multiple_linear and simple_linear.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>school</th><th scope=col>sex</th><th scope=col>age</th><th scope=col>address</th><th scope=col>famsize</th><th scope=col>Pstatus</th><th scope=col>Medu</th><th scope=col>Fedu</th><th scope=col>Mjob</th><th scope=col>Fjob</th><th scope=col>...</th><th scope=col>famrel</th><th scope=col>freetime</th><th scope=col>goout</th><th scope=col>Dalc</th><th scope=col>Walc</th><th scope=col>health</th><th scope=col>absences</th><th scope=col>G1</th><th scope=col>G2</th><th scope=col>G3</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>GP      </td><td>F       </td><td>18      </td><td>U       </td><td>GT3     </td><td>A       </td><td>4       </td><td>4       </td><td>at_home </td><td>teacher </td><td>...     </td><td>4       </td><td>3       </td><td>4       </td><td>1       </td><td>1       </td><td>3       </td><td> 6      </td><td> 5      </td><td> 6      </td><td> 6      </td></tr>\n",
       "\t<tr><td>GP      </td><td>F       </td><td>17      </td><td>U       </td><td>GT3     </td><td>T       </td><td>1       </td><td>1       </td><td>at_home </td><td>other   </td><td>...     </td><td>5       </td><td>3       </td><td>3       </td><td>1       </td><td>1       </td><td>3       </td><td> 4      </td><td> 5      </td><td> 5      </td><td> 6      </td></tr>\n",
       "\t<tr><td>GP      </td><td>F       </td><td>15      </td><td>U       </td><td>LE3     </td><td>T       </td><td>1       </td><td>1       </td><td>at_home </td><td>other   </td><td>...     </td><td>4       </td><td>3       </td><td>2       </td><td>2       </td><td>3       </td><td>3       </td><td>10      </td><td> 7      </td><td> 8      </td><td>10      </td></tr>\n",
       "\t<tr><td>GP      </td><td>F       </td><td>15      </td><td>U       </td><td>GT3     </td><td>T       </td><td>4       </td><td>2       </td><td>health  </td><td>services</td><td>...     </td><td>3       </td><td>2       </td><td>2       </td><td>1       </td><td>1       </td><td>5       </td><td> 2      </td><td>15      </td><td>14      </td><td>15      </td></tr>\n",
       "\t<tr><td>GP      </td><td>F       </td><td>16      </td><td>U       </td><td>GT3     </td><td>T       </td><td>3       </td><td>3       </td><td>other   </td><td>other   </td><td>...     </td><td>4       </td><td>3       </td><td>2       </td><td>1       </td><td>2       </td><td>5       </td><td> 4      </td><td> 6      </td><td>10      </td><td>10      </td></tr>\n",
       "\t<tr><td>GP      </td><td>M       </td><td>16      </td><td>U       </td><td>LE3     </td><td>T       </td><td>4       </td><td>3       </td><td>services</td><td>other   </td><td>...     </td><td>5       </td><td>4       </td><td>2       </td><td>1       </td><td>2       </td><td>5       </td><td>10      </td><td>15      </td><td>15      </td><td>15      </td></tr>\n",
       "\t<tr><td>GP      </td><td>M       </td><td>16      </td><td>U       </td><td>LE3     </td><td>T       </td><td>2       </td><td>2       </td><td>other   </td><td>other   </td><td>...     </td><td>4       </td><td>4       </td><td>4       </td><td>1       </td><td>1       </td><td>3       </td><td> 0      </td><td>12      </td><td>12      </td><td>11      </td></tr>\n",
       "\t<tr><td>GP      </td><td>F       </td><td>17      </td><td>U       </td><td>GT3     </td><td>A       </td><td>4       </td><td>4       </td><td>other   </td><td>teacher </td><td>...     </td><td>4       </td><td>1       </td><td>4       </td><td>1       </td><td>1       </td><td>1       </td><td> 6      </td><td> 6      </td><td> 5      </td><td> 6      </td></tr>\n",
       "\t<tr><td>GP      </td><td>M       </td><td>15      </td><td>U       </td><td>LE3     </td><td>A       </td><td>3       </td><td>2       </td><td>services</td><td>other   </td><td>...     </td><td>4       </td><td>2       </td><td>2       </td><td>1       </td><td>1       </td><td>1       </td><td> 0      </td><td>16      </td><td>18      </td><td>19      </td></tr>\n",
       "\t<tr><td>GP      </td><td>M       </td><td>15      </td><td>U       </td><td>GT3     </td><td>T       </td><td>3       </td><td>4       </td><td>other   </td><td>other   </td><td>...     </td><td>5       </td><td>5       </td><td>1       </td><td>1       </td><td>1       </td><td>5       </td><td> 0      </td><td>14      </td><td>15      </td><td>15      </td></tr>\n",
       "\t<tr><td>GP      </td><td>F       </td><td>15      </td><td>U       </td><td>GT3     </td><td>T       </td><td>4       </td><td>4       </td><td>teacher </td><td>health  </td><td>...     </td><td>3       </td><td>3       </td><td>3       </td><td>1       </td><td>2       </td><td>2       </td><td> 0      </td><td>10      </td><td> 8      </td><td> 9      </td></tr>\n",
       "\t<tr><td>GP      </td><td>F       </td><td>15      </td><td>U       </td><td>GT3     </td><td>T       </td><td>2       </td><td>1       </td><td>services</td><td>other   </td><td>...     </td><td>5       </td><td>2       </td><td>2       </td><td>1       </td><td>1       </td><td>4       </td><td> 4      </td><td>10      </td><td>12      </td><td>12      </td></tr>\n",
       "\t<tr><td>GP      </td><td>M       </td><td>15      </td><td>U       </td><td>LE3     </td><td>T       </td><td>4       </td><td>4       </td><td>health  </td><td>services</td><td>...     </td><td>4       </td><td>3       </td><td>3       </td><td>1       </td><td>3       </td><td>5       </td><td> 2      </td><td>14      </td><td>14      </td><td>14      </td></tr>\n",
       "\t<tr><td>GP      </td><td>M       </td><td>15      </td><td>U       </td><td>GT3     </td><td>T       </td><td>4       </td><td>3       </td><td>teacher </td><td>other   </td><td>...     </td><td>5       </td><td>4       </td><td>3       </td><td>1       </td><td>2       </td><td>3       </td><td> 2      </td><td>10      </td><td>10      </td><td>11      </td></tr>\n",
       "\t<tr><td>GP      </td><td>M       </td><td>15      </td><td>U       </td><td>GT3     </td><td>A       </td><td>2       </td><td>2       </td><td>other   </td><td>other   </td><td>...     </td><td>4       </td><td>5       </td><td>2       </td><td>1       </td><td>1       </td><td>3       </td><td> 0      </td><td>14      </td><td>16      </td><td>16      </td></tr>\n",
       "\t<tr><td>GP      </td><td>F       </td><td>16      </td><td>U       </td><td>GT3     </td><td>T       </td><td>4       </td><td>4       </td><td>health  </td><td>other   </td><td>...     </td><td>4       </td><td>4       </td><td>4       </td><td>1       </td><td>2       </td><td>2       </td><td> 4      </td><td>14      </td><td>14      </td><td>14      </td></tr>\n",
       "\t<tr><td>GP      </td><td>F       </td><td>16      </td><td>U       </td><td>GT3     </td><td>T       </td><td>4       </td><td>4       </td><td>services</td><td>services</td><td>...     </td><td>3       </td><td>2       </td><td>3       </td><td>1       </td><td>2       </td><td>2       </td><td> 6      </td><td>13      </td><td>14      </td><td>14      </td></tr>\n",
       "\t<tr><td>GP      </td><td>F       </td><td>16      </td><td>U       </td><td>GT3     </td><td>T       </td><td>3       </td><td>3       </td><td>other   </td><td>other   </td><td>...     </td><td>5       </td><td>3       </td><td>2       </td><td>1       </td><td>1       </td><td>4       </td><td> 4      </td><td> 8      </td><td>10      </td><td>10      </td></tr>\n",
       "\t<tr><td>GP      </td><td>M       </td><td>17      </td><td>U       </td><td>GT3     </td><td>T       </td><td>3       </td><td>2       </td><td>services</td><td>services</td><td>...     </td><td>5       </td><td>5       </td><td>5       </td><td>2       </td><td>4       </td><td>5       </td><td>16      </td><td> 6      </td><td> 5      </td><td> 5      </td></tr>\n",
       "\t<tr><td>GP      </td><td>M       </td><td>16      </td><td>U       </td><td>LE3     </td><td>T       </td><td>4       </td><td>3       </td><td>health  </td><td>other   </td><td>...     </td><td>3       </td><td>1       </td><td>3       </td><td>1       </td><td>3       </td><td>5       </td><td> 4      </td><td> 8      </td><td>10      </td><td>10      </td></tr>\n",
       "\t<tr><td>GP      </td><td>M       </td><td>15      </td><td>U       </td><td>GT3     </td><td>T       </td><td>4       </td><td>3       </td><td>teacher </td><td>other   </td><td>...     </td><td>4       </td><td>4       </td><td>1       </td><td>1       </td><td>1       </td><td>1       </td><td> 0      </td><td>13      </td><td>14      </td><td>15      </td></tr>\n",
       "\t<tr><td>GP      </td><td>M       </td><td>15      </td><td>U       </td><td>GT3     </td><td>T       </td><td>4       </td><td>4       </td><td>health  </td><td>health  </td><td>...     </td><td>5       </td><td>4       </td><td>2       </td><td>1       </td><td>1       </td><td>5       </td><td> 0      </td><td>12      </td><td>15      </td><td>15      </td></tr>\n",
       "\t<tr><td>GP      </td><td>M       </td><td>16      </td><td>U       </td><td>LE3     </td><td>T       </td><td>4       </td><td>2       </td><td>teacher </td><td>other   </td><td>...     </td><td>4       </td><td>5       </td><td>1       </td><td>1       </td><td>3       </td><td>5       </td><td> 2      </td><td>15      </td><td>15      </td><td>16      </td></tr>\n",
       "\t<tr><td>GP      </td><td>M       </td><td>16      </td><td>U       </td><td>LE3     </td><td>T       </td><td>2       </td><td>2       </td><td>other   </td><td>other   </td><td>...     </td><td>5       </td><td>4       </td><td>4       </td><td>2       </td><td>4       </td><td>5       </td><td> 0      </td><td>13      </td><td>13      </td><td>12      </td></tr>\n",
       "\t<tr><td>GP      </td><td>F       </td><td>15      </td><td>R       </td><td>GT3     </td><td>T       </td><td>2       </td><td>4       </td><td>services</td><td>health  </td><td>...     </td><td>4       </td><td>3       </td><td>2       </td><td>1       </td><td>1       </td><td>5       </td><td> 2      </td><td>10      </td><td> 9      </td><td> 8      </td></tr>\n",
       "\t<tr><td>GP      </td><td>F       </td><td>16      </td><td>U       </td><td>GT3     </td><td>T       </td><td>2       </td><td>2       </td><td>services</td><td>services</td><td>...     </td><td>1       </td><td>2       </td><td>2       </td><td>1       </td><td>3       </td><td>5       </td><td>14      </td><td> 6      </td><td> 9      </td><td> 8      </td></tr>\n",
       "\t<tr><td>GP      </td><td>M       </td><td>15      </td><td>U       </td><td>GT3     </td><td>T       </td><td>2       </td><td>2       </td><td>other   </td><td>other   </td><td>...     </td><td>4       </td><td>2       </td><td>2       </td><td>1       </td><td>2       </td><td>5       </td><td> 2      </td><td>12      </td><td>12      </td><td>11      </td></tr>\n",
       "\t<tr><td>GP      </td><td>M       </td><td>15      </td><td>U       </td><td>GT3     </td><td>T       </td><td>4       </td><td>2       </td><td>health  </td><td>services</td><td>...     </td><td>2       </td><td>2       </td><td>4       </td><td>2       </td><td>4       </td><td>1       </td><td> 4      </td><td>15      </td><td>16      </td><td>15      </td></tr>\n",
       "\t<tr><td>GP      </td><td>M       </td><td>16      </td><td>U       </td><td>LE3     </td><td>A       </td><td>3       </td><td>4       </td><td>services</td><td>other   </td><td>...     </td><td>5       </td><td>3       </td><td>3       </td><td>1       </td><td>1       </td><td>5       </td><td> 4      </td><td>11      </td><td>11      </td><td>11      </td></tr>\n",
       "\t<tr><td>GP      </td><td>M       </td><td>16      </td><td>U       </td><td>GT3     </td><td>T       </td><td>4       </td><td>4       </td><td>teacher </td><td>teacher </td><td>...     </td><td>4       </td><td>4       </td><td>5       </td><td>5       </td><td>5       </td><td>5       </td><td>16      </td><td>10      </td><td>12      </td><td>11      </td></tr>\n",
       "\t<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>   </td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "\t<tr><td>MS      </td><td>M       </td><td>18      </td><td>R       </td><td>GT3     </td><td>T       </td><td>1       </td><td>3       </td><td>at_home </td><td>other   </td><td>...     </td><td>3       </td><td>3       </td><td>4       </td><td>2       </td><td>4       </td><td>3       </td><td> 4      </td><td>10      </td><td>10      </td><td>10      </td></tr>\n",
       "\t<tr><td>MS      </td><td>M       </td><td>18      </td><td>U       </td><td>LE3     </td><td>T       </td><td>4       </td><td>4       </td><td>teacher </td><td>services</td><td>...     </td><td>4       </td><td>2       </td><td>2       </td><td>2       </td><td>2       </td><td>5       </td><td> 0      </td><td>13      </td><td>13      </td><td>13      </td></tr>\n",
       "\t<tr><td>MS      </td><td>F       </td><td>17      </td><td>R       </td><td>GT3     </td><td>T       </td><td>1       </td><td>1       </td><td>other   </td><td>services</td><td>...     </td><td>5       </td><td>2       </td><td>1       </td><td>1       </td><td>2       </td><td>1       </td><td> 0      </td><td> 7      </td><td> 6      </td><td> 0      </td></tr>\n",
       "\t<tr><td>MS      </td><td>F       </td><td>18      </td><td>U       </td><td>GT3     </td><td>T       </td><td>2       </td><td>3       </td><td>at_home </td><td>services</td><td>...     </td><td>5       </td><td>2       </td><td>3       </td><td>1       </td><td>2       </td><td>4       </td><td> 0      </td><td>11      </td><td>10      </td><td>10      </td></tr>\n",
       "\t<tr><td>MS      </td><td>F       </td><td>18      </td><td>R       </td><td>GT3     </td><td>T       </td><td>4       </td><td>4       </td><td>other   </td><td>teacher </td><td>...     </td><td>3       </td><td>2       </td><td>2       </td><td>4       </td><td>2       </td><td>5       </td><td>10      </td><td>14      </td><td>12      </td><td>11      </td></tr>\n",
       "\t<tr><td>MS      </td><td>F       </td><td>19      </td><td>U       </td><td>LE3     </td><td>T       </td><td>3       </td><td>2       </td><td>services</td><td>services</td><td>...     </td><td>3       </td><td>2       </td><td>2       </td><td>1       </td><td>1       </td><td>3       </td><td> 4      </td><td> 7      </td><td> 7      </td><td> 9      </td></tr>\n",
       "\t<tr><td>MS      </td><td>M       </td><td>18      </td><td>R       </td><td>LE3     </td><td>T       </td><td>1       </td><td>2       </td><td>at_home </td><td>services</td><td>...     </td><td>4       </td><td>3       </td><td>3       </td><td>2       </td><td>3       </td><td>3       </td><td> 3      </td><td>14      </td><td>12      </td><td>12      </td></tr>\n",
       "\t<tr><td>MS      </td><td>F       </td><td>17      </td><td>U       </td><td>GT3     </td><td>T       </td><td>2       </td><td>2       </td><td>other   </td><td>at_home </td><td>...     </td><td>3       </td><td>4       </td><td>3       </td><td>1       </td><td>1       </td><td>3       </td><td> 8      </td><td>13      </td><td>11      </td><td>11      </td></tr>\n",
       "\t<tr><td>MS      </td><td>F       </td><td>17      </td><td>R       </td><td>GT3     </td><td>T       </td><td>1       </td><td>2       </td><td>other   </td><td>other   </td><td>...     </td><td>3       </td><td>5       </td><td>5       </td><td>1       </td><td>3       </td><td>1       </td><td>14      </td><td> 6      </td><td> 5      </td><td> 5      </td></tr>\n",
       "\t<tr><td>MS      </td><td>F       </td><td>18      </td><td>R       </td><td>LE3     </td><td>T       </td><td>4       </td><td>4       </td><td>other   </td><td>other   </td><td>...     </td><td>5       </td><td>4       </td><td>4       </td><td>1       </td><td>1       </td><td>1       </td><td> 0      </td><td>19      </td><td>18      </td><td>19      </td></tr>\n",
       "\t<tr><td>MS      </td><td>F       </td><td>18      </td><td>R       </td><td>GT3     </td><td>T       </td><td>1       </td><td>1       </td><td>other   </td><td>other   </td><td>...     </td><td>4       </td><td>3       </td><td>2       </td><td>1       </td><td>2       </td><td>4       </td><td> 2      </td><td> 8      </td><td> 8      </td><td>10      </td></tr>\n",
       "\t<tr><td>MS      </td><td>F       </td><td>20      </td><td>U       </td><td>GT3     </td><td>T       </td><td>4       </td><td>2       </td><td>health  </td><td>other   </td><td>...     </td><td>5       </td><td>4       </td><td>3       </td><td>1       </td><td>1       </td><td>3       </td><td> 4      </td><td>15      </td><td>14      </td><td>15      </td></tr>\n",
       "\t<tr><td>MS      </td><td>F       </td><td>18      </td><td>R       </td><td>LE3     </td><td>T       </td><td>4       </td><td>4       </td><td>teacher </td><td>services</td><td>...     </td><td>5       </td><td>4       </td><td>3       </td><td>3       </td><td>4       </td><td>2       </td><td> 4      </td><td> 8      </td><td> 9      </td><td>10      </td></tr>\n",
       "\t<tr><td>MS      </td><td>F       </td><td>18      </td><td>U       </td><td>GT3     </td><td>T       </td><td>3       </td><td>3       </td><td>other   </td><td>other   </td><td>...     </td><td>4       </td><td>1       </td><td>3       </td><td>1       </td><td>2       </td><td>1       </td><td> 0      </td><td>15      </td><td>15      </td><td>15      </td></tr>\n",
