GPA_lineer_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()\n",
    "\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAT</th>\n",
       "      <th>GPA</th>\n",
       "      <th>Attendance</th>\n",
       "      <th>Attendance_Map</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1714</td>\n",
       "      <td>2.40</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1664</td>\n",
       "      <td>2.52</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1760</td>\n",
       "      <td>2.54</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1685</td>\n",
       "      <td>2.74</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1693</td>\n",
       "      <td>2.83</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SAT   GPA Attendance  Attendance_Map\n",
       "0  1714  2.40         No               0\n",
       "1  1664  2.52         No               0\n",
       "2  1760  2.54         No               0\n",
       "3  1685  2.74         No               0\n",
       "4  1693  2.83         No               0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('dataset/SAT_and_GPA_with_Attandence.csv')\n",
    "\n",
    "data['Attendance_Map'] = data['Attendance'].map({'Yes':1, 'No':0})\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# discovery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAT</th>\n",
       "      <th>GPA</th>\n",
       "      <th>Attendance_Map</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>84.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1845.273810</td>\n",
       "      <td>3.330238</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>104.530661</td>\n",
       "      <td>0.271617</td>\n",
       "      <td>0.501718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1634.000000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1772.000000</td>\n",
       "      <td>3.190000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1846.000000</td>\n",
       "      <td>3.380000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1934.000000</td>\n",
       "      <td>3.502500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2050.000000</td>\n",
       "      <td>3.810000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SAT        GPA  Attendance_Map\n",
       "count    84.000000  84.000000       84.000000\n",
       "mean   1845.273810   3.330238        0.464286\n",
       "std     104.530661   0.271617        0.501718\n",
       "min    1634.000000   2.400000        0.000000\n",
       "25%    1772.000000   3.190000        0.000000\n",
       "50%    1846.000000   3.380000        0.000000\n",
       "75%    1934.000000   3.502500        1.000000\n",
       "max    2050.000000   3.810000        1.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()\n",
    "# 46% of students not attending %75 of lessons. not bad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.scatterplot(x='SAT', y='GPA', hue='Attendance', palette='Set2', data=data)\n",
    "# w/ Attendance, there may be an opportunity for better regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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l47iuy3EDWjszJNMSDIupZ7QHRUz2wROT/fpGm1KgAg/lJiHZht/Vhp9Kon1/XINhkIBYCDFOli6q4w+vO5/pFRFSGY/pFRH+8Lrzxy1/WKPpSru0d2ckRUJMWaM9KGKyD56Y7Nc3GvoGwbq7Fb+rFT/ZReC64x4E9yUpE0KIcbN0UV1ZFNC5fkBn0sH1gvFeihDjarQHRUz2wROT/fpGSikg8MJhGdkM2nPRZRT8DkQCYiHElKWBVNYjmZJ2akLA6A+KmOyDJyb79RUjDIJ9lO8QOGm0W/5BcF+SMiGEmJKCIKCjK0tX0pFgWIgce+kNEHhoN4vWGu1mz2pQxGifr9xM9usbHo3hZdDJdoKuVrzuDgLHmVDBMMgOsRBiytFk3ICupIMfTKwbthClNtqDIib74InJfn2D0yjfCX8RcDN4/sRPN5OAWAgxZQSBpjPtkM36MmRDiEGM9qCIyT54YrJfX57WYa/gXBAcTIIguC8JiIUQU4Am7fh0p1zZFRZCiGHTGL5DMEmD4L4kIBZCTGpBoOlMOWQcf7yXIoQQ5U9rjMAhcDNoNzsp0iGGQwJiIcQkNX67woHW7Gk+RTxqseqihjF9bSGEKN7EzwnWnkvQdhC/5QA0bCj64yUgFkJMGrv3tfDUSwdxvYCKuMWyRXWcP79mTNfw/tFOHt9+gEMnkwAsnj+dmqroGT5KCCHGWmEQPNHSIXSmG79lP37LAYKW/QSnjkCQeyfwGgmIhRBT1O59Lfz8t+9RWRHB9QIOnkiy/2gXN10JixtLHxSfPJXmiZ3NvLm/Pf/YyosaqK6MlPy1hRBiuJSfRXtZtDNxgmCtNbqrBb9lP0EuCNbdLQMeq6bNHNFrSEAshJgUnn/9GNGISUe3QxBoIpaJAzz76pGSBsTdaZetLx9i51vH6cnMmD+zkvWrmliyqBZDqZK9thBCDMRt3h22gku2YdbMoXvVjRBrQGczBEFQViOTB6J9j6D9cC4APoDfegCyyf4HGhbGjHmYdU0YdU2YtU2oaEX/44ZBAmIhxITnuD6HT3YTaFB9AlDbNGjvypbkNV0v4PnXj/Kb3x0h64Zv09VURbl+RSNLFs4oWIcQQowV9+BrOC/9FCOawJw+iyB5itbN38Ncsh579uLxXt6AtJPCb2nO7f7uJ2g/BL7X/8BIIhf8LsCsW4BRMxdljk4oKwGxEGLC0oQ7tKmMS9Q26Uy7RCwz/7zrB6Oevxtoze53W3nyxWZOdTsAxCIm1y6fx6qLG7BMGQAqhBgfKnAJ3nkOlEmQTaNTneHjaLw9z5RFQKy1Rifb8rm/fssBdOfxAY9VlbVh4FvXhFm3AFVVX7LNBgmIhRATUhAEdCTd/O7sVcvmsGnb+ziEO8OuH+D7AVctmzNqr/nekbBg7nBL+NadaShWXdzANZfNIxGT26kQYoz1DMvwsmgnSxAEOEf3gh0vCByVGcFPtg94CvfoHrw9zxAk2zEqarAWrxnVwFkHPsGpo/ngN2jZj8509T9QGRg1c/PBr1nXhIpVjdo6zkTu4EKICSfr+nSeNnp5cWMNNxHmDLd3ZampinLVsjmjkj984lSaJ3Y089aB3h8ol5wzg+tXNlI7LXbW5xdCiOFQCgh8lO/m+gQ7YU5wH0ZFDUGqC+zegl7tOxgV/e+F7tE9OLt+AcoCO06Q6gr/e/nNIw6KtZvBb20Oc39b9hO0HQTP6X+gHcWsbcoHwMaM+Shr/IqQJSAWQkwoXbkUiYFqQhY31oxqAV132mXLy4d4sU/BXGNDWDDX2DB2OxdCiKlLKcD3wnQIJ432PHQweHcIa/EanF2/QLuAZYPnggqwFq/pd6y35xlQFqoneLYj6IyDs/0xnEh8WDvGQaqjN/e35QBBx9EBi/ZUYnou9zeX/jCtAWWUT4pZSQPi+++/nyeeeAKlFJ/4xCf4zGc+U/D8e++9x3333UdHRwf19fX8/d//PdXV1aVckhBigvICTWfSwXFLP3HO8Xyef+0Yz7zSWzA3Y1qUdSsaufgcKZgTQpSaRgUe+A7ayYRB8DA7Q9izF8PymwvSIKZ/cC2ZygX9jg2S7WDHe