life_expectancy_eda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Life Expectancy Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Graphic of life expectancy](images/healthcare-costs-in-retirement.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "This report leverages data from the World Health Organization (WHO) to tackle significant global health challenges, particularly in enhancing health and wellness outcomes in Africa and Asia. Our aim is to utilize data-driven insights to reduce child mortality rates and increase life expectancy. This analysis reveals trends in under-five child mortality rates as well as life expectancy and explores different factors like health expenditure, vaccination rates, and schooling can affect them. Policy makers and other decision makers in healthcare and government can use our findings and reccomendations to adjust budgets and policies to increase life expectancy across all continents."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Business Problem\n",
    "We are leveraging data from the World Health Organization (WHO) to address critical challenges in global health, particularly improving health and wellness outcomes in countries across Africa and Asia. At this policy advisory meeting, we aim to explore how data-driven insights can be used to reduce child mortality rates and increase life expectancy. By analyzing trends in health indicators, disease prevalence, and access to healthcare, our objective is to provide policymakers with actionable strategies to enhance healthcare systems, inform decisions, and shape effective, evidence-based policies that will improve lives and promote long-term wellness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Understanding\n",
    "The dataset we are working with includes comprehensive health statistics reported by 193 countries between 2000 and 2015, published by the World Health Organization (WHO). To enhance the analysis, we have merged it with economic data from the United Nations. Key variables in this dataset include GDP (Gross Domestic Product), years of schooling, and health expenditure as a percentage of GDP per capita. These variables are crucial for understanding the broader socio-economic context that influences health outcomes, helping us analyze how economic factors and investments in education and healthcare impact child mortality and life expectancy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>...</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>Developing</td>\n",
       "      <td>65.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0.01</td>\n",
       "      <td>71.279624</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1154</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.16</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>584.259210</td>\n",
       "      <td>33736494.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.3</td>\n",
       "      <td>0.479</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>271.0</td>\n",
       "      <td>64</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.523582</td>\n",
       "      <td>62.0</td>\n",
       "      <td>492</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>8.18</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>327582.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.476</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>268.0</td>\n",
       "      <td>66</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.219243</td>\n",
       "      <td>64.0</td>\n",
       "      <td>430</td>\n",
       "      <td>...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>8.13</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>31731688.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.470</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.5</td>\n",
       "      <td>272.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.01</td>\n",
       "      <td>78.184215</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2787</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>8.52</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>669.959000</td>\n",
       "      <td>3696958.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.463</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.2</td>\n",
       "      <td>275.0</td>\n",
       "      <td>71</td>\n",
       "      <td>0.01</td>\n",
       "      <td>7.097109</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3013</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>7.87</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>63.537231</td>\n",
       "      <td>2978599.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>18.2</td>\n",
       "      <td>0.454</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2010</td>\n",
       "      <td>Developing</td>\n",
       "      <td>58.8</td>\n",
       "      <td>279.0</td>\n",
       "      <td>74</td>\n",
       "      <td>0.01</td>\n",
       "      <td>79.679367</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>...</td>\n",
       "      <td>66.0</td>\n",
       "      <td>9.20</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>553.328940</td>\n",
       "      <td>2883167.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>18.4</td>\n",
       "      <td>0.448</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2009</td>\n",
       "      <td>Developing</td>\n",
       "      <td>58.6</td>\n",
       "      <td>281.0</td>\n",
       "      <td>77</td>\n",
       "      <td>0.01</td>\n",
       "      <td>56.762217</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2861</td>\n",
       "      <td>...</td>\n",
       "      <td>63.0</td>\n",
       "      <td>9.42</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>445.893298</td>\n",
       "      <td>284331.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>18.7</td>\n",
       "      <td>0.434</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2008</td>\n",
       "      <td>Developing</td>\n",
       "      <td>58.1</td>\n",
       "      <td>287.0</td>\n",
       "      <td>80</td>\n",
       "      <td>0.03</td>\n",
       "      <td>25.873925</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1599</td>\n",
       "      <td>...</td>\n",
       "      <td>64.0</td>\n",
       "      <td>8.33</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>373.361116</td>\n",
       "      <td>2729431.0</td>\n",
       "      <td>18.8</td>\n",
       "      <td>18.9</td>\n",
       "      <td>0.433</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2007</td>\n",
       "      <td>Developing</td>\n",
       "      <td>57.5</td>\n",
       "      <td>295.0</td>\n",
       "      <td>82</td>\n",
       "      <td>0.02</td>\n",
       "      <td>10.910156</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1141</td>\n",
       "      <td>...</td>\n",
       "      <td>63.0</td>\n",
       "      <td>6.73</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>369.835796</td>\n",
       "      <td>26616792.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.1</td>\n",
       "      <td>0.415</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2006</td>\n",
       "      <td>Developing</td>\n",
       "      <td>57.3</td>\n",
       "      <td>295.0</td>\n",
       "      <td>84</td>\n",
       "      <td>0.03</td>\n",
       "      <td>17.171518</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1990</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>7.43</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>272.563770</td>\n",
       "      <td>2589345.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>19.3</td>\n",
       "      <td>0.405</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 22 columns</p>\n",
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      ],
      "text/plain": [
       "       Country  Year      Status  Life expectancy   Adult Mortality  \\\n",
       "0  Afghanistan  2015  Developing              65.0            263.0   \n",
       "1  Afghanistan  2014  Developing              59.9            271.0   \n",
       "2  Afghanistan  2013  Developing              59.9            268.0   \n",
       "3  Afghanistan  2012  Developing              59.5            272.0   \n",
       "4  Afghanistan  2011  Developing              59.2            275.0   \n",
       "5  Afghanistan  2010  Developing              58.8            279.0   \n",
       "6  Afghanistan  2009  Developing              58.6            281.0   \n",
       "7  Afghanistan  2008  Developing              58.1            287.0   \n",
       "8  Afghanistan  2007  Developing              57.5            295.0   \n",
       "9  Afghanistan  2006  Developing              57.3            295.0   \n",
       "\n",
       "   infant deaths  Alcohol  percentage expenditure  Hepatitis B  Measles   ...  \\\n",
       "0             62     0.01               71.279624         65.0      1154  ...   \n",
       "1             64     0.01               73.523582         62.0       492  ...   \n",
       "2             66     0.01               73.219243         64.0       430  ...   \n",
       "3             69     0.01               78.184215         67.0      2787  ...   \n",
       "4             71     0.01                7.097109         68.0      3013  ...   \n",
       "5             74     0.01               79.679367         66.0      1989  ...   \n",
       "6             77     0.01               56.762217         63.0      2861  ...   \n",
       "7             80     0.03               25.873925         64.0      1599  ...   \n",
       "8             82     0.02               10.910156         63.0      1141  ...   \n",
       "9             84     0.03               17.171518         64.0      1990  ...   \n",
       "\n",
       "   Polio  Total expenditure  Diphtheria    HIV/AIDS         GDP  Population  \\\n",
       "0    6.0               8.16         65.0        0.1  584.259210  33736494.0   \n",
       "1   58.0               8.18         62.0        0.1  612.696514    327582.0   \n",
       "2   62.0               8.13         64.0        0.1  631.744976  31731688.0   \n",
       "3   67.0               8.52         67.0        0.1  669.959000   3696958.0   \n",
       "4   68.0               7.87         68.0        0.1   63.537231   2978599.0   \n",
       "5   66.0               9.20         66.0        0.1  553.328940   2883167.0   \n",
       "6   63.0               9.42         63.0        0.1  445.893298    284331.0   \n",
       "7   64.0               8.33         64.0        0.1  373.361116   2729431.0   \n",
       "8   63.0               6.73         63.0        0.1  369.835796  26616792.0   \n",
       "9   58.0               7.43         58.0        0.1  272.563770   2589345.0   \n",
       "\n",
       "    thinness  1-19 years   thinness 5-9 years  \\\n",
       "0                   17.2                 17.3   \n",
       "1                   17.5                 17.5   \n",
       "2                   17.7                 17.7   \n",
       "3                   17.9                 18.0   \n",
       "4                   18.2                 18.2   \n",
       "5                   18.4                 18.4   \n",
       "6                   18.6                 18.7   \n",
       "7                   18.8                 18.9   \n",
       "8                   19.0                 19.1   \n",
       "9                   19.2                 19.3   \n",
       "\n",
       "   Income composition of resources  Schooling  \n",
       "0                            0.479       10.1  \n",
       "1                            0.476       10.0  \n",
       "2                            0.470        9.9  \n",
       "3                            0.463        9.8  \n",
       "4                            0.454        9.5  \n",
       "5                            0.448        9.2  \n",
       "6                            0.434        8.9  \n",
       "7                            0.433        8.7  \n",
       "8                            0.415        8.4  \n",
       "9                            0.405        8.1  \n",
       "\n",
       "[10 rows x 22 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/Life Expectancy Data.csv')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2938 entries, 0 to 2937\n",
      "Data columns (total 22 columns):\n",
      " #   Column                           Non-Null Count  Dtype  \n",
      "---  ------                           --------------  -----  \n",
      " 0   Country                          2938 non-null   object \n",
      " 1   Year                             2938 non-null   int64  \n",
      " 2   Status                           2938 non-null   object \n",
      " 3   Life expectancy                  2928 non-null   float64\n",
      " 4   Adult Mortality                  2928 non-null   float64\n",
      " 5   infant deaths                    2938 non-null   int64  \n",
      " 6   Alcohol                          2744 non-null   float64\n",
      " 7   percentage expenditure           2938 non-null   float64\n",
      " 8   Hepatitis B                      2385 non-null   float64\n",
      " 9   Measles                          2938 non-null   int64  \n",
      " 10   BMI                             2904 non-null   float64\n",
      " 11  under-five deaths                2938 non-null   int64  \n",
      " 12  Polio                            2919 non-null   float64\n",
      " 13  Total expenditure                2712 non-null   float64\n",
      " 14  Diphtheria                       2919 non-null   float64\n",
      " 15   HIV/AIDS                        2938 non-null   float64\n",
      " 16  GDP                              2490 non-null   float64\n",
      " 17  Population                       2286 non-null   float64\n",
      " 18   thinness  1-19 years            2904 non-null   float64\n",
      " 19   thinness 5-9 years              2904 non-null   float64\n",
      " 20  Income composition of resources  2771 non-null   float64\n",
      " 21  Schooling                        2775 non-null   float64\n",
      "dtypes: float64(16), int64(4), object(2)\n",
      "memory usage: 505.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>BMI</th>\n",
       "      <th>under-five deaths</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
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       "  </thead>\n",
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       "      <td>2938.000000</td>\n",
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       "      <td>2385.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2904.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2712.00000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2490.000000</td>\n",
       "      <td>2.286000e+03</td>\n",
       "      <td>2904.000000</td>\n",
       "      <td>2904.000000</td>\n",
       "      <td>2771.000000</td>\n",
       "      <td>2775.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2007.518720</td>\n",
       "      <td>69.224932</td>\n",
       "      <td>164.796448</td>\n",
       "      <td>30.303948</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>738.251295</td>\n",
       "      <td>80.940461</td>\n",
       "      <td>2419.592240</td>\n",
       "      <td>38.321247</td>\n",
       "      <td>42.035739</td>\n",
       "      <td>82.550188</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>82.324084</td>\n",
       "      <td>1.742103</td>\n",
       "      <td>7483.158469</td>\n",
       "      <td>1.275338e+07</td>\n",
       "      <td>4.839704</td>\n",
       "      <td>4.870317</td>\n",
       "      <td>0.627551</td>\n",
       "      <td>11.992793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.613841</td>\n",
       "      <td>9.523867</td>\n",
       "      <td>124.292079</td>\n",
       "      <td>117.926501</td>\n",
       "      <td>4.052413</td>\n",
       "      <td>1987.914858</td>\n",
       "      <td>25.070016</td>\n",
       "      <td>11467.272489</td>\n",
       "      <td>20.044034</td>\n",
       "      <td>160.445548</td>\n",
       "      <td>23.428046</td>\n",
       "      <td>2.49832</td>\n",
       "      <td>23.716912</td>\n",
       "      <td>5.077785</td>\n",
       "      <td>14270.169342</td>\n",
       "      <td>6.101210e+07</td>\n",
       "      <td>4.420195</td>\n",
       "      <td>4.508882</td>\n",
       "      <td>0.210904</td>\n",
       "      <td>3.358920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>36.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.37000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.681350</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2004.000000</td>\n",
       "      <td>63.100000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.877500</td>\n",
       "      <td>4.685343</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>4.26000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>463.935626</td>\n",
       "      <td>1.957932e+05</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.493000</td>\n",
       "      <td>10.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>72.100000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.755000</td>\n",
       "      <td>64.912906</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>43.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>5.75500</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1766.947595</td>\n",
       "      <td>1.386542e+06</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>0.677000</td>\n",
       "      <td>12.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>75.700000</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>7.702500</td>\n",
       "      <td>441.534144</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>360.250000</td>\n",
       "      <td>56.200000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>7.49250</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>5910.806335</td>\n",
       "      <td>7.420359e+06</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.779000</td>\n",
       "      <td>14.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>723.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>17.870000</td>\n",
       "      <td>19479.911610</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>212183.000000</td>\n",
       "      <td>87.300000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>17.60000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>50.600000</td>\n",
       "      <td>119172.741800</td>\n",
       "      <td>1.293859e+09</td>\n",
       "      <td>27.700000</td>\n",
       "      <td>28.600000</td>\n",
       "      <td>0.948000</td>\n",
       "      <td>20.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Life expectancy   Adult Mortality  infant deaths  \\\n",
       "count  2938.000000       2928.000000      2928.000000    2938.000000   \n",
       "mean   2007.518720         69.224932       164.796448      30.303948   \n",
       "std       4.613841          9.523867       124.292079     117.926501   \n",
       "min    2000.000000         36.300000         1.000000       0.000000   \n",
       "25%    2004.000000         63.100000        74.000000       0.000000   \n",
       "50%    2008.000000         72.100000       144.000000       3.000000   \n",
       "75%    2012.000000         75.700000       228.000000      22.000000   \n",
       "max    2015.000000         89.000000       723.000000    1800.000000   \n",
       "\n",
       "           Alcohol  percentage expenditure  Hepatitis B       Measles   \\\n",
       "count  2744.000000             2938.000000  2385.000000    2938.000000   \n",
       "mean      4.602861              738.251295    80.940461    2419.592240   \n",
       "std       4.052413             1987.914858    25.070016   11467.272489   \n",
       "min       0.010000                0.000000     1.000000       0.000000   \n",
       "25%       0.877500                4.685343    77.000000       0.000000   \n",
       "50%       3.755000               64.912906    92.000000      17.000000   \n",
       "75%       7.702500              441.534144    97.000000     360.250000   \n",
       "max      17.870000            19479.911610    99.000000  212183.000000   \n",
       "\n",
       "              BMI   under-five deaths         Polio  Total expenditure  \\\n",
       "count  2904.000000         2938.000000  2919.000000         2712.00000   \n",
       "mean     38.321247           42.035739    82.550188            5.93819   \n",
       "std      20.044034          160.445548    23.428046            2.49832   \n",
       "min       1.000000            0.000000     3.000000            0.37000   \n",
       "25%      19.300000            0.000000    78.000000            4.26000   \n",
       "50%      43.500000            4.000000    93.000000            5.75500   \n",
       "75%      56.200000           28.000000    97.000000            7.49250   \n",
       "max      87.300000         2500.000000    99.000000           17.60000   \n",
       "\n",
