',\n",
       "       '10.1', '17.1', '26.4', '3.6', '20', '12.6', '16.4', '9.9', '59.6',\n",
       "       '37.5', '8.5', '4.8', '40.3', '19', '18.9', '9.5', '7.9', '4.5',\n",
       "       '9', '2.9', '20.6', '3.7', '10.5', '10.2', '5.5', '15.2', '16.5',\n",
       "       '28', '8.1', '49.3', '12.9', '14.5', '8.3', '4.9', '29', '8',\n",
       "       '12.2', '29.4', '2.8', '7.1', '11', '13.1', '37.2', '44.4', '17.5',\n",
       "       '2.5', '6.3', '10.4', '6.5', '13.4', '3.4', '27.9', '27.3', '8.7',\n",
       "       '17', '3.2', '6.8', '20.3', '10', '10.7', '9.7', '13.3', '36.8',\n",
       "       '5.7', '7', '11.3', '25.6', '20.1', '7.8', '24.9', '16.1', '41',\n",
       "       '3.5', '30.7', '21.2', '9.3', '38.9', '46.7', '51.3'], dtype=object)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kcal['Undernourished'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_kcal[\"Undernourished\"].replace({\"<2.5\": \"2.5\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6088888888888888\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted Result</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual result</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted Result  0  1  2\n",
       "Actual result            \n",
       "0                 4  3  1\n",
       "1                 1  8  5\n",
       "2                 1  3  4"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating average accuracy\n",
    "predictions = 0\n",
    "avg_score = 0.0\n",
    "ntimes = 30\n",
    "clf = 0\n",
    "X = df_kcal[['Obesity', 'Undernourished']]\n",
    "y = df_kcal.y_2\n",
    "for i in repeat(None, ntimes):\n",
    "\n",
    "    # train model with 80% of the data\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "    # prediction\n",
    "    # Creating a random forest classifier\n",
    "    clf = RandomForestClassifier(n_jobs = 2, random_state = 0, n_estimators = 40)\n",
    "    # Training Classifier\n",
    "    clf.fit(X_train, y_train)\n",
    "    predictions = clf.predict(X_test)\n",
    "    # compute model accuracy\n",
    "    avg_score += accuracy_score(y_test, predictions)\n",
    "\n",
    "avg_score/= ntimes\n",
    "print(avg_score)\n",
    "#Confusion Matrix (for most recent model)\n",
    "array = pd.crosstab(y_test, predictions, rownames = ['Actual result'], colnames = ['Predicted Result'])\n",
    "array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using Regression To Directly Predict Confirmed% and Recovery% : Death% ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "df_kcal = df_kcal.replace('<2.5', '2.5')\n",
    "df_kcal['Undernourished'] = df_kcal['Undernourished'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking type of Undernourished Column\n",
    "type(df_kcal['Undernourished'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting into 80% training 20% testing\n",
    "df_train, df_test = train_test_split(df_kcal, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>Q(\"Confirmed\")</td>  <th>  R-squared:         </th> <td>   0.650</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.553</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   6.699</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 12 Jan 2022</td> <th>  Prob (F-statistic):</th> <td>7.71e-12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:10:17</td>     <th>  Log-Likelihood:    </th> <td> -297.70</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   116</td>      <th>  AIC:               </th> <td>   647.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    90</td>      <th>  BIC:               </th> <td>   719.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    25</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                <td></td>                   <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                     <td> 4.309e+04</td> <td> 2.98e+05</td> <td>    0.145</td> <td> 0.885</td> <td>-5.49e+05</td> <td> 6.35e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Alcoholic Beverages\")</th>      <td> -422.2866</td> <td> 2983.563</td> <td>   -0.142</td> <td> 0.888</td> <td>-6349.656</td> <td> 5505.083</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Animal Products\")</th>          <td> -414.8912</td> <td> 2980.044</td> <td>   -0.139</td> <td> 0.890</td> <td>-6335.268</td> <td> 5505.486</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Animal