Covid_19_Prediction_Spatio_Temporal_Data_Analytics_Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hWG5yontvIUW"
   },
   "source": [
    "### **MA-INF4325 - Lab Data Science in Practice - Spatio Temporal Data Analytics**\n",
    "\n",
    "Supervisor : PROF. DR. ELENA DEMIDOVA\n",
    "\n",
    "Assistant  : ALISHIBA DSOUZA          \n",
    "                      \n",
    "UNIVERSITY OF BONN\n",
    "\n",
    "\n",
    "**Lab Topic  is Prediction of Covid-19 Spread**\n",
    "\n",
    "\n",
    "\n",
    "*   219 Countries and Territories around the world have reported a total of 122,435,351 confirmed cases of the coronavirus(COVID-19) and a death toll of 2,704,440 deaths.\n",
    "*   The aim of this project is to predict the spread of this pandemic in the countries which are highly affected by this virus. And also identifying the factors affecting the spread like government policies, healthcare systems, etc in such difficult times.\n",
    "*   We will work as per below guidelines to achieve the results:\n",
    "![DSworkflow.PNG](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABTsAAAFGCAYAAACluUtaAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAFiUAABYlAUlSJPAAAP+lSURBVHhe7P3ne1RXvrWNnr/lnA/v8z5777ZNztERg8kICZBEzlHkKLIA5YAiCAUkoZxAIuecTTQZ5+xu29273WmcNWZpllaVSgQbYxDjvq5xSSpVlapWrTW15r1+c87/D4QQQgghhBBCCCGEEKIFINkphBBCCCGEEEIIIYRoEUh2CiGEEEIIIYQQQgghWgSSnUIIIYQQQgghhBBCiBaBZKcQQgghhBBCCCGEEKJFINkphBBCCCGEEEIIIYRoEUh2CiGEEEIIIYQQQgghWgSSnUIIIYQQQgghhBBCiBaBZKcQQgghhBBCCCGEEKJFINkphBBCCCGEEEIIIYRoEUh2CiGEEEIIIYQQQgghWgSSnUIIIYQQQgghhBBCiBaBZKcQQgghhBBCCCGEEKJFINkphBBCCCGEEEIIIYRoEUh2CiGEEEIIIYQQQgghWgSSnUIIIYQQQgghhBBCiBaBZKcQQgghhBBCCCGEEKJFINkphBBCCCGEEEIIIYRoEUh2CiGEEEIIIYQQQgghWgSSnUIIIYQQQgghhBBCiBaBZKcQQgghhBBCCCGEEKJFINkphBBCCCGEEEIIIYRoEUh2CiGEEEIIIYQQQgghWgSSnUIIIYQQQgghhBBCiBaBZKcQQgghhBBCCCGEEKJFINkphBBCCCGEEEIIIYRoEUh2CiGEEEIIIYQQQgghWgSSnUIIIYQQQgghhBBCiBaBZKcQQgghhBBCCCGEEKJFINkphBBCCCGEEEIIIYRoEUh2CiGEEEIIIYQQQgghWgSSnUIIIYQQQgghhBBCiBaBZKcQQgghhBBCCCGEEKJFINkphBBCCCGEEEIIIYRoEUh2CiGEEEIIIYQQQgghWgSSnUIIIYQQQgghhBBCiBaBZKcQQgghhBBCCCGEEKJFINkphBBCCCGEEEIIIYRoEUh2CiGEEEIIIYQQQgghWgSSnUIIIYQQQgghhBBCiBaBZKcQQgghhBBCCCGEEKJFINkphBBCCCGEEEIIIYRoEUh2CiGEEEIIIYQQQgghWgSSnUIIIUQA/vOf/7zUEUIIIYQQQohXEclOIYQQLy2BJJ9//v3vf/vkX//6V7P55z//6ZN//OMfj8wvv/zyRPn73//eJIHu9zQJ9Hps/N8HE+j9Mv7bJ9A29I8QQgghhBBCvKhIdgohhHghcMs0fwHHuAWdW+K5JZ8VgVYo/u///q83f/vb3/DFF1/g/oOPcf3GRzh1+iz2HTyOg0cvorr+JKp3nzHZtr0auUX12LK9Dilba5CasxsJmTXYlFKBmLRaxKTvQmR0GZZHl2BFTAXmROZjdmQuZq/IxdhZyRgXkWbC78c0ZPSsJO/3Y+c4v+Pv56RiyqJszFlZ5EqhkwLn+fIxy3m+Wcu3OcnBzGVbnb/n/M1NJVi2sRjRqbuwcXMVNiSXYnP2LiRlliMjpxrbCnYir/QQqurPoLzmAPYeOIlTZy7h5KlzOH/+Aq5du47PPvsc3333HX7++Wf89a9/NeG2sdspkJB1b2P3tmeak6aM+zMVQgghhBBCiOeBZKcQQojfBbfosrESLJC0tGLNykqKtx9++AFffPElbty8hSNHT6J+/xnk7qhH3o7dSMupQ2xGHVZEl2Dh6nxMXZiBCfMyET47EyNmbMWA8enoNTwB7QdE440+6/GnPnF444NUtBqYiVYDMtB6YBbaDMlBm8E5aDskz0mu5+vQ7Wg3rMBJIdoHFTV878T5vn3QDk+GM8VoH1yMDsElDSl1fe9Je5PSx6SsmXh+385meImTYud1MDtM2jLDipwUOilAG+e1txma77yvPLR23pf92mpQNt4YkIXXnff/Wr/Nztc0/On9JLQbEIOO/dfivfA0DBwbj3FzszB6TroRt8s3lmBjSiU2b6tHbvEBFJbtRXntYRw+dhbnzl/E1WvXcefOHfz0009ecUppasVpIGHq/rzdkjTQviKEEEIIIYQQvwbJTiGEEL8JiikrMd0y00otWxFoJaYVmZRitrKQlZY7SioQuTYRa2NyEDY9Bv+nQzj+T4/F+K+31uB/3tuE1/smeiTloCy0GZyNtkNy0G7YdrQLKkD7oEIjIDsEMxSQVkJSQLoSwpT96nQMKX/qdDCp+F3S3nnu35RgG5dkHV5q0m7YDrQZWoDWg3ONQKU0faN/Jl7rl4r/fi/a+VzW4/99c42T1ejw7lwMCF2F6Uu2ICYxB9uLynD58mX8+c9/xl/+8hf8+OOPPlKUcVeSWhnqFqJuGWojCSqEEEIIIYR4HJKdQgghmsVdaeeWTlZEuYWmuyLzwcOPceTYKVTvPonU7GpTgRkZU4bZy7chdFYahk3dgh5Do9B1WAL+591NeOODzZ5Ky8HbGiosPSKz/fAiIzF9KyhdCWGsxAyUJ5SVI55VKlxfPd93MKl4vgkgRjuEVDZJe+f2RyaYcUtRT9oFl/lUnLYdtgNth3oqS1sN2oZWA7eaCtI33t+Edv024p3QVARNTcfkhVswb1UONqbUYHN2HQpK9mDPwTM4d+4irl69hm+++canSvRpZKh7XxVCCCGEEEK8ukh2CiHEK8zjRKZbYn755Zd48OAhjp08j+q640jbthPLN+Rj4bodGB2xBR+MTUb7AZvwWt8EvNE/zcjLtkNz0W5YPtoPZ+VlETzSMoC4DCgqPeloJKVfRjwu/gLyj41HQla+AKl6wgR6rJMAwpR5pCgN8ROlw8vQLqjEpM3QQrQZkm+G2LNilGnbbwPeGhGLkKlJmLEsF2sSKpGeU4vSyn04d+FDXLlyxVsp2pwUdQtR974tESqEEEIIIUTLR7JTCCFaOG6h6RaZDOWQlZkPHn6K/YdOIbtwL+Iy67A4qhijZ2fgnZFxaNV3A157P95UYLYZvAVth+Y1SszgIvgMHw9QbfnkwtItKZ9lAkvI55mA8vC5J5DY/D3i93eNFHVud6W9kaQBYiRpgyg1crQUbYYVofWQfFM1SiHa+oN49ByehJDpWzB+Tgqi0+uRkr0LBaV7ceDgcdy+fcdHiD5KhjL2GJEMFUIIIYQQ4uVHslMIIVoAblkTqDqTcydyaPmBw6dQXX8C0alVWBxVihHT09AzKBqt+0Wj1YA0tBm81YjM9sMLTCVmU4npm45GZLoywp3fU17+mvgLSPdt/vf9jfEfJu8k4NBy5283yeN+/8hQLrq/94+VkX9EqptJgPs+TowGe6RoByNEOc+oZ/EmLi7VevA2vDEgE52CMtAzOAVBk5KwfFMpMvN2o2LXKRw7cQYPHz40MtQOmbcy1IpQe/zYalAbIYQQQgghxIuPZKcQQrxkuKWmFZu2Us1WaX7xxRe4cfM24hIzMHNJOv5/Hafhv99Zjzf6JXmGlw/JCSAzfYVmYIFp41+R2VzK/4AEeh2M3/0CCMlA8Z/30z9Pc98mCwQ1xLOIkU1FkwR6DGN/53//JqH89H4fSIK60yAcf5cEkp1PE9/n6xjSEPOz+z3Y99uwTYeXoM3gXLT6IA2v90vB/+mxFG8Hr8XytSmorq3H3bt3zcr/XEyJApRxV4Pa48sKUBsJUCGEEEIIIV48JDuFEOIloDmx+ec//8XMoZm3ow6bNldg6qIsfDA6Hv/z7ga8/kFyQ6VmLtpzxXKflcp9xeaj5aa/NHzalD9l/B4f0jTu4fHu4fJNYlZhd96fif25IQ3P1WlkOTo76RpaiW5hlegxuga9x+3E2xPr8c6k3Xh3yh7zXLxf9/AqvDl+J96dvBv9ZhzAoDlHELTgJEIWn0Z45Hl8MPMgBs4+gpFLz2LiumuYHHUdE9Z9iCkbrmPS+msYNv8Ehsw7gUERxzBg9lGMXX0RY5yEOY8dseQMghaewtD5x5z7HMXAOYfRd/p+vOW8jjcn1KP3+Dr0GrcLPcfu9P783tR9zu93m9t6jNmJrmHVZhsauep8NVWPFKN2pfWGoeFeYWq+pzB9fPzn6fTGRzK64ysmPQkkMH99OppUoePIhvB7V+xr6cg478ETZ/s4n3/7oCK0HZKHVgPS0XZQKnoExWLUjM1YumEHEjPKsb1kD86eu2iqou1K8pwf1H84vFuASn4KIYQQQgjxxyPZKYQQLwi2YpNxS01Kla+++hoHj5xFem4dVsVVYeKCrRgwMQN/em8T2gziHJq5aBe0HT5C84lkZtnvkAB/o0Es2jSRku4YGWkFJV+75/YuoxplY+eRZeg5pgYDZx9CeOQFhK04jckbLmJOwlUsSr2O1TnXsSzjOiKzrmDVlstYEH8YS5MPY03aEcTlncfKzc7PCfuxNqUey2MqsCaxEonZdUjfXoOMop3IKd+LrUW1iE0rRnRqAWJT8xGdnGWyiUlMxcb4FGyIS8Dy1euwZmMsVq3fhLiUrc7t6Vi1bhNWrNqAhUtWYv7C5Vi4eAVWrI7CytUbsdL53froRETFpCA6MROLlq3GkhVrsWxlFNZERSM6KcP5O5lOMhCfuhWJGVuRkJGNpKxsLF25HstWb8Da6ARExac5ry8Pafm1SNxSg5jMKmxM24nVSRVYuqkSC6IqsCFjL6KyjmJZwh7M21CLyM3HMT9mL8YtqcDIuSUYt3wXlmdcxapt97E4/RZmxFzH1E1XnO15DsPmH0e/GYfRNazG2f7leHPC7obvPVWTRoJaeeqVqL6ClKEQ9Xxf6Xxf5RsjJN2hxLRfnywe4elk5KNShU6M89w2RoAyIxu+jqhwbi/37J/Di80FgtYDM9H6/Y0YMikdY2anYn0iJehenD5z3khQOxTeSlD/+UDt8SyEEEIIIYR4fkh2CiHEc8YtNd3VmhQlf/nLjzhw+CTS83ZjTuQ2jJiRgbZ9V+G1vvGmSrNxUaDAUvP3E5p+z2n/TkPcr6ExDa+v4TV2ch7XeVQ5eo2tdVJjxGXfaXsxatlJTI++jIiEq1iS/hFWbbmCyKzziEw9hLi8c1iZfAjLY2uwZGMxNqWVIbOoDgkZuVi7KR7puQWIitlsZOGcuYsQMW8R5s5bgNlzIrBgwQKsWLECkZGRWLlyJVavXo01a9Zg7dq1WLduncn69etNoqKisGHDBpONGzdi06ZNiI6ORkxMDGJjY03i4uIQHx+PhIQEJCYmIikpqdkkJyebpKSkmKSmppqkpaU9Nva+mzdvNo+1zxXo7/C1MHxdfH2Mfb187XwPfC98T3xvfJ98v3zv3A7cHgy309JlyzB37jxEzF2AeQsWI3JdLBKd7bwyKgnr4rcgKnkHNqTWYmWck8Sd2LDlKFZuPoF5m3ZjfuwhLEi+7Hx+dzEn/gambLiGSVHXMXzRabw9aQ+6j65F1/AaJ7VgpaVn3s2Kxhgh2jSPF6PVzv7YkICSszGdAqYKnUa54vzcuSGdRlZ6YiSoR4RSvrcfth1tBmai48B4fDA2BZMXZiMuoxY19cdw6dIlrwS1c4HaKlAe57YK1N0GCCGEEEIIIZ4tkp1CCPE7YmVGILH55Vdfo7L2ANYllmPSojy8NXIz/vR+HFpz6HlQAdqZOTUfITQZr4AMJCifNO7ncfI4gWklZsP33cIq8NaEnRg45xBGLj2F8BXnEJFwDevz72BlxgnEFX6I1ckHsCymAtHplcgurUdsah6Wrt6AhNRMrFizEQsXr8SsWRGYMWMGlixZYgSllZNWSlLUUdq5JSTlHmUfxR/FIAUhRWF6ejoyMjKQlZWFrVu3Ijs7Gzk5OcjLy0N+fj62b9+OgoICFBYWoqioCDt27EBxcTFKSkpQWlqKsrIylJeXo6KiApWVlaiqqkJ1dTVqampQW1uLnTt3YteuXQFTV1fnTX19PXbv3o09e/Zg79692Ldv3xOF92f4WPfz+f8tvg6+Hr4uvj6+ToavmeF74Hvh+2L4PvmeGW4Dbgtuk9zcXGzbts1spy1btpjtlpmZabajFa9WuHJ7W5lKkUqJagXq0qVLsXxFJObNX4CZsyOwan084tMKjDBdn1CAtck1WBpdjYj11ZgXfRBzN+7FzA1HMSvmEqZuvIkRS85iUMRxvDN5H7qP5rD8Wmcfo/gMLEbtUHo7b6fJSHceJzsbMqoxnX1S5YmfAO1sUmHC6Q06OcdG+6B8tBmYgU5DU/DOqHhMnJ9uFkU6cuykz3ygrAINNAxe8lMIIYQQQohng2SnEEI8Y6y0sHKTodgwCwd9+RUi18aZRYP+T89leP2DFLQZmgNWarYPLvaE4sSIRc/w7Q4+MjKQrHzauJ6vQWwGlpseqdmxQWr2HF2F/jP3463xtZib/BGydv+EvdeBnZf+iuJDN5GYU4hFkXEYOGQExo6fhEWLFmHx4sVGgC1btsxUD9oKS1td6RaYtnKSVZO2GpLCktKNAo4yjlLOykoKO8o7SjwKPbeYpPyjCLTCkeKQEvHAgQM4dOiQyZEjR0yOHj2K48ePm5w4cQInT57EqVOncPr0aZw5cwZnz541OX/+vMmFCxdw8eJFU8X3qFy+fBkffvghrly5YnL16lVcu3btiWIfw8fzeQI9P8PXwdfD13Xu3Dnva2X42vke+F74nvjejh07Zt4v3ze3wcGDB8024bahjKVc5fayYpUilRLVilOKYEphK0spSimRKUkpSPl5WUFKOcrP0ladUo7ys+Znzs+eInvVqlVGbM+aPRujQkMROmYi5i5NwOL1WViZcgQ5+/+B9F1/xdKszxC24iLen3YQfaYdQJfQaq/87MCY4fJORtg5OhvEp0uAmirOQKKTaRCdTWVnc6lCF1c6j6psiEeAcg7YTs4x1X5YLt54Px5d+6/AxJkbkJu/A998842Rn+55QB9V/SmEEEIIIYR4OiQ7hRDiGeAvOFmx9fHHH+PgkTPYvKUci6N2oP/YFPzp/Vi0HpSFNkNyTeWmv+D0Ss4R7nCxmUDSsrk0lZlNRKaZD9NWZ5agW1g53puyG4PmHMa0TdewMOUq1my7ihWbj2LV5kNYGl2GxNxqpOXXYE10Ilaui8asuYuwcOFSzJ8/H8uXLzci0w4TZ6UfxRYFlx36TfnFCkHKMApMVlxSYFKYUZyxwpIijUKN4pKSzQpLSjjKuP379xtBZ2Ul5R0lJWUexR4lH6WfFZJu4UiJeOPGDdy8eRMfffQRbt26ZXL79m3cuXPHrMh979493L9/3+TBgwd4+PChCT9L5pNPPsGnn35q8tlnnz0yn3/+uQlXxv/yyy9Nvvrqq8fG3pfhY/kcgZ6fsa+Fr8u+Rsa+br4Hvhe+L74/vk++X75vbgNui+vXr/tIVrdg5Xa0EpXbl9uZ29stS20lKj8nW2lK4eyWo/xsrRzlZ055zX2Aw/Yptq0UZcWorRalEF2+fAUWLFyM+YuWY33sVqzctBXrk3Zi6aYKzFm/ExOXVWHqurMYG3kOIYtPYfDc4+g1dpepCuU8nEZ+GglaYX52xzt/pxWhozwJLDebpgsTylR5Ekh+WvHJaSeG5aLtoEz0DknElIVbkZhZhdLqw2Y/tYsguRdAUtWnEEIIIYQQvw7JTiGE+BX4y01WZX399XeoqT+ODZt3oW94PF7rm4jWg7PRdmheQLHpEY9Npebj0kRqBhKaFJlmcR/PXJldQ8vRZ0o9Qpefwqy4y1i8+SoWxO7FhqwTmLMyD9FpVYhLy8f66HhMmToDM2bO9FZjsvrODienhLJVmBRUFJgUVlZessqPlZes/GPFJYUXpSUFGCsFKS0pxyjKDh8+bGSlFZVWUlKyUbixEpJy0opJijorI63Ms/KR4o9S0C0UWUHHfPvtt/juu+9Mvv/+e1NVx1AwMRxezDkWGQonG1bd+YfzMNpQTD0qnK/RHUqsp4n7sYGen3G/nkCvl7Hvx75Hvl++b7sdGG4Xbh9uK7vdGLeAtdKV29wtUilRKVD5OfHzojzlZ8fP0C1MrSzlZ87P3opSVpJSktoKUgpSVuz6y1FWjbJilHKUw+m5D3JfpFxftXotlq1Yg5Xrk7A2NhtL1xdiTUodZq8ux6QVdRizdD+mbbzq7P9nETT/hEeIhtc4xwaHwFOEOhnpfO8dpt44dN07lL1Z2RkojQK0qxGglZ44z93FCNAydOKFBjP0PR3vhKZi0rxUZOTWobbuAO7df+Ad9s7P3135KfkphBBCCCHEo5HsFEKIJ8DKBVu1Sflw9fotpG2rwYzl+Wj7QRTe6J+K1oOy0S6oEO25mrORmyUmZoXxpxabrvsbmVnqkqRWaHqqM3uMrsK7k+swfOFxzIz9EIs3n0NKxW0sj9uNqNQKpORUYuX6GCxcshoRcxeZIeZWZNpKTFbUsbLOLTE5NNlWX3KOS0ooDhln5Z6tumTFJYdCs9qPVX8cNs1KQEouVghSfFGAsYqQ0pJyzApLViFSVrpF5ddff+0VlFZOuqWkW0a6xaMVg1YW8jNyx86R+Kjw832SUDi9KAn0+ppLoPfMcNsw/tvMLV7dgtVKVCtQrTh2i1PKUn6WblnKz5qfuRWlFNjcJ9yClJWOgeQo9zFWkFo5yn2Q+yL3Se6b3Ec5nJ7i3Q6jt/OLUtSviFxpFq5asToOEYujEBlbhalLCjBj3UFMijyAMSvPYtCcI3hrQh16jakFKzO9ixM1SNDGOTur0IWxUjOg8PSkq0888pNfu4RWNmZUhZMydAouRIeh29Bh8GaETMvCiqhc7Nx91GxLbltuc34G7v2Zn6vkpxBCCCGEEI1IdgohRAD85SaFzoHDp7AusQwjZ2Thv95ehzc+SEWbIaza3NEgNz1i00duhpQFziOlpqfy05uGalCuZN5zbDWGLTiGCesuYE7cGSxPOoTF0bVYl1SCmPTtWL4mGhMnTfIsFOMaWs6KTA4ptxWZlEFukWkrMVlZxypMKzFZfcc5HimdOAck54ZktR6HO9uqS1b1seKS8tJWWXL4ta2udEtLt7C0lYdWorllpZVtVsJZKecWd/7xl4D2M3yatHQCvedHxX+bBtrujP18/KWpFaVuWWo/d+4DtsrUXVlKQeqWo6wkpRzlPuauIOU+yH2R+yT3Te6j3FetFOU+zLlbOSUCh9L7C1FOqcBjwVaIrlgRiaUr1mP5ulQsXJuLeetKMW3VLoQv3oMxkWcwbN4xvDt5jxGVZoGiEazQbBiu3hAzjN1PgPrKzoaE2VR54ty/m5OuoZWejKow6Tyi2MjP1v0S0GPoeqyKKcOOin24e/ee2WY8liifuZ253flZuPd9IYQQQgghXkUkO4UQogG34KE0oDz4+utvsT4uF6+/tQCv90tB2yG5aB9k5aZHcBqx+Ti5GTDO/RsEp6di0/O1fYjnuTn8nNWaQ+cdweqcT1Fx9h9IKdyH5OxSTJ212MyVyQpNik1WaXKhF4pNLgDDajZWtXHIL+fIpODh8HLOnchhwqyK47Byd1UmF7ChNLJzXXIOR1Zj2uHjdtg4ZSZlFKWUlZmBRKYVmLY60C0wrRwLJDDdcs1G8ubFxn3s+Mf/83V/9laM2n3ESlEKPHflKPct7mOBhKitFLUylPssq0S5D7O6mPs0K445dQLnfKUIZWUoxT4FP0U/jw0eIxwqz2PGVoTyIgGnc5g2fQ4iFq7H7GVbsCj+JJalP8TAWYcwYOYB9Ain/KT4dEJB6RafDVWczcvOxnRzx0d+esRn11Fl6DKiCO2HZKB935WYuTwXW7flmW3hrvrkdrTHFbe/jh0hhBBCCPGqIdkphHjlsTLAipgr124gMaMSkxdvx/+8F402g7eh7dDtaEfB6argbBScj5OcDfdpkKL28Z44zxdSjK5hFfhg5n4MX3QM85M+xPzYPZi3pgCp23cjPj0fc+Yvwbz5C7xik8PP7dBzVqZxDkNbrckVy1nFZhf6YaUmBY+7SpPDzFkVZ+fF9K/OtIvquGWmHUpOqeIeOu6WmZRXVmZaueUvv+z29o9o2QT6zBn3vmH3GXss2v3pcULUDqH3F6G2KpRTJ7grQin0eQxQ8POY4LFhJSiPGStBeSzxmOKxxWOMiyjxYsL8BQuxYNlGrIopxozl2xA+rxCjlx7GwNkH8f7UPeg5psYjPkdyeDrFpydmCLsVoKaqsxnZaVLliZ/47BZahq4jS9ApeLsRn72Gb0JkdBF27jlmjldWfHK7cDtxu9lj0W5vIYQQQgghWjqSnUKIVw7b6bdS5ceffkZh+UFMX5qLN0Pi8caADFPB6T88/cnkZsPvA0lN57m4oBC/HzDrIKZEXcSsDQcxb125GYa+Mioes+ctxJyICFNNxiHonE+TcoVD0Dnk1j2PphWaXNiFVZqUNaxesxWarGrjHIicD5Eyk3MkUvy4h5nbIeaURZRGrMh0i0zKEitMbOx2c0squ00lU8Szxr1vufc5ux+6900rRZsTotzXrRDlMUCxT8FP0c+5QylDecz4V4RaEcpjjcccjz0eg1aE8tjcsGEjlppFklKxeM1WRKwuRdj8coQuPoxBs1gBWo2eTjzzczrhXJ0NMcPYjdwMJD3dqUL3sErna0VjQsvRZSSHu29Ft+A0BE9LQ9q2XTh/4UNzfPN987jmdqE4tseujlUhhBBCCNFSkewUQrwyWFlC+VFVuw+RsaUInpqMP70fjzZGbhZ55GaD4GyUm4Gkpk3zcpNVWD1HVyN02WnMjD6HRXGHsDy6BFEJ27FweRSmTptpKjWt1LTzalKccOg5VzfnHIMces5VzSlb3PNocgEXVqixOpNDd63QZDWbe85MCh5bkek/vNxWzlkJ0pzEFOJlwO6v7n3YLUTdVaI8BihDeUy4ZSiPGR47PIZ4LPGYcg+P5zHHY89dEcrqaR6jthqUq8dTgnI4/KpVa7B05SbMXbIR81fnY+KCAoTMrcaAmXvRa0w1ejhtBOWnGapuxGelt5qzqQCtQvdwm0pPrPxk1WdYuan85HD3doNS8c7ITZi+KA25RXW4feeuqX7l+3VXfdrjXAghhBBCiJaCZKcQosVixYeVHdc/uo/I2DJ0DUpA68GeVdPd1ZtPXLnJ+7nlZkPFZtfQcgyYdQDTNlzEnHV1WBlfiWVrEzB56izMmTPHVGva1c83btxohsRykRSKEUpNu9I5xQmlpp1Hk8NtOf+gu0KT8xRymC4r1NxzZlJoUuBYmUmhQcHD929lphWadvtIdIhXBfc+z1gZatuIQDKUx5R7zlAec3auUCtCeWxyWgj/alAukMRK0O3bt3sXRuIq8bywsXjpSsyatwpzV2RjzLx8BM2uRb+p9XhzbI2RmB756SdAjeyk5HRLT5tG+dk9rALdG4a8dxtVgs7D89BhUBxCZ6Rga16ZGdLP90LJy4sfEp9CCCGEEKIlIdkphGiRWJFBeVFRsx+d+i7Af7+7AW2G5qE9BWeDqHwywclYydlYuUnJ2WVkGQbOOoD1eV8gdts5zFmagMlTpmHBgsb5Ne1q6Jxbk3KTwiMnJ8dUbHKRFK4azUWCWCl26tQpUznGxYEoUTjXYCCxaYecU1QEkpp87zZuueOOEK86gY4L97HD48lKUB5nDI85Ww1qBSgrQTk9BIfE2/lBOTcoh8PzgoV7KDwvaHAYPCu3WcFN+cnFkFgBOmbSYsxaUYjJqw5h4My9Rnzaldmt+DQys1nhaVOJHoxzX6Z7WLlJt1HFaDcoBW+GxCFiabJ5zRzWz/dCscv3J+kphBBCCCFediQ7hRAtBisqKPs4FDU1uwp9R8fjjQ9S0WbI00pOP7k5vNh8z+GiwxccQ0TCdUxZVYf560qwdE2yWa158eLFpnpz9erVZp5Nyk2KDAoNyk1WblJucn7Nffv2GbnJSjBWbdpFglgpxnkEucAKJYR7CLp/xaYVMbYyzQoKSQohng32eLLy01aAugWouwLUPRSew+B5oYLzgbqHwPOCxuHDh7F7925T+cnh75SfrPzknLycxiJy5WosWh6NGYszETa3EB9MKsaA6XVm2LuRlmbIuieN8tNXgHKVeCM8G6Rn93DelylH11HF6BiUjbdD4rE8KhfHTp4zwpavnSLXSk/brgghhBBCCPEyIdkphHjpoYxgp5xz0sVl7MLI2dvw2vvxaGsE55MOUfeXmx7B2WtsDfpN24OxK45hyrJirIzOw9QZ80zl5pIlSxAZGYl169b5LCDEhUts1aYVm8eOHcO5c+fM4icUm3fu3DFVYKzY9J9X00rN5qo1JTSF+OMJJEJ5vDI8dnkMuytAefHCVoByoTAuiMQKbl7s4EUPTlvhnv/TVn+yGtzM/bkmCotWb0H47EwMmV6C9ybV4M2xHN4eWH56RKeVnk5GM57b3eKT1Z7tB6eh55C1WLgmB7v3nTBV5GyP3OLTtj1CCCGEEEK86Eh2CiFeSqxkYCf84pXbmLGiAK/1TUTbYdsbBadXbj5acNrKTabLqHK8P2U3Ri7Yj1GzsrB4TRbmLliNOXMizLB0zrlpV0dPSkoyqzFzPj4OTWWVFmUFKzY5z6ZbbLLCi5Ve7mpNioTmpKYVKTZCiJcDe8xaCcrj2gpQikMOg3dXgdo5QClA3fN/chEkVoDW19ebVeBLSkrM3L4ZGRmIi4/H8sh1mDl/I8Jmp2LIjDK8O5ELGJU5ofzksHWP0DRD2Sk+jex0xwpR3qfcpHtoCToOy8JbwyKxNb/avBYrPvm6+T7cbZQQQgghhBAvIpKdQoiXCnaw2dm+/tE9zFm+BW37rUWrAZngEHVWZTYvNm088tM7ND2kBG9P2IngeXswbv52LFydi5kRK33k5vr1643cZIUVh5qyarOiosJUbXI15pMnTxo5YRcQYsUmxSYrNt3za7rFJt8DY8WIxIEQLRf3cU5RyGPfSlArQNlGUCq6F0GiAOX8nxwCb+f/3L9/v3fhI1Z/8oJLfHwCFi1bi9BJyzEmIgP9JuTh7XFVDQLTM18nqzk90tNXfPY0qURPr/ikJC1Dl5B8dA9OR+iMVORsLzNtGtsz25ap2lMIIYQQQryoSHYKIV54rCBg57qkci8mzM/A6/1S0C6oyMjKxwtOhlWcHsHZeUQp+k6tQ+jCWkxasA1zFkZh9pwI77B0rpZu5SaHpBcWFpphpaywYqUVh5xaucn5+DjHpl08iNValAGUF4+q2BRCCNseNCdAWQHqHgL/ySefmEpxDn+n/OT8n7zgwoWPKD/z8/OxZcsWxCckYN6iVRg3OxV9QxPwztgd6BXeKD0983tWGPFpRKdXevqLTz6mFJ2DtuCdkI3YVliPq1evmdcSqNpTCCGEEEKIFwHJTiHECw1FADvSe/ftR6d+i/Ba3ySv5Ow0ogwdnQSSmx293zdIzuBidHTSf8YeTF29D9PmrMbcuXOxcOFCs6gQV0yPjo42Q9O5WjqHprN6k4uIUHBSKnBuPVZZsdqK8+7ZBT3sSsa2ctMtN22EEOJRWOlpY+Un2xS2LWxj2NbwogoXMKP45IJmnCqDCx+x6vPAgQOoq6sz02rk5uaaxdE43+fchSswYGwC+k6myKTALPOKT4rNpsLTJT1HsyKUw9xL0WlYJt4amYDkzBLz9ylhKWT5+ig9+bp1MUcIIYQQQvzRSHYKIV5IbMf/m2+/w/qkCrTpH4+2Q/PQgdLyEZKT6Tii3PnqGarO+/ebVofB08owdl42Zi3ciDkRc80Q9ZUrV5oh6vHx8WYePFZFsTqKVVKsluKceVeuXDErKbOCk4KTQ9NZvWkXEmIFlq1ssoKTr10dfiHEb8G2I4HEJ6vHKT55scVd8Wnn+7TD3TnXZ3FxsRGfyckpWLBsE0bNSEbfsVvw7vhy9AgvM9WbpoqzWfHZcJu5Txm6hxajW8gWTJiXjkNHTpnh7XwdbBPZHvK1qg0UQgghhBB/JJKdQogXCtu5//a7H5CSXY+Og5PRbliBqeLsNLLciMzGqk1fwekRoJScxeg5pgrBc+sxesYmTJkxD/PnzzfD1DkH56ZNm5CSkmKGe7KCs6qqyqyYzrk3WSFlh6dTbrJyyc67aYema1i6EOKPwLY39uKKHfJuqz7tgkdWfnKuz3PnzuHYsWOmSp3TcdiKz9VrN2JyxEb0G5OGN8eUNiM+q0xbyvQaU4leRnzydxVGenYZnoGIlXkoKa8zi7CxvaSIZTtp20ghhBBCCCGeN5KdQogXAis5L1y6hrCZqXi9HyXndnQeWY7OoyoaqjXL0T6krCGUm87tIz2/ax9cii6h5egzvhB9gldgagRXUJ9jKjjtEPXk5GSzwBArndyLC3HVdC4sREHA4aEcJmrlpq3cpOC0clMIIV4EbLtJsWgrP9lm2cWOWInOykuKSE7BwYs5x48fNxd3uMgR5yNOTU3DgqXrMT4iCW+NSECv8BL0CCtrkJ6eBY16UnQa4dkQr/QsR4/QEnQKysK7I2KQvqXYtKNsQ+3wdr4utZtCCCGEEOJ5ItkphPhDsZ31b7/7HovW5eON/mnoGFKCrqGV6BJahU4jK4zYdMcITicdGio53xxXiQETMjB+umcV9cWLF5uFhjZu3GgqOHNyclBSUoLa2loz/yYrnTjPnV1ciEKAYsDOu+kvN9VRF0K86Ni2yspPtmPuYe+sUOeQ94cPH5qLO6z65FQdHO5eWVlpxGd6egYWLt2E4ZMS0TM0z8zTaSo+Wek5msLTLT19Kz15vy5BWxE6IwNHjp0ygpVtK6WnKj2FEEIIIcTzRLJTCPGHwY45O8C3b99F8NRktBm8DV1GVaBbeA06h1ajwwhf0emp5Kw04QJFb0+oRsjcGoSEzTCLDVFycpj6hg0bkJiYaKo4uVDHnj17zDDO8+fPm/k32dl3Ly4UaHi6EEK8zLjFp6365AUdzjnM4eas+OQCR1zZ/cyZMzh06JCpeC8qKjKLtE2ftRgd3pmGXqH5RnoaqemEgtOnytMIUI/4NCu+jyxEpw/WYtrMuUaqslqeF5Nslby9iCSEEEIIIcTvhWSnEOIPgZ3dn3/+K5ZvyMP/fXMV2g3LR/fwGnR10mlUpY/o5PedKDmdr6zm7DKqFH3C0zFm2gozVH3BggVYsWKFkZys5LRD1evr681CHXaYOiuNOJ8dO/vuKk4rOPma1AkXQrQkbLvGNs5KT1vtyQs+lJHuVd2PHj1qFmljpWdSUhIi5i/H+yM3oseo7aZ6k0PXG6WnW3xSeDq3swo0tBjtByVh/Nw0HDp83Mx/bNtd/n1dUBJCCCGEEL8nkp1CiOeK7XSfOvsh3hyRiA7Di9BzTC16j69Hl7AadOD8m17JWW7EpxmyHsLKonL0HZOJMZOWGsm5aNEiIzmjoqKM5OTCG1x9mMMyOTyTCw2xitMKTnbuWV3kruCU3BRCvErYNphtIC/2sLKdFe6cZ5Pi88GDB6ba89SpUzhw4ICZ25MLuS1ath7vhqxF1+As9AgrCSg9ezP82dxebu7XcVA04jZvx927d001KYfTay5PIYQQQgjxeyLZKYT4XWAn1oYda9u5Zgc3M383/vR+PLqFVeGtiXvRc1wdOo2q8kpOyk1TydkgObuNzEfH9yPQtfdQDBg+DX0GT8H7w+ejz/DFeGvoUvQethod+6/AgNAV3gWH2LHmUHV24gNVcAohxKuObZ/dFZ9sMznMnfMZc0X3K1eumAr5uro6M8R9zdoNGDdjHboOTWwiPY3sNOH3FKAc+l6OLsHZGD0zHsePnzRtM+fy5MUnDWsXQgghhBC/B5KdQojfjFtqstPMsBPLzjMrKRlWDGXl1aLH0CgzZJ2VnG9O3GPm5vSRnGYIOxceKkG74YVoPywXbwzIQKuBW9Bq0Da0HpKLNkO3o21QEdoOL3buU+w8RyVe+2Az0rO24+zZs2Y4JjvqrObk36bkVGdaCCGax7bhbC/t/J52RXdOAcJKebugUVVVFdLS0hA0bjU6DU3zSk9WdHpEZxV6j2UapKdze4/QHegRnIZZS1JNG/355597L0apylMIIYQQQjxLJDuFEL8J20Gm3GQl5alTp1FasQurN2Zi6vx4DBi1DK/1jsD/fXM1Xu+Xgo4hpXhr0h68PXmfqd60w9U7c+X1URVoN5ySsyHBNqVN0j6YQrQc/WYeRbfwavQauMDMM3f8+HGzyjA75+yos9PO16iOtBBCPBrbVrqrPTnMnW0p2/d79+55FzTiwm+s9Jw5fz06DklBj1E7zAJFXuFpZGdDTJUnf1eGzkFZmLcsychTDmvnRSn+DQlPIYQQQgjxrJDsFEL8JtJy96LnsI3oMCgWr/Vx0i8JrQZmovXgbWg9KBtthuSi7bDtaBdUaFZa7zP9kBm6boaoj2hYYd0sRlTWrNT0xrmPDRct6jP9MN6ZvM/5GzlYtCLWLErEOea4yAbnnWMHnRJWHWghhHg63NKTFfIcds55PVmRSel5+fJlHD58GJWVldiwKR5B49ehW0gueoaVuYSnlZ4NFZ5jONy9DN2C0rBmY4a3Cp/PywpPVeELIYQQQohngWSnEOJXw07p5at30WpAOjoEF6N9iJPgkmZSaqQmFyGyw9bd8tITe/vj8+aEPUacthu+A936zUNMTIxZhX337t04f/68V3aqWkgIIX4b7uHt7kpPzr954cIFHDlyBLW1tdgUvwU9hm5Ej9BiTyXnmAqX8LTS0yM8e47Yiogl8aa95tygnCeUzy3hKYQQQgghfiuSnUKIXw07wKz4GTErGx2Cd6B9SIlZUKgDKy9deVqR+bh0CavGB7OOodPIMvzp7dWYOnWqWZE9JyfHVHZyGPtnn31m5pxjx1kIIcRvhxKSbSovIrESk0PQORSdlZ5cGO7QoUPYUVyKgaPXosvwregZXuIRnj7D2j3Cs/foMnQNzsaA8LUoKavCrVu3zPygdkg7/78IIYQQQgjxa5DsFEL8atgZZYd3R/ketB6UZaosOwT7ys5nLTqZ96YdxFsTd+ONPusxfsIkLFmyBPHx8SgrK8O5c+dw//59fP/99975OoUQQjw72K6y/ec0IbbS8+uvvzYVmmyDa2pqEBWdgreC1qJHWHGTeTzfHFuJN/nz6HL0Ci9Bz6HrUFpWjo8++qjJSu1CCCGEEEI8LZKdQohfDTu8rOy8fecu/u+bq9AuyFd2BhKVvzVdw2vRf85xdBxRih7vj8PcuXOxatUqZGZmmgUzOAccV37/6aefTHWQEEKIZw/bf8Y9pyeHovNiE4e2Hzx4ENsLdqDjgI3oGV7qM6T9TRsKT+f2HiO3Y0JEAk6dOmWqRPk8fD5NQyKEEEIIIX4Nkp1CiF8NO6GsnmTHNDGtAG/0TzPzc3Io++9R0clFid6ZvB/vTtmPVv0TMHnqTCxduhTR0dHYsWMHTp48aVZh/+GHH0znW1VBQgjx+8L/AxzazkpMTh3CxYY4ZzKnEzl+/DiiYlLReUgSeo4u8w5p95eddtGipNRtuHTpknk8n0ftuBBCCCGE+DVIdgohfhOsvPnxxx/NirpDJ6WZoexcjCiQrPwtoejsPWEP+s06ivbDi9Bn8CQsXrzYzNWZlZWFffv24ebNmz6r+gohhHg+WOnJC2B2ASNWaZ49exYxCVnoNCjeW+FJyekVnk6s8OwxLA4VldVm0SJWiHJOUEpUVXcKIYQQQoinQbJTCPGbYNUN52xjdee27dVoMyQX7YzsfLaVnT3G7sLAiJPoOXYnWr2zBBEREVi7di3S0tJQUVFhhk1+/vnnprJIQx+FEOKPwT2XJ6vsWW1/8eJFxCZmou0H0egZVmzkppm30ys8PRWePcN2IHjiWuzatcvM/cmLaJySRBevhBBCCCHE0yDZKYT4Tdih7KzuvHfvPjr0X4d2w0vQPvjZyc6OIyvRd+ZRvDNlH1oNzMKIsMlYtmwZkpOTUVxcjCNHjpgKIlUBCSHEH4+7ytNW/l++fBnZ27bjrRGJporTIzwbqzs9Q9rL0T1kC0aMX466ujozFJ4LH7FaX8PZhWiZsL14FhFCCCHcSHYKIX4zrKS01Z1zV+WgbVBRQ3VnYHn5tOk1vt4zfD24GK3emWcWJVq3bh1ycnKwe/duUzXERYn4GtjB1kmvEEL8sbAdZnvMeTdZ4fnw4UMjPBdHxqNHaFGA6k6u1M7h7KXoMCgBKZszcOLECfM4ClNV7Avx4mPFozu8UMGwPfAPj2uGF6p5ccQ/bD8CxX0fPtY+T6C/Yf9+oNcmhBCi5SLZKYT4zfAkkiecnKetouYg3hi45ZkNZWdV5/szDpvh669/kIqhIyZhyZIlSExMRGVlpVmU6Pbt26Yzzdegk1chhHgxYHtM2cDKTF4M48JDZ86cw9ujkkx1p//cnZ7h7OXoGpKN8MnLUV9fj+vXr5vHsn3n/xohxB+LWxb6S0y3uKSU5IXo+/cf4NSpUzh64hwOHL2Akqoj2FqwF5tSyrA+uRorYkoQGVOOBavzMXNpFmYv34bZkbmYs6oAEat2OCnElEVbMHlBFmYuy8aCNQVYGVeNqJQ6RKfuRMrWGuSXHUFV3UlU1B7B4aOncf78BdNu/Pzzz2Z6I14Md4tSvkYrSe1rDyREhRBCvLxIdgohfjM8IeSJIk8mv/jiC/Qfk4K2w4sbhrL/NuHZe8JuvD/9EN4YkIFefUZh3rx5ZlGivLw8M3ydixJxIQz+bXWEhRDixcP+f7BzeK6NzkaPUQUBqjsrTXVnby5WFBSNgoICc0GLj6G0oJiQgBDi+WCFn1toumUmL16cPX8Z9ftPIbtwL9YlVmDq0lyETE3DWyNi0PqDeLz+wWa83j8NrQZuQevB29BmSB5aO2k7rMBJEdo554qelHgSzGmQShvinD+aOOeDTto53/NCervhNs79g4rN87QZut35Wug8d77zd3LN33utbzLa9l2P98LTEDQ1HZMWZGJ1fAVSc/agsOIQ9hw8g1u375jKcStFeWHGytDmRKgQQoiXA8lOIcQzgSeAPDlkZ3Z9bLY58TTVnUZ4ugRmw0nrk6RzaLUZvt55VBl6DZhtFiWKjIw0ixJxAQvO58ZFiTTEUQghXlzYNlMYUCTwf8S16zfw1sgk9B5d3nRldspPVncGb0HkqvWmuvPGjRv4/vvvTVUWn0cI8fvAYzWQ3OSxxwsWu/cewKRZa/DG24vwX2+uxut9E/FG/zS0GbzNyEbPnO0uWWkueNs453Z+6WBS8YhUPjbtnfsFjPd80vnbfD3DS40YZVoNysFr/VLx3+9Eo2O/ZZi8YDMKimu987/zvJKLo/E9BxKgVn4KIYR4cZHsFEI8M3gCyCvjd+/dR5t+Gxuu0vOE155wPnl4kvvO5AN4e9JevPF+NKZNm2EWJYqJiTGLEp0+fRqffPKJOSnlSbhOOoUQ4sXFLTxZjT9nWSZ6hZeit5GdDcJznKe6803O3RlWhIkz15j2niuzc9SAqjuFeLbwWGKs4KTQ45REH310C3sOnMGmlHJMX5qLARPS8d/vbMAb/dPRdkgu2gUVof3wYnQILnFSatLk4vajROaIJ03lk4Xi0359lAgNdqfcOU8tM/Kz9eA8tBqYjdb9U/D2qGRMWrgFm9LqkZ6zE/X7jpuLNBSgdki8lZ/cZpKfQgjxYiLZKYR4ZvBEjyeA3333HZZvKjBDlTjUqL1zMukvMx8Vngi/OWEv+s08ivbDCtB3yAQsXrwYGzZswNatW7F//37cunXLVPqw48wTTSGEEC82/B9BWUlpUFi2Fz1GFXqGstvqTsrOcZV4i8PZx5Shb/Ai0+YfPnzYDJnV3MxC/Das2GR4LPJ4+uqrb1FddwwbNu9C/zHJ+NN7MWg1ILNRarqEZoeQMm86hpQ3ZkRjmojIZ5Kq35gAzxPiSXsjRRsSzFSgnXMuyvNXI0IHZZuL7kFTMzF1UTrSttWa+UfZHrH6kxdhbPWnKj+FEOLFQbJTCPHMcHdk6/ccxGt9NzsnyiVPLTu7he/EgDkn8Ob43fjv3iswZ84crFq1CikpKSgtLcXZs2fx6aefmhNMXlnXCaUQQrz4WNHCyqjzF66g6/CtAWRnlZGdHMre/YMIbN68GXv27DEXuHghjVKBzyGEeDLsccfzM54zXbtxB2nbdmLmigK0HxCNVg3Vmu2H73i01PQKzYrH5qmqMp84LlH5m1MdIA2/a5Cgnjh/18R5P842YPUqBXCrgVlo/0E0hk1MwKa03cgr2mVGNXG0kVt+2upPyU8hhHj+SHYKIZ4ptiN7//599AleYoYGmepODm8KIDYD5b0pB9F3xhF0DC7Gu4NnYNGiRYiNjTWLErHTy/nb2Onl8HWdQAohxMsD22tKgEuXr6JrcPYjZedbQ1chMTERO3fuNKuyf/311+b/i2SnEM1j5SYlGwXn1eu3kJhVjT4ha/GnPrFoPTAD7YZtd863OAw9gNh0VWkGEplPmsBD0d23P2GMaLQ/u5/LHYpK+/VJE0h4BkqV834aY/9mxwYBym3YlosuDUxB0KQUpOXU4fCxs7h7966ZEoDyk+erFJ/8PHTeKoQQzwfJTiHEM4UncDyh4xDzDz+8ijbvr3+q6s4eo3eh34wj6DyqAm+8sxwzZ87E2rVrzVBGLkrEuds+++wzM28ST+R1wiiEEC8PbLPZ4b9yhbKzobLTO2en87VhGDtlZ+e+8808zRUVFc7/kw+983Zq6hIhfOFxxVjJyQsKH926h/BpUfjvt9aizZCchsrNEnQIYUrRkXJzBPPbpGbz4ZB2KykpBgPFOfdrJu7Fi3x/x58b456j08QrQP3za2VntfNenIx0p8qkk/M8TEfn+T3vuQzthuXjjb6JeO3N+UjNKjFzFPOc2M75qeHuQgjxfJDsFEI8c3gCxw7pl19+ieUbC9BmKFfoLEU7Dv8JLjcJJDp5sth3+hH0GF2LP/VJwLAR47F06VIkJSWhqqrKLErEK+UcJqTh60II8XLC/xH7Dp5AtxG5RnZykSLzdawnXKCIc3a+OWghNm3aZKYvuXTpkrnQxTnyJDuFaITnQjwmKNBufPQA65Oq0Cc8Ga/3S0ZbH8nJKk635LSi0x1/YdlcXI8JYRqrQ71xzvlY9egZ2cPbnPuNLEenURXoFl6NHmN24u2Ju/HWxHq8O2UfBs89jncm7TFfRy496+Qchsw7hr4z9jeEo34O4v1p/HoIvcbVOc9T6zzPLud5OeemR676nFv6iVFfOfp04rOp8KxGJwrPBulphKfzOjo5r4HpEFyMNgOz0DtkM+av3ILtxbtw+84dM9enqj2FEOL3R7JTCPHM4Ukbr1xTSp48dRb//W4s2gb5yk537Elp7/F7nJPYQ84Jegr6DgzH/PnzzaJE27dvx7Fjx/DRRx+ZYYyas00IIV5eKGZq9l7A/MTrmJ90E3Pjr2F2zIdYmn4NEfGXMSv2IsavOoWh4UsQFxdnKjsvX76Mzz//XLJTCAdbxUlRVrXrECKji/FmSBxaD8pC++GFHrlphqhTbvpXcT4uzQlNVlr6ikyGv+85pgbdwqowfMFxjFl1DrPjr5jjedWWK1iVdgRrMs5iTep+rE6sRkJ2Fbbs2InknCokbClDQkYB4lK3YvXGGCxcHonkzDxExWU6X/MRnZyBtdHxiIpNxIbYJGxMTMWGhAxEp2xF7OZtiEkvRFJOPdam7MSs5XlYtLESi2IPYdrKakxddwTTNlzCtI0fYsSS0845Zj16jdtlBKln/k2KUQpQ1wJFTSRoowgNLDv9MsojPzsbCVrpiRGfJWg3eBva9I3G9CXbULXzoDmftYsc8bxW83sKIcSzRbJTCPHM4UkaT8BtdeeQSaloE1SMdsMDy06GJ4f9Zx1H19BqdOk7zyxKtHz5crMoUU1NjenoclEizn+kqk4hxK+B7caziPhtsEP/0Ue3UFO7C4U7ypz2PgKLlq5CxIIVmDZzAUJGjcOY8TOwYOFys0BRbW0trl69qmHs4pXGtj/c/69d/wgxqZV4c0QC2gze9pTVm4HScF8+zgpNIzWt0CxDj9HV6D2uFqNXnsPsuA+xKP4oIlNPYv76MiRmV2PVpiRsjN+M+QsiMXfBUkREzMWKFSuwcuVKrF692kxJtH79enMRmxXb0dHRZpoKXtCIj4838/MyHM2TnJxszv8Chb/jfRjePyEhwTyez8Pn4/Pyb/BvLVu2HEuXrcC8BYuxODIWq2IKsCy6HHNWlyB8fiHCFu/F+DWXEbTgJPpM3Y8uYTXgkHd/CWoXLPLO3TnyUcKzGp29aRSfnU1Y9VmKdkOy8daIVExemIat+VX4+OOPTYEAh7nbik9JTyGE+G1Idgohfhd4Ms4TNs5TtDauEK2HFqCtGcoeWHb2GrfbVHW2HrgVwaMmY+HCheZENTc3F4cPHzbD17koERenUEdXCGE7/u6wc8iwjfAPL8Aw7ESy8jxQ2Gb5x/6Oj2Ps8wT6G/bvB3ptohFuK3bqOSz9ypUrOHDggKngp8SgrNi4caORIRQZ+fn55vdcjf2bb77RAkXilYPtB/f5O3fuIi69FqGzt+GND1LQPqgQnH/TVG5aUfk0ctOITY8cNWmoBO0aWoF3JtVh2LwjiIi/gFVpJzF/3Xakbt+NdbFpWLx8LebNX4Bly5YhMjLSiMx169YZwcjFJCkfeSynpaUhMzMT2dnZyMnJMYtMFhYWYseOHWZqivLyclRWVpppinhRmwuRMXV1daivr39keB/O487782JIdXW1eR5WgZeVlaG4uNj8LbYfPI/ka8jKykJ6erq5gEJRSjnKdiZy5RrMnbsQy1bHY8mabExanIOxi2sRMn8fBs0+hO7hteAFeQrQDgzlp5mj04pPykxf2ekrPF3i08aIz3J0cj6H9sPy0DskGVEJO3D9xk3Tzlnxyf89bC/1f0QIIZ4eyU4hxO8CT8psdef1Gx+hbf84tA0qCVjd2TV8J/rOPOqcAFag6/szzfD1NWvWIDU11ZzIsjPM4T488eNz6oRPiJaP7dzZ+EtMt7iklGQV+c2bH+HI0ZM4cOQcqnYdQ17xHmzeWoWY9J2IjK3AnBXbMGtpNmYt34bZy3MxZ2UBpi7ZhnERGRg/L9P5momxs1IxelYyZkfmO8nFwnVFiIyuxLqkaqTm7nWerxY5RfWo3HUCR05cwIkTJ3H37j3T1rGN4nBEtyh1S1L72gMJ0VcJvn9un2+//Rb379/HxYsXsX//fiMrKEEoQygrKEJ4O+frtJX93Kav2vYSrybcz9leXL95BxErtuJPfaLRLqjAIzhHNAhObwIJTXes3GTFppWbJegyqgzdwioQNP8opm04g4gNe7AiuhSrNm5GxPylpuqaUnPVqlWmMtMKTcpCisMtW7YYkVlQUGCOWcpGikvKyL179+LQoUM4evSo006ewKlTp3DmzBmcP3/eHPMcscPzO1ZtX79+HTdu3HDa8JsmnLaIFziaC3/P+/ExfOy1a9fM8/D52F5cuHABZ8+eNX+Tf5uvga9l37592L17txGltr0pKioyUnTbtm1GzrJ6lJWia9etQ+SqKCxYHuv8r8jHmIVl6D8+B0HzjqB7eA06jqT4dGLEpyfeOTxHNSc8q9GFI5hCq5xt70nnUZXo4jxXx+GFaNN3I0KnxSMrt9pM28GCAVvtyf8hutAjhBBPjmSnEOJ3gydl7JiyQzttcTraDC1qslBR59Aa9J1xFG9P3o/XnRP5SZOnmiFPrOhhh5cnqhzewzmNKAx0oidEy8Mt/QLJzIcPP8bxUxdRWn0E0ZsrsS6xElOX5mLIpHS0fX8d/ue9WLz+QSre6J+O1oOy0XpIntPe5KPNkHy0HVaItsN3OG1PMdoHs/1hSsz3jfEsoBEo5v5st4aXoG1QsfN8RWbRNT5368G5aDUgC60GbsEbfaPRc3gSgqdmYOycdMxblYf4rHqkZu3AngOncfXaTXPRhlKU1YlWiD5KhLZU+N74nrkt+P/hk08+MfKCK65TglCGMJQWFBr8Pee2o0jmNmrJ20YI7t9sB1jhtz42G/+3dyTaDctDx5ASMwS608inlJwNotMtOSk4w1ecRXzJd1gWV4vZizZi4sRJZlSNrdik3GTlIys1efGZIpBCkGKT1ZNWalIgcgTO8ePHjcykaKTIpIDkcX3nzh3cu3fPXNh4+PChOZ5Z1U2Zx6kpuFo5w/aR4ftmu/Co8D4M728fzwtefD4+Ly+O8NzxwYMH5m/zNfC1UIxaIXru3DmcPHnSiNCDBw8aOcsL7LzIwvNPVqKyIpRSl+ekHB7PEUcz5yzEhPk5GBaxxwzr70RRyTlNzTynnKOTstOTgLKTMcKzUXx2NeKz0gmHuZegzcB0jJgaZ7Yj35t7USP3/wkhhBDNI9kphPjd4IkYT8p+/PFHnDx1Hn/qu9lUd9rh7FwV892ph/D+jCNoF1SIdz4Yi0WLFpmhRbzKzhNonpxy+DqFh0SnEC0H26FnG2HFJsPO3BdffoWEzbl4d+B4/H/bT8V/vbnGDNtsPWgr2gzOcTr+hWjvFZYNYtLpzDeG7YtvTAXOY2NX6Q0c/1V9G+P8DbZp5rV4XlM7tnXDdjivOQevf5CG/3orCu36R2PUlE0oKK41HX92YFmt6BagthrUCtCWKj/5vvg++b75P4IVTJQXVlowFBls/3mxi9uG20QdfNFSsW3izz//FTnF+82CQ20GbTELDnUa6S85/dNUcrrlJitBe4+twcBZ+zBu+T5MXpiDpauTMWnSJHPexTnSORydU0iwqpFDvSk3KfxYsckqSIpACkGKTUpCXpBwS01ON0SZSclImclj2IpLHt9s7zg8m8c7j2nGVsQzbAvc4cWNQPG/H8PH87kYPi//Bv8W/yb/NtsR275QiPL18XVShLJSlBWirAzlxRYKWy6KSQG6Z88eI3VZAcqpNig/MzIyTGXr6rUbMGnWaoydnYYhk7ei7+RadA/jUHZKTydmqDqHrDcMXW9WdjJu4ek8bpTzGQbl451RGUjNrjGCltuT74nvl22nLvwIIcSjkewUQvyu8MSdJ2Y8uewTTtlZ3CA7y9AlrBb9Zh0zJ3r/9+0osygRh0rxBJvzL/GEkxUAPHFVJ1eIlxvbkWes4GSH9NyFK9hWtBfLN+Zj+rJcDJiQjj+9F2sqNNsOzUP7oCKns19sOux2sQyuBOwrMh8hM7kS7mNT+XR5EhFqV/o18chQCtDWQ/LRamA2WvdPQo+geARNzcDSjSXIyK1D3Z4jXhnATjs79lZ+WvHJtIS20O4PVnRTZvpLC35vt4Fb/ArRknAfC7klB/H2yBRTyckqTkovI76ayM3mYyUnh6gPmLkPY5cdxKQF2Vi0Mhnhoz0XlSk3WbnJakVWLXJ+TVZtsqKRYpPzYlL2cQg4pSZlG4Xg7du3TbUkZSHFIQUiRSKlom23GB7LPHb9L+C4w2PaxrZtvyX2ufz/DsPXwPD1WGHK18nXzAtOfP2UsrzAwspQK0L5filzKUE50ujIkSPmQjzFLwUoL8xv3brVVL6uXLUacxasx5g5mfhg3Fb0Gu2c55p5OStgV2a34rOp7Kw2C3R6UmXSJdRT6Unp2WVIMqYvzsCx46fN+XSgOT2FEEL4ItkphPhd4QkYT8Z4MrwmttB09M3cnU7H/50pB/D2pH1mKOi7g2ea4VNcnIIn26wc4HAjnnzy5FQnckK8XNgOvO2EsrN77cYdZOTtwaqEnegbGm2kZptB2U7HfrtZTZjVml6haYZdemKGCDJ+1Uu/SlQ+NlW/MfY5XM/VsJKve3VftwjlXMZthxaZ4fCvv7cBfcekYOL8TGwr2ocz5y4HFKDswFtJ8DK3j3Y/saKCsYLC/vyyv0chmoP79s9//RtWbCrA/9tzGdoNzQGrOLuElpsFbALJTP94Kj7ZZpY4jylB/+l1CJ1bgOmL0zFrQRQWLPAsJsS50G3lJodms2qTw9G5wA8rNlnNyHkuKTY5fQRlHy84s6LQVmdSDPICtFtmukWmPV5teNza/JHY1+B+bYx93XwP7osuVoSy7eX5K8UuBS8rVzkkngKUw/VZ4crzVVaAcvg7F0XiPKasjF2zPgZTFyTi/dHp6BVeaj5PIz6dmHk6jfRsFJ+NstNJGFOFbkZ8VqLrKOcxIcVoPzABkyM24vLlK0bK8rVRevK12+0thBDCg2SnEOJ3hye/PDm+cPFDdB4UhTbDipwT80qzKBGHr7/RYyymTZtmTsR5hZwLUvBkmyeWPOHk44UQLzbuziQ7jz/++BMOHTuHzPy9mLm8AO0HbELrgVkesRnMSk1fqelZPMPp3PtIzYbhgI/Iiyk83akOENfvG0RoY5Wo835MdWgZ2gYVmTlBuw5LQN/wFCyJ2oGc7VW4d+++j3RoSfJTiFcBHqM8XrcV1qPr0Gh0GF6ArmEVTjiEmW1bo8z0j6fak9WC/LkEPcLL8f7kWgyZth2jp2/C7LlLsXjxYqxYscKcV/EiMisPuZCQrdyk3OQcm6xYpLjjUG6KPFYNUmzaSk13dbWVmm6hadv9loD9/8XwPbpFqFuCsu2l+KUA5dygnJKE249zDp8+fdoshMTKWMpPDn3naKW1UbGYMH0Fhk9OQrcR+ejsfHadR/pKT09Fp7/wrEa3JtKzCD1HpJrREIePnDDSk58XPye+Vr52/Q8QQgjJTiHEc4AnXTxR5HCnopIavPZBOjo7J3Ldx+xExwFrzerrHFLFoVQ8OeTJN4cQ8eSNJ9g6aRPixcR2Dtm5YieLi/Akb63HO8GReK1vAtoM2eYrN71i8+ml5qMSeDi6+/ZHhHKxyffu52kuLmH5yASSnc2lynk/jbF/q6Pzuri9OHy/7ZA8tB2YgpEzMpC0dTd27jlhLgyxA+4WE+ykWxkhhHgxsG0mj8+wSSvRemCGqeTsMaYG3cM5pyPbQ1+5acPfUXh1CW0QnSHFeGdCNWauP4ZhI6ciIiLCjJDhIo+s4OTCQqzg5FBrnltRcHIxHlZvcn5KzrFJWWcrN1nFaKvHbcUm23a33HyV2hT7WdlYAcr2lduH7S2FsB0CT+lI8cmqWC6AxKmYOBUAK2cpmTlNAKcLmBWxGG+PiEb30OIm0tNHeHplp41HenZzPv+uo8rQKTgPrd9Z5Hyu+00FLgUsPz++NglPIYSQ7BRCPAd4wsUTRJ4U8sQ6dFaGc+Jejtf6pWLK9Plmnk7OG8Ur4DwR59ApDs3hVXSeYAohXhx4PNtOIDtU7PhV1OxHx/fm4L/eWmMEJ4ekmzk2G+aOM1WbpsP+28Vm4HBIe8Ow9obKSN80zu/5qLjn/mwyB6cT9xyd3vgIUJtfLzw7MiPdqTLhCr+siPfEec/ONm03LB+t+qfhT71mYXNmkak0skNN7XxubHttx1edXyH+WNhuFpYfQNcPFqPtkK1mVfSeY2tNRWenAMPWOzHO7Zy/kaLTrMYeUozeY0rRb3QCxk1dYi4YL1myxCs5k5OTjVgrKioy858fOHDAVBxyxAwrEDnnJis4eZ71uOkx1GY0YrcHtw3DbeWu+mSlPcUn5/3kBShuZ85zyjlPeW7Loe6squW57ubUNEyJiMKAMXHoNnwLOo/kMPcKcHEiLlRkpGcY45adDcLTpALdONVBSCH6hKcip7DGiFb+D6C05ucp4SmEeNWR7BRCPBd40sXON6s703Lq0XrIdnR9f44ZXsWw+oArXnJlT1YZ8OSbJ5E6URPixYHHI49ldvJufPQAUclVTkcrCa0GpHkWE2pWcjK2ivNphWfDY0IaYytEvQlmGlZmN3/TeRw7jk6nsPuYWrw9aY+pnOk1bif6zjiIoIWn0Xf6QYQuv4Axqz/E6JWXMWzBcQydf8zJcQyKONaQ43h3yn7ncXXoMWYn3pywG91H72x4fs67We7KI+ToU8rPpsKTK/wyLunJBS+c19HJ2TYdnO3eekAGegUnYvriLcgp2Ik7d+56F7FwD2+0EkMI8fzgMffddz9g1PRYIzm7h1ei9/hdRnQGlJzObZRd3UfXGLnFCsBuoSV4OywDwRPWY8wETxVnZGSkEZwcGZOVlWUEJxfP4UI6dv5NzjFJ+cbzL7YJmv7i2cFtxnD7cTta+cl2l9uZF594TkvxyRXrOdSd83yyyra8vNxU3W6MjsWkiGh0H5aELiNKzKJGXJjICG4jNpsKT+4/3cMqne8r0DXU+b83JBPTF2Uhe1uOkZ78rO15NF+XPlshxKuIZKcQ4rnAEy2eWLPi6MKFi2j7QSzWbEg3J+eczJ1DfHhyzmFVPBm3w3CEEH8stiPHTnH1roOYFVloKjhbD9qC9sMLPXLTDFEPVMX5JGkqNAOJTH7PDmDvcbvQY3Q1QhadxLRNl7Bw8w2syLyCVVmXsCSuHusyTiAyvgqbMiqRnFOLbWX1iM8sQ2xqNhIzcrEpMQWR66KQlJ6L9dHJiFwbjeVrorFqXQI2JqQhOjkDa6PjERWbiNUborFyvZMNSYiK34YY5zmjkiuxfFMFItYUISKqHksTDmHyyl0Ys+wQZsRcQ3jkBbw7ea/zOuucjmqN87obRKiRoa4FiowIrfLET4Ia2eknPD2y05VRVSZmdV9K0Ab5ye3ffmgeWvWJwtxV+di9/6S34pOdbys++XlKcAjx+8Lji+cyR09fw9sjNzvHajHembwHb02sR7dwHuf+krPM3N7Laee6hfPYLkW3UcV4KyQaYRPmYfbs2aaKkyNieKE4IyPDCE5eLOZ85xw6zcUdKbw4rJrSi+ddVm7qosfvC7ep/cwZbm+2ubbqk58HRzixwpZTNnG1e67uzoWiMjOzMH3eBvQNS0SHIVlGcJtKz1BKTVvR6RGe3Z19wxP+zgmHto/cgbb9kzB+TgIuXbpsPn9b5cnXwc9cCCFeJSQ7hRDPDZ748YSbcxulpOeZSdx5gs75jDivEU/QOfxHw2+E+OOxHbYDh44jenM53gyJR5vB29CB82+GNDdE/UnScH8+jjLThkIzmMM2y02nbnDEYUzZcBmz4z7E4sRjWJZwEFFpu7A5vw5rohOwMT4Vs+cscBKBpUuXmnl/OYxz9erVWLt2LdavX4+oqChT9cRpMmJjYxEXF2dWImYVFMPhnjYpKSk+cf8uKSnJ3J+PZfg8FA2bNm3Chg0bzN/i316yZBkWLFqGles3Y/HqBETGlGHRxipMWVGL8EV1GL/qDEYsOYP3px1A51Ge4en+ItQKUDNvp/P7RwtPTzqb+IpPDonsNKIEbQdvRcfBCRgzJx1ZOSWmjWUH2A5dlfQU4veBx9Tpc1cwYnoaWg9Iw1sT6vD+9APoMbrGOUZdbaET/swqzjed+7ACnaKr64hcvBcai5Fj52DevHmmjWObxraIF4lLS0tNhSAFp7uCk8PT7YUNTWXxx8LtzfbVik9+Hqz6ZBvMi1Ccn/727dtmZXeu6s5pBwoKCrApJhlDx8ehU3A+uoxy9oVRrOCk1PRIz0bZ2ZAwVnpWoHtoObqMKMTbIRuxZ98RU1HKNp/TFfBvq60XQrxKSHYKIZ4bPMHiyRY72Twh5wqgHGZ17tw5Izp5FZon6Dwx18mYEM8f2zHjEOjY9Bq8PTLFVHC2CyoABWfHRw5Nby7O/bxis6ECtGGoe9fQCrw7qR4T113AgoQzWJtxGvPX5CI+oxQr1sdi9twlTid/vpGZHK7JlYXXrVtnBCNlIxfgoJjkog+scNq6dStyc3PN0MDCwkJTMV5SUmKGC3KBDnYk7QWWXbt2oa6uzqya+6jwPrwvh4bycXwOzrtWUVFhZENxcbGprOI8bPzb2dnZpmKdr2fz5s1GTFC0UoyuW7ceCxYuxqLlm7B4TTamL8nBxCXlGLlgDwbOPoRu4RzSWgUzBD64YRi8maPTik+KzOZkpztVDaH09IRz/7UfmoP3QpORnFWDU6fPmc62rfqyFV/cB9T+CvHb4DH07Xc/4P/pNs9p+3aYas7+s4+ix5haT5voCocrvzVxN96bus+ITt6/W8gWDB4+FnPmzDHD1VnJyYssdiQM2yOupk7Jaas4eSG5ueptHdN/LNz+/CwYfi52qDvbX1Z7coEhLmzEc2K7qBH/j02LWI1OQVvRZURxo/AMJDtNKtHDSM9y57470OqdRTh8+IiRqZyjVcJTCPGqIdkphHhu2JM9nmzxqjZPvh4+fGiubHNOI3sippMwIZ4vPOZ4EeLQ0bOIWLEVbQcmof3wAqfTXdKkYx5YaLrj3KdBbHoFZ3CJ00krx3tTdmPS+guYsPIg5kbtwbLoEqyMSsaEiZOwePFiIzVZIcnKTFZkUhJSFqampppOPmUiq17Y2afApLikkGR1EzuIXASCwwJPnTrlvZDCoYKcC5hSgCsQ88IKF41gx5LhcMJHhfOs8X58DB/LizR8Hs69xhV3ufgE/w7/Jv82XwOr1rkoyO7du42UoGjlMEW+bnZgKUTdMpTvdcWq9ViwPB4RqwowYVEBhkwvxsCZe80CFRzq71k4yS5QxMVMPMPYmxee1egSylR54pWfFegYVIDW/VPw3oi1yMwpN20xh1haUWIliRDi6WF7eu/Bpxg4MRPdwsrQf/YRDIw4hp5jOd+v0ya62lOuwt5n2gG8PbHeyKrOIdvx1ohohI6ZYiSnreZkW8G2jxdeOOUP2x22T5ScdooKXbR4OeDnYqUnz3n5uVF6sgKTlZj8P8P/KZyWgBfUoqJTMGpaLDoOTkUXVvwa4Vlp9hd/4dnD3Oap8mSFZ68hK5GdW2Ke0y087T4ihBAtGclOIcRzhSdXPMlih5ondzxJZ3gCpnk6hXj+sNPFypJBYcvxer8ktBuWZySnWfXX6VjZTnlgselOw/28FZycy7PErBgbuuw0Ynd8jeWxpQgZOQZz587FggULvHKTw8Ap/DhEnJWarIy0YpOVmaykpNRk549D/Y4dO2ZWF+bQP0pHykcKSVaw3Lt3z3QYeSGF1TKcH41CgB09XlRhNSM7lQzfN6uhHhXex96fj2X4PHw+Vqjzb/CCDf8m/zZfAwUp5SgFK2UrxSsXpeDrppTlqrys3KEE5ftjVSirUlmhympVDrtn9eqsecswdu42DJ2zB+9M3OV0cquc7cttzXlOWa3JSk+P9AwoOxkjPBvFJ1f6NYtfjKxA55ElRnoOn7DWvH6+L7bHFN+2Q6wqICGeHB4vp89dRbsBG/HWxDoELT6LvjMOodvoGiM5bbh42juT95pqTy5U1HF4LnoNjUJQ2HSzuvqKFStMO8A2gRdIeNGEF1N4sYXtjL1AwepAHasvJ/ysGCs+eV7Mc2FOQ8D/WZTZlJ6c05MjCeISNqPvqHXoFFxgLh6aFdn9pGcPbzzCk4tatR+cihGT1pn/Q/x/Zav5+Te1vwghWjKSnUKI54o9ubPDeHjCxVjRqRMvIX5/bAfr55//im1Fe9ArOA5th2zzSM6R/pLTP00lp0dueio4OdS915hqjFh0GBNW7MTUpduxdO1mTJo82VRvshPPeTUp8zgskxVLFH15eXmm8pGdOlYvUWyygokdfHb4WJ3J6hSKRM5NRznHjhs7hZR0lJGUk5R1rBxn2Klj55FVTxR4FAOMbXcYdjAfFfd9GT6ez8XwuRn+Hf49/m23IKWQoGyleKWg4OumlKUEpajlXHscispKUFaBcog85Scl77Zt25Cenm4qP1ev3Yi5S6MxPiIZQyZno++UXU5HlqKTVZ6eleE983Ta4evNyc5qI0w9wrPSpPPIcnQIykPvkEREbszDydMXvMPb+f7dlWJCiMDw+Pj2u++wKqEar/VLwgezD2PkikvoNb7OOT6dY4ztpJNOoyqM5Bwy/5RznyPOMei0qYPjEDrOMy8nLwCxkpOSk+0h20JeJOEFHbYjbF/Y5rAtUhVny8H+T3afG1NmW+nJi2YcNcALfzPnr0eP4cnoOrLYac/LjPQ0c3a6ZedoptL5vgI9wsrRJWQ7Bo1eZ/6f8n8n/0fx/yL3If5dIYRoiUh2CiH+EOyJHU/UVZEgxPODxxqHWK6Kr8BbI5NNJSerODuPKjdpKjebhvM/8quVnOxshSw6irHLD2LSgmzMXRqPqdOmY9GiRVi2bJmZa5NzVnKhn8zMTOTk5Jh5LlnZyIpNVmtaqclKTVZF3r171ys0KTPZOaNMZAeQIo4dNSsu2TlkWOFk56pzx7Yztq35rbHP5f93+Lf5GuzrsbKUYpSvl6+br5/CgkKRHVnKUM65R4lLoUEJyiHx7NhyeD6H6rurPymHI1euxswFUZg4LxX9J2xBz9HOZzey3PlcKoz0dItPf9npEZ42VQ2h+KxA55Ad6DB4M2YuycSx4yfNdudrdlePqZ0WwhceE9999wP6j0lEx+BCDFt4GmErr6DnuF0+1Zxdw6sxeP4pJyfRa1wt2gfl4p2hczF9+gzTTrLCnRc3WMnJuYLZDrBNYPvAiydsQ9iusK1hO6RjsWVi/8ewzWXby/8ZvHDGC2aUnrwQmJu3HaOnrkTPYErPEq/w9JWdHuHZM5xzeVag26gdGDoxAQcOHjb/b/j/R8JTCNGSkewUQgghXgHYmWEl5/rEMvzpvU3oMLwArOLsElrx5JLTVHxycaESMwT6g+m7EDq3CFMiYhE+erwZfsnKJFZuUm5ySDYrlDgkncKOQzE5lyUrlVjVSLHJTpeVmuzQUWr6C01KQ3by2SmzMjOQuGTn/48WAPY1uF+XW45aIWpFKN8fK7VYGWolKIfHs4qLQ+IpOzg/H8UHK11ZAcrqVys/OZ/pmvWxmLogGb2CE/Dm6BJ0dT5Pik+zOJGp4GyYt7OJ7HQSxlSZVX4pPbkIBqVnu/4xWLAyHZc/vGI+F4pmW01mt7UQrzo8Dr799jsjOt+eWIep0fcxZs11dB9d67STvCDkSe/x9Ri14rKZu7ND8Ha0/2AtRoRNN9N5sL3khSBeBOKxzQsdPO45PQbbQ1tlbSWneDXgvsX/G2xz2fZyP2BbzEpPTuPCi2GZWdl4f2SU02YXOG04hScrOStdsrMKPRkrPEfuwNvD1yIpOdX8/+X/Gv7/se26EEK0JCQ7hRBCiBYMOzDsJG8rrMfbozY7He1CdHU6Q1wJvfMoVgI2lZo2nmpPe58SdA8vR5+J5eg/Pg2jZ8Rg2ox5ZhENDk3nokKcY45zbnIBHq5SzmHZnJ+ScpPCjsPQWbFJkceqEis23VLTXZ3pFppWIrYE+D7s+2KsCHVLUFaDsnPL+dtYYckKUEphymEO6af8ZEWslZ+sBuO2X7tuIybOWImBYxPQfWQBuoxk1Sc/60bp6anmdMtOT7oxPtKzED2CErF0/TYcPnLcdLQpZfkaJT3Fq449jmcvyzQXj2bEPTSi862Je3xE53tT9iN81VWELL1gKj87vT8Xs2bNMqKTVe/Jycmm0p1V7pzbl1NdsH3ksaaq6lcbu4/x/wMv+PF/Jat8+X+U/wcoxvO3F+D9sBinzXb+R4eVN1R4NgpPIzut8HT+h3cbWYQewzahpLTK/E/mBTbuZxKeQoiWhmSnEEII0QKxnaTbd+6ia/9laDUww1RycvVfyk4Oew4kOBlKTnMfU/HJVdWL0WdyLWZGHcf4SbMQERHhXSnYDr3k6uKcd5OrpFPAUXByQQRWj7BCkZWb7FTZOTUp8yj12JFnR64ly83HYT8rhtvAX366qz8piFmNwyGNlCLcvhSfrJjlkHdKZlbScrj7tJmL0CsoFj3Cip3Pu9QrPTlnp1d4+stOE0+lJ4dGduWcgkHb0Pb9SJw6fcZIV36G/PzY+ZaEEa8i9pgtLD+AVgPSMH7dTUzeeNeITs5lbEUnFycav/4WQiM/ROcRxejYdzGmTZ9hpvig6ORxymOWC5dxCgu2lby4weNdQ9aFxe5vFJJsezkKgP9T+T+A8z5v3ZaPvmEx6Bycj25Om20qPDlnp1t2mjk8PcKza0g+PghdZ/Y77nN8Pis8ta8JIVoKkp1CCCFEC4Qdo/LaI2jXPwodgpwOkNP56Tm21kjMQIKT1ZudRpajC6v6nJgh7iNL8Pa4cvQbk4RJs1Zi9uzZZpj6qlWrzAJDHHrJodRcSIOijavGUryx4oQdKFYiskKJHSk79yM78LZ608pNdq7UwWrEbg9uGys+ub243SiIWd1D4cgKH25fzunHCh3OeUrJzM+B86FSPkdHx2Ly9EUYPikBHYdmorPzOVN62gWKjPRsIjsbhKezr3C/4Yr6HCb59ohY5GyvNJ8r/7YdXivhKV41uM+nZlei1cB0TNxwCzPiP/EZuk7hOWDOcTOsnUPX2w/LQe9+kzF37lysXLnSVMFnZWWZ6ndWc3JoMi9i2OkiJDlFILg/cN+zVZ7838r2n6K8pqYWkWuT0TskxWmzy9CdixNxzs4mwtM5FwgvR/dRRRg6do2RpawUtfPCct/TfieEaAlIdgohhBAtCHZSuFjGqGmxaDt4C3qMqcabE+qM6KTMbCo5WcVZ5XSMKLxY8VmGrqEl6D0qHSMnRiIoKMgMt+RQda4SzCGXW7ZsQWFhoRGcXEmcko1zzNkKTivC2HFyV29Kbv567DazApTykx1ebl9WgXG4O4eZ27k+KZzZibXD3LnC+8boOEyKiEeP4FSzqEUX5/PnwkRGcBu52VR4dg+vNMMibZVn15BtmLpwM/YfPGqkJytNWWlk5bU+V9HS4TG4PCoXrQdtwajlF7Eg7RszJ6cVnczAiBOYGnPfzOP52vtx+GDoJLPaOufnZDUnj0cOW6ek4nHE45fHEdtKHUficXAf5P9U/g9gxT9F+bVr10ybn7e9GL1DErzCk4LTX3j24m3O77qE5GLyvHgj3DlFCquKeVFS+6AQoiUg2SmEEEK0ANgxYQfl2OlreHtkiqnKfHfyHvQet8vIrECSs1t4NXo5v+8eXuX8XIKeYSV4Z0QcwiYs8FZx2sWGuNAQO+i1tbVm6JtdKZjDqSnZ3NWbgSo3xbOF25Sx8pPbnFWWFMzs/FI4c4ETdmAvXbpkOrOcP7W0tBTpGZlYuioWwycnoeOwbHQeRenpqej1DF9vFJ/cNzxprPLsOqIIbT5IMNKTUpXVpbbK01akCdES4TFXXstKzWyMXXMdK/N+Rp9pB30qOgfNPYkZ8R8b0dm+zzKMmzDZXDDivMac7qOqqsrMtXjlyhUjqXjsuIesC/GkcH/h/1qKck4Tw//HbO8Li0rRfVi0016XmkpODl93C89eY5wY4VmOTsOysDIq1YwKuHHjhrmApYp9IURLQLJTCCGEeMlhh+T02Q8xcnoaWg1IxVsT6tB3+gEzP2cgydljTC3emlhvRKep5ByRh8FjojBq9EQzzHLZsmVmLk5WcXKYOueUoyijMGP1CIe8ce5GCk5WFbJj5K7eVAfp+eMWn7bqkx1gLgBF8cmKW4pPLhRFWU1pzekH1m1IxODxcWZezi7cF0ZVuKSnW3Y2JIyVnhXoHlqOLiEF6Dl0FfbsO2wqStlJ5v7AfUHSRrQ0eIzx+Hpr5GYMX3QWSbVA+KprPhWdb0/ai5kJn2DA7KP4U58YjBs3CfPnzzdTf7Cis6ysDCdOnDDTTvCYtKLTtptqO8XTYNt9Kzz5P5ltMSuG0zK2od3gzegRVuYZuu6q8DSy04lnWHuZmZPZTqnASmPul3xOteNCiJcZyU4hhBDiJYadnW+/+wFt+65Gx+AdTme7HgPmHDVC0190ssLzrYm78c7kPegxutosPNQ1ZCv6Dgoziw5x0QzOJxcTE4PMzEyUlJSgrq7OVCGxgs9WcXLIJaUWh1D7V3Cqs/7HYbc/w8/DDnNktS0/L1vtSdFC6XnkyBHTweXcnmOnrkCbvhvQecSORuFphrEHitNBNtKzHF1HFuG1XrPMc925c8cMg2R1qe0oa38QLQXuyzGby539vxyRuT+aYeq9xtV5ReebE3Zj4obbGLf2hplCZFDINLOQG0VnQkKCubjA48S92rpbdArxa7HCk209/z/zf/WZM2ewYEUSOgxJR/eGCk8rPK3s9A5nD96GgaPXo6y8wix6x3bczh2rfVMI8bIi2SmEEEK8pLATcu/BpxgwIRVdQ8vQf/YRDIg4hp7jdhq52cElOruPrkGfafvx9qTd6DKqDJ2Dc/HuqFiMCp9iKo8iIyPNokOs5szPzzeVfxzWdvHiRVMRyEpOdqIosmwlp1tyihcPfi5WevLzYueVgoXDHVn9Q+nCFfP37t1rxHZqehZGjF+OTkOzGqo8KTwrjdz0F54cFump8ixHlxEFGDw+Hlu2FZphkBKeoqXBffjGrQdoNygFM+IfGNHZJbTKKzq7h9dgzJrrJp1DitBv2AwzdJ0V8qmpqaY6nu0pFyKy89yqAlo8S7iP8n8yBTor+tnGs31fvykZPYNTnLaaFZ6UnZWNsrOhurNneBm6jcjHiEkbsX//fvO/gVWiEp5CiJcZyU4hhBDiJYSd5NPnrprV1t+auAtBi8+i78xD6Da6xkhOm+5jas3t/WcfRe/xu9BxeC76jFiF4BFhZsGM5cuXY+PGjWYuOc7JyUWH2CnnfHIczsZKTq4Q7F48QwLr5YKfle0IU0DauT35uXLOQMpsdooPHDhgKj3jEtPw/shN6BRcgK6h5Z65Ov2kZw9vKDyd348qMSJowpxYUxlEOe6ex1P7i3hZ4b7LfXjopFSnHT2MFTk/+VR0dh5ViRHLLpqh7e2G5aFb3zmmbeUcnVxxnQuEsTqeC4dRdNqKeIlO8azhvsr9iv+reWGLwpOjMtK3FKLTkM3m4hRXaHfLzl5juGBRBXqFl6Nz0Bas2ZBsFjriKu+8wMk2XPuqEOJlRLJTCCGEeIlgZ+bbb7/DqoQqvNYvER84ne+RKy6h1/g6dBxZ7pWcnUZV4J0pezF4/kn0nXEQXULL0HFwHEaNnWUqOe28nJxHjiurc9VuziVHUWVX2fYfqi5h9fJjO8O22pPDHq305DB0dowPHz5sKtFmLYxG5yEJ6DqqxCs9rfD0ys7RDOd9q0APVnkG5yFkUqyZ+40dbVYHUZRzH9L+I15G2N4OHbMOnUcUYV7qVxi64IxXdDLvTtmHkKUX0GZQJrr2mYTp06eboevp6emmQp6LufGCgq2UU1sqfm/cwpNtO6cuWReTjU5B20xbzeHrPsKTPzPhZeg1bINZRIvzfrINZ5Uon0v7rBDiZUOyUwghhHhJYGfju+9+wMCJWegQXIigRWcQtuqKWWjIXc3ZNbwag+efcnIC3cLL0W7oFrwTtNDphM80K6xzuHpKSgpyc3Oxc+dOIzk5/JgdGw5xtosOUYipY95y4efKz9dKTzvEnQtQsaO7b98+bE7NwNjpa9ErJBVdR5V6hGcY5aZbdnqEZ89wj/TsOrIQQ8ZtwrFjx82wXVYHu4e1C/GywGMkf0cd3hiQgcHzTmB20udOm1rjFZ2dRlZg6ILTZnqQ9u/OxuzZs7FkyRLExcWZCwZsWzkkmMcApb+qnMXzgm07/49TVrLSnm36+2EJTvtdip6s5GRFp0t49m6o8OwSnI2lK2PNRS9KUu67VtILIcTLhGSnEEII8RLADjIrjPqPScLbE+swNeaemR+u++hanyqj3uPrMHL5JQyaexwdQwrw5tBlPisCJyYmmjk5OVyZnRkOV7cVeG7JKSn16mClJ2UkO7WUnlxAhdVorEpj1W9G1jb0DYtB55BCdAstN0PXOYS9UXZ6Vvk1wtP5HRcuGjA2HqVllWYfY3WRhKd42eC+OnxKMjqPqsDU2IcYOPck2jvtLNMhpBw9xu7E0Pmn0HbIVgwfNcUsSLRu3TpkZ2ebuXApi7jva3Vr8bxhu87/5WzTWb3Pi1hxKfnoNHyb006XN5m7sze/jq5Az7AS9Bm+1JwjnD592ix2ZPdfiXohxMuEZKcQQgjxgsMOBjvJs5dlomNwEWbEPcSY1dfx1sQ9PqLz3Sn7Ebbyqpk/jvfr9P58U2lkVwROSkoy83KyE85OjO2Is/KDHSJW+KmS89XE7mP8/LnABcU3qzw5tP3SpUtGjOdvL0SvYVGmwpNzv/kLTyM7rfB0OtNdR2xHlwErUVxcaiqH+XysIJX0ES8DPCYOHT2LVgOz8MGso5i48S46h1ajfUi5SbfRO9Fn+iF0Da1AzwFzTTvLeTq5IBGHr3NKCFbF8Vhi26p9XjxvuA/bCk9e0OQFrNEzE9F91A4jPN3D2Sk73dWdK9fGoa6uzixSaKs7tQ8LIV4mJDuFEEKIFxgroQrLD6DVgDSMX3cTkzfdNaKz44hyr+h83+l0T4i6hbDID9FlZAnavzcX06bPMEMqWWnEVdaLiorMSquUV6zyYAeGFRuUW+wQ8e9IdL7a2P2NQpLDbjl368cff2zEOBeuSs/cht5D16BLSAG6jeIq1BUwc3a6ZSd/dm7n6r9dQnIxaHQUDh06ZMQpF7ywwlP7mniRYZsYNjPVTBMyfv1t9J151Cs6O46sxNuT96HX+Hq0ej/KtLWrV68204OUlJSYi0k8bqzo5HMJ8UdghSdl5ZdffonDR4+j55A16B5a7LTVjcPZPbKT4rMCPcNL0W/UKnNxlAvXse1mxb/abSHEy4RkpxBCCPECQ/GUurUSrQakY8L6jzAj/pMmQ9f7zz6GqdH3PUPXh29H735TEBERgRUrViA+Pt4MqaysrDQrrNr549hxUZWdaA7bQWZFEIUNpSeHM547dw7VNbUYO2UpegZnoFtoqUd4hle6ZKdHePZ0bmf1ULeRBRg1eb2Zv5DPwefS/IXiRYb75Weff403nHZ3YMRJ9J9zAl3Caryys/uYXXhnyn50CN6BfsM8F5USEhLMYm8HDx40c9VS7PtfSHJHiOcJ21tOJfLJJ5+gvn43ugyJ9anutLLTU91Zjs6DE7EuKgbl5eVmvk+KUlvdqf1XCPEyINkphBBCvKCwU7F8Qx5aD9qCUcsvYWHGt3hzwm4f0Tlo7klMjbmPdybW4/W+CXi3fxjmzZtnhq2zmpOLZHDYOiXV/fv3zVA2Sk4NWRdPAvdBdpLdC11wDs6jR48iNSMHvUekeIWnEZx+wpMdaUrPLsE5mBwRbeYApQhix5n7ofZB8aLB/ZH7fWbBAXQJrTDzdHYNr/WKzg4jKtDbaYe7j6lFq74bMG3aNDN8PScnB/X19UYMcXoQXiSg7OQFJXd4PLkFqBDPA+5r/L/PdpzzdEdGF6J76A6nnfZUd3pl51jKzwr0cH7X7t152JyaZirz7dydukglhHhZkOwUQgghXkDYmSivPYb2w7Ixds11rMr7GX2mHfRKTg5hHzT3BGbEf4x3JtWj/fsrMHb8FLMQ0Zo1a8y8cRUVFTh58qQZgsxqDlZz2kojdVbEk8J9hWKGooaVQe4V2wuKytA9KAbdQsvM0HX/Ck9WDHmqPMvRcWgG1m1K91YYs8KTElX7o3hRcO/rU5YWmwXf3mO7O6LCKzs7h1Y5tzttbnCJd67OmJgYc2GJc9teu3bNWz1/9eo17NpVh7T0TKyNycXCyM1G8nO/59/g3xLiecB9m20t97+vv/4aR4+fdtrkdHMxyld28qvz8+gydB6WgQWLV2Lnzp1mv2a1suafFUK8LEh2CiGEEC8g7JS8NXIzghadRVItEL7qms8cnazwpOgcOOco/tQnDuPGTTaic+XKlaaik/PGcY5FVtFxZW1Wc7hFp+SSeBrcEojCk6LywYMHpmI4NiENbQcmoUdYuRGe7gpPu/gFq4d6hpfh7aD1qKmpMRWerC7SKtXieWP3ZbaF/uG+SJnDNrPNgCS8P+Mweozd5RWdTPfRO9E1rAqv9UtBh97h6Bu8EANDV+GDsHXoF74J/cduRs/hifivt6Pwp/cT8PoHm9F6YBY6Dk7E4ROXzLFDEcohwaqSE88T7mvcv9nuskp/8IRkp80uMxX4XtHplZ3Ovj6qAMFjF5jzifPnzxuJby9QCSHEi45kpxBCCPGCwQ5JbGoFuoeXIzL3R0yNeYAeY3Y2is7x9WYxonFrb6Dd0GwMDJ7mFZ2co5PzxnHYGSs6OVyYHRtV0IlnASURO8usDuKUCKzwpLicPj8aHYdtQffQUh/haWWnHc7eeXg2BoyKRFl5hRkOb+eBk/QRvyfct9j+cT9je/jgwUOcPnsRB4+eR/3+08gr3otNKU57G1uOOSsL0Cd4BToMLzKrrXcaVeUVnVyYqLvTFrcLKkLrIXloM3Q72g4rQtvhO5wUo93wEm86jao0j+cCRx2HpKKkYo9ZoZ2LvbgvQEn0i+cJjwOeD7BKMyqxyKzMzhXYfWTnWMrPCvQKL8Vbg5eaeb9ZtcypcDg9Ay8KqL0WQrzoSHYKIYQQLxDsQNy49QDtBqVgRtx9TIt9gC6hVV7R2S28BqNXX0P4qivoMrIYb/Ufb0Qn54zjSsBccZ2dEg4T5nBjOz8nO9TqnIhnAfcjSiOKmu+//950gM+cOYOomAx0HZaC7q4KT6/s9ArPcnQLyUPw+NVmld+PPvrIdLqt8BTiWXPs5EUMm5qJHkOj0G5ADP6nTzxe65eMNwZuQavB24y0NOJyWAHaBjVKS8rKtybt9anq5BD2Ds7XdsGl3rQ3KfOGQ94HRJzAlJgH6OocC396ayk2xW9BdXW1WbyIwpPHjHsaByGeF7b95iJxZ89dRqdhmejltNemmtMrO52McW4bXY6OgxOwaVM0du3aZS6gss23o0SEEOJFRrJTCCGEeEGwnZChk1LRf9ZhLMv+s5kbzorOTiMrELLsIoYvOov2w/LR7YMIzJ0718zRmZGRgbKyMiM6KZA4J5cVSKocEs8a7qvcr9jpZYUah7RfunQJSam5aD8ozQxpbyI7nc40K4h6hZej09AMrFobY1Zo52PZgab40b4qnjXcpwrLDzv7ZQLaDCv0VF66ZKU7bmnJUHi6ZWd7px32/dk3rAINWnzeVON3C3P29cELERcXh82bN2P79u3Ys8dT3ckpHOxq7drnxfPGtt0U7oMnZpg22V92vsmfx1SgW0gups6YZ4ayc55mexFVF6eEEC86kp1CCCHEC8K3336HoePWo/OIIszb/CWGzDvlFZ3MO5P3YsTSi2gzeAu69JliVgHm0HUuRsSqIYojVnRSdLIzomHr4vfGLTw5BxwXsVi9KRtdgvNMdSerOX2EJ39umL+z19BVZr+9ePEiPv74Y/McrELWPiueFfYCEtvDD6/eQtt+G0z1phGbRlw+Wl4+TTjEffiSi5iT/CXenbIPf3pzOSIjI7F+/XokJSWZqntOL8I2msPY7Xy12t/F84b7HPc9zh07e3m2qbj3yM5G4flmw1D2HqOKMHriXOTn55spS9jOc95m7btCiBcdyU4hhBDiBYCdhu3FdXijfwYGzzuB2UmfmSHr7qrOIfNP493Je/BG7+mYNWsWFi9ebFYB5hydXIyIQ8zYiebwNHVExPOA+xilOquIKSu56j8Xsug6cDW6h3kWvjAVnS7hyU41hWeX4VuweMVGU41s55fl82h4pHhWcP+0Q3Z5EWh7cS06DUnwE56B5eXThKIzaPEFzE7+AkGLzqHVgM0YNynCyM5NmzYhMzPTLMzFY4Nin5XMdl9XOy2eN7bd5nGxOroIPcNKnba5IqDs7BlWgpAxi7F161YcOXLEVCVTkurClBDiRUeyUwghhHgBYIVc8NQUdB5VjqmxDzFw7qmGjjRlZ7lZoGjI/FNoOyQbwWHTsGDBAjN8PSsrC7t378bVq1dNxYXthGhopHheWKFEecOhuVyAZUPcNnQJyUfP0RzOXuEnO1nhWYEeYcUYNHqdkUCc8/PevXuqdhPPHEodVnZy3+RcmWWVu9BlaBzaDtuBdsNLnXDIutPW+sclMx8VztHJoevTnHb7van70WpgGkaPn4MlS5YgOjratNEVFRU4ffq0+fu8KGCnGNF+Lv4ouO9x6pCC0n3oPqrIIztdQ9kpOzmUvdfoMvQNXmRGkHAaBrbvPJY07YgQ4kVHslMIIYT4g2Gn49DRs04nOQsfzDqKSZvuoXNotbcz3W10Ld6bdghdwyrRc8ACIzpXr15t5oGzw4A/++wzI4o0B5z4I7DCkx1grtLOeQlDpiahe2ixWYXdPZzdrPprOtHOvj08C2uj4lBfX2/24y+++MKIIO3D4lnBfYkXgDj0lvMNsoq4uKQCnQesRJsh+WgbVIL2RngGkJ4uqRkoFJ2D55/BzIRP0XNMDV7vswFh42aaqvuNGzciJycHtbW1OHnypJH57sVdJDrFHwn3P15YOnrsJLoG5/jIzjdt+PPoMvQctAgJCQlmX75x44Z3TnC100KIFxnJTiGEEOIPhh2GsJmp6DVuF8atv42+M496O9McHvn25H1moaLX3l2HqdNmmHk6OQfcjh07TCeaC7xY0alOtPij4H7H/Y+dYA5J37P3AHoMWYseoSUN1Z0e4emRnRSfniGSoVOjUVBQYFaqZtUQq5NV3SmeJdwvrfCkUL9y5Qrq6/dg4uxYtOqXhHZBXIHdLlDktL1PIDzZNg9deA7T4z9Bz7E16DZwmZleZNGiRV7RuXfvXjN0naLTLkikNlq8KPAC1a3bd9B5WJbTHjcuUuQjO53bO/Wdj9jYWFRWVppjh8cQq6UlO4UQLzKSnUIIIcQfCDu9n33+NV7vn4aBEScwYM4Jn6rO7mN24p0p+9EhpBh9hkw1FUPx8fFmZd99+/aZlddZSceKOnai2fngc7ojxPOC+xs70JRKnJtwW04BeoSk+1R3Wtlpqzs7DYrDuqhNKC8vN6v9uufu1P4rngXcj6zw5IUhzi176dIlHDhwAOOmr0arAVvNKu2eCk+n7X2M8PTM0XkeESlfoc+0g/jv3ssxe/ZsLFy4EGvXrkVGRoapgqPovHv3rln1WovGiRcN7o+ffPIpOg5JNRefeEGK7TIlpw1lZ5cPFpu5Z8vKysxxw7nB2cbz8UII8aIi2SmEEEL8QbDTSzmZWXAAXUMrMHDuSXQNr/V2qDlEsveE3eg+phavv7cB06ZNN/N0ZmdnY9euXTh37pyZp9NWdbIjz4o4G0ondkasABXiecD9jfsih+yy6nj6wmT0CCv1zt3plZ1jKT8r0CO0CB3emmKmZeBiRayC+/HHH81zqHJIPCus8OSFIc6byX2T0y3s2bMXo2fEofXgPLQL4hyeASo8XaLTDl2P2PwVgpdewBv9EzE4aIwRnWyfk5OTjRSyQ9c55FerV4sXER4PPIfoNCzdXHjyDGV34nzvDmUn558tLS31Tpsj2SmEeNGR7BRCCCH+AKzoZAd4ytJi9B5fh/emHjQdadupZoUnh69z5eAeA+aZuTpZXVFUVGSG/HI4GSssOOz36rUbqKvfi5iEDCxZm4Nho2aYSiJKUP4NdkrU0RbPAyuVuNIv50jcWX8AXYJzvEPZPaKzIc7PXACjw6AkRMydbyT+tWvXvAtgqDMtniW23WW7yHaT7Ser448ePYrJc2LxxoAtaBdU0qzwtKJzetzHeH/6QbzRPwkDhoZ5h66np6d7RScrOin82Q5LdIoXEbavn3/+BaIz9zg5gE1Z+5GQewzJ+aeQkHceUVtOIbHwQ8xenm7m7OQwdl4gUGWnEOJlQLJTCCGE+J2wHWt2CPzDzi8r1zj3VZv+iXh/xiH0GLvLKzoZDmHvElaF1/ttRqf3JuO9YQvQb+RKvBeyGv3HxKN3cCw6D4vHf72zAX96PxGv909Hm0HZzvMlYO/h82boJCvkKI1Y5akqOfG84L5vhRLn4ewTGo+e4awSaio7WTnUbUQexk+PRElJiRn6y6HslKXcb4V4lth2me0v9zG2k7dv38aJEyewZHUy/m/vNWgztNAjPN3D2p02eciCs5gR/yl6javFn95Zi/5Dwo3o3LBhA7Zt24aamhrzPBSdVthzH5boFC8iPA64j7K95cJdrKzn/MmsTmYlJ/drXmCl6OT+zdXYeT/e307LIIQQLyqSnUIIIcQzhJ1adgDYwWVV282bH+HYyfM4ePQ8iisPIrtoP9YlVmDhuiLMjsxF35Er0WF4EfpMP4ROo6q8opNzwlF2thtebFYMbjOsAG2DitDW+Zm3cX45m06jKtFv1hFTHdp2YCq2l9SbebXYgWenhMPc2aGR7BTPC3sccL+j9FkdU2BWZue8nT6ycyx/5rDJUrw/YpWZouHIkSO4f/++GWpMYSpRJH4P2B6ynbb7KIecnz59GmXlVeaCEttdt/BkReeMhE/Re9xOdHhvPsaOHecjOimCOLWIW3TyGND+K15UuG/aKUcePnyIDz/80MxjW11dbS48cRHE4uJiM5/y7t27zf7N+/H+OqcQQrzoSHYKIYQQz4h9B0/hnRGb0GtEElr1i8X/9Ik31ZatBm1F68G5aD0kF22G5qPtsEIzNN0rK0dW4K1Je72ik+kcSvFZhnbBpd54hlU2hkMqB849hSkxD9A9vBKvvROJDTGpqKqqwv79+83cWlwkhh1vu+CLEM8LdqQpkzjc8cChk+g8PBu9RnNOuEqX7HTSMEdch4FxppqIQ9lv3Lhh5jpU9ZD4PXFLeQocSnY7j2f49Fi0HpRr5vHsElptKjp7jqlBhz4LMH36dCxdutQrOimCKIpse2tFpxAvMnb/5/kB91vOxclpHXgMcLE4yk1W2vNcgtOLcJ5b3k9TMwghXgYkO4UQQohnBKscyneeRPuBcaYS08hMl6xslJZNxSWrM92yk6LT92ffsPJz2KLzmBr70IjON4csRVxcHFJSUpCfn4/6+nrTQeGqw6yQUxWG+CPgPsfqTC6C0X3IBvTiUHY/2elZ8bcC3UJysGBxpFkEgx1tPobTMGgou/g9sVKewufbb781lWucD3nfvn14O3gN2g7bgW7hteg34zBefy8K4ydMxLJly4zo5BydHLpuK96sCOLzSQSJlwG20RSX3G857QgvMlF68tyB8p7h95xyx+7frAalJNU+LoR4kZHsFEIIIZ4BtsPMjsDlKzfRtu96T/UmxeZjxOXThgsXDV9yAbOTv8B7U/fjtffWITIyEuvWrUNiYqKZc4tD0Ti3FofSs7KOnRN1TMTzhvscO9IU7uMi0tEzvKxZ2dljVCFGT5yL7du349SpU96h7KogEr837vabFZ6UO7xYtL2wBJ0HrkU7py1vOzAdweEzsHz5cjOPYUZGhhHz7jk6+Xjtr+JlwwpPXpjiPsyLTAynwGH4Pc8jeEGA95PoFEK8DEh2CiGEEM8A21nmghesjCgu34Veo7L8hOdvl56s6AxadN6IzqBF59BqYBrGT5mPFStWeFcD5jD2s2fPmiFnrNTQ3HHij4L7HPc9dpQXrs1zyc5G4flmw7ydPUKLMGLsAmzdutXM28k5Z1lpJ1Evnge2DafwodxhdRuHptfV7cbo6dGYNX+NuZjExVvy8vJQW1sbcDEiVdCLlxHu/9x32V4z3Jfd4W38Pe+n9lgI8TIg2SmEEEI8I9gZsHNfUTSWV+5C50HRaDusyCx0YRa7sCv7uhNAagYK5+gcuvAcpsd/gvemHUDrgekIHzcHixcvNqKTlUZlZWU4efKk6YCzQokdd3ZU1DkRfxTsIFMEZebuNEKzF+fobJCdb3plZyV6hZfgg+AFSEtLw969e80ccVxgi/uwBJJ4Hljhw+o1CnoO3WWFPNtULkBUUVFhLiYdOnTITLWgxYiEEEKIFxPJTiGEEOIZwU4yq9DYSebwcXaSi0sr8FbQOrQZnId2QSVob4Qn5+ksfyrhSdHJ1YBnJn6GnmNr8HqfjQgbN9u7GjBXseYKqsePH8edO3fUARcvDNz/eFwcPHIG3Ubke2Rnw1B2j+x0Qtk5ugw9B8411XM7d+7EpUuXzLydvIAg2SmeJ9xneZHIVurz4hUXzeLQdu6Xt27dMpWfdlVqtbNCCCHEi4VkpxBCCPEMcQtPVgVdvXoV9bv3YsKcjXjj/Ti0DSpuqPB8cuFpFiNqqOjsObYWnd6fg1mzZmHhwoWIiooyopOrAXPoOkUnh/6qAy5eJCiOzp2/hC7B2/xkZ6VXdnJF9u4fRCA2NhaVlZVmf+aiL5xDTrJTPG/YdrLCk8KTUpNVxnaxFgpQzmVIEa/KeSGEEOLFQ7JTCCGEeIaw00vJSOHJzjA7xqwEOnjwIKbPi0KrQdvMKu3thweQnQGEp5mjc/F5RKR8hT7TDuJ/3lyBMWPGYMGCBVizZo0Z8suKTq4GTNHJTjjlkDrg4kWC++PVa9fROSirUXY6oeS06T2mHL2GLEd0dDTKy8vNIkWsqOOFAx5TQjxv7JB2XjzifmgXbGEbqylChBBCiBcXyU4hhBDiGWOFJzvIXE2awoYLXXAewvGzYz3CM6gkcIWnS3TaoesRm79C8NILeKN/EgYMDTMVnatXr0ZSUhJKSkq8Q9ftyuvsnKsSTrxI8Hi4fuMjdBm+FVyMyCM8bcpNVWfvMWXoOXiJkZ127lmuyC7ZKf5I2J6zPeU+SLnJ8HveJtEphBBCvJhIdgohhBC/A7aDzApProj++eefm3nejh07hvEz1uKN/hmPFJ5WdE6P+wR9ph80orP/kFDv0HVWdLpFJ+foZLWRRKd4EaEcuv/gY8RvrUdu9YfIKj2PlO0nkJx/Aqs3HzJJ2XEFc5ZvNnN2chj76dOnJTvFCwPbdBshhBBCvNhIdgohhBC/I1Z4ct43zqVJMcmKtbXRWXjjvQ1oM7TAIzxdw9o7hHDV9bOYEf8peo2rxf+8vRL9h4R7RefWrVvNisBWdNo5OjWkUryo8Djg/Iacx5arrHM164KCAlOdvGnTJrPIFr8mJCQgJyfHrHzNxWA4DQQlvmSnEEIIIYR4UiQ7hRBCiN8Zih6KSApJVmDeu3cPZ86cQVl5JfqNXI7WXKndJTyHzG8UnR3em4uxY8f6iM76+nrz+Nu3b2sxIvFSwH2T0p/7Pxcdunz5Mg4cOGCkPSuUd+zYgeLiYjNXJxfbcs9By/1b1cpCCCGEEOJJkewUQgghngOUPRSSXNTCzuNJ4cN5PMfNikWrgdlmWHuX0GojOnuMqUaH9+Zj2rRpWLJkiY/o5IJHFEYUnayWU9WbeNHh/k/hzypNCs9PP/0UH330kTkGLly4YOTm+fPnTTUnKz95fFB0/vjjj2ZqBol8IYQQQgjxpEh2CiGEEM8JKzwpKCl8Pv74Y1y9ehX79+9HzwERaDusCN3Ca9F3xiG8/t56jBs/AUuXLvXO0ckquLNnzxoRRNHJofEaui5eFuyUDhSenMeWMvOzzz4zQ9V5LDCUoBzqzuODc3Xy4oCqloUQQgghxNMg2SmEEEI8R2yFG4Xn999/b0QPKzWLS8rxVkiMmcOzzcA0BIdNw/Lly7Fx40YjOjnEl4sb2Tk67WJEkkDiZcHKfu63lJjch1m5yfzlL38x4feUnDw+KEYlOoUQQgghxNMi2SmEEEI8Z6z0ofCh3GF125UrV1BfvweT5kRj1tzVZkXq5ORk5Obmorq62ixGZOfopAiiMNU8huJlhPs/910eAwz3ZXd4G3/PSHQKIYQQQoinRbJTCCGE+AOwwodVbqxk+/LLL3Hjxg2cOnXKzONZWVlphq0fPHjQzGXoXnVd1W5CCCGEEEIIERjJTiGEEOIPhNKS1Wycf/Obb74xCw/dvHnTLNTC4e23bt0y8xhyDkOJTiGEEEIIIYR4NJKdQgghxB8M5SUrPDmHIefxZJUn5/JkuIgLF3OxQ9clOoUQQgghhBCieSQ7hRBCiBcAO6Sd1Zus8rQLtvB7zu1J0cn7CCGEEEIIIYRoHslOIYQQ4gWBVZsUmu5FW+xiLaroFEIIIYQQQojHI9kphBBCvGBQbNoIIYQQQgghhHhyJDuFEEIIIYQQQgghhBAtAslOIYQQQgghhBBCCCFEi0CyUwghhBBCCCGEEEII0SKQ7BRCCCGEEEIIIYQQQrQIJDuFEEIIIYQQQgghhBAtAslOIYQQQgghhBBCCCFEi0CyUwghhBBCCCGEEEII0SKQ7BRCCCGEEEIIIYQQQrQIJDuFEEIIIYQQQgghhBAtAslOIVoQ//nPf5QWGiGEEEIIIYQQQjyeV1Z2BpIJSsvIqwbf80effI1j1x5g55mbJrWnb5jUnLruyUnfVD+DVD1pTnhS+RtT8ZIk0Gv3j9kmAbaVe/seunwXF29/ir/97W/4+9//jl9++QX//Oc/8e9//7vhk3918D/GlZaTV5VA20JpOXlVCbQtlJaRV5lA20NpGXmVCbQ9lJYR0cgrJTv//s9/4vwnX6D6+m0UXbqBwovXUdCQ7ReuNZv8J0jeEyb3cTnvm5xfmW0vQQK9bne828G1ffy3J7d92Yc3cfTOQ3zzl7+8UlKIjdn+S3cxO6MOYTHlCI+rNAmLrfAm1MnohGqMjm9MeEPC4qp8Ehrrm1GxlQEzMqYxI6IrfBLSkOBN5Y3Z6MnwZhIUKBt8M+wljPf1N7wn//ft3kYhDRkR7clIZxsy/H5O5i5UHvsQ33zzDf785z8b+cn9u6X/M/vlH//ElZufYe+xm6jedwVVez/0pmLPZd/sbj7lzaU+cMoelzp3LqH0t2TXJZS8JOFrDfgeXOH2MNvFb5t5t6+z3XcduobTF+/hu+9frfaa/PLLP3Hr1hc4dfoO6uo+xC5nm9rs3HnRpLa51AZOTXOpCZzqR6X6UbmAqidN1UuUAK+f79Xnvbu2kc/2bNjWhw5dx7nzd/HVV9/jf//3f7379L/+9a9XotPxD2e/vn3zE5w6dg2H913CoT3ONmnIwd0XfHIgYM7jQH3z2f+o1DWffU+SXf45FzB7X7I0fQ/Oewvw/n22mWu7crsfO/ghrl+5jx9ewbaa/MM5B7l/6xNcPHUVpw5dxMmDF7w5ceC8X87hxP7GHP812dc0x5rk7KOz98ly9CVOoPfTdFt4tpfP9mzYzhdOXsWt6/fw048/vXL7Nf8fffft97h9/Q7OHb+As8fOe3L0PM4cOWdy+sjZJ8/hx+fU4TPN59Czy4lDpz05+BLGed2B3lPANGw79za+cPIirl++iU8ffmbOQbhfv0rnII/jlZCdP/39FyPFxpbVY2hBDYYV1mL4jl0IKtqJYe4U7sRQvwzh16JdGLajzpuhfhnil8HO/ZlBATIwQAYwha4U7EL/gp34YHvT9HtE+gaK8zxPlECPfY5xvw/zXp3XZMNt0d/5HAa4MtDJoIYM3F6DgfnVzudXg5Sj5/Dg889bvBS6/dm3mJ2xy0jOcYk1mJd3DJEVl7Cy8jJWV18xWcVU+WZlM4msbJoVlR82yfKKwFkWKOWeLHVlceklbxYFSokrpZexuMx5zG8Mn+dp8iyeg+Hjltg4751xbwuzfVzby25Lz3a+jKlZBxCeUIORMRUYnbgTU9LqUX/8Ij799FOzf/OfWUv8R8b3c/j0bSyJdt7z8lJMXVGGaZHlztfGTDEp82S5J5NdmRpZ4dzPkyl+mbzcN5OWlZtMtFnaNBPcWcKUmYw3KcW4xUwJxi1qzNjmstCTMQFT+oQJ9NjnF/MeXO9p3CLn/TPOdhhvYreNZzsxE5d6MmFJifP7Ykx1Ptuc0pP4+JOW315TchaXnMH8BcWYO2+Hybz5JU6KfTJ3XnPhfUubZO48mxKfzIkoDpjZc5rPrNlNM3PWDidFJjNmMoXNZvqM5lL0BAn0uOcb3/fjeb+e99+YWbM9mT2nMRFzua0KTeY63ycl78bt2x+bffqvf/2r6UiznW6J8Fg9f+ojJEdXIGpZATauKHRShA3LCwMmalnTbFyxw/kdH9M0Uct8s35poU/WLWEKms3axb5ZwyzajtXMQk9WeZPfkLzGLHhUnPs+UQI99jnG+37ynffLeN43t4PN2sWN8d1++c7v87BxZSF2VhzHp5989kpccOX7On34ErbEF2PzujyT9A0FSIvajrT125EaKOvyveF90qJ4f1fWN8b/sZudx2xe2zQp7qxh8gImeXUuklYxOY9NIrPSk4QAsb97VAI97nnF/Tp831vDNnC2BbdHY1zbaq0nZnutzsHW2GIcP3D2lSkkuHbpBmqKdiErdhu2xueaZCfkITu+MfZ2b+Iasy0h3yfux/nHPCbWky2xOYET45usmG2NifYkc1P2I5OxaasnG7cifeMWTzY0DX//uAR63HOL87rN63DeS6D3mRntid0u7m3FbZkZ03ifosxinD95Ad999x1+/vnnFttXfBpavOw8ev8ThBXvwtDt1RhZuhszD53HknMfeXL+VmPO3cLiAFnkl4V+WdBM5gfIvACZy5z1TUQzmeNk1umbmHnqRsDMCJBZZz7CbOdxTxreP3BuPkEa7x/ouZ8kfI829n3b7eLebt5t6vzd8btPYnjJbgzaXoOg4nqMrjyIolPnW6wU4vuwojM8tgKzth5E3IH7yLn0F2Rf+MGbrcx532wJkCwnGWe+Q9qpb7xJPfV105z8GpnnfvBJxtnvTdIbksbnsTn9HVKbyeZT3yKlIZvNbd/7ZHMLi/e9nWlMWkPSmYbt592eZ7410nNS2l6MiqnE2JR6TN16FFMz9uDChQv4+OOP8ZeGSuaW1JG++/G3RnJOXlaC6SsrsCL5CGJyLyE27zLi86+YxJl86EleY2L9k/uh81jfROcEziZmW9NsdCfb5rLJBne2NibKlXVZF7E284JvMjxZEyDrsi5h/ZbLTxze/1kk0HM/SaIY1/u128OzjTzbzbs9nW28MfsSFscdxLSVVUZ6TnW+zt+0C7V7z7XY9vq7737C+g07ETG3CEuWVmLjpoNIz7iIjEzf8LZASUsPnNQ0dy6YbE4NnJTNj05ySqCcN0lK9k1C4lnEJ5xpkrj4wElIPIfEpPNPHN7/WSTQcz9p7Hv1bIPGbeK/3bhtk1POIXJVPRYuqsTsOUXO51yCJcurcPjwRTx8+NB0pNmJbmmdjQd3vzCSc/3S7YheWYzs5H0ozTmNstyzKM871yRluc3lrPO4pinZ1nyKs5kzzWbH1kenaIs7p00KMk9ie8YJn+SnN5+CzFMozDr9xOH9n0UCPfeTxr5Xxr09/Lcft/GOraeRFlOPTZHFRnpujCxFwsZqHD5wtkVfcL178yEyo4uQsjYXWdE7UJG9D/tKT2NP8UnsLz3jfN9MSk57szdQij3ZU3yqaXY0zW6bIuZkwNTbFDafXduPY2f+MSdHTWqZvObD+9cVnHji8P7PIoGe+0lTX8g0vmff7eTZjo3b1TnOU2qQHlWI5DW5yNjktF3x5Th5tOWeg5B9NYe8krMgrRgHKo/gSO0JHNt5yidHd570TW3g8LFHah6Rak8OVx8PnCrfHKo61jSVzWd/2WGTfWWHPCl9dA5WHH3iHCg/8swS6PmfOJVHG99zwzbxbjPXtuR2rivai+2bd3gFaG7KDuytPYgHDx7g66+/xk8//YR//OMfLWqffhpatOzkh2pF57CCGkzafQLrb36GtK/+ZpJq8+XfsDlAUvySHCBJThI+/xmxn/6I2E/+YhITINFOEnhfV+K/+CvimM8bE+uXmM9+NoluyKZPf/IJb4v54m+/Mc7fCRT+/V+TQM8V8O8GTqxf4hoS3xD3NoxztjvlcljNYQwqqMWwkj0Ys/s0Juw8hhPnzrVIKXT4yn2v6BwTX4U5OUeQdOQT7PsGqP/iP6j7HCa7mM98szNAaj9tmpoAqf4kcKr887EnlY9IhTsPfVPupPT+vx+bio//0yTlD58k//ZJ2QPfBH4M43ltgcL3Uem8d3d83mNDvNuA93H9bLaZcxvDbVp2719YX3cL41LqzHD2sSm7sWznHaysv4+MykNGeH722Wf48ccfTeVQS/gHxvdgRScrOhfF7EVq6S2UH/kLig98h9JDP5iUmHyPkoONKfbPge+xI0CK9n+P7Xu+QV79Vz7JZep8U7jvexS4sn3Pdw35Fvnu7G5MXv03znM1Jqfu68bs4lfnb+/+7neK8/d/VQI919Ml39kuNmYb7W1MwV7P9uP2zN/9tZHLEVH1mLi0FFNXVWNB/GEsituHU6dbXnvNfbrA6XxRdLJyk6IzN+8Gdjn7wU5nf+BXm527mqZ2Z+DU1Np87U15xWcoKw+c0rLPnN9/garqr31SWfWVSUVloHzpPOZL5/Ff+KS07HOflJR+7tzvq98pntfw6xLo+Z48/tvDs624zXy3IbdvYtIZrIjcZapt58wtwYrV+xC1sR5nzpzBrVu3jPDk0LKW0oHme7CikxWdaTG7UFd6FecOf43TB77AmYNf+uT0gebyBU7tD5yT+77A8T2f4Uj9xzhS55vDu5iH3pzY+7lPju3+LGCO1jfmSN2nDfnExPOcnhzaya+f+Ny/pcR3m3zubGPf2G3I7Uzxmbi+GuuWbMemVaXY7LTTybF1OHP6bIs8t+Z+nZNUbqr/WM25I30XTtVfw0enP8dHpzy5GSgnPzO5ESgnfHP9xKe4euxjfHjkAS4fvu/Joaa55OTa8U9x7RjzicnVox/75ApzxDcfHn7oE+/zHfTk8qEHTR7TEtK4XbidPLHbzRNnOzrb8/z+29hVcAzb4sqRvCYPmTGl2J62G9sSq1psIcFHV257KinjcpC/uQh7yw7i1rl7ePDhp3hwuTH3L3/im0uBc+/Sx7h3sWlunb2Hj87c9eT0Xdw8fadJeJ+75x/65M65B7h97r5vzvrm1pl7PvE+5ylP/O/fYuJsC24fn5x/4LsNLzzEyfqzKN1SaSpuKTvz08qwY2sNDu47hJs3b+Lzzz83VZ6sXH4VadGyk0PXrejksPWJ9ccRe/9b1PwbqHLasApXygOkzC+lAVLil+IA2eGXIhtnn3OnMEAKmsn2RyTvl8cn0OPy/fOPx8f/ec3tgZ7rEeHfDvQebdzbw3+bFTl/c8XVjzGy6qCRnUFlezHLaWgXX/sSUXuPtTgpxNdvqzoZys4p6XuQcOghjvwF2P2lJ/XMF42pC5BdjJWiDdnZTGopRf1iBGigNEi7QPEXgm7hF0gO/nH5j/P6PPEXqu74vx/7eP+fA4W/D7j9nFQ5zx21+w5GJ9ZiREwFxqftw/Jdd7Hb+YwTnJPk6upqXL582VyJ5r7dEk7Kjp+76xWdHLa+OG4/sqru48Rt4OiN/+DYTZgctbnRmCP+uQ4cDpBD15rmIHO1aQ7YXLH5j8l+/3z4H+xrLpf/g72Pye6L/3psAj1uz6V/Pyb/ahL/5/Xcj8/15OHfDvheG8LtYdKwfex2M9vuw38hecctzFq7CxOWOp/z6lqszbmO+IrPsHn74RbXXp9yTsIpOjlsfeGiMkTHHEZB4R2cO/9vXHS2/YWLjTl/oWl4v0A5ey5wzpwNnNNnAufU6UD5l8nJU83nxMlH5/iJf+LY8X80n2P/CPCYQPnnY+P/3J7bAz1X8+HfD/Q+/WO3DXP6jOfr8RP/QFr6JSxeUuWZKsDJpqQLSM+9h8TU/Th48Aju3LmDH374wVRWtIR54S6du+0VnRy2nh67C3srnQ7V7X/j4xv/dPIvnzy83lz+iQfXAuf+1cC5d6X53P3w0blz2Z1/+OT2pcfnowt/f2wCPe7WRf/88th8dME3TZ/j8eHf9n+fgXL3w6bh9qwu/BCxq8tNZeemVWXYnnsZFWX3UVxwsEVecD2+/3yD6Mw3Q8+L0nbiwoHb+PnTf+LHh//EXx78E39ukn/gz/c9+SFQ7vnme3fuNs137tz5JWC+tbn96Hxz6+9PlK9u/u2x+eYWn883X3/096fKVzf/t0kC3e9x4Wv+9nZjvnF977MNGraT3Wb8+vm1n3Cg7ByyNhWb4exZceWor7yKvbUfodrpP7bE/boit9pUdXLI+o7MMuyvOIwvb3+D//3mF/z9q394879f/dKYLwPnb8wXj8nnv+Cvn/+9+XzWmJ9N/rcxnzafn0z+hh8/eXz+8vFf8eeHPz8yvM9Pzn3d+fFj//z1sfnLQ994bg/0XI/Oz857c4fvNdB2aNxedvv9HddP3kJ5drV3SHvR1hqcPHIHtWUHcOTIEVy7ds1UePKi66sy57KbFis7uRiRrepkOD9neNUBRH30GWqdtsvIyn825B9AiV+K/bLDL0UBUuiXggDZHiD+ApHJa0huM8n5rfnlt8e8jgC3u9PkdQe4PeDrc8Lf2e3g3iZ2G3Hb5f3yHyy/+rGRnAO21yC44iBmnLptPt8ND79DWXVNi5JCXHXdik4m1Mno+CrMyj6E9FNfYdvFP2PL+e+x5ZxvsgIk82zTZJz9zjdnvkN6M/Efsv7IYev+OeVJymNih4G7b0s++a1P/J87+eQ3JnaYvH/s7+193I/1//2vi+/ray4pTMNr4rB+JpU5/S0Sj32BGdlHvIsWzcg7jajD3+CB8z/q7i//RnxWAfbs2YOrV6/iyy+/BK9Cv8wnZXzta1L2GNnJTI0sR0TULmwu/ggnb/8Lx27+B4ev/QeHTP6NQ1c9ORgoV/6NAwGy/8Om2cdcbpq9Npca00QmXvRkd0PqLwTI+X+j7pnkX785fD2BbnfH//UHuj3w6/P8zm4L93axP3u22b+QVHQL01ZVY/ySEsxYW4c12Vdx+GMga8+XKK9sOe31n//8VyM6bRYsLMWq1XXIy7+J02f+bgTn2XP/wpmzgUOZFiiNwu2fPjl5qmlOnGw+/tLQ5tjxxhw9FjhHjj6L/OM3pfG1BP69jft12/sfPeZ7e+DX5/mde3v4h9vryNFfzHD2+QvKMWt2EeY6X6OTL+AaL2gd+DOytlTj6NGjZjhZSxhKxtee7pxzUHYynKczcX0FdhZ/iM9u/dMIzPtX/+HNvSvNJ5BkYwIJOcYjD38JmEDC0MZfHN483zQ3zj3v/L3ZeF5P4N/Z3Dz/D5/Y5715nr/zJNDjGP7Of5u449lufzeyc1NkCdYs3o6YtZXI2XIWDz4F6nd/isqKlnXBlfu1Z/g653XMBefnzEkoN5Wdf/3s3w0i8pemccm0JnGJNwo5n/gJR+ZrmwCCz+Yr5mbgfHnjfxvyN5MvmOsvTsxranL7Xxvi+bnxPXjS9Hb3Y/3i/K5RpDZuD+9tzrb77NrP2FdypmEYO+eorERdxRV8//1/cHDv9RZXSHDvo/tGdDIcwp6XUojagnp8evML/PLtP70C8md3XBLNnZ8++19PPn1EPvnfgPLRJIDgo3D0xk8aMn9+8LM3Pzz4yZP7L07+7Lwexvf2H12x92t8H+a9NHNboBgx679NXLdx21478REK00o883nG5piqzo8f/oh7d39AYW6J6S+ywvP7779vMRddn4YWKztPPfzMKzqHNIQLE43fdRQx979F0mc/IuGTPyPh4z8j3i9xARLrlxi/RAfIJr9sZB76ZkOARDWT9QES9fFfAt7emB+c+/B+jVn/4Idms+7+9z7hbe7X4P/7JnlgE/j5A8Z5jYFfe2Pcr4HbqHH7/YBxe0+j//Ya9M+vwZi957D4xrc47RzHD52Tl+jy2hYlhQ5dvmskpx3CTtHJFdgpPWdsOYA1tdewuvrqYxclimSeYEGiFWbBHP7uincBnSYLEpXztivO943xLsLD753YBXq8aVj4x2ZJOe/TGPfvHh0uBPQiJ9Br9k2gBYwWlVzAqLhqBG8sM8IzovgyNl/5N27/A/jJ2Q+isktQUFCAY8eO4e7du2Z4wsv8z+vBp996RSczyQkXH5q3sR7ZNR+jcO/XKHCyfc9X2L77S2/y3an3JC9AcuuaJofZ1TTbbHbafIHsQKn9AlubyZaawNla+2XA293hfdzZUvN5s8mq/swnvM39Ovx/3zR8Dk/8n7v5BH7d7rhfA7cTt5fZjs73i2IPeRZxWlyCBQnHEVf2Ge7+8B989df/ICGzvMW013v3XfWRnXMiijB/QQlWRNZiW841VDnbvLLqM1RUNg2HpAeK7/D0T70pLQucktLAKS4JlE+wo7j5FO1omh3Fn5oE+p079n6N9//4kSkseugNf/Z9Hb6/D5SiIud5GNdzPjpNX7N/3K+B28put6IdD7Fs+S7MnMWFjQqwcGkN0nLv48//69kPCivOori4GOfPn8dXX33lnb/zZeXzT77xik5P8rFheQGSN1Rjf/VtHNv9sTdH6wPnSN3DZuMenu7OoZ3Mg4A5WPvoHKixud8k+6sDh/cPdLs7jc/rSaD77K++Z7KvirnrDW9zvw737wLHeXzDczWfQH//0XG/Br4Hu832Vd0xc3aaRY0W5SM14SAqyu/jhz//Bz/99B9kZRS1qHNrrrpuRadN6vp8FGyuwYX9t3GtYei5b+7jw8OeeIeku9MwhNwzLP1eYw42zUV3DjB3m+SCzf7mcgfnmX3Nh/cLdLs7/s8b6D7n9902Obf3lk94G1+Hjf/vm2SfJ/b5AifQ339MvK+h4X00bMNT9TeQl1SNxJXbzGJGO7YcxNF9nzhtMvC3n/+JLVm5LWa//rfTJzhcdxRWdjIcyp6TtB17Sg+Y4dAcJm2Gn5+92xg7FN2Vm2fueOIakt4kp+7gxqnbgXPSN6xE9MmJ5kORZ3I8cLz3C/A7d3yeM8DvPblpcvXYDZ9cd/6+O/6/bxo+h+e5AifQ336CmL/f+D647bg9OT8oFyfiQkdbYnOxs/QYvvrir84+8B+cPfkA27JzzEVXTtPAhRNfdon/tLRY2Vl97bZnCHthranqtBlSUIMxtYex8MwNzD91DfNPXsM8v8z1S0SAzHHn1HXMdr66M8svs537zGJOejLzxDVP+L0rM5xMd273j7m9IdOcn58sVxtz/AWP97UGeh9+26Ih3H4znMcO2lGHfvnV+GB7DcYfuIz1X/4Hx53/Tf909oNNO/e2GCnEf7i7ztw0snN8Ui0mpdZjasZekwkpu4z4nOjcNjP7EGY4/8BtZmYfNpmx1bndyXRmyyFMyzroDX+ewfs0ZJrzuKmZBzAlQCb7J+MAJj0iE91Jb8yE35z9zzUT/eP/3h6bg4/MJCbzoLNNDyIsvhbDN5YheFM5QqIrMKfoErbeBe40/H+KKdiJrKws7N69G9evXzeTqr/Mc7F8eONTIzntEHZWdnLV9cnLShGxfhc2ZV/Apm0XsTH7PDZubcwGd7Z4EhUg67MaE7Xlgvm6jsn0zdqGrMu80BDPz2syzpmY32d4sqYhq9PPNSbNfs/bPVnl3Pb0OfuCJ9Br9o17u6xhnO23Ov0sJi6rwNiFxWYV98XJZ7DlwC+4/YNzYu7sB4k5u1pEe822ekfxaa/opOS0q6/PiSjEosXlSEw6iZTNZ5Cc0jRJznbxD+eFbMxpVzy3JSSe9kl8gm8SEnkf94JCvN3+fNr87E5s3CmTmFjP19g43tYY3v50OfmCJ9Br9o3dJkxcPMPtcNKs4D59RoH5unBJLfIqf8B3P3v2hX3H7yA1NRX79u3DvXv3TFvNyoqXletXHhjJaYews7KTK63ztsT1ldix9SSKs0+Zr0VbAuUECrOaT0FmYwqzTnq/9ywadDxA/BcVOm7i+d7Gc1te2jFvclPt97zdE972bHL0D0yg1/PouLdLvrMduK34XFyRfdWCXCM8M1OOY/+Bn/Gd01ZT/eTk1LSoC64XnH6aFZxm1fWGbF6bh/ykKtQXnUBd4XFXjqGuoDGcB9In2xuzc/tRn5jbGhYMCrxw0JEmv69xbqvJPeK6T2N4e03uYVTnNMT53nObJ97bf3MO/YEJ9HoeE7MdGrcFt3H5ln1m9faElduQuCoXpbkncPnC3/GPX/h/G0hLzmkx+zVfd6XTL6bkNKuuN4QrpXPF9F2Fe8yiN1ysyJuKwOHQ9/3lzYeyjV+9Cwa541ooyL2w0N7Sgybu37uzp+SAJ8We7C1x7u+Kvf23Z/8fmECv59Hx3w7chttTdjSs6r4VmdHbcGDXZedcwyPpv/v2FyQlpKCystIMZ+eUOi1lioYnpUXKTn6ARZdumLk6R5TuNsPXx9QcMhlZvhfDinaar2Nqj5iM3XnU+/3ohoTXHDbhwjdMOH+/6xhGNyTceUyYc1togIzyy0ibmsaMCJCQAAn+VTnsSfVLGvP6A70v323DbTakdC/6NojO/gW1GL33HKK/B8407Aurdx9sMVKI+/XO0zc8sjN5Jyan7cb0rP0m/H5sUi3C4ioRHleFMQk1JqMDJDzeN2Hx1U0SGhc4rDb0JtaTkQEywj8xnoT8plQ9u0Q/owR67kcm0PvyxGwjZ1uFRFciaEPZ/7+993CLIln/t/+89/2esHvi7jmbs7tmxYCCIgqSBRUUyTnnnIMCIoKYMYs5x1Xw6OLzq6c6dz890z2BMNTH675wenq6q3uKmp6bqi74T2KdKjvXl5+C7KsAV+RrrsSKLsjIyOBDbk6fPg3Pnj1b0vX62NhVLjvX7Wnkw9e3JHXClsRO2Li3FVbtqoeVUXWwNbkLtiZ1QXhyN/t/N1unS2UzW3dTgsbmRLZucg97TmJzYjdb3sUJQ+KNbNQTp9AJG8zs7YT1dsR2wjq/6FiiUMeioZwfPHcrdzXBD1sl0fnT9hrYun8EioY+wvSMVBfSywdCor3GLxk5OYNcdOLw9R07myB6dxvv1Rm5o5Ev2x5Rz5a1c3bv6eDg/3dF69jVxtnJiNqF63bKdPAJcXZGWcFehhSRO+xohYhImu0R/tKyRKGORUN/jjZvqVdF59r1FbBlWxOUt76Ex7LsPH7yFiQnJ0NjYyNMTU3B06dPl2xvISzzqbHLXGym7K7iw9czEpt4r87UmFouP1N2V8OhpBa2vIX9bOXg/xVw3YPxGukJzfI6EukJLWx5s8qBOKSJZP9ee9Ji9TQaSI0JBg2LFKqsNNo5aoKEnVUQvQVFZ7Hcs3MQjg5/gJdyvS4q6Qmpa+vRgUneszMnuZIPXy8+2ADFBxogL6UaDseVQi77WZLexMHZvEvx/wcbOcWMIrauyn58bSNbp1l9DV+HLTdTqJCG1FsoUEi1o46Tvy9Y1PpFXoozqNd6hiorjXSO8Hw28Pt07t+VDweiC3nPzprCIbg09RHeyX9/ys+pDYl6jXUay12WVc2Hr5ceroSqnDqozq2HiqwaKE4v58ITHyvU5DVAdQ77v46q7Dp+n8/KLAmcBEdBeb4qi60jP2/hiJGKIzVGMmlwSDbncLCo8ouyQ86gXmsPVU57lHOF+8lKyoOsREZyPu/h2dt2Gt68leoCVt/k+H1QWlrKR5goEyYu5T9OuU3Iys7yyYvwb53s/KkVZeYgfF3fA/+t7oB/l7cwWuHzynaVz0z828S/CP5pAnsZIn+vaFP5m0K5xqcEnxD81TWtGmX2UPtaVLDz9UlFO8mnMp+w4/hzcRP8paQZ/lrazB63wPc9E5Ckk53R7X0hI4WwYWoZvUjKzl9l2Ymi8+tkvJdnPXyzr8HC1ylWvmLrmvky2QpKNwNJ9fBfG/6jJ1Hic7+o852EeYYqgwXqGOvhM/b6zxJqObgeF57sXHPZeUWTnXElbZCWlgY1NTVw8uRJ/uG1VHsLYXutyM61suxEsYlSE3t1rtndyIe0/8Z7fdZzUICqRKEMtfLbTppfd1hZEVkHvyhEKNTCz3q2S/xkx7Za+NEvamwh97eoqLPlZ4Sdz5/YcXy/tVKSneGy7EwbBvZdA6bli7L0soGQaK/NsnNnVDOXmbuiWwHFJ/bsxEmLNm2ugi1ba1RwNm+KTZv1VHPCNtFsDNPYsLFKZf0GikpYt96etev8pcIWan+LiyqP4PnD9VavKYPVa3EIOzuu9ZLsLGt5CU/kOo2yc+/evXwo+9mzZ9Wh7EvxiwYlOw8lNUNGYjPs31sPydFVvJcnkhhVaSFhZwVJ/A6auEgEe41qYG9DhdjtNDHbzJQa2BMeDEo45n0tDqhzQoPnD48HRWf0lmLYvVWRnQNcdj5/LdUFlJ2h9AfX4/2n+AzsiuwszZBkZv6+GshKrOCTFqH0PJJQDkfiy1RwIiMLcRK4voG9Vg7JZMSWQEZMsQW8ryRnjx1FnIO7g0UhR9mPN+htuIfathXqfNDguUTBmRaVD/t3Ya9OWXYWDcPFCwDv5aqbk10ZEvUa6zR+JyjLqlJlJwrN2vxGLiFLD1XyXp44rL3gQInEfitqj9A0D6RKvUUpclMKDeBM4QbwHpMEKOw4ScEij+0n3xH0691DbdsKfT5I2PnDc4qSMzMhh/fqzMJ9se30tk3C7/IfpjDxsYmQl5cHIyMj8ODBgyV/Ox23CUnZiReRZTgTu53srOpgz7XAP0qa4J+lzfAv9v9/msCZvfX8neBvOrBXoRmUb5+UGvmrib8Q/NkBfyppJmiCP7OfwQG37SvU9lzA3iPy+IvZthH2/78y8Hx/J8vOUbku7GnrDRkp5FV2HtBk51c2fIkT3pj4IgnvC2kE7xVpBodVG0AhZ4Mi7DjxEv/2mRrfifMdpdxmqHURal0OOwdk2dhzdkivk2VnUj2sKz8FWVcALsufTTFFzZCSksK/QJ84cYJ/gcZhCUsxWK+PnrhilJ1yD0697FwRWQUrIqrg1x01RiJr2HNWfokw8rPCdiMo3TgoFTnVgD0PDYRXww82fL/VO99tqSL5fmsoYnf8lRxc5wc+jL0GtqDsPKbJzgOl/SHRXuNFZFZOP2g9O1F2Ym9NTXaGbaqEDRvL+b088f/IxjArGzYqVKjgRDgU69ZbMUtGCRx2TbN6jTNWrS4j4fIv5KDOAR5vKQf/v0Y+t5sV2Sl/0RgZm4Zdu3ZBcXExjI+P85l+l+rQSCzzxIlLYOzZaZWdeyNKGHg/zzIdpXwZRex2mphtKA+tKGKRYvdWBUnU6UF5541dm4tIrOvisqWI+TiMSMdbqB4znjc8r3jPzsEhTXYWFnaF1LX1SN8EKTvzdLIzPUa6lycObUeke3zqkAWlJCmLjJhEHYo2FS74JKmogDKOE01RoILizgko+VR2aVjXxWXeMWxvHqHKImE+DiOpUXns9QjbDi5j5xHPe3XhMFw8r8nO7MyKkKjXtrIzzyg7ceZuhN/Pc1+hBYuc1CNLN1VM6tFJPt7rUA9KORvwnpN8OLZDMuNzLFDrHUEZ6AVqW/MFVR4J6lg0Dsdna9tg5w7PL57z3lZNdqKrj9kdB5mZmTA4OMgn38JrECE7l3jwg8uT7PxPVQcXnCg7Ff5O8DcTn5r4xATeN1IPDq/+C/Y81MHlnAmUdmb+zzGNGkXBpMERf2LloKC2SR23HnVdfD0+JsD1UHZKvTtb4NueCUjUy87WHosUWuqyEycjQtm58kiXJjsPe5edTkQnJTkRs+S0E52Bk5yUHPQCISAXFVSZOdTx4zmUeoFy2Vkmyc5L8mdTrCw7KysrYXR0lN9IfSnLzsHRy5rsjNNk50YuOxsk2Rkhyc5fzGyvgp8Jftpm5UcERZsOFG8clHAcFHImUNLJss7Mt5t9pWJRQB+Ts/X0aOvqj9EIX1eVndWwJXUYCo4B3JAvylB2hoLEt8rOJlvZqWf9Bis48Y1GGQcnwqHA4dQURnGnIIk6ipWrfKVkUUAfk/N1FbT19MdoBNfD88llMcrO8CYobTbKzqioKCgoKICxsbEl/UUD2+rx0SnASYlQdqLgpGRn7PYSgmKI2UbDh0oT4P0iKWiJJ6EJShR2RqI2BZKCeYU6HsSwjgKxnh76eIzwdfGcyrLzEJed7+HZK6kuFBZ1htQfXC2yUx56bpad6Xuwl6PEQTM6WSkJS+xBKKMTlFxS6uFCzirwuJA0yT4JRdzlQerOQJLrmH07AgNdjjxH6+rPg9NzwdeVZSfKZJSdF3SyM+tQeUjUa5SdWG6nspMUloys5DyrrFTQC0oKnZTDnocqOslnBuUdJy6QZDni0N7AQZWDWg/Rr5PJ5aUR/fN24Lnj55q/N5Ls1A9j37MrlvdY7uvr45MU/f7770u2J74vWTay80cPstOb5AyE6Aya5NTJw8BAS8zgQpWDQDlm07nAXp68dyfKzu4JSHgOcFyuC7tau0NKClGyc7WPstMsOu1kp0F0IrLYdCI650V2UmJxsUKVX8V4DhTZibcCWCvLzstyZyCUnXgfuIqKCjh+/HhIyE6cgX3tbk124n05N8a1SrJzpyQ7fRWdlORURadOclpE55ZAS06jQAxtqOPHcyf1AP1hWzVsNsnOtOK+kGiv9bITh6ej4NwTow1jD99mlZ3+iE5KcCJWwYlYpZ4CJfO8Y5SHoY/1HKzC8yr3jN0ky07lnp0oO3fu3An5+fn8y/OdO3f4F42lLjuTbWRn/A5KdtKSE6ElJy06KbmpoElOq+ikZJ57jPIxNKCOUwPPJe/dibIzrR8Gjmmys0iWnaFybW2QnWneZadj0SnLTXvR6UZyInm8lyIl8nzDKDEpKPG4mLCWmTpODTyPmuwcggvnAP5nkp1LvV6TsjNHk50lhyo02UlITsSb6CQFJ6KTnE5F50IITko8LiasZaaOUwbFKJ5vFNDs/ekhZGd6ejr09vYK2RkqMcvOb/2UnT6JTqTYnew0Szx7dOKPkoI+QUnI+YYqF4FJeCqyE28BQMlORQqFwgVZs4+y0yw6Kdm5OEQnJQE9QAlFV1QHGGofBNSxcHTngt+/U5OdRy4be3Yue9m5nZadZtFpJzu99ujcYi86vyMknjMoKRjqGM+BKjvDUXYOQcFRTXYeLOsLiXodeNkpiU4hOxcLxnPAh+/jLQBQdm5thNImY89ORXbiNYjyRWPJy85oq+xM2kXJTlpyIpToRNzKTiE6/YE6XglJduK5p2VnKF2DOJOdznp1eurRaZCdPvbopASeb1CiUIMSi4sVa/mp45Vh5xLPP94SoMokOzMzWNsWAvXaX9nJRWcAZKdj0RlQ2UmJQg1KLC5WrOWnjlcCzzGXnYye1lMG2Rm9c4+QnaEWj7Kzzp3sdCs6VdlZHGTRSUlA11DC0YbCAEPtQ4Uqqwm98CyR7lmKsvOb7gmIf8a+YMh1IXo5yM5so+w0i05KdgZSdCLzLjopgegKSlQGEmqfJqjj4sjnQ5adOHHRmtJTkBnCsnPAk+yMtpedZtHpVHaqohOhRKcsOynRaZZ3zqAkYDApDzDUPtygOxd4/052zr8Pr4ZN+4Yg/yjA9d+luhBqshPvx7lla7VBdkbusMpOf0Qn4kx04nBrq+BcOpKzOMBQ+3CD7lzg/UrxPp6y7Cxpont2LkfZSUlOBUp0IpToRMySExGiM1AQx47nFM/z1mLIkGXn05dSXQhZ2ZkkyU5lBnWD7NzjUnQSstOR6EQsohNBUWcSdz5DCUIJSiYuFazHQxw7nkt2/rnsLJBkpzKMPfOgkJ1BEZ3eZCch73yDEoQSlExcKhiPhTpueSg7fx/yoKdFyE59Ql52fqGXnc2D8JVJdnoSnb7ITm+i0yw5F0Z0UoLRBkpUBhJqnxyq3CYU4SnLTpy46GtZdg7LdWG5yc5vCdn5pUl2mkWnv7KT6tVplJhOoKQfASUNHUEJyfmEKpMMdZx4TgjZORWiw9hp2dllkJ1m0emkVyeXnIgsOd2ITlJ26qWdYyj5FywoURlIqH06RT4ffLIiTXbmDYao7MxC2Vmu3rMTZWfULqvs9Cw6NdlJSU7EmejUJtMxY5B2jqHkXzCgJGUwoPbtBPl8yLITCcOenS062XliecjOtFhKdtKSE6EkJ+J7r85Ai05KCIY6pnMgn1suO1P7of/oMujZScrOcv6cv706VdGJ6ATn/IpOSghqUAJxqWE9LtM5wPPJ79tZwGXn+bNaz85DoSo7D2mysyKrRpOdJtHpl+zUiU6L7DQJToXAiU6zEDRCCcSlhvW4jOeAn1M892bZ+V7ITiE7TfgtOpFiWnRSstMqNO2Q5R4l/RxDSUUTlJCcT6gykccig+eGnXM+O7ssO+OWiez87UgXH8LuSXbqJefyE52UdFxoqHLKmI+Xn1dJdq5G2XkpxGVnJCE799rLTm+iU5WdsuS0yE4uOgnZuWUpiU5KSM4nVJk8wc6JLDu/21oNYSlG2XlAyE5XspMSnYhVdi4l0UnJyPmEKpMn2DnRyc6NWxoN9+wcHg092YmzrfsjOynJiQjRuViQz4MiO7cUQ3pqH5edId2zM1aSnQVm2ZlQzp8jRScSgF6d9pITQUFnEnY+QYlACUoaLnWsxymfBzyn/D0ogKr8IT5B0f/keWuXlexM8yA7KcmJ+NKr0yQ4FYItOilhGAoYj1M7D/y8yue/u3lCyE5dQlp2/sskO783yU43otOb7DSLTie9Oq1C0w6UeibJ5wpKIOqgpONCQ5WTPDYGO0eK7PzKJDtD+Z6d8yY7E2jZSYlO97KTkHwUlBz0CCUaFxNUmRn6Y+bnVpOdh3WyMybEZeeWpC5Gpyo7f9PLTkJ0UrLTY69OO9Epy06L6GRYRaY3zKIvUFDScaGhymkDP59G2XlNkZ0loSs7d++xyk6nopOSnZTkRJam6KSk42KAKisBJTvlOj0UYrLz5HFJdqbsrvYqOynRiVCiE3EjOhFKdPovOyn5txxh50IvO/f1Qd/g/0Jedma7lZ0BEJ2eZWeg7tNJyT8JShSGEsbjZecCz6ssOyvzj8HUxRCXnama7KzJa/AoOz2KTsRBr07HojMgstMs/yQoSRhKWI/ZKjtfvZbqAsrOPdHxQnaGWnyRnXrR6UR26kWnKjuLnYtO57ITZZ5J7rmCkoY6KNG4mDCUlzo+BjtPetm59xn7giHXheUkO3/wRXbKctNWdCIJVtFpkJ3xGrTQtMMk9+ygpKAtlFhczBDHoBw3P7+a7DxyDeCikJ3uZKdOdDqSnVtsZCcpMz1hEnwBgxKNiwmqzCb4OdVkZ66QnV5lp1l02slOq+hEAiU7CckXECjJuJigymwCz6ledja9hEdCdpKiE6FF52Lo1UlJv+UKOx+E7AzpYewLJDvth6/LotNv2WkWfhqUHAxFtGNm58MkOy9eELLTkex00KvTIDq9yU6LuHQLJf0kKEEYahiPWchOT1k+srPFP9mpF52k7Cx2JzutUpMCRZ5J7LnCLAt1UGLRFfUW/uQF62uo7dpgOQbdcbJzxc/rMpKdP6HszPQsO8336qRkp1l0WmSnLDZtRScSL0ELTTt0Us8OSgTaQonEpYTpePD45fO73GTnRh9kp1l0UrJzaYtOSiy6YFMAoLZrC3UMMsp5VWTnAMA1ecjvQSE7LaLTP9lplZyLR3RSYtEdvwUAars01DHI4Hll5xtB2YkTFKmy8/jNkJSd3oexOxediG+i0yg7rfLSDZTwm392huX7DbVdn+DnFc+56NmJz9mJTovs1ElOb6JTlZ0W0YmYpaUv6EWfEUoKBo+cAEBt1znqsaNE1snOKZ3szDhQvDxkZ4ZVdnoTnQHt1WkRl24wiz4NSgoGlyMBgNquc6RjF7LTU5al7Pzcpez0KjqRYueiE7GKTTMo8XRCzxWUHJShRKIjJEFJSUx/cSU/DccjHy87X/y8ljRbZWdzaMvONTl9XHauoGSnF9FJyU6D6GQER3QistCzwyz/PELJw6WI6bjYeZJkZx2sLp2ArGUgO9eosrNTlZ2r9bKTEJ2U7PTeq7PSKDu3LFbRSYlEB1CyMtBQ+7VAHJNybgnZudx7dgZWdCJW0eledhJyzy8okegdSlQGGmq/VohjwvPKzrckOxsMPTuPHb8R8rIzPcEqOynRidCi07nsDDXRScnKQEPt1xEm2dmvm429MARlZwYpO6tp2akTnf7ITo+iM0g9OikRGHgoWRloqP16hp8DLjvx/C8v2VmVU+eX7PQmOp3IzqU/dJ2SlYGG2q9n8Pj5OZbfB73s/N87ITtDVnaW6mVnQx/8YJKd/9TJTk+i0yw7SdGJFDuTnVapSYECT8YgMZ1gFoI6KHnoFVpQBgNlX17Fp+G42DGzc6bKzq5xiHshZOe89OpEGWcQmE5AiecFs/SzhRKGoYB8fOx82cnO3YXLQHYmdsIGB7LTLDop2emkV6dFdDJooekJk9jzC0oeeoGSkvMBVRYV03Ep55e9Dyg781ljLWRnMGSnVXIuRdFJScn5gCqLhvG4+Lll55ySnYPDQnZ6Ep2IU9GJUKLTP9lJSL55gJKS8wFVFlvkc6vIzsHhP1TZmZ/ftjxkZ4p32elIdCI6yamKToQQnf736jQKTgVKAAYWSkoGG6oc9uD50cvOS7p7dgrZSbAEenVSAjBwUEJyPqDKQoPnIJOdWzz3Ftk5i7IzTsjOUMu8y85iZ6LTley0SEwnmEWgDCUMPaKJx4XEsfRk50svOxNeLYzsfPi7NPUZfsgggY4r2WkSnYGSnaroRFDGGUSmE1DiecEgNO2gJGGoIB8jO194fhXZmX1dk53R+fMnO1/8zj4pWZR6Hei6bZWdrbay0yw6KdnppFenWXRSspOWmXaYhJ5fUMLQA5R8XCio8qnIx6ecYy47jxlkZ9o8yc4nzyQTFay22iw7UXCaZefGMDvZGTzRidBS0w6j1PMdShh6hhKQCwFVNg3p+Pi5ZeddkZ1FDS802Tk0f7JzZuZ/hnY60HVbLzv19+y0yk6r6LSTnb716lyaopOSjwsFVT4L8vnFc2+WnTl5rfPSVj+6/5T/DEZ9VuJNdh5JKOfP+SU7Tb067UUnkudnr06r5EQo8RcYKAG5UFDlM4HniJ1nfB/MsvNg2vzIzrem74uBrtu4PSy3U9m5NHp1WiUnQsm/wEAJyIWCKp8RPEeS7MyRZadUp94J2SlkpzfZqRedbmWnWXQitNzU0yhBykw7eqHyMau0s7cgsUQnAX0QnZR0XBBqzsEN1li/vnYcPmGPqbLayk52MbYQsrP09BSUXbgB79+/5/vAn/iFJlAfYgbZedCd7DSLTq8TE8ly05vstMpMT6DA84JBaHqCkoTBosoHqO24gR0jO2eLQXb2nroBx87f4fX53bt3QanX8yI7g9qrUxGV/kJJQg9QwnExQJVVOUblHMuys0AvO4vnR3aOjN2A5q6poLXVi092lkNU62OYgzcwnl0NtNg0YxaWvoJSsFS3/0p5GQ0lHH2jDrJar8OtGeU9/QOeTp2GrMhSYl3vUGVVj3GRyM6j/Rfg2tUHvE4Hq62eD9lplpwKAZedceNwd+4jvJ3shd2U7CMpgtSOR7wuny0ok5ZZtlMPvXfYNfmba1C+VXodJRwDRxWUdVyFu2pd/wDPL5+Csj2FxLpGjMdmQj6/eO5Rdh4dnpt32TnSdwpGB88Fra3G2MvOBlp26kSnQXbqJKdBdCKy5PQuO5dar05KOC40VDl14DnCc83Of2X+0QWRnafH2Hfcy9NBa6txO7htUnYe8Ud21sLofXayZq5Ce5Jz0Yn4Jzurpf2+vQJtCSj2cqB44CF8hN/hUnUhKf98xyQaM0bhIWvfZ853QKb5uaBRCq0Dl+HhrNamv7w+Dq0Hc9hzVJnNsnNcyE5dQl92VnuWnR5FZ/0FuEG1Ox+fQVV98yLq1dkTANkZgN6cBT5AbQfRyc5P2WPbXp6E7ExcANmJHywlkxfhy+YhGJu+A69evYI3b97wD7FAfakJpOz0KDoRWW56Ep3uZCfKOw8YRKYnKDEYSChxGSio/XmAnRcuOxPqYHXJBGRe0WRnVH7jvMnO1tHLsD57EB49fW6p14G4MPNHdppFp3fZaRWdS7JXJyUYXVPmA9R2bKDKjceonOcFkp1YZ4dGr8GmPV1w5vytoLTVuB1Fdm7ZWu1IdjoZwk6JToQLzp390DrxBGbkMrALArg9cQ4qUqp8kJ16WekPihT0LjspwegbRYwSiKi4DTNzL2E8t5YvW5NyCqZnWAnG+2ANX0cPtR0aquwW2Vm/MLKzp/MMpKf1wOvXr3m9xv3g7w+2sYFqq32VnZToRHZvbYQ+FIO4/ScXIG+7jeiMHYGrXOZ9gHudDf6LTpR58yA7y7bQkjFwFEJyzS1e188UVvFl0Qcm4A6r629P9UC0ZX0r1mOUMcjOXjg6zz07eVvdOwE5qc1wZepmUNpqDNZrX2WnL706VdlpEZ2IWVy6Zb5EJyUZFxtUuRl4nvBcs/cAZee5M39IspM1L/MlO08OTUBVXrPaVmO9DmRbjdvA7SmyE2dft5OdufVX2dXCG7hSV0KIzgIoPfqIbe8RjB7J9y47TYJTwf9enfMlOwnxmDHfsjMbCtum4d3cC7hUU8qXZRaMwYPZObkMVLkl2XkYz7eQnZYsC9n5jY+y8599rEKwC6sbp/q89upcONEpCz8zlBgkIUSjEyhxaaEBVow/ZReFb2G4u4V43gbcvkl2KvslpSc7D39C4UnIzh3zKDuLJy/C5w0DED18Fqbv3IHHjx/Dy5cvYXZ2ltdJf4PbQNn5tZ+y0yw6LbJTlpu2sjPerehEajxjkZoUhBAMCJSYDDZUOUyw86KXnYem5l92Yr1G2bniUD8caTsN9+7d4/vCeh0o4Yn1WpOdDZLsTCRkp0l0UrJz/nt1mmWlr1BikICSio6gxGWgoPanw3wMWyqk87yAsvPY8auwNrIJ0vLG4PqNWwFvq73Jzq3h3mWnWXTayU4Unev2neYib+b2RcjeWS715lzXDhWDd+Hx9CmIciU7KWnpC4QUJKCEonPM0lKhEVpuS7Ipa628bG0fjL/5CHO3J2CnZX0z1L40zMewanXJgstOrNfd7achKb4HWpsn4e7du/z3B79