HR Analytics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from time import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('HR_comma_sep.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14999 entries, 0 to 14998\n",
      "Data columns (total 10 columns):\n",
      "satisfaction_level       14999 non-null float64\n",
      "last_evaluation          14999 non-null float64\n",
      "number_project           14999 non-null int64\n",
      "average_montly_hours     14999 non-null int64\n",
      "time_spend_company       14999 non-null int64\n",
      "Work_accident            14999 non-null int64\n",
      "left                     14999 non-null int64\n",
      "promotion_last_5years    14999 non-null int64\n",
      "sales                    14999 non-null object\n",
      "salary                   14999 non-null object\n",
      "dtypes: float64(2), int64(6), object(2)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years  sales  \\\n",
       "0                   3              0     1                      0  sales   \n",
       "1                   6              0     1                      0  sales   \n",
       "2                   4              0     1                      0  sales   \n",
       "3                   5              0     1                      0  sales   \n",
       "4                   3              0     1                      0  sales   \n",
       "\n",
       "   salary  \n",
       "0     low  \n",
       "1  medium  \n",
       "2  medium  \n",
       "3     low  \n",
       "4     low  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['low', 'medium', 'high'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['salary'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Number of People who have left VS. who have stayed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Number of Employees Left = 3571 \n",
      " Total number of Employees = 14999\n"
     ]
    },
    {
     "data": {
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eyw5Yp9Op1NRUPfroo+4nheLiYj355JP66KOP1LVrV919992aMGGC+5jdu3dr8eLFOnz4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc0fadd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('left').count()['salary'].plot(kind='bar', color='g', title='Stayed \"0\" VS. Left \"1\"', width =.2,stacked = True)\n",
    "print('The Number of Employees Left = {} \\n Total number of Employees = {}'.format(df[df['left']==1].shape[0], df.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show The Percentile of each Salary Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'high' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-82-6ea55dbc0cfa>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0morder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'low'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'medium'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'high'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'salary'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'sales'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'bar'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'rgb'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'The Percentile of each Salary Class'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwidth\u001b[0m \u001b[1;33m=\u001b[0m\u001b[1;36m.2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mstacked\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'The Number of Employees with low salary = {} \\n The Number of Employees with medium salary = {} \\n The Number of Employees with high salary'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'salary'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;34m'low'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'salary'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;34m'medium'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'salary'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mhigh\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'high' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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IiGdGxMmUhbmGGGNBJ2lDaKunobpE5xHKxJafVo/9KyUoLKSEhqc2egci4krgmsw8LSJO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbbc3f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "order = ['low', 'medium', 'high']\n",
    "df.groupby('salary').count()['sales'].ix[order].plot(kind='bar', color='rgb', title='The Percentile of each Salary Class', width =.2,stacked = True)\n",
    "print('The Number of Employees with low salary = {} \\n The Number of Employees with medium salary = {} \\n The Number of Employees with high salary'.format(df[df['salary']=='low'].shape[0], df[df['salary']=='medium'].shape[0], df[df['salary']==high].shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      mean                     std\n",
      "satisfaction_level    0.6007531437944199       0.254593761941733\n",
      "last_evaluation       0.717017495899394        0.17326060551822497\n",
      "number_project        3.799890650628759        1.281822006837944\n",
      "average_montly_hours  200.9965828321487        50.832214201477065\n",
      "time_spend_company    3.4382176052487696       1.3038181669165836\n",
      "Work_accident         0.14215418261344998      0.3492320742806306\n",
      "left                  0.2968835429196282       0.45691601116040653\n",
      "promotion_last_5years 0.009021323127392018     0.09455771250585635\n",
      "\n",
      "\n",
      "satisfaction_level    0.6218166304685063       0.24506911590044703\n",
      "last_evaluation       0.7173223704622999       0.17104288525984024\n",
      "number_project        3.8135277691591685       1.2012316138017844\n",
      "average_montly_hours  201.33834936394663       49.34418794966946\n",
      "time_spend_company    3.52901023890785         1.5064420799217257\n",
      "Work_accident         0.14536146447409246      0.35249224780337285\n",
      "left                  0.20431275209432206      0.4032298048056323\n",
      "promotion_last_5years 0.028079429103319887     0.16521261818015648\n",
      "\n",
      "\n",
      "satisfaction_level    0.6374696847211003       0.22679128756955622\n",
      "last_evaluation       0.7043249797898137       0.1586083321914513\n",
      "number_project        3.767178658043654        1.0886838694359524\n",
      "average_montly_hours  199.86742118027485       47.710445562322235\n",
      "time_spend_company    3.692805173807599        1.9778845973970403\n",
      "Work_accident         0.15521422797089734      0.3622552384589218\n",
      "left                  0.06628940986257073      0.24888792852167982\n",
      "promotion_last_5years 0.0582053354890865       0.2342260128959637\n"
     ]
    }
   ],
   "source": [
    "df_low = df[df['salary'] == 'low']\n",
    "df_medium = df[df['salary'] == 'medium']\n",
    "df_high = df[df['salary'] == 'high']\n",
    "fmt= '{:<22}{:<25}{}'\n",
    "\n",
    "print(fmt.format('','mean','std'))\n",
    "\n",