       "       Diphtheria      HIV/AIDS            GDP    Population  \\\n",
       "count  2919.000000  2938.000000    2490.000000  2.286000e+03   \n",
       "mean     82.324084     1.742103    7483.158469  1.275338e+07   \n",
       "std      23.716912     5.077785   14270.169342  6.101210e+07   \n",
       "min       2.000000     0.100000       1.681350  3.400000e+01   \n",
       "25%      78.000000     0.100000     463.935626  1.957932e+05   \n",
       "50%      93.000000     0.100000    1766.947595  1.386542e+06   \n",
       "75%      97.000000     0.800000    5910.806335  7.420359e+06   \n",
       "max      99.000000    50.600000  119172.741800  1.293859e+09   \n",
       "\n",
       "        thinness  1-19 years   thinness 5-9 years  \\\n",
       "count            2904.000000          2904.000000   \n",
       "mean                4.839704             4.870317   \n",
       "std                 4.420195             4.508882   \n",
       "min                 0.100000             0.100000   \n",
       "25%                 1.600000             1.500000   \n",
       "50%                 3.300000             3.300000   \n",
       "75%                 7.200000             7.200000   \n",
       "max                27.700000            28.600000   \n",
       "\n",
       "       Income composition of resources    Schooling  \n",
       "count                      2771.000000  2775.000000  \n",
       "mean                          0.627551    11.992793  \n",
       "std                           0.210904     3.358920  \n",
       "min                           0.000000     0.000000  \n",
       "25%                           0.493000    10.100000  \n",
       "50%                           0.677000    12.300000  \n",
       "75%                           0.779000    14.300000  \n",
       "max                           0.948000    20.700000  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2938</td>\n",
       "      <td>2938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>193</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Developing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>16</td>\n",
       "      <td>2426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Country      Status\n",
       "count          2938        2938\n",
       "unique          193           2\n",
       "top     Afghanistan  Developing\n",
       "freq             16        2426"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=\"object\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                              0\n",
       "Year                                 0\n",
       "Status                               0\n",
       "Life expectancy                     10\n",
       "Adult Mortality                     10\n",
       "infant deaths                        0\n",
       "Alcohol                            194\n",
       "percentage expenditure               0\n",
       "Hepatitis B                        553\n",
       "Measles                              0\n",
       " BMI                                34\n",
       "under-five deaths                    0\n",
       "Polio                               19\n",
       "Total expenditure                  226\n",
       "Diphtheria                          19\n",
       " HIV/AIDS                            0\n",
       "GDP                                448\n",
       "Population                         652\n",
       " thinness  1-19 years               34\n",
       " thinness 5-9 years                 34\n",
       "Income composition of resources    167\n",
       "Schooling                          163\n",
       "dtype: int64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Life Expectancy Data\n",
    "The dataset contains 2,938 entries, providing life expectancy data and related factors for various countries from 2000 to 2015. While most columns consist of numerical values, the `Country` and `Status` columns are categorical. Several columns, such as `Adult Mortality`, `Hepatitis B`, `GDP`, and `Schooling`, have missing values that will need to be addressed before analysis. Additionally, certain columns, like `under-five death`, show a highly skewed mean, indicating the presence of outliers that may require further investigation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation\n",
    "We wanted to create a new dataframe called `df_relevant` that consisted of only the columns we wanted to explore further. However, we first had to strip the column names for whitespace to simplify the data manipulation process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the relevant columns we want to use for our findings\n",
    "relevant_cols = [\n",
    "    'Country',\n",
    "    'Year',\n",
    "    'Status',\n",
    "    'Hepatitis B',\n",
    "    \"Polio\",\n",
    "    'Diphtheria',\n",
    "    'under-five deaths',\n",
    "    'percentage expenditure',\n",
    "    'GDP',\n",
    "    'Schooling',\n",
    "    'Population',\n",
    "    \"Life expectancy\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Year', 'Status', 'Life expectancy ', 'Adult Mortality',\n",
       "       'infant deaths', 'Alcohol', 'percentage expenditure', 'Hepatitis B',\n",
       "       'Measles ', ' BMI ', 'under-five deaths ', 'Polio', 'Total expenditure',\n",
       "       'Diphtheria ', ' HIV/AIDS', 'GDP', 'Population',\n",
       "       ' thinness  1-19 years', ' thinness 5-9 years',\n",
       "       'Income composition of resources', 'Schooling'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Year', 'Status', 'Life expectancy', 'Adult Mortality',\n",
       "       'infant deaths', 'Alcohol', 'percentage expenditure', 'Hepatitis B',\n",
       "       'Measles ', ' BMI ', 'under-five deaths', 'Polio', 'Total expenditure',\n",
       "       'Diphtheria', ' HIV/AIDS', 'GDP', 'Population', ' thinness  1-19 years',\n",
       "       ' thinness 5-9 years', 'Income composition of resources', 'Schooling'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stripping column names without whitespace\n",
    "df = df.rename(columns={'Life expectancy ':'Life expectancy', 'under-five deaths ':'under-five deaths','Diphtheria ':'Diphtheria'})\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>under-five deaths</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Schooling</th>\n",
       "      <th>Population</th>\n",
       "      <th>Life expectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>Developing</td>\n",
       "      <td>65.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>83</td>\n",
       "      <td>71.279624</td>\n",
       "      <td>584.259210</td>\n",
       "      <td>10.1</td>\n",
       "      <td>33736494.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>62.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>86</td>\n",
       "      <td>73.523582</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>10.0</td>\n",
       "      <td>327582.0</td>\n",
       "      <td>59.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>64.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>89</td>\n",
       "      <td>73.219243</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>9.9</td>\n",
       "      <td>31731688.0</td>\n",
       "      <td>59.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>67.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>93</td>\n",
       "      <td>78.184215</td>\n",
       "      <td>669.959000</td>\n",
       "      <td>9.8</td>\n",
       "      <td>3696958.0</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>97</td>\n",
       "      <td>7.097109</td>\n",
       "      <td>63.537231</td>\n",
       "      <td>9.5</td>\n",
       "      <td>2978599.0</td>\n",
       "      <td>59.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year      Status  Hepatitis B  Polio  Diphtheria  \\\n",
       "0  Afghanistan  2015  Developing         65.0    6.0        65.0   \n",
       "1  Afghanistan  2014  Developing         62.0   58.0        62.0   \n",
       "2  Afghanistan  2013  Developing         64.0   62.0        64.0   \n",
       "3  Afghanistan  2012  Developing         67.0   67.0        67.0   \n",
       "4  Afghanistan  2011  Developing         68.0   68.0        68.0   \n",
       "\n",
       "   under-five deaths  percentage expenditure         GDP  Schooling  \\\n",
       "0                 83               71.279624  584.259210       10.1   \n",
       "1                 86               73.523582  612.696514       10.0   \n",
       "2                 89               73.219243  631.744976        9.9   \n",
       "3                 93               78.184215  669.959000        9.8   \n",
       "4                 97                7.097109   63.537231        9.5   \n",
       "\n",
       "   Population  Life expectancy  \n",
       "0  33736494.0             65.0  \n",
       "1    327582.0             59.9  \n",
       "2  31731688.0             59.9  \n",
       "3   3696958.0             59.5  \n",
       "4   2978599.0             59.2  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now we can create a new df called df_relevant with relevant cols\n",
    "df_relevant = df[relevant_cols]\n",
    "df_relevant.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>under-five deaths</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Schooling</th>\n",
       "      <th>Population</th>\n",
       "      <th>Life expectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2385.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2490.000000</td>\n",
       "      <td>2775.000000</td>\n",
       "      <td>2.286000e+03</td>\n",
       "      <td>2928.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2007.518720</td>\n",
       "      <td>80.940461</td>\n",
       "      <td>82.550188</td>\n",
       "      <td>82.324084</td>\n",
       "      <td>42.035739</td>\n",
       "      <td>738.251295</td>\n",
       "      <td>7483.158469</td>\n",
       "      <td>11.992793</td>\n",
       "      <td>1.275338e+07</td>\n",
       "      <td>69.224932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.613841</td>\n",
       "      <td>25.070016</td>\n",
       "      <td>23.428046</td>\n",
       "      <td>23.716912</td>\n",
       "      <td>160.445548</td>\n",
       "      <td>1987.914858</td>\n",
       "      <td>14270.169342</td>\n",
       "      <td>3.358920</td>\n",
       "      <td>6.101210e+07</td>\n",
       "      <td>9.523867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.681350</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>36.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2004.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.685343</td>\n",
       "      <td>463.935626</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>1.957932e+05</td>\n",
       "      <td>63.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>64.912906</td>\n",
       "      <td>1766.947595</td>\n",
       "      <td>12.300000</td>\n",
       "      <td>1.386542e+06</td>\n",
       "      <td>72.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>441.534144</td>\n",
       "      <td>5910.806335</td>\n",
       "      <td>14.300000</td>\n",
       "      <td>7.420359e+06</td>\n",
       "      <td>75.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>19479.911610</td>\n",
       "      <td>119172.741800</td>\n",
       "      <td>20.700000</td>\n",
       "      <td>1.293859e+09</td>\n",
       "      <td>89.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Hepatitis B        Polio   Diphtheria  under-five deaths  \\\n",
       "count  2938.000000  2385.000000  2919.000000  2919.000000        2938.000000   \n",
       "mean   2007.518720    80.940461    82.550188    82.324084          42.035739   \n",
       "std       4.613841    25.070016    23.428046    23.716912         160.445548   \n",
       "min    2000.000000     1.000000     3.000000     2.000000           0.000000   \n",
       "25%    2004.000000    77.000000    78.000000    78.000000           0.000000   \n",
       "50%    2008.000000    92.000000    93.000000    93.000000           4.000000   \n",
       "75%    2012.000000    97.000000    97.000000    97.000000          28.000000   \n",
       "max    2015.000000    99.000000    99.000000    99.000000        2500.000000   \n",
       "\n",
       "       percentage expenditure            GDP    Schooling    Population  \\\n",
       "count             2938.000000    2490.000000  2775.000000  2.286000e+03   \n",
       "mean               738.251295    7483.158469    11.992793  1.275338e+07   \n",
       "std               1987.914858   14270.169342     3.358920  6.101210e+07   \n",
       "min                  0.000000       1.681350     0.000000  3.400000e+01   \n",
       "25%                  4.685343     463.935626    10.100000  1.957932e+05   \n",
       "50%                 64.912906    1766.947595    12.300000  1.386542e+06   \n",
       "75%                441.534144    5910.806335    14.300000  7.420359e+06   \n",
       "max              19479.911610  119172.741800    20.700000  1.293859e+09   \n",
       "\n",
       "       Life expectancy  \n",
       "count      2928.000000  \n",
       "mean         69.224932  \n",
       "std           9.523867  \n",
       "min          36.300000  \n",
       "25%          63.100000  \n",
       "50%          72.100000  \n",
       "75%          75.700000  \n",
       "max          89.000000  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_relevant.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2938</td>\n",
       "      <td>2938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>193</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Developing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>16</td>\n",
       "      <td>2426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Country      Status\n",
       "count          2938        2938\n",
       "unique          193           2\n",
       "top     Afghanistan  Developing\n",
       "freq             16        2426"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_relevant.describe(include='object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering\n",
    "We intend to analyze the data and its relationships by continent, but this dataset does not include that information. To address this, our team found a country-to-continent mapping on [GitHub](https://github.com/samayo/country-json/blob/master/src/country-by-continent.json), provided as a JSON file containing a list of dictionaries where each key is a country name and the corresponding value is its continent. We will use this to add a new `continent` column to `df_relevant`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'country': 'Afghanistan', 'continent': 'Asia'}, {'country': 'Albania', 'continent': 'Europe'}, {'country': 'Algeria', 'continent': 'Africa'}, {'country': 'American Samoa', 'continent': 'Oceania'}, {'country': 'Andorra', 'continent': 'Europe'}, {'country': 'Angola', 'continent': 'Africa'}, {'country': 'Anguilla', 'continent': 'North America'}, {'country': 'Antarctica', 'continent': 'Antarctica'}, {'country': 'Antigua and Barbuda', 'continent': 'North America'}, {'country': 'Argentina', 'continent': 'South America'}, {'country': 'Armenia', 'continent': 'Asia'}, {'country': 'Aruba', 'continent': 'North America'}, {'country': 'Australia', 'continent': 'Oceania'}, {'country': 'Austria', 'continent': 'Europe'}, {'country': 'Azerbaijan', 'continent': 'Asia'}, {'country': 'Bahamas', 'continent': 'North America'}, {'country': 'Bahrain', 'continent': 'Asia'}, {'country': 'Bangladesh', 'continent': 'Asia'}, {'country': 'Barbados', 'continent': 'North America'}, {'country': 'Belarus', 'continent': 'Europe'}, {'country': 'Belgium', 'continent': 'Europe'}, {'country': 'Belize', 'continent': 'North America'}, {'country': 'Benin', 'continent': 'Africa'}, {'country': 'Bermuda', 'continent': 'North America'}, {'country': 'Bhutan', 'continent': 'Asia'}, {'country': 'Bolivia', 'continent': 'South America'}, {'country': 'Bosnia and Herzegovina', 'continent': 'Europe'}, {'country': 'Botswana', 'continent': 'Africa'}, {'country': 'Bouvet Island', 'continent': 'Antarctica'}, {'country': 'Brazil', 'continent': 'South America'}, {'country': 'British Indian Ocean Territory', 'continent': 'Africa'}, {'country': 'Brunei', 'continent': 'Asia'}, {'country': 'Bulgaria', 'continent': 'Europe'}, {'country': 'Burkina Faso', 'continent': 'Africa'}, {'country': 'Burundi', 'continent': 'Africa'}, {'country': 'Cambodia', 'continent': 'Asia'}, {'country': 'Cameroon', 'continent': 'Africa'}, {'country': 'Canada', 'continent': 'North America'}, {'country': 'Cape Verde', 'continent': 'Africa'}, {'country': 'Cayman Islands', 'continent': 'North America'}, {'country': 'Central African Republic', 'continent': 'Africa'}, {'country': 'Chad', 'continent': 'Africa'}, {'country': 'Chile', 'continent': 'South America'}, {'country': 'China', 'continent': 'Asia'}, {'country': 'Christmas Island', 'continent': 'Oceania'}, {'country': 'Cocos (Keeling) Islands', 'continent': 'Oceania'}, {'country': 'Colombia', 'continent': 'South America'}, {'country': 'Comoros', 'continent': 'Africa'}, {'country': 'Congo', 'continent': 'Africa'}, {'country': 'Cook Islands', 'continent': 'Oceania'}, {'country': 'Costa Rica', 'continent': 'North America'}, {'country': 'Croatia', 'continent': 'Europe'}, {'country': 'Cuba', 'continent': 'North America'}, {'country': 'Cyprus', 'continent': 'Asia'}, {'country': 'Czech Republic', 'continent': 'Europe'}, {'country': 'Denmark', 'continent': 'Europe'}, {'country': 'Djibouti', 'continent': 'Africa'}, {'country': 'Dominica', 'continent': 'North America'}, {'country': 'Dominican Republic', 'continent': 'North America'}, {'country': 'East Timor', 'continent': 'Asia'}, {'country': 'Ecuador', 'continent': 'South America'}, {'country': 'Egypt', 'continent': 'Africa'}, {'country': 'El Salvador', 'continent': 'North America'}, {'country': 'England', 'continent': 'Europe'}, {'country': 'Equatorial Guinea', 'continent': 'Africa'}, {'country': 'Eritrea', 'continent': 'Africa'}, {'country': 'Estonia', 'continent': 'Europe'}, {'country': 'Eswatini', 'continent': 'Africa'}, {'country': 'Ethiopia', 'continent': 'Africa'}, {'country': 'Falkland Islands', 'continent': 'South America'}, {'country': 'Faroe Islands', 'continent': 'Europe'}, {'country': 'Fiji Islands', 'continent': 'Oceania'}, {'country': 'Finland', 'continent': 'Europe'}, {'country': 'France', 'continent': 'Europe'}, {'country': 'French Guiana', 'continent': 'South America'}, {'country': 'French Polynesia', 'continent': 'Oceania'}, {'country': 'French Southern territories', 'continent': 'Antarctica'}, {'country': 'Gabon', 'continent': 'Africa'}, {'country': 'Gambia', 'continent': 'Africa'}, {'country': 'Georgia', 'continent': 'Asia'}, {'country': 'Germany', 'continent': 'Europe'}, {'country': 'Ghana', 'continent': 'Africa'}, {'country': 'Gibraltar', 'continent': 'Europe'}, {'country': 'Greece', 'continent': 'Europe'}, {'country': 'Greenland', 'continent': 'North America'}, {'country': 'Grenada', 'continent': 'North America'}, {'country': 'Guadeloupe', 'continent': 'North America'}, {'country': 'Guam', 'continent': 'Oceania'}, {'country': 'Guatemala', 'continent': 'North America'}, {'country': 'Guinea', 'continent': 'Africa'}, {'country': 'Guinea-Bissau', 'continent': 'Africa'}, {'country': 'Guyana', 'continent': 'South America'}, {'country': 'Haiti', 'continent': 'North America'}, {'country': 'Heard Island and McDonald Islands', 'continent': 'Antarctica'}, {'country': 'Holy See (Vatican City State)', 'continent': 'Europe'}, {'country': 'Honduras', 'continent': 'North America'}, {'country': 'Hong Kong', 'continent': 'Asia'}, {'country': 'Hungary', 'continent': 'Europe'}, {'country': 'Iceland', 'continent': 'Europe'}, {'country': 'India', 'continent': 'Asia'}, {'country': 'Indonesia', 'continent': 'Asia'}, {'country': 'Iran', 'continent': 'Asia'}, {'country': 'Iraq', 'continent': 'Asia'}, {'country': 'Ireland', 'continent': 'Europe'}, {'country': 'Israel', 'continent': 'Asia'}, {'country': 'Italy', 'continent': 'Europe'}, {'country': 'Ivory Coast', 'continent': 'Africa'}, {'country': 'Jamaica', 'continent': 'North America'}, {'country': 'Japan', 'continent': 'Asia'}, {'country': 'Jordan', 'continent': 'Asia'}, {'country': 'Kazakhstan', 'continent': 'Asia'}, {'country': 'Kenya', 'continent': 'Africa'}, {'country': 'Kiribati', 'continent': 'Oceania'}, {'country': 'Kuwait', 'continent': 'Asia'}, {'country': 'Kyrgyzstan', 'continent': 'Asia'}, {'country': 'Laos', 'continent': 'Asia'}, {'country': 'Latvia', 'continent': 'Europe'}, {'country': 'Lebanon', 'continent': 'Asia'}, {'country': 'Lesotho', 'continent': 'Africa'}, {'country': 'Liberia', 'continent': 'Africa'}, {'country': 'Libya', 'continent': 'Africa'}, {'country': 'Liechtenstein', 'continent': 'Europe'}, {'country': 'Lithuania', 'continent': 'Europe'}, {'country': 'Luxembourg', 'continent': 'Europe'}, {'country': 'Macao', 'continent': 'Asia'}, {'country': 'North Macedonia', 'continent': 'Europe'}, {'country': 'Madagascar', 'continent': 'Africa'}, {'country': 'Malawi', 