07-07-Project HR Complete.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project HR\n",
    "\n",
    "Predict attrition of your valuable employees\n",
    "\n",
    "[IBM HR Analytics Employee Attrition & Performance](https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
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       "      <td>...</td>\n",
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       "      <td>49</td>\n",
       "      <td>No</td>\n",
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       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
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       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
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       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1   49        No  Travel_Frequently        279  Research & Development   \n",
       "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3   33        No  Travel_Frequently       1392  Research & Development   \n",
       "4   27        No      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                 1          2  Life Sciences              1               1   \n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "           ...           RelationshipSatisfaction StandardHours  \\\n",
       "0          ...                                  1            80   \n",
       "1          ...                                  4            80   \n",
       "2          ...                                  2            80   \n",
       "3          ...                                  3            80   \n",
       "4          ...                                  4            80   \n",
       "\n",
       "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  \\\n",
       "0                 0                  8                      0               1   \n",
       "1                 1                 10                      3               3   \n",
       "2                 0                  7                      3               3   \n",
       "3                 0                  8                      3               3   \n",
       "4                 1                  6                      3               3   \n",
       "\n",
       "   YearsAtCompany YearsInCurrentRole  YearsSinceLastPromotion  \\\n",
       "0               6                  4                        0   \n",
       "1              10                  7                        1   \n",
       "2               0                  0                        0   \n",
       "3               8                  7                        3   \n",
       "4               2                  2                        2   \n",
       "\n",
       "   YearsWithCurrManager  \n",
       "0                     5  \n",
       "1                     7  \n",
       "2                     0  \n",
       "3                     0  \n",
       "4                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data//WA_Fn-UseC_-HR-Employee-Attrition.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1470, 35)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
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       "Name: EmployeeCount, Length: 1470, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pop('EmployeeNumber')\n",
    "df.pop('Over18')\n",
    "df.pop('StandardHours')\n",
    "df.pop('EmployeeCount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Attrition', 'BusinessTravel', 'DailyRate', 'Department',\n",
       "       'DistanceFromHome', 'Education', 'EducationField',\n",
       "       'EnvironmentSatisfaction', 'Gender', 'HourlyRate', 'JobInvolvement',\n",
       "       'JobLevel', 'JobRole', 'JobSatisfaction', 'MaritalStatus',\n",
       "       'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked', 'OverTime',\n",
       "       'PercentSalaryHike', 'PerformanceRating', 'RelationshipSatisfaction',\n",
       "       'StockOptionLevel', 'TotalWorkingYears', 'TrainingTimesLastYear',\n",
       "       'WorkLifeBalance', 'YearsAtCompany', 'YearsInCurrentRole',\n",
       "       'YearsSinceLastPromotion', 'YearsWithCurrManager'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1470, 31)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
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       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
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       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
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       "      <td>Male</td>\n",
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       "      <td>1373</td>\n",
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       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "      <td>8</td>\n",
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       "      <td>7</td>\n",
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       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1   49        No  Travel_Frequently        279  Research & Development   \n",
       "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3   33        No  Travel_Frequently       1392  Research & Development   \n",
       "4   27        No      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EnvironmentSatisfaction  \\\n",
       "0                 1          2  Life Sciences                        2   \n",
       "1                 8          1  Life Sciences                        3   \n",
       "2                 2          2          Other                        4   \n",
       "3                 3          4  Life Sciences                        4   \n",
       "4                 2          1        Medical                        1   \n",
       "\n",
       "   Gender         ...           PerformanceRating  RelationshipSatisfaction  \\\n",
       "0  Female         ...                           3                         1   \n",
       "1    Male         ...                           4                         4   \n",
       "2    Male         ...                           3                         2   \n",
       "3  Female         ...                           3                         3   \n",
       "4    Male         ...                           3                         4   \n",
       "\n",
       "   StockOptionLevel TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  \\\n",
       "0                 0                 8                      0               1   \n",
       "1                 1                10                      3               3   \n",
       "2                 0                 7                      3               3   \n",
       "3                 0                 8                      3               3   \n",
       "4                 1                 6                      3               3   \n",
       "\n",
       "   YearsAtCompany  YearsInCurrentRole  YearsSinceLastPromotion  \\\n",
       "0               6                   4                        0   \n",
       "1              10                   7                        1   \n",
       "2               0                   0                        0   \n",
       "3               8                   7                        3   \n",
       "4               2                   2                        2   \n",
       "\n",
       "  YearsWithCurrManager  \n",
       "0                    5  \n",
       "1                    7  \n",
       "2                    0  \n",
       "3                    0  \n",
       "4                    2  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Yes\n",
       "1        No\n",
       "2       Yes\n",
       "3        No\n",
       "4        No\n",
       "5        No\n",
       "6        No\n",
       "7        No\n",
       "8        No\n",
       "9        No\n",
       "10       No\n",
       "11       No\n",
       "12       No\n",
       "13       No\n",
       "14      Yes\n",
       "15       No\n",
       "16       No\n",
       "17       No\n",
       "18       No\n",
       "19       No\n",
       "20       No\n",
       "21      Yes\n",
       "22       No\n",
       "23       No\n",
       "24      Yes\n",
       "25       No\n",
       "26      Yes\n",
       "27       No\n",
       "28       No\n",
       "29       No\n",
       "       ... \n",
       "1440     No\n",
       "1441     No\n",
       "1442    Yes\n",
       "1443     No\n",
       "1444    Yes\n",
       "1445     No\n",
       "1446     No\n",
       "1447     No\n",
       "1448     No\n",
       "1449     No\n",
       "1450     No\n",
       "1451     No\n",
       "1452    Yes\n",
       "1453     No\n",
       "1454     No\n",
       "1455     No\n",
       "1456     No\n",
       "1457     No\n",
       "1458     No\n",
       "1459     No\n",
       "1460     No\n",
       "1461    Yes\n",
       "1462     No\n",
       "1463     No\n",
       "1464     No\n",
       "1465     No\n",
       "1466     No\n",
       "1467     No\n",
       "1468     No\n",
       "1469     No\n",
       "Name: Attrition, Length: 1470, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['Attrition']\n",
    "tmp = df['Attrition']\n",
    "X = df\n",
    "X.pop('Attrition')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Yes', 'No'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Age</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>...</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>94</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>92</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age     BusinessTravel  DailyRate              Department  \\\n",
       "0   41      Travel_Rarely       1102                   Sales   \n",
       "1   49  Travel_Frequently        279  Research & Development   \n",
       "2   37      Travel_Rarely       1373  Research & Development   \n",
       "3   33  Travel_Frequently       1392  Research & Development   \n",
       "4   27      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EnvironmentSatisfaction  \\\n",
       "0                 1          2  Life Sciences                        2   \n",
       "1                 8          1  Life Sciences                        3   \n",
       "2                 2          2          Other                        4   \n",
       "3                 3          4  Life Sciences                        4   \n",
       "4                 2          1        Medical                        1   \n",
       "\n",
       "   Gender  HourlyRate          ...           PerformanceRating  \\\n",
       "0  Female          94          ...                           3   \n",
       "1    Male          61          ...                           4   \n",
       "2    Male          92          ...                           3   \n",
       "3  Female          56          ...                           3   \n",
       "4    Male          40          ...                           3   \n",
       "\n",
       "   RelationshipSatisfaction StockOptionLevel  TotalWorkingYears  \\\n",
       "0                         1                0                  8   \n",
       "1                         4                1                 10   \n",
       "2                         2                0                  7   \n",
       "3                         3                0                  8   \n",
       "4                         4                1                  6   \n",
       "\n",
       "  TrainingTimesLastYear  WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                     0                1               6                   4   \n",
       "1                     3                3              10                   7   \n",
       "2                     3                3               0                   0   \n",
       "3                     3                3               8                   7   \n",
       "4                     3                3               2                   2   \n",
       "\n",
       "  YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                       0                     5  \n",
       "1                       1                     7  \n",
       "2                       0                     0  \n",
       "3                       3                     0  \n",
       "4                       2                     2  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelBinarizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = le.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1470, 1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tmp = le.fit_transform(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = pd.Series(list(tmp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0]    1233\n",
       "[1]     237\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0]    0.838776\n",
       "[1]    0.161224\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.value_counts() / tmp.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1470 entries, 0 to 1469\n",
      "Data columns (total 30 columns):\n",
      "Age                         1470 non-null int64\n",
      "BusinessTravel              1470 non-null object\n",
      "DailyRate                   1470 non-null int64\n",
      "Department                  1470 non-null object\n",
      "DistanceFromHome            1470 non-null int64\n",
      "Education                   1470 non-null int64\n",
      "EducationField              1470 non-null object\n",
      "EnvironmentSatisfaction     1470 non-null int64\n",
      "Gender                      1470 non-null object\n",
      "HourlyRate                  1470 non-null int64\n",
      "JobInvolvement              1470 non-null int64\n",
      "JobLevel                    1470 non-null int64\n",
      "JobRole                     1470 non-null object\n",
      "JobSatisfaction             1470 non-null int64\n",
      "MaritalStatus               1470 non-null object\n",
      "MonthlyIncome               1470 non-null int64\n",
      "MonthlyRate                 1470 non-null int64\n",
      "NumCompaniesWorked          1470 non-null int64\n",
      "OverTime                    1470 non-null object\n",
      "PercentSalaryHike           1470 non-null int64\n",
      "PerformanceRating           1470 non-null int64\n",
      "RelationshipSatisfaction    1470 non-null int64\n",
      "StockOptionLevel            1470 non-null int64\n",
      "TotalWorkingYears           1470 non-null int64\n",
      "TrainingTimesLastYear       1470 non-null int64\n",
      "WorkLifeBalance             1470 non-null int64\n",
      "YearsAtCompany              1470 non-null int64\n",
      "YearsInCurrentRole          1470 non-null int64\n",
      "YearsSinceLastPromotion     1470 non-null int64\n",
      "YearsWithCurrManager        1470 non-null int64\n",
      "dtypes: int64(23), object(7)\n",
      "memory usage: 344.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>Gender</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>OverTime</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Travel_Rarely</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>Travel_Rarely</td>\n",
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       "      <td>Single</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Travel_Frequently</td>\n",
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       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>Married</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>Single</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Female</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>Married</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>Single</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>Healthcare Representative</td>\n",
       "      <td>Married</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>Married</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>Single</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>Single</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>Manager</td>\n",
       "      <td>Married</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>Single</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Other</td>\n",
       "      <td>Female</td>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>Single</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>Research Director</td>\n",
       "      <td>Single</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>Single</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>Single</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Other</td>\n",
       "      <td>Female</td>\n",
       "      <td>Manager</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>Single</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>Married</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Female</td>\n",
       "      <td>Healthcare Representative</td>\n",
       "      <td>Married</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Female</td>\n",
       "      <td>Manager</td>\n",
       "      <td>Single</td>\n",
       "      <td>No</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1440</th>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1441</th>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>Healthcare Representative</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>Married</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1443</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>Manager</td>\n",
       "      <td>Married</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Technical Degree</td>\n",
       "      <td>Male</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>Married</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1445</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>Married</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1446</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Female</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>Married</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1447</th>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1448</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Technical Degree</td>\n",
       "      <td>Male</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>Single</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>Single</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>Married</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452</th>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1453</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Female</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>Married</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>Single</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>Single</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>Healthcare Representative</td>\n",
       "      <td>Married</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Female</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>Married</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>Married</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Other</td>\n",
       "      <td>Male</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>Married</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Female</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>Single</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1462</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Female</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>Married</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1463</th>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>Single</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1464</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Other</td>\n",
