Employee Exit Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Data = pd.read_csv('human-resources-analytics/HR_comma_sep.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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": [
    "Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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": [
    "Data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "columns = Data.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "categorical=['number_project','time_spend_company','Work_accident','left', 'promotion_last_5years','sales','salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), <a list of 10 Text xticklabel objects>)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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B9qR6SGEVcNSgbSWpY/a0SVJnzgPmAhdExMC9bWcBiyNiW+Bx4LbM3BIRi6mKsmnA+Zm5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\nol0aWIX+VAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15fa5db5f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.subplots(figsize=(10,15))\n",
    "length=len(categorical)\n",
    "for i,j in itertools.zip_longest(categorical,range(length)):\n",
    "    #j+1 - gives the plot number\n",
    "    plt.subplot(np.ceil(length/2),2,j+1)\n",
    "    plt.subplots_adjust(hspace=0.5)\n",
    "    sns.countplot(x=i, data= Data)\n",
    "    \n",
    "plt.subplot(np.ceil(length/2),2,6)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3571"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Data[Data['left']==1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2380825388359224"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3571/14999"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's analyze the categorical & ordinal variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# here we will do it only for categorical variable\n",
    "categorical=['number_project','time_spend_company','Work_accident','promotion_last_5years','sales','salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LgOMz8/76Wv6J6t9sJPk34GURcTswLzPvbHdA+j2d2jZ3ajvbqe1mp7aBndqe\ndWrbtAj4ADCF6v/5QcCjVD+rtifXEXECcDRVfI8CK4Clmdmy3uWOu/3SYTZn5trMfAQ4AZgVEa+i\napBGmnGZeWf9AfS1zPx1Zj4GjLTd0w4BfgJclpmvAn6Yma8aaXcStrM1Mx8AyMxfMjJ/L5/MzCeA\n9cC/w1PXMhJ/V36bmbOAdwPnRcTqiPjHiDiv3YHpKZ3aNndqO9vp7WantYGd2p51atvUXffofxV4\nJDN/Uf/8nmxzXETENcCfAd8EPkOV8L+a6gtBy9hzPbCHImIhcHFmro+INwG3As9sc1y7IiPieuCM\nzHw7QES8F3i4rVHtpMz8dUS8BfhIRPzf7Y5niKZExD3AXhHx11S3Ra8Aft7esHbJsoj4J2ANsDwi\nbqVq0G5vb1i7pAsgM78P/HlETAGOpOrtU2fo1La5I9vZDm43O7UN7NT2rFPbpoci4n9R5ZSPR8Sl\nwDrgV+0NC4AXZ+b2d0GWRcSqVr5pu78ddrrTgB9Rf1vNzP8CXgV8qZ1B7aLTga9n5raGsv/NCNxB\nLTO3ZOa7qG5xjtj/w5l5MPBKqrGPdwHbqLaLHon/Jh8CFlI1/v8JTAeuysz3tjWwXbO48UlmrsvM\nr2fmR9oUj35fp7bNHdvOdmK72altYAe3Z4sbn3RQ23Qq1dj0uVR3kiYCE6h+T9ttXETMaCyIiCNp\nca+6ExolSZI06kTEC6i+KB1M9WVpG/AD4O/7hiK1gsm1JEmSVIhjriVJkjTqRMS3gH7Xms/MV7bq\nfU2uNSZExB3AJZl5R5tDeZqIOAsgM6/bydf9ETA3M/+6JYFJkjTyvZdqZZATGcZVe0yupTba2aS6\nwb7AC0rGIknSaJKZd0XEZ4EDM/Om4Xpfx1yr40TETOAiqk0PDqCaPX4RcFtm7lfXuQQgMy+JiIeB\nrwMzqJb++ThwHvB84O2ZeWfdc/1L4E+pJjX8bWbeERHPAK4BXgyMBz6cmV+IiLdTzYB+FtXs/4t2\nEOslVBtFvAD4v4BPZOaC7V8PXAl8CvhDqm/PF2XmLdtdx58B76eaZf0fwOmZ+UhEHE21PNU4qiWq\nTgZWAvsDSzLznF34MUuSpBboiOV4pH68EphFlVz/IXDMAHWfDSzPzD+tn5+YmTOAS4B3NdR7PDP/\nJ1XS+9mI2J1q6aB76iWhjgTmRMT+df3nAy/bUWLd4MXAa6hmI58ZEf+zn9d/DLg9Mw8E3gx8OiKe\n3XeCiJiE0V2bAAAgAElEQVQGfAg4JjNfRrVm74frGD8HnJqZL6FafuxUqi8P3zexliSpszgsRJ1q\nTWb+b4CIuA/4g0Hq31z//XOqXt2+x1Mb6nwKIDN/FBE9VL3YRwN7RkTfepx7AS+qH/8gM5sZo/WF\nzHy8jnUZ1e5P/73d619NtQYumfnvEXEXcFjDOQ6j+hLxrYiAqhf9N8BLgF9k5g/r115Uv8/MJuKS\nJEnDzORanWpjw+O+sUtdDWUTaFgEPjM3NxzbUULcWN5Vv3488LbM/AFA3Zv8G+Avgd82GWvjecc1\nPP/tduWNunj67994YGVmHl/HMRGYBDy38UX1jlyTmoxLkgZVtytLgL8Brs/M49ocUhGdOpFdo5/D\nQjRSPApMjYhp9VCJP9uFc/wlQEQcAkwGHqDazvbsuvw5VMMu/nAnz3tiROwWEVOBNwC39VPnduCv\n6/fZHzgc+E7D8buAV0TEC+vnFwMLgASmRcT/qMvfDZxFlcD75VhSCVOBl2bmL0dLYi21kx/OGinW\nUSWb3wP+C7h7F87xjIi4F9gKnJyZT0bEPODjEbGGqvf43Zn5s+23Sx3Eb6mGokwGLsvMn0TEodvV\nOQ/4ZES8g6on/p2Z+at6CAiZ+XA9NOVLETGeasvkt2Xmxoh4G3BDROwG/Aw4hWrdzmdGxGcz85Rd\n+FlIUp+rgOdGxE1U80T2i4jFwBPAEcAzqeavnAIcBHwtM8+v26oFwEyq9nNxZn50R28SEZOBLwB7\n10XzMnNZ3cN8H9XwuInAuzLztvpO4ieAfah21rswM/+5ngj+POBPqFZOuj4zL607Xq4HDgEeoppQ\nPqCI+FuqDoutVJPX31O/744moP9h/TOYTjVn59V13P8GvBU4CphHdWd0H6rPqndm5qaIuJRqfs4f\nUA0dfFPd9v8KWFr/rLcAb6GasP6BvrWYI+JU4OWZefZg16T2c7UQaQgaV/vYxdd/FPhlZi4oGJYk\nNS0i9gPuoEqS72hIrqdk5ol1Yncl1cpIvwV+QZU4/r/ACzPz7+rE9lbg4sxcsYP3ORU4NDPPiYgD\ngNMy84I6uf73zDwtIl5KNYdmX+AG4PN1Av4cqk6MlwLnA8dSrRD1TKpOh32AdwIHZeYpEfEnVHci\nj93RsJC6E+RzVMn4E8AtwHvqP9/NzIX1ncaVwMuo7nL+P8ArqO4+3k41L+Z+qi8Hb6a6C3BzHef9\nwJeo7lIuAz4MnJSZ2yLiBuDfMvOKiOilmoj/tYi4og7v7+vrem3d4XM71ZeLu/q7FnUWe66lQdQ9\nG6f2c+iX7FoPet95FwJvYuCVUCSpXRoniq/JzF8DRMRvqJLIo4GXRsSr63rPoEo2+02ugX8FPhgR\nzwP+P+ADDccWAWTmD+ue3APr8/9pRLy/rjOB363v/616rs2v63imUH05+ER9ngci4l8Hub4jqXqr\n19XPj66vb6AJ6N/MzC0R8XPgV5n5k/o1v+B3E+i/nZlZl38WOKNO1M8H3hnVLctXUCXPfW6p/14D\nHJmZvRGxBHhbRHwGeLaJ9chhci0Nor7NucNbnUM4798Bf1f6vJJUyGATxfuG0n0VICKeRdUD3K86\n4f1TqjkzbwDOr3uwtz9/38Tw8cCrM/M39fmfC/wf4I38/qT3rvrvxrlkg6329GTjk/r8Gxh4AvrO\nTp4fB2yJiIOphsQspBoCspWGSfqZ2Xc9vQ3li6mS7o1UvfgaIZzQKEnS2LarE6RvB06PiAn1hlwr\nefoSo08TEbOoxll/mWplkulUPc5QjVfum3A+lWrzsNvretSTun8E7DlAPP8MnBwR4yJiX6r9Egay\nAjg2Ip4REd1Uye8hDD4BfTBHRMTzImIc8FdUdwCOohpycx3wE+B1VF8edigzf041/+Zs4LM78f5q\nM5NrSZLGtv8D/CfwmZ183XVUqy7dC3wf+Mwgy97dAERErAa+TbVM3qP1sf0j4gfAJ4G/yMytwLnA\nyyPiR8AXgVMyc/0A5/848BjV+OdFVEMsdqhegvVqqsT536iGc/wz1QT0V9dxfo16AvpA59rOL+tr\n/QnV+PTr6/gPqq/ldqovCn/UxLm+CPwkM3+5E++vNnNCoyRJapvRtB51vcHXJZk5s8C5uql6rL/c\nN/RGI4NjriVJUhER8QLgKzs4/M7