fats\")</th>              <td> -446.6705</td> <td> 2979.002</td> <td>   -0.150</td> <td> 0.881</td> <td>-6364.977</td> <td> 5471.636</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Aquatic Products, Other\")</th>  <td> -445.9817</td> <td> 2979.145</td> <td>   -0.150</td> <td> 0.881</td> <td>-6364.573</td> <td> 5472.609</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Cereals - Excluding Beer\")</th> <td> -422.7542</td> <td> 2983.613</td> <td>   -0.142</td> <td> 0.888</td> <td>-6350.222</td> <td> 5504.714</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Eggs\")</th>                     <td> -446.1560</td> <td> 2978.872</td> <td>   -0.150</td> <td> 0.881</td> <td>-6364.205</td> <td> 5471.893</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Fish, Seafood\")</th>            <td> -448.8057</td> <td> 2979.056</td> <td>   -0.151</td> <td> 0.881</td> <td>-6367.221</td> <td> 5469.610</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Fruits - Excluding Wine\")</th>  <td> -422.7405</td> <td> 2983.583</td> <td>   -0.142</td> <td> 0.888</td> <td>-6350.150</td> <td> 5504.669</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Meat\")</th>                     <td> -446.9466</td> <td> 2978.976</td> <td>   -0.150</td> <td> 0.881</td> <td>-6365.202</td> <td> 5471.308</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Milk - Excluding Butter\")</th>  <td> -446.3484</td> <td> 2978.989</td> <td>   -0.150</td> <td> 0.881</td> <td>-6364.630</td> <td> 5471.933</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Miscellaneous\")</th>            <td> -425.0909</td> <td> 2983.494</td> <td>   -0.142</td> <td> 0.887</td> <td>-6352.323</td> <td> 5502.142</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Offals\")</th>                   <td> -450.2668</td> <td> 2978.435</td> <td>   -0.151</td> <td> 0.880</td> <td>-6367.447</td> <td> 5466.914</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Oilcrops\")</th>                 <td> -422.4476</td> <td> 2983.654</td> <td>   -0.142</td> <td> 0.888</td> <td>-6349.996</td> <td> 5505.101</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Pulses\")</th>                   <td> -422.8110</td> <td> 2983.645</td> <td>   -0.142</td> <td> 0.888</td> <td>-6350.343</td> <td> 5504.720</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Spices\")</th>                   <td> -423.1448</td> <td> 2983.768</td> <td>   -0.142</td> <td> 0.888</td> <td>-6350.921</td> <td> 5504.632</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Starchy Roots\")</th>            <td> -422.6779</td> <td> 2983.602</td> <td>   -0.142</td> <td> 0.888</td> <td>-6350.124</td> <td> 5504.768</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Stimulants\")</th>               <td> -416.8805</td> <td> 2983.732</td> <td>   -0.140</td> <td> 0.889</td> <td>-6344.585</td> <td> 5510.824</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Sugar Crops\")</th>              <td> -420.2630</td> <td> 2983.944</td> <td>   -0.141</td> <td> 0.888</td> <td>-6348.388</td> <td> 5507.862</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Sugar & Sweeteners\")</th>       <td> -422.5009</td> <td> 2983.606</td> <td>   -0.142</td> <td> 0.888</td> <td>-6349.956</td> <td> 5504.954</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Treenuts\")</th>                 <td> -422.2286</td> <td> 2983.692</td> <td>   -0.142</td> <td> 0.888</td> <td>-6349.854</td> <td> 5505.396</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Vegetal Products\")</th>         <td> -439.1038</td> <td> 2975.313</td> <td>   -0.148</td> <td> 0.883</td> <td>-6350.082</td> <td> 5471.874</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Vegetable Oils\")</th>           <td> -422.7243</td> <td> 2983.639</td> <td>   -0.142</td> <td> 0.888</td> <td>-6350.244</td> <td> 5504.795</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Vegetables\")</th>               <td> -423.8060</td> <td> 2983.623</td> <td>   -0.142</td> <td> 0.887</td> <td>-6351.294</td> <td> 5503.682</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Obesity\")</th>                  <td>    0.0999</td> <td>    0.074</td> <td>    1.356</td> <td> 0.179</td> <td>   -0.046</td> <td>    0.246</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Undernourished\")</th>           <td>   -0.0598</td> <td>    0.049</td> <td>   -1.230</td> <td> 0.222</td> <td>   -0.156</td> <td>    0.037</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 5.282</td> <th>  Durbin-Watson:     </th> <td>   2.