       "      <td>Female</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>Single</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1465</th>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>Married</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>Healthcare Representative</td>\n",
       "      <td>Married</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>Married</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1468</th>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>Married</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Male</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>Married</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1470 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         BusinessTravel              Department    EducationField  Gender  \\\n",
       "0         Travel_Rarely                   Sales     Life Sciences  Female   \n",
       "1     Travel_Frequently  Research & Development     Life Sciences    Male   \n",
       "2         Travel_Rarely  Research & Development             Other    Male   \n",
       "3     Travel_Frequently  Research & Development     Life Sciences  Female   \n",
       "4         Travel_Rarely  Research & Development           Medical    Male   \n",
       "5     Travel_Frequently  Research & Development     Life Sciences    Male   \n",
       "6         Travel_Rarely  Research & Development           Medical  Female   \n",
       "7         Travel_Rarely  Research & Development     Life Sciences    Male   \n",
       "8     Travel_Frequently  Research & Development     Life Sciences    Male   \n",
       "9         Travel_Rarely  Research & Development           Medical    Male   \n",
       "10        Travel_Rarely  Research & Development           Medical    Male   \n",
       "11        Travel_Rarely  Research & Development     Life Sciences  Female   \n",
       "12        Travel_Rarely  Research & Development     Life Sciences    Male   \n",
       "13        Travel_Rarely  Research & Development           Medical    Male   \n",
       "14        Travel_Rarely  Research & Development     Life Sciences    Male   \n",
       "15        Travel_Rarely  Research & Development     Life Sciences  Female   \n",
       "16        Travel_Rarely  Research & Development     Life Sciences    Male   \n",
       "17           Non-Travel  Research & Development           Medical    Male   \n",
       "18        Travel_Rarely                   Sales     Life Sciences  Female   \n",
       "19        Travel_Rarely  Research & Development     Life Sciences    Male   \n",
       "20           Non-Travel  Research & Development             Other  Female   \n",
       "21        Travel_Rarely                   Sales     Life Sciences    Male   \n",
       "22        Travel_Rarely  Research & Development     Life Sciences  Female   \n",
       "23        Travel_Rarely  Research & Development     Life Sciences    Male   \n",
       "24        Travel_Rarely  Research & Development           Medical    Male   \n",
       "25        Travel_Rarely  Research & Development             Other  Female   \n",
       "26    Travel_Frequently  Research & Development     Life Sciences  Female   \n",
       "27        Travel_Rarely                   Sales         Marketing    Male   \n",
       "28        Travel_Rarely  Research & Development           Medical  Female   \n",
       "29        Travel_Rarely                   Sales         Marketing  Female   \n",
       "...                 ...                     ...               ...     ...   \n",
       "1440  Travel_Frequently  Research & Development     Life Sciences  Female   \n",
       "1441         Non-Travel  Research & Development     Life Sciences    Male   \n",
       "1442      Travel_Rarely  Research & Development           Medical    Male   \n",
       "1443      Travel_Rarely  Research & Development     Life Sciences    Male   \n",
       "1444      Travel_Rarely  Research & Development  Technical Degree    Male   \n",
       "1445      Travel_Rarely  Research & Development     Life Sciences  Female   \n",
       "1446      Travel_Rarely                   Sales         Marketing  Female   \n",
       "1447         Non-Travel                   Sales         Marketing    Male   \n",
       "1448      Travel_Rarely                   Sales     Life Sciences    Male   \n",
       "1449      Travel_Rarely  Research & Development  Technical Degree    Male   \n",
       "1450      Travel_Rarely         Human Resources     Life Sciences  Female   \n",
       "1451      Travel_Rarely                   Sales     Life Sciences  Female   \n",
       "1452  Travel_Frequently                   Sales     Life Sciences    Male   \n",
       "1453      Travel_Rarely                   Sales         Marketing  Female   \n",
       "1454      Travel_Rarely                   Sales     Life Sciences  Female   \n",
       "1455      Travel_Rarely  Research & Development     Life Sciences    Male   \n",
       "1456  Travel_Frequently  Research & Development     Life Sciences    Male   \n",
       "1457      Travel_Rarely  Research & Development           Medical  Female   \n",
       "1458      Travel_Rarely  Research & Development     Life Sciences  Female   \n",
       "1459      Travel_Rarely  Research & Development             Other    Male   \n",
       "1460      Travel_Rarely  Research & Development           Medical  Female   \n",
       "1461      Travel_Rarely                   Sales         Marketing    Male   \n",
       "1462      Travel_Rarely                   Sales         Marketing  Female   \n",
       "1463         Non-Travel  Research & Development           Medical    Male   \n",
       "1464      Travel_Rarely                   Sales             Other  Female   \n",
       "1465  Travel_Frequently  Research & Development           Medical    Male   \n",
       "1466      Travel_Rarely  Research & Development           Medical    Male   \n",
       "1467      Travel_Rarely  Research & Development     Life Sciences    Male   \n",
       "1468  Travel_Frequently                   Sales           Medical    Male   \n",
       "1469      Travel_Rarely  Research & Development           Medical    Male   \n",
       "\n",
       "                        JobRole MaritalStatus OverTime  \n",
       "0               Sales Executive        Single      Yes  \n",
       "1            Research Scientist       Married       No  \n",
       "2         Laboratory Technician        Single      Yes  \n",
       "3            Research Scientist       Married      Yes  \n",
       "4         Laboratory Technician       Married       No  \n",
       "5         Laboratory Technician        Single       No  \n",
       "6         Laboratory Technician       Married      Yes  \n",
       "7         Laboratory Technician      Divorced       No  \n",
       "8        Manufacturing Director        Single       No  \n",
       "9     Healthcare Representative       Married       No  \n",
       "10        Laboratory Technician       Married       No  \n",
       "11        Laboratory Technician        Single      Yes  \n",
       "12           Research Scientist      Divorced       No  \n",
       "13        Laboratory Technician      Divorced       No  \n",
       "14        Laboratory Technician        Single      Yes  \n",
       "15       Manufacturing Director      Divorced       No  \n",
       "16           Research Scientist      Divorced      Yes  \n",
       "17        Laboratory Technician      Divorced      Yes  \n",
       "18                      Manager       Married       No  \n",
       "19           Research Scientist        Single      Yes  \n",
       "20       Manufacturing Director      Divorced       No  \n",
       "21         Sales Representative        Single       No  \n",
       "22            Research Director        Single       No  \n",
       "23           Research Scientist        Single       No  \n",
       "24           Research Scientist        Single       No  \n",
       "25                      Manager      Divorced       No  \n",
       "26           Research Scientist        Single      Yes  \n",
       "27              Sales Executive       Married       No  \n",
       "28    Healthcare Representative       Married       No  \n",
       "29                      Manager        Single       No  \n",
       "...                         ...           ...      ...  \n",
       "1440     Manufacturing Director      Divorced       No  \n",
       "1441  Healthcare Representative      Divorced       No  \n",
       "1442         Research Scientist       Married      Yes  \n",
       "1443                    Manager       Married       No  \n",
       "1444      Laboratory Technician       Married       No  \n",
       "1445     Manufacturing Director       Married       No  \n",
       "1446            Sales Executive       Married       No  \n",
       "1447            Sales Executive      Divorced       No  \n",
       "1448            Sales Executive      Divorced       No  \n",
       "1449         Research Scientist        Single       No  \n",
       "1450            Human Resources        Single      Yes  \n",
       "1451            Sales Executive       Married       No  \n",
       "1452            Sales Executive      Divorced       No  \n",
       "1453            Sales Executive       Married       No  \n",
       "1454            Sales Executive        Single       No  \n",
       "1455         Research Scientist        Single       No  \n",
       "1456  Healthcare Representative       Married      Yes  \n",
       "1457         Research Scientist       Married       No  \n",
       "1458         Research Scientist       Married       No  \n",
       "1459      Laboratory Technician       Married      Yes  \n",
       "1460         Research Scientist        Single       No  \n",
       "1461            Sales Executive      Divorced      Yes  \n",
       "1462            Sales Executive       Married       No  \n",
       "1463     Manufacturing Director        Single       No  \n",
       "1464       Sales Representative        Single       No  \n",
       "1465      Laboratory Technician       Married       No  \n",
       "1466  Healthcare Representative       Married       No  \n",
       "1467     Manufacturing Director       Married      Yes  \n",
       "1468            Sales Executive       Married       No  \n",
       "1469      Laboratory Technician       Married       No  \n",
       "\n",
       "[1470 rows x 7 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(['object'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ind_BusinessTravel = pd.get_dummies(df['BusinessTravel'], prefix='BusinessTravel')\n",
    "ind_Department = pd.get_dummies(df['Department'], prefix='Department')\n",
    "ind_EducationField = pd.get_dummies(df['EducationField'], prefix='EducationField')\n",
    "ind_Gender = pd.get_dummies(df['Gender'], prefix='Gender')\n",
    "ind_JobRole = pd.get_dummies(df['JobRole'], prefix='JobRole')\n",
    "ind_MaritalStatus = pd.get_dummies(df['MaritalStatus'], prefix='MaritalStatus')\n",
    "ind_OverTime = pd.get_dummies(df['OverTime'], prefix='OverTime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   BusinessTravel_Non-Travel  BusinessTravel_Travel_Frequently  \\\n",
       "0                          0                                 0   \n",
       "1                          0                                 1   \n",
       "2                          0                                 0   \n",
       "3                          0                                 1   \n",
       "4                          0                                 0   \n",
       "\n",
       "   BusinessTravel_Travel_Rarely  \n",
       "0                             1  \n",
       "1                             0  \n",
       "2                             1  \n",
       "3                             0  \n",
       "4                             1  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_BusinessTravel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Travel_Rarely', 'Travel_Frequently', 'Non-Travel'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BusinessTravel'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/anthonyn/anaconda3/envs/tensorflow/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
      "\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "df1 = pd.concat([ind_BusinessTravel, ind_Department, ind_EducationField, ind_Gender, \n",
    "                 ind_JobRole, ind_MaritalStatus, ind_OverTime])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>28</td>\n",
       "      <td>103</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2028</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>29</td>\n",
       "      <td>1389</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>51</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9980</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>32</td>\n",
       "      <td>334</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3298</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>22</td>\n",
       "      <td>1123</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>96</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2935</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>53</td>\n",
       "      <td>1219</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>15427</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>38</td>\n",
       "      <td>371</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3944</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>24</td>\n",
       "      <td>673</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>96</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4011</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>36</td>\n",
       "      <td>1218</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3407</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>34</td>\n",
       "      <td>419</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>11994</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>21</td>\n",
       "      <td>391</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>96</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1232</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>34</td>\n",
       "      <td>699</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>83</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2960</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>53</td>\n",
       "      <td>1282</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>19094</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>32</td>\n",
       "      <td>1125</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3919</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>42</td>\n",
       "      <td>691</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6825</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>44</td>\n",
       "      <td>477</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>10248</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>46</td>\n",
       "      <td>705</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>83</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>18947</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1440</th>\n",
       "      <td>36</td>\n",
       "      <td>688</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>97</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5131</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1441</th>\n",
       "      <td>56</td>\n",
       "      <td>667</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>57</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6306</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442</th>\n",
       "      <td>29</td>\n",
       "      <td>1092</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4787</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1443</th>\n",
       "      <td>42</td>\n",
       "      <td>300</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>18880</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444</th>\n",
       "      <td>56</td>\n",
       "      <td>310</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>72</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2339</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1445</th>\n",
       "      <td>41</td>\n",
       "      <td>582</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>13570</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1446</th>\n",
       "      <td>34</td>\n",
       "      <td>704</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>95</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6712</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1447</th>\n",
       "      <td>36</td>\n",
       "      <td>301</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>88</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5406</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1448</th>\n",
       "      <td>41</td>\n",
       "      <td>930</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8938</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449</th>\n",
       "      <td>32</td>\n",
       "      <td>529</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>78</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2439</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450</th>\n",
       "      <td>35</td>\n",
       "      <td>1146</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>8837</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>38</td>\n",
       "      <td>345</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5343</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452</th>\n",
       "      <td>50</td>\n",
       "      <td>878</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6728</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1453</th>\n",
       "      <td>36</td>\n",
       "      <td>1120</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6652</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>45</td>\n",
       "      <td>374</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4850</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>40</td>\n",
       "      <td>1322</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>52</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2809</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>35</td>\n",
       "      <td>1199</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5689</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>40</td>\n",
       "      <td>1194</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>98</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2001</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>35</td>\n",
       "      <td>287</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2977</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>29</td>\n",
       "      <td>1378</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4025</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>29</td>\n",
       "      <td>468</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>73</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3785</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>50</td>\n",
       "      <td>410</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10854</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1462</th>\n",
       "      <td>39</td>\n",