M/P5YjqdZEdFF1QP+Tarec40g9lxLkiRJhTjmWpIkSSrE5FqS\nJEkqxORakiRJKsTkWpIkSSrE5FqSJEkqxORakiRJKsTkWpIkSSrE5FqSJEkqxORakiRJKsTkWpIk\nSSrE5FqSJEkqxORakiRJKsTkWpIkSSrE5FqSJEkqxORakiRJKsTkWpIkSSrE5FqSJEkqxORakiRJ\nKsTkWpIkSSrE5FqSJEkqxORakiRJKsTkWpIkSSrE5FqSJEkqxORakiRJKsTkWpIkSSrE5FqSJEkq\nxORakiRJKsTkWpIkSSrE5FqSJEkqxORakiRJKsTkWpIkSSrE5FqSJEkqxORakiRJKsTkWpIkSSrE\n5FqSJEkqxORakiRJKsTkWpIkSSrE5FqSJEkqxORakiRJKsTkWpIkSSrE5FqSJEkqxORakiRJKsTk\nWpIkSSrE5FqSJEkqxORakiRJKsTkWpIkSSrE5FqSJEkqxORakiRJKsTkWpIkSSrE5FqSJEkqxORa\nkiRJKsTkWpIkSSrE5FqSJEkqxORakiRJKsTkWpIkSSrE5FqSJEkqxORakiRJKsTkWpIkSSrE5FqS\nJEkqxORakiRJKsTkWpIkSSrE5FqSJEkqxORakiRJKsTkWpIkSSrE5FqSJEkqxORakiRJKsTkWpIk\nSSrE5FqSJEkqpLvdAZTU07O+t90xSNKumDZtUle7YxhuttmSRqqB2mx7riVJkqRCTK4lSZKkQkyu\nJUmSpEJMriVJkqRCTK4lSZKkQkyuJUmSpEJMriVJkqRCTK4lSZKkQkyuJUmSpEJG1Q6NQzF7wbJ2\nhzDsrrzg+HaHIHWUb3zj6zzyyCOccsrbf+/Yl770BW65ZTmzZ/89Dz/8K4455rjhD1BPsc2W1Klt\ntj3XktSElSvv5EMfWsj06c/m1ltvbnc4kqQBtLPNtudakp6ml3/8xwU88MD9AJx33vn8/OcPcf/9\nP+V977uQfffdj/vu+zE33bSUE098c5tjlaSxrvPabJNrSWqwdetWtmzZwjXXLKKn59dccskcrrlm\nEcuWfZV58z7I5s2b6enpMbGWpA7QiW22ybUkNejt7eXee+9h1qwzAFi3bl2bI5Ik7Ugnttkm15LU\noLt7AjNmzOSss2bxxBOPs3TpF592vKuri97ebW2KTpLUqBPbbCc0StLT9PL4448za9YZzJp1Bvvs\ns+/Tjk6dOpW1a9fyhS/c2Kb4JEm/03ltdldvb++wvVmr9fSs3+WLcVknSe00bdqkrnbHMNxss3eO\nbbbUOQZqs+25liRJkgoxuZYkSZIKMbmWJEmSCjG5liRJkgoxuZYkSZIKcZ1rSaNW6RUlXK1Bklpr\nNLTbJteSVNC2bdu44ooP8eCDDzBhwgTe+96Lef7z92l3WJKkfrSizXZYiCQVtGLFHWzevJlPfOIz\nnHXWuVx99UfbHZIkaQda0WabXEtSQT/60Q857LBXAPDiF7+En/70vjZHJEnakVa02Q4LkaSCnnji\nCfba6xlPPR83bhxbtmyhu7uzmtuIOAz4cGbOjIg/BhYDvcAa4JzM3BYRpwNnAluA+Zm5PCL2AG4E\npgPrgVMzsyciXg5cWde9LTPnDf9VSdLOaUWbbc+1JBW01157sWHDhqee9/b2dmJi/W7gemBiXbQQ\nmJuZM4Au4ISI2Bs4DzgcOAa4LCJ2B84GVtd1bwDm1ue4DjgZOAI4LCJeNlzXI0m7qhVttsm1JBX0\nkpccxHe/uwqANWtWs//+f9zmiPr1M+BNDc8PBu6sH98MHA0cCqzKzE2ZuQ54EDiQKnm+pbFuREwG\nds/Mn2VmL3BrfQ5J6mitaLM7qztFkgpqxxJMRx75Kr73vbs466zT6O3t5aKL/mHYYxhMZn4lIvZr\nKOqqk2KohnpMASYD6xrq9FfeWPbYdnX3HyyOqVP3pLt7/K5cwpg0bdqkdocgtdznL//LYX2/P//z\n41m9+gece+7p9Pb28sEPfnDIv2sm15JU0Lhx47jggovaHcbO2tbweBLwKFWyPGmQ8sHqDmjt2g2D\nVVGDnp717Q5BGpXOPfeCpz1v5ndtoATcYSGSpHsjYmb9+FhgBXA3MCMiJkbEFOAAqsmOq4DjGutm\n5mPA5oh4QUR0UY3RXjGcFyBJncKea0nS+cCiiNgNuA9YmplbI+IqqiR5HDAnMzdGxLXAkohYCWym\nmsQIcBbwOWA81Wohdw37VUhSBzC5lqQxKDMfAl5eP74fOKqfOouARduVbQBO6qfud/vOJ0ljmcNC\nJEmSpEJMriVJkqRCWjYsJCImAEuA/YCtwOlUO3ctZgi7gLUqXkmjzwXL5w5eaScseP38oueTJD3d\naGi3W9lzfRzQnZmvBN4PXEqZXcAkqeP9+MdrmDXrjHaHIUlqQsk2u5UTGu8HuiNiHNUGA09STXZp\n3AXsdVS92qsycxOwKSIadwG7vKHuxYO9oRsS7Bw3JJB2TrO/M4sWLWLZsmXsscce/p5JUof73OeW\ncOut32DixD2KnK+VyfXjVENCfgo8C3g9cOQQdwEbkBsS7Bw3JJB2TrO/M8985jTmzfsQH/jA+5p+\njUm4JLXH8573fC69dAEf+MD7ipyvlcNC/ha4NTNfCBxENf56t4bju7ILmCR1vJkzX0N3tyudStJI\nULrNbmVyvZbf9Tz/BpjAEHcBa2GskiRJ0pC1smvlo8CnI2IFVY/1RcD3GfouYJIkSVJHallynZmP\nA2/p59CQdgGTpGa5dJ4kjSyjod12ExlJaoHnPOe5fPKTi9sdhiSpCSXbbJNrSZIkqRCTa0mSJKkQ\nk2tJkiSpEJNrSZIkqRCTa0mSJKkQk2tJkiSpEJNrSZIkqRCTa0mSJKkQk2tJkiSpEJNrSZIkqRCT\na0mSJKkQk2tJkiSpEJNrSZIkqRCTa0mSJKkQk2tJkiSpEJNrSZIkqZDudgcgSWq/iJgALAH2A7YC\npwNbgMVAL7AGOCczt0XE6cCZ9fH5mbk8IvYAbgSmA+uBUzOzZ7ivQ5LazZ5rSRLAcUB3Zr4SeD9w\nKbAQmJuZM4Au4ISI2Bs4DzgcOAa4LCJ2B84GVtd1bwDmtuEaJKntTK4lSQD3A90RMQ6YDDwJHAzc\nWR+/GTgaOBRYlZmbMnMd8CBwIHAEcMt2dSVpzHFYiCQJ4HGqISE/BZ4FvB44MjN76+PrgSlUife6\nhtf1V95XNqCpU/eku3t8idjHhGnTJrU7BElNMLmWJAH8LXBrZl4YEfsAtwO7NRyfBDwKPFY/Hqi8\nr2xAa9duKBD22NHTs77dIUiqDfRl12EhkiSAtfyu5/k3wATg3oiYWZcdC6wA7gZmRMTEiJgCHEA1\n2XEV1bjtxrqSNObYcy1JAvgo8OmIWEHVY30R8H1gUUTsBtwHLM3MrRFxFVXyPA6Yk5kbI+JaYElE\nrAQ2Aye35Sokqc1MriVJZObjwFv6OXRUP3UXAYu2K9sAnNSa6CRp5HBYiCRJklSIybUkSZJUiMm1\nJEmSVIjJtSRJklSIybUkSZJUiMm1JEmSVIjJtSRJklSIybUkSZJUSEs3kYmIC4HjqXb7+jhwJ7AY\n6KXaLveczNwWEacDZwJbgPmZuTwi9gBuBKYD64FTM7OnlfFKkiRJQ9GynuuImAm8EjicaoevfYCF\nwNzMnAF0ASdExN7AeXW9Y4DLImJ34GxgdV33BmBuq2KVJEmSSmjlsJBjgNXATcDXgeXAwVS91wA3\nA0cDhwKrMnNTZq4DHgQOBI4AbtmuriRJktSxWjks5FnAvsDrgT8ClgHjMrO3Pr4emAJMBtY1vK6/\n8r6yAU2duifd3eOLBD8WTJs2qd0hSJIkjSqtTK4fAX6amZuBjIiNVEND+kwCHgUeqx8PVN5XNqC1\nazcUCHvs6OlZ3+4QJNX8sitJo0Mrh4WsBP4sIroi4rnAXsC/1GOxAY4FVgB3AzMiYmJETAEOoJrs\nuAo4bru6kiRJUsdqWc91veLHkVTJ8zjgHOA/gEURsRtwH7A0M7dGxFVUyfM4YE5mboyIa4ElEbES\n2Ayc3KpYJUmSpBJauhRfZr67n+Kj+qm3CFi0XdkG4KQWhSZJkiQV5yYykiRJUiEm15IkSVIhJteS\nJElSISbXkiRJUiEm15IkSVIhTSXXEfGxfsqWlA9HktQs22ZJ6jwDLsUXEdcD+wOHRMSLGg5NoInt\nyCVJ5dk2S1LnGmyd6/nAfsCVwLyG8i1Um8BIkoafbbMkdagBk+vMfAh4CDgoIiZT9Yh01YefAfym\nlcFJkn6fbbMkda6mdmiMiAuBC4FHGop7qW5LSpLawLZZkjpPs9ufvxN4QWb2tDIYSdJOsW2WpA7T\nbHL9n3ibUZI6TdG2ue4JPx7YDfg4cCewmKo3fA1wTmZui4jTgTOpxnjPz8zlEbEHcCMwHVgPnGrS\nL2ksaja5fgBYGRHfAjb2FWbm+1sSlSSpGcXa5oiYCbwSOBzYE/h7YCEwNzPviIjrgBMi4jvAecAh\nwMT6/b8JnA2szsxLIuKtwFxg9lAuTpJGomaT61/Uf+B3k2YkSe1Vsm0+BlgN3ARMBi4ATqfqvQa4\nGXgdsBVYlZmbgE0R8SBwIHAEcHlD3YsHe8OpU/eku3v8EMMeO6ZNm9TuECQ1oankOjPnDV5LkjSc\nCrfNzwL2BV4P/BGwDBiXmb318fVUq5JMBtY1vK6/8r6yAa1du6FI4GNFT8/6docgqTbQl91mVwvZ\nRjXmrtEvM3OfIcQlSRqCwm3zI8BPM3MzkBGxEWg8zyTgUeCx+vFA5X1lkjTmNNtz/dQ26RExAXgj\n8IpWBSVJGlzhtnklMDsiFgLPAfYC/iUiZmbmHcCxwLeAu4FLI2IisDtwANVkx1XAcfXxY4EVuxiH\nJI1o4wav8nSZ+WRmfhl4dQvikSTtgqG2zZm5HLiXKjn+OnAOcD4wr57EuBuwNDMfBq6iSp5vB+Zk\n5kbgWuBFEbESOIOn7xwpSWNGs8NC/qrhaRfwImBzSyKSJDWldNucme/up/iofuotAhZtV7YBOGlX\n31uSRotmVwt5VcPjXuC/gb8oH44kaSfYNktSh2l2zPU76vF8Ub9mTWZuaWlkkqQB2TZLUudpasx1\nRBxMtVnBEuAzwH9GxGGtDEySNDDbZknqPM0OC7kK+IvMvAsgIl4OfAw4tFWBSZIGZdssSR2m2dVC\nntHXeANk5neptr2VJLWPbbMkdZhmk+vfRMQJfU8i4o1UGw5IktrHtlmSOkyzw0LOAJZHxKeolnvq\nBV7ZsqgkSc2wbZakDtNsz/WxwAZgX6qln3qAmS2KSZLUHNtmSeowzSbXZwCHZ+YTmfkj4GDg3NaF\nJUlqgm2zJHWYZpPrCTx916/NVLcfJUntY9ssSR2m2THXXwNuj4gv1c/fBPxTa0KSJDXJtlmSOkxT\nPdeZ+R7+f/buPc6uqjz4+C8hIQEJMWoAL1TEy1OqohWKFwhE5VIogloRpSJK5faixLcUEQivYIMo\nIJZ4AQwiAcQLUSxNDaDlmlREEJQoPhhbUNuiI4aQEJOYZN4/9h5yGCYzk2Tvs0/m/L6fz3yyzzr7\nnPWcOZN1nrP2uhTrqQawMzAzM8+sMzBJ0uBsmyWp8wy355rMnAPMqTEWSdIGsm2WpM4y3DHXkiRJ\nkoYw7J7rjRER2wH3APsBq4ErKCbbLAROzMy1EXEMcFx5/4zMnBsRWwFXA9sBS4GjMrOnzlglSZKk\nTVVbz3VEjAUuBf5YFl0ITM/MKRSbHRwaETsAJwF7AgcA50bEOOAE4P7y3CuB6XXFKUmSJFWlzmEh\nFwCXAP9T3t4NuK08ngfsC+wBLMjMlZm5BFgE7ArsBdzQ71xJkiSpo9UyLCQi3gf0ZOaNEXFaWTwq\nM/vWX10KTAS2BZa0PHSg8r6yIU2atDVjxmyxidF3j8mTJzQdgiRJ0ohS15jro4HeiNgXeDXF0I7t\nWu6fADwGPF4eD1beVzakxYuXb1rUXaanZ2nTIUgq+WVXkkaGWoaFZObemblPZk4F7gPeC8yLiKnl\nKQcCdwB3AVMiYnxETAR2oZjsuAA4qN+5kiRJUkdr51J8JwNnR8T3gS2BOZn5CMUGCHcANwNnZOYK\n4GLg5RExHzgWOLuNcUqSJEkbpdal+ADK3us++wxw/yxgVr+y5cBh9UYmSZIkVctNZCRJkqSKmFxL\nkiRJFTG5liRJkipici1JkiRVxORakiRJqkjtq4VIkjYfEbEdcA+wH7AauALopdiD4MTMXBsRxwDH\nlffPyMy5EbEVcDXFhmFLgaMys6eBlyBJjbLnWpIEQESMBS4F/lgWXQhMz8wpwCjg0IjYATgJ2BM4\nADg3IsYBJwD3l+deCUxvd/yS1Ansue5ip8ztvs++8w+e0XQIUie7ALgEOK28vRtwW3k8D9gfWAMs\nyMyVwMqIWATsCuwFnNdy7plDVTZp0taMGbNFddGPcJMnT2g6BEnDYHItSSIi3gf0ZOaNEdGXXI/K\nzN7yeCkwEdgWWNLy0IHK+8oGtXjx8goi7x49PUubDkFSabAvuybXkiSAo4HeiNgXeDXF0I7tWu6f\nADwGPF4eD1beVyZJXccx15IkMnPvzNwnM6cC9wHvBeZFxNTylAOBO4C7gCkRMT4iJgK7UEx2XAAc\n1O9cSeo6JteSpPU5GTg7Ir4PbAnMycxHgJkUyfPNwBmZuQK4GHh5RMwHjgXObihmSWqUw0IkSU9R\n9l732WeA+2cBs/qVLQcOqzcySep89lxLkiRJFTG5liRJkirisBBJkjYD7k0gbR7suZYkSZIqYnIt\nSZIkVcTkWpIkSaqIybUkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIqYnIt\nSZIkVcTkWpIkSaqIybUkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIqMqaO\nJ42IscDlwE7AOGAG8DPgCqAXWAicmJlrI+IY4DhgNTAjM+dGxFbA1cB2wFLgqMzsqSNWSZIkqSp1\n9Vy/B3g0M6cAfw18DrgQmF6WjQIOjYgdgJOAPYEDgHMjYhxwAnB/ee6VwPSa4pQkSZIqU1dyfS1w\nZnk8iqJXejfgtrJsHrAvsAewIDNXZuYSYBGwK7AXcEO/cyVJkqSOVsuwkMxcBhARE4A5FD3PF2Rm\nb3nKUmAisC2wpOWhA5X3lQ1p0qStGTNmi02OXyPX5MkTGqn3iI98pZF6m3TNeX/XdAiSJLVdLck1\nQETsCFwHfCEzr4mI81rungA8BjxeHg9W3lc2pMWLl29q2BrhenqWNh1C1/B3vWGa+uInSapWLcNC\nImJ74Cbg1My8vCy+NyKmlscHAncAdwFTImJ8REwEdqGY7LgAOKjfuZIkSVJHq6vn+nRgEnBmRPSN\nvZ4GzIyILYEHgDmZuSYiZlIkz6OBMzJzRURcDMyOiPnAKuCImuKUJOEqT5JUlbrGXE+jSKb722eA\nc2cBs/qVLQcOqyM2SdKA+lZ5OjIingXcV/5Mz8xbI+ISilWevk+xytPuwHhgfkR8l3WrPJ0VEe+i\nmGsz0OeAJI1obiIjSQJXeZKkStQ2oVGStPloYpUnV3jSUJzoq82RybUkCWj/Kk+u8KShuOqQOtVg\nX/wcFiJJcpUnSaqIPdeSJHCVJ0mqhMm1JMlVniSpIg4LkSRJkipici1JkiRVxORakiRJqojJtSRJ\nklQRk2tJkiSpIibXkiRJUkVMriVJkqSKmFxLkiRJFTG5liRJkipici1JkiRVxORakiRJqojJtSRJ\nklQRk2tJkiSpIibXkiRJUkVMriVJkqSKmFxLkiRJFTG5liRJkipici1JkiRVxORakiRJqojJtSRJ\nklQRk2tJkiSpIibXkiRJUkVMriVJkqSKmFxLkiRJFTG5liRJkioypukAJEmSOsm0869vOoS2u+iU\nQ5oOYcSw51qSJEmqSMf2XEfEaOALwKuAlcAHMnNRs1FJGq5T5k5vOoS2O//gGU2H0CjbbUnq7J7r\ntwLjM/P1wEeBTzccjyRpcLbbkrpeJyfXewE3AGTmncDuzYYjSRqC7bakrjeqt7e36RgGFBGXAd/M\nzHnl7V8BO2fm6mYjkyQNxHZbkjq75/pxYELL7dE20JLU0Wy3JXW9Tk6uFwAHAUTE64D7mw1HkjQE\n221JXa9jVwsBrgP2i4j/AEYB7284HknS4Gy3JXW9jh1zLUmSJG1uOnlYiCRJkrRZMbmWJEmSKmJy\nLUmSJFXE5LoLlVsUS5I2A7bZ0ubFCY1dIiJ2Bi6k2DFtNcUXq/uB/5uZDzYZmyTpqWyzpc1XJy/F\np2pdBpyWmT/oKyjXof0ysGdjUUmSBmKbLW2mTK67x/jWRhogM++MiKbi0QgTEbcA4/oVjwJ6M/MN\nDYQkbc5ss1Ur2+z6mFx3jx9HxOXADcASii2KDwJ+0mhUGkk+CswC3kZxGVvSxrPNVt1ss2vimOsu\nERGjgLcCewHbAo9TbFV8XWb6R6BKRMQpwKLMvK7pWKTNmW222sE2ux4m15IkSVJFXN5HkiRJqojJ\ntSRJklQRk2t1rIj4bkS8reX2BRGxLCK2bCn7n4h40TCfr61joCJi94i4bIDynSLioY18zhdFxJc2\nNTZJalpEnB0RU8rjyyJi94qf/6GI2GlT4hrknD+LiKURcV/5c+NGB6oRx9VC1Mn+HXgD0DfRYl/g\nTooJPjdHxEuAJzLzvxqKb1CZeTfwgYqf9oXAiyt+Tklqwj7ALQCZWXVbuSmejGsQfwVck5nHtSEe\nbWZMrtXJbgb+GSAing+sBK4FDijvmwJ8t9xY4SJgPPB74LjMXBQRtwJ/AF4OHN73pBHxBuAK4KDM\nXFiLGM8AACAASURBVDRQxRGxLfAl4AXA84DbgfeWd3+SdUsXXZqZF0XEq4FLga3LOv8OeAlwVmZO\njYi/LJ8P4Mct9WxfPm5HYC3FphHfi4izgOcDL6VIqC/LzHOAmcDOEfH5zDxxQ36ZktRfREwFzgb+\nRNEO3QXMAP6Foj1dAexP0Ra/GegFrsrMT5WPPYNibeQXA3Molg18a1l2UGb+NiIOLp9zNPCfwHEU\n7fjuwGXlFcrPUrSXt0bE6cB7gDXATcBHytiuAxYCfwn8FjgsM/8wjNe4vvb8+cBXgGdQtL8nAS9r\njSsz71/P0/4V8IqIuJtiJZdpwE/L17d/Zj4YEc8Afk7Rjk8FPg6MBf4LOCYzH42Iw4CTga3Knw9k\n5u39Pr/eU8b2irLuL2TmrKFet5rjsBB1snuAF0fEeIrG/aby54Dy/r2BW4GvAR/MzFcBlwBfbXmO\nn2RmZOZ9AGUS/CXgLetLrEt/A9yXma+naBhfD7wGeAfF7mivBPYA3h8RO1A00P+Uma8s45nW7/mu\nBD6Sma+haHz7XARcnpm7AYcAl0bEhPK+XcvX/VrgoxHxTIoG9m4Ta0kV2gM4Efhzik6KvwECeE9m\n7gscT5Hc7lqe+7cR8TflY18LvJ8iCTwB6MnM3SnW435XRGxH0YHw1szclWI5wc9l5pXA3RTJ5JMJ\nbEQcRNEW7kaRRL+krB/gVcCFmfkK4DGKTozhWF97/vfA3DLejwB7rS+uAawAriwfewHwbYoOy9kU\nyTDA3wJzKdYo/yRwQGb+JXAj8KmIGF2+toPLz69PAqe01PGTzAyKTptnlY/dF3fo7Hgm1+pYmbmG\nYhjI7hQJ9U3lEJCtI2ISRQOZwOLM/GH5mGuBl0TExPJpftDvaW8AvpeZOUTdX6XoFf8wRY/Ks4Ft\nKC4XfiMzV2bmssx8NUUP9nMzc2752Isz88kGMiKeAzwvM79XFl3RUtW+wMcj4j5gHkWvRt+wj1sy\nc1Vm/o6iB2MiklS927PQC1wFvAn4XWY+VN7/JuCKzFyTmcspOhPeXN63MDN/XZb/nmI4H8DDwCSK\nZPyuluf6YstjB/Im4KuZ+cfMXA1c3nL+7zLz3r56gWcN58UN0p5/D/jHiLiGohf7c8N5vvI5z8rM\nS8vj7wDLgF0otqc/ojztKIr2/rXAnwG3lG39B4GXZuZaiqugB0TEx4H3lXH16fv8WghEOa77PcCp\nw41TzTC5Vqf7d4pv6XsA3y/LvgccCjxKcYmyv1HAFuXxH/vddwTw9oh41WCVRsSHgPOBHorG+Gfl\n8/6p33k7DVA2PiJ2binqLR/bp3UnrC2AN2Xmq8tE/XVAX2/JikGeQ5Kq0tomjS5v/7FfWatRrBtW\numqQ5xrqsQMZ7PyNahPX155n5gLgLyh6kg8H/nU4z9f3nBHx7H5x/qn8EvFwRLwd2L7cwn4LYH5L\nO/9XwDsiYhvgh8CLKIaqzOz3mv4IkJmPUlwZ+CzFFYUflVcy1aFMrtXpbqYYG3d/2YsB8F2KMWrf\npei5fnZE/BVARLwTeHh94/Ay82bgNGBWeUluffajGE/9FYpG/NUUDeTtFMn52IjYmqInfHvg1xGx\nX/nYIynG1vXV+ShFY9t3GbWvV6Pv9f2fMva/oLiUuvUgca3GuRKSqrVXRDy/bBPfS3EVrdXNwFER\nsUXZ7v0dQ0/46/MD4HUtq3Yc2/LYgdqzm4F3R8RWETGGYsjJcOtanwHb84g4DzgyM2dT9Ca/ZpC4\n+tuHYlgJEbEPxefDz8v7LqdIlK8qb/8AeH1EvKy8fSZFsv8yirHen6B43QeyrmPoSRFxCHA18G8U\nQwOXUQzTUYcyuVZHy8yFFJfwbmopvplibOBNmbmSosfhcxGxkKKBPPxpT/TU57wSeAL40CCn/TPw\nsYj4EfAF4D+AF5VbxC4AfkTR43BRZj5IcanuY+Ulv8N56rg5Wu6/l6eu9vEhig+enwBfp2jolw4S\n1wPAMyPiqkHOkaQN8T8U80J+Bvw3xdXBVpcCv6GYjH0vcP1wt8vOzN9SJNTXRcRPKSb29Y2hvgG4\npJxk3nf+XIpxyndTTBB8mKLHdlMM2J6Xz/u3Zbt9HcWY8QHjGsA0YL/yc+cC4N3lMA+Ab1F8bl1V\nvqZHgKOBb0TE/RRJ/MkUv8/7KJLyH1EkzS8coK55FL3YP6WYcPqtIcaDq2Fufy5JUpcqV/w4KzOn\nNhzKiBARoyh6oI/PzEOajkfN8PKyulZEHE4xRORpynFxkqQOFhG3UEyc7O+SzLxkE5/7fIohJf3d\nPci63J8B3kKRYKtL2XMtSZIkVcQx15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIqYnItSZIkVcTk\nWpIkSaqIybUkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIqYnItSZIkVcTk\nWpIkSaqIybUkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIqYnItSZIkVcTk\nWpIkSaqIybUkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIqYnItSZIkVcTk\nWpIkSaqIybUkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIqYnItSZIkVcTk\nWpIkSaqIybUkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIqYnItSZIkVcTk\nWpIkSaqIybUkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIqYnItSZIkVcTk\nWpIkSaqIybUkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIqYnItSZIkVcTk\nWpIkSaqIybUkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIqYnItSZIkVcTk\nWpIkSaqIybUkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkioxpOoAq9fQs7W06BknaGJMnTxjVdAzt\nZpstaXM1WJttz7UkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIqYnItSZIk\nVcTkWpIkSaqIybUkSZJUkRG1Q2Oraedfv0HnX3TKITVFImlz8Z3v/CuPPvooRx75vqfd941vfJUb\nbpjLtGn/yCOP/C8HHHBQ+wPsUhvanjehGz9DOv196cb3pNt0apttz7UkDcP8+bfxyU9eyHbbbc+N\nN85rOhxJ0iCabLNHbM+1JG2cXv75n8/nF794EICTTjqZhx9+iAcf/Dn/7/+dxgtfuBMPPPBTrrtu\nDm972zsajrVaEfEj4PHy5n8B5wBXAL3AQuDEzFwbEccAxwGrgRmZOTcitgKuBrYDlgJHZWZPm1+C\npK7TeW22ybUktVizZg2rV6/m85+fRU/P7zjrrDP4/Odncf313+Lssz/BqlWr6OnpGYmJ9XhgVGZO\nbSm7HpiembdGxCXAoRHxfeAkYHdgPDA/Ir4LnADcn5lnRcS7gOnAtHa/DkndpRPbbJNrSWrR29vL\nvffewwc/eCwAS5YsaTiitnkVsHVE3ETx2XA6sBtwW3n/PGB/YA2wIDNXAisjYhGwK7AXcF7LuWe2\nMXZJXaoT2+zakuuI2AKYBQTFJcXjgRV4iVFSBxszZixTpkzl+OM/yBNPLGPOnK8/5f5Ro0bR27u2\noehqtRy4ALgMeClFgjwqM3vL+5cCE4FtgdZPr4HK+8oGNWnS1owZs0UlwTdt8uQJTYegfnxPRr4J\nE8azcuU2HHDA/px88sksW7aMq666ismTJ7DllmN49rO3YdWqVYwdO7qtfw919ly/BSAz94yIqRRj\n90bhJUZJHa2XZcuW8cEPHssTTyzjyCOPfsq9kyZNYvHixXz1q1fz7ne/p6EYa/EgsKhMph+MiEcp\neq77TAAeoxiTPWGI8r6yQS1evLyCsDtDT8/SpkNQP74nI9/SpStYtmwFv/vdHzj88Hc/2Wb39Cxl\n1arVPProMrbZZht++9seZs68uNI2e7BkvbbkOjO/HRFzy5svpGho98VLjJI61EEHvWW9933uc198\n8viKK65pRzjtdjTwSuD/RMTzKHqib4qIqZl5K3AgcAtwF3BOOUZ7HLALxZXIBcBB5f0HAne0/RVI\n6iqd2mbXOuY6M1dHxGzgbcA7gP069RKjl48kdbkvAVdExHyKoXtHA78HZkXElsADwJzMXBMRMymS\n59HAGZm5IiIuBmaXj18FHNHIq5CkhtU+oTEzj4qIU4EfAFu13NVRlxi9fCSpSU1/wc/M9SXE+wxw\n7iyKOTWtZcuBw+qJTpI2H7VtIhMRR0bEaeXN5cBa4O5y/DWsu2x4FzAlIsZHxESefomx9VxJkiSp\nY9XZc/0t4MsRcTswFvgwxWVFLzFKkiRpRKpzQuMTwDsHuMtLjJIkSRqR3ERG0og17fzrK32+i045\npNLnkyQ91Uhot02uJalCa9eu5dOf/iSLFv2CsWPH8tGPnskLXrBj02FJkgZQR5td24RGSepGd9xx\nK6tWreLSS7/M8cd/iM997jNNhyRJWo862myTa0mq0E9+ch+vfe3rAXjFK17Jz3/+QMMRSZLWp442\n2+Rakir0xBNP8IxnbPPk7dGjR7N69eoGI5IkrU8dbbbJtSRV6BnPeAbLl6/b0Kq3t5cxY5zeIkmd\nqI422+Rakir0yle+ijvvXADAwoX3s/POL2k4IknS+tTRZtudImnEamIJpr33fiM//OEPOP74o+nt\n7eX00z/W9hgkaXPV7na7jjbb5FqSKjR69GhOOeX0psOQJA1DHW22w0IkSZKkiphcS5IkSRUxuZYk\nSZIqYnItSZIkVcTkWpIkSaqIq4VIGrFOmTu90uc7/+AZlT6fJOmpRkK7bc+1JNXgpz9dyAc/eGzT\nYUiShqHKNtuea0mq2Fe+Mpsbb/wO48dv1XQokqQhVN1m23MtSRV7/vNfwDnnnN90GJKkYai6zTa5\nlqSKTZ36ZsaM8cKgJG0Oqm6zTa4lSZKkiphcS5IkSRXxuqWkEcul8yRp8zIS2m17riWpBs997vP4\n4hevaDoMSdIwVNlm19JzHRFjgcuBnYBxwAzg18Bc4BflaRdn5tcj4hjgOGA1MCMz50bEVsDVwHbA\nUuCozOypI1ZJkiSpKnUNC3kP8GhmHhkRzwLuAz4OXJiZn+47KSJ2AE4CdgfGA/Mj4rvACcD9mXlW\nRLwLmA5MqylWSZIkqRJ1JdfXAnPK41EUvdK7ARERh1L0Xn8Y2ANYkJkrgZURsQjYFdgLOK98/Dzg\nzJrilCRJkipTS3KdmcsAImICRZI9nWJ4yGWZeU9EnAF8jKJHe0nLQ5cCE4FtW8r7yoY0adLWjBmz\nxUbFPHnyhI16nCRJktSnttVCImJH4DrgC5l5TUQ8MzMfK+++DvgscDvQmtVOAB4DHm8p7ysb0uLF\nyzc63p6epRv9WEnaVJ3yBT8itgPuAfajuOp4BdALLAROzMy1zpWRpPWrZbWQiNgeuAk4NTMvL4tv\njIg9yuM3UzTedwFTImJ8REwEdqFowBcAB5XnHgjcUUeckqR1ysnolwJ/LIsuBKZn5hSKIX6HtsyV\n2RM4ADg3Isaxbq7MFOBKiiuWktR16uq5Ph2YBJwZEX3jpf8B+ExE/Al4BDg2Mx+PiJkUyfNo4IzM\nXBERFwOzI2I+sAo4oqY4JUnrXABcApxW3t4NuK08ngfsD6zBuTKStF51jbmexsCre+w5wLmzgFn9\nypYDh9URmyTp6SLifUBPZt4YEX3J9ajM7C2PB5oTs77yYc2V2ZR5Mp2mU4b1aB3fEzXFHRolSQBH\nA70RsS/waoqhHdu13D/QnJj1lQ9rrsymzJPpNM7b6Ty+J6rTYF/e3KFRkkRm7p2Z+2TmVIqVnN4L\nzIuIqeUpffNfnCsjSYMwuZYkrc/JwNkR8X1gS2BOZj4C9M2VuZlyrgxwMfDycq7MscDZDcUsSY1y\nWIgk6SnK3us++wxwv3NlJGk97LmWJEmSKmJyLUmSJFXE5FqSJEmqiMm1JEmSVBGTa0mSJKkiJteS\nJElSRUyuJUmSpIqYXEuSJEkVMbmWJEmSKmJyLUmSJFXE5FqSJEmqiMm1JEmSVBGTa0mSJKkiJteS\nJElSRUyuJUmSpIqYXEuSJEkVMbmWJEmSKmJyLUmSJFXE5FqSJEmqyJg6njQixgKXAzsB44AZwM+A\nK4BeYCFwYmaujYhjgOOA1cCMzJwbEVsBVwPbAUuBozKzp45YJUmSpKrUklwD7wEezcwjI+JZwH3l\nz/TMvDUiLgEOjYjvAycBuwPjgfkR8V3gBOD+zDwrIt4FTAem1RSrNtG086/foPMvOuWQmiKRJElq\nVl3DQq4FziyPR1H0Su8G3FaWzQP2BfYAFmTmysxcAiwCdgX2Am7od64kSZLU0Wrpuc7MZQARMQGY\nQ9HzfEFm9panLAUmAtsCS1oeOlB5X9mQJk3amjFjttiomCdPnrBRj9OG83ctSZJGqrqGhRAROwLX\nAV/IzGsi4ryWuycAjwGPl8eDlfeVDWnx4uUbHW9Pz9KNfqw2jL9r6en80ilJI0Mtw0IiYnvgJuDU\nzLy8LL43IqaWxwcCdwB3AVMiYnxETAR2oZjsuAA4qN+5kiRJUkerq+f6dGAScGZE9I29ngbMjIgt\ngQeAOZm5JiJmUiTPo4EzMnNFRFwMzI6I+cAq4Iia4pQkSZIqU9eY62kMvLrHPgOcOwuY1a9sOXBY\nHbFJkiRJdXETGUmSJKkiJteSJElSRUyuJUmSpIrUthSfJGnzERFbUMx/CaAXOB5YAVxR3l4InJiZ\nayPiGOA4ig3CZmTm3IjYCrga2I5if4KjMrOn7S9Ekhpmz7UkCeAtAJm5J8XGX+cAFwLTM3MKxW67\nh0bEDsBJwJ7AAcC5ETEOOAG4vzz3yvI5JKnrmFxLksjMbwPHljdfSLF5127AbWXZPGBfYA9gQWau\nzMwlwCJgV2Av4IZ+50pS1xnWsJCI+Gxmfqhf2ezMPKqesCRJG2tj2+zMXB0Rs4G3Ae8A9svM3vLu\npcBEYFtgScvDBirvKxvUpElbM2bMFsN4RZ3PHTY7j++JmjJoch0RlwE7A7tHxMtb7hrLMBpOSVL7\nVNFmZ+ZREXEq8ANgq5a7JlD0Zj9eHg9W3lc2qMWLlw8npM1CT8/SpkNQP74nqtNgX96G6rmeAewE\nXASc3VK+mmKXRUlS59joNjsijgRekJnnAsuBtcDdETE1M28FDgRuAe4CzomI8cA4YBeKyY4LgIPK\n+w+k2HlXkrrOoMl1Zj4EPAS8KiK2pej5GFXevQ3whzqDkyQN3ya22d8CvhwRt1P0dH+YIiGfFRFb\nlsdzMnNNRMykSJ5HA2dk5oqIuBiYHRHzgVXAEVW/PknaHAx3zPVpwGnAoy3FvRSXHyVJHWRj2uzM\nfAJ45wB37TPAubMolu1rLVsOHLYx8UrSSDLcda4/ALzYNUslabNgmy1JDRnuUny/wiEgkrS5sM2W\npIYMt+f6F8D8iLiFYscuADLz47VEJUnaFLbZktSQ4SbX/13+wLrJMZKkzmSbLUkNGVZynZlnD32W\n1F7Tzr9+gx9z0SmH1BCJ1FlssyWpOcNdLWQtxUzzVv+TmTtWH5IkaVPYZktSc4bbc/3kxMeIGAu8\nFXh9XUFJkjaebbYkNWe4q4U8KTP/lJnXAm+qIR5JUoVssyWpvYY7LOS9LTdHAS+n2IFLktRhbLMl\nqTnDXS3kjS3HvcDvgcOrD0eSVAHbbElqyHDHXL+/HLcX5WMWZubqWiOTJG0U22xJas6wxlxHxG4U\nmxLMBr4M/CoiXltnYJKkjWObLUnNGe6wkJnA4Zn5A4CIeB3wWWCPwR5UNuafysypEfGXwFyKBh/g\n4sz8ekQcAxwHrAZmZObciNgKuBrYDlgKHJWZPRv42iSpW21Umy1J2nTDXS1km75GGiAz7wTGD/aA\niPgIcFnLebsBF2bm1PLn6xGxA3ASsCdwAHBuRIwDTgDuz8wpwJXA9A15UZLU5Ta4zZYkVWO4yfUf\nIuLQvhsR8Vbg0SEe80vg7S23dwP+JiJuj4gvRcQEil6UBZm5MjOXAIuAXYG9gBvKx80D9h1mnJKk\njWuzJUkVGO6wkGOBuRHxJYplnXqBNwz2gMz8ZkTs1FJ0F3BZZt4TEWcAHwPuA5a0nLMUmAhs21Le\nVzakSZO2ZsyYLYZz6tNMnjxhox6nDdfk79r3WV1ig9tsSVI1hptcHwgsB14DvBj4OjAVeHAD6rou\nMx/rO6YY/3c70JrtTAAeAx5vKe8rG9Lixcs3IJyn6ulZutGP1YZp8nft+6xOVfEXvyrabEnSRhju\nsJBjgT0z84nM/AnFEI8PbWBdN0ZE32SaNwP3UPRmT4mI8RExEdgFWAgsAA4qzz0QuGMD65KkblZF\nmy1J2gjD7bkey1N391pFcZlxQ5wAfDYi/gQ8AhybmY9HxEyK5Hk0cEZmroiIi4HZETG/rOuIDaxL\nkrpZFW22JGkjDDe5/jZwc0R8o7z9duBfhnpQZj4EvK48/hHFqiD9z5kFzOpXthw4bJixSZKeaqPa\nbEnSphvWsJDMPJVi3dQAdgZmZuaZdQYmSdo4ttmS1Jzh9lyTmXOAOTXGIkmqiG22JDVjuBMaJUmS\nJA3B5FqSJEmqiMm1JEmSVBGTa0mSJKkiw57QKEmSBnbK3OlNhzCo8w+e0XQIUtew51qSJEmqiMm1\nJEmSVBGTa0mSJKkijrmWJBERY4HLgZ2AccAM4GfAFUAvsBA4MTPXRsQxwHHAamBGZs6NiK2Aq4Ht\ngKXAUZnZ0+7XIUlNs+dakgTwHuDRzJwC/DXwOeBCYHpZNgo4NCJ2AE4C9gQOAM6NiHHACcD95blX\nAp09w0+SamLPtSQJ4FrWbZc+iqJXejfgtrJsHrA/sAZYkJkrgZURsQjYFdgLOK/l3DOHqnDSpK0Z\nM2aLyl6A1m/y5AlNh9B23fia1RlMriVJZOYygIiYQJFkTwcuyMze8pSlwERgW2BJy0MHKu8rG9Ti\nxcsriV1D6+lZ2nQIbdeNr1ntM9iXN4eFSJIAiIgdgVuAqzLzGmBty90TgMeAx8vjwcr7yiSp65hc\nS5KIiO2Bm4BTM/PysvjeiJhaHh8I3AHcBUyJiPERMRHYhWKy4wLgoH7nSlLXcViIJAngdGAScGZE\n9I2XngbMjIgtgQeAOZm5JiJmUiTPo4EzMnNFRFwMzI6I+cAq4Ij2vwRJap7JtSSJzJxGkUz3t88A\n584CZvUrWw4cVk90krT5cFiIJEmSVBGTa0mSJKkiJteSJElSRUyuJUmSpIqYXEuSJEkVqXW1kIh4\nLfCpzJwaES8BrgB6KdZEPTEz10bEMcBxFFvtzsjMuRGxFXA1sB3FTl9HZWZPnbFKkiRJm6q2nuuI\n+AhwGTC+LLoQmJ6ZU4BRwKERsQNwErAncABwbkSMA04A7i/PvZJiG15JkiSpo9XZc/1L4O3AVeXt\n3YDbyuN5wP7AGmBBZq4EVkbEImBXYC/gvJZz+zY0qM0pczcsfz//4Bk1RSJJkqTNVW3JdWZ+MyJ2\naikalZm95fFSYCKwLbCk5ZyByvvKhjRp0taMGbPFpoQ9bJMnT2hLPSNRk7873zdJklSndu7QuLbl\neALwGPB4eTxYeV/ZkBYvXr7pUQ5TT8/SttU10jT5u/N9U6fyi58kjQztXC3k3oiYWh4fCNwB3AVM\niYjxETER2IVisuMC4KB+50qSJEkdrZ091ycDsyJiS+ABYE5mromImRTJ82jgjMxcEREXA7MjYj6w\nCjiijXFKHWva+ddv8GMuOuWQGiKRJEkDqTW5zsyHgNeVxw8C+wxwzixgVr+y5cBhdcYmSZIkVc1N\nZCRJkqSKmFxLkiRJFTG5liRJkipici1JkiRVxORakiRJqojJtSRJklQRk2tJkiSpIu3cREaSJKkt\nTpk7vekQhnT+wTOaDkE1sOdakiRJqojJtSRJklQRk2tJkiSpIibXkiRJUkVMriVJkqSKuFqIpGGZ\ndv71G3T+RaccUlMkkqSBbGg73YRu+GwwuVbbbejySC5VJEmSNhcOC5EkSZIqYs+1pI7W5HCUbhwK\nExGvBT6VmVMj4iXAFUAvsBA4MTPXRsQxwHHAamBGZs6NiK2Aq4HtgKXAUZnZ08iLkKQG2XMtSQIg\nIj4CXAaML4suBKZn5hRgFHBoROwAnATsCRwAnBsR44ATgPvLc68EOn97PEmqgcm1JKnPL4G3t9ze\nDbitPJ4H7AvsASzIzJWZuQRYBOwK7AXc0O9cSeo6DguRJAGQmd+MiJ1aikZlZm95vBSYCGwLLGk5\nZ6DyvrJBTZq0NWPGbLGpYWsYJk+e0HQIGkA3vi/d8JpNriVJ67O25XgC8BjweHk8WHlf2aAWL15e\nTZQaUk/P0qZD0AC68X0ZKa95sC8JDguRJK3PvRExtTw+ELgDuAuYEhHjI2IisAvFZMcFwEH9zpWk\nrmPPdcW6cXWBzYlrbEsb5GRgVkRsCTwAzMnMNRExkyJ5Hg2ckZkrIuJiYHZEzAdWAUc0FrUkNajt\nyXVE/Iji8iHAfwHnMMylntodq7Q+fonSSJWZDwGvK48fBPYZ4JxZwKx+ZcuBw9oQoiR1tLYm1xEx\nnmKCzNSWsusplnq6NSIuoVjq6fsUSz3tTrEk1PyI+G5mrmxnvJsbEz5JkqRmtbvn+lXA1hFxU1n3\n6Tx9qaf9gTWUSz0BKyOib6mnH7Y5XkmSJGnY2p1cLwcuoNik4KUUyfSGLPU0qHYu61TVUjJNLkmz\nuSyHMxJ+RyPhNVhvZ9ctSeoM7U6uHwQWlcn0gxHxKEXPdZ+hlnoaVDuXdapqKZkml6TZXJbDGQm/\no5HwGqy33rpNzCVpZGj3UnxHA58GiIjnUfRQ37QBSz1JkiRJHavdPddfAq4ol2rqpUi2f88wTXPa\n7gAAGdZJREFUl3pqc6ySJEnSBmlrcp2Z61v7dFhLPUmSJEmdzB0aJUmSpIqYXEuSJEkVMbmWJEmS\nKmJyLUmSJFXE5FqSJEmqiMm1JEmSVBGTa0mSJKkiJteSJElSRUyuJUmSpIq0e/tzSW12ytzpG3T+\n+QfPqCkSSZJGPnuuJUmSpIqYXEuSJEkVMbmWJEmSKuKY64Zt6HhYqG5MrGNxNRI1+X9KkiSTa0m1\n8MubJKkbOSxEkiRJqojJtSRJklQRk2tJkiSpIo65lqSKOM5ckmTPtSRJklQRe66lNrBHU5Kk7mDP\ntSRJklQRk2tJkiSpIh07LCQiRgNfAF4FrAQ+kJmLmo1KkrQ+ttuShrIxu+i2UxXDMju55/qtwPjM\nfD3wUeDTDccjSRqc7bakrtfJyfVewA0AmXknsHuz4UiShmC7Lanrjert7W06hgFFxGXANzNzXnn7\nV8DOmbm62cgkSQOx3Zakzu65fhyY0HJ7tA20JHU0221JXa+Tk+sFwEEAEfE64P5mw5EkDcF2W1LX\n69jVQoDrgP0i4j+AUcD7G45HkjQ4221JXa9jx1xLkiRJm5tOHhYiSZIkbVZMriVJkqSKmFxLkiRJ\nFTG5liRJkirSyauFtFVE7JiZv25TXXv3K/oT8OvM/E0b6r4mM4+ou55OEhEHZ+bcltvvzMxvtKHe\nD2TmZS23T8rMmTXX+f7M/HKddQxRf2N/25IEEBHbAuP7bmfm7xoMR0BEbAH8JbB1X1lm3t5cRPXq\n6uQ6Ik4BHgOeCbw/Im7IzH9oQ9UzgB2Aeyj+2FYB4yNiVmaeX3Pd4yJiV+BBYC1AZq6qq7KI+F+g\nl2JZrla9mfm8uuot6z4Y2BN4d0S8oSzeAjgEqC25joh3l3W8MSLe1FLvK4Bak2vgSKCx5JoG/7Yj\n4vJ+RX8Cfg18PjMX11TnrsA7gOcAvwGuzcxf1FGXOk9E3APcSLEr5T1NxyOIiCuBvSg+20dRfP68\nptGgBDCHItd6pLzdC5hcj1B/C+wN3JCZfxERN7ep3uXArpm5IiLGAd8E3k7xh1Z3cv0y4Nstt3uB\nF9dVWWY+t67nHoYfA88G/ghkWbYW+GrN9d4A/G9Z96Ut9f6y5noBto6Il/L0LzNk5oNtqL/Jv+2t\nKH7HdwCvA/4K+B0wm+LLTqUi4jDgVIr3+G7ghcA3I+LMzPyXqutTR3o98GbgAxHxWeDONnXQaP0i\nM3duOgg9zXMyc0rTQbRLtyfXayh62X5b3t56kHOrNDkzVwBk5sqIeE5mroqIdoyBnwl8mHWv9WlJ\nWB3K3dreD4wt63xeZh5QZ53lMJ/ZEfGqzJxdZ1396l0M3ArcGhHbse7yZDv+vwVFsve0KwXAm55+\neuWa/NuenJnvLo9vjIibMvPMiKird2QasE9mPtFXEBGzgX8pfzTyPaP82QIYB2zfbDgC7oqIyMwc\n+lS10cPtHH7btG5Prm8tf94TEZ8B/q1N9X47IuYDd1H0rl0fEScAC9tQ9/EU2xM/MtSJFbsYOI/i\nEvr9wJZtrHuXiHhmZj7WxjqJiM8DfwP8D+suT75h0Adtuvsysx1J9Po0+be9bUT8eWb+PCL+HJgQ\nEc8GtqmpvtWtiTVAZj4eEWtqqk+dp4eiPTsjM49tOhgBsAT4YUQso2x36x6CqPVrGRo6HnhnRPyh\nvD2i35euTq4z8wzgjIh4FnBqnWOP+9X7TxHxL8AuwOWZuTAiJgOXtKH632fmw22oZ6B6vxoR+2fm\nWRFxWxvr/gvg0Yj4PcXwjHb9p34tsHNmrm1DXX0a3XK14b/tDwJfiYjnAb8CTgQOB86pqb71va+u\nwtQ9dgQOoOig+TBwT2ae1nBM3e5NwLMyc3XTgajxoaGN6erkulzZ4AsUl/SujYiHM/NLbah3R+Cv\nKb7JRUS8PTM/XnOdnygPt4yIG4EfUSZimXl6nXWX1kbEyynGBAfwrDbUCUBmvrBddfWziOI9Xt7G\nOv8sIv6jX1lf703dveaN/G33ycy7gN36Fd9dY5Uvj4hr+pWNovgyp+7wW4r/5y+jGHO/U6PRCIrJ\n+tsD/910IFpngDltfRPOZ2TmQ+2PqF5dnVxTrGywN8Wkq08AC4Dak2vgWuB7FH9Y7ZL9/m23f6BI\nOmYC19Ce3zMAEfFK4HLgBRTDYY7OzHvbUPWfUYwzW1TebkeC++aan38oTfxtAxAR7wU+ylOX4Kpz\nYtM711Pejl56dYYEbgO+BZzVrqufGtRewEPllUoY4cMPNiMPU+RYd1BMBH4L8H2KXKDpz63KdXty\nvTYz/xARveXqBkvbVO/SzJzeproAaOeEvvV4BHhuZi6IiC8DV7ex7pnABzLzxxHxauDzFEv01e3d\nQ59SrYaG/LRq+992i1MpVgVpV2Lf6BAcdYQADgReTjFZ24msDcvMlzQdgwb0Z5n5/vI4I+LvMvNL\nZafIiNPtyfWiiDgXeHZEfJTim1U7LIyIdwH3sm5oRjuWSWvS14CLyuM/UCTXB7ep7lGZ+WOAzLwv\nIto1Fm818ClgO4oe3Z/Qvr+xpjT5t/2fmblo6NMqc0L574spJuj+kGJt72XA1DbGoeacA7wUmA8c\nFRF7Z+bJDcfU1crOm6d88c3MoxsKR+tsGREHUPRWvwEYGxE7075V2tqq25Pr44EPUDSMy4Bj2lTv\nq8ufPu1aJq1Jz+jbJTEzr4mIdv2uAdaUG8rcQTEMaGWb6v0i8GngTIp1nmdTrL88kjX5t708IuYB\n99GG+QR9y/5FxL8Bh2bm6nIXsnatOqTm7Z2ZewJExEXAnQ3Ho6IjB4r5D68BHBLSGd5HsdfBP1Os\nsHM0xefhiFwXviuT64jYv+Xmf5Y/UPQ23VR3/Zn5xrrr6ECrImI/ig+fPSjWGG+Xo4ELgHOBB2jf\nl6itMvPmiJiemRkRK9pUb2Ma/tv+TkP1ts6GH0NxpULdYWxEjC5XBBqNQ4Ual5k3tty8ISJq/0zX\n+kXEmHLlll8D72LdsrRkZv8J4SNGVybXrH8sbC81JtcRMScz39Gy7iN0zzqcH6BIcGcCPwOOa1fF\nmflwuVpKAD9t47jkFeVlsC3KTXRGfHIdETOAv6clyWjDNve7Z+bdFLtiNuFLwE8jYiHF2NtPNRSH\n2u9rwIKIuJNi6c2vDXG+atav8+y5uLFP064EjqCY/Nv65bPW3aGb1pXJdcug+qeIiFrXY8zMd5SH\ne7TuUlRueDEitXxr/RXF6gpPfmttYwwzgDdSbGxyUkRcl5l1b8UNcCzFF4rnAP/IujG6I9nBwE6Z\n2a6hN1DMNL+bp39prvXLcp/M/HxEXEvxQfGLzPz9UI/R5q2cq9PXjv03xcoH9+FVi07Q2g6soLhy\nqYZk5hHl4SdoYHfopnRlct0nIj5OkfBsSfGGP0jR81RXfa8Ang98KiJOofjjGg18kqeOUx1JBvrW\n2pdg17lMWqu/pvhCs7YcE/t9irFftcrM30TEsbQsDdcF7qV4vW1LrjOzr6d4QWZe1lceESe1o/5y\nBZon3+eIcALVyPfzluME/rWpQFRo6chp21VRbZCmdoduRFcn1xTLdr0A+AxwIcWGMnWaRDHmaHuK\nhBOKXd7qrrcxfd9aM/NFDYbxG2ACxba4Yyk2fqhdRFxJseTfEtZ9oXhNO+pu0ELgfyPiEdYNear1\nS1REvJvi//IbI6Jv8uRo4JUUw5DqdgXwORpY21vN6IClTfV0ndCRo/VranfoRnR7cv2/mbkyIiZk\n5qKI2LLOyjLzDuCOiHhNZv6ozro6TTmZ8f/y1A0+2rWKxPOAByPixxQb2azq28Ww5k1dIjNH7Jiy\n9TgceBHwWBvrvIFivPWzgUvLsrXAL9tU/yOtPeaS2q9DOnLUTwfsDt2Ibk+ufxMRRwNPlGPontmm\nep8dEd+hmUSzKZ+hGG/VRO/eYeW/vbR3nNddERGZ2dSumE14GHiinWOuM3MxcCtwa0S8mWLs850U\n66m3w0PlOvmta3u7QoHURhFxCwPP5+nNzBG3A+BmpOndoRvR7cn1PwHbUGz+8GPas2sfNJtoNuVX\nmfm9huoeRzHG+mXAT4GT23R5agnww4hYRvesCrMj8MuI6Fvesh1bvgNP9pC8ANiFYsz3abRnl8xx\nFCvRRHm7LRMpJT3F8eW/HwO+TbHV9h60b7MyDaBbh1B1e3J9FXAWcCLFWK1PUKwqUbcmE82m/C4i\nLuGpvXtfbFPdVwJnA/8B7EUxRrYd7/ObgGeVk2y6xeEDFUbEazPzBzXXvVdm7h0Rt2Tm7Ihoy+os\n/VcfqnvVIUlP13eFMCK2z8xvlMXXRcSHGgxLXarbk+u1FDvnnZGZX2vjroFNJppN+a/y3x3Kf9u5\nHN8TmTmvPP63iGjXjlAPUkxe/e821de4Qa4InEv9OzWOiYjxQG+5KkxbNipq96pDkgYXEX9PsfTq\nG4BVDYejLtTtyfVY4Dzg9oh4I8WHYzv0TzS7wTkUyw1uPdSJNfh1REwHbgZ2A1b2bTRQ89jYPSnG\n4z5K8WWiG4aFrE87xrp/BrgHmAz8oLzdDu1edUjS+v0dcAbFXJuflbeltur25Pr9wP9v7/6D9KzK\nM45/A1TAgHFqaEwCBRG4EAGhoo6tQKyitILVOiJaprWQFqWFqoy1UBlRClXSQX78Qca0xLYmFFAg\n4UcQECFTKlIoUGDkAqSk6EJCBEQhMUq2f5xnzc5K6B/sc87yvtdnZifPbt53z/1Osrv3nufc930I\nZcLaHwB/UmndxZXWmUouAWawqQ3eKOWuQQ1jk6DGOnesppzF7fVsrO3d+/rcL0G936mwfYmk64Hd\nKL/AVtm5pnLXoYjYPNuPSVpGab93C/BM45BiCA11cm37AeCB7t2LX+ixk+wiSrKxBaVt2QOUs8CD\nbKbtA1ssvLmJnH2T9HpgIaW/+deAe2xf2SKWYSDpPNvHU4pI3w2cRyli7dvErkMzKqwZEc+jYWFz\nxC8NdXLdiu23jl1LeiUw6OetAVZJ2mn82PdaJD3KpjZ8vw48ZPt1FZY+l3J3ZBHl7sgKYFiT6xrH\nQp6W9EVKB6C9gd/rczFJW1GOhCyl9NS+hNLL/f4+142IF9SksDlivC1aBxD8mAGeHiXpUUkjlLGn\n3+/eH/tYFbZn255jezZlJ/OWims/SDlr/Tjwk1rrtiLpsAnvH9FdLu17bdt/C2wJ7GZ7nu2+h8gs\nAT4AfJaSZB8EHAfc2fO6EbF5TQqbI8bLznUDkr7Dpp3UHYDr2kbUny6hBUDSdNvPSJpju1pyPSGe\nVZL2rLTcE5KOBaZLOpK6Uwur6pLq3wE+LGmsr/WWlKTzYtuLelx77M4ElK+pWWO/vPVcQPpa2wd0\nZ6xvp9yCfrvt7/W4ZkS8sLOA29hU2HxW23BiGCW5buPIcdfrba/e7CMHhKTPUYZtnAycI+k221+q\ntPaFbEq+ZgOP1VgXOIbyetcCB3TvD6q7KOPH1wH3UZLcjcCFfS889gucpN+1fUPf643zdLf+Bklb\nAO+yXWsqZEQ8vw2UY1qrKd/3j6LCnbOI8XIspI3ngH8ArgYWStqlbThVvNf2yQC2P0jZ0azlKsq0\nrpso32xPr7TufpR/4y8BywFJ2rHS2lXZfqSbxHUwMNJdvwJ4uGIYp1Zca6LVSawjpoQFlN3qTwEn\nUgoaI6rKznUbi4DzKa3o5lGK3d7RMqAKNkp6WbfL92vU/cXuz9g0ifMrlG++NSY0/h2ll/ntwP6U\nHZVtJC2yvaDC+i0sAc7prp+kdEmpNX54VNJlgCm75oz9QteT10taStmlH7umW/cjPa4bEZt3r+2b\nWgcRwy3JdRvb2F7eXV8u6ZNNo6ljIXCPpLuBPSm7ubW0msT5LLCv7fWStga+AfxhF8ugJtfTx9oN\n2l4qaX7FtS+ouBbAEeOuF1ZeOyKe37KurumXtQ+2j24YTwyhJNdtbCVpH9t3S9qndTA12P4nScsp\nnVG+b3ttxeVbTeLcwfZ6gG7IyMxx53MH1QZJh1A6sryZbge5kiXAmyj/3tOAXqdhZncsYko6gfL9\nfmALyGPqS3LdxgnABZJmAyPAnzeOp3cTB6pIqjlQpdUkzssl/TtwKyXpW971XL2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2055d503780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.subplots(figsize=(12,15))\n",
    "length = len(categorical)\n",
    "for i,j in itertools.zip_longest(categorical,range(length)):\n",
    "    plt.subplot(np.ceil(length/2),2,j+1)\n",
    "    plt.subplots_adjust(hspace=.5)\n",
    "    sns.countplot(x=i,data = Data,hue=\"left\")\n",
    "    plt.xticks(rotation=90)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Observations\n",
    "* Those who were promoted in last 5 years didn't leave the company\n",
    "* Those who have spent 5 years in company are more in proportion compared to people staying back\n",
    "* More than 6 years, people dont leave company anymore\n",
    "* People who have done more than 7 projects have left the company.\n",
    "* more than 6 years expr are not leaving company & more than 7 years are not staying back. So, folks who have done many projects faster are leaving company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lets Calculate proportions for the above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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publT82yn5sxOzX+dnM86NT9dAHwG2ITqf31HYAXV+9XWIjoi5gJ7UcW2AlgC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\npn8DjbJZSsPzEcUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2055cf9ec50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#categorical=['number_project']\n",