       "\t<tr><td>MS      </td><td>F       </td><td>17      </td><td>R       </td><td>GT3     </td><td>T       </td><td>3       </td><td>1       </td><td>at_home </td><td>other   </td><td>...     </td><td>4       </td><td>5       </td><td>4       </td><td>2       </td><td>3       </td><td>1       </td><td>17      </td><td>10      </td><td>10      </td><td>10      </td></tr>\n",
       "\t<tr><td>MS      </td><td>M       </td><td>18      </td><td>U       </td><td>GT3     </td><td>T       </td><td>4       </td><td>4       </td><td>teacher </td><td>teacher </td><td>...     </td><td>3       </td><td>2       </td><td>4       </td><td>1       </td><td>4       </td><td>2       </td><td> 4      </td><td>15      </td><td>14      </td><td>14      </td></tr>\n",
       "\t<tr><td>MS      </td><td>M       </td><td>18      </td><td>R       </td><td>GT3     </td><td>T       </td><td>2       </td><td>1       </td><td>other   </td><td>other   </td><td>...     </td><td>4       </td><td>4       </td><td>3       </td><td>1       </td><td>3       </td><td>5       </td><td> 5      </td><td> 7      </td><td> 6      </td><td> 7      </td></tr>\n",
       "\t<tr><td>MS      </td><td>M       </td><td>17      </td><td>U       </td><td>GT3     </td><td>T       </td><td>2       </td><td>3       </td><td>other   </td><td>services</td><td>...     </td><td>4       </td><td>4       </td><td>3       </td><td>1       </td><td>1       </td><td>3       </td><td> 2      </td><td>11      </td><td>11      </td><td>10      </td></tr>\n",
       "\t<tr><td>MS      </td><td>M       </td><td>19      </td><td>R       </td><td>GT3     </td><td>T       </td><td>1       </td><td>1       </td><td>other   </td><td>services</td><td>...     </td><td>4       </td><td>3       </td><td>2       </td><td>1       </td><td>3       </td><td>5       </td><td> 0      </td><td> 6      </td><td> 5      </td><td> 0      </td></tr>\n",
       "\t<tr><td>MS      </td><td>M       </td><td>18      </td><td>R       </td><td>GT3     </td><td>T       </td><td>4       </td><td>2       </td><td>other   </td><td>other   </td><td>...     </td><td>5       </td><td>4       </td><td>3       </td><td>4       </td><td>3       </td><td>3       </td><td>14      </td><td> 6      </td><td> 5      </td><td> 5      </td></tr>\n",
       "\t<tr><td>MS      </td><td>F       </td><td>18      </td><td>R       </td><td>GT3     </td><td>T       </td><td>2       </td><td>2       </td><td>at_home </td><td>other   </td><td>...     </td><td>5       </td><td>3       </td><td>3       </td><td>1       </td><td>3       </td><td>4       </td><td> 2      </td><td>10      </td><td> 9      </td><td>10      </td></tr>\n",
       "\t<tr><td>MS      </td><td>F       </td><td>18      </td><td>R       </td><td>GT3     </td><td>T       </td><td>4       </td><td>4       </td><td>teacher </td><td>at_home </td><td>...     </td><td>4       </td><td>4       </td><td>3       </td><td>2       </td><td>2       </td><td>5       </td><td> 7      </td><td> 6      </td><td> 5      </td><td> 6      </td></tr>\n",
       "\t<tr><td>MS      </td><td>F       </td><td>19      </td><td>R       </td><td>GT3     </td><td>T       </td><td>2       </td><td>3       </td><td>services</td><td>other   </td><td>...     </td><td>5       </td><td>4       </td><td>2       </td><td>1       </td><td>2       </td><td>5       </td><td> 0      </td><td> 7      </td><td> 5      </td><td> 0      </td></tr>\n",
       "\t<tr><td>MS      </td><td>F       </td><td>18      </td><td>U       </td><td>LE3     </td><td>T       </td><td>3       </td><td>1       </td><td>teacher </td><td>services</td><td>...     </td><td>4       </td><td>3       </td><td>4       </td><td>1       </td><td>1       </td><td>1       </td><td> 0      </td><td> 7      </td><td> 9      </td><td> 8      </td></tr>\n",
       "\t<tr><td>MS      </td><td>F       </td><td>18      </td><td>U       </td><td>GT3     </td><td>T       </td><td>1       </td><td>1       </td><td>other   </td><td>other   </td><td>...     </td><td>1       </td><td>1       </td><td>1       </td><td>1       </td><td>1       </td><td>5       </td><td> 0      </td><td> 6      </td><td> 5      </td><td> 0      </td></tr>\n",
       "\t<tr><td>MS      </td><td>M       </td><td>20      </td><td>U       </td><td>LE3     </td><td>A       </td><td>2       </td><td>2       </td><td>services</td><td>services</td><td>...     </td><td>5       </td><td>5       </td><td>4       </td><td>4       </td><td>5       </td><td>4       </td><td>11      </td><td> 9      </td><td> 9      </td><td> 9      </td></tr>\n",
       "\t<tr><td>MS      </td><td>M       </td><td>17      </td><td>U       </td><td>LE3     </td><td>T       </td><td>3       </td><td>1       </td><td>services</td><td>services</td><td>...     </td><td>2       </td><td>4       </td><td>5       </td><td>3       </td><td>4       </td><td>2       </td><td> 3      </td><td>14      </td><td>16      </td><td>16      </td></tr>\n",
       "\t<tr><td>MS      </td><td>M       </td><td>21      </td><td>R       </td><td>GT3     </td><td>T       </td><td>1       </td><td>1       </td><td>other   </td><td>other   </td><td>...     </td><td>5       </td><td>5       </td><td>3       </td><td>3       </td><td>3       </td><td>3       </td><td> 3      </td><td>10      </td><td> 8      </td><td> 7      </td></tr>\n",
       "\t<tr><td>MS      </td><td>M       </td><td>18      </td><td>R       </td><td>LE3     </td><td>T       </td><td>3       </td><td>2       </td><td>services</td><td>other   </td><td>...     </td><td>4       </td><td>4       </td><td>1       </td><td>3       </td><td>4       </td><td>5       </td><td> 0      </td><td>11      </td><td>12      </td><td>10      </td></tr>\n",
       "\t<tr><td>MS      </td><td>M       </td><td>19      </td><td>U       </td><td>LE3     </td><td>T       </td><td>1       </td><td>1       </td><td>other   </td><td>at_home </td><td>...     </td><td>3       </td><td>2       </td><td>3       </td><td>3       </td><td>3       </td><td>5       </td><td> 5      </td><td> 8      </td><td> 9      </td><td> 9      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllll}\n",
       " school & sex & age & address & famsize & Pstatus & Medu & Fedu & Mjob & Fjob & ... & famrel & freetime & goout & Dalc & Walc & health & absences & G1 & G2 & G3\\\\\n",
       "\\hline\n",
       "\t GP         & F          & 18         & U          & GT3        & A          & 4          & 4          & at\\_home  & teacher    & ...        & 4          & 3          & 4          & 1          & 1          & 3          &  6         &  5         &  6         &  6        \\\\\n",
       "\t GP         & F          & 17         & U          & GT3        & T          & 1          & 1          & at\\_home  & other      & ...        & 5          & 3          & 3          & 1          & 1          & 3          &  4         &  5         &  5         &  6        \\\\\n",
       "\t GP         & F          & 15         & U          & LE3        & T          & 1          & 1          & at\\_home  & other      & ...        & 4          & 3          & 2          & 2          & 3          & 3          & 10         &  7         &  8         & 10        \\\\\n",
       "\t GP       & F        & 15       & U        & GT3      & T        & 4        & 2        & health   & services & ...      & 3        & 2        & 2        & 1        & 1        & 5        &  2       & 15       & 14       & 15      \\\\\n",
       "\t GP       & F        & 16       & U        & GT3      & T        & 3        & 3        & other    & other    & ...      & 4        & 3        & 2        & 1        & 2        & 5        &  4       &  6       & 10       & 10      \\\\\n",
       "\t GP       & M        & 16       & U        & LE3      & T        & 4        & 3        & services & other    & ...      & 5        & 4        & 2        & 1        & 2        & 5        & 10       & 15       & 15       & 15      \\\\\n",
       "\t GP       & M        & 16       & U        & LE3      & T        & 2        & 2        & other    & other    & ...      & 4        & 4        & 4        & 1        & 1        & 3        &  0       & 12       & 12       & 11      \\\\\n",
       "\t GP       & F        & 17       & U        & GT3      & A        & 4        & 4        & other    & teacher  & ...      & 4        & 1        & 4        & 1        & 1        & 1        &  6       &  6       &  5       &  6      \\\\\n",
       "\t GP       & M        & 15       & U        & LE3      & A        & 3        & 2        & services & other    & ...      & 4        & 2        & 2        & 1        & 1        & 1        &  0       & 16       & 18       & 19      \\\\\n",
       "\t GP       & M        & 15       & U        & GT3      & T        & 3        & 4        & other    & other    & ...      & 5        & 5        & 1        & 1        & 1        & 5        &  0       & 14       & 15       & 15      \\\\\n",
       "\t GP       & F        & 15       & U        & GT3      & T        & 4        & 4        & teacher  & health   & ...      & 3        & 3        & 3        & 1        & 2        & 2        &  0       & 10       &  8       &  9      \\\\\n",
       "\t GP       & F        & 15       & U        & GT3      & T        & 2        & 1        & services & other    & ...      & 5        & 2        & 2        & 1        & 1        & 4        &  4       & 10       & 12       & 12      \\\\\n",
       "\t GP       & M        & 15       & U        & LE3      & T        & 4        & 4        & health   & services & ...      & 4        & 3        & 3        & 1        & 3        & 5        &  2       & 14       & 14       & 14      \\\\\n",
       "\t GP       & M        & 15       & U        & GT3      & T        & 4        & 3        & teacher  & other    & ...      & 5        & 4        & 3        & 1        & 2        & 3        &  2       & 10       & 10       & 11      \\\\\n",
       "\t GP       & M        & 15       & U        & GT3      & A        & 2        & 2        & other    & other    & ...      & 4        & 5        & 2        & 1        & 1        & 3        &  0       & 14       & 16       & 16      \\\\\n",
       "\t GP       & F        & 16       & U        & GT3      & T        & 4        & 4        & health   & other    & ...      & 4        & 4        & 4        & 1        & 2        & 2        &  4       & 14       & 14       & 14      \\\\\n",
       "\t GP       & F        & 16       & U        & GT3      & T        & 4        & 4        & services & services & ...      & 3        & 2        & 3        & 1        & 2        & 2        &  6       & 13       & 14       & 14      \\\\\n",
       "\t GP       & F        & 16       & U        & GT3      & T        & 3        & 3        & other    & other    & ...      & 5        & 3        & 2        & 1        & 1        & 4        &  4       &  8       & 10       & 10      \\\\\n",
       "\t GP       & M        & 17       & U        & GT3      & T        & 3        & 2        & services & services & ...      & 5        & 5        & 5        & 2        & 4        & 5        & 16       &  6       &  5       &  5      \\\\\n",
       "\t GP       & M        & 16       & U        & LE3      & T        & 4        & 3        & health   & other    & ...      & 3        & 1        & 3        & 1        & 3        & 5        &  4       &  8       & 10       & 10      \\\\\n",
       "\t GP       & M        & 15       & U        & GT3      & T        & 4        & 3        & teacher  & other    & ...      & 4        & 4        & 1        & 1        & 1        & 1        &  0       & 13       & 14       & 15      \\\\\n",
       "\t GP       & M        & 15       & U        & GT3      & T        & 4        & 4        & health   & health   & ...      & 5        & 4        & 2        & 1        & 1        & 5        &  0       & 12       & 15       & 15      \\\\\n",
       "\t GP       & M        & 16       & U        & LE3      & T        & 4        & 2        & teacher  & other    & ...      & 4        & 5        & 1        & 1        & 3        & 5        &  2       & 15       & 15       & 16      \\\\\n",
       "\t GP       & M        & 16       & U        & LE3      & T        & 2        & 2        & other    & other    & ...      & 5        & 4        & 4        & 2        & 4        & 5        &  0       & 13       & 13       & 12      \\\\\n",
       "\t GP       & F        & 15       & R        & GT3      & T        & 2        & 4        & services & health   & ...      & 4        & 3        & 2        & 1        & 1        & 5        &  2       & 10       &  9       &  8      \\\\\n",
       "\t GP       & F        & 16       & U        & GT3      & T        & 2        & 2        & services & services & ...      & 1        & 2        & 2        & 1        & 3        & 5        & 14       &  6       &  9       &  8      \\\\\n",
       "\t GP       & M        & 15       & U        & GT3      & T        & 2        & 2        & other    & other    & ...      & 4        & 2        & 2        & 1        & 2        & 5        &  2       & 12       & 12       & 11      \\\\\n",
       "\t GP       & M        & 15       & U        & GT3      & T        & 4        & 2        & health   & services & ...      & 2        & 2        & 4        & 2        & 4        & 1        &  4       & 15       & 16       & 15      \\\\\n",
       "\t GP       & M        & 16       & U        & LE3      & A        & 3        & 4        & services & other    & ...      & 5        & 3        & 3        & 1        & 1        & 5        &  4       & 11       & 11       & 11      \\\\\n",
       "\t GP       & M        & 16       & U        & GT3      & T        & 4        & 4        & teacher  & teacher  & ...      & 4        & 4        & 5        & 5        & 5        & 5        & 16       & 10       & 12       & 11      \\\\\n",
       "\t ... & ... & ... & ... & ... & ... & ... & ... & ... & ... &     & ... & ... & ... & ... & ... & ... & ... & ... & ... & ...\\\\\n",
       "\t MS         & M          & 18         & R          & GT3        & T          & 1          & 3          & at\\_home  & other      & ...        & 3          & 3          & 4          & 2          & 4          & 3          &  4         & 10         & 10         & 10        \\\\\n",
       "\t MS       & M        & 18       & U        & LE3      & T        & 4        & 4        & teacher  & services & ...      & 4        & 2        & 2        & 2        & 2        & 5        &  0       & 13       & 13       & 13      \\\\\n",
       "\t MS       & F        & 17       & R        & GT3      & T        & 1        & 1        & other    & services & ...      & 5        & 2        & 1        & 1        & 2        & 1        &  0       &  7       &  6       &  0      \\\\\n",
       "\t MS         & F          & 18         & U          & GT3        & T          & 2          & 3          & at\\_home  & services   & ...        & 5          & 2          & 3          & 1          & 2          & 4          &  0         & 11         & 10         & 10        \\\\\n",
       "\t MS       & F        & 18       & R        & GT3      & T        & 4        & 4        & other    & teacher  & ...      & 3        & 2        & 2        & 4        & 2        & 5        & 10       & 14       & 12       & 11      \\\\\n",
       "\t MS       & F        & 19       & U        & LE3      & T        & 3        & 2        & services & services & ...      & 3        & 2        & 2        & 1        & 1        & 3        &  4       &  7       &  7       &  9      \\\\\n",
       "\t MS         & M          & 18         & R          & LE3        & T          & 1          & 2          & at\\_home  & services   & ...        & 4          & 3          & 3          & 2          & 3          & 3          &  3         & 14         & 12         & 12        \\\\\n",
       "\t MS         & F          & 17         & U          & GT3        & T          & 2          & 2          & other      & at\\_home  & ...        & 3          & 4          & 3          & 1          & 1          & 3          &  8         & 13         & 11         & 11        \\\\\n",
       "\t MS       & F        & 17       & R        & GT3      & T        & 1        & 2        & other    & other    & ...      & 3        & 5        & 5        & 1        & 3        & 1        & 14       &  6       &  5       &  5      \\\\\n",
       "\t MS       & F        & 18       & R        & LE3      & T        & 4        & 4        & other    & other    & ...      & 5        & 4        & 4        & 1        & 1        & 1        &  0       & 19       & 18       & 19      \\\\\n",
       "\t MS       & F        & 18       & R        & GT3      & T        & 1        & 1        & other    & other    & ...      & 4        & 3        & 2        & 1        & 2        & 4        &  2       &  8       &  8       & 10      \\\\\n",
       "\t MS       & F        & 20       & U        & GT3      & T        & 4        & 2        & health   & other    & ...      & 5        & 4        & 3        & 1        & 1        & 3        &  4       & 15       & 14       & 15      \\\\\n",