       "      <td>722</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>12031</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1463</th>\n",
       "      <td>31</td>\n",
       "      <td>325</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>74</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9936</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1464</th>\n",
       "      <td>26</td>\n",
       "      <td>1167</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2966</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1465</th>\n",
       "      <td>36</td>\n",
       "      <td>884</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>41</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2571</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>39</td>\n",
       "      <td>613</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9991</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>27</td>\n",
       "      <td>155</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>87</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6142</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1468</th>\n",
       "      <td>49</td>\n",
       "      <td>1023</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5390</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>34</td>\n",
       "      <td>628</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>82</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4404</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1470 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  DailyRate  DistanceFromHome  Education  EnvironmentSatisfaction  \\\n",
       "0      41       1102                 1          2                        2   \n",
       "1      49        279                 8          1                        3   \n",
       "2      37       1373                 2          2                        4   \n",
       "3      33       1392                 3          4                        4   \n",
       "4      27        591                 2          1                        1   \n",
       "5      32       1005                 2          2                        4   \n",
       "6      59       1324                 3          3                        3   \n",
       "7      30       1358                24          1                        4   \n",
       "8      38        216                23          3                        4   \n",
       "9      36       1299                27          3                        3   \n",
       "10     35        809                16          3                        1   \n",
       "11     29        153                15          2                        4   \n",
       "12     31        670                26          1                        1   \n",
       "13     34       1346                19          2                        2   \n",
       "14     28        103                24          3                        3   \n",
       "15     29       1389                21          4                        2   \n",
       "16     32        334                 5          2                        1   \n",
       "17     22       1123                16          2                        4   \n",
       "18     53       1219                 2          4                        1   \n",
       "19     38        371                 2          3                        4   \n",
       "20     24        673                11          2                        1   \n",
       "21     36       1218                 9          4                        3   \n",
       "22     34        419                 7          4                        1   \n",
       "23     21        391                15          2                        3   \n",
       "24     34        699                 6          1                        2   \n",
       "25     53       1282                 5          3                        3   \n",
       "26     32       1125                16          1                        2   \n",
       "27     42        691                 8          4                        3   \n",
       "28     44        477                 7          4                        1   \n",
       "29     46        705                 2          4                        2   \n",
       "...   ...        ...               ...        ...                      ...   \n",
       "1440   36        688                 4          2                        4   \n",
       "1441   56        667                 1          4                        3   \n",
       "1442   29       1092                 1          4                        1   \n",
       "1443   42        300                 2          3                        1   \n",
       "1444   56        310                 7          2                        4   \n",
       "1445   41        582                28          4                        1   \n",
       "1446   34        704                28          3                        4   \n",
       "1447   36        301                15          4                        4   \n",
       "1448   41        930                 3          3                        3   \n",
       "1449   32        529                 2          3                        4   \n",
       "1450   35       1146                26          4                        3   \n",
       "1451   38        345                10          2                        1   \n",
       "1452   50        878                 1          4                        2   \n",
       "1453   36       1120                11          4                        2   \n",
       "1454   45        374                20          3                        4   \n",
       "1455   40       1322                 2          4                        3   \n",
       "1456   35       1199                18          4                        3   \n",
       "1457   40       1194                 2          4                        3   \n",
       "1458   35        287                 1          4                        3   \n",
       "1459   29       1378                13          2                        4   \n",
       "1460   29        468                28          4                        4   \n",
       "1461   50        410                28          3                        4   \n",
       "1462   39        722                24          1                        2   \n",
       "1463   31        325                 5          3                        2   \n",
       "1464   26       1167                 5          3                        4   \n",
       "1465   36        884                23          2                        3   \n",
       "1466   39        613                 6          1                        4   \n",
       "1467   27        155                 4          3                        2   \n",
       "1468   49       1023                 2          3                        4   \n",
       "1469   34        628                 8          3                        2   \n",
       "\n",
       "      HourlyRate  JobInvolvement  JobLevel  JobSatisfaction  MonthlyIncome  \\\n",
       "0             94               3         2                4           5993   \n",
       "1             61               2         2                2           5130   \n",
       "2             92               2         1                3           2090   \n",
       "3             56               3         1                3           2909   \n",
       "4             40               3         1                2           3468   \n",
       "5             79               3         1                4           3068   \n",
       "6             81               4         1                1           2670   \n",
       "7             67               3         1                3           2693   \n",
       "8             44               2         3                3           9526   \n",
       "9             94               3         2                3           5237   \n",
       "10            84               4         1                2           2426   \n",
       "11            49               2         2                3           4193   \n",
       "12            31               3         1                3           2911   \n",
       "13            93               3         1                4           2661   \n",
       "14            50               2         1                3           2028   \n",
       "15            51               4         3                1           9980   \n",
       "16            80               4         1                2           3298   \n",
       "17            96               4         1                4           2935   \n",
       "18            78               2         4                4          15427   \n",
       "19            45               3         1                4           3944   \n",
       "20            96               4         2                3           4011   \n",
       "21            82               2         1                1           3407   \n",
       "22            53               3         3                2          11994   \n",
       "23            96               3         1                4           1232   \n",
       "24            83               3         1                1           2960   \n",
       "25            58               3         5                3          19094   \n",
       "26            72               1         1                1           3919   \n",
       "27            48               3         2                2           6825   \n",
       "28            42               2         3                4          10248   \n",
       "29            83               3         5                1          18947   \n",
       "...          ...             ...       ...              ...            ...   \n",
       "1440          97               3         2                2           5131   \n",
       "1441          57               3         2                3           6306   \n",
       "1442          36               3         1                4           4787   \n",
       "1443          56               3         5                3          18880   \n",
       "1444          72               3         1                3           2339   \n",
       "1445          60               2         4                2          13570   \n",
       "1446          95               2         2                3           6712   \n",
       "1447          88               1         2                4           5406   \n",
       "1448          57               2         2                2           8938   \n",
       "1449          78               3         1                1           2439   \n",
       "1450          31               3         3                4           8837   \n",
       "1451         100               3         2                4           5343   \n",
       "1452          94               3         2                3           6728   \n",
       "1453         100               2         2                4           6652   \n",
       "1454          50               3         2                3           4850   \n",
       "1455          52               2         1                3           2809   \n",
       "1456          80               3         2                3           5689   \n",
       "1457          98               3         1                3           2001   \n",
       "1458          62               1         1                4           2977   \n",
       "1459          46               2         2                2           4025   \n",
       "1460          73               2         1                1           3785   \n",
       "1461          39               2         3                1          10854   \n",
       "1462          60               2         4                4          12031   \n",
       "1463          74               3         2                1           9936   \n",
       "1464          30               2         1                3           2966   \n",
       "1465          41               4         2                4           2571   \n",
       "1466          42               2         3                1           9991   \n",
       "1467          87               4         2                2           6142   \n",
       "1468          63               2         2                2           5390   \n",
       "1469          82               4         2                3           4404   \n",
       "\n",
       "              ...           PerformanceRating  RelationshipSatisfaction  \\\n",
       "0             ...                           3                         1   \n",
       "1             ...                           4                         4   \n",
       "2             ...                           3                         2   \n",
       "3             ...                           3                         3   \n",
       "4             ...                           3                         4   \n",
       "5             ...                           3                         3   \n",
       "6             ...                           4                         1   \n",
       "7             ...                           4                         2   \n",
       "8             ...                           4                         2   \n",
       "9             ...                           3                         2   \n",
       "10            ...                           3                         3   \n",
       "11            ...                           3                         4   \n",
       "12            ...                           3                         4   \n",
       "13            ...                           3                         3   \n",
       "14            ...                           3                         2   \n",
       "15            ...                           3                         3   \n",
       "16            ...                           3                         4   \n",
       "17            ...                           3                         2   \n",
       "18            ...                           3                         3   \n",
       "19            ...                           3                         3   \n",
       "20            ...                           3                         4   \n",
       "21            ...                           4                         2   \n",
       "22            ...                           3                         3   \n",
       "23            ...                           3                         4   \n",
       "24            ...                           3                         3   \n",
       "25            ...                           3                         4   \n",
       "26            ...                           4                         2   \n",
       "27            ...                           3                         4   \n",
       "28            ...                           3                         4   \n",
       "29            ...                           3                         4   \n",
       "...           ...                         ...                       ...   \n",
       "1440          ...                           3                         2   \n",
       "1441          ...                           4                         1   \n",
       "1442          ...                           3                         2   \n",
       "1443          ...                           3                         1   \n",
       "1444          ...                           3                         4   \n",
       "1445          ...                           4                         3   \n",
       "1446          ...                           4                         4   \n",
       "1447          ...                           4                         1   \n",
       "1448          ...                           3                         3   \n",
       "1449          ...                           3                         4   \n",
       "1450          ...                           3                         3   \n",
       "1451          ...                           3                         3   \n",
       "1452          ...                           3                         4   \n",
       "1453          ...                           3                         1   \n",
       "1454          ...                           3                         3   \n",
       "1455          ...                           3                         4   \n",
       "1456          ...                           3                         4   \n",
       "1457          ...                           3                         2   \n",
       "1458          ...                           3                         4   \n",
       "1459          ...                           3                         1   \n",
       "1460          ...                           3                         2   \n",
       "1461          ...                           3                         2   \n",
       "1462          ...                           3                         1   \n",
       "1463          ...                           3                         2   \n",
       "1464          ...                           3                         4   \n",
       "1465          ...                           3                         3   \n",
       "1466          ...                           3                         1   \n",
       "1467          ...                           4                         2   \n",
       "1468          ...                           3                         4   \n",
       "1469          ...                           3                         1   \n",
       "\n",
       "      StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "0                    0                  8                      0   \n",
       "1                    1                 10                      3   \n",
       "2                    0                  7                      3   \n",
       "3                    0                  8                      3   \n",
       "4                    1                  6                      3   \n",
       "5                    0                  8                      2   \n",
       "6                    3                 12                      3   \n",
       "7                    1                  1                      2   \n",
       "8                    0                 10                      2   \n",
       "9                    2                 17                      3   \n",
       "10                   1                  6                      5   \n",
       "11                   0                 10                      3   \n",
       "12                   1                  5                      1   \n",
       "13                   1                  3                      2   \n",
       "14                   0                  6                      4   \n",
       "15                   1                 10                      1   \n",
       "16                   2                  7                      5   \n",
       "17                   2                  1                      2   \n",
       "18                   0                 31                      3   \n",
       "19                   0                  6                      3   \n",
       "20                   1                  5                      5   \n",
