Life expectancy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Life Expectancy around the world (2000 - 2015 full Dataset)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Created by Jesús García García - Master´s Degree in Big Data Analytics UEM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "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",
       "\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",
       "\n",
       "     ...      Polio  Total expenditure  Diphtheria    HIV/AIDS         GDP  \\\n",
       "0    ...        6.0               8.16         65.0        0.1  584.259210   \n",
       "1    ...       58.0               8.18         62.0        0.1  612.696514   \n",
       "2    ...       62.0               8.13         64.0        0.1  631.744976   \n",
       "3    ...       67.0               8.52         67.0        0.1  669.959000   \n",
       "4    ...       68.0               7.87         68.0        0.1   63.537231   \n",
       "\n",
       "   Population   thinness  1-19 years   thinness 5-9 years  \\\n",
       "0  33736494.0                   17.2                 17.3   \n",
       "1    327582.0                   17.5                 17.5   \n",
       "2  31731688.0                   17.7                 17.7   \n",
       "3   3696958.0                   17.9                 18.0   \n",
       "4   2978599.0                   18.2                 18.2   \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",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import sys\n",
    "sys._enablelegacywindowsfsencoding()\n",
    "%matplotlib inline\n",
    "\n",
    "dataset_path = r\"C:\\Users\\Jesús García\\Desktop\\Life Expectancy Data.csv\"\n",
    "df = pd.read_csv(dataset_path,  error_bad_lines=False)\n",
    "\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Exploratory Data Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Change country name to id from 1 to 193\n",
    "df['Country'] = df['Country'].replace(['Afghanistan' , 'Albania' , 'Algeria' , 'Angola' , 'Antigua and Barbuda' , 'Argentina' , 'Armenia' , 'Australia' , 'Austria' , 'Azerbaijan' , 'Bahamas' , 'Bahrain' , 'Bangladesh' , 'Barbados' , 'Belarus' , 'Belgium' , 'Belize' , 'Benin' , 'Bhutan' , 'Bolivia (Plurinational State of)' , 'Bosnia and Herzegovina' , 'Botswana' , 'Brazil' , 'Brunei Darussalam' , 'Bulgaria' , 'Burkina Faso' , 'Burundi' , \"Côte d'Ivoire\" , 'Cabo Verde' , 'Cambodia' , 'Cameroon' , 'Canada' , 'Central African Republic' , 'Chad' , 'Chile' , 'China' , 'Colombia' , 'Comoros' , 'Congo' , 'Costa Rica' , 'Croatia' , 'Cuba' , 'Cyprus' , 'Czechia' , \"Democratic People's Republic of Korea\" , 'Democratic Republic of the Congo' , 'Denmark' , 'Djibouti' , 'Dominican Republic' , 'Ecuador' , 'Egypt' , 'El Salvador' , 'Equatorial Guinea' , 'Eritrea' , 'Estonia' , 'Ethiopia' , 'Fiji' , 'Finland' , 'France' , 'Gabon' , 'Gambia' , 'Georgia' , 'Germany' , 'Ghana' , 'Greece' , 'Grenada' , 'Guatemala' , 'Guinea' , 'Guinea-Bissau' , 'Guyana' , 'Haiti' , 'Honduras' , 'Hungary' , 'Iceland' , 'India' , 'Indonesia' , 'Iran (Islamic Republic of)' , 'Iraq' , 'Ireland' , 'Israel' , 'Italy' , 'Jamaica' , 'Japan' , 'Jordan' , 'Kazakhstan' , 'Kenya' , 'Kiribati' , 'Kuwait' , 'Kyrgyzstan' , \"Lao People's Democratic Republic\" , 'Latvia' , 'Lebanon' , 'Lesotho' , 'Liberia' , 'Libya' , 'Lithuania' , 'Luxembourg' , 'Madagascar' , 'Malawi' , 'Malaysia' , 'Maldives' , 'Mali' , 'Malta' , 'Mauritania' , 'Mauritius' , 'Mexico' , 'Micronesia (Federated States of)' , 'Mongolia' , 'Montenegro' , 'Morocco' , 'Mozambique' , 'Myanmar' , 'Namibia' , 'Nepal' , 'Netherlands' , 'New Zealand' , 'Nicaragua' , 'Niger' , 'Nigeria' , 'Norway' , 'Oman' , 'Pakistan' , 'Panama' , 'Papua New Guinea' , 'Paraguay' , 'Peru' , 'Philippines' , 'Poland' , 'Portugal' , 'Qatar' , 'Republic of Korea' , 'Republic of Moldova' , 'Romania' , 'Russian Federation' , 'Rwanda' , 'Saint Lucia' , 'Saint Vincent and the Grenadines' , 'Samoa' , 'Sao Tome and Principe' , 'Saudi Arabia' , 'Senegal' , 'Serbia' , 'Seychelles' , 'Sierra Leone' , 'Singapore' , 'Slovakia' , 'Slovenia' , 'Solomon Islands' , 'Somalia' , 'South Africa' , 'South Sudan' , 'Spain' , 'Sri Lanka' , 'Sudan' , 'Suriname' , 'Swaziland' , 'Sweden' , 'Switzerland' , 'Syrian Arab Republic' , 'Tajikistan' , 'Thailand' , 'The former Yugoslav republic of Macedonia' , 'Timor-Leste' , 'Togo' , 'Tonga' , 'Trinidad and Tobago' , 'Tunisia' , 'Turkey' , 'Turkmenistan' , 'Uganda' , 'Ukraine' , 'United Arab Emirates' , 'United Kingdom of Great Britain and Northern Ireland' , 'United Republic of Tanzania' , 'United States of America' , 'Uruguay' , 'Uzbekistan' , 'Vanuatu' , 'Venezuela (Bolivarian Republic of)' , 'Viet Nam' , 'Yemen' , 'Zambia' , 'Zimbabwe' , 'Cook Islands' , 'Dominica' , 'Marshall Islands' , 'Monaco' , 'Nauru' , 'Niue' , 'Palau' , 'Saint Kitts and Nevis' , 'San Marino' , 'Tuvalu'], [1 ,2 ,3 ,4 ,5 ,6 ,7 ,8 ,9 ,10 ,11 ,12 ,13 ,14 ,15 ,16 ,17 ,18 ,19 ,20 ,21 ,22 ,23 ,24 ,25 ,26 ,27 ,28 ,29 ,30 ,31 ,32 ,33 ,34 ,35 ,36 ,37 ,38 ,39 ,40 ,41 ,42 ,43 ,44 ,45 ,46 ,47 ,48 ,49 ,50 ,51 ,52 ,53 ,54 ,55 ,56 ,57 ,58 ,59 ,60 ,61 ,62 ,63 ,64 ,65 ,66 ,67 ,68 ,69 ,70 ,71 ,72 ,73 ,74 ,75 ,76 ,77 ,78 ,79 ,80 ,81 ,82 ,83 ,84 ,85 ,86 ,87 ,88 ,89 ,90 ,91 ,92 ,93 ,94 ,95 ,96 ,97 ,98 ,99 ,100 ,101 ,102 ,103 ,104 ,105 ,106 ,107 ,108 ,109 ,110 ,111 ,112 ,113 ,114 ,115 ,116 ,117 ,118 ,119 ,120 ,121 ,122 ,123 ,124 ,125 ,126 ,127 ,128 ,129 ,130 ,131 ,132 ,133 ,134 ,135 ,136 ,137 ,138 ,139 ,140 ,141 ,142 ,143 ,144 ,145 ,146 ,147 ,148 ,149 ,150 ,151 ,152 ,153 ,154 ,155 ,156 ,157 ,158 ,159 ,160 ,161 ,162 ,163 ,164 ,165 ,166 ,167 ,168 ,169 ,170 ,171 ,172 ,173 ,174 ,175 ,176 ,177 ,178 ,179 ,180 ,181 ,182 ,183 ,184 ,185 ,186 ,187 ,188 ,189 ,190 ,191 ,192 ,193])\n",
    "#Change status name to id 1 (Developed) or 0 (Not developed)\n",
    "df['Status'] = df['Status'].replace(['Developing', 'Developed'],[1, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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       "      <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",
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       "      <th>Total expenditure</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
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       "      <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>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country  Year  Status  Life expectancy   Adult Mortality  infant deaths  \\\n",
       "0        1  2015       1              65.0            263.0             62   \n",
       "1        1  2014       1              59.9            271.0             64   \n",
       "2        1  2013       1              59.9            268.0             66   \n",
       "3        1  2012       1              59.5            272.0             69   \n",
       "4        1  2011       1              59.2            275.0             71   \n",
       "\n",
       "   Alcohol  percentage expenditure  Hepatitis B  Measles     ...      Polio  \\\n",
       "0     0.01               71.279624         65.0      1154    ...        6.0   \n",
       "1     0.01               73.523582         62.0       492    ...       58.0   \n",
       "2     0.01               73.219243         64.0       430    ...       62.0   \n",
       "3     0.01               78.184215         67.0      2787    ...       67.0   \n",
       "4     0.01                7.097109         68.0      3013    ...       68.0   \n",
       "\n",
       "   Total expenditure  Diphtheria    HIV/AIDS         GDP  Population  \\\n",
       "0               8.16         65.0        0.1  584.259210  33736494.0   \n",
       "1               8.18         62.0        0.1  612.696514    327582.0   \n",
       "2               8.13         64.0        0.1  631.744976  31731688.0   \n",
       "3               8.52         67.0        0.1  669.959000   3696958.0   \n",
       "4               7.87         68.0        0.1   63.537231   2978599.0   \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",
       "\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",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2509da0e320>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ldJw9ZDaUdAt7+FGrj/90GtVujnB7a430uxfAxcrLoXEp4obZB3HR/hnxO2Vn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\nsaVFNN8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2509da0eb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check missing data\n",
    "sns.heatmap(df.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.fillna(df.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2509dc6f780>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k7o9zomXGJWm5iKiS5GcoJF0WEdtKOov0I/8/cnDWseBDi/xSpNnlSPK+EvDe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\njDE1wQrZGGNqwv8Hesy3n3R4djkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2509d9baf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country  Year  Status  Life expectancy   Adult Mortality  infant deaths  \\\n",
       "0        1  2015       1              65.0            263.0             62   \n",
       "1        1  2014       1              59.9            271.0             64   \n",
       "2        1  2013       1              59.9            268.0             66   \n",
       "3        1  2012       1              59.5            272.0             69   \n",
       "4        1  2011       1              59.2            275.0             71   \n",
       "\n",
       "   Alcohol  percentage expenditure  Hepatitis B  Measles     ...      Polio  \\\n",
       "0     0.01               71.279624         65.0      1154    ...        6.0   \n",
       "1     0.01               73.523582         62.0       492    ...       58.0   \n",
       "2     0.01               73.219243         64.0       430    ...       62.0   \n",
       "3     0.01               78.184215         67.0      2787    ...       67.0   \n",
       "4     0.01                7.097109         68.0      3013    ...       68.0   \n",
       "\n",
       "   Total expenditure  Diphtheria    HIV/AIDS         GDP  Population  \\\n",
       "0               8.16         65.0        0.1  584.259210  33736494.0   \n",
       "1               8.18         62.0        0.1  612.696514    327582.0   \n",
       "2               8.13         64.0        0.1  631.744976  31731688.0   \n",
       "3               8.52         67.0        0.1  669.959000   3696958.0   \n",
       "4               7.87         68.0        0.1   63.537231   2978599.0   \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",
       "\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",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
      "Country                            2938 non-null int64\n",
      "Year                               2938 non-null int64\n",
      "Status                             2938 non-null int64\n",
      "Life expectancy                    2938 non-null float64\n",
      "Adult Mortality                    2938 non-null float64\n",
      "infant deaths                      2938 non-null int64\n",
      "Alcohol                            2938 non-null float64\n",
      "percentage expenditure             2938 non-null float64\n",
      "Hepatitis B                        2938 non-null float64\n",
      "Measles                            2938 non-null int64\n",
      " BMI                               2938 non-null float64\n",
      "under-five deaths                  2938 non-null int64\n",
      "Polio                              2938 non-null float64\n",
      "Total expenditure                  2938 non-null float64\n",
      "Diphtheria                         2938 non-null float64\n",
      " HIV/AIDS                          2938 non-null float64\n",
      "GDP                                2938 non-null float64\n",
      "Population                         2938 non-null float64\n",
      " thinness  1-19 years              2938 non-null float64\n",
      " thinness 5-9 years                2938 non-null float64\n",
      "Income composition of resources    2938 non-null float64\n",
      "Schooling                          2938 non-null float64\n",
      "dtypes: float64(16), int64(6)\n",
      "memory usage: 505.0 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x2509dc6a5c0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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YAA/UOjCwRtsBV6KYAA8UHxclP5ut0jY/m03xcVFuSgT8E4sfAA9UscCBVXkwEcUEeKgB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\nU6ve/7NKy65Kkk7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2509dc77438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Alcohol and life expectancy in Russia\n",
    "#Create a new Dataset\n",
    "dfrussia = df[['Year','Country','Life expectancy ','Alcohol']].copy()\n",
    "dfrussia.drop(dfrussia[dfrussia.Country != 134].index, inplace=True)\n",
    "dfrussia\n",
    "sns.set_palette(\"GnBu_d\")\n",
    "sns.set_style('whitegrid')\n",
    "# Drink alcohol, life expentancy\n",
    "\n",
    "sns.jointplot(x='Alcohol',y='Life expectancy ',data=dfrussia)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x2509e63e0f0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Nv1x51/uYygMAGIViAgAYhWICABiFYgIAGIViAgAYhWICABiFYgIAGIViAgAY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2509e63ee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Alcohol and life expectancy in Spain\n",
    "#Create a new Dataset\n",
    "dfspain = df[['Year','Country','Life expectancy ','Alcohol']].copy()\n",
    "dfspain.drop(dfspain[dfspain.Country != 152].index, inplace=True)\n",
    "dfspain\n",
    "sns.set_palette(\"GnBu_d\")\n",
    "sns.set_style('whitegrid')\n",
    "# Drink alcohol, life expentancy\n",
    "\n",
    "sns.jointplot(x='Alcohol',y='Life expectancy ',data=dfspain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression Life Expectancy War countries (2015)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Country</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>65.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>62</td>\n",
       "      <td>584.259210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>75.6</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21</td>\n",
       "      <td>4132.762920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>2015</td>\n",
       "      <td>34</td>\n",
       "      <td>53.1</td>\n",
       "      <td>356.0</td>\n",
       "      <td>46</td>\n",
       "      <td>777.248751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>2015</td>\n",
       "      <td>37</td>\n",
       "      <td>74.8</td>\n",
       "      <td>143.0</td>\n",
       "      <td>10</td>\n",
       "      <td>644.525556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>2015</td>\n",
       "      <td>39</td>\n",
       "      <td>64.7</td>\n",
       "      <td>267.0</td>\n",
       "      <td>7</td>\n",
       "      <td>7483.158469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>2015</td>\n",
       "      <td>56</td>\n",
       "      <td>64.8</td>\n",
       "      <td>225.0</td>\n",
       "      <td>136</td>\n",
       "      <td>645.463763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1186</th>\n",
       "      <td>2015</td>\n",
       "      <td>75</td>\n",
       "      <td>68.3</td>\n",
       "      <td>181.0</td>\n",
       "      <td>910</td>\n",
       "      <td>1613.188780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <td>2015</td>\n",
       "      <td>78</td>\n",
       "      <td>68.9</td>\n",
       "      <td>182.0</td>\n",
       "      <td>32</td>\n",
       "      <td>4974.269300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266</th>\n",
       "      <td>2015</td>\n",
       "      <td>80</td>\n",
       "      <td>82.5</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>35729.372530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893</th>\n",
       "      <td>2015</td>\n",
       "      <td>119</td>\n",
       "      <td>54.5</td>\n",
       "      <td>344.0</td>\n",
       "      <td>483</td>\n",
       "      <td>2655.157977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942</th>\n",
       "      <td>2015</td>\n",
       "      <td>122</td>\n",
       "      <td>66.4</td>\n",
       "      <td>161.0</td>\n",
       "      <td>352</td>\n",
       "      <td>1431.244865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>2015</td>\n",
       "      <td>127</td>\n",
       "      <td>68.5</td>\n",
       "      <td>211.0</td>\n",
       "      <td>52</td>\n",
       "      <td>2878.338370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2135</th>\n",
       "      <td>2015</td>\n",
       "      <td>134</td>\n",
       "      <td>75.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>13</td>\n",
       "      <td>9329.298351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2377</th>\n",
       "      <td>2015</td>\n",
       "      <td>149</td>\n",
       "      <td>55.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>50</td>\n",
       "      <td>426.985370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2441</th>\n",
       "      <td>2015</td>\n",
       "      <td>153</td>\n",
       "      <td>74.9</td>\n",
       "      <td>138.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3844.891230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2457</th>\n",
       "      <td>2015</td>\n",
       "      <td>154</td>\n",
       "      <td>64.1</td>\n",
       "      <td>225.0</td>\n",
       "      <td>58</td>\n",
       "      <td>2513.884661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2569</th>\n",
       "      <td>2015</td>\n",
       "      <td>161</td>\n",
       "      <td>74.9</td>\n",
       "      <td>148.0</td>\n",
       "      <td>8</td>\n",
       "      <td>5814.863134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2681</th>\n",
       "      <td>2015</td>\n",
       "      <td>168</td>\n",
       "      <td>75.8</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15</td>\n",
       "      <td>1979.525690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2714</th>\n",
       "      <td>2015</td>\n",
       "      <td>170</td>\n",
       "      <td>62.3</td>\n",
       "      <td>291.0</td>\n",
       "      <td>66</td>\n",
       "      <td>693.896383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2730</th>\n",
       "      <td>2015</td>\n",
       "      <td>171</td>\n",
       "      <td>71.3</td>\n",
       "      <td>195.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2124.662666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2890</th>\n",
       "      <td>2015</td>\n",
       "      <td>181</td>\n",
       "      <td>65.7</td>\n",
       "      <td>224.0</td>\n",
       "      <td>37</td>\n",
       "      <td>7483.158469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  Country  Life expectancy   Adult Mortality  infant deaths  \\\n",
       "0     2015        1              65.0            263.0             62   \n",
       "32    2015        3              75.6             19.0             21   \n",
       "528   2015       34              53.1            356.0             46   \n",
       "576   2015       37              74.8            143.0             10   \n",
       "608   2015       39              64.7            267.0              7   \n",
       "882   2015       56              64.8            225.0            136   \n",
       "1186  2015       75              68.3            181.0            910   \n",
       "1234  2015       78              68.9            182.0             32   \n",
       "1266  2015       80              82.5             58.0              0   \n",
       "1893  2015      119              54.5            344.0            483   \n",
       "1942  2015      122              66.4            161.0            352   \n",
       "2023  2015      127              68.5            211.0             52   \n",
       "2135  2015      134              75.0            222.0             13   \n",
       "2377  2015      149              55.0            312.0             50   \n",
       "2441  2015      153              74.9            138.0              3   \n",
       "2457  2015      154              64.1            225.0             58   \n",
       "2569  2015      161              74.9            148.0              8   \n",
       "2681  2015      168              75.8             16.0             15   \n",
       "2714  2015      170              62.3            291.0             66   \n",
       "2730  2015      171              71.3            195.0              4   \n",
       "2890  2015      181              65.7            224.0             37   \n",
       "\n",
       "               GDP  \n",
       "0       584.259210  \n",
       "32     4132.762920  \n",
       "528     777.248751  \n",
       "576     644.525556  \n",
       "608    7483.158469  \n",
       "882     645.463763  \n",
       "1186   1613.188780  \n",
       "1234   4974.269300  \n",
       "1266  35729.372530  \n",
       "1893   2655.157977  \n",
       "1942   1431.244865  \n",
       "2023   2878.338370  \n",
       "2135   9329.298351  \n",
       "2377    426.985370  \n",
       "2441   3844.891230  \n",
       "2457   2513.884661  \n",
       "2569   5814.863134  \n",
       "2681   1979.525690  \n",
       "2714    693.896383  \n",
       "2730   2124.662666  \n",
       "2890   7483.158469  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a new dataset with War Countries: Afganistan (1), Algeria (3), Chad (34), Colombia (37), Ethiopia (56), India (75), Philippines (127), Iraq(78), Israel(80), Nigeria (119), Pakistan (122), Congo (39), Russia (134), Somalia (149), Sri Lanka(153), Sudan(154), Thailand (161), Turkey (168),Uganda(170),Ukraine(171),Yemen(181)  Source: https://www.20minutos.es/noticia/404456/0/mundo/guerras/activas/\n",
    "dfwarcountries = df[['Year','Country','Life expectancy ','Adult Mortality', 'infant deaths', 'GDP']].copy()\n",
    "#Drop not war countries\n",
    "dfwarcountries.drop(dfwarcountries[(dfwarcountries.Country != 1) & (dfwarcountries.Country != 3) & (dfwarcountries.Country != 34) & (dfwarcountries.Country != 37) & (dfwarcountries.Country != 56) & (dfwarcountries.Country != 75) & (dfwarcountries.Country != 127) & (dfwarcountries.Country != 78) & (dfwarcountries.Country != 80) & (dfwarcountries.Country != 119) & (dfwarcountries.Country != 122) & (dfwarcountries.Country != 39) & (dfwarcountries.Country != 134) & (dfwarcountries.Country != 149) & (dfwarcountries.Country != 153) & (dfwarcountries.Country != 154) & (dfwarcountries.Country != 161) & (dfwarcountries.Country != 168) & (dfwarcountries.Country != 170) & (dfwarcountries.Country != 171) & (dfwarcountries.Country != 181)].index, inplace=True)\n",
    "dfwarcountries.drop(dfwarcountries[dfwarcountries.Year != 2015].index, inplace=True)\n",
    "dfwarcountries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Training and Testing Data\n",
    "# Adult and infant Mortality Linear Regression \n",
    "X = dfwarcountries[['Adult Mortality','infant deaths']]\n",
    "y = dfwarcountries['Life expectancy ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Train Test Split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training the Model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "# Create an instance of a LinearRegression() model named lm.\n",
    "lm = LinearRegression()\n",
    "# Train/fit lm on the training data.\n",
    "lm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [-0.07256769 -0.0043782 ]\n"
     ]
    }
   ],
   "source": [
    "# Print out the coefficients of the model\n",
    "print('Coefficients: \\n', lm.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Predicted Y')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NDZUkTZ482eOToUaOHGlESV5TWVnZ5Wv0FXrhjn64ox+3Gd2L8vLydscN2UUTGxuro0eP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2509eaf6a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predicting Test Data\n",
    "# Use lm.predict() to predict off the X_test set of the data.\n",
    "predictions = lm.predict( X_test)\n",
    "# Create a scatterplot of the real test values versus the predicted values. \n",
    "plt.scatter(y_test,predictions)\n",
    "plt.xlabel('Y Test')\n",
    "plt.ylabel('Predicted Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 2.66630922298\n",
      "MSE: 9.87388030243\n",
      "RMSE: 3.14227311073\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the Model\n",
    "from sklearn import metrics\n",
    "\n",
    "# Calculate the Mean Absolute Error, Mean Squared Error, and the Root Mean Squared Error. \n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gNDRizMgo+A7wgq/3ALi7uTKYiXqYl6cHxo2MxLiRba/evfxQ5cXbOFTX1aOm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\niYhEhsFNRCQyDG4iIpFhcBMRicz/AR3YH7PtKzFAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2509edb2c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Residuals\n",
    "# Plot a histogram of the residuals and make sure it looks normally distributed. \n",
    "sns.distplot((y_test-predictions),bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coeffecient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adult Mortality</th>\n",
       "      <td>-0.072568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>infant deaths</th>\n",
       "      <td>-0.004378</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Coeffecient\n",
       "Adult Mortality    -0.072568\n",
       "infant deaths      -0.004378"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recreate the dataframe below. \n",
    "coeffecients = pd.DataFrame(lm.coef_,X.columns)\n",
    "coeffecients.columns = ['Coeffecient']\n",
    "coeffecients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Interpreting the coefficients:\n",
    "# Holding all other features fixed, a 1 unit increase in Adult Mortality is associated with a decrease of 0.0725 Life expectancy\n",
    "# Holding all other features fixed, a 1 unit increase in infant deaths is associated with a decrease of 0.0043 Life expectancy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression Life Expectancy All Word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Adult and infant Mortality Linear Regression \n",
    "X = df[['Adult Mortality','infant deaths']]\n",
    "y = df['Life expectancy ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Train Test Split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating and Training the Model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lm = LinearRegression()\n",
    "lm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78.0408862026\n"
     ]
    }
   ],
   "source": [
    "#Model Evaluation\n",
    "# print the intercept\n",
    "print(lm.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adult Mortality</th>\n",
       "      <td>-0.051842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>infant deaths</th>\n",
       "      <td>-0.011759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Coefficient\n",
       "Adult Mortality    -0.051842\n",
       "infant deaths      -0.011759"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame(lm.coef_,X.columns,columns=['Coefficient'])\n",
    "coeff_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Interpreting the coefficients:\n",
    "# Holding all other features fixed, a 1 unit increase in Adult Mortality is associated with a decrease of 0.051842 Life expectancy\n",
    "# Holding all other features fixed, a 1 unit increase in infant deaths is associated with a decrease of 0.011759 Life expectancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2509eecf8d0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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37CCxJhKGxNoCqJmQNPLzvMsnnZwSSabdDp7nVaejR45fy+Sq0+FIib9Z6utC\nz68OYvBCYm0BlKI+1CTHsFC7n9T5h2RlITfHFfWCAMCsNSJ1XUrnEMNxnGqhjkw8stlsCIVCcScg\nEYTRUDSIBVCK+pBzk8hV0lMb7107pxKZ9tj3frC/H7VzKqPilMvLSrC3YSPUeE4i/cxqY6QzVFbn\nEyceCVEkidSazs1xaVpOEFogsbYAcuF3gLQLQRAmqaL4auO9y8tKmOF7kQ0EImlu8YDj5B+9WTNu\niLJuWdfozIoVb6lzipGrexJvrekH598V89LKtNuprRehC+QGsQiR0Q3NLR5s3LID9c9eCKm7MAHI\nLiPHspzrn21Ew64m5GSz616w4r27e9h+WbGwCxatXFKKa+iQcARIJOIIjimzY7dhnTOebeKpNU0F\nmwgjIbG2GOLwMUC5hRaLzrNeSZdC51kvKmrrooRIrbArVfJzOhyYc8tNqsaYSJ1oJR94vHVRKJuR\nMApyg1iMhl1NuhT/B4ABGeu386wX9c82YsPmbWhu8UTVbxaI7JYioGSx3jKtFFMmjlc1PjUZmlr2\n1XoMgkgmqizrxsZGHDx4EMFgEFVVVZg0aRJWrVoFjuMwZswYrFmzJiltl4hYxCF5erTT0sKe/Qfx\n9p+OMGuDZNrtaNjVhLWbGsMuAaUxetqP4Zelk1SdOxG3gzjxiKJBCLOjKNZHjhzBn//8Z7z66qvw\n+/3YsmULnnjiCfzqV7/CT3/6U6xevRrvvPMOpk+fnozxEhGwQuu0kpvjwpCsrIREXiqO2N/XF+7W\nIkxe3jKtFPsOtUq6QqQsb6k48UTcDuSyINIJRXO4tbUVV111FZYsWYLa2lpMnToVJ06cwKRJF6yf\nsrIyeDzaw5yIxFHTyUWJB+ffhdo5lTE1mY2gNxCAp/0YVtXOl/wSY/mKpZrXxhNeRxDpiqJl/f33\n3+P06dNoaGjAN998g/vvvx88z4dTjLOzs9Hd3W34QIlY9OiOXV5WgoraOt383Eqc8XolizMJTQPm\nP/pklDtCKt77ma07KfKCGDQoivWwYcMwevRoOBwOjB49GllZWejs7Ayv7+npgdvtZu7b0dGh30gB\n9Pb26n5MsyN3zZe43fCe64r72Hm5bnR0dMi6QLKHONHj74WN45hdvMXH857rQl6uG33BIHzn/cwx\nb9ndhP97sCVmXf/AQNil0nnWi8dffBmnT5+WHN+5bl+4D2Pk9monKM0EPdvWJ9HrVRTr4uJi/O53\nv8P8+fPx3//93/D7/ZgyZQqOHDmCn/70p2hpacHkyZOZ+yoVINKKmqJGVkPumpdVV8XdrNbpcGBZ\ndRWKiopQkC896ZeTnY13tjcfke6AAAAUoElEQVRKxjQLFOTnhYv7A7H+9MhzNuxqQiAYOyEpDjEM\nBPvx5mGP7PhY29fcmX6RHPRsWx+119ve3s5cruiznjZtGoqKilBZWYn7778fq1evxsqVK7Fp0ybc\ncccdCAaDmDFjhvaREwnDyuqbNeMGZjjbrBk3SGY4yoWpCSIpF3fsdDhQUjwxKm0dQEJNDQTOeL2a\n2nHp4RoiCDOiKnRvxYoVMct27Nih+2AIdShV0Jswdgx7/UL28crLSvDM1p1hlwLrfFK1rZ1ZDnDg\novzPwgTgqtr5Uda2gJYQw+F5ecwQvfO9vaqzK+WgZgFEukAZjGmGmkp48YSkPTj/rnB6upjI/oWR\nwlZ42XC8f/xj5j6R9ajFglhSPDEmfM+ekQGO46LitSOTU8TXJOVm0ZLMEm81QoJIBZTJkmZIRUb8\n5rnNzMp5apETJ7FrgeeBM97vJIVaoPOslxl2t+9QK26ZVhrTEfyfFt+LgvwLlrFSD0Sl4lVq0KuL\nPEEkA7Ks0wwpn6y4xCeg3TqUmsgbnpcXY4WqqTdis9kkBdHTfozpIlHbHUfYNhELWK8u8gSRDMiy\nNhni+tJtx05ErVfjk5WzDuXqV7Mm8uwZGfD39aH+2UbNUSehUMjUgqhXF3mCSAZkWZuAyI4lkXSe\n9eKVN97CiBEjoiI31ITrscRQjY82K8sRXu/McmBgICQ58ahEQX4e/H19zP3drtQX5I+ni7waaNKS\nMAIS6xQhJdBiAsH+qMax4gJEUrDEUMlHKxau3r74U9kF0du4hR01FE/ZVr0xov40TVoSRkFinQJY\nkQxysDqzALHiGsm5bh82bN4WVcRfziWhR50RASFVHJBvIltRW5dy61PvYk5GdZonCBLrJKLWmhbD\n8qGqEdc9+w9iwtgxYZGQim/mOJtupVWFfoNSYYCRCOe0kvVpZh89kd7QBGOSEDdoVYsj0870oar9\n8UdONEplAsq12NLKuW5fXMJvlZA5mrQkjILEOknE42YoyM/DPbfezLQ21f74xaKeFdFkllNoMS5O\nUze6wYQVrM9EutcQhBzkBkkSWoSI4zjFCTi1USGCqG/YvC2mJKncOaI6pvw1TV2pmJMahKSXeHsn\nmh1qmksYBYm1jjS3eKJqbLhd2airuRvlZSWy9TDcrmz4e/vCqdaCiLJC9wSEmtB7DxxGKBQCq3+5\nYNE1t3hihFoOcQU9gUTbhkVamEaEzJkF6kBDGAG5QXSiucWD9S+8HBVT3OXrwWPPvxQuhMT6PK5/\n4D4MdTqZPQyBi6F7rPPtO9Qa9jfzuFC83+3Kjkm/1uILlhNNLdXvxESOR49UcYIYbJBlrRMNu5qY\ngts/MCBZCEn4PF67ST5yguVCYfnAg/39yHPm4g+vvKC4fyQ2mw08H1L8ZFcb4x153NVLFzKPR9Yn\nQWiDxFon5ARRWCclUEruhZzsbNXnYy1XOj7Ph9D2b9sk10cSeQ1KoYg8HyJBJgidIDeITshNjilN\nnCm5F1hRG0ohYpE1QPx9fbINceOd2CsvK8Heho3h2Go1x5WrTUIQhDQk1jpRO6cSmfbYDxV7Robi\nxJngw5WiyxdbW0NK4P19fdiweVtUWdJz3T5wHAdnVuz28U7sCaI7ubKaWfsj0x4bH05dygkifkis\ndaK8rAT/tPjeKCvT7crGI0sWqHIFlJeVhMPaxLAsVEHg3a5oF8m5bh/27D/I9GcPy8lB/QP3JTyx\npybBZ4gzK+a4VD+aIOKHfNY6kuikGSt2WiqDUThfw64myfobYs54vcwxaq0SpybBp7sndkyUik0Q\n8UNibSJYCRUzp8q/ALQInZQPWWuVODXnZJ1LaqLTCskwBGE0JNY6k2gtY7Hl29HRIbu9lkQVloUu\n5Zqof7YRDbuamONXOqfgB9+weVs4acdms+En46/GD13dlk2GIQgjIZ+1jqRiAk1LokrDrqaYschZ\nyVLjlzun4Af/8OSn2LP/YDhpJxQK4f3jH+OasX+b1skwFM1CpApVlnVFRQVycnIAAIWFhZg2bRo2\nbNiAyy67DACwbNkyTJo0ybhRpgmpqGUsdp24XS70nPejf2AgZluWi0PJSmaNX039i988t5l5vP93\n4hN4Xtuq8SrNATUWIFKJolj39fUBALZv3x5e9swzz+DXv/41ZsyYYdzI0pBUTaCJXSdyySpi8VVT\nEIo1fqXJVKmyq3qWY0021FiASCWKYv3JJ5/A7/ejpqYG/f39qKurw4kTJ9DR0YFt27ZhwoQJWL58\nOeyMGOPBRrIm0Fh+cSDW0l27qRGswnqR4qsmhdztcmnu6mKz2ZjCbHSZVSOhaBYilXC8Qi3OkydP\n4tixY5g9eza+/PJLLFy4EHfccQfKy8tRWFiINWvW4KqrrsLdd98dtV97ezuGDh2q62B7e3vhdDp1\nPaaetB07gVfeeAuB4MUaIY5MO+659WZMmTg+rmOKr5l1jgwbB46zRbk+HJl2ZNrt6PH3xhwzL9eN\nf16+WNX47RkZ4PkQBkIXHxM11/S7f9+PQ//x55jl0yb9L8z7pfwXmVnv8/J/fgHec10xy6X+nlow\n6zUbyWC7ZrXXe/78eRQXF8csVzSHR40ahZEjR4LjOIwaNQrDhg3DP/zDP4T91TfeeCP279/P3Leo\nqEhxYFro6OjQ/Zh6UlRUhBEjRuhay1h8zQ/96+YoMQXwVyGN9lEHgv0Y4nTC6XDERF8sq65i/h1Z\n4z/f2xsTxx0I9mN380G8edgjeZ1PFBXFRINUTJ8a1RNS7TWbhWXVVczSrlJ/Ty2Y9ZqNZLBds9rr\nbW9vZy5XFOumpib853/+J+rr63HmzBl0d3dj9uzZaGpqQkFBAdra2jB+fHxWoxUxupqclk/uLp8P\na5bdp+nlIR6/VMOBLl9PWMSlJtpWLKxWJc7pAjUWIFKJolhXVlbioYceQlVVFTiOwxNPPIHz589j\n6dKlcDqduPLKK3H77bcnY6wEtMVVD8/LS/jlofZ8g2WijUq7EqlCUawdDgeefvrpmOWlpaWGDIiQ\nhxW9Yc/IAMdxUfW0I7vEJGIJqm0fBtBEG0EYCYVwpBlSn+IAolqKORyZ+PDkp9h3qDUqLnj9Cy9j\n45Yd6O7pUe0WEZ/P39fHrLRHaeMEYRwk1mmIVDGmvr6L1m+Xr4fZdzHY34+g74IFrjapgxXHbeUe\nigRhRtI36JWIQk0lPBbxlCilHooEkXzIsk4RifqSxSTiL45nX5poI4jkQmKdAoyoMaElSoS1rzCu\nyBojPM+r9m0TBGEs5AYxGFaVNiM6prAq4TkdDsyacUPYXZGb44rpxRgZNSJuBdbl66H2WwRhEsiy\nNhApC1rKt5yIK0M2YWNh9JhY21TU1sn6vAdLHDVBmBUSawORsqClihzFE/qm1fct5WtW86KgOGqC\nSB0k1joiFk4pH3IoFGLW7NAa+qan71uNz5viqAkidZDPWidYXWKkEELdEg1909P3rdRxhuKoCSK1\nkGWtE2rjnAXR0yP0Tc/6yqyOMxQNQhDmgcRaJ+QEsiA/T/cqbc0tHnCcDTyvj+8bSF7stN4x5gQx\nGCCx1gkpn29Bfh72NmzU9VyCy4U1SanGXZFKsaQ+hgQRH+SzjgNW7LRUnLMRfl4pl4vNZlP0faei\nA3skRsSYE8RggMRaI1JiByBp9TKkXC6hUAgNu5qiXiJiUi2W1MeQIOKD3CAakRO7vQ0bk/IpLxdm\nJyyXci+kWiyT1VSYIKwGWdYaSbXYAcphdgIsi1lKFJMllsl0FxGElSCx1kiqxQ5glyiVQvwSSbVY\nUnlVgogPcoNohNXmKhWWoTjMrqK2TpV7wQxNX6m8KkFoh8RaI2YQOxZaXiIklgSRfphKrNMlWcKM\nYmfWlwhBEPqgSqwrKiqQk5MDACgsLMQdd9yB9evXIyMjA6WlpVi6dGnCA6FkicQx40uEIAh9UBTr\nvr4+AMD27dvDy2699VZs2rQJl19+ORYtWoQTJ05g/PjxCQ1ELiSOBIggiMGOYjTIJ598Ar/fj5qa\nGsybNw9Hjx5FIBDAFVdcAY7jUFpaira2toQHYoaQOIIgCLOiaFk7nU7ce++9mD17Nr788kssXLgQ\nbrc7vD47Oxtff/01c9+Ojg7VA7nE7Yb3XBdzuXCc3t5eTce0AnTNgwO6ZuuT6PUqivWoUaMwcuRI\ncByHUaNGIScnBz/88EN4fU9PT5R4R1JUVKR6IMuqq5jRDMuqq8LH6ejo0HRMK0DXPDiga7Y+aq+3\nvb2duVzRDdLU1IQnn3wSAHDmzBn4/X4MHToUX331FXieR2trK6677jqNw46FkiUIgiCkUbSsKysr\n8dBDD6Gqqgocx+Hxxx+HzWbD8uXLMTAwgNLSUkycOFGXwVA0A0EQBBtFsXY4HHj66adjlr/22muG\nDMhqpEvsOEEQ5sZUSTFWIxmx4/QyIIjBARVyMhCja0enupEAQRDJg8TaQIyOHU91IwGCIJIHibWB\nGF1OlRKJCGLwQGJtIEbXjjZDbW2CIJIDibWBGB07nupGAgRBJA+KBjEYcelSwZ+sh2BTWVSCGDyQ\nWBuM0eF7lEhEEIMDEmuDibf0qxA/3XnWi4J8spgJYrBDYm0w8URsUCMGgiDE0ASjwcQTsUHx0wRB\niCGxNph4IjYoflofmls8qKitw5TZ1aioraPMTiKtITeIwcQTsTE8Lw+dZ2OFmeKn1UOuJMJqkFgn\nAa0RG7VzKpmNGCh+Wj3U05OwGiTWJiTSGqdokPggVxJhNUisTYpgjQ+21kd6Qa4kwmrQBCNhSSgV\nn7AaZFkTloRS8QmrQWJNWBZKxSesBIk1YQmovRlhdUisibSHYqqJwYCqCUav14vrr78en332GU6c\nOIGf/exnmDt3LubOnYt9+/YZPUaCkIXS84nBgKJlHQwGsXr1ajidTgDAxx9/jPnz56OmpsbwwRGE\nGiimmhgMKFrWTz31FO688078zd/8DQDgo48+wuHDh3HXXXfh4Ycfhs/nM3yQBCEHtTcjBgMcz/O8\n1Mo9e/ags7MTixcvxty5c1FfX48PPvgAY8eOxTXXXIMXX3wRXV1dWLlyZcy+7e3tGDp0qK6D7e3t\nDVv4gwW6ZmXajp3AK2+8hUCwP7zMkWnHPbfejCkTxxsxRN2h+2x91F7v+fPnUVxcHLNc1g3y+9//\nHhzHoa2tDR0dHVi5ciVefPFFXHrppQCA6dOnY926dZL76515Nxiz+eialSkqKsKIESPSOhqE7rP1\nUXu97e3tzOWyYr1z587w/wuW9eLFi/Hoo49iwoQJaGtrw/jx6WG5JAKFhZkfiqkmrI7m0L36+nqs\nW7cOmZmZyM/Pl7WsrQCFhREEYQZUi/X27dvD/797925DBmNGqNQmQRBmgAo5KUBhYQRBmAESawUo\nLIwgCDNAYq0AldokCMIMUG0QBajUJkEQZoDEWgUUFkYQRKohNwhBEEQaQGJNEASRBpBYEwRBpAEk\n1gRBEGkAiTVBEEQaIFsiNRGkKkcRBEEQ8rBKpBom1gRBEIR+kBuEIAgiDSCxJgiCSANMn8Ho9Xox\na9YsbNmyBXa7HatWrQLHcRgzZgzWrFkDm81a75uKigrk5OQAAAoLC3HHHXdg/fr1yMjIQGlpKZYu\nXZriEepPY2MjDh48iGAwiKqqKkyaNMnS93nPnj14/fXXAQB9fX3o6OjA9u3bLXufg8EgVq1ahb/8\n5S+w2WxYt26d5X/LgUAADz30EL7++mu4XC6sXr0aP/zwQ2L3mDcxgUCAX7x4Mf/zn/+cP3XqFH/f\nfffx7733Hs/zPP/oo4/yf/jDH1I8Qn3p7e3lb7311qhlM2fO5P/rv/6LD4VC/IIFC/iPPvooRaMz\nhvfee4+/7777+IGBAd7n8/HPPvus5e9zJPX19fzu3bstfZ8PHDjAP/DAAzzP83xrayu/dOlSy9/j\n7du384888gjP8zz/2Wef8TU1NQnfY1O/ysSd1U+cOIFJkyYBAMrKyuDxeFI5PN355JNP4Pf7UVNT\ng3nz5uHo0aMIBAK44oorwHEcSktL0dbWluph6kprayuuuuoqLFmyBLW1tZg6darl77PA8ePHcerU\nKfziF7+w9H0eNWoUBgYGEAqF4PP5YLfbLX+PT506hbKyMgDA6NGjcfz48YTvsWndIHv27MEll1yC\nn/3sZ/jtb38LAOB5HhzHAQCys7PR3d2dyiHqjtPpxL333ovZs2fjyy+/xMKFC+F2u8Prs7Oz8fXX\nX6dwhPrz/fff4/Tp02hoaMA333yD+++/3/L3WaCxsRFLliyBz+eDy+UKL7fafR46dCj+8pe/4Oab\nb8b333+PhoYGHD161NL3uKioCIcOHcJNN92EY8eOobu7G5dffnl4fTz32LRizeqs/t1334XX9/T0\nRAmZFRg1ahRGjhwJjuMwatQo5OTk4Icffgivt+I1Dxs2DKNHj4bD4cDo0aORlZWFzs7O8HorXjMA\ndHV14fPPP8fkyZPh8/nQ09MTXme1a37llVdQWlqKf/zHf8S3336L6upqBIPB8HqrXS8A3Hbbbfjs\ns88wb948/OQnP8HVV18Nv98fXh/PNZvWDbJz507s2LED27dvR1FREZ566imUlZXhyJEjAICWlhZc\nd911KR6lvjQ1NeHJJ58EAJw5cwZ+vx9Dhw7FV199BZ7n0draarlrLi4uxh//+EfwPB++5ilTplj6\nPgPA0aNHUVJyoeyuy+VCZmamZe+z2+0OT5rn5uaiv78f48aNs/Q9Pn78OIqLi7F9+3bcdNNN+PGP\nf5zwPU6LpJi5c+eivr4eNpsNjz76KILBIEaPHo3HHnsMGRkZqR6ebggzyKdPnwbHcVi+fDlsNhse\nf/xxDAwMoLS0FA8++GCqh6k7GzZswJEjR8DzPB588EEUFhZa+j4DwEsvvQS73Y577rkHAPDBBx9Y\n9j739PTg4Ycfxv/8z/8gGAxi3rx5uOaaayx9j7/77jvU1dXB7/cjJycH69evx7fffpvQPU4LsSYI\nghjsmNYNQhAEQVyExJogCCINILEmCIJIA0isCYIg0gASa4IgiDSAxJogCCINILEmCIJIA0isCYIg\n0oD/Dz8PPMQ/5hG7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2509ede00b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Predictions from our model\n",