       "\t MS       & F        & 18       & R        & LE3      & T        & 4        & 4        & teacher  & services & ...      & 5        & 4        & 3        & 3        & 4        & 2        &  4       &  8       &  9       & 10      \\\\\n",
       "\t MS       & F        & 18       & U        & GT3      & T        & 3        & 3        & other    & other    & ...      & 4        & 1        & 3        & 1        & 2        & 1        &  0       & 15       & 15       & 15      \\\\\n",
       "\t MS         & F          & 17         & R          & GT3        & T          & 3          & 1          & at\\_home  & other      & ...        & 4          & 5          & 4          & 2          & 3          & 1          & 17         & 10         & 10         & 10        \\\\\n",
       "\t MS       & M        & 18       & U        & GT3      & T        & 4        & 4        & teacher  & teacher  & ...      & 3        & 2        & 4        & 1        & 4        & 2        &  4       & 15       & 14       & 14      \\\\\n",
       "\t MS       & M        & 18       & R        & GT3      & T        & 2        & 1        & other    & other    & ...      & 4        & 4        & 3        & 1        & 3        & 5        &  5       &  7       &  6       &  7      \\\\\n",
       "\t MS       & M        & 17       & U        & GT3      & T        & 2        & 3        & other    & services & ...      & 4        & 4        & 3        & 1        & 1        & 3        &  2       & 11       & 11       & 10      \\\\\n",
       "\t MS       & M        & 19       & R        & GT3      & T        & 1        & 1        & other    & services & ...      & 4        & 3        & 2        & 1        & 3        & 5        &  0       &  6       &  5       &  0      \\\\\n",
       "\t MS       & M        & 18       & R        & GT3      & T        & 4        & 2        & other    & other    & ...      & 5        & 4        & 3        & 4        & 3        & 3        & 14       &  6       &  5       &  5      \\\\\n",
       "\t MS         & F          & 18         & R          & GT3        & T          & 2          & 2          & at\\_home  & other      & ...        & 5          & 3          & 3          & 1          & 3          & 4          &  2         & 10         &  9         & 10        \\\\\n",
       "\t MS         & F          & 18         & R          & GT3        & T          & 4          & 4          & teacher    & at\\_home  & ...        & 4          & 4          & 3          & 2          & 2          & 5          &  7         &  6         &  5         &  6        \\\\\n",
       "\t MS       & F        & 19       & R        & GT3      & T        & 2        & 3        & services & other    & ...      & 5        & 4        & 2        & 1        & 2        & 5        &  0       &  7       &  5       &  0      \\\\\n",
       "\t MS       & F        & 18       & U        & LE3      & T        & 3        & 1        & teacher  & services & ...      & 4        & 3        & 4        & 1        & 1        & 1        &  0       &  7       &  9       &  8      \\\\\n",
       "\t MS       & F        & 18       & U        & GT3      & T        & 1        & 1        & other    & other    & ...      & 1        & 1        & 1        & 1        & 1        & 5        &  0       &  6       &  5       &  0      \\\\\n",
       "\t MS       & M        & 20       & U        & LE3      & A        & 2        & 2        & services & services & ...      & 5        & 5        & 4        & 4        & 5        & 4        & 11       &  9       &  9       &  9      \\\\\n",
       "\t MS       & M        & 17       & U        & LE3      & T        & 3        & 1        & services & services & ...      & 2        & 4        & 5        & 3        & 4        & 2        &  3       & 14       & 16       & 16      \\\\\n",
       "\t MS       & M        & 21       & R        & GT3      & T        & 1        & 1        & other    & other    & ...      & 5        & 5        & 3        & 3        & 3        & 3        &  3       & 10       &  8       &  7      \\\\\n",
       "\t MS       & M        & 18       & R        & LE3      & T        & 3        & 2        & services & other    & ...      & 4        & 4        & 1        & 3        & 4        & 5        &  0       & 11       & 12       & 10      \\\\\n",
       "\t MS         & M          & 19         & U          & LE3        & T          & 1          & 1          & other      & at\\_home  & ...        & 3          & 2          & 3          & 3          & 3          & 5          &  5         &  8         &  9         &  9        \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| school | sex | age | address | famsize | Pstatus | Medu | Fedu | Mjob | Fjob | ... | famrel | freetime | goout | Dalc | Walc | health | absences | G1 | G2 | G3 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| GP       | F        | 18       | U        | GT3      | A        | 4        | 4        | at_home  | teacher  | ...      | 4        | 3        | 4        | 1        | 1        | 3        |  6       |  5       |  6       |  6       |\n",
       "| GP       | F        | 17       | U        | GT3      | T        | 1        | 1        | at_home  | other    | ...      | 5        | 3        | 3        | 1        | 1        | 3        |  4       |  5       |  5       |  6       |\n",
       "| GP       | F        | 15       | U        | LE3      | T        | 1        | 1        | at_home  | other    | ...      | 4        | 3        | 2        | 2        | 3        | 3        | 10       |  7       |  8       | 10       |\n",
       "| GP       | F        | 15       | U        | GT3      | T        | 4        | 2        | health   | services | ...      | 3        | 2        | 2        | 1        | 1        | 5        |  2       | 15       | 14       | 15       |\n",
       "| GP       | F        | 16       | U        | GT3      | T        | 3        | 3        | other    | other    | ...      | 4        | 3        | 2        | 1        | 2        | 5        |  4       |  6       | 10       | 10       |\n",
       "| GP       | M        | 16       | U        | LE3      | T        | 4        | 3        | services | other    | ...      | 5        | 4        | 2        | 1        | 2        | 5        | 10       | 15       | 15       | 15       |\n",
       "| GP       | M        | 16       | U        | LE3      | T        | 2        | 2        | other    | other    | ...      | 4        | 4        | 4        | 1        | 1        | 3        |  0       | 12       | 12       | 11       |\n",
       "| GP       | F        | 17       | U        | GT3      | A        | 4        | 4        | other    | teacher  | ...      | 4        | 1        | 4        | 1        | 1        | 1        |  6       |  6       |  5       |  6       |\n",
       "| GP       | M        | 15       | U        | LE3      | A        | 3        | 2        | services | other    | ...      | 4        | 2        | 2        | 1        | 1        | 1        |  0       | 16       | 18       | 19       |\n",
       "| GP       | M        | 15       | U        | GT3      | T        | 3        | 4        | other    | other    | ...      | 5        | 5        | 1        | 1        | 1        | 5        |  0       | 14       | 15       | 15       |\n",
       "| GP       | F        | 15       | U        | GT3      | T        | 4        | 4        | teacher  | health   | ...      | 3        | 3        | 3        | 1        | 2        | 2        |  0       | 10       |  8       |  9       |\n",
       "| GP       | F        | 15       | U        | GT3      | T        | 2        | 1        | services | other    | ...      | 5        | 2        | 2        | 1        | 1        | 4        |  4       | 10       | 12       | 12       |\n",
       "| GP       | M        | 15       | U        | LE3      | T        | 4        | 4        | health   | services | ...      | 4        | 3        | 3        | 1        | 3        | 5        |  2       | 14       | 14       | 14       |\n",
       "| GP       | M        | 15       | U        | GT3      | T        | 4        | 3        | teacher  | other    | ...      | 5        | 4        | 3        | 1        | 2        | 3        |  2       | 10       | 10       | 11       |\n",
       "| GP       | M        | 15       | U        | GT3      | A        | 2        | 2        | other    | other    | ...      | 4        | 5        | 2        | 1        | 1        | 3        |  0       | 14       | 16       | 16       |\n",
       "| GP       | F        | 16       | U        | GT3      | T        | 4        | 4        | health   | other    | ...      | 4        | 4        | 4        | 1        | 2        | 2        |  4       | 14       | 14       | 14       |\n",
       "| GP       | F        | 16       | U        | GT3      | T        | 4        | 4        | services | services | ...      | 3        | 2        | 3        | 1        | 2        | 2        |  6       | 13       | 14       | 14       |\n",
       "| GP       | F        | 16       | U        | GT3      | T        | 3        | 3        | other    | other    | ...      | 5        | 3        | 2        | 1        | 1        | 4        |  4       |  8       | 10       | 10       |\n",
       "| GP       | M        | 17       | U        | GT3      | T        | 3        | 2        | services | services | ...      | 5        | 5        | 5        | 2        | 4        | 5        | 16       |  6       |  5       |  5       |\n",
       "| GP       | M        | 16       | U        | LE3      | T        | 4        | 3        | health   | other    | ...      | 3        | 1        | 3        | 1        | 3        | 5        |  4       |  8       | 10       | 10       |\n",
       "| GP       | M        | 15       | U        | GT3      | T        | 4        | 3        | teacher  | other    | ...      | 4        | 4        | 1        | 1        | 1        | 1        |  0       | 13       | 14       | 15       |\n",
       "| GP       | M        | 15       | U        | GT3      | T        | 4        | 4        | health   | health   | ...      | 5        | 4        | 2        | 1        | 1        | 5        |  0       | 12       | 15       | 15       |\n",
       "| GP       | M        | 16       | U        | LE3      | T        | 4        | 2        | teacher  | other    | ...      | 4        | 5        | 1        | 1        | 3        | 5        |  2       | 15       | 15       | 16       |\n",
       "| GP       | M        | 16       | U        | LE3      | T        | 2        | 2        | other    | other    | ...      | 5        | 4        | 4        | 2        | 4        | 5        |  0       | 13       | 13       | 12       |\n",
       "| GP       | F        | 15       | R        | GT3      | T        | 2        | 4        | services | health   | ...      | 4        | 3        | 2        | 1        | 1        | 5        |  2       | 10       |  9       |  8       |\n",
       "| GP       | F        | 16       | U        | GT3      | T        | 2        | 2        | services | services | ...      | 1        | 2        | 2        | 1        | 3        | 5        | 14       |  6       |  9       |  8       |\n",
       "| GP       | M        | 15       | U        | GT3      | T        | 2        | 2        | other    | other    | ...      | 4        | 2        | 2        | 1        | 2        | 5        |  2       | 12       | 12       | 11       |\n",
       "| GP       | M        | 15       | U        | GT3      | T        | 4        | 2        | health   | services | ...      | 2        | 2        | 4        | 2        | 4        | 1        |  4       | 15       | 16       | 15       |\n",
       "| GP       | M        | 16       | U        | LE3      | A        | 3        | 4        | services | other    | ...      | 5        | 3        | 3        | 1        | 1        | 5        |  4       | 11       | 11       | 11       |\n",
       "| GP       | M        | 16       | U        | GT3      | T        | 4        | 4        | teacher  | teacher  | ...      | 4        | 4        | 5        | 5        | 5        | 5        | 16       | 10       | 12       | 11       |\n",
       "| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |     | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |\n",
       "| MS       | M        | 18       | R        | GT3      | T        | 1        | 3        | at_home  | other    | ...      | 3        | 3        | 4        | 2        | 4        | 3        |  4       | 10       | 10       | 10       |\n",
       "| MS       | M        | 18       | U        | LE3      | T        | 4        | 4        | teacher  | services | ...      | 4        | 2        | 2        | 2        | 2        | 5        |  0       | 13       | 13       | 13       |\n",
       "| MS       | F        | 17       | R        | GT3      | T        | 1        | 1        | other    | services | ...      | 5        | 2        | 1        | 1        | 2        | 1        |  0       |  7       |  6       |  0       |\n",
       "| MS       | F        | 18       | U        | GT3      | T        | 2        | 3        | at_home  | services | ...      | 5        | 2        | 3        | 1        | 2        | 4        |  0       | 11       | 10       | 10       |\n",
       "| MS       | F        | 18       | R        | GT3      | T        | 4        | 4        | other    | teacher  | ...      | 3        | 2        | 2        | 4        | 2        | 5        | 10       | 14       | 12       | 11       |\n",
       "| MS       | F        | 19       | U        | LE3      | T        | 3        | 2        | services | services | ...      | 3        | 2        | 2        | 1        | 1        | 3        |  4       |  7       |  7       |  9       |\n",
       "| MS       | M        | 18       | R        | LE3      | T        | 1        | 2        | at_home  | services | ...      | 4        | 3        | 3        | 2        | 3        | 3        |  3       | 14       | 12       | 12       |\n",
       "| MS       | F        | 17       | U        | GT3      | T        | 2        | 2        | other    | at_home  | ...      | 3        | 4        | 3        | 1        | 1        | 3        |  8       | 13       | 11       | 11       |\n",
       "| MS       | F        | 17       | R        | GT3      | T        | 1        | 2        | other    | other    | ...      | 3        | 5        | 5        | 1        | 3        | 1        | 14       |  6       |  5       |  5       |\n",
       "| MS       | F        | 18       | R        | LE3      | T        | 4        | 4        | other    | other    | ...      | 5        | 4        | 4        | 1        | 1        | 1        |  0       | 19       | 18       | 19       |\n",
       "| MS       | F        | 18       | R        | GT3      | T        | 1        | 1        | other    | other    | ...      | 4        | 3        | 2        | 1        | 2        | 4        |  2       |  8       |  8       | 10       |\n",
       "| MS       | F        | 20       | U        | GT3      | T        | 4        | 2        | health   | other    | ...      | 5        | 4        | 3        | 1        | 1        | 3        |  4       | 15       | 14       | 15       |\n",
       "| MS       | F        | 18       | R        | LE3      | T        | 4        | 4        | teacher  | services | ...      | 5        | 4        | 3        | 3        | 4        | 2        |  4       |  8       |  9       | 10       |\n",
       "| MS       | F        | 18       | U        | GT3      | T        | 3        | 3        | other    | other    | ...      | 4        | 1        | 3        | 1        | 2        | 1        |  0       | 15       | 15       | 15       |\n",
       "| MS       | F        | 17       | R        | GT3      | T        | 3        | 1        | at_home  | other    | ...      | 4        | 5        | 4        | 2        | 3        | 1        | 17       | 10       | 10       | 10       |\n",
       "| MS       | M        | 18       | U        | GT3      | T        | 4        | 4        | teacher  | teacher  | ...      | 3        | 2        | 4        | 1        | 4        | 2        |  4       | 15       | 14       | 14       |\n",
       "| MS       | M        | 18       | R        | GT3      | T        | 2        | 1        | other    | other    | ...      | 4        | 4        | 3        | 1        | 3        | 5        |  5       |  7       |  6       |  7       |\n",
       "| MS       | M        | 17       | U        | GT3      | T        | 2        | 3        | other    | services | ...      | 4        | 4        | 3        | 1        | 1        | 3        |  2       | 11       | 11       | 10       |\n",
       "| MS       | M        | 19       | R        | GT3      | T        | 1        | 1        | other    | services | ...      | 4        | 3        | 2        | 1        | 3        | 5        |  0       |  6       |  5       |  0       |\n",
       "| MS       | M        | 18       | R        | GT3      | T        | 4        | 2        | other    | other    | ...      | 5        | 4        | 3        | 4        | 3        | 3        | 14       |  6       |  5       |  5       |\n",
