sponsorship-budget-prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How much should we prepare for the event sponsorship budget in 2018?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [Exploring the Data](#Exploring-the-Data)\n",
    "* [Creating a Visualization](#Creating-a-Visualization)\n",
    "* [Modeling the Data](#Modeling-the-Data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>revenue</th>\n",
       "      <th>number of employees</th>\n",
       "      <th>sponsor budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>7500000.50</td>\n",
       "      <td>75.0</td>\n",
       "      <td>69000.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>8500000.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48500.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>10500000.78</td>\n",
       "      <td>109.0</td>\n",
       "      <td>78500.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>11800000.19</td>\n",
       "      <td>113.0</td>\n",
       "      <td>89000.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year      revenue  number of employees  sponsor budget\n",
       "0  2014   7500000.50                 75.0        69000.14\n",
       "1  2015   8500000.75                  NaN        48500.50\n",
       "2  2016  10500000.78                109.0        78500.30\n",
       "3  2017  11800000.19                113.0        89000.84"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/sponsorship-budget.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>revenue</th>\n",
       "      <th>number_of_employees</th>\n",
       "      <th>sponsor_budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>7500000.50</td>\n",
       "      <td>75.0</td>\n",
       "      <td>69000.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>8500000.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48500.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>10500000.78</td>\n",
       "      <td>109.0</td>\n",
       "      <td>78500.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>11800000.19</td>\n",
       "      <td>113.0</td>\n",
       "      <td>89000.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year      revenue  number_of_employees  sponsor_budget\n",
       "0  2014   7500000.50                 75.0        69000.14\n",
       "1  2015   8500000.75                  NaN        48500.50\n",
       "2  2016  10500000.78                109.0        78500.30\n",
       "3  2017  11800000.19                113.0        89000.84"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = df.columns.str.replace(' ', '_')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Missing Data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See the package `missingno` at https://github.com/ResidentMario/missingno."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: missingno in /Users/zkan/anaconda/lib/python3.6/site-packages\n",
      "Requirement already satisfied: seaborn in /Users/zkan/anaconda/lib/python3.6/site-packages (from missingno)\n",
      "Requirement already satisfied: scipy in /Users/zkan/anaconda/lib/python3.6/site-packages (from missingno)\n",
      "Requirement already satisfied: matplotlib in /Users/zkan/anaconda/lib/python3.6/site-packages (from missingno)\n",
      "Requirement already satisfied: numpy in /Users/zkan/anaconda/lib/python3.6/site-packages (from missingno)\n",
      "Requirement already satisfied: six>=1.10 in /Users/zkan/anaconda/lib/python3.6/site-packages (from matplotlib->missingno)\n",
      "Requirement already satisfied: python-dateutil in /Users/zkan/anaconda/lib/python3.6/site-packages (from matplotlib->missingno)\n",
      "Requirement already satisfied: pytz in /Users/zkan/anaconda/lib/python3.6/site-packages (from matplotlib->missingno)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/zkan/anaconda/lib/python3.6/site-packages (from matplotlib->missingno)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=1.5.6 in /Users/zkan/anaconda/lib/python3.6/site-packages (from matplotlib->missingno)\n"
     ]
    }
   ],
   "source": [
    "!pip install missingno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import missingno as msno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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EBgZiMplYsWIF3bt357XXXuOxxx5j4sSJxMfHA+Dr60tsbKyCZpEcoLBZREREREQkD8kO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\nAAAAUOx/AeGiwmDNmfk3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d5b2320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fill in the missing data with some random value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.number_of_employees = df.number_of_employees.fillna(85)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>revenue</th>\n",
       "      <th>number_of_employees</th>\n",
       "      <th>sponsor_budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>7500000.50</td>\n",
       "      <td>75.0</td>\n",