    "for i,(mean,std) in enumerate(zip(df_low.mean() , df_low.std() )):\n",
    "    print(fmt.format(df_low.columns[i], mean, std))\n",
    "print('\\n')\n",
    "for i,(mean,std) in enumerate(zip(df_medium.mean() , df_medium.std() )):\n",
    "    print(fmt.format(df_medium.columns[i], mean, std))\n",
    "print('\\n')\n",
    "for i,(mean,std) in enumerate(zip(df_high.mean() , df_high.std() )):\n",
    "    print(fmt.format(df_high.columns[i], mean, std))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find Correlation Matrix between all features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eYmIiJUqU0GqR29raUrJkSRISEjA3N9cqe/PmTa26ajQa0tPTuX37NhcuXODj\njz/WqsvAgQOBzB4CIYTIN5Kc323GxsZa2xqN5oVd2mq1Wmu70HNTwGXXDf5s+adztBobG6NWq/H2\n9sbf31+r/LPLkz2/Ikp2nj0XMutu9ExXzrPXK1q06AuvoVarteaSBXjy5AlVqlRh/vz5WcqbmZll\niSuEEG/K607D+a4o2J3yeWRsbMz9+/e19v3zzz+5vp5Go+Hs2bPKdnR0NOXLl8fExAQbGxuSkpKw\nsrJSPnv27GHbtm25jnfnzh2t96pjYmL46KOPXljWxsaGO3fuaHVhx8fHc//+fa3W+9OyKSkpvPfe\ne0pdL168yOzZszEyMsLa2pozZ85ondOpUyd27twpI7WFEPnrLS18oW8Kdu1z6NlRxs9ycHDg1q1b\nrFq1ikuXLjF16lRl9HJurz1x4kSio6M5ePAgc+bMwc/PDwAfHx9OnjzJDz/8QFJSEtu2bWPWrFmv\n3ZX9fOwxY8Zw7tw5du/ezapVq/jyyy9fWNbW1paGDRsyYsQIYmJiiI6OZuTIkbi5uVG1alWtsg0a\nNMDS0pJvvvmGs2fPEhUVxdixYzE1NUWlUtG5c2eioqJYtmwZFy9eJCwsjISEBNzc3JSl1U6dOvXS\nNVCFEEK8mkEk55e15qytrRkxYgShoaG0adMGlUpF06ZNc31tlUqFj48Pffv25euvv6ZNmzbKROiW\nlpYsWLCAAwcO0KJFC+bMmUNgYCBeXl55uq+GDRvi4+PDtGnTGDp0KJ6eni8tP336dCpWrEj37t3p\n1asX1apVY968eVnKGRkZsWDBAiBzgvdBgwbx6aefMnr0aCDzOfzcuXP55ZdfaNGiBREREYSGhlK2\nbFmqVavGJ598QufOnTlw4ECu700IIQCDnYREpXlZs1LotcjISHx9fTl9+nS+XO/QoUP07t2bmJiY\nfLne64j99/V6K/KT5dZvdRZ7ZB/dTXMaFNRYZ7ErtG2vs9iP7f+fzmIbPXmks9gZhV885uRtKPa/\ngbO59fjw5lyfa1ynVZ5i65KM7NFDGo2G69evv7V4ycnJHDp0KF9XVBFCiHxRwJ8d55YkZz10/fp1\nGjRo8MLu+KejzJcvX55v8WbOnMnBgwcZMWJEvl1TCCHyg6qAd0/nliRnPWRhYZFlebIXya8u7e+/\n/z5friOEEPnOQJOzYfYXCCGEEHpMWs5CCCH0lzxzFkIIIfSLoc4QJslZCCGE/jLQZ86SnIUQQugv\nSc5CCCEvaykcAAAgAElEQVSEfino6zLnlmHetRBCCKHHpOUsdM6qhHH2hd6QjEK6+/1Ul1NoTpny\nm85iz+s1UGexdTlZsVHabZ3FzjAvwLP/Sbe2EEIIoWdUhtnBK8lZCCGE/pLkLIQQQugXjYEmZ8O8\nayGEEEKPSctZCCGE/jLQlrMkZyGEEPrrBUvnGgJJzkIIIfSXTEIi3rZLly5x4MABAJKTk7GzsyMl\nJSVP14yMjMTOzi4/qieEEDqnURnl+lOQFezaF3BBQUFER0cr26p86r7Jr+sIIYTOqYxy/ynACnbt\nCziNLqcrEkIIobdeKzkfPXoUHx8fnJ2dcXFxoU+fPly5coVGjRqxadMmrbLu7u5s27YNgKioKNq2\nbYuTkxMtW7YkIiJCKRcYGEhgYCDe3t7Ur1+fixcvEh8fT48ePahZsyaOjo506dKFxMRE5ZyTJ0/S\nsWNHnJyc6Ny5M3PmzKFr167K8T179uDl5YWzszMdOnTgyJEjOb5HOzs7wsPD8fT0xNnZmaFDh/LP\nP//g6+uLs7MzXbp04erVq0r533//nTZt2uDk5ETz5s3Zs2ePcqxr166EhobSo0cPnJycaNq0KQcP\nHlTu+8iRI8ybN49u3boB/yXr0NBQWrZsqVWvJUuW8OWXX+b4PtasWUOjRo1wcXEhMDCQx48f57jO\nISEhyvbz3e12dnbMmTOHunXr0q9fP548ecLo0aOpW7cuLi4u9O3blytXruS4nkII8UrScn61e/fu\n4e/vT8OGDdm5cydLliwhKSmJH3/8kWbNmmkl3GPHjnH79m2aNGnCtWvX8Pf3p23btmzfvp1evXoR\nGBjI0aNHlfJbt27l66+/JiwsDCsrK/r27UulSpXYunUra9euRa1WM2PGDKUevXr1okaNGmzZsoXm\nzZuzcOFCpSs3Li6OkSNHEhAQwLZt22jZsiW9e/fm0qVLOf5S5s6dy3fffcfChQvZvXs3nTt3pkuX\nLqxZs4Zr166xaNEiAA4dOsSAAQNo3bo1W7dupV27dgwZMoTY2FjlWmFhYbRo0YLt27djb2/PmDFj\ngMwubWdnZ7p3764kw6f34OXlxblz50hKSlKuEx4ejpeXV47qr9FoiIiIYOnSpcyfP5/w8HA2btyY\n4zo/7/lu8n379rF27VqGDh3KqlWriIqKYtmyZWzcuJEHDx7w7bff5qieQgiRLQNNzjkerf3w4UMC\nAgLw8/MDwNLSks8//5yYmBiGDBnCl19+yYMHDzA1NSUiIoJGjRpRrFgxFi5cyCeffIKPjw8AVlZW\nxMbGsnz5cmrVqgVAjRo1cHd3ByAtLY3OnTvj4+ODiYkJAK1bt2bx4sUA7Nixg+LFixMUFIRKpaJy\n5cr8/fffXLt2DchsYXbo0AFPT08AvvzySyIjI1m9ejUjRozI0b36+flRo0YNAKpXr46trS2ff/45\nAJ9//jlxcXEArF69mi+++EJptfv5+REdHc3ixYv5/vvvgcwehFatWgHQt29fWrVqxbVr1yhbtizG\nxsaYmppSokQJ7t69q7ScraysqFGjBuHh4fTp04fk5GRiY2MJDQ3NUf1VKhXjx4+nUqVKVKlShfr1\n679WnZ/3fPd7p06dsLa2BjJb6CYmJlSoUIGSJUvy7bffcuvWrRzVUwghslPQB3blVo6Ts4WFBd7e\n3ixbtozTp08THx/PmTNnlK7nsmXLsm/fPjw9PYmIiFASYUJCAnv37sXFxUW5llqtxsbGRtn+4IMP\nlJ+LFStGp06d2LRpEydPniQxMZHY2FgsLCwAOHv2LNWrV9dqzTk7OytdswkJCYSHh7NmzRrl+JMn\nT2jYsGGOv5SKFSsqPxctWlSrfiYmJqSnpyuxOnfurHWui4uL0koFlCQGYGZmptTnRZ69Jy8vLzZv\n3kyfPn3YtWsXderUoXTp0jm+BysrK+Vnc3NzHj16lOM6Z8fS0lL5uWPHjuzcuZP69etTp04dmjRp\nQps2bXJ8LSGEeCVJzq925coV2rZti4ODA/Xr16dDhw7s27ePEydOAChJ2dramps3byotYbVajbe3\nN/7+/tqBC/8XukiRIsrPDx48oG3btpQpUwYPDw+aN29OYmIiS5YsAaBQoazLhz3bslOr1fTq1Utp\nrT5VtGjRnN6qVt3g5aOfX3RNtVqNWq1Wto2NtZdD1Gg0ORoI5unpyfTp07l48SIRERF07NgxJ1V/\naZ2fxsyuzs+fp1ars+x79hpVq1Zl79697Nu3j3379jFr1ix27NjBqlWrXqu+QgjxQgb69kmOk/Ov\nv/7Ke++9p9W1umLFCuU/fS8vL7788ksqVaqEh4eH8h+4jY0Nx48f12rJLVmyhCdPntC7d+8scSIj\nI0lNTWXnzp1KUvjjjz+UOB9++CG///671jknT55UfraxseGff/7Rijd9+nRsbW1p165dTm83R57e\n27OD0Y4fP67VK/C8ZxPdq155Klu2LLVr1+aXX37hzJkzSrf6m66zsbEx9+/fV45dvHjxldfbvHkz\nRYoUwdPTk6ZNm3LixAk6derEjRs3XqulL4QQ4j857i8oVaoUKSkpHDp0iEuXLrFw4UL27NmjjAK2\ns7OjXLly/PTTT8rzXgAfHx9OnjzJDz/8QFJSEtu2bWPWrFlaXcXPx3nw4AEREREkJyezfv16Vq9e\nrXQle3l5ce/ePaZOncqFCxdYt26dViL38/Njx44drFy5kkuXLrFs2TJWrFhB5cqVc/sdvZSfnx+7\nd+9mxYoVJCUlsWzZMn799Ve6dOny0nOebTWbmpqSlJTEjRs3shyDzNbzsmXLqF+/Pubm5m+lzk+f\ndcfExBAdHc3cuXNfeb2nfxZP/15s3bqV8uXL89577+VLfYUQBs5AB4TluPbNmjWjZcuWDB48mHbt\n2hEZGcnIkSNJSEhQErSnpyeFCxfWer5raWnJggULOHDgAC1atGDOnDkEBga+dOSxs7Mz/fr1Y+LE\niXh7e7N582bGjRvHjRs3uHr1KqampoSGhhIVFUXLli3ZsmULLVu2VLqPnZycmD59OqtXr8bLy4sN\nGzYwc+ZMXF1dc3Sfz7dmX9W6dXR0ZPr06fz888+0aNGCTZs28cMPP1C7du2Xnvvsvnbt2nHgwAF6\n9uz5wvJNmzZFrVbneJR2TmRX5+7du1O9enW6du3KsGHD6Nev30vrD9ClSxdat27NiBEjaN68OXFx\ncSxYsEAmQhFC5AtDnSFMpSlgM2H8888/XLlyRRnpDTBx4kTS0tKYNm2aDmuW/y5cuEDr1q3566+/\nKFasmK6r88bcfZCms9gZP0/RWew75y/rLPaUKb/pLPa8C1t0FvtRuY90Ftv43tXsC70hT8zL6Sx2\nsf+9dZNb6TdyP6VxkdKW2RfSUwVu4Yt79+7RvXt3goODqVGjBidPnmTr1q3MnDkz23Nv3rypNVjr\neSVLlswygEsX7t+/zx9//MG6deto0aKFkpg1Gg3Xr19/5blPR7ULIcQ7oYC3gHOrwCVnOzs7xo4d\ny8yZM/n333+pUKECgYGBNGrUKNtzfXx8uHDhQpb9Go0GlUrFihUrcHNzewO1fn1jxozB2tqawYMH\nK/uuX79OgwYNXthl/PQeTp8+/TarKYQQb5Yk54KjXbt2uRp5vWvXrjdQm/xXvHjxF045amFhoUwm\nIoQQ4t1VIJOzEEIIA2GgLWfDvGshhBAFwtscrZ2ens6oUaNwc3OjYcOGLF269KVlY2Nj6dChA87O\nzrRv355Tp07l5TazkOQshBBCf73F95y/++47YmNjWblyJePGjSMkJERrUaen0tLS6N27N25ubmzc\nuBFnZ2f69OnDw4cP8+OOAUnOQggh9JlKlfvPa0hLS2PDhg2MHj0aOzs7mjRpQs+ePV84FfGOHTso\nVqwYw4YNw9bWlqCgIIoXL054eHh+3bUkZyGEECIuLg61Wo2zs7Oyr1atWkRHR2cpGx0drTXXBkDN\nmjU5duxYvtVHkrMQQgj99Za6ta9du0apUqW0Fj4qU6YMjx494ubNm1plr169Srly2hO7lClThitX\nruT+Pp8jo7WFEELorbc1DWdaWprWConw34qJT9d2eOrhw4cvLPt8ubyQ5CyEEEJ/vaXkXLRo0SzJ\n9en289Mnv6ysSR6nKn2WJGehc6kPnugsdqXG+buM6OsofvNfncWe12ugzmIHVPbWWewfHuhuBr1C\nd3U3t7ZOF4EwqZin0zVvaRGd999/n1u3bpGRkYGRUeb3lZqaiomJCSVKlMhS9tq1a1r7UlNTKVu2\nbL7VR545CyGE0FsaTe4/r8Pe3p7ChQtz/PhxZV9UVBQODg5Zyjo5OWUZ/PX3339rDSbLK0nOQggh\nDJ6JiQne3t6MGzeOmJgYfv31V5YuXYqvry+Q2TJ+9OgRkLmc7927d5k6dSoJCQlMnjyZtLQ0mjVr\nlm/1keQshBBCb2VoNLn+vK7AwEAcHBzw9fVl0qRJDBo0iCZNmgDQoEEDZX0GMzMzQkNDiYqKom3b\ntsTExPDjjz/m6zPnArees3j3nE+9q7PYle4l6Cy2RofPnFWlK+gstqE+cy5yOX+nd3wdT0qU11ns\nIhZ5e+acl/XezU2LZV9IT8mAMCGEEHorw0Cbj5KchRBC6C1D7dyV5CyEEEJvGWrLWQaECSGEEHom\nX5JzXFwcx44dIzIyEnt7+/y4ZIEVEhJC165ddV0NIYR4J2jy8CnI8iU5BwQEkJSURM2aNfnzzz/z\n45IFmuotzWgjhBDvugxN7j8FWb48c376wL5w4cKUKVMmPy4phBBCGOyAsDy3nLt27UpKSgqBgYF4\neHhgZ2cHQHJyMnZ2duzfvx8PDw9cXFyYMmUK586do23btri4uODv78+DBw+Ua61Zs4bGjRvj4uJC\nt27dOHv2bI7rsWLFCjw8PHB0dKRt27YcPXoUgMjISNzd3Vm5ciV16tShQYMGhIaGap37qrgeHh6s\nXr2ajh074ujoSKtWrYiNjVWOJyQk4OPjg7OzM35+flmWFsvO0qVLle+nZ8+e/PPPP0DmX8hFixbR\npEkTnJyc8PX11aqXnZ0d4eHheHp64uzszNChQ/nnn3/w9fXF2dmZLl26cPVq5ly+ISEhfP311wQG\nBuLs7EyzZs3Yu3evcq0rV64wcOBAateuTY0aNWjTpg1///038N+f4549e/jss89wdHTE39+fO3fu\nAJkz5Sxbtkzrnlq0aMEvv/zyWt+DEEK8SEYePgVZnpNzSEgI5cuXJygoiKCgoCxduj/++COhoaFM\nmTKFlStX0r9/f7755huWLFnCsWPHWL9+PQB79+5l3rx5jB07li1btuDq6oqvry9372Y/QcXp06cJ\nDg5m/PjxhIeH4+rqyuDBg5Xj169fZ8uWLSxfvpwJEyawaNGi14obEhJCnz592LZtG+bm5kyePBnI\nXIWkd+/eWFtbs2nTJj7//HPWrl2b4+9uzZo1zJ8/n+HDh7NlyxaKFy+u1DskJIRly5YxevRoNm3a\nhKWlJT179uThw4fK+XPnzuW7775j4cKF7N69m86dO9OlSxfWrFnDtWvXWLRokVI2IiIClUrFxo0b\nadOmDQMHDiQhIXMCjmHDhqHRaFi3bh2bN2+mfPnyTJgwQauuYWFhzJo1i1WrVhETE8OSJUsA8PLy\nYvfu3Uq5hIQEkpKS+Pzzz3P8PQghxMu8rbm19U2ek3PJkiUxMjLCzMwMc3PzLMcDAgKoVq0anp6e\nlClThhYtWlCvXj1cXFz45JNPSExMBGDx4sX4+/vj7u5OpUqVGDhwIBUqVGDr1q3Z1iE5ORkjIyMs\nLS2xtLRk8ODBBAcHk5GR+buTWq1m6tSp2NnZ0bhxY3x9fZUkmpO4bdq0wcPDA2tra7p3705MTAwA\nf/31F7dv32b8+PHY2Njg4+OjTPWWE+vWrcPPz48vvviCSpUqMXbsWOrUqcOjR49YtWoVgwYN4v/9\nv/+Hra0tkyZNolChQlr18vPzo0aNGtSuXZvq1atTv359Pv/8c+zs7Pj888+V7xagVKlSTJgwAVtb\nW3r16oWLi4vSuv3ss88YPXo0lStXpkqVKnTu3Jn4+Hitug4cOBAHBwccHR1p0aKF8h14eXlx4sQJ\nZZHxXbt2Ub9+/Rf+XRBCCJEzb/Q9Z5VKRcWK/03dVrRoUSwtLZVtExMTZU3MhIQEgoODmTFjhnL8\n8ePHnD9/Pts4DRo0oFq1ajRv3pzq1avj4eFBhw4dlGW/TE1NqVatmlLewcFBafm9LO6FCxeUbWtr\na+VnMzMznjx5opxrbW1N0aJFleM1atRg//792dYZ4Pz583z88cfKdpkyZRg2bBjXr1/n9u3bODo6\nKscKFy6Mg4OD0toFsny3H3zwgbL97Hf79J6NjY21tp8m706dOrFjxw6OHTtGYmIip06dUn6xgcw/\nx5d9B1WqVKFatWrs3r2bbt26ER4ejr+/f47uXwghslPQB3bl1hufhKRQoUJa208T5vPUajVBQUHU\nrVtXa3/x4sWzjWFiYsL69euJjIzk999/Z9OmTaxZs4aNGzcCmYnt+VhP65GTuM8mtec9P1jhVWWf\n93y9nno22T9LrVajVqtfev6rRok/Xy+1Wo1KpUKj0dC9e3fu3buHp6cnHh4ePH78mAEDBrzy/Gfv\n28vLi4iICOrXr09ycjKNGzd+aT2EEOJ1yICwPMiPV4dsbGy4fPkyVlZWymf+/PmcOHEi23OPHz9O\naGgotWvXZsSIEezatYtHjx4pg8Lu3LlDSkqKUj4mJoaPPvooz3E//PBDLly4wL1795R9p0/nfGJ9\na2tr4uLilO2bN29Sr1497ty5g4WFhVYdnjx5wqlTp7C1tc3x9Z915swZre2TJ0/y0UcfER8fT1RU\nFMuWLaN37964u7srXdQ51bx5c44fP86WLVtwd3enWLGCO9m8EEK/yICwPDA1NSUxMZFbt25p7X+d\n33j8/PxYvnw5W7Zs4dKlSwQHBxMeHk6VKlWyPdfExISQkBDWr19PcnIyO3bsIC0tTUnAGo2GMWPG\ncO7cOXbv3s2qVav48ssvXxm3atWq2cb95JNPsLS0JCgoiISEBDZu3MjOnTtzfM9du3Zl2bJl/Pbb\nb5w/f55x48ZRqVIlLC0t8fPzY86cOfz+++8kJCQwevRo0tPT8fT0zPH1n3Xp0iWmT5/O+fPnWbBg\nAbGxsbRv354SJUpQqFAhtm/fTkpKCuHh4YSEhAAo3eLZ/TlWqFABR0dHVqxYgZeXV67qJ4QQL2Ko\nA8LypVu7c+fOzJgxA2NjY61W9PMt6le1sD09Pblx4wZz5szh+vXrVK1albCwMCpVqpRtfDs7O6ZN\nm8a8efOYPHkylpaWBAcHY2trS2pqKiqVioYNG+Lj40Px4sUZOnSokuReFtfKyirbOhcuXJiwsDCC\ngoJo27YtH330EV26dOHkyZPZ1hnA29ubK1euMGHCBO7du0edOnWYPXs2AF999RX3799nzJgx3L9/\nHxcXF1auXEmpUqVeWK/sei+cnJy4efMmrVq1wsbGhh9//FF5Rj1+/HjmzZvHzJkzsbGxYcyYMQwf\nPpzTp09jYWGRo54RT09Pzp49i7u7e47uXQghxMu98+s5R0ZG4uvr+1rdze+akJAQIiMjWbFixRuL\nMWvWLK5evcq0adNe+1xZz/ntk/Wc3z5Zzzl38vL/g41FwX1rpECsSnX9+vVXdq2WLl36pQPNdCUt\nLY379++/9LiJiQlmZmZvsUZvxpkzZ4iNjWXNmjUsWLBA19URQrxj3unW4ysUiOT86aef8vjx4yz7\nNRoNKpWK3377TesVLX2wcuVKZs6c+dIu4VatWuWqlalvTp48yeTJk/nyyy+pWbOmrqsjhHjHGOqr\nVO98t7bQf9Kt/fZJt/bbJ93auXPuau7/f/iwnHRrCyGEEPkuw0A7tvXrQa0QQgghpOUshBBCfxnq\ng1dJzkIIIfSWoQ4Ik+QshBBCb0nLWQghhNAzhjogTJKzEEIIvWWoLWd5z1noXPqNlOwLvSFPipXW\nWex8WMwt13T5r95Ihy2hwab2OosdkJL9SndvykemWSdxeluKlLTI0/nRKbdzfa6jZck8xdYlaTkL\nIYTQWxkG2n6U5CyEEEJvqQv6wsy5JMlZCCGE3pKWsxBCCKFn1JKchRBCCP1iqC1nmVtbCCGE0DPS\nchZCCKG3DHVAmMG1nPv06UNQUJDWvu3bt2NnZ0dISIjW/vnz59O6devXjhEYGEhgYGCe6pmfQkJC\n6Nat20uPd+3aNcu951ZcXBzHjh3Ll2sJIUSGRpPrT0FmcMm5Vq1aREdHa+2LjIzk/fff5/Dhw1r7\nT5w4Qe3atd9m9d6IHj165FvyzU5AQABJSUlvJZYQ4t2n1mhy/SnIDC45u7q6kpiYSFpamrLv8OHD\nfPXVV5w4cYL09HRl//Hjx3Fzc9NFNfNVsWLFKFGixFuJJRPOCSHyU4Ym95+CzOCSc40aNShcuDCn\nTp0C4N9//yUlJYUOHTpgZmbG0aNHATh//jx3797Fzc2NO3fuMGbMGOrXr4+rqyvDhw/nzp07QGar\n28PDg/Hjx+Pq6sqiRYu04t24cYMvvviCUaNG5ah+8fHx9OjRg5o1a+Lo6EiXLl1ITExUjkdHR+Pj\n44OzszNffPEFO3fuVI4dOHCANm3a4OzsTKtWrTh06BCQ2a3dtWtXpdyePXto2rQpLi4uTJo0iYwM\n7Yc6a9asoXHjxri4uNCtWzfOnj2rHPPw8GD16tV07NgRR0dHWrVqRWxsLJDZPZ6SkqJ33fpCCFHQ\nGFxyNjY2xtHRUenaPnz4MA4ODhQrVgxXV1ela/vEiRN8+OGHlCxZkoCAAM6cOcPChQtZunQpCQkJ\nWsknJSWF9PR0Nm3ahJeXl7L/4cOH9O3blw8//JApU6ZkWzeNRkPfvn2pVKkSW7duZe3atajVambM\nmAFkJvoePXpQvXp1Nm/eTJ8+fRg5ciRnzpzh3Llz9OvXj6ZNm7J161Y8PT0JCAjg+vXrAKj+N5Fz\nfHw8Q4YMoUuXLmzcuJHHjx8rv5AA7N27l3nz5jF27Fi2bNmCq6srvr6+3L17VykTEhJCnz592LZt\nG+bm5kyePFnZX758eYKCgrI81xdCiNxQZ2hy/SnIDHK0tpubm1ZyrlOnDgB16tRh+/btwH9d2mfO\nnOHIkSNERERQqVIlAIKDg/H09OTChQtAZuLr3bs3VlZWSgy1Ws2QIUMwMTFh5syZSnJ8lYcPH9K5\nc2d8fHwwMTEBoHXr1ixevBjIHLhWqlQpRo8eDUDlypW5ffs2jx49YufOndSsWZM+ffoA0Lt3bx4+\nfKi08J/auHEjbm5uygCxsWPHsm/fPuX44sWL8ff3x93dHYCBAweyb98+tm7dSpcuXQBo06YNHh4e\nAHTv3p1BgwYBULJkSYyMjDAzM8PMzCzb+xVCiOwU9IFduWWQydnV1ZXNmzcDmcn5acuvdu3afPfd\nd6Snp3P8+HH69etHQkICJUuWVBIzgK2tLSVLliQhIQFzc3MALC0ttWLs2rULtVrNF198gbGxcY7q\nVaxYMTp16sSmTZs4efIkiYmJxMbGYmGRuarLhQsXsLfXXlXHz88PgHnz5vHxxx9rHRs4cGCWGAkJ\nCdjZ2SnbhQsX1rpmQkICwcHBSmsd4PHjx8ovIgDW1tbKz2ZmZjx58iRH9yeEEK9LbZi52TCTs4uL\nC1evXuXkyZNcvXqVmjVrAvDhhx9ibm5OVFQU8fHxuLm58ffff7/wGmq1WutZbZEiRbSOW1paMn78\neHr27MmhQ4eoV69etvV68OABbdu2pUyZMnh4eNC8eXMSExNZsmQJkJlIX+ZVx7Lz7C8ParWaoKAg\n6tatq1WmePHiLywvhBBvkqG2nA3umTNktlDt7e1Zu3Ytjo6OFC1aVDlWq1YtfvnlF2xsbHjvvfew\nsbHhzp07Wi3H+Ph47t+/j42NzUtj1KxZk3r16tGhQwcmTZqEWq3Otl6RkZGkpqaycuVKvvrqK+rV\nq0dycrIyAtra2pozZ85onTNkyBCWLFmCtbU1cXFxWsc6deqkNWAMMn8BiYmJUbY1Go3WeTY2Nly+\nfBkrKyvlM3/+fE6cyNlatDnpvhdCiJwy1GfOBpmcIbNre/v27VneY65Tpw579+5VXqGytbWlYcOG\njBgxgpiYGKKjoxk5ciRubm5UrVo12ziDBw/m+vXrynPjVylVqhQPHjwgIiKC5ORk1q9fz+rVq5XX\nu1q2bMmtW7cIDg4mKSmJjRs3snfvXurXr0/nzp05evQoy5Yt4+LFi4SFhZGQkJDlVbAOHTpw8uRJ\nwsLCOH/+PN9++y2XL19Wjvv5+bF8+XK2bNnCpUuXCA4OJjw8PEf3CmBqakpiYiK3b+d+gXQhhDB0\nBp2cHz58mCU5165dO8v+6dOnU7FiRbp3706vXr2oVq0a8+bNy1GckiVLMnDgQEJDQ7ly5coryzo7\nO9OvXz8mTpyIt7c3mzdvZty4cdy4cYOrV69ibm7OwoULOXLkCC1atGDx4sV8//33fPTRR1hZWTF3\n7lx++eUXWrRoQUREBKGhoZQtW1YrRqVKlViwYAHbt2+ndevWpKam0qhRI+W4p6cnQ4YMYc6cObRo\n0YLDhw8TFhamDHbLrmXcuXNnVq1axZgxY3L0/QghxKsY6gxhKo3MGiF0LP1Gis5iPylWWmexdfkE\nQJf/6o3QXfDBpvbZF3pDAlJy9mjoTfjI9LHOYhcpaZGn89dF5/7/hw6OltkX0lMG23LWhZs3b5Ka\nmvrSz+PHuvsHJIQQ+kifWs4zZsygXr161KlTh+Dg4FeW/eOPP/D29sbJyYlWrVpx4MCB14plkKO1\ndcXHx0drYNlTGo0GlUrFihUr3onpQoUQIr9k6MnAriVLlrBz507mz5/P48eP+eabb7CwsKB79+5Z\nyl68eJEBAwbw9ddf4+Hhwa+//kpAQAC7d+/O8trty0hyfot27dql6yoIIUSBoi/vOa9cuZJBgwbh\n4jTuWo0AACAASURBVOICwDfffMPs2bNfmJz//fdfOnbsqEz25Ofnx4IFC4iOjpbkLIQQQuSHq1ev\ncvnyZVxdXZV9tWrVIiUlhdTUVGWiqKdq166tDCp+8uQJmzZtIj09HUdHxxzHlOQshBBCb+nDqOtr\n166hUqkoV66css/CwgKNRsO///6bJTk/dfHiRZo1a0ZGRgZDhw7NcasZJDkLIYTQY29rXeZHjx69\n9HXXBw8eANozQT79+dllhp9XunRpfvnlF44dO8a0adOwtrbms88+y1F9JDkLIYTQW29rQNiJEyfo\n1q3bC+dy+Oabb4DMRPx8Ui5WrNhLr2lmZoadnR12dnbEx8ezcuVKSc5CCCEKvrc1IKx27dpZpkB+\n6urVq8yYMYPU1FSla/ppV/fzEz1B5hTPt27d0npGXaVKFSIjI3NcH3nPWQghhN7Sh/ecy5UrR4UK\nFTh69KiyLyoqigoVKrzwefPevXuzzJJ48uRJqlSpkuOYkpyFEEKIbHTq1IkZM2YQGRnJ4cOHmTlz\nJr6+vsrxGzduKM+mvb29SU1N5fvvvycpKYmffvqJ7du34+/vn+N40q0thBBCb72tAWHZ6dmzJzdv\n3mTAgAEUKlSI9u3bayXndu3a0aZNG/r378/777/P4sWLmTJlCqtWreKDDz5gzpw52NnZ5TiezK0t\ndG7QppjsC70h3zW11VlsVcYTncU2StPdqmGF7l7VWexTxT7UWex5lk46iz3x1imdxS5Xsnj2hV5h\n9sHEXJ87qL7u/n3nlbSchRBC6K2Cvi5zbklyFkIIobcMNTnLgDAhhBBCz0jLWQghhN4y1JazJGch\nhBB6S5KzEEIIoWckOQshhBB6xlCTswwIEy+UnJyMnZ0dKSkp2ZZdu3Yt9erVo2bNmiQkJPB///d/\nJCQkvIVaCiHedeoMTa4/BZkkZ/FSL1qd5UVmzJhBly5d2LlzJzY2Nvj5+XHjxo03XDshhHh3Sbe2\nyLO7d+9Su3Ztypcvr+uqCCHeMQW9BZxb0nIW2bp79y7Dhg2jVq1aNGrUiMmTJytrmdrZ2aFSqfD1\n9aVbt254eHgA0K1bN0JCQnRZbSHEO0C6tYV4iVGjRvHgwQPWrl3LvHnzOHnyJBMmTADgzz//RKPR\nMHfuXEJCQtiwYQMAc+fOpUePHrqsthDiHfAkQ5PrT0Em3drildRqNb/99huRkZGYmZkBMGHCBFq3\nbk1gYKCylmnJkiUpUaLE/2fv3uNyPv8Hjr9ulQ5zmuOURmFEqE01mtmSQ+V8zLk5NNIc5tgUhUK1\nbJavwxyanHIME0XWspyKSUYOJYcMU0gOpbv794dH90+KYffnvu9yPR+PHt/1uT/d76v79u19X6f3\npfy5qlWrYmhoqJE2C4JQfpT1HvDbEslZeCmFQkF8fDyFhYW0a9euxONXr16lWbNmGmiZIAjvCpGc\nBaEUcrmcKlWqsG3bthKP1alTRwMtEgRBKP/EnLPwUjKZjM8++4ycnBwATE1NMTU15dGjRyxcuFC5\nKEwQBEEqcoXirb/KMpGchZdSKBQYGBjQrl07pkyZQkpKCn/99RdeXl48fvxYOQf9IkNDQy5cuEBu\nbq6aWywIQnkjVmsLwguKipAEBQVRr149vvrqK0aMGEHDhg0JCQkpcV+RYcOGERQUJLZSCYLwn72r\nyVnMOQulMjEx4dy5c8rvv//++5fe+/x9AJMmTWLSpEmStU0QhHdHWU+yb0skZ0EQBEFryQsLNd0E\njRDD2oIgCIKgZUTPWRAEQdBaYlhbEARBELSMSM6CIAiCoGXKeo3styWSsyAIgqC13tWes1gQJgiC\nIAhaRvScBUEQBK31rvacRXIWBEEQtJZIzoIgCIKgZURyFgRBEAQtI5KzIGhIoKOpxmLLZToaiy3T\n1Vzswsq1NRZbIdPcOtQmek81FnvOvb80FntWteYai71MkfGffl7xjiZnsVpbEARBELSM6DkLgiAI\nWqvwHe05i+QsCIIgaC2FQiRnQRAEQdAq7+qcs0jOgiAIgtYSw9qCIAiCoGUUhZpugWaI1dqCIAiC\noGXKVXLOzs5m3759yu+bNm1KYmKi5HG9vLzw8vL6z8/zYvv/TVhYGE2bNsXCwkL5v4GBgf+5HYIg\nCNpCoVC89VdZVq6GtYOCggDo0qULAAkJCVStWlWTTXojL7b/36SlpTF48GDGjRun/IdoaGgoWfsE\nQRDUTcw5l0M1atTQdBMklZaWRq9evahevbqmmyIIgiCJd3W19hsPa2dmZtK0aVN+/fVXPv/8c2xs\nbAgICEAulxMaGsq4ceMYMmQIdnZ2JCUlkZ+fT1BQEF988QXW1taMHTuWmzdvFnuu33//HQcHB6yt\nrfH39+fixYv06dMHa2trxowZw6NHj5Txt2/fjrOzM61ataJv374kJSUBEBoayo4dO9ixYwcdOnQA\nig9rv0479u/fT8eOHWnZsiVjxowhJyfnrV7UZcuW0aFDBywtLWnXrh2hoaHKx1JTU3F1dcXKyor2\n7duzZMmSl7b/36SlpdGgQYMS1/Py8vjkk084cOCA8lpBQQF2dnYcPXoUgP379+Pi4oKVlRX9+/cv\nNvyfm5uLl5cXbdu2xdLSEicnp2LP1bRpUxYvXsynn36Kh4cHBQUFeHt78+mnnypf21u3br3RayYI\nglAaRaHirb/Ksreec16yZAk//vgjS5YsITo6msWLFwMQGxtL9+7d+eWXX2jRogWzZs3iwIEDBAUF\nERERQUFBAR4eHsWe6+eff2bZsmX4+/sTHh6Op6cnU6ZMYfXq1fz5559s2bIFeJaY586dy5gxY9i5\ncydt2rRh9OjR3L59mxEjRuDk5ISzszPbtm0r0d7Xacfy5ctZtGgR69atIyUlhdWrV7/x6xIZGUl4\neDgBAQHExMTg6elJaGgo586dA2D69Ok0b96cqKgo/P39WblyJfHx8YwcOfKV7X9RVlYW9+/fZ/v2\n7Tg4OODs7Kxsr76+Po6OjsXmrxMSEtDT08POzo7U1FRmzJjBuHHj2L17N927d8fd3Z1r164B4O/v\nz5UrV1izZg1RUVHY2Njg4+NDQUGB8vni4uKIiIhg8uTJrFu3jqSkJMLCwti+fTuPHj1iwYIFb/za\nCYIgCM+8dXKeNm0a1tbW2NraMmHCBLZs2YJCoaBmzZr079+fpk2bkpeXx65du/D19cXGxoaPPvqI\n4OBgLl++TEJCgvK5xo0bx0cffYSzszM1atSgW7dutGnTBmtra9q2bUt6ejoA69atY/jw4XTv3p0G\nDRowefJkmjRpwvr16zEyMsLAwAB9fX2qVatWrK05OTmv1Y7x48djaWlJy5Yt6datGykpKW/8uhgb\nGxMQEICdnR3GxsYMGDCAmjVrcvHiReBZL71atWrUrVuXzz77jLCwMJo3b46hoeFL21+a9PR0ZDIZ\ntWrVYvny5Xz99dcsXbqUX375BQAXFxfi4uLIz88HYN++fXTp0gWZTMbq1avp378/zs7OmJqaMmTI\nENq1a8eGDRsAsLOzY86cOTRp0oQPP/wQNzc37t27R1ZWljK+q6sr9evXp2HDhmRmZmJgYEDdunUx\nMzNjwYIFuLu7v/FrJwiC8KJCheKtv8qyt5pzlslkWFtbK7+3tLQkOzube/fuUa9ePeX1jIwMFAoF\nLVq0UF6rWrUqZmZmxYZkn/8ZfX19jI2Nld8bGBgoE0xaWhqenp7F2mJlZUVaWtor2/u67ahfv77y\n8UqVKhXrKb4uW1tbTp8+TUhICGlpaZw7d46srCwKC59t1hszZgzff/89mzZt4osvvqBHjx5vNTdu\nY2PD0aNHlQveGjduTHZ2Nhs3bmT48OHY29ujp6fHoUOHaN++PbGxsaxYsQJ49jru27ePTZs2KZ+v\noKCAdu3aAdCjRw8OHDjApk2buHz5MmfOnAFALpcr73/+PRowYABRUVHY29tjZ2eHo6MjvXv3fuPf\nSRAE4UVlfXj6bb31gjBd3f//0aLEI5PJqFixovL68//9PLlcrvwZAB2d4kfnVahQeodeX1+/1Od6\nPmmU5nXboaenV+zxt1mKv2XLFubPn0///v3p3LkzM2bMYOjQocrHR40ahZOTE/v37+e3337Dzc2N\nOXPm0Ldv3zeO9eJKdHNzc+Vcr46ODp07dyYmJgY9PT0qVaqElZUV8Oz3Hj16ND179iz280Wv79Sp\nU0lOTqZHjx4MHDiQWrVq4erqWuq9AI0aNeLgwYPExcURFxfHokWL2LNnD+vWrXvj30kQBOF572py\nfqthbYVCQWpqqvL7lJQU6tSpU2I49sMPP0RHR4fk5GTltbt373LlyhXMzMyAZwn9dZmZmRV7LoDk\n5GTMzc1f+XOqbserbNq0CU9PT2bMmEH37t2pWrUqd+7cQaFQkJ+fj7+/P7q6uri5ufHLL7/Qr18/\nYmJi3jjOli1bSmy5OnfuXLHXomvXrsTFxXHw4EGcnJyU183MzLh+/TqmpqbKr40bNxIfH09ubi57\n9uzhhx9+wNPTE0dHR+7duwe8/MNKZGQksbGxdO7cmfnz5/Pzzz9z4sQJsrOz3/j3EgRBeF5hoeKt\nv1QtODiYNm3aYGdnp9z6+jJ///03o0ePxsrKis6dO7N37943ivXWc87+/v6cOXOGw4cPs3jxYgYP\nHlziHiMjI/r168ecOXM4fvw4qampTJ06FWNjY9q2bQu8We/Uzc2N8PBwdu7cSUZGBsHBwZw/f55+\n/fop42VmZpZYKazqdrxKtWrVOHz4MBkZGZw5c4ZJkyYhl8vJz8+nYsWKnDhxgnnz5nH58mVSUlJI\nSkqiWbNmr2x/aezt7blz5w4LFy7k6tWr7Nmzh1WrVhWb623dujVGRkZERkbi4uKivO7m5saePXsI\nDw/n2rVrhIWFsXbtWszMzNDX18fIyIjo6GgyMzM5dOgQc+fOBVBOL7woNzeXgIAAjhw5wrVr19i1\naxcffPAB77///n95KQVBELSmCMnq1auJiorif//7Hz/99BO7d+9mzZo1pd4rl8txd3dHX1+fyMhI\nRowYwdSpU7l06dJrx3vr5Ozk5MTXX3/NlClTGDBgwEsXAE2fPh17e3vGjx/P4MGDMTIyYs2aNcoh\n5Bd7rK/qwTo5OfHtt9+yePFievToQVJSEqtXr1bOGffo0YP09HTlcO3zz/Wm7XhbM2fOJDc3l549\nezJ+/HgsLCzo2LEjZ8+eBeDHH3/k8ePH9OvXj1GjRmFra6tcNf5i+1/F2NiYFStW8Oeff9KjRw8W\nLVrE1KlT6dy5c7H7unTpQp06dZQfAABatWpFYGAgGzZswMXFha1btxISEsInn3yCnp4eQUFBREdH\n07VrVwIDA/Hw8KBWrVrKFecvvlaDBw+mV69eTJ8+na5du5KamsrSpUtV9poKgiBoWnh4OOPHj1cu\nhJ4yZcpLp+7i4uK4desWgYGBNGjQgAEDBvDFF1/w559/vnY8meINP15kZmbi6OhIbGxssUVBgnaa\nMmUKDRo0KLGQTpvkPbinsdhyXQONxX5XP7vo5N7RXHA9zb3f99Bc9b5Z1ZprLPYyRcZ/+vlWM6Le\n+meTFzj/p9hFbt++zeeff86BAweUC5iLcuGhQ4eoWbNmsfsXLFjAjRs3lFuM38ZbLQgr6zVL38Tj\nx495+PDhSx83MDCgUqVKKo2pUCiKbVsqzYv/GF6UnJzMmTNniI2NZc+ePapsniAIgtpoQ/nOf/75\nB5lMRu3atZXXatasiUKh4ObNmyX+Hl+7do169erx/fffs3PnTqpXr65cw/O63nor1bsiPDyckJCQ\nl/7OPXv2ZP78+SqNmZWVxWeffVZqTIVCgUwmUw4xv8yhQ4dYs2YNkydPFiMcgiCUWeparZ2Xl/fS\n9T5FVSpL241U2lqcR48eKatZLl++nKNHjzJhwgQ2b95M8+avN4rxxsnZxMTkXxNDeeLu7q72gho1\na9Ysthr+bXh6emr1ULYgCMLrUFdyTk5OZtiwYaV2iqZMmQKgXNhb9N9Q+mFDOjo6vP/++/j5+QFg\nYWFBUlISERERzJkz57XaU64PvhAEQRDKNnVV+rK1tX1pp+j27dsEBwdz584d5Uhk0VB3rVq1Stxf\nq1atEvU6zMzMuHDhwmu3p1yd5ywIgiAIqla7dm3q1q3LiRMnlNeSkpKoW7duqet/rKysuHjxYrH1\nWWlpaZiYmLx2TJGcBUEQBK2lLadSubq6EhwczPHjxzl27BghISEMHz5c+Xh2drZybtrFxYXCwkJ8\nfX25evUq69ev59ChQwwYMOC144lhbUEQBEFraUv5zlGjRnH37l2++eYbdHR06NevX7Hk3LdvX3r3\n7o2npyeVKlVi9erV+Pr60q1bN4yNjfnhhx9o2rTpa8d7433OgqBqYp/zu0Xsc1a/srzPufG4HW/9\nsxeX9PpPsTVJ9JwFQRAErfWu9h9FchYEQRC0lrYMa6ubWBAmCIIgCFpG9JwFjdOvXO3fbxLKD4N6\nmm6BRtT+91sk81/nfTVJG8p3aoJIzoIgCILWUhTKNd0EjRDJWRAEQdBaIjkLgiAIgpZ5V5OzWBAm\nCIIgCFpG9JwFQRAEraWQv5s9Z5GcBUEQBK31rg5ri+QsCIIgaC2RnAVBEARBy4jkLAiCIAha5l1N\nzmK1tiAIgiBoGZGcBaGMSExMpKCgoMT1/Px8Dhw4oNa2ZGdnq+W0oG3btvHgwQPJ42ij0NBQHj9+\nXOJ6bm4uCxYsUHt71PWev0hRKH/rr7JMJGdB6924ceO1v8qzYcOGkZOTU+L6xYsX+fbbbyWLe+vW\nLSZNmsS5c+fIy8tjyJAh2Nvb4+DgQGpqqmRxAcLCwrC3t2fs2LH8+uuvpSYrqXh5eZGbm1vi+v37\n9xk/frwkMdPS0khMTCQxMZElS5aQkJCg/L7oa+fOnWzatEmS+EU0+Z6/qLBQ/tZfZZmYcxa0noOD\nAzKZrNi1ok/wRdcVCgUymYxz585J2pazZ88yb948UlJSSu3Fqjr+hg0bmDNnDjKZDIVCgb29fan3\ntW3bVqVxn+fr68ujR4+oVq0a27dv58KFC2zatIldu3Yxd+5c1q9fL1ns3bt3k5aWxt69e1m2bBk+\nPj60b98eFxcX2rdvT8WKFVUa788//+TKlSsAREZG0rx5cypVqlTsnvT0dP744w+Vxi3yzz//4Obm\npvze09OzxD2GhoYMHz5ckvhFNPmev6is94DflkjOgtaLjY3VdBOUvvvuOypXrsyPP/5Y4o+2FAYN\nGkTjxo0pLCxk+PDhLF68mKpVqyofl8lkGBoa8tFHH0nWhqNHj7J9+3bq1q3LgQMH6NChA61ataJ6\n9ep07dpVsrhFGjZsiKenJ56enqSlpbFz506mTp2Krq4uHTt2pF+/fnz88ccqiWVoaMhPP/2EQqFA\noVCwcuVKKlT4/wFGmUyGkZERU6ZMUUm8F9WqVYuzZ89SoUIFHBwc2Lp1K9WrV5ck1qto+j1/nkjO\ngqClTExMSly7ePEiGRkZ2Nvbk5WVRb169Ur0rqWQnp7O7t27qV+/vuSxitjY2ADPPqTo6enx8OFD\nzMzMAIiKisLGxkblPcjn6evrk5eXx/379zl27Bjff/89ANevXy/2QUFKt27dIjo6mpiYGE6dOkXL\nli1xdnbmn3/+YezYsfTv35/Jkyf/5zhNmzZVfhgcOnQooaGhavsdAfr160dUVBQffPAB9erVQ1dX\nM3+iteE9LyIqhAlCGXD//n0mTJjA8ePHAYiOjsbf359r166xYsWKUhO5KllYWJCWlqbW5Fzk6tWr\njBs3Djc3N+Wc59q1a5k9ezbLli3jk08+kSSuo6MjEydOxMDAgKpVq/LFF18QFRVFQEAAvXr1kiRm\nkbCwMKKjo0lOTuajjz7CxcWFoKAg6tatq7ynQYMGzJkzRyXJ+Xnh4eEqfb7Xoa+vz9atW7G1tSUx\nMZHjx4+/NBkWfWiTgibfc+EZmUITy+8E4S1NnTqV3NxcFi5cSPv27dm1axfvvfceU6dOpWLFiixd\nulTS+Bs2bCA0NJTevXtTv3599PT0ij3es2dPyWL37NkTZ2dn3N3di11fvnw5MTExbNu2TZK4BQUF\nrFu3jszMTAYMGECjRo2IjIwkNzeXwYMHSzpi0blzZ1xcXHBxcaFhw4al3nP+/HnOnDlDnz59VBpb\n3esLACIiIggODubBgwfKdQalkXp9hSbf8xdV7zLnrX82e98sFbZEvURyFsqUTz/9lPDwcBo3boy1\ntTW7du3C1NSUS5cu4erqSlJSkqTxHRwcXvqYTCaTdH7cysqKXbt28eGHHxa7fvXqVbp160ZycrIk\ncT08PJg8efJLk2N51bNnTypXrsxXX31V6voCW1tbSeM3bdqUP/74g5o1a0oapzTa9J6/32n2W//s\n3Rg/FbZEvcSwtlDm5OXllbiWnZ2tlvm5gwcPSh7jZczNzdm7dy9ff/11sesHDx4skbBV6eTJkxqb\n+8zJyWH16tXK3uuLfYm1a9dKFlsT6wueV7RlKTc3l6tXr9KoUSPy8/PVshBRk+/5i8SCMEEoA7p2\n7Yq/v79ye9GjR484evQos2fPxtnZWS1tuH37NuvXryctLQ25XI65uTn9+vWjQYMGksadOHEiHh4e\nJCQk0Lx5c+DZkG5SUhI//fSTZHEHDRrEpEmTcHV1xdjYGH19/WKPSzn3OW3aNFJSUujWrZtaktLz\nNLm+AJ4Vl/Hz82PHjh3As/UVCxcu5PHjx4SEhEi6MEuT7/mLFIWFaoulTcSwtlCm5OfnExISwvr1\n63n69CkymQwdHR369u3LjBkzMDAwkDR+UlISo0ePpkmTJlhZWSGXy0lOTub8+fOsXr1askVZRS5e\nvMjWrVvJyMhAV1eX+vXrM3DgQExNTSWL2bRp05c+JvXcZ8uWLVm3bh0tW7aULMbLaHJ9AcC8efM4\nc+YMfn5+uLq6smvXLh49eoSXlxcNGzYkKChIstiafM9fVOWL6W/9szlxC1XYEvUSyVkok548ecK1\na9eQy+WYmpry3nvvqSVu3759adOmTYmVwcHBwSQlJUleuanI/fv3qVSpEhUqVFDr4hx169ixI4sW\nLcLS0lLtsTW5vgDg888/Z8mSJbRo0aLY+oq//vqLESNGcOzYMUnja4t3NTmLYW2hTClavevs7Ezj\nxo3VHv/ixYsEBweXuN63b1/Jt94oFAqWLVtGWFgYDx48IDo6mh9//BEjIyO8vb0l2+v8b2VRjY2N\nJYkLz4a1fX19GT9+fKm9Vylja3J9AcDDhw8xNDQscb2wsBC5Gvb+FhQUkJWVpYylUCjIz8/n3Llz\naptCAjHnLAhlwogRI4iJiWHFihWYmZnh5OSEi4uL2uYFTUxMOH36dIn55eTkZMlX1S5ZsoQ9e/aw\nYMECJk2aBECvXr2YNWsWgYGBeHt7SxK3qHzqiyVTi0g5xPnNN98A4O7uXiyuusq1PnjwgF27dpGR\nkcHYsWNJTk6mUaNGkk4jFHFwcGDRokUsXPj/vb9r164xb9482rdvL2nsAwcO4OPjw71790o8VqtW\nLbUm57JeI/ttiWFtoUy6f/8+sbGxxMTEcPToUczNzXFxcWHkyJGSxt2+fTtz585l+PDhynnQ5ORk\nwsPD+fbbbxkyZIhksTt06MCCBQuwsbEpNsyZlJTEhAkTSEhIkCRuZmZmse/lcjlXr17lp59+wsPD\nQ9JE8WLsF0lZdObChQsMHz6cunXrcuHCBfbu3cv//vc/9u3bx/LlyyXfSvXgwQO+++47YmNjKSws\npEqVKuTk5NCuXTuCgoKoVq2aZLGdnJywsbHBzc2NgQMHsmLFCu7du8fcuXPx8PCgd+/eksV+kVGb\ntz9k5NGRxSpsiXqJnrNQJlWtWpXevXvTsmVL9u7dy5o1awgNDZU8ORf9UVq3bh1r1qxBX18fMzMz\n/P39cXJykjR2VlYWtWvXLnG9SpUqPHr0SLK4pSXADz/8kCpVqjB16lRJk/PLkm/R8KqUyXnevHkM\nHDiQ8ePHY21tDcD8+fOpXr06gYGBbN26VeUxX5xC8PLyYtq0acTHxyOXy/nss88wMDBQHkohlWvX\nrrF8+XI+/PBDLC0t+eeff3B0dKRChQoEBgaqNTmLYW1BKCPOnj1LdHQ0+/fvJzMzk3bt2jFv3jy+\n/PJLtcTv3bu3Wv84Ffn0009ZtWoVc+b8f8Wk3NxcQkJCsLOzU3t7ZDIZt27dkjTGyZMn8fPz49Kl\nSxS+sKVGR0eHM2fOSBY7JSWFefPmlbju6uoq2alMpZ3ABsVPYVPHkH6VKlWUx3OamZmRmpqKo6Mj\n5ubmXL9+XbK4wv8TyVkoUxwcHLh9+zaffvopo0ePpmPHjpLvfy3qkRsaGhIaGvrKe0s74k9VfH19\n8fT0xN7enry8PDw8PLhx4wbGxsaSli0t7Xd++PAh+/bte+kRlqoyb948TExMmDJlChMmTCAwMJBb\nt24RGhqKj4+PpLGrV6/O5cuXSxR4OXnyJDVq1JAkpracwNa+fXv8/PyYM2cOdnZ2BAYG8uWXXxId\nHV3q6I2URM9ZEMoAd3d3OnfuzPvvv6+2mMeOHWPYsGEYGhq+cvuK1FuaPvjgA7Zu3cqRI0dIT0+n\noKAAMzMzPvvss2LHGqrai7+zTCZDT0+PHj168NVXX0kWF56tjg8KCqJhw4Y0b94cPT09Bg8eTI0a\nNfj5558lXZg0evRovL29GTNmDAqFgqNHj7Jjxw5++eUX5YI8VZP64JbXNXPmTPz9/Tlz5gw9evQg\nOjqavn37YmRkJOn+6tK8q8lZLAgTypyiFbSXL1/Gw8OD5ORkGjZsKGkJyyI3btzggw8+KJEM5XI5\nqampyspdgmrY2tqyefNmGjRowOzZszExMcHd3Z0bN27QtWtXTp48KWn8gwcPsmrVKmU1ODMzM9zc\n3NS6Wllb5Obmoq+vX2I7m9QqWo9465/N/3O1CluiXiI5C2WKplfQWlhYkJCQQPXq1Ytdv3Lln44x\nwQAAIABJREFUCt27d1f54RMWFhb88ccf1KhRg6ZNm76yd66rq0vt2rXx9PRU+bF+Z8+eZdWqVaSn\npyuT1ODBgyV/vcePH49CocDb25sjR44QFhZGWFgYv/76KytXriQuLk7S+O+ya9eusWHDBq5cuYKv\nry/x8fGYmZlJXgVPeEYkZ6FMGTZsGK1bt1auoC3aThQUFMSxY8ckWUG7ZcsWli1bBjzb2lO3bt0S\nPeecnBxMTU3Zvn27SmMfP36cjz/+GF1dXeUZ1i8jl8s5cuQImzdv5ujRoyprw/79+5k0aRKdOnXC\n2toauVzOqVOnOHDgAD/88AOOjo4qi/WiW7duMXXqVDp27IirqytfffUVSUlJ6Ojo4OvrS79+/VQa\n79/WFDxPyvUFmpaYmIi7uzvt2rXjt99+Iyoqio0bN7J27VpCQkLo1KmTpptY7ok5Z6FM0cQK2p49\ne6Knp0dhYSHfffcdX331FZUrV1Y+LpPJMDQ05NNPP1V57Od7pkX/nZGRQVpaGoWFhZiZmdGoUSPl\nPR9++OG/7g1+Uz/++CNTpkzBzc2t2PWwsDB++uknSZNznTp1ip08FR4ezqVLl6hSpQp16tRRebzn\n59cLCws5ceIEtWvXxsLCAj09PVJTU/n777/5/PPPVR5bmwQFBTF58mSGDBmi3EY2bdo0ateuzeLF\ni0VyVgORnIUyRRMraPX09JSHHNSrV0/Zk1W3nJwcvLy8iI2NpWrVqsjlch4+fIiNjQ1LliyhcuXK\nmJiY8P3336s07rVr10rdpvbll18SEhKi0lilSUtLY9u2baSnpyOTyWjSpInKe8xFni/BOnfuXBo2\nbMisWbOU77dCoWDBggXcuXNHkvja4sKFC6XuX+/QoYNa3nNBJGehjNHECtrn2djYEBsby8WLF4vV\nN87Pz+fs2bOsXLlSstjz5s3j5s2bREVFYW5uDsClS5eYMWMG8+fPJyAgQJK4DRs2JD4+nqFDhxa7\n/vvvv0u+uvjgwYPKKQxLS0vkcjnHjx8nLCyMn3/+WdKjC7dv38727duLfRCTyWS4urqqfE5f25iY\nmJCSklKiTGlcXJzWrCgv70RyFsoUV1dXateuzapVqzAwMCAwMBAzMzPmzp2rlhW0c+fOZevWrTRr\n1ozTp09jbW3N1atXuXPnDgMHDpQ09sGDB1mzZo0yMQM0atSIWbNmMXr0aMnifvPNN3zzzTckJyfT\nqlUrAE6dOkV0dDSBgYGSxYVnw6sTJkwo8fstXboUf39/IiMjJYtdu3ZtDh06hJmZWbHrMTExaqmt\nrUkTJ05kxowZpKSkIJfLiYyM5Pr16+zZs0fy91x4RiRnocxxcHB45XF+UoqKiiI4OJhOnTrRpUsX\nfH19MTMzY8aMGTx9+lTS2Pr6+qXuZ5bJZJKeUvTll1/y888/s2HDBjZu3KgsWbphwwbJz1n++++/\n6dChQ4nrXbp0US7Sk8qUKVOYNGkSv/32m/J845SUFM6cOSNp0Rdt0LFjR0xNTVm9ejWNGzcmNjYW\nMzMz1q9fr/yAJkhLrNYWtJ42raC1tLQkJiYGY2Njxo8fT/v27enTpw8XL15k5MiRxMfHSxbbx8eH\n8+fPExwcrJxzz8jIYPr06dSrV0/lc83awMvLC5lMhp+fX7H9tYGBgdy9e5f58+dLGv/SpUts376d\ntLQ0ABo3bkz//v3Vsqdek44ePYqdnV25Pitc24mes6D1XvdQeXX8ITE1NeXs2bMYGxvTuHFjTp8+\nTZ8+fVAoFDx48EDS2FOnTmXcuHF07tyZKlWqAChPKZKylOXTp0/Zvn07qamp5OXl8eLneSkTZF5e\nHjExMcTHx2NpaYmenh7nz5/n2rVrtGrVimHDhinvfX5Vt6o0atSIadOmqfx5td2ECRPQ09OjS5cu\ndO3aFSsrK0036Z0jkrOg9Z5fQfs6Hj16xOrVqyXpRY8YMYKpU6fi7++Ps7MzvXv3RldXlz///FPy\n4gz37t0jPDyc1NRU0tPTlcPLz89BS2HWrFnKOtrPbyFTB3Nzc8aMGVPsWpMmTSSLN2zYMEJDQ6lS\npQpDhw595Qc+KT4MaIuEhAQSEhLYt28f7u7uVKpUSXl2erNmzTTdvHeCGNYWyp07d+7Qrl07yU7t\nSUxMxMjIiObNm3Po0CG2bNlCtWrV+Oabb6hVq5YkMQHs7e1Zvnw5lpaWksUojbW1Nf/73/9o06aN\nWuNqgrYccqJNCgoKOHz4MAcPHiQyMpI6derQrVs3evfujbGxsaabV26JnrMgvKHnt++0a9eOdu3a\nqSVuzZo1ycrKUkus51WuXFntJxEVuX37NitXriQ9PZ38/PwSj6u69/p8wvX09CQrK4ucnBzliu2o\nqChsbGwk/RCmTfLz8zl06BAxMTHExcXx/vvv4+DgQEZGBi4uLspCJYLqieQsCG9Ak0OdzZo1w8PD\ngxYtWmBiYkLFihWLPS7V3K+Hhwf+/v74+vpiamqq1kVCkyZN4p9//qFTp04YGBioLS7AkSNHGDdu\nHG5ubowfPx549v7Onj2bZcuWlesa0wcOHGDfvn3ExcWhp6dH586dWbJkCa1bt1bes379ekJCQkRy\nlohIzoLwBuzs7Ip9X1BQwLVr1/j9998ZO3as5PG7d+8ueQygxCEbCoWCzp07l3qvVNMHAH/99Reb\nNm1SbmVSp4ULFzJmzBjc3d2V1zZt2sTy5csJCAhg27Ztam+TukyfPh1HR0dCQkKwt7dHR0enxD2W\nlpaSHxn6LhNzzkK5I/Wcc2m2b99OTEyM5Htv1eXfDtl4npQnUw0fPpzBgwdrpJazlZUVu3btKrFt\n6urVq3Tr1k3lJ5Bpk7y8PPT19TXdjHea6DkLggrY2Njg5+cneZwtW7YQERFBWloaFSpUoEmTJgwZ\nMkTl1dHeNOFK9YHI39+fgQMHcvDgQUxMTEoMqUu5KMvc3Jy9e/fy9ddfF7t+8ODBcrnPOTAwEE9P\nT4yMjJSJ+ZdffmHjxo3cunULc3Nz3N3dXzqCIqiWSM5CuSTVgNCNGzdKXHv48CGrVq2SvObwsmXL\nWLlyJcOHD2fcuHHI5XJSUlLw8fHh3r17DBo0SNL4/0aK13zRokXcvXuX9PT0EqdtST33PXHiRDw8\nPEhISKB58+YApKamcuLECX766SdJY2vCmjVrGDlyJEZGRgCsXr2apUuX4uHhgbm5OefOncPb25v7\n9+/Tv39/Dbe2/BPJWSh3DAwMGDBggCTP7eDgUCIpKBQK6tatK9nBE0XWrVvHwoULi5WzdHR0pFmz\nZsyfP1/jyVmKZBkbG8vq1aslHTp/mc8//5wdO3YoT8TS1dXFwsKCOXPmlMva2i9+uNq6dSve3t70\n6NEDgPbt22NqasqiRYtEclYDkZyFMuXp06dERkaSkpJCQUFBqdWqKlWqJNkQc2xsbLHvZTIZenp6\n1KxZU/Ke3NOnT0vtnZubm/Pw4UNJY2uKsbExhoaGGotfWFiIs7Ozsob46tWrefTokcbaIyWZTFbs\n3/CTJ09KFBxp0aKFRrbzvYtKVtEXBC02c+ZM/P39uXv3rmRD169iYmJS7MvY2JhatWqpZXuRp6cn\n3t7eXLhwQXntxo0bLFiwgHHjxkkeXxPGjx/PjBkz2LJlC0eOHCExMbHYl5SioqLo168fJ0+eVF5L\nSUmhf//+HDhwQNLYmqBQKFiyZAlbtmwhMTEROzs7du/eXeyejRs3SlqhTfh/YrW2UKZYW1sTGhqK\nvb292mK+uK3