       "| MS       | F        | 18       | R        | GT3      | T        | 2        | 2        | at_home  | other    | ...      | 5        | 3        | 3        | 1        | 3        | 4        |  2       | 10       |  9       | 10       |\n",
       "| MS       | F        | 18       | R        | GT3      | T        | 4        | 4        | teacher  | at_home  | ...      | 4        | 4        | 3        | 2        | 2        | 5        |  7       |  6       |  5       |  6       |\n",
       "| MS       | F        | 19       | R        | GT3      | T        | 2        | 3        | services | other    | ...      | 5        | 4        | 2        | 1        | 2        | 5        |  0       |  7       |  5       |  0       |\n",
       "| MS       | F        | 18       | U        | LE3      | T        | 3        | 1        | teacher  | services | ...      | 4        | 3        | 4        | 1        | 1        | 1        |  0       |  7       |  9       |  8       |\n",
       "| MS       | F        | 18       | U        | GT3      | T        | 1        | 1        | other    | other    | ...      | 1        | 1        | 1        | 1        | 1        | 5        |  0       |  6       |  5       |  0       |\n",
       "| MS       | M        | 20       | U        | LE3      | A        | 2        | 2        | services | services | ...      | 5        | 5        | 4        | 4        | 5        | 4        | 11       |  9       |  9       |  9       |\n",
       "| MS       | M        | 17       | U        | LE3      | T        | 3        | 1        | services | services | ...      | 2        | 4        | 5        | 3        | 4        | 2        |  3       | 14       | 16       | 16       |\n",
       "| MS       | M        | 21       | R        | GT3      | T        | 1        | 1        | other    | other    | ...      | 5        | 5        | 3        | 3        | 3        | 3        |  3       | 10       |  8       |  7       |\n",
       "| MS       | M        | 18       | R        | LE3      | T        | 3        | 2        | services | other    | ...      | 4        | 4        | 1        | 3        | 4        | 5        |  0       | 11       | 12       | 10       |\n",
       "| MS       | M        | 19       | U        | LE3      | T        | 1        | 1        | other    | at_home  | ...      | 3        | 2        | 3        | 3        | 3        | 5        |  5       |  8       |  9       |  9       |\n",
       "\n"
      ],
      "text/plain": [
       "    school sex age address famsize Pstatus Medu Fedu Mjob     Fjob     ...\n",
       "1   GP     F   18  U       GT3     A       4    4    at_home  teacher  ...\n",
       "2   GP     F   17  U       GT3     T       1    1    at_home  other    ...\n",
       "3   GP     F   15  U       LE3     T       1    1    at_home  other    ...\n",
       "4   GP     F   15  U       GT3     T       4    2    health   services ...\n",
       "5   GP     F   16  U       GT3     T       3    3    other    other    ...\n",
       "6   GP     M   16  U       LE3     T       4    3    services other    ...\n",
       "7   GP     M   16  U       LE3     T       2    2    other    other    ...\n",
       "8   GP     F   17  U       GT3     A       4    4    other    teacher  ...\n",
       "9   GP     M   15  U       LE3     A       3    2    services other    ...\n",
       "10  GP     M   15  U       GT3     T       3    4    other    other    ...\n",
       "11  GP     F   15  U       GT3     T       4    4    teacher  health   ...\n",
       "12  GP     F   15  U       GT3     T       2    1    services other    ...\n",
       "13  GP     M   15  U       LE3     T       4    4    health   services ...\n",
       "14  GP     M   15  U       GT3     T       4    3    teacher  other    ...\n",
       "15  GP     M   15  U       GT3     A       2    2    other    other    ...\n",
       "16  GP     F   16  U       GT3     T       4    4    health   other    ...\n",
       "17  GP     F   16  U       GT3     T       4    4    services services ...\n",
       "18  GP     F   16  U       GT3     T       3    3    other    other    ...\n",
       "19  GP     M   17  U       GT3     T       3    2    services services ...\n",
       "20  GP     M   16  U       LE3     T       4    3    health   other    ...\n",
       "21  GP     M   15  U       GT3     T       4    3    teacher  other    ...\n",
       "22  GP     M   15  U       GT3     T       4    4    health   health   ...\n",
       "23  GP     M   16  U       LE3     T       4    2    teacher  other    ...\n",
       "24  GP     M   16  U       LE3     T       2    2    other    other    ...\n",
       "25  GP     F   15  R       GT3     T       2    4    services health   ...\n",
       "26  GP     F   16  U       GT3     T       2    2    services services ...\n",
       "27  GP     M   15  U       GT3     T       2    2    other    other    ...\n",
       "28  GP     M   15  U       GT3     T       4    2    health   services ...\n",
       "29  GP     M   16  U       LE3     A       3    4    services other    ...\n",
       "30  GP     M   16  U       GT3     T       4    4    teacher  teacher  ...\n",
       "... ...    ... ... ...     ...     ...     ...  ...  ...      ...         \n",
       "366 MS     M   18  R       GT3     T       1    3    at_home  other    ...\n",
       "367 MS     M   18  U       LE3     T       4    4    teacher  services ...\n",
       "368 MS     F   17  R       GT3     T       1    1    other    services ...\n",
       "369 MS     F   18  U       GT3     T       2    3    at_home  services ...\n",
       "370 MS     F   18  R       GT3     T       4    4    other    teacher  ...\n",
       "371 MS     F   19  U       LE3     T       3    2    services services ...\n",
       "372 MS     M   18  R       LE3     T       1    2    at_home  services ...\n",
       "373 MS     F   17  U       GT3     T       2    2    other    at_home  ...\n",
       "374 MS     F   17  R       GT3     T       1    2    other    other    ...\n",
       "375 MS     F   18  R       LE3     T       4    4    other    other    ...\n",
       "376 MS     F   18  R       GT3     T       1    1    other    other    ...\n",
       "377 MS     F   20  U       GT3     T       4    2    health   other    ...\n",
       "378 MS     F   18  R       LE3     T       4    4    teacher  services ...\n",
       "379 MS     F   18  U       GT3     T       3    3    other    other    ...\n",
       "380 MS     F   17  R       GT3     T       3    1    at_home  other    ...\n",
       "381 MS     M   18  U       GT3     T       4    4    teacher  teacher  ...\n",
       "382 MS     M   18  R       GT3     T       2    1    other    other    ...\n",
       "383 MS     M   17  U       GT3     T       2    3    other    services ...\n",
       "384 MS     M   19  R       GT3     T       1    1    other    services ...\n",
       "385 MS     M   18  R       GT3     T       4    2    other    other    ...\n",
       "386 MS     F   18  R       GT3     T       2    2    at_home  other    ...\n",
       "387 MS     F   18  R       GT3     T       4    4    teacher  at_home  ...\n",
       "388 MS     F   19  R       GT3     T       2    3    services other    ...\n",
       "389 MS     F   18  U       LE3     T       3    1    teacher  services ...\n",
       "390 MS     F   18  U       GT3     T       1    1    other    other    ...\n",
       "391 MS     M   20  U       LE3     A       2    2    services services ...\n",
       "392 MS     M   17  U       LE3     T       3    1    services services ...\n",
       "393 MS     M   21  R       GT3     T       1    1    other    other    ...\n",
       "394 MS     M   18  R       LE3     T       3    2    services other    ...\n",
       "395 MS     M   19  U       LE3     T       1    1    other    at_home  ...\n",
       "    famrel freetime goout Dalc Walc health absences G1  G2  G3 \n",
       "1   4      3        4     1    1    3       6        5   6   6 \n",
       "2   5      3        3     1    1    3       4        5   5   6 \n",
       "3   4      3        2     2    3    3      10        7   8  10 \n",
       "4   3      2        2     1    1    5       2       15  14  15 \n",
       "5   4      3        2     1    2    5       4        6  10  10 \n",
       "6   5      4        2     1    2    5      10       15  15  15 \n",
       "7   4      4        4     1    1    3       0       12  12  11 \n",
       "8   4      1        4     1    1    1       6        6   5   6 \n",
       "9   4      2        2     1    1    1       0       16  18  19 \n",
       "10  5      5        1     1    1    5       0       14  15  15 \n",
       "11  3      3        3     1    2    2       0       10   8   9 \n",
       "12  5      2        2     1    1    4       4       10  12  12 \n",
       "13  4      3        3     1    3    5       2       14  14  14 \n",
       "14  5      4        3     1    2    3       2       10  10  11 \n",
       "15  4      5        2     1    1    3       0       14  16  16 \n",
       "16  4      4        4     1    2    2       4       14  14  14 \n",
       "17  3      2        3     1    2    2       6       13  14  14 \n",
       "18  5      3        2     1    1    4       4        8  10  10 \n",
       "19  5      5        5     2    4    5      16        6   5   5 \n",
       "20  3      1        3     1    3    5       4        8  10  10 \n",
       "21  4      4        1     1    1    1       0       13  14  15 \n",
       "22  5      4        2     1    1    5       0       12  15  15 \n",
       "23  4      5        1     1    3    5       2       15  15  16 \n",
       "24  5      4        4     2    4    5       0       13  13  12 \n",
       "25  4      3        2     1    1    5       2       10   9   8 \n",
       "26  1      2        2     1    3    5      14        6   9   8 \n",
       "27  4      2        2     1    2    5       2       12  12  11 \n",
       "28  2      2        4     2    4    1       4       15  16  15 \n",
       "29  5      3        3     1    1    5       4       11  11  11 \n",
       "30  4      4        5     5    5    5      16       10  12  11 \n",
       "... ...    ...      ...   ...  ...  ...    ...      ... ... ...\n",
       "366 3      3        4     2    4    3       4       10  10  10 \n",
       "367 4      2        2     2    2    5       0       13  13  13 \n",
       "368 5      2        1     1    2    1       0        7   6   0 \n",
       "369 5      2        3     1    2    4       0       11  10  10 \n",
       "370 3      2        2     4    2    5      10       14  12  11 \n",
       "371 3      2        2     1    1    3       4        7   7   9 \n",
       "372 4      3        3     2    3    3       3       14  12  12 \n",
       "373 3      4        3     1    1    3       8       13  11  11 \n",
       "374 3      5        5     1    3    1      14        6   5   5 \n",
       "375 5      4        4     1    1    1       0       19  18  19 \n",
       "376 4      3        2     1    2    4       2        8   8  10 \n",
       "377 5      4        3     1    1    3       4       15  14  15 \n",
       "378 5      4        3     3    4    2       4        8   9  10 \n",
       "379 4      1        3     1    2    1       0       15  15  15 \n",
       "380 4      5        4     2    3    1      17       10  10  10 \n",
       "381 3      2        4     1    4    2       4       15  14  14 \n",
       "382 4      4        3     1    3    5       5        7   6   7 \n",
       "383 4      4        3     1    1    3       2       11  11  10 \n",
       "384 4      3        2     1    3    5       0        6   5   0 \n",
       "385 5      4        3     4    3    3      14        6   5   5 \n",
       "386 5      3        3     1    3    4       2       10   9  10 \n",
       "387 4      4        3     2    2    5       7        6   5   6 \n",
       "388 5      4        2     1    2    5       0        7   5   0 \n",
       "389 4      3        4     1    1    1       0        7   9   8 \n",
       "390 1      1        1     1    1    5       0        6   5   0 \n",
       "391 5      5        4     4    5    4      11        9   9   9 \n",
       "392 2      4        5     3    4    2       3       14  16  16 \n",
       "393 5      5        3     3    3    3       3       10   8   7 \n",
       "394 4      4        1     3    4    5       0       11  12  10 \n",
       "395 3      2        3     3    3    5       5        8   9   9 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = read.csv('C:/Users/MANI/Documents/R sem 2/linear_regression/studentdata/student-mat.csv',sep=';')\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " school   sex          age       address famsize   Pstatus      Medu      \n",
       " GP:349   F:208   Min.   :15.0   R: 88   GT3:281   A: 41   Min.   :0.000  \n",
       " MS: 46   M:187   1st Qu.:16.0   U:307   LE3:114   T:354   1st Qu.:2.000  \n",
       "                  Median :17.0                             Median :3.000  \n",
       "                  Mean   :16.7                             Mean   :2.749  \n",
       "                  3rd Qu.:18.0                             3rd Qu.:4.000  \n",
       "                  Max.   :22.0                             Max.   :4.000  \n",
       "      Fedu             Mjob           Fjob            reason      guardian  \n",
       " Min.   :0.000   at_home : 59   at_home : 20   course    :145   father: 90  \n",
       " 1st Qu.:2.000   health  : 34   health  : 18   home      :109   mother:273  \n",
       " Median :2.000   other   :141   other   :217   other     : 36   other : 32  \n",
       " Mean   :2.522   services:103   services:111   reputation:105               \n",
       " 3rd Qu.:3.000   teacher : 58   teacher : 29                                \n",
       " Max.   :4.000                                                              \n",
       "   traveltime      studytime        failures      schoolsup famsup     paid    \n",
       " Min.   :1.000   Min.   :1.000   Min.   :0.0000   no :344   no :153   no :214  \n",
       " 1st Qu.:1.000   1st Qu.:1.000   1st Qu.:0.0000   yes: 51   yes:242   yes:181  \n",
       " Median :1.000   Median :2.000   Median :0.0000                                \n",
       " Mean   :1.448   Mean   :2.035   Mean   :0.3342                                \n",
       " 3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:0.0000                                \n",
       " Max.   :4.000   Max.   :4.000   Max.   :3.0000                                \n",
       " activities nursery   higher    internet  romantic      famrel     \n",
       " no :194    no : 81   no : 20   no : 66   no :263   Min.   :1.000  \n",
       " yes:201    yes:314   yes:375   yes:329   yes:132   1st Qu.:4.000  \n",
       "                                                    Median :4.000  \n",
       "                                                    Mean   :3.944  \n",
       "                                                    3rd Qu.:5.000  \n",
       "                                                    Max.   :5.000  \n",
       "    freetime         goout            Dalc            Walc      \n",
       " Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  \n",
       " 1st Qu.:3.000   1st Qu.:2.000   1st Qu.:1.000   1st Qu.:1.000  \n",
       " Median :3.000   Median :3.000   Median :1.000   Median :2.000  \n",
       " Mean   :3.235   Mean   :3.109   Mean   :1.481   Mean   :2.291  \n",
       " 3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:2.000   3rd Qu.:3.000  \n",
       " Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000  \n",
       "     health         absences            G1              G2       \n",
       " Min.   :1.000   Min.   : 0.000   Min.   : 3.00   Min.   : 0.00  \n",
       " 1st Qu.:3.000   1st Qu.: 0.000   1st Qu.: 8.00   1st Qu.: 9.00  \n",
       " Median :4.000   Median : 4.000   Median :11.00   Median :11.00  \n",
       " Mean   :3.554   Mean   : 5.709   Mean   :10.91   Mean   :10.71  \n",
       " 3rd Qu.:5.000   3rd Qu.: 8.000   3rd Qu.:13.00   3rd Qu.:13.00  \n",
       " Max.   :5.000   Max.   :75.000   Max.   :19.00   Max.   :19.00  \n",
       "       G3       \n",
       " Min.   : 0.00  \n",
       " 1st Qu.: 8.00  \n",
       " Median :11.00  \n",
       " Mean   :10.42  \n",
       " 3rd Qu.:14.00  \n",
       " Max.   :20.00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t395 obs. of  33 variables:\n",
      " $ school    : Factor w/ 2 levels \"GP\",\"MS\": 1 1 1 1 1 1 1 1 1 1 ...\n",
      " $ sex       : Factor w/ 2 levels \"F\",\"M\": 1 1 1 1 1 2 2 1 2 2 ...\n",
      " $ age       : int  18 17 15 15 16 16 16 17 15 15 ...\n",
      " $ address   : Factor w/ 2 levels \"R\",\"U\": 2 2 2 2 2 2 2 2 2 2 ...\n",
      " $ famsize   : Factor w/ 2 levels \"GT3\",\"LE3\": 1 1 2 1 1 2 2 1 2 1 ...\n",
      " $ Pstatus   : Factor w/ 2 levels \"A\",\"T\": 1 2 2 2 2 2 2 1 1 2 ...\n",
      " $ Medu      : int  4 1 1 4 3 4 2 4 3 3 ...\n",
      " $ Fedu      : int  4 1 1 2 3 3 2 4 2 4 ...\n",
      " $ Mjob      : Factor w/ 5 levels \"at_home\",\"health\",..: 1 1 1 2 3 4 3 3 4 3 ...\n",
      " $ Fjob      : Factor w/ 5 levels \"at_home\",\"health\",..: 5 3 3 4 3 3 3 5 3 3 ...\n",
      " $ reason    : Factor w/ 4 levels \"course\",\"home\",..: 1 1 3 2 2 4 2 2 2 2 ...\n",