       "      <td>69000.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>8500000.75</td>\n",
       "      <td>85.0</td>\n",
       "      <td>48500.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>10500000.78</td>\n",
       "      <td>109.0</td>\n",
       "      <td>78500.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>11800000.19</td>\n",
       "      <td>113.0</td>\n",
       "      <td>89000.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year      revenue  number_of_employees  sponsor_budget\n",
       "0  2014   7500000.50                 75.0        69000.14\n",
       "1  2015   8500000.75                 85.0        48500.50\n",
       "2  2016  10500000.78                109.0        78500.30\n",
       "3  2017  11800000.19                113.0        89000.84"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TdL+p9wEJ8FngDdba2BhzJ/Da9tffbq39kDGmArwfuAQ0gNdYa9eNMbcD72wf+xFr7duG\n+b5EpD8H3UqPXkWcju0tziOMYh7fdo+Unj6y2SSMup+USil/ZH3i1dUaS/USGQVFEREZAwfbbbih\nR4xC4zQY9pXHlwF5a+23GmO+C/hloAC8xVr7MWPMe4CXG2M+Afw08HygDNxnjPlz4PXAZ6y1v2SM\neSXpXlVvBN4DvAJ4GPhTY8xzrbUPDvm9icy8w+sPD/Y+zBZdNnbcmVh/2I8winlsyzmyh+Kjm07P\n8ttapZB2O129ERQX54oKiiIiMnaiOMIJXdzQ1dw/ZYYdHv8TkDfGZIF5IABuBz7e/vqHge8mvSp5\nv7W2BbSMMV8AngO8EPi1Q8e+1RgzD5SstV8EMMbcA7wYUHgUGYB0/eGh8tKDEtMTtrco+xHhLNad\nAkEY8+hW+2piu/PpY9tuz6BYrxS4spYGRPPUFeZLOeZrCooiIjLegihIt9uIWqMeigzIsMPjPmnJ\n6ueBVeB7gO+w1h78JtUAFkiD5e6h5x33+OHH9m469rZeA1laqpLP5876PibK2topd/+UI2bx/B2E\nwSiKCcL23odReiUxJoF8llwe+v0XtLxcG+h4z+I/PLzJv//762zsuKwuVvjW51zhG29bOfPr+UHE\n1x7f58uP7vHVRxt8+dEGj2w0iXvU4y7VSzz5cp0n31LnyZfnefLlOgtzpTOPQ55oZWX8/v6JiEwT\nP/JpqnPqTBh2ePzvgHustT9vjPk64C+B4qGv14Ed0jBY7/F4r2O72t52zvgWJsvaWp319caohzGx\npvX8DXN7i+XlGltbzQt8xfN76Gs73PPAVzv3H9lo8sG/fIj9fY9nPGmx5/NbfsT1zaN7KK7vuD3X\nbS7OFbm6Ondke4y5SuHIMZEfsrV1owPdOJ6/SbK8XGNzs8nKQmXUQxERmTpe6NFU59SZMuzwuE1a\nqgqwRbre8UFjzB3W2o8BLwU+CjwA/LIxpgyUgGeRNtO5n3Td5APtY++11u4ZY3xjzNNI1zy+BFDD\nHJl5SZIcaUgTRrO5vcVxPv35x098/Obw6PnhkUY219abbO56PQP2cr3UKT09WKdYKxd6PEtERGS8\nJUmC2+mc2n1PYZk+ww6P/wvwXmPMvaRXHH8B+DRwlzGmCHwO+IC1NjLGvAu4F8gCb7bWesaY3wHu\nNsbcB/jAq9qv+zrgD0ir6D5irf3UUN+VyIgct71FFCcnrj+U1Hbj+LUYm7seX7i2e2MPxY0mm3te\nz9dbXSh3mthcaW+TUSlpJyQREZkecRKnTXACdU6dZUP97cZauw/84DFfetExx94F3HXTYw7wA8cc\n+0nSxjsiU6cTDo9cRdT2FuexVC/x+I5HEEYEYdz5L4oT3vunnzvxeRlgdbHSuZp4da3GrStVykUF\nRRERmU7qnCqH6TcekRF7wvrDm64i6gf1+TUc/0jp6ZcfbdD0uq/PyGTg0mLl0B6Kc1xeqVIqzEaj\nLRERmW1BHOIEDq2opd9FpEPhUWQI0u0tblwx7LW9hZxNkiQ0nKATFK+tN7m+sc+e07v72/J8iafe\nOt+5qnh5pUpxRjoyi4iIHPAjHyd0aUX+qIciY0jhUeSCRHHcvnqYHNnaIopjlA8vXpIk7Db9ThOb\ng8C473YPirlshsvL1U4Tm6trNW5ZqlLIZ4c0chERkfHjtZvgBLE6p8rJFB5FTqFz5TBKCOP4yDpE\nrT8cnCRJ2G60jmyNcX2j2bP0NJ9Lg+LVtblOWLxlqUI+p6AoIiKSJAlelG63oc6p0g+FR5FDDtYf\nHoTEQqPFdqNFGMXEWn84FHGSsL3X4trG/pGg6La6T2qFXJbLK1WuHtpD8dJShVxWQVFEROSwOInT\n7TYCV51T5VQUHmXmnGZ7i0I5oBXok7hBiZOEzV0vDYiHtsfodc6L+Sy3HpSdtv9bXayQy2aGNHIR\nEZHJc6Nzqoc+EpezUHiUqXSw/jA8VFZ6UGqq8tLRiOOE9V2X64fWJ17fbOIH3T/xLBVyN/ZQbO+j\nuDpfJqugKCIi0pcwDmkGLq3IU2SUc1F4lIn0hO0tDm5re4uxEMUJj287XN9ostnwefjaDo9sOgRh\n96BYLt4Iigelp8vzZbIZBUUREZHT8qMAJ3TUOVUujMKjjK2D7S2OKzGN1L50bIRRzOPb7pH1iY9s\nNgmj7v+PqqV8JyAeBMaleomMgqKIiMi5eGELJ3TUOVUunMKjjNRx6w9DbW8xtsIo5tEtp7M1xvWN\nJo9uOT3DfK1S6KxNPNgeY6FWVFAUERG5IAedU53AJVTnVBmQmQ2PYRSrXf+QHN7e4kZQ1PYW4y4I\nYx7dah5pZvPYlkvc439avVo4FBLn+IanrxL7oYKiiIjIABx0TnVDlyhR51QZrJkNj3tNn0wmw3yt\noFb+53R4/eGNvQ+1/nCS+EHEI5tOp+z0+kaTx7ednld/F2rFzpXEg8BYrxaPHLNUL7O11Rzg6EVE\nRGZPFEfseQ023C11TpWhmdnwCNAKIjZ2I2rlArVyXldGukjXHx4qLz0oMT1mewsZby0/4vpmu+Pp\netrxdH3H7XkVeKle4spK7cg6xblKYTiDFhERESDtnOqELl7oEVdrCo4yVDMdHgGSBPbdAK8VUq8V\nKRVyox7SyESHrhreXGqqfDiZPD/sXE08WKe4udu7TffyfOlG6enqHFdWq1TLCooiIiKjEkQBTXVO\nlRGb+fB4IIwTthstysUc9ep0lrJqe4vp5nhhp+T02sY+1zccNve8rs/JACsL5SOlp7eu1KiU9KNB\nRERkHLQiHydw8ONg1EMRUXi8medHtIKIuUqB2gReaUmS5EhDmkKjxdaeR9QuL1VAnA5NL+hcSTy4\nsrjdaHV9TiYDa4sVrqzc6Hh6ZaVGqTi7V9tFRETGUdo5tYUTOOqcKmNF4fEYSQINJ8BthcxXixTH\nrJT1uO0tojg5dv1hoRzg99iYXcZbw/GPNLK5tt5kt9m9ZCWbgUtL1SN7KN66Uh27v8siIiJyQ5zE\neKGHo86pMqYUHrsIo4StRotKMUe9WiSbHV5DnU44PHQV8WAdora3mE5JkrDnBO2AuN8Ji3tO9zKV\nbCbDLcuVTki8ulbj8nKNQn76Sq9FRESmUZzEOIGLG3rEKDTK+FJ47IPrR7QCl7lKkWo5PWWf/YdN\n7vv7R1jfcVlbrPDC59zKs5+60vdrPmH9oba3mClJkrDb9I80srm20WTf7R4Uc9kMl5cPXVFcq3F5\nuao9S0VERCZQFEc0Qwcv7N3MTmQcKDz2KU5gz/FxWyFfXW/w/9z3pc7XHtt2+eDHHwY4EiDT7S0O\ndS7V9hYzKUnSZkxHSk83mjhe2PV5+VyGW1dqR0pPLy1VFBRFREQmXBAF6XYbUfd+BSLjRuHxlIIo\n5qN/e40gjMlkIJPJkMkACXz0b6/xpLU5bW8xw+IkYWvP61xRPAiMnt99sXshn+XWlWonJF5pB8Vp\n7PorIiIyq9Q5VSadwuMZHHS1TJL0qtKB9R23Z0iQ6REnCRu7aVC8figotoLufweKhSxXVm6ExCtr\nNdYWKkNdUysiIiLDcaNzqkuYdK86Ehl3Co9nsFQvsbn3xDKDpXppBKORYYjjhPUd98jWGNc3m/hB\n90XtpULuyB6KV1ZrrCyUyWYUFEVERKZZkiS4nc6purgg00Hh8Qye/8xL3PPAV499XCZfFCc8vu1w\nfaPJZsPn4Ws7PLLpEPTY8qRSyh0pO726OsfSfElBUUREZIbESYwTuriBOqfK9FF4PINnPGkRgE9/\n/nG2Gy2W6iWe/8xLncdlcoRRzGPb7qE9FPd5dMshjLovWK2W84dCYvrnUr1ERkFRRERkJkVxlIbG\n0FXnVJlaCo9n9IwnLSosTpgwinl0y+lsjXF9o8mjWw5Rj85Gc5XCjaC4lv65UCsqKIqIiAhBHOIE\njjqnykxQeJSpFIQxj2w2j6xRfGzLJU66B8X5WjFtZtMOid/w9FViP1RQFBERkSP8yKepzqkyYxQe\nZeL5QcQjm047JO5zbb3J+o7bc6uUhVqxExIPrizWq8UjxyzVy2xtNQc4ehEREZkkXrsJThCrc6rM\nHoVHmSgtP+L6wRXF9bTj6fqOS48LiizVS0dC4pXVGnOVwnAGLSIiIhNNnVNFUgqPMrbcVpgGxUN7\nKG7uej0Xoa/Ml4+sT7yyUqNa1l91EREROZ04idPQGLjqnCqCwqOMCccLubaxf2SN4tYxe2kelgFW\nFspP2EexXNRfaxERETm7G51TPRL1ThXp0G/ZMnT7bnBoa4y09HS70SMoZmBtsXKk7PTKSo1SMTek\nUYuIiMi0C+OQZuDSinpXOonMIoVHGaiG43PtICS2A+Nu0+/6nGwGLi1Vj6xRvHWlSrGgoCgiIiIX\nz48CnNChFXX/HUVk1ik8yoVIkoQ9J+D6+n6n7PTaRpOG0719dS6b4ZalSnolsV16enm5RiGfHdLI\nRUREZFZ5YQsndNQ5VaRPCo9yakmSsLPvH1mfeG2jSdPtHRQvr1Q7VxOvrta4ZblKPqegKCIiIsOR\nJAlelDbBCdU5VeRUFB6lqyRJ2G60OiHxYJ2i0+r+CV0+l+HWlaN7KF5aqigoioiIyEgcdE51Q5co\nUedUkbNQeJSOOEnY2vM6AfEgMHp+90/lCvksVw6CYnt7jLXFCrlsZkgjFxERETmeOqeKXByFxxkV\nxwkb7aB4eB/FVtA9KBYL2TQkrhysUZxjdaFMVkFRRERExkgYhzihixeqc6rIRVF4nAFRnLC+4x5Z\no/jIRhM/7F6yUS7mOttiHJSeriyUyWYUFEVERGQ8BVFAU51TRQZi6OHRGPPzwH8FFIHfBj4OvA9I\ngM8Cb7DWxsaYO4HXAiHwdmvth4wxFeD9wCWgAbzGWrtujLkdeGf72I9Ya9825Lc1NqI45vFtt1N6\n+tiOy9ce2yeIugfFSinPldWDZjZzXF2rsVwvkVFQFBERkQnQinycwMGPuzfwE5GzG2p4NMbcAXwr\n8G1AFfgfgHcAb7HWfswY8x7g5caYTwA/DTwfKAP3GWP+HHg98Blr7S8ZY14JvAV4I/Ae4BXAw8Cf\nGmOea619cJjvbRTCKOaxbffI9hiPbjmEUffijFo5n65NPLROcXFOQVFEREQmS9o5tYUTOOqcKjIE\nw77y+BLgM8AfAfPA/wjcSXr1EeDDwHcDEXC/tbYFtIwxXwCeA7wQ+LVDx77VGDMPlKy1XwQwxtwD\nvBiYqvAYhDGPbTlHtsZ4bMshirsHxflakVtXqkdKTxdqRQVFERERmVhxEuOFHo46p4oM1bDD4yrw\n9cD3AE8F/hjIWmsPElADWCANlruHnnfc44cf27vp2Nt6DWRhoUKPC3Qj4wcRX3t8n688usdXHm3w\nlccaXN9oEvcIiov1Ek++pc6TL9d58uV5nnxLncV6aUijnl7Ly7VRD2Hi6Ryej87f+ays6PyJTIs4\niXECFyd01TlVZASGHR43gc9ba33AGmM84OsOfb0O7JCGwXqPx3sd29XurtuzYcwwtIKIRzcdrm3s\nd9Ypru+49MiJLM4Vn9DMpl4tHjkmDkKgxNZWc3BvYMotL9d0/s5J5/B8dP7OZ3m5xuZmk5WFyqiH\nIiLnoM6pIuNh2OHxPuCNxph3ALcCNeAvjDF3WGs/BrwU+CjwAPDLxpgyUAKeRdpM537gZe2vvxS4\n11q7Z4zxjTFPI13z+BJgLBvmeH7I9Q0n3R6jXXq6seP2/CG4VC9x9dAeildWa9TKhaGMWURERGRU\ngihgy91h09se9VBEhCGHx3bH1O8gDX9Z4A3APwB3GWOKwOeAD1hrI2PMu4B728e92VrrGWN+B7jb\nGHMf4AOvar/064A/AHKk3VY/Ncz3dRy3FR7ZGuPaRpPNXa/n81YWylxZuREUr67WqJS0o4qIiJyP\nMeYFwK9aa+8Y9VhEejncOXU5VOm5yLgYeiqx1r7pmIdfdMxxdwF33fSYA/zAMcd+Erj9osZ4Wo4X\nHAmJ19ebbDVaXZ+TAVYXy1xdnetcTbyyWqVcVFAUEZGLZYx5E/BqQDXQMrZudE51CZNw1MMRkWMo\nqZzSvht01iYerFPc2e++CW0mA2uLlc7axKtrNW5dqVEq5IY0ahERmXFfBL4P+P1RD0TkZmnn1Fa7\nc6q22xCpWZMPAAAgAElEQVQZZwqPXew5PtfXb5SeXt9ostvsHhSzGbi0dGNrjKtrNS4vVykqKIqI\nyIhYaz9ojHlKv8cvLVXJ5y9+3lpbq/c+aEbN4rmJ45hm4NAMfApJwgLlE49V1+mT6dx0p/NzsrOc\nG4VH0jKJvabfKTs9CIsNJ+j6vFw2wy3L1U7J6dXVOS4vVynks0MauYiIyMXb3nYu/DXX1uqsrzcu\n/HWnwaydmyiOcEIXN+zdNBDUdbobnZvudH5OdnBurpzyc6uZDY+f+eImX358n+sb+1zbcGi63YNi\nPpfh8nL1yNYYtyxXyecUFEVERER6CeIQJ3Dwou59IURkfM1seLz7Hnvi1wq5LJdXqkfWKF5aqpDL\nKiiKiIiInIYf+TTbnVNFZLLNbHg8UMhnubJyIyReWa2xtlghl82MemgiIiIXxlr7JUbYmVxmjxd6\nNNU5VWSqzGx4/NGXPpPFuRKrC2WyCooiIiIi55YkCW7oqXOqyJSa2fD47Kcu44fxqIchIiIiMvHi\nJE6b4AQeMfr9SmRazWx4FBEREZHzudE51SPpq3eqiEyyvjrAGGPefcxjd1/8cERERAQ098p4C+KQ\n3VaDTW8LJ3QVHEVmRNcrj8aY3wVuA55vjPnGQ18qAAuDHJiIiMgs0twr48yPfJzQpRX5ox6KiIxA\nr7LVtwNPAd4JvO3Q4yHwuQGNSUREZJZp7pWx44UtnNAhiNU5VWSWdQ2P7bbeXwK+yRjzFOAbgT8D\nnmyt3Rr04ERERGaN5l4ZF0mS4EUeTuASqnOqiND/mscfAv4EeBewAnzCGPMjgxyYiIjILNPcK6MS\nJzHNwGHT22LP31dwFJGOvsIj8HPAtwJ71trHgecCPz+wUYmIiIjmXhmqKI5o+PtsuFvsB02iRFtu\niMhR/YbHyFrbOLhjrX0EtImPiIjIAGnulaEI1TlVRPrU7z6P/8EY85NAwRjzzcB/C/zd4IYlIiIy\n8zT3ykD5UYATOuqcKiJ96/fK4xuAq4ALvBfYI53EREREZDA098pAeGGLbW+H7daOgqOInEpfVx6t\ntU3SdRZaayEiIjIEmnvlIqWdU1s4gaMGOCJyZn2FR2PMV4ErwE77ocX27YeBO621KqMRERG5QJp7\n5SLESYwXejihqwY4IjMuSRKc0GW3tcdX/RbXNtd59tffdqrX6HfN48eBD1hr/28AY8xLgR8kbR/+\nW8C3neq7ysx76Gs7fPrzj7PdaLFUL/H8Z17iGU9aHPWwRETGieZeObMojnDboVENcERmQ5zE7PkN\ndlt76X/+3o3brT12/QZBHBx5zk/c/oOn+h79hsdnW2s7e0tZaz9sjHm7tfZBY0zlVN9RZt5DX9vh\nnge+2rm/udfq3FeAFBHp0NwrpxbGIU7o4oWeIqPIlPEjn91OONxlt9Vg199lpx0OG/7+wD8s6jc8\n7hhjXgu8n7TJzg8DW8aYZ9J/0x0RAD79+cdPfFzhUUSkQ3Ov9C2IAprqnCoysQ6XlB6+YrjT2mXP\nb7DT2sMN3TO9diVfZqE4z0Lpxn9PWr5ELiid+rX6DY8/DLwT+DUgAj4C/Avg+4H/6dTfVWbadqNF\nNgtJ+4ORgz+3G63RDUpEZPxo7pWeWpGPEzj4N5Wiich4OamkdOfQ/TAOT/26GTLUi3NpKDwcEIvz\nLLZvF3PFJzxvebnG1lbz1N+v326r10gnq5u9+9TfUWZWMZ+lVi5wZbXGY9tHPzmJ44Tl+uk//RAR\nmVaae+UkNzqnuoTJ6X/ZFJGLF0QBO09YY3j+ktJ8Jn/kimEaEOsslBZYLM5TL86Ry+YG8I5OGE+3\nLxpj/gFOfpfW2tO155GZkwHKxRzVcoFCPq2yeuFzbuWDH3/4yHHZbIbvfN5V5ioFmm6gdRoiMrM0\n98pJ1DlVZDSSJMEN3TQIPiEgpn86F1VSeuj2Ymmear5KJpO54Hd0dr2uPN5B+vv/vyRtDf4+ICQt\npXnqIAcmky2bgWq5QLWUJ5s9+hf+2U9dAeC+v3+E9R2PtcUyL3zOrZ3Hy8UcDSegFWgfKhGZSXeg\nuVcOiZMYJ3BxQ48YhUaRi3ajpLSRNqLpo0tpPw5KSueLdRZLC8cGxNIxJaXjrGt4tNZ+GcAY8xxr\n7Y8f+tJvGGP+ZqAjk4mUz2aolgtUSrmun5I8+6krnbD4hNfIZVmql/D8kIYTEMW6Dikis0NzrxyI\n4ggndHFDVxU5IudwtEvp0ZLSvVaDPb9xjpLS+hPXG5YWWCjWmS/Wh1pSOgz9NszJGGP+M2vtR6Gz\n15SK7KWjVMhRLeUpFS/uH0i5mKdUyLHvBjheqIlTRGaN5t4ZFcQhTuDgRWokJ9LLSV1Kd1t77Ef7\nbDo7F9ql9OD+OJaUDkO/4fEngLuNMbeStgf/EvDqQQ1KJkMGKJfy1Mp58rnBdI3PZDLUq0UqpTx7\nTR8/VLmOiMwMzb0zxo98muqcKnJEty6lexdQUnpSl9L5CSwpHYZ+u60+CDzHGLMCJNbarcEOS8ZZ\nt/WMg5LPZVmeL+O2QhpuQKxSVhGZcpp7Z4cbeGy62+qcKjPp5o3vd1p77Pl7F9KldKm6QD0/1y4j\nHW2X0mnRV3g0xnyUQ53fjDEAWGu/czDDknGUz2WolQuUi93XMw5SpV0au+8GuCplFZEpprl3uqXd\nG9POqUG5rOAoU2