Write-a-Data-Science-Blog-Post-.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Science Blog Post - Missing Migrants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import necessary libraries\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, CRISP-DM methodology will be followed with the below specified steps:\n",
    "1. Business Understanding\n",
    "2. Data Understanding\n",
    "3. Data Preparation\n",
    "4. Modeling\n",
    "5. Evaluation\n",
    "6. Deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Business Understanding\n",
    "\n",
    "In this step, main focus is to understand the project objectives and requirements from a business perspective. Then, this understanding and knowledge will be converted into a data mining problem definition and defines a study plan. \n",
    "\n",
    "In this research, I will focus on the questions which are stated in below:\n",
    "\n",
    "1. Distribution of total dead and missing number of migrants among the years to understand the conditions.\n",
    "2. Region of Incident to understand the route and road conditions\n",
    "3. Top causes of death to investigate the migrants death reasoning.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Understanding\n",
    "\n",
    "Data Understanding process starts with data collection and then proceeds with performing analysis to identify data quality, to discover first insights into data, or to detect interesting subsets to form hypothesis for hidden informations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Web ID</th>\n",
       "      <th>Region of Incident</th>\n",
       "      <th>Reported Date</th>\n",
       "      <th>Reported Year</th>\n",
       "      <th>Reported Month</th>\n",
       "      <th>Number Dead</th>\n",
       "      <th>Minimum Estimated Number of Missing</th>\n",
       "      <th>Total Dead and Missing</th>\n",
       "      <th>Number of Survivors</th>\n",
       "      <th>Number of Females</th>\n",
       "      <th>Number of Males</th>\n",
       "      <th>Number of Children</th>\n",
       "      <th>Cause of Death</th>\n",
       "      <th>Location Description</th>\n",
       "      <th>Information Source</th>\n",
       "      <th>Location Coordinates</th>\n",
       "      <th>Migration Route</th>\n",
       "      <th>URL</th>\n",
       "      <th>UNSD Geographical Grouping</th>\n",
       "      <th>Source Quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51591</td>\n",
       "      <td>Mediterranean</td>\n",
       "      <td>March 28, 2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>Mar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Presumed drowning</td>\n",
       "      <td>Off the coast of Chios, Greece</td>\n",
       "      <td>Hellenic Coast Guard via IOM Greece</td>\n",
       "      <td>38.362368696592, 26.172509473654</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Uncategorized</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51588</td>\n",
       "      <td>Mediterranean</td>\n",
       "      <td>March 26, 2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>Mar</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Presumed drowning</td>\n",
       "      <td>Off the coast of Ayvacık district, Çanakkale p...</td>\n",
       "      <td>Turkish Coast Guard via IOM Turkey</td>\n",
       "      <td>39.441975591614, 26.378816195919</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>http://bit.ly/2YmiPAN</td>\n",
       "      <td>Uncategorized</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51589</td>\n",
       "      <td>Mediterranean</td>\n",
       "      <td>March 26, 2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>Mar</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Presumed drowning</td>\n",
       "      <td>Body recovered on Playa del Tarajal, Ceuta, Sp...</td>\n",
       "      <td>Ceuta al día, El Pueblo de Ceuta</td>\n",
       "      <td>35.871901875921, -5.343037665842</td>\n",
       "      <td>Western Mediterranean</td>\n",
       "      <td>http://bit.ly/2uyj7qO, http://bit.ly/2uwj5zC</td>\n",
       "      <td>Uncategorized</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51590</td>\n",
       "      <td>Mediterranean</td>\n",
       "      <td>March 26, 2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>Mar</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Presumed drowning</td>\n",
       "      <td>Body recovered on beach near Tetouan, Morocco ...</td>\n",
       "      <td>El Pueblo de Ceuta</td>\n",
       "      <td>35.635115912988, -5.275650103548</td>\n",
       "      <td>Western Mediterranean</td>\n",
       "      <td>http://bit.ly/2uwj5zC</td>\n",
       "      <td>Uncategorized</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51587</td>\n",
       "      <td>Central America</td>\n",
       "      <td>March 25, 2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>Mar</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fall from train</td>\n",
       "      <td>Train tracks in Teacalco, Tlaxcala, Mexico</td>\n",
       "      <td>Megalópolis, Línea de contraste</td>\n",
       "      <td>19.334475177429, -98.069823987538</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://bit.ly/2uvDIvH, http://bit.ly/2TXAFLS</td>\n",
       "      <td>Central America</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Web ID Region of Incident   Reported Date  Reported Year Reported Month  \\\n",
       "0   51591      Mediterranean  March 28, 2019           2019            Mar   \n",
       "1   51588      Mediterranean  March 26, 2019           2019            Mar   \n",
       "2   51589      Mediterranean  March 26, 2019           2019            Mar   \n",
       "3   51590      Mediterranean  March 26, 2019           2019            Mar   \n",
       "4   51587    Central America  March 25, 2019           2019            Mar   \n",
       "\n",
       "   Number Dead  Minimum Estimated Number of Missing  Total Dead and Missing  \\\n",
       "0          NaN                                  2.0                       2   \n",
       "1          4.0                                  NaN                       4   \n",
       "2          1.0                                  NaN                       1   \n",
       "3          1.0                                  NaN                       1   \n",
       "4          1.0                                  NaN                       1   \n",
       "\n",
       "   Number of Survivors  Number of Females  Number of Males  \\\n",
       "0                 36.0                NaN              2.0   \n",
       "1                 11.0                3.0              NaN   \n",
       "2                  NaN                NaN              NaN   \n",
       "3                  NaN                NaN              NaN   \n",
       "4                  NaN                NaN              1.0   \n",
       "\n",
       "   Number of Children     Cause of Death  \\\n",
       "0                 NaN  Presumed drowning   \n",
       "1                 1.0  Presumed drowning   \n",
       "2                 NaN  Presumed drowning   \n",
       "3                 NaN  Presumed drowning   \n",
       "4                 NaN    Fall from train   \n",
       "\n",
       "                                Location Description  \\\n",
       "0                     Off the coast of Chios, Greece   \n",
       "1  Off the coast of Ayvacık district, Çanakkale p...   \n",
       "2  Body recovered on Playa del Tarajal, Ceuta, Sp...   \n",
       "3  Body recovered on beach near Tetouan, Morocco ...   \n",
       "4         Train tracks in Teacalco, Tlaxcala, Mexico   \n",
       "\n",
       "                    Information Source               Location Coordinates  \\\n",
       "0  Hellenic Coast Guard via IOM Greece   38.362368696592, 26.172509473654   \n",
       "1   Turkish Coast Guard via IOM Turkey   39.441975591614, 26.378816195919   \n",
       "2     Ceuta al día, El Pueblo de Ceuta   35.871901875921, -5.343037665842   \n",
       "3                   El Pueblo de Ceuta   35.635115912988, -5.275650103548   \n",
       "4      Megalópolis, Línea de contraste  19.334475177429, -98.069823987538   \n",
       "\n",
       "         Migration Route                                           URL  \\\n",
       "0  Eastern Mediterranean                                           NaN   \n",
       "1  Eastern Mediterranean                         http://bit.ly/2YmiPAN   \n",
       "2  Western Mediterranean  http://bit.ly/2uyj7qO, http://bit.ly/2uwj5zC   \n",
       "3  Western Mediterranean                         http://bit.ly/2uwj5zC   \n",
       "4                    NaN  http://bit.ly/2uvDIvH, http://bit.ly/2TXAFLS   \n",
       "\n",
       "  UNSD Geographical Grouping  Source Quality  \n",
       "0              Uncategorized               5  \n",
       "1              Uncategorized               5  \n",
       "2              Uncategorized               3  \n",
       "3              Uncategorized               1  \n",
       "4            Central America               3  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the data\n",
    "Missing_Migrants = pd.read_csv('MissingMigrants-Global-2019-03-29T18-36-07.csv')\n",
    "Missing_Migrants.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Row    number: 5333\n",
      "Column number: 20\n"
     ]
    }
   ],
   "source": [