      " $ guardian  : Factor w/ 3 levels \"father\",\"mother\",..: 2 1 2 2 1 2 2 2 2 2 ...\n",
      " $ traveltime: int  2 1 1 1 1 1 1 2 1 1 ...\n",
      " $ studytime : int  2 2 2 3 2 2 2 2 2 2 ...\n",
      " $ failures  : int  0 0 3 0 0 0 0 0 0 0 ...\n",
      " $ schoolsup : Factor w/ 2 levels \"no\",\"yes\": 2 1 2 1 1 1 1 2 1 1 ...\n",
      " $ famsup    : Factor w/ 2 levels \"no\",\"yes\": 1 2 1 2 2 2 1 2 2 2 ...\n",
      " $ paid      : Factor w/ 2 levels \"no\",\"yes\": 1 1 2 2 2 2 1 1 2 2 ...\n",
      " $ activities: Factor w/ 2 levels \"no\",\"yes\": 1 1 1 2 1 2 1 1 1 2 ...\n",
      " $ nursery   : Factor w/ 2 levels \"no\",\"yes\": 2 1 2 2 2 2 2 2 2 2 ...\n",
      " $ higher    : Factor w/ 2 levels \"no\",\"yes\": 2 2 2 2 2 2 2 2 2 2 ...\n",
      " $ internet  : Factor w/ 2 levels \"no\",\"yes\": 1 2 2 2 1 2 2 1 2 2 ...\n",
      " $ romantic  : Factor w/ 2 levels \"no\",\"yes\": 1 1 1 2 1 1 1 1 1 1 ...\n",
      " $ famrel    : int  4 5 4 3 4 5 4 4 4 5 ...\n",
      " $ freetime  : int  3 3 3 2 3 4 4 1 2 5 ...\n",
      " $ goout     : int  4 3 2 2 2 2 4 4 2 1 ...\n",
      " $ Dalc      : int  1 1 2 1 1 1 1 1 1 1 ...\n",
      " $ Walc      : int  1 1 3 1 2 2 1 1 1 1 ...\n",
      " $ health    : int  3 3 3 5 5 5 3 1 1 5 ...\n",
      " $ absences  : int  6 4 10 2 4 10 0 6 0 0 ...\n",
      " $ G1        : int  5 5 7 15 6 15 12 6 16 14 ...\n",
      " $ G2        : int  6 5 8 14 10 15 12 5 18 15 ...\n",
      " $ G3        : int  6 6 10 15 10 15 11 6 19 15 ...\n"
     ]
    }
   ],
   "source": [
    "str(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "FALSE"
      ],
      "text/latex": [
       "FALSE"
      ],
      "text/markdown": [
       "FALSE"
      ],
      "text/plain": [
       "[1] FALSE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "any(is.na(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(ggplot2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in library(ggthemes): there is no package called 'ggthemes'\n",
     "output_type": "error",
     "traceback": [
      "Error in library(ggthemes): there is no package called 'ggthemes'\nTraceback:\n",
      "1. library(ggthemes)"
     ]
    }
   ],
   "source": [
    "library(ggthemes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# interpretation: we only require numeric columns for modelling of a linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "num.cols <- sapply(df, is.numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "cor.data <- cor(df[,num.cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>age</th><th scope=col>Medu</th><th scope=col>Fedu</th><th scope=col>traveltime</th><th scope=col>studytime</th><th scope=col>failures</th><th scope=col>famrel</th><th scope=col>freetime</th><th scope=col>goout</th><th scope=col>Dalc</th><th scope=col>Walc</th><th scope=col>health</th><th scope=col>absences</th><th scope=col>G1</th><th scope=col>G2</th><th scope=col>G3</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>age</th><td> 1.000000000</td><td>-0.163658419</td><td>-0.163438069</td><td> 0.070640721</td><td>-0.004140037</td><td> 0.24366538 </td><td> 0.053940096</td><td> 0.01643439 </td><td> 0.126963880</td><td> 0.131124605</td><td> 0.11727605 </td><td>-0.062187369</td><td> 0.17523008 </td><td>-0.06408150 </td><td>-0.14347405 </td><td>-0.16157944 </td></tr>\n",
       "\t<tr><th scope=row>Medu</th><td>-0.163658419</td><td> 1.000000000</td><td> 0.623455112</td><td>-0.171639305</td><td> 0.064944137</td><td>-0.23667996 </td><td>-0.003914458</td><td> 0.03089087 </td><td> 0.064094438</td><td> 0.019834099</td><td>-0.04712346 </td><td>-0.046877829</td><td> 0.10028482 </td><td> 0.20534100 </td><td> 0.21552717 </td><td> 0.21714750 </td></tr>\n",
       "\t<tr><th scope=row>Fedu</th><td>-0.163438069</td><td> 0.623455112</td><td> 1.000000000</td><td>-0.158194054</td><td>-0.009174639</td><td>-0.25040844 </td><td>-0.001369727</td><td>-0.01284553 </td><td> 0.043104668</td><td> 0.002386429</td><td>-0.01263102 </td><td> 0.014741537</td><td> 0.02447289 </td><td> 0.19026994 </td><td> 0.16489339 </td><td> 0.15245694 </td></tr>\n",
       "\t<tr><th scope=row>traveltime</th><td> 0.070640721</td><td>-0.171639305</td><td>-0.158194054</td><td> 1.000000000</td><td>-0.100909119</td><td> 0.09223875 </td><td>-0.016807986</td><td>-0.01702494 </td><td> 0.028539674</td><td> 0.138325309</td><td> 0.13411575 </td><td> 0.007500606</td><td>-0.01294378 </td><td>-0.09303999 </td><td>-0.15319796 </td><td>-0.11714205 </td></tr>\n",
       "\t<tr><th scope=row>studytime</th><td>-0.004140037</td><td> 0.064944137</td><td>-0.009174639</td><td>-0.100909119</td><td> 1.000000000</td><td>-0.17356303 </td><td> 0.039730704</td><td>-0.14319841 </td><td>-0.063903675</td><td>-0.196019263</td><td>-0.25378473 </td><td>-0.075615863</td><td>-0.06270018 </td><td> 0.16061192 </td><td> 0.13588000 </td><td> 0.09781969 </td></tr>\n",
       "\t<tr><th scope=row>failures</th><td> 0.243665377</td><td>-0.236679963</td><td>-0.250408444</td><td> 0.092238746</td><td>-0.173563031</td><td> 1.00000000 </td><td>-0.044336626</td><td> 0.09198747 </td><td> 0.124560922</td><td> 0.136046931</td><td> 0.14196203 </td><td> 0.065827282</td><td> 0.06372583 </td><td>-0.35471761 </td><td>-0.35589563 </td><td>-0.36041494 </td></tr>\n",
       "\t<tr><th scope=row>famrel</th><td> 0.053940096</td><td>-0.003914458</td><td>-0.001369727</td><td>-0.016807986</td><td> 0.039730704</td><td>-0.04433663 </td><td> 1.000000000</td><td> 0.15070144 </td><td> 0.064568411</td><td>-0.077594357</td><td>-0.11339731 </td><td> 0.094055728</td><td>-0.04435409 </td><td> 0.02216832 </td><td>-0.01828135 </td><td> 0.05136343 </td></tr>\n",
       "\t<tr><th scope=row>freetime</th><td> 0.016434389</td><td> 0.030890867</td><td>-0.012845528</td><td>-0.017024944</td><td>-0.143198407</td><td> 0.09198747 </td><td> 0.150701444</td><td> 1.00000000 </td><td> 0.285018715</td><td> 0.209000848</td><td> 0.14782181 </td><td> 0.075733357</td><td>-0.05807792 </td><td> 0.01261293 </td><td>-0.01377714 </td><td> 0.01130724 </td></tr>\n",
       "\t<tr><th scope=row>goout</th><td> 0.126963880</td><td> 0.064094438</td><td> 0.043104668</td><td> 0.028539674</td><td>-0.063903675</td><td> 0.12456092 </td><td> 0.064568411</td><td> 0.28501871 </td><td> 1.000000000</td><td> 0.266993848</td><td> 0.42038575 </td><td>-0.009577254</td><td> 0.04430222 </td><td>-0.14910397 </td><td>-0.16225003 </td><td>-0.13279147 </td></tr>\n",
       "\t<tr><th scope=row>Dalc</th><td> 0.131124605</td><td> 0.019834099</td><td> 0.002386429</td><td> 0.138325309</td><td>-0.196019263</td><td> 0.13604693 </td><td>-0.077594357</td><td> 0.20900085 </td><td> 0.266993848</td><td> 1.000000000</td><td> 0.64754423 </td><td> 0.077179582</td><td> 0.11190803 </td><td>-0.09415879 </td><td>-0.06412018 </td><td>-0.05466004 </td></tr>\n",
       "\t<tr><th scope=row>Walc</th><td> 0.117276052</td><td>-0.047123460</td><td>-0.012631018</td><td> 0.134115752</td><td>-0.253784731</td><td> 0.14196203 </td><td>-0.113397308</td><td> 0.14782181 </td><td> 0.420385745</td><td> 0.647544230</td><td> 1.00000000 </td><td> 0.092476317</td><td> 0.13629110 </td><td>-0.12617921 </td><td>-0.08492735 </td><td>-0.05193932 </td></tr>\n",
       "\t<tr><th scope=row>health</th><td>-0.062187369</td><td>-0.046877829</td><td> 0.014741537</td><td> 0.007500606</td><td>-0.075615863</td><td> 0.06582728 </td><td> 0.094055728</td><td> 0.07573336 </td><td>-0.009577254</td><td> 0.077179582</td><td> 0.09247632 </td><td> 1.000000000</td><td>-0.02993671 </td><td>-0.07317207 </td><td>-0.09771987 </td><td>-0.06133460 </td></tr>\n",
       "\t<tr><th scope=row>absences</th><td> 0.175230079</td><td> 0.100284818</td><td> 0.024472887</td><td>-0.012943775</td><td>-0.062700175</td><td> 0.06372583 </td><td>-0.044354095</td><td>-0.05807792 </td><td> 0.044302220</td><td> 0.111908026</td><td> 0.13629110 </td><td>-0.029936711</td><td> 1.00000000 </td><td>-0.03100290 </td><td>-0.03177670 </td><td> 0.03424732 </td></tr>\n",
       "\t<tr><th scope=row>G1</th><td>-0.064081497</td><td> 0.205340997</td><td> 0.190269936</td><td>-0.093039992</td><td> 0.160611915</td><td>-0.35471761 </td><td> 0.022168316</td><td> 0.01261293 </td><td>-0.149103967</td><td>-0.094158792</td><td>-0.12617921 </td><td>-0.073172073</td><td>-0.03100290 </td><td> 1.00000000 </td><td> 0.85211807 </td><td> 0.80146793 </td></tr>\n",
       "\t<tr><th scope=row>G2</th><td>-0.143474049</td><td> 0.215527168</td><td> 0.164893393</td><td>-0.153197963</td><td> 0.135879999</td><td>-0.35589563 </td><td>-0.018281347</td><td>-0.01377714 </td><td>-0.162250034</td><td>-0.064120183</td><td>-0.08492735 </td><td>-0.097719866</td><td>-0.03177670 </td><td> 0.85211807 </td><td> 1.00000000 </td><td> 0.90486799 </td></tr>\n",
       "\t<tr><th scope=row>G3</th><td>-0.161579438</td><td> 0.217147496</td><td> 0.152456939</td><td>-0.117142053</td><td> 0.097819690</td><td>-0.36041494 </td><td> 0.051363429</td><td> 0.01130724 </td><td>-0.132791474</td><td>-0.054660041</td><td>-0.05193932 </td><td>-0.061334605</td><td> 0.03424732 </td><td> 0.80146793 </td><td> 0.90486799 </td><td> 1.00000000 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllll}\n",
       "  & age & Medu & Fedu & traveltime & studytime & failures & famrel & freetime & goout & Dalc & Walc & health & absences & G1 & G2 & G3\\\\\n",
       "\\hline\n",
       "\tage &  1.000000000 & -0.163658419 & -0.163438069 &  0.070640721 & -0.004140037 &  0.24366538  &  0.053940096 &  0.01643439  &  0.126963880 &  0.131124605 &  0.11727605  & -0.062187369 &  0.17523008  & -0.06408150  & -0.14347405  & -0.16157944 \\\\\n",
       "\tMedu & -0.163658419 &  1.000000000 &  0.623455112 & -0.171639305 &  0.064944137 & -0.23667996  & -0.003914458 &  0.03089087  &  0.064094438 &  0.019834099 & -0.04712346  & -0.046877829 &  0.10028482  &  0.20534100  &  0.21552717  &  0.21714750 \\\\\n",
       "\tFedu & -0.163438069 &  0.623455112 &  1.000000000 & -0.158194054 & -0.009174639 & -0.25040844  & -0.001369727 & -0.01284553  &  0.043104668 &  0.002386429 & -0.01263102  &  0.014741537 &  0.02447289  &  0.19026994  &  0.16489339  &  0.15245694 \\\\\n",
       "\ttraveltime &  0.070640721 & -0.171639305 & -0.158194054 &  1.000000000 & -0.100909119 &  0.09223875  & -0.016807986 & -0.01702494  &  0.028539674 &  0.138325309 &  0.13411575  &  0.007500606 & -0.01294378  & -0.09303999  & -0.15319796  & -0.11714205 \\\\\n",
       "\tstudytime & -0.004140037 &  0.064944137 & -0.009174639 & -0.100909119 &  1.000000000 & -0.17356303  &  0.039730704 & -0.14319841  & -0.063903675 & -0.196019263 & -0.25378473  & -0.075615863 & -0.06270018  &  0.16061192  &  0.13588000  &  0.09781969 \\\\\n",
       "\tfailures &  0.243665377 & -0.236679963 & -0.250408444 &  0.092238746 & -0.173563031 &  1.00000000  & -0.044336626 &  0.09198747  &  0.124560922 &  0.136046931 &  0.14196203  &  0.065827282 &  0.06372583  & -0.35471761  & -0.35589563  & -0.36041494 \\\\\n",
       "\tfamrel &  0.053940096 & -0.003914458 & -0.001369727 & -0.016807986 &  0.039730704 & -0.04433663  &  1.000000000 &  0.15070144  &  0.064568411 & -0.077594357 & -0.11339731  &  0.094055728 & -0.04435409  &  0.02216832  & -0.01828135  &  0.05136343 \\\\\n",
       "\tfreetime &  0.016434389 &  0.030890867 & -0.012845528 & -0.017024944 & -0.143198407 &  0.09198747  &  0.150701444 &  1.00000000  &  0.285018715 &  0.209000848 &  0.14782181  &  0.075733357 & -0.05807792  &  0.01261293  & -0.01377714  &  0.01130724 \\\\\n",
       "\tgoout &  0.126963880 &  0.064094438 &  0.043104668 &  0.028539674 & -0.063903675 &  0.12456092  &  0.064568411 &  0.28501871  &  1.000000000 &  0.266993848 &  0.42038575  & -0.009577254 &  0.04430222  & -0.14910397  & -0.16225003  & -0.13279147 \\\\\n",
       "\tDalc &  0.131124605 &  0.019834099 &  0.002386429 &  0.138325309 & -0.196019263 &  0.13604693  & -0.077594357 &  0.20900085  &  0.266993848 &  1.000000000 &  0.64754423  &  0.077179582 &  0.11190803  & -0.09415879  & -0.06412018  & -0.05466004 \\\\\n",
       "\tWalc &  0.117276052 & -0.047123460 & -0.012631018 &  0.134115752 & -0.253784731 &  0.14196203  & -0.113397308 &  0.14782181  &  0.420385745 &  0.647544230 &  1.00000000  &  0.092476317 &  0.13629110  & -0.12617921  & -0.08492735  & -0.05193932 \\\\\n",
       "\thealth & -0.062187369 & -0.046877829 &  0.014741537 &  0.007500606 & -0.075615863 &  0.06582728  &  0.094055728 &  0.07573336  & -0.009577254 &  0.077179582 &  0.09247632  &  1.000000000 & -0.02993671  & -0.07317207  & -0.09771987  & -0.06133460 \\\\\n",
       "\tabsences &  0.175230079 &  0.100284818 &  0.024472887 & -0.012943775 & -0.062700175 &  0.06372583  & -0.044354095 & -0.05807792  &  0.044302220 &  0.111908026 &  0.13629110  & -0.029936711 &  1.00000000  & -0.03100290  & -0.03177670  &  0.03424732 \\\\\n",
       "\tG1 & -0.064081497 &  0.205340997 &  0.190269936 & -0.093039992 &  0.160611915 & -0.35471761  &  0.022168316 &  0.01261293  & -0.149103967 & -0.094158792 & -0.12617921  & -0.073172073 & -0.03100290  &  1.00000000  &  0.85211807  &  0.80146793 \\\\\n",
       "\tG2 & -0.143474049 &  0.215527168 &  0.164893393 & -0.153197963 &  0.135879999 & -0.35589563  & -0.018281347 & -0.01377714  & -0.162250034 & -0.064120183 & -0.08492735  & -0.097719866 & -0.03177670  &  0.85211807  &  1.00000000  &  0.90486799 \\\\\n",
       "\tG3 & -0.161579438 &  0.217147496 &  0.152456939 & -0.117142053 &  0.097819690 & -0.36041494  &  0.051363429 &  0.01130724  & -0.132791474 & -0.054660041 & -0.05193932  & -0.061334605 &  0.03424732  &  0.80146793  &  0.90486799  &  1.00000000 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | age | Medu | Fedu | traveltime | studytime | failures | famrel | freetime | goout | Dalc | Walc | health | absences | G1 | G2 | G3 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| age |  1.000000000 | -0.163658419 | -0.163438069 |  0.070640721 | -0.004140037 |  0.24366538  |  0.053940096 |  0.01643439  |  0.126963880 |  0.131124605 |  0.11727605  | -0.062187369 |  0.17523008  | -0.06408150  | -0.14347405  | -0.16157944  |\n",
       "| Medu | -0.163658419 |  1.000000000 |  0.623455112 | -0.171639305 |  0.064944137 | -0.23667996  | -0.003914458 |  0.03089087  |  0.064094438 |  0.019834099 | -0.04712346  | -0.046877829 |  0.10028482  |  0.20534100  |  0.21552717  |  0.21714750  |\n",
       "| Fedu | -0.163438069 |  0.623455112 |  1.000000000 | -0.158194054 | -0.009174639 | -0.25040844  | -0.001369727 | -0.01284553  |  0.043104668 |  0.002386429 | -0.01263102  |  0.014741537 |  0.02447289  |  0.19026994  |  0.16489339  |  0.15245694  |\n",
       "| traveltime |  0.070640721 | -0.171639305 | -0.158194054 |  1.000000000 | -0.100909119 |  0.09223875  | -0.016807986 | -0.01702494  |  0.028539674 |  0.138325309 |  0.13411575  |  0.007500606 | -0.01294378  | -0.09303999  | -0.15319796  | -0.11714205  |\n",
       "| studytime | -0.004140037 |  0.064944137 | -0.009174639 | -0.100909119 |  1.000000000 | -0.17356303  |  0.039730704 | -0.14319841  | -0.063903675 | -0.196019263 | -0.25378473  | -0.075615863 | -0.06270018  |  0.16061192  |  0.13588000  |  0.09781969  |\n",
       "| failures |  0.243665377 | -0.236679963 | -0.250408444 |  0.092238746 | -0.173563031 |  1.00000000  | -0.044336626 |  0.09198747  |  0.124560922 |  0.136046931 |  0.14196203  |  0.065827282 |  0.06372583  | -0.35471761  | -0.35589563  | -0.36041494  |\n",
       "| famrel |  0.053940096 | -0.003914458 | -0.001369727 | -0.016807986 |  0.039730704 | -0.04433663  |  1.000000000 |  0.15070144  |  0.064568411 | -0.077594357 | -0.11339731  |  0.094055728 | -0.04435409  |  0.02216832  | -0.01828135  |  0.05136343  |\n",
       "| freetime |  0.016434389 |  0.030890867 | -0.012845528 | -0.017024944 | -0.143198407 |  0.09198747  |  0.150701444 |  1.00000000  |  0.285018715 |  0.209000848 |  0.14782181  |  0.075733357 | -0.05807792  |  0.01261293  | -0.01377714  |  0.01130724  |\n",
       "| goout |  0.126963880 |  0.064094438 |  0.043104668 |  0.028539674 | -0.063903675 |  0.12456092  |  0.064568411 |  0.28501871  |  1.000000000 |  0.266993848 |  0.42038575  | -0.009577254 |  0.04430222  | -0.14910397  | -0.16225003  | -0.13279147  |\n",
       "| Dalc |  0.131124605 |  0.019834099 |  0.002386429 |  0.138325309 | -0.196019263 |  0.13604693  | -0.077594357 |  0.20900085  |  0.266993848 |  1.000000000 |  0.64754423  |  0.077179582 |  0.11190803  | -0.09415879  | -0.06412018  | -0.05466004  |\n",
       "| Walc |  0.117276052 | -0.047123460 | -0.012631018 |  0.134115752 | -0.253784731 |  0.14196203  | -0.113397308 |  0.14782181  |  0.420385745 |  0.647544230 |  1.00000000  |  0.092476317 |  0.13629110  | -0.12617921  | -0.08492735  | -0.05193932  |\n",
       "| health | -0.062187369 | -0.046877829 |  0.014741537 |  0.007500606 | -0.075615863 |  0.06582728  |  0.094055728 |  0.07573336  | -0.009577254 |  0.077179582 |  0.09247632  |  1.000000000 | -0.02993671  | -0.07317207  | -0.09771987  | -0.06133460  |\n",
       "| absences |  0.175230079 |  0.100284818 |  0.024472887 | -0.012943775 | -0.062700175 |  0.06372583  | -0.044354095 | -0.05807792  |  0.044302220 |  0.111908026 |  0.13629110  | -0.029936711 |  1.00000000  | -0.03100290  | -0.03177670  |  0.03424732  |\n",
       "| G1 | -0.064081497 |  0.205340997 |  0.190269936 | -0.093039992 |  0.160611915 | -0.35471761  |  0.022168316 |  0.01261293  | -0.149103967 | -0.094158792 | -0.12617921  | -0.073172073 | -0.03100290  |  1.00000000  |  0.85211807  |  0.80146793  |\n",