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.071</td> <th>  Jarque-Bera (JB):  </th> <td>   6.865</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.175</td> <th>  Prob(JB):          </th> <td>  0.0323</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.139</td> <th>  Cond. No.          </th> <td>4.73e+07</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 4.73e+07. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:         Q(\"Confirmed\")   R-squared:                       0.650\n",
       "Model:                            OLS   Adj. R-squared:                  0.553\n",
       "Method:                 Least Squares   F-statistic:                     6.699\n",
       "Date:                Wed, 12 Jan 2022   Prob (F-statistic):           7.71e-12\n",
       "Time:                        20:10:17   Log-Likelihood:                -297.70\n",
       "No. Observations:                 116   AIC:                             647.4\n",
       "Df Residuals:                      90   BIC:                             719.0\n",
       "Df Model:                          25                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=================================================================================================\n",
       "                                    coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------------------\n",
       "Intercept                      4.309e+04   2.98e+05      0.145      0.885   -5.49e+05    6.35e+05\n",
       "Q(\"Alcoholic Beverages\")       -422.2866   2983.563     -0.142      0.888   -6349.656    5505.083\n",
       "Q(\"Animal Products\")           -414.8912   2980.044     -0.139      0.890   -6335.268    5505.486\n",
       "Q(\"Animal fats\")               -446.6705   2979.002     -0.150      0.881   -6364.977    5471.636\n",
       "Q(\"Aquatic Products, Other\")   -445.9817   2979.145     -0.150      0.881   -6364.573    5472.609\n",
       "Q(\"Cereals - Excluding Beer\")  -422.7542   2983.613     -0.142      0.888   -6350.222    5504.714\n",
       "Q(\"Eggs\")                      -446.1560   2978.872     -0.150      0.881   -6364.205    5471.893\n",
       "Q(\"Fish, Seafood\")             -448.8057   2979.056     -0.151      0.881   -6367.221    5469.610\n",
       "Q(\"Fruits - Excluding Wine\")   -422.7405   2983.583     -0.142      0.888   -6350.150    5504.669\n",
       "Q(\"Meat\")                      -446.9466   2978.976     -0.150      0.881   -6365.202    5471.308\n",
       "Q(\"Milk - Excluding Butter\")   -446.3484   2978.989     -0.150      0.881   -6364.630    5471.933\n",
       "Q(\"Miscellaneous\")             -425.0909   2983.494     -0.142      0.887   -6352.323    5502.142\n",
       "Q(\"Offals\")                    -450.2668   2978.435     -0.151      0.880   -6367.447    5466.914\n",
       "Q(\"Oilcrops\")                  -422.4476   2983.654     -0.142      0.888   -6349.996    5505.101\n",
       "Q(\"Pulses\")                    -422.8110   2983.645     -0.142      0.888   -6350.343    5504.720\n",
       "Q(\"Spices\")                    -423.1448   2983.768     -0.142      0.888   -6350.921    5504.632\n",
       "Q(\"Starchy Roots\")             -422.6779   2983.602     -0.142      0.888   -6350.124    5504.768\n",
       "Q(\"Stimulants\")                -416.8805   2983.732     -0.140      0.889   -6344.585    5510.824\n",
       "Q(\"Sugar Crops\")               -420.2630   2983.944     -0.141      0.888   -6348.388    5507.862\n",
       "Q(\"Sugar & Sweeteners\")        -422.5009   2983.606     -0.142      0.888   -6349.956    5504.954\n",
       "Q(\"Treenuts\")                  -422.2286   2983.692     -0.142      0.888   -6349.854    5505.396\n",
       "Q(\"Vegetal Products\")          -439.1038   2975.313     -0.148      0.883   -6350.082    5471.874\n",
       "Q(\"Vegetable Oils\")            -422.7243   2983.639     -0.142      0.888   -6350.244    5504.795\n",
       "Q(\"Vegetables\")                -423.8060   2983.623     -0.142      0.887   -6351.294    5503.682\n",
       "Q(\"Obesity\")                      0.0999      0.074      1.356      0.179      -0.046       0.246\n",