    "predictions = lm.predict(X_test)\n",
    "plt.scatter(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2509eed87b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Residuals\n",
    "# Plot a histogram of the residuals and make sure it looks normally distributed. \n",
    "sns.distplot((y_test-predictions),bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 4.66777122769\n",
      "MSE: 42.8226654566\n",
      "RMSE: 6.54390292231\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "# Calculate the Mean Absolute Error, Mean Squared Error, and the Root Mean Squared Error. \n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Linear Regression Life Expectancy Spain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Country</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2425</th>\n",
       "      <td>2015</td>\n",
       "      <td>152</td>\n",
       "      <td>82.8</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2426</th>\n",
       "      <td>2014</td>\n",
       "      <td>152</td>\n",
       "      <td>82.6</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2427</th>\n",
       "      <td>2013</td>\n",
       "      <td>152</td>\n",
       "      <td>82.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2428</th>\n",
       "      <td>2012</td>\n",
       "      <td>152</td>\n",
       "      <td>82.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2429</th>\n",
       "      <td>2011</td>\n",
       "      <td>152</td>\n",
       "      <td>82.1</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2430</th>\n",
       "      <td>2010</td>\n",
       "      <td>152</td>\n",
       "      <td>81.9</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2431</th>\n",
       "      <td>2009</td>\n",
       "      <td>152</td>\n",
       "      <td>81.6</td>\n",
       "      <td>66.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2432</th>\n",
       "      <td>2008</td>\n",
       "      <td>152</td>\n",
       "      <td>81.3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2433</th>\n",
       "      <td>2007</td>\n",
       "      <td>152</td>\n",
       "      <td>89.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2434</th>\n",
       "      <td>2006</td>\n",
       "      <td>152</td>\n",
       "      <td>88.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2435</th>\n",
       "      <td>2005</td>\n",
       "      <td>152</td>\n",
       "      <td>81.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2436</th>\n",
       "      <td>2004</td>\n",
       "      <td>152</td>\n",
       "      <td>81.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2437</th>\n",
       "      <td>2003</td>\n",
       "      <td>152</td>\n",
       "      <td>79.4</td>\n",
       "      <td>83.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2438</th>\n",
       "      <td>2002</td>\n",
       "      <td>152</td>\n",
       "      <td>79.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2439</th>\n",
       "      <td>2001</td>\n",
       "      <td>152</td>\n",
       "      <td>79.4</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2440</th>\n",
       "      <td>2000</td>\n",
       "      <td>152</td>\n",
       "      <td>79.1</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  Country  Life expectancy   Adult Mortality  infant deaths\n",
       "2425  2015      152              82.8             56.0              1\n",
       "2426  2014      152              82.6             58.0              1\n",
       "2427  2013      152              82.4              6.0              1\n",
       "2428  2012      152              82.0             61.0              1\n",
       "2429  2011      152              82.1             63.0              1\n",
       "2430  2010      152              81.9             64.0              1\n",
       "2431  2009      152              81.6             66.0              2\n",
       "2432  2008      152              81.3              7.0              2\n",
       "2433  2007      152              89.0             72.0              2\n",
       "2434  2006      152              88.0             73.0              2\n",
       "2435  2005      152              81.0             77.0              2\n",
       "2436  2004      152              81.0             79.0              2\n",
       "2437  2003      152              79.4             83.0              2\n",
       "2438  2002      152              79.5             83.0              2\n",
       "2439  2001      152              79.4             84.0              2\n",
       "2440  2000      152              79.1             86.0              2"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfspain = df[['Year','Country','Life expectancy ','Adult Mortality', 'infant deaths']].copy()\n",
    "dfspain.drop(dfspain[dfspain.Country != 152].index, inplace=True)\n",
    "dfspain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Adult and infant Mortality Linear Regression \n",
    "X = dfspain[['Adult Mortality','infant deaths']]\n",
    "y = dfspain['Life expectancy ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Train Test Split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating and Training the Model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lm = LinearRegression()\n",
    "lm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81.9429990873\n"
     ]
    }
   ],
   "source": [
    "#Model Evaluation\n",
    "# print the intercept\n",
    "print(lm.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adult Mortality</th>\n",
       "      <td>-0.000268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>infant deaths</th>\n",
       "      <td>0.423163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Coefficient\n",
       "Adult Mortality    -0.000268\n",
       "infant deaths       0.423163"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame(lm.coef_,X.columns,columns=['Coefficient'])\n",
    "coeff_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Interpreting the coefficients:\n",
    "# Holding all other features fixed, a 1 unit increase in Adult Mortality is associated with a decrease of 0.000268 Life expectancy\n",
    "# Holding all other features fixed, a 1 unit increase in infant deaths is associated with an increase of 0.42 Life expectancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2509ffe21d0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KVF9fL0kqLS1Vd3d33BcNAIgt5hl8enq69uzZE/XYypUrhx1bW1s75LGGhoYxLg0AcCv4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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2509ef14630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Predictions from our model\n",
    "predictions = lm.predict(X_test)\n",
    "plt.scatter(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZbFYuS25paRF79uwRQgjR0dEh0tPTRU1NjXjllVdEbm6uEEKIzz77TKxatUrY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\niEgyDG4iIskwuImIJPP/AZWkOlLf9+EVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2509efea0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Residuals\n",
    "# Plot a histogram of the residuals and make sure it looks normally distributed. \n",
    "sns.distplot((y_test-predictions),bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1.75760723781\n",
      "MSE: 5.05251642636\n",
      "RMSE: 2.24778033321\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "# Calculate the Mean Absolute Error, Mean Squared Error, and the Root Mean Squared Error. \n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regresion War Countries (Influence of GDP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Country</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>65.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>62</td>\n",
       "      <td>584.259210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>59.9</td>\n",
       "      <td>271.0</td>\n",
       "      <td>64</td>\n",
       "      <td>612.696514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>59.9</td>\n",
       "      <td>268.0</td>\n",
       "      <td>66</td>\n",
       "      <td>631.744976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>59.5</td>\n",
       "      <td>272.0</td>\n",
       "      <td>69</td>\n",
       "      <td>669.959000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>59.2</td>\n",
       "      <td>275.0</td>\n",
       "      <td>71</td>\n",
       "      <td>63.537231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>58.8</td>\n",
       "      <td>279.0</td>\n",
       "      <td>74</td>\n",
       "      <td>553.328940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>58.6</td>\n",
       "      <td>281.0</td>\n",
       "      <td>77</td>\n",
       "      <td>445.893298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>58.1</td>\n",
       "      <td>287.0</td>\n",
       "      <td>80</td>\n",
       "      <td>373.361116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>57.5</td>\n",
       "      <td>295.0</td>\n",
       "      <td>82</td>\n",
       "      <td>369.835796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>57.3</td>\n",
       "      <td>295.0</td>\n",
       "      <td>84</td>\n",
       "      <td>272.563770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "      <td>57.3</td>\n",
       "      <td>291.0</td>\n",
       "      <td>85</td>\n",
       "      <td>25.294130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>57.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>87</td>\n",
       "      <td>219.141353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2003</td>\n",
       "      <td>1</td>\n",
       "      <td>56.7</td>\n",
       "      <td>295.0</td>\n",
       "      <td>87</td>\n",
       "      <td>198.728544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2002</td>\n",
       "      <td>1</td>\n",
       "      <td>56.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>88</td>\n",
       "      <td>187.845950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "      <td>55.3</td>\n",
       "      <td>316.0</td>\n",
       "      <td>88</td>\n",
       "      <td>117.496980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "      <td>54.8</td>\n",
       "      <td>321.0</td>\n",
       "      <td>88</td>\n",
       "      <td>114.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>77.8</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3954.227830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014</td>\n",
       "      <td>2</td>\n",
       "      <td>77.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4575.763787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013</td>\n",
       "      <td>2</td>\n",
       "      <td>77.2</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4414.723140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>76.9</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4247.614380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>76.6</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4437.178680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "      <td>76.2</td>\n",
       "      <td>91.0</td>\n",
       "      <td>1</td>\n",
       "      <td>494.358832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>76.1</td>\n",
       "      <td>91.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4114.136545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>75.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>437.539647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2007</td>\n",
       "      <td>2</td>\n",
       "      <td>75.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>363.136850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2006</td>\n",
       "      <td>2</td>\n",
       "      <td>74.2</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1</td>\n",
       "      <td>35.129300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2005</td>\n",
       "      <td>2</td>\n",
       "      <td>73.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "      <td>279.142931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2004</td>\n",
       "      <td>2</td>\n",
       "      <td>73.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2416.588235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2003</td>\n",
       "      <td>2</td>\n",
       "      <td>72.8</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>189.681557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2002</td>\n",
       "      <td>2</td>\n",
       "      <td>73.3</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1453.642777</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2908</th>\n",
       "      <td>2013</td>\n",
       "      <td>182</td>\n",
       "      <td>63.0</td>\n",
       "      <td>328.0</td>\n",
       "      <td>29</td>\n",
       "      <td>185.793359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>2012</td>\n",
       "      <td>182</td>\n",
       "      <td>59.2</td>\n",
       "      <td>349.0</td>\n",
       "      <td>29</td>\n",
       "      <td>1734.936120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2910</th>\n",
       "      <td>2011</td>\n",
       "      <td>182</td>\n",
       "      <td>58.2</td>\n",
       "      <td>366.0</td>\n",
       "      <td>29</td>\n",
       "      <td>1644.619672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2911</th>\n",
       "      <td>2010</td>\n",
       "      <td>182</td>\n",
       "      <td>58.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>30</td>\n",
       "      <td>1463.213573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2912</th>\n",
       "      <td>2009</td>\n",
       "      <td>182</td>\n",
       "      <td>57.4</td>\n",
       "      <td>368.0</td>\n",
       "      <td>30</td>\n",
       "      <td>1139.112330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2913</th>\n",
       "      <td>2008</td>\n",
       "      <td>182</td>\n",
       "      <td>55.7</td>\n",
       "      <td>45.0</td>\n",
       "      <td>31</td>\n",
       "      <td>1369.682490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2914</th>\n",
       "      <td>2007</td>\n",
       "      <td>182</td>\n",
       "      <td>52.6</td>\n",
       "      <td>487.0</td>\n",
       "      <td>32</td>\n",
       "      <td>114.587985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2915</th>\n",
       "      <td>2006</td>\n",
       "      <td>182</td>\n",
       "      <td>58.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>33</td>\n",
       "      <td>13.154199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>2005</td>\n",
       "      <td>182</td>\n",
       "      <td>49.3</td>\n",
       "      <td>554.0</td>\n",
       "      <td>34</td>\n",
       "      <td>691.317816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>2004</td>\n",
       "      <td>182</td>\n",
       "      <td>47.9</td>\n",
       "      <td>578.0</td>\n",
       "      <td>36</td>\n",
       "      <td>53.277222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>2003</td>\n",
       "      <td>182</td>\n",
       "      <td>46.4</td>\n",
       "      <td>64.0</td>\n",
       "      <td>39</td>\n",
       "      <td>429.158343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2919</th>\n",
       "      <td>2002</td>\n",
       "      <td>182</td>\n",
       "      <td>45.5</td>\n",
       "      <td>69.0</td>\n",
       "      <td>41</td>\n",
       "      <td>377.135244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2920</th>\n",
       "      <td>2001</td>\n",
       "      <td>182</td>\n",
       "      <td>44.6</td>\n",
       "      <td>611.0</td>\n",
       "      <td>43</td>\n",
       "      <td>378.273624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>2000</td>\n",
       "      <td>182</td>\n",
       "      <td>43.8</td>\n",
       "      <td>614.0</td>\n",
       "      <td>44</td>\n",
       "      <td>341.955625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>2015</td>\n",
       "      <td>183</td>\n",
       "      <td>67.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>22</td>\n",
       "      <td>118.693830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>2014</td>\n",
       "      <td>183</td>\n",
       "      <td>59.2</td>\n",
       "      <td>371.0</td>\n",
       "      <td>23</td>\n",
       "      <td>127.474620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>2013</td>\n",
       "      <td>183</td>\n",
       "      <td>58.0</td>\n",
       "      <td>399.0</td>\n",
       "      <td>25</td>\n",
       "      <td>111.227396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>2012</td>\n",
       "      <td>183</td>\n",
       "      <td>56.6</td>\n",
       "      <td>429.0</td>\n",
       "      <td>26</td>\n",
       "      <td>955.648466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2926</th>\n",
       "      <td>2011</td>\n",
       "      <td>183</td>\n",
       "      <td>54.9</td>\n",
       "      <td>464.0</td>\n",
       "      <td>28</td>\n",
       "      <td>839.927936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2927</th>\n",
       "      <td>2010</td>\n",
       "      <td>183</td>\n",
       "      <td>52.4</td>\n",
       "      <td>527.0</td>\n",
       "      <td>29</td>\n",
       "      <td>713.635620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2928</th>\n",
       "      <td>2009</td>\n",
       "      <td>183</td>\n",
       "      <td>50.0</td>\n",
       "      <td>587.0</td>\n",
       "      <td>30</td>\n",
       "      <td>65.824121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2929</th>\n",
       "      <td>2008</td>\n",
       "      <td>183</td>\n",
       "      <td>48.2</td>\n",
       "      <td>632.0</td>\n",
       "      <td>30</td>\n",
       "      <td>325.678573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2930</th>\n",
       "      <td>2007</td>\n",
       "      <td>183</td>\n",
       "      <td>46.6</td>\n",
       "      <td>67.0</td>\n",
       "      <td>29</td>\n",
       "      <td>396.998217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2931</th>\n",
       "      <td>2006</td>\n",
       "      <td>183</td>\n",
       "      <td>45.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>28</td>\n",
       "      <td>414.796232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2932</th>\n",
       "      <td>2005</td>\n",
       "      <td>183</td>\n",
       "      <td>44.6</td>\n",
       "      <td>717.0</td>\n",
       "      <td>28</td>\n",
       "      <td>444.765750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2933</th>\n",
       "      <td>2004</td>\n",
       "      <td>183</td>\n",
       "      <td>44.3</td>\n",
       "      <td>723.0</td>\n",
       "      <td>27</td>\n",
       "      <td>454.366654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>2003</td>\n",
       "      <td>183</td>\n",
       "      <td>44.5</td>\n",
       "      <td>715.0</td>\n",
       "      <td>26</td>\n",
       "      <td>453.351155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2935</th>\n",
       "      <td>2002</td>\n",
       "      <td>183</td>\n",
       "      <td>44.8</td>\n",
       "      <td>73.0</td>\n",
       "      <td>25</td>\n",
       "      <td>57.348340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>2001</td>\n",
       "      <td>183</td>\n",
       "      <td>45.3</td>\n",
       "      <td>686.0</td>\n",
       "      <td>25</td>\n",
       "      <td>548.587312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>2000</td>\n",
       "      <td>183</td>\n",
       "      <td>46.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>24</td>\n",
       "      <td>547.358879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2938 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  Country  Life expectancy   Adult Mortality  infant deaths  \\\n",
       "0     2015        1              65.0            263.0             62   \n",
       "1     2014        1              59.9            271.0             64   \n",
       "2     2013        1              59.9            268.0             66   \n",
       "3     2012        1              59.5            272.0             69   \n",
       "4     2011        1              59.2            275.0             71   \n",
       "5     2010        1              58.8            279.0             74   \n",
       "6     2009        1              58.6            281.0             77   \n",
       "7     2008        1              58.1            287.0             80   \n",
       "8     2007        1              57.5            295.0             82   \n",
       "9     2006        1              57.3            295.0             84   \n",
       "10    2005        1              57.3            291.0             85   \n",
       "11    2004        1              57.0            293.0             87   \n",
       "12    2003        1              56.7            295.0             87   \n",
       "13    2002        1              56.2              3.0             88   \n",
       "14    2001        1              55.3            316.0             88   \n",
       "15    2000        1              54.8            321.0             88   \n",
       "16    2015        2              77.8             74.0              0   \n",
       "17    2014        2              77.5              8.0              0   \n",
       "18    2013        2              77.2             84.0              0   \n",
       "19    2012        2              76.9             86.0              0   \n",
       "20    2011        2              76.6             88.0              0   \n",
       "21    2010        2              76.2             91.0              1   \n",
       "22    2009        2              76.1             91.0              1   \n",
       "23    2008        2              75.3              1.0              1   \n",
       "24    2007        2              75.9              9.0              1   \n",
       "25    2006        2              74.2             99.0              1   \n",
       "26    2005        2              73.5             15.0              1   \n",
       "27    2004        2              73.0             17.0              1   \n",
       "28    2003        2              72.8             18.0              1   \n",
       "29    2002        2              73.3             15.0              1   \n",
       "...    ...      ...               ...              ...            ...   \n",
       "2908  2013      182              63.0            328.0             29   \n",
       "2909  2012      182              59.2            349.0             29   \n",
       "2910  2011      182              58.2            366.0             29   \n",
       "2911  2010      182              58.0            363.0             30   \n",
       "2912  2009      182              57.4            368.0             30   \n",
       "2913  2008      182              55.7             45.0             31   \n",
       "2914  2007      182              52.6            487.0             32   \n",
       "2915  2006      182              58.0            526.0             33   \n",
       "2916  2005      182              49.3            554.0             34   \n",
       "2917  2004      182              47.9            578.0             36   \n",
       "2918  2003      182              46.4             64.0             39   \n",
       "2919  2002      182              45.5             69.0             41   \n",
       "2920  2001      182              44.6            611.0             43   \n",
       "2921  2000      182              43.8            614.0             44   \n",
       "2922  2015      183              67.0            336.0             22   \n",
       "2923  2014      183              59.2            371.0             23   \n",
       "2924  2013      183              58.0            399.0             25   \n",
       "2925  2012      183              56.6            429.0             26   \n",
       "2926  2011      183              54.9            464.0             28   \n",
       "2927  2010      183              52.4            527.0             29   \n",
       "2928  2009      183              50.0            587.0             30   \n",
       "2929  2008      183              48.2            632.0             30   \n",
       "2930  2007      183              46.6             67.0             29   \n",
       "2931  2006      183              45.4              7.0             28   \n",
       "2932  2005      183              44.6            717.0             28   \n",
       "2933  2004      183              44.3            723.0             27   \n",
       "2934  2003      183              44.5            715.0             26   \n",
       "2935  2002      183              44.8             73.0             25   \n",
       "2936  2001      183              45.3            686.0             25   \n",
       "2937  2000      183              46.0            665.0             24   \n",
       "\n",
       "              GDP  \n",
       "0      584.259210  \n",
       "1      612.696514  \n",
       "2      631.744976  \n",
       "3      669.959000  \n",
       "4       63.537231  \n",
       "5      553.328940  \n",
       "6      445.893298  \n",
       "7      373.361116  \n",
       "8      369.835796  \n",
       "9      272.563770  \n",
       "10      25.294130  \n",
       "11     219.141353  \n",
       "12     198.728544  \n",
       "13     187.845950  \n",
       "14     117.496980  \n",
       "15     114.560000  \n",
       "16    3954.227830  \n",
       "17    4575.763787  \n",
       "18    4414.723140  \n",
       "19    4247.614380  \n",
       "20    4437.178680  \n",
       "21     494.358832  \n",
       "22    4114.136545  \n",
       "23     437.539647  \n",
       "24     363.136850  \n",
       "25      35.129300  \n",
       "26     279.142931  \n",
       "27    2416.588235  \n",
       "28     189.681557  \n",
       "29    1453.642777  \n",
       "...           ...  \n",
       "2908   185.793359  \n",
       "2909  1734.936120  \n",
       "2910  1644.619672  \n",
       "2911  1463.213573  \n",
       "2912  1139.112330  \n",
       "2913  1369.682490  \n",
       "2914   114.587985  \n",
       "2915    13.154199  \n",
       "2916   691.317816  \n",
       "2917    53.277222  \n",
       "2918   429.158343  \n",
       "2919   377.135244  \n",
       "2920   378.273624  \n",
       "2921   341.955625  \n",
       "2922   118.693830  \n",
       "2923   127.474620  \n",
       "2924   111.227396  \n",
       "2925   955.648466  \n",
       "2926   839.927936  \n",
       "2927   713.635620  \n",
       "2928    65.824121  \n",
       "2929   325.678573  \n",
       "2930   396.998217  \n",
       "2931   414.796232  \n",
       "2932   444.765750  \n",
       "2933   454.366654  \n",
       "2934   453.351155  \n",
       "2935    57.348340  \n",
       "2936   548.587312  \n",
       "2937   547.358879  \n",
       "\n",
       "[2938 rows x 6 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a new dataset with War Countries: Afganistan (1), Algeria (3), Chad (34), Colombia (37), Ethiopia (56), India (75), Philippines (127), Iraq(78), Israel(80), Nigeria (119), Pakistan (122), Congo (39), Russia (134), Somalia (149), Sri Lanka(153), Sudan(154), Thailand (161), Turkey (168),Uganda(170),Ukraine(171),Yemen(181)  Source: https://www.20minutos.es/noticia/404456/0/mundo/guerras/activas/\n",
    "dfwarcountriesall = df[['Year','Country','Life expectancy ','Adult Mortality', 'infant deaths', 'GDP']].copy()\n",
    "#Drop not war countries\n",
    "dfwarcountriesall.drop(dfwarcountries[(dfwarcountries.Country != 1) & (dfwarcountries.Country != 3) & (dfwarcountries.Country != 34) & (dfwarcountries.Country != 37) & (dfwarcountries.Country != 56) & (dfwarcountries.Country != 75) & (dfwarcountries.Country != 127) & (dfwarcountries.Country != 78) & (dfwarcountries.Country != 80) & (dfwarcountries.Country != 119) & (dfwarcountries.Country != 122) & (dfwarcountries.Country != 39) & (dfwarcountries.Country != 134) & (dfwarcountries.Country != 149) & (dfwarcountries.Country != 153) & (dfwarcountries.Country != 154) & (dfwarcountries.Country != 161) & (dfwarcountries.Country != 168) & (dfwarcountries.Country != 170) & (dfwarcountries.Country != 171) & (dfwarcountries.Country != 181)].index, inplace=True)\n",
    "dfwarcountriesall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Define coordinate axes\n",
    "X = dfwarcountriesall[['Year']]\n",
    "y = dfwarcountriesall['GDP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Train Test Split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:\n",
      "      Year\n",
      "2492  2012\n",
      "1222  2011\n",
      "1113  2008\n",
      "310   2009\n",
      "2552  2000\n",
      "2205  2010\n",
      "2273  2007\n",
      "1993  2013\n",
      "1215  2002\n",
      "1556  2013\n",
      "1850  2010\n",
      "2903  2002\n",
      "1192  2009\n",
      "1052  2005\n",
      "1413  2012\n",
      "1753  2010\n",
      "130   2013\n",
      "2206  2009\n",
      "225   2014\n",
      "2051  2003\n",
      "420   2011\n",
      "2876  2013\n",
      "743   2009\n",
      "2076  2010\n",
      "577   2014\n",
      "1666  2000\n",
      "2510  2010\n",
      "1043  2014\n",
      "710   2010\n",
      "1731  2000\n",
      "...    ...\n",
      "2323  2005\n",
      "1273  2008\n",
      "1580  2005\n",
      "1442  2015\n",
      "2107  2011\n",
      "1949  2008\n",
      "371   2012\n",
      "2184  2015\n",
      "1530  2007\n",
      "623   2000\n",
      "1875  2001\n",
      "49    2014\n",
      "2088  2014\n",
      "477   2002\n",
      "908   2005\n",
      "1728  2003\n",
      "732   2004\n",
      "2364  2012\n",
      "936   2009\n",
      "2623  2009\n",
      "1668  2014\n",
      "1576  2009\n",
      "973   2004\n",
      "75    2004\n",
      "2045  2009\n",
      "599   2008\n",
      "1599  2002\n",
      "1361  2000\n",
      "1547  2006\n",
      "863   2002\n",
      "\n",
      "[2056 rows x 1 columns]\n",
      "X_test:\n",
      "      Year\n",
      "1201  2000\n",
      "1628  2005\n",
      "1317  2012\n",
      "1392  2001\n",
      "1308  2005\n",
      "1998  2008\n",
      "235   2004\n",
      "1329  2000\n",
      "958   2003\n",
      "2511  2009\n",
      "2458  2014\n",
      "2291  2005\n",
      "212   2011\n",
      "2266  2014\n",
      "56    2007\n",
      "1934  2007\n",
      "913   2000\n",
      "1138  2015\n",
      "458   2005\n",
      "659   2013\n",
      "768   2000\n",
      "1081  2008\n",
      "1397  2012\n",
      "2548  2004\n",
      "198   2009\n",
      "2416  2008\n",
      "228   2011\n",
      "221   2002\n",
      "1816  2012\n",
      "2651  2013\n",
      "...    ...\n",
      "1265  2000\n",
      "564   2011\n",
      "1832  2012\n",
      "2006  2000\n",
      "2896  2009\n",
      "2572  2012\n",
      "2035  2003\n",
      "1933  2008\n",
      "2877  2012\n",
      "722   2014\n",
      "1807  2004\n",
      "207   2000\n",
      "633   2007\n",
      "1247  2002\n",
      "1899  2009\n",
      "252   2003\n",
      "109   2002\n",
      "2675  2005\n",
      "774   2011\n",
      "890   2007\n",
      "1242  2007\n",
      "2033  2005\n",
      "650   2006\n",
      "2132  2002\n",
      "1697  2001\n",
      "917   2012\n",
      "847   2002\n",
      "1886  2006\n",
      "2714  2015\n",
      "1844  2000\n",
      "\n",
      "[882 rows x 1 columns]\n",
      "y_train:\n",
      "2492     3851.515470\n",
      "1222     7483.158469\n",
      "1113     2576.124957\n",
      "310      7483.158469\n",
      "2552     1177.629268\n",
      "2205     3453.487434\n",
      "2273     5458.121718\n",
      "1993     4479.958580\n",
      "1215      899.555686\n",
      "1556      461.723722\n",
      "1850    33691.348370\n",
      "2903     7483.158469\n",
      "1192       19.317765\n",
      "1052     6754.512393\n",
      "1413     7483.158469\n",
      "1753     2834.247200\n",
      "130       554.715320\n",
      "2206      335.112838\n",
      "225      8318.429294\n",
      "2051     5693.524160\n",
      "420        26.479973\n",
      "2876     7483.158469\n",
      "743     58163.293590\n",
      "2076      736.227840\n",
      "577      7913.383432\n",
      "1666      477.476124\n",
      "2510     5276.255910\n",
      "1043     8569.776998\n",
      "710      7483.158469\n",
      "1731      474.213336\n",
      "            ...     \n",
      "2323    29869.853980\n",
      "1273    29657.442100\n",
      "1580       28.367384\n",
      "1442    13666.583360\n",
      "2107     7483.158469\n",
      "1949      139.312880\n",
      "371     47651.259900\n",
      "2184     7483.158469\n",
      "1530    12297.915350\n",
      "623      7483.158469\n",
      "1875      143.675800\n",
      "49        479.312240\n",
      "2088     7483.158469\n",
      "477       339.676984\n",
      "908      3658.638320\n",
      "1728      646.561295\n",
      "732      7483.158469\n",
      "2364     1858.689868\n",
      "936     41631.131410\n",
      "2623      499.646871\n",
      "1668     1153.938220\n",
      "1576       42.737966\n",
      "973      7483.158469\n",
      "75       1352.837400\n",
      "2045    11527.593230\n",
      "599       795.975190\n",
      "1599     4167.364387\n",
      "1361     1229.958000\n",
      "1547    89739.711700\n",
      "863        21.768798\n",
      "Name: GDP, Length: 2056, dtype: float64\n",
      "y_test:\n",
      "1201      438.864634\n",
      "1628      487.942556\n",
      "1317     4863.476650\n",
      "1392      734.949246\n",
      "1308      482.247620\n",
      "1998      359.992184\n",
      "235      2378.339270\n",
      "1329    38532.488000\n",
      "958      4892.116180\n",
      "2511     4627.592000\n",
      "2458     2176.898290\n",
      "2291     1192.517400\n",