       "| G2 | -0.143474049 |  0.215527168 |  0.164893393 | -0.153197963 |  0.135879999 | -0.35589563  | -0.018281347 | -0.01377714  | -0.162250034 | -0.064120183 | -0.08492735  | -0.097719866 | -0.03177670  |  0.85211807  |  1.00000000  |  0.90486799  |\n",
       "| G3 | -0.161579438 |  0.217147496 |  0.152456939 | -0.117142053 |  0.097819690 | -0.36041494  |  0.051363429 |  0.01130724  | -0.132791474 | -0.054660041 | -0.05193932  | -0.061334605 |  0.03424732  |  0.80146793  |  0.90486799  |  1.00000000  |\n",
       "\n"
      ],
      "text/plain": [
       "           age          Medu         Fedu         traveltime   studytime   \n",
       "age         1.000000000 -0.163658419 -0.163438069  0.070640721 -0.004140037\n",
       "Medu       -0.163658419  1.000000000  0.623455112 -0.171639305  0.064944137\n",
       "Fedu       -0.163438069  0.623455112  1.000000000 -0.158194054 -0.009174639\n",
       "traveltime  0.070640721 -0.171639305 -0.158194054  1.000000000 -0.100909119\n",
       "studytime  -0.004140037  0.064944137 -0.009174639 -0.100909119  1.000000000\n",
       "failures    0.243665377 -0.236679963 -0.250408444  0.092238746 -0.173563031\n",
       "famrel      0.053940096 -0.003914458 -0.001369727 -0.016807986  0.039730704\n",
       "freetime    0.016434389  0.030890867 -0.012845528 -0.017024944 -0.143198407\n",
       "goout       0.126963880  0.064094438  0.043104668  0.028539674 -0.063903675\n",
       "Dalc        0.131124605  0.019834099  0.002386429  0.138325309 -0.196019263\n",
       "Walc        0.117276052 -0.047123460 -0.012631018  0.134115752 -0.253784731\n",
       "health     -0.062187369 -0.046877829  0.014741537  0.007500606 -0.075615863\n",
       "absences    0.175230079  0.100284818  0.024472887 -0.012943775 -0.062700175\n",
       "G1         -0.064081497  0.205340997  0.190269936 -0.093039992  0.160611915\n",
       "G2         -0.143474049  0.215527168  0.164893393 -0.153197963  0.135879999\n",
       "G3         -0.161579438  0.217147496  0.152456939 -0.117142053  0.097819690\n",
       "           failures    famrel       freetime    goout        Dalc        \n",
       "age         0.24366538  0.053940096  0.01643439  0.126963880  0.131124605\n",
       "Medu       -0.23667996 -0.003914458  0.03089087  0.064094438  0.019834099\n",
       "Fedu       -0.25040844 -0.001369727 -0.01284553  0.043104668  0.002386429\n",
       "traveltime  0.09223875 -0.016807986 -0.01702494  0.028539674  0.138325309\n",
       "studytime  -0.17356303  0.039730704 -0.14319841 -0.063903675 -0.196019263\n",
       "failures    1.00000000 -0.044336626  0.09198747  0.124560922  0.136046931\n",
       "famrel     -0.04433663  1.000000000  0.15070144  0.064568411 -0.077594357\n",
       "freetime    0.09198747  0.150701444  1.00000000  0.285018715  0.209000848\n",
       "goout       0.12456092  0.064568411  0.28501871  1.000000000  0.266993848\n",
       "Dalc        0.13604693 -0.077594357  0.20900085  0.266993848  1.000000000\n",
       "Walc        0.14196203 -0.113397308  0.14782181  0.420385745  0.647544230\n",
       "health      0.06582728  0.094055728  0.07573336 -0.009577254  0.077179582\n",
       "absences    0.06372583 -0.044354095 -0.05807792  0.044302220  0.111908026\n",
       "G1         -0.35471761  0.022168316  0.01261293 -0.149103967 -0.094158792\n",
       "G2         -0.35589563 -0.018281347 -0.01377714 -0.162250034 -0.064120183\n",
       "G3         -0.36041494  0.051363429  0.01130724 -0.132791474 -0.054660041\n",
       "           Walc        health       absences    G1          G2         \n",
       "age         0.11727605 -0.062187369  0.17523008 -0.06408150 -0.14347405\n",
       "Medu       -0.04712346 -0.046877829  0.10028482  0.20534100  0.21552717\n",
       "Fedu       -0.01263102  0.014741537  0.02447289  0.19026994  0.16489339\n",
       "traveltime  0.13411575  0.007500606 -0.01294378 -0.09303999 -0.15319796\n",
       "studytime  -0.25378473 -0.075615863 -0.06270018  0.16061192  0.13588000\n",
       "failures    0.14196203  0.065827282  0.06372583 -0.35471761 -0.35589563\n",
       "famrel     -0.11339731  0.094055728 -0.04435409  0.02216832 -0.01828135\n",
       "freetime    0.14782181  0.075733357 -0.05807792  0.01261293 -0.01377714\n",
       "goout       0.42038575 -0.009577254  0.04430222 -0.14910397 -0.16225003\n",
       "Dalc        0.64754423  0.077179582  0.11190803 -0.09415879 -0.06412018\n",
       "Walc        1.00000000  0.092476317  0.13629110 -0.12617921 -0.08492735\n",
       "health      0.09247632  1.000000000 -0.02993671 -0.07317207 -0.09771987\n",
       "absences    0.13629110 -0.029936711  1.00000000 -0.03100290 -0.03177670\n",
       "G1         -0.12617921 -0.073172073 -0.03100290  1.00000000  0.85211807\n",
       "G2         -0.08492735 -0.097719866 -0.03177670  0.85211807  1.00000000\n",
       "G3         -0.05193932 -0.061334605  0.03424732  0.80146793  0.90486799\n",
       "           G3         \n",
       "age        -0.16157944\n",
       "Medu        0.21714750\n",
       "Fedu        0.15245694\n",
       "traveltime -0.11714205\n",
       "studytime   0.09781969\n",
       "failures   -0.36041494\n",
       "famrel      0.05136343\n",
       "freetime    0.01130724\n",
       "goout      -0.13279147\n",
       "Dalc       -0.05466004\n",
       "Walc       -0.05193932\n",
       "health     -0.06133460\n",
       "absences    0.03424732\n",
       "G1          0.80146793\n",
       "G2          0.90486799\n",
       "G3          1.00000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#install.packages('corrgram',repos = 'http://cran.us.r-project.org')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"package 'corrplot' is in use and will not be installed\""
     ]
    }
   ],
   "source": [
    "install.packages('corrplot',repos = 'http://cran.us.r-project.org')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(corrplot)\n",
    "library(corrgram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RlreeSGRHZAZHLoDLkyL5fnAGi33+dVx8pfA2B9/JZTJ4JrtdTLzq1hm0Nht615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\nzFZCyH/98aVexv+3B3avF3tJeuw2GvEHd385c+bMBla39hqlu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XerBJ25zOavAiDDO0RdD1ZepDfOK1v3iel3/cKbfJ9bb8roLlIPDfB/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",
      "text/plain": [
       "Plot with title \"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrplot(cor.data,method='number')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# interpretation of correlation the above correlation shows that G1,G2,G3 are highly correlated if the student got good grades in his G1 eventually he will get good grades in G2 and G3...the mothers and fathers education column are correlated and fathers education also correlated with grades...The failures column and grades column are inversely correlated..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EHcAZJ64aCoE0scApLcbuoKWO118EXGxoFUrx26EhxZINWLhyiQ5IYUQdLjnQJJ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\nTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrgram(df,order=TRUE, lower.panel=panel.shade,upper.panel=panel.pie, text.panel=panel.txt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# the above corrgram shows that full pie chart is perfectly correlated ...as you can see the G1,G2,G3 are correlated looking at their pie charts and failures are inversely correlated and fathers education ,mothers education are correalted and somewhat correlated with grades of students also"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PLMwEQkNxOR5JmBiUhuJiLJMwMTkdxM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\nAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(df,aes(x=G3)) + geom_histogram(bins=20,alpha=0.4,fill='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# we have build a histogram from final grades as you can see that there are many students who got zeros and mean value is around 10...so we will try to build a model and gain insights from model.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# building a model multiple linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(caTools)\n",
    "set.seed(101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# we are using the split function for testing and training the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample <- sample.split(df$G3, SplitRatio = 0.70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = subset(df, sample == TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = subset(df, sample == FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "model <- lm(G3 ~ .,train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = G3 ~ ., data = train)\n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-7.4250 -0.6478  0.2844  1.0442  4.9840 \n",
       "\n",
       "Coefficients:\n",
       "                 Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)       3.70763    2.69488   1.376  0.17019    \n",
       "schoolMS          0.66981    0.47436   1.412  0.15926    \n",
       "sexM              0.25730    0.29257   0.879  0.38006    \n",
       "age              -0.36163    0.12949  -2.793  0.00566 ** \n",
       "addressU          0.08123    0.35652   0.228  0.81996    \n",
       "famsizeLE3        0.12222    0.28709   0.426  0.67070    \n",
       "PstatusT          0.06807    0.43032   0.158  0.87444    \n",
       "Medu              0.11100    0.18757   0.592  0.55455    \n",
       "Fedu             -0.16373    0.15928  -1.028  0.30503    \n",
       "Mjobhealth       -0.63993    0.65314  -0.980  0.32820    \n",
       "Mjobother        -0.15730    0.42323  -0.372  0.71048    \n",
       "Mjobservices     -0.15872    0.46682  -0.340  0.73415    \n",
       "Mjobteacher      -0.04930    0.62335  -0.079  0.93702    \n",
       "Fjobhealth        0.17565    0.83034   0.212  0.83265    \n",
       "Fjobother        -0.29559    0.56012  -0.528  0.59818    \n",
       "Fjobservices     -0.76964    0.59476  -1.294  0.19692    \n",
       "Fjobteacher      -0.27009    0.73824  -0.366  0.71480    \n",
       "reasonhome       -0.41126    0.31857  -1.291  0.19799    \n",
       "reasonother       0.06767    0.45323   0.149  0.88144    \n",
       "reasonreputation  0.13478    0.34735   0.388  0.69834    \n",
       "guardianmother   -0.05442    0.31663  -0.172  0.86369    \n",
       "guardianother     0.01588    0.58375   0.027  0.97832    \n",
       "traveltime       -0.02353    0.19540  -0.120  0.90427    \n",
       "studytime        -0.04294    0.16910  -0.254  0.79979    \n",
       "failures         -0.17219    0.19668  -0.875  0.38220    \n",
       "schoolsupyes      0.20742    0.42358   0.490  0.62481    \n",
       "famsupyes        -0.05329    0.27753  -0.192  0.84789    \n",
       "paidyes           0.31311    0.28284   1.107  0.26941    \n",
       "activitiesyes    -0.26104    0.26687  -0.978  0.32901    \n",
       "nurseryyes       -0.05345    0.31236  -0.171  0.86428    \n",
       "higheryes        -0.94298    0.74005  -1.274  0.20385    \n",
       "internetyes      -0.15834    0.37029  -0.428  0.66932    \n",
       "romanticyes      -0.30048    0.28115  -1.069  0.28627    \n",
       "famrel            0.36601    0.14609   2.505  0.01291 *  \n",
       "freetime          0.08386    0.14247   0.589  0.55668    \n",
       "goout            -0.12457    0.13306  -0.936  0.35015    \n",
       "Dalc             -0.16995    0.20659  -0.823  0.41153    \n",
       "Walc              0.21053    0.14963   1.407  0.16074    \n",
       "health            0.07805    0.09341   0.836  0.40426    \n",
       "absences          0.09547    0.02382   4.008 8.24e-05 ***\n",
       "G1                0.14259    0.07892   1.807  0.07206 .  \n",
       "G2                0.98859    0.06929  14.267  < 2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 1.962 on 235 degrees of freedom\n",
       "Multiple R-squared:  0.8456,\tAdjusted R-squared:  0.8187 \n",
       "F-statistic: 31.39 on 41 and 235 DF,  p-value: < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# interpretation the lower the p value the higher statistical significant they are...so we keep these variables to fit in our model.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "res <- residuals(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "res <- as.data.frame(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>res</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td> 1.4684389</td></tr>\n",
       "\t<tr><th scope=row>2</th><td> 1.8826707</td></tr>\n",
       "\t<tr><th scope=row>3</th><td> 1.1866990</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>-2.2440152</td></tr>\n",
       "\t<tr><th scope=row>9</th><td> 0.5974865</td></tr>\n",
       "\t<tr><th scope=row>11</th><td> 0.8583062</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       "  & res\\\\\n",
       "\\hline\n",
       "\t1 &  1.4684389\\\\\n",
       "\t2 &  1.8826707\\\\\n",
       "\t3 &  1.1866990\\\\\n",
       "\t6 & -2.2440152\\\\\n",
       "\t9 &  0.5974865\\\\\n",
       "\t11 &  0.8583062\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | res |\n",
       "|---|---|\n",
       "| 1 |  1.4684389 |\n",
       "| 2 |  1.8826707 |\n",
       "| 3 |  1.1866990 |\n",
       "| 6 | -2.2440152 |\n",
       "| 9 |  0.5974865 |\n",
       "| 11 |  0.8583062 |\n",
       "\n"
      ],
      "text/plain": [
       "   res       \n",
       "1   1.4684389\n",
       "2   1.8826707\n",
       "3   1.1866990\n",
       "6  -2.2440152\n",
       "9   0.5974865\n",
       "11  0.8583062"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# residuals gives a basic difference between observed value and predicted value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n"
     ]
    },
    {
     "data": {
      "image/png": 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i5ms4SQqk+yjt9siNksIaTqk6zjt79j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\n",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(res,aes(res)) +  geom_histogram(fill='blue',alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# in the above graph we have negative values we will remove the negative values using to zero function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "G3.predictions <- predict(model,test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>pred</th><th scope=col>real</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>4</th><td>12.682507</td><td>15       </td></tr>\n",
       "\t<tr><th scope=row>5</th><td> 9.433677</td><td>10       </td></tr>\n",
       "\t<tr><th scope=row>7</th><td>11.312310</td><td>11       </td></tr>\n",
       "\t<tr><th scope=row>8</th><td> 3.101530</td><td> 6       </td></tr>\n",
       "\t<tr><th scope=row>10</th><td>15.564674</td><td>15       </td></tr>\n",
       "\t<tr><th scope=row>13</th><td>14.190360</td><td>14       </td></tr>\n",
       "\t<tr><th scope=row>21</th><td>14.601427</td><td>15       </td></tr>\n",
       "\t<tr><th scope=row>25</th><td> 9.191419</td><td> 8       </td></tr>\n",
       "\t<tr><th scope=row>30</th><td>12.352589</td><td>11       </td></tr>\n",
       "\t<tr><th scope=row>32</th><td>16.292243</td><td>17       </td></tr>\n",
       "\t<tr><th scope=row>39</th><td>12.553386</td><td>11       </td></tr>\n",
       "\t<tr><th scope=row>40</th><td>14.573362</td><td>13       </td></tr>\n",
       "\t<tr><th scope=row>42</th><td>13.361456</td><td>12       </td></tr>\n",
       "\t<tr><th scope=row>49</th><td>16.121757</td><td>14       </td></tr>\n",
       "\t<tr><th scope=row>54</th><td> 9.044686</td><td>11       </td></tr>\n",
       "\t<tr><th scope=row>57</th><td>14.614118</td><td>15       </td></tr>\n",
       "\t<tr><th scope=row>61</th><td> 9.508089</td><td>11       </td></tr>\n",
       "\t<tr><th scope=row>62</th><td> 7.571092</td><td>11       </td></tr>\n",
       "\t<tr><th scope=row>64</th><td> 8.847217</td><td> 9       </td></tr>\n",
       "\t<tr><th scope=row>65</th><td> 9.367014</td><td>10       </td></tr>\n",
       "\t<tr><th scope=row>71</th><td>15.645439</td><td>15       </td></tr>\n",
       "\t<tr><th scope=row>75</th><td>16.283467</td><td>11       </td></tr>\n",
       "\t<tr><th scope=row>77</th><td>11.485059</td><td>10       </td></tr>\n",
       "\t<tr><th scope=row>79</th><td> 7.454869</td><td>10       </td></tr>\n",
       "\t<tr><th scope=row>84</th><td>15.908265</td><td>15       </td></tr>\n",
       "\t<tr><th scope=row>86</th><td> 8.259935</td><td> 8       </td></tr>\n",
       "\t<tr><th scope=row>90</th><td> 8.116941</td><td> 7       </td></tr>\n",
       "\t<tr><th scope=row>91</th><td> 5.680344</td><td> 8       </td></tr>\n",
       "\t<tr><th scope=row>101</th><td> 7.217520</td><td> 5       </td></tr>\n",
       "\t<tr><th scope=row>105</th><td>18.387648</td><td>18       </td></tr>\n",
       "\t<tr><th scope=row>...</th><td>...</td><td>...</td></tr>\n",
       "\t<tr><th scope=row>302</th><td>10.445265</td><td>10       </td></tr>\n",
       "\t<tr><th scope=row>307</th><td>17.581928</td><td>18       </td></tr>\n",
       "\t<tr><th scope=row>308</th><td>10.142502</td><td> 8       </td></tr>\n",