    "# Print the number of columns and rows\n",
    "number_rows = Missing_Migrants.shape[0] \n",
    "number_cols = Missing_Migrants.shape[1] \n",
    "print(\"Row    number: {}\".format(number_rows))\n",
    "print(\"Column number: {}\".format(number_cols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Web ID</th>\n",
       "      <th>Reported Year</th>\n",
       "      <th>Number Dead</th>\n",
       "      <th>Minimum Estimated Number of Missing</th>\n",
       "      <th>Total Dead and Missing</th>\n",
       "      <th>Number of Survivors</th>\n",
       "      <th>Number of Females</th>\n",
       "      <th>Number of Males</th>\n",
       "      <th>Number of Children</th>\n",
       "      <th>Source Quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5333.000000</td>\n",
       "      <td>5333.000000</td>\n",
       "      <td>5142.000000</td>\n",
       "      <td>503.000000</td>\n",
       "      <td>5333.000000</td>\n",
       "      <td>749.000000</td>\n",
       "      <td>897.000000</td>\n",
       "      <td>2813.000000</td>\n",
       "      <td>644.000000</td>\n",
       "      <td>5333.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>43247.885805</td>\n",
       "      <td>2016.656104</td>\n",
       "      <td>3.287631</td>\n",
       "      <td>29.119284</td>\n",
       "      <td>5.916370</td>\n",
       "      <td>63.607477</td>\n",
       "      <td>2.012263</td>\n",
       "      <td>1.890864</td>\n",
       "      <td>2.476708</td>\n",
       "      <td>2.895556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5186.837212</td>\n",
       "      <td>1.237033</td>\n",
       "      <td>13.593407</td>\n",
       "      <td>67.913216</td>\n",
       "      <td>28.051811</td>\n",
       "      <td>146.520155</td>\n",
       "      <td>2.990100</td>\n",
       "      <td>5.510674</td>\n",
       "      <td>10.216220</td>\n",
       "      <td>1.423257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>30158.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>40876.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>43504.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>45747.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>51592.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>750.000000</td>\n",
       "      <td>750.000000</td>\n",
       "      <td>1022.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Web ID  Reported Year  Number Dead  \\\n",
       "count   5333.000000    5333.000000  5142.000000   \n",
       "mean   43247.885805    2016.656104     3.287631   \n",
       "std     5186.837212       1.237033    13.593407   \n",
       "min    30158.000000    2014.000000     0.000000   \n",
       "25%    40876.000000    2016.000000     1.000000   \n",
       "50%    43504.000000    2017.000000     1.000000   \n",
       "75%    45747.000000    2018.000000     2.000000   \n",
       "max    51592.000000    2019.000000   750.000000   \n",
       "\n",
       "       Minimum Estimated Number of Missing  Total Dead and Missing  \\\n",
       "count                           503.000000             5333.000000   \n",
       "mean                             29.119284                5.916370   \n",
       "std                              67.913216               28.051811   \n",
       "min                               0.000000                0.000000   \n",
       "25%                               2.000000                1.000000   \n",
       "50%                               7.000000                1.000000   \n",
       "75%                              25.000000                3.000000   \n",
       "max                             750.000000             1022.000000   \n",
       "\n",
       "       Number of Survivors  Number of Females  Number of Males  \\\n",
       "count           749.000000         897.000000      2813.000000   \n",
       "mean             63.607477           2.012263         1.890864   \n",
       "std             146.520155           2.990100         5.510674   \n",
       "min               0.000000           0.000000         0.000000   \n",
       "25%               5.000000           1.000000         1.000000   \n",
       "50%              15.000000           1.000000         1.000000   \n",
       "75%              61.000000           2.000000         1.000000   \n",
       "max            1950.000000          30.000000       135.000000   \n",
       "\n",
       "       Number of Children  Source Quality  \n",
       "count          644.000000     5333.000000  \n",
       "mean             2.476708        2.895556  \n",
       "std             10.216220        1.423257  \n",
       "min              0.000000        1.000000  \n",
       "25%              1.000000        2.000000  \n",
       "50%              1.000000        2.000000  \n",
       "75%              2.000000        4.000000  \n",
       "max            250.000000        5.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use describe to get first insight about the data columns and rows.\n",
    "Missing_Migrants.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5333 entries, 0 to 5332\n",
      "Data columns (total 20 columns):\n",
      "Web ID                                 5333 non-null int64\n",
      "Region of Incident                     5333 non-null object\n",
      "Reported Date                          5333 non-null object\n",
      "Reported Year                          5333 non-null int64\n",
      "Reported Month                         5333 non-null object\n",
      "Number Dead                            5142 non-null float64\n",
      "Minimum Estimated Number of Missing    503 non-null float64\n",
      "Total Dead and Missing                 5333 non-null int64\n",
      "Number of Survivors                    749 non-null float64\n",
      "Number of Females                      897 non-null float64\n",
      "Number of Males                        2813 non-null float64\n",
      "Number of Children                     644 non-null float64\n",
      "Cause of Death                         5333 non-null object\n",
      "Location Description                   5323 non-null object\n",
      "Information Source                     5331 non-null object\n",
      "Location Coordinates                   5332 non-null object\n",
      "Migration Route                        2660 non-null object\n",
      "URL                                    3192 non-null object\n",
      "UNSD Geographical Grouping             5322 non-null object\n",
      "Source Quality                         5333 non-null int64\n",
      "dtypes: float64(6), int64(4), object(10)\n",
      "memory usage: 833.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Dataset Column data informations\n",
    "Missing_Migrants.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Web ID                                   int64\n",
       "Region of Incident                      object\n",
       "Reported Date                           object\n",
       "Reported Year                            int64\n",
       "Reported Month                          object\n",
       "Number Dead                            float64\n",
       "Minimum Estimated Number of Missing    float64\n",
       "Total Dead and Missing                   int64\n",
       "Number of Survivors                    float64\n",
       "Number of Females                      float64\n",
       "Number of Males                        float64\n",
       "Number of Children                     float64\n",
       "Cause of Death                          object\n",
       "Location Description                    object\n",
       "Information Source                      object\n",
       "Location Coordinates                    object\n",
       "Migration Route                         object\n",
       "URL                                     object\n",
       "UNSD Geographical Grouping              object\n",
       "Source Quality                           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Missing_Migrants.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Web ID', 'Region of Incident', 'Reported Date', 'Reported Year',\n",
       "       'Reported Month', 'Number Dead', 'Minimum Estimated Number of Missing',\n",
       "       'Total Dead and Missing', 'Number of Survivors', 'Number of Females',\n",
       "       'Number of Males', 'Number of Children', 'Cause of Death',\n",
       "       'Location Description', 'Information Source', 'Location Coordinates',\n",
       "       'Migration Route', 'URL', 'UNSD Geographical Grouping',\n",
       "       'Source Quality'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the column names of dataset to use in the analysis process.\n",
    "Missing_Migrants.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Preparation\n",
    "\n",
    "Data prepation is defined as all activities to construct the final dataset from the initial dataset. Some data preparation steps need to be done before using the dataset for exploration, including：\n",
    "\n",
    "- Drop unused columns\n",
    "- Checking columns with missing values and analyze impact\n",
    "- Dealing with missing values\n",
    "- One-Hot Encoding for Categorical variables if needed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, I will drop the unused columns in the dataset in the analyzing process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region of Incident</th>\n",
