Predicting_student_grades.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = pd.read_csv(\"student-mat.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
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       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
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       "      <th>1</th>\n",
       "      <td>GP</td>\n",
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       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
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       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
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       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
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       "      <td>4</td>\n",
       "      <td>3</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob ...  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher ...   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other ...   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other ...   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services ...   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other ...   \n",
       "\n",
       "  famrel freetime  goout  Dalc  Walc health absences  G1  G2  G3  \n",
       "0      4        3      4     1     1      3        6   5   6   6  \n",
       "1      5        3      3     1     1      3        4   5   5   6  \n",
       "2      4        3      2     2     3      3       10   7   8  10  \n",
       "3      3        2      2     1     1      5        2  15  14  15  \n",
       "4      4        3      2     1     2      5        4   6  10  10  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>traveltime</th>\n",
       "      <th>studytime</th>\n",
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       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
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       "      <th>G3</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.696203</td>\n",
       "      <td>2.749367</td>\n",
       "      <td>2.521519</td>\n",
       "      <td>1.448101</td>\n",
       "      <td>2.035443</td>\n",
       "      <td>0.334177</td>\n",
       "      <td>3.944304</td>\n",
       "      <td>3.235443</td>\n",
       "      <td>3.108861</td>\n",
       "      <td>1.481013</td>\n",
       "      <td>2.291139</td>\n",
       "      <td>3.554430</td>\n",
       "      <td>5.708861</td>\n",
       "      <td>10.908861</td>\n",
       "      <td>10.713924</td>\n",
       "      <td>10.415190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.276043</td>\n",
       "      <td>1.094735</td>\n",
       "      <td>1.088201</td>\n",
       "      <td>0.697505</td>\n",
       "      <td>0.839240</td>\n",
       "      <td>0.743651</td>\n",
       "      <td>0.896659</td>\n",
       "      <td>0.998862</td>\n",
       "      <td>1.113278</td>\n",
       "      <td>0.890741</td>\n",
       "      <td>1.287897</td>\n",
       "      <td>1.390303</td>\n",
       "      <td>8.003096</td>\n",
       "      <td>3.319195</td>\n",
       "      <td>3.761505</td>\n",
       "      <td>4.581443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>20.000000</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "              age        Medu        Fedu  traveltime   studytime    failures  \\\n",
       "count  395.000000  395.000000  395.000000  395.000000  395.000000  395.000000   \n",
       "mean    16.696203    2.749367    2.521519    1.448101    2.035443    0.334177   \n",
       "std      1.276043    1.094735    1.088201    0.697505    0.839240    0.743651   \n",
       "min     15.000000    0.000000    0.000000    1.000000    1.000000    0.000000   \n",
       "25%     16.000000    2.000000    2.000000    1.000000    1.000000    0.000000   \n",
       "50%     17.000000    3.000000    2.000000    1.000000    2.000000    0.000000   \n",
       "75%     18.000000    4.000000    3.000000    2.000000    2.000000    0.000000   \n",
       "max     22.000000    4.000000    4.000000    4.000000    4.000000    3.000000   \n",
       "\n",
       "           famrel    freetime       goout        Dalc        Walc      health  \\\n",
       "count  395.000000  395.000000  395.000000  395.000000  395.000000  395.000000   \n",
       "mean     3.944304    3.235443    3.108861    1.481013    2.291139    3.554430   \n",
       "std      0.896659    0.998862    1.113278    0.890741    1.287897    1.390303   \n",
       "min      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "25%      4.000000    3.000000    2.000000    1.000000    1.000000    3.000000   \n",
       "50%      4.000000    3.000000    3.000000    1.000000    2.000000    4.000000   \n",
       "75%      5.000000    4.000000    4.000000    2.000000    3.000000    5.000000   \n",
       "max      5.000000    5.000000    5.000000    5.000000    5.000000    5.000000   \n",
       "\n",
       "         absences          G1          G2          G3  \n",
       "count  395.000000  395.000000  395.000000  395.000000  \n",