nYG98vFtvrDUt1qvkqKytzZ9rLUE7Wb9nqLx26XQBeDmxf+GhkLJUKOarldP3h\nOMhmMsxXi1SKeRqOSllFZGr90qHbmnunRJzEaROcQJ1TZfLFSUzD329fJXxil9KdIW98L4PXb9nq\nx2966P8zxnyKtI24TKFhrGc8r0L+UCmr46NKVhGZJpp7p8uNzqnemUrwREbhpC6l6ZYWe+pSOoP6\nLVt98qG7GeAbgeP3WZCJls1mqJbyQ13PeF6dUlYnwGmp7EdEpoPm3umQdk51aUWeIqOMlVFtfL9Q\nnKdWmL0updOi37LVw59+JsA68FMXPxwZlXFYz3ge2UyG+dqNrqxBpFIgEZl4mnsnmB/5OKFLK/JH\nPRSZUSd1Kb2oje+PC4WTuvG99K/fstWnDnogMhrjtp7xvAr5LCsLZRwvZN9VKauITC7NvZPJO2iC\nc4Z1XiKn4Uc+j+47fGXnsXS9Yatx8Rvft8tID26rS6n0W7a6QLrG4g4gAP4c+BVrrTO4ocmgZEhL\nPatjvJ7xvKrlPOVijoYb4KqUVUQmkObeyZEkCV7k4QQuYRKNejgyBbptfL/b3sZCG9/LKPRbtvpv\ngC8CP0qaPX4c+NfAjwxmWDIIk7ie8Tyy2QwLtSLVUo7dpk8Y6TKkiEwUzb1jLk7idLuNwFXnVDmV\nUW18ry6lcl79hsenW2u//9D9nzHG/P0gBiQXr5DLdq7EzeInSYV8jtWFCo4X0HADEmVIEZkMmnvH\nlDqnSi+D3Pg+DYTpZve3Lq5QjCrMt/c4VEmpDFq/4dEaY/6ptfYTAMaYbwIeOus3NcZcAv4G+C4g\nBN5H2gzgs8AbrLWxMeZO4LXtr7/dWvshY0wFeD9wCWgAr7HWrhtjbgfe2T72I9bat511bNOkVMix\nulihqIkNgGq5QLm9N6Trq6xIRMbehc69cn5hHNJU59SZ16tL6aA3vj98IWB5ucbWVvOi3ppIT13D\nozHmH0hDXQX4fmPM50kD2jdwxgnMGFMA/jfg4F/VO4C3WGs/Zox5D/ByY8wngJ8Gng+UgfuMMX8O\nvB74jLX2l4wxrwTeArwReA/wCuBh4E+NMc+11j54lvFNukwGKsUb6xmnpRHORclmMyzMlagEEXuO\nSllFZPwMYu6V8/GjACd01Dl1RtwoKW0cu/H9eUpK54q1dgnpQructN4OhupSKpOh15XHO3q9gDHm\nW6y1f3uK7/nrpGHv59v3n8eNduQfBr4biID7rbUtoGWM+QLwHOCFwK8dOvatxph5oGSt/WJ7PPcA\nLwZmKjxmsxlq5TyVUp7sDJamnlaxkGNlvozTCtlXKauIjJc7eh1whrlXzsALW7ihi3+GoCDjaygb\n39/UpVQb38u06BoerbVf7uM1fhf4ln6+mTHmR4F1a+09xpiD8Jix1h78C20AC8A8sHvoqcc9fvix\nvZuOva3XWBYWKkzDRadCPstcpUCllD9xPePaWn3Io5osURSz2/RP7Mq6vFwb8oimj87h+ej8nc/K\nymSdv4uee+V01Dl1sqlLqchg9bvmsZvT/Cv5cSAxxrwY+Gbg90jXLx6oAzukYbDe4/Fex3a1u+vi\nh5PbGa1UyFEr58mQo9mIaDaOP25trc76+glflCOSIKLR9AkPbQ6ptQTnp3N4Pjp/57O8XGNzs8nK\nQmXUQ7lo+g31gh10TnVDlyiZ3N8Ppp02vhcZrYsIj31fv7PWfsfBbWPMx4DXAf/KGHOHtfZjwEuB\njwIPAL9sjCkDJeBZpM107gde1v76S4F7rbV7xhjfGPM00jWPLwGmsmFOJtPen7E0vfszjlKpkKO4\nUKbphTTdQM0QRGSc6UfUBYniKN1uI3TVOXUM3NyldLfVwP1Kk8cbWxfQpfRwGemNjqUqKRXp30WE\nx/P6WeAuY0wR+BzwAWttZIx5F3AvkAXebK31jDG/A9xtjLkP8IFXtV/jdcAfADnSbqufGvq7GKBc\nNkNV6xmHIpPJMFcpUC7maDha4yIiMq3COMQJXbxQnVOHZdglpYvF+XQLC5WUilyYkYVHa+0dh+6+\n6Jiv3wXcddNjDvADxxz7SeD2Cx7iyB3sz1gpjUPGny35XJaleon6fJndHYco1q8WIiLTIIgCmuqc\nOhDdNr4/uB/Gx/cX6EYb34uMj75SiTEmZ609adW4Psa5QBmgVEzXMxbyKp8YtXIpz6pKWUVkBDT3\nXqxW5OMEjjqnnkMQBewcc8Xw4PbFdSm9UVK6WJzn6y/fwu6ON4B3JCKn1e8lrb/m5K5ur7igscy0\ng/WMtXKeXFbrGcfJzaWsrUDd90RkKDT3nlPaObXV7px6+ites6TXxve7rT2cM5aUlnPlzhXC464e\n1grdS0q1FlFkfPQbHh8zxnw78EB778UOa+3DFz+s2ZFr789Y1nrGsXdQyur5IXtOQKxSVhEZLM29\nZxQnMV67CY46p6YGufF9vTjHfLF+Y+N7dSkVmVr9hsfnAx8HMMYcPJZYa/VR0BkV8+l6xnJR6xkn\nTbmYp1jI0XQDHC9UKauIDIrm3lOKkxgncHFDj5jZCo1D2fj+SEMadSkVmUV9JRdr7dqgBzILtJ5x\nemQzGerVIpVSnr2mP9F7horIeNLc278ojmiGztR2TtXG9yIyLvptmFMFfhH4z9vP+UvgrdZa7Vzd\nh+zB/oxazzh18rksy/Nl3FZIw1Upq4hcHM29vQVRkG63EbV6HzzGojhip7Wrje9FZOz1WzP5m4AD\n/DjpBbQ7gfcArx7QuKaC1jPOjkopT6mYY79dyioicgE0955g0jqnHrfx/a6/29nCYhAb3y8W56kX\n51RSKiIXqt/w+Dxr7Tcduv+Txpj/OIgBTQOtZ5xN2UyG+WqRSjFPw1Epq4icm+bem3ihR3PMOqee\n1KV0p7XLnt9QSamITJV+003WGLNord0BMMYsAuPzk3sMZIByMUe1XKCQV2nqLCvkD5WyOj6qZBWR\nMxrZ3NuKfIrZwlgEkzScHXROHf5WSXES0/D321cJd9tXEHcvtEvppfoylUztCeFQG9+LyLjpNzy+\nA/hrY8wfk+akfwb8ysBGNUG0nlFOUinlKRXapawtfdYiIqc2srl3p7VLlizlfIlyrkQhVxjGtz0i\nTmKc0MUNBts59fiN7wfYpbR9+3CX0uXlGltbWsoqIuOv326r/9YY89fAi4As8M+ttZ8d6MjGXD6b\noVouUCnlxuKTWRlP2WyG+VqRSinHXjMgiFTKKiL9GfXcG5OGNyd0yWdylPNlyrnSwNfQRXGUhsbQ\nPXfn1EFufP+EktJTbnwvIjKJ+u22ugxcttb+ljHmF4BfNMb8orV25tZeFPNZauUCpaIWoEv/Cvkc\nKws5HC9k31Upq4j0Nk5zb5hE7AdN9oMmxWyBcr5EKVcim7m4ipsgDnEC51SdUwe98f2RUFhMG9Go\nS6mIzLJ+y1b/d+BPjDEJ8ArgfyXt+PYdgxrYONF6RrkoaSOlHA3Hx/WHv3ZHRCbKWM69fhzg+wEZ\n9inmipRzZUq54pmvsvmRT/OEzqknbXy/09pjr9W4mJLS9mb3B7fVpVRE5GT9hscla+1vGmPeDdxt\nrf19Y8wbBzmwcaD1jDII2WyGhbkSlSBiz/EJI12GFJFjXdjca4zJAr8NfBPQAn7CWvuF8wwuIW2s\n04p8smQp5dMgWbxpfeS7H7wLu/0FEhIyZDBLT+ennnsnbuCy4W2x4W5p43sRkQlxmm6rzwO+F3iR\nMeabT/HciaP1jDIMxUKO1YUKTS9g3w1IlCFF5KiLnHu/Fyhba/+pMeZ24DeAl1/QOImJcUMPN/TI\nZXKU8yUKmTy/9Xf/hi/ufalzXELC57cf4mc+9maAgWx8ry6lIiKD0+8k9HPAvwJ+w1r7sDHmk8B/\nP7hhjYbWM8oo1MqFdilrgKdSVhG54SLn3hcCfwZgrf2kMeb55x1cEAWdK4Q3l5T22vi+W2hMS0pv\nbHavje9FRMZHv91W/wL4i0P3bx/YiIYsA5RLeaqlvNYzysjkslkW50q0gohG0ydURx2RmXfBc+88\nsHvofmSMyVtrj91HaL5ewktabLu7bLu7bLk77dvpn1veLk3fOcdw4Nm3GJbKCyxXFlmq3Phzrlib\nyaqf5eXaqIcw1nR+TqZz053Oz8nOcm767bb6GtISl6XDj1trJ/ajv2w2w1ylQLWUJ5udvUlKxlOp\nkKO4UKbphTQ9lbKKzLILnnv3gPqh+9mTgiPAz//Fr565pHSuWGOxtMBqeZkH1z9z4rGvfNorjjbJ\niSFownbzfKF0Emmfx+50fk6mc9Odzs/JDs7NlXrvYw/rt2z1F4E7pmlvx8W50qiHIHKsTCb9YONg\nb8hWoFJWkRl1kXPv/cA/A/7P9prHk1MdJ5eVnrjxfbtj6Wp5mXqp3tnG4t0P3sXntx96wus8c+kZ\nLJUXieIIL2rhhR5hop91IiLjrt/weG2agqPIJMhlsyzVS7T8tCtrpFJWkVlzkXPvHwHfZYz596Qr\nNn6s28Fm6emn6lJayhWp5asUbuq0+lPPvfPEbqsAuWyOWrZKrVAliEO80KMVtYiS+ILetoiIXKR+\nw+PfGGM+AHwE8A4etNb+3kBGJSIdpWKO1UK7lNUNzrCjmYhMqAube621MfC6fo//IfPPex6TAcr5\nMtV8hXz25F8nDoLi2lqd9fXGiccVsnkKxTnqzNGKfLywRStqnWkfRxERGYx+w+MC0AD+6aHHEkDh\nUWQIDkpZD7qyqpRVZCaM5dybIUM1X6GSLw+s62kpV6SUKxIntXaQ9I6ujxQRkZHot9vqjxljCoBp\nP+ez3Rbai8hg5HNpKavnhzScQKWsIlNs3ObeXCZLpR0as5nhdCfPZrJU8mUq+TJRHNGKWrhhizDR\nryAiIqPQ10//9ibFDwF3A/8W+Iox5gWDHJiInKxczLO6UKZWzqNewSLTaVzm3nwmx3xxjpXyMrVC\ndWjB8Wa5bI5qocpKZYmV8hK1fJXciMYiIjKr+i1bfRfwQ9baTwG0O7W9G/gngxqYiHSXyWSoV4tU\nSnn2mj5+qAYTIlNmpHNvIZunmq9Szo9fd/J8Ns9cMc8cNfzIb3ds1fpIEZFB6/cju7mDyQvAWvtJ\noDyYIYnIaeRzWZbnyyzUitqzVGS6jGzuXSotslxeGsvgeLNirsh8sc5aZYWFYrpNiH4SiogMRr/h\nccsY8/KDO8aY7wU2BzMkETmLSiktZa2qlFVkWoxs7i3etOXGJMhkMpTzZRZLC6xWVqgX5ih06QIr\nIiKn1+9P1f8GeL8x5r2k3bm/ALx6YKMSkTPJZjLMV4tUVcoqMg00955RNpOlWqhQLVQI47BT1hol\n6lQtInIe/XZbfQh4gTHmKpC11n51sMMSkfM4KGV1WyENx0dNWUUmj+bei5HP5pnL5pkr1PCjAC/y\naIU+MfpwTUTktPoKj8aYbyLdV+oqkDXGfA54jbX2C4McnIicT6WUp1TMse8EOC21theZJJp7L14x\nV6CYK5AUknT/yMjDj3y12RER6VO/ax7fC7zZWrtqrV0Gfp20bbiIjLlsJsN8rcjKfJlCTm3tRSaI\n5t4BSddHljrrI+eLcxSzk7fOU0Rk2Pr9TTJjrf3QwR1r7R8Bc4MZkogMQiGfZWWhzHy1iJqyikwE\nzb1DkM1kqeQrLJUXWS0vM1eokc/kRj0sEZGx1G/DnL8yxrwV+NdACLwS+Jwx5skA1tqvDGh8InLB\nquU85WKOhhvgqpRVZJxp7h2yXDZHLVulVqgSREGn0Y7WR4qIpPoNjy8HEuDH239C2vnt4+37t138\n0ERkULLZDAu1ItVSjt2mTxhpxY/IGNLcO0KFXIFCrpA22okDvNCjFbW0PlJEZlq/4fGVwAuB3wT+\nBPgW4HXW2g8MamAiMniFfI7VhQqOF9BwAxL9ViQyTjT3joFMJkMpV6SUKxIncdpoJ/Tw42DUQxMR\nGbp+1zy+E/hr4PsAB3gu8HODGpSIDFe1XGBtoUKlqHU+ImNEc++YSddHljvrI+uFGvlMv5/Di4hM\nvn7DY9Za+1fA9wAfbO81pZ+WIlMkm82wMFdiuV4in1NHHZExoLl3jOWyOaqFKiuVJZbLS1TzFXIZ\ndbQWkenW7085xxjzs8B3Ah8yxrwRaAxuWCIyKsVCjpX5MvVqgYwypMgoae6dEIVsnnpxjtXKCkul\nBcq5Mhn0A1REpk+/4fGHgRrwCmvtNnAFeNXARiUiI5XJZKiVC6wulCmrlFVkVDT3TqBirshCqc5a\nZYWFYp1SrjjqIYmIXJi+yl+stdeA//nQfa25EJkBuWyWxbkSrSCi0fQJY3XUERkWzb2TLZPJUM6X\nKefLxEmMF3p4UYsg1hZJIjK5hrp2whhTAN4LPAUoAW8H/iPwPtK2458F3mCtjY0xdwKvJd3b6u3W\n2g8ZYyrA+4FLpKU7r7HWrhtjbidtLBACH7HWvm2Y70tk2pUKOYoLZZpeSNMN1KpeROQUspks1UKV\naqFKGId4YQsvahEl0aiHJiJyKsNe2f0jwKa19tuB/4K0/fg7gLe0H8sALzfGXAZ+Gvg24CXArxhj\nSsDrgc+0j/094C3t130PaSnPC4EXGGOeO8T3JDITMpkMc5UCKwtlSgWVsoqInEU+m2euWGO1ssxS\naZFKvkxW6yNFZEIMu2vbvwMO9qfKkF4pfB7phscAHwa+G4iA+621LaBljPkC8BzScPhrh459qzFm\nHihZa78IYIy5B3gx8GC3gSwtVcnnZ+MX4LW1+qiHMNF0/p7oVsBrhezst4j6KGVdXq4NflBTTOfv\nfFZWdP5kPBVzBYq5AqtzcwT7WbzIw498VXeIyNgaani01u4DGGPqpCHyLcCvW2sPfk42gAVgHtg9\n9NTjHj/82N5Nx97Wayzb286Z38ckWVurs76u5nxnpfPXXTZJcHuUsi4v19jaag51XNNE5+98lpdr\nbG42WVmojHooIidK10eWKOdL7fWRLVpRCz8ORj00EZEjhr4hkTHm64CPAr9vrf1DID705TqwQxoG\n6z0e73WsiAyYSllFRC5Wuj6ywlJ5kdXyMrVClXxGP19FZDwMNTwaY24BPgL8nLX2ve2HHzTG3NG+\n/VLgXuAB4NuNMWVjzALwLNJmOvcDLzt8rLV2D/CNMU8zxmRI10jeO5Q3JCIA5HNZluolFueKZLNa\nuyMichFy2RxzhRorlWWWS4tU8xWyw//cX0SkY9hrHn8BWCJdq/jW9mNvBN5ljCkCnwM+YK2NjDHv\nIg2BWeDN1lrPGPM7wN3GmPsAnxv7Xb0O+AMgR9pt9VPDe0sicqBczFMs5Gi6AY4Xat2OiMgFKeQK\nFHIF5go1/DjACz1aUUs/Z0VkqIa95vGNpGHxZi865ti7gLtueswBfuCYYz8J3H5BwxSRc8hmMtSr\nRSqlPHtNf9TDERGZKplMhlKuSClXJE5iWlELL9T6SBEZjmFfeRSRGZHPZVmeL1Orl9jZcYj76Moq\nIiL9y2ayVPIVKvkKURzhRS280CPU/pEiMiAKjyIyUNVygdWFMvvtUlYREbl4uWyOWrZKrVAliMNO\nWWuUxL2fLCLSJ4VHERm4bCbDfLVIpZin4fj4oX6ZEREZlEI2T6E4R505WpHf2foj0QpJETknhUcR\nGZpCPi1ldVshDcdHlawiIoN1Y31kjVbk04patCKtRxeRs1F4FJGhq5TylAq5tJS1pVJWEZFBS9dH\nlqnky0RxRCtq4YYtwkQ/g0WkfwqPIjIS2WyG+VqRSinHXjMgiFTKKiIyDLlsjmq2SrVQJYxDvLCF\nF3laHykiPSk8ishIFfI5VhZyOF7IvqtSVhGRYcpn88wV88xRw498XK2PFJEuFB5FZCxUy3nKxRwN\nN8BVKauIyNAVc0WKuSJJMpfuHxm18CNfMVJEOhQeRWRsZLMZFmpFKsUce45PGOlXFhGRYctkMpTz\nZcr5MnESd8pag1gf7InMOoVHERk7xUKO1YUKjhfQcAMSZUgRkZHIZrJUCxWqhUq6PjJq4YUtoiQa\n9dBEZAQUHkVkbFXLBUrFHPtOgOvrFxURkVHKZ/PMZfPMFWr4UYAXebRCnxg12hGZFQqPIjLWctks\nC3MlykFEo+kTqqOOiMjIFXMFirkCSSGhFfl4kaf1kSIzQOFRRCZCqZCjuFCm6YU0PZWyioiMg3R9\nZIlyvkScxGmjnbCFHwejHpqIDIDCo4hMjEwmw1yl0NkbshWolFVEZFxkM1kq+Qr/f3v3Gi1ZWhZ2\n/F/7VpdTp8/06W5HiUsJBF9RRyNiQGQEuYjBG+oy6OCFcQQ1KjKoLJdmyRjJUhJwglEUEII31LgI\nK3Ex6dcAAB0PSURBVNEIkmQc5CLKVUDxjUA0icQ4zAzTp8+p2lW1q/JhV08fevpcuvt03c7/96XP\n2VWn6ql3Ve93P3s/z7vbWZtqXE37I/uM7I+UVobJo6SlkyYJJ9eblIOKrR1LWSVp0aRJylrSYS3v\nMKyG9y+0Y3+ktNxMHiUtrWaRUuTTUtbe0F4bSVpAeZqTp3m90M54SH/Up6xK99nSEjJ5lLTUzpey\ntoqUrR1LWSVpUTUaDZppQTMtpv2RA/qjvv2R0hIxeZS0ErL0Qinr2Z0BlaWskrSw6v7IFu2sRTWu\nKKuS3qhkNBnNOzRJ+zB5lLRSmkXK6bzFud6Qnf7IsihJWnBpktJJOnTyDsPx6P6y1mpif6S0aEwe\nJa2cRqPBeqeg3cw4uz1gMPIARJKWQZ5k5EWXdboMqgG9UUlCY95hSZoyeZS0srI0YfNEi145Yqs3\nZGwpqyQtjSItKNKC090uw3MN+lVJWQ3mHZZ0rJk8Slp57WZGs0g51xvSs5RVkpZKo9GglbVoZS3G\nkzH9UZ9+VTIc2x8pzZrJo6RjIWk0ONEp6FjKKklLK2kkdPK6P3I0HtEflfSrkmriStvSLJg8SjpW\nPqmUdWeAlayStJyyJKNbZHRZY1AN6Vd9+qOSifUl0jVj8ijpWLq/lHVnyE5p6ZMkLbMizSnSnPW8\nS1nVVyMH1cA0UjpiJo+Sjq2k0eDE2oVVWYeVpayStMwe2B9ZUlYlg/Fw3qFJK8HkUdKxl2cJpzZa\n7PRHnOtZyipJq6Duj2zTydtU44retKzV/kjpypk8StJUp5XRKlK2ekN6lrJK0spIk5RuskY3X2NY\nDelXJf1RyRgrTqTLkcw7AElaJEnSYGOt4NSJJlnqjam1OkIIXx9CeO2845DmLU9z1osup9ubXNfc\noJU2cW8vHY5XHiXpEvIs5fRGm53+kK3ekImlrFpiIYSXAk8B3jfvWKRF0Wg0aKYFzbRgPBnXC+2M\n7I+U9uOVR0naR6eVc2ajTbtI5x2KdDXeDnzvvIOQFlXSSGhnbU62ruN0a5NuvkbWcL8vXcwrj5J0\ngCRpsNFt0h5WnN0ZMKq8DKnFFEK4Bbj1os03xxh/O4Tw+MO+zsmTHbLs6A+cz5xZP/LXXBWOzf7m\nNT7Dakhv2Kc36lNNFrM/cnNzbd4hLDTHZ29XMjYmj5J0SEWecupEi51yxDlLWbWAYoyvAl51ta9z\n7707RxDNJztzZp277to68tddBY7N/hZhfBJaDKvB/bf+mCzIHSQ3N9e4557teYexsByfvZ0fmwdd\n5nkZk0dJugyNRoO1Vl6vyrozpD9wyXdJOg4u9EeuUVYD+qO+/ZE6dkweJekKpEnCdd0m5bBia3vA\nyJtDStKxUPdHtmhnLapxRVmV9EYlo4m3