Differentiation Prototype.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster students by skill level.\n",
    "\n",
    "Inputs:\n",
    "\n",
    "- gradebook data (formative assessments)\n",
    "- assignment weight (100 - median grade)\n",
    "\n",
    "Outputs:\n",
    "\n",
    "- plot students in latent space\n",
    "- kmeans clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "from sklearn.mixture import GaussianMixture\n",
    "import string\n",
    "import itertools\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>Student ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Race/Ethnicity</th>\n",
       "      <th>SPED Status</th>\n",
       "      <th>Quiz 1</th>\n",
       "      <th>Quiz 2</th>\n",
       "      <th>Quiz 3</th>\n",
       "      <th>Final Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18503</td>\n",
       "      <td>M</td>\n",
       "      <td>16</td>\n",
       "      <td>American Indian or Alaska Native</td>\n",
       "      <td>False</td>\n",
       "      <td>11</td>\n",
       "      <td>97</td>\n",
       "      <td>5</td>\n",
       "      <td>37.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90106</td>\n",
       "      <td>M</td>\n",
       "      <td>16</td>\n",
       "      <td>Native Hawaiian or Other Pacific Islander</td>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
       "      <td>95</td>\n",
       "      <td>25</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65296</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>White</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>89</td>\n",
       "      <td>8</td>\n",
       "      <td>35.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11274</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>Hispanic/Latino</td>\n",
       "      <td>False</td>\n",
       "      <td>90</td>\n",
       "      <td>19</td>\n",
       "      <td>80</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36210</td>\n",
       "      <td>M</td>\n",
       "      <td>17</td>\n",
       "      <td>American Indian or Alaska Native</td>\n",
       "      <td>True</td>\n",
       "      <td>91</td>\n",
       "      <td>9</td>\n",
       "      <td>75</td>\n",
       "      <td>58.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Student ID Gender  Age                             Race/Ethnicity  \\\n",
       "0       18503      M   16           American Indian or Alaska Native   \n",
       "1       90106      M   16  Native Hawaiian or Other Pacific Islander   \n",
       "2       65296      F   16                                      White   \n",
       "3       11274      F   15                            Hispanic/Latino   \n",
       "4       36210      M   17           American Indian or Alaska Native   \n",
       "\n",
       "   SPED Status  Quiz 1  Quiz 2  Quiz 3  Final Grade  \n",
       "0        False      11      97       5    37.666667  \n",
       "1        False      12      95      25    44.000000  \n",
       "2        False      10      89       8    35.666667  \n",
       "3        False      90      19      80    63.000000  \n",
       "4         True      91       9      75    58.333333  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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No2P3blpeeIFQZ+eA3FsZ+dra2khISCAmJoYTJ06wZ8+ePssXFBRQVFTEv/7rvxIMBgFt\nfYsrLZJ0pfvPnz+fkpISmpqa8Pv9vPLKK13X3HLLLZeMfuqvpjSQVI1CGdY69uzBd/Ys9k9/GkO4\nOj5QhMGA/dZbMWRm0v6nP9H0m9+Q8LnPYUhMHNDPUTSRDGcdLtasWcMTTzzBjBkzmDRpEgsWLOj3\nmqeeeopvfetbFBQUkJiYiNVq5ZFHHrmq+2dkZPCjH/2IhQsXkpGRwY033tiVeB577DG+8Y1vMGPG\nDAKBAEuXLuWJJ54YuC/dB3EtywJGS1FRkdy/f3+0w1AGQW9j7AONjTT+6leYCwtx3H33oD405z17\nltaXXwYhSLjnHkw5OYP2WcPRYPwSP378ODfccMOA31fpW28/dyHEASnlNY8JVk1PyrAkpaS9uBhh\nNGK/7bZBf7LanJ9P0v33o7NaaX72WToPHx7Uz1OUkUQlCmVY8p44ge/0aWzLlw9Yv0R/DMnJJN1/\nP8bsbNq2bMG5Y8cV25gVZSyJKFEIIdYIIT4RQpQJIb7Ty/tmIcTL4ff3CiHywudXCyEOCCGOhF9X\ndrumJHzPg+FNDWhXAJB+P84//xlDaiox8+YN6WfrYmJI/Pznsc6ahbukhLYtW5B+/5DGMJqoRDu0\nBuvn3W+iEELogV8Ca4EpwD1CiCk9it0HtEgpC4BHgQs9OI3AbVLK6cAXged7XHevlHJWeKu/ju+h\njCIdBw4QbG0lbs2aqAxZFQYD9ttvx3bzzXiOHKH5t78l2N4+5HGMdBaLhaamJpUshoiUkqamJiwW\ny4DfO5JRT/OAMinlGQAhxEvABuBYtzIbgB+F918FHhdCCCnlx93KlAIWIYRZSum97siVUUn6fLg/\n+ABjXh6m8eOjFocQAtuSJRgSE2nbupWmJ54gfuNGNU/UVcjKyqKyspKGhoZohzJmWCwWsrKyBvy+\nkSSKcUBFt+NKYP6VykgpA0KINiAJrUZxwUbg4x5J4hkhRBDYAvxU9vKnhxDiAeABgJwxNhJlLOrY\nt4+Qy4Xjs58dFlODW6ZOxZCSQuvmzbQ8/zyxS5diW74c0cdUDYrGaDRe8iSyMnJF8q+9t/9be/5C\n77OMEGIqWnPUV7u9f2+4SWpJePt8bx8upXxSSlkkpSxKGeBx9MrwIn0+3Lt2YZowAVNubrTD6WJI\nTSXxgQewzJyJ+/33afntbwm0tEQ7LEUZMpEkikogu9txFlB9pTJCCAMQDzSHj7OArcAXpJSnL1wg\npawKvzqBF9GauJQxrPPgQUJuN7Zly6IdymV0JhOOO+7Afvvt+KurafrVr3Dv3YsMhaIdmqIMukgS\nxT6gUAiRL4QwAXcD23qU2YbWWQ2wCdgupZRCCAfwBvBdKWXXnLhCCIMQIjm8bwTWA0ev76soI5kM\nhXDv3o0xKwvjMG5ijJk9m+RvfANjdjbO4mKafv1rvGfPRjssRRlU/SYKKWUAeAh4CzgObJZSlgoh\nfiKE+HS42G+AJCFEGfAwcGEI7UNAAfCDHsNgzcBbQojDwEGgCvjfgfxiysjiPXGCYHMzsYsWDYu+\nib7oHQ4SPv95HJ/9LKHOTlqefZbm557Dd/58tENTlEGhpvBQhoWy1bcQcrlI/vu/H1EdxdLno2Pf\nPlw7dyI7OjCOG0fMvHmYb7jhume5HUojaR4m5epd7xQealJAJeo6j5bir6jQnpsYQUkCQJhMxN50\nE9aiIjyHDuHes4e2rVsRr7+OqbAQc0EB5oIC9PHx0Q5VUa6ZShRK1LW89HuE0Yg1vBLZSKQzm4mZ\nNw/r3Ln4KyroPHgQ78mTeMMLzehTUjBlZ2PMyMCQkYExLQ1hMkU5akWJjEoUSlQF29tpf/0NLNOn\no4vCgiwDTQiBKScHU04OUkoCDQ34ysrwnj6N5/hxOv/61wsF0SclYUxLw5CejiEtDWN6Ojq7fdj3\n0Shjj0oUSlS1/eGPSI+HmLlzox3KgBNCYExNxZiaSuyiRUgpCbW14a+pwV9TQ6CuDn91NZ7S0ovX\nWK2YsrMx5eVhnjgRfXKyShxK1KlEoUSNlJKWl1/GMmMGxszMaIcz6IQQ6B0O9A4Hlm7rBYQ8HgL1\n9QRqa/HX1OA7dw7vyZM4334bfXIy1pkzsc6ahd5uj2L0ylimEoUSNZ7Dh/GdPk36T358+bP+Y4jO\nYulqrrog2NqK9+RJOo8exfXee7h27MA6cyaxS5ZgSEqKYrTKWKQShRI1ra9tRVgs2Neto/2N4miH\nM6zoHQ5i5s0jZt48Ak1NdOzZQ8df/0rnoUPELFiAbdkydIMwS6ii9EYlCiUqQh4P7cXFxN2yesgW\nJhqpDElJ2G+9ldilS3Ft307H7t14Sktx3Hknpry8aIenjAEja9C6Mmo4332PkNOJ4447oh3KiKGP\niyN+wwYS778fYTDQ/OyzuNQqfMoQUIlCiYq2rVsxZGYQM7/njPVKf0xZWSR99atYZszApVbhU4aA\nShTKkAs0NeHevZv49beNuCexhwud2Uz8HXd0rcLX8rvfEfL5oh2WMkqp/0uVIed8+20IhbDfui7a\noYxoF1bhi9+4Ed+5c7S++CJSJQtlEKhEoQy59uI3MU2YgHnixGiHMipYZ8wg/s478ZWX0/Lyy8hg\nMNohKaOMShTKkPLX1dOxfz/2tWvVE8cDyDpjBvb16/GVldFeXKw6uJUBpYbHKkPK+dafQUrs69ZG\nO5RRJ6aoiGBLC+6dOzEkJxO7cGG0Q1JGCVWjUIZU+xvFmCdPxjx+fLRDGZVsq1ZhnjwZ59tv46uo\niHY4yiihEoUyZPxVVXQeOoR9rapNDBah0xF/++3o4+NpfeUVQh0d0Q5JGQVUolCGTPuf/wygmp0G\nmc5q1ZZpdblo27ZN9Vco100lCmXItL9RjGX6dEzZ2dEOZdQzZmZiW7EC7/HjeI4ejXY4yginEoUy\nJHzl5XiOHcO+Tj07MVRiFy3CmJVF+xtvEHQ6ox2OMoJFlCiEEGuEEJ8IIcqEEN/p5X2zEOLl8Pt7\nhRB54fOrhRAHhBBHwq8ru10zJ3y+TAjxmFBjJUe19jffBMC+5lNRjmTsEHo98XfcgfT7cYab/RTl\nWvSbKIQQeuCXwFpgCnCPEGJKj2L3AS1SygLgUeCR8PlG4DYp5XTgi8Dz3a75H+ABoDC8rbmO76EM\nc+1vv4N19myMGRnRDmVMMSQnY1u8GM/Ro3hPn452OMoIFUmNYh5QJqU8I6X0AS8BG3qU2QD8Nrz/\nKrBKCCGklB9LKavD50sBS7j2kQHYpZS7pdbT9hxw+3V/G2VY8lVW4T1+nLibb452KGNS7OLF6BMT\naX/jDWQgEO1wlBEokkQxDug+ILsyfK7XMlLKANAG9FyGayPwsZTSGy5f2c89lVHCtX07AHGrVvZT\nUhkMwmjEvm4dwaYm3Hv2RDscZQSKJFH01nfQc7xdn2WEEFPRmqO+ehX3vHDtA0KI/UKI/Q0NDRGE\nqww3zvfew1xYoBbZiSJzYSHmiRNxf/ABIbc72uEoI0wkiaIS6D6eMQuovlIZIYQBiAeaw8dZwFbg\nC1LK093KZ/VzTwCklE9KKYuklEUpKSkRhKsMJ4GWFjr278e2alW0Qxnz4lavRnq9uN5/P9qhKCNM\nJIliH1AohMgXQpiAu4FtPcpsQ+usBtgEbJdSSiGEA3gD+K6UcteFwlLKGsAphFgQHu30BeCP1/ld\nlGHI9f77EAwSt0r1T0SbITUV65w5dOzbR6CpKdrhKCNIv4ki3OfwEPAWcBzYLKUsFUL8RAjx6XCx\n3wBJQogy4GHgwhDah4AC4AdCiIPhLTX83teAp4Ay4DTw5kB9KWX4cL33Hoa0NCzTpkY7FAWwLV+O\n0Otx7dgR7VCUESSi2WOllMVAcY9zP+y27wE+08t1PwV+eoV77gemXU2wysgS6uzE9cFOHHfeoaYU\nHyb0cXHEzJuH+8MP8S9dijE1tf+LlDFPPZmtDBr37t1Ij0f1TwwzsYsXI0wmVatQIqYShTJonO++\nhy4ujti5c6MditKNLiaGmAUL8B47hr+2NtrhKCOAShTKoJCBAK7t27EtW4YwmaIdjtJD7MKFCJMJ\n965d/RdWxjyVKJRB0fnxxwRbW4m7WTU7DUc6qxXrnDl4jh4l0NIS7XCUYU4lCmVQON/bjjAaiV28\nJNqhKFdwYanUjt27oxyJMtypRKEMCldJCTHz56O3xUY7FOUK9PHxWGbMoOOvf1W1CqVPKlEoA857\n9iy+8nJsy5dHOxSlH7abbgK/n5bfvRDtUJRhTCUKZcBdmCLCtnxZlCNR+mNITcU8aRItL7yg1tdW\nrkglCmXAuUre1yYBzMrqv7ASdbE33USwtZXWLa9FOxRlmFKJQhlQQadTmwRQNTuNGKbcXKyzZ9P8\nzDNqvQqlVypRKAPKvWsXBAIqUYwwSfd9GX91Nc73tkc7FGUYUolCGVCuHSXo4+OxzpwZ7VCUq2Bb\nsQJDZgYtL74Y7VCUYUglCmXAyGAQ11/+QuzSpQhDRPNNKsOE0OtJuPseOvbuxXvqVLTDUYYZlSiU\nAdN5+DDBlhY12mmEcmzaiDCZaPn976MdijLMqEShDBhXyfug12NbvDjaoSjXwJCYiH3dOlr/8EeC\nTme0w1GGEZUolAHjKikh5sYb0cfHRzsU5Rol3HsvsqODtj+oBSeVi1SiUAaEv7oa7yefqNFOI5x1\n+jQsM2bQ8uKLSCmjHY4yTKhEoQyIrqexVyyPbiDKdUu893P4zp5VkwUqXVSiUAaEs6QEY04Opvz8\naIeiXKe4NWvQJybS/IIaKqtoVKJQrluoo4OO3XuIW7FcrY09CujMZhybNuHasQN/XV20w1GGAZUo\nlOvm3rMX6fOp/olRxLFpI4RCtG3dGu1QlGEgokQhhFgjhPhECFEmhPhOL++bhRAvh9/fK4TIC59P\nEkLsEEK4hBCP97imJHzPg+EtdSC+kDL0XCUl6GJjiZkzJ9qhKAPElJNDzIIFtL66BRkKRTscJcr6\nTRRCCD3wS2AtMAW4RwgxpUex+4AWKWUB8CjwSPi8B/gB8M9XuP29UspZ4a3+Wr6AEl1SSlwlJcQu\nXqzWxh5lHJs24a+spGPv3miHokRZJDWKeUCZlPKMlNIHvARs6FFmA/Db8P6rwCohhJBSuqWUO9ES\nhjIKeY4dI1Bfr5qdRqG41Tejj4+n9ZVXoh2KEmWRJIpxQEW348rwuV7LSCkDQBuQFMG9nwk3O/1A\nqF7QEcm1owSEwLZUrY092ujMZuwbPo3znXfVUqljXCSJordf4D2fxImkTE/3SimnA0vC2+d7/XAh\nHhBC7BdC7G9oaOg3WGVouXbswDpjBoakSP4uUEYax8ZNSL+f9m3boh2KEkWRJIpKILvbcRZQfaUy\nQggDEA8093VTKWVV+NUJvIjWxNVbuSellEVSyqKUlJQIwlWGir+uHk9pKbaVK6MdijJILJMmYpk5\ng9ZXX1VPao9hkSSKfUChECJfCGEC7gZ6/nmxDfhieH8TsF328a9KCGEQQiSH943AeuDo1QavRJer\npARQT2OPdo5Nm/CeKqPz4MFoh6JESb+JItzn8BDwFnAc2CylLBVC/EQI8elwsd8ASUKIMuBhoGsI\nrRCiHPi/wN8KISrDI6bMwFtCiMPAQaAK+N+B+1rKUHDt2IFx3DjMhYXRDkUZRPa16xAxMbS++mq0\nQ1GiJKLVZaSUxUBxj3M/7LbvAT5zhWvzrnBbNeh+BAt1duLevRvHZz6jnsYe5fS2WOzr1tL+RjFp\n3/0uepst2iEpQ0w9ma1cE/fuPUivVzU7jREJmzYhOztpLy7uv7Ay6qhEoVwT144d6GJjiZ07N9qh\nKEPAMnMm5sJCWl/dEu1QlChQiUK5ajIUUk9jjzFCCByf2YTn8GE8n3wS7XCUIaYShXLVPKXHCDQ0\nqGanMcZ+220Io5HWzepJ7bFGJQrlqrl27ACdDtuyZdEORRlChoQE4lbfTNvrrxPyeqMdjjKEVKJQ\nrpqzZAfWWbMwJCREOxRliMVv3EiorQ3nu+9GOxRlCKlEoVwVf20t3mPHVbPTGBW7cCHGzEzatqhO\n7bFEJQrlqlx4GjtuxYroBqJEhdDpiL/zTty79+CrrIp2OMoQUYlCuSrOHTswZmdjmjAh2qEoUeK4\n43YAtfrdGKIShRKxC2tj29Ta2GOacdw4YhcupHXra8hgMNrhKENAJQolYu7du5E+n2p2UnBs2kig\nugb3nj3RDkUZAipRKBFz7tipe83tAAAgAElEQVSBzmZTa2Mr2G7WVr9Tndpjg0oUSkS0p7HfJ3aJ\nehpbAZ3JhP2229Tqd2OEShRKRDyHDxNsbFTNTkoXx6aN2up3r78R7VCUQaYShRKR9rffAaMR2/Ll\n0Q5FGSYskydjmTqV1i1b1Op3o5xKFEq/pJQ433mH2AUL0Nvt0Q5HGUbiN96J98QJPKXHoh2KMohU\nolD65T1xAn9FBXGrb452KMowE79+PcJspnWLWv1uNFOJQumX8513QKcjbtWqaIeiDDN6u524W26h\n/fU3CHk80Q5HGSQqUSj9an/7bWLmzMGQlBTtUJRhyLFxIyGnU/uDQhmVVKJQ+uQ9cwZf2Wnibrkl\n2qEow1TMvLkYs7LU6nejmEoUSp+c72jTSav+CeVKhE6HY+OddOzdi6+iItrhKIMgokQhhFgjhPhE\nCFEmhPhOL++bhRAvh9/fK4TIC59PEkLsEEK4hBCP97hmjhDiSPiax4SaPGhYcr79NpaZMzCmp0c7\nlBFLSklQhvDJAB7pxyv9+GWQgAwRknJUDC2Nv+MO0Olofe21aIeiDAJDfwWEEHrgl8BqoBLYJ4TY\nJqXsPh7uPqBFSlkghLgbeAS4C/AAPwCmhbfu/gd4ANgDFANrgDev7+soA8lfVYWntJTUf/5mtEMZ\ncFJKvARw4cElPbjx4pZenHhwSy9uPHRKHx4CePFrmwzgwY+XAF6pnfPgxycDBAgR7LaFLjnuPxEI\nQIdAILq96jBh0DZhwIQe8yXHFzeLMBKLmVhhvuQ1ppdzZmEc8J+nMT2d2MU30bb1D6Q89BBCrx/w\nz1Cip99EAcwDyqSUZwCEEC8BG4DuiWID8KPw/qvA40IIIaV0AzuFEAXdbyiEyADsUsrd4ePngNtR\niWJYaQ93TsatXg1SQsCjbaGgtslurwA6A+iMoDeCTq/tG8za/iCSUtKJj1bZQSsdtEo3bbKTVtzd\nzmnn2+nEJT248BIk1O+9zRgwY9Q2YcCCUTsnjNiwYMGASRgxCj16dOgQ6NF128LHQjsWCCQgkYSQ\n2quUlx6HXwOE8BPARxCfDODj4uaRftrpxEcALwE8IT9uvPgI9PudLBhxiFgcxJAgYnGIGNIPVJJo\nTiTRmkiCOYFEayJJliSSrckYdJH8mgDHnRup+sd/xL1rF7alSyO6RhkZIvkXMA7o3vBYCcy/Uhkp\nZUAI0QYkAY193LOyxz3H9VZQCPEAWs2DnJycCMJV+iQldDSDsxqctdrW0QSdzdr5zhbt1dOGc3Mr\n5kSJ6YVF4O8A2f8v1l4ZrGCKDW82MNvAHAcxSWBNhJhEzM1nCRlsSL2dkCGekNEBOgud0kejdGob\nThqlq+u4STppRDu+0i9IE3ri0X4ZOkQM40jAprNgw0KsMHe9xmLGJizYMBMbfrVgQjfCWkT9Mhiu\nHWm1pA58uKSHDry4pBc3XtqkljhbcNMonZSFamk9dgx/yH/Z/XRCR2pMKukx6WTEZpAem961XTh2\nmB0IIYhbuQJ9YiItmzerRDHKRJIoevs/pWddOpIy11ReSvkk8CRAUVHRyG/MHWxSgrsBWsqh+Sy0\nnNX2W8qhPZwcgt7Lr9MZISax6xe3X59JZ10LyasL4MYZYIoBo1X7pa8zgE4HQq/VFkS4xhAKQMgP\nwcDFfb8H/G7whTevC3xOcDcSajxJo6eVaumhxmCgymCgxqCn2mCg2mCg3qDHpbu8G80q9SQLG8ki\nniliHMm6OBJEDI5wQogXMTjQEoMV05haO8Mo9F3f/Wo4PvsZ3H43zZ7mrq3J00Stu7ZrO9p0lHfP\nv3tZQrHoLWTaMsm153LLTdmMf2MHfz3yDnmFc0gwJ4ypn/9oFUmiqASyux1nAdVXKFMphDAA8UBz\nP/fM6ueeSl9CIWirgIZPoOFEt+2k9ou4iwB7JiTkQfZ8iEvXjuMywlu69pe9KRa6/Q/d/vQzIEuJ\nf/hxyMu75jCdPicVzgrOO89T0V5BpauSKlcVNa4QNW4n/pDjkvJ2aSJDmskJ6Znrl6QGAqT6PaT5\nnKT52kgNBIiVEgFIBCFjMkFzOiFTBkFTOkFzDCGTHWmIu+aYxyIhBDaTDZvJRo79yjX3kAzR7Gmm\nzl1HjbuGGncNte5aKp2VlLeX89/Z5fzfUIg//eIfeXWJDrvJTl58Hnn28BafxwTHBHLjctEPcpOk\nMnAiSRT7gEIhRD5QBdwNfK5HmW3AF4HdwCZgu+xjKIeUskYI4RRCLAD2Al8A/vsa4h8bgn4tIdQc\ngtrD4dcj4HNdLGNLg5TJMOseSJwAifmQkA+OHDBarvoj219/Hcv06Zj6SBJSSjp8QaraGylrKeds\n6zkqXJXUdVTS4Kmi0VuNO9B2yTUxegc2fSqxuiwmWmYTq08hzpCCudyJLRSPSZjRE+7cFdCJpEIH\nVRYwWQIkyWYSZSPxshF7sIG4YB02bw0W9wl08uJfuiG9jZApPZw80rV9czohYwoINSr8WumEjmRr\nMsnWZKYmT73s/UAowJmPv8TGE2Xk//1XOeeuoLy9nD3Ve9h2eltXOavBysSEiUxOnMwNiTcwOXEy\nBQkFmPXmofw6SoT6TRThPoeHgLcAPfC0lLJUCPETYL+UchvwG+B5IUQZWk3i7gvXCyHKATtgEkLc\nDtwSHjH1NeBZwIrWia06skFrOmqrgIqPoHKfttUevdhcZIyF9Gkw8x5Im6olh5RJWrPRdfAGgjS5\nfDQ4vbSeOEnqsWOc3HQfz/7xKC0dfpxeH83eWloDlbhDNXhFDUF9HcLUgM7Q0S18gQzEE/IlEfJN\nIuRLQvqTwsdJOKWJunBZg04gBARDkpCMjyBKMxDLpRVcjY4QmaKRAlHNBFFNQbCaiYFq8jsOkyh2\nXvyemKnXZ9NiyMZpysFjykaaM4k3mUg0SBwGLUEp18agM5DxN39L5Tce4o76LOJu/tuu99x+N+Xt\n5ZxqOcWJ5hMcbzrO62de5+VPXtauFQbyHfldiWNy4mSmJk0lxnh1zWjKwBMjaQx3UVGR3L9/f7TD\nGFihoFZLKN8FFXugYh+4arX3DFYYd6O2pc+EjJmQNOGqRxG5vQEqWzqpaO6goqWDypZO6p1eGpwe\nGpxeGl0+2jrDf42LAJ8/+QfuOraX+z63lI4kJ3pzA0F9A4iLHcZmYcdhyCLJnEV6TDaZMdnk2HPI\nsWcRa7Ji1AtMeh1GvQ6jQXfpsV477t523fLyZqSEIFquDAGhC6/hfb8UeEKEN22/MyQuHsuL73UE\nwRkUOIOCYKADe7CG1GA1ufI8E8Q5bhDnsItOAIJScEZmckzmcjyUQ4Uul3p9NnpjPIkGSaJBkmSU\nXfspRkmqKYRDf0lr3YiWcNdnB+xeMhCg7ObVmAsKyHnqf/ssG5IhKp2VnGg+oSWP5uOcaD5BY6c2\nDkYndBQ6CpmZMpOZqTOZmTKTnLgc1e9xlYQQB6SURdd6fWTj3pSBEwyEE8NOOLcLzn0I3nbtPUcu\n5C+BrHmQPRfSpmlDTfvhD4bCSaBbMmjupLJFO9fs9l1S3mLUkWY34bC3k5haT1JWLR5RTXuwghZv\nJSt3+CnNFXhz95Abl0W+fRL5jjXk2/PJj9e2eHMkNYCrI0T4H+QVfwfIK+z3R4/WDZYFzNMqbUFJ\nrbcJ2VmJwXMeq6+Clf5P2BD6sOuqJn8Cpb7xHAiO50CogMOhfNqxdb1vEpI0oyTFpL2mGUOkGCVp\n4eNUkyTJINGPsd9pwmDA8ZlNNP734/gqKjBlX14DvEAndOE/MHK4Je/iNDGNnY0cazrG4YbDHGo4\nxBtn32Dzyc0AJFoSmZM2hzlpcyhKK6IwoRCdak4cVKpGMRRayuH0dm078xfwhtvtkwogbzHkLYHc\nm8Ce0edtfIEQ5U1uTtY5OVXnoqzexal6J2cb3fiDF/87mvQ6xiVYyUqwkpUQQ3K8H525hg7O0ew/\nT4XrDGfazuANN2cJBFlxWRQ4CpjTZGfeD7ag+/7fk/+5+zDph2bZ05aXNw/J5/RHBN3oPRXh7TwG\nTzl6X23X+x2GNBoM+ZzX5XOCCRwO5VHpN1PvF9T7BX55aVbQI0k2SlLDCSQ1nFAuJJMMk/Z+tJPJ\nQNYoAPx1dZStXEXSl79E6jev/4HNYCjI6bbTHGo4xMd1H3Og7gDVbm38S5wpjjlpc1iYsZBFmYvI\nteeqGkcP11ujUIliMPg64Oz7UPaulhyaz2jn7VkwYQWMX64liLjep8UIhiRnGlwcq2nXkkGdlhDK\nmzoIhrT/XkJATmIMhak2CtPimJBiIyfRisHcQqPvLJ+0nOCT5k840XyCuo66rnunWFMoTCikwFFA\nYUIhhY5C8uPzu9qBa//932l96WUKd+1EHzd0I4eGS6LojQh2oPeUY+gsR995FoOnHF2gFQCJnqB5\nHEFrHn5LHi3GfCpFFnUBvZY8fII6v6DOp6PBL6j1CzpDlyeTVJMk3ShJN0nSTaFL9jOMEtsgN3MN\ndKIAqPy7v6PjwF8pKNmBbhDWWa92VXOg7gAH6g7wUe1HVDi1x73G2caxMHMhN2XexPyM+cSZ1Ag4\n1fQ0XLSeh5NvadvZv2idz8ZYrSlp/oMwYaVWg+jxf3swJDnb6OJIVRuHK9s4WtVGaXU7HT7taWed\ngLykWApSbaydlkFhmo2CVBt5SVZqOs5T2lTKsaZj/Kn+BCc/OYnLr42E0gs9+fH5FKUXMTlhMpMS\nJzE5cTIJloQrfgUZDNJe/Ca2ZcuGNEkMd1IfQyB2CoHYKV3nhL9Fq210lmPwnMXYvg9z61+wAVnC\nTMCSQ9CaT8CWR9CSR8iYDEIgJbiCUOcX1Ph01PkFtT5BrU9HrV9w2K3jnRY9gR7tbzG6cOIwhsgw\nabWTdKMkw3SxdmIaZq0vjrvuxvnOuzj//GfiP/3pAb9/pi2TTFsmt024DYCK9go+rP6QD6s/5M2z\nb/LqyVfRCz3Tk6ezaNwiFmUuYlrSNDUs9xqoGsW1klLrazi2DT55E+pLtfOJ42HiGpj4KchZBIaL\nf0mFQpKzTW6OVLZxpKqNI5VtlFa34Q4nBYtRx9TMeKaP07YpmXbGp8Ri0us47zxPaWMpR5uOUtpY\nyvHm43QGtM5Yq8HKpIRJXclgcuJkChwFWAxXNyzW/eGHnP/yfYx77BfYh3ha8eFco4iIDKHz1V+S\nPPSe8wipDQAI6W0ELXkErHkELPkErXlIQ+/LygYlNAUEdT4tidT4BLV+HXXd9lsCl1cvkg0h0ntJ\nIhlGrWaS0MeIrsGoUchQiDPrb0MXE0PeK5uHtDnIH/JzuOGwljiqPqS0qRSJxG6ysyhzEStzVrJk\n3BJsJlv/NxsFVNPTUAqFoGo/HPsjHP8TtJ7TnkrOXRRODmsg+eK0Vh5/kMOVbewrb2ZfeTMHzrXg\n9Gi/OMwGHVMz7VpSyHIwfVw8E1JiMeh11LnrONx4uCsxHGs6hjP8EJ1Zb2ZS4iSmJU1javJUpiVN\nI9c+MA8vVX/3ezjfeYfCXTvRmYd2PPuITxS9kQH0nir0nrMYOsu1JitvdXi2Jwgak7qSR9CST8CS\nC/rIkrsnhJZI/OHaSLf9Op+gxi/w9GjiutD5fqFJ60IfSbpJMmnDLWTEW4k1D2wjQ8tLL1H7ox+T\n++ILxNx444De+2q0elrZU7OHXdW7+KDyA5o8TRh0BuZnzGdVzipWZK8g2ZoctfgGm0oUgy0U1EYm\nHd8Gx1/X5kjSGbW+hhs+DZPWQay28ltbp5+/nmvho/Jm9pc3c6iiDV9Qmx+pMNVGUV4is3MczMiK\npyDFhkGvwx/y80nzJxysP8ihhkMcajhEjbsG0MaVFyYUdiWEqclTmeCYgFE38LN/hjo6OLV4CXFr\n1pD57z8b8Pv3Z1Qmit6EPBg857VaR+dZ9J5y9H5tKKhEEDKlX0wc1jyC5izt39tV0kZ20dWkVeu7\ntImr1ido8AtCPZq44q1GMuItpNktpMaZSbWbSbGZSb1wHGch1W7GYozsD5NQZyenlq8gdv58sh77\nxVV/j8EQDAU53HiY9869x3vn36PSVYlAMCt1FiuzV7IqZxXZ9iuP1BqJVKIYDEG/1hl9bBuceAM6\nGsFggYKbYcoGrVnJEk+Ty8uHp5vYV97MR2eb+aTOiZTag2TTxsUzLz+RuXmJzMlNIDFWa4Jq7Gzs\nSgiH6g9R2lTaNfooLSaNmSkzmZU6ixkpM5icOHnInlRtfW0rNd/7Hrkv/I6YOXOG5DO7GzOJohci\n4LzYWR6ufeiCWg1SCgNBc1Y4eeQStOQQNGVcU/LoKSDROth9AuechVS3eqhu7aS69cJzNl4aXN6u\nARTdxVkMpMSZLyaPC0ml23GSzYzDaqTx0Udp+s1vmPD225iyep37M2qklJxqPcV7599j+/ntnGg+\nAUBhQiGrclaxOnc1ExMmRjnK66cSxUAJBbVnG45s1pqVPG3aTKeFt8CUT0PBajw6K/vKm9l5qpEP\nTjVyrEZ7/iHGpGdObgJFuYnMzU9gdnYCVpMeKSWVrkoO1B1gf+1+DtQdoNKlTZpr0BmYkjiFGSkz\nmJU6i5kpM0mPjd7iQOV/8zcEG5sY/2ZxVIYWjuVEcRkp0QWaL6l1GDzliJD2B4U20iqDoCVbSxzm\nbIKWbKT+2p9gvlIfRSgkae7wUd/upT78gOaFJNL9uL7dS6c/eNn1OgETcPPoH3/MR7Nv5sCtXyQp\n1kRirIkkm7lrP9lmIjFWSyy6KD4aX+WqYvv57bx3/j0+rv+YkAxR4Cjg1vG3siZvDVlxWf3fZBhS\nieJ6SAk1B+HIq3B0CzhrtOQweT1M2UBo/AqON/r44FQjO081sq+8GW8ghFEvmJObwJLCFG4qSGZa\nph2DXoeUkrPtZ7uSwoG6A11DUx1mBzem3sjs1NnMSp3FDUk3DJt5bbxnz3Jm7TpSvvkwyV/5SlRi\nUImiH+HOcr1Xe8bDEH7WQxe8OJeW1udxMXEEzeMIGZMimtvqejuzpZS4vIFuScRLs8tLk9tHk9vH\nvN89Su6pg3zvnn+n2qe7OBNADzoBieHk0TOZXNhPCu8n20zEW42D9odNU2cT75x7h+KzxXxc/zEA\nM1NmsjZ/LZ/K+9SI6tNQieJaNJ3WksORV6DplFaNL7wFpm+iLmMF7591sfNUI7vKGmkKP9U8KS2O\nxYXJLC5MZn5+IjEmQ1e1dV/tvq7E0OzRJs1NtiZTlFbU9fToeMf4Yfv0aP1//RdNTz9DwY7tGFNT\noxKDShTXRgTauh4QNHi1hwR1vvquDnMpTARNaQTNGYTMGQRNGQTNmYRMKSAudlwPxqin7joPH6b8\ns3eR9r3vkfiFz+MPhmjp8NHk8tHs9tHo8tLsvrDvo9mtHTe5tERzpcRi0AmSbCaSYs0k2Uwkh5NJ\nclz41aadv5BkIu1b6anaVc2bZ9+k+GwxJ1tOohM6FmQsYG3+WlblrBr2z2qoRBEpZx2UbtWalqoO\nAALyFiOnbeK4YwVvnfWy/UQ9R6q0v9BS4swsLkjWtsJk0uzaaJQaVw17avawp2YPe2v20uRpAiAz\nNlNLCulachgp89FIn49TK1ZinTGD7P/5VdTiUIliAIU82mgrbzV6Xw16bw06Xw16f1NXEYmekClV\na8IypWNddoc2tNuRC7HJg/J0X/k9nyPQ1MSEN4uveqlUfzBES7h2ciGpNLp8NLm8NLm0c41u7bjR\n5cXj732RrTizoSuhpMVbSLdbSLObSbNf2LeQHm/pM6GUtZRRfLaY4rPFVLmqMOlMLMtexvrx61mS\ntWRQBptcL5Uo+uLrgBOvw6Hfw5kSbYW29On4p2xid8xy3jyvY/uJeuravQgBN+YksOqGVFZMSmVy\nehxCCNq8beyr3deVHM61nwMgyZLEgswFzE+fz7yMeYyzDa9Ouki1FxdT9fA3yX7y11FdlUwliiEQ\n8qL31qL31aDzVqP31qD31YZrIN1+sRpjISFXSxoJeZfvm2Kv6ePb//wWVf/4j4z7/x/FvmbNgHyl\nK+nwBbTZkMOJpCncDNYYPq53eqhv91Lb7ul6uLU7u8VAenj0V7rdQlZCDFkJVrITY8hOtJIaZ0En\n4EjjEYrPFvPm2Tdp9jSTaElk/fj1bCjYMKw6wVWi6ElKOL8HDr4ApX/QFvFx5OCceCclpmW8VmHj\nw9NNeAMhbGYDyyamsHJyKismp5IYa8If8nOw/iC7qnaxt2Zv14M6VoOVuelzWZCxgAUZCyhwFIyI\nGkN/zn3+C/hrapjw9luIXlaTGyoqUURRyE/CqjkXV0JsPQct5y7ud1/3BCAmWUsYcRnaOihx6Rdf\n49LBlq7VSno82yODQc6svw1hNpO/9bVh8f+PlBKnN0B9u4faNi1x1HXbatu91LZ1Utd+6aqQRr1g\nnEObSy070UqGw0SnvpTjru0cavqQgAwwJWkKtxfczrr8dYMyiebVUInigtbzcOglOPgitJxFGmNp\nn7Ced40reaYyg6M12j/23KQYVk1OY9UNqczNS8Rk0FHrrmVn1U52Vu1kT80e3H43eqFnRsqMrsQw\nPXk6xghmch1JvGVlnFl/W1Q7sS9QiSK6rthHcWGN9ZZyaC3vlkDOg6tOW1q3s5fFLIUOYlMhLk1L\nHLZUiEmk9a/11DzzPlnf+QJxSxeBNeHi1mOVxeHE4w9S3dpJRUt4Vubmzq4p+yubO7r6MgGE3o09\n+QhGxwG8ugp0GJiRuIgNE27ntonLMRuG/vfI2E4UPrf2rMPBF6D8AwBcGYt4P2Y1v6y7gWONWnV6\nTm4Ct0xJY9UNaUxIiSUgAxysP8gHVR+ws2onp1pOAdpzDIvHLWbJuCXMz5g/6h/vr/0/P6V182YK\n3i/BkHh9Cx9dL5Uoouu6OrMD3nDSqNPWUnGGN1ftxXOueuhsQfp9nH4jFb05RN7qxkvzgs54MWlY\n4rXEYbaBKa7bfqx23H3fFHtxM8aE13ePBf3QTWXn9gaoau3kfFMH5U1uTjdoszuXtZ6kw7wHg/0g\nOoMbGYgjzr+AybZVTE8toCAtjikZdvKTY9EP4rDgsTcpYCgE5z/Uag7H/gg+Fx5bDrszv8LjjUUc\nOBuHXidYOD6R/7M4nU9NSSPVbqGho4G/VL7FL0t3srtmN26/G4POwI2pN/LwnIdZPG7xqGlOikSw\nvZ3WrVuxr1sb9SShjHAGs7bkruPKa20DICXC30nShN9R+/89inv6I9imjoPOlss3T5vW5OWs1V59\nLvC6INT76Kde6U1gtGpJwxQTTiI9kknP813vXals+NVguaT2E2s2MDEtjolpPUc/LaLZ/TlO1rXw\n1tkd7K5/iyrve+wPvsPesvH49s8j4JyK1WBhcoaWNKZmavO8TUqLw2oaHhMYjqwaxaQsuf9ridB6\njoAhlo/jVvBE+3zec4/HZNCztDCZT01N5+Yb0nDEGDnVeoqSihJKKko40ngE0GoNS7KWsHjcYhZk\nLCDWeG0dcyNd02+epv7nPyf/tS1Ypkzp/4JBpmoU0TXYw2O7kz4fZbd8CmNmJrkv/O7q/jgLeLWW\nBK8znEAu7LvB3wl+tzaIxd8RPtcRPr7S+W7bVRFaIrHEg8URrgk5wvu9vF6oKdlSaQh08scz23j1\n5KtUuaqw6uMYZ1iMbJ/P6Wpb13xwOgETUmxMybR3JZAZ2fHYLVffdDW2mp4yDfL3Dy/jGfciXuuc\njc4Uw4rJqayZms6KyamYDZL9dfspqSjh/cr3qXJVATAjeQbLs5ezLHsZhY7CMVNruBIZCFC2+hZM\n2dnkPvfbaIcDqEQRbUOZKACaf/cCdT/9KTm//S2x8+cN6Wf3KhSCQGcfSaWX5OJzQ2creFovf+0r\n8RhjwJZGyJbKXmsMW3Ru3vPWEyDE7Lg8bk5ZQZppMcfabJTWuDlW3U51mwfQKjGFqTZmZydwY66D\n2TkJFKTY+n2afUwlCltGvsz9yv9w85Q01k3PYNnEFLwhFzurdlJSUcLOqp24/C7MejMLMxayPHs5\nS7OWkhKTEu3Qh5ULQ2KzfvUr4lauiHY4gEoU0TbUiSLk8VC2ejXmCQXkPvvMkH72kAj4Lk8gnc1a\nX42r7uJAgPBxs6+dbbZYtsTZKDcZiQuGuNXdwSZsTIrLxRuXTY1I4xNvIvtb49hRH0NZZywgiDMb\nmJXjYHa2g9m5CczOduCIuXShqCFJFEKINcAv0BYffkpK+R893jcDzwFzgCbgLillefi97wL3AUHg\n76WUb4XPlwPO8PlAJF+icOpMeeTgX3EFWth+fjvvnnuXfbX7CMgAiZZElmcvZ3nWchZkLsBqsEb6\nMxhTpJSc3bgR6e7Q5nWK4pDY7lSiiK6hThQATc8+S/1/PDJ8ahXR5PeAux7ZXsv+mj1sqfmAd9pO\n4iPEtJCRje4O1jbVEtvt93VIb8FlzaRGpHLKl8TH7kROhsZxKjSOmKRs5uYnsWBCIvPzkxiXEDO4\niUIIoQdOAquBSmAfcI+U8li3Ml8HZkgpHxRC3A3cIaW8SwgxBfg9MA/IBN4FJkopg+FEUSSlbIw0\n2PHTxsslP1/Cx/UfI5HkxOWwKncVq3JWMT15+rCdImM4cf3lL1Q88FUyfvZTHBs3RjucLipRRFc0\nEkXI6+X0p9ZgSE0l7+WXxnyTcE9t3jb+dPpPbDm1hbLWMmIMMazNWPT/2rv36KrKM4/j318uJJAE\nCAmBEKBQJQ1Ql0gZW5R2YbWIiGKXUpjSVrEOVaF0KtUq0gqorbYoaungoFah03IRSotjBcHSVkfl\nVrEtqIVyEeQaiCEEJCR55o+9U2LMTQI5Oec8n7VYOfvk3TvP2bw5T8673/28XNe+L31PVqL3d566\n5+X9ncEEgNBxtWGL5fF2eRe2WB5TfvT4WZ/1dCGw1cy2AUhaAIwANldrMwKYGj5eDMxS8L8+Alhg\nZieA7ZK2hsd77XSC3Vu6lyNlR7j5/Ju5tPul5Gfme+f6GMyMwtmPk5SbS7urrop0OC7OJaSk0PHb\nE9h79xRKVq5s9lUVW+0xvIgAAA/nSURBVLp2Ke34Wp+vMab3GN48+CaL/7GY53esYMmuVRR0KODa\nXtdy5SV3nqozVVoIB9+Gg2/T+uA7nHfgbXrv30zy8T8xpYmxNCZR5AG7qm3vBj5bVxszK5dUDGSF\nz79eY9+qWhcGvCjJgP82szkNBdKrfS+WjljaiJBdbY6tXcfxN96g05Qp6Cwsdu/cx9VuxAgOPf00\nB2c+QsYll6Dk2Lqp9UyQgkWV+uX04/sXfp/fb/s9i7cs5v419/PQ+ocY0mMII/NHcn7H81GPQdBj\nULAfkAzBDZPTspoUQ2PGamr7k73meFVdberb92Iz6w9cAYyXVGuhIUnjJK2XtL74cHFtTVwjmBmF\nP/sZidnZtL+u5Qw5ufimpCRyJk2ibPt2iuYviHQ4LV5GqwxGFYxi0fBFLLhyAcPPGc6qnav4+gtf\n55rfXcPcTXM5dPzQh3dq0/T7pBqTKHYD1dcF7ArsqauNpCSgHXC4vn3NrOrrAWApwZDUR5jZHDMb\nYGYDOnb02Uunq/SV/+PY+vVk33wzCamNW5fZueaQPngwaRddxMFZsygvKop0OFFBEn2z+3LPwHtY\n/ZXVTB04lfRW6cxYP4PLnr2M767+Ln/e/WcqKj9a8PB0NCZRrAN6SeopqRUwGlhWo80y4Prw8XXA\nHyy4Sr4MGC0pRVJPoBewVlKapAwASWnAEODvTX85rjZmxsGZM0nOyyPzKyMjHY5zHyKJTnfdSWVp\nKQcfeyzS4USdNsltuDb/Wn417FcsvXopX+39VTbs38D4l8YzZMkQHvtL089pg4nCzMqBCcAK4C1g\nkZltkjRd0tVhs6eArPBi9W3AneG+m4BFBBe+lwPjzawC6AS8IulNYC3wvJktb/KrcbUqWbGCDzZv\nJnvCBL824VqklF69yBw9mvcXLOT43/xvxtN1bua53P5vt/PSyJd4ePDD5Gfm89Tfn2rycaPqhruz\numZ2jKo8cYJtw64koU0bev526cdeMKa5+PTYyIrE9NiaKkpK+OewYSR3zKHHooUoKfpK0bVE+0r3\nkZue26TpsX7jQYw7/PQznHzvPTpNvqvFJgnnABIzMug8eTIfbN5M0a9/HelwYkbntM5NPoYnihh2\ncv9+CufMIeNLl5E2cGCkw3GuQRlDh5L2hc9z4JFHKXv33UiH40KeKGLY/vt/BOXl5NxxR6RDca5R\nJJE7bRpKTGTP5MlYxZmZteOaxhNFjDqyciUlL75I9vjxtOrWreEdnGshknNz6TR5MsfXb+Dw3HmR\nDsfhiSImVRw5wv7p95JSUEDWjWMjHY5zH1u7a0aQfumlHJg502dBtQCeKGLQvnvvo/zwYXLvvddL\nIrioJInc++4lKTub9267jYqSkkiHFNc8UcSY4uee48hzz5F96y20Pu/TkQ7HudOWlJlJ3kMzOLln\nD3sn341VVkY6pLjliSKGlO3axb5p02ndvz/Z3/pWpMNxrsna9O9PzqRJlKxcSeHs2ZEOJ275HS0x\norK0lN3jJ0BiIl1+8hO/WcnFjA5jb+DEO+9Q+LNZpJxzLm2HXh7pkOKOv5vEADNjz91TOLF1K93m\nzKFV17yGd3IuSkii8/RplO3cyZ477iCxQyZpF8b5injNzIeeYsDBh2dSsnw5OZNuI33QxZEOx7kz\nLiElha6z/4vkbt3Yfet4Pti8ueGd3BnjiSLKHZ47l0NPPEH7UaPocOONkQ7HubMmKTOT7k8+QULb\nDN4deyPHN22KdEhxwxNFFCuaP5/9P36AjCFD6PzDH/iysC7mJefm8ol580hISwuSxcaNkQ4pLnii\niFKHnnmGfdOmk37JJXSZ8VMv+OfiRquuXfnEL+eR2L49O6+/gSMrV0Y6pJjniSLKWEUF+3/8Yw48\n8CAZl19O10cfIcHXmHBxJjkvjx4L5pNaUMB7E79D4eOP+30WZ5EniihSXlTErltv5fDceWR+4+vk\nPTTDFyJycSupQwe6z32GtsOGcfCRR9l1yy2+lOpZ4okiSpSuXcv2a75M6auv0XnqPXSePNnvlXBx\nLyE1lS4zfkqnH0yh9NXX2Db8Kh+KOgs8UbRwFcXF7P3hPbz7jetJSE2lx4L5ZI4eHemwnGsxJNFh\nzBh6Ln6WpE45vPftiey65VbKdu6MdGgxwxNFC1VZVsbhuXP559AreH/JEjrccAM9f7OE1n37Rjo0\n51qk1E99ip4LF5LzvUkcW7OGbcOvYt/06Zzcty/SoUU9H7toYSqKiylatIiiX/4P5QcOkHbRReTc\n/j1Se/eOdGjOtXhKTibrpptoe9XVFP785xQtepaiZxfTbtgVZI4ZQ+p55/k08tPgiaIFqDh6lNLX\nXqPkhRcoWfUSVlZG2kUD6fLgA76EqXOnIblTDrnTp5E1bhyHn36a4qVLKf7dMpK7d6ft5ZfT9oqh\npPTu7UmjkWRmDTeShgKPAonAk2b2QI3vpwDzgM8Ah4BRZrYj/N5dwDeBCmCima1ozDFrM2DAAFu/\nfn2jX1xLVV5UxIl3/sHxN9+k9OWXObZxI5SXk9iuHW2HD6f9yOtILSiIdJjNqmjhokiHENcyR30l\n0iGcVRVHj3LkhRcoWb6C0tdfh4oKkrrk0uaC/rS+4AJaX9CP1Pz8mF2/RdIGMxtwuvs3+IlCUiLw\nc+BLwG5gnaRlZla92Mo3gSIzO1fSaOBBYJSkPsBooC/QBVglKT/cp6FjRrXyQ4coffVVygsPUXGo\nkPJDhzm5by8ntmylorDwX+1S+vQma+xY0j4/iDb9+vl0V+fOgsT0dDJHjiRz5EjKi4ooWbWK0pdf\n4di6dRx5/vmwUSLJXbrQqls3krt3Iym7I4kZ6SSkZ5CQkY6SkiFBpJx7Lq26do3sC2pmjRl6uhDY\nambbACQtAEYA1d/URwBTw8eLgVkKPtONABaY2Qlgu6St4fFoxDGjWtmOHey5/Y5gIzmZpA4dSMrJ\nIX3QIFLy80nJzye1dwFJWVmRDdS5OJOUmfmvpGFmlO/Zw7E3NnJiyxZO7tpF2a5dfPDCciqKi2vd\nv9Ndd9Lh+uubOerIakyiyAN2VdveDXy2rjZmVi6pGMgKn3+9xr5VNbAbOmZUS+3Th0/+/nmSsrJI\naNvWx0Kda4EkkZyXR7u8j5bmt4oKKo8epaLkKJVHS7CT5YCR3Llz8wcaYY1JFLW9w9W8sFFXm7qe\nr21abq0XSySNA8aFmyck+UrrgWygsMFW8cHPxSmndy5GjzrzkUSe94tTPtWUnRuTKHYD3aptdwX2\n1NFmt6QkoB1wuIF9GzomAGY2B5gDIGl9Uy7IxBI/F6f4uTjFz8Upfi5OkdSkWUCNueFuHdBLUk9J\nrQguTi+r0WYZUDVodx3wBwumUy0DRktKkdQT6AWsbeQxnXPOtQANfqIIrzlMAFYQTGX9hZltkjQd\nWG9my4CngF+GF6sPE7zxE7ZbRHCRuhwYb2YVALUd88y/POecc03VqPsoWgpJ48KhqLjn5+IUPxen\n+Lk4xc/FKU09F1GVKJxzzjU/LwronHOuXlGRKCQNlfSOpK2S7ox0PM1JUjdJqyW9JWmTpO+Ez3eQ\ntFLSlvBrZqRjbS6SEiW9Iel/w+2ektaE52JhOEEi5klqL2mxpLfD/jEwXvuFpO+Gvx9/lzRfUmq8\n9AtJv5B0oPqtA3X1AwUeC99L/yqpf2N+RotPFNVKiFwB9AH+PSwNEi/KgUlm1hv4HDA+fP13Ai+Z\nWS/gpXA7XnwHeKva9oPAzPBcFBGUlIkHjwLLzawAOJ/gnMRdv5CUB0wEBpjZpwkmyFSVEoqHfvEM\nMLTGc3X1gysIZp/2Irg/bXZjfkCLTxRUKyFiZmVAVbmPuGBme83sL+HjEoI3gzyCczA3bDYXuCYy\nETYvSV2BK4Enw20BXyQoHQNxci4ktQW+QDDjEDMrM7P3idN+QTCDs3V4H1cbYC9x0i/M7M8Es02r\nq6sfjADmWeB1oL2k3IZ+RjQkitpKiHz0fvs4IKkHcAGwBuhkZnshSCZATuQia1aPAHcAleF2FvC+\nmZWH2/HSPz4JHASeDofhnpSURhz2CzN7D5gBvEuQIIqBDcRnv6hSVz84rffTaEgUjSkhEvMkpQNL\ngP80syORjicSJA0HDpjZhupP19I0HvpHEtAfmG1mFwClxMEwU23C8fcRQE+CKtVpBEMsNcVDv2jI\naf2+REOiaEwJkZgmKZkgSfzKzH4TPr2/6iNj+PVApOJrRhcDV0vaQTAE+UWCTxjtwyEHiJ/+sRvY\nbWZrwu3FBIkjHvvFZcB2MztoZieB3wAXEZ/9okpd/eC03k+jIVHEdbmPcAz+KeAtM3u42reql025\nHvhdc8fW3MzsLjPramY9CPrBH8xsDLCaoHQMxM+52AfsklRV7O1SggoIcdcvCIacPiepTfj7UnUu\n4q5fVFNXP1gGfCOc/fQ5oLhqiKo+UXHDnaRhBH85VpX7uD/CITUbSYOAl4G/cWpcfjLBdYpFQHeC\nX5SRZlbzglbMkjQY+J6ZDZf0SYJPGB2AN4CvhWugxDRJ/Qgu6rcCtgFjCf74i7t+IWkaMIpgluAb\nwE0EY+8x3y8kzQcGE1TL3Q/cA/yWWvpBmEhnEcySOgaMNbMGCwZGRaJwzjkXOdEw9OSccy6CPFE4\n55yrlycK55xz9fJE4Zxzrl6eKJxzztXLE4WLSZIqJG2s9q+HpAGSHmvCMXdIyq7l+XRJsyX9Myyn\nsUHSfzQx/hskzWrKMZw7UxpcCtW5KHXczPrVeG4H0KRF5uvwJMF9DL3MrFJSR+DGmo0kJVYtBexc\nNPFPFC5uSBpcbQ2LqWEd/z9K2iZpYrV2vw0/FWySNK6BY55DUOF4iplVAoSlJB6s9jNXS/o1wU2T\ndR5f0lhJ/5D0J4JyJVXPd5S0RNK68N/FONeM/BOFi1WtJW0MH283sy/X0qYAuATIAN6RNDusFXRj\neBdra2CdpCVmdqiOn9MXeLMqSdThQuDTZrY93P7I8Qnurp4GfIag+ulqgruJIVh3YqaZvSKpO7AC\n6N2Ic+DcGeGJwsWq2oaeano+LOlwQtIBoBNB0bSJkqoSSzeCRV7qShQfIuluYCSQY2ZdwqfXVksS\n1HH8zsAfzexgeJyFQH7Y5jKgT1B9AYC2kjLC9UmcO+s8Ubh4Vr3uTwWQFNaQugwYaGbHJP0RSK3n\nGJuB8yUlmFllWIfsfklHq7UprXrQwPHrqqeTELY/3uhX5twZ5NconPuwdkBR+CZeQLD8bJ3MbCvB\nBfL7wmV7kZRK7XX/6zv+GmCwpKywrPzIavu8CEyo2giLATrXbDxROPdhywk+WfwVuBd4vRH73ESw\n0t5WSRuAVcD3P87xw1LPU4HXwv3/Um2ficAASX+VtBm4+eO+KOeawqvHOuecq5d/onDOOVcvTxTO\nOefq5YnCOedcvTxROOecq5cnCuecc/XyROGcc65eniicc87VyxOFc865ev0/02ZA9ALG0RIAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20352518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Simulate Data\n",
    "\n",
    "def simulate_grades(mu, sigma, n_students):\n",
    "    grades = []\n",
    "    for i in range(n_students):\n",
    "        g = -1\n",
    "        while g<0 or g>100:\n",
    "            g = np.random.normal(mu, sigma)\n",
    "        grades.append(g)\n",
    "    return grades\n",
    "\n",
    "\n",
    "def simulate_gradebook(n_students=20):\n",
    "    gb =  pd.DataFrame({'Student ID': np.random.choice(100000, n_students, replace=False),\n",
    "                        'Gender': np.random.choice(['M', 'F'], n_students),\n",
    "                        'Age': np.random.choice([15, 16, 16, 17], n_students),\n",
    "                        'Race/Ethnicity': np.random.choice(['Hispanic/Latino', 'American Indian or Alaska Native',\n",
    "                                                            'Asian', 'Black or African American',\n",
    "                                                            'Native Hawaiian or Other Pacific Islander', 'White',\n",
    "                                                            'Two or more races'], n_students),\n",
    "                        'SPED Status': np.random.choice([False,True], n_students, p=[0.8, 0.2]),\n",
    "                        'Quiz 1': [11,12,10,90,91,92,50,55,41,38], #simulate_grades(mu, sigma, n_students)\n",
    "                        'Quiz 2': [97,95,89,19,9,11,50,45,40,30],\n",
    "                        'Quiz 3': [5,25,8,80,75,90,50,65,43,40]})\n",
    "    gb['Final Grade'] = gb[['Quiz 1', 'Quiz 2', 'Quiz 3']].mean(axis='columns')\n",
    "    return gb\n",
    "\n",
    "gb = simulate_gradebook(10)\n",
    "display(gb.head())\n",
    "\n",
    "for col in ['Quiz 1', 'Quiz 2', 'Quiz 3', 'Final Grade']:\n",
    "    sns.distplot(gb[col], hist=True if col=='Final Grade' else False, label=col)\n",
    "plt.legend()\n",
    "plt.xlim([0,100])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def optimalK(data, nrefs=3, maxClusters=20):\n",
    "    \"\"\"\n",
    "    Calculates KMeans optimal K using Gap Statistic from Tibshirani, Walther, Hastie\n",
    "    Params:\n",
    "        data: ndarry of shape (n_samples, n_features)\n",
    "        nrefs: number of sample reference datasets to create\n",
    "        maxClusters: Maximum number of clusters to test for\n",
    "    Returns: (gaps, optimalK)\n",
    "    \"\"\"\n",
    "    gaps = np.zeros((len(range(1, maxClusters)),))\n",
    "    resultsdf = pd.DataFrame({'clusterCount':[], 'gap':[]})\n",
    "    for gap_index, k in enumerate(range(1, maxClusters)):\n",
    "\n",
    "        # Holder for reference dispersion results\n",