       "Q(\"Undernourished\")              -0.0598      0.049     -1.230      0.222      -0.156       0.037\n",
       "==============================================================================\n",
       "Omnibus:                        5.282   Durbin-Watson:                   2.001\n",
       "Prob(Omnibus):                  0.071   Jarque-Bera (JB):                6.865\n",
       "Skew:                           0.175   Prob(JB):                       0.0323\n",
       "Kurtosis:                       4.139   Cond. No.                     4.73e+07\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 4.73e+07. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generating Linear Model for predicting Confirmed% with all independent variables\n",
    "model_Confirmed = smf.ols('Q(\"Confirmed\")~Q(\"Alcoholic Beverages\")+Q(\"Animal Products\")+Q(\"Animal fats\")+Q(\"Aquatic Products, Other\")+Q(\"Cereals - Excluding Beer\")+Q(\"Eggs\")+Q(\"Fish, Seafood\")+Q(\"Fruits - Excluding Wine\")+Q(\"Meat\")+Q(\"Milk - Excluding Butter\")+Q(\"Miscellaneous\")+Q(\"Offals\")+Q(\"Oilcrops\")+Q(\"Pulses\")+Q(\"Spices\")+Q(\"Starchy Roots\")+Q(\"Stimulants\")+Q(\"Sugar Crops\")+Q(\"Sugar & Sweeteners\")+Q(\"Treenuts\")+Q(\"Vegetal Products\")+Q(\"Vegetable Oils\")+Q(\"Vegetables\")+Q(\"Obesity\")+Q(\"Undernourished\")', data = df_train).fit()\n",
    "model_Confirmed.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Q(\"Ratio of Recovery to Death\")</td> <th>  R-squared:         </th> <td>   0.181</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                          <td>OLS</td>               <th>  Adj. R-squared:    </th> <td>  -0.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                    <td>Least Squares</td>          <th>  F-statistic:       </th> <td>  0.7932</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                    <td>Wed, 12 Jan 2022</td>         <th>  Prob (F-statistic):</th>  <td> 0.740</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                        <td>20:10:12</td>             <th>  Log-Likelihood:    </th> <td> -566.37</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>             <td>   116</td>              <th>  AIC:               </th> <td>   1185.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>                 <td>    90</td>              <th>  BIC:               </th> <td>   1256.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                     <td>    25</td>              <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>             <td>nonrobust</td>            <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                <td></td>                   <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                     <td> 4.584e+06</td> <td> 3.02e+06</td> <td>    1.518</td> <td> 0.133</td> <td>-1.42e+06</td> <td> 1.06e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Alcoholic Beverages\")</th>      <td>-4.611e+04</td> <td> 3.02e+04</td> <td>   -1.525</td> <td> 0.131</td> <td>-1.06e+05</td> <td>  1.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Animal Products\")</th>          <td>-4.591e+04</td> <td> 3.02e+04</td> <td>   -1.520</td> <td> 0.132</td> <td>-1.06e+05</td> <td> 1.41e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Animal fats\")</th>              <td>-4.576e+04</td> <td> 3.02e+04</td> <td>   -1.515</td> <td> 0.133</td> <td>-1.06e+05</td> <td> 1.42e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Aquatic Products, Other\")</th>  <td>-4.577e+04</td> <td> 3.02e+04</td> <td>   -1.516</td> <td> 0.133</td> <td>-1.06e+05</td> <td> 1.42e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Cereals - Excluding Beer\")</th> <td> -4.61e+04</td> <td> 3.02e+04</td> <td>   -1.524</td> <td> 0.131</td> <td>-1.06e+05</td> <td>  1.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Eggs\")</th>                     <td>-4.575e+04</td> <td> 3.02e+04</td> <td>   -1.515</td> <td> 0.133</td> <td>-1.06e+05</td> <td> 1.42e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Fish, Seafood\")</th>            <td>-4.576e+04</td> <td> 3.02e+04</td> <td>   -1.515</td> <td> 0.133</td> <td>-1.06e+05</td> <td> 1.42e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Fruits - Excluding Wine\")</th>  <td> -4.61e+04</td> <td> 3.02e+04</td> <td>   -1.524</td> <td> 0.131</td> <td>-1.06e+05</td> <td>  1.