       "21                   0                 10                      4   \n",
       "22                   0                 13                      4   \n",
       "23                   0                  0                      6   \n",
       "24                   0                  8                      2   \n",
       "25                   1                 26                      3   \n",
       "26                   0                 10                      5   \n",
       "27                   1                 10                      2   \n",
       "28                   1                 24                      4   \n",
       "29                   0                 22                      2   \n",
       "...                ...                ...                    ...   \n",
       "1440                 3                 18                      3   \n",
       "1441                 1                 13                      2   \n",
       "1442                 3                  4                      3   \n",
       "1443                 0                 24                      2   \n",
       "1444                 1                 14                      4   \n",
       "1445                 1                 21                      3   \n",
       "1446                 2                  8                      2   \n",
       "1447                 1                 15                      4   \n",
       "1448                 1                 14                      5   \n",
       "1449                 0                  4                      4   \n",
       "1450                 0                  9                      2   \n",
       "1451                 1                 10                      1   \n",
       "1452                 2                 12                      3   \n",
       "1453                 1                  8                      2   \n",
       "1454                 0                  8                      3   \n",
       "1455                 0                  8                      2   \n",
       "1456                 2                 10                      2   \n",
       "1457                 3                 20                      2   \n",
       "1458                 1                  4                      5   \n",
       "1459                 1                 10                      2   \n",
       "1460                 0                  5                      3   \n",
       "1461                 1                 20                      3   \n",
       "1462                 1                 21                      2   \n",
       "1463                 0                 10                      2   \n",
       "1464                 0                  5                      2   \n",
       "1465                 1                 17                      3   \n",
       "1466                 1                  9                      5   \n",
       "1467                 1                  6                      0   \n",
       "1468                 0                 17                      3   \n",
       "1469                 0                  6                      3   \n",
       "\n",
       "      WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                   1               6                   4   \n",
       "1                   3              10                   7   \n",
       "2                   3               0                   0   \n",
       "3                   3               8                   7   \n",
       "4                   3               2                   2   \n",
       "5                   2               7                   7   \n",
       "6                   2               1                   0   \n",
       "7                   3               1                   0   \n",
       "8                   3               9                   7   \n",
       "9                   2               7                   7   \n",
       "10                  3               5                   4   \n",
       "11                  3               9                   5   \n",
       "12                  2               5                   2   \n",
       "13                  3               2                   2   \n",
       "14                  3               4                   2   \n",
       "15                  3              10                   9   \n",
       "16                  2               6                   2   \n",
       "17                  2               1                   0   \n",
       "18                  3              25                   8   \n",
       "19                  3               3                   2   \n",
       "20                  2               4                   2   \n",
       "21                  3               5                   3   \n",
       "22                  3              12                   6   \n",
       "23                  3               0                   0   \n",
       "24                  3               4                   2   \n",
       "25                  2              14                  13   \n",
       "26                  3              10                   2   \n",
       "27                  3               9                   7   \n",
       "28                  3              22                   6   \n",
       "29                  2               2                   2   \n",
       "...               ...             ...                 ...   \n",
       "1440                3               4                   2   \n",
       "1441                2              13                  12   \n",
       "1442                4               2                   2   \n",
       "1443                2              22                   6   \n",
       "1444                1              10                   9   \n",
       "1445                3              20                   7   \n",
       "1446                3               8                   7   \n",
       "1447                2              15                  12   \n",
       "1448                3               5                   4   \n",
       "1449                3               4                   2   \n",
       "1450                3               9                   0   \n",
       "1451                3              10                   7   \n",
       "1452                3               6                   3   \n",
       "1453                2               6                   3   \n",
       "1454                3               5                   3   \n",
       "1455                3               2                   2   \n",
       "1456                4              10                   2   \n",
       "1457                3               5                   3   \n",
       "1458                3               4                   3   \n",
       "1459                3               4                   3   \n",
       "1460                1               5                   4   \n",
       "1461                3               3                   2   \n",
       "1462                2              20                   9   \n",
       "1463                3               9                   4   \n",
       "1464                3               4                   2   \n",
       "1465                3               5                   2   \n",
       "1466                3               7                   7   \n",
       "1467                3               6                   2   \n",
       "1468                2               9                   6   \n",
       "1469                4               4                   3   \n",
       "\n",
       "      YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                           0                     5  \n",
       "1                           1                     7  \n",
       "2                           0                     0  \n",
       "3                           3                     0  \n",
       "4                           2                     2  \n",
       "5                           3                     6  \n",
       "6                           0                     0  \n",
       "7                           0                     0  \n",
       "8                           1                     8  \n",
       "9                           7                     7  \n",
       "10                          0                     3  \n",
       "11                          0                     8  \n",
       "12                          4                     3  \n",
       "13                          1                     2  \n",
       "14                          0                     3  \n",
       "15                          8                     8  \n",
       "16                          0                     5  \n",
       "17                          0                     0  \n",
       "18                          3                     7  \n",
       "19                          1                     2  \n",
       "20                          1                     3  \n",
       "21                          0                     3  \n",
       "22                          2                    11  \n",
       "23                          0                     0  \n",
       "24                          1                     3  \n",
       "25                          4                     8  \n",
       "26                          6                     7  \n",
       "27                          4                     2  \n",
       "28                          5                    17  \n",
       "29                          2                     1  \n",
       "...                       ...                   ...  \n",
       "1440                        0                     2  \n",
       "1441                        1                     9  \n",
       "1442                        2                     2  \n",
       "1443                        4                    14  \n",
       "1444                        9                     8  \n",
       "1445                        0                    10  \n",
       "1446                        1                     7  \n",
       "1447                       11                    11  \n",
       "1448                        0                     4  \n",
       "1449                        1                     2  \n",
       "1450                        1                     7  \n",
       "1451                        1                     9  \n",
       "1452                        0                     1  \n",
       "1453                        0                     0  \n",
       "1454                        0                     1  \n",
       "1455                        2                     2  \n",
       "1456                        0                     2  \n",
       "1457                        0                     2  \n",
       "1458                        1                     1  \n",
       "1459                        0                     3  \n",
       "1460                        0                     4  \n",
       "1461                        2                     0  \n",
       "1462                        9                     6  \n",
       "1463                        1                     7  \n",
       "1464                        0                     0  \n",
       "1465                        0                     3  \n",
       "1466                        1                     7  \n",
       "1467                        0                     3  \n",
       "1468                        0                     8  \n",
       "1469                        1                     2  \n",
       "\n",
       "[1470 rows x 23 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(['int64'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 = pd.concat([ind_BusinessTravel, ind_Department, ind_EducationField, ind_Gender, \n",
    "                 ind_JobRole, ind_MaritalStatus, ind_OverTime, df.select_dtypes(['int64'])], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1470, 51)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(df1, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=42,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_score(clf, X_train, y_train, X_test, y_test, train=True):\n",
    "    '''\n",
    "    print the accuracy score, classification report and confusion matrix of classifier\n",
    "    '''\n",
    "    if train:\n",
    "        '''\n",
    "        training performance\n",
    "        '''\n",
    "        print(\"Train Result:\\n\")\n",
    "        print(\"accuracy score: {0:.4f}\\n\".format(accuracy_score(y_train, clf.predict(X_train))))\n",
    "        print(\"Classification Report: \\n {}\\n\".format(classification_report(y_train, clf.predict(X_train))))\n",
    "        print(\"Confusion Matrix: \\n {}\\n\".format(confusion_matrix(y_train, clf.predict(X_train))))\n",
    "\n",
    "        res = cross_val_score(clf, X_train, y_train.ravel(), cv=10, scoring='accuracy')\n",
    "        print(\"Average Accuracy: \\t {0:.4f}\".format(np.mean(res)))\n",
    "        print(\"Accuracy SD: \\t\\t {0:.4f}\".format(np.std(res)))\n",
    "        \n",
    "    elif train==False:\n",
    "        '''\n",
    "        test performance\n",
    "        '''\n",
    "        print(\"Test Result:\\n\")        \n",
    "        print(\"accuracy score: {0:.4f}\\n\".format(accuracy_score(y_test, clf.predict(X_test))))\n",
    "        print(\"Classification Report: \\n {}\\n\".format(classification_report(y_test, clf.predict(X_test))))\n",
    "        print(\"Confusion Matrix: \\n {}\\n\".format(confusion_matrix(y_test, clf.predict(X_test))))    \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Result:\n",
      "\n",
      "accuracy score: 1.0000\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00       921\n",
      "          1       1.00      1.00      1.00       181\n",
      "\n",
      "avg / total       1.00      1.00      1.00      1102\n",
      "\n",
      "\n",
      "Confusion Matrix: \n",
      " [[921   0]\n",
      " [  0 181]]\n",
      "\n",
      "Average Accuracy: \t 0.7813\n",
      "Accuracy SD: \t\t 0.0407\n"
     ]
    }
   ],
   "source": [
    "print_score(clf, X_train, y_train, X_test, y_test, train=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Result:\n",
      "\n",
      "accuracy score: 0.7636\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.89      0.82      0.85       312\n",
      "          1       0.31      0.45      0.36        56\n",
      "\n",
      "avg / total       0.80      0.76      0.78       368\n",
      "\n",
      "\n",
      "Confusion Matrix: \n",
      " [[256  56]\n",
      " [ 31  25]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_score(clf, X_train, y_train, X_test, y_test, train=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is clearly not satisfactory. We will revisit this project after we covered ensemble model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "bag_clf = BaggingClassifier(base_estimator=clf, n_estimators=5000,\n",
    "                            bootstrap=True, n_jobs=-1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=42,\n",
       "            splitter='best'),\n",
       "         bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
       "         max_samples=1.0, n_estimators=5000, n_jobs=-1, oob_score=False,\n",
       "         random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_clf.fit(X_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Result:\n",
      "\n",
      "accuracy score: 1.0000\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00       921\n",
      "          1       1.00      1.00      1.00       181\n",
      "\n",
      "avg / total       1.00      1.00      1.00      1102\n",
      "\n",
      "\n",
      "Confusion Matrix: \n",
      " [[921   0]\n",
      " [  0 181]]\n",
      "\n",
      "Average Accuracy: \t 0.8584\n",
      "Accuracy SD: \t\t 0.0281\n",
      "Test Result:\n",
      "\n",
      "accuracy score: 0.8641\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.88      0.97      0.92       312\n",
      "          1       0.62      0.29      0.39        56\n",
      "\n",
      "avg / total       0.84      0.86      0.84       368\n",
      "\n",
      "\n",
      "Confusion Matrix: \n",
      " [[302  10]\n",
      " [ 40  16]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_score(bag_clf, X_train, y_train, X_test, y_test, train=True)\n",
    "print_score(bag_clf, X_train, y_train, X_test, y_test, train=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf_clf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_clf.fit(X_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Result:\n",
      "\n",
      "accuracy score: 0.9782\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.97      1.00      0.99       921\n",
      "          1       1.00      0.87      0.93       181\n",
      "\n",
      "avg / total       0.98      0.98      0.98      1102\n",
      "\n",
      "\n",
      "Confusion Matrix: \n",
      " [[921   0]\n",
      " [ 24 157]]\n",
      "\n",
      "Average Accuracy: \t 0.8584\n",
      "Accuracy SD: \t\t 0.0147\n",
      "Test Result:\n",
      "\n",
      "accuracy score: 0.8533\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.87      0.98      0.92       312\n",
      "          1       0.56      0.16      0.25        56\n",
      "\n",
      "avg / total       0.82      0.85      0.82       368\n",
      "\n",
      "\n",
      "Confusion Matrix: \n",
      " [[305   7]\n",
      " [ 47   9]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_score(rf_clf, X_train, y_train, X_test, y_test, train=True)\n",