       "mean     5.708861   10.908861   10.713924   10.415190  \n",
       "std      8.003096    3.319195    3.761505    4.581443  \n",
       "min      0.000000    3.000000    0.000000    0.000000  \n",
       "25%      0.000000    8.000000    9.000000    8.000000  \n",
       "50%      4.000000   11.000000   11.000000   11.000000  \n",
       "75%      8.000000   13.000000   13.000000   14.000000  \n",
       "max     75.000000   19.000000   19.000000   20.000000  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = pd.read_csv(\"student-por.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
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       "      <th>Fjob</th>\n",
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       "      <th>famrel</th>\n",
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       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
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       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>...</td>\n",
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       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob ...  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher ...   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other ...   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other ...   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services ...   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other ...   \n",
       "\n",
       "  famrel freetime  goout  Dalc  Walc health absences  G1  G2  G3  \n",
       "0      4        3      4     1     1      3        4   0  11  11  \n",
       "1      5        3      3     1     1      3        2   9  11  11  \n",
       "2      4        3      2     2     3      3        6  12  13  12  \n",
       "3      3        2      2     1     1      5        0  14  14  14  \n",
       "4      4        3      2     1     2      5        0  11  13  13  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = pd.concat([data1, data2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['school', 'sex', 'age', 'address', 'famsize', 'Pstatus', 'Medu', 'Fedu',\n",
       "       'Mjob', 'Fjob', 'reason', 'guardian', 'traveltime', 'studytime',\n",
       "       'failures', 'schoolsup', 'famsup', 'paid', 'activities', 'nursery',\n",
       "       'higher', 'internet', 'romantic', 'famrel', 'freetime', 'goout', 'Dalc',\n",
       "       'Walc', 'health', 'absences', 'G1', 'G2', 'G3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1044, 33)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = res.drop_duplicates([\"school\", \"sex\", \"age\", \"address\", \"famsize\", \"Pstatus\", \"Medu\", \n",
    "                           \"Fedu\", \"Mjob\", \"Fjob\", \"reason\", \"nursery\", \"internet\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob ...  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher ...   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other ...   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other ...   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services ...   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other ...   \n",
       "\n",
       "  famrel freetime  goout  Dalc  Walc health absences  G1  G2  G3  \n",
       "0      4        3      4     1     1      3        6   5   6   6  \n",
       "1      5        3      3     1     1      3        4   5   5   6  \n",
       "2      4        3      2     2     3      3       10   7   8  10  \n",
       "3      3        2      2     1     1      5        2  15  14  15  \n",
       "4      4        3      2     1     2      5        4   6  10  10  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 662 entries, 0 to 642\n",
      "Data columns (total 33 columns):\n",
      "school        662 non-null object\n",
      "sex           662 non-null object\n",
      "age           662 non-null int64\n",
      "address       662 non-null object\n",
      "famsize       662 non-null object\n",
      "Pstatus       662 non-null object\n",
      "Medu          662 non-null int64\n",
      "Fedu          662 non-null int64\n",
      "Mjob          662 non-null object\n",
      "Fjob          662 non-null object\n",
      "reason        662 non-null object\n",
      "guardian      662 non-null object\n",
      "traveltime    662 non-null int64\n",
      "studytime     662 non-null int64\n",
      "failures      662 non-null int64\n",
      "schoolsup     662 non-null object\n",
      "famsup        662 non-null object\n",
      "paid          662 non-null object\n",
      "activities    662 non-null object\n",
      "nursery       662 non-null object\n",
      "higher        662 non-null object\n",
      "internet      662 non-null object\n",
      "romantic      662 non-null object\n",
      "famrel        662 non-null int64\n",
      "freetime      662 non-null int64\n",
      "goout         662 non-null int64\n",
      "Dalc          662 non-null int64\n",
      "Walc          662 non-null int64\n",
      "health        662 non-null int64\n",
      "absences      662 non-null int64\n",