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Meat\")</th>                     <td>-4.576e+04</td> <td> 3.02e+04</td> <td>   -1.515</td> <td> 0.133</td> <td>-1.06e+05</td> <td> 1.42e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Milk - Excluding Butter\")</th>  <td>-4.576e+04</td> <td> 3.02e+04</td> <td>   -1.515</td> <td> 0.133</td> <td>-1.06e+05</td> <td> 1.42e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Miscellaneous\")</th>            <td>-4.611e+04</td> <td> 3.02e+04</td> <td>   -1.525</td> <td> 0.131</td> <td>-1.06e+05</td> <td>  1.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Offals\")</th>                   <td>-4.575e+04</td> <td> 3.02e+04</td> <td>   -1.515</td> <td> 0.133</td> <td>-1.06e+05</td> <td> 1.42e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Oilcrops\")</th>                 <td> -4.61e+04</td> <td> 3.02e+04</td> <td>   -1.524</td> <td> 0.131</td> <td>-1.06e+05</td> <td>  1.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Pulses\")</th>                   <td> -4.61e+04</td> <td> 3.02e+04</td> <td>   -1.524</td> <td> 0.131</td> <td>-1.06e+05</td> <td>  1.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Spices\")</th>                   <td>-4.609e+04</td> <td> 3.02e+04</td> <td>   -1.524</td> <td> 0.131</td> <td>-1.06e+05</td> <td>  1.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Starchy Roots\")</th>            <td> -4.61e+04</td> <td> 3.02e+04</td> <td>   -1.524</td> <td> 0.131</td> <td>-1.06e+05</td> <td>  1.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Stimulants\")</th>               <td>-4.608e+04</td> <td> 3.02e+04</td> <td>   -1.524</td> <td> 0.131</td> <td>-1.06e+05</td> <td>  1.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Sugar Crops\")</th>              <td>-4.616e+04</td> <td> 3.02e+04</td> <td>   -1.526</td> <td> 0.131</td> <td>-1.06e+05</td> <td> 1.39e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Sugar & Sweeteners\")</th>       <td>-4.611e+04</td> <td> 3.02e+04</td> <td>   -1.524</td> <td> 0.131</td> <td>-1.06e+05</td> <td>  1.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Treenuts\")</th>                 <td> -4.61e+04</td> <td> 3.02e+04</td> <td>   -1.524</td> <td> 0.131</td> <td>-1.06e+05</td> <td>  1.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Vegetal Products\")</th>         <td>-4.557e+04</td> <td> 3.02e+04</td> <td>   -1.511</td> <td> 0.134</td> <td>-1.05e+05</td> <td> 1.43e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Vegetable Oils\")</th>           <td> -4.61e+04</td> <td> 3.02e+04</td> <td>   -1.524</td> <td> 0.131</td> <td>-1.06e+05</td> <td>  1.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Vegetables\")</th>               <td>-4.611e+04</td> <td> 3.02e+04</td> <td>   -1.524</td> <td> 0.131</td> <td>-1.06e+05</td> <td>  1.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Obesity\")</th>                  <td>    0.3474</td> <td>    0.747</td> <td>    0.465</td> <td> 0.643</td> <td>   -1.136</td> <td>    1.831</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Undernourished\")</th>           <td>   -0.4243</td> <td>    0.493</td> <td>   -0.861</td> <td> 0.392</td> <td>   -1.404</td> <td>    0.555</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>11.634</td> <th>  Durbin-Watson:     </th> <td>   2.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.003</td> <th>  Jarque-Bera (JB):  </th> <td>  12.150</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.771</td> <th>  Prob(JB):          </th> <td> 0.00230</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.372</td> <th>  Cond. No.          </th> <td>4.73e+07</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 4.73e+07. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                   OLS Regression Results                                  \n",
       "===========================================================================================\n",
       "Dep. Variable:     Q(\"Ratio of Recovery to Death\")   R-squared:                       0.181\n",
       "Model:                                         OLS   Adj. R-squared:                 -0.047\n",
       "Method:                              Least Squares   F-statistic:                    0.7932\n",
       "Date:                             Wed, 12 Jan 2022   Prob (F-statistic):              0.740\n",
       "Time:                                     20:10:12   Log-Likelihood:                -566.37\n",