    "#length = len(categorical)\n",
    "\n",
    "\n",
    "# here we will do it only for categorical variable\n",
    "categorical=['number_project','time_spend_company','Work_accident','promotion_last_5years','sales','salary']\n",
    "length = len(categorical)\n",
    "\n",
    "\n",
    "fig=plt.subplots(figsize=(12,15))\n",
    "for i,j in itertools.zip_longest(categorical,range(length)):\n",
    "    # only counting the number who left \n",
    "    Proportion_of_data = Data.groupby([i])['left'].agg(lambda x: (x==1).sum()).reset_index()\n",
    "    \n",
    "    # Counting the total number \n",
    "    Proportion_of_data1=Data.groupby([i])['left'].count().reset_index() \n",
    "    \n",
    "    # mergeing two data frames\n",
    "    Proportion_of_data2 = pd.merge(Proportion_of_data,Proportion_of_data1,on=i) \n",
    "    \n",
    "    # Now we will calculate the % of employee who left category wise\n",
    "    Proportion_of_data2[\"Proportion\"]=(Proportion_of_data2['left_x']/Proportion_of_data2['left_y'])*100 \n",
    "    Proportion_of_data2=Proportion_of_data2.sort_values(by=\"Proportion\",ascending=False).reset_index(drop=True)#sorting by percentage\n",
    "    plt.subplot(np.ceil(length/2),2,j+1)\n",
    "    plt.subplots_adjust(hspace=.5)\n",
    "    sns.barplot(x=i,y='Proportion',data=Proportion_of_data2)\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.title(\"percentage of employee who left\")\n",
    "    plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_project</th>\n",
       "      <th>left_x</th>\n",
       "      <th>left_y</th>\n",
       "      <th>Proportion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1567</td>\n",
       "      <td>2388</td>\n",
       "      <td>65.619765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>655</td>\n",
       "      <td>1174</td>\n",
       "      <td>55.792164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>612</td>\n",
       "      <td>2761</td>\n",
       "      <td>22.165882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>409</td>\n",
       "      <td>4365</td>\n",
       "      <td>9.369989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>72</td>\n",
       "      <td>4055</td>\n",
       "      <td>1.775586</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   number_project  left_x  left_y  Proportion\n",
       "0               7     256     256  100.000000\n",
       "1               2    1567    2388   65.619765\n",
       "2               6     655    1174   55.792164\n",
       "3               5     612    2761   22.165882\n",
       "4               4     409    4365    9.369989\n",
       "5               3      72    4055    1.775586"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Proportion_of_data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_project</th>\n",
       "      <th>left</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>4055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>4365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>2761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>1174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   number_project  left\n",
       "0               2  2388\n",
       "1               3  4055\n",
       "2               4  4365\n",
       "3               5  2761\n",
       "4               6  1174\n",
       "5               7   256"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Proportion_of_data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_project</th>\n",
       "      <th>left</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   number_project  left\n",
       "0               2  1567\n",
       "1               3    72\n",
       "2               4   409\n",
       "3               5   612\n",
       "4               6   655\n",
       "5               7   256"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Proportion_of_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering\n",
    "* Find imp features for prediction\n",
    "* Use Randomforest to find imp features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.5,  1.5,  2.5,  3.5,  4.5,  5.5,  6.5,  7.5]),\n",
       " <a list of 8 Text xticklabel objects>)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vCsRZa9cG22FSgWustX5jTDdgJYE3I+IgVzRpyOLlKTzUezB+P/R9rAOffvM9WdnZ3Njq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yZwmbv/dy3/XQB8Os2z16FrDZYX1IodITUKCvQrId9VZ/m3sSOkxooaqZscLZqFBRvFjpAaH7fI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\nRERERFLq/wNV4UTkSFkNRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2055d2139b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot the correlation Matrix\n",
    "#Data.drop('Impact',axis=1,inplace=True)\n",
    "corr= Data.corr()\n",
    "\n",
    "plt.figure(figsize=(12,10))\n",
    "sns.heatmap(corr,annot=True,cbar=True,cmap=\"coolwarm\")\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# For changing categorical variable into int\n",
    "from sklearn.preprocessing import LabelEncoder \n",
    "from sklearn.metrics import accuracy_score \n",
    "le=LabelEncoder()\n",
    "Data['salary']=le.fit_transform(Data['salary'])\n",
    "Data['sales']=le.fit_transform(Data['sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "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>7</td>\n",