      "G1            662 non-null int64\n",
      "G2            662 non-null int64\n",
      "G3            662 non-null int64\n",
      "dtypes: int64(16), object(17)\n",
      "memory usage: 175.8+ KB\n"
     ]
    }
   ],
   "source": [
    "res.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>traveltime</th>\n",
       "      <th>studytime</th>\n",
       "      <th>failures</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>662.000000</td>\n",
       "      <td>662.000000</td>\n",
       "      <td>662.000000</td>\n",
       "      <td>662.000000</td>\n",
       "      <td>662.000000</td>\n",
       "      <td>662.000000</td>\n",
       "      <td>662.000000</td>\n",
       "      <td>662.000000</td>\n",
       "      <td>662.000000</td>\n",
       "      <td>662.000000</td>\n",
       "      <td>662.000000</td>\n",
       "      <td>662.000000</td>\n",
       "      <td>662.000000</td>\n",
       "      <td>662.000000</td>\n",
       "      <td>662.000000</td>\n",
       "      <td>662.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.812689</td>\n",
       "      <td>2.492447</td>\n",
       "      <td>2.293051</td>\n",
       "      <td>1.564955</td>\n",
       "      <td>1.927492</td>\n",
       "      <td>0.332326</td>\n",
       "      <td>3.938066</td>\n",
       "      <td>3.184290</td>\n",
       "      <td>3.172205</td>\n",
       "      <td>1.504532</td>\n",
       "      <td>2.282477</td>\n",
       "      <td>3.531722</td>\n",
       "      <td>4.930514</td>\n",
       "      <td>10.728097</td>\n",
       "      <td>10.708459</td>\n",
       "      <td>10.725076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.269194</td>\n",
       "      <td>1.130958</td>\n",
       "      <td>1.094027</td>\n",
       "      <td>0.742799</td>\n",
       "      <td>0.827405</td>\n",
       "      <td>0.716024</td>\n",
       "      <td>0.941930</td>\n",
       "      <td>1.060583</td>\n",
       "      <td>1.161907</td>\n",
       "      <td>0.926567</td>\n",
       "      <td>1.290121</td>\n",
       "      <td>1.434835</td>\n",
       "      <td>6.858060</td>\n",
       "      <td>3.082098</td>\n",
       "      <td>3.529588</td>\n",
       "      <td>4.106738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age        Medu        Fedu  traveltime   studytime    failures  \\\n",
       "count  662.000000  662.000000  662.000000  662.000000  662.000000  662.000000   \n",
       "mean    16.812689    2.492447    2.293051    1.564955    1.927492    0.332326   \n",
       "std      1.269194    1.130958    1.094027    0.742799    0.827405    0.716024   \n",
       "min     15.000000    0.000000    0.000000    1.000000    1.000000    0.000000   \n",
       "25%     16.000000    2.000000    1.000000    1.000000    1.000000    0.000000   \n",
       "50%     17.000000    2.000000    2.000000    1.000000    2.000000    0.000000   \n",
       "75%     18.000000    4.000000    3.000000    2.000000    2.000000    0.000000   \n",
       "max     22.000000    4.000000    4.000000    4.000000    4.000000    3.000000   \n",
       "\n",
       "           famrel    freetime       goout        Dalc        Walc      health  \\\n",
       "count  662.000000  662.000000  662.000000  662.000000  662.000000  662.000000   \n",
       "mean     3.938066    3.184290    3.172205    1.504532    2.282477    3.531722   \n",
       "std      0.941930    1.060583    1.161907    0.926567    1.290121    1.434835   \n",
       "min      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "25%      4.000000    3.000000    2.000000    1.000000    1.000000    2.000000   \n",
       "50%      4.000000    3.000000    3.000000    1.000000    2.000000    4.000000   \n",
       "75%      5.000000    4.000000    4.000000    2.000000    3.000000    5.000000   \n",
       "max      5.000000    5.000000    5.000000    5.000000    5.000000    5.000000   \n",
       "\n",
       "         absences          G1          G2          G3  \n",
       "count  662.000000  662.000000  662.000000  662.000000  \n",
       "mean     4.930514   10.728097   10.708459   10.725076  \n",
       "std      6.858060    3.082098    3.529588    4.106738  \n",
       "min      0.000000    3.000000    0.000000    0.000000  \n",
       "25%      0.000000    8.000000    9.000000    9.000000  \n",
       "50%      3.000000   10.000000   11.000000   11.000000  \n",
       "75%      8.000000   13.000000   13.000000   13.000000  \n",
       "max     75.000000   19.000000   19.000000   20.000000  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school        0\n",
       "sex           0\n",
       "age           0\n",
       "address       0\n",
       "famsize       0\n",
       "Pstatus       0\n",
       "Medu          0\n",
       "Fedu          0\n",
       "Mjob          0\n",
       "Fjob          0\n",
       "reason        0\n",
       "guardian      0\n",
       "traveltime    0\n",
       "studytime     0\n",
       "failures      0\n",
       "schoolsup     0\n",
       "famsup        0\n",
       "paid          0\n",
       "activities    0\n",
       "nursery       0\n",