      "212     15534.157400\n",
      "2266       62.173221\n",
      "56       2878.837144\n",
      "1934     1585.132260\n",
      "913       276.133800\n",
      "1138     2326.158560\n",
      "458       248.134590\n",
      "659       676.153440\n",
      "768       768.176767\n",
      "1081      437.446724\n",
      "1397    51264.713400\n",
      "2548      148.852738\n",
      "198       681.125368\n",
      "2416     1678.711862\n",
      "228      6519.717530\n",
      "221     11675.393950\n",
      "1816      681.792587\n",
      "2651     1967.832830\n",
      "            ...     \n",
      "1265    26241.918830\n",
      "564      5633.796160\n",
      "1832    49474.756100\n",
      "2006     1545.626423\n",
      "2896     7483.158469\n",
      "2572     5859.915622\n",
      "2035       11.553196\n",
      "1933     2275.353600\n",
      "2877     7483.158469\n",
      "722      7483.158469\n",
      "1807     3288.257373\n",
      "207        45.633710\n",
      "633       612.623418\n",
      "1247     7483.158469\n",
      "1899      197.661422\n",
      "252      3743.956930\n",
      "109       783.261699\n",
      "2675     3194.562243\n",
      "774      5759.643000\n",
      "890       243.326822\n",
      "1242     3129.224922\n",
      "2033     1194.697155\n",
      "650     11363.418450\n",
      "2132     2124.873613\n",
      "1697      731.378864\n",
      "917     47415.559870\n",
      "847      2711.171140\n",
      "1886      258.463873\n",
      "2714      693.896383\n",
      "1844    25921.127940\n",
      "Name: GDP, Length: 882, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train:\")\n",
    "print(X_train)\n",
    "print(\"X_test:\")\n",
    "print(X_test)\n",
    "print(\"y_train:\")\n",
    "print(y_train)\n",
    "print(\"y_test:\")\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5rKTEvi+YyBlGovdZCwcU0Qfl6lbBtWd1XNHJSPSkBQ1eZ6J8fXTG6kKNATrZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2509eff1cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#MLearning\n",
    "#Creating and Training the Model\n",
    "# Fitting Simple Linear Regression to the Training set\n",
    "from sklearn.linear_model import LinearRegression\n",
    "# We create the object\n",
    "regressor = LinearRegression()\n",
    "# We fit the data in the train set\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "# Visualising the Training set results\n",
    "# Show the points in the train set (in red)\n",
    "plt.scatter(X_train, y_train, color = 'red')\n",
    "# Show the Regression line for the model (in blue)\n",
    "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n",
    "plt.title('Year vs GDP  (Training set)')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('GDP')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8vOD9tqa6ulpwfZQWb9Dly7D2JAp2+TLOi4jt+dVX6LlmDQZdu4bau+/G9YUL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2509ee6f630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The real GDP\n",
      "1201      438.864634\n",
      "1628      487.942556\n",
      "1317     4863.476650\n",
      "1392      734.949246\n",
      "1308      482.247620\n",
      "1998      359.992184\n",
      "235      2378.339270\n",
      "1329    38532.488000\n",
      "958      4892.116180\n",
      "2511     4627.592000\n",
      "2458     2176.898290\n",
      "2291     1192.517400\n",
      "212     15534.157400\n",
      "2266       62.173221\n",
      "56       2878.837144\n",
      "1934     1585.132260\n",
      "913       276.133800\n",
      "1138     2326.158560\n",
      "458       248.134590\n",
      "659       676.153440\n",
      "768       768.176767\n",
      "1081      437.446724\n",
      "1397    51264.713400\n",
      "2548      148.852738\n",
      "198       681.125368\n",
      "2416     1678.711862\n",
      "228      6519.717530\n",
      "221     11675.393950\n",
      "1816      681.792587\n",
      "2651     1967.832830\n",
      "            ...     \n",
      "1265    26241.918830\n",
      "564      5633.796160\n",
      "1832    49474.756100\n",
      "2006     1545.626423\n",
      "2896     7483.158469\n",
      "2572     5859.915622\n",
      "2035       11.553196\n",
      "1933     2275.353600\n",
      "2877     7483.158469\n",
      "722      7483.158469\n",
      "1807     3288.257373\n",
      "207        45.633710\n",
      "633       612.623418\n",
      "1247     7483.158469\n",
      "1899      197.661422\n",
      "252      3743.956930\n",
      "109       783.261699\n",
      "2675     3194.562243\n",
      "774      5759.643000\n",
      "890       243.326822\n",
      "1242     3129.224922\n",
      "2033     1194.697155\n",
      "650     11363.418450\n",
      "2132     2124.873613\n",
      "1697      731.378864\n",
      "917     47415.559870\n",
      "847      2711.171140\n",
      "1886      258.463873\n",
      "2714      693.896383\n",
      "1844    25921.127940\n",
      "Name: GDP, Length: 882, dtype: float64\n",
      "Predicted GDP\n",
      "[ 5377.7961279   6656.96370338  8447.79830905  5633.629643    6656.96370338\n",
      "  7424.46424866  6401.13018828  5377.7961279   6145.29667319  7680.29776376\n",
      "  8959.46533924  6656.96370338  8191.96479395  8959.46533924  7168.63073357\n",
      "  7168.63073357  5377.7961279   9215.29885433  6656.96370338  8703.63182414\n",
      "  5377.7961279   7424.46424866  8447.79830905  6401.13018828  7680.29776376\n",
      "  7424.46424866  8191.96479395  5889.46315809  8447.79830905  8703.63182414\n",
      "  9215.29885433  7168.63073357  8703.63182414  9215.29885433  8959.46533924\n",
      "  9215.29885433  5889.46315809  8191.96479395  8959.46533924  8959.46533924\n",
      "  8191.96479395  7168.63073357  6145.29667319  6145.29667319  8703.63182414\n",
      "  6656.96370338  8447.79830905  6401.13018828  8959.46533924  7424.46424866\n",
      "  5889.46315809  6401.13018828  6912.79721847  7424.46424866  8959.46533924\n",
      "  6145.29667319  7680.29776376  6912.79721847  6145.29667319  7424.46424866\n",
      "  5633.629643    8191.96479395  6912.79721847  6656.96370338  9215.29885433\n",
      "  8447.79830905  7680.29776376  8191.96479395  8959.46533924  8959.46533924\n",
      "  6401.13018828  6912.79721847  8447.79830905  7424.46424866  7680.29776376\n",
      "  6401.13018828  5377.7961279   8959.46533924  7680.29776376  6145.29667319\n",
      "  5377.7961279   8959.46533924  6656.96370338  7680.29776376  8959.46533924\n",
      "  8191.96479395  8703.63182414  6656.96370338  6145.29667319  7168.63073357\n",
      "  7680.29776376  5889.46315809  7424.46424866  6656.96370338  6145.29667319\n",
      "  7680.29776376  7424.46424866  8959.46533924  5889.46315809  7168.63073357\n",
      "  6401.13018828  6912.79721847  7168.63073357  5377.7961279   7424.46424866\n",
      "  6656.96370338  6656.96370338  5889.46315809  7936.13127885  8959.46533924\n",
      "  6145.29667319  7680.29776376  5633.629643    7680.29776376  8191.96479395\n",
      "  6401.13018828  6912.79721847  6912.79721847  7168.63073357  5377.7961279\n",
      "  8703.63182414  7680.29776376  7168.63073357  7680.29776376  6656.96370338\n",
      "  8447.79830905  6912.79721847  8703.63182414  6656.96370338  8447.79830905\n",
      "  6145.29667319  8447.79830905  5377.7961279   6145.29667319  6401.13018828\n",
      "  8703.63182414  9215.29885433  5633.629643    7168.63073357  7424.46424866\n",
      "  8959.46533924  6656.96370338  7680.29776376  6912.79721847  5889.46315809\n",
      "  8447.79830905  7168.63073357  5377.7961279   8447.79830905  6401.13018828\n",
      "  8191.96479395  7424.46424866  7680.29776376  7680.29776376  5633.629643\n",
      "  5889.46315809  6145.29667319  7168.63073357  9215.29885433  7680.29776376\n",
      "  6145.29667319  9215.29885433  7936.13127885  5889.46315809  8447.79830905\n",
      "  6656.96370338  7168.63073357  5377.7961279   9215.29885433  7680.29776376\n",
      "  5633.629643    8447.79830905  8191.96479395  9215.29885433  7680.29776376\n",
      "  6912.79721847  6656.96370338  6145.29667319  7424.46424866  7168.63073357\n",
      "  5889.46315809  7936.13127885  6145.29667319  6912.79721847  8703.63182414\n",
      "  8447.79830905  7936.13127885  6145.29667319  5377.7961279   8959.46533924\n",
      "  6656.96370338  7424.46424866  5633.629643    7936.13127885  8703.63182414\n",
      "  7936.13127885  8959.46533924  6656.96370338  8703.63182414  7168.63073357\n",
      "  6401.13018828  6145.29667319  7680.29776376  5889.46315809  8959.46533924\n",
      "  7424.46424866  6656.96370338  8959.46533924  7936.13127885  8447.79830905\n",
      "  7936.13127885  7936.13127885  7168.63073357  6401.13018828  7680.29776376\n",
      "  6145.29667319  6656.96370338  6912.79721847  8447.79830905  8703.63182414\n",
      "  5889.46315809  7936.13127885  9215.29885433  7936.13127885  5633.629643\n",
      "  5377.7961279   7424.46424866  7168.63073357  8959.46533924  5377.7961279\n",
      "  7936.13127885  5633.629643    7424.46424866  8191.96479395  7680.29776376\n",
      "  7424.46424866  9215.29885433  6912.79721847  8191.96479395  6912.79721847\n",
      "  5633.629643    5889.46315809  8703.63182414  5633.629643    8191.96479395\n",
      "  8703.63182414  8191.96479395  5633.629643    7680.29776376  8447.79830905\n",
      "  6912.79721847  6656.96370338  7936.13127885  7680.29776376  7424.46424866\n",
      "  6401.13018828  6656.96370338  6401.13018828  8447.79830905  9215.29885433\n",
      "  5633.629643    7168.63073357  5377.7961279   7680.29776376  7168.63073357\n",
      "  8959.46533924  6401.13018828  5889.46315809  6912.79721847  5377.7961279\n",
      "  7936.13127885  8959.46533924  6401.13018828  6401.13018828  5889.46315809\n",
      "  8447.79830905  7424.46424866  8959.46533924  6145.29667319  8959.46533924\n",
      "  8191.96479395  5633.629643    7680.29776376  6401.13018828  7168.63073357\n",
      "  8959.46533924  5889.46315809  9215.29885433  6656.96370338  5377.7961279\n",
      "  7168.63073357  5633.629643    6912.79721847  8191.96479395  7680.29776376\n",
      "  6656.96370338  8447.79830905  8447.79830905  7168.63073357  5377.7961279\n",
      "  6912.79721847  5377.7961279   8447.79830905  7168.63073357  8959.46533924\n",
      "  8959.46533924  7680.29776376  8447.79830905  8959.46533924  5377.7961279\n",
      "  6656.96370338  6401.13018828  8447.79830905  6912.79721847  5633.629643\n",
      "  6145.29667319  6145.29667319  7424.46424866  9215.29885433  5889.46315809\n",
      "  8959.46533924  9215.29885433  6145.29667319  6145.29667319  5633.629643\n",
      "  8703.63182414  8959.46533924  8191.96479395  8447.79830905  7424.46424866\n",
      "  9215.29885433  7168.63073357  8959.46533924  6912.79721847  9215.29885433\n",
      "  5633.629643    8703.63182414  6401.13018828  5633.629643    6401.13018828\n",
      "  6145.29667319  8703.63182414  5889.46315809  6401.13018828  9215.29885433\n",
      "  6912.79721847  6145.29667319  5377.7961279   7424.46424866  7680.29776376\n",
      "  5889.46315809  6145.29667319  8447.79830905  8703.63182414  5377.7961279\n",
      "  7424.46424866  8191.96479395  6145.29667319  7424.46424866  7168.63073357\n",
      "  7680.29776376  8703.63182414  5377.7961279   5889.46315809  6912.79721847\n",
      "  6656.96370338  6912.79721847  9215.29885433  8703.63182414  6145.29667319\n",
      "  7168.63073357  7424.46424866  7168.63073357  9215.29885433  6656.96370338\n",
      "  5889.46315809  5633.629643    6145.29667319  7168.63073357  8959.46533924\n",
      "  7168.63073357  5377.7961279   6145.29667319  8703.63182414  8191.96479395\n",
      "  6145.29667319  8703.63182414  7680.29776376  7936.13127885  7936.13127885\n",
      "  8703.63182414  5889.46315809  7936.13127885  8959.46533924  6145.29667319\n",
      "  7680.29776376  5633.629643    9215.29885433  6401.13018828  5377.7961279\n",
      "  6401.13018828  9215.29885433  9215.29885433  6912.79721847  6912.79721847\n",
      "  8447.79830905  6912.79721847  7424.46424866  7936.13127885  6401.13018828\n",
      "  7680.29776376  9215.29885433  6145.29667319  8191.96479395  6145.29667319\n",
      "  7680.29776376  8447.79830905  9215.29885433  9215.29885433  7424.46424866\n",
      "  7680.29776376  7680.29776376  7168.63073357  7680.29776376  6145.29667319\n",
      "  7168.63073357  6656.96370338  5633.629643    6912.79721847  7936.13127885\n",
      "  8959.46533924  9215.29885433  9215.29885433  8447.79830905  8703.63182414\n",
      "  6656.96370338  6912.79721847  6656.96370338  5889.46315809  8191.96479395\n",
      "  7936.13127885  7936.13127885  6656.96370338  7680.29776376  9215.29885433\n",
      "  8959.46533924  6656.96370338  5633.629643    5377.7961279   5889.46315809\n",
      "  9215.29885433  5377.7961279   7680.29776376  6656.96370338  5889.46315809\n",
      "  8447.79830905  6145.29667319  7936.13127885  5633.629643    8959.46533924\n",
      "  6401.13018828  6145.29667319  5377.7961279   7168.63073357  6656.96370338\n",
      "  8447.79830905  6401.13018828  5633.629643    7936.13127885  7936.13127885\n",
      "  8959.46533924  8191.96479395  6401.13018828  8959.46533924  8447.79830905\n",
      "  5633.629643    8447.79830905  7936.13127885  5377.7961279   5889.46315809\n",
      "  9215.29885433  5633.629643    6912.79721847  7680.29776376  6401.13018828\n",
      "  8703.63182414  8703.63182414  8191.96479395  5377.7961279   7424.46424866\n",
      "  6656.96370338  7680.29776376  7424.46424866  7680.29776376  5889.46315809\n",
      "  5377.7961279   7936.13127885  8191.96479395  7168.63073357  8703.63182414\n",
      "  6912.79721847  7680.29776376  7168.63073357  8447.79830905  5889.46315809\n",
      "  7424.46424866  9215.29885433  5633.629643    7424.46424866  8191.96479395\n",
      "  6912.79721847  9215.29885433  8703.63182414  8447.79830905  8703.63182414\n",
      "  9215.29885433  8959.46533924  6656.96370338  5633.629643    7168.63073357\n",
      "  5377.7961279   5889.46315809  8703.63182414  8191.96479395  5377.7961279\n",
      "  7936.13127885  8959.46533924  5633.629643    7936.13127885  7680.29776376\n",
      "  5377.7961279   6145.29667319  7168.63073357  6401.13018828  7680.29776376\n",
      "  6145.29667319  8191.96479395  7680.29776376  6912.79721847  7424.46424866\n",
      "  8959.46533924  7424.46424866  5889.46315809  5889.46315809  8447.79830905\n",
      "  6912.79721847  7936.13127885  8959.46533924  7424.46424866  9215.29885433\n",
      "  6401.13018828  6912.79721847  6145.29667319  7936.13127885  5377.7961279\n",
      "  6145.29667319  5377.7961279   9215.29885433  7936.13127885  5633.629643\n",
      "  6401.13018828  8191.96479395  6145.29667319  6401.13018828  8447.79830905\n",
      "  8447.79830905  7680.29776376  6912.79721847  8447.79830905  9215.29885433\n",
      "  7936.13127885  6656.96370338  5633.629643    8447.79830905  6656.96370338\n",
      "  8447.79830905  6656.96370338  7168.63073357  9215.29885433  5377.7961279\n",
      "  8191.96479395  8191.96479395  6145.29667319  7680.29776376  8959.46533924\n",
      "  8191.96479395  8191.96479395  9215.29885433  5377.7961279   8959.46533924\n",
      "  8959.46533924  7168.63073357  5889.46315809  6656.96370338  8191.96479395\n",
      "  6912.79721847  7168.63073357  5377.7961279   5633.629643    7936.13127885\n",
      "  7168.63073357  8447.79830905  9215.29885433  5377.7961279   7168.63073357\n",
      "  5633.629643    8447.79830905  7680.29776376  8703.63182414  5377.7961279\n",
      "  7424.46424866  6912.79721847  7936.13127885  5889.46315809  5633.629643\n",
      "  8703.63182414  6401.13018828  6401.13018828  8191.96479395  8191.96479395\n",
      "  5377.7961279   5633.629643    8191.96479395  5377.7961279   6401.13018828\n",
      "  8703.63182414  8191.96479395  5633.629643    8191.96479395  5377.7961279\n",
      "  6656.96370338  7424.46424866  9215.29885433  7168.63073357  5889.46315809\n",
      "  7168.63073357  7168.63073357  8959.46533924  6401.13018828  7936.13127885\n",
      "  8703.63182414  8703.63182414  5633.629643    5377.7961279   5633.629643\n",
      "  7424.46424866  8191.96479395  8191.96479395  7424.46424866  9215.29885433\n",
      "  6401.13018828  5889.46315809  8191.96479395  5889.46315809  6656.96370338\n",
      "  8447.79830905  8703.63182414  5889.46315809  9215.29885433  6656.96370338\n",
      "  6145.29667319  8191.96479395  5633.629643    7680.29776376  6912.79721847\n",
      "  7424.46424866  8959.46533924  5377.7961279   6145.29667319  8703.63182414\n",
      "  7168.63073357  5889.46315809  5889.46315809  7936.13127885  9215.29885433\n",
      "  6912.79721847  8447.79830905  8703.63182414  5889.46315809  6401.13018828\n",
      "  8447.79830905  8703.63182414  5377.7961279   5889.46315809  9215.29885433\n",
      "  7680.29776376  7424.46424866  6401.13018828  6145.29667319  6656.96370338\n",
      "  5633.629643    8703.63182414  9215.29885433  5889.46315809  7680.29776376\n",
      "  5633.629643    7680.29776376  5377.7961279   7680.29776376  8959.46533924\n",
      "  7168.63073357  5377.7961279   7680.29776376  6401.13018828  5633.629643\n",
      "  5633.629643    8447.79830905  9215.29885433  5633.629643    7936.13127885\n",
      "  6401.13018828  5889.46315809  5377.7961279   7680.29776376  5377.7961279\n",
      "  8703.63182414  6912.79721847  6656.96370338  9215.29885433  9215.29885433\n",
      "  6656.96370338  8703.63182414  6912.79721847  5633.629643    8191.96479395\n",
      "  5377.7961279   8703.63182414  8191.96479395  8447.79830905  5889.46315809\n",
      "  6145.29667319  6912.79721847  6656.96370338  7680.29776376  8703.63182414\n",
      "  7424.46424866  9215.29885433  5889.46315809  7424.46424866  6401.13018828\n",
      "  6656.96370338  9215.29885433  5633.629643    7168.63073357  5377.7961279\n",
      "  5633.629643    5889.46315809  9215.29885433  8191.96479395  7424.46424866\n",
      "  7424.46424866  7168.63073357  7936.13127885  8191.96479395  8959.46533924\n",
      "  6912.79721847  7168.63073357  6145.29667319  7168.63073357  6401.13018828\n",
      "  7168.63073357  6912.79721847  8447.79830905  6145.29667319  8447.79830905\n",
      "  7680.29776376  6912.79721847  8959.46533924  7936.13127885  7936.13127885\n",
      "  7424.46424866  9215.29885433  8959.46533924  7936.13127885  7936.13127885\n",
      "  8959.46533924  5889.46315809  6656.96370338  5889.46315809  6656.96370338\n",
      "  7168.63073357  6656.96370338  7168.63073357  5633.629643    8703.63182414\n",
      "  6145.29667319  8447.79830905  7680.29776376  5633.629643    8959.46533924\n",
      "  6912.79721847  8191.96479395  7424.46424866  9215.29885433  5377.7961279\n",
      "  8959.46533924  9215.29885433  6145.29667319  7168.63073357  9215.29885433\n",
      "  8447.79830905  8959.46533924  5377.7961279   6145.29667319  7168.63073357\n",
      "  7168.63073357  8191.96479395  8191.96479395  8191.96479395  6145.29667319\n",
      "  7680.29776376  5377.7961279   6401.13018828  6912.79721847  9215.29885433\n",
      "  8959.46533924  8959.46533924  7424.46424866  7168.63073357  8191.96479395\n",
      "  6912.79721847  5633.629643    8959.46533924  7168.63073357  8191.96479395\n",
      "  8447.79830905  5377.7961279   6401.13018828  5889.46315809  6145.29667319\n",
      "  7936.13127885  7936.13127885  6145.29667319  7936.13127885  7424.46424866\n",
      "  8959.46533924  6401.13018828  6145.29667319  5889.46315809  8191.96479395\n",
      "  5889.46315809  8447.79830905  6145.29667319  7936.13127885  7680.29776376\n",
      "  6912.79721847  8447.79830905  8447.79830905  9215.29885433  5633.629643\n",
      "  7424.46424866  8959.46533924  5377.7961279   8191.96479395  8447.79830905\n",
      "  5377.7961279   7680.29776376  8447.79830905  6145.29667319  7424.46424866\n",
      "  8447.79830905  8959.46533924  6401.13018828  5377.7961279   7168.63073357\n",
      "  5889.46315809  7680.29776376  6145.29667319  5889.46315809  6656.96370338\n",
      "  8191.96479395  7168.63073357  7168.63073357  6656.96370338  6912.79721847\n",
      "  5889.46315809  5633.629643    8447.79830905  5889.46315809  6912.79721847\n",
      "  9215.29885433  5377.7961279 ]\n"
     ]
    }
   ],
   "source": [
    "# Testing the model\n",
    "# Predicting the Test set results\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "# Visualising the Test set results\n",
    "# Show the points in the TEST set (in red)\n",
    "plt.scatter(X_test, y_test, color = 'red')\n",
    "# Show the Regression line for the model (in blue)\n",
    "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n",
    "plt.title('Year vs GDP (Test set)')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('GDP')\n",
    "plt.show()\n",
    "\n",
    "print(\"The real GDP\")\n",
    "print(y_test)\n",
    "print(\"Predicted GDP\")\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 7859.22356675\n",
      "MSE: 182785516.265\n",
      "RMSE: 13519.8193873\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regresion War Countries (Influence of GDP in Adult Mortality)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Country</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>65.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>62</td>\n",
       "      <td>584.259210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>59.9</td>\n",
       "      <td>271.0</td>\n",
       "      <td>64</td>\n",
       "      <td>612.696514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>59.9</td>\n",
       "      <td>268.0</td>\n",
       "      <td>66</td>\n",
       "      <td>631.744976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>59.5</td>\n",
       "      <td>272.0</td>\n",
       "      <td>69</td>\n",
       "      <td>669.959000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>59.2</td>\n",
       "      <td>275.0</td>\n",
       "      <td>71</td>\n",
       "      <td>63.537231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>58.8</td>\n",
       "      <td>279.0</td>\n",
       "      <td>74</td>\n",
       "      <td>553.328940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>58.6</td>\n",
       "      <td>281.0</td>\n",
       "      <td>77</td>\n",
       "      <td>445.893298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>58.1</td>\n",
       "      <td>287.0</td>\n",
       "      <td>80</td>\n",
       "      <td>373.361116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>57.5</td>\n",
       "      <td>295.0</td>\n",
       "      <td>82</td>\n",
       "      <td>369.835796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>57.3</td>\n",
       "      <td>295.0</td>\n",
       "      <td>84</td>\n",
       "      <td>272.563770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "      <td>57.3</td>\n",
       "      <td>291.0</td>\n",
       "      <td>85</td>\n",
       "      <td>25.294130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>57.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>87</td>\n",
       "      <td>219.141353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2003</td>\n",
       "      <td>1</td>\n",
       "      <td>56.7</td>\n",
       "      <td>295.0</td>\n",
       "      <td>87</td>\n",
       "      <td>198.728544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2002</td>\n",
       "      <td>1</td>\n",
       "      <td>56.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>88</td>\n",
       "      <td>187.845950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "      <td>55.3</td>\n",
       "      <td>316.0</td>\n",
       "      <td>88</td>\n",
       "      <td>117.496980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "      <td>54.8</td>\n",
       "      <td>321.0</td>\n",
       "      <td>88</td>\n",
       "      <td>114.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>77.8</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3954.227830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014</td>\n",
       "      <td>2</td>\n",
       "      <td>77.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4575.763787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013</td>\n",
       "      <td>2</td>\n",
       "      <td>77.2</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4414.723140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>76.9</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4247.614380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>76.6</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4437.178680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "      <td>76.2</td>\n",
       "      <td>91.0</td>\n",
       "      <td>1</td>\n",
       "      <td>494.358832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>76.1</td>\n",
       "      <td>91.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4114.136545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>75.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>437.539647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2007</td>\n",
       "      <td>2</td>\n",
       "      <td>75.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>363.136850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2006</td>\n",
       "      <td>2</td>\n",
       "      <td>74.2</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1</td>\n",
       "      <td>35.129300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2005</td>\n",
       "      <td>2</td>\n",
       "      <td>73.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "      <td>279.142931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2004</td>\n",
       "      <td>2</td>\n",
       "      <td>73.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2416.588235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2003</td>\n",
       "      <td>2</td>\n",
       "      <td>72.8</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>189.681557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2002</td>\n",
       "      <td>2</td>\n",
       "      <td>73.3</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1453.642777</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2908</th>\n",
       "      <td>2013</td>\n",
       "      <td>182</td>\n",
       "      <td>63.0</td>\n",
       "      <td>328.0</td>\n",
       "      <td>29</td>\n",
       "      <td>185.793359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>2012</td>\n",
       "      <td>182</td>\n",
       "      <td>59.2</td>\n",
       "      <td>349.0</td>\n",
       "      <td>29</td>\n",
       "      <td>1734.936120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2910</th>\n",
       "      <td>2011</td>\n",
       "      <td>182</td>\n",
       "      <td>58.2</td>\n",
       "      <td>366.0</td>\n",
       "      <td>29</td>\n",
       "      <td>1644.619672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2911</th>\n",
       "      <td>2010</td>\n",
       "      <td>182</td>\n",
       "      <td>58.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>30</td>\n",
       "      <td>1463.213573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2912</th>\n",
       "      <td>2009</td>\n",
       "      <td>182</td>\n",
       "      <td>57.4</td>\n",
       "      <td>368.0</td>\n",
       "      <td>30</td>\n",
       "      <td>1139.112330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2913</th>\n",
       "      <td>2008</td>\n",
       "      <td>182</td>\n",
       "      <td>55.7</td>\n",
       "      <td>45.0</td>\n",
       "      <td>31</td>\n",
       "      <td>1369.682490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2914</th>\n",
       "      <td>2007</td>\n",
       "      <td>182</td>\n",
       "      <td>52.6</td>\n",
       "      <td>487.0</td>\n",
       "      <td>32</td>\n",
       "      <td>114.587985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2915</th>\n",
       "      <td>2006</td>\n",
       "      <td>182</td>\n",
       "      <td>58.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>33</td>\n",
       "      <td>13.154199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>2005</td>\n",
       "      <td>182</td>\n",
       "      <td>49.3</td>\n",
       "      <td>554.0</td>\n",
       "      <td>34</td>\n",
       "      <td>691.317816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>2004</td>\n",
       "      <td>182</td>\n",
       "      <td>47.9</td>\n",
       "      <td>578.0</td>\n",
       "      <td>36</td>\n",
       "      <td>53.277222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>2003</td>\n",
       "      <td>182</td>\n",
       "      <td>46.4</td>\n",
       "      <td>64.0</td>\n",
       "      <td>39</td>\n",
       "      <td>429.158343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2919</th>\n",
       "      <td>2002</td>\n",
       "      <td>182</td>\n",
       "      <td>45.5</td>\n",
       "      <td>69.0</td>\n",
       "      <td>41</td>\n",
       "      <td>377.135244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2920</th>\n",
       "      <td>2001</td>\n",
       "      <td>182</td>\n",
       "      <td>44.6</td>\n",
       "      <td>611.0</td>\n",
       "      <td>43</td>\n",
       "      <td>378.273624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>2000</td>\n",
       "      <td>182</td>\n",
       "      <td>43.8</td>\n",
       "      <td>614.0</td>\n",
       "      <td>44</td>\n",
       "      <td>341.955625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>2015</td>\n",
       "      <td>183</td>\n",
       "      <td>67.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>22</td>\n",
       "      <td>118.693830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>2014</td>\n",
       "      <td>183</td>\n",
       "      <td>59.2</td>\n",
       "      <td>371.0</td>\n",
       "      <td>23</td>\n",
       "      <td>127.474620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>2013</td>\n",
       "      <td>183</td>\n",
       "      <td>58.0</td>\n",
       "      <td>399.0</td>\n",
       "      <td>25</td>\n",
       "      <td>111.227396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>2012</td>\n",
       "      <td>183</td>\n",
       "      <td>56.6</td>\n",
       "      <td>429.0</td>\n",
       "      <td>26</td>\n",
       "      <td>955.648466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2926</th>\n",
       "      <td>2011</td>\n",
       "      <td>183</td>\n",
       "      <td>54.9</td>\n",
       "      <td>464.0</td>\n",
       "      <td>28</td>\n",
       "      <td>839.927936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2927</th>\n",
       "      <td>2010</td>\n",
       "      <td>183</td>\n",
       "      <td>52.4</td>\n",
       "      <td>527.0</td>\n",
       "      <td>29</td>\n",
       "      <td>713.635620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2928</th>\n",
       "      <td>2009</td>\n",
       "      <td>183</td>\n",
       "      <td>50.0</td>\n",
       "      <td>587.0</td>\n",
       "      <td>30</td>\n",
       "      <td>65.824121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2929</th>\n",
       "      <td>2008</td>\n",
       "      <td>183</td>\n",
       "      <td>48.2</td>\n",
       "      <td>632.0</td>\n",
       "      <td>30</td>\n",
       "      <td>325.678573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2930</th>\n",
       "      <td>2007</td>\n",
       "      <td>183</td>\n",
       "      <td>46.6</td>\n",
       "      <td>67.0</td>\n",
       "      <td>29</td>\n",
       "      <td>396.998217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2931</th>\n",
       "      <td>2006</td>\n",
       "      <td>183</td>\n",
       "      <td>45.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>28</td>\n",
       "      <td>414.796232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2932</th>\n",
       "      <td>2005</td>\n",
       "      <td>183</td>\n",
       "      <td>44.6</td>\n",
       "      <td>717.0</td>\n",
       "      <td>28</td>\n",
       "      <td>444.765750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2933</th>\n",
       "      <td>2004</td>\n",
       "      <td>183</td>\n",
       "      <td>44.3</td>\n",
       "      <td>723.0</td>\n",
       "      <td>27</td>\n",
       "      <td>454.366654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>2003</td>\n",
       "      <td>183</td>\n",
       "      <td>44.5</td>\n",
       "      <td>715.0</td>\n",
       "      <td>26</td>\n",
       "      <td>453.351155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2935</th>\n",
       "      <td>2002</td>\n",
       "      <td>183</td>\n",
       "      <td>44.8</td>\n",
       "      <td>73.0</td>\n",
       "      <td>25</td>\n",
       "      <td>57.348340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>2001</td>\n",
       "      <td>183</td>\n",
       "      <td>45.3</td>\n",
       "      <td>686.0</td>\n",
       "      <td>25</td>\n",
       "      <td>548.587312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>2000</td>\n",
       "      <td>183</td>\n",
       "      <td>46.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>24</td>\n",
       "      <td>547.358879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2938 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  Country  Life expectancy   Adult Mortality  infant deaths  \\\n",
       "0     2015        1              65.0            263.0             62   \n",
       "1     2014        1              59.9            271.0             64   \n",
       "2     2013        1              59.9            268.0             66   \n",
       "3     2012        1              59.5            272.0             69   \n",
       "4     2011        1              59.2            275.0             71   \n",
       "5     2010        1              58.8            279.0             74   \n",
       "6     2009        1              58.6            281.0             77   \n",
       "7     2008        1              58.1            287.0             80   \n",
       "8     2007        1              57.5            295.0             82   \n",
       "9     2006        1              57.3            295.0             84   \n",
       "10    2005        1              57.3            291.0             85   \n",
       "11    2004        1              57.0            293.0             87   \n",
       "12    2003        1              56.7            295.0             87   \n",
       "13    2002        1              56.2              3.0             88   \n",
       "14    2001        1              55.3            316.0             88   \n",
       "15    2000        1              54.8            321.0             88   \n",
       "16    2015        2              77.8             74.0              0   \n",
       "17    2014        2              77.5              8.0              0   \n",
       "18    2013        2              77.2             84.0              0   \n",
       "19    2012        2              76.9             86.0              0   \n",
       "20    2011        2              76.6             88.0              0   \n",