       "\t<tr><th scope=row>309</th><td>10.525108</td><td>12       </td></tr>\n",
       "\t<tr><th scope=row>312</th><td>13.384567</td><td>13       </td></tr>\n",
       "\t<tr><th scope=row>313</th><td> 9.774990</td><td>11       </td></tr>\n",
       "\t<tr><th scope=row>316</th><td>12.674051</td><td>11       </td></tr>\n",
       "\t<tr><th scope=row>323</th><td> 9.498840</td><td>11       </td></tr>\n",
       "\t<tr><th scope=row>329</th><td> 8.749197</td><td> 9       </td></tr>\n",
       "\t<tr><th scope=row>330</th><td>13.930318</td><td>14       </td></tr>\n",
       "\t<tr><th scope=row>333</th><td>-2.795582</td><td> 0       </td></tr>\n",
       "\t<tr><th scope=row>339</th><td>14.119452</td><td>17       </td></tr>\n",
       "\t<tr><th scope=row>344</th><td> 6.314688</td><td> 0       </td></tr>\n",
       "\t<tr><th scope=row>345</th><td> 9.031470</td><td>10       </td></tr>\n",
       "\t<tr><th scope=row>347</th><td>15.360903</td><td>16       </td></tr>\n",
       "\t<tr><th scope=row>350</th><td>12.771407</td><td>13       </td></tr>\n",
       "\t<tr><th scope=row>351</th><td> 5.398667</td><td> 8       </td></tr>\n",
       "\t<tr><th scope=row>356</th><td> 7.808663</td><td> 9       </td></tr>\n",
       "\t<tr><th scope=row>359</th><td> 8.793634</td><td>10       </td></tr>\n",
       "\t<tr><th scope=row>360</th><td>16.556011</td><td>16       </td></tr>\n",
       "\t<tr><th scope=row>361</th><td>12.628150</td><td>13       </td></tr>\n",
       "\t<tr><th scope=row>366</th><td> 9.875136</td><td>10       </td></tr>\n",
       "\t<tr><th scope=row>367</th><td>12.621700</td><td>13       </td></tr>\n",
       "\t<tr><th scope=row>369</th><td> 9.334932</td><td>10       </td></tr>\n",
       "\t<tr><th scope=row>379</th><td>14.160218</td><td>15       </td></tr>\n",
       "\t<tr><th scope=row>381</th><td>14.131274</td><td>14       </td></tr>\n",
       "\t<tr><th scope=row>383</th><td> 9.814664</td><td>10       </td></tr>\n",
       "\t<tr><th scope=row>386</th><td> 9.685128</td><td>10       </td></tr>\n",
       "\t<tr><th scope=row>390</th><td> 2.449392</td><td> 0       </td></tr>\n",
       "\t<tr><th scope=row>392</th><td>15.786871</td><td>16       </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & pred & real\\\\\n",
       "\\hline\n",
       "\t4 & 12.682507 & 15       \\\\\n",
       "\t5 &  9.433677 & 10       \\\\\n",
       "\t7 & 11.312310 & 11       \\\\\n",
       "\t8 &  3.101530 &  6       \\\\\n",
       "\t10 & 15.564674 & 15       \\\\\n",
       "\t13 & 14.190360 & 14       \\\\\n",
       "\t21 & 14.601427 & 15       \\\\\n",
       "\t25 &  9.191419 &  8       \\\\\n",
       "\t30 & 12.352589 & 11       \\\\\n",
       "\t32 & 16.292243 & 17       \\\\\n",
       "\t39 & 12.553386 & 11       \\\\\n",
       "\t40 & 14.573362 & 13       \\\\\n",
       "\t42 & 13.361456 & 12       \\\\\n",
       "\t49 & 16.121757 & 14       \\\\\n",
       "\t54 &  9.044686 & 11       \\\\\n",
       "\t57 & 14.614118 & 15       \\\\\n",
       "\t61 &  9.508089 & 11       \\\\\n",
       "\t62 &  7.571092 & 11       \\\\\n",
       "\t64 &  8.847217 &  9       \\\\\n",
       "\t65 &  9.367014 & 10       \\\\\n",
       "\t71 & 15.645439 & 15       \\\\\n",
       "\t75 & 16.283467 & 11       \\\\\n",
       "\t77 & 11.485059 & 10       \\\\\n",
       "\t79 &  7.454869 & 10       \\\\\n",
       "\t84 & 15.908265 & 15       \\\\\n",
       "\t86 &  8.259935 &  8       \\\\\n",
       "\t90 &  8.116941 &  7       \\\\\n",
       "\t91 &  5.680344 &  8       \\\\\n",
       "\t101 &  7.217520 &  5       \\\\\n",
       "\t105 & 18.387648 & 18       \\\\\n",
       "\t... & ... & ...\\\\\n",
       "\t302 & 10.445265 & 10       \\\\\n",
       "\t307 & 17.581928 & 18       \\\\\n",
       "\t308 & 10.142502 &  8       \\\\\n",
       "\t309 & 10.525108 & 12       \\\\\n",
       "\t312 & 13.384567 & 13       \\\\\n",
       "\t313 &  9.774990 & 11       \\\\\n",
       "\t316 & 12.674051 & 11       \\\\\n",
       "\t323 &  9.498840 & 11       \\\\\n",
       "\t329 &  8.749197 &  9       \\\\\n",
       "\t330 & 13.930318 & 14       \\\\\n",
       "\t333 & -2.795582 &  0       \\\\\n",
       "\t339 & 14.119452 & 17       \\\\\n",
       "\t344 &  6.314688 &  0       \\\\\n",
       "\t345 &  9.031470 & 10       \\\\\n",
       "\t347 & 15.360903 & 16       \\\\\n",
       "\t350 & 12.771407 & 13       \\\\\n",
       "\t351 &  5.398667 &  8       \\\\\n",
       "\t356 &  7.808663 &  9       \\\\\n",
       "\t359 &  8.793634 & 10       \\\\\n",
       "\t360 & 16.556011 & 16       \\\\\n",
       "\t361 & 12.628150 & 13       \\\\\n",
       "\t366 &  9.875136 & 10       \\\\\n",
       "\t367 & 12.621700 & 13       \\\\\n",
       "\t369 &  9.334932 & 10       \\\\\n",
       "\t379 & 14.160218 & 15       \\\\\n",
       "\t381 & 14.131274 & 14       \\\\\n",
       "\t383 &  9.814664 & 10       \\\\\n",
       "\t386 &  9.685128 & 10       \\\\\n",
       "\t390 &  2.449392 &  0       \\\\\n",
       "\t392 & 15.786871 & 16       \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | pred | real |\n",
       "|---|---|---|\n",
       "| 4 | 12.682507 | 15        |\n",
       "| 5 |  9.433677 | 10        |\n",
       "| 7 | 11.312310 | 11        |\n",
       "| 8 |  3.101530 |  6        |\n",
       "| 10 | 15.564674 | 15        |\n",
       "| 13 | 14.190360 | 14        |\n",
       "| 21 | 14.601427 | 15        |\n",
       "| 25 |  9.191419 |  8        |\n",
       "| 30 | 12.352589 | 11        |\n",
       "| 32 | 16.292243 | 17        |\n",
       "| 39 | 12.553386 | 11        |\n",
       "| 40 | 14.573362 | 13        |\n",
       "| 42 | 13.361456 | 12        |\n",
       "| 49 | 16.121757 | 14        |\n",
       "| 54 |  9.044686 | 11        |\n",
       "| 57 | 14.614118 | 15        |\n",
       "| 61 |  9.508089 | 11        |\n",
       "| 62 |  7.571092 | 11        |\n",
       "| 64 |  8.847217 |  9        |\n",
       "| 65 |  9.367014 | 10        |\n",
       "| 71 | 15.645439 | 15        |\n",
       "| 75 | 16.283467 | 11        |\n",
       "| 77 | 11.485059 | 10        |\n",
       "| 79 |  7.454869 | 10        |\n",
       "| 84 | 15.908265 | 15        |\n",
       "| 86 |  8.259935 |  8        |\n",
       "| 90 |  8.116941 |  7        |\n",
       "| 91 |  5.680344 |  8        |\n",
       "| 101 |  7.217520 |  5        |\n",
       "| 105 | 18.387648 | 18        |\n",
       "| ... | ... | ... |\n",
       "| 302 | 10.445265 | 10        |\n",
       "| 307 | 17.581928 | 18        |\n",
       "| 308 | 10.142502 |  8        |\n",
       "| 309 | 10.525108 | 12        |\n",
       "| 312 | 13.384567 | 13        |\n",
       "| 313 |  9.774990 | 11        |\n",
       "| 316 | 12.674051 | 11        |\n",
       "| 323 |  9.498840 | 11        |\n",
       "| 329 |  8.749197 |  9        |\n",
       "| 330 | 13.930318 | 14        |\n",
       "| 333 | -2.795582 |  0        |\n",
       "| 339 | 14.119452 | 17        |\n",
       "| 344 |  6.314688 |  0        |\n",
       "| 345 |  9.031470 | 10        |\n",
       "| 347 | 15.360903 | 16        |\n",
       "| 350 | 12.771407 | 13        |\n",
       "| 351 |  5.398667 |  8        |\n",
       "| 356 |  7.808663 |  9        |\n",
       "| 359 |  8.793634 | 10        |\n",
       "| 360 | 16.556011 | 16        |\n",
       "| 361 | 12.628150 | 13        |\n",
       "| 366 |  9.875136 | 10        |\n",
       "| 367 | 12.621700 | 13        |\n",
       "| 369 |  9.334932 | 10        |\n",
       "| 379 | 14.160218 | 15        |\n",
       "| 381 | 14.131274 | 14        |\n",
       "| 383 |  9.814664 | 10        |\n",
       "| 386 |  9.685128 | 10        |\n",
       "| 390 |  2.449392 |  0        |\n",
       "| 392 | 15.786871 | 16        |\n",
       "\n"
      ],
      "text/plain": [
       "    pred      real\n",
       "4   12.682507 15  \n",
       "5    9.433677 10  \n",
       "7   11.312310 11  \n",
       "8    3.101530  6  \n",
       "10  15.564674 15  \n",
       "13  14.190360 14  \n",
       "21  14.601427 15  \n",
       "25   9.191419  8  \n",
       "30  12.352589 11  \n",
       "32  16.292243 17  \n",
       "39  12.553386 11  \n",
       "40  14.573362 13  \n",
       "42  13.361456 12  \n",
       "49  16.121757 14  \n",
       "54   9.044686 11  \n",
       "57  14.614118 15  \n",
       "61   9.508089 11  \n",
       "62   7.571092 11  \n",
       "64   8.847217  9  \n",
       "65   9.367014 10  \n",
       "71  15.645439 15  \n",
       "75  16.283467 11  \n",
       "77  11.485059 10  \n",
       "79   7.454869 10  \n",
       "84  15.908265 15  \n",
       "86   8.259935  8  \n",
       "90   8.116941  7  \n",
       "91   5.680344  8  \n",
       "101  7.217520  5  \n",
       "105 18.387648 18  \n",
       "... ...       ... \n",
       "302 10.445265 10  \n",
       "307 17.581928 18  \n",
       "308 10.142502  8  \n",
       "309 10.525108 12  \n",
       "312 13.384567 13  \n",
       "313  9.774990 11  \n",
       "316 12.674051 11  \n",
       "323  9.498840 11  \n",
       "329  8.749197  9  \n",
       "330 13.930318 14  \n",
       "333 -2.795582  0  \n",
       "339 14.119452 17  \n",
       "344  6.314688  0  \n",
       "345  9.031470 10  \n",
       "347 15.360903 16  \n",
       "350 12.771407 13  \n",
       "351  5.398667  8  \n",
       "356  7.808663  9  \n",
       "359  8.793634 10  \n",
       "360 16.556011 16  \n",
       "361 12.628150 13  \n",
       "366  9.875136 10  \n",
       "367 12.621700 13  \n",
       "369  9.334932 10  \n",
       "379 14.160218 15  \n",
       "381 14.131274 14  \n",
       "383  9.814664 10  \n",
       "386  9.685128 10  \n",
       "390  2.449392  0  \n",
       "392 15.786871 16  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results <- cbind(G3.predictions,test$G3) \n",
    "colnames(results) <- c('pred','real')\n",
    "results <- as.data.frame(results)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing negative predictions\n",
    "to_zero <- function(x){\n",
    "  if  (x < 0){\n",
    "    return(0)\n",
    "  }else{\n",
    "    return(x)\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         pred real\n",
      "4   12.682507   15\n",
      "5    9.433677   10\n",
      "7   11.312310   11\n",
      "8    3.101530    6\n",
      "10  15.564674   15\n",
      "13  14.190360   14\n",
      "21  14.601427   15\n",
      "25   9.191419    8\n",
      "30  12.352589   11\n",
      "32  16.292243   17\n",
      "39  12.553386   11\n",
      "40  14.573362   13\n",
      "42  13.361456   12\n",
      "49  16.121757   14\n",
      "54   9.044686   11\n",
      "57  14.614118   15\n",
      "61   9.508089   11\n",
      "62   7.571092   11\n",
      "64   8.847217    9\n",
      "65   9.367014   10\n",
      "71  15.645439   15\n",
      "75  16.283467   11\n",
      "77  11.485059   10\n",
      "79   7.454869   10\n",
      "84  15.908265   15\n",
      "86   8.259935    8\n",
      "90   8.116941    7\n",
      "91   5.680344    8\n",
      "101  7.217520    5\n",
      "105 18.387648   18\n",
      "114 21.155541   19\n",
      "116 15.686609   16\n",
      "119  7.929231    8\n",
      "121 14.703735   15\n",
      "129  1.656006    0\n",
      "136  0.000000    0\n",
      "144 14.027183   13\n",
      "149  5.402422    0\n",
      "154  0.000000    0\n",
      "155 10.110241   12\n",
      "156  7.756995    8\n",
      "161  5.009636    0\n",
      "162  8.718964    7\n",
      "164 10.577394   10\n",
      "165  7.015286    7\n",
      "166 12.718437   12\n",
      "170 13.832836   14\n",
      "173 11.229190   10\n",
      "176  8.887442    9\n",
      "177 13.303192   11\n",
      "178  4.400549    6\n",
      "182 12.234432   12\n",
      "184 13.026417    8\n",
      "187 10.757342   11\n",
      "189  5.368688    9\n",
      "191 11.479617   13\n",
      "194  8.695959   10\n",
      "202 10.328584   10\n",
      "204  6.841389    6\n",
      "213 12.494765   14\n",
      "216 15.346879   15\n",
      "221  4.023249    6\n",
      "224 11.606746   13\n",
      "225 11.976024   14\n",
      "230  9.228981   12\n",
      "233  9.525680    9\n",
      "234 12.803778   13\n",
      "235  7.988283    6\n",
      "236  9.972518   10\n",
      "240  6.582129    0\n",
      "244 11.842815   12\n",
      "245  0.000000    0\n",
      "249  3.044462    5\n",
      "252 10.703928   10\n",
      "260  7.027985    0\n",
      "261 18.904091   18\n",
      "262  7.189106    8\n",
      "263 11.585034   12\n",
      "277 14.557157    9\n",
      "278  8.871446    9\n",
      "279  6.509502    8\n",
      "283 11.597208   12\n",
      "291 11.509667   11\n",
      "292 14.121174   15\n",
      "294 18.731743   18\n",
      "295 13.826496   14\n",
      "300 13.668455   16\n",
      "301  8.591545   11\n",
      "302 10.445265   10\n",
      "307 17.581928   18\n",
      "308 10.142502    8\n",
      "309 10.525108   12\n",
      "312 13.384567   13\n",
      "313  9.774990   11\n",
      "316 12.674051   11\n",
      "323  9.498840   11\n",
      "329  8.749197    9\n",
      "330 13.930318   14\n",
      "333  0.000000    0\n",
      "339 14.119452   17\n",
      "344  6.314688    0\n",
      "345  9.031470   10\n",
      "347 15.360903   16\n",
      "350 12.771407   13\n",
      "351  5.398667    8\n",
      "356  7.808663    9\n",
      "359  8.793634   10\n",
      "360 16.556011   16\n",
      "361 12.628150   13\n",
      "366  9.875136   10\n",
      "367 12.621700   13\n",
      "369  9.334932   10\n",
      "379 14.160218   15\n",
      "381 14.131274   14\n",
      "383  9.814664   10\n",
      "386  9.685128   10\n",
      "390  2.449392    0\n",
      "392 15.786871   16\n"
     ]
    }
   ],
   "source": [
    "results$pred <- sapply(results$pred,to_zero)\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 3.991675\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "1.99791756601486"
      ],
      "text/latex": [
       "1.99791756601486"
      ],
      "text/markdown": [
       "1.99791756601486"
      ],
      "text/plain": [
       "[1] 1.997918"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "0.804447675832027"
      ],
      "text/latex": [
       "0.804447675832027"
      ],
      "text/markdown": [
       "0.804447675832027"
      ],
      "text/plain": [
       "[1] 0.8044477"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#evaluate the model\n",
    "mse <- mean((results$real-results$pred)^2)\n",
    "print(mse)\n",
    "\n",
    "mse^0.5\n",
    "\n",
    "SSE = sum((results$pred - results$real)^2)\n",
    "SST = sum( (mean(df$G3) - results$real)^2)\n",
    "\n",
    "R2 = 1 - SSE/SST\n",
    "R2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RMSE is measure of how spread out our residuals are..In other words,it tells you how concentrated data is around line best of fit..\n",
    "\n",
    "R2 is used to predict the accuracy of model..Higher the value is better the model is..the value lies between zero and one..\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# simple linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "model <- lm(G3 ~ G1+G2,train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = G3 ~ G1 + G2, data = train)\n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-9.5811 -0.3163  0.3296  0.9949  3.7016 \n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept) -1.83232    0.41333  -4.433 1.35e-05 ***\n",
       "G1           0.15027    0.06907   2.176   0.0304 *  \n",
       "G2           0.99107    0.06152  16.110  < 2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 2.021 on 274 degrees of freedom\n",
       "Multiple R-squared:  0.8091,\tAdjusted R-squared:  0.8077 \n",
       "F-statistic: 580.8 on 2 and 274 DF,  p-value: < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# interpretation the lower the p value the significant attributes are...so the zero represents three stars and so on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "res <- residuals(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "res <- as.data.frame(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>res</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td> 1.1345350</td></tr>\n",
       "\t<tr><th scope=row>2</th><td> 2.1256094</td></tr>\n",
       "\t<tr><th scope=row>3</th><td> 2.8518496</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>-0.2878175</td></tr>\n",
       "\t<tr><th scope=row>9</th><td> 0.5886910</td></tr>\n",
       "\t<tr><th scope=row>11</th><td> 1.4010447</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       "  & res\\\\\n",
       "\\hline\n",
       "\t1 &  1.1345350\\\\\n",
       "\t2 &  2.1256094\\\\\n",
       "\t3 &  2.8518496\\\\\n",
       "\t6 & -0.2878175\\\\\n",
       "\t9 &  0.5886910\\\\\n",
       "\t11 &  1.4010447\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | res |\n",
       "|---|---|\n",
       "| 1 |  1.1345350 |\n",
       "| 2 |  2.1256094 |\n",
       "| 3 |  2.8518496 |\n",
       "| 6 | -0.2878175 |\n",
       "| 9 |  0.5886910 |\n",
       "| 11 |  1.4010447 |\n",
       "\n"
      ],
      "text/plain": [
       "   res       \n",
       "1   1.1345350\n",
       "2   2.1256094\n",
       "3   2.8518496\n",
       "6  -0.2878175\n",
       "9   0.5886910\n",