       "higher        0\n",
       "internet      0\n",
       "romantic      0\n",
       "famrel        0\n",
       "freetime      0\n",
       "goout         0\n",
       "Dalc          0\n",
       "Walc          0\n",
       "health        0\n",
       "absences      0\n",
       "G1            0\n",
       "G2            0\n",
       "G3            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['school', 'sex', 'address', 'famsize', 'Pstatus', 'Mjob', 'Fjob',\n",
       "       'reason', 'guardian', 'schoolsup', 'famsup', 'paid', 'activities',\n",
       "       'nursery', 'higher', 'internet', 'romantic'], dtype=object)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_features = res.select_dtypes(include = ['object']).columns.values\n",
    "cat_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['age', 'Medu', 'Fedu', 'traveltime', 'studytime', 'failures',\n",
       "       'famrel', 'freetime', 'goout', 'Dalc', 'Walc', 'health',\n",
       "       'absences', 'G1', 'G2', 'G3'], dtype=object)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_features = res.select_dtypes(include = ['float64', 'int64']).columns.values\n",
    "num_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res['Weekly_Alcohol'] = res['Dalc'] + res['Walc']\n",
    "\n",
    "plt.hist(res['Weekly_Alcohol'], bins=8)\n",
    "plt.title('Weekly alcohol consumption of students')\n",
    "plt.xlabel('Weekly alcohol consumption')\n",
    "plt.ylabel('Number of students')\n",
    "plt.xticks([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n",
    "#plt.yticks([0, 50, 100, 150, 200, 250])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no student who does not consume alcohol. All students at least 2 times in a week consume alcohol."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GP    434\n",
       "MS    228\n",
       "Name: school, dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res['school'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(res['absences'])\n",
    "plt.title('Absences')\n",
    "plt.xlabel('Days of absence')\n",
    "plt.ylabel('Number of students')\n",
    "plt.yticks([0, 100, 200, 300, 400, 500])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.93\n"
     ]
    }
   ],
   "source": [
    "Avg_Absence = np.average(res['absences'])\n",
    "print(round(Avg_Absence, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(res['failures'])\n",
    "plt.title('Failures')\n",
    "plt.xlabel('Number of failures')\n",
    "plt.ylabel('Number of students')\n",
    "plt.xticks(range(5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.33\n"
     ]
    }
   ],
   "source": [
    "Avg_Failures = np.average(res['failures'])\n",
    "print(round(Avg_Failures, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yes    591\n",
      "no      71\n",
      "Name: higher, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "Count = res['higher'].value_counts()\n",
    "print(Count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see mother's and father's profession with the box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e67feae668>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.title(\"Final grades and mothers profession\")\n",
    "sns.boxplot(y=\"Mjob\", x=\"G3\", data = res)\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.title(\"Final grades and fathers profession\")\n",
    "sns.boxplot(y=\"Fjob\", x=\"G3\", data = res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(res.studytime)\n",
    "plt.title(\"Students study time\")\n",
    "plt.xlabel(\"Study time\")\n",
    "plt.ylabel(\"Number of students\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'gender')"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(res.sex)\n",
    "plt.title(\"gender\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"sex\", y=\"age\", hue=\"famsize\", data=res)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"sex\", y=\"age\", hue=\"guardian\", data=res) #hue=class\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(res.reason)\n",
    "plt.title(\"Reason to choose a school\")\n",
    "plt.xlabel(\"Reason\")\n",
    "plt.ylabel(\"Number of students\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(res.health)\n",
    "plt.title(\"Students health situation\")\n",
    "plt.xlabel(\"Health situation\")\n",
    "plt.ylabel(\"Number of students\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e60a8fd9b0>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(res.age, bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature transformation\n",
    "Now we will transform our features so our model can work with it. We will transform numerical features to binary features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "res[\"age\"] = res.Dalc.astype(\"object\")\n",
    "res[\"Dalc\"] = res.Dalc.astype(\"object\")\n",
    "res[\"Medu\"] = res.Dalc.astype(\"object\")\n",
    "res[\"Fedu\"] = res.Dalc.astype(\"object\")\n",
    "res[\"traveltime\"] = res.Dalc.astype(\"object\")\n",
    "res[\"studytime\"] = res.Dalc.astype(\"object\")\n",
    "res[\"failures\"] = res.Dalc.astype(\"object\")\n",