1/VyUA6l85QMaPfjrEOAnTncfye3d+W/ejUqf6LUAqjelZv8VtdE0Zi+kg+EWOmZAHxzp072b59O5s2bcLzPNavX8+aNWtYuHAhECZV/+f//J/5i7/4C66++mr+x//4H3z3u9/l7rvvLtWShBATkAa6klnaOjMEJZ44F2jNq++08OSLB+lIhm/xxaMm11wmBXNCiP7y7c26TmJU1WMvvQG7cenITpbLB8Z3CJwsge+D1iPK8bVnLy44Jj6jgkxb/7Zl/dIrsl2gANNCKYW2LMg4uLt+gV9Vj9/aDG6m/wuaNkZtY28HiNpGlD2x0slKFhCvWLGCRx55BMuyOH78OL7vk0gk8s+/8cYbJBIJrr76agDuvPNOOjs7S7UcIcQE5AWarqSDiyp5MLzvSAePv3CAI60pICyYu/ySWVxz2VziUckuE0IUcpt3k932AzAsiFQQJE+F/82twwqKw3xgL5cPnA3zgbUuSDcoRY5vXwOmVwBgoDtPgB/+t4aCojwVq8qnPxh1CzCmz0YZZr/zTyQlvcvbts0DDzzAgw8+yLp162hoaMg/19zcTF1dHX/+53/OW2+9xcKFC7n33ntLuRwhxAShgVTWI5lyCbRmZG3Wh+dEe5rNOw7wdnPv235LFtZy/Yr5zJCCOTEBjOoupRg2d/fj6MCHTFfYM9e0IJLA3f34EJ//vqkQWbTnDpkKMWCOr8uotFDTWmNW1GDOuQj/4KuQbCW8+wLeabvApo3VdFlv+7OKyZc6pvRwk1LOQjqd5s4772T9+vXccsstAGzatIl7772XH/7whyxZsoRvfetbHDt2jG984xulXo4QooxlXZ+O7iyuV9qRop3JLP/nufd57pUj4a4MsGheNZ+49jzOmTM6tQxVcZtplaPbB1mIvpL7dtG6+fvhW9xWFO1lwfeoXXc7FYuWj/fyJrX937qNIN0NygjTC7QGHWDEK1nwhQfzx2kdoB0ntwucyRXEDS/0OvrD+1DRREHwqbVGZ1PM/qP/XtR6te/inDyIc/Q9ssffwzn2HkFmgOlvAKaNsmMo0wBlUHP1LcQbLy7q9caTYUexptUW9TEl2yHet28fjuNw4YUXEo/HWbt2LXv27Mk/X19fT1NTE0uWLAHgox/9KBs3bizqNVpbu4d8G7W+voqTJwfodTeJyTVPfpP1epWCtBO2Uzv9+3rGjAraBsh/GwnH89m2+xjPvHoYxw2D7tppMdatbOSiBTUopUbttbyKCNn0AO2GzqC+fmw6WMg9tL+Jds2p3/6MQBsoZYMfgLLROqDltz8jNe28YZ2jnK55LHa7R+N63ebdYe6tDkAFaGWCYYAGjcHJkx0Y+VSIDDrQIxqXrBPT8VNdvTvEgHYdjMR02s9wn9LZFH7rAYKWA6hTzTgnmiE48/Q3P5vEf+c5gmQ7KpeznKlcMGAOcrmqbSh+I6JkAfGhQ4d44IEHeOyxxwDYsmULGzZsyD9/2WWX0dbWxttvv80FF1zA1q1bufjiifPbhxBidHUkHdJZbyQ/M4YlCDSvvBsWzHXmCuYSUYtrPzCXFRdKwZyYmIKukxA5LanIioSPTzBnm5M7VvLrzN+sFMqyMSIJVCSKNkx0ZwveEK3RhmvAHF/t9Wuh1jv9bX++/6/uPDHgOVVlXUEArKrqCnagTYC5F5312ieakgXEa9asYffu3dx8882YpsnatWu58cYbueOOO9i4cSNLlizhf/2v/8U999xDOp1m1qxZ/M3f/E2pliOEKFNj0U7t3cMdPL79AEf7FMxdccksfk8K5sQEZ1TVEyRPgd1nR8xzMKrqx29RI+TufhwMC9VzLXYU7XKGnNyx5+5+HGXHUVW1KKVQpol2s2GPYN/DqKodsk9wMQZqoWYtXoPVcC5+26FhT39LzDsXt3LumE9/m0hK+pPgrrvu4q677ip47Hvf+17+78uWLeMnP/lJKZcghChbmpTj05108zm8o+14W4rHdzSz92Bvwdyyc2tZ+6H51FRJwZyY+OylN5Dd9oPcDmIknAgWeNhLbxjvpRWt3He7lQK0j1IBqmIaQQqCzhNo3wWtwvSJiuiAAzDOhj17MVZdU2762368Pb/Fef6H+Q4QhQfH+kx/a8pPf5s+o+KMKRZTnWyNCCHGnB8EdKVcMk5pdoW7Ug5Pv3SIl/acyL+ruWBWFetXNTFvZmVJXlOI8RDunN46KbpMlONud8+UOILeARmB5xMk21B2BB1JQLYbPA/sCJFRaodWOP1tP0HHsTNMf8ulP1TPRClJ/xoJCYiFEGNGa0g5Hsm0W5K+wo7r89xrR/ntK0dwcl0qaqtj3LCykQubaiZdmyAhIAyKJ2IAfLry2e3WqMAFz0G7Tr/WaNb5V/Xm9doxUCZob8TBsA4Cgs5jBCcP4Lf2TH/r6H+gUhjVs/Otz4y6BRgJme47WiQgFkKMCccL6E45+UB1NAWBZtfekzz90kE6U+HbiGHB3DxWXjQT05AdEyHK3XjtdvfsAqvAJXAdtJcNf2EfJJVrsLze4QbD2nMI2g7in9xP0HoAv/UAuNn+B1oRjBnh9DezbgFGbWNvfrUYdRIQCyFKSqPpTnukMm5JOki8c+gUj29v5lhbWDBnmYorLpnN7102h1hEbnFCTCRjt9utUX44JjncBfaGHJBxup7RyD1jlZ1dv8AbJDAO0l0Erb3Fb0H7kTDf+DQqPg2jtmf3t2lSTH+bSOSnhRCiZBwvoDOVxfNGPxI+1pZi844D7D3Y+9bipefWcd2H5lNTJbsoQohCKnAhcPE6HXTnqX5jkos14Fjll3+OvuAalFL5IFh3tw68nuoGzNoFfaa/SVrXeJKAWAgx6npGL3ennFHfFe5MOTz94kFe3nsyf+5zZldxw6om5tWXT8GcYSgMQ364CTEelAICH+W7BJ4TtkULAtCaIFIxKm3RvD3PACYonSusc8DN4u76ef+DTQujZl64+1u/ALO2ERVJnPUaxOiRgFgIMao8X9OVcsiOcl9hx/V5dvdRfvvqkfxY57pcwdwFZVQwZ5mKeMwmZpuYEhCLKaKYCXOlmEanFOhAo7QLvot2smjfG7V+wD10NhlOfzsZToAjGOQ+F63obX9Wfw7G9DkoU0KuUacUKvf/+f82jHC4S5HkX0cIMWq6My6ptDeqfYWDQLPt1SP84pl36eopmItZfOQD8/jQheVRMKcURCyTRMwiYptIGCymkmImzI3WNLqensBhSzQv3w3ibNMg+tJao7tbcwHwfvzWA4NOf8Owws4YykBVTCf+4c+XzS/pE0ruc5YPchVhHrUywDDBUChloAwTHT4JSqGVARj5uNiqLH74iATEQoiz5voBnUknv3M7WvYePMXj2w9wvD0NhLuvVy6ZzZpLR69gbk9zO8++eoT2riw1VVGuWjaHxY01w/pYw1DEIibxqEXEMko2dlqIclbMhLmzmUanAg8VeAS+i/YctO8PuQPcU/A23E4QOvAJ2o/09v9tPYDOdPc/0DAxps+B+DSClv1gxiASzY9Vti++ToLhvgbaxTVzQa4yUIaRKx5UaKXCNnZKoTFQuXfZ+t5bde7PQM7mHiwBsRBixHpyhZOp0Z02d7Q1yeYdzbxzqLdg7rLzwoK56ZWjVzC3p7mdTdvexzQNYlGLzrTLpm3vcxMMGRRbpiIRs4lFTAzV/4YtxFRSzIS54RyrFGgdoIK+u78OOtDD7gQxYMHbrl/A8pthxnIAtJMJd397xh+3HRx8+ltdE0btAsz6Joya+SjLzr/OSNuvTRp9Al5lmrmdXDMMdJWJNsLdWwwjDHJzcXHPP2WpAtxiSUAshBgRPwjoTLqjmivcmXR46qWD7NpzMn+DPGf2NP7j9Yupiox++6FnXz2CaRpErPDcEcvEyT1+ekAsaRFCDKyYCXP5YyMxlGmhDAvtexjTGzC8dG7310X7/lmlP3h7noEgALcDfB9ME8wo7qv/l/a2d0kfeoeg4zgDhWKqoiac/pZrgTbU9Lee9muTTk/qQkFurglG4a6uzu3yogy0MnO/zIQfMligW66bBxIQCyGKpEm/hMaZAAAgAElEQVQ7Pl2p0Zs2l3V9nn31CM/uPppPu6ifHmPdyiYuaJxObW0lbW3JUXmtvtq7ssSihbdB2zRo7+ptki9pEUIMbTgT5noCpcilN+K+8iswLDQQZNNoL4M572q87gGms41QcOoYOOneF/b8cPJctovkG33ygJWBMX122P+3fgFGbdPkn/6mVD7Qze/oKgNlmihl9Aa5KDAUGnPYu7oT+f4oAbGYVHbva2HzjmZaOjLUVcdYt7KRpYvqxntZk0YQBHSmXDJO4a7wSPNwg0Dz8t6TPP3iQbrS4VuVFTGLj3xwPh+8YGbJuzTUVEXpTLv5HWII86Frp8WwDEU8XtgtYiLf7IUolfyEudc2o1PtmDWzsC76CPac81FeBh244eAL38Wsqsc/90q8t3+TTzOwL7keu+G8s1qDdrPh9LeW/WH3ByeVe6L/N2103gUE1bkWaDPmT77pbwMEvEasArPSzOXnFrejC1Pj3icBsZg0du9r4dGn9mKaBomYxamkw6NP7QWQoHgUpB1vwF3hkeThaq3Ze/AUm3c05wvmbNPgyqWzuXrZ7DGbMHfVsjls2vY+Tu71laGoTtjcdNU51FbHkboYIYYW9vv1iMxdTGT2oly6gwdBgN/VNmAkZc86H3vW+Wf1ukG6k6DlQD4ADk4NPP0tt8qwbYEG7Dj1H/087SV4x6mk+qUw9HRfCIvQlGn2SWEwC/J1tQarogqd6p/2MRUC3eGSgFhMGpt3NGOaBlE73O2L2ibZ3OMSEI+c6wd0pwbPFS4mDxfgSEtYMPfu4fDtUQVcdn4d131wPtWjWDA3HIsba7gJeOntEzi+pqYywlXLZnPxgtoxXYcQ5Sz/i6H2IQhQ2kf7uTxfz4Eiit1GQusA3XkyF/zmpr8l2wZaaTj9ra4J78jbkO4kv++pLIgmMKaV6c+Cvp0YcsHtwIVpue4LfX5b13pypjCMNQmIxaTR0pEhESv8ko5YBi0dmXFa0cQWaE0y45HKuEPeVIeThwvQkXR46sWD/G5vb8HcornTuGFlE3PqTqs6HyOWqfjQRQ1ctWxOvluEEFNV73ALPwx+tR8Ot/DDABgdvrtT6ihL+y5B2+HeALj1QJgPfDrTwpgxP0x9qGvCrG1CReK4R/fAwdehp08thAV2OsBavKakax9STyqDYYRDOnKFaWHAm2s1pkyUUpM6V7dcSUAsJo266hinkk5+hxjA8QLqqmPjuKqJSJNxA7qTDt4A6RGn5woPlodbUxXu9mYdn9++eoTndh/F9cO3NGfWxLlhZSPnz58+5v06FWBZBhVxm6hlSlqEGFWlmMI22q/dG/h6qMDHS2pIdxAEPpxld4eR0NlkPvXBbzlA0H5o4Alw0QrMuqZcALxgwOlv7tE9ONsfA9fJ5coSplIYJsQqStsRoifgVeQCXitXqGbm++tq48wBrwS740MCYjFprFvZyKNP7SVLuDPseAG+H7BuZeN4L23C6Cmayzp+v5v0YLnCHzi/npf3nszn4bp++Hm/cslsdr51nKdfOkR3rmCuMm5z3QfnsXxx6QvmTqdUmEaTiNpE7PGfbicmn9GawjYqr53qxNn5Y5RphkGg9sOevn6fdIdc4BtEKgiyY/NOWn76W9/0hwH6FQOoqvqw/2/dgrD9WWXtkL9A53sPFwTDGhI1YUs4d4Bd5iKEwe5AvXb7dGbomarG4KkMEvCWJwmIxaTRkycsXSZG4syt1AbLFX7vSCc3XXlOfud4emWEhXOq2bzzICdP9RbMrV42m6uXziFagn7CQzEMRTxqEY+a2Ka0TROlczZT2M6GUuC//RuMihpUNIZSZhjoZpO4uzdjVtaVNMd3KNr3CE4dyQe/fssByA4y/a1mbmH6Q6yyqNfy9jwT5gpbVrjD3JMuke0K26tVDG8CZeFOr42ybJRpo4e5wztoqwZR1iQgFpPK0kV1EgAXyQ80XSmnXyu10w2VK7y4sYbFjTUcbkny+PYDbNl1GAg3aJafX89HPjSf6opIqS5hQJaVmyZnyzQ5MTaKmdg2Er0T3HKFbT07voGH330SbdioZCc68EDnCt26WsY0GNZOGr+1uXf8cduhgae/ReKYtT27v00YNfPy099GKki2gx2HaCWkOoAAtALPA+0Nmj+sjLAHL1YEw7TDfN4Bdnrz1yj3kUlJAmIhpiilIOP4dA2QKzyQoXKFT3Vnw4K5d1ryz507t5obVjUyu3bsCuZ6pslVxCxsmSYnxlgxE9tOlx98EGgUPmiN0gFa+2jfh8An8Nxw53egHF/DRqe6wO7zi6fnDn9XdAS01ujUqT67v/vRg05/m1GY/jCtftDpbyNlVNQQpLpQkXi4gmx3GAzbESLLb+7NH1YKw46g7AiYdvhHhe8eFTRuk8B3SpGAWIgpKNCa7pRLOusNe7fj9J69rh/geT4V8QR//2+v4PnhiWbWxFm/qonz5lWPWcFczzS5RNTGtpTs4IhxUTixLQqBhzIMIpd+tLeQLRfsooOwi0OukE0HQW5nNxdoQlFbkdbiNTi7fpF7bRs8d8hd0ZHQgU/QcSwfAAct+9Hpzv4H9kx/ywW/Rl0TRnzaqK2jh3t0D96eZ/IDPoz6hQT7Xw4/B3YMrCiGaRC57Pex51wQFrqZNvb0GlSQKvz0yj1jypOAWIgpZfAOEmfS07P32VeP0NaZwTINMk7Aa++F/UCr4jYfGeOCOdNQxGMW8Ygl0+TEuOn5vS/SeDGG+Wm8vc8RpE5hxKowF67EqplF0HlixMHucNizF8PymwsCRGvxmrPqqtB3+pvfcoCg9UA4lvl0ViQcfdzTAWIMpr/lC+iUBXacIJNEH36dyHlXoE8dQTspjEQ15uI12PMuLkh7UIYp9wnRjwTEQkwRnq/pTjsDdpAYrvPnTyfQsHnHAU6eCqvSbcvgqqWzuWrZnIKWd6WiFFi5aYSxiImSxAhRQgVvcugg18/WD9MZgt6dXe2Hu7uqsh77spsLzhG4A+TQloA9e/FZBcDh9Lf9faa/HR1w+puKT+vN/a1bgFE9KxwkMYb8d7dhxKZhxCrC1AetCJwUwamjJK7/fwsCXgl+xXBIQCzEJDfcARtncvhkN/++vZn3j4ZvkSpg+eJ6rvvgfKaNQcGcoRTRiEk8ahGxVG4FQpydsFCtZxhFgJ9JotwkOggg8NE6CAvUAn/MBlOMhb7T39p+d4j0kX2DTn8zqhsK0h9UYhz6hysFhkJZudxfNMQqwjSOZCfoIPy36W6dDP88YhxIQCzEBLZ7X8ugbeaKLZobzKnuLE/uPMgr7/YWzJ03r5p1K8emYM4yFJUxG7M6imlI/+CJajwGVpwe7KogAHR+Z5fAD4dRBEE+lcG3EvjJZEnXNR605xK0H8zn/votB8AN3+UpSIIw7dz0t1wBXG0TKlK64Uan5wFbi9eE+b5KoSwLLBvDsNGGhTbCkEUDKAvd3T6iAkYhBiIBsRAT1O59LTz61F7MXPrAqaTDo0/tRSm4+JzaoovmTpdxPJ555QjbXjuaL5ibNSPButyEuVLq6RYRj1nEbJPqqihOZoDcRTEhlHJgxenjhsNUhp52ZGGHhlLl7ZYznenGbz2Qm/62n6D98CDT3yqJz1mEP21eWPxWM3fM0h/co3twX/0/KDuBWd2AAoL3XkBPq8WYe3G+32//pI3TCxgjYW5z4GEvvWFM1i4mHwmIhThLQ+3SltLmHc2YppHP243aJlbM4vnXjzFrRgX+CHeF/SBg51sn2PLyIVIZD4CqhM11H5zP8vPrMUZYMDfQ2OfFjYUtoXq6RYRpETJEY7IY6cCK/Lvy2s/l7ga5VmRBvjtDzw7vWI8bLifh9LcW/JP7CVoPDD39bdrMXP/fsADO62rBf28bzrvPYxx966wL8YZDGUa4+3vsLcyqunDKm+8R+B46myL7u/9LYu4lQ/5zhl83t47bmGwx+ZQ0IL7//vt54oknUErxiU98gs985jMDHveb3/yGL3/5y2zdurWUyxFi1A22SwuUPChu6ciQiIXfwhE77L2byXq8e6hjRMGw1pq3DrTng3sIR2BftWwOVy2dTSQXeA8nsD3dYGOfbwIubJqBbRnEY2EQLEM0Jp+CgRVKgTJQsQp0tit8qCeloafvbuCD39OD15tUubujIT/97eR+/NZc+kN2gDQPw8SomRfm/tbnpr9Fe9Oc3KN7cH/3S0zbDjs1pLrCzg19e/YOYMA0h6GCaKVQufxfw46izQgYJu6xfRBJFOYjFzHIxG5cOqYB8Hik/YixU7KAeOfOnWzfvp1NmzbheR7r169nzZo1LFy4sOC4lpYWvvnNb5ZqGUKU1EC7tNnc46UOiOuqY3SmXGqroygVTpJLZtwRTYQ7dKKbf99xgP1HcwGKgg8unsmHPziPaYne8w0V2A4VFJ8+9jkesbAsxdvN7axeMhtLdoMnlfyQidxwCWvmQnS2O9wh1oD2CZwMqrIW3XkifEgC3kGF09/6pD+0HQTf639gJBHm/tb2tD+bizIHn/7WM+rYsKIEfgB2BO2Gjw8W4PZrdzZIEN2zC6wiMTAi+fzffPqDBqOqbsSDTMZaKdN+RHkoWUC8YsUKHnnkESzL4vjx4/i+TyKR6HfcPffcw5/8yZ/wd3/3d6VaihAl03eXtkfEMvI7rKV04+VN/N/tB+hMOjiuj+sH+H7AVcvmDPsc7V1ZnnyxmVffbc0/dv78atatbGLWjP7fr6cHthHLxMk9PlRA3N6VJR61iEVNEjELyzDIOB77j3ZhmhIMTySnB7thg1cfhUb7udzdnulqufHBat4S3F2/BG2AZeWHRkSW3xwWt5UJ9+geTjz3HE5Hy6j08R0JrTU62R6mPuR2gMPpb/2pytqC8cfFTn/Ljzruy7LDxwfRE0Srnol4uSDa3/c8kXkXoewohhVBmzacod/vRMoDHmnaz1iR3euzV9KUCdu2eeCBB3jwwQdZt24dDQ0NBc8/8sgjXHTRRSxbtmxE56+trTzjMfX1VSM690Qm1zx2ZtdX0t6ZJtan/27G8ZhdX1nSNVVPT3DJ+RaOhie2N9OddqmtjrN2ZSOXDGNnOpVx2fzCAba+dBDPDwOSeTMr2XDteVy4YMagH3cq5VIRNQve4jQNk46Uy4wZg3ecWDR/Oo7nYypF1vHpzrpkHI+ZtRXD/jxNxa/rUhvoHqp7pqgFAYGbpXaalevKEI4P1vmuDLlAVuf/B2wAI/enz85kzaWkK2y6XtmC39mKOa2Wqks/TLzx4pJeXzHSzW+QeWUTmCZWrALtdOO/somqqv+npOvUgY/bepjssfdwjr5H9th7BKmO/gcqA7tuHtFZC4nMXkR01kLMxNlNf3Or6/BTHYCJZYaBdOBlMavrqBnk+zmTOoUVTaBMK58CgRUhSHdRf845xbVjq7+S5PQ4HS/8Eu/UCazpM6m+/A+oWLT8rK5rWC9d5P2kOdmKFassuD5txtHJ1nG/NyX37aJ1+6Mo08JKTENnO/G2P0r19HjB53K811nuSl5Ut3HjRu644w7uvPNOfvzjH3PLLbcAsHfvXp588kkefvhhjh07NqJzt7Z2EwyRK1lfX8XJk10jOvdEJddcen2L6GK2QWfaw/M1EcvA8cJd2g9fNqcka9JAPBGh+UgHQaCZPT3OH68r3MFqaxu8ZZQfBOx48wRbXz5EKhu+5TqtIsJ1H5zHZeeFBXNDffz0hE1n2s3vEAM4nk91wqatLVmQXzyzJs5HPjifJYvqWLNsNj94Yg+GYYzo8zTVvq7H6gdX27Hj+I4L6FywG/SmLWjN9JoE7UN8PRSlcgH26s/mw+QMkBmtc4+C9EtPEmgD24qGvyQqG6017S89SaZywai9jnazBK3NvdPf2poHmf4W7ZP+0IRR24iyImggC2QzQOYsP3+LVuPv+gUAPr079+ai1QP+uyvDwKhtRPsOmBF838V3HIKOVoxENS0t3cWvYdp52Nf/f/mvixSQKvH3+kjuJ7qiFj95qmACn3azGBW1435vSv32ZwTaQCkb8l+7AS2//RmpaecBcg8djpIFxPv27cNxHC688ELi8Thr165lz549+ec3b97MyZMn2bBhA67rcuLECT75yU/yox/9qFRLEuKsnV5E53hhAGEZilTGK2mXCS/QdCUdXNSQvwgORGvNm/vb2byzmdaegjnb4Oplc1i9dHZBgDuUq5bNYdO293EA2zQK0jR68ovjMZs5MysJtGbTtvexLYOli+r45EfOH5duHGJwfiY9ZlPUyt1I0geGdd5URz79IWg9QHDqyIC50ipejVG/ALM2LIAzps1Clbjvds+4Z/Y9hz9QmkhPMZwdC9MgLBvr4uvIbHsEMMo+zWE0lXN6R0HRao8iihNFqGQB8aFDh3jggQd47LHHANiyZQsbNmzIP79x40Y2btyYP/ZTn/qUBMNi3Ay3ddpARXQAlXGbL392ZdHnGw4NpLIeyZRLoDXFjsI4eKKbf99+gAPHegvmPnTBTD78gXlUJYorwFvcWMNNMGCXiUc2v83MmgSxiEU665F1fVxf5wsMe/4IUY6MihqCVBf0/eXQczEqhu6g0pfWAbrjRG73NwyA9YABtcKYPivc/a1fgFG7AKNi5L29i+760Ic9ezE1Fy/v3RFWKiyIi0RRVq4jBCpfDGfNu5jo5X845fJVy7nNm1FVP2GKE8tZyQLiNWvWsHv3bm6++WZM02Tt2rXceOON3HHHHWzcuJElS5aU6qWFKEoxrdOGU0Q3mq3YenaFs+4ADfXPoK0zw5MvHmT3vt6CucWN01m3spGGmv4Fc8O1uLGmoIDOMhXxqBW2elPhZLseY1VgKMTZshavwdn1CwIvi+6TPmAtXjPox2jPIWg7FAa/LQfwW3unvxUwbYza+bnRxwswZzSO2vS34XZ9GJrCsCOoSBTMCNoIExgGex9qrNudlYtyve5y3r2eSEqaQ3zXXXdx1113FTz2ve99r99x8+bNkx7EYtwU0zqtrjrGqaSTPxbA8QLqqmMjOt9gAq1JZjxSGbfoDgzprMdvfneY518/lu9HPLs2wQ2rmjh3bnVxJxuEUmBbYe/jiG2iANNQZ/zcCFGuzpg+QG76W0/ub8/0N92/S4aKVeUHXxh1TRjT55Rs+ttgXR+Gap0GPW3RbIxIDLu6BqUz0u1lgirn3euJRCbViSmvmNZp61Y28uhTe8nmjukpDlu3snFE5xtIMuuRTLtF5wl7fsCON4+zdddh0n0K5tZ+aD6XnleXH3hxNgyliNgGiahNxFZA7zmH87kRopz1TR/QWqO7TuK+9yJBLgjW3S0DfpyaNjMf/Jp1C1AVM4rrtnAWisl9VqaBsqLhcAzLBmUSaFCWjdbyTs5EVq671xOJBMRiyhvOrm+Pnh3eofKDizlfL03WDehKO3he8QVzb7zfxhM7D9La2Vswt2bZXK5cOmvYBXNDsQxFPG4Tsw2sQfoGD+dzI0S50r5H0H6YruYjZJrfCdMfBpv+NmNebwB82vS3sZbPfbb71AP0yX1WpoGKxAbMBx40J0KIKUgCYjHlFbuzeabisGLP5/marlQ4XKPYn0/Nx7v49+0HaD4etjsyFHzowgauXT636IK5gViGIpGwiUdMFGceqSyFc2Ki0NnUadPfDkHg0W+ftO/0t/pzMGrmoszy+dHZk/sc5o/a4Hso2yZ26Y1YVTUERgSUkti3TMgAjfJVPt/VQoyT0d7ZHO75ziZPuK0zwxM7m3ntvbb8Yxc01rBuZSMza+JDfOSZKcCywoLAWJ9AWIiJKpz+1pbP/fVbDqA7B57+Zk2rhxmNvekPVfVjlv4wEj25z/5728FJY9Y3Yp5/FebciymfGYACZPxzuZOAWAhGf2fzTOdLO2EbNa/IPOFUxmPrlnf49csH8wVzc+oquGFVI4vmnF3BnFLhKOaKmE3ELm3/UyFKSQc+wamj+eA3aNmPzgwwlEAZGDVz88GvWdfEjDmzRm8YSYn15ATHz1uJvmD1GUcli/FV7uOfpzoJiIUYMxrH1XRnXJwi26h5fsD2N47z698dIp0NP7a6IsLaFfNZdu7ZFczlA+G4TcQqLJQTYiIonP62n6Dt4MDT3+woZm1Tb//fGfNR1tmnFo0lZRgoO4JhxwisKJITPHHIAI3yJgGxECWmFGTdgGTaxfH8onZwtNa8/n4bT+xopq0r7O8bi5isuXQOV1wyG9sa+U6uoRSxqEk8ahGxBi6UE6IcBamO3O5v2P836Dg68PS3xPSw729P+sO0hpJPfyuFvkGwtmw0ZsnSISTHtXRkgEZ5k4BYiBIKgoCutEfG8YoOOAcqmFtxYQMbPnI+bmbk43YNIxykURE1MXLBgQTDolzpIEB3Hi/o/6tTp/ofqBRG9axcAJzr/5sY+fS3cdV3ZLIdRZs2GqPkOcGS41paMkCjvElALEQRihnJnMy4JDNe0f2EWzszPLGjmdff7y2Yu7ApLJirnx6nKhGhbQQBsWEoEjGLeMTCNCQtQpSncPrbwdOmv2X7H2jaGLWNYQeIugWYtY0oe+IOgVFKoaxcfqkVzU+LG8vCOMlxLS0ZoFHeJCAWYpiGM5JZKcg6AV3pLG6R/YRTGZdf7zrM9jeP5wvm5tZXcMPKJhbOmTbidff0EI5HzFEZziHEaNKZLvye1mctB84w/W1BPgA2ps8u2fS3sdKbChHuAmNY4/pujeS4lp4M0ChfEhALMUxnGsnsBwHdI0iP8PyAF944xq93HSbjhAVz0ysjXL+ikSWLakccxJqGIhG3SUSldZooDz3T3/K7vy370d2tAx7bO/0tDILHcvpbKSnTGDwVYpxTlyTHVUxlEhALMUwDjWRORE1cP6Ar7ZLOeARFRMJaa157r5Undh6kvU/B3O9dNpfLL5414oI5w1BUxCziUUt2hMW46pn+FgbAYQ4wTqr/gYY1wPS3xNgvuESUYaAsGyMS798ZooxIjquYyiQgFuOqmJzc8dZ3JLOhwnxcrUAFmmS6uJze/cc6+dkz79HS0TsX64LG6Wz4vUVUxOwRrU8CYTHedDbZZ/rbgfz0t356pr/1FMCV2fS30RAGwRZGJB6OTDZMiiwnGHPjkeMqXS1EuZhcdyBR9voGwDHboDPtkYhZA+bknh4sX9A4nbebT404eD7b861b2ciPntqLGbOoSkToTGbp6M7y+1ecM+w1tHSkeWLHQd7Y31swF42YJKImx9tTHDrRzeLGmmGfDyQQ7msi/YI10fVOf+uT/tB5YsBjVWVdQQCsquomRfrD6cLCOBsVjYEZAcPqDYLLPBjuMZY5rpOhq4UE9JOHBMRizJxelHasNYUfBOF4YKUKcnKBwmPbUuw9dIrqiihVCbsgeP5wfVXRr328Pc3eQ6eYlogwrSIyYIHc6ZadW4dhKra8dIjDx7uYVhHh9684Z1gBbDLjsnXXYXa8cTyfVmGZiurKaD4n2fF8nn31yLAD4p6uEQkJhIHhFT2KkQunvx0hOLk/twu8H53p7n+gYWJMn9On/28TKnbm79GJTJkGRjQOVhzMPoVxEyQIHi8TvavFZAjoRS8JiMWYOb0oLeykoOhMOsSj4ZdixDJo6cj0Ozbt+CgU6azHtIpIQfD84VVn3qHtd76sF57P8amuVP0K5E7nBZrulEN9dZxbrj1v2NfsemHB3G9+11swV1MVxXE8qioi+T7AALZp5HOJh6IUVMRtElFpn9bXmYoeRXG0myFzsBnn/bfDXeDWg+APkBpkxzBrGzHqzgl3gWfMm3DT30ZCKYWKRMO8YDOC7ilclSB42CZ6V4uJHtCLQhIQizFzelGaZRn4foDn95aXOF5AXXWs37GeF2AoCo7tCZ5H8tqenzufN/T5/CAglfWLLpgLtGb3vlae3NnMqe5whGwsYnLN8rBg7uF/f4vOtEukT92c6wfUVEUHOWPvQI266jgdMkmjn4GKHov5GpnqgtSpfOFb0LKfoOMYqSGnv+XSH6pnotTEm/42IrlewUYkBla0MCVCFG2id7WY6AG9KCQBsRgzfYvSAKYlbNo6s5iGQmuN4wX4fsC6lY1s3tFccKxlGXheUNB5oSd4HslrW6aB6wVYp52vfnoMpcD1NKmsSybrFxUIA7x/tJPHtx/g0MkkELY/W3lRA9cun0siVzB31bI5bNr2Pg7hzrDrh9d+1bI5/c7XEwgnoiamYRCxJ3bv1VI5/d8YivsamYq8/S/jHHwjN/2to/8BSmFUzy7s/5uoHvuFjjNlGKhoDGXH0EakdxNYguGzMtG7Wkz0gF4UkoBYjJl1Kxt59Km9ZAl37kzToCJmMa0iQirj9SuC6ntsPGLS6fnEo1a/4Hkkrx2PWjhelnjERGuNH2gSUZN1K5to7czien7RDfJbTqXZvLOZN/e35x+7+JwZrFvRSO1pQdnixhpuAp599QjtXVlqqqJctWxOQf5wbyAsqRHDcfq/cbFfI1OR+/pT+B19drOsCMaMcPqbWb+AGYsuoKPbH78FjielMCwbIxpHW1E0hsS/o2yiT26b6AG9KCQBsRgzPYFu3y4A//HacwfM7zz92FkzEvzepXNG3GXi9PM11MRZs2w27xzqIO34zK6NsWRhLfXT4zhucQFAd9pl665D7HzzRH43ef7MStavaqJp1uDFRIsbawYsoJMRyyMz0NeXdJkYmopVYlbNCnd/68/BqJ5VMP3NiMSA5PgtcBwoQ2EkKsIx0IYtKRElNpEnt030gF4UkoBYjKmli+qKCmJPP/amUXxtjSad9UmlXbwR/NRzvYDnXz/Kb353hKzbWzB3/YpGliwsfqqWUhCPWlTEbAmER6iYry8BsQ9/nsAboE/wFKOUCkcoR+JY1TPAS4W7wRIMizOYyAG9KCQBsZiSUo5HKjWyQDjQmt3vtvLki70Fc/GoyTWXzWPVxQ1YZnEFRkqFHREq4xEsc3IFwtIXuMxN5XZ9SqFMM1cgFwMzLJDru0MuhJg6JE2j8UkAACAASURBVCAWU8rZBMIA7x3p5PEdBzjcp2Bu1UUNXLN8Xr8OB2eigIhtUhG3idpG0TnL5U76AotypAwDFYmGBXJmVArkhBCABMRiCgi0JuOMPDUC4OSpNJt3NPPWgd6CuUvOmcH1KxupnVZ8FwPLUlTFI0RtA1CTLhgG6QssyohSGJaFisbBiqIxJf4VQhSQgFhMWvkewlmPYISBcHfaZcvLh3jxreP54prGhrBgrrGh+OlbhqGoiNtURCf/t570BRbjTZkmRrQnJcKelL94CiFGx+T/qSymnEBrkhnvrAJh1wvY9tpRnnmlt2BuRlWU61c2csk5xRfMQbhDOq3CxjSmxhAD6QssxoMyDJQdRUViaJkgJ4QYJgmIJwgpTjozP9CkHY9UZuSBcKA1r77TwpMvHqQj2Vswd+3yeay8qPiCOQDLUCQSNonI1Pp2k77AYsxIz2AhxFkq6U/o+++/nyeeeAKlFJ/4xCf4zGc+U/D8008/zbe//W201sybN4+vf/3rVFdPvSlIZyLFSUNzXJ+utHtWO8IA+4508PgLBzjSmgLCgrnLL5nFNZfNJT6CFAfTUCTiNomoiWLqVfNLX2BRUkqhDAMjGi/oEiGEECNRsoB4586dbN++nU2bNuF5HuvXr2fNmjUsXLgQgO7ubv7qr/6Kn/70pzQ0NHD//ffz7W9/m3vuuadUSyoLI9npleKk/pQK335PZz1cFMm0O+JznWhPs3nHAd5uPpV/bMnCGVy/opEZIyiY6+knXBm3MaZyWyukL7AYfYUpEdIlQggxOkoWEK9YsYJHHnkEy7I4fvw4vu+TSCTyz7uuy3333UdDQwMAixcv5le/+lWpllMWRrrTK8VJfWkcT5NKu2Rz45VjieiZP2wAAxXMNTVUccOqxhEVzEHYRq0ybhOxpkaesBBjIRycYWNE4mgrIl0ihBCjrqQpE7Zt88ADD/Dggw+ybt26fPALUFNTw3XXXQdAJpPhu9/9LrfeemsplzPuRrrTW6ripFLnJY/m+TVhakQy4+HmAuGRcjyfbbuP8cyrh3HcAIAZ06KsW9HIxSMsmDMNRUXCJh6xpmByhBCjTymFsixUJAZmVFIiiuQ275aRwkIUQWld+kY06XSaO++8k/Xr13PLLbcUPNfV1cXnP/955s2bx9e+9rVSL2VcffarT1EVtwoCLq013WmP7//FdYN+3EtvHeeffrYby1JhEO36eJ7mcx9fygcvbBj044ZSinOW4vy+H5DKuCQzHv5Z/jQMtGbH68fY9Nt9tHdlAaiIWdy4eiFXXzZ3RAVzALGIxbQKG9uSCVdiYnHaj0Pgj/cyeimFsmwMO4ZhR1GWPd4rmpCS+3bRuvn7YFooK4r2suB71K67nYpFy8d7eUKUpZLtEO/btw/HcbjwwguJx+OsXbuWPXv2FBxz4sQJPvvZz7Jq1Sr+/M//vOjXaG3tHrKIqr6+ipMnu4o+b6nMqIz02+nNuj41lZEh19lUl+A/fvjcfrutTXWJfh833Gv+tyffBgWmYeD5Ovx/5fNvT75NU13ijB9f6vP7QUDa8UkPIxCeMaOCtrbkkMe8e7iDx7cf4GifgrkrLpnF7+UK5jo70sO/uJye9AitNKfaR57DXKxy+7oeC1PtmuvrR5ayU6yOjjSBO/jXbs2MCtrP8L11tgrSIcwIBCY6q4FM7s/Ymgxfa6nf/oxAGyhlgx+AstE6oOW3PyM17bx+x0+Gay7GVLtemHrXPJJ7aMkC4kOHDvHAAw/w2GOPAbBlyxY2bNiQf973fe68805uuOEG/st/+S+lWkZZOZs2VKNdnFSKvOS+KRId3VmmV0WhT/A/nPMHQUDyLIdp9HW8LcXmHc3sOdhbMLd0US3Xr5hPTdWZU072NLfz7KtHaO/KUlMV5f9v796joyrP/YF/996z91wyuUEmXBMuKXjBBFQUtAJSFEISqEqrVitVl0pdSLSy5KDFY9sj1eOhcvTQ1VVbPd4QyzoVRH8misQbULkVTI0oGC7hkkASck/m/v7+mGRIQgi5zJ6ZzP5+1uoqs+f2Ppnpm6dvnud9p08ajkvHDILdqsGsKpAAbvZP1FuSFDg0Q7Ocu0NEH//3xBKBs/wNlYAW1/GiSQtcH6D4+ZLedEuIZ8yYgeLiYtx0001QFAWzZ89Gbm4u7r//fuTn56OiogLffPMNfD4fPvzwQwDAZZddhpUrV+o1pIiLpm2oQl2X3LlhsL7JjTP1TkiSFNyy7HyvL0mAxyvQ7PLA6fLBH4IMs6HZjY93H8fu704HE9bRQ+Mxd+oopKXae/Qa35XVYNO2w1AUGVazCUICvvzmFFISLUgZbe33GImMRpJlSJoFkmoO6Q4RnrJiuLa9AcgmQIuDv6k2cBt3GTJpkuMd8DfVAmq7hmOvG3K8I3KD6gd+vhQOujbVLVmyBEuWLOlw7S9/+QsAIDMzE99++62ebx+VomUbqlAfmtC5YTDRruFMvQu1DS5YNOW8r+/2+NHs8sDl6V+j3NnX82Hrv8rx+b6TcHsDDXNt/8fjklHJvWqY++Krk1AUGQk2DQk2DV6/H2fqXXh/+1FcOnpw/wdLZAAdd4jQ59AMT3EBIJsgtSWAqhnCE7huxIRJzZoL17Y3IDwATBrgdQN+L9SsuZEeWp/w86Vw6HNCXFtbi6SkpFCOhcIo1KvVnUswbBYVQgjUNXrQ7PR2eH0BAafbh2anB16vCMkvR79fYO/BSmzedQz1zYGaSJvZhFlXjsTVl6b26bjkuiY3hgy2QVVkNLR44PX5YVIkg253R9QLrTtEyGHaISIWSwT6I5Ak3hUzJQb8fCkcep0Ql5aW4rXXXsOmTZuwb98+PcZEYRLK1equSjBMJgUZI6xYdkegq9nnF2hyetDi9MIbwt+O3xyuxvrNB1BxJtAwZ1IkXHvZMFx/+XBY+nhcsixL+MHIRFTWtqDJf/Z6KLa7I4pVktJaEmEKbUnEhcRaiUAoqOlZAzYB7oyfL4VDj5fNvvjiC9x3333Iy8vD4cOH8ac//UnPcdEAkz0lHT6fv7X0QcDl8cHv8yPv2tHw+Pyob3ajut6JhmZPyJLhijPNeLVgP178275gMjzpByn41a2TkD0lvc/JsEVVMDjBjGsvG4qGZk+HmPpTVkIUiyRJgqyZYbInQYp3AOYECKVvh+X0lZo1F/B7ITwuCCEgPK4BXSJAHfHzpXDoNmNwuVzYsGEDXn/9dVRXVyMnJwcOhwNvvPFGuMZHA0T7Eowz9S6McMRh5hUjMGxwHM7UO0O6E0N9a8PcnvYNc8PikTN1FEY6etYw1xVFlmC3qbBqCgAJmWNTcOeN0dEESRRVJAly26EZJjMgR/bQjFgrEaCO+PlSOHSbEF9//fWYOHEiHnnkEVx//fXQNA2ff/55uMZGA0xbCUaL24tmpwceb2BVNVTcHh++KC7HF191bJj76Q3jMXKQtU8nzAGBXS6sZhPiLCoUueNrREsTJFE0CJZEqBYIWYVoO5cxCrYejKUSAToXP1/SW7cJ8eTJk7F7927ExcXBbDZj2rRp4RoXDTACAi1uH5pbPPD6Qvvb0e8X+OeBSmzefQwNbQ1zltaGuUtS4UiJv+DBHF2REDg+227TYFJ44DJRVyRZhqRqkFUL/CYzACka8l8iopDqNiH+n//5H5w+fRrr16/Hk08+Cb/fD5fLhWPHjiEtLS1cY6QoJUmAx+dHSwgP0ujswLFaFO4o69Aw98PMYZgxqe8Nc22vY7dpsKgyACbDRO21bZWm2BIg+2wQkgL/hZ9GRDRgXTCjSE1NxUMPPYQHH3wQH3/8MdatW4e5c+fixhtvxOrVq8MxRooyfiHg9vrR4vTC7e3b/sGdT4CbNnE4LkpPDt5fXt2Ewh1lOHi8Lnjt8nEpuPGqNCTZ+96wI0sSbFYT4iwmSEyEic6SJMgmFZLZAigaIJugWO0QjcY57pWIjKvHS2yKomDOnDmYM2cODh8+jLffflvPcVGUEQA8Xj+cbi+cbl+/VoPbnwBnMZtQ3+LBpm2HMR/AsMFx2Lz7GP75XWXwz7JjhiUg55pRGJES193LdksCYNYUxNvUPu1JTBSLpLYjlM3W4BHKop9HKBMRDUQX3GXihRdewKFDhzB16lQsXLgQsixjzJgxePzxx8M1Roogv2g9RMPlgS9Eh2i0nQCnmQJ7FmsmBS4h8O7Ww2hyeuFpbZhzJFkwd8ooXJSe1OeGOSCwe0R8nAarpoR0twuigU62xUNADdt+wURE0arbhPg3v/kNmpubMX36dBQUFKCurg4PP/xwuMZGESJJgMcr0OL26lIbXNPggsUc+OoJIdDs8qKhyR3ctinOYsKsySNx1cVDztn1obcsmoIEmwZZlpgME3UiZA0R3S+NiChKdJsQf/3113jvvfcAALm5ufjFL37BhDiGCQAejw/NTi9cfawN7onkeDPqmt0QAqhvcgd3pZAAzLh8BKZPHNavhjkgcNKc3abC1s/XISIiotjXbbZgMp29OzExEYJLbDHJ5xdwenxocYZ+y7SuTBgzCAU7yjq8l2aSMf+60bhifGq/X9+sKkiIY60wERER9Uyvls9kJhgxQ5IAlyc0TXI9Vdfowubdx7H3wNmGOZMiYUiyFTdMTuuwy0RfmGQJ9jgNFlXp/2CJiIjIMLpNiOvr6/HRRx8Fbzc0NHS4PXv2bP1GRiFTXFqFwh1lqGlwYWRqHGZMGoHhKXZ4LlAWcaGt0XrK5fbh869OYmtxOTy+QMNcarIVc6ekY3xa/xrmgECpRZxVRZzFBLmfr0VERETG021CPHz4cLzxxhvB28OGDcObb74JIQQkSWJCPAAUl1bh/z4thd2mYuhgG6rqXXit8FvkXTO62+S2u63RepoU+/wCu789jY/3HEdTS+CEObtVxQ2TR+LKi1L73TAHACaThOQEMxrZHk9ERER91G1C/MYbb6C8vBzNzc3IyMjA6tWr0djYCEVRsHTp0nCNkfpIkoAvS04hzqrC5fGjvsnTel3CF1+d7Dax7WprNHfr9QslxEIIfNd6wtzpmhYAgKrIuC5rGKZPHA6zdrakoa+r0JIEWM0m2K0qrGYVjXBe8DlEREREXem2KLi4uBgLFixASUkJAKCgoADJycn4/vvv8dZbb4VlgNQ3bo8fNfUuHDpZh/omN1xuX/A+VZFR0+Dq9vk1DS6oSsevR0+ed7KqCS//v/14vfA7nK5pgQTgivEOPHr7JNx4Vdo5yfCmbYdR3+LpsAr9XVlNt+9hkiUk2c1IjNNYIkFERET91u0K8QsvvIDnn38eU6dOBQDExcXhoYcewokTJ5Cfn4977rknLIOknmtodqO63gmv1w+BQIlCfYsnuNILAB6fH8nx3R9/nBxv7tXzahtd2LzrGPYdrAoWL/xgRCLmTk3HsMFdnzDX21VoCYDFbEK8TYUscV9hIiIiCo1uE+Jjx44Fk2EAwW3XRowYgbq6On1HRj0iSYDX54fT7UOL04sESMGT3gBg2sTh2LTtMNwIrPB6fH74fH5Mmzi829ft6fOcbi8+33cSW/9VHtxGLTXZipypozBuZGK3DXPtD+hoc75VaJ42R0RERHrpNiHWNK3D7bVr1wb/nZCQoM+IqIcE3F6BFpcXLrcP/vNkiRelJ2M+0Os63Qs9z+f3Y9e3p7Fl93E0Ob0AgHirihuuSsOV4x2Qe9Aw19NV6MBpcypkWWYyTERERCHXbUJss9lQUVGBoUOHAgiUTABAeXk5LBaL/qOjcwgBuL0+NDm9F9w2rc1F6cl92i6tq+cJIfBtWS0KdxxFZW2gkU01yZiWNQzTJg6HuRd7AF9oFVqWWk+bM/O0OSIiItJPt5nGrbfeiqVLl+LFF1/E4MGDAQB1dXV4/PHHcccdd4RlgBQQ7tPkunKishEffFmGw+X1AAI1vVde5MANk9OQEKd1/+QudLcKrakKEuI0mEKwNRsRERFRd7pNiH/yk5+grKwMs2bNQkZGBiRJwqFDh7Bw4ULk5eWFa4yGJUmAxyvQ7PKE7TS5rtQ2uvDRzmPY931V8Nq4kYnInnL+hrme6rwKzdPmiIiIKNwu+LfoRx99FL/4xS+wd+9eAEBWVhZSU1N1H5ixBeqDm50euDw9K4vQg9PtxWf7TmJbu4a5oYNsyG49YS6U2u8rzK3UiIiIKJx6VJw5ePBg3HDDDXqPxfAEBFweP5p7UR+sB5/fj537T+Ojncfg8gT2L1ZkCVMuHYKcqaN61DDXGyZZQkKcBjN3kCAiIqIIYLdShAXKIvxocfvgcnnhjVBZBBBomNt/tAaFO8pQVXf25De7VYVZk7H/6BmMG5nYpwa9rnBfYSIiIooGTIgjpG01uMXphTuCq8Ftjp9uxAc7juJIeUPwmllTkGTXoMiBE+vcXl+Pjm7uCbl1Vdiiygikxt0rLq0KJuopiRZkT0lHVkZKv8fRF9E0FiIiIuo/XRPiF154AR9++CEkScJPfvKTc062279/P37961+jqakJkydPxm9/+1uYTLGbo0tSYJ/dFlfkV4Pb1DQ48dGuY/jq++rgtfFpSaioakScTetwsEZPjm7uCYumICFOhSx1e3J4UHFpFdZuPgBFkWGzmFDb5MbazQcAIOyJaDSNhYiIiEJDt+xz586d+PLLL7Fp0yZ4vV7k5ORgxowZGDt2bPAxjz32GJ5++mlMmjQJTzzxBNavXx+T27lF22owALS4vPhs3wls/7qiQ8Pc3KnpGDcyCX99r6RPRz53pWjPMWwtroBJkZCSZMXVFzuQPXV0j59fuKMMiiIH9zg2qwpcrdfDnYRG01iIiIgoNHq2RNcHV199NV5//XWYTCZUV1fD5/PBZrMF7z9x4gScTicmTZoEALjllltQWFio13DCTpIAr9+PhhYPqmudqG1wRXTHiDY+vx/bv67AH97eh8+/CuwekWBTsWDGWDx0SybGjQzsHjFt4nD4fP7WBF7A7fX16Mjnzor2HMMn+07CblNht6kor27C/31+CJu2Hurxa1TVOaGZOn5VNZPcoc45XKJpLERERBQautYnqKqKF198Ea+88gqys7MxZMiQ4H2nT5+Gw+EI3nY4HDh16pSewwkLAcDt8aHF6YUrSlaDgbMNcwU7ylDdmrxpJhnTJw3HdVnDOqwEA30/8rmzXfsrkZpkhdPtRX1T4IhnCRI+2nUc868be4FnB6QkWlDb5O5wCp7b60dKYvhPS4ymsRAREVFo6F6wm5+fj/vvvx+//OUvsX79etx2220AAL/f36E+VQjR4XZPDB5sv+BjHI743g24jzxeP1qcHjS7PJBUE2yqCbYLP00XgwZ1PCzj8Mk6/L3oIL4/XgcgsHp93cThyLtuLBLt5y+BuGZQHK6ZNLLP4zDJEqxWExqaXPD4EPx8FTlw6l5PP5vbZl+MP79TDJ/fHyhR8PgAEbje9hrh+px7MpZwCOd7RQsjxqy3aJpDowljjn1GixcwZsy9oVtCXFpaCrfbjUsuuQRWqxWzZ8/Gd999F7x/6NChqKysDN6uqqrq9YEf1dWN3Z7e5nDEo7Ky4bz395ckBVYHW1zeiJ4k196gQXE4c6YJQKBh7sOdx1BcerZh7qL0JGRPSceQZBt8bi/OnPGGfAxth2zE21Q0NLnhdPuhtNu72OcHLKrS489mVIoNt8/6wTk7O4xKsaGyskH3z7k3YwmHcMYbLYwWc7h+cUV6Do1GjDn2GS1ewHgx92UO1S0hPn78OF588UWsW7cOALBlyxYsWLAgeP+IESNgNpuxZ88eXHnllXj33Xcxffp0vYYTUkIEtiBrjqImufZaXF58ujfQMOdr/WU3fLANc6eOQsaIRF3f26RIiLdpsLQesnHj5JHYtP0IfH5AlgC/CDQZzr6qdyvPWRkpUdO0Fk1jISIiov7TLSGeMWMGiouLcdNNN0FRFMyePRu5ubm4//77kZ+fj8zMTKxatQorVqxAY2MjJkyYgIULF+o1nJDw+QN/6m9xeoI7M0QTr8+Pot3H8N4Xh9DiCqz8JsZpuPGqNEwal6LrkciSBNgsKuxWEyScPWSjrU74o13H4XR7YdFMmH3VyB7XDxMRERHpTRIi2tY3ey4cf+4TAvB4fYG9g73RURbRmRACJUdq8OGOMlTXtzbMqTKunzQC12YOPadhLtTMqoJ4mwaTol/C3R0j/inISPECxouZJRORw5hjn9HiBYwXc1SVTAxsAh6fgMvjg9MZHQdonE/ZqQZ88OVRlJ1qBADIkoSrLknFj64YgXibput7m2QJ9l6cNkdEREQUjZgQt+MXgSS42emF1+ePutrg9s7UO/HhzjL869CZ4LWL05Nx2+yLYNZtd+kACYCltWlOzzIMIiIionBgQozAThEujw8tLm9UlkS01+wMNMz9o6Rdw1xKHHKmpmPs8MQOu0zoQZElxHNVmIiIiGKIIRNiSQo0oDk9frS4PPD5RFSvBgOB8X5Zcgqf7D2OFpcPQKBhbvZVaZioc8McEPiZWTQT7Fa1wxZqRERERAOd4RJiIYCaRhfcUXCMck8IIfD14TP4cEcZzjS4AASa2K6/fDiuvWwYVJPO9REI1ArHx53dSo2IiIgolhguIYYk4PFGd31wm3Mb5oCrLhmCWVeOhN2qhmUMFlVBfJwGRZYGxM+MiIiIqLeMlxAPANWtDXNft2uYu2RUMuZMSUdqkjUsY5AkwG5VYbOorBQmIiKimMaEOIo0O734ZO9xfFlyKtgwNyIlDnNbG+bCRTVJiLeZoYWhHIOIiIgo0pgQR9h3ZTX4fN8JVJxpgcvjQ9smF0l2DbOvTkdWxuCwbW3W+bQ5IiIiIiNgQhxB3x49g79/VooWt7/Ddm9XjE/Bj68bG5aGuTZcFSYiIiKjYkIcIUcq6vG3ou/h8viD1+IsJpg1BbUNrrAlw5IExFlV2C3hadIjIiIiijZMiMOsqq4FH+44hpIjZxvmLJqChDgNJkWGEAI1rdur6U01yYi3aVwVJiIiIkNjQhwmTU4Piv55AjtKTsHfun+ZWZVhMZsQ12511uPzIznerOtYWCtMREREdBYTYp15vH78o6QCn+49Aac7cMJckl3DnKvTYVZlvLf9CNxeH1RFhsfnh8/nx7SJw3Ubj0mWkBCnQVMV3d6DiIiIaCBhQqwTIQSKS6vx0a5jwRIIi6Zg5uUjMHXC0GCNsCRJ+OKrk6hpcCE53oxpE4fjovRkXcZkMStIsGlh27WCiIiIaCBgQqyDw+X1KPjyKI5XNgEAFFnClEuH4EdXjICtU/PaRenJuiXAbWRZQrxNhVVTAJZIEBEREXXAhDiEqmpbULizDN8cqQlemzBmELKvTsfgREtExqSpChLjVCgyG+eIiIiIusKEOAQaWzwo+udx7PzmdLBhLi3VjpypozBqaHxExtS2nVocj14mIiIi6hYT4n7weP3Y/nU5Pt17Ei5PoGEuOd6MOVenI3PsIEgRqtUNNM6ZoalcFSYiIiK6ECbEfeAXAsXfV+OjXWWobXQDCDTM/eiKkZg6YQhMSuQSUc2kYFCihY1zRERERD3EhLiXDp0MNMydqDrbMDd1whDMvHwkbJbI/TglCbDbNKQkWVBV5Y3YOIiIiIgGGibEPXS6tgUf7ijD/qNnG+YuGzsIc65Ox+CEyDTMtTGZJCTYzNBMcsTKNIiIiIgGKibEF9DY4sGWPcexa/8p+AP9ckgfYsfcKZFrmGsjAbCYTYi3qSyRICIiIuojJsTn4fH6se1f5fhs39mGuUHxZsyZko7LxkSuYa5N6Yk6fH24GicrmxBnVZE9JR1ZGSkRHRMRERHRQMSEuBO/EPjqYBU+2nUMdU2BhjmrOdAwN+XSyDbMtSk7VY+t/ypHk9MDWZZQ2+TG2s0HAACzHJFdtSYiIiIaaJgQt1N6sg4FX5bhZLuGuWsmDMXMK0bAao78j0qSAJtFxe5vK1Hf5IamKgAAs6rABaBwRxlmTR0T2UESERERDTCRz/KiwOmaFhTuOIpvy2qD1zJbG+YGRbhhro0iS0iI02DRFBypaDhnRwvNJKOqzhmh0RERERENXIZOiBua3diy5zh2f3s62DA3akg85k5NR/qQ6Ck9MLcevyzLMoQAUhItqG1yw9y6QgwAbq8fKRE6HpqIiIhoINM1IV6zZg0KCgoAADNmzMCyZcs63F9SUoJ///d/h8fjwbBhw/Bf//VfSEhI0HNIAAC3x4cvvirHZ1+dgNvjBwAMTrBgzpR0TBidHPGGuTbnO345e0o61m4+ABcCK8Nurx8+nx/ZU9IjNVQiIiKiAUu3DrHt27dj69at2LBhAzZu3IiSkhJs3ry5w2NWrlyJ/Px8bNq0CWPGjMHLL7+s13AABBrmtv+rAn94ex827z4Gt8cPq9mE3GtG4eGfZkXF7hFtTIqE5HgL7J2SYQDIykjBnTeOR1KchmanF0lxGu68cTx3mSAiIiLqA91WiB0OB5YvXw5N0wAAGRkZOHnyZIfH+P1+NDUFGthaWlqQmJio13AAABu/OIz3tx8BEKjJvfayobj+8uhomGvPYlaQYNO63Vs4KyOFCTARERFRCEhCCKH3mxw5cgQ/+9nPsG7dOowePTp4fd++fbj33nths9lgtVqxfv16JCcn6zaO9R8fwJuF+zH5kiH48fQMpCRZdXuvvpAkIN6qwm7TomalmoiIiCjW6Z4QHzx4EIsWLcKSJUtw8803B687nU4sWLAAzzzzDLKysvC///u/+Mc//oGXXnqpx69dXd0Iv//8w3c44lFZ2RC8LYSA1+9HTYO72+dFgsWsIMEaaJzrj84xG4HRYjZavIDxYnaEaT/x3s6hRsCYY5/R4gWMF3Nf5lBdT5nYs2cP7r77bixdurRDMgwABw4cgNlsRlZWFgDgtttuw86dO/UcDiRJioqDNdpTZAlJ8WYkxWn9ToaJiIiIqPd0y8DKy8uxePFirFq1Crm5uefcP2rUKFRUVODQ6uce9wAAEq5JREFUoUMAgC1btiAzM1Ov4UQdCYDVbMLgRAssqtJ6hYiIiIjCTbduspdffhkulwvPPvts8Nrtt9+OoqIi5OfnIzMzE8888wweeeQRCCEwePBg/P73v9drOFFFkSXEx2mwagr0r+AmIiIiou6EpalOL32pfxMQqKx1RqyGuP0hG3owWp0QYLyYjRYvYLyYWUMcOYw59hktXsB4MfdlDo2u/cZimCQBdqsKWxf7ChMRERFR5DAhDgOTIiEhzgzNxKY5IiIiomjDhFhnVrMJ8Ta120M2LqS4tAqFO8pQVedESqIF2VPSeSgHERERUYgwIdaJLEuw21TYtP7tIFFcWoW1mw9AUWTYLCbUNrmxdvMBAGBSTERERBQC/Bu+DlSTjEHxFtg0E/q7nVrhjjIoigyzqkCSJJhVBYoio3BHWWgGS0RERGRwXCEOMZslUCIhhah1rqrOCZul48ekmWRU1TlD8vpERERERscV4hCRJQkJdg0JIUyGASAl0QK319/hmtvrR0qiJWTvQURERGRkTIhDQDPJGJQQmhKJzrKnpMPn88Pl8UEIAZfHB5/Pj+wp6SF9HyIiIiKjYslEP0gSYLOoiLequr1HW+Mcd5kgIiIi0gcT4j4yKRISbGZoqv6L7FkZKUyAiYiIiHTChLiXJABWiwl2a//2FiYiIiKi6MCEuBdkWUJCnAarpkCISI+GiIiIiEKBCXEPmVUFCXEqFFlmMkxEREQUQ5gQX4AkAXarCptFDfH+EUREREQUDZgQd0M1SYi3maGZuDsdERERUaxiQtyFtu3U7FZTSA/ZICIiIqLow4S4E1Nr45yZjXNEREREhsCEuB2r2YR4W2A7NSbDRERERMbAhBiB7dTibSqsmoJQH71MRERERNHN8AmxpipIiNNgkpkIExERERmRYRPitsa5eKsa6aEQERERUQQZMiE2KRLsFjM0ldupERERERmdARNiCUl2M2SJJRJEREREZMCEWAIgMRkmIiIiolasGSAiIiIiQ2NCTERERESGxoSYiIiIiAxN1xriNWvWoKCgAAAwY8YMLFu2rMP9hw4dwlNPPYW6ujo4HA48//zzSExM1HNIREREREQd6LZCvH37dmzduhUbNmzAxo0bUVJSgs2bNwfvF0LgwQcfxP33349NmzbhkksuwUsvvaTXcIiIiIiIuqTbCrHD4cDy5cuhaRoAICMjAydPngzeX1JSApvNhunTpwMAfvnLX6K+vl6v4RARERERdUm3hHjcuHHBfx85cgQFBQVYt25d8FpZWRlSUlLwxBNPYP/+/Rg7diyefPJJvYZDRERERNQlSQgh9HyDgwcPYtGiRViyZAluvvnm4PVNmzbhySefxJtvvonMzEz893//NyoqKvDss8/qORwiIiIiog50barbs2cP8vPz8cQTTyA3N7fDfQ6HA6NGjUJmZiYAIC8vD/n5+b16/erqRvj958/nHY54VFY29H7gAxhjjn1GixcwXswOR3xY3odz6LkYc+wzWryA8WLuyxyqW1NdeXk5Fi9ejFWrVp2TDAPA5ZdfjjNnzuDbb78FABQVFWHChAl6DYeIiIiIqEu6rRC//PLLcLlcHUogbr/9dhQVFSE/Px+ZmZn44x//iBUrVqClpQVDhw7Fc889p9dwiIiIiIi6pHsNsZ74575zMebYZ7R4AePFzJKJyGHMsc9o8QLGizmqSiaIiIiIiAYCJsREREREZGhMiImIiIjI0HTddo2Mq7i0CoU7ylBV50RKogXZU9KRlZES6WERERERnYMJMYVccWkV1m4+AEWRYbOYUNvkxtrNBwCASTERERFFHZZMUMgV7iiDosgwqwokSYJZVaAoMgp3lEV6aERERETn4ApxmBmhlKCqzgmbpeNXSzPJqKpzRmhEREREROfHFeIwaislqG1ydyglKC6tivTQQiol0QK319/hmtvrR0qiJUIjIiIiIjo/JsRhZJRSguwp6fD5/HB5fBBCwOXxwefzI3tKeqSHRkRERHQOlkyEkVFKCdpKQGK9NISIiIhiAxPiMEpJtKC2yQ2zqgSvxWopQVZGChNgIiIiGhBYMhFGLCUgIiIiij5cIQ4jlhIQERERRR8mxGHGUgIiIiKi6MKSCSIiIiIyNCbERERERGRoTIiJiIiIyNCYEBMRERGRoTEhJiIiIiJDY0JMRERERIbGhJiIiIiIDI0JMREREREZGhNiIiIiIjI0JsREREREZGhMiImIiIjI0JgQExEREZGhMSEmIiIiIkNjQkxEREREhmbS88XXrFmDgoICAMCMGTOwbNmyLh/36aef4ne/+x2Kior0HI6hFZdWoXBHGarqnEhJtCB7SjqyMlIiPSwiIiKiiNNthXj79u3YunUrNmzYgI0bN6KkpASbN28+53FVVVX4z//8T72GQQgkw2s3H0Btkxs2iwm1TW6s3XwAxaVVkR4aERERUcTplhA7HA4sX74cmqZBVVVkZGTg5MmT5zxuxYoVeOihh/QaBgEo3FEGRZFhVhVIkgSzqkBRZBTuKIv00IiIiIgiTreSiXHjxgX/feTIERQUFGDdunUdHvP666/j0ksvxcSJE/v0HoMH2y/4GIcjvk+vPZB1jvlMoxvxVhMkSQpeMykSahrdMfPziZU4espo8QLGjFlvnEO7xphjn9HiBYwZc2/oWkMMAAcPHsSiRYuwbNkyjB49Onj9wIED+Oijj/Dqq6+ioqKiT69dXd0Iv1+c936HIx6VlQ19eu2BqquYB9k11Da5YVaV4DWXx4dkuxYTPx+jfc5GixcwXszh+sXFOfRcjDn2GS1ewHgx92UO1XWXiT179uDuu+/G0qVLcfPNN3e4r7CwEJWVlViwYAEeeOABnD59GnfccYeewzGs7Cnp8Pn8cHl8EELA5fHB5/Mje0p6pIdGREREFHG6rRCXl5dj8eLFWL16Na655ppz7s/Pz0d+fj4A4Pjx41i4cCHeeustvYZjaG27SXCXCSIiIqJz6ZYQv/zyy3C5XHj22WeD126//XYUFRUhPz8fmZmZer01dSErI4UJMBEREVEXdEuIV6xYgRUrVpxz/Wc/+9k510aOHMk9iImIiIgoInhSHREREREZGhNiIiIiIjI0JsREREREZGhMiImIiIjI0JgQExEREZGhMSEmIiIiIkPT/ehmPcmyFJLHxBrGHPuMFi9gzJj1xjm0a4w59hktXsCYMfeGJIQ4/0H2REREREQxjiUTRERERGRoTIiJiIiIyNCYEBMRERGRoTEhJiIiIiJDY0JMRERERIbGhJiIiIiIDI0JMREREREZGhNiIiIiIjI0JsREREREZGgDMiFubGxEXl4ejh8/DgDYu3cvbr31VuTm5uLRRx+F2+0GAOzfvx+33HIL5syZg1//+tfwer0AgJMnT+LOO+9EdnY2HnzwQTQ1NUUslp5qH/Nnn32GH//4x8H/TJ06FYsWLQIQuzEDwNatWzF//nzk5eVh2bJlwc/5fLHV19fjgQcewNy5c3HnnXeisrIyYrH0ROd433nnHeTk5GDevHl4+umnL/hZDrR416xZg9zcXOTm5uK5554DAGzfvh3z5s3D7NmzsXr16uBjY+l7HQ04h3IO5Rw68OPlHBpiYoDZt2+fyMvLExMmTBDHjh0TDQ0N4oc//KHYv3+/EEKIX/3qV2Lt2rVCCCFyc3PF3r17hRBCPP7448HrDzzwgHj//feFEEKsWbNGPPfccxGIpOc6x9ze6dOnxaxZs8Thw4eFELEd8/Tp08X3338vhBBiyZIlYv369UKI88f229/+Vvz5z38WQgixYcMG8fDDD4c7jB7rHG9paamYNm2aOHXqlBBCiKeeekq88sorQojYiHfbtm3itttuEy6XS7jdbrFw4ULx3nvviRkzZoiysjLh8XjEvffeKz799FMhROx8r6MB51DOoUJwDh3o8XIODb0Bt0K8fv16PPXUU0hNTQUAbNu2DZMmTcLFF18MAFixYgVuvPFGnDhxAk6nE5MmTQIA3HLLLSgsLITH48GuXbswZ86cDtejWeeY23vuuedw++23Y/To0TEfs8/nQ2NjI3w+H1wuF8xmc7exffrpp5g3bx4AIC8vD59//jk8Hk/4g+mBzvF+9913mDRpUvD2zJkz8fHHH8dMvA6HA8uXL4emaVBVFRkZGThy5AhGjRqFtLQ0mEwmzJs3D4WFhTH1vY4GnEM74hwaG3MK51DOof1livQAemvlypUdbh89ehQ2mw2/+tWvcOjQIVxxxRVYvnw5vvnmGzgcjuDjHA4HTp06hZqaGtjtdphMpg7Xo1nnmNscOXIEO3fuDN5/+vTpmI75N7/5De666y7Y7XaMHDkS2dnZ3cbW/udhMplgt9tx5swZDBkyJHyB9FDneC+++GI8++yzKC8vR2pqKgoLC1FVVRUz8Y4bNy747yNHjqCgoAA///nPO3x/U1NTcerUqZj6XkcDzqFncQ7lHDpQ4+UcGnoDboW4M5/Ph61bt+LRRx/FO++8g5aWFrz00kvw+/2QJCn4OCEEJEkK/nd7nW8PFH/7299wxx13QNM0AIjpmCsrK7Fq1Sq8//772Lp1KyZOnIhnnnmmV7EJISDLA+MrP2bMGCxduhQPPvgg7rzzTlx00UVQVTXm4j148CDuvfdeLFu2DGlpaV1+f2P5ex0NOIdyDo2lOaUN51DOob0V3Z90D6SkpGDixIlIS0uDoiiYO3cuiouLMXTo0A4F8VVVVUhNTcWgQYPQ0NAAn88HIDBJdPVntIFgy5YtyMnJCd6O5Zh3796N8ePHIz09HbIs49Zbb8XOnTu7jS01NRVVVVUAAK/Xi6amJiQlJUUsht5wuVzIysrCxo0b8fbbb2PIkCFIS0uLqXj37NmDu+++G0uXLsXNN998zve3LbZY/l5HA86hnENjZU5pj3Mo59DeGvAJ8XXXXYeSkhKUl5cDAD755BNMmDABI0aMgNlsxp49ewAA7777LqZPnw5VVTF58mR88MEHAICNGzdi+vTpERt/X505cwZOpxNpaWnBa7Ec8/jx41FcXBycrLZs2YLMzMxuY5sxYwY2btwIAPjggw8wefJkqKoamQB6qbm5GXfffTcaGxvhdrvx5ptvIicnJ2biLS8vx+LFi7Fq1Srk5uYCACZOnIjDhw/j6NGj8Pl8eP/99zF9+vSY/l5HA86hnENjYU7pjHMo59BeC2cHXyjNnDkz2Dn7ySefiPnz54s5c+aIRx55RDQ3NwshhNi/f79YsGCBmDNnjnj00UeFy+USQghx/Phx8fOf/1zMnTtX3HvvvaK2tjZicfRG+5i/+uor8dOf/vScx8RyzO+8846YO3euyMvLE4sXLxbV1dVCiPPHVlNTIxYtWiRycnLEbbfddk53eTRqH+/69etFTk6OmD17tnjxxReDj4mFeP/jP/5DTJo0ScyfPz/4n7feekts375dzJs3T8yePVusXLlS+P1+IUTsfa+jAedQzqGcQwduvJxDQ08SQohIJ+VERERERJEy4EsmiIiIiIj6gwkxERERERkaE2IiIiIiMjQmxERERERkaEyIiYiIiMjQBtzRzUShtG/fPvzhD39AbW0thBAYOnQo/u3f/i14LKbH48HMmTNx8cUX469//WvweU8//TR27doFACgtLcWIESNgsVgABE6/avs3EVEs4xxKsYLbrpFhud1uTJs2Da+88gomTJgAILBh+erVq7FlyxYoioIPPvgA77zzDr7++musXbsWGRkZ57zOj370I7zwwgvIzMwMdwhERBHDOZRiCVeIybBaWlrQ0NCA5ubm4LX58+fDbrfD5/NBURSsW7cOOTk5SE9Px2uvvYbf/e53ERwxEVH04BxKsYQ1xGRYiYmJeOyxx3Dfffdh1qxZeOyxx/D3v/8d1157LTRNw/fff4+9e/ciOzsbN910E959913U1NREethERFGBcyjFEibEZGj33HMPtm3bhhUrVsDhcOAvf/kLbrrpJjQ0NGDdunWYOXMmkpOTkZWVhZEjR2L9+vWRHjIRUdTgHEqxgjXEZFh79uzB3r17cd999wWveb1e5OXlYdGiRVi5ciU0TQs2dzQ2NsJsNqOoqAiqqgafw/o3IjIizqEUS7hCTIY1aNAg/OlPf8Lu3buD1yorK9HY2AiXy4WkpCR88cUXKCoqQlFRET7++GM0NzejsLAwgqMmIooOnEMplrCpjgxrzJgx+OMf/4jVq1ejoqICZrMZ8fHx+P3vf4/nn38e99xzDxRFCT4+ISEBd911F1599VXMmzcvgiMnIoo8zqEUS1gyQURERESGxpIJIiIiIjI0JsREREREZGhMiImIiIjI0JgQExEREZGhMSEmIiIiIkNjQkxEREREhsaEmIiIiIgMjQkxERERERna/wf2SfCw8Ga7DwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.regplot(x=\"SAT\", y=\"GPA\", data=data);\n",
    "sns.lmplot(x=\"SAT\", y=\"GPA\", col='Attendance', data=data, hue='Attendance');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((84, 2), (84,))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we need OLS w/ constant **for lineer regression**\n",
    "y = data['GPA']\n",
    "x = data[['SAT', 'Attendance_Map']]\n",
    "\n",
    "x.shape, y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### standardization - _feature scaling_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "scaler.fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-1.26338288, -0.93094934],\n",
       "       [-1.74458431, -0.93094934],\n",
       "       [-0.82067757, -0.93094934]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_scaled = scaler.transform(x)\n",
    "\n",
    "x_scaled[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# do regression w/ default settings, copy_X, fit_intercept, n_jobs, normalize\n",
    "\n",
    "reg = LinearRegression()\n",
    "\n",
    "reg.fit(x_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>Weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bias</td>\n",
       "      <td>3.330238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SAT</td>\n",
       "      <td>0.145449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Attendance</td>\n",
       "      <td>0.111038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Features   Weights\n",
       "0        Bias  3.330238\n",
       "1         SAT  0.145449\n",
       "2  Attendance  0.111038"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get regression results\n",
    "\n",
    "summary = pd.DataFrame(data=[['Bias', reg.intercept_], \n",
    "                             ['SAT', reg.coef_[0]], \n",
    "                             ['Attendance', reg.coef_[1]]\n",
    "                            ],\n",
    "                      columns=['Features', 'Weights'])\n",
    "\n",
    "summary\n",
    "\n",
    "# print('r-squared:', reg.score(x_scaled, y),\n",
    "#       '\\nadjusted r-squared:', 1- (1-reg.score(x_scaled, y)) * (x_scaled.shape[0]-1) / (x_scaled.shape[0]-x_scaled.shape[1]-1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# validations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAT</th>\n",
       "      <th>Attendance_Map</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2400</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1700</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1670</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SAT  Attendance_Map\n",
       "0  2400               1\n",
       "1  1700               0\n",
       "2  1670               1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  alice w/ 1700 sat but no attendance, \n",
    "#  arthur w/ 1670 sat and w/ attandence of %75 of lessons\n",
    "# test_data = pd.DataFrame({'SAT':[1700,1670], 'Attendance_Map':[0,1]})\n",
    "\n",
    "test_data = pd.DataFrame(data=[[2400,1],\n",
    "                               [1700,0],\n",
    "                               [1670,1]], \n",
    "                         columns=['SAT', 'Attendance_Map'])\n",
    "\n",
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAT</th>\n",
       "      <th>Attendance_Map</th>\n",
       "      <th>predicted_GPA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2400</td>\n",
       "      <td>1</td>\n",
       "      <td>4.226018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1700</td>\n",
       "      <td>0</td>\n",
       "      <td>3.023513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1670</td>\n",
       "      <td>1</td>\n",
       "      <td>3.204163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SAT  Attendance_Map  predicted_GPA\n",
       "0  2400               1       4.226018\n",
       "1  1700               0       3.023513\n",
       "2  1670               1       3.204163"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['predicted_GPA'] = reg.predict(scaler.transform(test_data))\n",
    "\n",
    "test_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [x] max SAT 2400, students gets 4.22 which is not greater than 5.00"
   ]
  }
 ],
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