'continent': 'Africa'}, {'country': 'Malaysia', 'continent': 'Asia'}, {'country': 'Maldives', 'continent': 'Asia'}, {'country': 'Mali', 'continent': 'Africa'}, {'country': 'Malta', 'continent': 'Europe'}, {'country': 'Marshall Islands', 'continent': 'Oceania'}, {'country': 'Martinique', 'continent': 'North America'}, {'country': 'Mauritania', 'continent': 'Africa'}, {'country': 'Mauritius', 'continent': 'Africa'}, {'country': 'Mayotte', 'continent': 'Africa'}, {'country': 'Mexico', 'continent': 'North America'}, {'country': 'Micronesia, Federated States of', 'continent': 'Oceania'}, {'country': 'Moldova', 'continent': 'Europe'}, {'country': 'Monaco', 'continent': 'Europe'}, {'country': 'Mongolia', 'continent': 'Asia'}, {'country': 'Montenegro', 'continent': 'Europe'}, {'country': 'Montserrat', 'continent': 'North America'}, {'country': 'Morocco', 'continent': 'Africa'}, {'country': 'Mozambique', 'continent': 'Africa'}, {'country': 'Myanmar', 'continent': 'Asia'}, {'country': 'Namibia', 'continent': 'Africa'}, {'country': 'Nauru', 'continent': 'Oceania'}, {'country': 'Nepal', 'continent': 'Asia'}, {'country': 'Netherlands', 'continent': 'Europe'}, {'country': 'Netherlands Antilles', 'continent': 'North America'}, {'country': 'New Caledonia', 'continent': 'Oceania'}, {'country': 'New Zealand', 'continent': 'Oceania'}, {'country': 'Nicaragua', 'continent': 'North America'}, {'country': 'Niger', 'continent': 'Africa'}, {'country': 'Nigeria', 'continent': 'Africa'}, {'country': 'Niue', 'continent': 'Oceania'}, {'country': 'Norfolk Island', 'continent': 'Oceania'}, {'country': 'North Korea', 'continent': 'Asia'}, {'country': 'Northern Ireland', 'continent': 'Europe'}, {'country': 'Northern Mariana Islands', 'continent': 'Oceania'}, {'country': 'Norway', 'continent': 'Europe'}, {'country': 'Oman', 'continent': 'Asia'}, {'country': 'Pakistan', 'continent': 'Asia'}, {'country': 'Palau', 'continent': 'Oceania'}, {'country': 'Palestine', 'continent': 'Asia'}, {'country': 'Panama', 'continent': 'North America'}, {'country': 'Papua New Guinea', 'continent': 'Oceania'}, {'country': 'Paraguay', 'continent': 'South America'}, {'country': 'Peru', 'continent': 'South America'}, {'country': 'Philippines', 'continent': 'Asia'}, {'country': 'Pitcairn', 'continent': 'Oceania'}, {'country': 'Poland', 'continent': 'Europe'}, {'country': 'Portugal', 'continent': 'Europe'}, {'country': 'Puerto Rico', 'continent': 'North America'}, {'country': 'Qatar', 'continent': 'Asia'}, {'country': 'Reunion', 'continent': 'Africa'}, {'country': 'Romania', 'continent': 'Europe'}, {'country': 'Russia', 'continent': 'Europe'}, {'country': 'Rwanda', 'continent': 'Africa'}, {'country': 'Saint Helena', 'continent': 'Africa'}, {'country': 'Saint Kitts and Nevis', 'continent': 'North America'}, {'country': 'Saint Lucia', 'continent': 'North America'}, {'country': 'Saint Pierre and Miquelon', 'continent': 'North America'}, {'country': 'Saint Vincent and the Grenadines', 'continent': 'North America'}, {'country': 'Samoa', 'continent': 'Oceania'}, {'country': 'San Marino', 'continent': 'Europe'}, {'country': 'Sao Tome and Principe', 'continent': 'Africa'}, {'country': 'Saudi Arabia', 'continent': 'Asia'}, {'country': 'Scotland', 'continent': 'Europe'}, {'country': 'Senegal', 'continent': 'Africa'}, {'country': 'Serbia', 'continent': 'Europe'}, {'country': 'Seychelles', 'continent': 'Africa'}, {'country': 'Sierra Leone', 'continent': 'Africa'}, {'country': 'Singapore', 'continent': 'Asia'}, {'country': 'Slovakia', 'continent': 'Europe'}, {'country': 'Slovenia', 'continent': 'Europe'}, {'country': 'Solomon Islands', 'continent': 'Oceania'}, {'country': 'Somalia', 'continent': 'Africa'}, {'country': 'South Africa', 'continent': 'Africa'}, {'country': 'South Georgia and the South Sandwich Islands', 'continent': 'Antarctica'}, {'country': 'South Korea', 'continent': 'Asia'}, {'country': 'South Sudan', 'continent': 'Africa'}, {'country': 'Spain', 'continent': 'Europe'}, {'country': 'Sri Lanka', 'continent': 'Asia'}, {'country': 'Sudan', 'continent': 'Africa'}, {'country': 'Suriname', 'continent': 'South America'}, {'country': 'Svalbard and Jan Mayen', 'continent': 'Europe'}, {'country': 'Sweden', 'continent': 'Europe'}, {'country': 'Switzerland', 'continent': 'Europe'}, {'country': 'Syria', 'continent': 'Asia'}, {'country': 'Tajikistan', 'continent': 'Asia'}, {'country': 'Tanzania', 'continent': 'Africa'}, {'country': 'Thailand', 'continent': 'Asia'}, {'country': 'The Democratic Republic of Congo', 'continent': 'Africa'}, {'country': 'Togo', 'continent': 'Africa'}, {'country': 'Tokelau', 'continent': 'Oceania'}, {'country': 'Tonga', 'continent': 'Oceania'}, {'country': 'Trinidad and Tobago', 'continent': 'North America'}, {'country': 'Tunisia', 'continent': 'Africa'}, {'country': 'Turkey', 'continent': 'Asia'}, {'country': 'Turkmenistan', 'continent': 'Asia'}, {'country': 'Turks and Caicos Islands', 'continent': 'North America'}, {'country': 'Tuvalu', 'continent': 'Oceania'}, {'country': 'Uganda', 'continent': 'Africa'}, {'country': 'Ukraine', 'continent': 'Europe'}, {'country': 'United Arab Emirates', 'continent': 'Asia'}, {'country': 'United Kingdom', 'continent': 'Europe'}, {'country': 'United States', 'continent': 'North America'}, {'country': 'United States Minor Outlying Islands', 'continent': 'Oceania'}, {'country': 'Uruguay', 'continent': 'South America'}, {'country': 'Uzbekistan', 'continent': 'Asia'}, {'country': 'Vanuatu', 'continent': 'Oceania'}, {'country': 'Venezuela', 'continent': 'South America'}, {'country': 'Vietnam', 'continent': 'Asia'}, {'country': 'Virgin Islands, British', 'continent': 'North America'}, {'country': 'Virgin Islands, U.S.', 'continent': 'North America'}, {'country': 'Wales', 'continent': 'Europe'}, {'country': 'Wallis and Futuna', 'continent': 'Oceania'}, {'country': 'Western Sahara', 'continent': 'Africa'}, {'country': 'Yemen', 'continent': 'Asia'}, {'country': 'Zambia', 'continent': 'Africa'}, {'country': 'Zimbabwe', 'continent': 'Africa'}]\n"
     ]
    }
   ],
   "source": [
    "# country-by-continent.json src: https://github.com/samayo/country-json/blob/master/src/country-by-continent.json\n",
    "\n",
    "import json\n",
    "with open('data/country-by-continent.json') as file:\n",
    "    country_continent_converter = json.load(file)\n",
    "\n",
    "print(country_continent_converter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a mapper function to find continents for each country in the dataframe\n",
    "def fixed_mapper(country_from_df):\n",
    "    '''\n",
    "    Takes in the country and outputs the continent of the country or N/A if continent not found\n",
    "    '''\n",
    "    for country_continent_dict in country_continent_converter:\n",
    "        if country_from_df == country_continent_dict[\"country\"]:\n",
    "            return country_continent_dict[\"continent\"]\n",
    "        \n",
    "    # if country_from_df loops through converter and doesn't find a continent\n",
    "    # then return \"N/A\"\n",
    "    return \"N/A\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/8g/m6zcqnnd1p36dhf35mwkv0zw0000gn/T/ipykernel_47394/1841596260.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_relevant.loc[:, \"continent\"] = df_relevant[\"Country\"].map(fixed_mapper)\n"
     ]
    }
   ],
   "source": [
    "# using mapper function to create new column \"continent\" inside df_relevant\n",
    "#df_relevant[\"continent\"] = df_relevant[\"Country\"].map(fixed_mapper)\n",
    "\n",
    "\n",
    "df_relevant.loc[:, \"continent\"] = df_relevant[\"Country\"].map(fixed_mapper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>under-five deaths</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Schooling</th>\n",
       "      <th>Population</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>Developing</td>\n",
       "      <td>65.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>83</td>\n",
       "      <td>71.279624</td>\n",
       "      <td>584.259210</td>\n",
       "      <td>10.1</td>\n",
       "      <td>33736494.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>62.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>86</td>\n",
       "      <td>73.523582</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>10.0</td>\n",
       "      <td>327582.0</td>\n",
       "      <td>59.9</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>64.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>89</td>\n",
       "      <td>73.219243</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>9.9</td>\n",
       "      <td>31731688.0</td>\n",
       "      <td>59.9</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>67.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>93</td>\n",
       "      <td>78.184215</td>\n",
       "      <td>669.959000</td>\n",
       "      <td>9.8</td>\n",
       "      <td>3696958.0</td>\n",
       "      <td>59.5</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>97</td>\n",
       "      <td>7.097109</td>\n",
       "      <td>63.537231</td>\n",
       "      <td>9.5</td>\n",
       "      <td>2978599.0</td>\n",
       "      <td>59.2</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2010</td>\n",
       "      <td>Developing</td>\n",
       "      <td>66.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>102</td>\n",
       "      <td>79.679367</td>\n",
       "      <td>553.328940</td>\n",
       "      <td>9.2</td>\n",
       "      <td>2883167.0</td>\n",
       "      <td>58.8</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2009</td>\n",
       "      <td>Developing</td>\n",
       "      <td>63.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>106</td>\n",
       "      <td>56.762217</td>\n",
       "      <td>445.893298</td>\n",
       "      <td>8.9</td>\n",
       "      <td>284331.0</td>\n",
       "      <td>58.6</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2008</td>\n",
       "      <td>Developing</td>\n",
       "      <td>64.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>110</td>\n",
       "      <td>25.873925</td>\n",
       "      <td>373.361116</td>\n",
       "      <td>8.7</td>\n",
       "      <td>2729431.0</td>\n",
       "      <td>58.1</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2007</td>\n",
       "      <td>Developing</td>\n",
       "      <td>63.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>113</td>\n",
       "      <td>10.910156</td>\n",
       "      <td>369.835796</td>\n",
       "      <td>8.4</td>\n",
       "      <td>26616792.0</td>\n",
       "      <td>57.5</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2006</td>\n",
       "      <td>Developing</td>\n",
       "      <td>64.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>116</td>\n",
       "      <td>17.171518</td>\n",
       "      <td>272.563770</td>\n",
       "      <td>8.1</td>\n",
       "      <td>2589345.0</td>\n",
       "      <td>57.3</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year      Status  Hepatitis B  Polio  Diphtheria  \\\n",
       "0  Afghanistan  2015  Developing         65.0    6.0        65.0   \n",
       "1  Afghanistan  2014  Developing         62.0   58.0        62.0   \n",
       "2  Afghanistan  2013  Developing         64.0   62.0        64.0   \n",
       "3  Afghanistan  2012  Developing         67.0   67.0        67.0   \n",
       "4  Afghanistan  2011  Developing         68.0   68.0        68.0   \n",
       "5  Afghanistan  2010  Developing         66.0   66.0        66.0   \n",
       "6  Afghanistan  2009  Developing         63.0   63.0        63.0   \n",
       "7  Afghanistan  2008  Developing         64.0   64.0        64.0   \n",
       "8  Afghanistan  2007  Developing         63.0   63.0        63.0   \n",
       "9  Afghanistan  2006  Developing         64.0   58.0        58.0   \n",
       "\n",
       "   under-five deaths  percentage expenditure         GDP  Schooling  \\\n",
       "0                 83               71.279624  584.259210       10.1   \n",
       "1                 86               73.523582  612.696514       10.0   \n",
       "2                 89               73.219243  631.744976        9.9   \n",
       "3                 93               78.184215  669.959000        9.8   \n",
       "4                 97                7.097109   63.537231        9.5   \n",
       "5                102               79.679367  553.328940        9.2   \n",
       "6                106               56.762217  445.893298        8.9   \n",
       "7                110               25.873925  373.361116        8.7   \n",
       "8                113               10.910156  369.835796        8.4   \n",
       "9                116               17.171518  272.563770        8.1   \n",
       "\n",
       "   Population  Life expectancy continent  \n",
       "0  33736494.0             65.0      Asia  \n",
       "1    327582.0             59.9      Asia  \n",
       "2  31731688.0             59.9      Asia  \n",
       "3   3696958.0             59.5      Asia  \n",
       "4   2978599.0             59.2      Asia  \n",
       "5   2883167.0             58.8      Asia  \n",
       "6    284331.0             58.6      Asia  \n",
       "7   2729431.0             58.1      Asia  \n",
       "8  26616792.0             57.5      Asia  \n",
       "9   2589345.0             57.3      Asia  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_relevant.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "Africa           784\n",
       "Asia             624\n",
       "Europe           546\n",
       "N/A              368\n",
       "North America    322\n",
       "South America    160\n",
       "Oceania          134\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_relevant['continent'].value_counts()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some countries' continents couldn't be identified, requiring further investigation. This issue arises because the country names in the `Country` column do not exactly match the country names in the JSON file. As a result, we will need to relabel certain countries to align with the names in the JSON file and attempt to map their continents again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bolivia (Plurinational State of)', 'Brunei Darussalam',\n",
       "       \"Côte d'Ivoire\", 'Cabo Verde', 'Czechia',\n",
       "       \"Democratic People's Republic of Korea\",\n",
       "       'Democratic Republic of the Congo', 'Fiji',\n",
       "       'Iran (Islamic Republic of)', \"Lao People's Democratic Republic\",\n",
       "       'Micronesia (Federated States of)', 'Republic of Korea',\n",
       "       'Republic of Moldova', 'Russian Federation', 'Swaziland',\n",
       "       'Syrian Arab Republic',\n",
       "       'The former Yugoslav republic of Macedonia', 'Timor-Leste',\n",
       "       'United Kingdom of Great Britain and Northern Ireland',\n",
       "       'United Republic of Tanzania', 'United States of America',\n",
       "       'Venezuela (Bolivarian Republic of)', 'Viet Nam'], dtype=object)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding all countries where continent wasn't found\n",
    "test = df_relevant.loc[df_relevant['continent'] == 'N/A']\n",
    "test['Country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "relabel_countries = {\n",
    "    'Bolivia (Plurinational State of)': 'Bolivia',\n",
    "    'Brunei Darussalam': 'Brunei',\n",
    "       \"Côte d'Ivoire\": 'Ivory Coast',\n",
    "       'Cabo Verde': 'Cape Verde',\n",
    "       'Czechia': 'Czech Republic',\n",
    "       \"Democratic People's Republic of Korea\": 'North Korea',\n",
    "       'Democratic Republic of the Congo': 'The Democratic Republic of Congo',\n",
    "       'Fiji': 'Fiji Islands',\n",
    "       'Iran (Islamic Republic of)': 'Iran',\n",
    "       \"Lao People's Democratic Republic\": 'Laos',\n",
    "       'Micronesia (Federated States of)': 'Micronesia, Federated States of',\n",
    "       'Republic of Korea': 'South Korea',\n",
    "       'Republic of Moldova': 'Moldova',\n",
    "       'Russian Federation': 'Russia',\n",
    "       'Swaziland': 'Eswatini',\n",
    "       'Syrian Arab Republic': 'Syria',\n",
    "       'The former Yugoslav republic of Macedonia': 'North Macedonia',\n",
    "       'Timor-Leste': 'East Timor',\n",
    "       'United Kingdom of Great Britain and Northern Ireland': 'United Kingdom',\n",
    "       'United Republic of Tanzania': 'Tanzania',\n",
    "       'United States of America': 'United States',\n",
    "       'Venezuela (Bolivarian Republic of)': 'Venezuela',\n",
    "       'Viet Nam': 'Vietnam'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using a .replace to replace relevant country names\n",
    "df_relevant.loc[:,'Country'] = df_relevant['Country'].replace(relabel_countries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding continents again\n",
    "df_relevant.loc[:,\"continent\"] = df_relevant[\"Country\"].map(fixed_mapper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=object)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Yay! all continents are found\n",
    "test = df_relevant.loc[df_relevant['continent'] == 'N/A']\n",
    "test['Country'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Under-Five Child Mortality Rate by Continent Over Time\n",
    "We want to plot the annual death rate of children under five by continent to identify any trends over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = df_relevant[\"Year\"].unique()\n",
    "continents = df_relevant[\"continent\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent      Year\n",
       "Africa         2000    4292\n",
       "               2001    4232\n",
       "               2002    4154\n",
       "               2003    4071\n",
       "               2004    3984\n",
       "                       ... \n",
       "South America  2011     129\n",
       "               2012     123\n",
       "               2013     120\n",
       "               2014     113\n",
       "               2015     111\n",
       "Name: under-five deaths, Length: 96, dtype: int64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by year and continent, get the sum of under-five deaths\n",
    "under_five_deaths_by_cont_year = df_relevant.groupby(by=[\"continent\", \"Year\"])[\"under-five deaths\"].sum()\n",
    "under_five_deaths_by_cont_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Under-5 deaths per year by continent\n",
    "# x-axis: year\n",
    "# y-axis: under 5 deaths\n",
    "# each continent will have its own line in the graph\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "colors = ['blue', 'green', 'red', 'purple', 'orange', 'pink']\n",
    "\n",
    "for i, continent in enumerate(continents):\n",
    "    # for each continent, get the under five deaths for every year\n",
    "    under_five_deaths_per_continent = [under_five_deaths_by_cont_year[continent][year] for year in years]\n",
    "\n",
    "    # plot under five deaths for that continent\n",
    "    ax.plot(years, under_five_deaths_per_continent, label=continent, color=colors[i], linestyle='-', marker='o')\n",
    "\n",
    "\n",
    "ax.set_title('Under-5 Deaths per Continent (2000 - 2015)')\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('Under-5 Deaths')\n",
    "ax.legend()\n",
    "ax.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Median Percentage Expenditure Per Continent in 2014\n",
    "The graph above reveals that most continents have a low under-five child mortality rate. This led us to investigate the relationship between health expenditure by continent, in connection with the patterns observed in the above visual.Our team chose to concentrate on the health expenditure data from 2014, as it was the most recent year with comprehensive information available.\n",
    "\n",
    "We created a new dataframe, `df_2014`, containing all the data from the year 2014. However, we needed to remove 29 rows that had placeholder `0` values in the `percentage expenditure` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "percentage expenditure\n",
       "0.000000       29\n",
       "45.337887       1\n",
       "8.523486        1\n",
       "1491.703605     1\n",
       "1040.278436     1\n",
       "               ..\n",
       "941.756291      1\n",
       "97.677946       1\n",
       "2163.043414     1\n",
       "789.276461      1\n",
       "10.822595       1\n",
       "Name: count, Length: 155, dtype: int64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2014 = df_relevant.loc[ (df_relevant['Year'] == 2014)]\n",
    "\n",
    "df_2014['percentage expenditure'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>under-five deaths</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Schooling</th>\n",
       "      <th>Population</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>62.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>86</td>\n",
       "      <td>73.523582</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>10.0</td>\n",
       "      <td>327582.0</td>\n",