       "21    2010        2              76.2             91.0              1   \n",
       "22    2009        2              76.1             91.0              1   \n",
       "23    2008        2              75.3              1.0              1   \n",
       "24    2007        2              75.9              9.0              1   \n",
       "25    2006        2              74.2             99.0              1   \n",
       "26    2005        2              73.5             15.0              1   \n",
       "27    2004        2              73.0             17.0              1   \n",
       "28    2003        2              72.8             18.0              1   \n",
       "29    2002        2              73.3             15.0              1   \n",
       "...    ...      ...               ...              ...            ...   \n",
       "2908  2013      182              63.0            328.0             29   \n",
       "2909  2012      182              59.2            349.0             29   \n",
       "2910  2011      182              58.2            366.0             29   \n",
       "2911  2010      182              58.0            363.0             30   \n",
       "2912  2009      182              57.4            368.0             30   \n",
       "2913  2008      182              55.7             45.0             31   \n",
       "2914  2007      182              52.6            487.0             32   \n",
       "2915  2006      182              58.0            526.0             33   \n",
       "2916  2005      182              49.3            554.0             34   \n",
       "2917  2004      182              47.9            578.0             36   \n",
       "2918  2003      182              46.4             64.0             39   \n",
       "2919  2002      182              45.5             69.0             41   \n",
       "2920  2001      182              44.6            611.0             43   \n",
       "2921  2000      182              43.8            614.0             44   \n",
       "2922  2015      183              67.0            336.0             22   \n",
       "2923  2014      183              59.2            371.0             23   \n",
       "2924  2013      183              58.0            399.0             25   \n",
       "2925  2012      183              56.6            429.0             26   \n",
       "2926  2011      183              54.9            464.0             28   \n",
       "2927  2010      183              52.4            527.0             29   \n",
       "2928  2009      183              50.0            587.0             30   \n",
       "2929  2008      183              48.2            632.0             30   \n",
       "2930  2007      183              46.6             67.0             29   \n",
       "2931  2006      183              45.4              7.0             28   \n",
       "2932  2005      183              44.6            717.0             28   \n",
       "2933  2004      183              44.3            723.0             27   \n",
       "2934  2003      183              44.5            715.0             26   \n",
       "2935  2002      183              44.8             73.0             25   \n",
       "2936  2001      183              45.3            686.0             25   \n",
       "2937  2000      183              46.0            665.0             24   \n",
       "\n",
       "              GDP  \n",
       "0      584.259210  \n",
       "1      612.696514  \n",
       "2      631.744976  \n",
       "3      669.959000  \n",
       "4       63.537231  \n",
       "5      553.328940  \n",
       "6      445.893298  \n",
       "7      373.361116  \n",
       "8      369.835796  \n",
       "9      272.563770  \n",
       "10      25.294130  \n",
       "11     219.141353  \n",
       "12     198.728544  \n",
       "13     187.845950  \n",
       "14     117.496980  \n",
       "15     114.560000  \n",
       "16    3954.227830  \n",
       "17    4575.763787  \n",
       "18    4414.723140  \n",
       "19    4247.614380  \n",
       "20    4437.178680  \n",
       "21     494.358832  \n",
       "22    4114.136545  \n",
       "23     437.539647  \n",
       "24     363.136850  \n",
       "25      35.129300  \n",
       "26     279.142931  \n",
       "27    2416.588235  \n",
       "28     189.681557  \n",
       "29    1453.642777  \n",
       "...           ...  \n",
       "2908   185.793359  \n",
       "2909  1734.936120  \n",
       "2910  1644.619672  \n",
       "2911  1463.213573  \n",
       "2912  1139.112330  \n",
       "2913  1369.682490  \n",
       "2914   114.587985  \n",
       "2915    13.154199  \n",
       "2916   691.317816  \n",
       "2917    53.277222  \n",
       "2918   429.158343  \n",
       "2919   377.135244  \n",
       "2920   378.273624  \n",
       "2921   341.955625  \n",
       "2922   118.693830  \n",
       "2923   127.474620  \n",
       "2924   111.227396  \n",
       "2925   955.648466  \n",
       "2926   839.927936  \n",
       "2927   713.635620  \n",
       "2928    65.824121  \n",
       "2929   325.678573  \n",
       "2930   396.998217  \n",
       "2931   414.796232  \n",
       "2932   444.765750  \n",
       "2933   454.366654  \n",
       "2934   453.351155  \n",
       "2935    57.348340  \n",
       "2936   548.587312  \n",
       "2937   547.358879  \n",
       "\n",
       "[2938 rows x 6 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfwarcountriesall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Training and Testing Data\n",
    "# Adult and infant Mortality Linear Regression \n",
    "X = dfwarcountriesall[['Year','GDP']]\n",
    "y = dfwarcountriesall['Adult Mortality']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Train Test Split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training the Model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "# Create an instance of a LinearRegression() model named lm.\n",
    "lm = LinearRegression()\n",
    "# Train/fit lm on the training data.\n",
    "lm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4046.37952324\n"
     ]
    }
   ],
   "source": [
    "#Model Evaluation\n",
    "# print the intercept\n",
    "print(lm.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>-1.923714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>-0.002536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Coefficient\n",
       "Year    -1.923714\n",
       "GDP     -0.002536"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame(lm.coef_,X.columns,columns=['Coefficient'])\n",
    "coeff_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Interpreting the coefficients:\n",
    "# Holding all other features fixed, a 1 unit increase in Year is associated with a decrease of 1.923714 Adult Mortality\n",
    "# Holding all other features fixed, a 1 unit increase in GDP is associated with a decrease of 0.002536 Adult Mortality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression (Predict whether or not a country can be developed or not based off the features of country)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country  Year  Status  Life expectancy   Adult Mortality  infant deaths  \\\n",
       "0        1  2015       1              65.0            263.0             62   \n",
       "1        1  2014       1              59.9            271.0             64   \n",
       "2        1  2013       1              59.9            268.0             66   \n",
       "3        1  2012       1              59.5            272.0             69   \n",
       "4        1  2011       1              59.2            275.0             71   \n",
       "\n",
       "   Alcohol  percentage expenditure  Hepatitis B  Measles     ...      Polio  \\\n",
       "0     0.01               71.279624         65.0      1154    ...        6.0   \n",
       "1     0.01               73.523582         62.0       492    ...       58.0   \n",
       "2     0.01               73.219243         64.0       430    ...       62.0   \n",
       "3     0.01               78.184215         67.0      2787    ...       67.0   \n",
       "4     0.01                7.097109         68.0      3013    ...       68.0   \n",
       "\n",
       "   Total expenditure  Diphtheria    HIV/AIDS         GDP  Population  \\\n",
       "0               8.16         65.0        0.1  584.259210  33736494.0   \n",
       "1               8.18         62.0        0.1  612.696514    327582.0   \n",
       "2               8.13         64.0        0.1  631.744976  31731688.0   \n",
       "3               8.52         67.0        0.1  669.959000   3696958.0   \n",
       "4               7.87         68.0        0.1   63.537231   2978599.0   \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",
       "\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",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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",
      "Country                            2938 non-null int64\n",
      "Year                               2938 non-null int64\n",
      "Status                             2938 non-null int64\n",
      "Life expectancy                    2938 non-null float64\n",
      "Adult Mortality                    2938 non-null float64\n",
      "infant deaths                      2938 non-null int64\n",
      "Alcohol                            2938 non-null float64\n",
      "percentage expenditure             2938 non-null float64\n",
      "Hepatitis B                        2938 non-null float64\n",
      "Measles                            2938 non-null int64\n",
      " BMI                               2938 non-null float64\n",
      "under-five deaths                  2938 non-null int64\n",
      "Polio                              2938 non-null float64\n",
      "Total expenditure                  2938 non-null float64\n",
      "Diphtheria                         2938 non-null float64\n",
      " HIV/AIDS                          2938 non-null float64\n",
      "GDP                                2938 non-null float64\n",
      "Population                         2938 non-null float64\n",
      " thinness  1-19 years              2938 non-null float64\n",
      " thinness 5-9 years                2938 non-null float64\n",
      "Income composition of resources    2938 non-null float64\n",
      "Schooling                          2938 non-null float64\n",
      "dtypes: float64(16), int64(6)\n",
      "memory usage: 505.0 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
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       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
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       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>92.328455</td>\n",
       "      <td>2007.518720</td>\n",
       "      <td>0.825732</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>...</td>\n",
       "      <td>82.550188</td>\n",
       "      <td>5.938190</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>53.044716</td>\n",
       "      <td>4.613841</td>\n",
       "      <td>0.379405</td>\n",
       "      <td>9.507640</td>\n",
       "      <td>124.080302</td>\n",
       "      <td>117.926501</td>\n",
       "      <td>3.916288</td>\n",
       "      <td>1987.914858</td>\n",
       "      <td>22.586855</td>\n",
       "      <td>11467.272489</td>\n",
       "      <td>...</td>\n",
       "      <td>23.352143</td>\n",
       "      <td>2.400274</td>\n",
       "      <td>23.640073</td>\n",
       "      <td>5.077785</td>\n",
       "      <td>13136.800417</td>\n",
       "      <td>5.381546e+07</td>\n",
       "      <td>4.394535</td>\n",
       "      <td>4.482708</td>\n",
       "      <td>0.204820</td>\n",
       "      <td>3.264381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>0.000000</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>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.370000</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>46.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>63.200000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.092500</td>\n",
       "      <td>4.685343</td>\n",
       "      <td>80.940461</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>4.370000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>580.486996</td>\n",
       "      <td>4.189172e+05</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.504250</td>\n",
       "      <td>10.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>92.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.160000</td>\n",
       "      <td>64.912906</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>5.938190</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>3116.561755</td>\n",
       "      <td>3.675929e+06</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.662000</td>\n",
       "      <td>12.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>138.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>75.600000</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>7.390000</td>\n",
       "      <td>441.534144</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>360.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>7.330000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>7483.158469</td>\n",
       "      <td>1.275338e+07</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.772000</td>\n",
       "      <td>14.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>193.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>1.000000</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>...</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>17.600000</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",
       "<p>8 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country         Year       Status  Life expectancy   \\\n",
       "count  2938.000000  2938.000000  2938.000000       2938.000000   \n",
       "mean     92.328455  2007.518720     0.825732         69.224932   \n",
       "std      53.044716     4.613841     0.379405          9.507640   \n",
       "min       1.000000  2000.000000     0.000000         36.300000   \n",
       "25%      46.000000  2004.000000     1.000000         63.200000   \n",
       "50%      92.000000  2008.000000     1.000000         72.000000   \n",
       "75%     138.000000  2012.000000     1.000000         75.600000   \n",
       "max     193.000000  2015.000000     1.000000         89.000000   \n",
       "\n",
       "       Adult Mortality  infant deaths      Alcohol  percentage expenditure  \\\n",
       "count      2938.000000    2938.000000  2938.000000             2938.000000   \n",
       "mean        164.796448      30.303948     4.602861              738.251295   \n",
       "std         124.080302     117.926501     3.916288             1987.914858   \n",
       "min           1.000000       0.000000     0.010000                0.000000   \n",
       "25%          74.000000       0.000000     1.092500                4.685343   \n",
       "50%         144.000000       3.000000     4.160000               64.912906   \n",
       "75%         227.000000      22.000000     7.390000              441.534144   \n",
       "max         723.000000    1800.000000    17.870000            19479.911610   \n",
       "\n",
       "       Hepatitis B       Measles      ...             Polio  \\\n",
       "count  2938.000000    2938.000000     ...       2938.000000   \n",
       "mean     80.940461    2419.592240     ...         82.550188   \n",
       "std      22.586855   11467.272489     ...         23.352143   \n",
       "min       1.000000       0.000000     ...          3.000000   \n",
       "25%      80.940461       0.000000     ...         78.000000   \n",
       "50%      87.000000      17.000000     ...         93.000000   \n",
       "75%      96.000000     360.250000     ...         97.000000   \n",
       "max      99.000000  212183.000000     ...         99.000000   \n",
       "\n",
       "       Total expenditure  Diphtheria      HIV/AIDS            GDP  \\\n",
       "count        2938.000000  2938.000000  2938.000000    2938.000000   \n",
       "mean            5.938190    82.324084     1.742103    7483.158469   \n",
       "std             2.400274    23.640073     5.077785   13136.800417   \n",
       "min             0.370000     2.000000     0.100000       1.681350   \n",
       "25%             4.370000    78.000000     0.100000     580.486996   \n",
       "50%             5.938190    93.000000     0.100000    3116.561755   \n",
       "75%             7.330000    97.000000     0.800000    7483.158469   \n",
       "max            17.600000    99.000000    50.600000  119172.741800   \n",
       "\n",
       "         Population   thinness  1-19 years   thinness 5-9 years  \\\n",
       "count  2.938000e+03            2938.000000          2938.000000   \n",
       "mean   1.275338e+07               4.839704             4.870317   \n",
       "std    5.381546e+07               4.394535             4.482708   \n",
       "min    3.400000e+01               0.100000             0.100000   \n",
       "25%    4.189172e+05               1.600000             1.600000   \n",
       "50%    3.675929e+06               3.400000             3.400000   \n",
       "75%    1.275338e+07               7.100000             7.200000   \n",
       "max    1.293859e+09              27.700000            28.600000   \n",
       "\n",
       "       Income composition of resources    Schooling  \n",
       "count                      2938.000000  2938.000000  \n",
       "mean                          0.627551    11.992793  \n",
       "std                           0.204820     3.264381  \n",
       "min                           0.000000     0.000000  \n",
       "25%                           0.504250    10.300000  \n",
       "50%                           0.662000    12.100000  \n",
       "75%                           0.772000    14.100000  \n",
       "max                           0.948000    20.700000  \n",
       "\n",
       "[8 rows x 22 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
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       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
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       "      <td>95.000000</td>\n",
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       "      <td>95.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4132.762920</td>\n",
       "      <td>3.987153e+07</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>0.743000</td>\n",
       "      <td>14.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>4</td>\n",
       "      <td>2015</td>\n",
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       "      <td>...</td>\n",
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       "      <td>8.200000</td>\n",
       "      <td>0.531000</td>\n",
       "      <td>11.400000</td>\n",
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       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>76.3</td>\n",
       "      <td>116.0</td>\n",
       "      <td>8</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>93.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>13467.123600</td>\n",
       "      <td>4.341776e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.826000</td>\n",
       "      <td>17.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>74.8</td>\n",
       "      <td>118.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>96.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>369.654776</td>\n",
       "      <td>2.916950e+05</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.741000</td>\n",
       "      <td>12.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>8</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>82.8</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>74</td>\n",
       "      <td>...</td>\n",
       "      <td>93.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>56554.387600</td>\n",
       "      <td>2.378934e+07</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.937000</td>\n",
       "      <td>20.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>9</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>81.5</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>309</td>\n",
       "      <td>...</td>\n",
       "      <td>93.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>43665.947000</td>\n",
       "      <td>8.633169e+06</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>0.892000</td>\n",
       "      <td>15.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>10</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>72.7</td>\n",
       "      <td>118.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>98.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>55.313820</td>\n",
       "      <td>9.649341e+06</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>0.758000</td>\n",
       "      <td>12.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>11</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>76.1</td>\n",
       "      <td>147.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>95.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7483.158469</td>\n",
       "      <td>1.275338e+07</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>12.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>76.9</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>98.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>22688.878240</td>\n",
       "      <td>1.275338e+07</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>0.823000</td>\n",
       "      <td>14.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>13</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>71.8</td>\n",
       "      <td>129.0</td>\n",
       "      <td>92</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>240</td>\n",
       "      <td>...</td>\n",
       "      <td>97.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>121.158120</td>\n",
       "      <td>1.612886e+06</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>18.300000</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>10.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>14</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>75.5</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>97.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>15557.838100</td>\n",
       "      <td>1.275338e+07</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>0.794000</td>\n",
       "      <td>15.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>15</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>72.3</td>\n",
       "      <td>196.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>5949.116770</td>\n",
       "      <td>9.489616e+06</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.798000</td>\n",
       "      <td>15.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>16</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>81.1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>47</td>\n",
       "      <td>...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4356.875000</td>\n",
       "      <td>1.127420e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.895000</td>\n",
       "      <td>16.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>17</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>71.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>94.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4849.997495</td>\n",
       "      <td>3.592880e+05</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.706000</td>\n",
       "      <td>12.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>18</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>25</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>55</td>\n",
       "      <td>...</td>\n",
       "      <td>78.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>783.947910</td>\n",
       "      <td>1.575952e+06</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>0.481000</td>\n",
       "      <td>10.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>19</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>69.8</td>\n",
       "      <td>211.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>98.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2613.645177</td>\n",
       "      <td>7.873860e+05</td>\n",
       "      <td>15.400000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.604000</td>\n",
       "      <td>12.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>20</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>77.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>8</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7483.158469</td>\n",
       "      <td>1.275338e+07</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>0.671000</td>\n",
       "      <td>13.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>21</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>77.4</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>1677</td>\n",
       "      <td>...</td>\n",
       "      <td>74.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4574.978700</td>\n",
       "      <td>3.535961e+06</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>0.747000</td>\n",
       "      <td>14.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>22</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>65.7</td>\n",
       "      <td>256.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>96.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>6532.651000</td>\n",
       "      <td>2.291970e+05</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>0.698000</td>\n",
       "      <td>12.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>23</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>75.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>42</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>214</td>\n",
       "      <td>...</td>\n",
       "      <td>98.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>8757.262200</td>\n",
       "      <td>2.596218e+06</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.754000</td>\n",
       "      <td>15.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>24</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>77.7</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3967.895100</td>\n",
       "      <td>1.275338e+07</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>0.864000</td>\n",
       "      <td>14.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>25</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>74.5</td>\n",
       "      <td>137.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>91.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>6993.477360</td>\n",
       "      <td>7.177991e+06</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>0.792000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>26</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>59.9</td>\n",
       "      <td>26.0</td>\n",
       "      <td>38</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>99</td>\n",
       "      <td>...</td>\n",
       "      <td>91.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>615.592225</td>\n",
       "      <td>1.811624e+06</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.399000</td>\n",
       "      <td>7.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>27</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>59.6</td>\n",
       "      <td>288.0</td>\n",
       "      <td>21</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>94.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>33.681223</td>\n",
       "      <td>1.199270e+05</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.406000</td>\n",
       "      <td>10.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>28</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>53.3</td>\n",
       "      <td>397.0</td>\n",
       "      <td>57</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>65</td>\n",
       "      <td>...</td>\n",
       "      <td>81.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>83.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>7483.158469</td>\n",
       "      <td>1.275338e+07</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>0.627551</td>\n",
       "      <td>11.992793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>29</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>73.3</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>93.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2954.119467</td>\n",
       "      <td>5.329130e+05</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>0.646000</td>\n",
       "      <td>13.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>30</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>68.7</td>\n",
       "      <td>174.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>87.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>89.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1163.189770</td>\n",
       "      <td>1.551764e+07</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.558000</td>\n",
       "      <td>10.900000</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>2457</th>\n",
       "      <td>154</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>64.1</td>\n",
       "      <td>225.0</td>\n",
       "      <td>58</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>3585</td>\n",
       "      <td>...</td>\n",
       "      <td>93.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2513.884661</td>\n",
       "      <td>3.864783e+06</td>\n",
       "      <td>4.839704</td>\n",
       "      <td>4.870317</td>\n",
       "      <td>0.488000</td>\n",
       "      <td>7.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2473</th>\n",
       "      <td>155</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>71.6</td>\n",
       "      <td>176.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>89.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>89.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>8818.982566</td>\n",
       "      <td>5.532800e+04</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>0.723000</td>\n",
       "      <td>12.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2489</th>\n",
       "      <td>156</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>58.9</td>\n",
       "      <td>373.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>84.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>3136.925110</td>\n",
       "      <td>1.319110e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>0.541000</td>\n",
       "      <td>11.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2505</th>\n",
       "      <td>157</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>82.4</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>98.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>5585.258470</td>\n",
       "      <td>9.799186e+06</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.909000</td>\n",
       "      <td>15.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2521</th>\n",
       "      <td>158</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>83.4</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>80.940461</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>97.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>8989.842400</td>\n",
       "      <td>8.282396e+06</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.938000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2537</th>\n",
       "      <td>159</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>64.5</td>\n",
       "      <td>293.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7483.158469</td>\n",
       "      <td>1.873499e+07</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>0.553000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2553</th>\n",
       "      <td>160</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>69.7</td>\n",
       "      <td>161.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>96.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>918.677154</td>\n",
       "      <td>8.548651e+06</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>11.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2569</th>\n",
       "      <td>161</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>74.9</td>\n",
       "      <td>148.0</td>\n",
       "      <td>8</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>154</td>\n",
       "      <td>...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>5814.863134</td>\n",
       "      <td>6.865760e+05</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>0.738000</td>\n",
       "      <td>13.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2585</th>\n",
       "      <td>162</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>75.7</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>92.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7483.158469</td>\n",
       "      <td>1.275338e+07</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>0.746000</td>\n",
       "      <td>12.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2601</th>\n",
       "      <td>163</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>68.3</td>\n",
       "      <td>152.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>75.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1161.769350</td>\n",
       "      <td>1.249770e+05</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>0.603000</td>\n",
       "      <td>12.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2617</th>\n",
       "      <td>164</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>59.9</td>\n",
       "      <td>287.0</td>\n",
       "      <td>13</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>88.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>551.138346</td>\n",
       "      <td>7.416820e+05</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>0.484000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2633</th>\n",
       "      <td>165</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>73.5</td>\n",
       "      <td>133.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>493.775387</td>\n",
       "      <td>1.636400e+04</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.718000</td>\n",
       "      <td>14.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2649</th>\n",
       "      <td>166</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>71.2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17321.833730</td>\n",
       "      <td>1.369200e+04</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>0.779000</td>\n",
       "      <td>12.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2665</th>\n",
       "      <td>167</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>75.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>98.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3828.915890</td>\n",
       "      <td>1.127366e+07</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>0.723000</td>\n",
       "      <td>14.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2681</th>\n",