    "print_score(rf_clf, X_train, y_train, X_test, y_test, train=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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epNXMbNRgFBwAAAAYDm0C5o0k3Vd6vjAtq0zj7kskLZa0bkeat0m61d2f6lyBmR1lZrPMbNaiRYvalh0AAAAYcm0CZqtY5t2kMbOXKpppvK9qBe5+lrvv7O47jxkzpkWRAAAAgOHRJmBeKGmT0vONJd1fl8bMRkoaLemh9HxjSZdIeqe737W0BQYAAACGU5uA+SZJW5nZODNbVdJESdM60kxTdOqTpIMkzXB3N7O1JV0m6QR3v26wCg0AAAAMl8aAObVJPlbS5ZLmS/qxu88zsxPN7MCU7GxJ65rZAknHSSqGnjtW0paS/svMZqfH+oP+KQAAAIAhMrJNInefLml6x7JPl/5/UtLBFfk+L+nzS1lGAAAAYJlhpj8AAAAgg4AZAAAAyCBgBgAAADJatWEGgOeisZMvq33t3pP2H8aSAACWJWqYAQAAgAwCZgAAACCDJhkAMNimjK5Zvnh4ywEAGBTUMAMAAAAZBMwAAABABgEzAAAAkEEbZgBYDmx33naVy+dOmjvMJQEAdKKGGQAAAMggYAYAAAAyCJgBAACADAJmAAAAIIOAGQAAAMggYAYAAAAyCJgBAACADAJmAAAAIIOAGQAAAMggYAYAAAAyCJgBAACADAJmAAAAIIOAGQAAAMggYAYAAAAyCJgBAACADAJmAAAAIIOAGQAAAMggYAYAAAAyCJgBAACAjJHLugAAgN7M32bbyuXb3jF/mEsCACs3apgBAACADAJmAAAAIIOAGQAAAMggYAYAAAAyCJgBAACADAJmAAAAIIOAGQAAAMggYAYAAAAymLgEAJ5Dph49o/a1Y87caxhLAgArDmqYAQAAgIxWAbOZ7Wtmd5rZAjObXPH6KDO7ML0+08zGpuXrmtlVZvaomX1zcIsOAAAADL3GJhlmNkLSVEn7SFoo6SYzm+but5eSHSnpYXff0swmSjpZ0qGSnpT0X5Jelh4AgBXMVw89oPa1j17482EsCQAsG21qmHeVtMDd73b3pyVdIGlCR5oJks5L/18saW8zM3d/zN1/qwicAQAAgBVOm4B5I0n3lZ4vTMsq07j7EkmLJa3bthBmdpSZzTKzWYsWLWqbDQAAABhybQJmq1jmPaSp5e5nufvO7r7zmDFj2mYDAAAAhlybgHmhpE1KzzeWdH9dGjMbKWm0pIcGo4AAAADAstRmHOabJG1lZuMk/a+kiZLe3pFmmqRJkq6XdJCkGe7euoYZALByWTj52trXNj5pj2EsCQAsvcaA2d2XmNmxki6XNELSOe4+z8xOlDTL3adJOlvS+Wa2QFGzPLHIb2b3SlpL0qpm9mZJb+gYYQMAAABYbrWa6c/dp0ua3rHs06X/n5R0cE3esUtRPgDAc8SUKVN6eg0Ahhoz/QEAAAAZBMwAAABABgEzAAAAkNGqDTMAAMujK2dsUfva3nvdNYwlAbAyI2AGADznbHDV7Mrlf91zx2EuCYAVAU0yAAAAgAxqmAEAaGHs5Msql9970v6DmgfA8oeAGQCA5cmU0ZnXFg9fOQA8iyYZAAAAQAY1zAAArOC2O2+72tfmTpo7jCUBVk4EzAAAPAfN32bb2te2vWN+5fKpR8+ozXPMmXstdZmA5RVNMgAAAIAMapgBAMCQ+uqhB1Qu/+iFPx/mkgC9IWAGAADLnYWTr61cvvFJewxzSQCaZAAAAABZ1DADAICVwpQpU7paDrRFDTMAAACQQQ0zAAB4zrpyxhaVy/fe665hLgmWZ9QwAwAAABkEzAAAAEAGATMAAACQQcAMAAAAZBAwAwAAABkEzAAAAEAGATMAAACQwTjMAAAAXdjgqtm1r/11zx0rl4+dfFltnntP2n+py4ShRcAMAACwHCLIXn7QJAMAAADIIGAGAAAAMgiYAQAAgAzaMAMAAKwspozOvLZ4+MqxkiFgBgAAeI7b7rztKpfPnTR3mEuyfKJJBgAAAJBBDTMAAAC6Nn+bbSuXb3vH/GEuydCjhhkAAADIoIYZAAAAw2Lq0TMqlx9z5l7DXJLuUMMMAAAAZFDDDAAAgOXWVw89oPa1j17482EpAzXMAAAAQAYBMwAAAJBBkwwAAACsVBZOvrb2tY1P2qPr92tVw2xm+5rZnWa2wMwmV7w+yswuTK/PNLOxpddOSMvvNLM3dl1CAAAAYBlqDJjNbISkqZL2kzRe0mFmNr4j2ZGSHnb3LSWdIunklHe8pImSXippX0mnp/cDAAAAVghtaph3lbTA3e9296clXSBpQkeaCZLOS/9fLGlvM7O0/AJ3f8rd75G0IL0fAAAAsEIwd88nMDtI0r7u/p70/B2SdnP3Y0tpfp/SLEzP75K0m6Qpkm5w9++n5WdL+oW7X9yxjqMkHZWevkTSnTXFWU/SA918wJUwz3Cuizx8RytrnuFcF3mGd13k4TtaWfMM57qeS3k2c/cxje/g7tmHpIMlfaf0/B2STutIM0/SxqXnd0laV9GU44jS8rMlva1pnZmyzHqu51ney0ee5b985Fn+y7ey5Vney0ee5b985Fn+y7ey5el8tGmSsVDSJqXnG0u6vy6NmY2UNFrSQy3zAgAAAMutNgHzTZK2MrNxZraqohPftI400yRNSv8fJGmGR0g/TdLENIrGOElbSbpxcIoOAAAADL3GcZjdfYmZHSvpckkjJJ3j7vPM7ERFFfc0RVOL881sgaJmeWLKO8/MfizpdklLJB3j7v9aivKeRZ5hXRd5+I5W1jzDuS7yDO+6yMN3tLLmGc51kadDY6c/AAAA4LmMqbEBAACADAJmAAAAIIOAGcPOzNZe1mXA8sHMmse+rM87ajDLgmpmdnCbZVhxmdkLlnUZVhRmtoqZvWpZlwPDj4B5GKVRRor/x5rZG5bXqcJzB1Azu9TMptU9Wrz9zWb2IzN7wyAWubOMI8xsTg/5VjWzQ8zseDP7z+LRkOfKNsvQX5oN9PIe8u1qZnMl/TE938HMThvs8q0IzGx1MzvBzM5Mz7c0s/1a5Fuzi9Wc0HLZUjOzk9ssG4T1bFFccJnZ68zsQ0N5IW9mW3WZftd0HHp78Riicr3KzG6XND8938HMTh+ida1uZi8ZivceTu7+jKSvLs17mNkLzWz7FulWSRPDdfv+S7V/d3MBZWabmdnr0/+rtzm2mNmXzWwtM3uemV1pZg+Y2RFt19ni/UeY2a8H6/0KjaNkLA/MbAdJe6Sn17p7bSBkZi+S9EVJL3b3/cxsvKTd3f3shnVsLekMSS9y95elnflAd/98Q77VJW3q7nWzE5b9zsz+TdKakn4raa6kt0v6jxZ5ZWavkbSVu5+baubW8JhyfNA+U7py/o6kNSRtmrb9+9z9A6VkX2lT3oytJL1R0nvNbKqkH0k6z93vasrYdnu7+7/M7HYz28jd/7eLsl0i6UlJN0vKjuhiZqtJer6k9czshZIsvbSWpBdn8q3TUPaHuihvlpm9tWFdP83kO1nS+orPZZHc12pY39aSPi5pM5WOL+6+V8W63cxuMrPt3f22ps9S8g1JB0j6WXqfOWa2Z5uMZvZ8SR9V7EPvTYHMS9z955k8myl+d79O+99Id3+kYT2rS3oyfcYtFDOYXuHuSzJ5Wm+7knMUx5HXpOf3S7pI0i9y5ZM0x8yuk3Suu8+oKc9+kv5d0kZm9o3SS2spRj1qVLOvP+Lu/6zJso+kT3Qs269iWed6XiDpCXd/Jm3HbRSzytat5yeSdjazLRWjPE2T9EPF5x0KPzKzJySdK+lCd3+sLqGZfVfSeEmz1XcM8lS+ujw9/c4lnaI4Fk9L6eaY2Wtz75XW19W5xczepDhvrCppnJntKOlEdz+wYT1Vn2uxpLnu/veK9ONz7+fut1fk6eV4fIWZvU3ST73lyAlm9htJByp+27MlLTKzq939uMy6nzGzOWa2qbv/uc16kp7275bn/3L69ypmaV5H0haKuTbOlLR3Q/ne4O7Hm9lbFPN1HCzpKknfz5RtFUm3ufvLGt67OP8/bmaj3X1xU/rWlnbmk6F+SPqwpN9LOjE95kr6YCb9LyQdImlOej5S8eNqWs/VknaVdGtp2e8b8rxJMY33Pen5jpKmZdLfmv4eI+mE9P/sltvhM5IulfSH9PzFkq4bgs80UzHZTKs8klZXBBy9fr+vk/S/kh6RdKWkXQdxe/9K0j8UtZg/LR4N5clun4p98x5JT0m6O/1/j6Q5ko7N5LunI335cXeL9b46fbY/lN6nMp/iBH2upMskPaw4kP5EMfxj7baQtEDStj18n3MkvT/td68oHpn0syX9UzFb6I2Kcd9vbFjHjelveR+d07J8F0o6vvie0/5b+xuU9N5UprvS860kXdliPbMUF1MbKk4Il0r63mBuu2I9Fdui8ZiiGCL0TYoLxDslfVoxPWw5zQ6K8fX/lP4Wj7dKemHL7X2vIuh7QNKD6f+Fkm4pf7b0uedKekzSbaXHPZK+32I9N6ftvZGk+9Ln+kEm/S3p78eVziflbViRfm5Hufo9Wm6L7RQB6t2KC509atLdIWmVLn9352Ye52Tyzezlt6Quzy3p+xndkb5xuymOWw+p77j1YFr2R0nvqEh/febxu5p1lI/HnfvqPTV5HpH0jOLY9Y/0/B8Nn6U4/79H0me72AYz1Hd+nFY8GvJ0tX+X9wd1d/6frbgIKqdvE2/NS3+/LWnfLva7HygqO9r8Jn4s6c+KC4ZvFI82eeseK0IN85GSdvN0RZ5uz10vqe4W7Hru/mMzO0F6dhzpNmM/P9/db4y7xM9qqkWZojho/Cata7aZjc2kNzN7uaJW+ei0rG2TjLdIerniRCN3v7/FrY9ePpPc/b6OPJXbbylqDdaWdLikdyqCuP+nOMG9QhHQjKvJOkXdbe+TcuWocYOZjfeKmohO7n6qpFPN7IPu3rpJgLvXfb62zlZss8ZacHd/lySZ2c8ljXf3v6TnGyqmrq/zN3ef30PZlrj7GV2kP7SHddxnZrtKcosmTR9UXDy0sYW7H2pmh0mSuz9hHTt7h2MU+9zMlP6PZrZ+i/Ws4u6Pm9m7JX3T3U8ys9kNebrddpL0dLrT4ZJkMUHU002ZPMbDv1TSpemu1w8lHZ9qnf/T3W/2uJM3x8x+6KmmNt1J2cTdH25Zvl9KusTdL0/53yBpX8XJ7HRJu6V0P1RUdnxJ0uRS/ke83R0XS9v7SEmnufuXzezWTPp/pn1gkuLCQZKel0l/QPp7TPp7fvp7uKTHW5RP7j7XzI5T1KSdIWlPM3tM0mTvf4djnqT1JA2oQc2897vapu1wX6pVdIsmgx9Sap7RoNtzyxJ3X5z/qVV6RnHh/jfp2TvIZyj2m2vU9z1Iktx9925XUByPLZo1TXP36en5fpJeX5OnmyZNhZHpuHuIpE92ke+zPayr2/37WW3P/8lT7v50kd5ipuc2Ne6Xmtkdkp6Q9IF0x/zJFvk2lDTPzG5UXFwXZa6KOS5Lj0GzIgTMpv5f2L/Ud+u7ymNmtq76TiCvVNzCafJAunVa5DtI0l8a8nR7EPiY4jb3Fengubmk37XM+7S7u5kV5WvTxqiXz9TNAXSKugtgCzcpTpCHuPufSstvMLNvZ/J1tb3d/Uoz21hxO/2qFFg0XaDsJulWi0l4nlJfU4SdMus5LW2zsep/K/17uRWlQO1wSePc/XNmtqmkDdy9aTbMxe7edMu909giWE7+JmnrTPpZZnahotnDU8VCr7+1W7jUzD6guAAq56sMejw1rTGztSSt1vDehfcrags2VXyOX6dlbTydmksUv4ktyuWs0OsJYRUz20VxcXxUWta073W17ZITFUHpxmZ2nqR/U1QyZKXtPVFxQn1CURP1E0m7KJp0bF5K/isz6+pWcsnO7l5UDsjdrzCzL7r7cVbqtOlx23SxpMPSRdCL0vrWMLM1vPl2tJnZ7orfU/H5c+e3dykqLb7g7vekC43a28HFccrMXu3ury69NDldZJzYULit0zrfIuk6SQe7++8smvtcLakcMI+WNN/MblD//SDb7KK0rv0lvVSl35O715XvaEmnKmrmF0q6Qn0XBTndnlt+b9EOe4RFM6gPqd25b2wRLCd/l7S1uz9kZnXNbYoOwR9U3DX5YCrrFu5+RWZdu3Tsq78ws89l1nGgpKL5ym8806wrOVFxt/PD+NyeAAAgAElEQVQ6d78pnf//2JBH7n51ulDYJS260Suao3Toav8u6fYC6mqLPj6rm9k+kj6guBDPcvfJqfLzH56aT0ia0KJ8rS8e3P08667JbKs3Xa4fko5T3Kqckh6zJX0kk34nxQFpcfr7B0nbt1jP5ooT7+OKJgK/VfxYc3nOVpwQb1Pcqj1N0pk1aUcodt5et8PHJH1LcevovYpa9g/18Jk2a8iznuK2x98UB6fvS1q3Jm3V7bw2t5i6ut3Yy/ZO6d+tqJEvbqdvLenXDevYourRkOd8xcH/9FSm09Ti1o+ipmSqpPnp+Qsl3dQi30mS/lvS7ml/30nSTg15vqk4WP+HIkj6haImri59V7d2S/m6amaiaD95pyJo+4vignhBr7+TFuXbRxGgLEr7+b2SXpdJ/2VJ/6m4Tb6PIpht/B1L2kvSdEmfTM83l3T6YG67Ur4xipPNmyWt33I7LJD0hap9W9KnO573dCs5pbtC0f54s/Q4XtGcaITSbeOO9McqbonPUzSDmNtmXYoLhWmSPlHa3tnfoHpoTqY4/7ym9PxVatcEZqbiuL1GxWtHdjzfu+rRsnxnSvqeolnKZ9L2Ozu373Tz+Uv5ujpfKprLfEFRWXKTpM9LWq3Fek5XXExMSo9padkLJF2VyfcDRTOjounVC9TQJEFxfPyUouJjM0Ut8OU1aU9SNJF4d3r8StJJvWzLFtvgEEWzqPPSd3uPpINa5Otl/259/k/pV0n79UWSLk7/W4v1PD9t67PS860kHdCyjJtJen3pfdasSddVE85W6x6KL3gIdphXKK50Pizp5S3Sj1RcYb9M0vO6XNcL6r6Ami+98yAwKpP+qqXcDvsoAqWvSNqnRfpxnZ+pWDZI30tXAWwp35bpoDddcUK9QlHr3u32/oIyB1313r7qZYqr86MlvbRF+vltDhIV+Yp2Zt22H7yq4jGjRb63KNpQniLpLYO1HyzlPjRb0bGwCMr2lTS1Ju0pkr5W9+hinetK2l9xm329hrQ9nRBK+WuPB4O4DSeqLzDfRA3tnlO6EV28/1zFrdArFLVwUvuAeb10XLg1fdffVAT4q0rasiL9AmVO0C3W94KW6Xo6mSrORXMUF1r3pM+UvVgdzkfxvZT+rpE7tipqOK9Q1Mqv3cv2VovzpVqct2vymaSD0m//6+n/NgHZzelv62OrouPaqWlfvSWtb5267axSxY/iAjD7m1BU2FypviB+e0mfavFZ5qh0IZx+P02fZdCDxZr1vKWXY5y67EtSyte6T4mq2803nv9zjxWhSYbc/WbFh29kA3vVbm1mtb1qS/nWVrSpHatoa1Ss+0OZ1e3v7p9UqT2SxfikF9Wkn2VmFyna75Xb30zPrKN435Pd/ROKK9nOZXV+ojiYl3tkX6w46NetZ5ziVtZY9W9eUNVG6IOKz/6UoonF5YqLhiYXK4Lt76uhDW6H9Su29y6KH1CVJ73/7fTG9uJmdqzittLP0qIfm9lUd88NtfR7SRuoublLp3+mMnla9xhFm70sd281IkSFWxRtQn9tZs83szW9ZrSH1JTlNEUHQ1fUIH3Y3RfmVmBmz1M0j3j2VqWkb3n9aAVL3P3vFsMAmbv/0szqbrt1PbxSqVydTWqK72pTix7ot9RkXV1Rs/7t9D4j0rJsu1WLNtZnKw7YRW/z97j7BzN5ut12MrNvKtomvlZxAfmYopZxl5r0F6lvfxvwursfUpGtuJX8W+/iVnJ6vwcUx4kqCyqW3ad2Tej6Sc0xzlbL3v2qbk7W2LcgnYt2SE1azBt64JvZTapuwlM09dq1Is8uit/etpJGpbRPecMINckT6e/jZvZiRee12s/l7lulfXWipE9aDDF3gbtX3r63aINdtbx4v6/VrOprqf3uRen957X4LPKIci5Oj248nZplFPv6Zmpo2+/R9OnDqQnQoy3WsbaiQ6IUv/Mm31Y0ffpWWt9tZvZDNZ8zV+mIXR5U85DAU9TF/m0xLGfVfqqUvy4OOlDS183sGkkXKGrk24yg021fkkI3fUqqmnDWfsY2ltuA2cweUd+Hs9L/IyWt6u51ZT9Scav6qvT8dZJuUATOJ7r7+TX5pqd0c9UiaElO0MDguGpZYWNFx4hyUO9p3U1aD7dkZtsoathHd1xAtGkn+jPFiedSNW+Hl3QGsC094110kiv5qZm9ydMwcRbDH01V9D6vcp2ZHS9pNYthx45R/7aCVY5SjNTxaFrHF9XX3KLOepJut+iIUG5zmO38qGiHe4mk9c3sC4rak0815JGZjVbcbi0Cq6sVnS1rT942cPifjZQf/udcxUVQMUHFEWnZPg3FO0MRwBXb6x1p2Xtq0v/DYqi36ySda2Z/q0kn7xgaMrVPk7s/UZ2jn9y4qa5oQlHlSkXHn+IEurqiRq5p4oJehr7rdttJ0qvcfSdLHdw82naumkn/nYYyDODuF6l0THP3uyW9rU3e1Hb3Yxp4AV63ve+W9Bszu0z9f0t1gVjh6+pueLSeTqbW/bClvYwte3rKd4EiOPgPxZ2DNn6eKn/+W3GB7Gr4zj36TNyYjnVfU9z6r2vv2ktnN7n7nma2gaJ5wVnpguNCbx62tafhLRVB6C8VQyKerTjOvbdhXd0MqfYlRV+Xq1KZXqtoupXTUyd8Sb80s8sVw69K0VG6qQ9Lt/v3rBblGMDd35Uu9PdT3G0+3cx+5e65Y5bUfV+SQjd9SnptN19vaaqnh/Oh+KF+QnFA/Wom3aWKsSGL5y9SDCe2jvLDowxoT5dJu5+iBuBvKg1XIum7ahgSq4fP3fVwS4r2jOcqrkTPLT2+oTjB5tY3s4uyXaVo2/k5tWi6UMr3GUXwNkYRxK8laa0W+Yra5A0U40nOVvTYr0s/Im2/SxSBy/vV0H46betRpeej1HAbR9F+csCj5bbYRhHIH6uWw7gp7hx8VtGOcPO0PZuGy+uqeYoqbo9VLatIM+BWYdWy0mtrK10ES3qf4jbdixrWMT7tBwsV7Sdntt123T6WYjt0PfRdt9suvT5TUdtUNO9ZV+2Gj3p/0zJJx6e/p6n/ca718EzqfpjBz1Q9Wqynq+HR1Htzsp6GLU1pN1QaSi7t76vXpCuaE8wtLascEq1hfaMkjW5Is5b6+jT8QRGcNjbpWZqHooLjfEVH9qa0PQ1vmfK+SHFhd5CiM3XjPqTuhlTbUFHDOqHl+/9CUVlR/FYPUowV3uazvFVxMdOqOV2v+/dSfKfPUzQD+amkRS3Sd9WXpJSvdZ8S9W/COUsNTThbfc6h2oCD+EWsrbi9cLfiqjHbvq3z4KW4+ivayeTG2fx/iivQDRXB9Tqqb7/U0/ik6SB5ZNrxTy8eDZ9ntKJ25kfq6zizWV3ZOvLu3sP2frviJNWqQ5kieP2QooZwrtq1ybqv4vHnluXbPR0EblSPHVYa3v94RRu2T6XHzZI+1iLfZmrREaEjz6lquICpydd1EKeOgEJxoq9tc6fo0HOE4qJjRPq/zfjDt6jUkUwR0GcvRtU/kBilmkCilP63KrXhV9QA/7bltntrxWNv1XSWS/v1TqXnr5B0fYv1/EQRJN6Stt9HJF00BNvunYqTxn2Ki6j5kia2+Z6alkl6U/o7qerRcnvf3O3+nfK1aotcSn+xotb/FsVx9mOKW/916XvthHZT+tvtuNeTFBetRdvLbST9qibtNekzfF9Rm/3B3G+1I+8xKrVFVnQk/kAm/T2KIKyrc4XiTuUxinPYOcUjk35bxXn894pA6f11v7mOfNm5BjL5LlSX7WvVxUWXKo6FVcs6Xu96YIGU7+Q2ywZp/x6j6CM1XTH+8wxl+sco+px8V32dEv9dMbFTm+3dui9JKc9S9SlZ2sewrKSngsVt7i8pAuVPqeFKuZSv1161x0j6P/V15rhHDT3U1X2Hwh8pbpXdo6hNu1oxRms377G+YjitTdUwgHe3B7WU50uKmrur1V2Hsta1Bj3sC5eqNGC7otbhWjUM4K6+zhvlx1XpO6i94FDUZB+nmBFulxbl63Vyi0npwLQglWnnltvjevXvqf9qNQRx6nK0h7R/TVPUAPxdUUO/WYuy7a0YLP43aR+6V9KeDdugVSBRytN1TWwpXbcTIewi6a60v12bvqs2nerWV9xSfyA9LlBzB8Outl0p30sVHaI/IullDWmLPhYPKfpSFI9fSrq6Lk+bZTV5pyj6BDRWRKT0u0u6XekCWlE5ka1USOm66t3f6yN9N+uqr5bwlXXbrSPfrYqLwcY7PIrAajVFZdHnFDX6W7csX9XFdK6iqKdgI+1Dn0u/jUmKZkqnZtLfkPbRF3e5nlMVwe9hKl3ktsj3RsX57k+K89IBajhfq8VFV/pe1lHcOXlhaZ8eqzTaUYuytR5YIKWvurhtdQHVw/dadACdr7hTeo4ywbniuPZmddnxT3ExM7r0fG1Jbx7kz9IZN0xL+8KH1WNNs6U3Xu5YDOi+SNGUYEDHJK9p05Yajr9VfVPFPihpQ3fPji1pZncpJkh5oIsybqUIMMer/5iXm9ekn+3uO5rZbe6+fWpr+At3b5pGspgk5GuKGf7+rqjRnO/uL83kuUgRIL1d0XHn8JTnw5k8dyiG4Wuc/MDMtlW0pzpYfUHBT7x5jMiinXXndquc+tViYoVa7n51Tb4vK+4wFO87UdHJ8FFJr/RSG2Mze4G7P5ba1lWt4x+ZzzJbqSOCu788LZvr7nVtqzvzr6O4dThRcRG0VUP6HRVX86PT53tI0n94fsr4VRQHwjekPJd76sg22FJnm5ek9dzh7rVt01Lb21dKuqHttjOz/1GcgIv+CEcoauqb2ozLzC5VdL7rnAjhPZKu8YppV1MbvfLnyXXCe6W739BUjkz+brZdMTTbDl28/xaKC7oBE4QoZg0c8Ls3s1u8YxzyqmU167unYrFnjpEzFbeqp5X2h99XfS9Lw8x+pQj6/y89f6EiOHpjQ76dFLe3X6aoLR2jGOIrO7W7md3g7q80s1vd/eXpu7vV3bcfjM9TWs9tknbwdGJP67mt8zxhZl9394+k38OAIKDpt1T6HMW57HmKY0puGveumdm5FYvd3d/dMv/zFLWg71Lc8q+dBtvM1lME6K9X/P6uUHR0frCUprgwfbGilrhoJPwPSd92929m3v+Lkr7csc991N0r+62Y2fsVF5tbqH8H2TUVTXQOz6yr1/37Znd/RfG9pmVXu3v2HNytIhbqWHZr8ZvP5Hu14iJ8M8Vd0qJN+4DjiZmdqvh9ltt+/1XRD2Utd39Ht+Vebjv9KWrcih9y644G7u5F8Ktoa3aPoiapyTy1nLGp5FxF84VTJO2p+FHmenoWJ6PFqTPM3xVXpm18XhFY/DodqPZUXHXnbOnuB5vZBI9BvIuRLHLmKK722swyda5iZ9zH3e9vkV6SZGafUgRu26TyvFFxe6oyYK4LiFt4lbu/pvT8VjP7rbu/xszmdqS9WNE2fZ76n0CKDqebZtbT6+QWhS0V22KsonYty91nq6+nfjaYL3l5CpCfDZJTB8p+g8yb2fEes6RV9pr2mt7SZraXu8+wgaPUbGFm8voJT55y96es/0gmTdvu3YraraKz7DWK314bY737iRB2UV+ntZenz1M3Kc0ZqfPnJ4qTVZNet53HgP+3m9lGnjrCNnH3uxS1gr9sUa79FLdYNzKzb5ReWkvtOivJe5jV0ruYaaxuPy29V13v/vXK34+7P2wtZnB091vSBXxxUXNn7gKq5HqLESZGmdkeijt//b4DM/uRux+WLiKrfnuNFyiK4+mPLWatc8XQmFXfdXGx+ZUW71ml+Mz/Z2YvUwQiYzsTmdmP3f2QdLwdcFxtumDw3mcwLILl/RSB0k5qiANSZVltEJrS9DS7a7Kfuz/bMTDtc/+u+o7eSzP7ZU/7t/q+179YTIBzv2Kwgn5K59Hy4AxS+06ZVaN8tIlHW89yqzjnlTv+Xmpm17j7a82s1SgtvRRwmXD3KW3SmdkJ7v6lFIBOVASRDypu45i3H4LrX5JmW/R6LffOzg0rt7rHbHLmMRPUFDO7VhFEV/luutKbomgbtLraz1zzT3d/0MxWMbNVPGauO7kpT/qbPah1eJGkOyyGQ8qO+JBqTFZXPpiscqhiXMhb3P0dFsMNfasu8VL8ONc0s1d4DAVV1A4Vafud8N19v/S3bW/0squth9mO0vf3VkUAc6Gkz+WCLDM7wt2/bx1DO1nzkE6S9G0zm+Tuc1OewxQ1JZ3lLGZ16rbX9L8p9uk3Vbzmis4gVRoDiQFvFjU+/XquWwyD18a1FtOEF6M+vE3SNRYzZw7Y9mZ2vqKGZ7b6DtCumECgSjFm/I1m9jmvH5WnrNdtJ0VThPlmdr36D1WZnRXOzA5QnIg3UZy8qn5L9yv2gwPVf1jPRxQnrdz7110EFOWr+0zdzjTWU+9+Sc9YDCf451TezZQJvDOfZ+uGC8LCxxXtdu9SdF6/XANH3vl4+ntQ2w9R4ROK5n7vV18t6YBRMopjoqQdUxD4rFSL2lRJcVY6l/2X4lb3GoqJQjoVdzMPqHitVq8X7qX85ylGyLpK0VRnkjcMd5byfLijVvarNbXZz5jZ2h1pD/P88KMjzGyUp7tG6dw5qi6xp9kvU03pQ56GADWzNc1sN3efmVlXV/t3yectRmH6qOJOylqq+K0XFVHe2xThUgyx+zXFKFeuaKffZujgbma5HdOxDTZVHC+lhiEG6yy3TTLasnRr0MyeUbQxPNLdF6TX7q6qqq95n0lVy939vEye6yTtoaidnKG4RXOSu7+ky4/Rpny/VrQV+pLiS/+7on1t7fBWZvYexVX1doqG+WsoZvE6M5On8tZLVS2vRTORryiG+Rtn0VTgxKrguiPfje6+q5ndrDioPapozzfYt11fqWiD9TzFyeNpRbOE2yQd6O4/qshzhbu/oWlZx+sDmjtI+o43/LjM7GhJF3vLZkBm9j53/5aZVV2QuddPfyuLsXMvVtSgvEbRWewArxmKzswO9hhOLLusIt84d7+naVnptZGKk3t5253u7pW1BxZj1G6k6OT3gJm9VBEk7OXujUGzxdXF2xTtvk1xZ+Mndd+Vmc2XNL7pu6zIN17R1nwVxQmhseal222XXq9szuXuVzaUb4Hilu2tuXQp7fNa1qKW83zW3T9jXd5Stxa3xQeDme0r6Sz1BYavlXSUu1fegev186S8IxS369s2I9hU0t/d/cn0fHVFjeF9bfJ3w6qb2zTeGu9hPQPmDKhaVnrtTe5+aS/n5ZT/zZKme4umhaU8Az533bawHpoUWAxxeqDizqwr7pRNc/cvN5VL0fG4aGaziqL5VO0dh273716Z2fne0bShallFvhcoLrZenxZdoehT81hN+uKzHqLoRP1T9a/QGzCOvkXt/ZmKi1STNE5R0fIbSe919683fb4B77kSBMxFW6q3KGqYX6WoobpAEbS0viWYajS2Tk8bb7Wlk/d89XXOGK1oo1TZhjGdDD4raSN3f7NFG+Cd29RCpR3sCcUJ+PC0rh8M9okkravVvPUp4N1L0m+8r73hs22fMu//LUWQc7iiBukfirbV76xJX9vuTHp2wPnc+tZV7Ou1gWn67ldTXHS9Rn1Na9ZSNIPZJreOXqQD39slbe7uJ6YT5QYe46Lm8r3a3a9rWlaRb2tF5737FB0sascvrjmRNrZbrcl3s7u/omPZcZK+7u5txzyXmX1JEezOURz8LlHUYJ0s6Qx377ZJVZt1XqSYgr71pDRmdqTiFuqpilkLWx1k2267wWBmv8tdbHekbd1usCJv1xcBvbCY9OcTGtgvorZNbToev1Lxea5vunBNv9eD3P3HPZTvV4pb8o1NWcxslqI52dPp+ShJ13rFJCelPHVNHyRJncdkiztMb1cc664tvbSmpH+5++uVYR0TfZXWU9dkq2rfbnOu6OnCPaXbUgP3h9rvzszmKNo5P5yer6Po0DmgP4W1bCtekW9flS4G2wSwNcF57bZLlQIbK+KFVvu39W9yNUDb7zVVgNzm7uMz6xqhqFj8eF2aijxX5YtX/TtPv51tpGf7hDzZdp1VltsmGV1wSXL3SyRdkgLLNytuI7zIzM6QdIm7X5F7EzN7naIj1b2KjbuJxS3sa2pX7F7MMPeoUhtKi1sfdb6r6JH+0fR8geJWfGPAXLryekbSeWmnm6i43VT1eUYohrh7ID1fVdGb+Th337ZuPWZ2iKL9+G8U2+E0M/u4u1fNtFQ1OHojd39f+neqxYDsa1VdIZbcrL5aug0Vt4qLlbqiV3klM3ujYhSB1ayv6cIXK5IeoxgZY331b0f8D8VVai2L29uf08CAoqkd11TF97mXolPmI4o7ApWztJWcpmiT17RMFSfQdRRX6DMtbiV3nkh7ardq3U+W8xLFbbkP1F1gVpigOEk9kU5m96fnd7bML+t+IoSuJqUxs98pjiF7uPtfW5ap54mGzOxhDQyQFiuaKnzc3e+tyTrTzL6nuOgof66qSZS6aTfY6ScauF/WzjZq3c00WvYDxbF0f0W73UmKTuM5oxQdZkdKGp9+D7nj/TMWM4F2HTArRmG52sx+pv5NZ6pu4Y8s14x6tPGvvXWfdNv04XeK2S7XU/9JfR5R3IFr0mqiL+vruLZ5CjILayqGbGzS7eRgxXonK5o4ba0Yym0fxd2k3Hf3VUm/M7OLFb+pQxRDs1Vp21a8KM8IRafI1+fS1bjbzD6k6Jwsxfa8uy6xu7uZ/SxdaDdN1FU4WtGJ9cfqf36tZGYnKEZdWt3Mij40xV3cs3J5PfpedFUJ4KlprZlt7jFxUrksuQv3rRTnmtUkbW/5/ieNVoaAuf9UNhFY/kDSD9JJ9WBFbU82YFb8WN5QnHxTbdyPVH9g311xa/gaj6l9t0/r2UP1szK9yN2/Z2b/L5X1nxZNSeo/XHTsOiata5piauxjFO3dZqsiYDaziYo2wY+Z2R8VtUPnK4Y+y3ZqUMzat0tRq5xqbn6t6qlJe55Jx8wOVNRuuOJAVhswl+8SWBe3C83sdEXt/2sVt8HepjjIV63jFEmnmNlHerhV83VFW+S5bWsTk928/yxtD1tmlra0z71K0Tar3I55LUUQXKWrtoPqvd3qS9K61lb/triPqGKGLXd/n6UpgFPNzhkqnXi9etSBJ4tacY9Oend0EywnX1aML5xrF1s2pcv3/4y7/6opkaW+F+lpV9uuQzGB0g8Vx8KJip7hCxT7fF0fjg3S3/JU2K7qWUe7aTcoaakuArqZabRsXXc/28w+7NF87Gozq22Ha9F/4FBFJ99iPa7oQJrzKzP7mCI4Lwe+TZ2wFqf3LoYhy3nQzP69uHhJF+TZ9y/ugHj0pWmU0v1JMYxfL1Zz98ppsjv01HGt1wv3ksPV10/mMItpws/IZUjn5lmKCgxTDF9X1wm7VVvx0nv/y8weN7PR3jCdeoWjFUMLfkqxj16pmPgr5wYz26VUqddkQ0WsdKhi+16oaKr2cFXidOz6kpl9yd1PaLmOslvNbJriwqf8O2rqC3CxBl6AX6SKOM2i6eLrFHcZpis6gP5W9f1PGq0MTTL+s6bGsNv3GXCLo+62h5n9t+IEN1sxwsHPFVd9X5T0rbpq/3QAn6AY13gni3Y53/D+Izl05vkfSQ8r2kPurRj7cVVFu77ZNXl+r7jlviCt43rFZAaXZDdC5O03pJfFbcg5Nbelnq8IsIv2vZcrOq5lp7i06MgxXtFsRoqT9h3u/sEW5Ws1nFVKWwx5NMfddzCzNRUHgVx75OcpgpSid+1vFE17crWrV0na27toXpDyzVQEwDel/WGM4jZd5QWBRfvy1ykOoOVa70ckXeruf6zJt4riNlnrNuLpQu0xT22JUw3JKG9o9mBmu7v79V2s57WKIOl29dWUuvfv3Vyk/T9FXwEpTlJ7lp43dnRL73Gdu7+6bfmGStV+3O22S3lucPdXVi0r9vtBKOtJatlusJRnguJO34FK01UnjyiGt6q8sDazme6+Ww9lLD7z5Yrg4n5F/4AtatLfqRg+s810vOV8XQ2T14tUWfNDxXjPUtSUH+Huf8jk6ewQ3VnAyjsoHflWVfT3eKzp7liq9HlUce4r7xNNzePWV/8mEn+uSbeDIuA9Uf07Ez6imE+hMpAr5S/6ydwi6bXu/qhlhifs8Ri5umIY0FYX7Wb2Y0UTiV+pf5CY7cDYCzO7XVG7/qe0rlajkqS8GykGTzhOMdpP9g64RYfHrdT/e81eeFr3fRuKC/Avq69zrBQXUB/3iqYwFndXd1AM37iDRVPT77h7VefqVpb7GuZ08DhDUTv7slSTe6CnOegHI1hOZlnMOV/sHIervtfm/oohS55MO8v9ioNvZcBScrxikoRxZnal4rbjwQ15Ni+CVTP7jmK840099Zit8bSnjo8ewyDd0yZYTqrmra+qdVIKnj6ZHkpl/IpiwPecvRQTLBTtv85Ru9uA3Sra6D5pZhsoRk8Z25Dnm4qB5c9Jz49QXNHmruiPlzQ9XRCVTx65USukOLFfIml9M/uConf8f9UlLtWcfbdtTVLK94yZzbFSj+EWrlC0tXs0PV89LWtq93q0mc33hp7mFu1H/1sxA9jrc8FXyds6nteOeZoxy8wuVATp5e+qsmbDouPoaamcqyoCx8aAooWqW56ttl1FGd9alD/V5hbvnbtVvrnic23gMe7qdoo2tlUdkIoAdufSMlf8jiu5+/9I+p8eLgJOTTVDV6hlcJ606t1fcrciOGwdMKeg6ghv6CtQk3edVJ6Xqn9g8e+daVNgvLNFO2F5i+EJPY1WYGYnKkZDOl+xHxyuzLCs3jHKgUVnudq20iVPK36/n1TpQlc1zeOsZh4BxfaoKtccSXPM7IfeZYfT5La0/b6rGLHmH4q+D5W6PUZa3CH9b8UxYZy16/B+WXp0pSkGqrFft+tJ69pJESzvo7gzkB25wmJggQ8r2kzPVlwQXK/Msao7048AACAASURBVEFSL8MF9nIX7on0vS5JFUB/V6b5ZhvLfcCsGDf240rDjrn7bRbjCed2ll68X9HU4UOKA801GjjsT+GJohbZ4zb6nblg2czWcPdH3X2mme2lGPS+mLK7qRH6sweLdFvnnoZgWYoArHy7bI3y81wg5+4fN7PyKAJndRFsS1Fb3BQw/0HxAyt6fW+oaD9VqeOzdH623Of5RTpofkV9w4I13Y55ZUet3BUWTQZyvqAILFdTHEBbcfcfWHSc3Fuxrd/s7ZoKPJ7ucnSefHMHqQ0lzbNoi1uu3ag7wK/m7o+W0j2a7ig02d4Hjv9ZVWN+o+J7eXdx4dTE08gPZnasd0wQYNG2NDsyRLKWYrz18l2G3NBt31Q0c7hIETC+U1GbsrSqPnPbbVd2hKJZy3fSe94o6R3pu/pIJt93FM1NiiHFfq+4SB4QMHv7oTmrPJgqB9qe7LeT9A7FCbfcVKLpBFy01Vys+mYoZY8rhhG9Ui2HEU0n36+ot2YM31cES4cozjGTFKMqPcvMDnP3H1m0Vy0vL9af7ZiVvLGjhv6MdCcrOxJDwd1/ZtH+t8lxinH+20701cs8ApI01qKzb6vJwUpOSL+lb6QKoNHe0Jla3R0jP6O4sPhNSjPbzMbm3twbRvbI6CUG6nZUn88qAtL5iju/J3iLDqqKYHkXxcRTe6aa4MahclMNc1Xn1MrKgR4vwGel8/+3FYH/o4rjY89WhID5+e5+o/XvWNZq0PwujVRM7fk1qe8WdE3aLSza30gR6IwtPa/6gc02s8nufrHHLcA24w0WdkhXx8UGWL303Gtqur6t/rUKnc+z3L2YNrgXbXoAjlaMHVu0J95N0dnip2n9nbfWe/os3jeW90UWY++u7s1tDZ8xs7GeOkulg2BTU4t1PNPMo6GMdyhmY1Ra35/dvWlc66KD0wFq38Gp7XjfhcfMbKeiZs+ik0btqBolq5jZC71/T/Oq48zu7v63FOgOCH47A+IO7+7MoxjWr7HGuYeaDXk0bRrh0TzlXIuOfUur6nfSdtv1K5vqa5NyY+mu6e7XlIIxN7PK42q6lflFxbTG+1kMmbe7u5+dK1vS7cn+LYq7aq2GAzOz9ypG6fmjxYc5R9Gf4E+KsXfrhs0rpsrt1hWpQuGnbS/0kg3cfarF0JCXm9kVijGCy16Y/o7poVyFf5nZ4YqgxxVBaW7il/KxdhXFRWGbz9XtRF+9zCMgdT85mNJ+cLlSW1dv38+hm2Nk1x3eLZrzVAWJTcF/LzHQZerrKL+apHGS7lRNjb7izubdiiYMO0j6YlpfU1OOJz3utMtijOk7zKzNsLrlzoirKX732cnPLNq1n5COP65oxneyV3dUlrsXY/WfaWa/VAwusFR3sleEgPkBi+lci9v3Byl69w62K9X+FvSEjudNsyXto7jVeKSkD3gXQyq5e11nrlyez0pxwm0RICqlrWsDNyAwt/ph3opRB5rU9Tyu1MvnebZAZruq1OPeopds5YyCyfGKiSz+oPgsWyqCsZxfm9kbvGEklrZFbpGmqw5OUjTnsJbDBSYfUVxoFAexDRXNc5qUe5pL0eRowPftfbPttQ5+zexQRW3vuOLiKllTFZOOdOTtdSKExy06Ys62mGr9L4omO0urqpd/q21XZjF01lRFQLZDqsHd3/s6FNZ5yGIYw+K4+iZF58Eq31UELkXTqz8oLtjaBMzdnuy7mWlUihqu76b/D5O0veK268sVTZ72qMrkMfNpV21Qk+MU3/8SM3tS+YqLsuIC4G8WY2ffr44JnzyNmOHutc2yWni74q7BqYrv9rq0rE759vYSxQgvnee3Kt1O9PV/ZraG4s7tD8zs72pX8dXt5GDFxd9NZrZ9NwFSl8fIXjq8l5s0rab4fTd1AJV6iIG8o8+RRVOL99UklyKg7sXCVIv7M0WH2IfVEPim8vWrkDOzHykGF6iULozfpzg/F5MV7SzpJDPb2N0rR+awaI9djF4lM3utN7SvzlnuO/1ZtLU7SxG4PqyY6voIrx8uqdf1VI11OGDZ/2fvvcMmKcru4XN2YckZFJC0EkQM5IyoCKLAK6JEyQiSJCqIL1FQFAVFESTJipIzIpJzzrssyQCCgKKCIjmf749TvVPT06Gq51ne9fN3rmuu55mequ6enu6qu+5wTunz9WBy9KRirzApnQQzNcSMACnFSkcCOEX1VbtVff4ApyKMA3BZqkeE7QTsxUq5yrhTwoq52M/06KeOapR4zv0+JH8Bh/L6VNqilWddv+ngnFUCeEgNfMWh/YvwJPpGeKVOolX7avUwM7PAKfQp0wV+DC6WqGI/KfpMjZ4E8CNKzCWkxUQ+GfpdU3XPRsbvJ9DvaZsZwGhVpAHQlGMLo6LiHi7sqD0/dhRCoGki/w7nu+4FR0eOC57dWrBb3mHStSu1vx6mdzpWDnUXqV5tfLCLwmPR8nDO678AbCJLZ5fb3iVp+XhcaBsbo76XAfgqgHPlwtYNYXGpSq94+D4fhRl9Umj8Jp1H8FzfoaBcx4YCYXYUXeoKWifgOnjx/jP4Pj+46vmj8/u3wyC1XhszwruGDs9RlY7AaW0OEHYUByM5Hvam/h79RW9NXNbJYySrC96/rUyeXwYV25Y2VTbQ5sqoYwn7SS6Yb9jHbZIqU5LoovRZ4Lk5V+joAwAulbRIzecPAVitfL/QGgs3q4Iqlz0mnIfQP/93fsaneIO5QHjgRqk9f7fr/m8BsJv6Q9A/rbs5QpvT4Hy28wGMU0P+aZigjoXDWAX/LoBebmbL+W0Ph6Omgg3GM9VCTxMmzzXhwXcF2Cv0CzVUW4d+Qz9YLfv/MhySfRu+DsVg1mYoZn0fko/AKm054hijAHwGg5NVSv5g6jHq6JgIYH9JjV6HsFC7CaYvLAqcviWpNsRM52GvpRJdoBpYFGg59XLuYBIlD1uq4YcxfkcKdFHd86kLycx934CQihAZmbVV+qW+SUwCoW1nYza0TRH1uR4uuLwqGL0rwaHQSlXQUt8shwczlEZD+3vhIux/wWkYa0h6MHz2cNVEGj6rEl3qYwhq+E7ZrAA1+5kkk1zafgvsVOnjvZZ0dsI+sxZqzJODLvdN9tAzU+kvapMrDsbgYa40qJvONXWMZAfhjdAvnlOL9JedW8bgSWI5OTZQaY4ZBaenzCFp7ZxzrthvnzON3ZX+iog2w99n4LzpylTQlme58jN2ZMJpwhSfksGSohB7OXcjTcWSHYKWtAVdfbkZnNso9IzZSTc1yW/DDAj7SLqky8lJOhnAyWEg2BauAr4FllytVMEJhsBVcKjkk3DhyS5hYNhPmRRWVaBz4Ao+5ZskXZTQbT9YcCI17Aqg0/d5ED0Z8VRcDH+XRkL+GMGQ3xzAWEmHkZwfwDyqLzJpysH+ccNnADoVOAEeaOPr8Bw8kFaCHTks6erxo9BSDS+nJf2Jzgd+NUxyC8Me7bbIwfoAvheOkSI+ApIHAThHzrGbBq4AXwoOrX9J0tWl9pWKadH5t9EzZecdpl67Ep4Li48iXPt5ePJpBC3TG78HAKiaJWNvON934TDmzAWPZyl4WtKa8WTPBuVOOSw+N7wgFky52PR9DoJDtKNhmeHCWP44GsQdUJ2D2rpwYgdWgOAxnhtWM307GKW7wcw7VXLuM0j6WsX2FOTmjHcpNO3z0AMYy3YP/Vowd3GMz1Zs64PyxcHugWWkc/nZgcQxUh2ENwJigZgi/WXj6qaTjjVJLEc1stE1iOeYt+Cc5q51SX2nVHrfNzaFxUTrtVGJnSUBL5BcUmZPiY+3JOxkqUI2E04bpniDGYmKQsNC0l10hWdWCFrSCyTPh3Oe94ST1/ch+RNJx4RmU8M0dClFU7UIN+Pi4fUsnO+3N11IsmlF+zngKvot4fzE3eCJbyk4hzIWBInTQmYtva+k3aKFQRZBj4JuJ5JrSdq15as8BivoZSHn+wTExYVxeLcpBWahFC9TCcehp9h3GDy4H4saxT6FnOyu6OgVSqYLDNgQPQ7LbRk4LBNO7zDkVcPfCGB1mhLsBgD3wakalTLpAUcB2EDSxITzKbBJODfARZKEDb/FYIXPcv5cruBLGV1qL3KvHeB0h58DWJzkE+EYA2NBBaaO/p8WFomoZKqRqSk/jt7Y+LuMCMAFJNcvJvtgDF+KekGo7WEj+NpwrGNIHirplKr2kn4TDKiZ1M/Nezcih0cYl2JBma6iS1msACR3gY3VJ+CCvB/AY8O5MBNRFS5j95qI3IVadqFpwCEYZIkYyIPlEEp/7CYOll6FN4icMTJbeEPd2WayxXLUq/uZyW97jEcjAQ6h9Bft43OI9A4iR1AVvgbg1zS7RqH8uzw8lm9R0yebCacN/wkGc6qi0EhgefRC8UuzRUYxrLK3g0PLvwKwQniwp4c9Q8cAQBF2CjdIGf+Gcw6fazoxkj+ERQCuAXB45L08IoQeqnBbOK/PS3oq2n43LekZIy7+uKH0vo526+Po51M+FV7YtGE/ALdUGLJtv3PO9wEc7s/FFSTXkHRte9NJyFLsK0CH/HbAYPpHWzg02yukfLrArhyWudXwoyS9QnI7OAXqe3T+YRP+lmksA+YmL7wja8PiGW/DC6qBcVCZ+YEV2BWeOBYn+TRCKkJLn2wmATmXeo2w4KASOHtDv75iQpKHo/SMF89BefEMYLEwNrapcgEuBjov3HvzwwvcJtrJfWDnwnPhHOaADdlKgzl8l7fglIx4W9kbdwQcnSqwG5yD+jpsIF2B3oKqCbmsAF8FsHiYF94PMxV8Ss0pHDsB+AbJV9BfEzE5CsRy5KBjpHrouyr9xeJg36BZjgpxsKbxcS6WaPn6TrAhtS6MkUXEtG2MnB32QMeRhSZ6SoRn9GD0jMQbYK98m/Jf8X1jR1Qt53U41ofhuXL28P5ZmDWmlr41EQQADan0R4shLY+eUvEeJFet25ekm+kC/l0BbBPO40GYBrYuAtWVCacW/wkG86/oCsksRaFckPwVbPj2FYihOQS9EYAflQe/yAAoY2c457nIx1sd9p4vSvIgNbM3PADgAFUrrdUVMnwgMhL6IOmI0vtsui148F8A9p4AnhBTqpKPh70LOWkPowH8RlLlpFb+PmFbCi9vGTcBuIROr0mdrN4M51dMUnMh7XtdHI53NRqonyqQ7BUiuSd8re9THl1gVw7L3Gr4UXSe4pfQE4dpY4a5i+TpGBQfaRocXw+TyN/gNJbYaKvll2ZH4RJJjwHoS0Vo/koAMq8dzZCxAxxxAmz8n6yWgsQaTAVHi2J8HPb0ViljNRoHkxpJJ4XF40XwwnBH1aj8BTyF/hDri+jxtQ+DfsuuQnQpEbmsAK8VYX5Jj5H8fYuxDDiNrCuyFmrKk4OOkeShD8bgvwFsFsbI98L32oy0PkFdfn5XcbDR8PXL9jTT6oXnpiwEO86Xp8DzeJGGsSWcwtlY9C+pC4PFiQD2VkjXJPkJ9GoJKhF+nyskrdmw377cZEnfZImJImxvu8fXAbCUQn1RcLbdB6DW+JaZlQ6q+zzs53xJXwztKwtQh8F/gsGcpSg0BJaDC8SSC4Ak1YaNa4y1NwF8UNJfAYDkPPBkvBIc2howmNkrFBgPD4Ll49xbXqGSvAQ9463q3GqrRFnKGY/67B61KfZfpDwUhtQKSAtrvpMbFpHzxrJkfoMhVhg708CD6Ostxs6P4JBfTgpQlWLfAQn9pldL0UsNcujH5oPzohcPIdFbYQP6tqpFZ1jl3wJgL7lYIonDkr0CpvUBvAYzShTV8Ic2fJe94ZD2pZIeCF64mxraA5YMfgeOuBQQmr0Je8CV9nPBi9w/hfNeBx6o61AlXFJZyR2j/Byxofaiy7UjuSK84DoZXtQTplK7kU6BuKuqX9T/LvTG09HwfdKXvyzp4PA32zhgf9ER4cX0eAArkVxJ9WJDTwO4g2RRS7A+rNS2dziXNvXMOhTj4dGS9ozHyL5GLRX0kjYI/x5CU6rNAuDyhi7vo+kIC8wZv5e0b7lDGOvmhp0R8RzdOrZ2XKjNDi8Cx5Gci+RYtVOfxh76MxBYIuoa03m4h8AL1liQpq4WIEscLMJfJTUaVQ2YGY4w/hPmsT5PPfpLAM79Vzd6SgBYuDDmAr7VFE1riPAUx2oy7GdQVNsk6fpwT9Qi3HevkJylzutd9lAHT/GmKDFRwIv+NswKoJiDZklon4JJdmFYyHURvanFf4LBnKso1BUPwIUZrRzPzOAsLmFsYSzDDf9K8gOSnmWNaAD6CwXKqFPAauOFbkJKzvgw+weAa4IH/hL0ewjb8prHMy9v7DjYu3IWbMxvg/rctwJ/gL2xOQunrop9vyG5jmqI1xuOl+wVkvR1AAhevuVgD8N2AE4i+bykJUpdfgLnl96GHvH/4wmnVbQ/Xr0K6dYVvpz6cm30/jE49NrUp7ECu6bPHeh5YuPtv0WUp0hy67JnQt2ES3JqL7pcu4Nhaql4YX4eyavhBciA5HIJsdfxLdjQqBSioNM1vq/+nPmvSWpaFJaLei6s2V7Go+FV4OLEfqn4VfibNYaxulCxSAuaEb2Jv4yy8dZqzIXrvQUsaBQbIbW/KWuYd6KFWuVCgy7uXQ7OTx8H57afhpr8apJTSXqrg4d+Tzji2Zh6GCFXHAxR206Q836/RedKbwLz2z9V8rgW4/rdAztox6skV5N0M2DnBJrFoIaJ8DxG8kD07vct4GhDG14DMJHkVeifY+sWAhvAv2tuYd134Tzw6+DfbHU0eJczEM/b2aI3bZjiaeXCQ7Jp3WA+gse5Di4euxMJ/J8dj3E8zL5xTti0IZwbujfMXbh6Xd93C8yklGOeGEbRpyrEKrXTyo2r6VeZ00byHknLMqKLInmrpKaw1DjYK/hb9N8HtblvJH8M4Gw1h5qr+sX8zUURVdOCq+hXeZ0awptF/tzK8ES4Mry6n1j2HtJ55Q/Dk/MAjVXdwEnyATgSdBCch1ruVzm40wwCX8OgzHetciK7i3W0onz/k7wRpjI8GWaf+CuAbdRABVW1n5a22dcuhPYXq9nf79TOU9socx6Pt6zgZU/5fuxIvxX6ZhUrsYKiLd5G8gJFxb7B2/aqeiHh0QCmaVg0vAOnixSOjXji1TBeq7D/oxSYMeialCWVwekbzm88nC/8eun8aguNg4dzaQD3qkevd79qWGDi353kMZJ2Szy/62DKtiSVXtbQCxZQPc3gXJL+Udq2nWqKRmv2MTcctdsULiZtY8RJ3e9S8EJ4Fvj3+Sc8lkxo7NjtWLPBC+ciH/sGmHr0Xy39cvm1LwOwUepzWuo7D2w7EOZPb2X3SdhnfH9Wzf83SaoUM0rBf4KHOVdRqCsOyWlM8yPerwRe1Qi7wPlLReHV2TBdzDvoFQI0HXMVDKZKDORYkzxH0sasocZqGQCSc8Y5SPR+DMlGMYywrzYvb12/3NDwy8GzOiF4bf4Ke4Oa8FR45YiO3AvgAJoD9ULYeG71QCifWqdAIXsKmJ1lLGpkT0meGLa/COAOOKz7w4aBcz3YQFwDeRLuO8FpBLNi0CPS5A05Db5mG8D5l1ujnRbtZASxjvB+Ily4NbTBjEEPxJYwtdRX4VSJ+WFO4jbk1F50uXZNofYU+qlHAbwHriQfBS9W/gZM4kWdN2o7umR8TgenODUihHiz+NxZXay0lQJdXAMmRUSqtmmQGSdH2RVwatcn4HSmM2GxhJH0NsUMCn9CA+VjDZaBDbx14ef2TFj4pu0c35AkumajWEg0IX4+6lg+qvAYgOtJXor+56HS8x0bxMzgey4bywFfRUPRaHScnWHP8lxw+tYOVZG7YFTuAXvlATsYflI1F5fObTyAJeki6pSIKuiC0q8gqlOAixEbtRTC+L572MdoOEWj9XjKV8DsxEQRvOvjJf2a5BYA9iX5Yw1fbB3fn68FO+0PdErQ0/CY1xn/CQbzReE1WVG3Ym1o/w7JCSQXaPLslfvA6QFn5Z4f84oS9wh/u1Bj5eSM7w9geZWI3uHBphY0K8FXEFHKADi5zftAclpYNrnsjayrmt4GPWPna7DYQCN/rIIsbRg0oAQqwLD6PjWEbb8IM5csIGnRtr7Mo9Ypjpcje7oAbNz8AR4wnkKDjLSc+nQWTQaf7PkIYcabSd4tKUUyucBckk4guassgXst+pX/qjCDpFujcLNIjpTQSZ+BEQ3gr6GBPqwCyc9Rx2s3P82cUwZhKq42nAfgusJ7TSvRrShpv4q2p8FpVOPCd9gOCek2AblpVFXFSiehxpAN3sD3wfRWS6M3Yc6MhmJOmH1pkldM0ktNXndJe9A33CfgRdQxJK8E8DO15/umgCR/BF/fF+Fw9dVIZBEKxth4APsFx8pm4Ry/oeZi2HNIngBTie4A/7ZN9JFdFwl/Dq8x4ZUE5vM9V+4msd2CAPYM17LufLaC00v2hh0lhBcrP2A7q9YecJrAi3BK3DKwfkAlfSBNq3cB/EycGI61NLzw+IJqxFtC3zPghfjb8AJqFpI/lPSD2m+PTte7KxPFz+DFw5JwVO0U2JZpFUNqQVwTtCc8BuwOM+CsATtkOmOKN5jrQgEjBQ4qzkz6CO3h8XkAPEgXvcWTQZ2Ma7bgQoTkokSFPOmOq7WcnPEsMYwIx8KpCMWqfwt40GmTfv0VnNe3NlwMtTl6OWUDkPNhARs7ByacF0guARsD8/gtn4LDZik5yYvAnoCF4CKItmNVUeusVmO01ELmya3jfP5MmOg/BBsdXwPwYbqw5TaFwq4KbE5LoL8KFzUtCU8mp7WczgskZ5IFKg6Af9fDJNUV1hWG7jMk14ar4dsiEJ3EOhLBsM9FYWP3nwB+CBttH4M9s9urpagO3Wovcq5dU77f/yYca+U4nC7pQpKV+ahykdP9sEcW4ZyuSDgGkE+/lVustDa8MJ4P/p0KvIjm6/AyyWXUr+zauDgOY+91NH3kpvAk/Af43hgWQo8H+0E0FxLWIjgtlgbwEXhx3JgiJ+lIkmvBvPgfAHCQ+vmqyyiKhwnnGReFwMVcVhm9VI8XeAblCXAcgkG+54Uy+gPA55s+JDlz8L5+P7zvy1cvRYV2gTngH4+2XUvTJp6FZlat7ST9OIxz74FzasfBkY0qHARgM0nXR9suCk6Fg2HhlzosIWtEbA6nF34DNpwbDWYk8msX6OCRLvBWcHSsD3vnf86adBAAYKKQVLz4UIXozbCYYg1mDpdWkAx1D4sDeR4noJvgQoHkosQC7EaJ9SAcZklBmeh9UziHrg0rqT8H9Epara8Ni0jaiGYBODWsogcmbloutxZqzr08EcD/FpMGyTXhCXG1ug40V+4XYGPqHNigSOHDraPWaTSYWS17WhWKBDBpon+A5PMwxdO/4ejDCvDAW4VPS9o3eB6fgnP6roO9jU04UNK5JFeDjZkjYRrBFWvaH07nV38dXkjNjGaeXqC7WEcKCjGFcfDkNzOcylKIEn0MZs6o+z4Fcp6jAsnXLtOLX4XnaTGE0+DxdQv4vqiEpMtpZo3VYdGkJCg/jSqrWCmK7nxRNbK6NchSdg1G+/rohewvgBXlRoLyDgBY/KYhkvZGNC6MQotXluS24dymhaMHGyutnmSJMNZdFW37RMlIi1EpT5xwnJXhZ3ZGAAsEz+KOkhoLfFHN95xyvDGwobwQ+llqDq9ofgY8HhaCGH356eiPCs2siiJoSY8zpFo0nVb4uw6AcZImsPmLLVz1O8hqmG3iIFOTnBq+Bj+V9CZD2k0LshQwh4gAvEiLn2wJ4GN02sjUDe2LaHnBRV2MD5ujNM4yKg6tQmZ0og9TrMGM4dIKskHySACnKI2DEkB+Ggc6CC6wR380E4CHgjc7tSixCyVWcs64ekTvRU728UqTxn6H5ELFwBM8BikUboU38nk61/EZeEAsY0xoewac75tTwTtT7GGRdDXJJqYSwBP6ypnexAJdqHWSZU9pEv9V4N/oTQRKOdi733QvFoPXOrDU+z8TJ60iXWhdOFx9MclD6hpH4eL7YWMUdL5ZJcLAuqSkbLGO0D8Oi54Me+MmhUUlFceeUdKJoc9Oks4N26+iRRXa0KX2IuvaDYnNYBqw64BJNFB9qoK0WMR+Mt3fPHAI+m7Ys3iipKPbDsL8NKrtYEfEBfCYciPSvEO/oXmBF0J/jUclLZ/ylV3/DnuTzwTwR/iaLV9EdpQm4tKEWHjpOgCfRi9PfQbYMVBbrAwboxPhtIe1AXw6fl4b5olzSP4S9jxOC3tZl4MLgweQGrUkeZukeB9Hh/P6ddjPBJIpRe5dFRkvhCOL96CF417SeuFvCt9xUxSiLX3vHjqNZyyAb9KFrU3z3jB1CifA0tsTYKrJBZGmrpt7vQ9Bhkc6wiYw9/52kp6hC9lrx9XivqNpT+Pc+f1I3oJ++s2VYe72M2Fnx1DMGDGmZIP5VNjLNWwSeCoegfOKpoIn1DPVosDDfnq5MbCR0eTB7SK4MBSFm/IpsVpzxkvfO74ZdyD5Guxp3V/1wiH7wg/x70P/ReBJtQ0n0tW/B8ID74yooGmS9OFgUG8GpztMgI3nqwuvTQMeDyvf2MNVeQ+yV9B0J+w16WOvKMK9DehEraM8ae2FYI/TXoooDRNwCclH4ElglxDqTanaf5rOiVwTzuWeBvkFTPvCi70ByIVkewI4v+35rEEcFp0L9WHR+D4pTzQpi7sutRcjce2SIBdH7Uhy6gZDcax6vKvbArhK0lZhor8FNoLakJtGNalYKRMXwx7ye9CwQA4G7pOSnglet2XguoMnSB6iekGsc+Exb3EM0hPWppiQLPpVQtLG4e8J0ebpFPEnyyk6jawm6C8azMGKsArirfBC/HTkFfPVYdryBklPlhbdKWJNWXzPERZURkF+mPc/i95v+xAs4lGuq/kg+yW+J+0C7doQX4aZuB6Txc3mQPNicH6SVexMrXUKMqtT3PcJkin3SK4CZpZHOjq/Z0ieD9cVAY5afpjNRQAAIABJREFUNSnPFpiB/dR8q8ALyhhzA1gLnv+/BDuUzlR74XArplhaOVZQGb1Lx/0AfBNvBk8KJynKqWvp+3lYHrsyd44u3CtDahBAifoeoZLIRdW20uddKbHGACgoq37X4nkp9x0N4MMATm8asOi8pw/CD/9DSiiu6wqSm8Dh/iPUXvQwBzxAFCkYNwI4WBX8ocHQrYMkVXFkl/eRTK3DGrGF6IBNgjQLA3hK0ut0IdVHAfyyyTsbFicvBCN1ejgc2ZgrHNp9Bqas+0P4fh9RTWFLzT6eVAOTCp3f+xLMMhPXDqRUnd8v6aM0FeD1cu5uFW3aK7AnkXCxbaGeRwDvl9TGJpCNkbh2GcdaDvZKziapCJFvK2nPqM14SUuF/6+Bx8Kzyp+1HOc+SUtH131q2BBZo9Su870d+j+QYiDR6VprhojJ6nDe6W6wIfNBSY1FwQn731pR3U1YmNVCFbngwamxs0LRLR3mPl7SSsOcW9jXJCW08H4MLHq0FuyAOKD4jYc8Tpme8Tw4x/ynsFDX7gCWkzRSqVTl458MswGl1JLMC3v1/wqnxBXFdXMD+KSkv0RtF2zaV5ODj7YqN4fHj0ODg2VuSZUKqmzI6Q3Hqq3vouleDwcwr6TP0rU5K2v4VK7ycX4OM87sBy88dwcwtaSdWvrtANcszS5p4eDNPl7Sp1r6LQtHR4to7POwE6TSORWcDpvB3utDJR2T/OWq9jcFG8yPoSGXcQRCYFXHHA2ngGwLFx6dAxtOL6c+2CRvH4mBrWK/A7ynbODLDJ8vCFNFjYEpsWYBcJwapHODMXUqHM4hfB22VrvUZXk/O5a8JiC5GYDRKhWOkdwewIuSBnh/w+e11eEAKqmJ6Or5TWBWjJfg3/L8JqMq/P7fUWbRXS5ILi7pEdZQbjU8/EUF8Rfgwby4jpsBeLxuoRb6jodDrQvBXoNfw4Tz65TadVKXYiicYbXAQx2dWt25/lkNnNzs5/GeVLDb1CfqOw72zoyFCxlHw4bzsqV2nSZGdqi96HLt2GNUqDu/xmeG5G2w9+UC9fh3+4zOYMReCeewnwJ7nJ8Pi927JQ3QGFYc505JK4TF+y7wwv1OlXiL2ZF3N+p/IoBj1JLyRnJC4TAgeSyAf0g6JLxPWgS07L+Wnzp4MedTixAQreJ4JnqRrQXg4q8Uafq28+tbHNK1IxfDToI54FD+myOwcCgbzHPCiqNrws/rlQD2qHJElPZzFczzG4vmnCWpcSESnr/F4IVuwUutqt+G5C9girOjS9t3B7CspKGYFaL9/QyOTq0h6YPhu1wpqbJge8hjXQZHzvaXueqnggW5PlLTvtOCNSzy94dTiIjgkVYLh3iYj1aAnUTF+DOx7vwq+s8M26+VUcZgKK8Lz40LwfPdKZKeTtl/HabklIxZYOO1Kv+kTeUmGzRF0+fg1dLh0eB0BE0kX9UnNipGwQbJwE1H8muSjmI1DVTj5EZzQ+4C4P3sDwXNhJZcLklP0KH0nDD+UXAqzO/C8ReDB+9lG3sNHvuEis37wLRMZZwLKxpVGszIVPkK3rBZw363QVQQx15F9ACCJ3WFnGOFfU4NYGf00+SdoHrP/N7w6roqN7pOvXGS0UDyMPWL3FwSDJImvCPpLbqI72hJx9DV/mV0VZfKKZwByX+heoAmGn5vugBqIzVQKrUgKSwaG8TBeF5UzmefDs3jZpfai6xrF/BAxbYcjJL0JzaHyL8Mp1GsCWAT9aIRK8GTcQpS06g68e5GWA3ANjSzS2wglRcooxnU6mBlzpiZZyTmw8p8SZKfgUPkUwMYG7zG+0vaqNxW0h0kP4heBO5BSW+MwLkBg8/cl9XjjH8GwPoks5U0K9Afo3d9x+Yd9jNndN9BlslO4dJtZMYoYSVJ25Q3SvpJ3dzfEStKWqYYd8N3SabYy8Scks6h0wsRxv6mFJgi9bPSGVPXSZHiI3t8zympe69LeqMYf4JB31RcmKxkSRfPfxgmIPiWSnLew2BKNpifUH1hyOTAA3A4qqqyvc6Iio2Kt+Aba/2KdoXUa5ccmjPgH/676GdPeLHOaxdCPwfDbAIEMIqW3j5GNUUwEaaOJypJvw8G4UhgqipjVc6Bqj1GhrFf4APww7cr+mWWC+rAJk/kvSQvwCBvbFOe+c/gifC48H7LsG37qsaSikn6s+XBhS6SasNcJN+vQJtHF1nM1dLnzeDh3xq9+3bgmqtHM3eoSvyybKYXyimcAYA5E9uVj/MOyaNho60LilSbjzKhiDEOHcKpGfPBBVp1ocPs2osO124kWDL+EsKbCuPFruilnRTH+DvM5Vo+9nVo58ou2hacvjegPccT7F5130SxFeNMWPL4WTg//6Zw3EXQwBKSgbpJ/zvwPXsNMKk4akCqPZzLdPDCayFJO5FchOSiklIYiJJQRJIk3U1ybOlZbywoC4bRFeqXjC6jz+gm+X049ziXpvIdRloHYfGakiP7KF3HUjzvN6k+h7UpHXAkFYbfDNeuoMOcC2n1EF3wcnAGFMdaCc0sOJ2cMezI9ww/g/8L86evBc/TlzS0z3GabQnfw4sB2D0a53NofCsxJRvMI1bZ2HiQXlh8PExT1fe5pHvr3P5KpExSjzniKpVETtiihBWO/W8Am4WH7b3w7zYjyRnL+wvYEy7cWL4YCEm+H8DPSO4l6UcNh7ybzkuKaVtyFN+aMIbk9OVFCckZkaAcxsSKe0nzBSNgHkX5Z4l4L/ywxakKQjM5+/Lqzwu/lmk0ebdiUJ2salsZe8Hk9QXP9EKoFy4psC08sH0neBbHopki7vyK8zgPLZEGkteolIdWtU0uQgXNkPKX4G1YDc6tPg3NFd1X0dSCFzedSw1imeZp4cXwPajx6sOG5ApwtTXk3OImD1fbwqUWqdeu9Hm2tHjATnBe/1iYJ/laOEoyIqjzCBVQjcIbOvLuhmjaanAkYFwwRgZUPSV9J0Sg5oHD4YXxNQrOZR4WdfPWWxpkmqkz/AoGm8LY+wu8gB8Jg7k4gSPRe77Lz/oBaIjghkjcKyRnaZgby169rjSV+8OiPkUEYnW08/WDZtrZBb3C23NIHivpuIrms7A6BY3IU3xtw0/gwrb3kPwOnC54wAjuP8be8Jy1MM0iMRd8zduQ64zpyve8HzyXT4Tnrt+iQTAnx2kmKalQmuRsapEKL2NKNpi3TFzJDosmyrDa8DgAkJwP5jleNbS9Gc7LeqqmywUk/0eBrSAM8MfD4YNGhAHgEDgnuViVCjYwytgKwFqKaM4kPUZLUF4JoMlg3hk2EnYHJtE6VQ0yXXAKzH26Y3GNwjU8Dmkh3uSKe0mi87KyUkkA7KYMmrKAt0kuLOlRYNLipDb8xe7qZABQcOIuil5F9yMKssUNfR4i+Q0E73pYSH2v4twWh42v8iQyMyoq36N+04Zzn5MOwcffad66fvCEtjxdlPhLuKK5SFGow1fD+b0Oe4cKz0FlDnAMSX2pJiTnRxAsqEFW6BD1k29x/AFDZIhrB3SQFg/j6oYaMk+1BUfCTojL0EuRSEFX3t2D4ZS4D8BjydTwtRlgfFBFOo9apIYzcEvN9kfCfcFwz+0Bs+tUYVFJm5HcKJzbK8y9IPUoisTj/ZX3nXKs1wBMpHOM40hcHcNJJ5rKMNYtA3vnCbP9pNB3fgUuwH8JAEgeDjsjquayG1CdggZ4/hsAq2sU/g3TLn5bFbnZkk4neQ8cnSKAzytBECss/nbAIGVibQReFrP6OHq0ianF+7nOmE58zzJb1UmIRH9ouey656dosxgcvX2vzIb1UQCfk5TCnFLGNWh3TvVhijWYixVq20p2BI7TlY4H8MB8Bnorty3CtrVq2u8K4GKS68FVuN+HE9NTsCdcpNVYJBEwddWgIukfbEh9CBPpzyVtgX7VrBGBrBj2CoA7guEBmBv4e5IqacRKSBIuiXAnIzWvJpBcB8Avwv+vwcT/qXmy+8AKYI/Bg9OCaKYL6qRORnJfSYVx9zn1uIFB8nA1F/2lhro/ABurs6J/EnkRHrTrsCN8j84LexiK2fAF2JNZh3fCIPsFOLf6J6zOrY7RKZ2jBk+hecGaGzrsUnvR9doBHaTFg4dw44R9D4NlYA74deHvdCaAayKPbh268u5uAI+p9wKApL/Q9HcjCqbzeJexK8wvPRr2pl2BXr57GW+ERVQRTh8LS603nVeuElrcttwvhQng0vBKRVeaSsDRx3/C9soStAR1W80G0ePtR/i/0kJPjRSznwHlMtgpckZ4X5ACvADPI5UGuKRHYKcPSM5Kcn9J32k59MVw6tDVSKPiK471FkIaKMm1wvxRZ5sUfXKdMVl8z8HG2Bh2GF0uc7yvB89708HPUxNOgufbE8L53h/sgC4Gc/4iVNIU/YLZDf4MUyAV3II/mUzHWgWuHN+qeLW0H5+yrfT5avDNdRe8Sko9t+vgHOCUtvd2+Sx8fgWAMZP5N10QNshmi7YtkNDvzvD3RtjImRMu3qprPxHOLf8dPJHeV/f9w2/yoeg+uCHzO00De/uXBDBNYp8vZh7j3qr/E3/Xe2Bj7r74+jS0X7njb7tbZvs74QXn/TDdEgA80NJnlapX4vGOicaRn8JRodMa2o+CFwrnwikpO8DV2a2/0eS+dqHP7eHvlfBC7CMAHk3odxgcXVsewBLFq6btXPCEdiIcJToFrjhPPcdVwnV/GF7oNbWdHs73vSu8vg1g2pT7KL7+MDfr/V1/i4bjTAh/CxGOJVN+c5iyLvUYn4FTUv6OHmPRp1r6LBhe3w+vj4TX92C563L758P5XxL9X7z/V+J5Tgc7cVK/12wwS1Lx+8yd0OeI8P0vDed2CYBfJ/TbFx7vDwivewB8fcjfPh5/b6n4/Jbwd2Jp+/zh2fkNXNcyfXj2/g7gxwnHbbQpSm3XAPB7mBnqtPBc3x2+/xcS95FlB1X0r7VT4MXENXBN1rXw4vMR2Nuesu+7wt94Hku+PnW/Z+privUwR8hdyXYCzZG8MBxGLFZxQrM2/LMhzaGQht4Mzgcs7/tC9K/ap4cHqZ+F1XJtCDfCY3CopE+5TtW5gEuSrFrlEQ1h9YDHAdxCy0vGobaR9DhfqEF6n4vQHh6pqrg/sKF9TqX02wpFIZJuzfFO0fnKZwE4RyEtIxHX08T0q6GX0nOo6qMIw4RRcwnm7yO5K9IV2orPj6GLbZYo9at7jraDvbbfl9OGxqL3PNUh/s2nhdNu7oMZPtpwd/T/W3B4eCAMyF7u8HdlrvOTym1q0Dl03uHaAdXS4vs0tC9QpLnF6nFCtZpcJw8XMCmcvDRsvD0FGwlN+ICk/eHc1RycQ4u+zEoXam6Hlt+MFewnigRD6rqFv6nyxgVOoSm4zoFp0QbGCYbiNtnLdw/8WxDAPmqRuVaeEhrQX5xeFsdqFcvKiFgV7aeHvewLwOkS88LRrN+0HOrz8D2Ro9YKOZp5PTy2EsBOku7K2UcF4t95RpIrSroDAGh2pSJnvix28ks47eN8eDF0O+z5/ahaeO0DfkNyHUm/TWh7FHx9b4MLYW8HcKCkHyf0zbaDWMP3DDs4q7Ac/L3fCVGUZ+HIccp1AGxzLYxe9GVDmD/7XcEUbzDLofcuNEO5WA72sKSEowpsB3upCk7UW8O2MlLSDdrw5/AaE161kDR6iOP8JbxGIZPOrQ0h/+iDcJ5nPLA25scWUEbFfQj9XKAWkZYI76F5Nyvfy8pJdfgczPl8Dsl3YHq8c1RdkBnjLNhbXggJbB761uXsDxNGzQ11Zym0FQi5pJ+Ajb7fwoP2zagZcOWQ3J7oz61uDFFK6mNEoIvCDm87t4BZy5MHyT0qJpR5Qg7g50iehUGarLo0n861F7nXLpzHgLR4CtQvW9yG6dUgkFQFktvCz8S0sGd+4zajL+CHtGDLubBhmcQsJOnIkDLzAmyIHaRI4r7i/HLZTwrkyhsX57dyMNCLcULw94uN00lOA1mJsUtRa4oSGtTCa52AQ5AniTwO9nIWC7Kn4N+4zWB+DM5/zjKYwzndifo88S6Ix9jt4UXQjPDY8AKA7UnOAHtPY8yuwPUN4AqSf4MLxRu/E3uKugTwv3TNRpFaIlWzPUjS9eH/i0j+I9VYDsi1g36BwPcc3v8ensPqDOY3FNR2Jb1G8vcZxjLgRdeJMEHD0wD+BKfCdsH/L1My/gcOqf8pvF8KCSGZDsc5F2ZVmFzfo5hEh93PTABmnAznl5TuMeQxNoANsefC3+J1HICPtdwDC0bvD4JTKH4NYGxDvzMBvC/x3A5reB2a8R0XhQ2ctxPa3lOx7e6G9m/DA/OLsBfjhej9my3HikPdd4f/a0PdCCEvhLA2PGldm/CdJsKLrSJ0/V4AlzS0X7fi+b6ww72VFH5HRRgOUXgv2rYhnKf4IpwOFb9SrsOvAcyS+R2yrl1o83N4EVC8nw1W5Ktrf2L0/2aJ5/VtAOtkfpd3wjM6KYwev1r6zg0v6G4J1+SAjOPODBvBs8OGSl278bDjISlFKWozCjZqZw3v54A9ZjnXZrEwRrzVdh/mvuBoywQ4Uvh4+J7LDLvfiuPcUT7npmcQYVwrtZ+QcJzzYbrDE5CQkhme13hcLP5/CS7gHeY7V40Ts8TPX02/CeG5LO7Lvvcj/Ls8BnMpF6++9wn9s+wgZKZIwDR994fXxOj9xKb7p2I/MwCYKaHdarCCKeDUsrHRZ9nXfor3MCN/JZsF9hRuZgLwEMk70Z/yMBBiInkMmgssBiqF5UKbN9ggnNFynh+GjcvZw/tn4dyiofXRA+5E8G6QPEbSSFAs9UHShQAujD0gBWi2iDoUHKYIBQJbwOkvS8NeoTrVpzkBPEyrmsXpJQMpMJIODPtfSaViP5rDshHBy7kx7EF6G86ha8N1JDeFw7SAjbTa9CMNETlQRDCf2KUomHk+3HvPwBXTbXhVDre9Rasx/R3N0YBDAayIUKgWnu9Fmg7AfpW7UfB90Pgc0BzUX4LDxzFF4EyoSKOSdB6A80geKOmwpn3XIJdFAMi/doCNobKwQxMzzHLR//ugIf2lo4erQOdiatnj9BNaen5feIHcWNRDckf4XnoVNtYLzvW665fLflIgi8c7Or+x8NiwEXwNz8Zgge/7QopWJVrunaLNPXBKXqMS2gggN2L1RogUF6H0hZHmNS4WWUmQ1BcVDR7fneC0rzZvdhsmpW7RSnJfRGCuYE9Ao0rnYBb0F/MCoTgVzfdocawN4EX6v8P7WQF8Qj262hhlxo/4vdAu+DYnEu2ggCy+ZzjCnA2SW0g6jSW6SlYIl0SfHYwG5hxlqM8W+E8wmHNzL3PRmq9VgTgP8luwSEgKXgIwIYT04km0kbM04EQAe8uiAaAlrE9Cdc5hF8QXeICKaSShXrhwMbi6+EuwgVEnSyv1uJu/ADN53AOHR3ep6QNU0KYl4DgM5lIfiwZ6OpJ3wA/jubAK3WN1bUvYEebLLLhIR8ED0N5oN0iSQPJoSXuyRvq0YSDMzRcvcHcY0E+CJ4mX0BwWfVOWW+47rZZjxByvb8Ee6bYQ861wrtuc6KeSfBH2cPSBQb4cwKWs4EpXO/NKl9qL3GsHWJRolmgynQ0VgjRdUDY+MvsmhfxJni/pi9H7D6JnWD4Lpy19LWFXX4eLdlMox4B89pMCuTzeBS6Av8uGKgkCRXgVHTnvy4ZEtB1AWg0KraI5Y6JDZzd48f06vOi6Ao7G1eFgWLBkfpKnw3PMNm0HUY+VIgthwbA7nCJ5DlzE3JgSxDwGlIthw/AetBj+khbq8h0iHBycTcX+ng/G4IDBrETGjwYcktm+iu+5lq5SGYJOJRRpRTlj0ogz5/wnGMxdaYaSoJ7CzREq5emRPAJeoZX7nBq12TPjob46vLpghsJYDudwfVg9jxRGchFSC5p3eVPYQzwariBeUdIfm7txRjh88yn0c2nW5j5LuibjvFaEvdhzsT+XeWa0GyBbB+MqC8MYJBkoBGiyFobKVGiL+hULmONJXg5gZkkDBmmEh2mKs1HBC7cHXKgyAFqkYw6VVO5ILk5yDjVQLoaB+gm4ICUFneTLo+Odyszaiw7XDgCOBnAbybPDeW2KZl7p99Gqa4z+j89hIDKS6eHKRfneGgcbYGspT3ToUeSpsmUJJxRQPo930a+NLgsAnutqIKJjvQk7KrXlRKxoq/0R2NlR8CnvkbK4CXP+dzFYCFs5JpGcHeYS3hxOe1lW6eIU20n6Mcm1YcNvW/h+vLKi7XySPpO432FRJcQxWWw3STewvxh2enieHkBYYE0LF1vn8j3nnldBI5csYALnS4uBF3ok7CWGXI4pFuEH2x9AoVx1BUwMnsrhmHqce1VibiB5vwJ/ZU6/lvZTAShCzn+UuRJT+l0Ir5QKA2gLAMtJymGCaNr/K3CuGOEimMKALcKvjdch8Rg3AngPHI48S9LDJP+kFklgktvB4csXAPy9GKhCGseRqlFCi0LKgAeY0XAodsBzS/KTsBG0PfonzhcBXNxk9JTDc8X2mvBc0WcMPKh/KJzjQwBOl9TItzq5QVe/369e1f1B8Hd7Ap7kKr1jYZB9PjKqPglXuD8BE9pXfq8wiB0EP9+En+9vqUKiPkzuJ8ULx7D9swA2l/nD277fSjDF2QfhHNbRAF7O8eazIm2nos0kFgFJY9nAItD12kX9PwrfuwRwtaSJDW0bFSGLianUZ7ykpUrb7ks0AhtRM+5mF3mHsWAcrMgYh5J3L7W7RtKnqhwkXRCMwfslfaSl3VjYA1s2/JaI2twuqavkeycUvy2t1LYsglJb3XhfF6kqUBexInmPpFwRKZC8GfZO/whOLdgWtlsqo7phzH8Ozu0fSA1QQ/F2Md+T/DGA6yVdWHefkzwRwDFNz9pIgeQpMKvWsfC13w2mZN1mMhxrUjGspIXDguX4hjn2NuUVEHc9rysV1EtJflNSubCyqs/X4ZqiteBF13YAzpB0TOfz+A8wmJeW1CZkMMz+d4bDce+HvRQFZgJwq6TNW/onG8wkPwYbvE/Dk9vcALZUBa1VRd/Z4PSPgibnRgCHZKye2/a/YNPnQ4RS4mNcCvMnXwjfuHeSfKzOW1Dq+z7Y2J6gUGVLV9NPJenJhP6jYA/Hkgr5yjXtYllQwgwBL9e1D+0uRy88N4l2S1KliiRNvfNrOCeuyG1bBg5Trq+Ry0uPj7kqHG5bEDbqi4XQ+0vt7gewkqwuth4srFLki28kqTJfnE5L2SCEvZaCIynfhbmp35S0/Qh8hwclfajmswckpShm3g17Yc+F89u2gmmNkmnMSP5Z0gItbYow/fXFhEtyYpVh1eXakZxB0st06HkA6lAnUdr/UZK+Fv4fcBzUfZcOx+kbP3MWGqX93AkzikxExFpR9tiSfAhWMz0eTgVLZT8p+sf1K6PgNLLH2xZrwVlwRHh9ETb83mwai7qAmUpoJB8M3+EMeHF2A8kJqmEXopljAI+lc6OXTrYZfB0qxZNIHgvgF8qkdisM7fh+I3mTpEpGGJLfRrNB3zT2j4MFNcbC/Nqj4ed3wNAP99EiMEtDoWQ5Io6limPNAKfDFaw7VwL4Ttu81PFY4+E0ozvaxq3w2bfglLYLNBmNyXjhkmlzrYXIGaMG5pwkaAQrNCfHCy4GegRenX9oMux/FtgzeCZ65O8LornCOq6+LbMVvNDQ725E4gCwl6uWFeH/8JovCGDN8P90SKhGzdj37PAK9jp4gfIvJFZxw0phrdta9nF7y+e/RE+i+iG48Grvlj6NQhtV5wyHnMvb1wRw3WT6TR+BacreA1f2zwGnN5TbTYj+PwXAN6L3TYI490f/HwnzKgM2Kpqq55eBcwzvhCMo99YdBw731e2n9rNSu7srzvfWzGv5ZEKbZBaBLtcOwGXFuaBHOfnn4v0I3C+xSMMp8MJpYdix8CPY+BmJ+/K+0vsqgZ3W6vnU3xDDs59sHb02B7Bq4nHvCX8nRttuGolrWDrODbDBE1+/2vEJTnF8Gk5JKVRKW88LwI0p26LPHoKdCY8igxUBdiqMgnPAvwrnpSY96y373bdiWzIDCvpthUmvhOOOhjmoFyheCe1/0PE7ZguQlMct2LnSNH6/CC9Q30SCDRT1WxXAVTAN3WPwwqNJhKxWtOvdfE3xOcySPklybpiB4MTgUTlb3bTDq/b/b9g7uBnNn/pe+CaZkeSMquDSVffc0zGSHor283AIzdeC/RX9A1CL5yUX7M5PmgS5MvVE+LecF/b2HU/yvZIqvdw0wfn0AOYMnvbCKzQzPPjUfZf42oyCPYptpe0fkfQCnTd/JVylfzeapcJvJfkRpYfn3qeKla6cM9Y5XNSCf0u6LKEd2SFfHP3XdQ0A3wQAmfWh6XhnwOk2fZ7BGjxKcm1JfXLoJD8ND7gpeCU8c+Pp/N2/ooKntgUpnpSc2ovsa6fARS1p/tSTzkR84N1gD9fZ4f2VsHraSKCcFlFV5J2C60h+BS7ci1My+irhNTz7SSqPdxmvh4jVYyS3h43U93Y4fhumlyN38bbatD85RSFOU3gipAS1Ya5SNG4snPdbh882fNaEPeGxf3fYabYGvFgZFlW5/q0MKOyxXLUJ3FT13Q1OL/kbemOd4EhSJWR2rS6pLF2E2IDMYtghbKGfw7nmfVHZBrw/2EKM/o/PY9Jcz/5UzBgp7D6NmOINZqAbzVAuSH4VDlkn38wdcC+tRlXkIW8OK5Q1YWXYa3QmnJ/XWUksEbsihGQAQNIfSL5nchxIDkH/CDagm46xIzxwzot+ep4X4LyuOmwU/f8WzEu6fnXTSRhD55mvD+BnMvVUmyG3GoBtSKaG50aRnEYl4vqwMJhcz+R1JH8Ae2pig6Icgj4aHmRfAPCwpLvDuS2NZkWla0meE9rMBsueFmkzTTm4z0lqozoqsDeAS0jegB6bwHIAVkc/lVITtoQXT1+FB+z54fByH1ifq0nY89SGmEXgDITai5o3ilXQAAAgAElEQVS2Xa9dca5zw96qOH9+2MLoolBmNJz6laIeGJ/TRDQsLIpnQ4GBIELXIu8vhb/fjA+DUlEhh2c/2RpA2TjepmJbGV+HmWb2gNNtZoaLDgdA8khYRbBLalaWEhrzldoK7AUrlRaMQAvB43QlJD0RrnehanpLwrWGeikcL8FpLCOFqnk0hQHlDADrhe0q7WfgfithD1i1sLY4uQb3BQPxXPSzazWNm12E2ID+YtivALhUvQLwSpD8Anq/601KKwZOdeAUaFKm7MMQRnwr/hNymAuaoQ3hZP6zAZynNNWonOP8EWZryL2Zc44xLTwBxHnIx6ihgDFMWGvBOWIfhamqzuw4mKac4x2SVixyhoLxeG+D8dflGL+EDZa3YO/tnAC+pxbqI5K7aYiE/cRz2wseOB+A+Z3nh/OtV2voU+kZV03eN8kD4Grxr0p6PGxbCPb03K2GYsGuCIvNilPUANsD6/PFp66KuITPCT+ncwM4V9LTYfvSAN5T9gpH/T4N53RejX5DvjKyEp6hLeFceMD8y7+S9GpV+4r+A97Amm0fRwPUQpvGjNqL6NrNAytEJl270OZwuAD4EUSeJEnrpBy7Yb9xzuC1VfdJS//imdg1/I2dBK/U3ePsL/IuikAPaxojQ79py21qtp0o6Ss5z0PoV/B4rwbLhBeYCRYpqlV1DGP4oUrMkw8e6G3hBdA4eLxP4lMm+X7YAbEKnO72JwBbFONMRfvLwjH2l7RkGO/vU0J+Ol3svHh4+0jZAVBqexDswCiMvM/D40Qbv/Zy8P1Q1F4A6C24uoIJebAMDCiSNittJ4D568bChv1dB6fiJRX6R/3GVWyWpCpV4aLPuQB2l5QkG01yfZj549jw/k44YiA4feW8mn7HwbncBZ/7JgAelbRrVfuo3/fgdJM2B85QILkkegqoN6qddah5f/8BBvMdMOH49bCqzIiyY0TH6XQzJ+57PTjHLpsou7SfaWDD+QfwADzixmMIUz8P5zztBodkHkod7BOPURjjX4JX8fvChmLrIEhLvS6E/sHzl6U234ULUE4obd8LwJw53yUMjlOrgqmApjCqRdPvHSIa+8LhRsCegyMn94IgByXPwc2KuEAb+lTSM5a3RZ+dCi8EH0IU2ZG01VAnX39+VawMtYwPJJeVOb/jbf8jqZGzN4wn2RLPwVu8AnzN71KLbCzJ38GFrCPNGrRj8fyQPAquNs/xcBX7uUXSqm3bRuB8q37XXAajWvaTsAAYC3uH94s+ehHO8WycN0heJylLzIXkB2DDeTM4l3eAIaah7wwARklqTB0geZek5UsLpAFWlJq+rWNx1PZhAEsX9ynNhHKvpEYhi3B/74PBYs6hitCbnvmoTS0DCjuwfpD8OUy/din6jcRWjuxchPFnKbg2pFWAhOZQ3lShgJ4u/lsDjoqMUz1LxoMAPlx4suni+omqKc4unV8ZtQvWqF9S8XpouweAHdBbpG0AK512nmOn2JSMsNI9HM7D2QAOm84XVlv7a+S5/h6DQ0yT42beHsDJJP8FD3y3wiGpJNqkYCivCw+cC8GeyNQwdi468ZNmoirtoXXlxvS8rPXR80DG+AksS9poMNM8nB9Cf87u4RVNq8JyBRrDc5J+SvJkBI7nYmIjOfuwC6s6kFwXpe/V4Okrew52JLlmm+cAjoaUjePPVmwrsKwS2C1K57Y+LEozL3ztW3PTWK/0NzMqlP4inERya4X89LCfPdEicqEOtRckvwznOF4bvtMxJA+VdErDof6Eap7WumOci+ZUiY3D33ixOTt8jeLJTEgbg2ZgpOwZjKzKnHGSWyOErcOmh2EZ5Nq8y3CN3wfnXC4N9NU3TF/XrwbnwKktA1A+j3cZd4drfw76Fx2/rWocvNKLh9ez8Li1d1jIbFrRPlsJLSBXqa3Yb26O7OPwuFMs7KZBPytVHf5RF20aEgP3LqsZUCbU9L+d5PLKY/0oCnPHhFcS6KjalzE4dtd6mJEvQDJG/WxTN4d56J9s5i/+HfzMFAuY+VEhBFVG7uIxQk7u85fhrIGXATtuANwG04p2whRrMMNe1JkAjI2MiZnh/JUj4YF1JNHpZk6BAlcyLfm7SnjtEQb7O+pWfaHPqbDxdxnMT/tAXdsRwnQATpF0Ujj+6LAtRxSgDSfB1/oBuMhgAaQVUaTmZb2jkEoQQy6gaMwBD4birHBe7Dg4XaDS66QW/ugEnA/TyL0Vjj0PHE3JLvJoA8njYQPik/ACaEM0q8h9HP2eg1PhRVTd/ifRM9LUdAVmQnMO6h0kP5C6eAw4CqZhy+FAzVL6i7AhXCS2Oext3wo9TvhGKL/2Yl/YC/ccAARD5laYpaIOL8I5juWUljr10OzFr4ZTD/sygFNIzhLePw/zofaB5FbwQmRvmCmloFr8AclazyWcNrUNXJx8FNBX31BJcdaA1voQdufxng9OQYvz5QU7JMrH+CGck38tgMMlFc/pEcHjWoUuSmhAplJbhNwc2dcBPEhLxQteWN/MIAWuetnvg4Nj