       "      <td>59.9</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>98.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1</td>\n",
       "      <td>428.749067</td>\n",
       "      <td>4575.763787</td>\n",
       "      <td>14.2</td>\n",
       "      <td>288914.0</td>\n",
       "      <td>77.5</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>95.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>24</td>\n",
       "      <td>54.237318</td>\n",
       "      <td>547.851700</td>\n",
       "      <td>14.4</td>\n",
       "      <td>39113313.0</td>\n",
       "      <td>75.4</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>64.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>101</td>\n",
       "      <td>23.965612</td>\n",
       "      <td>479.312240</td>\n",
       "      <td>11.4</td>\n",
       "      <td>2692466.0</td>\n",
       "      <td>51.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>99.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2422.999774</td>\n",
       "      <td>12888.296670</td>\n",
       "      <td>13.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.2</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2811</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>95.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0</td>\n",
       "      <td>463.639782</td>\n",
       "      <td>16737.898270</td>\n",
       "      <td>15.5</td>\n",
       "      <td>3419546.0</td>\n",
       "      <td>76.8</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2827</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>18</td>\n",
       "      <td>0.442802</td>\n",
       "      <td>25.448414</td>\n",
       "      <td>12.1</td>\n",
       "      <td>37577.0</td>\n",
       "      <td>69.2</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2843</th>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>64.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0</td>\n",
       "      <td>564.816704</td>\n",
       "      <td>3148.365130</td>\n",
       "      <td>10.8</td>\n",
       "      <td>25885.0</td>\n",
       "      <td>71.7</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2907</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>86.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>41</td>\n",
       "      <td>196.667577</td>\n",
       "      <td>1738.882200</td>\n",
       "      <td>12.5</td>\n",
       "      <td>1562974.0</td>\n",
       "      <td>61.1</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>91.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>34</td>\n",
       "      <td>10.822595</td>\n",
       "      <td>127.474620</td>\n",
       "      <td>10.3</td>\n",
       "      <td>15411675.0</td>\n",
       "      <td>59.2</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Country  Year      Status  Hepatitis B  Polio  Diphtheria  \\\n",
       "1             Afghanistan  2014  Developing         62.0   58.0        62.0   \n",
       "17                Albania  2014  Developing         98.0   98.0        98.0   \n",
       "33                Algeria  2014  Developing         95.0   95.0        95.0   \n",
       "49                 Angola  2014  Developing         64.0   68.0        64.0   \n",
       "65    Antigua and Barbuda  2014  Developing         99.0   96.0        99.0   \n",
       "...                   ...   ...         ...          ...    ...         ...   \n",
       "2811              Uruguay  2014  Developing         95.0   95.0        95.0   \n",
       "2827           Uzbekistan  2014  Developing         99.0   99.0        99.0   \n",
       "2843              Vanuatu  2014  Developing         64.0   65.0        64.0   \n",
       "2907               Zambia  2014  Developing         86.0   78.0        86.0   \n",
       "2923             Zimbabwe  2014  Developing         91.0   92.0        91.0   \n",
       "\n",
       "      under-five deaths  percentage expenditure           GDP  Schooling  \\\n",
       "1                    86               73.523582    612.696514       10.0   \n",
       "17                    1              428.749067   4575.763787       14.2   \n",
       "33                   24               54.237318    547.851700       14.4   \n",
       "49                  101               23.965612    479.312240       11.4   \n",
       "65                    0             2422.999774  12888.296670       13.9   \n",
       "...                 ...                     ...           ...        ...   \n",
       "2811                  0              463.639782  16737.898270       15.5   \n",
       "2827                 18                0.442802     25.448414       12.1   \n",
       "2843                  0              564.816704   3148.365130       10.8   \n",
       "2907                 41              196.667577   1738.882200       12.5   \n",
       "2923                 34               10.822595    127.474620       10.3   \n",
       "\n",
       "      Population  Life expectancy      continent  \n",
       "1       327582.0             59.9           Asia  \n",
       "17      288914.0             77.5         Europe  \n",
       "33    39113313.0             75.4         Africa  \n",
       "49     2692466.0             51.7         Africa  \n",
       "65           NaN             76.2  North America  \n",
       "...          ...              ...            ...  \n",
       "2811   3419546.0             76.8  South America  \n",
       "2827     37577.0             69.2           Asia  \n",
       "2843     25885.0             71.7        Oceania  \n",
       "2907   1562974.0             61.1         Africa  \n",
       "2923  15411675.0             59.2         Africa  \n",
       "\n",
       "[154 rows x 13 columns]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding and dropping all rows with percentage expenditure == 0\n",
    "\n",
    "drop_index = df_2014.loc[df_2014['percentage expenditure'] == 0].index\n",
    "df_2014 = df_2014.drop(drop_index)\n",
    "\n",
    "df_2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "Europe           1144.661902\n",
       "South America     704.570108\n",
       "Oceania           564.816704\n",
       "North America     473.123812\n",
       "Asia              181.908378\n",
       "Africa             59.487449\n",
       "Name: percentage expenditure, dtype: float64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding percentage expenditure by continent\n",
    "median_expenditure_per_continent_2014 = df_2014.groupby(by=['continent'])['percentage expenditure'].median()\n",
    "median_expenditure_per_continent_2014 = median_expenditure_per_continent_2014.sort_values(ascending=False)\n",
    "\n",
    "median_expenditure_per_continent_2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "colors = ['blue', 'green', 'red', 'purple', 'orange', 'pink']\n",
    "\n",
    "continents = list(median_expenditure_per_continent_2014.keys())\n",
    "expenditures = list((median_expenditure_per_continent_2014.values))\n",
    "\n",
    "ax.bar(continents, expenditures, color = colors)\n",
    "\n",
    "\n",
    "ax.set_title('Median Percentage Expenditure per Continent (2014)')\n",
    "ax.set_xlabel('Continent')\n",
    "ax.set_ylabel('Median Percentage Expenditure')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like North America has the lowest percentage expenditure in health while still maintaining a very low mortality rate of children under five."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relationship of Vaccination Rates vs Under-Five Mortality Rate\n",
    "We saw an interesting phenomenon from the first graph: Under-5 mortality rates in Asia and Africa crossed paths in 2008. This prompted us to explore the potential relationship between vaccination rates for diseases like Hepatitis B, Polio, and Diphtheria, and their impact on mortality rates in these two continents.\n",
    "\n",
    "Our team opted to use the median as the measure of central tendency because the mean vaccination rates for each disease were significantly below the 50th percentile, indicating that the data for all three diseases was skewed lower and likely contained some outliers. Therefore, we believed the median would provide a more accurate representation of the central values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the median vaccination rate per country over time for different diseases\n",
    "\n",
    "hepatitis_by_cont_year = df_relevant.groupby(by=[\"continent\", \"Year\"])[\"Hepatitis B\"].median()\n",
    "polio_by_cont_year = df_relevant.groupby(by=[\"continent\", \"Year\"])[\"Polio\"].median()\n",
    "diphtheria_by_cont_year = df_relevant.groupby(by=[\"continent\", \"Year\"])[\"Diphtheria\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+hBCpjyRKQghhJGdnZ2rXrs3EiRNJly4dOXPmZPfu3Xh5eSXYMfz9/WnYsCE2NjYMGjSIkydPRni+cOHCODg40LhxYxYvXkzBggUpXrw4p06dYsqUKVFagvLkyYONjQ3Lly+nUKFC2NvbkzVrVrJmzRrl2AUKFODbb79l1qxZmJmZ0aBBA27dusWPP/5I9uzZ6d+/f4K9zpgMGjSI+fPnM2bMGFq1akXBggXJkycP33//PZqmkT59ejZt2oS3t3eUbYsVKwbAzJkzad++PZaWlhQoUAA3NzfGjh3LDz/8wI0bN6hfvz7p0qXj4cOHHD9+HDs7O8aMGcO5c+fo1asXX3zxBfny5cPKyoo9e/Zw7tw5vv/++w/GbmVlxdSpU3n9+jXlypXj8OHDjBs3jgYNGoR3JaxcuTLffvstHTt25OTJk1SrVg07Ozv8/Pw4ePAgxYoV47vvvkvYkwrY29sza9Ys2rdvz7Nnz2jZsiWZM2fm8ePHnD17lsePHzNv3jwAo68tU1u+fDmBgYH06dMnPBF9X4YMGVi+fDkLFy5k+vTpNGnShKJFi1K2bFkyZcrE7du3mTFjBjlz5iRfvnzRHiMu158QIvWRREkIIeJg6dKl9O7dm6FDh6LX62nSpAkrV66McYxDXD179gw/Pz+A8MHp7/Px8aFGjRrMnDkTS0tLJk6cyOvXryldujReXl6MGDEiwvq2trb88ccfjBkzhrp16xISEsKoUaMizGXzvnnz5pEnTx4WLlzInDlzcHR0pH79+kycODHaMUkJzd7enpEjR9KzZ0+WL19Ou3bt2LRpE3379qVbt25YWFhQu3Ztdu3aFV4qPUyNGjUYNmwYS5Ys4ffff8dgMISfr2HDhlG4cGFmzpzJypUrCQoKwtnZmXLlytG9e3dAJcJ58uRh7ty53L17F51OR+7cuZk6dWp46fHYWFpasnnzZvr06cO4ceOwsbGha9euTJkyJcJ6v/76KxUqVODXX39l7ty5GAwGsmbNSuXKlaMUx0hIX3/9NTly5MDT05Nu3brx6tUrMmfOTMmSJSMU8DD22jK1hQsXkjlzZpo3bx7t88WKFaNChQosW7aMyZMn4+7uztq1a1mwYAH+/v44OztTp04dfvzxRywtLaPdh6WlpdHXnxAi9dFpWqQZ2YQQQggRJx06dGDNmjW8fv3a1KEIIYRIIFIeXAghhBBCCCEikURJCCGEEEIIISKRrndCCCGEEEIIEYm0KAkhhBBCCCFEJJIoCSGEEEIIIUQkkigJIYQQQgghRCSpfh4lg8HA/fv3SZs2bfgM20IIIYQQQohPj6ZpvHr1iqxZs2JmFnubUapPlO7fv0/27NlNHYYQQgghhBAimbh79y6urq6xrpPqE6W0adMC6mQ4ODiYNJaQkBB27txJ3bp1Y5wFXCQ8Oe+mIefdNOS8m4acd9OQ824act5NQ857wvD39yd79uzhOUJsUn2iFNbdzsHBIVkkSra2tjg4OMgFnoTkvJuGnHfTkPNuGnLeTUPOu2nIeTcNOe8Jy5ghOVLMQQghhBBCCCEikURJCCGEEEIIISKRREkIIYQQQgghIkn1Y5SMoWkaoaGh6PX6RD1OSEgIFhYWBAYGJvqxxDth5z0oKAgACwsLKRUvhBBCCCFi9cknSsHBwfj5+fHmzZtEP5amaTg7O3P37l25UU9CYef9zp076HQ6bG1tcXFxwcrKytShCSGEEEKIZOqTTpQMBgM3b97E3NycrFmzYmVllagJjMFg4PXr19jb239wgiuRcMLOu52dHaGhoTx+/JibN2+SL18+eR+EEEIIIUS0PulEKTg4GIPBQPbs2bG1tU304xkMBoKDg0mTJo3coCehsPNuY2ODmZkZlpaW3L59O/y9EEIIIYQQIjK5WwdJWj4x8n4LIYQQQogPkTtGIYQQQgghhIjkk+56J4QQQgghjKfXw759Ovbvz4adnQ53dzA3N3VUxgkO0TN3ywGuP/QjTxYXejSqipVlCgk+BQsODGbu7D+57udLHpds9OjVDqs0KaOglrQoJQS9HvbuhZUr1b9S+hsANzc3ZsyYEes6o0ePpmTJkkkSjxBCCCHiz8sL3NygTh0Lpk0rS506Fri5qeXJ3ZBFXtgOd6P/WXdmP/iK/mfdsR3uxpBFKSD4FGzIsEnqvAd0ZbbDaPoHdFXnfdgkU4dmFEmUPlbYp4a7O3z1lfo3kT81OnToQPPmzaMs37t3LzqdjhcvXiTasaOzePFinJycoiw/ceIE3377bfhjnU7H+vXrI6wzaNAgdu/eHe9j63S68B8LCwty5MjBgAEDwudMEkIIIcTH8/KCli3h3r2Iy3191fLknCwNWeTFlNst0dtFDF5v58uU2y0lWUokQ4ZNYor1cPQOfhGW6x0eMMV6eIpIliRR+hgp+VMjCWTKlOmD1QTt7e3JkCHDRx1n0aJF+Pn5cfPmTebOncvSpUsZN27cR+1TCCGEEIpeD337gqZFfS5sWb9+ybNDTXCInmmX+gIaRJ4BRqeCn3apH8EhyTD4FCw4MJhpQb8Q63kPmkVwYHCSxxYXMkYpMk0DYyaf1euhT5+YPzV0OvWpUrv2u867BgMEBKjHkSuv2dqqbRLY4cOH+f777zlx4gQZM2akRYsWTJw4ETs7O0B1j+vcuTOXL19m48aNODg4MGzYMHr37h2+j2nTprFo0SJu3LhB+vTpadKkCZ6entjb27N37146duwIED4H1ahRoxg9ejRubm7069ePfv364ebmBkCLFi0AyJkzJ7du3WL06NGsX7+eM2fOAKpVbMiQIVy8eBFLS0uKFCnCihUryJkzZ4yv0cnJCWdnZwCyZ89O06ZNOX36dIKeRyGEEOJTdeBA1O+E36dpcPcu5MsH9vZJF5cxHtsdQF8/luB1Gnr7u8zdcoB+zWskWVyp3dzZf6J39It5BZ2G3vE+c2f/Sb9BXZIusDiSRCmyN28S5rdc09SniqNj+CIzwCmm9V+/hv+Sl4Ry/vx56tWrx08//cTChQt5/PgxvXr1olevXixatCh8vSlTpjB8+HBGjx7Njh076N+/PwULFqROnToqbjMzfvnlF9zc3Lh58yY9evRgyJAhzJ07l0qVKjFjxgxGjhzJP//8A6hWoshOnDhB5syZWbRoEfXr18c8mpGfoaGhNG/enK5du7Jy5UqCg4M5fvx4nCYBvnr1Kj4+PnTo0CGOZ0sIIYQQ0fGL5X73fTdvJm4c8VLUuOCvPzTyRQqjXPfzBQcj10vGJFFKoTZv3hwlIdFHavOeMmUKX331Ff369QMgX758/PLLL1SvXp158+aFT7ZauXJlvv/+ewDy58/PoUOHmD59eniiFLY9QK5cufjpp5/47rvvmDt3LlZWVjg6OqLT6cJbdaKTKVMmIGLrT2T+/v68fPmSxo0bkydPHgAKFSr0wXPRpk0bzM3NCQ0NJSgoiMaNGzNs2LAPbieEEEKIDzP2+8qff4YSJRI3lrhadFJjhRHDlvNkcUn8YD4hgaHGdanL45ItkSP5OJIoRWZrq1p3PmT/fmjY8MPrbd0K1aoBYDAY8Pf3x8HBIeqkpx8YyxOZu7s78+bNi7Ds2LFjfP311+GPT506xbVr11i+fHn4Mk3TMBgM3Lx5MzwJqVixYoT9VKxYMUK1Oh8fHyZMmMClS5fw9/cnNDSUwMBAAgICwrvwJYT06dPToUMH6tWrR506dahduzatWrXCxSX2D6/p06dTu3Zt9Ho9165dY8CAAXzzzTf89ddfCRabEEII8anRNPjjDzX+KDY6Hbi6qvWSS6lwTdP49dSvrNcP/G8BUcfKhNFbYJFcAk/hggOD+bxXPza7LFQLYjrvmg5zfxd6DG2XlOHFmRRziEynU13gPvRTt676VIjpaxadDrJnV+sZs784jk+ys7Mjb968EX6yZYuYlRsMBrp168aZM2fCf86ePcu///4b3mIT82lQ8dy+fZuGDRtStGhR1q5dy6lTp5gzZw4AISEhcYrZGIsWLeLIkSNUqlSJVatWkT9/fo4ePRrrNs7OzuTNm5cCBQrQqFEjxowZw6pVq7h27VqCxyeEEEJ8Cu7fh8aNoUsX9f1xgQLqViXy7UrY4xkzkk+SdPflXeotq8d3W77jTegbslsXBnSgRQpe++/HPJTep6rz2Q9DePE60AQRpw57tuwnU+8KbM4+DyyCSfOgINGfd/V4gHXvZD+fkiRK8WVuDjNnqv8n00+N0qVLc/HixSgJVd68ebGyendhRk5Ejh49SsGCBQE4efIkoaGhTJ06lQoVKpA/f37u378fYX0rK6so3f6iY2lpadR6pUqVYtiwYRw+fJiiRYuyYsUKY15uuLDxT2/fvo3TdkIIIcSnTtNg+XIoWlR1irG2hilT4OJFWLMGIn0ni6urWu7hYZp436dpGovPLKbovKJ43/AmjUUaZtafya2h5xmccw3mARGDNw/IzreZ/yTPq/ag0zhhNQXnkWX4c9dJE72ClCk0JJQvv+tPrcMN8Hf9G4Lsafd4CAGzLzI4aALm/hGHXJj7uzA4aAKeE783UcTGk653H8PDQ3069O0bsRyMq6tKkkz8qTF06FAqVKhAz5496dq1K3Z2dly+fBlvb29mzZoVvt6hQ4fw9PSkefPmeHt787///Y8tW7YAkCdPHkJDQ5k1axZNmjTh0KFDzJ8/P8Jx3NzceP36Nbt376ZEiRLY2tpGWxbczc2N3bt3U7lyZaytrUmXLl2E52/evMlvv/1G06ZNyZo1K//88w9Xr16lXbvYm2VfvHjBgwcPMBgM/Pvvv4wdO5b8+fMbNb5JCCGEEMqjR9C9O6xbpx6XLQtLlkDhwuqxhwc0awY+PqFs23aGBg1K4u5ukSxakvxe+fHt5m/ZfHUzABVcK7C42WIKZCwAgGdHD8aFNGPulgNcf+hHniwu9GhUFStLc+AbfljqwaQL3xLkeIn2Byqw4MBwtn4/Anub5N3iYWpH9hyn0fLePM9xHACnu2XZ1PoXqtRRwzo8J37PuMABzJ39J9f9fMnjko0eQ9sl+5akMJIofaywT40DB1RZGBcXqFo1WbQ/Fy9enH379vHDDz9QtWpVNE0jT548tG7dOsJ6AwcO5NSpU4wZM4a0adMydepU6tWrB0DJkiWZNm0akydPZtiwYVSrVo2JEydGSF4qVapE9+7dad26NU+fPg0vDx7Z1KlTGTBgAL///jvZsmXj1q1bEZ63tbXlypUrLFmyhKdPn+Li4kKvXr3o1q1brK/z/fLkzs7OVKtWjQkTJmBhIZe3EEIIYYy1a1WS9OQJWFjAqFEwdChYWkZcz9wcqlfXCAjwpXr1Eia/3dE0jVUXV9Fza0+evX2GlbkVY2uMZVClQZibRQzOytI8xhLg479pSod7lak1rSd3HVdxgJ/IPHwTf3osoWXV4knwSlIWg95A537DWZx2LuR4BSE2tHzclZXzpmJhGfH+yyqNVbIuAR4bnaZFNxFQ6uHv74+joyMvX77EwSFincLAwEBu3rxJrly5wivAJaZYizmYyPtzHaVWkc97Ur/vn6qQkBC2bt1Kw4YNsYz8l1YkGjnvpiHn3TTkvH+8Z8+gd28I6+VevLhqRSpZMuZtkst5fxzwmB5be7Dm0hoASjmX4s8Wf1I0c9GP2m//BauZea0Hms1T0FtSx3I0G4cOIY2Vab+ATS7n/fThM9T/vReP3Q4BkNa3JGsbT6NOU3eTxRQXseUGkSWPu3UhhBBCCJGktmxRY5FWrAAzMxg+HI4fjz1JSi7WX1lP0XlFWXNpDRZmFoyuPppjXY59dJIEML1LK852u4Dzi6ZgHoK34QcyDq3M1uNXEiDylMugN9BzwCjKbnRXSVKoNY3udufJL8dSTJIUV5IoCSGEEEJ8Ql6+hM6dVVU7Pz9V0e7wYRg/XhVvSM6ev33ON+u+ocWqFjwKeESRTEU41uUYo2qMwtI84VpZiuVyxnfqerpmXAKBjgQ4HafRxlI0mzSN4JAPF6ZKbS6eukT2zrWY6zgWzeYFtn5F2FB0HZsXzEsx443iQxKlT9ytW7dSdbc7IYQQQryzaxcUK6bmR9LpYMAA+PtvKF/e1JF92PZr2yk6ryjLzi3DTGfG95W/59S3pyjtUjpRjmdmpuO3nu043uECGZ7XBctANgYNJNPgGuw5cz1RjpkcDRk2iWKrqnM/117QW1DzVkceTzlO09YNTB1aopNESQghhBAilQsIgJ49oU4duHsXcueGfftg6lSwsTF1dLF7FfSKbzd9S4PlDbj/6j75M+TnUKdDTKw9EWuLODSB6fWwdy+sXKn+NWLKEoByBVx5NG07bR1/hWB7/NMdpNb/ivPl1LmE6g3xek0pwfVLN3BrV5cpaYah2T0hzaP8rMi1it2L/sA2bdTqxqmRJEpCCCGEEKnYwYNQogTMnase9+gBZ8+qIr3Jnc9NH4rPL87vp38HoF/5fvzd7W8quFaI2468vMDNDdzd4auv1L9ubmq5EczMdCzr9y37Wp/D8Xl1sHrDqtc9yTKwHkcu3YlbLCnAmDEzyP9HJW7n8QaDGZVutOXhTydo0zEZTJiVhCRREkIIIYRIhd6+hUGDoFo1uH4dsmcHb2+YMwfs7U0dXezehLyhz7Y+1PyzJrde3CKXUy72tt/L9PrTsbWMY2uGlxe0bBlxzksAX1+13MhkCaBa8Vw8mboHD5uZEGLDs3S7qLSsGB1/+QODIeUXkr534x4FvmnMaPpjSPsQq6e5WZBlKYeWLMMhfewV4lIjSZSEEEIIIVKZ48ehdGnVtU7ToGNHOH8eatc2dWQfdvjuYUrOL8ms47MA6FamG2e7n6W6W/W470yvh7591UmILGxZv35Gd8MDsDA3Y+2QPmxvfgb7FxXA2p/FzzvjMrAJZ677xT3GZGLKpPm4zarA1bxbQNNR5voX+I04QeceX5k6NJORREkIIYQQIpUIDoYRI6BSJbhyBZydYdMmVbzB0dHU0cUuMDSQId5DqLqoKv8++5dsabOxve125jeeT1rrtPHb6YEDUVuS3qdpatDWgQNx3nW9svl56nmQBhaTIdSKR05bKL2gCL1/XZmiWpce+T6ieLsWDAn6Dr2TLxbPczDLcSEn/1xN+szpTR2eSUmiJIQQQgiRCpw9C+XKqTLfej20aQMXLqgy4MndqfunKPNbGaYcnoJBM9C+RHsu9LhAvbz14r/TK1dg9mzj1r16NV6HsLI0Z+sPQ/CqfwqbF6XR0jxn9oOvyDGoFZfvPI7XPpPSvJlLyOb5GefzrAeg6LXm3B14jF79O5o2sGRCEqUEEM8iKiazd+9edDodL168AGDx4sU4OTmZNCYhhBBCxE9oKIwbp5Kkc+cgY0ZYs0ZNJJshg6mji12wPphRPqMov6A8lx5fIotdFjZ8uYHFzRfjlMYp7ju8dk1liiVKQKFCsHatcdt99x3Uq6ea3p49i/NhW1QuypOJR3FnDOgt8HVcQ5E5RRi6eF2c95UUXjx5Qdl2renxvCOh6W9j/jIrk63mcn7pOpyzO5s6vGRDEqWP9JFFVOKtQ4cO6HQ6dDodlpaW5M6dm0GDBhEQEBDnfbVu3Zqr8fwmRQghhBCmc+kSVKwIP/4IISHQogVcvAiff27qyD7s/MPzlF9QnrH7x6LX9LQu0poLPS7QtEDTuO3o5k2YPBnKlIF8+VTfw3PnwMICGjSAdOnUpFExsbQEgwF27lQz8To7Q6NG8OefanZeI9mmsWTPqJEsq3EM65dF0Wwf43nbg9wDv+Gm3/O4vaZEtOS3VWQZU5ZTeVaDTiPf9Ybc6HGEIcO+M3VoyY4kSh8hAYuoxEv9+vXx8/Pjxo0bjBs3jrlz5zJo0KA478fGxobMmTMnQoRCCCGESAx6Pfz8syrYcPIkODnBsmWqASW5/0kPNYQy8cBEyvxWhjMPzpDBJgOrWq7ir5Z/kdE2o3E7uXsXpk1TM+Xmzg3ffw+nT4O5OdStCwsXwsOHsHUrLFigtomcLOl06uevv1TXu3HjoHhxlXFu3Qrt26uT2ayZap579cqo0NrWLM2jn05SIfR7MJhx02EZeacXZezKbXE4Swnv9cvXVGn/DR182xKc8TpmrzLzo34qV//cQo68OUwaW3IliVIkmqYmZfvQj78/9OkTexGVvn3VesbsL7r9fIi1tTXOzs5kz56dr776irZt27J+/XqCgoLo06cPmTNnJk2aNFSpUoUTJ07EuJ/out7NmzePPHnyYGVlRYECBVi6dGncAxRCCCFEgrt2DapXh8GDIShINZpcuABt28becJIcXHlyhcp/VGb4nuGEGEJoWqApF3pcoFWRVh/e+P59+OUXqFwZcuSAgQNVeT8zM6hZE379Ffz8YMcO6NQJ0v9XiMDDQ/VFzJYt4v5cXdVyDw/VEvXDD2qg16VLMHq06roXHAwbN6qTmzmzaqpbvVrdvMXCwc6aIz9N5PeKh7D0z4/B7j6jrjak4OCu3HvsH7+T9xFW/7mBTMPKcSj3MjDTk/N6ba50OMTYsQOSPJaUxMLUASQ3b94kzNwCmqZamiJWmDEDnKJd//VrsLP7uGPa2NgQEhLCkCFDWLt2LUuWLCFnzpx4enpSr149rl27Rvr0H65esm7dOvr27cuMGTOoXbs2mzdvpmPHjri6uuLu7v5xQQohhBAiXgwGmDcPhgx5d78yfbrqLZbcEySDZmDm0ZkM3zOcwNBAHK0d+aXBL3xT/Bt0sQX/8KFqJlu1SlWmC/tmWadTM+a2aqWSF+cPjKvx8FAtQwcOqGTKxUVtb24edd1ChWDUKBg5UmWgq1er4//7r+ou5OUFtrbQpIk6foMGYGMT7WG71K9A84p/U3fSD/ydZgb/2C/AzXMnkysuYqBHTSPPXvwFvgmkSc9e7Mr+J2QJQReQgf66/kz984dEP3ZqIIlSKnH8+HFWrFiBu7s78+bNY/HixTRo0ACA33//HW9vbxYuXMjgwYM/uK+ff/6ZDh060KNHDwAGDBjA0aNH+fnnnyVREkIIIUzg9m3VSLJnj3rs7q7qDri5mTQso9x4foOOGzqy//Z+AOrmqcvCpgtxdXCNfoMnT1Qysno1+PioDDFMpUoqOWnZMmoL0YeYm0ONGsavr9NBsWLqZ+xYOHPmXdJ086b6d9UqlbE2a6biqlcPrK0j7Cajoy2nJ05n5obmDDrQkdC0Nxl0vhZLjvdi19BJZE73kd+Ux2Dz/3bw5e5BBLhdAMDlZjV29ppL0bJFEuV4qZF0vYvE1la17nzoZ+tW4/a3deu7bfz9Ddy79wJ/f0OU/dnGcZJpgM2bN2Nvb0+aNGmoWLEi1apVo3fv3oSEhFC5cuXw9SwtLfnss8+4fPmyUfu9fPlyhO0BKleubPT2QgghhEgYmqaG2xQrppIkGxuYNQt27Ur+SZKmacw/OZ/i84qz//Z+7Czt+LXxr2xvuz1qkvT8ucr86tdXrUPdusHu3SpJKldODci6fRsOHVJjG+KaJH0snQ5KlYKJE+H6ddXlb9Ag1QXw9WtYvlwlS1myQIcO6gYwODjCLvo2q87tYWcpFNANgPM2s3EdV5J5Ww4laKjBgcE06dKDJueaEeByAd1bR7q/+JF7C30kSYojaVGKRKczrgtc3bqqa6uvb/Tji3Q69Xzduu9adQ0GNfjSzk51p/1YYa1HlpaWZM2aFUtLS86ePfvf8SM2Y2uaFnvTdpT4P257IYQQQnyc+/eha9d3X85WqgSLF6vhNMnd3Zd36byxM943vAGonrM6fzT7g9zpcr9b6eVL2LBBtdLs3KmKKIQpVQpat1atNLlyJXH0H6DTqeStXDlVbe/YMfUaVq9Wb9qSJeonXTrV5a9VKzWGysKCrBnScslzPhNXe/Djic6EOFyjx4mq/HF4IN7DfsLJPs1HhbZ78z5abOzHq+xnAMh4uyLbOs2mbJXSCfDCPz3SohRP5uYwc6b6f3RFVABmzIi+62tCsbOzI2/evOTMmRNLS0sA8ubNi5WVFQcPHgxfLyQkhJMnT1KoUCGj9luoUKEI2wMcPnzY6O2FEEIIEX+aphooihZVSZK1NUyZAvv3J/8kSdM0Fp9ZTNF5RfG+4U0aizTMqDeDPe33qCTp9Ws18WTz5qo4Qvv2sGWLSpKKFVOV565eVRXshg5NfklSZGZmqj779OmqEt/+/dCzp2pZev5cNQfWq6fGRHXvrpoF9XqGtarL9UHnyfOqPeg0Tlr9TJaRpVniHXPxrdiEhoTSqns/ah9pyKtsZyDInnaPh/Dw94OSJH0EaVH6CGFFVPr2jVgi3NVVJUkeHkkfk52dHd999x2DBw8mffr05MiRA09PT968eUPnzp2N2sfgwYNp1aoVpUuXplatWmzatAkvLy927dqVyNELIYQQn7ZHj9T99Lr/5iktW1Y1ThQubNq4jOH3yo9vN3/L5qubAajgWoHFzRZTwDY7rPmvIMOWLRAY+G6jQoXetRyl9C9kzcxUgYiqVdW36fv3q9e8dq0ad/Xrr+onSxZo2ZKcrVtzzfMPfljuwaQL3xLseJkOByuy8OBwtn4/AnsbK6MOe2T3MRqt6MPzHMcBcLpblk2tf6FKnYqJ+Wo/CZIofaS4FFFJKpMmTcJgMPDNN9/w6tUrypYty44dO0iXLp1R2zdv3pyZM2cyZcoU+vTpQ65cuVi0aBE14jIAUgghRLKn1yevv1+fiuAQPXO3HOD6Qz/yZHGhR6OqWFmas3atSpKePFFzpY4apRpV/us0kizoQ4LZv3EWF47vwT70GjWa9sbMwpJVF1fRc2tPnr19hpW5FWOr/MigZwUx7zUKNm1SZfrC5Mv3LjkqWjT5l+yLD3NzVXHD3R1mz1ZFKVatUkUqHj6EOXPUT9asjP/iCzrUWkotn4XcdVzFAX4i8/BN/OmxhJZViwNq3NGsmYs4duEc1648oHffjlhYWtC53zAWp50HOV5BiA0tH3dl5bypWFjKLX5C0GlafGbwSRivXr3ixx9/ZN26dTx69IhSpUoxc+ZMypUrF77O5cuXGTp0KPv27cNgMFCkSBFWr15NjhzGTYzl7++Po6MjL1++xMHBIcJzgYGB3Lx5k1y5cpEmzcf1CTWGwWDA398fBwcHzBJikJIwSuTzntTv+6cqJCSErVu30rBhw/CuoSLxyXk3jZR43r28ou8RMXOmaXpExEdKPO9DFnkx7VJf9PbvTrzZa1cK3JjJZS914osXV61IJUuaKMgYeC0aQt9L07hnrw9fljXAjJwZ83Lk7VUAStnk5s8rhSi6Zn/ECVpz5VKJUevW6oWlxuTIGMHBqkjFqlWwfr0apxUme3b612jKzKx/odk8Bb0ldSxHUzQAfgmejd7RL3xVs1dZsA7IwFvnSwCk9S3J2sbTqNNUqhN/SGy5QWQmTTe7dOnChQsXWLp0KVmzZmXZsmXUrl2bS5cukS1bNq5fv06VKlXo3LkzY8aMwdHRkcuXL8vNrRBCCPERvLxUdeXIX5X6+qrlYXNwioQ1ZJEXU263BLuIJ95g58vlYi3h8hqGt/Bg5MgoFaZNzmvREFrenoIWqeDVfVsD999excwAI49ZM9z7BpaGG+rJ7NnfJUdly366ydH7rKzUvEsNGqjZgnfsUEUgNmyAu3eZvnQOne0yUqd5RR7kO4K34Qe80wCRbn0N9g95m/Yh6C1odL8LXrNnYpXGuK56wngma9Z4+/Yta9euxdPTk2rVqpE3b15Gjx5Nrly5mDdvHgA//PADDRs2xNPTk1KlSpE7d24aNWpE5syZTRW2EEIIkaLp9aolKbr+JGHL+vVT64mEExyiZ9qlvoAGkfMFnTrxZg37MWq0PtklSfqQYPpemoYG0cQOaJDpDYzYGYSlc1Z1gR0+DLduqbLe5cpJkhQda2to2hSWLVOD09auhdatKaq9wXf5ETp7VQJNp85xDOfd7E16SZISkclalEJDQ9Hr9VFah2xsbDh48CAGg4EtW7YwZMgQ6tWrx99//02uXLkYNmwYzZs3j3G/QUFBBAUFhT/29/cHVPN8yPtlJ/9bpmkaBoMBw/uTmSWSsF6OYccUSSPyeTcYDGiaRkhICObSGT/RhP2+Rf69E4lLzrtppKTzvm+fjnv3Yv7zr2mqeJePTyjVq5usd75RUtJ5n7Vxf4TudlHoNAxp72I9IZmOLbGP5TkdPLSHvZN7UK3PtHdzoOj1knEby8ICmjRRPwEB6LZupfCC/4Un0dHSgSHtI2bNXESfAZ2SLtYULi6fFyb7bUybNi0VK1bkp59+olChQmTJkoWVK1dy7Ngx8uXLx6NHj3j9+jWTJk1i3LhxTJ48me3bt+Ph4YGPjw/Vq1ePdr8TJ05kzJgxUZbv3LkT20izulpYWODs7Mzr168JjjQpWGJ69X6fXZFkws57cHAwb9++Zf/+/YSGhpo4qtTP29vb1CF8kuS8m0ZKOO/792cDyn5wvW3bzhAQ4Jv4ASWAlHDe9xy/APGYXD4lOfrkFq+3bzd1GKmDnR1Hs2U1atVjF86xNWyyLfFBb94vLPIBJv3aYunSpXTq1Ils2bJhbm5O6dKl+eqrrzh9+nR4i0uzZs3o378/ACVLluTw4cPMnz8/xkRp2LBhDBgwIPyxv78/2bNnp27dutEWc7h79y729vZJMu5J0zRevXpF2rRpZfLWJBT5vAcGBmJjY0O1atVkvFsiCgkJwdvbmzp16qSYQdapgZx300hJ593X17i/Pw0alKR69RKJHM3HSUnn3ftfewj48HqjC66haz1TlnXW0J0+jW7jRsw2b0bn94BD2aFFmw9vWeGzmlRr2DDxQ/xEXLvygP8Ffni98kWL01DOu9HCepsZw6SJUp48edi3bx8BAQH4+/vj4uJC69atyZUrFxkzZsTCwoLCkSYOiG4y1PdZW1tjHU3nXktLyygfonq9Hp1Oh5mZWZJUoQtL/sKOKZJG5PNuZmaGTqeL9poQCU/Os2nIeTeN5Hzeg4Nh/Hg1n6cxjhyxoEYN1SMouUvO5/3tW/jxR5gzrQYMzAx2j6KONwHQdJgHuDLMozlWlkncLVzT4NQpVYlt9Wq4c+fdcw4ONCnXGNdXf+Frb0CLJnadBq4B5tRo2hvzZPo+pES9+3Zk6PAx6B0eRN8FT9Nh7u9C76Edk+31nxzF5Vwli7t1Ozs7XFxceP78OTt27KBZs2ZYWVlRrlw5/vnnnwjrXr16lZw5c5ooUiGEECLlOX8eypeHsWPBYIDKldXY+sidG95/PHIkVKoEly8nbaypyfHjULo0TJ0KWL/C0lILH4QfwX/Zx4DCM5IuSdI0OHMGhg2DvHlVwYWff1ZJkr09fPWVqsT26BHmS5czs8hAIOr9etjjGYUHYG4pBQUSklUaKwZY91EPImeoYdeMdW8p5JCITJoo7dixg+3bt3Pz5k28vb1xd3enQIECdOzYEYDBgwezatUqfv/9d65du8bs2bPZtGkTPXr0MGXYQgghRIoQGgoTJ0KZMuqeOEMG1WBw8KAqAZ4tW8T1XV3V8qVLwckJTpyAUqXUjb6MyTdecDCMGKESzStXIIuzRqVJXQixfoyDeWbM3kQce2Ie4MrgnGvw7JgENdkvXFBNXAULqjd30iS4cQNsbVUp77VrVQW25ctVRbb/eul4dPRkTc7BZAuImMi5BpizJudgPDp6Jn7snyDPid8zOGgC5v7OEZab+7swOGgCnhO/N1FknwaTNqi/fPmSYcOGce/ePdKnT8/nn3/O+PHjw5vEWrRowfz585k4cSJ9+vShQIECrF27lipVqpgybCGEECLZu3IF2rdXrRoAzZvD/PmQJYt67OEBzZrBgQPg5wcuLlC1KoQVA3V3hy5dYPt2GDRIzY25eDHkyWOCF5OCnD0L7drBuXPqcZs2ULrbrwzeuxYLMwu8O26iZOYyzN1ygOsP/ciTxYUejaombkvSlSvvutVduvRueZo00LChmueoUSOws4t5H6hkqVnIOPZunMXR43uo8FnN/7rbSYtGYvKc+D3jAgcwa+Yijl04R/mixek9tKO0JCUFLZV7+fKlBmgvX76M8tzbt2+1S5cuaW/fvv2oY4TqQzWfmz7ainMrNJ+bPlqoPjTa9fR6vfb8+XNNr9d/1PHiAtDWrVtn9Po+Pj4aoD1//jzRj5VU+4583hPqfRexCw4O1tavX68FBwebOpRPipx300hO512v17Rp0zQtTRpNA01zdNS0P//UNIMh7vsyGDTt9981zd5e7cvWVtPmzFHHSA6S03kPCdG0n37SNEtLda4yZtS0NWs07eyDs5r1T9Yao9F+PvRz0gX077+aNm6cphUvrgIK+7Gy0rSmTTVt+XJN8/eP166T03n/lMh5Txix5QaRpYAhmsmb12Uv+m7vyz3/d3MjuDq4MrP+TDwKJV4TeocOHViyZAmgypynT5+e4sWL06ZNGzp06BBeLMLPz4906dIl6LFHjx7N+vXrOXPmTILuNzaJ8TqEECK1uX4dOnZUrUQA9erBggWqS1186HSqVal2bejUCXx8oGdP8PKCP/6AHDkSLvaU7NIl1Xp38qR63KKFar2zcwqg7O+tCdIH0TBfQ/pX7J+4gdy8qVqNVq+G06ffLbewgLp1VctR06aqX6UQ4oOSRTGHlMrrshctV7eMkCQB+Pr70nJ1S7wueyXq8evXr4+fnx+3bt1i27ZtuLu707dvXxo3bhw+P5Czs3O0VQBTirD5rVL66xBCiMSkaTBvHpQooZIke3v47TfYti3+SdL73Nxg1y745RewsYHdu6FoUZUsacl7TtpEpder8VulS6skyckJli1Tw3wyZ4Ze23px5ckVsqbNypLmSzDTJcJt1927Kojy5SF3bvj+e5UkmZur5GjhQnj4ELZsUX0CJUkSwmiSKEWiaRoBwQEf/PEP9KfPtj5oUUrXEL6s77a++Af6R9w2JPr9afH4S2NtbY2zszPZsmWjdOnSDB8+nA0bNrBt2zYWL14MqJLY69evB+DWrVvodDr++usvKlWqRJo0aShSpAh79+6Nsu9Tp05RtmxZbG1tqVSpUnj1wcWLFzNmzBjOnj2LTqdDp9OFHwvgyZMntGjRAltbW/Lly8fGjRsj7PfSpUs0bNgQe3t7smTJwjfffMOTJ0/Cn69Rowa9evViwIABZMyYkTp16kR5HQBDhw4lf/782Nrakjt3bn788ccUMTO7EEIktDt31P1wjx4QEAA1aqgqd127Rq1q9zHMzKB3bzUGp2JFePUKOneGxo3h/v2EO05Kce0aVK+uxm8FBUGDBqpOQtu26rwvO7eMxWcWY6YzY4XHCjLaZky4g9+/DzNnqvKFOXKoII4fV29SzZqqOcvPD3bsUE2B6dMn3LGF+IRI17tI3oS8wX6i/UfvR0Pj3qt7OE52NGr918NeY2cV+yBKY9SsWZMSJUrg5eVFly5dol1n8ODBzJgxg8KFCzNt2jSaNm3KzZs3yZAhQ/g6P/zwA1OnTiVTpkx0796dTp06cejQIVq3bs2FCxfYvn07u3btAsDR8d1rHDNmDJ6enkyZMoVZs2bRtm1bbt++Tfr06fHz86N69ep07dqVadOm8fbtW4YOHUqrVq3Ys2dP+D6WLFnCd999x6FDh2JMINOmTcvixYvJmjUr58+fp2vXrqRNm5YhQ4Z89DkUQoiUQNNUcYV+/cDfX7X0TJoEvXqp++XEki+farWaNk1Vdtu6VbUuzZ6tChek9vnUDQbVejdkCLx5o1rvZsxQ+UjYa7/69CrdN3cHYGS1kVR3q/7xB374UDVVrVql3oCwv486HVSporrVff45ODvHvh8hhNEkUUqFChYsyLmwcjvR6NWrF59//jkA8+bNY/v27SxcuDBCkjF+/HiqV1cf7N9//z2NGjUiMDAQGxsb7O3tsbCwwDmaD+MOHTrQpo2avnvChAnMmjWL48ePU79+febNm0fp0qWZMGFC+Pp//PEH2bNn5+rVq+TPnx+AvHnz4ukZe5nRESNGhP/fzc2NgQMHsmrVKkmUhBCfBD8/+PZb2LxZPa5YUSVN/32MJjpzcxg8WBVMa99ezVXatq26j583T3U7S41u31YJUdh3e+7uqvuhm9u7dQJDA2m9pjUBIQHUcKvBiGojou5Ir4+53OD7njxRA8JWrYK9e1WWFqZiRZUctWwZtc67ECJBSKIUia2lLa+Hvf7gevtv76fhioYfXG/rV1uplrMaAAaDAf9X/jikdQgvtvD+cROKpmnoYvlKr2LFiuH/t7CwoGzZslyONKNg8eLFw//v4uICwKNHj8jxgZG7729nZ2dH2rRpefToEaC68/n4+GBvH7XF7vr16+GJUtmyZWM9BsCaNWuYMWMG165d4/Xr14SGhuLg4PDB7YQQIiXTNHXP3KMHPH8OVlbw008wcGD099mJrUgROHJEtWSNHavu6Q8cUD2/PJJgSqCkomkqIerfX3U5tLEBT0/1PkRuvRu8czBnHpwho21Glnssx9ws0hvj5QV9+8K998Y3u7qqrnQeHvDsmarFvmqVGgz2/gRW5cqp5OiLL6SShhBJQBKlSHQ6nVFd4OrmqYurgyu+/r7RjlPSocPVwZW6eeqGf0gaDAb0lnrsrOyiJEoJ6fLly+TKlStO20ROrMLmsnr/OcP732TF4P3twrYN285gMNCkSRMmT54cZbuwZAxUghWbo0eP8uWXXzJmzBjq1auHo6Mjf/31F1OnTv1gfEIIkVI9fqxuzNesUY9Ll4YlS1S3N1OytFTzlzZurFqXzp9XPcDatoVZsyClFyy9f1+N99q6VT2uVEm13uXLF3XddZfXMfvEbACWNF9C1rQRJ5bFy0u1AEXuVu7rq05a6dLqBL4/5rZUKZUctWoFcfzbLoT4OFLMIZ7MzcyZWX8moJKi94U9nlF/RtRvkhLZnj17OH/+fHjXuugcPXo0/P+hoaGcOnWKggULGn0MKysr9PGYor106dJcvHgRNzc38ubNG+HnQ8nR+w4dOkTOnDn54YcfKFu2LPny5eP27dtxjkcIIVKK9etVQrRmjar0PGYMHD1q+iTpfaVKwYkTMHy4amVZvly1OIUlGCmNpqnXULSoeg3W1jBlCuzfH32SdPvFbTpt7ATAwIoDaZgvUq8TvV61JEU39jZs2enTKkkqVgzGjYOrV9WyoUMlSRLCBCRR+ggehTxY02oN2Rwi9g12dXBlTas1iTqPEkBQUBAPHjzA19eX06dPM2HCBJo1a0bjxo1p165djNvNmTOHdevWceXKFXr27Mnz58/p1KmT0cd1c3Pj5s2bnDlzhidPnhAUFGTUdj179uTZs2e0adOG48ePc+PGDXbu3EmnTp3ilHjlzZuXO3fu8Ndff3H9+nV++eUX1q1bZ/T2QgiRUjx/Dt98o+blefRI3bQfOwYjR6qWnOTG2hrGj4fDh6FAATUEp1EjNReTv7+pozPeo0eqgefrr9V7ULasylcGDYq+i2OIPoQ2a9vwIvAFn2X7jAm1JkRd6cCBiN3tYrJ4MZw7Bz/8EH1GJoRIMpIofSSPQh7c6nsLn/Y+rPBYgU97H272vZnoSRLA9u3bcXFxwc3Njfr16+Pj48Mvv/zChg0bMI+ls/qkSZOYPHkyJUqU4MCBA2zYsIGMGY0vW/r5559Tv3593N3dyZQpEytXrjRqu6xZs3Lo0CH0ej316tWjaNGi9O3bF0dHxzh1RWzWrBn9+/enV69elCxZksOHD/Pjjz8avb0QQqQE27erxGjZMtVC8/33aq6e0qVNHdmHlS8Pf/+txvTodGoqn2LF1JCb5G7tWtUStm6dar376SeV+BUuHPM2I31GcuTeERysHVj5+UqszK2iruTnZ1wAVtFsK4QwCRmjlADMzcyp4VYjSY+5ePHiCPMXxSS68tqFChWK0P3ufTVq1IiyTcmSJSMss7a2Zk1YJ/kPHOvFixcRHufLlw8vr5gn4o1uTqfo9u3p6RmlMl6/fv1i3K8QQqQU/v6qOMOCBepx/vxqLFKFCqaNK65sbFQJ8ebNoUMHuHkTateGnj1h8mSIQ4/rJPHsmZonasUK9bh4cXXeS5aMfbud13cy6dAkABY0WUDudLmjX/Hff40L5L0xu0II05IWJSGEECKZ8PFRN+hhSVK/fqplJqUlSe+rVk31JPvuO/V4zhwoUQIOHTJtXO/bskW13q1YoVrvhg9X87d+KEl68PoB36z7BoBuZbrxRZEvoq70/LnqwzdqVOw70+kge3ZVKlwIkSxIoiSEEEKYWEAA9OkDNWuquXpy5VLT5kyfDrYJN3uEydjbw9y5sHOnqoR9/brKBwYNgsBA08Xl7w+dO6uKfX5+ULCgKnc+frwabxUbvUHP115f8yjgEcUyF2N6velRV9q6VfXjW75cZWAeHiohijyFR9jjGTNMU+ddCBEtSZQ+IW5ubmiaRskPfUUmhBAiyRw+rFouZs1Sj7t1g7Nn4b85v1OVOnXgwgXo2FEVeps6VY25OnEi6WPZvVuNm/rjD5WnDBigCjZ89plx2086OIndN3dja2nLqparsLG0efekv7+qYNGokcrAChRQb/Tatap0YeQJYl1d1fLUNPmUEKmAJEpCCCGECQQGwpAhUKUKXLum7pV37FCTtaZNa+roEo+jo0pONm0CZ2e4fBkqVoQRIyA4OPGPHxAAvXqp8VJ37kDu3LBvn0rabGw+vD3AwTsHGbl3JACzG8ymUKZC754My8AWLlQZWP/+qv9k+fLqeQ8PuHVL9bNcsUL9e/OmJElCJEOSKBF9EQKResn7LYQwtZMnoUwZNS+Ppr2bqLVuXVNHlnQaN1atS23aqCmGxo9XrTlnzybeMQ8eVOOj5sxRj3v0UMeLy7Cgp2+e0mZtGwyagbbF2tKhZAf1ROQMLKz/5LRpUTMwc3OoUUO9+Bo1pLudEMnUJ50oWf43CcWbN29MHIlISmHvt2VynIRECJGqBQerOZAqVIBLlyBLFtiwQU2d4+Rk6uiSXoYMqlFl9Wr1/7NnoVw5lTSFhibccd6+VeOhqlVT46OyZwdvb5Uw2dsbvx9N0+i0sRP3/O+RL30+5jWah06nU5Up3s/AundXFSyqVUu4FyGESHKfdHlwc3NznJycePToEQC2trbqAy+RGAwGgoODCQwMjNO8QeLjhJ33t2/fEhgYyKNHj3Bycop1rikhhEho589Du3Zw5ox63Lq1uq/OkMGkYSULX3yhcoru3WH9etUNb8MGVZ67UKEPbh6r48dVi92VK+pxx46qSIajY9z3Nev4LDb+sxErcytWtVxFWs1SZWDTpqmmQVdX1eXuU2oaFCIV+6QTJQBnZ2eA8GQpMWmaxtu3b7GxsUnUhExEFPm8Ozk5hb/vQgiR2EJDVRe7UaMgJEQlRnPnQqtWpo4secmSBby81AS7vXurAg+lSqnWpX794t47LTgYxo6FSZNU1z5nZ/j9d9XlLz5O+51msPdgAH6u8zOl7oVCrdJqkBWoyaKmT/80mwaFSKU++URJp9Ph4uJC5syZCQkJSdRjhYSEsH//fqpVqybdvpJQ2HmvXr06NjY20pIkhEgyV66o1ozjx9Xjpk3h11/VTbuISqeDb75RZdK7dIHt21WDzfr1qntinjzG7efsWdV6d+6cetymjaoqGN/Wu1dBr2i9pjXB+mCa529Kr80PYWJFlYFlyaIysCZN4rdzIUSy9cknSmHMzc0T/Qba3Nyc0NBQ0qRJI4lSEgo779bW1pIkCSESXHCInlkb97Pn+AWuhdrTu2kNLMzNmTlTTVwaGKi6ef3yi0oCklOHAr1Bz4E7B/B75YdLWheq5qiKuZnpPyezZVNTEC1YoMp2HzyoJuKdMkV1zzMzUznKvn069u/Php2dDnd31ftt8mQYM0a13mXMqKoIfv55/GPRNI3uW7pz7dk1cti4sHDadXQnN6onv/wSZs+W/pNCpFKSKAkhhBDxNGSRF9Mu9UVvfw9sYesFGHLUFZczM/Hdpco9162rhq24upo42Ei8LnvRd3tf7vnfC1/m6uDKzPoz8Shk+lLVOh107armXurUSVXR7tlTdc/74gsYNw7u3bMAyjJtmmrYsbdXxRoAWrRQSVLmzB8Xx6Izi1hxfgXmmLFy3iPS3/JTidG8eSoQIUSqJRUFhBBCiHgYssiLKbdbore7F2G5wc4X38otsSzuxa+/qu5jyTFJarm6ZYQkCcDX35eWq1viddnLRJFF5eYGu3apFjkbGzVNUffucC9i6Dx8qJIkW1s1zmnt2o9Pki49vkSvLT0B+GmXgUq39NC8OVy8KEmSEJ8AaVESQggh4ig4RM+0S33BToPIXel0Gmg69PV7UKqhG+cemr4r2/v0Bj09tvRAI+qcchoaOnT0296PZgWaJYtueKC62vXurVqXSpSIfWJaJyfVI+5juzi+DXxN6/m1eGsIpM51GHreAf6cDV9/nbz6TwohEo0kSkIIIUQczd1yQHW3i4lOw2D7kM8Wlkm6oBKIhsZd/7scuHOAGm41TB1OBA8exJ4kAdy/DwcOqHlc4+36dfpNqsIF1wdkeQ1LX7hjdnGpGjwlhPhkSKIkhBBCxNH1h35GrWerc8LRziaRo4mbtyFveRH04oPr+b0y7jUmJT8jQzJ2vSgMBpg/n9UL+vNbs2B0GizL2Y8sntOkFUmIT5AkSkIIIUQcGAwaf/teAiN6pY0vvo5+zWskekxxsffWXtyXuH9wPZe0LkkQTdy4GBmSsetFcOcOdO7MjVO76NpNLRpWvAe1PabHY2dCiNRAijkIIYQQRrp85zE5BrXikPk4tSDqMJ//luswf52dHo2qJllsxqqaoyquDq7oogyuimjO8Tk8efMkiaIyTtWqqjBGTI07Oh1kz67WM5qmwR9/QLFiBPvsonUrHf5poHL2yoxpPjNB4hZCpEySKAkhhBBGGLZkPUXmFMHXcQ3oLcj+sjWgAy3SXft/jwcUnoGVZfIohvA+czNzZtZXCUDkZCnssZnOjDWX11BkbhE2XNmQ5DHGxNwcZv6Xu0ROlsIez5ih1jOKn5+aKLZzZ/D3Z9g3Lpx00UiXJh0rPl+BhZl0vBHiUyaJkhBCCBGLm37PyT3wGybdaoFm+xjrl0VZVuMYd6b9xeCcazAPiDjA3zzAlcE51+DZ0fRzEcXEo5AHa1qtIZtDxNhdHVxZ22otx7scp0imIjwKeETzVc1pv749LwJfmCbYSDw8YM2aqHUVXF3Vcg9jTrumwYoVUKQIbNkCVlZsmdiRaW5qcNPi5ovJ4Zgj4YMXQqQo8lWJEEIIEYNxf21n1OnOGBzug8GMCoYh7PhpNA521gB4dvRgXEgzZm3cy57jR6n5WQV6N62RLFuSIvMo5EGzAs04cOcAfq/8cEnrQtUcVcNLgp/69hSj9o5iyuEp/Hn2T3bf2M3Cpgupl7eeiSNXyVCzZuDjE8q2bWdo0KAk7u4WxrUkPX4M332nJloCKFOGe/Mm096nNQB9PutD0wJNEy94IUSKIYmSEEIIEcm9x/7U9hzIP/YLwA4s/fMzt84SutSvEGVdK0tz+jStRl6L1zRsWA3LFJAkhTE3M4+xBLi1hTWTak+iWYFmtF/fnn+f/Uv95fX5tvS3/Fz3Z9Jap03aYCMxN4fq1TUCAnypXr2EcUmSl5earfbxY7CwgB9/JHToYNqurM/Tt08p5VwKzzqeiR67ECJlkK53QgghxHumrfPBbXJxlSRpOkoF9uP+yL+jTZI+BRWzV+RM9zP0Ld8XgN9O/0bx+cXZe2uvaQOLi+fP1USxn3+ukqSiReH4cRg5kp8OT2L/7f3YW9mzquUqrC2sTR2tECKZkERJCCGEAB49D6DE930YeK4m+rS3sXiVi5ml9nJ64nQyOtqaOjyTsrW0ZUb9Gfi098HNyY1bL27hvsSdvtv68ibkjanDi922bSoxWr4czMxg2DA4eRJKlcLnpg8/7f8JgPmN5pMvQz4TByuESE4kURJCCPHJ+3XrYVzHleSczSwACgd0586wc/RpVs3EkSUvNdxqcK77ObqVURMN/XL8F0rOL8nhu4dNHFk0/P2ha1do2BDu34cCBeDwYZgwAayteRTwiLZebdHQ6FiyI22LtzV1xEKIZEYSJSGEEJ+sF68D+eyHIXQ/XoUQh2uYv3ZlfMEdXPSch0sGe1OHlyyltU7L/Mbz2d52O9nSZuPfZ/9SdVFVhnoPJTA00NThKXv2QLFisGCBqhvevz/8/TeULw+AQTPQfn17/F77UShjIWY1mGXigIUQyZEkSkIIIT5Jf+46ifPIMpywmgI6jTyvOnB90HmGt65r6tBShHp563GhxwXalWiHQTPgediTMr+V4dT9U0kTgF6Pbt8+su3fj27fPtDrISAAeveGWrXgzh3IlQv27oVp08DGJnzTaUemsf3adtJYpGFVy1XYWdklTcxCiBRFEiUhhBCflNdvg6k2aiTtD1QgyPESZm+yMDz3Bq79vIicWZxMHV6K4pTGiSXNl7C+9Xoy22Xm0uNLlF9QntF7RxOiD0m8A3t5gZsbFnXqUHbaNCzq1AEXF8ibF2bPVut89x2cOwfVInafPHbvGMN2DwNgRr0ZFMtSLPHiFEKkaJIoCSGE+GSsPXiezMPLc8DsJzDTk/1lay73vsj4b2TenI/RrGAzLva4SKsirdBresbsG0P5BeU5//B8wh/MywtatoR79yIuf/wYHjyADBlg506YOxfsI3affBH4gi/XfkmoIZQvCn/Bt2W+Tfj4hBCphiRKQgghUr3A4FDqjZtIy51leOt0Bt3bDPTLtoo70/4iv2sGU4eXKmS0zciqlqv46/O/SG+Tnr8f/E3Z38sy6eAkQg2hCXMQvR769gVNi3mdNGmgZs0oizVNo+umrtx6cYtcTrn4vcnv6HS6hIlLCJEqSaIkhBAiVdt6/AoZh1Zmp344mIfg/KIZZ7+9yPQurUwdWqrUumhrLva4SNMCTQnWBzNs9zCq/FGFf5788/E7P3AgaktSZL6+ar1Ifj31K2surcHCzIK/Wv6FYxrHj49HCJGqSaIkhBAiVQrVG2g+aTqNNpYiwOk4BDrybcY/8Z26jmK5s5g6vFTN2d6Z9a3Xs7jZYhytHTnme4ySv5ZkxtEZGDRD3Hd49Sr89BN8841x6/v5RXh47uE5+m3vB8CkWpP4LNtncY9BCPHJkURJCCFEqrPnzHUyDKzBhqABYBlIhuf1ON7hAr/2/AYzM+lulRR0Oh3tS7bnQo8L1M1Tl8DQQPrv6I/7EnduPL/x4R3cuAETJ0KpUmoOpJEjP9yaFMbFJfy/AcEBtF7TmiB9EA3zNaR/xf7xfEVCiE+NJEpCCCFSDYNBo83UedT6Xwn80x2AYHu+dvyNR9O2Ua6Aq6nD+yS5Oriyve125jeaj52lHftv76f4vOLMPzkfLfJYo9u3YcoUKFcO8uSB4cPhzBmwsIAGDWDhQsiaVc2NFB2dDrJnh6pVwxf12taLK0+ukDVtVpY0X4KZTm59hBDGsTB1AEIIIURCOHLpDo1/68yzdLvAChyf12BT50VULeZm6tA+eTqdjm5lu1EnTx06bujI/tv7+W7Ld6y7so4FZX8i+7ZDsHo1HD36biNzc1WUoVUraNFCVbMDcHJSVe90uohFHcKSpxkz1LbAsnPLWHxmMWY6M1Z4rCCjbcYkeb1CiNRBvlYRQgiRohkMGp1mLaLSsmIqSQqxwcNmJk+m7pYkKZnJnS43Pu19mF5xDGmwZOf1nRRdUp7Ffw5AO3pUJTvu7jBvnhpntHMndOnyLkkC8PCANWsgW7aIO3d1Vcs9PAC4+vQq3Td3B2BktZFUd6ueVC9TCJFKSIuSEEKIFOvMdT/qzf6WR06bwRrsX1RgTdsl1Cub39ShicgeP4a1azFbtYp++/bRIL1Gh+ZwNDt0bA5eXxTlty+X45y7+If35eEBzZoR6uPDmW3bKNmgARbu7uEtSYGhgbRe05qAkABquNVgRLURifrShBCpk7QoCSGESHEMBo3ev66k9IIiKkkKtaKBxWSeeh6UJCk5efoUFiyAOnVUgYXvvoO9e0HTKJCvAgeK/MzEct9jZW7FppALFFnjzqoLq4zbt7k5WvXq+Farhla9eniSBDB452DOPDhDRtuMLPdYjrmZeSw7EkKI6EmLkhBCiBTl8p3H1JnRA1/HNZAGbF6UZnnLJbSoXNTUoQmAFy9g/XpYtQp27YLQ9yabLVsWWreGL76AnDmxAL4HGpdtS7t17fj7wd98ufZLvK54MafhnHiNKVp3eR2zT8wG4M/mf5I1bdaEeFVCiE+QJEpCCCFSjGFL1jP5Ujc0x0egt6CG+Y9smTgM2zSWpg7t0+bvDxs3quRoxw4ICXn3XMmSKjlq1Qpy545286KZi3KsyzHGHxjP+APjWX1xNXtv7eW3xr/RrGAzo8O4/eI2nTZ2AmBQxUE0yNfgY16VEOITJ4mSEEKIZO+m33Nq/dyHmw7LwBasXxZlYeMltK1Z2tShfbpev4bNm1VytG0bBAW9e65o0XfJUX7jukJamlsyusZomuRvQvv17bn4+CLNVzWnXYl2zKw/E6c0TrFuH6IPoc3aNrwIfMFn2T5jfK3xH/HihBBCEiUhhBDJ3Li/tjPqdGcMDvfBYEYFwxB2/DQaBztrU4f26XnzBrZuVaW8N2+Gt2/fPVeggEqOWreGwoXjfYgyWctw6ttTjNo7iimHp/Dn2T/ZfWM3C5supF7eejFuN9JnJEfuHcHR2pG/Pv8LK3OreMcghBAgiVKS0eth3z4d+/dnw85Ox3vFeYSIll4PBw6oCrkuLmr+RLlmEl9wiJ65Ww5w/aEfebK40KNRVawsU8aJDw7RM2vjfvYcv8C1UHt6N62RcmIPDGbu7D+57udLHpds9OjVjkevAqntOZB/7BeAHVj652dunSV0qV/B1OGmHsZ80AQGqu50q1ap7nUBAe+ey5PnXXJUrFjME8HGkbWFNZNqT6JZgWa0X9+ef5/9S/3l9fm29Lf8XPdn0lqnRW/Qs+/2PvY/38/lI5eZdGgSAAuaLiBXulwJEocQ4tNm0kTp1atX/Pjjj6xbt45Hjx5RqlQpZs6cSbly5aKs261bN3777TemT59Ov379kj7Yj+DlBX37wr17FkBZpk1T0z3MnBk+3YMQEby7Zt4tk2sm8Q1Z5MW0S33R2/934h/AoEOuDCg8E8+OyfvER4jdFrZegKFHU0jswyYxLegX9I5+4AAEwICR3wM6NPsnAJQK7MfOkePJ6Ghr0lhTldg+aBo3Bm9vlRxt2KDGIIVxc1Nd6lq3hlKlEiw5ik7F7BU50/0Mw3YN45fjv/Db6d/YeWMnHUt25PfTv3PP/7/Yb6t/6uauS8vCLRMtHiHEp8Wk5cG7dOmCt7c3S5cu5fz589StW5fatWvj6+sbYb3169dz7NgxsmZNeZVrvLzUBOLv/x0C8PVVy728TBOXSL7kmjGNIYu8mHK7JXq7iCdeb+fLlNstGbIo+Z74FB37sElMsR6O3sEvwnLN7ima3RN0AZmYUXIvpydOlyQpIcX0QXPvHnz+OaRLp5KlpUtVkuTqCgMGwLFjcOMGTJ4MpUsnapIUxtbSlpkNZuLT3gc3JzduvbjFqL2j3iVJ7/G+4Y3X5eR7vQshUhaTtSi9ffuWtWvXsmHDBqpVqwbA6NGjWb9+PfPmzWPcuHEA+Pr60qtXL3bs2EGjRo1MFW686PXqyzpNi/qcpqm/L/36QbNm0qVKKHLNmEZwiJ5pl/qCnQaR7/t0Gmjw8z9deTb7FRZmyWv6uVCDgT98B4B1CoxdH8ofhslANLHz32K9Bd/Vq5jUoaVusX3QhHnzBpyd37UcVagAJr5+arjV4O9v/8Z1uisBIQExrtdvez+aFWgmcycJIT6ayRKl0NBQ9Ho9adKkibDcxsaGgwcPAmAwGPjmm28YPHgwRYoUMWq/QUFBBL1Xecf/v+4CISEhhLxfrjQJ7Nun+6+7XfQ0De7eBR+fUKpXj+UPlvgoYe97Ur//8ZGarpmUdN5nbdz/rrtddHSg2Txj4dMOSRZTnKSJ5bnkHntsjUQ6MDj4MWvmIvoM6JRkIcVHSrredfv2YRG5JSkaoUuWoLm7qwd6vfoxsZO+J2NNkjQ07vrfxeeGD9VzVk/CyD4tKel6T03kvCeMuJw/kyVKadOmpWLFivz0008UKlSILFmysHLlSo4dO0a+fPkAmDx5MhYWFvTp08fo/U6cOJExY8ZEWb5z505sbZO228b+/dmAsh9cr1evl7RocY3SpR9hZWVI/MA+Ud7e3qYO4YOMvWa2bTtDQIDvB9dLDlLCed9z/ELsN+z/sXxahDShmRM/oDgItHhESIaLH1wvOcYeZPaA4EyXP7jesQvn2Lp1axJE9PFSwvWebf9+Iz5l4MyOHfi+X9UuGdj/fL9R6207uI2AizEnVCJhpITrPTWS8/5x3rx5Y/S6Ok2Lre09cV2/fp1OnTqxf/9+zM3NKV26NPnz5+f06dMsXbqURo0acfr06fCxSW5ubvTr1y/WYg7RtShlz56dJ0+e4ODgkNgvKYJ9+3TUqWN8LurgoNGkicYXXxioXVvDSiqbJoiQkBC8vb2pU6cOlpbJe1JKY6+ZtWtDadIk+bcopYTzHhoKnw/cz7YstT+47s9Fd9GnabUkiMp4v2zcz6ALKTD24GB+adeHQcX/+OCqP6eZnyJalFLC9Q7/tSjVqfPB9UK9vdGqJ69WmX2391Fn+Ydj927rLS1KiSglXe+piZz3hOHv70/GjBl5+fLlB3MDk1a9y5MnD/v27SMgIAB/f39cXFxo3bo1uXLl4sCBAzx69IgcOXKEr6/X6xk4cCAzZszg1q1b0e7T2toaa+uoc2tYWlom+UXl7q7Gv/r6Rt8VXKeDzJmhTRtYswbu3dOxfLmO5cvNcHKCFi1U1/CaNUF+Hz6eKa6BuHJ3h0yZ4PHj2Nfr0cMCKys11jq5S87n/Z9/oH17OHa8BvRzBYd7MYyV0WEe4ErvpjWwTGbltns3rcHQo67o7XzVmKTIkmPs585Bu3b0PneOoTld0Ds8iDl2fxd6D+2YbK+hyJLz9R7uQ9+m6nTg6opFMpzHwj23O64Orvj6+6IR9ZrRocPVwRX33O4yRikJpIjrPRWS8/5x4nLuksXIXjs7O1xcXHj+/Dk7duygWbNmfPPNN5w7d44zZ86E/2TNmpXBgwezY8cOU4dsFHNzVWUVohYGCns8dy5Mnw63b8PBg9Cnj5rK4sULWLQI6tdXj7/9FnbvVt9+i9TrxQswxND7MuyayZoVHj6EJk2gUyd4+TLJwks1DAaYMQNKllRFvBzSmlNH5xn9ypo68QMKz0iWcxJZWZozoPB/HzRapA+a5BZ7aCiMHw9ly8LZs1ilT8+AJ/+VLo8p9h05sNq1M4kDTcV27IAvvnj3OKY/TjNmJLskCcDczJyZ9dX1rov0rUbY4xn1Z0iSJIRIECZNlHbs2MH27du5efMm3t7euLu7U6BAATp27EiGDBkoWrRohB9LS0ucnZ0pUKCAKcOOEw8P1VqULVvE5a6uannYnDhmZlC5skqs7t6FvXvhu+9U68LTp/D771C7ttpPjx6wb1+yGFcrEpCmqcTn6VOVHEeuhu/qCmvXwrVrqkqvTqeS6WLFYNcu08ScEt24oVru+vdX82jWqQMXLkCjLx6p1iRDxBss8wBXBudck6znIvLs6MHgnGswD4j4QZOsYr98GSpVghEjICQEmjaFCxfw/HM2g4MmYO7vHGF1c38XBh9ojuelo6pc9fbtJgo8Fdm1S5XMDA5Wf3xWrfrwH6dkyKOQB2tarSGbQ8TYXR1cWdNqDR6Fkm/sQogURjOhVatWablz59asrKw0Z2dnrWfPntqLFy9iXD9nzpza9OnT43SMly9faoD28uXLj4z244SGapq3d4g2YMAJzds7RAsNNW67kBBN27VL07p21bT06TVN3U6rHxcXTevdW9MOHtQ0vT5x40/JgoODtfXr12vBwcGmDiVWM2ao99XKStP+/ltdMz4+mrZihfo38jWzf7+m5c797nro0UPTXr0yQeAxSG7n3WDQtPnzNc3OTp0vOztNmzdPLX8b8lZz+dlFYzTa3GPztenrfLRe81do09f5aEHBRv6yJgNBwaHaz2t2aQ2HjNN+XrMrecQeGqppU6dqmrW1OvGOjpq2ZIk68e8JehukTZ/yu9ZrwGht+pTftaC3QZoWHKxpHh5qO2trTdu50zSvwQjJ7XqPYvduTUuTRp3LZs00LShILf/QB00yFqoP1bz/9dYGLBqgef/rrYXqU07sKV2yv95TKTnvCSMuuYFJE6WkkFwSJU37+As8OFjTtm/XtI4dNc3JKWLS5Oqqaf37a9rRo1HuPz55KeGD5eRJTbO0VO/lrFnGb/fqlUqQwq6D3LlVApUcJKfzfueOptWt++48Vaumadevv3t+9rHZGqPRsk/LrgWFBpku0ASQnM67du2aplWp8u7E162raXfvxm0fQUHqxh7Ujf7u3YkS6sdKVuc9sr17Nc3WVp3DRo00LTDQ1BElmGR93lMxOe+mIec9YcQlN0gWY5SEcSwtoV49+OMPNUZl82b45htIm1ZNpj59upoTMFcuGDIETp2KfT5BkTz4+6uiHSEhqoBHz57Gb2tvD3PmgLc3ZM+uupVVrw4DB0Iyq+prEpoGS5ao7ok7d0KaNOr3xMcHcudW6wSFBjHp0CQAhlUZhpW5lJv8aAaDGoBZvLgafGlvD7/+qrrPubrGbV9WVrB6tapcEhio/t23L3HiTo0OHoRGjVQBh/r1Vbe6aAoeCSGEiEoSpRTKykr97fvzT3j0CNavV9Xz7OxUYYgpU9R46Xz54Icf4OxZSZqSI02D7t3h+nXIkQMWLow6ttoYtWvD+fPQsaPa57RpULo0HD+e8DGnFA8eqOEYHTqoghfly8OZM9CvnxoTGGbxmcXc879HtrTZ6FQqeZegThHu3FHf6PTsqW7Oq1dXVe6+/TZ+FzeoD7w1a6BBA/UNQKNGcOBAwsadGh05os5ZQIAajOflpb4tEEIIYZQ4JUovX75k8eLFdO7cmVq1alGxYkWaNm3KqFGjOHz4cGLFKD4gTRp1Q7hihUqa1qxRRY1sbNQN+IQJqrpXoUIwahRcumTqiEWYP/6AlStVcamVKyFduvjvy9FR7W/TJnB2hitXoGJFlSi/N7XYJ2HVKihSRJ0LS0uYOFF9sR65DkywPpgJBycAMLTyUKwt5Jv2eNM0dQGGVRexsVHVafbsUc3cH8vaWt3o16mjbvwbNgT5uxOzY8dUwvr6tZpjYv169Z4IIYQwmlGJkp+fH127dsXFxYWxY8cSEBBAyZIlqVWrFq6urvj4+FCnTh0KFy7MqlWrEjtmEQtbW1UgavVqlTStXAnNm6t7jH/+gbFj1Q1k0aLw009w9aqpI/50XbwIvXur/48bpwqCJYTGjVUVtzZtVA+oCRPgs89Uq2Jq9+SJ6sb45Zfw7BmUKqW6oH7/PVhEM2vc0rNLufPyDs72znQp3SXpA04t/PxUvfrOnVVf0goVVPNdnz4Rm+8+Vpo0sGGDuvF//Vp1JTt2LOH2n1qcPKmSpFevVIvexo3qj4MQQog4MWrC2RIlStCuXTuOHz9O0aJFo13n7du3rF+/nmnTpnH37l0GDRqUoIGKuLO3VzeMX36p7l02blTftO/YoW7SR45UPyVKqJvLVq0gT56o+wkO0TN3ywGuP/QjTxYXejSqmjzmZEnB3rxR5/ztW6hbV40pS0gZMqgWxs8/V137zp2DcuXU+x1T0pDSbdigenc9eqRa6EaMUK1pMc0rF6IPYfyB8QAMrjQYG0v5tj3ONE19G9OrFzx/rrrI/fSTGiSXWHPw2NiopsJGjdQ8CvXqqRassmUT53gpzd9/qw+Vly+hShU1mNXOztRRCSFEimTUV30XL17k559/jjFJArCxsaFNmzYcO3aM9u3bJ1iAImE4OMDXX6v7i0eP3k1ma2GhWhqGD4e8edW9xpQpapwTwJBFXtgOd6P/WXdmP/iK/mfdsR3uxpBFXqZ9QSlcv34qWXV2VuPMEvJL9/d9/rk6TosWqljEjz+q7nipqfvlixfQvr1qOX30SLWYHjsGo0fHnCQBrDi/gpsvbpLJNhPdynRLomhTkcePVR/ftm1VklS6tGq+GzIk8ScqtbVVH2ZVq6qEoE4dOH06cY+ZEpw9qwYsPn+uftG3blXfmAkhhIgXo27PMmXKFKedxnV9kbScnNQA923b1ID3sMlszcze3ee4uUHWWl5Mud0Svd29CNvr7XyZcrulJEvxtGqVOuc6HSxbBlmyJO7xMmdWE9UuW6be+5Mn1T3tzz+n/EmLd+xQ3UjDks0hQ9TrK1Mm9u1CDaHhrUmDKg3Czkq+cY+TdetURrp2rfq2ZcwYOHpUvRlJxd4etmxRfVZfvFDJ0qfQvzQmFy6oD/Jnz1Tlku3bVUlUIYQQ8Rbv77FfvXrF4MGDKVeuHKVLl6Z37948efIkIWMTSSBDBujSRZWX9vODefOgRg1Ap8evRF9Ag8iFqnSqfN60S/0IDknhd9pJ7Pp16NpV/X/4cKhVK2mOq9OpL/4vXFBFsIKCYPBgNXzh2rWkiSEhvXoF3bqpVlFfX1Xd8eBBmDzZuKJeqy6s4t9n/5LBJgM9yvVI/IBTi+fP1ZwEHh6qRaloUdV8N3Jk7M13iSVtWvWNT/nyKkGoVUuVf/zUXLqkxm09eaK6BWzfrroRCCGE+CjxTpS6du3KkydPGDNmDKNGjeLGjRu0bds2IWMTSSxzZjWexccHRi86AI73oiZJYXQaevu7zN0iJXqNFRysxou9eqWGDowenfQxZMumvoRfsEDdYx46pMaozZ6tCj+kBHv3qul5fvtNPe7TR9UNqFjRuO31Bn14a9KAigOwt5KuSUbZtk0lRsuWqea7779/1zxpSg4OqmmxXDl4+lQlS6mpb+mHXLmikqTHj1X1kp07VdOxEEKIj2Z0ojR9+nS09ybiOXHiBAsWLKBhw4Y0a9aMqVOnckyqD6UaTwL9jFrv+kPj1hMwbJi6r0yXThVaMFVBBZ1OFSc7f17dX715o6rv1anzbmxacvTmjRrb5e4Ot25Bzpyq8vTMmXEr6LX28louP7mMUxonen3WK7HCTT38/VUzaMOGcP8+5M+vMuyJE5PPxKWOjipZKl1aJQw1a6oEIrX791/1Wh8+VN94eHt/3BwDQgghIjA6Ubp27Rrly5fn77//BqBOnTo0atSI+fPnM2vWLNq1a0e9evUSLVCRtPJkcUnQ9T51W7aoSWABFi+G7NlNGg6gEg1vb9WaZGurko5ixdSkt8ltcuIjR9RcYDNnqsddu6pEz909bvsxaAZ+2v8TAP0r9MfBWronxSrsoliwQGXY/fqpqmoVKpg6sqjSpVMXdMmSKnGoWTN1z39w/br6BfDzezd3VYYMpo5KCCFSFaMTpTlz5jBz5kw6derEgAEDmDhxIo0aNcLb25vdu3fzxRdfsHjx4kQMVSSlHo2qYvY2E8R0w6zp0Pln57uGVZM0rpTo3j1VlQ1UN7GmTU0bz/vMzKBnTzUGvnJl1S2wSxc1F9P9+6aOTo2l+v571VXx339V18Ft21S3u/iMU19/ZT0XHl3AwdqBPuX7JHzAqUVAgGpmrFUL7txRE8bu3QvTpyfv+XjSp1fJUrFiKoFwd0+Zg/A+5OZN9dp8faFwYZUkZcxo6qiEECLVidMYpYoVK3LixAnSp09PxYoVcXNzY+3ataxfv57BgwdjI7N+pxqvQl5ga29QY5QiJ0uaGrikbZvBiB/Mk13rQ3ISGqqKKDx9qnoFeXqaOqLo5c0L+/ap0vDW1qqqcNGisHy56VqXTp1S1esmT1bjp775RrUi1a8fv/1pmsbYfWMB6Fu+L05pnBIu2NTk0CHVKjN7tnocNhFXtWomDctoGTPC7t2qKt/9+yqhuHHD1FElnNu31Wu6excKFlStfpkzmzoqIYRIleJczMHCwoIRI0awadMmZsyYQcuWLXnw4EFixCZMRNM0OmzowGv9U9JZuGD2JluE580CM1Ln+Rq47MHPP6vqbZIsRe+nn2D/flXJ+K+/ks+QjuiYm8OgQWo6mrJlVYGzr79WczE9epR0cYSEqEIX5curOaAyZ4b161UJ8I8ZfrHp6ibOPjyLvZU9/Sr0S6BoU5HAQFVfvWpV1Qrj6qrG/cybl/Lm4smUSSVLBQuqJt2wgW0p3d276rXcvq3Giu3Zk/jzCwghxCfM6ETp/PnzfPbZZ6RNm5bKlStjMBjYvXs3DRs2pFKlSsybNy8x4xRJaOaxmWy+uhlrc2v2dN7K2/G3mV7Ch9zW5QCY0HgwO2d6MGuWWn/SJBg1yoQBJ1M+PipRAvj1V1XCOiUoXBgOH1axW1hEnDInsV24oBKkMWPUHE9ffKGSpWbNPm6/77cm9f6sN+lt0idAtKlI2ORTU6aobz06dFDNd3Xrmjqy+MuSRSUS+fOr7oM1a6p/UypfX5Uk3bwJefKo1+YiY0SFECIxGZ0odezYkSpVqnDixAm++OILunfvDkCnTp04duwYBw8epKKx9XlFsnXy/kmGeA8BYGrdqZR0LomVpTn9mtegc+XmAJx7dAaAXr3UkAVQN9Vjx5og4GTq0SPV5U7ToFMn+OorU0cUN5aWMGIEnDihSnE/eQItW6rX9OxZwh9Pr1dd7MqUUbUC0qdXLXCrVyfM0Itt17Zxyu8UdpZ2DKg44ON3mFoEB6s5kCpUUCW1s2SBDRtg0aLUUWLaxUUlFHnyqASjZk3VwpTShI23un5djRfz8VED9oQQQiQqoxOlf/75hx49elCwYEF69+7NzZs3w5/LlCkTy5cvZ8yYMYkSpEga/kH+fLnmS0IMIbQo2CLKRJylXdR8Kafunwpf1q8f/Pyz+v+oUTBhQlJFm3wZDKp4g58fFCoEv/xi6ojir2RJOH5cda80M1NlzYsWVVX8EsrVq6pYw/ffq/v2Jk1UK1Lr1gmz//dbk3qU60FGWxn0DqhxR+XLq2859Hp1wi9eTF7VRhJCtmwqsciVSyUaNWsmj0olxnrwQMX877+qVKWPT/IomymEEJ8AoxOlGjVq8O233/Lbb7/Rtm1bKleuHGWduim5m8YnTtM0um3uxvXn18npmJOFTRei00WcbTYsUbr69Cqvgl6FLx84UHW/A/jhh+RbsCCpTJ0K27dDmjSqRcTOztQRfRxraxg/XpXoLlhQJYCNG6u5mPz9479fg0GV+y5RAo4eVfOGLl6sGjScnRMsfLxveHPM9xg2FjYMrDgw4XacUoWGqm80ypZVM/VmyACrVqkmvNRaXjp7dpVg5Mz5bu6hlDC29tEjVXnwyhX1GvbsUa9BCCFEkjA6Ufrzzz8pXbo0GzZsIHfu3DImKZVZ+PdC/rrwF+Y6c1Z+vpJ0NlFHzWe2y4yrgysaGmcfno3w3NChMG7cu/+HzRn0qTl6VLW+gEoCihY1bTwJ6bPPVKGHAQPUlDp//KGqMO/eHfd9hfWC6tdP1RCoU0eNT2rfXu07oWiaxph9qqW7e9nuZLH/xAe+X7mi6sD/8IOqmtGsmWpFatXK1JElvvdbY/75R12ASVmlJK6ePIHatVWXyGzZVJKUO7epoxJCiE+K0YlSunTp+Pnnn9myZQsTJkzAwUEmaowTvR7dvn1k278f3b59qqtLMnHx0UX6bFNzyoyvOZ6K2WMeaxZd97swP/ygqpWBamVKyV3O4uPFC2jTRn1h36qVmhQ1tbGxUS1m+/ape7Y7d9S9XK9eauqdMHo97NunY//+bOzbpwu/3DVNFbYoVkztw84O5s9XxdUSozeRzy0fDt89jLW5NYMrDTZuI71ezRm0cqX6Nxn9rn5QTJ8zer369qJUKdWX0tFRlRFct+7Tqpr2/viey5dVa82TJ6aOKqpnz9Qv1vnz78ZZ5c1r6qiEEOKTY1SidCeOlYJ8fX3jFUyq5eUFbm5Y1KlD2WnTsKhTB9zc1HITexPyhlZrWvE29C318tRjcOXYbybLuJQB4PSD09E+P3KkKgIA0LcvzJmToOEmW5qmJmq9dUslEL/9lrAtI8lN1apqktoe/w1jmzNHdaE7eDD8cqdOHQumTStLnToWuLmpc1K/vpqWJyBATctz7hx065Z45ypsbNK3Zb7FJa0RFcLCgnd3VxU43N2Tze/qB8X0OTN3LtSoob69CAyEevVU890336TuizQmefKoZMnFRZ2H2rXVRGfJxfPnqon17NmIlfuEEEIkOaMSpXLlytG1a1eOHz8e4zovX77k999/p2jRonilhJuKpOLlpcqFRa605Ourlpv4XPXd1pdLjy/hbO/Mny3+xEwX+yURW4sSqPuusWPVwHxQLQ2//pqgISdL8+er8tmWlmqoh6OjqSNKfPb2KkHy9latQdevqwTq88+jXu737qmEaOdONXZr+nR1r5qYPYn23drHvtv7sDK3YkjlIR/eIJn/rsYqptjv3YOePVUGa2+vstVt29QcSZ+yfPnUBZgli0pI6tRRCYqpvXihSrKfPq3mgtqzRw0MFEIIYRIWxqx0+fJlJkyYQP369bG0tKRs2bJkzZqVNGnS8Pz5cy5dusTFixcpW7YsU6ZMoUGDBokdd8qg16tmlehmY9U0lVX066fGCZibJ3l4f134iwV/L0CHjmUtlpHZ7sOzu4clSpefXOZNyBtsLW2jrKPTqbHioaGqIl737mo+ns6dE/wlJAtnz0L//ur/kyZBuXKmjSephfUQ6tdPFWOIjZUVnDql5mpKbD/tV5NYdS7VGVeHDyQGyfx3NVaxxR7G2loVbsiTJ8nCSvYKFFCJiLu7qklft67K+k1VFt3fXzW5njypauLv2ZM0vyhCCCFiZFSilD59en7++WfGjRvH1q1bOXDgALdu3eLt27dkzJiRtm3bUq9ePYqmppHrCeHAgdjn7NA0NdP6gQOqa0wSuvbsGt9u+haAH6r+QK3ctYzaLmvarDjbO/Pg9QPOPTxHBdcK0a6n06nqd6GhMGOGGq9jbq7msUxNXr9WVZWDgqBRo3cJ06fG0VEVYvhQohQcrMbPJ/b936E7h9h9czeWZpZ8X+X7D29g7O/qV18lv9LMd+9+eG6goCC1niRKERUurKqRuLurBKVePdXsmdRNwq9eQYMGcOyYmkRs167UVQlGCCFSKKMSpTBp0qTBw8MDDw+PxIondfHzS9j1EkhQaBBfrvmSV8GvqJqjKqNqjIrT9qVdSrP1362cun8qxkQJVLI0bZpKlmbPVhOvWljA119/7CtIPnr1UgW0smVTScKnOOQjTHK63MNakzqU7EAOxxwf3sDYoFav/oioTCyJP2dSjKJF3yVLx4+rhGXHDkibNmmO//o1NGwIhw+r1qxdu9SAPyGEECYXp0RJxJGLEYPH47JeAvl+1/ec8jtFepv0rPh8BRZmcbsMyriUYeu/WzntF31Bh/fpdKr6XWioGsfTvr1qWWrTJr7RJx9Ll8KSJe8mYs34ic9jmlwu92P3jrHj+g7MdeYMqzIsYYP68kvIYUTilZTu3FED4z4kiT9nUpTixVWCUquWmjCsYUM1lsvePnGPGxCgJiU7eFC1Ynl7q8qEQgghkgVJlBJT1apq0LSvb8zjBywsknTMw6Z/NjHj2AwAFjdb/OGxG9EIL+jgF31Bh8h0OjXoPzQUFixQLUrm5il76pZ//oHvvlP/HzVKVXD71H3octfp1PNVqyZuHGGtSe1KtCNXulzGbVS1qury9OxZ9M+HBb9sWfIco3TwoOlPfEpXqpRKVGrVUuezUSPYujXxZox+8waaNlV18h0cVJe/smUT51hCCCHixeh5lEQ8mJurWUch5j5ZoaFqfNLQoap0byK6+/IuHTZ0AKBf+X40KdAkXvsJS5QuPr5IYKhxMZuZqep3HTqAwaCGeqxdG6/Dm1xgoBqXFBCgeuv88IOpI0oeYrvcwx7PmJG4ecap+6fY8u8WzHRmDK863PgN166NuepZUgUfX8nhxKcWZcqohMXBAfbvhyZNVEKT0AIDoXlzVbDB3h62b1czOgshhEhWJFFKbB4esGaNGsTyvuzZ1YSP7dqpzMHTU/2RPmVcK01chRpC+crrK569fUYZlzJMqj0p3vvK7pCdjLYZCTWEcuHRBaO3MzNTLUrffKO+BP/yS9iwId5hmMzgwarSXcaMybOBwZRiutxdXdXyxB7eGNaa1LZYW/KmN3KCzrVrVeauaSrzjVw6O6mC/ximPvGpyWefvRuj5OOjKh2+fZtw+w8KghYtVOuVnZ3q4lcx5km+hRBCmI4kSknBwwNu3SLU25uTAwYQ6u0NN2+qjGHJEli/HjJnhkuXoHx51ZcrODhBQxizdwwH7xwkrVVaVrVchbWFdbz3pdPpPjifUkzMzWHRInVfGhoKX3wBmzfHO5Qkt26dKkwBKs/NmtW08SRH/13ueHuHMmDASby9Q7l5M/Hv1c88OMOGfzagQ8cPVY1s5tuwQWXser360sLbWwXv46MGnvn4kCTBJ4SYPmdSQuzJTYUKKoGxs1Njlzw8EqbFPyhITTS2fTvY2qqufVWqfPx+hRBCJIp4JUpLly6lcuXKZM2aldu3bwMwY8YMNqTE5oGkYm6OVr06vtWqoVWvHrEZolkzuHhRDdrR69WMrRUqqMlpEsDuG7sZf2A8AL81+Y086T++RHAZlzIARhV0iMzcXOWHrVpBSIi6b9i27aNDSnS3b6vKfQCDBqniWCJ65uZQvbpGtWq+VK+uJUmr27j94wBoXbQ1BTIW+PAGmzerTD00VGXuf/yhAjc3V91h27RR/6akJsPYPmdE3FSurBIZW1uV2LRsqRKd+AoOVn12t2wBGxt1/cngRiGESNbinCjNmzePAQMG0LBhQ168eIFerwfAycmJGTNmJHR8n46MGWHVKlW9Kn16NQFimTIwcaK6kYunh68f8vW6r9HQ6FKqC18W/TJBwo1rQYfILCxUt7XPP1f3Dy1aqKEByVVIiLpvfvFC9cwZP97UEYn3XXh0gbWX1aC3EVVHfHiDbdvUxRcSom5elyyRpEJEVa3au8Rmyxb17U58WvvDPkA2bIA0aWDjRtXNUwghRLIW50Rp1qxZ/P777/zwww+Yv3djUbZsWc4nUAvIJ611a9W61KSJ+uM6fLjqmvHPP3HelUEz0G59Ox68fkCRTEWY2WBmgoUZliidf3SeYH38uglaWsLKlWpMc1CQaljbvTvBQkxQI0eqqsGOjiqXtbIydUTifWGtSS0Lt6RI5iKxr7xzp8rMg4NVK8GyZSpzFyI6NWqoxCYswfnyS/XZbKzQUGjbFry81AfH+vVQu3ZiRSuEECIBxTlRunnzJqWimefB2tqagICABAnqk+fsrL55XLxYVV86dgxKloTp01XhByP9fPhndl7fiY2FDatarsLW0jbBQszllAunNE4E64O59PhSvPdjaaka0po0UUMAmjSBvXsTLMwEsXMnTPqv9sWCBZDLyIrTImlcfnyZ1RfVRLAfbE3avVtl5GED6leskCRJfFjt2irBsbJSAxXbtjWupT9s7Nv//qc+7Natg3r1Ej1cIYQQCSPOiVKuXLk4c+ZMlOXbtm2jcOHCCRGTAFXWt317uHAB6tRRWcSAAaq7xo0bH9z86L2j/LBHDWj/pcEvH/6WPc7hxb+gQ2RWVuo+omFDVVyqcWM4cCAhovx4Dx6omhsA3burBgiRvIw/MB4NjeYFm1PCuUTMK+7dGzEj/+svdfMqhDHq1VOJjqWl+sD65pvYkyW9Hjp2VM3mFhaq+mDDhkkXrxBCiI8W50Rp8ODB9OzZk1WrVqFpGsePH2f8+PEMHz6cwYMHJ0aMn7bs2VWp2vnzVQWm/fvVLPLz58c4ie3zt8/5cs2XhBpC+bLol3Qu1TlRQivtrBKl+BR0iMzaWlVprltXzU/UsCEcPvzRu/0oer2aHPfRI3XKp00zbTwiqqtPr7LywkoAfqz2Y8wrHjigJhB9+1ZdXP/7n/SfFHHXsKH6oLK0VIl2x47qg0KvR7dvH9n270e3b5/qmtelCyxdqsa+rV6tJpcVQgiRosS5z0nHjh0JDQ1lyJAhvHnzhq+++ops2bIxc+ZMvvwyYQoFiEh0OujWTbUsdeyokqXvvlN93hcuVMnUfzRNo8umLtx+eZs86fLwa+Nf0cU02e1HKpP1v8p3Dz4+UQI1BGD9evVl/+7dUL++qtZcvnyC7D7OJk1Scdjaqu6BNjamiUPEbMKBCRg0A43zNw5v4Yzi0CFVovDNG9UqsHatysyFiI8mTdQHQqtWanzbvXtw7RoW9+5RFtQ3KnZ26hsfc3PVotSihamjFkIIEQ/xKg/etWtXbt++zaNHj3jw4AF3796lc+fEabUQ78mdW83rMn26yiq8vaFoUTWW6b/WpXkn5+F12QtLM0v+avkXDtYOiRZO2I3p2QdnCTXEvzLf+2xs1HjpGjXg1SvVwnTiRILsOk4OHlQFHADmzIGCBZM+BhG768+us+zcMiCW1qSjR1WSFBCgxpmsW6d+d4T4GC1aqATIzEx16bx3L+LzYeN1+/RRJeiFEEKkSHFOlGrWrMmLFy8AyJgxI5kzZwbA39+fmjVrJmhwIhpmZtCvH5w5o+Za8vdXrUzNmnHmwi4G7BgAwOTakymbtWyihpI3fV7SWqXlbehbrjy5kmD7tbVVU4xUrapeXt26cDphGq2M8vSpquRrMKiud+3bJ92xhfEmHpyIXtNTP299Psv2WdQVTpxQLUivXqmxfRs2SLOgSDgtWoCTU+zrrFmjuuYJIYRIkeKcKO3du5fgaOaRCAwM5EByGYH/KShQQI27mDgRrKx4vWMTrRfUI0gfROP8jelXoV+ih2CmM6OUi6qA+LEFHSKzs1PTllSurOYuql1b5YaJTdPUpLL37kG+fDB3rur5KJKXWy9useTsEgBGVhsZdYXTp1WG7e+v5sLZtEll4EIklAMH4Nmz2Ne5ezf5VKYRQggRZ0YnSufOnePcuXMAXLp0KfzxuXPn+Pvvv1m4cCHZsmVLtEBFNCws4Pvv4dQpen6dnqvpDGTzh0UbzdA9fZokISRkQYfI0qaFrVtVw9nz5ypZSuypun75RXX9s7JS46/Tpk3c44n4mXRwEqGGUGrnrk3F7BUjPnnmjLpYXrxQmfaWLSrzFiIh+fkl7HpCCCGSHaOLOZQsWRKdTodOp4u2i52NjQ2zZs1K0OCEcf7Un+bPHM8w03SsXGdGxpsbYU8R+PVXNZtrIkrogg6ROTjA9u2qjsWJE1CrlhqmVSRhq50DcOoUhBVunDpVTV0lkp+7L+/yx99/ANG0Jp0/r5Kk589Vhr1tG9jbmyBKkeq5uCTsekIIIZIdo1uUbt68yfXr18NLgt+8eTP8x9fXF39/fzp16pSYsYpo/PPkH3ps6QHAaPcxVP3fMZVFPHqk+tC3a6duGhNJWEGHv/3+Rm9InL74jo5q0tfSpeHxY6hZEy5fTthj+PtD69aqqm+LFtCzZ8LuXyQcz0OehBhCqOFWg6o5q7574uJFlUk/fQrlyqkMW5oERWKpWhVcXWPum6vTqYqkVatG/7wQQohkz+hEKWfOnLi5uWEwGChbtiw5c+YM/3FxccHc3Dwx4xTRCAwNpNWaVgSEBFAzV02GVx0OZcrAyZMwZIgq/LB0qaqMt317osRQIEMBbC1tCQgJ4N9n/ybKMUCNmfb2Vq08jx6pZOmffxJm35qmJpO9fh1y5FAV12VcUvJ0/9V9fj/9OxCpNenyZXVRPH6sfgd27lQZthCJxdwcZs5U/4/8gRH2eMYMtZ4QQogUKV7lwUGNU9q+fTsbN26M8COSzsAdAzn38ByZbDOxrMUyzM3++4OcJg1MnqxqXOfLB/fvqxLJ336rKoAlIHMzc0o6lwQSvqBDZOnTq2SpWDF48EDdF1+79vH7/eMPVek3bMqTdOk+fp8icUw5NIUgfRBVclShhlsNtfCff9TF8OiRyqR37vxwNTIhEoKHh6psF3l8rqurWu7hYZq4hBBCJIg4Tzh748YNWrRowfnz59HpdGj/zd8TNqmpXkqhJom1l9Yy9+RcAP5s8ScuaaPpB1+xohrYPmyYqlLw++8q0/jjD1UuOYGUdi7N4buHOe13mrbF2ybYfqOTMaOaBNbdXfW0cneHffvUFFPxcfEi9O6t/j9uHFSqlHCxioT14PUD5p+aD6jWJJ1OpzLlmjVV5ly8OOzapTJqIZKKhwc0a0aojw9ntm2jZIMGWLi7S0uSEEKkAnFuUerbty+5cuXi4cOH2NracvHiRfbv30/ZsmXZu3dvIoQoIrv14hadN6oJfodUGkL9vPVjXtnWVnUP8fEBNze4dUvdWPbtC2/eJEg8iV3QIbJMmVSyVLCgKuPt7q5eVly9eaPGJb19qypJDxmS4KGKBDT18FQCQwOp4FqB2rlrw40b6s2/f1+Ny9u1CzJkMHWY4lNkbo5WvTq+1aqhVa8uSZIQQqQScU6Ujhw5wtixY8mUKRNmZmaYmZlRpUoVJk6cSJ8+fRIjRvGeEH0Ibda24WXQSyq4VmBczXHGbVijBpw7p7rfgWphKlkSDh/+6JjCCjqc9juNQTN89P6MkSUL7NkD+fPDnTvqfvnOnbjto39/1aLk7Ax//qmGdInk6XHA4/AW1JHVRqK7fVu96ffuQaFCKnPOlMnEUQohhBAiNYnzraFer8f+v3K7GTNm5P79+4Aq9vBPQo2uFzEasWcER+8dxSmNEys/X4mluaXxG6dNq0qGb9um+tT/+6+qyDR0KAQGxjumQhkLYW1ujX+QPzee34j3fuLKxUUlS3nzqhalsPtmY6xeDb/9psZcL12qEi+RfE07Mo03IW8om7Us9a0Kv8uMCxRQF4G8gUIIIYRIYHFOlIoWLRo+8Wz58uXx9PTk0KFDjB07ltzxHSgijLL92nY8D3sCsLDpQtyc3OK3o/r14cIFVTrcYABPT1Up7FT8ijFYmltSwrkEkPgFHSLLlk31KsydO2JPrNjcuAFdu6r/Dxumpt0RydfTN0+ZfWI2ACOL9EBXs6bKjPPlU0mSs7NpAxRCCCFEqhTnRGnEiBEYDKp71bhx47h9+zZVq1Zl69atzAwrlSoS3P1X92m3rh0APcr2wKPQR1ZTcnKCJUtg/XrInBkuXYLy5WHUKAgOjvPuSju/636X1Fxd3w3BunZNJUt+ftGvGxysxiX5+0PlyjBmTJKGKuJhxtEZvA5+TckMRWjcYYLKdHPnVklS1qymDk8IIYQQqVScq97Vq1cv/P+5c+fm0qVLPHv2jHTp0oVXvhMJS2/Q87XX1zx+85gSWUowtd7UhNt5s2YqY+jZU/VHGzsWNm1SSVSxYv8FoIcDB1T24eKiuutFGqxcJmsZOJV0BR0iy5FDJUvVq8PVq2reUR8fyJhOz/5Z57iw5xX2186x+U4pTp40I106WLECLOL8G5B09AY9B+4cwO+VHy5pXaiao+q7EvCfiOdvn/PL8V8AGLn+Obp/76uM2MdHZchCCCGEEIkkQYavp0+fngcPHtCrV684bffq1Sv69etHzpw5sbGxoVKlSpw4cQKAkJAQhg4dSrFixbCzsyNr1qy0a9cufEzUp2TCgQn43PLBztKOVS1XkcYiTcIeIGNGWLUK/vpLlVb++2/VFW/iRPjf/9SNqbs7fPWV+tfNDby8IuwirKDDqfunwkvGJzU3N9XI4Oqq5h8tVzSAHDaPqT2oDCO2fk3tQWWY8Yu65BcvVslVcuV12Qu3mW64L3HnK6+vcF/ijttMN7wue31441Tkl2O/4B/kT7EX1jTbc/9dRpyc3zwhhBBCpApxSpQuXbrEnDlz+O2333jx4gUAT548oX///uTOnZs9e/bE6eBdunTB29ubpUuXcv78eerWrUvt2rXx9fXlzZs3nD59mh9//JHTp0/j5eXF1atXadq0aZyOkdLtv72f0ftGAzC30VwKZCyQeAdr3VqVgWvSBEJCYPhwaNUqaoUEX19o2TJCslQkUxEszSx5Hvic2y9vJ16MH5Anj0qW0tkHcfeJHfcN0Q3y1wg9eDTJYzOW12UvWq5uyT3/iOfd19+XlqtbfjLJ0svAl8w4Oh2AH3cGYZbtvT6WQgghhBCJzOhEafPmzZQqVYrevXvTvXt3ypYti4+PD4UKFeLMmTP873//49KlS0Yf+O3bt6xduxZPT0+qVatG3rx5GT16NLly5WLevHk4Ojri7e1Nq1atKFCgABUqVGDWrFmcOnWKO3GtA51CPXnzhK/WfoVBM9CuRDvalWiX+Ad1doYNG9SktDF1pQxrMerXT3XLA6wtrCmWRXXVM8U4pfflzqnH+s1LQAOivgYdGv2mZUcfnPwmR9Yb9PTd3heNqK1yYcv6be+H3pD8Yk9os/d58iLoJYUfwecvXN5V7RBCCCGESAJGj9AYP3483bt3Z/z48fz2228MGjSI7t27s3btWqpVqxbnA4eGhqLX60mTJmI3MhsbGw4ePBjtNi9fvkSn0+Hk5BTjfoOCgggKCgp/7O/vD6iufCEhIXGOMyGFHd+YODRNo/269vi+8iV/+vzMqDMjSePXZc+ORWxd6DQN7t7F8NlnkC4dAKXyPuC0C5z8eSDNb89Nokij2ncnNw8Mv8X4vIYZd/XZ2DvrFNX6FE/CyD5s3+19UVqS3qehcdf/Lj43fKies3oSRhZ3cbneI3v14A7T9k8GKxh+zhH9Tm/0OXOqlk4Rq4857yL+5Lybhpx305Dzbhpy3hNGXM6fTjNyQImTkxPHjx8nf/78hIaGkiZNGjZt2kSDBg3iHWilSpWwsrJixYoVZMmShZUrV9KuXTvy5csXZU6mwMBAqlSpQsGCBVm2bFmM+xw9ejRjoilltmLFCmxtbeMda1Lb+Ggjf9z/A0udJZ75PcllkytJj59t/37KTpsWp23ml4XvGkP9f2Hb8kQKzAgr+ZKvWPnB9cY1XEbRb9MmQUTG2/98P9Nuf/i8D8g5gGrp4v4FRUpg8fo1J1b3YUzpZ+R7ZsbsfDN4m13GJAkhhBDi471584avvvqKly9f4uDgEOu6RidKZmZmPHjwgMyZMwOQNm1azpw5Q548eeId6PXr1+nUqRP79+/H3Nyc0qVLkz9/fk6fPh2hG19IyP/bu/PwqOrz/ePvyWQlJGHNRkLYF9k3RXZUcAFFo0VBkYpVKFpBVARXtMUFWwTxV0HtF3ChqIAttQqCQFgEypIgO2UNCQlhT0gg6/n9MU0kIUCGzMyZmdyv68qlmTk585ynUyd3zvk8J5/f/OY3JCcns2rVqqseVHlnlGJjYzl58uQ1m+Fs+fn5LFu2jH79+uHnd+UbxW5J20Kvub3IL8rng9s/YFSnUS6s0saSkIBvv37X3K7wxRcxWrYE4D+5B+me9iZ1fUJIjf3AtCmICUtzue3vT15zu+V/ds8zSv2+vHbflz28zCPOKFXk/V7KuXNcvPt2GvXcyslg+FuHNxl25wTnFuplrqvvUmnquznUd3Oo7+ZQ3x0jMzOTOnXqVCgo2TUcedeuXaSnpwO2S8P27t1LdnZ2qW3atq34L56NGzcmISGB7OxsMjMziYqK4sEHH6Rhw1/PnuTn5zN48GAOHTrEihUrrnlAAQEBBAQEXPa4n5+f27yprlbLuYvnePgfD5NflM/9Le/n6ZueNidw9O1rGx+XmvrrmqRLWSwQE4N18uSSUeEd8i9gfXsyJ4qyOB5/GzGh5oxv7jOkkJivj5FaGIlRzjI8C0XEkEKfunuw+na88losE/Rt1JeY0BhSM1PLXacE4IMPpy6ecpv387VU+P97mZlw9918YLGFpEbBMTw6cCK+Pm48w92NudN/86oS9d0c6rs51HdzqO+VY0/v7Jp6d+utt9K+fXvat29PTk4OAwcOpH379nTo0KHkn9cjODiYqKgozpw5w9KlSxk0aBDwa0j673//y/Lly6ldu/Z17d9TGIbByO9GcvDMQeLC4vj0nk/NuzeV1QrFNxAuW0Px99OmlbqfUpBfEK3CWwHmDnSw+luZPs428MNCUannir+fxliswx+xTe/LyHB5jVdi9bEy/Y7yb9xs+d9giiKKeGjhQzy04CFO5ZxyZXnOc/483HUXOVs28F4P23G+fOsbCkkiIiJimgoHpUOHDnHw4EEOHTp02Vfx4wcPHrTrxZcuXcqSJUs4dOgQy5Yto2/fvjRv3pzHHnuMgoICHnjgATZv3syXX35JYWEh6enppKenk5eXZ/eBeoK/Jf6Nr3Z+ha+PL/MfmE+NwBrmFhQfDwsWQL16pR+PibE9Hh9/2Y9cej8lM8VP6cqCF/5DPWt6qcdjrGksGLee+Dc72O42u2gRtG592X2hzBTfMp6vH/i6JBgViwmN4asHvuKVnq9gtVj5audXtPprKxbvXWxSpQ6SnQ0DBsC6dXzSPYjjwQZxYXEMazvM7MpERESkCqvwn2vj4uIc/uLnzp1j4sSJpKSkUKtWLe6//34mT56Mn58fhw8fZvFi2y+A7du3L/VzK1eupE+fPg6vx0w7M3byzA/PADD5lsl0jelqckX/Ex8PgwbBmjWQlgZRUdCzZ6kzSZfqGNmROcxha7q5I8LBFpYG/amQVTO2sGHFbrre0pI+f2iP1b8e0B0GDoThw2H7drj/ftsNdWfMsN1012RNajfBwCDIN4iP7/6YmNAYetbvidXHyuBWg7mn+T0M/8dwdp/czaD5gxjebjjT7phmfri2V06O7b5dq1dzsWYI794eALkXeKnnS/hZdVmBiIiImMeuS+8cbfDgwRw4cIDc3FzS0tL48MMPCQsLA6BBgwYYhlHul7eFpJz8HAYvGMyFggvc3vh2nu/2vNkllWa1Qp8+MGSI7Z9XCEkAnaI7AeafUSpm9bfS65m2tH4yhF7PtMXqf0ntHTrApk22G+v6+MC8ebazS99/b17B/5NwOAGA3g1680jbR+jToA9Wn19r71KvC1tHbuX5m5/HgoW52+bS5qM2/HjgR7NKtt+FC7YQvnIlhITwt5lPkpZ7ktjQWIa3G252dSIiIlLFmRqUxGbMD2PYdWIXkdUj+ey+z/CxeO7/LO0i2uFj8SHtfBppWWlml3NtAQEweTL8/DM0b247azZgADz+uG24gEkSjvwvKF1lsl2gbyDv9X+P1Y+tpnHNxqRkpnD7F7cz6rtRZOVmuarU63Pxou1s5fLlUL06ud8v5p3UrwCY0GMCAb6XD2QRERERcSXP/Y3cS8zfMZ9PEz/FgoUv7vuC8OBws0uqlGD/YFrUaQFAYnqiydXY4aabIDERnn3WNqzi//4P2rSBn35yeSlFRhGrj6wGrh6UivWo34Nto7bxdJenAZi1ZRbtZrYrOSvldnJzbUM0liyBatXg+++ZE7iXlMwUokOiGdFhhNkVioiIiCgomWn/6f08+S/b/X5e7vkytza61eSKHMNdBjrYLSgIpk6FVaugYUNITobbboOnn7YNHHCRXSd2cerCKar5VaNzdOcK/UywfzAz7prB8mHLqR9Wn0NnD9F3bl+eXfIsF/IvOLliO+TlweDB8O9/2/r973+T1+0m3lr7FgAvdn+RQN9Ak4sUERERUVAyTW5BLg8teIisvCx61u/J631eN7skh+kYaQtK7jDQ4br06gW//AK//73t+//3/6BdO1i3ziUvX3wmqFtsN7sHGtza6Fa2/347j3d4HAODaRun0X5WezakbHBGqfbJz4eHHoLFiyEwEP71L+jTh8+3fU7yuWQigiN4ouMTZlcpIiIiAlxHUDp+/DjDhg0jOjoaX19frFZrqS8pX2FRIQlHElh9ZjUJRxIYv2w8W9K2UCuoFvPun+dV94txt4EO16V6dfjrX+HHH23j0A8csE37e/552/oaJ6rI+qSrCQ0I5dN7PuXfQ/9NVPUo9p3aR/f/687E5RPJLch1ZKlXVliIJSGBeqtXY0lIsF1uN3QofPutbV3YP/8Jt95KfmE+k9dMBmB89/EE+QW5pj4RERGRa7D7t/Pf/va3JCcn8+qrrxIVFWXeDVE9yKLdixizZAwpmSkATD0yteS5OYPmEBMaY1ZpTtE+sj0ARzOPciL7BHWD65pbUGX06wc7dtjWLs2eDX/5i20q3ty50KWLw1/OMIxKB6VidzW9ix2jd/DMD8/w5fYveWfdO3z33++Ye+/ckssjnWLRIhgzBt+UFDqD7XLGoCDblDt/f1tY6t8fgHnb53Ho7CHqVqvLyE4jnVeTiIiIiJ3sDkpr165lzZo1l93bSMq3aPciHvj6AQyMcp/PL8p3cUXOFxoQSrPazdh3ah+J6Yn0b9zf7JIqJyzMNtwhPh6eeAJ274abb4YJE+C112y//DvI3lN7ycjOINA3kBvr3Vjp/dUKqsUX8V9wf8v7GfndSHZk7OCmT2/ilZ6vOOdeRYsW2QY1GGXe7xf+t07quefgzjsBKCgqKDmb9Hy35wn2D3ZsLSIiIiKVYPeld7GxsRhlfwmSchUWFTJmyZgrhiQLFsYuGUthUaGLK3M+jx3ocDUDB9rOLg0ZAoWFtrHiN94I27Y57CWK1yd1jenq0BHZ97W8j52jd3J/y/spKCpgUsIkuv6tKzszdjrsNSgshDFjLg9Jl/riC9t2wFc7vuK/p/9L7aDajO4y2nF1iIiIiDiA3UFp2rRpTJgwgcOHDzuhHO+yJnlNyeV25TEwOJp5lDXJa1xYlWt4/ECHK6ld23Zj2m++gTp1bCGpSxdbaCooqPTuHXXZXXnqBtflm998w7z4edQMrMnWtK10/Lgj76591zFhfc0aSLny+x2Ao0dhzRoKiwr505o/ATDu5nFU969e+dcXERERcSC7g9KDDz7IqlWraNy4MSEhIdSqVavUl/yqojdc9Ygbs9qpeKDD1jQvC0rFHnjAdnbp3ntt09xeeQW6dbNdlnedHLk+6UosFgtD2gxhx+gdDGg6gLzCPCb8NIGes3uy79S+69/xxYu2KXYVkZbGgl0L2HNyDzUCa/D0jU9f/+uKiIiIOInda5SmTZvmhDK8U1RIlEO38yQdIjsAcPDMQc5cOEPNoJomV+QEERG2NTlffgl/+ANs2gQdOtjOLo0dC3ZOgTxw5gDHso7hb/Wna0xX59T8P9Eh0fxryL+YkzSHMUvGsD5lPe1ntued297h6RufxsdSgb+h5OXBsmXw1Ve2KXaZmRV67aLICP64+hkAnu36LKEBoZU5FBERERGnsDsoDR8+3Bl1eKWe9XsSExpDamZqueuULFiICY2hZ/2eJlTnXDWDatKwRkMOnT1EYnoitzS8xeySnMNigUcegb594Xe/gyVLbCPE//EPmDMHGjeu8K6K1yfdWO9Gl4zJtlgsPNbhMW5tdCuPL36c5QeXM2bJGL7d8y2zB82mQY0Gl/9Qfj6sWGELR99+C2fP/vpcvXpw7hycP3+lF4SYGL6te4qdq3cSGhDKMzc944xDExEREam067rhbGFhIQsXLuRPf/oTkydP5ttvv6Ww0PsGElSW1cfK9DumA7ZQdKni76fdMQ2rj3fef8or7qdUUfXq2caGf/KJ7R5Ma9dC27a2ezEVFVVoF86+7O5K6ofV58dHfuSvd/2Van7VWHV4FW0+asPHWz62DW4pKICffoInn4SoKLjjDtuo9LNnbd8/84ztZrzJybax6RaL7etS//u+6P2pvLnWtjZpzE1jqBFYw6XHKiIiIlJRdgel/fv307JlSx599FEWLVrEggULeOSRR2jVqhUHDhxwRo0eLb5lPAsGL6BeaL1Sj8eExrBg8ALiW8abVJnzee1AhyuxWGxnlbZvt51hysmBp56y3TMoOfmaP25WUALb2aXfd/k9v4z6hZ71e3I+7zwjvxvJnS/HkdIsEm67zRYCT52C8HAYPRoSEmzDGaZPt63P8vGxjVBfsMAWHC8VEwMLFvCvVn78cvwXqvtXZ2zXsS4/ThEREZGKsjsoPfPMMzRu3JijR4+ydetWEhMTSU5OpmHDhjzzjC6jKU98y3gOjznMsoeXMS5uHMseXsahMYe8OiRBFRjocCUNGsDy5fDBB7Ybrf70E7RubbsX0xVGZx8+e5jkc8n4+vjSLbaba+stVlRE493prExqx19+DiGgAJYGHKX1g6f4rHt1jCefsB1Xair8v/8HvXqVvw4rPh4OH6Zg2TI2jxtHwbJlcOgQxn338cfVfwTgDzf+gVpBGv4iIiIi7svuoJSQkMCUKVNKTbirXbs277zzDgkJCQ4tzptYfaz0jutNr5q96B3X22svt7tU8UCHfaf2kZlbsYX+XsPHxzbgYds2281ps7Lg8cdt92I6duyyzYvXJ3WO7uzaG68aBmzcaLsRbFwc9OiB9YMPGfdjFklfhnBjbh3OBcLwfue5r28Gx29qDb4VWNpotWL07k1qr14YvXuD1coP+39gS9oWgv2CGXfzOOcfm4iIiEgl2B2UAgICyMrKuuzx8+fP4+/v75CixDvUDa5LbGgsAEnpSeYWY5amTW33F5oyBfz9beuYWre23YvpkrNLLr3szjBgyxZ48UVo1Ai6doWpU233QAoJgWHD4LvvaLHnJOv+lMbkWybj5+PHP/f+k1Z/bcU3O7+5jpc0eDPhTQBGdxlNnWp1HH1UIiIiIg5ld1AaOHAgTz75JBs3bsQwDAzDYMOGDYwaNYp77rnHGTWKB6tSAx2uxGqFF16ArVuhUyc4cwYefth2L6aMDMAFQckw4Jdf4OWXoVkz6NzZFt4OH4bgYBgyxDapLyMDPvsMBgwAf398fXx5qedLbH5yM+0j23PqwikGLxjMkIVDOJVzqsIvv+zgMjambiTIN4jnbn7OOccoIiIi4kB2B6UPPviAxo0bc/PNNxMYGEhgYCDdu3enSZMmTJ8+3Rk1igercgMdrqZVK1i/Ht5803b52qJF0Lo1KV99wsEzB/Gx+NC9fnfHvuauXfD663DDDdCuHbz1Fuzfb1s79Zvf2AYvnDhhO8M1aBAEBpa7m7YRbdn4u4282utVrBYr83fMp/VHrfnX3mvfZNYwDN5IeAOAkZ1GElE9wqGHKCIiIuIMdt9HqUaNGvzzn//kv//9L3v27MEwDG644QaaNGnijPrEw1XZgQ5X4ucHr75qW6s0fDhs307C5CfhfuhYt51jbr66b5/tPkdffw07dvz6eEAA3HUXPPig7YxR9ep27dbf6s+bfd/knub38Oi3j7L75G7umX8Pv23/W6bdPo2wwLByf27VkVX8fPRnAqwBvND9hcocmYiIiIjL2B2UijVt2pSmTZs6shbxQh2jbGeU9pzcQ3ZetmsHFbizDh1g0yZ4800Str8FQO9l+yDue1uYASgstK1vSkuz3a+oZ8/yp8wBHDz4azhKSvr1cT8/232PBg+Ge+6B0MoHsc7Rndk6ciuvrXyNP//8Z+YkzWH5weX87Z6/0b9xf1vpRYUkHElg9ZnVLF++HIAnOj5BdEh0pV9fRERExBUqFJTGjRvHH//4R4KDgxk37urTqqZOneqQwsQ7RFaPJKp6FGnn09h2fJt5o6/dUUAATJ5MwntfQE4yvXdm2870PP449OkDEyfaBiwUi4mx3bMo/n9j5Y8csQWjr7+GzZt/3c7XF/r1s4Wje++FGjUcXnqgbyBT+k1hUPNB/Pafv2X/6f3c/sXtjOo0ih5xPZiwfAIpmSmlfqZtRFuH1yEiIiLiLBUKSomJieTn55f8u4g9OkV34rt937Hl2BYFpTLSstLYl5OMBQs9B/4e3v8I/vY321dZqam2ARC//S3s3g0bNvz6nNUKt9xiC0f33Qe1a7uk/u71u5M0MomJP01kxn9mMHPLTGZumVnutiO/G0ntarW9/v5hIiIi4h0qFJRWrlxZ7r+LVETHyI58t+87DXQox+ojqwFoF9mOGq//P7j7N3DbbbbL7soqHic+e7btnxYL9O5tW3N0//1Qt66Lqi4t2D+YD+78gLub3c2dX95JoVFO7f8zdslYBjUfVCXuIyYiIiKeze6pdyNGjCj3PkrZ2dmMGDHCIUWJd9FAhysrdyx4eSGprGeesZ1hWrkSRo0yLSRdys/qd9WQZGBwNPMoa5LXuLAqERERketjd1CaO3cuFy5cuOzxCxcu8NlnnzmkKPEuxQMddmbs5EL+5e+dquyyoJSWVrEf7NrVNuDBjaRlVaz2im4nIiIiYqYKT73LzMwsucFsVlYWgZfcb6WwsJDvv/+e8PBwpxQpnq1eSD3qVqvLiZwTbM/Yzo31bjS7JLdwIvsEu07sAqBnXE/bgxUNP24WkgCiQipWU0W3ExERETFThYNSjRo1sFgsWCwWmjVrdtnzFouFN954w6HFiXewWCx0iu7Ekv1L2Jq2VUHpf4rXJ7UOb02danVsD/bsaZtul5r665qkS1kstud79nRhpRXTs35PYkJjSM1MxeDy2i1YiAmNoWd996tdREREpKwKB6WVK1diGAa33HILCxcupFatWiXP+fv7ExcXR3S07pEi5esY2ZEl+5ew5dgWs0txG+WuT7JabSPAH3jAFoouDUsWi+2f06Zd+X5KJrL6WJl+x3Qe+PoBLFhKhSULttqn3TFNgxxERETEI1Q4KPXubftl7tChQ8TGxuLjY/fyJqnCitcpafLdr8oNSmC7T9KCBTBmzOX3UZo27df7KLmh+JbxLBi8gDFLxpS6j1JMaAzT7pim0eAiIiLiMSoclIrFxcUBkJOTQ3JyMnl5eaWeb9tWN5WUyxVPvtt+fDu5BbkE+AaYXJG5Tl84zfbj2wHoFdfr8g3i42HQIFizxjbgISrKdrmdG55JKiu+ZTyDmg9i5cGV/LD2B+7scSd9G/XVmSQRERHxKHYHpRMnTvDYY4/xww8/lPt8YUVGG0uVExcWR83Ampy5eIadJ3aWnGGqqtYcWYOBQYs6LYioHlH+RlYr9Onj0rocxepjpXdcb7J3ZtM7rrdCkoiIiHgcu6+fGzt2LGfOnGHDhg0EBQWxZMkS5s6dS9OmTVm8eLEzahQvUDzQAXQ/JbjKZXciIiIi4hbsDkorVqzg/fffp0uXLvj4+BAXF8cjjzzClClTePvtt51Ro3iJjpG2s0ga6KCgJCIiIuLu7A5K2dnZJfdLqlWrFidOnACgTZs2bN2qMwVyZRroYHPu4jmS0pMA6N1AQUlERETEHdkdlJo3b87evXsBaN++PbNmzSI1NZWZM2cS5YY3wRT3UXzp3bb0beQX5ptcjXnWJq+lyCiiSa0mRIdopL6IiIiIO7J7mMPYsWNJS0sD4PXXX+f222/nyy+/xN/fnzlz5ji6PvEijWo2IjQglMzcTHaf3E3biKo5IVGX3YmIiIi4P7uD0sMPP1zy7x06dODw4cPs2bOH+vXrU6dOHYcWJ97Fx+JDx6iOrDq8iq1pWxWUFJRERERE3Fal7xpbrVo1OnbsqJAkFVLVBzpk5WaVHLvWJ4mIiIi4L7vPKBUWFjJnzhx++uknMjIyKCoqKvX8ihUrHFaceJ+qPtDh56M/U2gU0qBGA+qH1Te7HBERERG5AruD0pgxY5gzZw4DBgygdevWWCwWZ9QlXqp4oENSehKFRYVV7kakuuxORERExDPYHZTmz5/P119/zV133eWMesTLNa3VlGC/YLLzs9l7ai831L3B7JJcSkFJRERExDPYvUbJ39+fJk2aOKMWqQKsPlbaR7YHYGta1br8Lic/h02pmwCtTxIRERFxd3YHpeeee47p06djGIYz6pEqoFOU7fK7qjbQYf3R9eQX5RMTGkPDGg3NLkdERERErsLuS+/Wrl3LypUr+eGHH2jVqhV+fn6lnl+0aJHDihPvVFUHOlx62Z3W9omIiIi4N7uDUo0aNbjvvvucUYtUEcUDHRLTEikyivCxVHpKvUfQ+iQRERERz2F3UJo9e7Yz6pAqpEWdFgT6BpKVl8X+0/tpVruZ2SU53cWCi2xM2QhAr7heJlcjIiIiItdSNf6UL27F18eXdhHtgKoz0GFjykZyC3OJCI6oEsFQRERExNNV6IxSx44d+emnn6hZsyYdOnS46vqKrVurxi++UjmdojqxMXUjW45t4aHWD5ldjtOVXHbXQOuTRERERDxBhYLSoEGDCAgIAODee+91Zj1SRVS1gQ5anyQiIiLiWSoUlF5//fVy/13kehUPdNiathXDMLz6LEteYR7rj64HFJREREREPIXda5Q2bdrExo0bL3t848aNbN682SFFife7oe4N+Fv9OXvxLIfOHjK7HKfafGwzFwouUKdaHW6oe4PZ5YiIiIhIBdgdlJ566imOHj162eOpqak89dRTDilKvJ+/1Z824W0A7x/okHDYdtldr7heXn3mTERERMSb2B2Udu3aRceOHS97vEOHDuzatcuufWVlZTF27Fji4uIICgqiW7dubNq0qeR5wzCYNGkS0dHRBAUF0adPH3bu3GlvyeKmOkXZLr/bcmyLyZU4l9YniYiIiHgeu4NSQEAAx48fv+zxtLQ0fH3tuy3T7373O5YtW8bnn3/O9u3b6d+/P7fddhupqakATJkyhalTp/Lhhx+yadMmIiMj6devH1lZWfaWLW6oKgx0KCgqYN3RdYCCkoiIiIgnsTso9evXj4kTJ3Lu3LmSx86ePctLL71Ev379KryfCxcusHDhQqZMmUKvXr1o0qQJkyZNomHDhnz00UcYhsG0adN4+eWXiY+Pp3Xr1sydO5ecnBzmzZtnb9nihkqC0v8GOnijrWlbOZ93npqBNWkT0cbsckRERESkguw7BQT85S9/oVevXsTFxdGhQwcAkpKSiIiI4PPPP6/wfgoKCigsLCQwMLDU40FBQaxdu5ZDhw6Rnp5O//79S54LCAigd+/e/Pzzz4wcObLc/ebm5pKbm1vyfWZmJgD5+fnk5+dXuD5nKH59s+twFy1qtcDXx5eTOSc5eOog9cPqO+V1zOz7ioMrAOge253CgkIKKXR5DWbR+90c6rs51HdzqO/mUN/Nob47hj39sxjX8af87OxsvvzyS7Zt20ZQUBBt27ZlyJAh+Pn52bWfbt264e/vz7x584iIiODvf/87jz76KE2bNmX27Nl0796d1NRUoqOjS37mySef5MiRIyxdurTcfU6aNIk33njjssfnzZtHtWrV7DtQcbqxe8Zy+OJhJjSYQNcaXc0ux+H+dPBPbM7czGPRjzEofJDZ5YiIiIhUaTk5OQwdOpRz584RGhp61W3tPqMEEBwczJNPPnldxV3q888/Z8SIEdSrVw+r1UrHjh0ZOnQoW7f+umal7JSwa91zZ+LEiYwbN67k+8zMTGJjY+nfv/81m+Fs+fn5LFu2jH79+tkdKr3VP4x/MGfbHCz1LNzV+y6nvIZZfS8sKuTR9x8FYOTtI0suNawq9H43h/puDvXdHOq7OdR3c6jvjlF8tVlF2B2U3n77bSIiIhgxYkSpx//v//6PEydO8OKLL1Z4X40bNyYhIYHs7GwyMzOJioriwQcfpGHDhkRGRgKQnp5OVFRUyc9kZGQQERFxxX0GBAQQEBBw2eN+fn5u86Zyp1rM1jm6M3O2zWFbxjan98TVfd+etp3M3ExCA0LpHNMZq4/VZa/tTvR+N4f6bg713RzquznUd3Oo75VjT+/sHuYwa9YsWrRocdnjrVq1YubMmfbuDrCdoYqKiuLMmTMsXbqUQYMGlYSlZcuWlWyXl5dHQkIC3bp1u67XEfdz6UAHb1N8/6Qe9XtU2ZAkIiIi4qnsPqNU9gxPsbp165KWlmbXvpYuXYphGDRv3pz9+/fzwgsv0Lx5cx577DEsFgtjx47lrbfeomnTpjRt2pS33nqLatWqMXToUHvLFjfVLrIdPhYf0s+ncyzrGNEh0df+IQ+h+yeJiIiIeC67g1JsbCzr1q2jYcOGpR5ft25dqaELFXHu3DkmTpxISkoKtWrV4v7772fy5Mklp8TGjx/PhQsXGD16NGfOnOGmm27ixx9/JCQkxN6yxU1V86tGyzot2XliJ1vTtnpNUCoyiliTvAZQUBIRERHxRHYHpd/97neMHTuW/Px8brnlFgB++uknxo8fz3PPPWfXvgYPHszgwYOv+LzFYmHSpElMmjTJ3jLFg3SK7lQSlAY2G2h2OQ6xI2MHpy+cJtgvuMoNcRARERHxBnYHpfHjx3P69GlGjx5NXl4eAIGBgbz44otMnDjR4QWK9+sY2ZHPtn3GlrQtZpfiMMXrk7rX746fVQsuRURERDyN3UHJYrHw7rvv8uqrr7J7926CgoJo2rRpuZPmRCrCGwc6aH2SiIiIiGe7rvsoAVSvXp0uXbo4shapotpHtseChZTMFDKyMwgPDje7pEoxDIPVR1YDCkoiIiIinuq6gtKmTZv45ptvSE5OLrn8rtiiRYscUphUHSEBITSr3Yy9p/ayNW0rdzS5w+ySKmX3yd2cyDlBkG8QXerpjwkiIiIinsju+yjNnz+f7t27s2vXLr799lvy8/PZtWsXK1asICwszBk1ShXgTZffFa9Pujn2Zvyt/iZXIyIiIiLXw+6g9NZbb/H+++/z3Xff4e/vz/Tp09m9ezeDBw+mfv36zqhRqoBOUZ0AvGKgg9YniYiIiHg+u4PSgQMHGDBgAAABAQFkZ2djsVh49tln+fjjjx1eoFQN3nJGyTAMBSURERERL2B3UKpVqxZZWVkA1KtXjx07dgBw9uxZcnJyHFudVBkdojoAcPjsYU7lnDK5muv339P/Jf18OgHWAG6KucnsckRERETkOtkdlHr27MmyZcsA2w1jx4wZwxNPPMGQIUO49dZbHV6gVA01AmvQuGZjABLTE02u5voVr0+6KeYmAn0DTa5GRERERK5XhYNSUlISAB9++CEPPfQQABMnTuT555/n+PHjxMfH87e//c0pRUrV4A2X3+myOxERERHvUOGg1LFjRzp16sRXX31FcHCw7Yd9fBg/fjyLFy9m6tSp1KxZ02mFivfz9IEOWp8kIiIi4j0qHJTWrVtHx44dmTBhAlFRUTzyyCOsXLnSmbVJFePpZ5QOnT1ESmYKfj5+3Bx7s9nliIiIiEglVDgo3XzzzXzyySekp6fz0UcfkZKSwm233Ubjxo2ZPHkyKSkpzqxTqoDioLT/9H7OXTxncjX2K16f1KVeF6r5VTO5GhERERGpDLuHOQQFBTF8+HBWrVrFvn37GDJkCLNmzaJhw4bcddddzqhRqoja1WoTFxYHeOZAB112JyIiIuI97A5Kl2rcuDETJkzg5ZdfJjQ0lKVLlzqqLqmiPPnyOwUlEREREe9x3UEpISGB4cOHExkZyfjx44mPj2fdunWOrE2qIE8d6JB8LpnDZw9jtVjpFtvN7HJEREREpJJ87dn46NGjzJkzhzlz5nDo0CG6devGjBkzGDx4cMkkPJHK8NQzSsXrkzpFdyIkIMTkakRERESksioclPr168fKlSupW7cujz76KCNGjKB58+bOrE2qoOKgtPfkXs7nnae6f3WTK6oYXXYnIiIi4l0qHJSCgoJYuHAhAwcOxGq1OrMmqcIiqkdQL6QeqVmpJKUn0aN+D7NLqhAFJRERERHvUuGgtHjxYmfWIVKiY1RHUrNS2Zq21SOC0rGsY+w/vR8fi49H1CsiIiIi11apqXcizuBpAx2K1ye1j2xPWGCYydWIiIiIiCMoKInb8bSBDrrsTkRERMT7KCiJ2ykOSrtO7CInP8fkaq5NQUlERETE+ygoiduJDokmIjiCIqOIX47/YnY5V3X8/HH2nNyDBQs943qaXY6IiIiIOIiCkrgdi8XiMZffrT6yGoA2EW2oFVTL5GpERERExFEUlMQtlQx0OObeAx102Z2IiIiId1JQErdUckYp3b3PKCkoiYiIiHgnBSVxS8VBaUfGDnILck2upnwnc06yI2MHAL3ieplcjYiIiIg4koKSuKX6YfWpHVSbgqICtmdsN7uccq05sgaAG+reQN3guiZXIyIiIiKOpKAkbskTBjrosjsRERER76WgJG7L3Qc6KCiJiIiIeC8FJXFb7jzQ4cyFM2xL3wZofZKIiIiIN1JQErdVHJR+Of4L+YX5JldT2trktRgYNK3VlKiQKLPLEREREREHU1ASt9WoZiPCAsLIK8xj54mdZpdTii67ExEREfFuCkrittx5oENJUGqgoCQiIiLijRSUxK25Y1DKzM0sqUdnlERERES8k4KSuLWSyXdp7jP5bl3yOoqMIhrWaEhsWKzZ5YiIiIiIEygoiVsrPqO0LX0bBUUFJldjo8vuRERERLyfgpK4taa1m1LdvzoXCi6w5+Qes8sBNMhBREREpCpQUBK35mPxoUNkB8A91ill52Wz+dhmQEFJRERExJspKInbc6eBDj8f/ZmCogJiQ2NpUKOB2eWIiIiIiJMoKInbc6eBDquPrAZs65MsFovJ1YiIiIiIsygoidsrPqOUmJZIkVFkai1anyQiIiJSNSgoidtrUacFQb5BZOdns+/UPtPquJB/gY2pGwEFJRERERFvp6Akbs/qY6V9ZHvA3HVKG1M3kleYR1T1KJrUamJaHSIiIiLifApK4hHcYaBDwuFf75+k9UkiIiIi3k1BSTyCOwx00PokERERkapDQUk8wqVnlMwY6JBbkMv6lPWAgpKIiIhIVaCgJB7hhro3EGANIDM3k4NnDrr89Tcd28TFgouEB4fTok4Ll7++iIiIiLiWgpJ4BD+rH20j2gLmrFMqXp/UK66X1ieJiIiIVAEKSuIxzBzooPVJIiIiIlWLgpJ4DLMGOuQX5vPz0Z8BBSURERGRqkJBSTzGpWeUDMNw2etuSdtCdn42tYJq0Sq8lcteV0RERETMY2pQKigo4JVXXqFhw4YEBQXRqFEj3nzzTYqKfp1qdv78eZ5++mliYmIICgqiZcuWfPTRRyZWLWZpHd4aPx8/Tl84TfK5ZJe97qXrk3ws+tuCiIiISFXga+aLv/vuu8ycOZO5c+fSqlUrNm/ezGOPPUZYWBhjxowB4Nlnn2XlypV88cUXNGjQgB9//JHRo0cTHR3NoEGDzCxfXCzAN4DW4a1JTE9kS9oW4mrEueR1tT5JREREpOox9c/j69evZ9CgQQwYMIAGDRrwwAMP0L9/fzZv3lxqm+HDh9OnTx8aNGjAk08+Sbt27UptI1WHqwc6FBQVsDZ5LaCgJCIiIlKVmHpGqUePHsycOZN9+/bRrFkztm3bxtq1a5k2bVqpbRYvXsyIESOIjo5m1apV7Nu3j+nTp5e7z9zcXHJzc0u+z8zMBCA/P5/8/HynHs+1FL++2XV4svbh7QHYnLq5wn2sTN+3pG0hKy+LsIAwWtZqqf/t7KD3uznUd3Oo7+ZQ382hvptDfXcMe/pnMVy5Kr4MwzB46aWXePfdd7FarRQWFjJ58mQmTpxYsk1eXh5PPPEEn332Gb6+vvj4+PDpp58ybNiwcvc5adIk3njjjcsenzdvHtWqVXPasYhr7Mvex/j/jifMN4w5reY4/Z5G/8j4B3OOzaFzaGdeafSKU19LRERERJwrJyeHoUOHcu7cOUJDQ6+6ralnlL766iu++OIL5s2bR6tWrUhKSmLs2LFER0czfPhwAD744AM2bNjA4sWLiYuLY/Xq1YwePZqoqChuu+22y/Y5ceJExo0bV/J9ZmYmsbGx9O/f/5rNcLb8/HyWLVtGv3798PPzM7UWT9U3vy8T/zyRcwXnaN+rPfVC6l3zZyrT90+++QSABzo/wF1d77qumqsqvd/Nob6bQ303h/puDvXdHOq7YxRfbVYRpgalF154gQkTJvDQQw8B0KZNG44cOcLbb7/N8OHDuXDhAi+99BLffvstAwYMAKBt27YkJSXx5z//udygFBAQQEBAwGWP+/n5uc2byp1q8TR+fn7cUPcGtmds55cTv9CgVgO7ftaevhcWFbLu6DoAbml0i/43u056v5tDfTeH+m4O9d0c6rs51PfKsad3pg5zyMnJwcendAlWq7VkPHjxuqKrbSNVj6sGOmzP2M7Zi2cJ8Q+hQ1QHp76WiIiIiLgXU88o3X333UyePJn69evTqlUrEhMTmTp1KiNGjAAgNDSU3r1788ILLxAUFERcXBwJCQl89tlnTJ061czSxUQdozoyd9tctqRtcerrFN8/qXv97vj6mPp/FRERERFxMVN/+5sxYwavvvoqo0ePJiMjg+joaEaOHMlrr71Wss38+fOZOHEiDz/8MKdPnyYuLo7JkyczatQoEysXM3WK6gQ4/4yS7p8kIiIiUnWZGpRCQkKYNm1aqXHgZUVGRjJ79mzXFSVur11kOyxYOJZ1jPTz6URWj3T4axQZRaw+shpQUBIRERGpikxdoyRyPar7V6dFnRaA884q7Tqxi1MXTlHNrxqdozs75TVERERExH0pKIlHcvZAh+L1Sd1iu+Fn1WQZERERkapGQUk8UnFQctZAB61PEhEREanaFJTEIzlzoINhGApKIiIiIlWcgpJ4pPaR7QFIPpfMyZyTDt333lN7ycjOINA3kBvr3ejQfYuIiIiIZ1BQEo8UFhhG01pNAcefVSpen9Q1pisBvgEO3beIiIiIeAYFJfFYzhrooMvuRERERERBSTyWMwY6aH2SiIiIiICCkngwZwx0OHDmAMeyjuFv9adrTFeH7VdEREREPIuCknisDlEdADh45iBnLpxxyD6L1yfdWO9GgvyCHLJPEREREfE8CkrisWoF1aJBjQYAJKYnOmSfuuxOREREREBBSTycoy+/U1ASEREREVBQEg/nyMl3h88eJvlcMr4+vnSL7Vbp/YmIiIiI51JQEo9WfEbJEZPvitcndY7uTLB/cKX3JyIiIiKeS0FJPFrxQId9p/aRmZtZqX3psjsRERERKaagJB4tPDicmNAYAJLSkyq1LwUlERERESmmoCQezxEDHVIyUzh45iA+Fh+61+/uqNJERERExEMpKInHc8RAh+L1SR2jOhIaEOqQukRERETEcykoicdzxECH4svuetXv5ZCaRERERMSzKSiJxys+o7Tn5B6y87Kvax8l65MaaH2SiIiIiCgoiReICokisnokRUYR245vs/vn07LS2HdqHxYs9Kzf0wkVioiIiIinUVASr1CZgQ6rj6wGoG1EW2oG1XRoXSIiIiLimRSUxCtUZqCDxoKLiIiISFkKSuIVKjPQQeuTRERERKQsBSXxCsVnlHZm7ORiwcUK/9yJ7BPsOrELgF5xmngnIiIiIjYKSuIVYkJjqFOtDoVGIb8c/6XCP1e8PqlV3VbUqVbHWeWJiIiIiIdRUBKvYLFYrmugg9YniYiIiEh5FJTEa1zPQAetTxIRERGR8igoidcoDkoVHehw+sJpth/fDmh9koiIiIiUpqAkXqP40rvtx7eTV5h3ze3XHFmDgUHz2s2JrB7p7PJERERExIMoKInXaFCjATUDa5JflM/OjJ3X3F7rk0RERETkShSUxGtYLBa7Lr/T+iQRERERuRIFJfEqFR3ocO7iOZLSkwCdURIRERGRyykoiVep6BmldUfXUWQU0bhmY+qF1nNFaSIiIiLiQRSUxKsUD3TYlr6N/ML8K26XcFjrk0RERETkyhSUxKs0rtWYEP8Qcgtz2XNyzxW30/okEREREbkaBSXxKj4Wn2tefnc+7zybj20GdEZJRERERMqnoCRe51oDHX4++jOFRiFxYXHE1YhzZWkiIiIi4iEUlMTrXOuMUsn6JF12JyIiIiJXoKAkXqd4oENSehKFRYWXPa8bzYqIiIjItSgoiddpVrsZ1fyqkZOfw75T+0o9l5Ofw39S/wMoKImIiIjIlSkoidex+lhpH9keuPzyuw0pG8gvyqdeSD0a1WxkQnUiIiIi4gkUlMQrFV9+V3agw6XrkywWi8vrEhERERHPoKAkXulKAx20PklEREREKkJBSbxS8RmlxLREiowiAC4WXGRDygZAQUlERERErk5BSbxSy7otCfQNJCsviwNnDgCw6dgmcgtziQiOoFntZiZXKCIiIiLuTEFJvJKvjy9tI9oCv65TWp28GtD6JBERERG5NgUl8Voll98dTwRgTfIaQJfdiYiIiMi1KSiJ1yoe6JCUnkR+UT7rU9YDCkoiIiIicm2+Zhcg4iwlZ5TSE9nvu58LBReoU60ON9S9weTKRERERMTd6YySeK1W4a3w8/HjzMUzrDizAoBecb20PklERERErklBSbyWv9Wf1uGtAVhxyhaUetbvaWZJIiIiIuIhTA1KBQUFvPLKKzRs2JCgoCAaNWrEm2++SVFRUantdu/ezT333ENYWBghISF07dqV5ORkk6oWT7Fo9yL2ndoHQCGFALyz9h0W7V5kZlkiIiIi4gFMDUrvvvsuM2fO5MMPP2T37t1MmTKF9957jxkzZpRsc+DAAXr06EGLFi1YtWoV27Zt49VXXyUwMNDEysXdLdq9iAe+foDs/OxSj2dkZ/DA1w8oLImIiIjIVZk6zGH9+vUMGjSIAQMGANCgQQP+/ve/s3nz5pJtXn75Ze666y6mTJlS8lijRo1cXqt4jsKiQsYsGYOBcdlzBgYWLIxdMpZBzQdh9bGaUKGIiIiIuDtTg1KPHj2YOXMm+/bto1mzZmzbto21a9cybdo0AIqKivj3v//N+PHjuf3220lMTKRhw4ZMnDiRe++9t9x95ubmkpubW/J9ZmYmAPn5+eTn5zv7kK6q+PXNrsPbJRxJICUz5YrPGxgczTzKyoMrNSrcifR+N4f6bg713RzquznUd3Oo745hT/8shmFc/md3FzEMg5deeol3330Xq9VKYWEhkydPZuLEiQCkp6cTFRVFtWrV+NOf/kTfvn1ZsmQJL730EitXrqR378t/yZ00aRJvvPHGZY/PmzePatWqOf2YxHyrz6xm6pGp19xuXNw4etXs5YKKRERERMQd5OTkMHToUM6dO0doaOhVtzU1KM2fP58XXniB9957j1atWpGUlMTYsWOZOnUqw4cP59ixY9SrV48hQ4Ywb968kp+75557CA4O5u9///tl+yzvjFJsbCwnT568ZjOcLT8/n2XLltGvXz/8/PxMrcWbJRxJoN+X/a653bKHl+mMkhPp/W4O9d0c6rs51HdzqO/mUN8dIzMzkzp16lQoKJl66d0LL7zAhAkTeOihhwBo06YNR44c4e2332b48OHUqVMHX19fbrih9A1CW7Zsydq1a8vdZ0BAAAEBAZc97ufn5zZvKneqxRv1bdSXmNAYUjNTy12nZMFCTGgMfRv11RolF9D73RzquznUd3Oo7+ZQ382hvleOPb0zdepdTk4OPj6lS7BarSXjwf39/enSpQt79+4ttc2+ffuIi4tzWZ3iWaw+VqbfMR2whaJLFX8/7Y5pCkkiIiIickWmnlG6++67mTx5MvXr16dVq1YkJiYydepURowYUbLNCy+8wIMPPkivXr1K1ij961//YtWqVeYVLm4vvmU8CwYvYMySMaUGO8SExjDtjmnEt4w3sToRERERcXemBqUZM2bw6quvMnr0aDIyMoiOjmbkyJG89tprJdvcd999zJw5k7fffptnnnmG5s2bs3DhQnr06GFi5eIJ4lvGM6j5IFYeXMkPa3/gzh536nI7EREREakQU4NSSEgI06ZNKxkHfiUjRowodZZJpKKsPlZ6x/Ume2c2veN6KySJiIiISIWYukZJRERERETEHSkoiYiIiIiIlKGgJCIiIiIiUoaCkoiIiIiISBkKSiIiIiIiImUoKImIiIiIiJShoCQiIiIiIlKGgpKIiIiIiEgZCkoiIiIiIiJlKCiJiIiIiIiU4Wt2Ac5mGAYAmZmZJlcC+fn55OTkkJmZiZ+fn9nlVBnquznUd3Oo7+ZQ382hvptDfTeH+u4YxZmgOCNcjdcHpaysLABiY2NNrkRERERERNxBVlYWYWFhV93GYlQkTnmwoqIijh07RkhICBaLxdRaMjMziY2N5ejRo4SGhppaS1WivptDfTeH+m4O9d0c6rs51HdzqO+OYRgGWVlZREdH4+Nz9VVIXn9GycfHh5iYGLPLKCU0NFRvcBOo7+ZQ382hvptDfTeH+m4O9d0c6nvlXetMUjENcxARERERESlDQUlERERERKQMBSUXCggI4PXXXycgIMDsUqoU9d0c6rs51HdzqO/mUN/Nob6bQ313Pa8f5iAiIiIiImIvnVESEREREREpQ0FJRERERESkDAUlERERERGRMhSUREREREREylBQstPbb79Nly5dCAkJITw8nHvvvZe9e/eW2sYwDCZNmkR0dDRBQUH06dOHnTt3ltomNzeXP/zhD9SpU4fg4GDuueceUlJSSm1z5swZhg0bRlhYGGFhYQwbNoyzZ886+xDdjqt6fvjwYR5//HEaNmxIUFAQjRs35vXXXycvL88lx+luXPlev3Tb9u3bY7FYSEpKctahuTVX9/3f//43N910E0FBQdSpU4f4+HinHp+7cmXf9+3bx6BBg6hTpw6hoaF0796dlStXOv0Y3ZGj+v7xxx/Tp08fQkNDsVgs5X5W6jP1V67quz5XS3Pl+72YPlcrR0HJTgkJCTz11FNs2LCBZcuWUVBQQP/+/cnOzi7ZZsqUKUydOpUPP/yQTZs2ERkZSb9+/cjKyirZZuzYsXz77bfMnz+ftWvXcv78eQYOHEhhYWHJNkOHDiUpKYklS5awZMkSkpKSGDZsmEuP1x24qud79uyhqKiIWbNmsXPnTt5//31mzpzJSy+95PJjdgeufK8XGz9+PNHR0S45Pnflyr4vXLiQYcOG8dhjj7Ft2zbWrVvH0KFDXXq87sKVfR8wYAAFBQWsWLGCLVu20L59ewYOHEh6erpLj9kdOKrvOTk53HHHHVf977U+U3/lqr7rc7U0V77fi+lztZIMqZSMjAwDMBISEgzDMIyioiIjMjLSeOedd0q2uXjxohEWFmbMnDnTMAzDOHv2rOHn52fMnz+/ZJvU1FTDx8fHWLJkiWEYhrFr1y4DMDZs2FCyzfr16w3A2LNnjysOzW05q+flmTJlitGwYUMnHYlncXbfv//+e6NFixbGzp07DcBITEx0/kF5AGf1PT8/36hXr57x6aefuvBoPIez+n7ixAkDMFavXl2yTWZmpgEYy5cvd8WhubXr6fulVq5caQDGmTNnSj2uz9Src1bfy6PP1V85u+/6XK08nVGqpHPnzgFQq1YtAA4dOkR6ejr9+/cv2SYgIIDevXvz888/A7Blyxby8/NLbRMdHU3r1q1Ltlm/fj1hYWHcdNNNJdt07dqVsLCwkm2qKmf1/EqvVfw6VZ0z+378+HGeeOIJPv/8c6pVq+aKw/EYzur71q1bSU1NxcfHhw4dOhAVFcWdd9552SUeVZWz+l67dm1atmzJZ599RnZ2NgUFBcyaNYuIiAg6derkqsNzW9fT94rQZ+rVOavvV3otfa7aOLPv+lx1DAWlSjAMg3HjxtGjRw9at24NUHLpRERERKltIyIiSp5LT0/H39+fmjVrXnWb8PDwy14zPDy8Sl6eUcyZPS/rwIEDzJgxg1GjRjn6MDyOM/tuGAa//e1vGTVqFJ07d3b2oXgUZ/b94MGDAEyaNIlXXnmF7777jpo1a9K7d29Onz7t1ONyd87su8ViYdmyZSQmJhISEkJgYCDvv/8+S5YsoUaNGk4+Mvd2vX2vCH2mXpkz+16WPld/5cy+63PVcXzNLsCTPf300/zyyy+sXbv2sucsFkup7w3DuOyxsspuU972FdmPN3N2z4sdO3aMO+64g9/85jf87ne/q1zRXsCZfZ8xYwaZmZlMnDjRcQV7CWf2vaioCICXX36Z+++/H4DZs2cTExPDN998w8iRIx1xCB7JmX03DIPRo0cTHh7OmjVrCAoK4tNPP2XgwIFs2rSJqKgoxx2Ih3F036+1j+vdj7dxdt+L6XO1NGf2XZ+rjqMzStfpD3/4A4sXL2blypXExMSUPB4ZGQlwWfLPyMgo+QtBZGQkeXl5nDlz5qrbHD9+/LLXPXHixGV/aagqnN3zYseOHaNv377cfPPNfPzxx844FI/i7L6vWLGCDRs2EBAQgK+vL02aNAGgc+fODB8+3GnH5e6c3ffiX8hvuOGGkucDAgJo1KgRycnJjj8gD+GK9/t3333H/Pnz6d69Ox07duSvf/0rQUFBzJ0715mH5tYq0/eK0Gdq+Zzd92L6XC3N2X3X56oDuWoxlLcoKioynnrqKSM6OtrYt29fuc9HRkYa7777bsljubm55S74/eqrr0q2OXbsWLnDHDZu3FiyzYYNG6rkwlNX9dwwDCMlJcVo2rSp8dBDDxkFBQVOPCr356q+HzlyxNi+fXvJ19KlSw3AWLBggXH06FEnH6X7cVXfz507ZwQEBJQa5pCXl2eEh4cbs2bNctbhuS1X9X3x4sWGj4+PkZWVVWr/zZo1MyZPnuyMQ3Nrjuj7pa41zEGfqTau6rth6HP1Uq7quz5XHUdByU6///3vjbCwMGPVqlVGWlpayVdOTk7JNu+8844RFhZmLFq0yNi+fbsxZMgQIyoqysjMzCzZZtSoUUZMTIyxfPlyY+vWrcYtt9xitGvXrtR/RO644w6jbdu2xvr1643169cbbdq0MQYOHOjS43UHrup5amqq0aRJE+OWW24xUlJSSr1WVeTK9/qlDh06VKWn87iy72PGjDHq1atnLF261NizZ4/x+OOPG+Hh4cbp06ddeszuwFV9P3HihFG7dm0jPj7eSEpKMvbu3Ws8//zzhp+fn5GUlOTy4zabo/qelpZmJCYmGp988knJVMHExETj1KlTJdvoM/VXruq7PldLc+X7/VJV/XO1MhSU7ASU+zV79uySbYqKiozXX3/diIyMNAICAoxevXoZ27dvL7WfCxcuGE8//bRRq1YtIygoyBg4cKCRnJxcaptTp04ZDz/8sBESEmKEhIQYDz/8cIVGb3obV/V89uzZV3ytqsiV7/VLVfX/oLuy73l5ecZzzz1nhIeHGyEhIcZtt91m7NixwxWH6XZc2fdNmzYZ/fv3N2rVqmWEhIQYXbt2Nb7//ntXHKbbcVTfX3/99WvuR5+pv3JV3/W5Wpor3++Xquqfq5VhMQzDqOBVeiIiIiIiIlWChjmIiIiIiIiUoaAkIiIiIiJShoKSiIiIiIhIGQpKIiIiIiIiZSgoiYiIiIiIlKGgJCIiIiIiUoaCkoiIiIiISBkKSiIiIiIiImUoKImIiIiIiJShoCQiIh7FMAxuu+02br/99sue++tf/0pYWBjJyckmVCYiIt5EQUlERDyKxWJh9uzZbNy4kVmzZpU8fujQIV588UWmT59O/fr1Hfqa+fn5Dt2fiIi4PwUlERHxOLGxsUyfPp3nn3+eQ4cOYRgGjz/+OLfeeis33ngjd911F9WrVyciIoJhw4Zx8uTJkp9dsmQJPXr0oEaNGtSuXZuBAwdy4MCBkucPHz6MxWLh66+/pk+fPgQGBvLFF1+YcZgiImIii2EYhtlFiIiIXI97772Xs2fPcv/99/PHP/6RTZs20blzZ5544gkeffRRLly4wIsvvkhBQQErVqwAYOHChVgsFtq0aUN2djavvfYahw8fJikpCR8fHw4fPkzDhg1p0KABf/nLX+jQoQMBAQFER0ebfLQiIuJKCkoiIuKxMjIyaN26NadOnWLBggUkJiayceNGli5dWrJNSkoKsbGx7N27l2bNml22jxMnThAeHs727dtp3bp1SVCaNm0aY8aMceXhiIiIG9GldyIi4rHCw8N58sknadmyJffddx9btmxh5cqVVK9eveSrRYsWACWX1x04cIChQ4fSqFEjQkNDadiwIcBlAyA6d+7s2oMRERG34mt2ASIiIpXh6+uLr6/t46yoqIi7776bd99997LtoqKiALj77ruJjY3lk08+ITo6mqKiIlq3bk1eXl6p7YODg51fvIiIuC0FJRER8RodO3Zk4cKFNGjQoCQ8XerUqVPs3r2bWbNm0bNnTwDWrl3r6jJFRMQD6NI7ERHxGk899RSnT59myJAh/Oc//+HgwYP8+OOPjBgxgsLCQmrWrEnt2rX5+OOP2b9/PytWrGDcuHFmly0iIm5IQUlERLxGdHQ069ato7CwkNtvv53WrVszZswYwsLC8PHxwcfHh/nz57NlyxZat27Ns88+y3vvvWd22SIi4oY09U5ERERERKQMnVESEREREREpQ0FJRERERESkDAUlERERERGRMhSUREREREREylBQEhERERERKUNBSUREREREpAwFJRERERERkTIUlERERERERMpQUBIRERERESlDQUlERERERKQMBSUREREREZEy/j/2iRtDj7DwmgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vacinnations per year In Asia\n",
    "# x-axis: year\n",
    "# y-axis: Vaccination Rates\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "\n",
    "# get the immunization rate for 2000 - 2015 for each disease in Asia\n",
    "immunization_rate_per_cont_hep = [hepatitis_by_cont_year['Asia'][year] for year in years]\n",
    "immunization_rate_per_cont_pol = [polio_by_cont_year['Asia'][year] for year in years]\n",
    "immunization_rate_per_cont_dip = [diphtheria_by_cont_year['Asia'][year] for year in years]\n",
    "\n",
    "# plot each vaccine immunization rate with different colors\n",
    "ax.plot(years, immunization_rate_per_cont_hep, label='Hepatitis B', color='red', linestyle='-', marker='o')\n",
    "ax.plot(years, immunization_rate_per_cont_pol, label='Polio', color='blue', linestyle='-', marker='o')\n",
    "ax.plot(years, immunization_rate_per_cont_dip, label='Diphtheria', color='green', linestyle='-', marker='o')\n",
    "\n",
    "\n",
    "ax.set_title('Immunization Rates per Year in Asia')\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('Vaccination Rate (%)')\n",
    "ax.legend()\n",
    "ax.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vacinnations per year In Africa\n",
    "# x-axis: year\n",
    "# y-axis: Vaccination Rates\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "\n",
    "# get the immunization rate for 2000 - 2015 for each disease in Africa\n",
    "immunization_rate_per_cont_hep = [hepatitis_by_cont_year['Africa'][year] for year in years]\n",
    "immunization_rate_per_cont_pol = [polio_by_cont_year['Africa'][year] for year in years]\n",
    "immunization_rate_per_cont_dip = [diphtheria_by_cont_year['Africa'][year] for year in years]\n",
    "\n",
    "\n",
    "\n",
    "# plot each vaccine immunization rate with different colors\n",
    "ax.plot(years, immunization_rate_per_cont_hep, label='Hepatitis B', color='red', linestyle='-', marker='o')\n",
    "ax.plot(years, immunization_rate_per_cont_pol, label='Polio', color='blue', linestyle='-', marker='o')\n",
    "ax.plot(years, immunization_rate_per_cont_dip, label='Diphtheria', color='green', linestyle='-', marker='o')\n",
    "\n",
    "\n",
    "ax.set_title('Immunization Rates per Year in Africa')\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('Vaccination Rate (%)')\n",
    "ax.legend()\n",
    "ax.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Immunization rates in Africa have increased over time, whereas Asia's rates have remained relatively stable. Additionally, the correlation between immunization rates and the deaths of children under five is not as strong as we initially anticipated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relationship between Schooling and Life Expectancy\n",
    "Our team sought to examine factors beyond healthcare that might influence global life expectancy. This prompted us to explore the potential relationship between the years of schooling in a continent and the life expectancy of its population. We will create six different scatter plots for each continent and include a trend line to visualize the relationship.\n",
    "\n",
    "Again we create a new dataframe `df_schooling` and drop the rows with null values in either the `Life expectancy` or `Schooling` columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>...</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>Cook Islands</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>98.0</td>\n",
       "      <td>3.58</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>Dominica</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>11.419555</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>96.0</td>\n",
       "      <td>5.58</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>722.756650</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.721</td>\n",
       "      <td>12.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1650</th>\n",
       "      <td>Marshall Islands</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>871.878317</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>79.0</td>\n",
       "      <td>17.24</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3617.752354</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1715</th>\n",
       "      <td>Monaco</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>4.30</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1812</th>\n",
       "      <td>Nauru</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>15.606596</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>87.0</td>\n",
       "      <td>4.65</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>136.183210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1909</th>\n",
       "      <td>Niue</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>7.20</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958</th>\n",
       "      <td>Palau</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>344.690631</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>9.27</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1932.122370</td>\n",
       "      <td>292.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.779</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2167</th>\n",
       "      <td>Saint Kitts and Nevis</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>8.54</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>96.0</td>\n",
       "      <td>6.14</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.749</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2216</th>\n",
       "      <td>San Marino</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.50</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2713</th>\n",
       "      <td>Tuvalu</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>78.281203</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.61</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3542.135890</td>\n",
       "      <td>1819.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Country  Year      Status  Life expectancy  \\\n",
       "624            Cook Islands  2013  Developing              NaN   \n",
       "769                Dominica  2013  Developing              NaN   \n",
       "1650       Marshall Islands  2013  Developing              NaN   \n",
       "1715                 Monaco  2013  Developing              NaN   \n",
       "1812                  Nauru  2013  Developing              NaN   \n",
       "1909                   Niue  2013  Developing              NaN   \n",
       "1958                  Palau  2013  Developing              NaN   \n",
       "2167  Saint Kitts and Nevis  2013  Developing              NaN   \n",
       "2216             San Marino  2013  Developing              NaN   \n",
       "2713                 Tuvalu  2013  Developing              NaN   \n",
       "\n",
       "      Adult Mortality  infant deaths  Alcohol  percentage expenditure  \\\n",
       "624               NaN              0     0.01                0.000000   \n",
       "769               NaN              0     0.01               11.419555   \n",
       "1650              NaN              0     0.01              871.878317   \n",
       "1715              NaN              0     0.01                0.000000   \n",
       "1812              NaN              0     0.01               15.606596   \n",
       "1909              NaN              0     0.01                0.000000   \n",
       "1958              NaN              0      NaN              344.690631   \n",
       "2167              NaN              0     8.54                0.000000   \n",
       "2216              NaN              0     0.01                0.000000   \n",
       "2713              NaN              0     0.01               78.281203   \n",
       "\n",
       "      Hepatitis B  Measles   ...  Polio  Total expenditure  Diphtheria  \\\n",
       "624          98.0         0  ...   98.0               3.58        98.0   \n",
       "769          96.0         0  ...   96.0               5.58        96.0   \n",
       "1650          8.0         0  ...   79.0              17.24        79.0   \n",
       "1715         99.0         0  ...   99.0               4.30        99.0   \n",
       "1812         87.0         0  ...   87.0               4.65        87.0   \n",
       "1909         99.0         0  ...   99.0               7.20        99.0   \n",
       "1958         99.0         0  ...   99.0               9.27        99.0   \n",
       "2167         97.0         0  ...   96.0               6.14        96.0   \n",
       "2216         69.0         0  ...   69.0               6.50        69.0   \n",
       "2713          9.0         0  ...    9.0              16.61         9.0   \n",
       "\n",
       "       HIV/AIDS          GDP  Population   thinness  1-19 years  \\\n",
       "624         0.1          NaN         NaN                    0.1   \n",
       "769         0.1   722.756650         NaN                    2.7   \n",
       "1650        0.1  3617.752354         NaN                    0.1   \n",
       "1715        0.1          NaN         NaN                    NaN   \n",
       "1812        0.1   136.183210         NaN                    0.1   \n",
       "1909        0.1          NaN         NaN                    0.1   \n",
       "1958        0.1  1932.122370       292.0                    0.1   \n",
       "2167        0.1          NaN         NaN                    3.7   \n",
       "2216        0.1          NaN         NaN                    NaN   \n",
       "2713        0.1  3542.135890      1819.0                    0.2   \n",
       "\n",
       "       thinness 5-9 years  Income composition of resources  Schooling  \n",
       "624                   0.1                              NaN        NaN  \n",
       "769                   2.6                            0.721       12.7  \n",