       "No. Observations:                              116   AIC:                             1185.\n",
       "Df Residuals:                                   90   BIC:                             1256.\n",
       "Df Model:                                       25                                         \n",
       "Covariance Type:                         nonrobust                                         \n",
       "=================================================================================================\n",
       "                                    coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------------------\n",
       "Intercept                      4.584e+06   3.02e+06      1.518      0.133   -1.42e+06    1.06e+07\n",
       "Q(\"Alcoholic Beverages\")      -4.611e+04   3.02e+04     -1.525      0.131   -1.06e+05     1.4e+04\n",
       "Q(\"Animal Products\")          -4.591e+04   3.02e+04     -1.520      0.132   -1.06e+05    1.41e+04\n",
       "Q(\"Animal fats\")              -4.576e+04   3.02e+04     -1.515      0.133   -1.06e+05    1.42e+04\n",
       "Q(\"Aquatic Products, Other\")  -4.577e+04   3.02e+04     -1.516      0.133   -1.06e+05    1.42e+04\n",
       "Q(\"Cereals - Excluding Beer\")  -4.61e+04   3.02e+04     -1.524      0.131   -1.06e+05     1.4e+04\n",
       "Q(\"Eggs\")                     -4.575e+04   3.02e+04     -1.515      0.133   -1.06e+05    1.42e+04\n",
       "Q(\"Fish, Seafood\")            -4.576e+04   3.02e+04     -1.515      0.133   -1.06e+05    1.42e+04\n",
       "Q(\"Fruits - Excluding Wine\")   -4.61e+04   3.02e+04     -1.524      0.131   -1.06e+05     1.4e+04\n",
       "Q(\"Meat\")                     -4.576e+04   3.02e+04     -1.515      0.133   -1.06e+05    1.42e+04\n",
       "Q(\"Milk - Excluding Butter\")  -4.576e+04   3.02e+04     -1.515      0.133   -1.06e+05    1.42e+04\n",
       "Q(\"Miscellaneous\")            -4.611e+04   3.02e+04     -1.525      0.131   -1.06e+05     1.4e+04\n",
       "Q(\"Offals\")                   -4.575e+04   3.02e+04     -1.515      0.133   -1.06e+05    1.42e+04\n",
       "Q(\"Oilcrops\")                  -4.61e+04   3.02e+04     -1.524      0.131   -1.06e+05     1.4e+04\n",
       "Q(\"Pulses\")                    -4.61e+04   3.02e+04     -1.524      0.131   -1.06e+05     1.4e+04\n",
       "Q(\"Spices\")                   -4.609e+04   3.02e+04     -1.524      0.131   -1.06e+05     1.4e+04\n",
       "Q(\"Starchy Roots\")             -4.61e+04   3.02e+04     -1.524      0.131   -1.06e+05     1.4e+04\n",
       "Q(\"Stimulants\")               -4.608e+04   3.02e+04     -1.524      0.131   -1.06e+05     1.4e+04\n",
       "Q(\"Sugar Crops\")              -4.616e+04   3.02e+04     -1.526      0.131   -1.06e+05    1.39e+04\n",
       "Q(\"Sugar & Sweeteners\")       -4.611e+04   3.02e+04     -1.524      0.131   -1.06e+05     1.4e+04\n",
       "Q(\"Treenuts\")                  -4.61e+04   3.02e+04     -1.524      0.131   -1.06e+05     1.4e+04\n",
       "Q(\"Vegetal Products\")         -4.557e+04   3.02e+04     -1.511      0.134   -1.05e+05    1.43e+04\n",
       "Q(\"Vegetable Oils\")            -4.61e+04   3.02e+04     -1.524      0.131   -1.06e+05     1.4e+04\n",
       "Q(\"Vegetables\")               -4.611e+04   3.02e+04     -1.524      0.131   -1.06e+05     1.4e+04\n",
       "Q(\"Obesity\")                      0.3474      0.747      0.465      0.643      -1.136       1.831\n",
       "Q(\"Undernourished\")              -0.4243      0.493     -0.861      0.392      -1.404       0.555\n",
       "==============================================================================\n",
       "Omnibus:                       11.634   Durbin-Watson:                   2.012\n",
       "Prob(Omnibus):                  0.003   Jarque-Bera (JB):               12.150\n",
       "Skew:                           0.771   Prob(JB):                      0.00230\n",
       "Kurtosis:                       3.372   Cond. No.                     4.73e+07\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 4.73e+07. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generating Linear Model for predicting Confirmed% with all independent variables\n",