    "        refDisps = np.zeros(nrefs)\n",
    "\n",
    "        # For n references, generate random sample and perform kmeans getting resulting dispersion of each loop\n",
    "        for i in range(nrefs):\n",
    "            \n",
    "            # Create new random reference set\n",
    "            randomReference = np.random.random_sample(size=data.shape)\n",
    "            \n",
    "            # Fit to it\n",
    "            km = KMeans(k)\n",
    "            km.fit(randomReference)\n",
    "            \n",
    "            refDisp = km.inertia_\n",
    "            refDisps[i] = refDisp\n",
    "\n",
    "        # Fit cluster to original data and create dispersion\n",
    "        km = KMeans(k)\n",
    "        km.fit(data)\n",
    "        \n",
    "        origDisp = km.inertia_\n",
    "\n",
    "        # Calculate gap statistic\n",
    "        gap = np.log(np.mean(refDisps)) - np.log(origDisp)\n",
    "\n",
    "        # Assign this loop's gap statistic to gaps\n",
    "        gaps[gap_index] = gap\n",
    "        \n",
    "        resultsdf = resultsdf.append({'clusterCount':k, 'gap':gap}, ignore_index=True)\n",
    "\n",
    "    return (gaps.argmax() + 1, resultsdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [],
   "source": [
    "# optimize clusters or take a set number of groups\n",
    "def cluster(data=gb[['Quiz 1', 'Quiz 2', 'Quiz 3']], n_groups=None):\n",
    "    start = 1 if n_groups==None else n_groups-1\n",
    "    n_iterations = 20 if n_groups==None else n_groups\n",
    "    \n",
    "    for i in range(start, n_iterations):\n",
    "        n_groups = i+1\n",
    "        \n",
    "        # K-Means\n",
    "        kmeans = KMeans(n_groups).fit(data)\n",
    "        gb['kmeans'] = [string.ascii_uppercase[i] for i in kmeans.labels_]\n",
    "\n",
    "        # Gaussian Mixture Model\n",
    "        gm = GaussianMixture(n_groups, n_init=100).fit(data)\n",
    "        data['gaussmix'] = gm.predict(data)\n",
    "        data['gaussmix'] = data['gaussmix'].map(lambda i: string.ascii_uppercase[i])\n",
    "\n",
    "        # comparison of mappings\n",
    "        #display(gb.groupby(['kmeans', 'gaussmix']).size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal k is 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20ec40b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. Cluster\n",
    "\n",
    "data = gb[['Quiz 1', 'Quiz 2', 'Quiz 3']]\n",
    "\n",
    "n_clusters, gapdf = optimalK(data, nrefs=10, maxClusters=gb.shape[0])\n",
    "\n",
    "print('Optimal k is', n_clusters)\n",
    "plt.plot(gapdf.clusterCount, gapdf.gap, linewidth=3)\n",
    "plt.scatter(gapdf[gapdf.clusterCount == n_clusters].clusterCount,\n",
    "            gapdf[gapdf.clusterCount == n_clusters].gap, s=250, c='r')\n",
    "plt.grid(True)\n",
    "plt.xlabel('Cluster Count')\n",
    "plt.ylabel('Gap Value')\n",
    "plt.title('Gap Values by Cluster Count')\n",
    "plt.show()\n",
    "\n",
    "kmeans = KMeans(n_clusters).fit(data)\n",
    "gb['kmeans'] = [string.ascii_uppercase[i] for i in kmeans.labels_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1eb3dac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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O4WQ5ZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a21101940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20d1fba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a208fb128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MO1L/1zJ/MrMS8jUEq1TvBJ5Pv6xPIPfY0g5FxAZyF6qvTY97RdIgCtedP9D6\nHwImSBqayp2fn7fM3cDMtolU1M6s1zgQrFLdRe5I4VHgGuDBDMtcTO7JbRskrQb+A/ifnVl/KkH8\nLeCBtPzDectcBtQr91D0dcAlnf1QZt3haqdmZgb4CMHMzBIHgpmZAQ4EMzNLHAhmZgY4EMzMLHEg\nmJkZ4EAwM7PEgWBmZgD8f0NzyUtkblcVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a218f1908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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wUFBjqNy9yd1fCh4fBOpIBMJlJMKLYPmZcCr8T2Y2A7gY+HHw3IALgE3BJqHW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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a214a3208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "explained variance ratio: 99.0% explained [0.9553566  0.03505838]\n"
     ]
    }
   ],
   "source": [
    "# 3. Visualize Clusters\n",
    "\n",
    "cluster_method = 'kmeans'\n",
    "\n",
    "# K-Means\n",
    "sns.heatmap(kmeans.cluster_centers_.T, cmap=sns.color_palette('RdYlGn_r')[::-1])\n",
    "plt.xticks(np.arange(n_clusters)+0.5, string.ascii_uppercase[:n_clusters])\n",
    "plt.yticks(np.arange(3)+0.5, ['Quiz 1', 'Quiz 2', 'Quiz 3'], verticalalignment='center')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Gaussiam Mixture Model\n",
    "for col in ['Quiz 1', 'Quiz 2', 'Quiz 3', 'Final Grade']:\n",
    "    for i in string.ascii_uppercase[:n_clusters]:\n",