       "      <td>168</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>75.8</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>342</td>\n",
       "      <td>...</td>\n",
       "      <td>97.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1979.525690</td>\n",
       "      <td>7.827147e+07</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>0.764000</td>\n",
       "      <td>14.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2697</th>\n",
       "      <td>169</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>66.3</td>\n",
       "      <td>215.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>6432.668768</td>\n",
       "      <td>5.565284e+06</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>0.688000</td>\n",
       "      <td>10.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2714</th>\n",
       "      <td>170</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>62.3</td>\n",
       "      <td>291.0</td>\n",
       "      <td>66</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>478</td>\n",
       "      <td>...</td>\n",
       "      <td>82.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>78.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>693.896383</td>\n",
       "      <td>4.144870e+05</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.488000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2730</th>\n",
       "      <td>171</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>71.3</td>\n",
       "      <td>195.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>105</td>\n",
       "      <td>...</td>\n",
       "      <td>51.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2124.662666</td>\n",
       "      <td>4.515429e+06</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>0.748000</td>\n",
       "      <td>15.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2746</th>\n",
       "      <td>172</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>77.1</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>347</td>\n",
       "      <td>...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3911.746890</td>\n",
       "      <td>1.275338e+07</td>\n",
       "      <td>5.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>0.836000</td>\n",
       "      <td>13.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2762</th>\n",
       "      <td>173</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>81.2</td>\n",
       "      <td>69.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10.660000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>80.940461</td>\n",
       "      <td>91</td>\n",
       "      <td>...</td>\n",
       "      <td>96.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7483.158469</td>\n",
       "      <td>1.275338e+07</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.627551</td>\n",
       "      <td>11.992793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2778</th>\n",
       "      <td>174</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>61.8</td>\n",
       "      <td>279.0</td>\n",
       "      <td>85</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>96.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>7483.158469</td>\n",
       "      <td>1.275338e+07</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>0.627551</td>\n",
       "      <td>11.992793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2794</th>\n",
       "      <td>175</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>79.3</td>\n",
       "      <td>13.0</td>\n",
       "      <td>23</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>188</td>\n",
       "      <td>...</td>\n",
       "      <td>93.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7483.158469</td>\n",
       "      <td>1.275338e+07</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.627551</td>\n",
       "      <td>11.992793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2810</th>\n",
       "      <td>176</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>77.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>95.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>15524.842470</td>\n",
       "      <td>3.431552e+06</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.794000</td>\n",
       "      <td>15.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2826</th>\n",
       "      <td>177</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>69.4</td>\n",
       "      <td>184.0</td>\n",
       "      <td>15</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2137.576852</td>\n",
       "      <td>3.129890e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>0.697000</td>\n",
       "      <td>12.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2842</th>\n",
       "      <td>178</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>72.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>39</td>\n",
       "      <td>...</td>\n",
       "      <td>65.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>285.834140</td>\n",
       "      <td>2.646300e+04</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.598000</td>\n",
       "      <td>10.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2858</th>\n",
       "      <td>179</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>74.1</td>\n",
       "      <td>157.0</td>\n",
       "      <td>9</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>87.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7483.158469</td>\n",
       "      <td>1.275338e+07</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.769000</td>\n",
       "      <td>14.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2874</th>\n",
       "      <td>180</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>28</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>97.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7483.158469</td>\n",
       "      <td>1.275338e+07</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>0.678000</td>\n",
       "      <td>12.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2890</th>\n",
       "      <td>181</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>65.7</td>\n",
       "      <td>224.0</td>\n",
       "      <td>37</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>468</td>\n",
       "      <td>...</td>\n",
       "      <td>63.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7483.158469</td>\n",
       "      <td>1.275338e+07</td>\n",
       "      <td>13.600000</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>0.499000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2906</th>\n",
       "      <td>182</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>61.8</td>\n",
       "      <td>33.0</td>\n",
       "      <td>27</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1313.889646</td>\n",
       "      <td>1.615870e+05</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>0.576000</td>\n",
       "      <td>12.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>183</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>67.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>22</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.0</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>87.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>118.693830</td>\n",
       "      <td>1.577745e+07</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>0.507000</td>\n",
       "      <td>10.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Country  Year  Status  Life expectancy   Adult Mortality  infant deaths  \\\n",
       "0           1  2015       1              65.0            263.0             62   \n",
       "16          2  2015       1              77.8             74.0              0   \n",
       "32          3  2015       1              75.6             19.0             21   \n",
       "48          4  2015       1              52.4            335.0             66   \n",
       "64          5  2015       1              76.4             13.0              0   \n",
       "80          6  2015       1              76.3            116.0              8   \n",
       "96          7  2015       1              74.8            118.0              1   \n",
       "112         8  2015       0              82.8             59.0              1   \n",
       "128         9  2015       0              81.5             65.0              0   \n",
       "144        10  2015       1              72.7            118.0              5   \n",
       "160        11  2015       1              76.1            147.0              0   \n",
       "176        12  2015       1              76.9             69.0              0   \n",
       "192        13  2015       1              71.8            129.0             92   \n",
       "208        14  2015       1              75.5             98.0              0   \n",
       "224        15  2015       1              72.3            196.0              0   \n",
       "240        16  2015       0              81.1             74.0              0   \n",
       "256        17  2015       1              71.0            175.0              0   \n",
       "272        18  2015       1              60.0            249.0             25   \n",
       "288        19  2015       1              69.8            211.0              0   \n",
       "304        20  2015       1              77.0            186.0              8   \n",
       "320        21  2015       1              77.4             88.0              0   \n",
       "336        22  2015       1              65.7            256.0              2   \n",
       "352        23  2015       1              75.0            142.0             42   \n",
       "368        24  2015       1              77.7             78.0              0   \n",
       "384        25  2015       0              74.5            137.0              0   \n",
       "400        26  2015       1              59.9             26.0             38   \n",
       "416        27  2015       1              59.6            288.0             21   \n",
       "432        28  2015       1              53.3            397.0             57   \n",
       "448        29  2015       1              73.3            114.0              0   \n",
       "464        30  2015       1              68.7            174.0             10   \n",
       "...       ...   ...     ...               ...              ...            ...   \n",
       "2457      154  2015       1              64.1            225.0             58   \n",
       "2473      155  2015       1              71.6            176.0              0   \n",
       "2489      156  2015       1              58.9            373.0              2   \n",
       "2505      157  2015       0              82.4             53.0              0   \n",
       "2521      158  2015       0              83.4             49.0              0   \n",
       "2537      159  2015       1              64.5            293.0              6   \n",
       "2553      160  2015       1              69.7            161.0             10   \n",
       "2569      161  2015       1              74.9            148.0              8   \n",
       "2585      162  2015       1              75.7             12.0              0   \n",
       "2601      163  2015       1              68.3            152.0              2   \n",
       "2617      164  2015       1              59.9            287.0             13   \n",
       "2633      165  2015       1              73.5            133.0              0   \n",
       "2649      166  2015       1              71.2             17.0              0   \n",
       "2665      167  2015       1              75.3              1.0              3   \n",
       "2681      168  2015       1              75.8             16.0             15   \n",
       "2697      169  2015       1              66.3            215.0              6   \n",
       "2714      170  2015       1              62.3            291.0             66   \n",
       "2730      171  2015       1              71.3            195.0              4   \n",
       "2746      172  2015       1              77.1             75.0              1   \n",
       "2762      173  2015       0              81.2             69.0              3   \n",
       "2778      174  2015       1              61.8            279.0             85   \n",
       "2794      175  2015       0              79.3             13.0             23   \n",
       "2810      176  2015       1              77.0            116.0              0   \n",
       "2826      177  2015       1              69.4            184.0             15   \n",
       "2842      178  2015       1              72.0             13.0              0   \n",
       "2858      179  2015       1              74.1            157.0              9   \n",
       "2874      180  2015       1              76.0            127.0             28   \n",
       "2890      181  2015       1              65.7            224.0             37   \n",
       "2906      182  2015       1              61.8             33.0             27   \n",
       "2922      183  2015       1              67.0            336.0             22   \n",
       "\n",
       "        Alcohol  percentage expenditure  Hepatitis B  Measles     ...      \\\n",
       "0      0.010000               71.279624    65.000000      1154    ...       \n",
       "16     4.600000              364.975229    99.000000         0    ...       \n",
       "32     4.602861                0.000000    95.000000        63    ...       \n",
       "48     4.602861                0.000000    64.000000       118    ...       \n",
       "64     4.602861                0.000000    99.000000         0    ...       \n",
       "80     4.602861                0.000000    94.000000         0    ...       \n",
       "96     4.602861                0.000000    94.000000        33    ...       \n",
       "112    4.602861                0.000000    93.000000        74    ...       \n",
       "128    4.602861                0.000000    93.000000       309    ...       \n",
       "144    4.602861                0.000000    96.000000         0    ...       \n",
       "160    4.602861                0.000000    95.000000         0    ...       \n",
       "176    4.602861                0.000000    98.000000         0    ...       \n",
       "192    4.602861                0.000000    97.000000       240    ...       \n",
       "208    4.602861                0.000000    97.000000         0    ...       \n",
       "224    4.602861                0.000000    99.000000         2    ...       \n",
       "240    4.602861                0.000000    98.000000        47    ...       \n",
       "256    4.602861                0.000000    94.000000         0    ...       \n",
       "272    4.602861                0.000000    82.000000        55    ...       \n",
       "288    4.602861                0.000000    99.000000        11    ...       \n",
       "304    4.602861                0.000000    99.000000         0    ...       \n",
       "320    4.602861                0.000000    82.000000      1677    ...       \n",
       "336    4.602861                0.000000    95.000000         0    ...       \n",
       "352    4.602861                0.000000    96.000000       214    ...       \n",
       "368    4.602861                0.000000    99.000000         4    ...       \n",
       "384    4.602861                0.000000    92.000000         0    ...       \n",
       "400    4.602861                0.000000    91.000000        99    ...       \n",
       "416    4.602861                0.000000    94.000000         9    ...       \n",
       "432    4.602861                0.000000    83.000000        65    ...       \n",
       "448    4.602861                0.000000    96.000000         0    ...       \n",
       "464    4.602861                0.000000    89.000000         0    ...       \n",
       "...         ...                     ...          ...       ...    ...       \n",
       "2457   4.602861                0.000000    93.000000      3585    ...       \n",
       "2473   4.602861                0.000000    89.000000         0    ...       \n",
       "2489   4.602861                0.000000     9.000000         0    ...       \n",
       "2505   4.602861                0.000000    67.000000        22    ...       \n",
       "2521   4.602861                0.000000    80.940461        35    ...       \n",
       "2537   4.602861                0.000000    41.000000        45    ...       \n",
       "2553   4.602861                0.000000    96.000000         3    ...       \n",
       "2569   4.602861                0.000000    99.000000       154    ...       \n",
       "2585   4.602861                0.000000    92.000000         1    ...       \n",
       "2601   4.602861                0.000000    76.000000        43    ...       \n",
       "2617   4.602861                0.000000    88.000000        20    ...       \n",
       "2633   4.602861                0.000000    78.000000         0    ...       \n",
       "2649   4.602861                0.000000     9.000000         0    ...       \n",
       "2665   4.602861                0.000000    98.000000        16    ...       \n",
       "2681   4.602861                0.000000    97.000000       342    ...       \n",
       "2697   4.602861                0.000000    99.000000         0    ...       \n",
       "2714   4.602861                0.000000    78.000000       478    ...       \n",
       "2730   4.602861                0.000000    22.000000       105    ...       \n",
       "2746   4.602861                0.000000    99.000000       347    ...       \n",
       "2762  10.660000                0.000000    80.940461        91    ...       \n",
       "2778   4.602861                0.000000    98.000000        30    ...       \n",
       "2794   4.602861                0.000000    92.000000       188    ...       \n",
       "2810   4.602861                0.000000    95.000000         0    ...       \n",
       "2826   4.602861                0.000000    99.000000        22    ...       \n",
       "2842   4.602861                0.000000    64.000000        39    ...       \n",
       "2858   4.602861                0.000000    87.000000         0    ...       \n",
       "2874   4.602861                0.000000    97.000000       256    ...       \n",
       "2890   4.602861                0.000000    69.000000       468    ...       \n",
       "2906   4.602861                0.000000     9.000000         9    ...       \n",
       "2922   4.602861                0.000000    87.000000         0    ...       \n",
       "\n",
       "      Polio  Total expenditure  Diphtheria    HIV/AIDS           GDP  \\\n",
       "0       6.0            8.16000         65.0        0.1    584.259210   \n",
       "16     99.0            6.00000         99.0        0.1   3954.227830   \n",
       "32     95.0            5.93819         95.0        0.1   4132.762920   \n",
       "48      7.0            5.93819         64.0        1.9   3695.793748   \n",
       "64     86.0            5.93819         99.0        0.2  13566.954100   \n",
       "80     93.0            5.93819         94.0        0.1  13467.123600   \n",
       "96     96.0            5.93819         94.0        0.1    369.654776   \n",
       "112    93.0            5.93819         93.0        0.1  56554.387600   \n",
       "128    93.0            5.93819         93.0        0.1  43665.947000   \n",
       "144    98.0            5.93819         96.0        0.1     55.313820   \n",
       "160    95.0            5.93819         95.0        0.1   7483.158469   \n",
       "176    98.0            5.93819         98.0        0.1  22688.878240   \n",
       "192    97.0            5.93819         97.0        0.1    121.158120   \n",
       "208    97.0            5.93819         97.0        0.1  15557.838100   \n",
       "224    99.0            5.93819         99.0        0.1   5949.116770   \n",
       "240    99.0            5.93819         99.0        0.1   4356.875000   \n",
       "256    94.0            5.93819         94.0        0.2   4849.997495   \n",
       "272    78.0            5.93819         82.0        1.0    783.947910   \n",
       "288    98.0            5.93819         99.0        0.5   2613.645177   \n",
       "304    99.0            5.93819         99.0        0.1   7483.158469   \n",
       "320    74.0            5.93819         82.0        0.1   4574.978700   \n",
       "336    96.0            5.93819         95.0        2.2   6532.651000   \n",
       "352    98.0            5.93819         96.0        0.1   8757.262200   \n",
       "368    99.0            5.93819         99.0        0.1   3967.895100   \n",
       "384    91.0            5.93819         91.0        0.1   6993.477360   \n",
       "400    91.0            5.93819         91.0        0.6    615.592225   \n",
       "416    94.0            5.93819         94.0        0.7     33.681223   \n",
       "432    81.0            5.93819         83.0        1.9   7483.158469   \n",
       "448    93.0            5.93819         93.0        0.2   2954.119467   \n",
       "464    87.0            5.93819         89.0        0.2   1163.189770   \n",
       "...     ...                ...          ...        ...           ...   \n",
       "2457   93.0            5.93819         93.0        0.3   2513.884661   \n",
       "2473   89.0            5.93819         89.0        0.4   8818.982566   \n",
       "2489   84.0            5.93819          9.0        7.1   3136.925110   \n",
       "2505   98.0            5.93819         98.0        0.1   5585.258470   \n",
       "2521   97.0            5.93819         97.0        0.1   8989.842400   \n",
       "2537    5.0            5.93819         41.0        0.1   7483.158469   \n",
       "2553   96.0            5.93819         96.0        0.2    918.677154   \n",
       "2569   99.0            5.93819         99.0        0.1   5814.863134   \n",
       "2585   92.0            5.93819         91.0        0.1   7483.158469   \n",
       "2601   75.0            5.93819         76.0        0.1   1161.769350   \n",
       "2617   88.0            5.93819         88.0        1.0    551.138346   \n",
       "2633    8.0            5.93819         78.0        0.1    493.775387   \n",
       "2649   88.0            5.93819         96.0        0.3  17321.833730   \n",
       "2665   98.0            5.93819         98.0        0.1   3828.915890   \n",
       "2681   97.0            5.93819         97.0        0.1   1979.525690   \n",
       "2697   99.0            5.93819         99.0        0.1   6432.668768   \n",
       "2714   82.0            5.93819         78.0        3.1    693.896383   \n",
       "2730   51.0            5.93819         23.0        0.2   2124.662666   \n",
       "2746   99.0            5.93819         99.0        0.1   3911.746890   \n",
       "2762   96.0            5.93819         96.0        0.1   7483.158469   \n",
       "2778   96.0            5.93819         98.0        1.4   7483.158469   \n",
       "2794   93.0            5.93819         95.0        0.1   7483.158469   \n",
       "2810   95.0            5.93819         95.0        0.1  15524.842470   \n",
       "2826   99.0            5.93819         99.0        0.1   2137.576852   \n",
       "2842   65.0            5.93819         64.0        0.1    285.834140   \n",
       "2858   87.0            5.93819         87.0        0.1   7483.158469   \n",
       "2874   97.0            5.93819         97.0        0.1   7483.158469   \n",
       "2890   63.0            5.93819         69.0        0.1   7483.158469   \n",
       "2906    9.0            5.93819          9.0        4.1   1313.889646   \n",
       "2922   88.0            5.93819         87.0        6.2    118.693830   \n",
       "\n",
       "        Population   thinness  1-19 years   thinness 5-9 years  \\\n",
       "0     3.373649e+07              17.200000            17.300000   \n",
       "16    2.887300e+04               1.200000             1.300000   \n",
       "32    3.987153e+07               6.000000             5.800000   \n",
       "48    2.785935e+06               8.300000             8.200000   \n",
       "64    1.275338e+07               3.300000             3.300000   \n",
       "80    4.341776e+07               1.000000             0.900000   \n",
       "96    2.916950e+05               2.100000             2.200000   \n",
       "112   2.378934e+07               0.600000             0.600000   \n",
       "128   8.633169e+06               1.900000             2.100000   \n",
       "144   9.649341e+06               2.800000             2.900000   \n",
       "160   1.275338e+07               2.500000             2.500000   \n",
       "176   1.275338e+07               6.200000             6.100000   \n",
       "192   1.612886e+06              17.900000            18.300000   \n",
       "208   1.275338e+07               3.800000             3.700000   \n",
       "224   9.489616e+06               1.900000             2.000000   \n",
       "240   1.127420e+07               1.000000             1.000000   \n",
       "256   3.592880e+05               3.500000             3.400000   \n",
       "272   1.575952e+06               6.900000             6.800000   \n",
       "288   7.873860e+05              15.400000            16.000000   \n",
       "304   1.275338e+07               1.200000             1.100000   \n",
       "320   3.535961e+06               2.300000             2.300000   \n",
       "336   2.291970e+05               6.400000             6.100000   \n",
       "352   2.596218e+06               2.700000             2.600000   \n",
       "368   1.275338e+07               5.700000             5.100000   \n",
       "384   7.177991e+06               1.900000             1.800000   \n",
       "400   1.811624e+06               8.000000             7.500000   \n",
       "416   1.199270e+05               7.300000             7.200000   \n",
       "432   1.275338e+07               5.500000             5.500000   \n",
       "448   5.329130e+05               6.600000             6.600000   \n",
       "464   1.551764e+07               1.900000             1.900000   \n",
       "...            ...                    ...                  ...   \n",
       "2457  3.864783e+06               4.839704             4.870317   \n",
       "2473  5.532800e+04               3.500000             3.500000   \n",
       "2489  1.319110e+05               4.000000             4.100000   \n",
       "2505  9.799186e+06               1.500000             1.400000   \n",
       "2521  8.282396e+06               0.400000             0.300000   \n",
       "2537  1.873499e+07               6.300000             6.100000   \n",
       "2553  8.548651e+06               3.600000             3.700000   \n",
       "2569  6.865760e+05               7.700000             7.700000   \n",
       "2585  1.275338e+07               2.100000             2.100000   \n",
       "2601  1.249770e+05               1.900000            11.100000   \n",
       "2617  7.416820e+05               6.500000             6.200000   \n",
       "2633  1.636400e+04               0.100000             0.100000   \n",
       "2649  1.369200e+04               5.700000             5.900000   \n",
       "2665  1.127366e+07               6.500000             6.400000   \n",
       "2681  7.827147e+07               4.900000             4.800000   \n",
       "2697  5.565284e+06               3.300000             3.300000   \n",
       "2714  4.144870e+05               5.600000             5.600000   \n",
       "2730  4.515429e+06               2.300000             2.400000   \n",
       "2746  1.275338e+07               5.300000             5.100000   \n",
       "2762  1.275338e+07               0.800000             0.600000   \n",
       "2778  1.275338e+07               6.700000             6.500000   \n",
       "2794  1.275338e+07               0.800000             0.600000   \n",
       "2810  3.431552e+06               1.500000             1.400000   \n",
       "2826  3.129890e+05               3.000000             3.100000   \n",
       "2842  2.646300e+04               1.500000             1.400000   \n",
       "2858  1.275338e+07               1.600000             1.500000   \n",
       "2874  1.275338e+07              14.200000            14.500000   \n",
       "2890  1.275338e+07              13.600000            13.400000   \n",
       "2906  1.615870e+05               6.300000             6.100000   \n",
       "2922  1.577745e+07               5.600000             5.500000   \n",
       "\n",
       "      Income composition of resources  Schooling  \n",
       "0                            0.479000  10.100000  \n",
       "16                           0.762000  14.200000  \n",
       "32                           0.743000  14.400000  \n",
       "48                           0.531000  11.400000  \n",
       "64                           0.784000  13.900000  \n",
       "80                           0.826000  17.300000  \n",
       "96                           0.741000  12.700000  \n",
       "112                          0.937000  20.400000  \n",
       "128                          0.892000  15.900000  \n",
       "144                          0.758000  12.700000  \n",
       "160                          0.790000  12.600000  \n",
       "176                          0.823000  14.500000  \n",
       "192                          0.575000  10.200000  \n",
       "208                          0.794000  15.300000  \n",
       "224                          0.798000  15.600000  \n",
       "240                          0.895000  16.600000  \n",
       "256                          0.706000  12.800000  \n",
       "272                          0.481000  10.700000  \n",
       "288                          0.604000  12.500000  \n",
       "304                          0.671000  13.800000  \n",
       "320                          0.747000  14.200000  \n",
       "336                          0.698000  12.600000  \n",
       "352                          0.754000  15.200000  \n",
       "368                          0.864000  14.900000  \n",
       "384                          0.792000  15.000000  \n",
       "400                          0.399000   7.700000  \n",
       "416                          0.406000  10.600000  \n",
       "432                          0.627551  11.992793  \n",
       "448                          0.646000  13.500000  \n",
       "464                          0.558000  10.900000  \n",
       "...                               ...        ...  \n",
       "2457                         0.488000   7.200000  \n",
       "2473                         0.723000  12.700000  \n",
       "2489                         0.541000  11.400000  \n",
       "2505                         0.909000  15.900000  \n",
       "2521                         0.938000  16.000000  \n",
       "2537                         0.553000   9.000000  \n",
       "2553                         0.625000  11.300000  \n",
       "2569                         0.738000  13.600000  \n",
       "2585                         0.746000  12.900000  \n",
       "2601                         0.603000  12.500000  \n",
       "2617                         0.484000  12.000000  \n",
       "2633                         0.718000  14.300000  \n",
       "2649                         0.779000  12.700000  \n",
       "2665                         0.723000  14.600000  \n",
       "2681                         0.764000  14.500000  \n",
       "2697                         0.688000  10.800000  \n",
       "2714                         0.488000  10.000000  \n",
       "2730                         0.748000  15.300000  \n",
       "2746                         0.836000  13.300000  \n",
       "2762                         0.627551  11.992793  \n",
       "2778                         0.627551  11.992793  \n",
       "2794                         0.627551  11.992793  \n",
       "2810                         0.794000  15.500000  \n",
       "2826                         0.697000  12.100000  \n",
       "2842                         0.598000  10.800000  \n",
       "2858                         0.769000  14.300000  \n",
       "2874                         0.678000  12.600000  \n",
       "2890                         0.499000   9.000000  \n",
       "2906                         0.576000  12.500000  \n",
       "2922                         0.507000  10.300000  \n",
       "\n",
       "[183 rows x 22 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a new Dataset with all countries (2015)\n",
    "dfdeveloped = df.copy()\n",
    "#Drop all dates different to year 2015\n",
    "\n",
    "dfdeveloped.drop(dfdeveloped[dfdeveloped.Year != 2015].index, inplace=True)\n",
    "dfdeveloped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x250a0065c50>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Zs6cWL16sP/zhD3rmmWdO+HxJuvjii/Vf//VfysjIUElJifbs2aOl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\nIdgAAEYh2AAARiHYAABGIdgAAEb5/2REmSXbOZ+bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x250a0065b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='Total expenditure',y='GDP',data=dfdeveloped,color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Logistic Regression\n",
    "#Split data into training set and testing set using train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X = dfdeveloped[['Total expenditure', 'GDP', 'Schooling','Income composition of resources']]\n",
    "y = dfdeveloped['Status']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train and fit a logistic regression model on the training set\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "logmodel = LogisticRegression()\n",
    "logmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.67      0.15      0.25        13\n",
      "          1       0.81      0.98      0.89        48\n",
      "\n",
      "avg / total       0.78      0.80      0.75        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Predictions and Evaluations\n",