    "model_RecoveryDeath = smf.ols('Q(\"Ratio of Recovery to Death\")~Q(\"Alcoholic Beverages\")+Q(\"Animal Products\")+Q(\"Animal fats\")+Q(\"Aquatic Products, Other\")+Q(\"Cereals - Excluding Beer\")+Q(\"Eggs\")+Q(\"Fish, Seafood\")+Q(\"Fruits - Excluding Wine\")+Q(\"Meat\")+Q(\"Milk - Excluding Butter\")+Q(\"Miscellaneous\")+Q(\"Offals\")+Q(\"Oilcrops\")+Q(\"Pulses\")+Q(\"Spices\")+Q(\"Starchy Roots\")+Q(\"Stimulants\")+Q(\"Sugar Crops\")+Q(\"Sugar & Sweeteners\")+Q(\"Treenuts\")+Q(\"Vegetal Products\")+Q(\"Vegetable Oils\")+Q(\"Vegetables\")+Q(\"Obesity\")+Q(\"Undernourished\")', data = df_train).fit()\n",
    "model_RecoveryDeath.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Determining 12 most important variables for predicting Confirmed% Levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False  True  True False  True  True False  True  True  True  True\n",
      " False  True False False False  True False False  True False  True]\n",
      "Columns = Animal fats| Aquatic Products, Other| Eggs| Fish, Seafood| Meat| Milk - Excluding Butter| Miscellaneous| Offals| Pulses| Sugar Crops| Vegetal Products| Vegetables| \n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso, LinearRegression\n",
    "inp = df_kcal.iloc[:, 1:24]\n",
    "out = df_kcal.Confirmed\n",
    "rfe = RFE(estimator=LinearRegression(), n_features_to_select=12)\n",
    "model = LinearRegression()\n",
    "pipeline = Pipeline(steps=[('s',rfe),('m',model)])\n",
    "pipeline.fit(inp, out)\n",
    "print(rfe.support_)\n",
    "string = 'Columns = '\n",
    "for i,j in zip(rfe.support_, inp.columns):\n",
    "    if i:\n",
    "        string = string + j + \"| \"\n",
    "print(string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>Confirmed</td>    <th>  R-squared:         </th> <td>   0.579</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.541</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   15.25</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 12 Jan 2022</td> <th>  Prob (F-statistic):</th> <td>9.48e-20</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:05:16</td>     <th>  Log-Likelihood:    </th> <td> -388.66</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   146</td>      <th>  AIC:               </th> <td>   803.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   133</td>      <th>  BIC:               </th> <td>   842.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    12</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                <td></td>                  <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                    <td> 1919.4966</td> <td> 1437.494</td> <td>    1.335</td> <td> 0.184</td> <td> -923.811</td> <td> 4762.805</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Animal fats\")</th>             <td>  -37.6694</td> <td>   28.750</td> <td>   -1.310</td> <td> 0.192</td> <td>  -94.535</td> <td>   19.196</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Aquatic Products, Other\")</th> <td>  -47.6185</td> <td>   30.238</td> <td>   -1.575</td> <td> 0.118</td> <td> -107.428</td> <td>   12.191</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Eggs\")</th>                    <td>  -35.9257</td> <td>   28.962</td> <td>   -1.240</td> <td> 0.217</td> <td>  -93.211</td> <td>   21.360</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Fish, Seafood\")</th>           <td>  -39.3899</td> <td>   28.673</td> <td>   -1.374</td> <td> 0.172</td> <td>  -96.103</td> <td>   17.323</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Meat\")</th>                    <td>  -37.7113</td> <td>   28.757</td> <td>   -1.311</td> <td> 0.192</td> <td>  -94.591</td> <td>   19.168</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Milk - Excluding Butter\")</th> <td>  -37.4373</td> <td>   28.719</td> <td>   -1.304</td> <td> 0.195</td> <td>  -94.242</td> <td>   19.367</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Miscellaneous\")</th>           <td>   -0.5242</td> <td>    2.034</td> <td>   -0.258</td> <td> 0.797</td> <td>   -4.547</td> <td>    3.498</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Offals\")</th>                  <td>  -48.3947</td> <td>   28.953</td> <td>   -1.672</td> <td> 0.097</td> <td> -105.662</td> <td>    8.873</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Pulses\")</th>                  <td>   -0.4082</td> <td>    0.299</td> <td>   -1.365</td> <td> 0.174</td> <td>   -1.000</td> <td>    0.183</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Sugar Crops\")</th>             <td>   -6.0894</td> <td>    4.651</td> <td>   -1.309</td> <td> 0.193</td> <td>  -15.288</td> <td>    3.109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Vegetal Products\")</th>        <td>  -38.3625</td> <td>   28.753</td> <td>   -1.334</td> <td> 0.184</td> <td>  -95.234</td> <td>   18.509</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Vegetables\")</th>              <td>   -0.1973</td> <td>    0.554</td> <td>   -0.356</td> <td> 0.722</td> <td>   -1.293</td> <td>    0.899</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>11.665</td> <th>  Durbin-Watson:     </th> <td>   2.077</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.003</td> <th>  Jarque-Bera (JB):  </th> <td>  23.521</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.286</td> <th>  Prob(JB):          </th> <td>7.81e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.881</td> <th>  Cond. No.          </th> <td>1.99e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.99e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              Confirmed   R-squared:                       0.579\n",
       "Model:                            OLS   Adj. R-squared:                  0.541\n",
       "Method:                 Least Squares   F-statistic:                     15.25\n",
       "Date:                Wed, 12 Jan 2022   Prob (F-statistic):           9.48e-20\n",
       "Time:                        20:05:16   Log-Likelihood:                -388.66\n",
       "No. Observations:                 146   AIC:                             803.3\n",
       "Df Residuals:                     133   BIC:                             842.1\n",
       "Df Model:                          12                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================================\n",
       "                                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------------\n",
       "Intercept                     1919.4966   1437.494      1.335      0.184    -923.811    4762.805\n",
       "Q(\"Animal fats\")               -37.6694     28.750     -1.310      0.192     -94.535      19.196\n",
       "Q(\"Aquatic Products, Other\")   -47.6185     30.238     -1.575      0.118    -107.428      12.191\n",
       "Q(\"Eggs\")                      -35.9257     28.962     -1.240      0.217     -93.211      21.360\n",
       "Q(\"Fish, Seafood\")             -39.3899     28.673     -1.374      0.172     -96.103      17.323\n",
       "Q(\"Meat\")                      -37.7113     28.757     -1.311      0.192     -94.591      19.168\n",
       "Q(\"Milk - Excluding Butter\")   -37.4373     28.719     -1.304      0.195     -94.242      19.367\n",
       "Q(\"Miscellaneous\")              -0.5242      2.034     -0.258      0.797      -4.547       3.498\n",
       "Q(\"Offals\")                    -48.3947     28.953     -1.672      0.097    -105.662       8.873\n",
       "Q(\"Pulses\")                     -0.4082      0.299     -1.365      0.174      -1.000       0.183\n",
       "Q(\"Sugar Crops\")                -6.0894      4.651     -1.309      0.193     -15.288       3.109\n",
       "Q(\"Vegetal Products\")          -38.3625     28.753     -1.334      0.184     -95.234      18.509\n",
       "Q(\"Vegetables\")                 -0.1973      0.554     -0.356      0.722      -1.293       0.899\n",
       "==============================================================================\n",
       "Omnibus:                       11.665   Durbin-Watson:                   2.077\n",
       "Prob(Omnibus):                  0.003   Jarque-Bera (JB):               23.521\n",
       "Skew:                           0.286   Prob(JB):                     7.81e-06\n",
       "Kurtosis:                       4.881   Cond. No.                     1.99e+05\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.99e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generating Linear Model for predicting Confirmed% with selected independent variables above\n",
    "model2_ols = smf.ols('Confirmed~Q(\"Animal fats\")+Q(\"Aquatic Products, Other\")+Q(\"Eggs\")+Q(\"Fish, Seafood\")+Q(\"Meat\")+Q(\"Milk - Excluding Butter\")+Q(\"Miscellaneous\")+Q(\"Offals\")+Q(\"Pulses\")+Q(\"Sugar Crops\")+Q(\"Vegetal Products\")+Q(\"Vegetables\")', data = df_kcal).fit()\n",
    "model2_ols.summary()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}