eNLqM3mUpKvQzFOKjRbb/RHbveGCFDJJDxRjvBO4c85h\nSCslTVI6SYdO3mE0HtEflfSrxe2PlK6WyaMkXaVGo0G3faGUtRxayipJx02WZHSLjC5rDKoBvQXr\nj5SOgsmjJB2RLE04ud6kHNSrslaWskrSsVSkBUVaMJl06/tHViWDamAaqaVn8ihJR6xZpJzOLWWV\npOOu0WjQylq0shbjyfj+stbh2P5ILaeVSR5DCAnwMuALgBL4rhjjh+cblaTjylJWSdJuSSOhk7fp\n5O26P7Iq6Y9Kqonzg5ZHMu8AjtDTgFaM8UuAHwVeMud4JOn+UtbrugVJ0ph3OJKkBZAlGd18jdPt\nTU42r6OdtUhW6rBcq2qVvqWPBd4IEGN8B/DI+YYjSRe0iowzGy3WWhmmkJKk84o050Sxzun2JhvF\nCZpp4TyhhbUyZavACeC+Xb9XIYQsxnjJovKTJztkWTqbyObszJn1eYew1By/q+cYXvApwHA05r5z\n5aFLWTc3165tUCvu1CnHT9Liq/sjm7SyJuPJuF5oZ1QyGA/nHZp0v1VKHs8Cu49Qk70SR4B77925\n9hEtgDNn1rnrrq15h7G0HL+r5xjurSpHbPWGjPdZlXVzc4177tmeYVSrZXNzjbvv3ubURnveoUjS\noSWNhHbWpp21qcbVtD+yz8j+SM3ZKpWtvg14KkAI4dHAB+YbjiTtr93MOL3RomMpqyRpD2mSspZ3\nONXeZLN5HZ2sbX+k5maVrjy+HnhyCOHtQAO4ec7xSNKBkkaDE52CdpGxtTNgMBrPOyRJ0oLK05w8\nzenmawzGQ9pZRoNtbwmlmVmZ5DHGOAa+Z95xSNKVyLOEzRMteuWIrZ0B+1SySpKOuUajQTMtONle\nZ9CGshrQH/Xtj9Q1tzLJoyStgnYzo5mnnOsN2Sm9ibQkaX91f2SLdtba1R9ZMpo4h+jomTxK0oJJ\nkgYn1grazZQss69FknQ4aZKylnRYyzsMxyP6oz5lVVJNbInQ0TB5lKQFlWcpZ0526J0rOdezlFWS\ndHh5kpEXXdbpMqgG9EYlZVUysUNSV8HkUZIWXKeV0SpStnpDepaySpIuU5EWFGnBZNKt7x9ZlZTV\nYN5haQmZPErSEkiSBhtrBZ1myn3bA0aVZ44lSZen0WjQylq0shbjyZj+qE+/KhmOPTGpwzF5lKQl\nkmcppzfa7PSHbPWGTMwhJUlXIGkkdPIOnbzDaDyiP6qvSFaTat6haYGZPErSEuq0cppFyrmdIb2B\nE70k6cplSUa3yOiyxqAa0q/69Ef2R+qBTB4laUmlScJGt0lrWLG1PWDkijqSpKtUpDlFmrOeX+iP\nHFQD00gBJo+StPSaeUqx0WKnHHHOUlZJ0hF4YH9kSb/q2x95zJk8StIKaDQarLXyelXWnSF9S1kl\nSUek7o9s08nbVOOK3rSs1f7I48fkUZJWSJokXNdtUg4qtnYsZZUkHa00Sekma3TzNYbVkH5V0h+V\njBnPOzTNgMmjJK2gZpFS5C22+yO2e0N7VSRJRy5Pc/I0p5uvUVYDyqqkrErnnBVm8ihJK6rRaNBt\nXyhlLYeWF0mSjl7dH9mklTUZT8b1QjujksF4OO/QdMRMHiVpxWVpwsn1Jv3BiK2dIZWlrJKkayRp\nJLSzNu2s7o+sy1r7jOyPXAkmj5J0TLSKjGaecq43ZKc/sqxIknRNpUnKWtJhLe8wHI/oj/r2Ry45\nk0dJOkYajQbrnYJ2M+Ps9oDByAlcknTt5UlGXnRZL7qU1YD+6Hx/pKcyl4nJoyQdQ1masHmiRa8c\nsdUbMraUVZI0I820oJkWjCdr00Syb3/kkjB5lKRjrN3MaBZ1KWvPUlZJ0gzV/ZEt2lmLalxRViW9\nUcloMpp3aNqDyaMkHXNJo8GJTkHHUlZJ0pykSUon6dCZ9keWo5J+1aeaOCctEpNHSRJwUSnrzgAr\nWSVJ81D3R2Z0WWNQDejZH7kwTB4lSZ/k/lLWnSE7paVDkqT5KdKCIi2YTLr1/SOrkkE1MI2cE5NH\nSdIDJI0GJ9YurMo6rCwbkiTNT6PRoJW1aGUtxpMx/WlZ63DsSc5ZMnmUJO0pzxJObbTY6Y8417OU\nVZI0f0kjoZO36eRtRuMR/aqkPyqpJtW8Q1t5Jo+SpAN1WhmtImWrN6RnKaskaUFkSUY3yejmawyq\nIf2qTzkaMMaKmWvB5FGSdChJ0mBjraDTTLlve8Co8jKkJGlxFGlOkeZM8gllNaCVZTTYtj/yCJk8\nSpIuS56lnN5os9MfstUbMnFWliQtkLo/sslme51hu1EvtDMqGYyH8w5t6Zk8SpKuSKeV0yoytnYG\n9Ab2mUiSFk/SSGhnbdpZm2pcTfsj+4zsj7wiJo+SpCuWJA02uk3aw4qzO5aySpIWV5qkrCUd1vIO\nw2p4/0I79kcensmjJOmqFXnKqRMtdsoR5yxllSQtuDzNydO8XmhnPKQ/6lNWpf2RBzB5lCQdiUaj\nwVorr1dl3RnSt5RVkrTgGo0GzbSgmRaMJ2PKakB/1Lc/cg8mj5KkI5UmCdd1m5TDiq3tASNvDilJ\nWgJ1f2SLdtaiGleUVUlvVDKaeIuq80weJUnXRDNPKTZabPdHbPeGlgJJkpZGmqR0kg6dvMNwPLq/\nrLWaHO/+SJNHSdI102g06LZz2s2Us9tDyqGlrJKk5ZInGXnRZZ0ug2pAb1RO+yOP32lRk0dJ0jWX\nJgkn15uUg3pV1spSVknSEirSgiItmEy69f0jq5KyGsw7rJkxeZQkzUyzSDmdW8oqSVpujUaDVtai\nlbUYT8b0R336VclwvNr9kSaPkqSZOl/Ken5VVktZJUnLLGkkdPK6P3I0HtEf1Vckq8nqzW8mj5Kk\nucjSupS1PxhxdmfI2FJWSdKSy5KMbpHRZY1BNaRf9emPVqc/0uRRkjRXrSKjmaec6w3Z6Y9WZHqV\nJB13RZpTpDnr+YX+yEE1WOp5zuRRkjR3jUaD9U5Bu5lxdnvAYHS8l0KXJK2OB/ZHlvSr/lL2R5o8\nSpIWRpYmbJ5o0StHbPUsZZUkrZa6P7JNJ29TjSt607LWZemPnGnyGELYAH4dOAEUwPNijH8cQng0\n8FJgBLwpxviT0+e/APiq6fbnxhj/NIRwGngt0AY+BtwcY9wJIXwN8BPT5746xvjKWX42SdLRaTcz\nmkVdytqzlFWStILSJKWbrNHN1xhWQ/pVSX9UMmZxq2+SGb/f84D/HmN8HPBM4Bem238JuAl4LPCo\nEMIXhhAeATwOeBTwzbue+xPAa2OMNwLvBb47hJADtwNfMf2bZ4cQrp/NR5IkXQtJo8GJTsHmiRZF\nNuvpSpKk2cnTnPWiy+n2JhvFCVppk8a8g7qEWc/GtwMvn/6cAf0QwgmgGWP8SIxxAvwB8CTqRPJN\nMcZJjPF/AVkI4cx0+xunr/GG6XMfDnw4xnhvjHEAvBX4spl9KknSNZNndSnrxlpBsogzqSRJR6Tu\nj2yy0TzB6fYpThRdiiSfd1j3u2ZlqyGEW4BbL9p8c4zxnSGET6UuX30udQnr2V3P2QIeAvSBuy/a\nvjF9/n37bNu9fU8nT3bIsvRyPtLSOnNmfd4hLDXH7+o5hlfH8btgPJ5wdnvAdn946L85dWrtGkYk\nSdK1kTQS2lmbdlb3R9ZlrX1Gc+yPvGbJY4zxVcCrLt4eQrgB+C3gh2OMb55eedx9ZLQOfAIY7LH9\n7PTn3iW2XfzcPd17785lfqLldObMOnfdtTXvMJaW43f1HMOr4/hdWmNUcXZ7yLDavy9kc3ONu+/e\n5tRGe0aRLZ691huYb1SSpMuRJilrSYe1vMNwPKI/6lNWJdVktv2RMy1bDSF8DvA7wE0xxjcAxBjP\nAoMQwkNDCA3gKcBbgLcBTwkhJCGEzwCSGOPHp9ufOn3Jfzp97oeAh4UQNkMIBXXJqhOjJK2oPEs5\ntdHiRMdS1kPYa70BSdISypNs2h95iuuaG7TS1sz6I2d9q46fBlrAS0MIAPfFGL8O+B7gN4CUus/x\nTwBCCG+hTgIT4Pumr/FC4FdCCM8CPk6diA5DCM+j7pdMqFdb/dvZfSxJ0jx0WhmtImWrN6RXLt/9\nsmbkdqCc/pxRt4VIklZAMy1opgXjyRplNaA/6jMYH76143I1JpPjuQD6XXdtHYsPbsnb1XH8rp5j\neHUcv8Mbjiru2x4wqi7s3jc319je6vNZDzl9LK5PHmK9gTdQ3/rqzfu9zmhUTY7LugCStGqqcUVv\n1Kc37DMc739i9UHr11/W/DjrK4+SJF0TeZZyeqPNTn/IVm/IcTw3etj1Bg56nWuxLoAnQvbm2OzP\n8dmbY7M/x6eAcUI5KulX/U/qj9zcXOOee7Z50GWuyWfyKElaKZ1WTrNIObdz7cp2lsmu9QaeHmP8\ns3nHI0manTzJyIuMLmsMqgG9UUlZlQf/4R5MHiVJKydNEja6TU5c16bcufJJckXstd6AJOkYKdKC\nIi2YTLqstwq208s/yWryKElaWc08Zb1TzDuMuTJRlCTt1mg0aOctrmtuXPbfzvRWHZIkSZKk5WTy\nKEmSJEk6kMmjJEmSJOlAJo+SJEmSpAOZPEqSJEmSDmTyKEmSJEk6kMmjJEmSJOlAJo+SJEmSpAOZ\nPEqSJEmSDmTyKEmSJEk6kMmjJEmSJOlAJo+SJEmSpAOZPEqSJEmSDmTyKEmSJEk6UGMymcw7BkmS\nJEnSgvPKoyRJkiTpQCaPkiRJkqQDmTxKkiRJkg5k8ihJkiRJOpDJoyRJkiTpQCaPkiRJkqQDZfMO\nQFcvhJAALwO+ACiB74oxfnjX488AfgiogFfHGH9xLoEusEOM4bcBPwLcB7wmxviquQS64EIIjwJe\nFGN