       "1650                  0.1                              NaN        0.0  \n",
       "1715                  NaN                              NaN        NaN  \n",
       "1812                  0.1                              NaN        9.6  \n",
       "1909                  0.1                              NaN        NaN  \n",
       "1958                  0.1                            0.779       14.2  \n",
       "2167                  3.6                            0.749       13.4  \n",
       "2216                  NaN                              NaN       15.1  \n",
       "2713                  0.1                              NaN        0.0  \n",
       "\n",
       "[10 rows x 22 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# since there are only 10 null rows in life expectancy, we can drop these rows\n",
    "\n",
    "df.loc[df[\"Life expectancy\"].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>...</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>Côte d'Ivoire</td>\n",
       "      <td>2015</td>\n",
       "      <td>Developing</td>\n",
       "      <td>53.3</td>\n",
       "      <td>397.0</td>\n",
       "      <td>57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>65</td>\n",
       "      <td>...</td>\n",
       "      <td>81.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>Côte d'Ivoire</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>52.8</td>\n",
       "      <td>47.0</td>\n",
       "      <td>58</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>76.0</td>\n",
       "      <td>5.72</td>\n",
       "      <td>76.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>Côte d'Ivoire</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>52.3</td>\n",
       "      <td>412.0</td>\n",
       "      <td>59</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>48</td>\n",
       "      <td>...</td>\n",
       "      <td>79.0</td>\n",
       "      <td>5.81</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>Côte d'Ivoire</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>52.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>59</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>137</td>\n",
       "      <td>...</td>\n",
       "      <td>83.0</td>\n",
       "      <td>6.14</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>Côte d'Ivoire</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>51.7</td>\n",
       "      <td>419.0</td>\n",
       "      <td>60</td>\n",
       "      <td>3.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>628</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>6.42</td>\n",
       "      <td>62.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2805</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>2004</td>\n",
       "      <td>Developed</td>\n",
       "      <td>77.5</td>\n",
       "      <td>111.0</td>\n",
       "      <td>28</td>\n",
       "      <td>8.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>37</td>\n",
       "      <td>...</td>\n",
       "      <td>92.0</td>\n",
       "      <td>15.14</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2806</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>2003</td>\n",
       "      <td>Developed</td>\n",
       "      <td>77.2</td>\n",
       "      <td>114.0</td>\n",
       "      <td>28</td>\n",
       "      <td>8.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>56</td>\n",
       "      <td>...</td>\n",
       "      <td>91.0</td>\n",
       "      <td>15.60</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2807</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>2002</td>\n",
       "      <td>Developed</td>\n",
       "      <td>77.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>28</td>\n",
       "      <td>8.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>41</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.55</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2808</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>2001</td>\n",
       "      <td>Developed</td>\n",
       "      <td>76.9</td>\n",
       "      <td>115.0</td>\n",
       "      <td>28</td>\n",
       "      <td>8.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>116</td>\n",
       "      <td>...</td>\n",
       "      <td>89.0</td>\n",
       "      <td>13.73</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2809</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>2000</td>\n",
       "      <td>Developed</td>\n",
       "      <td>76.8</td>\n",
       "      <td>114.0</td>\n",
       "      <td>28</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>85</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.70</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>163 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Country  Year      Status  Life expectancy  \\\n",
       "432              Côte d'Ivoire  2015  Developing             53.3   \n",
       "433              Côte d'Ivoire  2014  Developing             52.8   \n",
       "434              Côte d'Ivoire  2013  Developing             52.3   \n",
       "435              Côte d'Ivoire  2012  Developing             52.0   \n",
       "436              Côte d'Ivoire  2011  Developing             51.7   \n",
       "...                        ...   ...         ...              ...   \n",
       "2805  United States of America  2004   Developed             77.5   \n",
       "2806  United States of America  2003   Developed             77.2   \n",
       "2807  United States of America  2002   Developed             77.0   \n",
       "2808  United States of America  2001   Developed             76.9   \n",
       "2809  United States of America  2000   Developed             76.8   \n",
       "\n",
       "      Adult Mortality  infant deaths  Alcohol  percentage expenditure  \\\n",
       "432             397.0             57      NaN                     0.0   \n",
       "433              47.0             58     0.01                     0.0   \n",
       "434             412.0             59     3.15                     0.0   \n",
       "435             415.0             59     3.24                     0.0   \n",
       "436             419.0             60     3.13                     0.0   \n",
       "...               ...            ...      ...                     ...   \n",
       "2805            111.0             28     8.48                     0.0   \n",
       "2806            114.0             28     8.40                     0.0   \n",
       "2807            115.0             28     8.33                     0.0   \n",
       "2808            115.0             28     8.25                     0.0   \n",
       "2809            114.0             28     8.21                     0.0   \n",
       "\n",
       "      Hepatitis B  Measles   ...  Polio  Total expenditure  Diphtheria  \\\n",
       "432          83.0        65  ...   81.0                NaN        83.0   \n",
       "433          76.0        50  ...   76.0               5.72        76.0   \n",
       "434           8.0        48  ...   79.0               5.81         8.0   \n",
       "435          82.0       137  ...   83.0               6.14        82.0   \n",
       "436          62.0       628  ...   58.0               6.42        62.0   \n",
       "...           ...       ...  ...    ...                ...         ...   \n",
       "2805         92.0        37  ...   92.0              15.14        96.0   \n",
       "2806         92.0        56  ...   91.0              15.60        96.0   \n",
       "2807         88.0        41  ...    9.0              14.55        94.0   \n",
       "2808         89.0       116  ...   89.0              13.73        94.0   \n",
       "2809          9.0        85  ...    9.0              13.70        94.0   \n",
       "\n",
       "       HIV/AIDS  GDP  Population   thinness  1-19 years   thinness 5-9 years  \\\n",
       "432         1.9  NaN         NaN                    5.5                  5.5   \n",
       "433         2.0  NaN         NaN                    5.6                  5.6   \n",
       "434         2.4  NaN         NaN                    5.8                  5.7   \n",
       "435         2.9  NaN         NaN                    5.9                  5.9   \n",
       "436         3.3  NaN         NaN                    6.1                  6.0   \n",
       "...         ...  ...         ...                    ...                  ...   \n",
       "2805        0.1  NaN         NaN                    0.7                  0.6   \n",
       "2806        0.1  NaN         NaN                    0.7                  0.6   \n",
       "2807        0.1  NaN         NaN                    0.8                  0.6   \n",
       "2808        0.1  NaN         NaN                    0.8                  0.6   \n",
       "2809        0.1  NaN         NaN                    0.8                  0.7   \n",
       "\n",
       "      Income composition of resources  Schooling  \n",
       "432                               NaN        NaN  \n",
       "433                               NaN        NaN  \n",
       "434                               NaN        NaN  \n",
       "435                               NaN        NaN  \n",
       "436                               NaN        NaN  \n",
       "...                               ...        ...  \n",
       "2805                              NaN        NaN  \n",
       "2806                              NaN        NaN  \n",
       "2807                              NaN        NaN  \n",
       "2808                              NaN        NaN  \n",
       "2809                              NaN        NaN  \n",
       "\n",
       "[163 rows x 22 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there are approx. 10 countries that don't report schooling data, so we will drop these as well\n",
    "\n",
    "df.loc[df[\"Schooling\"].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>under-five deaths</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Schooling</th>\n",
       "      <th>Population</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>Developing</td>\n",
       "      <td>65.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>83</td>\n",
       "      <td>71.279624</td>\n",
       "      <td>584.259210</td>\n",
       "      <td>10.1</td>\n",
       "      <td>33736494.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>62.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>86</td>\n",
       "      <td>73.523582</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>10.0</td>\n",
       "      <td>327582.0</td>\n",
       "      <td>59.9</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>64.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>89</td>\n",
       "      <td>73.219243</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>9.9</td>\n",
       "      <td>31731688.0</td>\n",
       "      <td>59.9</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>67.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>93</td>\n",
       "      <td>78.184215</td>\n",
       "      <td>669.959000</td>\n",
       "      <td>9.8</td>\n",
       "      <td>3696958.0</td>\n",
       "      <td>59.5</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>97</td>\n",
       "      <td>7.097109</td>\n",
       "      <td>63.537231</td>\n",
       "      <td>9.5</td>\n",
       "      <td>2978599.0</td>\n",
       "      <td>59.2</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2933</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2004</td>\n",
       "      <td>Developing</td>\n",
       "      <td>68.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>42</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>454.366654</td>\n",
       "      <td>9.2</td>\n",
       "      <td>12777511.0</td>\n",
       "      <td>44.3</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2003</td>\n",
       "      <td>Developing</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>41</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>453.351155</td>\n",
       "      <td>9.5</td>\n",
       "      <td>12633897.0</td>\n",
       "      <td>44.5</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2935</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>Developing</td>\n",
       "      <td>73.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.348340</td>\n",
       "      <td>10.0</td>\n",
       "      <td>125525.0</td>\n",
       "      <td>44.8</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2001</td>\n",
       "      <td>Developing</td>\n",
       "      <td>76.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>39</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>548.587312</td>\n",
       "      <td>9.8</td>\n",
       "      <td>12366165.0</td>\n",
       "      <td>45.3</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2000</td>\n",
       "      <td>Developing</td>\n",
       "      <td>79.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>39</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>547.358878</td>\n",
       "      <td>9.8</td>\n",
       "      <td>12222251.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2768 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country  Year      Status  Hepatitis B  Polio  Diphtheria  \\\n",
       "0     Afghanistan  2015  Developing         65.0    6.0        65.0   \n",
       "1     Afghanistan  2014  Developing         62.0   58.0        62.0   \n",
       "2     Afghanistan  2013  Developing         64.0   62.0        64.0   \n",
       "3     Afghanistan  2012  Developing         67.0   67.0        67.0   \n",
       "4     Afghanistan  2011  Developing         68.0   68.0        68.0   \n",
       "...           ...   ...         ...          ...    ...         ...   \n",
       "2933     Zimbabwe  2004  Developing         68.0   67.0        65.0   \n",
       "2934     Zimbabwe  2003  Developing          7.0    7.0        68.0   \n",
       "2935     Zimbabwe  2002  Developing         73.0   73.0        71.0   \n",
       "2936     Zimbabwe  2001  Developing         76.0   76.0        75.0   \n",
       "2937     Zimbabwe  2000  Developing         79.0   78.0        78.0   \n",
       "\n",
       "      under-five deaths  percentage expenditure         GDP  Schooling  \\\n",
       "0                    83               71.279624  584.259210       10.1   \n",
       "1                    86               73.523582  612.696514       10.0   \n",
       "2                    89               73.219243  631.744976        9.9   \n",
       "3                    93               78.184215  669.959000        9.8   \n",
       "4                    97                7.097109   63.537231        9.5   \n",
       "...                 ...                     ...         ...        ...   \n",
       "2933                 42                0.000000  454.366654        9.2   \n",
       "2934                 41                0.000000  453.351155        9.5   \n",
       "2935                 40                0.000000   57.348340       10.0   \n",
       "2936                 39                0.000000  548.587312        9.8   \n",
       "2937                 39                0.000000  547.358878        9.8   \n",
       "\n",
       "      Population  Life expectancy continent  \n",
       "0     33736494.0             65.0      Asia  \n",
       "1       327582.0             59.9      Asia  \n",
       "2     31731688.0             59.9      Asia  \n",
       "3      3696958.0             59.5      Asia  \n",
       "4      2978599.0             59.2      Asia  \n",
       "...          ...              ...       ...  \n",
       "2933  12777511.0             44.3    Africa  \n",
       "2934  12633897.0             44.5    Africa  \n",
       "2935    125525.0             44.8    Africa  \n",
       "2936  12366165.0             45.3    Africa  \n",
       "2937  12222251.0             46.0    Africa  \n",
       "\n",
       "[2768 rows x 13 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping null values for schooling and life expectancy data and creating new df\n",
    "\n",
    "df_schooling = df_relevant.dropna(subset=[\"Schooling\", \"Life expectancy\"])\n",
    "df_schooling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1127    36.3\n",
       "2312    39.0\n",
       "2311    41.0\n",
       "2309    41.5\n",
       "2308    42.3\n",
       "        ... \n",
       "241     89.0\n",
       "1850    89.0\n",
       "2513    89.0\n",
       "2056    89.0\n",
       "995     89.0\n",
       "Name: Life expectancy, Length: 2768, dtype: float64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_schooling[\"Life expectancy\"].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_life_expectancy = df_schooling[\"Life expectancy\"].min()\n",
    "max_life_expectancy = df_schooling[\"Life expectancy\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_schooling = df_schooling[\"Schooling\"].min()\n",
    "max_schooling = df_schooling[\"Schooling\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Schooling vs life expectancy per continent\n",
    "# x-axis: Years of Schooling\n",
    "# y-axis: Life expectancy age\n",
    "\n",
    "fig, axs = plt.subplots(3, 2, figsize=(10, 12))\n",
    "\n",
    "colors = ['blue', 'green', 'red', 'purple', 'orange', 'pink']\n",
    "\n",
    "# for-loop to loop through continents\n",
    "for i, continent in enumerate(continents):\n",
    "\n",
    "    # use the mod and // to indicate row and col in the axis\n",
    "    # get schooling and life expectancy data for each continent\n",
    "    \n",
    "    x_axis = df_schooling.loc[df_schooling[\"continent\"] == continent][\"Schooling\"]\n",
    "    y_axis = df_schooling.loc[df_schooling[\"continent\"] == continent][\"Life expectancy\"]\n",
    "\n",
    "    axs[i//2, i%2].scatter(x_axis, y_axis, color= colors[i], marker=\"o\")\n",
    "\n",
    "    # creating line of best fit\n",
    "    coeffs = np.polyfit(x_axis, y_axis, 1)\n",
    "    trend_line = np.polyval(coeffs, x_axis)\n",
    "    axs[i//2, i%2].plot(x_axis, trend_line, color=\"black\", linestyle=\"--\", label='Trend Line')\n",
    "\n",
    "\n",
    "    axs[i//2, i%2].set_title('Schooling vs Life Expectancy in ' + continent)\n",
    "    axs[i//2, i%2].set_xlim(min_schooling - 1, max_schooling + 1)\n",
    "    axs[i//2, i%2].set_ylim(min_life_expectancy - 5, max_life_expectancy + 5)\n",
    "\n",
    "fig.text(0.5, 0.04, 'Years of Schooling', ha='center', fontsize=12)\n",
    "fig.text(0.04, 0.5, 'Life Expectancy (Years)', va='center', rotation='vertical', fontsize=12)\n",
    "plt.tight_layout(rect=[0.05, 0.05, 1, 0.95])\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a clear positive correlation between years of schooling and life expectancy across all continents. The longer children stay in school, the higher their life expectancy tends to be."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save cleaned dataset as CSV\n",
    "df_relevant.to_csv('data/Life_expectancy_clean.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "- Governments in Asia and Africa should increase healthcare subsidies, aiming to match North America's healthcare subsidization rate of 473% per capita. This increase could lead to a reduction in mortality rates of children under the age of five.\n",
    "- Further research is needed to understand the sharp decline in Under-5 mortality rates in Asia. Insights from this research could inform policies that may be implemented in Africa to reduce child mortality rates. Potential causes for the decline in Asia might include: \n",
    "    - Higher immunization rates for common diseases\n",
    "    - The effects of population density on child mortality\n",
    "    - Cultural factors influencing health practices\n",
    "- There is a clear positive correlation between years of schooling and life expectancy across all continents. Therefore, we recommend implementing policies that encourage keeping children in school for as long as possible. The longer children stay in school, the higher their life expectancy tends to be. Encourage longer school attendance to improve life expectancy, focusing on expanding access to quality education and incorporating health education into school curriculums."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next Steps\n",
    "- Continue discussions with policymakers to explore options for increasing funding and resources dedicated to health services. \n",
    "- Investigate the factors behind the decrease in child mortality rates in Asia, including vaccination rates, population density, cultural influences, and education.\n",
    "- Expand access to education by building more schools, especially in low-income countries, and increasing minimum education requirements. \n",
    "- Focus on improving access to quality education, as the strong correlation between education and health outcomes highlights its importance in enhancing overall well-being. "
   ]
  }
 ],
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