4Rr039+VizW6JqZpQbh3+FtV8Bkr+L4Fp8LUUb5+EnYiPAEvgFpp5ZQnuhHjV3DK1dqw/Pvm8GKyFkpU2owwW6l/rGrYVMw5Byw+Vdyjy8NCSr8O+2mybZIdOBFycp+JfqP6bSQ8402Ykg3m9QAsFj+QMnvBzvDNM6IGc3Ez09KJkvTSSO4/HOOPwV4rvGIfg42zJmwJP5CLAdg9sveGrviswTUwvVnx/aeDq+JHRII7GOBPSpo32vYU2qVlARvYc6O5+AwAXiO5sKQ+DwZdDNMWul5NJq+fIOlAOkXl/LYTY7fCh4tgY+yL8Mr813Bx0OTAKuF73S/pWyHM3uQhzPUcnAEv6gZC1i0e8xUA3E/XEMQFk02h9L9lGssDHsJgjK4O4KWmcLqkx0huCv9WTwL4tBLypVlde9Em8fwU+heOL4ZjNuHy8EqConzoEOWZTzX5rVG7Lh6uos09AJYMzg6qPhd3F3gRFJ/LteHZOAs1nktZUfFXADaTdHrb+bSdbkKbn6KCx7uuMcmdJf1MpTzYhvaEc4+XVIV4D/y8DEDdlNAg6V46Xz9XqS11LC5wYXgVuD6x37awl31q9Bfj141dwziUchhQslk/SM4FL4rLz1Hb3LeIpI1Irh/u96KIuOlYuQu7O0juUDjKov3siGbHykEt5153frkOnAKpxetAEDKiRd8A5803OR9aMSUbzKpavQYv4YgnXpP8MLySmz28fxbmLRy6uI7kvvBEPS+soHc7fJPs2jRZA4Ck5HDrCGHaeLEg6SW6EGdEEH6/PREZocEbnKJwNyeAh8Jqtikv62AAvyV5GPrZFPZHu0FaGEOvhQjAc3AaTC0q0hd2IrlWW/qCpJNoirOLwjF21PDsBnUovtcrNJ3fc3BOZh/YY4eYBf2egxXQ4ClWB3rGgC5KlXeRPB2+bq2FggBA8jcA9pP0QPDk3wt7lBamC8GOLrUvszzMDk86dwSPZ1u+/S/gaMHOSK+9eDrs/+Jw7PUB3FmE2avC6irJhKeC5GfgFKWp4TSVpeBUt40qmmd7uMqpAdH24rzL32XmKsNd0uOsEWeJ2rwTJvZWg5nDs58Ujo/Rkt4GMI5k0zO7AywkkgRJIvn5Gg9o8ZwNgJlKaGGBX4XFwv3dlm6TOhYX209lBwVHeOGQLJBTfh5YwUbUgBwGlC42yOnwwnk9WI58awD/SOhXLGCeD3bKM2iZk1C9sFu0of1eAC6i64oK43NZOHWmdozWoJT2dLCYWFvEONeBU2DF8He5+DRQ4XCT9EOS16NHsrCthhTBm5IN5odIblUOxZEsKsJHGifCAhXXheN8Ak4dGAnP6lfgUORFsNFx5+TwYI8QXia5TLFiozkgkxgIMnBFMJrPRn8+X5s62SEpO5d0afBa74seVdCDcFHD+Jbul5GcFU77KfLzTm3ukp2+EBsThL234wGsRBcejXgRCIDfhO/1A3hAFHx/l9FI2dMGJtIzMijVIW3CKGOOsO94chbsoa/DWPXSmbYFcJWkrUJE6RaYTi/Geh3Oa9jai0fRn9d5cfhbG2YPnqSDMcgisFjLqX4HZmq5JrQfT3LxmrbZHq6mc65B0xiTMv5cRUvhlseUcnSj6f5OufdHgse7DV3yY+Ow+UZwpKcJTTSMKfnphySc0yQwUnBEb4HWquAIX4slFOkXJB5vBTjfdRYAC9BsCdtL2q2ibV19w0yor2+4FL36lWlh58PvYO9xHeaQ9PPgtb4BTkdMSZ04kdYfOBAe42ZEgmc3Z2Ens46tQnKN6DtcKunapmOwu25DkgOn4jyzcp+DHVPYMrOS3F/Sd3L2EWOKZcmgqa0ugC9sUVy1PJwisIEC9dIIHm9ADrRq2xD7fw96+csrwiu3+2D2jF819X03QXJ5OAT6l7BpHgCbqMQUMOQxqsLMUovccOgbr2anBzA6YTXbCWG1PB1sbNV+f5IXANgrhP2Lc/xeXRiW5MFNx80Nq+aCLiKdtiE8XrR7L/zMAV7ktVI5MpGekeRlMu/rk+hNPJP+ptwLOWBU+U/yGphx4KzyZ6U+hdJeclEinUM5E3w/lGsvXpU0oqlkNPvAvhiUZf9bS78++siwbUACO2y/U9IKtLzzLrCH604lSNpnfI9X4OjbwEcA3i+p0SilOdDLUNdzJHm+pC9WbF8QXgyOgb1yswA4VqX0r6h9oQQ78FE4vwGmHVp2eTE4hSgpP5YRIwgz2UG6ImcsZoZUfKnfw7AhlspxX/S7HU6Juig63gNVzzKHZEAJ+1gGjhDWclGTvF3SSiSvgKM7f4Epchdu238uwrO6JhzJfgZe2G0zUvZMdJwsKe2o34FwysinYC0FAThZLVLxdC3EwXA6HWBly0PjuYymAzwQjuhfBKcLHgant545zBg8xXqYg0G8YrTiISwHe81kOuRj4UcsjNctkK4e1opgbFwUVrBLA/gEHK79SnTM/3NIuit4moqctkdavGJdjtFJnaxiNfs+NKxm6SLLvTFIfZRatPUqgFfDoDBgwLF7+sJkNYirQOeh7oKIJo7kz1STKkByY9gbfT0wibFhH9XwcUZ4EglV9nCoOuteYGJBT9150UpbT8HFZJeHfU6HwFRSsb93SE4guYDSOVezay9IHi1pT9akC7R44V5QC8VdDR6hw/IME8weqM8h7OThgne+GJyS8F5JHyb5UQCf0yBbSCO9WBs0fAFuGXWG9uflnNbXABR1L3ugXrhkIvrDxynoooqXrIRWBjsUX+WOxahWcEzx1n0moU0VRsliKfG2SlYFDc+AUuSCL9/S7NvB4PsabCzODC+6GsEacRk1p2NtCaeQxQJNAwvAEcDrMstVca5TIeF3VS/l6Hw6Xa7VgRNwCpynvnF4vyWcqxynGP0SNqTPh++f2+EI80fVQtPZhinWw/xuI0wI30K/qMghkv41AvteBz3v8lJw6OZW9OjlhvoRRxrM4Ncc4hiLA1gC/YP0GS19slazof3PMeh9uyPzXJ+sMuw4vLjFVQA2kvR8eD8bzNm7ds75pYBWknsRwGlh02YAZlN1zipIToB5yf8e3s8F4Oo2DwUTuUa7eMFo2rVaNE0gIcJzKBwxOVZBYY6WAV5WUmU4nuS1sJf9TvSH+uv4TH+vmnSIus8YuJ7r7qeq+ygYnoB/R2CwCKaR2omWP/8WeowfVwA4WIGCaaRAh5z3AXBCm6dvBI71YQyOKZ3Grbr7s2o7m3m8W/l+a/rFggs3SaqjNyvadxqLWFN8JanxWeswFv8cTv/ZDzbcdof57XdqOEZ2hCfqez6AI2AjfnlYU2DVuvEu9EkulGN/Wt0oeBE+x2QauzuLy0xusKNuA13nsi4G7Yw28bKBaGB5G0uZAST/BufOp+ay12KK9TC/2wiGcR21zbDYCTaOD8ZkFF8ZCbC7Bn3OMQ6AJ+rF4Yl6bQA3w6GTJuSuZt/RyAiBVB4jnoTYIX0BwFyFsRz29y9OJhlyWJI1NnavC0ZxHUaVvsNzSOP7TaVnzKb3qTKI6Zth+jZDL3yXgclZrlloEoPIjQZk114opPu0LbBKKEvCx0qUQi9kWYf5JbWxdgDo7OEqML2kO0uevskhDnUwHLVbAqZq+yw8pozIuMXuPN5JfPkk95X0/fB/WXDhNLowtXYsy7x3YnQtvsodi8tS8VfCYfJadIzwFNgZTntYAE6huTpsa0IOA0qco/8W7CBoZFMiORa+Dguh30hsy+OeU9I5JL8Z2r9FspGDmBZLOwyDAh8jzarVVbfhEjhK0ydIk4BX2c/rvioqahyC86kYdJ4BMD0Dn7SG0Dn4rzeYy6GrMhJu5laMxD7eRXTVoM/BJrCn/V5JW9KsBSe09AFcJPG/sEjBWvBqdiAczV5V/cUkvwLTGcXet4GcQpp6plP1/BDpC2/HkwGdSze5rvt9jJTMSK4IF7vV4XI6165g/tgUpo1rhHr0jEVRXx3eR/PN1u2nNr2CFdLqJBul1VnPjlAcr666/4bMxdCuAC4guR0qai8a+hWD/yFIULGS9LHytkycQuedngNHNZpEJH6B4OEK738PF9elGMzP0nSORUHshkinIsvBhgCWhD1v24bfbBjBpfKCrhOPt2rYLiqwKYDvh/87Cy7k3EMBnYqvkDgWF5Ap8vZHJBoVxrs2xb55YP7mpAhPhLdUIfDSBqUXyj0k6dx4A8mNYGO7DhfBz8wlyDMSu4jLHA2nKUycnHO5zHB1EqIC8nAPNs0tgOksu8ib7wzgVDq1hQD+CTOZxJgFHnvjZ7hg/mgUFGvDf73BDOctPQkbBnegg+erDbTCX50xJlUUfvwfIpdfswtelenl3qJZCp5B2k2cupp9EL0CMsC5lwWEajWvnzYct+kzwJPA8uX0BQBtBvP+cC5x4R1aHc4LnBxYEcBWJAtPzQJw3vVEVBTRSNqHznFdFb6OxyuBW5rkyvCkMCN61ek7Stql1PRV+Hfqgo/IecFfgj1V+8KGc1M4rxP7R+5iSMPVXuSoWBXndxiAo0ppPXtKaissXTkYLJsAOIem6jyrJjUl28MVYVeYgWhxkk/DdSFbJPYFAJCcQy1FpPCY8k4YU2YG8HcMMTGipEypjjzeGWDp/66CC7n3UCp7ThnxWPwVmFGhcoESxoT3AbhR0t/pdKL94JSTthqGrvUed9EiL2cDuEBpheE5DCjfxKBxXLUtxmuSfpJwHmV0EZd5EsADk8tYDikVG8O/6+UyXed6sFjQdHCdVhMuI/lphdS4VMgsVwWve6XzS9JCOfvMwX99DnP44deCcwE/CodWztQI8C+XjlGLsJqdIsBMDfqOxzgBnpA2h9NgXgDwsKStWvrNAA86b4f3owFMo2qCf5CcWqWCxaptFf3GwDlPVVX7Ve37cvfo3LsJKTlmJOeE6b0A4HZJz6YcMxfBOKqFegwfL6K3uCtP0q/BtGf71xmAJO+AB/NfqyFnlUNU8pN8EPYmng5Lq1/PGqaLYcGOudwdj3WHpBXbW/b1GciRzb22dGHeAQC+JGnAiUJzmX4RpuJbJni4jpDUmDdb2scMcJpPEqMNyUfhsfg0AL+QtERL++PgyXpTuKjqJQDjVVIp5CC/dh/qvF5s4PEGMMDjnQv2s1zsDXP0xoILp0r6UcJ+su+hqG8rew4tSz+fpGPD+zthA04A9i0vJGmRifXgFL9FYG7yXeAUnxOUkJ6YGeGJ+60C3w+fC8c/S4EZp6Z9KwMKyc8CWAc2Fs+Ous8MR2YrhWVC3y/BXMhXol10o9x3KmSIy9AFiIfBxW+1tSRdQfIX8GLnTtgZUywm90t0rGwAP9ujYJ7pxpQR1ki/F6j7XuwmKFaL/3oPczC+LodD0NPAhvP1JA/VyOS/DhjEJGdHVJiCHoXblIBDJvcB1KPeOTaE/WdOGTSQr0J4B1yM0bZtEuiK8R+iny/0YElN4fRO6QsBq6A/3/Q3if2yIFeMzwYPcnH+3L2ldk2cv6NhmfbTw9+6Yz3J9ur0YRaJVdLqSbzmJBeF6aPKxWF13siuudzJoCmpgDwVqwKjSY6R9EbY17Rozh0vjjkW9i5vBE9YZ6NeSjrbw1U3wbFeuKQPkhYmuRecitAqzR1FMI4neTk8plSlShT82oWoUMFQtDmAyoV3QC6Pdy4mPTAaTnAh6R4KBtWTCgXntDT5FwE8QfIQ1ed57guPbwXGwAIXM8JpO+XIy7qw3PtrYfz5C8xW8IeUL5Mb4Ykhi0DdSvIQ+Pc5HaZMrUMKA8pf4IXS59ATxQKcmtPGePERmNVhDfRz1Dcq/dGpHpdLepCu/1mG5LdbxoXvwGNi0njQAcvBv+M7Ycx5FuZrTyUwOAo2sFNTRpqk3yv7s6OgWBP+6w1mYNLKel3YWF4ILhZIKtbIPM66AH4Ek3s/B4czfg8Xv00RUPfikSzQcsMLS/oOyfkZmAJauiWpENKFc/PA+XUfQW8ymhmuCG/CofCK+bpwjPE0PV0thkhf+B7sOSkUyvYguaqkb7b1zQUdut8G9hAXA0zrYB0jLPwmkGxaSD4ZPDsKnvrdUaEKJ2n5cF5VHr1/w5P5QJ4fh5NWBzypHww/h5+EjZ+mcHd5MbQJnAo0kjiq9L5VxSrCWbBoxymh7ZeRoHgHj29nAdhQUiN9prrJJzdNcAMgeSWAHaJIx0pwkeaOsJHbWrxX8ibdjIrc4mj/q0paNfpov7AYqKNTi7/vpxDSFiS9SDInH7UOk+Ybkr+StCV6eZfxtjakKqGdADsfQHJ1AN+DC9KWglNo6hZEYyTFPPo3B+P6nyGKUMarhRdZLmr+XaqxHNAp3Y1mgVkfNu4/CIsAtYmQtSr9yWwlE+ial5fL0c6W/W8Ac4qnqNrGOFDSuSRXgwvkj4SpGpsiCbMrkT61I94oxuewGPp9hrEMAH9ARsqIgvQ7HN3ry4+mc6arkCUolgRJ/9UvWMXtHgDfDhd3ch5rPOyduS+8Xws2rv7Pr0N0jisBuAtenb4BewJfGOFj/BQesB8O72eH2UPa+t0CYJno/bIAbqtoty2Am+BV/03R67cwjVvTMW4Pf++Ltt1f0/ZFOJ3khfB//PoHzP/4qYZj3Q97MIv3o+uONQLX/HfwZDe57585YYPtb3Ae6Wkw3VJd+7tgY2Q8gAnhnrsXNuwrrx0cWut6fveEvxOb9gdgT3gxMzVcPPND2MjeYHJfww7faT3Yg/ZjAOtOhv1vBGCm8P8BsHG3zAgfY3z0/7oAHoL5rJE4NhwHR5u2Da/L4XB67fEArBa9XyU+h4r2l8AG5QYA/gVg1rB9OgAPNvT7Ubh3Kl81fe4tvR8NF5mN5PWeEP1/LEyhOvBbVPT7Y8Nnj1Zsex6OThSvvvcJ5zmx9H5UeVtNv8fhIsmPJbTdLPy+/yqd6/WwgVbV53YAM0bvZ4RFyJqOczaA93T4rQp74btw2tSkbQ19vgfg0yN5z5T2/wo8f90PG6GvRP+3zmFwIfGNcN733sUrod+9KdvC9gsALBi9XxBOt+38vf+fh9khkpdhZaXdo1Dy5KBheUvSP0iOIklJV5HsLNM4mZCrQd8Fq8i5kPcBpnkJ3sg27AHgXJJ9KoTlRpLGwRXOG0s6J/PcHg5hwFEhbL0HPDgOQCOQvgBgVrjSF3DO3OTCA+FYSfl/XSHnYG+e0eUPAL6sED4PEYG94BzH82CPVxldpdUB4DU6x/wPtIz30wCqqPzmgw3QxeGJ4FZ4wXZb4vfKRgj/joMXXCfBqUP7qb0wZjzs7bqO5LRsZygpUjIOw2BqSlWucLaHi+SVCh4ukt+U1CbV/DrJreGUod3hMP7TdHFPivR0rjfpyzBTSPHMPQ9gu5b2h8Je2U3Uo4NcCf7N6vBAw2d9oIsqC+aJF9CLfLwBe31T9tGqhBYwmuRUcsHip9BfbNxkF9xBcgdJfYWBJHdEtfDN+qX35WhKG7pGeN6vighVDbowoCRFO0t4LywYdBfy6oOeput+1gRwRIiIt6WF7QpgX5KvIyFHuAOGEhqCi3//hHb6UQCTCkdXATBXKc1rZnhBGbftJCiWgv96g1nSiOYjtuDfIWx1M4Bfkvw78uhl3hUoQ4O+I94MRksxuc2BlusQ2o+BDZhGFUKSm0k6E8A8JAe4tdVcqfxVWMXsHXiFegXqcztrobT0he/CdG/Xwd9n9S7HSkRxrAcwmYo5gUmGWA7X6AcV5ZpKmkhymXAP1h2myIGPeYTr2E/K2BNOy9kdNhg/CS8K+yDp6+H7jIEXjqvABtVJJJ+vMSyHxXaSfkxybdiI3xY2xmoNZpq+7qvw5LAwfA2OQwi3N+BUWNjhCNhrui360w5iFPnm68JFlheHvNAmzBX9vxF8/zVhc5g54Y1wTqfS8r7rI4214Xfwdy9oyuZHM93bPehV27PCoCy378TjrRJXNclpVCOgEBYV3yX5XXVPy0pRQgNsgN5A8lmYseamcH6LoJmybC9YsfZL6KWMLAunI3y+3FgdU/yK66ReuluRz32ipAtbugPA7CS/hkEFw4E0BXVjQHk5jFP3hj7LooIPuIRG5poGbAwr1h0p6Xm66HSfpg5NzpyRQLhmw/TPZT8ZA3vxp0J/mtcLGEwf6sSIlIL/epaMdxN0gcgr8OpwK3iS+6UmEzNCF/Bd0KCnC0w2gA2RU+AB4VtqqGAO/W6T1CpfSnIXSceFvN0BqEWv/t1EGPyWhyeDOzSZVB9pZokTUCKK7zqhNRxnAkxtlXQckufB91jx228CYF7YgLpFUq6scNv5baQK/tTytuizWeCJdNXwd1Y4JNxaiNbh3O6XRSR+DOB6SReyRSmO1Ypr96uF45TkPZKWZcTwQvImVfA70wwRT8PjQmEY3Nk0JrCf9aGLquPS4Xj3Sbo6of0N6CkyIvx/G0IhX7FgY02VfQHVV9t34vGO+q8APxezSCroFreXtFtN+9ngyF5s7FIa64kAACAASURBVN3YdIzQr1UJLdq+Ehylu1I9zufF4FSDwhCcTRVqt+zRJgJOSbm27dxyUNwzTM/dLve/HGYZ2Qv2tm4N4BlJ+1a0zWZAoYsmz0KvYH8eOPLQVofTCSG6s6ikcXQe94xqqD2g89IHkHIPvRsITqKB50lSWwHkgjnGOjsyrNTu7/8ZzO8eSB4u6X/btv1fggnUOkPs+7cAdpH0OMkPwRMi4Tyx/6+98w63pKrS9/t1g5KmyYODhCajtiAoSlIxoKCCgCIgigqDIKjAqAyICirjIKAOg4OSJBtIKogCgtBIzk0QGH8kYRQVRUCC2Lh+f6xd99SpW1Wnqk6dcC/7fZ5++oQK+55Ua6+91vf1XLqU9EU8a3SulXxwJc2quDyft++FwI7WrWt7upm9s8nxepzrUjN7S6/HWjrXXKshA9bHeWrJWoVlzE/QySBdidcePotfFHKzXWpgrR72y7M2znvsODwgeBJXVrkWr2+fFDy0haST8EbgVXDZvJl44Pzqkn2uNbMNk8BaXgp0q/WQNAyrRpvgZgrn4wHxUZZv3b0InuG63cx+HYKKV1pJqYikv+A1isL1drsu1ANY2Sj9bCcTNrkjYNl2uZmvqscvGd+1+GTwR9bDIlzSv+KlYCvg5TYb4r0aPRtbJV0DfMa6ndCOrJJoKDheY/nHfggrYUfgq32TsqlmVtqUn5oQJpNQAZeZ2WY5295pZq8Itz8LrG0pBZSiyaekBemx2pnZPi3Z+SK8PyLXejuz38F4cmktM1tTbjBzlnU3rWb3SZvILIRPqm+q8hkaBiEjn7AQrtAyP29Ck9lvWVypJbtyMOnv0mSFldfj342eCitFvOBLMobMFkxecn9nzmOjpIq0TlNOBi6W1xcebvW1rv8Nr2d8XtIzFNdl3Sc3SUjqTq82s/sqnmM5m2xXvXzZDnWRy/AsgjvUpS08Z+HZ1UFwk6T/xJtZammA1uSo8ANfSWvUXEM7KQ3IUhQs17ZWV0c/9aWS0iU5s8i3al4JX2b+NR5MPozXuQ6S3fCa7fvM7OmwNNwrk32VpP2BhSS9Cc+mVZEm/DS+xLkPXi4xK5x/EmEsf8AnNb/GX69eSgfp2tWBLZEmmDsyroxn4S6RtDCwgGV0nxssBU8cv88hzjCXdkw/ViStuA+eFbvWzN4UJodVx13FCa0OgzDymoFPhsuSGnviq0xLAFtlnjN6q1glwesj8hKn31JsktJUAWUtOpP29SRhZoVqLpYpk5C0DR7I9mJb3Ajk5nCc34ZgvhAz63rNJK1Ix0myVcJ3bSUzu6fqPjmZ+KvUMfAq4wy8d+Vd+GfkQ3iDfR5NDcUKiQHzEJA3ROwJrCkpHTj8E770M070lNZpirlb2AV41uBGSafRvWzfS5u1Ul2WmS0j6eV43embgc/JaxWvwTMGZef5h6QVzOxhALnGb9vsgdfSLs9kLc//GcD5oOO8tGHqsVqychWppTUaloUPpmPl6zvkZDpTNLFWr6WfamZbhKzUK/DP0aeAOZL+jGf7mtYjFmKuafp74OVyo4Iq7I83bN2NB1oX0eO1CFnod5rr1D6JqwSUbT+R4cLrYRfE1U8KM1wtBJi1kLQ7/joshS+lrwB8Gw+A8rZfE29cXM7M5sjlDbc2s0N7nKeujnfCQ6Esw8Lr/wlcUjSPZ82lupJa3rslrdXj+Mk4ejqh1aSVJWhJ38Wvgc/j37/FJX3dzI7IPanZlWEV5GEza9IY/5Uwafg0/ps6i+K634ckfQKfFK+PK6wkgeCCBX/PwcBm+Ofgp8CWhN6kqgM0sx9JOqDCps+ZmcndOFG+fF8vHqa8+bwRkrbCJ8Rp34Iv9VpBkntRJMzAS71eUuGUS5vZiZL2Cb8xc0sC7dY19GPAPBzOxE03/hNvbEl4st+amraQtBPwfvxDf17qqVn4B60t/o4rG7wYnzBUbnoMAczOwCpm9uUwa/4XM5vUnW1mv8KlqU4Imaet8CA1MSYp4gv4bDepyXsTnrVpk6vxz8R7zexouTrAe3AppJ5lBU0wszcN4rg51NUaPQkP+irbQdPAWt06+qlnWEUr41D2c0coL3g8/HsXnhVqPWCW9FV8MvArOq+FkSlnCNuebGYfNm8u/Vb4V4nw2vXSpE1TO8OVGucmuBlSMiFKVoX6sa3OY29CLXcY46/leuxFHI8HUMeG7W8LQV1pwEx9He+Ej+H6/ivhJW+XUPy78rDcrvpHuMb2Y/Qwt1KfRjFD4OXmdvY74wHmv+Pf+dyAGSYmkO/CTTgqEyYks83sPLyEb1JdfoYmCijvxcumbjGzj8hrZXOtwVPjSjdezsAnoVUmJGfKVTKWCBPDXSuc6+jUsWfgCYZ5Fc5Vl0Pw793lMOFbMLvCfjfh4xO+YnU/BStcGZLVgN/JfS1+i0+O8+jHUCyXGDAPgVD3+BiwvaQ5+NImeGfyWATMNJPWqYWkLfBg9Txcx7XMWSuPY/AA+824wsFf8ezBBumNQiZn4/BvNu4Kdy2eKS/N6JvZBWH/jfAv878PYFJzLPDWECy/AZ9IVTENaIyqy031yzzqydc9YWbn996si1tCQPEd/P18gpTJQx6SzjSz94V985pN1sls/0n887MJ/iOdSMp9h37F74vZBq9TzFVRyFDa1FeBGyWdhU/c0tJ8eZJd/WS4TsQz+JUmRH0E2H8zs+eSADFk6MuCkUXM7PpMiUSVidTCZnapJJk3Hx0i6Zf0nkDNN7Mde2wDgHVcRQ+RN0ctTsh6llDbCa0ibZVkLCiv+d0G+KaZ/T3ve5jDxZLeQ4++lTRhQrgdPkGpsn0TBZRnQkA/P2Tz/0CPSTvdpSXz8QRJVnYvb3xHStoc/51bC/iCmf28x27p69x8XH/4qqKN+2C+mT2uYkWjXMxslYbnOzRcyz6F97rMosBh0RoaipURA+YhImlvPBOSvGlnSvofMztmhMMCOtI6kt5K58dgTbxOtK0A4SDcOKRu7XLC66xbv/kx5es3X4sHUN8Azq4YgKQRPnNdAFhd0uph+botZlrHenYHvBP7HOAcuerBIKgqN9UvdbVGfyGvrc5a+ZZJgjWxVt8n/P+u0q06zMZr3fYzs99V3Kdf7sOXgKt8XheRK0nkXqkqvB4r4BfS9Ptv5Gvc1s5wpXjczOpkdWoF2Cnmyhu2Fg7BxV54M2MRj0pajY605XvxhEEvqup4Z7lB0j14/eW52drqhHDs2yw0A1YtbbFmTmjJ82UKDG01IB+LB4jzgCvCql+VcpGqfStZrpSrzXyf7gnhpN8VNVNAuTFM2o/HP6t/JV+HOn2cxso6IUD+eRjvTEk7m1mho6eZndL0XDW5Qy4xOFNervRJSrSOM1n2SViPZk4zS/ozHsdXePLOkW6uTP8+7i7pWdwU6yAzu7TsXLnHrjhpi7SApNtw046/hvuJO1C/2aLWkHQTvoS1JB543gg8bWZ1zCgGgqTr8KzfDSFwXhaXRFovs91L8Vnlxnj22fC/4xq8/vQ3Jef4CvAB3M55ogbXzN7R4t9xB/AqM5sv6W7goxbkflTQOd/COSvLTfV5nlw1gaILf8jO5WxuubJIqf26rNVxB61SSaewVHuRmfXSKB4Jks7Bl3kvpXvyMElLPFwUbiA/YDYr6IaX9DEzq1y+kdpvc7zRUvhr2CvDlex3GK72kZ0Q5Qb0qqmyktpvBr6kOzFG4ISirKSkVfHVnI3x1b/7gQ+Y2QM9zrMB/tuwBL7KNQtvYL6uwhg3xpeFt8bVL75vOVKaks4ADiz7nSo5RyUVmNRztRUY2kId45RBHLvy70rRb1Zqp7mpbTcxs6uU0tMOJQiziib5mfKIvONP+n6H/WbhCbaX4quyPw/3P4Mr4UzKTku6veBcyUSj1VhDrqBzEP69A//eHWrBDj1n+3SJy1Z0T2rNzMrMg5ALBuxj3SpWX+u1X2r/CUOxJtfZGDAPkfBhfk3qi/Zi4EbrIQE1TNTRv/wEvvx4uHpowQ5xbDvjGdn1ceOF9wKfswIN3dR+i+K1hvvi9c8zS7a9B1i36AvfBpIOwhUbHsVrGtcPS96rA6cM4mKlluWmRomkb+KZ2DeY2cvkDSQXmdkGPXZFXp//QWu/FKVv5LXsk8jLFjX9TpYFTzWOMROXXizMcKW2zVvSnhTQS0rG9D5qBNiZYywbti3qms/bZ1G8OSg345uzfS0d74JjLIVbme+c91sk759INKXT2dHCRip1nND2xVfWEmbhdu65mtlhRWs9vIG2so53XeQ1vl8BljezLeVN2RtZxtglZ7/KfSth++16ZSn7QR25usrfo8z3+otkyneKssGSfoxP5q7BM/1L4s11+5g3d+bts3JyE7gAv86kz9WX4UjmXDOBw8ys1ESlZP/av2F5+zQ8zh6pVZnKxJKMIZCaSZ8GXBsySeDNNMNaOqmKwo/vznSK8Mfic2JmZ4QM+FvwH4RtzOyu7HYhc78hnTrm1+BOTj/Da1HLuJ8+O2l7EbKil9IxDUg3Z+QaGbTAnri7ZGIF/Bj9yU3lEpbcvoovU4uCJVQFN0blODHiO5TVHza1VgeXS7xd0s/pDkZyxzFMzOyU8HckCiH3WA9t10HSK8OFSzyVYtWbTbOWyWnDmjKVFeEByMcJnzdJzwNHm9mXcrbvtznuQCAbHOc9lj3vYni96o64rfCP8d+mPJpI39VxQkvThgJDFU7GS8AOCvf/Fy9PKQ2Yqdi3kuJz9Jacy0XVFFD+HrKkWXnKZNs8Z9mJa7ykfYsC5BxWtY6p0AmEBEvZ5C4dEEv6W5sBcs65nle3nnLtQzTYZ4ZSZjph8lk7PmkSLNPkRJFGXI9nEQ8PGZfX4z/ue5rZDaMd2iT2xS8APzSzO8PSZaH16zCQ6xbvCayO11Mf22Mp70H8Nb8a78K+1oKTVQWexBvDLqE7w1XqEFYXM7s257Eimak2zjePduWmijgc2CpvIpNhyfD/sqVb5VPbWj3FBeHf2CFpM3wC/QD++7CipA9ZvjvXv1c85jlm9p7UQ+vIpfEmbYpPbNJyT6fRyXD9Kx4ovwh4d1GGK+f8lZpNawTWWfbFy682sFB3G36zviVpPzP7Rmb7sua4QlRfxzvLHfjy8+FmllcuMIG5pnQthzLrSGydXDNIyqtPr2JFXpdlzGVFDwzjnR8mNr2o2rfSBlUUUN6Fq2m8mW55yqrUCRInJsshOL2/6krIELklrNqdRXcCYlBZ/q8BV8sdYgG2p6aKSj/EkowhMC4lDVMVST/Afzx+ietdPmBm+5ZsL8t8sEMmapFegbOkIvOGXpmQsSRk0h7Pjj+U3My0HNvXPs931SBKSjLnaGStPu6E1ZP3WzAAkDfdfs9KnP4qHLPrtycEHoV24+Yydcm2advsmVTIcOWc/xw8WEyyah/ES55ym3/kPQSHW3eN4qfM7HNFfx+wuZk9mnk8t7+hKXIr61fh8mNfSD31JO4gV+oAKWmGmVWa1KkPhzLVcEJL7dOoPr0Oki7HpTN/HgLgDYGvWg/3UVXsW0lt/zTw//KeokcNr+rZxa8bkhC1qFnK8TydIFTAwrjVe2HjozqlTeArQO8nFfRby0ZV6q5JTp0mv6ZY3Q2Wb6CBA2go53kz/nddai4hOxRiwDwEJD1MifZvhWXAgSPpv8xsXxV0DFf5IA+KzA/YAnjGpeePjqRT8aXa+XjT3zJ4zdXIX+9hIW8wXN8yusjy+vkbyi4gDc93FC5A/yO6M/S5GQdJy+BZrdl0G5d8NGfbvqzVwzGaGk8MHOXUjuY9VvOYXRfoOpP3nH1r1z+rZrNp3vjKzquSJtm85yRdbGZvC7cPNLP/rPn3NGpUC5/zTzE5kH1bzrbz8ElAl0OZFdQhZ/a9GC91+DQpJzQzq7QiMShCIHc03nB1B76y9F4rUcMJ+9XqW5F0J5m63TRl2XdJV+GTk7OBX+AKKIeZ2STTmBAo5l0nJwWK6lZtWAQPeqEk8K1DpkShbDXYyiZOw0D9W8znGolZgwbZJsSSjOEwE68va91mtEVOC/8P3Ma2Aemlqfmqrvn4SnOx/PfjVs3744HzpIBZ0vfMbKeQscr7IeyrUWqEWDZYDg/+TTVeyBrMwi8I6UCgzMr2x7gay5X0lhE7mf6s1aG58cQwuFHSiXS+izvTbNm3jEpLpXK77XUlJaU7wiXbnqDehf4ZSZtad7PpMyXbz1S3AsHCuMlREWUGOXnPpUuAtscnTz1RTR3vHE4HfoivjOyNB7KPFGzbj0NZJSe0TBDX9RQtBHFZzOzmECytFc5RqT7fKvatpHiuZklKmn3xgPaTeL30m4BdCrZN288vhL+vueYyVtGhNh341uRSfEJRubRJ0uZtrCTIyyV3Y/JEMDfD3CsgTh03W0qWcAGdz+3CwCrAPeH8AycGzMPhd5bTgDJOWJDkMq+fq91tPmCaXrhfFDLS7wa+ZW5sULQsmnT6tm4aMmokLWdmv88+NohzWX2t0UXN7FMVj92XtXqgqfHEMPgYHkx9Ev9sX4E3PfVD12TAzL5ccb8drURNpusE5Rf6jwGnyGuZBfyZ8mbT04FLUxm8XSlvjE7/NnQNi9QFPEXTJdW6Ot5ZljWzYyXtHT5/v6C4NyTrULYD+frYeVRyQqsaxPWLinV315RUtvJUt28loR9zjtnmPUV/xSfSSNqe4B6Zxlw3Pz3e7+Hujf0wEfjWpMmE/6sEXec+OQ24G3g7Xq60My672C+5K36WURQLKxd75G07CGLAPBzGJYNVSMg2prvNZ0iaT0G3+TCpeuHO4Xjc5e8OPNOyEv5jmHeOh8P/98JE7eSmwG+a1KqNEUcAF0j6FB03vFfjzXmtryZIWgFfet0ED06uxGWQHi7Y5WeS3mZmF1c8RWNr9UBT44mBE7KqX6fcur0uTZfi6/xmFV7ozZsDKzebmjdG30an3ObLZnZRyfZ1fxtWlTcpKXU7fbzc0jMz+528jvtEa6bjnQSyj0h6Ox7IrlhwrsShbNMwzuPM7IcVz1PZCS1BXp+d1Ole0atMoiZblTxXtvJ0Ct19Ky/DM8ClmNnHASTtg68mPYmb7KwHHNDjd6aRAkpgDVwitB+axglNJoFtxSSrm9n2kt5trvLzXVyLuV+qujreLNdGHwoxYB4ObbklDZK63eZjTbi4PWRmy6cee5hieaof4bVxd0h6CR5czgNWkbsxHj2McbeNmZ0q6Y/47D+p57wDONjqObBV5STgu/hyN7gJzEnA5gXb7wn8u7xZ5zk6qwZLZTdU/9bqMHnZ9c348vjISJb7VWA6kLfcX7Rtdp8aE5FJh6ix7aSLrxrIt6nbWKaXFXRT0mYPtSaM5koFT0ta3OrreH8lBLKfxmXRZtFZ1crjarxE6R+4QU3VMfZ0QksTAsvd6QSuZ0g6rq3fuwYrTgkvt07fyon0cNHLYVczOypMTpbFM8Yn4aV5XaiBAkqqpEXh/0doPjlNGGZDWVvnSiaCf5E0B38dZrd07Elkfk9m4BP1oa2Ex4B5CFjHBnmc2YVMt7mZ3SfpA/iPzJQKmMPFbV/gnNRj/6C45nEN6zSOfQTvvv1gyIxdiWdrpiQhMB5EcJzHsmaW7pw+ObwPRSxT49j9WqtjHRnHiWXXMaDJcn+y7d7h/3Tdc5OJRJY6Gai8i2+ZfFvuxbrPgLQSVWsoS6it4x0mArPN7DzgNjrZ3KLt/xUvO/oF/j4cLelLZvadXoNTfSe03XDptqfC9l/FZQRb/70LJSLZWtei1cumfSsTpwv/vwM4yczmlfRs/Bbvbdma7p6BJynIzg+rpKUio1zBPi58xj6HJzIWo1tFpilFf1P6dZ+P1zSfU7Bt68SAOZKwoGWkmcDrmCUtOIoBtcBFIVj7Ad0Xt7xl4XQDylsIgvrmTYN1l/3HDkmr4KYos+lWo2hb/eTRMMlK6i93wpuWsuNZw8x+TXGzxqRlYcuRd6pKduk959gjU4Exs9+Fm3tZRs0gBDCTMldJY5Pcqjct43eAvNu/3zKqvnRUrWMMcImZddWVhsa/IoZiLBPGcAiwMv59SFY2eqml1NbxDhOB7YAyM540nwHWM7M/hbEujWecewbMwDpJsBzO/ZikMlUU0d1s+zwDCMAkfRtf2XkTXiLxXsqzxv02nN4kVwxZBThQUmH5Vii5myfpjIp10snf9FI6n5/kWHma6ZUPWXKuTfGkzkmhx2ixZCWYnBVspRpnCx57oI9xTmBmJ4SbV1BQd9yQ3Gy9mTUx9WmNKCsXAXpKN/VtpzsKJD2U87CZ2aRas9BM9hPgYeBU3GXpsdB8crOZvXywox0scqmqE/EGmnSjXL8Zt+x5VgK+CWyEZxKvxjNeD2a2O9HMdgsNd1nMzN6Q83g/4/oj8BAeyF/H5Ga4Vl+HJuR9z9RDVk5ubfxx66hQbAwcY8Wybd+gvJSjtkGPSqTqCv6mst+ayvbg/SDpbjx7eBOpgDEJUttG0qF4duz7dE8EJk0M5S6gW1pQt5Ebdfy0Su10+J5vZt1OaHMt0yyV2v7f8JKkpEZ6G+CUtkvwks9x6v/FgHMtR1avpfPNwHWz7zOzv4TXYYWC17tJSdRX8WbMX9H5/FiviXdZ4CtpqbzVaEkH49rpa5nZmpKWB86yEr37ut+7pqi+bnqlUrKc/XLlblP7DSXhETPMkYS63eZjj5nlNtUUsBtwKL7U/X7rdP1vzPjZlzfhWSu3m+6bsPT8nio/Xma2W/i/cda4Ji/B66h3wsX8L8BNQRqXd7SFpI8Be+FNaOkL+j/Ru+t/N+A76lie/wVXliiikl51zhjrZrg2wr87y2bqDmfhMpu5tB0Yl/C4NajhV3Md70R/Nh2wGB0HxHR95v8B10n6cdjm3VSv4a3lhGZmX5ebiiQNhh8xs1sqnqsOiZTg0yHg+xOe/R0UGwG3mtlTYcVrfeCogm2blERtgwewf+u5ZSAd+OL11AviqjCbQGnp5rZ40+LNYbvfhox53jleglvZLxxWFpLEwCw8w982W5rZZ5M7Icn0DrxEI4+mpWRJv8F2+G/56eH+TrSULa9CzDBHpjWS1mbyxe27Ffet5A44FZBrUa+B16OnDUXadn663Mw2q7lP4/eoCXLTlp1wBZEv2YgbOkOwuyQeiB2QeurJkoto9hiz8N/zWrW/eUu3Ods0yXC9EdgMb+r8duqpJ4HzQzlO3n5DMZaRdBgeuJ9Lje+DpCvp6HhvRdDxNrNcWUJJ21lFm+DwOhdSdTlafTihSVoC2NvMWrUblvR5vC76LXjTowHHm1kb9a5557sNWBdYBw/KTgS2swJnQXU3nFY5/s/wfopc1aWCfW4lBL7JikyvFaSwzfVm9tokQyxpUeCagsz3h3DZxtfgddkJTwInV/0sViW8zhtYt276jWZWqousHEfYvMdy9rsiu/qY99igiBnmyLRF0udwA421cambt+MNfIXBmHLcASVNB3fAV+K2xG+mU5JhFKiG9MFVkr7J5Lrx3ECkyXvUlBAovxMPlmfjNaWtXkCaEILcx/FxIemf8WBxMUmLWY6LVSZrm348OWbp51XSa/EgYnFgJbm02L+a2SdyNq+c4Ur9TYlpxsnZcpweDMtY5nXh/7RNeJXvQ10d789R8TPWRn2mOtKZ56Ufy36GJK0IfB5YHnfl/C6uGvNBOv0HrWEd/e9zJP0EWKju5K4m883MJL0bOMrczKVQDcfqN5w+DdwaymfSE66yWvvnwpgMIAS+VThT0rHAEpJ2x1eQji/4O07Bdc/fYxmt6AFRVzc9YVF1GxptTKdRuIxlJa1qZveF/Vah24xooMSAOTKd2QGvY7vZXPHiX4Bje+xT2R1wirEtXpdd5ozWBhuH/9NNZ2WBSJP3qDZy9YA5uFrIF62ilfYwkbQV/jlbHvgD3lB0F/mNkf126f83vjz6I/DGJ0lFMmRNL/TgS/BHMFkdoejzMBRjGavoiJbDwHW8Q8nL/lR/zdJUdUI7FZiLKwxsgbtt3ok3DRY5ENZGrpH7UHJMSbsA7wEelHRI1RWUBjwp6UB8AvD6kEHu1bxep+H0PFKTkopUDnzTmNmRkjYHnsDLOb5gvV36fhKuYbPpbkps1VPBXDf9djpOjKW66SnqlpIl7AdcLum+cH820bgkEmmFZ0LmYH7IiD1C707eOu6AU4l5wBJ4IDYwGgQiTd6jJnwQvwiuCXxSHYWpgVgBN+RQYENcWWK9EMDulLdhC9nIGWb2oLqVtoqsyRtd6ANn4KsN78LLMz5EuW7qUIxlwoX6YDo1xHPx8pxe2cW6Ot5rq7sufWII+Ocubzm+7ms2gVV3QlvKzA4Jty+S9HtSS+stcixuQoOkNwCH4Wo9rwKOY3DOqjvgvQq7mtkjIfN+RI99KiugNKm1bxj4Jvv+nHrOfD/GV61uIpUBHwTWQLbU3Fk4MTSqXEpmZheGsq21w0N3D+AzW0gMmCPTmVtCTd538CzxE3Tc7oqo7A44xVgOuFvSDXQvIbbSXawCo4rUeYoy9E3eo9qY2Yy2jzkA/m5mf5I0Q9IMM7tM3o1fiKQ1gW8By5nZHEnrAFub2aE9zvVQKMuwkH37BPC/eRv2c6EHlg7L4fukyjTKFEmyAembGIyxzHfw7/j7wv0P4uUgRVbOQCMd7/spd7vLo+5rVoiVOKHJFQ2SGdMjwCLJ6kGLmd+ZqWPtgLsWnoOXZtza0jkmEYLkc/C+DYBH6SiBFO1TOQhWw1r7OoGvOuYok56i9yR/BTPbosp5+kEul/hVfFKrXmNrWkomaX8zOzzc3drMzko99xVLNR4OkhgwR6YtZpZkVv5H0kXArKJaWpho/KjsDjjFaHVJO4cyo4pc5L+Sh5hLElV6j6Y5f5HLbV2BO679gQKnsRTH47q9x4LLlMntaXsFzB/DyzJWAn4PXBIey6VBhish0Tf/ndy44rfACiXnuQHAKzIau8RVYTUze0/q/hfLAjg11/F+rmYNafuwEgAAIABJREFUN9R8zdKouhPa4nj2Mb3EkHzvjPZWeWZKWsBc3/gtwEdTzw0s/ggrIR8FlgJWw5Ujvk2J627NILhyrX3TwNf6M0e5WtIrzez2Po5RhcOBrczsrorbN/2bdgzngsl25VsAMWCORPpF0o74xfE/JK0o6dVhOWgSVt8dcMpgA9YZto5RxTFmVnX52EID0KvD/f83qPFNEd6N11Huh8ssLU5vA5JFzOz6TGlFFfOF+Wa2Y9kGfWa4Eg4N5Q+fwlUSZlHgnhbOuRHejLgYnWbEPcxsrwrnqsMzmaajTehIn+WxESU63iX0kgXMI+81K3PLTFPJCc3MZjcYVxO+h2fIH8Vf318CSFodLxkYFHsDr8XfK8zs1/Jm2jLqNJxWrrXvM/AFIHwPEgnOKyxHTzrDpsCHJd2PryiWlQD1w+9rBMv9lJKp4Hbe/YERA+bItEWu1rAgXqf4H3gN67eB3CXKQB13wClDJvh5Ef