       "11  1.4010447"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# residuals gives a basic difference between observed value and predicted value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n"
     ]
    },
    {
     "data": {
      "image/png": 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v3lA4DkOUZpAlLwACB5jlGa9iDxU7uA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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(res,aes(res)) +  geom_histogram(fill='blue',alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# in the above graph we have negative values we will remove the negative values using to zero function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "G3.predictions <- predict(model,test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>pred</th><th scope=col>real</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>4</th><td>14.296743</td><td>15       </td></tr>\n",
       "\t<tr><th scope=row>5</th><td> 8.980031</td><td>10       </td></tr>\n",
       "\t<tr><th scope=row>7</th><td>11.863789</td><td>11       </td></tr>\n",
       "\t<tr><th scope=row>8</th><td> 4.024659</td><td> 6       </td></tr>\n",
       "\t<tr><th scope=row>10</th><td>15.137549</td><td>15       </td></tr>\n",
       "\t<tr><th scope=row>13</th><td>14.146475</td><td>14       </td></tr>\n",
       "\t<tr><th scope=row>21</th><td>13.996207</td><td>15       </td></tr>\n",
       "\t<tr><th scope=row>25</th><td> 8.590030</td><td> 8       </td></tr>\n",
       "\t<tr><th scope=row>30</th><td>11.563253</td><td>11       </td></tr>\n",
       "\t<tr><th scope=row>32</th><td>16.579429</td><td>17       </td></tr>\n",
       "\t<tr><th scope=row>39</th><td>11.863789</td><td>11       </td></tr>\n",
       "\t<tr><th scope=row>40</th><td>13.155400</td><td>13       </td></tr>\n",
       "\t<tr><th scope=row>42</th><td>11.863789</td><td>12       </td></tr>\n",
       "\t<tr><th scope=row>49</th><td>15.287818</td><td>14       </td></tr>\n",
       "\t<tr><th scope=row>54</th><td> 9.280567</td><td>11       </td></tr>\n",
       "\t<tr><th scope=row>57</th><td>15.137549</td><td>15       </td></tr>\n",
       "\t<tr><th scope=row>61</th><td>10.572178</td><td>11       </td></tr>\n",
       "\t<tr><th scope=row>62</th><td> 7.598955</td><td>11       </td></tr>\n",
       "\t<tr><th scope=row>64</th><td> 8.590030</td><td> 9       </td></tr>\n",
       "\t<tr><th scope=row>65</th><td> 9.581104</td><td>10       </td></tr>\n",
       "\t<tr><th scope=row>71</th><td>14.987281</td><td>15       </td></tr>\n",
       "\t<tr><th scope=row>75</th><td>11.713521</td><td>11       </td></tr>\n",
       "\t<tr><th scope=row>77</th><td>10.722447</td><td>10       </td></tr>\n",
       "\t<tr><th scope=row>79</th><td> 7.298419</td><td>10       </td></tr>\n",
       "\t<tr><th scope=row>84</th><td>15.287818</td><td>15       </td></tr>\n",
       "\t<tr><th scope=row>86</th><td> 8.139225</td><td> 8       </td></tr>\n",
       "\t<tr><th scope=row>90</th><td> 5.316270</td><td> 7       </td></tr>\n",
       "\t<tr><th scope=row>91</th><td> 6.157076</td><td> 8       </td></tr>\n",
       "\t<tr><th scope=row>101</th><td> 6.157076</td><td> 5       </td></tr>\n",
       "\t<tr><th scope=row>105</th><td>18.411309</td><td>18       </td></tr>\n",
       "\t<tr><th scope=row>...</th><td>...</td><td>...</td></tr>\n",
       "\t<tr><th scope=row>302</th><td>10.7224468</td><td>10        </td></tr>\n",
       "\t<tr><th scope=row>307</th><td>18.5615773</td><td>18        </td></tr>\n",
       "\t<tr><th scope=row>308</th><td> 8.2894930</td><td> 8        </td></tr>\n",
       "\t<tr><th scope=row>309</th><td>12.3145944</td><td>12        </td></tr>\n",
       "\t<tr><th scope=row>312</th><td>12.1643261</td><td>13        </td></tr>\n",
       "\t<tr><th scope=row>313</th><td>11.0229834</td><td>11        </td></tr>\n",
       "\t<tr><th scope=row>316</th><td>11.0229834</td><td>11        </td></tr>\n",
       "\t<tr><th scope=row>323</th><td>10.7224468</td><td>11        </td></tr>\n",
       "\t<tr><th scope=row>329</th><td> 8.5900297</td><td> 9        </td></tr>\n",
       "\t<tr><th scope=row>330</th><td>14.1464748</td><td>14        </td></tr>\n",
       "\t<tr><th scope=row>333</th><td>-0.7804446</td><td> 0        </td></tr>\n",
       "\t<tr><th scope=row>339</th><td>15.4380859</td><td>17        </td></tr>\n",
       "\t<tr><th scope=row>344</th><td> 7.4486870</td><td> 0        </td></tr>\n",
       "\t<tr><th scope=row>345</th><td> 9.7313724</td><td>10        </td></tr>\n",
       "\t<tr><th scope=row>347</th><td>15.4380859</td><td>16        </td></tr>\n",
       "\t<tr><th scope=row>350</th><td>12.7045955</td><td>13        </td></tr>\n",
       "\t<tr><th scope=row>351</th><td> 6.3073443</td><td> 8        </td></tr>\n",
       "\t<tr><th scope=row>356</th><td> 8.5900297</td><td> 9        </td></tr>\n",
       "\t<tr><th scope=row>359</th><td> 9.5811041</td><td>10        </td></tr>\n",
       "\t<tr><th scope=row>360</th><td>16.7296969</td><td>16        </td></tr>\n",
       "\t<tr><th scope=row>361</th><td>13.0051321</td><td>13        </td></tr>\n",
       "\t<tr><th scope=row>366</th><td> 9.5811041</td><td>10        </td></tr>\n",
       "\t<tr><th scope=row>367</th><td>13.0051321</td><td>13        </td></tr>\n",
       "\t<tr><th scope=row>369</th><td> 9.7313724</td><td>10        </td></tr>\n",
       "\t<tr><th scope=row>379</th><td>15.2878175</td><td>15        </td></tr>\n",
       "\t<tr><th scope=row>381</th><td>14.2967432</td><td>14        </td></tr>\n",
       "\t<tr><th scope=row>383</th><td>10.7224468</td><td>10        </td></tr>\n",
       "\t<tr><th scope=row>386</th><td> 8.5900297</td><td>10        </td></tr>\n",
       "\t<tr><th scope=row>390</th><td> 4.0246589</td><td> 0        </td></tr>\n",
       "\t<tr><th scope=row>392</th><td>16.1286236</td><td>16        </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & pred & real\\\\\n",
       "\\hline\n",
       "\t4 & 14.296743 & 15       \\\\\n",
       "\t5 &  8.980031 & 10       \\\\\n",
       "\t7 & 11.863789 & 11       \\\\\n",
       "\t8 &  4.024659 &  6       \\\\\n",
       "\t10 & 15.137549 & 15       \\\\\n",
       "\t13 & 14.146475 & 14       \\\\\n",
       "\t21 & 13.996207 & 15       \\\\\n",
       "\t25 &  8.590030 &  8       \\\\\n",
       "\t30 & 11.563253 & 11       \\\\\n",
       "\t32 & 16.579429 & 17       \\\\\n",
       "\t39 & 11.863789 & 11       \\\\\n",
       "\t40 & 13.155400 & 13       \\\\\n",
       "\t42 & 11.863789 & 12       \\\\\n",
       "\t49 & 15.287818 & 14       \\\\\n",
       "\t54 &  9.280567 & 11       \\\\\n",
       "\t57 & 15.137549 & 15       \\\\\n",
       "\t61 & 10.572178 & 11       \\\\\n",
       "\t62 &  7.598955 & 11       \\\\\n",
       "\t64 &  8.590030 &  9       \\\\\n",
       "\t65 &  9.581104 & 10       \\\\\n",
       "\t71 & 14.987281 & 15       \\\\\n",
       "\t75 & 11.713521 & 11       \\\\\n",
       "\t77 & 10.722447 & 10       \\\\\n",
       "\t79 &  7.298419 & 10       \\\\\n",
       "\t84 & 15.287818 & 15       \\\\\n",
       "\t86 &  8.139225 &  8       \\\\\n",
       "\t90 &  5.316270 &  7       \\\\\n",
       "\t91 &  6.157076 &  8       \\\\\n",
       "\t101 &  6.157076 &  5       \\\\\n",
       "\t105 & 18.411309 & 18       \\\\\n",
       "\t... & ... & ...\\\\\n",
       "\t302 & 10.7224468 & 10        \\\\\n",
       "\t307 & 18.5615773 & 18        \\\\\n",
       "\t308 &  8.2894930 &  8        \\\\\n",
       "\t309 & 12.3145944 & 12        \\\\\n",
       "\t312 & 12.1643261 & 13        \\\\\n",
       "\t313 & 11.0229834 & 11        \\\\\n",
       "\t316 & 11.0229834 & 11        \\\\\n",
       "\t323 & 10.7224468 & 11        \\\\\n",
       "\t329 &  8.5900297 &  9        \\\\\n",
       "\t330 & 14.1464748 & 14        \\\\\n",
       "\t333 & -0.7804446 &  0        \\\\\n",
       "\t339 & 15.4380859 & 17        \\\\\n",
       "\t344 &  7.4486870 &  0        \\\\\n",
       "\t345 &  9.7313724 & 10        \\\\\n",
       "\t347 & 15.4380859 & 16        \\\\\n",
       "\t350 & 12.7045955 & 13        \\\\\n",
       "\t351 &  6.3073443 &  8        \\\\\n",
       "\t356 &  8.5900297 &  9        \\\\\n",
       "\t359 &  9.5811041 & 10        \\\\\n",
       "\t360 & 16.7296969 & 16        \\\\\n",
       "\t361 & 13.0051321 & 13        \\\\\n",
       "\t366 &  9.5811041 & 10        \\\\\n",
       "\t367 & 13.0051321 & 13        \\\\\n",
       "\t369 &  9.7313724 & 10        \\\\\n",
       "\t379 & 15.2878175 & 15        \\\\\n",
       "\t381 & 14.2967432 & 14        \\\\\n",
       "\t383 & 10.7224468 & 10        \\\\\n",
       "\t386 &  8.5900297 & 10        \\\\\n",
       "\t390 &  4.0246589 &  0        \\\\\n",
       "\t392 & 16.1286236 & 16        \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | pred | real |\n",
       "|---|---|---|\n",
       "| 4 | 14.296743 | 15        |\n",
       "| 5 |  8.980031 | 10        |\n",
       "| 7 | 11.863789 | 11        |\n",
       "| 8 |  4.024659 |  6        |\n",
       "| 10 | 15.137549 | 15        |\n",
       "| 13 | 14.146475 | 14        |\n",
       "| 21 | 13.996207 | 15        |\n",
       "| 25 |  8.590030 |  8        |\n",
       "| 30 | 11.563253 | 11        |\n",
       "| 32 | 16.579429 | 17        |\n",
       "| 39 | 11.863789 | 11        |\n",
       "| 40 | 13.155400 | 13        |\n",
       "| 42 | 11.863789 | 12        |\n",
       "| 49 | 15.287818 | 14        |\n",
       "| 54 |  9.280567 | 11        |\n",
       "| 57 | 15.137549 | 15        |\n",
       "| 61 | 10.572178 | 11        |\n",
       "| 62 |  7.598955 | 11        |\n",
       "| 64 |  8.590030 |  9        |\n",
       "| 65 |  9.581104 | 10        |\n",
       "| 71 | 14.987281 | 15        |\n",
       "| 75 | 11.713521 | 11        |\n",
       "| 77 | 10.722447 | 10        |\n",
       "| 79 |  7.298419 | 10        |\n",
       "| 84 | 15.287818 | 15        |\n",
       "| 86 |  8.139225 |  8        |\n",
       "| 90 |  5.316270 |  7        |\n",
       "| 91 |  6.157076 |  8        |\n",
       "| 101 |  6.157076 |  5        |\n",
       "| 105 | 18.411309 | 18        |\n",
       "| ... | ... | ... |\n",
       "| 302 | 10.7224468 | 10         |\n",
       "| 307 | 18.5615773 | 18         |\n",
       "| 308 |  8.2894930 |  8         |\n",
       "| 309 | 12.3145944 | 12         |\n",
       "| 312 | 12.1643261 | 13         |\n",
       "| 313 | 11.0229834 | 11         |\n",
       "| 316 | 11.0229834 | 11         |\n",
       "| 323 | 10.7224468 | 11         |\n",
       "| 329 |  8.5900297 |  9         |\n",
       "| 330 | 14.1464748 | 14         |\n",
       "| 333 | -0.7804446 |  0         |\n",
       "| 339 | 15.4380859 | 17         |\n",
       "| 344 |  7.4486870 |  0         |\n",
       "| 345 |  9.7313724 | 10         |\n",
       "| 347 | 15.4380859 | 16         |\n",
       "| 350 | 12.7045955 | 13         |\n",
       "| 351 |  6.3073443 |  8         |\n",
       "| 356 |  8.5900297 |  9         |\n",
       "| 359 |  9.5811041 | 10         |\n",
       "| 360 | 16.7296969 | 16         |\n",
       "| 361 | 13.0051321 | 13         |\n",
       "| 366 |  9.5811041 | 10         |\n",
       "| 367 | 13.0051321 | 13         |\n",
       "| 369 |  9.7313724 | 10         |\n",
       "| 379 | 15.2878175 | 15         |\n",
       "| 381 | 14.2967432 | 14         |\n",
       "| 383 | 10.7224468 | 10         |\n",
       "| 386 |  8.5900297 | 10         |\n",
       "| 390 |  4.0246589 |  0         |\n",
       "| 392 | 16.1286236 | 16         |\n",
       "\n"
      ],
      "text/plain": [
       "    pred       real\n",
       "4   14.296743  15  \n",
       "5    8.980031  10  \n",
       "7   11.863789  11  \n",
       "8    4.024659   6  \n",
       "10  15.137549  15  \n",
       "13  14.146475  14  \n",
       "21  13.996207  15  \n",
       "25   8.590030   8  \n",
       "30  11.563253  11  \n",
       "32  16.579429  17  \n",
       "39  11.863789  11  \n",
       "40  13.155400  13  \n",
       "42  11.863789  12  \n",
       "49  15.287818  14  \n",
       "54   9.280567  11  \n",
       "57  15.137549  15  \n",
       "61  10.572178  11  \n",
       "62   7.598955  11  \n",
       "64   8.590030   9  \n",
       "65   9.581104  10  \n",
       "71  14.987281  15  \n",
       "75  11.713521  11  \n",
       "77  10.722447  10  \n",
       "79   7.298419  10  \n",
       "84  15.287818  15  \n",
       "86   8.139225   8  \n",
       "90   5.316270   7  \n",
       "91   6.157076   8  \n",
       "101  6.157076   5  \n",
       "105 18.411309  18  \n",
       "... ...        ... \n",
       "302 10.7224468 10  \n",
       "307 18.5615773 18  \n",
       "308  8.2894930  8  \n",
       "309 12.3145944 12  \n",
       "312 12.1643261 13  \n",
       "313 11.0229834 11  \n",
       "316 11.0229834 11  \n",
       "323 10.7224468 11  \n",
       "329  8.5900297  9  \n",
       "330 14.1464748 14  \n",
       "333 -0.7804446  0  \n",
       "339 15.4380859 17  \n",
       "344  7.4486870  0  \n",
       "345  9.7313724 10  \n",
       "347 15.4380859 16  \n",
       "350 12.7045955 13  \n",
       "351  6.3073443  8  \n",
       "356  8.5900297  9  \n",
       "359  9.5811041 10  \n",
       "360 16.7296969 16  \n",
       "361 13.0051321 13  \n",
       "366  9.5811041 10  \n",
       "367 13.0051321 13  \n",
       "369  9.7313724 10  \n",
       "379 15.2878175 15  \n",
       "381 14.2967432 14  \n",
       "383 10.7224468 10  \n",
       "386  8.5900297 10  \n",
       "390  4.0246589  0  \n",
       "392 16.1286236 16  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results <- cbind(G3.predictions,test$G3) \n",
    "colnames(results) <- c('pred','real')\n",
    "results <- as.data.frame(results)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing negative predictions\n",
    "to_zero <- function(x){\n",
    "  if  (x < 0){\n",
    "    return(0)\n",
    "  }else{\n",
    "    return(x)\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         pred real\n",
      "4   14.296743   15\n",
      "5    8.980031   10\n",
      "7   11.863789   11\n",
      "8    4.024659    6\n",
      "10  15.137549   15\n",
      "13  14.146475   14\n",
      "21  13.996207   15\n",
      "25   8.590030    8\n",
      "30  11.563253   11\n",
      "32  16.579429   17\n",
      "39  11.863789   11\n",
      "40  13.155400   13\n",
      "42  11.863789   12\n",
      "49  15.287818   14\n",
      "54   9.280567   11\n",
      "57  15.137549   15\n",
      "61  10.572178   11\n",
      "62   7.598955   11\n",
      "64   8.590030    9\n",
      "65   9.581104   10\n",
      "71  14.987281   15\n",
      "75  11.713521   11\n",
      "77  10.722447   10\n",
      "79   7.298419   10\n",
      "84  15.287818   15\n",
      "86   8.139225    8\n",
      "90   5.316270    7\n",
      "91   6.157076    8\n",
      "101  6.157076    5\n",
      "105 18.411309   18\n",
      "114 19.702920   19\n",
      "116 15.287818   16\n",
      "119  6.457613    8\n",
      "121 15.438086   15\n",
      "129  3.183853    0\n",
      "136  0.000000    0\n",
      "144 14.146475   13\n",
      "149  5.166002    0\n",
      "154  0.000000    0\n",
      "155 10.722447   12\n",
      "156  7.749224    8\n",
      "161  5.166002    0\n",
      "162  7.838688    7\n",
      "164  9.581104   10\n",
      "165  6.847614    7\n",
      "166 10.872715   12\n",
      "170 14.146475   14\n",
      "173 11.022983   10\n",
      "176  8.590030    9\n",
      "177 13.005132   11\n",
      "178  4.024659    6\n",
      "182 12.854864   12\n",
      "184  8.439761    8\n",
      "187 11.713521   11\n",
      "189  6.307344    9\n",
      "191 11.713521   13\n",
      "194  8.289493   10\n",
      "202  9.280567   10\n",
      "204  5.166002    6\n",
      "213 12.854864   14\n",
      "216 15.137549   15\n",
      "221  5.015733    6\n",
      "224 12.854864   13\n",
      "225 13.005132   14\n",
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      "307 18.561577   18\n",
      "308  8.289493    8\n",
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      "323 10.722447   11\n",
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      "356  8.590030    9\n",
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      "360 16.729697   16\n",
      "361 13.005132   13\n",
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    }
   ],
   "source": [
    "results$pred <- sapply(results$pred,to_zero)\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 2.962738\n"
     ]
    },
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       "[1] 1.721261"
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   ],
   "source": [
    "#evaluate the model\n",
    "mse <- mean((results$real-results$pred)^2)\n",
    "print(mse)\n",
    "\n",
    "mse^0.5\n",
    "\n",
    "SSE = sum((results$pred - results$real)^2)\n",
    "SST = sum( (mean(df$G3) - results$real)^2)\n",
    "\n",
    "R2 = 1 - SSE/SST\n",
    "R2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# # RMSE is measure of how spread out our residuals are..In other words,it tells you how concentrated data is around line best of fit..\n",
    "\n",
    "R2 is used to predict the accuracy of model..Higher the value is better the model is..the value lies between zero and one.."
   ]
  }
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