IK1+i/Q1uwyw7UXQaZWeZRXTayU7eq3N7H5zF92XmHbxDgVhYYRGd0VtKIK3zEczNvoNZWSru8Ut2yjIvaLJzPoes10MPF6tXNbG6pQfO4Dm9cxJSLevbYzxOhkl2HhtZANnZw659EmqgsWoYbt8K/HU1BrdDys4DLmSnTnIaZKdL0emf7BSSk8Z0DHiu5HNekTf/shPr9dixcajMbYaR/pNBaatxH7gtJ7Ize18PXGHXD+9uDEGJRXbWccn48f4YlNoMYVdFp43s5KLTZ9mJEg8xyk5K9vmOLBkzFqIHJ0UVHOfHehlaE+RlCR1w6e1H9j6MQjFfx3oc/HzJ57yrSZOd6DT3CNkZesGGgsvO0uDJzsD36myUsMhMT6DoIzDLQFs0iegISkx6xEfZiVTTslPBID3ZuZBkZxN81XUSknT37NzR3DUvshO/uBSOn4e/lbfBt90TsL17BM6fPw83b96EJ0+eBOTCDF9Pyc5VJtlpnpxILzp9lZ0L36uTkIF+Q0nI+YYqlww7N58xFNmZcRHgovz9eD5lZ/PIFHyb3AxrC05CbMUoDJ04xev1ixcveL3290s71muUnSvMsjOpS5OdO7zLTi46TbLT47067WQnKTXtoMSlWwghSEGJRI9QYjLYUOWQUY7DJDsLh+dXdmJ97R+6BL9troZtCcdg35GjAW+r9bJz8xbvslMTnZLsdCM6V69pgdbbH2Du8UVIXa8MWzeyctV8y06rEDRDSUTvUGKSogKyxlmlmnsELVEl7HEJRORehadzf8DjwQ6iZ6cnqHJIKMciyU52rgnZOTBPshPrbGfbJOyKqIW01GEoyO2F8fHJgLfVlOzMSGxRZWdcpBvZWaTJznvTcP7JB5i9PAKpZtm5Ddf5AE9P34T7OtlJi0xv6GRekGQnJRKDRymUnZLqes9eHLZeCMmFV+D5HDtfQ22OenaaUY/Vk+zMCb7sxPp6tGsM9u8uhoKMLqjM7wx4W43BbTiWnTrR6Ul2+tyr02fZaRWdiEX0+QUlEpcS8nHg+ZLPOcrOM6c/zKvsxPp2YvAk5O8vhbqSHmiu7oTJydMwPT3N2+pA9Fx2Izuzkkug4wJrQz4+ghNHCoyy88hJePTxPTw6WquJTl9kp0ViOsUqO1sDKjsVwcjIWCyyswBaz7MLiLmHcDwjmz3OhsKaS/CStekvxppMZdOOhZ8v+ZzrZSdmj5CdoRdsSBTZ+V+T7PzSg+zUi06Um3rZqRedquysmYSz79glz91J+LFYFp182Qd4cGEIPi1uhhUTz2Du4zOo6T8NR5+8k0v4Dq5fOAE/GERnE/zQex6GHyvrfIQ3j29CVksrl3n/V9QEv8jb6hi5BtffYSX+ANcnethz0jD2ucfn4RcuABvhF5SMfN0pOPNabjTfPYOjQ/3wj5ZTcFTZz8ffYXKkzyQUOyBx/Bbc4PvAvIMbV05BeBF7jstIWWLi9od1ZcZtDbNtFdSxdbqlofWGoLwckZ6vHIbKa8/Y5aIcVrZjQ/haeR962YmPDeWTUIUnyk6EvSfBlJ344WEHNhp5Y2fhk7JW+GdtD3zTPQkJXcP8w/LSpUvw9OlT/gHmz4UZvtab7PzGm+yU5aat6ERkwUnKzvgAy05SbJohRKDfUOJxISHKyM6PJDtrYXXJuCo78bc5ULKTqst68IKrcegCfJPYCD/s74S1+SdhV/EwHBsegRs3bsCzZ89gZmbG73ptkJ17nclOvehUZadOdHqVnWbJKUNLTQpKXLpFJwE9QclDWygJOd9Q5WLgsXDZySBk5758rb0OVr3Gtrp38CL8uqkS1mxv5MLzcEF/QNtq/L3RenYa79kZEelJdvrQqzPnBvsc/QC3W1vYY6vo9Cg7I/uhZVw39H3mCYxVtMFaVVqWQWT2KRi/LU1Shp/hM7evQEUKDkWX14k6BbfYRfgt9uVN29Yf8PTCGCSvVYSgPIx97jG0ROG+i+G3qAnb1yWx12lSsQKSKq6Adu9NJewaiRySLhM5AlPY23VqEsrbb3PR+ZRddyUpPT1doy+ThlF2lgZNdlJ1WQ9ur6NlAnaytm33zkZI3TcMGfvb+T5RDgWqrVZkZ3J0NRecRtlZScpOWnSaZOedU1CIvTfnHkFffIlOdpbAvtrbMMuW92aegnuU7Izpgd7Jx6DUUryFw92JEcjaqluHtUtZtZe1+1rOPIbTXddUearJzmoo77imu/8lbmuYbUt53rPslOSi1NtS61mpPL4ALbp7a849uwktB0oMojH6wDBM3tGORI2+56aZPcNwFev65VPQ2HWLi87nU6OQ5ccQekp2Dhx7r8rOI9mBkZ1UXVbAtrq/fRTSdhVBxt5KKMzohpqyroC21Rh8vVV2NnqVnaro9Cg7jaIToWUnCji9vHSLEJ3ekY8Fz5d83rnsnPwA77Dqsl/Lg2mFftdrqi7rwbZ6dGAM8vYVQVFGFdQV90BdWRuMjIwErK3G/WC5UXbmphZqsjOXkp15kFOHQ9lRatZJkpNjHsKOolMaxs57enLZmQ9Fteyz/IX8fX/2EUy1N0AWl255UDwoDTW/XF0ky0vGoROyVOyEI8qy9A4YPf8IFAPCt9NWx563k53yMHYuBOX7WB7sgBPXX+HbiBuAh2ePQrUqRnOgsPoEXLqvXcu8uz8FfQV57LlseUi8KUrPygyUoO/h4UAVW1eRjOZ7auq3J6+jvG5sBI6rx/YBXl4dYuVStmPDwaMwPTsH766fhL6j01x0en4dJTv5Dnn2CNkZesEGYl5kZ3Ez/DR8n13yvIOzwz3wZ9ZgZd9lH7yvrkJEKfbolGUnFurdM+jo7Yb/K26D8Ikn/MvJ9Yk+VXZ+0nYVHnx8B9cmBuETlJslA5B7+w3MvbsPWdVsHUV28m09gcqWFrYMRR9iIzv5uo+ldQu7IAsvoD6+hzfvXsFwbwf8qbAZIs6/hLmPT6GyRpmcSF7GX9fMxeOnzadh8tUHeMMuRn/Uy06+/afQ0d3OljXJ23rJLrjZsXPhWa/r2YnbwmWMohEYnnkNZ9kF5I98mfJaVo5q3D5bZpadCjrZqcLOAZed7Fx+1XkS4p/7LzvxgwLrEX4ovZ19B89/f2PLs9e/w+MXLyHr+CT8tawV/lHTDZ9W98C3fWdhY2MfdPT1w5UrV+D58+d837htX4LlcSQ77UQnwyw6LbIzIdCys8YzFrFphpCAfkPJRpfsdQD1Oo+YysnOTyBl5/8+/AG/z7yD129n4dWbGXj5+1t48foNzStWt1/9Ds9evIaKnkn4OqERvklqhq+TW2Blzijsype+cFy9epX3sPDnCwe+zons1ItOJ7JTFZ2IWXZusZGdpNS0g5KXbpDlnzcoaWgLJR4XEqKM7Ngl2VnJZWfOwJwqO5N9kJ36thov3pSJLOzALxGdfedgRVglrNnWACvDGyAieRRi09qhvaM3IG01lgVl54aN3mWnuVenO9lZAbtkiTmRU2ORnJLotJGda7thkF03zEyfhuS1uE4lJFdPw9O5GZiqYOdlVQmszb4Or5/egfbsev545dou9po/YO72KYgyyE486lm4NdgPkauKYW3qBXjMe1F2wlouBI2yk4tCLju110WwZWt0r1uD66wqkpfNwnRrGxeb0r03PzjooSn37mSZY8cwUNHiskenHUbZiaw0yM76gMhOc70212M92P5iL7eWxjHYwX6vdrD2bue2WkiK74eszLaAttV62bl/b0MAZecE7I+fgHsopYc7IEaWnbs2N0Lvnf/xHp/74sZV2blbkZhb2ZfkJ6xOojg8iPKxEHYfPMXvWYmvSZHX251xntXtWbg71MuWFbB1RuHyM3wP9LKzFHKGnsPczAPoPcjqrLoe9jgd5q/zKDvVe2TSshP3NXPnvDRx0Bb2vjxh6zw5Dznq8HNp2cytCUlUbmmGnluzxnVI5N6d+B49uwPHa5p86tFpBo9NLzv7Bt/5JTt9aat7WoYhdVchpMeWw4GYCijO7IP8ww3Q29MXkLYag2Wyk525/sjOebtXZzBFJyUNlzh4juTzbpadB1IL/K7X+jpsRmmrh3pGWN0q5MIzN7UEqvLboIp9j8XPiEC01Vgm3J9T2ZmV0g1XZj7Cx/snoUyRneQQdqPszGTX6Zdn3sOLky1ccGYVjsDNexehLdGF7EzohMtvXsCtox1QxNcpgNqTz7jIHD2Uwx47kZ2FvDfkx2dnoRafT6iDgat3YUq+n2dm9RV4+2IaTlSX8ceHEprgNGvXtWHhjAybnp18+Xud7MyD2jEs3yu42VEnrZtQD8fvzrLLmWnoO4i9MZXX4bsxCw/HOqCQLcssPAMvyB6aZuTenfhevrgFp9vk/dgiZKenLGvZaSc6jbLTnI/w+voJ+HuxNHz9L7LghHcPoWriIbz5+Ao62trk4euK7JyB4d52WWwifVDJL3IuwC+8V2cbJF57K8lKFJ0Ktefh+keUor3ssSI7Z+HMcJcsOb3Jzrcw3NMq9X5kfNJzC16jvB3vUUXhp+ZlxcdhePa99FiVjEpPTpSRDdpjs8TkghK31S0vs5GdBJ92T8vlkF9rJzsRRXIqmGTn3qf+yc6mi9cMdUJ/CwP9LQvMtyjAe4b+pawV/lXXC39mP//ddAx+GroMO5t6LF9yfLkww3rtVXbu81922opOJH6pik5KLjqEEpm+Qm3fglxmdo6MsvOjJjvz3MvOwTM34Zv4Os7XjK/iWB1hfLGX1Q3Gf2NrOP9BYiQ+x5+xtfBVQiN8v68N/rO3Dr7d1wFr8schq7IVTpw4AdevX+f30MIvLr4E67UqO6OdyU6z6OSyU5acFtkpi87Ayk5KXroBpZ8DKFloCyUbFwOmcrLjt5OdSXnuZCfOqr6SvR+/barg/BpWAStkftkos6Ecfpb5ab0Gys51EexzNawK1kV1QNT+CUg51BaQthpfF2zZKQ1hr4HsCTx5KDurXclOFJlvuHwsl9dBednM73VpkJkGquTh4fi6MmkZl53Yy7JfFptIk7ydCbYdT7JTep0kNhHtdTv54xLY2Sr19pOGo6NslO7H6fnem7WQM/6Sve41XL78ml83KbJUlaD6+3m6RimvhEV2Njz3S3b2dp2ByC2lELm5FCI2l8D2TYwwiW3IxhII31gM4RsktiLrJXawdiwqvJo9Xwq7Ihsgdd8IlJY0wtjYGO+Nj/eF8/VLDrbVnmRnYpRVdtqLTkJ2snahYlJ6byq2SbJzT+E1dgWJ9/Ish12E7NxdID9fwOq4vCxqU7FOSOLyciiXt6vMjm6QlIrs3NrLyvI/uNtRr62jyE3sWRpXpD3m23YpOw29MwvV7UrDz9myvSfZdj7w/duuY6EKKk5hXX8F16++4nX9TkezXA5ZgnrqFeoBPLZAyc7zk1chLZodz64CiagC2MfJh307JVJ2KORBcqRGalQh+yJfydYthMykeijPG4bS3IaAtNUYrNcW2XnQs+w0iE6T7PR5CDspMr1hFZ0ILS59gZCFQSQl0jvU61yB54idbzzvBtnJ4lZ2jg+fksSgjHmYt35ot3k4N8rOgrQStk4u5O8vh9ribqivCUxb7Vp2JhVAmdyLUx3KTg5h12RnCR5b5hg8mmPrDNaox6nhomeniSPVV2AG3sPDgWr22InsrOb3ufx4/wQU856c3jAPFT/iXHby+2cqkwWZ19O9Xvf4cOwR1oYgVXD8HivnvVEoUpdJqNvZWwpt519wmXr7BvZUnYUHA/XyvuRy6+/nKcPPnXzuu5tPCdmpS0jLzn8GRHZ+sPTstNyvkw9dxw/ZP+Th64oIa5YFpVl2tkPi9RmA2duQWIq9OEdhWO0KbY5Zdr5l28Kh7TrZWehEdhJiE+ETAWnLPu3GdWyi9ry0kZiqoJSHqtutx/i0eRxa9cPYeSjZqW3LgnIM7Bxw2LnlsvOJJju3NbmXnY0Xr8GX9b2w8fgF2MBYN3wO1o2ch3Xs/6uOnYW1Ixdg9dA5WM1+rjl+EVay/+NP5Of+U/AZq29/YfXrL+Xt8HnzCHzfOwm5rZ1w9uxZePToEf9rtS8XZqrsTF4g2RnvRnQiNZ6xyE0zelnpD5RQ9AAlKYMFtX8OKzc7R4GUne0nrsCK/e2wu/YcRNechaiqMxBVfRZ2sp8R5af4z8iK0xBZiZyB8NIJ2FZ2CiLYsk2Fo1x2/ndvHfw3rh5+OXwUNrEvHFUNrTA+zr6A3rsHb9++9fgF3i562bkWZ2MPkuxURaed7DTITE9Q8tItKP08YBaEHqEE42JELi87/u8QLjuPQnb/Hz7LzuET12HjjkZIPDTGiT84CvHpJ9jPE7D3wCjEpI3wn7H7j7OfJ9hPXCb9PyrpKGzc2QorNlXDis01sDG6CyKSh6Ckos3vthpf46vsdCI6Ndmp79lplZ2SxKRkZ7Xa65GKJjsrIbninG4YuxyHshPeXIcsPpTdnezUi0j3PTtlQcquu6Yq6uC3tW3QMs3Kr87M7kSWOkUu92p2HgIsOxP2NEHu4RHIPTQCWQeHIDt9mDECh9OOQuaBY5C5n3FgCI4cGIZDqUfZ8mP8/2kJPRC9vRbCN6AYLYO4mC7Yn9YBjY0tMDEx4XdbHTjZWUTIziKI4XITZ1xvhOgtFVxSYseA3PBCQnbKEpOLyGL2WJGdigRFcdggS0zcxzikqhJTJyll2Sm9xiY2spOLQS4pfZSdbDtn8kulZa57dirbmIGrNTXq+ljXpZnZzeVwiXyeFNnZOzDrs+w8d+oKZCZUQmPRUU59/gDUFzDYz+qcXvZzEOryBqA2t0/+2Q817PoW/1/Orm8z46u49EyNLoasfU1QcLgH6qqb/W6rMQsvO/O4fKNlpjeCJToJSRhgKJHpK9T2bcHzZJCdf/glO8uPVENP7QB01/RDZ1UvdFX1QVd1H7SVd0FHRQ90VvaqP/G5trIu/v+GghYo3F8qS9E8KMtuguqCVmiW2+r79+/73FY7lZ2S6JRR5SYOZZflJ866nqyITkJ2JlRAx/mX8JH9e3f/KpwZ7IQiWbgdjs9VZ0z3JjuPFAzCpH4YO897g+w87lF25sj3tWQvm30El8ZGofWgPLydkw/VbZO6YexyfJCdmdWXZRGrH9bOUCYRUkRmury9c1bZibKyJV4TnRrZUNSP52yGXfOUaz1G1ZnZpcmLDD1SZfh5ErKTjJCdMk5lJxedSLEE9vD7Gx+CjntmF+7y/Tudyc5bBtlp6dlpwB/ZKUlBr7KzQJGd2mMNTVTiY79kJw5jn2WXeI+vQGKltG37np3KtvToyiXLzj8jxY1cdsY+1mRneGOn+sHlRHbiB0Tr1DX4V2U7rBqchKRbLyCRkTD9AuJvPoc4mb0IWxbLfz6H2BvPIObGU4i5/hQ2n7oOf6vq5O/1n0pb4F+NrO51jkFVZzdcvHiRD0/ADzBsaNxcmFGyc7WfstMgOhFZcFpEJxLvRnbWeMYiNs2YhaWvUCLRBrOInC+osiDx1RbZOcWuefCyx63sxHrWNnoZvk9ugtWHe2Ff503IGHzISR94oHKQXTykDz5Sf6az5w/0P+BsLzvFh7Njb88v4hvgt6xhWJ8/CrlVbXDmzBmfv3BosrOKlp27GuBXL7LT1SzslOhk0GKTgpKXbkDh5wVVZHqCEoqLHVZudg647GTn3B/ZiXVsaPQarAmvgdj9w1DaeA9KGhj196CYUVR3Vwc+1iisvQMFNbfhYP4FWB3O2lPs+Yk9PaPaISJxEOobu/xqq/F3QJGd5gmK7GWn+/t1Gu/Z2exedqo9O1FsWuG9P0EZno7L3PXsVGTnb6wMOx3JTuss6lx2/vEWnvIhxxhWnrERD/fepGZiV4Tna5gavgNP8V6hrY2m1/mBLDsRs+zsO+ZOdmI96+k4DdGsncvY1wdNNdegveEWtNVLtNZPQ2vdNLTU4s9b/Kfy/+aam4wbXIhGsHYEe3xuZ79ziXG9kBjfBVWVLX631WbZiaJTkp11AZGd0VsaoY8LuglIi8Nh7ZL4xB6G8yM7pZ6V6joWNNl5RpGUAZWd7+DFs7dsOYZ95b1zGRpN9/XUILarCs9XcPX4HX7/Tq2nqEvkY8Zz74/sxDp2duIyHIwpgRqcOKv3OpzsYXRfgzGkS+JE11X1/+qyzqsw2nEFOitPwoHdrN7tyJN7eDZAIbsGbm3p8KutxvgtO3Wi0yg7nYhOhJKYTgmG7CQEYQCgJGWwoPavgudJJzvPnp7zSXZiPTt5bAIK9pdAS2kHnDl6AS6OXIELI5clhiXOD1+Sfg5d4uD/zx2bYlyEjopuyEku4L0+s9jPyrxWqC7qhIaaJr9EPq6L5S51IzvZd8YTXKThUHb8//9g9kI3ZKuik8G+jxhlp0RW4VGYHL8Nr1jDhUPJ6xJdyE4cxv6WvQf3z0Jbei6XduaenYfknpse79mJJNRB3+gFmMZ7iM49g9OF+Xw5DmOfYdcOynByf3p2mmWnKirjAyE7iefjFeH5CqYnbsFLw5B6DUl2ZtvIznghO0MtdrLzO4eyE0WnY9lZOgQdeD/Lu6dhm+7+ndLwZjvZqQxjV+RmrzSZD8rPErPkVEDZKYtDvezkctOz7FTEphPZaR0+bhaNkmz0R3ZaxCa1zKPsRLBsMuyYNdk5ZpKd7eoHl1PZ2Xn5Bped/6nvhc9qu2FF3zj8Nngafjt6hvMr+/+v7OcK9nPF4CSsGLDybcdxuQ40sjrSAv9pPQ4/s2UN3dI94fADzO1f7OxkJ0LJTrPo9Co72YeYXnRSspMWmxQ1nrHITT2UtPQFQiBSUAJyodCXyyQ7D019hEusuuDHky+ys+X4JfguqQlWHOiEn1LbICx/BLYUnYAtxWOczez/m4vwp/J/mUKNddnH4Iu4ei48v0xg28o8BmuyR6CmuQsuXLjAv3DghZmbD9FgyE5jr06r7DSLzkU1fF2VmZ6gROISgR2jKjuT/ZOdx45fgdXhNbBxZzNs2NEE0Wx72JsTe3Ii2IuTkzqiY9hAZHyfNMSd8UsYK9OeHtiR3AfNLd0+t9W4bjBlpyI6OesHYQJFDs7Gvs6p7DQ/1gSnBiE2SdmpSUv/ZCeKQ7OolIaczz2+ACm2ctMMITuRtS1QdQGHtLOYn/OX1eyYZdm5gctO7Z6dbmUntodd7ZOwi7Vze1nbtzeqAQ6lDnCBeXj/Ud6jk/8/7Rjv0akxaGBfXCdsDyvlwjOStXvxsV2wL7kHGhvaeVuNk7soX6KdBssWGNkpi05SdpbAfpyoiH0xvX/nd5hj71U59uqUZacmJ1FqmoerK7JTXq5K0Hro5VLQOIxduo+nJjstw9pJgic7o/Ovwtu553A83U5umqlj22VfjPl289hjmS3sd2AKh7SzqM9Rr/eCfMx47g/6KTvPnJzisvNIYjXv4Vl5pIv36KzJ7eO9ObEXJ1Kd3UvQwyli17g4rD2JgcIza18z5GW0QFen7201Buu1UXbW0rJTFp2BlZ15Id+rk5KR9mS7hNqGEUuZ8Fxx2Znnl+zEejN2dJxLw9JDlZzW0k7eo7O9vJv35lT+j705jbD1ZGpzG3nvzkNx2ZCdUgjl2U1QmdcG7a2SyPelrSZlZ3Yd1LLvDvayUzeUvWwcHn38HS7X4u+sd9kpDVvPgawalIrsdTUoN53JTqPYxHWMslMSmQ5lp0JCpzzUvBMy5ft5GsSmP8PYlfUMEpPB5SZbr79K91hZL5MtQyp1MlNZpkf/vG7b8XXQf1WefIk9Zx7CjmiyMxu6W4yyM1bIztALNkBuZKdedDqSncUKbRBxgV1QqPfp7IEsfv/O++wXvlknO1ksExS9gzPD7LEsM6UJiv6AR7fPQDgXnk3wQ+9FOPPyEVTWNvF1LLKTi01vsrPFnewsUCYt0s+s3g6JE/fgwaNzsEIWjc5kpyIxZ+HsUAd73Aw/Vjaq671hF7c4QdGnzZMw+QobcTeyU8EoO7/msvOjz7IT607HpevwT1l2/pPVn0/L2+Czul74vHFARz983qDxWX2f+vMz9rp/s3qmCmn23qHw/G/7CVjbMsguGAf4B9iDBw9cfYBh2TzJzu+DKTvjAyg7LXLTDCUuO6D2PmuY396E1ATqeT21sG7gCa+fx6sb2GOTRNSzV79dfBwIKollLlDKxmVndUBkJ687I1Oq7PxhXyt8ndAAP6a1w8/7O+EnCx3S8rQODv5f4Yu9dfD5HlYu7OGJQ9oPDcLG3CFobOuGy5cvw+PHj+HNmzeu6rUb2WkWnU5kpyo6bWQnLTYpKIGpUAVbOqV6N1ZYRzxf7h1SbJohBCJF3CRMswut3ycH4Ufq+YWCHadRdn7gshMv4tzITqw3gyOXYfVWSXau3d4AK7fUwIbIJj483UiLLRt2NMv38yyDnzaU8Xt4bortg92pXdDX51tbjetRsnNnlFF2aqLTXnaaRadFdq6pgB1516TZxqfOQPaOckl2rmuHisG78PQ1Ckez3CyVJyj6A+aeTkOVMrt6ZB+0Tz2D6dZm9rhMfo0yYZEygRE7wIDLTkUcmkVlCURU3GYlMGXmMYy39kKEXjjqWJN9jR2pfvb1cnVG97mZd+yKxN+Z2U1w2cnORYBkZ2fbKUl2RjVAdEQNn2E9dmc9o9FEg0fw3p5b1xdJPTy3VEAcq4Mpya3Q0eF7W+1GdlKiE/EsOxmxI3CNz1iO4rGPz7xOy05GzAhcxff12RWoiEGxWQwpBefhrmGCoiJIqb3N3ne8n2WL1Ivz4ATcUfehyE15gqK5V3C5vlleVg3lXbfh6S2lV2gQe3byWdXNvbdm4e6pk9KkRorM1MEFKbsqeD51HLK41Cxm3z8u8Rnfsa6/MzynoJTBC/x4ZdmZ0gM9/TM+yU6sN6fHLsqyE4VBORzYXQyZ7JorK7GGoJpzhF3rKT+RzPgqSIrIlYjMg5SoQshJa4OCzBbo7+v3qa3GYPncyE6D6DTJTk10zpfsrIWhuyhHrkJDNIrKfMjpecQF08XSEvbYLDIVauGY8rpd+uWEHPRKHtunJLUulhary83S0R5KYvoCtW0JQ3kNsnMQzp72bRg71psTgydV2YlD0nP3FUJJegWUZlQS4PIK/rwZLjtR0qH8S8yD0iMNUFnQCl0dXVzkY1vtRuQ7kZ15aUUm2cngQ9k/wOsXb4kh7GbZmQcl7BrjxfUJaMvIY7+fhVA7eB/ezT2E0cPYQ1MRmx/g5fkePvnQkYJhuMlnbtdkpyI/310/xtbJ0q3z3rnszDgBD55pExBlFpyEB7P/k1+vzLY+A9Nt5exxHlR3XJeGvOtlp3wvznfXj0Ihe03+wSJJemYYZWdGbDn04ehceWg5l5EHOuD0vVnWXE9D34FsaZlPsjMTDldhz1F59nW+Ditv6xSf+f3j7Dv4n/Kc5Z6dQnbaRchOhp3s/KT+AtwwX3fwzMBIbzv8XRaUeJ/OvxVLQ9c/5cs+8kmMPtH17Dxz4SqceS03Uu+ew9HhAfa8secml5vKOuzX8s3jW1DZi5MRSesERXZyqaiXnUgHl5uP1GN/BzeunVOHnLuRnShKU67JvSn4vlFm1sOP3VfgBr/PKcu7Z3BsYhqmeTl8kZ318GcUnrLs3Pt4DoalLfskO9unrmmys0qSnX8pZfWF/V+lsoOgXeWvbH3t/ZEmUPqkogO+6zsD0fXtMDQ0xL9sPHv2jJcHP5i8BcvWfJyWnb8c6vAuO2XBScpO9gFmkZ1JwzDKL7xn4Vx7uwvZWWOPKjTtoOQl4kZ21jiTnVwu+iI7UWb6C7VdHVg+VXbWBER2Ng1flGTnwS4uO7+Kr4cv4+r5LOsaTRKJFI38NZ/vqVL5T0w1fBnfAGvyxmBPbisvy7Vr1/hfop3OJInrBFd2tkMjftG05ANMd7YvHtlJik0zhDy0I+Cys5RY5gPsWL9DFNnZ57vsHBi+ZJCdv22u5hMPrd5aB6ss1PIh60Zq2WtYHVpfqvLzhjJYydaN3DcKB480+dRW62Wn/p6dwZGdSDlEZk/AsSnlsxYzC7fGz0FFShWstPTklHt8Rvazzzvda2aewHhrnzxkHamHrMHH7AoGwy7ab1+D9nF2TaPKzuLgy87cq/D0zW0oj1QmKCqHJC5dPQ1FZ6/LnoSpp9oXQ5yR/SgXpLrnAtXDU5WdJZLsbHqlys7Bkdu+yU7WpnHZub0GIjaXQQT73dnJ2jMjlZwo1tYZqYRI9ju2dX2hxIYiCGdEbKmAlKSjkHm4wee22k52psYEUHYqExXxnpklkui0k52M3QeHYJRPRCVn7jVc7epRZ2KXemSWQlb9TXgurzT37Db0F42wfbMW29CTU5KbynpcNk6OQ3kM3q8z2LKzH848Y2XH+2/ydfIh+oA0G7y0Lb2wVCiA5PxTcJWtowSP7UQHa0v1z5l7f6oo5SGQz4kiO33t2Yn1ZvLEBYPs3B9dDHgPzoN7SglKiGWlkLarSJOdDOzluX9PGRQd7oOSvDqf2moMlm9eZefuHpjCXmdne+AAJTpTT8KDuffwoKfO+pwBk+zk0tIf2WmSgo6xyk5KONJQ0tJfqP3oyhsg2YltuCo7M1i92V/C6k8+l4Y4y7oVafZ1M/gatUciAyUhDmmvKeyCsvxq/rnhtq32WXYmlUDHBdb2snfTMoTdIjtz+O0k+s9q99r8+OI2nGlvgCPxci/OOHYcbZKok56fhhO17PdU7nUp3bMzF4qqz6rr4D03p45egcdcMDrt2Yn35NT2w4d8jw1ANV8XZWAptI49lMuJ9xe9BCfOP5Ffr/T2zNbu+4lrKffFzNBkJ5eYSHwd9I3hkHJpXfw+8fL6SWjholMWlz7KzozYLCioOgnTz7U2/ePzW3Cmvx0KYrO150w9PPWys0fITkOWpez8rKoD/uFEdjKUHp1qz061V6c2K7d+Zm4Fabi63TB2RC86ndBgRCfSaCQJ6BFVbpoxC0Ufybej1gOmdantmmHHwmUnO0962YlNni+ys23qGvyjsh0+l2XnJ+Vt8OeSZj7buoT1/f4Tez85rAyI+b3An7jev+oH4LuucUhvkMTQrVu34PXr17zR8XZhhmXzKDv3e5adZtFplp0G0cn4snYafmdffs89ZBfa98/COlJsUtTYw4WmJyh56QZZFFrwtwcnJSwDBbU/BjseTXaehMNTAJcDJDu/T2nh4vK/sey9N6DNxG6kmqMXnZ/vxp/S8u9SO2B9wQkorO3gM0nevHlTnUnSSb02y87NiuyM9UN2ctHJ2H4MxviX6dMQvsWfnp1meWnGk+ws9wwpNs0Q4hAJmNREmekv1HZNsOOVZGeFLDv/gGu/S+21a9k5dAlWoezcoclOnIH95w1mtBnZNco4etH54zoGF57lsG5nG2xLGoScgkbXbfV8y0790HUFbQi7JxSp6QuK2KSR7sHpDUIcGkD5+T94M95nnIxobR+Mv5mzLrdQGACo7ZowyM66gMnO2KgG2LW9motOPgM7Tjq0sZT/VGZityKJTVV0Mrasw59SD8/oHQ2QlNALtbWtcPLkSddttd0ERU5lp+F+nTKS4DSya7OCLDplFMHpHEViBgZSCgYEWX4ahCjKyBLdjOpmURlI9GWRkY85YLJzTwkcYddf2DMKZadxBnaFPI4yE7sevehMisiRhCd7zeHEOig43AmVpXWu22oMls8gO1MDITsJ0Ynw3pyl0HiWvaezt6A7pcAkMAsgF2Xl3CMYSjU/pwdFJyU7nWKWnToZaAvdg9MMJRqNUIIyWBj3zcsYQNk5OjgG+amy7EyTZKcyrJtPPCQP70YBhUgC0IhedHJxhdKKLc8/UAHVhe3QWNfiuq3G57HcpVmVLmWnTKJuZnlVdGqow9flYzWD0s18nN7B4zZiPDdu0USgv6iS0yOUuAwWxn1jGfk5k+uZkJ3GCNnJWPyys8GKQaaZqbeKTTOk5EQImegGg6ykZKYvyNuj9oew41FlZ8cYxD2RZCf+wcUv2VnXC/+QZSdKTv6+2b03pvNPvgdsPaw3nzcd4z089zR28Q+wu3fv8qFk3j7AsGyBlJ2q6EQssrMJ0s6/gd/PH4eNg0/Zvp9C7eF6oOWmmRp7DGLTDCUv3YCC0A5fZSclJ4OJbt/smBTZuaZ0XJWd7PLUd9mZzNqkA51cdn4ZX88FJw5H5+DQdAsmuWkDCtEfD/TAxrzjkFvZrH6pd1qv/ZGdZtHpWnaSYpNCLy8pfJSdpNg0Q0hDBb9kJyUsAwW1PwY7Zr3szOmfg+t+yk4cir5mWz2XnSg3f1ovD0sn0eSmKjjNsPV+3lgBG6I7YXvyCOw/Iokhp221WXbu2Nk8r7KTFpsUZoHpBqvg1EPLTT2ENLQg9fSUhtqXy8tqIWvwEczMPYeBVGWZHkpYBhLz/hgeZOexUd9kJ/bOxKHou7ZJshMF59b1xZK0JDHKTRJ8fkMx7NnVAilJnVBa0mgok5O22t+enf7ITlpmesIqK/2BFIIBQ5adOIS+rhGiuYDEnpnn4O7Me3g61CYvCza6MsnHTcrOI81+yc606CIuNzWZiT019TKTAgWnCfaalJ35cARnaM/ohhL2PcZNW43B8mmys0KVnUVOZCclOhGd6FRlp274+oHyq+yb4Xt40FunE5hIHZeXH++ehFzDcj2K6AyU7DTKSnu8y06zXDRCycj5QisHHr8iOyvzj/ktO/NSi7g8VGUnyj6TwKNlHGIVawoorooyqqGmoB2qy2tdXVfjc1hur7LTRnT6Izt9E51IIEUnQp9Xt5jFohFKRM4XunKwcvLzJsvOruZxITt1EbKT4VR2Gu/X6Y/sbJSwiDM7ZKGmYBBrFIRo02MRnAqESHRKwAWnHfJ+9Ptmx/RnhJ0bRXaewHrA8FV2/r2ind+nE2XnX21lJzvXhveDkJzmc8zW+wurl581DMC3HSfgYFM7TE5O8vsMYePz/j27yLH5AMOyzZvszDwLN+fewmhtE3x2GP//AW4OdoFFbGYMQf313/m5Rl3x9v51yGfHKMnNBohqvw431aEJT6C7dgKOv52D388PwxdccNaZhpzL4vLQGbZPdkl1foith8skWTl3/wysU9ZJP6bb9zu4efYkRKHIZNvl25x7ArWH2D74tvhKusj7k4ex8+2qorEGVlWfhuP3lYEZLLNPYbitC75QpWS7/LrLUDhwUz3GuRe3obCAnReDwHSLTnYyuOwsm1BlJ9ZeX2Xnt0lNNrLTreSsNMLWw16iv2QMwJrcMdhf0grj4+Nw//59rzOkYtkMsjOuzSA7V7mUncYh7JUW2WkQnYxvN7dA8eVZgJm7UBxbzR5ry+aeXIK4cPY47jRMs9+B6e4RKJ54ymoPhtX3O1OQHlvF1vcgO2MHoGHyifwazDuYnjgBseHl7HkUmZWwpQOH+D6BvvpLMM1vHcH21dHKnwsrmISxO6x8PLjPq1B8kJVzU4X0OvkZNW9uQPrWMlmC4nZaNNEY028qyywryyjEbtVJyrhT0uuGxnXr/gFPpk4a13OFvH/EIDsHuey8ocjOHB9kJ96nU5adv27yJjs9CM51JUbYeig8sYdnePwA5BQ0OW6r7WRn1K42G9np4+REHKPodC47zfLSDVa5qYeWm3oIYWjDmpRhGDAMz5eG07dk1+rWo6RksNGV0yQ7i5teqrJzeMwH2dmKsrNKlZ3b2e+Nvez0JjkLjLD1wsNKIZbVx6SEHijMb+Bt9b179xy11UJ2BpEtjdA4pB9CzzLzGM7wIemUmAwmrDzycQdCdp6SZSfepzM9powPSTfLTseC0wwKz6gCOMyue/PTO6CipM5xW43B8s237Ezb3Q1TOHOzWWpahrBXQkPPVXig3st1Fh5MDkFZtCY7jT005WHsc4/g2L58LvaQtCNDcB5ndFa3cQYu8mHBetlZwfZ1xbSvY1DKn6+R9mMIa4vPdEHajjzIRgk69xCOpeRpYpFdxx0784htRYm8vShFQObKr3sEE80n4aJSvrlXcL25HlItwjIQsHLhOWHnlpadrC76ITtREpplp88iLu4I31bpkXqoLe6CqpJax9fVuBzLHXTZqZOcCqHUq9MgFC1QAnKhwPJqsrOz6aSQnboI2cmwk5160elGdmpS0w69MPOGLNQUVLFmBwo1D5jlG8ckD52iCk6EEpPBRC4Dwo5Lk50nIO4x+C07/1YhTUr0d73spN4X6rx7O69yWf9V2wvfdoxBSUs7nD9/Xr0w8/QlSC87fw2a7KyH9dibc2Ya0tjrPovvksWeaSh74gB0P/sAb+9fhtQM7PXZBqnH7sHDe7heDXxRfBkezr2DmyeHYRU+LjgFZ17gsaHE9Fd2NkDq+bcw9+wSROM9PBO6oPDqQxjm2zDJTo89O62y84vCS6zcf8DjqxMQlYDysRaiBh6xEs7CubYW9hiXSbITj+Xt/UuQehDL0MvOxx9ymfTy0hewzLLsZOduTalRdu7MqfNNdiY2ctn5HcrOOEV2OhWdJsFphq3/BTtXPx3shbU5I5Bf3Qrnzp1TZ2j3VK/NshNF5/zJTkbsGFyYYbXw8hiEba6GsNq78HbuBfRl1ErPc9mJpX0H00NH2ToV8OPBM3ADJ4Fg291iJzvDe6DvCdaJW1B6sIaLPul1uK8TEMZ7bcqyEzc/8xAacD25R+ePBTfg9bM70FzQIInCrd3S9u5MwhZFHtr17DTLTuW1vCxVbFkpK8tpuSyjrCyymOSyEwszy451gC//MeMiPGK/E4+Gutk+9BLTDXK5TLIzt/8Pv2Qnn5TIkeykRKdJcJrB12wogzWRLRAe1w/lla2O2mpcvlxlJy039egkYUCgROR8wspgkZ1az07fZWclxOysl2RnmCI7zaJTk50eBacZtv62sFKIiW6FhLhuqChrhLNnz/JJMLy11YrsTNpVtchlp1VW+oNFTAYUSjYuAjbl82MPlOzkkxLFo+yU7r+pyU67Xp2E2LQDt7EjDzLY9WD+wXZorG9x1FZjsHwBlZ2OJicqkYayG4arm4ewl0DDmZfw8uopaEiW1tmfcw6ecxlayx47lJ27WmHi6XuYvXsOGpLYsl2N0HNZmdn5iiw7i/m+XrDr3oakPPY4B9Kyz8r7quGP7SciImRnVAvb5wf4+PwGdGcWcNGYmnkS7uPEYVePwREuHnOl12E52LXPRGk5W1YAZcefsW09g4ks6XWBBWUnKzee76g8qCrwU3YOjPH7buIkQ3x2c4PstJN2VqlmC3t9ZmIuFB2qhuqCVmioaXJ0Xe1JdlZmsfrtRXZ6Ep2eZCcXnQsuO4nz6BJNaFJQsnGhwXKzY8dzibKzcUzITl2E7GRQojN4srNRwiDOPCFLNQWD2LSiCjcKg+DUQwg5byyo6FSQy8KObUFkp3re2TnU4+WcKmX9T9NR+KH9OBQVFfEbq1+9epXfgBrLR/21DssWLNlp6NWZ0AV1999LQpI9/iy+Htah/EQhyT5oJdlJLUOwRyfSyGUkvJ2G1ER5GcrNQ9hLFCUm2zY+9ll26h+jGNRjkp06qelddtbL5cb19PJRlpvqcvNjpFrbbwYro/paH2HHosjOtQGSnTjJkCI7cSZ157KTkJtmUHay93PFoX5YX3ASMnOL4OjRo/z+WXifIU/1WpGda3Y3BE12WvLmJmRsk2WnIjhhFi50nocLMx/g0VAv/MifU2Qnq4+TR9kyucemIjfZ+93A3itKdv5YeIN9LXjDHqM0lXtysu1t6US5yZYXsOWq7GT7rm1ijyXRqcpBAzWQzifueMz2ycqAyxzJzlL4seC6VJYC7BWqCMhyed+65Vx24vYGdGKzGRr4hBunYIu6zBdYuYIlO8P1spMWnW5lJ3/NhjJYF9UG2xKOQnZOsaO2Gr+AhK7stApOPbTg1GOWlf5Aycf5hpUjKLKzCrzLTqpXJyE3zbD1t28ug7iYDkhO6IOsIwUwODgI09PTXttq/2WnUXR6lp2a6BSycwHQyc701P6gyk6r6EQIqakjcbuGIjszsXdnRgcU5Dm7rsZg+eZfduZBmqUXp4Mh7NHdcJH3CB1j6ziTnWmlOGTePGGR8jpFdhLs6lL3lcOXOZedqaVX5H2y91kVjYrcVJabH8vrpZyQz0u1tiyQ8HLjuQuQ7MSenTay0yriEKtcUzBKNVyWBZkJuVB6pA7qS7EnfhEcO3aMf454aqtxGT6nyE6cLX4pyU7rOXOD9by6xfw+aFCicXHAj10+n0J2GrN8ZGfrsYDKTr3oXDyy0yTdzFgkJ0ILOa8sqOTUI5WHkp34VSK83nfZ+W+3stPROa1n9acRPmvoh+96JmBt8gE4dOgQdHR08L/W4V+hcbY9uy8aTmWnWXS6kp18CLs0bB1nYOfwoezskgQlJZeaTZIUtNzLs0YicRiOs4skqTeovCyOETDZWQuram/AYzTas0/h+MnTkJqO26ti+CI7T8M6FIwJx+Ryy49VZAmqikwPspMfCzsnhtf7gFl2XgyM7PzZLDsNolMnO72JzmiFCv74P+y1Px3ogQ3FpyAs7ggcPHgQ2tra+Oyo+GVjdnbWtl4HU3Z+76lnpyIzOfJwdiyTMnxdeY6UnYrMVOSmWXbW6cQkbkeRneXy67Sh6ppwRPmpl53VEFt7XjeMXY5r2VmtKwtuWxOQkgTF9ZqlZR5kJ86une7zUHaZzWWa7Ew56r/s3FzNZedqB7LTcY9OZH0JF51r2ba3Jw3B9qgMx211oGSnWXRaZWfpopKdtNzUY5aVvkJJxwVkdVHAZedO7Nm5ox6iHMhO56IzH7asL+Czs2OvzrTUIdgVlc7b6tbWVrh06RLvMeSprV4o2UkLTU/Q0tIXaEEZKAjJuFjgsjOfn//gyU5KciKe5Wbi9mwN9hxOVJQRXw2Fh3sgfs9+x201BssXTNnJRSclO6NMcpPLzzdwsaxEJzcboWv4mm5ouRRFdlonGjLLTuwdyhojfU9PO9nJrrW6hvVD5qXYyU7eS5Jjlp3FUM/3iY9zJcEoI0lQRWTmepCdOES+MzhD2fWyM38ocD07U/Wy007aeRNreoHFnmfbKEyvgvqSHjiYfJgLq/b2drhy5Qo8e/bMtq3GZVjuYMhOVXQGTHYaRSd93txgPcduML4fevTvzeKDl5+fv2zoQNn5Sq4MLEJ2hmDwg8up7NSLTl9lp150BlZ2NhgxiE0KWbpRqCJODyXjvLDgvTkJWLkU2flN50kIiDPIsLoAAF52SURBVOwsl2VndRchO4lzTp1T87mSy/mfhkH4aeAc/JySDuHh4bB//35obm7m9xjC+2Z5+qIRfNmJExNJd+kjw3tq2snOGg2z7IyTCZjslEnogvwTV+EcDo+fewHdRXVsuU52ZrB9eJSdyr03F7vsPMVl58VAyc5kO9lpFp2E7FQEp47/sOXf72uH1VlH4ZctsbB161Z2wZoCjY2NcOHCBXV4JJbDHFwWcNnJRadL2Rney4d58xju38nwSXbW+iE7JWGJw9h/58PJByGML/OlZydKzMUpOzftGzLIzsTsTveyU+7ZaS87nYrOYgM/bSiFdTtbYEfqcdiwPRO2bdvmuK12Jzsl0emr7Jxf0YnQohOhBaceSly6hZCNCw2XnewcMFB2FjW+0MnOW7737ETZGW4nO52KznwDWzcUQjTbbkJcJ6xft5O31fv27YOGhgZHbbXTe3ZSohNxLjqXg+wkBONiQpWdBQGSnSWQyWWn/p6dbkWnTnLKYK/Ow+y6tPhIH+yNOuiqrcZg+SjZWczgsjO+HDJiJNlpEJ1uZKdBdCJ5kBqlH7ZeKP0fpWW0LDr5MHb2uT3zCCbKKmX5aezZGTjZicPY2Xp8OHkFe4zy03PPTnvZWbT4ZSfvFYv37ByCM5MB6tlpkp3eJJwm0WiRhuIqf38ZVOQ1Q9T2aN5Wp6amQlNTE1y8eFEdyk611Y5kJyu3X7LTJDr5cQegVyd97pxiPMduMb4neqzvz2KDHwM/f9nQ0SBkpz5CdpqYF9lpEJp2NFgxiE0zsnSzwyA5EZ2Uc4Iq7hCdaFwMsPL9GWHnIXiyk713+vdGPu/GcyufK/V8GcuJovPftb3wXcsIfLc7HjZs2AC7du2C7Oxs3gBNTU3Bo0ePePncys4VhzsDIzuThmF0Rp5AiD1WenaizPyiZppdkryVh63Lw9jtZGd8pywD5WHscRJfFOF9PNmlTUBkZ5UGl5RY7mN8uz7LTlJiass9rxcM2VkFn8dXqz07UXZi/Lln58/7OzzLTjvRSUhOXI6i81u2vTU5I/Dr5l28Xu/YsQMOHz4MXV1dcO3aNXj+/LnHHssBv2enjezcapKdqszcXAtxQy9gDiXnwZO6+3ficxU2stPbMHbdcHVqGLsiQW1lJyE2XclOeaIhLjGJ4eoceblegs6L7GRsKSdkp9Zez5/sNEpORXSuiWyE8MQ+WLU+ltfp6Ohox2116MpOq+BUoOWmHkpcuoUQjYsBg+yshcKG537Jzg5SdponJ9KLTkp2GiUn79HJiNpeA0mJ3bB+bQRs3LiRly0zM9NxW73cZCctKQMBIRcXGzrZmZHa55/sHNX37HQnOz2KTvZ8Ovv8xaHr28KiXLfVGCxfYGWnJjo9y06GOpS9F4bu/g9mznTDfi469WJTGdae64PslB9zoagbxr5LuqemKjstYtNX2YlyM5eWmMpyb+vNm+zMlWXnB1V2HkwrDpLstBNrhLjC5xi5bHvVBW2szd7N2+qoqCj2e3cEenp64Pr16x7balyG5baTncXp5UJ2EmhyUw/xHi1C+DHI56+j4YSQnbosC9n5bWM/l53ftxxdYNnZKGGQmhS9UPmYVcLZW5BY0sAey6hi04pFburhIq5H22YRPtaJOScsRsmpwMonyc56+FaWnWOsHgRHdrLzbZad/Bybz5exjFi+T8ta4fueCfg+bh+sXbuW9+rE3m/V1dV8hj28t+HLly95A2T3RcNn2SkLTm+y88taSWjyWdhNstPYW5M9zpiAc7Nz8PbeeYhi2/h3fKtugqI6WNV+j23pHVwf7OUC8ouCc3Cdz1rOLm1U2cngYvMPeHx+BFbFVZsmMrKRnew115/dhfrqFi4Fvyg4y7b9Hm4OdPBtWmWnNKERzN6F/INs2f4mWMUlpVliVsOqNrx3I5ZnGFbxZc2QOoazUhMTFC1z2YmiEyclWpU1BL/tLYT169fzXhUJCQlQXl4OJ06c4L0q8IPVbkZULFuwZOf3DmXnjxmX5Al48D6duvt31raw5xXZiaU1T1CkSFGd/GSvlGRnOXwbewIu4CRGz67D4VhpX2GFUzDNX2eeoEiRnYqwVGZbx3I0ssfVEFt/C55gOfSyc+sgOz5lexWwPqZWkpQG2ckex4zKZbkGh2Nwv+UQVnBRLot5giL2uzcvsvPYopOdP60vgRXsvdyWOAirtxz2qa0WstMMJS7dQkhGxzRAy22sv9cgay31vJ+YZGdB/bMg9Oz0Q3auz+eiczv7/UtK6IGIbYfVtjoxMZH/vjltqxdUdsaNw13WNr2d7IXdFrlpxigtdx8chcvP5HPO6kH5VuPzdtCi0l8IsbgYscjOmQANY/dFdtKiM5VtpyCji/2eJPrUVmOwfGbZWZreFHzZyQWmPJT9xSt4ZR7CLj8HM7egCycoitYmFnIuO9mypCG4yT7nZ++MQymus6sRjim3xlF7dsqzreO+kvLYOg2GfSkCNI33zJyBGw2VgMPVjyTms59m2ZkNKYnH+ASOH59fgsZESWweKT0LD7AclgmKFk52lucMGGTnoYNlCy478TmUhuU5jbA/6Qhvq7dv3w5JSUlQWVkJY2NjfEZ2RVhR9RqXYbn1srM6p96Z7DxyEh6x189e6IbsBZCdxvPmBus5doNRcCpY3x9nZEFRv1S3L1XlE88HHn4c8jkUPTuNEbLThL+yk5acCo0SFrlpxq3slIWbHf7KzsXao1OBlVGTnWMQ9ygIspO9fxKN8Cf2Hkk08KHz2KOUD6NXkMujL99f2HpftgzDt+WtsGr9BtiyZQvExMRAXl4e9PX1qTdR9zYTalBkpyw61SHsyizsZtlJDF3/onAChu+/k0uIM5NPQ21NK3uuhtEAUZ13pPtqssy9uAf1HVdMw9hRZLIvdG3X4SYXofJ6teNyT027np3G18Dc73Du5BhEYa9Ntl2r7GQcHIHjXKLi+sbh6MZh67Vs25ekofFy5l6gWG2Wn0fmV3auLh7jsvOCXKSd2X4MY9fLzpgaHdUSexSqNHYjUi9OVXbiY/bcT/u7YE1mL6xau4F/0di7dy/vVYE9hfB+nfjXZxxC5qleB1V2bm2HRhR1lrB6OHmMvUYavm64T6cypP3NTUgPV2QnW//yJWi9/Jq9x5h3MD0xBrH4vJ3sZPzIvmT3qa9hmXsNF7pwlnNJONrLTqQB0ocesy1i2O/XnWvQPPmMnTN9b88KCCu6JklQFnUSIbPsZPx48DhRln5NdKqvw3OzVGVnuQmr+NTASYh0wpPfp7MUNu7uhC0xjbCG1WmlrUZZ5bStxuVCduqh5KVbCMnomKUnO9V7dupkp0YRRy8+jUi9OA29Otn6e3Y1QXxcM6xZs97QVnd3d/O2+sWLF7ynkKeyOb1nJyU6kfmRnWZpWQ+9rP2ae3IechxKTgVaVvoDIRUXK/MgO1N24ARFeqSZ2RXM8lOVnez/OCFRFrvmTU9mbena9T611Rgs38LJzjzYX4YTCLEYhrDLJHfDxF3lnt2z8GDyDFy8j/f5dCE7GWlHRuD6c/k6aO4V3Ozrhy4caq6/Zye75rLblyI7UyIroPHMS/55DXw4eg1bRshORmrmIExclWd9x7D93ujrkEUnkhsastNyz06nQk8v2mRpJQurwvRKKM9rgrANm/gfpeLi4iAnJ4f36sT7dSptNdZdKv7IziNHxnyXnZbjdgJ1bnxBO59usUpOBaNQdM4Cy05xz05DhOw04VF2FmvMu+y0CE49OrFJoQ6zVpAlphMWu+hEWDkp2Ym/xtsatXvAuZKd8gRFf6vqhD+XtvCenarslIUnF50Iew/MsvMveN6VsrH/f94wAN819MPK9ZoQysrKgs7OTn7vFf0Fmae/PgdXdmpw0YkYZKcnUG7aEKfDcs9OFJe+gFLTBkVueoSQiwHmXw6hXsthxxJo2an07MRh5//dWwf/idXLTll4krJTEp3/Zf9XROcXsbXwS3ovrMk+Bis3buNfNLBHZ25uLp/s4vz58/yeQsqXZ0/1OjiyE0WnDlOvTn3PTs/oZKfpnp32SKKTRJWcduhlp1sIubjY0MnOzak4jP1DQGTnqq118MvGSsCenW5k588bsLenJjrxPp3b4nth9drNBiGEbTUOiXTSVuNzwZCdRtE537LTKjgVaMGpQIlLNxBycbERZNm5bWOpSXZKwpOWnZLoDN+AvT0l0bltUwns3dMKiQldsG7tVt5WY49O/GMrTiKnb6tx/57a6gWVnY4xScutvXAWe79zSWp6zgO0rPQHQiguZoIkOw/uKYV9Owtcy058rIhO5T6dWfsbYZ1J3rtpqzFYPn9lpyo6TbKTi05CdvIh7Hqp6QhJ0OlRh6W7QpabPqANXddDCMXFDB6LfP7KcvoDKju5DOTiz4nQk0QbPlaGruPM68WHa6AivwU2b9rK79OJbTV+fuDERHhfZazX3tpqXI7l9kl2KoJTjxPZeXiM36Zs5nwnHDEcuyeM5wWxSkwnGOWlG7jUTB+Vyn6uAw5zyelUdM6/1LSDH498DoXsNEbIThNBl50WsUnhTnaSglPBIjoRncz0xjyIzv9zCPVaDiunJjtPctk5yuoB/hpHNHX7JjvL2+Cf1V3wl9IWLjv/zOrSn0sUpPcdZ1XnsPcMe25y2PvxCVuG8HKxMv0L79PZdhx+2WoUQi0tLXymSLyfkKeeb0qwbHrZ+ZuQnTSk3NRDiMUAQIlMX1C3yY5FkZ2rik4ETHb+lNYOX8Y3cNn5372sLsTqqYH/xihUa+ypgq/Yul/F1XHRictWHBpgdW8EVoTtgk2bNvEvGoWFhXwGVJwUAMuEXzQ8XZBh8PngTFAUCNkpy8t5k52UwHQDIRcXG4TsvOan7Fy7ndWRsCouOyUqdBjFp0Ypq7vl8OumClV04n06tyX1w+p1VnmPbTVO3uKkrcbnhexUoASmUwixGAxWOoR6LRKkYex7Iusggv1eo+zctrHEhFF8ahTC9rBi9roSLjrD2f/jYtshKaEXNqzfaWirUQqdPn3aVVu9JGWnoUeo6TkP0MLSVwiZuNjRyc70fb0BkZ2H9pZDalQhl50SKDz15KnoBSiuhyiiMz2uEgoOdUDYBkne+9pWY7B8S1F20iLTCVaJ6QSr5EQImbjYweORz2FAZGdqEasr5azu5OukoFkGZhtQRF9WYi4XjCg6cQh46ZF6qCnqhKiI3bytxh6dRUVFfKTUmTNn+PdFJ221kJ3OoWUnLRStLE7Z2WmSnXui44TsDLVgI7CUZOcnLZNw9LEyFPgdXL96GYbxxtFm2Vk1ApXXnsEbeU149wyODvXDp6rc7IDEU/fhEW//PsKbxzcgq7kZJMEpDWOfe3wOVigis6gPsq7otqfmLQx3s9fld8uvuQK5J6fhxjupYZ17dQdymxqt0tEhlMj0BXWbhOwcZuX0VXa2X7oOn7D68RcUm7xXJzsXCHsPJZSenXLvTvbe8B6dDJSd/6psh7+y5ShCeY/O7gn4MTIawsLC+JeMgoICVQg9ePAA3r59y/frLbiOkJ0KVTSk3NSjk4kBghKWgQCPx1521vgkO7+Or+f32ETJiffr5Djo2fllbA38kMzaU/nxzwd7YNXBNvhtbRi/cfru3bvVITZnz57l93578+aN43otZKcCJTCdQojFxYhOdoalHPVbdv62uQp+DZMkp9SrU5KcZsFp7tmJsnPNtlouPH/eWAbrolpgW+JRWLluj0UIKW218iXDW1zLzj2TcHvuA9zuHIaqk0+kIY3sjMzcvgg5URWa7IyW1zt6CgZvS8MN526fgigUnTv6oXVCeS3LzBMYq2iHtd5kZ9QpuMW2eav9GFSMGfedFVkmr1fM1puQ1hsc1+17AqJQaEb2Qsu4bt8wC7fGjkPSWr3sLIGI7EmYeqrIB1xnhK2jf34CxuVtS2W4DOUpZew5WSpG9kDblHIbBuX1inSsgaz22/CUP2l+rTSMfe72OOzExyuLYWfLI/ZePoaBqlMwfkveJ97WoaoF1ugE55rkERhTntdHPySekJ0P/ZSdkez3Y3sYSk7s1VnCQcnprWcnis5d2yrZ/3HoeiHERrdATEwdrFm9kbfVe/bs4VII22qUQthWu7kGWQjZqUpMLi0/wN2OBnlZgzQ8/c4FaOm4Bndn5GvUZzeh5WApe74IUjvY+8yXKtFJz5hu6J18zGqSnJnHcLqW/a6qsrMeepTtDz2S6vfcI+jZW8ieK4Tk/JNwRrn3Ida5O5eh8UAJe04WnHtPSuUdOgE9px7Lvx8f4PnUccjaoheLBWxbp+DqM+XL5yzcHR/SrVMJZR1X1eOzPh8kdLLzYAo7V37KzrRo9n7IolORmygwjT07Jbmp9eqU/p8eUyoJULY+9ugsyOiErZui/G6rMbieXnYWptWpsjNvX80ikZ1G0YnQItMbVonpFCE7jeGyc3AMcpILNBGok3+epB6KKRSiBWnF0mNGUUYVFB2phQ3rwvj3RaWtRkHltq32S3byYezv4dHRWkl04uM59vjkKIyefwySrfgAr66NQG0iHmselAyi8DPl7RVoS8Bjz4Wi6hNw+b7WVr67PwX97Luwek4OnYCHbB8P2T5Oy+t9vH8CivfmQPEA2/bcQzhRcwym1G3MwsOxTihUZWcVHMdbLtw/CwNjD6Uystccz8hmz2VDYfUoXDLtv68gj/3OZ8uy0pS3l6ElHiViCbT0X4aHyi3TWCv+8OwgVPPnKuH4vffyciXsc4ALU1mCYhnSs9i6spQ80AbHzz2SzyFGvz1Eed0jON0+BpfuyVdVeOuJ9lq2XWU9K0J22mfZyc6v6nsXl+wsOQYdLz/Am8dXIbGarVMyALm3f5cuzvSys1he79EFCC9G+dkC4SN3WYM0C2eHOrns/LTnFrz++BI6WpvZ42b2/B24duU4fErKziaIOP8S5t49hspmVnb2OHz8MbzB17ewx7xHpyQ78Zf3zePLkFiJQnWAleMPmHt5GSIKdcLRAWZZGSj0svPr9uMBkZ34fivvrfW9k+Uz25+5pyxKzn+z+oVlwZnXf+w/A98dLoJ169bxGaozMjJ4j068cfrNmzdZY/TKcYODZXMqOynhuShkpwVKZHqjyh6L3NRjFZX+QklKEvahaAu1PgOPJ9Cy8wvsuYlD1xmfx8jsoSYoMk5M9HVcHXyX2MhF57dJzbA2bwx+2biL3zg9IiIC9u/fDw0NDfyLBn6Q4oQAOMmFk2DZ9LIzbDHKTteU05CCUw8lMZ1ASMXFik52bkxmn3f9H+Cqj7JzcJjVm7AK+EUWmz+tVzAOXacmJ/plYxms3V4nSc/IRohMGYYN7Is11mlsqw8dOsR7CWFbPT097aqt9k124itn4fbRQYhaVwYbUk/DNE4odXsSog2yU1pvurUT1ik9O9d1wyD7rJ6ZPg3Ja1FmVkFy9S14OjcDUxWN7LE32Slt89ZgP0SyZWtTJuV9n4Iovp4iO5V9t8FaXIaic20nDDxm1wRPp6EqpQKwN+ca/vo5mJkagQhZdq5JvQCP53AffdKyyF5om3rGttVoeB63vQafX9sCVRdew9zMbSiPRGlaDlnjb9g1zHlIQcnIn78PY9nlXDiuyb4Gb+aew0AqCs4ySKq+AdNjvWxbKCRl2XlrHHZyiSnLTjycmUcwkFXDlpVByuBzVq/YNvaxbeB6azvYseF5PSVJ1bWt0DI9q5VBkaIm2YmzsfslO9tOqT04UWxuXW+enEgSnNTERJGbSyEqvIKLzp2sbdyXMghr12xT2+oDBw7wthp7dN65c8d1W63IzvmcoMib7MTWY/bOeSiPKYaorR0wyu/BrLs/J9Wzc2s7W+8Du8yegCy+Xilk1d+E5+x35mptDUjCUpKd/AssrrdF6aWZD9Hp59nv1yzc6WiGaFy2pQlapl6x+noLGvegDFVkJ565Wbg7xK5Fw/LY686x132Ap0Nt7HWSVJSWaevs3NMFfZefsm3XseeLIWeI1cuZ+9BzgNVbXP/Acbj87D3MXB6S1g8WOtl5INk/2Xl67CLsi9J6cOrFpiI19UPWpftzIiinUEqw32m2bkZ8FRQf6YPE6KyAtNUYLJ+QnZ4JGdGJ4DHJ5zAQslPf01EvN+16HipDpLOT8lj9KuSCKpv9rC3ugh1bo3i9joyMhIMHD0JjY6OhrXZarwMvO3Grs/DoZDcUsWPNKj4LL1g79uJkC2QpgtemZ+eRIlx3Fh4MNEjLE+phAO/lOjsN/enSZE6S7JT2getlqudNlp38qYdwurqMLcuH6oF78A7ew8OBank9SXZiW/3u7hhUJ2jnO7PwjLz/erZdtiyhTt1/34Fs6f0w9OxUBGI+tJx7AS+vn4SWA5KwPFyA22L77a+U17Hr2UnIznj2Xj77AB+fX4f+fGzT2Pbyx+DB7By8uz4IBYbtycdbVcKW5UHN2DO2rWdwmgtaZR9G+PEqsrPppJCduiwb2fmjjew0C8/5lp2f9N6C19iTsrdNt1w3mZAsOz/hIvMpVNaw57loQ3QCkz1eMf4U5vg68tB2ddg6YpadSq9NXU/P6nNw4+MHuMEaM0l2dsnlmIZEVWzWw4qT8n6q2Hb5Mu9QkjJgsLIHSnbiB8TT16/hzLXr0DowCLkVlZB46DBExMXDlh27YGvkDltWxCXznp3/qGiDv5W1wrddJ+FrVq5VGzby+wnpy3H9+nXDfVecBNcLLdlJiUwnVNljEZx6rLLSHyhBqUJJTaco25CP6bMAyc6Xr9mH8dXrMDg0AuWV7PX79sPOXXtgW8ROCN++w5YVOw4ADmH/Jp79fiU0wsojx+C3vcVc4Cszr5eUlMDQ0BD/ooETErmt1/p7ds6b7CTlphlKZDqhnIYUnAqUxHQCIRTniW/C7KHW59jIToxb2fns+Ss4e/4KtLX3QV4++5KSkg47o/ayersLwrfRbA2PgnWbEnlvz9XhNfDr5ioITxiEjVENsHr1Jt5W42y+OOspzlB948YNta32NGxMH3eyk8Fl50d4MzEIG9Rh7BUQ3faYHedjaN0t9e6UZCe7IL90AnasKVOHrq/Nvg5vcL1d5aAJzWa5NyMKS73oRMyyk+17vB/WqsvLIKpV2ndLFPbuVGQn2/fUcYiURSeyBvfN/o1nV4I2dL0UduLr+XIUoI26CYKUnpx6KiSReXsCdqrLCuG3qHGpN2lrA3ts6p1poJjtD3tqPmLlxf2bn2evvcVea5GdrHxZ5Zq0VPbXwtoc9vhX82O1R6i8H+V1rLyBlJ3PWb0+d+4y9PUOQWlJNSQnHYConTEQwer19m07SbaF74StYfu47IzcUgaRWysgIa6LvUaSQkpbXVpaCsPDw7yt9jbJhTm4nlPZaSc8gyI7+ezqynrFUm9O9h71xhWxx7Ts3F1wDd7q1+HIExndGYdUvezUC0xOKZSdYvX1zkl5PRmlJ2dHve4x2++pblVs7gyrY9t8L79WewxvrkIZ1VNzSzecefM/tk0Un8ryAvUYe/ZiD1Td+jbs2GgPtT4ngLLzxfOXMHXhMnR39kJxYSnsTz3IvoDHws7IXbCDXYNQRLB6vXtbMpedB/aUQGp0MeSnd0La3hJYv066rva3rcZg+TzKzoRy/hzKTovwlGWnQXgK2TlvJEfYQ63PwWOSz6E/slOq1y9Yvb4ERweOQVVFNaSx6+o90TEQFRnF6vZOkh0ROyAhKpEPYc9Jzoes5AKoyGliv2PZvK3evn07b6vLysp4W33r1i3XbbVFdh42ys6SjArIS6NlpzQbu1l2foTZ892QrQ5jr4FR3pNyDEo8ys4iaDv/u9RLU13GUHpyDlRzOSc9/gjvrh/V9dbUy072/aW6ULe8Wu7Jib0/8bEsO1FgHsTenIrsLIBWvv9Rtp6yjJGBcpPtv7+K/W7byU6C+A649PYjfLw3CkV8mXPZebjqMszYracuNz5Oj8mU4OXD8iqS1YpednY1jwvZqcuykJ3fNPQtUtnZBonX3rJW6RlU1rLn7WRnYau0nk0k2VkPf6o6AcOv8QL6Hdy4dgUquzs8yE4PPTvxsTfZyYe643pGqWmGlJOBhh+PJDu/apNk5xCeF4YvshOfx5v143BcbBhqamr4FxWcUOjIkSMkmZmZsO1IHu/Z+Y+Kdvim4wR8lyn16FTuJ4RfMlAIYY9OZYZqpx9cGFy3eXS5y05JANpiEZwKtLD0FYPYNEMJTF/AcrNjCoTsxHqNF0q4Ht7gfGBggH9BwAkqvNXrrexCjPfsTG6BNTkjsGZ/E6xbv5H3EkpKSuK3ZcDt4cyn+JdnLIfbem0rO/cuxZ6d5faQklOBEplOIIRikKCkplPU7QRIdip1Gu9hhT0fcEbp6upqR231bnZx/MvGUlgVXgNb4vpg445C/iUD22qcDED5kqEIIbdtNS07O13KzjJYn3uDi8SJ3BqD7MT11qmysxqyJ6w3o1Eiyc5ySV7Ky3jeXIestXays0QSqFxWVhlkp7SeIjsruaRkFxbQElUKmuxUJCiKykb4bW0fjL/5aJKZOuTn6Siysxgicq9Iw9RnHsP44EnI4j0+UTYyIgdgnA+Rn4Vb4+ehhbWR6nMrPctOSWQqchOPsRdW42NTz85fTT078TWq7FzNzgfDX9npT1sdtydb6tm5vRqSE3shZk81r9dKW41DfXF7165d87mttpOdabGLTHay9/ZsQRl7TMnOMiif9PA7Y5adXETqpOaWHjjjob46kZ2q3OQyk/1uqPLTSHT+VXa1bRMPspOSmk5Rt+NBdmZmNs5LW50ad1iWnaWQd6AdEvZkBrStxuD6muysFLKTYLHITkpqOkXdDh6TfA7Lc32fjd2ftvrQvkypZ2daCVTlt0J+RiWEbQzjPTqVtnpwcNDnthrLhq9B2YlSM1iykw9V50PZbWRnQidcfmvXVr63yE58baYqNBE72VnIJSbuv5X34pRl59vL8mOZBElO0mH79yY72ffbvtErumHsUtzLTuwlysqr7+kpI0lQLEslpMdkQaGt7JTKd0hZJqNsR8hO+wjZyVgystPSs5OanKgZwoemoOsODoX/Ax6cP+pxGPsfr3+HJ8rv8LtncOxYH3yqTkrkn+wkxWQwsJGdGLeyE4MNADYEd+/ehUuXLvGhufihh6Ly2LFjJPghl9bQymXnV63D8HN1O//rHH7JwBtM679kKH+hs/uyYxes12bZuSa333/ZyQgJ2WmQm2ZoaekWUm4qUMLSH7Dc7Li47Cweg0MXAS76ITuxXr9+/Zrfz+rKlSswMTHBvyRQ9VkBL7YS85vg6/gGLjrXH2iC8Igd/IJs3759UFxczL+44O/Js2fPeL325YuGkJ2UxHSKTiQGCUpe+gqWGWXn9wGQnUpbjUO8cPZdrNMjIyNe2+oj7IIfh7FviumCiDj2mbnN2FZjvcfe9/glw5c6TcnOPTGBl53SBEWy7LT07LRDE5qcoMnOa+zV7mSn9rwsKSnWtkB5/yXp3p/qsHXl+TJIqjgNR/l9Pf+Ax4Md8v03rbJzh2PZ+Q6ePX3L1sWwLxu3LkN5cpn2GoZBdm6WZKevExT501ZnHaqHHez3CkVnXGw1uwbZwYf5pqam8t73eJ/Oy5cv8z+2uv3yjMH1Q0p2Wnp2akhi07PstPTsNBMw2fne1LPTHkpe+goeg53sPHy4fl7a6qKsKi47s1Ob4fC+Gt5DOZBtNQZfo5edBWnaBEVCdlKSE9HJw3mAkpe+guUPlOz0ta2uKGSf20n5UJXfBoWHK2BHZGDbaiwbvq6U/f4sBtlp6dmpIsm5YMtOS89OhnQ7ARnbYexsH+pwcrbM556dVtmpyMpDfspOhO8Tj0vITjJCdjIcy06kWCIwsrMJfpl4JolD/TB2fh9PdiGsyk72xQyHqON6Pa1eZKdMQYskSHEbRfjYNGy9aASGZz/Ag/OD8KksCzmG2dd9l52klAwWctlRdn7Zdhz2+ik7sf7gPaxwghUUk9jLEz/I7t+/T4I3jcbGo2ToBPy9ogPWdR6H/YczeW9OHGKDf+VDIYQfXCiEcP9uRScGy+VRdh4QstMeWl66gRScCpSs9Be57FLPTkl2+tqzE4P1B9fBG5xjvcbZSqn6rID1Gr+YZDcMwYr0PtiR1czvIYT1Gi/IysvLoa+vj/9FG39H/PmiQcnOrezLFcrO1T7LzqplIDutYjLQUMLSL+Sy+9uzE6O01Xjhhl8KsE47aatrm4Zh5dZa2JU6APsPZEB8fDyX9yik8GIQv7T4+iUDExjZaR3GvmY3JTuVXpsoJquBFpx6dKITcTmM3Sg79cPVzcPYpWG2LVEl7LE0jN1WdhqGuSvi0gtre2H8DdvzuHJfTj3lkoRVtmeSnTgc3YnsXJ0l3wd0n1FuWsBj0MvOet9lJ8bXtrq6fAD27m6FtNR6Q1uNv0/9/f0BaatDQ3YWWdcxIQlLG9lpu9yEE9lpGdZugtwGDSUs/UI+jig/ZSfG17a6qaob9u8phcLMdjgYhLYag68TstMeq+hErEIyWFDC0i+w/Hgu2Dn0R3ZisO740lZ3NvVA8eEaKM2p4RIK2+q0tLSAtdWLRnay723qenyyIjO+yk79MHaUlzay02a5QXQilOy0iE1qmVPZaVxPE5Wy3GTrjbD1fJWdCB6HkJ10Qlp2/svunp2V7YtEdjKqJ+HMO3bJhRMPlbDHJYNQ+Uiep0uVnQw+QdEfMPf6LuS2tEjLqkag5s4LuDbeA3/C+3c+egFnT43Ajyg7i/r5diS5ScjOgg7IUmeRVMIu8h9PQ2V3mywtfZOdpJAMJgGWnRisQ9gQ4Lo4LAZndkTwA40C/7JXd+oc/KfpKOS3dkJRURG7GDzML8iampp471DcN24Dv+TgB5HbYJm47EwRspNEloM0tMB0w7xJTgW57J+x4zbLztiSNlcXZErc1mv8QCzqOAlrjwzwibVQ3ONkANijQpl5HXtA4x8GcLu+1muz7NwairKTlJwKZonpFFpQBgpSVvqLXPbv2DnZmOSf7MT40la3dZ+CDbs6IL+4hd+GAdtq/Nnc3Axnzpzhwy39aat9k534SvMERXP8/pw7UXTayk4Gn6AIJwm6BVUp2BOzFFZG9kM7nwCoWXqsohOdiO0ERTjB0Ch/bC87i+G3yOMwhZMZPb0KOZHYu7MUIrIvwC3DBEUlEFFxG2bYPtQJiMgJiv6ApxdOyDOsF0uztz++L90fM2ocph/fhjZ5ePqalFOsjP8D9X6e089gqr2H7Y+9Vhlurvbk1GTnDi4oHfbsjBzhx2YMDpMfhyORxRbZiWzYXOe37MT40lY31R2HxPhO27Yav2hjW+3PNchCyE5VeAZMdjL4BEXsd0aduZ0ti+mGfj4xkDwM3YPUlCYowpnVR+WJi3B29lNw9ck9ebZ2tsyR7CyA5Jpb/HdDmuyILaMmKJp7BZfrGuXtVEJZ1y14eksTpKSs9Bf5WPHc+Cs7Mb601Z2NR+FIciPUVzcHpa3GYLmoYewlPstOQnguCtlJy0xvLJTsJEVlIMDy4/lg57A8xz/ZifGlrR7sPAYVOY3Q2NDIPxsC3Vbja7A8KDuDec9Og+xM7ILLb3GynWNQFJcLBeklXHpKExR9gJdXh6FGnZ2dfdY+uwujh/QTFHmSnSyWCYpmYLqtnD22l5qINEER7n9InrgoGwqqTsI02//xdHmCIi4xlYmCsiDvQCEcZt/DjuNs67M3oQ8nKGLfdftxYiM8vzoBKg1Df8uuZcrY43woOJDDfpplJ1v3wCBMz36Ej8+noHW/JDbzq87AQ5yg6Nog5HNp6Z/szMBzxs5nd/OEkJ26hLzs/KKmy5Xs1AvPeZGdjE9azsAkv9cmy8c3cObUSchSemUqslOVm/JM7Zh3z2B4XJabjE9bTsGxx7IohT/g0Z0rkMUuCKR7dpplZzukXHsFr+9MwI+yLPxTUR/k3mLbVycfWlqyE8+BIjuPyWfBV9mJwQ8LrEv4YeMN/Otb09kpSBw8yT8g6+rq+D7xggyH6uD9hPDCDf+y7c8FmZCdVfaoYtMMLS/dMO+iE5HLr8rOCwAX5M8mX2UnBusfQtVjM1hfy3tPQ8vRSd6LE++zhffIwplP8d5b+KGJf83GfftTryXZWR042akKTyE7fYGUlIFCKTs7J4GQnRi3bXX3wDlIzx3kw83wnsy4PxRE+BmBkwH421bjfijZuSvaS8/OS1PQdQmHYGNm4fbJE7Bvgyw6PclOZAfKTeW1LDNPYLwV5aVedBLCU+nZOTUFHerrZ+HW2Cgkr1XW8yA7GWtShmFAv++51zDV3qvOxC5RDkkVV+CWKg9RGk5AViT2/MTnSyS5ye+7iWEX9rev6e69iUPUL2uvx30MDsliFOXnCIzdVv6A+wc8nTrPh5tLkwj5KjvxPqC/w1RFrbrO6uQTcIGVUdsWQyc7N26p57LzsZ+yE+O2re5oPQWd7WMe22p/r0HsZWe9Q9npXXjOi+xU5eYr3e/MYzjbwa4ZZcnnuQenLDefKV8YWX29cxV68qu0dRzJTqQYsmouwV3d78Zd9j2gbI9yP05Jbj5XC6o9T0rKQCEfR6BkJ8ZtW93bOgKNFf1Ba6sxWB6D7NwvZKeekBKdiCI7GWUBkJ0Yt231SN8oDA+eUNtqHCkVyLYaX4flLvMmO5MDKDvj86Co9hK8lNspbei6LDdfaG3lu/tXYLS6XBKdjmTnG5geOwvTyjZmH8FUW528rmfZiXKzsPqk9lrc/73LcLyqVBKEnGwoqJnSyq7IzAMdcPrejLQQZuDh2Um49IC9L/renrEl0HbuBdsq5r08iRAhOxmH8vph8pokTHnmXsH0YJssOpEAyE6GkJ3GLFvZ+c9FJDutNBhRZKcFSXKSqBMTKchCUIHPvI7CEmWovCy/Dj7tnpZmh3cw+RAFKSODjVJ+dtxftQdOdroJfoBNP3nKhzCcO3cOjh49yjl16hSfIRLvJ4QfXFg3fQ2+1pPs/EHIThtogekUUnQilKAMJHL5Ay073QTr3NMXr3jvzfHxcX4PLQQvyPAvz6/Ypynu1996LWSnXmA6hRaV/kIKykCilJ+dkw1JA5DT57/sdBO8wLtz9wnjPu9BgffPwrYae9/j5HFKW+3Pl2f8PLCTnZE7mu1l5ynjPTsRVXTKrOaSU0EnO2WsYpOCkJ2GYexmNLlpRhu6bkYvOp0iiUu/WEmjCk3HyEL0zTU4Ik9GJFEOR+Qh8upyLLtOdhYESHa6CbajOIt7sNvqBZWdjtEJTR/QhKa/KDIzOJCCMpAox8HOCcrOnv4ZePJCqgu+yk43wbb69s17vPdmsNpqDNZrITvtmW/ZSQrKQKKTneW5g3D61AeYlauur7LTTbC+PXrw2NBWY90OZFvtl+xMlOCiUyFBQ5KdiuA0Yh2m7g1ZdspoklPBbhi7HrPc9I4mOfUo8jI4UIIykPBjkM9JV9MEvBSyU82ykp0/uJSdZuG5dGWnLAP1cNn5Bzy6PQnhRfKyyhHoePwO5l5ehgi1J6dzSBE5H8jHxCcokmWnv8PY3QbrHG4XL76wIcEJW3AyIvzgUnq++fPBhcHXu5GdFuEpS05KdtrOyB6/RGSnQW6aoSWmUxZEdCJy+VF2ri46uSCyU7lgevr0KZ8E4OLFi/zes3jvIfywDFS9FrJTLzGdQstKfyEFZSCRy4/D2M2yMz4r+PUa6xv2GMK2GnsG4WQZgW6rF7fsNInMZSA7NVHpBll2zr2GC1UtUk9Ptmx71nm4NfOBT34kLWNg2RXZudUkO0/Mj+ycr7aakp2HklqF7LRAC8pAQcrJQCMfC56ThZCd89FWY3Ab8y0705as7KQFZaAg5WSgIWSnvz073cTcVmO9DnRbTcrOXKPszE8rFrJTxSonAwklJwONXna2N4wJ2alLSMvOf5Y2OZKdZuHpj+z0LDzNUtOOBiOk6ERksWnGIDoRWWYaaILwoStw9pX+gvsd3Lh6FhIr2WvyaaHpCVJEzgfyMS2k7MQPFmw48H4teGGGN5bGizG850ogPrgwuA2Und/oZOdaN7KToZedBuFpEp2LU3ZW2WOQm2ZoiemEBROdiFx+UnYWzZ/sxC/leJ8hnFwL6zVOBoCP/e2prAS3ocrOPYTsjDbKTlJ4OpSdZuFJC049lMz0RjkNKTkRs8R0Ci0r/YGUk4FGLr9yz875lp1KW4331MI2Wt9WB6pOK7JzY5hn2bl+g/+y0yw8acFpRicyQ0l2rrRilZguwJnfB+/AU32VmHkM4y09sF2/HpbdLDvfSKvPp+y0a6uxvgeqrVZkZ8puq+xM2mWUnZTwFLIzMJByMtDIx4LnZCFk53y01RjcjlF21tOyc48kO+2Epyo7ufDUZCctPPNc9u5cGNlpFJ0ILSkDBSknA40H2ZlxsGRe6nWwr6txH1hulJ15qfIERU5lpyw8DbLTofA8HE8JTU+EvuykxGQwELLTPkJ2yvgjO83CkxadiFlq2tFghBSdSL1VdCKOZCeBYSZ2d5AScr5Qys+O/av20QWRnRisd4rwxA8t/It0oC/IVNmZbiM7U4MjO70Lzxp7LJJTgRKanqiyxyA3zdAi0wnBFJ3/9ALKToTLzmJJdl6Uvx/Pl+zEYL3DeqzccB3rN9ZzvJhC/A1uH2Xnr4Ts3Mj+j7LzN2+ykxKesuz01LuTFpx6KJnpjXIaUnQiZonpFFpY+gopJoOBUn52TsyyMy5zfuq1UqeD1Va7lp2cMo5edHqXnZLw9Et2OoIWnQgtOhGzyPRGoX+stGIQl8EEyy/LzrCtDQsiOzHz0Vb7Kzsl4elZdmrCU8hOClJMBgP5WPCcHEjuMcjOjHmQnZhgt9UY3BbKzvSYIk12ZriXncbenUJ2uoUUk8HAk+w8EHzZibFrq3F5INpq3AaWe+nLTifQQtMTIS87608I2anLspSdn1d1OJadFuFZrCFkpwYpIQPE/+8NVnYuPNmxm2XntsaueZOdGKx7+CUGGxH8iY8DFdyWXnauPNLNZecanez8FmVnsm+y0yw8A3bfTlJ0IpTQ9ESVPQa5aYYWmU4IhuykxCYJKzvv3cmOfSFlJ140Yd3DOh2sem2QnfHzJzu/40LTE5TM9EY5DSk6Eb3AdAMtLX2FFJPBQCk/OycbkwYXRHZisN4Fq63G7c2X7NSLTiE7NUgp6Su/eWEllp8ds052Pnkr1YX5lJ3z0VbrZeeBOLPsrBKyU4UWlYGAFJM+kesZvj9ZdqYsjOzEBLOtxuD2rLKzmcvO/EUjO5HQlZ2klPSJLO/gPvnxoew8ymWnUnXnS3YGu622lZ35RtmZnZxvO5R9+chOq6AMFJSU9I3DXsD9Cdlpl5CXnV+aZOfXOtn59xJadpqFZ0BlpyPh2WCEFJ1IvVFyKoSI7CTFJgUruyY7T5hkZ+e8ys5gBuu1N9n5zb4GITsN0BLTCRbRicT6Dik0PcHKr5edGQskO/XBC6hAB+u1E9n5i1vZqROevstOhBKaniinIUUnoheYbqClpa+QYjIYKOVn52Rj8sLJzmAGv7h4kp3h2yjZKQlPs+z0LjyNstOZ8KSEpido0YlYJaeCWWZ6Q5aWvrLSCCkt3UKJTQpFdjLWb6pdMNmpT7Da6mDITkp4moey01LTDqvAdINVXPqCVVIGClpcuoEQmxTy/vCcLKTsDHawXvNh7AGVnUbhSclOf+/baZWZ3rDKTG8sLdlJiE0K3Cc/voWTnfoEo632W3Z6EZ6q7DQJTy47XQlPo+z0TXjSQtMTS0d2UmKThu+TH5+QneaE9gRFZc1CdjqVnVx0IrTM9AQlKv2BFJqeYOVH2Ynn48v2ExC7DGTnz7ph7GbZ+ZVOdpqFp63sRHSi0yA7EVJumqmhIUUnQglNT6D4s8EgOPXQItMJCyo6EVb+xSY7gxGj7GySZGeykJ2eoYWlr5BSMlgox8DOyXKQnVvDKdlZr8pOc+9OITt9YKUVUl46hRKantDLzrBaKKhbeNkZjFCy81CSkJ00VkkZCGh56RRCaHpC3mfUpnw4KMvOZ/IXaCE7admpCk8hOx1Di0s3EELTE7hf+fikYezvWd2V6sJCyM5gxCw7yw5XCdmpYz5kJy0v3UBLTTv4fuXjM8vOXTuihewMteAHV5lOdn7nQHaahaet7ESKJdzLToSSm2YajJCiE6kPOdlJykxvsPIrsvOrjmUkO4+4l53mGdkNwlOWnKTwJOWmmRoaUnQilND0Bso/AoPg1EOLTG9YRCcS6xukyHQCK/9ylp3hetm50yg7SeHpUHYG/76d5TSk6ET0EtMptLT0BVJIBhPlGNg5WY6yE//vSXbOz1B2Smh6ghadiFFw6jHLTG8U+s5KI6TAdAolM71hkJ01y1J24v07UXbG7zDKTkp4Bn+SIqvAdAMtL91iFZX+QgtMpxAy0xvyfjXZ+TbkZWdOcpUmO9PtZafX3p3zMCM7LTQ9YZWZ3lj8spOQmd7A/crHV5rdJ8nO91JdELJThw+yUxWepNikELKThhaanuD7lY+vvX5UyE5dQl921vovO/XCUy879cJTLzs9C09KbpppMEKKTqSelp3IPMlOSlj6CikyncDKvxxlpzKMHfnVg+w0C09b2cnwJDsXde9Og+DUQ8tMbwRKdpIS0yms/IrsXFl4gsvOKfn7cUxRKMvOdr9lpyo8ueys0mSnLDzdyU6EkpqeKKchZSeiF5lOoMWlL5BCMpgox8DOx3KRnTujPMtOTXgK2ekTK42QEtMplMz0BiE7H8hfNIZGp4Xs9EF2asLTF9mJWCWmG2iB6QarrPQXWmI6gRCZTpD3u3xlZ5NVdi7ofTuF7DRCiEwn4H7l41sOshOlpiI76wqaoDLbKDu58Fww2YkI2alBi0wn8P3Kx9feIHp26hPashPv2Ymys6kffnIgO/XC06PsRIolfJKdARSepOhElpjsJCWmU1j5Ndk5tnxkZ1bgZeeSvW+nQXDqoWWmN4TsnL/oZec6Qnau2d0oyc4Io+w0C0/7SYoQe9m5NIey0+LSF0ghGUyUY2DnIyz5qJCdBtkpCU+z7PQuPIXs9Ft2UhLTKR5k57HjN5eZ7CyH2O1G2elEeAZWdlrlpVtogekGq6z0F1pkeoOQmE6R97vcZWeWjew0CE9SdhqFp/9D2Y2ycz6E5+KVnYTEdAruVz4+ITtl2WnTu9MiOynhKWSnBVpkOoEWmU7g+5aPT8hOY5aP7GzTZOd/dLJz/oeyN0qQglNPgxFCdCKk6ESWuew8JteF8FCXnXk2sjPQ9+2MX+ayM9Y3SInpFFb+5Sg7N3HZ2c1lZ5hJdnrq3bm87ttJi0tfIIVkMFGOgZ0PRXZeeS3VheUiO7dtr4ewTfay033vTqPsnE/haRSceswy0xuyuPSFlUZImekNSmI6ZRnJzvFRJ7KzxLXspISneSg7LTbtsApMN9AC0w1WWekvtMz0BiExnSLvl5KdOXkdoSc7YwnZmSrJzkMuZKcqPENgKPtykZ2z76S6EMqysya3wbXsXIih7LTQ9IRVZnpjccpOWmI6he9bPr4OITsNEbKTMb+yE6HkppkGI4TolJDlphmD7ER0YpNCyM5FH7PsXGWSnT/qZGeghrKrshMh5aaZGhpSdCKU0PQEyj8bDJJTDy00PREI2UkKTDewsi9r2Znig+zUCU/HspNBC049lND0RDkNKToRvch0Ai0u3ULKyGCjHAM7H0J2CtmpIYtLt6y0QspMT1AC0w1CdrqWnZLwdCc757N3Jy0w3WCVlf5Ai0wnEBLTKfK+UXbuT+risvO53FYvH9lZK2RnEGUnLTKdQEhMp+C+5eNbDrIzL026Z6cn2cmF5yKRne6Fp1VmemM5yM4XL+XKwCJkZwhGkZ3/Vu7ZKcvOH3Wy8182slMvPPWyM7DCs1GClJwKDUZI0YnUB0Z2IgskO0mB6QZWdkV2ftk2GtKys8UkO9eZZOe3qY2OZKd5oiK97Jzf3p2U0PQEyj8bDIJTDy00PbEoZGesVXaelz+bQlJ27tBkJ/bqdCI7SeEpy06z8FRlJyE8acFphpKadpTTkKIT0YtMp9AC0w2kjAw2SvnZ+diQNBC6sjPHKDtjYj3LTk14SrLTLDzNstMsPIXs1CCFpicogemGZSY74yI12Xk4uZXjTXZSwnMxD2WnBaYbrMLSH2iR6Q1CYLpB3vdykp25TmSnA+GpDWXXZCctPPOW2H07rcLSH2iR6Q1CYLoB9y0f27KVnYf8kJ2U8PRLdiL+yE7EKjQ9YZSdgRWetMj0Bi0w3cD3Lx+fkJ3GLC/Z2UrLziXRu5MUnUi9kJ2s7Mtddv6W2WWVnSbhaZCdDKeyUxWe8cGQnQglNT2BApDAIjkVaKHpiQWVnTEyKDsZ/96LsnMMMs5rsnN3UWjJzkGnstPLfTu58LSRnR5nZSflphlKatpRTkOKTkQvMZ1CC0w3kDIy6JRJwpOdjw2JkuwMyXt2ctlZYZCd0bvbHchOunenN9npvncnJTS94UZ4mmWmNwp9Y6UVUmh6ghKYblhZBL/Kx71+U+2ylZ3J0dV+yU5KePo+lN0qMN1AC0w3WIWlP9Ay0xuEwHQF2wbuf5nIzkN7i7nsLDLLzkT3slMVnl5lJ0JJTTuE7CQFpivYNvBY2LGVZi0v2Vlf0AxV2XW07PTnvp0B7t1JC01PWIWmJ4IpOxFaaHqCFpiu4Ptmx8LOR3u9kJ36hLzs/MpP2akXngsqOz0IT1J2IstRdraHvuz8dl89/JLRBquzeyCs6BhsKBiEVVk98HN6G3yf1qTKTre9Ow3Sk5KdSHwwhCclNL1RZYUUnQgtNL3hr/AkRaYJcj1Sdp7gsvMc+2yaY3VhechOVr/j2mDt7kZYqZOdXmdlp4SnN9nJoAWnHkpqeqKchpSdiFlmeoMWmG6gZWRw+HqjDMpOLjwl2ZndG7o9O8M2+Sc7vQlP/2QnQglNT7iRnQglNT1R6BsrjZBC0xOUwHTACgVZdiLrNtVCvpCdDO+yUxKe7mTn0hGeVmHpD1aR6QRKYHoncoOMvB08nrTELujuWz6ys8wgOyu47EzXy06d8FRlJ9W70zSUnQtPv2Qn4o/wpIWmJ0JFdiZtl8FtyMdTmt0PkxMhfs/O/cVQlqmTnTmy7GTLnchOT0PZA9e7M3RkJy0zvUHIS4cc3CPBt4PHws4Hys6XQnaqCXnZ+XVdN/zSPgRr+sdhdd9J+KF5EL6s7YZ/l7fCP0qbVf6u428mPpX5xMRfSyT+YuLPMn/yiFGM2tOoUWRHA/zZTKGZeu8UIHWuIKWpQ5zITpSY1HIziuz8ou24QXZGNnWpH1yhIDt7T12BH/c3wZ6mc5Bz8im03noLfQ//By3TryB77AH8sL8Zvkmph691fJVshPf4lPmCC1CN/yok1sF/THyukFAHn3lFJ0j1xNtR45Jqmjg7qlzjXKbS4BB018iCUxWeDHy8umgC0ib/gLPsgmyG1YWtaXmQkpIClZWVIVGvUXb+tqMa1sc0w7aUHojOGILdh4b5rOwbYltg9a56WBFZLRFRDb/o+Hm7kZ+QbRI/KoRLGHt64tB2je+2eMMoR71TQbOZotwHyvzGKlDtoQSmGU/rqbKTocjO9QkoO/+Ay7IYSsxuDznZGb6tlovOfanHICGxTzcjex3v+blho8b6DQpm+amxdp3GmrVGjPITh7Z7wypIPVNCstKWYpcU+cYqDVqi2uNNdv6GIpNYjhhkpyw814VJPTvvy180jo1cDznZGb+jFPbtqeGSsyCjj4P37kyNqYOEnRVcdu6NKJWlp0bMNitW+WkkeosCJUC9oQlSX6AEqDvyA4ZVptrjRHZKApV+TpWdDL4dts20xE7o7puBJy+kupCTF1p/cFVkZ96+aig/3AL1eT1Qm9sNxQcbITelGg7HlfLZ2tP17JGgBKgkQWWiEbMA1WOWn96QZnFXoIWoJ4yy1Bv+ylJPGEWqZ5zITkmg0s8ZZKcsPEuwZ+fkB5jBC2uWjAPFISk7ceh6a1kndFX3QUNhC1Rk1UBRehnkpBQY0MtPlSRiiHuiBiVAVQlqEqCeyVahhagnjLLUG/7KUk9YRapnvMlOTaJan1NEp0V21o2KCYp0CVnZ2XDuEnxR3QFRp69D5qPfoWnmDxh49xHqX85A+s2HXHb+s7SZQwlPs/RUhKdZev4VKbFKT0V4epaelNykaNQgZOefOQ00i1x4IlxU+oEqPdn/v+4ch+h7H6D7IwB+PEXUNIeUFDp/4x78kNYIVRdfwG12GO/l5/BrU/PUQ96z87vURj6cXeEbZJ/G1/saNFIa4CsdX+pJboAvdPxXIakB/uOVeppEO6xy1TO6Xqh6EihqXPO5mXh3cCEbR/VI9cBeIyg6/yH38Pw1fxR29z+Fc7MAN95/hA27kni9rqqqghMnTsDjx4/h/XulNiytYL0+MXkdVkXVQmrxGSjrfgTDlz/A5K33cPzK79A8+hJWR9fzCYzshKdZeuqFp1l6qsJTlp6LR3gilNT0BC0x3WCWmsGAy00ZvfAMSxmCw50zcPYxqwesLsSk14dEe40iq7x8BDazOnMwfRRaWh7C1KX/wZUr7+DM2Zdw9NgLiNrVymWnWXhq0pPq8WkVnmbpuSyF5yorlNi0ZaV7uNyUUaXnb0WwflM9ZFc+hkfyF426jklVdmJbfefOnSUtO89OXoPEqHKoLRqDke47cOfyLDyYnoFbl1/C2dEn/L6dKDoVzMKTkp57wt1LT1puUtAi0ym0xHSKJisDASU2PWPtIeoNTZTmGYRnekoftHe+hIdPpLqQllYeUtfWk6PnAYer99ePw+UT9+HZrbfw8uEsPLzxAs4OXQcuO2OLrMKTkp5ehCctPfNthrjbsTyFpwTdS9QTiihVhScDH5flDMKJkVfwTpad8bH7Q6JeK7KztqQRavMb4cyxC/D64Vt4+3yG1evX8PjWMxhoGrLITi48UwjhyZhv4eleetJi045gCk+EEpv2WHuIOkHpGapJTxSeKDvH4OlTvKoGePH8A0RF7hKyM9SCH1zHrk3Df6vaoezZW3jAlikaAN/6wtuP4LOKNi48kX/p+KeOf+j4u46/6fgUKZP4RMdfdfzFlhb4S6kTmjVKaPRD7A0Um2n0TBHS4Ji/BABS0tpRWG8Zro+i8//j1MEXbaMQduEJ9LM3epo19uv2Z8K+fftUKfTo0aMlLYVevv4dVh5qhcrJu/JSLYWnH/H7dqLw5NJTx7dIqgYXoDJfcwGq8ZVCSiN8aeILhWSJ/+r4IqXJ8FiVo2aS7Kh3ia4nqq5HKo11yL43nIlUe/S3AXBEvMZncTWa7NxbDV+ltcPmukvQ9QIgaeAGbNqyFfbv3w91dXUwPj4OT548WdL1+gZrk1F21g4+hCfsolP6iJYyfP4JrIlu4EPZ8d6dnB218KuJFZFGfonQ+FlhO1IDPylsq4EfTfwQ7g0Upk7RiVU9W+2odEmF33y/JZhYh+trPTzLYH3iACRW3oWLjz/Co3cfYWv0wZCQ+CiyenrOwZat1VBcwr5kvP4oPyPl7t3/QeSOJti0uYoTtsnIxjCFSs6GjUbWbzCybr2eCli7TmPNWicYhalnrMPpkdW2lLqkxD1rjKxyw2p3cCGrDtOXUGXnr4WwYVM9pGVdhcfycN9DeX0QFRUFhYWFMDY2xt77u/DmzRve7i21YJlvXr8HCTvLYeLoHXgvCwIlU2fuq5MUKcRFllnYG2EkdjuCYlQjZpuRPeEKkhzdvdUpVnHqBn2PUt8oDBiUjPVMgQusslQvO9MSOqGq8jo8fvIRZmY/wo6IxJD6g+uVCzfgSEIZTJ9/BB9Nf4e4euY2e64cDseVwOG9JXDITKxERmyxRoxEusIeRC9GGbuNaFLUCZo45fLUIE6dYBxa7w1vstVXaBHrCVrG2oFy1ihKc3SyMweKDnVBZ9MV+N87gN9f/wGR23aFRL1G2Ynl7m8fhPbKbph9LY/T16W1vJPfzzMv1UhuaiHk7iNIKTSK0WQNVYomWdFLUe/kGjDLU8/kuMKbaPUHWsZ6ghaydphlqr6H56G92VBf2gcP77/h7/PJ47chPHwbZGRkQF9fn/oHVyE7l3i4FGJv5A81HVB666G8VMu+Gw/5fTs/r2znfKbj3zr+peOfOv6h4+8VEn8rb+N8quMTHX/Fn2w9/KlHvw6nzChKJVqMlFr5hGPsdapSosc46ZKFYqTRFaQ0dcFf/AR7dKLsxJ9/q+6CH4+eh1LWru+ZegBh27aHjBTCD6/Z2VmoGZiEsMKjMPDgnSrxZxkHj97m9+3EoezI9ya+S9Pg8lPmGy4/Nb5W2NcEX5n4UiGlictNO5wKT0Wa6nuQesKr7ERI2YlYhaY3/BGepND0hE52Iig6Fb5MbYNtzTch5ugTWBmbCdu3b4fDhw9Da2srnD17Fp4/f75kP7iwvX779i3EZnRCQcsVeamW9vHHsD62mQ9lX6UQJbFSx287jfyK7JBYoRBZD79E1mnyUxagP+nZVgs/Mn7aXsd/6jGsJ2OWpVZ0PUv1hNthvOeodyr9gpaogUO5P6rCN3LvTvy5YmczbD80AT3XP0JGy23YtGVbSLTXWKdv334EmzZX8p6d5kM4f+EFv3/nlq01nM1bjGzarFDNCdtkZGOYkQ0bq2H9hioVo/yshLXr8Ccur+L/12Ne1yxLaay9S5E1JGUuKfWNtUZokWrDGnesMqGITmQ1O6exyaNw6hpARfvvsHlrCuzevRtKS0vh1KlTcP/+fd7eYR1ZasEyv371O2QfaIGj7ZfkpVpOD9+DlN01kBhVqYLD2s3E77ASF4liVGNvhIQkQiVitiFWAYrLNRmqLdOvv4evr/QidQclQd1hvC+pr9Ay1RO0NLXDLEuV3p34MzayCrIOj8KV63+w9nkKNm8OrT+4vn79GnJTK+HSxE15qZazR29AdlIlHIkv42TqidPA3p+cvRqHFGIRnRg1SFCjDEXxiY/NMlRZblxX14uUlKKeMApTTwRCmNphFaneoMWpHXpZivJTlZ2ROZARVwlNlZPw6OFHaK6bhC1btoREvVZk56Xzl6H8SDXMvMZvibqwj5+2ii4o2F9iAYe94+RFFlKLjWJ0nwbKUI8iVBaf/LFOhCrLJPD/EtlJeRxaiHrCKEu94a8w9YRVpnqClqZ2mGWpvncnys7CjGqYvvESrl97DjG7Evl3xiNHjsDRo0fh3r17/BpkKY4u8TUhKTsVKVR8YhJ+7RyB9pezqhR6ythz/rZFdv4be3rK/EvHP/Ww9SyyE1GEp45PdaDktIcQnhZaOWbZqe9JyrETngbZiZgEp5l5Fp6UwHSDIjsRPKcrx25A+NVn8POhfP4LnpmZGRJSSBmWMH3nHmzIbINNRcdg4C770sSeG3/+HqIbz8L3+6Vh7Oah7Hw4+z4j+uHsroeyI0nU8HU99fYEcCi7E9kpCUh6yLon/BnObr3PqAfijODETXrZ+Z/kJvgxcxC+298O69atg127dkFBQQH/K93ly5fh1atXS/aDS2mv2/tPw6b4NugafwKz8qF8+AiQ23QLNsa1GWTnyqg6iZ118JuJX3fI6GSnKjwjkTouPDkREj/rQMnpGVp6WjHKTrU3qdqr1Cw5FSih6QlaYrqBkpSBwk52Ij9FNMCu3Iuwr+4RrN+RHTISXxH43d2TsGNnAxwflcd+yunueQTRu9tU2SkJT0lsWgWn1tvTk+zU0KSnJj+9QUlPCqPsxMcKazmU7EQoqekJWWC6Ya0RUmrascYdFtkpi07kt5UlsHffBKTn34TNEeWwcWMYxMfHQ01NDZw7d473FML2Dtu9pRalrT7WdwoOxjfC5clHMKd87LDD6aq/BKkxtaTs1Pf21Hp9SniSnZr0VDDLTydogpRijzqUXsLyvN/Ck5aXvkBLTTtoqWmHWXYqPTuRPREVUJB3Gmprb0NUZHpI/cFVqddDvWNQkdkKT+685vWZh11gD7WcBZykCHt3qsJTEZsmwanv8WkVnjrZqRCjRy8+nSAJT0WQmjHLTfp5Wm5SBEt2IrTUtIOWmnbYyU4EZWdr7QXobL4Ku7YnhEy9xjqN5cbyV+TVwvGeMfkZKc/vv+Q9PgsOlEgoolMWm7zHJ9Hr04JJeCo9QPVoMtMpeulpxCw3zc9LgpQWmxRW2YnQ8tIttNS0g5aadphlp7l3Z9GhWjjWewXyDlXChg0b1O+M2FsZb82A7Z2QnUs8+EvOpdC9e/BddTus7B6F9jfv4Xf2XOOrWQgbPgv/qWpXh7GHylB2nCF+IYayI9TwdKeQw9UdgpMz6WUnntPPGo/CZ1XdISeFMMqHV9PASVhxoAk2Fw9B+dn7cGT0Dmxi//8urZFPUKRgmKgIcTtREYKyUId+oiKfJyvCodoksvxzTDVNnB30ZESe8GeiInISIjvkyYkU9LLzv8lN8FVSA/y0dS//y3NSUhL/8oz3FLp169aS7SmEUdprvMXE3ox2iEjthYFzLwEvL688moPkgklYF9MMK6NqjUPZEXkIu91w9l90w9kDN5SdGq7uiSoachg7QktDe+jh6W6gh6D7D3Vf0m90svPHbbWwOqYLfotuCqn2WvnyjGKrsLAfYvd2wY2b0n2/7t0HyC84Dzt2asPYPQ9n938oO0IPX9dT7oIyEnoYO1LqEnl4uhvWGCGHrNtBDFe3w3xPUj6MXSc7cZb7LdtaYUMYO6dr1sDWrVshLS2Nf3m+cuUKPHv2DN69e8fryFKLvq0+nNoAWakdcPPCc/7c0wcfoDLvOCRHVwEOc7eKzcAPZZckpFMoCekMWiS6gZaLvmAWkp6hhqvTUPcc1d+3czc770nsczg2qiok/+CK9frunbuQf6AGanI6JeHJ8mj6BbSWHpOEpio2NaGpokpM61B243D2Ik1QkpLSKCi9401IeoIWj3b4Kx09oReS3qGHrFNo9xnV0MvOg3tKITulAdJiikKuXuP3RbxlyqmTk3xCooe3HsvPAFw/dYvPzO5JapLD2c1D2alenJSkdNVL04mQ9AQtHykCIR7tMAtJz1iHqtthvddolqFnJ5KbVgZFGTWwYX0YbN68Gfbu3cvvQzs5OalegyzV74y+JCRlJ0aRQvWj4/BNZRv81jUCB67chvjLt+GXjmH4d3kL/KtUmqBIP0kRNVmRo4mKShZ+oiKEEoIcNxMV8UmKEHpCIjuoyYecQk085BRFdCLY8/VvpU3wXXScKoVqa2tDQgphlB5DeIPh4dEx2FfSAj+l1cH3aY2cb1Lq4GskWeIrE18maXyRVKtiHr6N6Idu64dbm6Xlv71SQyP3YDQiT9bjmCqavXZUuuZfZmKd8083xBhRROe/Ysrh64g0fiGGH1qxsbF8sovBwUF+MYbDbPBCfSlHaa+7+k/CrrRm2JzQDjmNV6Gk4zbsPDDIe3H+uqOaT1JkmKiI4XqionCtB6VePOonKvreMjERBTUhkR0VNIQMlKAmI7KDnnjILdTEQoHgmzB6giJFduJ79OuGmJCS+MqX55cvX8KZM1Owa3cD7I3rhs6uW1BdMw2JSf2wNbwGlAmKPE9UZD87u34IOWIWkst9oiJyMiIKedIhp+gnJ+ITFOllJzs3azfiRFHbISwsjN+vMycnh7fX+LmNw2WX6nBfjNJWD/SegMzUOt7Ds6dxCgbbrkF2WifE70CZqU1QpOF+oiJaPBrlIz0pEQUlA51DyUB3GCcb8gfzvTU9Q09ERIFyU4++ZyfKzuhtxRC2Pjrk/uCKUer1sb7jkJ1cxnt4njp6CY63nWf/bwMUmprEJCYqMkjMQgndEHMuMVWRqZOTpKCkxKId/khDhJaEdhjFIT3hkK/o76vpHXoiIgplciJqkqIDu4sheVcubNoQHnL1GsuNf3TFz536imY+nP1oywjcmbrPZ2UvO1wFOfsKNEwS026iIlVe6pFFpTpRESEpabFox/yIQ8QqD+kJh3xBf19N79CTEFEoExPp0WRnJnufCiEmIh42btzIJ0g8cOAAtLW1qQIfr0GW4h9cfU3Iyk6DFBobg9TGNviqrAn+XdbMRec/S5s4/ygx8neZv+n4VId+uLe+9yT2qvxLscSfdehl5f/Z0ihR5I0GjUI7rBP4cArM0NKRk69Q64r/8wN1RnUfUETn/+VVw3/jQ1sKYbCRevHiBUxPT/N7ytTU1UNseh5sSsyC1XFH4LfYw/DrnkPwa3Q6rDDx8y7f+MkDP/pKlB0Hg8gBn/hez44gEWnk58gUWLktFsI2bYbw8HD+pTkhIYHX6e7ubrh06RLvYYPt3FIfjqBvr7FOZ+Y3w7o9VbwnJ6IJzipYEVEFv+jZXgU/6/hpm5EfkXANrVdlJXyvQAjKbzd7o8IF5TSmyXs0aHFIUxoQvgkLDnq5qeebjcXw84bkkG2v8XdyZmaG/46ePDkBKSnV7Pe4EvA+nsjGsHLYsNGKfhZ2TXCWcdauM2LpWamTm3y4tUlQYq9Dz5S4oNgW/eQ9GkUuKXTPSiO/uuE352izr8uosrMAfvstDdas2cy/OEdHR8PBgwehqamJD2F/+vQp//K5lKWQua0uzm+ApN2lXHIiKDUVrHKz2IBebGqC0/OwbUpeRm1yCiUhnUGLRDfQgtEXzFLSM3jPTWfo5aaeiA05sHl9fEhfW+vr9YnRE1BVXA+H47HXZjHvyWmUm4WQvkdCFZs6uXlgd4GGWWxyuanrLWnTY5IWk3Z4EpLeoMWjHb4KRyeYpaRncIIhZ+jlpp74bemwLWxXyNZrlFko8fH74tTUFNRXNkBeehHvyan02lSlptMZ2GWxaZCaOrHJZ19XMAnMw66kpTch6Q1aPlL4Ix29QUlJew47Rt+L00zyzjQI3yT9sRV7KuM9aMvLy/kQdrxn+FK/BvElISs7MRYpVF8Pe7PyYOPBTPj1QCasSDsMP+/LgJ+SD6r8aMP3LvnOhm8CSRLNd0kH/CcBSXPFNwvED3uTYMWuGAhjH1ahLoUw2EhhF3TsNYSzquGkB+3t7VBSUsJvQIxfrnAG+uTkZMESB2eFxCGQhw4dgtzcXD5D5MDAAL9wwQsxrNN4MRMKf6Ezt9e1tfWQlJYNEXvSYdOODNgQcRDWbt8Pa8PTLKwxs9XIKjObFVJtWRlwUoJIsv9sCg6/mlgVFgdrwqJDXuLj7yTWaRxGhj1ERkZG2AVnJWubs2E3q9Pbth1gX7DSYBOrGxs3pWhsdMcGMxs01qFMNrMu+KwNCEnzy1rfWbcujp3vKPZ+bOPD1nfs2AGJiYmQlZXFJ7o4efIknxQA5XcotNfmtrqutg4OpmXB3t0HYM+OAxAdkQZR4fsgMjzZylYvsLZIT4RHkjjb5otNCYuC8GCx0cjWsBjYEhbFRdByuLa21Gv2u3voQBYk7z0ACbv3Q9yuVIjdmQyxkUgSZ483IjR2G0gwsi2AhAePaAvxi54oEzu3xrDf550hX6/xcwa/L6Lcwvs04nfFjo4OKCoshoP70iFlbyokx6RA0p4UiN+dBPHRCTRRNHtdEw97d84Xe10RG7m02GNi9/bd7PN1B2zfGsH/0BoREcGHrqMTwJ7KODHRjRs3+MgSrNOh8J3RTUJadgoptHxYTlIIg40VfoBh3ca/RJ85c4YfL3ZTr6+v58eP9+eoqKgQLGHwPcQPKuwZhBdhx48f53UaL1ywTuPFeajUadFeLx+WS3uN5cffUazTN2/e5H9ZxzqNv9sowvAv7nguqHMkWHpg+4TtFA5br66uhp6eHpiYmODt2e+//x4y1yCirV4+LKdra1Gvlw/LqV7j90X8oyv+we3ChQswNDTERT4eN9bp1NRUcR0SIij1GidmLisrg66uLv6Hm2vXrvE/5GBP5VCo024T0rITI6TQ8kCRQs3NzQYppPx1LpSkEAaPBes2Nlx4/w3smn716lU+VA4v0MbGxvg9ZwRLG5Qj2CsI2y2szyhM8EIML1ywTuPFeShFtNfLg+Uo8fF+cPj7i3Uav2zgl2j8woF1mjpHgqUHvpfYTnV2dvI6jV8ssR3D9izUvmSItnp5sJzaaoyo18uD5VSv8TjwePC74oMHD/h3RfxugSKsoaGB/2FO1OmlD76H2D7hPCU4ISL25jx//jzvqR4Kt9DxJyEvO/GXXEih0MeTFMK/zoXqLzjWbzw+bMSwezp+ocZGDWcBxg9twdIGZT2+lzh7Hl58Y33GtgzbtFD6gqFEtNfLg+Uq8fEv6/hlA4cT4UUo9vrDc0GdI8HSA99LbKcuXrzIv2Bg+43tGMruUKvToq1eHiy3tlrU6+XBcqvXeDxYp/H48Dsi3loHP6eUOi2uQ0IDfB+xJye2V9ibE79H4ndHvIUOtmvLNSEvO5UIKRTaLDcpZI5ygYZ1HD+oETx+wdIH30t8X/H9xQuW5VKfRXsduizH9hp/d/F3Ges0DmlG8YlfrvBcUOdIsPTA9xLbKXxvsU6j5MR2LNS+OOsj2urQZrleW4t6Hdosx3qNx4XHh8eJvVfxuJU6La5Dlj74Hir1GtsrvM5cDtcgTrJsZKcS5Zcd33whhUKL5SiFRERCOaK9Dl2Wa3uNx4nHK+p0aKG8l0q9Xm7XIKKtDl2UOr0cr61FvQ5dlnO9xuPF41bqtKjXoYHyXir1Guv0cqrXdll2slNEREREREREREREREREREREREQkNCNkp4iIiIiIiIiIiIiIiIiIiIiIiEhIRMhOERERERERERERERERERERERERkZCIkJ0iIiIiIiIiIiIiIiIiIiIiIiIiIREhO0VERERERERERERERERERERERERCIkJ2ioiIiIiIiIiIiIiIiIiIiIiIiIREhOwUERERERERERERERERERERERERCYkI2SkiIiIiIiIiIiIiIiIiIiIiIiISEhGyU0RERERERERERERERERERERERCQkImSniIiIiIiIiIiIiIiIiIiIiIiISEhEyE4RERERERERERERERERERERERGRkIiQnSIiIiIiIiIiIiIiIiIiIiIiIiIhESE7RUREREREREREREREREREREREREIiQnaKiIiIiIiIiIiIiIiIiIiIiIiIhESE7BQREREREREREREREREREREREREJiQjZKSIiIiIiIiIiIiIiIiIiIiIiIhISEbJTREREREREREREREREREREREREJCQiZKeIiIiIiIiIiIiIiIiIiIiIiIhISETIThEREREREREREREREREREREREZGQiJCdIiIiIiIiIiIiIiIiIiIiIiIiIiERITtFREREREREREREREREREREREREQiJCdoqIiIiIiIiIiIiIiIiIiIiIiIiERITsFBEREREREREREREREREREREREQmJCNkpIiIiIiIiIiIiIiIiIiIiIiIiEhIRslNEREREREREREREREREREREREQkJCJkp4iIiIiIiIiIiIiIiIiIiIiIiEhIRMhOERERERERERERERERERERERERkZCIkJ0iIiIiIiIiIiIiIiIiIiIiIiIiIREhO0VERERERERERERERERERERERERCIkJ2ioiIiIiIiIiIiIiIiIiIiIiIiIREhOwUERERERERERERERERERERERERCYkI2SkiIiIiIiIiIiIiIiIiIiIiIiISEhGyU0RERERERERERERERERERERERCQkImSniIiIiIiIiIiIiIiIiIiIiIiISEhEyE4RERERERERERERERERERERERGRkIiQnSIiIiIiIiIiIiIiIiIiIiIiIiIhESE7RUREREREREREREREREREREREREIiQnaKiIiIiIiIiIiIiIiIiIiIiIiIhESE7BQREREREREREREREREREREREREJiQjZKSIiIiIiIiIiIiIiIiIiIiIiIhISEbJTREREREREREREREREREREREREJCQiZKeIiIiIiIiIiIiIiIiIiIiIiIhISETIThEREREREREREREREREREREREZGQiJCdIiIiIiIiIiIiIiIiIiIiIiIiIiERITtFREREREREREREREREREREREREQiJCdoqIiIiIiIiIiIiIiIiIiIiIiIiERITsFBEREREREREREREREREREREREQmJCNkpIiIiIiIiIiIiIiIiIiIiIiIiEhIRslNEREREREREREREREREREREREQkJCJkp4iIiIiIiIiIiIiIiIiIiIiIiEhIRMhOERERERERERERERERERERERERkZCIkJ0iIiIiIiIiIiIiIiIiIiIiIiIiIREhO0VERERERERERERERERERERERERCIkJ2ioiIiIiIiIiIiIiIiIiIiIiIiIREhOwUERERERERERERERERERERERERCYEA/D/h8JTNJGclsAAAAABJRU5ErkJggg==)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RZ3gsI_cvKbJ"
   },
   "source": [
    "**SECTION 1 : DATA ACQUISITION AND PRE-PREOCESSING**\n",
    "\n",
    "Pre-processing is a crucial step in Machine Learning project work, as the real-world dataset contains incorrect, inconsistent or even missing values.\n",
    "\n",
    "So its very important that before proceeding with model building, our dataset should be clean and correct."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KkjnDCPdbIcK"
   },
   "source": [
    "**STEPS INVOLVED IN PREPROCESSING**\n",
    "\n",
    "1. Acquire the dataset\n",
    "2. Import all the crucial libraries\n",
    "3. Import the dataset\n",
    "4. Identifying and handling incorrect or inconsistent values\n",
    "5. Identifying and handling the missing values\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ew05f95-1PMo"
   },
   "source": [
    "**STEP 1** : **Acquire the Dataset** \n",
    "\n",
    "The dataset is obtained from below source. \n",
    "\n",
    "\"https://github.com/GoogleCloudPlatform/covid-19-open-data\"\n",
    "\n",
    "This repository attempts to assemble the largest Covid-19 epidemiological database in addition to a powerful set of expansive covariates. It includes open, publicly sourced, licensed data relating to demographics, economy, epidemiology, geography, health, hospitalizations, mobility, government response, weather, and more.\n",
    "\n",
    "The size of the  dataset is around 2GB and has more than 100 features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2hA7G3EVRLBg"
   },
   "source": [
    "**STEP 2 : Import all the crucial libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "-TgBV5hztIDP"
   },
   "outputs": [],
   "source": [
    "# IMPORTING LIBRARIES\n",
    "import pandas as pd\n",
    "\n",
    "# MATHEMATICS\n",
    "import numpy as np\n",
    "\n",
    "# VISUALIZATIONS\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# MACHINE LEARNING\n",
    "from sklearn import preprocessing\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "OhtQEpXqh7l2"
   },
   "outputs": [],
   "source": [
    "# To display all the available columns and rows in the output :\n",
    "pd.set_option('display.max_rows', 400)\n",
    "pd.set_option('display.max_columns', 400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hoPlIWWFPoZc"
   },
   "source": [
    "**STEP 3: Import and understand the dataset**\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a3TC0j2h2jJZ",
    "outputId": "27c664e8-c9e8-4337-a1b7-3d7c4de4023f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Dimensions of the Dataset: (79781, 112)\n",
      "\n",
      " First Few rows are: \n",
      "                CD_KEY key        date wikidata  datacommons country_code  \\\n",
      "0  2020-01-01_Andorra  AD  2020-01-01     Q228  country/AND           AD   \n",
      "1  2020-01-02_Andorra  AD  2020-01-02     Q228  country/AND           AD   \n",
      "2  2020-01-03_Andorra  AD  2020-01-03     Q228  country/AND           AD   \n",
      "3  2020-01-04_Andorra  AD  2020-01-04     Q228  country/AND           AD   \n",
      "4  2020-01-05_Andorra  AD  2020-01-05     Q228  country/AND           AD   \n",
      "\n",
      "  country_name  subregion1_code  subregion1_name  subregion2_code  \\\n",
      "0      Andorra              NaN              NaN              NaN   \n",
      "1      Andorra              NaN              NaN              NaN   \n",
      "2      Andorra              NaN              NaN              NaN   \n",
      "3      Andorra              NaN              NaN              NaN   \n",
      "4      Andorra              NaN              NaN              NaN   \n",
      "\n",
      "   subregion2_name  locality_code  locality_name 3166-1-alpha-2  \\\n",
      "0              NaN            NaN            NaN             AD   \n",
      "1              NaN            NaN            NaN             AD   \n",
      "2              NaN            NaN            NaN             AD   \n",
      "3              NaN            NaN            NaN             AD   \n",
      "4              NaN            NaN            NaN             AD   \n",
      "\n",
      "  3166-1-alpha-3  aggregation_level  new_confirmed  new_deceased  \\\n",
      "0            AND                  0            0.0           0.0   \n",
      "1            AND                  0            0.0           0.0   \n",
      "2            AND                  0            0.0           0.0   \n",
      "3            AND                  0            0.0           0.0   \n",
      "4            AND                  0            0.0           0.0   \n",
      "\n",
      "   new_recovered  new_tested  total_confirmed  total_deceased  \\\n",
      "0            NaN         NaN              0.0             0.0   \n",
      "1            NaN         NaN              0.0             0.0   \n",
      "2            NaN         NaN              0.0             0.0   \n",
      "3            NaN         NaN              0.0             0.0   \n",
      "4            NaN         NaN              0.0             0.0   \n",
      "\n",
      "   total_recovered  total_tested  new_hospitalized  total_hospitalized  \\\n",
      "0              NaN           NaN               NaN                 NaN   \n",
      "1              NaN           NaN               NaN                 NaN   \n",
      "2              NaN           NaN               NaN                 NaN   \n",
      "3              NaN           NaN               NaN                 NaN   \n",
      "4              NaN           NaN               NaN                 NaN   \n",
      "\n",
      "   current_hospitalized  new_intensive_care  total_intensive_care  \\\n",
      "0                   NaN                 NaN                   NaN   \n",
      "1                   NaN                 NaN                   NaN   \n",
      "2                   NaN                 NaN                   NaN   \n",
      "3                   NaN                 NaN                   NaN   \n",
      "4                   NaN                 NaN                   NaN   \n",
      "\n",
      "   current_intensive_care  new_ventilator  total_ventilator  \\\n",
      "0                     NaN             NaN               NaN   \n",
      "1                     NaN             NaN               NaN   \n",
      "2                     NaN             NaN               NaN   \n",
      "3                     NaN             NaN               NaN   \n",
      "4                     NaN             NaN               NaN   \n",
      "\n",
      "   current_ventilator  population  population_male  population_female  \\\n",
      "0                 NaN     77142.0          58625.0            55581.0   \n",
      "1                 NaN     77142.0          58625.0            55581.0   \n",
      "2                 NaN     77142.0          58625.0            55581.0   \n",
      "3                 NaN     77142.0          58625.0            55581.0   \n",
      "4                 NaN     77142.0          58625.0            55581.0   \n",
      "\n",
      "   rural_population  urban_population  largest_city_population  \\\n",
      "0            9269.0           67873.0                      NaN   \n",
      "1            9269.0           67873.0                      NaN   \n",
      "2            9269.0           67873.0                      NaN   \n",
      "3            9269.0           67873.0                      NaN   \n",
      "4            9269.0           67873.0                      NaN   \n",
      "\n",
      "   clustered_population  population_density  human_development_index  \\\n",
      "0                   NaN          163.842553                    0.858   \n",
      "1                   NaN          163.842553                    0.858   \n",
      "2                   NaN          163.842553                    0.858   \n",
      "3                   NaN          163.842553                    0.858   \n",
      "4                   NaN          163.842553                    0.858   \n",
      "\n",
      "   population_age_00_09  population_age_10_19  population_age_20_29  \\\n",
      "0                9370.0               12022.0               10727.0   \n",
      "1                9370.0               12022.0               10727.0   \n",
      "2                9370.0               12022.0               10727.0   \n",
      "3                9370.0               12022.0               10727.0   \n",
      "4                9370.0               12022.0               10727.0   \n",
      "\n",
      "   population_age_30_39  population_age_40_49  population_age_50_59  \\\n",
      "0               12394.0               21001.0               20720.0   \n",
      "1               12394.0               21001.0               20720.0   \n",
      "2               12394.0               21001.0               20720.0   \n",
      "3               12394.0               21001.0               20720.0   \n",
      "4               12394.0               21001.0               20720.0   \n",
      "\n",
      "   population_age_60_69  population_age_70_79  population_age_80_89  \\\n",
      "0               14433.0                8657.0                3904.0   \n",
      "1               14433.0                8657.0                3904.0   \n",
      "2               14433.0                8657.0                3904.0   \n",
      "3               14433.0                8657.0                3904.0   \n",
      "4               14433.0                8657.0                3904.0   \n",
      "\n",
      "   population_age_90_99  population_age_80_and_older           gdp  \\\n",
      "0                 976.0                       4881.0  3.154058e+09   \n",
      "1                 976.0                       4881.0  3.154058e+09   \n",
      "2                 976.0                       4881.0  3.154058e+09   \n",
      "3                 976.0                       4881.0  3.154058e+09   \n",
      "4                 976.0                       4881.0  3.154058e+09   \n",
      "\n",
      "   gdp_per_capita  human_capital_index  open_street_maps   latitude  \\\n",
      "0         40886.0                  NaN            9407.0  42.558333   \n",
      "1         40886.0                  NaN            9407.0  42.558333   \n",
      "2         40886.0                  NaN            9407.0  42.558333   \n",
      "3         40886.0                  NaN            9407.0  42.558333   \n",
      "4         40886.0                  NaN            9407.0  42.558333   \n",
      "\n",
      "   longitude  elevation   area  rural_area  urban_area  life_expectancy  \\\n",
      "0   1.555278        NaN  470.0         NaN         NaN              NaN   \n",
      "1   1.555278        NaN  470.0         NaN         NaN              NaN   \n",
      "2   1.555278        NaN  470.0         NaN         NaN              NaN   \n",
      "3   1.555278        NaN  470.0         NaN         NaN              NaN   \n",
      "4   1.555278        NaN  470.0         NaN         NaN              NaN   \n",
      "\n",
      "   smoking_prevalence  diabetes_prevalence  infant_mortality_rate  \\\n",
      "0                33.5                  7.7                    2.7   \n",
      "1                33.5                  7.7                    2.7   \n",
      "2                33.5                  7.7                    2.7   \n",
      "3                33.5                  7.7                    2.7   \n",
      "4                33.5                  7.7                    2.7   \n",
      "\n",
      "   adult_male_mortality_rate  adult_female_mortality_rate  \\\n",
      "0                        NaN                          NaN   \n",
      "1                        NaN                          NaN   \n",
      "2                        NaN                          NaN   \n",
      "3                        NaN                          NaN   \n",
      "4                        NaN                          NaN   \n",
      "\n",
      "   pollution_mortality_rate  comorbidity_mortality_rate  hospital_beds  \\\n",
      "0                       NaN                         NaN            NaN   \n",
      "1                       NaN                         NaN            NaN   \n",
      "2                       NaN                         NaN            NaN   \n",
      "3                       NaN                         NaN            NaN   \n",
      "4                       NaN                         NaN            NaN   \n",
      "\n",
      "   nurses  physicians  health_expenditure  out_of_pocket_health_expenditure  \\\n",
      "0  4.0128      3.3333         4040.786621                        1688.12146   \n",
      "1  4.0128      3.3333         4040.786621                        1688.12146   \n",
      "2  4.0128      3.3333         4040.786621                        1688.12146   \n",
      "3  4.0128      3.3333         4040.786621                        1688.12146   \n",
      "4  4.0128      3.3333         4040.786621                        1688.12146   \n",
      "\n",
      "   mobility_retail_and_recreation  mobility_grocery_and_pharmacy  \\\n",
      "0                             NaN                            NaN   \n",
      "1                             NaN                            NaN   \n",
      "2                             NaN                            NaN   \n",
      "3                             NaN                            NaN   \n",
      "4                             NaN                            NaN   \n",
      "\n",
      "   mobility_parks  mobility_transit_stations  mobility_workplaces  \\\n",
      "0             NaN                        NaN                  NaN   \n",
      "1             NaN                        NaN                  NaN   \n",
      "2             NaN                        NaN                  NaN   \n",
      "3             NaN                        NaN                  NaN   \n",
      "4             NaN                        NaN                  NaN   \n",
      "\n",
      "   mobility_residential  school_closing  workplace_closing  \\\n",
      "0                   NaN             0.0                0.0   \n",
      "1                   NaN             0.0                0.0   \n",
      "2                   NaN             0.0                0.0   \n",
      "3                   NaN             0.0                0.0   \n",
      "4                   NaN             0.0                0.0   \n",
      "\n",
      "   cancel_public_events  restrictions_on_gatherings  public_transport_closing  \\\n",
      "0                   0.0                         0.0                       0.0   \n",
      "1                   0.0                         0.0                       0.0   \n",
      "2                   0.0                         0.0                       0.0   \n",
      "3                   0.0                         0.0                       0.0   \n",
      "4                   0.0                         0.0                       0.0   \n",
      "\n",
      "   stay_at_home_requirements  restrictions_on_internal_movement  \\\n",
      "0                        0.0                                0.0   \n",
      "1                        0.0                                0.0   \n",
      "2                        0.0                                0.0   \n",
      "3                        0.0                                0.0   \n",
      "4                        0.0                                0.0   \n",
      "\n",
      "   international_travel_controls  income_support  debt_relief  \\\n",
      "0                            0.0             0.0          0.0   \n",
      "1                            0.0             0.0          0.0   \n",
      "2                            0.0             0.0          0.0   \n",
      "3                            0.0             0.0          0.0   \n",
      "4                            0.0             0.0          0.0   \n",
      "\n",
      "   fiscal_measures  international_support  public_information_campaigns  \\\n",
      "0              0.0                    0.0                           0.0   \n",
      "1              0.0                    0.0                           0.0   \n",
      "2              0.0                    0.0                           0.0   \n",
      "3              0.0                    0.0                           0.0   \n",
      "4              0.0                    0.0                           0.0   \n",
      "\n",
      "   testing_policy  contact_tracing  emergency_investment_in_healthcare  \\\n",
      "0             0.0              0.0                                 0.0   \n",
      "1             0.0              0.0                                 0.0   \n",
      "2             0.0              0.0                                 0.0   \n",
      "3             0.0              0.0                                 0.0   \n",
      "4             0.0              0.0                                 0.0   \n",
      "\n",
      "   investment_in_vaccines  stringency_index  noaa_station  noaa_distance  \\\n",
      "0                     0.0               0.0  8.117100e+09      39.866801   \n",
      "1                     0.0               0.0  8.117100e+09      39.866801   \n",
      "2                     0.0               0.0  8.117100e+09      39.866801   \n",
      "3                     0.0               0.0  8.117100e+09      39.866801   \n",
      "4                     0.0               0.0  8.117100e+09      39.866801   \n",
      "\n",
      "   average_temperature  minimum_temperature  maximum_temperature  rainfall  \\\n",
      "0             6.201389             1.930556            10.743056  1.415143   \n",
      "1             5.340278             0.423611            11.041667  2.866571   \n",