    "        sns.distplot(gb.loc[gb[cluster_method]==i, col], kde=False, label=f'Cluster {i}')\n",
    "    plt.legend()\n",
    "    plt.xlim([0,100])\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "# PCA\n",
    "X = gb[['Quiz 1', 'Quiz 2', 'Quiz 3']]\n",
    "y = gb[cluster_method]\n",
    "cluster_labels = gb[cluster_method].drop_duplicates().sort_values().values\n",
    "target_names = cluster_labels\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "X_reduced = pca.fit(X).transform(X)\n",
    "\n",
    "for i, target_name in zip(cluster_labels, target_names):\n",
    "    plt.scatter(X_reduced[y==i, 0], X_reduced[y==i, 1], label=f'Cluster {target_name}', alpha=0.5)\n",
    "plt.legend(loc='best', shadow=False, scatterpoints=1)\n",
    "plt.title('PCA')\n",
    "plt.show()\n",
    "\n",
    "# Percentage of variance explained for each components\n",
    "print('explained variance ratio: {}% explained {}'\n",
    "      .format(round(sum(pca.explained_variance_ratio_), 3)*100, pca.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Group A: 18503, 90106, 65296\n",
      "Group B: 50146, 94006\n",
      "Group C: 11274, 36210, 35559\n",
      "Group D: 52440, 85755\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a208a8a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommendations:\n",
      "Group A should focus on Quiz 1\n",
      "Group B should focus on Quiz 2\n",
      "Group C should focus on Quiz 2\n",
      "Group D should focus on Quiz 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20e5a0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a215fc6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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88vLz82Xy5Mntv/vuu+ROnToVPfTQQ/7vvPNOizlz5lwCABcXF/PBgwcrHSXc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\nuRIR3aX2bThebZCqrV7RgTV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/NkU1dAm1Yk/71hb+/v7FWq1WHT9+3Gn37t2N7r333qvn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\n3Ab3PHFj60cZJwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a215fc860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a215fc198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4. Describe Clusters and Offer Recommendations\n",
    "\n",
    "# which students are in which group?\n",
    "for label in string.ascii_uppercase[:n_clusters]:\n",
    "    print(f\"Group {label}: {', '.join(gb.loc[gb[cluster_method]==label, 'Student ID'].astype(str).values)}\")\n",
    "\n",
    "# average scores by cluster\n",
    "palette = sns.color_palette()\n",
    "temp = gb.groupby(cluster_method)[['Quiz 1', 'Quiz 2', 'Quiz 3']].mean()\n",
    "recommendations = pd.DataFrame(temp.apply(lambda x: temp.columns[x.values.argmin()], axis='columns'))\n",
    "temp = temp.stack().reset_index().rename({'level_1':'quiz', 0:'grade', cluster_method:'cluster'}, axis='columns')\n",
    "b = sns.barplot('cluster', 'grade', 'quiz', data=temp, alpha=0.5, palette=palette)\n",
    "sns.despine(offset=10, trim=False)\n",
    "b.legend(title='', loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.ylim([0,100])\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.title('Average Assignment Grade')\n",
    "plt.show()\n",
    "\n",
    "# which assignment does each group need to focus on?\n",
    "print('Recommendations:')\n",
    "for label in recommendations.index:\n",
    "    print(f'Group {label} should focus on {recommendations.loc[label].values[0]}')\n",
    "\n",
    "    \n",
    "# similarities and differences on other characteristics (gender, age, race, SPED)\n",
    "for field in ['Gender', 'Age', 'Race/Ethnicity', 'SPED Status']:\n",
    "    temp = gb.groupby([cluster_method, field]).size()\n",
    "    temp = temp.reindex(pd.MultiIndex.from_product(temp.index.levels),\n",
    "                        fill_value=0.0)\\\n",
    "                .reset_index()\\\n",
    "                .rename({'level_0':'cluster', 'level_1':field, 0:'count'}, axis='columns')\n",
    "    temp['count'] = temp['count'].astype(int)\n",
    "    values = temp[field].drop_duplicates()\n",
    "    palette = itertools.cycle(sns.color_palette())\n",
    "    bottom = np.zeros(n_clusters)\n",
    "    plots = []\n",
    "    for i, value in enumerate(values):\n",
    "        g = plt.bar(temp.loc[temp[field]==value, 'cluster'],\n",
    "                temp.loc[temp[field]==value, 'count'],\n",
    "                width=0.5, alpha=0.5, color=next(palette),\n",
    "                bottom=bottom)\n",
    "        plots.append(g)\n",
    "        bottom = np.add(bottom, temp.loc[temp[field]==value, 'count'])\n",
    "    sns.despine(offset=10, trim=False)\n",
    "    plt.legend(plots, values, loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "    plt.title(field)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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