       "      <td>1</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>7</td>\n",
       "      <td>2</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>7</td>\n",
       "      <td>2</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>7</td>\n",
       "      <td>1</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>7</td>\n",
       "      <td>1</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      7   \n",
       "1                   6              0     1                      0      7   \n",
       "2                   4              0     1                      0      7   \n",
       "3                   5              0     1                      0      7   \n",
       "4                   3              0     1                      0      7   \n",
       "\n",
       "   salary  \n",
       "0       1  \n",
       "1       2  \n",
       "2       2  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=100, n_jobs=1, oob_score=False, random_state=None,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can select importance features by using Randomforest Classifier\n",
    "from sklearn.ensemble import RandomForestClassifier \n",
    "model= RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "#Using all features except for left\n",
    "feature_var = Data.ix[:,Data.columns != \"left\"]\n",
    "\n",
    "#Target feature\n",
    "pred_var = Data.ix[:,Data.columns=='left']\n",
    "\n",
    "#Training\n",
    "model.fit(feature_var,pred_var.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "satisfaction_level       0.359148\n",
      "number_project           0.181580\n",
      "time_spend_company       0.175395\n",
      "average_montly_hours     0.131213\n",
      "last_evaluation          0.126108\n",
      "sales                    0.011972\n",
      "salary                   0.008104\n",
      "Work_accident            0.005376\n",
      "promotion_last_5years    0.001103\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "featimp = pd.Series(model.feature_importances_,index=feature_var.columns).sort_values(ascending=False)\n",
    "print(featimp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Machine Learning Models\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ZekeLabs\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# Importing Machine learning models library used for classification\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.cross_validation import KFold\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier as knn\n",
    "from sklearn.naive_bayes import GaussianNB as GB\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Classification_model(model,Data,x,y): # here x is the variable which are used for prediction\n",
    "    # y is the prediction variable\n",
    "    train,test = train_test_split(Data,test_size= 0.33)\n",
    "    train_x = Data.ix[train.index,x] # Data for training only with features\n",
    "    train_y = Data.ix[train.index,y] # Data for training only with predcition variable\n",
    "    test_x = Data.ix[test.index,x] # same as for training \n",
    "    test_y = Data.ix[test.index,y]\n",
    "    \n",
    "    model.fit(train_x,train_y.values.ravel())\n",
    "    \n",
    "    pred=model.predict(test_x)\n",
    "    \n",
    "    accuracy=accuracy_score(test_y,pred)\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "All_features=['satisfaction_level',\n",
    "'number_project',\n",
    "'time_spend_company',\n",
    "'average_montly_hours',\n",
    "'last_evaluation',\n",
    "'sales',\n",
    "'salary',\n",
    "'Work_accident',       \n",
    "'promotion_last_5years']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Important_features = ['satisfaction_level',\n",
    "'number_project',\n",
    "'time_spend_company',\n",
    "'average_montly_hours',\n",
    "'last_evaluation']\n",
    "\n",
    "#Target Variable\n",
    "Pred_var = [\"left\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "models=[\"RandomForestClassifier\",\"Gaussian Naive Bays\",\"KNN\",\"Logistic_Regression\",\"Support_Vector\"]\n",
    "Classification_models = [RandomForestClassifier(n_estimators=100),GB(),knn(n_neighbors=7),LogisticRegression(),SVC()]\n",
    "Model_Accuracy = []\n",
    "for model in Classification_models:\n",
    "    Accuracy=Classification_model(model,Data,All_features,Pred_var)\n",
    "    Model_Accuracy.append(Accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.99111111111111116,\n",
       " 0.79878787878787882,\n",
       " 0.93414141414141416,\n",
       " 0.75979797979797981,\n",
       " 0.94828282828282828]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Model_Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "models=[\"RandomForestClassifier\",\"Gaussian Naive Bays\",\"KNN\",\"Logistic_Regression\",\"Support_Vector\"]\n",
    "Classification_models = [RandomForestClassifier(n_estimators=100),GB(),knn(n_neighbors=7),LogisticRegression(),SVC()]\n",
    "Model_Accuracy = []\n",
    "for model in Classification_models:\n",
    "    Accuracy=Classification_model(model,Data,Important_features,Pred_var)\n",
    "    Model_Accuracy.append(Accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.98888888888888893,\n",
       " 0.80949494949494949,\n",
       " 0.94747474747474747,\n",
       " 0.76161616161616164,\n",
       " 0.95353535353535357]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Model_Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#### Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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