8/EXbvwb4CWBE/R185RzCW3h7jd/0sQ7wX4FbYox/OevYlsU+38FvAZ5L/R38APDPY4zj2Ue4\n2g6xLz22+4JDjM2x/o4eND67nvcK4J4Y44/OOMS5OcR354uBnwUawN8B3xpj7M8j1nnwOPhgR3l8\n5pXH1fA0oBVj/BLgR4GXXPT4i4EnAV8K/FAI4eSM41sGe45hCOE08FPA44HHAc8IITx4DjEutBDC\n84FfBloXbc+B24GvoB6/Z4cQrp99hIttr/GbPvZI4I+Ah846rmWyz3ewDbwQ+PIY45cCG8BXzz7C\nY2G/felx3xfsNzZ+Rw8+liGE8N3ADbMObAHs991pAK8Ebo4xPhZ4I/CZc4lyfjwO3sdRH5+ZPK6G\n8zsLYozvAB550ePvp56IWtRnpSYzjW457DeGDwH+LMZ4z/Qs8DuBR88+xIX3EeAbLrH94cCHY4z3\nxhgHwFuBL5tpZMthr/EDaAJfD3jFcX97jWEJPCbGuDP9PQOOzVn5GdtvX3rc9wX7jY3f0QOOZUII\njwEeBbx89qHN3X5j81nA3cCtIYQ3A5sxxjj7EOfK4+D9HenxmcnjajhBXU55XhVC2F2S/EHg3cCf\nA78XY/zELINbEvuN4V8BnxtCuH5aOvhEYG3WAS66GOPrgOElHrp4bLeod+LaZZ/xI8b4thjj/55x\nSEtnrzGMMY5jjP8PIITwA0CXugRYR2+/felx3xfsOTZ+R4F9xieE8GnAC4Dvn0dgC2C//1engccA\nP099de2JIYQnzDi+efM4eB9HfXxm8rgazgLru35PYowjgBDC5wNfBfxD4MHAp4QQvmnmES6+Pccw\nxngvcCvwOuA3gfcAH595hMvr4rFdB47VjlvzF0JIQggvBp4MfGOM8bideZ6VPfell3jsuO0L9hsb\nv6P7j883USdJv09dlnhTCOGZsw1vrvYbm7uprx59KMY4pL4Cd/GVt1XncfCVuaJ9ssnjangb8FSA\nEMKjqRvtz7sP6AG9GGMF/D1wrGq9D2nPMZyevXoEcCPwz4DPnj5fh/Mh4GEhhM0QQkFdEvHHc45J\nx8/LqUuWnrarNFBHb7/56LjvC/YbG/A7uuf4xBh/Lsb4RdPFPn4GeG2M8TXzCHJO9vvufBTohhD+\n0fT3G6mvsB0nHgdfmSvaJ7va6mp4PfDkEMLbqWu5bw4h3AR0Y4yvCCG8HHhrCGFAXff8mvmFurAO\nGkOorzj2gZfEGL3yeICLxu95wB9Qn7B6dYzxb+cb3eLbPX7zjmVZnR9D4F3ALcBbgDum/59fGmN8\n/RzDW1UH7UuP875gz7HB7ygc8N2Zb2hzd9D/q1uA104Xz3l7jPG/zDPYOfA4+DJc7T65MZkct6oI\nSZIkSdLlsmxVkiRJknQgk0dJkiRJ0oFMHiVJkiRJBzJ5lCRJkiQdyNVWJUlLIYTwKOBF0+X6L/X4\nV1LfAw7qFfceC3xejPFDs4lQkqT5mNUc6WqrkqSFF0J4PvBtwHaM8dGHeP6PACdjjD92zYOTdFVC\nCHcCt8UY75zR+6XA7wOfDnzfrN53+t6vAe48Zvep1DU2yznSK4+SpGXwEeAbgF8DCCHcAPwc9dnT\nu4HvjDHeN33s06kn0S+eT6iSFtw/AG6IMT5o3oFIR2Rmc6TJo7TgQgiPB/41kAJ/DZwDPm/6+4ti\njL8ZQngP8OwY47umZ1T/BngE8JnA7UAH+Djw3THG/zk9y/unwI3AGeAHYoxvuPiMaAhhEmNshBC6\nwC9c/L4z+PgSADHG14UQHrxr0yupJ8O/mN4g+/nAj08fex5we4yxnHGY0sqbzkk/BuwADwc+MP39\nTTHGB0+fcxtAjPG2EMLfAb9LPd/8X+BlwHOor/o9M8b45ulLPzuE8LPUB7u3xhjv3GvuCSE8E/gO\n4DTwu3tdPQkhdKj3FV8AjIEXxxh/Ffg94HQI4V0xxkfu81m/HXgu9Roh76a+Stk/zGeazrMfAh4F\ntIDnxhjfdNHr3wz8EDCZvv73A08HnhhjvGn6nBcA/ek4XGosUuDfAI+fbn9NjPH2aYLwG8Da9LM/\nJ8b4jr0+q5bbLOdIF8yRlsNnAU8A/gp4d4zxi4AvA348hPAQ6jNN3zx97hOA9wOfAH4ZuCnG+Ajg\nJdQ7k/OKGOOXALcCLzzg/f/FHu8rzcvDgZdND9C+k/pKAiGEBPhq4LfmF5q08h5Dneg8HPgM4Cn7\nPPd64PdijJ89/f3rY4w3ArdRJ2bnnZvOVd8B/FoIocn+c8+nA194QNndbcDdMcbPo54bbwshfD7w\ntcDHDkgcPxd4FvCYGOM/Bv4e+OHL/EzN6We6CfiVEEKx6/VvoD6Yf1yM8QZgG3gB8NvAE0MI3RBC\nA3gG9Ry/11g8C2D6Pv8E+LoQwo3ALdMYH0mdODx2n3HS6rlmc6TJo7Qc4rTc4EnA94QQ3gf8EfUZ\nxc8FfhP4hulE8y3Ar1MnnA8F/vP0+S8Cdid8b5z++0Fg84D33+t9pXmJwLdPFwZ4PvWVBKjPyv9l\njLE3r8CkY+CDMcb/E2McU19dO2gOecP0378B7tj188ldz3kVQIzx/cBdwGez/9zznhjj6ID3fcKu\n1/048J+or9AdxpcDDwPeMX3/r5vGdDmf6ZXT934f9RXKz9/12OOor5rePf39FdRXHM9R92N+I3XC\n95EY48fYeyyeBHztdPufUCfVNwD/DfjhEMJrqROHnz/k59ZquGZzpGWr0nI4/588Bb41xvgegBDC\n9cA9McZhCOF/UE+KT6I+I/ww4KPTM6bnFwi4ftdr9qf/TqjLhD7p5xBCvuu5l3zfo/yA0mX6XuBX\nQwgZ9ff2lun2AHx0blFJx0N/18/nV15s7NqWA8Pzv8QYB7se2yvh2729Mf37veaeZ3BhXtzPxRdJ\nGhz+2DcF/kOM8TnT9+7u/tsr+EzJJX7fK7ZXU19p/Cjwml3xXGosvhN4fozxP063n6ZeNKUXQvgc\n6qtMTweeCTx530+sVXLN5kiTR2m53EG9Q3hWCOHTgPdRlw99hLqs5SXUPYs7IYS/BDZDCDfGGN9C\nPcE8g/3Pun6cC2d1n3bI95VmIsb418Cjpz+/m0t8l2OMvwP8zkwDk/QJ4GQI4QxwFvhK6p7Ay/EM\n4N0hhEcCJ6jbNPaaew7rDuqD5udMk6qnUS8qchh3Ul+5eyH1ldBfpJ7zbruM9/9m4J3Tz3SSuj90\n9+v/YAjhp2KM91CXn/4hQIzxLdOexc/kQhnsXmNxx3Tb7wJN4K3UVyifSl2a+29DCH8IvPcy4tYS\nmtUcadmqtFx+EmiHED5IPWE8P8Z4PoF7PfXVxl8HmDZCfxPwkhDC+6n7SG554Et+kl8EHjd9/pdS\nl9kc9L6SpOPtPupFW95JXS75p1fwGt0QwnuBX6Lu1R9y9XPPv6Q+ifoB6lLPf3X+yt1BYox/Nn3/\nO4A/pz5m/pnLeG+Ah0wXtHsF8PQYY7Xr9d8P/DTw5unJ3uuorzae93rgjl2Lmuw1Fr9EnWi/F3gX\n8O+ntx75d8A3TstZX0+deEpXzfs8SpIkSUfoSu9dOV27oKBOwn/wsMmuNCuWrUqSJGkphRBupa6s\nudjHYoxPPeBvHwq8bo+HvyvG+K6rje8KfCrwF8ArTRy1iLzyKEmSJEk6kD2PkiRJkqQDmTxKkiRJ\nkg5k8ihJkiRJOpDJoyRJkiTpQCaPkiRJkqQDmTxKkiRJkg70/wG92rVZyQVR8gAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113782240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(1, 2, figsize=(15, 6))\n",
    "sns.regplot('revenue', 'sponsor_budget', data=df, ax=ax[0])\n",
    "sns.regplot('number_of_employees', 'sponsor_budget', data=df, ax=ax[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll try with a very simple approach that uses the data in the previous month to predict the budget in the next month as feature. We then use the linear regression model to predict the budget in 2018. Let's have a look at the data again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>revenue</th>\n",
       "      <th>number_of_employees</th>\n",
       "      <th>sponsor_budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>7500000.50</td>\n",
       "      <td>75.0</td>\n",
       "      <td>69000.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>8500000.75</td>\n",
       "      <td>85.0</td>\n",
       "      <td>48500.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>10500000.78</td>\n",
       "      <td>109.0</td>\n",
       "      <td>78500.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>11800000.19</td>\n",