      "2             6.638889             2.000000            10.722222  2.540000   \n",
      "3             7.375000             4.597222            10.986111  1.632857   \n",
      "4             5.833333             0.750000            11.881944  1.270000   \n",
      "\n",
      "   snowfall  dew_point  relative_humidity  Confirmed  Recovered  Deaths  \n",
      "0       NaN   1.597222          74.345734        NaN        NaN     NaN  \n",
      "1       NaN   1.006944          75.648204        NaN        NaN     NaN  \n",
      "2       NaN   1.979167          73.984924        NaN        NaN     NaN  \n",
      "3       NaN   3.222222          75.926650      390.0       10.0    14.0  \n",
      "4       NaN  -0.833333          66.599954      745.0      468.0    43.0  \n",
      "\n",
      " Features of the dataset are: \n",
      " Index(['CD_KEY', 'key', 'date', 'wikidata', 'datacommons', 'country_code',\n",
      "       'country_name', 'subregion1_code', 'subregion1_name', 'subregion2_code',\n",
      "       ...\n",
      "       'average_temperature', 'minimum_temperature', 'maximum_temperature',\n",
      "       'rainfall', 'snowfall', 'dew_point', 'relative_humidity', 'Confirmed',\n",
      "       'Recovered', 'Deaths'],\n",
      "      dtype='object', length=112)\n"
     ]
    }
   ],
   "source": [
    "# READING THE DATASET\n",
    "data = pd.read_csv('Covid_dataset.csv')                                         \n",
    "# You can also directly download the dataset from : \"https://storage.googleapis.com/covid19-open-data/v2/main.csv\"\n",
    "\n",
    "data.drop(data.filter(regex=\"Unname\"),axis=1, inplace=True)              # To drop a column named 'unnamed:0'\n",
    "\n",
    "# BASIC INFORMATION ABOUT DATASET:\n",
    "print('\\n Dimensions of the Dataset:', data.shape)                       # No. of records(rows) * No. of features(columns)\n",
    "print('\\n First Few rows are: \\n', data.head())                          # Shows what first few rows looks like\n",
    "print('\\n Features of the dataset are: \\n', data.columns)                # Columns in the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kp-i-BgXiP8O"
   },
   "source": [
    "**STEP 4: Identifying and handling incorrect, inconsistent or unnecessary values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4-Pkfj9oPid1",
    "outputId": "0f0cf77d-3fc5-4b79-d802-7e1de996f73c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Columns are:\n",
      " Index(['CD_KEY', 'key', 'date', 'wikidata', 'datacommons', 'country_code',\n",
      "       'country_name', 'subregion1_code', 'subregion1_name', 'subregion2_code',\n",
      "       ...\n",
      "       'average_temperature', 'minimum_temperature', 'maximum_temperature',\n",
      "       'rainfall', 'snowfall', 'dew_point', 'relative_humidity', 'Confirmed',\n",
      "       'Recovered', 'Deaths'],\n",
      "      dtype='object', length=112)\n"
     ]
    }
   ],
   "source": [
    "print('\\n Columns are:\\n', data.columns)                                 # Name of columns(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BhOvqlGd4EQx"
   },
   "source": [
    "\n",
    "Columns **'3166-1-alpha-2' and '3166-1-alpha-3'** does not make any sense by their names.\n",
    "So we will check the values of these columns.\n",
    "\n",
    "We will check other column values as well like **'key', 'datacommons' and 'CD_KEY'**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QzTaNz7Kibyo",
    "outputId": "54d46cab-874e-4646-8d70-2936c087544a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3166-1-alpha-2 3166-1-alpha-3\n",
      "0                 AD            AND\n",
      "1                 AD            AND\n",
      "2                 AD            AND\n",
      "3                 AD            AND\n",
      "4                 AD            AND\n",
      "...              ...            ...\n",
      "79776             ZW            ZWE\n",
      "79777             ZW            ZWE\n",
      "79778             ZW            ZWE\n",
      "79779             ZW            ZWE\n",
      "79780             ZW            ZWE\n",
      "\n",
      "[79781 rows x 2 columns]\n",
      "\n",
      " Checking repeated values: \n",
      " \n",
      "          date country_name country_code key  datacommons              CD_KEY  \\\n",
      "0  2020-01-01      Andorra           AD  AD  country/AND  2020-01-01_Andorra   \n",
      "1  2020-01-02      Andorra           AD  AD  country/AND  2020-01-02_Andorra   \n",
      "2  2020-01-03      Andorra           AD  AD  country/AND  2020-01-03_Andorra   \n",
      "3  2020-01-04      Andorra           AD  AD  country/AND  2020-01-04_Andorra   \n",
      "4  2020-01-05      Andorra           AD  AD  country/AND  2020-01-05_Andorra   \n",
      "5  2020-01-06      Andorra           AD  AD  country/AND  2020-01-06_Andorra   \n",
      "6  2020-01-07      Andorra           AD  AD  country/AND  2020-01-07_Andorra   \n",
      "7  2020-01-08      Andorra           AD  AD  country/AND  2020-01-08_Andorra   \n",
      "8  2020-01-09      Andorra           AD  AD  country/AND  2020-01-09_Andorra   \n",
      "9  2020-01-10      Andorra           AD  AD  country/AND  2020-01-10_Andorra   \n",
      "\n",
      "  3166-1-alpha-2 3166-1-alpha-3  \n",
      "0             AD            AND  \n",
      "1             AD            AND  \n",
      "2             AD            AND  \n",
      "3             AD            AND  \n",
      "4             AD            AND  \n",
      "5             AD            AND  \n",
      "6             AD            AND  \n",
      "7             AD            AND  \n",
      "8             AD            AND  \n",
      "9             AD            AND  \n"
     ]
    }
   ],
   "source": [
    "print(data[['3166-1-alpha-2', '3166-1-alpha-3']])                 # Looks like the values are matching with country codes       \n",
    "\n",
    "# OTHER COLUMN VALUES LIKE-  'key','datacommons','CD_KEY'\n",
    "print(\"\\n Checking repeated values: \\n \\n\",data[['date','country_name','country_code','key','datacommons','CD_KEY','3166-1-alpha-2', '3166-1-alpha-3']].head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9UyvOtaq4niy"
   },
   "source": [
    "\n",
    "Columns having repeated values : \n",
    "\n",
    "1.   **\"country_name\" - \"CD_KEY\"**\n",
    "2.   **\"key\",\"country_code\" - \"3166-1-alpha-2\"**\n",
    "3.   **\"datacommons\" - \"3166-1-alpha-3\"**\n",
    "\n",
    "\n",
    "So we will only keep **\"date\", \"country_name\"** column and We do not require other columns as those are repeated values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yq8ekDob4pmo",
    "outputId": "684c436b-eb13-484e-d7da-8b99316090d4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Shape of the dataset after removing incorrect/inconsistent columns:  (79781, 106)\n"
     ]
    }
   ],
   "source": [
    "# DROPPING INCORRECT/INCONSISTENT COLUMNS FROM DATASET:\n",
    "data.drop(columns=['key','country_code','datacommons','CD_KEY','3166-1-alpha-2', '3166-1-alpha-3'], inplace = True, axis = 1)\n",
    "print('\\n Shape of the dataset after removing incorrect/inconsistent columns: ',data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xTBfl1mnSwtv",
    "outputId": "84e4df94-d502-4183-b973-8fe67525398b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['date', 'wikidata', 'country_name', 'subregion1_code',\n",
      "       'subregion1_name', 'subregion2_code', 'subregion2_name',\n",
      "       'locality_code', 'locality_name', 'aggregation_level',\n",
      "       ...\n",
      "       'average_temperature', 'minimum_temperature', 'maximum_temperature',\n",
      "       'rainfall', 'snowfall', 'dew_point', 'relative_humidity', 'Confirmed',\n",
      "       'Recovered', 'Deaths'],\n",
      "      dtype='object', length=106)\n",
      "       noaa_station  noaa_distance wikidata  aggregation_level\n",
      "0      8.117100e+09      39.866801     Q228                  0\n",
      "1      8.117100e+09      39.866801     Q228                  0\n",
      "2      8.117100e+09      39.866801     Q228                  0\n",
      "3      8.117100e+09      39.866801     Q228                  0\n",
      "4      8.117100e+09      39.866801     Q228                  0\n",
      "...             ...            ...      ...                ...\n",
      "79776           NaN            NaN     Q954                  0\n",
      "79777           NaN            NaN     Q954                  0\n",
      "79778           NaN            NaN     Q954                  0\n",
      "79779           NaN            NaN     Q954                  0\n",
      "79780           NaN            NaN     Q954                  0\n",
      "\n",
      "[79781 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "# CHECKING UNNECESSARY/IRRELEVANT COLUMNS : \n",
    "print(data.columns)\n",
    "print(data[['noaa_station','noaa_distance','wikidata','aggregation_level']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yGQ_Yvkw7fp7",
    "outputId": "7ca203f7-e8bf-44b1-f94f-f74c46c0a3b2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(79781, 102)\n"
     ]
    }
   ],
   "source": [
    "# DROPPING UNNECESSARY/IRRELEVANT COLUMNS :\n",
    "unnecessary_info_cols = ['wikidata','aggregation_level','noaa_station','noaa_distance']\n",
    "data.drop(columns = unnecessary_info_cols, axis = 1, inplace = True)\n",
    "\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mjuZh59xZ6Op",
    "outputId": "ea1414d0-1837-4e40-af71-e15a018ae877"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Andorra' 'United Arab Emirates' 'Afghanistan' 'Antigua and Barbuda'\n",
      " 'Anguilla' 'Albania' 'Armenia' 'Netherlands Antilles' 'Angola'\n",
      " 'Antarctica' 'Argentina' 'American Samoa' 'Austria' 'Australia' 'Aruba'\n",
      " 'Azerbaijan' 'Bosnia and Herzegovina' 'Barbados' 'Bangladesh' 'Belgium'\n",
      " 'Burkina Faso' 'Bulgaria' 'Bahrain' 'Burundi' 'Benin' 'Bermuda' 'Brunei'\n",
      " 'Bolivia' 'Caribbean Netherlands' 'Brazil' 'Bahamas' 'Bhutan'\n",
      " 'Bouvet Island' 'Botswana' 'Belarus' 'Belize' 'Canada' 'Cocos Islands'\n",
      " 'Democratic Republic of the Congo' 'Central African Republic'\n",
      " 'Republic of the Congo' 'Switzerland' 'Ivory Coast' 'Cook Islands'\n",
      " 'Chile' 'Cameroon' 'China' 'Colombia' 'Costa Rica' 'Cuba' 'Cape Verde'\n",
      " 'Curaçao' 'Christmas Island' 'Cyprus' 'Czech Republic' 'Germany'\n",
      " 'Djibouti' 'Denmark' 'Dominica' 'Dominican Republic' 'Algeria' 'Ecuador'\n",
      " 'Estonia' 'Egypt' 'Western Sahara' 'Eritrea' 'Spain' 'Ethiopia' 'Finland'\n",
      " 'Fiji' 'Falkland Islands' 'Micronesia' 'Faroe Islands' 'France' 'Gabon'\n",
      " 'United Kingdom' 'Grenada' 'Georgia' 'French Guiana' 'Guernsey' 'Ghana'\n",
      " 'Gibraltar' 'Greenland' 'Gambia' 'Guinea' 'Equatorial Guinea' 'Greece'\n",
      " 'South Georgia and the South Sandwich Islands' 'Guatemala' 'Guam'\n",
      " 'Guinea-Bissau' 'Guyana' 'Gaza Strip' 'Hong Kong'\n",
      " 'Heard Island and McDonald Islands' 'Honduras' 'Croatia' 'Haiti'\n",
      " 'Hungary' 'Indonesia' 'Ireland' 'Israel' 'Isle of Man' 'India'\n",
      " 'British Indian Ocean Territory' 'Iraq' 'Iran' 'Iceland' 'Italy' 'Jersey'\n",
      " 'Jamaica' 'Jordan' 'Japan' 'Kenya' 'Kyrgyzstan' 'Cambodia' 'Kiribati'\n",
      " 'Comoros' 'Saint Kitts and Nevis' 'North Korea' 'South Korea' 'Kuwait'\n",
      " 'Cayman Islands' 'Kazakhstan' 'Laos' 'Lebanon' 'Saint Lucia'\n",
      " 'Liechtenstein' 'Sri Lanka' 'Liberia' 'Lesotho' 'Lithuania' 'Luxembourg'\n",
      " 'Latvia' 'Libya' 'Morocco' 'Monaco' 'Moldova' 'Montenegro' 'Madagascar'\n",
      " 'Marshall Islands' 'Macedonia' 'Mali' 'Myanmar' 'Mongolia' 'Macau'\n",
      " 'Northern Mariana Islands' 'Martinique' 'Mauritania' 'Montserrat' 'Malta'\n",
      " 'Mauritius' 'Maldives' 'Malawi' 'Mexico' 'Malaysia' 'Mozambique'\n",
      " 'Namibia' 'New Caledonia' 'Niger' 'Norfolk Island' 'Nigeria' 'Nicaragua'\n",
      " 'Netherlands' 'Norway' 'Nepal' 'Nauru' 'Niue' 'New Zealand' 'Oman'\n",
      " 'Panama' 'Peru' 'French Polynesia' 'Papua New Guinea' 'Philippines'\n",
      " 'Pakistan' 'Poland' 'Pitcairn Islands' 'Puerto Rico' 'Palestine'\n",
      " 'Portugal' 'Palau' 'Paraguay' 'Qatar' 'Réunion' 'Romania' 'Serbia'\n",
      " 'Russia' 'Rwanda' 'Saudi Arabia' 'Solomon Islands' 'Seychelles' 'Sudan'\n",
      " 'Sweden' 'Singapore' 'Saint Helena' 'Slovenia' 'Svalbard and Jan Mayen'\n",
      " 'Slovakia' 'Sierra Leone' 'San Marino' 'Senegal' 'Somalia' 'Suriname'\n",
      " 'South Sudan' 'São Tomé and Príncipe' 'El Salvador' 'Sint Maarten'\n",
      " 'Syria' 'Swaziland' 'Turks and Caicos Islands' 'Chad'\n",
      " 'French Southern Territories' 'Togo' 'Thailand' 'Tajikistan' 'Tokelau'\n",
      " 'East Timor' 'Turkmenistan' 'Tunisia' 'Tonga' 'Turkey'\n",
      " 'Trinidad and Tobago' 'Tuvalu' 'Taiwan' 'Tanzania' 'Ukraine' 'Uganda'\n",
      " 'United States Minor Outlying Islands' 'United States of America'\n",
      " 'Uruguay' 'Uzbekistan' 'Vatican City' 'Saint Vincent and the Grenadines'\n",
      " 'Venezuela' 'British Virgin Islands' 'United States Virgin Islands'\n",
      " 'Vietnam' 'Vanuatu' 'Wallis and Futuna' 'Samoa' 'Kosovo' 'Yemen'\n",
      " 'Mayotte' 'South Africa' 'Zambia' 'Zimbabwe']\n"
     ]
    }
   ],
   "source": [
    "print(data.country_name.unique())                 # Checking names of the countries for the correctness:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6p97sPXeQIEu"
   },
   "source": [
    "**STEP 5 : Identifying and handling the missing values**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XgYwAQj5yyL-"
   },
   "source": [
    "If multiple features/columns are missing for any particular record/row. We will drop such a row.\n",
    "\n",
    "If multiple records/rows are missing for any particular column/feature. We will drop that column/feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-JPmRjqqh-RH",
    "outputId": "edef2f0f-5606-403a-e1bc-f754ea5925d9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Botswana                                        323\n",
      "Algeria                                         323\n",
      "Panama                                          323\n",
      "Réunion                                         323\n",
      "Faroe Islands                                   323\n",
      "Antarctica                                      323\n",
      "Saint Vincent and the Grenadines                323\n",
      "Ghana                                           323\n",
      "Germany                                         323\n",
      "Niue                                            323\n",
      "Morocco                                         323\n",
      "Wallis and Futuna                               323\n",
      "Guernsey                                        323\n",
      "Dominican Republic                              323\n",
      "India                                           323\n",
      "Zimbabwe                                        323\n",
      "Northern Mariana Islands                        323\n",
      "Macau                                           323\n",
      "Sweden                                          323\n",
      "São Tomé and Príncipe                           323\n",
      "Niger                                           323\n",
      "Burundi                                         323\n",
      "Falkland Islands                                323\n",
      "Macedonia                                       323\n",
      "Gabon                                           323\n",
      "Tonga                                           323\n",
      "Cape Verde                                      323\n",
      "Taiwan                                          323\n",
      "Turkey                                          323\n",
      "Somalia                                         323\n",
      "Slovakia                                        323\n",
      "Solomon Islands                                 323\n",
      "Liechtenstein                                   323\n",
      "Micronesia                                      323\n",
      "Norfolk Island                                  323\n",
      "Bahamas                                         323\n",
      "Bouvet Island                                   323\n",
      "North Korea                                     323\n",
      "Jersey                                          323\n",
      "Ivory Coast                                     323\n",
      "Guam                                            323\n",
      "Norway                                          323\n",
      "United Arab Emirates                            323\n",
      "Egypt                                           323\n",
      "San Marino                                      323\n",
      "East Timor                                      323\n",
      "Turks and Caicos Islands                        323\n",
      "Philippines                                     323\n",
      "Honduras                                        323\n",
      "Bolivia                                         323\n",
      "Nauru                                           323\n",
      "Lithuania                                       323\n",
      "Malawi                                          323\n",
      "Martinique                                      323\n",
      "Myanmar                                         323\n",
      "Kiribati                                        323\n",
      "Belarus                                         323\n",
      "South Africa                                    323\n",
      "Cayman Islands                                  323\n",
      "Namibia                                         323\n",
      "Greenland                                       323\n",
      "Papua New Guinea                                323\n",
      "Nicaragua                                       323\n",
      "Eritrea                                         323\n",
      "Czech Republic                                  323\n",
      "Singapore                                       323\n",
      "Bulgaria                                        323\n",
      "Afghanistan                                     323\n",
      "Ukraine                                         323\n",
      "Peru                                            323\n",
      "Curaçao                                         323\n",
      "Tanzania                                        323\n",
      "Serbia                                          323\n",
      "Croatia                                         323\n",
      "Zambia                                          323\n",
      "Moldova                                         323\n",
      "Caribbean Netherlands                           323\n",
      "Liberia                                         323\n",
      "Swaziland                                       323\n",
      "Denmark                                         323\n",
      "Brunei                                          323\n",
      "Vietnam                                         323\n",
      "Seychelles                                      323\n",
      "Equatorial Guinea                               323\n",
      "Bosnia and Herzegovina                          323\n",
      "Nigeria                                         323\n",
      "Tuvalu                                          323\n",
      "Tunisia                                         323\n",
      "Greece                                          323\n",
      "Central African Republic                        323\n",
      "Christmas Island                                323\n",
      "Palestine                                       323\n",
      "Monaco                                          323\n",
      "Thailand                                        323\n",
      "South Korea                                     323\n",
      "Heard Island and McDonald Islands               323\n",
      "Togo                                            323\n",
      "Bermuda                                         323\n",
      "Turkmenistan                                    323\n",
      "Georgia                                         323\n",
      "Jordan                                          323\n",
      "Venezuela                                       323\n",
      "Saudi Arabia                                    323\n",
      "Oman                                            323\n",
      "Belgium                                         323\n",
      "Gibraltar                                       323\n",
      "Yemen                                           323\n",
      "Laos                                            323\n",
      "Fiji                                            323\n",
      "United States Minor Outlying Islands            323\n",
      "Spain                                           323\n",
      "Cocos Islands                                   323\n",
      "Chile                                           323\n",
      "Jamaica                                         323\n",
      "Angola                                          323\n",
      "Bahrain                                         323\n",
      "France                                          323\n",
      "Ethiopia                                        323\n",
      "Colombia                                        323\n",
      "Republic of the Congo                           323\n",
      "Japan                                           323\n",
      "Kuwait                                          323\n",
      "Tajikistan                                      323\n",
      "Gaza Strip                                      323\n",
      "Nepal                                           323\n",
      "Cameroon                                        323\n",
      "Paraguay                                        323\n",
      "British Indian Ocean Territory                  323\n",
      "Argentina                                       323\n",
      "Trinidad and Tobago                             323\n",
      "Romania                                         323\n",
      "Latvia                                          323\n",
      "Tokelau                                         323\n",
      "Guinea-Bissau                                   323\n",
      "Guatemala                                       323\n",
      "United States of America                        323\n",
      "Vanuatu                                         323\n",
      "Saint Helena                                    323\n",
      "Italy                                           323\n",
      "China                                           323\n",
      "Benin                                           323\n",
      "Luxembourg                                      323\n",
      "Sri Lanka                                       323\n",
      "Comoros                                         323\n",
      "Iraq                                            323\n",
      "Senegal                                         323\n",
      "United Kingdom                                  323\n",
      "Democratic Republic of the Congo                323\n",
      "Antigua and Barbuda                             323\n",
      "Puerto Rico                                     323\n",
      "Mauritania                                      323\n",
      "Kyrgyzstan                                      323\n",
      "Albania                                         323\n",
      "Kosovo                                          323\n",
      "Estonia                                         323\n",
      "El Salvador                                     323\n",
      "Rwanda                                          323\n",
      "Mexico                                          323\n",
      "Netherlands                                     323\n",
      "French Polynesia                                323\n",
      "Mali                                            323\n",
      "Western Sahara                                  323\n",
      "Samoa                                           323\n",
      "Qatar                                           323\n",
      "Belize                                          323\n",
      "Netherlands Antilles                            323\n",
      "Iran                                            323\n",
      "Kazakhstan                                      323\n",
      "Lebanon                                         323\n",
      "Montenegro                                      323\n",
      "Palau                                           323\n",
      "Chad                                            323\n",
      "Poland                                          323\n",
      "Guinea                                          323\n",
      "Kenya                                           323\n",
      "Libya                                           323\n",
      "Hong Kong                                       323\n",
      "South Georgia and the South Sandwich Islands    323\n",
      "French Guiana                                   323\n",
      "Austria                                         323\n",
      "Portugal                                        323\n",
      "New Caledonia                                   323\n",
      "Saint Lucia                                     323\n",
      "Cuba                                            323\n",
      "Barbados                                        323\n",
      "Azerbaijan                                      323\n",
      "Grenada                                         323\n",
      "Saint Kitts and Nevis                           323\n",
      "Indonesia                                       323\n",
      "Pakistan                                        323\n",
      "Aruba                                           323\n",
      "Iceland                                         323\n",
      "Mozambique                                      323\n",
      "Svalbard and Jan Mayen                          323\n",
      "Bhutan                                          323\n",
      "Uzbekistan                                      323\n",
      "Ireland                                         323\n",
      "French Southern Territories                     323\n",
      "Marshall Islands                                323\n",
      "Finland                                         323\n",
      "Andorra                                         323\n",
      "Anguilla                                        323\n",
      "Syria                                           323\n",
      "Cambodia                                        323\n",
      "Switzerland                                     323\n",
      "Slovenia                                        323\n",
      "Russia                                          323\n",
      "Cyprus                                          323\n",
      "Ecuador                                         323\n",
      "Haiti                                           323\n",
      "Malaysia                                        323\n",
      "American Samoa                                  323\n",
      "Hungary                                         323\n",
      "Maldives                                        323\n",
      "Vatican City                                    323\n",
      "Burkina Faso                                    323\n",
      "Australia                                       323\n",
      "Isle of Man                                     323\n",
      "Mongolia                                        323\n",
      "United States Virgin Islands                    323\n",
      "Gambia                                          323\n",
      "Malta                                           323\n",
      "Lesotho                                         323\n",
      "Montserrat                                      323\n",
      "Bangladesh                                      323\n",
      "Uganda                                          323\n",
      "Canada                                          323\n",
      "Guyana                                          323\n",
      "Mayotte                                         323\n",
      "New Zealand                                     323\n",
      "Suriname                                        323\n",
      "Israel                                          323\n",
      "Sint Maarten                                    323\n",
      "Dominica                                        323\n",
      "Sierra Leone                                    323\n",
      "Madagascar                                      323\n",
      "South Sudan                                     323\n",
      "Mauritius                                       323\n",
      "Armenia                                         323\n",
      "Cook Islands                                    323\n",
      "Uruguay                                         323\n",
      "Brazil                                          323\n",
      "British Virgin Islands                          323\n",
      "Costa Rica                                      323\n",
      "Sudan                                           323\n",
      "Djibouti                                        323\n",
      "Pitcairn Islands                                323\n",
      "Name: country_name, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# SOME INFORMATION ON THE DATA FOR EACH COUNTRY\n",
    "print(data['country_name'].value_counts())               # So this shows that data is collected for 323 unique days for each country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tedQWhFfDkfF",
    "outputId": "3f5af529-b9d8-4be3-9c0a-c31d06a685e4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date                                      0\n",
      "country_name                              0\n",
      "subregion1_code                       79781\n",
      "subregion1_name                       79781\n",
      "subregion2_code                       79781\n",
      "subregion2_name                       79781\n",
      "locality_code                         79781\n",
      "locality_name                         79781\n",
      "new_confirmed                          5355\n",
      "new_deceased                           5358\n",
      "new_recovered                         75885\n",
      "new_tested                            57355\n",
      "total_confirmed                        5353\n",
      "total_deceased                         5357\n",
      "total_recovered                       76043\n",
      "total_tested                          57124\n",
      "new_hospitalized                      77841\n",
      "total_hospitalized                    77849\n",
      "current_hospitalized                  78301\n",
      "new_intensive_care                    78795\n",
      "total_intensive_care                  78777\n",
      "current_intensive_care                78197\n",
      "new_ventilator                        79781\n",
      "total_ventilator                      79781\n",
      "current_ventilator                    79250\n",
      "population                              323\n",
      "population_male                        3553\n",
      "population_female                      3553\n",
      "rural_population                      10982\n",
      "urban_population                      10982\n",
      "largest_city_population               30685\n",
      "clustered_population                  40698\n",
      "population_density                    11628\n",
      "human_development_index               19703\n",
      "population_age_00_09                   5168\n",
      "population_age_10_19                   5168\n",
      "population_age_20_29                   5168\n",
      "population_age_30_39                   5168\n",
      "population_age_40_49                   5168\n",
      "population_age_50_59                   5168\n",
      "population_age_60_69                   5168\n",
      "population_age_70_79                   5168\n",
      "population_age_80_89                   5491\n",
      "population_age_90_99                   5491\n",
      "population_age_80_and_older            5168\n",
      "gdp                                   11951\n",
      "gdp_per_capita                        12274\n",
      "human_capital_index                   29393\n",
      "open_street_maps                       3230\n",
      "latitude                                969\n",
      "longitude                               969\n",
      "elevation                             75582\n",
      "area                                    646\n",
      "rural_area                            21964\n",
      "urban_area                            21964\n",
      "life_expectancy                       13566\n",
      "smoking_prevalence                    32623\n",
      "diabetes_prevalence                   12274\n",
      "infant_mortality_rate                 17442\n",
      "adult_male_mortality_rate             18734\n",
      "adult_female_mortality_rate           18734\n",
      "pollution_mortality_rate              20672\n",
      "comorbidity_mortality_rate            20672\n",
      "hospital_beds                         71706\n",
      "nurses                                21641\n",
      "physicians                            26809\n",
      "health_expenditure                    19703\n",
      "out_of_pocket_health_expenditure      19703\n",
      "mobility_retail_and_recreation        43883\n",
      "mobility_grocery_and_pharmacy         43888\n",
      "mobility_parks                        44165\n",
      "mobility_transit_stations             43886\n",
      "mobility_workplaces                   43256\n",
      "mobility_residential                  44061\n",
      "school_closing                        21296\n",
      "workplace_closing                     21651\n",
      "cancel_public_events                  21604\n",
      "restrictions_on_gatherings            21600\n",
      "public_transport_closing              21605\n",
      "stay_at_home_requirements             21640\n",
      "restrictions_on_internal_movement     21330\n",
      "international_travel_controls         21285\n",