    "res[\"famrel\"] = res.Dalc.astype(\"object\")\n",
    "res[\"freetime\"] = res.Dalc.astype(\"object\")\n",
    "res[\"goout\"] = res.Dalc.astype(\"object\")\n",
    "res[\"Walc\"] = res.Dalc.astype(\"object\")\n",
    "res[\"health\"] = res.Dalc.astype(\"object\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>absences</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G1</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G2</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G3</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weekly_Alcohol</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>school_GP</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>school_MS</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex_F</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex_M</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>address_R</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>address_U</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>famsize_GT3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>famsize_LE3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pstatus_A</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pstatus_T</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medu_1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medu_2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medu_3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medu_4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medu_5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fedu_1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fedu_2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fedu_3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fedu_4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fedu_5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>famrel_1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>famrel_2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>famrel_3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>famrel_4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>famrel_5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freetime_1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freetime_2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freetime_3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freetime_4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freetime_5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goout_1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goout_2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goout_3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goout_4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goout_5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dalc_1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dalc_2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dalc_3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dalc_4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dalc_5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walc_1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walc_2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walc_3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walc_4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walc_5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>health_1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>health_2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>health_3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>health_4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>health_5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>108 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                0  1   2   3   4\n",
       "absences        6  4  10   2   4\n",
       "G1              5  5   7  15   6\n",
       "G2              6  5   8  14  10\n",
       "G3              6  6  10  15  10\n",
       "Weekly_Alcohol  2  2   5   2   3\n",
       "school_GP       1  1   1   1   1\n",
       "school_MS       0  0   0   0   0\n",
       "sex_F           1  1   1   1   1\n",
       "sex_M           0  0   0   0   0\n",
       "age_1           1  1   0   1   1\n",
       "age_2           0  0   1   0   0\n",
       "age_3           0  0   0   0   0\n",
       "age_4           0  0   0   0   0\n",
       "age_5           0  0   0   0   0\n",
       "address_R       0  0   0   0   0\n",
       "address_U       1  1   1   1   1\n",
       "famsize_GT3     1  1   0   1   1\n",
       "famsize_LE3     0  0   1   0   0\n",
       "Pstatus_A       1  0   0   0   0\n",
       "Pstatus_T       0  1   1   1   1\n",
       "Medu_1          1  1   0   1   1\n",
       "Medu_2          0  0   1   0   0\n",
       "Medu_3          0  0   0   0   0\n",
       "Medu_4          0  0   0   0   0\n",
       "Medu_5          0  0   0   0   0\n",
       "Fedu_1          1  1   0   1   1\n",
       "Fedu_2          0  0   1   0   0\n",
       "Fedu_3          0  0   0   0   0\n",
       "Fedu_4          0  0   0   0   0\n",
       "Fedu_5          0  0   0   0   0\n",