       "      <th>Reported Year</th>\n",
       "      <th>Reported Month</th>\n",
       "      <th>Number Dead</th>\n",
       "      <th>Minimum Estimated Number of Missing</th>\n",
       "      <th>Total Dead and Missing</th>\n",
       "      <th>Number of Survivors</th>\n",
       "      <th>Number of Females</th>\n",
       "      <th>Number of Males</th>\n",
       "      <th>Number of Children</th>\n",
       "      <th>Cause of Death</th>\n",
       "      <th>Migration Route</th>\n",
       "      <th>UNSD Geographical Grouping</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mediterranean</td>\n",
       "      <td>2019</td>\n",
       "      <td>Mar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Presumed drowning</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Uncategorized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mediterranean</td>\n",
       "      <td>2019</td>\n",
       "      <td>Mar</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Presumed drowning</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Uncategorized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mediterranean</td>\n",
       "      <td>2019</td>\n",
       "      <td>Mar</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Presumed drowning</td>\n",
       "      <td>Western Mediterranean</td>\n",
       "      <td>Uncategorized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mediterranean</td>\n",
       "      <td>2019</td>\n",
       "      <td>Mar</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Presumed drowning</td>\n",
       "      <td>Western Mediterranean</td>\n",
       "      <td>Uncategorized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Central America</td>\n",
       "      <td>2019</td>\n",
       "      <td>Mar</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fall from train</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Central America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Region of Incident  Reported Year Reported Month  Number Dead  \\\n",
       "0      Mediterranean           2019            Mar          NaN   \n",
       "1      Mediterranean           2019            Mar          4.0   \n",
       "2      Mediterranean           2019            Mar          1.0   \n",
       "3      Mediterranean           2019            Mar          1.0   \n",
       "4    Central America           2019            Mar          1.0   \n",
       "\n",
       "   Minimum Estimated Number of Missing  Total Dead and Missing  \\\n",
       "0                                  2.0                       2   \n",
       "1                                  NaN                       4   \n",
       "2                                  NaN                       1   \n",
       "3                                  NaN                       1   \n",
       "4                                  NaN                       1   \n",
       "\n",
       "   Number of Survivors  Number of Females  Number of Males  \\\n",
       "0                 36.0                NaN              2.0   \n",
       "1                 11.0                3.0              NaN   \n",
       "2                  NaN                NaN              NaN   \n",
       "3                  NaN                NaN              NaN   \n",
       "4                  NaN                NaN              1.0   \n",
       "\n",
       "   Number of Children     Cause of Death        Migration Route  \\\n",
       "0                 NaN  Presumed drowning  Eastern Mediterranean   \n",
       "1                 1.0  Presumed drowning  Eastern Mediterranean   \n",
       "2                 NaN  Presumed drowning  Western Mediterranean   \n",
       "3                 NaN  Presumed drowning  Western Mediterranean   \n",
       "4                 NaN    Fall from train                    NaN   \n",
       "\n",
       "  UNSD Geographical Grouping  \n",
       "0              Uncategorized  \n",
       "1              Uncategorized  \n",
       "2              Uncategorized  \n",
       "3              Uncategorized  \n",
       "4            Central America  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Preparation Step 1: Drop the columns which will not be used in this project\n",
    "Missing_Migrants.drop('Web ID',              axis = 1,inplace=True)\n",
    "Missing_Migrants.drop('Reported Date',       axis = 1,inplace=True)\n",
    "Missing_Migrants.drop('Information Source',  axis = 1,inplace=True)\n",
    "Missing_Migrants.drop('Location Coordinates',axis = 1,inplace=True)\n",
    "Missing_Migrants.drop('URL',                 axis = 1,inplace=True)\n",
    "Missing_Migrants.drop('Location Description',axis = 1,inplace=True)\n",
    "Missing_Migrants.drop('Source Quality',      axis = 1,inplace=True)\n",
    "Missing_Migrants.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second and third step in the data preparation are about the missing values and replacing them with NaN values. Since, the selected dataset is clean in the point of missing values, it is not needed to perform conversion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last step is dealing with categorical variables if needed. However, it is not needed to deal/convert the categorical variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution of total dead and missing number of migrants among the years\n",
    "\n",
    "Total dead and missing people among the years can be a good identifier for understanding governments policy change by the years. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x229dcb7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Number of Dead and Missing Number of Migrants by Year Count\n",
    "total_losses = Missing_Migrants.groupby(['Reported Year'])['Total Dead and Missing'].sum()\n",
    "total_deads = Missing_Migrants.groupby(['Reported Year'])['Number Dead'].sum().astype(int)\n",
    "#total_deads = pd.options.display.float_format = '{:,.0f}'.format\n",
    "\n",
    "ax = total_losses.plot(kind='barh', figsize=(20, 10) ,color=\"orange\", fontsize=12);\n",
    "ax = total_deads.plot(kind='barh', figsize=(20, 10) ,color=\"slateblue\", fontsize=12);\n",
    "ax.set_alpha(0.8)\n",
    "ax.set_title(\"Number of Dead and Missing by Year\", fontsize=15)\n",
    "#ax.set_xticks([0, 5])\n",
    "plt.ylabel('');\n",
    "# set individual bar lables using above list\n",
    "for i in ax.patches:\n",
    "    # get_width pulls left or right; get_y pushes up or down\n",
    "    ax.text(i.get_width()+.1, i.get_y()+.31, \\\n",
    "            str(round((i.get_width()), 2)), fontsize=12, color='black')\n",
    "    \n",
    "\n",
    "# invert for largest on top \n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above plot, the total missing and dead migrants are represented with orange and stateblue colors and the total count for each year is located at the right side of orange block. It is seen that there is a significant increase in dead migrants count at the year 2015 and also the total missing and dead migrants count is increased to 6584 from 5287. However, in 2014, only 1724 migrants were reported as dead, in 2015 these number is 4045. So, it might be said that the research and reporting about migrants are improved after the year 2014. Because, 2014 and 2015 have similar number for total counts. The peak for migrants life condition is observed at the year 2016. The number of deads and missings are reached their tops therefore the reasons, economical, political, etc, for this year should be investigated. And hopefully, the migrants life and people life conditions will have some value after the year 2019."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Region of Incident\n",
    "\n",
    "It might be a good indicator to check the region of incident to understand migration route and border security."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Zw3bbbcejjz7aUCH4zDPP8OijjzY8Y/DBBx+kd+/e/PWvf2X77bdfYN7jjjuOF198kSFDhtC27fzHSMycOZPRo0eTmbz++uv07duXE088kbXXXnvZ33BpZU4M3g7UUWyZbUf5LL4KawOtgN9TJK/qX7OA1YBNynEbAO9UXpiZM4BpSxBTc9esN6GJeeZrL59P+Lfy+h9TbJ3eDngWqD605IOqzx+V7/Xj1itjnFwV49UUFaQblhWCtwNfAs4CvlKud3cj603JzHn1HzKzej1JkiRJkqQV2sCBA2nbti2//OUv+dOf/kTbtm0ZOHAgX/7yl+nfvz8HH3ww7du356CDDuL0009nzz2LYxgGDBjA5MmT2Weffairq6Ourq7hBONx48Zx+eWXM3LkSDbYYIOG/muvvRYoEoO9evWirq6O7bffnh133JEBAwYs1/teKbcSA2TmhxFxB3AS8JfMrD6N+AMggf7AXY1M8Wb5/jbFc/galCcB1y1BWM1ds142Mqax9h0pKvC+lpkvVcS51hLEOImiAnAnisrBau8AmwHbAHtnZsN2YE9IliRJkiRJn0T9+/enf//+jfadcMIJnHDCCY32PfTQQ03O2aVLl/lOHa7WoUMHRo0atVhxLm0rbWKw9D9Aa+Cy6o4ycTgM+Gzl8/ka8TRwVESsUbGd+MBmrP0RVVVxi7Hm4qpPyM2qbyhPTu4KjFjMuR6kqBhcKzPva2xARQKwcr0uFMnElv0TK0mSJEmSpKVipU4MZubDzP98u2qnUhz6MQ+4ieKk300pTuL9eWaOAS4G+gF3RMRFwEbAz4AZi1j+JWD9iOgDjAbey8yxzVxzcQ2j2Nr8vxFxPkX1YH/gjcWdKDNfjojLgOvLuYZTJDi7A5tn5vfKexsPXBgRZwLtgXOWZD1JkiRJkiStmFbmZwwuUmY+RnE6bkfgjxTPITwV+Dflc/wy8w1gH4pn7/0VOJ7ipN/pjUxZ6UaK5/KdT1F12L+5ay7BfUwADqF4HuJtFCcpfx94ZUnmo0iEDgAOp9jyfDVF4vKRcr1ZFFWTcyiSmwOAXwBDl3A9SZIkSZIkrWBiYXudpWWpR/TI4Qxv6TAkSZIkSVKt+gSmxSJiRGb2aM7YT3TFoCRJkiRJkqTGrdTPGNRKbluwYFCSJEmSJKllWDEoSZIkSZIk1SATg5IkSZIkSVINMjEoSZIkSZIk1SATg5IkSZIkSVINMjEoSZIkSZIk1SATg5IkSZIkSVINMjEoSZIkSZIk1SATg5IkSZIkSVINMjEoSZIkSZIk1SATg5IkSZIkSVINMjEoSZIkSZIk1SATg5IkSZIkSVINMjEoSZIkSZIk1SATg5IkSZIkSVINMjEoSZIkSZIk1SATg5IkSZIkSVINMjEoSZIkSZIk1aBVWzoA1bARQLR0EJIWKVs6AEmSJEnSsmDFoCRJkiRJklSDTAxKkiRJkiRJNcjEoCRJkiRJklSDTAxKkhbLP//5T9q0acN3v/vdhrbrrruOLl260K5dO771rW8xadIkAGbNmsXRRx9Nly5daN++Pdtssw133313w3XXXnstdXV1Da811liDiGDEiBHL/b4kSZIkqdbUdGIwIvpHREbEvY303RQRDy/FtU6NiN0aac+IOOFjzBsR8a9yns2aGPPNiHgxIj6KiLGLmO/hiLhpSeOR9MnXr18/tttuu4bPzz//PMceeyx//OMfmTBhAmussQbHH388AHPmzGGTTTZh6NChTJ48mQEDBnDooYcyduxYAHr37s20adMaXr///e/51Kc+xf/7f/+vJW5NkiRJkmpKTScGK+wZEdstetjHciqw2zKYd0ega/nzd6o7I6IVcA3wLLA7cMAi5jse+NlSjE/SJ8j1119Phw4d2GOPPRrarr32Wvbff3923XVX6urqGDBgADfffDNTp06lXbt29O/fn65du7LKKquw33770a1btyYrAgcPHszhhx9OhEeWS5IkSdKyZmIQJgGjgJ8vi8kjou2ymLdCT+BD4Mny52obAmsC12XmY5n5TGOT1MeZmS9k5j+XVbCSVl5TpkzhrLPO4sILL5yv/fnnn+cLX/hCw+dPf/rTrL766owZM2aBOSZMmMCYMWPo3r37An3jxo3jkUce4fDDD1/6wUuSJEmSFmBiEBI4D/hGRHx+YQMj4osR8UBETI+I9yPi2ohYv6K/a7mlt3dEXBMRHwBDyu276wJnl/1Zta24VUScFxHvRsQ7EfG7iGi9qMDLasBDgNuBq4DPRcTWFf19gH+XH28r1+1f9mVE/DgiLo6Id4HnyvYFthJHxNYRMSQiPoiIaRHxVER8rexrFxGDIuLl8nv5Vxn/mouKX9LK5cwzz+Too49mk002ma992rRprLXWWvO1rbXWWkydOnW+ttmzZ9O7d2+OOOIItthiiwXmv+aaa9hll13o1q3b0g9ekiRJkrQAE4OFvwBjWEjVYER0BB4G1gB6AT8AvgzcFxGrVw3/NTCVIml3HsX23cnA/1Fs/d0R+EfF+JOBjYDvAhcAxwInNiPu3YH1geuBm4DZzF81eCdwYPnzT8p1r6zoP4WiovAw4IdN3PcWwOPluO+X93ILUJ8ZWANoRfHd7Q2cWcb1l2bEL2klMXLkSO6//35OOumkBfrq6uqYMmXKfG1Tpkyhffv2DZ/nzZvHYYcdxuqrr86gQYMaXeOaa67hiCOOWLqBS5IkSZKatGpLB7AiyMx5EfFL4P8i4qzMXHD/W5G8A/h6Zk4BiIgxFFt4DwL+XDF2WGb2q7w4IuYA4zNzWCNzj83MPuXP90bEThQJvfMXEXpP4APgnsz8KCLuA74TEadn4d2IqN86/HIja7+dmd9exBpnUyQ1d8nMGWXbffWdmfkucFzFfa4K/At4LCI2zczXKyeLiL5AX4BN2XQRS0taUTz88MOMHTuWTTct/t5OmzaNuXPn8sILL7DXXnvx7LPPNox97bXXmDVrFptvvjkAmcnRRx/NhAkTuOuuu1httdUWmP/xxx/nzTff5OCDD14+NyRJkiRJsmKwwp+A12n64I3tgb/VJwUBMvMpYCywc9XYOxdz7b9VfX4B6LywC8qtxgcAt2TmR2XznykOIvmvZq7bnDh3B26oSAo2FsthEfFMREyjqFp8rOzavHpsZl6RmT0ys0dHOjYzTEktrW/fvrz66quMHDmSkSNH8v3vf599992Xe++9l969ezNkyBAeffRRPvzwQ8466ywOPPDAhorB4447jhdffJEhQ4bQtm3jj10dPHgwBx100HxVhpIkSZKkZcvEYCkz51BU6H03Iro0MmRDYEIj7ROAdRppWxwfVH3+CGiziGv2BjoAd0VEh4joQLHVeRaNH0LSmObEuS7wVlOdEXEAxanHT1Bsnf4v/nPy8aLuQdJKYo011mCDDTZoeNXV1dGmTRs6duxI9+7dueyyy+jduzedOnVi6tSp/P73vweKA0Uuv/xyRo4c2XBdXV0d1157bcPcM2fO5MYbb3QbsSRJkiQtZ24lnt9VwBnAaY30vQV0aqR9fWBEVVsu5bgaU5/8a+xZfodGxEmZOXcRczQnzokUSdGmHAI8mZnH1zdExJebMa+klVj//v3n+9yrVy969eq1wLguXbqQufB/atq0acMHH1T//yOSJEmSpGXNisEKmTmL4uCQo1gwGfYk8PWIaNjnFhHbUWzdfYxFa04VYLNERB2wH8XW4a9UvX5Mkaz8ytJYC3iAItHYVOxtKaoUK/VeSmtLkiRJkiRpGTExuKDLKU4U/lJV+0Xl+70R8c2I6A3cDDwH/LUZ874E7BsRu0VEj8oE4xL4JsVpwJdk5sOVL2AQRZVfc7cTL8o5wFrAIxHx7Yj4akScEhFHlf33AbtGxM/LvouAPZbS2pIkSZIkSVpGTAxWyczpwG8aaX+XogpvJkWl3u+AR4GvVRz+sTCnAB9SHPjxNLDtxwizJ/DPzHyykThnAzcCB5YHlHwsmfkyxeEq7wFXArcABwPjyiGXAxcCJ1IkSrsAC+4nlCRJkiRJ0golFvXsJ2lZ6RE9cjjDWzoMSYvi/0xIkiRJ0kojIkZkZo/mjLViUJIkSZIkSapBnkqslrMtWDAoSZIkSZLUMqwYlCRJkiRJkmqQiUFJkiRJkiSpBpkYlCRJkiRJkmqQiUFJkiRJkiSpBpkYlCRJkiRJkmqQiUFJkiRJkiSpBpkYlCRJkiRJkmqQiUFJkiRJkiSpBpkYlCRJkiRJkmqQiUFJkiRJkiSpBpkYlCRJkiRJkmqQiUFJkiRJkiSpBpkYlCRJkiRJkmqQiUFJkiRJkiSpBpkYlCRJkiRJkmqQiUFJkiRJkiSpBpkYlCRJkiRJkmrQqi0dgGrYCCBaOgi1qGzpACRJkiRJql1WDEqSJEmSJEk1yMSgJEmSJEmSVINMDEpqUbNmzeLoo4+mS5cutG/fnm222Ya7774bgGHDhvG1r32NddZZh44dO3LIIYfw1ltvNVybmZx22mmsu+66rLvuupx66qlkFvuT33vvPXbaaSfWXXddOnTowI477sjjjz/eIvcoSZIkSdKKaKVJDEZEn4gYERFTI+L9iHgmIi5aRmttHxH9G2nvHxHvLYs1l0RE7BkRP1rMa7pFREbE6xGx2E/4i4iHI+Kmxb1OasqcOXPYZJNNGDp0KJMnT2bAgAEceuihjB07lvfff5++ffsyduxYxo0bR/v27TnyyCMbrr3iiiu49dZbefbZZxk1ahR33HEHl19+OQB1dXVcddVVvPvuu7z//vucdtpp7L///syZM6elblWSJEmSpBXKSpEYjIifAVcC9wIHAocDtwHfWEZLbg+cvYzmXpr2BBYrMQj0LN83AXZagjWPB362BNdJjWrXrh39+/ena9eurLLKKuy3335069aNESNGsPfee3PIIYew5pprssYaa3DCCSfMV/U3ePBgTj75ZDp37szGG2/MySefzNVXXw1AmzZt+OxnP8sqq6xCZtKqVSvef/99Jk2a1EJ3KkmSJEnSimVlOZX4BODyzDy9om1IRJzTUgGtxHoCw4DPlz8/tjgXZ+YLyyIoqd6ECRMYM2YM3bt3X6DvkUcema/9+eef5wtf+ELD5y984Qs8//zz812z9dZb89JLLzF79my+973v0alTp2UXvCRJkiRJK5GVomIQ6AC8Xd2Y9Q8TK0XEehExOCImRsT0cttrj6oxGREnVLU1bBGOiD7AbyvGZkQ8XDV+m4gYVq7xTETsUh1bRHwvIp6PiFkRMS4iTq3q3zEibo+INyPiw4gYGRG9q8Z0iIgryzEzy+2//1sfM3Ay0KUizqsX9iVGRHdgK+APwO3AIRGxavWYiLgnIiaVcb0YEf0q+ufbShwRW0TE9RHx7/L7eD4ifhQRK8ufLa1AZs+eTe/evTniiCPYYost5usbNWoU5557LhdccEFD27Rp01hrrbUaPq+11lpMmzaNyn8aRo0axZQpU7juuuvYeeedl/1NSJIkSZK0klhZKgb/AfwgIl4H7sjMiU2MuxXYDPgJ8B5wCvBQRGyTma80c607gQspkm47lm1TKvrXAAYDv6FIVp4N3BIRm2bmdICIOAU4DzgfeBjYFhgQEdMzc1A5TxfgceAyYCbFtt4/RMS8zPxzOeYi4EvASeVamwC7ln1XAp8BdgcOKNveXcS99QLmAH8F3qKoGNyDYot2vduBl4DvArOAzwJrLmTOjYGXgWuBqcAXgXOAtsAvFhGP1GDevHkcdthhrL766gwaNGi+vldeeYW9996bSy65hF12+U8evq6ujilT/vPXc8qUKdTV1VH9+Mw2bdrQs2dPttxyS774xS/OV2UoSZIkSVKtWlkSg/0okn5XAxkRL1Ikt36dmVMAImIviuTabpk5tGx7EBhLkSA8tjkLZea7ETG2/HlYI0PaAj/KzAfLNd4CnqFI2N0TEWtSJAsHZmb9Vuf7ImIN4IyI+J/MnJuZ19dPWB4C8gjQGTgGqE8Mbg/8LjNvqFj/T2Vs48u1ZzURZ2O+A9yXmRMj4l7gfYrk4L1lHOsBnwK+lZnPldc8sLAJM/OB+jHlfTxGkTw9hkYSgxHRF+gLsCmbNjNsfdJlJkcffTQTJkzgrrvuYrXVVmvoGzduHF/96lf35O8gAAAgAElEQVQ588wzOeyww+a7rnv37jz77LNsv/32ADz77LONbkGuN3v2bF577TUTg5IkSZIksZJsJc7MUcCWFIeN/B4I4ExgeETUlcO2B96tTwqW130I3AEszf2DsymqAOvVP3Ovc/m+I9AO+EtErFr/Ah4E1q8fFxFrR8SlETGunHM2RcJs84q5RwKnRMTxEVHZvtgiYgeKpN/1AJn5EXAzcEBEtCmHTQL+DVwWEd+OiEU+jC0i2kTEORHxCkWF4Wzgv4Fu1duUy3WvyMwemdmjIx0/zi3pE+S4447jxRdfZMiQIbRt27ah/Y033mD33XenX79+fP/731/gusMPP5yLLrqIN954gzfffJMLL7yQPn36ADBs2DAee+wxPvroI2bMmMGvfvUrJkyYwA477LC8bkuSJEmSpBXaSpEYBMjMWZk5JDNPyMzPAd+j2Ep7dDlkQ2BCI5dOANZZiqFMycx5FXF9VP5Yn1xbr3x/nv8k/GYDD5Xtm5TvVwPfBi6gOF14O+CqinmgOHTlVuAs4OWI+GdEfGcJ4+5ZxvFw+ezCDhTbptcE9invZV4Zy9tlLG9HxKMRsc1C5v0VxdbtK8p5tgMGln1tmrpIqjdu3Dguv/xyRo4cyQYbbEBdXR11dXVce+21XHnllbz22mucc845De11dXUN1x577LHsv//+fP7zn2errbZi33335dhji+LgWbNm0a9fP9Zdd1023nhj7rrrLu6880422mijlrpVSZIkSZJWKCvLVuIFZOb/RcT5QP0JBW8BjVW4rU9RCVdvFrB61ZilmTisX2s/Gk9UvlxW6O0LnJCZl9V3VB/YkZkfAD8EfhgRWwOnAtdGxKjFOR24nPdQYDVgXCNDelJUD5KZLwEHRcRqwC4Uib87I6JzZUK0wiHAbzPz/Ir19m1ubFKXLl3mOyyk2tlnn91kX0Rw/vnnc/755y/Q9+Uvf5lnn312qcQoSZIkSdIn0UpRMdjYltaI6AisxX+Sb08CnSJi14oxa1Ak4B6ruHQ8xbbk+jGrUBzgUemjsm9JKt6eAGYAG2Xm8EZeU4HWQCuKJGV9HO0ptko3qtxOfQrF76w+GfoRzavK242iovI04CtVr2uA/cr1K9ebXT5H8aLy2g5NzN226j5aUTzLUJIkSZIkSSuwlaVi8LmIuA34G/AOxYm+PwGmU5wQTGbeGxGPAzdExE+BieWYthTbdevdAvSLiGeA1yi2JFefuvtS+X5ieYDJlMx8uTmBZuYHEdEfuCQiulAcKrIKxbMDv5KZB2Tm5Ih4GjgrIqYA84CfApMrY4mIx8p4RwNJcaDHh8BTFXGuHxF9yjHvZebYRsLqCXwAXJKZsyo7yvUPB74VEc8CvwZuKL+btSmSic9m5iQadx/F9/kKRbVkP4rEpyRJkiRJklZgK0ti8Fzgm8ClFNt+3wb+Dnw7M/9VMe4A4ELgYopKuqeA3TPzlYox51BsOR5IUXE3iCKpdkLFmEcpkoknUpys+whF1V2zZOb5EfEmcBJwMjATGEORcKvXi+K5fNdQJDEHUZzmWxnHE0AfoCswl+L0470zc3zZfyNF1d/5QEeKJGmfyljKLcEHAX+pTgqWsf4jIl4o47mXogLz58BGFMnEhyiSg035AXAZ8DuKSsnBFMnMKxZyjSRJkiRJklpYLOzZXtKy1CN65HCGt3QYakn+8yNJkiRJ0lIVESMys0dzxq4UzxiUJEmSJEmStHStLFuJ9Um0LVgwKEmSJEmS1DKsGJQkSZIkSZJqkIlBSZIkSZIkqQaZGJQkSZIkSZJqkIlBSZIkSZIkqQaZGJQkSZIkSZJqkIlBSZIkSZIkqQaZGJQkSZIkSZJqkIlBSZIkSZIkqQaZGJQkSZIkSZJqkIlBSZIkSZIkqQaZGJQkSZIkSZJqkIlBSZIkSZIkqQaZGJQkSZIkSZJqkIlBSZIkSZIkqQaZGJQkSZIkSZJqkIlBSZIkSZIkqQaZGJQkSZIkSZJq0KotHYBq2AggWjoILRXZ0gFIkiRJkqTFZcWgJEmSJEmSVINMDEqSJEmSJEk1yMSgpKVi0KBB9OjRg9atW9OnT5/5+q688ko222wz6urq2GuvvXjzzTfn6//HP/7BrrvuSl1dHeuvvz6XXHLJAvMPHTqUiOCMM85YlrchSZIkSVLNWKqJwYh4OCJuaqJveERcXfF5tYj4cUSMjojpEfFeRDwZET9txjpXR0RGxH2N9LWNiKllf5+Pcz8LWXv40p63ibWy4jUvIt6MiBsiottyWHu+35e0KBtttBFnnHEGRx111HztQ4cO5fTTT+e2225j0qRJdOvWjZ49ezb0v/fee+y1114ce+yxTJw4kVdeeYU999xzvjlmz57NiSeeyA477LBc7kWSJEmSpFrQkoePDAJ6A/8NPAl0AP4L2B/4ZTOunwZ8JSLWz8wJFe37Le1AqwwA2i7jNSpdCNxEcUxHN+Ac4M6I2Doz5yzHOKSFOvDAAwEYPnw448ePb2gfMmQIhxxyCN27dwfgzDPPZOONN+bVV1/l05/+NBdddBFf//rX6d27NwCtW7dmyy23nG/uCy+8kD333JN33nlnOd2NJEmSJEmffC2ylTgi1gCOBM7JzF9k5oOZeXNmngrs3MxpXgZeBQ6pav8OcPvSi3Z+mflqZo5eVvM3YmxmDsvMJzLzOuBEYEtg848zaRTaLJUIF77OMl9DK7bMJDPn+wwwenTx12jYsGGss846fOlLX6JTp07sv//+vP766w3jx40bx1VXXcVZZ521fAOXJEmSJOkTrqWeMdgOWA14u7ojKzMIi3YDRSIQgIhoD+wDXN/Y4Ij4ZrlFdmZEvB0R50fEamXfahHxTLkdOiqu+W25zXmD8vMCW4kjoktE/LkcNz0iRkVEr4r+9SJicERMLPsfjogei3GflaaW76tVxXBCRPwzImZFxCsRcVJVf/8yvp0j4mlgJmVSNSK2iojHy+/lxYj4RhPf384RMbS8h4kR8b/ld17f36fc9rx9eY8zgFOW8D71CbHPPvtw4403MmrUKGbMmMG5555LRDB9+nQAxo8fz+DBg7nkkkt4/fXXF9hq/MMf/pABAwZQV1fXUrcgSZIkSdInUoskBjPzXeDfQP+IOLAyubSY/gx8KSI2LT8fALwPDK0eGBGHAjcDTwHfoNiS2xf4RRnTbOBwYEeKqjwi4itAP6BfZi6QxCzHdAKeALYDfkKxFfr/gE0qht0KfL3s/zbF9/5QRGzWjHtcJSJWLROXm5dx/xNoqFqMiGOA31JUSu4P/AW4sJHnNa4BDAauBPYCnoqItsC9QB3QCxgIXAxsWnlhROwEPECRzD0Y+BFFEvYPjcT8Z+COsv+OZtyjPsH22GMPzjnnHA466CC6dOlC165dad++PZ07dwagbdu2HHDAAWy33Xa0adOGs88+m7///e9MnjyZIUOGMHXqVL797W+38F1IkiRJkvTJ05LPGOxDUdn3V2BeRDxTfr40Mz9qzgSZ+WJEPEeRbLuAonrwRmBe5biyAvAC4JrMPL6ifRbwu4j4RWZOzMznIqI/cF5EPEaR9PpLZt6wkDBOAtYCts3Mt8q2ByrW2AvYCdgtM4eWbQ8CYymq6Y5dxG1eUr7qjQf2ycy55VyrAP2BqzPz5HLM3yJiLeBnEXFxZs4s29sCP87M2yriOx7oBOyQmePLtrHAY1Vx/BL4e2Z+u+LaN4AHImKrqu3Vl2bmgsfKFtf0pUjIsun8uUd9gvXr149+/foBMGbMGAYOHMhWW20FwNZbb01FkW7Dz5nJAw88wPDhw9lggw0AmDx5Mq1ateK5557jtttuQ5IkSZIkLbmW2kpMZj4IfBroCVwFrEuRvHuwTHYREa3KarlVI6KpJOb1wHciYh3gqzS+jXhzigq4G6vmexBoA2xVMfZ84FmKxFgb4PjqyarsDtxTkRSstj3wbn1SsLz3Dykq6ZrzPMULKKoRtwP2BUYBd0XExmV/Z2AjiirBSjcAawKfr2hL4O5G4htRnxQs43scaDjloXwm5I4s+P09BswGtq2a886mbiYzr8jMHpnZoyMdm75rrXTmzJnDzJkzmTt3LnPnzmXmzJkNbaNHjyYzef311+nbty8nnngia6+9NgBHHnkkt9xyCyNHjmT27NkMGDCAnXfemQ4dOjBgwADGjBnDyJEjGTlyJN/4xjc45phj+MMfGitUlSRJkiRJi2NpJwbnAK2a6GtV9jfIzKmZeX1mHgN8iuLE350otsNCcbjI7PpXRHRtZN7rgf8HnA68kZnDGhmzXvl+V+V8wL/K9oZtv2Ul3l+A1sANmTmxifupty7QVFIQYENgQiPtE4B1FjE3wOuZObx83QUcSJGwrH+G4IYV81XPT9Ua7zdSjbkBFUnACpVta1P8/n7P/N/fLIpnHW5SdW1j96tPuIEDB9K2bVt++ctf8qc//Ym2bdsycOBAZs6cSa9evairq2P77bdnxx13ZMCAAQ3X7b777px33nnsu+++dOrUiVdeeYXrrrsOgPbt27PBBhs0vNq2bUu7du1YZ53m/NWRJEmSJEkLs7S3Er8LdG2ib0MaT0ABxaEjEXEBcCawBXAbRYKwdcWwNxu57l8R8RRFouyCJqafVL73BZ5ppL8+QUj5vMKzy3Hfj4grM/O5puIGJvKf5Fxj3qLYqltt/Yq4mi0zZ0XEaxQnE9fPTyNrrF++V67R2MEub1N839Uq5/ugvLY/RXK1WvXvZXEOkNEnRP/+/enfv3+jfaNGjVrotccddxzHHXfcIte4+uqrlyAySZIkSZLUmKVdMfgosG3FNlcAImIHikTVo+Xn1SKiQyPXf6Z8nwCQmc9VVMsNX8izBy8EhgDXNNH/MvAG0LVqvvrXxDKuoNjW/BrF1tmngMH1Jxc34QHg6xGxfhP9TwKdImLX+oZya+6+LPgcv0WKiDYUW7D/XTaNp0jMHVI19FBgCrCwpCbA0xS/s84Va+xERWKw3Po8DPhsE9/fAglbSZIkSZIkrdiWdsXgNcCPgUciYiAwjqKy7Wzg7xSn30JxWMeYiBgMPARMBj4L/IwigXfL4iyamTdSHDrSVP+8iDgZ+GNErEnxnL2PKLYvfws4ODOnU5xAvAuwXVmZ14fieYM/p6iWa8xvKE4zfjQi/psiYbcl0C4zz8/MeyPiceCG8pTgiRSnE7el6QrHSl0j4r/KnztSPPNwLYqTj+vvrT9weURMBO4DvgwcB5xecfBIU/4AnAHcWc7TlmJL93tV406lOGhkHnATMJXiuY37Aj/PzDHNuBdJkiRJkiStIJZqYjAzp5WVcedRnGK7DkX13w0UyaP604KnUBzysQ/Qi+KQjDcoEocDM3Py0oyrjO2GiJhC8SzCo4C5FJWBdwAfRcRmwK+AczJzVHnNqxFxGnBxRNyemf9oZN53ywq784GLKbY+/xP4RcWwAyiqGi+meD7gU8DumflKM0I/uXxBkVR8DtgzM5+uiOF/I6I18CPgRIoqwpMz8zfN+F6mR8TXgcsontc4tlzvjKpxj5W/23OAP1I8c3AccA8+U1CSJEmSJGmlE5k+Dk4to0f0yOEMb+kwtDT4z4gkSZIkSSuEiBiRmT2aM3ZpP2NQkiRJkiRJ0kpgaT9jUGq+bcGCQUmSJEmSpJZhxaAkSZIkSZJUg0wMSpIkSZIkSTXIxKAkSZIkSZJUg0wMSpIkSZIkSTXIxKAkSZIkSZJUg0wMSpIkSZIkSTXIxKAkSZIkSZJUg0wMSpIkSZIkSTXIxKAkSZIkSZJUg0wMSpIkSZIkSTXIxKAkSZIkSZJUg0wMSpIkSZIkSTXIxKAkSZIkSZJUg0wMSpIkSZIkSTXIxKAkSZIkSZJUg0wMSpIkSZIkSTXIxKAkSZIkSZJUg1Zt6QBUw0YA0dJB1Ihs6QAkSZIkSdKKxopBSZIkSZIkqQaZGJQkSZIkSZJqkIlBqUYMGjSIHj160Lp1a/r06dPQPnbsWCKCurq6hteAAQMWuH7SpEl07NiRnXfeeb72K6+8ks0224y6ujr22msv3nzzzWV9K5IkSZIkaSloVmIwIvpExIiImBoR70fEMxFx0eIuFhFXR8TwxQ+z4fr9I+LxiPggIqZExPMRcVlE1C3mPA9HxE1LGseKJCK+EhEZEY820b9KRPwuIiaU4/ovZK6u5Zj9llnAajEbbbQRZ5xxBkcddVSj/R988AHTpk1j2rRpnHnmmQv0n3baaWy55ZbztQ0dOpTTTz+d2267jUmTJtGtWzd69uy5TOKXJEmSJElL1yITgxHxM+BK4F7gQOBw4DbgG8s2tAXi6AncDjwH9AQOBQYDuwAdlmcsK5j6LMxOEbFpI/0HAscDPwN2pPhdNuWtcsxjSzVCrRAOPPBAvvWtb7Huuusu9rVPPPEEo0eP5sgjj5yvfciQIRxyyCF0796d1VdfnTPPPJNHHnmEV199dWmFLUmSJEmSlpHmVAyeAFyemadn5n2ZOSQz+wOfWbahNRrHXZn5/cy8OzPvyczzM7M78MZyjqVJEdF2Oa61GnAQ8CDF+b7fbmTYFsD7mXlVZg7LzPFNzNUmM2eVYz5YdlFrRdWlSxc6d+7MkUceyXvvvdfQPnfuXPr168egQYOImP8Y6cwkM+f7DDB69OjlE7QkSZIkSVpizUkMdgDerm7MimxAROxWbkHdqnJMU1t2I+JbEfFSRMyMiMci4nNLGkcjsZwcEU9HxORy++yQiNissesioldEvFJuS747IjpX9f8yIp6LiGkRMT4iro2IDarGjI2ICyPizIgYD0wp23eMiNsj4s2I+DAiRkZE76pr+5Tf2+cj4r5y3EsRcWAzvg+ArwPrAL8CnuA/1YP18z8MDADWLtfJcrtw/brbl7+jGcApTW0ljohjyu9hZvmd3hQRazX3PrViW2+99Xj66acZN24cI0aMYOrUqfTu/Z9f4aWXXsoOO+zAtttuu8C1++yzDzfeeCOjRo1ixowZnHvuuUQE06dPX563IEmSJEmSlsCqzRjzD+AHEfE6cEdmTvyYa3YBLgLOBGYA5wD3RsRnMnPmIuLoGRHPADdnZlMnHHQGBgHjgDWB7wOPR8TmmTm5YtwOwEbAyUBb4BLgCmCfijGdgPOAN4GO5dgHI+LzmTm3Ylwv4HmKLbv132kX4HHgMmAmsBPwh4iYl5l/ror5unLtC4AfANdHxKeaqu6r0BN4l6Ji8LPApRGxRWa+VPYfD/wYOBjYq2x7q+L6PwP/Q/E7aLRKMCLOAM4Ffg+cAqwB7AvUAZMX8z61Aqqrq6NHjx4ArL/++gwaNIgNN9yQKVOmMG3aNC699FJGjBjR6LV77LEH55xzDgcddBCTJ0/mpJNOon379nTu3LnR8ZIkSZIkacXRnMRgP+BW4GogI+JF4K/ArzNzyhKsuR7wzcz8O0BEjABeBfpQJJeacjrweeC3wG8j4l9lXOdnZkMlYWaeVP9zRLQC7gPeAb4JXFMx35rAvpn5fjl2A+A3EdE2M2eUcx1VNdcTwHiK5NcjVfHtV5nYzMzrK66Ncnxn4BiKhFyl32TmVRXfxwRgv4V9HxGxBsVzHv+YmXMi4kbgN8B3gP5lDC+UVYxzMnNYxbX1P16amZdUtHetWqMDxfd+cWb+uKLr5iW8TyKiL9AXYFMaeySiWlr9n4/M5KmnnuKtt97ic58rinpnzJjBjBkz2GCDDXjjjTdo1aoV/fr1o1+/fgCMGTOGgQMHstVWWzU5vyRJkiRJWjEscitxZo4CtqRIQv2e4ll2ZwLDF/c04NI79UnBcv5xwAhge2g4RXfVileU4/4NbAt8FbgQmAScBIyq3AIcEf9VbsudCMwBplNUt21eFcfT9UnB0gvl+8YVc+0dEX+PiMnlXPUVfNVzPVBd7RgRa0fEpRExDphdvvo2ci3A3yq+j4kUicxFlVztX97X9eV1E4CHqdpOvAh3LqJ/R4pqyj80NWAx75PMvCIze2Rmj450XIxQ9XHNmTOHmTNnMnfuXObOncvMmTOZM2cOTz75JC+//DLz5s1j4sSJ/PCHP2S33XZjrbXWYu+992bs2LGMHDmSkSNHcu6557LNNtswcuRIWrVqxcyZMxk9ejSZyeuvv07fvn058cQTWXvttVv6diVJkiRJ0iI05xmD/H/27j1Mq6ru//j7K3IYDspDQKicNEORLk0bKA8/H+2gmJhKRpwUDaNMTaQeLQ84CvZYqVmZhpVnC8vygGdQUfTJFIQyNfE0JFaD4gEQOYjr98feM97czDAzMMOM3u/Xdd3XPXvttdf+7lv853OttVe+KcWMlNJJKaXdgOPJNh8Zvwn3XFJH23b531fyfsC0FhhXUMe6lNJ9KaXvppTKef8de98ByHflvZcsvPwG2cy+wfn4HYruWbx0dk3+3SEfazDZLsiLgaPJQrLPFPYpUFXLM11NthnIj4GD8jqurOXaumqprV+hUfl9n4yIrvnsvhnAgIjYq55rN1Z3oerta/+9kT5X0/DnVAuaOnUqZWVlXHDBBVx//fWUlZUxdepUXnzxRYYOHUqXLl34xCc+Qfv27fnd77LJnu3bt6dXr141n2233Za2bdvSq1f2qs1Vq1YxevRoOnfuzJAhQ9h7772ZMmVKSz6mJEmSJElqoIYsJd5ASuk3EfEjsh1vIXu3HEC7oq7dgNeK2nrWMmRPsnf0QbYM9tKCcy9tpI57I+KvBXUMJXsH3uEppbcBImLrvI7GOpLs/X1frd7cJCL61VVK4UFEdCB7D99JKaVfFrQ3KIitTx4CDgXak82cLDaK7J2M9Un1nK9+n+R2bPjfsdmfU02roqKCioqKWs+NGtWwiabHHnssxx57bM1x165d+dvf/tYE1UmSJEmSpC2t3gAnIjYI8iKiB7At7884q15iO7CgTx+yDTGK9YyIfQr69QX2Ah4DSClVppTmFnyWbqSODmRLbqvrKAPeI1v2W20EmxaAlgFrC3c8Bhq62257oA2wuqDWLmTLsZvC8Pwe44ADiz73AiOrl2Bvpj+TbRAzro7zzf2ckiRJkiRJaiYNCcyejIhbyQKnJWS70H6X7N191wCklBZHxOPAlIhYSRY4nkHts9leA66LiOpdic/Lx726njruiYh/kC2XfRnoBZwE/BcwLe9zP1lQdVVE/AYYlNda64679ZgJTIyIS/J77gOMbciFKaW38t9jckQsIwsrv0e2i+82m1BLsVHAP1JK1xafiIhuZJvD7AfM2ZybpJTejIgpwPkR0Q64kywMPBQ4N6X0SjM/pyRJkiRJkppJQ5Z8ngf0B35GFg5OIVv2OySlVLjMdzTwT+B64Af5dc/WMt4i4H/IlgxPB5YBBxdv3lGLH5EtE/4hMItsB963gP1SSn8GSCk9CRwHfBq4Pa/pK3m/Rkkp3QmcDnyZ7F2D/022U3BDjSZbBn0t8FOysG6DIK+xIuKjZDMDr6ujyx3AG/n9N1tK6X+BE8g2fbmVLITtCizPuzTLc0qSJEmSJKl5xforZaUtpzzK01zmtnQZpcH/zSVJkiRJKgkRMS/ftLdebhIhSZIkSZIklaBN2pVYahKfAicMSpIkSZIktQxnDEqSJEmSJEklyGBQkiRJkiRJKkEGg5IkSZIkSVIJMhiUJEmSJEmSSpDBoCRJkiRJklSCDAYlSZIkSZKkEmQwKEmSJEmSJJUgg0FJkiRJkiSpBBkMSpIkSZIkSSXIYFCSJEmSJEkqQQaDkiRJkiRJUgkyGJQkSZIkSZJKkMGgJEmSJEmSVIIMBiVJkiRJkqQSZDAoSZIkSZIklSCDQUmSJEmSJKkEGQxKkiRJkiRJJWjrli5AJWweEC1dRCuSWroASZIkSZJUSpwxKEmSJEmSJJUgg0FJkiRJkiSpBBkMSpIkSZIkSSXIYFBqRS699FLKy8tp3749xx57bE37mjVrOOqoo+jfvz8RwezZs9e7rqKigrZt29K5c+eaz4svvgjAwoULOfzww+nRowfdunXj4IMP5tlnn92CTyVJkiRJklqjD0UwGBEVEfFaHeeujoi5W7qmTRUR+0bEExGxKiLq3Y4iIo6LiBQR19VxvlNETI+IpXm/Yzcy1gF5n09sxiNoM2y//facddZZfO1rX9vg3H777cf1119Pr169ar32q1/9KitWrKj57LTTTgC8+eabfOlLX+LZZ5+lqqqKIUOGcPjhhzfrc0iSJEmSpNbPXYlbn2nAEuBgYHUD+o/Kv4+IiLKU0jtF508ADgOOAV4BXtjIWE8Ae9fTR81o+PDhAMydO5fFixfXtLdr146JEycC0KZNm0aNOWTIEIYMGVJzfOqppzJ16lSWLl3KRz7ykSaoWpIkSZIkfRB9KGYMbgkRUbaFbrUrcEtK6cGU0qP11NQT+CxwH9AZGFbHeM+mlP6YUno0pfRqLeNERHRIKS3L+xSHi/oAmDFjBt26dWPQoEFcfvnldfZ76KGH6NWrl6GgJEmSJEklriSDwYj4ZETcFxErI+KNiLghIj5acL5/vqR2TERcGxFvAjPyc5URcWFEnBoRi/Prp0dE1wbc97MR8Zd8mXBVRFwWEZ3zcwfkS4fbAD/N7391PUOOyPufRDYbcFThyYioBMYDe+bjpby9IiJei4j9IuJxYBXwldqWEkdEm4j4fkQsjIjV+TNfXXD+0IiYGRFLImJZRDwaEQfV91uoaY0YMYJnnnmGV199lV/96lecd955/O53v9ug3+LFiznxxBO5+OKLW6BKSZIkSZLUmnyogsGI2Lr4A0RRnx7AbKAjMBo4GfhvYGZEtCsa8kJgOfAV4AcF7SOAzwETgNPJZur9gI2IiN2Au4HXgC8D5+T3vynvUr2MF+Ci/O8p9TzyKGB+SukfwI3AFyNi24LzRwJ3Av/Ix9u74FxH4Brg18BQ4LE67jENOBf4ff6c3wE6FZzfkSw0PTp/rv8D7oqIfeupXU1ot912Y/vtt6dNmzbss+JyF8EAACAASURBVM8+nHLKKdx0003r9Xn11Vc56KCD+Na3vsWoUaPqGEmSJEmSJJWKD9M7Bj8CrK3j3LyCv7+Tfx+cUloGEBELgb+QBVuF06weTSmdWMt4a4EjUkrv5tfvBowEvrWR+iYDi4AvpZTW5de9DtwYEXunlP4MPBoRAJUNWEbcjyzo+17eNB2YRBYGXg2QUpofEa8CH61lvDJgUkrp1oIxtyu6x65kMw5PSSn9rODUjdV/pJQuLei/FfAAMCi/7pFa6p5AFqjSl74be0Rthoggpff3rnnjjTc46KCD+NKXvsSZZ57ZgpVJkiRJkqTW4sM0Y/AtYHAtn9uL+g0B7q0OBQFSSo8BlcB+RX3vqONeD1SHgrmngZ61zDgsvu/N1aFg7o/Au7XctyFG5t83AqSUHgeep2g58UYk4K56+hyYf19dV4eI6B0R10TEK2TPshY4CBhQ601TuiKlVJ5SKu9BjwaWWjreffddVq1axbp161i3bh2rVq3i3Xezf2qrV69m1apVAKxZs4ZVq1bVhH+33norb7zxBiklHnvsMX72s5/V7Dy8bNkyDj74YPbdd18uuOCClnkwSZIkSZLU6nyYgsF3U0pziz/A0qJ+2wFVtVxfBXSrpa02bxYdryFbsryxYHCD++Yh4dJa7tsQo8iWH78VEV3zdxzeBnwu35SkPm+klNbU0+cjwNuFIWqhfIbgbcA+ZDMiDyQLY+8COjTsMVRo6tSplJWVccEFF3D99ddTVlbG1KlTAdhll10oKyvjlVde4eCDD6asrIxFixYBMH36dHbeeWe6dOnCMcccw+mnn864ceMAuPnmm3n88ce56qqr6Ny5c83nn//8Z4s9pyRJkiRJankfpqXEDfVvoLbg7KOsv+QYsll1zXbfiGhDFr693piB8iW+e+SHb9TS5SvAL+oZpiHPthToFBHb1BEO7gzsCRySUrq7oL4ttYPzh05FRQUVFRW1nqusrKzzuto2Gqk2bty4mpBQkiRJkiSp2odpxmBD/QU4OCK6VDdExGCgP/BwM9/3yDwMrDacLJxt7H1HA+uAQ8lm6RV+/kbDlxPX5/78+5g6zlcHgKurG/J3H7rxiCRJkiRJUitXijMGLwZOAO6JiB8CnYELgCfJ3vnXXKYC84FbIuJyoDfwQ+CefOORxhgJzEwp3Vl8IiKuAS6MiH4ppUWbU3BK6dmIuAK4KF+e/BDQFTgqpTSSbLfjxfn5s4EuZDsYv7I595UkSZIkSVLzK7kZgymlV8lm1q0i24H4F8Ac4AsNeOfe5tz3KeAQsuXEfyILCn8HHNWYcSKiHPg4cF0dXX4HvMf7m5Nsrm+RhX1jgTuBS4B3AFJKq8lmPb4L3ARMAf4XeLCJ7i1JkiRJkqRmEtW7mkpbWnmUp7nMbekyWg//V5QkSZIkSZspIuallMob0rfkZgxKkiRJkiRJKs13DKq1+BQ4YVCSJEmSJKllOGNQkiRJkiRJKkEGg5IkSZIkSVIJMhiUJEmSJEmSSpDBoCRJkiRJklSCDAYlSZIkSZKkEmQwKEmSJEmSJJUgg0FJkiRJkiSpBBkMSpIkSZIkSSXIYFCSJEmSJEkqQQaDkiRJkiRJUgkyGJQkSZIkSZJKkMGgJEmSJEmSVIIMBiVJkiRJkqQSZDAoSZIkSZIklSCDQUmSJEmSJKkEGQxKkiRJkiRJJchgUJIkSZIkSSpBW7d0ASph84Bo6SKKpJYuQJIkSZIkactwxqAkSZIkSZJUggwGJUmSJEmSpBJkMCjVYvr06QwcOJBOnTrxsY99jDlz5rBmzRqOOuoo+vfvT0Qwe/bs9a558803GTduHD179qRnz55UVFS0SO2SJEmSJEkN0WqCwYgYHhH3R8SbEbE6IhZGxNSI6N4M9xoQERUR0bWJx62MiAsb2HfHiEgR8c+IaDVv2st/l9dauo6WNHPmTE4//XSuuuoqli9fzkMPPcROO+0EwH777cf1119Pr169Nrju1FNPZeXKlVRWVvLYY49x3XXXcdVVV23p8iVJkiRJkhqkVQSDEXER8AfgReBo4CDgJ8BhwK+a4ZYDgHOAJg0GG2lU/t0H2LcF6yj2a+Dgli6iJZ1zzjlMnjyZz3zmM2y11VbssMMO7LDDDrRr146JEyey33770aZNmw2umzFjBqeddhodO3akf//+jB8/niuvvLIFnkCSJEmSJKl+LR4MRsRhwCTg6yml41NKM1JKD6aULgf2Aq5o4fo6NNPQo4BHgbd5PyRsMRHRNiLapJQWp5TmtXQ9LWXdunXMnTuXV199lZ133pnevXtz0kkn8c477zTo+pTSen///e9/b65SJUmSJEmSNkuLB4PAqcATKaUNplallNallO6qPo6IDhHxo4h4OV9u/NeI+GLhNdXLeSPi1IhYHBFvRMT06mXDEXEAMCPv/lK+nLcyP3dsfjwkImZHxDvA/+TnLoiIJyNiRT7uDRGx4XrSBoiIQcAngKuA24CvRMTWRX2ujoi5EXFoRDwdESsj4o6I6BYRO0fEAxHxdt5n96Jrt4qI70XE8wXLsscV9ZkdETdFxISIeAFYBWxf21LiiPhIREyLiH9HxKqIeDYiJhac/05EPB4Rb0VEVUTMiIidN+W3aWlVVVWsXbuWm266iTlz5rBgwQLmz5/P1KlT67126NChXHDBBSxfvpznn3+eK6+8kpUrV26BqiVJkiRJkhqvRYPBiGgL7APc3cBLbgKOBX5Atsz4ceC2iPhkUb8RwOeACcDpwLD8GoAngO/mfw8H9gaOLLr+d8DtwBfzb4Ce+RiHAhOBnYD7I2LDNaX1Gw28C/wxv1ePvN5ifYHzgLPyZ9mHbAbl9PxzFLA1ML3oPYU/z6+5Iq/3ZuDKiBhWNP6+wAlkv9FhwFvFBUREGTAbOAKYQvabXARsX9CtN3ApcDjwdaAN8EhEbFvfD9HalJWVAXDyySez3Xbb0b17dyZNmsSdd95Z77U/+9nPKCsr4+Mf/ziHH344o0aNonfv3s1dsiRJkiRJ0ibZuv4uzeojQHvgn/V1jIjPkYVcB6SUHsyb742IAcCZwFcKuq8FjkgpvZtfuxswEvhWSmlZRDyb95ufUqqs5XY/Syn9tLAhpfS1glraAH8GFpOFaw/VV3+RkcDMlNLSiLgHeINsOfE9Rf26AXunlF7I77s72QzGcSmla/O2AO4AdgWeyWfqnQAcl1K6Jh9nVkRsR/ZexdsLxu8K7JlS+k/BsxXXegwwCNgrpbQgb7u/sENK6dSC69sAM4ElZEHhtYV9I2ICWchJX/rW9fu0mP/6r/+id+/etf0O9erWrRs33HBDzfEZZ5zBkCFDmrI8SZIkSZKkJtMalhIDpPq78HngP2Qz0bau/gD3AeVFfR+oDgVzTwM9I6JdA+u5o7ghIg6JiP+LiLfIZvstzk8NaOCY1eN8mmy24XSAlNIa4E/AkbW8z7CyOhTMPZ9/319L2w759+eA94Cba/mdPlk0w3FeYShYh8+SBagL6uoQEZ+JiJkRsZTst1kJdKaW3yaldEVKqTylVN6DHvXcumUcd9xx/PznP2fJkiW88cYbXHLJJQwblk22XL16NatWrQJgzZo1rFq1qua9gi+88AJLly5l3bp13HXXXVxxxRWcddZZLfYckiRJkiRJG9PSMwaXAquhQVPHugO9yGYDFltXdPxm0fEaIIB2+d/1qSo8iIjBZO8CvBm4gGw2XCLbPKSxm5OMInuG2dXvPSQLIseTLdP9U0Hf2p6juL26rbqO7mRLeTdYFpzbjvdDzao6+hT6CPDvuk5GRF/gXuAx4BvAv/Ka7qDxv02rcPbZZ/Paa68xYMAAOnTowIgRIzjzzDMB2GWXXVi0aBEABx+cbd780ksv0b9/f+bNm8fEiRN58803GTBgADfccAODBg1qseeQJEmSJEnamBYNBlNKayPiEeBgsnfibczrwCtk77prbsUzGI8EXgW+mvLpYRHRr7GDRsRWZO8/bAssqqXLKNYPBjfF62Sz9vYlmzlYbEnB3w2ZqbkU2NhGIkOBjsDhKaW3AfIZit0aVG0r1LZtWy677DIuu+yyDc5VVlbWed2IESMYMWJEM1YmSZIkSZLUdFp6xiDAJWQbiIwreCceUBOkHZRSuptsKex3gBUppX9s5j2LZ9nVpwxYWx0K5sZswn0PIJuxdzrZDLtCxwEjIqJLSmn5Joxd7X6yGYPbppRmbsY41e4j2zV595TS32o5X0YWQBYu3R5B6/i3JUmSJEmSpDq0eHiTUpoRERcDv4mIfYFbgRVkm2l8E6gk27V4JtnmHDMj4ofAU8A2wCeBDiml7zfittWbj3wjIqYDK1NKT26k/0xgYkRcAswg2x14bCPuV20U2TLgn6aUVheeiIhlZBt9HAFctwljA5BSejYifkm2U/GPgLlkAeggYEBK6fhGDnktcCLZRi8VZL/djvlY3+P9IPKqiPhNfp/vsuEyaEmSJEmSJLUirWLzkZTSd4CvAh8HfksWxH2HbLbaCXmfBAwHrgQmkoWE04C9gYcbeb9FZOHVcOARsrBvY/3vJJvl92Wydw3+NzCsMfeMiLb59X8oDgXzezxBtknK6MaMW4cTgSlkQeOdwNVkOzo3dvdkUkqryDYgmQGcB9wFnEb2LkHyQPU44NNkOx6PJtshuq53HEqSJEmSJKkViPVXx0pbTnmUp7nMbeky1uf/DpIkSZIk6QMsIuallMob0rdVzBiUJEmSJEmStGUZDKrlfIpshl5r+kiSJEmSJJUIg0FJkiRJkiSpBBkMSpIkSZIkSSXIYFCSJEmSJEkqQQaDkiRJkiRJUgkyGJQkSZIkSZJKkMGgJEmSJEmSVIIMBiVJkiRJkqQSZDAoSZIkSZIklSCDQUmSJEmSJKkEGQxKkiRJkiRJJchgUJIkSZIkSSpBBoOSJEmSJElSCTIYlCRJkiRJkkqQwaAkSZIkSZJUggwGJUmSJEmSpBJkMChJkiRJkiSVIINBSZIkSZIkqQRt3dIFqITNA6IF7pta4J6SJEmSJEmtjDMGJUmSJEmSpBJkMChJkiRJkiSVIINBlbTnnnuODh06MHbsWABSSpx//vn07duXbbbZhpEjR7Js2bKa/q+88gqHH3443bp1o3fv3vzyl79sqdIlSZIkSZI2i8HgZoiI4RFxf0S8GRGrI2JhREyNiO5NMPbVETG34PjYiEgR0bme62ZHxE2be/9SceKJJzJ48OCa42uvvZbrrruORx55hH/961+88847nHzyyTXnx44dy4477khVVRV33HEHZ5xxBg888EBLlC5JkiRJkrRZDAY3UURcBPwBeBE4GjgI+AlwGPCrJrjFFODYJhhHdZg+fTpdu3blc5/7XE3bjBkzGD9+PH369KFz586cfvrp3HjjjaxcuZIVK1Ywe/ZszjzzTNq2bcsee+zBUUcdxZVXXtmCTyFJkiRJkrRpDAY3QUQcBkwCvp5SOj6lNCOl9GBK6XJgL+CKzRi7DCCl9EJK6e9NU7GKLVu2jMmTJ3PRRRet155SIqW03vHq1at57rnnatqLz//97/5nkiRJkiRJHzwGg5vmVOCJlNIGU8VSSutSSncBRMQFEfFkRKyIiMURcUNE9CrsHxGVEXFRRJwdEYuBZXn7ekuJCwyMiDkR8U6+dPnI2gqMiAn52O9ExB0RsUPR+Q4R8aOIeDlfBv3XiPhiUZ9jIuLhiHg9It6IiAcioryoz9URMTcivhARf4uIt/NrBjXgd2wxZ599ds3MwEKHHHIIv/71r6msrOStt97ihz/8IQArV66kS5cu7LvvvkyZMoVVq1bxxBNP8Mc//pGVK1e2xCNIkiRJkiRtFoPBRoqItsA+wN0N6N4T+AFwKDAR2Am4PyLaFPUbDfw38C3gq/WMeSNwKzAceBL4Q0TsUdRnb+BkslmN44HdgVuK+txEtlT5B2TLnx8HbouITxb06Q9cC3wlr3Ex8FBE7FQ0Vl/gx8D5wKj8uX8fEVHPs7SIBQsWMGvWLE499dQNzn3ta19j1KhRHHDAAQwaNIgDDzwQgN69ewNwww038NJLL9GnTx9OOOEExowZU3NOkiRJkiTpg2Trli7gA+gjQHvgn/V1TCl9rfrvPAz8M1m4ti/wUFH3YSmlVQ24/69TShfmY94DPA18HxhZ0KcnsE9KaVHebxHwcEQMTSndHRGfIwsrD0gpPZhfc29EDADOJAsCSSmdV1D/VsBMYDAwFqg5B3QD9k0pPVfQ92ZgF+AfhcVHxARgAkBf+jbgcZve7NmzqayspG/f7P4rVqxg3bp1PP300zzxxBOce+65nHvuuQDce++97LDDDuywQzbhsl+/ftx+++01Y40ePZohQ4Zs+YeQJEmSJEnaTM4Y3HSpvg4RcUhE/F9EvAW8SxYKAgwo6npfA0NByAK3rICU3iObPVicTD1RHQrm/R4BlhT0+zzwH+CRiNi6+gPcB9QsFY6IgRFxc0RUAeuAtWRhX3H9ldWhYO7p/HuDqXQppStSSuUppfIe9GjgIzetCRMm8MILL7BgwQIWLFjAN7/5TQ499FDuueceXn/9dV544QVSSjz99NNMmjSJyZMns9VW2f8qzzzzDMuXL2fNmjVcf/313HvvvUyaNKlFnkOSJEmSJGlzOGOw8ZYCq2Hj090iYjBwG1mQdwFZMJeAR4EORd2rGnH/JbUcb1dPn+J+3YFeZEFfsXUAEdEFuDevbRKwCFgF/JoN63+z6HhN/l3cr1Xo2LEjHTt2rDnu3LkzHTp0oEePHixcuJDDDjuMl19+mR49enDKKacwYcKEmr733HMP559/PitXrmTPPffk7rvvpkePlgk4JUmSJEmSNofBYCOllNZGxCPAwcBZG+l6JPAq8NWUb2MbEf3qGrYRJfQkCycLj/9dS5/arqvu9zrwCnDERu6zN9mMvy+klGqWA0fEto2o9QOhoqKi5u8BAwbw7LPP1tl34sSJTJw4cQtUJUmSJEmS1LxcSrxpLgHKI2Jc8YmI2CoihgJlwNrqUDA3pgnuXbMLcf4uv8OBx4r67BURfQv67UsWDFb3u49sxuCKlNLc4k/epyz/Xl0wzj5kG5JIkiRJkiTpA84Zg5sgpTQjIi4GfpOHbrcCK4BdgW8ClcCvgIkRcQkwg2wn47FNcPvjI2IN8Hfg68DOZDsBF1oC3B4RFWTLeX9I9t7B6p2UZwL3ADMj4ofAU8A2wCeBDiml75MteV4B/CoifkQ2e7CCbKahJEmSJEmSPuAMBjdRSuk7EfF/wEnAb8lm2FWSvVfwwpTSfyLidOBksgDvz8AwYOFm3nok8BNgKtlmJl9NKc0v6vNnYBbZzMYewGzynYDz2lNEDAfOACaSvS/xdWAB8PO8T1VEfAW4kCz4fI4s9DxtM+uXJEmSJElSKxDrr3SVtpzyKE9zmVt/x6bmP3lJkiRJkvQhFRHzUkrlDenrOwYlSZIkSZKkEmQwqJbzKbLZe1v6I0mSJEmSJINBSZIkSZIkqRQZDEqSJEmSJEklyGBQkiRJkiRJKkEGg5IkSZIkSVIJMhiUJEmSJEmSSpDBoCRJkiRJklSCDAYlSZIkSZKkEmQwKEmSJEmSJJUgg0FJkiRJkiSpBBkMSpIkSZIkSSXIYFCSJEmSJEkqQQaDkiRJkiRJUgkyGJQkSZIkSZJKkMGgJEmSJEmSVIIMBiVJkiRJkqQSZDAoSZIkSZIklSCDQUmSJEmSJKkEbd3SBaiEzQNiC9wnbYF7SJIkSZIkfcA4Y1CSJEmSJEkqQQaDkiRJkiRJUgkyGFTJee655+jQoQNjx47d4Nxxxx1HRPD8888DsHr1asaPH0+/fv3o0qULe+65J3fdddeWLlmSJEmSJKnJGQy2QhFRERGpjs+GaZYa5cQTT2Tw4MEbtD/88MO88MIL67W9++679OnThwcffJC33nqLKVOmMGLECCorK7dQtZIkSZIkSc3DzUdar7eAobW0P7+lC/kwmT59Ol27dmWfffapmRUIWQB48sknc80117DHHnvUtHfq1ImKioqa42HDhrHjjjsyb948+vfvvwUrlyRJkiRJaloGg63XuymlR5tywIhoA7RJKa1pynE/KJYtW8bkyZO57777+M1vfrPeuZ/85Cfsv//+7L777hsdo6qqioULFzJo0KDmLFWSJEmSJKnZuZT4AygiDsiXFX+iqH12RNxUcHx1RMyNiCMi4ilgFfDp/NwnI+K+iFgZEW9ExA0R8dGCa/vn9xgdEddFxPKIWBIR59RSzyci4o68z/KI+ENE9Gq+X2DTnH322YwfP54+ffqs1/7yyy8zbdo0zjvvvI1ev3btWsaMGcO4cePYddddm7NUSZIkSZKkZueMwVYsIjb475NSereRw/QHfgScB1QBL0VED2A28AwwGugMXADMjIjyohmFPwZuB44C9gfOiYjXUkq/yGvcGXgEmAscDbQBpgAzImJISik1st5msWDBAmbNmsX8+fM3ODdx4kQmT57MtttuW+f17733HkcffTTt2rXj0ksvbc5SJUmSJEmStgiDwdbrI8Da4saI2HETxvl8SmlBwRgX5H8enFJalrctBP4CfBn4XcH1T6WUvpH/fU9E9ATOiIjLU0rvAecA/wEOqQ4UI+JvwD+ALwJ3FNU/AZgA0Je+jXyUTTd79mwqKyvp2ze754oVK1i3bh1PP/00L774Ig8//DCnnXZaTf+9996bn/70p4wePZqUEuPHj6eqqoo777yTtm3bbrG6JUmSJEmSmovBYOv1FvD5Wtr/RTYLsKFeKQwFc0OAe6tDQYCU0mMRUQnsx/rB4M1F1/4JOB7oDfwzr/Ea4L2CGY4vAZVAOUXBYErpCuAKgPIo32KzCSdMmMDIkSNrji+88EIqKyu5/PLLSSnx3nvv1ZzbbrvtmDFjRs0mJCeccALPPPMMs2bNoqysbEuVLEmSJEmS1KwMBluvd1NKc2s7ERGNGaeqlrbtgKfq6NutqG1JHcfbkQWD3YHT80+xPrW0tYiOHTvSsWPHmuPOnTvToUMHevToUWv/7t27U1ZWxqJFi5g2bRrt27enV6/3X5s4bdo0xowZ0+x1S5IkSZIkNReDwQ+mVfl3u6L2bsBrRW21zcr7N9CzlvaPAvOK2or7VR//O/9+nWxW4a9rGa+4llajoqKiznOFr0Xs168freQ1iZIkSZIkSU3KYPCDaXH+PRB4AiAi+gC7AAsbcP1fgBMioktKaXl+/WCyJcoPF/U9Eri84Hg4WShYXcN9wCeAea1loxFJkiRJkiTVz2Cw9do6Ij5TS/vLKaXFEfE4MCUiVgJbAWeQzd5riIuBE8g2E/kh7+9K/CTwx6K+gyJiWt6+PzAeOCXfeASgAngMuCMiriSbJbgD8AXg6pTS7AbWJEmSJEmSpC1oq5YuQHXaFvhzLZ/j8vOjyd7xdz3wA+A84NmGDJxSehU4kGxJ8u+AXwBzgC9U7yxc4DRgG7Jg8BvAFODSgrEWAp8BVpJtKnIXcC6wGni+Ec8rSZIkSZKkLShc/anaRER/st2FD0sp3d4c9yiP8jSXWvdXaVr+E5ckSZIkSSUiIuallMob0tcZg5IkSZIkSVIJMhhUy/kU2Wy+5v5IkiRJkiRpA24+olqllCqBaOk6JEmSJEmS1DycMShJkiRJkiSVIINBSZIkSZIkqQQZDEqSJEmSJEklyGBQkiRJkiRJKkEGg5IkSZIkSVIJMhiUJEmSJEmSSpDBoCRJkiRJklSCDAYlSZIkSZKkEmQwKEmSJEmSJJUgg0FJkiRJkiSpBBkMSpIkSZIkSSXIYFCSJEmSJEkqQQaDkiRJkiRJUgkyGJQkSZIkSZJKkMGgJEmSJEmSVIIMBiVJkiRJkqQStHVLF6ASNg+IZhw/NePYkiRJkiRJH3DOGJQkSZIkSZJKkMGgJEmSJEmSVIIMBiVJkiRJkqQSZDCYi4iKiEgR8Vwd55/Pz1cUtF0dEXMbMPZrhdfV0WdYPn7//Lh/fjysMc9Rx9iz87Fq+/Te3PEL7jMg/x27NtWYTem5556jQ4cOjB07tqbtt7/9Lf369aNTp04cccQRvP766+tdM336dAYOHEinTp342Mc+xpw5c7Z02ZIkSZIkSc3CYHB9q4AdI6K8sDEiBgP98vOFpgDHbpnSNtsDwN61fJY04T0GAOcArTIYPPHEExk8eHDN8VNPPcU3vvENrrvuOqqqqujYsSPf+ta3as7PnDmT008/nauuuorly5fz0EMPsdNOO7VE6ZIkSZIkSU3OXYnX9zbwBDASKJwJOBK4H/hUYeeU0gtbrrTN9npK6dGWLqKlTJ8+na5du7LPPvvw/PPPA3DDDTdw2GGHsf/++wMwZcoUBg4cyPLly+nSpQvnnHMOkydP5jOf+QwAO+ywQ4vVL0mSJEmS1NScMbih6cCIiAiA/HtE3r6e2pYSR8T+EfHXiFgVEfMiYp9arot8ye2SiFgeEdcC2zSkuIg4PiKeiojVEbEoIk7blIesZdztIuLKiHgxIt6JiIURMTUi2hX1+36+rHpVRFRFxN0R0SsiDgBm5N1eypcpVzZFbZtr2bJlTJ48mYsuumi99qeeeoo99tij5vhjH/sY7dq1Y+HChaxbt465c+fy6quvsvPOO9O7d29OOukk3nnnnS1dviRJkiRJUrMwGNzQn4CPAvvlx/8P6AHcXN+FEbE9cBfwOnAUMA24AehY1PXbwGTgirzfO8CPGjD+/wCXA7cAw/K/p0TESfVdm10eWxd92hSc757XPQkYCvwYOA74ecEAxwBnABcDBwMnAM8DnchmWn437zqcbJnykQ2oq9mdffbZjB8/nj59+qzXvmLFCrbddtv12rbddluWL19OVVUVa9eu5aabbmLOnDksWLCA+fPnM3Xq1C1ZuiRJkiRJUrNxKXGRlNKbEXE32fLhOfn33Xl7fZdPJHsP4aEppZUAEfE2cH11hzyMOx2YLoen2wAAIABJREFUllI6K2++JyJmAnWuVY2Ibcje3zc1pXRu3jwzIjoCZ0XE5SmldRupbTiwtqhtEdA/f+4neT/YIyIeIVtafWVEnJxSWgMMAe5NKV1WMMafCq55Nv9zfkqpso7nmABMAOhL342U2zQWLFjArFmzmD9//gbnOnfuzLJly9ZrW7ZsGV26dKGsrAyAk08+me222w6ASZMmMXXqVM4///xmr1uSJEmSJKm5GQzWbjpwSURMIpvR9+0GXjcEmFkdCub+VNSnD7AdcGtR+5+Az29k7L3JZub9ISIK/7vdD5wN9CYL+upyP1kgWWh19R/5kulTyEK7HYEOBf36ks0MXACMj4hzgTuAefWEkRtIKV1BNlOS8ihPjbl2U8yePZvKykr69s1CyBUrVrBu3Tqefvpphg4dyl//+teavi+++CKrV69mwIABdOnShd69e9OAMFiSJEmSJOkDyWCwdrcBvwbOJwvjZmy8e41ewN8KG1JK70TEiqI+sOFuwPXtDtw9/36qjvN92Hgw+EZKae5Gzk8ELgQuAB4E3gAGA7/g/ZDwSqALWXg4GVgaEZcDFY0NCLeUCRMmMHLkyJrjCy+8kMrKSi6//HKWLFnC3nvvzZw5c9hrr72YPHkyw4cPp0uXLgAcd9xx/PznP2fo0KG0bduWSy65hGHDhrXUo0iSJEmSJDUpg8FapJTejojbgVOBP6SU3m7gpf8BehY2REQZ0LmoD8X9ajku9nr+PQyoquX8s7W0NcZXyJ71zOqGiNitsENK6T3gJ8BPIqIPMIYsPH0F+OVm3r9ZdOzYkY4d33/FY+fOnenQoQM9evSgR48e/PKXv2TMmDEsXbqUz3/+81x11VU1fc8++2xee+01BgwYQIcOHRgxYgRnnnlmbbeRJEmSJEn6wDEYrNvlQHsaF3g9DnwtIjoWLCceXtTnZbJw8HDg7oL24n7F/ky2Scn2KaU7GlFTQ5VRsLQ4N6auzimll4ELIuI4oDpAXJN/d6j9qpZXUVGx3vHo0aMZPXp0rX3btm3LZZddxmWXXVbreUmSJEmSpA8yg8E6pJRmA7MbedklwInA7RFxMbA98H2yQK963HUR8SPgwoh4jWyDky8DA+up582IqAB+GhH9gIfIdpUeAByYUqpvB+BuEfGZWtqfSiktB2YC346IvwAvkIWCOxd2jIhpZDMXHwXeAg4EPs777y6snrX4jYiYDqzMNzWRJEmSJElSK2Mw2IRSSq9ExBeBnwF/BJ4BxrLhRiOXAN2Ab5K92+824DTghnrG/1FE/ItsifN3yHZAXgjc2IDyDiSbdVjs/wEPA+cBPYCpefufyDZdKXy/4p+BrwPfIJsV+Dzw9ZTSLXl9iyLiu/l1JwOLyXc9liRJkiRJUusSKTX7xrBSrcqjPM1lY/uhbCb/aUuSJEmSpBITEfNSSuUN6btVcxcjSZIkSZIkqfUxGJQkSZIkSZJKkMGgWs6nyJb7NtdHkiRJkiRJdTIYlCRJkiRJkkqQwaAkSZIkSZJUggwGJUmSJEmSpBJkMChJkiRJkiSVIINBSZIkSZIkqQQZDEqSJEmSJEklyGBQkiRJkiRJKkEGg5IkSZIkSVIJMhiUJEmSJEmSSpDBoCRJkiRJklSCDAYlSZIkSZKkEmQwKEmSJEmSJJUgg0FJkiRJkiSpBBkMSpIkSZIkSSXIYFCSJEmSJEkqQQaDkiRJkiRJUgkyGJQkSZIkSZJK0NYtXYBK2DwgNnOM1BSFSJIkSZIklR5nDEqSJEmSJEklyGBQkiRJkiRJKkEGg/rAW716NePHj6dfv3506dKFPffck7vuuqvm/O9//3sGDhxIly5d2G233bjlllvWu/7FF19k2LBhdOnShe7du3Paaadt6UeQJEmSJEna4gwGGygijo2IeRGxPCLeiIj5