      "income_support                        21983\n",
      "debt_relief                           22908\n",
      "fiscal_measures                       23655\n",
      "international_support                 23307\n",
      "public_information_campaigns          21591\n",
      "testing_policy                        22069\n",
      "contact_tracing                       22048\n",
      "emergency_investment_in_healthcare    23714\n",
      "investment_in_vaccines                23392\n",
      "stringency_index                      21645\n",
      "average_temperature                    6952\n",
      "minimum_temperature                    6955\n",
      "maximum_temperature                    6958\n",
      "rainfall                               7749\n",
      "snowfall                              75797\n",
      "dew_point                              7875\n",
      "relative_humidity                      7878\n",
      "Confirmed                             36635\n",
      "Recovered                             36635\n",
      "Deaths                                36635\n",
      "dtype: int64\n",
      "\n",
      " Percentage of missing data for each feature \n",
      " date                                    0.000000\n",
      "country_name                            0.000000\n",
      "subregion1_code                       100.000000\n",
      "subregion1_name                       100.000000\n",
      "subregion2_code                       100.000000\n",
      "subregion2_name                       100.000000\n",
      "locality_code                         100.000000\n",
      "locality_name                         100.000000\n",
      "new_confirmed                           6.712124\n",
      "new_deceased                            6.715885\n",
      "new_recovered                          95.116632\n",
      "new_tested                             71.890550\n",
      "total_confirmed                         6.709618\n",
      "total_deceased                          6.714631\n",
      "total_recovered                        95.314674\n",
      "total_tested                           71.601008\n",
      "new_hospitalized                       97.568343\n",
      "total_hospitalized                     97.578371\n",
      "current_hospitalized                   98.144922\n",
      "new_intensive_care                     98.764117\n",
      "total_intensive_care                   98.741555\n",
      "current_intensive_care                 98.014565\n",
      "new_ventilator                        100.000000\n",
      "total_ventilator                      100.000000\n",
      "current_ventilator                     99.334428\n",
      "population                              0.404858\n",
      "population_male                         4.453441\n",
      "population_female                       4.453441\n",
      "rural_population                       13.765182\n",
      "urban_population                       13.765182\n",
      "largest_city_population                38.461538\n",
      "clustered_population                   51.012146\n",
      "population_density                     14.574899\n",
      "human_development_index                24.696356\n",
      "population_age_00_09                    6.477733\n",
      "population_age_10_19                    6.477733\n",
      "population_age_20_29                    6.477733\n",
      "population_age_30_39                    6.477733\n",
      "population_age_40_49                    6.477733\n",
      "population_age_50_59                    6.477733\n",
      "population_age_60_69                    6.477733\n",
      "population_age_70_79                    6.477733\n",
      "population_age_80_89                    6.882591\n",
      "population_age_90_99                    6.882591\n",
      "population_age_80_and_older             6.477733\n",
      "gdp                                    14.979757\n",
      "gdp_per_capita                         15.384615\n",
      "human_capital_index                    36.842105\n",
      "open_street_maps                        4.048583\n",
      "latitude                                1.214575\n",
      "longitude                               1.214575\n",
      "elevation                              94.736842\n",
      "area                                    0.809717\n",
      "rural_area                             27.530364\n",
      "urban_area                             27.530364\n",
      "life_expectancy                        17.004049\n",
      "smoking_prevalence                     40.890688\n",
      "diabetes_prevalence                    15.384615\n",
      "infant_mortality_rate                  21.862348\n",
      "adult_male_mortality_rate              23.481781\n",
      "adult_female_mortality_rate            23.481781\n",
      "pollution_mortality_rate               25.910931\n",
      "comorbidity_mortality_rate             25.910931\n",
      "hospital_beds                          89.878543\n",
      "nurses                                 27.125506\n",
      "physicians                             33.603239\n",
      "health_expenditure                     24.696356\n",
      "out_of_pocket_health_expenditure       24.696356\n",
      "mobility_retail_and_recreation         55.004324\n",
      "mobility_grocery_and_pharmacy          55.010591\n",
      "mobility_parks                         55.357792\n",
      "mobility_transit_stations              55.008085\n",
      "mobility_workplaces                    54.218423\n",
      "mobility_residential                   55.227435\n",
      "school_closing                         26.693072\n",
      "workplace_closing                      27.138040\n",
      "cancel_public_events                   27.079129\n",
      "restrictions_on_gatherings             27.074115\n",
      "public_transport_closing               27.080383\n",
      "stay_at_home_requirements              27.124253\n",
      "restrictions_on_internal_movement      26.735689\n",
      "international_travel_controls          26.679285\n",
      "income_support                         27.554180\n",
      "debt_relief                            28.713603\n",
      "fiscal_measures                        29.649917\n",
      "international_support                  29.213723\n",
      "public_information_campaigns           27.062835\n",
      "testing_policy                         27.661975\n",
      "contact_tracing                        27.635653\n",
      "emergency_investment_in_healthcare     29.723869\n",
      "investment_in_vaccines                 29.320264\n",
      "stringency_index                       27.130520\n",
      "average_temperature                     8.713854\n",
      "minimum_temperature                     8.717614\n",
      "maximum_temperature                     8.721375\n",
      "rainfall                                9.712839\n",
      "snowfall                               95.006330\n",
      "dew_point                               9.870771\n",
      "relative_humidity                       9.874532\n",
      "Confirmed                              45.919455\n",
      "Recovered                              45.919455\n",
      "Deaths                                 45.919455\n",
      "dtype: float64\n",
      "\n",
      " Columns with more than 60% missing data are:\n",
      " subregion1_code           100.000000\n",
      "subregion1_name           100.000000\n",
      "subregion2_code           100.000000\n",
      "subregion2_name           100.000000\n",
      "locality_code             100.000000\n",
      "locality_name             100.000000\n",
      "new_recovered              95.116632\n",
      "new_tested                 71.890550\n",
      "total_recovered            95.314674\n",
      "total_tested               71.601008\n",
      "new_hospitalized           97.568343\n",
      "total_hospitalized         97.578371\n",
      "current_hospitalized       98.144922\n",
      "new_intensive_care         98.764117\n",
      "total_intensive_care       98.741555\n",
      "current_intensive_care     98.014565\n",
      "new_ventilator            100.000000\n",
      "total_ventilator          100.000000\n",
      "current_ventilator         99.334428\n",
      "elevation                  94.736842\n",
      "hospital_beds              89.878543\n",
      "snowfall                   95.006330\n",
      "dtype: float64\n",
      "\n",
      " No. of columns with more than 60% missing data:   22\n"
     ]
    }
   ],
   "source": [
    "# CHECKING MISSING VALUES FOR DATASET\n",
    "print(data.isnull().sum())\n",
    "\n",
    "percent_missing_data = data.isnull().sum() * 100 / len(data)\n",
    "print('\\n Percentage of missing data for each feature \\n', percent_missing_data)\n",
    "\n",
    "# Checking for columns that has more than 60% missing data\n",
    "sixtyPercentMissingData = percent_missing_data[percent_missing_data > 60.0] \n",
    "columns_to_remove = list(sixtyPercentMissingData.index)\n",
    "print('\\n Columns with more than 60% missing data are:\\n', sixtyPercentMissingData)\n",
    "print('\\n No. of columns with more than 60% missing data:  ', len(columns_to_remove))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xcz7JuMq51tM"
   },
   "source": [
    "Theoretically columns/features having more than 25-30% of missing data should be removed. \n",
    "\n",
    "But practically its not feasible as that column or feature can be crucial while doing the prediction. \n",
    "\n",
    "So we will look at category of columns together for missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5TbBOfMBORan",
    "outputId": "f741dace-c295-41d8-d989-e9770082266d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Shape of the reduced data: (79781, 80)\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "There are 22 features having more than 60% of the data missing. \n",
    "And percentage of data missing ranges from 70% to 100%. \n",
    "\n",
    "We will go ahead and remove these columns.\n",
    "'''\n",
    "data = data.drop(columns=columns_to_remove, axis=1)             # Dropping the columns that has more than 60% missing data\n",
    "print('\\n Shape of the reduced data:',data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ewHkCYi48fnP",
    "outputId": "78f81bf6-1050-4aa6-ec5b-470b19705df3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Records for which Confirmed cases are missing (36635, 80)\n",
      "\n",
      " Countries and number of missing data days country_name\n",
      "Afghanistan                                      69\n",
      "Albania                                          83\n",
      "Algeria                                          70\n",
      "American Samoa                                  323\n",
      "Andorra                                          76\n",
      "Angola                                           93\n",
      "Anguilla                                        323\n",
      "Antarctica                                      323\n",
      "Antigua and Barbuda                              86\n",
      "Argentina                                        77\n",
      "Armenia                                          75\n",
      "Aruba                                           316\n",
      "Australia                                        39\n",
      "Austria                                          70\n",
      "Azerbaijan                                       74\n",
      "Bahamas                                          95\n",
      "Bahrain                                          69\n",
      "Bangladesh                                       82\n",
      "Barbados                                         90\n",
      "Belarus                                          73\n",
      "Belgium                                          50\n",
      "Belize                                           96\n",
      "Benin                                            89\n",
      "Bermuda                                         323\n",
      "Bhutan                                           80\n",
      "Bolivia                                          85\n",
      "Bosnia and Herzegovina                           79\n",
      "Botswana                                        103\n",
      "Bouvet Island                                   323\n",
      "Brazil                                           70\n",
      "British Indian Ocean Territory                  323\n",
      "British Virgin Islands                          323\n",
      "Brunei                                           83\n",
      "Bulgaria                                         82\n",
      "Burkina Faso                                     84\n",
      "Burundi                                         104\n",
      "Cambodia                                         42\n",
      "Cameroon                                         80\n",
      "Canada                                           41\n",
      "Cape Verde                                      322\n",
      "Caribbean Netherlands                           323\n",
      "Cayman Islands                                  320\n",
      "Central African Republic                         88\n",
      "Chad                                             92\n",
      "Chile                                            77\n",
      "China                                            37\n",
      "Christmas Island                                323\n",
      "Cocos Islands                                   323\n",
      "Colombia                                         79\n",
      "Comoros                                         133\n",
      "Cook Islands                                    323\n",
      "Costa Rica                                       80\n",
      "Croatia                                          70\n",
      "Cuba                                             86\n",
      "Curaçao                                         323\n",
      "Cyprus                                           83\n",
      "Czech Republic                                   75\n",
      "Democratic Republic of the Congo                323\n",
      "Denmark                                          72\n",
      "Djibouti                                         91\n",
      "Dominica                                        323\n",
      "Dominican Republic                               75\n",
      "East Timor                                      322\n",
      "Ecuador                                          75\n",
      "Egypt                                            59\n",
      "El Salvador                                      92\n",
      "Equatorial Guinea                                88\n",
      "Eritrea                                          94\n",
      "Estonia                                          72\n",
      "Ethiopia                                         86\n",
      "Falkland Islands                                323\n",
      "Faroe Islands                                   316\n",
      "Fiji                                             92\n",
      "Finland                                          44\n",
      "France                                           39\n",
      "French Guiana                                   310\n",
      "French Polynesia                                323\n",
      "French Southern Territories                     323\n",
      "Gabon                                            87\n",
      "Gambia                                           90\n",
      "Gaza Strip                                      323\n",
      "Georgia                                          71\n",
      "Germany                                          43\n",
      "Ghana                                            87\n",
      "Gibraltar                                       316\n",
      "Greece                                           71\n",
      "Greenland                                       317\n",
      "Grenada                                          95\n",
      "Guam                                            317\n",
      "Guatemala                                        87\n",
      "Guernsey                                        315\n",
      "Guinea                                           86\n",
      "Guinea-Bissau                                    98\n",
      "Guyana                                           86\n",
      "Haiti                                            93\n",
      "Heard Island and McDonald Islands               323\n",
      "Honduras                                         85\n",
      "Hong Kong                                       275\n",
      "Hungary                                          78\n",
      "Iceland                                          73\n",
      "India                                            45\n",
      "Indonesia                                        76\n",
      "Iran                                             64\n",
      "Iraq                                             68\n",
      "Ireland                                          74\n",
      "Isle of Man                                     323\n",
      "Israel                                           66\n",
      "Italy                                            46\n",
      "Ivory Coast                                      84\n",
      "Jamaica                                          85\n",
      "Japan                                            37\n",
      "Jersey                                          315\n",
      "Jordan                                           77\n",
      "Kazakhstan                                       86\n",
      "Kenya                                            86\n",
      "Kiribati                                        323\n",
      "Kosovo                                           92\n",
      "Kuwait                                           69\n",
      "Kyrgyzstan                                       91\n",
      "Laos                                             97\n",
      "Latvia                                           76\n",
      "Lebanon                                          66\n",
      "Lesotho                                         145\n",
      "Liberia                                          89\n",
      "Libya                                            97\n",
      "Liechtenstein                                    78\n",
      "Lithuania                                        73\n",
      "Luxembourg                                       74\n",
      "Macau                                           275\n",
      "Macedonia                                       323\n",
      "Madagascar                                       93\n",
      "Malawi                                          106\n",
      "Malaysia                                         39\n",
      "Maldives                                         82\n",
      "Mali                                             98\n",
      "Malta                                            81\n",
      "Marshall Islands                                308\n",
      "Martinique                                      309\n",
      "Mauritania                                       87\n",
      "Mauritius                                        91\n",
      "Mayotte                                         317\n",
      "Mexico                                           72\n",
      "Micronesia                                      323\n",
      "Moldova                                          82\n",
      "Monaco                                           74\n",
      "Mongolia                                         84\n",
      "Montenegro                                       90\n",
      "Montserrat                                      323\n",
      "Morocco                                          76\n",
      "Mozambique                                       95\n",
      "Myanmar                                         323\n",
      "Namibia                                          87\n",
      "Nauru                                           323\n",
      "Nepal                                            40\n",
      "Netherlands                                      72\n",
      "Netherlands Antilles                            323\n",
      "New Caledonia                                   323\n",
      "New Zealand                                      73\n",
      "Nicaragua                                        92\n",
      "Niger                                           323\n",
      "Nigeria                                          73\n",
      "Niue                                            323\n",
      "Norfolk Island                                  323\n",
      "North Korea                                     323\n",
      "Northern Mariana Islands                        323\n",
      "Norway                                           71\n",
      "Oman                                             69\n",
      "Pakistan                                         71\n",
      "Palau                                           323\n",
      "Palestine                                       323\n",
      "Panama                                           84\n",
      "Papua New Guinea                                 93\n",
      "Paraguay                                         82\n",
      "Peru                                             80\n",
      "Philippines                                      44\n",
      "Pitcairn Islands                                323\n",
      "Poland                                           78\n",
      "Portugal                                         76\n",
      "Puerto Rico                                     317\n",
      "Qatar                                            74\n",
      "Republic of the Congo                           323\n",
      "Romania                                          71\n",
      "Russia                                           46\n",
      "Rwanda                                           87\n",
      "Réunion                                         323\n",
      "Saint Helena                                    323\n",
      "Saint Kitts and Nevis                            98\n",
      "Saint Lucia                                      87\n",
      "Saint Vincent and the Grenadines                 87\n",
      "Samoa                                           323\n",
      "San Marino                                       72\n",
      "Saudi Arabia                                     76\n",
      "Senegal                                          76\n",
      "Serbia                                           80\n",
      "Seychelles                                       87\n",
      "Sierra Leone                                    104\n",
      "Singapore                                        38\n",
      "Sint Maarten                                    323\n",
      "Slovakia                                         80\n",
      "Slovenia                                         79\n",
      "Solomon Islands                                 293\n",
      "Somalia                                          89\n",
      "South Africa                                     79\n",
      "South Georgia and the South Sandwich Islands    323\n",
      "South Korea                                      37\n",
      "South Sudan                                     109\n",
      "Spain                                            47\n",
      "Sri Lanka                                        42\n",
      "Sudan                                            86\n",
      "Suriname                                         87\n",
      "Svalbard and Jan Mayen                          323\n",
      "Swaziland                                       323\n",
      "Sweden                                           46\n",
      "Switzerland                                      70\n",
      "Syria                                            95\n",
      "São Tomé and Príncipe                           323\n",
      "Taiwan                                           38\n",
      "Tajikistan                                      133\n",
      "Tanzania                                         89\n",
      "Thailand                                         37\n",
      "Togo                                             80\n",
      "Tokelau                                         323\n",
      "Tonga                                           323\n",
      "Trinidad and Tobago                              87\n",
      "Tunisia                                          78\n",
      "Turkey                                           85\n",
      "Turkmenistan                                    323\n",
      "Turks and Caicos Islands                        323\n",
      "Tuvalu                                          323\n",
      "Uganda                                           94\n",
      "Ukraine                                          77\n",
      "United Arab Emirates                             44\n",
      "United Kingdom                                   46\n",
      "United States Minor Outlying Islands            323\n",
      "United States Virgin Islands                    323\n",
      "United States of America                         37\n",
      "Uruguay                                          87\n",
      "Uzbekistan                                       88\n",
      "Vanuatu                                         321\n",
      "Vatican City                                     80\n",
      "Venezuela                                        87\n",
      "Vietnam                                          38\n",
      "Wallis and Futuna                               323\n",
      "Western Sahara                                  109\n",
      "Yemen                                           114\n",
      "Zambia                                           91\n",
      "Zimbabwe                                         93\n",
      "Name: date, dtype: int64\n",
      "\n",
      " Countries and their missing data count(no. of days for which the data is missing)\n",
      "  {'Afghanistan': 69, 'Albania': 83, 'Algeria': 70, 'American Samoa': 323, 'Andorra': 76, 'Angola': 93, 'Anguilla': 323, 'Antarctica': 323, 'Antigua and Barbuda': 86, 'Argentina': 77, 'Armenia': 75, 'Aruba': 316, 'Australia': 39, 'Austria': 70, 'Azerbaijan': 74, 'Bahamas': 95, 'Bahrain': 69, 'Bangladesh': 82, 'Barbados': 90, 'Belarus': 73, 'Belgium': 50, 'Belize': 96, 'Benin': 89, 'Bermuda': 323, 'Bhutan': 80, 'Bolivia': 85, 'Bosnia and Herzegovina': 79, 'Botswana': 103, 'Bouvet Island': 323, 'Brazil': 70, 'British Indian Ocean Territory': 323, 'British Virgin Islands': 323, 'Brunei': 83, 'Bulgaria': 82, 'Burkina Faso': 84, 'Burundi': 104, 'Cambodia': 42, 'Cameroon': 80, 'Canada': 41, 'Cape Verde': 322, 'Caribbean Netherlands': 323, 'Cayman Islands': 320, 'Central African Republic': 88, 'Chad': 92, 'Chile': 77, 'China': 37, 'Christmas Island': 323, 'Cocos Islands': 323, 'Colombia': 79, 'Comoros': 133, 'Cook Islands': 323, 'Costa Rica': 80, 'Croatia': 70, 'Cuba': 86, 'Curaçao': 323, 'Cyprus': 83, 'Czech Republic': 75, 'Democratic Republic of the Congo': 323, 'Denmark': 72, 'Djibouti': 91, 'Dominica': 323, 'Dominican Republic': 75, 'East Timor': 322, 'Ecuador': 75, 'Egypt': 59, 'El Salvador': 92, 'Equatorial Guinea': 88, 'Eritrea': 94, 'Estonia': 72, 'Ethiopia': 86, 'Falkland Islands': 323, 'Faroe Islands': 316, 'Fiji': 92, 'Finland': 44, 'France': 39, 'French Guiana': 310, 'French Polynesia': 323, 'French Southern Territories': 323, 'Gabon': 87, 'Gambia': 90, 'Gaza Strip': 323, 'Georgia': 71, 'Germany': 43, 'Ghana': 87, 'Gibraltar': 316, 'Greece': 71, 'Greenland': 317, 'Grenada': 95, 'Guam': 317, 'Guatemala': 87, 'Guernsey': 315, 'Guinea': 86, 'Guinea-Bissau': 98, 'Guyana': 86, 'Haiti': 93, 'Heard Island and McDonald Islands': 323, 'Honduras': 85, 'Hong Kong': 275, 'Hungary': 78, 'Iceland': 73, 'India': 45, 'Indonesia': 76, 'Iran': 64, 'Iraq': 68, 'Ireland': 74, 'Isle of Man': 323, 'Israel': 66, 'Italy': 46, 'Ivory Coast': 84, 'Jamaica': 85, 'Japan': 37, 'Jersey': 315, 'Jordan': 77, 'Kazakhstan': 86, 'Kenya': 86, 'Kiribati': 323, 'Kosovo': 92, 'Kuwait': 69, 'Kyrgyzstan': 91, 'Laos': 97, 'Latvia': 76, 'Lebanon': 66, 'Lesotho': 145, 'Liberia': 89, 'Libya': 97, 'Liechtenstein': 78, 'Lithuania': 73, 'Luxembourg': 74, 'Macau': 275, 'Macedonia': 323, 'Madagascar': 93, 'Malawi': 106, 'Malaysia': 39, 'Maldives': 82, 'Mali': 98, 'Malta': 81, 'Marshall Islands': 308, 'Martinique': 309, 'Mauritania': 87, 'Mauritius': 91, 'Mayotte': 317, 'Mexico': 72, 'Micronesia': 323, 'Moldova': 82, 'Monaco': 74, 'Mongolia': 84, 'Montenegro': 90, 'Montserrat': 323, 'Morocco': 76, 'Mozambique': 95, 'Myanmar': 323, 'Namibia': 87, 'Nauru': 323, 'Nepal': 40, 'Netherlands': 72, 'Netherlands Antilles': 323, 'New Caledonia': 323, 'New Zealand': 73, 'Nicaragua': 92, 'Niger': 323, 'Nigeria': 73, 'Niue': 323, 'Norfolk Island': 323, 'North Korea': 323, 'Northern Mariana Islands': 323, 'Norway': 71, 'Oman': 69, 'Pakistan': 71, 'Palau': 323, 'Palestine': 323, 'Panama': 84, 'Papua New Guinea': 93, 'Paraguay': 82, 'Peru': 80, 'Philippines': 44, 'Pitcairn Islands': 323, 'Poland': 78, 'Portugal': 76, 'Puerto Rico': 317, 'Qatar': 74, 'Republic of the Congo': 323, 'Romania': 71, 'Russia': 46, 'Rwanda': 87, 'Réunion': 323, 'Saint Helena': 323, 'Saint Kitts and Nevis': 98, 'Saint Lucia': 87, 'Saint Vincent and the Grenadines': 87, 'Samoa': 323, 'San Marino': 72, 'Saudi Arabia': 76, 'Senegal': 76, 'Serbia': 80, 'Seychelles': 87, 'Sierra Leone': 104, 'Singapore': 38, 'Sint Maarten': 323, 'Slovakia': 80, 'Slovenia': 79, 'Solomon Islands': 293, 'Somalia': 89, 'South Africa': 79, 'South Georgia and the South Sandwich Islands': 323, 'South Korea': 37, 'South Sudan': 109, 'Spain': 47, 'Sri Lanka': 42, 'Sudan': 86, 'Suriname': 87, 'Svalbard and Jan Mayen': 323, 'Swaziland': 323, 'Sweden': 46, 'Switzerland': 70, 'Syria': 95, 'São Tomé and Príncipe': 323, 'Taiwan': 38, 'Tajikistan': 133, 'Tanzania': 89, 'Thailand': 37, 'Togo': 80, 'Tokelau': 323, 'Tonga': 323, 'Trinidad and Tobago': 87, 'Tunisia': 78, 'Turkey': 85, 'Turkmenistan': 323, 'Turks and Caicos Islands': 323, 'Tuvalu': 323, 'Uganda': 94, 'Ukraine': 77, 'United Arab Emirates': 44, 'United Kingdom': 46, 'United States Minor Outlying Islands': 323, 'United States Virgin Islands': 323, 'United States of America': 37, 'Uruguay': 87, 'Uzbekistan': 88, 'Vanuatu': 321, 'Vatican City': 80, 'Venezuela': 87, 'Vietnam': 38, 'Wallis and Futuna': 323, 'Western Sahara': 109, 'Yemen': 114, 'Zambia': 91, 'Zimbabwe': 93}\n",
      "\n",
      " Number of Countries with more than 3 months of missing data: 108\n",
      "\n",
      " Countries with more than 3 months of missing the data: ['American Samoa', 'Angola', 'Anguilla', 'Antarctica', 'Aruba', 'Bahamas', 'Belize', 'Bermuda', 'Botswana', 'Bouvet Island', 'British Indian Ocean Territory', 'British Virgin Islands', 'Burundi', 'Cape Verde', 'Caribbean Netherlands', 'Cayman Islands', 'Chad', 'Christmas Island', 'Cocos Islands', 'Comoros', 'Cook Islands', 'Curaçao', 'Democratic Republic of the Congo', 'Djibouti', 'Dominica', 'East Timor', 'El Salvador', 'Eritrea', 'Falkland Islands', 'Faroe Islands', 'Fiji', 'French Guiana', 'French Polynesia', 'French Southern Territories', 'Gaza Strip', 'Gibraltar', 'Greenland', 'Grenada', 'Guam', 'Guernsey', 'Guinea-Bissau', 'Haiti', 'Heard Island and McDonald Islands', 'Hong Kong', 'Isle of Man', 'Jersey', 'Kiribati', 'Kosovo', 'Kyrgyzstan', 'Laos', 'Lesotho', 'Libya', 'Macau', 'Macedonia', 'Madagascar', 'Malawi', 'Mali', 'Marshall Islands', 'Martinique', 'Mauritius', 'Mayotte', 'Micronesia', 'Montserrat', 'Mozambique', 'Myanmar', 'Nauru', 'Netherlands Antilles', 'New Caledonia', 'Nicaragua', 'Niger', 'Niue', 'Norfolk Island', 'North Korea', 'Northern Mariana Islands', 'Palau', 'Palestine', 'Papua New Guinea', 'Pitcairn Islands', 'Puerto Rico', 'Republic of the Congo', 'Réunion', 'Saint Helena', 'Saint Kitts and Nevis', 'Samoa', 'Sierra Leone', 'Sint Maarten', 'Solomon Islands', 'South Georgia and the South Sandwich Islands', 'South Sudan', 'Svalbard and Jan Mayen', 'Swaziland', 'Syria', 'São Tomé and Príncipe', 'Tajikistan', 'Tokelau', 'Tonga', 'Turkmenistan', 'Turks and Caicos Islands', 'Tuvalu', 'Uganda', 'United States Minor Outlying Islands', 'United States Virgin Islands', 'Vanuatu', 'Wallis and Futuna', 'Western Sahara', 'Yemen', 'Zambia', 'Zimbabwe']\n"
     ]
    }
   ],
   "source": [
    "# WE WANT TO FIND THE RECORDS(COUNTRIES) WITH MOST OF THE MISSING VALUES FOR FEATURES - RECOVERED, CONFIMED AND DEATHS \n",
    "\n",
    "missing_data_confirmed = data[data.Confirmed.isnull()]\n",
    "missing_data_recovered = data[data.Recovered.isnull()]\n",
    "missing_data_deaths = data[data.Deaths.isnull()]\n",
    "\n",
    "print('\\n Records for which Confirmed cases are missing', missing_data_confirmed.shape)    \n",
    "# print(missing_data_recovered.shape, missing_data_deaths.shape)   \n",
    "# print(missing_data_confirmed[['country_name','Recovered']])\n",
    "\n",
    "countries_missing_data = missing_data_confirmed.groupby(['country_name']).count()['date']\n",
    "print('\\n Countries and number of missing data days', countries_missing_data)\n",
    "\n",
    "missing_data_count = countries_missing_data.to_list()\n",
    "countries = list(countries_missing_data.index)\n",
    "dict_missing_countries = dict(zip(countries,missing_data_count))\n",
    "print('\\n Countries and their missing data count(no. of days for which the data is missing)\\n ',dict_missing_countries)\n",
    "\n",
    "countries_to_drop = [k for k, v in dict_missing_countries.items() if v > 90]\n",
    "# print(countries_to_drop)\n",
    "\n",
    "print('\\n Number of Countries with more than 3 months of missing data:',len(countries_to_drop))\n",
    "print('\\n Countries with more than 3 months of missing the data:',countries_to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OYbgrfKEIyKj",
    "outputId": "597d76f0-1e64-4cb8-ea34-bb27d63df2e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of countries to drop 138\n",
      "\n",
      " Shape of the data after removing countries for which many records are missing (35207, 80)\n"
     ]
    }
   ],
   "source": [
    "manualy_noted_countries = ['French Southern Territories','Yemen','Cameroon','Venezuela','Uzbekistan','British Virgin Islands','Somalia','Puerto Rico',\n",