       "      <td>113.0</td>\n",
       "      <td>89000.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year      revenue  number_of_employees  sponsor_budget\n",
       "0  2014   7500000.50                 75.0        69000.14\n",
       "1  2015   8500000.75                 85.0        48500.50\n",
       "2  2016  10500000.78                109.0        78500.30\n",
       "3  2017  11800000.19                113.0        89000.84"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting a Set of Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_rmse(scores):\n",
    "    mse_scores = -scores\n",
    "    rmse_scores = np.sqrt(mse_scores)\n",
    "    return rmse_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = df[df.year >= 2015]['sponsor_budget']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39541.999418465071"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[df.year <= 2016][['revenue', 'number_of_employees']]\n",
    "lr = LinearRegression()\n",
    "scores = cross_val_score(lr, X, y, cv=2, scoring='neg_mean_squared_error')\n",
    "get_rmse(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39541.999417865081"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[df.year <= 2016][['revenue']]\n",
    "lr = LinearRegression()\n",
    "scores = cross_val_score(lr, X, y, cv=2, scoring='neg_mean_squared_error')\n",
    "get_rmse(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45541.966467816324"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[df.year <= 2016][['number_of_employees']]\n",
    "lr = LinearRegression()\n",
    "scores = cross_val_score(lr, X, y, cv=2, scoring='neg_mean_squared_error')\n",
    "get_rmse(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalizing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler_y = StandardScaler()\n",
    "y_scaled = scaler_y.fit_transform(y.values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.4556130301959431"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[df.year <= 2016][['revenue', 'number_of_employees']]\n",
    "scaler_x = StandardScaler()\n",
    "X_scaled = scaler_x.fit_transform(X)\n",
    "\n",
    "lr = LinearRegression()\n",
    "scores = cross_val_score(lr, X_scaled, y_scaled, cv=2, scoring='neg_mean_squared_error')\n",
    "get_rmse(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3041660512343558"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[df.year <= 2016][['revenue']]\n",
    "scaler_x = StandardScaler()\n",
    "X_scaled = scaler_x.fit_transform(X)\n",
    "\n",
    "lr = LinearRegression()\n",
    "scores = cross_val_score(lr, X_scaled, y_scaled, cv=2, scoring='neg_mean_squared_error')\n",
    "get_rmse(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.6537922863401189"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[df.year <= 2016][['number_of_employees']]\n",
    "scaler_x = StandardScaler()\n",
    "X_scaled = scaler_x.fit_transform(X)\n",
    "\n",
    "lr = LinearRegression()\n",
    "scores = cross_val_score(lr, X_scaled, y_scaled, cv=2, scoring='neg_mean_squared_error')\n",
    "get_rmse(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Final Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "selected_features = ['revenue']\n",
    "dependent_variable = 'sponsor_budget'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = df[df.year <= 2016][selected_features]\n",
    "y = df[df.year >= 2015][dependent_variable]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler_x = StandardScaler()\n",
    "scaler_x.fit(X)\n",
    "X_scaled = scaler_x.transform(X)\n",
    "\n",
    "scaler_y = StandardScaler()\n",
    "scaler_y.fit(y.values.reshape(-1, 1))\n",
    "y_scaled = scaler_y.transform(y.values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(X_scaled, y_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: [[ 0.89549625]]\n",
      "Independent Term: [ -5.18104078e-16]\n"
     ]
    }
   ],
   "source": [
    "print(f'Coefficients: {lr.coef_}')\n",
    "print(f'Independent Term: {lr.intercept_}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making a Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11800000.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       revenue\n",
       "3  11800000.19"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = df[df.year == 2017][selected_features]\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.37862447]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_scaled = scaler_x.transform(X_test)\n",
    "X_test_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = lr.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's prepare the event sponsorship budget (in 2018) around [[ 108554.51277154]] baht!\n"
     ]
    }
   ],
   "source": [
    "print(f\"Let's prepare the event sponsorship budget (in 2018) around {scaler_y.inverse_transform(results)} baht!\")"
   ]
  }
 ],
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