EXFxM91rSERU1NJeERGvbebYx0VEiojrNvH6FBEnbU4NTe3dd9+lT58+PPjgg7z11ltMmTKFESNGUFlZySuvvMLYsWO5+OKLWbZsGT/+8Y8ZPXo0S5YsAWDNmjV84Qtf4LOf/Sz/+c9/WLx4MWPHjm3hJ5IkSZIkSWp+BoMNEBHfB34N3AMMB44BbgW+1Ey3HAKc00xjj8q/j4iIsk24fm/gD01Yz2br1KkTFRUV9O/fn6222ophw4ax4447Mm/ePBYvXkzXrl055JBDiAgOPfRQOnXqxAsvvADA1Vdfzfbbb8+kSZPo1KkTHTp0YPfdd2/hJ5IkSZIkSWp+BoMNcxIwLaV0RkppZkppRkqpAvh4C9fVKBHRE/gscB/QGRjW2DFSSo+mlKqauramVFVVxcKFCxk0aBDl5eUMHDiQ2267jXXr1nHLLbfQvn37mvDv0UcfpX///hxyyCF0796dAw44gCeffLKFn0CSJEmSJKn5GQw2TFfgP8WNKaVUeBwR3SPimohYGhErI2J2RJQX9dlgKW7hEuGIOBb4eUHfFBGzi/rvGRGP5veYHxH/r4HPMQJoQxZ0vsL7swcLx/5SvmT67XzJ9F8i4r/rqj8iDo2ImRGxJCKW5XUd1MB6mtzatWsZM2YM48aNY9ddd6VNmzYcc8wxjB49mvbt2zN69GimTZtGp06dAFi8eDHTp0/n29/+Nv/617849NBDOfzww1mzZk1LPYIkSZIkSdIWYTDYME8AJ0fEuIj4yEb63QIcDHwX+CrZ7/tAROzciHvdAVyU/713/vlWwfmOwDXANODLwGrg5ojo2ICxRwHzU0r/AG4EvhgR21afjIiPATcB9wOHAWOA24FuGxlzR2AGcHRez/8Bd0XEvg2op0m99957HH300bRr145LL70UgFmzZnHaaacxe/Zs1qxZw4MPPsjxxx/PggULACgrK2O//fbjkEMOoV27dnz3u99l6dKlPPPMM1u6fEmSJEmSpC1q65Yu4APiRLLQ72ogRcQzwB+BC1NKywAiYiiwL3BASunBvO1+oBL4H+AbDblRSunViKjM/360li5lwMSU0v35Pf4NzAf2B+6ua9yI6EcWMn4vb5oOTAKOzJ8LYE9geUrpfwouvbOeei8tuMdWwAPAIGA88EgtdUwAJgD0pe/Ghm6UlBLjx4+nqqqKO++8k7Zt2wKwYMEC9t9/f8rLs4mbgwcP5tOf/jSzZs3ik5/8JLvvvjuPPLJBmZIkSZIkSR96zhhsgJTS34CBZJuNXAYEcDYwNyI6592GAK9Wh4L5dW+TzbjbrwnLWQvMLjh+Ov/uXc91I/PvG/PaHgeeZ/3lxE8C2+bLoQ+KiE71FRMRvfP+rwDv5vUdBAyorX9K6YqUUnlKqbwHPeobvsFOOOEEnnnmGWbMmEFZ2ft7qgwePJg5c+bUzBCcP38+c+bMqXnH4NixY3n00UeZNWsW69at45JLLqF79+4MHDiwyWqTJEmSJElqjZwx2EAppdVkS2ZnAETEeLKdiscDPwW2A2rblKOKjS/FbaxlKaX3CupaExEAHeq5bhTZkui3IqJr3nYbcEpE9EwpLUkpPRsRh5PNKrwTWBsRNwOnpJReLR4wnyF4G9AFmEwWNL4NnAf03JyHbIxFixYxbdo02rdvT69evWrap02bxpgxY6ioqOCoo46iqqqKHj16cMYZZ3DQQdlrEHfZZReuv/56vvnNb7JkyRL22msvbrvtNtq1a7elypckSZIkSWoRBoObKKX0m4j4EbBr3vRvag/DPgq8XnC8GihOnZoyONxAROwK7JEfvlFLl68AvwBIKd0B3JG/e/BQ4BKyzVBG1nLdzmTLjw9JKdUsY46Islr6Npt+/fpRtA/Mek466SROOumkOs8PHz6c4cOHN0dpkiRJkiRJrZZLiRsgIjYI/CKiB7At788S/AvQMyL2L+jTkSxce7jg0sVky5Kr+2wFfLZo+DX5ufpmATbUaGBdXsuBRZ+/UcvuxCmlt1JKvwVuBnarY9zqAHB1dUP+LsMtvvGIJEmSJEmSGscZgw3zZETcCtwLLAH6ke08vJJsh2BSSvdExCPAjRHxPWBp3qcM+HHBWDcDJ0bEfOBF4Hhgm6L7/SP/PiXfwGRZSunZzah/JDAzpbTBRiIRcQ1wYR7oDSXboORu4F/Ax8lmE15bx7j/IAs6L4qIs8mWFJ8LvLIZtUqSJEmSJGkLcMZgw5wH9Ad+RhYOTgGeAoaklF4q6HckMJNs+e0fyDYp+WxK6fmCPufm56aS7Qa8ALiy6H5zyMLEU8hmIk7b1MIjopws4Luuji6/A94jCw//BvQALiZ7zrOAXwGn13Zh/t7F4WSbjtxE9rv8L/Bgbf0lSZIkSZLUesTG3s0mNafyKE9zmbt5g/jPV5IkSZIkqUZEzEsplTekrzMGJUmSJEmSpBJkMKiW8ymyGX+b85EkSZIkSdImMRiUJEmSJEmSSpDBoCRJkiRJklSCDAYlSZIkSZKkEmQwKEmSJEmSJJUgg0FJkiRJkiSpBBkMSpIkSZIkSSXIYFCSJEmSJEkqQQaDkiRJkiRJUgkyGJQkSZIkSZJKkMGgJEmSJEmSVIIMBiVJkiRJkqQSZDAoSZIkSZIklSCDQUmSJEmSJKkEGQxKkiRJkiRJJchgUJIkSZIkSSpBBoOSJEmSJElSCTIYlCRJkiRJkkrQ1i1dgErYPCBqaU9buhBJkiRJkqT/396dh8lV1fkff38MmhADREgQHAR0EBhhHB2CrA4Iyr4jSxAG0B+I4II6OKJxWEdRQGFAGRgEURRQVARZA5iAOCAJOIBikCUoCMgaEmJWzu+PexsqRaW7Oul0p1Pv1/Pcp7rOPffcc6vPc+v2t8/SeewxKEmSJEmSJHUgA4OSJEmSJElSBzIwqKXW/fffzzbbbMNKK63EOuusw89+9jMAbr/9dj74wQ+y8sorM3r0aPbZZx+eeOKJAa6tJEmSJEnS4DLoAoNJDkkyOcn0JM8nuTvJN5bQud6b5PgW6ccneWYxyz40SUny/cUpp68lmZDk8oGux7x589h9993ZZZddeO655zjvvPM48MADeeCBB3j++ec5/PDDmTp1Ko8++igrrLAChx566EBXWZIkSZIkaVBJKYNnpYckxwInAV8HfgkMAzYCDiylrLMEzvcJ4KxSSprSjwc+UUoZtRhl3wB8EJgBrFpK+dvi1LWvJHknMLeU8sclfa4xGVMmMem1Owrcd999bLrppkyfPp2k+vi32247NtlkE0466aQFst91111stdVWTJ8+fUlXWZIkSZIkaamWZHIpZUw7eQfbqsSfAM4tpXyxIe2qJCcMVIUWRZJVgW2Am4BtgV2AHw9wnZYvpfytlPL7gaxHl1YB61IK991332vSb7nlFjbYYIP+qJYkSZIkSdIyY7ANJR4JPNmcWJqiSElGJbkoybNJZtbDY8c05Sl1j8DGtFeGCCc5BDirIW9JMqEp/3uS3F6f4+4k72vzOvYFhlAFOh8HxjZnSDI1yWlJvpDkiSTTkpyeyk5JflcPp74iyZuajl05yblJnkoyK8mvk2zS4vo/m+SMJE8D99bprxlKnORdSa5K8kKSGUl+k+SD9b43Jjk7yZT6c3gkybeSrNjmZ9HS+uuvz6qrrsqpp57K3LlzueGGG5g4cSIzZ85cIN8999zDiSeeyKmnnro4p5MkSZIkSeo4gy0weBfwySQHJ1mlm3xXANsD/wbsR3Wdv0zSm+HGVwOn1z9vVm9HNuwfDlwEnAvsDcwGfpZkeBtljwXuLqX8AbgM2CnJSi3y7Q+8FziUavj0Z4FvUA2n/jJwBLAV8NWuA5IMBW6kGqZ8DLAH8DRwY5LVmso/BlgdOAj4VKuKJlkfuK3OdwSwJ/Az4K0Nn8MQ4EvAjnW9tmExe0C+/vWv54orruDqq69mtdVW4/TTT2ffffdljTXWeCXPgw8+yI477siZZ57J+97XbkxWkiRJkiRJMPiGEh9FFfT7LlCS3A/8BDitlPIiQJIdgC2ArUspE+u0m4GpVIGwj7VzolLK00mm1j/f3iLL8sDRpZSb63M8AdwN/Atw3cLKTbIWVZDxC3XSpVQBvz3r62o0C9inlDIfuC7J7sAngXeUUh6py/sn4GCqoB3AgcCGwAZd8wQmuRGYAnyu/gy6PFlK2a/bDwKOA6YB72uYB3F8185SytPAxxuubzngEeBXSdYspfyp6foPBw4HWJM1uz3xu971LiZOnPjK+80335yDDz4YgEcffZQPfOADfPnLX+aggw7q4RIkSZIkSZLUbFD1GCyl3AP8A7Ab8G0gVD3UJiUZUWd7L/B0V1CwPu4l4BfAln1YnbnAhIb3XXPzrfHarAvYv369rK7bncCDtBhODEyog4JdHgSmdgUFG9JGJ3lD/f4DwGTgkSTL1YE6gIlA88STV/dQV6h6/13W3eIoSQ6qh1LPoPpcflXvWrc5bynlvFLKmFLKmNGM7vbE99xzD7NmzWLmzJmcdtppPPHEExxyyCE8/vjjbLPNNhx11FEcccQR3ZYhSZIkSZKk1gZVYBCglDK7lHJVKeUTpZR3Av8PeAfw0TrL6sBTLQ59Cli5D6vyYinl5YZ6zal/HNbDcWOphkRPSzIyyUjgSmDbelGSRi80vZ+zkLQAXYHBUcCmVAG6xu1QXh3+26XV59RsFeCJhe1MsifwPeB/gX3qc+9Z7+7ps+jW97//fVZffXVWXXVVbrrpJsaPH8/QoUM5//zzefjhhznhhBMYMWLEK5skSZIkSZLaN9iGEr9GKeU7Sb4OrF8nPQE0B9gA3gw81/B+Nq8G07r0ZeDwNer5+v6pfvt8iyz7AN9azNM8B0yiYXhvg9lN71+79O9rPUsVbF2YfYA7SimvzL+YZKs2yu3Rqaee2nJRkeOOO47jjjuuL04hSZIkSZLUsQZVj8EWPepIMhpYiVd7v90BrJrkXxryDAd25tUhrgCPUQ1L7srzOqphs43m1PsWq+dbgwOA+XVd3t+03UPr4cS9dROwDvCnUsqkpu3eRSxv324+g+V5bcDxw4twHkmSJEmSJPWjwdZj8N4kPwduAP4KrEW18vBMqhWCKaVcn+Q24LIkX6Dq8fZvVAGsxu5nPwOOSnI38DDVkOQVm873h/r10/UCJi+WUqYsRv33B8aXUq5p3pHkIuC0JGuVUh5djHN8j2ohkglJTqO6tlWo5l58spTyzV6WdwJwJ3BLktOpPs/3AM+WUi6gWojkW0m+RBWU3QnYdjHqL0mSJEmSpH4wqHoMAicCawP/RRUcPAn4HfDepgU59qQKWJ0B/JhqDr5tSikPNuQ5od53MtVqwL8FLmg6361UwcRPUwW9zl3UiicZQzUX4vcXkuUS4GVeXZxkkZRSZlH1QBxPdY03AGfW5/7NIpQ3hWrRlmeA86kCqh8CuoKX5wKnU31GP6UK1h6wONcgSZIkSZKkJS+ltDPNnNT3xmRMmcSk1+6wSUqSJEmSJC2SJJNLKWPayTvYegxKkiRJkiRJ6gMGBjVwNqLqHdi8SZIkSZIkaYkzMChJkiRJkiR1IAODkiRJkiRJUgcyMChJkiRJkiR1IAODkiRJkiRJUgcyMChJkiRJkiR1IAODkiRJkiRJUgcyMChJkiRJkiR1IAODkiRJkiRJUgcyMChJkiRJkiR1IAODkiRJkiRJUgcyMChJkiRJkiR1IAODkiRJkiRJUgcyMChJkiRJkiR1IAODkiRJkiRJUgcyMChJkiRJkiR1IAODkiRJkiRJUgcyMChJkiRJkiR1IAODGjiTgdSbJEmSJEmS+pWBQUmSJEmSJKkDGRiUJEmSJEmSOpCBQS1Vtt56a4YNG8aIESMYMWIE6623HgBXX301W265JSNHjmS11VbjsMMOY/r06QNcW0mSJEmSpMGr4wODSfZKcnOSF5LMTvJAkpOTjFoC51o3yfFJRvZxuVOTnNZm3rclKUn+lKTXs/slmZDk8t7Xsn1nn302M2bMYMaMGUyZMgWAadOmMW7cOP7yl79w//3389hjj3HMMccsyWpIkiRJkiQt0zo6MJjkdODHwMPAQcB2wDeBXYH/WQKnXBc4DujTwGAvja1f3wpssQjHHwkc23fVac8BBxzADjvswPDhw3nTm97EYYcdxm233dbf1ZAkSZIkSVpmdGxgMMmuwGeBw0op/6+UclUpZWIp5Rzgn4HzBrh+w5ZQ0WOB24GXeDVI2LZSyu9LKX/s81o1OPbYYxk1ahRbbLEFEyZMaJnnlltuYYMNNliS1ZAkSZIkSVqmdWxgEPgMcFcp5YLmHaWU+aWUa7veJxmW5OtJ/lwPN/6/JDs1HtM1nDfJZ5I8luT5JJd2DRtOsjVwVZ39kXo479R63yH1+/fWQ3X/BhxT7zslyb1JZtTl/iDJaotywUk2ADYELgSuBPZJslxzniTXJXkuyUtJ7k9yVMP+BYYSJ1m/vs4/J5mZ5HdJjk6ySG3ra1/7Gg8//DCPP/44hx9+OLvuuisPPfTQAnnGjx/PRRddxIknnrgop5AkSZIkSRIdGhhM8npgc+C6Ng+5HDgE+ArVMOM7gSuTvLsp377AtsDhwL8Du9THANwF/Fv9817AZsCeTcdfAvwC2Kl+BVi1LmNn4Gjg7cDNSYa0WfdGBwDzgJ/U5xpd17fRlcB84EBgN+AsYIVuyvw7YArVEOOdqIZgn0B1/b22ySabsMIKKzB06FAOPvhgtthiC6655ppX9t9+++0ccMABXH755ay77rqLcgpJkiRJkiQBy/WcZZm0CjAU+FNPGZNsSxWU27qUMrFOviHJusCXgH0ass8F9iilzKuPfSewP3BkKeXFJFPqfHeXUqa2ON1/lVLObEwopXykoS5DgP8FHqOaH/CWnurfZH9gfCnl2STXA89TDSe+vi5/FFXgcY9Syr31MTd1V2Ap5aauPPViJr8ChgOHAV9tzp/kcKrAKWuyZo8VTkIpBYC7776b3XbbjQsuuIBtt22OZ0qSJEmSJKk3OrLHYIPSRp4PAE8CtyVZrmujCoaNacr7y66gYO33wKpJ3tBmfa5uTkiyY5JfJ5lG1dvvsXpXr7rLJdmEKuh3KUApZQ7wU2DPhvkMnwP+DPx3kv2SrNpGucOSnJDkQWA2VXD0P4G3NQ9Trs97XillTCllzGhGL7DvhRde4Prrr2fWrFnMmzePH/zgB9xyyy1sv/323Hfffeywww6cddZZ7Lrrrr25dEmSJEmSJLXQqYHBZ6mCWD13WYNRwGpUAa/G7XiqlX0bvdD0fg4QoN3A4FONb5JsTDW09zGqVZM3Azatd/d2cZKxVPWekGRkPffh1cCKVEOAKaW8TLUy85PABcCTSW5N8p5uyv0a1RDp8+pyNgZOXpQ6zp07l3HjxjF69GhGjRrFWWedxRVXXMF6663H6aefztNPP81HP/pRRowYwYgRI1x8RJIkSZIkaTF05FDiUsrcJLcB2wPjesj+HPA4sMcSr9hrezDuCTwN7Ffq8bRJ1uptofVCIPsCrwcebZFlLFXvQUopfwD2rudhfB9V4O/qJGvUgcNm+wBnlVK+3nC+nXtbR4DRo0dz5513ttx34YUXcuGFFy5KsZIkSZIkSWqhIwODtTOoFhA5uJRyUeOOOpC2XSnlOqohw58DZtRBs8Uxp35ttyfd8sDcrqBg7cOLcN6tgdWpFgT5TdO+Q4F9k6xQSpnelVhKmUu1yMk3gB8CI6mCpK3qOLvrTT0P4v6LUEdJkiRJkiT1o44NDJZSrqqDXt9JsgXwc2AGsD5wBDCVatXi8VSLc4xP8jXgd1TDb98NDCulHNuL03YtPvKxJJcCMxsW+WhlPHB0kjOAq6hWUj6wF+frMpZqmPOZpZTZjTuSvAj8K7BHkv8DTgMuAx4G3kQVTPy/UkqroGBXHY+q5xh8DjiKamEXSZIkSZIkLcU6dY5BAEopnwP2A95B1StuPFXvwJuAj9d5CrAX1Zx7R1MFCc+lmu/vV70836NU8/HtBdxGFezrLv81VIG5vanmGtwK2KU356yHBO8N/Lg5KFif4y6qRVIOoJpb8Cmq1ZavBb4N3A/s1s0pPgncCnyL6jO6jxarEUuSJEmSJGnpkgVHqUr9Z0zGlElMqt7YDCVJkiRJkhZbksmllDHt5O3oHoOSJEmSJElSpzIwqIGzEVVPQXsLSpIkSZIk9TsDg5IkSZIkSVIHMjAoSZIkSZIkdSADg5IkSZIkSVIHMjAoSZIkSZIkdSADg5IkSZIkSVIHMjAoSZIkSZIkdSADg5IkSZIkSVIHMjAoSZIkSZIkdSADg5IkSZIkSVIHMjAoSZIkSZIkdSADg5IkSZIkSVIHMjAoSZIkSZIkdSADg5IkSZIkSVIHMjAoSZIkSZIkdSADg5IkSZIkSVIHMjAoSZIkSZIkdSADg5IkSZIkSVIHMjCogTMZyEBXQpIkSZIkqTMZGJQkSZIkSZI6kIFBSZIkSZIkqQMZGJQkSZIkSZI6kIHBJSDJ8UnKQrYD+/A8b6jP9e5eHndRXZePLsI5t66P3bC3x/bkwAMPZPXVV2fFFVdk3XXX5fzzzwdgzpw5fOhDH2LttdcmCRMmTOjrU0uSJEmSJHUcA4NLzjRgsxbbdX14jjcAxwFtBwaTDAP2qN+OXYRz3kV1HQ8twrHdOvbYY5k6dSovvvgiV155JePGjWPy5MkAbLnlllx88cWsttpqfX1aSZIkSZKkjrTcQFdgGTavlHL7QFeihZ2BFYGbgPcnWa2U8mS7B5dSXgSWyHVtsMEGr/ychCQ89NBDbLTRRhx99NEADBkyZEmcWpIkSZIkqePYY3AAJTklyb1JZiR5LMkPkqzWlGe3JJOTvJTk+SR3JNmq3j29fr2wYajy2j2cdizwOPAJqt//vi3qdWySB5PMSvJUkuu66tVqKHGSzyW5M8m0Ov9VSdZZlM/kyCOPZPjw4ay//vqsvvrq7LTTTotSjCRJkiRJknpgYHAJSrJc89aUZVXgK1S9+I4G3g7cnGRIffzfA5cDNwO7Ah8GfgHS6KqxAAAV4klEQVSsXB+/Tf16Mq8OVX6im/qsUJ/rR6WUP1ANCx7blOdfgS8C3wC2Bz4OPAi8sZtLXQM4G9gdOAwYAtyWZKVujmnp29/+NtOnT+fWW29lr732YujQob0tQpIkSZIkSW1wKPGSswowtzkxydtKKVMBSikfaUgfAvwv8BiwBXAL8B5geinlmIYirmn4+c769aE2hy3vCQwDLq3fXwp8va7TI3Xae4EbSinfbjjup90VWkr5TNN1jAf+ShUo/F5j3iSHA4cDrMmaLcsbMmTIK3MKnnPOOXzqU59q49IkSZIkSZLUG/YYXHKmARu32P7SlSHJjkl+nWQaMI8qKAiwbv16L7BSvYrwdkm667XXjrHAw6WU39TvLwUKsH9Dnt8COyU5Icl7u3ovdifJpknGJ3m2vo6ZwIiG63hFKeW8UsqYUsqY0Yzuttx58+bx0EN9vsaJJEmSJEmSMDC4JM0rpUxqsc0BSLIxcCVVMPAgqmHAm9bHDgMopUyh6nX3dqqegs8k+WGS7iNqLSQZBXwAuCrJyCQjqeYovBM4oCHrBVRDifcF7gCeSnLSwgKESdYEbgACfIyqt+PGVD0Gh7Vbv7/+9a9ceumlzJgxg/nz53P99ddzySWXsM021Wjp2bNnM2vWLADmzJnDrFmzKKX04hOQJEmSJElSI4cSD5w9gaeB/Uod4UqyVnOmUsrVwNX1fH07A2cAZ7FgL7927EP1+/50vS0gyYallPtKKS8D3wS+meStVPMa/ifVgiX/3aLcHYDhwO6llJfqspbj1XkQ25KEc845hyOOOIKXX36ZtdZaizPOOIPdd98dgPXWW49HH30UgO233x6ARx55hLXXXrs3p5EkSZIkSVLNwODAWR6YWxbs9vbhhWUupUwDflivSLxZnTynfm2nZ95Y4H7gyKb0ocBVVIHGcU3n/DNwSpJDgXd2cx0vUw0h7rIvvWxbo0ePZuLEiQvdP3Xq1N4UJ0mSJEmSpB4YGFxylkuyaYv0P5dSHqdaoOPoJGdQBeY2Bw5szJjkY1RBwOuo5iZ8B1XPv+8BlFLmJHkE2DfJfcAs4J6u4coN5awBbAkcW0qZ0FyhJNdRBQ7HJTkXeA64nWqexPfX5/33hVznzVSrEF+Y5DvABsC/AS8s/KORJEmSJEnSQHOOwSVnJapVhpu3QwFKKddQBdv2ppprcCtgl6Yy7gFGA9+gmsdvHPA/LBikOwIYBdxINV/gW1rUZX+qRUZ+sJC6Xgy8PckmdR3/BbiQal7DPYHDSilXtDqwlHJvfU2bAL+gmq9wH6qgoiRJkiRJkpZScQEHDZQxGVMmMakKWUqSJEmSJGmxJZlcShnTTl57DEqSJEmSJEkdyMCgBs5G2FtQkiRJkiRpgBgYlCRJkiRJkjqQgUFJkiRJkiSpAxkYlCRJkiRJkjqQgUFJkiRJkiSpAxkYlCRJkiRJkjqQgUFJkiRJkiSpAxkYlCRJkiRJkjqQgUFJkiRJkiSpAxkYlCRJkiRJkjqQgUFJkiRJkiSpAxkYlCRJkiRJkjqQgUFJkiRJkiSpAxkYlCRJkiRJkjqQgUFJkiRJkiSpAxkYlCRJkiRJkjqQgUFJkiRJkiSpAxkYlCRJkiRJkjqQgUFJkiRJkiSpAxkY1MCZPNAVkCRJkiRJ6lwGBiVJkiRJkqQOZGBQkiRJkiRJ6kAGBjXgzj77bMaMGcPQoUM55JBDBro6kiRJkiRJHWHQBQaTHJ+kJLm+xb7Lk0zow3N9PsnWLdJLkk8sRrlJ8khdzjqLVck+lGTruk4b9ud53/KWtzBu3Dg+8pGP9OdpJUmSJEmSOtqgCww22C7Jxkv4HJ8Htl4C5W4GrF3/vP8SKH9R3UVVt4f686R77bUXe+yxB6usskp/nlaSJEmSJKmjDdbA4HPAPcCXlkThSZZfEuU2GAu8BNxR/zyg6h6Mw0opL5ZSbi+l/G2g6yRJkiRJkqQla7AGBgvwFWC3JP/YXcYk705yU5KZSZ5P8oMkb27Yv3Y9fPbDSb6X5AXgqiRTgVWA4+r9pWlY8ZAkX0nydJK/JvlWkqE9VTzJEGAf4ErgAuCdSd7VlOeQ+nz/nGRCXfff1u/fmOTCJNOSPJzkNYHFJLsnmZRkVpInk3w9yesb9h+f5JkkWya5E5gF7NNqKHGSIUmOTfJAktlJHkvy3Yb9OycZX38GLya5Pcl2PX0OkiRJkiRJGliDNTAI8GPgAbrpNZhkNDABGA4cAHwS2AoYn+QNTdlPA6ZTBe2+AuwJTAO+QzW8djOqobZdPge8BTgQOBX4GPDpNuq9DfBm4FLgcmAuC+81eBFwCbA3kDr/d4C/AB+i6nH4vSRrNFzzvsBPgd8AuwEnAIcDX20qe3hd/vnADnX+Vs6ty/gRsEt93W9s2P824CrgoLqevwauTbLFwj8CSZIkSZIkDbTlBroCi6qU8nKSU4DvJPmPUsoDLbJ9rn7dvpTyIkCSB6gCantTBd263F5KOarx4CTzgMdKKbe3KHtqKeWQ+ufr60DYXsDXe6j6WOAF4LpSypwk44H9k3yxlFKa8p5WSrmorkuAq4EJpZQv1Wm/oQoQ7gqcU+c5FfheKeXIhuuYDXwryVdLKc/WycsDny2l/Lwh3+pN178+8FHg06WU/2rYdVnXD6WUsxvyvw74JbBBfdxtzRef5HCqQCVrsmb3n5QkSZIkSZKWmMHcYxDgYuBPwLEL2f9e4IauoCBAKeU3wFRgy6a8V/fy3Dc0vf89sEarjF3qocZ7Aj8rpcypky+hWohk0xaH3NTw84P1681dCaWUacDTwN/VSesCawI/SrJc11YfMwxoXG24ANd2V1/g/fXrd7u5pjWSXJTkcWAeVQ/I7eq6vEYp5bxSyphSypjRjAZg3rx5zJo1i/nz5zN//nxmzZrFvHnzeqiaJEmSJEmSFsegDgyWUuZR9dA7MMlaLbKsDjzVIv0pYOUWab3xQtP7OVTBt+7sCIwErkkyMslIqqHOs2k9nLjxHHNapDWfd1T9eg1VgK5re6ROf2vDcc83BCcXZhXgpcbAaqO6h+CVwObAf1AFEjemCjj29Fm84uSTT2b55ZfnlFNO4eKLL2b55Zfn5JNPbvdwSZIkSZIkLYJBO5S4wQXAOODfW+x7Ali1RfqbgclNac3DeJeEruDfj1vs2zfJZ0op8xej/Ofq18OBu1vsf6Th53au91ngjUlWXEhwcB3gPcCOpZTruhJ7u6rz8ccfz/HHH9+bQyRJkiRJkrSYBnWPQYBSymyqhUM+QtVDsNEdwPZJVuhKSLIx1dDdX7VRfDu9ANuSZATV4h2XUPWsa9w+SxWsfP9CC2jPFOBxYO1SyqQW27M9FdCka9jyvy5kf1cAcHZXQt1z04VHJEmSJEmSlnLLQo9BqFbO/SLVkNaJDenfAD5OtTjI14ARwCnAvcBP2ij3D8DOSa4DZgBTSinTF7GOu1OtBHxmKeWOxh1JbqNaXXkscOMilt+1IMvngO8nWZFqSO8c4O3AHsCHSikze1HelCTnAacnWRW4hWoo9IdKKftTfT6P1fu/DKxAtYLx44t6DZIkSZIkSeofg77HIEAd7Ppmi/SnqXrhzaLqqfct4Fbgg23MrwdwDPAS1cIkdwIbLUY1xwJ/bA4K1vWcC/wI2KteoGSRlVIuowpCvptqyPJPgSOBu3h1nsLeOJIq2Hcg1dyFZwB/q881m2ol5nnA5cBJwFdZMDgrSZIkSZKkpVBK6Y+p9aTXGpMxZVKZNNDVkCRJkiRJWmYkmVxKGdNO3mWix6AkSZIkSZKk3jEwqIGzOAOzJUmSJEmStFgMDEqSJEmSJEkdyMCgJEmSJEmS1IEMDEqSJEmSJEkdyMCgJEmSJEmS1IEMDEqSJEmSJEkdyMCgJEmSJEmS1IEMDEqSJEmSJEkdyMCgJEmSJEmS1IFSShnoOqhDJZkOTBnoemjQGwU8M9CV0KBnO1JfsB2pL9iOtLhsQ+oLtiP1BdvRwFmrlDK6nYzLLemaSN2YUkoZM9CV0OCWZJLtSIvLdqS+YDtSX7AdaXHZhtQXbEfqC7ajwcGhxJIkSZIkSVIHMjAoSZIkSZIkdSADgxpI5w10BbRMsB2pL9iO1BdsR+oLtiMtLtuQ+oLtSH3BdjQIuPiIJEmSJEmS1IHsMShJkiRJkiR1IAODkiRJkiRJUgcyMKh+l+SdSW5KMjPJX5KcmGTIQNdL/SvJPkmuTPJ4khlJJicZ25RnQpLSYhvWlO/vkvysLueZJGcnGd7inIcl+WOSWfX5tl3S16klK8khC2kjRzTkSZIvJvlzkr8luSXJu1uU1eO9qd2yNLh0c68pSTar80xtse/JFmXZjjpAknWSnJvk/5LMTzKhRZ5+v/f4jDW49NSOkqye5NR6/4z6939Rkrc05dt6IfevU1qcs8dnobT5XKWlQ5v3o37/DvN+NLi0cT9a2H2mJLm+IV+Pz+Z1PtvRUmS5ga6AOkuSNwE3Ar8Hdgf+HjidKkg9bgCrpv73WeAR4DPAM8BOwA+TjCqlnNWQ75fAF5uOnd31Q5LlgOuBOcB+wEjgG/XrgQ359gf+Gzge+BVwKPCLJBuXUu7r0yvTQNgG+FvD+4cbfv4C8GXgGOAPVG3vxiQbllKehF7dm3osS4PSkcCKTWknAu8B7mxI+yHQeH+a03iA7aijbED1vXU78IaF5OnXe4/PWINST+1oI2BP4HzgDuDNVM8xv65/9zOa8n+YBb//Hm/c2c6zULvPVVqqtHM/gn78DvN+NCj11I7uAjZrSlsTuAy4tkX+7p7NwXa0dCmluLn12wYcCzwPrNiQ9nlgZmOa27K/AaNapP0QeKTh/QTg8h7KGQvMB97WkLYv8DLwjoa0KcAFDe9fB9wLXDzQn4XbYrWjQ4ACjFjI/mHANOA/GtLeCDwNnNyQ1uO9qd2y3Ab/RvVA/BxwTkPaVOC0Ho6zHXXIBryu4efLgQlN+/v93uMz1uDb2mhHI4HlmtLWrb/3Dm5I27pO27CH8/X4LESbz1VuS8/WUzuq0/v1O8z70eDb2mlHLY75fH2/eEtD2iF082xuO1o6N4cSq7/tCFxfSnmxIe1SYHlgq4GpkgZCKeWZFsl3A6v2sqgdgTtLKY80pF1B9V/QHQCSvJ3qQfpHDed/GfhxfbyWXZtT9QRr/N2/BFzFgr/7du5N7ZalwW8H4E3AJb08znbUIervkO4MxL3HZ6xBpqd2VEp5oZQyryntAao/inv1vNSLZ6Een6u0dGnjftQu70cdbBHb0f7AxFLKX3p5nO1oKWNgUP1tfaquwq8opfyJ6gFn/QGpkZYmm1N1FW+0XT2nxMwk1yd5V9P+Vm1qDvAQr7aprtcF8gH3AysnGb34VdcAeyjJvCRTknysIX19qv9k/rEp//0seM9p597Ublka/PanGoJ3a1P6R5LMSTItyeVJ1mrabztSl4G49/iM1QHq56DhvPZ5CeDmem6wqUnGNc3D1e6zUDvPVRqc+vM7zPvRMi7JO6imXFnYP1EX9mwOtqOljnMMqr+9CXihRfrz9T51qHry692BjzQkTwQuAh4E1gK+BNya5J9KKVPrPO20qa7X5nzPN+x/enHqrwHzBNX8JL8BhlANgfrvJMNLKd+k+t3OKKXMbzrueWB4kjfUf/C0247aKUuDWD3B/q7AeaVUY1ZqP6ead+cx4B+A46juR/9YSplW57EdqctA3Ht8xlrGJXkdcCbVH9M3NOyaBpxC9c+MOcAuwAnAaODTdZ52n4VsR8um/v4Osx0t+8YCc4GfNKX39GwOtqOljoFBDYTSIi0LSVcHSLI21fyCPy+lfLcrvZRyXEO2W5PcSPVfo6Pr7ZWsrYptkd78Pt0cr0GglHI91STpXa5NMhQYl+TMrmwtDm31u2+nHbVblgavXYERNP0HvJTy6Ya3tyb5NfBbqsn7z2jM2qJM21FnGoh7j89Yy7avUk3+v1UpZW5XYinlbqrpWLrcmGQ28NkkJzVN39LOs5DtaBkzQN9htqNl2/7ADaWU5xoTe3o2bxiybDtaijiUWP3teaqJlJutROv/BmgZl2RlqpWs/kQPq92VaoWq24B/bkheWJsayatt6vmGtOY8YNtb1lwOrAysTfW7X6FpOBVUv/uZDX9YtXNvarcsDW77Aw+WUiZ1l6lUK3hOob37ke2o8wzEvcdnrGVYkiOpVu88uJRyRxuHXE7VCaRrCpZ2n4Xaea7SINcP32Hej5ZhSf6Jqudpu3MxNz6bg+1oqWNgUP3tDzTNB5DkrVSrEDXPeaJlXD1k7xdUK4DuXE86247G/xC1alNvAN7Oq22q67V5Lor1gedKKQ4jXjYVqt/9EGCdpn3Nc5a0c29qtywNUklWoproujeLjvR0P7IddaaBuPf4jLWMSrI3cBbw+VLKZb08vOse1e6zUDvPVVp2LKnvMO9Hy7b9gb9RDVHvjcb7ke1oKWJgUP3tWmD7JCs0pO1HdWOZODBV0kBIshzVSnjvAHYspfy1jWPeDGwBTG5IvhbYuGkC5d2AocB1AKWUh4EHgH0aynpd/f7axbsSLYX2Bp4BHgV+DbzIgr/7rjnkGn/37dyb2i1Lg9eeVPeOHgODSTYE1uO19yPbkWBg7j0+Yy2DkmwN/AA4u5RyWi8O3RuYB9wDvXoW6vG5SoNfP3yHeT9atu0HXFVKmdFm/sZnc7AdLX1KKW5u/bZRTRL6BDAe+ABwODADOHmg6+bW723hPKr/Gn0K2LRpG0o19OVq4BDg/cDBVP8Zeg5Ys6Gc1wP3UT3Y7EQ1we2TwMVN5xtLtfrVuLq871J9qWw40J+F22K1o58A/07Vy2sX4Pt1u/pkQ55jqVYvOwrYtm5XzwBvbsjT1r2pnbLcBu9G9Ufvb1uk70wVLPxwff/4ONWqxQ8DK9qOOm+jWhX2Q/X2v8DvGt4Pb/f33Jdtpt2y3Jaerad2RDVU7wWqueA2Z8Fnpb9vKOcc4ESqP6q3p1qgZD5wetP5enwWos3nKrelZ2ujHfX7d5j3o8G3tfO9VufblOpZe4+FlNPjs7ntaOnbBrwCbp23Ae8Ebq4fRJ4ATgKGDHS93Pq9HUytvyRabWsDfwdcU7eROcCz9RfN+i3KWgO4ov6ieBb4VuMXWEO+w6hWOJ4N3AVsO9Cfg9tit6OvUM2RM7O+p0wGDmrKE6oVrR+r89wKvKdFWT3em9oty23wbcAoqtX1vtBi37uAm6hW7JxL9Ufyd4G32I46c6u/pxb6Hdab33NfthmfsQbX1lM7ovrn6ML2f7ehnE9R9QycXj/j/I5qkbbXtThnj89CtPlc5bZ0bG20owH5DvN+NLi2dr7X6nxnUP3DYuhCyunx2dx2tPRtqT9sSZIkSZIkSR3EOQYlSZIkSZKkDmRgUJIkSZIkSepABgYlSZIkSZKkDmRgUJIkSZIkSepABgYlSZIkSZKkDmRgUJIkSZIkSepABgYlSZIkSZKkDmRgUJIkSZIkSepA/x9mf4cIDEP93AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x229df5390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "region_incident = Missing_Migrants.groupby(['Region of Incident'])['Total Dead and Missing'].sum().sort_values(ascending = False)\n",
    "\n",
    "ax = region_incident.plot(kind='barh', figsize=(20, 10) ,color=\"magenta\", fontsize=15);\n",
    "plt.ylabel('');\n",
    "ax.set_alpha(0.8)\n",
    "ax.set_title(\"Region of Incident\", fontsize=15)\n",
    "\n",
    "\n",
    "# set individual bar lables using above list\n",
    "for i in ax.patches:\n",
    "    # get_width pulls left or right; get_y pushes up or down\n",
    "    ax.text(i.get_width()+.1, i.get_y()+.31, \\\n",
    "            str(round((i.get_width()), 2)), fontsize=12, color='black')\n",
    "    \n",
    "\n",
    "# invert for largest on top \n",
    "ax.invert_yaxis()                                          "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is seen that most of the accidents is taken place in Mediterranean borders and it is reasonable if we think that the people want to migrate the European countries. However, it is also observed that migrants can survive during the road but they had to face the most serious conditions when they have reached the final point."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top Causes of Death"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first illustrate the top 10 cause of deaths in whole dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x229dbfe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Missing_Migrants['Cause of Death'].value_counts().head(10).plot(kind = 'pie', figsize = (8,8), autopct='%1.0f%%')\n",
    "plt.title('Top 10 Causes of Death', fontsize = 14)\n",
    "plt.ylabel('');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the Pie Chart, **Drowning and Sickness** are the most common reason behind missing and dead migrants life story. We can say that migrants choose to escape via seaway and most of the time it ended in a worse scenario. And, even if they can survive in the road, they have to face with the illness and try to rescue without any access to medicines.\n",
    "\n",
    "It is seen that the **Unknown** has a huge percentage, 26%, and it is quite sad because it indicates that tough circumstances that migrants faced, even their body didnt identified. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}