    "                 'Curaçao','Azerbaijan','Jersey','Eritrea','United States Minor Outlying Islands','Macau','French Guiana',\n",
    "                 'Nauru','Swaziland','Palau','Sierra Leone','Heard Island and McDonald Islands','Kazakhstan','Guernsey',\n",
    "                 'Samoa','Kosovo','Montenegro','Wallis and Futuna','Democratic Republic of the Congo','Antigua and Barbuda',\n",
    "                 'Malta','Equatorial Guinea','Georgia','Christmas Island','Kiribati','Bermuda','Chad','Comoros','Central African Republic',\n",
    "                 'Taiwan','Antarctica','Cocos Islands','United States Virgin Islands','East Timor','Saint Helena',\n",
    "                 'Turks and Caicos Islands','Saint Kitts and Nevis','Malawi','French Polynesia','Martinique','Cape Verde', \n",
    "                 'Albania','Caribbean Netherlands','British Indian Ocean Territory','Saint Vincent and the Grenadines','Liechtenstein',\n",
    "                 'Tajikistan','Lesotho','Marshall Islands','Greenland','Andorra','Zimbabwe','Solomon Islands','Cambodia','Cayman Islands',\n",
    "                 'Hong Kong','Seychelles','Bouvet Island','Niue','Madagascar','Ukraine','Western Sahara','Isle of Man','Mayotte',\n",
    "                 'San Marino','Grenada','Micronesia','Svalbard and Jan Mayen','Faroe Islands','Syria','Maldives','Bahamas','Dominica',\n",
    "                 'Republic of the Congo','Mauritania','Northern Mariana Islands','Saint Lucia','Armenia','Sint Maarten','Niger',\n",
    "                 'Gaza Strip','Montserrat','Tokelau','Anguilla','Cook Islands','Monaco','Falkland Islands','New Caledonia',\n",
    "                 'Zambia','Vatican City','São Tomé and Príncipe','Djibouti','Hungary','Ivory Coast','Libya','Tuvalu','Vanuatu',\n",
    "                 'South Sudan','South Georgia and the South Sandwich Islands','Gibraltar','North Korea','Myanmar','Papua New Guinea',\n",
    "                 'Netherlands Antilles','Norfolk Island','Guam','Kyrgyzstan','Guinea-Bissau','Tonga','Sudan','Pitcairn Islands',\n",
    "                 'American Samoa','Aruba','Réunion','Turkmenistan','Palestine','Macedonia']\n",
    "\n",
    "extra_countries = list(set(manualy_noted_countries) - set(countries_to_drop))\n",
    "# print(len(extra_countries))\n",
    "\n",
    "countries_to_drop.extend(extra_countries)\n",
    "print('No. of countries to drop',len(countries_to_drop))\n",
    "\n",
    "# DROPPING THE ROWS/RECORDS WITH MOST OF THE MISSING DATA\n",
    "data = data.drop(index=data[data['country_name'].isin(countries_to_drop)].index)\n",
    "print('\\n Shape of the data after removing countries for which many records are missing',data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eq8T2AcUWqOv",
    "outputId": "4e106283-31e8-4d03-db99-08c35eaca59e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Percentage of missing data for each feature after month of Feb \n",
      " date                                   0.000000\n",
      "country_name                           0.000000\n",
      "new_confirmed                          1.013134\n",
      "new_deceased                           1.023035\n",
      "total_confirmed                        1.013134\n",
      "total_deceased                         1.023035\n",
      "population                             0.000000\n",
      "population_male                        0.000000\n",
      "population_female                      0.000000\n",
      "rural_population                       0.000000\n",
      "urban_population                       0.000000\n",
      "largest_city_population                8.256881\n",
      "clustered_population                  22.935780\n",
      "population_density                     0.000000\n",
      "human_development_index                0.917431\n",
      "population_age_00_09                   0.000000\n",
      "population_age_10_19                   0.000000\n",
      "population_age_20_29                   0.000000\n",
      "population_age_30_39                   0.000000\n",
      "population_age_40_49                   0.000000\n",
      "population_age_50_59                   0.000000\n",
      "population_age_60_69                   0.000000\n",
      "population_age_70_79                   0.000000\n",
      "population_age_80_89                   0.000000\n",
      "population_age_90_99                   0.000000\n",
      "population_age_80_and_older            0.000000\n",
      "gdp                                    0.000000\n",
      "gdp_per_capita                         0.000000\n",
      "human_capital_index                    6.422018\n",
      "open_street_maps                       0.000000\n",
      "latitude                               0.000000\n",
      "longitude                              0.000000\n",
      "area                                   0.000000\n",
      "rural_area                            11.009174\n",
      "urban_area                            11.009174\n",
      "life_expectancy                        0.000000\n",
      "smoking_prevalence                     9.174312\n",
      "diabetes_prevalence                    0.000000\n",
      "infant_mortality_rate                  0.000000\n",
      "adult_male_mortality_rate              2.752294\n",
      "adult_female_mortality_rate            2.752294\n",
      "pollution_mortality_rate               0.000000\n",
      "comorbidity_mortality_rate             0.000000\n",
      "nurses                                 0.000000\n",
      "physicians                             0.000000\n",
      "health_expenditure                     0.000000\n",
      "out_of_pocket_health_expenditure       0.000000\n",
      "mobility_retail_and_recreation        16.045146\n",
      "mobility_grocery_and_pharmacy         15.886740\n",
      "mobility_parks                        16.464260\n",
      "mobility_transit_stations             16.160649\n",
      "mobility_workplaces                   15.619431\n",
      "mobility_residential                  15.629331\n",
      "school_closing                         1.966867\n",
      "workplace_closing                      2.016369\n",
      "cancel_public_events                   1.966867\n",
      "restrictions_on_gatherings             1.966867\n",
      "public_transport_closing               1.989968\n",
      "stay_at_home_requirements              1.980067\n",
      "restrictions_on_internal_movement      2.019669\n",
      "international_travel_controls          1.986668\n",
      "income_support                         2.003168\n",
      "debt_relief                            1.993268\n",
      "fiscal_measures                        2.115372\n",
      "international_support                  2.059270\n",
      "public_information_campaigns           1.996568\n",
      "testing_policy                         2.079071\n",
      "contact_tracing                        2.032869\n",
      "emergency_investment_in_healthcare     2.164874\n",
      "investment_in_vaccines                 2.184674\n",
      "stringency_index                       1.986668\n",
      "average_temperature                    1.478450\n",
      "minimum_temperature                    1.478450\n",
      "maximum_temperature                    1.478450\n",
      "rainfall                               1.818362\n",
      "dew_point                              1.478450\n",
      "relative_humidity                      1.478450\n",
      "Confirmed                             15.903241\n",
      "Recovered                             15.903241\n",
      "Deaths                                15.903241\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# WE ARE AGAIN  CHECKING FOR NULL VALUES FOR REMAINING COLUMNS:\n",
    "'''\n",
    "Data is missing for multiple features for starting few days, so we will consider the data from Month of Feb\n",
    "'''\n",
    "\n",
    "# print(data.shape)\n",
    "# percent_missing_data = data.isnull().sum() * 100 / len(data)\n",
    "# print('\\n Percentage of missing data for each feature \\n', percent_missing_data)\n",
    "\n",
    "data = data[((data.date>= '2020-02-15') & (data.date <= '2021-01-07'))]\n",
    "percent_missing_data = data.isnull().sum() * 100 / len(data)\n",
    "\n",
    "print('\\n Percentage of missing data for each feature after month of Feb \\n', percent_missing_data)\n",
    "# print(data.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "chgYIhzE-V-g"
   },
   "source": [
    "We can see below that there is no relation for **'Confirmed', 'Recovered' and 'Deaths'** columns with any other related (**'new_confirmed', 'new_deceased',  'total_confirmed',  'total_deceased'**) columns.\n",
    "The values does not add up. There are irrgularitites in these values. So we will drop these incorrect values.\n",
    "\n",
    "However there is a relation between **\"new_confirmed to total_confirmed\" and  \"new_deceased to total_deceased\"**. \n",
    "But we do not have information about **\"Recovered\" cases and \"newly recovered\" cases**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "30jYLLUBbI9T",
    "outputId": "a25cca19-26d5-4f2b-e1d2-8eb28d0372f6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in the dataset are : \n",
      " Index(['date', 'country_name', 'new_confirmed', 'new_deceased',\n",
      "       'total_confirmed', 'total_deceased', 'population', 'population_male',\n",
      "       'population_female', 'rural_population', 'urban_population',\n",
      "       'largest_city_population', 'clustered_population', 'population_density',\n",
      "       'human_development_index', 'population_age_00_09',\n",
      "       'population_age_10_19', 'population_age_20_29', 'population_age_30_39',\n",
      "       'population_age_40_49', 'population_age_50_59', 'population_age_60_69',\n",
      "       'population_age_70_79', 'population_age_80_89', 'population_age_90_99',\n",
      "       'population_age_80_and_older', 'gdp', 'gdp_per_capita',\n",
      "       'human_capital_index', 'open_street_maps', 'latitude', 'longitude',\n",
      "       'area', 'rural_area', 'urban_area', 'life_expectancy',\n",
      "       'smoking_prevalence', 'diabetes_prevalence', 'infant_mortality_rate',\n",
      "       'adult_male_mortality_rate', 'adult_female_mortality_rate',\n",
      "       'pollution_mortality_rate', 'comorbidity_mortality_rate', 'nurses',\n",
      "       'physicians', 'health_expenditure', 'out_of_pocket_health_expenditure',\n",
      "       'mobility_retail_and_recreation', 'mobility_grocery_and_pharmacy',\n",
      "       'mobility_parks', 'mobility_transit_stations', 'mobility_workplaces',\n",
      "       'mobility_residential', 'school_closing', 'workplace_closing',\n",
      "       'cancel_public_events', 'restrictions_on_gatherings',\n",
      "       'public_transport_closing', 'stay_at_home_requirements',\n",
      "       'restrictions_on_internal_movement', 'international_travel_controls',\n",
      "       'income_support', 'debt_relief', 'fiscal_measures',\n",
      "       'international_support', 'public_information_campaigns',\n",
      "       'testing_policy', 'contact_tracing',\n",
      "       'emergency_investment_in_healthcare', 'investment_in_vaccines',\n",
      "       'stringency_index', 'average_temperature', 'minimum_temperature',\n",
      "       'maximum_temperature', 'rainfall', 'dew_point', 'relative_humidity',\n",
      "       'Confirmed', 'Recovered', 'Deaths'],\n",
      "      dtype='object')\n",
      "\n",
      " Relation between above mentioned columns: \n",
      " \n",
      "       new_confirmed  new_deceased  total_confirmed  total_deceased  \\\n",
      "79085         1767.0          67.0         674339.0         16734.0   \n",
      "79086         1745.0         132.0         676084.0         16866.0   \n",
      "79087         1749.0          43.0         677833.0         16909.0   \n",
      "79088         1883.0          29.0         679716.0         16938.0   \n",
      "79089         1573.0          38.0         681289.0         16976.0   \n",
      "79090          926.0          40.0         682215.0         17016.0   \n",
      "79091         1027.0          87.0         683242.0         17103.0   \n",
      "79092         1913.0         145.0         685155.0         17248.0   \n",
      "79093         1736.0         160.0         686891.0         17408.0   \n",
      "79094         1641.0         139.0         688532.0         17547.0   \n",
      "79095         2364.0         126.0         690896.0         17673.0   \n",
      "79096         1575.0         107.0         692471.0         17780.0   \n",
      "79097            0.0           0.0         692471.0         17780.0   \n",
      "79098         2066.0         248.0         694537.0         18028.0   \n",
      "79099         1877.0         123.0         696414.0         18151.0   \n",
      "79100         1770.0         158.0         698184.0         18309.0   \n",
      "79101         2019.0          61.0         700203.0         18370.0   \n",
      "79102         1928.0          38.0         702131.0         18408.0   \n",
      "79103         1662.0          63.0         703793.0         18471.0   \n",
      "79104         1461.0          21.0         705254.0         18492.0   \n",
      "79105         1050.0         164.0         706304.0         18656.0   \n",
      "79106         2055.0          85.0         708359.0         18741.0   \n",
      "79107         2156.0         102.0         710515.0         18843.0   \n",
      "79108         1897.0          48.0         712412.0         18891.0   \n",
      "79109         1834.0          53.0         714246.0         18944.0   \n",
      "79110         1622.0          24.0         715868.0         18968.0   \n",
      "79111          891.0          40.0         716759.0         19008.0   \n",
      "79112         1092.0          45.0         717851.0         19053.0   \n",
      "79113         1863.0          58.0         719714.0         19111.0   \n",
      "79114         2056.0          53.0         721770.0         19164.0   \n",
      "79115         1912.0          66.0         723682.0         19230.0   \n",
      "79116         1770.0          46.0         725452.0         19276.0   \n",
      "79117         1371.0         135.0         726823.0         19411.0   \n",
      "79118          772.0          54.0         727595.0         19465.0   \n",
      "79119         1241.0          74.0         728836.0         19539.0   \n",
      "79120         1712.0          46.0         730548.0         19585.0   \n",
      "79121         1866.0          92.0         732414.0         19677.0   \n",
      "79122         1761.0          72.0         734175.0         19749.0   \n",
      "79123         1731.0          40.0         735906.0         19789.0   \n",
      "79124         1372.0          20.0         737278.0         19809.0   \n",
      "79125         1247.0          36.0         738525.0         19845.0   \n",
      "79126         1729.0         106.0         740254.0         19951.0   \n",
      "79127         2140.0          60.0         742394.0         20011.0   \n",
      "79128         2338.0          65.0         744732.0         20076.0   \n",
      "79129         2213.0          77.0         746945.0         20153.0   \n",
      "79130         2237.0          53.0         749182.0         20206.0   \n",
      "79131         1842.0          35.0         751024.0         20241.0   \n",
      "79132            NaN           NaN              NaN             NaN   \n",
      "79133            NaN           NaN              NaN             NaN   \n",
      "79134            NaN           NaN              NaN             NaN   \n",
      "\n",
      "       Confirmed  Recovered   Deaths  \n",
      "79085   674339.0   608112.0  16734.0  \n",
      "79086        NaN        NaN      NaN  \n",
      "79087        NaN        NaN      NaN  \n",
      "79088        7.0        0.0      0.0  \n",
      "79089     2003.0      410.0     24.0  \n",
      "79090    10015.0     4173.0    194.0  \n",
      "79091    55421.0    31505.0   1210.0  \n",
      "79092   250687.0   118232.0   3860.0  \n",
      "79093   563598.0   417200.0  10621.0  \n",
      "79094   644438.0   573003.0  15265.0  \n",
      "79095   690896.0   622153.0  17673.0  \n",
      "79096   740254.0   683194.0  19951.0  \n",
      "79097        NaN        NaN      NaN  \n",
      "79098   694537.0   625574.0  18028.0  \n",
      "79099   696414.0   626898.0  18151.0  \n",
      "79100   698184.0   628301.0  18309.0  \n",
      "79101   700203.0   629260.0  18370.0  \n",
      "79102   702131.0   630436.0  18408.0  \n",
      "79103   703793.0   634543.0  18471.0  \n",
      "79104   705254.0   635257.0  18492.0  \n",
      "79105   706304.0   639568.0  18656.0  \n",
      "79106   708359.0   641706.0  18741.0  \n",
      "79107   710515.0   642560.0  18843.0  \n",
      "79108   712412.0   643523.0  18891.0  \n",
      "79109   714246.0   644641.0  18944.0  \n",
      "79110   715868.0   646170.0  18968.0  \n",
      "79111   716759.0   646721.0  19008.0  \n",
      "79112   717851.0   647833.0  19053.0  \n",
      "79113   719714.0   648654.0  19111.0  \n",
      "79114   721770.0   649935.0  19164.0  \n",
      "79115   723682.0   653052.0  19230.0  \n",
      "79116   725452.0   654182.0  19276.0  \n",
      "79117        NaN        NaN      NaN  \n",
      "79118        NaN        NaN      NaN  \n",
      "79119       13.0        0.0      0.0  \n",
      "79120     2028.0      410.0     25.0  \n",
      "79121    10652.0     4357.0    206.0  \n",
      "79122    58568.0    33252.0   1284.0  \n",
      "79123   264184.0   127715.0   3971.0  \n",
      "79124   566109.0   426125.0  10751.0  \n",
      "79125   646398.0   574587.0  15378.0  \n",
      "79126   692471.0   623765.0  17780.0  \n",
      "79127   742394.0   686458.0  20011.0  \n",
      "79128        NaN        NaN      NaN  \n",
      "79129        NaN        NaN      NaN  \n",
      "79130        NaN        NaN      NaN  \n",
      "79131        NaN        NaN      NaN  \n",
      "79132        NaN        NaN      NaN  \n",
      "79133        NaN        NaN      NaN  \n",
      "79134        NaN        NaN      NaN  \n"
     ]
    }
   ],
   "source": [
    "# CHECKING THE VALUES FOR \"CONFIRMED\", \"RECOVERED\" AND \"DEATH\" CASES\n",
    "print('Columns in the dataset are : \\n',data.columns)\n",
    "print('\\n Relation between above mentioned columns: \\n ')\n",
    "print(data[['new_confirmed', 'new_deceased','total_confirmed', 'total_deceased', 'Confirmed', 'Recovered', 'Deaths']].tail(50))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gszpvh25_0BX"
   },
   "source": [
    "We will drop these columns as some of the values does not addup.\n",
    "\n",
    "**And we will import these features from JHC(Johns Hopkins CSSE)**\n",
    "\n",
    "COVID19 Daily Updates. This dataset is a curated version of 2019 Novel\n",
    "Coronavirus COVID 19 (2019 nCoV) Data Repository by Johns Hopkins CSSE\n",
    "https://www.kaggle.com/gpreda/coronavirus-2019ncov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wUtViYRr_vtZ",
    "outputId": "1c6c53fa-2a82-4a69-d9cc-1dfab3fb73b8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the data after removing above columns\n",
      " (30302, 73)\n"
     ]
    }
   ],
   "source": [
    "# DROPPING COLUMNS SUCH AS - 'Confirmed', 'Recovered', 'Deaths', 'new_confirmed', 'new_deceased','total_confirmed', 'total_deceased'\n",
    "data.drop(columns=['Confirmed', 'Recovered', 'Deaths', 'new_confirmed', 'new_deceased','total_confirmed', 'total_deceased'], axis=1, inplace = True)\n",
    "print('Shape of the data after removing above columns\\n',data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t9v6JVteAsJw"
   },
   "source": [
    "Looking at features : **\"clustered_population\" and \"largest_city_population\"** for null values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3cK-bXGIAw6-",
    "outputId": "83caa9f4-26b8-4394-bd59-cd0eca09a94c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      " Features \"clustered_population\" and \"largest_city_population\" are missing for below countries respectively: \n",
      "\n",
      "clustered_population -  ['Barbados' 'Brunei' 'Bhutan' 'Cyprus' 'Guyana' 'Iceland' 'Luxembourg'\n",
      " 'Slovenia' 'Suriname']\n",
      "\n",
      "largest_city_population -  ['Bosnia and Herzegovina' 'Barbados' 'Bahrain' 'Benin' 'Brunei' 'Bhutan'\n",
      " 'Cyprus' 'Estonia' 'Gabon' 'Gambia' 'Guyana' 'Croatia' 'Iceland'\n",
      " 'Jamaica' 'Sri Lanka' 'Lithuania' 'Luxembourg' 'Latvia' 'Moldova'\n",
      " 'Namibia' 'Qatar' 'Slovenia' 'Slovakia' 'Suriname' 'Trinidad and Tobago']\n"
     ]
    }
   ],
   "source": [
    "print(' \\n Features \"clustered_population\" and \"largest_city_population\" are missing for below countries respectively: ')\n",
    "print(\"\\nclustered_population - \",data[(data.largest_city_population.isnull())].country_name.unique())\n",
    "\n",
    "print(\"\\nlargest_city_population - \",data[(data.clustered_population.isnull())].country_name.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZpNwWFbYBL5Q"
   },
   "source": [
    "Looking at features : **\"human_development_index\" and \"human_capital_index\"** for null values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e-DN0IAeBJR6",
    "outputId": "2d981f0f-325c-4586-ded9-567f781a9562"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      " Features \"human_development_index\" and \"human_capital_index\" are missing for below countries:\n",
      "\n",
      " human_development_index -  ['Ireland']\n",
      "\n",
      " human_capital_index -  ['Barbados' 'Brunei' 'Bolivia' 'Bhutan' 'Belarus' 'Cuba' 'Suriname']\n"
     ]
    }
   ],
   "source": [
    "print(' \\n Features \"human_development_index\" and \"human_capital_index\" are missing for below countries:')\n",
    "print(\"\\n human_development_index - \",data[(data.human_development_index.isnull())].country_name.unique())\n",
    "\n",
    "# Ireland's HDI value for 2019 is 0.955\n",
    "# So we will fill this above value in plave of NaN values.\n",
    "data['human_development_index'].fillna(value = 0.955, inplace = True)\n",
    "\n",
    "print(\"\\n human_capital_index - \",data[(data.human_capital_index.isnull())].country_name.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7F-sa_zXBe7I"
   },
   "source": [
    "Looking at features : **\"rural_area\" and \"urban_area\"** for null values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QPI3U7ZfBi7_",
    "outputId": "714cc1ba-da93-46d6-bb61-55b47337c6e1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      " Features \"rural_area\" and \"urban_area\" are missing for below countries:\n",
      "\n",
      " rural_area -  ['Afghanistan' 'Austria' 'Burkina Faso' 'Bolivia' 'Bhutan'\n",
      " 'Czech Republic' 'Mongolia' 'Nepal' 'Paraguay' 'Serbia' 'Rwanda'\n",
      " 'Slovakia']\n",
      "\n",
      " urban_area -  ['Afghanistan' 'Austria' 'Burkina Faso' 'Bolivia' 'Bhutan'\n",
      " 'Czech Republic' 'Mongolia' 'Nepal' 'Paraguay' 'Serbia' 'Rwanda'\n",
      " 'Slovakia']\n"
     ]
    }
   ],
   "source": [
    "print(' \\n Features \"rural_area\" and \"urban_area\" are missing for below countries:')\n",
    "print(\"\\n rural_area - \", data[(data.rural_area.isnull())].country_name.unique()) \n",
    "print(\"\\n urban_area - \", data[(data.urban_area.isnull())].country_name.unique()) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X5y5WQGQB-HO"
   },
   "source": [
    "Looking at features : **\"smoking_prevalence\",\"adult_male_mortality_rate\" and \"adult_female_mortality_rate\"** for null values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QePAg7XlCB-m",
    "outputId": "6527d9e7-4331-4481-db15-0d22449ca289"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      " Features \"smoking_prevalence\", \"adult_female_mortality_rate\" and \"adult_male_mortality_rate\" are missing for below countries:\n",
      "\n",
      " smoking_prevalence -  ['Afghanistan' 'Bolivia' 'Bhutan' 'Gabon' 'Guinea' 'Guatemala' 'Guyana'\n",
      " 'Iraq' 'Jordan' 'Trinidad and Tobago']\n",
      "\n",
      " adult_male_mortality_rate - ['Italy' 'New Zealand' 'Russia']\n",
      "\n",
      " adult_female_mortality_rate - ['Italy' 'New Zealand' 'Russia']\n"
     ]
    }
   ],
   "source": [
    "print(' \\n Features \"smoking_prevalence\", \"adult_female_mortality_rate\" and \"adult_male_mortality_rate\" are missing for below countries:')\n",
    "print(\"\\n smoking_prevalence - \", data[(data.smoking_prevalence.isnull())].country_name.unique()) \n",
    "print(\"\\n adult_male_mortality_rate -\", data[(data.adult_male_mortality_rate.isnull())].country_name.unique()) \n",
    "print(\"\\n adult_female_mortality_rate -\", data[(data.adult_female_mortality_rate.isnull())].country_name.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "usZsXGprCQlf"
   },
   "source": [
    "\n",
    "So instead of dropping records for above features, we will drop these columns as we do not require them in our prediction.\n",
    "**'largest_city_population', 'clustered_population','human_capital_index'**,  **'rural_area', 'urban_area',\n",
    "'smoking_prevalence', 'adult_male_mortality_rate', 'adult_female_mortality_rate'**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "z3qSor3rf3sD",
    "outputId": "c86bef7f-dd18-4f15-a811-ad07bf4a5a9e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Shape of the data (30302, 65)\n",
      "\n",
      " Percent Missing Data date                                   0.000000\n",
      "country_name                           0.000000\n",
      "population                             0.000000\n",
      "population_male                        0.000000\n",
      "population_female                      0.000000\n",
      "rural_population                       0.000000\n",
      "urban_population                       0.000000\n",
      "population_density                     0.000000\n",
      "human_development_index                0.000000\n",
      "population_age_00_09                   0.000000\n",
      "population_age_10_19                   0.000000\n",
      "population_age_20_29                   0.000000\n",
      "population_age_30_39                   0.000000\n",
      "population_age_40_49                   0.000000\n",
      "population_age_50_59                   0.000000\n",
      "population_age_60_69                   0.000000\n",
      "population_age_70_79                   0.000000\n",
      "population_age_80_89                   0.000000\n",
      "population_age_90_99                   0.000000\n",
      "population_age_80_and_older            0.000000\n",
      "gdp                                    0.000000\n",
      "gdp_per_capita                         0.000000\n",
      "open_street_maps                       0.000000\n",
      "latitude                               0.000000\n",
      "longitude                              0.000000\n",
      "area                                   0.000000\n",
      "life_expectancy                        0.000000\n",
      "diabetes_prevalence                    0.000000\n",
      "infant_mortality_rate                  0.000000\n",
      "pollution_mortality_rate               0.000000\n",
      "comorbidity_mortality_rate             0.000000\n",
      "nurses                                 0.000000\n",
      "physicians                             0.000000\n",
      "health_expenditure                     0.000000\n",
      "out_of_pocket_health_expenditure       0.000000\n",
      "mobility_retail_and_recreation        16.045146\n",
      "mobility_grocery_and_pharmacy         15.886740\n",
      "mobility_parks                        16.464260\n",
      "mobility_transit_stations             16.160649\n",
      "mobility_workplaces                   15.619431\n",
      "mobility_residential                  15.629331\n",
      "school_closing                         1.966867\n",
      "workplace_closing                      2.016369\n",
      "cancel_public_events                   1.966867\n",
      "restrictions_on_gatherings             1.966867\n",
      "public_transport_closing               1.989968\n",
      "stay_at_home_requirements              1.980067\n",
      "restrictions_on_internal_movement      2.019669\n",
      "international_travel_controls          1.986668\n",
      "income_support                         2.003168\n",
      "debt_relief                            1.993268\n",
      "fiscal_measures                        2.115372\n",
      "international_support                  2.059270\n",
      "public_information_campaigns           1.996568\n",
      "testing_policy                         2.079071\n",
      "contact_tracing                        2.032869\n",
      "emergency_investment_in_healthcare     2.164874\n",
      "investment_in_vaccines                 2.184674\n",
      "stringency_index                       1.986668\n",
      "average_temperature                    1.478450\n",
      "minimum_temperature                    1.478450\n",
      "maximum_temperature                    1.478450\n",
      "rainfall                               1.818362\n",
      "dew_point                              1.478450\n",
      "relative_humidity                      1.478450\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "columns_to_drop = ['largest_city_population', 'clustered_population','human_capital_index', 'rural_area', 'urban_area',\n",
    "'smoking_prevalence', 'adult_male_mortality_rate', 'adult_female_mortality_rate']\n",
    "\n",
    "#DROPPING COLUMNS\n",
    "data.drop(columns=columns_to_drop, inplace=True)\n",
    "print('\\n Shape of the data',data.shape)\n",
    "\n",
    "#AGAIN CHECKING MISSING DATA PERCENTAGE AFTER REMOVING COLUMNS AND ROWS\n",
    "percent_missing_data = data.isnull().sum() * 100 / len(data)\n",
    "print('\\n Percent Missing Data',percent_missing_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mnc6yAPPCoRK"
   },
   "source": [
    "Now we will check for further missing features and their records. We still have some missing data for **mobility, weather related features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "es01IDBzmHZK",
    "outputId": "90b01862-95a0-45f9-b9df-8ec5aeb6771f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['mobility_retail_and_recreation', 'mobility_grocery_and_pharmacy',\n",
      "       'mobility_parks', 'mobility_transit_stations', 'mobility_workplaces',\n",
      "       'mobility_residential', 'school_closing', 'workplace_closing',\n",
      "       'cancel_public_events', 'restrictions_on_gatherings',\n",
      "       'public_transport_closing', 'stay_at_home_requirements',\n",
      "       'restrictions_on_internal_movement', 'international_travel_controls',\n",
      "       'income_support', 'debt_relief', 'fiscal_measures',\n",
      "       'international_support', 'public_information_campaigns',\n",
      "       'testing_policy', 'contact_tracing',\n",
      "       'emergency_investment_in_healthcare', 'investment_in_vaccines',\n",
      "       'stringency_index', 'average_temperature', 'minimum_temperature',\n",
      "       'maximum_temperature', 'rainfall', 'dew_point', 'relative_humidity'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Only checking the columns that has missing data :\n",
    "index_cols = percent_missing_data[percent_missing_data > 0].index\n",
    "print(index_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U4oUZa6sEfUx"
   },
   "source": [
    "**STATISTICAL IMPUTATIONS**\n",
    "\n",
    "We will use Statistical Imputations to fill the NaN values of these remaining features.\n",
    "\n",
    "1. **Weather related features** : We will use the countrywise\n",
    "mean for 'average_temperature', 'minimum_temperature','maximum_temperature', 'rainfall', 'dew_point', 'relative_humidity' features.\n",
    "\n",
    "2. **Govt. Policy related features** : We will use the countrywise mean value \n",
    "\n",
    "3. **Mobility related features**: We will use the countrywise mean value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "KqTy-g7CEwrf"
   },
   "outputs": [],
   "source": [
    "weather_related_features = ['average_temperature', 'minimum_temperature','maximum_temperature', 'rainfall', 'dew_point', 'relative_humidity']\n",
    "for i in weather_related_features:\n",
    "  # print(i)\n",
    "  data[i] = data[i].fillna(data.groupby('country_name')[i].transform('mean'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "4UwPbuwoFqj_"
   },
   "outputs": [],
   "source": [
    "govt_policy_features = ['school_closing', 'workplace_closing','cancel_public_events', 'restrictions_on_gatherings',\n",
    "                        'public_transport_closing', 'stay_at_home_requirements','restrictions_on_internal_movement', \n",
    "                        'international_travel_controls','income_support', 'debt_relief', 'fiscal_measures',\n",
    "                        'international_support', 'public_information_campaigns','testing_policy', 'contact_tracing',\n",
    "                        'emergency_investment_in_healthcare', 'investment_in_vaccines','stringency_index']\n",
    "for i in govt_policy_features:\n",
    "  # print(i)\n",
    "  data[i] = data[i].fillna(data.groupby('country_name')[i].transform('mean'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FF9asLmiF3ue",
    "outputId": "0d0e8595-ffa5-4210-d57b-357fec579e1c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " All Mobility data is missing for below countries for all the days:\n",
      "['United Arab Emirates' 'Afghanistan' 'Argentina' 'Austria' 'Australia'\n",
      " 'Bosnia and Herzegovina' 'Barbados' 'Bangladesh' 'Belgium' 'Burkina Faso'\n",
      " 'Bulgaria' 'Bahrain' 'Benin' 'Brunei' 'Bolivia' 'Brazil' 'Bhutan'\n",
      " 'Belarus' 'Canada' 'Switzerland' 'Chile' 'China' 'Colombia' 'Costa Rica'\n",
      " 'Cuba' 'Cyprus' 'Czech Republic' 'Germany' 'Denmark' 'Dominican Republic'\n",
      " 'Algeria' 'Ecuador' 'Estonia' 'Egypt' 'Spain' 'Ethiopia' 'Finland'\n",
      " 'France' 'Gabon' 'United Kingdom' 'Ghana' 'Gambia' 'Guinea' 'Greece'\n",
      " 'Guatemala' 'Guyana' 'Honduras' 'Croatia' 'Indonesia' 'Ireland' 'Israel'\n",
      " 'India' 'Iraq' 'Iran' 'Iceland' 'Italy' 'Jamaica' 'Jordan' 'Japan'\n",