    "predictions = logmodel.predict(X_test)\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ESEpMbNwbH9zqh3foPx/q4eGEhr3W3h8SFk5M7Ps/2M0yZSJrFluyZs6MXWZbCn6Rl6xZ\nMmOXOTPZsthilznzW49bWpj/96RGDx9CzpywZAmmdevCJw4xoZcg2L59O1FRUbRr147Ro0fTs2dP\nFEWhVatW5M6dWx8lCCHEO4JfvuL2/YdvfWv//2/woeGviYqJee/ymUxN//4gtyVblszY58ur/dae\nNXNm7LLYqj///RwrC4vPm60uMRH8/GD/fti4EfLnh9On4TNnwNNZEOTPn197eWizZs2099etW5e6\ndevqarNCCJEsRVHYsGcfPsvXEhcfr73f1NREPRXz94d3/jy53vogVz/k//3WbmX5mR/sH+Ovv6Bv\nX/WDv359eP0aMmf+7BCAVD76qBBCpLRXYeF4zV/M0bPnqfJNGbq2bEY2u8xky5wZayvL1De/dEQE\njBsHPj7qaaDVq6FduxQJgH9IEAghMowT5y/hMXchEZFRDO3RiTaN6qe+D/732bJFPRrw8gI7uxRf\nvQSBECLdi4uPZ/7KdazZuZfCBb5gzvgRFPmygKHLStrduzB1KsyaBTY2cPmy+r+OSBAIIdK1uw8e\n4Trbj5tB92ndqD4DOrXDwtzM0GW9X1wczJgBEyeCsTF07QqVK+s0BECCQAiRTimKwqa9B5izbDVW\nFhbMGPMT1cqXNXRZSQsIgH794OpVaNlSPRoooJ+jFgkCIUS6Exr+Gq/5izl85hyVy5Zm/I+9yJ41\n5c+tpxhFgeHDITIStm+Hpk31unkJAiFEunLywmU8fPwJj4hkSLcOtG3shLFxKhxxX1FgxQpo0gSy\nZ4d169Srgqyt9V5KKtw7Qgjx8eLi45mzbDWDJ04js401v052w6Vpw9QZAlevQu3aahuAv796X8GC\nBgkBkCMCIUQ6EPToMa4zfbkRdJ+WDesyqIsLFubmhi7rXVFR4OkJ06apncEWLYLu3Q1dlQSBECLt\nUhSFg6fPsXbPQSzMzZg6ajA1vy1n6LKSNmIEzJ+vHglMm6aeCkoFJAiEEGlS2OsIvH0Xc+jUX1Qs\n8xWuA3uTIzU2CD96BPHx6qmfMWOgTRv1tFAqIkEghEhzTl+8gsdcf0LCX+PiXJdBPTqnvraAxESY\nN08dHqJGDdi5Ux0kLn9+Q1f2DgkCIUSaER+fgN+ajfy2bTf2+fIwffRQNLFRqS8EzpyBPn3UgeIa\nNoQ5cwxd0X+SIBBCpAn3Hj3BbbYf1+4E8b1THQZ3a4+FuXnqm51s82Zo3Rpy54a1a9VTQal8PCMJ\nAiFEqqYoCtv2H2LmklWYZcrElJGDqFWxvKHLepuiwIsXauOvkxOMHg0jR0KWLIau7INIEAghUq2w\n1xFMXrCEgyfOUKFUSVwH/kCu7NkMXdbbbt+GAQPUgeIuXFDHBfLyMnRVH0WCQAiRKp29HIj7nAWE\nhIczoHM7OjRzTl1tAbGxMH262i8gUyb1w980bX6kps2qhRDpVkJCAv5rN7Fiyy4K5M3NtFHjKe5Q\nyNBlve3BA2jQAK5dU9sAZs6EL74wdFWfTIJACJFq3H/yFLdZfgTevst39WrxU/eOWFqkoh7CiYlg\nYgL58sFXX6lDRjdubOiqPpsEgRDC4BRFYceBw/yyZCWZTE3xHj6AupW/NXRZ/9Jo4Ndf1d7Ax46p\ng8Rt2GDoqlKMBIEQwqDCIyKZsmAJ+4+fptxXxXEb2JvcObIbuqx/Xb6sThN59KjaMez1azUI0hEJ\nAiGEwZy7eg33OQt4ERJG/45t6PhdY0xMUkmDcGIi/Pwz/PKLehnokiXqGEGpvE/Ap5AgEELoXUJC\nAgvXbWH55h18kTsXC73GUbJIYUOX9TYTE7h1C7p0UecPTmdHAW+SIBBC6NXDp89wneXH1Vt3aFqn\nBkN7dMLK0sLQZakePlRnCvPwAEdHtWdwGr0k9GOk/1cohEgVFEVh16GjzFi0AhMTY7yG/ki9qhUN\nXZYqIQF8fMDVVT0l1LKlGgQZIARAgkAIoQevIyOZ6r+MP46e5JuSxXAb2Ic8OVPJqZaTJ9UB4i5c\ngEaN1BFDC6Wyfgs6JkEghNCp84E3cJ+zgOcvX9GnfSu6tGiaehqEQT398/y5ejloy5bpsjE4ORIE\nQgidSEhM5Nf1W1m6aRt5c+Zggec4Sjk6GLosdYC4tWvVnsA1aqjtAe7u6tSRGZQEgRAixT1+9hy3\n2X5cunGLxrWrM6xnJ6wtLQ1dFty8CT/+CH/8AR06qEFgY2PoqgxOgkAIkaJ2Bxxl2sLlGBsZM3FI\nP5yqVzZ0SeoAcVOmgLc3mJvD3LlqJzEBSBAIIVJIRGQUUxcuZ++R43xd3BH3wX3ImzOHoctSrVwJ\nbm7Qrp06QFzevIauKFWRIBBCfLaL127iNseP4Bev6N2uJV1aNsXUxMSwRT17BtevQ82a0K0bFC2q\n/izeIUEghPhkCYmJLN24jSUbtpE7R3b8Jo6ldLEihi1Ko4FFi2DUKLCyUieMMTOTEPgPEgRCiE/y\nOPg57nMWcPHaTZxrVmVEry5YWxm4QfjiRfXc//HjULs2+PqqISD+k06CQKPR4O7uzvXr1zEzM8PT\n05Mvv/xS+/jixYvZuXMnRkZG9O3bFycnJ12UIYTQkb1HjjPFfxmKouA+qA/ONasauiQIDIRy5SBb\nNli+HDp1ypB9Aj6FToJg3759xMXFsXbtWs6fP8/kyZPx9fUFIDw8nBUrVrB3716io6Np0aKFBIEQ\naURkVDTTFy1nd8AxSjsWYcLgvuTLndOgNWUKCoISJdR/c+aAi4saBuKD6SQIzp49S40aNQAoW7Ys\nly9f1j5maWlJvnz5iI6OJjo6GiNJbCHShMs3buE2248nz1/Qs00Lurf+zrANwvfvw6BBFN61C65e\nhSJFoH9/w9WThukkCCIiIrB5o5OGiYkJCQkJmP49gFPevHlp0qQJiYmJ9OnTJ8n1BAYG6qK8NCcm\nJkb2BbIf3qTPfaHRaNgRcJytB4+QNbMto3t0pOiX+bl544Zetv+O+HiyrVhBznnzAHjSvz/hUVHq\nqSHxSXQSBDY2NkRGRmpvazQabQgEBAQQHBzM/v37AejZsyflypWjTJky76ynRIkSuigvzQkMDJR9\ngeyHN+lrXzx9/hL3OX6cD7xBg+qVGfFDF2ytrXW+3STFxUGVKvDXX9C0KcydS3hUlLwv/nb27NlP\nWk4nIz+VK1eOgIAAAM6fP4+jo6P2sSxZsmBhYYGZmRnm5ubY2toSHh6uizKEEJ/hj6Mn6TRsHDeC\n7uM2sDcTBvc1XAhER6v/m5lB69awaRNs2wZvXIQiPp1OjgicnJw4evQoLi4uKIqCt7c3S5Yswd7e\nnnr16nHs2DHatm2LsbEx5cqVo1q1arooQwjxCSKjo/nl15XsPHiEr4o64DGkL1/kzmWYYhQFVq2C\nYcNg3TqoVQvGjDFMLemYToLA2NgYDw+Pt+5zcPh31MFBgwYxaNAgXWxaCPEZrty8jdtsPx4HP6d7\nq+/o2aa59rSu3l2/rjb+HjgAFSvKlUA6JB3KhBAkJmpYsWUnC9dtJmdWO+a7j6FsyWKGK2jqVBg/\nHiwt1U5hP/ygziEsdEKCQIgM7tmLl7jP8efc1WvUq1qR0X26GbZBGMDCQm0LmDED8uQxbC0ZgASB\nEBnYgeOnmbxgCfEJCYz7sRdNalc3TN+ep09h6FB1qsjOnWHgQJDTx3ojQSBEBhQVHcPMJavYfiCA\nkkUKMWFwPwrkza3/QhITwd9fbQCOjoZvv1Xvl46meiVBIEQGE3jrLq6zfXn4NJiuLZvxQ9sWhmkQ\nvnABeveGU6egbl2YPx+KGbBdIgOTIBAig9BoNKzcupsFazaS3S4Lc91GUb6UATti3bsHQUHqpDEd\nOshRgAFJEAiRAQS/fMUEH3/OXg6kbuVvGdWnG1ls9TxXr6LAli3w+LE6b/B336lHAjJnsMFJEAiR\nzh08cYZJfr8SFx/P2H49aVq3hv4bhO/dgwEDYMcOtU9A377q5aASAqmCBIEQ6VR0TCyzlq5i675D\nFC9cEI8h/bDPp+dLMePj1TmCJ0xQT/1Mnw6DB0ufgFRGgkCIdOjanSDcZvlx/8lTOrdoQu92LcmU\nyQB/7oGB6hVB330Hs2eDvb3+axDJkiAQIh3RaDSs3r4H39UbyJo5s2EahF++hO3b1Qnjy5RRp4/8\n6iv91iA+ygcHQVhYGFmyZNFlLUKIz/D8VQgecxdy+uIValUsz8/9eui3QVhR1Ckihw+H0FCoU0cd\nHVRCINVLNghOnTqFh4cHiYmJODs7ky9fPtq0aaOP2oQQHyjg1F94+S4mNi6O0X2607x+Lf02CF+7\npjYAHzqkzhfg5ydDRKchyc5HMHv2bFauXEmOHDno27cvq1ev1kddQogPEBMbyxT/pYycOpvcObKz\nbKoHLZxq6zcEoqKgenX1FJC/Pxw5op4SEmlGskcExsbG2NnZYWRkhLm5OdaGHoxKCAHAjbv3cJ3t\nR9DDx3T8rhF92rfCLFMm/RVw/DhUrgxWVuqcAd98A7kMNG+B+CzJBoG9vT0zZswgNDQUf39/8uXL\np4+6hBBJ0Gg0/H7sFBv/CCCLrQ1zXEdSsYwez8M/eQI//QRr16r/2raFhg31t32R4pI9NTRhwgTy\n5ctH+fLlsbS0xNPTUx91CSHe40VIKD95zWDN7gNU+aYMK2d46i8EEhNh7lwoXlztIezhAc2b62fb\nQqeSPSLw9vbG1dVVe3vkyJFMnTpVp0UJId515Mx5POcvIjomli7NGtKvS3v9tgW0aQObN4OTkzpA\nXJEi+tu20Kkkg2DVqlX4+voSGhrK3r17tfe/OeWkEEL3YmLjmLtiDRv27KdoQXs8hvQj5nWYfkIg\nPBzMzdV/vXurp4HatZMB4tKZJIOgY8eOdOzYET8/P/r27avPmoQQf7sZdB+32X7cefCI9s2c6deh\nNWaZMhEYGKbbDSsKbNyoDgfRpw+4uoKzs263KQwm2VNDLi4u7Nixg4SEBBRFITg4mD59+uijNiEy\nLEVRWLfrD+atXIettRWzxw2nUtnS+tn4nTvqAHG7d0PZshIAGUCyQTBo0CAKFizIjRs3MDc3x9LS\nUh91CZFhvQwNY+LchZw4f4lq5csyrn9PsmbJrJ+Nr1ypThRvaqoOFjdggPqzSNeSvWoIwMPDg0KF\nCrFkyRLCwnR8SCpEBnbsrwt0GjaOc1evMbxnZ6aPHqKfENBo1P9LloSmTdXB4oYMkRDIID7otxwb\nG0t0dDRGRkZERUXpuiYhMpzYuDjmrljH+t1/4GCfn3nuoyhcIL/uN/ziBYwcqTb+Ll4M5crB+vW6\n365IVZINgo4dO7Js2TKqVatGrVq1KF++vD7qEiLDuH3/Ia6zfLl9/yHtmjSgf8c2mJuZ6XajigJL\nl8KIERAWpg4UpyhyNVAGlWwQNHyjx2CjRo3kiECIFKIoChv27MNn+VqsrSz55eehVC33te43fPs2\ndO8Ohw9DtWrqAHGlSul+uyLVSrKN4MqVK/Tt25dRo0bx6tUrALZu3UqrVq30VpwQ6dWrsHCGT5rJ\njMUrqVC6JCtneOonBEDtE3D/PixaBAEBEgIi6SOC8ePHM3ToUB4/fszMmTOJiooiODiYVatW6bM+\nIdKd4+cuMnHeIiIioxjaoxNtGtXXfeew3bth3Tr49VfInx9u3ZKGYKGV5DvB0tKS6tWrAzBv3jxa\ntGjB9OnT9T/ptRDpRGxcHL6r1rNm514KF/iCOeNHUOTLArrd6KNH6tU/GzaoYwQFB0Pu3BIC4i1J\nvhtM3phcOleuXPz00096KUiI9Ojug0eMn+XLrXsPaN2oPgM6tcPCXIcNwomJMG8ejBunTiDv6ak2\nDOu6EVqkSUkGgaIoxMfHoygKFhYW2p8BzOTNJMQHURSFTXsPMGfZaqwsLJgx5ieqlS+r+w1HR8P0\n6VC1qhoIMkaY+A9JBsGjR49w/rtruaIoODs7oygKRkZG7N+/X28FCpFWhYSF4+37K4fPnKNy2dKM\n/7EX2bPa6W6DYWEwaxaMHg02NnDyJOTJI5eEimQlGQQHDhzQZx1CpCsnL1zGw8ef8IhIhnTrQNvG\nThgbf1BH/o+nKGpD8JAh8OwZVKqkjg+UN69utifSHZ20GGk0Gtzd3bl+/TpmZmZ4enry5RsTWR86\ndIh58+YBULJkSdzc3KQRWqQLcfHx+P62gdXb91Aofz5mjRtO0YL2utvg7dvw44/w++9qr+Dt26FC\nBd1tT6RLOgmCffv2ERcXx9qcc6JAAAAgAElEQVS1azl//jyTJ0/G19cXgIiICKZNm8by5cvJli0b\nCxcuJCQkhGzZsumiFCH0JujRY1xn+nIj6D6tGtZjYJd2WJib63aj3bvD+fMwZw707w9vXOQhxIf6\noCCIiIjg0aNHFChQACsrq2Sff/bsWWrUqAFA2bJluXz5svaxc+fO4ejoyJQpU3jw4AFt2rSREBBp\nmqIobPnjT2Yt/Q1LC3OmjhpMzW/L6W6Dhw5h8s8FGwsXqu0BX3yhu+2JdC/ZINizZw9+fn4kJibi\n7OyMkZER/fv3/89lIiIisLGx0d42MTEhISEBU1NTQkJCOHnyJFu2bMHKyoqOHTtStmxZChUq9M56\nAgMDP+ElpT8xMTGyL0id++F1ZBRLtuzm3LWbfOVQkF6tmmJnY6mTOk1evSLXtGnYbd1Kli5dCLT7\nu+E5PFz9l0GlxvdFWpNsECxdupR169bRs2dP+vfvT6tWrZINAhsbGyIjI7W3NRoNpn93YLGzs6N0\n6dLkzJkTgAoVKhAYGPjeIChRosRHvZj0KjAwUPYFqW8/nL54hYn+ywkJf82gru1xadJANw3CGo3a\nI3jkSIiIgLFjCWnVKlXtC0NKbe8LQzp79uwnLZfsu9bY2BgzMzOMjIwwMjL6oIlpypUrR0BAAADn\nz5/H0dFR+1ipUqW4ceMGr169IiEhgQsXLlBEJsEWaUh8fAI+K9YyaOI0rCwtWeztRodmzrq7KsjN\nTZ0spnRptT3A0xPFwkI32xIZUrJHBBUqVGDo0KE8e/YMV1dXSpdOfro8Jycnjh49iouLC4qi4O3t\nzZIlS7C3t6devXoMGzaMXr16AeDs7PxWUAiRmt179AS32X5cuxPE9051GNytvW4ahCMj1X4B+fKp\ncwY7OEDXrtInQOhEskEwdOhQAgICKFmyJIULF6Zu3brJrtTY2BgPD4+37nN4o2djkyZNaNKkySeU\nK4RhKIrCtv2HmLlkFWaZMjFl5CBqVdTR3Bw7dqhTRDo4wL596iBx3brpZltC8AFB0LJlS1q1aoWL\ni8tbDcBCZBRhryOY5LeEP0+eoULpkrgN7E3ObFlTfkMPH8KgQbB5szplpLu7HAEIvUg2CPz9/dm6\ndStdu3alaNGitGnTRmYpExnG2cuBuM9ZQEh4OAM6t9NdW8ChQ+pcwYmJMHky/PSTDBAn9CbZd3SO\nHDno2bMnPj4+xMbG0q9fP33UJYRBxccnMH/VOgZMmIKlhTmLvMbTqXnjlA+B6Gj1//LloU0buHoV\nRo2SEBB6lewRwZYtW9i8eTMajYZWrVoxadIkfdQlhMHcf/IUt1l+BN6+S/P6tRjSrSOWFincIBwa\nCj//DH/+CefOqZ3Cfv01ZbchxAdKNgiuXbuGm5sbhQsX1kc9QhiMoijsOHCYX5asJJOpKZOGD6RO\n5RQet0dRYPVqGDoUnj+HgQMhIUGdPlIIA0kyCA4ePEidOnUoWLAgp0+f5vTp09rH2rVrp5fihNCX\n8IhIpixYwv7jpylfqgRuA3uTK3sKD33y8iW4uKhXAlWoALt2qQPFCWFgSQZBaGgoAC9evNBbMUIY\nwrmr13Cfs4AXIWH079iWjt81wsREBw3CdnbqEcG8eWrfABkgTqQSSQbB999/D6h9At4cUmLGjBm6\nr0oIPUhISGDhui0s37yDL3LnYqHXOEoWSeFToAcOgKsrbN0K2bPDH3/IJaEi1UkyCNavX8+GDRu4\nffu2drgIjUZDfHw8w4YN01uBQujCw6fPcJ3lx9Vbd2hWtyY/de+IlWUKDtvw7BkMHw4rV6odwx4+\nVINAQkCkQkkGQfPmzalSpQoLFiygb9++gHp0kD17dr0VJ0RKUxSFXYeOMmPRCkxMjPEa+iP1qlZM\nyQ2Av786XWRkJIwfD2PGwAeM0SWEoSQZBNevX6d06dI0aNCAu3fvau+/ffs21atX10txQqSk15GR\nTFmwjH3HTvJNyWK4D+pD7hwp/MXGyEidLaxsWfD1heLFU3b9QuhAkkFw/PhxSpcuza5du955TIJA\npDXnr17Hfc4Cnr8KoW/71nRu0STlGoQjIsDDA3r1AkdHWL4crK3lNJBIM5IMgt69ewMwadIkEhMT\nURSF8+fPU6ZMGb0VJ8TnSkhM5Nf1W1m6aRv5cuXE32scXxV1SH7BD7V1q9oX4MEDdXA4R0e1c5gQ\naUiyHcqmTZtGgQIFePz4MVeuXCFnzpxMnjxZH7UJ8VkePQvGbbYfl2/cpnHt6gzr2QnrlDpXf/++\nOkDc1q1QqpTaSaxatZRZtxB6luyx8dmzZ3FxceHcuXMsXryYJ0+e6KMuIT7L7oCjdB4+nqCHT5j4\nU39cB/yQciEAMHeueino1Knw118SAiJNS/aIQKPRcPHiRfLnz09cXByvXr3SR11CfJKIyCimLlzO\n3iPH+bq4I+6D+5A3Z46UWfnx4+p5/8qV1b4BP/4IX36ZMusWwoCSDYLmzZszceJEvL29mTZtGl26\ndNFHXUJ8tIvXbuI2x4/gF6/o7dKSrt83S5kG4ZAQ9XJQf39wcoK9e9V2AGkLEOlEskHQsWNHGjdu\nzIMHD+jXrx/ZsqXw+CtCfKaExESWbNjGko1byZMjB34Tx1K6WArMg60osGqVOkDcq1fq/xMmfP56\nhUhlkg2CXbt2MXv2bBwcHLh58yYDBgygefPm+qhNiGQ9Dn6O22w/Ll2/RaOaVRneqwvWVinUFrBh\nA3TuDJUqqUcBZcumzHqFSGWSDYJly5axadMmrK2tiYiIoGvXrhIEIlX4/fBxpi5cBsCEwX1pWKPK\n5680JgYCA+Gbb6BlS/VqoLZtQRezkgmRSiQbBEZGRlhbWwNgY2ODuYybLgwsMiqaaYuWsyfgGKWL\nFWHCoL7ky53z81f8xx/Qrx+EhUFQkNopzMXl89crRCqXbBDY29szefJkKlSowJkzZ7C3t9dHXUK8\n1+Ubt3Cd5cfTFy/o1bYF3Vp9h+nnDuf89Kl6/n/1aihaFNasUUNAiAwi2SDw9vZm7dq1HDt2DAcH\nBxl5VBhEYqKGbQePsu3Po+TMng1fj5/5urjj56/44UO1Q1h0NLi7q/MFW6TgKKRCpAFJBkFUVBSb\nNm3CysqK9u3bp/yk3UJ8oKfPX+I+x4/zgTdoUL0KI3/ogo211eet9MULyJFDHRZi+HC1HcAxBYJF\niDQoySAYPXo09vb2hIeHExQUxNChQ/VZlxAA/HH0JFMWLEWjaPihVVN6tm/zeSt8/VrtDObvD+fP\nq6eCxo1LmWKFSKOSDIKQkBDmzJmDRqOhR48e+qxJCCKjo5mxeCW7/jxCKUcHJgzuS/irl5++QkWB\nzZvV8YEeP1anisyRQj2OhUjjkgwCo7+H0DU2Nkaj0eitICGu3LyN22w/Hgc/p0fr5vRo/R2mpqaf\nHgQajXop6Nat8PXXav+AypVTtmgh0rAkg0BRFOLj41EU5a2fAczMzPRWoMg4EhM1rNiyk4XrNpMz\nqx3z3cdQtmSxT1+hRqNe/29sDKVLQ82a6hGBabLXSAiRoST5F/Ho0SOcnZ0BNRScnZ1RFAUjIyP2\n79+vtwJFxvDsxUvc5/hz7uo16letxKg+XbH9nEs4jx6Fvn3Bxwdq14aJE1OsViHSmySD4MCBA/qs\nQ2RgB46fZpLfryQmahg/4Aca16qmPTX50V6+VAeIW7QIChSAxMSULVaIdEiOkYXBREXHMHPJKrYf\nCKBkkUJMGNyPAnlzf/oK16xRZwsLCYERI9Srg2SEUCGSJUEgDCLw1l1cZ/vy8GkwXVs244e2LTD9\n3HP3wcHq5aB+fiBTqgrxwT7oLy8iIoJHjx5RoEABrKw+syOPyNA0Gg0rt+5mwZqNZLfLwjz30ZT7\nqvinrSw6Gry8oHhx6NRJnShmwAAZIE6Ij5RsEOzZswc/Pz8SExNxdnbGyMiI/v37/+cyGo0Gd3d3\nrl+/jpmZGZ6ennz5fzM5aTQaevfuTb169Wjfvv3nvQqRJgS/fMUEH3/OXg6kbuVvGd23O5ltPrFB\n+PffoX9/uHMHBg9Wg+BzxxwSIoNK9qvT0qVLWbduHXZ2dvTv3599+/Ylu9J9+/YRFxfH2rVrGTZs\n2Hsnu581axZhYWGfVrVIcw6eOEOnYeO4eusOY/v1xGvYj58WAo8fQ7t24OwMmTLBgQMwa1bKFyxE\nBpLsEYGxsTFmZmYYGRlhZGSE5QdMAH727Flq1KgBQNmyZbl8+fJbj+/ZswcjIyNq1qz5iWWLtCI6\nJpZZS1exdd8hSjgUYsKQvtjnzfPpKzxzRu0Y5uEBI0eCDIsuxGdLNggqVKjA0KFDefbsGa6urpQu\nXTrZlUZERGDzxtUaJiYmJCQkYGpqyo0bN9ixYwdz5sxh3rx5/7mewMDAD3gJ6V9MTEya3BdBj5+y\nYP02nr18ReMalfm+bg0iQ0MIDA35qPVYXLmC+fXrxDRuTGDRopj+/jsJuXKpp4UyqLT6ntAF2Ref\nL9kgGDp0KAEBAZQsWRIHBwfq1KmT7EptbGyIjIzU3tZoNNorQrZs2cKzZ8/o2rUrjx49IlOmTHzx\nxRfvPTooUaLEx7yWdCswMDBN7QuNRsNv2/fgt3oDWTNnZq7baMqX+oT6w8Nh/HiYOxcKFCC8aVOK\nlygBaWhf6Epae0/okuyLf509e/aTlks2CLZs2QJAjhw5CAsLY8uWLbRo0eI/lylXrhwHDx6kcePG\nnD9/Hsc3hvcdOXKk9mcfHx9y5Mghp4jSkeevQpjg48+ZS1epXakCY/p2J4vtR17LryjqeECDB6uT\nxvTvD56eKE+e6KZoITK4ZIPg9u3bgDrMRGBgIHZ2dskGgZOTE0ePHsXFxQVFUfD29mbJkiXY29tT\nr169lKlcpDoBp/7Cy3cxsXFxjOnbne/q1fq0HsK3bqlTRH79tdoe8O236v0SBELoRLJB8OaMZIqi\n0KdPn2RXamxsjIeHx1v3OTg4vPO8gQMHfkiNIpWLiY1l9tLVbP7jIMUKf4nH4H58+UXej1tJXJx6\nSWizZmqnsAMHoFo1GSBOCD1I9q8sLi5O+/Pz5895+PChTgsSacuNu/cYP8uXe4+e0LF5Y/q6tCJT\npo/88A4IUAeICwyES5fUqSNr1dJNwUKIdyT7F/tPJzJFUbCwsKBnz576qEukchqNhjU79+K7aj1Z\nbG2Y4zqSimW++riVvHihXgK6ZAkULAg7dqghIITQq2SDYPDgwTRv3lwftYg04kVIKBPnLuTkhcvU\n/LYcP/frgV1m249bSUICVKwIDx6oo4WOHw8yfIkQBpFsEKxfv16CQGgdPnMOr/mLiY6JZVTvbrRw\nqv1xDcK3b0Phwuq5/+nT1Qnj5ShACIP6oDaCFi1aUKhQIYz/HsxrxowZOi9MpC4xsXH4LF/Dxt/3\n41jQnglD+lEof74PX0FUlDo5zPTp6qmgTp3U6SOFEAaXbBAMHz5cH3WIVOxm0H1cZ/ly9+Fj2jdz\npl+H1phlyvThK9i1Sx0ZNCgIunWDhg11VaoQ4hMkGQRDhgxh1qxZVKxYUZ/1iFREURTW7fqDuSvW\nktnGmtnjhlOpbPJDjLxl8GCYM0ftDfznn3I1kBCpUJJB8OrVK33WIVKZl6FhTJy7kBPnL1G9QlnG\n9utJ1iyZP2zhhAR14ngzM3Bygty5Yfhw9bYQItVJMggePHjAL7/88t7Hhg4dqrOChOEd++sCE+cu\nJComhuG9utCqYd0PbxA+fVrtE9C8uTpVZNOm6j8hRKqVZBBYWFhQqFAhfdYiDCw2Lo65K9axfvcf\nFPmyAPOG9KVwgfwftnBYGIwdC/PnQ548ciWQEGlIkkGQI0cOvv/+e33WIgzo9v2HuM7y5fb9h7g0\naUC/jm0w/9BTOX/8AV26qHMGDxgAnp6Q+QNPIwkhDC7JICgl3+gyBEVR2LBnHz7L12JjbcXMscOo\n8s1HTvyePTvY28P27VChgm4KFULoTJJBMGrUKH3WIQzgVVg4XvMWcfSvC1Qt9zXjfuxFtg9pEI6N\nhWnT1Gkj58+HcuXgxAn4lJFGhRAGJ0M7ZlDHz11k4rxFRERGMaxnJ1o71/+wBuGDB6FfP7h+XZ07\nOCFB7SUsISBEmiVBkMHExsXhu2o9a3buxcE+P3PGj6DIlwWSX/DFCxg2DJYvV4eI2L1bnUBeCJHm\nSRBkIHcfPGL8LF9u3XtAm0ZO/NipLRbmH9ggHBMDO3eqVwaNHQuWlrotVgihNxIEGYCiKGz8/QA+\ny1djZWHBLz8PpWq5r5Nf8NIldVygGTMgf364exdsP3KUUSFEqidBkM6FhIXj5buYI2fOU7lsacb/\n2IvsWe3+e6HISPDwgF9+gSxZ1EtCCxeWEBAinZIgSMdOXriMh48/4RGR/NS9I20a1deOIJuk7dvV\nD/7796FnT5gyRb08VAiRbkkQpENx8fH4/raB1dv3UCh/PmaNG07RgvbJLxgTo44SmjkzHD4M1avr\nvlghhMFJEKQzdx8+xm2WLzeC7tOqYT0GdnH57wbhhAT49Vfo2hUsLNRewoUKyQBxQmQgEgTphKIo\nbPnjT2Yt/Q1LC3OmjR5CjQrf/PdCJ09Cnz5w4QJYW0PHjlCsmH4KFkKkGhIE6UBo+Gu8fX8l4PRf\nVPq6FOMH/ECO/2oQDgmBn3+GBQsgXz7YuBFkXCkhMiwJgjTu9MUrTPDxJ+x1BIO6tselSYPkG4Q7\nd1Y7hA0ZAhMmyNVAQmRwEgRpVHx8An5rNvLbtt3Y58vDLz8PxbHQl0kvcPMmZM0KOXLApEnq/MHf\nJHPqSAiRIUgQpEH3Hj3BdbYv1+/c4/sGdRjctT0W5ubvf3JMjHoJ6KRJ0KOHOkhc6Y+cblIIka5J\nEKQhiqKwbf8hZi5ZhbmZGVNGDqJWxfJJL7BvH/Tvrx4NuLjA+PH6K1YIkWZIEKQRYa8jmOS3hD9P\nnqFC6ZK4DexNzmxZk17AxwcGDQIHB/j9d2jQQH/FCiHSFAmCNCDwzj1GzlxASHg4Azu3o30z5/c3\nCGs0EBoK2bKpVwG9egUjR8oAcUKI/yRBkIrFxyfgv3YTK7fupEDePEwb7UrxwgXf/+QLF9RJ462s\n1FNC+fODm5te6xVCpE0SBKnU/SdPcZvlR+Dtu9Sq8DXuQ37E0uI9DcIREeDuDrNmqUcCM2bovVYh\nRNomQZDKKIrCjgOH+WXJSjKZmjJ5xEByZ7Z+fwhcvAhNm8KDB/DDDzB5shoGQgjxESQIUpHwiEgm\n+y3hwInTlC9VAreBvcmVPRuBgYFvP1GjAWNjdWjoUqVgzRqoWtUwRQsh0jwJglTiryvXmOCzgBch\nYfTv2JaO3zXCxOT/GoTj49VTQGvWwLFjYGMDu3YZpmAhRLqhkyDQaDS4u7tz/fp1zMzM8PT05Msv\n/+31unTpUnbu3AlArVq1GDBggC7KSBMSEhJYuG4LyzfvIH+eXCz0GkfJIoXffeKxY2pj8KVL0KwZ\nvH4NSXUiE0KIj6CTINi3bx9xcXGsXbuW8+fPM3nyZHx9fQF48OAB27ZtY/369RgZGdGhQwfq169P\n8eLFdVFKqvbw6TNcZ/lx9dYdmtWtyU/dO2JlafH2k6KiyOPmBuvXQ4ECsGULNG9umIKFEOmSToLg\n7Nmz1KhRA4CyZcty+fJl7WN58uRh0aJFmJiYAOo3YvMM9s1WURR2HTrKjEUrMDExxnvYAOpW+fb9\nTzY3x/zGDRg2TL06yMZGr7UKIdI/nQRBREQENm98YJmYmJCQkICpqSmZMmUiW7ZsKIrC1KlTKVmy\nJIUKFXrvet5pJE0HoqJjWLbtd05dDqRYwQL80LoZ2bPYvPVaze7eJefs2Tx1dyfRzo6YhQuxsLFR\nrw7KwGJiYtLle+JTyL74l+yLz6eTILCxsSEyMlJ7W6PRYGr676ZiY2P5+eefsba2xu0/Oj2VKFFC\nF+UZzPmr15nov4LnIaH0bd+azi2avN0gHB2tDg43ZQpYWZE5NhZKlCAwMDDd7YtPIfvhX7Iv/iX7\n4l9nz579pOWSGbj+05QrV46AgAAAzp8/j6Ojo/YxRVHo378/xYoVw8PDQ3uKKD1LSEzEf80m+rtP\nwtTUBH/PsXRr1eztENi7Vx0VdOJEaNMGrl2D2rUNVrMQIuPQyRGBk5MTR48excXFBUVR8Pb2ZsmS\nJdjb26PRaDh16hRxcXEcPnwYgKFDh/JNOh0b/9GzYNxm+3H5xm2a1KnO0B6dsH7f2D8LFqh9A/bt\ng3r19F+oECLD0kkQGBsb4+Hh8dZ9Dg4O2p8vXbqki82mOrsDjjJt4XKMjYyZ+FN/nKpV+vfBxET1\nw79ePXWeYH9/dd5gC4ukVyiEEDogHcp0ICIyiqkLl7P3yHG+Lu6I++A+5M2Z498nnDunThp/+jSM\nHq22C2TPbriChRAZmgRBCrt47SZuc/wIfvGK3i4t6fr9G20Br1+DqyvMmQM5c8Jvv6kTxgghhAFJ\nEKSQhMRElmzYxpKNW8mTIwcLPMdSyrHI20+aPh1mz1Z7CHt7g52dYYoVQog3SBCkgMfBz3Gb7cel\n67doVLMqw3t1wdrq7wbhoCB1gphy5WDECGjcGCpV+s/1CSGEPkkQfKbfDx9n6sJlAHgM6UuD6lXU\nB+Lj4ZdfYMIEKFlSbQ+wsZEQEEKkOhIEnygyKpppi5azJ+AYZYoXxX1QH/Llyqk+eOSIevrnyhVo\n0UJtEzAyMmzBQgiRBAmCT3Dp+i3cZvvx9MULerVtQbdW32H6T8e4ffvAyQns7WHrVvjuO8MWK4QQ\nyZAg+AiJiRqWbdrO4vVbyJUjG34eYylTvCgoCty+DQ4OUKeOekqod2+1X4AQQqRyEgQf6MnzF7jP\nXsCFazdoWKMKI3p1wcbaCgIDoV8/uHwZbtxQp4r86SdDlyuEEB9MguAD/HHkBFP8l6FRNLgN6k2j\nmtXUAeLGjYOpU9VG4KlT5XJQIUSaJEHwHyKjo5mxeCW7/jxCKUcHJgzuyxe5c8HLl1CxIty5A126\nwLRpkCuXocsVQohPIkGQhCs3b+M224/Hwc/p2aY53Vs3xzQuTn0we3b1aqCmTdU2ASGESMMkCP5P\nYqKG5Vt2sGjtZnJmy8r8CT9T1tEB5s9Xh4g+cgQcHWHGDEOXKoQQKUKC4A3PXrzEfc4Czl29jlO1\nSozs3RXba9fUTmBnz0KDBpApk6HLFEKIFCVB8Lf9x04xecESEhM1uA74gUY1q2I0fLg6NlCuXLBm\nDbRtKx3DhBDpToYPgqjoGH75dSU7Dh6mZJHCeAzpS/48udUHjY2hf3/w9IQsWQxbqBBC6EiGDoLA\nW3dxne3Lw6fBdGvVjF7ly2Das4c6OFzt2urVQHIEIIRI53QyZ3Fql5ioYfnmHfQaO5G4+Hh8xw6l\n790bmH79NQQEwKNH6hMlBIQQGUCGOyIIfvmKCT7+nL0cSL0q3/JzKUesO7ioPYRbtVLbBL74wtBl\nCiGE3mSoIDh44gyT/H4lPiGBcf170qRODYzmzlV7Ce/YAU2aGLpEkY6cPHmSNWvWMHPmzE9eh7+/\nP5UrV6ZMmTLvfXzlypV06tSJgIAAnjx5Qrt27ZKtaciQIRQpok6aFBkZSf78+Zk+fTpmZmafXOfn\nGjBgAHPnzv2sdWzduhUrKyucnJxSqKpPc/78eby8vDAxMaF69eoMGDDgrcdDQ0Np2LAhjo6OANSv\nX5+KFSvi7e391jrmzZuHoig8f/6c1q1b67TmDBEE0TGxzFyyim37D1GycEGm5bEj+/Mn6qmf/v2h\nZ0+wsjJ0mUK8o3fv3v/5uK+vL506daJmzZofvM7KlSu/FU7Dhg3jwIEDODs7f3Kdn+tzQyAqKopt\n27axePHiFKro07m5ueHj40OBAgXo3bs3V65c4auvvtI+fvXqVZo2bcr48ePfWm7FihUA7N69m1y5\ncml/p7169aJhw4bY2trqrOZ0HwTX7gThOsuXB0+eMfjbMrTbvhnjo0fh+++hXTswMZEQyAB2/XmE\n7QcOp+g6m9WtQePa1T96uaNHjzJr1izMzc2xs7PD29sbW1tbJkyYwOXLl8mRIwePHj3C19eXuXPn\n0rhxYwoUKMCYMWMwNTXFxMSEH374AV9fX8LCwnB3d6dMmTLcuXOH4cOHM3/+fPbt20diYiLt27fH\n5T/mxY6LiyM4OJgsf18VN2PGDE6fPo2iKHTr1o1GjRpx8eJFJkyYgLW1NdmzZ8fc3JwBAwbQr18/\n7OzsqFmzJjVr1sTT0xNA+5ri4+MZMmQIiqIQHx/PhAkTKFiwIIMHDyYiIoKYmBhGjBhBpUqVqFat\nGkePHuXq1atMnDgRExMTzM3NmThxIhqNhmHDhpEnTx4ePHhA6dKlmTBhwluvY/v27VSrVg2AiIgI\nxo4dy+vXrwkJCaFNmzZ06NCBzp07kzVrVsLDw/H398fd3Z179+6h0WgYMmQIlSpVYs+ePaxatUq7\n3tmzZ5MtWzbt7ZUrV/L777+/te0pU6aQL18+7bbj4uKwt7cHoHr16hw/fvytILh8+TJXrlyhU6dO\nZMuWjXHjxpHr7yFqoqKi8PHxYeXKldrn16pVi82bN9OlS5cPfId9vHQbBBqNht+278Fv9QbyWFmy\nKUsm8o4brV4G+uuv0LWroUsUGZCiKIwfP57Vq1eTO3duli1bhq+vL+XLlyc0NJQNGzbw6tUrGjRo\n8NZyx44d46uvvmL06NGcOXOGsLAw+vXrx8qVK3F3d2fTpk2A+m0zICCA9evXExcXx4wZM1AUBaM3\nLnw4ceIEnTt35uXLlxgbG9O2bVuqVKnCoUOHePjwIWvWrCE2Npa2bdtSrVo13NzcmDp1KkWLFmXm\nzJk8e/YMgOfPn7Nx40bMzMxo27Yt3t7eFClShPXr17No0SK++eYbbG1tmTFjBrdu3SIiIoL79+/z\n4sULli5dysuXLwkKCnrrdY4bNw4vLy9KlCjBvn37mDx5MiNHjiQoKIjFixdjaWlJ/fr1ef78OTlz\n5tQud+rUKVq2bAnAvXCEm3EAABEPSURBVHv3aNKkCQ0aNODZs2d07tyZDh06ANCsWTOcnJz47bff\nyJo1K97e3oSEhNCpUyd27txJUFAQ/v7+WFpa4urqypEjR/jujTlFOnXqRKdOnZL8/UZERGBjY6O9\nbW1tzYMHD956TuHChSlVqhRVq1Zl27ZteHp6MmfOHAA2bNiAs7PzW+FTrFgxli9fLkHwsYJfvsJj\n7kLOXLpKncoVGFfoC6xbt4Lu3dVRQnPkMHSJQs8a167+Sd/eU1pISAg2Njbkzq32Vfn222/55Zdf\nyJo1K2XLlgUgW7ZsFC5c+K3lWrduzcKFC+nVqxe2trY0b978veu/e/cuZcqUwcTEBEtLS8aNG/fO\nc/45NRQSEkKPHj3Inz8/ADdu3ODKlSt07twZgISEBB4/fkxwcDBFixYFoHz58uzatQuA/Pnza9sV\nbt++rf2WHh8fT6FChahZsyZBQUH0798fU1NT+vXrR9GiRenYsSNDhw4lISFBu61/BAcHU6JECe2+\nmfH3UC729vbaD9icOXMSGxv7zn7Nnj07ADly5GDZsmXs3bsXGxsbEhIStM8rVKiQ9rWePXuWixcv\nal/rP+sYNWoU1tbW3LlzR/s7+UdyRwQ2NjZERkZqH4uMjCRz5szv7H9LS3VOcycnJ20IgHpk8+bt\nf15vaGgoupTugiDg1F94+S4mc2gIPmWKUWHYAPXb0KVLUKqUocsTGVzWrFmJiIggODiYXLlycerU\nKQoWLEjRokXZunUrAGFhYe98U96/fz/ly5dnwIAB7Nixg02bNuHk5ISiKG89r3DhwqxevRqNRsP/\n2rv3qKiqvoHjX4aLchMQ85KGCqw084JY4SV9TC3xslqCgMOIpOhSKmtpUhiRYOIlAW+oLNFIFkig\nKxal9mZvthQvyws8iJUaD3jFlo1vgs6IDjKz3z98nEBtQAOGy/78I3LO2efnDzy/Ofvss7der2fu\n3Lls2bLlsQ+CXVxciI+PJzQ0lNzcXNzd3fHx8TF2x2zevJkePXrQtWtXSkpK8PT0pKioyHi8QvHX\n6PPevXsbL4gFBQVcv36d48eP07lzZ1JTUyksLGTNmjVER0dz+/ZtUlJSUKvVKJVKXqsxcWPnzp05\nd+4cffv25eTJk/Tq1Qug1h3N43Ts2BGNRgNAamoqXl5eqFQqjh07xsGDB437PWjH3d2drl27Eh4e\nzt27d0lOTsbKyooNGzZw4MABAGbNmvVIfuu6I3BwcMDa2prLly/z3HPPcfjw4UceFkdHR/PGG28w\nceLEWt1GGo2GqqoqunXrVmv/W7du1bpDaAytphDc1elYv/0rvt23n3cq1ASfOILigDVEfACOjrII\nSGZx5MgRY5cF3O+Dj4uL47333sPCwgInJydWrlyJi4sLeXl5KJVKOnXqRPv27bGuMa9V//79+fDD\nD0lKSkKhUBj7/T08PIiIiGD48OEAvPDCC4wcOZLg4GAMBgPBwcEmRwN5enoyY8YM4uLiWL9+PSdO\nnEClUlFZWcm4ceNwcHAgJiaGqKgo7OzssLa2Nt7N1BQbG0tkZCR6vR6A5cuX4+zszMKFC0lLS0Oh\nUPDuu+/Sq1cvNm3aRG5uLtbW1rz//vu12omLi2PZsmUIIbC0tKw1ksYUHx8fioqKePnll3nttdeI\njY1l9+7dODs7Y2lpSdWDmYP/S6lUEh0dTUhICFqtFpVKhYODA97e3vj5+WFnZ0eHDh1Qq9X1On9N\nS5cuJSIiAr1ez6uvvsqgQYOoqKggOjqajRs3smjRIqKiovjqq6+wtbU1Plu5cOEC3R8zdL2oqIhh\nw4Y9cRxPRDRT+fn59d73XOlFEfR+pAgbM1H80bOXECDEhAlClJY2YoRN58yZM+YOoVlozXkoKSkR\ne/bsEUIIcePGDTF8+HCh0+n+dv+mzEVGRob4888/hRBCrFmzRiQlJTXZuevjzJkzQqvVitDQUHOH\n0ijCwsKERqOp175Pct2sqUXfERgMBrL2/sDmHTvxsLRga94PKDp3hl277r8cJt8MllqIbt26kZCQ\nQFpaGnq9noiICLOO66/J1dWVsLAw7OzscHR0ZNWqVeYO6RH29vZMmTKFffv2MX78eHOH02AOHDjA\n+PHjaz2AbgwtthD8X3kFy5JS0B44wAjf8US9PRtFwGQYOxYeejgjSc2dnZ0dycnJ5g7jsXx9fc36\njkF9+fn5mTuEBjd69OgmOU+LLASH8gtJXZXI20cO8Mq1q4jln2Lh6HD/3QBJkiTpibSoQnBXV8Xm\nL9Kx37SRLb/9jJWtLSQlYfHKK+YOTZIkqcVqMYXgPxcvs2TtZqLTt/Fi+Z/oAwNRrFsH/x2/K0mS\nJD2dZl8IDAYDuVlfs/ab/8GpgyPtIhaB92AsW0CfpSRJUkvQKIXAYDAQGxvLb7/9ho2NDXFxcfTs\n2dO4fefOnWRlZRnfNqz5QklNf94o53/D5jLhu2/RTZmKb8I6XJzkg2BJkqSG1CiF4Mcff6Sqqors\n7GxOnTrFqlWrjCMirl+/Tnp6Ol9//TU6nQ6VSsWIESMeO1Tu2gv9UKqvcX3AQJQxn2Ahi4AkSVKD\na5QVygoKChg5ciQAXl5e/PLLL8Ztp0+fZvDgwdjY2ODo6Iibmxvnzp17bDvPaW5xPXEtzxSdwqLG\n7H2SJElSw2mUO4KHZ+CztLSkuroaKysrtFptrXm17e3t0Wq1j23nP4fyALj87383RpgtSkFBgblD\naBZkHv4ic/EXmYt/plEKwcMz8BkMBqysrB677fbt249dcGHIkCGNEZokSZL0kEbpGvL29iYv7/6n\n+VOnThmXZAMYOHAgBQUF6HQ6NBoNpaWltbZLkiRJTctCiIfmWW0AD0YNFRcXI4RgxYoV5OXl4ebm\nxtixY9m5cyfZ2dkIIZg3b16rmhtEkiSppWmUQlBfDTXMtDWoKxfbt29n7969wP2l6x6e47w1qSsX\nD/aZO3cuY8eOJTg42EyRNr66cnHw4EE2bdoEQL9+/YiJialz7v6Wqq5cfPHFF+zduxcLCwvCw8PN\nvoh9UygqKiIhIcG43vEDP/30E5s2bcLKyoqpU6cSFBRkuqGnmrO0gezbt09ERkYKIYQoLCwU4eHh\nxm1qtVpMnjxZ6HQ6cevWLePXrZWpXFy+fFn4+fmJ6upqodfrxbRp08TZs2fNFWqjM5WLBxITE0VA\nQIDIzMxs6vCalKlcaDQaMWnSJOMU0SkpKcavWyNTubh586b417/+JXQ6naioqBCjR482V5hNJiUl\nRUyePFkEBgbW+n5VVZUYN26cqKioEDqdTvj7+wu1Wm2yrUZ5RlBfDTXMtDUwlYuuXbuybds2LC0t\nUSgUVFdX065dO3OF2uhM5QLg+++/x8LCglGjRpkjvCZlKheFhYU8//zzfP7556hUKjp16tToK1mZ\nk6lc2Nra8uyzz3Lnzh3u3LnTau+KanJzcyMpKemR75eWluLm5oaTkxM2NjYMGTKE/Px8k22ZdYqJ\nhhpm2hqYyoW1tTUdO3ZECMHq1avp16+fce3V1shULoqLi9mzZw8bNmwwdom0ZqZyUV5ezvHjx8nN\nzcXOzo7p06fj5eXVan83TOUC7q/pMGnSJPR6PfPmzTNXmE1m/PjxlJWVPfL9p7l2mrUQNMQw09bC\nVC4AdDodUVFR2NvbExMTY44Qm4ypXOTm5vLHH3/w1ltvcfXqVaytrenevXurvTswlQtnZ2cGDBjA\nM888A8BLL73E2bNnW20hMJWLvLw81Go1+/fvB2D27Nl4e3szcOBAs8RqTk9z7TRr15AcZvoXU7kQ\nQvDOO+/Qp08fPvvsMywtLc0VZpMwlYuPPvqIXbt2kZ6ejp+fHzNnzmy1RQBM56J///4UFxdz48YN\nqqurKSoqwtPT01yhNjpTuXBycqJ9+/bY2NjQrl07HB0duXXrlrlCNSsPDw8uXbpERUUFVVVV5Ofn\nM3jwYJPHmPWO4PXXX+fIkSMolUrjMNMvv/zSOMx0xowZqFQqhBAsXLiwVfeLm8qFwWDgxIkTVFVV\ncejQIQA++OCDOn+4LVVdvxdtSV25WLRoEXPmzAHuryTWmj8s1ZWLo0ePEhQUhEKhwNvbmxEjRpg7\n5Ca1e/duKisrmTZtGosXL2b27NkIIZg6dSpdunQxeaxZh49KkiRJ5mfWriFJkiTJ/GQhkCRJauNk\nIZAkSWrjZCGQJElq42QhkCRJauOa/eL1UttQVlbGm2++yYs1VqLz8fH528n1Fi9ezMSJE5/6HYIx\nY8bQrVs3FAoFQgicnZ1ZtWpVrTdX65KSksLQoUPp06cP3377LYGBgeTk5ODk5PTUw1xrxqXX66ms\nrGTZsmUMGDDgb4/JyMggJCTkqc4nSSALgdSMeHp6PjKLYmNKTU01vpsSHx9PTk4OoaGh9T5+7ty5\nwP0itmvXLgIDA/H392/QuA4dOsTGjRvZsmXL3+6fnJwsC4H0j8hCIDVrer2eJUuWcO3aNcrLyxk1\nahQLFiwwbr9w4QIff/wxVlZWWFpasnr1arp06UJiYiInT55ECMHMmTOZMGHC357DYDCg0Wjo3bs3\n9+7dIyoqiitXrqDX65k1axYTJ05kx44d5ObmGl9WioyMNN6V/PDDD5SUlLBx40aEEHTq1ImLFy/S\nt29f/Pz8uH79OvPmzSMnJ+eJ4gL4/fff6dChA3B/sr0dO3YYt61fv57s7Gxu3rxJbGwsn3zyCTEx\nMVy6dAmDwcCCBQvw8fH5hz8BqS2QhUBqNkpKSpgxY4bx7wkJCdy7dw8vLy8CAwPR6XSPFIKjR4/y\n4osvsnjxYvLz87l58ybnzp2jrKyMrKwsdDodQUFBjBgxwnhBfSAsLAyFQoGFhQUDBw5kypQpZGVl\n4eLiQnx8PFqtFn9/f4YOHUpOTg6ffvopXl5eZGZmUl1dbWwnPDyc4uJi5s+fb5wNMigoiKVLl+Ln\n58c333yDv78/Bw8erHdcOp0OtVrNyJEjiYyMBODixYukpKRga2vLkiVLOHz4MG+//TYZGRnExsaS\nmZmJi4sLK1asoLy8nJCQEOMaFpJkiiwEUrPxuK4hrVbLzz//zLFjx3BwcKCqqqrW9oCAALZu3cqc\nOXNwdHRk4cKFFBcX8+uvvxqLSnV1da1P1g/U7IJ5oLS0lOHDhwP3J+/y8PDgypUrrFy5ktTUVBIS\nEvDy8qKuF/I9PDzQ6/VcvXqV7777ju3bt5Odnf1Eca1Zs4aysjJcXV0BcHV1JTIyEnt7e86fP4+X\nl1et44qLiykoKOD06dPG9svLy3FxcTEZqyTJUUNSs5aTk4OjoyOJiYmEhYVx9+7dWhfh/fv3M2TI\nENLS0vD19WXbtm24u7vj4+NDeno6aWlpTJgwgR49etTrfB4eHsa527VaLcXFxfTo0YOdO3eydOlS\nMjIyOHv2LIWFhcZjFAoFBoPhkbYCAgKIj4/H09OTDh06PHFcCxYsQK1Wk5mZiUajYcOGDaxdu5a4\nuDjatWtnzMODP93d3Zk0aRLp6els3boVX19fnJyc6vXvlto2WQikZm3YsGHk5eWhVCqJjY2lZ8+e\nqNVq4/b+/fuzbt06VCoVWVlZhISEMGbMGOzs7FCpVMaHt/UdDRQUFERFRQXBwcGEhoYyf/58XF1d\n6dOnDwEBAYSGhtKxY0cGDRpkPMbV1ZV79+4RHx9fqy1fX18OHz5MYGAgwBPHpVAoWL58OcnJyVRW\nVuLt7Y2fnx/Tp0+nffv2xjx4eHgQERGBUqnk/PnzhISEoFQq6d69OwqF/C8u1U1OOidJktTGyY8L\nkiRJbZwsBJIkSW2cLASSJEltnCwEkiRJbZwsBJIkSW2cLASSJEltnCwEkiRJbZwsBJIkSW3c/wOG\nlbN53lRNKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x250a0163b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "logit_roc_auc = roc_auc_score(y_test, predictions)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, predictions)\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K Means Clustering (under-five deaths,infant deaths,Adult Mortality)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2509d9bae48>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NmTJFGRkZ6t27tyTppptu0q5duyrdV1ZWVo3rcbGCggK/7q++oh1stMMFtIWN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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2509db97860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exploratory Data Analysis\n",
    "df['Status'] = df['Status'].replace([0, 1],['Yes', 'No'])\n",
    "df.rename(columns={'Status':'Developed'}, inplace=True)\n",
    "#Show all infant deads\n",
    "sns.set_style('whitegrid')\n",
    "sns.lmplot('under-five deaths ','infant deaths',data=df, hue='Developed',\n",
    "           palette='coolwarm',size=6,aspect=1,fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x250a014e0f0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qX7SW7YCqQrHY5AaLDTr80Fq29yn5Dhw4EIsWLcL999+Pn//859i/fz+WLVuG\n5uZmrFixAgAwYMAArFy5En6/H3PnzoWu6/D7/Vi2bBnq6+tTOk/S5DtixAh4vV6Ulpam/csAwNVX\nX42rr74aALBs2TL84Ac/QFNTE375y19ixowZof3+9re/wefz4cUXX8TWrVvx8MMP46mnnurTuYmI\nqH8RXe2ANWYVBdUqt/eRMafzggUL8J//+Z9QFAWLFi3CypUrMWbMGKxbtw7PPPMMzj33XFRWVuKx\nxx7DF198gY6OjpTPkTT5ulwufOtb3wqVPvta7bx9+3Z88cUXWLJkCX784x/D6XTi+eefx4QJEzBv\n3jw0Njbi4osvBgCcc845+PTTTxMey+l0ph1HJI/Hk7FjmYlxm4txm4txmytXcY8bNy6tn1NLKyG8\nnYBR8gUAEYBaWpmRuK666ip4PB6ceuqpAIBdu3Zh2bJlAAC/34+6ujpccsklaGlpwc9+9jNYrVbc\ncccdKR8/afLN9LCi3/3ud7jzzjsBABdddBGmT5+O2tpaLFmyBGvXrkVHRwcqKipC+1ssFgQCAVit\n3UNN902L5XQ6M3YsMzFuczFuczFucxVa3JaRZ0M4N0KHH1CtgAgAQsAy8uysnK+urg6PPPIIhg4d\nisbGRhw+fBibNm3C4MGD8dxzz2HLli14/PHHsWbNmpSOlzD5rlu3Dtdeey3Wrl0LRVGivvfzn/88\nreDb2tqwe/duXHDBBQCAH/zgB6iqqgIAfPvb38abb76JyspKuN3u0M8IIeImXiIiKl5Gu26mezsn\nsnTpUsyfPx+apgEAHnzwQQwYMAD33HMPnn/+eaiqGipYpiJhVhsyZAgAYNSoUX0MOeyjjz7ChRde\nCADQdR3f+973sHbtWgwZMgQbN27EmWeeiUGDBuHdd9/F5Zdfjq1bt+L000/P2PmJiKj/sJwyLGvJ\nNnZ1o7POOituqfaPf/xjWsdPmHyNdtfvf//7aR04nj179qC2thaAbDtesWIF7rrrLpSUlGD06NG4\n7rrrYLFY8Pe//x2zZs2CrutYuXJlxs5PRESUD0ytz/3xj38c9XratGmYNm1at/0eeOABs0IiIiIy\nXd8G7xIREVGvMfkSERGZjMmXiIjIZEy+REREJmPyJSIiMhmTLxERkcmYfImIiEzG5EtERGQyJl8i\nIiKTMfkSERGZjMmXiIjIZEy+REREJmPyJSIiMhmTLxERkcmYfImIiEzG5EtERGQyJl8iIiKTMfkS\nERGZjMmXiIjIZEy+REREJmPyJSIiMhmTLxERkcmYfImIiEzG5EtERGQyJl8iIiKTWXMdQH/jahXY\nvg9o98jXFSXAhOFATTWfc4hn6XEYAAAgAElEQVSISGJGyCBXq8BHu4G2LkDo8l9bF/BBM9C0X+Q6\nPCIiyhNMvhniahXY9AXg9QN6nO9/dlDuQ0RExOSbAa5WgS0tQEBLvI+uA80u00IiIqI8xjZfAO3+\nMry3Q8DtBcodQH1N79pom12AqgAWFQgkKNzqANzezMRLRESFreiTr6tV4GDXqShxAHYL4PEBW1oA\nQKScgN1e+bMOGxDoIcGWOzIRMVHPXK0CzS6EHibLAmW5DomIYhR98m12AYqiw2qRr60WWX3c7AJq\nqlM7RrlDJm27FejyAvEKv6oiS9RE2WAk3BOd8vPrsMqHQY8POO49Fa7W1B8miSj7iv6v0e0FlJgu\nUha1d1XE9TWyZ3NAA0rtgBLcriry/6oCnDGUw40oO4w+Bx4fIITsX+ANyM+j1SIfLtnfgCi/FH3J\nt9wBnPArUds00bsqYplUw1V9VaWyjTcg0mtDJuqN7ftk4tUhHwKNT7PHD9is8uGS/Q2I8kvRJ9/6\nGmDT5woCmizxakLewHpbRVxTraZcTU2Urtj23FMq5VhyAFBVALpMwgrk5xgAdCjsb0CUZ4o++dZU\nqxhaegid1mFp93YmMkPTfoHPDspqZVUBdAEcbQeUiIobBeESsEWRVc+6rrC/AVGeKfrkCwCVtk6c\nP47JlvKXqzUi8arhdl09WLpVFPl/4ysAWK1AiR0YqB5CTfWI3AVPRN0w+RIVgGZXOKlqMd3pFciO\nfl5/sMSryhqcGRPlA6XT2WlusESUFJMvUQFwe2WpVsSbuxSyGrqiJNxnYcJwc+Mjot5hXStRASh3\nJE68JTZZvezT5NdzR7LPAlG+M7Xke9VVV6GyshIAUFtbi5kzZ+LBBx+ExWLBtGnTcNddd0EIgaVL\nl6K5uRl2ux0rVqzAiBFsr6LiZPRuPtYR//tWBVBU4J/GM9kSFRLTkq/XKwcarlmzJrTtyiuvxKpV\nqzBs2DD85Cc/QVNTE7788kv4fD68+OKL2Lp1Kx5++GE89dRTZoVZ8GKHorDndmFytQp8sg9o7wr2\nbE6wnw5OW0pUiExLvp999hm6urpwyy23IBAIoKGhAT6fD8OHy8apadOmYePGjTh8+DAuvvhiAMA5\n55yDTz/91KwQC54x05GqpD9PNeVeaMYqf7D3MrpXOavB4UXpjEknotwzLfmWlJTg1ltvxbXXXouW\nlhbcdtttqKqqCn2/vLwc+/fvR0dHByoqKkLbLRYLAoEArNbuoTqdzozE5vF4MnYsM8XGvaejFn7d\nClXR4Q9uE7qCLV8EcLziQG6CjKO/XO90tfvLcMQ7ED5hg131Y5DjGCpt4R7JxvuoCRsAY8YqYzCv\nnEJD6DoU6LArfhz/ai+Of5X9uM3GuM2Vq7jHjRtn+jnzgWnJt66uDiNGjICiKKirq0NlZSWOHz8e\n+r7b7UZVVRU8Hg/cbndouxAibuIFMvemOZ3OgvwAxMa9e4tAmSV60gVdB3yaI69+v/5yvdPhahXY\n0wKoNqBcBTRhxxGtHMOGhZsHjPdReILTRSrBOZsBKFCgKkCZQ4HQgXNHWlBT3XNMxXy9c4FxUypM\nq4t8+eWX8fDDDwMADh06hK6uLpSVlWHfvn3QdR0ffPABJk+ejEmTJmH9+vUAgK1bt+L00083K8SC\nV+7oPga0t/NUU3YZaz9bgw9JVot8HbnwgfE+OmTBNzyRBoI/Y2WvZqJCZ1rJ95prrsG9996L2bNn\nQ1EUrFy5EqqqYt68edA0DdOmTcPEiRNx9tln4+9//ztmzZoFXdexcuVKs0IsePU1so23r/NUU/YY\naz9Hil1Fy3gfVUUOIzJmsqoqBc4ezoRL1B+Ylnztdjsee+yxbttfeumlqNeqquKBBx4wK6x+JXZ1\nJfZ2zj/G2s/WiAQcWzsR+z4OrOD7SNTfcIarGIU+VIerK+W3VGsn+D4S9W9MvhE4VIeyjbUTRAQw\n+UaJ7AwDyK8BTW5nKYQyhaVaIuLjdgS3V1YFRortDENERNRXTL4ROFSHiIjMwOQbob5Gdn4JaHJo\nR0DjUB0iIso8tvlGYGcYIiIyA5NvjMjOMMawo49bBBMxERFlDJNvAhx2RERE2cIskkAqc/ASERGl\ng8k3AQ47IiKibGHyTYDDjoiIKFuYfBPgsCMiIsoWdriKw+jl7A8mXlWVy7mxtzMREWUCM0kMo5ez\nxweU2WU7rz8AtHXKzlauVpH0GERERD1hyTdGZC9nX0D+M6qdOdwodYW+NCMRUTYx+cZwe+W4XgDw\n+uVXVZUJmKschZPqiU55TXRdDsVSFaCqTCbZYx3AZweDVfYKoAs+tBARRWLyjVHukCVcq0V2sALC\nSQQo7uFGTfsFPjsYvi6xDrfJf5EEAG8AcFiL+6GFiChS0SdfV6vAno5a7N4ip5AssQJH24OluuA+\nqgKU2OT/i3W4UdN+gR1f9v7ndB2AIqvvi/WhhYgoVlEnX6NzlV+3oswCdHQBR/yAVQV0BQgE+1ZZ\n1XCVczEON3K1CjgPpv/zui5LwMX40EJEFE9RJ1+jc5Wq6FAUObQIkCXeylL5/y6fLO36tOLtOPTx\nnmAJtg8UpfgeWoiIEinq5Gt0rgr2q4IIdh6KbNMsscnE+6/nFlfCNbhaBTp9fT/OGUOL76GFiCiR\nor4bxk4hqSrRnauA4m3jNWRiIYnxpwFnDivqjxoRUZSiviMaU0gKXYGuA7bgECOrhVNKGtzexB+S\nUhswrT583SIpkA8xTLxERN0VdbWzrAYV2PJFAD7NgYpSoG4wcLg9vyeHMMbaHm+vw6EdIqsxljsA\nIQCPv/v3zhslzzt1TPTYX07HSUTUs6JOvoBMDscrDmDcuHG5DiUlRg9t2VFMZH3WrfoaefwSm6wJ\n0IRsF49sw62pVjl+l4ioF4o++RaayOkv/Ur2Z90yagc4VSQRUeYw+RaYyOkvDdmedYslWyKizGLx\npcDE9tAG2CObiKjQMPkWmFMqZSn3uBvwajZ4fOyRTURUaJh8C4irVWDvEcBulVXNOhR4A8CIQWyD\nJSIqJGzzLSBGZyu7Xb7u8vhgs5XgcHtu4yIiot5hcamAuL2yxBupmJc4JCIqVCz5FhCbCrR3yYUf\nVAVQobKzFRFRAWLJt0C4WgU8/vCiD5oAfMIKn8bOVkREhYYl3wLR7JIdrawWwOuX6+NC1+GwsrMV\nEVGhYfItEMbkGlZFJmEA6OzyIyDirGpARER5jcm3QJQ7AI9PLlxgVD/rug1VLPQSERUc3roLRH0N\n4NNkCdiY4UqHAo9ftgcTEVHhYMm3QNRUq3BYBQJacNk+BbAgALvVnrVFFaiwGEtNcgEMovxnWvL1\n+/1YuHAhvvzyS/h8Ptxxxx0YMmQIbr/9dowcORIAMHv2bFx++eV48skn8d5778FqtWLhwoWYMGGC\nWWHmLVerQIdHJl6LGlziLyA4zrdIJEuskUtN2i2IWmqSiPKPacn3tddew4ABA/Cb3/wGra2t+P73\nv48777wTN998M2655ZbQfk1NTdi8eTPWrVsHl8uFhoYG/OUvfzErzLxk3FgVRb4WOtDpA6wKx/kW\ng54Sq5GAI5eaBKKXmjxVyVHgRJSQacn3n//5nzFjxozQa4vFgk8//RR79uzBO++8gxEjRmDhwoVo\nbGzEtGnToCgKhg4dCk3TcOzYMQwcONCsUPOOcWMtsQFdPrlN1wG/boGNiyrEFVlStKqAAsAvov+v\nC9mOrgm5zWoBBpTLxSsOtwMnOuV11nX54CPEaBzaIUyvzt2+L9jZDuHPgapEr+Hc41KTJaaFSkQp\nUnRd1808YUdHB+644w5cd9118Pl8qK+vx1lnnYWnnnoKbW1tqKysxIABA3D99dcDAG644QasXLkS\nI0aMiDpOY2MjysrKMhKTx+NBSUl6d6h2fxmOeAfCJ2ywq34MchxDpa0zI3EZmtvqAACaboGAUYxR\noEBgeNnBjJ8vVen+7n253qnGdbDrVCiKDiEUBILPmCo0CBgZSgDoPkxLgYAu5w6DCPVHVCBTH2BV\nNCjQMbT0ECptnd2uQZmlE51aWcLXvf18tPvLsLezFrJ7XZhVCQAA6qv2AAD2dNTCr1uhKuE/Z6Er\nsCkB1Fi/yOr1zpZsf06yhXH3zrhx40w/Zz4wtcOVy+XCnXfeieuvvx7f/e530dbWhqqqKgDAZZdd\nhuXLl+Pb3/423G536GfcbjcqKyvjHi9Tb5rT6UzrWE37BfYfDLfDaqodR7RyDBuW2ZLR/m0CbV2y\n9GVRwqUxu+rH+RNGJD9AFrhaBfa0AKoNKFcBTaT+u6d7vVP13g6BEocsybZ3AWowH+m6CjVUdR8/\nRh2WYKq1QlXCM4oZDztQbCixA53WYRg2BFHXwOO344ivHCW24NCwmNeasOMrXzmO60CXP9xxrqos\nfhtusws42hU+u6oavwcgYMfAyvDfwICIqmmLKkvzQgfOHenA8a9KCvIGl+3PSbYwbkqFaXVnR44c\nwS233IJf/vKXuOaaawAAt956Kz755BMAwMaNG3HmmWdi0qRJ+OCDDyCEwMGDByGEyMsqZ1erwGcH\nI6okdcAXkDe9ZldmzxVZNRFVT2FqnUW0yDZGJfjVqArNtcgFKIzkqSjhy6UkaQON3DeW0MPVubHX\nIKDJffxa/NfGZ6StS34voMnXHV2yDdcYMma08RpVzYD8KkT4/Y9dw7mmWsW5I4ESu6xKL7ED545k\nb2eifGVayfc//uM/0NbWhtWrV2P16tUAgAULFmDlypWw2WwYNGgQli9fjoqKCkyePBkzZ86EEAKL\nFy82K8ReaXYFSy4R9zZdlzfUTPc+9vrlVyORWBR5c/X7zb+xGiWyI20y8ZTaAVvwU5QvPa+NCUmM\nBwJhtNsGv5+soSVy31iqglAnt9h2VhHxIBbvtdcffq1AfnZ0XSZnK4BNXwA2q4A/INum7Xb5XgtE\nlsDlMSpKuifWmmqVQ86ICoRpyff+++/H/fff32372rVru21raGhAQ0ODGWGlze2VN2Kj5GvIdO9j\nV6uAP1iCsgRv1jrC1c5m9qZxxana7PQBZZAJOF96XtfXyJJkQJNTcRqd1OwWWSoE5DSdgThJ2BJM\njlYV0GK+rwOwWcKlzmZXOMkD4cRslLpjXxsPAZEUBQiI8PfKHUCXV8ZuUQFHsJOd8Vkrd8h9JwzP\nxJUiolzhJBtpKnfI3rLeQDgBG18z2fvYWFDBFwjfuHVdnndY6TEA8dvDsyGy1y0iYunyhUt0+dDz\nWpYIw72dK0vDPZwr7eH/23rd21mgolSNaJ8VoSRvUeXPa0ImaF3v/jr0GUGwJBuRiI1+A0rEg43H\nL2MH5P8BWePByTOICh+Tb5qM0pUjmBiFkDfOM4Zm9sbY1ilv7kZVJSBvzqoKU3s5u1plpy8gWF2K\ncIchoedfUshGFazTuSuqQ0q8JD9qsEzc8V6XO4BOryzpxtIhhxAB4dKuJsLXuMTGNlyi/oTJN02x\nN95sTOdnVDkbpSKj5GuzABWlGTtNSnFs+iKi80+wFKdCPhCcXAX80/jiTAqJkrzRNt5yRH42Jo2U\n+/5tm5ypLNSGGywNqwi3nduDVfhasGTOqSKJ+h8m3z7IdgeXnqqcz6sBjn+VvXMbjHZeo+cuECyF\n6/lV1ZxPepqRyi+C1eAR/QS8flnSNaqvjXbiyaOYcIn6KybfDMjWhPZur+wABD08Q68xtKWmWjUl\n+Ua180YwZluK1+sWKO5J/nua6jGyJ7bBosqE7LAV5/UiKkZMvn2Uyry76bKpiDu5RqktA4GnILKd\nNzzHU7jtucQWv9etq1Xgo91AICBLxl4f8FEXMGVU369JIehpqsdJIxHVSSs0GcYIJluiYsLk20c9\nlXL6WiWdaHINM+bWiG3nVYPZ1+ika7Mk7gC0fV94bDIgh+xofrm9p/bRfCj1ZSKWeKVbYxiWGX0F\niCj/Mfn2UY8T2vdRosk14vWWzaSE7bwIjzedOiZxwmjzxD9uvO3ZrDlIJFGCzVQskeOMI0u3Rts4\nJ8MgIibfJJKVhHoq5fT1vIkm16jI8rwa6bbzhvZLUDSPtz2bNQfx9JRgMxULS7dElAyTbw9SKQkl\nK+WkK1lP52xJt503XdmsOYj34NRTgs1kLCzdElFPmHx70OySk2d4g5NcqIps64wsCWWrlNPT5BrZ\nLEE1u6LnIzYka+dNlbF4gCFTNQexifaUSmDvke4PTgFNzkcdyUiw2arFICKKxeTbg7ZOWfJUlHBC\n8vjDw34MmS7l5HJyjbbO7onX0FM7b6SqUoRKz5EUyOR+asQY13RrDiKTrVWVNQJ2SzjRfnZQzj5m\nDyZao4QrdHmOeAk2W7UYRESxmHx7ELkcnfFV12VpOJtyWeXsC8T/nqqkXuKtHQjs+LL7drslWIUb\n0Wbd25oDV6vAJ/uC6/QGp110e+V7YlPDyxvqweX7SiJKucbcyUKPn2DZVktEZmHy7YFR4o3sKGRs\nyya3VyYViyp7PBtryGazytlo344t9CoIJ7RUtRyOv92vAVXl3benWnPQtF+uoRxasg/hRekBWSsR\nubyhFvOQpAngpLJw22+8BMu2WiIyA5NvD04qkyWsQESbr9USXmkmW4y2R7tV/gNkDCX2nn8uXcaY\n3oAW3cnKYLem/js37Rfo9MX/noBMdKnOzBVbtdzuiQ5O1xFqEI9tp7Zaei7hMsESUS6xPq0H9TXy\nxl1ql+2YpXb5OtttgPU14cSh6+Hkn43zRo7pVZTo3s1qMLGl+js37Rdxq5sjpVpyN+Ly+MLV1fGG\nKkUOwVIQvl4WVa4wVWKXixOU2LkqEBHlD5Z8e5CrNkAzzxs5pjdyiUAgvCZtKkkrlcRb1Ysag9gh\nQaG2b3QvnVuC+5XYuAoQERUGJt8EYoeuGEvCmcWMqtF4Y3ojhzaVOTKXeIHejQ+OHXMb+VAQ2w5f\nUQKcPZzJlogKB5NvHLmY8jAXml2yE5exPq8Qqc3dHCnVxDv+tN4lx9gxtw4boAUnuzCmuFQUWbV8\n5rD+854UolzOzd3TudP9HpEZmHzj6Ms0g037BXZ+Jfe3WoAhJwGeQP79kbtaBY62R5Qmg8nMaDdN\nZUyvq1X2Pk5m/Gm9T5CxY25VRXb8KrEBfpFf17KY5fJBtadzA0jre/w8kVmYfONId5rBpv0CzmAp\nUFHk0Jr9x+T40/KS/Pkjjx2yAwTbUHVZEk42d7Oh2ZV4HmfDsIHplUzjtXtP5LJ7ecd4UNV1oMMT\nbrZItIKVIRMlz54ekoH0vsde8GQWJt840p1msNkV7ggUmZT8IjxWNtd/5D1VExuTVqTaNuv2dm9/\njWRTZak/XRwS1DvZqEqNN23n4XbgRKd8343FPwxG23xbl/zZeOfPVGk52UNyut8jMgOTbxzpTDPY\ntF90m9Qhkj8gJ4DI5R95svZZHb0bjlPuADp7+F3KS3hD66tEyc/tlQ83OuTkIpEzk6kAdBFdzZro\neMlmE4tMkh1dwJE2OeZa0+NPQxpaelJN/JCZqdWjkj0kp/s9IjMw+QJo95fhvR0i6oZ07sjEsyAZ\nQlMdepJXvxqzL+XqjzxZ+6wC4OTK3o3D7Ygzf7NBVQr7hma8tx3BNYgrS4ABSlnc/SInAgkEwqX9\nyiS9sGP7B4wdEl1FH5v82ruAw23xJ0KJJCBjKLFGz6UdeTxFB461A39vAypLBSbEiTM2SRql3IDo\neZY3HXJe7UQPXplaPSrZQ3K63yMyQ9EnX1erwMGuU1HiiK4CO3dk9DSEsq1IRPWW/GhXcP7lFM4T\nENmdLCOZZO2zipJ6XMZN3Kcl3kdB/t/QEpUqj3V0n5ryRBfQhqEY1hr9GTCSGXREDdsC5OuPdgNT\nRnWvTo3tHxDQEHwtQgk4sj21rSti/HUKv5sxt3XkXNqRx+vyh8/t9so4HVYRms7U6LOA4P+Nhylj\nJrFkM6xa1MQzsmVq9ajk4+HT/R5R9hV98pVL6OndqsA+2SdvCInapZpd4RJAslKvocSeuz9yt1fe\nEAMJqsbPGNpzXJGJyh+Qpbyefu98H3trJE4hZJLq9MpSpbydx6dDxcd7gH8NVo1Glgy7fJH7hVej\nCgTiV6fu/Cp+/4CdXwEDK2SpO97KUL0h9OiEZpQ4jdJ85EIhPiHf13hVyXpEFbOeJPEaM6P19OCV\nydWjeuoXkO73iMxQ9MlXroijoL0rPH+z3Sqricsdidul3N7USgCA3MduA/5pfO4SUblD3mRFnLa6\nZEOBjN7RkTNgBTT5f6MkZRzSospzfWdi5n9X4wHA6OyjR5TKqsp692CzfV/8as5kC1ZFzlsdWX0a\ne00jS85GEo16gElQa+DXgA+akwSRIqM2w5hL2yhxaiLcuz20b5zfIeFxE2w3PgvJHry4ehQRky+s\nKtAJK1RjpRxdlmKM0kskTQBH24HXtwj4A6klXiCYHLK8GEMyRmnDbpWJ06hCTDZJRWwnLYHwPVuL\nuVlbetlbOt65YodAxRPb5qlAdgba0gIc6xChDklWVX7PL6I7JxnV/30VWX0aOQNXLF8A+H+NAh5/\nuEe5GYzaDCP51tfI6uV4YfbmcihquGQfegBSZRuzDnm9t+8DPtknEIi49oGY8dkseVIxK/rkG5lA\nI6uQjeowq0XePLu84VKRpgUXPUjxHJoub0y5lE5pw9Uq4IzppBVb1RyZCDVdJrdjHb0fSvXh5wL7\nj6W2b+x11xFuwzQeFJJ1SkqXGvGBMR5ounw9J3OjjVWBvEbuBKs+ZdqBY7IK21BTrcJhFfD5+3Zt\nNAEMrIjueW0NDiuzW+TfR6Je8B4v8FFX/HZwomJS9MnXLwALNAhdDVUj260AlHAp2BeIro70pjF2\n9cvWDAVsElerwIbPk7dnx35bE0YCFClPruFqTT3xpiobiReQpVZj/GpNtYpjHeEq+XgLPoRWXcpi\nTJGxeQPh96ytS1ZhW1GHAcGYvX1MvIb24LCjEpuc+vNEpzyu19/zz4ngPh/vBspLo0cYAEDj7ojO\nYBgDsT/1zxFRISn65GtTgU5YotovfQFZTXz2cGDTF5mposzEMdLlahX4eDfQGbqpRY8DjZ3vNl5v\n395qdgFnDku+X6pzQ+cLfyB6/OzOr8Jt/90eREx8zxXIWod4ArDhw53ABWNFwrbm3goEj+PXgvOD\n9/LnO4MPAUat0pG2eDUaaq8f5MzAeaEpE4o++Ub+wUeW8nTIPyhFET2256XLrD9gY0hL1O8JWboo\ntSGYbFNra+2NVJJ35HCbQhEQQIkie8N7A+GOUzl8tgKQvNd9QMiYM/UeG73mAyK84EVvGTVIqtrz\n52XnV6k9yJkh0exckX0NlEBtqKbB+Bkma4pV9Mk3IAAVGnRdDa0Va7PIUsR7O0TUzEGZ0rRftqUa\nN8tOL3CkHRg3NLNP+MbEGonut16/vAEebsvYKZPG0+wCjrfX4dAOgWMd5pw3k4yOeCe6kPuMG6Sm\n2PPPGGLU5/MhuhmmL5ehp8k6DIEMldYzId7sXB4f8NlBmVjtFqDTbw3VjhzrQNTDb2ewlD/utPwq\nzZP5ij75WlVABKudjfuAMXlEJpOSLfh35mrtXhIFZCJ2fik7yGTqqbjZ1XNJRwBZSyClMT16I0sM\nqiJCQ15S7TGeTzSRfLyrmUqDk1lkcyrPMnvEWOaIOvbYEndvOroZiTdZvwKrJfH30i1VGj/X1hme\nVOSkBMPVIs/h8cm/ZaOTnRqxFKcRp6roUIO1I+2e+B0EnQcz+7dOhafok29sb+e+tnUmIhAeopTo\nXqMjs4suHG3PzHHSoQD42zY5vEbowbbB4J1Zhx1a8EaeJ4XHXjFu1sbyi7mWatKtLJGdsNIJOXJ8\ns/E7l9hkqTQQ01zTE0uwb4VfpP63NnaI/NptKk8tPCwQCE+UYlcFrLaep4WNnGAFkO9ne1f8fhBR\n1cw64I0oiRsPt7G1DxY1eK0TXBBdj/5bZ9V08Sn65OsXgE0JQNPt0HTgFP8BjPP8A5XiOJSYW