66PDWA2t2rw4/0D3Bjgsd6bH+9pPVfwFnlCaxbvrDq8vCjklYjvLeS3gv8rnwXAG6QdA+d9+nJnPH0faE3s5+Em4/jQUgv/gtXSjkv7D8v1L62zcdwNYHF8Qvun3E5riIa6Xib2ccBJO2DB2RP4k536wEHmNnFOftMes1Ubi+f3rd2QBKW1TfFP0O/NLMf1T1GyXj+Q64k8S/AxWYTOrYz8DKgZAxLVvitqMPfQv9JcvwFKDG+CNRpOG1Ua98g8E0+O7vTUVs5Q9JxVi6HuWWv4/ZD+MwA3CjpB3jzcLrUr1QZpkEpmRXczrs/MKIOc2Taoo5u5YQTmaR5ZrZuyT6V3QGnMpK2AV47iNqvUBu7Iy7u/yvg+2Z2emabBcxsvrzD+mXAvfgEJcmETDlnyabkZHIV7vfM5EpaFW+e2hh4DK+Z/YCZPVDhvBvj79PWwK34+/T9gm1rX+jDfqfgTo9pJ7CvmVluR7yk68zsdXW+s/0Qmo5qTYjVQMc7+RskvR3Pfn4eOKnq51zSb8p+g9TQCU3SMcDqdKTkdgDuNbO987YfFGrZhU7S4bjywi54YL4X8CszO6hkn6vwz/jZwC/wIPgwM1srZ9sNcAWbJfBa+8Vxx7trS46fDXy3xWu6Sz8/8qbRjZIJtUp0mDP7lZkN9YVcRq4IK/p+p/afSyglS33P7zCzOQXbP0/n+rAwHZMT4RKFvRRQWiFmmCPTmb+HLECSfVualC10HlbPHXDKYmY/knRA7y0bHft6PHP8FVwm7RQ6zkwJ1+P1ldsMYgxTiX4yueZ6pG8NF9EZeZnikn2vxlcEDsEzu2fg1s1dNMxwJayTBMvhnI/JHciKeCgE8ia3hP4kHpi0ggqaU3s1HYVt+tHxTpaN34EHyvOUqaOpuH8RTZ3Q3gjMSTK/YYIz6LrXPNpeVj8Aly67HVcJ+Sme2S+jsgKK1W/+JIzndanA96vANXjZTRmiW8HmeXq8XurhKtgvSX+BpE3MrKvsKJQ39aJWKZmZFbqDDpMYMEemM/+D1+8tK+mLeEd8zyVLDdl5bhikltDAl0NfwwCWskLGbls8c7ka3pn+2rxNAczs3rbHMJXJZIWWAf4pLyvUT+AXtlsMr5neEc/w/5iOhnaWphd6gBnp5XZJS1F+3dkTtzB+KfAwroXeZrazrDm18Pug/nW8b5J0Ma6LfKBcQrGOXGXpdzXpUZD0Zet2PTtf0hUlu96DN34mjYkrApVWD1qm1d+i0HtyPNXlDysFwWre/AkNAt/ASbhVeqLysQ2u8lJGbbOhhhxNt9170WNZmpaSjZQYMEemHZJ+CuxlZqdKugnXARVuZVp6odMQneeGTFraaj6edXr3AM4zD69n+5KZXVOy3bLZYC9Nr4BvOpKTFXoRxVmh2qokGe4AzseXkX/Za2g0u9ADfA3PYp8d7m+P9xPkYmaP4g2PA8E6zamX1MyM9avjvRuuPXyfmT0dJg5dQVlOaU76HAv3OH5CJSe0VAnH4sBdkq4PT70WuLriucaW8F4egpv/LEDnfZqkeFEzCG7a/AnNAl/M7OuSLsfrzAV8xMxu6bFbP2ZDPZE3527M5N/xWbjlfC/2xkvJ1pb0f4RSsjbHOAhiwByZjpwMXByyQof3aszJMBTnuWFjg5XoSrNqqrGnjJm4EsLQOpynAJWzQkng16TJK7BqyMJVodGFHiBMWm/El7cFbGdmvyraPtSeHoorKlwIrAvsm62Bb4FamTHrX8d7I+BWM3tK0gfCeY7KnKPS5KdHg1xVJ7Qjcx4bJW3/DpyIvxY3UWzIk1AnCG7U/AmNA99k35sJvwuSlpB0kJkVTjzpz2yoCi/Cf78XoHvS/gQVzGj6KSUbJbHpLzItCV/EL+AajaeRWv7sUad4vZm9NmSmN8OX524vakYYdyR9oeRpM7Mvt3y+NYFPM9mS9c2Z7Vpt8pkOpD57SbNqYXOPpIvN7G3h9oFm9p81z7UMLluWtV9+W8H269O50F9R9UIvN/6ZhJn9pmD7W83sVZK2xQPz/YDL2mr6S2XG9sXlwxJmAdsOsLnwNjz4Xwf/PToRnzy8scGxSr87oda6shOapOXoKAddb2YDcQMta0KTtJS1aJOt0DxacduZdILgdagYBKtB82dm/yWAvYsCX0kr4s2hy+Ordt/Fa6s/GMa3T4/jb46vlgq4yKqbDVVG0spWQ2e8qJQsYdxXFmOGOTJd+Tu+hPpifAZcNZs2FOe5IfJUzmOL4kvES+M/wG1yFi7ddwLlmZ2YWZ5MXlaoqFEpvcy+PW64UIfT8frybfHl0Q/hbm+5NMhwJVxAp8xgYbyG9x48UM8j6XZ/Bx4U/Lleb1xP+sqM9cH8sET+buAocye/pg6Gk14QNXRCk/Q+POC7PBz3aEmfMbOz87ZvSq8mtDaD5cBlko7AmzLTcmeTfsvN7Hl8NePCVBB8uaTcILhu82evwLfkbzgVt2w/B0/8XAvciTfSFn5XU3/XzyVdR4jz2pyUpEp6yPt+ltRy91tKNlJihjky7ZC0Ba7OcB4+83+6xy5Fx1mdaeQ8F5b398GD5TNxea9Ws0mSbjKzV1fYrtWM0nShalYonWVskq1P3idJt5nZOvKr3mVmtllqm74yXAXnXR83IskrE0DSYXhm+Rm8nnYJ4CdVs4U1xlErM9bC+ebiQdmuuHTZH/ESjVc2ONak97vs81D2+ZA0D9g8+R0Imd9L2s60y10U18NL3RIZsdvyVk9aOt9lOQ9bdqUrtX02CD4P+I6Z/V9mu3Tz5/d79cSkxjIXb5TdAncbvBPYryzwVUZOUdLvgZV6rRiEbffATY+ewZNFhTXcTZBUujJiAzbKGhUxwxyZjhyEN/jVqV2eQDXcAacCocHo3/BmqlOA9UtqIPvlfEl74dnLdGanKziOwXI+lrKgljRT0s5mdkbOpquGZiWlbqePU9atDx375Ufk2sC/xRUS0vSV4crDzG6Wa9gWPX+AXIXjCXPnzacZTHPq0yEDmS1JyQ2oWmAHvOZ1VzN7JJSqHNHi8Zs6oc3ITJr/hKvotM1Am9CymFkVkxzCWOoooDRp/lzKzA4Jty8Kge8GFQPfJem8f48AiySvXY/f0E8DrzBvom2dpgFxv6VkoyYGzJFph5m9vvdW+aiZO+DYEoKC7fCO5Fea2V8HfMpkmfkzqccMaCWzMR2RS/HtjUupnYcHzHvjr+GtuD5ylnQQ2aSB6ytyl7tP4/KLs+h+z6CPC31CplZxBt7sVmidLmkR/G9fCfgont1eC/hJ0T4NOQN3OXwXLmX3obJx9UsIks8B1ggPPYpPKpuQFwA3dUK7UNJFdEoDdsQDx7YZdBMa0LhGtnIQbBWbP5VpzGwY+C6ONy2m3+9ktbPXb+q9dMw9BoakNfBysKwMa9HY+i0lGymxJCMSSaEG7oDjjKR/4Jne+eS4yRVkRCJDRNKPcZe+a/Dl2iXxWtt9zOzWAZxvJt5s9N89tpuHN74mF+zL0verrBKE2tWERM7wHDN7tmD7H+BBwi7mlrkL442Pr+p1rjpkS1LCY3ObNOFVPN/u+ARgKTNbLQQa3zaztxRsX6tBTn04ock12jeh09DZmjV25jzDaELbw8yOzXzuJrDmqjJ1x5EukXmATllEzpDaKZPInH89groN3St9n2z5PFfi1uHfwKVLP4LHlbmvf7+lZKMmBsyRSIrQJLERcGMInJfGa/rK3Mle8KSbjiRtX7XpKAKSbk9qWUMw+yheq9hTakk19GYz+12erlcu2OYBhn+hv9HMXjPoCauka81sw5Bd/W+8JOVsM1utzfOkzncrXpN9XervmnjfM9tONMiZ2ZqSlgfOMrNWXNrCOdKaz9n391k8Q3mQmV3a1jlfSKQ/vy0dbztcpcaAX/aa1Mh1ta/EnQ7TClGntDWmcJ5k4pn+Dftl0SqvpL8AV+CfudeH2xNUKCUbKbEkIxLpppE7YIQdgaRL/0BcLSNhCyAGzMUk9cSEut37qwTLgTp6s2mulHQUboU9oaRiZrelbs+ucbwulOqiz6PkwvhcyConta6rkcqQtcihoSTlU7j+8iz8dRwUfzOz55LlfkkLUPz6DNylzUo0n8OkbQ5ettKXnKbKzVgGtsKl4el5F5H73tYNfMM+xwCr0ymb2VPS5mZW5oA538wKjaFa5FlJM4BfS/o48H/AP5ds328p2UiJAXMkAqgPd8AI0LzpKALrSnoi3BawcLhfJah43Mya1JwmpQfpJVHDa/cn0eBCn1wMt8PNHpJAZSe8LKOIg/EAZ0VJZ+ClAh/uca7amFlSE/04ULlBrA/mSvos/t5uDuyFOy3mMdQGuSzmMmvzJNXSFS441qjkw95mZvvL9bwfxutlL6PzORw6DQNf8O/qHAvlAKFJ8fYe+1wm6aP4Z6yw+boF9gUWAT6JK+i8iU4fyySaNguOC7EkIxKBRI/0UFxF4nAz+3uPXSIpymrTpmKt2lRBLsM2kwp6s2H77cysUDO2YJ/shX4H4N4KF3okXWFmb+j1WOb5pYEN8QnDtYPo9A9Bxz5m9pdwf0lcZnHXts8Vjj8Dl3OcqOEFTrCcC7CkT+PNgZvjTVG7At+1msYY44akdfFlePBa6dvKtu/zXHea2SskHY/XzF84zF6UvJIMSXfSHfjOwE2xijTJk/3OxSXoHgz3VwYOM7OdSva5P+fhgZRRhfMtamZ5mv9F2zcqJRs1MWCORAJq6A4Y6a/pKNIc1debbaLZ3OhCH7a9C3inuRUuklYBfmpmL6t47rWAT5vZ7nXGXOG4eQFNq3Wn/TCMBrlhImkfYHc6Bh/bAscNahKgIeh5N2jMrBX4psqaFsdVmq4PT70WuNrM3loytoUs01ib91i/yJ0zT8T/9pXCpGgPM9urx353k1NKZmZ/anN8bRNLMiKRDk3dAV/wmNnMUY/hhYjV0Jvtg3twmbfE6GNFoGp2cD/cNe2+cH82MMm0RNI6eBlHYpJyNHAM8Drga00HXsIMpaS/5FrlA7se1s2oWUqPe5qwG/C6JAsp19q+Bn+fW8cm63k/RYt63qrhXJgJfO8KDXkQAt+S0/RT43s13eVWRY/1y38Bb8flMDGzeZIKV49SNC0lGykxYI5EAHW7A65vDd0BI5FhEhrXDqZTezwXd7d8vGCXtSXlBbtJADfhvNbHhX6CsBS+BrB2eOhuy9dxPh74Fh03tJtxZ8Gd286KBb4GXC0psYDeHtddHxQ9mzNH1SA3JET33/08A+xtkLRL6nb6qVNbOkWdxsxGgW+63lfScnS8AK63AodWSS/B9dwXlkvLJX/8LLzWuHXM7KHMa1yl+biydfk4EQPmSMTpyx0wEhkR3wHuwNVcwE0YTsKb7fK4H9dLrULjDJdSMoPA1tZbZvDFZnZyuH1PqOM9IDSgtU5o7r0ReDMeVGxnZr8axLkCPTNqI2yQGwYnAddJSsxatsE/u4MibTS1EK5vfjPtBcyVGzObBL5pQn/NEcDl+Gf1aEmfMbOzczZ/O94kuwI+KUwi2ScYjFLRQ5I2BkzSi/Dmv7sq7JeUxrwm9Zjh38exJdYwRyKRyBRF0q2WMfXIeyz1XKM63boX+rpNoKGmcSc6F/gzcCvpxCSl1cyT3Jp6Emb2mzbPkzpf3ebMoTXIDQtJ6+NKK4lByi1DPPfiwGnWks5vk8bMnMD39UBR4Jvebx6wefKdC/XSlxQ1MIYeg53MLM8htFUkLQMcRUdV6mK8mXasa5GbEjPMkUgkMnV5RtKmZnYlTNTKPlOy/VV1T1AzwzWxW8HtvPsAv8NLohIeSd0fRObpAjrlDwsDq+C12j0bGRtSOaOW0yB3hqSBNcgNizA5uBlA0hKSDjKzQZbBpHmaji1535jZkaEx8wm8jvkLFRozD8Kt5bsCX6A0YAZmZCaof8Jt5ovG9g9Je+CTzoESFGx2rrtfg1KysSAGzJFIJDJ1+RhwSrgACfgzJbrFZvZxmAjKTgKeBE7A6zEPMLOLc3ZrcqG3gtt59ys3L8p1a/tuhrOMw17Ifk5qRmyLms2ZQ22QGySSVgQ+T6eZ87u4Xu8H6cgUDuK8aeOcGcDLgTPbPEeDxsxagW+KC+WOlMnrtSPQq2Hu5yEL/gO6jYla0WGWa3SXGRP1suCuW0o2FsSSjEgkEpniSJoFYGZP9No2bD/PzNaV9HZgbzyoOSlPck4ZC+ew5DsvG3Rm9hmIzGATWbxRHlvSB8zsdEm5rmt5cpWSbscnKM+G+wsBN5S93uNKkD2cS6eZ8y3Anbi82iMDPO8bU3fnAw+a2cMtHLdxY2ZocluH7sD3NjPbv8J5t8MVOJJyll7W2APVYZaUNif5Ip4tTp+o1IK7binZuBAzzJFIJDLFKArEkm71vEAse4jw/zvwQHmeMq3uKWpnuAYoM9iKskLmdZuBy239sY1jZ0iaweo09A27QW6QLGVmh4TbF0n6PT4ZGITd+QSZRrtl8GxuG8dt3JhpZp/JBL7fLgt8M8F5+nO/u6RngXuBg8zs0pxzrdJ0nFVIB8SS9u0VIOdQt5RsLIgBcyQSiUw9ygKxKsuGN0m6GK/dPTBIYuXqjte90A+YtpZE06/bfLym+ZyWjj2BmR0b/v9ijX2+LulyOg1yHxlmg1zbyF0Uk4DvEWCRRFWirRKB1Lk2BA7DS5O+jBtQLYPrbu9iZhe2eK5KjZlNA9+y4FzSTGAOXqc8p2CbOXgpykKpY7alEpKmyXeyVinZuBBLMiKRSGSKImkTM7uq12M5+80AXgXcZ2Z/kRt3rJC+6Jdc6AFKM1yDYpAlGYNE0uHAoXgW7UJgXWBfMzu9wr5LAHsPsUGuNSQ9gE/E8lYGWisRSJ3vRlw+bXHgOGBLM7tW0trA95ooxBScpxXnwnTga2a5gW+P/fdIJmWZxw8GNsMD5p8CWwJXmtl7656jwhgafyfrlpKNmhgwRyKRyBSlQKKt5wUsLIHeamZPSfoAXpJwlAXb3grn7etCX3LcF2eX69OPSTrXzBo3BmWawSbRluxYznlvNbNXSdoWL7HYD7gsLQ3Wq0HOzPYZxNimE+k6WEl3WcqCvamkYsF5bgM2SjVmLgpcYynjn5rHyw18+xjf7fik7JbQq7AccIKZVdVg73X89GR6Ebp7FApruZvU9I8TsSQjEolEphiSNgI2BpbNXHxm4Xq/vfgWsG5YVt4fd6I7FXhj6V4Bc0OReaFbvk2uYbJ978Rj/QTLgcSMZTvgJbidMbgG9AN9HruMpMnxHXjw++eckvFT8Qa5c/AGuWvxBrl1BtkgNyxCWc+meKD1ywGV9aTLirI1sW1mB1t1LmwzWA48E+Tl5ocs7h+A1rL5VWu5lbKfD/RbSjZSYsAciUQiU48XAYvhv+Hpi88TQJVl1/lmZpLejWeWT8x0vleirQu9hmTpmzSDSfqymb0h9dT5kq5o6zw5nC83Z3kG2CtI82Utv0fSIDcMJB0DrE6ncXTPIBG4d8unWlfSEwR1lnCbcH+h4t1qM+6NmTeGUp7jcTv2vwLXl+8yEC4lNQFO/V5ckldKNsyBNSGWZEQikcgURdLKVcsoMvvNxWtpd8Ubl/6Il2iMRLosBOsfxo09bkw99SRwspmdm7dfH+e7C3inmd0X7q8C/DS9hN82ofntCTN7XtIiwKx05lju6LYZncnCZen7bTfIDRNJdwJzLAQcoYb+djMblFHMwNEInQvrIGk2/lkbultkURlM01KyURMzzJFIJDJ1eTrou76C7m74Xs54O+DW07ua2SNyq+gjBjfMcoIs1SmS3mNmratV5LAfcLmk+8L92QzQuETSLqnb6afSqgWL49nA9AaJdbbR4pL6CLgHWAlIJncrAlPa7ttG61zYk0wJzJWM5vXuysi2UEo2UmLAHIlEIlOXM3A3r3cBewIfooKecAiSz6FjF/wo8MOSXYbFTyS9Hw9gJ65PZvalNk9iZhdKWgNYOzx094BLHzZI3V4IN/C4mVTAbGazB3j+kZBqslwcuEtSUhbwWuDqkQ2sIb0aM0c4tC5ySmD2kPTWAZTA1KXfUrKREksyIpFIZIoi6SYze7Wk25IOfUlzzay0eU/S7sBH8brZ1ULw+G0ze8sQhl02rguBx/FM60RTlZl9raXj729mh4fb25vZWannvmJmn23jPBXGsThwWpEqx5Aa5AaOuh33JpE2GJkKaETOhXUZlxKYkpKMRqVkoyZmmCORSGTq8vfw/+8kvRP4LbBChf32xrN81wGY2a8l/fNghliLFcxsiwEef0fg8HD7QOCs1HNb4Bq+w+BpOtn9LobYIDdwrNtxbzk6mfbrzewPoxlVX0yVxsyhlcBI2hRYw8xOCs2si5lZYs1dNAFvWko2UmLAHIlEIlOXQ0O28lPA0Xgt4H4V9vubmT2X1NNKWoDxkHW6WtIrzez2AR1fBbfz7rd30m795xm4ocSZBZu/ke7s4CnAoF6PoSDpfXiN/OX463y0pM+Y2dkjHVgDNETnwj5Ymu4SmA2AaySdB+3pjQeDlNcAa+HKIQviUo2bhPMUvR6NSslGTQyYI5FIZIpiZj8JNx8H3lRj17mSPotLb20O7AWc3/b4GrAp8GFJ9wN/o2OE0MgQIgcruJ13v02OTN2eDzxoZg8XbDvtGuSAg/BM7B8AQibyEmCqBcxTpTHzC0M6z7bAeoTXwMx+K6mKRvPSQcpyn7AKMTco94w1MWCORCKRKUrIPu5jZn8J95cEvmZmu/bY9QBgNzxzuQdun3vCIMdakS0HfPxh6fR2kSlNWAb4U3ab6dYgl2FGpgTjT3imfUoxVRozzWyupJXxUolLJC0MLGBmT7Z8queCnnuyGrJorx0CTUvJRkoMmCORSGTqsk4SLAOY2WPB+KMUM/sHbmpw/CAHVxczezCvJrLF4w9VukrShsBhwJ9xNYXTgGWAGZJ2MbMLU5sfmXOI6cKFki6iU5e9I/CzEY6nb8a5MTPd1Aushgej36a4prgpZ0o6FlginHNXqv2mNC0lGylRJSMSiUSmKInZRWI/K2kpYG4vA5LgqnUIsDKeOElKH0a6pJyuiTSzNSUtD5xlZmPvApaHpBvxRsLFgeOALc3sWklr4xbZuZObadIg10UIMDehY/QxNgFmXXIaM3cA7h2XxkxJtxKaepPPmKTbB2FMFEq63oa/rxeZ2c/bPse4EDPMkUgkMnX5Gt4ol9SCbg9UMU84Ec/odMm3jQFNayLHlQXM7GIASV8ys2sBzOzujIHJBNOsQe5JOrXh6T94d0nPAvcCB5nZpUMfXH+Me2Pm0Jp6Q4BcK0juo5RspMSAORKJRKYoZnZqyGK+GQ9ItjOzX1XY9XEzG8cl8aY1kePKP1K3n8k8VxTATJcGOcyscLIjaSYwB1dMmDO0QbXDuDdmDrSpNzMR6noKX6ma1eMQjUrJRk0MmCORSGSKEiyt/wqcl37MzH7TY9fLgg7qubgaBTBh9ztKmtZEjitNmgynRYNcL8zseWCepKNHPZaqTKHGzIE29ZZNhCoyQ9KSmVKysY9HYw1zJBKJTFEk3U4n07MwsApwTy9Hr+BYlsXGwTjghVQTmUeYyKxDd4PcbWa2/+hGFYGp5VwYViYws4HqG0taF3h9uHuFmfXMtEvaBTcO6iolM7PTBjPKdogBcyQSiUwTJK0P7GFme4x6LP0gaRapjNMYGUIMhenUIDddGcfGTHnR8sHAx/HPjvAehaPN7EsDON8+wO74ShV4D8JxZtZz1UDSy+mUkl1asZRspMSAORKJRKYRkm42s/ULnvuAmZ0u6d/ynjezrw92dOVI2gP4El7v+w/GRL1jGJQ0yAFM5Qa5aUdOY+brgZE3ZkraD3gH8NHEnlrSqsC3gAvN7Bstn+82YCMzeyrcXxS4ppfRUCglm0SFUrKRMvY1I5FIJBLJJxP4zgDWp9xiNmmiG1fliU8DrzCzR0c9kGEzjRvkpiPj2pi5C7B5+vtjZvdJ+gBwMdBqwEwng53wPNUs5i8gp5QMKC0lGzUxYI5EIpGpSzrImo9fiM4p2tjMjg3/f3HA42rKvcDTox7EuDEVG+SmOePamLlg3mTTzP4oacEBnO8k4DpJPwz3twG+02unrB50UkrW/vDaJZZkRCKRyAsMSYcDh+KlDxcC6wL7mtnpIx7XeoSLMN3qHZ8c2aAikQzj2pjZoxyr8Lk+z7k+7niY1Nvf0vA4Axlfm8SAORKJRKYYKXmrXMxs6x7732pmr5K0LZ4V2g+4zMzWbXek9QgyXVficlgTGsZmdsrIBhWJ5DCOjZmSngeeynsKWMjMBpFlTp9/CWBvMys1TyooJVvazN4+yPH1SyzJiEQikanHkeH/7YCXAElmeCfggQr7JxfOd+AWzX8ucp4bMvPNLLchMRIZNePuXGhmM4dxHkkrAp8Hlgd+BHwX+DLwQTpZ9zJqlZKNCzHDHIlEIlMUSVeY2Rt6PZaz32F4ZvkZ3HRhCeAnZva6gQ22ApLmdUVtAAAGYElEQVT+A3dPO5/ukowXlKxcZOqRbsw0s2ndmBl03OcC1wBbAG8B7gT2M7NHRjm2QRID5kgkEpmiSLoLeKeZ3RfurwL81MxeVmHfJYEnzOx5SYsAs0Z9sZN0f87DLwhZucj0QNIeSXPtdEXSvHT5lqTfAyuZ2d9Kduu7lGzUxJKMSCQSmbrsB1wu6b5wfzYVus2D01ZyO/3UqW0OrgEvM7Nn0w9IKrKQjkTGjukeLCeECXfy4/EIsEjQYS5bEeq3lGykxAxzJBKJTGEkvRhYO9y9u1eWJ+yTlidbCF9SvdnM3juAIVYmr1N+KnTPRyIvJCQ9QMdYKEvPFaGmpWSjJmaYI5FIZIohaX8zOzzc3drMzko99xUz+2zZ/mb2iczxFgdOa3+k1ZD0EuClwMJBWi65EM8CFhnVuCKRyGTMbHafh1hW0qqZUrJl+x7YgIkZ5kgkEplipLOu2Qxsk4xsMDW4rUrt8yCQ9CHgw8BrgBvoBMxPAKeY2bmjGFckEiknyOttitcm/7KKvJ6kLYDjgK5SMjO7aFDjbIOYYY5EIpGphwpu592fvHN3880M4OXAme0MrT5mdoqk04CdzOyMUY0jEolUR9IxwOp0pOT2lLS5me1dtp+ZXShpDWqWko2aGDBHIpHI1MMKbufdz+PI1O35wINm9nDfo+oDM/uHpD2AGDBHIlODNwJzLJQqSDoFNx3Kpd9SslETSzIikUhkipFy9BKwMPB08hQ1Hb0kLQP8ycbgYiDp87g29A9IOZZFHeZIZPyQdC6uvfxguL8ycJiZ7VSwfaulZMMmZpgjkUhkitHU0UvShsBhwJ9xZ67TgGWAGZJ2MbML2xtlI3YN/6eXdA2IOsyRyJiQKulaHLgrWNqDmyBdXbZrwe28+2NHDJgjkUjkhcM3gc/iF7pfAFua2bWS1sbrEEcaMJvZKqM8fyQSqcSRvTfJpd9SspESSzIikUjkBYKkW83sVeH2XWlVDEm3mNl6oxvdxDjm4E2IE4YlZjZqQ5VIJJKDpOWADcLd683sDyXbtlZKNgpihjkSiUReOPwjdfuZzHMjz55IOhjYDA+YfwpsCVzJ6B0II5FIBknvA44ALseD3qMlfcbMzs7bvmkp2bgQM8yRSCTyAmHcMzySbgfWBW4xs3VD9uoEM9tqlOOKRCKTkTQP2DzJKktaFrjEzNYd7cgGQ8wwRyKRyAuEKZDheSbIy82XNAv4A7HhLxIZV2ZkSjD+hOu6T0tiwByJRCKRceFGSUsAxwM3AX8Fri/fJRKJjIgLJV1Ex7hkR+BnIxzPQIklGZFIJBIZOyTNBmaZ2W0jHkokEikgWGNvgpd1XVHFGnuqEgPmSCQSiYwN4QK8Kd6EeKWZ/XDEQ4pEIikkPUmnSTirn/wscC9wkJldOtSBDZgYMEcikUhkLJB0DLA6nSXeHYB7zWzv4r0ikci4IGkmMAc4w8zmjHo8bRID5kgkEomMBZLuBOYkNt2SZgC3m9krRjuySCRSB0l7mNmxox5Hm0zbbsZIJBKJTDnuAVZK3V8RiDXMkcgUY7oFyxAzzJFIJBIZEyTNxV3DEmWMDYBrCHrRZrb1iIYWiURe4ERZuUgkEomMC18Y9QAikUgkj5hhjkQikcjYIGllYA0zu0TSwsACZvbkqMcViURe2MQa5kgkEomMBZJ2B84GkvrHFYBpq+saiUSmDjFgjkQikci4sDdugvAEgJn9GvjnkY4oEolEiAFzJBKJRMaHv5nZc8kdSQvQMUiIRCKRkRED5kgkEomMC3MlfRZYWNLmwFnA+SMeUyQSicSmv0gkEomMB8GoZDfgbbjl7kXACRYvVJFIZMTEgDkSiUQiY4OkZQHM7I+jHkskEokkxJKMSCQSiYwUOYdIehS4G7hH0h8lRV3mSCQyFsSAORKJRCKjZl9cHWMDM1vazJYCXgdsImm/0Q4tEolEYklGJBKJREaMpFuAzc3s0czjywIXm9l6oxlZJBKJODHDHIlEIpFRs2A2WIaJOuYFRzCeSCQS6SIGzJFIJBIZNc81fC4SiUSGQizJiEQikchIkfQ88FTeU8BCZhazzJFIZKTEgDkSiUQikUgkEikhlmREIpFIJBKJRCIlxIA5EolEIpFIJBIpIQbMkUgkEolEIpFICTFgjkQikUgkEolESogBcyQSiUQikUgkUsL/B4rVpPmDZjw/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(rf_clf.feature_importances_, \n",
    "         index=X_train.columns).sort_values(ascending=False).plot(kind='bar', figsize=(12,6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ada_clf = AdaBoostClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
       "          learning_rate=1.0, n_estimators=50, random_state=None)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada_clf.fit(X_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Result:\n",
      "\n",
      "accuracy score: 0.9020\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.91      0.98      0.94       921\n",
      "          1       0.82      0.51      0.63       181\n",
      "\n",
      "avg / total       0.90      0.90      0.89      1102\n",
      "\n",
      "\n",
      "Confusion Matrix: \n",
      " [[901  20]\n",
      " [ 88  93]]\n",
      "\n",
      "Average Accuracy: \t 0.8729\n",
      "Accuracy SD: \t\t 0.0255\n",
      "Test Result:\n",
      "\n",
      "accuracy score: 0.8723\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.89      0.96      0.93       312\n",
      "          1       0.65      0.36      0.46        56\n",
      "\n",
      "avg / total       0.86      0.87      0.86       368\n",
      "\n",
      "\n",
      "Confusion Matrix: \n",
      " [[301  11]\n",
      " [ 36  20]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_score(ada_clf, X_train, y_train, X_test, y_test, train=True)\n",
    "print_score(ada_clf, X_train, y_train, X_test, y_test, train=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AdaBoost + RandomForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME.R',\n",
       "          base_estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "          learning_rate=1.0, n_estimators=50, random_state=None)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada_clf = AdaBoostClassifier(RandomForestClassifier())\n",
    "ada_clf.fit(X_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Result:\n",
      "\n",
      "accuracy score: 1.0000\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00       921\n",
      "          1       1.00      1.00      1.00       181\n",
      "\n",
      "avg / total       1.00      1.00      1.00      1102\n",
      "\n",
      "\n",
      "Confusion Matrix: \n",
      " [[921   0]\n",
      " [  0 181]]\n",
      "\n",
      "Average Accuracy: \t 0.8466\n",
      "Accuracy SD: \t\t 0.0153\n",
      "Test Result:\n",
      "\n",
      "accuracy score: 0.8614\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.86      1.00      0.92       312\n",
      "          1       0.86      0.11      0.19        56\n",
      "\n",
      "avg / total       0.86      0.86      0.81       368\n",
      "\n",
      "\n",
      "Confusion Matrix: \n",
      " [[311   1]\n",
      " [ 50   6]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_score(ada_clf, X_train, y_train, X_test, y_test, train=True)\n",
    "print_score(ada_clf, X_train, y_train, X_test, y_test, train=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "              presort='auto', random_state=None, subsample=1.0, verbose=0,\n",
       "              warm_start=False)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbc_clf = GradientBoostingClassifier()\n",
    "gbc_clf.fit(X_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Result:\n",
      "\n",
      "accuracy score: 0.9701\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.97      1.00      0.98       921\n",
      "          1       1.00      0.82      0.90       181\n",
      "\n",
      "avg / total       0.97      0.97      0.97      1102\n",
      "\n",
      "\n",
      "Confusion Matrix: \n",
      " [[921   0]\n",
      " [ 33 148]]\n",
      "\n",
      "Average Accuracy: \t 0.8648\n",
      "Accuracy SD: \t\t 0.0177\n",
      "Test Result:\n",
      "\n",
      "accuracy score: 0.8641\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.88      0.97      0.92       312\n",
      "          1       0.62      0.29      0.39        56\n",
      "\n",
      "avg / total       0.84      0.86      0.84       368\n",
      "\n",
      "\n",
      "Confusion Matrix: \n",
      " [[302  10]\n",
      " [ 40  16]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_score(gbc_clf, X_train, y_train, X_test, y_test, train=True)\n",
    "print_score(gbc_clf, X_train, y_train, X_test, y_test, train=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_clf = xgb.XGBClassifier()\n",
    "xgb_clf.fit(X_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Result:\n",
      "\n",
      "accuracy score: 0.9564\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.95      1.00      0.97       921\n",
      "          1       1.00      0.73      0.85       181\n",
      "\n",
      "avg / total       0.96      0.96      0.95      1102\n",
      "\n",
      "\n",
      "Confusion Matrix: \n",
      " [[921   0]\n",
      " [ 48 133]]\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/anthonyn/anaconda3/envs/tensorflow/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/anthonyn/anaconda3/envs/tensorflow/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/anthonyn/anaconda3/envs/tensorflow/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/anthonyn/anaconda3/envs/tensorflow/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/anthonyn/anaconda3/envs/tensorflow/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/anthonyn/anaconda3/envs/tensorflow/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/anthonyn/anaconda3/envs/tensorflow/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/anthonyn/anaconda3/envs/tensorflow/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/anthonyn/anaconda3/envs/tensorflow/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/anthonyn/anaconda3/envs/tensorflow/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/anthonyn/anaconda3/envs/tensorflow/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Accuracy: \t 0.8720\n",
      "Accuracy SD: \t\t 0.0219\n",
      "Test Result:\n",
      "\n",
      "accuracy score: 0.8668\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.89      0.97      0.92       312\n",
      "          1       0.63      0.30      0.41        56\n",
      "\n",
      "avg / total       0.85      0.87      0.85       368\n",
      "\n",
      "\n",
      "Confusion Matrix: \n",
      " [[302  10]\n",
      " [ 39  17]]\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/anthonyn/anaconda3/envs/tensorflow/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/anthonyn/anaconda3/envs/tensorflow/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/anthonyn/anaconda3/envs/tensorflow/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/anthonyn/anaconda3/envs/tensorflow/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/anthonyn/anaconda3/envs/tensorflow/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "print_score(xgb_clf, X_train, y_train, X_test, y_test, train=True)\n",
    "print_score(xgb_clf, X_train, y_train, X_test, y_test, train=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  }
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