      " 'Kenya' 'South Korea' 'Kuwait' 'Lebanon' 'Sri Lanka' 'Liberia'\n",
      " 'Lithuania' 'Luxembourg' 'Latvia' 'Morocco' 'Moldova' 'Mongolia' 'Mexico'\n",
      " 'Malaysia' 'Namibia' 'Nigeria' 'Netherlands' 'Norway' 'Nepal'\n",
      " 'New Zealand' 'Oman' 'Panama' 'Peru' 'Philippines' 'Pakistan' 'Poland'\n",
      " 'Portugal' 'Paraguay' 'Qatar' 'Romania' 'Serbia' 'Russia' 'Rwanda'\n",
      " 'Saudi Arabia' 'Sweden' 'Singapore' 'Slovenia' 'Slovakia' 'Senegal'\n",
      " 'Suriname' 'Togo' 'Thailand' 'Tunisia' 'Turkey' 'Trinidad and Tobago'\n",
      " 'Tanzania' 'United States of America' 'Uruguay' 'Vietnam' 'South Africa']\n"
     ]
    }
   ],
   "source": [
    "mobility_related_features = ['mobility_retail_and_recreation', 'mobility_grocery_and_pharmacy',\n",
    "                              'mobility_parks', 'mobility_transit_stations', 'mobility_workplaces','mobility_residential']\n",
    "\n",
    "print('\\n All Mobility data is missing for below countries for all the days:')\n",
    "print(data[(data.mobility_retail_and_recreation.isnull())].country_name.unique())\n",
    "# print(data[(data.mobility_grocery_and_pharmacy.isnull())].country_name.unique())\n",
    "# print(data[(data.mobility_parks.isnull())].country_name.unique())\n",
    "# print(data[(data.mobility_transit_stations.isnull())].country_name.unique())\n",
    "# print(data[(data.mobility_workplaces.isnull())].country_name.unique())\n",
    "# print(data[(data.mobility_residential.isnull())].country_name.unique())\n",
    "\n",
    "for i in mobility_related_features:\n",
    "  # print(i)\n",
    "  data[i] = data[i].fillna(data.groupby('country_name')[i].transform('mean'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mONS55a7KYSf",
    "outputId": "e630a1c9-b178-44a0-c3c8-08622330327f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Percent Missing Data date                                   0.000000\n",
      "country_name                           0.000000\n",
      "population                             0.000000\n",
      "population_male                        0.000000\n",
      "population_female                      0.000000\n",
      "rural_population                       0.000000\n",
      "urban_population                       0.000000\n",
      "population_density                     0.000000\n",
      "human_development_index                0.000000\n",
      "population_age_00_09                   0.000000\n",
      "population_age_10_19                   0.000000\n",
      "population_age_20_29                   0.000000\n",
      "population_age_30_39                   0.000000\n",
      "population_age_40_49                   0.000000\n",
      "population_age_50_59                   0.000000\n",
      "population_age_60_69                   0.000000\n",
      "population_age_70_79                   0.000000\n",
      "population_age_80_89                   0.000000\n",
      "population_age_90_99                   0.000000\n",
      "population_age_80_and_older            0.000000\n",
      "gdp                                    0.000000\n",
      "gdp_per_capita                         0.000000\n",
      "open_street_maps                       0.000000\n",
      "latitude                               0.000000\n",
      "longitude                              0.000000\n",
      "area                                   0.000000\n",
      "life_expectancy                        0.000000\n",
      "diabetes_prevalence                    0.000000\n",
      "infant_mortality_rate                  0.000000\n",
      "pollution_mortality_rate               0.000000\n",
      "comorbidity_mortality_rate             0.000000\n",
      "nurses                                 0.000000\n",
      "physicians                             0.000000\n",
      "health_expenditure                     0.000000\n",
      "out_of_pocket_health_expenditure       0.000000\n",
      "mobility_retail_and_recreation        13.761468\n",
      "mobility_grocery_and_pharmacy         13.761468\n",
      "mobility_parks                        13.761468\n",
      "mobility_transit_stations             13.761468\n",
      "mobility_workplaces                   13.761468\n",
      "mobility_residential                  13.761468\n",
      "school_closing                         0.000000\n",
      "workplace_closing                      0.000000\n",
      "cancel_public_events                   0.000000\n",
      "restrictions_on_gatherings             0.000000\n",
      "public_transport_closing               0.000000\n",
      "stay_at_home_requirements              0.000000\n",
      "restrictions_on_internal_movement      0.000000\n",
      "international_travel_controls          0.000000\n",
      "income_support                         0.000000\n",
      "debt_relief                            0.000000\n",
      "fiscal_measures                        0.000000\n",
      "international_support                  0.000000\n",
      "public_information_campaigns           0.000000\n",
      "testing_policy                         0.000000\n",
      "contact_tracing                        0.000000\n",
      "emergency_investment_in_healthcare     0.000000\n",
      "investment_in_vaccines                 0.000000\n",
      "stringency_index                       0.000000\n",
      "average_temperature                    0.000000\n",
      "minimum_temperature                    0.000000\n",
      "maximum_temperature                    0.000000\n",
      "rainfall                               0.000000\n",
      "dew_point                              0.000000\n",
      "relative_humidity                      0.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "percent_missing_data = data.isnull().sum() * 100 / len(data)\n",
    "print('\\n Percent Missing Data',percent_missing_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RfmGjOMDKgVW"
   },
   "source": [
    "We still have some missing data for mobility related features. We will check into that further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "T1OlT_OTJAJR",
    "outputId": "d8258550-1b3c-4a2d-8634-b82f82c67a3e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Brunei' 'Bhutan' 'China' 'Cuba' 'Cyprus' 'Algeria' 'Ethiopia' 'Gambia'\n",
      " 'Guinea' 'Guyana' 'Iran' 'Iceland' 'Liberia' 'Suriname' 'Tunisia']\n",
      "['Brunei' 'Bhutan' 'China' 'Cuba' 'Cyprus' 'Algeria' 'Ethiopia' 'Gambia'\n",
      " 'Guinea' 'Guyana' 'Iran' 'Iceland' 'Liberia' 'Suriname' 'Tunisia']\n",
      "['Brunei' 'Bhutan' 'China' 'Cuba' 'Cyprus' 'Algeria' 'Ethiopia' 'Gambia'\n",
      " 'Guinea' 'Guyana' 'Iran' 'Iceland' 'Liberia' 'Suriname' 'Tunisia']\n",
      "['Brunei' 'Bhutan' 'China' 'Cuba' 'Cyprus' 'Algeria' 'Ethiopia' 'Gambia'\n",
      " 'Guinea' 'Guyana' 'Iran' 'Iceland' 'Liberia' 'Suriname' 'Tunisia']\n",
      "['Brunei' 'Bhutan' 'China' 'Cuba' 'Cyprus' 'Algeria' 'Ethiopia' 'Gambia'\n",
      " 'Guinea' 'Guyana' 'Iran' 'Iceland' 'Liberia' 'Suriname' 'Tunisia']\n",
      "['Brunei' 'Bhutan' 'China' 'Cuba' 'Cyprus' 'Algeria' 'Ethiopia' 'Gambia'\n",
      " 'Guinea' 'Guyana' 'Iran' 'Iceland' 'Liberia' 'Suriname' 'Tunisia']\n"
     ]
    }
   ],
   "source": [
    "# We will check for the countries having missing mobility data\n",
    "print(data[(data.mobility_retail_and_recreation.isnull())].country_name.unique())\n",
    "print(data[(data.mobility_grocery_and_pharmacy.isnull())].country_name.unique())\n",
    "print(data[(data.mobility_parks.isnull())].country_name.unique())\n",
    "print(data[(data.mobility_transit_stations.isnull())].country_name.unique())\n",
    "print(data[(data.mobility_workplaces.isnull())].country_name.unique())\n",
    "print(data[(data.mobility_residential.isnull())].country_name.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xo4NZ5NfL8hX"
   },
   "source": [
    "**All mobility related data is missing for above countries for all the days. So we can not use any imputation method.** \n",
    "\n",
    "So, We will remove these countries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "5h5mhanTL8wt"
   },
   "outputs": [],
   "source": [
    "mobility_country = data[(data.mobility_retail_and_recreation.isnull())].country_name.unique() \n",
    "data.drop(data[data['country_name'].isin(mobility_country)].index, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "62RWHSJjMRxL"
   },
   "source": [
    "**Finally Checking missing values again :**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZZRbFCPFMbS2",
    "outputId": "eaef262b-8a4a-444b-d39b-ea4246ea9fca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Percent Missing Data date                                  0.0\n",
      "country_name                          0.0\n",
      "population                            0.0\n",
      "population_male                       0.0\n",
      "population_female                     0.0\n",
      "rural_population                      0.0\n",
      "urban_population                      0.0\n",
      "population_density                    0.0\n",
      "human_development_index               0.0\n",
      "population_age_00_09                  0.0\n",
      "population_age_10_19                  0.0\n",
      "population_age_20_29                  0.0\n",
      "population_age_30_39                  0.0\n",
      "population_age_40_49                  0.0\n",
      "population_age_50_59                  0.0\n",
      "population_age_60_69                  0.0\n",
      "population_age_70_79                  0.0\n",
      "population_age_80_89                  0.0\n",
      "population_age_90_99                  0.0\n",
      "population_age_80_and_older           0.0\n",
      "gdp                                   0.0\n",
      "gdp_per_capita                        0.0\n",
      "open_street_maps                      0.0\n",
      "latitude                              0.0\n",
      "longitude                             0.0\n",
      "area                                  0.0\n",
      "life_expectancy                       0.0\n",
      "diabetes_prevalence                   0.0\n",
      "infant_mortality_rate                 0.0\n",
      "pollution_mortality_rate              0.0\n",
      "comorbidity_mortality_rate            0.0\n",
      "nurses                                0.0\n",
      "physicians                            0.0\n",
      "health_expenditure                    0.0\n",
      "out_of_pocket_health_expenditure      0.0\n",
      "mobility_retail_and_recreation        0.0\n",
      "mobility_grocery_and_pharmacy         0.0\n",
      "mobility_parks                        0.0\n",
      "mobility_transit_stations             0.0\n",
      "mobility_workplaces                   0.0\n",
      "mobility_residential                  0.0\n",
      "school_closing                        0.0\n",
      "workplace_closing                     0.0\n",
      "cancel_public_events                  0.0\n",
      "restrictions_on_gatherings            0.0\n",
      "public_transport_closing              0.0\n",
      "stay_at_home_requirements             0.0\n",
      "restrictions_on_internal_movement     0.0\n",
      "international_travel_controls         0.0\n",
      "income_support                        0.0\n",
      "debt_relief                           0.0\n",
      "fiscal_measures                       0.0\n",
      "international_support                 0.0\n",
      "public_information_campaigns          0.0\n",
      "testing_policy                        0.0\n",
      "contact_tracing                       0.0\n",
      "emergency_investment_in_healthcare    0.0\n",
      "investment_in_vaccines                0.0\n",
      "stringency_index                      0.0\n",
      "average_temperature                   0.0\n",
      "minimum_temperature                   0.0\n",
      "maximum_temperature                   0.0\n",
      "rainfall                              0.0\n",
      "dew_point                             0.0\n",
      "relative_humidity                     0.0\n",
      "dtype: float64\n",
      "\n",
      " Final Shape of the data after removing all missing data (26132, 65)\n"
     ]
    }
   ],
   "source": [
    "percent_missing_data = data.isnull().sum() * 100 / len(data)\n",
    "print('\\n Percent Missing Data',percent_missing_data)\n",
    "print('\\n Final Shape of the data after removing all missing data',data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M7wnJ9mrMg6t"
   },
   "source": [
    "\n",
    "Now we have a dataframe which do not have null values. \n",
    "However we do not have the important features like **\"Recovered Cases\", \"Confirmed Cases\" and \"Deaths\"**. \n",
    "\n",
    "The integration part was done by rohil as I was having memory issue on the system.\n",
    "\n",
    "Also on google colab the session was getting crashed. So for EDA part we will be using the new/updated dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 135
    },
    "id": "EAb6J-HPS3xg",
    "outputId": "36156f5b-eab3-430b-fa86-5e2400ff61a7"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'\\nconfirmed = pd.read_csv(\\'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\\')\\ndeaths = pd.read_csv(\\'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv\\')\\nrecovered = pd.read_csv(\\'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv\\')\\n\\ndef meltData(df,name):\\n    melted = pd.melt(df,id_vars=[\\'Province/State\\',\\'Country/Region\\',\\'Lat\\',\\'Long\\'],value_vars=confirmed.columns[4:],\\n                     var_name=\\'Date\\',value_name=name)\\n    return melted\\n\\nmelt_confirmed = meltData(confirmed,\\'Confirmed\\')\\nmelt_deaths = meltData(deaths,\\'Deaths\\')\\nmelt_recovered = meltData(recovered,\\'Recovered\\')\\n\\ndef merge(df1,df2):\\n    merged = pd.merge(left=df1,right=df2, how=\\'inner\\',\\n                      on=[\\'Country/Region\\',\\'Province/State\\', \\'Date\\', \\'Lat\\', \\'Long\\'])\\n    return merged\\n\\ncovid19 = merge(merge(melt_confirmed,melt_deaths),melt_recovered)\\n\\ncovid19.rename(columns={\\'Country/Region\\': \\'country_name\\', \\'Date\\': \\'date\\'}, inplace=True)\\nprint(covid19.shape)\\n\\n\\ncovid19[\"country_name\"].replace({\"US\": \"United States of America\"}, inplace=True)\\ncovid19[\"country_name\"].replace({\"Czechia\": \"Czech Republic\"}, inplace=True)\\ncovid19[\"country_name\"].replace({\"Korea, South\": \"South Korea\"}, inplace=True)\\n\\n\\ncovid19 = covid19.groupby([\\'country_name\\',\\'date\\']).sum()\\ncovid19 = covid19.reset_index()\\n\\nprint(covid19.shape)\\nprint(len(covid19.country_name.unique()))\\nprint(len(covid19.date.unique()))\\nprint(covid19.isnull().sum())\\n\\n\\nprint(\"number of columns in df_final dataset obtained from google api \" , len(data.columns))\\nprint(\"number of columns in covid19 dataset obtainedy directl from john hopkins \", len(covid19.columns))\\n\\n\\nprint(covid19.columns)\\nprint(data.columns)\\n\\n\\ncovid19.date = pd.to_datetime(covid19.date,infer_datetime_format = True)\\ndata.date = pd.to_datetime(data.date,infer_datetime_format = True)\\n\\n\\nfinal_merged = pd.merge(left=data,right=covid19, how=\\'inner\\',on=[ \\'country_name\\', \\'date\\'])\\nprint(final_merged.shape)\\nprint(final_merged.columns)\\nprint(final_merged.isnull().sum())\\n\\ndataset = final_merged.drop([\\'Lat\\', \\'Long\\'], axis=1)\\n\\nprint(dataset.shape)\\nprint(len(dataset.country_name.unique()))\\nprint(len(dataset.date.unique()))\\n\\n\\n# Now to obtain new_recoverd we must subtract todays total count from yesterday\\n# For this we create a new column \\'Recovered Shifted\\' with values shifted p by 1 \\n\\ndataset[\\'Confirmed_shifted\\'] = dataset.groupby([\\'country_name\\'])[\\'Confirmed\\'].shift(1,fill_value = 0)\\ndataset[\\'Deaths_shifted\\'] = dataset.groupby([\\'country_name\\'])[\\'Deaths\\'].shift(1,fill_value = 0)\\ndataset[\\'Recovered_shifted\\'] = dataset.groupby([\\'country_name\\'])[\\'Recovered\\'].shift(1,fill_value = 0)\\n\\n\\ndataset[\\'Recovered_New\\'] = dataset[\\'Recovered\\'] - dataset[\\'Recovered_shifted\\']\\ndataset[\\'Deaths_New\\'] = dataset[\\'Deaths\\'] - dataset[\\'Deaths_shifted\\']\\ndataset[\\'Confirmed_New\\'] = dataset[\\'Confirmed\\'] - dataset[\\'Confirmed_shifted\\']\\n\\ndataset = dataset.drop([\\'Confirmed_shifted\\',\\'Recovered_shifted\\',\\'Deaths_shifted\\'],axis = 1)\\n\\nprint(len(dataset[dataset.Recovered_New < 0]))\\nprint(len(dataset[dataset.Confirmed_New < 0]))\\nprint(len(dataset[dataset.Deaths_New < 0]))\\n\\n\\ndataset.loc[(dataset.Recovered_New < 0),\\'Recovered_New\\'] = 0\\ndataset.loc[(dataset.Confirmed_New < 0),\\'Confirmed_New\\'] = 0\\ndataset.loc[(dataset.Deaths_New < 0),\\'Deaths_New\\'] = 0\\n\\n\\ndataset.to_excel(\"Final_Covid_Dataset.xlsx\")\\n'"
      ]
     },
     "execution_count": 95,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code for integration part for features \"Recovered Cases\", \"Confirmed Cases\" and \"Deaths\" from JHC.\n",
    "'''\n",
    "confirmed = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv')\n",
    "deaths = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv')\n",
    "recovered = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv')\n",
    "\n",
    "def meltData(df,name):\n",
    "    melted = pd.melt(df,id_vars=['Province/State','Country/Region','Lat','Long'],value_vars=confirmed.columns[4:],\n",
    "                     var_name='Date',value_name=name)\n",
    "    return melted\n",
    "\n",
    "melt_confirmed = meltData(confirmed,'Confirmed')\n",
    "melt_deaths = meltData(deaths,'Deaths')\n",
    "melt_recovered = meltData(recovered,'Recovered')\n",
    "\n",
    "def merge(df1,df2):\n",
    "    merged = pd.merge(left=df1,right=df2, how='inner',\n",
    "                      on=['Country/Region','Province/State', 'Date', 'Lat', 'Long'])\n",
    "    return merged\n",
    "\n",
    "covid19 = merge(merge(melt_confirmed,melt_deaths),melt_recovered)\n",
    "\n",
    "covid19.rename(columns={'Country/Region': 'country_name', 'Date': 'date'}, inplace=True)\n",
    "print(covid19.shape)\n",
    "\n",
    "\n",
    "covid19[\"country_name\"].replace({\"US\": \"United States of America\"}, inplace=True)\n",
    "covid19[\"country_name\"].replace({\"Czechia\": \"Czech Republic\"}, inplace=True)\n",
    "covid19[\"country_name\"].replace({\"Korea, South\": \"South Korea\"}, inplace=True)\n",
    "\n",
    "\n",
    "covid19 = covid19.groupby(['country_name','date']).sum()\n",
    "covid19 = covid19.reset_index()\n",
    "\n",
    "print(covid19.shape)\n",
    "print(len(covid19.country_name.unique()))\n",
    "print(len(covid19.date.unique()))\n",
    "print(covid19.isnull().sum())\n",
    "\n",
    "\n",
    "print(\"number of columns in df_final dataset obtained from google api \" , len(data.columns))\n",
    "print(\"number of columns in covid19 dataset obtainedy directl from john hopkins \", len(covid19.columns))\n",
    "\n",
    "\n",
    "print(covid19.columns)\n",
    "print(data.columns)\n",
    "\n",
    "\n",
    "covid19.date = pd.to_datetime(covid19.date,infer_datetime_format = True)\n",
    "data.date = pd.to_datetime(data.date,infer_datetime_format = True)\n",
    "\n",
    "\n",
    "final_merged = pd.merge(left=data,right=covid19, how='inner',on=[ 'country_name', 'date'])\n",
    "print(final_merged.shape)\n",
    "print(final_merged.columns)\n",
    "print(final_merged.isnull().sum())\n",
    "\n",
    "dataset = final_merged.drop(['Lat', 'Long'], axis=1)\n",
    "\n",
    "print(dataset.shape)\n",
    "print(len(dataset.country_name.unique()))\n",
    "print(len(dataset.date.unique()))\n",
    "\n",
    "\n",
    "# Now to obtain new_recoverd we must subtract todays total count from yesterday\n",
    "# For this we create a new column 'Recovered Shifted' with values shifted p by 1 \n",
    "\n",
    "dataset['Confirmed_shifted'] = dataset.groupby(['country_name'])['Confirmed'].shift(1,fill_value = 0)\n",
    "dataset['Deaths_shifted'] = dataset.groupby(['country_name'])['Deaths'].shift(1,fill_value = 0)\n",
    "dataset['Recovered_shifted'] = dataset.groupby(['country_name'])['Recovered'].shift(1,fill_value = 0)\n",
    "\n",
    "\n",
    "dataset['Recovered_New'] = dataset['Recovered'] - dataset['Recovered_shifted']\n",
    "dataset['Deaths_New'] = dataset['Deaths'] - dataset['Deaths_shifted']\n",
    "dataset['Confirmed_New'] = dataset['Confirmed'] - dataset['Confirmed_shifted']\n",
    "\n",
    "dataset = dataset.drop(['Confirmed_shifted','Recovered_shifted','Deaths_shifted'],axis = 1)\n",
    "\n",
    "print(len(dataset[dataset.Recovered_New < 0]))\n",
    "print(len(dataset[dataset.Confirmed_New < 0]))\n",
    "print(len(dataset[dataset.Deaths_New < 0]))\n",
    "\n",
    "\n",
    "dataset.loc[(dataset.Recovered_New < 0),'Recovered_New'] = 0\n",
    "dataset.loc[(dataset.Confirmed_New < 0),'Confirmed_New'] = 0\n",
    "dataset.loc[(dataset.Deaths_New < 0),'Deaths_New'] = 0\n",
    "\n",
    "\n",
    "dataset.to_excel(\"Final_Covid_Dataset.xlsx\")\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "L2AClo4CaKD7",
    "outputId": "bb266549-c2a1-49f3-e713-9cdd7557ba46"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Dimensions of the Dataset: (34111, 69)\n",
      "\n",
      " First Few rows are: \n",
      "              date          country_name  mobility_retail_and_recreation  \\\n",
      "0  2/15/2020 0:00  United Arab Emirates                             0.0   \n",
      "1  2/16/2020 0:00  United Arab Emirates                             1.0   \n",
      "2  2/17/2020 0:00  United Arab Emirates                            -1.0   \n",
      "3  2/18/2020 0:00  United Arab Emirates                            -2.0   \n",
      "4  2/19/2020 0:00  United Arab Emirates                            -2.0   \n",
      "\n",
      "   mobility_grocery_and_pharmacy  mobility_parks  mobility_transit_stations  \\\n",
      "0                            4.0             5.0                        0.0   \n",
      "1                            4.0             4.0                        1.0   \n",
      "2                            1.0             5.0                        1.0   \n",
      "3                            1.0             5.0                        0.0   \n",
      "4                            0.0             4.0                       -1.0   \n",
      "\n",
      "   mobility_workplaces  mobility_residential  population  population_male  \\\n",
      "0                  2.0                   1.0     9770529          6766808   \n",
      "1                  2.0                   1.0     9770529          6766808   \n",
      "2                  2.0                   1.0     9770529          6766808   \n",
      "3                  2.0                   1.0     9770529          6766808   \n",
      "4                  2.0                   1.0     9770529          6766808   \n",
      "\n",
      "   population_female  rural_population  urban_population  population_density  \\\n",
      "0            3003721           1290785           8479744           135.60911   \n",
      "1            3003721           1290785           8479744           135.60911   \n",
      "2            3003721           1290785           8479744           135.60911   \n",
      "3            3003721           1290785           8479744           135.60911   \n",
      "4            3003721           1290785           8479744           135.60911   \n",
      "\n",
      "   human_development_index  population_age_00_09  population_age_10_19  \\\n",
      "0                    0.863                870683                786876   \n",
      "1                    0.863                870683                786876   \n",
      "2                    0.863                870683                786876   \n",
      "3                    0.863                870683                786876   \n",
      "4                    0.863                870683                786876   \n",
      "\n",
      "   population_age_20_29  population_age_30_39  population_age_40_49  \\\n",
      "0               1841983               2722996               1907265   \n",
      "1               1841983               2722996               1907265   \n",
      "2               1841983               2722996               1907265   \n",
      "3               1841983               2722996               1907265   \n",
      "4               1841983               2722996               1907265   \n",
      "\n",
      "   population_age_50_59  population_age_60_69  population_age_70_79  \\\n",
      "0                889205                225625                 58028   \n",
      "1                889205                225625                 58028   \n",
      "2                889205                225625                 58028   \n",
      "3                889205                225625                 58028   \n",
      "4                889205                225625                 58028   \n",
      "\n",
      "   population_age_80_89  population_age_90_99  population_age_80_and_older  \\\n",
      "0                  9915                  2478                        12394   \n",
      "1                  9915                  2478                        12394   \n",
      "2                  9915                  2478                        12394   \n",
      "3                  9915                  2478                        12394   \n",
      "4                  9915                  2478                        12394   \n",
      "\n",
      "            gdp  latitude  longitude   area  life_expectancy  \\\n",
      "0  4.210000e+11      24.4       54.3  83600           77.814   \n",
      "1  4.210000e+11      24.4       54.3  83600           77.814   \n",
      "2  4.210000e+11      24.4       54.3  83600           77.814   \n",
      "3  4.210000e+11      24.4       54.3  83600           77.814   \n",
      "4  4.210000e+11      24.4       54.3  83600           77.814   \n",
      "\n",
      "   diabetes_prevalence  infant_mortality_rate  pollution_mortality_rate  \\\n",
      "0                 16.3                    6.5                      54.7   \n",
      "1                 16.3                    6.5                      54.7   \n",
      "2                 16.3                    6.5                      54.7   \n",
      "3                 16.3                    6.5                      54.7   \n",
      "4                 16.3                    6.5                      54.7   \n",
      "\n",
      "   comorbidity_mortality_rate  nurses  physicians  health_expenditure  \\\n",
      "0                        16.8  5.7271      2.5278         1357.017456   \n",
      "1                        16.8  5.7271      2.5278         1357.017456   \n",
      "2                        16.8  5.7271      2.5278         1357.017456   \n",
      "3                        16.8  5.7271      2.5278         1357.017456   \n",
      "4                        16.8  5.7271      2.5278         1357.017456   \n",
      "\n",
      "   out_of_pocket_health_expenditure  school_closing  workplace_closing  \\\n",
      "0                        256.034485             0.0                0.0   \n",
      "1                        256.034485             0.0                0.0   \n",
      "2                        256.034485             0.0                0.0   \n",
      "3                        256.034485             0.0                0.0   \n",
      "4                        256.034485             0.0                0.0   \n",
      "\n",
      "   cancel_public_events  restrictions_on_gatherings  public_transport_closing  \\\n",
      "0                   0.0                         0.0                       0.0   \n",
      "1                   0.0                         0.0                       0.0   \n",
      "2                   0.0                         0.0                       0.0   \n",
      "3                   0.0                         0.0                       0.0   \n",
      "4                   0.0                         0.0                       0.0   \n",
      "\n",
      "   stay_at_home_requirements  restrictions_on_internal_movement  \\\n",
      "0                        0.0                                0.0   \n",
      "1                        0.0                                0.0   \n",
      "2                        0.0                                0.0   \n",
      "3                        0.0                                0.0   \n",
      "4                        0.0                                0.0   \n",
      "\n",
      "   international_travel_controls  income_support  debt_relief  \\\n",
      "0                            1.0             0.0          0.0   \n",
      "1                            1.0             0.0          0.0   \n",
      "2                            1.0             0.0          0.0   \n",
      "3                            1.0             0.0          0.0   \n",
      "4                            1.0             0.0          0.0   \n",
      "\n",
      "   fiscal_measures  international_support  public_information_campaigns  \\\n",
      "0              0.0                    0.0                           0.0   \n",
      "1              0.0                    0.0                           0.0   \n",
      "2              0.0                    0.0                           0.0   \n",
      "3              0.0                    0.0                           0.0   \n",
      "4              0.0                    0.0                           0.0   \n",
      "\n",
      "   testing_policy  contact_tracing  emergency_investment_in_healthcare  \\\n",
      "0             0.0              2.0                                 0.0   \n",
      "1             0.0              2.0                                 0.0   \n",
      "2             0.0              2.0                                 0.0   \n",
      "3             0.0              2.0                                 0.0   \n",
      "4             0.0              2.0                                 0.0   \n",
      "\n",
      "   investment_in_vaccines  stringency_index  average_temperature  \\\n",
      "0                     0.0              2.78            18.683333   \n",
      "1                     0.0              2.78            20.916667   \n",
      "2                     0.0              2.78            22.700000   \n",
      "3                     0.0              2.78            22.022222   \n",
      "4                     0.0              2.78            23.083333   \n",
      "\n",
      "   minimum_temperature  maximum_temperature  rainfall  dew_point  \\\n",
      "0            12.683333            23.644444       0.0  12.800000   \n",
      "1            14.250000            26.500000       0.0  11.627778   \n",
      "2            15.727778            29.283333       0.0   8.266667   \n",
      "3            16.133333            28.205556       0.0  11.055556   \n",
      "4            15.950000            29.588889       0.0  10.450000   \n",
      "\n",
      "   relative_humidity  Confirmed  Deaths  Recovered  Recovered_New  Deaths_New  \\\n",
      "0          69.124779          8       0          3              3           0   \n",
      "1          57.479017          9       0          4              1           0   \n",
      "2          42.372791          9       0          4              0           0   \n",
      "3          50.902287          9       0          4              0           0   \n",
      "4          46.757393          9       0          4              0           0   \n",
      "\n",
      "   Confirmed_New  \n",
      "0              8  \n",
      "1              1  \n",
      "2              0  \n",
      "3              0  \n",
      "4              0  \n",
      "\n",
      " Features of the dataset are: \n",
      " Index(['date', 'country_name', 'mobility_retail_and_recreation',\n",
      "       'mobility_grocery_and_pharmacy', 'mobility_parks',\n",
      "       'mobility_transit_stations', 'mobility_workplaces',\n",
      "       'mobility_residential', 'population', 'population_male',\n",
      "       'population_female', 'rural_population', 'urban_population',\n",
      "       'population_density', 'human_development_index', 'population_age_00_09',\n",
      "       'population_age_10_19', 'population_age_20_29', 'population_age_30_39',\n",
      "       'population_age_40_49', 'population_age_50_59', 'population_age_60_69',\n",
      "       'population_age_70_79', 'population_age_80_89', 'population_age_90_99',\n",
      "       'population_age_80_and_older', 'gdp', 'latitude', 'longitude', 'area',\n",
      "       'life_expectancy', 'diabetes_prevalence', 'infant_mortality_rate',\n",
      "       'pollution_mortality_rate', 'comorbidity_mortality_rate', 'nurses',\n",
      "       'physicians', 'health_expenditure', 'out_of_pocket_health_expenditure',\n",
      "       'school_closing', 'workplace_closing', 'cancel_public_events',\n",
      "       'restrictions_on_gatherings', 'public_transport_closing',\n",
      "       'stay_at_home_requirements', 'restrictions_on_internal_movement',\n",
      "       'international_travel_controls', 'income_support', 'debt_relief',\n",
      "       'fiscal_measures', 'international_support',\n",
      "       'public_information_campaigns', 'testing_policy', 'contact_tracing',\n",
      "       'emergency_investment_in_healthcare', 'investment_in_vaccines',\n",
      "       'stringency_index', 'average_temperature', 'minimum_temperature',\n",
      "       'maximum_temperature', 'rainfall', 'dew_point', 'relative_humidity',\n",
      "       'Confirmed', 'Deaths', 'Recovered', 'Recovered_New', 'Deaths_New',\n",
      "       'Confirmed_New'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#READING IN THE FINAL DATASET THAT WE WILL USE FURTHER:\n",
    "covid_data = pd.read_csv('Final_Covid_Dataset.csv')\n",
    "covid_data.drop(covid_data.filter(regex=\"Unname\"),axis=1, inplace=True) \n",
    "\n",
    "# BASIC INFORMATION ABOUT DATASET:\n",
    "print('\\n Dimensions of the Dataset:', covid_data.shape)                       # No. of records(rows) * No. of features(columns)\n",
    "print('\\n First Few rows are: \\n', covid_data.head())                          # Shows what first few rows looks like\n",
    "print('\\n Features of the dataset are: \\n', covid_data.columns)                # Columns in the dataset"
   ]
  }
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