4mA\nis/tE/FF6Tm9Po8m5B9vV5Ib5dH2xKvB9Hj8w/vh37EB8HSEor4cwGHlVGyqurzX8fZVpx9ATAeg\nyFJ4Ljug9dXAcnlzbe/jDFRmUgCcNhBoO4iMPvFUBsevd/kSd/hSICfeiJzN6iQbcCSFZgdjScrY\nqTzbe3iI8AkAAcCjxF8tyag69mrBh6hg6T2gySanyKQYW82c6Hmhp0lWEjEemvq6ylOyxB1vnvJU\nZ59ztQpsjyjBG81yvZ3Uhror+uRrVYFOXV6GU/wHcE7XB7DrXXGrFFUInOHbAgBpJWBF6XmKR0B+\nuDfuBDQR3slhBUafKsdtRk85OBb7twmcW3UAlbvfhx7oPoD0FP0Qpra9kZMEnJgib3p6YZZ8v2yV\nE1gUUi9tHcEpLTN4wT1+WfrVRPcJaWLPXWKPTg7NLtk00RUnYRsTeFgVHzwBOT9m7FSeyTqY+TW5\nqla81ZKMmcmMGgyD0Lsv8BBvEYhEjOkyha5A6D3XNCgIT/3Zl1WekiXuyDnojWvW05zjscf+aDei\nxoXrkP1EOuLUElDvFPVVc7WKYDuW/Asc7d0Oq+5L2pY32teU1vla3T0nXiB+1bc3IG/03ef6VXCi\nC/Dt2Q4RJ/EaTtEPpRVvNmWret8Mug60fJ3rKHrHWCgh0+3UxzuDpaIk72d9jUw2xiIlbZ1yfLBV\n7V5la7Sp67oSSoTH3fI8xt9QspoT49vxVksqd4TnbY9M4PFW4jL2jccSnOlLgby+JXbZX8SmBHDu\nSFm6tCeYzcxuDY9tNuZdjz12Ks0JkYnbWDNcVYyFYBA1B70xE5mihOccT3bsQCB+pzijGSnZMSix\nok6+za7oOXbLRAeUpLP7AtbY+tQMsSjJk3M8pYGOvOn80xuFWOoFZNyd2fkIZI2RZLJ1zeOVYCNt\naZGlMqN05tdkb/uEU20qgF+3wqbK0QG9HZ9s/D3EWy2pvkYmb5slvLAEIBNX7AIPxr4BLTy3OdC9\ns1hViZy7/V/PVVFXcTxWNZcAABK8SURBVCA0CcuUUcBJpdEJrKoUmDI6XGUbL8GnuspTssRtzEEf\ndW2CQyeTJffQ/PVxbi7xagmod4q62rmtM/z/MV1bUaqnlsQCSH9y3lP8B3B25waUwp1WwpQzJano\nUE+Cs+Q8HLYNg1utQImWXgI+xX8Ao73bUam1Qg3Nw6RAQQAWiNAxvXBAgxUOeKBAR4d6Eg5aR6LG\nvwdV+olQ+3gAFnxhn5BWtTyZIxtTbyY7XrybdKKErSAco1Fd3luqElyGMdix681tImpuaWO9boHw\ngiN+TValf9AMKIpAVbBtNHJt7xJrcIIQPXoSlRNd8hyx85rHm8PaaKP9uEWElrLc9bXsMGb0oLZa\ngYk10fu7vd3nybap8iHGr4V/VlXkPonmoDceIpIl93IH4PXFr94v9PW684Gi6/nQXzOaEAJLly5F\nc3Mz7HY7VqxYgREjRkTt09jYiPPOO69P5/nLJoFRnVsx1rcNlhRKvAYdgICCgOJAuzoAuxxn47Ct\nNmqfiR3vo1bbnfUSqQ7giHIqBuitsMGkSYN7weikIaCgC6XQVAesuh+dakXUdTvFfwBndH2ESv1E\n8CEg3rEUHLDUYVvFN837BWIiyJ8BRr1ROHEbUVpUAMIHu8OOLm96DwsWJTw3OyCHZMk2WZlQo9bm\n3h1/akyHDZgyClHtp4mmlzWmph1i34/zJ4yIu09kG63RAc2nyfmwdUQkX1WWjgHZPhsIRCd7BbLW\nwK8Fq5URLtECMinbrbKXuh7xMwarRV4Pjz+cjK26G+eOKe92XeLNBV5qk1XYkdeReicvr9rbb78N\nn8+HF198Eb/4xS/w8MMPZ+U8ozq3ot63tVeJF5AfYgt02HQPykUbzu76EKf4D4S+P7HjfQwzIfEa\nsZyiH0IezUMQxbgGKnSUoxMVolV2wNG7QtftFP8BnNP5/6Gqh8Qrj6VjmLYbEzveNyX2/sRYWWvY\nQHPPa0tyh4lt7zXaJktsgKrqUct6JmMkLYsqE29VWXj1LkWRSTi2TRQINj8lKIX7/d3bTxNNaqIE\nm42OeBNf5HhttIHg8MOqUmBAmfxqt8p9t++TyS/2DmV0fDKmNjWqno3QhB5czCNi/gI9uEOpXcbg\nCwSHimny/15hx5YWmXQBhKrNq0rl/sbynQ4bUFHKxNtXeVnt3NjYiIsvvhgAcM455+DTTz/NynlG\n+5qgQ+k2pChVKgCb7oNHLcdo7/ZQKa5W25PBKFNTkoel3ngUAA7dA7d6EoAARnu3AzDa0VN7H2q1\nPdiGXJV+zWd0VIrHqoaH+7S6Ex+j1C6Xemx2ZW/FJ0DeoAeUyRu62yt7HB/vTLx/vI5Tui57NKtQ\nUF8j5xpPpWe5UfLTgdAvaLRZ6nr4XPF6NCccQoTu7ac9PVQLHfDpiZul4vWejncNjBg9xnj4BG+Y\nEY/xGTjRGV49y0iYQLh3dUlwQhaPLzxpiqoa10cNPZgYVeVc+jF78jL5dnR0oKKiIvTaYrEgEAjA\nao0O1+l09uk8I+CPaNVMjwoBDRaUiQ4Yf/HpJvNioEBeMwAR101es1TfCcWUNYISMe+8svOfAr/f\nD8AesV0PXi0dmgC6jDs0EjfADbLsx/GvOnG8vQ5WBfD3kCDSp0PRdXR5/MHeynZ0evwAbEil2luB\nDujydxM6MNh6AMe/8suHXKUuacxC6FF/e10eP6DbIHS5VYWMTegKbEoATqesrVICtQDKEscV6ITT\neUDup5cE7xndfx+hy/NbFS+czvgP4EqgFp1+K1Ql8nNkA/RgvKFjyRh1vTR0V4klWwxlo07oZ3Vb\nKD5d10OraqnQ4fP54fHKkqqqCASE/EwZoehQ4PN54PGqCePPhnHjxpl2rnySl8m3oqICbnf4MV4I\n0S3xAn1/09r2/QMW9G3yZgEVFmjoVCtg/IH0pTTd38n2cnkDCF83wKF1pdwyqSe4+WWfeW2nsk3N\nghGDgMPtDhxtl2cutQM2q4L2LkATCiwqUFpSAgDwJCj5qgiE2iAP7ZBTezqCpctUV14ySlEiuMb1\n1DEItV12GVOFKgrK7IDNakFAkwtQBIQDul9OZpNMmUOB3SpLzSV24GTFH/obHxAxK5Un4bKIChy2\nYElSAWwWCywW2e6pQB7TolqCbb4O1FSHj/335sTjcWU76DgMiBgzC3TfX4GMf7D9eMJ704A4bb66\nKg9ms1qiJiI5d6RDTlGZcEIR+Vm02yzyd1UBxR852YkSqnaWVc2WqJKv5ouuGVB0DXZ7CUrsxZsQ\nzZSXFfaTJk3C+vXrAQBbt27F6aefnpXz7LKf2ackKQD4FTtUXWCX4+zQ9gOWugxE1zvuHp7c84kO\nwKuUwKIHQtdtl+PsYA/y1BJbLq5vpPGnyX+ZVGYPtnPGtKmdOUzFP41XceHpMnkYN0qjHdQYLhPQ\nZBU0EKxdUMK9hk+2hxeTNobOKIqsqqwskdWR40+Tk7kY41bjMYbkjB0iqyPPHSljMsaOOqzhCSKE\nHu4pPKBCxhbZmcpuia4WLbHJ38X42cjhPkD4fBWlcuyszSKPabPI49kscntFcBjPlFEyNj3Y1l1Z\nKi9Gib17W2VNtYpxp3UfUqMowLjTwvvWVKuYMloeKzRuNvgzavB6ThkNVNoS17NHXjdf8CFjyij5\nc5HbjBgnDJftv/HamRVFvm/G7+rT5O8/7jT5Oxv7GJ8r47oan4HQrF3C6HQl4l57yo687u38+eef\nQ9d1rFy5EqNHj47aJxO9nQFgy7sfY7SvqVc9hXvq7VxqA84bBVTvex/4anfUz2SDDsBlHYV9p34T\np32VuR7WqZbxNKhQQmXZ7j/XU2/nwyedjROltTjSURi9na3w4YJ6e1Rv0NDk+T28wYMqgG+dFf85\ntzdz+sbue0olQsvYGT97rEMOzQlo8uY6dgigdjRHlWQSnTPe9njHi7caT1/mJk70s06n09QSWKbm\nV8503KFFIIJDp3paBCLez8X7fYzvnegMJl4VsIro3s6UXXmZfFORqeQLZP6PxSyM21yM21yM21yF\nGneh4iMOERGRyZh8iYiITMbkS0REZDImXyIiIpMx+RIREZmMyZeIiMhkTL5EREQmY/IlIiIyGZMv\nERGRyZh8iYiITMbkS0REZDImXyIiIpMx+RIREZmMyZeIiMhkTL5EREQmK+j1fImIqPBlam32QlKw\nyZeIiKhQsdqZiIjIZEy+REREJmPyJSIiMpk11wHkkhACS5cuRXNzM+x2O1asWIERI0bkOqxutm3b\nhkcffRRr1qzB3r17sWDBAiiKgrFjx2LJkiVQVRVPPvkk3nvvPVitVixcuBATJkzIWbx+vx8LFy7E\nl19+CZ/PhzvuuANjxozJ+7g1TcP999+PPXv2wGKx4KGHHoKu63kft+Ho0aO4+uqr8dxzz8FqtRZE\n3FdddRUqKysBALW1tZg5cyYefPBBWCwWTJs2DXfddVde/p3+7ne/w//93//B7/dj9uzZOP/88/P+\ner/yyit49dVXAQBerxdOpxNr1qwpiOvdL+lF7M0339Tnz5+v67qub9myRb/99ttzHFF3v//97/Ur\nrrhCv/baa3Vd1/Wf/vSn/397dx9T4//Hcfx5SZJKzWKxRLXZ3E2juc1NQzUtJqeEsYkRbZgiMZa5\nv92wFd9NtHIz0pD7m7GUIg2TYROLRJtCTpNuzuf7h3V965ub/L5+dU69H/99rmvXdV7nvXP1Pp/r\ntM+lcnJylFJKrV27Vl25ckXl5+er2bNnK5PJpN68eaOCg4NbMrJKTU1VGzduVEopVVZWpsaOHWsR\nua9evapWrVqllFIqJydHRUREWERupZSqqqpSixcvVn5+fur58+cWkbuyslJNmTKlwbbJkyerwsJC\nZTKZ1Pz581V+fr7ZXac5OTlq4cKFqra2VhmNRrV3716LqHd9cXFx6vjx4xZR79aqTd92zsvLY/To\n0QB4eXmRn5/fwokac3NzY9++ffr48ePHDB06FIAxY8Zw+/Zt8vLy8PHxQdM0evToQW1tLWVlZS0V\nmYCAAJYuXaqPraysLCL3hAkT2LBhAwDFxcU4OztbRG6Abdu2ERYWRrdu3QDL+Jw8ffqUL1++EB4e\nzpw5c8jNzaWqqgo3Nzc0TcPHx4fs7Gyzu04zMzPp06cPkZGRREREMG7cOIuod51Hjx7x/PlzAgMD\nLaLerVWbbr5GoxF7e3t9bGVlRU1NTQsmaszf35/27f/5dUAphaZpANjZ2fH58+dG76Nue0uxs7PD\n3t4eo9HIkiVLWLZsmUXkBmjfvj0xMTFs2LABf39/i8idlpZGly5d9D+YYBmfk44dOzJv3jwOHjzI\n+vXriY2NxdbWtlE+c7tOP3z4QH5+Pnv27GH9+vVER0dbRL3rHDhwgMjIyB/mM7d6t1Zt+jdfe3t7\nKioq9LHJZGrQ6MxRu3b/fF+qqKigc+fOjd5HRUWF/jtaS3n79i2RkZHMnDmToKAgduzYoe8z59zw\nbRYZHR1NaGgoX79+1beba+5Tp06haRrZ2dk8efKEmJiYBjMsc83t7u5Or1690DQNd3d3HBwc+Pjx\nY4N8nTt3prKy0qyuUycnJzw8POjQoQMeHh7Y2Njw7t07fb+51hugvLycFy9eMHz4cIxGY6N85ljv\n1qpNz3wHDx5MRkYGAA8ePKBPnz4tnOjX+vXrx507dwDIyMjA29ubwYMHk5mZiclkori4GJPJRJcu\nXVos4/v37wkPD2fFihUYDAaLyX369GkOHDgAgK2tLZqmMWDAALPPfeTIEVJSUkhOTqZv375s27aN\nMWPGmH3u1NRUtm7dCkBJSQlfvnyhU6dOvHr1CqUUmZmZem5zuk6HDBnCrVu3UErpuUeMGGH29QbI\nzc1l5MiRwLfJh7W1tdnXu7Vq019nJk6cSFZWFmFhYSil2Lx5c0tH+qWYmBjWrl3L7t278fDwwN/f\nHysrK7y9vZk+fTomk4l169a1aMb9+/dTXl5OfHw88fHxAKxZs4aNGzeadW4/Pz9iY2OZNWsWNTU1\nrF69Gk9PT7Ov9/dYwufEYDAQGxvLjBkz0DSNzZs3065dO6Kjo6mtrcXHx4dBgwYxcOBAs7pOfX19\nyc3NxWAwoJRi3bp1uLq6mn29AV6+fImrq6s+rrttbs71bq1keUkhhBCimbXp285CCCFES5DmK4QQ\nQjQzab5CCCFEM5PmK4QQQjQzab5CCCFEM5PmK0QT/fXXX/j4+DRYeKPOsWPHGiwD+m/79u3j2LFj\nAKSkpHx3f9++fSkpKdG3lZaW0r9/f9LS0pqc8Xvnrm/27NkUFBSQlpbG9evXm3SMEOLPk+YrRBOl\np6czadIkzp8//5/Ok5CQ8N3tvXv35uLFi/r4woULdO/e/Y+c+9+Cg4MZP378bx0jhPhzpPkK0QR3\n7tzBzc2NsLAwjhw5AsC9e/cIDg5m7ty5XLt2DYCioiJCQ0P140JDQykqKtLHCQkJfPr0ibi4uEav\nMWnSJC5duqSPb9y4ga+vrz7eunUrISEhhISEkJSUBMCqVauIiIggLCyswbmNRiNLly4lPDycqVOn\ncvTo0QavVTcTr39MVFQUN2/eBKCgoIAFCxb8t6IJIX5Imq8QTXDy5ElCQkL0NX0fPnzIli1b2LVr\nF4cOHWqwatDPLFq0CEdHx+82X2dnZ2xtbXn9+jWFhYW4uLhgY2MDfGvERUVFnDhxgqNHj3Lu3Dme\nPXsGwPDhwzl+/HiDcxcWFhIYGEhiYiL79+/n8OHDv8wTEhKiP+81NTVVXxpUCPHntenlJYVoik+f\nPpGRkUFZWRnJyckYjUZSUlIoKSnB3d0d+LZO+KtXrxod+7sLyAUGBnL+/HlqamoICgoiKysL+DYT\n9fb2RtM0rK2tGTRoEAUFBQB6hvqcnZ1JSkriypUr2NvbN+mpNMOGDWPTpk2UlpaSlZXF8uXLfyu7\nEKLpZOYrxC+cPXuWadOmkZiYyMGDBzlx4gRZWVl06NBBb4CPHj0CwMbGhtLSUmpraykvL29wy7nO\nzxqyv78/169f5969ewwbNkzf7unpSV5eHgDV1dXcv3+fXr16AeiPsqt/7sTERLy8vNi5cycBAQE/\nfc26fZqmERQUxKZNmxg1ahTW1tZNqo8Q4vfJzFeIXzh58iTbt2/Xx7a2tvj5+eHi4kJMTAx2dnbY\n2dnh6OhI165dGTVqFAaDATc3N71B1ufp6Ul0dDQ7d+5stM/BwQEXFxd69uzZ4PGRvr6+3L17l+nT\np1NdXU1AQAD9+/f/4bkNBgNxcXGkp6fj5OSElZUVVVVV331/9fMEBwczbtw4zpw587+USgjRRPJg\nBSGErqSkhJUrV+r/0CWE+P+Q285CCAAuX77M/PnziYqKaukoQrR6MvMVQgghmpnMfIUQQohmJs1X\nCCGEaGbSfIUQQohmJs1XCCGEaGbSfIUQQohm9jffznjopL96kAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x250a01442b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Show all Adult Mortality\n",
    "sns.set_style('whitegrid')\n",
    "sns.lmplot('Adult Mortality','infant deaths',data=df, hue='Developed',\n",
    "           palette='coolwarm',size=6,aspect=1,fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t3v99bTYnZPu2/pFexk42bareazXcp0xvjK1Xcja8wHyG32B/LjSbE7Jp02+HeDXsLfOp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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x250a02a2860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "g = sns.FacetGrid(df,hue=\"Developed\",palette='coolwarm',size=6,aspect=2)\n",
    "g = g.map(plt.hist,'Adult Mortality',bins=20,alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AQgQWAABAIQILAACgEIEFAABQiMACAAAoRGABAAAUIrAAAAAKEVgAAACFCCwAAIBCBBYA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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x250a02eeb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "g = sns.FacetGrid(df,hue=\"Developed\",palette='coolwarm',size=6,aspect=2)\n",
    "g = g.map(plt.hist,'infant deaths',bins=20,alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QiMACAAAoRGABAAAUIrAAAAAKEVgAAACFCCwAAIBCBBYAAEAhAgsAAKAQgQUAAFCIwAIA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\nAAoRWAAAAIUILAAAgEL+H4DOps8GIG3TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x250a052d710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Population Developed vs not Developed countries\n",
    "sns.set_style('darkgrid')\n",
    "g = sns.FacetGrid(df,hue=\"Developed\",palette='coolwarm',size=6,aspect=2)\n",
    "g = g.map(plt.hist,'Population',bins=20,alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+lK9//etH3P+f/umfsnXr1ixatCiDg4O5/PLL86Y3vSk33nhjVqxYka997WsZ\nGhrKHXfccdwztbW1ZcWKFfnIRz6Szs7O1Gq1TJs2reTTBqCFVer1er3RQwBAM/nyl7+cD3/4wxkz\nZkyWLl2auXPn5sorr2z0WACMAHawAOBlJkyYkPe///0ZN25c/uZv/ibvete7Gj0SACOEHSwAAIBC\nXOQCAACgEIEFAABQiMACAAAoRGABAAAUIrAAAAAK+X9UarGb3gc7FwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x250a08c6908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Schooling Developed vs not Developed countries\n",
    "sns.set_style('darkgrid')\n",
    "g = sns.FacetGrid(df,hue=\"Developed\",palette='coolwarm',size=6,aspect=2)\n",
    "g = g.map(plt.hist,'Schooling',bins=20,alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PPTcPPPBAV/0MAAAAPcJGA+vII4/MyJEj27+uq6vLwoULc9BBByVJRowYkTlz5mTIkCEZ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\n+pVNOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x250a0526080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Total expenditure Developed vs not Developed countries\n",
    "sns.set_style('darkgrid')\n",
    "g = sns.FacetGrid(df,hue=\"Developed\",palette='coolwarm',size=6,aspect=2)\n",
    "g = g.map(plt.hist,'Total expenditure',bins=20,alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YAAAAhQgsAACAQgQWAABAIQILAACgEIEFAABQiMACAAAoRGABAAAUIrAAAAAKEVgAAACF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\nAEAhAgsAAKAQgQUAAFCIwAIAACjk/wNm5vft5L9duQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x250a05b6e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Alcohol Developed vs not Developed countries\n",
    "sns.set_style('darkgrid')\n",
    "g = sns.FacetGrid(df,hue=\"Developed\",palette='coolwarm',size=6,aspect=2)\n",
    "g = g.map(plt.hist,'Alcohol',bins=20,alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x250a001a860>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QYlCKbYvlhr8HqjQMvBaJvcDbVTnez4BsE1IVOSbwjPGFTmVlKdW2xXLDtlKq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\niIi4SMErIiLiog8teSTgEqKIGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x250a001a0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Show diseases Developed vs Not Developed countries\n",
    "sns.set_style('whitegrid')\n",
    "sns.lmplot('Hepatitis B','Measles ',data=df, hue='Developed',\n",
    "           palette='coolwarm',size=6,aspect=1,fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=2, n_init=10, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Kmeans cluster creation\n",
    "from sklearn.cluster import KMeans\n",
    "#Define number of clusters\n",
    "kmeans = KMeans(n_clusters=2)\n",
    "#Fit all the model less Developed column\n",
    "kmeans.fit(df.drop('Developed',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  9.23579952e+01,   2.00751926e+03,   6.92316568e+01,\n",
       "          1.64851335e+02,   2.79836345e+01,   4.60780941e+00,\n",
       "          7.39456135e+02,   8.10426441e+01,   2.32925878e+03,\n",
       "          3.83617192e+01,   3.89706785e+01,   8.25749927e+01,\n",
       "          5.94092425e+00,   8.23478205e+01,   1.74459598e+00,\n",
       "          7.49483017e+03,   1.07615559e+07,   4.80178995e+00,\n",
       "          4.83098901e+00,   6.27689065e-01,   1.19962582e+01],\n",
       "       [  7.50000000e+01,   2.00720000e+03,   6.52800000e+01,\n",
       "          1.32600000e+02,   1.39140000e+03,   1.70000000e+00,\n",
       "          3.14926763e+01,   2.10000000e+01,   5.54092000e+04,\n",
       "          1.45800000e+01,   1.84000000e+03,   6.80000000e+01,\n",
       "          4.33400000e+00,   6.84000000e+01,   2.80000000e-01,\n",
       "          6.36536468e+02,   1.18115454e+09,   2.70800000e+01,\n",
       "          2.79400000e+01,   5.46600000e-01,   9.96000000e+00]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Guessing cluster center vectors\n",
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Developed</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>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",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>No</td>\n",
       "      <td>65.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>71.279624</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>1154</td>\n",
       "      <td>...</td>\n",
       "      <td>8.16000</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>No</td>\n",
       "      <td>59.9</td>\n",
       "      <td>271.0</td>\n",
       "      <td>64</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>73.523582</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>492</td>\n",
       "      <td>...</td>\n",
       "      <td>8.18000</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>No</td>\n",
       "      <td>59.9</td>\n",
       "      <td>268.0</td>\n",
       "      <td>66</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>73.219243</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>430</td>\n",
       "      <td>...</td>\n",
       "      <td>8.13000</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>No</td>\n",
       "      <td>59.5</td>\n",
       "      <td>272.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>78.184215</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>2787</td>\n",
       "      <td>...</td>\n",
       "      <td>8.52000</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>No</td>\n",
       "      <td>59.2</td>\n",
       "      <td>275.0</td>\n",
       "      <td>71</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>7.097109</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>3013</td>\n",
       "      <td>...</td>\n",
       "      <td>7.87000</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>No</td>\n",
       "      <td>58.8</td>\n",
       "      <td>279.0</td>\n",
       "      <td>74</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>79.679367</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>1989</td>\n",
       "      <td>...</td>\n",
       "      <td>9.20000</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>2009</td>\n",
       "      <td>No</td>\n",
       "      <td>58.6</td>\n",
       "      <td>281.0</td>\n",
       "      <td>77</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>56.762217</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>2861</td>\n",
       "      <td>...</td>\n",
       "      <td>9.42000</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2008</td>\n",
       "      <td>No</td>\n",
       "      <td>58.1</td>\n",
       "      <td>287.0</td>\n",
       "      <td>80</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>25.873925</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>1599</td>\n",
       "      <td>...</td>\n",
       "      <td>8.33000</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>2007</td>\n",
       "      <td>No</td>\n",
       "      <td>57.5</td>\n",
       "      <td>295.0</td>\n",
       "      <td>82</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>10.910156</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>1141</td>\n",
       "      <td>...</td>\n",
       "      <td>6.73000</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2006</td>\n",
       "      <td>No</td>\n",
       "      <td>57.3</td>\n",
       "      <td>295.0</td>\n",
       "      <td>84</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>17.171518</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>1990</td>\n",
       "      <td>...</td>\n",
       "      <td>7.43000</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>2005</td>\n",
       "      <td>No</td>\n",
       "      <td>57.3</td>\n",
       "      <td>291.0</td>\n",
       "      <td>85</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.388648</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>1296</td>\n",
       "      <td>...</td>\n",
       "      <td>8.70000</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>25.294130</td>\n",
       "      <td>257798.0</td>\n",
       "      <td>19.3</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0.396</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>2004</td>\n",
       "      <td>No</td>\n",
       "      <td>57.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>87</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>15.296066</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>466</td>\n",
       "      <td>...</td>\n",
       "      <td>8.79000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>219.141353</td>\n",
       "      <td>24118979.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>19.7</td>\n",
       "      <td>0.381</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>2003</td>\n",
       "      <td>No</td>\n",
       "      <td>56.7</td>\n",
       "      <td>295.0</td>\n",
       "      <td>87</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>11.089053</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>798</td>\n",
       "      <td>...</td>\n",
       "      <td>8.82000</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>198.728544</td>\n",
       "      <td>2364851.0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>19.9</td>\n",
       "      <td>0.373</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>2002</td>\n",
       "      <td>No</td>\n",
       "      <td>56.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>88</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>16.887351</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>2486</td>\n",
       "      <td>...</td>\n",
       "      <td>7.76000</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187.845950</td>\n",
       "      <td>21979923.0</td>\n",
       "      <td>19.9</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.341</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>2001</td>\n",
       "      <td>No</td>\n",
       "      <td>55.3</td>\n",
       "      <td>316.0</td>\n",
       "      <td>88</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>10.574728</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>8762</td>\n",
       "      <td>...</td>\n",
       "      <td>7.80000</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>117.496980</td>\n",
       "      <td>2966463.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.340</td>\n",
       "      <td>5.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>2000</td>\n",
       "      <td>No</td>\n",
       "      <td>54.8</td>\n",
       "      <td>321.0</td>\n",
       "      <td>88</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>10.424960</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>6532</td>\n",
       "      <td>...</td>\n",
       "      <td>8.20000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>114.560000</td>\n",
       "      <td>293756.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.338</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>2015</td>\n",
       "      <td>No</td>\n",
       "      <td>77.8</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>364.975229</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3954.227830</td>\n",
       "      <td>28873.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.762</td>\n",
       "      <td>14.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>2014</td>\n",
       "      <td>No</td>\n",
       "      <td>77.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.510000</td>\n",
       "      <td>428.749067</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.88000</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4575.763787</td>\n",
       "      <td>288914.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.761</td>\n",
       "      <td>14.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>2013</td>\n",
       "      <td>No</td>\n",
       "      <td>77.2</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.760000</td>\n",
       "      <td>430.876979</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.66000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4414.723140</td>\n",
       "      <td>289592.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.759</td>\n",
       "      <td>14.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>2012</td>\n",
       "      <td>No</td>\n",
       "      <td>76.9</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.140000</td>\n",
       "      <td>412.443356</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>5.59000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4247.614380</td>\n",
       "      <td>2941.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.752</td>\n",
       "      <td>14.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>No</td>\n",
       "      <td>76.6</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.370000</td>\n",
       "      <td>437.062100</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>5.71000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4437.178680</td>\n",
       "      <td>295195.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.738</td>\n",
       "      <td>13.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>No</td>\n",
       "      <td>76.2</td>\n",
       "      <td>91.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.280000</td>\n",
       "      <td>41.822757</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>5.34000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>494.358832</td>\n",
       "      <td>291321.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.725</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2</td>\n",
       "      <td>2009</td>\n",
       "      <td>No</td>\n",
       "      <td>76.1</td>\n",
       "      <td>91.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.790000</td>\n",
       "      <td>348.055952</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.79000</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4114.136545</td>\n",
       "      <td>2927519.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.721</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>No</td>\n",
       "      <td>75.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.610000</td>\n",
       "      <td>36.622068</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.87000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>437.539647</td>\n",
       "      <td>2947314.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.713</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2</td>\n",
       "      <td>2007</td>\n",
       "      <td>No</td>\n",
       "      <td>75.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.580000</td>\n",
       "      <td>32.246552</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>6.10000</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>363.136850</td>\n",
       "      <td>29717.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.703</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>No</td>\n",
       "      <td>74.2</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.310000</td>\n",
       "      <td>3.302154</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>5.86000</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>35.129300</td>\n",
       "      <td>2992547.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.696</td>\n",
       "      <td>11.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2</td>\n",
       "      <td>2005</td>\n",
       "      <td>No</td>\n",
       "      <td>73.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.160000</td>\n",
       "      <td>26.993121</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>6.12000</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>279.142931</td>\n",
       "      <td>311487.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.685</td>\n",
       "      <td>10.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2</td>\n",
       "      <td>2004</td>\n",
       "      <td>No</td>\n",
       "      <td>73.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.540000</td>\n",
       "      <td>221.842800</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>6.38000</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2416.588235</td>\n",
       "      <td>326939.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.681</td>\n",
       "      <td>10.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>No</td>\n",
       "      <td>72.8</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.290000</td>\n",
       "      <td>14.719289</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>6.27000</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>189.681557</td>\n",
       "      <td>339616.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.674</td>\n",
       "      <td>10.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2</td>\n",
       "      <td>2002</td>\n",
       "      <td>No</td>\n",
       "      <td>73.3</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.730000</td>\n",
       "      <td>104.516916</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>6.30000</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1453.642777</td>\n",
       "      <td>3511.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.670</td>\n",
       "      <td>10.7</td>\n",
       "      <td>0</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>2908</th>\n",
       "      <td>182</td>\n",
       "      <td>2013</td>\n",
       "      <td>No</td>\n",
       "      <td>63.0</td>\n",
       "      <td>328.0</td>\n",
       "      <td>29</td>\n",
       "      <td>2.410000</td>\n",
       "      <td>20.623063</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>4.99000</td>\n",
       "      <td>79.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>185.793359</td>\n",
       "      <td>1515321.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.565</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>182</td>\n",
       "      <td>2012</td>\n",
       "      <td>No</td>\n",
       "      <td>59.2</td>\n",
       "      <td>349.0</td>\n",
       "      <td>29</td>\n",
       "      <td>2.590000</td>\n",
       "      <td>196.915250</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>896</td>\n",
       "      <td>...</td>\n",
       "      <td>4.91000</td>\n",
       "      <td>78.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1734.936120</td>\n",
       "      <td>14699937.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.554</td>\n",
       "      <td>12.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2910</th>\n",
       "      <td>182</td>\n",
       "      <td>2011</td>\n",
       "      <td>No</td>\n",
       "      <td>58.2</td>\n",
       "      <td>366.0</td>\n",
       "      <td>29</td>\n",
       "      <td>2.570000</td>\n",
       "      <td>183.046170</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>13234</td>\n",
       "      <td>...</td>\n",
       "      <td>4.26000</td>\n",
       "      <td>81.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1644.619672</td>\n",
       "      <td>14264756.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6.4</td>\n",
       "      <td>0.543</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2911</th>\n",
       "      <td>182</td>\n",
       "      <td>2010</td>\n",
       "      <td>No</td>\n",
       "      <td>58.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>30</td>\n",
       "      <td>2.470000</td>\n",
       "      <td>184.364910</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>15754</td>\n",
       "      <td>...</td>\n",
       "      <td>4.41000</td>\n",
       "      <td>83.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>1463.213573</td>\n",
       "      <td>138533.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.533</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2912</th>\n",
       "      <td>182</td>\n",
       "      <td>2009</td>\n",
       "      <td>No</td>\n",
       "      <td>57.4</td>\n",
       "      <td>368.0</td>\n",
       "      <td>30</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>143.869887</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>4.73000</td>\n",
       "      <td>94.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>1139.112330</td>\n",
       "      <td>13456417.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.518</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2913</th>\n",
       "      <td>182</td>\n",
       "      <td>2008</td>\n",
       "      <td>No</td>\n",
       "      <td>55.7</td>\n",
       "      <td>45.0</td>\n",
       "      <td>31</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>153.678375</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>140</td>\n",
       "      <td>...</td>\n",
       "      <td>4.87000</td>\n",
       "      <td>87.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>1369.682490</td>\n",
       "      <td>1382517.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.504</td>\n",
       "      <td>11.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2914</th>\n",
       "      <td>182</td>\n",
       "      <td>2007</td>\n",
       "      <td>No</td>\n",
       "      <td>52.6</td>\n",
       "      <td>487.0</td>\n",
       "      <td>32</td>\n",
       "      <td>2.080000</td>\n",
       "      <td>10.851482</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>535</td>\n",
       "      <td>...</td>\n",
       "      <td>4.37000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>114.587985</td>\n",
       "      <td>12725974.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.492</td>\n",
       "      <td>11.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2915</th>\n",
       "      <td>182</td>\n",
       "      <td>2006</td>\n",
       "      <td>No</td>\n",
       "      <td>58.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>33</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1.860004</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>459</td>\n",
       "      <td>...</td>\n",
       "      <td>6.11000</td>\n",
       "      <td>81.0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>13.154199</td>\n",
       "      <td>12383446.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.479</td>\n",
       "      <td>10.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>182</td>\n",
       "      <td>2005</td>\n",
       "      <td>No</td>\n",
       "      <td>49.3</td>\n",
       "      <td>554.0</td>\n",
       "      <td>34</td>\n",
       "      <td>2.330000</td>\n",
       "      <td>121.879331</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>7.56000</td>\n",
       "      <td>82.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>691.317816</td>\n",
       "      <td>1252156.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.467</td>\n",
       "      <td>10.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>182</td>\n",
       "      <td>2004</td>\n",
       "      <td>No</td>\n",
       "      <td>47.9</td>\n",
       "      <td>578.0</td>\n",
       "      <td>36</td>\n",
       "      <td>2.460000</td>\n",
       "      <td>8.369852</td>\n",
       "      <td>80.940461</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>7.33000</td>\n",
       "      <td>83.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>53.277222</td>\n",
       "      <td>11731746.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.456</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>182</td>\n",
       "      <td>2003</td>\n",
       "      <td>No</td>\n",
       "      <td>46.4</td>\n",
       "      <td>64.0</td>\n",
       "      <td>39</td>\n",
       "      <td>2.330000</td>\n",
       "      <td>65.789974</td>\n",
       "      <td>80.940461</td>\n",
       "      <td>881</td>\n",
       "      <td>...</td>\n",
       "      <td>8.18000</td>\n",
       "      <td>83.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>429.158343</td>\n",
       "      <td>11421984.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.443</td>\n",
       "      <td>10.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2919</th>\n",
       "      <td>182</td>\n",
       "      <td>2002</td>\n",
       "      <td>No</td>\n",
       "      <td>45.5</td>\n",
       "      <td>69.0</td>\n",
       "      <td>41</td>\n",
       "      <td>2.440000</td>\n",
       "      <td>54.043480</td>\n",
       "      <td>80.940461</td>\n",
       "      <td>25036</td>\n",
       "      <td>...</td>\n",
       "      <td>6.93000</td>\n",
       "      <td>84.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>377.135244</td>\n",
       "      <td>111249.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.433</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2920</th>\n",
       "      <td>182</td>\n",
       "      <td>2001</td>\n",
       "      <td>No</td>\n",
       "      <td>44.6</td>\n",
       "      <td>611.0</td>\n",
       "      <td>43</td>\n",
       "      <td>2.610000</td>\n",
       "      <td>46.830275</td>\n",
       "      <td>80.940461</td>\n",
       "      <td>16997</td>\n",
       "      <td>...</td>\n",
       "      <td>6.56000</td>\n",
       "      <td>85.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>378.273624</td>\n",
       "      <td>1824125.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.424</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>182</td>\n",
       "      <td>2000</td>\n",
       "      <td>No</td>\n",
       "      <td>43.8</td>\n",
       "      <td>614.0</td>\n",
       "      <td>44</td>\n",
       "      <td>2.620000</td>\n",
       "      <td>45.616880</td>\n",
       "      <td>80.940461</td>\n",
       "      <td>30930</td>\n",
       "      <td>...</td>\n",
       "      <td>7.16000</td>\n",
       "      <td>85.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>341.955625</td>\n",
       "      <td>1531221.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.418</td>\n",
       "      <td>9.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>183</td>\n",
       "      <td>2015</td>\n",
       "      <td>No</td>\n",