       "...            .. ..  ..  ..  ..\n",
       "famrel_1        1  1   0   1   1\n",
       "famrel_2        0  0   1   0   0\n",
       "famrel_3        0  0   0   0   0\n",
       "famrel_4        0  0   0   0   0\n",
       "famrel_5        0  0   0   0   0\n",
       "freetime_1      1  1   0   1   1\n",
       "freetime_2      0  0   1   0   0\n",
       "freetime_3      0  0   0   0   0\n",
       "freetime_4      0  0   0   0   0\n",
       "freetime_5      0  0   0   0   0\n",
       "goout_1         1  1   0   1   1\n",
       "goout_2         0  0   1   0   0\n",
       "goout_3         0  0   0   0   0\n",
       "goout_4         0  0   0   0   0\n",
       "goout_5         0  0   0   0   0\n",
       "Dalc_1          1  1   0   1   1\n",
       "Dalc_2          0  0   1   0   0\n",
       "Dalc_3          0  0   0   0   0\n",
       "Dalc_4          0  0   0   0   0\n",
       "Dalc_5          0  0   0   0   0\n",
       "Walc_1          1  1   0   1   1\n",
       "Walc_2          0  0   1   0   0\n",
       "Walc_3          0  0   0   0   0\n",
       "Walc_4          0  0   0   0   0\n",
       "Walc_5          0  0   0   0   0\n",
       "health_1        1  1   0   1   1\n",
       "health_2        0  0   1   0   0\n",
       "health_3        0  0   0   0   0\n",
       "health_4        0  0   0   0   0\n",
       "health_5        0  0   0   0   0\n",
       "\n",
       "[108 rows x 5 columns]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = pd.get_dummies(res)\n",
    "res.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.linear_model import LinearRegression, Lasso, Ridge\n",
    "\n",
    "from sklearn.model_selection import KFold "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Scores(res, model):\n",
    "    y = res[\"G3\"].values\n",
    "    X = res.drop([\"G1\", \"G2\", \"G3\"], axis=1).values\n",
    "    SKFold = KFold(n_splits=5, random_state=1, shuffle=True)\n",
    "    scores=[]\n",
    "    for train_index, test_index in SKFold.split(X, y):\n",
    "        X_train, X_test = X[train_index], X[test_index]\n",
    "        y_train, y_test = y[train_index], y[test_index]\n",
    "        model.fit(X_train, y_train)\n",
    "        scores.append(model.score(X_test, y_test))\n",
    "    return scores, model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.10526315789473684,\n",
       "  0.18045112781954886,\n",
       "  0.09090909090909091,\n",
       "  0.12121212121212122,\n",
       "  0.14393939393939395],\n",
       " DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "             max_features=None, max_leaf_nodes=None,\n",
       "             min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "             min_samples_leaf=1, min_samples_split=2,\n",
       "             min_weight_fraction_leaf=0.0, presort=False, random_state=1,\n",
       "             splitter='best'))"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(random_state=1)\n",
    "Scores(res, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_list = [DecisionTreeRegressor(), LinearRegression(), Ridge(), Lasso()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = res[\"G3\"]\n",
    "X = res.drop([\"G1\", \"G2\", \"G3\"], axis=1)\n",
    "X_train, X_other, y_train, y_other = train_test_split(X, y, train_size=0.6)\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_other, y_other, test_size=0.5)\n",
    "#X_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset size:  397\n",
      "Val size:  132\n",
      "Test size:  133\n"
     ]
    }
   ],
   "source": [
    "print(\"Train dataset size: \", X_train.shape[0])\n",
    "print(\"Val size: \", X_val.shape[0])\n",
    "print(\"Test size: \", X_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.6271753681392236\n",
      "0.003101512666518036\n",
      "0.008743929163382291\n",
      "-0.002997786597755514\n"
     ]
    }
   ],
   "source": [
    "for clf in clf_list:\n",
    "    clf = clf.fit(X_train, y_train)\n",
    "    print(clf.score(X_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-6.752242298624367e-07\n"
     ]
    }
   ],
   "source": [
    "clf = Lasso()\n",
    "clf = clf.fit(X_train, y_train)\n",
    "print(clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.396140581376252\n"
     ]
    }
   ],
   "source": [
    "clf = DecisionTreeRegressor()\n",
    "clf = clf.fit(X_train, y_train)\n",
    "print(clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.004262635976071127\n"
     ]
    }
   ],
   "source": [
    "clf = LinearRegression()\n",
    "clf = clf.fit(X_train, y_train)\n",
    "print(clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00299947116090149\n"
     ]
    }
   ],
   "source": [
    "clf = Ridge()\n",
    "clf = clf.fit(X_train, y_train)\n",
    "print(clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.09774436090225563, 0.17293233082706766, 0.09090909090909091, 0.12878787878787878, 0.15151515151515152]\n"
     ]
    }
   ],
   "source": [
    "scores, model = Scores(res, model) \n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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