oVaRcId6+fXuysrKQy+UYGRmhq6tLTk4OCoWiRPvUuVIdpFsQ9qotVDKZTNLfs0uX\nLnz99dclzm7evn07q1atYs+ePZLF1oSwsDDS0tKUX/fv30cmk3H06FGqVq1Kly5duHPnDitWrODj\njz/WdHPLPTGsLZQplStXpk6dOmqNqS01lIOCgjTdBLVLTU3VWOybN29ibW1d4vonn3yCr6+v+hsk\nMTc3t2LfZ2VlkZ6eTtWqVQEYOXIk9vb2omSnmoies1CmbNy4kZiYGLy9valfvz66utrz+fL27dsa\nK3NZRBN7vIvifvbZZ5Ik0ydPnrBr1y7S0tKQy+WYm5vj7OxMtWrVVB7reYMGDaJZs2Z4e3sXux4U\nFERiYiKbN2+WNL6209S/tXeFSM5CmeLg4MDt27eRy+WlPi71H4r09HSCg4O5dOmSsg0KhYL8/Hyy\ns7M5e/aspPH/jRRJcsiQIbi4uNC5c2eqV69e6j0KhYKsrCxq1qypsrgAFy5cYNSoUejo6GBpaYlc\nLuevv/4iPz+f8PBwGjVqpNJ4zzt9+jQjR46kWrVqWFhYAHD+/Hnu3bvHihUraNWqlWSxywIpP5AJ\nIjkLZcy/Va6SesvN4MGDkcvl9OrVi4CAAKZNm0ZmZiYbNmxg9uzZJeYn1U2K3szq1avZt28fZ8+e\nxc7ODmdnZzp16qSW4yOHDx+OsbExc+fOVY6SFBQU4O3tze3bt1m9erWk8bOzs/n111/JyMhAV1eX\n+vXr0717d7UfnamNRM9ZWiI5C2VSRkYGaWlpFBYWYmZmJmkP6nktW7YkIiICCwsLBg4cyPjx42nT\npg1btmwhMjKS9evXq6UdLyPlH8zMzEz27t1LTEwM58+fx97eHhcXFxwcHCTb7tSqVSt27NhR4szq\ntLQ0+vbty59//ilJ3H+jDVMYmiaSs7S0Z8JOEF5DTk4OXl5exMbGUrVqVeRyOQ8fPsTGxoYlS5ZI\n3qPR1dVVxiiqmtSmTRvatm3LwoULJY2taSYmJowaNYpOnTqxbds2wsLCiI+PR09Pj27dujFx4sSX\nDnu/rVq1anH16tUSyfnq1au89957Ko31Im2fwhDKN7HPWShT5s2bx82bN4mKiuLYsWMkJSWxe/du\nHj16JNmRic+ztrZm1apVPHnyBEtLSw4ePIhCoeDMmTPl+qCAK1eusHz5cnr16oWTkxOnT5/G29ub\nw4cPs2PHDq5fv17s9CZVcXV1xdvbmy1btnD+/HnOnz/P5s2b8fHxoV+/fiqP9zwfHx+ys7MZOXIk\nd+7cYcSIEXTp0oXc3Fz8/f0ljS0IoucslCkHDx5kzZo1xXpSjRo1YtasWYwePVry+F5eXowdOxZT\nU1NcXV1Zu3Yttra2PHr0CA8PD8nja0L37t25ePEiLVq0oGfPnjg7O1OrVi3l41WqVKF///74+Pio\nPPbIkSN5/PgxwcHB3L9/H4CaNWvi5ubGiBEjVB7veSkpKcopjMjISMzNzRk8eDBmZmZs3bpV4+sL\nhPJNJGehTNHX16dChZIDPjKZ7KUruFWpUaNGxMTE8OTJEwwNDdm2bRvHjx+nWrVqWFlZSRr76tWr\nr3UakqqXkTg7O+Pi4vLKetLt2rUjLi5OpXHh2fv6zTff4O7uTm5uLvr6+uTk5Khlr+27PIXxusSS\nJemIYW2hTHFwcMDPz4+rV68qr2VkZDBv3jzat28vefwRI0awfft2nj59CoCRkRFffPGF5IkZYODA\ngZw5c+aV99SoUYM//vjjP8e6ceOG8qt79+7o6OgUu/b8F8B7770nyRzw9evX6du3L4sXL6ZGjRpU\nqlSJPn36MGDAAG7evKnyeM97V6cwADp06MC9e/dKXL916xZt2rQBVPdvTSidWK0tlCk5OTmMGzeO\npKQkqlSporzWrl07AgMDJS9MERISQkxMDJmZmdjb2+Pk5ISjo6Pki5MAevTowbfffquWDyHPlywt\nKg/6YpnQou+lXK07atQo3nvvPWbNmkWNGjUAuHv3LrNnz+bp06csXbpUstiXLl1i7NixDBw4EFdX\nV/r06cOdO3eUUxjlrZ75vn37+P333wHYsWMHzs7OJT6EZGZmkp6eLpKyGojkLJRJqamppKeno6+v\nj5mZWYnVvFI7f/48+/fvJzo6mqtXr9K+fXucnZ3p0qWLZDG9vLzYtWsXLVq0wMTEhIoVKxZ7XJUL\n4l48O/lVpDzH2tramp07d5YYzr98+TJ9+vQpdiiFFBQKhXIK49GjRyWmMJ4+fcqpU6ewsbGRtB3q\nkJ2drSwRu2PHDpycnDAwMCh2j5GRET169FCe0iVIR8w5C1rvxo0b1K1bF5lMphxGrVKlSrGh5KLr\n6qr726RJE5o0acLw4cPZuHEjy5YtY//+/ZImZ3i2OEsdnk+4w4YNIzQ0VDlSUSQ7O5tRo0axfft2\nydrx/vvvc/bs2RLJOT09nUqVKkkWt4hMJlPu4S6awnje/fv3GTZsWLnY61u9enXlBzwTExNGjhyp\n0eM633UiOQtaz8HBgYSEBGrUqIGDg0OpJ0SpY4i1SHZ2NrGxscTExHD06FEaNWrEmDFjcHFxkTSu\nOraKFYmPj+f06dMAJCYmsmzZMoyMjIrdc+XKlTfqYb+NoUOH4uPjQ1paGs2bNweejZqEhYVJvlr7\ndZXHwUdPT0/i4+Np3rw5NWrUYOvWrcTExNCsWTM8PDxKjNoIqieGtQWtl5mZSd26dalQocK/JgMp\nh1jhWbI4efIk9evXx9nZGWdnZ7UOqZ84cYJffvmFK1eusGzZMnbv3o2JiYnKPxhcu3aNmTNnolAo\nSExMxMrKCj09PeXjMpkMIyMj+vbti6Ojo0pjv2jTpk1s3ryZy5cvK0toDh06lB49ekga93WU1ypZ\nS5YsYeXKlYSFhZGXl4ebmxv9+vUjKSkJW1tbZs+erekmln8KQShDhg4dqrh//36J61lZWYpevXpJ\nHj84OFhx7tw5yeOUJjo6WvHxxx8rFixYoGjRooXi6tWrilWrViksLS0V69evlyzujBkzFA8ePJDs\n+f+rBw8eKGbMmKGR2P/884+iadOmGoktpc8//1xx6NAhhUKhUHz33XeKYcOGKRQKheL06dMKW1tb\nTTbtnSGGtQWtpy1DrACTJ09GLpcTFxdHRkYGvXv35vLly5ibm0teOjQ0NBRfX1+6devGpk2bgGdb\nu2rVqsXixYsZNGiQJHGLhtP/+ecfCgoKSgzjavp83ydPnhAZGanWYf/y7v79+5ibm6NQKIiLi1MW\n+KlUqZJa6gkIYs5ZKAPMzMxYuXIlCoUChULByZMnSx1iVUdJxb///puRI0dy79497t+/T4cOHVi5\nciV//vknq1atokmTJpLFvnLlSqn7qVu2bMmtW7cki5uQkICPjw9///03UHJrVXkb0hWebaVbtWoV\n1apVIzs7m44dO3Lr1i1CQkLUsqdfEMlZKANMTU1Zu3Yt8Gw70cyZM9WyUrc0fn5+fPLJJ/j6+tK6\ndWvg2d7nmTNnMm/ePMLDwyWL3ahRIw4dOlSih7xjxw5JT+WaM2cOLVu2ZOnSpRp73QX18vX1Zfr0\n6WRmZjJ58mRMTEzw9/cnMzOTH3/8UdPNeyeI5CyUKX5+fvzwww+YmJgwePBgAHr37k3btm2ZMGFC\nsR61FE6cOMHmzZvR0dFRXtPT08PDw0PyWsteXl6MGTOGo0eP8vTpU5YtW8aVK1c4c+aMpMU4bt68\nycqVK19ZvvNdc+vWLerUqYOOjg4NGjTQdHNUrmnTpuzcubPYtalTp4pV2mokyncKZcq8efP4/fff\nadq0qfKah4cHcXFxaql3bGBgQFZWVonrly9flrxX2bp1a/bu3UvDhg1xcHDg3r17WFlZERUVpSyp\nKFXcEydOSPb82uq7774rMb8ul8tZsWIFTk5OwLN92Hv37tVE8yR37tw5du3aRWRkJJGRkezZs4eI\niAixUltNRM9ZKFNiYmJYs2YNFhYWymuOjo7UqVOHr7/+Gm9vb0nju7q6MmvWLKZNmwY8S8rHjx9n\n0aJFkh9hCM/ON54wYQIPHjxAT0+vRAUnKdjY2ODn50dcXBz169cvMTrh6ekpeRs04eTJk4wfP56Q\nkBD09PQ4cuQIc+fOJSsri8mTJ2u6eZIKDQ0lNDSUmjVrkpWVRZ06dbhz5w5yuZyOHTtqunnvBJGc\nhTJFoVCQl5dX6vWiwyikNG7cOKpUqYKvry+PHz/G3d2dGjVq4ObmxsiRIyWN/fTpU5YvX86mTZuU\nvfcPPvgANzc3hg8fLlnchIQELC0tycrKKjFqUFpBGE14sYerChs2bGDs2LGMGjWKGjVqEB0dTf/+\n/Zk4cSJVq1ZVeTxtEhERgZ+fHwMGDMDBwYFffvmFqlWrMmnSpNc6GU1QAU3t4RKEt+Hj46Po2rWr\nIjExUfHw4UPFw4cPFSdOnFD06tVL4eXlpda2PHz4UJGTk1Pq9Z9++knl8Xx8fBRffPGFYseOHYrz\n588rzp07p4iIiFDY29srvv/+e5XHKysePXqkWL58uSTP/fjxY8WYMWMUFhYWisOHD0sSQxs1b95c\nkZmZqVAoFAoPDw/Fzp07FQqFQpGSkqL48ssvNdm0d4aoECaUKY8fP2bmzJlER0dTWFgIQIUKFejZ\nsyffffedWk6H+jdSVY365JNPWL58uXKVeJGEhAS+/fZbjh07ptJ4zzt79iyrVq0iPT0duVyOmZkZ\ngwcPxtbWVuWxhg4d+to98qJV/Kri5eVV4ppcLmfv3r0YGxvz8ccfK6+X533VHTp0ICAgADs7O0JC\nQsjPz2fGjBlkZGTQs2dPTp06pekmlntiWFsoUwwNDQkJCSEnJ4crV66gp6dHvXr13oktPpUqVUJX\nt+T/ZStXrlzqdVXZv38/kyZNolOnTvTu3Ru5XM6pU6cYMWIEP/zwg8rLd9rZ2Sn/++7du0RERODo\n6EiLFi3Q09Pj3LlzREVFKVfrS01HR4euXbuqJZa26NevH99++y0BAQE4Ojri5uZG7dq1OXz4cLHF\nmIJ0RM9ZKHOys7O5fPmysuesUCjIz8/n7NmzuLu7a7h1qu05F522BbB37142b97MzJkzadGiBTo6\nOly4cIE5c+YwaNAgXF1d/3O80nTt2pW+ffvi5uZW7HpYWBg7duwoseVGldzc3OjUqVOJvd3bt28n\nIiKCiIgIyWKvXLkSFxcX6tatK1kMbRYZGYmxsTG2trZs2bKFTZs2Ua1aNWbOnKn2I1rfRSI5C2XK\n5s2bmTNnDgUFBcoqVfBsYVLLli0l/WP9ulSZnJs2baoc4n3+/6ovXpOyUlerVq3YtWsX9evXL3b9\nypUrdOvWTVlaVQpWVlbs2LEDMzOzYtfT0tLo06ePpMOrrVu3ZseOHWJ/t6ARYlhbKFOWLVvGmDFj\ncHd3x8HBgS1btvDw4UOmTZtWLrd4xMbGaroJNGzYkPj4eIYOHVrs+u+//y75KWDNmjVjxYoV+Pr6\noq+vD0Bubi6LFy+WvIxk165dWbp0Ke7u7hgbG5f7Ahylzbe/THmeb9cWIjkLZcrt27fp2bMnFStW\npHnz5pw6dQonJye+++47Zs6cyahRozTdRJUqLfnduXOH/Px8tbXhm2++4ZtvviE5OZlWrVoBcOrU\nKaKjowkMDJQ09ty5c3F3d8fe3p769eujUCjIyMjA2NiY5cuXSxo7Pj6eGzdusGPHjlIfFzXFBSmJ\n5CyUKdWrVyc7O5t69ephbm7OuXPncHJyok6dOpIe/vCmpJgt2rdvH7NnzyYnJ6dELCmHtb/88kt+\n/vlnNmzYwMaNG9HX18fMzIwNGzbQsmVLSWIWadiwIXv37uXw4cOkpaUB0LhxY9q2bSvpIjiABQsW\nSPr82uZNe8O5ubn4+/uLXrRExJyzUKbMnz+f+Ph4/P39efLkCdOmTcPHx4fffvuNc+fOSbo4CZ4t\nknF2di4xxPno0SO2bt3KsGHDyM3NJSgoCD8/P5XGbt++PQ4ODgwZMqTUymBSDzG/qzIyMkhLS6Ow\nsBAzMzNJDxkpS6TaMig8I3rOQpkyZcoUKleuzN27d+nQoQN9+vRh9uzZVKtWTbJP8NnZ2Tx58gR4\nNi/XuHFj3n///WL3nDt3juDgYIYNG0alSpVUnpjh2QeAYcOGlVgcJbXbt2+zcuVK0tPTSx1OV/Ve\n4+edPXuWefPmkZKSQkFBQYnHpUwMOTk5eHl5ERsbS9WqVZHL5Tx8+BAbGxuWLFki+fndwrtN9JwF\nrTds2DBCQkKoWbMmkZGRODk5KRcHqcO+ffuYOHHiSwtjFP1fqHv37pLOwS5atIi7d+/i7e2t1sVJ\ngwcP5p9//qFTp06l9tilrK3ds2dPKleuzFdffVXqXnYpiqAUmTZtGmlpaQQFBSm3Dl26dIkZM2bw\n0UcfERAQIFnsskD0nKUlkrOg9Vq1asWmTZuwsLDAwsKChIQEqlevrtY23Lhxg8LCQhwdHdmyZUux\n+DKZDENDwxK9aVU7d+4cw4cP58mTJ9SsWbPEhwWpVnZbWVmxadMmjRSfaNmyJbt37y6xjUsdWrdu\nzZo1a2jRokWx66dPn2b06NGSVmQrC0RylpYY1ha0nr29Pa6urtSoUQOFQkGfPn2oUKH0006lSlDG\nxsYApKamFruel5fH+fPn1TLUPHXqVBo3bkzXrl3VchpVkVatWnH16lWNJGcLCwvS0tI0kpz19fVL\n/Xcmk8mQy+Vqb4/wbhHJWdB6P/zwA/Hx8Tx48AAvLy+++uorjc33paWl4eXlxYwZM2jUqBEDBgzg\n8uXLGBoasnTpUj799FPJYl+/fp2lS5eqvSiGv78/AwcO5ODBg5iYmJTosUs5rN2jRw+8vb3p3bt3\nqcdV9uzZU7LYDg4O+Pn5ERwcrDyJKSMjg3nz5tG+fXvJ4goCiOQslAEVK1YsVr/ZxcVFYwUhfH19\nMTU1pUGDBmzdupUHDx7wxx9/sG3bNhYuXPjSPbGq8OWXX3L48GEGDBggWYzSFM11p6enk5mZWewx\nqY+MXLlyJQYGBkRFRZV4TCaTSZqcp06dyrhx4+jUqZPyiMicnBzatWuHj4+PZHHLEjErKh0x5yyU\nKbm5uSxbtozevXvToEEDZsyYQUxMDM2aNSMoKEjy7UStWrXi119/xdTUlEGDBtGkSRNmz55NZmYm\nzs7OJCcnSxY7ODiYtWvX0rx5c0xNTdHR0Sn2uFSr1a2srFixYoWki6+0Ra9evbC1tcURZXT5AAAW\nDklEQVTGxgYbGxuqVq3K+fPnSUtLU+7vFnWln3n8+DHh4eFaUc++PBI9Z6FM8fPzIzU1lT59+rB7\n925iYmIICAhg3759+Pn5sWLFCknjV65cmTt37qCrq8upU6f4+uuvgWeLtWrUqCFp7KysLFxcXCSN\nURpjY2MMDQ3VHrfIgwcP2LVrFxkZGYwdO5bk5GQaNWokyfB+x44dSU5OJjIykgcPHtC4cWNsbGyw\ntbXl448/lnzRn7bIyclh9erVyi1sL/bh1q5di6GhoUjMEhI9Z6FMsbW1Ze3atTRt2hQPDw/09fVZ\ntGgRGRkZ9OrViz///FPS+CEhIWzevJmKFStiYGCgPCkqMDCQCRMmlDi5qTzYt28fP/30E25ubtSr\nV69EZS4bGxvJYl+4cIHhw4dTt25dLly4wN69e/nf//7Hvn37WL58uaS9+cuXL5OcnMypU6c4deoU\nly5dwszMTJmsu3TpIllsTRszZgwpKSl069at1C1sUq4zEJ4RyVkoU2xsbNi0aRMmJia0adOG2bNn\n07NnT5KTk/Hw8CAhIUHyNuzfv5/MzEy6du1KzZo1+f333yksLOTLL7+UNG5oaOgrH5fqD+arVmlL\nWTYUnu1xb926NePHj8fa2ppdu3ZhampKUFAQx44dY+vWrZLFfl5+fj5JSUls27aNgwcP8uTJk3K9\nhahly5asW7dO8vKswsuJYW2hTPn000/x8fHByMiIChUq4OjoyJEjR5g7dy4ODg5qacOLp18Vrdy9\nffs2tWvXlizui/tq5XI5169fJycnh86dO0sW98XtY6V5+vQpp06dUnkvOiUlhXnz5pW47urqyvr1\n61Ua63n5+fmcOHGCY8eOcezYMf766y8qV67MJ598wpQpU8r9/HudOnVeul1RUA+RnIUyJSAggB9/\n/JEbN26wZMkSKlWqxPnz52nfvj0TJkyQPH56ejrBwcFcunRJuddVoVCQn59PdnY2Z8+elSx2eHh4\nqdcDAgIkXzX9b+7fv8+wYcNU3pusXr06ly9fVm5lKnLy5ElJ5vhDQ0M5duwYycnJVKpUidatW9O1\na1fmzJlD48aNVR5PW02bNg1fX1/Gjx9f6ha2on3/gnTEsLYgvIHBgwcjl8vp1asXAQEBTJs2jczM\nTDZs2MDs2bPp1auX2tt07do1evfuTWJiotpjF7lz5w6fffbZa/Wy38SmTZtYsmQJY8aMISgoiJkz\nZ/L333/zyy+/MGnSJIYMGaLSeE2bNqVOnTqMGDGC/v37a3QhnCaVNpUhk8kkPwFN+H+i5yxoPS8v\nL2bOnEmlSpX+9UB4qY+vS0lJISIiAgsLCyIjIzE3N2fw4MGYmZmxdetWjSTn33//Xa21xl9Git67\nq6srtWvXZtWqVRgYGBAYGIiZmRlz587F2dlZ5fGCg4M5fvw4GzZsIDg4GEtLS+zs7LCzs+Pjjz/W\nitdZHaSqtCe8PpGcBeEN6OrqKquTFZ0n3aZNG9q2bcvChQslje3g4FAiAT58+JB79+4xffp0SWNr\nkoODg9rWE3Tt2pWuXbsCcPPmTeWcs7e3N7dv36Zly5bY2tpia2tLmzZt1NImTSiqF5CQkFDsuMy2\nbduWGOIWpCGGtYUyJTExESsrqxJ/IPLz84mPjy9WSUwKI0eO5MMPP2T69Ons2LGDPXv2EB4eTkxM\nDHPmzJF0tfiL1cdkMhl6enpYWlpqpPb086Q8BGHLli1ERESQlpZGhQoVaNKkCUOGDJGk5/wqZ86c\nISIigl9//bXcr9a+efMmHh4eXL58GTMzM+RyOVeuXMHY2Jg1a9ZQp04dTTex3BPJWShTXnYq1dmz\nZ3F1deX06dOSxr906RJjx45l4MCBuLq60qdPH+7cucOjR4/w8PBg3LhxksW+f/8+a9aseWVhCE2R\nKjkvW7aMlStXMnz4cCwtLZHL5aSkpLBu3TomT57MoEGDVBqvyOPHj/nrr784ffq08uv27dtYWFjQ\nunVrWrduTYcOHSSJrQ3Gjh1LQUEBwcHBytKld+/eZerUqRgZGbF48WINt7D8E8lZ0HobNmxgzpw5\nxRaklKZt27asWrVK5fFv3LhR7PvCwkLy8vIwNDTk4cOHHD9+HEtLS2rVqiXpKlZtLgwhVXL+7LPP\n8PPzK5EIo6OjmT9/PnFxcSqNN3PmTE6fPk16ejq6urq0bNkSGxsbWrdujbW19TuzQMza2pqIiAg+\n+uijYtdTU1MZPHgwJ06c0FDL3h1izlnQeoMGDaJx48YUFhYyfPhwFi9erPw0D/9/nvKLf0hUpbS5\n3hepYxXr4cOHtbYwhI6ODg0aNFD58z59+rTUeunm5uY8fPhQ5fHu3LlDt27daN26NS1atHhn51er\nVq3K/fv3S1zPycl5Z18TdRM9Z6FMyczMxNjYWK37el88ielVpDx4o2PHjixatAhLS0vJYrzMtWvX\n2LBhA1euXMHX15f4+HgaNGhA69atJY0bHh7Ozp07CQgIUH74unHjBj4+PrRr165clkvVBkFBQRw4\ncABfX19atGgBQHJyMnPmzMHe3p5Zs2ZpuIXln0jOQpny+PFjIiIiihUBgWcLws6ePcvevXs12Dpp\n7d+/n+XLl6u9MERiYiLu7u60a9eO3377jaioKDZu3MjatWsJCQmhU6dOksSFZ9XXsrKykMvlGBkZ\noaurS05OTqnTG+V5gZa65efnM2vWLHbt2qVc26Cjo0O/fv2YPn06BgYGGm5h+SeSs1CmTJ48mSNH\njtCmTRv27duHk5MTV65cISUlBU9Pz3JdkP/5whDPJyaph9T79+9P9+7dGTJkSLH61mFhYWzdupVf\nf/1VkrgAx48ff+17y3tJTU3IyckhIyODihUr8uGHH2JkZKTpJr0zxJyzUKbEx8fz448/0rZtWy5e\nvIibmxuWlpYsWLCAixcvarp5ktJUYYgLFy4o64c/r0OHDoSEhEgauyjhFhYWUqFCBW7fvs2JEydo\n0qSJOFdZxRITE7G2tkZXV7dEtbm8vDz++usv5fdSnkQmPCOSs1Cm5OXlKRceNW7cmDNnzmBpacmA\nAQNUXspR20g5n/1vcVNSUkqcnxwXFyd5m06cOMHEiRMJCgrC3Nyc3r17k5eXx+PHjwkKCsLJyUnS\n+O+SoUOHkpCQQI0aNRg6dOhL7xPlO9VDJGehTGnYsCGHDx+mb9++NG7cmBMnTuDq6sqDBw/Iy8vT\ndPPKpYkTJzJjxgxSUlKQy+VERkZy/fp19uzZQ2BgoKSxAwICcHZ2plWrVqxatQp9fX0OHjzInj17\nWLx4sUjOKvR8XXRV10gX3pw4E0woUzw9PfH19WXz5s306NGD2NjY/2vv/mOiruM/gD9PkDj6AQqK\n3KELoo1jQLIKNxNQKBwgEJop4I9oYQTOFEkgE8zSMXO23aiQRKKAFHEkROIR/UTjVxkeUSm/rgWI\nDUVBQhDu+weTxY7v+f1u97kPcs/Hxiafj+P1GvzxuveP1/uNuLg4bNu2DcuWLRM7vRnpueeeQ0FB\nAXp7e/H444+jqqoKw8PDKCgoEPyUrsuXL2Pz5s2QSqX45ptvEBgYCAsLC3h7e+v0n5PhBAQEoK+v\nT+d5T0/PjD62dDrhyJnuKwEBAThz5gzGxsbg4OCAI0eOICUlBVFRUdi0aZPY6c1Yrq6ugo+Sp2Jn\nZ4eWlhYMDg6iubkZKSkpAMZ7vh0cHIyez0xWUVGB77//HsB4++C+fft0Lvro7OyEmZmZGOmZHBZn\nuq/8dw1SKpVix44duH37NpRKJRYuXMhpTgFs3Lhxyr7yu2d7z5s3D0FBQfD19TV47JdeegkJCQmY\nNWsWPDw84O3tjaysLGRmZgp+A5mp8fb2nijOAHSOhwXG93kkJSUZMy2TxVYquq+sWbMGTz31FLZv\n346cnByUlJTgzJkzKC8vR3Z29ozucxaLUqlEdnY2Vq5cicWLF0Or1aKpqQlfffUVwsLCYGlpidLS\nUqSmpmLNmjUGj//777+js7MTy5Ytg6WlJX799VdYWlpOtJaNjY3hypUrgh6damoyMzPx8ssvs3VK\nRCzOdF/x9PRERUUFZDIZVq9ejSVLliA5ORmdnZ0IDg5GY2Oj2CnOOJGRkVi1ahWio6MnPS8qKsLp\n06dRUFAAlUqFw4cPo6Kiwuj5CXkjlim7evUqCgoK0NraitHRUTg7O2Pt2rWCHNNKurghjO4rd9cg\nW1pa0NzcjBUrVgDgGqSQmpub8cwzz+g89/b2hlqtBgC4u7uju7vb2KlN4BjDsBoaGrBy5UrU1tbC\n0dERjo6OqK+vR3h4OC+9MBKuOdN9hWuQxufq6or8/Hzs3r170tpzYWEhXFxcAABqtRoLFiwQK0Wj\nnrVuCjIyMrBhwwbs3Llz0vNDhw7hvffew/Hjx0XKzHRwWpvuO/dagyTDampqQmxsLB588EG4ubkB\nGP8bDAwMICsrC3fu3EFMTAzS0tLwwgsvGD0/Tmsb3hNPPIHTp0/rTGF3dHQgPDycy0dGwJEz3XcU\nCgUUCsXE94sXLxYxm5nP3d0dlZWVKC8vx6VLl2BmZgYfHx+EhITAysoKf//9N4qKivjhaAaRy+W4\nePGiTnFubGyEnZ2dOEmZGBZnIrqnhx56COvWrdN5fvXqVTg6OoqQEQnplVdeQXp6Otra2ibuD29s\nbMRnn32GxMREkbMzDSzORKRXW1sbDh06NOmaTq1Wi+HhYVy7dg3Nzc0iZ0iGtnr1agBAfn4+cnNz\n8cADD8DJyQn79+/nWQJGwjVnItIrOjoao6OjiIiIwIEDB7Br1y50dnaisLAQ6enpiIiIEDW/3t5e\nrFixAhcvXhQ1DyJD4siZiPRSq9U4ceIEFAoFvvjiCzg7OyM6OhpOTk4oLi4WvDj39/ejtLQU7e3t\niI+PR2NjIx577DEsWrQIAGBra8vCbGCDg4M4efIk2traMDw8rPOenRHCY58zEellbm6Ohx9+GADg\n7Ow8sSt66dKl+PPPPwWNfenSJQQGBuLUqVM4fvw4bt26BZVKhfDwcNTV1Qka25QlJibiww8/xM2b\nN8VOxWRx5ExEenl5eSEnJwfJyclwd3dHeXk5YmJi0NTUpHMxgqG9++67iIyMxLZt2+Dl5QVgfNQ2\nd+5cHDx4EMXFxYLGN1W1tbU4duzYxO+cjI8jZyLSKzU1FdXV1SgsLER4eDh6e3vh7e2NxMREREVF\nCRpbrVbj+eef13m+fv16tLS0CBrblDk7O2NoaEjsNEwaR85EpJeFhQVUKhWGhoYglUpx6tQp1NXV\nwcbGRvAe87lz56K9vX1iffmuX375Bba2toLGNmUZGRnYunUrQkNDIZPJMGvW5HHcVB+YyLBYnIlI\nr8jISBw5cgTu7u4AACsrKyxfvtwosWNjY/HWW28hLi4OWq0WNTU1KCkpQV5eHnbs2GGUHExRUVER\nNBoNPv/8c52lC4lEwuJsBCzORKSXnZ0dent7RYm9fv16zJ8/Hzk5ObC0tMTBgwfh5OSEd955B8HB\nwaLkZAqKi4tx+PBh/o5FxOJMRHq5ubkhPj4eHh4ekMvlsLCwmPRe6LYaf39/+Pv7CxqDJpszZ87E\npSYkDhZnIrqnsLAwUeKy31Yc6enp2LdvHxISEuDo6AgzM7NJ72UymUiZmQ6eEEZE01ZcXBwuXLiA\npUuXwtLSUuc9i7MwprrERCKRQKvVQiKR8AYwI2BxJqJ7+vnnn5GXlweNRoOsrCyUlZVBLpcjJCRE\n0LheXl7stxVBZ2en3vdyudxImZgu9jkTkV4qlQpbtmyBXC5He3s77ty5A3Nzc6SkpKCwsFDQ2Oy3\nFYdcLodcLkdHRweqqqpQWVmJlpYWzJ8/n4XZSDhyJiK9wsLCEBsbi9DQUHh5eaG0tBQLFy5EWVkZ\nlEolKisrBYt9+fJl9tuK4MqVK4iPj0d7ezucnJwwOjoKjUYDmUyG3Nxc2Nvbi53ijMcNYUSkl0aj\nmfKwEU9PT/T09Agam/224nj77bdha2uL3NxcWFtbAwCuX7+ON954A/v374dSqRQ5w5mPxZmI9HJx\nccGPP/6oc1RnSUmJ4O027LcVR01NDU6cODFRmIHx9qqkpCRER0eLmJnpYHEmIr1SU1MRFxeHmpoa\njIyMICsrCxqNBk1NTfjoo48Ejc1+W3FYW1vjxo0bOs9v3ryJ2bNni5CR6THbu3fvXrGTIKLpSyaT\nISIiAp2dnbCxscG///4LhUKBjIwMuLm5CRr70Ucfxfvvvw8HBwcAwK1bt9Df3z/xdfcqSzKsf/75\nB0ePHoWLiwvmzJmD4eFh1NfXY+/evQgICICfn5/YKc543BBGRHqVlZXh2WefhVQqNXrs//bbSiSS\niX+z31ZYw8PDSEtLQ2lpKe6WCDMzM6xduxbJyclT9pyTYbE4E5Fefn5+uHHjBnx9fbFq1Sr4+fkJ\nfo/zXey3FUdDQwM8PDxw+/ZtdHR0wMLCAosWLYKVlZXYqZkMFmciuqcLFy5ApVJBpVLh+vXr8Pf3\nR3BwMHx8fLgGOQMtWbIEeXl5U54URsbB4kxE/y+//fYbzp49i4KCApibm6O2ttagP1+hUKC6uhq2\ntrZwdXWdNJ39XxKJBM3NzQaNTeM2bNiAsLAwvPjii2KnYrK4W5uI/k8GBwfx3XffQaVSobq6Gvb2\n9oK0OOXl5U208MhkMuzevVtn41dfXx/27Nlj8Ng0ztraGmlpaVAqlXB0dNS5iezTTz8VKTPTweJM\nRHqVlJRApVLh3LlzmDdvHoKDg5Gfny/YlOfs2bPx5ZdfAgC6u7vR3d2N/v7+Sf+nra0NIyMjgsSn\n8dkLhUIBrVaLvr4+SCQS2NjYiJ2WSeG0NhHp5evri6CgIISEhMDT03PSu5GREYOvOf/xxx9ISEiA\nVqtFV1cXFixYMOnYTolEAisrK0RGRuocjEKGMTo6CqVSiZMnT+LatWsAAHt7e0RHR2PLli0iZ2ca\nWJyJSK+enh58/PHHaG1txejoKIDxVqaRkRG0traivr5esNgbN25EZmbmpJOqSHgHDhzA2bNn8frr\nr8Pd3R1jY2NQq9VQKpVYt24dtm7dKnaKMx6LMxHp9eqrr0Kj0SAwMBDHjh1DTEwM/vrrL1RWViIl\nJQWbNm0SO0UysKeffhoffPABvL29Jz0/f/48kpKScP78eZEyMx1ccyYiverq6ibuVD537hyWL1+O\nJ598EtnZ2fjhhx9YnGcgqVQ65XLFI4888r/unifD4n3ORKSXVquduCLQxcVlon0pKCgIarVazNRI\nILt27cKbb76Jb7/9Fn19fRgYGEBDQwP27NmDzZs3o6ura+KLhMFpbSLSKyoqCj4+PnjttdfwySef\noKamBllZWfjpp5+wfft2g/c5k/imOjb1v6VCIpHwCFWBcVqbiPTauXMn4uLiIJVKER4ejqNHjyI0\nNBRdXV0ICwsTOz0SQFVVldgpmDyOnInongYGBjA0NAQ7Ozv09PTg66+/ho2NDYKCgia1ORGRYbA4\nExERTTP8yEtERDTNsDgTERFNMyzORERE0wyLMxER0TTD4kxERDTNsDgTERFNMyzORERE0wyLMxER\n0TTzPw9aP8Gz+0KUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc3efcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = df.corr()\n",
    "f, ax =plt.subplots(figsize=(4,4))\n",
    "# Draw the heatmap using seaborn\n",
    "sns.heatmap(corrmat, vmax=0.8, square = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# As a Result:\n",
    "    • the avg monthy hours and # of projects are highly corrleated, in the other hand, they both have high correation with unsatisfaction levels of the employees.\n",
    "    • the period of time that the employees spend in the company is a very important factor which reflect on unsatisfactin employee level ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Satisfaction Level by Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbc45160>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gIiJSYkoERERESkyJgIiISIkpERARESkxJQIiIiIlpkRARESkxJQIiIiIlJgS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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbc807f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('sales').mean()['satisfaction_level'].plot(kind='bar', title = 'Satisfaction Level by Sales', colormap='hot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Left Level by Slaes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbcb2160>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JUi2TBEmSVMskQZIk1TJJkCRJtUwSJElSLZMESZJUyyRBkiTVMkmQJEm1TBIk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\ny5AgSZJqGRIkSVKtX0WGFb1Pm1Z7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc218358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('sales').mean()['left'].plot(kind='bar', title = 'Left Level by Slaes', colormap='gist_rainbow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# As a Result:\n",
    "    • Although, Accountants have the highest level of unstasifaction but HR have the highest level of departure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sales by Salary all feature plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xc3cd358>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xc45dd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(\"sales\", col=\"salary\", col_wrap=3, data=df, kind=\"count\", size=10, aspect=.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Predict who will left from other features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales_IT</th>\n",
       "      <th>sales_RandD</th>\n",
       "      <th>...</th>\n",
       "      <th>sales_hr</th>\n",
       "      <th>sales_management</th>\n",
       "      <th>sales_marketing</th>\n",
       "      <th>sales_product_mng</th>\n",
       "      <th>sales_sales</th>\n",
       "      <th>sales_support</th>\n",
       "      <th>sales_technical</th>\n",
       "      <th>salary_high</th>\n",
       "      <th>salary_low</th>\n",
       "      <th>salary_medium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years  sales_IT  \\\n",
       "0                   3              0     1                      0       0.0   \n",
       "1                   6              0     1                      0       0.0   \n",
       "2                   4              0     1                      0       0.0   \n",
       "3                   5              0     1                      0       0.0   \n",
       "4                   3              0     1                      0       0.0   \n",
       "\n",
       "   sales_RandD      ...        sales_hr  sales_management  sales_marketing  \\\n",
       "0          0.0      ...             0.0               0.0              0.0   \n",
       "1          0.0      ...             0.0               0.0              0.0   \n",
       "2          0.0      ...             0.0               0.0              0.0   \n",
       "3          0.0      ...             0.0               0.0              0.0   \n",
       "4          0.0      ...             0.0               0.0              0.0   \n",
       "\n",
       "   sales_product_mng  sales_sales  sales_support  sales_technical  \\\n",
       "0                0.0          1.0            0.0              0.0   \n",
       "1                0.0          1.0            0.0              0.0   \n",
       "2                0.0          1.0            0.0              0.0   \n",
       "3                0.0          1.0            0.0              0.0   \n",
       "4                0.0          1.0            0.0              0.0   \n",
       "\n",
       "   salary_high  salary_low  salary_medium  \n",
       "0          0.0         1.0            0.0  \n",
       "1          0.0         0.0            1.0  \n",
       "2          0.0         0.0            1.0  \n",
       "3          0.0         1.0            0.0  \n",
       "4          0.0         1.0            0.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy = pd.get_dummies(df)\n",
    "df_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1 = df_copy\n",
    "y = df1['left'].values\n",
    "df1 = df1.drop(['left'],axis=1)\n",
    "X = df1.values\n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation accuracy:  0.7784\n"
     ]
    }
   ],
   "source": [
    "log_reg = LogisticRegression()\n",
    "log_reg.fit(Xtrain, ytrain)\n",
    "pred_log = log_reg.predict(Xtest)\n",
    "accuracy = accuracy_score(pred_log, ytest)\n",
    "print(\"Validation accuracy: \", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RBF Kernel, C=10000\n",
      "training time:  22.006 s\n",
      "predicting time:  0.898 s\n",
      "0.9576\n"
     ]
    }
   ],
   "source": [
    "clf= svm.SVC(C=10000,kernel=\"rbf\")\n",
    "t0=time()\n",
    "clf.fit(Xtrain,ytrain)\n",
    "print(\"RBF Kernel, C=10000\\ntraining time: \", round(time()-t0, 3), \"s\")\n",
    "\n",
    "t1=time()\n",
    "pred=clf.predict(Xtest)\n",
    "print(\"predicting time: \", round(time()-t1, 3),\"s\")\n",
    "accuracy= accuracy_score(pred, ytest)\n",
    "print(accuracy)"
   ]
  }
 ],
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