       "      <td>67.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>22</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>87.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>118.693830</td>\n",
       "      <td>15777451.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.507</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>183</td>\n",
       "      <td>2014</td>\n",
       "      <td>No</td>\n",
       "      <td>59.2</td>\n",
       "      <td>371.0</td>\n",
       "      <td>23</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>10.822595</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.44000</td>\n",
       "      <td>91.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>127.474620</td>\n",
       "      <td>15411675.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0.498</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>183</td>\n",
       "      <td>2013</td>\n",
       "      <td>No</td>\n",
       "      <td>58.0</td>\n",
       "      <td>399.0</td>\n",
       "      <td>25</td>\n",
       "      <td>6.390000</td>\n",
       "      <td>10.666707</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.88000</td>\n",
       "      <td>95.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>111.227396</td>\n",
       "      <td>155456.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.488</td>\n",
       "      <td>10.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>183</td>\n",
       "      <td>2012</td>\n",
       "      <td>No</td>\n",
       "      <td>56.6</td>\n",
       "      <td>429.0</td>\n",
       "      <td>26</td>\n",
       "      <td>6.090000</td>\n",
       "      <td>92.602336</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.69000</td>\n",
       "      <td>95.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>955.648466</td>\n",
       "      <td>1471826.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.4</td>\n",
       "      <td>0.464</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2926</th>\n",
       "      <td>183</td>\n",
       "      <td>2011</td>\n",
       "      <td>No</td>\n",
       "      <td>54.9</td>\n",
       "      <td>464.0</td>\n",
       "      <td>28</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>63.750530</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.31000</td>\n",
       "      <td>93.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>839.927936</td>\n",
       "      <td>14386649.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.452</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2927</th>\n",
       "      <td>183</td>\n",
       "      <td>2010</td>\n",
       "      <td>No</td>\n",
       "      <td>52.4</td>\n",
       "      <td>527.0</td>\n",
       "      <td>29</td>\n",
       "      <td>5.210000</td>\n",
       "      <td>53.308581</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9696</td>\n",
       "      <td>...</td>\n",
       "      <td>5.37000</td>\n",
       "      <td>89.0</td>\n",
       "      <td>15.7</td>\n",
       "      <td>713.635620</td>\n",
       "      <td>1486317.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.436</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2928</th>\n",
       "      <td>183</td>\n",
       "      <td>2009</td>\n",
       "      <td>No</td>\n",
       "      <td>50.0</td>\n",
       "      <td>587.0</td>\n",
       "      <td>30</td>\n",
       "      <td>4.640000</td>\n",
       "      <td>1.040021</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>853</td>\n",
       "      <td>...</td>\n",
       "      <td>6.26000</td>\n",
       "      <td>73.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>65.824121</td>\n",
       "      <td>1381599.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.419</td>\n",
       "      <td>9.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2929</th>\n",
       "      <td>183</td>\n",
       "      <td>2008</td>\n",
       "      <td>No</td>\n",
       "      <td>48.2</td>\n",
       "      <td>632.0</td>\n",
       "      <td>30</td>\n",
       "      <td>3.560000</td>\n",
       "      <td>20.843429</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.96000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>325.678573</td>\n",
       "      <td>13558469.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.421</td>\n",
       "      <td>9.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2930</th>\n",
       "      <td>183</td>\n",
       "      <td>2007</td>\n",
       "      <td>No</td>\n",
       "      <td>46.6</td>\n",
       "      <td>67.0</td>\n",
       "      <td>29</td>\n",
       "      <td>3.880000</td>\n",
       "      <td>29.814566</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>242</td>\n",
       "      <td>...</td>\n",
       "      <td>4.47000</td>\n",
       "      <td>73.0</td>\n",
       "      <td>23.7</td>\n",
       "      <td>396.998217</td>\n",
       "      <td>1332999.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.414</td>\n",
       "      <td>9.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2931</th>\n",
       "      <td>183</td>\n",
       "      <td>2006</td>\n",
       "      <td>No</td>\n",
       "      <td>45.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>28</td>\n",
       "      <td>4.570000</td>\n",
       "      <td>34.262169</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>212</td>\n",
       "      <td>...</td>\n",
       "      <td>5.12000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>26.8</td>\n",
       "      <td>414.796232</td>\n",
       "      <td>13124267.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0.408</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2932</th>\n",
       "      <td>183</td>\n",
       "      <td>2005</td>\n",
       "      <td>No</td>\n",
       "      <td>44.6</td>\n",
       "      <td>717.0</td>\n",
       "      <td>28</td>\n",
       "      <td>4.140000</td>\n",
       "      <td>8.717409</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>420</td>\n",
       "      <td>...</td>\n",
       "      <td>6.44000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>30.3</td>\n",
       "      <td>444.765750</td>\n",
       "      <td>129432.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.406</td>\n",
       "      <td>9.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2933</th>\n",
       "      <td>183</td>\n",
       "      <td>2004</td>\n",
       "      <td>No</td>\n",
       "      <td>44.3</td>\n",
       "      <td>723.0</td>\n",
       "      <td>27</td>\n",
       "      <td>4.360000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>7.13000</td>\n",
       "      <td>65.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>454.366654</td>\n",
       "      <td>12777511.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.407</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>183</td>\n",
       "      <td>2003</td>\n",
       "      <td>No</td>\n",
       "      <td>44.5</td>\n",
       "      <td>715.0</td>\n",
       "      <td>26</td>\n",
       "      <td>4.060000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>998</td>\n",
       "      <td>...</td>\n",
       "      <td>6.52000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>36.7</td>\n",
       "      <td>453.351155</td>\n",
       "      <td>12633897.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.9</td>\n",
       "      <td>0.418</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2935</th>\n",
       "      <td>183</td>\n",
       "      <td>2002</td>\n",
       "      <td>No</td>\n",
       "      <td>44.8</td>\n",
       "      <td>73.0</td>\n",
       "      <td>25</td>\n",
       "      <td>4.430000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>304</td>\n",
       "      <td>...</td>\n",
       "      <td>6.53000</td>\n",
       "      <td>71.0</td>\n",
       "      <td>39.8</td>\n",
       "      <td>57.348340</td>\n",
       "      <td>125525.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.427</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>183</td>\n",
       "      <td>2001</td>\n",
       "      <td>No</td>\n",
       "      <td>45.3</td>\n",
       "      <td>686.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.720000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>529</td>\n",
       "      <td>...</td>\n",
       "      <td>6.16000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>42.1</td>\n",
       "      <td>548.587312</td>\n",
       "      <td>12366165.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.427</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>183</td>\n",
       "      <td>2000</td>\n",
       "      <td>No</td>\n",
       "      <td>46.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>24</td>\n",
       "      <td>1.680000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>1483</td>\n",
       "      <td>...</td>\n",
       "      <td>7.10000</td>\n",
       "      <td>78.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>547.358879</td>\n",
       "      <td>12222251.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>0.434</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2938 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Country  Year Developed  Life expectancy   Adult Mortality  \\\n",
       "0           1  2015        No              65.0            263.0   \n",
       "1           1  2014        No              59.9            271.0   \n",
       "2           1  2013        No              59.9            268.0   \n",
       "3           1  2012        No              59.5            272.0   \n",
       "4           1  2011        No              59.2            275.0   \n",
       "5           1  2010        No              58.8            279.0   \n",
       "6           1  2009        No              58.6            281.0   \n",
       "7           1  2008        No              58.1            287.0   \n",
       "8           1  2007        No              57.5            295.0   \n",
       "9           1  2006        No              57.3            295.0   \n",
       "10          1  2005        No              57.3            291.0   \n",
       "11          1  2004        No              57.0            293.0   \n",
       "12          1  2003        No              56.7            295.0   \n",
       "13          1  2002        No              56.2              3.0   \n",
       "14          1  2001        No              55.3            316.0   \n",
       "15          1  2000        No              54.8            321.0   \n",
       "16          2  2015        No              77.8             74.0   \n",
       "17          2  2014        No              77.5              8.0   \n",
       "18          2  2013        No              77.2             84.0   \n",
       "19          2  2012        No              76.9             86.0   \n",
       "20          2  2011        No              76.6             88.0   \n",
       "21          2  2010        No              76.2             91.0   \n",
       "22          2  2009        No              76.1             91.0   \n",
       "23          2  2008        No              75.3              1.0   \n",
       "24          2  2007        No              75.9              9.0   \n",
       "25          2  2006        No              74.2             99.0   \n",
       "26          2  2005        No              73.5             15.0   \n",
       "27          2  2004        No              73.0             17.0   \n",
       "28          2  2003        No              72.8             18.0   \n",
       "29          2  2002        No              73.3             15.0   \n",
       "...       ...   ...       ...               ...              ...   \n",
       "2908      182  2013        No              63.0            328.0   \n",
       "2909      182  2012        No              59.2            349.0   \n",
       "2910      182  2011        No              58.2            366.0   \n",
       "2911      182  2010        No              58.0            363.0   \n",
       "2912      182  2009        No              57.4            368.0   \n",
       "2913      182  2008        No              55.7             45.0   \n",
       "2914      182  2007        No              52.6            487.0   \n",
       "2915      182  2006        No              58.0            526.0   \n",
       "2916      182  2005        No              49.3            554.0   \n",
       "2917      182  2004        No              47.9            578.0   \n",
       "2918      182  2003        No              46.4             64.0   \n",
       "2919      182  2002        No              45.5             69.0   \n",
       "2920      182  2001        No              44.6            611.0   \n",
       "2921      182  2000        No              43.8            614.0   \n",
       "2922      183  2015        No              67.0            336.0   \n",
       "2923      183  2014        No              59.2            371.0   \n",
       "2924      183  2013        No              58.0            399.0   \n",
       "2925      183  2012        No              56.6            429.0   \n",
       "2926      183  2011        No              54.9            464.0   \n",
       "2927      183  2010        No              52.4            527.0   \n",
       "2928      183  2009        No              50.0            587.0   \n",
       "2929      183  2008        No              48.2            632.0   \n",
       "2930      183  2007        No              46.6             67.0   \n",
       "2931      183  2006        No              45.4              7.0   \n",
       "2932      183  2005        No              44.6            717.0   \n",
       "2933      183  2004        No              44.3            723.0   \n",
       "2934      183  2003        No              44.5            715.0   \n",
       "2935      183  2002        No              44.8             73.0   \n",
       "2936      183  2001        No              45.3            686.0   \n",
       "2937      183  2000        No              46.0            665.0   \n",
       "\n",
       "      infant deaths   Alcohol  percentage expenditure  Hepatitis B  Measles   \\\n",
       "0                62  0.010000               71.279624    65.000000      1154   \n",
       "1                64  0.010000               73.523582    62.000000       492   \n",
       "2                66  0.010000               73.219243    64.000000       430   \n",
       "3                69  0.010000               78.184215    67.000000      2787   \n",
       "4                71  0.010000                7.097109    68.000000      3013   \n",
       "5                74  0.010000               79.679367    66.000000      1989   \n",
       "6                77  0.010000               56.762217    63.000000      2861   \n",
       "7                80  0.030000               25.873925    64.000000      1599   \n",
       "8                82  0.020000               10.910156    63.000000      1141   \n",
       "9                84  0.030000               17.171518    64.000000      1990   \n",
       "10               85  0.020000                1.388648    66.000000      1296   \n",
       "11               87  0.020000               15.296066    67.000000       466   \n",
       "12               87  0.010000               11.089053    65.000000       798   \n",
       "13               88  0.010000               16.887351    64.000000      2486   \n",
       "14               88  0.010000               10.574728    63.000000      8762   \n",
       "15               88  0.010000               10.424960    62.000000      6532   \n",
       "16                0  4.600000              364.975229    99.000000         0   \n",
       "17                0  4.510000              428.749067    98.000000         0   \n",
       "18                0  4.760000              430.876979    99.000000         0   \n",
       "19                0  5.140000              412.443356    99.000000         9   \n",
       "20                0  5.370000              437.062100    99.000000        28   \n",
       "21                1  5.280000               41.822757    99.000000        10   \n",
       "22                1  5.790000              348.055952    98.000000         0   \n",
       "23                1  5.610000               36.622068    99.000000         0   \n",
       "24                1  5.580000               32.246552    98.000000        22   \n",
       "25                1  5.310000                3.302154    98.000000        68   \n",
       "26                1  5.160000               26.993121    98.000000         6   \n",
       "27                1  4.540000              221.842800    99.000000         7   \n",
       "28                1  4.290000               14.719289    97.000000         8   \n",
       "29                1  3.730000              104.516916    96.000000        16   \n",
       "...             ...       ...                     ...          ...       ...   \n",
       "2908             29  2.410000               20.623063    79.000000        35   \n",
       "2909             29  2.590000              196.915250    78.000000       896   \n",
       "2910             29  2.570000              183.046170    81.000000     13234   \n",
       "2911             30  2.470000              184.364910    83.000000     15754   \n",
       "2912             30  2.300000              143.869887    94.000000        26   \n",
       "2913             31  2.120000              153.678375    87.000000       140   \n",
       "2914             32  2.080000               10.851482     8.000000       535   \n",
       "2915             33  2.250000                1.860004    81.000000       459   \n",
       "2916             34  2.330000              121.879331    82.000000        45   \n",
       "2917             36  2.460000                8.369852    80.940461        35   \n",
       "2918             39  2.330000               65.789974    80.940461       881   \n",
       "2919             41  2.440000               54.043480    80.940461     25036   \n",
       "2920             43  2.610000               46.830275    80.940461     16997   \n",
       "2921             44  2.620000               45.616880    80.940461     30930   \n",
       "2922             22  4.602861                0.000000    87.000000         0   \n",
       "2923             23  6.500000               10.822595    91.000000         0   \n",
       "2924             25  6.390000               10.666707    95.000000         0   \n",
       "2925             26  6.090000               92.602336    97.000000         0   \n",
       "2926             28  6.000000               63.750530    94.000000         0   \n",
       "2927             29  5.210000               53.308581     9.000000      9696   \n",
       "2928             30  4.640000                1.040021    73.000000       853   \n",
       "2929             30  3.560000               20.843429    75.000000         0   \n",
       "2930             29  3.880000               29.814566    72.000000       242   \n",
       "2931             28  4.570000               34.262169    68.000000       212   \n",
       "2932             28  4.140000                8.717409    65.000000       420   \n",
       "2933             27  4.360000                0.000000    68.000000        31   \n",
       "2934             26  4.060000                0.000000     7.000000       998   \n",
       "2935             25  4.430000                0.000000    73.000000       304   \n",
       "2936             25  1.720000                0.000000    76.000000       529   \n",
       "2937             24  1.680000                0.000000    79.000000      1483   \n",
       "\n",
       "       ...     Total expenditure  Diphtheria    HIV/AIDS          GDP  \\\n",
       "0      ...               8.16000         65.0        0.1   584.259210   \n",
       "1      ...               8.18000         62.0        0.1   612.696514   \n",
       "2      ...               8.13000         64.0        0.1   631.744976   \n",
       "3      ...               8.52000         67.0        0.1   669.959000   \n",
       "4      ...               7.87000         68.0        0.1    63.537231   \n",
       "5      ...               9.20000         66.0        0.1   553.328940   \n",
       "6      ...               9.42000         63.0        0.1   445.893298   \n",
       "7      ...               8.33000         64.0        0.1   373.361116   \n",
       "8      ...               6.73000         63.0        0.1   369.835796   \n",
       "9      ...               7.43000         58.0        0.1   272.563770   \n",
       "10     ...               8.70000         58.0        0.1    25.294130   \n",
       "11     ...               8.79000          5.0        0.1   219.141353   \n",
       "12     ...               8.82000         41.0        0.1   198.728544   \n",
       "13     ...               7.76000         36.0        0.1   187.845950   \n",
       "14     ...               7.80000         33.0        0.1   117.496980   \n",
       "15     ...               8.20000         24.0        0.1   114.560000   \n",
       "16     ...               6.00000         99.0        0.1  3954.227830   \n",
       "17     ...               5.88000         98.0        0.1  4575.763787   \n",
       "18     ...               5.66000         99.0        0.1  4414.723140   \n",
       "19     ...               5.59000         99.0        0.1  4247.614380   \n",
       "20     ...               5.71000         99.0        0.1  4437.178680   \n",
       "21     ...               5.34000         99.0        0.1   494.358832   \n",
       "22     ...               5.79000         98.0        0.1  4114.136545   \n",
       "23     ...               5.87000         99.0        0.1   437.539647   \n",
       "24     ...               6.10000         98.0        0.1   363.136850   \n",
       "25     ...               5.86000         97.0        0.1    35.129300   \n",
       "26     ...               6.12000         98.0        0.1   279.142931   \n",
       "27     ...               6.38000         97.0        0.1  2416.588235   \n",
       "28     ...               6.27000         97.0        0.1   189.681557   \n",
       "29     ...               6.30000         98.0        0.1  1453.642777   \n",
       "...    ...                   ...          ...        ...          ...   \n",
       "2908   ...               4.99000         79.0        4.8   185.793359   \n",
       "2909   ...               4.91000         78.0        5.6  1734.936120   \n",
       "2910   ...               4.26000         81.0        6.3  1644.619672   \n",
       "2911   ...               4.41000         83.0        6.8  1463.213573   \n",
       "2912   ...               4.73000         94.0        9.1  1139.112330   \n",
       "2913   ...               4.87000         87.0       11.9  1369.682490   \n",
       "2914   ...               4.37000          8.0       13.6   114.587985   \n",
       "2915   ...               6.11000         81.0       15.9    13.154199   \n",
       "2916   ...               7.56000         82.0       17.0   691.317816   \n",
       "2917   ...               7.33000         83.0       17.6    53.277222   \n",
       "2918   ...               8.18000         83.0       18.2   429.158343   \n",
       "2919   ...               6.93000         84.0       18.4   377.135244   \n",
       "2920   ...               6.56000         85.0       18.6   378.273624   \n",
       "2921   ...               7.16000         85.0       18.7   341.955625   \n",
       "2922   ...               5.93819         87.0        6.2   118.693830   \n",
       "2923   ...               6.44000         91.0        6.3   127.474620   \n",
       "2924   ...               6.88000         95.0        6.8   111.227396   \n",
       "2925   ...               6.69000         95.0        8.8   955.648466   \n",
       "2926   ...               6.31000         93.0       13.3   839.927936   \n",
       "2927   ...               5.37000         89.0       15.7   713.635620   \n",
       "2928   ...               6.26000         73.0       18.1    65.824121   \n",
       "2929   ...               4.96000         75.0       20.5   325.678573   \n",
       "2930   ...               4.47000         73.0       23.7   396.998217   \n",
       "2931   ...               5.12000          7.0       26.8   414.796232   \n",
       "2932   ...               6.44000         68.0       30.3   444.765750   \n",
       "2933   ...               7.13000         65.0       33.6   454.366654   \n",
       "2934   ...               6.52000         68.0       36.7   453.351155   \n",
       "2935   ...               6.53000         71.0       39.8    57.348340   \n",
       "2936   ...               6.16000         75.0       42.1   548.587312   \n",
       "2937   ...               7.10000         78.0       43.5   547.358879   \n",
       "\n",
       "      Population   thinness  1-19 years   thinness 5-9 years  \\\n",
       "0     33736494.0                   17.2                 17.3   \n",
       "1       327582.0                   17.5                 17.5   \n",
       "2     31731688.0                   17.7                 17.7   \n",
       "3      3696958.0                   17.9                 18.0   \n",
       "4      2978599.0                   18.2                 18.2   \n",
       "5      2883167.0                   18.4                 18.4   \n",
       "6       284331.0                   18.6                 18.7   \n",
       "7      2729431.0                   18.8                 18.9   \n",
       "8     26616792.0                   19.0                 19.1   \n",
       "9      2589345.0                   19.2                 19.3   \n",
       "10      257798.0                   19.3                 19.5   \n",
       "11    24118979.0                   19.5                 19.7   \n",
       "12     2364851.0                   19.7                 19.9   \n",
       "13    21979923.0                   19.9                  2.2   \n",
       "14     2966463.0                    2.1                  2.4   \n",
       "15      293756.0                    2.3                  2.5   \n",
       "16       28873.0                    1.2                  1.3   \n",
       "17      288914.0                    1.2                  1.3   \n",
       "18      289592.0                    1.3                  1.4   \n",
       "19        2941.0                    1.3                  1.4   \n",
       "20      295195.0                    1.4                  1.5   \n",
       "21      291321.0                    1.4                  1.5   \n",
       "22     2927519.0                    1.5                  1.6   \n",
       "23     2947314.0                    1.6                  1.6   \n",
       "24       29717.0                    1.6                  1.7   \n",
       "25     2992547.0                    1.7                  1.8   \n",
       "26      311487.0                    1.8                  1.8   \n",
       "27      326939.0                    1.8                  1.9   \n",
       "28      339616.0                    1.9                  2.0   \n",
       "29        3511.0                    2.0                  2.1   \n",
       "...          ...                    ...                  ...   \n",
       "2908   1515321.0                    6.4                  6.2   \n",
       "2909  14699937.0                    6.5                  6.3   \n",
       "2910  14264756.0                    6.6                  6.4   \n",
       "2911    138533.0                    6.7                  6.5   \n",
       "2912  13456417.0                    6.7                  6.6   \n",
       "2913   1382517.0                    6.8                  6.7   \n",
       "2914  12725974.0                    6.9                  6.8   \n",
       "2915  12383446.0                    7.0                  6.9   \n",
       "2916   1252156.0                    7.1                  7.0   \n",
       "2917  11731746.0                    7.2                  7.1   \n",
       "2918  11421984.0                    7.3                  7.2   \n",
       "2919    111249.0                    7.4                  7.3   \n",
       "2920   1824125.0                    7.4                  7.4   \n",
       "2921   1531221.0                    7.5                  7.5   \n",
       "2922  15777451.0                    5.6                  5.5   \n",
       "2923  15411675.0                    5.9                  5.7   \n",
       "2924    155456.0                    6.2                  6.0   \n",
       "2925   1471826.0                    6.5                  6.4   \n",
       "2926  14386649.0                    6.8                  6.7   \n",
       "2927   1486317.0                    7.1                  7.0   \n",
       "2928   1381599.0                    7.5                  7.4   \n",
       "2929  13558469.0                    7.8                  7.8   \n",
       "2930   1332999.0                    8.2                  8.2   \n",
       "2931  13124267.0                    8.6                  8.6   \n",
       "2932    129432.0                    9.0                  9.0   \n",
       "2933  12777511.0                    9.4                  9.4   \n",
       "2934  12633897.0                    9.8                  9.9   \n",
       "2935    125525.0                    1.2                  1.3   \n",
       "2936  12366165.0                    1.6                  1.7   \n",
       "2937  12222251.0                   11.0                 11.2   \n",
       "\n",
       "      Income composition of resources  Schooling  Cluster  \n",
       "0                               0.479       10.1        0  \n",
       "1                               0.476       10.0        0  \n",
       "2                               0.470        9.9        0  \n",
       "3                               0.463        9.8        0  \n",
       "4                               0.454        9.5        0  \n",
       "5                               0.448        9.2        0  \n",
       "6                               0.434        8.9        0  \n",
       "7                               0.433        8.7        0  \n",
       "8                               0.415        8.4        0  \n",
       "9                               0.405        8.1        0  \n",
       "10                              0.396        7.9        0  \n",
       "11                              0.381        6.8        0  \n",
       "12                              0.373        6.5        0  \n",
       "13                              0.341        6.2        0  \n",
       "14                              0.340        5.9        0  \n",
       "15                              0.338        5.5        0  \n",
       "16                              0.762       14.2        0  \n",
       "17                              0.761       14.2        0  \n",
       "18                              0.759       14.2        0  \n",
       "19                              0.752       14.2        0  \n",
       "20                              0.738       13.3        0  \n",
       "21                              0.725       12.5        0  \n",
       "22                              0.721       12.2        0  \n",
       "23                              0.713       12.0        0  \n",
       "24                              0.703       11.6        0  \n",
       "25                              0.696       11.4        0  \n",
       "26                              0.685       10.8        0  \n",
       "27                              0.681       10.9        0  \n",
       "28                              0.674       10.7        0  \n",
       "29                              0.670       10.7        0  \n",
       "...                               ...        ...      ...  \n",
       "2908                            0.565       12.5        0  \n",
       "2909                            0.554       12.3        0  \n",
       "2910                            0.543       12.0        0  \n",
       "2911                            0.533       11.8        0  \n",
       "2912                            0.518       11.6        0  \n",
       "2913                            0.504       11.4        0  \n",
       "2914                            0.492       11.1        0  \n",
       "2915                            0.479       10.9        0  \n",
       "2916                            0.467       10.7        0  \n",
       "2917                            0.456       10.5        0  \n",
       "2918                            0.443       10.2        0  \n",
       "2919                            0.433       10.0        0  \n",
       "2920                            0.424        9.8        0  \n",
       "2921                            0.418        9.6        0  \n",
       "2922                            0.507       10.3        0  \n",
       "2923                            0.498       10.3        0  \n",
       "2924                            0.488       10.4        0  \n",
       "2925                            0.464        9.8        0  \n",
       "2926                            0.452       10.1        0  \n",
       "2927                            0.436       10.0        0  \n",
       "2928                            0.419        9.9        0  \n",
       "2929                            0.421        9.7        0  \n",
       "2930                            0.414        9.6        0  \n",
       "2931                            0.408        9.5        0  \n",
       "2932                            0.406        9.3        0  \n",
       "2933                            0.407        9.2        0  \n",
       "2934                            0.418        9.5        0  \n",
       "2935                            0.427       10.0        0  \n",
       "2936                            0.427        9.8        0  \n",
       "2937                            0.434        9.8        0  \n",
       "\n",
       "[2938 rows x 23 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluation\n",
    "#Create a new column for df called Cluster which value 1 for devoloped country and 0 for not developed country\n",
    "def converter(cluster):\n",
    "    if cluster=='Yes':\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "df['Cluster'] = df['Developed'].apply(converter)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2421    5]\n",
      " [ 512    0]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.83      1.00      0.90      2426\n",
      "          1       0.00      0.00      0.00       512\n",
      "\n",
      "avg / total       0.68      0.82      0.75      2938\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Create a confusion matrix and classification report\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "print(confusion_matrix(df['Cluster'],kmeans.labels_))\n",
    "print(classification_report(df['Cluster'],kmeans.labels_))"
   ]
  }
 ],
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